From de8712ddf9bfc7bee9af49c350800cd890f134e0 Mon Sep 17 00:00:00 2001 From: paulfogeladvestis Date: Tue, 9 Apr 2024 11:57:11 +0200 Subject: [PATCH] new commit due to failed github connexion --- README.md | 47 +- adilsm/adilsm.py | 124 +- adilsm/adilsm_bck.py | 350 +++ examples.bck/abis_915.csv | 916 +++++++ examples.bck/abis_all.ipynb | 1617 ++++++++++++ examples.bck/abis_biomed.ipynb | 403 +++ examples.bck/abis_gfa screeplot.ipynb | 1081 ++++++++ examples.bck/abis_gfa.ipynb | 1156 ++++++++ examples.bck/abis_mofa.ipynb | 537 ++++ examples.bck/abis_mofa_screeplot.ipynb | 2351 +++++++++++++++++ examples.bck/abis_mvmds_screeplot.ipynb | 227 ++ examples.bck/easi3.ipynb | 949 +++++++ examples.bck/getting_started_mofa.ipynb | 593 +++++ examples.bck/mofa_template_script_matrix.py | 153 ++ examples.bck/simulation_biomed.ipynb | 97 + examples.bck/uci_digits_all.ipynb | 2061 +++++++++++++++ examples.bck/uci_digits_biomed.ipynb | 542 ++++ examples.bck/uci_digits_gfa.ipynb | 1151 ++++++++ examples.bck/uci_digits_gfa_screeplot.ipynb | 1062 ++++++++ examples.bck/uci_digits_mofa.ipynb | 845 ++++++ examples.bck/uci_digits_mofa_screeplot.ipynb | 2222 ++++++++++++++++ examples.bck/uci_digits_mvmds_screeplot.ipynb | 216 ++ examples/abis_all.ipynb | 1617 ++++++++++++ examples/abis_biomed.ipynb | 107 +- examples/abis_gfa screeplot.ipynb | 1081 ++++++++ examples/abis_gfa.ipynb | 1156 ++++++++ examples/abis_mofa.ipynb | 537 ++++ examples/abis_mofa_screeplot.ipynb | 2351 +++++++++++++++++ examples/abis_mvmds_screeplot.ipynb | 227 ++ examples/easi3.ipynb | 949 +++++++ examples/getting_started_mofa.ipynb | 593 +++++ examples/mofa_template_script_matrix.py | 153 ++ examples/simulation_biomed.ipynb | 89 +- examples/uci_digits_all.ipynb | 2061 +++++++++++++++ examples/uci_digits_biomed.ipynb | 145 +- examples/uci_digits_biomed_bck.ipynb | 446 ++++ examples/uci_digits_gfa.ipynb | 1151 ++++++++ examples/uci_digits_gfa_screeplot.ipynb | 1062 ++++++++ examples/uci_digits_mofa.ipynb | 845 ++++++ examples/uci_digits_mofa_screeplot.ipynb | 2222 ++++++++++++++++ examples/uci_digits_mvmds_screeplot.ipynb | 216 ++ setup.py | 1 + 42 files changed, 35509 insertions(+), 200 deletions(-) create mode 100644 adilsm/adilsm_bck.py create mode 100644 examples.bck/abis_915.csv create mode 100644 examples.bck/abis_all.ipynb create mode 100644 examples.bck/abis_biomed.ipynb create mode 100644 examples.bck/abis_gfa screeplot.ipynb create mode 100644 examples.bck/abis_gfa.ipynb create mode 100644 examples.bck/abis_mofa.ipynb create mode 100644 examples.bck/abis_mofa_screeplot.ipynb create mode 100644 examples.bck/abis_mvmds_screeplot.ipynb create mode 100644 examples.bck/easi3.ipynb create mode 100644 examples.bck/getting_started_mofa.ipynb create mode 100644 examples.bck/mofa_template_script_matrix.py create mode 100644 examples.bck/simulation_biomed.ipynb create mode 100644 examples.bck/uci_digits_all.ipynb create mode 100644 examples.bck/uci_digits_biomed.ipynb create mode 100644 examples.bck/uci_digits_gfa.ipynb create mode 100644 examples.bck/uci_digits_gfa_screeplot.ipynb create mode 100644 examples.bck/uci_digits_mofa.ipynb create mode 100644 examples.bck/uci_digits_mofa_screeplot.ipynb create mode 100644 examples.bck/uci_digits_mvmds_screeplot.ipynb create mode 100644 examples/abis_all.ipynb create mode 100644 examples/abis_gfa screeplot.ipynb create mode 100644 examples/abis_gfa.ipynb create mode 100644 examples/abis_mofa.ipynb create mode 100644 examples/abis_mofa_screeplot.ipynb create mode 100644 examples/abis_mvmds_screeplot.ipynb create mode 100644 examples/easi3.ipynb create mode 100644 examples/getting_started_mofa.ipynb create mode 100644 examples/mofa_template_script_matrix.py create mode 100644 examples/uci_digits_all.ipynb create mode 100644 examples/uci_digits_biomed_bck.ipynb create mode 100644 examples/uci_digits_gfa.ipynb create mode 100644 examples/uci_digits_gfa_screeplot.ipynb create mode 100644 examples/uci_digits_mofa.ipynb create mode 100644 examples/uci_digits_mofa_screeplot.ipynb create mode 100644 examples/uci_digits_mvmds_screeplot.ipynb diff --git a/README.md b/README.md index f144fac..b0bedb0 100644 --- a/README.md +++ b/README.md @@ -6,12 +6,57 @@ pip install adilsm # Description -ILSM is Integrated Longitudinal Multi Source Model. +ILSM is Integrated Latent Multi Source Model. # Usage ```python +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt import adilsm.adilsm as ilsm + +max_noise_level = 0.1 +# Generate a random non-negative matrix with 100 rows and 10 columns +A = np.random.rand(100, 10) +# Swap the columns of the A and add some noise to generate B +B = np.random.permutation(A.T).T + np.random.uniform(low=0, high=max_noise_level, size=A.shape) +# Add noise to A +A += np.random.uniform(low=0, high=max_noise_level, size=A.shape) + +# ISM is expected to recognize that A and B convey the same information up to some noise, +# albeit with the columns of B swapped around. Heatmaps of the loadings of A and B columns +# on ISM components show the effective permutation. + +Xs = [A, B] +n_embedding, n_themes = [10,10] + +ilsm_result = ilsm.ism(Xs, n_embedding, n_themes, norm_columns=False, update_h4_ism=True, + max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8) +hv = ilsm_result['HV'] +hv_sparse = ilsm_result['HV_SPARSE'] +hhii = ilsm_result['HHII'] +w_ism = ilsm_result['W'] +h_ism = ilsm_result['H'] +q_ism = ilsm_result['Q'] +Xs_emb = ilsm_result['EMBEDDING'] +Xs_norm = ilsm_result['NORMED_VIEWS'] + +fig, ax = plt.subplots(1, 2, figsize=(10, 5), constrained_layout=True) +ax[0].imshow(hv[0], cmap='viridis', aspect='auto') +# Add labels and title +ax[0].set_xlabel('Component') +ax[0].set_ylabel('Column') +ax[0].set_title('Loadings of A columns on ISM components') +ax[1].imshow(hv[1], cmap='viridis', aspect='auto') +# Add labels and title +ax[1].set_xlabel('Component') +ax[1].set_ylabel('Column') +ax[1].set_title('Loadings of B columns on ISM components') + +# Show the plot +plt.show() + ``` diff --git a/adilsm/adilsm.py b/adilsm/adilsm.py index e4f70af..d7eaea5 100755 --- a/adilsm/adilsm.py +++ b/adilsm/adilsm.py @@ -2,6 +2,8 @@ import pandas as pd import numpy as np +print("coucou") + def format_loadings(h4, list_columns): # Format loadings df_h4 = pd.DataFrame(data=h4) @@ -55,7 +57,7 @@ def integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4_ism, h4_ism, q4_ism, n_s h4_score = h4_sparse[i1:i2, :].copy() m0_score = m0_nan_0[:, i1:i2] m0_weight_score = m0_weight[:, i1:i2] - i1=i2 + i1 = i2 # Apply multiplicative updates to preserve h sparsity for _ in range(0, max_iter_mult): @@ -100,23 +102,19 @@ def integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4_ism, h4_ism, q4_ism, n_s return h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score -def ism(m0:np.array, n_embedding:int, n_themes:int, n_scores:int, n_items:list[int], norm_m0:bool = True, max_iter:int=200, tol:float=1.e-6, verbose:int=-1, random_state:int=0, +def ism(Xs:list[np.array], n_embedding:int, n_themes:int, norm_columns:bool = True, max_iter:int=200, tol:float=1.e-6, verbose:int=-1, random_state:int=0, max_iter_integrate:int=20, max_iter_mult:int=200, fast_mult_rules:bool=True, update_h4_ism:bool=False, sparsity_coeff:float=.8): """Estimate ISM model Parameters ---------- - m0: NDArray - Matrix of views, concatenated horizontally. + Xs: List of NDArray + List of matrices of views. n_embedding: integer Dimension of the embedding space. n_themes: integer Dimension of the latent space. - n_scores: integer - Number of views. - n_items: integer - List of numbers of attributes (features) per view - norm_m0: boolean + norm_columns: boolean Scale each column of the concatenated matrix max_iter: integer, default: 200 Maximum number of iterations. @@ -144,7 +142,15 @@ def ism(m0:np.array, n_embedding:int, n_themes:int, n_scores:int, n_items:list[i Returns ------- - ISM decomposition W, H*, Q and view mapping matrix H + Dictionary + ilsm_result['Hv']: View-mapping + ilsm_result['Hv_sparse']: Sparse view-mapping + ilsm_result['HHII']: Number of non-negligable values by Hv component + ilsm_result['W']: ISM meta-scores + ilsm_result['H']: NTF loadings in latent space + ilsm_result['Q']: NTF view loadings + ilsm_result['EMBEDDING']: Embedded views (concatenated) + ilsm_result['NORMED_VIEWS']: Normed views (concatenated) Example ------- @@ -157,21 +163,38 @@ def ism(m0:np.array, n_embedding:int, n_themes:int, n_scores:int, n_items:list[i ---------- Fogel, P., Boldina, G., Augé, F., Geissler, C., & Luta, G. (2024). ISM: A New Space-Learning Model for Heterogenous Multi-view Data Reduction, Visualization and Clustering. - Preprints. https://doi.org/10.20944/preprints202402.1001.v1 + Preprints. https://doi.org/10.20944/preprints202402.1001.v2 """ - + + Xs_concat = Xs[0].copy() + for X in Xs[1:]: + Xs_concat = np.hstack((Xs_concat, X)) + + m0 = Xs_concat + + n_items = [Xs[i].shape[1] for i in range(len(Xs))] + n_scores = len(n_items) + m0_nan_0 = m0.copy() # create m0_weight with ones and zeros if not_missing/missing value m0_weight = np.where(np.isnan(m0), 0, 1) m0_nan_0[np.isnan(m0_nan_0)]=0 - if norm_m0 is True: + if norm_columns is True: #Scale each column of m0 max_values = np.max(m0_nan_0, axis=0) # # Replace maximum values equal to 0 with 1 m0 = np.divide(m0, np.where(max_values == 0, 1, max_values)) m0_nan_0 = np.divide(m0_nan_0, np.where(max_values == 0, 1, max_values)) + Xs_norm = [] + i1 = 0 + for i_score in range(n_scores): + i2 = i1+n_items[i_score] + Xs_norm.append(m0[:,i1:i2]) + i1 = i2 + else: + Xs_norm = Xs # Initial Embedding my_nmfmodel = NMF(n_components=n_embedding, leverage=None, max_iter=max_iter, tol=tol, verbose=verbose, random_state=random_state) @@ -188,10 +211,10 @@ def ism(m0:np.array, n_embedding:int, n_themes:int, n_scores:int, n_items:list[i # Embed using scores w4 found in preliminary NMF and initialize themes through NTF h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \ integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4, None, None, n_scores, n_items, n_themes, update_h4_ism=True, - max_iter_mult=max_iter_mult, fast_mult_rules=True, sparsity_coeff=sparsity_coeff) + max_iter_mult=max_iter_mult, fast_mult_rules=fast_mult_rules, sparsity_coeff=sparsity_coeff) error = np.linalg.norm(m0 - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0) - # print('error ism before straightening: ',round(error, 2)) + print('error ism before straightening: ',round(error, 2)) # Iterate embedding with themes subtensor until sparsity becomes stable flag = 0 @@ -202,11 +225,11 @@ def ism(m0:np.array, n_embedding:int, n_themes:int, n_scores:int, n_items:list[i if iter_integrate == 0: h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \ integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, np.identity(n_themes), q4_ism, n_scores, n_items, n_themes, update_h4_ism=update_h4_ism, - max_iter_mult=max_iter_mult, fast_mult_rules=True, sparsity_coeff=sparsity_coeff) + max_iter_mult=max_iter_mult, fast_mult_rules=fast_mult_rules, sparsity_coeff=sparsity_coeff) else: h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \ integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes, update_h4_ism=update_h4_ism, - max_iter_mult=max_iter_mult, fast_mult_rules=True, sparsity_coeff=sparsity_coeff) + max_iter_mult=max_iter_mult, fast_mult_rules=fast_mult_rules, sparsity_coeff=sparsity_coeff) if (hhii_updated == hhii_updated_0).all(): flag+=1 @@ -216,20 +239,44 @@ def ism(m0:np.array, n_embedding:int, n_themes:int, n_scores:int, n_items:list[i if flag==3: break - error = np.linalg.norm(m0 - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0) - # print('error ism after straightening: ',round(error, 2)) - - return h4_updated, h4_updated_sparse, w4_ism, h4_ism, q4_ism, tensor_score, m0 + Xs_emb = [] + i1 = 0 + for i_score in range(n_scores): + i2 = i1+n_embedding + Xs_emb.append(tensor_score[:,i1:i2]) + i1 = i2 + hv = [] + hv_sparse = [] + i1 = 0 + for i_score in range(n_scores): + i2 = i1+n_items[i_score] + hv.append(h4_updated[i1:i2,:]) + hv_sparse.append(h4_updated_sparse[i1:i2,:]) + i1 = i2 -def ism_expand(m0, h4_sparse, h4_ism, q4_ism, n_themes, n_scores, n_items, max_iter=200, tol=1.e-6, verbose=-1, random_state=0, - max_iter_mult=200): + error = np.linalg.norm(m0 - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0) + print('error ism after straightening: ',round(error, 2)) + ilsm_result = {} + ilsm_result['HV'] = hv + ilsm_result['HV_SPARSE'] = hv_sparse + ilsm_result['HHII'] = hhii_updated + ilsm_result['W'] = w4_ism + ilsm_result['H'] = h4_ism + ilsm_result['Q'] = q4_ism + ilsm_result['EMBEDDING'] = Xs_emb + ilsm_result['NORMED_VIEWS'] = Xs_norm + + return ilsm_result + +def ism_expand(Xs:list[np.array], h4_sparse:float, h4_ism:float, q4_ism:float, n_themes:int, norm_features:bool = True, max_iter:int=200, tol:float=1.e-6, verbose:int=-1, random_state:int=0, + max_iter_mult:int=200): """Expand meta-scores to new observations Parameters ---------- - m0: float - Matrix of views, concatenated horizontally. + Xs: List of NDArray + List of matrices of views. h4_sparse: float View-mapping matrix H. h4_ism: float @@ -238,12 +285,10 @@ def ism_expand(m0, h4_sparse, h4_ism, q4_ism, n_themes, n_scores, n_items, max_i View loading Q. n_themes: Dimension of the latent space. - n_scores: integer - Number of views. - n_items: integer - List of numbers of attributes (features) per view leverage: None | 'standard' | 'robust', default 'standard' Calculate leverage of W and H rows on each component. + norm_features: boolean + Scale each column of the concatenated matrix max_iter: integer, default: 200 Maximum number of iterations. tol: float, default: 1e-6 @@ -278,17 +323,28 @@ def ism_expand(m0, h4_sparse, h4_ism, q4_ism, n_themes, n_scores, n_items, max_i Preprints. https://doi.org/10.20944/preprints202402.1001.v1 """ EPSILON = np.finfo(np.float32).eps - #Scale each column of m0 + + Xs_concat = Xs[0].copy() + for X in Xs[1:]: + Xs_concat = np.hstack((Xs_concat, X)) + + m0 = Xs_concat + + n_items = [Xs[i].shape[1] for i in range(len(Xs))] + n_scores = len(n_items) + m0_nan_0 = m0.copy() # create m0_weight with ones and zeros if not_missing/missing value m0_weight = np.where(np.isnan(m0), 0, 1) m0_nan_0[np.isnan(m0_nan_0)]=0 - max_values = np.max(m0_nan_0, axis=0) - # Replace maximum values equal to 0 with 1 - m0 = np.divide(m0, np.where(max_values == 0, 1, max_values)) - m0_nan_0 = np.divide(m0_nan_0, np.where(max_values == 0, 1, max_values)) + if norm_features is True: + #Scale each column of m0 + max_values = np.max(m0_nan_0, axis=0) + # Replace maximum values equal to 0 with 1 + m0 = np.divide(m0, np.where(max_values == 0, 1, max_values)) + m0_nan_0 = np.divide(m0_nan_0, np.where(max_values == 0, 1, max_values)) i1 = 0 diff --git a/adilsm/adilsm_bck.py b/adilsm/adilsm_bck.py new file mode 100644 index 0000000..56f0c4c --- /dev/null +++ b/adilsm/adilsm_bck.py @@ -0,0 +1,350 @@ +from adnmtf import NMF, NTF +import pandas as pd +import numpy as np + +print("coucou") + +def format_loadings(h4, list_columns): + # Format loadings + df_h4 = pd.DataFrame(data=h4) + n_comp = len(df_h4.columns) + df_h4.columns = ['theme_' + str(i) for i in range(1, n_comp + 1)] + df_h4.insert(loc=0, column='label', value=(list_columns)) + + # Add description index + df_h4['description'] = df_h4['label'] + + return df_h4 + +def generate_h4_sparse(h4, q4_ism, n_items, n_comp, n_scores, sparsity_coeff=.8): + # Calculate hhii of each h column and generate sparse loadings + hhii = np.zeros(n_comp, dtype=int) + h_threshold = np.zeros(n_comp) + + if q4_ism is not None: + i1 = 0 + for i_score in range(0,n_scores): + i2 = i1+n_items[i_score] + h4[i1:i2,:] *= q4_ism[i_score,:] + i1 = i2 + + for i in range(0,n_comp): + # calculate inverse hhi + if np.max(h4[:,i]) > 0: + hhii[i] = int(round(np.sum(h4[:, i])**2 / np.sum(h4[:, i]**2))) + # hhii[i] = np.count_nonzero(h4[:, i]) + + # sort the dataframe by score in descending order + h_threshold[i] = np.sort(h4[:, i], axis=0)[::-1][hhii[i]-1] * sparsity_coeff + + + h4_sparse = np.where(h4 < h_threshold[None,:], 0, h4) + + return h4_sparse, hhii + +def integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes, update_h4_ism=False, + max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8): + + EPSILON = np.finfo(np.float32).eps + + # Generate w for each score, based on sparse loadings and create tensor_score + + # Extract score-related items + i1 = 0 + for i_score in range(n_scores): + i2 = i1+n_items[i_score] + w4_score = w4_ism.copy() + h4_score = h4_sparse[i1:i2, :].copy() + m0_score = m0_nan_0[:, i1:i2] + m0_weight_score = m0_weight[:, i1:i2] + i1=i2 + + # Apply multiplicative updates to preserve h sparsity + for _ in range(0, max_iter_mult): + # Weighted multiplicative rules + if fast_mult_rules: + m0_score_est = (w4_score @ h4_score.T)*m0_weight_score + h4_score *= ((w4_score.T @ m0_score) / (w4_score.T @ m0_score_est + EPSILON)).T + w4_score *= (m0_score @ h4_score / (m0_score_est @ h4_score + EPSILON)) + else: + h4_score *= ((w4_score.T @ m0_score) / (w4_score.T @ ((w4_score @ h4_score.T)*m0_weight_score) + EPSILON)).T + w4_score *= (m0_score @ h4_score / ((m0_weight_score*(w4_score @ h4_score.T)) @ h4_score + EPSILON)) + + # Normalize w4_score by max column and update h4_score + max_values = np.max(w4_score, axis=0) + # Replace maximum values equal to 0 with 1 + w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values)) + h4_score = np.multiply(h4_score, max_values) + + # Generate embedding tensor and initialize h4_updated + if i_score == 0: + tensor_score = w4_score + h4_updated = h4_score + else: + tensor_score = np.hstack((tensor_score, w4_score)) + h4_updated = np.vstack((h4_updated, h4_score)) + + # Apply NTF with prescribed number of themes and update themes + my_ntfmodel = NTF(n_components=n_themes, leverage=None, init_type=2, max_iter=200, tol=1e-6, verbose=-1, random_state=0) + + if q4_ism is None: + estimator_ = my_ntfmodel.fit_transform(tensor_score, n_blocks=n_scores) + else: + estimator_ = my_ntfmodel.fit_transform(tensor_score, w=w4_ism, h=h4_ism, q=q4_ism, update_h=update_h4_ism, n_blocks=n_scores) + + w4_ism = estimator_.w + h4_ism = estimator_.h + q4_ism = estimator_.q + + # Update loadings based on h4_updated (initialized by multiplicative updates) + h4_updated = h4_updated @ h4_ism + h4_updated_sparse, hhii_updated = generate_h4_sparse(h4_updated, q4_ism, n_items, n_themes, n_scores, sparsity_coeff=sparsity_coeff) + + return h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score + +def ism(m0:np.array, n_embedding:int, n_themes:int, n_scores:int, n_items:list[int], norm_m0:bool = True, max_iter:int=200, tol:float=1.e-6, verbose:int=-1, random_state:int=0, + max_iter_integrate:int=20, max_iter_mult:int=200, fast_mult_rules:bool=True, update_h4_ism:bool=False, sparsity_coeff:float=.8): + """Estimate ISM model + + Parameters + ---------- + m0: NDArray + Matrix of views, concatenated horizontally. + n_embedding: integer + Dimension of the embedding space. + n_themes: integer + Dimension of the latent space. + n_scores: integer + Number of views. + n_items: integer + List of numbers of attributes (features) per view + norm_m0: boolean + Scale each column of the concatenated matrix + max_iter: integer, default: 200 + Maximum number of iterations. + tol: float, default: 1e-6 + Tolerance of the stopping condition. + verbose: integer, default: 0 + The verbosity level (0/1). + random_state: int, RandomState instance or None, optional, default: None + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. + max_iter_integrate: integer, default: 20 + Max number of iterations during the straightening process. + max_iter_mult: integer, default: 200 + Max number of iterations of NMF multiplicative updates during the embedding process. + fast_mult_rules: boolean, default True + Use common matrix estimate in w and h updates + update_h4_ism: boolean, default False + Update or not the NTF factoring matrix H*. + sparsity_coeff: + Enhance H sparsity by a multiplicative factor applied to the inverse HHI. + ntf_kwargs: dict + Additional keyword arguments for NTF + + Returns + ------- + ISM decomposition W, H*, Q and view mapping matrix H + + Example + ------- + >>> import ILSM_functions + >>> n_embedding, n_themes = [9,10] + >>> h4_updated, h4_updated_sparse, w4_ism, h4_ism, q4_ism, tensor_score = ism(m0, n_embedding, n_themes, n_scores, n_items, update_h4_ism=True, + max_iter_mult=200, sparsity_coeff=.8) + + References + ---------- + Fogel, P., Boldina, G., Augé, F., Geissler, C., & Luta, G. (2024). + ISM: A New Space-Learning Model for Heterogenous Multi-view Data Reduction, Visualization and Clustering. + Preprints. https://doi.org/10.20944/preprints202402.1001.v1 + """ + + m0_nan_0 = m0.copy() + + # create m0_weight with ones and zeros if not_missing/missing value + m0_weight = np.where(np.isnan(m0), 0, 1) + m0_nan_0[np.isnan(m0_nan_0)]=0 + + if norm_m0 is True: + #Scale each column of m0 + max_values = np.max(m0_nan_0, axis=0) + # # Replace maximum values equal to 0 with 1 + m0 = np.divide(m0, np.where(max_values == 0, 1, max_values)) + m0_nan_0 = np.divide(m0_nan_0, np.where(max_values == 0, 1, max_values)) + + # Initial Embedding + my_nmfmodel = NMF(n_components=n_embedding, leverage=None, max_iter=max_iter, tol=tol, verbose=verbose, random_state=random_state) + estimator_ = my_nmfmodel.fit_transform(m0.copy()) + + w4 = estimator_.w + h4 = estimator_.h + + error = np.linalg.norm(m0 - w4 @ h4.T) / np.linalg.norm(m0) + # print('error nmf: ',round(error, 2)) + + h4_sparse, hhii = generate_h4_sparse(h4, None, n_items, n_embedding, n_scores, sparsity_coeff=sparsity_coeff) + + # Embed using scores w4 found in preliminary NMF and initialize themes through NTF + h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \ + integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4, None, None, n_scores, n_items, n_themes, update_h4_ism=True, + max_iter_mult=max_iter_mult, fast_mult_rules=fast_mult_rules, sparsity_coeff=sparsity_coeff) + + error = np.linalg.norm(m0 - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0) + print('error ism before straightening: ',round(error, 2)) + + # Iterate embedding with themes subtensor until sparsity becomes stable + flag = 0 + + for iter_integrate in range(0, max_iter_integrate): + hhii_updated_0 = hhii_updated.copy() + + if iter_integrate == 0: + h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \ + integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, np.identity(n_themes), q4_ism, n_scores, n_items, n_themes, update_h4_ism=update_h4_ism, + max_iter_mult=max_iter_mult, fast_mult_rules=fast_mult_rules, sparsity_coeff=sparsity_coeff) + else: + h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \ + integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes, update_h4_ism=update_h4_ism, + max_iter_mult=max_iter_mult, fast_mult_rules=fast_mult_rules, sparsity_coeff=sparsity_coeff) + + if (hhii_updated == hhii_updated_0).all(): + flag+=1 + else: + flag=0 + + if flag==3: + break + + error = np.linalg.norm(m0 - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0) + print('error ism after straightening: ',round(error, 2)) + + return h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score, m0 + + +def ism_expand(m0, h4_sparse, h4_ism, q4_ism, n_themes, n_scores, n_items, max_iter=200, tol=1.e-6, verbose=-1, random_state=0, + max_iter_mult=200): + """Expand meta-scores to new observations + + Parameters + ---------- + m0: float + Matrix of views, concatenated horizontally. + h4_sparse: float + View-mapping matrix H. + h4_ism: float + Factoring matrix H*. + q4_ism: float + View loading Q. + n_themes: + Dimension of the latent space. + n_scores: integer + Number of views. + n_items: integer + List of numbers of attributes (features) per view + leverage: None | 'standard' | 'robust', default 'standard' + Calculate leverage of W and H rows on each component. + max_iter: integer, default: 200 + Maximum number of iterations. + tol: float, default: 1e-6 + Tolerance of the stopping condition. + verbose: integer, default: 0 + The verbosity level (0/1). + random_state: int, RandomState instance or None, optional, default: None + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. + max_iter_mult: integer, default: 200 + Max number of iterations of NMF multiplicative updates during the embedding process. + ntf_kwargs: dict + Additional keyword arguments for NTF + + Returns + ------- + Expanded meta-scores + + Example + ------- + >>> import ILSM_functions + >>> n_embedding, n_themes = [9,10] + >>> h4_updated_sparse, w4_ism, h4_ism, q4_ism, tensor_score = ism(m0, n_embedding, n_themes, n_scores, n_items, update_h4_ism=True, + max_iter_mult=200, sparsity_coeff=.8) + + References + ---------- + Fogel, P., Boldina, G., Augé, F., Geissler, C., & Luta, G. (2024). + ISM: A New Space-Learning Model for Heterogenous Multi-view Data Reduction, Visualization and Clustering. + Preprints. https://doi.org/10.20944/preprints202402.1001.v1 + """ + EPSILON = np.finfo(np.float32).eps + #Scale each column of m0 + m0_nan_0 = m0.copy() + + # create m0_weight with ones and zeros if not_missing/missing value + m0_weight = np.where(np.isnan(m0), 0, 1) + m0_nan_0[np.isnan(m0_nan_0)]=0 + + max_values = np.max(m0_nan_0, axis=0) + # Replace maximum values equal to 0 with 1 + m0 = np.divide(m0, np.where(max_values == 0, 1, max_values)) + m0_nan_0 = np.divide(m0_nan_0, np.where(max_values == 0, 1, max_values)) + + i1 = 0 + + for i_score in range(n_scores): + i2 = i1+n_items[i_score] + non_missing_rows = np.where(np.sum(m0_weight[:, i1:i2], axis=1) > 0)[0] + w4_score = np.zeros((m0.shape[0], n_themes)) + w4_score_non_missing = np.ones((len(non_missing_rows), n_themes)) + h4_score = h4_sparse[i1:i2, :].copy() + m0_score = m0_nan_0[non_missing_rows, i1:i2] + m0_weight_score = m0_weight[non_missing_rows, i1:i2] + i1=i2 + + # Apply multiplicative updates to preserve h sparsity + for _ in range(0, max_iter_mult): + # Weighted multiplicative rules + m0_score_est = w4_score_non_missing @ h4_score.T + w4_score_non_missing *= (m0_score @ h4_score / ((m0_weight_score*m0_score_est) @ h4_score + EPSILON)) + + w4_score[non_missing_rows,:] = w4_score_non_missing + + # Generate embedding tensor and initialize h4_updated + if i_score == 0: + tensor_score = w4_score + else: + tensor_score = np.hstack((tensor_score, w4_score)) + + # Impute rows with missing views + temp = np.where(tensor_score > 0, 1, 0) / n_themes # will be used to find the number of non-missing views by rows + + # Normalize q4_ism by the mean weight of each component across all views + q4_ism_norm = q4_ism / np.mean([q4_ism[i_score,:] for i_score in range(n_scores)], axis=0) + + for i_score in range(n_scores): + i1 = i_score*n_themes + i2 = (i_score+1)*n_themes + include_mask = np.logical_or(np.arange(tensor_score.shape[1]) < i1, np.arange(tensor_score.shape[1]) >= i2) + n_scores_non_missing = np.sum(temp[:, include_mask], axis=1) + missing_rows = np.where(np.sum(tensor_score[:, i1:i2], axis=1) == 0)[0] + if len(missing_rows) > 0: + # Estimate missing view using the weighted average other non-missing views, which is an estimate of the meta-score, where the weights are derived from view-loadings. + for j_score in range(n_scores): + if j_score != i_score: + j1 = j_score*n_themes + j2 = (j_score+1)*n_themes + tensor_score[missing_rows, i1:i2] += q4_ism_norm[j_score, :] * tensor_score[missing_rows, j1:j2] + # tensor_score[missing_rows, i1:i2] += q4_ism[j_score, :] * tensor_score[missing_rows, j1:j2] / (q4_ism[i_score, :] + EPSILON) + + tensor_score[missing_rows, i1:i2] /= np.repeat(n_scores_non_missing[missing_rows,np.newaxis], n_themes, axis=1) + tensor_score[missing_rows, i1:i2] *= np.where(q4_ism[i_score, :] > 0, 1, 0) + + # Apply NTF with prescribed number of themes and update themes + my_ntfmodel = NTF(n_components=n_themes, leverage=None, init_type=2, max_iter=max_iter, tol=tol, verbose=verbose, random_state=random_state) + estimator_ = my_ntfmodel.fit_transform(tensor_score, h=h4_ism, q=q4_ism, update_h=False, update_q=True, n_blocks=n_scores) + w4_ism = estimator_.w + + return w4_ism \ No newline at end of file diff --git a/examples.bck/abis_915.csv b/examples.bck/abis_915.csv new file mode 100644 index 0000000..6b46a4c --- /dev/null +++ b/examples.bck/abis_915.csv @@ -0,0 +1,916 @@ +gene_id,Cell type,9JD4 B Memory,9JD4 B Naive,9JD4 Basophils LD,9JD4 CD4+ effector,9JD4 CD8 activated,9JD4 MAIT,9JD4 mDCs,9JD4 Monocytes C,9JD4 Monocytes NC+I,9JD4 Naive T cells,9JD4 Neutrophils LD,9JD4 NK,9JD4 pDCs,9JD4 Plasmablasts,9JD4 VD2-,9JD4 VD2+,925L B Memory,925L B Naive,925L Basophils LD,925L CD4+ effector,925L CD8 activated,925L MAIT,925L mDCs,925L Monocytes C,925L Monocytes NC+I,925L Naive T cells,925L Neutrophils LD,925L NK,925L pDCs,925L 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Naive,2.0724647059,16.334012715,0,0,0.0086000328,0,0,0,0,0,0,0,0,0,0,0,1.261743687,18.447956183,0,0,0,0,0.4123,0.0904,0,0,0,0.1047,0,0,0,0,2.8111104558,12.767412779,0,0,0,0,0,0,0.1573532258,0,0.2,0,0,0,0,0,0.0987770335,14.951795556,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +AC002511.3,Neutrophils LD,0,0,3.7206,0,0,0,0,0,0.610294958,0,10.9472,0,0,0,0,0,0,0,2.3908,0,0,0,0,4.9293,2.2988263158,0,27.6862,0,0,0,0,0,0.901416622,1.5306571347,5.4884,0,0,0,0.4265,6.341,3.8490677419,0,39.48,0,0,0,0,0,0,0,4.4202,0,0,0,0,0.6685,0.6035453216,0,26.8835,0,0,0,0,0 +AC007381.3,pDCs,2.2906244344,1.5048591065,0,0,0,0,0,0,0,0,0,0,14.9014,0.2954,0,0,2.568071725,3.3423627656,0,0,0,0,0,0,0,0,0,0,21.0952,0.3099,0,0,0.0392549598,1.7157112894,0,0,0,0,0,0,0,0,0,0,13.5655,0,0,0,0.3774325359,2.222489697,0,0,0,0,0,0,0.2231491228,0,0,0.0545,18.0048,0.1571,0,0 +AC008074.5,Neutrophils LD,0.2078153846,0,0,0,0,0,0,0,0,0,15.6323,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.3815,0,0,0,0,0,0.4512975871,0,0,0,0,0,0,0,0,0,6.5179,0,0,0,0,0,0,0.7568993939,0,0,0,0,0,1.5092,0.5468517544,0,34.8641,0,0,0,0,0 +AC008697.1,B Memory,14.68438009,3.2830010309,0.1753,0.8325856357,0.2131634827,0.2441,0,0.3901,0.2802605042,0.2404725506,0.3125,0.3765,0.4066,0.2582,0.0378,0.1294,12.640587255,0.9782153547,0.4541,0.9883119673,0.0495213622,0.1136,0.5119,0.6128,1.8488578947,0.5902459672,0.2217,0.2784,0.267,0.0577,0.0718,0.1654,5.7766048257,1.251692149,0.6255,0.6013478413,0.5348140047,0.342,0.1835,0.2938,0.1160709677,0.1380887963,1.0885,0.2002,0.0763,0.0256,0.1444,0.0914,12.764222488,2.7457123232,0.2044,0.8231003837,0.1900059934,0.3706,0,0.1357,0.2955283626,0.3628858026,0.5623,0.1766,0.8723,0.0714,0.159,0.2163 +AC009237.17,Basophils LD,0,0,18.9767,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,31.4467,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,40.1594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17.9643,0,0,0,0,0,0,0,0,0,0,0,0,0 +AC009238.7,Basophils LD,0,0,18.9767,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,31.4467,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,40.1594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17.9643,0,0,0,0,0,0,0,0,0,0,0,0,0 +AC010468.2,Naive T cells,0,0,0,20.525527927,6.2360922698,11.5395,0,0,0,46.172161064,0,0,0,1.0961,3.1781,9.4904,2.819836396,0.0377007418,0,19.189626303,2.9426807992,18.715,0,0,0,46.659563672,0,0,0,0,0.7649,10.0764,0,0,0,17.517509476,2.8088835563,7.7258,0,0,0,41.40877488,0,0,0,0,0.6835,2.9193,1.413791866,0.1418505051,0,18.767843165,6.3368027843,7.5383,0,0,0,28.592827977,0,0.4532,0,0,3.5925,8.2379 +AC011380.8,Monocytes C,1.5386986425,0.5753862543,0.2629,1.6322919627,1.1037293944,0,2.5672,22.9226,2.4464155462,0.7698508291,13.7933,0.5905,0,2.0905,0.6041,1.0135,1.5649397155,2.6533680447,1.3814,1.659343905,0.8317600653,0,0.9583,26.8764,2.1214236842,0.9794811803,22.3312,0,1.8731,0.2264,1.1589,0,2.0909423592,1.4830726648,1.1772,1.1989736061,0.8494685287,0.0934,0.5286,16.0124,1.3736177419,0.6100098564,9.9625,0.723,1.3571,0.3586,1.9911,0,1.2694722488,2.7558686869,2.1233,1.3844836223,0.6993550967,0.0775,1.0445,29.824,3.8762865497,1.0418943878,14.2996,2.2565,0,0.1835,0,0 +AC011893.3,pDCs,0,0.0603,0.0834,0.0569051609,0.0055637617,0,0,0,0,0,0.1496,0.1533,80.2341,0.0584,0.1669,0,0,0,0,0.0450198589,0.0733808746,0.0678,0,0,0,0.1566691148,0.1763,0.0675,74.9992,0.0306,0.0594,0,0.0048924933,0.9384731232,0,0.0786179579,0.0780304485,0.084,0,0,0,0,0,0.082,96.3698,0.0253,0,0,0.0079311005,0,0,0.1929544165,0,0,0,0,0.1020067251,0.609254301,0.1449,0,70.2901,0,0,0 +AC011899.9,Monocytes NC+I,0,0,3.7678,0,0,0,7.2001,13.5326,51.484690336,0.159598899,7.041,0,0.122,0,0,0,0.001392709,0.0183032625,4.3392,0,0,0,12.1666,22.9068,48.050636842,0,7.4886,0,2.2812,0.0861,0,0,0.5679884718,1.6912582808,3.6705,0,0,0,7.0744,13.377,49.343677419,0,4.3111,0.6905,0,0,0,0,1.1143741627,1.7514254545,2.8072,0.0147369835,0,0,6.9528,17.4037,42.023715789,0,6.3016,0.4044,0,0,0,0 +AC012314.19,Neutrophils LD,0,0.6209457045,29.6226,0.634945168,0.7807985885,4.7479,13.5516,46.3888,18.658292437,0,506.737,0,0.9192,0,0,0,0,6.5830698884,3.9265,0.7699153452,0.7612795175,0,10.7742,17.5763,15.697847368,0,246.229,0,2.1209,0,0,0,0,0.5138939828,7.7849,0.8324034499,0,3.5131,18.9263,16.512,15.381046774,0,167.176,0,0.8394,0,0,1.3327,10.488526794,5.2688816162,2.7973,0,0,0,15.121,52.7146,32.171042105,0,542.953,0,4.2142,0,2.1775,0 +AC018643.4,pDCs,0,0,0,0.1553015788,0,0,0,0,0,0.829842128,0,0,20.9032,0,0,0,0,0,0,0.3040380104,0,0,0,0,0,1.8331081311,0,0,18.2956,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20.0093,0,0,0,0,0,0,0.1837614199,0,0,0,0,0,0,0,0.5069,37.2561,0,1.3393,0 +AC022816.2,Neutrophils LD,0.0682099548,0.6059869416,0.4117,1.0115206016,1.16376258,0.356,1.4519,0,0,1.7280347375,20.4987,1.4186,1.7885,0.7316,1.0239,0,0.1142247184,0.9395738567,1.4469,0.8055065776,0.0515098266,0.9274,1.0793,1.199,0.2707894737,1.2287266885,15.0153,0,0.667,0,0.2561,0.1909,0.5281013405,0.2287402865,2.5922,1.0239726857,0.4227884343,0,0,0,0.7628758065,0.9225770431,11.7391,0,0.8495,0.4332,0.9112,1.0933,1.6391837321,0.1868668687,2.5975,0.5145746728,0.4909306277,0.876,0.1753,0.4018,0.2280628655,0.5859647904,18.1587,0,2.6459,0,1.5223,0 +AC023590.1,pDCs,6.8498289593,6.141452921,3.3885,3.5537070207,0.9384688987,4.9557,2.8192,2.1959,3.4172537815,4.8687941562,1.2902,2.8144,266.6494,3.885,1.7989,1.3583,6.2169065797,1.148206561,1.7876,3.7478509948,1.3206111335,3.9425,5.3232,1.5598,2.1112184211,5.9712148197,2.8875,3.4277,214.7262,1.7639,1.3578,0.6646,12.911382038,5.7272633811,2.4791,7.9784790955,1.3076894571,4.1784,2.4986,2.9119,4.2192032258,7.7532077469,2.6056,1.184,197.6185,2.1243,0.8106,1.3478,8.483638756,6.2106779798,5.9308,9.1074275543,2.3589172015,9.6681,3.3588,0.7286,0.694724269,5.3215039085,3.335,5.6781,240.734,7.2667,2.7995,7.0807 +AC068724.1,pDCs,18.966733937,5.9739261168,0,0,0,4.961,0,0,0,0,8.921,0,38.7969,1.9932,0,0,14.068825074,5.033799323,0,0.0456566295,12.876498241,0,0,0,0,0,0,0,34.2204,2.1395,0,0,9.6039018767,15.196167106,0.0003,0,0,0,0,0,0,1.0446390002,0,0,67.4769,14.5713,0,0,1.5793875598,32.585376768,0,0,0,0,0,0,0.4114546784,0,0,0,35.0878,0,0,0 +AC099552.2,Monocytes NC+I,0,0,0,0,0,0,0,0,28.580913445,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,57.120360526,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.3382,32.229119355,0,0,0,0,0,0,0,0,0.3364242424,0,0,0,0,0,0,24.458381287,0,0,0.839,0,0,0,0 +AC103563.5,Plasmablasts,9.1336221719,1.7225869416,0,0.8633509987,0,0,0,0,0,0,0,0,6.547,57.1522,0,0,0.337946473,1.2679236946,0,0,0,0,0,0,0,0,0,0,0,73.6051,0,0,11.618975871,0.9250748424,0,1.1410158787,0,0,0,0,0,2.3414842404,0,0,0,39.3266,0,0,12.261338278,0,0,1.2108424313,0,0,0,0,0,0,0,0,0,67.2733,0,0 +AC103563.9,Naive T cells,0,0,0,9.8223709066,0.6741825373,0,0,0,0,22.51335144,0,0,0,0,0.7364,0.3065,0,0,0,11.223796897,0.4462106057,0,0,0,0,21.489584918,0,0,0,0,4.5059,0,0,0,0,7.0656738247,0,0,0,0,0,12.402225421,0,0,0,0,1.1812,0,0,0,0,7.2566850682,0,0,0,0,0,14.914884834,0,0,0,0,0,0.3395 +AC104809.4,Monocytes NC+I,0.2340959276,0.2726323024,0,0.0106829386,0,0,0.3152,0.2626,160.05109118,0.0791037488,0.6432,0,0,0.0643,0,0,0,0.6163215484,0,0.0212951893,0.0415593868,0,2.9087,0.4458,168.45148158,0,0,0.0539,0,0,0,0,1.420941555,0.2011541547,0,0,0.0156132179,0,0.4051,1.7298,213.06208871,0.3679421379,0.0517,0,0,0.071,0,0,0.804337799,0.8728389899,0.3961,0,0.2336129542,0,0.0506,1.2531,132.4222614,0.0385141139,0.4149,0.3789,0,0.1638,0.2225,0 +ACSL1,Neutrophils LD,10.188795023,8.7441408935,65.3833,8.1991413049,17.668181093,17.3642,27.2087,125.5934,63.953576891,3.8627506954,1044.9702,9.2735,8.1748,3.4097,11.6544,10.0066,21.084390279,9.2111804537,59.5734,8.3386773942,12.224532546,4.8387,19.1255,107.2237,75.323097368,5.4724980984,1916.062,22.3031,9.7569,3.8499,12.0058,7.7275,25.71875067,22.008713467,54.4388,8.3400205469,6.9699033832,6.4448,34.5183,165.2637,104.31755484,5.8440777167,1619.7767,9.8852,10.1089,3.3427,6.8617,8.0195,20.006645455,14.177276566,51.723,5.2632945453,6.7572969797,8.0641,28.5675,178.0003,88.639547953,5.1297140304,1239.4048,13.9279,5.2163,3.0054,6.4638,3.1394 +ADAM12,MAIT,0.048240724,0.0387226804,0,4.7806571224,0.0535675201,41.1672,0.1053,0.7632,0.0602907563,0,0,0,0,0,0.0884,0.0148,0.2687231772,0.7222799064,0.0797,2.6131837342,0.6774378487,40.528,0.0396,0.2476,0.0490763158,0.7035991475,0,2.8007,0,0,3.5514,0.3079,0,0.0910234957,0.0149,7.6344988346,0.2562825334,62.3066,0,0.0339,0.0151387097,0,0,0,0.3051,0,0.0375,1.8353,0.1434961722,0.0905955556,0.4376,9.8873641678,0.2311766871,31.1099,0.067,0.7732,0.0556304094,0,0,0.4715,0,0.0073,0.0329,0.6828 +ADAMTS1,NK,0.5425733032,0.9193594502,0.3211,1.2576748176,7.4429036435,3.0163,0.8811,1.0032,0.8717827731,1.6898626059,0.3745,45.1068,0.1237,0.106,7.1685,2.0652,0.3426113219,1.8746046093,0.3244,0.1275038382,4.898546469,0.1668,1.2836,0.6933,0.60715,0.2785430164,0.3883,26.1467,0.2824,0.0448,7.3756,3.517,0.4512463807,2.6352221777,0.343,0.3396556085,2.6926967742,1.4841,0.9249,0.794,1.8856387097,0.6613458607,0.2548,33.7283,0.161,0.0191,6.5756,14.2545,0.6433636364,1.2630646465,2.2065,0.4287986432,3.8795082586,0.459,0.6165,1.2683,0.9794116959,1.1519602472,0.221,34.7393,0.2562,0.5061,1.0719,4.2296 +ADM,Neutrophils LD,0.6926226244,0.1237989691,0,0.079738249,1.1290645002,0,0.761,14.2068,12.514240336,0,275.3177,0.9131,0.1691,1.656,1.4483,1.5706,1.7128870184,1.8003316097,1.1163,0.0383724722,0.1875289017,0.0784,2.5732,44.8176,19.34585,0.3103269508,510.7348,0,0.4695,0.1772,0,0.667,7.1749463807,6.6985676218,0,0.0267437955,0.0671683714,0.389,9.4728,57.5087,18.531487097,0,222.7462,0,0,2.6573,0,0,2.2483,1.8912589899,0.1149,0.2193629411,0.132451109,0.6428,2.402,26.7341,57.156763158,0.0558454652,511.4705,0.1517,0,1.301,0.5401,0.0886 +AEBP1,pDCs,8.2519638009,19.919070447,0.352,4.3009917474,0.8267039389,0.9863,0,0,0,17.779000883,0,3.2394,56.1929,0.0246,0.6402,3.3965,2.0596354475,12.085216262,2.0164,2.7258205345,0.0039263131,0.3986,0.2201,0,0,12.143263541,0,0.8459,51.0624,0,0.8412,2.4425,1.6942461126,11.38418086,0.3535,2.6998375646,0.3700493312,0.1413,0,0.0272,0,10.585547616,0,1.1646,22.0668,0,0.803,0.5602,2.3770674641,11.188436768,1.1405,2.0489560543,1.2196801321,0,0,0,0,9.9791379322,0,1.5079,65.6081,0.2843,0.758,2.1086 +AF001550.9,pDCs,0,0,0,0,0,0,0,0,0,0,0,0,5.5955,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.2202,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32.4872,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18.6078,0,0,0 +AF064858.10,Basophils LD,0,0,11.9021,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.4829,0,0,0,0.8984,0,0,0,0,0,0,0,0,0,0,0,18.3062,0,0,0,0.8426,0,0,0,0,0,0,0,0,0,0,0,5.2579,0.2173341122,0,0,4.4464,0,0,0,0,0,0,0,0,0 +AIF1,Monocytes NC+I,4.6742927602,7.0265065292,1.6035,3.7216540426,1.5210149188,0,221.5183,273.3191,382.31988109,15.156838353,205.8312,2.9849,19.5101,0.6222,2.7435,6.0642,6.8070407825,11.686734908,0.8104,3.0926031997,0.1510142247,0,279.619,169.0217,514.13719474,32.912546164,245.5392,5.6068,32.5526,0.6179,0,0.9455,17.527676944,24.699498223,0.3845,1.7621059197,0.5365170732,0.4624,90.1428,219.8762,644.53763548,9.4748909236,264.2781,2.2516,9.7617,1.6717,0,0,25.403450718,28.174557576,2.8604,2.354925382,0,0,199.3936,359.9458,647.57883655,26.746487089,185.2729,2.8105,13.1395,3.259,0,3.3791 +AIM2,B Memory,110.8042009,36.324446392,1.2172,2.7546282024,2.2654031183,0.5623,5.5473,10.0347,5.9673542017,0.5461815548,24.4499,1.3441,0.3913,5.9202,3.739,0.6182,225.10451251,20.300734123,5.0337,7.4908273274,4.7530463936,3.0962,4.5696,7.0716,8.3819973684,1.8563352131,11.4084,3.6952,0.139,21.2625,0.6087,6.9189,220.43299437,83.837556103,11.0043,11.158541173,7.9551678993,5.2216,6.4224,29.7796,31.657896774,1.4759601135,61.7257,0.3247,0.8107,38.1945,3.9823,2.5066,201.94383589,53.939154343,16.5302,4.3604249092,2.7345329637,3.7793,4.247,12.7015,13.777861404,0.3427708034,29.3287,1.9426,0.4034,22.2772,1.0437,2.9142 +AK8,B Memory,11.846026697,1.1231628866,0,0,0,0,0.0512,0.1102,0,0,0,0.9639,0,0.3255,0,0,19.504733254,0.2002039755,0,0.0426134373,0,0.0629,0,0,0.0505973684,0.1898297049,0,0.5985,0,0.7087,0,0,10.004433244,0.3960621203,0,0.009476811,0,0,0,0,0.3137580645,0.039130367,0,0.6093,0.0557,0.3764,0,0,23.974855502,2.3570111111,0.0922,0.0661497019,0,0,0,0,0,0.6090955069,0,0,0.2807,0.4763,0,0.0711 +AKAP12,Basophils LD,0.0488180995,0.2044962199,111.6328,0.0944488399,0.0349734613,0.0144,0.0225,0.2634,0.1320063025,0.0723730588,0.0318,0.4001,0.0807,0.0059,0.0194,0.339,0.3135620036,0.7762484912,131.4752,0.2541754343,0.0098228701,0.0725,0.0329,0.0244,0.2920605263,0.0659543607,0.4822,0.6348,0.0313,1.0398,0.0216,0.0119,0.0009820375,0.4940913467,147.0928,0.0704103496,0.0406243116,0.018,0.0427,0.0906,0.0207854839,0.0496549548,0.6388,0.0164,0.0506,0.0153,0.0706,0,0.0287564593,0.0599892929,117.5372,0.1624212362,0.1190256017,0.0421,0.0256,0.0257,0.2028798246,0.0435319359,0,0.0244,0.1001,0.0144,0.063,0.0313 +AL122127.25,B Naive,157.52337059,319.81235189,0,0,0,0,0,0,0,1.4171637693,0,0,2.1342,75.6472,0,0,102.98655175,320.86651292,0,0,0,0,0,0,0,1.7000575738,0,0,3.2844,31.9266,0,0,101.7970622,260.61149484,5.8728,0,0,0,0,0,0.344583871,2.2417362702,0,0,15.6517,56.6656,0,0,125.75988134,330.39362404,2.4449,0,0,0,0,0,0,0.5798106063,0,0,17.3859,45.0484,1.9097,0 +AL450992.2,MAIT,0,0,0,19.820917097,17.853285344,60.6204,0,0,0,2.0185157484,0,4.9637,10.425,0.6726,4.3756,9.6937,0,1.8645657472,0,12.899170973,7.0670633325,69.8644,0.9165,0,0,0,0,2.3612,6.8749,0,5.6057,30.8203,0,0,0,16.298176142,4.0832518489,43.272,0,0,0,1.931996685,0,0,4.9026,0,0,9.4658,2.5157267943,0.6025377778,0,18.322256883,10.10221017,63.2587,0,0,0,1.5245996826,0,4.5052,6.3727,1.7837,3.1984,23.358 +ALDH1A1,Monocytes C,2.120941629,3.9540274914,0.7303,0.0060591556,0,0,0.713,137.1384,10.237528151,0,0.5648,0,0.4026,0.5592,0,0,5.1922101956,2.2591555708,0.1636,0,0,0,3.6492,157.748,11.887968421,0,0,0,3.1833,0.7994,0,0.0501,24.100417962,27.157571175,0.3961,0,0,0,3.4576,250.3703,13.878998387,0,0.906,0,0,1.6086,0,0,10.568337321,9.7958529293,0.4329,0.0633588775,0.0393965314,0,2.7531,221.8549,22.257637135,0.2373238517,0.3645,0,0.2628,0.9642,0.0675,0 +ALG1L13P,Neutrophils LD,12.942188688,6.8753460481,7.2411,1.1523159072,0.5674704743,0.348,0.7945,3.4733,7.1518605042,1.053458398,147.6896,1.6172,1.8513,0.8714,0.7629,0,1.2735065797,4.2709821102,13.4795,1.1968057832,4.1480363157,2.7689,0,1.9257,3.0247894737,2.6156899016,48.9633,0.4857,1.178,1.1784,2.698,0.3183,2.8108836461,2.2935210888,21.1798,1.3646730168,0.5844744296,0.5682,0,0,1.6683596774,0.5888186314,28.9587,0.4146,1.3441,1.7979,0.4081,0.4363,14.060442584,4.1057973737,15.4346,0.6033882341,0.3805298254,0.3551,3.5917,2.4577,4.7826429825,0.2503193586,84.681,0.3,3.078,2.0622,0.4338,1.2801 +ALPL,Neutrophils LD,2.2356479638,5.0625443299,0.2715,0.0083379022,0,0,0,0,0.2834021008,0,548.8059,0,0.2139,0,0.2696,0,6.0783167753,9.480657861,0.4499,0.1096149369,0.0508087459,0,0,0,3.2715815789,0.3146923279,922.4467,0.2319,0,0,0,0,2.693863807,8.9161634957,0,0.0938155675,0,0.5441,0,0.2184,1.5280177419,0,374.775,1.5821,0,0,0,0,0.3634966507,2.2667082828,0,0.0746094223,0.0166753185,0,0,0,0.6352704678,0.1669118423,838.5182,0.3545,0,0.3267,0,0 +ALS2CL,CD4+ effector,0.179340724,0.1521367698,0.0982,25.407998385,0.7659862465,1.7219,5.8294,2.9073,1.6466071429,23.779240318,0.2053,0.03,0.1569,0.0114,0.8564,4.5095,0.8139263189,0.1728046597,0.9187,35.38446997,1.123678864,1.0118,0.9026,3.7086,1.3991289474,13.830362623,0.0796,0.6589,0.9059,0.024,1.2698,0.9053,0.6182042895,0.256594384,0.1375,21.676548861,0.2538457907,0.1018,4.7422,3.1719,0.2979951613,19.764793015,0.0775,0.032,0.0707,0.01,0.0694,0.5079,0.6756028708,0.2519367677,0.2776,26.932531611,0.7282694667,0.807,13.9601,7.0264,1.7234915205,29.655860247,0.0302,0.0704,0.2672,0.0136,0.9203,2.0172 +AMIGO3,pDCs,0,0.6023591065,0,0.2974790638,2.1985547842,0,0.1616,0,0.1727760504,2.4612177588,0,2.0792,26.5974,0,1.096,0.7329,0.0280724956,0.0493839467,1.7233,0.7018338382,1.0534631063,0.1751,0,0.6417,0.1870105263,1.1267166562,0,0.9196,25.0207,0,1.0454,1.2994,1.1072493298,1.1073230946,0.7746,0.9321320156,0.8658704957,0,0,0.2479,0.0026112903,0.5989068782,0.0404,3.4219,25.8004,0.6048,1.1284,0.2913,2.5620172249,0,0.3115,1.7706184815,2.1367373525,0.0015,0,0,0.5681464912,1.9064856857,0,1.2443,30.0551,0,4.2603,2.9096 +ANG,Monocytes C,1.5671936652,1.5634838488,0.3892,1.7572148068,2.4001254719,0,3.664,17.8949,0,0.5182365205,0,0,0.435,2.0282,0,0,1.6981943094,0.1858647389,0.2992,0.7879281514,0,0.2157,3.6502,7.6609,0.4521815789,0.0669163279,0,0,1.1426,1.0512,0.1944,0,1.0705630027,0,0.2226,0.5418836909,0.1840476003,0,3.5025,3.2151,0.1810564516,0,0,0,0.9524,1.5501,0,0,2.3844588517,2.5081189899,2.2921,0.7120323032,0.3632338367,0.6674,5.3811,13.3029,1.4730304094,0,0,0,3.0787,2.9552,0,0 +ANKRD53,pDCs,0.3983162896,1.1147766323,0,0.6005855819,0,0,0,0,1.157094958,0,0,0,18.1805,0,0,0,0.2870782454,0.0478139575,1.072,0.4462558575,0.3544596632,0,0,0,0.1767105263,0.0773570492,0,2.3021,18.2739,0.4405,0.5344,0,0,0,0,0.5159625745,0.4042983478,0.4069,0.0575,0,0.1374854839,0,0.4875,2.1698,16.4695,0,2.155,0,0.4556449761,1.7802018182,0.1128,0.4736990886,0.2901024776,1.0183,0,0.1054,0.077298538,0,0.3519,0,22.3776,0.1641,0.1945,0 +ANXA3,Neutrophils LD,1.3368542986,0.9455910653,1.3448,0.4040018479,0.2161947481,0.1787,0.3915,0.4585,0.7669886555,0.2572740884,229.3105,0.2352,0.2519,2.3271,0.11,0.4095,2.5644882632,0.8911876918,0.2642,0.2866918114,0.5010064086,0.3329,0.3845,0.1465,1.1799263158,0.6921659016,385.5901,0.4909,0.1291,0.5776,0.2951,0.1759,9.4576088472,11.911273696,0.1417,0.4684487095,0.3108836349,0.181,0.1339,0,2.5972354839,0.4367119482,361.1888,2.0701,0.2322,5.4592,0.3403,0.294,4.2656277512,0.630269697,0.6603,0.4478061399,0.3935751062,0.1014,0.17,0.5594,0.2426973684,0.3901718724,380.9653,0.6378,0.5916,6.1112,0.4108,0.2175 +AOC2,Neutrophils LD,0.6107443439,0.4331185567,0.2906,0.1478545389,0.5820648777,0,0.2307,1.0389,0.0276802521,0.7722756709,17.8951,1.3402,0,0,2.0312,0,0.0303384707,0.150313475,0,0.7484652042,0.0064493591,0.5923,0,0,0.1283052632,0.1377960656,25.9426,0.7534,0,0,0.4387,0,0.0406541555,1.8452451576,0.5864,0.4893200172,0.1716626279,0,0.3184,0,0.1294548387,0.4733618507,18.2093,0.5515,0.3228,0,0,0.0664,0.4098047847,0.2499545455,1.5342,0.5176592476,0.1070799906,0.4372,0.6387,0.5722,0.2660804094,0.5261704861,14.1208,0.5375,0,0,0.5744,0 +AOX2P,pDCs,0.4781466063,0.4170467354,0.4414,0.4737492704,0.3565408502,0.5517,0,0.0796,0.412552521,0.4103183472,0.3144,0.9158,27.8747,0.0821,0.2252,0.284,0.1993183758,0.706277112,0.5004,0.3545233705,0.3591186982,0.5447,0.3824,0.5591,0.3728026316,0.6697665574,0.1238,0.8053,12.7682,0,0.4933,0.7809,0.5864520107,0.2394095702,1.392,0.4212779301,0.4405468922,0.4536,0.2774,0.1878,0.3625403226,0.5711241801,0.192,0.2167,17.372,0,0.2413,0.2502,0.1207325359,0.414999596,0.2667,0.5697743507,0.5282612553,0.5589,0.5423,0.092,0.1994643275,0.7018316853,0.1958,0.4818,5.9215,0.0968,0.8507,0.1008 +AOX3P-AOX2P,pDCs,0,0,0,0,0,0,0,0,0,0,0,1.2036,8.9321,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.5982,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.3865,0,0,0 +AP001434.2,Neutrophils LD,0,0,0,0,0,0,0,4.068,0,0,102.338,0,0,0,0,0,0,0,0,0,0,0,0,2.0675,0,0,71.3528,0,0,0,0,0,0,0,0,0,0,0,0,1.1252,1.8062822581,0,171.275,0,0,0,0,0,0,0,0,0,0,0,0,3.4096,0,0,133.97,0,0,0,0,0 +AP002954.4,Monocytes C,0,0,0,2.8362035163,0.918672969,0,6.1855,26.372,14.804267227,16.78004593,0,6.9062,0,0,2.6969,0,2.4894344991,0,0,0.2752480104,0,4.413,7.587,23.8493,5.8135842105,2.1943081311,0,5.4589,0,0,4.0504,0.4648,0,2.601366533,0,3.8044648391,0,0.6831,1.5944,2.553,1.389766129,3.3808544762,0,0,0,0,0,1.5488,0,1.2438949495,0,7.0697216131,3.6110493157,0,18.1472,40.4817,21.346715205,8.6560170703,0,2.4049,0.4914,0,4.4482,0.6243 +APP,pDCs,26.154292308,39.410863918,2.0922,38.29736452,4.1452530937,8.4831,79.4856,111.5464,40.189404202,43.19780756,4.4179,10.6431,1074.6587,8.9086,3.0922,9.6603,7.5204430943,79.126707778,2.6964,28.632149562,1.1636544107,6.1349,59.9757,92.3832,43.438234211,23.014792066,3.6077,9.7511,455.6348,4.7911,2.9249,13.9514,17.138860858,21.958729513,2.8914,16.514346087,2.1636624705,4.1817,60.8063,62.845,20.395491935,20.938337493,3.1408,3.0252,618.7711,3.5374,1.8026,2.6035,5.177615311,34.741221212,1.5513,22.721485007,3.6250613497,6.9335,56.1784,88.1237,40.550032164,34.192178821,5.7279,5.6592,795.6522,2.5641,3.0971,8.989 +AQP9,Neutrophils LD,1.995459276,1.8396597938,0.7407,0.0206032293,0.1097854587,0,0,87.3198,32.907772689,0,868.7942,0.2677,0,0.2213,0,0.0443,3.42150492,5.0275987685,2.2715,0.0598283593,0.3320591857,0,4.9469,149.3906,56.530628947,0.1005320656,1249.6799,0.405,0.5449,0,0,0,11.995750938,16.368449799,0.5462,0.1803825983,0,0,5.1159,146.5726,33.903004839,0,1039.7343,0.1211,0.0381,0.768,0,0,3.83034689,8.1233165657,37.1541,0.0783597067,0,0,4.0833,159.9832,65.731894444,0.0306031067,1042.0624,2.1491,0.0893,1.9268,0,0 +ARHGEF10L,Monocytes C,1.0982027149,1.0747498282,0,0.0082080493,0,0.0404,31.6729,55.7456,31.717363866,0,0,0,0,1.4159,0,0,1.393220984,1.9886064026,0.2755,0.0048401188,0,0,28.6679,61.6034,23.945947368,0,0,0.3561,1.4886,0.3233,0,0,3.3283214477,6.4375522063,0,0.0752152761,0.0114656176,0,32.9718,55.6827,30.187562903,0,0.0379,0,0,1.0075,0.0712,0,1.6905952153,4.0948640404,0.1594,0,0,0,48.3106,67.3664,40.057879532,0,0.8737,0,0.0286,0.659,0,0 +ARL5C,pDCs,1.4118624434,1.1317257732,1.5141,1.1290232687,0.7108825702,0.5392,0.8838,1.9836,1.4170441176,1.7075636311,2.8239,0.306,10.2632,0.3309,0.4567,0.3077,1.3007060462,1.4312306374,0,1.838163712,2.3766938678,1.0378,1.9256,1.8748,1.5166842105,1.8398328525,0.7405,1.1838,29.5548,0.1859,1.5632,1.2076,1.0399983914,2.1338708309,2.3292,0.9203039333,0.3239425649,1.0964,1.4958,1.6094,3.5588870968,1.9904015423,1.3168,0.6551,30.6446,0.3242,1.0433,0.4922,3.188492823,1.5688381818,0,1.4215980059,0.9484845918,1.3985,1.124,1.4589,0.455627193,2.0533787707,3.2317,2.5506,33.6057,0.6086,2.9576,0.2919 +ARVCF,NK,0.7072199095,0.7526460481,0,1.8809015309,1.4688853767,0,1.077,0.4245,0.6061147059,0.7811848712,0,14.2574,0,0,2.6235,0,0.2950776526,0.7614721066,1.2107,0.7863071269,0.4327418949,0,0,0.6181,1.3308394737,1.8832108197,0,35.473,0.1452,0.0647,4.4941,0.0406,1.1759624665,0.0966711748,0,0.234283181,1.7426982691,0.2366,1.1537,0.4779,0.8547887097,0.2427814926,0,18.796,0,0.0109,1.0672,0,0.522437799,0.8125579798,0,0.3625964298,0.1969976404,0,0.0446,1.7237,0.558205848,0.2189055955,0,24.6148,0,0,9.1517,0.3199 +ASGR2,Monocytes C,1.4547352941,0.6490123711,0,0.0768688195,0.2898850156,0,54.368,85.6846,3.8876911765,0,0,0,0.1224,0.2448,0,0.1285,1.375559751,2.1081487793,0.7088,0.1010511433,0.0167966826,0,34.3556,74.9109,6.5907526316,0.1066579672,0,0.9446,3.237,0.2997,0,0.1002,3.8564136729,7.2065806304,0.6409,0.0272079923,0.1031793863,0,43.4939,55.8867,2.9207435484,0,0,0,0.1244,0.6882,0.1664,0.1598,2.3982813397,2.4715470707,0,0,0,0.2463,65.1796,59.9258,6.8156187135,0.1731514782,0,0,0,0.472,0,0.268 +ASIP,pDCs,0,0.1039175258,0,0,0,0,0.2494,0,0,0,0,0,15.2881,0,0,0,0.0312117368,0,0.4288,0,0,0,0,0,0,0,0,0,25.5057,0,0,0,0,0.4044575358,0,0,0,0,0,0,0,0,0,0,11.6575,0,0,0,0.2796406699,0.2450626263,0,0,0,0,0,0,0.4884137427,0,0,0,25.2663,0,0,0 +AZIN2,pDCs,1.2415683258,0.8243161512,33.6926,9.1277608241,6.3224160348,11.2182,0,0,0.037605042,20.062007221,0.2013,2.8998,93.5702,0.6036,3.0949,5.1,1.1428980439,0.0655996327,27.6841,7.0398405642,8.7284138728,9.6092,0,0,0.0097973684,35.300246557,0,6.7254,92.8674,0.8853,4.7996,10.9839,0.5035640751,2.6948033238,38.5767,8.9990590452,3.7974406766,11.6761,0.0335,0,0.3389645161,9.743661638,0,4.0799,132.3539,0.6785,6.8596,4.4872,4.170622488,2.4728983838,46.3075,6.7945442815,8.0933325625,10.1983,0.1136,0,0.0352067251,14.284453182,0,11.7922,130.3912,3.1881,4.8155,5.7407 +B3GNT5,Neutrophils LD,0.6947638009,1.08144811,0.6259,3.4898617928,0.297864451,0.527,7.2898,9.2125,3.0406222689,0.4994725729,64.2325,1.493,0.7664,0.7219,2.1052,0.3043,0.5675788975,0.3763563054,18.0922,4.5376715961,0.9811806233,4.4088,12.4988,14.0902,4.0588684211,1.6262872787,67.2563,1.3632,1.2157,0.0653,3.4574,2.5646,2.0658013405,2.8955144986,12.1982,2.7273723745,2.0457946499,2.0311,6.8684,20.3978,4.3873693548,0.8369403652,121.602,0.3524,1.2829,0.3298,1.2816,1.0038,0.3813205742,0.8537850505,2.4771,2.6336918934,1.065819278,4.4386,3.3158,5.3253,1.3756783626,0.5208085018,32.5275,2.1472,0.2066,0.1242,1.1857,1.166 +BAIAP3,B Memory,80.62578009,5.539467354,1.2262,12.696952255,10.43171057,5.1396,0.1465,0.3932,0.0136029412,0,8.4784,4.3388,0.5629,3.8581,7.8364,15.016,139.78025169,3.9314402953,1.5186,2.9399982257,1.3730487308,3.8871,0,0.7645,0.0547105263,0.5148428197,1.5022,4.5101,0.137,4.5317,11.1922,5.5075,55.194034048,3.0104443553,2.4771,4.6004412661,1.2584869394,2.1644,0.0574,0,0,0,7.5171,6.5247,0,4.0589,2.5448,1.0265,94.119858373,6.8884276768,3.8419,7.4553821284,4.1474307456,4.0755,0.4053,0.5482,0.0338230994,0.3319398864,19.2566,1.8375,0.744,5.0841,2.2964,6.0873 +BASP1,Neutrophils LD,8.0336153846,7.6133216495,12.3767,0.3291886497,0.1636897916,0.1521,14.0265,6.1779,1.5600991597,0.4936517875,68.1486,0.9294,0.8986,2.8623,0.3677,0.1538,2.8810740368,5.6601611235,9.61,0.1364790572,0.1830278965,0.3733,13.0929,2.0809,5.2023868421,0.2432162623,88.3372,0.3125,3.4072,2.3359,0.1152,0.0635,9.0828592493,1.065487106,13.1735,0.1714428354,0.421377498,0.1852,15.5761,3.0933,0.4919048387,0.2767753413,51.8848,0.0903,0.0661,1.9498,0.0977,0.1049,4.415354067,6.7033379798,8.2013,0.2052325224,0.1773162813,0.1537,7.3285,4.6618,3.1381611111,0.1446871555,87.2853,0.4575,0.4788,0.6442,0,0.0844 +BCL2A1,Neutrophils LD,19.670926244,11.292434364,18.3391,15.153832879,13.454612539,7.5617,17.2973,60.426,131.33218655,11.347280998,553.57,5.622,1.3427,0.9799,18.1163,8.4106,25.415054475,27.516325711,19.5909,17.68232291,9.2676390299,9.7442,122.8873,207.543,447.05839474,8.9724847213,828.693,8.3081,4.6247,0.9434,12.3255,12.4447,58.508093298,60.52341937,35.2744,14.177280281,30.585416444,5.7345,157.3274,267.2353,504.65551613,15.012661727,1940.235,5.159,1.8541,6.4296,10.8124,20.4967,39.472952153,21.424848889,14.0397,20.205760632,17.417697286,10.4319,9.4614,59.8171,165.20044678,12.890634124,659.576,5.6968,1.5321,2.2967,22.778,20.8298 +BCL11A,pDCs,399.99418235,709.99665498,4.9002,0.309075296,0.6446682915,0.186,239.5309,78.5168,85.183369748,1.5584397878,8.7803,4.8741,1791.4578,10.9452,1.0966,0.0891,459.94176497,635.31856372,1.8414,0.2371287899,0.2564456145,0.1023,158.4221,54.5439,73.950007895,2.187324918,9.9321,5.1816,1614.423,16.6792,0.4709,0.0503,402.75924987,674.65784229,2.6815,0.4248587671,0.1833194335,0.1854,204.7632,55.9842,71.809524194,1.5848425102,9.7294,9.7922,1672.3398,6.691,0.5502,0.1216,534.63837656,732.75660828,3.2723,0.5041582745,0.2943869986,0,216.9388,57.2997,82.476426023,3.1615683147,8.9224,3.5505,1659.4764,6.8405,0.3755,0.473 +BEND6,pDCs,0.6779728507,0.7939178694,3.6654,0.7927916816,0.2464976202,0.4307,3.9884,0.2972,0.6340823529,0.8676204957,0.6487,0.7658,22.104,0.2192,0.0834,0.4191,0.4004321873,0.6305281959,2.116,0.5418825984,0.7845567731,0.7786,4.3491,0.2044,0.4941078947,0.8999407213,0.176,0.8685,26.9758,0,0.9452,0.5478,0.3153739946,0.5622395415,8.4098,0.7101330817,0.4809426436,0.7139,1.0624,0.16,1.1768548387,0.868022496,0.5183,0.6557,18.1967,0.0301,0.5311,0.2855,0.4518416268,0.8186909091,2.4591,0.6459317961,0.5835446673,0.4178,3.5211,0.3471,0.5247377193,0.5788208786,0.724,0.397,20.6336,0.1368,0.5043,0.6718 +BHLHA15,Plasmablasts,0.6857628959,0,0,0,0,0,0,0,0.3555226891,0,0,0,1.1415,13.4448,0,0,0.0469907528,0,0,0,0,0,0,0,0,0,0,0,0.5913,6.5554,0,0,0,0.2699831519,0,0,0,0,0,0,0,0,0,0,0.6143,10.4304,0,0,0.9299976077,0.1367757576,0,0,0,0,0,0.2159,0,0,0,0,5.5861,13.7142,0,0 +BMX,Neutrophils LD,0,0,2.8197,0,0,0,0,0,0,0.088209236,19.9361,0,0,0,0,0,0,0,3.3236,0,0,0,0,0,0,0,17.6829,0,0,0,0,0,0,1.5518757593,5.5961,0,0,0,0,0.0921,0.16445,0,27.6369,0,0,0,0,0,0,0,4.3624,0,0,0,0,0,0,0,17.9888,0,0,0,0,0 +BNIP3P41,pDCs,0.2733375566,17.904304811,0,0,0,0,0,0,0,0,0,0.5953,87.6806,1.6792,0,0,0.2020105513,13.51577475,0,0,0,0,0,0,0,0,1.5633,0,88.2633,0,0,0,6.3502396783,30.835052951,0.3326,0,0,0,0,0,0.1779322581,0,0,0,88.6627,0,0,0,0,20.951454343,0,0,0,0,0,0,0,0,0.1874,0,120.6646,0.484,0,0 +BSPRY,pDCs,0.9215669683,1.436124055,0.6866,0.4253485109,1.3900942393,0.2273,0.0908,0.1954,0.1275739496,0.3611613845,0.923,2.195,23.138,0.2035,0.6182,0.4024,0.3873569057,0.1695156788,0.1549,0.401459807,0.3780084946,0.4461,0.1757,0.0481,0.2286131579,0.3982040656,1.6678,1.2025,3.5124,0.4982,1.1359,1.3765,0.2713624665,0.8711522636,0.2885,0.6040849954,0.8280827695,0.3458,0.2088,0.0527,0.4254983871,0.4483791349,1.0653,1.1422,5.8954,0.0209,0.2917,0.3918,0.3554105263,0.3645987879,0.0817,0.5008809018,0.9618599575,0.1721,0.343,0.2079,0.2889248538,0.1309674461,0.4771,1.6986,17.6796,0.1688,0.831,0.3151 +BTNL8,Neutrophils LD,0.0512470588,0.115780756,0.3224,0.1202663019,0.2214985885,0,1.1462,0.6811,0.3512079832,0.2415493358,113.8609,0.5919,0.2549,0,0,0,0.2557784825,0,0,0.1150758426,0.4773741895,0,0.5117,0.7857,0.5825842105,0.1310255082,150.2832,0.6504,0,0,0.1001,0.222,1.1854485255,1.0987585673,0.135,0.0356668918,0.2987982691,0,0.1943,0.4915,0.9342903226,0,77.5753,0.316,0.1155,0,0,0,0.2496339713,0.1831765657,0.1915,0.1877976496,0.1149152902,0.2692,0.1222,1.0955,0.5401809942,0.0531808752,170.7832,0.2286,0,0.1081,0.4498,0.1477 +C1orf186,pDCs,382.62610633,189.08538625,1096.9938,22.358097058,18.416561037,17.3791,52.8338,15.0671,7.2723932773,17.000003196,32.1096,28.6636,3154.5081,266.1376,18.6694,17.8297,122.31743254,90.13687444,1111.9829,15.383276756,14.962911862,16.7136,20.9324,7.2877,7.5518578947,15.931043803,41.3519,27.9938,1443.381,77.1755,15.5765,6.6325,318.07391635,288.01497931,558.4076,41.355100152,15.220728718,17.066,18.2105,9.6303,6.9506951613,17.074912976,59.0059,32.2824,2891.5908,195.6794,20.2839,13.2232,293.54287177,276.16998162,1432.7505,19.787300528,18.081862246,24.4476,21.4613,6.4691,5.5717836257,12.057082095,46.1122,20.2721,3018.3867,161.0458,18.7722,9.148 +C1QA,Monocytes NC+I,0.0870443439,0.4437652921,0,0,0,0,1.3217,2.9552,54.930753782,0,0,0,0,0.0636,0,0,0.0226221103,0.3782768527,0,0,0,0,3.6772,3.052,42.161713158,0,0,0,1.8652,0.3276,0,0,3.3042235925,2.0723459599,0.1531,0,0,0,11.8814,10.3022,220.59966129,0,0,0.248,0,0.3752,0,0,0.0321129187,1.7064862626,0,0,0,0,1.5253,0.1911,80.176181579,0,0.6593,0.1296,0,0.0745,0,0 +C5orf64,pDCs,0.6230235294,0.744504811,1.0804,1.0997936311,0.5025946496,0.4598,0.4292,1.1245,0.6800105042,0.7614149193,2.0097,3.1782,35.3938,0.2643,1.434,0.4375,0.4402977475,0.7470732877,0.9819,0.9827795546,1.5994404876,2.2671,0.7599,0.5025,0.8776105263,2.4239982295,0.4437,1.9181,34.8132,1.122,0.6541,0.4955,0.4764895442,1.9091598854,1.9829,1.0705580254,0.8133765539,0.3075,1.2349,0.6096,1.0316193548,0.9387147846,0.9156,0.2727,48.9091,0.0333,0.7771,0.2839,0.4190502392,0.9505715152,1.5433,0.6075097033,1.1197672959,0.8575,0.3485,0.5915,0.4934371345,0.4814875898,0.6377,1.2699,23.5565,0.0654,2.7885,0.5572 +C10orf105,Monocytes NC+I,0.2055122172,0.2088350515,0.1512,0.1278990731,0.0041276875,0.4801,3.8616,2.5157,12.913473529,0.132028537,6.5382,0.1028,0.7097,0,0,0,0,0.0352470436,0,0.2654954714,0,0.3212,2.9847,1.2368,14.485907895,0.0994407213,5.3429,0,0.5262,0.1893,0,0,0,0.3654778223,0.3817,0.0851378029,0.0210586153,0.0307,1.5749,0.9161,12.411191935,0,5.2045,0.1232,0.234,0,0,0.0691,0.0662511962,0.0112739394,0.1372,0.31121376,0.0253621992,0.6105,1.7257,1.9195,12.717947953,0.4567836479,7.2451,0.6412,0.334,0,0.2637,0.3055 +CA4,Neutrophils LD,0,0,0,0,0,0,0,0,0.1992693277,0,136.0601,0,0,0,0,0,0,0,0,0,0,0.1554,0,0,0.2778026316,0,90.9281,0,0,0,0.2374,0,0,0,0.1606,0,0,0,0,0,0,0,57.1024,0,0,0,0,0,1.3053650718,0.6916735354,0,0,0,0,0,0,0,0,119.518,0,0,0,1.2489,0 +CA6,Naive T cells,1.2687791855,1.4045670103,1.9509,2.6381116075,1.2853593304,1.2852,0.4294,1.2387,0.5255092437,28.472252853,0.968,1.2971,0.6925,0.0709,3.7491,2.5402,1.4976719028,3.3077045589,1.5074,2.8609095249,1.2179857502,1.4841,0.3721,1.0059,0.6528184211,30.314204,0.7953,2.2406,0.4994,0.3168,0.7879,1.8684,0.9834356568,0.7280497994,2.2061,3.7363988346,1.5400257278,1.2049,0.5963,0.3706,0.4248806452,20.716498653,0.5082,1.4535,1.7487,0.1849,0.968,1.1568,1.3566401914,1.4409769697,1.5179,2.6751255054,1.5584095564,1.9145,0.8347,0.7939,0.5812166667,14.682877251,0.9107,1.4789,0.5913,0.1008,1.2755,2.802 +CACNA1E,Neutrophils LD,0.9074,0.4451175258,0,0.0100938644,0,0,0,0,0,0,10.7575,0,0,0.0609,0,0,0.9036273266,0,0.0193,0.0074297105,0.0067328474,0,0,0,0.4898842105,0.0049766557,10.5706,0,0,0,0.0054,0,0.9137635389,0.435457765,0,0.0638314131,0.001997797,0,0,0,0,0.0101978904,17.4651,0,0,0,0,0,1.0057808612,0.0959343434,0,0,0,0,0,0,0.0370979532,0,14.1057,0.0061,0,0,0,0 +CACNA2D3,mDCs,0.1066004525,0,0,0.0064970398,0,0,97.2701,34.8132,3.9101231092,0,1.4375,0,8.6644,0.1851,0,0,0.0329281565,0.2388436298,0,0,0,0,75.4614,20.9107,2.8628,0,0,0.5709,17.8478,0,0,0,2.4554742627,1.6686295702,0,0,0,0,94.5886,11.9855,1.389033871,0,0,0.523,4.4172,0,0,0,0.6313631579,2.0868911111,0,0,0,0,83.2189,27.6988,4.9979584795,0,0,0.0298,9.5742,0.5635,0,0 +CAMP,Neutrophils LD,0,0.5302749141,3.1817,0,0,0,0.1735,0,0.4753554622,0,14.7103,0.9722,0.209,0.1833,0,0,0.0543532899,1.861080749,2.0823,0,0,0,0.6675,0.3671,0.4505736842,0,17.6369,0,2.1098,0.1926,0,0,4.6312608579,7.1517232665,2.2134,0,0,0,0,0,0.6796225806,0,28.516,0,0,0.2348,0,0,0.023230622,0.0583545455,4.0912,0,0,0,0,0.7985,0.3284128655,0,9.1832,0,0,0,0,0 +CAV1,Plasmablasts,1.1934570136,0.2518780069,0.1914,0.0565139337,0,0,0,0,0,0,0,0,0,30.8608,0.0957,0.3685,0.1057705987,0,1.5553,0,0,0,0,0,0,0,0,0,0,15.0322,0,0,1.3059552279,0.1180059599,0.0477,0,0,0,0,0,0,0,0,0,0,11.1025,0,0,5.814530622,2.2376377778,0.989,0.0793316864,0,0,0,0,0,0,0,0,0,24.2855,0,0 +CCDC50,pDCs,105.78681086,69.727416838,1.1922,16.228576785,17.893510487,7.1717,42.4242,23.7855,23.825752941,5.7153007355,12.7018,19.3437,619.6858,32.3312,6.8937,8.755,49.423075222,58.870865185,4.0971,7.9630564068,11.857154084,4.561,51.631,21.4764,24.870115789,4.4807020328,21.4328,15.0083,491.9111,28.2164,18.0039,7.5247,79.589237534,57.629912665,6.3223,8.6178564826,8.2643533438,5.2302,54.2944,24.372,28.02888871,6.0875260415,14.8646,14.9042,591.3794,36.7442,11.6554,10.0356,47.26634067,45.154058384,16.6553,13.584708278,13.235605356,4.4809,47.6166,21.4028,16.180982164,7.3727228829,10.0375,16.2699,630.813,25.4721,8.4698,11.6239 +CCDC149,Monocytes C,0.2340751131,0.6367127148,0.2704,0.1861539469,0.0509019531,0.0857,2.3908,13.7013,3.3896466387,0.2202217215,1.9495,0.2135,0.2855,0.0561,0.1309,0.4175,0.1079339063,0.2899858912,0.1347,0.1568851745,0.1951541845,0.1554,2.9614,6.4126,3.0607552632,0.1133881967,0.558,0.3104,1.7753,0.0487,0.1031,0.1074,0.7475238606,0.8447797135,0.1443,0.2058577937,0.1245730134,0.1416,1.352,8.6149,5.5124870968,0.1873945222,0.2247,0.0758,0.1326,0.0081,0.1431,0.0316,0.3367047847,0.9246606061,0.1302,0.158185541,0.1938703634,0.3362,3.7412,15.2924,2.928378655,0.2060072156,2.6307,0.1297,2.5885,0.1831,0.0259,0.2403 +CCDC183,pDCs,2.673161086,1.740442268,0.7734,2.5471862337,3.353403742,1.2729,2.9567,1.8218,3.1235336134,6.348220353,1.2427,0.5481,46.9751,0.8842,5.2461,1.8637,1.0162007113,1.3102676125,1.7356,1.4057455679,5.5145221664,2.5716,0.4852,2.2806,1.69165,4.3006032787,0,2.881,34.2561,1.0955,3.1253,2.5644,1.4399530831,2.249552149,1.2014,3.1994347173,0.9750905586,0.88,0.2298,1.3314,0.8824822581,1.7611246765,0,1.5532,49.381,0.8277,1.3778,0.4512,1.1061478469,3.4233480808,0,2.4841012951,2.0629835064,1.7559,1.2431,2.0703,0.7313070175,1.802206631,0.4111,1.5405,46.2462,0.7458,4.4748,3.2069 +CCL3L3,Monocytes C,2.372561086,4.8203869416,0.9858,0.6458154886,6.3149457574,10.0783,43.1534,301.6311,79.207034034,0.0876067576,85.2018,7.0147,0,1.0215,12.4099,5.3154,9.9404857143,9.7921309831,0,0.0472464143,1.4180499874,2.5307,50.9281,381.9789,84.759076316,0.0594334426,35.9473,1.9111,7.6834,1.7069,2.3361,1.9068,60.452217962,92.811735989,0.2091,0.4104843663,2.5981107002,0.9908,133.4289,519.7848,156.02899355,0,105.4562,1.5128,0,5.0175,1.7023,1.2133,18.23042201,12.475997374,2.3145,1.8647124786,14.652896838,41.492,49.787,220.611,85.78734269,0.4959472858,72.7779,17.2204,0,1.2283,37.622,36.1511 +CCNA1,Basophils LD,0.5669307692,0.4822749141,160.0392,0,0,0,0,0,0.444012605,0,0,0,0,0,0,0,0,1.4105988765,93.9891,0,0,0,0,0,0,0,0.3917,0,0,0.8845,0,0,0.2997957105,1.0428266476,178.2088,0,0,0,0,0,0.2256370968,0,0,0,0,0,0,0,0,0.2639183838,89.9335,0.0624180086,0,0.5422,0,0,0,0,0,0,0,0.1518,0,0 +CCNJL,Neutrophils LD,0.83569819,0.6121415808,1.3784,0.622681629,0.0657092565,0.3545,0.8398,0.6333,0.4332457983,0.1691325756,21.9035,5.745,0,0,0.5096,0.0541,0.0401627149,0.3163094418,1.5778,0.4719385672,0.4252861774,0.0415,0.2685,0,0.8687131579,0.4447483279,39.0328,4.4217,0.2644,0.3478,0,0,0.1081745308,0.2513465903,0,0.1089290558,0.321449882,0,0,0,0,0.457982184,27.6868,14.277,0,0.2733,0.4641,0,0.0393177033,0.7956715152,0.6299,0.1132560543,0.1422422133,0,0.3898,0.9789,0.9076909357,0.1502395691,46.2945,8.5663,1.0294,0,0.8288,0.1827 +CCR3,Basophils LD,2.8054936652,2.5626061856,3273.7734,3.0661470936,3.2356351551,0.0636,0.6175,0.1982,1.4430584034,0.0285953018,234.9622,4.5283,18.3153,1.6813,0.1539,0.0644,4.5070560166,1.5793951458,3740.5327,1.9515571269,1.7285712239,0.6227,0,0,1.1404394737,0.6769946885,62.2756,2.1792,19.7006,0.8314,2.3595,0.2658,4.3610689008,4.140537765,2227.3391,2.6712080519,0.7216173092,0,0,1.0032,2.1516241935,0.1151982804,49.7072,2.8913,12.9061,4.066,0,0,1.8636301435,0.7724624242,3265.9832,1.7017376276,2.4190613497,0,1.1478,0.5792,1.0422222222,0.0766303324,151.5181,0.1094,20.7823,0.1261,2.5295,0 +CCR4,CD4+ effector,0.5642357466,0.9499371134,1.5,43.42228505,4.4936136714,0.4239,0.5779,0.5459,0.7589773109,1.4480910939,1.2114,1.5739,0.7861,0.3153,1.4307,9.128,2.5167117961,0.5044619157,0.7293,43.042132784,4.9775971098,2.4924,0.63,0.4685,0.3181210526,2.2489482623,0.6736,1.0112,0.5944,0.0615,0.8448,3.4045,0.6019557641,1.0721766762,1.4099,41.845307886,4.1032699449,1.5732,0.3921,0.4426,0.7895983871,1.6846762631,0.6159,0.7593,0.3724,0.1205,2.499,1.2637,0.6487397129,0.8260294949,1.2777,46.368769869,6.8530081406,1.3216,0.279,0.7189,0.3155798246,2.9124811759,1.1699,1.092,0.6055,0.1019,1.1834,4.4826 +CCR7,Naive T cells,129.70064072,437.57221581,0.994,563.3380931,40.620696225,20.5618,4.9946,0.0588,1.290837395,1604.0301532,0.5196,56.8683,27.4506,0.509,83.8364,218.544,118.59576218,332.08584341,0.2752,531.19627796,14.357993717,30.4825,5.9074,0.4747,0.9445578947,1306.8540016,0.37,22.0731,43.2523,1.0813,38.909,136.298,136.42458418,284.97272086,0.819,536.75098825,7.3326059009,20.9422,11.7207,1.6729,1.1593306452,1361.9522163,0.7491,28.0691,29.6135,0.9433,44.7064,13.4972,92.876930144,372.80034263,0.394,524.01302125,19.75685899,13.0887,1.2513,0.1881,0.5284415205,1601.1304904,0.6695,7.9723,25.4787,0.2986,55.7957,174.9266 +CCR8,CD4+ effector,0,0,0,20.772507553,1.2669949122,0,0,0,0,0,0,0,0.1272,0,0,0,0,0.1032659993,0,22.355851618,3.0538353355,0,0,0,0,0,0,0.7786,0,0,4.7547,1.5134,0,0,0,8.6424476228,0.587591188,0,0,0,0,0,0,1.7705,0,0,0.6825,0,0,0,0,21.111666861,10.50781017,0,0,0,0,0,0,0,0,0,1.7868,0 +CD1B,mDCs,0,0,0,0,0,0,9.2308,1.0876,0.4612109244,0,0,0,0,0,0,0,0.0726918791,0,0,0,0,0,16.959,1.554,0.4869236842,0,0,0,0,0,0,0,0,0,0,0,0,0,13.6981,1.9631,0.7812919355,0,0,0,0,0,0,0,0,0.0665494949,0,0,0,0,15.7918,0.5306,0.4397833333,0,0,0,0,0,0,0 +CD1C,mDCs,93.172413122,112.25835567,0,0,0.149440407,0,834.044,34.2879,15.990971849,0.1711423776,0,0.2003,0,0.5923,0,0.1814,105.45051583,133.44396521,0,0.0345195991,0.0125018346,0,645.396,37.6235,21.395581579,0,0,3.7317,1.6738,4.7316,0,0.156,120.18807399,213.02834521,0,0.0292083433,0,0,864.185,16.588,7.8912774194,0.1629223719,1.2607,6.1044,0.1562,0.7138,0,0,91.003878947,131.68782909,0.5187,0.0690082985,0,0,984.426,19.5713,23.269088012,0,0.1892,3.0917,0,0.8785,0.1052,0 +CD1E,mDCs,0,0,0,0,0,0,70.8946,1.5904,3.514362605,0.0758571454,0,0,0,0,0,0,0,0,0,0.0185806088,0,0,113.1396,1.7014,6.5229368421,0,0,0,0,0,0,0,0,1.3028946132,0,0,0,0,133.3951,2.219,2.196316129,0,0,1.9837,0,0.6321,0,0,0.9431397129,1.1794335354,0,0,0,0,112.9858,3.3565,3.2922289474,0,0,0.5458,0,0,0,0 +CD8A,CD8 activated,0.1477963801,1.0474054983,0.0484,19.203907511,1403.7972937,623.8899,0.1291,0.0554,2.0537798319,368.42413409,0.1739,97.4335,0.0618,0,124.5391,89.9178,2.2784201541,1.1639576954,0.3516,16.338077409,1544.9876805,790.3093,0.3097,0.17,2.0199368421,399.7210617,0.785,204.2294,0.22,0.5725,291.162,46.0373,1.8705930295,1.3221488252,0.3274,23.913054695,1326.4359446,812.9891,0.3622,0,1.521783871,366.25690447,0.7251,119.3282,0,0.1082,89.9268,52.6689,1.3557602871,1.58738,0.3592,19.183189735,1644.6506285,497.4973,0.0763,0.0368,1.0003792398,370.80556917,0.0895,75.1474,0.3952,0.1916,166.0813,37.1945 +CD14,Monocytes C,13.651800905,30.451970103,0,1.1947171511,0,1.4295,103.8361,1583.4456,162.47607437,0.7160877552,271.3054,3.4123,0.8335,0.8396,0.827,0.4283,34.568327149,29.061613151,1.7809,0.8631751967,0.204499246,0,133.7817,1837.1331,107.48805789,1.351420459,425.0838,0.843,23.5101,4.2049,0.3007,0.7189,149.37646997,242.88353461,0.8873,2.6049959078,0.3345637293,0.4969,278.3987,1401.9524,57.031179032,1.2945040418,280.662,0.8193,0.069,18.4362,0.6659,0,73.310990431,103.14054141,4.2259,1.1211493113,0.2055185701,0.963,194.0897,1673.6976,246.55859064,0.8471883414,376.984,1.122,0,13.0457,0,0.5282 +CD36,Monocytes C,7.1644208145,15.969585911,0.6728,0.4698092333,0.314334991,0.4096,230.5897,709.9711,76.638926891,0.6234085049,0.8683,1.4176,394.548,1.2333,0.2294,0.2522,21.25095987,25.115393994,0.6985,0.3203953898,0.440647801,0.5114,285.1768,961.3451,107.59826316,0.5041557377,1.993,2.8471,555.0378,4.059,0.2532,0.5495,71.38169571,69.257811347,0.9569,0.4573253145,0.5207162864,0.3173,250.1086,674.7114,71.17631129,0.4019583407,2.8867,3.2502,286.1258,7.2157,0.0248,0.2532,21.998052153,47.676981414,2.5285,0.8667966285,0.263201463,0.5033,506.9875,1062.0052,159.32881637,0.706399215,4.4931,2.4891,413.0041,6.2888,0.4767,0.2146 +CD72,B Naive,103.96660452,452.81829863,2.1578,5.3562107583,13.413876186,19.6988,37.8637,4.4066,4.8091033613,5.4322635821,0.5312,16.402,17.2293,1.6601,12.4917,23.7169,44.058796621,295.28872977,7.5435,3.6854427988,6.0757770043,17.9494,19.0435,2.2805,2.3069973684,5.1285904262,0,26.2992,34.22,3.369,15.1741,1.8896,77.459190349,366.73422986,4.9295,8.4736281353,14.921208733,10.6627,34.3154,6.4096,4.7489870968,9.8602947704,0.5548,48.383,14.8253,1.5519,14.0046,14.4971,63.64576555,334.68875636,0.5426,6.1923669842,22.014859155,14.3035,34.5039,4.6167,3.3467397661,3.6879408719,0.1981,29.956,25.447,2.9403,17.7687,15.8528 +CD80,B Memory,16.67008733,2.2131103093,0,0.5678175756,0.1527162317,0,1.1566,0.352,1.4788672269,0,0,0.0992,0,1.7475,0,0,19.270624066,0.313020605,0,0.6066901707,0,0.457,1.0447,1.6775,1.0589763158,0.1583220984,0,0,0,0.3168,0.2026,0,9.4713632708,2.0026859026,0,0.2095187591,0.1299436664,0.4349,1.8155,1.2398,2.3605306452,0,0,0,0.0776,0.6957,0,0,6.4317660287,1.9494739394,0,0.4708098266,0.5817372581,0,0.6559,0.7445,1.2187874269,0,0,0.7138,0,0.1362,0,0 +CD177,Neutrophils LD,0,0,0.9363,0,0,0,0,0,0,0,42.1878,0,0,0,0,0,0.3110752816,0.5629189197,2.4875,0.2678237565,0,0,0,0,0,0.0558511475,83.6483,0,0,0,0,0,1.014175067,2.3068423496,0,0.0856380612,0,0.0689,0,0,0,0,26.1954,0,0,0,0,0,0,0,0.1626,0.0669362023,0,0,0,0,0,0,4.2322,0,0,0,0,0 +CD200,B Naive,11.574576471,147.57817354,5.6984,5.0536365925,3.4506015592,8.4671,2.0814,1.4525,0.956407563,7.1899469154,3.9144,0.2584,5.6539,1.4886,5.7128,0.7466,13.051408121,134.72783107,2.9262,4.0911675056,0.5158495099,9.7102,0.6412,1.0213,1.7967394737,4.4248994098,5.9688,3.0467,4.4378,1.2378,2.5028,0.4491,15.966159786,131.79052436,5.5345,4.5012911734,4.5858719119,7.9632,4.5171,0.5279,1.176383871,6.6828094664,2.7594,2.3662,2.6096,1.6467,2.9144,1.4876,14.562460287,109.64258242,4.5413,7.7906384499,5.1651638273,5.7768,1.2667,2.25,0.5472061404,7.3548119258,2.7857,0.9789,6.3858,1.9618,4.0579,3.1938 +CD207,mDCs,0,0,0,0,0,0,14.3798,0,0.1085609244,0,0,0,0,0,0,0,0,0,0,0,0,0,2.23,0,0.2021526316,0,0,0,0.308,0,0,0,0,0,0,0,0,0,19.2241,0,0,0,0,0,0.0597,0,0,0,0.0365473684,0.1312670707,0,0,0,0,20.9369,0.0629,0.2771464912,0.0275734926,0,0,0,0,0,0 +CD248,Naive T cells,0,0,0,0.2657154647,0.8061739045,0.8714,0,0,0.0288672269,39.277835197,0,0,0,0,3.0431,9.4531,0.3551843509,0.034840857,0,0.1859834892,0.8438644634,0.2375,0,0,0,49.605561902,0,0,0,0,0.8827,0.4044,0,0.1322004585,0,1.29219363,0.8464825334,1.7677,0,0.4936,0,12.97532824,0,0,0,0,0.559,0.4673,0,0,0,0.5139618995,0.3847804625,0.5375,0.0444,0,0,32.529883815,0,0,0,0,1.3611,2.523 +CDA,Neutrophils LD,1.2266773756,0.8774487973,20.4615,0,0,0,1.7118,76.4605,28.770134034,0,240.241,0.2353,0.1471,0.4439,0,0,2.2314553053,2.123743637,42.7287,0,0,0.2076,7.1928,89.3808,44.676494737,0.2076763934,359.4573,0,0.9149,0.4662,0,0,6.0647675603,11.077545501,8.7245,0.0942944709,0,0,1.0734,32.4094,5.5834612903,0,259.0851,0,0,0.389,0,0,8.9806205742,7.6177286869,34.8862,0,0,0,3.8882,107.6051,45.698880702,0,226.4137,0.1815,0,1.4403,0.2384,0 +CDC42EP1,Monocytes C,1.183640724,0.6864460481,1.368,0.2121488877,0.379934794,0.2601,7.1116,23.5044,2.6859340336,0,0.2543,0.3631,0.2571,0.0963,0,0.4614,0.778561233,1.3743015268,0.3329,0.3336020119,0.0613652928,0.2253,6.4427,14.6742,1.3346342105,0.1376636066,0.2942,1.0832,0.5006,0,0,0.1909,2.3298541555,0.6703264756,1.2349,0.1439412859,0.4401172305,0.2792,4.1583,8.2415,0.4688467742,0,0,1.3141,0.2189,0.1033,0.2953,0,0.1567464115,1.2882622222,0.8107,0.2481022271,0,0.359,5.4032,17.0113,1.9294116959,0.2877518624,0.4188,0,1.13,0.0286,0.335,0 +CDH1,pDCs,2.3913330317,1.9648580756,2.4955,2.0758539589,2.350829099,1.2107,10.9122,0.7761,1.8112008403,3.3318062049,0.9746,3.1277,37.303,2.7462,1.447,1.6919,1.6158823948,1.6109879078,0.7212,2.3394221158,1.5500330988,1.0295,2.2195,1.339,2.6058131579,3.3584029508,0.7751,1.6607,25.3901,0.1289,0.5399,1.0057,1.2638321716,2.466327851,1.2847,2.5059732155,0.7405739575,1.7129,8.9715,0.5471,1.024483871,2.2394732672,0.7726,2.3798,41.8058,0.7255,1.3795,0.7718,1.7180760766,1.6808246465,2.0565,2.3999340369,2.7765132138,0.6968,3.916,1.889,1.4694035088,1.7472791548,1.3448,1.1524,28.3616,1.1876,1.8911,1.7635 +CDK2AP1,Monocytes NC+I,1.3094493213,1.9312501718,2.9152,0.8205907368,0.0678893813,0.7385,30.6463,30.7355,101.4919479,0.2677394268,0.9489,2.1288,3.0428,0.7138,0,0.2476,0.5837747481,0.8152490169,1.584,1.2480311507,3.0317531289,2.298,24.8399,30.3633,48.462407895,0.7873791475,0,1.2754,15.2331,0.5454,0.6929,0.8556,4.7818707775,5.0198690544,3.9644,0.1113780426,1.240311251,1.0497,33.0665,22.6432,48.6885,0.8998206169,1.3072,0.3128,1.3859,0.058,1.7402,1.7566,3.8761660287,6.1325692929,2.57,0.478329891,1.3455617036,1.046,21.6085,25.8535,121.5552576,0.5129149157,6.1139,0.798,3.2451,0.0815,0.201,1.0938 +CDK15,Basophils LD,0.0254325792,0.0577,22.6298,0.0384247757,0.0549845232,0,0.08,0,0.0386588235,0,0.0356,0.0392,0.0315,0.014,0,0,0.0344836989,0.1745620742,23.88,0.015589265,0.0699478512,0.0646,0.0256,0.0279,0.0259921053,0,0.2257,0.0322,0,0,0,0,0.0279839142,1.5536220057,29.6646,0.0048754668,0.0556605035,0.04,0,0.0916,0,0.0202112037,0.031,0.0782,0,0,0.0422,0,0.0615267943,0.0403008081,27.4572,0.0384364284,0.0350340727,0,0.0316,0.0301,0.066555848,0.0231605144,0.1382,0,0,0,0,0 +CDKN1C,Monocytes NC+I,0.1093746606,0.13155189,0.2754,0.2677965495,0.0274085016,0,0.6275,0.1341,13.511896639,0.2007740751,0.2467,0,0.151,0,0.1374,0.1148,0.1265411381,0.0120944112,0.4651,0.1091492502,0.0210348329,0,1.2262,0.5313,27.917010526,0.0993540984,0.1454,0.1112,0.1174,0.0697,0.2572,0,0.1991798928,0.1470205158,0.8475,0.2831276718,0.0955074744,0.1919,0.1149,0,25.231587097,0.2376658217,0,0,0.1368,0.0414,0.2923,0,0.3153406699,0.4217836364,0.454,0.0356913863,0.0317791411,0,0,0.104,21.900885088,0.1104938701,0,0,0,0.0778,0.3553,0.35 +CEACAM3,Neutrophils LD,0.1039574661,0.425804811,3.1876,0.1118374178,0,0,6.7422,9.4909,9.2015957983,0.2030930775,514.2296,0,0.2994,0,0,0.7788,2.3626556017,2.5174996399,4.8235,0.1646700965,0.0106875094,0,3.3933,7.1101,64.3548,0,362.5392,0,0,0,0,0,0.9400785523,4.8931860745,5.426,0.1079028407,0,0,3.0644,3.4432,85.463509677,0.0836917568,271.2402,0,0,0.1108,0,0,1.2259311005,0.9244771717,2.5923,0.0273277325,0,0.7389,4.4898,5.6087,65.429165497,0.1996192417,459.14,0,0,0,0,0 +CEACAM4,Neutrophils LD,0.3751438914,1.087137457,0,4.3854240043,0,0,30.7752,77.2941,21.45075084,0,274.6363,0,0,0,0,0,0.473265086,1.5156554987,0,5.0640289235,0.0154359387,0,35.3689,59.6249,18.736905263,0.3745022951,306.1474,0.1139,0.2449,0.1552,0,0.1927,6.2778230563,4.9577601146,0,17.938076275,0,0,33.1805,13.2782,8.5757451613,1.7740235419,129.0975,0,0.6017,0.2116,0.8519,0,5.2719229665,5.5316961616,1.0972,6.3935646748,0,0,47.221,64.5678,24.237166959,0.5116799232,391.2541,0.5669,0.2947,0.3447,0,0 +CFAP58-AS1,Neutrophils LD,0.0381665158,0,1.2082,0.0338665172,1.6510961924,2.3838,3.6405,1.3497,0.2235819328,0,22.9257,2.4374,0.7623,0,0.2403,2.8055,0.1804125667,0.1544404465,0,0.3266052042,3.4610031918,7.0385,4.5714,9.5459,1.2028657895,0,38.2686,0.5832,0,0.7029,0,7.2925,2.1630563003,0,0.4037,0.2151502847,2.4534025177,0.4847,3.7586,2.9325,1.1311096774,0,53.3676,0,0,0,0,2.7449,0,0.6690416162,0,0.6020578497,13.132468098,17.5403,2.7461,1.4564,0.7060856725,0,40.8151,2.2218,0,0,5.6128,9.9473 +CFH,MAIT,0.0129411765,0.039095189,0.4465,15.54348795,23.055670934,35.6095,0.0535,1.4405,0.1658840336,0.0951685567,0.8159,0.6822,0.3504,0.0071,9.4055,4.0313,0.0042007113,0.0487845085,0,21.403749161,17.797701809,64.4614,0.2652,0.695,0.0338105263,0.7539548852,0.1076,0.9287,0.0874,0.0074,23.7858,13.6815,0.0069273458,0.0370041834,0.1187,16.356780777,5.0118114083,56.6417,0.0246,1.3468,0.1064419355,0.9704411984,0.0471,0,0.5595,0.0062,11.6482,0.758,0.0182727273,0.0734957576,0,18.465225594,14.101643228,33.0677,0.3619,1.3096,0.3978763158,0.5666771338,0.0883,2.3839,0.0292,0,12.3136,6.2615 +CH17-296N19.1,Neutrophils LD,0,0.1312285223,0,0,0,0,0,0,0,0,21.4115,0,0,0,0,0,0.0037716064,0,0,0,0,0,0,0,0,0,19.3261,0,0,0,0,0,0,0.48141851,0,0.0178128725,0,0,0,0,0,0,29.8898,0,0,0,0,0,0.0294736842,0.5125416162,0,0,0,0,0,0,0,0,30.4334,0,0,0,0,0 +CH17-373J23.1,Monocytes C,4.912499095,4.5916845361,4.1842,6.2224937388,1.1564624159,11.7706,19.3229,136.288,21.641577311,9.8813371044,1.8635,1.4181,18.7179,15.0693,5.103,7.5951,9.3797995258,17.37845099,8.09,4.5191789755,5.5162424981,35.1512,96.6475,379.706,84.648426316,4.0946287213,3.3105,7.4437,20.0017,7.4362,10.3197,14.7504,50.25308445,60.730828596,8.6073,10.029642312,4.0407196696,26.9556,158.241,513.904,56.674987097,6.0675478816,5.393,16.1584,42.2736,21.3372,12.1573,16.4798,6.8850239234,13.808561414,14.8281,7.3526296306,11.735436951,27.7679,12.5794,84.8044,23.689071345,7.317881226,0.8992,2.1886,15.6449,17.4416,14.4511,20.8206 +CHI3L1,Neutrophils LD,0.4140515837,0.3600178694,0.9043,0.3221505861,0.1627821927,0.4327,0.1296,0.6926,0.8864642857,0.4334727913,714.0988,0.5501,0.2329,0.1434,0.5724,0.334,2.7989400119,2.7196481239,1.1492,0.6166107053,0.5964236492,0.3453,0.3143,0.361,0.1986394737,1.1272872787,606.3414,1.1306,0.2026,0.7896,0.4142,0.7535,5.0471104558,5.9608848711,0.8237,0.3376411005,0.1422208497,0.3281,0.5295,0,0.4220725806,0.2889259883,135.5347,0.7272,0.0625,0.518,0,0.093,0.1282444976,1.6517331313,0.1993,0.4021037347,0.2733987494,0.0747,0.0618,0.8937,0.8340116959,0.2416500418,762.3977,0.7958,0,0.1676,0.8159,0.2285 +CIB2,pDCs,0.1015547511,1.0094501718,0.3877,0.4207079117,0.3017264238,0,0,0,0,1.367613426,0.3992,0,24.6633,0.0743,0.193,0,0,0.0114809867,0,0.5007124573,0,0,0.4728,0,0,0,0,0,16.5765,0,0,0,0,0.2351387966,0,0.3724320818,0.885840834,0,0.555,0,0,0,0,0,12.9087,0.2312,1.0279,0.1213,0.3191866029,0.6105692929,0,0.307317015,0.6867858188,0,0,0,0.0453362573,0.2603896776,0,0,23.2114,0,0,2.2287 +CKB,Monocytes NC+I,0.8394778281,1.369137457,9.0267,0.1415595742,0.0265140325,0,3.7472,2.6739,151.20497395,0,3.7144,0,0,0,0,0,0.4155114997,0.3358333669,7.0757,1.9961174239,0.7978212616,3.2614,7.2452,1.6087,163.03868947,0,1.0945,5.3289,1.4149,0.6875,1.9794,1.8249,2.2602252011,2.2396032665,7.5305,0.0834194478,0.2878106216,0.4363,4.0288,2.0632,132.78910645,1.6312352774,0,1.3866,0.6411,0.6925,1.4638,1.9284,2.311654067,3.8779981818,9.533,0.6766069622,1.7105180038,0,3.6321,1.269,221.41661228,0.9912179723,0.8689,2.9957,0.2932,0.5389,0.9623,3.771 +CLC,Basophils LD,2.2089742081,3.0684635739,4931.71,0.0149948332,0.0440997866,0.2762,0.2198,0.2331,0.6227806723,0,3.0152,3.3973,0.2652,0.9283,0,0,1.8583569057,4.0148865466,2998.6499,0.2860880104,0.0368251319,0.2723,0.4221,0.2325,2.0173026316,0,4.2704,0.8109,13.3721,0.3659,0,0,7.4448967828,9.7582419484,4966.0801,0.2281599788,0,0,0,0.2543,1.1835629032,0,7.6725,2.6297,0.7209,0.1971,0,0,1.9577947368,4.3960020202,7645.6802,0.1489094429,0.2403058518,0,0,0.7602,0.286498538,0.3898252881,1.7497,0,0.2429,0.5451,0,0 +CLCN5,pDCs,0.850579638,1.1019202749,0.1145,1.2473949647,0.2990422452,0.6448,15.9743,4.441,5.2858445378,0.5227497682,0.502,0.9267,37.1823,0.2613,0.0558,0.8893,2.87699016,3.4125142312,0.1425,0.377822294,1.8756919578,0.057,10.673,8.3155,3.5035736842,2.249706623,0.03,1.4523,31.8636,0.0109,0.4003,0.0888,0.0863045576,2.2345954728,0.0489,1.3866028274,1.8236494886,0.3383,8.5242,4.3872,1.4254225806,0.8254636235,0.3349,1.4872,32.8173,0.5085,0.7736,2.781,0.2288392344,1.5599793939,0.0668,0.5846876653,0.3085085654,1.4512,5.9216,3.9655,2.9114473684,0.7878507767,0.5315,1.2964,19.6446,0.0992,0.2337,0.2558 +CLDN10,Neutrophils LD,0,0.0633127148,0.0776,0.1508071762,0.1608721484,0,0,0.4189,0.0358193277,0.1410881029,13.415,0.1984,0,0,0,0.2975,0.0048556609,0.2603660497,0.0312,0.0516689504,0.2117693893,0.0606,0.6749,0.9469,1.4419815789,0.171148459,21.413,0.3366,1.5156,0,0.1496,0.601,0.0191420912,0,0,0.1187239637,0.0829771833,0.0007,0.5415,1.9969,0.9278725806,0.1911758376,7.1308,0.0457,0,0.0725,0,0,0.0007368421,0,0,0.0531117317,0,0,0,0.2925,0.2630976608,0,12.065,0.2734,0,0,0.1266,0.1937 +CLEC4C,pDCs,1.4622316742,2.9838512027,13.7228,0.814436449,0.5790614968,0.3119,3.7548,1.2481,0.6386584034,1.2563981724,8.4788,1.3936,354.5382,0.2633,0.8816,0.7347,1.6650546532,5.03916,1.5596,0.8417435709,1.0475594873,0.7169,0.4806,3.8681,1.5153684211,1.3827612459,0.6647,1.6319,358.0987,0.2345,0.5591,0.6077,2.7978844504,4.5163045272,15.2133,0.9930931665,0.6126890637,0.7695,3.4994,3.651,1.8853419355,1.0216361815,8.2513,0.4943,373.7706,0.6846,0.6673,0.7173,0.5011837321,3.1920274747,1.0487,0.9615759474,0.8783264512,0.8982,0.3815,2.8831,0.7977423977,0.8106881243,1.0919,0.7189,338.8848,0.3639,0.7527,0.2308 +CLEC5A,Monocytes C,0,0,0,0.1124655783,0,0,0.6497,9.4957,0.7359718487,0,0.5527,0,0,0,0,0,0.1015171903,0.3490500828,0,0.1598181069,0,0,2.8171,31.2414,1.0458236842,0,3.2856,0,0.2387,0,0,0,0.0764927614,2.6665506017,1.9563,0.0694560191,0.0910083399,0,3.0849,12.8871,1.4984806452,0.3782824322,3.5391,0,0,0,0,0,0.1752880383,2.4576274747,0,0.2046891592,0,0,1.2414,26.7834,1.5371345029,0.3817609988,1.3264,0,0,0.1021,0.7275,0 +CLEC6A,Monocytes C,0.2691040724,0.0260563574,0,0,0,0,0.5574,17.7041,0.3483659664,0,0.0841,0,0,0,0,0,0.7137983995,0.1210578322,0,0,0,0,1.6171,19.2131,0.6563921053,0,2.0805,0,0,0,0,0,1.7432136729,2.5986329513,0,0,0,0,5.0648,27.4378,0.3048709677,0,3.1226,0,0,0,0,0,0.5952899522,1.2955175758,0,0,0,0,4.6274,21.2194,1.0781538012,0,0.4073,0,0,0.2304,0,0 +CLEC9A,mDCs,0,0,0,0,0,0,23.0578,0.4499,0.3548319328,0.0336358295,5.2085,0.1664,0,0,0,0,0,0.0799723299,0,0,0,0,16.7604,0,0.7205552632,0,3.5713,0.2198,0,0,0,0,0,0,0,0,0,0,9.5926,0.3615,0,0,4.3006,0.3558,0,0,0,0,0,0,0,0,0,0,3.7266,0,0,0,5.9148,0.4505,0,0,0,0 +CLEC10A,mDCs,1.6235,1.6215969072,0,0,0,0,491.2149,26.378,18.292545798,0,0,0.3098,1.9056,0.1284,0,0,1.3623135744,0.5776733525,0,0.0116358575,0.0292276451,0,463.0144,33.5428,18.433436842,0,0,1.7884,45.7715,0,0,0,2.1500005362,4.1776402292,0.4717,0,0,0,501.9024,18.4678,5.993466129,0,0.1738,5.2099,0.6245,0,0,0,1.5941129187,2.8001434343,0,0.0450643459,0,0,473.5423,27.5199,24.124881579,0,0.7807,0.8746,5.7696,0.8534,0.347,0 +CLEC17A,B Naive,73.434879186,148.07348797,11.0323,0.25670903,0.1027173314,0.6762,43.9706,2.7482,2.8347802521,0.2319899438,15.7738,0.2734,0.2484,5.0299,0.1558,0.1874,52.062258566,138.38774989,11.5496,0.2560976021,0.1501766776,0.1876,9.9298,2.6576,1.7255447368,0.5824842623,17.9958,0.7167,1.4001,2.438,0.1409,0.1549,59.256858713,136.35971032,8.3671,0.2325986227,0.0590870181,0.0749,12.4773,1.2477,0.1856596774,0.1837575784,8.1389,0.0731,0,4.5959,0.0794,0.0853,45.371193301,114.21846202,9.8312,0.3997479134,0.0780492449,0.3206,34.7145,2.1415,2.2834818713,0.1709672791,16.8814,0.1617,0.0713,2.2062,0.3472,0 +CLIC2,mDCs,1.7583276018,2.3268006873,0.5782,1.0342323047,1.7591612342,0.7745,108.4498,4.2189,15.267055042,0.1042032183,1.2898,1.436,12.1873,0,0.4885,2.5584,0.1004481921,1.1684907382,0.5628,0.3503106162,0.9148745916,1.1489,71.981,5.7934,11.355373684,0.9755975738,1.5068,1.7272,9.2467,0.074,1.221,3.2341,3.7746495979,2.0271558166,0.2808,1.5841996623,1.5258998426,1.0709,80.5761,5.4494,20.74726129,1.1679450275,3.0954,1.8969,2.7569,0.2736,1.5386,0.8011,0.1311311005,1.3964777778,0,0.8905323374,0.4734099103,0.1425,94.9989,4.6336,10.093484503,0.268922148,0.8291,1.6119,7.4579,0.9953,0.9443,0.4003 +CMTM2,Neutrophils LD,0.5533452489,0.3608243986,0.4304,0.0613403062,0,0,0,3.5401,0.931039916,0,203.5143,0.3038,0,0,0,0,0.160387374,0.8511454375,0.4805,0.0537076837,0,0,0,2.9544,1.3969789474,0,366.3488,0,0.2615,0,0,0,0.5837426273,0.3152646418,0.7147,0.0266380612,0,0.2032,0.4987,1.6149,0.6596967742,0,478.8027,0,0,0,0,0,0,2.1845882828,0.5099,0.1357649489,0,0,0.976,1.9358,0.1746663743,0,197.118,0,0,0,0.5646,0 +CNTN4-AS1,Basophils LD,0,0.0268261168,17.5659,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.3858,0.0499722049,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5983827507,14.915,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3236,0,0,0,0,0,0,0,0,0,0,0,0.6531,0 +COCH,B Memory,235.48715611,12.853840893,0.1434,1.2246401627,0.4218139012,0,0.2735,0.306,0.9389533613,0.4087770527,0,0,0.6823,23.6018,0.1283,0,328.52319816,4.6279416133,0.2426,0.5989360653,1.0807031415,0.4814,0,2.5986,0.1869210526,1.9793373115,1.483,0,1.5783,27.6722,2.2244,0,243.17242145,19.162037421,1.539,0.3054196596,0.2887143981,0.2379,0,1.6007,0,1.7557091119,0,0.1409,1.4106,28.8314,0.2622,0,269.29356411,11.079581616,1.1463,0.6133357089,0.619218051,0.1198,0,2.0139,0.257348538,0.4848898781,0.1246,0.1618,1.1794,40.0009,0,4.0653 +COL4A3,B Memory,79.114190498,16.996620275,0,0.5143225511,0.5285490563,2.5089,2.3184,0,0.6894432773,1.9470681822,0,0,0,8.4735,0,0.0726,82.139745821,6.5396430609,0.0195,0.7235815145,0.2816031164,1.1748,0.1,0,0.3759552632,0.8578107541,0,0.1263,0.4307,11.1,0.2781,0,66.84816622,15.223005845,0.0437,0.5156738445,0,0.4624,0.5659,0,0.0950758065,0.0609675589,0,2.1933,0.0374,7.2,0,0,68.981908134,34.611123636,0,0.7660926814,0.1648083766,0.8383,0.0394,0,0.1497222222,1.1417602138,0,1.2203,0.3329,12.107,0.0161,0.8704 +COL4A4,B Memory,16.72850905,3.0803852234,0.0281,0.2651911195,0.0047103233,0.3735,0.1586,0.0201,0.0977731092,0.2170080547,0,0.2588,0.3801,5.5112,0.0141,0.0827,20.67487297,1.118131264,0.0159,0.3195752784,0.0172077406,0.0114,0.0181,0.1581,0.1806842105,0.4902474754,0,0.3426,0.3315,4.597,0,0.1658,7.5526466488,2.7279291117,0,0.1303969739,0.02627262,0,0.3269,0.13,0.047816129,0.1108191278,0,0.3741,0.5475,2.9246,0.1496,0.1448,12.3817,3.0090567677,0.2847,0.1055872816,0.195458235,0.0235,0.3634,0.0213,0.0477745614,0.0328963755,0,0.3709,0.1123,4.9869,0.0524,0.0647 +COL9A2,mDCs,16.454057014,8.8552453608,0.8582,1.5960974345,0.0600771377,3.745,50.9717,1.9536,0.5527621849,1.6612416332,5.2398,0.3327,0.4924,1.5594,0.3031,1.0572,0.8406123889,8.33004157,1.267,1.1389411878,0.4551330736,0.3458,13.4188,2.6379,3.6994210526,4.1310382295,7.8157,2.3886,0,5.009,0.2779,0,2.7713849866,0.4770512321,1.7484,0.6665072375,0.0343751377,0,12.5647,4.5262,3.1308016129,2.5364590498,5.9677,3.7136,0,0.4737,0,0.8012,0.9109076555,6.9851066667,0.745,1.2893747893,1.0127398537,0.1297,13.973,3.1301,5.5763605263,1.5203739937,9.4251,0.6472,0.2249,0.0424,0.0482,0 +COL13A1,NK,0,0,0,0,0,0,0,0,0,0,2.8939,6.3476,0,0,0,0,0,0,0,0,0,0,0,0,1.0507973684,0,0,6.0084,0,0,0,0,0,0,0,0,0,0,0.211,0,0,0,1.7174,15.3346,0,0,1.0168,1.9395,0,0,0,0,0,0,0,0,0,0,1.5261,12.7393,0,0,0,1.2011 +COL19A1,B Naive,16.268135747,70.673076976,0.2745,0.8836817127,0.2943102905,0.3542,0.46,0.8594,0.7622966387,0.645704306,1.0856,1.023,0.7745,1.2679,0.7984,0.7281,10.590021755,114.47356755,1.8052,0.6275862064,0.633637899,0.6207,0.0697,0.3831,0.5300684211,2.060628459,0.4479,1.2593,0.0682,0.3681,0.5735,0.684,18.101007507,25.964602693,1.4788,0.9038625149,1.139213454,1.0671,0.179,0.6426,0.4243209677,0.734271654,1.1042,0.855,0.5262,0.735,0.2293,0.1209,6.637708134,66.30998404,0.9005,0.6521322141,0.8724777961,0.8094,0.1609,0.6139,0.5501988304,0.9380858193,1.1965,0.9485,0.4824,1.0903,1.2622,0.234 +COL24A1,pDCs,0.1381520362,0.2238415808,0,0.0449582048,0,0.0586,0.0311,0.8492,0.3017777311,0.035644914,0.021,0.1293,22.6624,2.093,0,0,0.0206441612,0.0789245733,0.0265,0.1590421826,0.0284082433,0,0.106,1.266,1.4839289474,0.0579054426,0.0256,0.5009,15.5219,0.5662,0.033,0,2.5266882038,0.0807125501,0.0835,0.4730843862,0,0.0244,0.0146,0.1388,0.5033564516,0,0,0.1421,9.7349,0.8605,0,0,0.0632976077,0.0052135354,0,0.2557512026,0.3513124587,0.0392,0,0.696,0.1008836257,0,0,0.4352,19.4924,1.4786,0.0218,0 +COLQ,MAIT,2.9028742081,4.226647079,3.0971,17.014297333,6.420555572,163.6324,0.9901,2.725,1.1878861345,5.1135187572,0.5853,54.2454,6.5052,0.8113,11.0104,28.1278,3.1204323059,0.9620547209,8.5721,11.324484714,3.5266602413,200.5925,0.2144,2.2478,1.1668236842,17.406937705,0.9003,47.3576,5.2508,1.9701,16.0733,20.2492,1.2902450402,1.6592429799,3.9003,25.947746179,5.641145712,229.3725,1.5408,2.5484,1.6491080645,7.4204501507,0.586,77.3954,5.1067,1.1008,18.0047,55.0099,1.788962201,1.3138234343,3.3027,20.098208367,7.6680683577,135.1686,1.7598,3.5353,2.6872049708,3.2030754468,0.3351,58.2976,2.1202,1.4411,7.3504,22.7258 +CORO6,Basophils LD,1.0325615385,0.9344257732,171.9898,3.2267599928,5.3057251928,7.2455,0,0.7064,0.7539815126,1.7961565526,0.4632,4.8989,0,0.2226,9.9088,9.6804,0.0171641968,0.7360744761,72.9145,3.5227639941,3.9821167379,1.9634,0.7416,0,0.5300052632,1.5952967213,3.5035,4.7579,0.6356,0,1.9283,5.0653,2.8784820375,1.6240614327,401.4226,3.8092895047,7.646311487,2.7706,0,0,0.6300532258,1.6980700762,0.0984,7.896,0.2426,0.0347,9.9771,5.6792,1.3930842105,1.107470303,144.7167,2.1204683615,11.05450538,4.2412,0.392,0.2288,0.5270207602,2.2185480541,0.3952,4.0818,1.6522,1.2925,10.7738,3.0428 +CPA3,Basophils LD,1.0146475113,0.863685567,1283.6899,0.0222976737,0,0,0,0.2959,0.7471638655,0,0.6238,1.5191,0,0.0304,0,0,2.273234914,2.1122091178,1323.86,0.0992884781,0,0,0,0.3043,0.2721710526,0.0710015738,0.2757,1.38,1.2899,0.6217,0,0,0,4.7492836103,884.943,0.0634592173,0,0,1.0168,0,1.0063177419,0.0424382911,0,3.3577,2.7619,0.1246,0,0,0.0765511962,0.0947963636,1246.0238,0,0.0145581642,0,0,0,0,0,0,0,0,0,0.162,0 +CPNE5,B Memory,213.15410317,106.81800515,0.5751,0.6472124686,0.0981830297,0.0487,0.1383,1.7141,0.9332613445,0.0998412231,3.0978,0.1623,0.598,102.4361,0.0698,0,335.13321494,92.948621548,0,0.6423771641,0.6736967077,0.1726,2.0118,1.953,1.8155052632,0.0740206557,1.6285,1.0106,0.5342,91.6284,0.1053,1.3452,224.23827721,125.7908557,0.6232,0.5162196795,0.1100189614,0.8588,3.6299,4.5759,1.3357741935,0.0862229746,1.4283,0.0579,0.2541,100.7413,0,0.0672,375.83850096,114.4517501,0.7922,0.4398874323,0.4753896885,0,0,1.6808,2.0830362573,0.0403357274,1.2197,0.1752,0.6742,104.9378,0.8093,0.0541 +CPNE7,MAIT,0,0,0,2.10431025,0.2000656491,16.255,0,0,0,0,0,0.8493,0,0.3362,2.1451,2.4476,0.002597214,0,0,2.4426063252,0.5409240513,15.7691,0,0,0,0,0,0.0506,0,0,0.3141,5.3299,0.3552225201,0,0,10.883531532,1.9656118804,22.118,0,0,0,0,0,0,0,0,0,3.5962,0,0.2310892929,0,5.5096020832,1.5023969797,31.3233,0.0474,0,0,0.6479218974,0,0,0,0.0256,1.1474,5.3263 +CR2,B Naive,41.247749321,93.965757732,1.5873,2.8784769346,1.5002130313,0.3042,0.2973,0.6343,0.7282021008,17.997608804,0.4394,2.0829,1.4315,5.108,15.7069,3.1524,52.964814938,131.14461874,1.5279,4.6042713437,0.0747275195,0.9176,0,0,1.3641947368,12.493944262,0.2394,0.5476,0.2781,2.4736,1.8315,0.3113,57.428237802,115.85903553,1.3267,7.221147305,0.6567129032,4.883,0.5896,0.6886,1.1341048387,15.912496455,0.6823,0.887,1.1364,5.6428,6.9295,1.5461,46.275935885,61.695556364,0.6273,5.1118440005,6.2011394526,4.298,0.1976,0.3418,0.3563906433,16.463379923,0.3929,0.8023,0.8187,9.8202,12.1017,1.9138 +CREB5,Neutrophils LD,3.0649855204,3.0994226804,2.5175,0.1333855878,0.1721377482,0.2021,101.8865,164.6546,28.445632773,0.3368709414,804.1514,0.1848,20.0693,0.632,0.0857,0.1965,7.5077887374,7.3610809723,5.3587,0.1578219673,0.458618849,0.1279,76.177,102.8769,16.220705263,0.2544217705,805.4904,0.3559,5.3403,0.2791,0.0865,0.118,13.533444772,17.767454212,2.372,0.1637890213,0.1084543666,0.0701,61.1714,154.9304,14.25573871,0.1853745258,456.203,0.8587,4.8109,0.5856,0.0583,0.0191,8.5971555024,7.5405717172,0.3018,0.2133178853,0.1416148655,1.2714,119.8478,186.626,28.173958187,0.4209145816,553.9119,0.1949,2.8837,0.8458,0.1711,0.1275 +CROCC2,Monocytes NC+I,0,0.2855333333,0,0,0,0,0.0608,0.2076,18.427828992,0,0.0254,0,0,0.0208,0,0,0.2455946651,0.0684160173,0.3694,0.0314603489,0,0,0.3753,0.6034,19.278831579,0,0.3482,0.1928,0,0,0,0,0.0828922252,0.1527412034,0,0,0,0,0.1959,0.2872,18.03468871,0,0.2503,0.3217,0,0,0,0.3737,0.0092976077,0.3173175758,0,0.3113050572,0,0,0,0.1401,12.947398246,0.19069691,0.5937,0.094,0,0.0434,0,0 +CRYM,pDCs,0.2658809955,3.7471886598,0,0.0052153391,0,0,0.139,0,0,0,0,0,90.5253,0.0383,0,0,0.6535909899,2.5491771408,0,0.0335148033,0,0.0945,0,0,0,0,0,0,90.5003,0,0,0,1.5519329759,2.0343778223,0,0,0,0,0.1271,0.3359,0,0,0.1624,0,123.1522,0,0,0,0.7441760766,1.2614355556,0,0.0311555266,0,0,0,0,0,0,0,0,29.0932,0,0,0 +CRYM-AS1,pDCs,0.7549361991,2.5778876289,1.4218,1.1091843201,0.6961821763,0.3528,1.7112,1.2932,1.039839916,1.1675571454,1.1567,0.714,44.883,0.1948,1.2225,1.3303,0.6362915827,1.5055568959,1.4806,0.5659701336,0.3436651671,0.4714,2.5764,0.6772,0.6328631579,1.8991182951,1.101,1.3313,27.5853,0.2213,0.8882,0.4462,1.3797458445,3.1872582235,1.0473,0.999184075,0.8769912667,0.7945,0.8669,0.4261,0.3706709677,0.9533581634,0.3694,0.4519,32.9962,0.1036,1.5045,1.2474,0.2746497608,1.5248175758,1.3122,1.1393352909,0.954079613,0.3626,0.496,3.9955,0.3615397661,1.1395907967,1.6605,0.8262,35.2945,0.137,0.1078,0.3182 +CSF1R,Monocytes NC+I,11.045382353,14.810628179,4.4019,3.0494846789,2.4985316593,1.0664,385.3864,403.8613,1309.7035235,0.9956029286,70.128,4.0969,82.9661,2.3834,3.2225,0.9682,13.057906876,22.126066878,12.3437,2.3531921307,4.9313438552,3.1963,240.8297,357.1428,1154.1496263,0.9842116721,37.7588,10.4818,110.7338,1.4833,0.243,2.5958,31.171245576,59.391222808,6.8677,2.6769812872,1.7882431157,2.3307,355.4351,415.3122,2119.1940984,4.5043271051,34.2511,2.5289,50.7395,5.355,3.6843,0.943,31.391820096,38.360190303,9.3716,2.7349636264,3.5101898301,6.1591,450.1152,454.0559,1518.3143892,4.2949928178,65.0407,8.3821,113.3145,5.1149,1.8681,2.4706 +CSF2RBP1,Neutrophils LD,0,0,0.822,0,0,0,0,0,0.3964021008,0,47.4027,0,0,0,0,0,0.0325366924,0,0,0,0,0,0,0,0,0,31.0211,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25.4811,0,0,0,0,0,0,0.1782923232,0,0,0,0,0,0,0,0,58.5485,0,0,0.6538,0,0 +CSF3R,Neutrophils LD,12.923113575,21.739717869,3.325,0.9934504126,0.6536471689,0.7622,279.0218,847.7866,123.37476555,0.9386821298,5495.2568,5.7837,17.2817,1.3938,1.8298,0.5021,17.56846882,34.339746172,4.1295,1.2582861321,1.4401846193,0.7752,323.1684,770.8546,122.12482895,2.5592531148,7693.3979,1.9785,26.779,2.6225,0.5252,1.3948,58.73219571,112.28955129,3.6331,0.8978301947,1.0636303698,0.2266,273.4291,806.5759,102.92015161,1.0958315724,4532.5342,5.6638,10.7158,11.7165,0.6045,0.5107,40.669028708,59.600191111,7.9383,1.2324038991,0.7557410335,1.007,407.44,1055.9523,172.33345585,0.946426925,6647.1411,5.9294,16.2318,12.7088,0.9572,1.0881 +CST3,mDCs,11.145800905,19.808606873,5.3463,0.3400302237,7.0433091088,0.3731,1902.6479,710.0261,779.20263361,0.1359026968,59.4741,11.4901,402.7911,1.6779,4.6025,3.2393,8.8768432721,29.620575196,1.3144,0.6303667706,4.4964245539,0.581,1873.3892,566.1866,581.75168684,0.6245758033,60.4428,14.1023,1103.3826,3.1506,4.9102,2.4783,80.617625737,100.37782963,4.9322,1.3794669249,9.0337434304,1.5197,1482.7009,550.8657,849.30931613,0.7428855168,75.4257,24.1473,261.6531,7.5373,4.1261,10.1149,45.040456459,85.895496364,3.4408,0.6383985404,3.4028194195,0.3784,1780.2379,785.9358,1136.7022035,0.6826058794,77.0977,21.8315,483.1403,7.1906,7.6961,2.939 +CTA-286B10.7,Neutrophils LD,0,0.3022845361,1.2309,0.6214521887,0,0,0,3.8024,0.5650210084,0.3769151912,150.644,0,0,0,0,2.1696,2.5270705394,0.7589959237,9.1857,0.4169617446,0,0.7403,0.8222,1.1377,0.7177552632,0,213.583,0,0,0,0.4568,0,0.6108150134,0.2125189685,4.3566,0.5373577804,0,0,0.4314,0,0.7707322581,0.7873602021,102.435,0,0,0,0,0,1.0776861244,0.4818036364,0,1.2985623e-8,0,0,0,1.4234,0.7834350877,0,156.243,1.7634,0,0,0.8217,0 +CTB-50L17.14,Neutrophils LD,0.7587190045,0.858685567,1.5053,0.573122593,1.0443787297,0.1387,0.9103,0.3816,0.0959033613,0.4477345725,11.236,0,0.6515,0.4204,0,0.5422,0,1.1985920922,2.1386,0.2886433408,0.4185947977,0.4085,0,0.5225,1.5446868421,0.622926623,16.9358,0.4992,0.2407,0,0.3579,0.3876,1.4722686327,0.7720165616,1.572,0.5284930208,0.3429615264,0.8505,0.6235,0.8574,0.331016129,0.6083867754,12.2676,0,0.6001,0.44,0.1911,0.5006,0.0585990431,0.5904230303,0.3943,0.2496038169,0.3758034686,0.844,0.2407,1.0492,0.5575596491,0.5967724236,19.9878,0.3933,0.0002,0.201,1.0024,0.1794 +CTB-61M7.2,Neutrophils LD,1.9061217195,4.8178556701,1.5584,1.294334948,0.1270116855,0,3.5381,165.105,87.064021008,1.4085976732,289.772,0.5757,0.5662,0.0244,0,3.556,3.9019648488,4.475525776,2.0167,0.6486857387,1.4232970344,2.8838,0.6792,151.316,14.099944737,2.7103379672,486.506,1.4364,0.9748,0.6792,0,0.5489,23.436575335,33.982575129,2.7478,0.6758101841,1.9086424076,0.9394,9.8443,237.265,95.816579032,1.2670715831,472.758,3.0281,1.2293,2.6374,2.9805,0.1512,11.003725837,5.8652721212,0.8682,0.8860991366,2.4154364323,1.9051,4.9944,180.988,71.602759064,2.1046814097,281.703,2.0019,0.4315,2.022,0.3506,3.8036 +CTB-180A7.6,Neutrophils LD,1.3460809955,1.7097030928,6.8153,2.1897653989,0,3.2735,0,1.0218,0,3.698616894,74.6989,0,3.3549,0,4.0173,0,0.0486243628,2.5911821534,0,0.4740618189,7.0022519477,5.034,9.6308,2.6952,0,5.3158127213,99.7162,4.4382,2.0414,0,0,3.7631,0.1175348525,7.4420394842,7.5106,5.3291647927,2.7163265146,9.1729,0,1.3907,0,3.8083591385,76.1587,1.7569,4.9915,0.4208,2.9389,5.5202,1.7280736842,0.3414733333,0.0001,2.6834498458,6.2611634969,0,3.3938,2.3906,1.6212008772,0,63.6755,3.6431,2.4558,0,0,3.0569 +CTC-510F12.4,Neutrophils LD,0.2001461538,0,53.6903,0,0,0,2.2413,13.3517,3.877560084,0,135.6929,0,0,0,0,0.3546,0.0525615886,0.5456773497,35.6708,0,0,0,4.015,21.9287,9.9740078947,0,175.4803,0.345,0,0,0,0,1.1710402145,0.7771923209,28.7445,0,0,0,3.3047,2.9158,2.3923548387,0,60.5143,0,0,0,0,0,1.3716909091,0.9425614141,48.5306,0,0,0,3.9161,11.595,7.3666809942,0,141.6951,0,0,0.1738,0,0 +CTC-546K23.1,Basophils LD,0.2537090498,0,10.2311,0.5168076366,0.0247907435,0,0,0,0.2047294118,0,0,0,0,0,0,0.3132,0,0.0490905438,10.3289,0.2785920787,0.0414459915,0,0,0,0,0.1813794098,0,0.3044,0.2315,0,0,0,0,0.1316750716,15.3477,0.1846147199,0.2611581432,0,0,0,0.6647403226,0,0,0,0,0,0,0,0,0,3.8473,0,0.2708717555,0.3487,0,0,0,0,0.6572,0,0,0,0,0 +CTD-2003C8.2,pDCs,1.4963737557,1.4951835052,3.9889,1.534615955,0.6339199902,3.7875,5.5058,1.1072,2.3887210084,1.1367110413,0.6039,0,28.0812,1.3583,4.2421,2.8292,2.5501885003,0.1316873317,0,2.1535395694,0.79807238,1.1412,3.4435,5.7758,3.4819921053,1.709296918,2.9706,1.3533,25.4644,0.5314,0.4952,0.9992,1.7413356568,0.2939210888,0,1.5591144881,1.4551499607,1.7913,3.4633,5.2741,0.7988919355,3.3538222656,2.1916,0.6662,37.3468,0.7791,4.2342,0.8125,2.6117229665,5.3753379798,0,1.875059275,1.180997546,0.6903,0.582,2.0566,1.2031099415,1.0652734926,1.7595,0.5765,24.5492,0,1.5622,1.2405 +CTD-2006K23.1,Monocytes NC+I,1.1330248869,2.2783601375,0.0429,0.0846224016,0.0028639422,0,44.5473,27.9207,169.88373361,0.0179336186,24.3887,0.0771,0.3608,0,0.0424,0,0.5581449318,3.7030194671,2.7603,0.0387080624,0.0918190249,0,66.0777,34.5894,126.66097368,0.0210881311,9.9185,0.3677,1.4962,0.4968,0.0265,0.3131,4.7210101877,3.8251676791,0.382,0.0762749702,0.3251333596,0,73.3887,32.9086,156.59033871,0.0613954973,15.9118,1.0275,0,0,0,0,3.5479368421,9.1763672727,1.0803,0.0160900911,0,0.0357,88.9457,40.7314,222.66097105,0,34.7103,3.0491,0.6079,0.3703,0.0398,0 +CTD-2311M21.5,Basophils LD,0,0,21.2239,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26.2911,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.8377,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.5921,0,0,0,0,0,0,0,0,0,0,0,0,0 +CTD-2509G16.5,B Memory,59.694049774,21.961701718,0.9065,1.9217804688,0.0864680781,1.0679,0.3605,2.4585,2.481760084,1.7828079656,2.162,0.5135,1.78,5.3376,0.3924,1.7264,79.919282395,8.4303411883,0,1.8692168968,1.4736226187,0.9536,0.6104,3.1155,1.9850815789,4.3242663607,1.6661,0.3861,3.1097,8.53,0.9951,1.3609,38.477382842,14.045561547,0.4153,1.3218241888,3.208531786,2.5959,3.114,0.5695,0.3317096774,1.2366599539,4.5631,0.4748,0.8583,2.143,0.4627,0,54.772081818,21.815193737,4.2659,1.7556495717,0.8869350637,1.4792,2.2796,2.7369,0.4413780702,1.9140273426,1.6732,1.7136,1.7127,6.1042,0.9092,0.9169 +CTD-2530H12.2,Neutrophils LD,0.1758782805,0,0.223,0,0,0,0,2.1844,0,0,21.7639,0,0,0,0,0,0.0825387078,0,0.2505,0,0.1951673787,0,0,1.5474,0.5871289474,0,75.3558,0,0,0,0,0,0,1.1854326648,1.4916,0,0,0,0,0.6775,0.1896112903,0,48.4853,0,0.319,0,0,0,2.1167473684,0.3021535354,0.2649,0.0212747139,0,0,0,1.3449,0,0,51.597,0,0,0,0,0 +CTD-2531D15.4,Basophils LD,0,0,15.0187,0.1382116314,0,0,0.6296,1.6591,0.2103466387,0,0,0,0,0,0,0,0,0,37.6569,0.1381622272,0.4751907012,1.1745,2.7122,0.9993,0.9531131579,0,0,0,0,0,0,0,2.6293941019,0,54.163,0.4914711892,0,0,2.8159,1.0921,4.3441725806,0,0,0,0,0,0.5141,0,0,0,10.3663,0,0,0,4.777,0.3641,1.1415084795,0,0,0.3417,0,0,0,0 +CTD-2545M3.8,Monocytes C,0,0,0,0,0,0,10.9129,10.7177,3.8403672269,0,0,0,0,0,0,0,0.0176761114,0,0.5173,0,0,0,9.7277,13.1545,2.9830105263,0,0,0,0.226,0,0,0,0,0,0,0,0,0,7.383,9.3555,0.5122435484,0,0,0,0,0,0,0,0,0.8458422222,0,0,0,0,3.8899,20.1969,5.2815315789,0,0,0,0,0,0,0 +CTD-2547E10.3,pDCs,4.3872904977,0.3492783505,0.5235,0,0,0,1.7421,0,0.862710084,1.3792144112,0,0,136.002,0,0,1.5059,0.3516176052,3.3792826215,0,0.2579328508,1.745692385,0,13.9429,0,0,4.1657252459,0,0,82.3995,0,0,0,8.4333672922,4.2324541547,0,0.3479437823,1.559824941,0,0,0,0.7267629032,0,0,0,91.0709,0,0,0,0,2.7542286869,0,0,0,0,4.825,2.3289,3.783380117,0,1.7028,0,104.909,0,0,0 +CTD-2547E10.6,pDCs,0.3885190045,1.8512783505,0,0.0185675457,0,0,1.3958,0,0,0,0,0,43.5084,0,0,1.9233,0,0,0,0,0,0,2.8905,0.2875,0,0,0,0,23.822,0,0,0,0.3577747989,0.8506012607,0,0,0.2869921322,0,0,0,0,0,0,0,36.3581,0.4841,0,0,0,0.6126040404,0,0.0525951209,0,0,0.6329,0.3139,0,0,0,0,23.4734,0,0,0 +CTD-2583P5.3,Neutrophils LD,1.8532140271,0.5608883162,1.7621,0.6351967707,0.1163958641,0,3.8563,3.1591,11.445842857,0.5746686324,54.5172,0,2.1648,0.4079,0,0,0.0967819798,0,1.9804,0.679389948,0,0,11.2766,9.6583,7.5290236842,0.635547541,14.4695,1.2622,2.4047,0.9992,0,0,0.393991689,4.3264816046,9.1735,0.4966123096,0,1.5724,2.5877,7.7436,7.9981645161,0,19.2254,0,4.4847,0,0,0,1.0006095694,0.2587448485,5.1162,0.6402276365,1.9679893346,0.9805,5.7882,14.1769,8.2023116959,0.1132002672,66.0177,1.664,3.8194,0,1.274,0 +CTD-3088G3.8,Neutrophils LD,3.5296868778,1.116967354,4.2342,1.588686455,4.5340354505,1.0144,2.1256,0.9942,0.2557941176,4.4539680218,41.584,8.0935,0.8372,1.0509,1.7889,0.3798,1.6629527564,1.1905772128,5.4743,2.6255626503,4.4487290274,1.4055,1.9746,0.2335,0.7145289474,4.8267424918,56.8495,7.2699,0.5034,0.7056,2.9623,1.526,0.4680812332,1.4464648138,7.1116,1.7088883327,4.5771576711,1.5361,0.609,0.5748,0.1342177419,2.9571619571,58.2014,4.2527,2.5321,1.0062,2.5296,2.6479,1.0916344498,2.2143591919,5.301,1.6326419174,2.5506827513,2.0774,0.2468,0.4505,0.3598526316,3.1335169534,67.0671,4.7786,0.3957,0.6612,1.246,0.624 +CTD-3116E22.8,mDCs,0,3.883395189,0,0,0,0,13.6301,0,0,0,0,0,1.9171,0,0,0,0,0,0,0,0,0,14.8673,0,0.1667289474,0,0,0,0,0,0,0,0,0.2571683668,0,0,0,0,6.6069,0,0.1648693548,0,0,0,1.7387,0,0,0,0,1.6555014141,0,0,0,0,18.133,0,0.4063859649,0,0,0,0,0,0,0 +CTLA4,CD4+ effector,0,0.0455463918,0,56.035982813,4.7022382406,2.6163,0.1545,0,0.199894958,12.887418218,0,0,0,0,0.4558,0.1304,0.0323861292,0,0,52.196572116,9.5170596884,1.9349,0.2867,0,1.8621815789,8.6128857049,0,0.0631,0,0.4866,0,3.5691,0.0088648794,0.2046053295,0.0655,66.26379706,10.494337923,9.2174,1.1104,0,0.0628822581,17.762507055,0,0,0,0.4452,43.3356,8.7933,0,0,0,14.054728308,0.1667444313,0,0,0,0,1.8951025889,0,0,0,0,0,0 +CTNNA2,Basophils LD,0.2100488688,0.1746797251,24.6152,0.1678989834,0.0313103233,0.1131,0.0708,0.1701,0.0891,0.1955474949,0.068,0.1667,0,0.2892,0.2107,0.1717,0.1674266153,0.1769543464,27.416,0.1355416333,0.213204373,0.217,0.1112,0.051,0.1616315789,0.2360241311,0.0362,0.1614,0.0622,0.0128,0.2236,0.0767,0.2291096515,0.4739481948,10.1976,0.1363716925,0.1780133753,0.0666,0.1486,0.0512,0.0460725806,0.2555056018,0.1043,0,0.0496,0.0309,0.0394,0.1194,0.0357100478,0.1573543434,21.3392,0.193890468,0.1348089429,0.0552,0.0777,0.1667,0.0409687135,0.1748738934,0.2423,0.1294,0.0479,0.0407,0.1265,0.1798 +CTSL,Monocytes NC+I,0.6629936652,3.4264900344,2.2779,13.767954551,0.4218300837,0.3197,6.6464,34.5739,315.73037185,17.592439922,0.5116,3.7637,0,0.0674,0,2.2781,0.9301532899,0.8178102989,0.4363,7.6417704974,1.9779665996,0,19.8975,33.1581,289.62134737,7.1374095738,0.4099,1.2628,2.533,0.3341,0.12,0.6671,5.2733726542,7.6305627507,0.1684,17.264677957,0.0453413061,0.1808,12.9533,48.5737,303.45412419,26.125190232,1.1351,2.498,0.2779,0.6499,1.0144,0.7728,3.7980822967,5.2236119192,1.601,7.4811768108,0.170648537,0.162,13.2924,45.2231,304.15693947,11.330566344,1.7166,2.8472,0.1403,0.8066,0.1804,0.1777 +CTSV,pDCs,0.6557443439,0.6580652921,0.5338,0.3626466272,0.2551389299,0.2033,4.5464,0.3934,0.2846420168,0.5189738433,0.7053,0.5246,22.3453,0.3255,0.2273,0.3603,0.8566461174,0.8645118977,0.294,0.5770226429,0.257674667,0.5218,3.202,0.3673,0.3005,0.9121450492,0.36,1.0995,21.0168,0.1419,0.409,0.1671,0.4455737265,0.4972487679,0.6449,0.2545756721,0.2280918175,0.1502,2.1144,0.4787,0.3708887097,0.4810646517,0.2983,0.1784,8.2614,0.2817,0.2932,0,0.4027172249,0.7648105051,0.4223,0.4453028027,0.3245380368,0.2863,3.0714,0.2379,0.3572961988,0.359536312,0.8242,0.2543,34.9526,0.2541,0.1618,0.2259 +CUEDC1,pDCs,0,0.4955189003,0.1547,0,0,0,1.3561,2.5693,2.4901983193,0,4.6939,0,45.671,0,0,0,0.6186366331,0,0,0,0.0465204323,0,3.7771,2.0074,7.8265631579,0,5.7464,0,52.7514,0,0,0,2.0277855228,1.047747851,0.1166,0,0,0,4.3593,1.5966,2.8895596774,0,6.6029,0,67.0429,0,0,0,0.3762564593,0,0.6599,0.1204635647,0.0793605474,0,3.2767,0.8757,3.8179555556,0,7.1469,0.4267,91.0479,0,0,0 +CUX2,pDCs,0,0,0,0,0.0754515181,0,0,0,0,0.1473869707,0,0,24.2036,0,0,0,0,0,0,0,0,0,0.0396,0,0,0.2460803279,0,0.0167,30.4521,0,0,0.228,0,0.0465952436,0,0.0967517084,0,0,0.1633,0,0,0.1145301542,0,0,26.7894,0,0,0,0.0451200957,0.1172888889,0,0,0,0,0.0312,0,0.0167488304,0,0,0,46.6052,0,0,0 +CXCL1,Neutrophils LD,0,0.043452921,0.3146,0,0,0,0,3.3225,0.6833428571,0,255.5783,0,0,0,0,0,0.2235172496,0.3871594022,0,0.0121710468,0,0,0,21.6697,0.7341,0.1278798689,536.8489,0,0,0,0,0,1.5811994638,5.4948893983,0,0.0209445504,0,0,2.008,14.4099,0.930066129,0,364.029,0,0,0.0477,0,0,0.6585511962,0.436,0,0,0,0,0.121,10.0837,0.507755848,0,275.6185,0,0,0,0,0 +CXCL6,Neutrophils LD,0,0,0,0,0,0,0,0,0,0,13.0413,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.1142,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9922,0,0,0,0,0,0,0,0,0,0,0,0,0.0693,0,0,15.6811,0,0,0,0,0 +CXCL8,Neutrophils LD,14.698981448,37.111634021,61.2209,0.2069248236,0.0821523387,0,136.644,1467.993,214.55215084,0,6319.6509,1.2725,0.0732,2.6872,0.8505,0.3948,105.38170966,81.532191624,174.406,0.8779107424,0.5812484041,0,1714.3846,3697.6311,997.65929474,2.4145443279,17426.3398,6.7955,21.3887,11.3219,0.2892,0.2551,395.8653193,529.08224183,135.2699,1.7484634287,0.0216500393,0.5821,1964.597,4300.2129,913.86709516,0.2700863322,9961.6611,12.4647,0.0663,53.6699,0.7838,0,31.057727273,53.618845051,200.7719,0.749363181,0.0308842614,0.0771,240.2904,1161.621,180.38265673,0.6563533656,5837.8281,2.8863,0.5327,13.6797,0.1719,0.3389 +CXCL16,Monocytes NC+I,18.624800452,17.122640206,3.6048,3.1726824901,1.1719730675,3.3231,151.0353,147.6483,476.11851134,2.1108142106,264.6715,2.3285,32.7958,1.7316,1.8384,3.3896,10.102153231,17.69071722,6.7574,2.9490272383,1.3071607439,3.7077,113.6334,102.1301,487.82826316,6.5095262951,194.24,5.9217,32.4736,4.6315,1.2976,4.4062,16.122625469,24.501790315,6.6018,4.7228991392,2.298259166,3.7251,108.1597,102.8283,435.17521613,5.8275193405,109.154,4.7891,7.914,1.1163,4.0443,1.4587,10.704488517,18.595371515,2.8224,1.8913834441,1.8452878952,3.6174,101.3233,127.1283,315.92136287,3.1281945382,116.259,4.13,18.5389,1.9981,0.7407,3.1844 +CXCR1,Neutrophils LD,0.067481448,0.5532704467,93.258,0.2707210382,10.571696406,0,0.0404,2.2551,2.1397827731,0,1803.9399,50.1284,0.3896,0,28.1394,0.5607,2.3215125074,2.0661453799,94.0039,0.0489983445,11.545828374,0,0,0.6822,3.9172078947,0.2956259016,2144.27,59.9731,3.4832,0.1108,13.9018,0.5482,1.8999774799,1.6044316332,91.6007,0.2166898292,6.0265998426,0,1.5024,1.3563,2.5558967742,0,891.388,51.2145,0.0437,0,27.4138,13.6692,3.5322397129,3.244199596,117.835,0.4613386213,24.7824668,0,1.6476,2.6321,0.7611622807,0.2091732754,1967.12,86.3792,1.1554,0.3214,34.187,2.6504 +CXCR2,Neutrophils LD,0.4865040724,0.7588041237,63.6626,0.6830359706,11.803845478,0.1278,5.9127,15.3661,7.6647680672,0.0444923509,3332.9316,89.4656,21.9758,0.0616,42.4882,4.8672,5.3798892116,3.2635788477,62.6505,0.3852593987,29.755983061,0,9.3972,29.1928,8.7254842105,1.1464141639,3440.0742,105.1644,53.2091,0.0641,31.7228,3.1691,3.0801364611,6.252948596,69.457,0.8055943716,17.415465618,0.1177,16.2652,24.3711,5.4983935484,0.9831421202,1453.519,96.2886,12.8897,0.5579,67.7486,35.2405,6.972969378,2.8131680808,64.4876,0.4642362228,37.887994502,0.9769,10.4999,48.1863,7.9171239766,0.3238483548,3680.74,134.4931,19.281,0.8465,76.6915,10.224 +CXCR6,MAIT,0.796999095,0.7570491409,0.4345,22.852533913,17.166795076,285.4842,0.2218,0.3391,0.3331243697,3.2504172506,0.128,18.689,0.5529,0,21.5933,159.4826,0.1128997629,0.4485576882,0.0597,8.1538755605,36.776372606,280.0817,0.1449,0.0996,1.3442105263,0.3786700984,0,6.499,0.1255,0,38.0995,124.6773,0.1431790885,0.6400269341,0,24.656138909,39.871595515,232.1373,0,0,0.4832225806,1.6725348165,0,7.5313,1.4338,0,8.0869,37.215,1.2820909091,1.9792543434,1.1078,30.439659549,21.897576876,257.5357,0.542,0.9716,0.246447076,2.3802366461,0,12.0198,2.3299,0,13.2921,123.669 +CYB561A3,pDCs,486.82825023,736.4629945,41.5732,61.692798182,66.868964041,70.1042,87.6537,94.0133,79.207337395,57.229213462,29.3692,91.3208,2323.5244,23.1326,67,69.5108,417.44399905,857.03962835,46.7547,64.447266941,78.43495994,57.1732,100.3942,68.4683,84.398657895,90.307066164,17.868,90.2289,2242.6074,16.2052,79.7171,49.2054,407.02876113,766.28435794,53.2039,57.827680638,51.362419512,54.5565,78.5534,115.2429,122.8006129,53.006275341,15.9326,71.6893,2174.1528,23.6229,72.2518,45.9335,438.87763349,771.15678323,38.9714,54.312867745,72.282257433,54.8644,124.7659,121.502,88.155036257,63.125733556,19.0432,91.2754,2736.4063,25.3141,70.1478,48.6674 +CYP1B1,Monocytes C,1.2908126697,4.8727611684,0,0.0690182095,0,0.3309,0.7791,198.3024,14.251815126,0.1857240617,4.341,0,0,1.4182,0,0.0617,5.6165069354,10.302519942,0.8896,0.0286710616,0,0,2.0867,173.9414,18.526813158,0.2411188852,4.1762,0.3239,0,1.3508,1.7088,0.3512,16.258906434,15.895478109,0,0.1218510926,0.0063625492,0.4045,2.4253,167.0603,11.099767742,0.0549578621,6.1864,0,0,0.3954,0,0,10.066697129,22.086212323,0,0.0461199068,0.2637057574,0.0227,3.3286,424.7024,39.317981287,0.3963830967,29.8135,1.1515,0,4.5694,0,0 +CYP2S1,mDCs,1.7348153846,0.7614735395,0,0.0075703983,0,0,111.7845,23.9725,2.1122848739,0.0211286886,0,0,0.7776,0.1659,0,0.3984,1.0631662715,0.1035411307,1.264,0.0051668003,0,0,82.3313,27.5425,5.9528894737,0.0261721967,0,1.1643,7.4698,0,0,0,0.975969437,0.9177617192,4.0339,0.2941653887,0,0,84.3493,10.5573,2.1975612903,0,0,1.3921,0.0767,0,0,0,1.7520095694,2.1751591919,1.9031,0.0067270883,0,0,133.5671,44.9171,5.3527988304,0,0,2.8426,2.3965,0.0446,0,0 +CYP4F3,Neutrophils LD,1.7306828054,4.3384171821,4.9627,2.1644437089,3.8328349089,2.5615,1.5438,4.0583,2.2017516807,2.9525036418,253.4985,3.7851,1.2702,0.3886,3.3747,1.0985,1.2367725548,3.0674573208,7.0884,2.2627226429,2.9747978387,4.8846,1.2988,3.6324,4.2314657895,6.0632201967,266.4311,1.9637,4.7549,0.0366,3.5117,1.6094,3.0630731903,3.365999255,8.8032,2.2180720501,2.8648804091,1.5405,2.3548,1.5295,1.0459612903,1.4793975004,206.8698,1.3244,2.6539,0.5346,2.3431,2.2978,2.483830622,8.8363743434,10.0195,4.2497296718,1.9719600991,2.1708,2.307,3.0845,1.8608637427,2.451173693,216.7628,2.9926,4.0085,0.4812,1.1066,6.0245 +CYP4F22,Monocytes NC+I,0,0,0.2458,1.1580243571,6.6934868374,6.6384,0,0,19.722747059,0.3664560622,0,1.3556,0,0,3.0752,2.8381,0,0.438220144,1.4016,0.6944003044,5.9734619251,12.6135,0.5062,0.6724,26.542378947,0.9143744262,0,1.842,0,0,3.8641,6.0352,0,0.6261636676,2.1183,0.8358216594,2.2168800944,15.9772,0.0374,0.39,27.844306452,0.2794498848,0,2.9608,0,0,3.917,1.1604,0.04494689,0.8425634343,0,1.1740050504,3.8065610901,6.2751,0,0,35.222931871,0.8421831802,0.5887,1.224,0,0.0504,2.9438,1.6362 +CYP7B1,Basophils LD,0,0,19.646,0.1605935295,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.4706,0.1982152784,0,0,0,0.0483,0,0,0,0,0,0,0,0,0.0117932976,0.2960559312,24.4146,0.1876815654,0,0,0.3778,0.0529,0,0,0,0.2715,0,0,0,0,0,0,25.9612,0.1461741931,0.2522251062,0,0.1572,0.3659,0,0,0,0,0,0,0,0 +CYP11A1,Basophils LD,0.0903420814,0.1081323024,65.6408,0.2115663318,0.013006729,0.6112,0.2762,0,0,0,1.4336,1.1181,0,0.1288,0,0,0.0077013041,0.0110416421,23.278,0.1427702598,0.0216301583,0,0,0,0,0.1498993443,0,0.3188,0,0.3672,0.3671,0.4038,0.0106520107,0.0850469914,31.5264,0.1050524897,0.0449070024,0,0.1188,0,0,0.0403301188,0.1821,0,0,0.4022,0,0.3079,0,0,36.2815,0.1052335846,0.0704335772,0,0,0.1512,0,0.6517780859,0,0,0,0,0.1771,0 +CYP46A1,pDCs,1.3468864253,1.3099876289,2.6389,1.503577096,0.5435688331,1.4042,0.8434,0.8963,0.9441352941,1.6428537844,1.6209,2.078,45.5331,0.382,0.9674,1.5688,0.8199108477,1.3285819085,1.7011,1.1312813734,1.0333046243,1.0906,0.8783,0.8226,0.7003026316,1.9849505574,0.9301,1.7614,32.9423,0.1961,0.7948,0.3908,0.5527812332,0.699725616,3.0167,1.0373599921,2.1870549174,1.3564,0.5069,0.4561,0.6417354839,0.9963152101,1.127,0.4564,28.5925,0.2052,0.7981,1.422,0.8018617225,1.1430583838,2.1937,1.0043401014,1.2253243275,1.2521,0.4529,1.2262,0.5688157895,2.9400629531,1.0824,1.552,31.6333,0.2466,1.2284,1.2157 +CYYR1,pDCs,0,0.305419244,1.6224,0,0,0,3.0067,0.1882,0.2346638655,0,0.7258,0,66.6814,0,0,0,0,0,0,0,0,0,0.0304,0.3742,0,0,0.0501,0,78.0148,0,0,0,0,0.7097879656,1.3541,0,0,0,0.1155,0.2048,0,0,0.8247,0,73.6108,0,0,0,0,0,0.6419,0,0,0,1.0238,0.2036,0,0,0.4682,0,78.3601,0,0,0 +DAB2,pDCs,1.3961877828,2.0068680412,5.0737,1.2638655603,7.7673860167,0.2823,27.8374,9.6066,6.7301672269,2.314963279,1.3994,43.4474,331.7027,0.1245,4.132,0.316,0.8790957321,5.7988246237,14.2848,1.296073853,4.3888307112,1.7115,17.9087,9.4241,12.686865789,1.3349480656,2.1383,35.7317,266.03,0.5517,12.3138,0.4655,2.1768930295,8.2235519198,6.4358,2.8977595087,8.0891442172,0.6879,17.6366,11.527,4.3999629032,1.3005526857,3.2583,64.2521,376.8684,0,27.5211,4.2994,0.8426650718,1.9623218182,7.7083,2.3236905982,6.8688574563,0.8953,21.245,7.1562,5.3931982456,1.6443965425,1.6631,43.5698,280.9198,1.1643,11.9667,1.3276 +DBNDD1,B Naive,2.2123977376,26.403199656,0,3.233115046,0.0961152962,0.6111,0.901,0.0396,0.1033680672,6.2790689712,0.1029,0.0514,5.7534,0.4409,0.0568,0.0467,0.9610500296,57.995455333,0,0.7217000445,0,0.0916,0,0.1609,0.0182526316,3.697723082,0,0.0459,0.8792,0.4334,0.0353,0.0529,1.7431367292,14.969927908,0.094,1.5571627798,0.0520321007,0.0745,0,0.0564,0,1.3679642971,0,0.0555,0.8998,0.3752,0.0592,0,1.7198607656,55.98324404,0,8.1361088056,1.2490705757,0.1888,0.0425,0,0,12.090991248,0,0.1218,0,0.2241,1.8548,1.7697 +DEPTOR,mDCs,0.3350986425,0.3102800687,0.4062,1.295796693,0.8095451173,0.3318,14.9663,0,0.2856533613,1.6113041277,0,1.115,2.5499,1.3638,1.6059,0.975,2.2020640782,1.2852306518,0.7223,0.5763931255,1.3135304599,0.6146,12.0262,1.246,1.0703368421,1.5655057705,0,1.0373,3.7572,0.7443,1.5619,1.429,0.9595107239,0.0312309456,0.6548,1.0425298504,1.0243261998,1.6448,14.7305,0,1.7242548387,3.2930061691,0,1.1856,1.6326,0.3829,1.2982,0.3987,0.3783062201,0.9775545455,0,1.1060046187,1.7356117508,2.1797,28.5365,0.0441,0.9974137427,1.8154018874,0,0,2.0445,1.6367,1.1297,0.8566 +DFNA5,mDCs,1.4328063348,3.480757732,0.1901,1.9953193219,1.380356409,0.0285,8.1335,2.4812,0.2530857143,0.3898832576,0.1242,0.7443,0,0.5923,1.4271,0.036,1.5717365145,1.1851189269,0.0779,1.0968662732,2.3180556673,0.112,14.5678,6.8924,0.6490657895,1.8715020328,0,2.1517,0.6105,0.0127,0.0657,0.6429,6.1702536193,10.428302178,0.4091,0.4513750761,0.726879229,0.0357,20.6253,5.6308,1.1718758065,1.1639451161,0,0.4738,0.3309,0,1.4138,0.4769,0.5565287081,1.1179890909,0.1433,0.5809716028,0.2735296602,0.8103,19.6707,3.8714,0.7878763158,1.108820628,0.0305,0,2.9481,0.1656,1.34,0.0951 +DGAT2,Neutrophils LD,16.342780543,21.831495533,5.8449,0.4068117869,0.2106173478,0.3288,22.6942,33.5099,3.7934794118,0.301106713,342.7085,0.3591,0.9302,4.9948,0.3345,0.158,5.578540901,8.6737035362,1.9301,0.3410555457,0.1765785122,0.1418,6.3019,14.6948,6.6319210526,0.6734928525,526.8259,0.3436,4.1125,1.949,0.2824,0.2472,11.72216756,1.3379366762,6.7193,0.3334340021,0.3905148702,0.4873,5.0565,11.677,2.4743306452,0.3454826449,213.1554,0.5659,0.6666,3.8095,0.1846,0.8167,6.4303827751,13.114614949,4.187,0.4638818612,0.3248246815,0.0674,7.214,43.5244,9.1715108187,0.3510614832,481.0501,0.6967,1.2835,1.4904,0.2082,0.1681 +DKK3,MAIT,0.0466927602,0,2.9501,2.892481928,2.9113587559,53.2094,0,0,0,4.7004375457,0,0.6279,0,0,6.9197,11.8428,0.2216550089,0.1382076918,0.3989,2.8460883519,11.034470621,76.2293,0,0,0,15.925396197,0,1.543,0,0,3.1762,22.0232,0,0,0,0.732320388,1.8732173879,23.9078,0,0,0,1.7210439816,0,0,0,0,7.112,0,0,0.3306608081,0.5389,1.8600129788,1.8836176262,36.6689,0,0,0,4.0007648906,0,0,1.1313,0.2398,5.7973,11.1436 +DNAJC5B,B Memory,12.14561267,2.3058457045,0,0.2929310071,0.1830876416,0.3316,0.1595,0.4514,0.3640962185,0.2461323928,0.168,0.4614,0.3832,0.0666,0.3208,0.4433,33.957983165,2.2629856608,0.2633,0.4652621752,0.4795,0.3335,0.1127,0.0751,0.1932894737,0.6093700984,0.3504,0.3889,0.3913,1.3704,0.2246,0.1448,25.317563539,2.967457937,0.5926,0.5758759303,0.5180505114,0.6927,0.392,0.9506,0.5504580645,0.4521458252,0.4071,0.1895,0.229,0.1311,0.5184,0.2747,15.855898086,3.3794490909,0.0839,0.5704579182,0.4267942898,0.5307,0.9642,1.9864,0.4301032164,0.3358981961,0.0658,0.227,2.3139,0.7668,0.8399,0.2475 +DNASE1L3,pDCs,1.0208904977,2.8595707904,0.0834,0.4440616792,0.3906484655,0.4531,54.4384,0.5775,2.3727,0.5489229963,0.5483,0.2711,442.648,0.0263,0.3653,0.0646,0.4742686426,0.6422493986,0.6373,0.2527772903,0.1672105554,0.3026,42.3381,0.3811,2.4619210526,0.4617128525,0.0933,1.03,215.9611,0.122,0.3308,0.3118,3.0460965147,3.4678356447,0.2362,0.4119954046,0.1060935484,0.5504,16.6199,0.7248,0.4509580645,0.5397736926,0.1876,1.6096,276.4045,0.0741,0.3214,0.2539,0.3070674641,1.1452066667,0.4628,0.3627990612,0.4423663049,0.562,18.3793,0.5199,0.4498333333,0.2747406715,0.658,3.0919,289.9692,0.1533,0.2063,0.2949 +DOCK4,Neutrophils LD,0.1126963801,0.152643299,0.0903,0.2521010106,0.2552767602,0.1643,1.6109,6.7195,4.0630533613,1.0836412945,40.6064,0,2.1736,0.0314,0.1784,0.1219,0.142133195,0.0889776377,0.1164,0.1556836748,0.1159850465,0.1918,2.7192,6.5733,4.4947657895,0.1230556721,20.3725,0.1928,0.0277,0,0.1858,1.9278,0.154410992,0.2687674499,0.2569,0.3010152364,1.6447509835,0.0898,2.3232,4.3431,8.4314548387,0.2586897359,62.1168,0.0899,0.3807,0.1376,0.1595,0.1198,0.1576617225,0.5279026263,0.1093,0.5900243747,0.4492984663,0.2775,0.4875,4.8134,4.1097002924,0.4498471522,12.1449,0.5486,0.2118,0.0269,1.3282,0.2715 +DPP4,MAIT,1.8852918552,0.496371134,1.1636,117.4317469,4.9303362219,281.0107,7.3873,0.8927,0.5904432773,77.66915066,0.7688,4.1465,66.7144,0.2437,5.1479,110.8526,0.0659090101,0.8085640331,1.2987,108.40997812,6.1781524755,307.961,8.95,0.451,1.3595315789,79.449344459,0.0715,6.5119,51.8931,0.4733,37.9688,158.0203,0.4785348525,2.0426783954,0.0569,115.21558229,11.318473013,381.425,4.1249,1.477,1.1756370968,49.729141322,1.1267,2.3957,23.5995,0.7004,2.6434,29.4058,0.9574971292,0.4033414141,0.0805,88.160350387,6.7155561114,208.4996,3.4432,0,0.6389175439,69.580764289,0,1.2079,39.4854,0,3.1642,78.1139 +DPPA4,pDCs,1.9002027149,7.7012041237,1.9295,2.3460740701,2.3430655999,1.4206,1.2336,1.1818,1.1275638655,3.6253513283,1.4771,3.4403,39.0057,0.3034,2.4813,1.4318,3.6877404268,6.7824317393,1.1983,1.3591747884,1.1155951244,1.8492,0.8892,1.1881,1.1623289474,3.9098674098,1.0529,3.1698,28.2915,0.582,2.6429,0.9003,1.3630664879,8.3926882521,2.7758,2.0311750828,1.2408446105,1.2332,1.4037,1.3181,1.5301645161,2.8512674526,1.7937,1.8985,52.965,0.6301,0.6767,0.5136,0.8867899522,5.8907624242,2.7098,2.1628876311,2.1612240208,1.0182,1.2126,1.8584,1.2013143275,3.3017124269,1.2406,2.1853,35.8924,0.3228,1.8719,1.7651 +DSC1,Naive T cells,0,0,0,1.2598566619,0.0984642048,0.0286,0,0.0535,0,18.371654917,0,0.0318,0,0,0,1.7624,0,0.0042691033,0.7063,1.279993853,0,0,0.03,0,0,27.883705443,0.0371,0,0,0,0,0,0.0254316354,0,0,2.4610945901,0.5474681353,0,0.0221,0.027,0.0060177419,15.956049725,0,0,0,0,0,0.0393,0,0,0,1.1481911807,0.1784223926,0,0,0,0,21.632851679,0,0,0,0,0.283,0.4436 +DSC2,Neutrophils LD,0.0769859729,0.3026800687,0.3552,0.0587474764,0.0150284589,0.0488,0.0788,6.4961,2.045210084,0.1037558616,33.7617,0.1116,0.0285,0,0.0693,0.1376,0.2369672199,0.1118998992,0.6611,0.0883627914,0.0694729078,0.1171,0.083,10.6793,0.8043842105,0.1598581639,43.3524,0.1222,0.1235,0.0172,0.0587,0.0567,0.7594128686,2.4193672206,5.182,0.1047951662,0.0602892998,0.0955,0.542,22.4754,3.047533871,0.0644820954,137.262,0.1907,0.0424,0.2172,0.1124,0.105,0.292545933,1.2653428283,0.1418,0.1217235798,0.1053981831,0.0873,1.5286,12.5281,1.942369883,0.1158832303,60.8053,0.102,0.0751,0.231,0.0435,0.0964 +DSG3,Basophils LD,0.0445900452,0.1318934708,12.1653,0.05960543,0.0051616609,0.0749,0.0678,0.0933,0.0144957983,0.0404814835,0.038,0,0.0745,0.0118,0.0311,0.0272,0.0812827504,0.0225607778,15.005,0.0375289013,0.014117718,0.049,0.0621,0.1099,0.0339315789,0.0423160656,0.2816,0.1902,0,0,0.0201,0.0484,0.0056533512,0.0986154728,29.4183,0.1272767779,0.0152440598,0.0314,0.0908,0.0247,0.0513016129,0.1215686226,0.0611,0.0336,0.0551,0,0.0692,0.1695,0.0518244019,0.0625389899,23.8709,0.0501217433,0.0483632846,0,0,0.0288,0.0904154971,0.0945745114,0.1524,0.1367,0.2021,0.0132,0.0287,0 +DYRK3,pDCs,0.2929149321,0,0.3016,0.3282965495,0,0.5843,0.1167,0,0.2358651261,0,0.7123,0,14.8988,0.2962,0,1.2399,0.0029284529,0,0.1992,0.299724974,0,2.0718,0.0564,0,0.3698789474,0,0.3731,0,5.0941,0,0,0,0,0,0,0.3341713018,0.148683871,0,0.1467,0,0.0151177419,0,0.2736,0,8.7032,0.2537,0,0,0,0.0142909091,0.105,0.4658517371,0,0.178,0,0,0.0376608187,0,0.7984,0,16.7384,0.2887,0.7929,0.1242 +DYSF,Neutrophils LD,0.4104117647,1.3167666667,0.0215,0.0408525894,3.2470865748,1.3351,1.6891,25.6104,2.3219138655,1.0339626772,209.7294,7.8037,0,0,3.6341,1.8516,2.3487996443,3.3307803889,0.073,0.0502336748,1.6539870319,0.0876,3.3985,62.2399,7.5299578947,2.3107609836,274.3051,5.6898,2.0931,0.0077,5.0264,0.0149,3.6595504021,6.167177192,0,0.1925825983,1.8750730134,0.0433,4.6353,55.881,23.82156129,0.3158115937,346.8356,7.2256,0,0.6814,2.8511,1.9624,2.8340253589,3.3211579798,0.3576,0.0493112246,3.5930254837,0.4157,2.1087,56.9209,5.3085666667,0.4898011358,232.7428,7.6435,0,0.1509,5.5609,3.1092 +EBI3,B Memory,12.851936652,2.7656333333,0,0.0174022246,0,0,2.1596,1.107,4.435589916,0,0,0,0.7156,0.0994,0,0,26.965501067,1.2019225279,0,0,0,0,0,0.6223,0.5379394737,0,0,0,0,1.3096,0,0,9.0992892761,1.7687566189,0,0,0,0,3.875,0.1088,2.3767064516,0,0.3297,0,0,0.2571,0,0,36.6490311,8.9095711111,0,0,0,0,0.3245,0.1077,4.2235163743,0,0,0,0,0,0,0 +EDAR,Naive T cells,0.016418552,0,0,6.2064341227,0.1206035943,0.0283,0,0,0,16.769386494,0,0.4082,0,0,1.129,3.2084,0,0.0221022254,0,7.3765065553,0,0,0,0,0,17.163948262,0,0.0277,0,0,0.0862,0.9459,0,0,0,6.0111591975,0,0.0689,0,0.0263,0.00585,19.775912906,0,0,0,0,0,0,0,0,0,4.0264294662,0,0,0,0,0,13.779473392,0,0,0.3219,0,0.3817,2.8971 +EGLN3,pDCs,0.9433859729,1.8204649485,0.1559,9.5820778137,0.3627334482,2.4137,2.0607,0.43,0.1757878151,5.7670214585,0.3937,1.3125,68.084,0,0.3298,0.334,0.3304018969,1.3461688801,1.0702,4.5405727914,2.3801909022,0.1338,4.3581,0,0.0764842105,0.4232375738,0,0.8449,46.9336,0,0,0,1.3742134048,2.5920895129,1.9576,8.1187128791,0.2009740362,1.2172,5.2792,0,0,4.6766581103,0,0.054,59.0869,0.3386,4.5119,2.8982,0.3362789474,2.7036688889,0,1.536479497,0.7765415998,0,0.6253,0,0.2968660819,0.9575984466,0,0.9144,62.5728,0.3598,0.2001,0 +EGR2,Monocytes C,2.8231429864,0.7635130584,0,0.4339959933,1.4465919744,0,1.8047,26.8229,5.3193983193,0,18.3575,1.4814,0.5105,0.3128,1.7334,1.2099,1.9911722584,2.9286838603,2.5421,1.9412585672,3.5076063835,0.1308,7.9916,10.1006,7.1459105263,0.0128891803,11.4283,1.9535,0,0.3538,3.224,0,6.8026506702,10.202963324,6.8642,2.0082446232,12.163516601,2.4943,24.3703,74.3252,52.010704839,3.4789525793,11.9224,0.7908,0.9254,0.2902,1.7124,0,2.5512354067,1.8022694949,0.4993,0.9048983759,2.7221492213,0,5.7525,24.7379,7.1633184211,1.3998839486,5.8987,0.7093,0,0.1975,1.9513,0.0456 +ELL3,B Naive,3.7193280543,54.465785567,1.0746,2.7504575888,3.911362301,0.1556,1.5418,1.6858,0.1644957983,1.7510987341,0,0.9911,15.9114,0.0646,0,0.1573,8.5525090101,40.139228686,0.2164,5.2826725538,1.7836633325,0,2.0949,0.5332,0.6450131579,1.4037007869,0.6598,1.4293,11.7788,0.4233,0,1.547,10.023949598,63.213438281,2.4461,1.9910081049,1.2201243116,0.1941,1.2685,2.2136,0,6.4596026591,0,0,15.6981,0,3.9415,0.8753,8.476884689,40.811702222,2.161,2.0118406017,3.2893349457,0.7813,0.4756,0.1444,2.3882426901,1.6266299148,0.1708,0,13.456,0,3.3156,1.1074 +ELOVL4,MAIT,0,0,0.0484,4.8521934099,4.2409663877,20.4447,0,0,0,1.9046359098,0,1.6461,0,0.0849,1.8899,3.1235,0,0,0,7.0483629176,4.3787240513,27.2933,0,0.0339,0,0.4749273443,0,0.0785,0,0,0.1524,2.2103,0,0,0,8.3794679314,2.1075819827,37.7109,0,0,0,0.7206500089,0,0,0,0,0.977,0.7739,0,0,0,4.8666165627,3.1486826805,21.3576,0,0,0.0206725146,1.2742676299,0,0.1722,0,0,0.3155,1.1555 +EMP1,mDCs,0.3414257919,0.8381233677,0.1203,1.6825202607,0.0016001969,0.3816,30.9267,10.4343,2.0430088235,0.0184484666,0.0214,0,1.5083,0,0,0,0.0404915827,0.2322524019,0.0545,1.1402635783,0.1203547876,0.0389,18.1601,5.2085,1.8745052632,0.0706624918,0,0.3061,7.5376,0,0.0302,0.034,0.084150134,0.5250606877,0,1.4857511853,0.2935426436,1.4642,25.8815,3.879,1.3399016129,0.2690674171,0.0931,0,3.5861,0,0.0261,0.0546,0.156445933,0.0144129293,0,1.3414785719,0.0630562765,0.02,27.3247,3.4567,1.0039754386,0.4820661433,0.0831,0.7013,2.4872,0.1179,0.456,1.093 +ENHO,mDCs,0,0.0928694158,0.3201,0.0195194953,0,0,15.7939,0.1127,0.3848033613,0.2518951948,0,0.1469,0,0.0557,0,0,0.0066097214,0.7198355492,0,0.0154134595,0.035294295,0.1297,22.662,0,1.0908921053,0.0388823607,0,0.1292,0.8863,0,0.1988,0.1102,0.0089887399,0.4628119198,0.1341,0.0398512118,0,0.1609,10.1319,0.122,1.3674096774,0,0,0,0,0.048,0,0.1822,0,0.0909632323,0.3804,0,0.2282671071,0.4009,21.0001,0,0.9484277778,0.4230011525,0,0,0,0.0653,0.1489,0 +ENPP3,Basophils LD,1.0346651584,1.3854109966,463.0014,1.3074471893,1.6132771213,4.5159,1.1071,1.069,0.5287659664,3.0308740439,0,2.7893,0,0.0171,0.7822,1.4034,0.5482655602,0.8085195823,535.8161,0.6554973422,0.1912330988,0.9904,1.4832,0.0682,1.2176842105,3.6238718689,3.2817,3.0099,2.6677,0.1379,4.9241,0.3301,0.7849871314,2.9565826934,614.2697,0.8812560389,0.7078773407,0.3925,0.6968,0,0.3423145161,0.0721494948,1.9568,0.4146,0.2735,0,0,0,0.0325014354,2.3291763636,498.2768,0.6018144727,1.5357241387,0.9093,0.99,0.2811,0.5380482456,0.4536716218,0,0.9041,0.3194,0,1.6177,0.3511 +ENTPD3,pDCs,0.6114298643,1.7437646048,2.1826,1.2011670853,0.9834794354,0.37,0,0.292,0.5822928571,0,1.148,0,11.8362,0.0369,1.4935,0.7431,1.7008714878,0.9536839323,1.3247,0.7137057461,0.1054434783,0.5365,1.5157,0,0.5004868421,2.5837579016,0,0.954,10.4045,0.1543,0.2694,0.4174,0.9512235925,0.3370356447,0.9539,0.7267215071,0.1338608183,0.4257,0,0,0.4979822581,0.131959493,0,0.5079,10.8642,0,0.4764,0.5173,1.4260894737,1.9999919192,0.301,0.445028918,1.1842967909,1.2932,0.3951,0.1593,0.2839964912,0.6448844997,0.0457,1.4055,8.5322,0,0.7761,2.6108 +EPAS1,Basophils LD,0,0,292.7789,1.155786742,1.863374643,2.7561,0,2.4401,0.3033848739,0,1.8525,3.1761,0,0,5.3553,3.5137,0.0209911085,0.0356753763,229.0295,0.8078805048,0.1315385775,0,0.9262,1.7819,0.2648052632,0,2.169,0,0.6051,0.1034,1.0239,3.8433,0.5485404826,2.1247456734,434.4776,0.4199319295,1.0482336743,3.8511,0,3.4271,0.3498677419,0.3873523666,2.6398,1.6139,0.6842,0,2.0959,1.8092,0.0441569378,1.3473147475,263.7901,0.8955859933,1.5387894762,1.0536,0.1218,1.7223,0.4557944444,0.2820168866,1.4627,0.5887,0,0.0494,0.5829,1.1347 +EPHA2,pDCs,0,0.1495601375,0,0,0,0,0.4749,0,0,0,0,0.067,69.0288,0,0,0,0,0,0,0,0,0,0,0,0.3376026316,0,0,0.5146,46.7324,0,0,0,0,0,0,0.0467206198,0,0,0,0,0,0,0,0,30.3207,0,0,0,0,0,0,0.0178688412,0.0259536574,0,0.3874,0,0.0900584795,0,0,0.2996,48.9969,0,0,0 +EPHX2,Naive T cells,9.7439846154,6.5531594502,4.4528,65.068556536,13.849832496,16.2074,1.6267,2.8058,1.9641189076,175.35728715,2.0383,3.7591,3.9786,3.5956,31.7659,27.9008,6.2748458803,4.0013183435,4.4921,99.203629852,18.667186177,26.8479,4.8527,3.3207,2.4049052632,226.53461528,1.8446,18.821,1.452,2.0875,29.0474,23.6002,8.5219664879,1.9417641261,5.9438,73.095989869,7.3191053501,10.1112,2.7678,2.0119,0.5596048387,151.00760661,7.3713,4.0148,2.2616,3.7206,11.4797,10.4556,7.2346382775,8.8759183838,7.0984,73.658596875,17.427476097,12.5841,2.1176,5.4551,1.8795611111,138.67975709,3.7629,11.3888,1.8893,3.9098,16.5272,19.726 +EPS8,Monocytes NC+I,3.0716909502,2.3409646048,2.5634,4.6720144959,1.8095254554,1.5921,13.9731,9.6691,61.099926471,4.1677515601,1.6077,2.0346,2.1191,0.7024,1.562,0.8676,5.0427239478,1.6664337919,1.8579,3.7332086637,1.0894236743,1.9035,8.5471,5.9145,53.972002632,4.9280759344,1.4968,2.0816,0.6443,1.8159,1.3728,1.6713,3.073616622,1.891583553,1.1626,4.5055957754,1.3270236035,0,12.2309,6.3864,74.392851613,2.6958493352,1.6921,0,1.2658,0.6091,1.7526,3.2533,4.5098880383,1.4762608081,1.6631,2.701708134,1.2712274422,2.4486,17.3906,9.8006,53.346479532,2.4731298647,2.1989,3.2795,0.4906,0.8835,0.7926,6.5012 +ERBB2,NK,3.9126294118,4.5850010309,3.7883,3.2823397022,23.054786558,1.258,2.2984,3.0234,2.5681596639,8.0516420924,2.6078,56.592,4.1365,0.9385,15.1081,3.731,6.1822106106,5.5496518113,2.5024,3.0841244246,14.08601136,1.0094,2.7404,0.8848,4.5477657895,2.4128810492,3.035,98.6097,1.0097,0.6045,13.2286,5.5224,5.1976112601,6.9445762178,0.7865,5.5351808502,13.345932101,6.9377,0.4039,2.8684,3.7005983871,3.6160948236,2.1546,82.8145,2.2219,0.8493,32.0691,34.7762,5.1563047847,7.4772153535,3.097,4.3341697663,30.687925177,4.0938,1.2153,1.9554,2.5478134503,4.8046016035,4.5119,95.1655,2.7326,1.079,20.184,3.5667 +F2RL1,Neutrophils LD,2.1643624434,0.3199917526,0.7245,7.8407266954,0.3363405383,1.3869,2.83,15.2784,1.2945260504,2.6173931755,75.6023,0.0487,0.2088,0.0536,1.0757,1.4177,0.9730329579,0.326434973,1.6208,8.5269090794,0.0458825836,0.0873,0.5097,14.9667,1.8949763158,7.0725544262,64.5874,0.0832,0.0642,0.056,1.5369,1.2359,5.1314353887,3.6939386246,0.1818,4.886229612,0.0851745083,0.5056,3.86,28.9183,4.2664564516,4.8732478284,77.594,0.0504,0.2613,0.0157,0.6137,0.1765,1.4159124402,4.2096032323,2.1475,4.0484530734,0.284273832,0,6.0284,13.0549,3.0423599415,1.6535620845,75.8334,0,0.0756,0.4387,1.0081,0.2377 +FAM81A,pDCs,1.8024366516,10.711908591,0,0.0662955149,0,0,9.4068,0.9386,0,0,0,0,45.9126,2.1466,0,0,0.8643285122,12.526952366,0,0,0,0,8.4107,0.7767,0.6555315789,0,0,0,27.6974,0,0,0,1.1845798928,8.2922756447,0,0,0.1059049567,0,12.0994,0.0323,0,0,0,0.6098,40.2229,0.4827,0,0,0.5225023923,11.002615556,0,0.0061875077,0,0,12.5265,0.3506,1.1281359649,0,0,0.1497,50.4245,0.338,0,0 +FAM111B,B Naive,3.9478950226,21.887679038,4.0116,2.6707322928,1.2526172657,0.7887,0.8177,1.0659,0.8439079832,1.6156329946,1.4453,1.9743,1.3275,1.8409,1.3727,0.9955,8.4519732662,93.813570738,2.6202,2.2955091166,2.035664187,1.5079,0.7326,0.6831,1.3290684211,1.808005377,0.8079,2.3461,0.5767,1.714,0.9211,0.956,6.1857077748,31.914400917,2.8309,1.7117798702,2.5493548387,0.5309,0.7162,0.4479,1.176616129,1.4999413756,1.0539,1.2529,1.7474,2.3131,0.8012,0.8255,1.4508851675,14.316348687,1.9655,2.6188410813,1.469066942,1.2788,0.6627,1.0326,0.9859520468,1.6172107399,0.7877,1.2095,1.8991,1.1495,1.546,1.213 +FAM157A,Neutrophils LD,0.712078733,0.8673474227,0.7472,2.1816237531,0.3704766453,0.9161,0.2258,10.0818,10.64075,0.8292934742,81.9037,2.0336,0.0678,0.1071,0.0992,0.3688,3.4981823355,1.210034901,0.2643,1.0007767632,0.8034639105,0.7363,0.6187,4.9413,5.6089684211,1.4741425574,63.1254,0.1743,0.0672,0.0545,0.191,0.3271,1.101847185,2.5215494556,0.459,0.3003064495,0.0964301338,0.2518,0.7196,11.6494,6.8964725806,0.9181522957,92.9526,0.3978,0.3407,0.0232,0.0499,0.1319,0.2218875598,0.7551684848,0.738,0.6440500308,0.2051262152,0.0613,0.0399,5.6255,3.6600608187,0.745686287,82.2861,0.3596,0.2237,0.1941,0.167,0 +FAM157B,Neutrophils LD,0,1.5243278351,1.1204,1.3965747698,1.3704251e-7,0.8701,0.9496,1.3575,0.925105042,0.6451219711,25.1363,0.6749,0,0.3611,0.1518,1.3803,0.8703548903,0.9206287216,0.7114,0.7611891388,0.4457617241,1.6959,0,5.4286,3.8640657895,4.0781640656,16.3841,0.6847,0.1156,0,0.6276,0,1.0819495979,1.1054944986,1.4883,0.9518602304,0.4345282455,0,0,0.9578,0.2090435484,1.1188521539,21.7517,0.7993,0.6711,0.2251,0.9297,0.623,0.2297679426,1.9713660606,0.5145,0.4003604194,0.5164665408,0.6719,0.7129,5.0331,3.0522181287,0.8451571405,40.4695,0.5251,1.2324,0.1646,0.5991,0.3121 +FAM160A1,pDCs,0.0055746606,0.1015910653,5.3333,0.5691137962,0,0.9816,9.0518,0,2.7641096639,0.5181166711,0.7314,1.3898,51.6811,0,0,0,0,0.004136471,11.1365,1.3650696882,0,0,6.2761,0,0.0845526316,1.7087797377,0,0,20.4108,0,0,0,0.2576211796,0.1063579943,15.7879,0.4339696199,0,2.0915,7.7665,0,1.4746177419,1.0423127814,0.3074,0,59.6377,0,0,0.2772,0,0.2337654545,18.5215,0.9686970397,0,1.4454,5.3946,0,1.0892429825,0.1962753132,0.0301,0,44.1749,0,0,0.0318 +FAM198B,Monocytes C,0.8573312217,1.5975061856,0,0.0382580014,0,0,15.8507,76.5094,44.134969328,0,0.0783,0,0.2716,0,0,0,5.1165069354,1.8964807274,0.1781,0.0030553675,0,0,11.1155,101.6079,32.716315789,0.0516141639,0.2332,0.3429,4.8927,0.3089,0,0,9.1710091153,11.015090487,0,0.000613528,0.1068169945,0,19.1191,104.8263,45.682351613,0,0.7258,0.3725,0.4139,0.5414,0,0,4.678377512,7.000259596,0.0375,0.0046448229,0,0,24.4688,122.2237,39.534439474,0,1.1114,0.2021,2.8296,1.3839,0,0.0268 +FAM213A,pDCs,2.9297126697,7.2341164948,0.8455,4.239667169,1.3901868702,5.9554,3.0276,3.6137,0.9135705882,14.69872371,0.8538,2.4611,94.1698,0.2081,4.8495,5.3126,5.3180196206,4.8160735614,0.7303,4.7799584039,4.376383614,10.3437,3.4432,4.2127,0.7011052632,13.119028197,0.5077,3.6876,116.5759,0.1862,3.1227,10.9515,9.7221147453,7.5894469341,0.5649,5.3054945438,2.4477944925,6.0914,3.6516,3.3957,1.0075967742,15.765204804,0.2444,2.9574,89.0143,0.6762,3.6708,6.7371,3.4119397129,8.4258086869,0.8348,4.1825667786,3.2569004955,6.7818,4.2949,3.8167,0.7347269006,8.5619707533,1.5804,2.7849,89.2809,1.032,2.7891,8.7146 +FAM221B,pDCs,0.6544556561,1.7883319588,2.3911,1.7129760256,0.5129369276,0.3474,5.041,1.0949,2.4615558824,1.6448882678,2.8672,0.9856,46.891,0.1592,0.4198,0.8958,0.686048607,2.4657159165,2.5617,1.2256231255,1.2250003518,2.1073,2.3947,1.7191,1.9155078947,1.5805861639,1.7527,1.7771,28.1981,0.2048,2.6034,1.312,1.4725458445,1.0497553009,4.9587,2.6639786717,2.8084413061,1.46,3.174,2.5805,1.1547354839,2.0275392838,1.4563,3.0652,20.5249,0.1155,1.3458,0.5329,1.0763827751,0.7846664646,3.6951,1.0426635442,2.7920655498,1.7054,3.3929,2.5689,1.0268254386,0.3344784533,2.1622,1.8984,45.5776,0.139,1.0947,2.1989 +FBLN2,mDCs,3.6896588235,15.120346392,0,0.1368943966,3.4882716396,0.0289,42.0015,0,0.392892437,9.2873178167,0,2.1365,0.6965,0,8.3305,0.9525,0.2060155305,3.9874410515,0,0,0.7724302589,0,21.4809,0.0934,1.8259631579,8.5502971148,0,1.2345,3.2024,0,0.9031,0,1.010608311,13.818578395,0,0.4668638194,0.3061228167,0,18.0635,0,1.7941709677,4.8580258288,0,11.4272,0,0,1.7437,0,0.2081110048,8.3881458586,0,0.3788578565,2.1320907739,0,24.9099,0.1058,0.6860090643,14.5287692,0,2.1731,2.4971,0,4.6266,1.2961 +FBLN7,CD4+ effector,0.228160181,0.105332646,0.6304,24.483633776,0.1950733629,2.3046,0.1176,0.142,0.146687395,8.1558861148,0.216,0.1828,0.066,0.0334,0.0672,1.3251,0.0824412567,0.3051501548,0.4268,28.048949206,0.0881597638,1.5012,0.0617,0,0.1768947368,6.3054828197,0,0.1612,0.2282,0.109,0.1153,0.2112,0.1557723861,0.1134033238,0.2547,25.303810264,0.7664083399,1.652,0.0811,0.1236,0.0794274194,14.72481599,0.1805,0.5687,0.8307,0.1207,0.2479,0.0378,0.3098665072,0.2632353535,0.3868,24.187169033,0.3157721567,0.1469,0.1043,0.0745,0.1701076023,10.040241139,0.3186,0.1713,0.0929,0.0131,0.1515,0.4033 +FCAR,Neutrophils LD,4.3371714932,8.6335817869,3.07,1.5964139457,0.6943009683,0.6343,2.5439,185.4739,97.413659664,2.3012281448,400.1266,1.9405,1.0073,0.3513,1.1336,2.1539,3.4664066983,5.095499856,1.8868,1.4502457461,1.2639457653,1.4262,1.8953,161.2395,12.383410526,2.3394662295,480.106,2.9292,0.3815,2.1578,1.3747,1.0868,31.551117158,47.05208384,1.6349,1.8781855119,0.9985690008,0.9502,7.0701,319.5302,134.87767419,1.3673449743,572.2885,2.205,1.5993,4.8218,1.3294,0.927,14.806460766,9.4217634343,3.5307,1.3995830193,1.062929731,1.7163,6.4596,210.6531,105.48198392,1.0006704359,358.6273,1.4107,0.371,3.7239,0.9025,1.0547 +FCER2,B Naive,54.493417647,398.0849921,0.4205,0.0999831719,0,0,2.7477,4.9748,2.6240403361,0.2329195016,1.5003,0.2193,0,11.0309,0,0,145.64004185,507.3494854,0.7868,0.2186902895,0,0,4.8609,6.4958,1.0338105263,0.3548152787,0.7573,0.497,0,21.6083,0,0.328,127.31871367,388.83588034,0,0.0179559595,0,0.3285,4.6781,3.9251,1.8087580645,0.4787932636,0.2524,0.8813,0,13.2283,0,0.1241,55.240995694,283.23594485,0,0.0932516412,0,0,0.2477,2.5003,2.2525473684,0.0629237348,2.0971,0,0.4806,6.5447,0,0 +FCGR1B,Neutrophils LD,2.3037651584,0.9889505155,0,0,0,0,4.4232,38.1978,18.086832773,0,101.0741,0,0,0.0583,0,0,0.0106147007,0,1.5221,0.0317713808,0,0,2.2438,40.0608,14.454781579,0.0608297705,91.3362,0.6089,0.758,0,0,0,9.8639072386,10.50663639,0.1517,0.0312280493,0,0,22.9072,84.902,34.458751613,0,247.3276,0.7813,0,0.6464,0,0,0.8991181818,3.707139798,0,0.1350605222,1.5730886975,0,9.1536,54.5379,35.837384211,0,140.9247,0.5939,0,0.4794,0.0938,0 +FCGR2A,Neutrophils LD,43.939153394,36.192065636,21.4503,0.5786451381,4.4078301822,0.1034,264.8094,807.5697,647.50793613,0.3206299278,2554.3459,18.5062,4.3941,3.6169,10.2572,5.0674,41.430635803,41.627976759,71.0008,0.4498146845,3.0314695401,0.2204,290.302,690.3573,596.72400789,0.5817238033,3418.8494,20.4684,16.1619,5.1262,4.9365,0.4776,53.448509115,105.78421175,28.2853,0.4593967554,7.0977655389,0.8378,186.0394,770.0403,420.93959677,0.1835940791,2044.6877,26.0259,2.0082,14.6534,14.0487,6.676,38.976021531,44.760040404,35.1831,0.3452709792,3.0072902312,0.2741,233.3435,646.0238,453.35592895,0.1161201102,3196.5002,16.2418,6.2629,7.9277,7.1696,1.6169 +FCGR3A,Monocytes NC+I,13.467564706,19.935899656,1.0243,7.8530095383,895.90023575,2.9969,9.8762,95.0569,5737.8325584,9.0409607337,1813.0492,2360.8196,0.2679,3.0527,1308.9583,292.0464,20.764009899,26.68966018,0.0827,6.1315192576,583.3328533,1.6683,128.7203,221.7457,5507.3005211,5.7722292459,2075.9697,2192.2817,4.17,4.3749,937.6054,88.2003,41.235676676,87.387949284,0.5414,14.190245305,728.11253312,18.0599,14.0116,171.4856,6439.7624629,7.6291053714,953.271,2191.8608,0.6785,9.27,2025.3621,742.581,60.701989474,73.049963232,4.035,4.4377827177,672.50079677,6.1726,4.8516,105.4953,4530.0231459,3.0613139135,1762.4613,1670.9033,0.1329,11.2176,884.4739,145.6187 +FCGR3B,Neutrophils LD,2.6524180995,6.6371323024,0.083,0.7963160447,125.14666032,0.3109,1.7037,9.9705,981.64344706,1.0121422929,4657.9141,359.7177,0.4999,0.6566,218.547,48.546,7.3236620628,6.1048609723,0.1742,0.2090197773,10.408018774,0.13,7.2981,7.0733,158.41093684,1.2130603934,6368.3359,32.1165,0,1.0304,13.0443,0.5853,10.506499196,13.804160802,0.1718,0.9056752086,22.388039811,0.1517,0.1518,9.4818,368.42976935,0.4955801454,6248.6631,76.3788,0.06,0.3071,73.2731,24.3097,7.0212492823,12.959251313,0.2279,0.3652780374,31.143956701,0.1633,0.6088,6.3857,371.11129035,0.3086183397,4218.7876,102.5346,0.0449,1.0549,56.0616,5.3738 +FCN1,Monocytes C,55.650035747,91.150130928,0.6421,0.5868501854,0.534128065,0.6663,1348.6892,5759.4858,2792.1300433,0.6693013863,419.0567,11.3169,1.1627,8.4916,0.3815,0.685,89.37352786,103.18073749,3.1031,0.8621411952,0.6704087962,0.3452,1178.434,4931.855,1578.9267711,0.4459171148,199.8958,9.7463,74.0904,12.0072,0.8747,0.832,410.31521877,621.66666069,1.5194,1.5450003576,0.2930707317,0.2596,1268.2828,3327.666,931.51867258,0.6275512852,354.0579,7.1679,0.3906,54.0814,0.617,0.1471,256.73755263,343.5927301,12.0293,1.231880566,0.5512627655,0.5674,1366.713,4983.208,3232.3708173,1.449736479,572.1569,6.3123,1.0786,33.0553,0.7806,1.1908 +FCRL1,B Naive,611.19854887,1842.1017893,5.6358,1.9483579177,1.9062020679,2.1051,1.1638,0.7918,5.112842437,1.5217979718,4.0226,0.8811,1.0069,24.7624,2.9209,0.8964,496.68442359,1422.885561,17.8406,6.9186061173,1.3231079417,0.8688,0.9918,1.0354,5.3250105263,1.5287375082,4.1229,2.7004,1.6136,35.7733,2.9201,0.761,529.18280295,1394.4527629,8.3334,3.103399212,1.4071575138,1.6191,1.2557,1.2701,35.913712903,1.3767430775,8.2219,0.4897,0.461,27.5939,4.327,0.975,607.36809091,1518.7269921,4.4088,2.5418738094,4.0378574799,1.3982,0.973,0.8058,5.6607774854,0.8050761149,2.8376,1.6993,0.5675,32.854,3.0132,0.9146 +FFAR2,Neutrophils LD,9.3210529412,12.221623711,20.4975,9.76655964,6.5756798785,8.2436,6.4154,20.6511,31.066518487,9.7634815013,647.8284,13.7056,6.0381,2.3196,4.2134,5.8738,10.270320925,12.589943234,17.1924,9.4153414402,11.902342473,8.3442,29.1076,58.1288,105.43415526,13.505847475,1072.7299,12.3951,5.8365,0.58,7.1507,14.5123,18.822129491,34.9802149,38.1585,10.663798888,6.4914745083,6.9414,43.5829,185.807,298.66048871,9.7258813154,2421.6924,8.0382,2.1636,5.6509,8.5396,8.913,11.097649282,10.899730101,14.1238,9.841083588,7.919580722,6.9834,6.4964,29.2766,33.010727778,6.8306587941,989.1669,2.616,7.223,2.4428,8.4142,9.8548 +FLT4,MAIT,0.2206058824,0.2338718213,0.1845,2.7355971235,2.3309432628,33.7187,0.2219,0.174,0.1011008403,1.9388501783,0.4804,0.2782,0.5732,0.0353,0.8858,6.5158,0.1869624185,0.3988261649,0.4539,2.8142916407,1.8622601156,40.9269,0.3573,0.376,0.2869078947,9.977244459,0.3308,0.2908,0.3161,0.0456,2.5208,10.2431,0.1432801609,0.1919998854,0.6938,1.1826699046,0.2551601888,27.9932,0.0449,0.309,0.2607419355,2.3035591739,0.2995,0.1763,0.2873,0.1098,0.5572,0.8134,0.2232330144,0.1972650505,0.1136,2.9755542589,0.8846090845,28.9866,0.178,0.2914,0.2062222222,1.7329842158,0.4642,0.4612,0.5792,0.1238,1.3873,5.0271 +FOLR2,Monocytes C,0.8966221719,1.1270494845,0.5824,0,0,0,10.2517,33.1161,13.989435294,0,0.4089,0,1.9702,0,0,0,1.6711120925,0.7852447605,0.4676,0,0,0,10.759,35.1169,5.7962684211,0,0,0,0.5813,0.7592,0,0.2893,3.2744077748,4.3600311748,0,0,0,0.19,9.4565,18.2815,5.7952741935,0,0,0,0,0.3487,0,0,4.0709,3.8883525253,0,0,0,0,7.9474,44.9729,25.457341228,0,0.3281,0,0,0.6845,0,0 +FOXP3,CD4+ effector,0.1178932127,0.0383725086,0,20.245520327,0.3039877564,0.8012,0,0,0.0801995798,0.5395646073,0,0,0.1097,0,0,1.0055,0,0.2157030465,0,11.829481767,0.2142888163,0.4625,0.3528,0,0.0480236842,1.7286367869,0,0.112,0,0.0245,0,0,0,0.1480951289,0,18.353305218,0.1320106216,0.4184,0.2513,0.5294,0.3708177419,0.4746573303,0,0,0,0.0817,0.071,0.076,0.0973416268,0.157659798,0,21.500503858,0.1977793771,0.3474,0,0.0465,0.2614023392,0.4811141473,0,0,0.1003,0,0,0.6114 +FPR1,Neutrophils LD,7.4283606335,11.352368041,64.243,3.0892834529,1.1440761037,0.8772,98.7351,593.0198,209.01894538,2.6306176117,3214.2878,2.2588,0.6764,1.1608,2.49,1.236,10.629335092,17.73828255,87.473,1.7246485152,1.1811047248,3.2969,97.207,703.7341,187.80211316,2.8410556066,1854.3069,4.414,3.3911,2.0257,2.2819,1.4264,52.00547882,75.788531003,61.6502,2.0098153291,0.7537292683,0.9092,198.9678,445.0948,163.70180323,1.5908397447,1780.0741,1.5445,1.2725,5.4163,0.9234,0.9906,28.58227177,38.586182626,245.6971,2.5046114438,1.9090492921,1.7125,120.7874,644.2368,249.1218038,2.0735037414,2951.1018,4.1236,2.2797,6.4271,2.5328,1.4559 +FPR2,Neutrophils LD,2.9205090498,4.3833924399,3.3014,0.8816330642,0.1807885606,0,3.1912,107.8151,74.570190336,0.7468218418,968.5047,0.838,0.8787,0.2174,0.1313,0.2502,6.2921172496,10.54504875,3.4148,0.9401917966,0.3163512189,1.673,6.8549,116.892,113.53266842,2.1601767213,1261.0502,3.5072,1.3857,0,0.2597,0,14.585430831,21.554723438,6.7074,0.5955325983,0,1.361,18.0991,166.0262,108.87174355,0.2427651658,715.8031,3.7775,0.748,1.5963,0.6261,0,5.7841641148,8.3387446465,5.5088,0.3871948057,0.4405208825,0,11.463,146.8389,59.310451462,0.3748360114,657.7871,1.342,0,2.0704,0.725,1.3383 +FPR3,mDCs,0.2062085973,0.8825993127,0.8202,0.1948777419,0.6233097489,0.1131,47.8032,6.1267,11.916536134,0.0907886244,0.0613,0.7537,1.1925,0.0239,0.2051,0.6294,0.0170512745,0.0883171912,1.0023,0.0541375501,0.2632048756,0.1665,22.6387,8.118,2.4747078947,0,0.0722,0.5535,0.0845,0,0.1717,0.0945,3.7778549598,1.566020745,1.6659,0.0273168322,0.1426349331,0.1377,27.4254,7.8151,1.4557516129,0,0.1061,0,0,0.5376,0.2178,0.078,0.018045933,0.378010101,1.8695,0.1239924073,0.2895153138,0.0571,23.608,5.6976,4.9463602339,0.1412911642,0.0594,0.1458,0.0495,0.0851,0.0636,0.1882 +FRAT2,Neutrophils LD,2.4031090498,4.0564219931,3.0896,1.3211748774,2.2136194157,1.0228,7.5342,5.9106,2.0719252101,1.6510324998,43.01,1.3881,1.2523,0.1443,2.1294,1.2645,0.9788357439,1.098826381,1.8587,0.9079490349,0.6899023373,0.1115,8.6536,4.2297,4.5204394737,1.0487896393,94.2547,1.8348,1.0187,0.9334,3.1052,1.5663,1.4939691689,1.4066711748,1.7888,0.7166252947,1.4689450039,1.0375,4.2585,4.6863,1.3919758065,1.8334991491,15.8735,1.6872,0,1.0643,1.7502,2.5078,2.3941023923,1.507579798,1.4697,1.0194400946,0.4227295186,1.2622,4.0651,5.8215,5.5662418129,1.8653227159,91.9634,2.3435,0.2985,0.1407,3.3877,1.1343 +FSCN1,mDCs,2.5097117647,0.5946865979,0.1572,0.6506175996,0.6038104054,0.0568,15.2242,3.9966,1.4728920168,0.0593956717,2.4907,0.7802,0.0546,0.2069,0.1991,0,1.2615908121,0.8124159597,0.0818,1.0779827914,0.8811943453,0.1175,10.8149,4.1073,0.9203842105,0.377724459,1.1875,1.2684,0.7837,0.0255,1.3807,0.1848,1.816591689,0.4121221203,0.7081,0.2820020461,0.0988040913,0,10.9997,3.8407,0.3716048387,0.448688034,0.9564,0.0526,0.2112,0.1232,0.0877,0,1.0397602871,0.2197670707,0.1636,0.6320122182,0.0365372817,0.1898,13.6401,5.9093,0.9786040936,1.0049793219,3.282,0.4182,0.1606,0.0438,0.3483,0.1882 +FXYD6,Monocytes C,0.6019705882,3.1131202749,0.6221,0.6011473807,0.4844054325,1.5305,5.0045,70.1166,5.7475428571,0.4677797361,0,0.285,1.9586,0.238,0.617,0.7712,1.848974511,3.1968955059,0,0.7285592576,0.3735221161,1.0053,4.4032,53.8596,2.7141078947,0.0745782295,0,0.9985,0,0.2253,0.9483,0.4255,3.4106152815,8.2492618911,0,0.558992458,0.0723767113,0.3115,9.3668,66.7206,2.8301209677,0,0.7093,0.3035,4.1578,1.1383,0.659,0.3526,5.9268191388,3.7439769697,0.2343,0.8326509148,0.2572888155,0.2594,7.882,94.6103,9.3510915205,1.609489544,1.615,0.8784,1.3278,0.2518,0.2888,0.5685 +G0S2,Neutrophils LD,2.977241629,7.5974656357,0,0.3647778256,0.4407023305,0.1766,25.0575,317.047,179.81090546,0,4034.02,0,0.5082,1.1908,0.6407,0.3565,14.504016242,11.323496169,0.2411,0.5253315887,0.4701034933,0.6949,121.112,168.62,199.79965789,1.5660790164,7541.54,1.3826,5.0035,1.7974,0.397,0,139.9649866,180.13081605,0.5384,0.4382616077,0,0,612.536,716.091,473.24235484,0.1041030668,13126.9004,4.8288,0.1535,13.2075,0,0,15.07478756,17.892053737,0.7646,0.6932421846,0,0,37.0848,160.985,145.74039912,0.2482078503,4111.0098,0.4558,0,6.1963,0,0.1964 +GALNT14,Neutrophils LD,0.2835511312,0.7070817869,0,0.0149326157,0,0,0,0,0,0,11.2626,0,0,2.9915,0,0,0.1483347955,1.1316416997,0,0.1029889903,0,0,0,0,0,0,38.3341,0,0,2.2334,0,0,6.2598729223,0.8059433811,0,0,0,0,0,0,0,0,11.0554,0,0,2.2005,0,0,1.8038143541,3.0624943434,0,0,0,0,0,0.0742,0,0,18.2283,0,0,2.329,0.1667,0.4926 +GATA1,Basophils LD,0.4785443439,3.5479676976,83.7595,0,0.0143032989,0,0,0,0.0651785714,0,0,0,0,0.1127,0,0.1797,1.2183721399,1.4047531941,80.2666,0.0260522569,0,0.6108,0.0952,0,0.1493210526,0,0.3164,0.087,0.7379,0.0788,0,0,3.2578013405,3.7974947851,53.8705,0.0967517084,0.2713351692,0,0,0,0,0,0.4617,0,0.2149,0.3574,0,0,0,0.4918725253,103.0171,0,0.1068634025,0,0,0,0,0.1732614665,0,0,0,0,0,0 +GCA,Neutrophils LD,72.525801357,112.01279656,93.1518,18.473922067,7.8527893484,5.7641,231.0898,450.4407,233.43833866,22.329551859,3186.0715,12.2074,92.3938,7.26,8.7088,3.6778,70.232670124,64.388321181,97.575,12.845588604,4.6104546368,4.7079,189.337,441.4225,172.01411316,18.478371148,2612.0715,6.9894,66.8486,7.7898,9.3896,7.1187,83.781983914,134.33003719,77.4545,18.378993716,11.150214477,8.3398,217.2356,429.3143,140.69748387,27.807526236,2072.28,9.1297,98.5548,9.1922,10.4584,1.4475,77.463536842,97.55537596,98.8266,15.465793696,10.939585559,7.4068,253.872,604.1398,225.3024962,21.061648889,3384.8645,10.2439,85.5088,12.3057,8.9108,8.3049 +GCSAML,Basophils LD,0.4139533937,0.8739865979,563.2353,0.7068275625,0.3964976038,0.4162,0.3968,0.2281,0.5272252101,0.3275315013,2.1202,0.5992,2.9376,0.0268,0.5076,0.6132,0.9027522229,0.6266794022,628.293,0.5896017001,0.3193346067,1.4723,0.3436,0.9596,1.2522,0.9547050492,1.5408,1.5593,1.8724,0.1116,0.3148,0.4247,0.9898227882,2.9050664756,1045.7999,0.5924321348,0.4691923682,0.7659,1.0676,0.3872,0.9854516129,0.5157724694,1.181,0.5277,4.0048,0.0919,0.6681,0.1183,0.443477512,0.5416822222,482.3084,0.5284138697,0.7072353705,0.5014,0.1999,0.3977,0.5559921053,0.5756337899,0.5642,0.3354,2.2446,0.1153,0.4439,0.2885 +GDPD3,Neutrophils LD,1.7089511312,2.9270439863,2.2186,4.8772348284,0.7931005416,3.569,2.6403,5.2882,2.2667243697,6.2125124187,102.7211,1.9679,0.1003,0.2853,4.0114,1.7108,1.8240796088,3.1615975513,4.3126,7.0325692873,4.424640186,3.1843,2.5985,3.1031,2.9192973684,6.3376406557,90.8153,0.9438,1.5121,0.2567,2.5038,1.4405,1.463458445,0.453442063,2.7101,3.6725431598,0.4913494886,3.5834,1.0602,0,0.715166129,5.1948924659,41.0539,1.2606,1.9678,0.0382,1.6322,0.2887,9.5546961722,3.2982618182,3.9142,6.7579197423,1.6235109486,5.1693,1.5947,3.3556,1.713522807,7.4747769835,94.2701,3.764,1.1832,2.5143,2.173,2.8359 +GLOD5,Basophils LD,0,0,59.6918,0,0,0,0,1.3454,0,0,0,0,0,0,0,0,0,0.951129377,48.5444,0.2086449295,0,0,0,0,0,2.7687403934,0,0,0,0.2143,0,0,0,0.4363994269,117.2889,0.1203450536,0.3374075531,0,0,0,0.2248467742,0.4743071441,0,0,0,0,0,0,0,0,71.7775,0,0,0,0,0,0,0.6712333389,0,0,0,0,0,0 +GLT1D1,Neutrophils LD,0.5245895928,0.8341264605,0,0.0045679943,0,0,0.1818,18.7814,2.1200021008,0,89.4416,0,18.4077,0.153,0.3823,0,0.7175186129,1.0395866691,0,0.0049094284,0,0,0.4543,19.1013,3.5571394737,0,116.0052,0.1596,9.2024,0.0803,0.2645,0.7152,1.3982265416,1.5192888252,0,0.0072916038,0.1276042486,0,0.8326,13.7878,1.3932225806,0,49.3594,0,7.3394,0.075,0,0.4361,0.6455555024,0.8943492929,0,0,0,0,2.1027,27.6682,3.6730339181,0,91.9168,0.5027,14.5238,0.0404,0,0.4367 +GLYATL1,B Naive,2.315159276,9.8589735395,0.3275,1.9879054778,0.021434433,0,0.2632,0,0,0.2835098021,0,0,0.1663,0.1128,0.3752,0,7.2389147007,22.507666936,0,1.7253881811,1.2307098015,0.1704,0.2051,0,0,0.1568541639,0.3457,0,0.083,0.7821,0,0.8953,2.1507868633,7.5935666476,0,1.6758510859,0.0880116444,0.6556,0,0,0,0.5909239142,0,0,0,0,0,0,3.576438756,12.247151515,0.7771,1.6644133626,0,0.5461,0,0.4923,0.5565716374,0.3960753633,0.8499,0,0.076,0,0.0977,0.3461 +GLYATL1P1,B Naive,3.3062167421,8.063556701,0,0,0,0,0.0653,0,0.536560084,0,0,0,0.2351,0,0,0,6.374283936,16.239412856,0.5575,0,0,0,0,0,0.1290210526,0,0,0,0.1221,0,0,0,3.3095458445,12.805910602,0,0,0,0,0.8398,0.2272,0.1184258065,0,0,0,0,0,0,0,0.6701861244,11.70577798,0,0.0145548962,0,0,0.4504,0,0.2455166667,0,0,0,0.2865,0.1619,0,0 +GNA14,Basophils LD,0.3759113122,0.1690223368,39.8597,0.0488197823,0.801727589,0,0.04,0,0.0789915966,0,0,0,2.6933,0,0.0605,0,2.1877913456,0,29.1883,0.0369684113,0,0,0,0,0.0197289474,0.2377846557,0,0.1469,0,0,0,0,0,0,26.4653,0.1086173355,0,0,0.0736,0,0,0,0,0,0.6087,0,0,0,0,0.0689026263,14.4805,0.0729953608,0,0,0,0,0,0,0,0,0,0,0,0 +GNA14-AS1,Basophils LD,0,0,13.4742,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.1467,0,0.0397167127,0,0,0,0,0,0,0,0,0,0,0,0,0,10.6024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.6355,0,0,0,0,0,0,0,0,0,0,0,0,0 +GPM6B,pDCs,7.7397126697,13.730392784,3.1203,10.022261548,7.5085637617,13.921,4.866,0.4184,0.5990063025,11.803527802,0.697,6.6552,145.7128,0.132,13.9171,9.2589,5.3366471844,5.2300824343,6.3645,7.2462023534,7.4542958784,5.994,2.6305,1.8584,0.6985947368,13.657636197,2.4995,5.6159,174.4367,0,7.7928,3.9182,5.533908311,6.9433315186,2.6157,5.2745121308,4.5284503541,1.6644,2.6688,0,0.8546,9.0570073746,0.0288,4.4704,142.4642,0.3429,2.8028,1.5679,10.766433014,10.739486869,4.6251,10.292744878,9.6450682633,7.7733,1.7986,0.9607,1.0307102339,8.8900595791,0.7365,10.3263,129.1037,0.114,12.2212,9.6118 +GPR20,Monocytes NC+I,0,0,0,0,0,0,0,0,12.711897899,0,0,0,0,0,0,0,0.6411997629,0.2733889377,0,0.0134458055,0,0,0,0.5925,25.725060526,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.3700774194,0,0,0,0,0,0,0,0.0559330144,0.6092862626,0,0,0,0,0,0,22.343044444,0,0.092,0,0,0.2168,0,0 +GPR27,Neutrophils LD,0,0,0,0.2342317127,0.2451027244,0,1.0152,0.8744,0.0606306723,0,7.7595,0,0,0,0,0,0,0,0.2771,0.1095370824,0.1565422468,0,0,0.7741,0.4129578947,0,55.3482,0.1492,0,0,0.5019,0.6794,0,0,0.1549,0.1279565289,0.0720292683,0,0.411,1.7903,0.0946693548,0,4.2902,0,0,0,0,0,0,0,0,0.0245451312,0.6568933223,0,0,0.4185,0.0250011696,0,22.6029,0,0,0,0,0.2818 +GPRC5C,pDCs,0.4021343891,0.3794810997,1.9634,0.2000555017,0.2583747907,0,1.4863,0.4576,0.8158701681,0.1394411072,0.4949,0.2674,39.1283,0.2718,0.1449,0.1239,0.2304823948,0.7145280735,2.5372,0.0965384113,0.7122468459,0.2984,3.7745,0.6382,3.8504342105,0.0706398689,0.1602,0.3393,55.9379,0.3001,0.0895,0,1.1179978552,4.5212371347,4.9399,0.2015886439,0.5466628639,0.2111,2.9457,0.3161,1.7187145161,0.8994413934,0,0.1439,38.6307,1.0022,0,0.1609,0.1185631579,0.2627145455,3.9592,0.1149225519,0.2546116328,0.062,3.5219,3.0504,2.2401719298,0.6740311842,1.9453,0.9617,46.9531,0,0.1414,0.3845 +GRIP1,mDCs,0.4567162896,0.8021079038,13.4021,8.1328184906,3.1397862301,4.3945,45.6993,0.9057,0.093644958,4.0966438174,0,0.5934,9.7401,0.4486,0,2.6862,0.0908648488,3.21509426,4.7504,7.3504606978,2.2902393315,1.629,60.8954,0.0205,5.1751184211,6.9600072787,0.683,0.2224,4.969,0.157,0.4894,5.8092,1.3780482574,0,7.5817,4.6901138922,0.2629926042,1.2482,68.2312,0,5.1370177419,1.8304000709,2.3323,0,8.4926,0.1203,1.8119,0.2451,0.1793473684,0.4358882828,5.9315,4.9896368601,3.4325150543,2,97.894,0.6026,3.2091026316,8.7911018039,1.6816,1.7569,3.5695,1.0834,1.4644,2.0399 +GUCY1B3,Monocytes NC+I,0,0.9684261168,1.7297,0.0180866045,0.0424087642,0.1046,2.3409,1.1445,28.895023529,0,0,4.4306,6.1484,0.1884,0.0612,0,0.7515443983,0.8942638963,4.4088,0.1733037788,0.053898467,0,0,0.3626,38.013265789,0,1.5648,3.7911,4.3054,0.0581,0.0496,0,1.3278463807,2.9638333524,0.1542,0.078102205,0.0425869394,0,1.3182,0,85.040677419,0,0.1227,5.9671,6.6281,0,0,0,0.4527368421,2.2779640404,1.919,0.1426639485,0.0872418122,0,1.1065,0.3558,30.934484795,0.0406649073,0,1.5036,6.2552,0.1755,0.0569,0 +GUCY2D,pDCs,0,0.0054845361,0,0,0,0,2.6066,1.669,0,0,0,0,17.2781,0,0,0.0329,0,0.2779744616,0.2215,0.0041027171,0,0,0.151,0.1376,0.0385184211,0,0,0,11.9056,0,0,0,0,0.0210892837,0.033,0,0,0,1.3529,0.1206,0,0,0.6928,0,3.3437,0,0,0,0,0.0447094949,0.0934,0,0,0,2.5219,0.6595,0.0591336257,0,0,0.0279,12.423,0,0,0 +HBEGF,mDCs,1.645241629,1.393752921,0.1363,0.7998276881,0.0126374528,0,37.5955,45.67,15.779518067,0.0536977891,7.4565,0,1.0017,0,0,0.5817,0,0.3947632481,0.6087,0.8275761024,0.2222024378,0,19.2076,3.4932,12.821642105,0.0516141639,6.9259,0,0.3124,0,0,0.7423,1.2272541555,1.5884010888,0.4777,0.8600589988,0.0444292683,0.3181,76.3747,5.8132,13.632206452,0.5329604148,1.5684,0,0,0,0,0,1.8092966507,0.4945121212,0,0.3556678407,0,0,52.0086,31.9026,11.428434211,0,8.0652,0,0,0.3702,0,0.058 +HCAR2,Neutrophils LD,0.8599104072,1.3531731959,40.6669,0,0,0.0619,5.8708,33.6192,22.451036975,0,228.552,0.3334,0,0.0524,0.0748,0,0.0934997036,0.8479173641,37.548,0.0297946102,0.0657423976,0,11.395,25.9546,27.018910526,0.1221021639,422.923,0,0,0.236,0.0469,0,3.5945868633,9.2592460745,71.6642,0,0,0,18.4211,54.7056,18.799043548,0.0742063464,391.207,0.2032,0.7525,0.5314,0,0,1.6646684211,1.388500202,44.9572,0.0505034469,0,0,10.5524,24.3872,7.8925991228,0,285.836,0,0,0.5159,0,0 +HCG27,Neutrophils LD,0.7570719457,1.5583439863,2.5701,1.2197630307,0.8750268012,0,1.7623,1.2945,1.0449672269,1.7102413792,46.53,0,0.8209,0.6385,2.0268,3.0356,2.0524125667,0.3538190277,0.6042,1.490358389,0.1970669012,3.3578,0.2465,0,0.3559973684,1.9954969836,19.6854,0.4819,0,0.5439,1.2043,0,1.7618072386,1.6156560458,3.171,6.5872295656,2.2776232101,4.3439,0.7611,3.5764,1.4161790323,8.1192862436,22.3015,3.1854,0.0693,1.1529,3.207,0.6807,0.2344674641,1.593260202,1.9513,4.5522880765,1.6796209533,3.276,2.2719,3.4702,2.8611719298,6.100557391,103.9568,1.5839,5.3853,1.1347,2.0087,4.1366 +HDC,Basophils LD,3.8223791855,8.6400838488,11138.7344,0.3529941694,0.080567077,0.6636,0.6279,1.3445,0.9043466387,0.0714109477,6.6166,10.5456,2.1106,0.9678,0.075,0,10.899489804,10.862762852,8392.1641,0.2787009354,0.0136499623,0.7231,0,1.0263,3.3802289474,1.3790610492,14.7817,13.6645,18.9691,1.7313,0,0,10.850545576,23.47722235,11144.3818,0.1089792412,0.6108459481,0.1243,0.0911,0.1418,1.8454645161,0.8740258288,10.7409,3.2687,2.9496,3.1318,0,0.2514,3.4676311005,1.3566278788,10255.7012,0.1588940588,0.4517804153,0,0,0,0.8625763158,0,0,1.5156,0.4006,1.2094,1.0024,0 +HIST1H2BC,Neutrophils LD,1.1895040724,1.8383237113,0.6506,0.9877545987,1.0682631709,0,2.0085,0,3.0420218487,0.593649893,150.9462,1.7499,9.7355,1.7738,0,0,0.9231232958,6.3467138135,8.0939,1.1706260802,1.0450981402,1.8248,0.6405,2.5575,1.3712447368,0,148.037,0.6532,5.9203,3.6302,0,0.2891,0,4.3391377077,2.5691,0.2791681896,1.6727206924,0,0,1.2949,2.5273241935,1.7598497784,135.0966,0.9315,9.1653,3.5134,0,1.0824,1.9324210526,5.5611151515,4.4717,1.8256858631,0.9440363615,0.3406,0.9688,3.9823,2.3094923977,0.6289373977,228.3849,0.289,8.9495,7.6857,0.7571,0 +HLA-DQA1,mDCs,547.51267919,647.66617148,3.7574,7.0519848762,34.275928607,2.3401,2545.6748,224.7288,360.04982857,1.5141826558,3.9405,26.2795,382.2361,43.4776,106.5746,9.4569,523.50599022,455.28858629,4.7855,8.7865953378,76.599144232,7.1499,1816.0546,277.095,165.78928947,4.5806158033,2.8545,25.2615,500.812,28.634,28.1596,14.1966,344.66612493,333.45403765,5.7232,3.7922881208,10.437957986,2.0359,853.0373,35.6921,54.477929032,6.1223976777,1.2807,10.6444,58.7346,6.4821,3.9294,3.4243,435.33340239,368.76421838,2.817,5.1463043445,17.365797404,4.9793,1039.2787,21.0002,82.947063743,3.901140304,6.2022,13.5942,125.3024,9.29,10.1096,6.8257 +HMOX1,Monocytes NC+I,32.358659276,14.016999656,12.5766,0.4840092333,0.7300249959,0,68.1928,177.506,487.88060168,0.0460193144,39.803,9.7312,0.4108,1.1973,3.1319,0.9221,29.145882573,3.6977442492,7.4642,0.5149129993,2.3342504398,0.8296,38.213,108.8576,323.87102368,2.0614451803,17.0773,9.72,0.4402,2.1311,2.9194,0,29.477436997,22.843382235,3.8111,0.6781074427,3.6170692368,0,37.9298,82.2264,213.93606613,0.1952269101,7.6513,8.6266,0.8642,3.1242,4.1789,0.2263,23.489189952,15.058585859,10.928,1.1994945248,1.4232138981,0,33.4379,140.2691,400.16888333,0.5364391515,56.1755,7.6559,0.4182,2.3252,10.0362,0 +HPCAL4,CD4+ effector,0,0,0,18.831207637,0.2423518792,0.0996,0.2143,0,0.0160004202,7.3458101007,0,0,0.0239,0,2.0093,1.8655,0.6154925904,0,0.0365,20.316521678,0.9518630309,4.2598,1.1626,0,0.1917078947,9.594243082,0,0.6558,0.02,0,3.7768,4.3684,0,0,0,16.087720256,1.3766354839,2.818,0.2961,0.2736,0,6.1439491402,0,0,0,0,0.3094,1.4077,0,0,0,13.542298924,0.2448386031,1.5412,0.4913,0,0,7.0621005345,0,0,0,0,0.3638,1.4856 +HPGDS,Basophils LD,0.3679357466,0,72.2643,1.1166606506,0.0773838831,0,0.5144,0.4123,0.0421743697,0,0,0,0.3105,0,0,0,0,0.0691511847,49.7954,0.9093455531,0.0956347826,1.4155,0.1237,0,0,0,0,0,0.4784,0.0711,0,0,0.1508798928,0.1555502579,54.2606,1.4730428619,0,0.2926,0.8813,0,0.1178370968,0,0,0,0,0,0,0,0,0.1494987879,77.9022,0.4413012335,0,0,0.147,0.2199,0.1652266082,0.1605976115,0,0,0.5692,0,0,0 +HRASLS2,Plasmablasts,2.2652054299,0.1823945017,0,0.1942233285,0.7277417364,0.2191,0,0.1857,0,0.3125499911,0,0.9779,0,27.1959,0,1.1044,1.3871497333,0,0,0.3466275204,1.398613722,0.2157,0,0,0,0,0,0.643,1.6323,23.1449,0.9806,0.5482,1.4981924933,0.4242707163,0.2226,0.0969277513,0.7360338316,1.3366,0,0,0,0,0,1.3025,2.2858,35.3409,0,3.3301,3.893645933,0.3272373737,0,0.0391875488,0.3801585418,0,0,0,0.2268347953,0,0,0.9425,0.7697,60.0311,0.4955,0.2439 +HRH4,Basophils LD,0.3981606335,0.7275515464,137.772,1.0422886317,0.2895120958,0.2637,0.4275,0.4913,0.3911260504,0.351309918,0.6172,0.5274,0.3952,0.146,0.3822,0.1934,0.3858033788,0.6285440907,105.9295,0.7692503935,0.5006549133,0.2846,0.2808,0.2531,0.5331526316,0.326875082,1.0376,0.366,0.7881,0.0335,0.366,0.5195,0.3100844504,0.9965040115,142.7441,1.254413283,0.3350198269,0.2134,0.2816,0.3281,0.5610354839,0.5544985641,0.9224,0.3887,0.4991,0.0499,0.4911,0.2857,0.4113980861,0.2419292929,134.523,0.876550908,0.5652373761,0.4763,0.4423,0.4643,0.2615590643,0.4046902455,0.6301,0.3394,0.3903,0.0705,0.2429,0.205 +HSPA6,Neutrophils LD,7.2199104072,4.6912917526,1.245,0.3536771499,3.8036516166,2.2399,20.2489,46.5932,36.580818067,0,268.384,7.0691,0.353,0.4328,3.6576,3.0178,4.1490596918,2.4646759381,5.1282,0.1211714774,4.9796501634,0,42.3516,31.9303,48.346955263,0.1039344918,267.254,7.229,0.1461,0.9581,2.784,0.8354,6.6567399464,6.4620051003,1.2501,0,4.1425560189,0.0641,10.7582,26.8239,29.89496129,0,155.858,9.8957,0,0.8562,6.7432,2.0906,4.3597923445,4.1723852525,0.8318,0.1311947166,2.2807578811,0.797,21.4985,46.5385,36.16376345,0,334.376,7.7141,0,0.4902,2.8272,0.1351 +IDO1,mDCs,0.0623565611,0,20.4177,0.0659786389,0,0.3261,98.203,0.2815,2.3467886555,0,4.759,2.2292,0,0.1368,0,0,0.0980617664,0.0234261865,13.1612,0.0701771492,0,0,87.0056,3.414,4.6698394737,0,0.0973,1.2855,0,0,0,0.272,0.1086699732,1.882236447,3.3255,0.3076300556,0,0,69.4148,4.3536,1.8003225806,0.3907084737,19.369,0,0,0.1391,0,0,2.1248014354,0.7714593939,12.8501,0.1382707737,0,0,15.8795,1.8349,0.4264795322,0.4614927677,4.4529,0.6811,0,0.1609,0,0 +IFNL3P1,Naive T cells,0,0.1349580756,0,4.3822042937,1.144049959,4.086,0,0,0,12.010200865,0,0,1.5722,0,0,2.055,0,0,0,3.9216221307,1.7443191757,8.0812,0,0,0,10.001656066,0,0,0,0,0,4.0915,0,0.2634817192,0,8.8496602304,0.1161328088,2.5028,2.3548,0,0,16.326680411,0,3.8998,4.6335,0,0,0,0,0.1101987879,0,4.3413609402,0.3574881784,1.6725,0,0,0,15.606040571,0,1.7637,0,0,0.4651,5.0296 +IGF1,Plasmablasts,1.8432769231,1.755128866,0.1573,0.2032928119,0,0,0,0,0,0,0,0,0,25.5425,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3198,0,0,0.1091099196,0.6577304298,0,0.4395360614,0,0,0,0.5386,0,0,0,0,0.4464,12.9715,0,0,0.730169378,0.3539991919,0,0,0,0,0,0,0,0,0,0,0,11.7092,0,0 +IGHA1,Plasmablasts,5890.9758611,161.32352749,6.8345,4.0667697644,0.6173984901,2.9716,2.3058,1.5959,3.7896067227,0.9619761656,23.2311,0.2838,4.1144,36316.3984,0.1542,1.8964,4716.7357803,76.760215499,12.7088,4.024893608,0.8380676552,10.8828,2.4293,0.6461,4.7366973684,0.5275937049,34.0244,11.5153,22.7436,56780.8008,0.1921,0.3194,6349.5022879,859.51364585,21.0192,1.7163764534,2.4978099921,0.4122,28.0458,10.0459,3.9389322581,5.7852758199,43.1133,64.0766,61.5321,43847.3008,2.1194,3.021,6543.8724368,395.48665455,62.4307,3.0108749332,5.8920596744,0.1291,1.8761,0.467,4.6258809942,0.382870152,8.9527,0.6498,24.452,36659.3984,3.5304,0.1417 +IGHA2,Plasmablasts,877.18490045,26.939424055,2.0749,0.849304222,0.0238470376,0.6282,0.4623,0.1545,1.9373117647,0,0.3245,0,2.0964,10363.5996,0,0.3079,592.39886366,7.210426273,6.1936,0.9134029844,0.0832888665,1.0735,0.2329,0,0.3403184211,0.0394194098,1.2994,1.926,1.4937,8098.8198,0,0,997.48088472,241.57924183,7.4376,0.2141544895,0.7154449253,0.2196,10.4819,0.7169,1.1717741935,1.0302595285,0.9756,10.0403,15.0871,15165.5996,0.1859,2.1858,1515.7485124,61.121518384,1.5766,0.6017723155,1.3191556866,0,0.246,0,0.7061853801,0.1604537498,0.456,0.2385,0.2704,6765.7002,1.1233,0 +IGHD,B Naive,33.554085068,2108.9509639,0.4703,0.1973192321,2.9158953225,0,0.097,0,0.7475882353,0.3445640412,0.9242,0.9472,27.4426,2.2077,1.0343,0.1229,33.235139301,1926.6300407,0,0.1596183222,0,0.239,0.0937,0.6162,0.8651657895,0.3474278033,1.4008,0.714,18.6493,0.1617,0.825,0.2031,46.737683378,2001.9309971,1.606,0.7797777579,0.7255627065,0.2964,0.6228,0.5623,0.3486887097,0.2175716185,2.0441,6.0529,17.4357,3.676,0.1564,0.3357,24.718116746,2091.1138404,0.3503,0.7250517097,5.1975614441,0.1572,0,0,0,1.1199148321,0.3836,0.4182,31.2228,2.1422,0.5484,0.1351 +IGHG1,Plasmablasts,2549.3998231,243.08333162,2.8248,1.6559020751,0.3601870507,1.6501,0.6164,0.3738,4.0548340336,0.358875127,14.0074,1.2028,7.4012,15588.1357,0.0946,0.4742,2239.8710581,119.78236346,5.01,4.0825468226,0.30598952,10.3828,5.2539,0.6899,0.7757052632,0.4985876721,2.0353,0.4231,1.4632,25130.041,0.1434,0,1602.510337,471.40665903,3.384,0.9911997682,1.1672344611,0.173,0.3812,2.7898,1.4646806452,3.0952708385,19.6516,7.2835,10.9271,12847.3848,0.4237,2.0333,2658.0397086,389.26489091,23.8293,1.4978949085,3.7862490562,0,2.1829,2.3251,1.0464388889,0.3117771672,0.7636,18.7548,4.6322,45609.582,1.2575,0 +IGHG2,Plasmablasts,1816.6059819,154.04935567,2.8946,1.8740527568,0.3357677335,1.4714,0.4337,0.5985,2.2930147059,0.0671995364,15.0665,4.2872,0.9149,22000,0,0.3779,1195.3155519,79.600195564,3.2138,1.0120164439,0.1326005278,5.6612,1.2223,0.6728,1.9957736842,0.4226070164,0.3682,0.6771,1.4581,18515.4004,1.19e-9,0,1243.7515094,397.45904183,2.1646,0.7199259105,0.9152642014,0.6078,2.0154,4.4592,0.9976096774,2.4848131005,2.1927,6.8055,37.858,14261.2998,0,2.0434,2766.3239191,283.36564242,27.8399,1.838421524,4.6464028315,0.2525,1.2577,3.237,1.8185692982,0.2572944714,0.3361,0.5729,7.8325,33910.1992,0.9842,0 +IGHG3,Plasmablasts,592.92806787,169.0695945,0.6155,0.2963803732,0.1226311013,0,0,0.128,0.4613184874,0.114970986,0.4311,0.8678,0.4868,2392.24,0,0,484.64518239,107.14679087,0.9348,0.238608686,0.0161163106,0.9991,0.5435,0.2539,0.1124052632,0.1201762623,0,0.2649,0.46,3258.1699,0,0,283.10019035,150.57463209,0.129,0.1391082638,0.1147655389,0,0.1638,0.5857,0.2154435484,0.543575749,0.783,9.1767,1.411,1415.26,0,0,957.31636842,129.1223596,1.5955,0.2759848215,0.737215597,0,0.944,0.1127,0.2139862573,0.056154017,0.1986,1.6363,0.8119,6670.2402,0.1339,0 +IGHG4,Plasmablasts,237.19451584,12.641190722,0.3874,0.2004591257,0,0,0.1565,0,0.0925420168,0,0.3346,0,0.2874,1884.28,0,0.1601,249.1455163,7.5638527764,0.2531,0.2090221381,0.0225373209,1.0047,0.2269,0.1407,0,0,0,0.1859,0.2823,3350.5,0.0953,0,128.47676139,27.381896619,0.1475,0.2078395577,0,0,0,0,0.3909725806,0.1851864563,0.5359,0.7715,0.3751,1170.74,0.1628,0.2395,445.08608612,34.852635354,2.3615,0.016314226,1.1521222747,0,0.3023,0.1434,0,0,0,1.4149,0.2135,4341.23,0.1427,0 +IGHJ1,Plasmablasts,218.29699095,196.45794502,0,0,0,0,0,0,0,0,0,0,150.515,2122.3401,0,0,72.634394191,92.482808052,0,1.1408440535,0,0,0,0,0,0,0,0,65.995,1588.65,0,0,275.08746649,298.6584361,0,1.9027181168,0,0,0,0,0,0,0,0,156.654,1661.8,0,0,215.14492344,276.35603232,0,0,0,0,0,0,0,0,0,0,138.839,3389.3401,0,0 +IGHJ2,Plasmablasts,417.38933937,354.04918213,0,0,0,0,0,0,0,0,0,0,57.8904,5895.9399,0,0,269.68026912,278.83623219,0,0,0,0,0,0,0,0,33.2702,0,103.706,4456.3999,0,0,371.56326005,575.20883152,13.0813,2.0264502318,0,0,0,0,0,0,0,0,190.223,4594.1099,0,0,464.14785167,378.45723232,0,0,0,0,0,0,6.189254386,0,0,0,74.7593,4921.75,0,0 +IGHJ2P,pDCs,42.951154299,53.636878007,0,6.8852220787,0,0,0,0,0,4.1077877998,0,0,615.013,22.5624,11.4771,0,34.687189627,8.7005486208,0,0,0,0,0,0,0,0,0,0,434.884,21.0781,7.2577,0,41.579906971,53.53017851,0,2.9706513574,0,0,0,0,0,0,0,11.0314,720.384,27.2549,0,0,28.883230144,41.492900202,0,9.2748252039,0,0,0,0,0,0,0,0,939.374,17.4233,0,0 +IGHV1-3,Plasmablasts,420.22329864,633.77887629,2.6169,14.916026964,9.578365567,1.2887,0.5117,0,2.6335663866,24.678053566,0,6.2356,116.006,5858.0498,7.6166,0,148.49867398,254.36100014,0,1.4120166295,2.5294427746,3.6129,0,0,0,0,0,0,11.1527,2108.3701,0,3.5544,214.83935121,451.74824126,3.5829,10.240949907,5.2331987411,0,0.4783,0,0.1332080645,13.68026318,25.5921,1.6086,139.408,1002.11,10.0643,0,393.11601435,492.01917576,3.4445,1.5121928527,8.2151024068,0,0.6012,0.5943,0.9983005848,1.3846344413,0.6863,7.7826,14.9998,7209.3501,1.4731,0 +IGHV1-12,Plasmablasts,47.443405882,15.131995189,0,1.2996003648,0,0,0,0,0,4.4196578541,0.3849,0,0,156.971,0,0,42.160303082,16.307537314,0,0.8013084781,0,0,0,0,0,0,0,0,4.6795,379.859,0,0,32.85059008,15.165404871,0,0.3659071779,0,0,0,0,0,0,9.4651,2.6288,0,220.339,0,0,19.037401914,2.2125238384,0,0,0,0,0,0,0,0,0,0,0,489.292,2.3632,0 +IGHV1-46,Plasmablasts,135.5511629,273.23075945,0.3213,1.1219732508,0.0212046611,0.2634,0.386,0,0.2919642857,0,0.8622,0,1.2724,2411.6599,0,0,83.104256669,225.07867528,1.0798,0.0759907795,0.2872303594,1.0382,0.2014,0,0.11025,0,0,2.1076,0.1996,1040.79,0,0,163.09145308,296.62690201,0,0.0399631638,0.9223516916,1.6084,3.5617,2.7424,0.2176241935,0.63569727,18.2046,7.5364,5.7764,3811.47,0,0.7283,199.89152632,273.91227475,0,0.6071942096,0.053125295,0,0,0.2983,0.6529602339,0.8242107566,0,1.1646,0.4774,1147.1801,0.5965,0.5871 +IGHV1OR15-2,Plasmablasts,7.1446420814,0.7440494845,0,0,0,0,0,0,0,0,0,0,0,30.6204,0,0,3.5889675756,0.1269457256,0,0,0,0,0,0,0,0,0,0,0,88.1276,0,0,6.5117107239,1.5148892837,0.8307,0,0,0,0,1.4872,0,0,0,0,0,37.2199,0,0,0.6030985646,2.4366608081,0,0,0,0,0,0,0,0,0,0,0,42.7109,0,0 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+IGHV3-7,Plasmablasts,613.56526244,615.88624399,0.7122,0.474340372,0.0515755785,0,0,0,0,0,0,0,1.023,10971.4004,0.6589,1.0929,375.03797036,367.27618502,0.7955,0.0509606236,0,3.8247,0,0,0.1134552632,0,0.708,0.5335,0,6046.0898,0,0,540.11031099,885.59357708,3.3248,0.3903671037,1.0620900865,0,1.1954,0.9982,0.8037145161,0.9083840808,4.9346,29.2252,9.8499,8859.4004,0,1.9283,656.72294258,691.48626263,0,0.5235993559,1.5920074328,0,0.5066,0,0.2841447368,0.3882639051,0,0,0.53,7443.4702,1.2409,0 +IGHV3-11,Plasmablasts,277.69586878,297.05965292,1.3635,0.127094881,0,0,0,0,0.7277865546,0.1975983819,0.0000785,0,0,2492.0601,0.5493,0,72.523583877,158.94220092,2.0969,0.1141003563,0,1.6413,1.8755,0,0.3789394737,0,0,0,0.3578,485.641,0,0,280.90193029,437.7319467,0,0.2182245597,0,0,0.7293,0,0.3384951613,0,0.8424,0.8605,0,2350.3501,0.5898,0,269.08137799,406.10091919,0,0.1362054341,0,0,0,0,0,0.6456839151,0,0,0.433,2691.45,0,0 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+IGLC1,Plasmablasts,1388.6535294,355.77569759,3.4752,1.0850118766,0.0314623338,0.285,2.2737,0,1.240802521,0,8.5047,0,0.8073,10451.4004,0,0,956.37750605,466.6125951,4.5852,1.3495560208,0.063628952,3.8333,0,0.8033,2.0143421053,0,56.9363,0.4685,2.9384,12723.4004,0.3492,0.637,1288.7114129,559.26527278,0.3731,0.5959939213,2.946483871,0,6.0182,5.0248,0.5911435484,2.3355218756,5.7001,13.5251,6.2696,8630.2695,0,2.6582,1348.924967,434.90177172,2.9338,2.0737086069,1.015198655,0.9796,0.1978,0,2.5522426901,0,2.0293,3.2015,8.5585,6938.5498,0,0.5354 +IGLJ2,Plasmablasts,118.75016244,64.473347423,0,0.4149499522,0,0,1.9196,0,0,0,0,0,0,1461.79,0,0,61.576150563,73.106733172,0,0,0,0,0,0,0,0,0,0,0,721.77,0,0,96.472573727,54.221429398,0,0,0,0,0,0,1.0109806452,1.4191515334,0,0,0,769.954,0,0,250.46735885,120.18776566,0,0,0,0,0,0,0,0,0,0,0,842.617,0,0 +IGLV2-8,Plasmablasts,130.63749774,50.740680412,0.4747,0,0.4657307402,0.5321,0,0.3242,0,0,0.4238,0,0,2219.8301,0,0,63.752009484,76.464889363,0.5295,0.0955765256,1.2298928374,1.1113,0,0,0.2181789474,0,0,1.1147,0.2879,1257.63,0,0,130.83315442,95.50149467,2.2449,0,0.8041416208,0,1.131,0,0,0.2503751463,0,1.6549,0,1454.1899,0,1.3047,211.9728756,90.405024242,5.8306,1.6893538683,1.6352367626,1.4304,0.3587,0,0,0,0,3.4287,0.4057,1148.14,0,1.0267 +IGLV2-11,Plasmablasts,255.92611765,140.72064261,0.7767,0.0342839612,10.622730904,5.3998,0.9976,0,1.0073004202,0,0,11.0414,0,2288.79,21.2517,2.6553,95.183008892,110.67047944,0,1.2637282851,10.44782855,3.4021,0,0,0,1.6480582295,0,11.4623,0,1083.8101,10.4314,1.311,190.98018499,107.63918682,0,0.1094728314,7.0593588513,0,2.4075,0,0,0,4.606,12.8332,1.8366,824.938,5.3412,10.9887,472.03145933,279.42294949,27.0076,1.1523889399,14.872969302,0,0,0,0,0,2.6753,12.3604,0,2333.46,18.291,0.8863 +IGLV2-28,Plasmablasts,7.1371936652,0.5606350515,0,0,0,0,0,0,0,0,0,0,0,23.1029,0,0,2.3564841731,0.1609078934,0,0,0,0,0,0,0,0,0,0,0,65.7993,0,0,4.6583536193,0,0,0,0,0,0,0,0,0,0,0,0,15.575,0,0,7.2878248804,2.6915868687,0,0,0,0,0,0,0,0,0,0,0,25.3702,0,0 +IGLV2-33,Plasmablasts,3.524040724,0.1010886598,0,0,0,0,0,0,0,0,0,0,0,29.9662,0,0,4.095866805,0.0896843284,0,0,0,0,0,0,0,0,0,0,0,40.7785,0,0,6.4402522788,1.0147518052,0,0,0,0,0,0,0,0,0,0,0,22.6821,0,0,2.9169990431,1.4567729293,0,0,0,0,0,0,0,0,0,0,0,13.341,0,0 +IGLV3-1,Plasmablasts,203.16673891,90.185238488,0,0.1074275565,0,0,0,0,0,0,0,0,0,705.547,0,0,65.74797866,105.4365608,0,0.2832535709,0,0.6291,0,0,0.2647105263,0,0,0,0,413.53,0,0,81.596602681,93.995067507,0,0.0947888359,0,0,0,0,0.1311951613,0,0,0,0.5562,347.695,0.8289,0,295.5277512,242.1130303,5.5701,0.456539005,0,0,0,0,0,0.2499836813,0,0,0,2488.27,0,0 +IGLV3-10,Plasmablasts,61.403664253,37.673712027,0,0,0,0,0,0,0,0,0,0.7643,0,587.241,0,0,63.45046473,64.059267224,1.8544,0,0,0,0,0,0,0,0,0,0.5016,1801.28,0,2.2569,56.558175603,62.268601032,0,0.2141405774,0,0.8305,0.4822,1.8576,0.6981209677,0,0,0.8092,0,1617.66,0,0,117.23197751,62.32746,0.9892,0,3.5783995989,0,0.6305,0,0,0.2659043761,0,0,0,170.947,0,0.7589 +IGLV3-17,VD2+,1.2124411765,0,0,0,4.5032879862,0,0,0,0,0,0,0,0,3.3404,1.9739,12.7859,0,0,0,1.5382603563,1.5246993717,0,0,0,0,0,0,6.4304,0,0,0,17.7216,0.1050160858,0,6.6916,0,0.9259137687,0,0,0,0,0,0,5.8824,0,1.1097,4.2624,0,0,0,4.7992,1.7597585418,11.806153752,0,0,0,0,0,0,0,0,1.5837,0,9.0971 +IGLV3-19,Plasmablasts,306.7359819,226.91651546,7.904,0.6565501375,0.1680206795,0,0.5625,0.5872,0.8074277311,0,0,0,0,1639.48,0,3.5581,66.74085655,199.96936853,6.8103,0.4492169488,0.094316562,0.7034,0,0,0.2958789474,0,0,0,0.5264,664.329,0,3.5541,300.31171314,277.00204298,2.1725,0.2699926301,0.7975118017,0,0.5058,1.9489,0,0.4193204751,0,0.85,0,3477.73,0,0,378.90645072,317.23469495,2.0784,0.3756433153,4.5289290231,0,2.647,0,0,0,0,0,0.6272,5643.5498,0,0 +IGLV4-69,Plasmablasts,91.107576923,59.993217869,0,0,0,0,0,0,0,0,0,0,0,1064.08,0,0,62.196481921,105.41856032,0,0.1072404454,0,0,0,0,0,0,0,0,2.1272,506.96,0,0,96.171489812,123.13441948,0.5833,0.4643233082,0,0,0.8197,0,0,0,1.0498,0,0,1047.22,0,0,120.41548325,109.92531111,0,0.1028467964,0,0,0,0,0,0,0,0,0,647.755,0,0 +IGLV7-46,Plasmablasts,107.90552534,47.11412543,0.8406,0.0370881593,0.0268258657,0.3393,0,0,0,0,0.7494,0,0,1463.11,0,0,132.99445287,74.962864372,0,0,0,0,0,0,0,0,0,0,0.2522,1489.17,0,0,120.72499651,48.626964642,0.3466,0,0,0,0,0,0,0,0,0,4.5949,577.062,0,0.9476,169.76852153,104.78183354,0,0,0,0,0,0,0.1702716374,0,0,0,1.2006,494.763,0,0 +IGLV8-61,Plasmablasts,94.20620905,91.734134021,0,0,0,0,0,0,0.3130252101,0,0,0,0,1287.65,0,0,41.438016598,72.460191674,0,0.144474343,0,0,0,0,0,0,0,0,0.4356,1828.16,0,0,140.6523319,160.02327622,0,0,0,0,0.8393,0,0.2423870968,0.3462921468,0,0.7012,6.1567,709.86,0,0,94.441255981,64.691823434,0,0.1061769616,0,0,0,0,0.2935195906,0,0,0,0,499.197,0,0 +IGLV10-54,Plasmablasts,17.313471041,11.734764605,0,0,0,0,0,0,0,0,0,0,0,356.8657,0,0,8.7504893894,15.960445164,0,0,0,0,0,0,0,0,0,0,0,258.6357,0,0,47.338824933,32.175035244,0,0,0,0,0,0,1.2663258065,0,0,0,0.666,599.205,0,1.0583,67.614113397,49.549207071,0,0,0,0,0,0,0,0,0,0,0,223.9479,0,0 +IL1R1,Neutrophils LD,0.1147081448,0.1256003436,1.931,1.6811007655,0.2363322501,0.1189,3.6221,1.1717,0.1600331933,0.2513381207,19.6945,2.1599,0.1563,0.0198,0.2274,0.1439,0.6477321873,0.3061530645,0.6047,2.9633279065,0.1639786127,1.0901,4.1889,1.13,0.1788631579,0.109078623,20.3952,0.0911,1.443,0.0213,0.1117,0.156,0.4259627346,0.147093639,3.4855,1.9371090253,0.0987627852,0.9521,0.8112,0.087,0.3740225806,0.2698449566,23.8354,0.3582,0.0817,0.0254,0.0605,0.2621,0.2731100478,0.2567313131,0.7032,2.2400820736,0.2216889335,1.5471,1.2297,0.9441,0.2239298246,0.2347613329,18.806,0.2004,0.1008,0.0809,0.1849,0.0262 +IL1R2,Neutrophils LD,0,0,1.7283,0.1128977156,0,0,73.5025,4.7976,2.0246352941,0,563.8909,0,3.4719,0,0.2415,0,0.913379668,0,0,0.8037494803,0.1365485046,0,79.9092,7.4077,4.9981921053,0.2592905574,461.096,0,2.4767,0,0.1957,0,0.0394230563,1.6919694556,0,2.2764752417,0.1820166798,0,47.5372,2.6686,1.0469935484,0,366.9946,0.2375,0.5081,0.3362,0,0,2.3413760766,0.8265870707,1.6629,0.2643147536,0,0,74.4466,10.8834,1.293824269,0,693.6937,1.0303,0.1754,0,0,0 +IL1RL1,Basophils LD,0.1418900452,0.0387628866,289.535,0.6428295599,0,2.6253,0.0373,0.0788,0.0532563025,0.1996744005,0.2479,1.7895,0.2642,0.0196,0.0561,0.1391,0.0536626556,0.1174811235,260.4329,0.0922053007,0.158635235,0.4939,0,0.0387,0.0748973684,0.1186175082,0.2151,0.045,0.0343,0,0.1415,0,0.0126780161,0.2661070487,268.6212,0.106688379,0.0129639654,1.444,0,0.1279,0.0674145161,0.0824037227,0.3009,0,0.4516,0.0337,0.1174,0,0.4899990431,0.1832161616,281.1715,0.5029848832,0.1160728646,1.3459,0.2517,0,0.0235891813,0.0827391849,0,0.0394,0.321,0.0913,0.0519,0.1015 +IL2RB,NK,9.9827886878,5.6765975945,1.395,84.706572904,324.14445444,128.6399,3.1112,0.308,7.1038747899,31.437553829,3.4405,767.1074,1.3368,9.155,256.0057,274.9642,12.245992116,9.179085884,2.5284,74.759250772,256.73954984,126.6521,2.3192,0.3462,12.197942105,78.049033902,1.4019,874.3648,1.737,5.5535,331.8046,174.3639,13.343307239,19.038634499,2.3689,78.329043802,201.98692895,156.3963,2.7488,1.2472,34.694791935,33.042314767,6.6661,1415.6975,1.6624,6.0726,358.2129,220.6615,19.033208134,14.65224101,4.2654,59.36815554,205.41697869,100.7884,0.5261,0.3657,8.4807385965,26.057174678,0.4454,829.9004,0.9093,7.4707,227.2895,173.134 +IL4,Basophils LD,0,0,209.7819,0.2836598314,1.1834370425,0,0,0,0,0,0,0,0,0.4526,3.9807,0.5887,0.2164871962,0,123.1501,0.2865940312,2.5723728826,0,0,0,0.4555894737,0,0,0,0.6861,0,2.5564,0,0.038308311,3.5433882521,385.3031,0.0876495828,0,0,0,0,0,0,8.7916,0,0,0,0,0,0,0.1999426263,200.5246,0.5079277325,0,0,0,0,0,0,0,0,0,0,0,0 +IL12RB2,VD2+,0.3379674208,0.2718185567,0.2633,11.560844008,4.2398357131,35.5518,0.0508,0.1915,0.8217142857,2.5857964607,0.3091,46.0623,0.0305,0.1254,5.8147,161.9886,0.2714288085,0.8547796111,0.0433,9.7180223682,2.5290224931,38.9979,0.9338,0.0269,0.1586236842,0.4717430164,0,22.9203,0.0476,0.2166,8.23,105.6483,0.7591903485,0.138073639,0.2901,26.474778308,5.4641058222,25.5524,0.1148,0.0883,1.4586370968,3.5375243219,0.0298,51.5842,0.0552,0.0234,2.6633,46.6403,0.311130622,0.3861743434,0.0918,13.848491345,3.7723319726,31.0029,0.2328,0.0582,0.5237397661,1.2205516118,0.1332,30.5022,0.2154,0.0619,3.8847,73.1564 +IL13,Basophils LD,0.2615113122,0,17.966,0.7794366822,0,0,0,0,0,0,0,0,0.5504,0,0,0,0.6382406639,0,2.15,0.1905260431,0.2397744157,0,0,0,0,0,0,0,0,0.357,0,0,0,0,13.345,0.4812166269,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.532,0.1178657301,0,0,0,0,0,0,0,0.6761,1.1239,0.2952,0,0 +IL17RE,MAIT,1.555019457,0.4550841924,2.6395,5.0770205777,0.2556268669,15.1463,0.1995,0.4452,0.1874109244,0.3019042837,0.5811,1.0326,0.3942,0.0985,0.8188,1.0032,0.071015412,0.3533032913,5.0241,3.6274619748,0.1889921588,13.4983,0.5475,0.2971,0.3351236842,0.6409540984,0.7328,0,0.3914,0.1462,1.7256,0.1729,0.1062096515,0.2623079083,1.345,7.3213306516,0.9030498033,24.5309,0.2716,0.7403,0.2630516129,0.2018567453,0.4506,0.0783,0.3862,0.1106,0.0843,0,0.0198277512,0.4189288889,1.1052,2.8307701021,0.9327638509,9.087,0.4237,0.5844,0.3159815789,0.5114899115,1.0187,0.4098,0.3247,0.092,0.2178,1.4889 +IL23R,MAIT,0.1869312217,0.0697340206,0.2809,4.2686996532,0.4865650583,63.9592,0.1198,0.0384,0.1995768908,0.2290790185,0.2306,0.5914,0.1933,0.1229,0.6479,6.9675,0.1866237107,0.2944695211,0.2606,0.3395571641,0.3010119125,38.1607,0.132,0.2038,0.2209,0.2964165902,0.1077,0.3147,0.0527,0.038,6.5322,7.9488,0.1739646113,0.1314738109,0.3407,6.5271056019,0.6367147915,98.1079,0.166,0.1577,0.0409387097,0.5296190746,0,0.9623,0.1828,0.0171,0.3277,2.6242,0.1504105263,0.1166525253,0.2801,5.0027069348,1.43679311,58.2775,0.2253,0.241,0.1272576023,0.5346286454,0.2492,0.8501,0,0.1202,0.6124,17.082 +ILDR1,B 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cells,0.4342968326,0.1209003436,0.3797,1.051162062,0.0890776629,0,0.053,0.1127,0.1572218487,6.5590669965,0.1896,0.0858,0.1254,0,0.0928,1.5238,0.1637054535,0.2083662081,0.2388,3.454790438,0.2827948731,0.0321,0.1088,0.1764,0.1401815789,14.39749941,0.1651,1.8774,0.2427,0.0152,1.5,0.0893,0.0167565684,0.081906361,0.6907,3.4973885711,1.2331819827,0.4544,0.0257,0.2408,0.1423790323,13.075903404,0.0321,0.9814,0.0843,0.0119,1.223,0.3766,0.0877803828,0.1579478788,0,1.8049269102,2.9866492213,0.3893,0.0298,0.182,0.0593567251,30.324967079,0.106,1.7471,0.1451,0.0165,0.8789,0.8779 +KRT81,Basophils LD,0,0.0223037801,25.776,0,0,0,0,0,0,0,0,0.248,0,0,0,0,0.0807279194,0.0477061937,32.6698,0,0,0,0,0,0.0370815789,0,0,0.3374,0.5447,0,0,0,0,0.1266838968,22.9607,0.0100411204,0.2229551534,0,0,0,0.0976048387,0,0,2.3588,0,0,0.0925,0,0,0,52.0861,0,0.0134013686,0,0,0,0.2081067251,0,0,2.8048,0.1742,0,0.1684,0 +KY,Neutrophils LD,0.1716764706,0.0590453608,8.6773,0.0777369872,0.0542692762,0,0.0711,0.3839,0.3797436975,0.1694975707,71.106,0.0328,1.6,0.2073,0.0715,0.0299,0.6278948429,0.1850717465,9.7664,0.1846735486,0.0628243277,0.7563,0.0246,0.2434,0.3923921053,0.3404474098,25.0512,0.6649,0.2654,0.0576,0.0675,0.0247,0.0609541555,0.0927582808,7.3836,0.0296484108,0.4729297404,0.1799,0.0467,0.0592,0.0564241935,0.052887059,26.2422,0,0.0771,0,0.0402,0.0408,0.0385861244,0.151359798,9.3623,0.1713907147,0.0315432043,0,0.0542,0.6091,0.5705070175,0.0482837481,67.5185,0.3899,0.0259,0,0.0335,0.1322 +LAMB2,mDCs,0,0.6937749141,0.6034,0,0,0,12.4101,12.3422,4.7874478992,0,0,0,5.2165,0,0,0,0,1.4260598272,2.529,0.0592829547,0.0325586328,0,33.7456,22.8669,3.3843605263,0,0,0,8.9272,3.7623,0,0,2.6113589812,2.2770454441,0,0.0031656734,0.4971868607,0,18.0682,10.8511,0.7402241935,0,0,0,5.732,0,0,0,0.1922220096,0.9522082828,1.4652,0,0,0,17.173,11.6785,4.0772125731,0,0.6814,0,4.5443,0.9448,0,0 +LAMP5,pDCs,1.021600905,0.8357439863,0.2258,0,0,0,3.83,1.7124,0.3692676471,0,0.1132,0.7608,274.4537,11.5644,0,0,5.5149128038,0.6903273749,0,0,0,0,1.4921,1.3168,0.1570578947,0,0,0,247.529,10.5913,0,0,4.0265120643,0.2024302579,0,0,0,0,7.7634,1.154,0.1818806452,0.036729587,0.0584,0.7436,278.7717,6.2459,0,0,4.1624799043,1.7102577778,0,0.0094882067,0.0900560642,0,7.4273,1.0825,0.1562143275,0,0,0.5351,338.2188,4.9193,0,0 +LARGE1,B Naive,9.2794556561,36.24272268,1.082,0.7401032472,0.5497605285,0.397,0.6937,0.5441,0.5397537815,0.373401052,0.4105,0.5065,0.3279,1.1756,0.5845,0.5316,19.344318731,84.150388931,0.9882,0.5022662584,0.8206206585,0.7742,0.6211,0.3507,0.7092631579,1.2742316721,0.4502,2.6126,0.4879,2.0203,1.0441,0.3591,7.749950134,22.759208711,1.1645,0.5137006622,0.7261587726,0.4752,0.574,0.1752,0.4076580645,0.8741094841,0.4995,0.6203,0.278,1.3457,0.6843,0.2529,6.1742430622,35.191290505,1.023,0.4083260604,0.5633458707,0.3506,0.8101,0.3412,0.2911654971,0.5054613162,0.8288,3.6073,0.2881,4.673,0.5725,0.3038 +LDB2,NK,0,0.417104811,0,0.1251094726,0,0,0,0,0,0,0,27.0909,0,0,4.307,0,0,0,0,0,0,0,0,0,0,0,0,8.3526,0,0,5.354,0.3434,0,0,0,0,0.3089853659,0,0,0,0,1.5617614253,0,9.9449,0,0,0.4092,0,1.3339440191,0,0,0,4.0924851345,0,0,0,0,0,0,29.5461,0,0,0.4428,1.9511 +LEF1,Naive T cells,0.7901624434,3.0471790378,0,578.55483992,149.07330128,11.357,0,0,0.2280138655,1123.5208189,0.4098,41.0764,0.3324,5.3825,147.2562,171.5053,0.3813525193,1.2615393878,0,491.77218336,64.582873913,4.2568,0,0.3506,0.8618710526,1019.3982625,0.1152,26.2743,0.2079,6.9458,161.0379,109.4904,1.2819093834,8.0365804585,0.3317,494.83012598,19.931407789,2.9224,0.2697,0.485,0.0723387097,1061.4196098,0.0835,10.1198,0,7.9874,94.0495,13.6608,1.1050985646,4.3867668687,0.1297,419.81881224,101.72774677,2.4288,0.3468,0.3177,0.3556459064,947.55275599,0,11.2835,0.1533,4.6108,126.117,111.1494 +LEF1-AS1,Naive T cells,0.4913393665,0.2766213058,0.6338,8.2648169956,0.3823478746,0.2913,0.1019,0.3324,0.2222915966,41.563116649,0.1168,0.3665,0.0438,0.0909,2.6918,5.3659,0.5994100771,0.1990065538,0.1072,5.0112001559,0.2419164866,0.0935,0.3287,0.1344,0.1663763158,33.29788059,0.3962,0.3181,0.1211,0.0384,1.8578,0.1396,0.2298965147,1.1102506017,0.4202,14.131859687,0.2944372148,0.203,0.316,0.1862,0.2514467742,38.706597979,0.3212,0.4558,0.5015,0.1672,2.0533,0.8713,0.5486751196,0.1965628283,0.9638,5.5671127595,4.7768691128,0.1185,0.6182,0.2761,0.2901005848,33.122901136,0.2286,0.2533,0.1527,0.088,5.8028,4.4229 +LGALS2,mDCs,1.5311963801,3.5947975945,0,0,0,0,181.6169,138.592,24.737992017,0,0,0.3462,1.1879,0.3894,0,0,2.2605103734,7.2183618797,0,0,0,0,192.1534,58.5559,10.904784211,0,0.399,1.211,3.6847,0.273,0,0,3.3381193029,3.5705732951,0,0,0,0,40.2536,34.1526,5.0363370968,0,0,0.3683,0,0.2198,0,0,5.9673937799,8.2258529293,0,0,0,0,122.137,117.817,35.506482456,0,0,1.5986,0,0.3051,0,0 +LGALS9B,NK,1.5134031674,1.9212254296,2.8099,1.4521309114,3.8574048252,0,2.0903,1.8671,1.7122462185,0,6.1664,123.7983,1.4648,0.8132,19.2027,1.4391,0.4784493183,0.9999540511,2.5358,1.0603171195,0.8386655944,0.8998,0.5673,0.7168,1.4204578947,0,0,54.2439,0.0856,0,7.7732,5.7699,2.6350820375,0.6844915186,0,0.2036276718,1.2432411487,0,0.0965,0,1.8111596774,0,1.7744,59.372,0.6531,0.3684,2.1647,0.9977,0.1581779904,2.6407870707,2.2757,0.3625286096,3.7803504483,3.0112,2.0016,0.6069,1.8026853801,0,1.3887,97.9306,1.3266,0.1108,5.6113,1.4658 +LGALS9C,NK,0.1791841629,0.2094137457,5.0883,1.2971097177,1.6981552765,0.0882,0.6425,1.1958,1.4662079832,0,26.4455,100.8668,0.308,0,4.4233,1.5599,0.6352024896,0.107540641,3.328,0.3497806904,0.4826236492,0,0.2855,1.6659,4.0214078947,0.0271388852,16.9742,47.7892,0.111,0,2.7683,0.0406,0.1713080429,6.7666847564,17.1304,1.6847794199,0.5475634933,0.0507,0,0,1.406416129,0,20.9999,94.1667,0,0.4563,2.6856,1.4962,0,0.9695985859,3.5557,1.2979445282,2.0856540349,0,0.2853,0,5.5964859649,0,21.1823,90.6423,0,0,1.0981,1.2285 +LGALS12,Monocytes C,1.1395683258,0.1798175258,5.3649,0.0515033608,0.005818152,0,4.7992,22.2914,1.5887336134,0,5.3608,0,0.0692,0.0306,0,0,0.1133966212,0.0919888153,5.3093,0.0512053378,0.076813898,0.1418,2.3977,19.6463,0.0569947368,0.0713225574,1.445,0.2828,0,0.2288,0.0547,0,1.4399466488,2.3113384527,10.9672,0.1242226659,0.1017038552,0,4.5366,14.8299,0.8486709677,0.0866676653,3.1503,0,0,0.0344,0.0928,0,3.2215650718,2.5674072727,6.8466,0.1746565614,0.2492176498,0.073,9.98,41.3477,2.5552611111,0.0289641557,2.3007,0.3239,0,0.2008,0.0813,0.0802 +LGALSL,Neutrophils LD,0.4334289593,0.6220474227,0.6284,0.2998413826,0.1097078615,1.6536,0.0294,0.0949,0,0.0176759517,11.0783,0,0,1.2538,0,0.0744,0.1797772377,0.7595037883,0.1001,0.2577554937,0.5273465946,0,0.0569,0.0311,0.0444631579,0.2649180328,18.7725,0.036,0.0275,0.1308,0,1.2279,0.660436193,1.8200241261,0.1119,0.3207797047,0,0,0.0541,0.0681,0,0.4549576494,5.6235,0.0873,0.0638,0.8677,0,0.2533,0.9377186603,0.6254672727,0,0.2266540602,0.0074427796,0.6206,0.101,0.6432,0.5069926901,0.0660872557,38.006,0,0,0.3829,0,0.4101 +LGMN,pDCs,97.618211312,33.544613402,22.2164,15.537026193,5.3344266864,9.3924,106.7,7.5615,7.8707331933,37.224962307,13.8708,15.5288,518.5305,23.9399,12.5287,8.5492,68.884711855,28.575134843,15.7894,15.66821948,3.891988188,8.228,112.6656,8.7694,13.947897368,34.885198951,22.8744,13.8408,538.2664,30.0101,7.194,9.3755,57.580469169,28.816397307,26.1621,18.026070825,6.5315774194,6.2495,113.706,10.8031,24.903419355,36.501600195,11.5587,22.3151,768.5641,18.3808,6.2155,1.621,62.531922488,23.178316364,11.3283,12.272709443,8.8789806984,7.7071,51.2276,5.6426,8.8582640351,21.881802405,11.3439,11.3775,518.2874,22.4396,11.4447,8.0573 +LILRA1,Monocytes NC+I,8.1468113122,5.1935982818,7.6606,0.0651076067,0.0625431315,0,179.8563,351.5019,861.01177143,0.1854163279,283.9766,2.6926,35.6948,0.9019,0.175,0.2426,4.4075133966,15.990340735,11.2077,0.0532348998,0.0181574265,0.1062,124.2863,309.0826,743.59163421,0.0661868197,170.8033,3.2041,57.8919,1.2049,0.0733,0.0452,26.160698391,52.928501375,10.1554,0.0776205072,0.2339440598,0.0658,175.8907,368.9002,1053.129721,0,140.6727,1.7503,27.8166,2.6534,0.1762,0,16.833492344,24.97354303,14.0043,0.2237726513,0.1075117744,0.1223,209.184,384.9857,847.52158801,0.0685646066,245.6374,1.5582,42.1645,4.7252,0,0 +LILRA4,pDCs,7.7272384615,10.10701134,0.4277,0.0692191843,0,0.3491,16.6792,1.8264,2.6291823529,0,2.1278,2.3379,3629.0349,0.1463,0,0,12.08449917,13.791920058,0,0.1223016481,0.0698620256,0.1313,7.6441,1.1491,3.2960315789,0,1.2322,3.0999,2488.874,2.1718,0.6751,0,10.95599571,25.378518625,0,0.0315137068,0,0,8.7807,1.7622,2.7983645161,0.2848735508,0.1249,19.1225,3577.4709,0.3617,0,0.7554,28.981948325,35.529122424,0.1909,0.1691812513,0.1098448561,0,15.0485,7.3432,11.536686842,0,9.3186,4.6063,5821.27,0.1722,0,0.1458 +LILRB2,Monocytes NC+I,37.683928959,30.467356357,144.996,0.304809036,1.9556588052,0.055,198.9344,556.617,1327.3333605,0.2761228894,605.942,4.0379,71.9352,2.1035,3.0531,0.2825,19.923122999,22.342689968,197.0711,0.1824699777,2.0605816537,0.3616,207.8267,586.3914,1394.9216868,0.3886327869,576.9363,8.9924,103.4275,2.2824,1.7198,0.1503,57.648114209,72.621960974,156.5488,0.1500184744,1.7799823761,0.1141,175.1673,565.535,1691.5723419,0.3320501861,447.1071,2.5314,82.0654,6.8732,1.0697,0.3085,28.882029665,38.579632929,65.8467,0.340156671,2.299167579,0.2751,180.63,542.8893,967.28661111,0.342119676,359.7312,3.3532,48.9284,5.3404,2.459,0.3604 +LILRB4,pDCs,3.7442665158,2.8279419244,3.1372,0.6456454491,0.4640792385,0.4312,118.9812,121.4746,121.95016681,0.7478659401,13.0933,2.5324,694.0947,39.3998,0.9448,0.4279,5.2017362774,5.9572835074,2.4202,0.8008273422,1.3977940437,0.4073,91.917,108.9073,88.239176316,0.951420459,9.3795,1.3829,540.8602,28.4443,1.313,0.2384,7.5527683646,19.780953181,1.4385,0.718376023,0.3187691581,0.1103,108.3591,84.1879,141.73562097,0.6506876795,8.2798,0.9305,490.0162,30.166,0.4871,0.4986,4.6707976077,8.9739913131,2.1655,0.8805327691,0.5117733837,0.5291,160.0976,112.7561,123.45132632,0.989525522,15.3784,1.3974,639.3086,26.5119,0.8205,1.3015 +LINC00211,Neutrophils LD,1.6507561086,1.9812120275,3.2525,0.7887570028,0,0.6953,0.9567,2.6409,0.3607983193,0.4612547606,22.9852,0.7258,0,0,1.6017,0,0.3058043865,1.4870795175,3.6354,0.5415157684,0.6044695903,3.4279,0.2174,1.1695,0.2774289474,1.7082856393,13.1142,0.318,0,1.0272,0.4321,0.4599,1.2144892761,1.0145487106,9.3362,0.502468726,0.8148666404,2.6362,1.0817,5.3436,0.4399612903,0.1905414643,38.5042,0,0.2912,0.0172,0.4172,0.3946,0.5102861244,1.257720202,4.2691,1.1251347907,0.4755194195,0,1.1626,7.9145,0.8833201754,0,28.9021,0.9571,0.2665,0,0,0 +LINC00298,NK,0.0976452489,0.037795189,2.3343,0.5242632939,0.1592975546,1.4416,1.0618,0.0483,0.414355042,0.2859995721,0.1216,13.0487,0.0538,0.0915,0.0679,0.9104,0,0,1.1876,0.1801379584,0.0225656698,2.1402,0.0434,0.095,1.0683736842,0.2162434754,0,13.8691,0.042,0,1.531,3.1231,0.2412281501,0.2336155301,3.9123,0.2903183221,1.5225223446,1.6066,0.632,0,0.2552403226,0.1281765644,0.1053,12.1475,0,0.0781,1.8133,3.4474,0.0463674641,0.0897945455,2.4759,0.7116989927,1.5570702218,4.7704,0.8588,0.4624,0.7020657895,0.0225383331,0.1769,7.8783,0.0492,0.0278,1.0346,0.7617 +LINC00475,pDCs,0,0,0,0,0,0,0,0,0,1.6961698939,0,1.2944,55.0246,0,0,0,0,0,0,0,0,0,1.4743,0,0,0,0,0,32.5437,0,0,0,0,0,0,0,0,0.6863,0,0,0,0,0,0,25.0491,0,0,0,0,0,0,0.0574549716,0,0,0,0,0,0,0,0.2978,57.1393,0,0,0 +LINC00694,Neutrophils LD,1.014638009,0.1184467354,9.9467,0.2897644421,0.1845379452,0,1.6107,5.306,1.3911945378,0.5897701212,24.0928,0,1.1733,0.2954,0,0,1.3860036159,0.780223745,5.9282,0.5275516778,0.9114708721,1.4962,1.5301,1.9978,0.2904842105,0.7255312131,26.6334,0.4158,0.3042,0,0,0.5952,0.4206702413,1.0483167335,9.5347,0.1408891074,0,0.6143,0,1.6129,0.222216129,0.1371408615,29.6413,0,0.5476,0,0,0,0.0833971292,1.1354290909,10.0601,0.6959642089,0.1337299434,0.4543,2.1475,5.3682,0.637944152,0.4704434942,26.9244,0.5658,0.7357,0,0.5083,0.976 +LINC00865,pDCs,1.1079719457,0.2565391753,0,0.4121008073,0,0,0.7907,0.8757,0.3276159664,0.3610152403,0.0333,0.205,32.9501,0.0441,0.1887,0,0,1.669405848,0,0.2357614254,0,0,0.0477,0.1564,0.0218842105,0.5777704918,0,0,27.4336,0,0,0,0.2381924933,1.2485644699,0,0.0581307178,0.2094429583,0,1.1561,0,0.1083612903,0.3669755717,0,0.1236,31.0567,0,0,0,0,1.3095476768,0,0.5493553964,0.4362647475,0,1.477,0.338,0.3302111111,0.6438987807,0,0,53.6982,0,0,0 +LINC00892,CD4+ effector,0.5677791855,0.294967354,1.8519,23.911809831,4.4272490727,10.4852,0.978,0,0.3896533613,0,0,0.8344,0,0.1558,1.7994,4.3538,0.0739114404,0,0,27.890423125,1.3743456396,1.4256,0,0,0,1.4523958033,0,1.9847,0,0,1.8309,3.095,0,0,0.0767,12.030486995,2.2619500393,0.7233,0,0,0,1.0972560184,0,0,0,0,0,2.2792,0.0632076555,0,0,29.659235174,11.235476994,8.8778,0,0,0,1.6788344246,0.2592,1.7331,0,0,2.0406,7.6947 +LINC00929,Basophils LD,0,0,26.3465,0,0,0,0,0,0,0,0,0,0,0.1636,0,0,0,0,15.2927,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32.5916,0,0,0,0,0,0,0,2.1604,0,0,0.0833,0,0,0,0,35.1163,0,0,0,0,0,0,0,0,0,0,0,0,0 +LINC00968,Monocytes C,0,0.4043931271,0,0,0,0,2.5527,6.5041,4.7216151261,0,2.2297,0,0,0,0,0,0.1033766449,0,0,0,0,0,6.3036,10.4605,5.9194184211,0,0.4712,0,1.0027,0,0,0.0441,0,0.7799825788,0,0,0,0,1.9913,12.1421,5.7917387097,0,1.5684,0,0.3003,0,0,0,0.1822411483,1.206260404,0,0,0,0,1.3953,14.0435,6.5952321637,0,1.1313,0,0,0.0261,0,0 +LINC00996,pDCs,24.002009502,10.159253952,2.1547,3.5644905454,2.4984937469,2.2409,0.2577,0.6432,0.6992936975,1.3713149594,3.1392,5.7744,268.365,1.1362,3.2636,12.7794,12.004116064,4.6221574217,0.8849,3.1366677209,1.9337016838,7.7776,0.4501,1.5053,1.3145657895,3.0075736393,3.5246,8.6508,180.79,0.2709,2.5646,13.7632,12.318106166,10.871383381,2.4308,2.0590488876,2.3053585366,1.9328,0.6949,1.5298,3.2031209677,2.6458231165,3.6403,18.1912,220.2118,1.1855,1.3531,7.707,12.447997608,7.0153494949,0.6876,2.7303443911,1.3719555687,0.6864,0.7333,0.5067,1.9852733918,1.6233545348,1.3846,4.5141,340.35,1.1039,2.9585,4.0185 +LINC01013,B Naive,2.2957977376,20.248048797,0.3736,0.4192103158,0.2100114722,0.5381,0.3103,0.2829,0.2236268908,0.6738126103,0.4848,0.3617,0.142,0.262,0.0945,0.3366,3.2827270302,27.244003205,0.3588,0.2880377951,0.4820632571,0.1641,0.3169,0.6013,0.2270973684,0.5825051803,0.3693,0.1624,0.0562,0.4354,0.1253,0.6182,1.519530563,18.427295301,0.816,0.3837405311,0.0886791503,0.1807,0.0633,0,0.1596548387,0.145954423,0.3915,0,0.1308,0,0.4791,0,0.7069956938,9.7781131313,0.114,0.232137285,0.1676647947,0.1523,0,0.1478,0.1154538012,0.528500618,0.0851,0.2786,0.2106,0.0733,0.2803,0.1829 +LINC01032,Neutrophils LD,6.9083850679,0.9140773196,6.6101,0.6273045748,0,0,1.0451,9.8345,0.8040567227,0,62.9587,4.5134,0,0,0,9.2234,1.1795708358,0,9.1087,0.248886637,0,0,2.9866,5.5383,3.1522447368,6.6828786885,127.265,0,0,0,0,0,0,0,2.7162,0,1.4909937057,0,2.8027,3.6244,0,1.5694089346,30.2468,4.7766,0,0,0,0,1.3384942584,1.2704961616,11.6908,1.4259733776,0,0,0,10.9818,2.0983114035,0.5213548021,40.9794,1.1466,0,0,0,0 +LINC01127,Neutrophils LD,0.2375828054,0.1865817869,0.4915,0.1444623669,0.1254435418,0.0353,0.423,4.5729,0.0233121849,0.1107043862,11.1525,0.0394,0.0675,0.1704,0.4324,0.1632,0.0903915234,0.1495095859,0.1021,0.1282780252,0.0748389545,0.1664,1.6956,3.6673,0.0608289474,0.0549580328,14.5315,0.4351,0.186,0.0143,0.0265,0.0337,1.5218549598,0.0385210888,0.3809,0.2004527215,0.2204472069,0.0469,0.3935,2.5035,0.1517967742,0.2131385747,9.3733,0.1032,0,0,0.1282,0.0889,0.2207933014,0.0680543434,0.2589,0.1358417597,0.1640244691,0.2145,1.2924,6.9748,0.5149292398,0.0632991482,23.5779,0.069,0.1092,0.0347,0.0725,0.0818 +LINC01225,pDCs,0.2239330317,0.6743934708,0.0667,0,0,0,0.2647,0,0.0356567227,0,0.1196,0.6746,102.0189,0,0,0,0,0,0,0,0.0080085449,0,0,0,0,0,0,0,23.8046,0,0,0,0,2.0315767908,0,0,0,0,0,0,0,0,0.1702,0,97.0936,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,91.8713,0,0,0 +LINC01229,Basophils LD,0,0,21.9408,0.0592384822,0,0,0,0,0,0,0,0,0,0,0.8301,0,0,0,33.8143,0.0329745212,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4542927221,30.6115,0.3011489604,0,0,0,0,0,0,0,0,2.7704,0,0,0,0,0,9.0713,0.2534027547,0,0,0,0,0,0,0,0,0.201,0,0,0 +LINC01270,Neutrophils LD,0.6343167421,0.3005487973,2.5034,0.4682529243,0.6938959133,0.1576,0.6783,0.7184,2.1884663866,0.1453638005,17.5565,0.7024,1.3606,0,0.7624,0.3183,0.1435163011,0.056667166,1.5058,0.5163564588,0.3353205579,0.465,0,0.9374,0.3472315789,0.1854967869,11.9172,0.617,1.058,0.0698,0.1183,1.0525,0.0856319035,0.538312894,5.1248,0.5926083102,0.4866968529,0.5765,0.4601,1.542,1.8103758065,0.2850075164,6.41,1.3114,0.9591,0,0.203,0,0.2794674641,0.5854373737,2.9554,0.1428035771,0.1910022416,0.1597,0,1.0388,3.1279353801,0.3778767329,24.379,0.2711,1.5212,0,0.8895,0.1753 +LINC01374,pDCs,0,0.5957962199,0,0,0,0,1.8525,0.4847,0,0,0,0,41.6039,0,0,0,2.9542173681,7.7370045517,0,0,0,0,0,0,0,0,0,0,40.7547,0,0,0,0.2669461126,2.3522705444,0,0,0,0,0.4176,0,0,0,0,0,60.2439,0.2023,0,0,0,6.3784719192,0,0,0,0,1.0878,1.0748,0,0,0,0,54.0731,0,0,0 +LINC01375,pDCs,0.0801755656,0,0,0,0,0,0.3863,0,0,0,0,0,12.0532,0,0,0,0.632877297,3.9477202593,0,0,0,0,0.1068,0,0,0,0,0.1352,8.3608,0.0307,0,0,0.0569493298,0.2237210315,0,0,0,0,0.0507,0,0,0,0,0,10.3274,0,0,0,0.0146966507,0.9745428283,0,0,0,0,0.2536,0,0,0,0,0,12.7077,0,0,0 +LINC01478,pDCs,0,0,0,0,0,0,0.0979,0,0,0,0,0,44.1761,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,38.3377,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19.1308,0,0,0,0,0.0490125253,0,0,0,0,0,0,0,0,0,0,48.6802,0,0,0 +LINC01506,Neutrophils LD,0.4978425339,0.0689127148,0,0.1891100825,0.0663548334,0,1.4066,5.1424,8.2176012605,0,100.2618,0,0,0,0,0,0,0.3230960173,0,0.0752500668,0,0,1.3429,0.7458,9.0459973684,0,73.2487,0.8187,0.3108,0.1842,0,0,0,0.6844061318,1.2758,0.1981995431,0,0.5115,1.2648,5.9116,11.017241935,0.5077257401,206.4771,0,0.3643,0.6083,0,0.5792,0.4695736842,0,1.3061,0.0752729596,0.0842932987,0,0.8301,9.0331,9.9399760234,0,108.5909,1.802,0.3682,0.2061,0.9506,1.402 +LINC01644,MAIT,0,0,0,3.2993384942,0,33.9457,0,0,0,0,0,0,0,0,0,0.7527,0,0,0,0.9722828137,0.0199292284,7.672,0,0,0,0,0,0,0,0,0,0,0,0,0,6.1150340021,0.6596659323,58.6434,0,0,0,0.8297009041,0,0,0,0,0,0,0,0,0,5.5282817515,0.1289865503,36.6199,0,0,0,0,0,0,0,0,0,5.0781 +LINC01724,pDCs,0,0.1897649485,0,0,0,0,0,0,0,0,0,0,37.0603,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,56.0107,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24.135,0,0,0,0,0,0,0,0,0,1.0571,0,0,0,0,0,53.3226,0,0,0 +LINC01781,B Memory,84.353667873,14.883504811,0.5921,0.2770006578,0,0.4757,0,0.3925,0,0,0.6003,1.5588,0,20.9577,0,0,48.25001719,1.9996089521,0,0.1165340163,0.0634446846,0,0,0,0.2010473684,0.630795541,0,0,0,12.5137,0,0,49.517662198,5.549296447,0,0.1377232817,0.2591055862,0,0,0,0,0.2841104591,0.4262,0,0.8104,12.3345,0,0.6332,92.798125359,21.458687475,0,0.172045001,0,0.4822,0,0,0,0,0,0,0,45.621,0,0 +LINC01986,Basophils LD,0,0,17.4466,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.2987,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.0383,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19.7811,0,0,0,0,0,0,0,0,0,0,0,0,0 +LINC02085,Monocytes NC+I,0,0,0,0,0,0,0.2979,0.7631,7.9880621849,0,0.268,0,0,0,0,0,0.0488050385,0,0,0,0,0,2.0673,4.8618,27.3043,0,1.0352,0,0,0.0549,0,0,0.4966589812,0.5400147278,0,0,0,0,1.7796,2.985,28.626798387,0,1.499,0,0,0,0,0,0.1234143541,0.8169777778,0,0,0,0,0.5172,0.9703,11.120724854,0,0.2672,0,0,0,0,0 +LINC02185,Monocytes NC+I,0.4849479638,0.0352742268,0,0,0,0,0.9558,0.5387,10.715695378,0,0,0,0,0,0,0,0,0,0,0,0,0,1.1357,0.5352,9.8195105263,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2383,1.1714,19.639095161,0,0,0,0,0,0,0,0,0,0,0.1446745837,0,0.2149,0.1951,1.7461,13.796064327,0,0,0.2843,0.1859,0,0,0 +LINC02218,Neutrophils LD,0,0,0,0,0,0,0,0,0,0,31.3066,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27.4077,0,0,0,0,0,0,0,0.3783,0,0,0,0,0,0,0,20.2085,0,0,0,0,0,0,0,0,0,0,0,0,0.3416,0,0,30.1843,0,0,0,0,0 +LINGO2,NK,0,0,0,0,0,0.6409,0,0,0,0,0,8.981,0,0,0.6934,0,0,0,0,0,0.4036418447,0,0,0,0.4865578947,0,0,13.3577,0,0,2.3759,0.6504,0.1040053619,0.2187907736,0,0,0.1033359559,0,0,0.038,0.232133871,0,0.0384,7.8968,0,0,2.3121,1.146,0,0.0281640404,0,0.027271733,0.2501079519,0,0,0,0.0211330409,0,0,15.1739,0,0,1.216,0 +LIX1,B Naive,2.1465665158,13.357727491,0.7279,0.3289343918,0.1373930412,0.3714,0.1274,0.5196,0.293212605,0.2684765891,0.2941,0.4362,0.116,0.0541,0.1474,0.5494,1.0325247184,14.16149583,0.2547,0.3077907572,0.3391898216,0.1174,0.1444,0.3062,0.1163078947,0.5128085246,0.1138,0.8106,0.2704,0.0268,0.0952,0.5959,0.8878930295,7.577117765,0.719,0.4609196001,0.3881512982,0.2169,0.0705,0.1409,0.2286354839,0.2267165573,0.2408,0.6462,0.1843,0.0497,0.309,0,1.4193698565,14.054547273,0.7511,0.2476828,0.204051463,0.3433,0.1361,0.1146,0.2005002924,0.1586947052,0.1883,0.1531,0.1856,0.0589,0.3056,0.2083 +LIX1-AS1,B Naive,3.9077167421,17.494309278,0,0.0413184906,0.0837811751,0,0.3453,0.0619,0.0381302521,0.3129004547,0,0,0.5754,0,0.2684,0,2.8355505631,28.595038826,0,0.2042005122,0.4991426238,0.0852,0,0,0.0568605263,0.2004983607,0,0.073,0,0,0,0,0.6997308311,10.227852779,0.2196,0.2483098066,0.0816653029,0,0.1141,0.0667,0,0,0,0.1755,0.1285,0.0266,0.0933,0.4793,0.7139358852,15.910904242,0.2072,0.0722790927,0,0,0.1327,0,0,0.1754153666,0,0,0,0,0,0 +LLNLF-173C4.1,Neutrophils LD,2.3861190045,2.220756701,2.7339,3.024004503,4.5072373708,4.5213,0,7.1816,0.0025210084,2.8933237541,73.813,1.9897,0.0152,0.36,6.4556,3.263,2.9710630113,0.0140341592,0.007,1.4895959317,0,1.9938,0,13.2473,13.232960526,4.0670548852,256.469,0,0,0.2635,0,2.1644,0.5387796247,4.6453919771,0,0.3208778308,0,0,0,11.3014,11.214919355,2.3008770431,69.2185,1.2879,0,0.9936,1.8072,2.5389,2.4937598086,1.2298650505,0,2.3063910299,0.0536271354,0,2.9774,16.1734,7.0873114035,0,114.0139,1.4129,1.6137,0,4.4454,0.816 +LLNLF-173C4.2,Neutrophils LD,1.857058371,0.6092646048,0.362,1.0773070745,2.9935836205,1.0102,1.1629,0,1.2867542017,4.4507647544,55.6291,0.2056,1.2953,0.3387,1.6086,0,0,2.1137431185,0,2.0574135264,2.821133149,2.9394,0.5891,0,0.7808368421,6.887680918,115.418,0,0.8123,0,0.4296,1.401,0.2244530831,4.3136354155,0,0.2889648854,0.4802313139,1.451,0.8118,11.6448,3.8572403226,0.8952917213,103.668,0,0.6265,0,0,0,2.2076803828,2.7827248485,1.1994,1.4293908449,0.1106376829,1.2073,0,7.6713,3.5478137427,2.0165986805,163.643,2.041,1.5964,0.6905,0.7296,1.9488 +LOXL3,Monocytes NC+I,2.6164647059,0.6508955326,1.0369,0.3509228382,0.5741422944,0.1458,14.5561,21.1767,49.849059244,0.449061915,3.6491,0,0.2334,0.4781,1.0966,0.2359,0.6491305276,1.9437311559,0.3556,0.282535761,0.5533367429,0.2405,11.4798,18.615,30.947171053,0.8446362623,2.0709,0.428,0,0.5822,0.7892,0.1596,6.7798348525,5.4187632092,0.2937,0.4049072308,0.3202526357,0.4653,10.4994,27.1076,47.216067742,0.3960009573,3.728,1.2317,0.1698,0.2418,1.4568,0.3432,5.1761004785,2.4972559596,0.2703,0.3263475296,0.9043241859,0.0586,15.2562,21.3018,44.725673099,0.5148650075,4.2314,0.5204,0.2779,0.9363,0.3834,0.0642 +LPAR1,Monocytes C,0,0.176209622,0.0602,0,0,0,2.0724,13.7554,1.6485651261,0,3.1152,0.7197,0.2389,0,0,0,0,2.379976291,1.5909,0,0,0,4.0195,19.8212,3.2975657895,0,1.4161,0.4391,1.0705,0,0,0,1.918747185,1.9456186246,0,0,0.0418823761,0,2.1779,12.2985,4.1419,0,0.8418,1.3812,2.2434,0.2069,0,0,0.9749215311,2.0210642424,0.1433,0,0,0,5.3124,33.8062,4.965654386,0,6.9811,0.0428,0.0873,0.3234,0,0 +LRG1,Neutrophils LD,1.2280054299,2.1030295533,3.4504,1.3434328848,0.841464172,0.4797,8.5538,13.4226,3.1420466387,0.7250668093,320.45,0.6931,0.3466,0.4005,1.1788,0.9217,2.823205987,1.6928876701,1.8947,0.7887921604,1.1213812516,0.9916,3.9606,7.7259,2.0395710526,1.0508335082,751.786,1.1336,1.9481,0.1599,0.7222,0.7591,1.4819563003,6.1708840688,1.4126,1.1525726924,0.7557083399,0.4591,12.7296,16.1434,4.1298241935,0.7521352243,399.744,1.7876,0.6175,0.5011,0.7502,0.5616,1.5436090909,3.6784317172,2.9969,0.9874116768,0.7050294714,0.9108,7.0299,19.757,3.4529766082,0.9762036913,535.209,0.6917,0.3795,0.484,0.4969,0.582 +LRP1,Monocytes C,2.7922678733,3.1552587629,5.193,2.0217730953,0.4137295913,0.34,37.0355,89.9686,60.35942521,2.1604518766,0.9277,0.5896,1.966,0.3473,0.7299,1.5543,3.7411234143,4.5305684624,2.9387,1.2473456867,1.6700707464,1.5906,47.9607,66.4275,62.712965789,3.8073539016,0.9715,1.1091,5.2135,0.3769,2.065,1.7671,9.4617579088,10.843129226,2.8913,1.1465779566,1.0137781275,2.3469,27.9613,60.0639,40.649974194,1.5446120191,0.4095,0.329,0.3606,0.7163,0.9745,1.0317,3.4589650718,5.1527822222,3.7254,1.2543221065,0.5862916942,3.146,42.0249,95.7325,43.779392982,1.4475249875,1.6423,1.8353,1.6788,1.4393,1.4806,2.4547 +LRRC4,Neutrophils LD,0.1650561086,0.047532646,0.5294,0.1816300443,0,0.6581,0.0501,0.432,0.9053684874,0.4786478693,17.7241,0.1043,0.4803,0.1195,0,0,0.2908342027,0.271932265,0.9821,0.11924683,0.3713378236,0.0615,1.1887,0.8753,1.9841236842,0.0402516721,17.74,0,0.2342,0.0557,0.1908,0.1047,0.2955297587,0.0171079083,0.6682,0.0598795524,0.2460062156,0,0.7123,0.1744,1.3011725806,0.075198635,4.8251,0.0372,0.2178,0.0231,0.3617,0,0.0066583732,0.0897466667,2.2944,0.3150848215,0.2697773242,0.2213,0.3155,2.0919,1.2180921053,0.1411534324,22.4818,0.0269,0.4379,0.3882,0.3874,0.1737 +LRRC36,pDCs,1.8274361991,3.8631512027,2.0451,1.4920137723,1.5046103397,1.3681,2.7589,1.7778,1.5737260504,2.3023424222,0.6005,4.5422,42.2792,0.5332,2.9025,1.2949,0.859390575,1.1594586244,1.3623,0.9980578471,1.0310861272,1.664,1.3785,0.8922,2.1277052632,1.4476090492,0.6894,1.5177,44.9973,0.0834,1.8646,1.3764,0.3201246649,1.7787632665,2.5749,2.3611791816,1.7634648308,1.8401,2.3483,2.2145,1.0717387097,2.9170920227,1.0311,1.8716,50.7326,0.2797,0.5183,0.9775,1.6990516746,2.0981369697,3.596,1.4455142603,1.1327506371,1.5393,1.5933,1.2969,1.3599488304,2.2936703023,2.7913,2.058,56.9025,0.1956,2.3953,1.617 +LRRK2,Neutrophils LD,19.709930317,19.33977457,0.0956,0.0923146872,0.0211125882,0,17.7971,72.0405,44.9596,0,228.8979,0.1965,5.4216,0.2225,0.0221,0.0253,10.911637878,20.87376507,0.0688,0.2120527394,0.1140781352,0,27.3643,59.7421,41.293357895,0.2260366557,207.9204,1.6804,4.9316,0.4,0.0279,0.0153,11.625635389,23.778873352,0.3565,0.058965733,0,0,15.5789,62.4129,46.074408065,0,301.1599,1.4539,4.9411,1.0587,0.1015,0,11.151741148,20.249730303,2.3354,0.3330069485,0,0,18.2333,77.0917,44.105528363,0.0147218473,240.3921,0.1293,4.4229,1.4906,0,0.0277 +LRRN3,Naive T cells,1.4962669683,1.2780649485,0.7661,14.956700706,4.5891191695,1.4784,0.4395,0.0604,0.3046815126,194.50991312,0.1858,0.4692,0.6528,0.0582,4.9185,16.4255,0.4797118554,1.1800133165,0.0468,20.338270698,0.6539871827,0.8866,0.0532,0.0595,0.7557789474,226.46689449,0,0.4368,0.1026,0.0458,0.1306,6.9271,0.5467310992,0.5134064183,0.4882,16.711549212,1.9289470496,0.8571,1.1625,0.3503,0.3156709677,131.12872794,0.1612,0.8094,0.9216,0,1.9886,0.9407,0.6221822967,0.9485927273,0.6073,18.204484109,3.0193993157,1.3512,0.2833,0.1256,1.7589230994,241.27185141,0.072,0.5856,0.9015,0.0681,5.2812,13.5034 +LTK,MAIT,5.1358058824,1.6845945017,0,5.6916832197,0.2531540456,90.8237,0.8647,0,0,0.4510202015,0,0.0923,34.2906,7.0245,0,0.6313,4.5446298162,0.0631239251,0,2.9603399183,0.235181905,68.8248,0.4827,0,0.0279157895,0,0,0.3755,23.0975,9.3371,3.6131,3.1728,3.2034324397,0.2584574785,0.0356,11.315889061,0.8406541306,64.5891,0.5197,0,0.4646306452,0.475761248,0,0.2083,18.2174,13.9614,0.4545,1.606,4.4040287081,1.1253692929,0,6.95100209,1.0346770175,72.5075,0.4194,0,0.0348649123,1.3453396359,0.892,1.3038,27.4892,10.7844,1.3095,4.7388 +LUCAT1,Neutrophils LD,0.4907475113,1.1156790378,1.4,0.8919211159,0.9020208928,1.023,0.2559,18.0472,1.8037420168,0.2608270571,291.2587,1.2339,0.4232,0.068,0.7167,1.6248,0.3607632484,0.9761655384,0.2178,1.0059394061,0.8215812516,0.3164,3.6139,14.2845,3.4724342105,0.8394918689,261.1275,0.2606,0.3607,0.1124,0.4504,0.2995,2.614391689,6.6307141547,1.0701,1.0890574825,1.4111380016,0.1875,1.6143,38.6779,4.8223435484,0.2058068605,160.7077,1.3258,0.1448,0.1465,2.4028,4.9785,0.2840827751,1.2964670707,0.2366,0.6194723361,0.6238762388,0.5395,1.2512,10.6303,0.9590400585,0.1152380491,284.2847,1.2448,0.2839,0.3557,0.3273,0.8999 +LY96,Neutrophils LD,8.7377927602,5.7676316151,1.449,6.2744972551,2.7493278845,0,18.5256,21.4874,23.23159958,2.0295909067,115.8125,0,15.6517,3.4842,0.6589,0,5.0778261411,3.0415999208,2.9846,10.089585419,0.0724170897,1.078,16.7307,13.1556,16.996597368,2.8746971803,124.5462,0,14.6476,4.1865,0.3961,0.7415,5.3312876676,4.5089075645,2.4107,5.0495052443,0.4978131393,0.3622,13.2954,20.5655,30.333232258,5.0427232937,232.915,0,7.0364,1.1605,2.8356,0,10.240547368,7.1913973737,19.3278,7.1519005414,1.3309167296,0,22.9619,35.2894,37.007873684,1.5699548856,158.639,0.2553,21.3358,5.5568,0.6209,0.61 +LYPD2,Monocytes NC+I,0.1746773756,0,0,0,0,0,0,0,12.230920168,0,0,0,0,0,0,0,0,0,0,0.2242703786,0,0,2.1074,0,28.081728947,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,42.021248387,0,0,0,0,0,0,0,0.3063478469,0.2118553535,0,0,0,0,0,0,14.069638596,0,0,0,0,0,0,0 +LYZ,Monocytes C,236.72432941,405.5957457,532.3767,6.6219037316,1.1221453635,1.0203,10734.1455,22326.3555,3330.3485483,3.0925938486,1711.4637,49.8142,158.951,34.0506,0.3261,1.792,556.56101725,527.64131353,1326.4918,10.862736674,2.2621258105,0.3215,15053.1025,26337.0371,3066.2752947,7.5622032787,1297.2915,68.7536,1351.7338,82.9253,0.8174,3.4556,1344.8863491,2346.8611852,272.0986,7.2043624354,1.5053313926,1.246,10532.6484,17456.1504,1292.0185452,3.3765836022,1114.4955,101.094,30.6881,214.805,2.1621,1.6006,597.70856316,612.92870606,261.7827,5.272185822,0.4259845446,2.1006,6004.439,11014.6055,1444.9598289,3.8958145816,225.46,32.2957,86.5281,86.5751,2.3103,1.4644 +MANSC1,Neutrophils LD,0.6971393665,1.5322515464,4.3044,2.0622862636,1.0620081733,0.9819,2.2179,1.6143,2.6726092437,3.0160675715,108.1893,1.5454,2.488,0.261,2.2848,1.1339,1.6610793124,1.445468736,1.8549,1.6420639866,1.1608024127,1.8053,1.2261,1.7967,1.3657868421,2.285545377,139.3883,2.2359,0.7551,0.151,1.209,1.0894,0.6291324397,1.5023959885,4.5598,1.6095404715,0.9785073958,1.1024,1.3889,3.7018,2.0322419355,2.6272074809,40.1791,0.8674,1.0342,0.1833,2.2218,0.6509,1.7163129187,1.528719596,5.561,1.8825244021,1.388516824,1.3427,1.6784,2.1633,2.7160842105,3.020555704,118.7117,1.6562,0.909,0.7232,2.8201,1.3939 +MAP1A,pDCs,0.0321339367,0.0055230241,0.1976,0.7698928059,0.016236665,0.3934,0.131,0.0094,0.0772382353,0.3362903004,0.4252,0.2584,72.851,0,0.1981,0,0.1852713693,0.0124583075,0.5061,1.1940653304,0.2816948228,0.7393,0,0,0.1054473684,0.0594232787,0.2088,0.2032,39.2947,0,0.3334,0.2009,0.2955174263,0.0709132378,0.0888,0.6166006622,0.1136346184,0.7843,0.1567,0.0835,0,0.026207215,0.0308,0.2595,70.1542,0,0.196,0,0.0095655502,0.2019410101,0.0314,0.718458624,0.099539429,0.1322,0.0824,0.01,0.0520906433,0.284599549,0.6071,0.0751,60.7295,0.0056,0.0123,0.1332 +MAP1LC3A,Neutrophils LD,4.4038683258,1.3314938144,5.3459,2.4973521289,4.7621806335,2.6918,0.2883,1.692,2.0252079832,0.2919789159,47.9058,3.1066,1.6086,1.481,7.111,4.4067,5.7251035566,1.1653640115,2.5531,5.979578248,1.9391190751,1.6868,0.388,0.7626,1.3741421053,0.1895783607,26.2711,1.4327,1.4909,0.7378,2.594,3.8576,3.4351552279,4.0507112321,1.6423,2.2000083896,5.533902439,1.2063,1.2167,1.1034,0.6225935484,0.9041021627,45.4798,1.113,2.524,1.147,0.8212,2.3118,10.923103828,2.0465810101,1.385,2.4281574454,3.1984130722,3.3136,0.7436,1.2408,0.9855710526,0.4707050109,89.5546,4.3961,3.3913,0.591,4.3041,1.8105 +MARCO,Monocytes C,0,1.3605773196,0,0.0037956405,0,0,7.3908,28.9323,25.280555042,0,0,0,0.0671,0,0,0,0,0,0,0,0.0455423976,0,3.6797,4.5599,2.2932184211,0,0.0894,0,1.5265,0,0,0,3.3046844504,5.0050105444,0,0,0,0,10.8553,33.4997,18.424819355,0,0,0,0,0.4841,0,0,4.837823445,2.5980525253,0,0.1115578154,0,0.0708,18.1268,67.9861,57.987738012,0,0.0736,0.2946,1.5043,0.4162,0,0 +MARVELD1,mDCs,0,0.681661512,0,0.0698272515,0.465100476,0.3769,9.5167,9.4802,4.5899537815,0.6898367344,0.0408,2.1766,0.6139,0.1024,0,0,0.9554388856,0.1274845445,0,0.0032922049,0,0.4798,18.2377,12.6627,2.6083078947,0.437093377,0,0,6.8948,0.0167,0,0,0.7351474531,0,0,0.330174957,0.0690977183,0.4899,11.6663,8.7538,1.3912967742,0.4446926254,0,2.3179,0.6356,0,0,0,0.7823995215,0.8458121212,0,0.4253609402,0.2135270175,0.9907,18.4273,9.3801,3.9669769006,0,0,0.2914,1.3522,0.1494,0.3334,0.2713 +MDS2,Naive T cells,14.154639819,12.727541237,0.6481,39.804114442,0.550013655,2.1222,0.4902,0.3816,0.558555042,93.515123286,0.4332,0.6161,0.7744,0.4732,0.7532,16.8278,27.779886366,29.583977206,1.114,58.429767186,0.9127593616,3.6097,0.8595,0.3254,0.7238447368,150.30189134,1.0546,0.8864,0.5602,3.3681,0.4299,3.9725,20.15758445,15.20848384,2.7755,50.01138928,0.6175825334,7.4673,0.6152,0.5976,0.2861548387,111.27307027,0.5846,0.5521,0.7967,0.3955,2.5571,1.2738,24.761340191,32.895128081,0.5597,37.93763736,0.2149620104,2.2829,0.3878,0.402,0.5819163743,104.03103469,0.7932,0.8068,0.226,3.404,1.7467,4.8832 +ME1,MAIT,0.3330963801,0.4569175258,0,0.9933788901,0.0868701789,37.5076,0.4589,4.7605,1.0723029412,0,0,0,0,0,0.564,3.269,0,0.1682887288,0,0.585760052,0.1153797437,44.9971,0,2.5516,0.4968131579,0.2980622951,0,0.281,0.2414,0,2.5666,15.2029,0.8375372654,0.960994957,0,0.9638686267,0.6916286389,44.9,0,2.6293,0.5938467742,0.0423305797,0,0,0,0,0,2.1775,0.0457779904,0,0,1.6713994929,0.0726162105,29.2587,0.0329,2.8885,0.405055848,0,0,0.37,0.5967,0,0,7.8777 +MEG3,Monocytes NC+I,0.1281294118,0.2619969072,0.6221,0.3093011841,0.0872691449,0.1543,0.3416,0.0787,7.5152773109,0.2442851297,0.3112,0.1437,0.3294,0.0546,0.263,0.0382,0.9657258447,2.4360426647,0.499,0.1438995917,0.1844508419,0.3613,0.1626,0.0807,50.629536842,0.157756918,0.5653,1.5091,0.0876,0.0163,0.2185,0.1786,0.027175067,0.1011653295,0.3453,0.1673867633,0.0916365854,0.0644,0.0389,0.0812,12.705872581,0.1956250133,0.1667,0.0889,0.4383,0.0285,0.2976,0,0.3938444976,0.2047614141,1.2903,0.2060355924,0.2489420009,0.1653,0.3283,0.4855,12.757997953,0.156816018,0.2873,0.2351,0.1192,0.0535,0.3788,0 +MEIKIN,Monocytes NC+I,0.813761086,2.5712900344,0,1.2333350078,0.8911114065,1.7424,2.3435,0.7756,14.668182353,1.2535220201,0,0,0,0,0,0.4883,0,0,0.2829,1.4786529324,0.5906994974,0,2.7376,0.7808,16.870715789,1.2646792131,0,0,0,0,0,2.3609,0.4019276139,2.1624305444,0,2.8605329559,0,0,1.6696,0,5.9753516129,2.7406060982,0,0,0,0,0,0,0.1990009569,0.2508054545,0,1.1722957925,1.1511854176,0,5.238,0.4275,9.8137833333,1.0741673626,0,0,0,0.3722,0,0 +MERTK,Monocytes NC+I,0.441859276,0.6659140893,0,0,0.0033152798,1.4518,0,11.3651,26.880813866,0,0.4535,0,1.2377,0,0,0,0.4664387078,1.6118116673,0,0.3041050334,0,0,0.0318,5.8259,40.03805,1.8440508197,0,0.1118,0,0,0,0,0.2520209115,1.9100291117,0,0.007981625,0,0,0,4.7697,30.252832258,1.1087847367,0,0,0,0,0,0,0.9337555024,1.0166787879,0,0.7951845268,0,0,0,7.7867,40.864316082,0,2.1856,0.4867,2.058,0.3073,0.2998,0 +MGAM,Neutrophils LD,0,0.5181305842,2.1942,0,0,0,0.2522,0.7621,0.3357668067,0,139.4505,2.3359,0.0121,0,0,0,0.063889508,0.1648995823,9.1706,0.0182883148,2.088937723,0,0,4.3709,0.4020947368,0,199.3217,4.8813,0,0.1443,2.5456,0,0.2893286863,0.3913247564,4.7799,0.0003197259,0,0.0219,0.1196,0.5834,0.89045,0,205.4023,3.6377,0,0,0,0,0.1218334928,0.3101721212,8.8518,0,0.015478386,0,0.0447,6.6203,0.0975143275,0.4567565726,160.0666,2.57,0,0.0447,0,0.02 +MGAT3-AS1,Basophils LD,36.572047511,7.3977134021,198.931,0.2091826337,0,0,0,0,0.2479827731,0,0,0,0,7.6112,0,0,27.782889093,5.3425529276,147.691,0,0,0,0,0,0,0,0,0,0,2.1871,0,0,18.224435121,2.9160395989,429.949,0,0,0,0,0,1.2103,0,0,0,0,0.2905,0,0,24.390585167,11.431226061,257.214,0,0,0,0,0,0,0,0,0,0,6.5245,0,0 +MIR181A2HG,NK,0.1976615385,0.3624329897,0,0.0453761512,0.6449436731,0,1.0314,0,1.0964810924,0.8185687572,0.2828,6.9511,2.2388,0,0,0,0,0.0558630825,0,0.1666768077,0.1382430761,0,0.1982,0,0.1018657895,0.2551819672,0,14.2034,2.8958,0,0.3853,0,0,1.1181862464,0,0.0389443782,3.2706920535,0,0,0.7162,0,0.1528424747,1.9213,11.101,0.2255,0,0,0,0,2.6432240404,0,0,0.5027889099,0,0.2393,0.7131,0.128472807,0.6981423751,0,9.3713,0.6835,0,0,0 +MIR606,Monocytes C,0,0,0,0,0,0,3.4827,30.5604,16.99542605,0,0,0,0,0,0,0,0,0,6.2691,0,0,0,0,22.2525,12.637057895,0,0,0,0,0,0,0,2.0413640751,0,0,0,0,0,3.2621,4.2524,3.320066129,0,0,0,0,0,0,0,0,5.9936460606,0,0,0,0,16.0386,40.6115,16.069165497,0,0,0,0,0,0,0 +MIR650,Plasmablasts,90.552909502,6.7568714777,0,0,0,3.911,0,0,0,0,0,0,0,1246.02,0,0,97.812785418,4.7444079078,0,0.7043638085,0,0,0,0,1.6615421053,0,0,0,0,990.412,0,3.7093,153.18453646,20.262668138,0,0,1.2469076318,0,0,0,0,2.3429383443,0,9.9449,0,754.323,0,0,207.4854067,35.89277697,0,0.151210601,0,0,0,0,0,0,0,0,3.4586,640.114,10.035,0 +MIR2276,pDCs,0,0,0,1.4216892417,0,0,0,0,0,2.2979252518,0,0,28.1961,0,6.1399,0,0,0,0,0,0,0,0,0,0,0,0,0,14.8053,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25.8929,7.0457,0,0,0,2.5491012121,0,0,0,0,4.4493,0,0,0,0,4.2164,29.8003,0,0,0 +MIR4432HG,pDCs,2.2317402715,5.8647017182,0,0.0350074752,0,0,0,0,0,0,0,0,40.0187,0,0,0,2.9084358032,9.1276106662,0,0,0,0,0.1814,0,0,0,0,0,53.0417,0,0,0,1.0321946381,1.5948117479,0,0,0,0,0,0,0,0,0,0,45.23,0,0,0,0.8974526316,6.1297866667,0,0,0,0,0,0,0,0,0,0,49.6317,0,0,0 +MIR4537,B Naive,75.914670136,323.97196907,0,2.8240742076,0.5815279829,0,0,0,0,0,0,0,126.048,98.2042,8.737,0,33.734473918,325.97824203,0,5.9637644543,0,0,0,0,0,0,0,0,60.9647,27.9063,0,0,28.598093834,209.23410372,0,5.9507782943,2.0073516916,0,0,0,0,0,0,0,96.0803,41.4321,9.0232,0,40.834767943,336.01226465,0,6.956557185,19.338624823,0,0,0,0,0,0,0,169.654,9.8435,7.905,22.7914 +MIR4538,B Naive,36.841351584,102.57633746,0,1.4901682574,0,0,0,0,0,0,0,0,17.0184,11.9341,0,0,6.26991885,40.78515874,0,1.165206637,0,0,0,0,0,0,0,0,4.3456,2.5378,0,0,16.85082118,61.090264814,0,0.9432708648,1.6955216365,0,0,0,0,0,0,0,20.4812,19.3751,0,0,12.03485311,101.19700242,0,4.6837495374,0,0,0,0,0,0,0,0,20.6888,8.2922,6.6861,0 +MIR4539,B Naive,247.32575566,867.36173883,0,31.549116206,0.7724766125,0,0,0,0,0,0,0,87.8591,184.26,0,0,133.16933017,483.27706244,0,6.9893291017,0,0,0,0,0,0,0,0,40.1432,0,0,0,175.19553083,564.91545444,0,26.769877453,5.1602228167,0,0,0,0,0,0,0,113.319,57.9166,11.6303,0,101.56825694,769.25116566,0,12.082229103,33.006749646,0,0,0,0,0,0,0,212.38,34.8467,0,0 +MIR6774,pDCs,53.24409095,44.337089691,0,0,0,0,0,11.9445,23.711018067,0,0,0,252.096,0,0,0,86.941540605,102.18822859,0,0,0,0,64.2814,11.712,0,0,0,0,304.823,0,0,0,31.212896247,46.07438894,0,0,0,0,35.3032,13.5817,30.770422581,0,0,0,240.201,0,9.0232,0,12.488373684,68.907879394,0,0,0,0,19.1709,18.6959,21.425380994,0,0,5.9043,364.443,0,0,0 +MIR8071-1,B Memory,134.69668778,15.982912715,0,0,0,0,0,0,0,0,0,0,0,4.8596,0,0,203.62010409,24.758443479,0,0,0,0,0,0,0,0,0,0,0,17.7928,0,0,106.79127882,26.82814149,0,0,0,0,0,0,0,0,0,0,0,7.3027,0,0,252.07784211,18.872124646,0,0,0,0,0,0,0,0,0,0,0,21.8827,0,0 +MIR8071-2,B Memory,134.69668778,15.982912715,0,0,0,0,0,0,0,0,0,0,0,4.8596,0,0,203.62010409,24.758443479,0,0,0,0,0,0,0,0,0,0,0,17.7928,0,0,106.79127882,26.82814149,0,0,0,0,0,0,0,0,0,0,0,7.3027,0,0,252.07784211,18.872124646,0,0,0,0,0,0,0,0,0,0,0,21.8827,0,0 +MLC1,NK,0.087721267,0.9529175258,0,0.9314347088,15.056735533,1.7052,1.3539,2.8224,21.160903782,0.8900813185,0.1809,77.0341,0,0,20.3357,0.8462,0,0.7835619589,0,0.587882487,14.483605278,0,1.4657,5.4035,20.825076316,0,0.1279,106.9059,3.0289,0,11.8463,1.4602,5.9578600536,2.1406406304,1.2955,0.6065947557,13.019175452,2.6005,0.3692,1.5624,13.728535484,0.8722378656,0.2826,143.9877,0.7618,0.0246,34.217,11.628,1.3072425837,1.1639159596,0,0.7875028507,15.764170009,0.863,0.7835,1.6067,24.128137135,0.149528445,0.2239,95.7529,0,0.4319,37.3545,8.3167 +MME,Neutrophils LD,0.6466959276,4.5865017182,3.9213,1.3698841706,1.0861939603,1.2651,0.3899,1.486,1.0167273109,1.7058567086,776.9953,1.7911,6.6516,0.1315,12.0282,0.6808,6.7814272673,6.292866561,5.832,1.4407728211,1.3839882383,0.9486,1.0309,2.1158,4.5472078947,2.2311790164,630.4064,1.8092,14.0509,0,10.6923,0.8396,0.6286664879,8.4962837822,5.7914,1.349424361,1.8273855232,0.5678,0.5413,0.2538,0.8635467742,2.5218187733,701.4983,2.1488,24.6564,0.3707,10.7607,1.5548,4.0943607656,5.6355335354,2.2185,3.9920847667,5.482441411,1.016,0.6851,3.2487,0.5710722222,1.4894295306,642.9773,1.6183,18.475,1.5185,6.8087,0.6603 +MMP9,Neutrophils LD,0,0,0,0.0395755591,0.0533282619,0,0.2565,0.8742,0,0,186.941,0,0,0,0.0647,0,0.5886757558,1.0177002521,0,0.0672270973,0.0569023624,0.0525,1.0757,0.679,0.2969973684,0.0528339016,140.624,0,0,0,0,0,2.3703587131,5.0831666476,0.0543,0.0157452457,0,0,1.573,0.4463,0.7680806452,0,157.671,0,0,0.2359,0,0.0738,0.0113741627,0.0796080808,0.3844,0.0571404029,0.0922449269,0,0.4907,0.9299,0.0275804094,0.0644500418,99.8935,0.046,0,0,0,0.1187 +MMP28,Naive T cells,0,0,0,1.5644292429,0,0,0,0,0.762657563,17.343408541,0,0,0,0,0,2.7424,0.6775832247,0.0563024271,0,0.5200236823,0,0,0,0,0.4179473684,13.349586885,0,0.5192,0,0,0,0,1.2054959786,1.1799252722,0,1.6788705536,0,0,0,0,0.3130887097,10.197734249,0,0,0.4183,0,0,0,0,0.1290151515,0,0.5987461043,0,0,0,0,0.2788704678,10.811230349,0,0,0,0,0.1471,1.3971 +MMRN1,pDCs,0.4795819005,3.025443299,0,0.7285312642,0,0.738,0.3298,0,0,1.7923827271,0,5.0806,17.6057,0.2644,0,0,0.0688915234,9.0200344833,0,1.1323646028,0.3352674541,0,0,0,0.0500684211,0.2455388852,0,1.836,21.2981,0,0,1.2224,0.0402895442,1.2230309456,0,0.5254877169,0.1276201416,0,0,0,0.0561725806,0.370257933,0.9723,2.6293,23.8805,0.4518,0,0,0,1.468059798,0,0.6548733639,0.1815192544,0,0,0,0.310854386,0.1660791047,0,1.5474,18.1999,0,0,0 +MNDA,Neutrophils LD,17.768574208,29.429214089,260.7829,0.0460753259,0.8030127031,0.962,394.9209,808.7825,241.22475294,0.183570986,3521.6592,0.0822,0.922,1.2832,0.3836,0.3193,31.243093005,29.280500871,179.4854,0.2342568745,1.0830968334,4.141,472.722,705.9285,240.41159737,1.6179457049,4538.1753,4.9963,20.9439,6.5348,1.332,0.1652,92.273612332,198.68455937,263.4221,0.6065957886,0.9573808025,0.7751,530.1232,1559.0988,343.91824032,0.1744924659,2959.5178,6.6805,8.2943,17.1353,0.4751,0,69.976708134,76.211943636,349.1168,0.5104708833,0.0639293299,0.5233,663.4937,1245.6643,391.24708918,0.1689067814,3642.8181,3.8792,2.7866,8.6096,0.1666,0.4926 +MPP3,mDCs,0,1.4893872852,0.133,0.1916840928,0,0.1098,13.5733,1.3642,0.1233466387,0.1474354373,0.1191,0,0,0,0,0.3328,0,0.0855658768,0.2995,0.4894673497,2.9667500126,0.4237,8.7278,5.16,0.2821631579,0.1407018361,0,0.1074,0.0819,0,0,0,0.0075024129,0.0299470487,0.223,0.2720305324,0.123415657,0.1337,7.0666,0.6953,0,0.9515612657,0,0,0.3786,0.04,0,0.1515,0,0,0.4742,0.609132728,0.0665703162,0.3437,13.2577,0.3838,0.3124345029,0.7678256556,0.1154,0.0944,1.3771,0,0.2474,1.0625 +MRAS,Monocytes NC+I,0.0996968326,0.3310178694,0.3575,0.3144003169,0.1336952569,0.2307,4.6179,9.9191,26.65602479,0.1804605599,0.1423,0.7176,0.2697,0.0695,0.3021,0.2496,0.1750510966,2.3059089809,0.1899,0.1666703192,0.2284800452,0.165,5.6465,4.5952,28.354686842,0.3778194754,0.2786,0.3342,0.1307,0.0931,0.1322,0.2105,0.8843367292,0.1756203438,0.1729,0.1890959542,0.1115254917,0.3265,2.8759,4.4761,44.629459677,0.1853218401,0.464,0.0972,0,0.0345,0.2097,0.1511,0.3326028708,0.6458589899,0.3646,0.2256640238,0.1583263332,0.3747,6.3729,2.4142,33.563387427,0.5966818273,0.2702,0.0832,0.0734,0.1287,0.064,0.1208 +MRC1,mDCs,0,0.0160704467,0.0274,0.3027917653,0,0,23.3866,0,0.5133403361,0,0,0,0.5654,0.0192,0,0,0,0,0,0.3541525316,0.0030474994,0,25.1421,0.3075,0.9355078947,0.0067310164,0,0,1.001,0,0,0.019,0.3475820375,0.7976012034,0.2996,0.214121176,0,0,13.1069,1.4952,0.9197774194,0,0,0,0.3155,0.3267,0.0291,0,0,0,0.2281,0.2424268828,0.0137724162,0.0229,17.947,0.4566,0.6875821637,0,0.2618,0.195,1.1319,0,0,0 +MRVI1-AS1,Neutrophils LD,8.0368819005,14.165003093,1.1473,0.0546576366,0,0,2.2388,1.8433,3.6080214286,0,39.1258,0,0,0,0,0,2.6647946651,6.4001900324,0,0,0,0,0.4661,1.8494,2.5758052632,0,19.2499,0,0,0,0,0,1.6870176944,3.7721476218,2.7816,0.0732109058,0,0,0.2261,0.519,1.5417532258,0,20.0172,0,0,0,0,0,11.771142584,13.396821212,4.604,0,0,0,0,0.287,2.8848394737,0,30.7314,0,0,0,0,0 +MS4A2,Basophils LD,2.7948393665,1.226285567,1434.6884,0.7521654647,0.560044707,0.2953,0.2976,0.2929,0.5407302521,0.3864527815,3.9022,1.7365,0.1816,0.4226,1.031,0.3855,0.7620517487,1.5370906446,1858.1639,0.6780053081,0.4238709223,0.8618,0.4718,0.2773,0.9691894737,0.571654623,4.5205,2.7579,2.1189,0.3767,0.8609,0.3147,2.2810764075,8.0761782235,1758.793,0.5843826116,0.7075028324,0.3497,0.7068,0.4994,1.1648064516,0.6720610885,1.4226,1.1944,0.6434,0.0511,0.6962,0.4709,0.3976023923,0.8021173737,1330.3038,0.8258945385,0.3332054743,0.9657,0.406,0.7269,0.4096739766,0.5262658928,1.1523,0.5279,1.2352,0.3952,0.3992,0.713 +MS4A3,Basophils LD,4.1625180995,4.6058147766,5073.2632,0.1660019555,0.01413097,0.2988,0.1293,3.4253,1.8351659664,0.103375974,6.7638,2.7517,0,0,0.5257,0.2192,2.1572390634,3.5124625423,5090.876,0.3050831626,0.0861520985,0.3,0,0.3366,1.5495236842,0.0636940328,1.0069,4.7458,8.4618,1.1854,0,0.544,10.721334853,13.449709628,2960.3899,0.1681578268,0.123320535,0,1.9534,0.8916,0.8042645161,0.5151020209,4.7426,4.4457,0.141,1.1321,0,0,2.763776555,2.6400610101,3911.1223,0.2573201398,0.0877418594,0,0,0,0.982921345,0,0.3414,1.2427,4.3503,0.0433,0,0 +MS4A4A,Monocytes NC+I,1.812980543,2.0129024055,10.689,0,0,0,25.4183,20.6348,140.8259,0,0.1777,0.6859,41.9003,0,0,0,0.2506574985,0.4610246597,9.8468,0,0,0,25.9968,20.751,81.857297368,0,0.3081,1.0618,41.9931,0,0,0,2.898383378,5.5096370201,10.4351,0,0,0,34.7312,16.5453,132.84274194,0,0,0.8214,29.496,0,0,0,0.2816631579,1.6148959596,11.5142,0,0,0,26.6905,14.7728,118.99773626,0,1.6355,0.0704,67.3977,0.3342,0,0.0909 +MS4A7,Monocytes NC+I,41.059699095,36.163006873,2.3485,1.2857695252,0.7095289841,1.1559,105.4523,338.8573,1358.3873958,0.7624844121,2.3147,8.7165,2.3034,0.7169,1.0628,0.4326,26.630472555,43.344506323,2.215,0.5070470304,0.6008487811,0.6156,119.7654,324.2619,1225.3435105,1.2543189508,1.6367,7.417,19.6743,4.0468,0.3944,0.537,37.654824129,46.227667507,2.054,0.5614629917,1.0814485445,0.0978,65.4545,257.4017,1340.9348145,1.5932202092,1.0206,1.7487,2.4835,3.102,0.3962,0.7267,44.842270813,63.394322626,2.2242,1.2800017885,0.732597546,0.935,89.7875,276.4536,1457.8512953,1.0219419576,7.483,6.2851,6.1492,4.516,1.5141,1.6722 +MS4A14,Monocytes NC+I,6.9739221719,3.2029154639,0,0,0,0,14.3558,29.8102,62.517513445,0,0,0.0558,0,0.0554,0,0,5.7077450504,3.8988411811,0,0,0,0,12.3437,23.0639,44.026526316,0.0144645246,0,0,1.3977,0.3324,0,0,0.7614458445,2.9042487106,0,0,0.0410995279,0,10.1324,8.4757,52.161833871,0,0,0,0,0.707,0,0,3.5625143541,4.0396242424,0.8637,0.005646769,0.0098873289,0,5.944,24.3278,74.734174854,0,0.3167,0.1286,0.1283,1.058,0,0 +MSRB1,Neutrophils LD,9.9647072398,9.0965680412,139.3521,10.864596603,5.8891591334,13.4054,75.6989,206.3251,86.285778151,10.158387091,1274.4126,10.6042,36.8682,9.3719,11.8644,10.3012,10.734266153,12.667966777,104.6724,9.3958957016,12.867417467,11.3908,49.21,222.285,61.389115789,3.709689377,737.0705,11.7773,41.4912,5.4403,7.1144,12.6575,34.711440751,46.893016963,65.4367,11.487543239,19.191144138,9.485,79.5279,132.8469,51.831040323,9.0878071087,723.7952,18.1367,39.0714,13.5339,21.8331,19.4672,35.707803349,31.615098384,174.0458,13.384164243,22.684721496,14.6924,83.0276,236.9418,114.93737924,8.3823815266,1366.1429,25.6231,37.3372,9.0623,18.9629,15.6475 +MTCO3P23,Neutrophils LD,0,0.3965993127,4.5519,0.1232938404,0,0,0,27.1037,13.504616387,0,235.744,6.9046,0,0,0,0,3.3639320095,22.621182297,3.3466,0,0,0,9.1564,155.024,105.43733684,0,222.347,8.3875,1.8121,0,0,0,26.258886327,11.419009513,4.9806,0.5232441134,0,0,7.734,105.579,27.490541935,0,493.74,0,0,0.8232,0,0,0,0.8387442424,1.7904,0,0,0,0,48.2037,12.380495906,0,195.606,1.0522,1.0771,0,0,0 +MTND3P9,Neutrophils LD,0,0,0,0,0,0,0,0,0,0,39.8509,0,0,0,0,0,0,0,0,0,0,0,0,0,0.7330105263,0,29.2455,0,0,0,0,0,0.2928064343,0,0,0,0,0,0.657,3.2173,0.6197709677,0,15.4411,0,0,0,0,0,0,0.585929697,0,0,0,0,0,0,0,0,74.6336,0.7341,0,0,0,0 +MTND3P12,Neutrophils LD,0,1.0799532646,1.1771,0,0,0,0.748,31.252,7.2402302521,0,110.462,0,0,0,0,0,2.013173029,2.9076807346,1.3005,0,0,0,8.5669,78.6343,64.957736842,0,166.018,4.648,0,0,0,0,10.769461662,6.4486312894,0.9671,0.2818438617,0,0,6.6566,61.223,17.515232258,0,286.553,0,0,0,0,0,0,3.9267878788,2.7796,0,0,0,0,19.2119,8.2205277778,0,143.001,0,0,0,0,0 +MTND4LP12,Neutrophils LD,0,0,0,0,0,0,0.9242,0,0.7076743697,0,41.4356,0,0,0,0,0,0,0,0,0,0,0,0,0.9811,0,0,30.7216,0,0,0,0,0,0.1510568365,0,0,0,0,0,0.8267,0.0000000394,0,0,10.7042,0,0,0,0,0,0,0.8067563636,0,0,0,0,0,0,0,0,73.6051,0,0,0,0,0 +MTND4P22,Neutrophils LD,0,0,0,0,0,0,0,0,0,0,15.7365,0,0,0,0,0,0,0.2154197839,0,0,0,0,0,0,0.0502894737,0,7.3595,0,0,0,0,0,0.0163495979,0,0,0,0,0,0.5259,1.3296,0.3523258065,0,6.2696,0,0,0,0,0,0,1.8983151515,0,0,0,0,0,0,0.1237280702,0,25.3421,0.206,0,0,0,0 +MTND5P32,Neutrophils LD,0.0414868778,0.0481676976,0,0,0,0,0,11.9958,3.1875159664,0,65.439,0,0,0,0,0,1.0308502667,2.1251152179,0.3907,0,0,0,1.2176,43.5007,35.181571053,0.0706805902,80.5195,1.1919,0,0,0,0,4.4580045576,3.4259660745,0.3638,0.0156100649,0,0,1.7882,25.6166,6.9397725806,0,121.856,0,0,0,0,0,0.6329803828,0.4025444444,4.5327,0,0,0,0,17.2411,4.8530038012,0,93.8104,0,0,0,0,0 +MXRA8,Naive T cells,0.0087402715,0.1329463918,0,0.8587062552,0.064850599,0.1354,0.0721,0.1244,0.3289344538,23.914665628,0,0.1002,0.5835,0.0764,0,3.211,0.0136033788,0,0,0.943218634,0,0,0.1745,0.0764,0.1665552632,23.664391934,0.1868,0,0.2707,0.0807,0,0.9215,0.0163142091,0,0,0.9948544895,0,0.3765,0.1065,0.1347,0.2648,10.624307853,0.1693,0.1421,0.0784,0,0.1158,1.0447,0.3002574163,0.0600747475,0,0.8460008771,0,0.2277,0,0.0413,0.117974269,19.048784967,0,0,1.1873,0,0.329,3.3315 +MYBL2,pDCs,10.693552941,15.015081443,0,1.167958139,0.1282701461,0,0.1121,0,0,0,0,0,68.4959,18.4156,0,0,1.545339182,0.873317076,0.1275,0.6293080921,0,0,0.5428,0,0,0,0,0,59.4198,15.1453,0,0.2736,9.8044785523,8.0397567908,0,0.7651924712,0.5833784422,0.6264,0.2408,0.0434,0.0677677419,0.0285633221,0,0,71.3528,29.8123,0,0.0645,6.4895851675,10.798954949,0,0.6025546838,0.6650290939,0,0,0.0428,0,0.1127396192,0,0.5627,72.5595,9.5248,0.1578,0 +MYO1E,pDCs,59.954153846,83.300804124,0.3148,1.8388539349,0.7455049073,0.7851,61.0629,28.5036,13.296740756,0.1175856958,0.2309,6.7791,261.5986,9.3144,4.1195,0.014,26.431566568,123.20345296,0.3312,0.8722823237,0.2393795426,0.0137,27.9199,15.8392,7.7061947368,0.0479211148,0.1642,16.7409,212.1337,9.7988,0.2806,0.4766,34.039121716,65.609377249,0.0713,0.6665848431,0.8702737215,0,75.4854,19.4679,10.446664516,0.1733366424,0.9305,21.2894,267.3974,2.5691,0.9035,0.7019,39.993490909,67.136743838,0.1214,1.867387213,4.1203409391,0.3038,61.0876,20.3422,13.656476023,0.1303244029,0.1401,13.3566,214.8604,3.1031,4.4515,2.3561 +MYOM2,NK,7.1806746606,15.984889003,0.5494,7.8131903181,634.37464507,5.1435,11.8972,1.7655,33.192027731,11.501718695,3.0773,2816.8018,7.6969,1.0072,520.3314,88.0243,15.303093361,10.566330407,1.742,84.570569154,645.45850741,0.5918,2.2996,1.7133,30.118555263,22.83461141,0.3752,1958.0726,9.1545,2.9035,999.744,81.9762,32.298513405,43.500043496,0,42.087416455,65.29807915,1.9438,0.4549,3.7033,30.66836129,15.639060415,21.257,3532.668,3.8082,19.2602,673.8155,560.9178,51.599921531,73.304367677,3.7176,13.710506181,705.97797244,5.0408,3.6999,2.0784,54.289819883,8.0322365458,0.625,5744.9673,2.8403,9.8966,971.7952,391.6948 +NAMPT,Neutrophils LD,64.614812217,78.928371134,2601.7981,66.858436294,46.714547481,45.1647,441.2329,1554.8153,1376.900805,36.010061162,9262.2266,30.8842,90.7222,38.2091,44.1953,62.5557,106.38127421,146.30759244,1809.4502,65.685013341,52.486396431,87.0199,1004.7305,1724.8759,2913.7959868,40.459623344,13141.5469,80.1521,70.2862,23.5161,54.8367,53.2598,212.23038123,361.41879438,3702.6064,87.575219282,68.367029111,100.5338,1096.6517,2956.1846,4294.9946419,41.543730438,13065.834,75.2775,179.2646,42.6933,96.8446,52.0712,83.554390431,107.35668889,2011.0206,51.617417419,46.297285913,48.966,474.8281,1434.8597,988.68268918,29.583390947,8493.3271,32.1819,97.2091,33.6315,34.8345,35.0312 +NAMPTP1,Neutrophils LD,3.1939420814,5.6622123711,128.08,3.3028582406,3.060467438,2.3879,22.832,53.9288,70.982489496,2.0743462111,647.652,1.6887,7.3532,2.9557,1.7898,2.8625,1.9944943094,6.7968322362,75.562,2.5440693244,0.9701543604,2.5376,27.0893,52.295,85.981521053,1.3527197377,518.17,1.1213,3.6678,0.7681,3.0495,1.9842,15.517576676,28.59574361,288.742,5.0683409615,4.0241918175,6.8784,58.4507,218.159,290.28219355,2.87249181,926.313,1.8717,13.4646,2.9148,6.1949,1.6436,5.1554162679,4.8755430303,96.8508,2.9297894196,3.1152015573,2.3965,18.6826,66.6647,48.566114327,2.2864658928,563.128,2.2216,8.008,1.9152,1.2302,1.0048 +NAPSB,pDCs,248.09424615,221.82556392,0.5358,0.173101818,0,0.3367,607.1931,0.3119,3.7204273109,0.2162079032,0.9429,2.607,1368.4448,1.3765,0.3024,0.504,362.40414878,153.40941152,0.1559,0.2594857164,0.1293391556,0,586.1451,168.204,207.79799211,0.2179846557,0.4349,4.4528,1174.8701,4.4259,0.0917,0,412.06639196,300.29803181,0.685,0.0178962919,0.3783549961,0,748.8235,76.8333,78.635280645,0.0616487857,1.2291,9.5961,1545.4518,2.0711,0,0.3456,367.24509856,248.97566,1.3087,0.108607113,0.1272693487,0,662.0735,122.611,214.84415877,0.1335995657,0.7992,4.7002,980.867,2.9669,0,0 +NCAM1,NK,0.1440542986,0.4008896907,0.0275,0.8461290396,34.35960558,11.6628,0.7143,0.2347,2.0841890756,1.4368343274,0.4466,153.1897,0.0771,0.0256,14.1678,3.214,0.0910868405,0.2431161037,0.0274,0.3245208834,25.18110294,5.3849,2.1377,0.017,5.2882026316,0.3714089836,0.4057,221.5538,3.502,0.3279,37.6527,4.7199,0.1709541555,0.6952509456,0.0408,1.8692287578,18.418781039,23.6721,0.6992,0.2707,1.8546096774,0.3197732849,2.7402,147.0083,1.9504,0.0074,59.4043,27.6765,0.3501564593,1.1655436364,0.1142,1.2597167203,19.853069396,10.9251,4.0663,3.3288,0.7839497076,0.0161604643,0.3336,133.3698,1.7572,0.1316,26.8727,5.3297 +NCR1,NK,0.0370316742,0.9002749141,0.3089,0.3437418251,40.284831544,0,0,0.9473,1.751192437,0.4564014621,2.2236,167.3177,0.1136,0.7924,88.1136,1.5448,1.9904755779,0.7134158156,0.1228,0.5734951967,31.468229806,0.8658,0.0683,0,2.2394105263,0,3.4629,112.7829,0.2242,0.1455,40.8949,2.3269,1.3211184987,3.7247411461,0.362,0.7099895113,44.719725256,0,0.0786,1.4176,2.4801451613,0.3893228506,2.553,142.9926,0,0,55.4884,17.3112,1.2653684211,3.2537913131,1.4225,0.3705223737,14.665542095,2.9412,0,0.2794,2.070980117,0.0676629531,3.3859,170.7798,0,1.1754,89.469,3.9296 +NDRG2,mDCs,16.196866063,19.631279725,6.1298,38.61168124,26.50753056,25.7845,510.8811,12.5543,7.3504327731,43.275106062,10.1075,11.3777,19.6487,1.9661,14.8084,23.0615,5.7663428571,11.745071336,7.4305,38.690145538,19.296658608,26.0445,411.9024,22.4768,31.345068421,46.978394623,4.1636,24.9159,32.7205,3.5717,13.8212,48.2651,12.037509651,13.607240458,5.6652,40.492075447,22.181501574,19.2793,355.0737,4.9114,8.1209145161,32.566681209,7.0999,9.2574,12.0003,1.374,18.4412,14.8735,6.7617210526,13.156877778,8.7883,37.050673803,25.903569113,30.71,534.6104,16.6562,13.828016082,44.646057341,6.9984,30.948,25.6079,2.1748,13.311,37.3059 +NECAB2,Neutrophils LD,0,0,0,0,0,0,0.4597,0.4872,0,0,64.1257,0,0,0,0,0,0,0,0,0,0,0,0,0.7293,0,0,37.5163,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.0097,0,0,0,0,0,0.1574698565,0.1551622222,0,0,0,0,0,1.0602,0.7010710526,0,43.1319,0,0,0,0,0 +NELL2,Naive T cells,0,1.2990412371,0.0485,121.07532007,39.074184605,82.9524,0,0,0.0218487395,400.63496009,0.3237,1.6767,0,0,50.4768,52.8715,0.0110588619,3.3269810443,0,164.44835788,37.658215707,100.4954,0.0321,0,0.0928421053,755.35948538,0,6.84,0,0.0185,66.0298,92.2466,0,0.087253467,0,88.191963654,9.8021043273,37.1981,0.2446,0.0385,0.2369387097,281.98893891,0,3.2669,0,0,19.1862,9.851,0.0145492823,0.9102654545,0,37.637083019,24.44006496,35.6507,0.4616,0,0.1897561404,346.47588677,0.4348,1.3758,0,0,26.8822,39.706 +NEURL1,Monocytes NC+I,2.0922746606,1.817095189,0.6862,2.1125350437,0.5327565075,0.562,4.8008,4.1506,34.494146218,1.7443264598,2.881,1.0879,3.4603,0.5997,0.7799,1.1567,1.8607202727,1.217092193,0.7682,1.4226942465,0.4892802966,1.2915,3.973,6.3623,116.43025789,2.6625022951,2.7424,2.3143,2.1135,0.4949,1.7089,0.786,1.4010203753,3.05216149,1.4562,2.9040948219,0.7002673485,1.411,1.1792,10.8152,106.27416452,2.9793468357,0.1037,0.7016,2.6089,0.0509,1.142,1.196,1.3094674641,2.3224240404,4.6491,1.5731231138,0.9328163521,0.885,0.5837,4.6146,69.225551462,1.9711041423,0.9409,1.1869,3.8203,0.0684,0.6661,0.4357 +NFE4,Neutrophils LD,0.9716945701,0.7811439863,2.7066,0.726439152,0,0,0.7346,1.309,0.5073609244,0.6981122181,36.0766,0.3421,0.8836,0.1295,0.557,0,1.6560004149,0.2722141448,0.4191,0.6553578619,1.163721337,0.604,0.3542,0.3887,0.6282921053,1.1906092459,52.1744,0.7842,0.229,0,0.1153,0,0.8050975871,0.9254752436,1.6893,1.0375228844,0.2612553895,0.3744,0.4483,0,0.1127790323,0.920468073,21.4598,1.095,1.0677,0.1669,1.1862,0.8481,0.8493076555,0.569879798,0.8045,0.6513614062,0.4297009202,1.556,0.5653,0.4219,0.1587280702,0.4460244029,43.433,0.5282,0.2694,0,1.0397,0.3414 +NLRP6,Neutrophils LD,1.1029800905,0.0619848797,0.1112,3.0221507834,0.7945727556,1.4727,0,0.0396,0.6472609244,3.494452728,29.8615,0,0,0.0195,0,1.6119,0.3121976882,1.301738538,0,7.7019465702,0.8819251571,2.4905,0,0.0352,3.3401026316,11.165248262,45.2342,0.856,0.1556,0,2.2032,3.4104,1.8646608579,0.3060139828,0,3.6097376573,1.1280269866,3.0807,0.2369,0,7.2722806452,4.2565037227,21.7467,0.5466,0,0.0767,0.4133,0.8616,0.4893368421,0.3571018182,0,1.2827770781,0.2820397121,0.2787,0,0.1263,1.9910426901,2.391416135,56.0626,0.3163,0.0806,0,0.4684,1.0716 +NLRP7,pDCs,0.1832271493,0.0288298969,0,0.2727818562,2.3787696209,0.0378,0,0,0,0,0.1228,8.5319,44.0281,4.5481,5.7071,0.3579,0.0794295791,0,0,0.0394293541,1.2531825584,0,0,0,0.1396236842,0,0.2189,4.8012,52.2759,10.3321,1.3967,0,1.1394101877,0.289947851,0,0.0089827771,0.77006845,0,0,0,0.0557177419,0,1.3124,8.2101,31.6634,5.7031,2.388,0.7827,0.0671564593,0.2670856566,0,0,0.7782993865,0,0,0,0.019495614,0,0.3582,6.999,47.7576,2.4671,2.1359,0.4806 +NMUR1,NK,0.2964226244,0.3108845361,0.2872,0.9595426325,28.306955145,9.4181,0.5113,0.5015,1.5905021008,0.4459754079,0.6804,68.3998,0.3325,0.0657,21.9792,8.0022,0.9546123889,1.1371219662,0.6802,0.8288537268,26.271032546,7.4613,0.2337,0.1661,1.9464973684,0.5582440656,0.2862,85.3706,0.1492,0.4826,20.2852,7.365,0.4084600536,1.222590086,0.8111,2.1172104754,18.926427852,10.1765,0.3472,0.3807,1.2503548387,1.0555603794,1.3287,82.2669,0.5155,0,51.6424,22.0855,0.6187172249,1.840259596,1.4215,1.6537377099,29.740431312,4.8768,0.3496,0.6648,0.6169169591,0.5567939035,0.6995,81.6119,0.1512,0.1993,42.4827,15.3434 +NOG,Naive T cells,0.1438737557,0.2736189003,0.2458,4.1140220129,0.2992881339,0,0.2165,0.3493,0.1165184874,17.538979152,0.4406,0.527,0.065,0.0287,0.4916,2.0564,0.2066546532,0.2311730284,0.3696,2.453520386,0.0635013823,0.1331,0.1047,0.1146,0.1958736842,12.272786295,0.6923,0.5306,0.2025,0.0602,0.2569,0.6227,0.0232857909,0.1617963324,0.4818,3.3885023838,0.0192179386,0.2476,0.2986,0.5023,0.4270887097,12.946796579,0.6984,0.0805,0.2944,0.0746,0.087,0.561,0.1486023923,0.1188963636,0.0974,3.2287337491,0.0858877773,0.1369,0.0622,0.186,0.1032976608,15.254672858,0.2135,0.1749,0.1187,0.0336,0.6864,1.8798 +NOTCH4,pDCs,3.1399524887,3.9402725086,1.7506,0.884825296,0.2800841457,0.3099,2.5852,7.8668,24.251284454,1.4636015601,4.8597,1.1601,74.3025,0.456,0.3126,1.0804,3.0135329579,3.4379500756,2.3565,0.6341885895,0.5552752953,0.8232,8.7565,19.5198,36.327005263,1.171408918,3.5822,2.5239,41.324,0.4633,1.3421,0.8885,8.0570418231,6.4678032092,1.8588,1.391186472,1.3855767899,1.1448,4.3726,7.7704,32.616001613,0.4588556462,4.0798,1.6993,74.8602,0.2111,0.8445,1.3665,7.8297961722,3.7319036364,1.6773,0.8587979853,0.5878819962,2.9815,1.4006,9.3759,23.691330409,0.7570251211,2.9433,1.3602,78.5963,1.1119,1.6506,1.0043 +NOV,Neutrophils LD,0,0,0.6533,0,0,0,0.5499,2.7917,1.2311777311,0,46.3989,0,0,0,0,0,0.6763390634,0,10.5361,0,0,0,5.5792,0.7488,1.1990447368,0,33.5812,0,0.5877,0,0,0,0,0,4.1,0,0,0,1.1753,0,1.0229741935,0,14.7109,0,0.1709,0,0,0,0.8679339713,0,1.3624,0,0,0,0.7214,0.9445,0.6348739766,0,39.5405,0,0.3121,0.0487,0,0 +NRCAM,Naive T cells,2.875560181,0,0.0522,0.1149666667,0,0,0,0,0,10.439736659,0,0,0,0,1.5659,1.5961,2.8661911085,0.1260101981,0,0.0354419599,0,0,0,0,0.2959026316,29.038269443,0,0,0,0,0,1.1929,0.2086785523,0.4330019484,0,0.9051077142,0.1091290323,0,0.5091,0,0,11.024784063,0,0,0,0,0.5728,0.8879,0.9444526316,0,0,1.0454682588,0.0347091081,0,0,0,0,16.222833756,0,0,0,0,0.3406,1.8195 +NRG1,Monocytes C,0.3043945701,0.7228223368,2.2307,0.4198284834,0.3371052683,0.7112,8.1951,57.0659,2.5559386555,0.1765533833,0.9445,0.4912,0,0,0,1.6236,2.5380619443,4.472715009,0.5751,0.7147026726,0,0.3876,4.3818,101.5251,5.4786684211,0.4993481967,0.4596,0,0.4079,0.2305,1.7729,0,8.9122785523,10.434740172,3.0525,1.0457756787,0.1009538946,0,8.452,69.6152,1.8693403226,0,2.5764,0.9473,0.4552,1.3009,0,0,3.4715301435,4.9316826263,1.0327,0.5847577263,0.397358353,1.5509,15.8467,112.6067,4.3831283626,1.5923118089,0.836,0,0,1.1592,1.3172,0 +NRP1,pDCs,0.6867285068,0.8462714777,2.3983,0.8918203803,0.4659190547,0.662,2.8271,6.1064,1.3062621849,1.3720369662,0.9799,1.0016,309.3926,0.129,0.9714,0.5137,0.8755750445,0.5733242348,0.8027,0.7870326578,0.6319062579,0.9529,4.0864,8.2205,1.6315026316,1.4321095738,0.7237,1.3599,280.3211,0.0563,0.3323,0.5512,1.8675238606,2.6065920344,0.6467,0.691015978,0.7425716758,0.3704,4.1938,11.5034,2.745216129,0.9198630917,0.5638,0.3503,417.0163,0.4077,0.4136,0.3664,0.3610617225,1.4467856566,0.7768,0.6797655794,0.5821091317,0.7207,8.5133,7.7065,3.0853429825,0.8639804911,0.4207,2.2945,342.3772,0.0651,0.8987,0.9586 +NSUN7,Neutrophils LD,0.6442438914,3.4662140893,0,0.1993404676,0,0,0.6603,2.7519,0.2429886555,0.7739527369,9.8827,0,0,0,0,0.2123,0.0537133966,1.772405171,0,0.2327432888,0,0,0.6827,1.2864,0.3600789474,0,11.5889,0,0,0.1634,0,0,0.3730884718,2.5658041261,0,0.0943806913,0.1768453973,0,1.0272,0.2729,0.7035258065,0.303869438,13.9571,0,0.448,0,0,0,0.1558980861,2.0107670707,2.0726,0.2599273282,0,0,0.2028,1.135,0.7961704678,1.0369664941,17.2672,0,0.4516,0.104,0.1529,0 +OLFML2B,Monocytes NC+I,0,0.0194364261,0,0.0882623968,0,0,0.7779,3.3338,16.958494958,0.6397869974,0.0833,0.1019,0,0,0,0,0,0,0,0,0,0,1.4958,5.1465,6.8993157895,0.4098960656,0,0,0,0,0,0,0.3137160858,0.0125436103,0.0466,0.0105135081,0,0,1.7975,1.7216,8.9627677419,0.2155843645,0,0,0,0,0,0,0.3725655502,0.1679547475,0,0.1244027822,0,0,2.3181,3.459,16.640023099,0,0.4412,0,0,0.2281,0,0 +OR2A4,Basophils LD,0,0,19.6534,0,0.041391761,0,0,0,0,0,0,0,0,0,0,0.8069,0,0,23.5403,0.0822216778,0,0,0,0,0,0,0,0.3129,0,0,0,0,0.2823002681,0.4543939255,20.5224,0.0355874056,0,0,0,0,0,0,0,0,0,0.1835,0,0,0,0.2137814141,24.8104,0,0,0,0,0,0,0,0,0,0,0,0.3387,0 +OR2AQ1P,Basophils LD,0,0,28.9785,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,31.9596,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,82.5179,0,0,0,0,0,0,0,0,0,0,0,0,0,7.6303191388,0,62.0597,0,0,0,0,0,0,0,0,0,0,0,0,0 +OR2I1P,Basophils LD,0,0,38.9587,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,41.8719,0,0,0,0,0,0,0,0,0.1532,0,0,0,0,0,0,40.396,0,0,0,0.1142,0,0.1618080645,0,0,0,0,0,0,0,0,0,37.4745,0,0,0,0,0,0,0,0,0,0,0,0,0 +OR6K3,Basophils LD,0,0,22.615,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,30.441,0,0,0,0,0,0,0,0,0,0,0,0,0,2.4974959786,0,18.1304,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,41.6846,0,0,0,0,0,0,0,0,0,0,0,0,0 +OR6K4P,Basophils LD,0,0,29.1712,0,0,0,0,0,0,0,0,0,0,0.1308,0,0,0,0,25.3888,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1301662198,0.2112383381,13.5457,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,38.2299,0,0,0,0,0,0,0,0,0,0,0,0,0 +OR6K5P,Basophils LD,0,0,9.3513,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.9374,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2639785523,0,17.8869,0,0,0,0,0,0,0,1.6112,0,0,0,0,0,0,0,20.1762,0,0,0,0,0,0,0,0,0,0,0,0,0 +OR6N1,Basophils LD,0,0.0529979381,11.1629,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.0043,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.1386,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17.6038,0,0,0,0,0,0,0,0,0,0,0,0,0 +OR6N2,Basophils LD,0,0,21.2962,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25.0001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2961211461,23.6588,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24.6264,0,0,0,0,0,0,0,0,0,0,0,0,0 +OR7M1P,MAIT,1.0418972851,1.0218865979,0,2.2316037795,0.0876005252,24.2216,0,0,0,0,0,0,0,2.9219,0.4441,0,0,0,0,0.0343606682,0,14.8622,0,0,0.7672184211,0,0,0.3594,0,0,0,0,1.5239839142,0,0,1.4709635346,0,6.7346,0,0,0.8821854839,0,0,0,0,0,0,0,2.2957555024,0,0,2.4168361269,0,18.3647,0,0,0,0,0,0.9494,0,0.181,0.417,2.4602 +OR14L1P,Basophils LD,0,0,25.4156,0,0.0129164615,0,0,0,0,0,0,0,0,0,0,0,0,0,31.6412,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.2282,0,0,0,0,0,0,0,0,0.9568,0,0,0,0,0,0,26.9378,0,0,0,0,0,0,0,0,0,0,0,0,0 +ORM1,Neutrophils LD,0.8326,2.890367354,0,2.0571552386,4.0884194485,1.1868,1.6129,2.9736,1.9616394958,2.0130648525,40.6067,8.5429,1.6318,0.5405,4.5973,3.5119,0.0476155898,1.1700668851,0,1.2836603415,0.3952252325,0,2.5641,2.061,0,3.2433088525,70.6488,6.3091,0,0,2.5744,1.1736,1.903216622,3.0769524928,0,1.2165396173,2.1306210071,0,0.1692,1.5514,0.4055580645,0,234.5645,4.4598,1.2099,0,0.7358,0,0.4356880383,0.3053755556,0,1.0730967724,1.8318652666,2.8984,3.9412,0.5948,0.8888157895,6.1247798397,46.9584,4.0868,0.9968,0,3.8668,2.5324 +OSGIN1,Monocytes NC+I,1.6088484163,0.7407814433,1.3174,0.6316703744,0.1139914656,0,0.1578,5.9806,24.147668487,0.2307149728,8.4948,0.4409,0,0.1394,0.4261,0,0,0.8389688657,0,0.0242651967,0.0700336768,0,3.4828,3.7303,16.116810526,0.8839491148,4.3886,1.2956,0,0,1.8121,0.9153,0,0.5913228653,0.7796,0.0272556218,1.0490586939,0.8588,2.1685,4.7511,19.252429032,0.3478180287,1.547,0.8856,0,0,0,0.2573,1.0506645933,0.5639426263,0,0.2227377167,0.6096764276,0.8063,1.3561,9.9208,18.963597368,0.5441578754,4.8587,0.1698,0.9435,0.3609,0.4327,1.9225 +OXCT2,Plasmablasts,1.3404986425,0.0277690722,0,0,0,0,0.3945,0.0606,0,0,0,0,0,9.5988,0,0,0.4928726734,0.165262283,0,0,0,0,1.9627,0,0.1531789474,0,0,0.2762,0,10.0829,0,0,1.0807302949,0,0,0.1147594822,0.0593398899,0,0.8476,0,0,0,0,0,0,13.5302,0,0.0974,1.6091909091,0.0878890909,0,0,0,0,0.4418,0,0,0,0,0,0,3.7285,0,0 +P2RY2,Monocytes NC+I,0,0.2259821306,2.4677,0,0,0,14.3891,13.7413,22.857915546,0.0287040965,0.26,1.0665,0.6902,0.4318,0,0,0,0.0993416205,3.1091,0,0,0,5.6479,11.5274,16.469978947,0,0.1532,0.2836,1.2554,0,0,0,0.4057211796,3.3872350716,5.6723,0,0,0,8.4238,13.6467,17.523380645,0,0,0,0.1612,0,0,0,0.1659636364,0.5384741414,2.5879,0,0,0,9.0115,9.8772,17.604591813,0,1.1972,0.361,1.1846,0.0892,0,0 +P2RY6,pDCs,0.4227823529,0.9430752577,0.3181,0.3650190468,0.034859675,0.3386,37.9304,2.6614,1.949439916,0.4866577472,0.4032,0.9798,134.4889,0.134,0.6367,0.3202,0.4422405453,0.3120692762,0.2249,0.3310871121,0.2547221664,0.3106,30.4729,2.9624,2.7735710526,0.2132542951,0.2004,2.3597,71.9328,0.0236,0.2403,0.2209,0.2670957105,1.3900510602,0.5498,0.2337417362,0.0446223446,0.3205,49.1676,5.7585,3.1041129032,0.095149548,0.2012,0,115.7377,0.0781,0.2228,0,0.5102832536,0.5248115152,0,0.2355879942,0.4062709061,0,43.0553,1.1697,2.9290663743,0.2592643895,0.9681,1.0984,107.4675,0,0.4183,0.5252 +P2RY14,pDCs,10.651599095,42.6809,46.3021,2.7300288901,0.3766366814,51.3782,90.6135,1.6497,1.717089916,0,21.025,1.7948,314.9083,0.2883,0.8354,1.4983,16.678852934,51.342047152,52.8527,2.4765245954,0.2379600905,69.4364,26.6536,0.7373,1.4575657895,0.5412381639,15.3553,3.5744,172.0597,0.4968,4.9872,8.0284,5.193663807,9.8613426361,26.2096,1.0359852006,0,35.3349,21.7529,3.7947,3.7900870968,0.0301322283,32.2825,1.1725,207.4793,0,0,0.7489,1.792162201,42.105119192,73.0535,2.2076949428,0.15439521,33.225,42.2655,0.4654,1.2076035088,0.7098192417,9.7657,2.0744,273.8224,0,0,2.6876 +P3H2,pDCs,2.3320226244,0.5385666667,1.3703,1.1259649085,0.359708518,1.1861,12.7925,1.7426,0.024842437,1.1612024115,1.4352,1.5211,173.6936,0.1387,1.9714,0.2864,0.9567589212,0.23821269,2.5953,1.0951747587,0.4529243529,0.5172,6.7732,1.2836,0.7261236842,1.1783833443,0,3.2426,118.3479,0.3155,1.1653,0.3765,0.4973664879,0.6658613754,0.8718,1.0562171898,0.3013189614,1.6416,2.9591,1.8007,0.7385870968,0.6974062046,2.5179,0.8405,65.9525,0.4052,0.394,0,1.4703248804,0.6435329293,2.2059,0.9805997807,1.1873001652,0.5244,13.8645,1.1609,0.8800780702,0.7155890763,2.2793,1.7937,191.4112,0.2121,1.1588,1.5365 +PACSIN1,pDCs,1.063441629,0.3599587629,0.0341,1.5210348822,0.1778188085,0.6306,0,0,0.0148109244,0,0,4.1954,117.6248,1.2241,0,6.0161,0.6942786011,0,0,0.3563201633,0.9658477758,0.0277,0,0,0.3834210526,0.8017500328,0,1.1586,57.6431,0.0376,1.0947,8.079,0.0365123324,0.1848048138,0,1.5313281949,0.5751701023,2.314,0,0.0261,0.037133871,0.0172118241,0,6.126,104.5918,0.3511,0,1.8355,0.164108134,0.1164064646,0.4571,0.4563881861,0.5333082586,0.729,0.1033,0,0.0983523392,0,0,1.1866,124.956,0.3492,0,3.9369 +PADI2,Neutrophils LD,1.1312393665,0.5630621993,1.5158,0.1344206076,0.004332841,0.0432,34.3461,23.401,1.6951857143,0.0156547161,101.2602,0.3114,0,0,0.0788,0,0.0729628927,0.6150882247,0.2814,0.0644934373,0.0781667002,0.0561,23.2421,29.5995,3.5794868421,0.2685770492,150.1637,1.098,1.3954,0.2001,0,0,3.1304268097,5.4019993123,18.5665,0.0465715534,0.0305310779,0,29.3963,51.8916,4.7003370968,0.0309628257,316.737,1.5706,0,0.3511,0.0448,0.0477,1.4511124402,2.8854688889,18.2283,0.0968643459,0.1053781029,0.1194,40.2336,61.0952,4.5090040936,0.0758734258,203.6007,0.2478,0.0468,0.3754,0.087,0 +PALD1,pDCs,0.2120574661,1.2687302405,0.1573,0.1413737292,0.2503776301,0.3129,3.2507,0.0225,0.1343726891,0.0485484666,0,1.6775,36.7412,1.3586,0.1261,0.1583,0.2873100178,0.7712187829,0.071,0.2371862064,0.1974527268,0.0256,3.6948,0.2649,0.4366657895,0.133941377,0.0332,0.1276,27.1622,0.3592,0.1787,0.0218,0.0036008043,0.1832340401,0.2383,0.2800258972,0.1608214005,0.349,3.014,0.0484,0.3288741935,0.0927718667,0,1.2693,22.8978,0.0962,0.468,0.0719,0.0570655502,0.4339129293,0.1871,0.0929069211,0.1174611845,0.0789,3.2727,0.0238,0.0903143275,0.2487743945,0.0273,0.5823,44.2957,0.155,0.1758,0.3467 +PAPSS2,Monocytes NC+I,0.8127099548,3.7033934708,1.2008,2.0874004724,0.7064840144,1.7413,17.5635,14.7142,95.490640756,1.9063628555,3.5734,2.2083,1.8172,0.4238,2.1425,2.0141,1.494505987,3.1408943104,1.3558,1.7874242094,1.8853966826,1.037,12.9573,11.4083,61.272494737,4.4779443934,2.2039,2.5862,0.6971,0.2487,0.8995,1.0132,2.6754297587,1.5424562178,1.9087,1.3814090981,1.3911708104,0.1892,9.3954,5.0511,77.773409677,2.2662816876,1.0259,1.2554,1.0265,0.0458,0.1595,0.686,1.0457354067,2.1100666667,1.2187,1.9882124101,2.092021496,1.2243,15.5482,7.4679,46.244276901,1.6415457491,2.9056,4.6155,0.6529,0.6333,2.5862,1.6888 +PCDH9,B Naive,6.329660181,43.034062199,1.3767,0.1563553163,0.0669143443,0.7453,0.0513,0.0485,0.1633651261,0.1590692164,0.0966,0.4365,2.379,0.0637,0.1189,0.0586,2.5484666271,49.49493058,1.287,0.1104922866,0.155216386,0.0702,0.0519,0.0622,0.0767842105,0.0886658361,0.0763,0.4019,3.3354,0.0061,0.0489,0.0438,1.9020715818,33.444790888,2.8693,0.1149380016,0.0904041699,0.0482,0.0411,0.0467,0.0918096774,0.1030946286,0.131,1.4657,3.1695,0.0101,0.0802,0.0358,4.5355813397,37.986765859,1.3057,0.3363498253,0.4000257905,0.2177,0.0692,0.0643,0.0341409357,0.1195555704,0.0915,0.1893,4.6188,0.0221,0.1899,0.0678 +PCDHGC3,pDCs,5.1578447964,1.7251690722,0.4488,0.386707541,0.0824470704,0.2782,2.544,1.9314,0.5849407563,0,0.4878,0.7966,33.6479,0.2526,0.0178,0,2.8718737996,0.8631790277,0.7607,0.6783858352,2.2649150289,0.0000000279,0.1235,0.6903,0.8452921053,1.7503180984,0,3.769,49.6478,0.342,0.1367,0.6961,3.1047790885,0.9146695129,0.8585,0.3891925043,1.9833817467,1.0072,1.4537,0.5124,0.2134725806,0.4490869881,0.763,1.6922,32.7863,0.1309,0.1104,1.6655,3.1116837321,0.6370216162,0.4029,0.7772656959,4.0498075271,1.1486,1.1169,0.4871,0.7322856725,0.3003832303,0.2652,1.8763,72.1293,0.4768,2.5427,1.0461 +PFKFB2,pDCs,10.648861086,14.136500687,5.7089,2.8745909281,1.4902258329,0.9278,7.5276,9.601,19.009257143,5.6830717928,16.3431,5.3817,215.2286,4.3544,0.915,1.3176,10.225147659,19.796794454,2.4972,0.8162205642,1.6048761247,0.8025,3.4788,7.2298,10.794152632,7.7992325902,3.7644,6.0778,178.3385,2.6186,2.1573,1.1136,21.504465952,18.105462063,7.3498,2.665968726,0.9452247836,0.7627,3.285,20.2978,16.704508065,14.890408899,22.0787,9.0174,253.8297,2.2689,0.4859,0.1359,16.624519617,21.930496364,6.1538,3.0550274858,3.343122487,1.5874,6.4731,5.9889,12.482837135,4.5235491732,8.8366,5.0135,206.5488,1.9234,1.5127,0.4238 +PGBD4P1,VD2-,0,0,0,2.3375487501,1.1918356967,3.6386,0,0,0,0,0,15.7683,0,0,15.436,1.2183,0,0,0,1.4860777134,4.5345947474,0,0,0,0,0,0,7.7387,0,0,18.0646,0,0,0,0,0.4533343266,6.6392459481,0,0.434,0,0,0,1.112,8.7074,0,0,14.2529,0,0.1300995215,0,0,2.869819667,0.5341606654,0,0,0,0,0,0,5.7681,0,0.2994,22.1706,0 +PGLYRP1,Neutrophils LD,0,0,0.5574,0.1753258043,0,0,0,0.1938,0,0,72.0599,0.2555,0,0,0.8301,0,0,0.727194332,0.3125,0.2901374239,0.2434871827,1.1266,0,0,0.3657236842,0,100.925,0.6717,1.3639,0.9102,0,0,2.381477748,4.3381625788,0,0.1345597802,0.7687568843,0.2792,0,0,0,0,141.637,1.6327,0,0.4105,0.5906,0,0.4067736842,0.3419155556,0.9922,0.5506300007,0.3832900661,0.2325,0,0,0.231844152,0.1611347252,89.7355,1.1815,0,0.2259,0.2588,0.2548 +PHEX,pDCs,0.093300905,0.0202945017,0.0238,0.1560471056,0.0160639258,0.0193,0.0614,0.0168,0,0.0612837523,0.2099,0.0216,34.6008,0.4658,0.023,0,0.0195043865,0.016416579,0.4565,0.533000683,0.027994848,0.1496,0.0148,0.1963,0.1310789474,0.0114570492,0.0728,0.0563,15.3755,0.0085,0.0146,0.0484,0.2600099196,0.1426829799,0.0396,1.3772522315,0.0756416994,0,0.0844,0,0.06045,0.0346048573,0.0905,0,48.3494,0.0071,0,0,0.0376478469,0.2720410101,0,0.7172873912,0.1967631194,0.0579,0,0.0353,0.078002924,0.0231617338,0.7059,0.0492,40.4216,0.038,0.0215,0.0642 +PHOSPHO1,Neutrophils LD,5.0687257919,5.7595872852,0.2803,0.8863817845,3.4293854915,4.0255,7.854,3.0715,39.174081933,0.296324873,340.7354,8.7862,1.0133,1.0429,3.8409,2.6413,7.0050432128,7.625152157,1.8065,0.8735479362,2.1856257351,4.6575,6.9675,4.1937,33.842368421,3.5230793443,205.9865,13.1137,1.2018,0.2577,6.1866,2.2864,26.616190617,43.34695788,2.3122,0.3632612369,3.6892660897,4.0424,3.3783,4.9068,48.359077419,2.7774010105,142.7194,14.0022,0.4751,6.1037,5.1608,3.6707,1.9451574163,3.2232010101,0,0.7597731515,6.028176286,4.1391,6.1401,5.6869,22.779639766,0.8391843661,259.0197,9.7076,0,0.2227,5.682,2.9559 +PI3,Neutrophils LD,0,0,0,0,0,0,0,0,0,0,64.6746,0,0,0,0,0,0,0,0,0.153017023,0,0,0,0,0.3788315789,0,97.0961,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1328306452,0,113.192,0,0,0.2282,0,0,0,0,0,0,0,0,0,0,0,0,108.075,0,0,0,0,0 +PI16,CD4+ effector,0.5002927602,2.276271134,0,32.177911219,7.6785396028,0,0.1457,0,0,1.4543043238,0.5122,5.7753,0.1102,0,6.4977,0.0615,0.6093604624,0.928176543,0,32.189473526,2.0544512692,0.2257,0,0,0,3.314755082,0,3.9918,0.6939,0.081,7.4017,0.8163,2.0677855228,0.297869341,0,37.446445126,1.6626414634,3.8489,0,0,0,2.7896482539,0,1.6699,1.2213,0.7464,0.8854,0,1.665222488,3.0739557576,0,31.103292058,10.876686975,1.9122,0,0,0,6.4557666945,0.495,0.5927,0.2129,0,2.3928,1.0447 +PID1,mDCs,0.0259493213,1.8692274914,0,0.1109481641,0.401537223,1.7722,71.4791,59.307,27.026278571,0,0.8878,0,0,0.1327,1.6101,0.4456,4.0832002964,4.5905464602,0,0.2880437713,0.5320928877,0.4385,106.7051,78.539,11.398539474,0,0,0.6149,1.8419,0.3263,0.3247,0.3698,6.9426,5.5933783381,2.0917,0.3243082307,0,0,97.9911,44.087,18.120204839,0.2617925545,0,0,0.3868,0.9876,0,0,1.9109870813,2.3368472727,0.5006,0.6554534503,0.0894661397,0,87.2074,44.543,21.152915205,0.3506389177,0.559,2.3345,0,0.1776,2.5242,0.4963 +PKIB,mDCs,3.9275429864,10.176588316,1.3238,2.8448967767,4.053009831,0,25.1392,0,1.6349789916,5.1025624053,0,2.5482,0,2.5142,2.3144,0,3.0847665679,0.5269204537,0,1.7126465924,1.0400613973,0,68.4166,1.4551,7.8645526316,4.4757908197,0,2.1594,2.7538,1.1797,0.8938,0,0.2078646113,0.0009851003,5.7897,2.2056525626,1.6619083399,1.7293,26.4374,3.8709,7.0521435484,0,0,0,2.3208,0.7224,0.8493,0.7596,6.1585033493,9.7230232323,5.0994,2.5516477969,3.152743983,0.4833,53.0716,5.3915,2.9586356725,3.0095584266,0,0.3743,1.7026,1.131,2.8079,0 +PLA2G7,Monocytes C,0.9509117647,1.9213587629,1.4095,0.8282181796,0.2065624323,0.2619,13.3399,58.5447,5.4263491597,0.5608260631,0.3518,0,0.5195,0,0.766,0,5.5103469472,3.5770130284,0.9745,0.0996775204,0.0326995476,0.4737,9.5651,46.3898,2.0966473684,0.4314212459,0,0.5395,0.8449,0.365,0.2997,0.1994,5.9842407507,9.2794693983,0.6533,0.4995514965,0.39234524,0.8217,18.4468,52.1828,3.3034774194,0.4622411806,0.5941,0.7444,0,2.161,0,0,2.5454334928,5.0555250505,1.0608,0.2808891386,0.3243947617,1.0678,15.1743,47.9691,3.9054687135,0.0737530984,0.5236,0.5003,0.6218,1.0365,0.4945,0.7642 +PLBD1,Monocytes C,14.100190498,17.652185223,0.7817,1.5955995096,0.1930612834,0.2053,426.805,836.7072,190.22367353,0.6458978336,663.2826,3.7309,3.7939,6.6709,1.2054,0.6194,24.33163604,27.155312582,3.3125,0.7150196362,0.2761723297,1.1958,427.1857,1038.0444,193.78036579,1.3251330492,500.742,3.4685,18.9402,6.9936,1.2759,1.8617,76.182996247,106.62061496,0.6558,0.6044594756,0.9616592447,1.2894,415.2305,1042.792,139.75310161,1.1780462329,298.483,3.3014,2.4886,10.6369,0.8936,1.1699,43.269347368,55.625910101,2.181,1.6500924964,0.7564137801,1.0832,522.699,1210.3887,169.09475497,1.5758109404,409.7659,1.7428,2.7153,12.8709,0.643,0.9147 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Naive,43.885162896,81.386181443,0.1233,4.5661110992,9.6618896603,1.4457,0.3934,0.1793,0.1278058824,1.6604535081,0.3891,0.0571,0.181,0.8736,4.8754,1.0681,30.472524956,104.83721521,0.0469,2.821489703,4.7555975873,2.0441,0.3017,0.2193,0.2055394737,0.3631196721,0.1963,0.1139,0.0387,0.7214,9.2173,1.1895,43.600693566,75.247976504,1.0765,1.5197439147,1.8713970102,1.5589,2.0751,0.092,0.0908548387,0.186717621,1.7214,0.4294,0.0573,1.8847,0.6226,0.0458,22.3789311,67.562332727,0.102,1.4520247173,5.8393792591,0.7307,0.1056,0.2136,0.2406596491,0.109492467,0.1258,0.0297,0.0792,0.2282,5.9406,0.3835 +PLEKHG7,B Memory,25.358333937,1.6362725086,0.0455,0.0226032054,0.0062859019,0.0383,0.3455,0.1966,0.2120042017,0.3123650976,0,0.0415,0.5734,1.2195,0.1878,0,31.721024007,2.293950054,0,0.1507978471,0,0,0.048,0.032,0,0.038710623,0,0,0.3333,1.9876,0,0.0312,15.563904826,4.8053947278,0,0.0878406171,0.0217151849,0,0.1671,0,0.0273451613,0,0,0,0.2994,0.8807,0,0,12.774026794,1.0734882828,0,0.1248507709,0.0456825153,0,0.1807,0.1784,0.075530117,0.5063164356,0,0.2158,0.0786,0.8042,0,0.1383 +PLS3,pDCs,0.0556470588,1.229766323,0.3971,1.4566119483,0,0.4753,2.1163,0.9602,0,0,0,0.739,22.3615,0,0,0.7318,3.9489176052,0,0,0.4063526652,0.1606528273,0,0,1.6218,0.1938631579,0,0.6134,0.3245,24.3936,0,1.4048,0,0.7218399464,0,0.4551,1.2464238511,0.5383411487,0,0.2954,0,0.0437806452,0.5234773976,0.4002,0,16.5598,0,0,0,1.8903674641,0.2322981818,1.6836,0.3825668951,0.0590184049,0,0.3807,0.9774,0.2411245614,0.0358215634,2.3224,2.0176,17.0344,0,1.7419,0.4979 +PLVAP,pDCs,0.0630533937,0.5738986254,1.0505,0.0223235857,0.2376543082,0,1.1279,0,5.6869588235,0,0,0,53.1163,0.3218,0,0,0.0824898044,4.1246083759,0.3701,0,0.029085901,0,0,0,2.0096631579,0,0,0,56.1512,0,0,0,0.9558284182,1.3041029799,0.386,0.0128761621,0,0,1.6602,0,1.556866129,0.0319902854,0,0,96.0374,0.4086,0,0,0.5108703349,0.1121414141,0,0,0,0,1.0598,0,2.7865961988,0,0.057,0,200.9756,0.4243,0,0.3197 +PLXNA4,pDCs,0.1198280543,0.1155876289,0.7699,1.5351059502,0.1387724766,1.4431,0.007,0.0978,0.139542437,1.570148431,0.1216,5.3144,50.1338,0,0.8405,0.1901,0.2311046829,0.2264217645,0.5166,1.3095499332,0.2508877607,0.3735,0.1497,0.0323,0.1949921053,2.8784288525,0.141,5.8851,53.3514,0,0.2826,0.2562,0.0554201072,0.4619264183,0.4175,0.8914789233,0.2972890637,0.471,0.0811,0.2052,0.2228532258,1.0205100691,0.3148,5.3182,51.6133,0,0.3266,0.2523,0.0797162679,0.2716747475,0.1077,0.7090462345,0.1876487966,0.2118,0.271,0.1519,0.0397479532,1.0520723568,0.0789,4.6702,45.8205,0.0277,1.0983,0.222 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LD,3.7950678733,2.2478219931,0.6472,1.2344731073,0.7099477761,1.5662,1.5486,5.0012,2.1261978992,1.191142703,60.5763,3.038,3.4095,0.3484,2.686,0.8019,2.3778189686,3.2234788621,3.5223,0.390121069,0.8967813772,0.0368,2.7649,0.017,1.3920026316,4.9808952131,44.5233,6.1371,1.0144,0,3.0932,0,0.7650965147,1.6960939828,0.2039,1.0821002119,0.1505079465,0.0228,0,0.3824,1.6809241935,2.2621702535,32.9833,0,1.0888,0,1.6215,0,0.3725655502,6.1139224242,2.686,2.0688268279,1.9782580698,1.8725,0.9462,1.7774,2.2411175439,3.9873418072,64.4716,2.3764,1.1815,0.0832,1.2057,0.9312 +PPM1J,pDCs,3.8558615385,3.4158542955,0,0.2791072479,0,0,12.3953,1.4183,2.5284336134,0.0619887983,0,0,239.5896,0,0,0,7.0383987552,3.100933547,0,0.0111531849,0,0,9.2251,0.1275,1.8079105263,0,0,0,260.0067,0.0646,0.3289,0,4.2126117962,6.7022144986,0,0.4931265395,0,0,5.7751,0,0.7972677419,0.229102145,0,1.7218,241.5605,0,0,0,3.6132483254,5.6523628283,0,0.1056825944,0.4123430864,0.481,9.8216,2.1467,2.1713830409,0.1550349591,0,1.6086,320.3746,0.2497,0,1.5798 +PPP1R14B,pDCs,2.1821950226,0.4654323024,0.3691,0.6229811326,0.2608505334,0.6351,1.4304,0.5088,1.2141945378,0.1675975484,0.3474,0,40.2134,0.1267,0,1.0924,1.4007197985,1.7161699172,0.4128,0.1881754714,0,0.5956,1.844,1.0093,1.8898657895,0.8913714754,1.5574,0,26.8651,0.5296,0.2247,0,0.2718126005,0.2762414327,0.307,0.2882189246,0.1256471282,0,1.3101,0.4144,0.8507870968,0.7234179401,0,0,12.6753,0.4296,0,0.4177,0.1802899522,0.7659632323,0,0.5382776057,0,0.9227,1.4549,0.6845,1.1666397661,0.4646271421,1.7256,0.6489,31.7172,0.1489,0.4756,0 +PPP1R17,Monocytes NC+I,0,0,0,0,0,0,0,0.0951,15.286208403,0,0,0,0,0,0,0,0,0.0039789701,0,0.0628159465,0,0,3.261,0.3742,46.959563158,0,0,0.3229,0,0,0,0,0.8231099196,0.0357980516,0.3355,0,0,0,0,1.5173,31.585064516,0,0.3047,0,0,0,0,0.0763,0,0.8261058586,0,0,0,0,0,0.302,34.198262573,0,0,0.2835,0,0.0821,0,0 +PRAMENP,B Memory,15.602997738,5.4180776632,0,0,0,0.0401,0,0,0,0,0,0,0.0769,1.8953,0,0,19.967434025,3.0132385452,0,0,0.0053721035,0,0.0311,0,0.0168552632,0.0118866885,0,0,0.06,1.4622,0,0,9.9739050938,3.5096558166,0,0.0240019401,0,0,0,0,0,0,0.113,0,0.0697,2.2342,0,0,15.401548325,6.4529125253,0,0.0104755157,0.0162387919,0,0,0,0,0,0,0,0,1.1422,0,0 +PROC,pDCs,0.8211710407,0.2725525773,0,0.1169673424,0,0,0.8143,0,0,0,1.3806,0.375,57.4399,0,0.9888,0,0.2279430943,2.9523593014,0,0.3224010393,0,2.6436,0.1567,0,0.0856578947,0.3294575738,2.2936,0,66.1867,0.2381,0,0,0.0552152815,0,2.0249,0,0.0950256491,0.7481,0,0,0,0.5925742599,2.2697,0,46.2593,0,0,0,0.0357837321,0.6939531313,0,0.1078821216,0,0,0,0,0,0.2369138467,0.5365,0,32.1695,0.676,0,0 +PROK2,Neutrophils LD,0.5448235294,0.9498522337,11.735,1.1169970578,21.020400377,0,0.5452,32.9766,1.7014096639,0.331050477,513.5373,3.7001,0,0.3263,1.5481,0,0.7028784825,0.333000749,14.2644,1.6559714477,48.866631239,0.2791,1.6472,21.5494,2.8815157895,0.5270600656,761.531,20.4815,0.6597,0.0629,10.7278,5.1331,3.2552782842,1.4676970201,5.2957,1.1480638127,5.9234996853,0,1.0806,17.6276,0.452483871,0,318.7945,0,0,0.4372,0,5.6517,0.1708535885,1.1922575758,0,0.7447577195,28.349334733,0.5177,0,6.1391,0.1320687135,0,690.353,1.3567,0,0,3.7651,0.2842 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+PTCRA,pDCs,0.8508882353,0.7357728522,0.2581,0,0,0,0.1689,0,0.2387962185,0,0,0,60.2095,0.1785,0,0,0.0635301126,0.5857611883,0.2897,0.0261332739,0,0,0,0,0.0833894737,0,0,0,193.4749,0,0,0,0.9017697051,1.9889941547,0,0.0498505959,0,0,0,0,0.510666129,0.3872891154,0.788,0.5044,63.8183,0,0,0,0.5933578947,0.0775678788,0,0,0,0,0.5865,0,0,0,0,0,109.2196,0,0,0 +PTGDS,pDCs,1.5500574661,0.1622460481,0.8449,2.1133645916,24.351917413,2.0691,0,0,0.9576512605,0.820514692,1.5943,18.8616,1201.4973,0.2892,6.3555,2.4156,0,1.416919251,0.2924,10.142990356,50.323229806,5.4308,0.5249,0,3.5041421053,3.0309855738,2.5416,130.7295,819.9515,0,39.5455,14.6194,4.3326281501,4.2549776504,0.3687,13.131609164,22.222490087,9.7824,0,0.3873,3.6456483871,0.1776160964,2.8241,68.3052,455.0891,0.4207,32.9995,61.8326,4.9577952153,4.1196375758,0,10.875763455,50.606639358,6.7991,0,0.9903,1.7148853801,1.4741837815,0.9933,153.5978,1295.5247,0.2964,264.4929,48.7986 +PTGS2,Neutrophils LD,1.2880036199,2.8500814433,18.028,0.0650460411,0,0,58.9849,59.6531,26.599168487,0,1010.2393,0,0.1464,0.0323,0.0923,0.0266,3.4927545347,17.561759878,40.2466,0.0237634892,0.1385224931,0.0258,186.8247,233.296,169.39389737,0.6555073443,878.2487,1.1954,2.2427,1.7072,0.0401,0.2681,36.220872386,47.326437937,31.842,0.01875441,0,0.0397,387.1885,406.9289,283.38077258,0,2414.9673,4.5951,0.8625,4.1949,0.0418,0,1.5771373206,5.1357284848,10.4188,0.1935453985,0.0065915526,0,46.2996,47.2862,29.430959064,0.0306425422,549.8394,0.3943,0.283,0.5292,0,0.3588 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+PTPRS,pDCs,3.1672131222,1.5472395189,1.2166,0.4257787107,0.4118563433,0.36,0.6078,0.6637,0.5835256303,0.3402659312,1.1216,0.3805,113.4636,0.1497,0.1669,0.4699,0.7072418494,0.6199873245,0.9226,0.4633925167,0.6000529279,0.405,0.5438,0.3591,0.4826421053,1.3361351475,0.7504,0.8671,51.3498,0.1365,0.3746,0.409,0.5613013405,0.8155272206,0.8511,0.6710357569,0.3413191975,0.2901,0.5822,0.5906,0.4029177419,1.0324612126,0.8706,0.3406,88.0943,0.0432,0.436,0.3188,0.7875966507,0.5518579798,1.1226,0.4574352977,0.297832185,0.3824,0.481,0.7433,0.3051052632,1.0023256055,1.0682,0.7362,123.362,0.1785,0.4193,0.5103 +QPCT,Neutrophils LD,5.6145538462,2.4917099656,0.4684,0.1751744648,0,0.0746,43.0446,129.0322,6.6891718487,0.0658176607,585.0067,0.2013,0,6.2689,0,0,6.7774896858,8.4463566871,4.4369,0.1826265033,0.3026835637,0,40.23,198.3708,9.0780789474,0.1778891148,590.752,1.1685,3.4999,2.4933,0.1352,0.904,7.3759329759,18.409344126,0.7182,0.3995562177,0.161736428,0.3247,55.6619,134.153,4.1491387097,0,305.9906,0,0,3.0898,0,0,9.3400296651,9.0441422222,2.8666,0.228160762,0.0643369278,0.2705,65.6286,245.6899,15.106186842,0.3138379823,607.0803,0.6866,0,3.9717,0,0 +RASD1,pDCs,0,0.2072508591,0.7187,0.5600681318,1.2620260627,0.0959,1.3051,1.3788,1.6920147059,0.4219066283,0.0805,0.533,53.8187,3.1906,1.0778,2.4792,0,0.2319358012,0.1308,0.049216778,0.6516488314,1.0359,0.6312,1.1307,2.0600315789,0.0733126557,0.1898,0.7997,52.9497,1.3842,0.394,0,0.1633367292,0.5266987966,1.5449,0.2063031585,0.2316358773,0.6332,0.9814,1.5826,0.4019967742,0.2831631448,0,0.5295,114.0875,1.2793,1.908,1.2299,0.0816287081,0.1832256566,0.5341,0.2046669842,0.4405533034,0,0.9546,1.2679,0.982348538,0.3117040922,0.3022,0.5108,53.2982,1.5029,0.7526,1.0007 +RCAN2,CD8 activated,0.1172524887,0.0515223368,0,1.0995407128,38.422134039,0,0,0,0,0.0726620754,0,6.2169,0,0,11.5006,3.9421,0,0,0,1.4811954343,22.706331264,1.1964,0,0,0,0,0,1.4543,0,0,29.15,0.987,0,0,0,0.2519758045,9.7547765539,0,0,0,0,0,0,0,0,0,3.1167,0.5583,0,0,0,1.087688611,12.335807574,0,0,0,0,0,0,0.5566,0,0,6.6019,0.0561 +RHOXF1P1,Basophils LD,0,0.5683608247,293.985,0.0222282562,0,0,0,0,0,0,0.4509,0,0,0,0,0,0.7349662122,0,226.459,0,0,0,0,0,0.2443078947,0,0,0,0,0,0,0,1.5598069705,1.4927972493,537.909,0.0302513905,0,0,0,0,0,0,0.9618,0,0,0,0,0,0,0,267.655,0.0370244364,0,0,0,0,0,0,0,0,0,0,0,0 +RHOXF1P2,Basophils LD,0,0,26.5134,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.714,0,0,0,0,0,0.3197789474,0,0,0,0.6062,0,0,0,0,0.6638590831,43.1291,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24.8829,0,0,0,0,0,0,0,0,0,0,0,0,0 +RN7SL343P,Neutrophils LD,0,0.430766323,0,0.0632584798,0.0286148039,0.3635,0,0,0,0.5128087323,32.7705,0,0,0.2998,0,0,0,0.2805423551,0,0.0421665553,0.0488450364,0,0,0,0.1435157895,0,6.7208,0.3594,0,0,0,0.9179,0,0,0,0,0,0,0,0,0.2681564516,0.2224155292,11.6725,0.4395,0,0.1237,0,0,0,0.0985808081,0,0,0.3995921661,0,0,0,0,0,20.1069,0,0,0.1784,0,0.4083 +RN7SL462P,Neutrophils LD,0,0.430766323,0,0.0632584798,0.0286148039,0.3635,0,0,0,0.5128087323,32.7705,0,0,0.2998,0,0,0,0.2805423551,0,0.0421665553,0.0488450364,0,0,0,0.1435157895,0,6.7208,0.3594,0,0,0,0.9179,0,0,0,0,0,0,0,0,0.2681564516,0.2224155292,11.6725,0.4395,0,0.1237,0,0,0,0.0985808081,0,0,0.3995921661,0,0,0,0,0,20.1069,0,0,0.1784,0,0.4083 +RN7SL494P,Basophils LD,0.211239819,0.183452921,150.42,2.3329377706,0.1812571804,0,1.7594,0,1.243539916,2.5800249666,0,0,2.2389,0,1.349,0,0,0,149.597,0.7612683519,0.1650610706,5.4762,0,0,0,0.9662588852,1.5182,3.3241,0,0,0,0,0.4989016086,1.3049295129,195.685,2.4723549133,0,0,0,0,0.2284016129,0,0,1.3299,0,0.9255,0,0,0,1.3803917172,127.16,0.3034759474,0,0,1.1189,0,0,0,0,1.9117,0,0,1.2608,1.245 +RN7SL640P,Neutrophils LD,0,0.430766323,0,0.0632584798,0.0286148039,0.3635,0,0,0,0.5128087323,32.7705,0,0,0.2998,0,0,0,0.2805423551,0,0.0421665553,0.0488450364,0,0,0,0.1435157895,0,6.7208,0.3594,0,0,0,0.9179,0,0,0,0,0,0,0,0,0.2681564516,0.2224155292,11.6725,0.4395,0,0.1237,0,0,0,0.0985808081,0,0,0.3995921661,0,0,0,0,0,20.1069,0,0,0.1784,0,0.4083 +RN7SL668P,Neutrophils LD,0.2100452489,0,4.147,0.8300335307,0.9979360906,0,0.8772,0.9156,0.5961659664,0,14.7201,0,1.0744,0,0,0,0,0,1.5266,0.5314785895,0,1.1027,0,0,1.3589,0.3185417705,18.9347,0,1.6507,0.4895,0.7995,0,0.0717595174,1.4227206877,0,0.9480526619,0.3170416994,2.7317,0,3.0424,3.7103870968,0.6746758199,13.2061,2.6631,0.9799,0,0,0,0,0.3010882828,3.262,0.9206671692,2.421927159,0,0,1.0219,0.5834356725,0,18.9405,0.9622,0,2.1793,0,0 +RN7SL854P,pDCs,0.2214334842,0,0,1.5229928358,0,0,1.8484,0,1.9639243697,0.5493974369,0,0,18.1162,0.4828,0,0,0,0.16823583,0,0,0,0,0,0,0,0,0,1.1509,9.5802,0,0,0,0.9595656836,0.631613467,2.3943,0.8719286849,0,0,0,0,0,1.4218904804,0,0,3.1052,0,0,0,0.1255712919,0.3169406061,0,0.670745933,0,0,0,0,0,0.9201873893,0,0,27.9128,0.5735,0,0 +RNA5SP151,Neutrophils LD,4.0472932127,0,0,4.9923734362,0,0,8.4824,12.1896,12.367510504,0,249.019,0,0,0,0,0,0.173855246,0.2554084336,14.9962,1.8242117372,0,0,13.5709,8.8978,11.521444737,0,196.78,0,18.8722,0,0,0,0,0,25.6412,2.5471153357,0,0,10.2341,9.9768,9.4026467742,0,398.823,0,0,0,0,0,0.7353684211,2.4147539394,5.0928,0,0,0,3.2337,6.5711,5.5567105263,0,210.82,0,0,0,0,0 +RNA5SP373,pDCs,0,0,0,0,0,0,0,0,0,0,0,0,17.1221,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.253,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23.5877,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,35.2857,0,0,0 +RNA5SP452,Basophils LD,0,0,25.8924,0.6001443248,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19.6453,0.9702722494,0,0,0,0,0,0,0,3.4444,0,0,0,0,0,0,50.2559,1.2351419348,0,0,2.5237,0,0,0,3.352,0,0,0,0,0,0,0,35.1349,0,0,0,3.1788,0,0,0,0,0,0,0,0,0 +RNASE4,Monocytes C,6.2076040724,2.5483474227,2.3272,2.9535116493,1.3762024947,0.9014,17.2393,51.0079,2.5357827731,0,0.3923,0.2187,11.3514,7.0293,0,0.8567,11.000781683,2.0681164854,0.0671,1.9327331477,0.449077331,0,20.0317,51.4893,2.3572105263,2.1365819672,0,0,4.9201,3.6809,1.4978,0.9539,7.6527991957,4.5695634384,0.1999,1.2965404119,0.849852085,1.0992,11.8198,22.0699,0.4090306452,0.5489787449,0.0462,0,8.8409,5.9077,0.5208,0.249,27.796550718,1.7269808081,1.4581,1.840226499,4.0869341435,1.975,15.8219,54.2512,6.5378128655,0.2216933189,0.4258,0.5225,5.742,4.0059,0.1107,0.0549 +RNF157-AS1,Naive T cells,0,0,0,8.1899642925,0.0058509765,1.0904,0,0,0,25.031915338,0,2.794,0,0.2073,1.2013,2.8234,0.0651926497,0,0,6.2430164068,0.3874166876,0,0,0,0,19.763298951,0,0,0,0,2.015,0.0614,0,0,0,4.827081029,0.345288749,0,0,0,0,26.071085144,0,2.858,0,0,1.5258,2.0678,0,0,0,5.1786984102,1.1246979943,3.126,0,0,0,35.096672941,0,0.9189,0,0,4.4029,2.2038 +RNU2-2P,Monocytes C,11.777643891,8.8550677718,0,4.8522855759,3.2351622846,20.1766,47.6809,170.289,41.504116807,2.6774142551,8.93,0,14.6855,14.4136,0.6264,0,12.075006283,31.667625747,0,6.0751844766,0.4830263885,7.3158,68.9728,349.987,59.869421053,0,1.1475,3.9018,20.6225,5.6455,6.4744,4.2546,13.025302949,47.418838109,6.5341,1.5751926434,6.9087132179,20.9573,99.6958,229.567,55.896498387,0.4240059209,17.1855,8.2666,48.5024,7.0267,5.8161,17.1452,8.3693129187,28.238362626,0,3.0285472007,5.6053297546,10.992,37.9434,198.627,18.608860819,2.6340597795,0.1079,0,23.2365,12.6661,2.7917,16.0784 +RNU2-6P,Neutrophils LD,11.30468914,16.215820619,0,0,0,0,37.336,0,0,0,571.249,0,20.1996,0.9966,0,0,2.5169950207,17.761252733,0,0,0,0,22.8498,0,0,2.8709415738,913.495,0,16.3063,0,0,0,0.4614603217,27.074180172,0,0,0,0,1.6513,0,11.570312903,0,557.563,0,14.4856,0,0,0,0.2905301435,16.534724444,0,0,0,0,0,0,2.4221125731,0,625.004,0,20.4266,0,0,0 +RNU4-90P,Neutrophils LD,0,0,0,0,0,0,0,4.9895,0,0,19.8247,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,43.2819,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.4762,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4633008772,0,32.0601,0,0,0,0,0 +RNU5E-1,Monocytes C,1.108640724,0,5.3177,0.1906209365,0.2790825538,0,0,25.9464,5.7745848739,1.496891348,0,0,3.3554,6.061,31.7465,0,0.3393015412,0,0,0,1.3887100276,0,22.4836,40.9086,13.558260526,0,0,0,2.8681,0,0,2.8779,0,2.7384502006,0,0.8026225864,11.056399292,0,21.4132,32.3279,14.919437097,0,22.36,0,0,1.3152,13.612,0,0,4.6972913131,4.9754,0,0,0,0,0,7.2868567251,0,6.162,0,0,0,0,0 +RNU6-29P,Basophils LD,0,0,34.2675,0,0,0,0,0,0,0,0,0,0,0,0,8.2249,0,0,39.6645,0,0,0,0,0,1.8791131579,0,0,0,0,0,0,0,0,0,72.7701,0,0,0,0,0,7.2793451613,0,0,0,0,0,0,0,0,0,81.2153,0,0,0,0,0,0,8.2001648739,0,0,0,0,0,0 +RNU6-123P,pDCs,0,7.1167353952,0,2.4940456345,0,0,3.2225,3.5641,5.0458298319,9.2339680128,0,0,107.499,4.933,0,0,3.8005002964,16.378666143,0,12.066920252,0,0,0,3.441,9.5739078947,6.1085405902,0,0,56.2943,0,0,0,0,0,0,0,0,0,9.0104,0,3.0539645161,7.325775359,0,0,65.2723,1.4528,0,0,0,8.251990303,0,3.5636963818,0,0,0,3.8226,4.3436330409,0,13.3093,0,107.25,0,0,0 +RNU6-355P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNU6-447P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNU6-785P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNU6-791P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNU6-831P,Basophils LD,0,0,38.8571,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32.7438,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,72.6865,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,50.8098,0,0,0,0,0,0,0,0,0,0,0,0,0 +RNU6-860P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNU6-951P,Monocytes C,8.9365778281,8.772728866,0,0,0,3.9098,0,84.3798,12.865839496,8.5450031693,4.3199,8.7246,0,0,0,0,0,0,0,0,0,11.9094,0,29.355,14.698678947,0,0,3.9424,0,0,14.8373,0,0,3.5439787966,0,0.5879084227,0,0,0,95.6835,14.500206452,0,3.7871,0,0,2.7319,0,0,3.4170028708,4.6857840404,0,2.5618629206,3.3540411751,0,21.2848,123.072,30.53111345,7.3334037581,41.9097,0,3.4302,0,0,8.5582 +RNU6-1076P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNU6-1100P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNU6-1118P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNU6-1128P,Neutrophils LD,0,0,0,3.912076803,0,0,0,0,0,0,72.1104,0,0,0,0,0,6.9856004742,0,10.6417,0.3648383964,0,0,0,6.4114,1.6386631579,0,149.015,0,0,0,0,0,0,4.910780745,11.8617,1.1509531188,0,0,0,14.3617,3.6614677419,0,346.394,0,0,0,0,0,0,0,5.5372,0,0,0,0,7.067,0,0,98.6455,0,0,0,8.6231,0 +RNU6-1138P,Basophils LD,0,0,9.7823,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.5633,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32.6567,0,0,0,0,0,0,2.3868139514,0,0,0,0,0,0,0,0,17.0527,0,0,0,0,0,0,0,0,0,0,0,0,0 +RNU6-1174P,Neutrophils LD,0,0,0,0,0,0,0,0,0,0,64.7767,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,71.3643,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32.6222,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22.4623,0,0,0,0,0 +RNU6-1217P,Neutrophils LD,1.04269819,0,1.3708,0.6508691723,0,0,0.432,4.7563,1.6419705882,0,24.3489,0,0.4863,0.4877,0,0.6886,0,0,1.3034,0.4369404603,0,0,0,7.9406,6.6447368421,0.2451672787,28.1182,0,0,0,1.3866,1.2115,0.6012439678,0.0894142693,0,0.0921078202,0.5756436664,1.7791,0.4901,2.775,4.313616129,0,27.4767,0,0.296,0.0834,0.3116,0,1.964862201,0.5128276768,0.3618,0.3772904269,0,0.2528,0,9.5415,4.9207602339,0.4719019876,49.2679,0,0,0.32,1.1515,0 +RNVU1-15,Monocytes C,0,2.5769756014,0,1.5186117091,0.4033398982,5.1059,0,17.0999,4.6229819328,0,0,0,4.9876,6.1848,6.2262,5.0603,2.5528250741,1.9923184444,0,1.7859845657,1.37931239,15.3199,30.9658,94.145,15.408963158,1.4747012459,0,0,9.7059,2.2025,0,4.248,4.417586059,16.75586447,0,3.4520720236,2.1283292683,12.5362,23.6943,71.1046,11.53521129,0,0,6.1493,2.2836,16.452,0,0,0.5400650718,4.8243062626,0,0.9985148976,0,2.6226,4.6859,30.7416,10.639958772,0,2.7516,2.1967,11.2603,0,0,2.8571 +ROR2,MAIT,1.621700905,0.2471312715,0,0.9638464239,0.3973001805,25.2656,0,0,0,0,0,0,0,0.2169,0,0.4723,0.2471644339,0,0,0.3647806756,0.2070740387,10.1829,0,0,0,0.2250236066,0,0.8011,0,0.8059,0.1918,1.0235,0.0281782842,0,0,2.4227300291,0.2314187254,23.9251,0,0,0.6662419355,0,0,0.4849,0,0.3115,0.14,0,0.5411100478,0.1194878788,0,0.2016035496,0,2.7653,0,0,0.0150438596,0,0,0,0,0.3035,0,0 +RORC,MAIT,0.2634289593,0.4155828179,0.3571,27.177797668,0.5788867225,153.0113,0.2831,0.1993,0.3268180672,2.2370676607,0.32,0.8785,0.1582,0.0122,1.5359,1.7666,0.2492433314,0.6022630537,0.5142,27.725704766,1.4050006534,151.6838,0.3719,0.1464,0.6927078947,0.4643192787,0.1137,0.3769,0.2156,0,15.6299,13.3907,0.0871300268,0.2173901433,0.3806,42.185738723,1.6050948859,223.9168,0.1277,0.2975,0.3485564516,0.4810761035,0.2372,0.4615,0.2004,0.0213,0.8325,2.8662,0.0683588517,0.383290303,0.2581,43.127361509,3.0794231477,153.0817,0.1585,0.4482,0.3779774854,1.2981650242,0.5155,0.7407,0.4288,0.1,1.6681,16.8206 +RP1-45C12.1,pDCs,3.3925099548,5.282542268,2.1511,0.9983351274,0.5250101264,0,0.4609,0.9648,0.9696033613,1.5984531827,7.6736,1.9665,31.3324,1.6983,0,1.7492,5.2461329579,9.961020569,2.3896,0.6478635412,1.2399222166,1.151,1.3186,0.9756,0.6829973684,2.6816655738,0.7564,2.8479,23.3002,2.0482,1.2663,1.455,0.5930624665,7.5166060745,1.7756,0.8041161899,0.4878709677,0.7128,0,0,0,0.3527270342,1.597,0.6945,47.7499,0.2014,0,0,1.0199803828,4.7892220202,4.2422,0.8966653944,3.0653458235,0,0,1.0697,2.3776181287,0.4577679305,2.471,1.5059,69.181,2.864,0,0 +RP1-228H13.2,Monocytes NC+I,1.1902841629,2.5823841924,0,0,0,0,2.7389,29.1482,66.715113025,1.9918324017,2.5329,0,0,0,0,0,7.7418328986,6.2120092906,0,0,0,0,8.7081,16.427,49.348389474,1.2311362623,0,2.2567,0,0,0,0,2.2371064343,7.1884443553,0,0,0,0,2.8822,24.2642,43.831077419,0,8.9643,0,0,0,0,0,4.5439397129,6.6860775758,0,0.0672796272,0,0,3.7529,23.3089,57.335138889,0,5.5065,0,0,0,0,0 +RP1-229K20.5,Neutrophils LD,0,0,0,0,0,0,3.6075,17.5516,1.727752521,0,240.366,0,0,0,0,0,0,0.583278646,0,0,0,0,0,7.638,2.5099394737,0,191.342,0,0,0,0,0,0,2.6942433238,0,0,0,0,2.1618,15.962,2.6046548387,0,128.371,0,0,0,0,0,0,0.7305991919,0,0,0,0,2.832,8.4007,0.7961988304,0,147.178,0,0,0,0,0 +RP1-229K20.8,Neutrophils LD,0.9081122172,0,0,0,0,0,0,0,0,0,26.4473,0,0,0,0,0,0,0,2.204,0,0,0,0,2.6728,5.1356368421,0,14.735,0,0,0,0,0,0,0.8623896275,3.2812,0,0,0,0,5.848,0,0,10.2278,0,1.4234,0,0,0,0,0,0,0,0,0,0,4.4078,0,0,13.6933,0,0,0,0,0 +RP1-229K20.9,Neutrophils LD,0,0.418652921,0,0,0,0,0,2.1674,0,0,34.6648,0,0,0,0,0,0,0,17.9031,0,0,0,7.8226,15.1668,3.0553921053,0,44.8771,0,0,0,0,0,0,0,2.6595,0,0,0,1.84,0,1.6136145161,0,19.3296,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,33.4613,0,0,0,0,0 +RP1-244F24.1,pDCs,0,0,0,0.3018957003,0.9391430002,0.1612,0.1285,0,0,0.5746686324,0,0.8981,10.6694,0.2719,0,0,0.024195495,0,0,0.3467399926,0.0430760493,0,0.2478,0,0.0675868421,0,0,0,15.9914,0.7851,0,0.942,0.2707238606,0,0,0.1214877433,0.0457104642,0,0.1176,0,0,0.3897328133,0,0.1916,9.9504,0.7588,0,0.8906,0.0859157895,0,0.2326,0.3065215103,0.6234242803,0.1634,0.4452,0,0,0,0,0,10.1851,0.5578,0,0.1793 +RP3-388E23.2,pDCs,0.6717411765,0.1065154639,2.7537,0.539494875,0.1281142294,0.643,0.7673,0.8118,0,1.1304837523,0,0,11.4242,0.1349,0,0.9712,0.0639968583,0.9965260641,1.3192,0.3540285375,1.0272464187,0.6346,0,0,0.1345105263,0.3164898361,0,2.5178,8.8572,0.5675,0,0,0,1.4515337536,1.9619,0.4765587604,0.2700043273,0,0,0.8874,0,1.3685015246,0,0,10.076,0,0,0,0.5737440191,1.0120379798,0.9325,0.6864559378,1.1438976876,0.6556,0.2975,0,0.1669663743,0.3550467847,0,1.108,21.2235,0.4757,0,0 +RP3-416J7.4,pDCs,0,0.4018092784,0,0.0062481761,0,0,0,0,0,0.3897685032,0.067,0,11.5673,0,0,0,0.0528551867,0.7307503925,0,0,0,0,0,0,0,0.3115647869,0,0,8.2762,0,0,0.0517,0.0170219839,0.9664103725,0,0.1993688849,0.0175458694,0,0,0,0,0.2644068428,0,0,7.2069,0,0,0,0.0263253589,0.9141143434,0,0.072207949,0,0,0,0,0,0.4720581928,0,0,13.766,0.1226,0,0.0687 +RP3-477M7.5,Naive T cells,0,0,0,0.6317509389,2.0798180699,0,0,0,0,78.613815904,1.8074,0,0,0,10.7316,16.1497,0,0,0,0.2566487008,0,4.0628,0,0,0,107.02939016,0,0,0,0,1.3429,0,0,0,0,2.5362919282,0,0,0,0,0,108.41813927,0,0,0,0,1.0723,0,0,0,0,0.7848179538,0.1709874941,0,0,0,0,46.576639268,0,0,0,0,0,0.919 +RP3-522P13.1,mDCs,0.2384294118,0.1388646048,0,0.2895655663,0,0,29.7131,5.1157,12.94735042,0.1990734733,0,0,2.0227,0,0,0,0.0208906343,0.3558040835,0,0.0364221826,0,0,16.6163,0,3.5533,0,0,0,2.6566,0,0,0,0.6213289544,0,0.4371,0.0630661502,0,0,10.0936,3.4855,3.5826048387,0,0,0,4.5542,0,0,0,0.3766358852,0.113559798,0,0.1453185774,0,0,10.8587,5.283,8.8692859649,0.1666398029,0,0,5.3093,0,0,1.4128 +RP3-522P13.3,mDCs,0.3997004525,0.914932646,0,0.7419639696,0.9784624159,1.0098,22.1854,2.9862,15.083953782,0.4025293617,0,0.6254,3.5911,0.3098,1.2123,1.8197,0.0463358032,0,1.4812,0.1060171492,0.1287320432,0.798,16.6626,1.1878,4.4008421053,0.0735399344,0.525,0.3348,2.8391,0,0,1.2442,1.7741099196,0.6519272779,0.4914,0.5702466561,0.4985738002,0,11.1228,4.1486,7.5515774194,0.5628326006,0.3047,0.4971,2.1191,0.1565,0,0,0.2595354067,0.1807535354,0.8942,0.781335798,0.4638825389,0.2271,12.4219,2.9825,9.8688479532,0.1533278103,0,0,6.8535,0.2246,0.2584,0.9515 +RP4-647C14.2,pDCs,0.278700905,0.5045054983,0.2859,0.2631419746,0.2262830461,0.0834,0.1144,1.0388,0.1828407563,0.2374563609,0.2155,0.1682,45.4874,0.0578,0.0548,0.2216,0.5420750445,1.5864554843,0.1234,0.1606673942,0.4767339533,0.1432,0.9509,1.034,0.3532526316,0.4411126557,0,0.0957,43.8903,0,0.1005,0.1234,0.5326426273,1.1573027507,0.4584,0.2385895312,0.230615657,0.0531,1.4267,1.9099,0.1925774194,0.1090415706,0.1513,0.0576,44.0216,0.3554,0.6796,0.0691,0.1679937799,1.7572266667,0.1997,0.2110768656,0.3711634497,0.1321,0.6772,2.0756,0.5308394737,0.1163,0.3528,0.3579,63.1451,0.1072,0.1096,0.1203 +RP4-669L17.8,Neutrophils LD,2.7150095023,1.469919244,0,1.9361010286,0.4076340883,0.6868,0.4094,4.945,2.513689916,2.7607784791,23.0848,2.3712,1.9844,0.585,0.0963,3.245,0,1.945415758,0.3758,0.329299562,0,1.3577,0.3,3.9994,7.5374578947,0.1615801967,28.1216,1.2262,1.005,0.6173,0,1.1933,0,0.3028198281,0.4347,0.5792546405,0.9851001574,0,1.0699,1.3735,0.5416806452,0.2908746322,12.0773,1.3604,0.283,7.13e-10,0,0,0,1.5432129293,0,0.7941170698,0.4957455403,0,1.2835,8.6607,5.0567666667,0,47.3186,1.0813,0.8109,0,0.2504,0.7026 +RP4-673D20.3,Neutrophils LD,1.0250642534,0,1.4085,0.2341174202,0.4928243394,0,3.0707,6.1818,0.2698231092,0.3652443969,24.5936,0.3941,0,0.2416,0.5553,1.1706,0.5131262596,1.2921703925,3.3257,0.4021842242,1.2251335763,1.3109,0.7289,2.9484,0.0415578947,0.6299624262,21.1633,0.4492,0.1949,0,0,0.3746,1.283466756,1.3009353009,0.7496,0.3964082108,1.3886943352,0.3141,2.2767,3.1323,0,0.319170289,7.2634,0,0.7455,0.3253,0,0.5996,0.6704200957,1.0243725253,1.7746,0.1546704858,0.2239675555,0,2.6494,4.7792,1.0232894737,0,34.1625,0.2343,1.5139,0.6855,0.3216,0.348 +RP4-718D20.3,Basophils LD,0,0,25.0297,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.943,0,0,0.2491,0,0,0,0,2.1051,0,0,0,0,0,0,0,22.8246,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19.455,0,0,0,0,0,0,0,0,0,0,0,0,0 +RP4-799P18.3,pDCs,0,0,0,0.7634712176,0,0,0.5303,0,0,0,0,0,28.3749,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.3464,0,0,0,0,0,0,0,0,0,0,0,2.4440725806,0,0,0,9.9616,0,0,0,0,0,0,0,0,0,0,0,0.3349766082,0,0,0,17.1292,0,0,0 +RP4-799P18.4,pDCs,0,0,0,1.715716565,0,0,0,0,2.2060693277,0,0,0,35.658,0,0,0,0,0,0,0,0,0,1.4984,0,0,0,0,0,33.4337,0,0,0,0,0,0,0,0,0,0,0,2.8917112903,0,0,0,23.2959,0,0,0,0,0,0,0.0764076749,0,0,0,0,1.0315277778,0,0,0,30.9604,0,0,0 +RP4-799P18.5,pDCs,0,0,0,0.3117646514,0,0,0,0,0.868802521,0,0,0,14.4911,0,0,0,0,0,0,0,0,0,0,0,0,0.3228453115,0,0,8.5475,0,0,0,0,0,0,0,0,0,0.2369,0,0,0,0,0,11.7083,0,0,0,0,0,0,0.1127548962,0,0,0,0,0,0,0,0,7.5065,0,0,0 +RP5-906A24.1,pDCs,0.0599873303,0,0,0,0.0642376498,0.4425,0,0,0,0,0,0,10.3138,0,0,0.6237,0,0,0,0,0,0.4342,0,0,0,0,0,0,14.557,0,0,0,0,0.9746741547,0,0,0.297638867,0,0,0.1936,0,0,0,0.0526,7.5184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.2294,0,0,0 +RP5-1011O1.2,Basophils LD,0,0,14.272,0.2711569729,0.1520925652,0,0,0,0,0,0,0,0,0.2677,0,0,0,0,14.0794,0.2433226578,0.1705749183,0,0,0,0,0,0,0,0,0,0,0,0,0.1737060172,8.5035,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.3166,0,0,0,0,0,0,0,0,0,0,0,0,0 +RP5-1027G4.3,Basophils LD,0.0975909502,0,22.5388,0.0934813539,0.005818152,0.1445,0,0,0.0380252101,0.0324726308,0.1566,0,0,0.0917,0,0,0,0.0823895931,15.5617,0.1220272309,0.0096669515,0.0709,0.0558,0,0,0.1140716721,0.7381,0.2828,0,0,0.219,0,0.0758621984,0.0788736963,16.5092,0.0252751953,0.1821687648,0,0.1061,0.1338,0.0531887097,0,0.0677,0,0.1883,0,0,0.299,0.2553052632,0.0770385859,18.282,0.0875783938,0.0146018169,0,0.0663,0.1322,0,0.050536763,0.2276,0.1242,0.0633,0,0.4066,0 +RP5-1031D4.2,pDCs,0.7813882353,0,0,0,0.027137699,0,0,0,0,0,0,0,18.3338,0.4286,0,0,0,0,0,0,0,0.3364,0,0,0,0,0,0,12.4292,1.2026,0,0,0.2040868633,0.4394496275,0,0,0.2901800157,0,0,0,0,0,0,0,15.4307,0.1207,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22.4958,0.8403,0,0 +RP5-1158E12.3,CD4+ effector,0,0,0,22.608879075,0.6793041195,0,0,0,0,8.7669363734,0,0.8118,0,0,3.4761,5.7565,0,0,0,19.551207216,3.0586664237,0,0,0,0,5.2278296393,0,0,0,0,0,4.7903,0,0,0,8.4917926698,3.6526898505,0,0,0,0,9.4116881581,0,0,0,0,0,1.0003,0,0,0,19.410539307,7.8382185701,0.738,0,0.6618,0,7.1659420745,0,0,1.2683,0,4.1006,1.6107 +RP6-91H8.3,mDCs,0.5874153846,1.1108292096,6.4217,2.4379335785,1.1123554078,4.2671,48.142,5.4899,1.3263294118,0.7726028929,4.524,1.9408,1.665,0.4342,1.8221,1.9826,1.1695324244,1.2299350306,4.217,0.9979139421,1.0846104549,6.4119,7.8976,1.0497,2.3190368421,2.3701693115,1.1048,2.5864,1.3895,0.1294,3.0051,3.0907,1.6011922252,1.873105788,6.4669,2.0624719706,3.6304387884,6.4534,33.8507,1.966,1.5833596774,1.0627990427,1.6797,2.9271,2.4907,0.3874,2.9944,2.2873,0.6255722488,0.7696375758,7.3078,2.0576946413,1.3613928504,6.1857,34.2868,2.1026,2.1363576023,1.79390309,2.1064,1.884,3.231,0.2837,0.7706,3.2823 +RP11-4C20.4,Monocytes C,0,2.0282680412,1.8163,0,0,0,8.1396,23.0534,5.4023483193,0,0,0,0,0,0,0,0,0,2.012,0,0,0,3.323,20.9182,13.935894737,0,1.9149,0,2.7218,0,0,0,3.6216836461,0.1985962178,2.9959,0,0,0,0,16.1056,9.2333870968,0,0,0,0.6436,0,0,0,2.6398287081,1.8101624242,1.0741,0,0,0,8.2107,13.5299,3.0792836257,0,2.3441,0,1.2965,0,0,0 +RP11-6B19.1,Monocytes NC+I,0.264699095,0,0,0.1019777718,0,0,2.2317,0.7816,16.277884874,0.4250895382,0,0,0,0,0.5671,0,0,0.067277753,0,1.5565348701,0,0,0,0,14.043315789,2.7687403934,0,0,0,0,0,0,0.7752546917,0,0,2.6582409284,0,0,0,0,10.625096774,0.56909766,0.4305,0.5589,2.0439,0,1.827,0,0.4700095694,0.2528432323,0,0.4472371959,0.0945080227,0,0.8709,1.2928,6.1233783626,0,0,0,0.4132,0.2311,0,1.5734 +RP11-7F17.3,Neutrophils LD,0.0820927602,0.4641443299,0.0858,0.4113179823,0.1872594781,0,0.0567,0,0.0796936975,0.1019683026,81.3401,0,0,0.03,0,0,0.003560818,0.1788977026,0.0968,0.0858153378,0.0744298567,0.0697,0,0.06,0,0.1281041311,62.3219,0,0.053,0,2.7149,0,0.0097495979,0.020785616,0,0.3347409813,0,0,0.0521,0,0.0522403226,0.0524554512,27.216,0.0843,0,0,0,0.1459,0.4522598086,0.0488274747,0,0.0970877681,0.5681731005,0.0717,0,0.1299,0.2761622807,0.3547249708,82.5376,0,0,0,0,0.0787 +RP11-10J21.4,Neutrophils LD,0,0.1045718213,0,0.6986146633,0,0,0,0,0,0.8259680218,4.9232,0,0,0,0,0.6404,0,0,2.6265,1.1014171047,0.6817126162,0,0,0,0,0,12.4795,0,0,0,0,0,0,0,1.9521,0.4437186532,0.5355464201,0,1.3676,3.5092,1.0547403226,0.3768283283,10.5207,0,0,0,3.3336,0,0,0,0,0.3209910779,0,1.3113,0.595,0,0,0,8.8313,0,0,0,1.4578,0 +RP11-13A1.1,Basophils LD,0.0884285068,0.029395189,232.689,0.0368584559,0,0,0,0.179,0.0656512605,0.0187565793,1.035,0.1846,0.0398,0,0,0.1262,0.5340248963,0.9420500396,273.389,0.0052576837,0.0442001759,0,0,0.1759,0.2098342105,0.0821076066,0.053,1.3426,0.5592,0,0,0,1.2149032172,0.3007439542,170.659,0.0791555026,0.0349150275,0,0,0,0.0085967742,0.0245043432,0,0,0.1084,0,0,0.0573,0.0044210526,0,113.16,0,0,0,0,0.1901,0.3426827485,0,1.3959,0.5001,0.1819,0,0,0 +RP11-13A1.2,Basophils LD,0,0,354.365,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,444.705,0,0,0,0,0,0,0,0,0,0,0,0,0,6.2458820375,0,375.972,0,0,0,0,0,0,0,0,2.285,0,0,0,0,0,0,275.944,0,0,0,0,0,0,0,2.0243,0,1.6729,0,0,0 +RP11-13A1.3,Basophils LD,0.2302714932,0,534.37,0,0,0,0,0.2551,0,0,1.6535,0.6776,0,0.1271,0,0,2.1871440427,0.7039696363,575.274,0.1133823163,0,0,0,0.7642,0.2533263158,0,0,2.6664,0.9017,0.2672,0,0,0.2521286863,0.8533892837,437.365,0.1802236856,0,0,0,0.5572,0,0.178585499,3.6393,0,0,0.2153,0,0,0,0.0813523232,239.987,0,0,0,0,0,0,0,1.2793,0,0,0,0,0 +RP11-20I20.4,NK,0,3.5323487973,0,1.4597363055,44.243601001,4.4638,0,1.0581,1.5365151261,2.2026137024,0.7033,204.839,14.7639,0,13.9177,18.5707,0,0.1834139431,1.751,2.0611694655,27.40985818,5.6916,0,0,3.03285,1.6559374426,0,191.654,10.9016,0,12.0288,17.5799,1.3017525469,4.7524481948,0,2.6819307774,45.999697797,7.0535,0,0.5849,6.9375693548,1.1414533593,1.169,366.42,1.118,0.4409,85.8393,68.4533,1.6953555024,5.0127856566,0,1.2368259645,53.038849033,2.6226,0,0,3.2695040936,0,0,278.485,3.9442,0.63,30.613,15.0442 +RP11-21J7.1,MAIT,0,0,0,3.639761135,0,26.8683,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7781083519,0,37.1737,0,0,0,0,0,0,0,0,0,0.5222,0,0,0,6.5912087671,0,41.4745,0,0,0,0,0,0,0,0,0,0,0,0,0,9.2238125266,0,42.4023,0,0,0,0,0,0,0,0,0,0 +RP11-22N19.2,Neutrophils LD,0.1049828054,2.0009879725,6.9222,0.3072424232,0.5270403906,0,2.6113,34.2091,14.360691176,0.0866791566,56.1821,0.4697,0,0.0767,0,0,0.3309567279,1.5571522218,2.0994,0.4321288048,2.7105436039,0,8.9433,37.1611,34.671234211,0.320797377,98.5624,0.8906,0,0.2416,0.4089,0.6075,6.8419281501,6.0455330086,2.538,0.42399216,0.5817617624,0.8879,18.7203,39.5467,22.900124194,0.2217233647,151.091,0.6492,0,0.2628,0,0.426,0.4137555024,1.4985349495,1.9194,0.3067071884,0.0367555451,0.1846,5.3651,17.8431,12.233851754,0.4358050443,67.1995,0.1566,0.6391,1.0792,0.2056,1.0123 +RP11-34P13.9,Neutrophils LD,3.1773226244,4.9856780069,5.0167,5.7472050353,2.1887939439,0,6.6497,14.1995,27.734511345,2.2195881163,127.836,2.9,8.1443,0.4702,2.3434,0,0.8730500296,5.6505577386,1.158,1.8776059837,0.386479618,2.8688,5.7618,23.1549,21.208892105,3.1860819672,86.3987,3.2592,2.9628,2.3038,0.002,4.9329,1.9345418231,3.7526152436,2.3282,2.8026490068,0,2.5617,3.1276,17.1525,13.62036129,1.166929906,111.484,0,7.1809,0,6.4048,0.8392,4.0445655502,4.8620941414,0,5.8266176934,2.0874777489,4.1653,6.2992,28.7764,19.025155263,5.0237969434,164.162,2.0829,6.2034,0.4776,3.6326,3.253 +RP11-34P13.15,Neutrophils LD,3.9308312217,3.7660051546,1.802,1.2864460053,3.1587056951,2.5657,2.9984,26.9288,15.939187815,7.2362353125,210.468,0,2.2233,0,2.5014,1.7852,3.9289375815,3.7815885128,0,0.9149636897,0.635426665,0,4.7036,26.8357,32.005805263,1.4805047869,281.306,0,1.5543,0.8086,0.2491,0,2.5225522788,2.948826361,1.5553,2.2553753311,0.1301391031,1.0711,2.6229,21.8935,13.626232258,3.683917036,167.75,0.4628,2.5396,0.8709,2.8206,1.5286,6.9958511962,2.9675086869,1.5211,0.6417547249,0.6346465786,6.3543,2.9672,49.056,17.680508187,0.0039034575,247.568,2.6373,6.4354,0,2.8127,2.4706 +RP11-34P13.16,Neutrophils LD,2.7109832579,4.8970185567,0,0.4053121576,0.1146889874,12.5508,0,9.682,11.342793277,0,134.034,0,3.9672,0,0,0,9.2569863663,9.7140548866,0.2432,1.7554295323,0,0,0,9.6835,26.538765789,8.953131541,187.766,0,10.1505,0,0,0,1.1474375335,4.4307233811,0.505,1.7817987949,0.9051106216,3.4439,0,20.2872,20.037375806,2.7962523311,139.561,0,0.55,0,0,0,2.7278827751,0,0,0.34653919,0,0,8.1193,40.8085,22.830475146,1.3428414231,168.119,0,3.4613,0.2358,0,0 +RP11-44F14.1,Neutrophils LD,0.9093298643,0.9020773196,23.1787,0.6965354324,0.4412075989,0.5188,1.4175,12.5975,2.1287764706,0.6206593207,70.0486,1.4266,0.6197,0.3485,1.1317,0.8659,1.0968193242,0.8572851855,25.458,0.6681922866,0.6203696657,0.8475,2.7022,14.6574,3.1185184211,0.9744573115,72.5329,0.7272,0.8194,0.2129,0.7878,0.708,1.3421407507,2.0441981662,16.477,0.9652830354,0.9030996853,0.8651,3.2929,5.9728,1.2390854839,0.7718661585,58.4927,0.9306,0.3153,0.1629,0.6635,0.6041,0.9928727273,2.3636284848,40.9222,0.7262561022,0.4657987966,0.8613,1.702,14.3593,3.8381216374,0.9743884249,104.3614,0.7627,0.5063,0.3634,0.7566,0.4989 +RP11-61O1.2,Naive T cells,0,0,0,14.85846873,2.5427067126,0.7203,0,0,0,115.55484761,0,0,0,0,10.486,7.9576,0,0,0,13.765959146,1.5416849711,0,0,0,0,77.616423016,0,0,0,0,2.0842,0,0,0,0,20.813646027,1.4157831629,0,0,0,0.1490387097,104.881507,0,0,0,0,0,2.0115,0,0,0,15.674495642,0,2.2265,0,0,0,76.721181226,0,0,0,0,4.9488,12.9553 +RP11-71G12.1,pDCs,0.2442954751,0.3455343643,0.2534,0.2571810788,0.1357318562,0.152,0.1118,0.1541,0.2070588235,0.2232298119,0.481,0.0758,283.7481,0.46,0.0274,0.119,0.1247775341,0.2691244653,0.2154,0.1229900742,0.0783777331,0.1643,0.1105,0.2576,0.1641789474,0.0503394098,0.5104,0.3158,244.5516,0.0203,0.1707,0.0412,0.4274930295,0.3269404585,0.375,0.1956455834,0.0641484658,0.0496,0.0503,0.1869,0.3441935484,0.1908833008,0.3219,0.0259,366.4969,0.2096,0.11,0.0617,0.0925492823,0.1297753535,0.0948,0.2215805934,0.1861848513,0.0897,0.046,0.1951,0.2497137427,0.066472457,0.3511,0.116,270.9486,0.7516,0,0.1625 +RP11-71L14.3,pDCs,0.4906959276,0.5259573883,0.2024,0.36466324,0.2986972263,0,0.6437,0,0.0570697479,0.1568160961,0,0.3513,12.6469,0.0264,0.0854,0.1696,1.2916770006,0.0268331797,0.6294,0.0445911804,0.4134183715,0.0577,0,0,0.0206236842,0.166882623,0,0.9848,4.6095,0.1998,0.7183,0.051,0.283983378,0.0304724355,0.1449,0.1292485962,0.2518831629,0,0.1114,0,0.2926983871,0.064842262,0.1082,0,13.8067,0,0.3988,0.1576,0.1040497608,0.1723860606,0.275,0.1869237169,0.865357362,0,0.0872,0,0.2873774854,0.1878251545,0.00000196,0.8766,12.8446,0.0304,0.4012,1.081 +RP11-73G16.2,pDCs,0,0.5954948454,0,0,0,0.4283,0,0,0,0,0,0,144.646,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,179.177,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5112,192.523,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,200.646,0,0,0 +RP11-84C10.2,mDCs,0,0.6178642612,1.1121,0,0,0,7.9276,2.2525,0,0,0.9901,0,0,0,0,0,0.0441676941,0.688012013,0,0,0,0,12.7769,4.5009,0,0,0,0,3.9852,0,0,0,0,0.5742464183,0.9143,0,0,0,3.0182,0,0,0,0,0,3.3898,0,0,0,0.8056665072,0,0,0,0,0,11.6913,10.6084,0.5126918129,0,0,0,1.5837,0,0,0 +RP11-98G13.1,MAIT,0,0,0,4.2492441395,0,10.8687,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5366882183,0,12.4748,0,0,0,0,0,0,0,0,0,0,0,0,0,2.1045828235,0.2054907946,9.4989,0,0,0,0,0,0,0,0,0,0,0,0.3746626263,0,2.8918550469,0.1462800849,18.7388,0,0,0,0,0,0,0,0,0,0 +RP11-100G15.10,pDCs,0,0,0,0,0,0,1.4709,0,0,0,0,0,75.6624,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,97.5444,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,30.2104,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0765,96.508,0,0,0 +RP11-103H7.1,Basophils LD,0,0.7357680412,11.764,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0483859345,6.7863,0.0824163474,0,0,0,0,0,0.8748087213,0,1.2909,0,0,0,0,0,0,8.165,0.1839423917,0,0,0,0,0,0,0,0.6978,0,0,0,0,0,0,15.2116,0,0,0,0,0,0,0,0,0,0,0,0,0 +RP11-109G23.1,Basophils LD,0,0,11.6204,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.9407,0,0,0,0,0,0,0,2.0372,0,0,0,0,0,0,0,11.1215,0,0,0,0,1.4141,0,0,0,0,0,0,0,0,0,0,34.142,0,0,0,0,2.8498,0.8172807018,0,4.9595,0,0,0,0,0 +RP11-117D22.2,pDCs,10.836426697,8.7920154639,0.4492,2.312343685,0.7003926309,0.5434,7.9298,3.6972,1.7179088235,1.3597437104,0.1984,0.8478,108.255,0.2409,2.1957,3.5761,8.9856371666,7.6438221966,0.8111,2.2118262212,1.1246433023,0.9823,14.0463,3.3608,3.5967447368,2.2171666885,1.2043,0.1848,130.836,0.0811,0.4311,1.9864,6.4992780161,4.8865137536,0.3681,1.4745648788,0.4584840283,0.5849,2.651,1.3376,0.3369516129,0.9739369615,0.1723,0.8782,97.8962,0.1982,1.194,0.2521,11.603016268,8.7841612121,0.533,1.795735565,0.5000897593,1.0167,13.0142,6.3063,3.3282883041,1.0680311675,0.1909,0.5062,106.218,0.1585,0.4233,4.5283 +RP11-138I18.2,B Memory,15.803708145,5.1813982818,3.0196,1.7769083363,1.0125325948,0.5226,0.5677,1.6575,0.2329319328,2.2368798743,2.2617,0.502,0.7743,1.2141,0.8456,1.0117,57.07658281,8.6187940655,3.5908,1.244438196,2.4857376225,1.7672,1.2195,1.5246,1.5690973684,2.7708559344,2.4421,1.6207,0,3.5373,1.3706,1.0725,33.961318499,4.1025417765,4.8983,1.6404436498,1.1449475216,2.4753,0.7444,1.5551,2.0504774194,3.7781971636,1.006,0.0228,3.1072,7.8046,2.8636,1.412,19.508382297,6.1819626263,3.9931,0.7703316453,1.2754527843,0.3998,1.2186,0.9904,1.7584032164,1.5587080007,1.8764,0.3544,1.0205,4.7675,1.8214,1.3622 +RP11-144A16.8,Neutrophils LD,2.5650461538,1.7765037801,5.9653,1.8579024279,2.436897571,1.507,1.6917,1.6249,1.5852268908,3.6673808772,176.882,1.1266,0,0.2869,1.0635,2.2828,1.540992709,3.5679453583,3.241,2.7765972457,5.8981916311,7.2313,1.2277,2.3972,1.26665,2.5632523934,38.9745,3.8639,3.5273,0,3.0099,2.0284,1.4086911528,1.7755119198,5.7342,2.5680698715,3.3302360346,1.1452,0.4462,3.1092,2.4366387097,1.506242262,46.9377,1.1676,1.2581,0,1.265,0,0.1055827751,1.3520523232,7.1686,2.0545530117,2.9256323974,2.6751,0.9277,1.558,0.5341081871,2.4763844663,192.683,4.1927,2.4118,1.2984,2.8222,1.2772 +RP11-162P23.2,Monocytes C,0.8168271493,2.0238264605,0,0.0610556632,0,0,39.8849,39.4791,14.718659664,0.2645307747,0,0,23.0618,0,0,0,0.0941523912,1.0840953259,0,0.0230338827,0,0,13.5753,34.8041,7.7917842105,0.5041101639,0,0,18.6478,0,0.1514,0.2787,5.4557117962,0.0041641261,0.8459,0.0857174546,0,0,29.3741,69.2352,10.622872581,0,0.0217,3.8175,9.7813,1.6895,0,0.2799,0,6.0247276768,1.3452,0,0.1849588957,0,24.5347,42.893,5.882648538,0,1.764,0,29.2766,0,0,0 +RP11-164P12.5,pDCs,0.264438009,0.4825979381,1.3992,0.259391263,0.7629905794,0,0.7318,0,0.6548747899,1.1189486984,0,0,23.2816,0,0,0,0,0,1.2552,0.3914950186,0.4889483539,0,0,0,0,1.2110459016,0,0,13.5372,0,0,0,0,0.4375815473,5.6035,0.1659305589,0,1.1215,1.2549,0,0,0.1354470839,0.6396,0,16.9224,0.2473,0,0,0.2287129187,0.9066416162,6.974,0.2957535805,0,0.49,0.8475,0,0.4551482456,0.3447554535,0,0,29.6128,0,0.2599,1.0233 +RP11-178F10.2,Basophils LD,0,0,76.7876,0.1531172348,0.0761119317,0,0,0,0,0,0,0.5404,0,0,0,0,0,0,96.686,0.6596753898,0.4472169892,0,0,0,0,0,0,0,0,0,0,0,0,0,106.54,2.6984184015,0,0,0,0,0,0,0,0,0,0,0,0,0,0,135.662,1.0553055712,0,0,0,0,0,0,0,0,0,0,0,0 +RP11-191A15.1,mDCs,0,0,0,0,0,0,40.313,9.6045,22.292169328,0,0,0,9.505,0.7439,0,0,0,0,0,0,0,0,8.1043,11.2522,10.615157895,0,0,0.8789,0.6642,0,0,0,0,2.2970528367,0,0,0,0,31.1762,16.3658,23.106375806,0,0,0,0,0,0,0,0,0,2.628,1.1369741383,0,0,53.4461,6.6088,28.12537807,0,0,0,8.7103,1.3235,2.0475,0 +RP11-203M5.6,pDCs,0,0,0,0,0,0,1.6872,0,0,0.3468755505,0,0,1107.363,0.1435,0,0,0.2448758743,2.2615804105,0,0,0,0,5.6831,0,0,1.3050551475,0,0,1131.198,0,0,0.1421,0.9025490617,2.3629989112,0,0.1204769567,0,0,2.4707,0,0.035183871,0,0,0,1763.614,0,0,0,0.6124124402,0,0,0.147658761,0.7147418358,0,1.3816,0,0,0,0,0.7321,1637.8879,0,0,0 +RP11-213H15.4,Neutrophils LD,0.400439819,0.6769676976,5.8881,0.5850433381,1.8724502216,0,2.0553,2.7959,1.0253894958,0.0720952082,47.671,0,0,0.1864,2.3479,2.6495,2.9521644932,2.3896329204,1.7872,1.1659921678,0.6018077406,0.0576,0,2.5035,1.7801368421,1.1367891803,28.6496,1.2952,0.0783,0,0,0,0.5130080429,4.0287510602,0,0.2241842604,0,0,0.6268,1.2307,0,0,44.8711,1.5139,0.6087,0,0,2.0363,0.2123444976,0.2009725253,1.1712,0.8426245529,1.2241553563,0.5465,0,7.1974,1.5860555556,0.3910223484,44.0699,1.8302,0.4304,0,0,0.4078 +RP11-221J22.2,Monocytes NC+I,0.3383425339,0.5548793814,0.165,0.2617812223,0.1194717381,0.2846,0,0,33.083844538,0.1935559998,1.3209,0.2354,1.0984,0,0,0.2088,0.3204149378,0.4893404969,0.4654,0.1312670007,0.1271350842,0.4116,0.611,0.3378,34.417931579,0.3728840656,0,1.1089,0,0,0.4671,0.3449,0.1595147453,0.8858272779,0.1388,0.5438585949,0.0771789929,0.1708,0.2892,0,25.598459677,0.0822907995,1.1101,0.0808,1.5319,0,0.2621,0.0941,0.2613985646,0.8190185859,1.3774,0.3300282327,0.0591774658,0.0689,0,0,39.799169006,0.0558183397,0,0,0.238,0.0674,0.8392,0 +RP11-242C19.2,Neutrophils LD,1.5269253394,0.5673907216,0.8222,0.8088518,0,0,2.1361,4.799,3.4726289916,0.294822408,17.6249,1.1285,0,0.1408,0.4062,0,0.0167049793,0,2.2963,0.5150507944,0.0447344559,0,0.5119,0.5649,0.6934789474,0.3304205246,7.8038,0,1.7496,0,0,0,0,0,2.3903,0.3638722355,0.2819036192,0,0.7273,0.6177,1.4377258065,0.2040962418,18.9127,0.3999,0,0,0.4348,0,0.1076593301,0.9280161616,1.4614,0.2583060783,0,0,0.9323,2.1579,0.6976973684,0.7128754301,14.2021,0.5786,0,0,0.3811,0 +RP11-256L6.3,Neutrophils LD,0.5070529412,0.3862707904,19.3271,0,0,0,0,1.0418,0.1319915966,0.3256956405,53.4507,1.5794,0.2127,0,0,0,0.210411559,0.0756170112,13.5401,0.1631605791,0,0.3697,0,0.2562,0,0,38.8687,0,0,0,0,0,0.0459965147,1.4736380516,13.1413,0,0.1758278521,0,0,0.3055,0,0,28.1497,0,0,0,0,0,0.0557856459,0.0604717172,19.1925,0.034986007,0,0,1.1397,0.6617,0.3653368421,0,77.0575,0,0,0,0,0 +RP11-265P11.1,B Naive,0.1077217195,14.258681443,0,0.5335439361,0,0,0,0,0,0.0893588928,0,0,0,0,0,0,0.197041968,7.0483671372,0,0.3264019005,0,0,0,0,0,0,0,0,0,0,0,0,0.7946517426,7.9804833811,0.3816,0.5772715336,0,0,0,0,0,0.3321280979,0,0,0,0,0,0,0.7066129187,21.750213333,0,0.4802255191,0,0,0,0,0,0,0,0,0,0,0,0 +RP11-294C11.2,Plasmablasts,18.722824887,8.2080546392,0,7.6590724435,4.3637081897,0,0,0,0.4358193277,3.9442317732,0,6.1425,0,130.588,1.2909,0,39.071239597,25.690893885,2.2245,9.4983285449,0.1622120131,0.459,0,0,0.3387078947,0,0,15.0815,4.2879,316.67,3.9425,0,28.675868901,4.3088825788,1.246,2.4996714938,0.5947970102,0,0,0,0.8917209677,0,0,6.7844,1.3445,52.4571,3.5378,2.2711,21.055380861,14.671025253,0,2.6526672035,0,0,0,0,0,3.1503971772,0,3.4547,0,383.324,0,0 +RP11-295P22.2,Plasmablasts,1.6959190045,0.2498254296,0,0,0,0,0,0,0,0,0,0,0,14.4025,0,0,2.9883200948,0,0,0,0,0,0,0,0,0,0,0,0,29.8169,0,0,3.1650514745,0.4106883095,0,0,0,0,0,0,0,0,0,0,0,19.0268,0,0,3.2402052632,1.9410632323,0,0,0,0,0,0,0,0,0,0,0,16.9832,0,0 +RP11-297B17.3,B Naive,12.106084163,37.595778351,0.3767,0.8462394809,1.4084923026,1.7692,0.1783,0.6084,1.500447479,0.6350985647,1.442,1.6483,1.0599,0.1868,1.3001,1.0396,10.182336337,25.351411883,0.9823,0.7591267706,0.2731268158,1.9926,0.8493,0.557,0.5260894737,1.0917845902,0.2839,1.2907,0.9514,0.1015,1.0924,1.9728,7.1579613941,20.856954957,0.7945,1.0773875116,1.1853357986,0.3876,0.9607,0,0.4268532258,0.5444163269,0.4112,1.0131,0.976,0.1583,0,0.9677,15.0186,32.742271111,1.8401,0.8058327212,0.4076573148,0.2243,0.5496,0.2788,0.2191997076,1.2526596125,0.703,1.7101,0.9873,0.1264,0.911,0.246 +RP11-306K13.1,pDCs,1.8564176471,3.0675171821,0,0,0,0,0,0,0.6707831933,0,3.68,0,12.8933,0,0,0,0,1.0136646813,0,0,0,0,0,0,0,0,0,0,4.952,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17.6385,0,0,0,0,2.3012153535,0,0,0,0,0,0,0,0,0,0,36.3313,0,0,0 +RP11-321E2.5,Neutrophils LD,0,0,0,0,0,0,0,0,0,0,23.3407,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,33.2334,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20.2774,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22.3631,0,0,0,0,0 +RP11-321E2.12,Neutrophils LD,0,0,0,0,0,0,0,0,0,0,5.5792,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.6215,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.3284,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.1254,0,0,0,0,0 +RP11-324E6.6,Neutrophils LD,0.3708352941,0.152790378,2.7661,0.3355145617,0.0516904645,0.4306,0.9693,5.872,4.5961323529,0.4911344299,42.7536,0,0,0.1812,0.0907,0.6513,0.1953930053,0.0059933237,1.3889,0.1422001559,0.7111507917,0.212,1.534,1.5505,4.4180473684,0.424083541,66.7568,0.8436,0,0,0,0.1796,0.4226852547,1.8540230372,8.0808,0.1879319627,0,0,0.3404,8.1164,5.4963548387,0,41.0695,0.256,0.1872,0.4489,0.5555,0,0.4961411483,0.1989876768,7.1137,0.2560550469,0,0.2186,0.589,6.3634,3.5368535088,0,29.8778,0.3492,0,0.1405,0.2435,0 +RP11-330H6.5,pDCs,22.444920814,17.177114777,0,0.5564780469,0.0168307894,0.9356,0.7793,0.6793,0.5308298319,0.8559774182,2.4411,0.4934,146.13,3.2535,0,0.3915,15.11790332,15.901667692,0.719,0.7997871269,0.1177625283,0.14,0.027,0,0.2129473684,2.2719590164,1.5027,0.7901,138.279,2.9266,1.6196,0.0294,14.343873458,12.270587622,0.4998,0.4601532314,0.0197608183,0,0,0,1.6780241935,0,1.3643,0.3339,145.375,2.5273,0.0446,0,20.14381244,16.024691919,0.5189,0.5356516795,0.2880402784,0.5223,1.1163,0,0.5082216374,0.8671333723,1.1856,0,188.535,2.4892,0.0969,0.151 +RP11-330H6.6,pDCs,4.784119457,3.4818676976,4.67,0.6279054898,0.2889266864,0.5268,0.057,0.4856,0.3374621849,0.3538119462,3.6822,0.804,17.963,0.3589,0.3961,0.1981,5.0161966805,3.5941115376,2.6383,0.2322270527,0.2063247047,0.1478,0.2488,0.1624,0.7165578947,0.1628532459,1.9113,1.0164,12.5098,0,0,0.1695,3.5080077748,3.1601312321,4.927,0.4918596212,0.2890845004,0,0.8626,0.1883,0.14905,0.2341645807,1.1493,0.3294,19.1435,0.0975,0.602,0.19,7.7884736842,2.8517577778,3.5371,0.5439359213,0.4647289051,0.5089,0.1171,0.4189,0.3596552632,0.7024865041,2.6101,0.5673,19.1395,0.2238,0.2911,0.1563 +RP11-342D11.3,CD4+ effector,3.6044977376,0,0,50.086031808,6.1618549975,0.3459,0,0,0,0.7709720335,0,5.04,0,0.7251,1.674,1.3925,20.388366509,0.1242267627,0,61.895495575,7.9708658457,1.0246,0,0,0,3.8313919344,0,1.0161,0,2.7467,4.594,0,3.4501340483,0,0,34.301407708,2.3319529504,1.6922,0,0,0,0.4082800567,0,0,0,0.2449,1.7926,8.1425,8.8809411483,0,0,43.243476427,10.00862277,0,0,0,0,0.9796637381,0,0.2982,0,0.3412,7.8563,0.3863 +RP11-344B23.2,B Naive,2.1096167421,6.8462398625,0,0.0387596938,0,0,0,0,0,0,0,0,0,0,0,0,2.334039182,15.429815974,0,0,0,0.3784,0,0,0,0,0,0.3751,0,0.1689,0,0,1.7029855228,11.839351117,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.4903004785,7.8885193939,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +RP11-356N1.2,Monocytes C,1.2818479638,5.4155381443,0.5377,0.2506369214,0.1178224192,0.2079,3.6727,26.0618,0.6069647059,0.33497198,2.0272,0.2877,0.1472,0.7956,0.0637,0.0529,1.9842876111,21.24063691,0.1408,0.1438974536,0.2719281478,0.411,1.1689,24.5923,2.8081,0.3656535082,1.0081,0.302,3.4336,0.0701,0.8024,0.4604,7.5653378016,8.2349533524,0.9095,0.2289239306,0.2865435877,0.2551,12.2103,23.6501,0.7323290323,0.2523683212,1.47,0,0.1364,0,0.0668,0.5844,0.3432315789,6.0425973737,0.6649,0.2295240252,0.293151463,0.4179,11.1292,22.9623,6.3802438596,0.3749689828,5.4021,1.3617,0.2257,0.9438,0.1752,0.2325 +RP11-362F19.1,Monocytes NC+I,1.3652909502,4.0505810997,0.6469,0.1409604114,2.3453706548,0,1.1188,87.2096,257.76446639,0.1985829634,0.2611,1.4772,0.0444,1.9472,0,0,2.2446064019,3.8521848614,0.9398,0.5978715664,0.1353940186,0.4755,9.3449,59.2311,280.17334211,0.7729177049,2.2876,0.5053,0.2343,1.0754,1.0076,0.4053,13.927796247,10.175188481,2.0002,0.8782690902,0.4281649882,0.2798,3.3903,73.6968,254.02361774,0.4837290729,0,0.8911,0,4.6079,0,1.4476,6.1426971292,8.3256117172,0.6908,0.5827470774,0.0393995281,0,5.6832,93.8011,287.68891959,0.2972182729,1.0666,0.7579,0,1.0618,1.9885,0 +RP11-362F19.3,Monocytes C,0.2797882353,1.3586391753,0,0.1755345533,0,0,0,27.7665,5.1907848739,0,0,0,0,0,0,0,0.1214517487,1.8661964926,0,0,0,0,1.1147,34.1723,7.1159631579,0.7086900984,0,0,0,0,0,0,0.0644163539,3.5516105444,0,0,0,0,2.8145,22.4912,2.6952548387,0,0,0,0,0,0,0,0.7543090909,1.7537440404,0,0,0,0,5.9265,58.1591,12.388094152,0,2.0807,0,0,0,0,0 +RP11-389C8.2,mDCs,0,0,0,0,0,0,20.6807,1.0317,0.1051995798,0,0,0,0,0,0,0.0506,0,0,0,0,0,0,16.3102,0,0.5883315789,0,0,0,0.672,0,0,0,0,0,0,0,0,0,8.8906,0,0.2275419355,0,0,0,0,0,0,0,0,0.0665585859,0,0,0,0,15.3065,2.377,0.4484842105,0,0.0524,0.0429,0,0,0,0 +RP11-424I19.2,Neutrophils LD,0,0,0,0,0,0,0,2.7543,0,0,16.3048,0,2.5657,0,0,0,0,0,0,0,0,0,0,0,0.5031157895,0,19.6058,1.6899,0,0,0,0,0,0.6605097421,0.5482,0,1.116498033,0,0,0,0,0,3.3373,0,0,0,1.7389,0,0,0,0,0,0,0,2.6088,1.7213,0.8001839181,0,39.2334,0,0,0,1.8256,0 +RP11-426C22.4,mDCs,0.3839538462,2.3702570447,0.6557,0.767127401,1.3797085836,0,30.4046,1.8651,2.5793798319,0.8361807346,0.3917,3.6559,0,0,0,0.6098,0,0.7059872668,0,0.6460932146,0.1770959789,0,6.4655,2.0395,2.6576078947,0.6128410492,0,4.9312,0.3602,0.0803,2.3638,0,1.4283828418,2.057550086,2.0891,0.3151578268,1.6904486231,0.451,14.8145,1.7874,2.7669790323,0.1066342847,0.4081,1.074,0,0,2.3,0,0.3494607656,0.8686927273,1.3862,1.0430724114,0.3868493865,2.6784,21.7874,2.4816,2.9685578947,0.0726022048,1.9616,1.6057,0,0.5373,1.6277,1.7384 +RP11-426L16.10,pDCs,0.8321402715,0.3061140893,0,0.0627757924,0.0263006729,0,2.3502,0.35,0.5817865546,0,0,0.3746,32.0441,0,0,0,0.6539693539,0,0,0,0,0,0.8582,0,1.3550868421,0,0,0.3277,25.8873,0,1.2388,0,0.1458281501,0.1359438395,0.2569,0,0.2878532651,0,0,0,0.9044290323,0.1053956036,0,0,32.3882,0,0,0,0,3.0003125253,0,0.2577289796,0,0.2313,0,0,0.4795614035,0.0042199432,0,0.302,36.1839,0,0.3661,1.6379 +RP11-440I14.2,Basophils LD,0,0.0807560137,56.1986,0.3355771439,0.0615519449,1.3142,0.502,0.5403,0.3667558824,0.2909357716,0,0.6979,0,0.0242,0,0.6358,0.5236680498,0,57.8643,0.0990157832,0.8501882383,2.8038,0,0.0483,0.0240394737,1.0208896393,0,0.3914,0,0.4312,0.1735,1.3364,0.0965308311,0.0311948424,25.2897,0.285024222,0.1770313926,0.3478,0,0,0,0.0673734799,0,0,0,0.1679,0,0,0,0.0393414141,29.4763,0.4512561365,0.4079674847,1.0961,0.4193,0.3659,0.2061111111,0.0229219643,0,0.3928,0,0.283,0.1928,1.3942 +RP11-455F5.3,Neutrophils LD,0.7338081448,0.0932563574,0,0.155802105,0.1468570491,0,0,0.2722,0.1725516807,0.864928069,39.7961,0.2644,0.097,0.3354,0.0959,0.6415,0.1362710729,0.2760508246,0,0.3479467706,0.0106166373,0.3893,0,0.4023,0.3230184211,0.2345472787,30.5953,0.6208,0,0.397,0,0.5299,1.6060214477,0.0865574785,0,0.3993868494,0.089095594,0.3863,0.5236,0,0.0583258065,0,15.4425,1.2247,0,0.2323,0.3056,1.6412,0.811592823,0.6271337374,0.4562,0.5804149318,0.342087966,0,0.1456,0.508,0.3662426901,0.2226499582,29.2346,0.4089,0,0.2275,2.0534,0 +RP11-456H18.2,Monocytes C,0,0,0.5326,0.3226205059,0,0,1.1649,9.8296,1.4570109244,0,2.3774,0,1.7492,1.2691,0,0,0,0,0,0,0,0,3.9964,16.8874,4.2628789474,0,5.2364,0,0,0,0,0,1.7031903485,1.174283553,0,0.1870886969,0,3.8659,4.6126,13.4701,1.6773822581,0,5.3504,0,3.7038,0,0,0,0,0.9778731313,0.6306,0.1546124717,0,0,0,3.1882,0.5058418129,0,2.2976,0,2.2927,1.0693,0,0 +RP11-467L13.7,Neutrophils LD,2.1716457014,2.9303189003,4.3818,2.1865388052,1.0603607582,0.7084,3.7347,4.4467,5.6950004202,2.8888096282,83.9957,3.0628,1.1257,0.518,0.3914,0.9164,1.8291739775,1.9025194022,4.3447,2.4983218931,0.9691883639,0.4411,3.7937,10.5777,9.8340894737,1.9725841967,90.566,1.8721,1.4596,0.7506,2.5711,1.9138,4.1457544236,3.0600468768,2.5006,2.7607371342,2.0174044847,0.7308,4.1473,13.4685,6.4860354839,2.8045931572,85.5054,1.5992,1.2352,0.1645,0.7987,2.4702,2.957737799,3.0135769697,5.0568,2.5844007469,1.4009323266,1.1742,3.2431,3.9037,4.6514418129,3.4806834976,73.2815,2.5319,1.8092,0.5745,2.6441,1.6256 +RP11-482G13.1,Neutrophils LD,1.4886633484,4.7926209622,1.0631,1.6870127258,0.0614147382,1.3925,1.0217,27.6478,23.936922689,1.1006590711,118.7834,0.8273,0.9356,0,2.923,2.4722,2.2506886189,2.3035879798,0.5635,2.9310834892,0.7076223674,1.3409,0.0265,20.4904,21.493823684,0.558208918,101.874,0.7895,0,0.4563,1.5004,1.0981,3.7449227882,6.8563939255,1.2854,2.6066537346,0.9164498033,0.7885,0.8245,27.694,17.740922581,3.2273209892,140.9295,0.476,2.4488,0.3424,2.5385,2.5115,0.3802291866,5.3491644444,5.8827,2.0820641335,0.0950973336,0.5029,0.8813,27.7539,13.810331871,2.8003090864,128.8319,0.7523,1.4671,1.5041,1.8436,2.4987 +RP11-501J20.5,Basophils LD,0,1.2955728522,80.4782,0,0,0,0,0,0.5217941176,2.4090633859,0,0,0,0,0,0,0,0.9573497947,92.0324,0,0.2038564463,0,0,0,0.8581421053,0,3.0854,0,0,0.1929,0,0,1.4604504021,1.4612729513,46.3257,0.3504462058,0,0,0,0,0,0,0,0,1.5377,0,0.2815,0,0,0.8609357576,50.2898,0.0448900158,0,0,0,0,0.2959122807,0.6954754134,0,0,1.5453,0,1.2339,0 +RP11-511B23.1,B Memory,8.1139809955,6.4907075601,0,0.1092089882,0.1803583128,0,0,0.2864,0.0887605042,0,0,1.4994,0.1613,0.2209,0,0,15.486602786,6.0878592078,0.2296,0.1999220564,0.2013269666,0,0,0,0,0,0.4314,0.1646,0.1254,2.531,0,0.1404,7.4138391421,3.5979317479,0,0.2957833863,0.0477082612,0,0,0,0.0347645161,0,0.3145,0,0,1.7033,0.4333,0.2323,12.703494737,4.5376955556,0,0.2539559104,0.2912750826,0,0,0.1541,0.1706581871,0.1181446467,0.8853,0,0,1.4962,0,0.3884 +RP11-529A4.7,Basophils LD,0,0,17.614,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.0837,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0212453295,7.933,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.1259,0,0,0,0,0,0,0,0,0,0,0,0,0 +RP11-568J23.8,B Naive,3.9520977376,18.027660137,0.4486,1.0864627138,0,0,3.4907,2.7632,0.8519289916,1.8669277079,0,0,0,1.2263,0,0,4.9194863663,36.871686237,0,3.6404334447,0,0,3.6218,4.3076,0.4494868421,3.6256718689,0,0.7165,0,4.6795,0,0,1.2634337802,12.131458567,0,0.7518498543,0,0.4475,1.0552,6.0531,0.0755887097,4.3637601666,0,0,0,4.7812,0,0.5067,1.7518746411,7.2941147475,0,0.7278905297,0,0,1.6951,2.6893,0.1901973684,1.6477045265,0,1.8927,0,4.6916,0,0.4087 +RP11-588H23.3,B Memory,26.229611765,4.1558914089,1.6333,0.9250388829,0.6115875103,0.3695,0.5994,0.6168,0.8212428571,1.0353251137,0.8493,0.8974,1.0161,6.8274,0.6443,0.5834,30.853790516,2.4801763414,0.7696,0.8896883073,0.7656119628,1.4483,0.5312,0.3413,0.6376447368,0.9619043279,0.7675,1.464,0.419,6.6708,0.8656,0.8406,15.004880161,1.0128659599,1.4559,0.8007769633,0.595493391,0.9438,0.3946,0.5469,0.5958516129,1.0419253856,0.4558,0.7292,0.5568,6.984,0.5038,0.5901,20.836550239,3.004330101,1.5679,1.1699694991,0.581234403,0.8625,0.4546,0.7496,0.9131052632,0.8307456322,0.9728,0.6951,0.4263,5.1447,0.7829,0.9671 +RP11-588L15.1,Neutrophils LD,3.9860239819,0.9731876289,0,0,0,0,7.5349,19.6769,5.5346138655,0,52.8362,0,9.2422,0.9834,0,0,1.0603746295,0,3.2808,0,0,0,0,12.0081,2.4527,0,28.218,0,3.5533,0,0,0,0.0769970509,0,0,0.177920739,0,0,4.2129,8.7173,5.9813080645,0,39.2865,0,4.2211,0.8077,0,0,1.0750014354,6.0788571717,0,0,0,0,7.7811,12.0801,8.6831783626,0,53.4022,2.0648,1.0563,2.3376,0,0 +RP11-588L15.2,Neutrophils LD,1.0885420814,1.1099731959,0.216,0.0385606566,0,0,0.9916,3.7798,0.8122701681,0,11.4076,0,1.1933,2.6961,0,0,1.669529757,0,0.2426,0,0,0,0.6823,4.197,1.8261894737,0,13.9092,0,1.7225,0.5505,0,0,0.1971900804,0.3388416619,0,0,0,0,0.3885,3.9375,6.9376580645,0,28.9454,0,1.3904,0.0642,0,0,2.4597555024,0.9584537374,1.2824,0.0182436511,0,0,1.6368,6.8383,3.3958935673,0,9.7314,0,1.4039,0.3513,0,0 +RP11-597D13.9,Monocytes C,0.5351823529,0.6869054983,0.0682,0.1426483256,0.0834406204,0.042,7.9873,33.1496,22.807413025,0.1301480387,0.2192,0.0466,0.847,0.1706,0.0508,0.0851,1.8283962656,0.1180313864,0.365,0.0347588716,0.1710821563,0,8.2304,40.6562,14.052063158,0.2685740328,0,0.3083,0.2169,0,0,0.2618,1.0443225201,3.3107453868,0.1977,0.0754110317,0.0359327301,0.0687,13.0373,28.9459,8.7127564516,0.1192023755,0.1587,0,0,0.2607,0.0724,0,2.6035334928,3.2806038384,0.0702,0.1190986432,0.0228303445,0.2377,19.5416,59.387,15.878182456,0.1283168198,0.219,0.1917,0.8722,0.1064,0.0482,0 +RP11-603B24.1,Monocytes NC+I,0.9960855204,0,0.8733,86.50998011,3.5021171837,98.3607,0,0,330.2167563,0,0,28.0423,0,0.2942,5.6663,98.8324,7.5970886781,0.4562552395,0,63.699802094,9.76995142,134.289,7.6276,0,364.60342105,6.8233367869,0,49.6057,0.5237,0,7.5123,42.8526,0,0.906906361,0,56.685866435,22.395181747,53.8988,0,0,266.10725645,0,0,24.9903,3.7137,0,9.9626,24.4356,2.2221430622,3.24396,6.0838,21.561947201,1.1936372581,11.3483,0,2.6035,168.03408333,0.2775571739,0.441,16.5918,0,0,0,17.3413 +RP11-603B24.6,Monocytes NC+I,0,1.0184041237,0,0.5244372443,0,0,0,0,5.5555147059,0,0,0,1.6668,0,0,0,0,0,0,0.1635872457,0,0,0,0,24.083392105,0,0,0,0,0,0,0,0,0,0,1.3542219838,0,0,0,0,13.882095161,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5162921053,0,0,0,0,0,0,0 +RP11-605P22.1,NK,0,0.6050426117,0.2271,0,1.7087427376,0,0.1489,3.3353,0,0.0830233084,2.8466,15.4154,0,0,2.7114,0,0,0.1880312063,0,0,2.9319897964,0,0,1.7329,0,0,1.199,28.3384,0,0,1.3984,0,0,0,0.5695,0,2.0404937057,0,0,1.5519,0.1544483871,0,5.3875,5.7754,0,0,0.7225,0,0,0.1000363636,0.2697,0,3.7065482775,0,0.6885,2.5678,0.0966593567,0,5.3131,11.8932,0,0,2.7437,1.4548 +RP11-610P16.1,pDCs,0,0.5459914089,0.9787,0.4006703385,0,0,11.2599,0,2.1153361345,0,0,0,111.273,0,0,0,0,0,0,0,0,0,1.789,0,0,0,0,0,36.3229,0,0,0,0,0,4.8391,0,0,0,6.751,0,0,3.3621660344,0,0,102.037,0,0,0,0,0.5462941414,3.4724,0.2749013088,0,0,1.4741,0,4.4020078947,1.5503975447,0,0,120.146,0,0.9026,0 +RP11-619I22.1,Neutrophils LD,0,3.4457158076,2.3089,2.4594762469,6.04866473,0,1.4619,13.7621,10.074563025,6.7799444504,65.6091,2.1179,0,2.2445,0,7.3544,5.9473375222,0.9390452359,0,1.0843083667,1.9988778588,5.5191,0,1.5499,9.1311473684,1.9099808525,60.9837,1.83,1.3966,0.8085,6.6833,0,0.3596911528,1.1899326074,0,5.6441479804,0.5282870181,0,0,0,12.614146774,0,30.5011,0,0,0.6229,2.4195,0,6.7002909091,0.4988056566,0,2.4984788118,4.5504630722,1.9236,0,13.6432,9.5582494152,0,67.3251,3.1973,1.6367,0,2.1252,2.0714 +RP11-676B18.1,Neutrophils LD,0.1176321267,0.0679158076,0.0386,0.0492462205,0.0319736747,0,0.0512,0.0276,0.0190865546,0.0306623652,9.1744,0.0356,0.092,0.0136,0.0387,0.0326,0.1549853586,0.0546828952,0.2181,0.029212925,0.0382817793,0.1256,0.0248,0,0.2299473684,0.0219832131,13.589,0,0,0,0.0244,0.027,0.0336989276,0.0415069341,0,0.0379902265,0.0181321794,0.0779,0,0,0,0.0188494948,5.4098,0.038,0.0556,0,0.0821,0.0883,0.0351454545,0.0220606061,0.046,0.0556357295,0.0065479,0,0,0,0,0.0386028896,10.2411,0.0831,0,0,0,0 +RP11-692N5.2,Basophils LD,0.1414570136,0.7317718213,6.4662,0.1261933321,0,0,3.5474,3.6994,0.3994747899,0,4.9368,0,0,0,0,0,0.6648725548,0.5733739143,7.1617,0,0,0,5.6327,0,2.4137052632,0,0,0,0.5536,0,0,0,0,0,58.6553,0,0,0,1.0632,3.4129,1.6358887097,0,2.0466,0,0,1.0251,0,0,0,0,19.671,0,0,0,3.4801,0,1.1235388889,0,3.1792,0,0,0,0,0 +RP11-693J15.5,B Naive,7.8134850679,15.856607216,0.3729,0.2242541622,0.0591979649,0.221,0.2059,0.2432,0.1904260504,0.1593772132,0.4099,0.2025,2.0626,0.5903,0.2582,0.1817,6.9480292828,18.819807267,0.1627,0.2098570527,0.2510889419,0.1346,0.1934,0.0773,0.2327921053,0.372386623,0.3111,0.2577,1.0319,0.3117,0.2835,0.1634,4.8950991957,8.552533639,0.324,0.2128108992,0.1355668765,0.1339,0.2214,0.1198,0.1893790323,0.2330885481,0.3516,0.1473,0.593,0.1875,0.2016,0.0562,6.4446755981,17.665149899,0.349,0.2212471116,0.161866588,0.1973,0.1481,0.2584,0.173654386,0.1082332053,0.3176,0.221,1.7273,0.709,0.1754,0.2413 +RP11-701P16.2,Neutrophils LD,0.534418552,1.4672391753,7.5484,1.1457550293,0.2329728213,0.562,2.4597,27.7414,8.6588672269,0.2477613266,70.5598,0,0.2698,0,0,0,1.5920348548,8.2809059849,8.0665,0.1883470007,1.8319042976,0,9.446,41.8647,37.507497368,0,154.977,1.3749,2.7207,0,1.459,3.7494,5.9134691689,8.0934440115,5.9984,0.0596449808,1.2628109363,1.0299,15.2617,69.5937,21.618285484,0.8546847367,165.966,0.6689,0.9779,0.8016,0.7264,0.7773,2.1224296651,1.6165012121,1.6278,0.4760207908,0.5844596744,0.286,5.7121,24.4917,9.0863152047,0.1983476867,80.1055,1.4517,1.2356,0,1.2735,0.6266 +RP11-704M14.1,Basophils LD,0.0451149321,1.2502646048,425.046,0.0119320536,0,0,0,0.2406,0,0,1.7983,0.7818,0,0,0,0,1.3580445762,0.4665271804,470.223,0.0058782628,0,0,0.0361,0.0996,0,0,0,1.7387,1.6674,0.0207,0,0,0.0657434316,0.2733653295,315.913,0,0,0,0,0,0.2409693548,0.0549328133,0.2764,0,3.0856,0,0,0,0.3372851675,0,383.542,0,0,0,0,0,0,0,0,0,0,0,0,0 +RP11-704M14.2,Basophils LD,0,0.2975859107,79.279,0,0,0,0,0,0,0,0,0.475,0,0,0,0,0.0082358032,0.0674518329,57.1663,0.0431413957,0,0,0,0.0901,0,0,0,0,0.5137,0,0,0,0,0,75.9954,0,0,0,0,0,0,0,0.7686,0,0,0,0,0,0.0283483254,0,59.246,0,0,0,0,0,0,0,0,0,0,0,0,0 +RP11-736I10.2,Basophils LD,0,0,16.9675,0,0.0177006401,0,0,0,0,0,0,0,0.6368,0,0,0,0,0,16.6203,0,0,0,0,0,0,0,0,0,0.1648,0,0.165,0,0,0.1204120917,18.2083,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.6312,0,0,0.2247,0,0,0,0,0,0,0.1943,0,0,0 +RP11-750H9.5,Monocytes NC+I,4.4321778281,9.0499780069,10.1365,0.1475094606,0,0,130.699,190.471,360.50085294,0,145.995,0.4132,1.0666,0.7747,0,0.334,5.088282217,4.2452108534,17.9619,0.0741587676,0.3534297562,0.1823,94.4456,172.973,341.30732368,0,163.862,3.4636,2.4864,0.6558,2.451,0,6.7111726542,17.715411461,10.5441,0.2759215005,0,1.3559,102.163,154.091,246.43186613,0.1957654671,83.4324,0.6155,1.1276,1.003,0.4297,0,7.6048129187,11.545272525,12.5719,0.0596113548,0,0,122.225,158.0521,331.86349415,0.073944914,162.7581,1.3689,0.1627,1.7337,0,0 +RP11-770G2.2,Neutrophils LD,1.594281448,0.7745127148,1.7362,0.4396099749,2.3674840801,0.4698,2.2408,7.0629,2.5665857143,2.4548339574,22.7209,0,1.3572,0,0.5694,0,3.3756846473,0,0,0.958243029,0.1878110078,0,0,1.5805,0.5537078947,0,24.4185,0,0,0.2074,0,2.354,0.4643887399,0,6.2246,0.8273219838,0,0,0,0,1.4736903226,0,23.7738,0,0,0,1.223,0,0.8430789474,0,1.3699,0.084864435,0,0,0,0.4327,1.6859532164,0,7.4856,0.406,0,1.3922,0.5359,0 +RP11-779O18.3,Neutrophils LD,1.5283674208,2.2306010309,5.0742,1.8947502153,0.1289676678,0.8165,0,6.0786,1.3155462185,0.7018450254,44.2814,3.7072,0,0.681,0,0.8198,0,1.2573506662,0,3.0066231997,0,0,9.2604,10.2763,5.7321815789,5.4681535738,67.4721,0,0,0,1.1793,1.3638,10.139773995,0.5252843553,8.3547,2.0626158588,0.4658341463,0,10.471,14.9612,10.062441935,4.3546102819,60.5689,0,0,0.2802,0,0,0.1758909091,0,5.9968,1.5645308161,0,3.3698,2.2902,6.7949,5.8878260234,1.8702252213,49.72,1.4164,0,0.4036,1.8708,0.919 +RP11-785D18.3,B Memory,19.174370136,0.3412536082,0,0,0,0,0,0,0,0,0,0,0,5.1364,0,0,35.104010314,0.5352460857,0,0,0,0,0,0,0,0,0,0,0,11.2365,0,0,19.392833512,1.6837662464,0,0,0,0,0,0,0,0,0,0.1588,0,7.2193,0,0,20.098721531,3.3138490909,0,0,0,0,0,0,0,0,0,0,0,3.4125,0,0 +RP11-812E19.9,Plasmablasts,2.3368579186,2.2107766323,0,0.0614878603,0.0328081405,0,0,0,0,0,0,0,0,66.9228,0,0,9.6499119146,0,0,0,0,0.4935,0,0,0,0,0,0,0,118.644,0,0,4.0800911528,2.8032998854,0,0,0,0,0,0,0,0,0,0,0,32.5743,0,0,20.957067943,6.3908923232,0,0.0944559104,0.0594112789,0,0,0,0,0,0,0,0,147.47,0,0 +RP11-829H16.3,B Memory,37.565959729,3.5176948454,0,2.0439119663,1.6769820614,0,0.1022,0.2995,0,0.1564017607,0,0.8227,0,1.2369,0.5103,0.4529,72.443228394,0.8560093338,0.4881,0.0216169488,0.7602430259,1.3147,0.5584,0,0.9904368421,0.6256980984,0.3598,0.4382,0.3819,0.9671,0.5514,0,43.636198123,4.2971510029,1.577,0.0065068534,0.053781668,1.3089,0,3.4188,0,0,0,1.2749,2.8412,0.756,0,0,60.131711005,3.4725064646,1.5808,1.4835447886,1.1575166824,0.5558,0,1.9939,0.3005964912,1.6725361283,1.1588,0.2103,1.4344,3.3406,0,0 +RP11-864G5.7,Basophils LD,0,0,32.9376,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,36.3479,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.6715,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,30.4838,0,0,0,0,0,0,0,0,0,0,0,0,0 +RP11-875O11.3,Neutrophils LD,0,0,1.6828,0,0,0,1.0367,4.3602,0,0.4328068557,38.9299,0,0,0,0,0,0,0,0.5959,0,0,0,0,0,0.2079,0,48.5109,0,0,0,0,0,0,0,1.3285,0,0,0,0,7.9914,0.269666129,1.0648887431,14.2021,0,0,0,0,0,0.0465818182,0,0.633,0,0,0,0,3.0339,2.6000769006,0.6184638049,49.9118,0,0,0,0,0 +RP11-883A18.3,pDCs,6.0967099548,2.9612718213,2.2143,1.3610110453,0.7121486624,0.7553,1.3017,1.6145,1.2705743697,2.5285934073,2.7406,1.4022,41.1217,3.4353,0.218,0.545,4.3741917605,1.9149230681,1.9625,1.5226041871,0.5272289017,3.0359,0.6991,1.0683,1.3925815789,2.8156834098,1.6192,3.5,12.4592,1.1132,1.1286,0.9864,3.6992273458,2.5105036676,6.0198,1.6555377169,1.4693625492,1.3148,0.2619,0.6672,0.8897193548,0.8841880163,4.1616,0.441,20.6154,3.1943,0.2532,1.4893,3.5831669856,3.4925008081,2.3371,1.6774074008,2.1524684993,1.1357,1.9862,1.4817,1.5868660819,1.4385300651,2.4096,0.1546,26.7648,2.4862,1.431,2.1988 +RP11-903H12.5,Monocytes C,3.9628615385,0.9436979381,0.9592,1.2639484571,0,0,15.6534,32.8886,1.1534659664,0,0,0,0,0.6745,0,0.3226,0.3673213397,0.8063634066,0,1.4336898812,1.0744998995,0,5.8303,22.8873,0.3025105263,0.187824,0,0,3.9502,2.3917,0,0,6.674530563,3.0296680802,0,0.2756883194,0.6513663257,0,3.2897,12.9044,0,0,0,0,3.9467,0.0884,0,0,6.2023100478,4.7812183838,0,1.7530009594,1.4304051675,0.8178,13.8367,18.5954,0.5675219298,0,0,0,2.0822,3.7519,0,0 +RP11-925D8.3,Neutrophils LD,0,0.2417429553,0.4356,0,0,0,0,12.4971,7.0346214286,0,76.5991,3.4012,0.0863,0,0,0,1.1411908714,1.8954721786,1.9634,0,0,0,2.3611,50.8099,43.826747368,0,159.185,2.1135,0,0,0.0681,0,6.9553144772,3.8710479083,0.3656,0.0022870481,0,0,1.7154,40.4876,12.559303226,0,167.714,0,0,0,0,0,0.7160325359,0.5918155556,4.6611,0,0,0,0.1653,19.6835,6.7067403509,0,112.584,0,0,0,0,0 +RP11-978I15.10,Basophils LD,0,0,23.0024,0,0,0,0,0,0,0,0.3954,0,0,0,0,0,0.6508168346,0,16.8071,0,0.3849277457,0,0,0,0,0.105082623,0,0.3537,0,0,0,0,0,0,10.6588,0,0,0,0,0.3321,0,0,0,0,0,0,0,0,0,0,9.5595,0.0649604468,0,0,0,0,0.6586733918,0,0,0,0,0,0,0 +RP11-1008C21.1,Monocytes NC+I,0,0,0.1487,0.093662349,0.1022583621,0,0,0.1049,13.906021429,0,2.1114,0.273,0,0,0,0,0,0.3823409867,0.8367,0.0590073497,0,0,0,2.4115,30.009842105,0,1.754,0.7896,0,0,0,0,0,0,0,0.0627876771,0,0,0.0897,0,21.855941935,0,0,0,0,0,0.1578,0,0,0.3380420202,0.3534,0.0761739601,0.0885494337,0,1.1422,0.5647,16.299856433,0.1472904627,1.1683,0,0,0.9679,0,0 +RP11-1166P10.8,Plasmablasts,33.070639819,4.56875189,0,0,0,0,0,0,0,0,0,0,0,367.463,0,0,10.251186426,10.667855499,1.276,0.274789369,0,0,0,0,0,0,0,0,0,247.189,0,0,3.4902729223,13.30513192,0,0,0,0,0,0,0,0,0,0,0,262.495,0,0,39.697648325,21.796721212,0,0,0,0,0,0,0,0,0,0,0,267.153,0,0 +RP11-1228E12.1,Neutrophils LD,1.8852972851,2.2745597938,1.064,2.4894178747,0.9267789595,1.4694,2.4933,5.547,4.8701134454,2.2987013462,27.9282,1.3442,0.7501,0.3853,1.0148,1.1984,0.7472649674,1.1006515376,0.6856,0.5733420416,0.9463613722,1.0602,0.4837,8.0033,6.2557815789,1.1208036721,47.247,1.2586,0.1813,0.1337,0.3007,0.6317,1.3801219839,0.4885497994,1.4089,0.8932954576,0.1691830055,0.6492,0.409,2.5968,1.8875612903,0.7738219287,35.4828,0,0.759,0.192,1.1889,0.4447,0.8365507177,0.5192626263,1.847,0.6187537107,0.9124430392,0.2426,0.788,10.7952,4.5016394737,0.4389900952,37.795,0.7153,0.6515,0.4113,0,0.2399 +RP13-644M16.4,Basophils LD,0,0,17.3279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.9795,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.3129,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.6345,0,0,0,0,0,0,0,0,0,0,0,0,0 +RPL7AP64,Monocytes C,0.0495547511,1.2656082474,0,0,0,0,14.4068,29.9397,2.9390445378,0,0,0,0,0.1087,0,0,0.1546735033,0.0557304501,0,0,0,0,11.0736,23.5155,1.7608973684,0,0,0,1.3483,0.1142,0,0,0.7690214477,2.1871216046,0,0,0,0,6.5627,12.3864,0.8305403226,0,0,0,0,0,0,0,1.4083942584,1.9134739394,0,0,0,0,14.8008,33.9126,6.1306751462,0,0,0,0,0.7656,0,0 +RPL12P18,Naive T cells,0,0,0,7.6432664633,0,0,0,0,0,16.50031089,0,0,0,0,0,0,0.1428542976,2.962523601,0,10.451663177,3.0926391053,0,0,0,0,33.65552518,0,0,0,0.4261,1.3885,5.6388,0,1.2362081375,0,9.0688344524,0,0,0,0,0,33.844923294,0,0,0,0,0,0,0,0.261550101,0,5.8483244569,8.6670185701,0,0,0,0,27.302308786,0,0,0,0,4.4122,0 +RPL12P47,B Naive,9.9021104072,51.036626117,0,0.0668922378,8.2176618907,0,5.1927,0,0,0.689486039,0,0,0,0,0,0,7.0075609366,46.305015391,0,3.2996905791,0.4377768283,0,0,5.5064,0,0.9416620328,0,3.242,0,0,0,2.7285,3.5368219839,17.586905043,0,4.0511193286,0,0,0,0,0,11.710969704,0,0,0,0,4.2836,0,0,32.311478182,0,5.8145560611,0,3.2964,0,0,0,0,7.1026,13.7988,0,0,3.7003,0 +RPS15AP30,Monocytes NC+I,0,0.4578058419,0.8185,1.1263539947,3.268136862,1.9831,0,3.8359,38.356833193,1.5588849514,1.4594,12.7165,0,0,0.8019,0,1.1804030824,1.5302810515,0,2.1272986414,2.916019427,4.5902,11.0043,6.6653,70.151778947,3.8408692459,9.4919,0.6496,0,0,5.2716,0,0,2.9787605731,0,1.5496035624,4.6309253344,0,3.7783,19.4103,61.214420968,1.6084980323,19.3899,0,0,0,2.5943,0,0,0.4581252525,0,1.054897259,3.0512711892,0.6803,0,3.6604,24.761126316,1.562242158,4.2284,3.4313,0,0,6.0476,1.4858 +RRAS,Monocytes NC+I,0.185678733,0.8420498282,0,1.8652251465,0.6860303791,2.0722,1.7629,6.9703,18.917580252,1.8881649193,0,0.6192,0.6623,0.2915,1.9276,0.5579,0.3141436277,1.4781481527,0.1885,1.6710223311,2.1120320935,0.679,2.412,5.8309,18.886771053,1.1284830164,0,0.4055,0,0.8567,1.2472,0.4613,1.390080429,1.0911216046,0,1.0077412793,2.4011977183,0.6734,2.8709,2.5536,8.080783871,2.6556039887,0,1.3134,1.0806,0,2.4888,0.9536,1.8455220096,2.8500268687,0.401,1.5458666141,0.6811289759,2.3783,2.033,4.0478,28.140423392,1.0368740271,0,1.0691,1.7899,0,0.3137,3.0702 +RTN1,mDCs,0.054061086,0.6180329897,0,0.0069028824,0.0076645331,0,53.1057,23.3131,23.046871429,0.0650130427,1.2324,0,0,0,0,0.0472,1.3219356846,0.2387081383,0,0,0.0422539331,0,110.2592,23.4808,28.956723684,0,1.1402,0,0,0,0,0,0.9088193029,1.4764145559,0,0,0,0,66.9194,27.5651,7.4495548387,0,0.9947,0,0,0.3131,0,0,0.7009966507,0.898969697,0,0.0390148085,0,0,70.018,27.5379,17.005859064,0,2.3675,0,0,0.5448,0,0 +RXFP2,Monocytes C,0.2612552036,0.0545835052,0.3265,0.0073272874,0,0,0.2916,6.9808,0.4503806723,0,0,0,0,0.0191,0,0,0.3946664493,0.0356780987,0.8598,0,0,0,0.6624,14.49,1.4449973684,0,0,0,0.0337,0,0,0,0.3753565684,0.4979904298,0,0.0136519666,0,0,0.3314,6.768,0.1171274194,0,0,0,0,0,0,0,0.8576440191,0.7523028283,0.3237,0.048074755,0,0,0.4958,15.1685,0.6691926901,0,0,0,0.0394,0.0223,0,0 +S1PR3,Monocytes C,0.2689674208,1.2501127148,0.3469,0.3967889367,0.1715668472,0.2894,0.2366,22.2529,0.9475794118,0.3207179371,0.1535,0.4069,0.1459,0.0974,0.5032,0.2431,0.2672556017,1.5261396255,0.5086,0.310505078,0.2933932646,0.2355,1.2544,31.507,1.1705421053,0.4115768525,0.2451,0.4746,0.1789,0.0269,0.2605,0.2475,0.3507565684,2.4989748997,0.5733,0.3969894517,0.3594012589,0.2351,0.936,23.4166,0.7524596774,0.4859284879,0.205,0.2919,0.2206,0.1215,0.369,0.176,0.7800066986,1.788849899,0.2312,0.3290009868,0.2455915054,0.3162,0.6101,43.0559,3.6595871345,0.1939412895,0.3803,0.2382,0.2306,0.0321,0.2998,0.2273 +S100A8,Neutrophils LD,20.906447964,31.500465292,8.6314,0.2385967408,0.1911148203,0,111.3917,935.0775,47.640936134,0,2615.6296,1.7024,0.3644,5.4003,0.2784,0,18.405371606,94.472433749,7.0378,0.3446284929,0,0,247.3202,533.0323,139.18366053,4.4791978361,5236.8481,2.8653,12.5765,5.3513,0.6128,0.6324,73.683567828,101.60112688,19.8976,0.3476794133,0,0,96.2911,989.5746,92.390166129,0.2466052473,9608.3828,1.4944,0,8.1443,0,0,67.937247368,115.50681455,25.0429,0.2681975057,0,0,315.6511,2199.0251,138.72015936,0,3446.8918,1.0812,0,16.0825,0,0.4673 +S100A9,Neutrophils LD,54.701668778,78.715668385,21.6365,0.9919022067,0.0256195634,0.643,488.241,3312.96,174.21011345,0.5874803379,7055.5098,4.3198,0.6175,11.7352,0.778,0.6475,47.993053942,176.90389759,9.2345,1.0858840238,0.7276515959,0.3173,717.115,2102.3101,413.94278947,5.7546381639,8271.4297,5.9798,37.5832,13.4773,0.4754,0.8044,230.8281689,324.89422235,30.0818,1.3234726394,0.1825378442,2.7511,424.262,2623.55,225.5260129,1.5175458961,15017.7002,4.9781,0,25.1028,0.4162,0.445,239.84530144,378.01953333,59.6804,1.6747401768,0.0648460359,0,996.816,6433.9302,543.4175614,0.8098776516,8647.7402,8.0332,0.283,49.6351,0.3648,1.0765 +SCAMP5,pDCs,2.7246298643,2.2983824742,1.2844,1.3483380277,1.5982124405,0,0,0.2757,2.0632063025,0.7732966257,0.9917,3.2236,336.6638,8.9735,0,1.6703,1.4266803201,0.8650999208,0.4143,0.7973971269,0.0454006534,0,0.2597,0.509,0.8957394737,1.2505312787,0,1.3823,154.8989,6.2076,0.6001,0,1.0938469169,0.7304518625,0.5859,0.447842842,0.4164103855,0.7448,2.2403,0.1411,1.7872322581,0.9251685871,0.7318,1.6334,204.7891,5.084,0.4554,0,2.1931291866,0.6253860606,0.996,0.8167361612,0.6029464842,0.1547,0,1.7544,1.0727783626,1.41218617,0,1.8707,353.9479,8.83,0,0 +SCARA5,pDCs,0,0.714495189,0.0503,0.0076034804,0,0,0.744,0,0,0,0,0,105.6413,0,0,0,0,0,0,0,0,0,1.1669,0,0.0526105263,0,0,0,56.7481,0,0,0,0,0.5964502579,0,0.006314859,0,0,0.8482,0,0,0,0,0.8416,87.1686,0,0,0,0.4046086124,0.2127579798,0.1796,0,0,0,1.4022,0,0,0,0,0.5014,131.4778,0.0325,0,0 +SCARB1,pDCs,26.687220814,11.993709966,1.8725,2.394663557,1.9334752011,3.9444,38.428,24.3666,6.3993563025,4.9166119194,5.0837,3.7372,151.2135,10.1054,2.221,6.7823,34.223447362,17.048956039,1.9639,2.2561844618,1.0998472732,4.5493,57.1619,26.9867,12.729786842,2.0866847213,4.6639,2.4764,168.6202,6.3277,3.5821,4.4868,22.317179893,10.658330315,4.4308,3.3278783274,4.2475988198,7.2691,24.9073,16.1667,2.7433709677,4.9865093955,1.5476,0.3883,148.4595,10.8416,1.2677,1.5181,17.736609569,11.58477899,0.4509,2.9456450353,2.9555316423,5.4665,53.5605,24.8827,8.5064783626,2.667510907,6.2436,3.9554,183.9804,7.1693,1.4589,4.2069 +SCARF1,Neutrophils LD,1.0772475113,1.2017676976,1.4586,0.4248492585,0.3688946332,0.283,12.9538,11.7969,0.5245739496,0.5604196265,32.0877,0.4229,1.4862,0.0852,0.3118,0.4776,0.3354633669,0.3023395895,1.7759,0.2659985078,0.1480799196,0.3258,5.1789,6.8664,1.9829815789,0.4031622951,59.9242,1.0185,1.2885,0,0.1192,0.2808,1.4495702413,1.0932097421,3.2481,0.3976145146,0.3148501967,1.0515,6.4624,21.5238,2.4463370968,0.3261749335,48.9182,0.1823,0.9058,0.7202,0.3144,0.4191,1.0656995215,0.6033812121,1.5609,0.3303169259,0.2391574092,0.2731,6.5122,11.386,2.3026880117,0.4780146651,35.6548,0.7884,0.9593,0.074,0.2354,0.3109 +SCART1,MAIT,0.8333823529,0.837138488,1.3097,2.6182672228,0.7625446906,31.0096,0.4859,0.3844,0.9191634454,1.0578595034,0.5123,0.5304,0.5673,0.2792,1.031,0.4274,0.6835208654,1.1887587396,1.487,2.7033421381,0.7763154813,28.3272,0.4983,0.2345,1.7010263158,1.3916872787,0.4211,1.161,0.3478,0.4636,2.8233,0.8023,0.9630246649,1.0308843553,1.1508,7.683021772,1.0744084186,69.1747,0.3696,1.1399,2.227783871,0.7153319447,1.0892,0.885,0.2827,0.1083,0.5633,1.9508,0.6824057416,0.7313131313,1.1507,2.6272085041,0.9666468853,21.8734,0.3637,0.3205,4.345248538,0.7838511776,0.6143,0.6644,0.7247,0.1454,0.4134,0.8131 +SCML2,pDCs,0.4385257919,1.3262597938,1.2414,0.9684066679,0.1102330543,0.2345,3.3424,0.0485,0.5060512605,1.9616182669,0.0611,0.3222,22.1495,0.3068,0.3362,1.784,0.4182237107,0.9334309471,0,0.9705081366,1.9772498115,1.2853,1.499,0.2517,0.0702578947,2.7891642623,0.3819,0,24.6126,0.027,0,0.2649,0.1057372654,0.9139523209,0.252,0.9872196464,0.6687918961,1.6125,0.9217,1.3497,1.8563645161,1.1011184719,0.2268,2.4539,23.5474,0.4208,0.5134,0.3204,1.2940818182,1.1753363636,0.7611,0.6960749743,0.6610924257,0,1.6594,0.4462,0.5776900585,1.6732904126,0.1302,0.8374,18.159,0,0.0362,1.0395 +SCN9A,pDCs,0.2141366516,0.1196312715,3.3991,0.1071691066,0.0514807156,0.0237,5.4295,0.5242,0.1249348739,0.1333986226,0.9766,0.5564,64.8939,1.7498,0.0715,0.097,0.1271529935,0.0960061505,7.1361,0.0958267632,0.1482450113,0.0581,6.2587,1.2369,0.8067263158,0.1777422295,0.3109,0.209,55.2723,0.3776,0.0457,0.0495,0.1186174263,0.3588729513,3.5357,0.1406257383,0.0638354052,0.0868,4.2474,0.2653,0.2022354839,0.0940634462,0.6222,1.2247,79.4868,1.394,0.0909,0.0163,0.0599133971,0.1227882828,5.1743,0.1867424313,0.1457354176,0.0835,7.7975,1.2927,0.6559505848,0.1229625856,1.222,0.4707,69.1812,0.4998,0.0666,0.1441 +SCT,pDCs,0,0,0,0,0,0,0,0,0,0,0,0,59.6805,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,43.1167,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.8503,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,60.6897,0,0,0 +SDR16C5,B Memory,6.2145678733,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19.515942205,0.06040574,0,0,0,0,0,0,0,0,0,0,0,1.1488,0,0,5.4041697051,0,0,0.0788626407,0.0147228954,0,0,0,0,0,0,0,0,0,0,0,31.927343062,0.1751430303,0,0.0091900363,0,0,0,0,0,0,0,0,0,0.4873,0,0 +SERINC2,pDCs,0.4017977376,0.32894811,0.2704,0.1051502093,0.2182698506,0.2041,1.0693,10.2113,1.3553151261,0,0.3453,0.3998,11.2418,0.1618,0.2654,0.1058,0.2481609959,0.852446309,0.1721,0.0856753823,0.0069454637,0,0.0985,3.752,0.8723368421,0,0.8565,0.1791,11.5238,0,0.0484,0,0.4689187668,1.7558324355,0.5474,0.1140846775,0.0993316286,0,0,0.5907,0,0.187902535,0.0597,0.0609,14.2916,0.0000324,0.0688,0,0.743154067,1.4953266667,0.9474,0.0544912698,0.203862034,0.1194,0.5264,6.6374,0.882554386,0,0,0.1774,13.8997,0.1292,0.196,0.2847 +SERPINF1,pDCs,24.402772398,28.762570103,5.9371,10.249515172,10.601988807,5.7215,63.263,14.1159,6.3797953782,23.930973103,7.2139,9.4368,1661.1593,20.6673,11.2481,6.7873,15.412842976,7.5506619373,3.7372,9.5671011284,10.636467203,2.2163,48.4041,8.5326,8.5697842105,25.257303607,6.1122,9.5076,2551.8655,7.7551,6.4663,9.7671,29.297125737,24.285751576,5.1693,6.1306010462,7.2239917388,5.4321,58.2029,10.5468,7.8294612903,13.943033381,6.9867,8.1107,1967.4255,19.2308,9.3018,3.9737,30.213126794,25.696568485,4.1845,11.016962564,9.9726620576,6.4186,56.0014,16.869,12.026821053,32.744474829,5.9796,11.6731,1646.4615,22.3627,5.9686,10.2435 +SERPINF2,pDCs,0.0963063348,0.5715945017,0.2245,0.1178128214,0.0740029542,0,7.6518,1.8797,0.5068315126,0,0,0,31.0391,0.2812,0.8033,0.3695,0.8761707172,0.0239069788,0.1246,0.1005669636,0,0,5.9505,0.9836,0.1629157895,0,0,0,27.1653,0.1634,0,0.6124,0.5147399464,0.4766204585,0.2793,0.0491854324,0,0,6.2254,0.9304,0.4831225806,0,0,0,19.2427,0.2004,0.1174,0,1.6569593301,0.6111351515,0.8752,0.3436490098,0.8002638273,0,5.9926,1.7221,1.15775,0.2752875564,0,0.0952,22.3456,0.1645,0,0 +SF3A3P2,pDCs,0,0,0,1.0763914484,0.207596734,0.0897,1.9834,2.6654,2.3867172269,0.3607370019,0.3871,1.5007,8.9859,0,0,1.4512,0,0,0,0.5898339124,0.9759863534,0,0,0.1507,1.88,0.5815526557,0,2.0071,6.8409,0.0397,1.3541,1.2616,0,0.3704902579,0,1.2764045623,1.7932237608,0,2.1714,5.0203,0.4034612903,0.2634082255,0.9245,0,17.5321,0,0,0.2485,0,0.1473957576,0,1.062938402,1.7008244691,0.8078,2.5497,2.9464,1.7061929825,1.0482512611,1.3164,1.8335,16.5287,0,1.5997,1.2893 +SFTPD,Monocytes NC+I,0.1446624434,0,0,0,0,0,0.2077,0,20.241752101,0.0511211197,0,0,0,0,0,0,0.0130086544,0,0,0,0,0,0.1995,0,13.771394737,0,0,0,0,0,0,0,0.1803914209,0,0,0.0699164283,0,0,0.1568,0.7211,9.8307677419,0.0652941854,0,0,0,0.0933,0,0,0.3993712919,0.2138333333,0,0.0124012198,0,0,0.9838,1.2741,15.431325146,0,0,0,0,0,0,0 +SGMS2,Monocytes C,0.6536846154,1.1812989691,1.8628,0.5707083244,0.9006813721,0,13.9392,21.8485,7.097944958,0.4990607159,0.206,0,0.5158,0.2006,1.1622,0,0.8851831061,1.2476153115,0,0,0.0076541845,0,15.0435,16.295,3.8848763158,0.0188096393,0.5208,0,0,0,0.3673,0.0159,1.6090262735,0.7735335817,0.5198,0.2339421401,0.1494656176,0,7.0092,20.5181,1.1948709677,1.5230167346,0.4425,1.8399,0,0.0492,0,0,1.0267047847,0.8443735354,0,0.5748102858,0.4614591553,0.0192,9.0617,12.4081,5.549971345,0,2.6659,0.7131,0.279,0.1511,0,0 +SH2D1B,NK,0.0569443439,0.7780841924,0,0.2648394331,7.6903979485,0.1475,0.8398,0,55.324798319,0,1.9355,614.1254,0,0,22.1899,0.0497,3.3349466509,2.5543684624,0.4491,0,1.5148215632,0,4.7757,1.2059,107.44350789,0.0485327213,0,546.3478,0,0.5456,22.6771,0,4.3618319035,10.001456619,0,0.1127820487,8.6676743509,0,0.0723,0.2917,104.5943371,0.1179363233,3.8259,773.8689,0.0427,0.7409,34.0241,0,5.1055043062,6.5315020202,0.6355,0.0333196738,4.0422821378,1.0418,0.4957,0.6271,63.060174269,0,0,415.7735,0,1.254,30.5296,0.218 +SHD,pDCs,0.3406117647,0,0,0.2058627616,0,0,0,0,0,0.5712991263,0,0,147.9863,0,0,0,0,0,0,0,0,0,0,0,0.0296157895,1.3974019672,0,0,81.6645,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2677,81.6671,0,0,0,0,0,0,0.4244478449,1.0788747758,0,0,0,0,0,0,0,104.0201,0,0,0 +SIGLEC10,Monocytes NC+I,103.20861176,89.312885911,18.8539,1.571243972,2.0857819301,1.6854,56.8271,62.633,428.34369034,1.0052469867,175.501,7.4508,11.844,1.5901,5.0939,1.543,116.01456479,65.05481534,13.2941,1.5774520713,4.411350666,0.9458,36.0018,58.8479,461.65093421,1.6932874098,135.486,16.2959,5.1097,1.7195,2.6069,3.7204,79.717071314,105.47183788,26.4407,1.3910798967,5.2099480724,1.3754,35.6702,101.2013,680.00312097,1.0600124801,68.1179,13.2465,2.7701,2.3005,7.3716,5.3719,90.685077033,72.418944444,16.0816,1.4975788529,5.9599848277,2.399,46.1422,74.0008,518.35608246,1.360788308,194.5902,14.3391,4.2675,1.4758,4.4496,5.8123 +SIGLEC20P,Monocytes NC+I,0,0,0,0,0.008370261,0,4.736,0.6247,12.701392437,0,0,0,0,0,0,0,0,0,0,0,0,0,3.2145,0.088,13.931931579,0,0,0,0,0,0,0,0.0071396783,0.9886175358,0,0,0,0,5.8711,0.7714,9.0613419355,0,0,0,0,0,0,0,0,0.2798636364,0,0,0,0,6.9946,1.7158,16.472097368,0,0,0.362,0,0,0,0 +SLAMF8,mDCs,0,0,0.0685,1.7665306781,14.580896685,1.272,50.5673,1.2279,2.4323466387,2.4913478648,1.0575,14.1212,0,0.0212,9.1239,8.0732,0.9747957321,0.9795596255,0,2.2582629176,9.7412520734,1.7831,29.1468,3.6467,1.8264236842,2.5626045246,0,5.2928,1.0533,0,5.0207,5.2228,0.1291954424,0.9730009169,0.3311,1.8219485962,9.281070653,0.2096,56.6531,4.6275,4.5042483871,0.968645772,0,9.8717,0,0,8.0933,5.9805,0,0.441800404,0.0585,1.5295765024,12.906298985,0.8265,43.155,5.6462,4.8698754386,2.4867357608,0.1136,7.7437,0,0.4771,6.5511,7.1514 +SLC4A3,pDCs,0,0.0116041237,0.0834,0.0506889726,0.0055637617,0.069,27.7051,0.0759,0.0968092437,0.3417368013,0.0329,0,40.4059,0.0292,0,0,1.2662169532,0.0049654231,0.3475,0.0347590646,0,0.0298,16.8874,0.2335,2.5766763158,0.4747497049,0,0.0675,46.389,0.0306,0,0,0.0582466488,0.4520323782,0,0.0046166071,0,0,13.0146,0,0.7225919355,0.1500958341,0,0,33.7666,0,0.0886,0,0,0,0.0992,0.0406720894,0.0139688532,0,12.6493,0.188,0.8402248538,0.2221704192,0,0.2352,51.9561,0,0,0 +SLC4A10,MAIT,0.441378733,0.2579347079,4.5702,5.9783476737,1.6886552109,218.3172,0.2947,1.2436,1.331437395,2.7796526879,3.2459,3.8962,0.8138,0.1553,0.6277,8.5282,0,0.3416143752,1.0144,3.0646774165,1.5816798191,261.3252,1.4107,0.3116,0.5818921053,1.8045203279,1.4376,2.9751,0.2917,0.1792,9.3472,29.0033,0.0854815013,0.345356447,1.8853,14.770556714,1.5008344611,227.6549,0,0.0197,0.6193467742,1.4874057614,1.7085,3.3691,0.3413,0,2.0122,6.933,0,1.5520046465,2.1535,12.818609011,2.0050401605,213.6301,0.6868,1.0215,0.7379251462,2.1337121096,1.9614,2.3058,0.3838,0,1.2005,18.9012 +SLC7A5,pDCs,2.5935642534,2.2908556701,2.6522,2.4865418849,4.8939765304,12.9157,3.2259,0.9579,0.4713722689,1.205389039,0.4225,1.5666,66.6274,6.7831,1.5933,4.5003,2.1727889745,0.2262082679,2.8245,1.9083395843,1.8298502388,6.6323,5.1638,1.9995,1.8859789474,0.7441912787,0.5157,1.9966,40.2773,8.0359,7.2516,7.5984,1.0670504021,0.9441603438,9.1464,2.9356177659,4.2473800944,23.438,3.1537,2.0704,0.3773903226,1.2014021805,1.5937,0.9704,36.4681,8.6054,4.6188,7.7316,0.8636641148,1.0246092929,2.487,2.0901703488,2.1349004955,6.8031,1.1012,0.3408,0.3353947368,1.2668078503,0.2243,3.1278,62.7695,3.811,4.3957,4.613 +SLC12A3,pDCs,2.6853918552,0.1797024055,0.2849,0.1952219292,0.1578623995,0.2426,1.3924,0.1511,0.1661134454,0.0807959035,1.107,0.1273,84.9827,0.0245,0.0873,0.3461,1.5811248963,0.3482372488,0.1637,0.1280881811,0.2371628047,0.0568,0.209,0.0487,0.0676421053,0.1008829508,0.1454,0.7887,61.9284,0.156,0.044,0.2397,0.2629587131,1.5489055587,0.3324,0.1502963912,0.0251678993,0.1065,0.5398,0.0266,0.1924016129,0.0760992732,0.0536,0.1244,84.2098,0.0315,0,0.1203,0.1943014354,0.7515377778,0.1678,0.1280587542,0.0958283624,0.1446,0.0262,0.0531,0.0150885965,0.0918796726,0.1342,0.5201,116.5446,0.2126,0.1646,0.0319 +SLC22A4,Neutrophils LD,1.0687846154,1.9513704467,0.0691,0.5890600347,0.7046542262,0.1206,7.6078,28.0539,2.363157563,0.3967995721,105.8576,0.9054,0.3169,0.1054,0,0.1157,1.0351765857,0.2405784804,0,0.4453111433,0.0686738376,0.1143,5.6075,22.7226,3.8409473684,0.6435805246,116.5991,0.0572,2.0615,0,0,0.6814,5.4160600536,3.7874772493,0.9869,0.4397333135,0,0,5.4384,14.8826,4.7093016129,0.1118372097,82.8869,0.0679,0,0.0213,0.1542,0,0.4168397129,0.9398153535,0,0.2083742548,0.2019154082,1.7022,5.9831,28.1493,4.1222888889,0.1391154,138.0058,0.2876,1.6666,0.3293,0.538,0.0634 +SLC23A1,pDCs,10.358886425,9.4664484536,1.6152,0.7348749372,1.3692460528,0,0,0.2341,0.4661592437,2.4340968485,0.8845,1.3301,31.1579,6.8962,0.1317,0.1653,10.270771488,9.6636062225,0.668,0.5065528062,0.3691214375,1.2291,0.1684,0.1842,0.4839526316,1.3762583607,0.5559,1.0124,31.9559,6.7145,0.7028,0.3639,5.1876522788,8.278365043,2.6867,1.062483181,0.4438922895,0.1326,0.6003,0.1009,0.4009306452,1.0486741358,2.5407,0.388,29.1638,5.8152,0.4193,0.1502,12.397114833,14.345621212,1.3301,1.1644643939,1.2609634261,1.1444,0,0.3985,0.3279622807,1.1416068983,0.1143,1.0293,55.1162,8.2179,0.3062,0.4375 +SLC24A3,Basophils LD,0,0,12.5189,0,0,0,0,0.1682,0,0,0.2113,0,0,0,0,0,0,0.0023459345,13.3633,0.0041199555,0,0,0.0472,0,0,0,0.0415,0,0,0.0145,0,0,0,0,10.1051,0,0,0,0,0.2415,0,0,0.0917,0,0,0,0,0,0.0286655502,0.0224282828,10.6977,0,0,0,0,0.2084,0,0,0.5121,0,0,0,0,0 +SLC25A37,Neutrophils LD,142.70568688,187.88942543,9.444,27.39976693,17.303639012,21.107,45.7039,61.1279,41.903561345,35.91955579,1111.6602,22.907,11.231,10.8815,21.0809,28.0752,126.99580978,170.54620436,19.3689,29.313646771,20.446192335,23.2231,40.0466,48.8619,39.613707895,36.129963148,1538.4581,20.3348,10.2826,11.2345,17.2587,28.8642,169.6072815,265.03325181,11.8163,29.173823745,18.843235799,23.6635,51.8298,47.9517,37.511872581,31.806238096,922.9926,24.693,11.0671,29.4729,16.8374,15.8487,68.301698086,73.711719192,5.8852,32.799843192,19.70841647,22.8246,60.853,81.8856,43.393799123,37.238677401,1665.4169,15.8721,14.735,8.6451,20.9544,26.4997 +SLC25A47P1,pDCs,0,3.7505487973,0,0,0,0,0,0.9741,0,0,4.5207,0,23.7279,0.2449,0,0,0,2.9244415124,0,0,0,0,0,0,1.8533894737,0,0,0,16.5532,0,0,0,0.9817648794,5.8598802865,0,0,0,0,0,0,0.9788516129,0,7.5244,0,18.4701,0,0,0,0.0628593301,4.0612105051,0.8565,0,0,0,0,0,0,0,0.6238,0,35.7038,0,0,0 +SLC35F3,pDCs,0,0,0,0.2180531456,0,0,0.1778,0,0,0.2343223099,0,0,20.6361,0,0,0,0,0,0,0,0,0,0,0,0.1065973684,0,0,0,7.509,0,0,0,0,0.6711406304,0,0,0,0,0.1964,0,0,0,0,0,16.7452,0,0,0,0,0,0,0.0972664017,0,0,0.1224,0,0.1136719298,0,0,0,21.3995,0,0,0 +SLC38A11,B Naive,2.9178452489,44.217645704,0.0388,0.0082299845,0,0.0321,0.1914,0,0,0,0.0348,0.107,2.6521,0,0.2908,0,2.8475516301,45.743932294,0.3285,0.1723471715,0.5118637849,0,0.0249,0,0.2990210526,0.079912918,0.1639,0,0.048,0,0,0.0537,5.4043493298,28.988600229,0.0653,0.1817303933,0,0,0,0,0.1941596774,0.1418020564,0,0.286,3.0718,0.1723,0.3104,0,3.5101655502,19.32039697,0,0.0648168163,0,0.0649,0.2513,0.2499,0.0165789474,0.2651260398,0.0337,0.207,0.8165,0.1998,0,0 +SLC41A2,pDCs,1.8731343891,0.4451752577,1.0003,1.1573455508,1.5716616937,0.027,30.1945,0.1216,3.5041466387,0.2869917491,1.8689,0.0951,90.6199,9.2188,0.3074,1.4886,1.7022883225,8.4391505366,2.0538,1.0149548404,2.3197664991,3.6332,22.9074,1.2455,2.6376078947,0.0534051803,0.0695,1.2251,72.6783,5.0912,0.1245,1.5947,1.4451954424,1.1097179943,0.4664,0.411115594,0.2644439811,1.9791,22.6372,0.1513,0.2313064516,0.2631349229,0.0788,3.9787,80.3439,4.1077,0.7086,0.265,1.9656952153,4.4504555556,6.0141,0.361819756,1.521143983,0.4782,22.9886,0.5044,0.5174023392,0.0593325539,0.3529,1.8791,75.7,3.2967,0.7649,0.3497 +SLC45A3,Basophils LD,2.6762882353,4.0036450172,297.8121,0,0.004094863,0,0.1448,0.0936,0.0966722689,0,0.33,0.1692,0,0.1228,0,0,2.859253112,2.6986819878,412.977,0.0091535412,0,0.0499,0,0.0307,0.0572184211,0.1815055082,0.1945,0.2983,0.4332,0.0902,0,0,1.5778294906,2.5244922063,270.738,0.025591425,0,0,0.1866,0.0942,0,0.0620179401,0.102,0.6638,0,0.2801,0.1304,0,1.0537488038,2.0848705051,290.9873,0.0206091962,0.0073336479,0,0.2989,0,0,0,0,0.0623,0.0445,0.0755,0,0 +SLC45A4,Neutrophils LD,0.2826751131,0.331028866,4.4232,0.5519245844,0.8433990481,1.2597,0.7956,0.6709,0.803792437,1.0367352189,16.8512,0.6036,0.7914,0.2211,0.2068,0.2519,0.3102375222,0.163152942,3.939,1.3278623831,2.3062106308,0.8845,1.2504,0.5531,0.2424526316,0.677023082,24.0197,0.5022,0.3212,0.1382,0.0265,0.4418,0.1212485255,0.0290869341,8.0646,0.4786990332,0.27364524,0.7189,0.8899,1.1978,0.8252935484,0.6479455416,13.846,0.8309,0.1891,0,1.3102,0.8146,0.1507755981,0.586209697,6.0741,0.7289595491,0.6421027843,0.3136,0.6609,0.8247,0.2197967836,0.333703541,23.3046,0.5083,0.4871,0,0.648,0.9164 +SLC46A2,Monocytes C,2.2530425339,2.2347123711,0,0.011615955,0.0065649106,0,26.7584,134.7848,69.822254622,0.0411966212,0.4554,0,0.2263,0.2664,0,0,1.6362664493,3.7373546633,0.205,0,0,0,43.8959,105.9422,63.568276316,0,0.0847,0.1941,1.4067,0.2338,0,0.0667,4.6396048257,10.785557135,0.078,0,0,0.4558,18.0783,62.9632,39.410777419,0.1318926077,0,0,0,0.138,0.1023,0,1.7731253589,3.1839664646,0.0713,0.109987679,0,0,28.7988,98.7877,42.563060234,0,1.132,0.4112,0,0.9656,0,0.0753 +SLPI,Neutrophils LD,0,0,0,0,0,0,0.2428,0,0.547955042,0,35.3393,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8547105263,0,35.4229,0,0,0,0,0,0,0.6943970201,0,0,0,0,0,0.2808,0,0,41.4773,0,0,0.217,0,0,0,0.6322569697,0,0,0,0,0.2821,0,0.3029926901,0,58.36,0,0,0,0,0 +SMIM5,pDCs,0.6326484163,3.1992305842,1.0238,0.2289821493,0.0047923847,0.0719,0.4126,1.1317,1.2947726891,1.615950633,0,0.7413,45.3606,0.2341,0.4138,0.3358,1.0826711915,1.7079345769,0.3542,0.276527654,1.2628372707,2.1172,3.5733,1.0212,1.6591052632,0.6418792787,0.8746,0.6283,63.7773,0.0759,0,0,3.9409828418,1.5443567908,0.5541,0.5256563833,0.218577262,0,1.1798,0.2376,0.953883871,1.313186864,0,0.0396,34.2506,0.0216,0,0,0.4172712919,0.6272565657,1.2812,0.6257223326,0.0356271826,0.2594,0,0.1566,0.7705333333,0.8043692333,0.5523,0.1627,54.8744,0.0896,0.3741,0.1549 +SMIM6,pDCs,0.7112036199,0.4604735395,0,0,0,0,0.7039,0,0,0,0,0.1969,9.4869,0,0,0,0,0.253347843,0,0,0,0,0,0,0.0695210526,0.1556726557,0,0,14.4174,0,0,0,1.7629865952,0.2285761032,0.3589,0.3842432261,0,0,0,0,0,0,0,0,9.5686,0,0,0,0.4299267943,0,0.223,0.0270087576,0,0,0,0,0,0,0.186,0,9.8988,0,0,0 +SMIM25,Monocytes NC+I,1.2348524887,8.3218065292,2.0713,0.5057553463,0.3024906942,0.0733,8.3799,97.5947,957.95766933,0.4223833556,430.5776,3.471,0.8142,0.9095,0.1877,0.2282,3.7395254298,2.9830355275,2.4719,0.3888867409,0.1748995476,0.6809,38.6833,119.9087,998.96110526,0.3825845902,371.0299,3.299,0.2681,0.3175,0.5048,0.1134,14.576971046,13.520549914,4.8258,0.4517184015,0.2246966168,0.5426,14.6769,106.6608,921.46846935,0.5359767594,373.3895,6.0172,0.5357,0.8387,0.2918,0.4986,5.5149545455,12.585065051,1.9857,0.3446798054,0.2803421425,0.2532,17.1049,128.3755,762.03049971,0.522398931,620.9879,3.5462,0.342,2.1447,0.1675,0.8445 +SMPD3,pDCs,17.040627602,11.503913746,9.2615,9.8032101304,13.947132874,5.7135,10.3174,6.8782,7.1253159664,5.2187713827,9.746,9.941,287.441,5.0964,21.7283,9.3259,6.715684588,3.9008006338,8.1224,4.0371978693,9.5984185725,3.0107,3.8225,2.52,3.8193815789,3.5050789508,4.4561,10.9496,406.4519,2.9871,18.5152,6.2876,11.649362735,7.0380766762,13.2171,5.3547990001,4.5686810386,4.2131,4.9424,2.1717,9.4713516129,2.6929718667,7.5044,2.4548,333.0723,3.1513,11.6253,6.3484,12.116700957,9.5363410101,3.6435,7.1324424998,8.2084514866,6.9947,7.1212,5.0676,4.9933450292,4.7614449474,9.6322,24.8364,318.2297,1.8869,14.8452,7.2183 +SNED1,B Memory,106.68319683,21.440045361,0.3062,19.298925284,1.5372065649,0.5102,0.2445,1.493,19.210271849,13.283639017,0.3471,0.3266,0.4472,3.0071,1.5908,2.9305,126.41631938,7.7805869427,0.5364,26.136778708,3.8969869565,4.5985,0.4216,2.1687,19.25495,17.565374689,0,1.9671,0.6392,3.3555,0.5733,1.5255,79.478784718,43.551451633,1.8051,36.477149152,0.863202203,10.0996,0.102,0.7083,11.525291935,35.614426485,0.4204,0.6668,0.4337,5.6618,3.8615,0.784,83.7733,20.275650505,0.2497,19.673951538,2.6527517225,1.833,0.1461,3.2958,11.35977193,9.2911579756,0.0557,0.3259,0.2993,4.6659,4.2419,2.907 +SNORD3B-1,Monocytes C,1.391680543,1.4328350515,1.1445,0.1093534266,0.3678180043,1.1251,0.4859,6.5284,2.0957021008,0.1895529241,0,2.1262,2.8347,1.9185,1.1638,0.2836,0.5597072318,1.6023312423,1.4902,0.3930626578,0.0283630058,1.1592,13.5302,32.6807,6.6669131579,1.0822102295,0,2.0134,3.0172,1.6737,0.2223,0.1951,1.761947185,4.6229262464,2.0764,0.2880926434,1.3243495673,0.4664,11.8486,23.438,4.1922758065,0,0.8691,2.7172,4.22,1.5422,0.4604,0,2.2417789474,0.8690830303,2.1312,0.3287477215,0,0.4241,3.0744,15.3436,5.2880827485,0.5797138467,0,1.6421,6.3891,0.6084,0.7529,0 +SNRPD2P1,pDCs,0.6254882353,1.709524055,0,0,0,0,1.3109,0,0,0,0,0,23.5218,0,0,0,0.0814019561,18.976365459,0,0,0,0,4.9932,0,0,0,0,0,37.9764,0,0,0,3.234533244,1.0378748424,0,0,0,0,2.3014,0,0,0,0,0,27.906,0,0,0,9.4677722488,4.0851830303,0,0.1520886041,0,0,2.396,0,0,0,0,0,47.4267,0,0,0 +SNX29P2,B Naive,33.486926244,192.40892509,2.3033,2.6190632939,6.3633778106,0.6979,14.8817,4.1268,3.6293802521,1.7603177008,2.5204,8.2232,6.9339,1.2639,5.5425,2.9352,55.979215353,255.17848223,3.7444,3.4901463697,4.0881957024,1.7582,10.2385,2.3073,1.5308789474,3.4090273443,0.5581,7.4852,9.5345,1.5694,2.4429,2.7608,49.452319035,228.62872143,4.9194,3.1490006489,2.4376211644,0.876,16.6374,3.9949,3.9232225806,2.9449734976,0.3568,7.7138,13.1915,1.2331,1.7579,0.9898,22.658521053,195.36403212,5.3752,3.0165766463,3.2961636857,2.5018,15.1333,7.734,3.8786809942,4.8271342576,1.5476,9.669,11.2275,2.842,5.6275,3.1881 +SOCS2,Basophils LD,2.2398045249,1.3998353952,960.4977,38.343346902,43.904343722,56.6816,0.6556,0.8319,1.3390361345,33.677028114,1.4991,51.742,2.5857,1.5612,27.206,25.0105,4.861377297,4.2469432841,1900.493,29.227185301,21.511291505,29.1913,0.4983,0.7726,1.5701052632,25.060143279,1.1078,54.855,5.3979,5.6033,31.4198,20.7019,7.1129458445,6.468022235,2382.1995,31.232382473,23.107569473,56.8187,0.5685,1.0149,0.7592096774,11.933801028,1.682,46.9896,0.9771,2.2551,30.0665,22.1379,4.2530995215,1.4893149495,428.3356,19.285485349,13.479750425,34.5149,0.4719,0.6181,0.7851269006,18.203521229,0.6414,41.7784,2.1631,1.1543,19.6534,15.9478 +SPATA20P1,Neutrophils LD,0,0,0,0,0,0,0,3.4936,0,0,94.6433,0,0,0,0,0,0,0,0,0,0,0,0,5.2921,0,0,101.026,0,0,0,0,0,0,0,0,0,0,0,0,1.4448,0.7677983871,0,135.837,0,0,0,0,0,0,0,0,0,0,0,0,3.3809,0.2740643275,0,137.211,0,0,0,0,0 +SPIB,pDCs,373.78701855,333.77777801,3.2912,2.4445995096,1.9132491219,1.2794,22.8133,1.9235,0.9027945378,2.3934533431,3.4676,6.2383,947.6772,7.0934,1.26,3.0032,405.9581639,197.52340787,3.7494,1.805949317,0.8160642875,2.0265,21.2256,1.3964,2.5060210526,3.5606737705,1.0373,4.8365,659.5562,10.0868,1.0788,2.6609,345.34488311,318.92246556,2.5461,2.5389172825,1.3902272227,2.1051,21.6316,1.2791,0.9752209677,1.4253173374,1.5808,2.8155,977.446,3.1264,1.7938,0.6684,463.4327756,393.09064081,2.2362,2.3354141712,3.3114127419,2.4765,20.0059,0.761,1.6901906433,1.171058627,1.2342,1.6869,960.2938,2.828,2.526,1.4415 +SPON2,NK,7.100359276,7.1452982818,17.9041,17.11974469,199.21759473,35.9937,3.0774,1.6068,7.5342369748,9.2678963582,2.0441,790.8682,33.468,1.9372,124.4733,105.4096,5.1801128631,3.7750620958,8.0379,13.021473838,172.61735059,29.36,0.8302,1.8795,13.974023684,27.240765115,0.7473,755.9564,59.8166,1.2,91.2286,111.2556,18.454248794,26.642893181,10.465,22.500850649,230.77846153,54.8271,3.6582,0.9187,27.316882258,9.997389009,6.6372,1768.8118,12.3122,3.4703,519.0203,475.0781,7.8763870813,14.842613131,10.3069,15.050251477,284.23331041,33.7949,1.9267,1.6506,9.9547432749,15.915185335,0.5381,1014.9136,36.5105,4.4242,136.9568,131.8416 +SPRED1,Monocytes NC+I,0.2755090498,0.1916776632,0.1543,0.2189696029,0.1733656163,0.0159,1.2426,0.9233,5.4625210084,1.1699886244,0.6638,0.1034,0.0777,0.0476,0.0972,0.1154,0.1048880854,0.590592265,0.1543,0.1068102895,1.6040605177,0.0327,1.4081,1.5734,16.066028947,0.1259883279,0.0954,0.0798,0.2375,0,0.0971,0.3121,0.2381514745,0.7215139255,0.109,0.0888648722,0.1228660897,0.1518,0.9238,1.3758,9.1262225806,0.224445967,0.5623,0.0639,0.0486,0.0444,0.2037,0.1079,0.0766272727,0.1705583838,0.2789,0.1024889331,0.0165225342,2.2935,0.6919,1.3754,11.284170468,0.2366842993,0.1543,0.1254,0.2289,0.0081,0.0903,0.8364 +SPTSSB,NK,0,0,0,2.2222057469,0.0415640899,13.6427,0,0,2.8058823529,0,0,94.4715,0,0,0,14.0335,0,0,0,0.8232448701,0,1.7322,0,0,0.2843,0,0,38.7072,0,0,0,17.5625,0,1.5246746132,0,1.135981327,0,18.3174,0,0,0.3662225806,0.5094749158,0,80.3081,0,0,0,0.9548,0.0736708134,1.2115684848,0,2.0210099705,1.2528508259,15.8904,0,0,0.0404239766,0,0,28.9648,0,0,0,28.7194 +SRPX,pDCs,0,0,0,0,0.1600607254,0,0,0,0,0,0,0,44.6575,0,0,0,0,0,0,0,0,0.7398,0,0,0,0,0,0,20.993,0,0,0,0,0,0,0,0,0.5309,0,0,0,0,0,0,16.686,0,0,0,0,0,0.4333,0.2889722949,0,0,0,0,0,0,0,0,13.6031,0,0,0 +SSPN,B Memory,97.871137557,9.9235683849,2.2246,0.0628955627,0.0297954374,0,1.1284,0.0236,0.1030621849,0.038950214,0.863,0.0916,0.0525,30.108,0,0.0554,83.26657866,1.4173178178,0.4838,0.0501309651,0.1239628047,0.1074,0.6821,0.0464,0.0455210526,0.0622167869,2.061,0.108,0.0409,15.7837,0.0209,0.0458,49.745548525,3.9765868195,2.2275,0.0348824262,0.0926390244,0.2262,0.1008,0.0254,0,0.1141843113,2.7015,0.065,0.6007,9.533,0.1755,0.0776,96.30629378,9.9375725253,1.0126,0.0878839992,0.1068159981,0.0277,0.0568,0.0501,0.0545055556,0.0764036746,1.6916,0.0472,0.0719,21.9435,0.0309,0.0303 +ST6GALNAC1,CD4+ effector,0.3457361991,0.8074075601,0,10.76925604,0.4915605941,0,0,0,1.0683109244,4.278817108,0,0,0,0.3825,0,0.9192,0,0.0656861649,1.8796,23.311460416,0.1312550892,0.4839,0.3535,0.8087,0,0.5153881967,0.55,1.9075,0.3402,0,0,2.3547,0.4123300268,0,0,10.390814707,0,0.614,0.6927,0.0723,0.2835048387,6.7302784613,0.9448,0,0,0,1.1889,0.6348,0.0683119617,0,2.1165,9.5770796272,0.2083323502,0,0,1.6828,0.2282821637,0.4682587606,0,0.826,0,0,0,0.512 +ST6GALNAC2,Neutrophils LD,2.9877656109,0.4252838488,0,1.4281719292,2.4243752339,4.345,3.7596,18.9697,12.922913866,0.7285036329,93.0129,0,0.161,0.1229,0.7671,2.3038,1.8199328986,0.5368731437,4.7833,2.4622782925,1.2288784368,6.263,0.4997,7.2438,6.8188605263,1.2176154754,76.4178,0.3016,0.2134,0,5.2758,1.2214,3.4690616622,2.4260740974,0,2.7827585883,2.102688749,3.8889,3.6073,15.2035,8.6761629032,0.0073243751,40.0725,1.1793,0.0051,0,0.9449,2.0922,3.544577512,1.2187951515,0,0.9773549853,2.6829181925,2.5103,0.2205,2.6041,2.9151119883,1.0643220478,85.5414,0,0.112,0,0.9207,9.9891 +STAB1,Monocytes C,1.4528149321,2.1427161512,0.2703,0.0145134673,0,0.6362,12.4138,69.02,13.73317395,0,0,0.2837,0,0.1861,0,0,1.2892436277,2.7655309615,1.3008,0.0940065702,0,0,18.8855,78.2856,8.1792578947,0.0875001311,0,0.0571,0.8385,0,0.0671,0.2118,4.8625101877,12.727530372,1.3381,0,0,0.2445,34.086,99.252,9.5740596774,0,0.083,0,0,0.1824,0,0,2.7608138756,2.5926482828,1.7439,0.0150271774,0,0.0507,23.3888,72.3655,14.032912281,0.1862209454,0.8971,0.1272,0,0.4195,0,0.0557 +STEAP1B,B Naive,1.3615330317,16.002330584,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3577497333,9.2831032697,0,0,0,0,0,0,0.08295,0,0,0,0,0,0,0,0.7966801609,13.629101146,0,0,0,0,0,0,0,0,0,0,0,1.1051,0,0,0.0789090909,11.604721616,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +STEAP4,Neutrophils LD,0.6288529412,1.3131127148,1.4443,0.5641010824,0.3317383555,0.2419,0.2984,20.7027,0.5740680672,0.5256093162,292.0235,0.5405,0.1966,0.1995,0.8249,0.5484,2.0116142264,1.8695593158,0.7569,0.4531055085,0.558062478,0.3946,0.5401,49.8802,2.1967394737,0.7684945574,385.9512,0.9726,0.3993,0.0674,0.3787,0.3578,0.4783949062,3.3523279083,0.6799,0.4760289697,0.439695594,0.3943,0.6617,17.6884,1.0822435484,0.6826494948,352.1179,0.5737,0.5477,0.7289,0.389,0.3517,1.2969837321,2.2591107071,0.8334,0.601936819,0.5937778905,0.3036,1.4799,56.175,0.8434201754,0.6380715884,311.9821,0.564,0.4176,0.3698,0.5482,0.3301 +SUMO1P1,Neutrophils LD,0,0,0,0,0,0,0,0.8981,2.5593655462,0,63.7701,0,0,0,0,0,0,0,14.9647,0,0,0,0.8211,15.5311,12.551076316,0,79.9755,0,0,0,0,0,0,1.3954523782,0,0,0,0,2.3132,5.9708,7.5063403226,0,41.8023,0,0,0,0,0,0,0,1.5987,0,0,0,0,7.019,6.1441883041,0,92.844,0,0,0,0,0 +SYBU,B Memory,13.431337104,2.6976666667,0.1795,0.350073496,0,0.0352,0.0574,0,0,0.4156777302,0.3042,0.2012,0,0.5632,0.0777,0,40.087742798,7.5368589125,0.1994,0.5030162584,0,0,0,0.1583,0.0443763158,0.0212313443,0.047,0.0354,0,0,0.0269,0,20.364876676,2.1369780516,0.5232,0.6500563237,0.288920535,0.4561,0.0793,0.5954,0.0407903226,0.0640261124,0,0.0418,0,1.1553,1.3536,0,14.232843541,8.3421939394,0,0.1475207428,0.5604270175,0.0722,0,0,0,1.3504180725,0.1107,0.0389,0.0629,0.0174,0.3881,0.1224 +TAS1R3,B Memory,28.309268778,4.6740137457,0.7222,1.1000311027,0.8499277203,0.176,0,0.4547,0.3492722689,1.299992284,0.1523,1.3279,0,4.6659,3.0634,2.3153,39.250080557,0.263449269,0.7194,0.5779758352,1.3236276954,0,0.1362,0,0.1906421053,0.6777340328,0.4039,0.7921,0,5.7386,0.6694,0,12.207103217,2.7044255587,0.0357,0.3931881406,0.4149469709,0.6853,0,0.2611,0.5184516129,0.6335038823,0,1.3793,0,0.7131,0,0.5339,43.322310526,5.1368208081,0.4043,0.4260363668,0.6435272062,0.5682,0.3226,0.0966,0.5778473684,0.5374814264,0.0738,0.3628,0.308,2.6333,0.8704,0.3902 +TCEAL9,pDCs,0.1697918552,0.0987024055,0.3581,0.7845379799,0.0214754636,0,0.9412,1.6898,0.4790155462,0.5693913569,0,0,13.4631,1.0247,0,0.149,0.7163885596,0.2297347497,0.1824,1.4322406533,0,0,2.1562,2.0322,0.0618947368,1.3181325902,0,0,21.4619,1.2443,0.302,0,2.0517525469,0.0953719198,0.4497,1.2926336313,0.5940188828,0,0.6464,0.9889,0.0305080645,3.0639127814,0.2496,0,7.6705,0.4816,0.1899,0,1.15584689,0.3944026263,0.5781,1.4941306106,0.8094101463,0,0.9502,0.4053,0.3015318713,1.8320733756,0,0,10.223,1.4548,0.1665,0.9839 +TCF4,pDCs,368.01302172,318.24495361,20.3329,11.148721977,6.5221536846,7.2497,52.5139,30.4562,49.325212605,8.7497076268,26.1995,38.7946,1612.0634,103.7711,17.8366,5.8183,406.16803124,261.13497163,24.3819,6.9592125687,4.3482227444,14.5459,56.1917,23.4667,32.066047368,7.018067541,4.4391,16.4144,1337.1406,99.3426,9.6188,2.3407,358.21727185,358.66030544,28.1397,7.3860270097,1.6657801731,13.3087,47.1084,32.2108,43.288546774,14.362948803,14.6442,13.6443,1465.8497,93.8361,2.1901,4.439,379.85937225,322.16081657,24.5237,8.6261264099,5.0488892402,9.772,70.2454,37.4352,42.102545322,17.288758844,24.422,22.85,1485.4116,93.6877,8.5367,16.9714 +TCF7L2,Monocytes NC+I,0.5380294118,1.204847079,0.1714,3.2033163617,7.3619008699,16.4288,3.1335,10.8492,320.74619874,5.0714899082,19.4494,8.8573,2.1885,0.1204,12.397,4.1923,0.1474690575,3.1532367087,0.3464,2.2935456941,12.504993943,17.7386,17.8902,40.693,469.26242895,5.6363982951,17.3097,16.9925,2.7169,0.2399,5.2688,7.6661,4.8992651475,4.8601197135,0.9615,2.7420330354,7.4081138474,2.853,7.9711,46.4797,474.21712419,3.37388986,12.8621,11.3679,0.1136,0.7886,4.7084,8.4184,8.0466105263,2.9565668687,0.8144,2.9573065374,6.8292347806,8.1359,5.9001,25.7412,312.20502865,5.3644610489,7.9519,7.6356,3.2326,0.6608,12.185,8.1196 +TCN1,Basophils LD,0,1.86804811,1520.5945,0,0,0.2178,0.3929,1.1429,0,0,12.7972,1.7893,0.209,0,0,0,0.1489445762,1.143747058,1594.5851,0.0806925167,0,0,0,0,0,0,7.5671,1.166,4.2286,0,0,0,8.0681865952,17.861463438,677.9903,0.105433916,0,0,0,0.2008,0.5321903226,0,4.5375,0,4.2807,1.4145,0,0,0.9561009569,0,684.1302,0.1620553759,0.4287043889,0,2.8702,0.8251,0,0,9.9913,0.0716,0,0,0,0 +TEX2,pDCs,5.2421552036,6.0570298969,6.4887,8.0898719651,12.887837108,5.501,8.1389,6.3047,5.6063983193,6.9023051039,2.0315,14.1747,109.3264,5.7598,7.01,10.7099,3.6522991108,9.9627334174,6.3848,6.2453589903,12.944445916,7.2872,4.6768,5.7587,5.7489473684,9.4314621639,2.3992,12.2365,105.9794,2.721,6.4583,4.7461,1.6029849866,6.0286618338,12.3676,9.1576880281,5.5851952006,12.0773,11.087,8.1178,3.110516129,8.3574856054,1.7748,13.7463,123.4852,2.4782,8.2366,7.6141,3.2866617225,2.6513042424,4.215,6.20567801,4.717216588,11.5038,13.6176,9.0516,5.160554386,9.1425236847,1.1024,12.652,125.4613,3.1251,4.3865,5.8933 +TGM3,Neutrophils LD,0.1895742081,0,0,0,0,0,0,0,0,0,16.6913,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.1463,0.0914,0,0,0,0,0,0,0,0,0,0,0,0,0.1375951613,0,7.7142,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.7584,0,0,0,0,0 +THEM5,MAIT,0,2.2215979381,0,11.453485654,5.598857804,31.2954,0,0,0,1.2212104707,0,2.9887,4.655,0.566,1.2619,8.856,0.1358411381,0,0,7.44481585,3.1229536316,39.1149,0.6067,0,0,0.4619697049,0,1.1671,4.1741,0,3.3612,9.3818,1.8549512064,3.6682453295,0,5.6480496292,2.1102040126,14.9023,0,0,0,0.4888527211,0,0,2.1194,0.0541,3.1599,0.8234,2.2587578947,0.595930101,0,11.163610532,6.2000827513,19.0367,0,0,0,0.9388666611,0,7.6551,2.8441,0.2211,1.8376,10.8368 +THSD7A,Basophils LD,0.0063271493,0.0018474227,37.2931,0,0.0017889381,0,0,0,0,0.1568160961,0,0.1329,0.0104,0,0,0,0.0005495554,0.0008040835,27.9106,0.0022040683,0,0.3672,0,0,0.0045868421,0,0,0.0107,0,0,0,0,0.0114713137,0,20.1078,0.006556178,0,0,0,0,0,0,0,0.0129,0,0,0,0,0.0096129187,0.0075006061,41.3748,0.0378649558,0.0137897593,0,0.6124,0,0,0.0317454819,0,0.0094,0.0286,0,0,0 +TIFAB,pDCs,0,0,0,0,0,0,9.4455,0.1025,0.2197357143,0,0,0,27.1183,0,0,0,0,0,0,0,0,0,8.1606,0.4025,0.4940526316,0,0,0.6203,17.3831,0,0,0,0,0.1669008596,0,0,0,0,7.3738,0.7352,0.1353258065,0,0,0,22.0269,0,0,0,0,0.0263587879,0,0,0,0,6.8541,0.037,0.8954678363,0,0,0.051,20.8827,0,0,0 +TLR9,pDCs,9.9176832579,6.3623068729,3.3643,0.6983828549,0.025885869,0.3901,0.849,0.2277,0,1.0755464607,2.9411,0.5597,87.2102,2.1658,0,0,6.376038115,3.9015448974,0.4663,0.477313801,0,1.1828,0,0.0787,0.2480210526,0.5712234754,1.8845,0,87.3916,2.2172,0.592,0,6.3785855228,6.016233467,2.9918,0.6305070256,0,1.5077,0.1596,0.6033,0.2686177419,1.4957729303,1.8854,0.4398,92.6704,1.0118,0,0.0427,6.6825851675,5.6606606061,2.3531,0.6950431782,0.1288068664,1.5304,0.7432,1.2747,0.3815233918,1.7306946551,3.3466,0.1596,105.084,2.0023,0.5067,0.8963 +TMEM63C,Monocytes NC+I,2.9177914027,0.5479051546,0.5787,1.1913308516,2.1992623338,0,0.6384,1.707,27.367857563,0.8544619996,2.8394,0.0735,0.0847,0,0,0,1.2050110848,0.3940545265,0,0.7892866444,0.1885622518,2.8995,0,5.1525,19.074557895,0,2.6897,1.4827,0,0.0491,0.8179,0,0.0030522788,0.3990118052,0,0.587483426,0.9573656176,0,0,0,15.346514516,0.3614944513,0.4752,0,0,0,0.0915,0,0.2290952153,0.0152218182,0,0.129453464,4.4317044361,0.2674,0.0202,1.4366,7.7634926901,0,0.4264,0.4368,0,0,0.0248,0 +TMEM150B,Monocytes C,0.2594099548,0.9670120275,7.0452,0.5132267731,0.134786066,0.1532,1.0127,18.0661,3.1915168067,0.2926649238,0.6524,0,0,0,0.5067,0.3182,1.5465977475,1.4168566943,3.4087,0.3465595249,0.1365367932,0.2445,0.7452,16.1845,2.3951552632,1.0368486557,0.584,0.1242,0.1215,0.1879,0.4649,0.1065,1.2270053619,4.7796436676,2.318,0.3790244007,0.9664147915,0.3906,4.9379,19.5069,1.7751774194,0.2695475802,2.2817,0.2066,1.1272,0.1273,0.1774,0.1601,3.4753655502,1.8839468687,6.5152,0.2709483451,0.5026536574,0.5064,2.5548,24.6344,4.7427295322,0.2736591615,0,0.8882,0.5341,1.0875,0.8415,0.3665 +TMEM183AP1,Basophils LD,0,0,22.6066,0.6279234302,0,0,0,0,0,0,0,0,0,0,0,0,0.2987021932,0,18.2844,0.185627654,0,0,0,0,0,0,0,0,0,0,0,0,0,2.3284852722,19.7838,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,39.1023,0,0,0,0,0,0,0,0,0,0,0,0,0 +TNFAIP6,Neutrophils LD,0.2908452489,0.5982405498,0.5021,0,0,0,0.3248,8.8469,1.0854382353,0,79.4364,0,0,0,0,0,0.7861172496,1.1283468707,0.1286,0,0.1002442825,0,5.38,70.4398,16.476665789,0.0930210492,131.0373,0,0.1408,0.0418,0,0,6.6214294906,5.2867917479,0.0958,0.0285075487,0,0,4.7389,48.3132,11.192029032,0,407.955,0,0,0.8267,0,0,0.4469229665,0.8861676768,0,0,0,0,0.3463,14.8409,1.2981932749,0,230.4077,0,0,0.0467,0,0 +TNFRSF4,CD4+ effector,0.0258624434,0,4.3014,30.557224836,0.7436559166,1.9415,0.2141,0,0,0.8961655969,0,8.8373,0.1287,0.4131,0.7895,0.4066,0,0.104549946,1.7876,21.185842146,0.0102055793,0,0,0,0.2218184211,0.1977057049,0,3.5455,0.9675,0.2972,1.8472,0.1274,0,0,2.7815,43.566992908,0.2635835563,1.3648,0,0.597,0.0277403226,0.7911940791,0,3.8979,0,0.6926,2.5532,0.9264,0.0698660287,0.1764133333,0.3868,26.158517611,0.522756135,1.423,0,0,0.6526672515,0.4265698346,0,6.8197,0.575,0,0.3027,1.8466 +TNFRSF8,Monocytes NC+I,1.6925316742,0.3111945017,0,0.4906845593,0.0672575086,0,1.6847,24.1706,99.559015546,0,14.2429,0,0.2474,0.7603,0,0,5.6024307647,0.4244339215,0,0.7359182628,0.1839130435,0,5.722,32.3328,116.14070263,0,15.0653,0.9333,3.3546,0.116,0,0,0.8060469169,1.6386889398,0,0.0730674414,0,0,2.9898,20.9076,92.126191935,0.0363617089,6.7371,1.8823,0.5049,1.0483,0,0,3.2294822967,1.5196094949,0.0759,0.398075173,0,0,8.6221,41.3615,122.25865936,0,24.9112,0,1.9423,0.6018,0,0 +TNFRSF10C,Neutrophils LD,0.2455723982,0.74055189,18.2715,0.1867026432,0,0,6.2914,23.4045,11.095360924,0.826294963,1615.7263,0.6856,0.1623,0.1426,0.2046,0,3.3350140486,3.1151763342,14.2979,0.2511815887,0.3596091983,0,3.13,14.2732,13.910873684,0.7187636721,2196.3447,0.7041,0.4522,0,0.1269,0,0.7028790885,4.912266361,45.1075,0.2740602702,0.5308538946,0.0922,4.2837,15.7238,9.4417209677,0.6246722567,1180.8578,1.5284,0.2768,0.5268,0,0,4.8264760766,4.2549242424,16.1114,0.1619898376,0.0994332232,0,5.9997,13.5871,13.816110234,0.8680248539,1669.9044,0.2739,0,0.0836,0,0.084 +TNFRSF21,pDCs,0.2879253394,2.8872357388,0.8662,0.0471033309,0.0056212047,0.1737,7.2837,1.0972,3.3006743697,0.016387617,0.1499,2.0085,331.268,0.0344,0.0415,0.14,0.0354280972,0.8938404897,1.3464,0.0884340906,0.0046208595,0.0352,6.4706,1.208,1.5842868421,0.0680485246,0.0435,0.6188,303.971,0.0155,0.0788,0.1227,1.0786967828,3.0612548424,0.5293,0.0487827771,0.0685768686,0.0844,8.0348,1.9925,0.1741919355,0.1446479702,0.13,1.3919,383.383,0.0249,0,0.0976,0.488361244,0.9161272727,0.1128,0.1332774001,0.1435847806,0,3.6987,0.2227,1.2077359649,0.0490274261,0.0715,0.3345,371.574,0.1893,0.6305,0.1191 +TPM2,pDCs,0.246358371,0.0477443299,1.6187,2.7894890922,0.5860634663,8.0732,0.1586,0.0851,0.6268382353,5.2436797941,0.5774,5.9107,251.0377,0,1.0774,4.2444,0.9394418494,0.147919856,1.2493,1.7435012472,3.0365648907,5.281,2.2182,0.1204,0.1562157895,1.891736,1.1247,0.9236,209.4565,0,1.0688,2.5915,6.6640447721,1.0871990831,0.2976,3.4179515031,1.1088579072,3.9878,1.6505,0.6389,0.9436451613,3.5438926786,0.2661,1.6766,315.91,1.1607,0,3.0143,0.2317138756,0.3572119192,0.2057,2.2269603303,0.5194043417,6.9522,0.9826,0.6156,0,2.2260812427,0.2277,2.0095,282.0823,0,1.0004,3.4433 +TRABD2A,Naive T cells,5.6954696833,13.288423024,2.5461,101.29340774,34.783958904,16.2643,9.5198,1.0751,2.1275336134,297.97979758,2.4643,5.6817,2.7726,3.2121,31.1593,46.2071,8.2564117961,23.275294159,1.8471,115.95329857,14.76420568,23.1528,5.6007,1.1049,2.8353473684,261.66131298,2.5545,4.5173,1.7671,0.7185,32.792,37.8495,3.3004710456,8.4018045845,1.9604,106.93040148,12.019635405,12.9113,12.9268,1.1263,1.8851854839,217.81377038,1.5878,2.3446,2.3207,2.3635,12.4102,15.7253,5.1002392344,11.019368889,2.7034,83.706620188,30.989050637,10.8874,8.5054,1.0623,1.4125599415,259.98735433,2.4533,2.4979,4.0415,2.2628,14.4349,27.6502 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+TRGV9,VD2+,0.0432488688,0.5658817869,0,2.8808776343,260.122226,44.9016,0.5378,0.0642,0.3071722689,1.7584816841,0.2433,22.2824,0.0717,0.0316,34.2883,560.521,0.2815071725,0.5363608426,0.102,6.2354994209,50.406084544,70.3094,0,0.5062,0.4171157895,8.1394739016,0.2867,15.8901,0,0,103.912,749.541,0.7579356568,0.0969166762,0,6.3582075156,12.350610543,53.4872,0,0.3465,0.1015290323,3.5734872186,0,9.2449,0,0.0274,677.892,828.792,0.0238870813,0.6747969697,0,5.8883805523,306.71569325,50.5827,0,0,0.5581491228,1.8323588776,0,18.1357,0,0,238.969,536.314 +TRIB1,Neutrophils LD,6.402160181,1.5467391753,5.2819,0.9646666248,0.1797554571,0.0955,28.7919,27.8426,10.373608403,0,138.4893,0,0.8536,40.9636,0,0,0.8548335507,2.3986834498,1.2516,1.4334945954,0.287967429,0,22.2248,15.8067,48.207413158,0.0326761311,430.3722,0.2998,1.4035,42.1253,0,0,14.533678016,3.1285646991,8.2741,0.7109191763,0.160483871,0,17.3087,95.1957,41.984043548,0,212.8456,0,0,54.9969,0,0.1409,1.0262688995,2.2971048485,1.7713,0.7847813815,0,0,10.8297,23.0316,19.797624854,0,145.1455,0.4103,1.7426,18.2937,0.4603,0 +TRIP10,pDCs,4.3639809955,2.7284443299,0,2.4674596639,1.8239692434,4.1986,10.6498,1.4808,0.9706932773,6.4940618124,0,4.0541,49.7744,1.6943,0.5641,1.7052,5.6244497333,1.7712468707,0,6.1028666073,0.6148858256,1.7043,14.1578,0.6912,1.1836105263,5.5201072787,0.2408,2.0005,52.4922,1.7314,1.5153,3.8419,3.1678136729,1.9453896848,0,1.8632800821,1.081191188,5.1815,9.5644,4.8128,0.3373096774,5.394655983,0,1.2219,29.7445,1.3943,1.1789,2.5621,2.0666483254,2.0661074747,0,2.9298498801,6.0314504719,1.4303,8.8827,1.9445,1.1350304094,3.1345190079,0.4199,1.2628,50.7899,1.5536,1.6977,4.321 +TRO,pDCs,0.5724832579,3.1222704467,1.5756,1.4731497548,3.9481852125,0,0.2214,0,0.5353647059,0.2965187394,0,3.7514,18.8378,0.5265,0.1538,1.4966,4.2281596918,7.8772208931,2.1115,0.0741894284,2.9160519729,1.8391,0,0,0.1164394737,2.5556487213,0,4.9067,28.729,0.4921,1.8579,0.2407,0.6737104558,1.3789762178,2.2596,1.4530741425,0.8709185681,0,0,0,0.1869903226,1.2745504698,0,3.7115,12.6975,1.211,0,0.3973,1.2311559809,5.4808436364,1.0691,0.5583659768,2.9373589901,0.6627,0,0,0.062747076,3.4722824119,0,6.2588,35.8635,0.4118,3.8506,2.6513 +TSPAN1,pDCs,0,0,0.2223,0.3714648128,0.0272936156,0,0.4296,1.3041,0,0.0757533164,0,0.5691,11.1195,0.7907,0.4036,0,0.4645785418,0,0.4465,0.3551808537,0.1810919829,0,0,0,0,0,0,0,18.9312,0.0361,0,0,0,0,0,0,0,0,0,0.1688,0,0,0,0.3863,5.2779,0,0,0,0.754830622,1.1174282828,0.7368,0.8720697595,0,0,0.1567,0,0.5385140351,0.4782516118,0,0,7.974,0,0,0 +TSPAN13,pDCs,29.147247964,77.285031615,0.2193,0.1443860304,0,0.7257,1.3525,0.4158,0.1591386555,0,5.8948,5.9772,789.9292,6.0392,0.7311,0.3321,53.015326497,126.99224364,0.7419,0.0843728582,0,0.0594,0.8881,0.5551,0.6099789474,0.3426086557,5.249,0,422.667,4.8861,0,0.3031,69.564828418,133.64321948,0.1228,0.0786933254,0.5763217152,0,0,0,0.0820274194,0.035652473,0.7935,1.7957,640.8206,7.7254,0.3105,0.0834,26.408312919,104.28257576,1.4357,0.3075204002,0.3559874941,0.3053,0.9159,0,0.5140497076,0,3.2376,1.2471,424.628,4.4923,0,0 +TSPAN15,MAIT,0,0,0,9.7673666667,0.9877661086,58.5325,3.525,0,0.9646289916,0.6092987162,0,1.6219,0,0,1.1707,1.4739,0,0,0,8.6361460134,0,47.1168,1.1848,0,0.9053078947,0.8498982295,0,1.4263,0,0,5.2174,1.4232,0,0.4168155874,0,12.012983241,0.4953289536,53.3363,1.6357,0,0.2122806452,3.38188908,0,0,0,0,0,0.895,0,0,0,10.484015891,0.8644801557,39.4657,4.8586,0,1.4300818713,0.8047888425,0,2.6666,0.4703,0,1.3223,3.9158 +TSPAN16,Neutrophils LD,0.3196411765,0,14.2406,0,0,0,2.5096,4.0137,1.4377105042,0,28.1169,0,0,0.0586,0,0,2.1149517487,0.2162533093,3.0703,0.0120829696,0.0185543101,0.2729,1.3412,3.35,3.9825921053,0.1368674098,19.3777,0,0,0,0,0.9059,0.1166756032,0.147666533,6.1301,0.0301896901,0,0,0.5151,2.3928,0.5828403226,0,14.9882,0,0,0,0,0,0.1239100478,0.4454771717,4.4484,0,0,0,1.2912,1.9931,0.6332815789,0,22.6642,0,0,0,0,0 +TTC24,pDCs,10.636249774,31.151864948,0.0783,3.2577735319,19.986214656,10.461,2.4209,0.5045,0,42.216222907,0,6.9372,198.3742,0,22.7897,26.5421,9.4880554238,46.875741808,1.6944,4.4156655308,12.68445622,16.8712,0.96,0.5491,0.2811263158,65.390990557,0,11.2147,192.4265,0,24.9822,21.5037,3.2020426273,24.712147221,0.4605,4.232832254,15.440756884,1.4017,1.4656,0,0.1280887097,43.564505566,0,12.9891,180.2071,0.0475,9.4513,1.2646,5.420654067,28.33472404,0,2.3003059892,12.696089523,1.7498,0.982,0.4482,0,37.40581054,0,2.8344,214.1396,0,18.6263,17.4879 +TTC39A,pDCs,0.2243230769,0.2831742268,2.5611,0.5553920404,0.1459668144,0.0189,0.1785,0.5255,0.5262155462,0.4175961442,1.0503,0.3513,93.3075,0.0796,0.2042,0.171,0.232672377,0.1907886784,0.526,0.8076993022,0.3711400101,0.1488,0.0728,0.7093,0.7963131579,0.2403655738,0.9712,0.2592,96.7504,0.3721,2.9731,0.0806,0.091908311,0.2768237822,1.89,0.1982633227,0.5382475216,0.8547,0.5053,0.3417,0.1379451613,0.2015314661,1.4882,0.1561,80.8688,0.0141,2.8729,0.351,0.1022746411,0.0489282828,1.0186,0.2493119235,0.26359311,0.5831,0.3818,0.5037,0.1223356725,0.1570823117,0.235,0.149,91.5229,0.0187,0.3578,0.3713 +TUBB6,pDCs,111.59154751,77.064788316,3.1893,1.140006339,0,0.3026,47.4077,51.7701,16.365748739,0,0,1.0989,271.9867,8.9312,0.3069,0.4356,12.588455127,23.355268621,0,1.0320236303,0.138807615,1.0675,21.7376,17.2032,3.0454078947,0,0.3894,3.5216,142.7262,0,0,0,60.624215013,61.327805387,0.4412,0.3453385644,0.7399232101,0,41.1204,18.9923,7.9295709677,0.0419913136,0.0834,3.3437,175.0465,6.7327,0.1603,0.2779,23.871521053,25.766342222,0,1.6679518125,0,0,17.8781,15.166,6.6228172515,1.8816460331,0,2.7171,231.7588,0.309,0,0 +U1,Monocytes C,14.536079186,30.719776632,11.6246,11.395269142,7.2115625144,15.4043,28.6428,147.486,38.625418908,10.462956878,4.118,16.8819,72.7582,14.2203,12.523,18.3384,5.7609437463,21.544608218,8.7306,7.0227661767,8.2357471727,38.2998,114.33,303.518,62.967094737,10.059334033,13.7105,7.651,31.2302,11.1045,22.6575,8.5353,59.290457909,61.461589284,5.3508,8.2488450404,13.563441621,18.0611,113.627,517.012,50.321720968,13.015954033,30.0251,9.2543,35.3723,12.9884,3.3829,17.7929,12.164960287,9.9103088889,18.6747,7.3480216405,9.9835856536,26.2256,25.9087,115.885,45.42288655,8.4772433272,4.1558,11.2087,43.0307,19.667,17.4477,14.2854 +UBD,Basophils LD,0,0.0923848797,81.0007,0,0,0,0.9421,0,0.2767331933,0,0,0,0,0,0,0,0,0,66.6768,0,0,0,0.2021,0,0.0550973684,0,0.8402,0,0,0,0,0,0,0,79.1454,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,74.9078,0,0,0,0,0,0,0,0,0,0,0,0,0 +UGCG,pDCs,51.269778281,53.229600687,6.3373,29.343425362,25.503312441,12.5963,18.2832,4.4705,8.5955365546,22.937104168,9.604,15.8484,934.2249,5.258,14.9566,19.52,38.082016123,29.726560929,4.5149,25.139033259,15.117400251,16.3143,17.7506,4.523,9.1812868421,21.104675541,5.7459,18.5808,739.9048,3.8268,7.3467,15.0623,28.616728686,32.219893295,5.7421,25.344301576,16.744285287,19.9291,16.2153,1.9382,5.6851806452,10.726519429,7.0357,9.7111,933.0368,2.8194,20.3018,13.486,38.461866986,44.386638586,4.1999,22.88330466,15.992507055,27.126,13.7051,5.2687,6.9093769006,20.886546801,13.7488,11.2551,810.283,3.6741,15.697,8.762 +UGT2B11,Basophils LD,0.3456886878,0.0856821306,236.9845,0,0,0,0,0,0,0.0272867032,0,1.1353,0,0,0,0,0.3235716657,0.1551486856,306.3534,0,0.0081219402,0,4.3962,0.0513,0,0,0,0,0.0478,0,0,0,0,0.0196363324,147.2704,0,0,0,0,0,0,0,0.3118,2.0115,0,0,0,0,0,0,213.9275,0,0,0,0,0,0,0,0,0,0,0,0,0 +UPK3A,mDCs,0.3751778281,0.2197219931,0,0,0,0,18.8907,7.3775,12.768334874,0,2.5397,0,7.6923,0,0,0,0,0.0404694418,0,0,0,0,23.0594,3.2377,3.7715421053,0,0,0.134,17.1434,0,0,0,0,1.1513808596,0,0,0,0,14.3407,0.3798,5.5528016129,0,0.3833,0,1.9237,0,0,0,1.0289162679,1.318010303,0,0,0,0,25.743,7.948,22.652749415,0,2.017,1.5311,4.725,0.0677,0,0 +VAV3-AS1,B Naive,1.8581705882,21.805878007,0.3511,0.146621391,0,0,0,0,0,0,0,0.9644,0,0,0,0,2.3211673977,19.685162989,0.7858,0,0.6118505906,0,0,0,0,0,0,0,0,0,0,0,0.7202613941,21.611902407,1.753,0,0,0,2.4965,0,0,0,0.798,0,0,0,0,0,1.9354344498,11.420171919,0.8327,0.1551109573,0.2500775602,0,0.5312,0,0.1425847953,0,0,0.99,0,0,0,0.3205 +VCAN,Monocytes C,15.61761086,27.807330584,2.839,0.1419453175,0.064618513,0.0137,260.9615,1243.0477,120.76495168,0.1048189088,1.3198,1.3431,0.2008,2.3941,0,0.663,22.654555779,65.781880094,5.6438,0.6503182183,0.053496381,0,334.6925,1598.0597,105.46943684,0.5351049836,1.5139,3.9613,15.3648,2.9344,0.0535,0.3858,89.485308043,110.44219186,16.6579,0.4686247252,0.0046253344,0.2378,346.7693,907.9208,45.483882258,0.4649513739,1.3118,1.9854,0,10.9542,0.2846,0.0415,33.409073206,62.094956768,15.9987,0.4853970739,0.167760689,0,361.3881,1542.5187,139.0751269,1.6349211792,1.4511,0.3919,0,10.7707,0.7068,0.0783 +VCAN-AS1,Monocytes C,0.3150760181,0.1837223368,4.8304,0.0856371726,0,0,15.0928,46.9423,4.1082865546,0.2137377641,0,0,0,0,0,0,0,0,0,0.0383744024,0.0568535813,0,20.3237,38.0564,1.8238289474,0,0,1.0969,0,0.2466,0,0,1.1061643432,4.5860760458,0,0,0,0,16.0175,37.2136,0.7557983871,0,0,0.5308,0,0,0,0,2.3095330144,2.0025173737,2.4497,0,0,0,18.2319,83.9263,6.3899114035,0,0,0,0,0,0,0.5176 +VEGFB,pDCs,7.4789760181,2.7959879725,0.6308,2.4609815512,1.0258701953,11.8286,1.3164,0,0.838405042,3.0510593965,0.3262,6.1387,50.7158,0.6964,4.0657,2.4894,10.100218139,5.9683270076,1.5171,4.7920762435,0.6985927117,3.822,3.3616,0.579,3.9274289474,4.7328503607,0,0.8812,48.7693,0.571,3.1766,2.8871,11.177929759,2.3563404585,0.5256,1.3526269236,3.7972399685,0.9538,1.6875,0,0.7894258065,3.5714033859,0,1.8459,41.8001,0.7396,3.3404,1.7875,12.410057895,7.4193672727,1.1225,5.2047218324,3.6307811468,5.0964,1.4827,0,0.4653362573,5.9609252046,0.2729,4.8907,67.5074,1.276,4.4918,5.7581 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LD,1.2251687783,1.7783539519,110.9747,1.5703721265,4.0471727064,1.3902,0.9291,38.1537,6.5860096639,1.9514006731,671.947,0.8916,1.8083,0.936,1.3774,1.7331,2.7336840545,1.9863400864,158.7854,0.9071825241,2.3427368686,1.5845,0.138,11.3438,5.5997973684,1.4917930492,406.8618,4.4457,2.4031,0,2.8099,0.9162,0.4720439678,1.9659159312,96.121,0.5705308237,1.9605710464,0.8031,0.3442,10.3069,2.0230387097,1.4630936891,225.9882,2.2236,1.3368,0.3314,0.5238,1.1514,4.702608134,3.0705125253,89.0848,2.0867687658,3.2418125295,1.7091,1.2998,50.2077,5.8053675439,2.1922793219,425.3285,3.8596,0.8578,1.0826,2.4755,0.7458 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activated,0.1742678733,0.6720972509,0.2486,3.7393974046,420.66583954,1.1424,0,0,0,1.0134040519,0,10.6762,0,0,108.4465,15.8716,0.412862537,0.1694809651,0.2579,4.0797813957,252.41782073,1.4266,0.1224,0,1.5621947368,1.8804761967,0,67.063,0.1587,0,211.3319,11.3501,0.3930418231,0,0.1918,1.4347751821,57.176861841,0,0,0,0,0.9742036696,0,14.8626,0,0.0286,100.4411,62.7298,0.2397282297,0.0978662626,0,4.6091071061,370.38464127,0.0825,0,0,0.1297423977,2.513561216,0,8.607,0.3361,0,101.2375,73.9485 diff --git a/examples.bck/abis_all.ipynb b/examples.bck/abis_all.ipynb new file mode 100644 index 0000000..07a62d9 --- /dev/null +++ b/examples.bck/abis_all.ipynb @@ -0,0 +1,1617 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "# from umap import umap_\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import tensorly as tl\n", + "from tensorly.decomposition import non_negative_parafac_hals\n", + "\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "from mofapy2.run.entry_point import entry_point\n", + "\n", + "RESULTS_PATH = r'C:\\Users\\paul_\\OneDrive\\Pro\\George\\Wise\\analysis\\results\\abis'\n", + "\n", + "list_solutions = None\n", + "predefined_solution = ''\n", + "\n", + "# ISM algorithmic options\n", + "embed = True\n", + "max_iter_integrate = 20\n", + "update_h4_ism = True\n", + "\n", + "# Grid search limits\n", + "min_embedding = 10\n", + "max_embedding = 25\n", + "min_themes = 16\n", + "max_themes = 16\n", + "\n", + "# list_solutions contains one ore more solutions selected because of their low condition numbers\n", + "list_solutions = [[16,16]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'\\abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'\\cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "\n", + "m0_nan_0 = m0.copy()\n", + "\n", + "# create m0_weight with ones and zeros if not_missing/missing value\n", + "m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])\n", + "\n", + "data_mat = [[None for g in range(1)] for m in range(4)]\n", + "\n", + "for m in range(4):\n", + " data_mat[m][0] = Xs_norm[m]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -554078.45 \n", + "\n", + "Iteration 1: time=0.03, ELBO=-80263.41, deltaELBO=473815.045 (85.51407166%), Factors=12\n", + "Iteration 2: time=0.03, ELBO=-72448.31, deltaELBO=7815.094 (1.41046700%), Factors=12\n", + "Iteration 3: time=0.02, ELBO=-67384.30, deltaELBO=5064.013 (0.91395231%), Factors=12\n", + "Iteration 4: time=0.03, ELBO=-63970.45, deltaELBO=3413.847 (0.61613056%), Factors=12\n", + "Iteration 5: time=0.04, ELBO=-62161.20, deltaELBO=1809.254 (0.32653396%), Factors=12\n", + "Iteration 6: time=0.04, ELBO=-61281.89, deltaELBO=879.315 (0.15869856%), Factors=12\n", + "Iteration 7: time=0.03, ELBO=-60660.21, deltaELBO=621.672 (0.11219925%), Factors=12\n", + "Iteration 8: time=0.03, ELBO=-60037.75, deltaELBO=622.469 (0.11234304%), Factors=12\n", + "Iteration 9: time=0.03, ELBO=-59598.16, deltaELBO=439.582 (0.07933568%), Factors=12\n", + "Iteration 10: time=0.04, ELBO=-59364.67, deltaELBO=233.496 (0.04214138%), Factors=12\n", + "Iteration 11: time=0.03, ELBO=-59204.15, deltaELBO=160.515 (0.02896971%), Factors=12\n", + "Iteration 12: time=0.03, ELBO=-59080.27, deltaELBO=123.882 (0.02235817%), Factors=12\n", + "Iteration 13: time=0.03, ELBO=-58999.55, deltaELBO=80.719 (0.01456811%), Factors=12\n", + "Iteration 14: time=0.03, ELBO=-58949.40, deltaELBO=50.155 (0.00905203%), Factors=12\n", + "Iteration 15: time=0.05, ELBO=-58913.41, deltaELBO=35.983 (0.00649417%), Factors=12\n", + "Iteration 16: time=0.04, ELBO=-58883.82, deltaELBO=29.589 (0.00534023%), Factors=12\n", + "Iteration 17: time=0.04, ELBO=-58857.76, deltaELBO=26.060 (0.00470338%), Factors=12\n", + "Iteration 18: time=0.04, ELBO=-58833.88, deltaELBO=23.887 (0.00431114%), Factors=12\n", + "Iteration 19: time=0.05, ELBO=-58811.25, deltaELBO=22.625 (0.00408333%), Factors=12\n", + "Iteration 20: time=0.04, ELBO=-58789.14, deltaELBO=22.108 (0.00399001%), Factors=12\n", + "Iteration 21: time=0.04, ELBO=-58766.88, deltaELBO=22.266 (0.00401863%), Factors=12\n", + "Iteration 22: time=0.07, ELBO=-58743.80, deltaELBO=23.080 (0.00416554%), Factors=12\n", + "Iteration 23: time=0.03, ELBO=-58719.23, deltaELBO=24.565 (0.00443343%), Factors=12\n", + "Iteration 24: time=0.03, ELBO=-58692.46, deltaELBO=26.771 (0.00483167%), Factors=12\n", + "Iteration 25: time=0.03, ELBO=-58662.65, deltaELBO=29.812 (0.00538040%), Factors=12\n", + "Iteration 26: time=0.02, ELBO=-58628.74, deltaELBO=33.911 (0.00612020%), Factors=12\n", + "Iteration 27: time=0.02, ELBO=-58589.24, deltaELBO=39.500 (0.00712890%), Factors=12\n", + "Iteration 28: time=0.04, ELBO=-58541.91, deltaELBO=47.333 (0.00854274%), Factors=12\n", + "Iteration 29: time=0.02, ELBO=-58483.39, deltaELBO=58.519 (0.01056151%), Factors=12\n", + "Iteration 30: time=0.03, ELBO=-58409.34, deltaELBO=74.048 (0.01336420%), Factors=12\n", + "Iteration 31: time=0.03, ELBO=-58316.53, deltaELBO=92.808 (0.01674999%), Factors=12\n", + "Iteration 32: time=0.02, ELBO=-58209.32, deltaELBO=107.211 (0.01934936%), Factors=12\n", + "Iteration 33: time=0.03, ELBO=-58107.17, deltaELBO=102.146 (0.01843532%), Factors=12\n", + "Iteration 34: time=0.03, ELBO=-58034.88, deltaELBO=72.291 (0.01304710%), Factors=12\n", + "Iteration 35: time=0.01, ELBO=-57997.19, deltaELBO=37.690 (0.00680233%), Factors=12\n", + "Iteration 36: time=0.04, ELBO=-57980.63, deltaELBO=16.558 (0.00298839%), Factors=12\n", + "Iteration 37: time=0.02, ELBO=-57972.92, deltaELBO=7.719 (0.00139310%), Factors=12\n", + "Iteration 38: time=0.02, ELBO=-57968.42, deltaELBO=4.497 (0.00081157%), Factors=12\n", + "Iteration 39: time=0.01, ELBO=-57965.26, deltaELBO=3.162 (0.00057070%), Factors=12\n", + "Iteration 40: time=0.02, ELBO=-57962.82, deltaELBO=2.440 (0.00044033%), Factors=12\n", + "Iteration 41: time=0.03, ELBO=-57960.85, deltaELBO=1.962 (0.00035412%), Factors=12\n", + "Iteration 42: time=0.02, ELBO=-57959.24, deltaELBO=1.615 (0.00029146%), Factors=12\n", + "Iteration 43: time=0.02, ELBO=-57957.89, deltaELBO=1.352 (0.00024403%), Factors=12\n", + "Iteration 44: time=0.03, ELBO=-57956.74, deltaELBO=1.149 (0.00020738%), Factors=12\n", + "Iteration 45: time=0.03, ELBO=-57955.75, deltaELBO=0.990 (0.00017864%), Factors=12\n", + "Iteration 46: time=0.02, ELBO=-57954.89, deltaELBO=0.863 (0.00015584%), Factors=12\n", + "Iteration 47: time=0.02, ELBO=-57954.12, deltaELBO=0.762 (0.00013755%), Factors=12\n", + "Iteration 48: time=0.01, ELBO=-57953.44, deltaELBO=0.680 (0.00012273%), Factors=12\n", + "Iteration 49: time=0.02, ELBO=-57952.83, deltaELBO=0.613 (0.00011059%), Factors=12\n", + "Iteration 50: time=0.02, ELBO=-57952.27, deltaELBO=0.557 (0.00010056%), Factors=12\n", + "Iteration 51: time=0.02, ELBO=-57951.76, deltaELBO=0.511 (0.00009218%), Factors=12\n", + "Iteration 52: time=0.03, ELBO=-57951.29, deltaELBO=0.472 (0.00008513%), Factors=12\n", + "Iteration 53: time=0.02, ELBO=-57950.85, deltaELBO=0.439 (0.00007915%), Factors=12\n", + "Iteration 54: time=0.03, ELBO=-57950.44, deltaELBO=0.410 (0.00007402%), Factors=12\n", + "Iteration 55: time=0.02, ELBO=-57950.06, deltaELBO=0.386 (0.00006960%), Factors=12\n", + "Iteration 56: time=0.02, ELBO=-57949.69, deltaELBO=0.364 (0.00006576%), Factors=12\n", + "Iteration 57: time=0.02, ELBO=-57949.35, deltaELBO=0.346 (0.00006241%), Factors=12\n", + "Iteration 58: time=0.02, ELBO=-57949.02, deltaELBO=0.329 (0.00005946%), Factors=12\n", + "Iteration 59: time=0.03, ELBO=-57948.70, deltaELBO=0.315 (0.00005685%), Factors=12\n", + "Iteration 60: time=0.02, ELBO=-57948.40, deltaELBO=0.302 (0.00005453%), Factors=12\n", + "Iteration 61: time=0.01, ELBO=-57948.11, deltaELBO=0.291 (0.00005245%), Factors=12\n", + "Iteration 62: time=0.02, ELBO=-57947.83, deltaELBO=0.280 (0.00005059%), Factors=12\n", + "Iteration 63: time=0.02, ELBO=-57947.56, deltaELBO=0.271 (0.00004891%), Factors=12\n", + "Iteration 64: time=0.05, ELBO=-57947.30, deltaELBO=0.263 (0.00004739%), Factors=12\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "ent = entry_point()\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(4)])\n", + "ent.set_model_options(\n", + " factors = 13, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + ")\n", + "ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + ")\n", + "ent.build()\n", + "ent.run()\n", + "factors_mofa = ent.model.nodes[\"Z\"].getExpectation()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/10: 49 iterations with final cost -62237.691583\n", + "Run 2/10: 74 iterations with final cost -62236.513594\n", + "Run 3/10: 48 iterations with final cost -62235.318998\n", + "Run 4/10: 51 iterations with final cost -62240.281221\n", + "Run 5/10: 49 iterations with final cost -62233.905370\n", + "Run 6/10: 48 iterations with final cost -62236.270922\n", + "Run 7/10: 52 iterations with final cost -62234.902403\n", + "Run 8/10: 59 iterations with final cost -62238.596242\n", + "Run 9/10: 48 iterations with final cost -62238.554169\n", + "Run 10/10: 64 iterations with final cost -62237.835234\n" + ] + } + ], + "source": [ + "model_gfa = gfa_experiments(Xs_norm, K=12, Nrep=10, rotate=False, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def format_loadings_merged(h4, list_solutions, list_columns):\n", + " # Format loadings\n", + " df_h4 = pd.DataFrame(data=h4)\n", + " list_themes = []\n", + " for i_solution in range(0, len(list_solutions)):\n", + " list_themes = list_themes + ['theme_' + str(i) + '_' + str(list_solutions[i_solution][0]) + '_' + str(list_solutions[i_solution][1]) for i in range(1, list_solutions[i_solution][1] + 1)]\n", + " \n", + " df_h4.columns = list_themes\n", + " df_h4.insert(loc=0, column='label', value=(list_columns))\n", + "\n", + " # Add description index\n", + " df_h4['description'] = df_h4['label']\n", + " \n", + " return df_h4\n", + "\n", + "def format_loadings(h4, list_columns):\n", + " # Format loadings\n", + " df_h4 = pd.DataFrame(data=h4)\n", + " n_comp = len(df_h4.columns)\n", + " df_h4.columns = ['theme_' + str(i) for i in range(1, n_comp + 1)]\n", + " df_h4.insert(loc=0, column='label', value=(list_columns))\n", + "\n", + " # Add description index\n", + " df_h4['description'] = df_h4['label']\n", + " \n", + " return df_h4\n", + "\n", + "def generate_h4_sparse(h4, q4_ism, n_items, n_comp, n_scores):\n", + " # Calculate hhii of each h column and generate sparse loadings\n", + " hhii = np.zeros(n_comp, dtype=int)\n", + " h_threshold = np.zeros(n_comp)\n", + "\n", + " if q4_ism is not None:\n", + " i1 = 0\n", + " for i_score in range(0,n_scores):\n", + " i2 = i1+n_items[i_score]\n", + " h4[i1:i2,:] *= q4_ism[i_score]\n", + " i1 = i2\n", + "\n", + " for i in range(0,n_comp):\n", + " # calculate inverse hhi\n", + " if np.max(h4[:,i]) > 0:\n", + " hhii[i] = int(round(np.sum(h4[:, i])**2 / np.sum(h4[:, i]**2)))\n", + " # hhii[i] = np.count_nonzero(h4[:, i])\n", + " \n", + " # sort the dataframe by score in descending order\n", + " h_threshold[i] = np.sort(h4[:, i], axis=0)[::-1][hhii[i]-1] * .8\n", + "\n", + " h4_sparse = np.where(h4 < h_threshold[None,:], 0, h4)\n", + " \n", + " return h4_sparse, hhii\n", + "\n", + "def integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes):\n", + " EPSILON = np.finfo(np.float32).eps\n", + "\n", + " # Generate w for each score, based on sparse loadings and create tensor_score\n", + "\n", + " # Extract score-related items\n", + " i1 = 0\n", + " for i_score in range(n_scores):\n", + " i2 = i1+n_items[i_score]\n", + " w4_score = w4_ism.copy()\n", + " h4_score = h4_sparse[i1:i2, :].copy()\n", + " m0_score = m0_nan_0[:, i1:i2]\n", + " m0_weight_score = m0_weight[:, i1:i2]\n", + " i1=i2\n", + " # # Normalize w4_score by max column and update h4_score\n", + " # max_values = np.max(w4_score, axis=0)\n", + " # # Replace maximum values equal to 0 with 1\n", + " # w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " # h4_score = np.multiply(h4_score, max_values)\n", + " # h4_score0 = h4_score.copy()\n", + "\n", + " # Apply multiplicative updates to preserve h sparsity \n", + " for _ in range(0, 200):\n", + " # Weighted multiplicative rules\n", + " m0_score_est = w4_score @ h4_score.T\n", + " h4_score *= ((w4_score.T @ m0_score) / (w4_score.T @ (m0_score_est*m0_weight_score) + EPSILON)).T\n", + " w4_score *= (m0_score @ h4_score / ((m0_weight_score*m0_score_est) @ h4_score + EPSILON))\n", + " # if i % 10 == 0:\n", + " # # Normalize w4_score by max column and update h4_score\n", + " # max_values = np.max(w4_score, axis=0)\n", + " # # Replace maximum values equal to 0 with 1\n", + " # w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " # h4_score = np.multiply(h4_score, max_values)\n", + " # if np.linalg.norm(h4_score-h4_score0)/max(np.linalg.norm(h4_score0),EPSILON) < 1.e-10:\n", + " # print(i)\n", + " # break\n", + " # else:\n", + " # h4_score0 = h4_score.copy()\n", + "\n", + " # Normalize w4_score by max column and update h4_score\n", + " max_values = np.max(w4_score, axis=0)\n", + " # Replace maximum values equal to 0 with 1\n", + " w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " h4_score = np.multiply(h4_score, max_values)\n", + "\n", + " # Generate embedding tensor and initialize h4_updated\n", + " if i_score == 0:\n", + " tensor_score = w4_score\n", + " h4_updated = h4_score\n", + " else:\n", + " tensor_score = np.hstack((tensor_score, w4_score))\n", + " h4_updated = np.vstack((h4_updated, h4_score))\n", + "\n", + " # Apply NTF with prescribed number of themes and update themes\n", + " my_ntfmodel = NTF(n_components=n_themes, leverage=None, init_type=2, max_iter=200, tol=1e-6, verbose=-1, random_state=0)\n", + "\n", + " if q4_ism is None:\n", + " estimator_ = my_ntfmodel.fit_transform(tensor_score, n_blocks=n_scores)\n", + " # hals_decomposition = non_negative_parafac_hals(tensor_score.reshape((tensor_score.shape[0], int(tensor_score.shape[1]/n_scores), n_scores)), rank=n_themes, init='svd')\n", + " else:\n", + " estimator_ = my_ntfmodel.fit_transform(tensor_score, w=w4_ism, h=h4_ism, q=q4_ism, update_h=update_h4_ism, n_blocks=n_scores)\n", + " # hals_decomposition = non_negative_parafac_hals(tensor_score.reshape((tensor_score.shape[0], int(tensor_score.shape[1]/n_scores), n_scores)), rank=n_themes, init='svd', fixed_modes=[1])\n", + "\n", + " w4_ism = estimator_.w\n", + " h4_ism = estimator_.h\n", + " q4_ism = estimator_.q\n", + " # w4_ism = hals_decomposition[1][0]\n", + " # h4_ism = hals_decomposition[1][1]\n", + " # q4_ism = hals_decomposition[1][2]\n", + "\n", + " # Update loadings based on h4_updated (initialized by multiplicative updates)\n", + " h4_updated = h4_updated @ h4_ism\n", + " h4_updated_sparse, hhii_updated = generate_h4_sparse(h4_updated, q4_ism, n_items, n_themes, n_scores)\n", + "\n", + " return h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "condition number(16, 16) = 3.43\n", + "condition number (primary NMF): 1.3\n" + ] + } + ], + "source": [ + "if predefined_solution != '':\n", + " max_iter_integrate = 0\n", + " # Read pre-defined themes\n", + " df_h4_updated = pd.read_csv(DATA_PATH + predefined_solution)\n", + " h4_updated = df_h4_updated.values.astype(np.float_)\n", + " h4_updated_sparse = h4_updated.copy()\n", + " list_solutions = [[h4_updated.shape[1],h4_updated.shape[1]]]\n", + "\n", + "if list_solutions is not None:\n", + " perform_grid_search = False\n", + "else:\n", + " perform_grid_search = True\n", + "\n", + "if perform_grid_search:\n", + " # Perform grid search first to select solutions with low condition numbers\n", + " cond = np.ones((max_embedding+1, max_themes+1))*999\n", + " list_solutions = []\n", + " for n_embedding in range(min_embedding, max_embedding+1):\n", + " for n_themes in range(min_themes, max_themes+1):\n", + " list_solutions += [[n_embedding, n_themes]]\n", + "else:\n", + " h4_updated_merged = None\n", + "\n", + "for n_embedding, n_themes in list_solutions:\n", + " if predefined_solution == '':\n", + " # Initial Embedding\n", + " my_nmfmodel = NMF(n_components=n_embedding, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=0)\n", + " estimator_ = my_nmfmodel.fit_transform(m0.copy())\n", + " \n", + " w4 = estimator_.w\n", + " h4 = estimator_.h\n", + "\n", + " # my_nmfmodel = NMF(n_components=n_embedding, init='nndsvd', solver='cd', beta_loss='frobenius', max_iter=1000, tol=1.e-6, random_state=0)\n", + " # w4 = my_nmfmodel.fit_transform(m0)\n", + " # h4 = my_nmfmodel.components_.T \n", + "\n", + " # hals_decomposition = non_negative_parafac_hals(m0.reshape((m0.shape[0], m0.shape[1], 1)), rank=n_embedding, init='svd', n_iter_max=200)\n", + " # w4 = hals_decomposition[1][0]\n", + " # h4 = hals_decomposition[1][1]\n", + " \n", + " h4_sparse, hhii = generate_h4_sparse(h4, None, n_items, n_embedding, n_scores)\n", + "\n", + " my_ntfmodel = NTF(n_components=n_themes, leverage=None, init_type=2, max_iter=200, tol=1e-6, verbose=-1, random_state=0)\n", + " estimator_ = my_ntfmodel.fit_transform(m0.copy(), n_blocks=n_scores)\n", + " w4_ntf = estimator_.w\n", + " h4_ntf = estimator_.h\n", + " \n", + "\n", + " # hals_decomposition = non_negative_parafac_hals(m0.reshape((m0.shape[0], int(m0.shape[1]/n_scores), n_scores)), rank=n_themes, init='svd')\n", + " # w4_ntf = hals_decomposition[1][0]\n", + " # h4_ntf = hals_decomposition[1][1]\n", + "\n", + " if embed:\n", + " # Embed using scores w4 found in preliminary NMF and initialize themes through NTF \n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4, None, None, n_scores, n_items, n_themes)\n", + "\n", + " else:\n", + " h4_updated = h4\n", + " h4_updated_sparse = h4_sparse\n", + " hhii_updated = hhii\n", + " w4_ism = w4\n", + " h4_ism = np.identity(n_themes)\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + "\n", + " else: \n", + " w4_ism = np.ones((m0.shape[0], n_themes))\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + " w4 = w4_ism\n", + " h4 = h4_updated.copy()\n", + " h4_sparse = h4\n", + " n_themes = list_solutions[0][1]\n", + " h4_updated_merged = None\n", + " if embed:\n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes)\n", + " else:\n", + " h4_updated = h4\n", + " h4_updated_sparse = h4_sparse\n", + " hhii_updated = hhii\n", + " w4_ism = w4\n", + " h4_ism = np.identity(n_themes)\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + "\n", + " if embed:\n", + " # Iterate embedding with themes subtensor until sparsity becomes stable \n", + " flag = 0\n", + " for iter_integrate in range(0, max_iter_integrate):\n", + " # print(iter_integrate, hhii_updated)\n", + " # indices = np.nonzero(q4_ism[:, 0])[0]\n", + " # non_zero_elements = q4_ism[indices, 0]\n", + " # print(iter_integrate, np.column_stack((indices, non_zero_elements))) \n", + " hhii_updated_0 = hhii_updated.copy()\n", + "\n", + " if iter_integrate == 0: \n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, np.identity(n_themes), q4_ism, n_scores, n_items, n_themes)\n", + " else:\n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes)\n", + " \n", + " if (hhii_updated == hhii_updated_0).all():\n", + " flag+=1\n", + " else:\n", + " flag=0\n", + " \n", + " if flag==3:\n", + " break\n", + " \n", + " if perform_grid_search:\n", + " cond[n_embedding, n_themes] = np.linalg.cond(h4_updated)\n", + " # cond[n_embedding, n_themes] = np.linalg.cond(normalize(h4_updated, axis=0, norm='l2'))\n", + " elif len(list_solutions) > 1:\n", + " # Construct merged solutions\n", + " if h4_updated_merged is None:\n", + " h4_updated_merged = h4_updated\n", + " else:\n", + " h4_updated_merged = np.hstack((h4_updated_merged, h4_updated))\n", + " \n", + " print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated), 2)) \n", + "\n", + "if perform_grid_search:\n", + " row, col = np.unravel_index(np.argmin(cond), cond.shape)\n", + " print('minimum condition number achieved for '+ str(row) + ' embeddings and ' + str(col) + ' themes')\n", + "\n", + "if len(list_solutions) == 1:\n", + " # print the condition number achieved by NMF alone\n", + " print('condition number (primary NMF): ', np.round(np.linalg.cond(h4_sparse),2))\n", + " # print(h4_ism)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[760.5816119426518, 787.6095590397154, 1008.5321028509333, 704.4503744654254, 66697.78954415832, 1123.1274864519983]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "n_comp_pca_mvmds = 10\n", + "\n", + "\n", + "# MVMDS reduction\n", + "mvmds = MVMDS(n_components=n_comp_pca_mvmds)\n", + "Xs_mvmds_reduced = mvmds.fit_transform(Xs)\n", + "\n", + "# PCA reduction concatenated views \n", + "pca = PCA(n_components=n_comp_pca_mvmds)\n", + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "Xs_pca_reduced = pca.fit_transform(Xs_concat)\n", + "\n", + "# NMF reduction concatenated views \n", + "\n", + "my_nmfmodel = NMF(n_components=n_themes, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=0)\n", + "estimator_ = my_nmfmodel.fit_transform(m0.copy())\n", + "\n", + "w4_nmf = estimator_.w\n", + "h4_nmf = estimator_.h\n", + "\n", + "# my_nmfmodel = NMF(n_components=n_themes, init='nndsvd', solver='cd', beta_loss='frobenius', max_iter=1000, tol=1.e-6, random_state=0)\n", + "# w4_nmf = my_nmfmodel.fit_transform(m0)\n", + "# h4_nmf = my_nmfmodel.components_.T \n", + "\n", + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1, spread=1, n_neighbors=15, init='pca')\n", + "# mds = umap_.UMAP(n_components=2, init='random', random_state=0)\n", + "\n", + "n_marker_genes = 915\n", + "\n", + "stress = []\n", + "\n", + "w4_gfa = model_gfa['Z']\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "w4_mofa = factors_mofa\n", + "w4_mofa_mds = mds.fit_transform(normalize(w4_mofa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "w4_ism_mds = mds.fit_transform(w4_ism[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "Xs_mvmds_reduced_mds = mds.fit_transform(Xs_mvmds_reduced[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "w4_nmf_mds = mds.fit_transform(w4_nmf[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "w4_ntf_mds = mds.fit_transform(w4_ntf[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "# Xs_pca_reduced_mds = mds.fit_transform(Xs_pca_reduced[:n_marker_genes,:])\n", + "# stress.append(mds.stress_)\n", + "\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "w4_gfa = model_gfa['Z']\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14 11.46\n", + "0.9958\n", + "12 11.19\n", + "0.9927\n", + "13 8.78\n", + "0.9878\n", + "11 8.39\n", + "0.9875\n", + "13 12.13\n", + "0.986\n", + "14 12.83\n", + "0.9967\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(16)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(16):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + " \n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(cell_label, xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', n_std=2, **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " \n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " if l==14 and n_std==.8:\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=n_std, edgecolor='black')\n", + " else:\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4)) # Output: 1.0\n", + "\n", + "fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=16\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes)\n", + "plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (16-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (16-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_ntf_mds[:, 0], w4_ntf_mds[:, 1], title=\"NTF Reduced Data (16-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_mofa_mds[:, 0], w4_mofa_mds[:, 1], title=\"MOFA+ Reduced Data (16-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[], n_std=.8)\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"GFA Reduced Data (16-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples.bck/abis_biomed.ipynb b/examples.bck/abis_biomed.ipynb new file mode 100644 index 0000000..c009944 --- /dev/null +++ b/examples.bck/abis_biomed.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "# scipy==1.12.0 not used (due to changes in SVDS) to reproduce presented results in ref paper" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coucou\n" + ] + } + ], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error ism before straightening: 0.39\n", + "error ism after straightening: 0.39\n", + "condition number(16, 14) = 2.78\n", + "error: 0.39\n" + ] + } + ], + "source": [ + "n_embedding, n_themes = [16,14]\n", + "\n", + "h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0 = False, update_h4_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated_sparse), 2))\n", + "error = np.linalg.norm(m0_norm - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0_norm)\n", + "print('error: ',round(error, 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[871.060394080838]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "mds = MDS(n_components=2, random_state=0)\n", + "n_marker_genes = 915\n", + "\n", + "stress = []\n", + "# w4_ism_mds = mds.fit_transform(w4_ism[:n_marker_genes,:])\n", + "w4_ism_mds = mds.fit_transform(normalize(w4_ism[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "# m0_mds = mds.fit_transform(normalize(m0[:n_marker_genes,:]))\n", + "# stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11 9.59\n", + "0.9919\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(16)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(16):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + " \n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(cell_label, xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " \n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4)) \n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=16\n", + "\n", + "# plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes)\n", + "# plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (16-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (16-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[1])\n", + "# plot_scatter(w4_ntf_mds[:, 0], w4_ntf_mds[:, 1], title=\"NTF Reduced Data (16-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[4])\n", + "# plot_scatter(Xs_pca_reduced_mds[:, 0], Xs_pca_reduced_mds[:, 1], title=\"PCA Reduced Data (16-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(m0_mds[:, 0], m0_mds[:, 1], title=\"Original Data (16-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[10])\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes)\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/abis_gfa screeplot.ipynb b/examples.bck/abis_gfa screeplot.ipynb new file mode 100644 index 0000000..b2e02c1 --- /dev/null +++ b/examples.bck/abis_gfa screeplot.ipynb @@ -0,0 +1,1081 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/10: 25 iterations with final cost -79133.383391\n", + "Run 2/10: 24 iterations with final cost -79133.068141\n", + "Run 3/10: 27 iterations with final cost -79133.535069\n", + "Run 4/10: 17 iterations with final cost -79133.633315\n", + "Run 5/10: 19 iterations with final cost -79133.023092\n", + "Run 6/10: 20 iterations with final cost -79133.614160\n", + "Run 7/10: 22 iterations with final cost -79133.412946\n", + "Run 8/10: 22 iterations with final cost -79132.977388\n", + "Run 9/10: 18 iterations with final cost -79133.572912\n", + "Run 10/10: 27 iterations with final cost -79133.710666\n", + "Run 1/10: 27 iterations with final cost -77535.355524\n", + "Run 2/10: 26 iterations with final cost -77491.636013\n", + "Run 3/10: 28 iterations with final cost -77541.221338\n", + "Run 4/10: 33 iterations with final cost -77513.677929\n", + "Run 5/10: 31 iterations with final cost -77511.939634\n", + "Run 6/10: 27 iterations with final cost -77501.682313\n", + "Run 7/10: 36 iterations with final cost 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iterations with final cost -55851.374305\n", + "Run 9/10: 70 iterations with final cost -55849.519401\n", + "Run 10/10: 70 iterations with final cost -55852.645281\n", + "2 0.22694591404422354\n", + "3 0.3447063278586807\n", + "4 0.43491089381450376\n", + "5 0.5234815263248173\n", + "6 0.6080527439185863\n", + "7 0.6774380319837394\n", + "8 0.7241360875226066\n", + "9 0.7707051314744143\n", + "10 0.832421378820104\n", + "11 0.856068009678558\n", + "12 0.8696563070710126\n", + "13 0.8502564109925649\n", + "14 0.8439898525994537\n", + "15 0.8348636396739824\n", + "16 1.0102458494221684\n" + ] + } + ], + "source": [ + "gfa_cov = np.zeros(17)\n", + "for k in range(2,17):\n", + " model = gfa_experiments(Xs_norm, K=k, Nrep=10, rotate=False, verbose=1)\n", + " gfa_cov[k] = np.trace(model['covZ'])\n", + "\n", + "for k in range(2,17):\n", + " print(k, gfa_cov[k])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca = PCA(n_components=16)\n", + "\n", + "# Concatenate views then PCA for comparison\n", + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "Xs_pca_reduced = pca.fit_transform(Xs_concat)\n", + "\n", + "\n", + "# Plot the scree plot\n", + "plt.plot (np.arange (1, pca.n_components_ + 1), pca.explained_variance_ratio_, 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(2,17), gfa_cov[2:17], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/abis_gfa.ipynb b/examples.bck/abis_gfa.ipynb new file mode 100644 index 0000000..5c46f8e --- /dev/null +++ b/examples.bck/abis_gfa.ipynb @@ -0,0 +1,1156 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/100: 52 iterations with final cost -62234.366388\n", + "Run 2/100: 48 iterations with final cost -62238.057050\n", + "Run 3/100: 85 iterations with final cost -62236.052869\n", + "Run 4/100: 48 iterations with final cost -62236.163082\n", + "Run 5/100: 48 iterations with final cost -62237.015366\n", + "Run 6/100: 84 iterations with final cost -62238.479998\n", + "Run 7/100: 52 iterations with final cost -62238.542473\n", + "Run 8/100: 63 iterations with final cost -62241.274993\n", + "Run 9/100: 70 iterations with final cost -62239.231260\n", + "Run 10/100: 47 iterations with final cost -62239.043010\n", + "Run 11/100: 50 iterations with final cost -62234.862528\n", + "Run 12/100: 49 iterations with final cost -62237.187186\n", + "Run 13/100: 50 iterations with final cost -62237.780570\n", + "Run 14/100: 54 iterations with final cost -62237.436951\n", + "Run 15/100: 48 iterations with final cost -62237.784498\n", + "Run 16/100: 52 iterations with final cost -62234.562732\n", + "Run 17/100: 48 iterations with 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final cost -62340.284289\n", + "Run 97/100: 81 iterations with final cost -62239.102161\n", + "Run 98/100: 47 iterations with final cost -62238.662903\n", + "Run 99/100: 48 iterations with final cost -62233.406714\n", + "Run 100/100: 89 iterations with final cost -62238.114494\n" + ] + } + ], + "source": [ + "model = gfa_experiments(Xs_norm, K=12, Nrep=100, rotate=False, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[764.641798261622]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "mds = MDS(n_components=2, random_state=0)\n", + "n_marker_genes = 915\n", + "\n", + "stress = []\n", + "w4_gfa = model['Z']\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "\n", + "stress.append(mds.stress_)\n", + "\n", + "# m0_mds = mds.fit_transform(normalize(m0[:n_marker_genes,:]))\n", + "# stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14 12.31\n", + "0.9952\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(16)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(16):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + " \n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(cell_label, xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " \n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4)) \n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=16\n", + "\n", + "# plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes)\n", + "# plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (16-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (16-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[1])\n", + "# plot_scatter(w4_ntf_mds[:, 0], w4_ntf_mds[:, 1], title=\"NTF Reduced Data (16-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[4])\n", + "# plot_scatter(Xs_pca_reduced_mds[:, 0], Xs_pca_reduced_mds[:, 1], title=\"PCA Reduced Data (16-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(m0_mds[:, 0], m0_mds[:, 1], title=\"Original Data (16-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[10])\n", + "\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes)\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/abis_mofa.ipynb b/examples.bck/abis_mofa.ipynb new file mode 100644 index 0000000..0d48f26 --- /dev/null +++ b/examples.bck/abis_mofa.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "from mofapy2.run.entry_point import entry_point\n", + "from scipy.stats import trim_mean\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])\n", + "\n", + "data_mat = [[None for g in range(1)] for m in range(4)]\n", + "\n", + "for m in range(4):\n", + " data_mat[m][0] = Xs_norm[m]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -554078.45 \n", + "\n", + "Iteration 1: time=0.04, ELBO=-80263.41, deltaELBO=473815.045 (85.51407166%), Factors=12\n", + "Iteration 2: time=0.03, ELBO=-72448.31, deltaELBO=7815.094 (1.41046700%), Factors=12\n", + "Iteration 3: time=0.05, ELBO=-67384.30, deltaELBO=5064.013 (0.91395231%), Factors=12\n", + "Iteration 4: time=0.05, ELBO=-63970.45, deltaELBO=3413.847 (0.61613056%), Factors=12\n", + "Iteration 5: time=0.05, ELBO=-62161.20, deltaELBO=1809.254 (0.32653396%), Factors=12\n", + "Iteration 6: time=0.05, ELBO=-61281.89, deltaELBO=879.315 (0.15869856%), Factors=12\n", + "Iteration 7: time=0.03, ELBO=-60660.21, deltaELBO=621.672 (0.11219925%), Factors=12\n", + "Iteration 8: time=0.05, ELBO=-60037.75, deltaELBO=622.469 (0.11234304%), Factors=12\n", + "Iteration 9: time=0.03, ELBO=-59598.16, deltaELBO=439.582 (0.07933568%), Factors=12\n", + "Iteration 10: time=0.03, ELBO=-59364.67, deltaELBO=233.496 (0.04214138%), Factors=12\n", + "Iteration 11: time=0.03, ELBO=-59204.15, deltaELBO=160.515 (0.02896971%), Factors=12\n", + "Iteration 12: time=0.04, ELBO=-59080.27, deltaELBO=123.882 (0.02235817%), Factors=12\n", + "Iteration 13: time=0.04, ELBO=-58999.55, deltaELBO=80.719 (0.01456811%), Factors=12\n", + "Iteration 14: time=0.03, ELBO=-58949.40, deltaELBO=50.155 (0.00905203%), Factors=12\n", + "Iteration 15: time=0.04, ELBO=-58913.41, deltaELBO=35.983 (0.00649417%), Factors=12\n", + "Iteration 16: time=0.04, ELBO=-58883.82, deltaELBO=29.589 (0.00534023%), Factors=12\n", + "Iteration 17: time=0.04, ELBO=-58857.76, deltaELBO=26.060 (0.00470338%), Factors=12\n", + "Iteration 18: time=0.03, ELBO=-58833.88, deltaELBO=23.887 (0.00431114%), Factors=12\n", + "Iteration 19: time=0.07, ELBO=-58811.25, deltaELBO=22.625 (0.00408333%), Factors=12\n", + "Iteration 20: time=0.06, ELBO=-58789.14, deltaELBO=22.108 (0.00399001%), Factors=12\n", + "Iteration 21: time=0.05, ELBO=-58766.88, deltaELBO=22.266 (0.00401863%), Factors=12\n", + "Iteration 22: time=0.04, ELBO=-58743.80, deltaELBO=23.080 (0.00416554%), Factors=12\n", + "Iteration 23: time=0.04, ELBO=-58719.23, deltaELBO=24.565 (0.00443343%), Factors=12\n", + "Iteration 24: time=0.04, ELBO=-58692.46, deltaELBO=26.771 (0.00483167%), Factors=12\n", + "Iteration 25: time=0.04, ELBO=-58662.65, deltaELBO=29.812 (0.00538040%), Factors=12\n", + "Iteration 26: time=0.04, ELBO=-58628.74, deltaELBO=33.911 (0.00612020%), Factors=12\n", + "Iteration 27: time=0.04, ELBO=-58589.24, deltaELBO=39.500 (0.00712890%), Factors=12\n", + "Iteration 28: time=0.03, ELBO=-58541.91, deltaELBO=47.333 (0.00854274%), Factors=12\n", + "Iteration 29: time=0.04, ELBO=-58483.39, deltaELBO=58.519 (0.01056151%), Factors=12\n", + "Iteration 30: time=0.04, ELBO=-58409.34, deltaELBO=74.048 (0.01336420%), Factors=12\n", + "Iteration 31: time=0.04, ELBO=-58316.53, deltaELBO=92.808 (0.01674999%), Factors=12\n", + "Iteration 32: time=0.04, ELBO=-58209.32, deltaELBO=107.211 (0.01934936%), Factors=12\n", + "Iteration 33: time=0.03, ELBO=-58107.17, deltaELBO=102.146 (0.01843532%), Factors=12\n", + "Iteration 34: time=0.10, ELBO=-58034.88, deltaELBO=72.291 (0.01304710%), Factors=12\n", + "Iteration 35: time=0.04, ELBO=-57997.19, deltaELBO=37.690 (0.00680233%), Factors=12\n", + "Iteration 36: time=0.03, ELBO=-57980.63, deltaELBO=16.558 (0.00298839%), Factors=12\n", + "Iteration 37: time=0.03, ELBO=-57972.92, deltaELBO=7.719 (0.00139310%), Factors=12\n", + "Iteration 38: time=0.03, ELBO=-57968.42, deltaELBO=4.497 (0.00081157%), Factors=12\n", + "Iteration 39: time=0.04, ELBO=-57965.26, deltaELBO=3.162 (0.00057070%), Factors=12\n", + "Iteration 40: time=0.04, ELBO=-57962.82, deltaELBO=2.440 (0.00044033%), Factors=12\n", + "Iteration 41: time=0.04, ELBO=-57960.85, deltaELBO=1.962 (0.00035412%), Factors=12\n", + "Iteration 42: time=0.04, ELBO=-57959.24, deltaELBO=1.615 (0.00029146%), Factors=12\n", + "Iteration 43: time=0.04, ELBO=-57957.89, deltaELBO=1.352 (0.00024403%), Factors=12\n", + "Iteration 44: time=0.04, ELBO=-57956.74, deltaELBO=1.149 (0.00020738%), Factors=12\n", + "Iteration 45: time=0.03, ELBO=-57955.75, deltaELBO=0.990 (0.00017864%), Factors=12\n", + "Iteration 46: time=0.04, ELBO=-57954.89, deltaELBO=0.863 (0.00015584%), Factors=12\n", + "Iteration 47: time=0.04, ELBO=-57954.12, deltaELBO=0.762 (0.00013755%), Factors=12\n", + "Iteration 48: time=0.03, ELBO=-57953.44, deltaELBO=0.680 (0.00012273%), Factors=12\n", + "Iteration 49: time=0.03, ELBO=-57952.83, deltaELBO=0.613 (0.00011059%), Factors=12\n", + "Iteration 50: time=0.03, ELBO=-57952.27, deltaELBO=0.557 (0.00010056%), Factors=12\n", + "Iteration 51: time=0.04, ELBO=-57951.76, deltaELBO=0.511 (0.00009218%), Factors=12\n", + "Iteration 52: time=0.03, ELBO=-57951.29, deltaELBO=0.472 (0.00008513%), Factors=12\n", + "Iteration 53: time=0.03, ELBO=-57950.85, deltaELBO=0.439 (0.00007915%), Factors=12\n", + "Iteration 54: time=0.04, ELBO=-57950.44, deltaELBO=0.410 (0.00007402%), Factors=12\n", + "Iteration 55: time=0.03, ELBO=-57950.06, deltaELBO=0.386 (0.00006960%), Factors=12\n", + "Iteration 56: time=0.04, ELBO=-57949.69, deltaELBO=0.364 (0.00006576%), Factors=12\n", + "Iteration 57: time=0.03, ELBO=-57949.35, deltaELBO=0.346 (0.00006241%), Factors=12\n", + "Iteration 58: time=0.04, ELBO=-57949.02, deltaELBO=0.329 (0.00005946%), Factors=12\n", + "Iteration 59: time=0.03, ELBO=-57948.70, deltaELBO=0.315 (0.00005685%), Factors=12\n", + "Iteration 60: time=0.03, ELBO=-57948.40, deltaELBO=0.302 (0.00005453%), Factors=12\n", + "Iteration 61: time=0.03, ELBO=-57948.11, deltaELBO=0.291 (0.00005245%), Factors=12\n", + "Iteration 62: time=0.04, ELBO=-57947.83, deltaELBO=0.280 (0.00005059%), Factors=12\n", + "Iteration 63: time=0.04, ELBO=-57947.56, deltaELBO=0.271 (0.00004891%), Factors=12\n", + "Iteration 64: time=0.04, ELBO=-57947.30, deltaELBO=0.263 (0.00004739%), Factors=12\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "ent = entry_point()\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(4)])\n", + "ent.set_model_options(\n", + " factors = 13, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + ")\n", + "ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + ")\n", + "ent.build()\n", + "ent.run()\n", + "factors = ent.model.nodes[\"Z\"].getExpectation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[787.6095590397154]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "mds = MDS(n_components=2, random_state=0)\n", + "n_marker_genes = 915\n", + "\n", + "stress = []\n", + "w4_gfa = factors\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "\n", + "stress.append(mds.stress_)\n", + "\n", + "# m0_mds = mds.fit_transform(normalize(m0[:n_marker_genes,:]))\n", + "# stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13 12.13\n", + "0.986\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(16)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(16):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + " \n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(cell_label, xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', n_std=2, **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " \n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " # print(l,list_cell_types[l])\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " if l==14:\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=.8, edgecolor='black')\n", + " else:\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=n_std, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4)) \n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=16\n", + "\n", + "# plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes)\n", + "# plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (16-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (16-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[1])\n", + "# plot_scatter(w4_ntf_mds[:, 0], w4_ntf_mds[:, 1], title=\"NTF Reduced Data (16-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[4])\n", + "# plot_scatter(Xs_pca_reduced_mds[:, 0], Xs_pca_reduced_mds[:, 1], title=\"PCA Reduced Data (16-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(m0_mds[:, 0], m0_mds[:, 1], title=\"Original Data (16-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[10])\n", + "\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes)\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/abis_mofa_screeplot.ipynb b/examples.bck/abis_mofa_screeplot.ipynb new file mode 100644 index 0000000..cf110a3 --- /dev/null +++ b/examples.bck/abis_mofa_screeplot.ipynb @@ -0,0 +1,2351 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "from mofapy2.run.entry_point import entry_point\n", + "from scipy.stats import trim_mean\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -180060.33 \n", + "\n", + "Iteration 1: time=0.01, ELBO=-82808.81, deltaELBO=97251.517 (54.01051845%), Factors=1\n", + "Iteration 2: time=0.01, ELBO=-81284.48, deltaELBO=1524.330 (0.84656651%), Factors=1\n", + "Iteration 3: time=0.01, ELBO=-81074.24, deltaELBO=210.245 (0.11676381%), Factors=1\n", + "Iteration 4: time=0.01, ELBO=-80994.47, deltaELBO=79.763 (0.04429780%), Factors=1\n", + "Iteration 5: time=0.01, ELBO=-80950.31, deltaELBO=44.161 (0.02452584%), Factors=1\n", + "Iteration 6: time=0.02, ELBO=-80920.83, deltaELBO=29.479 (0.01637199%), Factors=1\n", + "Iteration 7: time=0.01, ELBO=-80897.48, deltaELBO=23.349 (0.01296705%), Factors=1\n", + "Iteration 8: time=0.01, ELBO=-80875.66, deltaELBO=21.819 (0.01211769%), Factors=1\n", + "Iteration 9: time=0.01, ELBO=-80852.09, deltaELBO=23.573 (0.01309169%), Factors=1\n", + "Iteration 10: time=0.00, ELBO=-80823.69, deltaELBO=28.399 (0.01577220%), Factors=1\n", + "Iteration 11: time=0.00, ELBO=-80787.06, deltaELBO=36.630 (0.02034302%), Factors=1\n", + "Iteration 12: time=0.01, ELBO=-80738.34, deltaELBO=48.727 (0.02706138%), Factors=1\n", + "Iteration 13: time=0.01, ELBO=-80673.57, deltaELBO=64.766 (0.03596904%), Factors=1\n", + "Iteration 14: time=0.00, ELBO=-80589.68, deltaELBO=83.893 (0.04659147%), Factors=1\n", + "Iteration 15: time=0.00, ELBO=-80486.00, deltaELBO=103.677 (0.05757896%), Factors=1\n", + "Iteration 16: time=0.00, ELBO=-80368.21, deltaELBO=117.787 (0.06541527%), Factors=1\n", + "Iteration 17: time=0.01, ELBO=-80253.96, deltaELBO=114.250 (0.06345122%), Factors=1\n", + "Iteration 18: time=0.00, ELBO=-80166.37, deltaELBO=87.597 (0.04864844%), Factors=1\n", + "Iteration 19: time=0.01, ELBO=-80113.64, deltaELBO=52.729 (0.02928390%), Factors=1\n", + "Iteration 20: time=0.00, ELBO=-80086.70, deltaELBO=26.936 (0.01495962%), Factors=1\n", + "Iteration 21: time=0.00, ELBO=-80073.85, deltaELBO=12.849 (0.00713593%), Factors=1\n", + "Iteration 22: time=0.02, ELBO=-80067.76, deltaELBO=6.090 (0.00338242%), Factors=1\n", + "Iteration 23: time=0.00, ELBO=-80064.78, deltaELBO=2.979 (0.00165418%), Factors=1\n", + "Iteration 24: time=0.01, ELBO=-80063.24, deltaELBO=1.546 (0.00085833%), Factors=1\n", + "Iteration 25: time=0.00, ELBO=-80062.37, deltaELBO=0.867 (0.00048170%), Factors=1\n", + "Iteration 26: time=0.01, ELBO=-80061.84, deltaELBO=0.532 (0.00029524%), Factors=1\n", + "Iteration 27: time=0.01, ELBO=-80061.48, deltaELBO=0.356 (0.00019786%), Factors=1\n", + "Iteration 28: time=0.00, ELBO=-80061.22, deltaELBO=0.259 (0.00014406%), Factors=1\n", + "Iteration 29: time=0.01, ELBO=-80061.02, deltaELBO=0.203 (0.00011254%), Factors=1\n", + "Iteration 30: time=0.00, ELBO=-80060.85, deltaELBO=0.167 (0.00009290%), Factors=1\n", + "Iteration 31: time=0.01, ELBO=-80060.71, deltaELBO=0.144 (0.00007982%), Factors=1\n", + "Iteration 32: time=0.00, ELBO=-80060.58, deltaELBO=0.127 (0.00007051%), Factors=1\n", + "Iteration 33: time=0.00, ELBO=-80060.47, deltaELBO=0.114 (0.00006343%), Factors=1\n", + "Iteration 34: time=0.02, ELBO=-80060.36, deltaELBO=0.104 (0.00005775%), Factors=1\n", + "Iteration 35: time=0.00, ELBO=-80060.27, deltaELBO=0.095 (0.00005298%), Factors=1\n", + "Iteration 36: time=0.02, ELBO=-80060.18, deltaELBO=0.088 (0.00004885%), Factors=1\n", + "Iteration 37: time=0.00, ELBO=-80060.10, deltaELBO=0.081 (0.00004518%), Factors=1\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -211701.49 \n", + "\n", + "Iteration 1: time=0.01, ELBO=-82599.17, deltaELBO=129102.316 (60.98318740%), Factors=2\n", + "Iteration 2: time=0.00, ELBO=-81320.30, deltaELBO=1278.877 (0.60409462%), Factors=2\n", + "Iteration 3: time=0.02, ELBO=-81041.84, deltaELBO=278.454 (0.13153136%), Factors=2\n", + "Iteration 4: time=0.00, ELBO=-80891.22, deltaELBO=150.627 (0.07115059%), Factors=2\n", + "Iteration 5: time=0.01, ELBO=-80747.44, deltaELBO=143.776 (0.06791438%), Factors=2\n", + "Iteration 6: time=0.01, ELBO=-80576.60, deltaELBO=170.844 (0.08070049%), Factors=2\n", + "Iteration 7: time=0.00, ELBO=-80324.91, deltaELBO=251.689 (0.11888885%), Factors=2\n", + "Iteration 8: time=0.01, ELBO=-79847.19, deltaELBO=477.712 (0.22565343%), Factors=2\n", + "Iteration 9: time=0.00, ELBO=-79155.07, deltaELBO=692.120 (0.32693190%), Factors=2\n", + "Iteration 10: time=0.02, ELBO=-78707.62, deltaELBO=447.454 (0.21136085%), Factors=2\n", + "Iteration 11: time=0.00, ELBO=-78526.66, deltaELBO=180.966 (0.08548149%), Factors=2\n", + "Iteration 12: time=0.01, ELBO=-78402.88, deltaELBO=123.776 (0.05846719%), Factors=2\n", + "Iteration 13: time=0.00, ELBO=-78272.38, deltaELBO=130.499 (0.06164309%), Factors=2\n", + "Iteration 14: time=0.01, ELBO=-78143.21, deltaELBO=129.167 (0.06101355%), Factors=2\n", + "Iteration 15: time=0.00, ELBO=-78042.86, deltaELBO=100.358 (0.04740525%), Factors=2\n", + "Iteration 16: time=0.01, ELBO=-77983.16, deltaELBO=59.696 (0.02819813%), Factors=2\n", + "Iteration 17: time=0.00, ELBO=-77953.61, deltaELBO=29.554 (0.01396023%), Factors=2\n", + "Iteration 18: time=0.00, ELBO=-77939.69, deltaELBO=13.913 (0.00657207%), Factors=2\n", + "Iteration 19: time=0.02, ELBO=-77932.70, deltaELBO=6.990 (0.00330185%), Factors=2\n", + "Iteration 20: time=0.00, ELBO=-77928.70, deltaELBO=4.001 (0.00189009%), Factors=2\n", + "Iteration 21: time=0.02, ELBO=-77926.07, deltaELBO=2.632 (0.00124343%), Factors=2\n", + "Iteration 22: time=0.00, ELBO=-77924.14, deltaELBO=1.926 (0.00090954%), Factors=2\n", + "Iteration 23: time=0.02, ELBO=-77922.64, deltaELBO=1.503 (0.00071012%), Factors=2\n", + "Iteration 24: time=0.00, ELBO=-77921.42, deltaELBO=1.217 (0.00057499%), Factors=2\n", + "Iteration 25: time=0.01, ELBO=-77920.42, deltaELBO=1.006 (0.00047524%), Factors=2\n", + "Iteration 26: time=0.01, ELBO=-77919.57, deltaELBO=0.842 (0.00039782%), Factors=2\n", + "Iteration 27: time=0.01, ELBO=-77918.86, deltaELBO=0.711 (0.00033600%), Factors=2\n", + "Iteration 28: time=0.00, ELBO=-77918.26, deltaELBO=0.605 (0.00028583%), Factors=2\n", + "Iteration 29: time=0.00, ELBO=-77917.74, deltaELBO=0.518 (0.00024468%), Factors=2\n", + "Iteration 30: time=0.00, ELBO=-77917.29, deltaELBO=0.446 (0.00021067%), Factors=2\n", + "Iteration 31: time=0.00, ELBO=-77916.91, deltaELBO=0.386 (0.00018241%), Factors=2\n", + "Iteration 32: time=0.02, ELBO=-77916.57, deltaELBO=0.336 (0.00015879%), Factors=2\n", + "Iteration 33: time=0.00, ELBO=-77916.28, deltaELBO=0.294 (0.00013899%), Factors=2\n", + "Iteration 34: time=0.01, ELBO=-77916.02, deltaELBO=0.259 (0.00012231%), Factors=2\n", + "Iteration 35: time=0.00, ELBO=-77915.79, deltaELBO=0.229 (0.00010822%), Factors=2\n", + "Iteration 36: time=0.02, ELBO=-77915.59, deltaELBO=0.204 (0.00009628%), Factors=2\n", + "Iteration 37: time=0.00, ELBO=-77915.40, deltaELBO=0.182 (0.00008613%), Factors=2\n", + "Iteration 38: time=0.03, ELBO=-77915.24, deltaELBO=0.164 (0.00007749%), Factors=2\n", + "Iteration 39: time=0.00, ELBO=-77915.09, deltaELBO=0.148 (0.00007010%), Factors=2\n", + "Iteration 40: time=0.01, ELBO=-77914.96, deltaELBO=0.135 (0.00006378%), Factors=2\n", + "Iteration 41: time=0.03, ELBO=-77914.83, deltaELBO=0.124 (0.00005835%), Factors=2\n", + "Iteration 42: time=0.01, ELBO=-77914.72, deltaELBO=0.114 (0.00005369%), Factors=2\n", + "Iteration 43: time=0.01, ELBO=-77914.61, deltaELBO=0.105 (0.00004968%), Factors=2\n", + "Iteration 44: time=0.00, ELBO=-77914.52, deltaELBO=0.098 (0.00004622%), Factors=2\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -246185.54 \n", + "\n", + "Iteration 1: time=0.00, ELBO=-82064.64, deltaELBO=164120.903 (66.66553199%), Factors=3\n", + "Iteration 2: time=0.02, ELBO=-80051.96, deltaELBO=2012.686 (0.81754824%), Factors=3\n", + "Iteration 3: time=0.00, ELBO=-79577.71, deltaELBO=474.251 (0.19263965%), Factors=3\n", + "Iteration 4: time=0.01, ELBO=-79249.40, deltaELBO=328.308 (0.13335805%), Factors=3\n", + "Iteration 5: time=0.01, ELBO=-78767.53, deltaELBO=481.864 (0.19573194%), Factors=3\n", + "Iteration 6: time=0.02, ELBO=-78001.16, deltaELBO=766.378 (0.31130100%), Factors=3\n", + "Iteration 7: time=0.01, ELBO=-77332.79, deltaELBO=668.363 (0.27148732%), Factors=3\n", + "Iteration 8: time=0.01, ELBO=-76988.97, deltaELBO=343.827 (0.13966171%), Factors=3\n", + "Iteration 9: time=0.00, ELBO=-76740.25, deltaELBO=248.720 (0.10102960%), Factors=3\n", + "Iteration 10: time=0.00, ELBO=-76482.49, deltaELBO=257.759 (0.10470116%), Factors=3\n", + "Iteration 11: time=0.02, ELBO=-76245.11, deltaELBO=237.379 (0.09642274%), Factors=3\n", + "Iteration 12: time=0.02, ELBO=-76050.49, deltaELBO=194.621 (0.07905443%), Factors=3\n", + "Iteration 13: time=0.00, ELBO=-75911.40, deltaELBO=139.083 (0.05649539%), Factors=3\n", + "Iteration 14: time=0.01, ELBO=-75832.30, deltaELBO=79.107 (0.03213303%), Factors=3\n", + "Iteration 15: time=0.01, ELBO=-75795.39, deltaELBO=36.902 (0.01498932%), Factors=3\n", + "Iteration 16: time=0.00, ELBO=-75779.08, deltaELBO=16.315 (0.00662693%), Factors=3\n", + "Iteration 17: time=0.00, ELBO=-75771.13, deltaELBO=7.948 (0.00322827%), Factors=3\n", + "Iteration 18: time=0.02, ELBO=-75766.53, deltaELBO=4.602 (0.00186931%), Factors=3\n", + "Iteration 19: time=0.01, ELBO=-75763.41, deltaELBO=3.121 (0.00126755%), Factors=3\n", + "Iteration 20: time=0.01, ELBO=-75761.07, deltaELBO=2.344 (0.00095209%), Factors=3\n", + "Iteration 21: time=0.01, ELBO=-75759.20, deltaELBO=1.862 (0.00075635%), Factors=3\n", + "Iteration 22: time=0.00, ELBO=-75757.68, deltaELBO=1.524 (0.00061916%), Factors=3\n", + "Iteration 23: time=0.02, ELBO=-75756.41, deltaELBO=1.270 (0.00051570%), Factors=3\n", + "Iteration 24: time=0.01, ELBO=-75755.34, deltaELBO=1.069 (0.00043433%), Factors=3\n", + "Iteration 25: time=0.01, ELBO=-75754.43, deltaELBO=0.908 (0.00036873%), Factors=3\n", + "Iteration 26: time=0.01, ELBO=-75753.66, deltaELBO=0.775 (0.00031499%), Factors=3\n", + "Iteration 27: time=0.01, ELBO=-75752.99, deltaELBO=0.666 (0.00027048%), Factors=3\n", + "Iteration 28: time=0.00, ELBO=-75752.42, deltaELBO=0.574 (0.00023331%), Factors=3\n", + "Iteration 29: time=0.02, ELBO=-75751.92, deltaELBO=0.497 (0.00020208%), Factors=3\n", + "Iteration 30: time=0.01, ELBO=-75751.49, deltaELBO=0.433 (0.00017571%), Factors=3\n", + "Iteration 31: time=0.00, ELBO=-75751.11, deltaELBO=0.377 (0.00015333%), Factors=3\n", + "Iteration 32: time=0.02, ELBO=-75750.78, deltaELBO=0.331 (0.00013428%), Factors=3\n", + "Iteration 33: time=0.00, ELBO=-75750.49, deltaELBO=0.291 (0.00011801%), Factors=3\n", + "Iteration 34: time=0.02, ELBO=-75750.23, deltaELBO=0.256 (0.00010407%), Factors=3\n", + "Iteration 35: time=0.01, ELBO=-75750.01, deltaELBO=0.227 (0.00009210%), Factors=3\n", + "Iteration 36: time=0.00, ELBO=-75749.80, deltaELBO=0.201 (0.00008179%), Factors=3\n", + "Iteration 37: time=0.02, ELBO=-75749.63, deltaELBO=0.179 (0.00007289%), Factors=3\n", + "Iteration 38: time=0.00, ELBO=-75749.46, deltaELBO=0.160 (0.00006519%), Factors=3\n", + "Iteration 39: time=0.02, ELBO=-75749.32, deltaELBO=0.144 (0.00005853%), Factors=3\n", + "Iteration 40: time=0.00, ELBO=-75749.19, deltaELBO=0.130 (0.00005274%), Factors=3\n", + "Iteration 41: time=0.02, ELBO=-75749.07, deltaELBO=0.117 (0.00004771%), Factors=3\n", + "Iteration 42: time=0.01, ELBO=-75748.97, deltaELBO=0.107 (0.00004332%), Factors=3\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -285543.94 \n", + "\n", + "Iteration 1: time=0.01, ELBO=-80743.70, deltaELBO=204800.234 (71.72284484%), Factors=4\n", + "Iteration 2: time=0.01, ELBO=-76978.08, deltaELBO=3765.626 (1.31875549%), Factors=4\n", + "Iteration 3: time=0.00, ELBO=-76009.11, deltaELBO=968.970 (0.33934174%), Factors=4\n", + "Iteration 4: time=0.02, ELBO=-75560.09, deltaELBO=449.015 (0.15724895%), Factors=4\n", + "Iteration 5: time=0.00, ELBO=-75382.06, deltaELBO=178.034 (0.06234920%), Factors=4\n", + "Iteration 6: time=0.02, ELBO=-75301.64, deltaELBO=80.419 (0.02816333%), Factors=4\n", + "Iteration 7: time=0.01, ELBO=-75252.00, deltaELBO=49.637 (0.01738338%), Factors=4\n", + "Iteration 8: time=0.01, ELBO=-75214.45, deltaELBO=37.555 (0.01315197%), Factors=4\n", + "Iteration 9: time=0.01, ELBO=-75178.04, deltaELBO=36.405 (0.01274932%), Factors=4\n", + "Iteration 10: time=0.01, ELBO=-75129.89, deltaELBO=48.153 (0.01686370%), Factors=4\n", + "Iteration 11: time=0.01, ELBO=-75050.65, deltaELBO=79.235 (0.02774876%), Factors=4\n", + "Iteration 12: time=0.02, ELBO=-74918.97, deltaELBO=131.685 (0.04611729%), Factors=4\n", + "Iteration 13: time=0.01, ELBO=-74739.60, deltaELBO=179.373 (0.06281783%), Factors=4\n", + "Iteration 14: time=0.01, ELBO=-74565.87, deltaELBO=173.723 (0.06083934%), Factors=4\n", + "Iteration 15: time=0.01, ELBO=-74439.25, deltaELBO=126.624 (0.04434484%), Factors=4\n", + "Iteration 16: time=0.01, ELBO=-74350.81, deltaELBO=88.435 (0.03097060%), Factors=4\n", + "Iteration 17: time=0.01, ELBO=-74288.45, deltaELBO=62.360 (0.02183912%), Factors=4\n", + "Iteration 18: time=0.01, ELBO=-74249.15, deltaELBO=39.299 (0.01376269%), Factors=4\n", + "Iteration 19: time=0.01, ELBO=-74226.94, deltaELBO=22.217 (0.00778045%), Factors=4\n", + "Iteration 20: time=0.01, ELBO=-74213.90, deltaELBO=13.035 (0.00456485%), Factors=4\n", + "Iteration 21: time=0.01, ELBO=-74204.67, deltaELBO=9.238 (0.00323515%), Factors=4\n", + "Iteration 22: time=0.01, ELBO=-74196.59, deltaELBO=8.073 (0.00282733%), Factors=4\n", + "Iteration 23: time=0.00, ELBO=-74188.45, deltaELBO=8.138 (0.00284997%), Factors=4\n", + "Iteration 24: time=0.02, ELBO=-74179.51, deltaELBO=8.943 (0.00313184%), Factors=4\n", + "Iteration 25: time=0.00, ELBO=-74169.09, deltaELBO=10.423 (0.00365019%), Factors=4\n", + "Iteration 26: time=0.01, ELBO=-74156.35, deltaELBO=12.734 (0.00445961%), Factors=4\n", + "Iteration 27: time=0.01, ELBO=-74140.14, deltaELBO=16.211 (0.00567724%), Factors=4\n", + "Iteration 28: time=0.00, ELBO=-74118.75, deltaELBO=21.393 (0.00749194%), Factors=4\n", + "Iteration 29: time=0.01, ELBO=-74089.69, deltaELBO=29.060 (0.01017722%), Factors=4\n", + "Iteration 30: time=0.01, ELBO=-74049.49, deltaELBO=40.201 (0.01407868%), Factors=4\n", + "Iteration 31: time=0.00, ELBO=-73993.75, deltaELBO=55.740 (0.01952061%), Factors=4\n", + "Iteration 32: time=0.02, ELBO=-73917.85, deltaELBO=75.897 (0.02657964%), Factors=4\n", + "Iteration 33: time=0.01, ELBO=-73818.53, deltaELBO=99.320 (0.03478283%), Factors=4\n", + "Iteration 34: time=0.05, ELBO=-73696.47, deltaELBO=122.065 (0.04274811%), Factors=4\n", + "Iteration 35: time=0.02, ELBO=-73561.79, deltaELBO=134.682 (0.04716681%), Factors=4\n", + "Iteration 36: time=0.02, ELBO=-73439.50, deltaELBO=122.283 (0.04282468%), Factors=4\n", + "Iteration 37: time=0.01, ELBO=-73354.96, deltaELBO=84.538 (0.02960606%), Factors=4\n", + "Iteration 38: time=0.01, ELBO=-73309.48, deltaELBO=45.488 (0.01593032%), Factors=4\n", + "Iteration 39: time=0.01, ELBO=-73288.02, deltaELBO=21.456 (0.00751415%), Factors=4\n", + "Iteration 40: time=0.01, ELBO=-73277.93, deltaELBO=10.091 (0.00353387%), Factors=4\n", + "Iteration 41: time=0.00, ELBO=-73272.76, deltaELBO=5.166 (0.00180901%), Factors=4\n", + "Iteration 42: time=0.02, ELBO=-73269.76, deltaELBO=3.000 (0.00105054%), Factors=4\n", + "Iteration 43: time=0.02, ELBO=-73267.80, deltaELBO=1.967 (0.00068875%), Factors=4\n", + "Iteration 44: time=0.00, ELBO=-73266.39, deltaELBO=1.410 (0.00049378%), Factors=4\n", + "Iteration 45: time=0.02, ELBO=-73265.32, deltaELBO=1.070 (0.00037470%), Factors=4\n", + "Iteration 46: time=0.01, ELBO=-73264.48, deltaELBO=0.840 (0.00029435%), Factors=4\n", + "Iteration 47: time=0.01, ELBO=-73263.80, deltaELBO=0.675 (0.00023639%), Factors=4\n", + "Iteration 48: time=0.01, ELBO=-73263.25, deltaELBO=0.551 (0.00019283%), Factors=4\n", + "Iteration 49: time=0.01, ELBO=-73262.80, deltaELBO=0.455 (0.00015928%), Factors=4\n", + "Iteration 50: time=0.01, ELBO=-73262.42, deltaELBO=0.380 (0.00013306%), Factors=4\n", + "Iteration 51: time=0.01, ELBO=-73262.10, deltaELBO=0.321 (0.00011238%), Factors=4\n", + "Iteration 52: time=0.01, ELBO=-73261.82, deltaELBO=0.274 (0.00009595%), Factors=4\n", + "Iteration 53: time=0.00, ELBO=-73261.59, deltaELBO=0.237 (0.00008285%), Factors=4\n", + "Iteration 54: time=0.02, ELBO=-73261.38, deltaELBO=0.207 (0.00007235%), Factors=4\n", + "Iteration 55: time=0.01, ELBO=-73261.20, deltaELBO=0.182 (0.00006390%), Factors=4\n", + "Iteration 56: time=0.01, ELBO=-73261.03, deltaELBO=0.163 (0.00005708%), Factors=4\n", + "Iteration 57: time=0.01, ELBO=-73260.89, deltaELBO=0.147 (0.00005154%), Factors=4\n", + "Iteration 58: time=0.01, ELBO=-73260.75, deltaELBO=0.134 (0.00004703%), Factors=4\n", + "Iteration 59: time=0.00, ELBO=-73260.63, deltaELBO=0.124 (0.00004333%), Factors=4\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -325441.90 \n", + "\n", + "Iteration 1: time=0.00, ELBO=-80473.38, deltaELBO=244968.520 (75.27258206%), Factors=5\n", + "Iteration 2: time=0.01, ELBO=-75531.62, deltaELBO=4941.757 (1.51847599%), Factors=5\n", + "Iteration 3: time=0.01, ELBO=-74265.08, deltaELBO=1266.541 (0.38917570%), Factors=5\n", + "Iteration 4: time=0.01, ELBO=-73848.62, deltaELBO=416.462 (0.12796828%), Factors=5\n", + "Iteration 5: time=0.01, ELBO=-73639.81, deltaELBO=208.811 (0.06416216%), Factors=5\n", + "Iteration 6: time=0.02, ELBO=-73480.16, deltaELBO=159.647 (0.04905547%), Factors=5\n", + "Iteration 7: time=0.01, ELBO=-73323.97, deltaELBO=156.193 (0.04799403%), Factors=5\n", + "Iteration 8: time=0.03, ELBO=-73133.93, deltaELBO=190.037 (0.05839354%), Factors=5\n", + "Iteration 9: time=0.01, ELBO=-72856.02, deltaELBO=277.915 (0.08539620%), Factors=5\n", + "Iteration 10: time=0.01, ELBO=-72435.29, deltaELBO=420.730 (0.12927974%), Factors=5\n", + "Iteration 11: time=0.02, ELBO=-71964.81, deltaELBO=470.480 (0.14456652%), Factors=5\n", + "Iteration 12: time=0.01, ELBO=-71663.07, deltaELBO=301.740 (0.09271707%), Factors=5\n", + "Iteration 13: time=0.00, ELBO=-71505.62, deltaELBO=157.444 (0.04837856%), Factors=5\n", + "Iteration 14: time=0.02, ELBO=-71400.10, deltaELBO=105.521 (0.03242401%), Factors=5\n", + "Iteration 15: time=0.02, ELBO=-71323.96, deltaELBO=76.141 (0.02339625%), Factors=5\n", + "Iteration 16: time=0.01, ELBO=-71274.16, deltaELBO=49.802 (0.01530279%), Factors=5\n", + "Iteration 17: time=0.00, ELBO=-71244.96, deltaELBO=29.193 (0.00897026%), Factors=5\n", + "Iteration 18: time=0.02, ELBO=-71228.33, deltaELBO=16.633 (0.00511086%), Factors=5\n", + "Iteration 19: time=0.02, ELBO=-71217.86, deltaELBO=10.469 (0.00321691%), Factors=5\n", + "Iteration 20: time=0.01, ELBO=-71209.93, deltaELBO=7.935 (0.00243826%), Factors=5\n", + "Iteration 21: time=0.00, ELBO=-71202.75, deltaELBO=7.175 (0.00220469%), Factors=5\n", + "Iteration 22: time=0.02, ELBO=-71195.41, deltaELBO=7.337 (0.00225462%), Factors=5\n", + "Iteration 23: time=0.02, ELBO=-71187.27, deltaELBO=8.140 (0.00250112%), Factors=5\n", + "Iteration 24: time=0.00, ELBO=-71177.71, deltaELBO=9.565 (0.00293901%), Factors=5\n", + "Iteration 25: time=0.02, ELBO=-71165.99, deltaELBO=11.724 (0.00360250%), Factors=5\n", + "Iteration 26: time=0.02, ELBO=-71151.20, deltaELBO=14.788 (0.00454392%), Factors=5\n", + "Iteration 27: time=0.01, ELBO=-71132.30, deltaELBO=18.900 (0.00580758%), Factors=5\n", + "Iteration 28: time=0.01, ELBO=-71108.30, deltaELBO=24.000 (0.00737467%), Factors=5\n", + "Iteration 29: time=0.01, ELBO=-71078.81, deltaELBO=29.491 (0.00906192%), Factors=5\n", + "Iteration 30: time=0.02, ELBO=-71044.93, deltaELBO=33.878 (0.01040990%), Factors=5\n", + "Iteration 31: time=0.01, ELBO=-71009.99, deltaELBO=34.935 (0.01073452%), Factors=5\n", + "Iteration 32: time=0.00, ELBO=-70978.86, deltaELBO=31.130 (0.00956548%), Factors=5\n", + "Iteration 33: time=0.02, ELBO=-70955.37, deltaELBO=23.490 (0.00721778%), Factors=5\n", + "Iteration 34: time=0.02, ELBO=-70940.20, deltaELBO=15.176 (0.00466326%), Factors=5\n", + "Iteration 35: time=0.00, ELBO=-70931.42, deltaELBO=8.780 (0.00269774%), Factors=5\n", + "Iteration 36: time=0.02, ELBO=-70926.55, deltaELBO=4.870 (0.00149654%), Factors=5\n", + "Iteration 37: time=0.01, ELBO=-70923.76, deltaELBO=2.787 (0.00085631%), Factors=5\n", + "Iteration 38: time=0.01, 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deltaELBO=0.186 (0.00005711%), Factors=5\n", + "Iteration 50: time=0.01, ELBO=-70916.48, deltaELBO=0.165 (0.00005063%), Factors=5\n", + "Iteration 51: time=0.02, ELBO=-70916.34, deltaELBO=0.147 (0.00004525%), Factors=5\n", + "Iteration 52: time=0.01, ELBO=-70916.20, deltaELBO=0.133 (0.00004076%), Factors=5\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -360073.29 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-79742.48, deltaELBO=280330.809 (77.85381961%), Factors=6\n", + "Iteration 2: time=0.01, ELBO=-73224.56, deltaELBO=6517.922 (1.81016541%), Factors=6\n", + "Iteration 3: time=0.01, ELBO=-71327.33, deltaELBO=1897.225 (0.52689980%), Factors=6\n", + "Iteration 4: time=0.01, ELBO=-70643.16, deltaELBO=684.172 (0.19000904%), Factors=6\n", + "Iteration 5: time=0.01, ELBO=-70285.33, deltaELBO=357.827 (0.09937627%), Factors=6\n", + "Iteration 6: time=0.02, ELBO=-70032.38, deltaELBO=252.949 (0.07024928%), 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ELBO=-68886.69, deltaELBO=0.181 (0.00005014%), Factors=6\n", + "Iteration 152: time=0.05, ELBO=-68886.51, deltaELBO=0.180 (0.00005004%), Factors=6\n", + "Iteration 153: time=0.00, ELBO=-68886.33, deltaELBO=0.180 (0.00004994%), Factors=6\n", + "Iteration 154: time=0.02, ELBO=-68886.15, deltaELBO=0.179 (0.00004984%), Factors=6\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -395353.66 \n", + "\n", + "Iteration 1: time=0.03, ELBO=-80148.56, deltaELBO=315205.101 (79.72737609%), Factors=7\n", + "Iteration 2: time=0.01, ELBO=-73687.08, deltaELBO=6461.484 (1.63435532%), Factors=7\n", + "Iteration 3: time=0.01, ELBO=-71326.64, deltaELBO=2360.438 (0.59704474%), Factors=7\n", + "Iteration 4: time=0.02, ELBO=-70242.29, deltaELBO=1084.344 (0.27427198%), Factors=7\n", + "Iteration 5: time=0.02, ELBO=-69524.49, deltaELBO=717.802 (0.18155949%), Factors=7\n", + "Iteration 6: time=0.02, ELBO=-68937.65, 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deltaELBO=0.205 (0.00005177%), Factors=7\n", + "Iteration 184: time=0.00, ELBO=-66675.47, deltaELBO=0.204 (0.00005165%), Factors=7\n", + "Iteration 185: time=0.02, ELBO=-66675.27, deltaELBO=0.204 (0.00005152%), Factors=7\n", + "Iteration 186: time=0.02, ELBO=-66675.06, deltaELBO=0.203 (0.00005140%), Factors=7\n", + "Iteration 187: time=0.01, ELBO=-66674.86, deltaELBO=0.203 (0.00005127%), Factors=7\n", + "Iteration 188: time=0.02, ELBO=-66674.66, deltaELBO=0.202 (0.00005115%), Factors=7\n", + "Iteration 189: time=0.04, ELBO=-66674.46, deltaELBO=0.202 (0.00005102%), Factors=7\n", + "Iteration 190: time=0.01, ELBO=-66674.26, deltaELBO=0.201 (0.00005089%), Factors=7\n", + "Iteration 191: time=0.01, ELBO=-66674.05, deltaELBO=0.201 (0.00005077%), Factors=7\n", + "Iteration 192: time=0.01, ELBO=-66673.85, deltaELBO=0.200 (0.00005064%), Factors=7\n", + "Iteration 193: time=0.02, ELBO=-66673.65, deltaELBO=0.200 (0.00005051%), Factors=7\n", + "Iteration 194: time=0.01, ELBO=-66673.46, deltaELBO=0.199 (0.00005038%), Factors=7\n", + "Iteration 195: time=0.00, ELBO=-66673.26, deltaELBO=0.199 (0.00005025%), Factors=7\n", + "Iteration 196: time=0.01, ELBO=-66673.06, deltaELBO=0.198 (0.00005012%), Factors=7\n", + "Iteration 197: time=0.02, ELBO=-66672.86, deltaELBO=0.198 (0.00004999%), Factors=7\n", + "Iteration 198: time=0.02, ELBO=-66672.66, deltaELBO=0.197 (0.00004986%), Factors=7\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -428343.35 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80130.64, deltaELBO=348212.712 (81.29289483%), Factors=8\n", + "Iteration 2: time=0.02, ELBO=-72541.06, deltaELBO=7589.586 (1.77184621%), Factors=8\n", + "Iteration 3: time=0.02, ELBO=-69592.66, deltaELBO=2948.395 (0.68832525%), Factors=8\n", + "Iteration 4: time=0.01, ELBO=-68431.01, deltaELBO=1161.654 (0.27119687%), Factors=8\n", + "Iteration 5: time=0.06, ELBO=-67728.20, deltaELBO=702.804 (0.16407500%), Factors=8\n", + "Iteration 6: time=0.02, ELBO=-67244.14, deltaELBO=484.062 (0.11300802%), Factors=8\n", + "Iteration 7: time=0.02, ELBO=-66938.53, deltaELBO=305.607 (0.07134626%), Factors=8\n", + "Iteration 8: time=0.02, ELBO=-66752.63, deltaELBO=185.899 (0.04339944%), Factors=8\n", + "Iteration 9: time=0.01, ELBO=-66620.06, deltaELBO=132.571 (0.03094974%), Factors=8\n", + "Iteration 10: time=0.02, ELBO=-66503.28, deltaELBO=116.784 (0.02726408%), Factors=8\n", + "Iteration 11: time=0.02, ELBO=-66389.04, deltaELBO=114.237 (0.02666956%), Factors=8\n", + "Iteration 12: time=0.01, ELBO=-66277.71, deltaELBO=111.334 (0.02599176%), Factors=8\n", + "Iteration 13: time=0.02, ELBO=-66176.22, deltaELBO=101.492 (0.02369406%), Factors=8\n", + "Iteration 14: time=0.02, ELBO=-66091.59, deltaELBO=84.625 (0.01975634%), Factors=8\n", + "Iteration 15: time=0.02, ELBO=-66025.11, deltaELBO=66.476 (0.01551944%), Factors=8\n", + "Iteration 16: time=0.02, ELBO=-65970.88, deltaELBO=54.238 (0.01266234%), Factors=8\n", + "Iteration 17: time=0.02, ELBO=-65917.62, deltaELBO=53.259 (0.01243372%), Factors=8\n", + "Iteration 18: time=0.04, ELBO=-65850.36, deltaELBO=67.256 (0.01570145%), Factors=8\n", + "Iteration 19: time=0.01, ELBO=-65753.54, deltaELBO=96.818 (0.02260281%), Factors=8\n", + "Iteration 20: time=0.01, ELBO=-65624.10, deltaELBO=129.446 (0.03022005%), Factors=8\n", + "Iteration 21: time=0.02, ELBO=-65492.53, deltaELBO=131.570 (0.03071601%), Factors=8\n", + "Iteration 22: time=0.02, ELBO=-65403.56, deltaELBO=88.970 (0.02077069%), Factors=8\n", + "Iteration 23: time=0.01, ELBO=-65361.03, deltaELBO=42.530 (0.00992904%), Factors=8\n", + "Iteration 24: time=0.02, ELBO=-65342.44, deltaELBO=18.591 (0.00434027%), Factors=8\n", + "Iteration 25: time=0.02, ELBO=-65332.70, deltaELBO=9.736 (0.00227291%), Factors=8\n", + "Iteration 26: time=0.02, ELBO=-65326.20, deltaELBO=6.497 (0.00151680%), Factors=8\n", + "Iteration 27: time=0.02, ELBO=-65321.23, deltaELBO=4.972 (0.00116081%), Factors=8\n", + "Iteration 28: time=0.02, ELBO=-65317.22, deltaELBO=4.016 (0.00093752%), Factors=8\n", + "Iteration 29: time=0.01, ELBO=-65313.90, deltaELBO=3.311 (0.00077299%), Factors=8\n", + "Iteration 30: time=0.02, ELBO=-65311.15, deltaELBO=2.757 (0.00064357%), Factors=8\n", + "Iteration 31: time=0.02, ELBO=-65308.84, deltaELBO=2.309 (0.00053913%), Factors=8\n", + "Iteration 32: time=0.02, ELBO=-65306.89, deltaELBO=1.944 (0.00045386%), Factors=8\n", + "Iteration 33: time=0.01, ELBO=-65305.25, deltaELBO=1.644 (0.00038379%), Factors=8\n", + "Iteration 34: time=0.01, ELBO=-65303.85, deltaELBO=1.396 (0.00032593%), Factors=8\n", + "Iteration 35: time=0.01, ELBO=-65302.66, deltaELBO=1.191 (0.00027800%), Factors=8\n", + "Iteration 36: time=0.02, ELBO=-65301.64, deltaELBO=1.020 (0.00023815%), Factors=8\n", + "Iteration 37: time=0.00, ELBO=-65300.77, deltaELBO=0.878 (0.00020492%), Factors=8\n", + "Iteration 38: time=0.01, ELBO=-65300.01, deltaELBO=0.759 (0.00017715%), Factors=8\n", + "Iteration 39: time=0.01, ELBO=-65299.35, deltaELBO=0.659 (0.00015387%), Factors=8\n", + "Iteration 40: time=0.02, ELBO=-65298.77, deltaELBO=0.575 (0.00013431%), Factors=8\n", + "Iteration 41: time=0.02, ELBO=-65298.27, deltaELBO=0.505 (0.00011783%), Factors=8\n", + "Iteration 42: time=0.01, ELBO=-65297.82, deltaELBO=0.445 (0.00010391%), Factors=8\n", + "Iteration 43: time=0.02, ELBO=-65297.43, deltaELBO=0.395 (0.00009212%), Factors=8\n", + "Iteration 44: time=0.05, ELBO=-65297.08, deltaELBO=0.352 (0.00008212%), Factors=8\n", + "Iteration 45: time=0.01, ELBO=-65296.76, deltaELBO=0.315 (0.00007361%), Factors=8\n", + "Iteration 46: time=0.01, ELBO=-65296.48, deltaELBO=0.284 (0.00006635%), Factors=8\n", + "Iteration 47: time=0.00, ELBO=-65296.22, deltaELBO=0.258 (0.00006014%), Factors=8\n", + "Iteration 48: time=0.01, ELBO=-65295.98, deltaELBO=0.235 (0.00005483%), Factors=8\n", + "Iteration 49: time=0.02, ELBO=-65295.77, deltaELBO=0.215 (0.00005026%), Factors=8\n", + "Iteration 50: time=0.01, ELBO=-65295.57, deltaELBO=0.198 (0.00004633%), Factors=8\n", + "Iteration 51: time=0.02, ELBO=-65295.39, deltaELBO=0.184 (0.00004293%), Factors=8\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -459964.46 \n", + "\n", + "Iteration 1: time=0.01, ELBO=-80534.93, deltaELBO=379429.534 (82.49105448%), Factors=9\n", + "Iteration 2: time=0.02, ELBO=-73189.49, deltaELBO=7345.435 (1.59695705%), Factors=9\n", + "Iteration 3: time=0.02, ELBO=-69769.28, deltaELBO=3420.213 (0.74358199%), Factors=9\n", + "Iteration 4: time=0.06, ELBO=-67905.27, deltaELBO=1864.008 (0.40525052%), Factors=9\n", + "Iteration 5: time=0.04, ELBO=-65877.24, deltaELBO=2028.027 (0.44090945%), Factors=9\n", + "Iteration 6: time=0.01, ELBO=-64436.06, deltaELBO=1441.185 (0.31332529%), Factors=9\n", + "Iteration 7: time=0.02, ELBO=-64011.12, deltaELBO=424.941 (0.09238564%), Factors=9\n", + "Iteration 8: time=0.02, ELBO=-63824.82, deltaELBO=186.295 (0.04050194%), Factors=9\n", + "Iteration 9: time=0.02, ELBO=-63700.04, deltaELBO=124.778 (0.02712785%), Factors=9\n", + "Iteration 10: time=0.01, ELBO=-63608.05, deltaELBO=91.991 (0.01999955%), Factors=9\n", + "Iteration 11: time=0.02, ELBO=-63540.97, deltaELBO=67.083 (0.01458449%), Factors=9\n", + "Iteration 12: time=0.01, ELBO=-63493.89, deltaELBO=47.082 (0.01023607%), Factors=9\n", + "Iteration 13: time=0.01, ELBO=-63461.28, deltaELBO=32.607 (0.00708908%), Factors=9\n", + "Iteration 14: time=0.01, ELBO=-63438.02, deltaELBO=23.263 (0.00505758%), Factors=9\n", + "Iteration 15: time=0.02, ELBO=-63420.54, deltaELBO=17.478 (0.00379988%), Factors=9\n", + "Iteration 16: time=0.01, ELBO=-63406.79, deltaELBO=13.750 (0.00298931%), Factors=9\n", + "Iteration 17: time=0.02, ELBO=-63395.64, deltaELBO=11.154 (0.00242487%), Factors=9\n", + "Iteration 18: time=0.04, ELBO=-63386.41, deltaELBO=9.223 (0.00200512%), Factors=9\n", + "Iteration 19: time=0.01, ELBO=-63378.68, deltaELBO=7.735 (0.00168159%), Factors=9\n", + "Iteration 20: time=0.02, ELBO=-63372.10, deltaELBO=6.581 (0.00143084%), Factors=9\n", + "Iteration 21: time=0.02, ELBO=-63366.38, deltaELBO=5.715 (0.00124249%), Factors=9\n", + "Iteration 22: time=0.01, ELBO=-63361.25, deltaELBO=5.132 (0.00111583%), Factors=9\n", + "Iteration 23: time=0.02, ELBO=-63356.37, deltaELBO=4.879 (0.00106079%), Factors=9\n", + "Iteration 24: time=0.02, ELBO=-63351.30, deltaELBO=5.072 (0.00110276%), Factors=9\n", + "Iteration 25: time=0.02, ELBO=-63345.36, deltaELBO=5.942 (0.00129186%), Factors=9\n", + "Iteration 26: time=0.01, ELBO=-63337.45, deltaELBO=7.904 (0.00171835%), Factors=9\n", + "Iteration 27: time=0.02, ELBO=-63325.79, deltaELBO=11.659 (0.00253486%), Factors=9\n", + "Iteration 28: time=0.01, ELBO=-63307.50, deltaELBO=18.298 (0.00397803%), Factors=9\n", + "Iteration 29: time=0.02, ELBO=-63278.26, deltaELBO=29.231 (0.00635507%), Factors=9\n", + "Iteration 30: time=0.02, ELBO=-63232.77, deltaELBO=45.491 (0.00989000%), Factors=9\n", + "Iteration 31: time=0.02, ELBO=-63167.31, deltaELBO=65.463 (0.01423217%), Factors=9\n", + "Iteration 32: time=0.01, ELBO=-63086.08, deltaELBO=81.230 (0.01765999%), Factors=9\n", + "Iteration 33: time=0.02, ELBO=-63006.16, deltaELBO=79.917 (0.01737454%), Factors=9\n", + "Iteration 34: time=0.01, ELBO=-62947.39, deltaELBO=58.776 (0.01277832%), Factors=9\n", + "Iteration 35: time=0.02, ELBO=-62914.12, deltaELBO=33.273 (0.00723381%), Factors=9\n", + "Iteration 36: time=0.02, ELBO=-62897.50, deltaELBO=16.613 (0.00361186%), Factors=9\n", + "Iteration 37: time=0.02, ELBO=-62888.69, deltaELBO=8.812 (0.00191581%), Factors=9\n", + "Iteration 38: time=0.01, ELBO=-62883.13, deltaELBO=5.563 (0.00120950%), Factors=9\n", + "Iteration 39: time=0.02, ELBO=-62879.05, deltaELBO=4.079 (0.00088675%), Factors=9\n", + "Iteration 40: time=0.01, ELBO=-62875.82, deltaELBO=3.233 (0.00070286%), Factors=9\n", + "Iteration 41: time=0.02, ELBO=-62873.17, deltaELBO=2.648 (0.00057575%), Factors=9\n", + "Iteration 42: time=0.02, ELBO=-62870.97, deltaELBO=2.200 (0.00047831%), Factors=9\n", + "Iteration 43: time=0.06, ELBO=-62869.13, deltaELBO=1.841 (0.00040034%), Factors=9\n", + "Iteration 44: time=0.01, ELBO=-62867.58, deltaELBO=1.549 (0.00033685%), Factors=9\n", + "Iteration 45: time=0.02, ELBO=-62866.27, deltaELBO=1.310 (0.00028477%), Factors=9\n", + "Iteration 46: time=0.02, ELBO=-62865.15, deltaELBO=1.113 (0.00024187%), Factors=9\n", + "Iteration 47: time=0.02, ELBO=-62864.20, deltaELBO=0.950 (0.00020644%), Factors=9\n", + "Iteration 48: time=0.01, ELBO=-62863.39, deltaELBO=0.815 (0.00017711%), Factors=9\n", + "Iteration 49: time=0.02, ELBO=-62862.69, deltaELBO=0.703 (0.00015278%), Factors=9\n", + "Iteration 50: time=0.01, ELBO=-62862.08, deltaELBO=0.610 (0.00013256%), Factors=9\n", + "Iteration 51: time=0.02, ELBO=-62861.55, deltaELBO=0.532 (0.00011572%), Factors=9\n", + "Iteration 52: time=0.02, ELBO=-62861.08, deltaELBO=0.468 (0.00010168%), Factors=9\n", + "Iteration 53: time=0.02, ELBO=-62860.66, deltaELBO=0.414 (0.00008996%), Factors=9\n", + "Iteration 54: time=0.01, ELBO=-62860.30, deltaELBO=0.369 (0.00008014%), Factors=9\n", + "Iteration 55: time=0.02, ELBO=-62859.96, deltaELBO=0.331 (0.00007192%), Factors=9\n", + "Iteration 56: time=0.01, ELBO=-62859.67, deltaELBO=0.299 (0.00006502%), Factors=9\n", + "Iteration 57: time=0.02, ELBO=-62859.39, deltaELBO=0.272 (0.00005921%), Factors=9\n", + "Iteration 58: time=0.02, ELBO=-62859.14, deltaELBO=0.250 (0.00005433%), Factors=9\n", + "Iteration 59: time=0.02, ELBO=-62858.91, deltaELBO=0.231 (0.00005020%), Factors=9\n", + "Iteration 60: time=0.02, ELBO=-62858.70, deltaELBO=0.215 (0.00004672%), Factors=9\n", + "Iteration 61: time=0.02, ELBO=-62858.50, deltaELBO=0.201 (0.00004376%), Factors=9\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -490385.10 \n", + "\n", + "Iteration 1: time=0.07, ELBO=-79676.91, deltaELBO=410708.191 (83.75217564%), Factors=10\n", + "Iteration 2: time=0.03, ELBO=-71159.28, deltaELBO=8517.626 (1.73692595%), Factors=10\n", + "Iteration 3: time=0.01, ELBO=-67093.62, deltaELBO=4065.668 (0.82907648%), Factors=10\n", + "Iteration 4: time=0.02, ELBO=-65066.90, deltaELBO=2026.713 (0.41329015%), Factors=10\n", + "Iteration 5: time=0.05, ELBO=-63975.03, deltaELBO=1091.868 (0.22265528%), Factors=10\n", + "Iteration 6: time=0.01, ELBO=-63283.67, deltaELBO=691.367 (0.14098450%), Factors=10\n", + "Iteration 7: time=0.02, ELBO=-62875.78, deltaELBO=407.885 (0.08317647%), Factors=10\n", + "Iteration 8: time=0.02, ELBO=-62606.50, deltaELBO=269.280 (0.05491199%), Factors=10\n", + "Iteration 9: time=0.02, ELBO=-62326.47, deltaELBO=280.031 (0.05710439%), Factors=10\n", + "Iteration 10: time=0.01, ELBO=-61959.39, deltaELBO=367.082 (0.07485586%), Factors=10\n", + "Iteration 11: time=0.01, ELBO=-61620.23, deltaELBO=339.159 (0.06916171%), Factors=10\n", + "Iteration 12: time=0.02, ELBO=-61442.95, deltaELBO=177.280 (0.03615115%), Factors=10\n", + "Iteration 13: time=0.03, ELBO=-61373.45, deltaELBO=69.498 (0.01417203%), Factors=10\n", + "Iteration 14: time=0.02, ELBO=-61340.61, deltaELBO=32.845 (0.00669789%), Factors=10\n", + "Iteration 15: time=0.02, ELBO=-61319.41, deltaELBO=21.200 (0.00432311%), Factors=10\n", + "Iteration 16: time=0.02, ELBO=-61303.34, deltaELBO=16.072 (0.00327743%), Factors=10\n", + "Iteration 17: time=0.02, ELBO=-61290.33, deltaELBO=13.006 (0.00265216%), Factors=10\n", + "Iteration 18: time=0.02, ELBO=-61279.49, deltaELBO=10.835 (0.00220945%), Factors=10\n", + "Iteration 19: time=0.01, ELBO=-61270.33, deltaELBO=9.165 (0.00186896%), Factors=10\n", + "Iteration 20: time=0.02, ELBO=-61262.50, deltaELBO=7.827 (0.00159613%), Factors=10\n", + "Iteration 21: time=0.03, ELBO=-61255.77, deltaELBO=6.731 (0.00137262%), Factors=10\n", + "Iteration 22: time=0.02, ELBO=-61249.95, deltaELBO=5.821 (0.00118704%), Factors=10\n", + "Iteration 23: time=0.02, ELBO=-61244.89, deltaELBO=5.058 (0.00103149%), Factors=10\n", + "Iteration 24: time=0.02, ELBO=-61240.48, deltaELBO=4.414 (0.00090017%), Factors=10\n", + "Iteration 25: time=0.02, ELBO=-61236.61, deltaELBO=3.867 (0.00078866%), Factors=10\n", + "Iteration 26: time=0.02, ELBO=-61233.21, deltaELBO=3.401 (0.00069349%), Factors=10\n", + "Iteration 27: time=0.01, ELBO=-61230.21, deltaELBO=3.001 (0.00061192%), Factors=10\n", + "Iteration 28: time=0.04, ELBO=-61227.55, deltaELBO=2.657 (0.00054172%), Factors=10\n", + "Iteration 29: time=0.00, ELBO=-61225.19, deltaELBO=2.359 (0.00048110%), Factors=10\n", + "Iteration 30: time=0.03, ELBO=-61223.09, deltaELBO=2.102 (0.00042858%), Factors=10\n", + "Iteration 31: time=0.02, ELBO=-61221.21, deltaELBO=1.878 (0.00038294%), Factors=10\n", + "Iteration 32: time=0.02, ELBO=-61219.53, deltaELBO=1.683 (0.00034317%), Factors=10\n", + "Iteration 33: time=0.02, ELBO=-61218.02, deltaELBO=1.512 (0.00030843%), Factors=10\n", + "Iteration 34: time=0.02, ELBO=-61216.65, deltaELBO=1.363 (0.00027800%), Factors=10\n", + "Iteration 35: time=0.02, ELBO=-61215.42, deltaELBO=1.232 (0.00025129%), Factors=10\n", + "Iteration 36: time=0.02, ELBO=-61214.31, deltaELBO=1.117 (0.00022780%), Factors=10\n", + "Iteration 37: time=0.02, ELBO=-61213.29, deltaELBO=1.016 (0.00020710%), Factors=10\n", + "Iteration 38: time=0.00, ELBO=-61212.36, deltaELBO=0.926 (0.00018882%), Factors=10\n", + "Iteration 39: time=0.02, ELBO=-61211.52, deltaELBO=0.847 (0.00017264%), Factors=10\n", + "Iteration 40: time=0.02, ELBO=-61210.74, deltaELBO=0.776 (0.00015831%), Factors=10\n", + "Iteration 41: time=0.02, ELBO=-61210.03, deltaELBO=0.714 (0.00014559%), Factors=10\n", + "Iteration 42: time=0.02, ELBO=-61209.37, deltaELBO=0.659 (0.00013429%), Factors=10\n", + "Iteration 43: time=0.02, ELBO=-61208.76, deltaELBO=0.609 (0.00012422%), Factors=10\n", + "Iteration 44: time=0.02, ELBO=-61208.19, deltaELBO=0.565 (0.00011524%), Factors=10\n", + "Iteration 45: time=0.02, ELBO=-61207.67, deltaELBO=0.526 (0.00010723%), Factors=10\n", + "Iteration 46: time=0.02, ELBO=-61207.18, deltaELBO=0.491 (0.00010006%), Factors=10\n", + "Iteration 47: time=0.01, ELBO=-61206.72, deltaELBO=0.459 (0.00009365%), Factors=10\n", + "Iteration 48: time=0.03, ELBO=-61206.29, deltaELBO=0.431 (0.00008789%), Factors=10\n", + "Iteration 49: time=0.02, ELBO=-61205.88, deltaELBO=0.406 (0.00008273%), Factors=10\n", + "Iteration 50: time=0.02, ELBO=-61205.50, deltaELBO=0.383 (0.00007808%), Factors=10\n", + "Iteration 51: time=0.04, ELBO=-61205.14, deltaELBO=0.362 (0.00007390%), Factors=10\n", + "Iteration 52: time=0.02, ELBO=-61204.79, deltaELBO=0.344 (0.00007013%), Factors=10\n", + "Iteration 53: time=0.02, ELBO=-61204.47, deltaELBO=0.327 (0.00006673%), Factors=10\n", + "Iteration 54: time=0.06, ELBO=-61204.15, deltaELBO=0.312 (0.00006365%), Factors=10\n", + "Iteration 55: time=0.01, ELBO=-61203.85, deltaELBO=0.298 (0.00006087%), Factors=10\n", + "Iteration 56: time=0.04, ELBO=-61203.57, deltaELBO=0.286 (0.00005834%), Factors=10\n", + "Iteration 57: time=0.02, ELBO=-61203.29, deltaELBO=0.275 (0.00005605%), Factors=10\n", + "Iteration 58: time=0.02, ELBO=-61203.03, deltaELBO=0.265 (0.00005397%), Factors=10\n", + "Iteration 59: time=0.02, ELBO=-61202.77, deltaELBO=0.255 (0.00005207%), Factors=10\n", + "Iteration 60: time=0.02, ELBO=-61202.53, deltaELBO=0.247 (0.00005034%), Factors=10\n", + "Iteration 61: time=0.02, ELBO=-61202.29, deltaELBO=0.239 (0.00004876%), Factors=10\n", + "Iteration 62: time=0.01, ELBO=-61202.06, deltaELBO=0.232 (0.00004732%), Factors=10\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -522301.62 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80174.18, deltaELBO=442127.439 (84.64983095%), Factors=11\n", + "Iteration 2: time=0.01, ELBO=-72643.69, deltaELBO=7530.490 (1.44178943%), Factors=11\n", + "Iteration 3: time=0.02, ELBO=-68223.26, deltaELBO=4420.435 (0.84633759%), Factors=11\n", + "Iteration 4: time=0.02, ELBO=-65303.95, deltaELBO=2919.311 (0.55893206%), Factors=11\n", + "Iteration 5: time=0.02, ELBO=-63540.86, deltaELBO=1763.085 (0.33756070%), Factors=11\n", + "Iteration 6: time=0.01, ELBO=-62339.31, deltaELBO=1201.553 (0.23004965%), Factors=11\n", + "Iteration 7: time=0.02, ELBO=-61445.24, deltaELBO=894.067 (0.17117832%), Factors=11\n", + "Iteration 8: time=0.02, ELBO=-60953.60, deltaELBO=491.644 (0.09413035%), Factors=11\n", + "Iteration 9: time=0.02, ELBO=-60704.83, deltaELBO=248.769 (0.04762945%), Factors=11\n", + "Iteration 10: time=0.05, ELBO=-60537.62, deltaELBO=167.207 (0.03201358%), Factors=11\n", + "Iteration 11: time=0.02, ELBO=-60423.57, deltaELBO=114.051 (0.02183619%), Factors=11\n", + "Iteration 12: time=0.02, ELBO=-60351.79, deltaELBO=71.778 (0.01374269%), Factors=11\n", + "Iteration 13: time=0.02, ELBO=-60302.79, deltaELBO=48.997 (0.00938092%), Factors=11\n", + "Iteration 14: time=0.02, ELBO=-60263.85, deltaELBO=38.943 (0.00745596%), Factors=11\n", + "Iteration 15: time=0.02, ELBO=-60229.62, deltaELBO=34.227 (0.00655310%), Factors=11\n", + "Iteration 16: time=0.01, ELBO=-60197.74, deltaELBO=31.888 (0.00610529%), Factors=11\n", + "Iteration 17: time=0.02, ELBO=-60166.71, deltaELBO=31.027 (0.00594044%), Factors=11\n", + "Iteration 18: time=0.02, ELBO=-60135.35, deltaELBO=31.359 (0.00600395%), Factors=11\n", + "Iteration 19: time=0.03, ELBO=-60102.54, deltaELBO=32.814 (0.00628252%), Factors=11\n", + "Iteration 20: time=0.02, ELBO=-60067.06, deltaELBO=35.475 (0.00679209%), Factors=11\n", + "Iteration 21: time=0.02, ELBO=-60027.44, deltaELBO=39.626 (0.00758682%), Factors=11\n", + "Iteration 22: time=0.02, ELBO=-59981.58, deltaELBO=45.852 (0.00877883%), Factors=11\n", + "Iteration 23: time=0.01, ELBO=-59926.45, deltaELBO=55.132 (0.01055563%), Factors=11\n", + "Iteration 24: time=0.02, ELBO=-59857.78, deltaELBO=68.671 (0.01314782%), Factors=11\n", + "Iteration 25: time=0.02, ELBO=-59771.07, deltaELBO=86.713 (0.01660203%), Factors=11\n", + "Iteration 26: time=0.02, ELBO=-59666.09, deltaELBO=104.976 (0.02009870%), Factors=11\n", + "Iteration 27: time=0.02, ELBO=-59555.81, deltaELBO=110.278 (0.02111395%), Factors=11\n", + "Iteration 28: time=0.01, ELBO=-59466.71, deltaELBO=89.099 (0.01705892%), Factors=11\n", + "Iteration 29: time=0.02, ELBO=-59414.57, deltaELBO=52.149 (0.00998446%), Factors=11\n", + "Iteration 30: time=0.02, ELBO=-59390.53, deltaELBO=24.035 (0.00460167%), Factors=11\n", + "Iteration 31: time=0.05, ELBO=-59379.71, deltaELBO=10.824 (0.00207239%), Factors=11\n", + "Iteration 32: time=0.02, ELBO=-59373.79, deltaELBO=5.920 (0.00113341%), Factors=11\n", + "Iteration 33: time=0.02, ELBO=-59369.77, deltaELBO=4.016 (0.00076897%), Factors=11\n", + "Iteration 34: time=0.02, ELBO=-59366.72, deltaELBO=3.055 (0.00058497%), Factors=11\n", + "Iteration 35: time=0.00, ELBO=-59364.28, deltaELBO=2.437 (0.00046656%), Factors=11\n", + "Iteration 36: time=0.02, ELBO=-59362.29, deltaELBO=1.988 (0.00038055%), Factors=11\n", + "Iteration 37: time=0.02, ELBO=-59360.65, deltaELBO=1.645 (0.00031492%), Factors=11\n", + "Iteration 38: time=0.02, ELBO=-59359.27, deltaELBO=1.377 (0.00026366%), Factors=11\n", + "Iteration 39: time=0.06, ELBO=-59358.10, deltaELBO=1.165 (0.00022305%), Factors=11\n", + "Iteration 40: time=0.03, ELBO=-59357.11, deltaELBO=0.995 (0.00019052%), Factors=11\n", + "Iteration 41: time=0.03, ELBO=-59356.25, deltaELBO=0.858 (0.00016419%), Factors=11\n", + "Iteration 42: time=0.01, ELBO=-59355.51, deltaELBO=0.745 (0.00014268%), Factors=11\n", + "Iteration 43: time=0.03, ELBO=-59354.85, deltaELBO=0.653 (0.00012496%), Factors=11\n", + "Iteration 44: time=0.02, ELBO=-59354.28, deltaELBO=0.576 (0.00011024%), Factors=11\n", + "Iteration 45: time=0.02, ELBO=-59353.77, deltaELBO=0.511 (0.00009792%), Factors=11\n", + "Iteration 46: time=0.02, ELBO=-59353.31, deltaELBO=0.457 (0.00008754%), Factors=11\n", + "Iteration 47: time=0.02, ELBO=-59352.90, deltaELBO=0.411 (0.00007874%), Factors=11\n", + "Iteration 48: time=0.02, ELBO=-59352.53, deltaELBO=0.372 (0.00007122%), Factors=11\n", + "Iteration 49: time=0.02, ELBO=-59352.19, deltaELBO=0.338 (0.00006477%), Factors=11\n", + "Iteration 50: time=0.02, ELBO=-59351.88, deltaELBO=0.309 (0.00005920%), Factors=11\n", + "Iteration 51: time=0.02, ELBO=-59351.59, deltaELBO=0.284 (0.00005438%), Factors=11\n", + "Iteration 52: time=0.02, ELBO=-59351.33, deltaELBO=0.262 (0.00005018%), Factors=11\n", + "Iteration 53: time=0.02, ELBO=-59351.09, deltaELBO=0.243 (0.00004651%), Factors=11\n", + "Iteration 54: time=0.02, ELBO=-59350.86, deltaELBO=0.226 (0.00004329%), Factors=11\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -554078.45 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80263.41, deltaELBO=473815.045 (85.51407166%), Factors=12\n", + "Iteration 2: time=0.01, ELBO=-72448.31, deltaELBO=7815.094 (1.41046700%), Factors=12\n", + "Iteration 3: time=0.02, ELBO=-67384.30, deltaELBO=5064.013 (0.91395231%), Factors=12\n", + "Iteration 4: time=0.02, ELBO=-63970.45, deltaELBO=3413.847 (0.61613056%), Factors=12\n", + "Iteration 5: time=0.02, ELBO=-62161.20, deltaELBO=1809.254 (0.32653396%), Factors=12\n", + "Iteration 6: time=0.02, ELBO=-61281.89, deltaELBO=879.315 (0.15869856%), Factors=12\n", + "Iteration 7: time=0.02, ELBO=-60660.21, deltaELBO=621.672 (0.11219925%), Factors=12\n", + "Iteration 8: time=0.02, ELBO=-60037.75, deltaELBO=622.469 (0.11234304%), Factors=12\n", + "Iteration 9: time=0.02, ELBO=-59598.16, deltaELBO=439.582 (0.07933568%), Factors=12\n", + "Iteration 10: time=0.03, ELBO=-59364.67, deltaELBO=233.496 (0.04214138%), Factors=12\n", + "Iteration 11: time=0.02, ELBO=-59204.15, deltaELBO=160.515 (0.02896971%), Factors=12\n", + "Iteration 12: time=0.02, ELBO=-59080.27, deltaELBO=123.882 (0.02235817%), Factors=12\n", + "Iteration 13: time=0.02, ELBO=-58999.55, deltaELBO=80.719 (0.01456811%), Factors=12\n", + "Iteration 14: time=0.02, ELBO=-58949.40, deltaELBO=50.155 (0.00905203%), Factors=12\n", + "Iteration 15: time=0.02, ELBO=-58913.41, deltaELBO=35.983 (0.00649417%), Factors=12\n", + "Iteration 16: time=0.01, ELBO=-58883.82, deltaELBO=29.589 (0.00534023%), Factors=12\n", + "Iteration 17: time=0.03, ELBO=-58857.76, deltaELBO=26.060 (0.00470338%), Factors=12\n", + "Iteration 18: time=0.04, ELBO=-58833.88, deltaELBO=23.887 (0.00431114%), Factors=12\n", + "Iteration 19: time=0.02, ELBO=-58811.25, deltaELBO=22.625 (0.00408333%), Factors=12\n", + "Iteration 20: time=0.02, ELBO=-58789.14, deltaELBO=22.108 (0.00399001%), Factors=12\n", + "Iteration 21: time=0.02, ELBO=-58766.88, deltaELBO=22.266 (0.00401863%), Factors=12\n", + "Iteration 22: time=0.02, ELBO=-58743.80, deltaELBO=23.080 (0.00416554%), Factors=12\n", + "Iteration 23: time=0.02, ELBO=-58719.23, deltaELBO=24.565 (0.00443343%), Factors=12\n", + "Iteration 24: time=0.02, ELBO=-58692.46, deltaELBO=26.771 (0.00483167%), Factors=12\n", + "Iteration 25: time=0.02, ELBO=-58662.65, deltaELBO=29.812 (0.00538040%), Factors=12\n", + "Iteration 26: time=0.02, ELBO=-58628.74, deltaELBO=33.911 (0.00612020%), Factors=12\n", + "Iteration 27: time=0.02, ELBO=-58589.24, deltaELBO=39.500 (0.00712890%), Factors=12\n", + "Iteration 28: time=0.02, ELBO=-58541.91, deltaELBO=47.333 (0.00854274%), Factors=12\n", + "Iteration 29: time=0.02, ELBO=-58483.39, deltaELBO=58.519 (0.01056151%), Factors=12\n", + "Iteration 30: time=0.02, ELBO=-58409.34, deltaELBO=74.048 (0.01336420%), Factors=12\n", + "Iteration 31: time=0.02, ELBO=-58316.53, deltaELBO=92.808 (0.01674999%), Factors=12\n", + "Iteration 32: time=0.02, ELBO=-58209.32, deltaELBO=107.211 (0.01934936%), Factors=12\n", + "Iteration 33: time=0.02, ELBO=-58107.17, deltaELBO=102.146 (0.01843532%), Factors=12\n", + "Iteration 34: time=0.06, ELBO=-58034.88, deltaELBO=72.291 (0.01304710%), Factors=12\n", + "Iteration 35: time=0.02, ELBO=-57997.19, deltaELBO=37.690 (0.00680233%), Factors=12\n", + "Iteration 36: time=0.04, ELBO=-57980.63, deltaELBO=16.558 (0.00298839%), Factors=12\n", + "Iteration 37: time=0.02, ELBO=-57972.92, deltaELBO=7.719 (0.00139310%), Factors=12\n", + "Iteration 38: time=0.02, ELBO=-57968.42, deltaELBO=4.497 (0.00081157%), Factors=12\n", + "Iteration 39: time=0.02, ELBO=-57965.26, deltaELBO=3.162 (0.00057070%), Factors=12\n", + "Iteration 40: time=0.03, ELBO=-57962.82, deltaELBO=2.440 (0.00044033%), Factors=12\n", + "Iteration 41: time=0.02, ELBO=-57960.85, deltaELBO=1.962 (0.00035412%), Factors=12\n", + "Iteration 42: time=0.02, ELBO=-57959.24, deltaELBO=1.615 (0.00029146%), Factors=12\n", + "Iteration 43: time=0.02, ELBO=-57957.89, deltaELBO=1.352 (0.00024403%), Factors=12\n", + "Iteration 44: time=0.03, ELBO=-57956.74, deltaELBO=1.149 (0.00020738%), Factors=12\n", + "Iteration 45: time=0.01, ELBO=-57955.75, deltaELBO=0.990 (0.00017864%), Factors=12\n", + "Iteration 46: time=0.02, ELBO=-57954.89, deltaELBO=0.863 (0.00015584%), Factors=12\n", + "Iteration 47: time=0.03, ELBO=-57954.12, deltaELBO=0.762 (0.00013755%), Factors=12\n", + "Iteration 48: time=0.02, ELBO=-57953.44, deltaELBO=0.680 (0.00012273%), Factors=12\n", + "Iteration 49: time=0.02, ELBO=-57952.83, deltaELBO=0.613 (0.00011059%), Factors=12\n", + "Iteration 50: time=0.02, ELBO=-57952.27, deltaELBO=0.557 (0.00010056%), Factors=12\n", + "Iteration 51: time=0.02, ELBO=-57951.76, deltaELBO=0.511 (0.00009218%), Factors=12\n", + "Iteration 52: time=0.02, ELBO=-57951.29, deltaELBO=0.472 (0.00008513%), Factors=12\n", + "Iteration 53: time=0.03, ELBO=-57950.85, deltaELBO=0.439 (0.00007915%), Factors=12\n", + "Iteration 54: time=0.02, ELBO=-57950.44, deltaELBO=0.410 (0.00007402%), Factors=12\n", + "Iteration 55: time=0.02, ELBO=-57950.06, deltaELBO=0.386 (0.00006960%), Factors=12\n", + "Iteration 56: time=0.04, ELBO=-57949.69, deltaELBO=0.364 (0.00006576%), Factors=12\n", + "Iteration 57: time=0.01, ELBO=-57949.35, deltaELBO=0.346 (0.00006241%), Factors=12\n", + "Iteration 58: time=0.02, ELBO=-57949.02, deltaELBO=0.329 (0.00005946%), Factors=12\n", + "Iteration 59: time=0.03, ELBO=-57948.70, deltaELBO=0.315 (0.00005685%), Factors=12\n", + "Iteration 60: time=0.02, ELBO=-57948.40, deltaELBO=0.302 (0.00005453%), Factors=12\n", + "Iteration 61: time=0.02, ELBO=-57948.11, deltaELBO=0.291 (0.00005245%), Factors=12\n", + "Iteration 62: time=0.02, ELBO=-57947.83, deltaELBO=0.280 (0.00005059%), Factors=12\n", + "Iteration 63: time=0.02, ELBO=-57947.56, deltaELBO=0.271 (0.00004891%), Factors=12\n", + "Iteration 64: time=0.01, ELBO=-57947.30, deltaELBO=0.263 (0.00004739%), Factors=12\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -585197.23 \n", + "\n", + "Iteration 1: time=0.03, ELBO=-80405.65, deltaELBO=504791.589 (86.26007768%), Factors=13\n", + "Iteration 2: time=0.02, ELBO=-71662.97, deltaELBO=8742.675 (1.49397071%), Factors=13\n", + "Iteration 3: time=0.02, ELBO=-65404.79, deltaELBO=6258.183 (1.06941434%), Factors=13\n", + "Iteration 4: time=0.02, ELBO=-61082.29, deltaELBO=4322.501 (0.73864001%), Factors=13\n", + "Iteration 5: time=0.02, ELBO=-59423.91, deltaELBO=1658.377 (0.28338769%), Factors=13\n", + "Iteration 6: time=0.03, ELBO=-58864.74, deltaELBO=559.171 (0.09555254%), Factors=13\n", + "Iteration 7: time=0.02, ELBO=-58605.61, deltaELBO=259.130 (0.04428072%), Factors=13\n", + "Iteration 8: time=0.02, ELBO=-58438.71, deltaELBO=166.898 (0.02852002%), Factors=13\n", + "Iteration 9: time=0.02, ELBO=-58314.20, deltaELBO=124.508 (0.02127626%), Factors=13\n", + "Iteration 10: time=0.05, ELBO=-58218.41, deltaELBO=95.793 (0.01636936%), Factors=13\n", + "Iteration 11: time=0.01, ELBO=-58145.01, deltaELBO=73.398 (0.01254250%), Factors=13\n", + "Iteration 12: time=0.03, ELBO=-58088.96, deltaELBO=56.054 (0.00957867%), Factors=13\n", + "Iteration 13: time=0.02, ELBO=-58045.99, deltaELBO=42.967 (0.00734235%), Factors=13\n", + "Iteration 14: time=0.07, ELBO=-58012.68, deltaELBO=33.314 (0.00569284%), Factors=13\n", + "Iteration 15: time=0.02, ELBO=-57986.33, deltaELBO=26.343 (0.00450156%), Factors=13\n", + "Iteration 16: time=0.04, ELBO=-57964.99, deltaELBO=21.338 (0.00364626%), Factors=13\n", + "Iteration 17: time=0.02, ELBO=-57947.31, deltaELBO=17.680 (0.00302112%), Factors=13\n", + "Iteration 18: time=0.02, ELBO=-57932.40, deltaELBO=14.915 (0.00254873%), Factors=13\n", + "Iteration 19: time=0.02, ELBO=-57919.65, deltaELBO=12.752 (0.00217915%), Factors=13\n", + "Iteration 20: time=0.02, ELBO=-57908.64, deltaELBO=11.012 (0.00188180%), Factors=13\n", + "Iteration 21: time=0.03, ELBO=-57899.05, deltaELBO=9.584 (0.00163768%), Factors=13\n", + "Iteration 22: time=0.02, ELBO=-57890.66, deltaELBO=8.394 (0.00143438%), Factors=13\n", + "Iteration 23: time=0.02, ELBO=-57883.27, deltaELBO=7.392 (0.00126325%), Factors=13\n", + "Iteration 24: time=0.02, ELBO=-57876.72, deltaELBO=6.543 (0.00111800%), Factors=13\n", + "Iteration 25: time=0.02, ELBO=-57870.91, deltaELBO=5.816 (0.00099388%), Factors=13\n", + "Iteration 26: time=0.02, ELBO=-57865.71, deltaELBO=5.192 (0.00088719%), Factors=13\n", + "Iteration 27: time=0.02, ELBO=-57861.06, deltaELBO=4.652 (0.00079500%), Factors=13\n", + "Iteration 28: time=0.05, ELBO=-57856.88, deltaELBO=4.184 (0.00071497%), Factors=13\n", + "Iteration 29: time=0.02, ELBO=-57853.10, deltaELBO=3.776 (0.00064521%), Factors=13\n", + "Iteration 30: time=0.02, ELBO=-57849.68, deltaELBO=3.419 (0.00058416%), Factors=13\n", + "Iteration 31: time=0.03, ELBO=-57846.58, deltaELBO=3.105 (0.00053056%), Factors=13\n", + "Iteration 32: time=0.02, ELBO=-57843.75, deltaELBO=2.828 (0.00048332%), Factors=13\n", + "Iteration 33: time=0.02, ELBO=-57841.17, deltaELBO=2.584 (0.00044158%), Factors=13\n", + "Iteration 34: time=0.02, ELBO=-57838.80, deltaELBO=2.368 (0.00040458%), Factors=13\n", + "Iteration 35: time=0.02, ELBO=-57836.62, deltaELBO=2.175 (0.00037169%), Factors=13\n", + "Iteration 36: time=0.03, ELBO=-57834.62, deltaELBO=2.004 (0.00034239%), Factors=13\n", + "Iteration 37: time=0.02, ELBO=-57832.77, deltaELBO=1.851 (0.00031623%), Factors=13\n", + "Iteration 38: time=0.02, ELBO=-57831.06, deltaELBO=1.713 (0.00029280%), Factors=13\n", + "Iteration 39: time=0.02, ELBO=-57829.47, deltaELBO=1.590 (0.00027178%), Factors=13\n", + "Iteration 40: time=0.02, ELBO=-57827.99, deltaELBO=1.480 (0.00025289%), Factors=13\n", + "Iteration 41: time=0.02, ELBO=-57826.61, deltaELBO=1.380 (0.00023587%), Factors=13\n", + "Iteration 42: time=0.02, ELBO=-57825.32, deltaELBO=1.290 (0.00022052%), Factors=13\n", + "Iteration 43: time=0.02, ELBO=-57824.11, deltaELBO=1.209 (0.00020663%), Factors=13\n", + "Iteration 44: time=0.03, ELBO=-57822.97, deltaELBO=1.136 (0.00019406%), Factors=13\n", + "Iteration 45: time=0.02, ELBO=-57821.90, deltaELBO=1.069 (0.00018265%), Factors=13\n", + "Iteration 46: time=0.03, ELBO=-57820.89, deltaELBO=1.008 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(0.00004952%), Factors=13\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -615307.48 \n", + "\n", + "Iteration 1: time=0.04, ELBO=-80589.10, deltaELBO=534718.371 (86.90262875%), Factors=14\n", + "Iteration 2: time=0.02, ELBO=-72596.29, deltaELBO=7992.815 (1.29899520%), Factors=13\n", + "Iteration 3: time=0.03, ELBO=-67510.49, deltaELBO=5085.800 (0.82654606%), Factors=13\n", + "Iteration 4: time=0.02, ELBO=-63999.43, deltaELBO=3511.065 (0.57061952%), Factors=13\n", + "Iteration 5: time=0.02, ELBO=-61788.12, deltaELBO=2211.306 (0.35938231%), Factors=13\n", + "Iteration 6: time=0.03, ELBO=-59763.05, deltaELBO=2025.070 (0.32911510%), Factors=13\n", + "Iteration 7: time=0.02, ELBO=-58514.85, deltaELBO=1248.195 (0.20285711%), Factors=13\n", + "Iteration 8: time=0.02, ELBO=-57966.07, deltaELBO=548.785 (0.08918881%), Factors=13\n", + "Iteration 9: time=0.03, ELBO=-57502.26, deltaELBO=463.806 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"\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -645535.19 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80045.18, deltaELBO=565490.011 (87.60018376%), Factors=15\n", + "Iteration 2: time=0.04, ELBO=-69744.16, deltaELBO=10301.020 (1.59573328%), Factors=14\n", + "Iteration 3: time=0.01, ELBO=-63151.18, deltaELBO=6592.979 (1.02131987%), Factors=13\n", + "Iteration 4: time=0.03, ELBO=-59310.89, deltaELBO=3840.287 (0.59489976%), Factors=13\n", + "Iteration 5: time=0.02, ELBO=-57762.93, deltaELBO=1547.965 (0.23979556%), Factors=13\n", + "Iteration 6: time=0.02, ELBO=-57255.89, deltaELBO=507.036 (0.07854501%), Factors=13\n", + "Iteration 7: time=0.06, ELBO=-57035.37, deltaELBO=220.523 (0.03416126%), Factors=13\n", + "Iteration 8: time=0.03, ELBO=-56898.02, deltaELBO=137.347 (0.02127652%), Factors=13\n", + "Iteration 9: time=0.03, ELBO=-56796.95, deltaELBO=101.070 (0.01565670%), Factors=13\n", + "Iteration 10: time=0.02, ELBO=-56715.73, 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ELBO=-55735.44, deltaELBO=0.316 (0.00004895%), Factors=13\n", + "Iteration 66: time=0.03, ELBO=-55735.14, deltaELBO=0.305 (0.00004727%), Factors=13\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "total_variance = np.zeros(17)\n", + "for k in range(2,17):\n", + " data_mat = [[None for g in range(1)] for m in range(4)]\n", + "\n", + " for m in range(4):\n", + " data_mat[m][0] = Xs_norm[m]\n", + "\n", + " ent = entry_point()\n", + " ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(4)])\n", + " ent.set_model_options(\n", + " factors = k, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + " )\n", + " ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + " )\n", + " ent.build()\n", + " ent.run()\n", + "\n", + " total_variance[k] = np.sum(ent.model.calculate_variance_explained())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(2,17), total_variance[2:17], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/abis_mvmds_screeplot.ipynb b/examples.bck/abis_mvmds_screeplot.ipynb new file mode 100644 index 0000000..4d6d454 --- /dev/null +++ b/examples.bck/abis_mvmds_screeplot.ipynb @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "mvmds = MVMDS(n_components=16)\n", + "Xs_mvmds_reduced = mvmds.fit_transform(Xs)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.25888715 0.7012003 0.9441707 1.24957556 1.95817956 2.45557595\n", + " 2.70811699 2.92816196 3.13355755 3.24058277 3.28982273 3.3577562\n", + " 3.49285628 3.53937728 3.56071788 3.57073439]\n" + ] + } + ], + "source": [ + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "\n", + "p = Xs_concat.shape[1]\n", + "variance_explained = np.zeros(16)\n", + "\n", + "for k in range(16):\n", + " variance = 0\n", + " for i in range(p):\n", + " variance += np.var(np.dot(Xs_concat[:,i], Xs_mvmds_reduced[:,k])*Xs_mvmds_reduced[:,k])\n", + "\n", + " if k==0:\n", + " variance_explained[k] = variance\n", + " else: \n", + " variance_explained[k] = variance_explained[k-1]+variance\n", + "\n", + "print(variance_explained)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(1,17), variance_explained, 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/easi3.ipynb b/examples.bck/easi3.ipynb new file mode 100644 index 0000000..47945ef --- /dev/null +++ b/examples.bck/easi3.ipynb @@ -0,0 +1,949 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coucou\n" + ] + } + ], + "source": [ + "# from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "\n", + "DATA_PATH = r'C:\\Users\\paul_\\OneDrive\\Pro\\Galderma\\Vevey\\NEMO Phase 3\\AD\\EASI\\data'\n", + "RESULTS_PATH = r'C:\\Users\\paul_\\OneDrive\\Pro\\Galderma\\Vevey\\NEMO Phase 3\\AD\\EASI\\results\\ISM'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(DATA_PATH + r'\\adeasi3_by_zone.csv', na_values=' ', index_col='USUBJID')\n", + "\n", + "m0 = df.values[:,3:].astype(np.float_)\n", + "\n", + "list_columns = df.columns[3:].to_list()\n", + "score_pref = ['Head And Neck', 'Limb, Lower', 'Limb, Upper', 'Trunk']\n", + "list_items = ['Area of Involvement', 'Erythema', 'Excoriation', 'Induration/Papulation', 'Lichenification']\n", + "\n", + "n_scores = 4\n", + "n_items = [5, 5, 5, 5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "condition number(4, 6) = 2.33\n" + ] + } + ], + "source": [ + "\n", + "n_embedding, n_themes = [4,6]\n", + "h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0=True, update_h4_ism=True,\n", + " max_iter_integrate = 20, max_iter_mult=100, fast_mult_rules=False, sparsity_coeff=1)\n", + "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated), 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save ISM results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calinski_harabasz_score: 217.20840085533604\n", + "condition number(w4_ism) = 10.317792797819772\n" + ] + } + ], + "source": [ + "# Save\n", + "df_h4_updated_sparse = ilsm.format_loadings(h4_updated_sparse, list_columns)\n", + "df_h4_updated_sparse.to_csv(RESULTS_PATH + r'\\h4_updated_sparse.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "df_h4_updated = ilsm.format_loadings(h4_updated_sparse, list_columns)\n", + "df_h4_updated.to_csv(RESULTS_PATH + r'\\h4_updated.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "df_q4_ism = pd.DataFrame(q4_ism)\n", + "df_q4_ism.columns = ['theme_' + str(i) for i in range(1, n_themes + 1)]\n", + "df_q4_ism.insert(loc=0, column='score', value=score_pref)\n", + "df_q4_ism.to_csv(RESULTS_PATH + r'\\q4_ism.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "cluster = np.argmax(w4_ism, axis=1)+1\n", + "# cluster = np.argmax(normalize(w4_ism, norm='max', axis=0), axis=1)+1\n", + "\n", + "calinski_harabasz_score = metrics.calinski_harabasz_score(w4_ism, cluster)\n", + "print(f'calinski_harabasz_score: {calinski_harabasz_score}')\n", + "# silhouette_score = metrics.silhouette_score(w4_ism, cluster, metric='euclidean')\n", + "# print(f'Silhouette Score: {silhouette_score}')\n", + "\n", + "df_w4_ism = pd.DataFrame(np.column_stack((w4_ism, cluster)))\n", + "df_w4_ism.columns = ['theme_' + str(i) for i in range(1, n_themes + 1)] + ['nmf_cluster']\n", + "\n", + "def calculate_value(row):\n", + " for i in range(1, n_themes+1):\n", + " if row.iloc[n_themes] == i:\n", + " return row.iloc[i-1]\n", + "\n", + "# Apply the function to each row\n", + "df_w4_ism['nmf_cluster_loading'] = df_w4_ism.apply(lambda row: calculate_value(row), axis=1)\n", + "\n", + "for k in range(1,7):\n", + " df_w4_ism['pred_'+str(k)] = df_w4_ism['theme_'+str(k)] / df_w4_ism[['theme_' +str(i) for i in range(1,6) if i != k]].max(axis=1)\n", + "\n", + "df_w4_ism.columns = ['theme_' + str(i) for i in range(1, n_themes + 1)] + ['nmf_cluster', 'nmf_cluster_loading'] + ['pred_' + str(i) for i in range(1, n_themes + 1)]\n", + "\n", + "df_w4_ism.insert(0, 'TRTP', df['TRTP'].to_list())\n", + "df_w4_ism.insert(0, 'ASEX', df['ASEX'].to_list())\n", + "df_w4_ism.insert(0, 'AGEGR1', df['AGEGR1'].to_list())\n", + "df_w4_ism.insert(loc=0, column='USUBJID', value=(df.index.to_list()))\n", + "df_w4_ism.set_index('USUBJID', inplace=True)\n", + "df_w4_ism.to_csv(RESULTS_PATH + r'\\w4_ism.csv', sep=',', na_rep='.',index=True)\n", + "\n", + "df_easy_by_time = pd.read_csv(DATA_PATH + r'\\adeff_easi_no_dtype_no_doubles.csv', na_values=' ', index_col='USUBJID')\n", + "merged_df = pd.merge(df_w4_ism, df_easy_by_time, on='USUBJID')\n", + "merged_df.to_csv(RESULTS_PATH + r'\\w4_ism_adeff_easi.csv', sep=',', na_rep='.',index=True)\n", + "\n", + "print('condition number(w4_ism) = ' + str(np.linalg.cond(w4_ism)))\n", + "\n", + "# Save the tensor\n", + "df_tensor_score = pd.DataFrame(data=tensor_score)\n", + "df_tensor_score.columns = [score_pref[j] + ':theme_' + str(i) for j in range(len(score_pref)) for i in range(1, int(tensor_score.shape[1]/n_scores) + 1)]\n", + "df_tensor_score.insert(loc=0, column='wise_id', value=(df.index.to_list()))\n", + "df_tensor_score.set_index('wise_id', inplace=True)\n", + "df_tensor_score.to_csv(RESULTS_PATH + r'\\tensor_score.csv', sep=',', na_rep='.', index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additional tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "calculate difference in %success between treatment and placebo as a function of predictor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df_W16 = merged_df[(merged_df['AVISIT'] == 'Week 16')].copy()\n", + "ncols = 3\n", + "nrows = int(np.ceil(n_themes/ncols))\n", + "irow = 0\n", + "icol = -1\n", + "xmin, xmax = 0, 1.1\n", + "ymin, ymax = .25, .55\n", + "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12, 8))\n", + "\n", + "for k in range(1,7):\n", + " merged_df_W16['PRED_'+str(k)] = merged_df_W16['theme_'+str(k)] / merged_df_W16[['theme_' +str(i) for i in range(1,6) if i != k]].max(axis=1)\n", + " count_Y_Nemo = np.zeros(len(merged_df_W16))\n", + " count_Y_Placebo = np.zeros(len(merged_df_W16))\n", + " count_Nemo = np.ones(len(merged_df_W16))\n", + " count_Placebo = np.ones(len(merged_df_W16))\n", + " \n", + " for i in range(len(merged_df_W16)):\n", + " cutoff = merged_df_W16['PRED_'+str(k)].iloc[i]\n", + " count_Y_Nemo[i] = merged_df_W16.loc[(merged_df_W16['PRED_'+str(k)] >= cutoff) & (merged_df_W16['TRTP'] == 'Nemolizumab') & (merged_df_W16['CRIT1FL'] == 'Y'), 'CRIT1FL'].count()\n", + " count_Y_Placebo[i] = merged_df_W16.loc[(merged_df_W16['PRED_'+str(k)] >= cutoff) & (merged_df_W16['TRTP'] == 'Placebo') & (merged_df_W16['CRIT1FL'] == 'Y'), 'CRIT1FL'].count()\n", + " count_Nemo[i] = merged_df_W16.loc[(merged_df_W16['PRED_'+str(k)] >= cutoff) & (merged_df_W16['TRTP'] == 'Nemolizumab'), 'CRIT1FL'].count()\n", + " count_Placebo[i] = merged_df_W16.loc[(merged_df_W16['PRED_'+str(k)] >= cutoff) & (merged_df_W16['TRTP'] == 'Placebo'), 'CRIT1FL'].count()\n", + "\n", + " pct_respY_Nemo = np.divide(count_Y_Nemo, np.where(count_Nemo == 0, 1, count_Nemo))\n", + " pct_respY_Nemo_err = np.sqrt(np.divide(pct_respY_Nemo * (1-pct_respY_Nemo), np.where(count_Nemo <= 1, 1, count_Nemo-1)))\n", + " pct_respY_Placebo = np.divide(count_Y_Placebo, np.where(count_Placebo == 0, 1, count_Placebo))\n", + " pct_respY_Placebo_err = np.sqrt(np.divide(pct_respY_Placebo * (1-pct_respY_Placebo), np.where(count_Placebo <= 1, 1, count_Placebo-1)))\n", + "\n", + " merged_df_W16['COUNT_NEMO_'+str(k)] = count_Nemo\n", + " merged_df_W16['COUNT_PLACEBO_'+str(k)] = count_Placebo\n", + " merged_df_W16['PCT_RESPY_NEMO_'+str(k)] = pct_respY_Nemo\n", + " merged_df_W16['PCT_RESPY_NEMO_ERR_'+str(k)] = pct_respY_Nemo_err\n", + " merged_df_W16['PCT_RESPY_PLACEBO_'+str(k)] = pct_respY_Placebo\n", + " merged_df_W16['PCT_RESPY_PLACEBO_ERR_'+str(k)] = pct_respY_Placebo_err\n", + "\n", + " # Drop duplicates, sort and drop values with counts smaller than 10\n", + " merged_df_W16_sorted = merged_df_W16.drop_duplicates(subset=['PRED_'+str(k)]).sort_values(by='PRED_'+str(k))\n", + " merged_df_W16_sorted = merged_df_W16_sorted[(merged_df_W16_sorted['COUNT_NEMO_'+str(k)] > 10) & (merged_df_W16_sorted['COUNT_PLACEBO_'+str(k)] > 10)]\n", + "\n", + " # Superiority test\n", + " super_test_delta = .05\n", + " super_test = (merged_df_W16_sorted['PCT_RESPY_NEMO_'+str(k)] - merged_df_W16_sorted['PCT_RESPY_PLACEBO_'+str(k)] - super_test_delta).values\n", + " super_test_err = np.sqrt((((merged_df_W16_sorted['COUNT_NEMO_'+str(k)]-1) * merged_df_W16_sorted['PCT_RESPY_NEMO_ERR_'+str(k)]**2 + \\\n", + " (merged_df_W16_sorted['COUNT_PLACEBO_'+str(k)]-1) * merged_df_W16_sorted['PCT_RESPY_PLACEBO_ERR_'+str(k)]**2) / \\\n", + " ((merged_df_W16_sorted['COUNT_NEMO_'+str(k)] + merged_df_W16_sorted['COUNT_PLACEBO_'+str(k)]-2))).values)\n", + " \n", + " merged_df_W16_sorted['SUPER_TEST_'+str(k)] = np.divide(super_test, super_test_err, out=np.zeros_like(super_test), where=super_test_err!=0)\n", + "\n", + " icol = icol+1\n", + "\n", + " merged_df_W16_sorted.plot.scatter(x='PRED_'+str(k), y='PCT_RESPY_NEMO_'+str(k), s=2, ax=axes[irow, icol], color='blue')\n", + " merged_df_W16_sorted.plot.scatter(x='PRED_'+str(k), y='PCT_RESPY_PLACEBO_'+str(k), s=2, ax=axes[irow, icol], color='red')\n", + " \n", + " axes[irow, icol].fill_between(merged_df_W16_sorted['PRED_'+str(k)], merged_df_W16_sorted['PCT_RESPY_NEMO_'+str(k)]-merged_df_W16_sorted['PCT_RESPY_NEMO_ERR_'+str(k)], \n", + " merged_df_W16_sorted['PCT_RESPY_NEMO_'+str(k)]+merged_df_W16_sorted['PCT_RESPY_NEMO_ERR_'+str(k)], alpha=0.2, color='blue')\n", + " axes[irow, icol].fill_between(merged_df_W16_sorted['PRED_'+str(k)], merged_df_W16_sorted['PCT_RESPY_PLACEBO_'+str(k)]-merged_df_W16_sorted['PCT_RESPY_PLACEBO_ERR_'+str(k)], \n", + " merged_df_W16_sorted['PCT_RESPY_PLACEBO_'+str(k)]+merged_df_W16_sorted['PCT_RESPY_PLACEBO_ERR_'+str(k)], alpha=0.2, color='red')\n", + " axes[irow, icol].set_title('PCT_RESPY_'+str(k)+' by PRED'+str(k), fontdict={'fontsize': 8})\n", + " axes[irow, icol].set_xlabel('PRED_'+str(k), fontdict={'fontsize': 6})\n", + " axes[irow, icol].set_ylabel('PCT_RESPY_'+str(k), fontdict={'fontsize': 6})\n", + " axes[irow, icol].axhline(y=merged_df_W16_sorted['PCT_RESPY_NEMO_'+str(k)].iloc[0], linestyle='--', color='grey')\n", + " \n", + " X = merged_df_W16_sorted['SUPER_TEST_'+str(k)].values\n", + " Y = merged_df_W16_sorted['PRED_'+str(k)].values\n", + " test_X = np.nonzero(X < 1.645)\n", + " if len(test_X[0]) > 0:\n", + " x = Y[test_X[0][0]]\n", + " axes[irow, icol].axvline(x=x, linestyle='--', color='grey')\n", + " axes[irow, icol].text(0.95, 0.95, \"Cutoff=\"+str(round(x,2)), transform=axes[irow, icol].transAxes, \\\n", + " fontsize=8, ha=\"right\", va=\"top\")\n", + " \n", + " if icol==ncols-1:\n", + " icol = -1\n", + " irow+=1\n", + " \n", + "for ax in axes.flat:\n", + " ax.set_xlim(xmin, xmax)\n", + " ax.set_ylim(ymin, ymax)\n", + "\n", + "plt.rc('xtick',labelsize=6)\n", + "plt.rc('ytick',labelsize=6)\n", + "plt.subplots_adjust(wspace=0.3, hspace=0.3)\n", + "fig_name=RESULTS_PATH + r'\\predictors.png'\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predict on phase2b data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df2b = pd.read_csv(DATA_PATH + r'\\easi2b_by_zone.csv', na_values=' ', index_col='USUBJID')\n", + "\n", + "m02b = df2b.values[:,1:].astype(np.float_)\n", + "m02b_nan_0 = m02b.copy()\n", + "\n", + "# create m0_weight with ones and zeros if not_missing/missing value\n", + "m02b_weight = np.where(np.isnan(m02b), 0, 1)\n", + "m02b_nan_0[np.isnan(m02b_nan_0)]=0\n", + "\n", + "max_values = np.max(m02b_nan_0, axis=0)\n", + "# Replace maximum values equal to 0 with 1\n", + "m02b = np.divide(m02b, np.where(max_values == 0, 1, max_values))\n", + "m02b_nan_0 = np.divide(m02b_nan_0, np.where(max_values == 0, 1, max_values))\n", + "\n", + "my_nmfmodel = NMF(n_components=n_themes, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=0)\n", + "estimator_ = my_nmfmodel.fit_transform(m02b.copy(), h=h4_updated_sparse, update_h=False)\n", + "w42b = estimator_.w\n", + "h42b = estimator_.h\n", + "scale = np.linalg.norm(h4_updated_sparse, axis=0) / np.linalg.norm(h42b, axis=0)\n", + "h42b *= scale\n", + "w42b /= scale\n", + "\n", + "cluster = np.argmax(w42b, axis=1)+1\n", + "# cluster = np.argmax(normalize(w4_ism, norm='max', axis=0), axis=1)+1\n", + "\n", + "calinski_harabasz_score = metrics.calinski_harabasz_score(w42b, cluster)\n", + "print(f'calinski_harabasz_score: {calinski_harabasz_score}')\n", + "# silhouette_score = metrics.silhouette_score(w4_ism, cluster, metric='euclidean')\n", + "# print(f'Silhouette Score: {silhouette_score}')\n", + "\n", + "df_w42b = pd.DataFrame(np.column_stack((w42b, cluster)))\n", + "df_w42b.columns = ['theme_' + str(i) for i in range(1, n_themes + 1)]+ ['nmf_cluster']\n", + "\n", + "def calculate_value(row):\n", + " for i in range(1, n_themes+1):\n", + " if row.iloc[n_themes] == i:\n", + " return row.iloc[i-1]\n", + "\n", + "# Apply the function to each row\n", + "df_w42b['nmf_cluster_loading'] = df_w42b.apply(lambda row: calculate_value(row), axis=1)\n", + "df_w42b.insert(0, 'TRTP', df2b['TRTP'].to_list())\n", + "df_w42b.insert(loc=0, column='USUBJID', value=(df2b.index.to_list()))\n", + "df_w42b.set_index('USUBJID', inplace=True)\n", + "df_w42b.to_csv(RESULTS_PATH + r'\\w42b.csv', sep=',', na_rep='.',index=True)\n", + "\n", + "df_easy2b_by_time = pd.read_csv(DATA_PATH + r'\\easi2b_by_time.csv', na_values=' ', index_col='USUBJID')\n", + "merged_df = pd.merge(df_w42b, df_easy2b_by_time, on='USUBJID')\n", + "merged_df.to_csv(RESULTS_PATH + r'\\w42b_easi2b_by_time.csv', sep=',', na_rep='.',index=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot word clouds of the survey items\n", + "ncols = 2\n", + "nrows = int(np.ceil(n_themes/ncols))\n", + "\n", + "irow = 0\n", + "icol = -1\n", + "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8, 6))\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_h4_updated=df_h4_updated_sparse[df_h4_updated_sparse.columns[[0,i]]].set_index('label').T.to_dict('list')\n", + " for k in sub_df_h4_updated:\n", + " sub_df_h4_updated[k] = sub_df_h4_updated[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, relative_scaling=1).generate_from_frequencies(sub_df_h4_updated)\n", + "\n", + " icol = icol+1\n", + " title = 'theme ' + str(i)\n", + " axes[irow, icol].imshow(wc)\n", + " axes[irow, icol].axis('off')\n", + " axes[irow, icol].set_title(title)\n", + " if icol==ncols-1:\n", + " icol = -1\n", + " irow+=1\n", + " \n", + "fig_name=RESULTS_PATH + r'\\word_clouds_items.png'\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot individual word clouds of the survey items\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_h4_updated=df_h4_updated_sparse[df_h4_updated_sparse.columns[[0,i]]].set_index('label').T.to_dict('list')\n", + " for k in sub_df_h4_updated:\n", + " sub_df_h4_updated[k] = sub_df_h4_updated[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, relative_scaling=1).generate_from_frequencies(sub_df_h4_updated)\n", + "\n", + " title = 'theme ' + str(i)\n", + " plt.imshow(wc)\n", + " plt.axis('off')\n", + " plt.title(title)\n", + " fig_name=RESULTS_PATH + r'\\word_clouds_items_theme '+str(i)+'.png'\n", + " plt.savefig(fig_name, dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot word clouds of the surveys\n", + "ncols = 2\n", + "nrows = int(np.ceil(n_themes/ncols))\n", + "\n", + "irow = 0\n", + "icol = -1\n", + "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8, 6))\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_q4_ism=df_q4_ism[df_q4_ism.columns[[0,i]]].set_index('score').T.to_dict('list')\n", + " for k in sub_df_q4_ism:\n", + " sub_df_q4_ism[k] = sub_df_q4_ism[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, relative_scaling=1).generate_from_frequencies(sub_df_q4_ism)\n", + "\n", + " icol = icol+1\n", + " title = 'theme ' + str(i)\n", + " axes[irow, icol].imshow(wc)\n", + " axes[irow, icol].axis('off')\n", + " axes[irow, icol].set_title(title)\n", + " if icol==ncols-1:\n", + " icol = -1\n", + " irow+=1\n", + " \n", + "fig_name=RESULTS_PATH + r'\\word_clouds_surveys.png'\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot individual word clouds of the surveys\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_q4_ism=df_q4_ism[df_q4_ism.columns[[0,i]]].set_index('score').T.to_dict('list')\n", + " for k in sub_df_q4_ism:\n", + " sub_df_q4_ism[k] = sub_df_q4_ism[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightyellow', relative_scaling=1).generate_from_frequencies(sub_df_q4_ism)\n", + "\n", + " title = 'theme ' + str(i)\n", + " plt.imshow(wc)\n", + " plt.axis('off')\n", + " plt.title(title)\n", + " fig_name=RESULTS_PATH + r'\\word_clouds_surveys_theme '+str(i)+'.png'\n", + " plt.savefig(fig_name, dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lVhwAAAAAACi/SjQR56ZNm1S3bl3NmDFDJ0+e1MmTJzV9+nTVrVtXv/32W2nXCAAAAAAAyiHD4XA4LvRJnTp1Ur169fTuu+/K1TW3s0Z2drYeeeQR/fHHH/rxxx9LvVAAV6bk5GQFBAQoKSlJ/v7+ZV0OAFxx+DtaHmVJipd0wf9MB3BZVZDE3+VLrUTDQzZt2uQUWEiSq6urRo4cqVatWpVacQAAAAAAoPwq0fAQf39/xcXF5dt+8OBB+fn5XXRRAAAAAAAAJQot+vbtq0GDBumTTz7RwYMHdfDgQS1evFiPPPKI7rvvvtKuEQAAAAAAlEMlGh7y+uuvyzAMDRgwQNnZ2ZIkNzc3Pf7445o6dWqpFgjg6tB43ArZPLzLugwAhYid2rOsSwAAAHBSotDC3d1ds2bN0pQpUxQTEyNJqlu3rry9+UACAAAAAABKR4lCizze3t5q0qRJadUCAAAAAABgKnZocdddd2n+/Pny9/fXXXfdVeixX3755UUXBgAAAAAAyrdihxYBAQEyDENS7uoheb8DAAAAAABcCsUOLT744APz9/nz51+KWgAAAAAAAEwlWvK0W7duSkxMzLc9OTlZ3bp1u9iaAAAAAAAAShZarFmzRpmZmfm2p6en66effrroogAAAAAAAC5o9ZBt27aZv+/cuVNHjx41H+fk5Gj58uWqXr166VUHAAAAAADKrQsKLZo3by7DMGQYRoHDQLy8vPTmm2+WWnEAAAAAAKD8uqDQ4sCBA3I4HKpTp442btyoKlWqmPvc3d1VtWpVubi4lHqRAAAAAACg/Lmg0KJWrVqSJLvdfkmKAQAAAAAAyHNBocXZ9uzZozfffFO7du2SJEVGRmrw4MFq0KBBqRUHAAAAAADKrxKtHvLFF1+ocePG2rx5s5o1a6ZmzZrpt99+U5MmTfTFF1+Udo0AAAAAAKAcKlFPi5EjR2rMmDF66aWXnLaPGzdOI0eOVO/evUulOAAAAAAAUH6VqKdFfHy8BgwYkG/7/fffr/j4+IsuCgAAAAAAoEShRZcuXfTTTz/l275u3Tp16tTpoou6UIZhaMmSJZf9vHnGjx+v5s2bl9n5L7cuXbro6aeftkw7pS0sLEwzZ868qDbK23sCAAAAAC6FEoUWt99+u0aNGqXBgwfr//7v//R///d/Gjx4sEaPHq0777xTX3/9tflTmIEDB6pXr14lKaHMFBSQjBgxQqtWrbok51u7dq1CQ0Ml5d4vwzBkGIbc3d1Vr149vfTSS8rOzr4k5y4ta9askWEYSkxMdNr+5ZdfauLEiZe9nqIChV9//VWPPvro5SuoEOcLUAhFAAAAAJQHJZrT4oknnpAkvfXWW3rrrbcK3CflfsDPycm5iPIuj5ycHBmGIZutRBmOfH195evrW8pV5frvf/+r2267zXzco0cPffDBB8rIyNC3336rJ598Um5ubhozZswlOf+lVLFixbIuoUBVqlQp6xKuGJmZmXJ3dy/rMgAAAABcpUr0Kd1utxfr50IDiy5dumjIkCEaOXKkKlasqODgYI0fP97pmH379um6666Tp6enGjZsqJUrVzrtL+hb/ejoaBmGodjYWEnS/PnzFRgYqK+//loNGzaUh4eH4uLi9Ouvv+rGG29U5cqVFRAQoM6dO+u3334z2wkLC5Mk3XnnnTIMw3x87rfedrtdL730kmrUqCEPDw81b95cy5cvN/fHxsbKMAx9+eWX6tq1q7y9vdWsWTOtX78+3z35+uuvdfvtt5uPPTw8FBwcrFq1aunxxx/XDTfcYPZomT59upo0aSIfHx+FhobqiSeeUEpKivncvOtesmSJwsPD5enpqe7du+vgwYPmMQX1fnn66afVpUuXfLXlWbRokVq1aiU/Pz8FBwerX79+SkhIMK+1a9eukqQKFSrIMAwNHDhQUv7hIadOndKAAQNUoUIFeXt76+abb9a+ffvy1b9ixQpFRkbK19dXPXr0KPV5VM7t3WAYht555x3deuut8vb2VmRkpNavX6/9+/erS5cu8vHxUYcOHRQTE5OvrXfeeUehoaHy9vZWnz59lJSUVKq15unSpYsGDx6swYMHKyAgQJUrV9bYsWPlcDicrmvixIm677775OPjo+rVq2vOnDlO7SQmJuqRRx5RlSpV5O/vr27dumnr1q3m/rz3+nvvvafatWvL09PzklwPAAAAAEglDC0upQULFsjHx0cbNmzQq6++qpdeeskMJux2u+666y65u7trw4YNevvttzVq1KgSnSctLU2vvPKK3nvvPe3YsUNVq1bV6dOn9eCDD2rdunX65ZdfFB4erltuuUWnT5+WlDtsQJI++OADxcfHm4/PNWvWLE2bNk2vv/66tm3bpu7du+v22293+gAuSc8//7xGjBih6Oho1a9fX/fdd5/TUI8dO3YoISFB3bp1O+91eHl5KTMzU5Jks9n0xhtvaMeOHVqwYIH+97//aeTIkfmue/LkyVq4cKGioqKUmJioe++998Jv4FmysrI0ceJEbd26VUuWLFFsbKwZTISGhprL4O7Zs0fx8fGaNWtWge0MHDhQmzZt0tdff63169fL4XDolltuUVZWllP9r7/+uhYtWqQff/xRcXFxGjFihLk/L7TKC6hKy8SJEzVgwABFR0erQYMG6tevnx577DGNGTNGmzZtksPh0ODBg52es3//fn366af65ptvtHz5cm3ZssWpJ1JpW7BggVxdXbVx40bNmjVL06dP13vvved0zGuvvaZmzZppy5YtGj16tIYOHeoU/N1zzz1KSEjQd999p82bN6tly5a6/vrrdfLkSafr+uKLL/Tll18qOjo6Xx0ZGRlKTk52+gEAAACAkij28JA33nhDjz76qDw9PfXGG28UeuyQIUNKXFDTpk01btw4SVJ4eLhmz56tVatW6cYbb9QPP/yg3bt3a8WKFapWrZok6eWXX9bNN998wefJysrSW2+9pWbNmpnbzg0H/vOf/ygwMFBr167Vrbfeag4bCAwMVHBw8Hnbfv311zVq1CgzDHjllVe0evVqzZw50+mb7REjRqhnz56SpAkTJqhRo0bav3+/GjRoICl3aEj37t0L7H7vcDi0atUqrVixQk899ZQkOfVaCAsL06RJk/Tvf//baQhPVlaWZs+erbZt20rK/aAbGRmpjRs3qk2bNkXfuAI8/PDD5u916tTRG2+8odatWyslJUW+vr7mMJCqVasqMDCwwDb27dunr7/+WlFRUerQoYMk6cMPP1RoaKiWLFmie+65x6z/7bffVt26dSVJgwcPdlp619vbWxEREXJzcyvRtZzPQw89pD59+kiSRo0apfbt22vs2LHq3r27JGno0KF66KGHnJ6Tnp6uhQsXqnr16pKkN998Uz179tS0adMKff+UVGhoqGbMmCHDMBQREaHff/9dM2bM0L/+9S/zmI4dO2r06NGSpPr16ysqKkozZszQjTfeqHXr1mnjxo1KSEiQh4eHpNz38pIlS/T555+b83xkZmZq4cKF5x1GM2XKFE2YMKHUrw8AAABA+VPs0GLGjBnq37+/PD09NWPGjPMeZxjGRYcWZwsJCTGHGuzatUuhoaFmYCFJ7du3L9F53N3d853rr7/+0gsvvKA1a9YoISFBOTk5SktLU1xcXLHbTU5O1pEjR9SxY0en7R07dnTqZi85X2tISIgkKSEhwSm0OPfb+6VLl8rX11dZWVmy2+3q16+fOYTmhx9+0JQpU7R7924lJycrOztb6enpSktLk7e3tyTJ1dVVrVu3Nttr0KCBAgMDtWvXrhKHFps3b9b48eO1detWnTp1Sna7XZIUFxenhg0bFquNXbt2ydXV1QxTJKlSpUqKiIjQrl27zG3e3t5mYCE5vz8kqU2bNtq9e3eJrqMwZ79WQUFBkqQmTZo4bUtPT1dycrL8/f0lSTVr1jQDCyn3vWq327Vnz55LElq0a9dOhmE4nW/atGnKycmRi4uLue1s7du3N4fCbN26VSkpKapUqZLTMWfOnHEa+lKrVq1C5/0YM2aMhg0bZj5OTk42J5MFAAAAgAtR7NDiwIEDBf5e2s79htwwDPNDcHHkTaZ59lj+s4cX5PHy8nL6gCdJDz74oE6cOKFZs2apVq1a8vDwUPv27c3hF6Xt7GvNqyXvWuPj47VlyxazJ0aerl27au7cuXJ3d1e1atXk6pr7EsbGxurWW2/V448/rsmTJ6tixYpat26dBg0apMzMTDO0KIrNZnO6d1LB9y9Pamqqunfvru7du+vDDz9UlSpVFBcXp+7du1+S+1bQ++Pcei+Fgl6rwl6/0uLv71/gPBiJiYkKCAgo1XOlpKQoJCREa9asybfv7B4yPj4+hbbj4eFh9tQAAAAAgItxwXNaZGVlqW7duk7ffl8ukZGROnjwoNPEi7/88ovTMXnfAJ99TEHj7gsSFRWlIUOG6JZbblGjRo3k4eGh48ePOx3j5uZW6ASj/v7+qlatmqKiovK1XdxeB5L0zTffqEOHDvlW2PDx8VG9evVUs2ZNM7CQcns72O12TZs2Te3atVP9+vV15MiRfO1mZ2dr06ZN5uM9e/YoMTFRkZGRknLv37kTWxZ2/3bv3q0TJ05o6tSp6tSpkxo0aODU80GSObylsPsWGRmp7Oxsbdiwwdx24sQJ7dmz54Lum5XExcU5vQa//PKLbDabIiIiit1GRESENm/enG/7b7/9pvr16zttO/ve5Z0vPDzc7GWRt+3cY/Je+5YtW+ro0aNydXVVvXr1nH4qV65c7JoBAAAAoLRccGjh5uam9PT0S1FLkW644QbVr19fDz74oLZu3aqffvpJzz//vNMx9erVU2hoqMaPH699+/Zp2bJlmjZtWrHaDw8P16JFi7Rr1y5t2LBB/fv3l5eXl9MxYWFhWrVqlY4ePapTp04V2M6zzz6rV155RZ988on27Nmj0aNHKzo6WkOHDi32tZ67akhR6tWrp6ysLL355pv6448/tGjRIr399tv5jnNzc9NTTz2lDRs2aPPmzRo4cKDatWtnDg3p1q2bNm3apIULF2rfvn0aN26ctm/fft7z1qxZU+7u7uZ5v/76a02cONHpmFq1askwDC1dulTHjh1zWtEkT3h4uO644w7961//0rp167R161bdf//9ql69uu64445i34eNGzeqQYMGOnz4cKHHnTlzRtHR0U4/Ba3+cTE8PT2d3qtDhgxRnz59LmhoyDPPPKNly5Zp8uTJ2rVrl7Zv367nn39e69evz/d+iouL07Bhw7Rnzx59/PHHevPNN/MdExUVpVdffVV79+7VnDlz9Nlnn5nH3HDDDWrfvr169eql77//XrGxsfr555/1/PPPOwVdAAAAAHC5lGj1kCeffFKvvPKK00oXl4PNZtNXX32lM2fOqE2bNnrkkUc0efJkp2Pc3Nz08ccfa/fu3WratKleeeUVTZo0qVjtz5s3T6dOnVLLli31wAMPaMiQIapatarTMdOmTdPKlSsVGhqqFi1aFNjOkCFDNGzYMA0fPlxNmjTR8uXL9fXXXys8PLxYdaSmpmrVqlUXFFo0a9ZM06dP1yuvvKLGjRvrww8/1JQpU/Id5+3trVGjRqlfv37q2LGjfH199cknn5j7u3fvrrFjx2rkyJFq3bq1Tp8+rQEDBpz3vFWqVNH8+fP12WefqWHDhpo6dapef/11p2OqV6+uCRMmaPTo0QoKCso3T0eeDz74QNdcc41uvfVWtW/fXg6HQ99+++0FTaqZlpamPXv2FDqkRZL27t2rFi1aOP089thjxT5PcdSrV0933XWXbrnlFt10001q2rSp06SoxVnppEOHDvruu+/03XffqWPHjurSpYt+/vlnrVq1So0bN3Y6dsCAAeZ/G08++aSGDh1qTp6ZZ/jw4dq0aZNatGihSZMmafr06eZkooZh6Ntvv9V1112nhx56SPXr19e9996rP//805zHAwAAAAAuJ8NRggkB7rzzTq1atUq+vr5q0qRJvjHuX375ZakVWB59+eWXeuGFF7Rz585SbXf+/Pl6+umnlZiYWKrtomQ++OADvfzyy9q5c+dFr3bSpUsXNW/e3JxUsyBhYWF6+umnnVaZuRySk5MVEBCg0Kc/lc2jeHOrACgbsVN7Fn0QLru8v6NJSUnmZM+42mVJipd06eftAnAxKkji7/KlVuyJOM8WGBio3r17l3Yt+Juvr69eeeWVsi4Dl9i3336rl19+udSXZwUAAACAq0WJQosPPvigtOvAWW666aayLgGXwWeffVbWJQAAAACApZVoeMiBAweUnZ2db46Gffv2yc3NTWFhYaVVH4ArHMNDgCsHw0OsieEh5RHDQ4ArA8NDLocSTcQ5cOBA/fzzz/m2b9iwQQMHDrzYmgAAAAAAAEoWWmzZskUdO3bMt71du3aKjo6+2JoAAAAAAABKFloYhqHTp0/n256UlKScnJyLLgoAAAAAAKBEocV1112nKVOmOAUUOTk5mjJliq699tpSKw4AAAAAAJRfJVo95JVXXtF1112niIgIderUSZL0008/KTk5Wf/73/9KtUAAAAAAAFA+lainRcOGDbVt2zb16dNHCQkJOn36tAYMGKDdu3ercePGpV0jAAAAAAAoh0rU00KSqlWrppdffrk0awEAAAAAADCVqKfF8uXLtW7dOvPxnDlz1Lx5c/Xr10+nTp0qteIAAAAAAED5VaLQ4tlnn1VycrIk6ffff9ewYcN0yy236MCBAxo2bFipFggAAAAAAMqnEg0POXDggBo2bChJ+uKLL3Tbbbfp5Zdf1m+//aZbbrmlVAsEAAAAAADlU4lCC3d3d6WlpUmSfvjhBw0YMECSVLFiRbMHBgAAAICSMJTbIdpe1oUAKJRR1gWUCyUKLa699loNGzZMHTt21MaNG/XJJ59Ikvbu3asaNWqUaoEArg7bJ3SXv79/WZcBAMAVwEVScFkXAaBIJZptAReoRKHF7Nmz9cQTT+jzzz/X3LlzVb16dUnSd999px49epRqgQAAAED5YugiFvkDgKuK4XA4HGVdBICrV3JysgICApSUlERPCwAoAf6OAgDKsxL3Z4mJidELL7yg++67TwkJCZJye1rs2LGj1IoDAAAAAADlV4lCi7Vr16pJkybasGGDvvzyS6WkpEiStm7dqnHjxpVqgQAAAAAAoHwqUWgxevRoTZo0SStXrpS7u7u5vVu3bvrll19KrTgAAAAAAFB+lSi0+P3333XnnXfm2161alUdP378oosCAAAAAAAoUWgRGBio+Pj4fNu3bNliriQCAAAAAABwMUoUWtx7770aNWqUjh49KsMwZLfbFRUVpREjRmjAgAGlXSMAAAAAACiHShRavPzyy2rQoIFCQ0OVkpKihg0bqlOnTurQoYNeeOGF0q4RAAAAAACUQ4bD4XCU9MkHDx7U77//rpSUFLVo0ULh4eGlWRuAq0BycrICAgKUlJQkf3//si4HAK44/B0FAJRnrsU9cNiwYYXuP3vVkOnTp5e8IgAAAAAAAF1AaLFlyxanx7/99puys7MVEREhSdq7d69cXFx0zTXXlG6FAAAAAACgXCp2aLF69Wrz9+nTp8vPz08LFixQhQoVJEmnTp3SQw89pE6dOpV+lQAAAAAAoNwp0ZwW1atX1/fff69GjRo5bd++fbtuuukmHTlypNQKBHBlYyw2AFwc/o4CAMqzEq0ekpycrGPHjuXbfuzYMZ0+ffqiiwIAAAAAAChRaHHnnXfqoYce0pdffqlDhw7p0KFD+uKLLzRo0CDdddddpV0jAAAAAAAoh4o9p8XZ3n77bY0YMUL9+vVTVlZWbkOurho0aJBee+21Ui0QAAAAAACUTyWa0yJPamqqYmJiJEl169aVj49PqRUG4OrAWGwAuDj8HQUAlGcl6mmRx8fHR02bNi2tWgAAAAAAAEwlmtMCAAAAAADgUiO0AAAAAAAAlkRoAQAAAAAALInQAgAAAAAAWBKhBQAAAAAAsCRCCwAAAAAAYEmEFgAAAAAAwJIILQAAAAAAgCURWgAAAAAAAEsitAAAAAAAAJZEaAEAAAAAACyJ0AIAAAAAAFgSoQUAAAAAALAkQgsAAAAAAGBJhBYAAAAAAMCSCC0AAAAAAIAlEVoAAAAAAABLIrQAAAAAAACWRGgBAAAAAAAsidACAAAAAABYEqEFAAAAAACwJEILAAAAAABgSYQWAAAAAADAklzLugAA5UPjcStk8/Au6zKAyyp2as+yLgEAAOCKRk8LAAAAAABgSYQWAAAAAADAkggtAAAAAACAJRFaAAAAAAAASyK0AAAAAAAAlkRoAQAAAAAALInQAgAAAAAAWBKhBQAAAAAAsCRCCwAAAAAAYEmEFgAAAAAAwJIILQAAAAAAgCURWgAAAAAAAEsitAAAAAAAAJZEaAEAAAAAACyJ0AIAAAAAAFgSocUlZBiGlixZUqxjx48fr+bNm1/Ses53nvHjxysoKMisd+DAgerVq9clr+VC7s/lEhsbK8MwFB0dfVHtdOnSRU8//XSp1AQAAAAA5ZVrWRdwJRs4cKASExPP+8E7Pj5eFSpUuLxFFWHEiBF66qmnzMe7du3ShAkT9NVXX6ldu3aqUKGCunbtKofDUWrnHD9+vJYsWZIvCCir+9OlSxc1b95cM2fOzLcvNDRU8fHxqly58mWv61yxsbGqXbu2tmzZki9oKuwaAAAAAOBqQWhxCQUHB5d1Cfn4+vrK19fXfBwTEyNJuuOOO2QYhiTJw8PjstRixfvj4uJiybqsKjMzU+7u7mVdBgAAAICrFMNDLqFzhz8cOnRI9913nypWrCgfHx+1atVKGzZscHrOokWLFBYWpoCAAN177706ffq0uc9ut2vKlCmqXbu2vLy81KxZM33++efm/jVr1sgwDK1atUqtWrWSt7e3OnTooD179pjHnD08ZPz48brtttskSTabzQwtzh0eYrfb9eqrr6pevXry8PBQzZo1NXnyZHP/qFGjVL9+fXl7e6tOnToaO3assrKyJEnz58/XhAkTtHXrVhmGIcMwNH/+/ALvz++//65u3brJy8tLlSpV0qOPPqqUlBRzf15dr7/+ukJCQlSpUiU9+eST5rlKw7nDQ/Lu6YoVK9SiRQt5eXmpW7duSkhI0HfffafIyEj5+/urX79+SktLc2orOztbgwcPVkBAgCpXrqyxY8eWag+WsxmGoblz5+rmm2+Wl5eX6tSp4/TeyLuuxYsXq0OHDvL09FTjxo21du1ap3a2b9+um2++Wb6+vgoKCtIDDzyg48ePm/u7dOmiwYMH6+mnn1blypXVvXv3S3I9AAAAACARWlw2KSkp6ty5sw4fPqyvv/5aW7du1ciRI2W3281jYmJitGTJEi1dulRLly7V2rVrNXXqVHP/lClTtHDhQr399tvasWOHnnnmGd1///35Png+//zzmjZtmjZt2iRXV1c9/PDDBdY0YsQIffDBB5Jyh2rEx8cXeNyYMWM0depUjR07Vjt37tRHH32koKAgc7+fn5/mz5+vnTt3atasWXr33Xc1Y8YMSVLfvn01fPhwNWrUyDxH3759850jNTVV3bt3V4UKFfTrr7/qs88+0w8//KDBgwc7Hbd69WrFxMRo9erVWrBggebPn2+GIFJuEBMWFlbgdVyM8ePHa/bs2fr555918OBB9enTRzNnztRHH32kZcuW6fvvv9ebb77p9JwFCxbI1dVVGzdu1KxZszR9+nS99957pV5bnrFjx6p3797aunWr+vfvr3vvvVe7du1yOubZZ5/V8OHDtWXLFrVv31633XabTpw4IUlKTExUt27d1KJFC23atEnLly/XX3/9pT59+uS7Lnd3d0VFRentt9/OV0dGRoaSk5OdfgAAAACgJBgecpl89NFHOnbsmH799VdVrFhRklSvXj2nY+x2u+bPny8/Pz9J0gMPPKBVq1Zp8uTJysjI0Msvv6wffvhB7du3lyTVqVNH69at0zvvvKPOnTub7UyePNl8PHr0aPXs2VPp6eny9PR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PHqyXXnpJkuTi4qJ7771XH330kQYNGiRJWrVqlRITE9W7d29J0vTp03X99debHyLr16+vnTt36rXXXtPAgQPzna979+7y8fHRV199pQceeECS9NFHH+n222+Xn59fse5hnkmTJqljx46SpEGDBmnMmDGKiYlRnTp1JEl33323Vq9erVGjRl1Qu5MnTzYfjx49Wj179lR6erq8vLzk6+srV1fXfPc5JCREdrv9gu59UbKysjR37lzztbn77ru1aNEi/fXXX/L19VXDhg3VtWtXrV692qn3SseOHTV69GhJua9HVFSUZsyYYb5PS9vgwYPN98PcuXO1fPlyzZs3TyNHjjSPGTdunHn+BQsWqEaNGvrqq6/Up08fTZkyRf3799fTTz8tSQoPD9cbb7yhzp07a+7cufL09JQkdevWTcOHDz9vHRkZGcrIyDAfJycnl/alAgAAACgnLN3TYs+ePdq4caPuu+8+SZKrq6v69u2refPm5Tu2VatW5u/79+9XWlqabrzxRvn6+po/CxcuVExMjHncnDlzdM0116hKlSry9fXVf/7zH8XFxZ23nl27dql9+/ZO8x107NhRKSkpOnTo0EVdq7e3t/mhWMr98J2QkGA+7t+/v9asWWPO6fHhhx+qZ8+eCgwMNGvLCw7Orm3fvn35hi1IufeyT58++vDDDyVJqamp+u9//2v23CjuPZSkpk2bmr8HBQXJ29vbDCzytuVdS0nbDQkJkSSne1KQvF4cpenc1yYoKEhhYWHy9fV12nZubXmhzNmPd+3aVaq1ne98rq6uatWqVb7znX1MxYoVFRERYR6zdetWzZ8/3+l16d69u+x2uw4cOGA+7+z/1goyZcoUBQQEmD+hoaGlcXkAAAAAyiFL97SYN2+esrOzVa1aNXObw+GQh4eHZs+e7bTCg4+Pj/l73pCHZcuWqXr16k5tenh4SJIWL16sESNGaNq0aWrfvr38/Pz02muvacOGDZfyks7Lzc3N6bFhGE7DYFq3bq26detq8eLFevzxx/XVV19d9DwO/fv3V+fOnZWQkKCVK1fKy8tLPXr0kFS8e1hQ7YZhFHgteb0fLqZdSaXei6I4Crqewq6xtPj7+0uSkpKSzHAqT2JiYqmvcJKSkqLHHntMQ4YMybevZs2a5u9n/7dWkDFjxmjYsGHm4+TkZIILAAAAACVi2dAiOztbCxcu1LRp03TTTTc57evVq5c+/vhj/fvf/y7wuQ0bNpSHh4fi4uKchhucLSoqSh06dNATTzxhbjv3m/5zRUZG6osvvpDD4TA/REdFRcnPz081atS4kMsrkf79++vDDz9UjRo1ZLPZnJaAjYyMVFRUlNPxUVFRql+/vlxcXApsr0OHDgoNDdUnn3yi7777Tvfcc4/5Ybw497AkSqtdd3f3AnuQWMkvv/yS73FkZGSxnx8eHi6bzabNmzerVq1a5vY//vhDSUlJql+/fr72r7vuOkm5//1s3rw533wUv/zyixlAnDp1Snv37jVratmypXbu3Kl69eoV/yIL4OHhkS+AAgAAAICSsGxosXTpUp06dUqDBg3K941y7969NW/evPOGFn5+fhoxYoSeeeYZ2e12XXvttUpKSlJUVJT8/f314IMPKjw8XAsXLtSKFStUu3ZtLVq0SL/++qtq16593pqeeOIJzZw5U0899ZQGDx6sPXv2aNy4cRo2bJhstks/0qZ///4aP368Jk+erLvvvtvpg+Hw4cPVunVrTZw4UX379tX69es1e/ZsvfXWW4W22a9fP7399tvau3evVq9ebW4vzj0sidJqNywsTAcOHFB0dLRq1KghPz8/eXh4aMyYMTp8+HCRQ0QOHz6s6Ohop21nBwOlISoqSq+++qp69eqllStX6rPPPtOyZcuK/Xw/Pz898sgjGj58uFxdXdWkSRMdPHhQo0aNUrt27dShQwen4+fMmaPw8HBFRkZqxowZOnXqlDl5aZ6XXnpJlSpVUlBQkJ5//nlVrlxZvXr1kiSz3cGDB+uRRx6Rj4+Pdu7cqZUrV2r27NkXfT8AAAAA4EJZdk6LefPm6YYbbiiwC3zv3r21adMmbdu27bzPnzhxosaOHaspU6YoMjJSPXr00LJly8xQ4rHHHtNdd92lvn37qm3btjpx4oRTr4uCVK9eXd9++602btyoZs2a6d///rcGDRqkF1544eIutpjq1aunNm3aaNu2bebcE3latmypTz/9VIsXL1bjxo314osv6qWXXipwEs6z9e/fXzt37lT16tXzzYlR1D0sqdJot3fv3urRo4e6du2qKlWq6OOPP5YkxcfHFzovSZ7XX39dLVq0cPq5kEChOIYPH65NmzapRYsWmjRpkqZPn67u3bub+4uz0smsWbP04IMPatSoUWrUqJEGDhyopk2b6ptvvnGaW0WSpk6dqqlTp6pZs2Zat26dvv76a1WuXDnfMUOHDtU111yjo0eP6ptvvpG7u7uk3DlE1q5dq71796pTp05q0aKFXnzxRafhWQAAAABwORmOgtYPBXDJde7cWV27dtX48eMvqp3Y2FjVrl1bW7ZsUfPmzQs8Zs2aNeratatOnTqVb36MSy05OTl3Qs6nP5XNw/uynhtXr9ipPYs+CLhK5P0dTUpKMuc7AgCgvLDs8BDgapaUlKSYmJhS790BAAAAAFcTQgugDAQEBFz0MrkAAAAAcLUjtACucGFhYSpqlFeXLl2KPAYAAAAArIbQAgCAq5DdbldmZmZZl4FicHNzO+/y5AAAlHeEFgAAXGUyMzN14MAB2e32si4FxRQYGKjg4OB8K0MBAFDeEVoAAHAVcTgcio+Pl4uLi0JDQ2WzWXZ1cyj39UpLS1NCQoIkKSQkpIwrAgDAWggtAAC4imRnZystLU3VqlWTtzfLDF8JvLy8JEkJCQmqWrUqQ0UAADgLX78AAHAVycnJkSS5u7uXcSW4EHkBU1ZWVhlXAgCAtRBaAABwFWJuhCsLrxcAAAUjtAAAAAAAAJZEaAEAACxnzZo1MgxDiYmJZV0KAAAoQ0zECQBAORA2etllPV/s1J4XdHyXLl3UvHlzzZw589IUVAYcDoduueUWLV++XF999ZV69epV1iUBAHDFoacFAADAJTBz5kzmqgAA4CIRWgAAgDI1cOBArV27VrNmzZJhGDIMQ7GxsZKkzZs3q1WrVvL29laHDh20Z88ep+f+97//VcuWLeXp6ak6depowoQJys7ONvcbhqF33nlHt956q7y9vRUZGan169dr//796tKli3x8fNShQwfFxMRcULtFiY6O1rRp0/T++++X/MYAAABCCwAAULZmzZql9u3b61//+pfi4+MVHx+v0NBQSdLzzz+vadOmadOmTXJ1ddXDDz9sPu+nn37SgAEDNHToUO3cuVPvvPOO5s+fr8mTJzu1P3HiRA0YMEDR0dFq0KCB+vXrp8cee0xjxozRpk2b5HA4NHjw4Atu93zS0tLUr18/zZkzR8HBwaVwhwAAKL+Y0wLAZbF9Qnf5+/uXdRkALCggIEDu7u7y9vY2P+Tv3r1bkjR58mR17txZkjR69Gj17NlT6enp8vT01IQJEzR69Gg9+OCDkqQ6depo4sSJGjlypMaNG2e2/9BDD6lPnz6SpFGjRql9+/YaO3asunfvLkkaOnSoHnroIfP44rZ7Ps8884w6dOigO+6442JvDQAA5R6hBQAAsKymTZuav4eEhEiSEhISVLNmTW3dulVRUVFOPSBycnKUnp6utLQ0eXt752sjKChIktSkSROnbenp6UpOTpa/v3+x2y3I119/rf/973/asmXLRV45AACQCC0AAICFubm5mb/nTWppt9slSSkpKZowYYLuuuuufM/z9PQstI3SaLcg//vf/xQTE6PAwECn7b1791anTp20Zs2aQp8PAACcEVoAAIAy5+7urpycnAt6TsuWLbVnzx7Vq1evVGu5mHZHjx6tRx55xGlbkyZNNGPGDN12222lVSIAAOUGoQUAAChzYWFh2rBhg2JjY+Xr62v2eijMiy++qFtvvVU1a9bU3XffLZvNpq1bt2r79u2aNGlSiWu5mHaDg4MLnHyzZs2aql27dolrAgCgvGL1EAAAUOZGjBghFxcXNWzYUFWqVFFcXFyRz+nevbuWLl2q77//Xq1bt1a7du00Y8YM1apV66JquVTtAgCAC2c4HA5HWRcB4OqVnJysgIAAJSUlsXoIcBmkp6frwIEDql27dpHzL8A6Cnvd+DsKACjP6GkBAAAAAAAsidACAACgmD788EP5+voW+NOoUaOyLg8AgKsOE3ECAAAU0+233662bdsWuO/sZVQBAEDpILQAAAAoJj8/P/n5+ZV1GQAAlBsMDwEAAAAAAJZEaAEAwFWIxcGuLHa7vaxLAADAkhgeAgDAVcTNzU2GYejYsWOqUqWKDMMo65JQCIfDoczMTB07dkw2m03u7u5lXRIAAJZCaAEAwFXExcVFNWrU0KFDhxQbG1vW5aCYvL29VbNmTdlsdIIFAOBshBYAAFxlfH19FR4erqysrLIuBcXg4uIiV1dXesUAAFAAQgsAAK5CLi4ucnFxKesyAAAALgp9EAEAAAAAgCURWgAAAAAAAEsitAAAAAAAAJbEnBYALimHwyFJSk5OLuNKAODKlPf3M+/vKQAA5QmhBYBL6sSJE5Kk0NDQMq4EAK5sp0+fVkBAQFmXAQDAZUVoAeCSqlixoiQpLi7uqvrHdnJyskJDQ3Xw4EH5+/uXdTmlgmu6MnBNV47Sui6Hw6HTp0+rWrVqpVgdAABXBkILAJeUzZY7dU5AQMBV9WEkj7+//1V3XVzTlYFrunKUxnVdTaEvAAAXgok4AQAAAACAJRFaAAAAAAAASyK0AHBJeXh4aNy4cfLw8CjrUkrV1XhdXNOVgWu6clyt1wUAwOVkOFg/CwAAAAAAWBA9LQAAAAAAgCURWgAAAAAAAEsitAAAAAAAAJZEaAEAAAAAACyJ0ALAJTVnzhyFhYXJ09NTbdu21caNG8u6pBIbP368DMNw+mnQoEFZl3VBfvzxR912222qVq2aDMPQkiVLnPY7HA69+OKLCgkJkZeXl2644Qbt27evbIq9AEVd18CBA/O9dj169CibYothypQpat26tfz8/FS1alX16tVLe/bscTomPT1dTz75pCpVqiRfX1/17t1bf/31VxlVXDzFua4uXbrke63+/e9/l1HFRZs7d66aNm0qf39/+fv7q3379vruu+/M/Vfi6wQAgJUQWgC4ZD755BMNGzZM48aN02+//aZmzZqpe/fuSkhIKOvSSqxRo0aKj483f9atW1fWJV2Q1NRUNWvWTHPmzClw/6uvvqo33nhDb7/9tjZs2CAfHx91795d6enpl7nSC1PUdUlSjx49nF67jz/++DJWeGHWrl2rJ598Ur/88otWrlyprKws3XTTTUpNTTWPeeaZZ/TNN9/os88+09q1a3XkyBHdddddZVh10YpzXZL0r3/9y+m1evXVV8uo4qLVqFFDU6dO1ebNm7Vp0yZ169ZNd9xxh3bs2CHpynydAACwEpY8BXDJtG3bVq1bt9bs2bMlSXa7XaGhoXrqqac0evToMq7uwo0fP15LlixRdHR0WZdSKgzD0FdffaVevXpJyu1lUa1aNQ0fPlwjRoyQJCUlJSkoKEjz58/XvffeW4bVFt+51yXl9rRITEzM1wPjSnHs2DFVrVpVa9eu1XXXXaekpCRVqVJFH330ke6++25J0u7duxUZGan169erXbt2ZVxx8Zx7XVJuT4vmzZtr5syZZVvcRahYsaJee+013X333VfF6wQAQFmipwWASyIzM1ObN2/WDTfcYG6z2Wy64YYbtH79+jKs7OLs27dP1apVU506ddS/f3/FxcWVdUml5sCBAzp69KjTaxYQEKC2bdte0a9ZnjVr1qhq1aqKiIjQ448/rhMnTpR1ScWWlJQkKffDsCRt3rxZWVlZTq9VgwYNVLNmzSvqtTr3uvJ8+OGHqly5sho3bqwxY8YoLS2tLMq7YDk5OVq8eLFSU1PVvn37q+Z1AgCgLLmWdQEArk7Hjx9XTk6OgoKCnLYHBQVp9+7dZVTVxWnbtq3mz5+viIgIxcfHa8KECerUqZO2b98uPz+/si7voh09elSSCnzN8vZdqXr06KG77rpLtWvXVkxMjJ577jndfPPNWr9+vVxcXMq6vELZ7XY9/fTT6tixoxo3biwp97Vyd3dXYGCg07FX0mtV0HVJUr9+/VSrVi1Vq1ZN27Zt06hRo7Rnzx59+eWXZVht4X7//Xe1b99e6enp8vX11VdffaWGDRsqOjr6in+dAAAoa4QWAFBMN998s/l706ZN1bZtW9WqVUuffvqpBg0aVIaVoShnD21p0qSJmjZtqrp162rNmjW6/vrry7Cyoj355JPavn37FTd/SlHOd12PPvqo+XuTJk0UEhKi66+/XjExMapbt+7lLrNYIiIiFB0draSkJH3++ed68MEHtXbt2rIuCwCAqwLDQwBcEpUrV5aLi0u+WfL/+usvBQcHl1FVpSswMFD169fX/v37y7qUUpH3ulzNr1meOnXqqHLlypZ/7QYPHqylS5dq9erVqlGjhrk9ODhYmZmZSkxMdDr+SnmtznddBWnbtq30/+3dv0sjaxTG8XORTFDQ4EgwUUgIxFaLkUCaNAFFwUIsglVQsfEHaFDBIogg2Cr+AXbTiqVC1FI7sQsYhCBqY6FGxWbOrVYI11Xvsu68Lt8PpMlM8RxO9wzzjojRu7IsS5LJpDiOI+vr69LT0yObm5vffk8AAJiA0gLAl7AsSxzHkVKp9Pqf53lSKpUknU77mOz3qdVqUqlUJBqN+h3lt0gkEhKJROp2dn9/LycnJ3/Nzn64vLyU29tbY3enqjIzMyM7OztycHAgiUSi7rrjOBIIBOp2VS6XpVqtGr2rj+Z6y4+Db03d1Vs8z5OXl5dvuycAAEzC6yEAvkyhUJB8Pi+9vb2SSqVkY2NDHh8fZWxszO9ov2RhYUGGhoYkHo/L1dWVrKysSENDg4yOjvod7dNqtVrdE+uLiws5PT0V27YlFovJ3NycrK2tSVdXlyQSCSkWi9LR0VH3JQ4TvTeXbduyuroqIyMjEolEpFKpyNLSkiSTSenv7/cx9c9NT0+L67qyu7srzc3Nr+cfhEIhaWxslFAoJBMTE1IoFMS2bWlpaZHZ2VlJp9NGf5Hio7kqlYq4riuDg4PS1tYmZ2dnMj8/L5lMRrq7u31O/7bl5WUZGBiQWCwmDw8P4rquHB0dyd7e3rfdEwAARlEA+EJbW1sai8XUsixNpVJ6fHzsd6RflsvlNBqNqmVZ2tnZqblcTs/Pz/2O9b8cHh6qiPznl8/nVVXV8zwtFova3t6uwWBQs9mslstlf0N/wntzPT09aV9fn4bDYQ0EAhqPx3VyclJvbm78jv1Tb80iIrq9vf16z/Pzs05NTWlra6s2NTXp8PCwXl9f+xf6Ez6aq1qtaiaTUdu2NRgMajKZ1MXFRb27u/M3+DvGx8c1Ho+rZVkaDoc1m83q/v7+6/XvuCcAAEzyj6rqnyxJAAAAAAAAPoMzLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJEoLQAAAAAAgJH+BS2cX9RaQP7PAAAAAElFTkSuQmCC", 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ucLkcASr0FisjP1tNQmJlSlZLmJAAl6JdIdZ+c4oLrGl38z1FevG3b60WOUdS4C1W/sH3mDKVX0FXI+v4ZCjqkP3CnggtAAAAANQIh2FUeCPsqEYTfJcjQI6jhBYOGdaUnFLJ4JEo4TVNn/MRcoSpZQ/ndgRagYHH9FrbMAxDkYHBCnW6DoYRJS0tTJnadnCWkGhXqNzOAEW5QhTqdGuPJ0fpBwfo9Jhe7TrYOiMmMNS6bqZpKt9TqKKDoYNXprbk7T6mYzVNyVtBV6NSpeN2wN7oHgIAAACgRhgH/6vo9ao6WisLSQenWf3rF/niSs4gURuYMn3OR3mzsJTHccg5NSUrTJCk8MAghR9s2bKrYL+8MlXk9Sr9QKakkjEvnIZDYU63olwhMiVtPxhoFHqLtacwR5KslhilCr2eak9WW9nxV6sTpOHEoKUFAAAAgJMCt5fVUTOzqBy6FZcjQHVc4Uo7OCtJsdejfE+RNb5Fo+BoGTLkcgSonjtCm3IylH4gy2qxUdp9Jz44yqeFzqFjmzhkqH5QRJmxUCriNBwKdtKC4lRBaAEAAADA9jxmya1sRcGFaZo+M5Acy43uqc6QIech441UthXK4ec08JCWLAGGU/WCwiVJ2UUHlO8pUlbRAeV4CuQ0DDU4OI2pwzAUf3B2kN2F+5XvLdL+g+tLsl77a7t/1RkeGKTH212mGFfoMRytFHzYgLA4eRFaAAAAALC9Qq9HXtMrh3HkARm9MpXvLbKec+P6F4fhUMgh56Oy430Uej0qOjhNqtPwHTPk0GlPc4oLlOcp1N7CXOV7iuQyAlT/kDCiYUj0wWlP8w5OSXpABd5iBRgO1Q+K8Jly1H1wlhmP6ZXXNBXocCoiMLgaR4+TGdEjAAAAANvL8xSo+CiDK3pMr/YX5UsquaEunUkEJecj+pDWCplFeRVOMSuVtLLI8xRYLSICHU6FBwb5BAxxQZFyyFCht1jZRQe0Mz9LHtNUeGCwogJDrHXjgiIVYDiV7ynS3sJc7SnMORhIBKiOO/yvOg1DoQEuBR0c46LI61F2Ub41MwlqH0ILAAAAALaXXXRAhUeZxrKke0KepJLuELGusBNR2knj0G4YO/KzfLp9HMm+gy0nJCnI4VJ0oG83jbrucAU6nCoyS6ZD3ZGfJVOmYlyhCj04Q4lhGKrrDpfbWTLt6Z6CHO0pyJUpU+EBboUH+IZL4QFBCju4rNBbrJ0HZxxB7URoAQAAAMD2sovylVmYd8Rf3E3T1O6CHO0vLmlp4XIEqK474kSWaHuJoXWsMSl25mcr++BAmBX5PXe3ig92D6kXFK6wwL+mSjUMQzHuMAU5A+U1vdpXmKed+dmSSgKSQMdfXXnCA4MVERAsr0ztyM/W3oMzh0QFhljhRqmQAJc1HoZXpjZk76ihYURxMiK0AAAAAGB7eZ4C/Z67q8J1NubstFoFRAQGq25QuE9XhoqYpimv6ZXnkMep1CXBMAw1ColRlCtEUsnYEptyMio8xmLTq1WZaVZgkBRWX26H76wcoU63IgODZUraU5ijXYUlM4c0OGxGkGBnoGLdJS1fduZnaV9hSYuYekERPtPUSiUDfLaKiLeer8pK0/6iA6fU9UDlEVoAAAAAsD2PaWrpnt+POK5FobdYP+5OtW6wE0PrlOl2UBFT0ud//qxn1s7WM2tna8K6L7Que7vMU+g3/ujAELU5GAYUmx6lZKy3Btk8nGmaSsvbo7XZ2yWVzOhxWnSTMrO3BDkDFeP6K4zIKjwghww1DI72WS/AcFjdU3YV7Neegy0t4oIi5SwnWOoS1diatnRr3l4t3pN61CthmubB8OnUuWYgtAAAAABOOqb+ukGrzONUsWzfZv2atc3nuEr/XrHvD63O2iapZDyLbtGJPlNnHo3H9Gr5vi36Yc8m/bBnkxbvSdWewtzjchxVVRqgVPW6Ow2H+tZtbXURWbZ3s5bt2SzvYeuXDMBZqI+3LlPWwS4kDYOj1SaiQZmWK07DoXoHu+HsyM/S/uIDCnQ4fWYOkUquSYODQcbO/GxlFub5zD7is65hqHlYPbUOLw1YvPpg61Ktz04vW6tkBRX7i/O1YNdvSsvbcyynFTbHlKcAAADASWRHfrb+u/1nOSrx+2PjkBh1iW5SZrl1w3/wNtg0JY/pUV5x4V83xjKVW1ygAk+RnIZThlEyA4Vx8Lf2yna7qClOw6Gc4gK9vmmehjbro/aRDeV2BKrIW6xfs/7U279/b013Wscdpm4xicdUY5G3WJkHuyxIktsRoEbB0dbx+p+pn/b9oZzigqOu6TAMnR7brExwIMNQp6gEtYmI1y9Z25TnKdSbv6eowFusbjGJCnG65JWpjPxsfbpthRbu3liyPRk6r367cqcdNSTFB5fsJy1vr3KKCxQRGKw67jCf828YhtVlZGdBlgwZchiOsjUe5HYE6OIGnbU2e7sKvMXakZ+l5zZ8pSsadlXX6CbWQJ0F3iLtKcjVr1nbtHhPqtLy9mhUm4uUGFqnMicVJwFCCwAAAOAkkpa3R2//vqBS6/ar31ady2nSn+8p0v8y1mlnfrayivKUVXRAucUFyi7Ot8aEyCnO13Prv1JYQJDCAtyKCAxWRGCwYl1hOi+uXZnBE48nQ9I59dpo6d7ftfXAXk1c94Uah8YqKjBEOcX52py7W3mewoPrGupXr43qBR3bIJz5nmKrVYEkxbrCfKbi9DdTUsqu9Zq/a8NR1w0wHGoQHFVOawcpxOnSwIQe2py7W/uL87WrYL9e2viNGgXHqI47XIXeYv15YJ92F+y3umO0iYhXv/ptjxjflE57mnlw5pbowFCFlfP5qB8UoUCHUwcOfsZCnW7FukLLrCeVhBynRScquW5rfbPzV5kqacnxZmqKIg9+Fg1JeZ5CZRUdUKHXI1NmmfExcPIjtAAAAABOMt5KjrNwpK4hOZ4CvZ+2RLsPjitQHo9paks5zexDnW71iG12QkOLQIdT59Rro7igSP0n7Ucd8BZpw/4d5a7bKjxOFzXo5DMIZGVkFeUp/2DwIUmNQ2MV4nRVq+6aZkqVGmPDW8FahmGoY1RjXd2om95LW6xCr0eFXo9+z91V7kCncUERurVpH0UGBpfbcsUwjJLBNB0OFXr/mmXE7Qwss25UYIjCAtwqKCyZujY0wKVIV8gRW8S4HE7dlNhTuwr26+fMP2QePLZ9RXnaV5RX7nsObQ2EUwNjWgAAAAC1UFVHuvDHwJShTrfig6N0WcMuuiCuo1yOsr+9GpKSwurp7qRzFOsKO6auIaZpam9hrgq8xda2WofHy3GCu8CcKE7D0GUNu+imxr0UcYTBSg0Zah5aVw+27K/Wh8zkUZ7owBAFHxLwNAyOLjc0CgsIUlRgyF/vc4VWGAwZhqFYV5geaHme+tVrK3c51/2veqWIgCD1q9+GriGnGFpaAAAAADbXPKyezq/f7pjf1zaiQbm/OQc5AtW3bivlFOcf8zZdjgCfG03DMNQ5qrHCA4LkdgYoJMD3JjQswK2z67VRgadILcLjpMMqSjp4bA7DUN0g3+4YiaF1dH79dop1hSkqMFguR4BuaXamWkXEaV7GOqXl7VWR16NYd5hOi26iAXEdFBcUWaXxNnYWZMtzcGYSlyNArSPijnkbNS3A4VDPOs3VPKzuMb3PYThU5+CMHuUxDEMuR4CuaNRVrSPi9O3Otdqwf4eyi/IUYDgVHxxZ0jWjXhvVcx992tjIwBCdX7+9sg62fuhazjgqUsk4Ff3qt1Xz3N2SpKZhdRXoqLg7h2EYquMO110tzlHvui20YNdv+j1nl7KK8uSVqbAAt+oHRapNeLx6xDZTYmgda6DR8gQ6nOpVp4VaF+TIYTgUfYTuKbAPwzyVhhMGAAAATnpFktKlQ2aKkKrWMqL0VvPwm07TrF57CeOQbR5e36GvHb6vw+up6L1Heq10+QFPkfI8BfKaptzOQIUFuOWQUaXAwjRN/XvzAn28bbkkqUFQlCZ3vk5RrpCjvPP4qs51Ovw6HGn7UsnsHDnF+SryeuQwDAU7XQp2uiq1jfLqrOhzJ+mIn4fK1ppbXKDCg61iAgynggMC5XYEVvqYy9RqVLVLSbSkYxs7BceOlhYAAACAjZXehNVkRwXDqLle/0err6J9VfTeI71WujwkwFWmVUdVeWVqR36W9bxFeP1yB5I80WryOh1p+5IUaDir1eKgsnVW57N8aK3VCZOO9zlFzWNMCwAAAAC1WpHXo5352ZJKbqg7RSXIaXCrBNgB/ycCAAAAqLVMSTnFBdZ0nWEBQWodXvHAkwBOHEILAAAAALWXaSqzMNcalLRxSKzigqs2mCeAmkdoAQAAAKBW25GfrUKvR5LUOSpBQY5AP1cEoBQDcQIAAACo1TymRy3D6svpcKh7TFN/lwPgEEx5CgAAANiK75SnOL5KpsA05T14W1Q6ACfdQ3B0THl6ItDSAgAAAECtVTIFpiEHGQVgS4xpAQAAAAAAbInQAgAAAAAA2BKhBQAAAAAAsCVCCwAAAAAAYEuEFgAAAAAAwJYILQAAAAAAgC0RWgAAAAAAAFsitAAAAAAAALZEaAEAAAAAAGyJ0AIAAAAAANgSoQUAAAAAALAlQgsAAAAAAGBLhBYAAAAAAMCWCC0AAAAAAIAtEVoAAAAAAABbIrQAAAAAAAC2RGgBAAAAAABsidACAAAAAADYEqEFAAAAAACwJUILAAAAAABgS4QWAAAAAADAlggtAAAAAACALRFaAAAAAAAAWyK0AAAAAAAAtkRoAQAAAAAAbInQAgAAAAAA2BKhBQAAAAAAsCVCCwAAAAAAYEuEFgAAAAAAwJYILQAAAAAAgC0RWgAAAAAAAFsitAAAAAAAALZEaAEAAAAAAGyJ0AIAAAAAANgSoQUAAAAAALAlQgsAAAAAAGBLhBYAAAAAAMCWCC0AAAAAAIAtEVoAAAAAAABbIrQAAAAAAAC2RGgBAAAAAABsidACAAAAAADYEqEFAAAAAACwJUILAAA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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot horizontal bar charts of the themes\n", + "for i in range(0,n_themes):\n", + "# for i in range(0,1):\n", + " # plot the horizontal bar chart with ordered values\n", + " df_temp = df_h4_updated_sparse[['description','theme_'+str(i+1)]].sort_values(by='theme_'+str(i+1), ascending=False).iloc[0:hhii_updated[i],:]\n", + " df_temp.plot.barh(x='description', y='theme_'+str(i+1))\n", + " fig_name=RESULTS_PATH + r'\\theme_' + str(i+1) + '.png'\n", + " plt.tight_layout()\n", + " ax = plt.gca() # get the current axes object\n", + " ax.invert_yaxis() # invert the y-axis\n", + " # fig = plt.gcf()\n", + "\n", + " # Insert word cloud\n", + " img = image.imread(RESULTS_PATH + r'\\word_clouds_surveys_theme '+str(i+1)+'.png')\n", + " # create a new axis for the image\n", + " # ax = plt.gca()\n", + " newax = ax.inset_axes([1.1, 0.25, 1.2, 1.2]) # adjust the position and size of the image\n", + " newax.imshow(img)\n", + " newax.axis(\"off\") # turn off the axis\n", + "\n", + " plt.savefig(fig_name, dpi=300, bbox_inches=\"tight\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot individual word clouds of the survey items\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_h4_updated=df_h4_updated[df_h4_updated.columns[[0,i]]].set_index('label').T.to_dict('list')\n", + " for k in sub_df_h4_updated:\n", + " sub_df_h4_updated[k] = sub_df_h4_updated[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, relative_scaling=1).generate_from_frequencies(sub_df_h4_updated)\n", + "\n", + " title = 'theme ' + str(i)\n", + " plt.imshow(wc)\n", + " plt.axis('off')\n", + " plt.title(title)\n", + " fig_name=RESULTS_PATH + r'\\word_clouds_items_theme '+str(i)+'.png'\n", + " plt.savefig(fig_name, dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot horizontal bar charts of q4\n", + "for i in range(0,n_themes):\n", + " # plot the horizontal bar chart with ordered values\n", + " # df_temp = df_q4[['score','theme_'+str(i+1)]].sort_values(by='theme_'+str(i+1), ascending=False)\n", + " df_temp = df_q4_ism[['score','theme_'+str(i+1)]]\n", + " df_temp.plot.barh(x='score', y='theme_'+str(i+1))\n", + " fig_name=RESULTS_PATH + r'\\score_' + str(i+1) + '.png'\n", + " plt.tight_layout()\n", + " ax = plt.gca() # get the current axes object\n", + " ax.invert_yaxis() # invert the y-axis\n", + " # fig = plt.gcf()\n", + " plt.savefig(fig_name, dpi=300)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot interaction network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weight_cutoff = .6\n", + "show_edge_cutoff = .6\n", + "\n", + "rows_with_nonzeros = np.where(np.amax(h4_updated_sparse, axis=1) > 0)[0]\n", + "\n", + "list_network_items = [list_columns[i] for i in rows_with_nonzeros]\n", + "list_themes = ['Theme '+str(i) for i in range(1, n_themes+1)]\n", + "\n", + "# create a new list of lists with items that start with each search sequence\n", + "new_list = [[item for item in list_network_items if item.endswith(seq)] for seq in score_pref]\n", + "\n", + "# get the number of items in each sublist\n", + "n_network_items = [len(sublist) for sublist in new_list]\n", + "row_labels = list_themes+ list_network_items + score_pref\n", + "influence_matrix = np.vstack((h4_ism, h4_updated_sparse[rows_with_nonzeros], q4_ism)) @ w4_ism.T\n", + "\n", + "weight = np.corrcoef(influence_matrix)\n", + "\n", + "# Define a color map\n", + "node_colors = []\n", + "for i in range(0, len(row_labels)):\n", + " if i < len(list_themes):\n", + " node_colors.append('lightyellow')\n", + " elif i < len(list_themes)+n_network_items[0]:\n", + " node_colors.append('lightgreen')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]:\n", + " node_colors.append('lightblue')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]:\n", + " node_colors.append('orange')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]:\n", + " node_colors.append('red')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]+len(score_pref):\n", + " node_colors.append('yellow')\n", + "\n", + "G = nx.Graph()\n", + "G.add_nodes_from(row_labels)\n", + "\n", + "for i in range(len(row_labels)):\n", + " for j in range(i + 1, len(row_labels)):\n", + " if np.abs(weight[i][j]) > weight_cutoff:\n", + " G.add_edge(row_labels[i], row_labels[j], weight=np.round(weight[i][j],2))\n", + "\n", + "weights = nx.get_edge_attributes(G, 'weight')\n", + "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if np.abs(d['weight']) > show_edge_cutoff]\n", + "\n", + "plt.figure(figsize=(16, 12))\n", + "pos = nx.spring_layout(G, iterations=200)\n", + "nx.draw_networkx_nodes(G, pos, node_color=node_colors)\n", + "nx.draw_networkx_edges(G, pos, edgelist=elarge, width=[.25*weights[edge] for edge in elarge])\n", + "\n", + "# nx.draw_networkx_labels(G, pos)\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [score_pref[0]]}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='lightgreen', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [score_pref[1]]}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='lightblue', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [score_pref[2]]}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='orange', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [score_pref[3]]}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='red', edgecolor='none', boxstyle='round,pad=0.2'))\n", + " \n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in list_themes}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='lightyellow', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node not in score_pref and node not in list_themes})\n", + "\n", + "fig_name=RESULTS_PATH + r'\\interaction_network.png'\n", + "plt.title('Interaction network')\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot interaction network including patients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weight_cutoff = .6\n", + "show_edge_cutoff = 1\n", + "\n", + "list_themes = ['Theme '+str(i) for i in range(1, n_themes+1)]\n", + "\n", + "# Sort the DataFrame based on 'nmf_cluster' in ascending order and 'nmf_cluster_loading' in descending order\n", + "df_w4_ism = df_w4_ism.sort_values(['nmf_cluster', 'nmf_cluster_loading'], ascending=[True, False])\n", + "w4_ism_ordered = df_w4_ism[['theme_' + str(i) for i in range(1, n_themes + 1)]].values\n", + "w4_nmf_cluster = df_w4_ism[['nmf_cluster']].values\n", + "\n", + "list_network_items = df_w4_ism.index.values.tolist()\n", + "# get the number of observations in each nmf cluster\n", + "unique_values, n_network_items = np.unique(w4_nmf_cluster, return_counts=True)\n", + "\n", + "row_labels = list_themes + list_network_items\n", + "influence_matrix = np.vstack((h4_ism, w4_ism_ordered)) @ w4_ism.T\n", + "\n", + "# save influence_matrix\n", + "df_influence_matrix = pd.DataFrame(influence_matrix)\n", + "df_influence_matrix.columns = df.index.to_list()\n", + "df_influence_matrix['nmf_cluster'] = np.vstack((np.zeros((len(list_themes),1)), w4_nmf_cluster))\n", + "df_influence_matrix.insert(loc=0, column='wise_id', value=(row_labels))\n", + "df_influence_matrix.set_index('wise_id')\n", + "df_influence_matrix.to_csv(RESULTS_PATH + r'\\influence_matrix.csv', sep=',', na_rep='.',index=True)\n", + "\n", + "weight = np.corrcoef(influence_matrix)\n", + "\n", + "# Define a color map\n", + "node_colors = []\n", + "for i in range(0, len(row_labels)):\n", + " if i < len(list_themes):\n", + " node_colors.append('lightyellow')\n", + " elif i < len(list_themes)+n_network_items[0]:\n", + " node_colors.append('green')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]:\n", + " node_colors.append('magenta')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]:\n", + " node_colors.append('orange')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]:\n", + " node_colors.append('red')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]+n_network_items[4]:\n", + " node_colors.append('brown')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]+n_network_items[4]+n_network_items[5]:\n", + " node_colors.append('indigo')\n", + "\n", + "G = nx.Graph()\n", + "G.add_nodes_from(row_labels)\n", + "\n", + "for i in range(len(row_labels)):\n", + " for j in range(i + 1, len(row_labels)):\n", + " if np.abs(weight[i][j]) > weight_cutoff:\n", + " G.add_edge(row_labels[i], row_labels[j], weight=np.round(weight[i][j],2))\n", + "\n", + "weights = nx.get_edge_attributes(G, 'weight')\n", + "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if np.abs(d['weight']) > show_edge_cutoff]\n", + "\n", + "plt.figure(figsize=(16, 12))\n", + "pos = nx.spring_layout(G, iterations=200)\n", + "nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=[10 for node in G.nodes()])\n", + "nx.draw_networkx_edges(G, pos, edgelist=elarge, width=[.25*weights[edge] for edge in elarge])\n", + "\n", + " \n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[0]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='green', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[1]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='magenta', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[2]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='orange', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[3]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='red', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[4]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='brown', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[5]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='indigo', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "\n", + "fig_name=RESULTS_PATH + r'\\interaction_network_patients.png'\n", + "plt.title('Interaction network')\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples.bck/getting_started_mofa.ipynb b/examples.bck/getting_started_mofa.ipynb new file mode 100644 index 0000000..0f29d79 --- /dev/null +++ b/examples.bck/getting_started_mofa.ipynb @@ -0,0 +1,593 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MOFA+: training a model in Python\n", + "Author: Ricard Argelaguet. \n", + "Affiliation: European Bioinformatics Institute, Cambridge, UK. \n", + "Date: 13/11/2019 \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook contains a detailed tutorial on how to train MOFA using Python.\n", + "A template script to run the code below can be found [here](https://github.com/bioFAM/MOFA2/tree/master/inst/scripts)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Load libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:16.898360Z", + "start_time": "2020-02-06T20:25:16.836870Z" + }, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "from mofapy2.run.entry_point import entry_point\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# initialise the entry point\n", + "ent = entry_point()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Load data\n", + "\n", + "To create a MOFA+ object you need to specify four dimensions: samples (cells), features, view(s) and group(s). MOFA objects can be created from a wide range of input formats:\n", + "\n", + "### 2.1) pandas data.frame format\n", + "A pandas data.frame with columns `sample`, `group`, `feature`, `view`, `value`. This is the most intuitive format, as it summarises all omics/groups in a single data structure. Also, there is no need to add rows that correspond to missing data.\n", + "\n", + "For example:\n", + "```\n", + "sample group feature value view\n", + "sample1 groupA gene1 2.8044 RNA\n", + "sample1 groupA gene3 2.2069 RNA\n", + "sample2 groupB gene2 0.1454 RNA\n", + "sample2 groupB gene1 2.7021 RNA\n", + "sample2 groupB promoter1 3.8618 Methylation\n", + "sample3 groupB promoter2 3.2545 Methylation\n", + "sample3 groupB promoter3 1.5014 Methylation\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we load a simulated data set with the following dimensions:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:20.952029Z", + "start_time": "2020-02-06T20:25:18.586476Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sample group feature view value\n", + "0 sample_0_group_0 group_0 feature_0_view_0 view_0 -2.05\n", + "1 sample_1_group_0 group_0 feature_0_view_0 view_0 0.10\n", + "2 sample_2_group_0 group_0 feature_0_view_0 view_0 1.44\n", + "3 sample_3_group_0 group_0 feature_0_view_0 view_0 -0.28\n", + "4 sample_4_group_0 group_0 feature_0_view_0 view_0 -0.88" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D = [1000,1000] # Number of features per view\n", + "M = len(D) # Number of views\n", + "K = 5 # Number of factors\n", + "N = [100,100] # Number of samples per group\n", + "G = len(N) # Number of groups\n", + "\n", + "data_dt = pd.read_csv(\"http://ftp.ebi.ac.uk/pub/databases/mofa/getting_started/data.txt.gz\", sep=\"\\t\")\n", + "data_dt.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2) List of matrices\n", + "A nested list of numpy arrays, where the first index refers to the view and the second index refers to the group. Samples are stored in the rows and features are stored in the columns. All views for a given group G must have the same samples in the rows. If there is any sample that is missing a particular view, the column needs to be filled with NAs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading the same data above in matrix format:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:21.324421Z", + "start_time": "2020-02-06T20:25:21.058184Z" + }, + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "data_prefix = \"http://ftp.ebi.ac.uk/pub/databases/mofa/getting_started\"\n", + "data_mat = [[None for g in range(G)] for m in range(M)]\n", + "for m in range(M):\n", + " for g in range(G):\n", + " data_mat[m][g] = np.loadtxt(\"%s/%d_%d.txt.gz\" % (data_prefix,m,g), delimiter=\"\\t\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "### 2.3 Define data options\n", + "- **scale_views**: if views have different ranges/variances, it is good practice to scale each view to unit variance. Default is False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:21.371541Z", + "start_time": "2020-02-06T20:25:21.359823Z" + } + }, + "outputs": [], + "source": [ + "ent.set_data_options(\n", + " scale_views = False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4) Add the data to the model\n", + "\n", + "This has to be run after defining the data options\n", + "- **likelihoods**: a list of strings, either \"gaussian\" (default), \"poisson\" or \"bernoulli\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:21.862319Z", + "start_time": "2020-02-06T20:25:21.805782Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=100 samples and D=1000 features...\n", + "Successfully loaded view='view0' group='group1' with N=100 samples and D=1000 features...\n", + "Successfully loaded view='view1' group='group0' with N=100 samples and D=1000 features...\n", + "Successfully loaded view='view1' group='group1' with N=100 samples and D=1000 features...\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# option 1: data.frame format\n", + "ent.set_data_df(data_dt, likelihoods = [\"gaussian\",\"gaussian\"])\n", + "\n", + "# option 2: nested matrix format\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\",\"gaussian\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) Set model options\n", + "\n", + "- **factors**: number of factors\n", + "- **spikeslab_weights**: use spike-slab sparsity prior in the weights? default is TRUE\n", + "- **ard_weights**: use ARD prior in the weights? Default is TRUE if using multiple views.\n", + "\n", + "Only change the default model options if you are familiar with the underlying mathematical model!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:26.955109Z", + "start_time": "2020-02-06T20:25:26.914220Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: True\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: True \n", + "\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n" + ] + } + ], + "source": [ + "ent.set_model_options(\n", + " factors = 10, \n", + " spikeslab_weights = True, \n", + " ard_weights = True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4) Set training options\n", + "\n", + "- **convergence_mode**: \"fast\" (default), \"medium\", \"slow\".\n", + "- **dropR2**: minimum variance explained criteria to drop factors while training\n", + "- **gpu_mode**: use GPU? (needs cupy installed and a functional GPU, see https://biofam.github.io/MOFA2/gpu_training.html)\n", + "- **seed**: random seed" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:29.519460Z", + "start_time": "2020-02-06T20:25:29.509712Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "GPU mode is activated, but GPU not found... switching to CPU mode\n", + "For GPU mode, you need:\n", + "1 - Make sure that you are running MOFA+ on a machine with an NVIDIA GPU\n", + "2 - Install CUPY following instructions on https://docs-cupy.chainer.org/en/stable/install.html\n", + "\n" + ] + } + ], + "source": [ + "ent.set_train_options(\n", + " convergence_mode = \"fast\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = True, \n", + " seed = 1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6) Build and train the MOFA object \n", + "\n", + "After training, the model will be saved as an hdf5 file" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:27:21.866240Z", + "start_time": "2020-02-06T20:27:18.843347Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -5381458.89 \n", + "\n", + "Iteration 1: time=0.06, ELBO=-575232.56, deltaELBO=4806226.336 (89.31084362%), Factors=9\n", + "Iteration 2: time=0.07, ELBO=-475536.26, deltaELBO=99696.298 (1.85258867%), Factors=8\n", + "Iteration 3: time=0.06, ELBO=-467044.81, deltaELBO=8491.451 (0.15779088%), Factors=7\n", + "Iteration 4: time=0.05, ELBO=-462583.09, deltaELBO=4461.713 (0.08290899%), Factors=6\n", + "Iteration 5: time=0.06, ELBO=-459116.18, deltaELBO=3466.915 (0.06442334%), Factors=5\n", + "Iteration 6: time=0.04, ELBO=-458285.22, deltaELBO=830.956 (0.01544108%), Factors=5\n", + "Iteration 7: time=0.04, ELBO=-457814.07, deltaELBO=471.158 (0.00875520%), Factors=5\n", + "Iteration 8: time=0.04, ELBO=-457487.58, deltaELBO=326.485 (0.00606685%), Factors=5\n", + "Iteration 9: time=0.06, ELBO=-457202.99, deltaELBO=284.587 (0.00528829%), Factors=5\n", + "Iteration 10: time=0.04, ELBO=-456926.93, deltaELBO=276.062 (0.00512988%), Factors=5\n", + "Iteration 11: time=0.05, ELBO=-456666.10, deltaELBO=260.835 (0.00484693%), Factors=5\n", + "Iteration 12: time=0.11, ELBO=-456437.43, deltaELBO=228.665 (0.00424912%), Factors=5\n", + "Iteration 13: time=0.08, ELBO=-456246.66, deltaELBO=190.774 (0.00354502%), Factors=5\n", + "Iteration 14: time=0.04, ELBO=-456087.59, deltaELBO=159.067 (0.00295584%), Factors=5\n", + "Iteration 15: time=0.05, ELBO=-455950.33, deltaELBO=137.263 (0.00255067%), Factors=5\n", + "Iteration 16: time=0.06, ELBO=-455826.84, deltaELBO=123.490 (0.00229474%), Factors=5\n", + "Iteration 17: time=0.04, ELBO=-455710.86, deltaELBO=115.979 (0.00215515%), Factors=5\n", + "Iteration 18: time=0.05, ELBO=-455597.76, deltaELBO=113.095 (0.00210156%), Factors=5\n", + "Iteration 19: time=0.05, ELBO=-455485.19, deltaELBO=112.574 (0.00209190%), Factors=5\n", + "Iteration 20: time=0.05, ELBO=-455372.67, deltaELBO=112.518 (0.00209085%), Factors=5\n", + "Iteration 21: time=0.06, ELBO=-455259.13, deltaELBO=113.542 (0.00210987%), Factors=5\n", + "Iteration 22: time=0.05, ELBO=-455141.61, deltaELBO=117.515 (0.00218370%), Factors=5\n", + "Iteration 23: time=0.05, ELBO=-455017.31, deltaELBO=124.306 (0.00230989%), Factors=5\n", + "Iteration 24: time=0.05, ELBO=-454890.60, deltaELBO=126.711 (0.00235458%), Factors=5\n", + "Iteration 25: time=0.06, ELBO=-454777.30, deltaELBO=113.294 (0.00210527%), Factors=5\n", + "Iteration 26: time=0.07, ELBO=-454687.38, deltaELBO=89.924 (0.00167099%), Factors=5\n", + "Iteration 27: time=0.08, ELBO=-454621.75, deltaELBO=65.626 (0.00121948%), Factors=5\n", + "Iteration 28: time=0.06, ELBO=-454575.85, deltaELBO=45.908 (0.00085308%), Factors=5\n", + "Iteration 29: time=0.24, ELBO=-454540.91, deltaELBO=34.939 (0.00064925%), Factors=5\n", + "Iteration 30: time=0.06, ELBO=-454510.81, deltaELBO=30.094 (0.00055922%), Factors=5\n", + "Iteration 31: time=0.06, ELBO=-454483.09, deltaELBO=27.720 (0.00051509%), Factors=5\n", + "Iteration 32: time=0.07, ELBO=-454457.65, deltaELBO=25.444 (0.00047280%), Factors=5\n", + "Iteration 33: time=0.09, ELBO=-454435.35, deltaELBO=22.304 (0.00041445%), Factors=5\n", + "Iteration 34: time=0.04, ELBO=-454416.89, deltaELBO=18.454 (0.00034292%), Factors=5\n", + "Iteration 35: time=0.06, ELBO=-454402.44, deltaELBO=14.450 (0.00026852%), Factors=5\n", + "Iteration 36: time=0.06, ELBO=-454391.52, deltaELBO=10.926 (0.00020303%), Factors=5\n", + "Iteration 37: time=0.04, ELBO=-454383.33, deltaELBO=8.189 (0.00015217%), Factors=5\n", + "Iteration 38: time=0.05, ELBO=-454377.13, deltaELBO=6.200 (0.00011521%), Factors=5\n", + "Iteration 39: time=0.06, ELBO=-454372.32, deltaELBO=4.807 (0.00008932%), Factors=5\n", + "Iteration 40: time=0.06, ELBO=-454368.46, deltaELBO=3.858 (0.00007169%), Factors=5\n", + "Iteration 41: time=0.04, ELBO=-454365.23, deltaELBO=3.230 (0.00006002%), Factors=5\n", + "Iteration 42: time=0.05, ELBO=-454362.40, deltaELBO=2.828 (0.00005256%), Factors=5\n", + "Iteration 43: time=0.05, ELBO=-454359.82, deltaELBO=2.585 (0.00004804%), Factors=5\n", + "Iteration 44: time=0.05, ELBO=-454357.37, deltaELBO=2.452 (0.00004556%), Factors=5\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "No output file name provided as a training options or to the save method. Saving to /tmp/mofa_20210223-153612.hdf5 .\n", + "Saving model in /tmp/mofa_20210223-153612.hdf5...\n" + ] + } + ], + "source": [ + "ent.build()\n", + "\n", + "ent.run()\n", + "\n", + "# Save the output\n", + "ent.save(outfile=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7) Downstream analysis\n", + "\n", + "This finishes the tutorial on how to train a MOFA model from python. To continue with the downstream analysis you can either use the [mofax](https://github.com/gtca/mofax) python package or the [MOFA2](https://www.bioconductor.org/packages/release/bioc/html/MOFA2.html) R package. Please, visit our [tutorials](https://biofam.github.io/MOFA2/tutorials.html) webpage for more information." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "latex_metadata": { + "affiliation": "European Bioinformatics Institute, Cambridge, UK", + "author": "Ricard Argelaguet", + "title": "MOFA+: training a model with Python" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples.bck/mofa_template_script_matrix.py b/examples.bck/mofa_template_script_matrix.py new file mode 100644 index 0000000..9f6147b --- /dev/null +++ b/examples.bck/mofa_template_script_matrix.py @@ -0,0 +1,153 @@ + +###################################################### +## Template script to train a MOFA+ model in Python ## +###################################################### + +from mofapy2.run.entry_point import entry_point +import pandas as pd +import io +import requests # to download the online data + +############### +## Load data ## +############### + +# The data format is a nested list of matrices, where the first index refers to the view and the second index refers to the group. +# samples are stored in the rows and features are stored in the columns. +# Missing values must be explicitly filled using NAs, including samples missing an entire view + +datadir = "/Users/ricard/data/mofaplus/test" +views = ["0","1"] +groups = ["0","1"] +data = [None]*len(views) +for m in range(len(views)): + data[m] = [None]*len(groups) + for g in range(len(groups)): + datafile = "%s/%s_%s.txt.gz" % (datadir, views[m], groups[g]) + data[m][g] = pd.read_csv(datafile, header=None, sep=' ') + +########################### +## Initialise MOFA model ## +########################### + +## (1) initialise the entry point +ent = entry_point() + + +## (2) Set data options +# - scale_views: if views have very different ranges, one can to scale each view to unit variance +ent.set_data_options( + scale_views = False +) + + +## (3) Define names +views_names = ["view1","view2"] +# groups_names = ["groupA","groupB"] + +# samples_names nested list with length n_groups. Each entry g is a list with the sample names for the g-th group +# - if not provided, MOFA will fill it with default samples names +samples_names = (...) + +# features_names nested list with length NVIEWS. Each entry m is a list with the features names for the m-th view +# - if not provided, MOFA will fill it with default features names +features_names = (...) + + +## (4) Set data matrix +ent.set_data_matrix(data, + views_names = views_names, + groups_names = groups_names, + samples_names = samples_names, + features_names = features_names +) + + +## (5) Set model options +# - factors: number of factors. Default is 15 +# - likelihods: likelihoods per view (options are "gaussian","poisson","bernoulli"). Default and recommended is "gaussian" +# - spikeslab_weights: use spike-slab sparsity prior in the weights? (recommended TRUE) +# - ard_weights: use automatic relevance determination prior in the weights? (TRUE if using multiple views) + +# using default values +ent.set_model_options() + +# using personalised values +ent.set_model_options( + factors = 5, + spikeslab_weights = True, + ard_weights = True +) + +## (5) Set training options ## +# - iter: number of iterations +# - convergence_mode: "fast", "medium", "slow". Fast mode is usually good enough. +# - dropR2: minimum variance explained criteria to drop factors while training. Default is None, inactive factors are not dropped during training +# - gpu_mode: use GPU mode? this functionality needs cupy installed and a functional GPU, see https://biofam.github.io/MOFA2/gpu_training.html +# - seed: random seed + +# using default values +ent.set_train_options() + +# using personalised values +ent.set_train_options( + iter = 100, + convergence_mode = "fast", + dropR2 = None, + gpu_mode = False, + seed = 42 +) + +#################################### +## Build and train the MOFA model ## +#################################### + +# Build the model +ent.build() + +# Run the model +ent.run() + +#################### +## Save the model ## +#################### + +outfile = "/Users/ricard/data/mofaplus/hdf5/test.hdf5" + +# - save_data: logical indicating whether to save the training data in the hdf5 file. +# this is useful for some downstream analysis in R, but it can take a lot of disk space. +ent.save(outfile, save_data=True) + +######################### +## Downstream analysis ## +######################### + +# Check the mofax package for the downstream analysis in Python: https://github.com/bioFAM/mofax +# Check the MOFA2 R package for the downstream analysis in R: https://www.bioconductor.org/packages/release/bioc/html/MOFA2.html +# All tutorials: https://biofam.github.io/MOFA2/tutorials.html + +# Extract factor values (a list with one matrix per sample group) +factors = ent.model.nodes["Z"].getExpectation() + +# Extract weights (a list with one matrix per view) +weights = ent.model.nodes["W"].getExpectation() + +# Extract variance explained values +r2 = ent.model.calculate_variance_explained() + +# Interact directly with the hdf5 file +import h5py +f = h5py.File(outfile, 'r') +f.keys() + +# Extract factors +f["expectations"]["Z"]["group_0"].value +f["expectations"]["Z"]["group_1"].value + +# Extract weights +f["expectations"]["W"]["view_0"].value +f["expectations"]["W"]["view_1"].value + +# Extract variance explained estimates +f["variance_explained"]["r2_per_factor"] +f["variance_explained"]["r2_total"] diff --git a/examples.bck/simulation_biomed.ipynb b/examples.bck/simulation_biomed.ipynb new file mode 100644 index 0000000..11e1867 --- /dev/null +++ b/examples.bck/simulation_biomed.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -e .." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coucou\n", + "Relative error: 0.06\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import adilsm.adilsm as ilsm\n", + "\n", + "max_noise_level = 0.01\n", + "# Generate a random non-negative matrix with 100 rows and 10 columns\n", + "A = np.random.rand(100, 10)\n", + "# Swap the columns of the A and add some noise to generate B\n", + "B = np.random.permutation(A.T).T + np.random.uniform(low=0, high=max_noise_level, size=A.shape)\n", + "# Add noise to A\n", + "A += np.random.uniform(low=0, high=max_noise_level, size=A.shape)\n", + "\n", + "# ISM is expected to recognize that A and B convey the same information up to some noise, albeit with the columns of B swapped around.\n", + "\n", + "m0 = np.hstack((A, B))\n", + "\n", + "n_items = [A.shape[1], B.shape[1]]\n", + "n_scores = len(n_items)\n", + "n_embedding, n_themes = [10,10]\n", + "\n", + "h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0=False, update_h4_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "error = np.linalg.norm(m0 - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0)\n", + "print('Relative error: ',round(error, 2))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples.bck/uci_digits_all.ipynb b/examples.bck/uci_digits_all.ipynb new file mode 100644 index 0000000..6b04a75 --- /dev/null +++ b/examples.bck/uci_digits_all.ipynb @@ -0,0 +1,2061 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from mofapy2.run.entry_point import entry_point\n", + "\n", + "RESULTS_PATH = r'C:\\Users\\paul_\\OneDrive\\Pro\\George\\Wise\\analysis\\results\\uci_digits'\n", + "\n", + "list_solutions = None\n", + "predefined_solution = ''\n", + "\n", + "# ISM algorithmic options\n", + "embed = True\n", + "max_iter_integrate = 20\n", + "update_h4_ism = True\n", + "\n", + "# Grid search limits\n", + "min_embedding = 8\n", + "max_embedding = 10\n", + "min_themes = 10\n", + "max_themes = 10\n", + "\n", + "# list_solutions contains one ore more solutions selected because of their low condition numbers\n", + "list_solutions = [[9,10]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " scaler = StandardScaler()\n", + " Xs_norm[i] = scaler.fit_transform(Xs[i])\n", + "\n", + "data_mat = [[None for g in range(1)] for m in range(6)]\n", + "\n", + "for m in range(6):\n", + " data_mat[m][0] = Xs_norm[m]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "Successfully loaded view='view4' group='group0' with N=2000 samples and D=47 features...\n", + "Successfully loaded view='view5' group='group0' with N=2000 samples and D=6 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "- View 4 (view4): gaussian\n", + "- View 5 (view5): gaussian\n", + "\n", + "\n", + "\n", + "Warning: some view(s) have less than 15 features, MOFA won't be able to learn meaningful factors for these view(s)...\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -9709296.32 \n", + "\n", + "Iteration 1: time=0.35, ELBO=-1404936.80, deltaELBO=8304359.518 (85.52998325%), Factors=9\n", + "Iteration 2: time=0.31, ELBO=-1304107.77, deltaELBO=100829.032 (1.03847930%), Factors=9\n", + "Iteration 3: time=0.31, 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Factors=9\n", + "Iteration 380: time=0.42, ELBO=-1234821.17, deltaELBO=4.838 (0.00004983%), Factors=9\n", + "Iteration 381: time=0.37, ELBO=-1234816.35, deltaELBO=4.819 (0.00004963%), Factors=9\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "ent = entry_point()\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(6)])\n", + "ent.set_model_options(\n", + " factors = 10, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + ")\n", + "ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + ")\n", + "ent.build()\n", + "ent.run()\n", + "model_mofa = ent.model.nodes[\"Z\"].getExpectation()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "X_car_p = Xs[2].copy()\n", + "X_car_p[X_car_p<0] = 0\n", + "X_car_n = -Xs[2].copy()\n", + "X_car_n[X_car_n<0] = 0\n", + "\n", + "Xs_concat = Xs[0]\n", + "Xs_concat = np.hstack((Xs_concat, Xs[1], X_car_p, X_car_n))\n", + "\n", + "# Xs_concat = np.hstack((Xs[0], Xs[1]))\n", + "\n", + "\n", + "for X in Xs[3:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "\n", + "m0 = Xs_concat\n", + "m0_nan_0 = m0.copy()\n", + "\n", + "# create m0_weight with ones and zeros if not_missing/missing value\n", + "m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "\n", + "max_values = np.max(m0_nan_0, axis=0)\n", + "# Replace maximum values equal to 0 with 1\n", + "m0 = np.divide(m0, np.where(max_values == 0, 1, max_values))\n", + "m0_nan_0 = np.divide(m0_nan_0, np.where(max_values == 0, 1, max_values))\n", + "\n", + "\n", + "list_columns = [str(i) for i in range(m0.shape[1])]\n", + "score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-kar-p', 'mfeat-kar-n', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", + "n_items = [Xs[i].shape[1] for i in range(2)] + [X_car_p.shape[1], X_car_n.shape[1]] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", + "# score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", + "# n_items = [Xs[i].shape[1] for i in range(2)] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", + "n_scores = len(n_items)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def format_loadings_merged(h4, list_solutions, list_columns):\n", + " # Format loadings\n", + " df_h4 = pd.DataFrame(data=h4)\n", + " list_themes = []\n", + " for i_solution in range(0, len(list_solutions)):\n", + " list_themes = list_themes + ['theme_' + str(i) + '_' + str(list_solutions[i_solution][0]) + '_' + str(list_solutions[i_solution][1]) for i in range(1, list_solutions[i_solution][1] + 1)]\n", + " \n", + " df_h4.columns = list_themes\n", + " df_h4.insert(loc=0, column='label', value=(list_columns))\n", + "\n", + " # Add description index\n", + " df_h4['description'] = df_h4['label']\n", + " \n", + " return df_h4\n", + "\n", + "def format_loadings(h4, list_columns):\n", + " # Format loadings\n", + " df_h4 = pd.DataFrame(data=h4)\n", + " n_comp = len(df_h4.columns)\n", + " df_h4.columns = ['theme_' + str(i) for i in range(1, n_comp + 1)]\n", + " df_h4.insert(loc=0, column='label', value=(list_columns))\n", + "\n", + " # Add description index\n", + " df_h4['description'] = df_h4['label']\n", + " \n", + " return df_h4\n", + "\n", + "def generate_h4_sparse(h4, q4_ism, n_items, n_comp, n_scores):\n", + " # Calculate hhii of each h column and generate sparse loadings\n", + " hhii = np.zeros(n_comp, dtype=int)\n", + " h_threshold = np.zeros(n_comp)\n", + "\n", + " if q4_ism is not None:\n", + " i1 = 0\n", + " for i_score in range(0,n_scores):\n", + " i2 = i1+n_items[i_score]\n", + " h4[i1:i2,:] *= q4_ism[i_score]\n", + " i1 = i2\n", + "\n", + " for i in range(0,n_comp):\n", + " # calculate inverse hhi\n", + " if np.max(h4[:,i]) > 0:\n", + " hhii[i] = int(round(np.sum(h4[:, i])**2 / np.sum(h4[:, i]**2)))\n", + " # hhii[i] = np.count_nonzero(h4[:, i])\n", + " \n", + " # sort the dataframe by score in descending order\n", + " h_threshold[i] = np.sort(h4[:, i], axis=0)[::-1][hhii[i]-1] * .8\n", + " \n", + "\n", + " h4_sparse = np.where(h4 < h_threshold[None,:], 0, h4)\n", + " \n", + " return h4_sparse, hhii\n", + "\n", + "def integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes):\n", + " EPSILON = np.finfo(np.float32).eps\n", + "\n", + " # Generate w for each score, based on sparse loadings and create tensor_score\n", + "\n", + " # Extract score-related items\n", + " i1 = 0\n", + " for i_score in range(n_scores):\n", + " i2 = i1+n_items[i_score]\n", + " w4_score = w4_ism.copy()\n", + " h4_score = h4_sparse[i1:i2, :].copy()\n", + " m0_score = m0_nan_0[:, i1:i2]\n", + " m0_weight_score = m0_weight[:, i1:i2]\n", + " i1=i2\n", + " # # Normalize w4_score by max column and update h4_score\n", + " # max_values = np.max(w4_score, axis=0)\n", + " # # Replace maximum values equal to 0 with 1\n", + " # w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " # h4_score = np.multiply(h4_score, max_values)\n", + " # h4_score0 = h4_score.copy()\n", + "\n", + " # Apply multiplicative updates to preserve h sparsity \n", + " for _ in range(0, 200):\n", + " # Weighted multiplicative rules\n", + " m0_score_est = w4_score @ h4_score.T\n", + " h4_score *= ((w4_score.T @ m0_score) / (w4_score.T @ (m0_score_est*m0_weight_score) + EPSILON)).T\n", + " w4_score *= (m0_score @ h4_score / ((m0_weight_score*m0_score_est) @ h4_score + EPSILON))\n", + " # if i % 10 == 0:\n", + " # # Normalize w4_score by max column and update h4_score\n", + " # max_values = np.max(w4_score, axis=0)\n", + " # # Replace maximum values equal to 0 with 1\n", + " # w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " # h4_score = np.multiply(h4_score, max_values)\n", + " # if np.linalg.norm(h4_score-h4_score0)/max(np.linalg.norm(h4_score0),EPSILON) < 1.e-10:\n", + " # print(i)\n", + " # break\n", + " # else:\n", + " # h4_score0 = h4_score.copy()\n", + "\n", + " # Normalize w4_score by max column and update h4_score\n", + " max_values = np.max(w4_score, axis=0)\n", + " # Replace maximum values equal to 0 with 1\n", + " w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " h4_score = np.multiply(h4_score, max_values)\n", + "\n", + " # Generate embedding tensor and initialize h4_updated\n", + " if i_score == 0:\n", + " tensor_score = w4_score\n", + " h4_updated = h4_score\n", + " else:\n", + " tensor_score = np.hstack((tensor_score, w4_score))\n", + " h4_updated = np.vstack((h4_updated, h4_score))\n", + "\n", + " # Apply NTF with prescribed number of themes and update themes\n", + " my_ntfmodel = NTF(n_components=n_themes, leverage=None, init_type=2, max_iter=200, tol=1e-6, verbose=-1, random_state=0)\n", + "\n", + " if q4_ism is None:\n", + " estimator_ = my_ntfmodel.fit_transform(tensor_score, n_blocks=n_scores)\n", + " else:\n", + " estimator_ = my_ntfmodel.fit_transform(tensor_score, w=w4_ism, h=h4_ism, q=q4_ism, update_h=update_h4_ism, n_blocks=n_scores)\n", + "\n", + " w4_ism = estimator_.w\n", + " h4_ism = estimator_.h\n", + " q4_ism = estimator_.q\n", + "\n", + " # Update loadings based on h4_updated (initialized by multiplicative updates)\n", + " h4_updated = h4_updated @ h4_ism\n", + " h4_updated_sparse, hhii_updated = generate_h4_sparse(h4_updated, q4_ism, n_items, n_themes, n_scores)\n", + "\n", + " return h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "condition number(9, 10) = 7.99\n", + "condition number (primary NMF): 4.57\n" + ] + } + ], + "source": [ + "if predefined_solution != '':\n", + " max_iter_integrate = 0\n", + " # Read pre-defined themes\n", + " df_h4_updated = pd.read_csv(DATA_PATH + predefined_solution)\n", + " h4_updated = df_h4_updated.values.astype(np.float_)\n", + " h4_updated_sparse = h4_updated.copy()\n", + " list_solutions = [[h4_updated.shape[1],h4_updated.shape[1]]]\n", + "\n", + "if list_solutions is not None:\n", + " perform_grid_search = False\n", + "else:\n", + " perform_grid_search = True\n", + "\n", + "if perform_grid_search:\n", + " # Perform grid search first to select solutions with low condition numbers\n", + " cond = np.ones((max_embedding+1, max_themes+1))*999\n", + " list_solutions = []\n", + " for n_embedding in range(min_embedding, max_embedding+1):\n", + " for n_themes in range(min_themes, max_themes+1):\n", + " list_solutions += [[n_embedding, n_themes]]\n", + "else:\n", + " h4_updated_merged = None\n", + "\n", + "for n_embedding, n_themes in list_solutions:\n", + " if predefined_solution == '':\n", + " # Initial Embedding\n", + "\n", + " my_nmfmodel = NMF(n_components=n_embedding, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=0)\n", + " # estimator_ = my_nmfmodel.fit_transform(m0.copy(), sparsity=.5, regularization='components')\n", + " estimator_ = my_nmfmodel.fit_transform(m0.copy())\n", + " \n", + " w4 = estimator_.w\n", + " h4 = estimator_.h\n", + " \n", + " h4_sparse, hhii = generate_h4_sparse(h4, None, n_items, n_embedding, n_scores)\n", + "\n", + " if embed:\n", + " # Embed using scores w4 found in preliminary NMF and initialize themes through NTF \n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4, None, None, n_scores, n_items, n_themes) \n", + " else:\n", + " h4_updated = h4\n", + " h4_updated_sparse = h4_sparse\n", + " hhii_updated = hhii\n", + " w4_ism = w4\n", + " h4_ism = np.identity(n_themes)\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + "\n", + " else: \n", + " w4_ism = np.ones((m0.shape[0], n_themes))\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + " w4 = w4_ism\n", + " h4 = h4_updated.copy()\n", + " h4_sparse = h4\n", + " n_themes = list_solutions[0][1]\n", + " h4_updated_merged = None\n", + " if embed:\n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes)\n", + " else:\n", + " h4_updated = h4\n", + " h4_updated_sparse = h4_sparse\n", + " hhii_updated = hhii\n", + " w4_ism = w4\n", + " h4_ism = np.identity(n_themes)\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + "\n", + " if embed:\n", + " # Iterate embedding with themes subtensor until sparsity becomes stable \n", + " flag = 0\n", + " for iter_integrate in range(0, max_iter_integrate):\n", + " # print(iter_integrate, hhii_updated)\n", + " # indices = np.nonzero(q4_ism[:, 0])[0]\n", + " # non_zero_elements = q4_ism[indices, 0]\n", + " # print(iter_integrate, np.column_stack((indices, non_zero_elements))) \n", + " hhii_updated_0 = hhii_updated.copy()\n", + "\n", + " if iter_integrate == 0: \n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, np.identity(n_themes), q4_ism, n_scores, n_items, n_themes)\n", + " else:\n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes)\n", + " \n", + " if (hhii_updated == hhii_updated_0).all():\n", + " flag+=1\n", + " else:\n", + " flag=0\n", + " \n", + " if flag==3:\n", + " break\n", + " \n", + " if perform_grid_search:\n", + " cond[n_embedding, n_themes] = np.linalg.cond(h4_updated)\n", + " # cond[n_embedding, n_themes] = np.linalg.cond(normalize(h4_updated, axis=0, norm='l2'))\n", + " elif len(list_solutions) > 1:\n", + " # Construct merged solutions\n", + " if h4_updated_merged is None:\n", + " h4_updated_merged = h4_updated\n", + " else:\n", + " h4_updated_merged = np.hstack((h4_updated_merged, h4_updated))\n", + " \n", + " print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated), 2)) \n", + "\n", + "if perform_grid_search:\n", + " row, col = np.unravel_index(np.argmin(cond), cond.shape)\n", + " print('minimum condition number achieved for '+ str(row) + ' embeddings and ' + str(col) + ' themes')\n", + "\n", + "if len(list_solutions) == 1:\n", + " # print the condition number achieved by NMF alone\n", + " print('condition number (primary NMF): ', np.round(np.linalg.cond(h4_sparse),2))\n", + " # print(h4_ism)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/100: 142 iterations with final cost -1337315.771801\n", + "Run 2/100: 142 iterations with final cost -1337269.733267\n", + "Run 3/100: 142 iterations with final cost -1337257.988570\n", + "Run 4/100: 142 iterations with final cost -1337252.456298\n", + "Run 5/100: 141 iterations with final cost -1337452.024950\n", + "Run 6/100: 141 iterations with final cost -1337308.567830\n", + "Run 7/100: 141 iterations with final cost 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generate N visually distinct colours\n", + "\n", + "# n_comp_pca_mvmds = 12\n", + "# n_comp_pca_mvmds = 10\n", + "# n_comp_pca_mvmds = 9\n", + "n_comp_pca_mvmds = 10\n", + "\n", + "# MVMDS reduction\n", + "mvmds = MVMDS(n_components=n_comp_pca_mvmds)\n", + "Xs_mvmds_reduced = mvmds.fit_transform(Xs)\n", + "\n", + "# PCA reduction concatenated views \n", + "pca = PCA(n_components=n_comp_pca_mvmds)\n", + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "Xs_pca_reduced = pca.fit_transform(Xs_concat)\n", + "\n", + "# NMF reduction concatenated views \n", + "\n", + "my_nmfmodel = NMF(n_components=n_themes, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=1)\n", + "estimator_ = my_nmfmodel.fit_transform(m0.copy())\n", + "\n", + "w4_nmf = estimator_.w\n", + "h4_nmf = estimator_.h\n", + "\n", + "stress = []\n", + "w4_ism_mds = mds.fit_transform(w4_ism[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "Xs_mvmds_reduced_mds = mds.fit_transform(Xs_mvmds_reduced[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "w4_nmf_mds = mds.fit_transform(w4_nmf[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "Xs_pca_reduced_mds = mds.fit_transform(Xs_pca_reduced[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "w4_mofa = model_mofa\n", + "w4_mofa_mds = mds.fit_transform(normalize(w4_mofa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "w4_gfa = model_gfa['Z']\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "# stress = []\n", + "# w4_ism_mds = mds.fit_transform(normalize(w4_ism[:n_marker_genes,:], axis=0, norm='l2'))\n", + "# stress.append(mds.stress_)\n", + "# Xs_mvmds_reduced_mds = mds.fit_transform(normalize(Xs_mvmds_reduced[:n_marker_genes,:], axis=0, norm='l2'))\n", + "# stress.append(mds.stress_)\n", + "# w4_nmf_mds = mds.fit_transform(normalize(w4_nmf[:n_marker_genes,:], axis=0, norm='l2'))\n", + "# stress.append(mds.stress_)\n", + "# Xs_pca_reduced_mds = mds.fit_transform(normalize(Xs_pca_reduced[:n_marker_genes,:], axis=0, norm='l2'))\n", + "# stress.append(mds.stress_)\n", + "# m0_mds = mds.fit_transform(normalize(m0[:n_marker_genes,:]))\n", + "# stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# n_comp_pca_mvmds = 13\n", + "\n", + "# # MVMDS reduction\n", + "# mvmds = MVMDS(n_components=n_comp_pca_mvmds)\n", + "# Xs_mvmds_reduced = mvmds.fit_transform(Xs)\n", + "\n", + "# stress = []\n", + "# Xs_mvmds_reduced_mds = mds.fit_transform(Xs_mvmds_reduced[:n_marker_genes,:])\n", + "# stress.append(mds.stress_)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 5.81\n", + "0.9233\n", + "8 4.01\n", + "0.8983\n", + "9 5.84\n", + "0.9261\n", + "4 1.91\n", + "0.881\n", + "7 2.91\n", + "0.867\n", + "9 4.39\n", + "0.8998\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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R5eXlymw2q2nTpimllPryyy+Vpmlqz549avLkyUdVJmLBggWqpKRE7du3T82aNUvFxsYqi8Wi9u3bF2g7fPhw1aVLF+V0OgPb/H6/GjBggGrbtm1g25133qkAtXLlysC24uJiFR4e3miqFoeYupSamtpgWtVDDz2kADV79uxGbQ9MOZo2bZrSdV0tXbq0wetTp05VgPr++++VUvVTAwF18803N2g3fvz44y4TccAFF1ygAFVVVaWUUsrpdDZ6L3NycpTFYlGPPPJIYNvhykQ0NT3u8ccfV5qmqb179x62z6+//roC1MaNGw/b7nBlIkaNGqXS09Mbba+rq1OAuu+++w577J07dypd19WFF17Y6F4cPG3s12UivF5vo/IpFRUVKj4+Xl199dWBbXfccYcKCws7bLmKbt26NZiqejgjRoxQHTp0aFZbIYQQ4lRzcDz74osvqtDQ0ECscumll6phw4YppVSjMhFKKXXPPfcoQG3fvj2wraqqSlmt1kZT5A8uvXDJJZeo4cOHK6WU8vl8KiEhQU2ZMqXJ2OxwZSIO6Natm4qMjAz8ft68ecpgMCiDwaD69++v/v73v6tvvvkmUE7rSA70taSkRJWUlKhdu3ap//73v0rTNNW5c+dAvFNTU6MiIiLUdddd12D/wsJCFR4e3mB79+7dVWJioqqsrGzQT6BBzHaosgwH7s3BseXgwYNVaGhoo/jx4HjsmmuuUYmJiaq0tLRBm3Hjxqnw8PDAe/3ss88qQH344YeBNnV1dSozM/O4y0QcEB4ernr06BH4fVMx8fvvv68A9d133wW2Ha5MRFPHGDlyZJOx7q9dccUVKjo6+ojtDlcmIjg4uEEce8CXX36pAPX1118f9thvvvmmAtTTTz/d6LWD38dfP9s0dd0rVqxQgHr33XcD244UM1dUVBzxeehgZrNZ3XTTTc1qK8QfhYwMFkIct6uvvpqvv/6aoUOHsmzZMh599FEGDRpE27ZtWb58eZP7REZGcvbZZwdGYsyYMYMBAwaQmpp61Oc/88wziY2NJSUlhUsuuYTg4GA+++wzkpOTgfoyB4sWLWLs2LHU1NRQWlpKaWkpZWVljBw5kp07d5KXlwfAV199Rb9+/QIjRqF+2lNTU/6a6+OPP6Zbt25ceOGFjV47MBXro48+okOHDmRlZQX6V1payhlnnAEQKCvw1VdfAfULihzszjvvPOb+/VpISAhAYAVki8US+Obf5/NRVlZGSEgI7du3b/aqwTabLfDruro6SktLGTBgAEqpI47KPTBt7uARKUfL4XA0WATmgAOLthypXMWcOXPw+/089NBDjUZBHG46ncFgCJRP8fv9lJeX4/V66dWrV4N7FxERQV1dXaOSDweLiIhg8+bNzVqYIzIyskEJEiGEEEI0bezYsTgcDr744gtqamr44osvDjtT7YorrgBoMDr4448/xul0HjZeHD9+PEuWLKGwsJBFixZRWFh4XDPiQkJCArEa1E9nX7FiBeeffz7r16/nySefZOTIkSQlJTVZFq0pdXV1xMbGEhsbS2ZmJnfffTenn346n376aSDemT9/PpWVlVx++eUNYlaDwUDfvn0DMWtBQQHr1q1jwoQJhIeHN+hnx44dj+maS0pK+O6777j66qtp3bp1g9cO9E8pxccff8zo0aNRSjXo48iRI6mqqgrEYF999RWJiYlccsklgeMEBQU1WrD4ePz6fTo4JnY6nZSWltKvXz+AY4qrq6qqKC0tZciQIezevZuqqqrD7ltWVnZcMTUcf1z98ccfExMTE1hs72CHi6sPvm6Px0NZWRmZmZlEREQ0iqsPFzPbbDbMZjNLliyhoqLisH0FiavFn5Mkg4UQJ8TIkSP55ptvqKys5LvvvuOWW25h7969nHfeeRQXFze5z/jx45k/fz65ubnMmTPnmAPil156ifnz5zNr1izOPfdcSktLGwQou3btQinFgw8+GAhwD/xMnjwZINDHvXv30rZt20bnaN++/TH1DepruB2pDvLOnTvZvHlzo/61a9euUf90XScjI+OE9e/XDpTuCA0NBeqTmM888wxt27bFYrEQExNDbGwsGzZsOGLAeUBubi4TJ04kKiqKkJAQYmNjGTJkCECzj6F+njp3LGw2W5P1y5xOZ+D1A30pLCwM/JSXlwP176Gu68f08PLOO+/QtWvXQM2y2NjYQF3BA26++WbatWvHOeecQ3JycuALloM98sgjVFZW0q5dO7p06cI999xzyBWqlVLNqvkmhBBCnOpiY2M588wzmTFjBrNnz8bn8zVIDv5a165d6dy5c4PSEjNmzCAmJoaRI0cecr9zzz2X0NBQZs6cyXvvvUfv3r3JzMw85n7X1tYGYrUDevfuzezZs6moqODHH3/kH//4BzU1NVxyySVs2bLliMe0Wq3Mnz+f+fPn89Zbb9GhQweKi4sbJOEOJNjOOOOMRnHrvHnzGsSswAmNqw/ULT5cXF1SUkJlZSWvvvpqo/5NmjQJaBhXZ2ZmNoqZTnRcffD7VF5ezh133EF8fDw2m43Y2FjS0tKA5sfE33//PWeeeSbBwcFEREQQGxvLP//5z2Yf43hiamh+XF1SUtIgrj7wjJGdnU379u0xGo1HdV6Hw8FDDz1ESkpKg2eSysrKBtd9pJjZYrHwxBNPMHfuXOLj4xk8eDBPPvkkhYWFTZ5X4mrxZ3R0f/qEEOIIgoKCGDRoEIMGDSImJoYpU6Ywd+5cJkyY0Kjt+eefj8ViYcKECbhcLsaOHXtM5+zTpw+9evUCYMyYMQwcOJDx48ezfft2QkJCAou/3X333YcM0o8nGP81n8931Pv4/X66dOnC008/3eTrKSkpx9utZtu0aRNxcXGEhYUB8Nhjj/Hggw9y9dVX8+ijjxIVFYWu69x5551NLqz3az6fj7POOovy8nLuvfdesrKyCA4OJi8vj4kTJx7xGNHR0QBUVFQERnsfrcTERBYvXtwomCsoKADqF6IDuOOOO3jnnXcCrw8ZMqTRYiZHY/r06UycOJExY8Zwzz33EBcXh8Fg4PHHH2+wQEZcXBzr1q3jm2++Ye7cucydO5e33nqLq666KtCfwYMHk52dzaeffsq8efN4/fXXeeaZZ5g6dSrXXnttg/NWVFQQExNzzP0WQgghTiXjx4/nuuuuo7CwkHPOOafJRdgOdsUVV3DfffexevVqkpOTWbx4MTfccMNhk1sWi4WLLrqId955h927dze5CHFzeTweduzYccikqNlspnfv3vTu3Zt27doxadIkPvroo8AgiEMxGAwNFhYbOXIkWVlZ3HDDDYHRxQfitmnTppGQkNDoGEeb4INDjwY91pga6t+jpp4/oD6h/3vYv38/VVVVDZ4zxo4dy/Lly7nnnnvo3r174Fnl7LPPblZcnZ2dzfDhw8nKyuLpp58mJSUFs9nMV199xTPPPNOsuLo5o2EPJzExMRBDH+zXcXXv3r0DXwoATJ48+bg+97fddhtvvfUWd955J/379yc8PBxN0xg3blyD625OzHznnXcyevRo5syZwzfffMODDz7I448/zqJFixrVU66srJS4WvzpSDJYCPGbOZCgbSpYgPpvjceMGcP06dM555xzTsg/sgcSbQcWDLjvvvtIT08HwGQyNbly7sFSU1ObnFK0ffv2RtsiIyMbrAoN9StQ//p6MzIyjriYR0ZGBuvXr2f48OGH/eY5NTUVv98f+Eb9cP07FitWrCA7OzswBRLqVwweNmwYb7zxRoO2vw6MDtXvjRs3smPHDt55550GC8YdriTCwbKysgDIycmhS5cuzb6Wg3Xv3p3XX3+drVu3Nhjdu3LlysDrAH//+98bXPuBaXQZGRn4/X62bNkSaNscs2bNIj09ndmzZze4P009jJnNZkaPHs3o0aPx+/3cfPPN/O9//+PBBx8MPERERUUxadIkJk2aRG1tLYMHD+bhhx9ulAzOycmhW7duze6nEEIIcSq78MILueGGG/jhhx+YOXPmEdtffvnl/OMf/2DGjBmkpqbi8/maVVJs/PjxvPnmm+i63uQCdc01a9YsHA7HYUciH3CkePxwEhMTueuuu5gyZQo//PAD/fr1C8xOi4uLO2xcfaD0W3Pi6gPx1q/j6oMTiUAgpj9cXB0bG0toaCg+n69Zcf+mTZsaDRY4UXH1tGnTAALvU0VFBQsXLmTKlCk89NBDgXZN3aNDxdWff/45LpeLzz77rEGpjAPlOY4kKyuL9957j6qqqgblO45G9+7dWbp0KX6/v0H5tJUrVxIUFBSY2fjee+81KBlx4P3LyMhg5cqVeDweTCZTs887a9YsJkyYwFNPPRXY5nQ6G31uoHkxc0ZGBn/729/429/+xs6dO+nevTtPPfUU06dPD7TJy8vD7XY3WoRaiD86KRMhhDhuCxcubHL7gfq2h5tqdffddzN58mQefPDBE9afoUOH0qdPH5599lmcTidxcXEMHTqU//3vf00GwiUlJYFfn3vuufzwww/8+OOPDV5/7733Gu2XkZHBd99912Dbq6++2mgUw8UXX8z69ev55JNPGh3jwDStsWPHkpeXx2uvvdaojcPhoK6uDoBzzjkHgOeff75Bm4NXRj5We/fuZeLEiZjNZu65557AdoPB0Gg62UcffRSos3xAcHAw0DiQNxgMQMMpaUopnnvuuWb1q2fPnpjN5uNaefqCCy7AZDLx8ssvN+jD1KlTSUpKYsCAAQB07NiRM888M/BzYAXhMWPGoOs6jzzySKMRF4ebatfUta9cuZIVK1Y0aHegLvIBuq4HRq0cmIb36zYhISFkZmY2mqZXVVVFdnZ24JqEEEIIcXghISG88sorPPzww4wePfqI7Vu3bs2gQYOYOXMm06dPJy0trVn/7g4bNoxHH32UF198sclRtc2xfv167rzzTiIjI7nlllsC2w/MgPq15sTjh3PbbbcRFBTEv//9b6A+sRkWFsZjjz2Gx+Np1P5AXJ2YmEj37t155513Gkzhnz9/fqOSFampqRgMhkZx9cFxG9QnegcPHsybb75Jbm5ug9cOXLvBYODiiy/m448/bjJp/Ou4Pz8/n1mzZgW22e12Xn311UPfkGZatGgRjz76KGlpaYEvCpqKC6HpOP5o4uqqqireeuutZvWrf//+KKVYs2ZNs9o35ZJLLqGoqIjZs2cHtpWWlvLRRx8xevToQLm+008/vUFcfSAZfPHFF1NaWsqLL77Y6NhHiqt//foLL7zQ6NnrSDGz3W4PlLQ4ICMjg9DQ0EZx9YH7JHG1+LORkcFCiON2wQUXkJaWxujRo8nIyKCuro4FCxbw+eef07t378MG1d26dftNRjDec889XHrppbz99tvceOONvPTSSwwcOJAuXbpw3XXXkZ6eTlFREStWrGD//v2sX78eqB8ZOm3aNM4++2zuuOMOgoODefXVV0lNTW1Un/Xaa6/lxhtv5OKLL+ass85i/fr1fPPNN41GON9zzz3MmjWLSy+9lKuvvpqePXtSXl7OZ599xtSpU+nWrRtXXnklH374ITfeeCOLFy/m9NNPx+fzsW3bNj788EO++eYbevXqRffu3bn88st5+eWXqaqqYsCAASxcuJBdu3Yd1f356aefmD59On6/n8rKSlatWsXHH3+MpmlMmzatwfS58847j0ceeYRJkyYxYMAANm7cyHvvvRcI6A7IyMggIiKCqVOnEhoaSnBwMH379iUrK4uMjAzuvvtu8vLyCAsL4+OPP272FDWr1cqIESNYsGABjzzySIPXNmzYEJi2uGvXLqqqqvjXv/4F1H+2Dnz2kpOTufPOO/nPf/6Dx+Ohd+/ezJkzh6VLl/Lee+8FAutDyczM5P777w8sjnjRRRdhsVhYtWoVrVq14vHHH29yv/POO4/Zs2dz4YUXMmrUKHJycpg6dSodO3YM1E2D+s9SeXk5Z5xxBsnJyezdu5cXXniB7t27B0YidOzYkaFDh9KzZ0+ioqJYvXo1s2bN4tZbb21wzgULFqCU4oILLmjW/RVCCCEEhywpcChXXHEF119/Pfn5+dx///3N2kfXdR544IFmn2Pp0qU4nc7AAr7ff/89n332GeHh4XzyyScNEsq33XYbdrudCy+8kKysLNxuN8uXL2fmzJm0adMmUC/3aEVHRzNp0iRefvlltm7dSocOHXjllVe48sorOe200xg3bhyxsbHk5uby5ZdfcvrppweSfI8//jijRo1i4MCBXH311ZSXl/PCCy/QqVOnBnFQeHg4l156KS+88AKappGRkcEXX3zR5Lojzz//PAMHDuS0007j+uuvJy0tjT179vDll1+ybt06AP7973+zePFi+vbty3XXXUfHjh0pLy/np59+YsGCBYE1Ia677jpefPFFrrrqKtasWUNiYiLTpk0jKCjoqO7R3Llz2bZtG16vl6KiIhYtWsT8+fNJTU3ls88+CyysFhYWFqhP6/F4SEpKYt68eeTk5DQ65oEBCffffz/jxo3DZDIxevRoRowYEZhNdsMNN1BbW8trr71GXFxcs0Z/Dxw4kOjoaBYsWBBYqPqAzz//PPBM5PF42LBhQyCuPv/88wPPB5dccgn9+vVj0qRJbNmyhZiYGF5++WV8Ph9Tpkw5Yh+uuuoq3n33Xf7617/y448/MmjQoMDz480333zIGPa8885j2rRphIeH07FjR1asWMGCBQsCJeUOOFLMvGPHDoYPH87YsWPp2LEjRqORTz75hKKiokYj9ufPn0/r1q0blY4Q4g9PCSHEUXjrrbcUoFatWhXY9v7776tx48apjIwMZbPZlNVqVR07dlT333+/qq6ubrA/oG655ZbDnmPy5MkKUCUlJUfdlwN8Pp/KyMhQGRkZyuv1KqWUys7OVldddZVKSEhQJpNJJSUlqfPOO0/NmjWrwb4bNmxQQ4YMUVarVSUlJalHH31UvfHGGwpQOTk5Dc5x7733qpiYGBUUFKRGjhypdu3apVJTU9WECRMaHLOsrEzdeuutKikpSZnNZpWcnKwmTJigSktLA23cbrd64oknVKdOnZTFYlGRkZGqZ8+easqUKaqqqirQzuFwqNtvv11FR0er4OBgNXr0aLVv3z4FqMmTJx/2nuXk5Cgg8GM0GlVUVJTq27ev+sc//qH27t3baB+n06n+9re/qcTERGWz2dTpp5+uVqxYoYYMGaKGDBnSoO2nn36qOnbsqIxGowLUW2+9pZRSasuWLerMM89UISEhKiYmRl133XVq/fr1DdoczuzZs5WmaSo3N7fB9gOfgaZ+fv0e+Hw+9dhjj6nU1FRlNptVp06d1PTp04947oO9+eabqkePHoH3Z8iQIWr+/PmB1399T/x+f+CcFotF9ejRQ33xxRdqwoQJKjU1NdBu1qxZasSIESouLk6ZzWbVunVrdcMNN6iCgoJAm3/961+qT58+KiIiQtlsNpWVlaX+7//+T7nd7gZ9vOyyy9TAgQOP6rqEEEKIU8nhYsiDpaamqlGjRjX5Wnl5ubJYLApQW7ZsabLNhAkTVHBw8GHPcSA2+89//hPYtnjx4gYxjclkUrGxsWrw4MHq//7v/1RxcXGj48ydO1ddffXVKisrS4WEhCiz2awyMzPVbbfdpoqKig7bhyP1NTs7WxkMhgax1eLFi9XIkSNVeHi4slqtKiMjQ02cOFGtXr26wb4ff/yx6tChg7JYLKpjx45q9uzZjeIgpZQqKSlRF198sQoKClKRkZHqhhtuUJs2bWoyVty0aZO68MILVUREhLJarap9+/bqwQcfbNCmqKhI3XLLLSolJUWZTCaVkJCghg8frl599dUG7fbu3avOP/98FRQUpGJiYtQdd9yhvv76awWoxYsXH/ae/ToONZvNKiEhQZ111lnqueeea/QcpJRS+/fvD/Q9PDxcXXrppSo/P7/JOP7RRx9VSUlJStf1Bs8hn332meratauyWq2qTZs26oknnlBvvvlmo2eVQ7n99ttVZmZmo+0TJkw4ZFz96/egvLxcXXPNNSo6OloFBQWpIUOGHPHP08Hsdru6//77VVpaWuD9ueSSS1R2dnagza/vSUVFhZo0aZKKiYlRISEhauTIkWrbtm2Nnr2OFDOXlpaqW265RWVlZang4GAVHh6u+vbtqz788MMGffT5fCoxMVE98MADzb4uIf4oNKWOcylJIYQQ4jfm8/no2LEjY8eO5dFHH23p7py0CgsLSUtL44MPPpCRwUIIIYQQopHdu3eTlZXF3LlzGT58eEt356Q1Z84cxo8fT3Z2NomJiS3dHSFOKEkGCyGE+EOYOXMmN910E7m5uYSEhLR0d05K9913H4sWLWpQ81oIIYQQQoiD3XTTTezatavZCzqfivr378+gQYN48sknW7orQpxwkgwWQgghhBBCCCGEEEKIU4De0h0QQgghhBBCCCGEEEII8duTZLAQQgghhBBCCCGEEEKcAiQZLIQQQgghhBBCCCGEEKcASQYLIYQQQgghhBBCCCHEKcDY0h34M/D7/eTn5xMaGoqmaS3dHSGEEEKIU4pSipqaGlq1aoWuy1iHliIxsRBCCCFEy2luTCzJ4BMgPz+flJSUlu6GEEIIIcQpbd++fSQnJ7d0N05ZEhMLIYQQQrS8I8XEkgw+AUJDQwFYsyaHkJDQFu6NEEIIIcSppba2hp490wIxmWgZEhMLIYQQQrSc5sbEkgw+AQ5MgwsJCSU0NKyFeyOEEEIIcWqS0gQtS2JiIYQQQoiWd6SYWIqqCSGEEEIIIYQQQgghxClAksFCCCGEEEIIIYQQQghxCpBksBBCCCGEEEIIIYQQQpwCJBkshBBCCCGEEEIIIYQQpwBJBgshhBBCCCGEEEIIIcQpQJLBQgghhBBCCCGEEEIIcQqQZLAQQgghhBBCCCGEEEKcAiQZLIQQQgghhBBCCCGEEKcASQYLIYQQQgghhBBCCCHEKUCSwUIIIYQQQgghhBBCCHEKkGSwEEIIIYQQQgghhBBCnAIkGSyEEEIIIYQQQgghhBCnAEkGCyGEEEIIIYQQQgghxClAksFCCCGEEEIIIYQQQghxCpBksBBCCCGEEEIIIYQQQpwCJBkshBBCCCGEEEIIIYQQpwBjS3dACCGEECcXpRT5dUVkV+5GAenhqSSHtELTtJbumhBCCCGEOEhFBWzdquF0QqtWirZtwWBo6V4JIU5mkgwWQgghRIBSigV7v+XrvYuoctWgASHmYM5IGcSo9LPQNZlUJIQQQghxMtiwAWbO1CkqAk3TMJngtNMU48f7CQpq6d4JIU5W8kQnhBBCiICdlbv5PGceOjpZkZm0j8zEYrDw9d5FbC7b1tLdE0IIIYQQQFVVfSK4vBzatoX27RWxsYoVKzSWLpXZXEKIQ5NksBBCCCECNpdtw+6xkxAch6ZpaJpGrC0aj8/D+pItLd09IYQQQggBbNumUVwMbdr8UhYiNBRCQhQrV2r4/S3aPSHESUySwUIIIYQIcHicTZaCMGpGHB57C/RICCGEEEL8mssFSmnovwrbLBZwOJBksBDikCQZLIQQQoiA1PAUlFK4fe7ANo/fi9vvJiMirQV7JoQQQgghDkhOVths9eUiDlAKyss12rVTGGWFKCHEIchfD0IIIYQI6B7bmY7R7dhYuo1wSygaGpWuatpHZdIzvltLd08IIYQQQlBfHqJvXz9LlmhUVtaPCK6s1IiLUwwdqlq6e0KIk5gkg4UQQogWtLtqL8vzV5FTtZcoawR9EnrSM75rk6Uafg/BpiAmdR7P0v0/8FPxBpTyMyipP4OT+xFuCWuRPgkhhBBCiIZ0HcaOVSQnww8/aNTWQrdufgYPVrRp09K9E0KczCQZLIQQQrSQbeU7eWvTDMqcFYSZQ8mvLWRz2TZKHWWckza8xfoVYQlndMZIzksfAYCmyYrUQgghhBAnG7MZhg6tHwmsFEjIJoRoDqkZLIQQQrQApRTz9y6hwlVFVmRbkkISyYxII9gYzKJ9Syl1lLV0F9E0TRLBQgghhBB/ABKyCSGaS5LBQgghRAuoclezt3o/cbaYBgnXuKAYKl1V7KvJb8HeCSGEEEIIIYT4M5JksBBCCNECTLoJg27A6/c22O71+zBoBky6qYV6JoQQQgghhBDiz0qSwUIIIUQLCDYF0T22M8XOMlw+FwA+5WNvzT6SQhLIiGjTsh0UQgghhBBCCPGnIwvICSGEEC1kZJszKLQXs61sJ37lR6FICI7n0nYXYDNaW7p7QgghhBBCCCH+ZCQZLIQQJwkvXvIMuSgUSb7WmDj5ygR4/B52V+7F5XORFJJItC2qpbv0hxZljeCmrpPYUr6dYnsJwaZgOkVnEWWNaOmuCSGEEEIIcdRqPFUU2vdR7anAZggm3pZMlCVWFiUW4iQiyWAhhDgJbDduZo71Q/YZ9oAGrbwpjHZeTBdvj5buWsCe6n18uH0Oe6r34fF5iLCEMyi5H6PSzsKgG1q6e39YVqOF0+K6tnQ3hBBCCCGEOC4VrlI2V67G7qnFqJspV8UUOfbTLrwLScFpLd09IcTP/nA1g1966SXatGmD1Wqlb9++/Pjjj4dt/9FHH5GVlYXVaqVLly589dVXjdps3bqV888/n/DwcIKDg+nduze5ubm/1SUIIUQDhXo+7wT9j93GncT644nzJZBrzGFa0Gv1yeGTgN1jZ/rWj9hZmUNScCLtIjPQNY2vcubzQ+Galu6eEEIIIYQQogUppdhTux2Ht45ISyxh5ggiLbGgaeTUbsf98xoZQoiW94dKBs+cOZO//vWvTJ48mZ9++olu3boxcuRIiouLm2y/fPlyLr/8cq655hrWrl3LmDFjGDNmDJs2bQq0yc7OZuDAgWRlZbFkyRI2bNjAgw8+iNUqtRqFEL+PtaYfKdQLyPS2J1iFEKSCyfC2o0wvYbXph5buHgBbynewr3o/GeGpWI0WdE0nLigWo2Zked6PKKVauoviD04pxf6afFYVrmNL2XbcPk9Ld0kIIYQQQjST02enyl1BkDG0QUmIYGMoDm8d1Z7KluucEKKBP1SZiKeffprrrruOSZMmATB16lS+/PJL3nzzTe67775G7Z977jnOPvts7rnnHgAeffRR5s+fz4svvsjUqVMBuP/++zn33HN58sknA/tlZGT8DlcjhBD1SvVSTJjQ+CVo0tCwKCvFemEL9uwXdR47fhQmvWEd42BTEBWuSvzKj0GTUhHi2Lh8bmbt+IxVhWup9dRh1I2khacyPusiUkKTWrp7QgghhBDiCDRNR0ND4W+wXeFHQ0eXmsFCnDT+MCOD3W43a9as4cwzzwxs03WdM888kxUrVjS5z4oVKxq0Bxg5cmSgvd/v58svv6Rdu3aMHDmSuLg4+vbty5w5cw7bF5fLRXV1dYMfIYQ4VrH+WDx4UPwyulahcGlO4v2JLdizX8TYojFqRuweR4Ptla4qUkKTqDPU8rXlUx4LeYD/C7mfryxzqNQqWqi3f1zV7hq+3b+cNza9x/vbPmFT6Tb8yn/kHf/gluxbxuJ9ywgxBZMV2ZaUkCR2Vuzmva2zcPncLd09IYQQQghxBBbdSrQlDru3NhC/KqWo9VQRYgojzCQLTwtxsvjDJINLS0vx+XzEx8c32B4fH09hYdMj5woLCw/bvri4mNraWv79739z9tlnM2/ePC688EIuuugivv3220P25fHHHyc8PDzwk5KScpxXJ4Q4lfXw9CHR34qdxq3UajXUabXsMm4n1h9PL0+/lu4eAO0i0+kU056c6lxK7KVUuarZVZlDkCmIXsndeDPoZT60TafQkEeRoYBZtvd4I+hFarWalu76H0a5s5JXN7zLtC0fsrpwHYv2fcfU9W8xN2fBn7oMh8fvYUXBakLNIURaI9A0DavRQnp4Knuq97GzIruluyiEEEIIIY5A0zTSQrMIN0VR6S6l3FVChbsEi8FGZlhnjPofamK6EH9qp/SfRr+//tuqCy64gLvuuguA7t27s3z5cqZOncqQIUOa3O8f//gHf/3rXwO/r66uloSwEOKYxfsTmWi/kTnWD8k17AEU6d62jHZeTLIvtaW7B4BJN3Flh7HE2KJZW7SRWk8d6eGpnJk6BHtiGZtM68jwtsOCBQA3braYNrLa9AND3We1cO//GJbl/cCWsu20i8wIlOMosZeyIPc7usZ2Oq5yCT6fj7q6WurqaqmtraGurhan04Gm6RgMhp9/jFitFqxWGzZbEFarjeDgEHT9t/3e2O3zYPfYCTLaGmw36yZ8ykedx/6bnl8IIYQQQpwYwaZQekQPoMRZgN1bh8VgJcaaQJAxpKW7JoQ4yB8mGRwTE4PBYKCoqKjB9qKiIhISEprcJyEh4bDtY2JiMBqNdOzYsUGbDh06sGzZskP2xWKxYLFYjuUyhBCiSW29Hfhr7QPkG/ahULTypWA8yf6KDreEMa79hZyXNgKXz0WEJRyDbmC64XV0tEAiGMCMGRMmso3bT7lkcIm9lFqPnWhbJGHm0Gbto5RibfFGIizhDeoyx9ii2V6xi+zKPU0mg51OJ/v372X//r3s27eXffv2kJu7h/3791JRUU5tbQ21tTU4HMeWUDUajcTGxhMfn0hcXALx8YmBn7i4BBISWpGR0Y6goOBjOj6AzWglMSSBHeXZRFkjA9ur3TXYDFbigmKP+dhCCCGEEOL3ZTZYSQpOa+luCCEO4+TKNByG2WymZ8+eLFy4kDFjxgD1I3sXLlzIrbfe2uQ+/fv3Z+HChdx5552BbfPnz6d///6BY/bu3Zvt27c32G/Hjh2kpp4co/GEEKcOAwZSfG1auhtHFGIOJoRfkn8WZcVP47q2PrxYVdDv2bUWVe2uYc6ur1hfshmn10WYOYSBSf0Y2WZYs6fFHVw3+uBtfr+fnTu3sn79GtatW8PGjWvJzc2hqKgg0E7TdaLiYkhKaU27jPbExsQTEhJKSEgoQUEhhISEBH4fHByC1WpDKYXf78Pn8+H1+nC5nDidDhwOOw6HncrKCoqKCiguLqSoqIC1a1dRXFxISUlRoHSFpmmkpqaTldWJrKxOtG/fiQ4dOpOW1haTydToen5N13SGJQ9kT9U+dlXmEG2NxOFzUumqZmBSX9qEycwbIYQQQgghhDhR/jDJYIC//vWvTJgwgV69etGnTx+effZZ6urqmDRpEgBXXXUVSUlJPP744wDccccdDBkyhKeeeopRo0bxwQcfsHr1al599dXAMe+55x4uu+wyBg8ezLBhw/j666/5/PPPWbJkSUtcohDiFORwONi7N5ucnF3k5+dRVVVBZWUFVVUVVFSUU1VVGdhmt9fh9/tRSqGUQtM0TCYTRqMJs9mM1WojOjqGqKgYYmLiiI6OITo6lpiYWKKjY4mLSyAtrS02m+3IHWumTt6ufMt8SvRiYvz1ozjL9TJMykwXT/cTdp6TmVKKmdvn8EPBahKC4oiyRFLqKOPdLTPZXbmHkWlnkBHeBoNuaHJ/TdM4La4rn+z6ijibB0dpNYU7c9m9ZTulu/P5du+71NXVApCenknXrj0ZNOgMUlLaUGWz85N7K64gP0ajEaNupEt8V67ocAk2Y/Pf51JHGRtKtlDldhBtjaJP7CAiLOFNtvV6vZSWFlNQkMf27VvYvn0zW7du4v333w4kqM1mM5mZ7enWrRcDBgymX7/BJCU1ndjtFtuJiZ3GsXjfMvJrC7EZbQxLGcjw1oPQZOVpIYQQQgghhDhh/lDJ4Msuu4ySkhIeeughCgsL6d69O19//XVgkbjc3NwGtQ0HDBjAjBkzeOCBB/jnP/9J27ZtmTNnDp07dw60ufDCC5k6dSqPP/44t99+O+3bt+fjjz9m4MCBv/v1CSH+3AoK8ti4cS05ObvIydnF7t27fk4A7wuMsrRYLERERBEeHkF4eCQREZG0aZMe+HVQUDC6rqNpGpqmoZTC4/Hg9XrweDw4HHbKykopKyth27ZNlJWVUlpajN1eF+iHpmm0bp1G27ZZZGZm0bbtLz/h4RFHfV1Z3s6MdJ7PAstXbDduRgHBKoSzXKPo7O1+Ym7eSS63Zj8bS7eSHNyKMEsoNe5a9tbsI6+2kP21BWwu30732M6M73Bxk6UjXC4XhhwnJZ9sYemP71NXWAlAUHQoWZ27MvKC6+nevRdduvQgIuKXUgqFdcU8teZlbL5QMkMS0TSNOo+dlQVrSAtP5czWg5vV/x0V2by7ZSaFdcWBba3DkpnU6fImy1MYjUYSElqRkNCKHj16N3itvLyM7ds3s23bZrZu3ciaNT/w/vtvAdCmTQb9+w9mwIDB9O8/hFatkoH6z2SPuC50i+1EjbsWq9GKxWBuVt+FEEIIIYQQQjSfpv7MS5T/TqqrqwkPD2f79lJCQ8NaujtCiJOAx+Nh8+b1rF79A6tXr2DNmpXk5eUCYLMFkZaWQVpaJunpbUlLa/vzrzOJiYn7TUZCOhwOyspKKCjIY+fObezcuY1du+r/v2/fnkAyOjExmT59BtCv3yD69j2ddu06NmsBMYVir2E3u4zbUSgyvO1I82WicWqM6lxfspkX1r5GVmRbFIqVhT9R4iglyGDDp3z0jO9Gkb2Us1KHcnnWhQDk5e1j8eJvWLhwLkuXLsJuryM+oRWd+/WkVfd02nXtwoB2/ciKbHvIz8S3+5czbctHtI/MqP+C4Of7vbd6P61C4rmvzx1H7LvH7+HpNa+wu3IvmRFp6JqOz+9jR+VuesR15uZuVx/3Z7KsrJSVK5eyfPl3rFjxLVu3bgLqk8ODBw/n3HMvZMCAIRiNf6jvqMVJpKammvbtY6iqqiIsTGKxliIxsRBCCCFEy2luTCxPXUIIcQL4/X7WrFnJggVf8uOPy1m/fg1OpwOz2UzXrj0577yL6dWrL92796ZVq+Tffeq7zWYjObk1ycmt6d27f4PXHA4H2dnb2blzGxs3ruPHH5fx5Zez8Xq9REZG0atX/5+TwwPp2vW0JhN2GhptfBm08WX8Xpd0UomwhBFkCqLGU4vP76PCWUG4KQyHz4nFYCHCEo5SipW7V1KxNIfPPv6QDRt+Qtd1evXqzx133Mfw4efQoUOXo/ps1HnsFNtLKHGU4vF5iLFF0yYsBaNuwOlzNesYudV55FbnkRLaCl2rT/wbdAOtguPZVZlDsaOU+ONcxC06OoZzz72Qc8+tT4SXlZXwww9L+f77b1m4cC7vvvsqUVExnHPOBYwadRGnnz60WfWGhRBCCCGEEEIcHUkGCyHEMXI4HCxbtohvvvmcefO+oLS0mKioGAYMGMy9906hV6/+dO7cHYvF0tJdbcTr91Lo2EeJswC/8hHTJoFzO4zhwgvHAWC31/HTTz+ycuUyVq78nv/+9xEcDjuRkdGMHDmac88dw6BBwwPX5sGDR3NjU0GnzGjgg7UOTaZzdBY/FK7BrJtw+zwYNBcev4fMsDbsX7+Ltd98z54ft6CjMXz4Odx4410MGXIWkZFRx3ROn9/HhpLN5NcWEmQKwma0sqd6H6WOMsItYQxo1adZx/ErP0r50bWG9Yx1Tcev/Pj8vmPq3+FER8cyatRFjBp1EUo9y4YNP/H557P44ovZvPfeG0RGRnH22Rdw3nkXMXDgGZIYFkIIIcRJwemzk1+XS6mrEF3TibMm0SqoNUZdYhUhxB+HJIOFEOIo1NRU8/XXn/L115+xZMl8HA476emZXHrpFYwceT49e/bFYGh6kbCThV/52Fa5lgJHLrpmQEOjzFVEibOArpF9MBusBAUFM3DgMAYOHAbUl73YsOEn5s37nC+//IQPPnibkJBQhp01glYXpOA4244/xE+aL5MzXCNp7+3Uwlf5+9I0jXFZF2I2mFlVuBav8lFbXIlpo51F36+gprSS0KRo+l9xNs/d8QKt4pOP+5w7K3eTW51HRkQbiuwleHwezLqJgroiwixhDEru16zjJIcmEhsUS0FdEW3C6hd4U0pRaC8mI7wNcUExx93Xw9E0jW7detKtW0/uv/8xNm5cxxdf1CeG33//LWJi4rjiimu44orrAjWGhRBCCCF+by6fg43lqyh3F2PWLSilKHcVU+EuoXNELwy6pFeEEH8MUjP4BJD6aEL8+a1fv4Zp015jzpyZ2O119OzZlxEjRnP22aPJzMz63cs+HI9iRz4bKlYSbAzFpNcv0uVTPqpcZWRFdKd1SOZh91dKsWPHFr74cjbvff0GhZvyMVgNJI1sTcK1rcgc0p4bnHfQ1tvh97ick853q5fwyJP/YMv3azBazaSf3oWEgZnEZCRxZcdL6ZvY84ScZ2Hud7y/7RPaRaSTV1dAQV0RHp8Xo26gdWgy/x0ypdmfyx8Lf+KDbZ9Q46klyGCjzmsnyhrJVR0vo3NM1gnp79FSSrFp0zpmzHiLWbOm43Q6GDlyNBMn3sTppw/9Q/2ZE789qRl8cpCYWAjxZ7a3difbq9YTYY4OzKjy+j3UeKroGtWXeFvjRXePls8Hug4S5gghjoXUDBZCiONUW1vDJ598wPTpr7Nx41patUrh5pv/xrhxE//QIxSr3RX4/b5AIhjAoBkw6EbKXEWNksEuXOwybsOluUj2tSbOn0D79p1QnRTZj+zAssNK/mf72PL+Rn4c/T2bM9dRMamMpy58lYiIyN/78lrM2rWrePbZx5g//0tap6Zx7T/uRu8ajgMXCSFxDE7qz2lxXU/Y+SwGC5pWP7K2dWgyrUPrP5N7q/cRFxx7VMnSPgmnEWEJZ1XhWortpSSFJNAnsWdgpHBL0DSNLl168PjjPbj//v9j1qz3ePvtVxg7diRt22YxYcKNXHrpFZJwEkIIIU6g/NpC1hStp7CuiBhbDD3ju9I67I8b955I5c5iDJqxQWkto25C4afaXXFcyeDdu2HJEo1duzSCgqBfP8XAgQqr9UT0XAghGpJksBBC/MrOndt47bXn+eSTD3A47Awffg733DOZYcNGnvQlIJpD13WaKuurUBi0hv8s7DJsZ6btXXKNOfjwEq4iGewaznnOiynUC3DjJj29LUl3ptDrjv7krdjHyjeW8c3Dn7Po0VTGXHAZEyfeSLduJ2Y07Mlo5cplPPPMY3z33QIyMtrx/PNvMmbMOIxGIz6/D7ffg9VgOa6RrEopcqpz2Vy6DYfXQXJoEm3Ckom1xbCneh+pYckYNANVrmocXid9E0476nO0i8ygXeTJuQBgSEgoEyfeyIQJN7BixXe8/fYrTJ78Nx5//AEmTLiBm2++m6io6JbuphBCCPGHtr18F+9s+YAiewlWgwWXz82Kgh+5osOldI09tUqANcWgm2hyYrWqX3z3WGVnw2uv6ZSUaERFKYqL4YMPNPLy4MorFbreeB+/30929nZ27dpBTs4ucnJ2UVCQR3FxIcXFhdTW1mA2mzGbLT//mLFarZjNFlJSUunQoQsdOnShU6euJCW1lhlXQpxiJBkshBA/y87ewTPP/B9z5swkLi6BG264k8svn0RSUsuNjvwtRJpjMWpmHN46bMZgANw+F0opYqwJgXa1Wg3vBb1BniGXVG8GZsyU6sV8Zf2EGH/cz4vFgRcvRoxomkbygNb4BvXjzIJziHk9nunTXmfmzHfo0aM3d911P8OHn/OnCTY3bVrH5Ml3s2LFd3To0JmpU99j1KiLGnxhYNAN2I7j4eCApXkr+GTXV1S7a9CpT+Z3jGrHOW2G8/Weheys2A2A1WhhcMoA+rfqfdznPBlpmsaAAUMYMGAIBQV5vP32K7z55su8++6rXHvtbdxww52Eh0e0dDeFEEKIPxyf38cXu7+hzFFOh8h2aJqGUordVXv4bPc3tI9qi8VgPvKB/sTirIkUO/bj8jmxGKwopXD46jDpZqIsccd83G+/1Sgt1cjKUoHyENXVsGqVzumn+8j8edLevn17+O67RSxdupBlyxZTXl4KQHBwCGlpmSQlpXDaaX2IjY0nNDQMt9uN2+3C7XbhctX/2ul0sGdPNq+++hyVlRUAhIaG0aFDF3r06M2oURfRs2ffP028LoRomtQMPgGkPpoQf2y7d+/k2WcfY/bs94mPT+S22+7l8ssnYbFYjrhvqV5MrmEPRmUi09ueIIJ+hx4fH6UUOTXb2Vu7A49yA6BjoFVQKu0jumH4eerbStP3vB78PBne9hgP+u5wt2EX6b5Mbqi7k/+GTKHQkE8bbyZGjFRo5ZQYCvmL/RqGukfg8/lYuHAuL7/8FD/++D09e/bl7rsnM3jw8D9ckOnwOthVmUN5ZTmfvj6dD6e/TWZme+6771FGjDivfsT1b6DEXsp/Vr+I1++jVXACmqbh9nnYWZnN6PSzGZpyOtvKd+D2e0gKSSQ9PBVd+236cjIqKyvl5Zf/y1tvvYLFYuHGG+/immtuJSQktKW7Jn5HUjP45CAxsRB/XHm1hTy56nkiLRGEmkMC2x1eJwV1RdzV80YyI9JasIctz6987KjaSJ59Lz7lBRRm3UKbkPakhrQ9ptjW64UHH9TxeiE+vuFrW7Z46djxC0pKvmHp0kXs2ZONruv06NGbgQOHMWDAUNq370hsbPxRn1spRUFBHlu3bmTLlo1s3bqRFSu+o6iogKSk1owefQkXXHApXbue9oeL2YU4lUnNYCGEOII9e7J59tnH+PjjGcTGxvPII08zfvzVWJtRnMuPn28sn7PI+jUVWhk6Oq18KVzi+Audvd0D7XYYt7DKtIISvYhkXyp9PANo7WvZQFrTNNJC2xNljaXCVYrCT5gpiihLbIMkYp1eA9AgEQwQrIIp18sIUaGMd1zNB7Z3yDHuwo+fEBXCma5zGeAeCoDBYGDEiPM466xRfPvtAv7zn4e5/PJz6ddvEH//+8P06zfod7vu47G9fBczt3/CyvnfsuX9pficXsbccBX//fvzWC2/bTG3nZU5lDsraR+ZGQjGzQYTkZYI1pZs4ILMs/+0I4GbIzo6hgcf/DfXX38HL7zwJM8883+89toL3HLL3UyYcCNBQb98QePyucmrLcCg6SSFJGKUVb+FEEII4EAFsUMn/bTDvHaq0DUD7cO7EW9LospdgaZpRJpjCDVFHHPC1GCAoCAoKfllW03NPtavf53169/g668Lychox7BhIxk06Az69x98QmZBaZpGq1bJtGqVzPDh5wDg8/n48cfv+eyzj/joo2lMnfo0qanpjBkzlmuuuZWYmGMf/SyEOLnIyOATQEZBCPHH4nA4eO65x3jllaeJjIzm1lv/zhVXXNusJPABa0wreT3oRYJVMHH+BHz42GvcTZQ/mr/VPki0P5aVpmV8EPQ2NVoNNmXDrtUR449jov1GOni7/IZXeGJsMq7jxeD/kuRPxqbqE2oKxQ7jFvq4T+c6++0A2LU6dhi34tQcJPlSSPalHvKBQSnFggVf8eSTD7N583oGDRrOP/7xKN279/rdrutoVbmqeWDOwyx59RPKtubR7vRudBk/FE+oYkLHy05IItav/GhoTT5ILM37gXc2f0BWZMMRJwV1RVgMZh4ZcN9x1an7s8nL28dzzz3OBx+8TWJiMo899hzDh5/DT8Ub+Gr3fArqitE1ndahSVzY9lwyI9JbusviBJCRwScHiYmF+OPyKz/PrX2VrWU7aBuRjq7pKKXIrsohJTSZu3vdgtlgaulu/inNn6/x/vsKmM/Onf9j9+4v0PUg2ra9kieeuJbevX//5wav18vy5d/y2Wcf8dlnH+H3+7n66pu56aa/ERkZ9bv3RwjRPM2NiSUZfAJI4CvEH8eSJfP5xz9uo6BgP7fe+nduvvnuBiMHm2tq8DOsNa2irTcrsM2Pn+3GzVxpv54+ngH8O+RBKvRyUn31ySaFYqdxG1nejtxR+08MnNwJPA8e/hf8LD+ZVhLtj8WsLJQaiglRoVxXdxtZ3s7HfGy/38/cuXP4738fYceOrUyYcCP/+MejJ93foV6vl789eiuz3nyb8Pgoht9wCW161L/nu6v20DYygztPu+GYj59XW8B3+1ewuWw7Zt1ISmgS6eFtiAuOoW1EOkbdSF5tIU+tfgmzbiI2KAaor+u3rWIXI9sM5bL2F56Qa/2z2b17J//85x18990Cho4YSfgFmehhZhKD4/ErP3m1+cQHxXHHadcTbZOHmj86SQafHCQmFuKPbVdlDm9v/oCCukJMugmP30ucLZorOo6lU3T7lu7en5LX6+Xtt9/g2Wefpbw8m9DQrqSm3kCPHuO58spgOh97uH3ClJeXMXXq07zxxksYjUauu+52rr/+DsLCwlu6a0KIX5Fk8O9IAl8hTl4lehG7DbuoLCrnowen89Unn3D66UN5/PEXyMw8clCrUGw2rme9aQ3VWhXpvkx6uQfwv+BnKTYUkOxLbdB+m3ETFzrHkeltzzMhj9Ham4aZXxbbqNGqqNIrua/mURL8rU749Z5o1VoV31g+5yfzStyamxRvKsNd59DJ241s43Z2GXYAkOltT4avXf3iZkfB6/Xy1lsv88QTkwkLC+df/3qGc84Zc1LUJtu/fy+33DKBNT/9QMZ5p3HuVeMwmn4pK1BQV0SQycYjA+47puMX1BUxdf077K/NI9gUzM6KbErsZYRbw2gbnkb7qLZc0eESYmzRfJUzn6/2LMTj82DSTTh9TtLD23BtlyuID4o9UZf8h1XnsbO+ZDP7avYTZAyiY3R70sPr/2zOmTOTex+4DafDzqCrRtP9nIHoBh2/8rOtYhd/ybqYM1r/McqViEOTZPDJQWJiIf74iu2lrC3eQJG9lBhrFN3jOtMqJOHIO4qjopRi4cK5PPLIvWRn7+D88y/lrLNuJipqADZb/WJy4SdZrrW0tJiXXvov77wzFavVxs03380NN9yJySQjxoU4WUjNYCHEKU2hWGD5im/Mn7PhrZ/Y9uAmDEYjt770d/4x5tFmJxvnWb7gc+ssXJoTkzKzyryCH03LifPHk23cjkIFSiK4cKGhE+dLRFcGdHT8+Bocz4cPDf2YRgUrFHn6Psr1MiJV5OHLMaAo0PMo1UsIV+G09qU1u9ab/+f/jBgJU+Fc6ryCUa4LceEiXEWgUMy2vs8Sy3wcmh2AIBXEMNdIxjgvO6qE8IHRBeecM4b777+Da6+9jLPOGsX//d9zJCe3bvZxTrSvvvqEv/71esLCIvjvW2+yhJ/waN5A/WSlFJWuarrEdDzmc3yf9yP7avLoENWWTWXbqPPYiQmKxuVzYTFY2FS2lQ93fMrN3a7mnLQzSQlNYn3JZmo8tWSEt6F3Qg+irJGB4+VW72d9yWaq3DVEWMJICUkiKTSBGFv0cd+Pk1mlq4q3Ns1gS9kONDR8+FiY+x0XZJzDsNYDufDCceyILuDzV2ew+NXZbF28mjNvHktYagxOj5NCe8mRTyKEEEKcIuKCYhjZ5oyW7saf2ubN65ky5V6WLVtEvwGDePaF1zmtW9+DWpyc4/ViYuKYPPlJbrjhTp5//gmeeOIh5s6dwwsvvE16etuW7p4Q4ihIMlgI8ae0xbiBD2um8dP1KymYl0enq7qR8a92lEYXs7d2N218GUc8RpFewDzrF1iVLdDeh4/txs1EeKKI9cez3biFOF8CXs1DqV5MZ093Onu6Y8BAkrc1ucbdZHjbo6Pjw0eBIY9env7E+I9uAYY6rZYPrdNYb15NrVZLkAqmi6c7Yx1XEa4iGrS1Y+dj2wx+Mv/wc73iIDp6uzDOPpFIdejp8HbsfGeZz4/m5Tg0O+08HRnmHkEbXwZBKpggggFYb1rDQsvXRPmjaKPqS2CU66UssMylrTeLLt4eR3VtAMnJrXn77dnMnTuHBx64i6FDu3HvvVO45ppb0fWjG218PLxeL4899gBTpz7NeeddxH/+MxVbSDClG+z8VLKRGGsUJt1IqaOMKGs4pyf1OeZz7ajYRbglFJfPTWFdMSGmYGxGGw6PA7ffQ0pIEjsqstlfm09KaBJdYzvRNbZTk8daVbiOmds/odxZSamjjGJHKRbdTMfo9gxo1Zsxmec2WBn8z+TbfcvZWLaNtuHpgVqG+bWFfJWzgE4xWcQFxZAWn0bWVafTZ8QQ5r/8IdP/+hSxI9tiHBqP2WgmzhbNsJSBUntZCCGEEL+ZwsJ8nnhiMh9++C6paWn887kpdDy9E7V6GVsr15IemoXFYDth56t0lVLkzMPhtRNqiiDBlkywKfS4j5uQ0IrHHnuOSy75C7fdNpERI/owZcpTjB8/6aSY3SeEOLLf7wlbCCF+R7N+eI8F/b6i8qcKLvz4cka+OJqMiHZUaRVsNK1r1jF2G3dSpVUS708MbDNgINofS75hH1fZr6eHpzdu3YWGxpmuUUyw34gNG2bMXOy8nBh/HNuNm9lm3MQO41ba+DIY7bz4qFdk/sw6i6WWRYT4Q2nv7Ui4P4IV5u/42Po+6lejB+Za57DE8g1B/hDaezsS6Y9khek73re91ajtAV68zAh6g49s71Gml+LFyzLLIl4Lfp5cQ06DtpuM6/FqbiLVLyNOo/wxeDQXW0wbjuq6DqZpGueeeyFLlqxn7NirePjhe/jLX0ZTUlJ0zMc8GqWlxYwdO5LXXnuOhx/+D//73/uEh0dgNpi4suNYzm1zJkbdiNvnoUtsJ67u/BcyI9KO+XzBpmDcPjcevwef34tRN6KUAg0MmgGr0YrL58bucRz2OLXuOj7NnovL58aim6l21xBqCsGnfBTWFbNk//d8uH0Of8aqUH7lZ03xBiLN4Q0WtUkMjqfCVcmOimwAeiecRqQlAnsitLtvOBEj0iiZt5OqVzdDpYePdn7G0rwfWuoyhBBCCHGU7N5acmt3sat6M3l1Obh9zpbu0iEppZgx4y0GDuzE/Plfct/kKUyZ8RgdTu+A1WBDQyO3NpstlWvxK9+RD9gMBfZc1pWvYG/tLspcRWTXbGZd+XIq3WUn5PgAp53Wh3nzfuTCC8dxzz03cvXVl1BWJjOuhPgjkJHBQog/jSqtkn36HmY9/x6vPf480QNjuei1ywlJrP8GXPu5QEOtVt2s4+mq/vuyg0tBHPi9rnTaejvQwduFaq0KEyaCVHCD/bO8nbmz9p+sN62hSqskxh9HN09PIlQkR6NcK+Mn80ri/PFE/DyyN1xF4Pa5WGT9CrfmJNYfT2dvNxJ9yawyLyfaH0ukiqJILyDHsIsyQwl7jNkEE8Kl9isJouGieTuNW1ljXkmKL5UQVX+/4vwJbDNuYql5EX9xXBNo68aN3kSZCw0dF+6jujYnTpyag1AVFiidERYWzmOPPcfIkaO57baJnHVWb1544W0GDfrtpizu27eHcePOpba2hlmz5tO378AGr4dbwrik3WhGpZ+Fx+8h1BTS7JEPXr8Xp9eFzWhtMPK0V0J3tpRtw+1zYzVaqfPY0dCwGazE2qIpc5QTbg4l7gg1gXdX7aHYXkJqaDIrClZj1k2EmkMx6kYcXgex1gw2lG4NjDD+M1FKUeuuw+614/X7MDYY2avhV34A0sJbc1WnsczYOpv1JZuIObsdKV3bsvutH1jywAx6XjeCb4OX079VbywGc9MnE0IIIcRJocxZzNaqn7B7a9HQUEC4KZJOkb0IMTWvXnm1u4IyVzFev4dQUzgx1gSM+omvfVteXsbf/34TX301h7NPH8Ml/SfhS6/BUeskMSYJTdcACybdTJmziApXKV6/ToWzkghLOPFBsUc92tbjd7O7Zht+FFGW+jhSKUWlu5Scmu10j+p/2GMqv8Ln8WEwG4547uDgEP7zn1cYPvwc7r77Rs444zRefPGd3zRuF0IcP0kGCyH+8BSKhZa5fOWaw5Lr51H4aT6Zf29PwkNJ2Py/JD09eFAoknzNq0Wb6WtPlD+afMN+knwpaGh48FCml3Cu80JM1AeMvy7TcLBYfzxnus5t9rVUaOWUGAoJ9ofSyp+MhkatXk2JXoQLN1tNG7GqIOJ8CeQZ9rLXkIMXHyEqhCVqHn3dg6jT6ojxx1KsF7LOvAoPHqzKRrleypfW2fiVn4mOGxskuPMM+/DgDiSCoT55HuGPYptxc4M+tvW153sW48KFBQtQn9RV+Mn0tWvWddqxs9A6lx9MS3HqDhJ8rRjmGklPT99Av4YMOZPPFn7Lzbdexbhx53D7vffx99sePuHTz3bs2MK4cedisVj57LNvSU1NP2Rbm9GKDWuzjuv1e1mWt5JleT9Q7a4hxhbN4OT+9E3oiaZp9EnoQW51Hivyf8SoG6lyV2PRzXSIbke5swKnz8no9LOJtB5+9ZAD433dPg8unxurof490QA/EGIKocxZQYWz8k+VDC6xlzIney45VXvZV5PH/pp8MiPSSQ5JpNRRRpg5hIyINoH2XWI6cnE7N7k1+8kITyMsIwRXjyHMe3Emy5/9lPJdBdzYeQLxoUdXwkUIIYQQvx+v38uu6o04vQ4izfWJUr/y/5zo3EaKox35GwuoLqghKCqIVl0SiU6LahA/5tXlsLN6My6/E+3n/6Kt8XSO6InZ0Lw4rzmWLVvMHXdcjb3Ozh3nP0Sn2NMwek1UOCtROWCpqyA6rX6gh1E34fF7+DLna9YVZWP32gky2ugR14WL2p5HsCnoCGf7RbW7Aru3lnDzLwNQNE0jyBhClbscp8+OzRjcaD+/18++dXns/2k/zhoXIbHBpPZKIb5D/BHj77PPPp+uXftwxx2TuOKK0bzwwtucf/6lze6zEOL3JclgIcQf3k+mH3mv/E1+OP877PvtnP3++Xgv8FJsKGSTfx2JviQUijK9hPbeTvTw9G7WcaP9sYx2XsJs2/tsM25Cx4AfPx28XTjDPfKEXoMHD19bP2WpeRHVehUWZaWTpysXO/5CuV7KXn03dXot4SqCKq2SbMN2arVawlQYwSqYVr4UnJqDlealGJWRcr2MfH0/HjxE+WOwa3YiVBStvWn8ZP6RIe4zSff9stCDVdXXJ/Pjb7AAnEtzNiiTAXCapw9rPD+wwfQTNlUfmDo0O908vejuPvK9VSg+sk3jO8sCwv2R2PxB7DbsJDdoD5pdo6enfgGNdabVzE6fQdJXydT+XzXP/ftxvt+wmOnPfE5Y6IlZXnnt2lVcccVoEhJaMWPGl8THJx55p2b6KmcBX+yeh81oJcQUzN7q/by75UM8fi+Dkvph0k2Maz+GvomnsbdqH7uqcthfU4DDayfcEsbprfoyMKnvEc+TFt6aGFs0Zc4KLAYzLp8Lk26izmMnKSQRl89FkMlGhOUkW5L6OLh8bt7d8iFbyreTHp6Ky++isK6YEkcZqWHJxAbFcG6b4bQKbrj6ebQ1imhrJLpW/+BnDQli9L0TWfzR56ybsYQbSi7jf1NnnNDPgRBCCCFOnGpPOTWeajBDjnEXCkWEiiTIGEx+6T4Kvq7EXeLFZDNTllNOwZZCOp6dRUqPZKC+vER2zVZAEfVzMtnn91LqLGBfXQ4ZYR2Ou49ut5snnniIqVOfYcCAIVw7/K948xVRbaJAA7s1BLtWQ1V+FWHxoZiCTPiUj4K6ItYV7SXUGEl8UAw17lqW7PseXdP5S4dLmn1+TasfWvHrAmGK+sEeh0rs7lqaza5vszGYDIH7V7Gvki5eP0ldWx3yfHY7LF6ssXJlK9q0+YLq6mu56aYrqKys4Kqrrm92v4UQvx9JBgshTig/ftaYVvKj+XvK9RLSvG053T2UNF/mb3bOr/bNYemFCzD4jPxl8dVEtYvB6/Xiw09bX3vsWv30+5Gu0Qx3nUOoat70MYCB7mEk+VLYZFpHrVZLii+V7p5e1Gm1zLK+xzbTJmwqiF7u/vRzDwqMlD1a8y3z+Nj6ETH+KFK96di1On4wL8Op1dc/C1Gh+JUfpcBG0M8jgj2YlZkdxm3sM+6lg6czds1OR19nthg3km/Yj1VZqdVqcGoOMnztiPcnst24hSJDQYNkcAdPF+L8CewxZJPqS0dHp0qrpFwvI9ofy+MhDxLnj6e3+3S6eLtztf0WVpqXsc60GoDunl70cw9qVH6iKbmGHNaYV9LKl0z4zyUzIn1RZBt2sMQyjx6e3hTq+cywvUmtVkO6rx2Z/8xizWk/8MO1yxgxug8fv7eApKSUY7rXByxduohJky6mQ4cuTJv2KRERR1e+43DKnZUsy1sZmN4HEGWNJLd6P4tzl9I7vgdWowVN00gPTyU9PJVhDMTn9+HwOrEaLRj15v0THWYOZVT6Wcza8RmaplHuqqTKXU2UNYoYWxT59kL6Jfb6U40K3la+k52V2WSEt8FisBBpjSC/rpAdFbsINYVwQ5er6BzTodHDTmpYMh2i27GqaB1JwYkEm2xUOCtpdVYHzh5wLm9OeYpRowby/vtf0Lbt8T8MCiGEEOLE8is/hXo+ey17cOkuAIw+jVaOeMLyo7HazcSmxQVigKqCarKX7ia+fRzmIDOV7lKcPgeR5phAG4NuxKxbKHLsJz0067hmoeXk7OLGG//Ctm2buP/+x5h05S18P3UF5ig9cNyQqiic8XZcDifOOidYodxVTHFdBSGGcKKsP8fH1gj8SrG2eCMj2wwjxhZ9uFMHhJkiCTaGUeOpJNwUFRg9bffWkGBLwaI3XqTOUekgd/V+LCEWgqPrRw0HRdqozKsi54c9JHSMx2BsXCbO54MZM3SWL9cID1dYLCZSUt7G7Y7ivvtupaKijNtvv08WlhPiJCPJYCHECfW15TM+t85CaQqbCmKPZR4bTWu52n4z7b2dTvj5tmzZwNvjX8EYbmTcpxMITa4f/WjESBA2+roHMcR1FhraMSVqNTTSfW0bJE4L9Dz+F/wsuYYcwlQEHtxsC9rEHsNurnBcE6h9eySFej7Zhj2sMbn4zPo5Dt1IpT8Cr08j2ReB0Wdko2ktAJ093anUK8g15FBiKEbhx6hMxPrj60cL65VsNW4i3p9IZ08Punp68t+QR6jWq4j0R5Hl7US6tx0ePBjQG9U3jlYxXOaYwIfWd9lp3AYo3LhxaHZyDXsIU+FsMq1lnvULRjrP53L7JM50nXtUJTAOKNILsWt1pKjUBtuj/NEUGPKo1WrYaFpLqV5CB2/nQNmIPmefjuE7A+tH/8SYMcP44IOvyMhoXlmKX5s37wuuv34cAwYM5fXXZxIU1Hiq3PEoshdT5aomPbzhNUbboih1llPurKBVSEKj/Qy6gRBzfV8c3vqpi1bjkT+3AxJ7E2eLYXXRWn4oWEOJvQyr0YpRNzEoqT8XtR110gbhNe5a1pVsYn9NPsGmIDrHdCAtrPVh+1vmKMenFJafS2LYjFYywtsQYgrGpBvpHNMBXWu8Rq6u6YxrfxFG3ciWsu2UOErrk+lpZ3Fu2llc0vdi/vKX8xgzZhjvvDOHXr36/WbXLYQQQoijV2GtYJ81F7PbRKgxFFVRjtNRRrHRjqlgF233tMEddQbKVL8GQEhsMFV5VVQX1hCTHo3/EAvqaugo/MfVtzVrVjJhwoVERETw+efL6Nq1B16XF4NRx+/95djB1ZG4NRfl5kJq9Sr8Xg8WPZTCGjux1vgGxww1B7OvtooqV02zk8FG3URmeCe2Vq6j3FWCrukoFGGmCNIOkeyuLanFVesksnXDwRG2CBv2MjvOKmcgSXyw7GxYu1YjJUUR+nO1uehoDXiGpKQYnnhiMuXlZUye/CS63jg2E0K0DEkGCyFOmFK9mEXWrwlRocT76qdZtyKZHcatzLd8RTtvxwZ1ao/X6tU/cOWV5xORGkWnz7oSEvXLiF8XLjR0Yv3xWJtZ47W5lpkXk2vIIcvbOVBSoVqr4kfLMvp7BtLO2/Gw+3vx8rl1Fksti9hsLKVYc+PUc4nxdaRG87PRWD/KIckXjOfnxdp8mo8MXzta+9qwxDIPLx5qtJrA/Qzzh1Og5xFFDG29WaT72uLCxQdBb5Pka02MPxYPbnKMu0j1ptPO07iP3Tw9SfWms924GYfmYIHlK4oppI0vg83G9RQY8qnRqnkt6Hly9F1c47iFNr6Mo75/wSoYozLgcbuxuS0oHfxWsJvqCPNHYFU2arRq9J9ruB0sum0sl38zkQUXfMWYMcN4770v6Nq1x1Gdf82aldx443jOPPNcXn55OmbziV8wLMhow2Iw4/A6CTWHBLY7vA6sBjNBpsYjMg7Iqy1kwd5v2Vq+HQ2NrrGdOLP1YGKDYhq19Ss/a4rW82PhT5Q7K0kLT+XO024kyhpBqaOcUHMIcU3sd7Ioc5Tzxqb32FmxOzBqZfG+ZVzU9jwGJR06ERthDUdHw+3zYDb8sthLraeOrMi2TSaCD4i0hnNt5yvIryuizlNHjC2aKGsEAImJScyevYiJEy9i7NiRvPrq+5x55tF/4SGEEEKI38Yuy3aKo4rJKM6A0hp8NXaCjOFUmOvYb85m0PZ8HCadmtNGAKB8Ck3X0A31MWW4ORKzbm5QN9ev/Dh9DtoEtTvmL8/nzp3DLbdcRdeuPXnzzVlERdUnbo0WIwkd49m1dDeWEAtGi7F+UYeNVloltqPj4LZYrTaU38CC3WupcldjNf6yeHClq5oQU1BgtHBzxVoTsUUHU+IswOVzEGwKJdbaCquh6RjUaDVhMBnwuXzotl/iKJ/bi8FkqO93E/LzNVwuAolgAE2DyEgwGu/n//4vigceuJ3q6iqefvrVk3ZwghCnGvlqRghxwuQa9lCplRPr/+UbbQ2NOF88e4zZVGtVJ+xc3323kMsuO5v27Tvx6uwZxMTGkW3cQY1WRZleSrZxO+29Heng6XLCznnAZtN6IvyRDWrrhqlwXDjJNew54v4rzcv42vopHoz4yCTOn4GuPJQbNmBRddi1WjYaSynSiwhVYaR727LHkE2NVo0RExoGDMpIqArDqTko18so10vxaG66eHoESnKc5RrF2c7zcWoOthu3sNe4mzbeTP7iuAYbTQeCESqSvp6BdPJ2pU6vJd6XSI5hF7mGPQSpIJJ8KRgxst20mfdtb+HCddT3L93dlvaF7VEFTgxFCnOBjr/QicvlpL97EGbMJPhboaivpXyAHz8OzU6XhB7Mnr2IlJRULr30LH74YWmzz713724mTryILl1O48UX3/1NEsEAKaFJtIvMYF9tHg5vfamPWncdRY5SusV2PmT93lJHGW9snMZ3eStAgU/5WbD3W97cPIMad22j9nNzFvLW5hlsKt1GhbOSJfuW8b8Nb1NYV0xGRJuTOhEMsGjfMraV7yQjog3tIzPpEFU/0vuL7G8oc5Qfcr8OUe1IC2/NrqocKlxVlDsryanai4ZG/1ZHrlutaRpJIQm0i8wIJIIPiIiI5P33v2LIkDOZNOliZs5897iuUQghhBAnjkfzUB1WiyvaiasmFz9O3HoVZZZdOMK8OPRgrHs3YqirRPkV1YU1hCWEEd6qPvYKMYaTHJyO2++iwlVKtbuCSncpEeZIUoIPvYjw4bzxxktce+1lnHXWeXzwwdxAIviAtAFtiM+Ko7qwmrKcMsr2lBMUaaPL8C4khCUTYY4m0hpB/8ReVLgqKawrxu5xUFhXTLmzgn6JvY64oHBTQkxhpIW2JyuiOynBGYdMBAOEtwojMiWSqoIqvG4vAB6Hh7pyO/Ed4rGEND1TLSgINE3h9Tbc7nTWJ4gnTryBF154i5kz3+Hpp/911NcghPhtyMhgIcQJY1Km+lGseNH5Jcnm0TwYlAETpsPs3XyrV//AxIkX0r//EF57bSZBQUEY7Sa+sXxOvmE/RowMcg1ntOviQyY9D+bGzW7jDty4SfK1JlodPoFmVTbK9JIG2xQKBZjUkZOLP5iXYlAmLCoGD07CCSLe3409xqXsMi5HpxWl+CjXd5HkS8CNmxJDMfsMXxDtj8WPDx2d011D8GheyvUSXJqL1iqN8Y5JgdG0Fiz8xXENg93DKdDzsakg2nk7NGuktFGZMGDApbnIM+RiUzbMyoIXLwaMJPvasNe4m13GbXTydjvi8Q5WVLef9Jp0cq17KA8qQylFiDOYXgW9GBQ+HPT6Ucptve3ZYdxKrD8BgzJQZCggwZdEH/fpREfH8OGH33D11Zcwfvwopk6dwYgR5x32vJWVFVx55QWEhoZxw2P/4LWt0/H43HSMzqJfq54ndIE1XdMZ234MHr+XHRXZeJUXi8FCn/gejM4YiV/5KbaXoms6sbbowCiJVYXr2Fuzn/aRmRi0+nIjUdZIdlXksLZ4I4OT+wfOUWIvZcm+7wkxhRBljaDYXopf+dlctp2ZO+bwQJ+/ntTT8Tx+D+tLNhFtjcKk//J3Q6vgBHZUZrOrModoW1ST+9qMVq7sMJanfnqZZXk/4PQ6CTLaGJw8gKzI469PbrPZeO21mfzzn7dz113XUlJSxC233C2jWYQQQogW1tqXhkkzUauVYqrdhRYcghedSlsVA+2dMMSE4s3PpXpHLvbwVoTGhJB1VjsMpvq4StM00kM7EGaKoNiZj9vnIsIcQ2JQSmCkcHP5/X4effQ+/ve/Z7nxxr/ywAOPNRl7WUOtnDa2B6W7S7GX2THZTMRmxmANaxiTn5N2Jrqms6JgFSWOUoLNwZyXPoKz04YDUOetocpdBmhEmKMJMoY0Otex0g06Hc/JYuNnPir3V+L3KwwmA4mdEmg7+NAzATt0ULRqpZGTA23agNEIlZX1yeD+/RWaBhddNJ79+3P5978fIi0tg4suGn/C+i2EODaSDBZCnDAZ3vYk+VLYa9xNurcdOjounJQYihnpPK9RndpjsWdPNhMnXkTXrj15442PsFrrg6hunp508nSjTC/BrCxEqqaTSL+2y7Cdj2zT2GvMwYsXDYjyxZLoTyTVl0Ef9+kk+RsuVNbHM4AZxu3UajWEqFAUin2GvUT5Y+jg7XzY8ykUFVoZQSoIMxoGwIPCRCyoUDQMaLixKCM6kG/YT7o3k2HOEWQbd1ChlzPUNYJqrZJiQyEhKpRwFQEKznSOwouXT60fYtfqaO1Lo7unF619aYT4w9hkWsci61xifQl08nQ77GJvESqSDp4ufGuZj1tzY1ZmFIpqvZJwfwSx/jiy9QocmqNZ9/kAv/JRYM8lTIugr38Q5Z5SPHiwaTaUw4/DVkuwNYRQFcYk+818bfmczaZ1uHUvvT39GeE8j1b++tWgQ0JCeffdT7nlliu59tqxvPvupwwdelaT53W73Vx77VjKykqZ+PQ9zMn/BqNmxKgb2Fy2jQ2lm7m+61WBhLBf+Q+72jKAUorNZdtZW7yBSlc1aeGt6Z3QI7BgXFxQDLf2uIZdlTlUu2qItkWSHt6GXZU5fJkzn71V+9A0jYyINEanjyA1LIWc6r1YDdZAIhjApBvRNI38usIG599Xm0+lq4rUsGTWFG2gxFGCUuDyuZm3ZzGnxXblvIwRR/X+/J6U+vk+/+oea2gopfCrw9fty63Zj8PjJDM8jRBTMAZNZ1/NfmbumMM1nf9yyPcupyqX7/N/ZHdlDpHWCPoknEav+O4Y9Ia1vo1GI0888RKxsfE89tj9eDxu7rrr/uO7aCGEEEIcl46erpzm7svKkMUYk+wY/A5qI3Qyi2MYkJ9JSGotTlsC2pBOmJNbEdculqDIhjGvrunE2ZKIsx374rpOp5M77pjEF1/M5l//eparr775sO2NZgMJWfGHbWM2mBidMZJhKQOpclcTZg4l1ByCUoo9NTvYW7sTl9+JBph1K2mh7UkJzjiuL6vdfhflzmI8ykNQWAi9ruxJ5d5KXHUugiJsRLaORDccenBBaCiMH+/n/fd1du+uj++CgzWGDfNz+ukKn9dHRW4ll5wxgW2bt/DXv15PcnIb+vQZcMx9Ppn4lZ9SZyFlziL8+IgwxxBna4VJ/21mHwpxokgyWAjRLJVaBcvN37LOtBoDBk7z9KG/ezAh6pcCUUEEMdZxJdNtb7DDuBUAHZ2u7h6McI4+7j6Ul5dxxRXnExERwZtvzgokgg8wYiTen9js41VrVUwPep18w35SvWkUGPJZa16J0+SkrbcDG9U6VpmXM6nuZtr6sgL79XcNIceQzWrzCvazF4Uiyh/DRc5xxPkbLwp2MA2NdG87lluW0NabSJTfQInuw6mVYSSCRO+leGhFon83ZcZ8XLiw63aC/MF08fZgr2E3CsVtdfeyxvwD241bCPWHc5qnDzVaFS+F/Ofnerv1C0V08HZhsGs4n1o/pNCQH+hDO29HJtlvIsYfd8h+nue8iGK9kL2GHMr1UkJUKCH+MDp4u1KjVROqwkjwtWr2/QbwKR8e5caomdDRA+dXKCpVKR6/O9A23p/IBMf11Dpr8OEjTIU3qiFstVr53//eZ+LEi7j++nHMnr2Qzp27N2ijlOKee25k9eoV/PvV//GtYR1JtsTAQm0ev4ft5btYWbCGLjEd+Xb/cjaVbsVsMNMrvjtDkgcE2h5sYe5SPs3+CpfPjcVgZl3xRlYVruPaLn8hJbT+4cKkmwKlD6C+HvDbm9+nzFFOQnA8SinWFW+k1FHG7T2uI9wchsfvaXAepRQ+5SPY2PBBxqyb0HWdnKpciuzFRFkjMGhG6jx12L0OFuR+S6+E7iQEN/0etzSzwUTn6A4s3PcdMdaoQDK2yF5ChCWcjIi0Q+7r9XtZsn85Bs1AZvQv7ULcIWwo2cye6n2khbdutN/Oit28sek9Sh1lhJvDKLSXsKVsO0X2Es7POBulFE6fC7NuwqAb0DSNe+6ZjNls5oknJhMTE8eVV1534m+GEEIIIZrFjJkr7NeRaWnPSuMbOHO3MXRDG/rndyS80Im/rIjw4WcSf3H/Ix/sGLlcLiZNupiVK5fxxhsfcfbZ55/Q44eYgxvEnuWuEnbXbMWgG4k0189gtHtryK7ZQqgpgkjLsZUFq3SXsbVyLbWe+lJ+mqYTa0mgQ8ZpmA3NX3i7fXu45x4/27fX1w9u1cpP69ZQU1TNpi+31o809vk5L+VydrfZydVXX8KXXy4jNfXoynKonxf/O9Eztbw/x95G/ehmsSql2Fm9iX112YG+5dn3UuzMp3NkL0kIi5OaJIOFEEdUrVXxRvCLbDKuJ1SFofCzw7iFbcbNXFt3W4NSDB28Xfhb7YNsMq2jTqsl3p9IR0/X417EzeVycc01l1JZWcHnn3/XqBbXsdhi2sB+Qy6Z3vb4NB/Zxu3YVBBmrHjxkunNYp1pNe8G/Y+7ayfXj8AFbNiYYL+BVG8aSy2LqNYqaevtQJzv8IngAwa5z2CreSO7jFtJ9sVSq9VSZMhHVx0x0YFUXwXh7KMYPyZMePglQRrqD6dSLydcRXC+89LA9mK9kCdDH0ZXOh189XWS3bjZYPqJ9cY12LDRztsRHR03LrYaNzLX8ilXOg6d2Ir3J3J73b2k+FKZY5uJDx+p3nTsWh12vY7hrnMajZo+EqNmIsQYRrmrBNtByU2334VBMzU53e3gLxyaPKbRyP/+N4OLLhrOlVdewOefLyU5+ZdE4Pvvv81HH03npZfexZQZhWeXp0GAbdJNBJuCWFmwhhUFq8mt2U+0JQq718HsXV+wpzqXa7tcicXwS0BX6ihj3t5FWAwW2oTVn8uv/Gwr38mCvd8yqXPT099WF66lyF5Kh8i2gUA21BzCtopdrC3eSI+4LqwsWEN+XSEJQXEoFHm1BURYwuka23DRv4yINrQKTmDB3m+xGCwYNCNevxe710l6eBtqPXZ2VGSftMlggDNaD2J39V62V+7CarDi8XuwGMyMTh952HrHdR47ZY7yRqU9Qk0h5NcWUuoob5QMVkoxP3cJZY5ysg66/yX2Ur7dv5woayQbSrewt3ofQUYb/RJ7MiT5dKxGC7fffh8lJcXcd9+tREZGcd55F5/4myGEEEKcZFw+Jx6/G6shCKN+8qQOggjiDNfZDIsdgmvFx3hW/YCqK0QFBWMeMhzLhZce+SDHyOPxcNNNf2HlyqVMn/45AwYM+c3OdUCpqwCv8hJm/GURuWBTGOWuYkqdhceUDPb6vWyvXE+tp4pwczS6puPxuyly5hFUG0rb8M74lY9SZxFV7nI0NCIsMURb4ppMxgYHw2mnqcDvfR4f6+eup6SmiNDMEGzeULyVfib2vpOnFz0QiNnDwyOO2Fe3z8X+uhyKnPvxKz8x1gRSgtOPu0xGraeavbU7KXcVAxBjSSA1tG2zj1vhLmF/XQ42QxCWn+sx+/xeSpz5FNj30Trk6BfaFuL3cvL8jS6EOGmtMf/AFuNG2nmzMP1cC9iJkw2mNWwwraGvZ2CD9lEqmsHu4cd0rhzDLn40L6dIzyfe34o+7gGketK5665rWbduFR99NJ+0tOOvCQpQo1WjUT+iuFwrxa7VEemPxq7VUa6XsML8LWV6CTnGneQZ9tPHM4BBrjNo5+3IDuMW5lm/oFQvIUgF8aN5GdtNmxlvv5qenr6HPW9bXxZX193MAstc9hp3090bSntvf/YYquni3US4clKum9DQ8OImTP2S8KrWK0n2tcamGo4S3WncRqVWTntfp8A2M2YMysB24xZGHjQy24yFOH8CG0xrqXZWNTj+rwWrEK5wXEtXT0++tcxjvyGXEH8o/d2DGew6s9FI3SPRNI3k4HSqPBVUusuwGoLw+T24fE5aBbchzHR0KyUH+hkcwrRpnzJ69GCuuGI0c+YsISIikn379jB58t8YN24iF144jq/3LATqE4MHB7Jev49CezF2r5NIczjF9lKgPlG7rngTm8u2cVpc10D7nKp9VLiqaBfxS5CnazpxQTFsK9+F3WMnyNS4DEdeXQE2g6XBuXVNx6gZKLaXckbKIM7POId5exexoyIbTdOItkVxfvpIUsMaJt5tRhuXtjufZXk/UO2qweP3oGsa8UExZIa3YW/N/iOWWjgWdR47W8q2U+2uIdYWTfuotg0S5UcjITiOm7tNYlXhWnKqcgk1h9A9rgsdDxpN3RSb0UaoOYRKZ1WDBVUcXicmg4kwc+MgvtZTR05VLnFBMQ3uf4wtmlVFa3lt4zSsBjNR1kgqnVV8tOMzCutKuKrjWDRN45FHnqK8vJRbb51AREQUAwcOO6ZrFkIIIU52br+LnJrtFDn24/N7sRptJAelkxycfkJGZfqUj1JnIZXuUkAjyhJLtCUeXTu6tQ40mw3rZVdgPmME/vIy9IhItLj436zGv9/v5667rmXBgq94662Pf5dEMNS/H03dGw29way6pnj8boodeVS6y9A1IzHWeKIt8VS6y6j0lGMzBKOUHzQdk27GYrBR5NxP6+AMdtZsptCxD6fPgcNbh095iTTH0jmyJ4lBqYd9v3bs2UxuzDb0TIVbr8TkMRMeEk+0I4a/nj+FKR/cxa23TuDdd+cc9v3y+r1srlxDsTMfs25BR2NPzQ4qXSV0jep31HWeD3B67WyqWEWVpwKbIRhQ5NZlU+OtpFtUfyyGIw9kqnSX41MeLIZfnl8MuhGDZqLUWSDJYHFSk2SwEOKIdhi2YlbmQCIYCIz0zTHuapQMPlbrTWuYFvQaFVo5NmVjvbaGVeblBP8nmDlzZvLqq+/Tq1e/E3IugFh/PDoGnDjr68Oi4cdPnVaLW3PhV36sykadXsdG01o2mH7iW/N8err7UaNXU6VX0sHbub7GKYocQzZfWD+mk6fbEUdCd/B2IcvbmRqtGpMy49DsvBz8FNnGH3H9vEicBmgYsPhtOLBTZijFh49B7uEYaFjf1I8f1cR56rQ6qrQKllkWY1ImEvxJpHkzMSojbt2Oh/ppUWV6CVuMG3FpThJ8iYT7IzFhJs6fUF/qw9uDLt7uOHHWJ5l/df6juu/WRDpF9GRfXTa1nmqMupnk4AxSQzKPK3iPi0tg+vTPuOCCIVxzzaVMn/45f/3r9URERDFlyn8BaBeZSbApmBJHKbG2+qRgrbsOr/Ji0kwU15Wwpyo3cEwNDaNuYG/1vgbJYIOuB2rbHpwPr6+Bq6MdIjiOtcXg9G1okIxWSuFVXiIs4Wiaxpmpg+ke14mcqtz6msLhaYdcQbpjdHuu6DCWT3Z+QVxQHGGWEGJt0VS5qgk2BTUYHVvmKKfIXkKwKYiU0KSjfuAC2FO9j2lbPmRfTR4KMGg6WVFtuarjZURZI476eFC/QN7INmcc1T5mg4kBib35cOenlNjLiLJFYPc42FebR5eYjmREtGm0j1E3YtJNePwNl7v2+D2UOsqJsUXRJbpD4H2pclWzpmgdg5L7kR6eiq7rPPvsG1RUlDNp0sX8770P6dipK3FBMcd0L4UQQhyay+eg1FmI2+/GZgwmxhJ/1FO4TyV+5afIsZ8CRy5Or51wcxRJQW2IOIYRo0optldtIN++B5shGKsxCKfPzvbqjeiagaTgNsfVV5/fy9aqtRTY98HP0ev+umySgtJoF971mP5N1WPj0GN/25lQSin+8Y/bmDNnJq+8Mp3hw8/5Tc93sHBTFAV1ufiVP3B//MqHwk+Y+dADKdw+F5sqV1PmLKQ+YFXk2/eQFNSGancVZc4ijJoJo24kxBROpDkGg2bE5/dS6NxPgT0Xg2bA5XWglB8dnVJXIevLVuDwOcgM69jkeSvdZeR4tuEzeAlyhoMGHouTith8wspaEVOVyDP/fZ2rr7uYd999lQkTbjjkNZS6Cil1FhJuigqMTrcZQ6h0lVJg30d6WNYh9z2cQsd+qj0VRJljAnG71RBEpbuUYmc+KcHNLWHR1LNLU09lQpxcJBkshDgiq7Lh07yNtisUFnV85R8OcOPmc8vH1FFLlrdTIMG6dvsqvntsIdfecNsJn5rdwdOFLE9nNpp+Isofg0mZyTfsw6hMGDAQ7o8kx7gLgAR/K6q1Snz4+Mm8kjK9lH7uQYGRsRoarXzJ5Bv3sd+wl0xf+yOeX0MLjMq1KRvX2W9jkeUbNpjWoCszE+tuolarYbdpJ0VaAZG+KM53X0J/9+BGx0r3tiVMhVOiFwXqFldpFfX918Cu2bFhY4dxK1VaBeEqgi6e04hUUfxk+pEPbe9SqhdTrVVRYijCpoLI8LajnbcD5zsvJdWXjobWoCTIsdI0jThbK2KtiT+XhzAeduqhHTu7jTvwaT5ae9MOuzhg27ZZvPXWbMaNO5vLLhvJ6tU/MHPm14SGhgGQFtaas9ucwTd7FrGtYieK+sRi/8RebKvYRYmjjISgeMyG+gdOt89FXl0RhXUlgXOUOyux6hYiLGHsr82ndWgymqYFkopnpg7BZmz6z0XP+G78ULCaPdW5tApJRCnF/tp84mwxdI/7ZfHBGFs0MbbmlUI5N204+2vz2V21B6fXxe6qvRh0/f/ZO+84yaoCbT/npspd1dU5x8k5MAMMQ5IoQZCsCCKwhjXvruEzu664rqususqKKEFEQCUoOUhmAsPkmZ7QPdPTOVR3deWqG873Rw09NJOHGWDXevj5w666uarpc9/7nvflzPrTqA/UYjomf21/kld6VzKWi+FWXUwJt3L55A9QdoAohrdiOib3bXmQXfEeWoKN6IpGxsqyfmgTj+54mqunXXrQbfQm+tkyuh3TsWgI1NIaatqruO1QObV+CXEzwSu9K9ke3YFbdTGvbBaXT7lon98nj+ZmXvksHtvxNEGjCLfmwpFOXnQH6gM1Ex5GFBkB+pID9CUHaA42AGAYBl/+j+/zsasv5fprLuWM717NrElzuKD57H1mFBcoUKBAgcNnNDvM5uhqklZs9yuCYqOU6cXz3/a08P+r7Ii3sSOxBQBdGPSkdhLJDjIjtJAS9+GJpHEzynC6D78WHHdH6opBLDdKd2oHVd46FHHkpoDB3VPo/XrReKZq1s7Qk9pJibuCMveh93+8U0gp+dd//Sp33XUrP/7xrVxwwcHHPEeTCk8N/ekuRnPDuJX8WDzrpCk2yig/wPXqS+9iON1H0AijKhrSkYz1xFg9tArNqyDKFRQ93zMSzQ6DlChCIeyuIJqNIBAkrTi2tHGrXhAgrSQ2Dj3JHVR76/f5OzmU7kMaDq6sB1s6aC4VI+sh400Q0yPUlbWy+NxTufbaj/Od73yJJUtOpbV13/dOCXMMiTNhbKcIBU3RdzvLj4yYGUUR6gQDhyIUBArx3BgcguE4ZJSgCY2Mnca9OybCcixsaVHmee99jwsUeDMFMbhAgQIHZZY1j2XGi4yKCMWyBIlkWBnELb1Ms2YefAOHQK/aRb/WQ7VdOy6wOjmH9devxtfi4+r/d8NR2c+bceHi2tTHecz9IGv1VdTYdWhSIyVSxESUIWUQkJTblehSR0PHEhZ1ViM7tPbxErU3cISNQKBwZC7BCqeKq9If5dL0hwHQ0cevdUokKXMq8Mp9j0yqnVrOyLyfRz0PsEXZhC51OrRt6FLnOPNEOrUOTJFDR6ND28ZscwFnZ88nKka533MXcRGnwq6my+gkR46syBBXYqzVVxFRhvls4iuE5dvPaX4zQoiDTsHaoK3hT57f06t2YeMQdko4K3s+78ueu9+IisWLl/C5z/0/fvjDb3H66eewdOke16kQgnMaT2dKuJWtu0XJpqJ6poYn8f3lP0EisaUFaEggbWcxFA1HOiRySR7ueJzVA+vJ2BnSdpZELkHCTKIKFQRMCbdyZsP+pww2Bxu4auoH+WvHk3TFexBCUBOo4qKW91PlO3DD9P4o85byyTkfZUXfarZFO/AbPuaUzWBO6QyEEDzX+TKP7nyaElcxrcEmUlaa1wfXYTk2n557/SGLsTvHutgZ66IhUIu+e0Du1lyUe0tZN7SBseazCLqK9rv+C92v8nD740SzY4DApRosrprPlVM+OC6+Hw66ovPBSedzcu0JDKSG8ele6gM1ZO0cG4bbsKVNfaB2gqv6zIZT6EsOsCnShi0dpJSUe0tx6S4sx56wfUtaKELg1fY8AOlJ9HHvzr8w+7Nn8up3/8SKnz+K81WFSHqUz867gRLP/h9UFChQoECBg2NLm61j60lacUJGCUIo2NImkh1kR3wLM4oXvNuH+J4jZSXoTu7AUFzjwpxX+onmIuxKbiPsKjusmVdpO4UpTfzKxFlJLtVDxkqRc3LjwteREMkOIBATyrVcqpukFWc0O/yeFIN/8pN/45Zbfsx3v/tjrrzy2gnvZe0Mg5le4rlRdMVFmbuSoFFyVKMqXKqHmcXH0ZXsYDjTB0CVdwp1vmaMA4ylhzJ9aIqOqmhYKZuBZ0aItSdJpNPomgtPjQdzaQYtlBeEI7lBKty11Pta6Ul24EiHnJ1FV/QJBlhDcZFzMiTM2D7F4KyTweVxQZnGWO8YjqWhaAqmY6F6HBpn1yMUwTe+8QNefPEZPvvZ63j44RfQtL3lKU3kX5sQ8SYhZSexMxarh18maJRQ5a07rMgIl+rKx2O8CSklUjqHXJ5XbJRS62umK9lO2krwhvu63F1NpefwelUKFHinKYjBBQoUOChzzAW8L3suz7ueol/kByB+GeDczAeYYs04yNqHhoKCkAKbPYLMsh+8wMimCKc/fzZetxfsA2zgCAnLEj6cvp4LM5eRE1kyZPij524edz9EuV1Fp9aOBx8SiSlyFNlBwk4pHumlV+2i0qlGQcHBoUftoslqpc5ufFvHpLNHGBMIypyDi4QCwbnZD1BnN7JOX0VcxMajIybZUwjLUnqULtIiiSZ0TsqdxjRrFq8YzzOkDDLZmkabtpG0SFHuVBJTxhgREZaYp7JV28xa4zVOy579ts7rDWwOTTQfUgb4vfc3jCgRGq0WFFQGlD4e8PyBcruS2db8fa4npeS5557E6/Xx+uvL6e/vpbKyes+1EoLmYMO423M0M8aueA8l7hLKvWXk7BzJdAoEeFQPNf5qQq4i7tv6IC/3rqDcU0aFq5zRTJSsmWVK8SRaQg1U+SuZUzpjQjndvlhYMZcZJVPZFetGEQr1RbVHnLn7BmF3Mec0nc45TIxbyNkmL/etwK/5xl3AAcNPY6COraPttI/tZHLxoeWZZe0clmPtJdwaqkEsFydr7z+zrifRx0PtjyGlHC9vi+XivNSznOZgIyfVHDhn+0C82UW9MbKFP259mN5EPw4OYVeIMxtO4331SxFCEHIF+fjsa9kc2cpAahCv7mVGyVRW9r/O/VsfZiwbo8gIYEmLHWOd1AaqmfKm67OyfzVDqWFm1s2g7MtF3PPl/2LwL22IixRWD23gjPq9XfsFChQoUODQGcuNELeiBPTQuGNPFSpezU8kO0DWTo8XNRXIkzDHyDoZio09s32EEHg0H7FclJyTOaxr5lJcqELFkia62DM+MZ0shuJGF28vrkMi9z2z/o333mP87ne/5kc/+i5f/vJ3uOGGT094L2Ul2DC6kmgugkBB4tCV7KClaCr1vklHVRD2an6mBGczqShvxDncOI3IsjFim5NopQpqscQlNZxuBXWZH+NcB5McmhBMCs6g1F1B2k7Sl+5GInFwUMk/rBFCwVBc2NJGFfuWkwJ6kF52UtoSRndrxAbi2JaNHlKZVDuZimn5exuv18dPf3o7F1ywlFtv/Smf/OQX99pW2FWOobhJWjF8WhFIGMz2MpYbIagXM5IbZijbx2Cmh1nFi/DpBy6efoMydzW9yU4SZgyfFgAkCSuGobopcx9aKbgQgtaiGYRdZUSyAziOTchVSpm7qhBrU+A9T0EMLlCgwEFRUflg5irmmgvp0LYhEEyypo5HBxwNaux6GuxmtmqbaLWm0r+8lxU/foVJ35jGvJnHUZWsPSr72R8BWTQe73R96h9JiQRt2kZUqTEmRhFC4JU+au0GRpRhppkzKXKCbNE2jovBxU6YBbnFjCjDlDuVR+3aHCpvZPvOtuYBcIv3ZtYYKwCosmuosmuQSDZrG2i08zlYObKAREEhroyhyXxxnSpVTGHmRXoEw8rQ/nZ7yHSrnfzNeJLN+noM6WJRbgmn5M7AJ/c95XODvoYBpZ8p1vRx4bjaqWWrspmVxiv7FYOffvpRVqx4mV/96g984xtf4HOfu5577nkERZk4aM7aOR7peJJlfatImimSZhKkw4zwFCT5aXJuzc1oJkrYHeLxnX+jxlc17n71+CtxpEPKSnN+89m4tUNzEUA+smBK+OgUIR6IlJUikUvuJVB7NA9ZO0ssFz/kbdX4qyh2BRlKRaj07ZlyOpQapinYcMDM4M0j24hmY0wr3nNjVGQEGE6P8PrgurclBr/BcDrC3Zv/yGg2SlNRPYqiMJAa4sHtj1DmLWFOWf7BlUs1JkRyONKhNlBDXaCG9uhOdEVDU1Rq/NVcNfWDE4oA+5IDuHcXAFZOqufkay/gudseZFGTn6H6I5+qWKBAgQIF8jjSzmeTvkXoUkTeVWjLY+AM+F+OIjQUoewW7PbM9nGkjSKUw450KDLChF1lDKb7COhBNEUnY6cxnRyN/imoB4j2OhRKXOX0p7qwHHNcMHujIO3Ngvah4kiHWG6EnJPFo/nwa8GjJsKuXPkqX/va57j22o/z2c9+Za/3u5LtjOYiFBulCARxM0okO8DQYC/d3h3U+pup9TYfcSTWvjgcEbjcXc1IdpBMIkt8awo9qCM9FsJUUHUVvdwg12tRHKtElpkoQqHCXQNApaeW4UwfKStOykpgKSZCCHxaEaZjEjTChIx9z4iqcNfQl+pkzBzBU+vDVWWQMuOEPDVMLZk54fOZP38RN974Wf7jP77NOedcuFdReEAP0Vo0g474ZkZzw1iOSSw3SpEeosKTj2qT0mE0O0xXsp2pobmHdG2KjVImBWfSEW8jmhtGAl7NR3NgGsH9nNe+UIRCqbuS0kMUkAsUeK9QaDwpUKDAIaGg0GpP4azs+ZyZPY9Gu+Woip0qKhelr6DCrmaTvY6HP3k/wYUhTvr8qXwgc/nbKiw7XKLKCM3WZMJOKV58ZEUGTeo0Wq1ElRESSpzzM5fwz4lv8eH09ZycPZMSu5wt2ia+U/QlrgifwxeLbmSb2vaOHfO+WGAuRiAYUgbz8QfYdKodlDhlzDDnAHkR3i09jIkoHsdLQkkQUYaIKEMUOUXjboBi5+1Nf+9Vuvkf73/xnOtJcuSIKqP80XM3d3p/NV5i91biIoaAvRzEHukhouSFt55EHw+3P86t6+/i4fbH6Yr18MMffpsTTjiZ8867mJtvvo0XX3yGW2/96V7bf2zH0zy642kEghp/Zd4V7JhsjbaTsbOkrTTRbJTjqxdS5i0jaSYpMia6DYKuImK5GAkz8bauz9FCSslwOkJXvIe0lcGv+wi7i4lmYxOWi5sJvJqHEvehf67F7iCn1S8laSZpj+6kPznIltHtuDU3ZzSccsDc55ydyxcivuXmTFc0UmbqsM5xf6wf3sxAaojmogZ0VUcVKtW+Skxp8Vr/6n2ukzRT/GbD77ll7W93l+I5+HQvl0y6kH9e+ClaQxPLQ/IFgNl8cSAw/8JTaD5uOqt/9RRybN/f4wIFjhX//d//TWNjI263m8WLF7NixYr9Lrtx40YuueQSGhsbEUJw8803v+1tFihwLAjoQdyqj5S15++qlJK0ldz9nvcAa783sR2L4cwA/elu4ubY+N+Qo0XIKCGgFRHPjeLsnvZuOSZpK0W5p2ZCHMOhoAiFqcG5VHpqyNppxnIjOI5Nna+VGl/T2z7ecncN5Z4a4maU0eww0ewwSTNOlaeOUvfhRWalrARrRl5lVeQl1ows47XhF9gUXYXp7H+20sGwHJOhTB/rdr7GDTdcxty5x/Gd7/znXmMYW9oMpfvwqF4UoRDNDTOcHcBxHDJWio5EG68MPMkLA48wtDva4Z2m0ltHmbuaWGKUTDZNTksjpSSgBXGkja3bOKZDOp0i66Sp9NaPx07oisHM4uOYW3IiYVc5mqLhVr3oQiOgB5kSmr3fBwNuzcvM4kVUexuRSIQKdcEWZoUX49b2/h3+0pe+TXl5Ff/yL5/c6/dDCEGtr4mFpSczIzSfGl8DQaOYCnft+GcihIJL8zCc6R//HTgY+e02s6j0VGaFFzMnvJiFpadQ7W04nEtcoMD/WgrO4AIFCrxnaLWn8PnkV/n333yLdEeKb93x77w/ezElTtkhrT8iIvSou3DjpsmahHYE/4l7wXiGBz33MiZGkUCJXUarOYUSWUZMRAk5YZbmTufE3CmoqJyePYfbPb9kuetF0iJNwCnCFDmecz3NoDLAv8f+m1rn3SmWmm8uoidzIc+7nmaLtgkBlDkVXJy+iion/9S/xZ7M4txJPON6nF3qDkaVIYawMHDTq3Zji5eZYk1njjkxoy9LFoHA4NBuMF42nqNH3cVUa+a4uJsSSdbqr7FZWz/B5Wtj0612khNZbGxMcui79yORJEScBruZTZEt3LnpPobSEdyqQcbOcc+f72DjxrU88MCzCCE45ZQz+Id/+Bw33fR1li49nenTZwMwlo2xrG8VYXcx5bvjEyq8ZSwon0t/apB5ZbMIuAJMDbcyu3QGvcl+vJqHhJkkYOxxMsdycfy6D59+6Bllx4qRTJSH2h9jw/BmcnaOsLuY0+uWcnLtCdzd9kd2xXsIu0OkzTRDmQgnVB1HY9Hh5Zmd1XAqxa4gr/a9xlBqmIUVczmpZjHTSw5cmFgfqEFXdZJmCt9up63t2MRyCU6vX3rI+8/ZJtujHXmh2/ChoOA3fFR6y0mYSRTEXo4Zj+pmODOyz+09vvMZXu1dSV2ghoDhJ2vnC/jWD2/i9PqT9lr+jQLAHbFd1PgrcaRkykeX0P/Nbn73bz/j8j9fss+8uwIFjjb33nsvX/ziF7nllltYvHgxN998M2effTZbtmyhvHzvsqhUKkVzczOXXXYZX/jCF47KNgsUOBa4VA8N/la2xzYSzQ6jKcZ4PEGDf/JhT41/t4nlRtk8toa4GcWRDoZiUOmtZ1LRzHzfwFFAUzSafdNZ2fkiG4fWkEokSSczqBmNXgZ5TX0NVVVRVQVFUVAUFVVVURQFn89PeXklFRVVhMOl47OoPJqP2eHjieWiDGV6GckOMpTpZSwXodrXQI238YhL5DRFZ0ZoAWXuKiLZQRQEJa4KyjzVh7VNKSVbxtYxnO4jYITQhE7OydKd3ImhuJkUPPxek2guQlt0DaOpCDd94rtYWHz5R19H1Q98XJZj5gvJUMjKDDY2biU/3hlM97KB15gTPp6w69DuaY4WhuJiVvFxlGqVrAqvITdmURouxaW6GckOER2O4nhstJBCnX8yTf6J4zldMWgOTKXRP4mR7DApK4GhGITd5RjKgWfE+fUiZhYvJGdn88dygBxer9fHD37wcz70ofN4/PGHOPfci/ZeRvPj1fwIoTCQ7t1LnJdS7jUL8FBwa14q9yFQFyjwf53CHUuBAgXeU7hibh790YNcecVH+UjTP8AhPNy1sXnC9Rf+5nqSqBJBx6DRauGK9DU02M0H38Bu+pVeHvbcj8RhqjUTgSAlknSqOzgnfSFLc6fjku4JLuUBpY+n3Y+RFIlxJ7HiKBjCRbu+leddT/Hh9PVHcineNioqH8hcznG5fIGcKjWmWNMJyeLxZRQULk9fw6DST7u2hQq7GktYaGjkRJYxEeWC9KXjucUDSh/PuB5nvf46sCdP+s25xo50EIgJg7St2iaKZGiCy9crfVjY9KhdzLbm06f08DfXEzzneoqYGMMlXQyq/USUYaZZs9CkxqDaR5lTwcLMCfyx4zGi2bHx6AHbsvn1vffQMG8KrXNm8GLPMkbSo5x49ft56ulH+Na3/pn77nsCIQRj2RgJM0mld6LAUewKEsvFOaPhFCYV7/nuNARqmVEylRX9r1Plq8SvexnNRombCc5uPA2PduAivKNB1s6xbTQvhFb6yqj1V49fY8uxuHvz/awe2kCVt5yQESSSGeHerQ/ykWmXc9WUi/lb18uMZEZxqy7OajiV85rOOuxplIpQWFy1gMVVCyYWeRyEKeFWFlbMYVnvKtyaG01RiWXjNIcaOKFq4SFtY1esm9+3/YmdY7voTvYTy8YIGAFaQo3MLp1OY1EdQghM20TfnWsspSRhJmkq2vuBTNJM8drAWko84XGB36W6aCyqo31sJzvGdk34DgA0Bev50LRL+Gv7E3THexFCobm6lbN+/DP++WMf4+c//yGf//z/O6TzKVDg7fDjH/+YG2+8keuuuw6AW265hUceeYTf/OY3fOUre09lPu644zjuuOMA9vn+kWyzQIFjRZ2vBbfqpT/dRcpKUu6uptrXQMg4ukW2xxrLMdkcXU3MHKVIL0YRKlknza7Edryqj3r/4cVFxeMx2tu3sm3bZrZv38K2bW3s3NnO0NAgIyPDb9txrGkaZWUVVFRUUV5eSWVlNVWNVbjqdWpaawkVFZO0E7RF15Cx0kwKziRtJRnK9JO1U7g1L2Wuqn06P/fal6JT7W14Wy7MmDnKaHYIvxEcdz+7VDe2tOhPd9MQmHRQwfLNmE6OtmheuL//5j+wc1MH377tJhLeMXqSO/b6vFShUuqupDOxHalKbMdCCAXTyaIJHZfqQUqJ5Zhk7BS9qc53XAyG/LWuCzbDqQYb/7qJbL+FCJgYKR/BnErtkiqmt0w74OemCHW3a/vwy44PtYzt1FPP5OSTz+AHP/gmZ555/n4frhcbpbhVDwkrhl8ryt8DSIucnaHG1/SeeGBkZkyiPWMgIVQTRPcU8oMLvPcoiMEFChR4T/E//3MzyWScL37x64e8zgrjZR723IffKaLVmkqOLNu0zdzluZUvJr+OVx6aa7NN28ioGGGqNWM8AsMrffikj1X6cs7OXrDXOqv1lWzS1hFX4mREBgNjd8GchwRxNmvr91onLmJs0TaRE1nq7AZq7YZjli8sENQ4ddTk9u8ANTBIixTzzEXU241kSNOrduEgSYsUpshPfx8VI9zq/Rnt2lZKd7u1n3D9hXZ1GxelL6d7aICtXZ30xwcp9YQ5sXoRx1XOQxEKfllEN7sm7FfuDqFw42a1vpI7Pf/DcuMVYkoUiYMqNfwygCIUBpU+wrKUOeZCzs6ejzbmpjveR7WvclyQ3PrSahK9o8y64XT+fcVPGc1EAYkDtF62iKf+/fc888xjnH7GOQi3xKUZxHPxCUJuLBfHq3sI7c4FHr+OQnDF1IvQVY31w5uJZEYIGH7OrD+FlmATg6lhyjz55uiRTJQNw5uIZmOE3cXMLpu+V7zEwTAdk8HUMKpQqfCWsSveze/b/kxnrAvbsfDpPo6rnMelky/EpRpsj+5g88g2mgL1ePV8SYxXr6FjrJOXepbxTws/xaLKBYxmo3g1D66cwdCGYfqTA3hDHsomlaK7D2+gejhCsq7ofGjqpTQHG1k1sJaMleHkmhNZUrNovNjuQGSsLL/bfD87YrtQUYhn4yhCEMvFiGVjvNq3knguTnOwgS0j7ZR5SzAUncH0EGWeEo7fh+CcsTJkrSz+t7i63aob0zFJWel9Hsv88tlMD0+hK96DEIL6QC2GqrPt4+v46U9/wKWXfpja2sIUwwLHjlwux6pVq/jqV786/pqiKJxxxhm8+uqr7+g2s9ks2Wx2/OdYLLbfZQsUOFSEEJR7qin3VB984fcwo7lhYmaUIiM87gJ2q15MJ0dvqpNaX/N+have3m6WL3+J115bxrZtbWzf3kZ/f+/4+9XVdbS2TuH440+moqKS0tJySkvLCYdLCYVCFBWFCASCeDz5MYHjONi2jW3bOI49/nMiEWdwsJ+Bgb7d/+5lYKCfwcF+Vq58lW33tGGZ+azYqvpqmqe2Uje5nobJm5BLBMOil4Q1BgiQki69gxnFCw4rc/VIMZ0ctrQmlNwBaEIn62SwHPOwxOBIZpCR7BCv/vUlHr/vr/zD1/+RWXPnkrDG6Et1Uudr2WvsU+9vZSw3wnC2H1OaOLaNROLR8tERljRRFAWX4iaeix6N0z5iaufWoKgKu1Z2kRxJ4S320HjSZAZkHbf/WsVxYOZMycKFEu87aJRNRdPseq2LwS2DnDP5Uv7fC5/gD3ffwdXX7ttM49F8tASmsy22gdHc0Pg9VIm7gnrfoZuAjhX9mwfY8sw2ksMJJOAv8THp1FaqZ1W924dWoMAECmJwgQIF3jNEIkPccstP+OhHP0VNzaFNX5dIXjaeQ5EqlU7+psGDl2ZrMju0bWzS1rPQPP6QtmULK59r+hZhVt3tkt3reMUwT7j+giksFClwSRemMBlU+gk5YYQUlDsTywTWaau5z3MnA2ovEolPBjgxdwofTF+Fzrv31DgjMuhSZ0Dpo03fSFzEAElO5NikrWNp7nRWGcvo0LYxxZo+HsEhEDzhfogXBl5mbLPEZbqZpE4mGh1j22gHsWycMxtP5TjzBDbqaxkVEUIyjESyS91BiVNGvdXEb72/2B1LkS+ts5HkRJbs7muiSJWvxb5PsQwjEOyie8KnJKVk2X1P0nDcVBJlFt3xXuaUzURTVBzpsG1aO7UzW/j6977AleetpjvYRXdLhNhmh2liGqVGKfFcnMF0hPfVLx0XKC3HYsNwG1tGt+FIyezSGZxRfwppK8PW0XZW9L/OCz3LMBSdGSVTmFcxm4e2P0ZPoo83vkkNwTqum3EVNf5DGwSuHdrIozuepi/Rj6qoNBbV0Z8cZDgzSlNRPYaiE82O8beulyjzlHBW42mMZKKYjjkuBL9BQPexK95Db6KfGn8VVb4KRneNsvzhlcQHE/lrKKC4Psyci2biKzl2cRcezc1pdSdxWt3e8QsHY8vodjpj3TQE6ljZ/zq6qhM0ihjLxhjNjDGvfBZbR9qZUTqVtJ1hRf/rBAw/x1XO46opH6S+aO8CyqCriDJvKd3x3vFSQIBIZpQiIzChJO+tuDXXXq7hz3/+//GnP/2e73znS9x6672HfY4FChwqw8PD2LZNRcVEh1ZFRQVtbUeWVX+k27zpppv4zne+c0T7LFDg/zqmk0Mi94qDyMcZ5MYL3qSU7NixnWXLXmLFipdYtuwldu3aAUBz8ySmTZvJlVdeS2vrFFpbp9LSMhmfb9/lu/sjHxOxd9RBKFRMbe2+48wydoqXep5gYOcA3du66Ni8nfa27ax6aSXpZIofqv/G5DlTWHzyEhYuXUxdSwNjZoTtsY3MK1lyzB2aXs2PobjI2Ck82p7xS8ZJ41V9uJRDn7UlpaQ72cHa1au4/Qe3cfyFJzLj3GkMZwfwqv7xz0sVE+UTr+ZnTskJDKS62DT2OrFcFFVqGMKNI21MJ0fQCONIZ8IxvhsIIaiZXU3VjEqsrIUtNe66W2PlSnjDhPv664INGwTXXefg8Rx4e0eDTCzD6vvXMNI5ijvgotpTz4LGE/nhv3+biy6+An/Rvr/nNb5GAnqQSHYAyzHx60FK3ZWHnY99tIn1xdjw101YGZNgdRAExAcSbHxsM95iD6Ha0Lt6fAUKvJmCGFygQIH3DD/96b+jKAqf+cyXDnkdiWREGcYnJw4WdHQcJHFl7JC3VW814ZYeomJ0PErBxmZMjLLEPHWv5dfrrzOmRJliTmO9voa0SOGWXuIiSkpJUelUUW5XYWKiozMiItzrvZ0REaHFmoKKyqgY4RnXo9TYdZyUO+2Qj/VoIhDMtObwsPt+hpUhTJEj6ITIkCGtplhpvMqZ2fPYqXbgkq5xITglkmzQ15BwkmS2B/DYAZRSiy7ZziJzCal4lme7XmRR1XwWsYQudScvG88zIPJCaZlTznnpD7LCeJkN+hp0XCRFAhB4pQ9T5LCFTcApol3fSkakETIvAVf7K6kNVNMe3cmkUDP9WzsZ6R6k8YqFZKRDU7ABbXdzsyIU6otqGbhuKiv/6REe+/3DLPzo8dRMChOVHbTt2khDogWf5uPM+lO4sOVsIC8E37flIV7sWYYlLQSC57tfZlHlfCaFmnls59O4FBeV3nIyVoaXepbz2M5nKXYHmVzcgipULMdmW7Sdh9of45Ozrzuom3Z7tIM7N91L2kxT6avAcixe6lnBQGqQ0+uW4lLzg9xid4iEmWJZ32ucXr+UoKsITaikrcy403kwNcxrA2sA+PGqXzKtZDLvrz+Dzic7SQwlCdcXo6gKtmkT2RFh2/PtzLl41lFr4D6aJM0UDg5CCDJ2Ftfuwb6u6GTtHIaqsz26g8H0MHWBWqq85YxkR0mZ6f0W22mKxhn1J3PXpvvYHt1B2B0iaaZIWinObjiNCu/hTeX0+wN84xs38elPf5QXX3yWpUtPf9vnXaDAe52vfvWrfPGLXxz/ORaLUVd3eFnkBQr8X8Wr+dGERs7OjJdyAWTtNF6niEf+8iBPPPEwr7zyPIOD/SiKwvTpsznrrPM5/viTWLRoCaWl715etyo03C4PdZPrmTJ9Ou/7QH58lLNybO3YSNtrm9j06ib+cMvvuOPmX1NWVc68JQuZdeJs6s+bRFmg8iB7eHt4NT+V3no6E9uwpY2uGGTtNBJJna95v+Vm+yKai9A13M5d37yDukn1fPifr0EIQTw3iqVlqfdN2m+esVv10BCYTIm7kg0jK9mZ3EbciqIrBj41L1hLJFXed6dD5K0oqoLhNVi+XPDaa1Bfz7jwm07D6tUwd67gxBOPbtHhvujd0Mdo5ygljWEUNf/w4CPnfYIv/uKj/PLHP+Zfvv3N/a5bZBRTZBTv9/13g/62QTJjaUqaS8bH08HqIiI7RujfNFAQgwu8pyiIwQUKFHhPEIkMc8cdt/C5z32VcPjQM+EUFOrsRlbrK6lw9jgv06TQpEapfeiD6BZ7MifmTuU515MMM4ghDRJKnCazlZNz79tr+TElioJgnrkIU+TYrm5lVIlg7RZ/dXTu8t7Ki65nuTJ9DTlyDCj9TLamjWfnhmUJUTnKcuOld00MBjg5ewaPuh5iUO2n1C4npkRxkEw1Z+Jg87q+goAsGo+MAOhVu4koQ9gxgZXMEfa58TpuhpVBepVuJnmmsWOsk95kP9Nck7k8fQ2LcyexS92Jjk6RHeJhz32sMpbTqe5ARSUhEhTJ4O4ACRux+zqpUqVX7abayTs8NUXjAy3ncseme9k8upXNj7+Eq9hH89xpxMwE+ltuAFShklmSoPyKKtr/dSunfPAMigMlFE8rZkfzDs4YOAERcTOSHuUvHU8wu2wGWSvHiz3LKPeWjsc8JM0Uy/pWsW5oEwKF2sBuN7rmJmtn2RjZQo2/atwFpCoKXs3LC12vUuOrYnHVggM6Tpf1vU4sF2dKqHV8EFnpK6d9bCcjmdEJ5XVezU3STJGzc0wubmZycSsbIpup9VWTsbO80reCtJVhXtlMvJqHZX2riOyMMKN7MsVVofFBt6qr+Et9DLcPk4ll8QSPff7x4VLuLcWtushYGdyam2QuhVtzk7EzlHlKGUgMETMTzCidSt3uz6TGX0Xb6Dae736FpuC+b8AWVsxFEQrPd79Cf3KQEk+YC6rPZmnNCUd0nBdffBV33PErvvGNL/DUU6+h64WMuAJHn9LSUlRVZWBgYMLrAwMDVFYemfhypNt0uVy4XIc+DbtAgb8ngnqYck81vclOXE6ObDLHq397ideeXc6GZevIZrPMmjWPyy//CMcfv5SFC0+gqCh4xPvLWRnaYuvoTXXiSJtKbx1Tg3PwaofnIn4DXTGocNfSkdiMpugYigtbWiTsMVqbp1DXWM9FV16OY0o2rlrHay8sZ+WLy3jyj4/y6+/cwuWXX8PVV1/PpEnTjvicDkZrYDqG4qI31Ynl5PDpRdT5mqjyHF5c02huiN/96C6yySzX/OJapOogUMjJHLrjos7ffNCH5X69iOPKTqHSW0tHvI2snUFTNHTFRb2vhXL324s9sXI2wx3DJAYTaC6NstbStzWja/t2kFLg8ewRfT0eEALa2uDEE9/W4R4SIztH0Vza+JgUoL66mSWTz+C23/+cT/zT5wkEig6whfcWmXgGRVMmfFeEEKi6SjqWeRePrECBvSmIwQUKFDggW7VNvKK/QJe2g3K7iuPNpcw25x/1jNsHHrgHKSXXXPMPh73uSdnT2KJtol3dSplTQVZkGVL7mZtbyFTr0JuEFRQuS19Nk93CKn05KZFkanYmJ+SWTihIe4MSpwwQqKiclDudGqWeFa5XyJGlwqnEEZJRJUKf2k2v2kWd3YCFOaFEDcAt3YyJUYaUAXLkKHcq3/HIiGqnlsW5JQyrg+hSR3cMqp1aauw6OtUOhpVBTsudxcvG3+hVuilxymjTNjKsDKIaBlJ10yO7qFaq0NBJKHFydg5d1XHtLo6wsEiIOBKJIV085n6Q7dpWppkziYkxcuQYUgaJihF0jPEiO10YVNu1eGTetpAhw0Z9LX3V3SwItJLa2cJzK+7itAvP5cuLPsPv2/5Mx9hO/LpvfDDWmxzALsmw6Fsn8tiDD/L6L1dw/JeW4pFeTFeWx8eexOnw4FJ0TGnxcs8Kwu5iTGlNyPv16V4c6bBjbBczSvY0LqetNP2pIeK5BDuiO/HrXoKuIjaPbKUj2knKSnPf1od4qWc5F7aew9KafUeX9CR68Wm+CYPIoBFAFQojmSgNRXscdyPZKNNLpuDRPAghuHrapdy/7S9sGdnOttF2LMdiYflcWkKNKELBrbnZtrmDcDxIcU1own4VVcHMWDiW/fa+SMeI5mADc8tn8krvawR0H8OpERJmAq/mpdgdZEd8Fz7dOy7OQ37wXewKsW20HUc6+5yuKoRgQcUc5pXPIm1lcKnGfp3Eh4IQgu997yecc87x3HHHLdxww2eOeFsFCuwPwzBYsGABzzzzDBdddBGQzwN95pln+PSnP/2e2WaBAn/vCCGolPU8+9RTPPHoX1i3fC22ZTF73jy+/OXv8v73X0R9fdNR2Zfl5Hh+4FEG0t0gBALBSHaAvlQnp1VduJcg7Eib/nQ3g+keTGkSNsqp8tbttVxDYBJpO18Sl5AxFKEQNMJMCc5mS3QtCStGkVHMvBMXMveEBVz2xSsZ646x6cnN3HfvXdx6609ZvPgkrr76es477xLc7qP7wFlVNJoCU6jztWDJHIbi2q+D90A889gTvPrYS3zue1+ipWEqCTOGg41b8VDuqSbsOjRziapoNAamUO9vJZYbxZY2fj2IS317551N5lj/0HoGtw7h2BKkxFPsYfo506iacWQPAVUV3ugctCx4I/LdcQSaduxdwQC6R8Ox924LP3/uFSzveJ5bbvkJ//Iv33pHjuVo4C/149gS6UiEkh/LS0di5ywC5Uf2UKZAgWNFQQwuUODvGBub7VobA0o/fhlgqjkTL3saA9bor3Gn91fExRgBp4huo4v1+mouTV/Nqbkzj9pxSCm5557fctZZ51NScvgtu7OseVyT+geecj1Cn9qDLg3elzmX92cuPmxRVUfnhNzJnJA7efy1uIgxJqIUySAODh3aVmIiRsgpptFqZpu2mSq7hriSHziWyFKyIodHeqiU1YwoEVSp0Kv0YIocKZEcL7WTSHrUXVhYXF18ISmRpNyu5JLMh7kk/SFcvHOOqyn2dF63lzPFmkFMROlRu1hudBITY8wy5zPJmsYHMpfzuPthlhkvMKj0oaJRa9SSKXcR6UzRb/QRUP24TS+74j3MKJlMQ6CWERHhTu+vaNM2YAuLrJMlnU4wfWwmISXMpMAUNrs3YEiDlEgilSya1HBJg4xI4ZFeWq2pjIkov/X+kk36OhxspFuSejVFOp7gS9f/P2oC1Zzb9D7u2HQvbaPb8Ok+UmYKn+5llncm2VCS6R+azbrfvM6iLy7B0RwG08N4ojonhE4ej5boSw6wbngTPn3vBg1VqHh1D7FcghJPmFguwZrBdQymhrGlza54D2k7S5m3hN54P7ZjU+uvZk7pDPpSAzy4/VGaiuonCJdvUOEtZ3t054TXAoafkKuIuJmgN9GPR3MTyYzi0dycWnviuHBc5i3lk7M/Sm+yn5+t/vXuQrXGfA5evJdt0R1EZIQSM0Rqa4qpLZMJuYJIKUlGkhTXF+MJvQMBcUeAIhSumvJBStxhVvSvxpYOsVyckFGET/cyr3wWnbEuHOlMyGbM2jnCnhACge3Y9KcGAaj0lqMq6oTt7+uzPhJmzZrH1VffwI9+9F0uv/yat+XyKlBgf3zxi1/k2muvZeHChSxatIibb76ZZDLJddddB8A111xDTU0NN910E5AviNu0adP4/+/p6WHNmjX4/X5aW1sPaZsFChQ4dNauXcXtt9/CQw/dSzabZdGiJXzjm9/nvHMvOeRejMOhPdbGYKYHj+Ybz021HYvR7DBbxtYyr2TJ+LJSSraMrac72ZF3LQqVkcwQw5leZoUXTxCEDcXFrOJFRHMR0nYCXXFRbJShKRpNgalsjq4mmhtGFRqWNHEpHk6efRaXLLqWL/3Ld3j88Ye4++7b+MxnruOb3/wnLr/8Gj7xiS9QUXF0y7Q0RRuPMTtcBgb6+PG3v8/C0xex9LxTMVSDkFGCLU3GcqPUHUEpmSJUQq6DF+QeKp0rdtG3aYBQTQjdrSGlZKw3RtuTWyiuC+EuOnyxedo0eO45yfbtgt5eiMcFpglCSM4++6gd+gGpmFJO7/o+0tE07t0z05LDSSrKq/jQZR/j1lt/yqc+9U+HnZH9blE5vYKu17uJ7BzBX5q/10sMJwlUBI5YtC9Q4FhREIMLFPg7JSHi3O29jbX6KnLkUBDUW018OH09TXYrFhZPuP5CmhSTrel5J7AD3WonT7n/ykLzePwycPAdHQJr165i8+YNfO1r3z/ibcw3FzHbnM+IMoxLugnK0Ns+rj6lh8dcD7FZX48UkmqrjrgyxqDaT5YMXumjwWpiljmfTq2DpJLALwPU2HXsUneO5xgrUiCEQoVdTb/Sy061g6AMYUiDbrWTXeoOsiKLW3pwSzcd2jb+w/8d1ugr+UTyCzTZrXs5scdElNX6CnapnRTJImaZ82i2J70tx/a83CJeNJ5lpf4KQ+ogWdK73bk6q40VLLde4n3Zc5ltzuebgX/CJd3ElChZkaVomko2YxAdTiBsA9sSTAnWc9nkD6AqKn9x/5F1+us0WS14TS+5oSTRXATFkegSpo5NxlPuZTQ0giUs/E4At/SgoqJKBQkkRYLnXE+yTl9FszUZN24kknvvuYPwnFKqp9WBhJml0/jUnI+xrG8VvYk+yr2lHFc5j4jRw53iV9Tf2MT6365my2MbERepWKOCxnQrmr5HHKzwltEZ6yJjZUjkkviN/IAubaVxcDit7iRWD66jN9FPT7KPwXQETdVoLKrHkTZjuRh9yQEMRafEU0yNvwpb2tT4qtg8uo1NI1v3KQYvrprPmsH1dMa6qPSVYzk23YleFlctYGbJNDZG2shYWSaHWzi9bimzy2ZMWF8IQY2/iinhVl7tXQnAcDrC+shmpJQoXpXcVJuxjXHWb9zE9KopyIzE7XfRclLzhGl67zW8upeLWt/POY3vI2NnUIVKJDOKT/Ogqzo/eu0X7Ix10RioQxEKY7k4aSvF8VUL2TK6nb+0P0FXojdf6ldUx4UtZ9MaOjat01/4wtf4wx9u5557fsvHP/75Y7KPAn/fXHHFFQwNDfHNb36T/v5+5s6dy+OPPz5eALdr1y4UZc/vc29vL/PmzRv/+Uc/+hE/+tGPOOWUU3juuecOaZsFChQ4MOl0moceuo877/wf1qx5jZqaer7wha9xxRXXUl5+bIWgwXQPUsoJBVqqoiGEQn+qG96UwBbNRehLdeLV/OOOVUdzGM0N05PcyaTgxFl1+Zk2pRQzUdws91TjUt30pbtImXF8ehGVnlqCRhjIx8h84AOX84EPXE57+1Z+//vf8Pvf/4Y77vgfbrjh03zqU/9MKPTuZr5KKfnSlz6FoRv8y3e+QcIaQ7VVhFCwnBwlrgoqPHuX0L4V27EYyHQzmO7DlhYlrgqqvHW41PxD9pydZTjbT9pKYqguSl2Vh1wo59gOfRv7cAdc6O7dBc5CUFQVYLRzlMjOEWpmH34ExcyZklmz4He/U8jlJG63RFXB64WVKwVLlkiq316yxUEpn1JO0wmNdL7WRXI0BYDL72LSqa1Mr/sid/7hf/jTn35/RDNH3w28IQ9zL5nN9hfaGekcBSmpnF5B69LmY1rSXKDAkVAQgwsU+DvlSddfWa6/RL3dhF8GMDHp0LbyB8/t/FPim0SUIXrVLsqdygkCY4VdzU6tnW6187AiGA7EPff8lqqqWk455cBuYwuLjEjjkV5U9p4CpqFR7hydwXZUjHKb97/p0LZS7lRhSZM/eG4nI9LMtuYzyZqKjcVGfT1nZs/jw/GPsV5bzb2eOyfk6trYWMKizKpAQ2W6NZPjzCWs1F8hKzKEnVI61O0EZBFhp5SoMoItbJIiwWPuhxhRhjk3cxEXZa4YP+cBpY8f+b/LRm0dCPA4bmpc9VyW/ghLc0deWlUiS7k29XG+WvQZUiQpIkixHWaSNY2EiPGo6wHmmAsoccoolmF8th/dNtiibWTMP0rRiRJlSOWMyOlcypVML5mMR/MwIiKsN1ZT4VThkV60EYGR9jHg7SOmxvCYfrwZD8ExP0bQYKo1nSZrEm36xrxLGEGbvoE7Pb9iQO2h2CnBTf7mxUyYDDzWx+R/nc5WbTMLzXz8QlOwfq+cWNtsZigzwItzniW0qJiVt73KJRd8GKOjEj3n4q0m8iIjQG2gmt5kP6TyRXuOdFhYMZcPT7uU5mADT3Y+x65YN5pQqfVXM614Eik7Q3e8l9WD68YjB9pGt9E+tpMafyWO45C1s/v8DKYWT+LKqRfz2I5n6U70oQqFycUtXNx6Hi2hRrJ2jqydxa/7DtjSfVzFPFYPrKMr3sNAaoislUVVVEKuIkrnhTEqDCKbIsSNJNNnTKF2bg3hhvdWCcf+cGsu3FreMf/mDOUrp1zEvVseZPvYDhwpcasuJhe30hXv5rYNdwPQGsxPx20b2cZIZpTPzLvxsIviDoXKymouuugKfv3rn3P99Z9G0wrDrQJHn09/+tP7jXB4Q+B9g8bG/CyBt7PNAgUK7JsdO7Zz552/4t5772BsLMppp53NHXc8wOmnn4OqHn5kwZEghIJkX7/jcq/xQtyMYkmLojdFFyhCwaW4Gc72M4lDH98HjfC4+HsgWlom841v/IDPfvYr/PKX/8mtt/6MO+/8FZ/61D9xww2fwes9dkJZKgXxOBQV7SlKe4MHH7yXp556hNtuu58Tmk6nL92VF9aRlLgqqPbWHzTiwZEObWNr6UnsxEyZSBv6jG4Gi/qYXbII27HYGH2NsdwIkJ8V6NMCTAvNJ+w6+BhESoltOns9sBdCICU41t4xC4eCqkJFBdTWSoqK8lnB4bDE74fNmwXLlwsuvvjYxkUoqsKUMyZTOb2SaHcUoQjCDcUEyvOGozPPPI877riFj3zkxvdkwfG+CFYVMf/yuWTGMkjAE3T/rzn2An9fFO5OChT4OyRFiteMVwk7pePuXh2dRquFTq2D7Vob5U4lKhoW1oR1TZHL57hKY1+bPvxjSaV48MF7uf76f9zvgNnC4kXXM7xk/I2YGKPUKefU7JksMpcc9eziN1ijr2SHtp16q4kBtY+N+lpGlQiOcNikrSOiDDHbnE+ZU87r+gren7mIpbn3sVPt4Gn3o2REhj6lB4Aqp4Zyu4pdWgenm+dyQeYSzs18AAuLn/l+wCpjOR7HR1SM0Kf0IBBo6NjYDClDPOD+A/V2E8eZJyCR3Oy7iWddT+CRXhQEKTVBSknxoPteZphzCMtDL+B7K17po9KpocFuxif9eKUPgSBAgE6tg13qDqZaM5lmzuI515NMtWZyfG4pcTFGTIxhlVjc4LqOFnvy+DYzIk2OHH4ZQFigpgXogmLC9NJNVB3BcQexMiaGbVAmK+lRu7CxKLcrsTCJKzFWGC+jCoVqe8/0yv5VPdg5m9L3lWO9SYTfFyoqF2Wu4ITcyVReW81P/vHfuHzdR1jv3s7jmWcp8YTHi+fyZWLFfGL2tXQn+tgY2YKUDlPDk5hXPhu35uKMhlOYVTqNWDaOoerUB2oRQuDDR9gVYle8m9HsGEXSj0fzYDombSPb8Os+an37tloIITi+aiFzymbQk+hDU3Rq/VXjorJLNXCpB//dm1EyhcumfIAndj7L60PrMaVFiSucF+h1DzRAJDBG0aQgs5uOzkOdd5uZpdOoL6qlbWQbiVyKtcMb2DbSwXNdL9OX7KfYFcKneZlU3MwkvZm20W2sHljPOU17P0AZTA2zdmgj0cwYpd4wc8pmEnaHDut4brzxs9x//+949NEHuPDCy47SWRYoUKBAgfcK27a18ZOf/BsPPXQfoVAxV111HR/5yI00Nra8o8dhOjl8mh8pJSkrgVf1g4Cck0NKSbV3Ypma2C0OSykniFT5qKVjKw8EgyG+8pV/5frrP81//dcP+M///Fduu+2/+dznvso11/zDUX14ms3CU08JXn1VkEgIgkHJ0qWS006TaBpEIkN84xtf5ANnnc6lVVtRXn2OoL+epppTsUrm5tXRQ2AkO0TXaAfJnRnMUQvHkQhdkKreTkiUkFVTRHMRQkYJilDzEQ+5CNvG1rOw7JQJ8Vb7QtVUSptL6Fy5C2+xdzyLNjOWwfDqBKuOvGAtEhGUlUmam/O5wdu2CdauhZERwe9+p+B2O5xxhkTX89+zlJVAEzpezX/UBE4hBKGaIKGavWO1PvrRT3LVVe9nxYqXWbz4pKOyv3cCIcR7NnqtQIE3KIjBBQr8HZIT2XwsgTPxj5SBCwuLtEhT4pQxzZzJq8YL+C0/OgY2Nl3qTiZZ02iwj8706hdeeJp4PMZll31kv8s86n6Av7r/hFt68DtFdKo7uMP7K8y0yUm5047KcbyVHrWbUWWEHlcXfUr37jxgB0O60NBJizSbtPXMNRcypoySFAmCMsSH09dT49Rxs+8mdmrb0dAZoJ81xkpOyJ3MSdn88Wq7/ylzqpBSMqj0k1BiZMmgoJATJjoa/WoPvarNrd6fMXNsLrvUHbzoegaf9FHq5MssTHKMKVG2a21s17awyDzy+l919z+6NPBJPw4OEWWIMRElLuIg8wO/U3Jn0KZtZLO2npATJiuy5ESWU7Jn0GS3TthmqVNOuV1Bn9pLQAZA5jejY1DhVFFil5ElQ5VZxdmpC3jdt5IhZYBSpxwbm7gSo85uoESWMij6GVIGKXHKUFDoXd6NHjSonlJLQ/LQvpMVThWfOffL3F58C/fddSf/+KUvsyO2i22jHahC3V324ePC5nOo8ldS5a/kuMp5+96Wr5zF1Qt4qXvZhLzaXfEeVEWl2BUk55gotoqDg+Xki8yKDyIsejTP24owEEKwtOZ45pXNImD4WTO4gXnls8aPz3LyD3mKXf938mwd6eDVPCyqnM8rvStpi2yl0lfBUHoYEAykh+lPDTGUjjCzZCq6ojOQHtprO20j27hr8/0MJAcQ5J1Wz3e/wsdmfIj6ooNPFX2DmTPnsmTJqfzP/9zMBRdcWnCFFChQoMD/ETo6tvGTn/wbDzzwByoqqrnppp9x2WUfwfNW2+k7wEh2kLboWhLmGJrQSZr5iCRN0VGEoMJTy5Si2RPWCRtluBQ3SSuGTytCCIHp5LBkjspDiEQ4GpSVVfC97/2Ej3/8c/znf36Pb37zi/zhvt/y7R/+kBlT51CkF7/tv5t/+YvgsccEoRCEQpKxMbj/foHjwNlnS772tc+DneO/LixCH16NoxehD65AG1lPatr1mJVLDrYLAMYyEaL9UZyIijvgQtEU7JxNcjjGNjbjr/Pi1QLjxXZCCPx6iLg1Riw3QvEhuIMbF9UzumuU4R0RXD4XlmmBI2k6oZFA5ZHH9lVVSZYvV5BS0tYm6OgQuN0SXZeoquSBBwRSSmad3M6uZDsZO4UqVIpdZUwqmrlX4eDRZunS02lunsTtt9/yv0oMLlDgfwMFMbhAgb9DimSQGquOLfomiq09U7siyjBFMkSNXYdAcGHmMkaUYdq1rTi7p57V2vVcmv7wEZc0vJVly16ktraBpqbWfb4fEcO8aDxLyAmPR0CE7RJ2qTt41vU4x+VOPCYla1mRoUvdgc/xYwoz75IWadIiScgOEnRCjCmj7FJ30GS3EnbyOWoqKm3aBty4abRbyJDBxkZFZXHupL1cuyeYJ3GX/B8G1D40NBRUTJFD4qCgUmqXMaZE2apt4gXXU1jYWJgUvSkTWaBgSZshZXA/UwQPnVKnnMnWNFbqr6DbGhv1dQwpA8RFDL8M8Ij7z5Sly6m3m/hk6gu8YDzLFm0DVU4Nx+VO5MTcKShMnMZmYHBm9jx+572NbZ42Gt31iLTE1HJMN2fTak8hbo5hKAbT0gv4T+N7dLl3ElVG0dGoseuYbs4mocQpdcrxO0W0aRsIyCDbV26h+Lgwp5lnUeXUHPJ5ulwuzv/gpdxz7+18+DOf5OOzr2Xt0EY6Y90EdB+zyqbREjy0hu+zG06jJ97Llmg7hqJjOiaGolPtq6TaV8lIZpSB1BCqojIp1EzGzhDLxQ7rczlS/IaPD7Scy0ByiM5YN5XeMkzHojfZT1OwgVml09+R4ziWZKwsL/UuZ1nfayRzKVpCjfQm+lGEikd1M5gaJm4m8Goeco5JZ6yLrJUl5A5S5p74+5izTR7Y/iiR9AhTiycjhMCWNltH23m44wn+cc7HDuvm9B/+4XNce+3FvPbaMo477oSjfeoFChQoUOAdpLOzg5/85Pv86U93U1ZWwb/+60/40Ic+hsv1zpX9vpmsnaEtuoaUlSTkKiGklzKc7WMsN0LIVUpL0XSaA1PQlIkzinx6gJai6bTHNjKaHQIhUIRCpaduLxfxsaaurpHv/cePWXThcfzn12/iygvP59JPXMm1N9zI9PD8CRnIh0MkAsuWKZSWSsp2a62BAPT2wgsvCFT1GR5++H5+/Zn3UVYcwArkz9vxVaOOtePe+VfMsoWgHvyzTY9kyGWzuIp9IAAJqqGiCZXkYApPlYFmTMwiU0ReZHUOIb4HoKiqiPlXzKV7dQ8jnaO4/AZVM6qomlE5YVySSsErrwhefz1fBjdrlmTJEknJfiYNLlggefVVyYYNgs5Oga5LslkoLoYZM2BsDJ56PoWcsgmvF3xaEba0GEh1Yzo55oVPRFWOnaSkKArXXvtxvve9rzI42E95eSVSwpo1gldeEfT3Q20tnHiiw8yZh2zmLlCgAAUxuECBv0sUFM7Ivp8urZOt2maKnTApkSQrMpyVuYBKJz+FvcKp4jPJL7NBW0tEGaJIBplpzj0q5Wxv8Morz3PiiSfv9/0BtZcxJUqzNWnC62GnlGFliIgyRLVz9F0MEolAIaqMklKSeTcrEhuHUWWUYqeMpEiQI8up2TPHM2w3amt4xP0AKZFC2R1hoaGTkzlW6cs4JXfGhGiLydZ0plgzGFQHSInkbiFY4pYeXNJFSqQQKJTJcpYZL7EwdwJFMkhapPBKH6PKCGMiSkKJoUuDl1zP0mJPGncNHy4CwfnpSxgSAzzneooRZRiP9FHhVDHFms5GfS1/kvdwY+oz1NoNfCh9aC3zi8wluFJuXjSeYTjUT1m2hIZEMxVUMuoMowqVBv9kwkop16RvpEfbhc/xU+yECcpiBIIuZSczzXlcnL6SZcaLbFPbiK+IcdGNV3B+5pJDPse0leHP2/5KX12CWDTKt/74HU5ctJQrp1zMKbWH76qu9lfyqbnX8/rgOrrjPQSNIiYXt/D7LX8mmUtR4immyBXAr/tQhUIkEyVgHJ3yxUNhangSH552KY/vfJb+5CCqUJhXPouLWt8/Xoz3vxVHOty39UFe6H4Vn+7DrRos619Fd7yXSm85A6khLMfCq3mROPlpe6qb/tQgAZefueUTIzJ2xbvpjvdS668ev7lShUq1r5L26A4G08OHlTH8vvedS3PzJH79658VxOACBQoUeA/wxhT94cwApjQp0oOUeaoxlP2Lfv39vfzoR9/l3nvvoKSkjG996z+4+uobcLsPnCV7rBnJDpKw4rvjBxRQodxbg1vzEtCDTCqaud8HmLW+JoJGmP50F0kzjk8LUO9vQVP0fS5/rMjZWTaOvkbJ5DA//sMvufcXd3Hvz37Ha88t5zs//CGnzX7/EW13eDifE9zYOPH1UAgGBx2++91/5ri5c7lqYTG2d2LfiOOtQEn1oab6sQMHFsdzdpaIOUjWn8Z0ZQBQLQ0j40baEmOnHy8BElYUQ3GNfx5JK45X8xEw9p6hZTsWOSeLobonREgEygNMO3vqfo8lm4Xbb1dYtUrg9UoUBR58UGHTJsnHP+5QvI9qiJoa+OhHHW6/XWHzZoHfD2VlMGWKxOfLi6tbutMkxnQqgl4gP7tRFRrRXITR3DCl7mNbjnj55dfwgx98k7vvvo0vfOFrvPCC4P77FUwzn2+8apVg0yaVD33IYfHiY5txXKDA/yUKYnCBAn+nzLbmc33yH3ne9TRd6k6q7FpOzJ3MkrfELnil723FDhyIaHSUjRvXcv31+y+r8UofLukiLVIE5J5MrLRI45IuvPLYiFku6aLULmenth0hBY5wcEk3Ds7uLN8BSpxSrk1/gpNzZ4yv97LreYaUQWrtOnTyboaUSDKqRNimtZEhg4c90wgFgnKnklOzZ5IVGVbrK4kow/iknxxZxpRR6u1Gaqw60iJFvd1Erd1Aj7KLbrWTmBjbvRVBk9XCRm0td3l+zWeSXzpi93atU89V6evYrG+g3Kkk7JRQ4VThlT58IsBmfR0DSt/4Q4NDQSCYay5kjrkAC4tEYIy+1C7iVhSv6qfSW0epKz+YbLWmcmL2FJYZL+IRXiDKsDqIR3o5JXsmtU49l2Y+zPbtW/jZ6L9zwdxL93IjH4gnd/6NZ7tepGZKM4bXTXzzAK83rgPgk3OuO2Ax2/4Iu0OcUT/xoUZzfwO/3fB7HOmgKRoCgaEZnN90Fg2HETdwONiOTSyXwK258Gh7blIXVMxhVul0BlKDaIpGpbf8f0VsQcbKEsmM4tU8FLv3vmHaGevitf41VPsqCbry/30o85QykByiK96DIgSaolHlK2cwPUzWzuEgCRpFNBc1UO2feAMjpURKZ6/vgCIUHCmxHfuwjl9RFK644lpuvvn7tA1spT+Xd4hPLW6lzFt68A0UKFCgQIGjSleynfb4JkwnNx4FFE53MbN4IW7VO2FZy7K4/fZf8sMffhvDcPH1r9/ERz7yD3i93v1s/Z3FkiYC9vqbpSoaWWffRbVvIKUkmovQn+oiY6cZFn0MZfqYFJxJmbvqGB51Htux6ExsZ2diC/3pbjyqF93l4rp/+jjHv+8kbv76v3PdRZfzta99nxs+9pnDHrMEAvmyuGQyLwC/QSpjMpj4D7Zv38B/3fFDdnq3UmXn0N7sALZzoOjIg7iCpZRsja0nro2gShVb5EABWzOxjBz6sIeQGqYlPJXN8dWM5obQFQPLMdGETqN/8oSHEI602ZVopzfVSc7J4lLd1PqaqPE27fUZ2zZ0duYF4OpqCAZhwwbBmjXQ2CjHi/JMU7J1q2DlSsFZZ+1bKJ0yBT71KYdIRODzSaqr9zhsY3GJ6k4TLJq4f03RkNIhY6cPeI2OBsFgiA9+8Cp+97tf87GPfZmnnjJQVTku9FdVSXbsyOdDz50reZeM+gUK/K+jIAYXKPB3zExrLjOsOZiYuyMKDl8EezssX/4SUsoDOoPr7SYmW9NZo6+k0WrFg4eEiDOk9HNG9jxCch+PuY8CtXYDCElQFlPmVDCsDGHvDmnQMAg5xXwp8W3OzJ43Yb1uZRcqyoRr6ZU+hpRBTGHuM9JimjWLJ1wPM9WaScgJ8zfXk2REGnV3REK5XUWP2s3S3OlMs2byvuz7ecL1F4bVITQ0DFxUWtWcYJ4CSLbqm9iutTHVOngxWFIkWKuvok/pwSt9zDLnUevUo6LilwEUqZAWafqUHsqdStzSw4gYJiWSE7YTEcOsNlbQq/RQLMPMMxfmr+FbEAh0dIpdpRS79i2Gqah8KP0xSpwyVhnLSCoJaq0Gzsqezyxr7vhyq1YtRwjB/PmLDnqetmOTc0xsx2Z5/yrCrmJKfSXUz55M77oO5l92GltGttMZ66YpWH/Q7R2MtJVmV6ybgOHHtC1Mx0QisRybWn/VEQnOkfQIqwbW0hnrosgVYG7ZLCYXt+xuk5a8NrCGZ3a9SGesC0PVOb5qAec3n0XONlk/vJmxXJxST5jZpdPf80KwlJIXe5bxt66XiGRGcakGc8tmckHL2RS9yVXdk+gjZWWoD+wR14UQtAQbaRvZykBqkJSVRhUqfs1HfaCWaeHJDGcitBbvHQNSG6im3FdGb6KfxqK68Wvblxpgcqj5sFzBb3DGWe/nppu+ztd/9w2KZ1UhgbC7mA+0nsuS6oN/dwsUKFCgwNEhZSXYEd+CgkLYlZ9BZUubSGaQruQOJhXNGF925cpX+epXP8Pmzeu55pqP8+Uvf4dQ6NiMOd+M6eToT3czlO4FoNRdSaW3bp/OZd/uHFrTyY3HKUgpydpparyN+/1bL6WkI97GhpGVCCEIGsW4VQ8JK05bdA3eUj8+LbDXOrbMj2dSVhxFqASN4vEc3MNle3wTnYmtOI6DQGA5FpFMP7gk0+fN5D/u/Rl3/ddtfOsb/8zG9Wv5wQ/++7Cc2FVV+YiEl18WCCEJBCCWMHEqnmf1Az/ixHOXUjy9gdXRJEOxXmY77rwr2s6ipnrIVS3F8VQccB9JK8ZQpg+Px4vu0SEhQROgOcgcCEfgX+imzFeFS3fTl95F3IziUf1UemrHv4NvsCO+hY74ZnTFQFdcZOwUbdG12I5NY2BPMXN3N9x3n0JHRz4KorhYcvrpkngcbFvg8ewRfXUdPJ58HvD+xGDIRy0cf7zDSy8peL356xWNwuiIYO7JKTRvEthz/S3HQggFl/rOuOOvvvoG7r77Nh599HkikbOorZ14LuXlMDAgGByETAZeeimff1xcDCecIFm0SLKfnvICBf5uKYjBBQr8nSMQGBxZHtfbZdmyF6mpqSdcX0qcfCbtmyMUIB9pcUX6GnJk2aa3YWHhxs0icwnnZz54zI5tnnkcFXYVvWo3RU6QoBNkUBnAEQ66hBKnlAW54/dar1iGCThBRpURimQITWqkRQqJwxRr+j4F96XZ09moraVN20DICVPmVNCrdOHGw7AyRLfahYHBTrudXeoOLk9/hBq7jh/5v4sU0GA3Um3X4Zb5AZmJSVQZPeg5RpQhfuP9BVu0TYDEQfKM+zEuT11DuVPBLnUHEWUYj8zbCzrkNiqcKursBiqcvGukX+nlPvedPOS5n5zIUOpUEHRCvGA8zYfT1zPXXHhE198vA1ycuZJiJ8zfjCeJqEM86n6AqDLCadmz0dHZtWsHFRVVFBXtvwTNcixe6lnOy70riGXjBFx+uuK9NBflherG+VN45pY/oecU0laGhJk4ouN9K1tG2ulPDnJKzYlknRwpM42hGsSycTZG2viAcy7aPjLWRjKjrOxfzfboTny6h7lls5hdNp3+5CC/3vA7OmNduBQXpjR5pXclH5x0PqfUnshrA2v5xZrfsCvezVg2TtJM8sTOv/GbDfdQF6hBAEJREBIag/VcN+OqvVyx7yWW96/iD1seQFc0St1h0laGZ7peJJ5L8A+zrxkX092qG0Xkb+a1tzSgz6+Yw5TiVu7efD8ODrNKplHjryaaHcNQDRZUzN1rvx7NzXlNZ3FP259pG92OR3OTslKUeUp5f/OZqMrh30mMBTL4KkIMr+7k+JNPRiLpSfTzwLZHaCyqo8Z/7B1YBQoUKFAARrPDZJ0Mxcaeh9GqUHGpbobSPbQGpjMyEuH73/8a99zzW+bMWcCjj77CnDkL3pHjsxyTTaOrGMj07o4HEAxn+4lkB5hZfNxe+bkho5RSVyWdie3Y0sSRNhIIGmFqfPvuPZBSsj2+iQ0jy0laCXTFIJNJ49eKKHFVMGZGGM70o3k0RrPD2NLGlhaR9AB96S5SdgJN0fFpAUJGmEnBWYSMfCBtykrQn+omZo5gKG7KPdWUuCrGRemMlSLrZJDSoT/VhUf1oWk6CSuGIhRsaRMzR/HrQTAcPvX/vsD5J13Ov/zzJ9i+fQu33XY/FRWH9jdTCLjkEgfbVti4MZ8tWz65l51bfoGZyXLDFz5FyFWCE5xOj2NRPdpJbTYJKFjFM8i0XH7QANqMncFycoDA8Oq4hJtsIodt2QiXJFRZjCzLO7SLjGKKjP0/TMjaaXpSO3GpnvFSNpfqJmHG6EntoMbXiK4YpFJwxx0KO3YI6uokhgFDQ/DAA4LJkyX7iiC2LDhYt6EQcOmlEnBYv15heBj8fjjzTIfjz/LSnhHEzTE8qg9bWiTNGGF3OWHj8B+SHwmzZ8+ntraB5168j5xczGAyR0jR8Wn+/AMREzQt75Z+6CGV0VEIBiWRCGzdKhgaggsvLERIFCjwZgpicIECBd41Xlv7KiWLyvhO4EtIJFOs6ZyVPZ96e+IAttyp5LPJr7BdayMmxgg7pbTYk4+pkzkoQ1yb/CQDSi9jyhhjShQ/AcJWCUmRICMy/M57K59IfnGCmD7HXMBqfSVpkWJMiWILG0Uq1DkNnJv5wIR92NjsULeTVBJcnL6SLfomNmvrODF7Mru0nazXXseDl0arhVq7jqgS5fee3/DPiW+xNHc6y3MvsUvdSZPdMr7NpEhgYBB29tMU8Saedj3KZm09k6yp6BhIJLvUHTzo+QP1VjM6Ol7pRUXDkAYjSoSkSHBZ6mp80k+Huo2vF32B1foKciKH2/GQUTN4pJeEiPGg+14mm9PxcmTTKV8y/sb9nt+hohJyiokqI9zvuYuESPDBzJVEIkOUlh44G/mvHU/y6I6n8ahuvLqXrlgPu+LdONJhfvlsGudORToOm19fS/n8Rko9B79uh0LKSuOQj4fQVR2/no8zsRyLpJnGdKy9xODB1DC3rr+LjrGdeFQ3pmOyon81ZzecxmhmjM6xLqaEW8fz43oSfTza8RTTw5N5tOMptkd3kLLSpK0MqqJh2iZbR9sZSA5yUs3xtIQasRyLbdEO/trxJDfO+sh70iFsOzbPd7+Kghh3/Pp0L27VxYZIGx1jnbSG8v+NmBaeRJWvkp2xXTQWNaApKrFcnISZ4Nym0zm78XRmlE7hgW2PMpQeYsfYToKuIBc0nc3s/ZTnHVc5l2J3kNf61zCUjlDtr2BR5XzqAodeUPhmXh9cR9WCZrpf2QZSoigKtf4qNo9uZWNkS0EMLlCgQIF3jH2LQQKBIx3u/v1t3PT9r2PbNjfd9DOuvvoG1HfQTjiU6WMw00eRHhrP7rUdi6FMP4Ppnr0EXkUoBI0wEoesk0EgUIW6eyaSuc99jOaG6EpsBwQu1YNLdWNLm4Q1hkfLj9eG0/10JTtIWwmydpaEObZ7poyDRIzHHERzETaPrmZ+6UmYTo71oyuIm1FUoeNIi/50F82BadT4GmmPb2Yg1Y0lzd1iYpwabyOKohLQg0RzIwjypXgj2UEMxUW9r4VFl5xKa8sUrrvuEi644GTuvvsvTJq0/9zcNxMKwY03OnR3512u22Oruevnj3Lp9VdRWpkXMRV3CVbxNHqLZlAivDjuUszwLNAOop4CbtWDrhik7RQIgSvgwh1wk7UyaKqGV/cesnM6ZSXJ2VkCRmivfaSsBGkrhW4YbN4s6OyElhaJvjveuboa2tshEhEEAtDfDxUVeYE3Gs3/e86cAwuhuRxs3pwfE9bVOVRVwUknSerqQMoa1GSWXcl2klYMVahUeGuZVDTzmJbHvRkhBEvPOI3HH3mIUz74WTq2llDVOIzf5SGkVtPba7BokeT11xWi0Xzm8RtD3J4e+POfFXw+h/nz5T6zkwsU+HukIAYXKFDgXSEmxtjW10bJSeX5wSsqrxovskvdwT8m/2XcefoGOjrTrFnH7HgcHDrUbXRqHWhSZ4o1nZPMU+lKf4zbvb/EkC6KZAhL2NQ7zUw3Z7JJX0ebtpHZ1rzx7RyfW8p6bTUb9bUUySA5crilm9Oz50w4/gGlj3s8t7NN30yWDAEZZFFuCV+Jf49OtYMfBL5Bvd1EizWFYhlGICh2StipdbBV28xsax5Lc6dzp+dWdqoddP9qJ1t+vZnErjgqKrsm7+CLX/g6p59+zj7PN0OGNfprlDhl49nGAkGt3UCbtoFN7vXY0kYKSJPCxqLSrkJDp95uwsHhx75/Y4O2BoHAJ/1IIUmRpF3byknZ0+hTe+jU2o/oc8uQ4W+uJ9B37w8gZIcZUgZ4xfUcp+Tex9DQwD7F4P7kIK/2rmT14HpeG1hDhbeMSaFmFKFQ6gkzkhmlO9FL2B2iJBzGXxGifc0m3v/+i6j0Hlnx3lup9JXjUd3EcvHxLFspJSOZUWaVTce9jxy657tfoSO6k8nFrWi7HahD6QhP73oeB0mZp3RCkUiVr4Kt0XY2RrbSNrqNnJPDdmxcqoGu6OSERtrKIMkXo9UX1aArOpXecraMbieSGTmo+L1s2Yv84hf/yfr1qxkY6OO22+7n3HM/sM9lx7IxNkW2kLTSVHhLmVI8CUM9/CKatJVhOB0h5Jro+PYbPrqTvYxkRoGm8deunHoxv91wDy/2vEosF8dQdBZXLWBxZd7JtbBiLpOLW9g22oEtbRqL6ik/SF5va6hpXHB+uyTMFHULJ7P90dfp29pJ9dQmhMjnfGftA2c6FihQoECBo0fQKMFQXKTt5Lj70pEOg8P93PNvv2PZCy9z2WVX8/Wv30RZ2YEjAo4F0dwIAJqiY0ubnJ0BBELCaG54LzE4Z2fpTnUQNIqp1hry5cdSMGZG2JnYSthVvlcs1Uh2GEua+LQAo7lhkHl3tAkkzTia0BjK9qErBkG9hEGrB4kkZ2fQFB2/HsR2LNJ2kqBRTNwaI5LtJ5odIW5GKTbKJpSkdSa25d3NmQG8mh+/WkTcHCNlJYhkByjzVBN2laMKjdHsEAgodpXS4J9MhTv/EHbu3IX89a8v8eEPn89FF53K7bc/cMilrEJAXV3+f//20V/gDwX44Ecvf8tCKjLUSjY457A+L58WoMxdxc7E1vw1crIoQkEKB4/qx8GhwnNoD5INxYWmaFhODlXdI0SbTg5VaBi7XeGxGEgp0PWJ4m4gAIlEPj932TLB1q15MbyoCE49NS+C7g/ThLvvFixbtscgsHkzRKOCj37UweMR1PlbqPTWkbISaELHq/nfUUNBxkoxeclk7rl9hKlTlqPmzqa3q4Q+O4ffSDN3qs4ZZzjccotKefkeIbi7G7ZsEQwMwC9/qdDSIjnvPMnSpXKC8TvWH6N3Qz/J4QS+sI+qmZUEq/c/87BAgf8LFMTgAgUKvCu8riwn3hNjQc3xlDn5AXexU0KbvpEVxitckLnkHTuWHDl+6vsBz7qeICkSePHRZLdwVeo6rkhfy/Oup/HKAH7po9SpoNqpQZcGvXQzpPaDtWdbRTLIjanP8prxKpu0dRjSxRxzAfPNxeOFbhYWv/f8lvX6ahrsJrzSx6gS4WnXI2zU1hJRh9isrUcIhYg6TKs1mRZrCjoGDg5pkQJgUW4JK/VXeMT9AP2NvXhu8nBcwwncmPgsL9/7N6677hKefHIFU6bM2Oucnd3/KEx0LAgE/WovfUovXunDL/0gQBcGreZU4soYUkg61Q7WG69TJIPERQwFBU3qZESahIgTE2MoCOR+XDgHY1SJEFGGCTsTRbuwU0q7tpUBpY+hoUGamlomvN+T6OdX6+6kO96DJW0GU0OkrDRCCKaHpyCEYFrJZPRRDZ/mZSwXI1xfiXdM5eLW847awLaxqI55FbN5uWc5CTOJW3UTyY7g032cWrtkr/040mHd0EZC7uC4EAxQ6g4zkBrCdmz82sSyRIkECS7NwJY2pmNhS2dcaH7j2qtCIWNlSFsZdENHUzRs08Y6hDK0VCrJjBmzueqqj3L99Zfvd7mto+3cvfmP9Cb78zeWisrMkql8ZPrl42L4oeLWXASNAEPpCMXu0PjraSuDLrQJmcEA5Z5SPJobXdGp8VdhKDoDqSEe2P4IH5l+OZqSX2dBxeHd5B0tpoZbaWvcgifoZ/vyDVRPbSJtZdAUlVr/oZcwFihQoECBt4dPC9Dgn0RHvI3R7BBCKKx7dTW//vb/oKBy110P8b73nXtM9m05FpFsPwkzhqpolLgqCOgTxaZ8WalD3IwSzUUw7RzsduQGjTAj2UFydhaP5qNILyZujpG2UgSN4vGHjAjwagHi5hgZOzUuer+BlA5I8OtBklacjJ1CU3QcxyYlk5S4yzGdHEV6cV7glPkis6yTwZb5Aa8qNEwnh7X757SVJpIdwK1694xvJDiOw0C6C0tauBQPilBxqR6CRpiYGWU0FyGgF+NS3Xg0L7Yspt7XytTQ3L3GSTU1dTz44HNcd90lXHHFOfzhD4+xaNGhFVxLKXnulSd54clnue5bN2DpJkgPiLwTWRHKXvm9h4IQgknBWQgUOuKbiZlRADyqF1VRqPTUUempO6RteTU/Je5K+lKdCBR0xSDnZEnbSep8Lbh3u7ZLSiSKIslk4M0Ryl1dkEgIYjHw+fKisapKLrvMYelSUA4wmXLDBsHy5YKqqnw0hJQOmYzg9dcFs2cLlizJjyd1xSBohA/7Oh0NRnPDNM5qIFgcYuv6v3HFx6ewY4ufyIhJKGxz+ZKFCFQ0TZLL5deJRmHjxnyuciCQL9bLZuFPf1KorLSZvDuGeWj7MOsf2kAqmkIzNPpyA3Sv62XWBTOomHJ0TCIFCrwXKYjBBQoUeFfYMrwZaUmCNaHx1xQUPI6X9dpqcEO/0kOZU8ECczF1duMxO5Zf+37Gvd470KQ+HnGwQVvLXd5babJbmG7OxtAMmu1J4+tYuxVgv7O30FUkg5yePYfTs/t25e7QtrFN30yj3YxX5gW+sFNKj9rF31xPsCR7GnVKI0PKAKpU2aa1UeyUoEgVn/RT7eRdBiuMl2nTNjLJmsrsM+djYhFXomww1/Clpu9w552/YtWqfYvBXrxMNWfykutZSpzS8ciNLnUnCSVOuVNJRqTwST8+6WdUibBBX8NccyENVjPbtS1IQMfALfOlfhoamlRJKVmGlUFmWXNptFv22veh4JU+3NJNWqTwyz3iX0okcUk3PulneHhwL2fI810v053oYWp4EtHsGF3xHjShsSveQ42/ipArSNbKUVdUy78s/AyOtPn5/AxPPPYXPNrRK8FQhMKVUy6m0lvOsr7XSFsZZpZM5bS6pcws3Xt6Y35qp4LcR9ibKhQagrXsincTdhejKSpSSnoT/ZR6S5gWnsSc0pm0jWzPZ/s5NopQMG0zvyz5ZnFd0ZFS0p8aZFKoibJDiMQ4/fRz9usuf4O0leHeLQ8ykBpkUrAZVVFJWxleH1pPRWc5l02+8JCvG+QbqpfULOaetj8zkBqixF1M2srQlehhRngqzcGJxYSv9r1GV7yX46sWjgvpsWycx3f+jZ5EPwHDz6RQM4ur5lPieedvYo6vWsiaoQ2snVrJzg1baIovIJ6Ls6ByLtNLprzjx1OgQIECf68IIWj0Tyagh+iNdXLrzf/Nn26/h5OWnsbPf3YH5eXHJks/Z2fYGH2d4Uz/7qiF/PT/ScGZVHv3/E0rcZXTHtvEYHoYRagYqhvbsUg5aXYmtzKaiyCEQBMaZe4qKjy14wLym+MIHOzdVcZ7K4AhVwlKUkUIQZmnmrFshJSVxMGh1ltPmaeazsS2vBgr82NzS1poQsOS1sRxipQIBB7Ni3iLASCxu1zNlDkECgIYyQ5iOSZl7irK3FUMpHtIWjFSdj67uNbXTGtwxn4fzIdCxfz+949w9dUXcO21F/GnPz3N9OmzD3jtpZTsjG/lW9/9JxqnNTP3jHn0pTrxaD68qh9V0aj1NVPiOjInuKG4mF48n0b/ZEZyg6StJIpQCeghStwVE2Z0HQghBJOKZuLsLjRMWDF0oVPlqaclsCfWaurU/P/WrxdUVuYzgwcGoL9fUF4umTYt74a2bdi2TbBuncLJJzsH3Pf27WBZAs2TYjA9QsZOowiFjCzj+Re99PQo49s/7jhJy5EN7d8WEomiKiw67QRefeYlrv38DUyfl3eYCxTcHlAFLFggeewxhaIiycBAvkxOUfIO6dLSfK5wWxusXp3PWHYsh23PbycTz1DSVDJeHDzaFWXbc9spbS5B1QvNcwX+b1IQgwsUKPCukOvKT48O1E0UUyPqEP1qD+3aFgzpIieyvGw8z0dSN06IYzhajIoRHnH/GQWVqt0iq5SSiDLEdrWNjdo6TjCXslXfxIDSR5lTQY4cnVoHtXYDM6wDD0L3RVzEyZHFIydm6SZFAkuYBGSAZmsSY0aUjEiTERnatA2UOOWclj2bWrsBG5sXjKdRUCdkBidkMRuU1fzq4f8ilUqycOHi/R7HGblz2am106ZtxCu95ESWhEgQtIuZbc1jjf4aw8oQOjo5smSVLCdlT8uX5MkAZU45fUovfhnAFDlSIklWZFGkQrms4PzMpeNi9+ESlCHmm4t50vVXXNJNQBaRFim61E7mm4uosxsZGhqYMI3TkQ4bI22EXcUoQiHkChJ2hxhMDiOFZCwbQ1M0hjMRzm44jRJPPjRscus0buv+OblcDsM4emWKHs3NuU3v44yGk8nZJl7Ns98bHCEECyrm8mD7o5TaJbhUAyklfckBil0hLp18IX/peJxt0XZUoWJLmyIjwAXNZxNyBfnwtEt5fWgdmyNb81EJqoGqqIT1YmxpI8hHTiTNJGF3iHOazjiiMjSQjHSOMtQ+jJ2zCFYHGSodoTvRS2OgfnybHs1NqTvM6sF1nN98Jp7d2XtpK8NIJopf9x7QMby05ngSuSQv9SyjY6xz3N0byYzw7Vd+SGuomffVL6UhWMfGSBsBwzcuBEsp6Un20R7dQcbKUF9Uw9qhjbw+tI4bZ32ECu/RLTvpTw6yM7YLRSi0hpoJv8nNDFDuLeXGWR+ha/Z6nrr7AcLuIGc3nsbJtSfgUt+d8s4CBQoU+HtFCEGsN8aXPvVZNm9ezze+8QM+/vHPoxzIOvk26U7uYDDTQ1AvQVM0pJQkrDG2xzZRbJTi2T3zp8RVgUfzMZodQlPypWICgaG4yNpZFENQ7CojZ2fpS+9CVwyK9BCj2WF0xYWDjUDBdDLU+JpwqXvn3pa4yqn01NGX2oUQArfqRVVUSlyVzCk5nlhulK5kO6Zjou+OhRjO9AMCXehk7DRS2miKQdbJUOqqpNxdTdyMsjOxDbfqQaAwlh3Bkjk8mh/HcVAUFRWNpBWjyCnGwabCU83U4FwkEo/mw68VHXSGlsfj4be//ROXXnomH/rQ+Tz00HM0NDRPXOiNmU+Kykh2iEee+yPb12/lm//9bzQEJjFqDpM2U5R7aqjzNVPsKtsrTuNw8ep+vLr/4AseALfqYXbxYmLmKFk7g1v1ENBDE66JYcA11zj85S+CDRsUEgnw+STl5TBz5p7OO1XNR0a0t+cjEioP8JxDiHwcxWC6B1Oa48J/33CK3h5B+zY/Xi+sW6ewciV8+MMHjp04FgSNMIbiZv6pC3jqz4+xa/tO6lsbyFgp6nwt46L7WWdJ+vslGzdCZ6cgmYSqKpg5U6LtVr4MQxKNCkCSGEoQ64/jLw+MX2chBIFyP/HBBLGBOMW1oXf0XAsUeKcoiMEFChQ4ZtjYeXcrKqVOPhv4Dfyd+QFTtGGEYkoQCPrUHoaUfsrsSqZaM8ddBh3aNh5238/UxIwJZW1Hg51aO3ERwyf3DODeyMCNKMOMKaOckX4/IyLCc+4n2aptRkOj2WrlivS1E1yrbyCRdKudJETeYVviTBSfypxyfDLAmBglJPc4FeMihlt6cOHG43iZn1vEDq2dXWoHbunlivQ1nJw9A4EgJZJE1GFCTmh8/aGNg/zhjN9iZSye8P2V2267n8mT912SBVBnN/Kp5D+x3HiZHeo2gk4xxTLMo+4H8Tl+jsudSI/axZgyiolFmVPGabmzgXy0huZoRLURooALFwIFQxosyZ7GlxPfneCkPhLOz3yQhIixVn+dXtGNgcEscx5XpK9BSEEymSAQ2CMoCgSGapAy00DenTujZCqWs5GueA/d8V5s6XBcxTzObTpjfL2mplYcx2HXrh20th59p6au6OjKwbNzT649gfaxnWyK5F3XzpsE35mlU6kLVLN6cD3d8V4Chp9ZZdPHXbKNwTq+e8JX+PX6u1jR/zopK41X99AQqGNqeBLl3jLiZpxafw3HVy2gKVh/ROcysGWIlatXkUvlEEr+N3q0JYZVbqG/pUTEUHQydpacbeJSXTzX9TLPd7/CaHYMQ9FpDTVxev3JtAQb9hKmNUXjgpazWVKzmL5kP4/veJaNkTZ6E/1EMqM82/UiD7Q/wmfm3oBLcU2IvIjl4nSOdaEpGjWBKhqL6rEdm7bR7bzQ/ephO5X3h5SSx3c+y9O7nieWjQMQ9hRzUcu5nFB93PhyaStNJD3Kotkn8Mit9/CxusuprW3Y32YLFChQoMAx5E9/upsvf/nTVFZW8Ze/vMjs2fOP6f6klPSnu3Ep7vHiWCEEfi1INDfMaG54XAwWQhDQgpS6K3cXweWduSPZQTShj/tuDdWFW3oZzPTR4GulK7mDlNU7vs+AHqTCXYsQAsuxyNppdEXHUN0oQmVacC5hVxnDmX4caVPirqTCU4OhuAi7yna7drt359jmxzBC5gvncnYGIbR8TrG3gabAVDRFp97XSiwXJZobxpY2STuGKnRKjHIyTpqYOYqOgeVYxM0xFKHQEphGmefwS1TdPhe/ueuPXPHBc7jyyvfz4IN/o6KiCpGN4up6En1wGcKxMMvmEwvW8tBv/kTT1BYWnLQIIQQlrnJGGNwdzfDOZ0MfCCHEQaMYSkrgox+VjIzYZDL5OISf/UzdKwpCUUBKcA5sDGbyZIn5WJyxGJSE8kaVZEJlbDBAUWmKhkkKXs2LlJIdO+CRRwQzZkhce9dfHDN8WoB6fyuZhWncPg/PPfU0F9R/gCIjRJ1/jykmGISPf9yhrQ2eflrhxRcF8+ZJvLv9N44DmYygoWH3RVFEXkB33iJuy/xn8V4sWn6D5EiK1GgKl88gUBF4Tx9rgfcmBTG4QIECx4RN2joedT9It5rPvppsTeOC9KXUOnkRyh6wMTwuSgMVtKtbAYkhXZTY5Uy2po0LxwJBtVVLn9ZNl7oTl3Sz0niFbnUXpU45C83FTLKmHfFxqlKlyAkyqo7kizd279cUJgoKdXYjKirnZS/mePMkepQuDAyarcn7FKYjYpg/eG9nhfEySZGkxCnhzMz5XJi5bHz5WruB+bnFvOB6ioyTwSt9jCjD+GUAj/Ri757eV+ZUEMgF8WsBrk99miW5U8b345FeipwgQ8oAxXZ+un94UgmXvXw1PYkuqu+r43Ofu54///npAwrCFU4VF2YuHf85S5YN2lo6tXaarclMtqbRp/SwQ2tnljkfvwywVl/F7d5bcAk39XYjA2ofWbJU27Vclb6Oj6RuPCqivV/mz3un2k5EGaZIFtFiTUFFBQGapmFZewKbhRAcVzGPP23/K2krjUfzEND91AaqKfOU8MFJ59MaaqL5LeJjU1MrADt2bD8mYvChEnQV8fHZ17B2aCNdsV7cmosZpVNpKqoff//UuiX7XX9G6RT+/eRvsXNsF7viPXg0N5W+cpqC9W/b8fIG/Zv6qZ3USllLPsvZylmMto+huBWGfRHKdhezSSkZTA8zq3QaRUaAF3uWcf/Wh3CpbgSCDcObea77FR5uf5wz6k/mwtZz91nYFnaHGEoN0zHWSdbKMpAaQhMaIVeQ3kQ/P1v9ay6edB6mYxLPJQgYfkYzUWJmgiLdT+nuWAhVUQm7Q6wb2sglk84/Ktdj/fAmHtnxJF7Ny5Ti1vxDoEQff9z2F2r8VdQX1bJuaCN/3v4IfYkBkuYYAH986Y987oovFgbtBQoUKPAOYts23//+1/jlL3/MZZddzfe//1N8vrfn5DwUJBIHB7GPvzv5B78TlbqAEWQkN0jQyE9ZT1mJ/PqALvY8WNaETtZO05/uxqN5CRrF43ERppOlO9lB2krRk9pBxk6hKjqVnhqaAlPRFYNqb8OEiIo3UITK9NB8AlqI/nQXtrSZEpxLsasUIfKFzx7Nh6G6cb/JeezRfMwpOZ7hTB9j2VGklGiKTsAI4ZUBBIK4OYYtLVSh0BiYSqN/8mFdy5SVYEe8jUh2ECkl37zle3zl2i/woQ+dx5/u/Ss1u36LPrQax1UMQsW1868MdmXYuGIDX/rRNyb83RUoWE7usPb/XiO8WzMuLobKSklvLzQ25p2+UkJfX17orTiI3j1tmmTqcT2sXVZKYlhHANmsgsfnUNsaxZIq4EWIvMu2r0/Q3c0xjYuQMh9fsWmTIJuFhgaYNWsygcogx5+8hPUvreULn/8aVd668Ycpb2AYMHs21Nc7pNMK7e2CsrJ8YdzQkKC+XrJwYV789Zf6CFYHieyIEPaEEUo+JiI+mCBUF6SoYm/Tz7uNlbXY+uw2etf3kU3m0NwaZS2lTD9nKu6ioxd5V+D/PgUxuECBAkedTrWD2723MKaMUmlXY2Pzmv4qw2KQzya/QlCGsCwLt+7mK4nv0q5txcHB5bj5lf/mCQ5igDd+3Km287T7UQaVfjzSyzqxihXGS1yZ+iiLzZOO6Fhb7MlMtWaxQnmJIWUAj/RiCYtRJcJxuRNYkDt+fNkSp2wvl++bcXD4le+/eMz9EA42IOhTutju24qGxkWZK3afjuCy9NUEZZAVxsvElTH8MsBp2bNoV7fTpm8g5ISROCRFgrnmQubljpuwLw2NpdnT+b33NwwofYSdUjKuFCOTR1hknsRnmr9E1+ud/PrXP+eHP/zFIV8PFy6uSF/D77y/ZqO2ll3aDpIigV8G2Kiv5dfenzOk9JMWKWab85FIosooA0ovbunhA28SvY8GAkGT3UqT3brXe7puYJrmhNdOqTuRnfEu1g5twJE2UkrC7mKumX45iyr37f6pqKjC7fbQ2dlx1I77SPFoHo6vWsjxh2+UAcBQdSaHW5gcPjYjdDNt4SvZE2+iGRpl7lImRRvpKOkibibw/H/2zjs+0uo+99+3Ty/qXdpdaXtvsEtvBoONwRiXAG4Yx7Gx4xInsRM77cZObm4SOze+cXfs2LhhwAUDBkxfloXtTVr13jUzmv62c/8YrXaFtA0wmGS+fPbDR6P3PW8bzZzznOc8P9VLPD9N1BPmysZLsIXNkwM7SFkZRjMTdCV6kJAI6SHSVoY9YweYysf52IYPUuErm3fMwdQwCXOa8ewUXtWLbyZyQghBLB9nLDPBtuotPDv8PJl4hkQ+CQKWRpfMKTbnCAdFUuZ/vrxE9o4fwnQsFoUKnwkSEvWBGo5OHePQZCu6ovOD1p+RzKdYFG5ACkk85TP49c77ueqqN7GqmBdcpEiRIq8KqVSSj3zk3Tz66AP89V//E3fc8bFXbUJOlmTKjEr6Uu34FP+sKJxzsjMFuaJztq/2NjCaHSRhTuJTA7jCxRE2uuyZUwwu52TQZI20nSCslaArJ2yaeSfHULaP0dwghuLBo/qwXJOe1DEs12RlZNNpr1+TdRaHltMUXAqIOXnEp0OXDWp8TdT4mvCoPtqnD5Jzshiyh4heisAl6K1nY9mF8wrbnQnTyXMo9jzx/AReNYAsK0glOf7s3/+Sv7n9L/jEne/invdXYkeWwcy9cH2VfOPfvkldbZTzLj9RY8IVDgJB6CUWQ0tZ04xmB0haCbyqj0pPLRFjfv/l1cLrheuuE9x1l0xbG3g8gmy2IIBed52LcobHp6pw1VsmqG2ZYKKnGteVCIZNnn64AkliTva06xYcx2dq8+UgBDz8sMSvfiWTThfEbUkSrFmj8L73VXHNJTfwF3/xx1Sp84Xgk4lE4PbbXR56SOLwYQnXhe3bBVdf7VI287hkRWbZ5S0cuC/HRPcksiLjOi6B8gDLLm9BVn938TEvla4d3XTt6MEX9RGtj2BlLQYPDIEQbHj7/OKLRYqciqIYXKRIkVecnfrTTMrjs1EPAAE7RLvayn5tNxebV8zkh8mYUp5yp5JytxIFhUZ7MUe1Q7TYy5GREQgGlT7q7UXs13Yz8aJ2e5UufuW5hzX2hpeUT+sXAW7Jvp+clKFdbSMpJ5CFzOb8Nv4w83ESUpyQCGNw5rVQncoxHjUewMKi3C1HRcOU8ozIw/zI+12uyb0FD4UZWx8+bsi9g8vyV/NT7/c5oO1hn7YbGwshBLKQqHDr2GhtYbt5KT588453oXk5KTnJk/qj9KidGMLDZut8bs7eioqKEC6mmT/ne9LsLOOPU5/h74OfJe5Osc7ZRI1TT0ZK8YT+MGk5xWprHVAQwKJuCWE3wjH1CINKP2Xuq1N5V9cNxpPjjGbGqfAWHCt+zccHVt/K0aljDKaGMRSdlaXLqPbPtUUIBIfV/ezRnicmT2BEDAaTA6/Kef93Q5IlznM2ctHK7ewc2c1ULs6qsuVcVHsezZHFTOXiHJ5sYyA5TMbOkLNzqJJKPJ/Aq3mo8JUznB5l39hB3tB02bz2DdUgb+fJ2TnKfSeK3tkzMRqjmTEWhxvQZmIpvJqXKn9Fwf0tAAkyVobuRC81/iq+uOtLLC9pYXvNVqr8L/29Op1PostzJz4kSUKWZDJWhgMTR5jITrIiunS2Y17eWMNU/xh7RvcXxeAiRYoUeRUYGhrg3e9+C/39vXzve/edsSjq74I6/2Ji5gRT+Qk0Rcd1HZCg0d9CQA3P2TakR1kV2UxPqo1pK46ERKW3DtPJFwqtCWM2S7jSW09/unOeWCsjkbKmieilBLUIUBB4FUllLDdEg908+/rpOJdVNEKI2RV2kiRR719Czskwmh0gY6eQJZlyo4blkfXnLAS7wqUreZTRzCBhoxSv4gcJDNkDjYI/++Jf8dkPf4KvN67l9ptXz+53tH+KX+8d53/90YVM23EMUeiD550sJUYFFZ6aczoPgHh+gsPx3aTtJIqk4uQchjP9LAuvpdr30iK4Xgk2bxZEIg67d0uMjUnU1rps2SKorz/zvpIkUReqJ7V8L4tXTpOzM6SsFP7DaxntqKW5vHDfXBcGByWamwV1db+7axkchAcflNF1QcPMLc3nYd8+iaeflti6dTuO47B37/NccMGlp22rqgre8x5BOi0QAnw+gXBdhJBn+2bRhiibb93MaOsomakM3oiXqhWV+EtfWt2T3yUF4XcYT9DAFy2YI4yAQQgY75xgeniacE349I0UKTJDUQwuUqTIK86g0odfBOY48FRUJGBSHgcgTYqcyPGF4F9iYVLhVHFV/jrelLuJSWWcVvUwGhoWFhVuJRfnL+ce3w+pcmrmtFvj1NGjdtGrdLHCXvOSzneDtYXPJv+eA9oektI0Y/IIx9Qj/HXo06RIUunW8Obs23hj/i2UilPP/B9TjxCXY1S61agUlvLpwiDoBhlU+hiVh2l05y6F36e9wC79acrcCuqdRmxsetVOAiLIezJ/iIqKR8wvAHL8nr4pdxMX5C/j77/4GS6+/ErWV29mIjXGN+79N3bseIK77rr/Jd2TmDxJTs6y2do2m6ccEhHK3Qr61R7Scpqwc8LNYpJHQ8cjXp3lSQcnjiAMid+0/ZaxXQ7NkUW8ZckbqQvWoCsa68pXsa581Sn3f8x4iHs8PyIv5TCEgekxedZ5gh6lkyZnvqv2+DLOVypu4fVCOp2iu7tj9udYfpzW9kOUVZRTHqnCtV3yqTxNWxtYWtMyJyv3OI5rM56dxBUOhqJjORYe1UPaypCxshiKgSqpjGUnFzyHFSVLKfOW0DPdj+nY6IpG3sljC4cqT4SxzAQP9T5GRA+ztmwVSTNFR7yLgeQQGTuLEIX4BtM1qfCVM56ZpDPew4GJI/zh2vfMmyg4W5aEm9g7fnDWcQxgOhZIUBOoZiQ9OjsoPk5ZQxV9rZ1M5qZe0jGLFClSpMjZc/ToQW699XpkWeEXv3iCZctO3S94OViuOZuDG9Ii88TZgBZiXcn5DGf6iZnj6LJBhbeWCk/Ngi6+Uk8FUaOMrJNGRkaXDQYzPQxmejCdPCEtSp1/EWVGFRO5EbJOiqAcmd0/ZU0jSdI80VWXDdJ2kqydOa0YnLFTBUev4sGnBE7rNBRCMJ4bZjDTTdpK4lX91PqaqPTWsTy8nnr/YtJ2Ck3WiOilZ3QZu8IhZU3P3reck6U1vo++dCcpK0HOzeJT/ZQZVSiyiirprLlwLbe/9Wo+86OH2bblPFY2Fb7X/+XuZ6gr8fK+a6+lI7ic8VwhV7nW10S9f/EcN/VxHOFguyaarM87VyEE3ak2MnaKqF4+e1+mzRhdyVbKPFVo8mtXGLa5GZqbBXDuxd1qfI1M5sZoS+zHdPNIrsLybZ3ER8I8udNHmU/H65WprhbceKM7W4ztZIRpYu95HvvQfoRloa5Yhbb5PKTA2UUtODi0q0d5qNuiO9nEpmV+mDHRGAYEg4LduyWuvnol4XCEXbueOaMYfByv4dK/b5D9ewfJp3KEa8I0bK6fjT3zl/hYvH1+XNmrTXoyzWjbGNl4Dl+Jl8rllfgiJ8aBZsbEyph4gnPHW7pPJzmeIp9+fUefFHl1KYrBRYoUecUpcyo5qh6a85qLi0AQFhHy5NkXeAHTNDFcDyHCjChD/MD3LW7P3MnHUn/OXm0Xo/IIZaKCDeYWdHQkfoyLO69dCWnOEqaXQo1bR02+jqf03/Jrz710qe1YWKhodKsdfMf//xhRB/lw+lOndCDrwkACxIvy3xzJQUFFftHydIFgp/4UujAocQudEQ2NWruBZ4zH6Vc/RsQtodyp5ArzGs4zL1xwiXtUlOCOCf7XnZ9lbGyYYDDMihVruOuu+7nkkivnbX82pKUUOXJ4xVxHckhECLsRRuVhom4JXuHDwqJH7aLZXsZi+9zy347TobSxT3uBmDxJvdPIZmvbKR3GnfEevnf4J0heBSUnCGoB9o4dJJZP8LENd8yJBliIKWmSB41foAuNJqdQgdrr8TGVnOKro1/juvyNLA4vosZfSdJK8eTAs7wwshdbOKwuW8ElddtfsoD4emP//t287W1Xzf78o6e/yY+e/ibbWi7nA1d+Etu0KW0qoX7jqS0io5kJfKoX0zExHRNHuNiug8BFkTQMWccWNqWeKJZr8dzwHp4f2UPCTLI0soQLa8/j/atvpXu6n8H0EH7Vj65oVHnLkZGwhUPECFPjL5TK9ms+ZEkmYU5zef1FDKVGiOenaY6sno2hcIVL61Q7Tw3s5O3L3vKS7s3W6g28MLqXtqkOyrwlOMItuKJLl7KufNXM0l6X7kQf49lJHNcmq9tkk2kag2dh1SlSpEiRIi+Zp59+jNtvv5mGhkX813/9nKqqc3eBngkhBMPZPrqTbWTtNJIkEdIiNIdWE31RbIBPDbAktAI4c50LIQoOW796oj/TEGimzr8I27VRZW12crop2EJr/ADx/ASabGC6eWRJIaKX4gh7Tru2sFAldUERFMB2LTqSRxjNDGC5JqqsUeGppjm8Gl1eeJ/hbB+t8X24wkVXDGLmBHFzEtPN0xBoJqCFCWhn51Scyo/ROX2E5EzGvl8N4QiLlDWNT/GTd7IokkrKmkaRVMo8VTjCwqv4+Nzn/5Edu57j/f/7pzz2L3cwEktz95OH+cdb1qPXX0JLeDXNocJkwELititc+lOdDGZ6yNgpJKDcW8vS0Bo8M/FUWSdNwozhU+cW6wpoIRJmjGkzTqnn1Vkd90qjSAp+NUBAC+Jmq+joUEmN+Vm6YZTY1CSJ9pWcd16Ut771RMTCyQjbJvfD72HveKqQIyHL2Lt3Ye/fi/f2D51REM5IaX7g/RZ79ecZ0FfRr15JTo+x1l5PuVvoc0vS8ZgKmU2bzufZp5/kunVvJ5/KE6wMUrWi8pSZucd+207nM10omorm1Rg5MspUzxTrblxLxdJTRwC+mkx2T3LwF4dJjaeQFBnhCvqe72fdjWuI1EWAggvYCBrkknl0f2HiYSoXZ2h0iFw+jzstcX5mM2qujFisUFBvoedVpAgUxeAiRYr8DthqbWOPvpM+pZtqpxYbhwG1lyq3hrXWRtrUw0z6x3CzDlG3pLC83wnQrh7laf0xPpL+E67JzxVoBIIWezkvaM8SssMoKAgE/WovtXY9Tfb8TNnTYWNjYuLFe6JoHBZP6A8Tl2O4CCrdaiQkcmTJyln2arvYr+1mm3nxgm2utzdT5dYypgwTcUvRhEZOypKTsmywtlDhzg+CTcjxOc5fgeCodpBReZgyt5ywG2ZYGeD7vm8iC4Wt1vZ5bVhYvPffPsRVynV48LDSWkvlAsc6FyrdasIiwpQ8MUeUnZInWGttoswtp1U7jI2NjESDs4h3Zt+DhjavrQ6ljV36DoaVQaqdWraa22l2TiyR36E/wU+9/8W0NI0udJ7Vn+JZ/Sk+kLmTeqdpXnu7RvYQyycIhSPYaZOwEcKv+emId7F//DAX1Z4/b5+T6VE7icmTs4UHhRDYtkNq0OKxg88xYmco0aJcXLed3ul+DowfImyEkSWZ3/Q8xrFYJ3+09r2zxdL+O7N9+yV0Hp3Gztv4ol4kWWKsbYzRtnGsvEVJQwk1a6rmORRORpNVavyVRI0IfckBEvkkGTuDT/US1IP0pQao8lewrnwV97bfz6N9T6HKKh7F4NH+pzg82coH1tzGP13013z1wHcYSo3iUQxKPBEaQ/V0T/cRNeYONKOeMFO5KSayk+wZ209/chBHOJiuSa2/GlmSiRhhDk+2Ai9NDC7zlvKBNbfx2/6nOTxxFE3RuHbRlVzecCF+zceq0mXk7Rz7xw/hVbwoksyoO4GVzrO0pOUlHbNIkSJFipyZxx9/mPe9762cf/7FfOMbPyJwls7EcyVmjtOWOIBAENQjCOESN6c4Et/DptIL8ajzI75ejBCCpBXHdPM4rs1kfowpcxxN0qj2NVDja0KVC0N2WVLQXxTWWu1tRJMNhtK9pO1CNEStv4mkGacjeZisncZQvNiuRdJKENai5O0c08QIapE5omZ3so2+VDteJUBIj2C6JgOZbkBiRWTDPBHVdm16U+0ARIxCjJOPAClrmr50J1Xe+lMKzy8mbSU5Et9Dzs7g10KAxJQ5xrQ5ReVMOyl7GmdG0E5Z0zPRFxqV3jr0aCVf+6d/4Kp3f4S/+vrPsB2XaMDgne/+GFb5JmBhEfjEtbfSOX2UrJMh52SwXZPhXD9DmR42ll5Ahbe2sNqHQl99zjOcjcc4q0t9RTDdPGPZIeLmJOqMMF5qVL6svNi4NYlPCdDWFSU2IlFWWii6VrdsiAFPlMHBKL5TvKWdo4ewn3sGuaZ2VvgV+Tz2oX1Yzz+HftnpzSmP6w+zU3+KeqeJaFOAlM8gPiVzuHQ/281LkUydRELi8ssLhpvlDWv4zl1fofXRVlRNw3UE/XsHWf/WtQQr5jriU+Mp+vcO4A178UULF+CLeon1xeje2UN5cxmSfPb3TQjxiufyOrZD26PtZGJZSheVFgrZuYLJ3imOPdbBlls2IckSqqHSsLmeow+2Mj2aJKbEaR/uwk25ZJtzvDB8jB/+xiY8cgWSGcDng40bXW64QeD//Uu9KPIaUxSDixQp8oqzzF7FO7Lv4dfGffSqXcgzWcA35d5FmVvBQW0vsr/QmbUzFtrMzGbIjTCo9OHiznP6Ski8OXcT4/Iox9SjyEg4uFS6VdyYe+dZZfoCZMjwuPEbntOfIitlWeQ0c1n+DSy3VzMtJZhSJrGx0NBmRWIDDylS5MkzLA+esu1yt5J3pd/L9/xfJylNz7o26uwmbsl8YN45Skg0W8vYYTw+KzzHpSkGlH78IkCd00hQhAk6YbqUdp40Hmazdf6ce5OWUnzb9/94yniUhJRAAurtRm7PfJTzX2JRPYAKt4rz8xfxkOeX5KQsfjdIXJ5CAG/Mv4Wt5gW0qYeZkMfwiyCr7LVzHNMWFu3qUfZou3jSeBQHG78IcFQ9wG59J7dmPsAGawvTf2bHIwABAABJREFUUoJfeX6Gg8sKu5Dz5uLSph7mAeMX3JH56Dw39EBqCL/mI1pdxlBrD65wSVkpklaakfTYGa9NoVBE7Pj7bGoqST5n4pF8REr9LDWXkMhM8+O2+xDCZU3ZSjxq4dlV+MponerguZE9vGnxG17y/X09kE3kaH+8g7FjYziWi7/Ex6JtTURXRumJDtAe60KVNVbmlrLav7yQ0bsAi8KNNITq6J0e4JK67QymRuid7mcyO4lP87I0uoS3NL8R27XZMfQ8Zd4SSjyFCJJqUUlrrJ3H+p/i/atv4f9c8rcci3WSstJUeMso9ZbwD7u+TMpM41VPTKok8ymG02NkhnYhI6HKCtNmkgPjR5CRqQlUYbs2hqLTO93PsVgXrnBoCjXQEl181nEgNYEqbl3xNvKOiSxJaPKJyZCh9CiG6mFxuImUmUIgqCyroiPfTedkF6tKX5qLvkiRIkWKnJonn3yU97//Ji666Aq+8Y0fYxhn1z98KYxk+rFdk6gx4yycceTGzAnG8yPUq4tPua8rXHJOhvbEQSbz42SdDCkrgSqrlBoVmORpTexn2oqzMrLxlN9LkiRR7qmm3DPXBBA1yrGFxVCmj4Q5Vej3CIeklWB/bCeapFHmqWJZeC264iHv5BjO9uNRfHhnRGyPUvheHcsN0ei0zHEqQ8Epm7Uz84p4eVU/STNO2k7OisGucEmYkzNxGgolRvmcGIvR7CBpK0WJcSJ+wa8EmXRHyTtZAlqIMk8VU/kxck4WRzhISCwNr6bEKJgWmi95P5//7Dh/8TefQ9c0PvGRO5FX/gFnUmlzTpbBTA8uDnk3OzMh7cV08sTzkxycep7N5V5CWpSIUcZodhBDNpAkGSEEKWuagBYkpL20gnTnSt7JcSj2PJP5MWSkQn2VTA8NgRaagytPK1TmcnD0qEQsViiwtmKFwDvTfdIlg0zeYWpSIhAQM7dNgASlUY32NomeHli5cn67dlcnwrLmOIAlw0AyvNiH959WDLaxeU5/ipAIExBBxOIpFl3eSedDLQyPpzkgJfFny1i+XHDBBYJ82qQ0X0XWzJD2TbOopgXXcZnsmaLrmW7W3Tg3NjA5lsJMm5Q0nXg+kiThi/pIjiTJp/OnNTUcZ6p3ir7dA8QH4njDXmrX1VCzphpZefkRcsmRJNMj0wQrA7PCtCRLBMsDxAfipCbSsyJ345YGENDxXBc9PX24iktgUwD/mgBjz26jdUcNtZUDbF+8jGQSHn1UxnVdbrvt3ONDivz35nUnBn/lK1/hn/7pnxgZGWHdunX83//7f9m6despt//pT3/K5z73OXp6emhpaeEf//Efufbaaxfc9kMf+hBf+9rX+Nd//Vc+/vGP/46uoEiR//5ISGwzL2adtYk+pRtFqDQ6i9E5Ifr6GgodzURfgrIVhU50Skqy1F4xK3YWXKfy7M91TiMfTf0Z+7UXGFNGCbsR1lmbqHLPbumfi8sPfd9hh/44IRHBEAZ7tF10qe3ckf4oDfZi/G4AoRRyq45jYaGgIKOcsUjdzbnbKBeV/Nq4l2FliEqnmrfm3sVF5uULbn+xeQVHtUO0qUcocysYkPtISgmWO6tmoyMAIm4Jo/IIGSlNQJzoaD2i/5p7PT/ElEzkGbf0fn0P/yL/HV+J/xfl4tyXq5mYyMi8Jfd2QiLMDv0J0nKKBmcRl5hXzsZVrLbXL7j/kDzAD3zfol09yhH1AKZk0WIvo9leVojdUDq437iXVdY6utUOxuVRltgnnMIyMpVuNce0I0xLCcIiMqf9Kl8F7bEuyhprOPTILnYMPE/CniaRn+bBnkepC1RzfvXmU3aGl9hLqXCq6Vd6aHKWMDo6heM65L0ZMnKaPrWbWn8905NJbNeaFYKhsIwuoPk4Fus85/v6esKxHQ7+4hCjbWMESv3oPp3URJp9v9hPd+8gB7xHZgX1pwZ3cFn9hdzU8uYFB6u6onHz0rfwX0d/wkBqCJBoCNWwqXIdNy19M+vKV6LJGs8M7SJlpakLnPh7liSJUk8J7bEu8o6JV/XMy4LeUrWeB3seQ5EVwkaIpJmmc7oHF8GScMFRNZ6dxBYOruvQM91HUA+QstLUyjV8ac/XmMxOkbLSqLLGpXXb+cCaW9GVs8/9MxbYtne6H0WS2Va9mbyTxxWCnt7DdPAsB0cPc/3SV7+IUZEiRYr8d+bppx/jve+9ke3bL11QCHZcm5xTEPsMZeF6DOdCxil8b5xMoe8hMJ3cwvvYKfrTnYxlhpg0x3CFQ5mnCtmRcIWN5biYrkmJUY7p5BnNDlDja6TEOLel7Iqk0BJeQ91MZm9vqo3x7Ch+LYgm61hunuFsH4qksiKygVh+nGkzhlfxYsvG7HXpsk7OzmA6uXlisCIpyJKMI5w568Ic10aRFBRJnf25NbGfkWw/jij0rz2Kj6Wh1VT5CrFJOSeNIp0o6nW8YJ4tbGL5CYJaBJ8awKN4mcqPoUseziu/HL8295ze+8E/5avf/ib9/b28491/fEYhGCBrpzCdPJZrIlEotocojBtyTpbhbB/Pjj1CS2g1Df4WMnaKWH4CSZJxhYtP9dMcWj3r4P5dM5TpZSI/SkQvna1ZkLUzDKQ6qfBUE9YXFqVHR+G735Xp6AAhJCRJsGSJxLvf7VJdDZXeWganh1E8aSSn8Pch+eII04uTOP14QlJkJLGA2Oi6oM5fNXgyNjY5KYc+U3dEkmDZ2w4SWTLBgQMyDbFy3rCkZKZIHoy2xqn01KPICq19B1lU04KsyPhLfEx0TpBPmxj+E/0y1VCRZAnHclH1E8YF27RRdAVVP/NzG++YYP+9B8gn8xhBg6m+GBPdkwzsG0QzVHIpk5KGCHUbaglWBBFC4DgOpmliWYV/pmniug5erw+v14/X6519vwtXINz57nVJlgrFGU+6t7Iis2hbE9MNaXp3DtFYVo/wS+TTOkOH6wlHTEzvEI7cSGmpB0kS7N0r84Y3OFT+z0i5K3KWvK7E4B//+Md88pOf5Ktf/SrnnXceX/rSl7j66qtpa2ujomL+B9SOHTt417vexRe/+EXe9KY3cdddd3HDDTewZ88eVq9ePWfbe++9l507d1JT88rnSRUp8j8Vn/Cz3F497/UV1hpWNK1hB08y0jlIeEWYcXkUgO3mpXQp7TyuP0y7dhSv8HGeeSEX56/Ei5eoKOFS8w3Y2HSorXSrHUy5E7TYKxaMKDiZTrWNPfpzM47bEAClbjlt6hEe1x/hg/bH2GZezFHtEDEmSErTCAQJOY7X9VHr1rPaXnfaY8jI5KU8kiTjER4ycponjIepdKsWvBfNzjLuyNzJo8aDdCsdBEWIOreRpdbKuQ5gOUmZWz4vUuJX3p+RlJNUOdVoM2J7RkrTpbbzW8+DvCP77tOe78kMKL08qj9Eq3YQVahstrZxWf5qLstfTVbKEBBBFOa7P20KmXQqKjY2P/R+h1b1ECVuGRoGfjdAn9KLV/hYbq+myq1hRB1kSOlHEhIskIMsEEhCmucKBthStYHdo/uZKhW4jsNgTw96dYAKbxmapPGjtvsIGSFWlS6bty9AQAR5a+5d/ND7nxzRDnKIbuykiRYMICNzRD3AsDyIpGrYOXvecjDTtU6ZS+wKl1guXsjrM0Kv+DKyV4upnhiTXZNEaiNonkJXQffptB5tZ2D3IA3X1OHVCp32WC7B4/07WFO2kuWniD9oiS7mExv/iIMTR0hZacq8JawuXY5PO7HeUJd1JEnCFe7s4AbAci2CegDlFK6o6xZdRd422TN2gPHsJD7Vy7LoEnqTg4T0Qq7f8pIWjk61k7YzpFPDRL1RmqOL6Ev2kzTTjGUmyNo5LNfiu0d+TMbK8olNHzql2/lsUKXC5IwQAmPGGaXOVFxRxf+sQoRFihQp8rtmx44nePe7b+D88y/mm9/8CYZhIIQgbU8Ty08ylR8jYcVwXAtFVin3VLM4uAJDeemFbwNamKn8+Jx+wvGCsx5l/nr6nJPl4NQu4uYUsiSTtdMAxHITONhoioGERMpKENFLZqIREqSs6XMWg4/jVf0IBElrmoAemnXq6ooHr3AZyw1BXGIk20fSipMwp/BaMaJGOUEtTN7Jock6HsWH7VqM54aJ5yeRZZkSvYISo5yRbD+qpKHKKo6wSVgxQloYIdzZXOWhTC9+NYCueGaiMRIcmz5ISI/iUwN41QDuzPZZJ814bhjLMWfuW4b+dCelniokJDTZoCW0Zp4QfBxlJkpjx44neOtb33XKe2M6eeLmBGm7sHqnIAYXnmPezZF1UoCELCnYwqIzeQTLNVlfso2J/AhpK4mheCn3VBPQQi/p+bwUxnND6LI+p6/kUbzEnDRxc3JBMVgIuOcemdZWiSVLBIYhME04dkziZz+T+chHXCq8tTSXJOiN9hBPpZGCgOnDHFhNf0eYykpBU9PC56S0LAevD3dqErmkEBki0ilwbLQ1px87GRgssZeyS99BmVteqAWjCDxbDrHyvGk+krqURueEGCoQ6IrB4ppltPYe4I3nv7XwC6ngkuZForQ34kXRVUZbRyhvrkD3aZhZi2wiR8slS1CN00tiwhV07eghP+MuNm2T4VwfrQePMProEKYnRyIfYzI+TtKMk8wnyJu5OQLuQkiSRDAYIhIpIRKJIqdVIp4yGusXUVVaS2W0BiPto3JxFYHS+WYkxVBwQi7Hh4b5tIGZ09CDSeDEGCoYhKkpiMcpisFF5vC6EoP/5V/+hTvuuIP3ve99AHz1q1/l/vvv59vf/jZ//ud/Pm/7L3/5y1xzzTV8+tOfBuDv/u7vePjhh/n3f/93vvrVr85uNzg4yEc/+lEeeughrrvuulfnYooU+R+MDx8f9H+c7wW+wVDXAB7VQ9Qt5drcjZS65Xzd/2Um5DFK3DIm5XF+4v0vBuV+3pv9UGFZvzTJ933f4Kh2CBsLBZVl1kpuy95xyqJjAMPyEHnys0IwFL4qS9xSutV2bGyuyL+RlJTkR77/5Kh2CJMcslBRJBld6PjcwCnbB3hB28n9np8RdMOsdNbiYNOjdvF977f4VOpzRMX8DtpSeyUt9gqyUgYh4D8C/8xR9SANziK8wsekPEFGSrPdvBn1pI9tF5cReQhFKLNCMBREeAeHXqVrdrsD2h72qM+TkhM028vZam2fc6+G5UG+7vsyg0o/pW45eSnPzz0/pVfp4oPpj89z50KhENtjxkPs1XbhSi5rrY002Ivo1I7R5CzBkgoOYw0Dr4BBpZ8l9jIcHGQho6Ky2GmhwqliUOmjwVmEhISDw5g8wnbz0jnP6jjLos28a/lb+UrsmwBY4ymqWupYE1lJiSdKW6yDXSN7TikGA2y0tlLl1PCI8Wu6jG9D0qWypJYytwIXl3HGMIwwFXIVA6lhagOFAchULoaExPqK+cJ+e6yLB3oepSfRjyxJrChp4drFV70ui81l4llcR8wKwcdJqNN4UwbekwrJRD1hRjKjdMS7TykGH9/u4rptp/z9spIlVPoq6E320xRqQJZkMlamUAiu4aJTOm58mo9bVryNKxovZioXJ2KEiecS/Pu+b5B3TDyqQX2wlognTOtkO17Nw53r3k9fcpBDE61MZKdwhEOptwQJGE6P8Zvex7mk/gK2VK0/p/t2MktLmgnpQUYz45R4IkzmYgzmCpNeLcFTLx0uUqRIkSLnxs6dT3HbbW9h69YL+Pa378bjKQiOPalj9KbambYKxb0USSGql+GXgvSnOjGdHGtKzjuraKC4OclIpp+UPY1fDVLlrafaW89YdpCEOYVPDSBwSdtJwloJ5Z6qeW2MZgdIWFNEjTJyTgZZUjBkDzk3WygyLEkoUkFQLcQgyEgzUUcvB9PN4wgbrzRXoNZknel8jL50O341SKlRScycwHRyTGRHcF0HB5vGwFJUWeNQ7HnGc8Mwk5w7IHVT6amb3c91HdJ2Cse1cFyL5yeeoNRTQdZOo0gKunLc+SkR1MLEzAmm8uP41AAV3hqGMj3EzAlSVgLbtZBlmZASwa8EiVuTJMxJ6nyLqQ8sptrbsOC17tv3Aj09XWzcuJV/+IfPc911b10wKmQsO0j79GEyM0Jw2pom52QL91squKGFEHhkD4qsElQjKLLCSHaAev8S6v1LXtYzeXmcymhwavFxdBTa2iRqawXHb4euQ22toL1dYnAQ6upkWkKrsJvquOeBBO1jCs50GdlpP+EwvPnN7ikzg5WWZWhXvAHr0d9gjw6DJCOpCup521E3nXeGq5G4LH81HWobbephIm4JeSlHTspyWf4aGpxFc7aP1kbwRX00lbZwsH83o6OQzQiceJpl22tnC6sJIejbPUDHE51kYhlS42mSo10EygP4S/3UrKlm8QWLFjqlWZLJafbuep5f/Po++qe66P15JwPjvbhuweGuyToRfylhPUpYj1JrNFG1rIY1V68mVBZC03R0XZ/9vyTJZLMZMpk0mUya6ekE8fgUiUScga4+OtqO8mzbb8nbJ1YWlJVU0Hz/Mtat28zGjVvYuPE8amrqWBxuoMpfwUByiKZQPUYgj+bJM5UQLKmPzq5snJ4Gnw+i0dNeapH/gbxuxGDTNNm9ezef+cxnZl+TZZkrr7ySZ599dsF9nn32WT75yU/Oee3qq6/mvvvum/25kJ9yG5/+9KdZtWoVZ0M+nyefz8/+PD09fQ5XUqRIEYB60ciyppUsbm3hE6nPUu3UERJhvuP9DybkMZbZq2ZnNJNSgj3Gc1xkXUazvZx7vT9in/YCTc4SfMJPlgwHtb3czQ/4w/THF3STAniFD4mCk/VkUTUnZalwqlFQUFF5e+42JuUxLGEREiHK3Sq8wsuoMsLd3h8smGN7nOf0pwGJCrcwCJDRWWy30K62cljbz4XmZQvuJyHNRlDckrmdH3n/kw6tjTx5wiLCG3M3cHF+bt6WgkKZW8mgMjBbvOL49SgolLplCAT3e+7lAeM+bMlGFzr7tN3s1nfygfRHqXZrAXhWf5IBpY/l9upZR3KJW8YhbT+Htf1ssuZ25FJSkm/5/52j2kGiTikSEg8ZvyKgBUmRxCO8eISXiBtlXB4j4AawZIu8lGNQ6WeltYYap342juLHvu/Sqh7CxcXGZqmznGty1y94nyVJ4rzqTezc8CyPRXTMbIx8Y4wBtxuvbeDXfAynRxe8zydT49ZR7zZSYYVpByxDIpnMAJDPC0KV8J7wO/lt31O0xToACGh+3tB4GRvK5+aRDSSH+Pbhu5jMTlLpq8AVLjuGnmckM85HN3zglE7i31eMgA4yOJaDop00CM0JHL8DvwNja0gP8val1/Ojtvtoi3UgSRKqrLKlcgOX1Z8+/1qSJKr9lbPCe4WvlJpANTtHXkCVVHyqB6/mJaD7ePvSG1hfsYbuRB9JM0XWzlLuLeX44MqrerCFzd6xAy9LDG4M1vHGpiu4u/2XPDv8PFk7R25iAoDW6Q6mcnFKPJGX3H6RIkWKFIG9e5/n1luvZ9Om8/nOd36Gx1MQHCfzY3QnWwtRBUKgywaKrJCyEwS0EEE9wkR+lIQ5eSLz9xSM54Y5EttD3s2hyRqx/Dij2UFWRNazMrKJ7mQrKTuBhEylp44loRWzwufJTOXHsF0b082joKJICi4OEqDKGpZrYoochuJBQWHaiuFV/ZToJybvLbfgllUl7axXH3kVP7pskHOz+OUT/ZG8k8F2LTRFxav6MZTCsvWkFSdrp8m7OVpCq1kUXMZwto+x3BAhrQRXOKTsaZJmnEQ+xqroJqq9jQxn+4gnjyBwydhpsk6GpBUHIKKXzjmn4+fuiMLqMr8aZGVkI4fje5jIjaBKKh7FR1Qvw6v6CeoRMnaK5ZH1hPVTq1rf//43qa1t4P/8n69x1VWb+c53/h8f+tAn5myTtpO0Jg5guXnCesmMAKxh5Uws1yJpx7FcE1XWkCQZrxLAq/qRkMjYE2Sd9Cldya8G5Z5qYokJcq6DrinIcsE9rcn6nPucz8P4OBhGISvYsgoC8MnoeuH149KGJEmsXByh9G0Rdu+WGByUKClx2bhRsOg0uqkkyxjX34S6bCX2sVZwbJRFzair1iC9+KAL0OIs5w/TH+dJ/VHataOUOeWcb15UKB73orGAETRouWQJ5c/WMzr1S/Y8NY6CjK2HGe1YQtOkRFkZTHZP0fqbNmRZonJ5BWVLSpnomgIEK65eRsPm+nl5v47jsH//bh599AEee+wh9u/fjRACTdGoL1/M8oY1XHv+TVQYtTCsogkdWZXRfTqaR8PMmJgZi6XyEs5/y3lndB2/mNhAnKFDQwz2DDLtTJLWk4zFh2hvP8qvfvUzvva1fwWgsrKajRu3UtFSTzKa4EhDBl3VkRdraLsvIGrVkctBMgkTE4XCewsspC/yP5zXjRg8MTGB4zhUvsjbXllZSWtr64L7jIyMLLj9yMjI7M//+I//iKqqfOxjHzvrc/niF7/I3/zN35zD2Rcp8rvBRHBMNZmWXEpcmWZHRz3lbPHvH4sWNTPRVRB+oeBgbdeOEnVL53zxB0WYQfoZlAeIyCUc1vZT7dTNiqdefNQ6DbSqhxiWB6lx6xY83gp7NTVOPd1qB032ElRU4lKMjJRhu3Xx7DEnpQk6tXZWO+sodU8MEBRH5Yh24LTHmJIn8Lpzp80LBctgyp7iiYEdHBgvdJRXl67gvOpN+LW521e7tXws/ed0Kx1kpAzVbg3l7sLu0mty19OqHmJMHsaLHwebPCaVbjXnmRcxKPfzW+NBgiJEuVNow8GhTT3ME8bDvDP7XgDa1VYCIjQnmsKDBweHIaV/nhi8T3uBNvUILdbyWVdyuVvJfnU3tmwzJU9S6pax3F5NXnuBYWUQn/DRq3TT4DRxY+6ds8faam3Hzljc5fs2fUoPfuHHJwLYkrXgNQO0qYd5vuQp1AYPTncOCZlupYMUKSJmJRsq1pxy35ORhYI5WugBt2xZxLRsIgTUNUZY0bCEq93L2Fy5jrZYJ45rsyjcSF2gZt4A7LmR3YxlxlkRXTr7u5AepCPezf7xw1xUe/5Znc/vC2WLSonWRZjqnSJUFULVVTKxDGEtTFdDP4bjm3UcxPPTGIrBkkjTyz7u2vJV1AaqOTJ1jKydo8ZfybKS5jmF2c6GRH6anJ1jOj9Nzs5jCwdN1rio9nwuqbsAgMZwPQKB67ocF4Jd4WK5FpW+cqbN5Mu6FkmSuKz+Qp4a3MlwapRl0TLG1U72au2053r5VddDvHvlO17WMYoUKVLkfzKDg/28971vZeXKtfznf96D13siSmsiN4wjbPxaiIn8CKqsosoaWTtDxk5R4qnAFQ5ZJ8PpDHOucOhOtmIJk6heNvsdnzCn6E62sbnsEjaVXUTWTiNJEl7FP6+PIEShwNdguoe4OUnaTmLIHgzFS8ZOYrvObB5vxknjQSJuTeFT/SwNrcWj+sjYKXqTx5jIjyIQlBjlNAZaCGqRM94nQ/FQ42uiK3kUIVw02cB089iuhV8NIs/EDciSTNQoI6RHmMyNsji4nJZwYSXUeHYYRVKxhcV4dgjTzeMKl4ydYuf4o5QZVaTtJKabw6v6Cw5n1ybv5EA6XmQtPCcTWJEUglp49jyjRjmrIptIW9N4FD8+9aR7KZ3IKD4VqVSSn//8J3z4w59i+fJV3HLL7Xz5y1/kHe94D9HoiZV5k7lRck6aqF5+Up8tgiNsPLIXWZbpT3UVIr/0gutTlmRMJ48qqYVM4deQdP8inn7QQ1e3wOO1WbZpjLXn2bSUNBPSoggBO3dKPPKIxNiYhKZBS4sgEBCMj0PDSabq8XEoLRVUz60/SGUlXHutYCG38cgItLZK2DY0NgqWLAFZLgjC6opVqCsK4zohYHKyIERXVMwXol/MEmcpS7JLcbPuTMjBqce0tetrkSuvQPDv5IIWFbXryPmrOdTl5557BHfc4TLaOoqVsyhbVBDIZUOmankFk12TmFlrVgienk7wyCO/5re/fZDHHvsNsdgkkUiUiy++kttu+yDr12/C6hT07hyYjU9LDCfoHxgsvC9VFcNf6BNLsow35CE+NM1E5wRVK+evEDgd0boI0boIq1igSh8wOjrM3r3Ps2fPc+zd+zxPfutR0ukUvmCApVvWsvXCIDU3XEXnER/DwxI+H1x1lcv117+84nFCCNKTGbLxLEZAJ1gZfN1G4RU5wetGDP5dsHv3br785S+zZ8+ec3ozf+Yzn5njOJ6enqa+vv53cYpFipySEdnm+54EnaqFi0BFYqVtcEs2RES8vOVkrxaLFzfz3HNPz2atSUj43QCjyvCc7RwKS+W8wktGSmNK+XlxCx7hZUIaIyOlT3m8gAhyS/b9/ND7n3Sr7bgI/MLPZfmruTB/osBbWk6SIzsvGsEv/ExIY6TlJLinuCZ7KU8YD1Pt1p5w6pJF2BLPHzjCyNDT6IqOBBwYP8KhyVY+sPqWOdmpUBCQm51Txxwc57r8jRxU9/K45zfEpEl0oVPt1vGW7NtZYa/mWf1JklKCZc6JlQ8KCiVuGQe0PbwteysqKmER4Zh0lFFlGIEg6pagCwMQeMX8NWH9Sk8ht+2keAoFhbCI4DoO4/IoWSmD3/VT6dTgFT625i9gi7WdtdZGSsQJ58K4PMpDnl+SlTKssTYAcEQ9wLd9CT6a/lPK3AqEEHTEuzk6eYy8Y3KwcheUW5SurGTkF93ISYWAHqI300/IW8K26i1nvHdAoWBhvwwSrL1oGYqmkCNLj9bJRdlLkPISZd5Syrylp22nb3oQvzp3AKjKKgLBeGZiwX0G5D726s8TkyapdKvYaJ03T/QXCLqVDnrUTmQh02KvoNZ9ad83jutwZKqNo5PtuLg0RxaxpmzlgsXPVENlzfWrOPpgK1P9cRzLwRvysOWNG0mFsuyfPIyMjECgSAqX1m9naeSVWTJZ6i152eL5UwM7Gc6McU3TlaSsFKZjzbixTIbSIywKN7CqdDnrylbxQM8jTGanUGUV0zUp9ZRgqB5aIi8/yqEvOUgsF2dr1QZ8mo9nMp34wgGqfRUcHD9KLJcg6gmfuaEiRYoUKTKHTCbNe9/7VgzDw7e//VN8L1q/bgkTSSoUIValwue7ioYEOMLBdm0kScaQT58ZnLaTpKxCNMTJ3/F+NVj4nZ0gopee1ik6kR/hWOIAqqyhyx5AkHUy6LKOIfuADKqsEzXKCzEWWhBV1igxyvEoPkwnx6HY88Tyk7MO1cFML0kzwbrS8/Gpp48wA1gUXIoiKQxlejHdPB7FS01gOaZr0pU8iuu62MIk62RwXAcB+JUgQ5le0laSuDmJ7dhk7YlZd3LGSePiYLmCsewgDg6qpKNKauHeKzpIkHeyaIrBVH4MQ/YUVoEJmxpfIxG9bM55hvUoUaOcaXMKKJg+hCjEOESN8jlF7I5nsh5/Lvfd92NyuSzvfOd7AfjUpz7HT37yPX7wg29x552fBiePNrkfpo+giDSy5EWctHJLlVW8mp/NZRdzzHOQ7mQrPs2PLCmYbp6UnaDCU0NIe+3W27e2wn9+20ss3kQ0lCKZtNj96yrCGcEb3hNAkiT27ZO46y4ZEJSXC/J5eP55iVBIYNsSHR0Cn68QHaEo0mnjH17M009L3HefTCxW+NnrhW3bXN749mn2Bp7mgLYHCYmmiU0M/+Ri2vcFsCyorBRcdZVg61Zxxpp+8lksPxselkiYWwGIh2XKq5dgADU1gqNHJcbGIDedn7u6jcJ7RVJk8skczz33NHfd9W1++cufkctlWb16Pe9+9x1cdtk1bNy4dbbOA0CuLkc+bjHeOYHruLiWgydkkE+bKHrhfF3bxcnbhBcVYsfyqcLfSTaRIz2ZRvdpL1tErays5pprrueaa64HCk7mAwf28Mgjv+bhh+/n63/79yjKP7Bhw3bOO+9arrvuBtavf3n9cztvc/ThNkYOj2BmLFSPSnlzGSuvWY4n+NIz14u89rxuxOCysjIURWF0dO7S39HRUaqqFp5xqaqqOu32Tz31FGNjYzScND3mOA6f+tSn+NKXvkRPT8+C7RqGsWD2UJEirxYugp96pmlVTZY4OgYSGVz2qjlChsxtuddGXHAQ7FfzHFTz5CSXFkdnk+UhfApxevPmbXz5y/9AZ+cxmpuXISFxvnURP1T/k4QUJywiODj0qJ1UOoUCbJrQiTqlTCrj1DmNs21NyuNE3RIq3eoFj3WcpfZKPp36K9rUI+SkLDVO3WxW7XHK3AoibglT8iS1zone0ZQ8ScgNU+6cepb3QvNSDmp7aVePUu5UYkomk8o4Jd31DA9P0hRqwKsWvjjzTp6DE0fYPXbgJYtfw8oAruxQ5laQlBKoaLTYK3hT7iYUlBnBjjkxElBwYStCnX3N6/o4qh7ExUVHxyf8hN0oDU4Tq6z5hR/8IoiDM+91W7K4IH8p1W4dO/QnSMspVtiruCT/BjZb5y84y79He45+pZfl9qrZDmDYjtCqHma39hxvyL2Jh3of49fdj5CxMkiSxJHhA4SqPGz8g4386kftTOwZwb+mBE9E4/yGdbREz07Iq3Zrqe2qx1Pp5ZjvKBISCjIbrC1cZF5xVm0AlHlLODTZiu3as9m2QggEguACEREH1L183/cNJuRxNDQsLJ7WH+d9mT9isVPI3XVwuM/zY54wHiYtpZGAkIhwXe5GLs9fc1rHxItxXIe723/Ow72Pk3fyKLKKIRtsrtrAbSvePuvyPZlgRZDNt2xienga23QIlPvxBD0stpvYO3aIY7EONFljVelyVpctf1nF1l5pDkwcIaKH0BWNEqUwaBNC0BprpzvRy6JwA4ai86nNHyFppdkzdgAVqPFXockaSyJNnF+z+ZTtZ6wM+8cPM5Qewat6WV26nIbQ/NUCeSeP5dqzBXvS8SS+SBBd0cnYWfJOft4+RYoUKVLk9Liuy8c+9j66uzv4xS+eoKxs/vrnsFbKcKYPgUtQK0RCmE5+pkipyrQ1RYleQcQoW+AIJ5CQkSRpXjGo4/2qsxGuhtK9uMKhzKhClTQS5hSOsEjbSco8VawLn0eFpwaP4kVZIB9/LDdM3JwkapTNOmM9io9YfpyRzACLQ8vPeA6ypNAUXEqdfzGWm5+JzVDJ2CnGs0MMZXrIuTkcYeMKB0P2cDC+C0XSkCWJtJ0ibU0XxHVFJ2UmZiIuZHTZwHJNBAJHWJiueaIwnxDYro2CTNbJkLBiGIqHRn8Ly0Lr5jl9ZUmhObSSI/E9TOXHUSQZFxe/GmRJcGWhnoCdYiDdzVh2kJybJaCGqfbW873/+jqXX34NNTWF7+Py8kre9Kab+OEPv8NHPnAHgaNfR5vYS4nuQQmWoUz3IUrX4gTqEUJgOjn8niBCCBYHl+MIm9HsIBl7AlVSqfDUsCy87jVzRAoBjz0mk0jA8mUSklToX8bjcHivRN9lDk1N8NRTEpYlaG4uREDYNtTVCUZGCi7RPXsknn9eIpstuHW/8x2JiQm44QaB5zTa3sAA3HuvjOMIli8HSYJEAh59Ls3uq79Conw3XuHDcQX3JPcjlx7hvNCHCUtehofhBz+Q8Xod1q59+fcilwNZrkTXQ8RibbOvGwbEYpDNQrg2xNChYYQrkOTCM5tKTPDgvnt5/v4n6RvqpqlpCZ/4xF/wtrfdQnV17SmP5wl62PiO9Ux2T80Iuzq25fDsN3eSmkjjmA6SJOEv8xMsD5CezKAHdNp+207/ngHMVB7FUClbXMqKq5fji3hPeaxzQVEUNmzYwoYNW/j0p/+K4eFBHn30AR5++H6+9a2/5Stf+Qzbtl3MO9/5Hq677q34fPML0Z2Jjic76dnZS6DMj7/Mj5WxGNw/VKih8ra1RYfw65jXjRis6zqbNm3i0Ucf5YYbbgAKHYFHH32UO++8c8F9tm3bxqOPPsrHP/7x2dcefvhhtm0rFLC57bbbuPLKuRmcV199NbfddttskboiRX4f6VNsOhSLBlfDmBGEfMhUuCoHtTyxvEP0VXYHuwjuNZI8ZmRwKHy4vKDl2KvmeH82suD5bNt2MZqm8cQTj9DcXHDBXpi/nAG5n+f1HQxLg0hApVPDO7LvnnXqXp6/mp94/4supYOwiJCUEtiSxTXZ6xcsOPZifMLPBuvUzlGf8HOJeSU/8/6APqWbsBshKU+TkdK8Ofc2IuLUjoDFTgu3Zz7Mw8av6VW7UIXK1bnrSY5I7GLvrBAMYCgGiqRwbKrjJYnBOXL82PM9JuRxzjcvQkUlK2XoVjp4zHiIt+VuYam9glK3jCGln1qnAQkJkzxxeYpLcleioNCrdLFPf4EKt5KknMQkz6Q8QVpK8c7su2dzhU9mtbWO3xoPMqj0Ue3UISExJo+gY7DJOp819gYuy19NXsrhE34UTv1+HFT60dHnDKhkZHR0hpQB+pODPNTzW7yKQWOw0MGfkAfpGxpAj3rRSj2k9owTXF9GpMrD6tLlp3RuL8T0vgSrW9Zxc/YWclKOOqeRNdYGPJzdbHc8nyCWT9Ae6+TYVCcNwVoaQnUkzGkqfOWsLZ+71CtHju/J3+Ro6gha3I9f9VEdqGQoNMCvPD/jzvSfIiOzT3uBRzz3E3VLqXeaABiRh/il526a7CUscZae9TXuGz/AfZ2/xFAUgh4P8sx/O4dfYGXpMi6o2brgfrIiE6mLzHnNq3rZXrOF7TVn575+LVBkZbai+0K/O07UE+bvLvhzHu9/hhdG92G7DitLl3FZ/YVU+hbOkIzlEvzn4bs4MnUMKPRFHjGe5Kalb5p3H2sCVUQ9EcazE1T7K8nEk/gjQcazk9QGqin1Fqt5FClSpMi58k//9Nc88MDP+fa372blyoWVpUpvLaPZfqbyE+iygUf2kbTiaLKGLMmUeipYGl6LIp2+v+xXg4T1UiZyI0TkUmRJRohC7EHUKCOgndmAkXFSqHLBJRsxSvGrQXJuhqSVYFFwGYuCp18JlrISBeH5JOFUkiQUWSVpxWZfm8qPMZzpI2kl8KkBqn0NlBlV81YtnVyQ1acGWBRazlh+CAnwyj4CepicnWYqP06Nr5GQHiWkRelz2knZSWTXxMWezTqWJWWmn1eYBM87OVRZw3ZMUvY0rhCMZAeQJRmP4sOj+JjMjzCc7aUh0DzvekuMCjaUXMBodpCck8arBqj01uJTA+TsDAendjFpjpGxUphujmHRz7N7H+PQwX384Uf/eE5bt9xyO3ff/QOe/8WXeUP4IHa4hVLVS6kiMS4yeONHyCsGk840rnAYyvaSd7M0BlpYHl5PvX8JWSeNLhuEtOhrKnzlctDTU4h1OPk0wmEYGREMDUnU1wuGhyEQgI4Oib4+yOclVFUgy5DJCPL5QnyAZUmYZsFt3NUl0dsr+OhHF3YJC1FwBff3w5o1zB4/HIajNc/RxV6uspdiYDAyIiHas9ibniOf20p590UsWgTt7YKnn5ZZu/YcOumnoKoKSkogEFhGLHZs9vXpyQwrKgep8WtYKysZ3D/MRPckaXman+/8AU8dfgRFVrj2jTfwz1/+Gtu2XYwsn35Cx87bDOwbZPjQCLZpU95cRsWyCvwlPoTjsvtHe3Ftl0BFAN2nkRxNUt5STnoiTccTnXiCBpHaCFbOYvjgMK7lsuldG+blFb8SVFfXcuutH+DWWz9AJpPhwQfv40c/+i5//Me38xd/8XGuv/5m3vnO97Jp03ln9V7Op02GDg7ji3jxhgsCthEwCAoY7xgnOZYiVPn6qotS5ASvGzEY4JOf/CTvec972Lx5M1u3buVLX/oS6XR6Vrh997vfTW1tLV/84hcB+OM//mMuueQS/vmf/5nrrruOH/3oR7zwwgt8/etfB6C0tJTS0rlLfzVNo6qqimXLzrw8u0iR14osLqYk8LhzP8Q9SEzhkpPE6QrK/k7oViye0rOUuMqs8GshOKqaPKtnuTY/fwmbz+dn69YLeOKJh7n99o8AYGBwW/YDXGheyqDSjyE8rLBXExJhBuV+9ui7GJNGWWavJCZPkZUy1DkNXGheznbzklfsei7PX4MuDJ40HiEmTxFxS3iTeROX5K86477L7dUss1cxLSXQhI4PHz+Vf4Er5j8UIVxU5dzyUI/Trh6lX+2ZzT+GQpG8UrecPfpzXJu7kTK3gutzN3OP94ccVQ/N1KWWWGNtmL2W/doepqU4281LSUlJ4nIMBZlJaQLzFLm9i5xmbsy+k1957uGYehQQhESEN2VvYrW9HgAdHV2cOVct6pZiYc5xLwsEFhZRt4Rj8S6mzRQroi2z+zRJi2nP9tI22UtgUxnpPZMkb0+QP+JhKB2Ds1zh7zgOzz33NH/0R5/kjfkbzrh9zs4zbSYJaD58mo+sneM7h3/IwfEjNITqGEwOczTWTm+ynwtqt3LL8pvmiYpPpB5nh74DaVrDC8RycUbSYzSKGrpDHYzLo1S61ezXXsBFUOKecC1VuTUcVQ/Rqh06azFYCMFTQ4+TslJU+RuRJQVH2JhujpyT59DE0VOKwa9XNlWs4+72X5Cz83hUAyEEw5lRwkZoXvxDSA9y/ZJruG7RVQjEnEHyQjze/zQHJ1ppiSxGVzSEEAykhvlFx4MsjzZT6j0RYRMxwlxafwE/73iArkQviakpvNVhVFnlqsZLzzkLuUiRIkX+p3PPPT/ky1/+Bz772b/n6qvffMrtDMXD6ugWBtLdjOeGMJRKWsKrC5EOaoCgFjkrMUSSJJqDK8k7WeLmBIWMeUFADdMcKhQ7njZjxMwJBIKIVkJYL53TdkALk7ISsz9rio4sK9iuTVg/fQwVgC4buIjZWLXjOMLBUAoCzVh2iCPxPVhuHk02SFkJJnIjLA2vpc5/mspfFArJeVU/Nd7GggsalwErgSKpZO00IT2KIqvU+ZvpSbVhujlkoSJJoEkatrALjmZXwsEGBFkrRd7NIwR4ZC+6aqDLOjkni+PaGKqH/nQnVb56dHn+6iSv6qfMU4WLQ0ANoc58Xw5n+0lYUyAKxed8M7ERv37gV4RKw1RvrMBx7VmH9datF9DcvIz/+slPuOrO7aB60YD1lqBL9TJixYnlhnAVL1GjDEPxMJ4bZtqKs67kfMJ6CQHtzEaTVwNNA4+n4MY9GdsuZPZ6vYX/l5XBk09KxGIF56/PJ8hmC/nAv/2tRC4HqZSE11uIixCiEBnx5JOwdavEZZfNHa9MTsJPfyrz8MMS3d0SiQQ0NBScx4oCmaajuJaKQeE5ZrMg5XzIskSs/Bh13RcBEAoV3MVCcMaoiDPh98MVVwgefngZw8NtjI8Lmtzf8vay+1m7ZIzyQyp2ZDnVG8/je//6XX7z7C/we4Lc/taP8cFPfJTaxfNXc7mOi2u7KLpyosCh7XDwV4cZ3D+EqqvIqkz7E52Md06y8e3radzagObR6NrRTWYqg+sI6jbU0nzxEvb8dB+aR8NfWnDjGgEDWZGZ6J4kPhCnpLFk3jm8kvh8Pt761j/grW/9A/r6uvnxj7/Hj3/8Pe6669u0tCzngx/8Y972tltPu+LdTJtYOQtfZO4Mge7TSE+myafyUBSDX7e8rsTgd7zjHYyPj/P5z3+ekZER1q9fz4MPPjhbJK6vr2/OzM727du56667+Mu//Es++9nP0tLSwn333cfq1atfq0soUuQVocpVCQuZCdmhyj3xZzwpOZS7CiXuq79cu0uxyEiCxpOOrSERFDIH1PyCYjDAJZdcxZe+9AVM00SfqSwgIxeKCJwkdh1S9/F93zcZl0fR0DExqXKruTVzO6vs9ad1nr4UFBQuMa9ku3kJKSmJT/hnOzlng4Q0J3N4RUkLj/U/TSwXJ+opvJ7ITyNLcsHJ+hIwJRMHB425gpIudPJyHksyQfi40LyMeqeRw9p+MmSocxtYa27CR+GLPSHFUVCRkQmJMCGn4HLJK3kSUmxO23nySEjo6FxiXskqey3taisuLovtlgVdxGdig7WFp43H6FW6qHUKsT0DSh8REWWDtYU20YcEcwZAJflytJQHoeXwXhoi9psBIhM+FpWsYs/QIa6pu5KgfuYMvcOH95NMTrNt2+knEhzX4YmBHTw1uJNYLo5f83Fe9SZKPFGOTh6jObIYQ9FZWbKUWC5Bb7KfTRXrWF22Yk47rnB5cuhZzAaTGk8lmlt4dtNmkt7pfpYFFiNmZnLSUhpVzP2aLixJlciSPeO1HWfaipGykrPuHQBFUlEll7wTx3LMs27r1UQIwVB6hIyVpdxXSsQ4+/ibC2vPozPezYGJI7jCRQhBUA1wlfcScgdy9Gp9lC0une2gA2cVc2G7NrvHDhD1hNFnJnEkSaI2UMWxWCft8a45YjDAGxovJawHeWZwFw+MJ1i+ZT23r/4D1pQtXBykSJEiRYoszKFD+/jUpz7IzTffykc+8idn3N6r+mkJr6Y5VKib8FJdnSE9ysbSCxjPjZBzMngUL2WeKgzZS3eyjd50O5abByRUSaXOv5jm0KpZJ2+tr5HJ3AgJcwqfGsARDhk7SVQvo8xYuEDwyZR5qulPd5G04oUibEik7SSapFHhrcEVDj2pY9jCImqcmIBOWgm6kkeBQv/Dq/ooMSrmuaEd4SDB7DjadWdiMCQJ96SlVqqsEtFLsByLmDWB4zqYbh5FUvEqPjIig4SEEIXMZkfYeJVCMTlN1pAkGU3WydppwnqUnJMlY6fQ9bn962kzRvv0IRJmDIFbcC8HllHlqyeen0BCJuukZ/o1Baf2gScPsOnSzWREioQ1RYlRiA6RJIlbb3kfX/j7zzKW2kLpTNfQC6yyBKXpKZLRpfj9i9FmvtcN2UvMnGAo3UNY/90KdueCqsL55wvuvltmeloQChViIHp6oLYWli0rOIY3bxb87GeF93okAo4Dpgnl5TAyIqGqBQE5Eim0K0kFoRkk9u1jjhjsOHDXXTJ790pUVBQK0Nk2HDsmoWmCxkYwUzqh8In3iWGAJAlc4SK7J8YoqRQsW/byheDjXHqp4JJLlnH33fezyHie6yu/S3mFQrS+loHxKf75W9/iO09+nmAoyp/96d9y2y13EC6NzGvHsR36dw8wsHeQfNokWBmgcUsDFUvLmeyeYuTQCKGqELq3cC1umZ/JnikG9w2y9PIWatfVULmikkwsg2qoeMMe4oNx4oPT6F5tTkyF6lFxLGc2T/jVoqFhEZ/+9F/xyU/+JU8//Rjf+97X+NM//TD//M9/xwc/+Mfcdtsd+P3zx02ekIEnYJBL5tC8J55lLplH9+uvWNxFkdeG15UYDHDnnXeeMhbi8ccfn/fazTffzM0333zW7Z8qJ7hIkd8nokLh4ryPez1JJlWHsCOTlwUycIXpn42OeDUpdB/FAtm0nDZN7ZJLruQLX/gLXnjhWbZvX1iQMzH5heenxOUYy+3VhY4mgmPqUR72/Jp8bjkvaCaTskOjo7Hd8rLEeWmVfodlm91aljG5IKxvtDzUui+/I7iydBlXNFzEY/3PMJIZA0BXdC6u28668lVn2Hth6pwGQiLChDw2W3hMIBhXRlltrZ8TmVFl1tHbO0r72EE6nb1MlGbZVr2ZUm8JdW49zsx/x0X1mDRFr9JN1C3hW76vkCdPr9pJSkoSEmHWW5u5Iv9GytwKysz5WX3nQpOzhHdm3ssvPT+lW+2YiQap5vrc22l0FmOFC9EEJwvpKTOFbKlsiGyksjbC3d4Ogk97aLipgeH0CJO52FmJwTt2PIHH42X9+lPnwwI8PvAMPz32czyKl4gRJmWl+UXng5R5S3GFO1uEzVAMqvwVmK5JX3JwXjtTuRjJ0TylVeWk/HEi6bJC4UTNT5/eSTi1lQpRyKReai9nr/Y8Nvas8ztHDhmZBuf0Lp+TsVyLykAUfVwjY2XxaYWOm+0WCugsLXllCr+9kkxmp7j72C85OnWMvJMnbIS5sOY8rll0+RmduwBBPcDta27l0MRR+pOD6K6Gb6+HfHueI9ZREOAJe1h+5VLq1tcSzyfYP36Y8cwEISPEuvJVC8ZECASucOYNpI9/JjkLRFPIksy2mi3U2mX8S+ITfODqD7D2Jf7NFylSpMj/VHK5HHfe+R6WLFnG//7f/3FOwu4rsbTfULzzHLZT+XF6Um2oskZAL0eSJHJOlr50JxG9hApvYYK8xKhgRWQjPaljZKwkkiRT5a1nSWjlrOP1dIT0CMvCa+lIHiFhTiEQeBQfi0IrierlpO0kaWt6XiE5RVIZzvSStOLosoEkyZTo5ayMbsSjnHD6BbUIsqRguRaarKFICobsJWunMOQTYk/GTuLXQiwtXcu+yR3ErUkcUagfkXOzSDIElSjl3iqyTobx7DCSBLYwcYWOLClIyLjY2K6NLCmo0tzrzzs5Dsd2k7ITM8K3TMZOcjSxD10x0BQdV9gIIWYnuPtae4mNTLHlsvNxcWfP6Tg3XXsTX/j7v+D79+ziY7fXIRkz5pPcBFk9CJp/VgiGwvtFlw3i5tQZn82rzaWXCoaHXfbskRkaAlkW1NTAu951It5h8eLCa/F4wUWsKFBZWRCL9+2TyGQK7lzXLfxTlMLPXq/AeVE5kK4uaG2VaGwUeL2FNgcHC+LykSOFtpbXrCNd8hhxKUZERCkvFxi1k0zGDPwda7EsGBsriMAXXPDKLV2VZbj00hZ+8IMYt1z6K+ocEzO0lP93/wv83fcfQ1cVPnfjCm77+D9jLLr8lO20PdpO944eVENF86hMdEwQ74+z9i2rC3nAtjsrBEMhQk336Ux0TbL08sKqRVVXCFUGcW2X9sc66H2+j1jvFI7lkBpPUd5chhEwMDPmrGD8WqAoCpdcciWXXHIl7e1H+cpX/pkvfOEv+Ld/+wfe974Pc/vtd1JScmK1gubRqN9cT+tv2kiOJjGChWvITedZtK1pjqmiyOuP150YXKRIkUKhNpmC0NqjWDiKYJGt8b5MhPPt1+bLpdnWCQqZcdmhwj0uXLlkJJcN1qnPadWqdZSVVfDIIw+cUgweUHoZVPuptetnhWYJiRqnjp1aO4flY3hENT5k+pQsB7U8782GWWmfW6HHNsXke94Eo7KNgYQJ7NBy3JoLseoc23oxsiRzY/N1rC5dQXu8C4FgSbiJZdHmcy68lXMyJMwYErBdvZiHffeTlJL4hI+EHCMsolyZv3Y2g9d2be5q/RnPDj2PJmuoskrbVCf7xw/zobXvYb20hWe0x2lTD1PhVjMiD3JYO4AsZB43fsP90r2YkomKSsgNs9huYcQYokfp5MPpPzmrnOYzscXaxip7Ld1KB1CIofCJQgdjcbiJi+vO57d9TzOenUSVVRLmNCWeKJVGOTVKFYs3r6Ltyb20vHkLHtVD6CyEYIBnn32SzZvPP+0Sqayd48mBnXgVL3XBGqAgNuqyRtd0L6qszi7dFAhMNUdSmSaoz48bUiQVw/XQ2L+S3pZDTISGUR0VUzbxTAe4zLoaOVB4blvMC9ij7eKYeoSwiOLikJSSbLC2sMbacNb31q8GWBJupK9knLapXiZzcSQkck6e5SWL2FZ9dhERruMSH4iTTeTwBA2i9VFk9aXnneXsPH3JAYQQNITqZvO0HdfhrtafsW/8ELX+anxqBZO5GL/sehC/7uOy+gvPqn2v6mFL1Qa2VG2g94V+Dh05TKDcjxEoxEZMjyRpffgY2UiOH47cS9/0ABIFF9Tj/U9z64qbWVk69xlqssbq0hU80v8EZd6SWVF4PDtJSA+xKNSw0KkAsHv3cwBs2vTSikUWKVKkyKuFEIJpK0bcnAQgrJcQ1kpe07zUf/iHz9HT08mDD+78vSnkPZUbwxY2IfVE/rtH8ZK104znRmbFYChkGJd7qsjYKWRJwav4F7yfOTtDwppCQiKil6LPFGKr8tVTYpQTnxGDw3p0VtCVJRlJkudk5QshmMqPYrsWIS2KV/Vjuzbj+RG6pltZGd04u22pp5ISvZzBTA+qpGEoHiRJwqcGsLGZNmM4MxOhdd5GNFlnbcl5jGUHmTLHyNgZhBAz+b4+FFklJEdIWnEs18QRDnmnMJltCRNdMsg5Waq8tfjVuUvMJ3LDJO0EEb101lkd0qPE8uMMZ/op99QwnBlAlhRsYaK4Ci/8dhe+kJ/lm1biUbwEtchse9bOZ/D8+hdcVlbBPc8M8h7pFwQubMbT4C1E6jVcjlC9COEinZTJ7AgbQ/39cz16vfDe9wouvthheFjC64UVKwT+kzS5cBiamwWZDPh8BUdxKFQQhmtqIJUS7NsHk5MSBTO4oKwMPB5pXp7v9LREPl+Ik5AkWLtWEI1KdHVBPg9XXOFy+Rs28LT0Bp5UHmWYAVChbpWfxt9cCwfW0WtKlJQI3vQmwebNr5wYbJsOUb1gSOlpP0zCL/Phv/8uLxwb5I5rN/P52y6lNN9JVslxqnK9qYk0g/sG8Ya9+KKF5+2L+oj1xeje2UvF0nJYIKLFtV1Uz3wprX/fIB1PdmIEPVQur2C0bYzp4Wkc06Z0cSm5RI6adTWEa16bYu8n09Kygi996Zv8yZ98nq997Uv8x3/8C1/96r9yxx0f5c47/5RAoPC32bS1ULC9f/cA2ekcmkej5bJmllxw9saUIr+fFMXgIkVehzylZ/m5J0VUKCzKqyQlwaTs0K6aXGS/Nh2XBlflDXk/Dxhpjir5mWxa2GB72G6d+pxkWeb662/m7ru/z5/92d8s2ME/vmz+xeQlwbBiU+5INLuFWf5qBB2yxW/0NMttHfksXdIOgl8aSaZkhxWOPuv065QtfmmkaLF19JfpuJYlmWUlzSwrmV8s42wQQjCQ6aY72UbOySABFdNRriu/gfbQMWLSJCvMi7nYvJwW+0Q8wbFYJ8+P7KXGXzXrlrVdh7ZYBzuGnufNS67m/ZmP8JDxS/Zou+hS2yl3KgiJCINqHwERZEQeptQpw8BgTBnhPPNCjqmt7NWe52Lzipd1X47jE35W2evmvX5cSF8cbuLAxBGydpalkWY6Et3sGt6DX/Ox9o0X8NPP/jtHdu3hnW+6lRJPFCEEPdP9DKaG8agGy6MtBPQTveW8mefZnU9y7R+8nX1jh1hW0jynwN9x4vkE8XyCkhlX8nEinjB6SkNXDHqTA0TKfHRVH6bf34XlWtT4vPQonTQ5J5y3ESPEytKlPD0YY6O4lPGSQdL6NOlJk5W59Vyy+rLZbUtEKXdkPsZT+m/Zr+1GFRpvtN7CBeZleDn7v3Ov6qc+uITtdTnqQuUMTI+Td3LUh6q4runa2feEEILR1jEG9g2SGk8RrAxSv6GO8pYy8qk8h351hInOCWzTQVEVSheVsPrNq17SErED44f5eecDDKVGAUGlv4I3L76aTZXr6Er00jrVQVOwHp9WGOgW3NYWTw8+x4W1551z1u7woWEUTcYIFD5fJEkiVBVksnuK3+x8jL6SAZZGlqDICkIIOhPd3NNxP0sii2Zd38e5rOFCOhLdtMU68KleTNdClVSuXXQF1f5TL/d94YWdLF7cTGnp6SvXFylSpMhriRCCruRR+tIdWK5JIfpAo34m+uC1EISffvoxvv71L/NXf/W/Wb785cX9mRmTsbZxMrEMmk+jYmmhENRLwZkpovZiJEnCEfNrLsiScsqCc0II+tOd9KSOkXOyhWJuaoDm4EqqfPUA6IqHCm/NvH29ip8So5yRbD+arKNIClknTdpK4leDs6KxKqv4FD8T+RHyThZD8c5k6veSshO4wmHaTqM5GouDy6n3NxMzx5k2Y2iyTt7NMZTtpTfdjiprlBoVLAmupDt1jKSVwHTzxM1Jsk6ack81Yb2EidwoIKFIKik7AUh4dR+lRgUt4TXz3k95Nzdzr+ZONquyTtpOssKzgaZAC53Jw0zlM+TcGPse38v6i9ejaRoN/mY8MznKdnsbuZ/8AGyHC1du4K8fe4CJbBTn6THEH1yJu/oqgqXr8E3tIGHGCOoR5JkICoGg2lt/mqf/6pLLFdy4gUDBEdvcXBB8FyIQgNpawc9/LiPLhXiIaFSQTkts3+7S1ydx5EghtkGSCu1NTxdygM8/f26bJSUCr1cmlRIEg4U4icWLxazb+J3vFGiays3Z21hnbaJTPYaERItYTtPFyxhcJDBNh+pqCL6C0bKTPVMcfaiNwe4hAP7Pjw/weHsPi6pL+M0/vIfzV9aDY4Ip4RqnzuVOjafIp0xKF801kXijXlITaZZctBgj6CE1liJQEUCSJPKpPK7jUr2yas4+whUM7BlA0ZRCcTlReB9O9cVIT2XwlfpZdH4TLZc1z8ZG/D5QV9fA3/3dv/Dxj3+Gr3/9y3z961/mRz/6Ln/+53/H299+G7Iqs3j7Iuo31pGfiYfQfS9tBW6R3y+KYnCRIq8zTARPaRk8QqJmxoHrE+B1JQ5qefpMm0b31S9KJCFxtelnsaNzVM2TlwRNjsYay8B72qAIeM97Psi3v/0Vfv3re7nxxnfO+32900S1Xceg2sdiu2VWqO1WBlDcBhqdEx1jCYkKodCvWMQkl1KxsOvWwaFLPcakPEHQDeFzWxhQbKpddY77uNZVGVQsBhWbRc5rW+wpZo7TkTiEJMlE9TIEhWrW4dEAd9gfJaiH50R0HKd3egDTMefEJqiyQkgPcHiylTcvuZoat473Zf+IereRaSXOcms1TxmP4nV9JOUEqlBISylKnXKm5Emm5QQKMv1K76ty7aqssqlyHZsqT4jF55kb0WWVA+NHSNcIoouriD/ew/Ufu4a8Y/LTY79g1/BuMnYOWYJqfxXvWHYDK0uXkTLT/PV3/4pUMslQdZL/2P8dGkN13LLibTSG5nb+g1oAn+ohbWUIaCfE5LSVIeqJcFXjpTw+/iSP1fyKdChO0IywyN/EcLSHbzlf4c70p6l0q4HCAPHaxVcxlp2gY7gbhkL4RIBaT4Sbl791nvBY5lZwY+6d3Jgr/F04rsNEbgpLjs8Tp0/HkuByDNkgYvSwvGQxPjVAnX8RVScNdPp3D3DkwaO4tovu1xk9OspE5ySr37SSya5Jhg+PEK4Jo3s1rJzFWNsYrbrChrevPydxYDA1zA+O3k3STFEfrEFCYjA1zA9b76HUW8JUNkbeyuPV5orMQS1AIj9NxsoSNs7tb9HKWsjai6IdJAnTMRmKj1JdXznr0JckibpALYPJIXqn+1kanRujUe2v5MPr3seu4b10JroJ6kE2VKxhTdmK096H3bt3/s5dwcIVTHROMNE9iWsLog0RKpZWoOqvfo58kSJFXp9M5cfoSbWjyzoBfaaGgJulL91BxCil3FP9qp5PIhHn4x//ABdccCl33PGxl9VWejLN/nsPEuuLcbymb/ezvax500rKW+ZHA52JkBYFJGzXno0wcoSDKxwi+rm1N5Ufp2P6CPKcPl6CtsQB/Fpwjtv1xUiSxJLQ8SJ3Beew6eRQZJVST9Wc7yZFUsm7udkohcn8GMcSh5AliWpf4+xxU/Y0XtVHqacQa9QxfYThZB9eJYBPD2C5JsOZPvrcDgzZS5lRheXmEcLFck2mzHGqvQ3knRymk0dGRpIKmcpexU/UKJ8TVXGc40KuK9xZQVgIgeWaBNQQsiTTHFpFpbeWwUwv+488z3j/GHf++adYHd1ChefEmMDevQuRnEZdvoptoSCOEOwKN3GpZZN0tqJXXYAHWBFZT1viANNmDBDosodFgWVUeucXGXu1yWTg4Ycldu2SyOclamoEV17psnbtqfd55hmJo0dlVBUSCYnxcYHPJ/PGNzqsXSt44QWZt7xFMDYmGB+XUJSCy7eqquAgPpnGRlizRrBzZyEz2OeDqalCVvHFF4uZrOFCvZfl9mqW2ydN1ijQ1AQin8d+4TkyB/eBbaGuXIu65Tzk4EtbWZibznHol4dJT6YR/sL7+JG2Hv7o/AY++471hBpKkfJxlHQ/dnQFVumpb5ZqFIrCOaaDapyQxuy8g6IpROsjLL28hfbftjPZXYgNUXSF+k311KyZ+1noWA65ZH42W1eSJCK1EUJVIcbax1l59XKWXHSWFa5fA0pLy/nMZ/4Xt912B3//93/BJz95B9/5zv/jb//2nznvvAvRPBqap1j4+L8TRTG4SJHXGSnJJS47hMRcgTUoZIZkhynZeU3EYCiIp0sdnaXnmNfb0rKC7dsv4Xvf+/qCYrCOzvW5m/m+7xu0qofQhUFeylPiVqLxZtwXFY+zABWJU92FaSnBD3zf4pC2D5M8ilCpcDeQlW4CMXfaWsxc2e8DY9khLGFRMjPAkJAIamFi5jgTuWFCemTB/VRZRcC8JU6Wa+NR5jqxDeFBE9qs4C4hoQgVJBDS8TxoQUpK0q/08BvPLxmVh9libeM880J0XrmZYoHAwkKdKW73YoJ6gPet+gMGU8PE8wm237mcv/zkR+nv7GLYH+eJgWeo8lXSEKzDEQ490/38sO1ePr35Izzc+ziP/vyXlDZVsXXDNizXpmu6hx+13ccnNn5otjgYQED3s7VqI7/qfhhd1mYzgwdSQ2yt3Mh1i66CJWm6jF3U5tZSGiwhoPlxbZdW7TAv6Du5LnfjbHuVvnI+su52Dk4cYffoftpiHWTtHD859nOOxbp446LLFyyWdmD8MA/1PMZQegRFUlhVuow3LX4D5b4zO01lSaEh0Eydf9HMoFWb47qxshZdO7qRFZlIbaTwYhnEBxO0PdqOlbMIlPlnM9M0j0awMshE12TBRVxx9naPvWMHmchNsSK6dPb92BRqoG2inWcefhZ1TCXuJugY6qKmrgp/SUGAnzanqfJX4tfO3cFVvqSMjic7EeWBWTeGmTGRVAkrYs1zWiuSjIuYs+T2ZMq8pVy7+MqzPn4mk+HIkQPccsvt53zuZ4twBW2PHKP7uV4c0wEJep6TqF5Vxdq3rJ4zyClSpEiRUzGRHy0U/poTfeAja6eZyI286mLw5z73CZLJBP/6r9+cUyj8pdDxZBeTPVMEGv3E9EkMxyDXn6P14WNEG6Ln/DlZ7qmm3FPFWG4IVdKQJAnLNSkxKqjynltB3bHcII6wCemF7/RCHy9CzBxnPDtyWjEYwK8GWV+6ncncKDknA0j0pNpwX5Sfm3FShLQTERNj2QEcYRE6SbwOadGZvuUIDYFmLNdkJNuHoXjxqoX9DMWD6eaYzI9R64uiKdrs+QoEGSvFpDyKoXgIaRHSTpKoUl5wGDsZupJH0WSNhsDc1XJlRhVhLULcnCSgBpEkuVBkTjaonnFIS5JESI8S0qM8+Nz9BAJB3n3th+etLnQnxpE8he/3xkgJNcEwO/u7ubSmCTcRn92uxKhgS9klxM1JHGET1CLz8pdfC1y3ULxtxw6JaFTg8Qja2qC/X+H973dYs2b+PqkUPPCAhKoKrrgCMplCXMTwsMAwJJLJQgG4YLAQOVFRURB0EwkYGSkc8+Q/M1ku5BH7/RL79smMj0MkInjzmwUXXnjmyAdhWeTu+i72zqcL4cSygr1/D+qBvXhu/9BLEoTH2sdJjqVozxzk3+/5ArIkc815N3Ltpo3kMocpyQwjZA2r8nyyze+C08R9RBsiRGrDxHpjROojKJqCmTbJJrK0XLwEzaPRuLmekoYok92TuI5LuDpEtCGKrMz9PFJ0hUB5gMnuSXzRE31V23QwAgaRutc+GuJsqKtr5D/+4/u8730f5q//+k+48cbLefOb38bnP/+P1Nb+/rjli7x8iiODIkVeZ/iFTFAoTEsO4ZNcr2lJ4EEiIl5eR/m14t3v/iAf+tAttLYeWnAJ4Fp7Ax9L/xl7tF2MyaNUutWsNc/jLq+XPsVisaOhIJFHMKbYXJL3ETqFK/hXnnt4XnuWBqeJgAiSI0uXuouUaEGSt9JyUkzEoGzT6GjUOa/9x2Xezc0vXCVJSMiY7qnSsGBFSQthPcRwZpRqXyWSJJEy05iOycbKwmy5hcWUPEGpU05QhIjJU5S7lXQrHXiFH1dykd2CO1hC5qh6kJyUo9ZpoENto1U7SK/SzR9k37egcHsm+pUentIfo109SlCEqHJqicmT9Cs9+N0A261LuDB/OQZzO/qSJFHlryBr51h38XlUVlXz9a//G2XvXIlP8RIxCp1MVVJpCjXQkehi/9ghnm7bwdj+Xi69/YZCoRBFoynYQE+ij85ENytKls45zjWLLidtZ9kzup/RzHghj7ZyAzcvewuSJDHtiVNmlNConugkych4XC99cve86w3ofqKeCN3TfViOTbmvDNMxebj3cSayE3xo3XvnRCG0x7r47pEfk7YyVPrKsV2bpwefYzIX4yPr34/3FB1dC4tW9RD9ag+6MFhuraZOmp9rmxpPkY1nCVbO7ZT7S33EBxMgmBcHoRoq6akMVtZe8NinYioXR5sZNB9HAkqPRIn1xWiuWkRjoI7WZDvp1jR1zbXkvHks1+Lium1nVUDuxdRtrGO0fZzO1m4SShLTNPELH0u3NFO9pJLWeDtBPTB7TkPpUSp8ZTQEz20wfyqef34Htm2zefPvzhk81TtFz65ePEHPbGESK2cxdGiYssWlNGwuduCLFClyZhzXXnCVkSTJOOLcPu9fLg88cB933/0D/u3fvk1d3akz2c+GfDLPWMc4vZWdtIUOkZUyyChULKpmbfcmYgNxypecW4yPKmusim4mmullLDuEQFBmVFHrb5zN+j3r83NyswXRjlP4TpKwhHlWbWiyPhspcZyO5BHi5iS6bJB3cmiyRlOgZXZCOOtkUF4UvXT8uMf7lqZb+A42Tr4mAQoqQri4M++LiF6KKqvEzElMO0dIj1LlqaMneYygGp69J6ocwrUSDGZ6qPU1oZz0va4rHlZGN9ExXThvIVwCaohFwWVEjflu6/vvv5crr7x2wZg5ua4ee9/uWTPE9obFPNvXhaiqIxYSxGN7UWSVMk8lUb2cMk/VvDZeSzo7Yd8+ifp6MRuxEI3CsWOCxx+XWb3a5eQFSQMDcO+9Es88I1FTA4YhqKgo5AbrOgwMSKxeLQBBdzf09RUKwKlq4d+2bYKFFjgFg3DLLYI3vtEhk4GSEmaL1Z0J58hB7Od2INfUIwVmYsnMPPbh/djPP8fgVYvYoT9Bt9pBiVvGVvMCNlpbTzuWMNMmv9hzF7/a+2O2rb6U3pFOZFlhUtnIvuwmgpsbEYoH11fDghd0EoqqsOq6lRz65WHiQwmEK1B1hdq1NSy+8EQmbrAiQLDi9BMEkiTRuKWeeH+M+EAcb9SLnXfIxDJUr6oi2hA97f6/b2zdup1f/epp7rnnLr7whb/k8ss38Ld/+y+8/e23vab58UVeOV57daNIkSLnhIHEBaaHuz0pRmWbElchLbkMyTabLA+Nr3GUwUvlmmveQnl5Jd/73jf4whe+vOA2dU4jdU7jnNfeljP5gXeadsUCBDISKy2Da/MLf2EnpDh7tecpdysJzLiAPXhpcBrpUp7CcFdxVAEdCRNBhatyfT6A9hLdwVPSJAe1vUzLcUrdctZaG2ePe64E1QijYmCOw9cVLgL3lBl0ALWBat60+Cp+1fUwR2PtSBQKYW2v2cKWqo08pz3DI8avGVdGUIWOJnQm5TGgIGiOyyME3DAgmJQn0IWGI7lsMy+i0Sksd0pIMXbqT3G+eSHNzvzCaaejV+ni6/4vMywPEXEjHFNa6fAeJeqWss7axLgyxg/V/2REHuIPsu+fM0gdSo3wo7Z76Iz3knfyVF22nLvv/gFvvfxOjPDcCreqrICAhJmi7Yk9yLLMiks2zf7eUHRs1yZr5+ado1f1csvym7i8/kImc1OE9CANwbrZ5xAWEWycWTf1cUwpR1QsnFX21OBOps0kyyLNs+0E9QCHJ9tom+pgddmJ3OdnhnYxnU+yLHpi24AW4Fisk8OTbWyuXD+v/SxZfuD7Ji9oz2FJhYFkxBPlhuw7uMicW1VZ0RRkRca1HDgpUsCxHHSvhqLKZOM5tKoTny/ZeBZP0FgwazGfNpnomCCfzuMNeylvLpt1XFX5yrGFPXcJaAyMHg1/uZ9IVYRrxRWE3CBHUm30DPXRsmIxb66/mu01Wxa8l2ciUOYnvT3LYbMNdURF8cn01gzQVzPM5ZUXMZaboDXWjlf1knNyBDQ/1y26ajaz+OVy770/ZPHi5peddXk6pnpj2HmbSO2Jwbrm0VBUhbFjY0UxuEiRImdFxChhINON49qzIl0h+sAlor96mee5XI6/+qtPc+WV13LTTbe87PZcx6VTbuOIZz+mlMcjvDg4DGq9SLLM+c7ZFVN9MZqs0xhooTHQ8rLOL6RFGc0OvqiPV3D1BtSX1m9sDLRgKB6GM31knDQV3hrqfIso9ZzItw9pUSbzo/OOKwE+tdCPMmQPhuzBdHLokkHKTjBtxsk5aWxhk7Cm8KshZFkmoIaxHYuQt4nNZRczmR/FxkaT54q1x8Vp083jfdEkb1CLsL5kG2k7iStc/FpwnhkCoKenkyNHDvDxj39mwevXtm7D3rUT51grclU155dVcPfhvXQEBfF6GzfTjUAwkO5iUXA5TYGlv1ci19iYRD4/P2s3GIQ9eyQefliioUHQ0gJdXfDtb8t0dhYE3r4+idFRiRUrBE1NAscpuHxXrhQ4jsTTT0v4fAVxOZ0uZAYnkyDEqfXTkpLCv3PB7u5C2PasEAwg6QaS4WWk7VH+44ZpJuUJQiLMgNLLQXUv47mbeWP+LQu2l81m+V9f/VN+s/eXvOvyD/C2y9/D5755J9PpOPm0SeWKepzQkgX3PRXh6hDnvWcLk91TmFkTX9SLJMv07xlAuIJwTZjSppKzKppcubyCNdevpntnD+nJDIoms3h7E80XL5nnJD5XXNtlrGOcya5JhCsoaSqhclkFiva7iwKTZZm3ve1W3vCGN/P5z3+ST3ziAzzwwH380z/9B+Xlp66TUeT1QVEMLlLkdcilpp+cJHhGz9KrWHiExDbTy1vzwbMumPb7hq7r3HLL+/nGN/4vn/rUX1JaenZZay2OzsfTUQ6reZKSoMJVWGHrp8wpzkqZgkDnzhXovMKHhzZuymVIS1WMyQ7lQmG95ZnNZj5X2tWjfM/7DYaUARwsMlKGBmcRH0v9OQ1u0yn3E7aNvXsX1t4XIJVCWboc7fwLqCqpYzQ7QMwcx6v4EQiydoboGTL8JEnisvoLaYkupnWqA8u1aAjWsbykmYPGXr7v+yYuDmVuBTkpx5gyTJVTyyK7mWXyShxcdHQCbpCoKOVx/SEM4aHRLQjBjnDIpE065E5+NPZjbpLeybJo87ziH6fit/pDjMhDrJjJGRtQ+lGEgimZaOiUO5XEpCl26c9wkXk5DU5hpt5yLe5q/RlHp9pZFKrHo3gIXxXg0D1PcfDnO1j89i1UeMtmO/ZJM4Wh6CwJNdL3xGGqNi3GGzwhGE/mYgT1IDX+hZ0hkiRRE6iiJjD/9+usjTxu/IYepYs6pwEZmSGln4AIstGaL2AKIehJ9BHRw3MGHl7VgyMcxjITc7bvmx4gpAfnbKsrGkIIJrNTC57vs8YTPKs/RYPThF8ECk53pZ+fe39Cs72MaveE6zVYGSTaEGWsfZxofQRFVXAsh+RYito11UQao7T9po34YAIjYGBmTOy8zbIrWjCCcwd48cEEB39xiOnhJFAYVZQ0Rll7wxr8JT42Vq7lmaFdtMU6qZkpuJYYTFDjVFDuKS0sw3MF24ObWa+uIhPP8MZVbyASeenL6yazUzyefAZ3o6DMX3Bm+EWQtlgHnYkePrzu/Tw/upfB1DBl3lI2V66nJfrK5LplMmnuv/9ePvzhT815fmkrw87hFzgwfgSAteUrOa9q05wih+eCEAsv15QkcN1Xrnp3kSJF/ntT7qmhzDPIRG4YVS6UzjVdkzKjcsHiZb8rvvGNLzMyMsgPf3j/KyLQ6SGd1qZDGK1+jIAHJNCAwGiIyfIxdjc9SwMvz338cqjy1TGS7f//7J13mGRVgb7fc1Pl6gqd43RPzpkhDBkkSBYVFEUFw5pdXeMG80931TWsq7uiqKiIoIKgkpQMAwyTQ8/0dM6xqitX3XB+f9ymh2ZmYIYZ1N3tl4eHh+qqe2/dDnXOd77zfUyURvHPGOOVU+F9+fvuSIeMOYlEEtTCqIqGEIIafyM1/sZDosKep8bfwHChj0RpDL8WxJEOk6VxPKqXnJUlVUoS0suo889hf2onw/k+MlZqyowg8asBcmaGfroI6WU40savB5lfthRVUfGqPjShYzoljBdEk5lOEUPxHiISP48QgqD+0hEC9977O7xeL+ecc+Fhv67W1uN9+7vI/v7XpNt3M9/nfhbe3xzk7PJ6tClHdM7K0J3ZT7m36mXjOP6SBAIghMQ0mc7mnZiAzZsFtg233abg8cCKFZJsFkZHBWvWSEolQSLhvtf2drdAbmhIsH69ZPt2QUeHIJcT5POuCFxeDkuXSjIZQUeHW053ohCaihCHjkGkbdHq3U9SMVhkLZ02UgwpA/zZcy8nmacSd2bOBXO5HG996+Vs2fIM//CmLzLPu4x8Mk/ACDE+Poo35KVh1Svb0aV5NKoWVbolwo930vF4B6Wca6RQdZW6lXUsvXjxywqvQgiqF1chNMFI6wiKplIxr+K4s3Ydy2H3vXvpfa4P6TiAoPvZXmpX1LD80mWvejdEOFzGN7/5Qy688HI+/vH3cvbZq/nqV/+D1772qlf1vLO8usyKwbPM8j8QHcGlxRAbS37GFJugVKh21MNu6zsR2EhMJB7Eq3YOgBtu+AA//OF3+cY3vsSXvvTNo35dRKqcZh6dgy/mlFPuVDKiDBG0Dy61jyujlDlRllq1xOXx54SZmNzh/QXD6iAe6aVT6yUjMhzQ9tOtdvKl9L+zwFpyyOuklBTvvB3zT/eBUEDXsVp3Y217Du8738ey8vV0Z9qYKI4gEDQGWmgKLZi5de8wCCFoCNXR8IJt7xLJI8aDmKLEPMt184ZkmIAMMKj2c0bpnJlFEFOv6VIPMKwOAmA5FttHdzOQHSJdNs6z/TsYGstxfuOZXDb3wpedwJmY7Nf3EHcqEAhMSkwqCcIyQk5kSYkkEaJEZJRhMciA2jctBrclOmhPdtISbsKruZOJ2vJallx2Crt+8wRzz1lFqzhAuSdK0S6RMtOcVnsSE/sHmegb4YzrzqQt2UHEU0bOzFOwC7ym6WyqA5Uv8909lHq7iWtyb+NO7210am3uVlGnktcWrmK+tfiQ5wshiPtitCe7ZjxuOe5WyxcLgpX+cgayQzMesx3XMRQyDu8Y2qw/hV/6CUz9PAsEdXYDrdpu9mm7qSkd/FkQimDRaxZi5k2SfUk3LFsI4s1xFp63AH/Uj+7R6NncSz5ZIBDz07CugYbVMwfdjuWw975WUoNpoo0RFFXBNm3GOsZpe6iNlVetoNwX5+3L3sQ97ffRMdmNRNISbyJiB0nsSSCnhEshBJpPp2pBBUH/8f1OdqZ6SBQmZ5TBKUKh0ldOW6KDaxdexVXzLzmucxyJe++9i2w2w+te96bpx/JWnpt3/YJto7vwae7v7u7xVvaM7+OGZW9+RY7kaL2bdVfMFPEE3d8Hq2RhlWwqX0Ex0iyzzPJ/E10xWBZZx0Cum5HCAABzvDXU+pswjiDcnWhGRob49re/ytvf/l7mzl3w8i84CrJKlvbzdtE8spRwWwQ7YKEUVKTh0HNRG4ORRZA7Iac6KkpOkbHCIDkri64YlHurWR5bT3fmwMExXnAeTcF5M0TUw5EojnEgtZu0mZwSg0M0hxbPEO+PNB4L6mUsi6yjK7OfRHGcydIENhaq0OlI76Uv20lLaBENwbnY0mLL+BM40saj+ghqYSKe+JRLOE/cU0WZHqXSVzct5Ib0CHFvJUO5PgKE0BWdgp3HdErMCS58RdFPz/P44w+xYcPp+P1HXkTNz6lmzxvXkR+pQqgaxiMP05lOsN5ME/G4NlefGiBRGiVRHP+bEoMXLpQ0Nrri7Zw5EkWBZ591c39POkkyb56bEfz4466wu2iRRNNg8WJX9E2nIZGAXbtg1SpJfb3kzjsFjgPNzRLHgVRK4PNJliyRdHTA+Lhg3rwTt4Cszl8EXj/O+BhK3N1ZIDNpTFlk71qHCrt6xtyy0qmmTdtLj9o1QwzO5/O8/e2vY9u2zdx66x9YvnANBx5pZ7Rt1BWDM6OsvHL5cUcxJPsmaX+8A01XiTfHEUJQzBbp3dJHrDFK/eqXFpsdy2HPvXvp3dKHbbq9E31b+qhfU8/SixYflbv4cIweGKPvuT4CMf/0GM/MmwzsGKByXgV1K/8yC3UXXngZ69efwic+8T7e+c5ruPLKa/h//+87hMP/M/KQZ5nJrBg8yyz/g4lKlaj96q0EFnB41MjxtF4gJxyabIOzSj4W2a/OZCAeL+eDH/wkX/3qP/OOd5y4CcALMTA4t3gRP/f/kHZ1PxEZIyvSFESeSwqvIy5PzBbILrWdXq0Lr+Nll7EdBYW4U45P+OjROviB/9t8Ov1lonLmfiunuxPz8YcRsXKUmOtelraNvW8P5qMPEXrjm1kWXYfpuKvVunJ0hW158rRprRRFgXq7kWqnlhIlBtU+Is7MgZNfBjAxGVVGWPSi4wgEJ5mn8UvtJ2RFhrFMgv7sIErEplJWsY715HWTP/U8ypL4wpd1V6qoeKSXpJIAQEFFkzpFUQAE6tTHlIXpZvDKg7m1GTOL5dh41Jn3YPXlZ9D5yE4Gf72Dy/7lRjomu4l4y7hgzjmcUX8K173xEpYsWc6nrvkUj/Vvoi8zQFWgglNr17OxbsNR3c/DsdbcwCJrKe3qfobzo1QV6pjnbUHoh5+AnVKzjv2JdoZzo1T44piOSVeql/pgLUtiM6M2NtSsZdf4Xvozg1T5KzEdk550H3WhGpbGDx/LURAFtBd9zD+/nGMK85Dnh6tCnPSWdYy2j1FIFfGVeWfEOzSsrqduRS1mwUKfal9+MZODKSYHJgnXhKa3w6m6SrA8wFj7OIXJAr6IjznhBt636gZG8+MAKEPwx98+gFk0CcYDKKpCKVciM5qhcW39cTseNOE6pF4YTQFgT/2/eoylRLZpk+xL4lgO4Zrw9MD8cNxxxy/YsGEjjY0Hs+e2juxi+9huml+wkFG0i+wY3cPW0V2cVnvs25XjLXHqV9fTu6WPzFgWoQgc26FyYcUhjdezzDLLLC+FoXqYE1rAnNCJH4cdDf/2b59D13U+/OFPn7BjBgmiNivsvGETC59Zga8viBktMrZqkMGl3VRl/nJ/J3NWht2JzW4mLgCSnkw7iyIrWRpZiyVNBGLaufpS5K0se5JbyFlpgnoZAkHGSrM3uRWP6qXMePl9/RFPOSuNOB3pVvandhIxYuiKgZSSrJWmM91KzFNBha+WiBHHUD14FO90jEjIiEAJ5gQXEPXMHEcLIVhUthJVaIwVhijaeQzFQ0toMY3BY9vO/0JM0+Tppx/ngx/8xEs+ryfTRtpJEa11Y7bKaytIDEwwaY4T0EMv6Gf429tZ6fPBddc5/OIXCj09MD7uFr0tWQLNU0OKYBCiUUlfn8CcGtrF427+b38/9PcLrrhCctllkptuUlDVg9EQZWUQDEoSCTdPWNchHD5YnGuakEy61xF8hWvy6vyF6OddgPmn+7BHhpBCIDQN7eRTGV6/G1uYz7d1A2BhoaBQKlo8O7KNol2k0hPncx/6MM8++yQ///ndbNhwGgArLl9GMVPkqewD7L97FxXzjn8ON945TilbouwF+eGaoSFth6HW4RlicDFdxLYdfGHvdDnxSNsovc/14X+BaFvMFOl9rpeKuXGql7yyXOrxrgls25kx3tR9bv/G6IGxYxaDHcvBzJtoXu2YYybi8Qp+8IPb+O1vb+VTn/og27c/x49+dDsLFhxqcprlb5tZMXiWWWY5LBLJ7d40jxl5glLglQpb9Dxb9TwXFYOsn4pPONFO4RtueD8//vH3+fKXP8MPf3j7CT3285xSOgNN6jzqeZABZYSI3cBZpY2c8aIc1ePBFhY2NuPqKDb2tOCqS4OgDDGg9LNT38oZpXNnvq67E7I5RP3BbGShqijROPau7cjXX4tQlKMWgcGNq7jV9xP61W5sbEKyjI3Fs7m0cDVRJ0af2kM5rhtWIulWO+hSD3Cn9zbGlBFOKZ1BtXNwkHFa8Wy61HaeM56mVWkjG01TLitZ3L8OfymE3wfD+VHakh0vKwYrKJxUOo07fD8nJ7L4ZYBap54t+tNEnThxpwITk06tnXq7kQXmwYFGha8cn+YlXcoQ9hx0x6acLOe+83Xc9aUf8v6Bj3L9+Z9AEyqqovLQQ/exadNj3HLLXaysXMaKiqUU7RKGqh91rMWRkFLSOdbHPR2PMpgewkES80Y4v+lMTq875RBXzknVa5goJHi490n2J9vRhEpzWRNvWHD5Ic7gVRXLuGreJTzQ8wgdk12oisrcyByunn8ZZZ7Db6Ncaq7k997fUE0dKu5ALyWSePDSZB8UJh3pkCxOois6IV+Q2mWHToilI0GAoip4Akf+2bNNG9tyULWZA0tVUynlTWzzYKu5EIJKvzvY7hjsJBj3U8qZFNJFkBJFVQjE/Sdke/C8SDNV/gp60v00hxsp2EW6JntoTbbREp7D4/3PcGrtegIvcuTalk0pU0L36dOi+HjXBK337yM1lMKxJb6Ij5ZT59B0UuMh1zo8PMijjz7IV7/63RmPH0h2uIsb2sFBvUf1oAiFtkTHKxKDFVVhyUWLiDfHGG0bw7Fs4s1xoguj7Ejvob23E1WoNIbqyVo52pNdeFSDZeWLWVG+BFV5dbcYzjLLLLMcDXv27ODWW2/ms5/9GtHoMQaUvgQKChcXruS/677JriueJerEKYoCo8oQTfZczi+8OrtDDkdneh+J4hgRTxxFqEgpmTQnODC5m2hF+TGN8UYLg2SsFDGjYvozKKxHSZRGGcr3HZUYDO5ncqI0iqF4ps8vhCCghUiURpkojlLhrUFXjKlF1IMygu2YqIp6RPHao/pYGllL3s66GcFq4GV3tL0cO3duJZvNcOqpZx3xOaZTYrzoRqs9f2+q62sY6x/DckyKdn7KqZxDVwwiR3mv/pLMmQMf/ajDgQPw3HOC++6DFSvc/N/nCYUgHHajIKJRia6D1+vGRK1dK7nqKonX64rJoRBommTXLkE26wq9pino7ZWcdZbrNpYSnnlG8Kc/CUZHBYYhWbNGcvHF8pD84pdDKAqeS69CW7wMe/9epGWjtcxFXbyMZcrPeFD5PWEnggcPDg7dWgeewQh3bf0TY9kJLNNi+3fvZ3R3Lz/5yW855ZQzZhzfE/RQXVfDxMToEaNQjgXHdqbntmbBZKInQXY8SzFdopQ3aT65CW+Zj/ZH2xlpG0M6DuGaMuZubKa8Jc5YxzjOi0XboE5qfJLHuh+hZlUli61lh0RgHA9Higk70nMHdgzS9UwP+UQO3W9Qv7qOOSc1HpMoLITgqqvexKpV67nxxjdw8cWn8flPfZOzT76AQHmAUGXwhIzfZ3l1mRWDZ5nl/xgFHCYUB78URKRKHoedepEexcRAsMTyMNfW6VJNNusFahyVMqmSEQ5JYdOlWbSrFsstg7Wml9cVQkfM530leL1ePvWpL/D+91/P008/zoYNG0/YsZ9HIFhpnsKEWE7SSJMXKts0jTLHYo11YuI26u0myu1K9ht70XEHxxJJVskQdWL48JFWJg+9Nk1zF8hf1OAgbQuh69OPZUpZJktpop7wS24pT4sUP/P9kEG1nznWXHR0xpUx7vPeTaVTzamls/iZ/yaGlUHKnUp2alvZo+8kJMMURIG7vb9mm76Zd2U/RJ3jFlD58fP23Hs5pXQG/9n334ymJ1hmrSRYPLhFSOC6MI+GM0vn0at1sVV/FgsTC4sGuwm/DNKltqMgqLMbuTb3dvwcfK/RshDNDbVs726lwqrAp3mZKCRQFYW3XPUuUk/28dnPfpyzzroA1WvgOA5f/vI/smHDxul8OSHEDEHulTKaG+P2/Xfz2wP3kChOUu6NMbesmVQxw23778Kn+VlfvWrGa1RF5bUtr2FDzVr6M0MYqs7csmYM9dDJlBCCcxpPZ23VSvoygxiKzpyyhhc4Wg7l9NI57NF3sE/bQ0iGMDGxhMnpxXOZOxULsnt8H/d3P0RfegBNaKysWMqFzecS80YASA2n6Xm2h7H2cVRDpWZZDU3rGtB9hz9vqDKIr8xHdiJLqPLgjCE7kSNcE8YX9R32dY4j0f0GVYuqyCVy2JaD4dMpZksv2wR9NASNAFfNv4Rftv6WneN76JzsJW1miHsi+DQvv9z3W/YnDvCOZW/Gp3mRUtK/feDgYNmrU7eyluol1ez83W5yE7lp93N2LEvrA/vxRXxULZwZMfLLX/4YXde55JLXzXjcUA0cDv39cHAOcbofC6qmUrusZlrQL1hFbtn7KzYPbUPiULJNetP9bpt8uBEHhycHn+XchjO4av5rj3tBZJZZZpnleJBS8tnPfpzm5nlcf/27T/jx35F7L2PKCPd77mZQ7UNFY6G1lE+lv0iYl86nPVGU7CLjxWF8WgBlqhRNCEFYjzBZSpAsjVPufXn3oJSS8eIIbandJItjOI5NSC/Dq7mLqJrQyZnpY7q254vjsmYaU5ZQhYZfDcDUmM6nBSj3VtOf60QVGrpiYDkmGTNFla+OoBZ+0fEcpHRQhIoQAr8WxM/xR7EBPPHEw/j9AVasWDN9rkMyk6dG9PIF1tO6xkaefvgJTKdExkphOkUUodIUXPAXi4jIW7BjVNCVAo8KS+KS+ZEjD3c8Hli6FCIRydatComEJD5VfSIljI0JTjlFIqWbEQxuFERVleR1r3MwDBgZgcpK2L5dEIlIAgE3EiKRkEgpWb5cct11Drruis633KIgpaS8XJLPw733uud917scjnEzFUJR0BYsQlswc7/hhYXLGFL6adV34+CaBaLZCnLbvCTzKVrCc3jgW79keGc36z58MaLl8HEggUCIQqGAbdto2rHLW2beJJ8q4AkYlNWUoWgK+ck8Y+3j5JI5dI8OSKyCxZZfbUf1qGTHsgTiARRVZax9lMxomrXXrMGxZ87fLCx26lsZ08dJq+NM+Icodyp4ff4trDWPfjdirClK19PdlLIljClThlkwkY6kYu7RO6L7tvWz65497vwn5KGYKdJ6XyvFTJElF7x4P+jL09Iyn1/86I984N1v52P/9E4uXX0NV218K3Urall0/sJXPct4luNjVgyeZZb/IzhIHtNzPOLJMyFsPAgWmQYJxWa/ZuLgDpb+bOS4uBggKBXywqHR0XCQ7FaLjKgOUUdBQeKVgkeMHGVS4dLiK2s5zog0W/Vn6VY78Ek/y61VzLcWc8UVb+QHP/g2n/vcx7nnnsdRjnXU8TJIJL/1pvmzkSMoVQJSoV0z6VYnkfky1lnH51YACMoQFxUvZ7uxmX7RC0JSEiZe6aXZmktaSVFuH5pNqy5YjBKL4fT3otS7TkNZyEM6hfaaiynaJe7t+hNPD20ha+YIG0FOq93AeU1nHFYY3KPtZEDtZa61cDoyoNypIC1SPGU8ykcz/8SkkuBR40/s0LdwQNtHmROlwqmgT+3GL/3s0rbxkOd+rsvfMH1cDY1l1iqucq7h1t7fYJT5mDKfkiqlMVSD5rKmQ67ncARkkHdk38c+bTcDah9e6WOxtYykSDCkDuCXARZZSwnIIBOFBLvTe3ky8hCD0S5Ka4qYzVmGO7PE++upD9VyTuPprK9axec+9zXOPXcN3//+N/jwhz/N7353O7t3b+fOOx8+oavVBavIT/bcxlMDm8maear8FRSsIvsSB1hduRzTMnli4GnWVa087HnLfXGklDw19gy3Z36F4dM4JbSBjd4zph29z1PmCR/RCfxiKp1q3pP9CE8aj7BH24lf+llrnsxJpdNQUTmQ7ODHu24lbWao8JVj2iYP9j7KcH6U9658B6XxElt/tY3UcBpfmZdSrjSVB5xi5VXLD3H/guvQaD6liX0P7GeiZwLDZ1DMFtF9BnNPm3PY1wBE6spQdZVSziRY7k4UbdMmM5alcsGJcU+srlxOdaCSX+z9NaO5cVZWLKE2UIOh6hSsIttHd7NzbA8nVa+hf/sAu+7eDULgK/Ni5k32/amN/l2DpEdSxJpiCCFQVIVQVYjx7gkGdgzMEIMnJ5N8//v/zpvffCNlZZEZ17IktoBH+54iUUgSnRLeE4UkuqIdMfbjlbB5eBvPDG2hMViHX/dzINlJzszj0SQRTxkV/jiJQpJH+p5kVeVS5kVOTGneLLPMcmLJkcMRNgEZfFW7G/7aPP304zz++J+5+eZfo+vHV7h0ODQ0Pp35Im/JvZPd+nbCThknmacdEqn0auLgIKVEedFujIOy5dG5/Abz3exL7iBjTmJLi8lSgqyVpsJbg18LYkmLwFRur+1YjBdHyNkZDMVD3FOJRz10cTasR+lM75vuDQBQhUpACxE2IgDMDS+h5BSZKI5MiccKcW8V88uWT49xLMeiL9vBUL4H0zEJG1EaAi3EPMfeyXAknnrqUTZs2Iiu64fJTA7THFpEpa+Wcm8NPZkDeFUfilCpbahjfHCMmF5Jjb8Br+qnwltD3FP1F3EyZkpw826FHWMgEUgJf+qB17Y4XNAkX3L9u6YGFi1y+MMfFGzbjW6wbaivh8suc2hshE2bBNu3g6rCSSc52DZ84xsK/f2CVAr27IFiUeD1uiV0xaJbHvfpTzsEg+A48MgjAsuS00VyoRD4/ZKdOwXt7TB//om5FzEZ573Zj7Fb386IMkhAhsj3wB2Ze5gfmcPOPz7F3oc3c/FH34JvVSVPDz3HmfWnHvJ90qbGlscqBjuWQ+embnqf66WYKaJ5NeItMYQC7Y93UswUMfw6juUQrAhSvbiKob3DmAWLpnUN005aT9DDeOcEfdv6ic+J0vtcD2bBRPfq9Gpd9Jm9lDlR/HOqKLei9Kid3O67hTl2y1E7hCvml1O/spa+bf3IUffXU0qoWVpN9ZKqozqGbdp0bepGUQRlta6Bxxv2kkvkGNg+QNO6BgLxYysxtk2bjge7eOv6D9BQ3swvH7qJ0fwQ16Xei6/MS8OaekYPjJGbyGEEDCrmV+CPHN4YMstfnlkxeJZZ/o/wtF7gV94MBhCXKnkkv/GmyQs4u+TFNyU6DSkW93mynF/0YwJpHEwhGVdtIo4rEBtSIS5VLAnP6AXOKwaO2R08Icb5UeC77NV2oaLiYPOIfIBLC1dzfvG1fPaz/8aVV57DTTd9h3e960Mn9F4MKTbP6nkqHZWgLDCp9BCRGglRy0OeLKstD+oJmGydVjqL92c+zn8Gvk5SSVBt11DhVJFUEiy0lrDcWn3Ia5SKSjyXX03hN7/Cbt0DQiJUDXXNeozTz+JX7X/kge6HiXmjlHtjTBZT/PbA75FILm4+75Dj5ZQMwCETHb/0k1QmEAguLVzNqcWz+L33NyR9CUqiyJA6gCE9TCoJSqLEg57fc23+bYeIkxuq17BzbA+7x1oxFNftKJGcXncKi6JHX0eso7PMWsUya9X0YxVUMd92V6mllPyp91Hu7fozW5sfZZx+IiMxFoUWUh4vIx/Lcf68k3iNvHi6jGTu3AW87R3v4Vvf+grnnnsR//qvn+X881/LSSedOn2OrJnDkQ5BPfCKJwGtE220JTqo8McYL4zjVb14VS8ThQl60/00hesZyY3iSAdVHCqGdqd6+Wbnf/BM3UMUQxlsHO7il5xvX8AntH+CMUGyfxKhCGJNUbyho1+sqHCquLzwBi7nDYd87fH+Z0gWJ1kYnTf93kNGkNbxNvaM78O7VWeiJ4GvzIddsvGGvXjDXob3DjPeWXfEUrI5G5rwhrz0bx8gO5El3hKnYU095S3xI15nrClKw5p6ep7tITeRRVEVrJJNeUuc+lXHX4phOiZPDWzmmaEtPN7/NAiIeMqmXdhezYMEuiZ7WFe+iq5nekAIInXuYNkT9KB5NEZaRyjlS2THckhH4ot4idRF0D06uWR+xjn/+7+/RbFY5AMf+Pgh17M0vohzGk7nkb4nGMwNIxD4NC/nNJzB0vixOzOOxM6xvehCm949MJwbIaAHMB2T8cIEFf44UW+EodwI7cmuWTF4lln+xhgXYzzgvYdt+mZsHOZbCzm/+Fqa7aP/fP2fxH/91zdZsGAxr3nNqxvZ0OA00VA8ugXrE41H8RIxYowUBvAo3unP36yVwasGCB+FO9V0SnSm94OAKl8dg3mHkl3EkibjxRFMp4hX9VHtq6dg59iT2MJ4cWRaaA5qYRZHVh+S71t0Cu5zhCsCS+lQsHP41AAhzb0ur+pjZexkkqVxCnYOj+Il6imfdjlLKWmb3EFvrhNd0VGFzki+n2RxnOWx9SdEEDZNk2eeeYIPf/jT5KwMe5LPkbOyBPUwAkHampzOTG4KziNjJkmU3I6CQJUf27KpLjWxOn7KcV/LsfLEgGDbqKA5LPFq7vdjOAf3dSksjds0HMFfUyzCr38teOwxha4uyOVAVQVVVZJlyyRNTdDf75bJDQyAlIItW1QmJqCmRlJbK8lm3WgITXPFXU1z4yX8fjhwQLBqlaRQgKEhN1f4hQSD0NcnGRsTzJ9/4grmvHhnOGQfsh5HShhp6+Ohm37LqteezuIz1zKUHSFVPLzTXVUPisHHQtcz3bQ+sA/Dr+OP+ilkiuy4cxeKquKLeDELJo7lILzu+Fv3uhm9Zs6cEakghMDwG0wOpFh03gJqltYwuGsIoQoGtAF8TgBtOThL3TzkRruZ/doe9mq72Fg6+6iuVdVUlr52CeXzyhlrH0c6DuUt5VQtqpyOMXs5CqkC+WQeb9lMMdZX5mOie4LsRO6YxeCJ7gTJ/kmiDRHeMPdtNNY0863bv8BEaoyPBj/P0J5hkn3JqV2vECwPsOzSpS85J5jlL8esGDzLLP+L6FZMnjTydKkmMUdlvellleXBAR4xcqgCGmxX/AgAUkBOsSkI8E19rlc5Klu0In80svSoJvu0IjFHpSAlXuFQFDDX1lAQBKQgLRxyQk6//mh5xPMAe/QdzDcXoeNudxlU+rnX+zuWmavYsGEj73rXh/jylz/Dxo1ns2TJihmvTwuHNrWEJSSNtk61c/R/zoYUi5RwsMUzbNP+SEYZQUHB49RjchUpcQpRefzbWgSCC4uXUW83cr/3bnrVblQ0lpdWM99axGZ9ExVOJfOtxTPEWv3U01Ga5mDv2YUsFsg2xdmxokQvv+X3zp+pDNdRpblCXED305cb4A7zNnb5niCvZZlnLWRj6Wya7Baq7BpUtOk8XnB9JwllgpNLG1GmRPy4LKfZnkdamUQgiDsVrktFwqDaT5/aQ1ZkCMuZbbEhI8g7l72FzcPb2DuxH0PRWVGxlFWVy05oBunu8X3ceeCPFMMZnJo8NcV6CjmTjnwPJ6vrsH3DbAs9zWsyFwOQKI7SmdnPaW89lXsfvIs3Xnshk4kkP/zhrwA31uG+7ofZObYHKR3mRVp4zZyzmRNuOOZrmygkcJBEDHcSYksLVWgYqodUKc1kKc2S2MLDbsGXUnJX1x95tvYR1LhDfaYJRWqM2qM8EP4jLU8toP6xZgrpIgLwR/0sOHc+dSuOXyDtSvUQ9oRmiOBezYMjHUZyY5iP55kcSJEeTgMCVVeINESQtkN6KH1EMVgIQc3SamqWHn1JhqIqLL5gIbHGKMP7RrBLNvGWGLVLa/CEji/GQ0rJb9ru4U89j6ELnVK2QN9YH4XRLMvLF1FmhEFANpkk7U0woPeTHU/jDc8cLOt+nexYllKuRKA8gKqppAbT5CcLeMu81Cw/+H7Hx8f4wQ++zdvf/ndUVR2awawIhaXxhQznRuhLD1LpL+ecxtNZXr74hEY1ONJ50SKH6zx78f2RSMRsRMQss/xNkRNZbg78J7u17VQ4VWioPGM8QbfWwXszH5uObvrfQkdHG/fffw//+q/fO+bFWTcyYZjBXA9ZK01IL6PG33hCnagnCiEETaEFpK0UidIommK4mbtCY254yWEduy8mY6bIW1lCRhmq0Kjw1pAojpK3shTsHJW+WhaVrSRsRGlNbmOsOERYj6EpGo50mCxNsH9yB+vK3fzV0cIgY4UhutNtlOlRVKFRcHIoqMTUSqSUpK0kMdW9n4pQiHkOPwZIm0mG8n0EtNB0LrBP9ZMsjdGb6SD6gmxjYDovOVkaBwkRI0aZEX/Jn4Ft2zaTy2U57bSzGC0MkrXSM45bpsdIlMYYzvexoGwFK2OnMFoYIGOlKFa7gqHffmW7Go+X50YEAV3ifcG0pdIHrQloSwgaQoefUD30kODeewWDgxCLQUODWyYXCkFnp+APfxDs3i0YGoLaWkgkJF1d7v9XVwvCYUlXF0QiAkWRLFggqa93Iyj27RPs2SNZtcr9/1AIRkeZjqIAV4xWVbdw7kQgpSQzmsEqWATKAxh+dy5YE6hC5ix+95WfU9VSz1nvuBwpJYli8rD9G8C0y95xjk4MTvQk6N7cw957WxGKQvnccjSvhsgUcUwHoQgitVHXCFHmpZAsuEJpLICUoOoK0pHTpXHgRjb4I140j8aKy5dRMa+ckQOjbDUy5Bal8C6LwNRwVkFBICiJ4jHdM1WfGQX2QqyixeiBMbITOXSfTuW8cnwvcuDqPh3Vo2EVzBn9H2bBRDW0I0bAvRSlXAlpO9Pi+MlLzyQaivOFH3+UL//i43zwgn+hcXEjqqYiHUmiN0Hr/fs4+R0bZiMk/gaYFYNnmeV/CfvVEj/2TTKqWISkQo9islMrcmkxwCmmj3HFdfa+ENVysAoZkrkiRlHiWBY5VXLAUyQpdBb5yjgQVOhXLPJCgqMw1zZomhKUJxSHWlsjLI9NRHBw2KI/Q8SOTgvBANVOLfu03RzQ9lFTquOTn/wCjz/+Z973vrfyhz88hc/nfqht0wr8xptmRLFxgLBUOKvk4+JiEOUoHL1+qZAXrRzQfokKRJw5OFiMKF2Y2i1kxXyi8ui23BwNy6xVLM4sZ1wZZUgZ5G7v7WzxP4ODg47OUnMlb8m9kzIZQSIpUUKrq8Woa6BP7eZm3/fo1jrIlLK0ze0gUaon0H0GZfk4EslYcxf7qrbhKAuJiTgPee5jl76NG7MfYIG1hJXmWp7RnyTulGNID2PqMBEZPaS8rtypwMGZHqTAVKuvVPHhp1/tIWwtP+T9BY0AZzWcxlkNp52we/Zito7soGgXCUW92KqJ1/bh9fgZy48zkhsl7okxro6REWnUksLu5HPkrSwBf4jX/92b+MbHvsKcBc3MXbCAyVKKH+7+OfsnOqj0laMKnWeGttCb7ue9q95BTeDYvvdlnjAKUGaEiXmjjObHCRkh8lYer+ZFEyqn1Z102AFsophkm3wOJ1oknq1Cle7HclyJU9gRpPvPfdQYjcTnxNzJ2FCavfe1EqoMEq4+vnzDuDfKSG5sxmP21EBaG1EZG0ghpcQXcV2lZsFiojuBP+I7ahfCyyEdyeRgilKuhD/io2ZZNbXLj6/RfXIyyc6dW9m5cyt9fT109Xewo3MHxckchWQWq+TWbY8Brdw347UP8zP+CXchJ+gLEwlFiYcriZdVElTCeFJB5lTOwyh5UHUVPeAKxJpXp37VwYbp733v60gpee97P3boe5aSe7v+zB87HyRn5VGEwlhhHCEEzWWNhI0TN0FdVr6IrSM7KFhFvJqH6kAlQ9kRdEWbjqcYL0wQ1APMizS/9MFmmeVviCFlgC36MwyqfVQ4Vaw219Ngz/lrX9YJZae2lX3abuZbizGmxkoxp5xWbRebjMd4XeFNf+UrPLHcdNN/EIuVc9VV1x7za5+PTLClhaYYpM0ko4UhFkdWUeWrfxWu9viIGHFWxU5mMNdLykzgU/1U+uqJH6V4rQgFRShTO46YcgE3kDYnSZtJdKHTn+0ia6YYzvfhVf3Tu6YUoRDWI6TNScaLIwzn+xjK92FLi4w1iSJUIkacGl+TG1UmJcnSGLZ8aaHNkQ4pM8FArpuCnSeov6BHQgi8qp+UmXC/R2KqS0NK2tN76c0cwJQlQKAJjfpAC/PCS4+4OPrUU48QDIZYvnw17dk9bjbwC8ZYbmayRtZyd8cZqoe6gPsZVwhbAIzlhhnIhfCpfsqM+F8sM3+qj/cQBBymScClVIKnnnIjJSzLdfKmUlAsSnp7IZuFb31LwXGguho6OlzxdmwMSiU3NqK5WeI4oChutrDjuMIvuJEDztTJVRU2bpTcequYFoQLBejuFixYIFmw4PjvQXYiR+sD+xjvGMc23Z1nTSc10nzyHOaVNdP2wycoFguc/YFrmDCTjKUTVPjinFF/eCf389EQR+MMHmkbZcedO8mMZSlkSghFMLRniPK55TiWjaIpSMcVTg2/QTFdRGgKhWSBzGgGX5kXf9RHojdBuKYMRRVkxrMomkLtcteooXk0GtbU07Cmnn2+bTzo+T0RKzxtwEmLSQw81NuNx38zcR2/2+/c6TqGpQQpCcQDLLtkKRXzDrr/Db9B7dJqDjzWgebRMAIGVtEiNZSmcmEFkdqylzjL4QnEA2gejVK2NF2at7BxGR++4HN884+f5dt//hyfa/kmYS2CUAThmjJSQymSfclZd/DfALNi8Cyz/C9AInnAk2VcsVlkG9NCXufYID/b8Qj7WkdpnehmYngIhkbJDY+SGR4mNzqGtG36DnPMXmAroAcDGOUxrMoYoxXlECtnpKICdU49oSWLuGLuOnTPsW+vd7eqzXxdRqTpVXv4if/7PGE8xHrPqXz9uz/gigvP5Etf+jRf/OK/M6JY3OZLkRGSFltHBUYVmz8aOWpsnbVHkffbYusItjKpZGmwF6EisNAwZAsG7RzQtlNfes0xv6eXQkUl6sT5sf/7dGoHaLEWYGCQE1m26E8T91awrnQyD3nup0Pbj1f6WF86hVZtN91aB/OsRRTsIqPpNONlI+yte44NB84j7Z2kq7wVfynIfGcRHuGhxqmjVdvNnzz38s7cB7gudyNVnho2G09RUoosN1dzbvEiFlhLZlxjpVNNsz2PHrWTUWUEgbt6XelUUeFUoskTn993tCQKSTyqB4+pozoallpCtz3uyrpjklFSbh6XDNCV20fOzBD1VGCWTO743i8pr66ga38nH/v1u0lfm6etpYullauIjofRHJ2ot4zWRBvPDm3lsrkXHtO1LYkvYE5ZE+3JTuZFmlGESn9mEFtaLIkv5I2LrmRN5Yojvt5WLaRwUF/kbq/YWwWmxFPrQUh3ohOuCTPWMc5o29hxi8En16xj78R+hrIjVPrLMR2L7lQvtb5qvPt1hOJm4tolG9VQ0b2au70s7KF87vEP4PLJPLv/uJfxjnHMooUnYFC9tJrF5y88arHZNE2ee24T27ZtZvv259ixYwudnQcAMLweKmqrCcejeCtDtCxfRDBWhi8aYkRO0D3RS6lkUl6K4pEeWkqNrIkup3pNNe3b2ziwrY28zDJZmKBzoI2RiUEyxRTsA4/mpSbUSF24kdpQE8uaVhOucUXckZEhfvSj/+Q97/kI8fihpR59mQEe6H4Yn+alacqJXrRL7BzdzeN9T3Nxy6FxL6+UdVWr2Dm2h+2ju9GERskp4dO8aIrGRCHBRCGJV/PwmqazaA6fmEnJLLO82rRpe/mx//sMqgN4HA8lUeQx4yHekruRFdaav/blnTCG1AEc5LQQDO5CVUAG6dDa/opXduJJJCa47baf8Hd/9/fTC/9Hi+WYdKXbkAIixsG/uZOlCbrS+yn31hw2oumvTVAvY37ZsYsvACE9QliPkiiNTguZtrQYLw4BgpSZJG1NMpjvIWdlqfC6i6xSOuSsLAUrR97J0pNtZ6IwTMiIoAvDLVUz06TNpFv2pgXJ2Rk8qo+QduRrzVpp9iW3kyiNk7Pc1zvSxqP6KNg5hHBNBhE9Nh0nATBRHKE7sx9D8RJUyxBCULDz9GbbiXrKqfDWIKUkZ2eQ0sGvBVGEyhNPPMKGDRvRNA2f6neTlqWcFoSllFiOeUihHUBRupFOeya2kEskUYVKubeaxWWrMdTjLxR+OVaUS+5sVzAdiT6lPyeK4NOgpezwrttiEXI5ga67MQ6Tk2CabhREJiNJpwXhMKiqZO9eNwaiudkVkZNJSSYj6OoSlJdDT4+bM/y8EJzPA8gZOcAbN0oSCXjySYX9+93nLlkiueYaZ/p1rxTbstl1925GDowSrgyhGhr5ZI59D+zH8Blsan+IA8/t4sNf/wK5WkHRKnJyzVrObthIc9nhxylHiokoZor07xhkZP8IiqpQuaCC/h39lHIlylvi5JN5pCOnHavh6jC26aD7NLxBD5ULKhhtHyMzkgFcJ/CSixYRqgqx74H9JHoSZMYzOCWbcE2YycFJInVlM3a0nVE6h73aDlq1XUSdOCVRJC/ybCyePV3mfLy0P9HJyP5Rog0RNENz309fkr33tRKp34DuPTh/m3t6C8VMkeF9I6RHMqi6SsW8cpZetHiG0/losIoWQoFIU4R9bW3oRZ0KvRxz0qS5aj4fufDzfOdPn+eff/hBPvuObxIJxlBUgbQdHPvoSsZneXWZFYNnmeV/AUlsdnTsI7NjN4/v2Mvwjp2M7txNdngEgIc1jbLKSpyaCoJVVVSsXkF9dSXZ6hhOJERAM/AaHhxNZRgTzZEsLWmYmSy5sXFyo2MMTowgRyZI793P8GNPkevtR9o2jwCNjc0sWrSUxYuXsWjRMjZs2Eh19cFt7P2KyQ6tyKTiUO1orDI9rDbXc4/311Q61WhoZESaxzx/Ji0mmWO10K/20qbdzCmrz+DT//hl/uWfPso551yAuPB0xoTDIlufFr0rHY1JpcRzeuGoxGAdQYszyYATICUcnKnHGm0DHY1JJfkqfJegSztAl9ZOk9UyPbnzywAVThWPeB7gGf0J0sokcaeCpJLg5/4fkVAmWF86BQ2NoK5R46+mI5VjONBPwjPOoNpPRkmzSl2HZ2oQKxBUOJUc0FrJkyckw1xVuJaLC1dSEkVCMnzYApqYU87pxXN52HM/QekKWwEZJC0mabEWMMee+6rcl6NhTlkj28d2U5+eSzxdzVCkl2A2giMcTF+BrDC5uHQlBq4jSFMNhBD87Ds3M9DTz+dv+wo//9HN3PnpX7F87XoK1Vn2xp8j60+xqvs0FKHg1/x0TvYc87X5NB9vXfJG7tj/O9qTnZT7YjSEajmpeg2Xzb1gOq/1cEQ9ERZrS9mTf5a8kcFfCiFxS/jKzUpCnhAeeXBQKYRAKAKzYL6S2ziDtVUrGctP8FDvY+xPtqMKlaZSPSvbFtO7o49cIo+iKeSSeVRdcQvTNIWKueXHnCn2YqQj2XNvK4O7hwhXhwn7dArpAt1Pd2P4dBaee2TrSS6X5aGH7uPee3/Hgw/+gcnJJD6fn2VLV7Jo/krCyxopVakEKyJ4Ih78cT95p8CaypVoU1sJW3JF2LEZG5sLvWfToNRTbkVJ9CSZXzaXS/7xSvbev4/B3UOUsiVUQ0XRFPq7+hgt9tM90k7fZBcdyVae7P4Tt++SfPPef+HMM8+jt7cLwzB497s/fNjrP5DsJFVKsyh6cOblUQ1CRoitoztOqBgc0P28Y+mbeG5kB63jbWiqxtuXvgmQ9KT68agGS8oXzsiNnmWWv2VsbO72/JoRZZjF5jIEAomkQ23jLu+vWJhZiodXX9D5SxCQQZ4vFHvhZ3ZBFIg5/7scVT/72U04js3117/7mF+bMSfJ29lDhD+/FiRrZchZGUL6KxNd/1ZRhML8smXsSWxx4xWAgu12INT4G/FrbhGraZdImUkmS2MYSi1jxSGyZhrTMRFAV3ofutCJeipAQJkRp2QXydtZksVxTMfdxt4SWoJXO/xYxpEOrcltjBeHCekRglqIvJVjuDCAruh4VR+2Y2FK0xVu5UFr7FC+n6JdcLN+pz6DvKqPvJVlLD+EV/XTntpNsuQ6HgN6mEb/PJ57bhMf/vCnAajw1tCX7SRZGiOgucfJmmm8mv8QV7gjHfryHe558BPzVGA6JYbyvXhVPwvLjrxwf6LYWCdpnZDsSwg8qsRyBKoiOa/RofkIa/yBAFRVSYaGXPHWNCEQEOTzU+NC4QrAuu5mAkspyWbdPOCJCYGmwcAArF8v0XWBZUEmA1u3uv9dutR1Dj+PrsNVV0lOP91mcNB1Ijc3u67h42WiK8F41wTReARvv4YyJPATZtyTYN9DrXzhJ5/i0kuv5uPXfgLbsbGlM93vcCQOFxNRzJbYdsd2RtrH0D06SMng7iFyiTzViyvRDI1QVYiJrgk0Q8PMmzi2jXQcwB3vGgGD+Jw4ht9g3plzmXf6XHxlU9EnES+bfvQsxWyRYH0ERVPZ96c2Er1J1rx+1XTkQr3dxLtzH+ZR48/s1XZS4VRyknkapxbPQtiCib4JchN5jIBBbE7smKMTzILJ8N5hd9ee4Up7QhGU1YZJ9k2S6EnOKGI2/AYrr1pBsn+Sie4E+WSOUEUIx3ZmLKi8FNKR9DzXS9fT3fSk+nnat5WR8lGkkMTsKKfXbeCck86gfGs5H/N+mW/e+8/84w/ex+dv+A56wYMv4qPsOA0ts5wYZsXgWWb5H0oiMcGjjz7IQw/dx0MPP8DoyBAAobpaKpYvZflb30xs+RJKqxbyscqVLHa8/NGT4QkjT0o4eKRggW1wQcFPl2axXy3hk4IJxWaPVmK+fXAyJZG0qiZXFAOcUwyQFxI9X6KrrZXW1l3s3bub1tZd3HbbTxkaGgBg4cIlnHnm+dSdfTp7z15ByutBQ2AjeULXubxwFm1aK23aXgzpoUvrYFIkWGuezDx7IQJBRqR5ztjE+971Mc7+8wV86EM38NH770MsLD9EzPQhSCpHXxyw2GqhR91LpePFFG50RNBxOKApR93seqzkKVCihIeZgrVHeuhVuokRY4W1dvq9CQkHtH2kxeR0Vu/i2ALMRJFuuujO9qAGBFX+SuZ6ZhahlCjhl/4ZWcRevHjloWJ5zsqQtdJoQuMScSUTyijt2n7A3cpUazdwdf7N6Pz1nMEnVa9h89A29ifaqdu/mHxTkaFgL/5yLzFfhDMK53F28QIAfGqAieIIuzZv565b7uBtf/8urIUmp3/6dAb3DtL+7lbKf9hMSAQZiHXSOD6feKaaol0k4n1lE8a6YDUfWH0jfekBSk6Jan8VQePlBVMhBNfUXs325LO0VmxF1yYQpgIRG3+Lh8oDNe6+wSn3iG26P+PBiuArus4XogiFi5rPZUPNGnrTA4gCjN81Rm40R6AyMC0CI6GstgxvyEMhXWLOhuMv3UkNpRhrd93Nz+eW+cp82KZD/45Bmk+ZM50fB5DP57nnnjv4/e9/y6OPPkihUGDRoqW8/e3v5cILL2PJkhV0Pt7FfzzzQ3S/wlytHkxJaajEpJMhGZykI9nJvGgLilBIJJLYls15wTNYr04VOergDXkZah1hwTnzWXbJEppOanQbkP06yYEUw98bJp6vo7aiGadcoigKekwjuFZnT/9WfvObX9LT0wnAG95wAeeccwHnnHMR69adPMOxdDgEAkeeeKeEX/dzet3JnF538swv1B3++bPM8rfMsDJIt9ZJrV138LMSQZ3dwIDWR6/axTz7xLit/tosM1dR7qmkUz1Ao92MgsKIMoQuddaaJ7/8Af6HYFkWN9/8n1x11ZuoqDj2iC5FqO7fT5wZFbeOdFCE+Itt//9LU2bEWFO+kbHCEEWnwFCul7SSnBaCAXTVwK8GsaXFUL6XjJlGUzQM1SBilJMxU2TtDCWniEf14tcCVPnqGMr3oQmdmKeSGl/jS0ZtJEvjJEvjhPUomuKOE4NaiIw1ieVYmMLEctwF7MF8D8+MPsTiyGrGi8N0pPeQKiUo2DlCetm0y1kIQdHJszuxmZSZJKCFUIRCykzw0I4/kMtlWbHC3QXg0wIsia6hPeUeSwJhI0pLaBFhIzLjWlOlCbJyqoRs6uNWVwx8aoCRfD/NwYWvujs45oV3r3DYPCzYnxD4NIcVFbCyXPJCHc5x3EgIw3D/e+qpkl27FGxb4DhuNESh4EY8BIPg9bqlcGNjAscRpFISv18Qj7tjjkQChocFZ50laW6WPPSQwuioKzQPDkq+/W2Fa65xWLbs4DVUVLj/nkgK6QKy6ODfo6OMwPN+B/+4hx9v/z6ZTJp//uevAqAq6iHF1YfjeWfw0L5hhjaNYps2pZzJ8P5R4nNi03m2+VSBiS63KM1X5iPWGEXaDpMDKcy8SSFVxBf1U8oU2f/QATxBD+XNMZZcuIj5Z81DUQ/+LRltGyM/mad2Wc3043bMz9iBcYZbR6hffXCQVW838ab822cs7BWzJbbds53h/aM4pu3mFNdHWHbpUsJVRx8X5liuy/aFhXYAiqKAlNPzhheT6EnQ8UQnxbS74OMJGrSc1kzLac0vKwgP7Bxkzx9byRt5Ho1tYtQZJ5QLEoj4sWsdtvn3cGrjycwNtJAZzfD3F36Rr//hM3z+hx/lk1d+hZUXrzruPpBZTgyzYvAss/wPQUrJjh1bePDBP/Dww/ezdeuzOI7D4sXLuPp1b8I+9xTa181jabQaA4GDpEMxaXQ05mc96AguK4bYWPIzrFgEpEKDoyEQzC15OBdXuOpUSwyoSbpVk1pbQwJ9qkVMKqw0vfhQ3LI4r4/ly1ezfPnqGdc5NjbCE088zCOPPMjv7r6Dof/+FophULdhPc3nn8PC119JZ0OcZ3Q/785+mK3GM3SobUwqSSrtauZbi6Y/KIMyRC/d9Gk9fPvbP+Kyy87ku294A8v//GuKkUo8U8+TSNLC4TTL4GjZUDqN5/RNJJU2qu1aLGzatH6arfmsMF+dbaa1Th0RGWVEGSIq4+hSn5rcDWMLi0qnxnU5ScnwcIKB/izpksHm8F78tVGi0RAe1aCiKkJjYSPXLXwPIW+QH/v/g36lh2Z7HgoKeZEjoUxwRuHcGdtLX4wjbdrTrQxkuyg5BRShUqZHeVv03XT7uhhTRwg7EZaZq4jK2KtyT46W6kAlNyx/Mw90P8L+RDtL95/MBfWXsrZhOQtzi2YI+FW+evb37eLrn/5/LFmzjIvffClPm49hhH2c+ePz+N0Fd+B8qhP96wuwqkwmvRMUh8CjelhbufIVX6MiFBrDx55NOCfcyNe1b/CL/C08630SGbZZrq3g4hWXk9leYqxzHH/Uj3Qk+ck85S3xGav8x0vMGyXmjdK7tY+ekR4Mv8FE1wRWwaSQcgeomkdD1VUq55dTs+Toi+GORDFbwipaGC8qqzCmHMJm3sTwGwwNDfDjH3+PW265iWRygrVrT+Yf/uGzXHjhZTQ3z5t+XSlXYu+2fYz4xqj0lKMLzR3hCAhPhpBlElXR2J9sBwmlosmiiXnMzTaR0TP4Ij5UXXUnYo47cRJCEK4KEa4KYRUtdv9hL6HKIIV0EbNggZSYeZNoIMKFrz+fk1On8tOf/oBzz72Qyy57PQ8//AA//ekP+Na3vsKcOXN5wxvewtVXX0dLZA4BPcB4IUG5z/29Mh2TSTPNOY2nH/e9nWWW/80cblcLvLgW8X8H1U4t1+bfzh3en08t0EoiMsZlhatZaa79a1/eCWPTpscYGhrgLW955yt6fVAvo8yIMV4cITIlJjrSJmulqPLW4VcPiqPjYoyUkiTmlFMmIyfoHfz18Khe6gJzAChaebJWyv2ChLydJWOmyFlpIno5GXsSkGiKTsSIE9ajqKhkrRRZKzVd9qYIlYgnzsrYydPxEi8ka6UZzvWTsVL4VP9URIXtlhVbGbJmmqQ5jiYMVOG2UOiKgVf1UbDzDBX6SI6MgxBoio4qNGxpM1EcRUoo80SnFkbduIvIC/J8y/QYm/Y9AcDSpQddvBEjzur4aeSsNBI5JR4fKiJa0jq4uP6CSAE3RqmIJU2Mv8DOgjIPnNsoObfx0L9c+Tw8+qjg6acFyaQb9QCuOzcel0Sjbg6wZYGuuwJyVRVYlmDhQkkqJUgm3cdDIUl1tXvMJUscNm6U1NXBs88KxsehsRHq6iR+P3R1wS9/qfDxjzuEX0XTpjfsRU0p2MMmlOtIBSwH9ucOcG/vnXzyDZ+lru7YyjGfL6zeftcOIr4YQgjGuxMIAbE5B+cv3rAHPWCQGkwTrY+g6irxljhTMbsoyvMxhmCOZcmOZ9H9OsHK4AwhGFwxVTO0GY+7gqwkOTg5Qwx+nhd+frU/1s7AzkHCNWEMv4FVspnoSbDnj3s56bp1KNrRLWIZAYNIfYThvcN4w95pITeXyOEJeQjXHPrNnOiaoO2hA6iGSrzZvT+5iRxtD7cTrglTMffQiLPnkY6kZ3MvQsBIxTjjToImtR4ZlJRSJg1NtfSU+nl2aAtvXHgla69ZQ/X2Kj5T8TX+6Ufv59ad3+e1//i7o3pvs7z6zIrBs8zyN87o6DB33PFzfvnLH9PW1ko4XMYZZ5zLm950A2eddT41Ne6HzYSw+Ylvkla1hMT9MKtyNK4uhvBx8AMlJlVi9pFXWZttgzcWQvzek6FLdVfyq22Ny4tB6p2Xd4aWl1dy+eVv4PLL38BOtcBX+jYj//QkPX9+hCe//G889tkvUXPW6Uxe93pee9abOEdeyDlcSEqZpF3dP+OD8vkPZA8e4vEKfvaz33HppWew8w03Iu/5GRXeAJoUjCo21Y7GyebLR0QcfJ/zeFvuPfzRexd9ajcKKuvNU7k0f/W0C/dEU+ZEidox/ui9C1vYhJwwIVlGnd3AQnPJdKtsZ+cg+1p7sRwbw/CTGjf50+AmFq9ugKoiCgoXcyUrou4W2dfnr+NW/83s1/YCoKGy1tzAucWLXvJ6+nPddKX34VV9RIxybGkxURrDSTisVU6fLhs5HAUKHNBaKYgCtXY9NU7dESfpJ4qmcAM3LHszWTOHIsTB+IUXmCkzpSylvM13/+Hb2JbNDZ99Nzk7g+m12Fz9DBMLElTdVsvAlb30f2Uf/q9GGEyNMF+p4OKW81ga/+s4ymr9NXzM/3EcHExMDNtAlAlSV6fo2tTNaMc4qq4wd2MLzSc3zXDNniiKmRKlbInJ/kmkIwlVhSimi+QSObITOVZcuZy5G1uOeTW/ZJvsHm+lPzOIR/WwJL6AsmgYI2BQSBfwlR3MhyykC3jDXvZ37eFHn/0uv/vd7RiGh2uueRs33PA+5sw5fFRJIV2kmCuixMSMn0PN0CikiwTw85Ylb8DBIZvLUXw4z+iBUcadcZIeDU/AoHxunHyqwNyNzYcM+lNDaTKjGSrmuyJ8frKAtB2EKnAsSS6R5+Ofei8A//7vN1FeXsnrX/8WbNvm6acf51e/+inf+c6/8m//9jk2bjybeWetZGzOBOOFCTShUnJMFkfnc1rdhmO6t7PM8n+NKqeGOVYLe/QdzLdCKChIJP1aD/VWE432/64ixFXmOuZZCzmg7cPCYo7dQrlzdCVjf2uoqQ6M/ofQkq1Io4xS9WmUak7nnnt+TX19EytXvjKBWxEK88PLMJNbmJyKTABXIJwbXooQgpzIcpf3dp4zNpEXOUJOmFNKZ3BR4YqXXDT/W+drX/s83/jGF2c8Vjunnn+945tMFIcxHRNHOkyURig4OVQ0LMdksjSBKjSCRhmeko+clUXgFsoKoNY/h7jnUJf2ZGmCXYlnyZppVMUVcQUCyzEZKw6RsVJI6WA5FiWniCY0FNVLQAsjkW4klxqYFu7LvdXYjkXWyrjXWRzBxqLKW4dPC7jdFS9wdgsh6GvrJVYZJx6fuSiuCGVGad3hCGghDMUdw7xwk07ezhHSI3jVY8urPtE4Dtx6q+CJJwQ+H7S3CwYH3biHJUskpZKbDRwOS1aulCSTrrCbSkF1taS+Hnp73Yxgx5GMjrrxEF4veL2CwUGFgQE4cAAURTAx4RbDzZsnmTNHcuAA7N0r2LDh6JfXTBP27YNkUhCJSBYudGMmjkSsKUqVFmfYGiWXCtKXVsmV8tzV+wPKjQbevegjx3zfcuNuDrQe0Ik3uhE6+XSRRPcE6aEU0YYoMFUOXB5A1RSSfUl3limhrDqE4zhkJ/LkJ3KYuRLBigCO5ZAZSbPpR8+w8srlzDl5znSMg+bVcV6woGAVLdLDaSa6E2hejVhjlOpFVYcVdUu5EoO7h/FF/dPjec1QKasJk+hNkuyfJNYUPar3LoSg5dQ5pAZTjHdO4AkYmEULIWD+mfMIxA6NdxnZP4pZMCl7QWFcIB5grHOckf2jLykGW0WLXDKHJ+ghJd2CRkUooIF0ithFm4Dfz0BmGIBIXRmRujKWXryE2o0VvOUtl/PP//xRvvzlb83Gk/0NMCsGzzLL3yCmafLnP9/LL3/5Yx588A9omsaFF17O5z73dTZuPHu6NfV5ssLBQXJjrox9msmoauGXCstNDzF57AFP600fS00PXZqJkDDH1mcIykeNEAQXzmPuvMWse/eNFCdT7LvzbrbfehtPvOP9nB76NJdf9nre8Ia3svbUk9kb2EVKJAnLCBJJr9pNTMZZbC4HoLl5Hj/96Z1cffX59L/976m45T9xNJWTS17ONQPUHYVY/UKWWitZlFnGqDKMhkbcqXhVBc17vL+hQ2uj3m4iqSRIi0kcbC4rXU2D08StvpsZKg7T0TGIUAVKzKRe1rDAXMKu5F462weJVEFABLnfew99ajevK7yJ5dZqatMN7NF3kBNZauw6FppLKZo5uktuyUyZEaNMj83Yqj6Q7UIVGj7NdYVrQqfMiJIykyRKo4d1hAC0q/u51XczvVo3NhYhWcYpxTO4snDNqx4lIYQ4bPzCaG6Me7sfYsfwbh799zsY2t/Nf/z0Z5yy9GSKosgdFbfT7umgwqmk/vxGxE2C/rf1UFYb5sM3fpBlC5YR80ZOyDVajkl/rstt53Ysyr3V1AXmENBeftuXgjIj7zJcHWbFFcuxipbrojnGLLFjwVfmJT+ZxyraBOLu4FHzaAjVPW+0PoI/cmwTpayZ4yd7bmPH6C4sx0YCUU8ZV857LTVLa+h6uhvbtDF8rjDcPdjBPY/eyrNfeoL6+iY+/ekvce21byccfukJnifoIeqNUFGK0+sbJCD9CCGwTZukkWJesJkVFUsI6H46nuxkT/de4g1RUsNpHEuSGc2Qn8zTfEozTesPLShxf20ESPeehKZiOoqZIsVMkbv/cDv33vs7brrpNsrLDwo1qqpy6qlncuqpZ/LFL36Te+75Dbfd9hNu/sK/EwgFWXXeaZx05TmsW7yetVUrCRtHvzVwlln+L6KgcEnhdYypI+zTdqNjYFIi7lRweeH1/6OFvSMRlCFWmev+2pdxXKiTbQR2fhslN4RjRFFyw/gndiNTvfzhD3fyute9+biEgbARZU38NMaKwxTtAl7VR7m3Gl0xkEh+7b2Vhz33UeFUUWPXM6kkuNt7ByoqlxRedwLf6V+ehQuXcNtt9+JIhwOTe+grdtCX7UAiMRQPfi1AySmioOLg4FV8lGSJ8aLbKxI1ymkIzsVySggEcW8VFd6aQ+I1pJR0pFvJWW5BrxDuTrZ0KUnWKZJ9PoJC8WBJE1taOFK6blzhjo1UobrfE+lMC70Vvlp85iRpc5KSXaQxMI/54aWMFAYOKYYD6NrfybxFr2zh3qcF8BXdz2/hh7yVpeQUEKg0BuYe1k38l6S9HbZsUaivd0vhcjnXvZtMQjotWLnSFXqllLS1CYSQ2LZbLjc2JnnqKVcsrqpyncGG4TqKe3th927B8uWS8XFBsSjQddc5bJqwf7+grEwipaBQOHoheGwMfvpTt2TOsgSaJlmwAN76VofyI+iJqqayatUy7m9rY+/oKA4FkmY/3dktrCv7Bfe0ebmCY4vMSgwmAAhGDu4CCFcGmexNkhyYnBaDc4k8hk+ncW0DxWwJobjO4WhjlGdveQ7pSDcqoswHwhVtS9kSYx3jPHPLZob2DLPkokXEmmJULaykf/sAuUQOzasxtGeYzGgGhCCXyLPtju3MObmJxa9ZdEgxm1W0sE17OibteTRDwzZtd8x/DMSaYqx542r6tvWT7E0QLfNRu7zmiDv5zIJ1iOkBQFEVzFzpJc+lGirekJf0SIZQOABS4kgHabtzNMVQyVp5qgOHLlqeddZr+MpX/oN/+Ie/o6mpmfe859iF/1lOLLNi8Cyz/A2RSk3ynz/6D35+8/cZHx1mxYo1fP7z3+CKK95INHroNv2McLjPyLJZz1MUkkpH45ySnwuLgeMWNf0oLLGOb6tUk61R7qgMKhYNjo6nLMyy69+E/o6rad43iHbL77j99p/x85//kJWr17HyY2sYu2KUAdEHQNQp53WFa6l2DpbRxdet5qof/he3vuVtmJ/6HG/90le4pBSgQr4yEVJFnXH8V4txMcaTnoeJOxUsdpYjkTg49KnddGntXJm+lkFlgHvT9zFSGCUYMwjLMpaZq6hwqkj50uxO7qA2M5c6by15kWeT8Rg5keP92X8gLss5vXQO4A7YO9P76M62TZd/aMKgIdDC3PCS6S19JaeIPpXvJpG0e9vYFdhBn9rNFnUrF8orWW6tmvGzlBUZfu7/IX1qN3OsuegYTChjPOC9h0qnirNKr3nV7+WLyZo5bt59K60TB+i+Ywt9m9tY8+GL2cQu1lgnMRQdxFJsFlvLGFIHmBBj+N/kJzZWTv/Hetgb2cwZ7954Qq7FkTZ7klsYyvehKzoChc70PiaKw6yInTwjy+9Y0DwajnR4dmgbmwafZTQ/TmOontPqTmJx7Mhlaw4Oe7Qd7NK3kRM5Wqz5rDU3EJKHbhurnF+BZmgUU8WphmBBKWeieTR8ZT4KqcIxX/djfZt4bng7zeEGfJoPKSV9mUHuav8jHznt3eh+nYHtA4yPjnL3ttt44Lm7aGxq5r/+6xdcdNEVhyx8HQlPwKBhRT3LnlhEQknRbfShmip5s0BFNMYVSy4moPtxbIf+bQMYfoNQS4hgZZDMWBazYOGYNk0nNRKIB7CKFiNto6SHM2gelVhTlFBViNRQimhDxJ0EO5LMWAY7WuILX/0EV111LRdffOX0NUnpOobtooU/5icYDHHNNddzzTXX09l5gNtu+wm33PIDnr77T1x11Zuof9/HCM9fdMz3eJZZ/q8x317EBzOfZIv+NEPKEOWygtWl9dQ7h2+an+WvjJR4eu5FyQ1jRZc9v7qGUhjjuYd+xdjYCJdeevyCrKF6qfUfmmk/ogyx1XiGKruWmHRdg16nBgeHJ41HOKv4munS3L8UEkmf2k2n2o6KygJrMRXOseclA6iqRmVlNVJKMt4E48kB8sX09O6uol1AEQoBLUTWSpO3c6hCJe9kSJs6C8pWsCC8/GXF+IKdY7I0gV8LTT9XCEHACJOxU+iKgTblFvarIbyq3xV4nSJ5K+vGTxhxvIp/qvhx6vqFSpkRw1A8ONKhJbQIQ/VS7q0mqJWRLI0T1MMoKGStNL37e7j69W9+RfcKwJt3x2HV5TXuArVRQX2gmQrvqzcXKKQL9G3rZ7RtDKEIqhdXUbeyFt07c+7S3y8oFCAUgsFBkNIVWH0+mJhwn1NTIykrg5NOcrjnHlf8ra52sG2m/21qEixZ4t7h/fvdrGAQ9Pe7x4jH3WzhdFpQXi4ZG4OuLkF5uRsbcTRICb/5jcKuXYLmZonPJ8nnYdcuwR13KLz73Q5H+pFS1nhp/fFKxko55lRYPNb5IcJ6AyujV/PYhODkIag+hkSyQtF1BnsML8VMkey4O65TPRpmzmSsw3W9O7ZEOg5dz3SDECiqgmM5VC6ocLsh+pIgJEIRFLMl8okCIPCEDBRNITWUYtuvd1C9pIrxjglKuRKZ0QylvEluKoc4PidKpCFCMVWk57k+apZUE22c6fL1hrwE4n7SQ2k8wYNz7XwyjzfoIVB+7CXNzztwj4ayujJ6NvdgWzaqNlW+Zzk4lk2kPvKSr1VUhYa19ey6Zw9VExXEyqL02P2Es0GCkQDDyihhLcT66lWHff2b33wDPT2dfP7zn6CpqZmLLrriGN7lLCeaWTF4lln+BkgkJvjWD7/FT2/6LqVikblveSOXXX89b12wgYX24V0uDpJbvSme1guUS4WwVOhXLX7uTaFJwRrLi4Nku1Zki14goTi0WDonmz5qnVfvVz8hbAZUC48UzLF1XlsMcrs3RatawoMgj0Odo3Ft/XKaP76Gj33sn3nkkQf49re/yk/f/N+0LJ7PhR+9nNMuPZOlciWVzsHRwKBi8SN/ksnLz+Lkr32Rp/7+09wcCzP68Y/z3nyMoPzbLQkZVgeYFEla7PmAu1VJRSXuVDCujJFRUrwp/3aqC018z/kJtaUqakQthvS4orHoQREqtdTjwcAjvXhsD/v03RzQWllkHWx8SJRG6crsQ1MMgobbrFywc/RkD1BmxKj01aIKlYAeZqIwgk8LsCuwnQei91IURQxTY493F31KL9fm384ppTOmj71X20Wf2k2zNX/aBRx3KsiINE8aj3Jm6fxjXogoUuRBzx/4g/dOBtU+au0GLilcxdnFC46qFX7H6G7aEh3knxhg7++f5tz3XM3Ks06jNdHGpsHNxOIGCoLV5noS9gQpMYmGhvw7SevQLj73uY8Tj5dz9dXXTR/TkQ77E+3sn2jHljYtkTksiS+YFs+PxHhxhJHCgNtcrghGlWEyIsVYfgRvPsjK0AYmxDhSOMSccpRjcNz/uecxfnPg91N5eH6eGdrCnvF9vHXJG1lVueyQ50skf/DeyR+9d1LCdQU9aTzMs9aT3Jj9wCE50LpPZ87JTbQ9cgDbspEOeEMeyurKKOVKxxwPIaXk2eGthPQAPs11FAshqAtWsy9xgAO5Lk49cz1Pdf2Jf7vpc5RKRT716S9y440fwDCO3d039/QWbNshvDPE3mwbSc8k9dV1XHTGeSypdV1Eji0xCyaa4ealB2IBArEAUkrGuyZQNYVipsj2O3cy2jaGdFxXkjfspXpJFaVskbHOcVRVwbElImjz1ds+RTxewRe+8O/T15JP5tn35zZGD4xhl2x8ES/NJ8+hYW09tulQW9nIJz7+eT7wgU/w85//kO9//9+5/fZbeO1rr+T97/8EK1asPtLbnGWWWXDjIi4qXvHXvoxZjga7iJZsxfFW8EJ1yPHE+e1T91NXVcHq1etftdMnlHGyIk2lnKkulckIo8oISZH4i4rBNjZ3e+/gYc/9ZEQaEESdGJcVXj+9qH8sdHYeYPXqJnRDp3FpI1e/7xoC0SAe1YcjbVJmAkPxoik6PjVAWI+SszMUrSJeNUBIj2JLGwUFW1qoQnuZ0r1DxUIFhYAWJOatAiSacDtHhvP9JItjeFU/EU8cj/CStVOEpordCnYOQ/FScgrk7SyNgXnTu9W8qp8l0TW0Te4ibSaRSMxJm8ToBGtXvPICxYkJN0rk7PmXoukqqtBe1e3qxXSRrbdvZ6xjHMNvIB3JWMc4E10TrLhyxYwdXz4f027fF0YtWJZbEieEWxq3ZAnU1LiC7Lp1klIJxsddIXnfPqivP/g9SiYF2az7tUTCjYsoL5ckk27MhNcLhYIrCF90kUNLy9G9r+FhN1KittYVq5+//ro6SWurYGjIvcbDMV4HfRWC2HCQwugIe8du44yqzxNbpjJoSwYHBdXVR+9QVgLu9y/TnyM7nMUqWEjc3VvxljhNG5rw+A16NvdSyBSJNUZdF2zBZHDPMIF4gLpVdQzvG8EuuYVshVQB27LxBD0oioI35CVUHabrqS7GOsaJ1JXhK/NimzZ2Mk+0IULFvHI8AXes7A17yYxlSfRNHiIGK5pC88lz2HHXLob2DqPqynQJ3LwzDh/tcCKpXlzFwI4BxtrH8YY9gKCQLhCfEzuqXpD6VXWYOZPuzb2cMrKOzcFtTMbSWDUOdYFqLm45n3mRI/8gfeITn6e9fT8f+cg7Wb58DfX1swu5fy1mxeBZZvkrMj4+xn//9zf50c3fo2ibNNx4HSd94L2EqqsYVmx+7EzynlyEpsPEH3SqJjv0Io2ONi2Chm2VdrXEI0aOVZaHe40sv/dkcZB4EbR6imzVC9x4hGMeDw6S+40sD3tyJISDDjRbOq8rhPi7XJSteoEJYdPgaKwzfVRPCdKKonD22Rdw9tkXsGnTY3zrW1/hP2/8Gve23MX73/9xrr76zdPuwGf1PL2KSZ2jUXfj21ATKR7/wle4MzHJaZ/9BqfZx76S+pciIIN4pNfNqnuBKzMvcnikl4AMIhBsDJ3OU4Ht9CUH0Mp0EJBz8iRzKermxPF6D4pkPunHwiSpJGaca6wwjCUtwtrBwYdX9ZO3cowVhqj01SKEoCHQwmRpnBFzkCeDj2E7FpXFCoJamEpZRw+d3O+5h9Wlk/Di5jFnRQYHeUgchE/6SSuTWFjHFBVhYvLfgW/yK99PySpZdGnQrXawU99Cp3KAd+Y/iPYyH1UjuTH6N+1n+48eZO0VZ7PqYtflG9D8dE32Mt8+BRWVIgViTpwYrjNov7aXS//xapb1r+IjH3GLa66++joc6XDngT/w597HyVuuG1ZXNDbUrOVNi16HRz2yUJkxJ3GkjaVabNM3M6a42zANx2CvuoffhX7DpDKJg0OzNZeLClcw3355N+hkMcWfeh7Fp3mpDbgDtZpAFe3JTu7rfohl5YsOyXjuFp38XrsTv+WjmXlT97vEXm0nj3n+zGWFqw85T8tpzST7kph5C3/ULVXLjGQoqw1TOf/YS+tKdmm62ON5nl8s6Ovq5pIbP8L27c/xhje8hU996otUVR1h5nAUaB6NJRcsYs6GJs6bPAtP0IM/5p/eyjqYHSZZnESpUMi0ZfFFfdMTwFK2hObRCFYE6dncy3DrCNGGCJqhIaVkcjBFx+OdlNWGUVQFT8hL+aI4//JfHySZSnD33Y9O796wLZudd+9meN8IwfIg3pCHXCLPrnt2M7RniPxkAbNgEaoKMWdDI+9614e4/vr3cMcdP+O73/0aF164gbPPvoDPfOZLLFmy4iXe8SyzzDLL/wAUDakaiFJmxsOObXHn5n6uvOTyV1WMizgx/DLIsDJIQcmREVn80o8qVcpkhIg8umzOE8U2fTP3ee6mTEapsxuRSAbUPn7r+yVNdvMx5V6vWXMS3/zmTcydu4Btnc/w/W//O//vXZ/nU7f8IyVfAY/iQ50qRxMIQnoEVahTu8YkRTvProln8age9Kl4B58aoC4whxpf44zvi1f1EzXiDBf6MRTv9GdrxpwkYsRxkOStDCHd3T3jSBtD9bAgsgLLMclbGUxRIqSFWRpdz0TBXTzPWxPoikG939299kIiRpy15ae7YrB02Nr2HABLl77yst+hoX5isXK8nqPvGTkeBnYNMtY5TqwpOu3CNAsmg3uHqVk2Ss3Sg+Lb4sWS6mpBZydUVEi8XhgZcUXgujo3A1hRYO1aN9/Xtl3Rta1NTBfNpVKSAwcEa9ZILMt1GKdSEkUR+HyCyUk3LzgchrIyN05C1+G1r5W88Y0S5Sg9Cvm8W24Xj8983OOB8XH360fC6wN9niRdLunc/30UVWPJ+e8gE5LoAwKf79hiIoRPoms6ya4kVtFG97kL/rGmKJquIi2HaEOEA492EK2PTEck6F4df8TH4J5hTr1xA5nRDDvu3ElqMIVZtNAMFd2roegqoeoQuUSOYqZItClKuNqdywXiAdLDaYSiTAvB4BoiABT18H/bYk1RPEGDsY4xzJyJYqhUzq+gdvnxlzS/HJ6AwaqrVtD9bA/Dre48pWFNHY3rG4/K+KGoCnNPb6F+dR2rxpdzpXEJk/40jnSoC9bg1V76GIqi8LWv/RfnnbeOD3zgbdxxxwOo6l83ouX/KrNi8Cyz/BUoFAp897v/xve+9w0AXnPDjeQ+8lYWx2vRp3Ipy2yFVrXE00aepsKh4tqIYlNCHuKGjTgqQ6pFj2LykCdHEEHVlPDrIGlVTf5kZHl7oeyoHJzCzKCN70AppbB9lVixZXAYMewZvcDd3gwBqTDP1ikh2aeZ/MKX5sPZKAvsl6+mPfnk0zn55NPZtm0z3/72V/j7v38nN930bb785e9w0kmn8qSep0u16NUshIToJ9/NadEwT3z0M/zbeJaT/u1m9JdqLfgr0mDPYaG1hC36M8yxW/DLAGkxyYgyzGuKl0y3WntUg9fPv4xb9t7OvsQBQCAE1FRWEF0w877nRBYdg4gzcyLzfKHHixFCuNltU5R7qlkSWcum0sMktAnKSxVEjHIiRhyBoNKuZkgbYEjtZ47tFndFShHwSBJynKhwR4ASSVKZYI254WWF2xezR9/B/Z7fYwmbRqvZdaZgM6IM8YDv95xpnscS66UFsad//xDb/usBlpy9njPfdun043m7QMwXZZG1lIXmMnbqW6hyajCkh1FlBJ/0c0bpPN7z1Y8gpeSDH3wHo6MjnPXGS/hz7+OUGSGaw+5qdaaUZdPAZhZF53FK7ZEdTKpw33+b0sqoMkzMiaOiUSDHo7FHyXgybCyeg47ODn0rQ+oA78v8A3XOSzcn92UGGS8kmFs2c5JY6a9gMDPEeCFBld8Va6WUbB7ezs2FH/Bc/RbiqWomgznmRuagKwZhGWGb/uxhxeDyljjLLllK+2MdZCdyKIqgfH45i85fOGMr29EghGBZ+SLu63qIKn8F6lQWX7I4yfhzPfzTj35CdVUtv/vdo6xb98pdPi/GH/HNyDbOlLLc0XY320d3kTPzRDxh5lh1OJ02wWgQq2hTypVoWFNPWU2YnXftxBv0oBlTP8sSCpN5xjomsIoWwfIA2YksP/iPr7Ntz7Pc9qv7aGmZP32+ie7ElGskgu51jxGu1ujc1M1YxzjVS6vRPRrjnWNMDkyy6qoVVC6o4M1vvoE3vvF67r77Dr7xjS/ymtecxHXX3cg//MNnicePXOYxyyyzzPI3jaJhVp2G98AvkZ4YUg+AdNi2fTPDk0UuuvL6V/X0VU4NdVYDd/h/jpQSDwYFUURB8Nbsu47ZFZwnT5u2l4IoUGc3UOvUv+x4WiLZq+1ks/40D3juJqFMUGlWI6b+qbMb2KvtYre2/ajEYCklOSvD2o0n4deC6IqBXqfgm/tx/uGyD9H66H5WXLiMvJ1FSonEzd1VFZXR4iASh5inggpvLeOFEXqzHZQZMaKeclJmglQyge3YNAQPuvuEEDSHFpGzskyURqfKGx28qp8FZSuwpc3e5FZG84OoioYiBDGjgmXRdWiKTtpMAoIyPYqqaFR562iy51O083hUH341eNhFAUUolBnuYuu+vXvw+wPMmTMXG5sOtY1JJUnMiTPHnntUu616e7v/ok7EsY5xdEObFoLBFSGlLZkcmJwhBofDcO21Dr/8pVv0ZhgwOenGRuTzrqv34osd1qyRdHYKslkYHRUkk99lbOxrmOYQur4S2/4WmzadhM8nyeVA0wSqKqmqch29w8Ou07i8HDQNTj9dcsMNDt5j0MerqiAWcx3F9fUHHx8fdx+veonUE78f1q2X/O53Nlu6vsfipW9BRGJ0dwgWLJDMPXxX8BHJZrP4fH5CVSFUQwXp9mAEygMU00VGD4xRVl+GY9uHFLqpujqd0bvyiuVUzCtnz72tDO4ZopgqovsN4nNi+KM+hvcMgxB4XzAeVjUVf8RPLpHDKlpoHnfclx3P4gkaRyyCa3ukncxYlsa1DW5WsGUzOZCi9YH9rH/z2kNyhk8YVgF9YgfeTC+RhT5KJ6/ACdS9ovN5gp7puUGMY1tUi0SifPe7P+F1rzuP73znq3z4w58+5vPPcvzMisGzzPIX5qGH7uMzn/kw/f09vPOdH+Tv/u6jPFnr5W5PBt0++IdYIAhKhW7VPOxxQlJBBbI4ZBUHCwhLhaxw3Jxe1WZSOCy0D4qjCoJKR2W/XiJfkPgRlJCkhYNfikNK4tRUB769/42W6py6KBUztozckncjvTOXgp/S8wiYdvz6EMy1dTpVkz1aiXXW0Y8wVq1ax49+dAfbtz/Hpz71Aa644iwueOObGf7qh8jURKi1NRxgRLUJv+eNLIsEeO5dH+XGRJbvf/8X+HwHhSATSatWols1USUstA2abf1VLYo7HAoKb8y/FROTNn0vJUp4pY9TSqdzSeGqGc9dGJvH36/9O3aN7SVjZqnwxUnXjHJ74Kf000PMrqAg8gyqfawy1zPPmuksLTOi9GUltmOhTrlFbWnjSJuo56CoJISgylfHmsBp3O+5j5gep+wFH+amKKFJHUN6sB2L9vReJnIDlJlBdoafo9yppNKoJaGOE5Rhziiee8z3tUttJ6GME5CB6UG8iooXHxNijG618yXF4O9851/58Ve/yaIL1zP/mlOxsFElDGVHMFSDdVUrMTC4Pvdu7vb+mp3GFnIiR4PdxHnFi91MZE3w9a//F5WVVXzhC5/kqf2b0C6omRaCAYJGACWnsHNs70uKwTFPJYqqMWGP4lcDqGhgwaBvgLzXbfKWwiHiRCmzIrRqu3jWeJK6whtf8j55VB1d0TGdEppy8Oe7ZJvoio6hHFwo2Dq6k1v23MZI9SiqULGlzf5EOwWrwKrK5Rxui+fzCCGoW1FL1cJK0iMZVE0hVBWaHiRaRYuR/aNkRjNoHo3yeeWEq448kT6j/lT2JdppTRwgpAcoFAps/+lD9Dyym40rzuWjN36O5vJ5L/nej5fftv2ezc9spWaoAm+uiny4wL7qDnQMAmYQb9hDy8Zm5pzUCMKNkhAvcHLkkjlSw2k0QyVYHqCstoxbH7iJR7ffx7988Gts2HDajPMVJgs4tjMtBIO7TbSULaEaKsF4AEVV8JX5GO+eoOuZbirmlyOEQNM0rrzyGi655HXcfPN/8o1vfJG77voVH/3oP3P99e/+m13smmWWWWZ5KYoNF6BmetDGtiAcE6RkU2cOr8fDqvVnvarnlkgsbMJOBImDJUwijh8dnXFlDBPzJXc0uYXGXXSrnQyrA2zRnmVcHcHGxpAequ1alljLKXeqWGmunY5gsrBo0/YyqSTpUNt4yniEoigyqA6QVCZ4RjzBavMkYk58ShKGgnj5bP6claEttYtEcRRb2nhVPw2BFsJ6hFA4TE1jHeP9Y9T4G8mYKdJmkjr/HAJamL5cB0IIKjx1hA03HiJnp1GF20vgVf1TWb9JerPt1Pgb0F4QjxU2oqyKn8JIYYCsmcar+qjw1aKg0JHeiyNtHGw0odMQmMvc8BL0qfFJzDOzUEoIQUALHVW57vPs3r2dxYuXkdQS/Nz/Q/bpuylSwCv9rDBXc03+bYTlS+em9vX1/EXFYM1QcZxDna5SSlT9UDfkkiVw3XUON92kkE67QnAgIFm7Fi6/3KZ2Ktp44UI3o3dk5DbGxz9Kc/N/4vFsYGDgW2QyF+H37yWRqMTrhTlzJMUi5HKgqq4zGCQTExCNulERx2rM9PvhnHMkv/qVoKPDFbJTKTe64tJLJYGX2ax5wQWS5557gnx+mFDobXR3w9y5kmuvdTjWoU4mkyLgD2EEXPFVeYG9uZgtkRpMUcqbJPsnsYo2lQsqUFQFKSXZRI6KuQfjHWqX1VC9uIq+bf3svHs30pEYfp18Ik9usoDh1/G/KMbBG/bgi/qYHEy5iy+OxBMwmHfmXEKHGSMX0gWGW0cIxAIYfvf3Q9EUwtUhJnoSTA6mjjr/91gQpUn8u/8LfWwrSBuQeLzl5Be8BbP61BN+vpdjw4aNfPCDn+DrX/8CGzeec0KNIbMcHbNi8Cyz/IUYGOjjs5/9GPfc8xs2bjybn/zkt8yfKgsKytxUoZhEeYGYlhOScvvwn87zLYMyR+EBTxYABQlSEJIKlxRjeKQ7sLSZ+YtuCYkx9bVH9RyPGjkSik1QKpxc8nFOKYAHAY6Fb99P0VKdWGULQNHByqOPbsHb8RvyS945fUwHybhiE3iRS1lH4CBJK/YrumcrV67lnnse5xe/+BH/8uVPY953N+Wf+3vyN7yJkKIRcQT9qs3iN17OjUYDn7zxzVx33aXcfPOvCYfLKCL5hXeSZ/UCpgCQBKTCBcUAF5aOv2TvaJBIRpQhciJLhVPFB7Ifp13bx6SYpNypOKKTIeaNcEb9KdP/70gHWbB5yLifIa0fQ3o4rXQWV+avPcSNW+mtZdhbw2h+AE0xEAJMp0TMU0mVt+6Qc9U5jSyyl7FN34zfCqKjY2IyMCU21zh1dGb20ZXZj1f1c1nqap5yHmeXbzvDDLBSW8e5xYte1sH7PLZjUXQK6IqBOvWP86LmYIlEII44QZNS8uUvf4bvfvdrfOQjn+E117+O3xz4PR2T3YAk6olyUfN5LIsvdu+njHN9/l0kChMURYG4UzHj2EIIPvnJL1BRUc0///PfU9Mzn0X/MG+Gk0MRAtN56YbfoB6mMTyPLYWn8eQNFEAqMBlKg+b+xDnCfa8CgVf66VN7XvaezQk30hSupy3ZybyyOWiKRtEuMpwf4fS6U4h63UGjIx0e7n0C07FYIpcz6QygBB3CuRCD2RFqi6OkApOcY170kufTPBrRhsiMx4rp57N0R5HS/R74yrwsvmARdSvc2YmZNxltH6OYLuKL+KhoifOeFW9j0+BmNu3exCNf+SXjvUNce+q7OH/VZYzuGGfyQJqlFy+ePsaJZDQ3RuuT+6nfWYUhPAgDApN+VE2lY20Pl11yMeXB2IxW5Yr55XQ82YU/5kdRFArpInbRxggYKGGF+zffxe0P/5jXnXw9K6o2HHJOT8iDoipYJWvaXVzKlbBLFv6Yb4b7wlfmIz2cxipaM4pkdF3nXe/6EFdddS3/+q+f5V/+5aP87Gc/4HOf+zpnnnneCb9Ps8wyyyyvJtIIkV3+AbSJXajZfmzVxyO/+hYLly1l3BwiplRiqMdXWnwkBpV+BrVeTimdPh0Z5ZV+EJIhbYBetWu6z+HFWFjc5f0Vj3r+RFIkaNP2IoXDutIphGQZm/THecp4jOfspyl3KnnYvp+35d5DUIa4xXcT+/U95MhxQN9L0Cnj9OLZgGCv2ElGZGjX9hErnUqRAioqdfZMkdKRDpOlCUynhE8L4FMD7E1uZbwwTEAP4xUaBTtLW2oXi8pWEnaiDPUN4IueRt7KInFoCs5nefQkDNVDUA+zP7WDiMc1dBTtApa00ITOCxeKvWqAvJUlb+cIKTNFKZ8WoCl48H6VnCLbxp8kURonoIXwaQHyVoaRwgC1/ib0qQ4AKSUFO4cQCp6pmIljpa2tlWXLVnG7/xa26ZtpspsJyCBpkWKT8RgBGeTN+Rte8hjt7fu54oo3HPO5XymVCysZ3D1EMVPEE/RMl8safh0lEmPTJoGU0NLiumnzebeYbWJCsGiRxDBgdBR27YL1692MXoClS2HRIsnWrf9OOHwjivJ2bBtWr/4emzb9Adv+ERdf/Al27YIFC8BxoL8ftm4VaBqsXg3z5kkKBdi7FzZtEpx77tHn9AKceabE74fHHhOMjkJzs+syPumklz9OIAAez++Jxar40IfWEA47LFjAMbmTn2dyMkk0FsMf8ZEZzU6bFHKJHIO7hzD8OrpPwwh4GOsYJ5/KUzG33P2eBDw0n9zEC6eEiqrQuLYBX5mPjic7SQ2mUDSFllOaGOtKkE/mpwXhfDKP5tVZdeVyEILJgUlUQ6O8JU6kvuywP+dWwcI2bQz/zDmO5tGwSzZm4fBGsOPF03s/+ugzWOH5oPlAStR0J74Dt2JFFiG9h5bVvxjHdkj2TVLKFvFF/YSrQ8cV8/ORj/wjjz32Z97//ut58MHNBIN/2TLP/+vMisGzzPIqY1kWN930Hb72tc8TCAT5j//4CVdeec2MP5zLTYMqQ6NDMWl0dFRgSLHxIFhv+Q573JKQWIAmhetmA6SADA4PGBkqHI0ikna1xALbQCAoIpkQNheWAmwy8tzuTWNIQUQqZITkt94MWSG5uhhCTR1AS7VjhZpdIRhA8+H4azDGtlAoJpAe10WqIGiwdbbqBV64K6iAg4qg/DgK6xRF4brrbuTA1WfwwGe/SOeHPsvkT+6g+sffwLOgBUMK1pV8vOGcS2i69Q9cf/0VXH75mfzwh7czuLCOTUaBOkcj6Lhiz4hicb8ny0LboOUI5Xwnigkxzm99v2SXvo2iKBB1YpxZPJ9zixehcmxL8AoK5xYv4pTSGYwoQ/hlgAqn6rCCtqboLI2uZdBTwXC+H5BUeGup9TcedrKloHBV/lrSIkWHth9whcq51gKuyl+L7VgM5rvxKB78WgAknJe+kJMnTyXrpDkt9hoiRvyQ474YKSUDuS56sx0U7ByaouMp06n0VdOjduMXQQxpUKJInizznEUsspYechzbtvn0pz/ILbf8gM997mu8850fBGB+tIX2yS4sx6Yp3EDMGznktVEZeyljLDfc8D6yRpF//cynuePz3+fKT92I4fNQtEsU7RJL4wtf9n3O9y/FF4nQZbZRbzUiPaAHvORFnqgTo8w5eF2lKWH65dAUjdcvuJyf7f0VByZdp76CwtL4Qi5tec3083JmnqHsMDFvhHA+QsvwEg7U7CQXyZIpZTmg7uM063TOKJ77sud8MZ1Pd7tZuo0Hs3RTQ2laH9xPrCmGmSux43e7mexP4v5ZEsTmxFhx+TIaUnE++cnv41V8fObyf2PlmnXTfwOT/ZMceLSdygUVMwRRs2Aysn+U9HAazatTMTdOWe2xOSXGEhPorSq6of1/9t47zq6qUN9/1m6n1+ktk5n03khCIEDoRZoggoIKKiqoqCjYe+8dRa4goKiAgCAgIL1Dep30zEymt9PbLuv3xx4mDOkB7vf+7p0nn/yRmbPL2efknLXf9a73Rbxmepeg92pomwT9xw0iuxwUTXEzgj0ajQvHMbhriIGdgxg+nWx/lgEtQdekXlatfo6Wh19g5sLFLJ13xj5dPWXj48TGxRjYPkCoKojm0SikiwCEK0cPmq2CiTfiGzXp8HrKyyv50Y9u4H3vu5Kvfe2zvOc9Z/HOd17Cd7/7S6LR/96cyzHGGGOMw6FAgRXGS6zTVmELmxnmbBZWHIMRn0FLYjWvrl7BgmULWTf0CgEtzNTonL3co28Vr33t+2UAP65lMS/zyOE/+2OF/hKPef9FzClHINDQUKXKZm2ju2pKmESdGALBJGsq27XN3OP9K158rNNX0WRPICWSbJMtZEWGFn0Dk61pdCsd9CrdtKo70TQdW9gcXVrKLHNPeWjOytCSWM1QqR9b2uiKQUALkSgNEDbiaIrGzT+5kUXLjsZb7qFz47954Kb70FWDs845n5ARotxbTY2vYWTsF/WUoQmDgp3Hq/pQhGtBsWSJqLZnHGc5JqpQ0YX7nSylJG+75hOfGhj1PdZf6CZRGiQ2PA4s2Hk0YZA103Tm2wgbMYaK/ezKbCZZGnLHBp4KmkPTCOiHJ/zs3t3GMWctY5O2nnq7gYAMAhCSYSqdGlYZr3Jm4Xzict9j0p6eLjo725k796jDOu6boXp6FUNtDbSv6iDd6+Zm636dQmwCv/9zGUMJ91pGIgqnn+5QVgY7drji8GtduvX1sG0bPP+84Kij3PerqsKFFxa47bYV1Nd/nvp6N54hFhNs2XIymcxLLF4s2bBBkEhANArFoltAFw5DZaWbF+zzuX9XrRotBnd1QVeXwOdzYxv21eurKLB4sSv+FovuYw41cxjg8ccf4rTTzmTp0jdnznlNDJ5w/ARaHt3sFv1qCv07B1EUQd2cWnxhH4FYgP6Il2RHEqtoUTurlnB1iN1rOtnw0CZ8UR/1c+uomVmNoipUTCynvLmMQqqAoqvoPo0ND21i14utpPsyaIaG4ddpPraJ6unVCEWMiv3YH76oD3/MR24wN+IMBldY9oY8BMuDb+p67BPHRu95EceIuUIwgBDYoUa0xGa0oY2YNUsPuIvsYI4ND25kYNcgdslG9+tUT61i+hlT0X1HtnJN13V+85tbOfHEefz859/lq1/9wRHtZ4wjY0wMHmOMt5Hu7k6uvvp9vPLK83zgAx/j85//JuHw3mJGudS4NB/mH940raqJA5Q5KucUgzTYGs/qOTpUi6AUzDK9NDo6m7QiKcXhtJKfjJAUhcNuxWKHavKqXmCm7SUrbHoUhwJgIFCBGZaHY0s+bgwk8UpB/XCecFiCIWxeNvIsK/motgrgmKCMFg/dEpA8wi6OGj4vNX1s1opsV0tUDgvRvYrFLMvDFOvIRdc2xeQZI8eGiB/1D9/m+A9czJqPfpb2Jedz1C++T+S95zPPcs9xytFH86MHH+M7l7+XM85cwpk3/gbt3GWjcpUrHJUWtcRmtfSWi8ESSatqsVMt4eDwkvFXtmlPU2vXUSYrGFT6+IfvDnzSf0SN0eDeyLyW33sgDMVDY3DSKPfGgahzGvh09ous19aQUAaJOnFmWnPwywA5J4PplDCU0dP1ARGiZBexnEObwe7MtdKSXI0QKl7FhylLOP1FjtNP4OHKB+lS27Gl646vtRv4QPaj1NuNo/aRSAxxzTVX8MQT/+ZnP7uJSy7ZkzXo1/3MKp/+xsMeNldd+kl6GeS2r/+c267/CYuvOQetzMus8ukcVTX3oNsLBKc753Bz6AbWK+sJyTA5kUOXOn4ZRJXqsPO6Haco8HVF2SFaGR9uOGCDd1NkHJ+ZfxUbBlpIlTKU++JMi08eVdTg0Qz8up9kIUXcG2NS9xxi2Uo6gjvptfo433kvF/gvPOyMRMdy6N7UgzfsHXG7CiEIV4UYaB2kf0c/nWs6SexOEBsXRdVUrJJN//Z+/v67P/O9Wz7PlEkz+NDCz1JeWTHqRjJYESTVnSLdkybe6LoSRruQJVLCzpCHqadOoWF+/T7PcV94kgaegod8vEgAv6sIlKAoSmg9Gptvb0Ev6gjFfS7TTp9KeXMZ8y+ZR+faTgZah7AqLVYNrmfT8pfpfn4TtfOnoZ5Qw7+LT/C+2otJlzKEjD0Dd1VXmX3uDDY90sJA6xD2YA5/3E/VtCos08Yq2ai64kZH5EwmHD9hrwy7NzJr1jzuuedx7rnnDr785U/z0kvP8vOf/xfHH3/4ov4YY4wxxttNiRJ/9v8XLxvPoaKiSIVV+iusNVdxavdptLSvo7+zjznz5hMxykiVhticWMtRFcePxAq8VVQ7tYyzx7NF20TQCiEQw6Vt7dRZ42iwx+932xXGy4CgzClnl7odEMScMrrU3dg4VDrVmJQoCtfZW2s3sFFfhwDq7Qa3XFek0NDxSg89SheTmcZscwHPeB6jV+lhu76FWrseBZWiKOCTPhzp0JJcQ3+hm5ARRRM6RadAV64NR9rEPe4k8kBvHz/5/PdIJZKEYxGOPfpEHnrwBcaP3/c4MaLHqQ80sSuzhYyZdKOsAEWo+FTX6Wg6JXJWhoZAM17NT6o0xPb0JpKlAcCNimgOTiU6HDuWMVMU7Dy9+U4yZhpwEEJF4tCe2U6tr5ENieXkrSx+LYTEoSvfRs7KMK/sGAz10KyguVyWwcF+4g1xOkUbPjl6NZFf+ukTPeSUDHF732LwypWvADBv3qJDOuZbgaqpTD9zGtXTq0l0JFEUQZoYt9wZRyiua1cI6OmR3H+/wlFHOaRSgi1bXOE2EoHaWkko5Gb9Os4ewbWmph+wiUYr8XolpRLs2CGoqamkVGphwQLJzp3w3HMKXV3u9uA6iiOvux1VFLCHF3GaJtx3n+CFFxTSaYmmKTQ1ufEN4/aTriHE4Tt629p2snVrC5///DcPb8N9kEwmCIcjjFtQTyDup3tjD7mhHPlUAX/cjy/sip9CEZQ3lyEUwfQzp+ENeVlz3zpKWde1Pdg6xMCuQXJDOSYtmziyjS/qc8el96yjd0svtm0jHUmoKsTMs6cTb4wdljtW1VWajh7P+gc3MNQ2hCfsxcyVsEo2k06ciC/ydpQbOm5Ej/JG+U8BOfy7AyAdycaHN9GzuZdobQTNq1HMlGhb2Y4nYDD1tIObZfZHY2Mzn/jEdfzyl9/n0ks/NKqHY4y3lzExeIwx3iaeeupRPvnJK9B1nbvvfoyjjz7ugI+fZnv4XNbN2LUEjLM1Skh+70+wXTWHIx8kTxl53lUIkReum8FAIS6hT0h6VTfuwYeg0daoQ2OdWmCmZTDV9lDtqMw0PSQUh4SwKZOj3WgxqbBdMelTbCoCDeBYeHY/CkLBMSLYofGIUgo7MgHHO7rQaIbl4bJ8hP94snQpFjqCE0p+zi4GMQ4hjqFb6eR54yk26evwywBHlZZQbx3NLf4cXYpFXKp0YbN96QxmPn8/Pdd8jZeu/BRTn36RCd/8Dc+GczzsydI/r5qZz9zHpo98lrve+36mfu7jNH3xiyjasICFAAT2m5uE3gsHyf2eDE8bOTLCISPStGrVTLGWEZUDCKDWaaBV7OBZ43GWlI4/7LK1txu/DLDI3DszylC8GIqXolNAaIK8ksfn+LFtC0Mx8Kr+fextNI602Z3dgUBB8ah06934HC/BQoiZvbM4RjmJ5YGX6FW7qLHrObV4FuPtiZQo0aptx8Qktz7HNR+8gkRikD/+6U6OPn4pBTt3SMc/HHRF56vv/RrTG6bznWs/xzNf/xuf/NZX+NCySwkaBwlBG2ayNZ2rstfygvEMO9VtTHVmcnbhAjbp62jTdlKwSwz2pfFtKefBrqd5XH2ZmWVTuWTqO4l49l+2GDQCLK5ZcMBzP6ZmIXdtvZ/BwhAxT5TgUBxPa4aTY0dzSf2lGPLwZ+8lEsdy9m5FHv5npi/L0O4k4ZrwiMtVM1S2pTby81u/zjFLTuDXP/0Tq/6yDscZ7cKSjoNQBOJ1dpLWV9vo2dxLrCGGZqhIKUn3ZNjyxFbKmuL4Y4f2mseCEcoDMVpLHUhH4uvzIFMSLa/gswwsn0XVrEqklCS7kqx/YAOLP7CQQNzPpGUTmQTcu+VBNl7/PD2vbKV57nwaps2k2FVkW7yVP/Tezn9eeZYT64/l5MbjR8T8QFmABe+ZPxIBESgPkk/k2fDgRpKdKRzHwfDpNC4eR+PCQ8suFEJw4YWXcvTRx/OZz1zJJZecyQc/+HG+9KXv4ve/tf8HxhhjjDHeDOv11bxqPE+9PW7EvVmkwFptJdXFcto3tQIwedY0FKEQNqIkS0MMFfuo9O0dZ/VmUFE5p3Ahf/L/nk3aerzSS1EUiTtlnFt4Fwb7F5/TIolHuhOuARlCRaFECQAHByGhoOSpsxsQCFRUShRB4EZRADGnjJAMkxRDIzFcHWo7KZFmqjmTo0vHkRUZVhuv4pVersxdQ8ocYqjYT8iIjojjXtWHV/UzVOqjZBcwVC/X/egrACRLg0SMOAvKjjugKGVLG1WoCAR5K4cqNMYFJ4GEtJUgX8yiCJUqXx0TwtPIW1nWDy0nY6VGsn0HCj3krSxz48egKToduV0kSwMjxgBVaPi1ICXHIm0m2JpaR87MEPPsmQg2FC+J0gB9hS7qAvsuzHOk7bqMFR1D8dDR4UZqTa2dSauzi0GlnyqnZuTxg0o/USdO3N7/aqtVq16hurqW2tpDn1R+K1BUhfLmMsqbXZH6vvsE6Yxg2rQ946Hqati8GV55xc3gNQyBrks6OgQdHa7rd9Gi0c7b4VsbZs2C3buhVIJFixza2iRr1ri/f/e7JQsW2LS2Crq64MknFaJRd5K9u1sMO4ChoUGSTsOrrwoefVRQXi6prYViUbJtm+D22xWuvdbBt+8Fq4eMLJWwlr/MQzf9Cl1ROdpycBJDKG9itVMyOURV1XSEECPX2bEdCqkChVRx9PEdicAVZHe8sBMzVyLeGB95b2YHsrS+2k7d3Dp8Ya87jitZ7HhhF10buglVBgmWBxlqT9CxuoNSpsj0M6dRNbVypDzuUKibW4tqqLS+2kZ2IEuwIkjD/Hrq5x3e55+U8tCEaEXHjM/Gs/sRHF8VDI9XlUI/0ghjh5sPuHmyM8nArkEiNeERF7A35MEuWXSu76J5adMol/PhctVVn+Wvf/0T3/jGddx2231HvJ8xDo//WUrEGGP8L8CyLH7842/w61//iGXLTuPXv76FsrKDLwMH8KIwzd7j8vurN8XW4ZgHbdjJ0K5YPODJcG4xiIYgh4MfhYRwsHGX/ERtbThvFcqk2+h7TnGPc82UEi+CrHBG5fxmhcQr3eI6vW85SnEQJd+LVDS0fB9aYgtWfDq5xnfsY2YR5lteZlseBhQbjxRE5aFFIXQpHdwY+AVt6k7CMkqJDjb51+FzNpLlAqbbXgSCkFTZqBVpjxpM/a+fMum443jiui9xwaurmXr7b4hNm8IkW0cGy/D/9Q8oP/8tm7/1U5LPvcI5//U7wg31ZISDLgXjbZ08Div1Aps0d2A/w/Iwz/TgPYQ24jeyTivymJEjKhXqHY3dSp5dmOxWJ1PtbKRMJgEIOxGG1EHyIkdI7l/02xcD9POK5zkECkuKxxMhetjneSTk1Cw9ZT08qjxIv7cPAw8By8/U5DROtc4eVf4hpSSfzzEw0Mfg4ACDg/0MDvbT09fF2o7ldNBOv96PKUwURyFshanIVVKrN2BLsIWDHYHHyh8iH8uxrmIViYoEAy/0svX7LdQ1j+M3f7gZtQyW9z+DrhhU+xpoDk1D28d78kjxqAbvXfZeznriTK655gp+fO2XMNvSXHvtV9G0QztOo91MY3704CpfyLNT3cY92x5gy87dTPA1EYwFSJcyvNKzEp/u5f3TD1wmdzCWNSxlsJDgle6V9Oa2oas6U+OTeM/UCzDUI1vGpWoqFRPL2fVyK/6YfyT3Np/IY/gNAnEfju2gvs7hunLzi/z83q8za9wCfvvz24nXxok3ROlu6XWz8lQF6UjSPWmi9VHCNe77yLIsNq7cTEbNIC0o02OoQiVUGWRg1yCDu4YOWQwO14aZOnkyciMkehPIlARD4JUeDNUgnyyQ6kkTrYsQq48xsGuA3m39NB7VAIBpmvz6q9+h55WtzD5lGY1zZjCQH6KEiaKAP+GlWF3knm0PEjQCo8oFhRCEq/f8H/cEDBZfvojB1kGsgkWwPEDoCLLW6uoa+NvfHuLmm2/ge9/7Es888x9+/es/MWfO/icJxhhjjDFeQyLpV3opigLldhVe3noX2nZtCzb2iBAM4MGL4RgM0c+O9duIlsWoqHFjIQSKW/QmD5zLf6RMsWbwicz1rDBeplvpoMKpYoG5mHH2voXI15hkTWOzthEHhzKnnCq7hja1FRsbr/TSpXYSd8potJuRSLqUDpqsCeSVPANKHzVOHTo608yZvGg8Qx53DLBNa6HcqWCueRQaGhEZBQs26GvYrbbidTw4I1m+ewhqYTJmgsSw+KsKbSS+oc4//oDfJ1JKtqU30JbZhkfxUOmrpWjnyZhJJodnEdBnULQLeFU/ESOOIhRaM1vJWElixuuFXA9DpT568rtxcMhbWTShYVJCFx4kNlkrhVcNYCheevOdeFTfqHNzJ04lWTPDULGfgp3Do3qJGm4cR3e+nbbsdvJWBlVoVPnq2dXmTiBMrZtBrpTlAe/dlESJkBMiqSQwhcm5pYvws//xwcqVrzBv3sI3lXH6VpDNgqruHU8ipaS1VRAOuyKszweKIunuFlgWHHvs6H6NYLAcRVFZsaKXigqBELBmDXR09FJe7gb3KQpMmgSTJkkcBwzD4YknBCtWCPr7XUdwJALr1sGNNyokEm5sRNmwudrrhaYmSWsrbNkimDPn8HKFRz0/y6Lw19uwXniGlRvWMCtWhn7/PRS2bMb74auOWBDu6elm6dITR/1MURVqZ9fS8uhmilk3G9ixHRIdSYIVQYIVQdI9afxxP0KI4VVoEl/Mx2DrEN0bu+nfPsBQ+xDFTGl45VsMw2/Qv72fZFcKq2Cxe20nhUyRulk1zD5/1iELwkK4kRLV06qwShaqro7qrzgYqa4UbSvaGdg5iObVqZtdQ/28ejRj//fepYbT0BKb0AY3II0QwnaF8kLj2djBAxsTSnkT+w39FgC6V6eQLmDmzTclBvt8Pr72tR/w0Y++lyeffIQTTzz9iPc1xqEzJgaPMcZbSFdXB1dddRkrVrzEF7/4HT7+8c+NajQ9HPI4rNUKlEsVbdh+JxDUORrbVBMkzDI9rNALxKVKVjhkhKTMURhn7/mgtgXob/jeDkuVBSUvj3iyGEIQkQpZIdmtWCw0vTQUcnjbH8IONWGHm5GZXeTNNCUs+oJROqqmUovJNs11MTfaOo3DArSGoOowM4Kf8TxOm7qTqdbMkTK1lEjwlOd5ppbmI3BLwGodjcqSyhqtyBlmgHdfcDVbZyzjko9dwmMnnM1ZN/wS9YLzAGiSBunPXU3dkkUs/+A13HLcycz92Xepetc5HGP6aLQ0bvOlWKnnUYev76t6gQ2al/flI26J3mGwVitiCUmZ434J+/HhJ0ce6FeilNmuGJxSklTbdfjk4Tn5bvPdxO3+PzCkuMv0KpwqPpy9hgsL7zms/Rwum7UN/MV3Myv0l2hXWhEOxIpxoukYnbs7GdiYwt9yH1u3trBt22Y6O9spFPZuw/b5/ehRjVLQHK6MU5CKZIA+dst2WkqbKdglrJKJXTCx8yZmsbTXfnZvbeOzH72KhnHjqB1XT0VDJfHaGAumHsOxU05BPdw65IMQjcb405/u4Te/+RE/+tE3eOGFZ/jNb2494iZqHz7iqSr6W7MEK3W2Vq8k60kRzscJdZazpm8D/fkByn37XuIopaQr20PRLlEdqMCn7W3RMFSdi6ecz/H1S+jJ9eHXfDRHG9GVIxOCX2P84nEMtQ3Rv3MAw6djmw5CgealzVROrcL33E5ygzlCVSE2ta7lB3/5ErMaFnDdpd8mWhlFCMHkkyaRTxYY2DlIKV/CKtoE4n6ajhmPqqmkSmn+vOEuert7kZaD7TjEvTFmVUwjqLmu7H21cgNYJZu+rX0kO5MomuvCiY2LMeuMGRT6C+xqA2EIdF3D9Fh4AgZCUUh1pQjXhNzPaiEwc+77LpVK8olPfIAtL6yi6ZzFTD3qKLJWjrxTwFANkALvgIdafxU702081/Eyi2sWHDDqQzNUKicd2uTggVAUhQ9/+BOccMLJfPKTV3DOOcfx5S9/j4985FP/z29yxxhjjP+5DCh93Of9Oxv0tZiUKHcqObX4DpaUjn9LS3VVqe4ziddWbIRHY8eW7UyYPmnk88otldUJaoeXC3841DkN1BUaDvo4B4cepQsHh0WlY1mtL2eztoG4U065U0lSSaA5OjEZp1fpJuxEyIoMfVoPERnjncX30KV0cJ/vb7SK4nBUVJZJ1lRmmnOJOnEsYdFsTxoV2RSUIbpFJymRJKY2oSseik5+1OqnopOn2tdAQA+TKPVTkO7qqIbABKp9DTjSpr/QzdBwpEPMKKfcW40iFHJWhu5cO34tMLJPj+olVUrQkdvFwoplqGL0GCprZhAoo75XhBCoQiNtJslaKfxaECkdik4RBxspJQhJUA/jU/0U7Dz2G0R+KSW2tOnM76I9twNHWgihEDPKKfNWsyO1EQn4VD+WNNmZaWHV1lWoqkp1dR1nFs7HLwM8bzxFSklSZddyQukUji0t2+/rats2a9as4FOf+uJB3wNvNw0NrghrmqAPD80sC4aGBKoqOeYY2Lp1j1gbjUrKyyUej6StDWprXdfvhg0ewuH55HJPMHWqe/+TSDg88siTnHfeVeTzjHLyKgpceKGbRfyHPwhqalxHcG2tG/Wwbp2gVJI0jk5owzDAtgWZzJEJwaY5/Fx3bMR6+XmUmjq2v1RgTl0DysQpWC0bMF9+Ac/p7zik/UnLAkVBKAqWZdHd3UFd3d7j8vGLxpHtz9C1oZt0TwYhBIGKANPPmoYv4kXRFKyiRSKVINWddkvdfDpCVdj+3E6KmaLb9aAqmNstErsTgCTZlUb36Rh+A7Ng4Y/56drQTfnEckIVQfp3DGBbDpGaMLHGGIVEHkVXCVUER5UIAyBAGepDFgvIqmqE7+D3h6muFCvvXE2mP4M35KWQKrD+wSFSXSlmnTtz72MMY4cayc75LEbnM2iJTUgjSqnqaMzKxe4b4AAE4n6MoId8soA/tudNlU+5//aE3vyk4tlnX8iSJcfz9a9/jqVLT0LX39x9yxgHZ0wMHmOMt4j161fzvvedh6Io3H33f1i8+Ng3v1MBQu71IyQSFcFlhQhVjsYKvYBfKpQ7Co22TvmwIzeBTR6HuaaXPmHRoVp4pGCCbfCOYpCckKzWC3QrNl4E8y0PFxVCaNnNKPl+rFATBU1nZayCAWERMHOE8gPcTAs9kVoijrvUzC8FS0s+zi+GRoTrw2GjtoaoEx8RggHCMgrsYEhtA3vayM8VICAV6m0dBcGUKTO4/Il/c8dnPsO/rvgo6Y5OjvrExxBC4JUKJx51PO958jl+dd2nePWKTzD39vtY+r3fsH6Ch5V6gXG2jm/4uDkclusF5ppejrIO70utIOSoD9SYE6fCrmSrliXpFChQYFDpxxQljiuddFgREU8aj3Jj8OeYmFTbdYCkR+nil8Hv02xNZJ618KD7OBJy5Pi77zZ2FrYz9Owg4jlB4cU821u2Yne54WIreZW6+gYmT5rOySefSUPDeMrKyonHy1GDCkNaD0pQoPgEf6v5CyktQYM9Hk3RsaVNWiboFD2EX5rIVGsGYSNE2sywOruG3sHNDP2ijVxvlgWfWEzFtCoSm/sptZewWm1effplejq6cGxXHDQMg8bGZmbOnMu8eYuYP38RM2bMweN5cw3liqJwzTVfYPHi4/j4x9/PqacexTe+/2PmnTiPol0gpEep8tXh0w4tQiJjZuko207/lF1IzUazDAaDPWgRD9UbJ5E1c/sUg3tyfdyz9V9sHtyG6ViU+eKcMu54jqs7ei8BUAhBbbCa2uDBiywOlVBliAWXzKNjTSeDrUMIBcLVYaomV+DxG4xfMp7Nj25m28Yt/OjeL9NUMYmPn/VFJh83eaRoLVwTZvYFs3jltuXkBnMIBWzTZssTWzH8Bo8WnmJ53xomNjbia/FgaTb9+QE29m9mhmcqRsAgWh/d69zMvMna+9fTvbHHvRGVkp0v7GLCcc1MOK6ZySdPJtGZJFgeQPPqZAeypLpSaB73RsCxHKRwl9r54342blzLlVdewsBAH5+5/ls8Z61jUCZQpIKNQ1rN4Ct6qTTLQUDICDJQGKRkm6MynN9uJk2axgMPPMsPfvBVvvnN69m0aR0/+MFv8R5JHfcYY4zxv5oSJW71/YH1+ipq7DqixOhTerjDdzNe6WO++dblqE6xpvMfHmJIDBAbLvNKixRSSKb4ZzLQ9TumLphKzspgORa2NGkITCCkH54YnCPHLm0bAOPtCfjloX0P74+d6jb+6b2Lndo2JJIGu5GTimewW21lo74Wv1PB6dlzOaZ4PAYeupQOlhsv0qV2UmvXs6h0DBPsycxiHpaw+JfnHnaoW6mx63l3/gMcVzqRIkVatR2klCRx9nzXJ5UhQjJMmVNBQAtR7aunLbsdy7HQFYOi7RbeNYWnUONrJGdnsBwTvxZEVwxsadOSWEVXvh1HOgigXWynzjeeKdE55OwMJac4ajUXuPETBTtH0c7j14J7/U7ijFqKLqXEdiw3T9hMoAiFgB4mY6XQFB0pwZYlV8h1TKp9DSRLA6TNJIHhzOCMmcR0SkgpiXnK0RUDyzHpL3TTlWvDq/qJetxrY+BBEzq72rdTWV09sjrrlOJZHF88hZzIEpBBdA4sHG3ZspFsNsP8+f99ecH7Y948yYsvClpaBPG4u6pzcFBQVydJJsHvhxkzXDduMumKsO3tgl/9SkXXXQH3vPMcNmyA5ubPsGbNFdTXL6CiYiHPPPMrisUsW7Z8kO99T+G44yQnnSRHIiU0zRWIa2sZFVMBEIlIdu8W7NolKRYFoZAkHIZ0Gjwet3TucMjn4amnBC+9JMjnoTarc+xALVOb/OwY6ufCmXMRuo7wB7A3rIODiMF2605KTz2OvWUTwuNFW7SE3gkTsG17nyYNzaMx+7xZNMyvJ92bQfNolDeX4Qm647TKyZWs/scazII1IgIP7U6iGirSdqieUe1GnyluJIKZM0nsTiIdiWZoFLNFNEPFG/ZQSBdoeWwLUkrMrIlEUkwXkUgCMT+qRyM+LsqUU6YQrnL/Dzq9PRTvvROrZSOYJkpZBfopp6EvXXbAif3WFe1k+jOUjS8bEX6LmSKd67uon1c30r+xL5xAHYVJh28kCpQFqJtTw47nd2GbFrrPoJAqIB2HxkXjDuhIPlSEEHzrWz/ltNMWceedt3HppR960/sc48CMicFjjPEW8NRTj3HllRfT3DyJ22//J5WVb16A8aEwzfTwjJEjbqsj7tUexSYmVZpsnZBUuKAY4sxigKJweEHP86gnxyo1T49qkxUO5Y7KvZ4Uf/eBifuffpytc3EhxPsLYU4q+RlQbALD8RACkKoHqegIp0SbCv2KTZnUCFo2CINOXadbsZhkGcSlypBweMKTY5yjs8jcd5hUVjiUkESkgvIGwdgr/QwOO15fQyKJOQq21EgKm4hUsZG0qRbljso0a4/g0uwNM/OPv6CmvoGnv/JN0rs7OO7730Sq0GwbHB+o5503/IPH3vkgX/ziNZy2bB7LvvA59E9dju91RSV+FCSwRSsdthg80dZ5Rc9jItHdZFymmHMZFG3orKNTayfmxDkjf+4BnQv74iHvveTIMt6eMOLcqXca2alu51/ee5iXOTIxOC1SrNaX0612EnLCzDLnUec0kE6nWL78Rf758l089vK/SKwYQloSrUwjdEyE+OXl+Kf6md98NAPTejlKX4ItbMbZTSwpHcd4ewIFO8eK/mfxWV7CRpScksc0LDymj5JTxJQmCgohESVhbqcu5COSc5fUawVB+y0r6X5qB9WL6rjw3kuJTYyjFMBekEfXvSy13OVglmnR1dHJzp3b8AwF6Ni1m7VrV/Dgg/dQKpUwDIMZM+Ywd+5C5s9fyLx5i2hqmnhE7snFi4/lP/9Zzic+834+c/WVHHXyYt73ucuJlEfoyrcxK7aQ4CHcyPr9HgbGt1OyS1Tn3WwwiaTT28bg+Haivr33UbCK3Lbx77QMbqUuUINH9dCX7+fOzfcR0P0sqJpz2M/nSAiUBZhwXDO2tZWO1Z307xikbXk7ZU1xpp0xFXTJdz/2OXRN5+sf/xkzj59FxcTR+eKdazoppArUzq5B9+rusr3dSVb9aw2rpq2j0leOZ7oH2Q9ar0pMj5AeyDBUnmD2iTMJVe7dstyxtpOu9d1EaiPoXndokx3MseP5nZRPKCdUGSRUGcQf82P4DXSvRm4oR24ojy/ipZgukkvkqZhQxjNrH+FLX/kUzc2T+Pe/X6IsUIV9+x9ZVVxPSsuQVjKEi0Fm9E0mPCWEFIJkMc3EWBMe9a0tPtof5nA2o67o6LrOV7/6A2bMmM3nPvcxtm3bzB//eBdVVTUH2csYY4zxf4kWbQNb9A00W5Pw4o7VGu1mtmmbedZ4gnnmwrfMHTzNmsXJhTN5wvtvuukEQJcGxxaXcbSyjMHuAcaPm4SCQlAPUeMfR+1BYg7eyAr9Ze733kW32glIquxazi1cxFHm0Ud0zgNKH7f4f0enuptaux6BYKvWwoDSxyey13FJ/nKU4T+vEbLDTM5P22tfG7S1vGg8jSmK6I5BgkFWai8x11xAREY5rnQSd/n+TIfaRtSJkxFpBpUBTi6eQbVTCwImhmdiKF668m2YTomAHqYh0EyNb5zrcHyDqNuX76Qz10ZAD2EMlz+X7AId+V2UeavwqF40oWFJE13s+a4yZQlN0fdZ3Ffpq2V3bicpc4iAFkYAGSuFR/VR42vAdix253YQ0eP41MBIZAUIinaekBFjQmg6SXOA1sw2kqUBBApe1Yel2CNCNoCm6Pi0IIPZnXtNrhuqh76uXqprR5fGGRgY8tC+dx9//N94vT7mzj3qkB7/dhIKwYc/7MY1rF4tkBJOO81h4ULJzTcrbN8OfX2CwUE3B7i3V6DrbmRDMAgdHYL+foXqaqirezeRSB8vvPANMpluDGMu5eUPEw5XkclI7r5bkE7DKae4wq4Qe0ygUo42hKbTkEoxUmAXDCrE45JoFI45RjLh4P3VIzgO/O1vgueec2MvvF5Y3x5ne/fpnF72PEXLYmLcjYnBtsE4sJhvt7eSv+kGnO5OlHg5Tm6Q4t1/ZUeVG4exvxV7QhHEG+N7CaTJziT92/rI9udwbIdSroTHbxCqCGIWTVK9aaINUXwRH4bPIFQRon9HP1bGRvO4QrBtOsQaYm5pcsEi1ZWiclIFkQlhsoM5Eu0JSrkSvoiPQMhDz+Y+ihmTRe9bgK5KCrf/EWvTBpTaeoTHg9PXS/HvdyD8AfQF+560kFIysH0Ab8g7ygHsCXrI9GVIdacPKAa/GSafOBnD73GzkvMmqkfB8PvYvbqTZFeKulm1xBuPPPsZYMaMOZx++jnceOMveM97rjjiFdZjHBpjYvAYY7xJ7r33b3zqUx/khBNO5fe//wuBwN5CxZFycslPq2qyWS3hQ6GIxCcF5xWDI+VvvYpF+3Bh27KSnypH4wZ/Ah2H6ZaHEpJnjQJlUmFpyYeGwk7V5C/eFJ/JxWlwdPoVm3u9GboUCw3BDCPOuytmUNu1nH5fAx5UdKtIJNPFK+MWMxCIoUtBUnEoszXiUmVQ2qzUCnuJwQlh86gnyyq9gAk02BqnlALMeJ2Yu8g8hju0W8iINEEZcrOR1VYmWlVMNOfSojl0Ka4TtcJReVchNKr8bqHpY7leQPnWdSypr+HFz36ZnuQg5//ml8x53XFOPfUdHHPMCfz4x9/kpm99l9DddxP6xU+oXfj6rE3Jkcxtzje9LNcKbNZKhKUrKmeExtnFaZxbaMIRWSqd6lFLAg+VHqULDd11U4gsJVEcXoIp6VN7juBs3X3e4r+BrdpmBFAYKJC5N0PhrjwbXliNbdtEK2L4jvez5OJZdC/rIjQ1hE/xkxc5HBwS9iC79B3opkFMxtmibWKV/gpX5K4inA2StdIjOXM+6aPMKafL2I3P9BPzVKAKlX5nEJkXxJxypJSs/8/LPPOn+7GlTeQjjZz8hbOIlbuDGkeVFNUSZVblyPPQdI2y+jhl9YtYXHEiHtV9/xWLRTZuXMvKla+watUrPP30o9xyyw0A1NTUceKJp3PyyWewdOlJhEKHnt3sDXn5yPeuYtrx0/jLz27lC+/6LJd94nKWvPMYWjNbmRE7+E1G2j9E0O/F6rZJksajGhTtIqrlJVjhIStTRJzR59QyuJVtQzuZENkjODaE6tiW3MnzHa/8t4nBUkp2vdzK9ud24o/6iI2LYuYtujb24NiSW1/6FR39rfzzn08xa9a8vba3ihbdG3vwR30juWOKqhCpDdPT1ouMO/jG+yEikMsk7ASlRyEts9SdWMfkxfsW8rs29qB5tBEhGMAf89G/c4DB1kHGL26kfEK5W/5REUTzaoSqQtimgy/qAwEVs8u49fHf8M8H/s673/0+vve9X48Us110zHlU3VfOjkQr+XyeUCqIJ+Ihf1SR3nQ/QhH7dGi/1fTk+niy/TnW9m1EAHMqZnDSuOMo95VxwQXvpbl5Mh/84Ls488wl3Hzz3f8jbnrHGGOM/xkMKQNu1i2jx2kRJ0q32oGFdVB35aGioPDOwiVMt2axRWtB4tBsTWa6NYtMMk0um+WoicdwTNVpCMRhf3a2qTv5q/8W8uQYbzUDgt1qG3/130J5poLx9t6qVb/Sy3ptNVklQ5VdywxzDr7XXYvV+nI61DamWDNGBN+gFaJFW8+r+ou8077kkM4tI9Lc7fsz/fQRz8QpWjkKFHna9yghgnxIXsNJxTMAeMZ4nAGlH7/0847CBZxZOG9kP5qi0RyeyrjgBCxpYigeFLH/EepgsRcBI0IwgKF6yVpphkp9TA7PJuapoC/fSciIIoTCBu9aNkXWE/CFyaolFpWWEpN7hKSgHmZ6dB7bUhvIWElsx0YRgoinkoKdo9bfSKI0QMIcxK8FsKRJ0SkQ0iLU+BuJesrYlWkZdhCrVPvGUe2vRxceVg48t6frQULaSpIsDlKwc/TmOwFBWHcjpizHYqB7gEmNewvvh8qDD97DySefgd//5tzjbxVlZXDRRZLzz3fdtq+tiD/vPMn3v6+wa5crGhcKrmPScaC1FSoq3McPDAjOOENi2zB16seZMuXjPPec+zjLcp2/Ph+0tAh+/WvBc89BVZUkGJR0dgp6ekBVYfJk97iZjJsJXFbmupLb2wUDA5Lubpg7V3LppQ6Ho8vt3AkrVijU1bkiNEBspodN3WH+vdbNqp1QVo7MZpBmCX3ugXsPzOeeRnZ3ok6dscelns3Q9srzAPuMidgfhXSBNfetZ2DXIJ6QgebVKGVNUASekEEukSc7mKN9VQe6R8MT8CBUgVAVDI+CVbJB4saR1UcxCybFdBHN0PDH/ZgFk8HWAaQj8QQ95BN5ypvK0Bt0htqH6NvWTxU92Nu2oDZPRHhcA5La0Ii9fSvmc0+jzd9/trXm0ylmR0fpScctBVTfAofu/tAMlYnHNTN+0TjaV3Ww5cmtpHvS6F6dwdZBujf2MOucGVRPq3pTx/noRz/DO995Ik888W9OOeWst+jsx9gXY2LwGGO8Cf785//i85//OBdddBk/+cmNh1wsdagEpcJi04tfCgpC0mTrzDO9TLUNbCQPezI8beRJChsVQY2t0WBrKMCykh8FeFEvEJGCopD0qjZNtsoEW2e7arJJLRGQgj/7khSEpNLRMJE85ymQmHshnyskqRraQASJD5Wu8mk8M/VspBC89uc1DARpZXSWZwnJ7b4ka7Qi5VLFLwUtaondPotL82HXQSthfvF4dqrbWW68iInreIvLMi4svJdFpYnsUk3aVQtDCqZaBrE3FNNVOxpX5KM8YmTZfuX7UWMRXrjyU2xMWnh/+xfw7Hl8IBDkG9/4MdPefSHfuO7j3HHq2cx+/6Ucff1noKEaVQqmWIfmMhgqJFnVu5b2TCdhPcgpldOYWlnBGq2IguA008Oxpo+wfHMztOPtiaw2ltOh7iYvskhcJ2mRAl55ZMvB/+15gPWZtSj/FGz7x2bantqJ40hqT6jja9/9EScecxreST5+Gv42nuGyrXa1FdVRyYoMISfMemMVqtToVbvJyDTj7CaGlAH+7bmf85wLEQgcHHJmmoKdo3mwmfaKXfToPcT0SjIiywC9VKUbSKxJ8PQt99CxcQfTTzyKSZcsZnv5Vrqj7RgCVDR6fF3UBWuI9sXIq1kMxUPRKVCwczQFp44IwQAej4e5c49i9tz5fJCrEUIwNDTIypUv88wzj/PEE//mjjtuRtM0Fi9eyoknns5JJ53OlCkzDnhTmioNUnTynHb2WRx7wgnc9ss/ctMPb+DxBx7lyq9czeTjZ+/TXfN6NKlTF6yhsgx6kv0U7SJ+zU9DrJpIIISe2ftmPFFMIpF7OU/DepDuXC+2Y6Mqb88A0Cpa7F7dQcfaLkrZEkPtg3hD3pESN0/AIFob4Y77/ot7X/wbv/vdn/cpBIMbCWFbNqo++rNS0RQ0oRHTonQVegkaAQgJ5GzozQ0igUnzmmnVd1ASRWrthlETK9J29spIE8L9hJK2RFEVZr5jOppHo29rH/lUgWBZgOmnTaV6RjXrNq3i2usup6+vh1//+hYuvPDSUfsKVgSJazF8uhcREvSU9ZPKpbGWm8RPi7KkdiGB1V6ea30BT8CgZmYNtTNrULTRd06pUppVvetoT3UQNALMKp9Oc6TxkISQoUKS/1r3Z7YndlHmdZ0Xj+x6kp2pNq6acwVhI8TcuUfx8MMv8uEPvyd+y9oAAQAASURBVJsLLjiJn/zk91xwwXsPuu8xxhjjfz8RGUVBoUQRgz2CYVqkmGhPPqz4qkNBQWGaNYtp1qxRP+/rcyexKyurD5ixfiBW6a8yJAaYas0cGYuOt5tp0dazSn91LzF4nbaKv/pvoUfpRuAW1k0zZ3J57iriwzEWfUrPcKOBe05FUWBA6ScrsmzRNh3yuW3VWuhSdxNIB8haKXTFQ4AgWSvDM8p/OC13NvW+Jo4tLWNJ8XhSSpKQDO+3VFhTdLRDEOklEvbj7H4t5mFKZDYgGSj28nT8STZE1uJRfcS0Incqt7NSf4WP5D5FmVOB5Viuu1ePclT5CezObmd7ugXTKdJb6KKv0E2Zp5Kp0TkMFPtIlgao9NVR5qkiosdJmwk2p9ZiOiW8qo+SU6A734am6EwMzcCn+SnYOXTFIGUmGCh2Yzmu6G07Nr353diOSciIkjYTDHYP0HzcpEN+HV5PW9tO1qxZwUc/+ukj2v7t5I2xqHPmSOrqJLou8PlcATiXg0DAdQl7vW6MRHu7YHBQMn8+rFzpPmZgwP3duHEQCEhWrBAkkwJFkfT1SZ5/XkHXJbNmuYLy2rUKfX2S8nJIJt0oiPnzJcEg1NVJikXo7nazhg83ca27W1AouIL2ayjxOOUT02xYO4RP1Shvb8MxDPSjj0U76sCOfnvzRojERudXB4J0ZlPEQ+HDEvl7Nve5fRF1UXKJAp6AB2/IS7ovTf+OQVRdwQgYmHmT3EAOoQh0v4YQgoqJZUTrIiQ6UkjbYbBtCJDEGqLkEnn6tvWT6cuQ7kkjJSPZwuCOdSVQSBVw7EGkbY8IwSPPKRTG6el23dL70BWEENTNrmHDQ5soZkt4AgbSkSS7Uvjjfsqb9t058laze+VupOWMHE9KSWJ3km3PbqdiYvlIPNyRsGjRMcyfv4jf//7nY2Lw28yYGDzGGEfITTf9iq9//XN88IMf51vf+ulbvoxhnVbkTm+KXsVGAl4E42ydJltHIFih53nYkyUkFabYBhbQqpis0YvEHQUVgUSSFw6eYVdxSrhirTb8u4zisEYpkRGSKfYekSlgK7QEBMsXfhY5sIJXnG6CnkoGKmZiGxomefwS4o77nG0kGeHsJaJu1kps1Eo02zre4cF1RCq8qBf4drCfCsdNGK62Nc4pXs6S0nG0q6148DDNnOUulcONemi2Dyywjbd1PpKPkBIOyhkf4uWb67jyyou55por+N3v/rzX63PB9MXkH3mUv97yezb98Fes/+udNH3oMi675rPMjBx8RrMr28PN6+9gR7IVXdGwHItwx0tcOPkcvlL71uaRvTN/MQ96/0G/0ktABlER5EQOQxoUKJAVmVGN3QdCSsm/n76f397xYzofbccxHeqOGceyH55G83mT6KrtYHHmWCZaU5BScmxxGY96HyDghAiJMN1qJyoqQhEUKFLlxBAIEsoQCTHEeLuZNm0XJaOEg6Q7107RzoOA6oFKZloz6azsplfpxouPhTuX8Oy3nuHhf95MoCLCO752BaGplaTNDJdVXUpJ6WeTsh4bm0nWVE71vgNfUKc7v5uMlUJXDJqCU2gKTR31HPuL3ezO7iRjJvGoPmr9jdRGGzn55DM5+eQz+eY3f0Jr6w6eeOIRnnzyEX7yk2/xne98kdraBs4663wuvPC9zJ49f78inQSC4RBXf/XTnHjOqfzmWz/jS5d9jm0f2sEXrv/WAVcIjLObGG9PYFtsM0uCC3FsB0UVbNc3M9WcQblTudc2UU8EgaBol0YJwikzw7TY5LdNCHZshw0PbRxxR0gJid1JPMECgbLASO5a+9BO7n7pT1x+8VWcd96797s/I2AQqQ3Tu6Ufb9gzcn3zQ3l8IR/HzFjE3d33sz2xi6gnQsbMUrQLLJ42m9vLfs8ObRuWMClzyjm1cDYnlE5BIKicXMHArkFXaNbca1FIF9A8GtGGKADesJc575xFbjBHKW8SiPvJFjN8+4fXc/vtNzF37lHccce/GD9+b0dZ14ZuFEUwbr5bPlRPHdlijqFdQ0wyJ5J8Isn2np14gh7SPRn6tg2Q6k4x7fSpI8+xL9fPzRvuYOvQDlShYkubp3e/wAWTzua4uoMva17Ru4YdyVamxCaiDb/eZb44W4d2sKp3HSfUHwNAVVUNd931GF/4wif4xCcup6urk49//HOH8nKPMcYY/4uZas6k2ZrEFm0T9XYjHumlX+nBQXJs8cS3tEDuQAwO9gNQVlZ+kEceYB/KADrGqHMWCAzpYVDpH/XYnMhyt+/PDIlBpljTUVAoUmSdsZrHnH9xcf4DAMScMixsJJIutYMWbT0ZkSYtUhRFkYc893FG8dxRMRH7okSJol1AtxQ8im9E8Pbhrqx6jAcYCiTpVXsIO1GWlk5kaenEI74WrxH3VNKZa8NyTLThwljTKSEQxDzutfZrQebEl7BGvkp3uIeJTKeSanDAciy26Bt5Tn+SBYMLac9up2DlUBWNck81Q6V+pHQo81QhhMB2LPoK3QT0EFMi7uqkgpVjV3Yr21Mb6S9240iHKl89Ps2dPC7Yebrz7dQHxtMQmMDm5BoSxQESpQEsx0RVdMr0MkAyVOynv9gDCOKeShL9Q9TU1B/RtXnwwXvxer3/vxCWpHSjIPx+SWUldHYKTNPVBuVwZK/bdyvJZODyyx1mz3ZzeRMJ1xE8aZJkxw5BKiUIBt2NEgmBzydxHIGmwQknSNatc53Fxx0nMU147jlBILDnGD6fK+ZmMq4QfTieJ78fFEViWaMF71JFI2ptiLATxnv+u1Abm1CnzUQcpCxMhCKu2v36a+U4dOay1JbvPW4+EIVkAQQE4348QYNCuogv7MUpOVhFG93no7wuwuAutx8DKTF8BlVTqzDzJfyxABOOm8DAjgEcR1I2Po7m1XjiZ0+TT+bxhbzoPoNipkAxXSBYPlyAbLlZ3t6wF0XGEKqKLBYRr1ParVSGZOVUdj+5HVVXiY+PE28cLYI3zKsn1ZWic3036Z60e73jfqafPsVd7fY2k+7LkBnIEqrYc68jhCBYHiDTlyXTnyVSc+irLt+IEIKPfvTTfPSj72Xt2pXMnj3/rTjtMfbBmBg8xhhHwN13/5mvf/1zXH31Z/nyl7/3li8NTgibO70pBhWbibaOiiAlbJ418tQ5GieVAryqF0BCpeP+N9aBZkdnl2oyoAC2OzCOOipdqoWNgyHd88zhoCGodFSeNCwicvTA1oPAAfoNg6PLT+J5X5JntSISsLAJSwVdQkrY5IVkUNg02jpHvyEiok+xsGFECAYYFA69iokiFRbZGhJBu2pypy/HNdkZzLCOfLm7QBAZdg2fcspZ3HDDn7nyyov5+tc/x7e+9dNRr5OO4DIrzrwrPsv6iy/nqRv/wFO/vYEf3vJXeq+4iquv/txeNyqvL9B4rPUpdiR3MSU6cUSIa0938K8djzKjbApRz+E3YtuOzYaBFjYMbKZol5gYbWJ+5SymMINp1iw2aGsoKAVsoMquZp65iIKSY5u2mTnmgZdXFYtF7rvv79x4489padlAbE4ZC759NHPPO4pQrfuFbWMDElvYI9fz/MLFVDk1vGg8Q7msJFqK0Wg18aD3Hgp6gZgsQ0XFLwMMKv1s07Ywx5xPuVHNbnaQNAfJ+nNkjRyGqTEjOZPFxWOJOJXc97u7+OlfvoXfH+RdH7mC4HH15MgT1AOcPv5EThp3HFpOY1AMYAmTcqcSFRUiMC44kaJdwKP68Kqj33e9hU42JlZiOyaG6iNjJtmUWEXBzjMxPH3kcY2NzVxxxVVcccVVFAoFXnrpGf7zn4f55z/v5L/+69dMnDiFd73rUi644D3U17vVyhGjDJ/mun2CWsR12syZxtdv+zYv3/0yt9/wR/51/z/41Ke+yHvec8U+y+t0dC7Mv5fb/H9gh74ZdPc91WxN4tz8Rfu8IZ8an8TEWNNemcEqCsfWvX1lKIOtQ3Su7yZUGcITMHAch2RXgExvhuRwNprt2Nxwzw+piTXwmc98FXCFz2QpRdQTGVWGJ4Sgfn49neu62fnCLnS/jubRMPw6E4+fwOTpkwiUBXhm9/P05vqpClSwoGEWr0x+jDZtFw12I7o06FW7uMt/OxEZZZ65kPq5dfRt66d/xwC6R8O2HYQQNC5sID4uNur4gbIAPsfhzjtv5zvf+SKlUpFvfvOnXH75x/a7uiM3kEPz7rlREQiCngB5I8/GjZtpLbQTHB+kTqmmhioKqQLtqzqom11LpNb9LHi8/Vm2DG5nUmwC+vDS2N2ZTv613f3MiHsPnLO2K9mGR9FHhGAAXdFQhUpbaveox3q9Xn7+85uora3nu9/9EqlUgi984dtve4zFGGOM8T8XL17en/sod/n+zDathZIoEXfKOKvwThaax7wlx5BI2tSdbNTWURQF6uxxzDLn4WWP+21goA+AeLziiI9Ta9dTooSDMyLOOjgURZE6u2HUY7epm+lSO2myJo481oOHCruSVfqrnJO/CD9+5ppH8bTnMdZpq+hQ27GEhYJKuVNJlV3Nv7z3UOvUM9c8cPxOo92E3/GT1JJUSf/IdRnShwgVg/w7+gDlWjVRWUaP2skd/pvJKCnOLlx4xNcDoMJbS5Wvjp78bkBgSwsQ1PnHU+7dkyGvCIUB7wBSk1RaezpONDTCTpQXxFMEkwaKUPBqfkzHZEd6EwUnT72/eeR7RFU0fJqf3nwnzaFpCATrE8sZLPZiKB4sx8SWFn2FTqp99RiqF4/iJWdlyJgp6vzjUYXKjnQL/YVuPKqPqFFGaDgaIqCHSRT7mRydRVxWYVkW0eiR5ZE++OA9LFt2GsHg4Ue1/Xfj9brFbo88orB+PezcKSgW94ix+fweh3BtrSu6Ll0qOfZYSTSq8PTTgl27oKMDbFtSKEBdHfT2usJuOi1Jp92s4ClTYPduwZIlDqoKq1crpNN7Yh0ABgdh1ixXGD4cpkyR1NcLdu4UNDW5BXbJpCssVzY4DGRieM654JD3py1cjL15E05iCBGJguPgtO2iy7apnzz1oNu/Hm/YMxyYDJWTKujd0kc+laeYK6HpKuHqEIG4n0xvhmBFgEKyQGxcjEhNmFwiT//2fiJ1YQy/gSfkId4YxyqYqJqCogisooWiulnQQlUo5U1yiTy5wSzR+pjrnFXLUJsnYW/eiKitR3i8lHr72DxURr9RC8/sACSaV2fCsU1MPGHCyP89zaMx69yZ1M+tI9WTRjM0yprL8P83CMHgxrsJRYwUeL+G47ir9BT1zY81zzzzfMaNa+LGG3/Bb39725ve3xj7ZkwMHmOMw+TJJx/h2ms/wiWXXP62CMEALVqJHsVm0rAQDBCWKgnp8LJe4MSSn0Fh43uDO0FBEB0uWtutmNQ4GvW2RqtqYgMBqdCrWPQLm/mWl0mWQaWjslG1Ru3HQiKAkFQISoUP5SKs04vsUk08UtBo63QqJsv1IpaQnGoGOL7kHxGmXyMgFUBiIdGGn0eXalEQMN5W0YfPv9nW2aSWWKsVqC29dZnLZ555Ht/73q/4whc+QXV17V7uOA3BHMvLHKOOSz/5TRLv+zR/+MMvuOmmX3PbbX/gwx/+BJd/6Co2F3byUtcKksUUE6NNLKyez4aBzVR4y0c5MmuD1WxL7GB7YtdhZ7g60uG+7Q/xeNuzmI5brvZ8x8us7F3Lu2eeTY1dR709DolEQSHulCORbNU2jURr7IudiW38+K/f5NGb/kWuO8uc0xdw+3f/ybaTNvOq5wUC1p7r3a12EnfKGW/tcUZqaBxXOonjSichkcOFKpt4gkcotyvd9msnwoDaR0okyYksISfECu/LKIZkZdlKdgV2UlSLGNJDdWsNiZ8kePmeF/D7A1x77Vf44Ac/TjAYomAVSZfSBI0gPm3PjWOZLHetuK/Dq/rxqv59Xse2zDZsaRH1vCbmB8hZGTqyO6nzN+5VTAKueLZs2WksW3Ya3/jGj3nuuSe4++6/8Mtf/oAf/OBrHH30cVx44aWcffYFTAhNZ3NyLUPFPoRQkDiU+Su5/lPf4sMXf4of/egbfPnLn+KGG37Kpz/9JS666DL0NzgeJtpT+Ezmy6zVV5JSEpQ5Fcwy5+13mahX8/D+6Rdzz9Z/sXlwG6YzRJkvzinjjmd+5ez9vv5vlnRPGtu08QSGl7gpCtG6CNmBHIndCaJ1ER584W52dm3hJ9f+EX+1jzta/sGKnjXkzPxwud1czp94Bj7Nh5k36VjdiWM5SCDbn0XzaFQsaWTi8e5Ad2H1XBZUzaZgFfCoHpZ7XqJd28UEa/JInmW93chWrYUXjWeYZy7EG/Yy/6K5dK7vYmDnIKqhUjWlkuppVXvFR2zcuJYvfvEaXn31Bd75zkv42td+eNCytVB1iP4dA6MmhGzLZo1nA6uVDZSiJhTBsHRm2lM5yb8UczBHsjNFpDZC0S6xtm8DZb74iBAMUBuoZsvQdrYldrKo+sA3uSEjgOlYe/3cljZBfe/3tBCC66//BuFwhG996/Ok02m+852fj5VxjDHG/2FqnDo+kb2O3WorRVGg2q7b7/fOkfCs8QT3+v5GUiQQCFQUZpnzuTz3sZHj5PM5gFHLurerW3jOeJId2hZiThmLzaUsKh3rTgDvgwXmYl4wnmaLtpEq211F1qN2Um83sOANBXKmMHGw99qXhoYpTGxhgYQqp4b35a7kZ4HvklAGCcsIQSfMZGsaNU4dW7RNrNRfPqgYXO3UsjR/Ev/Q/8xu0YZHesmqGSJWlLzIoQsPE+wpAJRRTo/SxdPG4xxTXDYSWXEkaIrG9OgC/GqQ7emNFKwcHtWH5ZhkzRRhY893jIo6Ejn2+gloG5tcKYNAENKjAOiKQckukCwNYjmlPTm/gCJUbMfCkTZDxX6Giv1EjDgCBV31oDgqplMiZSbxS4usmSZnZxgq9lHurabGP46oUUbJKbrGAv11TkMEPi1I3Kgg1ZsEOCIxePfuNlaufIXf/ObWw972/xXHHiv5y19g+3YAiaoKbNt1B/f0CBobJVVVkuOOGz04LiuTJBKClhaFTMZ9/KxZkgkTJH19bp6w4wg8Hnc7y3JzgzUNGhth/nyH555TyGQkqgpbtkA6LTAMyd/+JjjhBIfyqhJCiINGovn9cOmlDn/5i8LOnW6hnN8vWLbMYePGPLt2HZ5wqR+9FKdjN+ZLzyG7O5FCoFRU0aXASc2HFx9SObmSUFWYobYhQpUhqqdVMdA6hBACI2BQNbWS3GAOhDvuVTQV3auT7kkztDvhxkD0ZdzVcVISqgzRsKAef8xPpDZCbiiHbdqEqsIUUnmK2RL5RI6qqVVMOWXySGyE9/0fonDP37E3b0Ja/fSJOnpjE4lMGYfH5z4mN5Rjx7NbqArvprzKwQ7UY0cmoqgKZU1llA3HNBRSBdpX7qaUKxEoC1A+oRztEPODS7kSZt7EE/Ie0jahyiDRugj9OwaIN8ZQFAXHdsj0ZKiYXEGw/M3fy2uaxqWXfohf/OJ75HLZ/zFZ3//bGBODxxjjMFi9ejlXXnkJy5adxo9+dMPb5rIqCPdLWn2DQ9BAkBWuiNJsGzyl5qh53UCugENYKhxd8rJZL7FNdUXCGaaHAAIFgZBweinAaaUAOoKjSz42+Up0KhaVjkoJSbtq0WBrIyVvPhQWmb5R5XBz8XJmKYjEFaH3xXTLQ52ts0M1abRdGadPWChSUP864di9YXAL6d5q3v/+j9Dd3cl3v/slqqqqede7LtvvY6PRGNdf/00+9KFP8rvf/ZQbb/wlv73hp1QtbGbSafOondzE852vsL5/E1krv0/3r3yDaDkg+hlSBvBbQXr6BmlNtaMpGlPjE5kQaRp5D21P7OKp9heIeSIjDsGiXWRN3wamdU1mQnQyy/UXmWLNGHm9e5VuIjJGo92013mkUkl+9KtvcPutf8C2bBrf28ykT05Fnaqy0VzHaYV3sFtrpUXbgF/6KYoCXunnvMJFo8pDXs9rx/VJPz7po8meyHaxhZ3aNgqigJCCgAxQbddyr++vKFWCdqOVGqsWa4PFlttaWP6XV9E8Kpdf9VE+d/U3RpW3eTUPXu0wQ8neQNHOk7XS+NTRgwafGmCo1E/GSu1TDH49mqaNCMPZbIaHH76Pu+++g89//mq+8pVPcd55F/PeKy6nvilOyS4Q1CNU+mrxqj4CDSF+/es/8clPXs9Pf/ptPve5j/Lb3/6Ya6/9MueffwmqumeQFZNxTiidcsjPrcpfwcdmX05XtoeiXaI6UIFPe2tcAPlEfjj3DGIN0ZE8YFVXQbrFFK+JquHqMJn+LGbeZHdHO3c9dwtnn3gRF179bv6x40Eeb32GKn8FFaEyUqU0/2l7GkUILp5yPp3ruuje2EPFxHI0j+ZmX6eKZHozJHYnRga1ilDw6+45JBX3vN5YbBR0QvQonSP/9oQ8NC0ZT9OS8ft8jgMD/fzqVz/g5pt/S3PzJO6661GOPXbZIV2fulk1dG3oZqh9CH88QCGVp7WvnR1VbXgVD4F+P9KW5NQ8L+sr8e32MEk0j8pMGzah7BP5xg+OfTC7YgbPd7xCV7aHan8lEkl3tpegHmBWxehSnayZY2XvWjdu48TxfJqv8ctvfxvbtvj+9389JgiPMcb/YRQUxu1j3PBm6VG6uN93FyCZNpzlWyDPKv1VJhiTOat4PgCq6o7/HMddhdSireeP/hsYUvqJODF6tR5a9PX0Kt2cV9h37FC5U8kHc1fzoPdetmmbAZhnLuIdhXdS4YyO+hpnjycq4/QpPVQ57sSfRNKr9LDAXDwqf36aNYsziueQVlI0W5MIy8hIlrJHekgMfx8djHdb78dJl1jheRnTsJiWmUFZrpz/VPybBkZf+zKngp3aVnrUTuLWm8v7LDkFegodCKFQ7nOfa1+xk8JQjrllS0bGP5PMaUS8UbqVTqqdWgSCPHkypJiVmYWhjs4xDWghhFDIWGm8w5EPUkryVoZybw2G4iVnZwCJKjQsx8SjeElagyhCIVkaIFUapOQU0RWDtuwOCnaembGj8GkBanwNtGa3otk6huq6ijNmkkpfLSE9SnuyDYBI5PDF4H/+804Mw+DUU9/xJq7sfx9SuqVv2azEMARerysEl0pgmgASRYFly2DRoj1jh5UrBY89Jpg0STJ7tlv8tnq1oKsLPB7B0BCkUhCLQSzmOoZXrxboOjz4oMKcOZLzzpNUVDi8+KJg9WpBOg3hsKSlRbBitcnfHuznhHPamHdsP1WBKsaHJuPX9i/8TZgA113nsGWLIJ+HmhqHcePguuvyeL2HN34VhoHnkvehLz4Wu6MNoRsoU6bR8debqK8/9PI4AF/Ey+zzZ7L5P1tIdCSRjqRiQhmzzplO+4rdDLUN4Y34UBSFVG+aYHmAdG/aLZbryyKEGzURqggSrAwy1DZE+4rd6H4dj98YFZGQ6klhmw6L338UsYbYKIOCUlWN72PX4HR1QKFI8pk+9LbUiBAMEPRmuP2h33HF7x7j46c286MPHE2p+hjyk98Pw8aZ/h0DrP/XBjLD54YiqJhQzuzzZuIN779bxiyYbH92B53ru7EKJr6oj8aF42iYX7+XkeL1KKrC1FMms+a+9QzuGnIHtxLCNWGmnDzpgNsCDBbghU7B2n6BrsD8SsmSWolfA6enG3vbZpCSMxcdw/fzOZ588hHe8Y5Dd5GPceiMicFjjHGIbN++hcsuO5dp02bx+9/f8ZaXxb2eOlvDA6SFQ2g4wkEiGRIOx5k+FATHmD7Wa0W2qCYVjoopJAPCZrbl4T2FCLmiw85hMbjZ1glJhZRwMBDDjl2X+ZaHRDHI40aOHaqJDky2dC4shgnKAwsGoyvk9iYkFS4rhLnTm6Zt2J3sR6HcgRpnj0hiI3GQVDlvzzW97rqv09PTxbXXfoTy8kqWLTvtgI8vKyvnK1/5Pue9771c96tr2fH4Gp564W9UNNUx56xjGZxjoRk6fbl+4p7oiDu4K9tD3BejOTKeAgUe8N7NK57nScoEnUO92FmdyrYJaJZGQA9wxviTOHP8yQgh2J7cSd7KMz7cAEiEkCiKQ97M8Y8tD3Ba8Fhi47awSVtPSIYpiDyKVDmncOGoGx/Hcfj732/j+9//CqlcknGfbGLZR04jUhEFoGDlWaevZFHpWK7Ofo5XjRfYpW4n5pQz31zIdOvgLtM6exxTrBks11+i2ZrEoNJH0AlhCYtJ1hSm27PZrm1mrWc1+h0aL9/+An0v9eKJe5j4iUnEr4rzLs9lhJTRbqSCnWOg0IslTQJaiLinAkWoIw7oDdpaiqLAeHsCs0sL8LO3M1gdXjbvLo/cgy0tVKGiicN7jwUCQd71rst417suo7u7k7vu+jN/+tPvufPO21i69ESuvPIaTj557l7i2uTJ07nxxr/yyU+u5ic/+Raf/OQV/OpXP+Szn/0qZ531ziP+/BBCUBusRkpJKVOiIAt4Qp4jnpiSUtK+Yjdbn95GIVlw88nDXiYe10zjonGUTyjDF/WR7EoRqQkjFEEpW8IT9DD3gtl844bPEI5F+PHvfktaZFjRs5pKfwVlPndCocwXx0GyvGc1pzYuo2dzL5qhonnc5y8QeMNeMv1ZBtv2iMGvp8ypQCAoUcJgzwA5rSSZUppx0OfY29vN7373c2677UaEEHz+89/iIx/5FLqmk+hIUkgX8EV8hKtD+72O0fooc86byeYnttK+cjeFVIGcUaAhVwMatOq7ETFBRAuR0/Nso5X6RA3h4RsCj2ows3wqT7Q/S5k3PvKZ0Z3rJeqNMCE6fuRYRbuE7Vj4NN+o85kam8S5E87g37ueYNPgFop2CUUIjqldRMR43Y1HKc0f1/+Fjf2bUYSCg4MxyeB9X/gkt//g19i2zY9+dMOYIDzGGGO8pWzWNjIkBpn6uklrLz5CMsxy40XOLJ7nTv4PT4paloVE8h/PwySVwVGT3X1KD894Hufo0nEjAu4babSbuSp77UhGcNwp3+eotMKp4qTCGTzg/QcprQWf9JESKaqcak4pnLXXNvV2IyEZIiiDI0Kwg0NWZGm2Ds2BaCge3uX5ALOT8xgq9mNLG8tjUaZWoOo6vM77UBB5DOnBL4/c+dardLNd20KX1YZNkfHGxJHvD4/iZajUR0++g/GhyQDUOQ2cVTiff3n/wSZtPQoCBYX5pcXMyE0bKX0bQQgCWggpHRLFATRFp+QU8ao+GoMTR5yitnQYKPSSsZLYjoUtLQp2CQCv6iOohyn31qArOn3FbjpyrTSFptAcmkrRKTBQ6CFjpVCEQpm3ismR2QghSCYTAITDhxfBZlkWf/rT7znvvHePMh/8v0ZK1/W7caNbsjZunCu6btzoZv+uXCno7XXdtKoKkYgkEIChIUGpJJg92+GDH3TweocnmgW8/LIrGte6RnliMdfx+/TTr5XDCXRdkstJXnxRIIQrLk+YINmwAdasESxYILj8coeKCujqcuMltm0ToFjEx3WTykoeuWs8dklnzolbyVhJ5saX7DV58Hp8PrcU7/Xk87nDFoPBHQOrzRNQm90VjG1tOykU8jQfpjMYIFYfZdH7jiLdm8GxHUIVQTSPRnxcjO3P7STZmSRcE0JP6pgFk0xvBtVQ0Twq/rgfx3Ho2z6AL+ojXBUmM5Al1hClb1s/0pEYATeLuJQzmXziROKN+zHaCIFa62ZhO88NjRZS7SJb1z7EUztfZWJVFY63DMcTx7P7PziBOoqN78AsmGx6pIXcYI74eNela5Usejb3sv35ncw4c9o+jyulZNO/W2hbsRtfxIsn5CU3lGfDgxsRiqBh/oHzuaP1URa//yh6tvRRTBXwhr1UTqnAGzpwsflQAW5ap7B5SBDUJY6EzYOCrV153rPqvxAvPI20LEQgQFU4wvSG8fzrX/8YE4PfJsbE4DHGOAQGBwe49NJzKCsr59Zb78Xv31uEeiuZYOssKHl5wSgQkAIDwaBwqHBUjjPdYzfZOlfkIzxuZNmpmehScEYpwCmlAB4EHqkSs0Yv9YjJvZd+CAQnlwIcZXrpVCwM3BgI7YAy7+E8F4Nrs3F2qCWKQqJJwd99KbaqJtWOhgN0KxZ1tsYU8+BtyUeCEIIf/vC39PZ2c/XV7+Oxx5ZTV9dw0O2yRpH602dw8rvPpXX1FlY//ByP/+4uNI9Bw9JpzD7laFrkVryah5JjEtQDnN18GjFvhHu8f+MR7/2UOZWoSQ8DmSGURofKQAXT25bQl+vn0V1PMjU+ieZI4+vO1UHTStgUyBWTlAUVerJdPLziGco7Klk8ewaDgR5iThkLzSXMMxeObPvqqy/y1a9+hrVrV7o5t99tpG3cTiJ2dOQxXnw4OLRqO1hsHsu5hYsO/3oieFf+UvLkeNl4HhOTCFHKrSqmm3MY3NJPy582sOuO7TiDDuXHVHDUDYuoOaMWxaOQCKUgL8Des8/+Qg8tydXkrLQ7ySAUKjzVTI3O4wn/IzzkvY+8yI7ctM3S53FF7uq9lrcaiodKXy2t6a3oijF8c2KTMhPEjXIixpG7bqqra/nkJ6/nYx/7DA8+eA833fQrPvCBd9LcPIkrr7yGiy66bK9lTDNnzuVPf7qHVate5cc//iYf+9il1NWN433v+zDvfe8HKT/M0guAdG+G7c9up3/HAEiIjYsxYWkT0froYe9rqC1By2ObEUKMDFQz/Rk2P76VUGWQsqYypp0+hU2PbmZg1yAAmkdl3Px6Ou1WHn/qYX7/+78QDkfYnthFzspTHxzt3AnpQTqz3SSLqb3iPvYg97bVDzPdnM1Eayot2gZq7DoMDHqUbvwywLGlE/b73Do62rnhhp9yxx1/xDA8fOQj1/DhD19DWVk5hXSBVfeupm9bP2bBQvfpVE6uYPqZ00YiMd5IxaQKerf20b+tn4pJFTi0098/iK/NQ5VeQVewF8cyAYn0O3ill9xgjlCl65o5edzx7Ey1sSWxHUMxMKWJT/Vy3sQzKfe5LurHW59hRe8aTNtiQnQ8J487fkQoFkJwSuMJzCyfxh2b7mZV3zoAVvS4xXLnTDid4+qO5oWOV1nft4mJ0SaM4bLB/vwguZk5vvS9H/K9L30ej8fLd77z87EM4THGGOMtw8He58hRleqoOKvXJkNt2yYtUuzStlNuV40SZcudSjZrG2hXW/crBoM7HilzDp49fEbxXKqcGl41XmBQDLDEPp4lpeP36ZCeZc1jqjWTDfoa4nY5Kir9Si91dgNHl4476LFe47WytoyVwnLcSe4BUjyu/Buf9BGQQQrkaVN3Mdc8igZ7/CHv+zVcMf0hHvE+QEIMkdQH0bwqyzKnMD+zcHg85Qq9OSs9sp1AcHLxTJqsibTo6ylSpMEezyxzHt2eNrYW15GzMnhV/4hLtz7QTI2vgd5CJwU7R5W3ltrAeCLG8OSvpwrbMUmUBvCoPgzFiyMlpjDRhUGNvxG/FkAR7r2IoRj0FTppCk3BUL3Mji0maQ5SsHIYqoeoUT5SvpdKuTERhysGP/TQvXR0tHHlldcc9rUF1zkpHYnu09/S78snnhDcf79COu0WtQ0NuVm6oRDs3AnJpCsSu0VxYFkCr1fi8UikhJ4euP56BSldsbWpCVpa9s71VRTweNz83vHjJT6fmx28caNbIrd0qSQ2PGTL52HlSpg3T9Dd7cZJdHa6x4/VJVBDGdSUH6sg2fhqDfOXZEiIbnoLXdQHDn+lwaGsiDoYGzasAWDGjCPrm1FUZa+is7KmMuKNcfLJPIqqkE/lefrXz2H4DHxRHwM7BihmS9glG7tk07W+i4rJFSAlTUePJxD3093SS6E7heE3mHjcBJqPbT6k86mYWE7vll5s071B6lzfwg8fvZdzG9/Ni4OPkRxScfQoip7A6HqW4rgzGWpPkO5JE6mNjEzya4aGP+qjZ1MPk0+ciO7d+/463ZOme1MPwfLAiHvYEzBIdiZpfaWN2tk1I4XM+8Mb9tJ41MHvp1/PK92CzYOCSTGJPuxJyHb3sevBl+h//C/ErDSEQqh1DQhF4bRQlJsefZB8Po/vcIOrxzgoY2LwGGMcBCkln/70h0inU9x990vE429uCdf+GBA2u1UTQwqabYNLCmHqHZ1X9AJZ4XCs6eUEM0CTvecDfbJtMCmvkxYOuhR7ZQgfDhGpErEPLVvocPEgmGbvWf7vz0d42JNlp2qSFW6z6m7V4vpwH7W2xkklP8eV/PjfxPN5I5qm8atf3cKppy7kk5+8nLvuenTUkv19nrfqQREKtpA0LZhG04JppHoHef6Bx9jx1Fp2PLaa8ppqJi+Zw6KTjuO8Ey5gcnwCSZHgJeMZ4k45FU4lOzLt+EpBPCWVrtguJvTMoFyWsWloK1uHttMcaWRCpAm/5sMSSTyqTnd6iIyZw294WRCqRpFhNne10eg08YXZHx81KO3u7uTb3/4C9977N2bPns8///k0Cxcu4Wbf79ghtu71vCQSjzzwzO3BqHSquSb7Bf5jPcQtgRsIbQmTeSDDPx/4O50v7cYo91D2gQpmv38+TeMmULBzKEKloOepVSJUyT03dyWnyJbkWop2nphRgRACyzHpLuwmVczwaPwBfNI3EodRpMBqfQXPG09yRvG8vc6tKTiFgpWjv9iDI21AENHjTI7MGbmxeDPous7551/MOWe/i6ce/g+33HYDX/7yp/jBD77GBz7wET72sWv3yrWbN28hd9zxL9auXcktt/yOX/zie/zsZ9/h7LMv5PLLr2LBgsWHdKNRSBdYfc9akh0JAvEACEHXhm4yvWkWvHfBSGPxodK5rpNkZwp/zEeyM4m/LECoMkT/zgF6NvdS1lRG7cyakWww27QJV4eJ1Ic5993LqGiq46XYZjrW3MLs8hkENB+pYooK/57yxVQpTVD3E/NGEFMkvVv7sEoWmuEOQQqpAqqhERsX2+c5+vDxgdxH+af3Llr09aRJUmc3cFrx7H0WTra27uA3v/kxd955G35fgA++5xNc+dFPUt3o2mWklLQ8toWOdV2Eq8OEa3RK2RIdqzvQvBqzzt6327iUK9Hd0kuoKkSgLEA0E0GqkkKgSDDvJ+6PkfVmUXSVGfGpeLo9WKU9DvXqQCVXz/kgr+5exa6tu/ALP7OapjOnYSZFu8RtG/7Oyt51xL1RdEXjle6V7Eq18bHZlzMuvMeh0ZPrY3uyldpADRW+MiSSzmw39259kIZQHav61hE0AiNCMECZN0bfUD+Tl83jhz/8LddffzX19eO46qprD+PdcuhIKbGlPSpbcowxxvjfzXh7An4ZZEgZIO643wE2NgllkCWF40fE3tdiImzbwif9GNLAFKVRk4UWJgoqhnxz0VGvoaCwwFzMAnPxQR/rlwGuyF7N456HWWW8go3NscUTOaV0JtVO7WEdVwhBSN8jYJ5duJCkkmCDvoYSRVSpMd2axcX591OgQEZJEXIi+Dg00WOztoH7vXdjSIMp9nQGSj3sZBvPRJ6k0qyiodiIlBIHB88bynYFggn2ZCbYk0f9fFxwAqY06czuIlkaRBUalb5aJkdm49eC1AYa2Rea0DBUD7riwZE2RWmjqzqGEiPv5DAUz4gQvL9rFTXKYB+T9rlcFnBXax0qUkpuvPEXLF16IjNnzj3k7QCygzl2PL+T3q19ICVl4+M0H9M0strnzdDVBQ89pKBpkunTXXfu008L+vtBVSX5vBvdYFmMxEOUSm4hnONAIOA6fTdvFlgWVFe7JW/d3a7wW1fnCsZdXbB6tSvySgnBoJvjGwrBtm2g64wIwfCakCzYulVSVuYeM593fy70PEgF6QhCEZNMykeiz0donCBrpQ77GkQiMTZv3vCmr+WGDWspK6s4aO/DG5FSkhvKA+CP+fYafwtFjMSlGcOxD7bpoHpUujf2YBZMdK8GSNK9WYo5k7o5tZQ1xamaWknz0hylTBFf1HfAmIY3Uje7hr4tvfRs7SPTm+HPL9/KlMgUFk6YwIuDj9Hfo9C6Q6O51oudy1JM5bBLNtKRKOobeoQ0Fdu0cEwH9nEKuaE8pZxJqGp0qaI37CWfzFPKlPC9DYV0LUMCn7ZHCJbFAsam1YQ7WyjqPpTqGBQLOK07UcIRTquq5RcbV/P0049xxhnnvuXn83+dsVH6GGMchD/+8Tf85z8Pceut91Jfv+8B0JvBRvJvT5anjRwJ4aABDbbOxYUQp5QCnFzy47B3fvBrCAThfTh+D4XXiuL2t++3i2bb4OqcTota4lZfkq1aiQFhUxSSjVqRV/QCq7QiH81H9+lmPlJisTi//e2tXHjhKfzyl9/n2mu/st/HSinxqh40obKhfxMzy6ahqRpqzEvjO+fyiU9fT7zHy8MP38fDD/+TF+55hL9W/ZYzzjiPRecsIXNqmhpRB4AtHQQCj+kjYfRT0HMEimHE8O8AJkTHc2z9PHZlN9KZTjFQSKAJFSH9+DUfUkpqhsumevP9VPldJ8xDD93Lddddhapq/OQnN3LJJR8YmRmeay3gVc9zDIkBYrJsOCOvm4AMMt2a+aaupeM4rF+zmlWPvErLYxvo39SL6lFpOHE8S24+geg7Y8xTFrJRW8sAA8ScGBklQ0ZkOL1wLhVO1UhxSaLYT9ZKu6UjrzXlKjpexccaXiUpEkyzZ40c24OXoAyxUn+Fk4pn0qKtp1/tJeREmG7NIqAGmRVfTKLUT87KYige4p4KNOWtc54XM0XWP7iR0hbBe6Z9jNMaL+T51se46aZfc/MtN/DeD32Ya6/+EuHgaBfL7Nnz+fnPb+KrX/0Bf//7rdx22x+4556/MnPmXC6//CrOP//iA6486NnUS7IjQbwxPjLw84Y89O8aoGt9F5OWTdzvtq9FSwAYQYPsQI6tT28n2ZUin8yDBN2fpHJyBYqqUMqWRrZVNZVAWQDdqxGqCvH7f9/I6pdeYeHHz8SSNqt717N5cBv1wRpahrYhkYSMEKlSmsHCEGc1nULUE8E/y0fftn56N/ciFOEWsikKjYvGUTZ+30vobMvGPxjkYvkBCpU5TNWk0qkeFRlRLBZ57LF/8be/3cpTTz1KNBLnouOvYEnDSXhUH+vvbCGzOM+Epc1kB3P0bukjVBEccQF7gh5sy6FnUw8TlzbvcwBsFS3s0p4yvUp/BdXBSnal2iADWTVHf3iIZjGeSZkmDL8x4gp+DdErCTzupaGrBseR9K3tY/P8reRmFFg/sInmSONIeWKZN86moa083/nKKDF4Ve86TMekclhwFwjqAjW0DG1lQ3/Lfl//17jssg/T2dnOt7/9BWpq6jj//IsPus2hUrJNnut4iRe7lpMpZWmONnJC/TFMjk04+MZjjDHG/69ptJs5oXgKj3kfZEDpQ5cGOZGlyZrI8aWTRx73mjPYsix8+JhXWsTD3vsIO1F8+LCx2aXtoN4ex0Rryv+T5xKXZVxUuIxzCxdhY+8zlgrc+Ijt6hZ2a63o0mCaNfOATuWIjPLR7KfZrm1mQOkn7ERosibxtOcxXjCeIq2kCDlhlpZO4qTiGXtl5b+RNfoK8iI3Mlke0iJU5qtoVXex1buZ2kI9GTOJR/FR4T00IVsRKpPCM6j3jydnZdEVnZAePeiEddEpoAqN+sB4LGkhpcSjejGdEu3Z7WTMNDHFQAiBLS1KTonxh3hOr5UOHk60wPLlL7Fq1avcdtt9h7wNuOO7NfesZWDXIP6YHyEE7as6SHalOeo98wiUvbkiqy1bBIkETJ3q/juRgHxeEIlIursFpZIr0uo6ZLPD51R0/5aXwzHHSDZvFkQirlCbTEI4DKWSpLNT0NICmYygvd0ViYVwc4OLRViwwI2bANc1/EYcx42WmDZNEo1CW5t7HtgatuN21uiGgiMcPF4LkOji8CdsotEoiUTisLd7Ixs2rGH69NmH5dpO7E6w9entDLW7x481RJh4wkRi9VEyfRn6tvVjFS2CFcGRfouKieXsfKkVpETVFaSjYhVtEAJFFZRyJeLjYiMRaIG4n0D88FcRe4Ie5r5rDlue2sZtN9xET6mD7518GeUVGbTVrkt720adfJegdWgyhRWvuONX2yE7lCM4/N50xe4cVZMrMIL7Xu3mCRhoHhVreHXca5RyJoZPH/WztxKfCvbrJv5kXx9ONoMh3G4SIQCvF1ko4Oxuo7m2jokVVTz66L/GxOC3gTExeIwxDsDatSv59re/wJVXXvO2FQ+8qhd40JMhJBUm2zomsEs1+bM3xWdyw8u2hUPMUfC+RU7ZLsXiaSPHeq2IimCB6eGEkp/IWyi8HgyBYJtWolO1yAsHHUG5o+IAA4rDK3qeSbbBhcXQQfd1OCxevJTPfObL/Oxn3+HYY5exePHSvR6TLmX42+Z7Wde3kYHiEJ2ZHjqy3TQEawkYARZXL+DU5hMJTPFz/PEn853v/IIVK17moYfu5aGH7uPWW3+PETUoP7qK5iWT0GZplOqL5INgWB58pQCpUhpDNYYzgt3CrMU1c1H7svRoCXJWnpg3SsQIIQQIIdEVFcuxsByLXC7LV796LX/96y2ceeZ5/PjHv9/LtT7HXMDJhbN42vMfukUXAGEZ5qzCO5loTT2s6yalZEfrVp5++VFefelFnn/yafp7e4nF4hx/6in4vuTHPL2EHbSJOFGOL57Cqfl38KrxAk8Zj9Gv9hJxohxnnoSDwzdC1wEw31zE5MKUvRqt3WuiYmHvM1pAIMiKDL8N/IQWfT328PLUcVYT789/hEa7mbinkvhbYyrai23PbKdzXReRmjCG3yBQCrI1PpWjjlVpW7mOm379S/5y6x/5+DXXc/UVn8YwRg/G4vEyrrrqWj760U/z1FOP8qc//Z7rrvsYX/vatZxyylmcc86FnHjiGXsJw+n+DEJRRjkAhCLQDY1k9/7dGeneNNuf3UH/jkFAEhsXw8ybmLkSulfDF/GBcF26fdv68MV9RGojOLbDjud30ra8nUK6iKqr+Bv83Pr3GwhWx1h6yqkoqkKVv4IdyV0UnBKnN57I8p41dOd6R6JTzmxyi/IMv8HcC2bT3dLD4K5BFE2lYlI5lRMr9iqckFLSua6LtfetY2DHILbtEKkNM/vcmehLdFBg48a1/O1vt/KPf9zB0NAA8+cv4tvf+Dm1qYlYKZtobQRFVcgN5dj61Db8MT/esBe7ZKG/YaBu+HQy/UVKuX27IbwhL4EyP+nuNJ6gB13RmFsxE1/SS0eqE2FJFmbnMDnXjCgK6o6uHeW4KOVKrHtgA+neDNG6CIqmkE/k2fHcTgZEAttxRoRgcG/gIkaIbYmdo1/LUgbjDS3eQrgJ7jkzz7yKWdw1tIOSXRpxBw8Uhgjo/pHIieuu+wYdHe18+tMforKymmOO2X/cxqEipeQfWx/gibZn8Ws+vJqHV7pXsmVoOx+eeRlT4vufqBhjjDH+/49AcH7hYsbZTazWl5NR0kwxp7PYPHaUQPqaM9g03QnH04pn0612sl5fjT2cH1Vj1/Lu/Pv2K8K+3Tg4KCh42P8gokSJv/tu5WXjOQrCdRmWORW8K38ZC80lI4/rUNpp0ddTGo5imGrNZLI1feT393vv4gHv3YScMBEnRkpJcrfvz5iixNmFCw94nhmRHsk1lkhKmonXF0CXOoPOAKnSEAEtxMTwdMJG9LCugU8LHLRw9/V4FC+6YmBJa1SpWNbKoKDSV+hkoNSNV/Hh0wJUemup9R+aySaXy+Lz+Q8r6/4Pf/gFEyZM5qSTzjjkbQB6WnoZbBuirDGOornH80V8DOwcoHN9F5NOeHPfZftKR3DH+a/9dbN/VdV181ZUSHp7IRKBWbPcqIdCAaJRV9CVUpBISJqHkwgiETcGwuORTJ0Kg4MSv9+Noti1SxCPS6qr3e/sVMoVksEVlTVNMm0aNDbCRRc5dHQotLZCzoxQ1pwiECqRTUSZPCuFt6IbXfFR4Ts8Vy5APF7BwECvawh4E/EbGzeuO6ws2exgjjX3riPdlxleSSfo2dxHbiBHw1EN7HxhF/lEHoRAKIKKieXMOX8WjYsaGWxLsPPFXQhFYPgNrKKFETAI14ZxSrY7jn4L8AQ9WIEi97f8hW98+BeUaUnU1E6EtFAVi45tJgPBOrwNtWiGRro3QylTxCpalHIldI9OMVvEF/bSdMz4UdfXKlpuV0ayQGYgSz5RoG/7APHGGNG6CMVMiWK2SNPRjSPC9lvNnArJil5BsggRDzimSa8WxROrIuwUkLaGUFWEpuEUCqiFAotmzmPFqlfelvP5v86YGDzGGPshnU7xsY9dyrRps/jSl777th3nJT2PAiPlaR7czOAtaonf+4ZoVy36FIugVDil6OfCYgjtTYjC/cLij74Eu1STuFRxkNzvybBTNflILrrfqAlnWJFT3kIX8Q7VxEaSF5K4o7rFIoACqAJW6QXOKQYx3mLn8qc+9UWee+5JPv7xD/DYY68Si412JN6//d+81LWc+mAt40L1DEYSbBrcQlWggstnvIfJsQmjlj+rqsqiRcewaNExfP3rP2LdulX89ukf8+Irz/LSj57ByloIj4I216Bsei2rK1YQHl/BSVOWMSW2Z1Dp1wJEPBGq/XXkrTwFu4QiFIRwsG1BT66XumAt6a5BLvvImbS17eInP7mR97zn8n0OplRULii8h3nmQnZoW1FQmGRNpcEef5DqPygUCqxbt4oVK15i+fIXWb78RXp7ewCoHlfDglOOYulJJ/DOZe8j7i9HIulxusimM1Q4VYSl64hdUjqehaVjyIg0JiZ/CvyOTfo6Io67Nu2fvjuZICYxLzmXgp3H9/qWajtLo9nEOmsN7c4uakQdumJgYpIWSXRhsE5fRbM1ES8+TEx2aFv4u+82rs18ZeQG6a2mmCnSvamHQMyP4XfFtk3aFtbHN+MveDnzA+8hc+EQT95+Pz/61lf52y1/5Prrv87551+y142MoiicdNIZnHTSGbS27uCf/7yTBx74B1deeQk+n59TTjmLs8++gJNPPhO/P4AnYCAdudcA2jJtfOF9D0TzyQKr715LojPpugYE7F7VQao7TfmEOAhBLpFH92oIVZDpzRBvjFM9vZrdqzrY8sTWkWVyVtFi+XMv07ayhZOvumiUKF3hK6c/N8CVs97HaeNPJFlMEfGEiXhGL6vUfToN8+ppmHfgcoreLX28ePPLJDtTaB4NRRX0bx/g0RseJfNIH/95xY3dKC+v5OKLP8All7yfyZOn075qN2vuWTdSpAEQKAtQzJboWNvJrHNnuMUeqcIoh08+WcAT8u53WZyiKTQdPZ61969nsG0Ib9iLmTep9VQz9/TZaIZKYncCo8Kgfm4d9fPqRr1G/dsHSPdmiDX8f+y9d5xcZ339/35umz6zs7037a56l4vkXuWObYwppjhAgPA1EFogCQQSkjgESIAAP0MIJYAxGIwN7lVucpOLZJVVW62215mdnX7b8/vjrlZaa1Vtk7bHr33JO3P73J37ec5zPueUTF+3YDyImTXJ7ssiGyWudGdYmRSc4mHXr62khVdGX8VxnekgOtOxQEB9pIbF5QvYkdw1M0BOMbi46Vxaol7ithCCr33tFoaGBnn/+6/jzjsfY8GC19ct0Jvu59nBF6kKVhD3lwDePbFrYi+P9j5JR3zenEfxHObwvxwqKqdYa2eQoa9FVVU1AIODAzQ2tkyrZTu1rQyrg173krWMEjm7ddCbBYnkRf05njY2MKwOUOs0cIZ5LiusNbPWTM8YT/CE7xGqnVpisgQXl161m9sDP6PZaaXCreIp4zF+F7iNCZEEQEdnjbmWG3IfwI+fTnUbv/XfOuV9XIkPHxEnypAywNPGBs4uXjhdT82GZmceG3mcYWWQvdouJpUJXJ/EljaXWVezquxMYkb8De2MOhIM1U9NsJGudCe2a+EiKdp5JqwEPsWg1KigYOcpugX8aoCFJSvxHSV47FDkctnDMhmOhp6efdx331384z9++4TDUjOjGQRME8HgTbprPo1U/4lbIrwWLS2ScNizcRDCs3owDMn4uBf+ZllQLEosSxAMeoreaBTa2yW27ZHEisLUeh65rKqerUNFBZx1lmR0VDJ/vsTn8+wkurs9D+LOTsnatYL3vMdldBSeekrQ3z8VNOiDc85xWbxYIgScd56kttbhBz9Q2NcdRKTrsf2T1LSPsObSLgJ6gLboohk2KMeL2to6isUiicQ4ZWXlx15hFqRSE/T2drN48dJjL4wndth+3w5Gdo1SOb/yYGdYyGBk9yiJ371KIBagrLXM+1ymQtj2PbefBRd2sPodKymmC57PbkWYSEWY0JQ123jX+BuqpN3Tu4N0IcVnv/sBEIAEV7psHezj/s5N/OzjtxKo8mpof9SPdF18YT+x2gj5VJGqhVU0rKojfkiWyFjXODse3MlE3wTJniS26RAqDQCSoe3DpPpTlM8rp+X0ZprXNr9h5/JarKqS7E3B0/0Kg1lwlEpC2iALwkVC5SXI0WGkz4+cnEBEoqht81lZUcZtX/4M2WzmhKxi5nBszJHBc5jDEfCXf/kxxsZG+MUv/oDP9+bICyWShOIQlDMLFRXoUy32qCYScITEBrZqJt2axWezZSdNyj5vFOhWLeY7xrQ9RBkq2zWTV/Uip1oziZBBxeYJI8erWhFdClbbPs42gydtTXEoYq6CKYBDymyJRAKGFGSEy27VpNbV3lC7CE3T+M53fspFF63h7/7uc/zrv/779HuJwgSvjGylMlBB1PBUfWX+OEvKFpKxMpT4Ykf1wRRCsGzZKr697Cfc67+TZ5TH6Xu1h+ST40w8lmLkt4M8M3knAC9W3MVd7T+gvX0BbW0LaG1rwylzyZakaIrWsyuxl7SVQlc1+lMZfKqPyF7Jle8+i+rqWu6992nmz5/d3/QAFJRZPeEOIJlMsG/fHvbt201X1x727dtDV9duOju3Ypomfn+AlStP4ZwrL6B2US2rVp1KvKwUV7pMmON05zqJB85ACHFELz0NjRIZZ4PxEJ3aNtqthdOtj5VOFXt8u2gpnYc+omNOtRkWnTyWaxJIGzSMNrC55CV6lS6iShxV02l25jGmjFDpVuOf8tbT0Wl0WujW9rJP3UO7c2Lq5wOQSBwcVNRZB4DTdgFTibmOdHlVdhJQ/ITtEDiSqro6rvns+9m6YwvFB/u56aYb+d73vsE//MO3ZlWjAzQ1tfLxj3+ej3/883R17eaee+7gD3/4LR/+8LsIBIJccMElnHXahRhKBK1fJVodBeENXIyATvWiqlm3O7RjiInBFGXNB60lVF2dCsGwqF5UxURfiux4FgWFYGmQjvPb8YUNel7qQ9VVwhXh6fU2dj9EyB+lvWOmX2/RMdFVHZ9qzEoCn9BnICX7Nu4jPZLBFzEYNvvY1vcy2wZeoneiC2WDwllrL+CHP/w1F110Obp+SItb1kQIDhsA6n6dfCqPP+KnYWU9uzfswbVdjJBBcUpV0bqueZrgnw01S6pRVMH+F3rJjGUIlgZpWFlHw+p6VE3FddypNrfD7xszZ4I83NdND+jUZCqpDJSzL9VDU7QeVaiMFRJIKTmtehWO60xNDAlOrV7Ji8Ov0JncTZnf+1tMFidYWr6Q5RWLCepBPrT0vbw0soW9E/vwa36WlC1gUdn8GcdlGAY//OGvuOaa87jhhqu4996nT9h371D0ZwbJWTkawge/B4QQlPlL6U71UnAKBLS58I85zOH/OhobPUuDnp4uTjvtDAAMDJbZq8A+2ppvLh43HuL2wM9xhEPEjbJVf5lObRvvzN/IGea5hy2/yXgGQxrklCy7lU7yIkdMxhljlO3aq3TYNncGfoWDzQJ7MQJBRqR51niSZqeFSTHJvf7fsVV/hYAMMaoMs9BeSrVbS4lbyqDWx7gyStQ5Mtm22jydh417edR/PyAIyiAFCmhCY3dwJ0iB5r75RLCUkrHiEHkrS97KMmlNgPAmOKWUVBjVlBoV4APHtZm0JkiZCQJaiLydZaQwQNZK41eDVAZqCL+GYDRNE8M4/rHYN77xFeLxMq677t0nfC56UEfKwyfdHcvBF33948GGBqipkfz+9wdD4hzHs3wIBiEclkxMQDAoqajw/q2tFaxcKbnnHoVMxrNuSKXA7/eI4HweNm8WrFolSSQ8YlhVPaXxggWSmhrJrl2eFcWnP+1SV+ftc/lyyZ493jm2t3tK4kPjVObPh3/+Z5euLhgfD+ALCyobbQxjJTGjFEM5uetRXe3Z6A0M9J40Gbxjhxeiezzhcf2bB9j56G4Gtw2RT+Wx8hYlDSXEG0oQisAxbfKpAlULKg/a1Rka/qifoe3DdJzbhqortJ7Rgpmz8IUMQmUhpCtJDU4SiAeo7Dh2kOXxYv1bruBbwz9jvDtBuCyE5tP4t9/8I2W+Ss5vuxJ/Zd2M5QMlQaTrsuLa5bOS0rmJPFv/sI3cRA4hBK4rMYI6Vt6mZlE1ru2STeRYePF8GlYdXajxeqEr8I75kjVVDl0pgSIDNCe7qHruMURVFa6qIPt6EaEwvmvejv9t72Tl6BCu6/Lqqy9z+unHH+I5h2NjjgyewxxmwWOPPcAdd/ySb3/7R7S0vHmtrQJBk63zvJGn6hDiaUw4jAsXTUIMlbDrvT6qONxn5Di/GOIU++QG1XtUixDKDJ9gHwIXSb8yswofFTY/DEywf0pFnBeSO30ZulWLD+ZKXrdtxSo7wFNunhHFJiNcQlJhUrgYEgamXvtuKElEKqw1A1x2nCphG8kuzaRbtVCloMMxaHa0GcReXV0Dn//8V/j852/ife/7MCtWrAEga2UpOAWqfDMf6iEtwFh+jKyVO65z8+HjmsLbOV+sJ9kxTqwtTvzGUqSU7NnTybZtW9i9u5M9ezp57rmn+OUvf4xpei2TwXCIeGWcYCyEHvGhhQOUl9fgjhT51QP3s3bt2Xz601/EcRx2796BpukUnDyDuV6ShVEUoRKWUYxigMxkmomJ5NRPglRqgomJBAMDfezbt4dkMjF9zJWV1bS0tLFo0TLe9rb3cMopa1m4cClF8rww9jg+xYcxpeJQhEJYjzJhJsjYKSJ6yTGvSZe2Gw1thgeejoGGhhm1WWytYijXR9EpEFCDJMxRwnqUS7JX0uEuZJuxBVMpco6xngViKd8Jfw2fnEncGdKHhUVRFI7rczoULi7PG0/ztLGBMWWEWqeeM83zD1MF+aN+gvEgmbEsvpCBjUVO5lBNFc3QpslEn+ojVFfCx77+UfQBhy9/+TNcc835vOtd7+ev//ofD1OkH4rW1nY+9rHP8bGPfY7u7r3cffcd3HPPHXzu7o8CUFlSQ3vlYubXLGXFgjWcdtVaSptmV1GlRzKo6kxrCT2gY4R9ZEczVHVUUNlegdtaRnokjR4wqFlUhV20KU4WMEIHr7FpFXlp10ZWtJ7FWDqJ4frQFZ28XWAkP8o59eso8Z24SuS1SI4nuP+xP/DCjqfYPbGVTHGSgB6irWQRKxrWsaBsGa3tbZSOVFJMmejlB++pUFkIFDEjpE5KSTFTpGpBJQDzzm5F9Wn0vdxHMWPij/lpWFVP05rGox6XEILqRdVULajCNm1UXUW6kmLGxAjoR22rC5WFEKoyFTyiTx9XIV2gYUk9rQtb+fWu37M31Y2UkogRZkn5Al4efpW79t5HzBdlbc0prKs9hQ8ufQ+P9T7Nq2PbURWVM+tO59yGMwjqnrI+pAc5q+50zqo7/ajnE4lE+fnP/8Cll67lppvex2233XfMYM0jwa/5UYTAlja6OPh5FJ0iESM8FyY3hzn8L0JqIMXIrlGsvBdAVLWg8qgTaYciGAxRWVlNd3fXm3yUx4+syPCw7z50dFpsr+6vcmvYr3bxsO9eVpun439NClNGpBlVRxhVhpG4aOgkGcekyD5tNxKXpEhME8EAYRnBL/383v8bTFEkJCPEZRmq1MgpWbbpm4maMfIih18GjqoKBiiRcRbYS3jJfQ6BioKg0Wkh6sbYo+3iaWMDVxXe9uZctEPQndnFvnQneSdH1kl7ORlKEEUILLdIyk4QcIIEtBCqoiGRZO00k2aSrclNZOwUAgWJS1+ui4UlK6nwH5ycVBQFOZWxcSxs3vwit9/+c7761e8eNYPhSKjqqGT/8z1MDk4SqYoghCAzlkX1aVQvnH3S/USwdy8MDgra2jz1bzIJuZxH8La1uVRUCLZvFxSLnvK3vR3mzZNs3apQKEBfn8Cy5LS/7wG7B02Dhx4SaJrANAUjI55HcHW1t51QCK69VlI3xSMWCpBMChIJTxVs21MS1NdAVb1jaG+XeP2rJxaiOBvmzfMEKjt2vMrSpStPahtbt27GMAzmzTu6r/jk4CQ7HujEsV1i1RHsguWJsboT+EIGwdKgFw6nK4dN5Cuqgl202P5AJ8M7hjHzJmbepDBZID9ZQFEVQmUhFlzcccIBzkdDNBbj8g9eyfb7OxnfN45ddPDpfqobamhvXYBru6j6wVrNLlj4Y4EZrx2K0V2jZMaylDWX0relH81Q8Uf85CfyZEYyVC+qxsxZFDPFN+wcjgZFQEccOuISEMgbrsGsj2E/txElWoJy4SUY512E1ubdJx3xUvz+AK+8smmODH6DMVeZz2EOr4Fpmnzxi59i3bpzeOtbb3jT93emFaRTN9mjWlS4KpaQ7FFNEBIdiLgHCZyYqzKi2LygF06aDI64AvM1D3tPiyvwy5kPwWeNPPsPUxErbNVMtmkmq+3jT0idDUttg+sLEX4cSLFXtRgXLgEpcITnH9tqG1S7GknF4V5fFkMKLjOP3h5iIrnNP8lzRh5ryok2LAWXFENcbIZmkHo33PAB/vM/v88Xv/hJ7rrrcRRFocwfp8QXI1GYoC588BonihNEjQjlgcNTjo+GmCwh5pRM/y6EoL19Ie3tC2csZ9s2PT37pgnivsEexsZGSCUnSHSP8fwTj5DJpAHYuPFxNm58/LiPQQhBNBqjpKSUWKyEWCzOvHkdXHTR5bS0tE3/hMOz+zPnzSxSurjSJW/nUIXqpUKjInFxpHNcxxGSoWk/wEPhhbOEqA02UxtsBuDVxPOkrQn8qlfIdxQW0J6fT9IcpT26lIbIPGqcOrrVvUQOUc2MKEPE3VLqnKOTerPhYd+9/M5/GwJBWEbYpm9ml7aDG/If4HTzYPGh6iota5t59Q/bSPYkMaI+QjJAvzJEfV3tNNGXMbP4VB8VwTLa1rRy112P87Of/Ts33/wFHnzwbr785a9xzTXvOGbrfHPzPG666bPcdNNnSSTGee65J3nqicd4+qkNPP34w/A4NN3fytq1Z3PKKetYuHAJHR2LpgdB/rAP13FnqFyEEARLvGDC8a4EelDHLtpohsq8s1rxhX1IVxKIB5gcmJz2Qnt593PkiznOX7SeVKXFvtR+JN7kwPxQG4sG23n2hRfwBfUp0rTyMCXsa1EoFNi2bTObN7/I5s2b2Lz5RXbv7kRKSXWonlMbz2Fp/RoqqMOcNHEdl1BpkGhVhLG9Y2y/bwdr3rVqej/l88qpaC1jeOcowdIgqqaQTWSnCV/wAvHmndFC05oGzLyFETTQjINFtHQlZtZENVQ0n+fJ1r+5n2TfBP6wj5olNVS2V9D3cj/7X+ghnypgBHTqV9TRvLYZzVCxChYDW4cYPZBGPq+c8pYyhneNECzxivZsMocR8tG4uoHy8jJaok3smtiL5VjkrDx3dz1I2soQ98XoSw/wi4nbGcuPc237FbxzwTW8zb3Ss9dRTr5zorq6lu9+9z+5/vr1/Ou//gOf+czfnNR25sfnURuuoXuyl5ZoI5qikTYzpMw0FzSejf5HaFOewxzm8Oaj7+V+djzYSTFTRAiBBMqaS1lx7bLjTp9vamqlp2ffsRf8I2FQ6WdcHaXenlk7VLrVDCuDDKsDNDmtM96rt5u4x38HMTdORHqdMCYmg2ofe9Xd1DoNCOC1HUY6OvvUvdQ4dTQ4TSSUMa+WkVHSIs0+dQ8ChQuKlxwWRieRDCr9ZJRJypxKymQ5WSVDm72ARqeFMWWETn0b+7UuJkWKnwa/T4lbylnm+ce0BztZZO00PZndaIqO5uqoQiVkRCi4OaQUKIqGK11ydpaAFkJKCUhUodGV3kHGniRuVHj3kpSkrAR7J7cRNyqmJxFVVcVxjl1rSin50pc+w4IFi3nnO//kpM4nWhNl0SUL2fnILpI9nr2HP+qn7dwOyltPbAxwKAoF2LpV8LvfCfbtgxUrvIC24hT/Nj4OjzyiEA57xKuUMDkpyOfhpZc8Ve8FF8DEhKS/H7q6vOA5IaCqSpJOe/9vWYLycs924sknBW1tEApJVq2SnHmmZGzMI6TvuUehv98jkV1X8NRTkgsvhGuukbOGy72RiMVKaG1t55VXXuT66997UtvYvn0L8+cvRtd1bNMhP5FDNTSCr/kOGtkzRiFdoKylDDNrMjmcxrFdXNtlciiNlbcIV4RwbJf8RJ5gfMquzpXkJnIYfp19z3QTjAeJVEQwAgaZsSyVHRU0ndpIaWP8uCfCTgTBeJDV71g5fYzRV6JUdlRSUl9CojuBHvLkUVKCVbRoO2feDGuTQ2HmTBAc7FybogEUTcEq2NN/k6/N7jgR2C5sT8Du5JTSvESyqAyOcEgzIAJBfJdcgXHRpeA4iNfkrGiaxtKlK3nllU0nfXxzmB1zZPAc5vAa/Pu/f5v9+7v44Q9/9UfxN5zvGLw3H+NhI0u/aqMB5xRD5HwZJpWZs+CmkBgo2LMlah0nVth+NulFxoRDmVSQeCrcmFRYbM9s99mjWoTkTBWxHwUHSb9qs/p1tvMJBBebYZZbPh715dijmpjADq1Ii6NTI6esBFwNR7F5xshzrhkkeBRF8ia9wEYjT42rEZEKEsmI4nC/L0uHY9DiHHzAqKrK3/3dv3DddRdxxx23ct117yaoBzmnfh2/2f0HetJ9xIwoaTND1s5xRevFxP2vX/U4GzRNo7W1ndbWdtavv3L6dSkl//iPf813v/t1PvWpL/CBD9xEIjFGLpfFtm0sy2J/ajcDuW4ieslUa7xANRSUsOD0xvNormg/abUfeKEgRSfPcL4fVWgoQiGohTEUg5AeJawdnx3AUmslTxqPMqIMUeF66ooRZQi/DLDUWjFjWcs1UcTMYz7w92i7FhoaFxUv52fBH7BL207MjZMRHln+lsL1xOWRVbezYVKkeMz3AAEZpNb1CMNKt5pudS8P++5lpXnqjCCZ2mU1KJrC/hd6yI5nWRNeSaH0OSbjaYyiQcEpMFpIcHr1alpjzYB3v91440e45JKr+NKXPs1NN72P22//OTff/G2am+cd13GWlpZx6aVXc+mlVwMwPj7G888/xcaNT/DMM4/zq1/9dJr0bWpqZcGCxTTXtaEO+qnONtA+fz6a6pGb4cowCy/qIDOWJTUwSTAeoGZxNeVtXsueUARNaxrZctdWJoc8QviJFx+kLt7EaWvXsuDi+XSm9pA2MwQLAXIPZRgcGMII6NiWw9COYVrWtTD/gnaEEFiWxcBAL729+9m3bw9btrzE5s0v0tm5Fdu20XWdRYuWsXbt2XzkI5+irXQRO2/bSzFbxB8KkBlJ4zoOiqoSrYkSiAVQDY3E/iSpwclpbzTNUFl29VL2PtXFUOcIdtGmor2C1nUtxBtKZlxPzacdpuYd3jlC97P7SQ+nUQ2VWF2MZO8E+UQOPWiQNCcY6hwhVhsl1ZdC0VX8ER9mzqLzkV2YBZOOc9vZctdWBrcNoWrefTzUOUJ5axkta5sZ2TWKXbQpby2nZW3T9AAzbIRYVbkMV7p86+UfkLVzdJQc9Nody4/z9MDzrKs9lepQ5Rumtl237hw+/ekv8vWv/x2nnXYmZ511/glvI6gHeeeCa7l1x288dTMSn2KwtmYN5zfObo0yh9eH7373u3zta19jaGiI5cuX82//9m+ceuqpR1z+9ttv54tf/CLd3d20t7fz1a9+lcsuu2z6/RtvvJGf/vSnM9ZZv349999//5t2DnP4n4V8qsDOR3cjXUlZi+et6VgOY13jdD+3n4pLS9mj7QSg1W6n0q2edTtNTS2vWxlsYrJd30Kv2o0hfSy2l1HvHF8g2Wvhx48uDUxRxCcPCh2KFNExZrx2AI1uM5rUp8PjXFwsYdJoN5NR0sTdUnz4mRSpaYWvg0NKTBCR0Wml8UJ7CS4Ow+ogaZFiVBlmffEq3lK4fsb+UmKC3wR+wVb9ZfIiR0TGWFs8m7hTRkEvkBc5tuqvkBd5om4JtmIjENwe+DllbjmL7WO3058MUmYC0y1SYpSTtTxVsBACTejY0kIgcKSD7VpI6ZK2UvjVICE9QndmFyEtMmOiOqLFSFspJq0kpVNdesdLBt9zzx08//zT3HrrPWjayT8fa5fWUNZSSrIniZRQUhc77omO2TAxAT/5icL27bB3r6cGTqU89W9pqWf3YNuQywmEkPj9goYGz6ri8ccFuRxceKFH/CmKt1wq5RHMtbUH1MIwb57nN+w4cO65kq1bBfPnSy6/XNLRIXnwQcHGjQq7dkF/PzQ2elYR4bAkmYTHHlNYtsyhvf2kT/W4sWLFGjZvPnlyb+vWV1i8eDn9mwfo2riPXCKHqqtUtFfQcV7b9Odl5UyYuid9YR+VHRWM70uQGc+SHc9S1lJK+7ltTA6n6Xqqi0K6gKprWDmTQCyAmSsSKg0RjHvb03waUkJ2PEe84c0hgg9ACEGsxhtj3XnXowB0P9tNz4u9ZHePIl2JZmjULqmmblndEbcTLA0i8KxOwhVhcokctmXjWC6+iI9cMktttIs2dzOh57M40TbM2rNxoq1H3OahsF341U7BUwMCy/EmCHVVckat5B3zJfpxTi4IVZ3pU3IIFi1axvPPP3V8G5rDcWOODJ7DHA7B4GA/3/zmP3LjjX/2usN0TgRLbR+LbIOUcDEQBKWgT7V42Jcjh4sfQUFI8sIl6ioscmb3aEoLFxtJTCpH9BRebvtYbwZ53MixU7EBQalUuLIQpvE1vmJhqcyqIpaATwocJC9rBZ7T8wyqDuWuykrbz2mm/4QsJKqkzjsLXqG8VSvyb2qSKjnz6ykiFZLCYVJxCbpH3vZmrYA6tTx4hHOlq7JDNelUzRlkMHgkyBVXXMs//MNfc8klbyEcjnBew5noqs7jfRu9ECx/lMtrL+KchnXHfU4HkLfzZK0cMV/0hJVxruvy13/9CX760+/z5S9/jQ996BMAh9sLjJqU2/HDrBoSxRGUEK+LCAbozXVhSRtFKFNKYEmiOEJQi7CoZA2aolOgQKe+lQmRpMwtZ769GIOZ13qhvZTLCtfwkP8eOrVtCCAqS7iicA0L7Jl/b3FfBaPFwRmBWrZrI1CITCVir7JOxZf18ZTxGPu1fbTbCznDPIdTrBP/nAbUXhLKOM32TFK2wq1iRBliTBmhzm2Yfl0IQc3iaqoXenYBiqawZGwRj/Q8wXBuFJ9qcGnzBVzafMGMQDDwlJjf//4vue66e/irv/oE55+/kk9+8gv82Z996oQHL2Vl5TPI4Vwuy65dO+js3EZn51Y6O7dxxx9uZWRkCABV0YgF48QjZdQ3N9KcaqWqqprKyhqqItUoRQdrsIjfHyAQCFKzpBrbtOl+bj+JkQQv7X2Wd1z6fpZcuRjDMFgQmkeWDC8/+Ap7tuzGKNcppvKk85P0D/QysmGA4jez9A31MDLkeX6B1/I5f/4ili9fw7vf/UGWL1/NggVLZvizu45LwA6x5a6tZEYzWHkL1adR2hinYp5HWOs+Dce0sfLWjOuiGiqx2pj3+tRnVdZ87AmC0d2jbP7dq9gFi2A8iFWw2HbvDgTQekbLtPo4PZKm66l9xJvixKcKdV/Yh5pU6d88iC/oY2j7MLGaGLrf+0ztos141zj1K+qYf0G75z8Y8s2qxJg00/SnByn3l86YlCzzl7IzuYf+zCDVocrjuUWOGx//+Od55pknuekmL1izsnJ2Eudo6IjP4zNrbmJHYjd5O0d1qIp5sebXpVyew+z41a9+xac+9SluueUWTjvtNL75zW+yfv16du7cSWXl4ffGxo0beec738nNN9/MFVdcwa233srVV1/NSy+9xJIlB79/L7nkEn784x9P//5mZSbM4X8mEvsTFFJ5SpsOfjepuoo/5uMh+156gruY0Dw1ZYmMs75wJRcWLztMldrU1Mrjjz980seRE1l+Gvw+r+ibcLBxhaTEjXNN/h2cZZ74ZFat00C7vYBX9BeYZ8/HwKBIkQG1j1OtdVS5h/up1zh1zHM6cKUkqYyjo1FnNxKQQVRU2u2FLDaXs9G3gZAbwU+AtEjR7MyjyqnhOeNpapEY0scyazUv8ixJLYGOwT5tD3f5f81bCtcTlhEkkl8FfsqzviepteuplDVMKAnu9d/JGcVzKJFxXtVfZlKkKHXLmVQ8wnmJtYJ+tYdN+rNvGhmsoEyPFHyqn0nLIzElEl0xCKkRxsxhbEwmzHECWoi26BJCWoTZbAmYfuYdfE9RVFz36GRwoVDg7//+r7jggks599yLXvd5+cI+qhed+HNwNjzyiGDLFkFrq0RVPYVwOi3J5aCy0lMHFwoCVZUYBgwPe97C+Tyk0zAwILj/fo8wdhyYmPAsHYQQxOPednI5weSkJBj0LB+iUUltLXR0SFavljzyiOC++wSlpRJFEYRCMD4uePVVOO00STwOvb1wxx0K0ainJl6xQrJkCSelFC4WYds2wcCAR3YvXiypOeTPaMWKNdx992+n/KBPjFCdnEyxffsWrrn4Xbx69zaQHuHpmDY9L/ZSTBdY/c5VqLpKpDKCEB4Rquoq4fIwgViAoc5hWk5vZtlblqD5NKoWVBKpDDO4bZjMSBojqGPmLJI9E5TPU5Guf7pe84UNsokcxcnCdBAdeAKe7FiW/EQeX8Q3bTMikWSERAFC8uRl18Wsyf4XeolURymfV46cqqnNrMXA1gFaTm+edb2KtnLiTXHG9yUIlgQIxAKkhiZRVQW7aFMjn2Zp/UuEzQA4AbSJneijm8gt+X/Yca+TNTOWZeDVQVL9KfxR72+jfJ43IfjqmOCpfkFFAKI+7+82bcLT/YJFpbC66uRFbAdQW1vH0NDA697OHGZijgyewxwOwd///V/i9wdOuk329UBFUHpISNpN2Ti9qs1uzUSRYCAIuYLVlp+Yq9CtWjQ4GiqChHC4z5flVb2ADTQ6GhcVQyychTRWEFxZDLPa8rNPtVARtNs65fLwr4OVlp9X9ALjwqH0EBVxXKrUOCpfD47zoC/LhCLxSyiRKq9qRfYqJu8pxCgIyQ6tSE5Iqh2V9kPsJo6EUlclJAWTwqXkkOuREi5RqRA7ChEMUBQS7TX7EFP/2UfY9d/8zVc5++ylfOc7/8znP/8V9ukOXW2LyLc3EykWOFOUco4TQz+BFru8XeCh/Rt4bugl8laeskCccxvOZG3NmsPIwSPhb/7m0/znf/6Ar3/9+7zrXUdud9MVHfc1Xmpeyw+o4vV9zZtukaF8DzEjTtwoI2OlMN0iAS2IofiI6FEGlD5+FvwBXdpuXFxUVDqsxbwv/6EZLY4CwaXFt7DMWsVebRcAbfZ8at36wwaKNYEGRvL9JM0xfIofiYvlmlT6ayn3HSzOF9vLWWwvnzIEOXklv18G0KVBURTQ5UHSPu/mcNIuie1JgtEIJbWxGeSdUMS0LcQp1StZWbmUiWIKv+onbBzdP+yiiy5n3bpz+PrX/46vfvVveOSR+/jOd35Cff3JKZvA82JcsWLNtAf2AYyPj7F18ytseeFlRsaGmCxMMDo+zAsvPM3w8BDj46NH3Kbf70dVNRzHoWgVuOvxX3L7yp+Sz+em77PZEI+UEQ+WU11dxSnLT6W8poLy2goq66pY1rqGxeWrj/q3oKgKiy9bRPPpzfS93EfnQ949U9lRMU1CHCi4Q2UHr7WZM9n8u1cZ2TniDSpdyeDWIVrWNjH/gg6EIkgNpBjdO45dmPK7nF+Jaqjsf74HK29NE8d6QEcIj8gtZorTdhlGyKA4FVQ341rFfCT2JxneNQyCaSIYPDUJQpDoTlC/om76vpkNPtXAUA2KjjnjddO1UBUVv/b6LHpmg6qqfPe7P+Wii07hpptu5Je/vOekJpLCRohTqle84cc3h5n4l3/5F/70T/+UP/kT79lwyy23cM899/CjH/2Iz3/+84ct/61vfYtLLrmEz372swB85Stf4aGHHuI73/kOt9xyy/RyPp+P6uo3hgCZw/9CyCmK7jXffcOV/Ty9/BFqqKHDXohAMKQM8Hv/7dQ7jSywlzCkDJBRJil3qujoWMjo6DBDQwNUV5+4B+mTxqNs0p+h2ZlHQAaRSPrVXu4K/Jp2e8ERA22PBAWF6/I3kBMZ9k75/SooLLSXcG3+nbPWF/Ps+bTbCxhQe1lmrkRBwcJkj7aT08yz+G3gVnbpO7CExZA6QIPTxGWFa2hx5lEQefZqu+jUtlLuVrJP3UOnvpWYE6fGqUVIeMR3HxYW78t/mF61m636ZurtRqKyBPAmqy0s9ug7uS73br4X/jp5kSejpIm5npdwWEYIyCBj6iiDSj+v6i+TEWmq3BqWW6sJy9ntwU4EJb4yAmqQjJ0iqIYJqEFydhZHOkT0GCiCpnA7DaF5+BQfcV85PtWzqYoapYwXhjAU37RNRMZKEdIiRPWDOQjHowz+j//4Dv39PfzsZ3e+7nN6I2Gans1DPO5ZP4Bnw2CagkJB0tXlWTsI4Vk2+P0H13v5ZTFF/MLYGFgWqKr3u+N460xMHFjHs4qQ0rON8OwfoKrKW+/ppwWBgEfsTk562y8tlYyPCxIJj0T2guIETU0Sy1J49lnJpZdKrrhCHlbvHA2Tk/DTnyq8+qpnQSGlt6/rr5eccopXNy5fvgbTNOns3MqyZatO6Jo+99xTuK5LrdaMm3UobZya7A/o6H6dsX0JxrsTVLZXUDm/gtLmUsa6xgnEAghFkJ/IUTGvnPkXdkx3hymqQu2SGiKVYV769WYyY1kUVWBmTQa3DWHlbcpavEkwM2ti+HV84YPjbCtvseOhnQxtH8bKm+h+nYr2CsKXzmNDuc0ezUQAiy0f680Qle6Jj8/Gu8bJjGYobYzPsGBLDU7Sv3mA5tOaZu1qNoIGy69eyt4nuxjZM0asNkppc5xoTYyKSpv5xbvwhatwQlPfm1KiJbfj2383dskCUoOTvPLbLUwOp9H9Oo7p0P/qIPMv7KDltCZ2JMCWYpoIBogYMJgVbB+XrH79VttUV9eSTCbI5/MEAnNhxG8U5sjgOcxhCi+99Dy/+91tfOMbPyAWK/mvPhzqpc53Jyu505fhZb2IMUW2jguHHwVT6Ajm2TpXFELc5c+yTStS6aoEEWzXTPpVm4/kSg5TwoJHytW5OnXHSBheafvoLYZ4wsgxqnhFWKlUuKIQ5glfjqeMPKaAJkfFEp4y2UKyyShQ6Wq8qBcYUD0vCR1Ybvl5VyF61FnRGldlueXnKSOH7UJICiYUl4xwWV8MEziG4nihbbBNK2JzkBTOChcdaHJmP9/6+ibe//7/x49//P9x8cc/wa+qXJLCJS41knqI2ymQMBXeVogcF+EopeS3u+/msd4niftKiOhhhrOj/GLHbwA4o/bIbbwArnT5zn98gx/96Lt8+StfPyoRDFAVqGOsMETGTBPQgihCIWNP4lMClPoqcXCQSLRZvvJd6dKV2s9IboywHqQj3oZfO6S4cUwsx8SvBZEKFHwmWZHDcA2UgkrBLXB74Gfs1jpptdsx8FF08nQqW7jT+DXvL3x0xjUTCOrdRurNo3v6+rUgS0tPpT/bzWhhEEUoNIfnUxdqnrU1/vV64TU6LcyzO9iqv8K8qfNIZSfoTO6g7eVF9DzWT79vmOoFlSy+bNGsab0AmqKdkK90KBTmS1/6Zy655CpuuulGLrxwDf/8z9/jqqve2MCXsrJyzjn/Qs45/8JZ3zdNk9HRYUZGhkgmxykU8hQKefJ578d1HX79658x1D/A+YuvQJUqoVCY6tYa2k5po/vxXjRHo6ymAr8RIOQPo6sG/cVu9HMsqipqpz+3olNgxOqnzmyi1HdsdWuoNMj8CzoIlYbYevc2JvpT+CN+rLyJlbeYd1YrodKDITH9WwYY7hympL5kOkAun8rT/XwPlR2VZMYy7Hx4F4V0ETHloVbRVsGSyxcxOZwmEDtItApFoKgKjuVSyBQxoj6GGWFSyZAJZLDNgynYaSvDvuH9JCcnYERQnokTdSMzOwKkRBzDQxkgoAVYXbWMe/Y9TMQIE9KDWK5N92QPzdEG2kpajrmNk0FFRRXf+c5PefvbL+F73/s6H/vY596U/czh9cE0TV588UX+8i//cvo1RVG48MILeeaZZ2Zd55lnnuFTn/rUjNfWr1/PnXfeOeO1DRs2UFlZSTwe5/zzz+fv//7vKSs78ndasVikWDwYOjM5OXkSZzSH/ykoqY/hD/vIjuemA5Nc16WzbBtumU09B5/tNW4dndo2ntGfZKPx+LSFQcSNsew8j/x5+unHTiqfY5P+DCEZJiC9736BoM5poFPbxk5tO9XmiRPMNW4dH8/8JTv0V+lT9uMKl6XWymlbK/CsrQbVfgIyQIvdznX5G/hF8D/Yre2YOg6F+fZiRpVhurRd1Dh1LHKXMaoMkxZpnjGeZIN4EFe4+N0AdU4jaWWSvdpukAJXSLbpWwjJMPV2E5uNTQwWryAlJsiJLA1y5mRxREZJKUlanTben/1/fD/0TZrsVkplGSqqF9QmMggJ/xr+B8aU0enOwTZ7Ae/PffSIVh7HC78apC26mF2pV5m0k+iqD0N6FhUxo5SaYCMNoVYC2swJciEErZEF5O0MieIomqLhSAef4qc1uhDtkGenpmnY9pG96fr6evjWt27mve/98GGZHH9MOA7s3g1DQ4JgEBYt8pTAtu15Am/b5lk+HFD4WhakUhAIQDjsqYBHRmDJEhgeFgwPQyQiCQS89Q3D8xB2Xc9ewrIkExNwICdvclISjUJFhWTXLo/UXbFCTofF9fZ6+0ulPMI2nxf4/ZJiUUx7EJ9xhktFBYBkdBQefVRh5UqH+vrjvw4bNgheftlTQvv9Hgne0wN33OEF58XjsGTJCnRd5/nnnz5hMnjjxsepqakj7MYgPLP+13wa0nHJp7wgaSNosOLaZex7dj/DncNIV9K4ppHm05tm1I4H0P1cD5NDk5S3lCEUgV10SHQnSHQnCJYGQHp2OW1nt+KLHBwv7X5iDz0v9GKEDfwRP44j2bu5j13+DPve2Uql1HCRPG7k6Fdt/l8uPt3FeiTIqcm3AxoUq+gZRb42i0MzVMychXQkQpt9PBQqC7Hs6qUU00Vcx8Uf9ZTO+sjzBDZnsYOHjMuEwAlWo6X2QnGCrqd7SA9npq8JHOiQ66J6QRWunJ2cFUJyfLGPx0ZFhfc9PD4+Sn39iefCzGF2zJHBc5jDFL7znX9m3rwO3v72kzOyfzNQIjVuLJTwvoLkBT3PT/1p/AiaHY2ikGzVTLpCFjnh0uYY+KYKvJijsFO12KgXZiWDjxcqgquLYdZYfrqnVMQdtk5akfxaS6NMhc6pKKgSbGBcdcCF3/jTBKRgnqOjIcgIl+f0PNWuypXFIysRBILrihF8CF7SCwwpLjFX4epimPPNYye1nmoF2KIV2amZhKSCI8DEZa0ZYKF95GvxgQ/cxA9+8C2+ddu/4//zP2G+o08TjAnh8IyeZ50ZoP4YBDrAQHaYF4dfoSZYRdxfAkDUF2H/ZC8bep/i1OqVR7SMGMwO8y+//Qa3/u23ablgGfsXZHik5wnOazjziCrKZL7A7sQAeWcCVVGJ+2LURxqojTfyaOQBNunPYAubRdYyziuun7Y7yFo5ftl5B5tHt1JwiqhCoSnayLsXXkdDxPOe8ql+DNVPyk2y07+dhDIOgGophIwwStDPHm0XdU4jPsuHlhT4cyHm08FgoJt9xi5a9aMn/R4JQS1Me2wJbdHFALPOdr9RUFC4Pv8eCiLPPm0PruswMZ6iZlcDF/RdRryljEK2wN7+XYxt6SU2L0KpUUFNsJGgdvRQw+PBaaedycMPb+Iv/uKjfOQjN/Dkk4/yla/8K37/G68AnQ2GYVBX10BdXcMRl7nlO//Kqvp1XL3uBnxhg2LGJJfM0ag003rpInY+sot4xCNgpZSkh9Ow0MQfCs4g8H2qn6w9yYQ5flxk8AHUrahFqIL9L/SQS+TwxwK0n9tG4ykzj3m4cwTNp08TwQCBWIDseJbB7UMMbhtCSihv9drbbNNmeOcIsboYRsggO54DPIIjNZCikC6QT+Xp2tfN9sAexiIJcnYOfYlGf3GECyfPRvgFL/Zuxhwp4jZBrr5AvidPbn+BVc3LUIVKMVNEURUq2so5HlzcdB6juXG2jG3Hdi1A0BCp5e3zr8GnvnkedWeeeR4f/ein+Zd/+XuuuOKttLS0vWn7msPJYWxsDMdxqKqaKbepqqqis7Nz1nWGhoZmXX5oaGj690suuYRrr72WlpYW9u7dy1/91V9x6aWX8swzzxxRJX7zzTfzt3/7t6/zjObwPwWhshAtZ7Sw+7HdjHcnUDUFq2ijLBVE4ofnB2hS40nfwwihTNkbVJNUEjxR/xANi5t48smTI4NNYaK9pqNNTFVttrBmX+k4oKGxX93HRmMDk4qXJbDQXsrVuXew0beBp30bSIkkOgYtdhvvzN/Ix9Kf47eBW9lmbEaVGopU2a1vod1eOO0LLFzBy77n6ZH7ONe8CFWqDKj9SCFpMxfwlPEoFW41URlFIkkpE+zXuqhyakgq48TdMsIywoRIzshESCkTRN0Som4Ja6zTecZaxR5tJ4qroEqVYXWQEreUXm0/RQostJcgEFhY7NK284DvD7wn/6cnfb0OoCbYSFiPMl4YxnJNwnqMUl8lumIctQOoxChjRdk6hnP9ZOxJAmqQykAtMWOmrdPRbCJc1+XP//wDRCIx/uIvvvy6z+Vkkc3Cz3+usHmzwDQ9IqyuTvCud7lUVnp+vJGIpKzMI4JHR6c6+FSB63oKX1X1rCL6+z2LiFwO5s/3bFRN0/MYtm2PJEynQVE8j2FNg2gUamo8JbDfL1i6VHLFFS7xuLe/XE7S0yOoroZIBAYHIZGQZLOC3l7J0BA0NUnKDylRysth507P57i+/tit/pZrUrQtXtgUpKRETKuchYD6eo8o37VLcNppkkAgwFlnXcC9997JBz/4sRO61hs3Ps66decSigdJ9qVmkLqO5YAQ+MIH66RASYBFlyyg4/x2T/Xqm50Cc22Xsb1jBEsC06RneWsZUkoS3UkS+5OU1MVoPbOZtrMP2soV00X6XxkgnyqQGpz0LCk0hbFaH8XOBPMHG1GqPOI47qjsUS02awXOtA4nowEKNjw1IHh2UJCzoD0uObdeEqsIoRkqxWwRX8jbnpSSQqpA3YraIwbIHYpDCWwAqeggFJCO9+8UhGsjFRWzIBnfnyBYFpzRGRkuD5PYnyDVP0F7aYAn+iQ5C4JTw9v81H06P84bgljM21AqlfyjkcGZDPT1ga5DU5On3P/fhv+FpzSHOZw4du/u5IEH/sDXvnbLVADXfyNIiT7RSXFiA1eao4hIK/trTyETqqDN0dmo5/FJMU0Eg1cQR6RCt3qwxVgiSQkXdeq944VA0ODqNBxCgg6JIgVcglKQOYSf0xHkcUgJr3VpoRWYVueGpUKJVHlBL7C+GMY4ipIzJBXeUYiyvhgiLVxKpUr4OI85LlU+mC/hOT3PNt1El7DC8nOq5T+qzUNNTR1XXH09D33/B7z1ozciVHHINhVGFIc+1T4uMng0N0bazFAXmukxF/eVMJ5PMllMUxY43L+0YBf5zmP/H7/++1uoWdjEpR95Jwlrgt/u/gMRI8yp1YfPnO9K7uUnW28jZU5SEypDKib7k91kLI0tbVvZYrxEiYyjSY1HffezR9vJn2U/RZVbw4PdG3hm8AUawnVEjDCmY9I10c0vO+/gk6s/gq7oaIpOXbCZh7J3kbZTxEUZmqsibBgoGeTB8L0URB6/7ccYESh5gdRBCAVfVmNXZgvl8SqiUz6/J4MjkcC26eAUbYyQ8boScA+gzm3gzzN/Rae+lZ6h/QzeNcICdzF+NYBEkmtKUAiOY+YURKGKZHGUkcIAy+KnEdJff6tlLFbCLbf8grPPvpAvfOETvPLKJn7wg1/+tyDjevfsZ2hsgOtOXzodouH9K+l5qY+axdWYWZPuZ/djhH0Eoj78sQDl88pxjOLRN36cEEJQt6yWmsXVWAUbzadOh7MdiqM4V5AeSVOcLMzwu9QMDX/Yx/D2IRrWNLL93u3kJvLkklnG9yVxHYke1niqbBMDuSGqchU0lNSit+nsS/XywvjLVHSWUTALROZF4DRBJBDBmjBJdk7QXegh5oug6ioNaxqobK848gEegogR5oNL383uiS5GcmOE9CALSzsI6bMPHt5IfPKTX+APf/gtn//8x7jttnv/KGGqc/ivxzve8Y7p/1+6dCnLli1j3rx5bNiwgQsuuGDWdf7yL/9yhuJ4cnKShoYjTyrN4X8+Wtc1E6kMM7RjGDNjUtIQw1jhcru/C8u20PHqJBubCSWJjc1iexkR6ZHFlW41lrCInlvCk797dDr09ESw1FrJvf7fUeXWouI9B5IiQUAGabKPL/RoNjzme4B7/XcQd8totueRF1me1zeyM7KdCSWBg4MrXPIU2GQ8g4lJndPAZmMTmtRRUHjC9zBJZYwWpw2mgueGlUEsYRORPoIyhEDQ4sxjl7ad531PYUgfyrSpmaDEjTOk9BMXZZS4pdS69aww1/CE7xFs1yIsIyRFgpzIcLl5DQECIAO8P/dR7vH/jh36qxSFy1JrBS1OG3/w/4YWu31a5KCjU+FWe7YRhfQx7SKklGTtSUy3iF8NzjoJHtFLDsuuOB6EtAit0QVHXeZoNhH//u/fZuPGx/n1rx/4L+3sfOghwXPPCRobJeGwR9p2dQluu02hvl6iKJ4CN52W0zYNiiKm/XiF8H4qKz3yKZ/36plCAXw+T90LAl33yF3vd28b6bRk3Tr46lcd8nlv35HIQcWwEJ4KWUpPERyLQUmJZzUhpecp3N/vkV2z/Skea2hsuSbd6V0MF/owbZvw0jDsbwMaOOAp421DYNsHi7TLL7+Wz3zmw4yMDB13TsFAVz9bt77ChSuuRKgKVt4iPZohGA/gmA6poUnKmkopbzm8o0Uzjmx9JV3J4I5hxrrGMbMmsdookaoovpBBVUclQgg6zmun6bRGgq8JEyxmiyR6kuSSefwRH0bAj2M5jAsXkSwg0hZMzcVqU3+FQ4rNRH+Kwa2DZBNep0XtkhrC1VF+tVPwZL8gqINPhaf6FXYlJX+6OE7N4mp6X+qnGCyiGhr5VJ5AiZ/GNSdHkNol83FC9WjpLuxou0cIO0WU/DDFpsvBCHvfz+5rMoSk9D5aRbCiQrKmSvDCsKdiFoAjYXUlrKh4/X7BANGgJwgbf/YpnGAYpbn1TatNpYQnnhA8+KBgfFygqtDcLLnuOpeWN6cx778Mc2TwHOYAfP/736Siooq3vvVdJ70NF8lLWpHn9TxJxaHZMVhnBWiZxZrAQtKlWhSFpNZRZ/XrPQBjYAOBXT+nlXFMVSfS9yKt/c/y1MoPkIw1oSEwhcRFzgiNy+HSPkVcdqkmDxo59momCgf9ispdlV7VxkRS62rHTbhWuCpRqeIAEociEh+CHA4CMb2d156VTwqKQmIJiSGP/QUelypxeeKelXGpcokZ5hLz2Mseig99+JPc+Ztb2fv7e6i85trp121AwTv+40HYCOFXfeTs/AziJmvnCOoBgvrs7TQv9b7Cb/7u+wTDIa7+/AcJ+ALU+QLsnejmqf7nOKVq5WEPvif6nmHCTDG/pO0guSVzPOk8iWCCxfayaWVKhVtFp7aVZ40nuThzJZuGX6bUFydieEW9oRo0RxvpTvWwd6KbBaVenHAsUk5/cIDQZBDNVkEBq0QSiscZVHaiS41MIUW0UIUbAARkRYaQGoKMZDC3n4AWRBXacfslHw1WwaL72f30bxnALtqEy0M0n9ZE1cKq4y4MpJQU3TwKCoZ6UH0bIMBK6xSqhxp4sftlfM3ee6YvT6YkgZY3EBmVsIiiGIJEcYy+XBfzY29MMIsQghtueD/Ll6/iwx9+F5dccjr/9m8/4eKLr3hDtn+yeP55r/V8UdvM81Q0hdFdo+QSOYLxAEKAmbcIloVY/fYVpEsS7Jh4Gdu1pls+C04eVeiUGMe20yikC/S+1MfwzhEEgqqFVTSsqp8R2PFaVM2vZLxrHNu0kVPFq23aaD6dcFmYsb3jh/ldCkXgOpL6lbXkUzn2P9/L6K4xAKLVEcxWi5yWpyZdTUD1U7u4hkA0gBrXSNVmoUmQIU+kMTrdz6efZtAfH2S+3k5HRTvxxjhlLaWHtfcdCtdxGe8aZ7w7getI4g0ldHTMY2FpxzGv1YkgPZJhaPsQ2fEswXiQ6sXVRKsOkgHBYJCbb/42N9xwJb/73S+59tqTfzbO4Y1HeXk5qqoyPDw84/Xh4eEj+v1WV1ef0PIAra2tlJeXs2fPniOSwT6fby5k7v8YhBCeF+chE1uVopyX7efZqW2jzK1AIBhXRqlyq0mKxGFkY8SNUnpeGdu+u5l9+/bQ2tp+Qsdwlnk+O7RX2alvI+xGsISFi8N5xfW0Oge3JZGYmBgYx7STKlLkaWMDIRmZtk7QZQmNjsojvvu8c8cLaHancgoe9z1ImVtBzPVUa6pUaLZb6PPtp0vdwzJ7JQAFkcfGRkWdIoYtXFyyIsukmKDWrWdYGUIhg18GMEWRgijQYDdP5yq8rfBu/AR4UX+WYWWImIxxcf4Kziuunz6HareW9+c+yoRI4gqHuFvGZv1FJBLlNTZrqlQxFRuHo3vxFp08u1KvMlYcxnYtDMVHTbCB1siiWW273gwciQzu7NzKP/3TF/nQhz7BmWee90c5ltdCSk/V++yzni9weIon1zRoaZF0d0M8Dq2t4PdLEgmPrNU0jxBWVY+YtW1IJmH/fs8GYuFCiesK+vs9wti2vdA41/W8f8Fbv7FRUlfnEcZjY/DKK4IXXvDUyW1tcNZZLlu3CvbsUXAcbx/pNJSXS5Yu9SwczjjDs5u4804F0/RC7MBTD8di0N5+ZEJPSsnO1GYGcvvxqQF8mkFZbQpHfQkxLpApb3JwfNyzwmhqOrit9euv5C/+4s+47767eN/7PnzMa53Yn+TWf/lPpJRUWvUM7xzBsV2snEkqb6FoClUdlSxcv+CI6t8jYc8Te9nzxF5c26UwWcQqjJMZzVK1sBIzaxGuDFO/0rOfsU1nBrGsKApm1kRRlek8CM2nESzYJIKCQECdjtSWSBwkTl+aTbfupDBZRDNUhrYPMbBlkNj5S3ghWUVtSBKderRWBiSdScHjAwrvuWwRofIwA1NjoNqlNTSf2kRp00lKcLUg+fYbCHb+CC25HfBmJeyy5RSar8TwG1S0V7D/+f34o34UTUFKyeRQmlBZiNLGOIYK713ksqRcsG1M4AJLyiWrKiX+N+ArwunvpfiTHwBQvOcucps3o61cjf/t70YEj901fKLYskVw++0Kmian/LM9RftPf6rwqU+5RA9vgvkfizkyeA7/5zE8PMhvfvNzPvOZL72uAc0DRpa7fVkkkgAK3WqOrVqRG/Mx5h9i1dCtWvzaP8l+1cICSqTCWWaAy4rhw8LVRHEC/747AEiXLmJYsSlzBVVjO1m89wEeXvkBoq7ALxX2KhZNro4GjCgOKoJTrQADis2PAilGFJsqV8MBnjBy7NSKRFyVAc3GRlLqqlxkBjnbDB6zYK5yNU4z/TzgyxKRCknFoYBEAh2OxuWFEJuMAskpVS94D79xxWG55SN4nKTqHxsrFi9nwdlns/Xfvs+Sa95CBBUbSbdiUetozD+KzcShaIk20h6fx5axbTRFGghqAYadDF2xEKsb17LDL1hou4f5H9/yL18nNzbJe77+aQLRgw+3qBFmNDeOIx20QwLhHNehK7WfuK9kBgka0oOkAxNoTh6/cpDoVFAIywi7tB2cba+n4BQJajNVhj7VwHJthuUQu/1b2KxtoiiK7NS30xJpo9LyIVVAA3XqvzXWOrqsHWRFBleBoiigSJUWpx3XddiZ2sxwoR+/GqAu2ExVoIG0laToFvGrAWJ66QmRuDvu76TnxV58YT+6XyPZM0FqMM1yIaheeOyUgoniGN2ZXaTMJEIIynyVtEQWzFC6hMpDGEGDwmSBQCyA6c/jKg5uWiUYM1ANFQT41QBjhWE6oieubDoalixZwf33P8snPvF+3v/+6/jKV/6VP/mTP3vDtn+i2L5rM7FQKWElNuP1xP4kVsGivLUMI2gQb4hTzBbJJfLYRYfqQD2JwjAjhQEEXqKyIlQaQq3EDY9IkK5kvDvB2N4xHMuhpL6EqvmVOLbLK7/dwuieMXwhAykh8UAnie4EK69bfkTf5rrltfS93Mu+Z7qxChZILxV8/kUdNKysY2DrIPmJPMG4d++7rkt+skDrGS3oPp2FFy8gWhUlP5EjUhkhVBZir+hGulBqlGDlrOnPOqyHyJBDbzLIpQoHjd0AKSBfaVK2oJyOxmMTHa7j0vnwTvY/14tjOQgB+xRB3dIally55KiKlhPB+L5xNt+5lex4Fk1XcWyHvpf7WXrVYioOIXfOO289b3nL9XzpS5/lvPMuIR4/vJthDv81MAyD1atX88gjj3D11VcD3n38yCOPcNNNN826ztq1a3nkkUf48z//8+nXHnroIdauXXvE/fT19TE+Pk5NTc0Rl5nDHACiMsYHczfxqO8BXtFfQCK5uHgFHfZCfhL8PpMiRWwq+Aw8e4N5azvYqD7O009vOCYZbGGxWX+RTm0rLpL59iLen/soLxoeAR2SYVZZp7LaPB0FBQeHZ4wneNrYQFIZp9qp40zzPIIyyE5tO7awabXbWWKtwIdX+2dFhrQyScSdSVz7pJ8JJYkqVZqc1mklckqZoFftYVgZJC4PTm5G3RgRN8oerZNWpw2f9JESE2TEJKOKRq//bkxhYkgDVSrE3Dh+N0iHvYA+tYdJJeVNfro1vLVwMLwuJMO8I/8+1heuJKNMUuqWE5KHK3QFwrOSmOLcmuxW4m4Zw8oAtVM2YRLJsDrIKdZaojJ22DYOwCP6tjCY6yWsRwlpEYpOnu7MblShMS+66Kif2xuFQMALnDs0PKpYLHLTTTfS1NTK5z//lT/KcbwWu3fDww8r7Nrlhb1FImCakljMI1E1zVPxxmIQDksaG2HRIo9s6uvzVLiK4i2TzYJtS6QUFAqwf7+grk4yPu6FzAkhp1XBiuL5DEcikpISj6zt6hL8+McKPT2CkhKP0H3+ecHjjyu4LtMBduEwTEwIolGor5ekUt5+li2DPXskO3YAeNYV4TBcdZXL0TJFJ60kI/lBQloU35S4orXGTyadIJHsItVXR7HgjXcuvtilru7guqWlZZx55vncc88dxySDXcdl12O72bLrRSpKqpm/dBFSSpI9SUIVIRauX4AvaBCtjp5wt2BmLEv38z0YQYO65bUMd46QHc+RGc1gmzaV8yu9YLlfvUIxa+KP+mla00DD6noUVcF1XfwxP7nxHMVMEc2n4VgOsaxDz7IyciWCpqlxcp9qE7cVfA/tnxFKd+BchjfspbCwgmj0kI5fAaV+ye6kQOgaLWubqF1SjWpoGEF9xvjjwHbGusZxbZdYbYyK9oqj1pB2+Qoyq7+INvYyipXBCVZjla+AqTHivDOaSQ+nSfQkvVxmVxKIBZh/QQdG0Bsb+zVYVytZV/vGKIGnz8eyKP7yZ2S79gAQbGlDhKNYTz+OKC3D/5br3tD9ATzzjMCyJM3N3u+6DvPmSfbsga1bBevWvbHn+F+JOTJ4Dv/n8R//8R0Mw8d73nPynlljwuYxX44wgqopNW4tkl2qxUO+LB05z382h8utU0Rwk6NhIBhTHO71ZSl1Vc54jXeQltqDkh/Fjs2nwYExxSGpgBGuIF0c5Sk1iVADtNoGOeHQpZqAIC4VriqG6LB1vh1K8oyep8pVCQmXGlcj6Og85MtS7mqssH3oUjCi2PzGlyHqqqy0j+1TelUxQkQqPG3kGFQcFGCp7efKQohGV0NH8LAvRxoXvxQkhUOJVDnvOMjm/0p85kOf5oPvfgudLzxH5PTVCKDG1bm+ECF4jPC6A1AVlXctuBY6YfdEFwm/wdCCVfjK6tkXruEHYoJ2W+e9+RgVU6rwTZue5ZE7fs/iG84mVDtzdjdlpVkQb0cVMx/kilCI+SL0pQdnvG67DqqjoSgKckq9cgBFUSTqxogaYWpCVXRNdFPiOzjFmSgk8YV0Hqn9PX3+fURkDInLiDLMaGCYi5QrMKbmt4eUfsqdCq7L38Am+yl2uVvIyCwVbiWNTiuBQoChYg9+LYRAIWNNsjW5iZ2pLTjSmUqbNglpURbEVh4xHG7GtRiYZHD7EOGKCP4p3yt/1E+iJ8n+53uoml951CIwbaXYOrGJnJ0lqIWR0qU/203WzrCibC2G4m0zUhmmblkN+57dj5W3sFQTM2phqBol9SXTylIX97DP5XjQPdnLswOb6M30UxEo55TqlSwq7ZhR0EUiUf7933/FV77yef76rz9BT083X/zizf8lVjZbtr3IkvkrKKTyCCHwhQyKmSLpkQyRyvB0MQjgC/lID2dI9acoby1jcXwNlYVBksUxVEWl1FdJma9quvjd88Re9j61D7t4gGTdT9WCSkoaShjbO05pYxxV966xbdqM7h5juHOE+pV1sx6rXbCwTRdf2O+pGBQBCDIjGVS/RtMpDXQ9vY/CZAFFU7EKFiV1MZpO8VrshBDEG0qIVEbQ/TqKqhCTUQL4mbTShNTg9PEkiylqQlWcXrOGvRPdJAspSnye52NfZpC4r4RFx6nqHd+XYP/zvQRi3nGDl07dv2WQ8tbyI57vicAbTO2hkMpPeyZLKUn2TrDrsT2UNpdOnxvA3/7t1zn77KX8wz/8FV//+i2ve/9zeOPwqU99ive9732sWbOGU089lW9+85tks1n+5E+8wNH3vve91NXVcfPNNwPwiU98gnPOOYdvfOMbXH755dx2221s2rSJH/zAU9tkMhn+9m//lre+9a1UV1ezd+9e/uIv/oK2tjbWr19/xOOYwxwOoNyt5Pr8e7gm/w4kEgMDieRlaxNPGxuwXJOQDJMU4+RFjrf438a2VVt44IE/HLUG71b2ckv4m+zQtuCXASIyxlO+RzjFXMd7ch/iKg4nAx7w/YG7Ar9GkzphGWGH/iqP+R7Aj5+wG0UAqk9ljbmWd+f+lAABIjJKqVvGiDJEzDlYh+VEBvAm1FVUXFzG1BEmRIKMkkaRCiEnRIVbg0CQVMY9MtepZlwZxRQmGZEmKEOYFLEookmNAnlixClzK0gpCUJOmAX2EjIiTUHkWWeewyrrtMPOLS5LiTvHPzlXKsu4pHgVd/hvY6e2Hb/0kxUZatw6Li5ccdSaPGOnGC8ME9YPEn0BLUSeHJuKT7NV30qVqGGptXJWYvqNQmmpZ2R7aHjUN77xd+zevYO7737qj5avcCi6uuCHP1QYHxeEQpJUyiNwu7uhogKqq6G2VhKJeKFsExNeqFo8LnFdieMo+P2eondy0vMDFkJMKYUljgNjY3DKKZ69RC4H550neeYZTzls214Luxf85hHPxSK0tclpe4hYTPKHPwjKymDZMsmLLwqyWU+h3N8v8Pkkl13mtb4rCnzkIy6vvCLo6fFC6xYvlsybN/O8TbfIeGEY0y0SUENYroktLSKHiAWiUVjUEWAwlmZowiIS8LFmjWdH8VrdxBVXXMvnPvf/GBsbobz8yDkS2fEsqYFJdo9sY0mrZ5snhCBSFSE7nsXw68RqjzyxcTSkBlIUM0XKmj1itmZxNdnxHOmRNEJRKGsqZWj7MEZIxxcyKKTybLt3O47t0LquBX/UT1lTKaqmYmZNrIKNqqtUhv20+0P0Gxp7FBM1Y1MhVc4d1yjsnSRcEZ6u+4UQhCvCJAbT6Ok0TmmU6UYyKXHGJilNJdi0d4T0SBqrYKP7NVrWNtNyevO0YnfvE13sfarLE0MgEIqgZlEVS9+yZFq1PBvcQAVmw8WzvhcqC7HmXasY7hwmM5rFFzKo6KiY0VH2ZsHp2oOzbw/mlI1IQNdRojHIlWM//yzy4ssQgTfWPm10FEKvERx7fsGCVOp/DxEMc2TwHP6Po1gs8rOf/Ts33PDB1+Uz1avapIRL+yGWEAJBpauyX7VICZcSqbJDM+lRLVodfdq/ttLVyKqWF1BmBWYUZXK6pV5S5WostXT2izx7gyXc1XoKrqKx0DIQAiSCJlvnUjNEi2Pgk4IfBVM8bOTICZdhBQZVhzHHocxVyArJPFcQmrJ0qHd19igmz+j54yKDfQguMcOcawbJC0lYKjM8ea8uRqiUGs/qeSaFy2lWgLOtIO2vI9Duj4FLzl1PdU0d5bfey3vmdaDqUdrVONETtKuoCJZz04oPsGdyPz8sMdGCfhYTRncVTCQ7NJO7fRluLMSwLZvPfe6jLF26kouuu5rtE7uoCVbhU32M5cdRhcqZdacdpjwVQrC2Zg0/n/gN4/kkpf4SbNemO91Dh74ISx2mV+2m3mmaatkcQ0GwyjoNVVE5v/EsetMD7JnoIu4rIWvnyVk56lZG6Qlsp91eOO39p0uDp40NPG9spNlsRp/UKE+X0G4tYFTvZ7XvdALCIJ/OETXiICX7C3sQQqHCX01AC+KXAXqze8nbOcJ6lKKbx3VdcvYgGXuSlDXOopLVRyWEs+NZ7IJNtHrmfRSI+UmPZLAK1gxi8rUYzPWQszLEfRXT19On+pkwxxkrDFIbbJ6+tgsumk+gJEDfKwPIlEOwJUSoPjAdVGG7FpZbpDnccUKq4O3jO/nJtttIFJL4NR+bR7fwaO+jXNp6Hpc0XUiJUT69PVVV+fKXv0Z9fRNf+tKn6e/fz7e+9eNpZcwfA1JKtmx5iT/78KdoWzqP/i0DZMYy6AGdspZS1NcEVsip+GMxVcVqik5NsJGa4OF+Zqn+FF3PdGMEdUrqvCLeLtoMbh9maMcwk0NpchN5fGGDaFWEQCwAAib6J45Ijg5uHyaXyNKwsm56YkC6kvF94wzvGKbjvHZiNVGGdoxg5kxKm+LULq2ZVgoDBEuDVHZU0PNiH0IVlAdKaTWbeMneglKqklcKDEwO4UiHcxvO4JTqFQxkh9jY/zzDuWEkUOaPc3XbZdSGj88HL7E/gWPa00QwgB7QEYpgZPfoG0IGZ0YzTA4dPgCJVIZJj6SZHE4Try+ZXr6yspq//ut/5HOf+3+8/e3v45RTjqwincMfF29/+9sZHR3lb/7mbxgaGmLFihXcf//90yFxPT09MyaO1q1bx6233soXvvAF/uqv/or29nbuvPNOlixZAnjfNVu2bOGnP/0pExMT1NbWcvHFF/OVr3xlzgZiDieEA3UDeLXw2/PvJSADvGQ8z7AYJCZLOK1wFkIKlt6wkh9/8nv09O2jsf5wM8bHjYf5ceh7bNFewi/9+PCjOTrz7A5eMDay1F7J6eZZM9ZJigQbfA8ScaNUu15bt4LgZf0FStwSOpxFJNQxsmR40H838+z5nG+uR0fn7OKF3Br8D/rU/ZS65WRFlhFliDq7gZQ6QVIZJ0+epEggcVGliiZ1xtUECiqVbg0BGWRcGeXDhT/nDPNc9qg7+XXgZyy0l/KE7xEQAiEV/DJAUIZYZq1ml76NVruNvJIj6sZYY63l/OL66cn314vziuupcKp4SX+ehDpGk93KaeaZ04HCR0LRKWJLa0ZX0KgyzFb/K5hOkT59ENuwabMX8Ce5P5u21zheWK6J5Zr41QDKUSbWy8q8rpVEYpz6+kaefPJRvve9b/AXf/Flli5deUL7fKPw5JMeGdvRIdm0SWAYnkdvoSDI5yU7d0IyKbjxRpfWVnjf+1yamz0Lh+pqaGx0SacFPp8X7qYoAik9ondoSBAOez7BL77oEcRlZR7pW1cHL73kEb+q6tVc3d0eAev3CwIBz4piclJMhc0JCgVJPA6rV0u6uwWJBFiW5Pzz4frr5bQncDAI69Z5/sOzIWV61l9pa2LqFYFfDSKle1j3oh4w6WgN8J7TFRThHvE6XnLJW/jiFz/Jrbf+iI9//PNHXE5KyORT7B/Zy1VnveOQd7zrdjIUnZQS13YRijf6lq5EqAJFVYhUhrEKFtnxLDse6ETza5QGSzGCBr6wj/Rwmt5NvTSsrMcIGjSuaSCfKhAqC00rg82syfpQGZG+IK88tYf8vgmiUsEfD5IpWoeFXEgJMT+UBwXdk9AcAxVJ4eW9RF7dR2k2yZaJNK7toOoqiq7St3mAkd2jnH7jqV5NvXEf2iHEuFWwGNg6RFlLGU2nnnzwmi9k0Lj6j58HIHM5sCyKAa829mtTzxd/AJnLIAuFN5wMrquT9PQoU3723muW5anzy47tcPc/CnNk8Bz+T2PDhgdJpSZ4xzve+7q2oyNQAAdmaEctIdEk0/64GcVFTi1/KEJSkFRcXOBAKTSi2GypqGF+NE5JdjdxV6El00uLled7i87D9ce50Ip6HqwSSoTLoGYTKno+u0/qObZoRapdlUEVSqWKKSX9io0zdZSvtSkIozCsHt077LXwo+Cf5QmsIzjHDHK2GZg6r/++auBDoSC56qyV3H3nL/nJeTmEHsSsXkex5RqkfmK+RKqiopfVUwgl6HA09KnrbSCodjW26ybJossvf/Atdu7czn33PUNNWyN37rmP7eO7SJlpygOlXNh0Dqsql826j7W1pzCcG2XjwAvsTI6hCIX6SA1vb76GZHGQOwK/ZJe2A5CEZZSLC1eyyjoVgJUVS1GXqGzofZqBzBAVgVLWzltPZ+MLDEvfjAFdjVtHm9NBlVVL01ADgYyPMiooleXsK+wkURyhJTyf3mwXaTOJ6Zq4OJT7qgio3nUz3SK2tL0wNjtDQAuiqCq2a+FIh4FcDxX+mllJwwMwAjpCVXAsB804+AizCzZKUDBs9aFkFUqMsllD3dLWBJpqzCBvDwxAcnZ25uenq7Sua6H51CYcy2HE7WfP5DaS1igHcssr/XXUhZqPfBO8Bo7rcH/3o6TMSVpjjYwWBoj4DcZzE9y372FiAY1lpaupD80MwHnf+z9MWVUFn/7Eh7j++vX85Cd3UFZWfoS9vLFIpydJpydpbp3Hgovm03x6E8V0EX/Ez9COYV69extm3sII6F7IzHgWI2xQ1nJs5VKiJ4mVs2aoCzSfhpW3SHQnQAgCMUkhlSc7lqVqfiVSSs+m4wjIjGZQNHWGQlwoAkVXyYxkEIqgelE11YuOPGgVQrDgwvk4lsvo7lHSwxlOCaygrKmUwdoRJs00VaEKzm04k7U1axBCcH3HWzi1ehX7J3vRFI0F8TYqgsf/GR0p+E4IMe19/Lpx4L4/0r5mee2GGz7Af/7nD7j55i/w298+PBcm998IN9100xFtITZs2HDYa29729t429veNuvygUCABx544I08vDn8H0LGmmSsOITlFAlqYSr8tRiqN4kQlhHelX8/lxTeQkaZpEvZwz2B35FUxrDfZaN+QeWvf/vn/PATv562bADoV3r5feB2JkSSsIxQ7lZiCpNedT9xtwyBwnbt1cPI4EG1jwklSat90HpiXBlDlRppJc3TxmPYwgYgJ7L8R/C7nGqtIywjnGmehy0sNhgPMaqM4Jd+LipejiY1bgv8mKJSZFgdQkfHJ30oIkOZW86EMsGYOooENDRK3TKWWqtoclpJiRSOcKh0qil3K7DcEgLSC6Y1FRMpXAIyxIXmZawx16Kioh1hiJ4Wk2zWX2RQ7SfsRlhmrTomoQtexbLUXslS+8SI04AaRFd8FN0CfjWAjc0O7VUsyySiRJlHFMs22alt4z7fXbwvf2zfV5gZOGa7FgEtTGNoHtWBhlmfMQeUwYnEKF1du/nwh9/JmWeez0c/+pkTOp83ApYFzz3neYrmch7pOjTkqYFLS+W0x29Zmfdz4YUeoRSLwVVXSc45R3LXXYK+PoVUyiN4HcdbxzA8y7FCAdJpgaJ4HsF+v6StzVMLT05K8nnPxgG8MLpgUCCEZHTU8wweHDxABEuSyYMBcPG4ZxcxMQGplGD9epfjnetzpcOu1BbS1gQxowxFKDiuTdIcR0qXSStJVC9BFRoFJ4/lmrSE5x+V5AcoKyvnuuvezY9+9D0+/OFPHnHyMVwWYl9mFwDamadw1xI/lgLlO1KsCkROSKUqpWRo+zA9m3q9mjVk4LqS1OAkJXUxhBBkxrMMdw6jGhp20cJ1JcOdw5hZk7KWUvwxP9lEjnwqjx7QaVnXDELQ91IfxUwRPaDTtGYetUtrePk3W2AwRXlpCCSMdY2TTeRACsrnlU3XeZnRNJWt5Vx3SpBf74bdE+AbTlC9pYvKsCBcdMlJietIpHQIR/1YRZtdD++mdnGNR0DnLMoOuRa6X0fzaQx1Dh8kgy1QRgAD3HJmL/7+m0CpqoZwhNzYCHCQDJaJMdSmFkTkjTfwPeMMydatnvq/stLz8x4cFMyfL1myZE4ZPIc5/K/B739/O/PnL2L+/MWvazvzbJ1aR2O/YtHq6igIikhGhcPFVmjaXqDC9ZzGcrgzLAcmhMsK24865am5US/wi0CKCeFy2qLLedeTX0VNdmMLHR2dPaX1VGXG0JRB3LCnFgtLhT4kA6rNfMegUzPRgUZXZ1RxmBQuYSlwBAwrLj4Jpe7MB/S4cGh0FTrVoqcufgOeDgLBG+N0+ceBr+8hrmub5Ae/zvJcr83preDfdxfCzpFf+Kezx+weBUUkNh4BfCgMBFlcunr28Y1vfIU//dOPT6sbPrDkBkbz4xSdIhWBcvzakSs1XdG5rv0q1tWeSn9mEL/qoz3eSkALgDWPNnsBu/Tt2Ni0OPOocxqn1edCCJZXLGZZ+SIKTgFDMVAVlT524EwNlGZcG+lnWW4FtakqInpsOhAspIVJFMeo8BdYU342KTNJ3smwK7UFQ/VPF/audHBdx5tpVcR0gagIBUc6gEuyOHpUMjjeVEpJXYxET5KSuhiqrlJIF0imxvEtgh2ZpHesip+mcDtN4fYZAwu/GiRRHJ2xTSklIKctIl4LRVNQNIU6mokZccaLIzjSJqzFKPNVop5AgMpYIUFvup/qYCWTVpKiUyCgBakJ+RnMjjCWS9Gld1Luq8avBTEdiw29T7Fx8AWysRzX/v2Huecf/5MrrzyL2267l8bGNz/WdmioH4CaGu+7xh/x4494M/R1y2tJ9CQZ3DaEa3sTSUbIR9vZrUSrj12YHyA5D/2MHMshPZJB82lohoaiKehBnUKqwPDOEeKNcSraKo60SfxRP47tzEio99QfDv6S41dU+yI+Vr5tOamBSQppzzv6yuoIeTtPzs4TNaIY6sEJE0UotMaaaI01Hfc+DkW8vgRlqsXQCBkUHZOB5CATyRSTwQylqTJaYiev6gAIl4eI1UQZ705SGoxP20SkR9LEamJEZvnMFEXhs5/9EjfeeC1PPvkoZ589e5DYHOYwh/+bGMkP0Jl6hYKdm6qRJCVGN0vip8zw4i+VZVjS5J7A7yiKPPPtxQi/YP9b9/HUzx7jgffezRXRa6ZDNjv1rUyIJCVunITiBXoa0kATGoNqH+VuhWfO/hr4ZQBDGhRFAW3KukAisTHJiRw+YVDueu3o48oIfWo3j/se5vLCNSgonF+8hNOLZ5NQxgjJMHFZyoRI0qt184K+Eb8SmFIp+wg4QXQMmpxmRpURWpx5aFKn1m2g1WkDoMKtJCKjpJUUcVnGoNKHT/pJiQmCbpAiRQIySK1TP4MMP+w6K0P8KPg99mg7AYkrJI869/OO/I2snsVO4o1AUAtT5a+jN7sXVzpMaikKVp6wHcEtE6BIdAwq3Rq26q+QKkzM8IaeDVJKdk5spj+/n4AaxFB8ZK1Jtk+8jEBQHTyc3D6gDO7t3c8Xv/gpysoquOWWX6Bpf1wqQ0q44w7BI494tg25HHR3C9JpCIUkfr9n8bBihUcMJ5NekNtr13/ySYW6OkkgINi2zfMB1nWoqQFF8fx/PSWi8PxiSz0yKhTyPIJDIaislGiapw62LEk266mFEwnvvVjMOz5dl+RysGePt/1cDkZHBWee6dIwdakPWFJomrev2YY6KTPJpDVBRC+ZDoNWFY2QFsZ0i4T1KBlrEkc6GIqPpnA7DeF5h29oFnzoQ5/g5z//Ib/73W284x3vm3UZRVPYOvYipS0dPLmuFlW6CNtl95klWJESTjcgepw8Xd/L/Wy9ZzvSlfjDPq+zMG9hF23GTa9+nBycRDVU6pbVMLR9GFVXka4kNZgiUhXGMR00nzbdjahqKm1ntdK0poFCuoAR8uELGex9eh+pgZQXIDzFyvtjPsy8het6ocFCVZCOS6QqQsf57cQrBU0xl86EYGB8BDNsUVUTYn9vEcdx0YM6TtHBsRxCpUHSIxm6nummfoVXpx/WSaoIXNtTZ+svCXz3C9QhASpYCyWFq1xOUNT/R4NSU4t+2jom/9OztApkMzgjw6Br6OdfhHgTvgPmz4f3vtfl/vu9iRVVhdNPl7zlLe60Bcv/FsyRwXP4P4t8Ps+DD97NRz/66de9rQAK1xUi3BqYZKdqIZAoCJbZPi4uHlSTttsGS2wfL2kFyqWKT3qewUEpONsKMCkcbvWnuNOfYUy4lKDwSmUbl8YacRQDRfXRopQT9NcxoYGW2o0ZqgGh4Ex5wwamCmNNgis9Anqh42OvapJQXLK4dLg6tY6PQcUGF3TgVa3IiHDIKS7fVm0aHJ23FiJ0/De3dXhDYRcw+h/h9IUNVJaEuPO5/Zy65CKkYmCMPE+x8VLc8Im1yNS5GqWuyrDiUOce/ModVmwaHZ0f//PfE4+X8pnP/M30e0IIKk9AUSiEoC5cQ1348JCfMlnOWvPsY64f0A6SZMvsVTxjPElCGSPuev0wo8owQRmmPt+AQ3GaCPbWV9BVg6Q5yjxlIWX+SqSsIGUm6M91owoNXfGSvF3poAplRqq17dpoQkdBndLYHxmaobLk8kVsvWc7E/0pXNtF+h3EYpvoighhw5shzjkZutKdRI0SSn0HPciqAvUM5/vJWJOEtDAukoyVIqiFKfcfuxIK6zHC+sl5kmXMLOP5BK6UWI5FzsmgKfrUdXFRhEJ4KpxlwkpQpQa4Y/fdPNL7BGEtREgPkqjMcfoXrmHzvzzA9ddfwm9/+zB1dW9u29bQkOdJXey1eXrzMwBULaykYUU9voiPZVcvoXZpDZODkyiaQllLGbHa6HEpSOMNJWg+jcJkYdoeITeRw8qbVC2oRKAwMZDCyls4tottFqlbWnNU1XHNoir6Xu4j1Z8iMqWOSA+n8Uf9xxUweCiEEFP2FTFc28XMmPgCPoJHaEmTUpIeTmPlLULloWnS/HhQ3lZG/fJa+l4ZoDicYN9kD5lChnytyYvKVp57+WXe2nElZ9SeekLncCgUVaH9vHbyd21lvGscRVenBxMd57ejarNP3V100eWsXHkKX/valznrrPPn1MFzmMMcAE/huWdyG5ZrTtsvudJlojjGvvROFsdXz1h+h7aVpDLmEcEIJgcnqTu1iZ0/3caPfv3/UeVrYMHF84nVxvBilqHMraCbLooU8eFDQSFPFkk5i5wlhx1Tk9PKPLuDrfpmWu12j7R1A+SVHEIqxF2vvnKwkXidTy/oG7m08Jbp2kRBIS5LCUqvhi+RcT6c/XOWGMu5JfSvgKDDXkhRFHlVf5mESOCbIqGDMsL6whXTHrrVbi2nmGt51Hc/YTeMoqj0qt1oUqfcrWREHeLc4kU0O0cnzn7nv42njEfxywB+AlQ51WRFlt/5f8l8exFhOfsE7B51J9v0LeRFjjqngRXWGiSSl/Tn6NL2EJIhllorWWgvPcw/WAhBe2wJmqIxmO/DtIvYfhs7JnEPKYU0qWIqRWwOFxK8FpNWkpHCIBEtijHlQ2yoflJmgt5sF5WBummy8QACgQDBYIjvf/+bJBJj3H33U5SUxGfb/JuK/fvh6acVKiokfr9kyxaBpkkmJgQjI4JIRBIOe2TsyAiUlUlKDylX+vvh+ecVMhmPRE4mPRXwgUdqMulZTBwgkKX0SNrxcYGmwdCQFxxnmgLblqTTnkVCKORZSoTDngo4l/MC6QwDVq/2CGLb9rbj90vWr3e54gpPsbx1K9x/v0Jfn0d6LVwoufJKl6rXlEuOdHClc5jSVxUqqlBZXLJm2j84qIUJacev1G1rm8/FF1/B97//r7z97e+dtcYoFAo8+eyjNHz8Q8wLhdEmTfSQjr8iRE9M8EKhwAXmsTs47aJN18ZuLw+i3hszBEuDaIaKlNB8WiOO5bDn8b0ESgIES4MES4NMDqXxhQycgidYkK6kdV3zDGsv8Oy99KlOObtoMzmYQtPVGbZN0pXofp3KjgoqOyrITeQJlgSpXlg5bVlW4oPTaySvhmx6fAJtKs9BOi6K4om7pOvZXGg+jWKmSKQqjGqoFDNFfGFvYsmxHcyc6YXIdULwZwoUJW6lBAuMZwVKQiH7cRf5X0V0OqAkADmKCGRxApXT4XVCCHxXv43eDQ9Q1rmFiBAobR3o51yAtmrNm3ZIK1ZIFi/21PaG4an8/zeWvv/jyODvfve7fO1rX2NoaIjly5fzb//2b5x66pEHRrfffjtf/OIX6e7upr29na9+9atcdtllAFiWxRe+8AXuvfdeurq6iMViXHjhhfzTP/0TtbW1f6xTmsN/ER599D6y2QxXXTV7y+SJYqHj41PZUrZqRbJCUumqLLIN/IeQWzqCG/JRyn0qL+sFMsKlxda50AwhJHwmPMJmo0gOSVwKAq5gSJjc17iKcxKjpBSXiBVg3USCn9U1MmFPEHDyjOs+XtWKCARb1AIlrsIix8fzRoGskDQ7OtWOSq9qkxYun8zGaXB1fu/L0KmZdAubceHS4hossnUcBD2KxS8CKf48W0r8OP1yHSQK/LcOiDsaFHMCpZjEDZRx5doF/P6ZTv7h/ReCL47IDaIUxk6YDI5KlQvMIL/zZdijmIRQmBQOYamwat8E373r13zhCzcTCr15wRuO6zBpZvBrPgLascmpZdYqLipexuO+hxjRhpBAVEa5rHA1rVY7u3l1huoSQEoHXRycOBBCMC+6GNM1SRRHcKWDQKEiUEvOzpC1Jyk6RTy9jiSieQOkUv+RwyMOIFoT5bT3nUKiJ4ldsBk2ehgJFIj4D45MQlqERHGUscLwDDK4zFdJe2wJ3eldTJjjgCCsRWmPLZmhYJq+dv292C+/iEyMI6qq0VedglJx7GM8FHm7wP37HuH5oZfI2Xl60wOYjkljtAxN1XClZDg3TnWonOpwOVl7AoCh3AjPD71Ehb+csoA36In7S+gS+7n0S+/l4b/7BW9/+3p++9tHqKo6fCLgjUJfTw8A45smCEa9a5R8YCfjXQlWvm05RtCgan4lVfNP7LoAxBviNJ3SQPdz+8kmciiKID9ZwB/xE64I44/4CZYGvJa6sSwgSfZPMLhtiJrF1bMOGGK1MRZftohdj+0hNZACIQiVhZh/fhuxmmO3k0k5U63sOi59L/fT82IvhckCvrCPxtUNNKyqRznELzmXzLHjgZ2M7RvHMR18YYOG1Q20ndk6Y7kjQdVUFl++iLLWMh54+hGGjFHKm8sIhSKUT5QxMj7GH6wHWFjaQam/5Pgu8Cwoay7llBtWM7R9iOx4jmA8QPWiKiKVRx64CSH47Ge/zLvedTmPPHIfF1542Unvfw5zmMP/HqTMBFk7TcyIT39nKkIhoIUYLw5jOsVpuwgAUxQBr07MjucY2TNGpLmE8IIo+x/Zw9iCcbbctZVT33sKTVoLPnwEZZAGp4letZs0KdJikjoaONU8k5Xm4WNAG5smZx5P+h7lQX8nYRml0q2i1e6gR91HQhlDQcHBodKpodqpwRRFXFwmRYqHfffykvEcFjbznHYuLFxGmzOfsIywvngVlrC4y387NjalbhlN9jy6td1UOFWssk5jnXnutB3XgXO9Nv8uwjLKc8aTuI5LihQhGaLNmc9a82zOLl541Nq5V+3mt4FfkFImCMowLg79ag/NdhtDapE92k5WWDOJkR51Hz8MfIcN/gexMCmVFZS7FbTYbV7IqdqDjo6NzZPGo1xRuJb1xasOOw5N0WmPLaUp3M6YHOWF2ItMqH3UTtlTSCQj6hBLrBXE5bHtofJ2Flta6MrMiXWfGiBnZ7Bda8Y9cwCKorBv3x5+9av7aW1tP+z9PwZ6ez1FcEODZ/uQyUBPj2fvMDHhkbHNzZKhIU9te955coYNw8gI7NjhqYWzWTBNbzkpvZ9cziN6D8UBCynH8ULjwCOL+/o8n2Lb9pS+iiIpLZUsWeKpjB3H8xGORGDnTsG110pWrHAJhbxjB9i7F37yE5V0WlJVJbFt2LhRMDam8LGPuTNCtCJ6FL8aJG9nZ9iw5ZwsMb2UoBY+jMQ/EXzkI5/k2msvYMOGBznvvMNDS5944mHy2QzVb72choaZ91kAi+2aeVxkcG7K2uFA/scBBONBUoOTlLeWEauNMdw5gl2wEQgq5pUjXUk2kcXMmdhFm+bTmmg/t+2w7UspGXh1kN4Xe8km8uQSWQqTRUoaSpCuJNmbZHI4Qy6RxbEcKtorWHTxglnrRLtoEywJIF0XoSoES4PkEnls0wYpUVTF6yYL6IQrwlTMK6dhZd20/YWiKtimQ3lrGfUratFvF4gM2PMP2UdYou0FbavAOvWPb4GgvyLwPTSBkfs1SvBFZGkBq6WMQtvFmA0XgVAQPh9drk3H8jWE/uYfIBhC/BHCtHUd/rdTgv+jyOBf/epXfOpTn+KWW27htNNO45vf/Cbr169n586dVFYePgjduHEj73znO7n55pu54ooruPXWW7n66qt56aWXWLJkCblcjpdeeokvfvGLLF++nGQyySc+8QmuuuoqNm3a9F9whnP4Y+L3v7+dxYuXM2/e8SW9Hw/iUuUs6+jTajGpcn0hymXFMEUkJVKhUzP5XjDJVr2I3xVkFIdRATlVElNUXqhqY9lkAsV1EcDZ48MMKiYbKxrZbki69TwK0GbrvGgU2aGbXFuIsNYM8Kyex1IkIPABbymEpy0pPpCPMSYcfhpIEZQK812PzNOAVldnt2qxVSse85x2qSZP6jm6NIuoVDjdCrDODBzmjXwyEGYafewllNwgUg9jla/EDb3+IKXZ4BpRpB5CWGmuXreA/7jvRTZ3DbGyIYjUgkjj5BSh55lBSlyVZ/U8I6rDAstgnRXgtz/4JsFgiHe96/1v8Jl4kFKyaXgzG3qfYig3QkDzc1r1ai5sOnuGEvi1UFG5tvBOVlhr6NJ2o6DQbi+gwWkm45ukR9lN1p4kpHnEWtEtIKWkMjDzielXAywvPZ0Jc5yCk8OnBIgZcfqy3WxNvkDamvAGjWoQVajUBZqoOA51Lnh+vhXzptKlk30oOWXqnF1saeNpjwWWa85YTwhBQ6iVSn8Nk9YEAoUSo3SG0vkA7FdfofDzH+OOjyF0HWla2BufwP++D6G2Hl/rm5SSO3bfzaO9T1Hmj1Pqj1Owi+xI7GJ3Mo9f1zAUg7JAnHMbT6Ho5vCrQfxKkHu7H2Nncg8N4Tp0VSNqeMV3mT9ORs3y7z+/nT95x7Vcf/16fvvbh4+awvx6sHfrLkK+CJWtVahTygTHchjdM8bQ9mEa15y8MlkogvkXdhBvjDOya5SJQopAVRh9e4rscA4jZJAZy5KfKCBdSbgyTHY8x5Y7t4KE2qWzk+A1i6spn1dGamASgFht9KgpyuCFE/Zs6mVk9yiKplKzuIrG1Q30vdJP54M7UXUVX9hHLumlSFsFi7azvfvAtV22/mEbw7tGiVZF0Pwa+YkCux/bgy9oHHdoh6qrlC8uZ8/kfkReRduiInoBB6ooIxGY4NXSbZyz+ozjv8hTsE1PHWIEDcLloeljP16cc86FnHrqGfzzP3+ZCy64dE4dPIc5zAFXeq3HsylKXddFMjM4qsFpQscgLSbJDOeQjotR4qP8XZV0f2kPk8sTMAiju0fpWLGI04pn8pTvUUJuhHoaGVYHqbMW8yf5j3JO8aLDbBVsbG4N/IiNvg1UONUEZZi0MkHcKecjuU/y7cg/8f+z995xdlXnuf937Xr6mTNnetNoNOoFSQiEQPRuwGBjwLiXxHZunMR27k3sFOeXdpPcm3YdxzWxY+OKKxhTjQFTZIF6l2Y0vdfTy27r98cejTTSqAFu9+rho4/EObusfco+73rW8z5PngIBTKq8GurcBrq1Ti4pX46Ly1dCn2OXsZ2kW4WOzjZ9C71qF7+T/xgtrm/LdHPpjQip8KL5DOPKKDVeDbfn3sytpTsJMn9tFSDAG0tv4YbSGygoOaJeHIFAQ5tVI8sZM/f5SOGfGU+TEtMkvOSs4rgg8vRp3dS59bjMzfsYUPr4VOgfeD7wUzSpEZZRMiJFQATYYvwMieTa8s2z2RCjyjBPBn7MGvtiGrymea/BUAM00MyNzm18X/8mHcpBAjJETmSo9mq5uXzH7LVYWOzRd9ChHURBZZmzkpX2RWhoGKqv7nal3xV2DMeC5OYLEf7qV79ALpfl0ksvZ/Pma+cd3y8DhgEg8TxmVbTNzXDkCJRKsHw5lMuCZFJyzTWSyy6TSAkDAzA2JtizB/J5iWEIXJdZstWywPP8uZqi+Opezzv+NxwnjY8Rwp7nq4rB384nkwVDQ7Bx4/Hgq2zWH/eiRfIUYmvLFkEq5bfFH9s+GpUcPQp79wouu+w4OWiqQZrDiziaPUDamkQTBpZXQp+xhHgtRDDAxo2bWbt2A//8z3/LNdfcdEqN8eijP6Ru8WKiy9o56eOOi0Q/Rx5TNTUUVaGYLiEl6EENgcCxHFRdRTM1VF2lbkUdHc92EogF0EyNuuW1TBydQF2gcek7N1DVlpy3DurfPsD+xw6CBDNi4HmQHc8xuGcII2Qw3T+NdCVGyMAI6hx68hDSk7RfeTwrxLVdun/ey+CuQUrZMsVUkcJ0kXAyTDqUoZwtoxkqCF+JHKmOsGBDM6qu0nJJM/mpAmNHxrFLNrXLallx8zIC0QDagMCLnvRC6YAUKFO/fCJYOwihr0i06H+iVG7BsxsQ/ZXo5TGE8wBoAayGqwHo6DjExo2bEZFzV5xfwNnxG0UG//M//zO//du/zXvf+14APve5z/HjH/+YL33pS3z846emT/6f//N/uOWWW/gf/+N/APDXf/3XPPXUU3z605/mc5/7HPF4nKeeemrOPp/+9Ke59NJL6evro6XltXnzXcCvLyzL4qmnHuWjH/2TX9kYIlIhgl/8PWMUSAuPEIKwFGRQSCkelvQomgGmkwt4bqqX20f6qPAUdCvFew7uZqN4K/+7xsBzXS5y/OY5gD7F5idmnj/MVbLWCdClWghgiWOwxDVmw9wEgmqpYQtJ5KT2/GPb5JTTJ8ACHFTLfDmUJiVcElJlSHH4hpphTDi8pRx9TSphpTBKaP9n0KYP4SceSczeRykuex92zS+gNUQLYdVfTaDz21y+sBJDU9m69wgbEgns2stxo6/On1UgWO8EWO8cV+UO5vv5ytc+z13vvI9Q5PyC6c4V28d288CBb+NKl8pAgqJT4odHH2OyNM27V9x3RjJHIGgoN6KmIOekKSt50sEpKowk7bFVHM0eYNryPfw0odMcaac2eOoEQhEKleZcf9fW6GKawq0zdg1phFBImFUkzdpXVUzGjUoG891krGmydnqWABYI2mIr5t3HVINUq6cnxGWpRPmh7yGzWdRlK2fCHTzcI4co//gHBH/3Y+e0Kj1SGGP76G7qQtVUBnx175LEIjRFo+QUWZJsRFMlrfFGIqa/fFJlNPNf+7/D1uHtjObHSZUydGd6WZFcSlOkgaJTIqAFWLxwKQ8++Dh3330D9913C9/5zlNUVr7+MbcDvf1UhCtniWAANBgyR5g+mmZd61qWVi5CnyHUS9kSIwdGyY7lMCMmtUurZ1ON54OiKiSXVvKKsYsXh14mY2VRFggqZJSVnUspdBdQFIVITYS65bUYIYPUQIqel3upW157WtWtHtCpaju316OYKrLzu7uZ7k8RiJpIT/oq36OTFKYK6EF9VjkbiAXIjefo29ZP8/omzIjJVN80kz1TVDTGZ0nnSFUY13bp295/ior4TJBSIqUkcFhHdAFJwARc0Ic0Rp8ZxVnloJnnVsJ5jkfvtj76t/uhJkbYoHl9E62XLjjnMYFP7vzxH/8ld999A4899kPe8IY3nfO+F3ABF/CrxZgyws+N5zmk7Scio6y3L2WDtem0QWXniriRIKCGyDvZWQslKSUFJ09toBFDmduNtMRZwSXly3nJfI6MmUVUK5QTRVZeexGFT+X5+lNf4ENXfZxytoyKyluL72Gh2852fStFUeCe4ju53Lqaam9+y5/D2n5eNl+kwW0mKmPg+gTxEf0AY9oIdxbv4+nAowgUVDS6tA6a3AVcU76JA/oeXjFeQpcGk+oEUS/GQncxXeoRXjSeo6Xo14A6OreV38Q11o1MiyliMk5MHv+Nk0hGlWFsYVHrNmBwvGsqRIiQN1dgMS2m+Jn5E3boLyPxWGtfwtXWDSQ9v3ZycNhi/AwDk0llAt01MDAIyhCTSh/1NLLQaZ9z/kcC32OPsR0FQbVXi0CgoDCsDuJKFxNzTn1e49VxWNtPp3aYBqsJ27MYLvQxWhzEw6PKrKMxtICAFuLa8s1UuTW8YrzEhDJGq3sVl1lXssD1yawyZb4W+g9eNl7Ew5uZ5zzBldb13Fd8FxVGkgojyZQ1RlSvQBM6Jbdw2sCxxx9/mD/7s4/Q2NgyGyT3q8LSpZJYTLBtG1RV+crbYNAPcHvnOz1uuMH3541EfP/dQgG+8x3Bjh0K+bxkclJQLkO5LHEcMUPu+spiRfG9f4+FyZ0YKiulT9Yee1zT/P0CAZ+UBt8/2DB8UvrgQUFlpZxVIF9xhUf7qSJW+voEkYic0/6u6yClYHLyVHJwQWQxATXIULGPkpOn0qymMdw6pwPv1UIIwZ/8yd9y770388Mffps3vemts8/Zts1TTz3Cbe96H2WpMCFcqma6VnPCwwUucs7Nlis/mSc/mWeqdxojrBNJhok3xslPFqhfUTtrL7bwsgVkR7OMd4z7RL2ESE2UFbcumxWjnAyn7ND98x5UTSFWN2NBkQghXY/MSIbMUAahKgRiAZKtlcTqomTHc/TvGKDl4qZZ/+HDP+2g68VuzLCBGTEJJ8PkJnKgCNoubyU3kccpu6i6QrQmQuulC2i8qIHp/hS7f7CH6b4UuYk8Vr7MZM80E12TrLhlGatq2jAGxdwlOgdAIs+gd8rbsHNM0JOBoAarqySLK167bYLxvILiHELU7sb12kCNIDVQxhag1HZiDPwEq24zjic5evQI73jHb7+2E17AKfiNIYMty2L79u184hOfmH1MURRuuOEGtmzZMu8+W7Zs4WMf+9icx26++WZ++MMfnvY86XTa9wisqDjtNuVymXK5PPv/mUzm3C7iAn5tsG/fLkqlIps3X/erHgoFIelXbeo8jSnpkhESV0g0CY4AB4HQguytXcato4MEpg+AGsBquBKt/jrAYrl7nAgGqPc0elSbIdVhjWOyxjlzVGyLa9CtFpAzvsMA1ozlQ5V3eosIieSnZoGM8FjqGrP7TgmXLUaRy+0gjd6Z1Xhngtn7I7Sp/TiJ5aDoICVqppNA5zdwEsuQ+utvrVBacBs4eQLDL7CmJcb2wwNY976R4pJ3vi5mQS4uT5g/4nMP/AuFQp7pP5ji38P/yL3Fd1LrvX6t/q7n8mz/iziey6KKVgAqzDhBLcjOsT1c23wFC2KnV3Rm7RT7praRdVIoQsWTHoOFHpbG19AYbiVhVjFVHkfiEdMTxPTEeSkFNUWnMdz6Gq/SR22ggaPKfgYLPShCQRUajrRRhUaqPIEX8c6bZHb7e/GGBlEajydbC0VBqW/A7e5Cjo8has+uYp4sTpG189SHa5GFPO7gADI9TaUhSCci3L/pfoRqk7XS6IpBdbCeZ3u3smf8ACuTy7Bcm4niJJZrc2DyCIaiM1Wa5taF1+NIi6bWZh588HHe/OYbuP/+N/Dgg08Qj1e8mpfxtHA8Z07LZlbmeNJ7jo7IUcyyyUu7trM4sZB3LL+XYC7A7u/vITWYRlEFnuvR+0ofK25ZRuOa0/daPdP3Aj/qeoIKM05TpIGCWWCgdQitQmdpeiGVzQkiVZFZQjoQD5KfyFPOWwTj5+7LezoM7h1iuj9FsrVyNrzIKTuMHBhFet4pgXXBiiCZkQyFqQJmxKScK+M53inqYyNkUM5Z2GUHUzs3//WAZrI8vpSO7iN4YYlizizMeQWUKgU9rTHZM3XOthxdW3o4/JMj6AFtZqwWBx8/hGO5LJlpcUwPpRnYNcj0QJpgLEDD6nrqltcilLnf6U2bruKyy67ki1/8twtk8AVcwG8IhpVBvhj+FD1qFxEZwRIWu/Xt9Krd3Ft852tatDfVIAujSziS3sd0eXz29zesRWmNLjmlLlBReVvxfbR6bTxiP8REdoK1YxtY3buB5Teu5R+/+eccat/DuvhFAGi2xuXWNVxlnFtw5V5tJ/1KL3lyBAlR5zYQlTEiXoz92m7+e+4vaPFaeUXfQk5kucq5nsutq6n3Gvlu6Gsc0Q4RkAHAD1eq8qqpcxvo0o6ccq6wjMyqdCWSMWWEPrWHLcbP6NY6cHCodeu5ufxGLrE3ze43qgxzSNuPLSySXhU/MR/joLaXuEwgpOBHwe/RoR3kQ/mPYUiTbwa/xB59B3mRoySKdGqHScgkutSQeFxibaJS+gufeZHjwcADPBj6KhNinJIoUdRKCAmOcCiLEkhJwkvOKpFPhus5HEjtYLQ4gCZ0hFDoLO9nqjzK6sqNBNQga5z1rHHWz7v/TuNlthrP0+i2zL4+GZHieeOnrLbXssZZz/KKdRxK7yJlTeFKB1MxaY0sOSVw7MknH+GDH7yfW255I4FAiO7uznP6HPyi0N0tyGR8u4jOTlAUn3S9807J1VdLDOOYetjHU08Jnn1WQQjIZn0Vrm2DafqK33LZJ3YjEaithdFR//uSzfpkr+cdJ4UDAX8aYhgS2/aD4UIhnyCORn3FcE2NJJmEiy/26OkRJBK+SviKK+QMaTwXNTXQ2+srjY/BVyBLovMIMIXwA/7mC/l7PbB587W84Q138Td/8yfcfPMdhEK+WGbLlp8xPT3FO259C31WiKeMAuOKAwh0CZdZAdbbZ68FUwMp9jy0D1VXiVSFKaVLTHRPkR3N0XZFK8tuWjZ7zzIjJuvuWcvE0QlyE3n/vW5NnFHckJ8qUEyVCCfnLvhUt1fhuR52ySbRXEEoEfaVvUAgapKfLFBKlzBCBvmpAkO7hwhVBGf9gwNRE9XQCFYEuPz9l6Hqqp9PUXaIJMOYMyKGjuc6yY7msIoW0vMIV4WxCzbTfSkOPHYQs0Wl3WpE69WQ9QJhgzogcFsk9qr57wfTJfjSfoWDU6AAnhQ8NwB3tHncuOA1qIklaL0gK8aBMjAzp9fwg0HtBGppAuHk6e4dwrZtFi9e9urPdwHz4jeGDJ6YmMB1XWpPcjOvra3l0KFD8+4zMjIy7/YjIyPzbl8qlfjjP/5j7r//fmKx0/sK/t3f/R1/+Zd/eZ5XcAG/Tti2bQuBQICVKy963Y6ZFf76d0wq51VY69IPfcsJyQJX52W9hIOvHM7jYUhYKENEYyvYt6qBK0cncUO1uPElCMVDZQr3pILORaIC2jmOY5MdYI9eolOxqZEqDn7A2WLHIIfHv4emKQnJCtvgMjs46yFcEJI+1SbpqXOuOSEVxhSXQdV51WSwsHPoEzvxgrU+EQwgBG60FS3diZY6jF198ZkP8mqgmpSWvBOr6SbWXlbgpy9tpbD6918zEVxwckyVx9mj7uBx42EOfX4fS968nMb6Znaqr1AWJX4/9/E5CpLXgrxTYDQ/foq3aNyIMlIYZawwcVoyWEpJT/YIWSdFwqieLYwydoqu7EGSZi0hLTKvx+7pjudIG4Eyb/vfa4WumAS0EGEtOju5qdCSBLUw09YkaWuKhHmeapJj1ffJ77sQx83dzgFRI0JQC5CdGiG47yAynQJdJ6MUMUd1ItE9VN5wO8yIwx3PYfv4HhJmnKAWYHXVcvZMHGCqNM14cYK+7CAX160gEYZXxp9FU3Tqapv4+rd+xP33voHf/u238o1vPPK6JmwHYwGEEBSmCwQrgjzvbuWw1UnSq2RhXQtqRGP/5GG+c+Qhrui6hOmB46TqsUTmjmc6qVpUhRk+9fNddEq8MLiViB6mNuSTrqZqoAqVEW+ctqYWQonwHGWyU7LRAzp64OzXebIH8HyY6plGD+izRDDgK2+FwLX9Av5EJa5dtFENDS2gkRpMkxpIUc5bMyEgxxXnfqBH9JzGeSKurtvEqDJMykkhygqu56IpGkuSbejTOq7lnv0gQDlv0betHzNsEKn2v69mxCQ3kWNgxwALLvbbCXd9bzeF6QJG2CQznGbsyBiLr2mf10riPe/5EB/60Ns5dGgfy5adGt50ARdwAb9e+Jn5E7rVoyxzVqLi30enxRQvGs9wqXU5C915JIPngcbQQoJqhNHiICW3QMyooC7YTESffy4VIMB15VtYY13Cjkd2+SGWyTDrWi5jYfUSfrDzAd6f/CD7HzvI6KExQFK9uJqFGxfM3sfmQ6d6mMcCDzGg9pJVMkg8+tRu1tjrcXAIyBAaGpdZV3KZdeWcfafFFNv1rXh4JL1qP9gVj3FllBIlVjrH5wsZkaYkiiS8JDo6E8oY3w98i336TvboOyhTZrmzihZ3IcPqIF8P/SfhfJgVzhp+bjzP9wLfZEqZQABpJUVKTHN1+QYCMxYT1V4tR7SDbDd+TkakeTTwQyrdKlzVpdarZ0IZo0yRhNdAu7eMe4rvnB3bI4Hvz9pqWIqNo0wwJSYwpEHMq8DBxsYhp2TJieysx++oMkxMVtDuLGWyPMpYcYiYnpi10PI0j2lrguFCHwujJxiOzoMD2h4EyiwRDBCTFQyJAQ7rB1jjrCesR1mXvIKMPY3tWYS16Ck15dNPP8YHPvBWbrzxNv793x/g3/7tH3jmmSfOeO5fJKanfZWvacItt/gq31xOks3CwoVyjr8u+KrgLVsEExOQyRxX9XqeIJ+H6mpfBRwI+FYS8bj/t6r6vsG27ZPBQvh/LMsniMPh40FxdXU+mVwsQqkkicWgslLyjnf4AXfH9j0dLr3UY9culYEBn4x2HJ/obmyUrF79y7cNAPjkJ/+Bq69ew6c//b/5oz/6/wB47LEf0tS0gNWr17G6DMsckyOahQu0ujrLHeOc5ryDe4cppUtUt1chpaSYKmEVLApTBZrWNxGpmvsmaoZKvD7GZM8UowdH6f55D5ULKlm4qXUmXHgufIsJBddy54gDnLKDGTExIyZG0JglggHsgo0W0DBm6uPCZJ5yvkyiZW5AYjAeoJguUcqWiVSFiZ2UgVGYLpAaSKPoCuVcmUA8gKIoKKpCYarAeOckzw68yIS+hLbeJqomKgkmAzjLJMW7PU6TP8nzg4L9k4L2uMSYUaGP5OGxHoWVSZeGM0wFpZQcnu5k/8Qhim6ZhfEW1lWvIqSHQIBbBUpfAt+rIg+E8WXLEpQMrlmL1EK8+OKzaJrGRRf9Aub9/4/jN4YM/kXDtm3uvfdepJR89rOfPeO2n/jEJ+YojjOZDM3Nv9g09wt4fbFt2xYuumgDhvHaibdRxeEJI88BvYwHLHIMbrLCLHTPjQQ1EFxiB/mhmaXe02h2NY6oFmUhiUiFJY4/xh7dxausZJO5mOWOr8KtkoLFjsE2vUTEVdAQ5HDZrpWQQvCdQJZL7QCbrCAhTq+MbHMN3l2M86SRp1+1URFstoIUhOTbwSwqUELypJFDk4JNdpCrrRDr7QCmFBSFPHFRGQd/9dCUr4FAlS5Cekhx0usoVMADeW5kyKuFF6rlok238B/f+DbTqWkSiXlCMaREyQ8inBxeqP60nsLDhT46MvsougWOagcwd2jkurKs/MeLiMoYbU47nephjmgHWOWsfV3GH1ADhPQQWStLgorZx8uuhSZUwvrpfaAtr8y0NUFQjcwh0CJajLQ1Sdqeolo9NxVzxkrRl+9gqjyBQFATrKfGbGDSGmeyPIomNGqDjdQFW141UexIG8srkwzUEZixfjg27lK5QNHNk+D8yGC1pRWlrh5vsB9l4SLfJkJKvOEhtJWrETXzt6iejJZoEysql/Dzl39AXT5NpKqWjGKRFoJbx6IYTz2Ft34Tyoy9g0Tieg6q4heKUSPCZXUXM1mapjPdzc0Lr6AhFkFVFEw1iO2V6c4eobmxjS9+8du89a238jd/8wk+/mf/m5ILUR3U83femINALIAZNbHLDqO9fRyKdxBXY9Q1+6nHQgiaIg0cGu+gqruC6srkLKkqhCBaG2W6f5pUf4raZaeqWXNWjqydI27MLWyjRoRBYxi1XiXdnaKisWImJdmilC2zeEPzGa0SsmM5+rb1Md45gWpoNKyqo+XiZvTgqfdmPaDhOnPvKVJKNF0hUl1BbiKPqqsYIQOraJMdz9Gwqp7eV/oZ3D2EVfQnE+nBNNVLqonXxSimi0jPY8GG5jkk87mgpbqJS1eup3NfN4VgkYBmUhuuIVIO4YRcorXnthBTnC74hHTN3O0DsSDZ0Qz56QJdL3VTTBVJLjzuf5efzNOztZf6VfWnhKzccsudVFfX8sAD/8Hf/u2/ntd1XcAFXMAvFxLJXm0XCa9ylggGqJAJRsUQvWrXayaDhRAkAzUkzyEA9kRUtSVZfcdKjr7YRX48j1AVPvjWP+Tj//ZBvvAP/8aK2HpCFf79p2dLL6mBNBveuo5gxakWTx4eDwe+iyscqmQNQgoiMsq0MskefQcNbtOcULeT0aEdAukrgVPKNHEvjpipm1PKJGutDaRFih8HfsAu/RUsUabGreOa8k1sMX7Gfn03KioONkEZpEc7SoVXyUK3ncPaAbYYP6PSq+J7gW9QFmWWOSsRCF7WX2RSHWdEHaZ1xmZBR0eXBk+aj7Bd30pKmSIgg5REEUfYBGWYnMhQISt5e/F9sx6/aZFiu/5zqrwaojLGLmUbmqeCIrGxyYk0Ciqtbht5kWOXvo0mrwUHh5AMcXvpbuq9Ro7aB5DSm5OloAgFTehMl8fPSgZ7p1EcM0Own3jMCmN+K6dnnnmC97//Hq677hY++9mvo+s6ixYtYXJynFRqmoqKxLz7/SJx6JBgbAwWL/YJ20jEv87+fti+XfCGN8y1WygWYWREMDUFicRxxbBhSLq6fBJX0yS2LaiqkrS3wzXXeGQy8MMfCvbsUbAsiZS+N7CUPtHb0ODvOzrqnzsUEgjhj2d0FC65RBIMnpuGZfVquOcejyeeEPT0+PYUCxdK3vIWj8Qv/yUGoKVlIR/60Ef53Of+mfvvfw+NjS089thD3HXXcXu7dteg3T3/OXx2JIse1BFCIIQgXBkiXBnCtV3KWeuU7e2Sze4f7mW8c4JQRRChKAzuHiI9mObi+9efUluFEkGq26vo3zmIamjoAQ2n7JAdzVG3shbVUBnYOQhCYIYNyrky+VSBRZcvJBDzlc1G2EA1NJyiM0sQgy9C0Axt3hr2RLi263OpM1Z2ru1SOuYxHNbJrC+xdWgfsXKEFXcuJ7IxBqdpApYSto8K4sYxIthHbQgOT0NnStAQmf/7LqXksZ6neaz7aYpOCVUoPNf/Ii9XLeV9K99G3IxhXS7RjyzDS61Eie/As5tR0mFIjCGjRazG60DR+MlPHmXjxs3EYq8uu+cCTo/fGDK4qqoKVVUZHR2d8/jo6Ch1dfO36tbV1Z3T9seI4N7eXn7605+eURUMYJompnnmtvsL+PWFlJJt237O3Xe/7TUfKyNcvhxM06la1Hi+89o2vcSQavOhQoJ679y+YteVQ4wJh51GGU1CUCokPYU6V2NAc3CkpIQkKzy+GEzx9mKcDU4AgeD2cpgJxaFTtbHw6FJtHCFZ7piMKA7fCmToVG3eU4xjzrNqKpEMKg6jisMKx2CzFWSRq7Nftfg/kSmiUiXgCYZVh5KQlIXHTr3EgOowqDpssAM8bOaICoWIVHCQ9Cg2Ta42S2S/Gkg9hhNfgj76El6gEmba/JX8EJ6ZxImdX/jRMUwKlz7VRkewyNEJnoEkX7v2EgB2797ONdfcOOc5pThOoOPr6JN7EW4Z16ygL3ERncl1xMwKFlW0oikaeSdLR2YfrnSIGhXkAwV6X+xBD+gsWNMKQIAgrnDIKOlXdU3zwVB1NtVv4LsdDxMsBUiYFZTcMj2ZfpZWLmJxou20+06XUuwZO0LBLlMVSrKoopmwHsT2LHJ2hkOp3YybQ9QEG0matadVXOadLHunXybvZAiqYSQeRzMHOOTuwtSCmGoQT3pMlcdIWVOsqFh/XnYO/r7jpMoTFJ08EklQ9VebwVfZCqFgKOd3vy6mS9gFC+2mOxHfewD30H6EYSKtMkpdA+Ztd55ziq0QgntbbsL68UMcCuuMq1kiUuc6p5EbIy3I4Q7crs5ZMlhXdFYkl/FM//NUB5O+7YXiK+9bo000xOIYqjaruNIVHUWojJWG2HDpVfzpJ/+Jv/qLj3DQWEvTFe+iISK5rlmytlq+enG7gEDMZOM7N3Dg6GHCI2EaK+upSMRnOwKCaoCya1EW1rwzECk5bUtq1IgSM6KkrQxR43hhnbGyhI0w669dR4oppvtTOGU/4KNmaTVGUKfz+S5CFUGqF1fNUWHkJvLseHAXmeEMwXgAK29x4PFDpAfTXHT3GlRtbuVbu7yW4QOjFNPF2YI8N57DiJisfuNK+rcPMHF0guxoFtVQqVteS7whzuGnjxCuDBGrixJviDG4a4ipnimEEESqwyy4tIXGi84/ilgIwYorl2OP25SyZYKxAPaUQ9EusfDyM6vjToQRNtBMbXYScQx20Vc6CyA1kCZUGZrzPQ4lQkz1TpEZzpxCBhuGwf33v4cvf/mz/Omf/u1sG+cFXMAF/HrCmAlsOxHH7sfqr3gqWL+yjpol1eQn/QW3UOV1PPrKd/na45/nX3//q4Rn5JbBiiCTPVMM7R9h0RWn5jcMK4P0akdZ5CwlLhMc0Q4yKcZxccgrea4t3cJGa/NpxyHxMAmwyl7LIW0f08rU7HMt7kJW2+t5IPwFtus/p9qrJe5VMKj285nIP2NTZrW9jgGtDwWNClnJtJikT+2m3msk6sUYUPs4pO1jSpmcJYIBIjKGQGFYHZglgwGmlUkGlD5KokSFV0lYhlEVFQeHZreFtJLmjtLdXFe+ZXafnMhSFEWqvGqSXjUZkWFan0KTBp5wCcgQy5yVrHLW0qMepdltpc1ZTEiGWe2sZenMuFThB1NJKef8LngnEcSnw3JnFVuNFyhSIEhodmwaKkuc5Wfd/9lnn+R973sL11xzE5/73DfQdf+cxwK/jx49wsUXbzzrcV5vWBb4AW9zaxld9316j4XKHUMsBkJISiUFwzi+j6r6fsPr1kne9S6JEL4lQ2MjhGZ+bm+5RbJnj8fLLys8/LCvAl69WnKMwujshKYmyOWgowNyOUGhAPE47Nmj8MgjHrfd5vsQnwlCwNVXS9avl/T3+9eyYMFcq4tfBT784T/i29/+Kp/85B/yoQ99lLGxkdfFmiqUDDHZPTnnMTnjB2xGTr3osSPjTHZNUtmSmO1OC8YDTHZPMrh7kGU3zl0YEUKw9PolWAWbia5JPNdDURWq2pMsv2mpL2CQfkBmfjKHHjRYsKGFxdccX5CL1cWoWljJ8IFRKhriaAGNcs7vPFt0Zdu8HXbg122J5ji9r2QB8FwPoQgKqSJSSvSwQTAeRDd1tFaNse4pwulBVqpn5r38WMP5Hz+TdnwgN8RTvc8S0oK0znSiWq7FvomDvDC4ldvabsS+RFKcUjGf+y30wtdQI/uwK0fpq6ngWfFWjgzfwOpikRdffJaPf/yvzjjOC3h1+I0hgw3D4OKLL+bpp5/mrrvuAsDzPJ5++mk+/OEPz7vPpk2bePrpp/nIRz4y+9hTTz3Fpk3HfZuOEcEdHR0888wzJJOvf/jOBfx6YXCwj5GRITZs2HT2jc+C3VqZLtVmiXu8PaXCVTisWmzTi9xR9tvWS0gMxGwo28kIovDuUpyrbJsBxeFpI0+vZtOt2bhINKHQ6GlcbJsMqy5PmjnWOCYGgkZP5/cKCfZoZZ428kwrHussk9jMMl8Rj116iQN2gHUnmetLJD8x8jxu5skI/7YekgqNrsZOvUS3ahOVHmnNt8BY6GrkhX/rr/ZUtupFPpCvYKMSZLdeYnDmuA2uxr2l2BmJ1rNCCEqtd6DmetCm9yO1MMItIVWT0sK7kIHz+656SJ408vzU9MP6xMw431KOsuI0nsoLF7YTj1ewa9crc8lgzyF48D/QJ7bjhpspC42e0e2U+p7l5egy9ofbWJJYxNuX3U1JZii5RRJGFQjfY+7o8520XrYQ4Xoc0Y4wKcYpK2UKIv/qX695cFXTJiZLk2wb3c1YYQJd0VhW2c79y+6eDfs6GZ2pLr66/9scmj5EySuiC4OGSA3Xt26kLKfxpEfZLTJY6GWkOMCi6Apqgg0MFHqYLA0jhEptoJHGcCvDhT7yTmaO1UTByZN1xogZFURnwmYsr8xIoZ9KswYpXYpuwQ94M+sIaPMrmD3pcji9l8FCN550KbkFck4G27WoDTbh4pC10ySMKhInBdidDuVcmSPPdjJ6cBTHcglETZo2v50Gtw8xNYlS34B+8aUodefn7ZwIJHhvdgGD+SL5qjhJGaBGBpHSxUPMLnQcw7XNm+lMdXFwqoOIHsbyyihC4YaWK9EV5vj3AphKgKKTo+AU0S77XRqv3M2Ln/8Qb2hYSkfTpfRlFN63yuOi6vNr+fOkx0RpmJQ1ScHJkamYYOVlS2nZ3kyqnJ6jOJ8oTZEMJ2hraiW1d9q3lpjxm82N5/witbFi3vMENJPNDRv5TsdDDOdHSZgV5O0Co8UxNtVfwrLWdkr3lNn70H5GD41SzpU5+rOj9G7t89vgVEHlgkouetPqWX+1gZ0DZEYyftrzzDjsos3IoTEaj06e4rdbt7yWhZctoG/7APmpAkifAF9yTTs1i6upaa8mNZCimCkRiAaoaIrz8gPbUDUFoSpMdk9RzpYIxoOohkrbFa0svrr9nEPe5kP1oirW3bOWvlf6SQ2miNVFaVzbSNPahnP25w4lQtQuraH3lT4UVcEIG1gFi9xEjpaLmwlXh31vZ2duUKg305t6uoC5t7/9/fzbv/0vfvCDb/P2t7/vVV/jBVzABfxiIRBcal3Og8GvzZJzEsmg2k/Cq2KpM3/I6i8Tqq7Ohi0BvPe23+WD2+7jv574NL/7Zj8gXFEVNEMjPZia9xhy5j+BYKHTTtKrYkIZp0SRsihzV+leTE6/MLzQbadC+lLIy61rmFImsIXNuBjllvKdDGl97Nd3s8Btm7U/iLpxXlJ/xpQYZwObCMggAl+lbEiTnJLDwyOnZFlqraAsygiYY6tW69VhYJBSpmavYUIZI6tkiMoYCa+SXrVr5t9JJpVxNGmw0GnnEuvyuRZtXpIKL8G0MuUTXEBQhiiIHAmviptLd1Apq2bUuYIrrGu4qXz7Ka9FpVmDrpgU3ByhmQ6xslsCJNWBs9c/662N7NV3sl3/OQrq7MLDpvJVrLLXnnHf5577Ce9731u48srr+fznvzmng7OtzSeDOzsP/0rI4KYmX3GbTsOxaCEpYXpacNVV3imevLoOa9ZI9uyRTE/7QXO27fsEJxKwZAlcfPH8dVkoBJddBvG4x/79Kq2tEv2Esr2uDtJpwb33unzlKyqBgKSuTlJZCVNT8MQTCosWuaw4x693NMo5b3s2DCkDvGy8SK/aRcKr4mL7UlY4a87LQjEcjvC3f/uvvP/99zI1NUlT0wI2bLhs3m0dz6Ej1UXWylEZqKAt3npaYUnDqnpG9o+QHs4QqQojPUlmJEOkOkzNPDkM+Ym8b91xgk2ZUAR6yGB6IDXvOYIVQS6+fx1TvdOU0iXMqEmy9XgI89q715AdzVLOlgnEg0Sqw3NqOqEIVty6HM+TTPVM4cxYTjSvb6L9yuMLRqVMCatoE4wH0AM6QhG0X91ObjxPYSpPejiDpqtIV6JqCpruW16AT1prpkZh8szzTiFgbbXkR10K1SGJPvOyThQhZsCi+OnnFZ2pbrJWjmWJxbOPGapBVI+yc3wvt7XdCAqUb5VYG6tQB/6AVLGfb2aL7LEb0AJx7Cw89+wzlMslrr3u1jOO9QJeHX5jyGCAj33sY7z73e9mw4YNXHrppfzrv/4r+Xye9773vQC8613vorGxkb/7u78D4A/+4A+4+uqr+ad/+iduu+02vvWtb7Ft2za+8IUvAD4R/Ja3vIUdO3bwyCOP4LrurJ9wZWXl62IhcAG/fti+fSvAaX9UzgejioNgrjevgiCIQrdqs0cr85yRZ1B1iHgKm+wgV1ohBHBYsxhXXKKewnLXICwVFrkGi1yDS+wA3wtk+XIwRYXUqPdUml2dAAo1HowqLuPKcT/emFTZbIfYoZdZ4HmzRDD4RLOD7+17Mhncpdo8aubRESxzNQSCz67eQL5v4JRrNT70NjL/8pcIIVBnfI57VJd+zeZ9xTgdVpBR1SUkBcsck6h8jb3pgBtvJ3fR/8AYfgEtcxQ3kMSu3YiTXHvex9qllfmRmSeMYLGr4wJ9is03Ahk+mq8kKU/tkVEUhfb2ZfT0dM15XEsdRp8+gBtdhNTDHJk4yFGrxCItyHUyRypYxd6JA3ynw+DWtsuB47YFyckaRnaOcOWfLGWIfnbr27GFRcyr4MeB7xPzKrjUvvyUscgZZbguxVlJ9rJbpC/fxXhxkJaKCBXBS+hJjSFRWF65hLB+aoslgO3ZfK/jEUYL41xUvZqJ0ggFp0BPpp8nuotsbllBQ6iV6IwdRt7O0JU9yGChm6ydwVRNJJIj6T2krElsz0ITxpwip+wWEajY0pl9zFBMUt4ke6dent227Bb818uopT68gLpgE0HtuAJxvDTMYKGboBrGVANIQzJWHCJjTyNKg4S0CEmzhqXxi3yVy1kgPcn+Rw8yuGeIcDJMOGJSSpc4srOAfvsmau9MMlToZ7LcgTbZQ02wntpg8zkdWwSD6KvX0fD0E6ixRoSm+5YTg/2I6mrU9sVztm+M1PHfLnofW4e305HqIm5EWVe7hpXJJbwy8Ry2a81RO1teGU0xGMmH2DWucNN/+zSPjx3gmX98C2//562MUM8z/YI1VeeuDpZS0pnZT1/+KHkni+3ZHErtImFWc03zZXz3yI/pSvcQN2Jk7Ry253BX+xtY076SnWN7mOyZRNV96wUjqLP46kWY0dNPxK9t2Yzl2bw4tJWh/AhBLcC1TZu5s90v/g4/eZjRQ6MEogFyYzmKqRJ6yCPeGCdcGWKia5LO546y5q7VAEx2T2GGjTkBaHpQx3M8smO5U8hgRVVYdtNS6lfWkRpMI1SF5ILEcQWugERLghO7Jq28hWt7DO8bxspbKLqKdItYBYvpgdRrIoKPIdlaSbK1clbhcT4hjcew9PrFOJbDeMc42bEcekCjcXU9y25YghE0qF1eS9cL3ZjRAJqhIj1JejhDtCZC5UmedcfQ3NzK9dffygMPfOECGXwBF/Brjiut6+nSOtmtb8fFQSKpkJW8qXQfNd7Zg1B/2WhduJi3XvbbfPWFT7Nu8aVcvtoPe/YcFyM8/+9InddAs9tKh3aIxc4yYl4FUS9Ol9pBm7OYBe7pu6EAarw6bizdxo+C36Vb6cSQBkVRZKm7guvLt9CtdWJhzfHBBUh6SYb1fgrkMWUAC4sO7RCKVKjzGuhXezGlySbrKjQ0dAxyIktkxqCzwqsk4SZxhcOj5g8pKgViXpyEl6TKq6ZS+gTwhDJGQAYpiDxj6gg3FG6lzZ1bO4QIcY11I18Jfp6d5suUsfCEi4ekKAr0ql3ors6QOkCj28Q6+5J5X4uYnqAtsozu3GGmrQkANKHRFG6jNth41vcvSJB35z/ERcbFHNT2IqTKSmcNa+0N6JxeWfy9732dj33sA1x11Q188YvfPqULNxQK0djYwtGjh886hl8EFi6EjRs9nnvOD4IzDJ+QrauTXHvt/KTY7bdLXn4ZJid9ItgwoL5eommwdu3ZF+g1zVcSOw5zyGDH8R/v7xdEo1BVJZmeFqTTvpVEsQj79wtWrPjl+v52q538R+jTjKiDhGSYktjNK8aLvLl4P9daN5/XsW699S7uuecdfOc7X+P97//wrO3BiRhOjfJfu79Fd74HqYGpmaxKLuNty+8mZpxqgptcWMmKW5dz9Pku0sMZhCKIN8ZZduPS2byH6b5phvYNz4bB2UXrFJW8azkEYycJrKQk1Z9iqncagIrmCprWNp4SxCuE8Be/znDrDSVCbLh/PamBNFa+TDDhd6AJISjnLTqe7WTk4ChO2R9Hy6UttF7SQqKpgkvfdQm1y2vp2dJDMVXEsV1K6RJV7VWEZjq9pJQ4ZYdozWmMgk/A1U2SIylJx7TAUCSOFBgK3LrQozEC/VnYNyHIWVAf8cnjiHE8r+NkCEDKuQIEWQlOpeCJjgVsSSksrZKoMy/bwf2PEa5tx6lcctaxXsD54zeKDL7vvvsYHx/nk5/8JCMjI6xdu5bHH398NiSur69vzo3i8ssv5xvf+AZ/9md/xp/8yZ+wePFifvjDH7JqlR94Mjg4yMMPPwzA2rVr55zrmWee4ZprrvmlXNcF/HLR29tFIpEkmTw3teCZEJUq7glqhGMoIykg+XIwRQlJQqpMKA7fDmToU2xyistB1cYVfvNFs6vx9lJ81mc4hMJVVohXjBJVnkrkBGLVRqIB+jwrrEEpcE66+R5bkTfm2f6QZpETHstcg5yQFIXLFc89zB5RpNFVySuS8oEjdN/2bow330xKkUSkxEXheaPAlPD4djBLXkhuLYdZ5r7+9ilepInS4re+5uO8opfwkNTNEOgKsNDTOaxa7NPLXG3Nr0Ctq6tndHR4zmOiPA2ehdTDlNwyw/lRInoIF4+QUyCuajRHGjk01cEVDetQhILtWeiKweiLw0hX0nJFM32RfmIyTp3TyBJnOaPKEI8Evssq5yJC8jjxeUgt87RZoFe10aVgvR3gBitEfB4C2/Fs9k9vZ6I0gqEGyFgZjqa7ydgFRtJFdo3t5aWhrbx35dtois5tX+/NDNCXGaQ52kRADVAXbCbvZBHoTBTHCaqJWSIYIKRFGSx0ozgqdcFmBIKSW6Dg5TiS3kNYj6KgwAkKUgUFiTdn1d7zPPJOBl0xaQguIGVPUnQKWJ5FySmSddKMFQdYXblxNmBkojSKlBJT9YsxIQS1oUaUoqAm2Mii2ApieuKcbSfSQ2nGOsaJ1UUxwz6prWqCyYEUW557jmhSQaqSkBbxQ2VKw6TKUyyrWHtO5zBuvBWvrxen84ivBPZcREUC8413o8QrTtm+JlTFHYtOLZzrg80czR5EOAoBNYDlWRTsLPWhFrKlKCVH0hozueMT3+XrH9vIj/7+Hm7482cYyukUHQidocPTGxvF3v4y3mA/2doo/UsVAvEk4UAUz/aoMJNMlEeoCTTy5qU3sGv0IGP5FI2RejY3Xsam+g2oisqGt61neP8ImeE0gViAuuW1pwRhnAxN0bit7UauatrERHGKqBGmKuir/1ODaQb3DuO5HtMD0+SnCoQqg3i2R3owTbQ6QrgyzFjHBKWsr9w1QjqZkZOKzZl744nBHSdCCEFFUwUVTae+H/MhsSBB37Z+XNcjlAgihMC1XZyyw1TPNMVUcV5vy1eD8/UcPhFmxGTdWy4iPZSZfX3iDbHZiU3b5QvJjeWY6JqcVZOFkyGW37T0jN5073znb/Pud7+JAwf2sGLFmlc9vgu4gAv4xSIsI7w//2EO6HsYVPswZYDl9mqavJZf9dDmRW1zNTc0voFD9bv57Pf/F4sjKwhGIqimNq/vPICGxh2lu/mv0Oc4qO0jIAOURImkrOKO8lvOqAo+hpvKt9PgNrFT30ZGSdHmLOZS+3KqvBom5BgqKhZljBOOpUuDGreevfouppQJPOHh4VFSCoyJEZrdVt5X+G+0uYsZUPpot5dyUN9LUIbQpU5KmWaBu5CckiUvcjTazahoTCnj9KpZFlhtXGxfRq/axZgyQlCGuK30Ju4rvnumvpqLa8o38ZzxFJ3aYSJEMF2TRXIpk8oEXXoHQUKstC/ijaV7qPbmzz4QQtASaacyUM1Bey+d6iFSwRQpvUDESdDkLjjraxkkyCbrKjZZV511Wykln/70/+Lv/u7Pue++d/O//tdnZq0hjj0/XhpmvDRMTUsNew/voOQWCKinz774RUBR4N57Jc3NsHWrHwK3YYPHlVf6j82H9nZ4xzs8HntMIZXyyd1AADZulGzYcCpZNjICXV2+d297u6S1FZqbJd3dgkWLJKrqk8pdXYKaGsnWrQoHD4I208XjOKBpgmhUUir94l6L+SCRPBH4ESPqEEudlbOfzyGln8cDD7PWvmQ2sPBcsXr1Or7zna/x4ovPYFnWrFBPSknv9j7+7eX/4LDTSZ2spiJRQaA5wMujO4kaEd6+/C0AOJbL6KFRxjsnKE4XMCMmLRuaCcQDmGGTeGNs1jpseP8I+x7ZTzlvoZsaxXSJ7FgOoYxRs8TvcsxPFUDxCd2Rg6PoQZ2KxjidPztKz897sUsOINFMjZZLmll2w9Kz1nB2ySY1kEZKSbwhjhk2UFSFygVza2cpJQceO8jArkHf7zgRopgucfCxg6iqQsuGZsKVIVbcvIwVNy+jmCqSm8iz/9GD5MdzWHkLoQhy4zmCFQHqV8+v9C/nLZySTSAWIBlU+Z01HttHBZ0pQVj3WFMtWZmEl0cFDx5WmC4z825LliQE71vlsaiilYgeZrI0NVvP255N2s5yXcuV8573wKQgbh4ngu1Snq4Xv03j1b9Nf1ZhRfJXE2r4fzN+o8hggA9/+MOntYV49tlnT3nsnnvu4Z577pl3+9bW1tOuWlzA/70YHOynsfH1Cfxb7Zj8VKr0qA7Nrq8PHlFcAhIyiktZSGo8lV7VZkJxcaSkI2hR52qscs1Z1e5R1eLBGYXqMdK2ydNoc3QOaBbtro6GwEIypDhstINUe6cSGhc5Jjv0EinhUiH91qxRxSUqFZbN499bwsNFskcrM6I4WALK9RHKSgjD1lkgdZ579POobS24V19CEY/kDLHtIKj0VOKewqNmHgfJPeUz+w79KjGluARPIsQVBAJBQXin2Qtqaxvo7HxmzmMykATFRNg5bAmOdAmpBkE7zbSRwFIMgkKjnC+jKUHqAk0MFXvRhMGuV7ZT11qPsSBAPlpASIGHS1HkaXRb6NE66VGPssLxyZWjqsWXQ2nSwqPKU7GE5DEzx7Dq8IFCxSkk/0RphMnyGHGjElDYmTpAplyiKhSnQq+mbKkcTh3lR11P8KE175m70u25eNKdVbtqiu4fR6qkyym/3W/mnimEQCKxPIuIFkURClk7zWRpFFe6OJ5F3sniei6KolJp1PiLJsL3o9PF8YIuY0/jei7VZhJHWmSsaTRFx1BNn0QXJsPFAdxJycrEemJ6Ag9vXpWkpuiEtehpQ0lOh2K6hFNyMOr9cR27FjvgYKdtrLRvARDR48T0BJZbZrjYR22w6ZxCc5TqGgL/7Q9wdu/AGxxERCNoq9eitrSe1zhbI0twpctwoY+MlUJTNBrCC1gcW81h1w+Ls1yIVNZzx8cf5Nsfv5qdD/0jV9z3CU7DgQLg9nRR+vLncQcHEIbJVLNBWasi0rCcisoK0lMpym6JjJUiY02TMKtZV7+AZGAjK+Lr0NXj95dwZWhOK9v5IGpE5vgGA4x3jjPeMeF7GLoSK1/Gcz0CURO7aOM6Loqm4NrOrN1B3co6nxzO+C16SMiMZgnEAlS1vT52UPXLa9nlSTzbxS7aSE/ydMfDPN75Xa4bv51lNy6hYVX9rHVFfjLP6KExStmSb9+wrJZgPHCWs7w+EELMJF+fGr4RjAe4+K3rGO+cID+ZRw/qVC+unlXJnA5XX30jkUiUJ5740QUy+AIu4NccJibr7EtOqwb9dYFIQcOPEqwfW86Haj/K/5j4IP/4rU/yyVv/kSX3tZ/x/r3MWcWHc3/EduPnjChDVHu1XGxvPKsqePbcCFY761jtrDvluaX2SlqdRRzVDtPiLsSUgZl6rYtGt4UO7SAZkSYuK6j0ktR69QgpqPKqyIosfx/5JGPqMGmRIi/yhGWYaq+WG0pvYJe+jXFlFA2dslKm1ktgeDqd6hH2abtochfQ4DajSY12ZxnvLnzwtOS2ROIIh8usK6mQCXRpoKPj4rJH38Ftpbu5s3TPvETynNdCCDpDHTwY+jrTYpKADLJdvMzL3ou8q/CB2Rr1tcK2bf7kT36fr3/9P/nDP/xzPvaxP5tT20kpOZo9QG+uA096VLVUsf/lveyZ2sqqxCWzAoFfFgzD99i9+upz4w+EgDe8QbJ0qcuRIwLH8QPali/3ieFCAQ4cEExPw+HDgr17BVNTviVEY6Pgjjskd9/t8bWvKXR0AAgyGd92QlEE4+M+gRwI+MdzXf/P6CgMDQnO7Or6+iIj0nRqh6n16uZ8vmq9Bjq1Q/RoR0nY50cGP/jgA2zceCU7dvycf/iHT/Lnf/73AIwcGOX5J1+iNzJAnVFD0A2QHc3i2i417Ul2je/jDQtvJKZG2PejfQzsGiQznKWYKSE9j2A8SMOaelbfsWqWCHbKDh3PHcW1XJKtlb6Ctz6G53gUM0Wm+qYRgB7SCUQDdP7sKFbRRtUUjLBJfipPtCpCvGGme3Iyz6EnjuDaHm2bWgkn589XGDsyzqGnDpMbzyGlJFQZpv3KNprWNZ4yz0kPZRg7PEasNooZ8e8BelAnPZSmb1sfjWsb5mRiBCuCBCuCrL17DUee6SDVn0JKSDQnaL960axtxDFYBYvO548yvG8U13IIJYIs2LiAprWNXNcC17Uc/zylyvBQp8ByJcsT/mfd9uDQlOAnfYL7ljRxbfNmnux9lsnSNKpQsT2bFZVL2Nw4f3d2UPOPcQwHfvoAVjFDy3W/g3n2RszzgmXBzp2CPXsEtg0rVvgLNJFf7i3lV47fODL4Ai7gteL1JIMbPI23FmM8FMhyVLWRQJWncr0d4RkjT0AKduglssIjKBVsIRlQHVQEpuvf4DUEC1ydPtWmS7Vm1bUqgrtLUQrBzOyxFWCpa/DGcmRe76X1doAu1eYlo8gwLgBxqXBbOUKre6rCq8XTGVZccopHhacQlYI0MKa4pBXJxpwk982HaP+936IgNQIIcsIvLZKeykrXoNbTmBAurxglrrPC89otvJ5wkHSpNgXhUetp1HnqOflQtbo6Xao1R8VdRqIANe7pb4W1tacqg534EuzkavSxlwkHaokrGkZxDEXT6YgtRQqFqeIkcTNGfbiWmNFGzEgwUuxntG+ExJIkLza/gGXYhGWEfrWHCWWMVfbaGXr6+PW8oBeZFh5LXd1/XEIchQNamcOaxeqT/I5zTgaJh6poTBWnyVo5YkYUKQWK4qIqBg3hGnoy3fRle2mJLpgtNpqjDVQFk4wURmmJ+unUUkrGi5M0RRuwZJ7+3FEQEFRCKIqGqQTQhI4rXVLW5GyIWxFJhV5F0c2TtdIgBYpQiBmVVAXqyDtZpsrjgERBIaLHMVSTklvElS6GYuJJD8u1GC8P43g2fe4Rik6e5shCKowkw4U+HM9BU/z3z/FsQJw3EQwQiJpopopdclADCilrEgRoZQM35BAMB5BI0tYkIS2CoZrknAwZe/qcE9SVWBzjymvPe2wnQlU0lsRX0xxuo+gWMBSTsOa3ji1JwIIodKUFrTFJ/dLLWPXGP2Tf9/6Kt95xO5qyct5jSikpP/Yj3KFB1KUrEIqCWi1B5nGPHCYei5CeTjFW8N+HsB4lYVRheSXGigMkjCpaIscDHT3Xm7e1zfVcym6ZgBY4Z8W2lJKR/SMz7Wy+d6FTdnAsh2K6SKwujlAE+ak8Va2VBOM+gdmwup7McIaBnYPkJvMIIBAPsOzGpfOGrxWmC4wfncSdOU/lwspTQuZORrwhTlV7FbnRLI7tMpjr4ZXRn9FU2UopW2Lfjw7QvaWX2iXVJNsqOfx0J/nJPIoikBL6tg+w9k2ridX/6hfRNFOjfuX5tYsbhsG1197Mk08+wkc/+qe/oJFdwAVcwC8aSm4ALXUQ4bk48Xbc2KJ5g0B/GTB/JjD2KbSsbKaKKrT6z/Cex+5j977nufHjN+CdZVhNXgtNpVeveJZIutQOduvbyYg0LV4r662NVMgE7yz8Nt8OfoUuvYNxZYxhZYCoF0dIQUEUCMogi5wlLHKXYkgDB4ftxs8ZVP6DClnBhJhgVB2kIApEZJS4TJARafYYO8iINCb+4uCoMkSNV0utV0ebu4SckkFIhavLN3Jz+Q5i8tRFvWNQUNClTlEpEPKOk08CQVCGqPPqTyGCJZIiRQwMtBlqoESJRwLfo0CeZc4qBL4AoFM7xI8DP2BJbsXstq8WU1OTfOADb+WVV17iX/7lP7jvvnedsk3WTtOf78JQTIJamLZFi3nmez9hojDGoNHD4viq1zSGXwaE8BXC7e3HiTTPg5074cEHFcbHIZUSHDjg1wYVFQJdh64uyY4dgjvv9Lj9do9CQZBKSZ56ShCPS5YsgW3bBKYpyGZ9/2Ih/D+qCs8/L7jnHmh7dWvz543BPpUuUyGX8Ui6gsZGqKuTsx7V6nnOD/fs2cG+fbv4yld+QGfnYf76rz/O5s3Xcs01N9G3vZ8SZQhCiCCqpqJoCsVUETNvUg6UKThFSkeLDO0dxvMkjuUQToYQiqCcLTPVM8X+Rw+w6X0bMUIGmZEs+Ykc0dro7LxICEHVoiT5yTxLr19CIB5gqm+ani29hJMhorVRnLLLwI4B7LI9u1iVG/e7rXITefY+tI/RA6O0blzAoivb5thG5MZz7P3RforpIoqmYGUt8pPjZEezBCsCVLVVzXlNSukiTtnBOClEzowGKGbK2AUbNXbq61zRGOeSt11MfjKPlJJwZRhFUyjYsHdCMFaEiCYRP+8gu6eXUCKEGQ2Qnyyw/5EDKKpC45q53aSdKcF4AdoTx38udAWSQcnuMcFdiwR3LLqZtooF7Bk/QNEp0V6xkPW1a+ZYeBQd6JiGkitojUsOTQuyFkQ0jx0/+jcaN7yJxqYFLKt0516UBDEFwgMvCWdZ35oDx4FvflPw4ou+Cl8I2LFDsGuX4Ld+yzsrISzsPProS+jjO0EI7Kp1WHWXw2lybn6dcYEMvoD/5zA42McVV7w2QuZErHMCLMkbPmHrlmnPjKMrBbbUBDmoW2RnFJ0CQRGf/M0Jj0nFpdrzv4IGAhsoi7kruM2ezh8UEuzXyqSFR6WnssIxTusZqyG4txRlgx2gW7XRgMWOQaOnzUuYNjoaqgAXSVlILCSW8Eh6Clnh8dyjj2ClMzS+4x7arDCrLZMHQ1mqXZVGT5sdR1wq9Co2U4pL0v3FkcFDM1YbRzULC4hKwWVWkDvL0XltME7E5VaQvVqZI6pNjadiC8mEcFnpmKyYRzV9DHV19aRS0xSLRYLBGaWcolJY9n4CegRjYidLdZO9lsaLgVZ6tCTZ7CAFp8htC2+kMlABQEuknZZIO+OD48QvTxDWYrikCMkwERllQhljt7GdzeVraXWOp8p2aTZxqcx5/3xFue9ZvfokdYgmfNJfSomHnPW5EgI8T6AoDhVhgSs19qdeZtIepD26gpiRIKSHuKX1er51+PscnDpCRA+Ts/NUmDHW17YjZYHSjNdv3skSUEIsjq9iojxCzk5jexaG8NW8qtAI6xEieoy8k6UhuJCe9DBp2yJmBFkQX4qpqShCocJIcji9h7Q1NWeSUvaKONLGwMRUA6hCw1BN+vJHWRa7iOpAPeOlIVSh+WYt0qM22ETVOQScnIx4Y5zkwiQjB0cxqjRsr4zI63hlj+A6FTQHXTGwPMu/TsWEmXMO5LuZKI0gECQDtdQFm84pcfu1IKiF53goAwQ0eMdyj28eVuhOCxxPsvSuv2Bs54948H//Fu+6+nk07dSffTk9hXu0A6W2HjFjtRTPgVEXpECaeFDHcz2mpiaIVUaJzJDPphqk7JUZLvTSHG6bbZ/b/+MDTPZM4VouekinammSyTVpXp7cQbacpS5Sy9VNl7OuevVZPXBLmTKlTJlwMkw5V8aMmJhRA2fSwS76rXjT/SkC0QBtm48X2aqmsuKW5TSsaSAzlEHRFJILK2dVuidi5MAIBx4/RGG6CICiKdQtr2X1HSvPaJOgB3Wa1jbSs7WXQK3BP37h47z3ut/nu898dcZyIo70JD0v93HkmQ6CiZAfaCcEnucx1TPNkWc7ufit616VF/BrgZSS9FCGia5JnJJNtDZKzZJq9MD5fW5vvvl2Pvzh9zA8PEh9/dm9JC/gAi7g1whSYvY/QaD7B779FSD1MOXGGyi1vxWU4/WcO7MQP6m4xD2FdteY167stY0H9B0CGZdg+CTPXcvfxCdSn+Rvt/x/tP5HO2/+83e+vuc8CS8az/K94NfJiDQaOs/zNI+ZD7HO3kBQhrnSuo6bSrfzlfDnCSthVjhrKFGk1+uiIPKMqsO0O8sAcHGZUiaolQ0gYUIdJelVU43ih7wheSrwY8aUEZJeNVHpLww6OAwpQzR5zbyj8H6qvBpU1NnnzwQFhQ325fwg8C0qKRIkiETSr/aQ9Kpot5cxqgwjkdR4dezXdvOs+ST9ai9BGWKddQlXl29kUh1nWB2kwWmarT8Fgnq3iX61hxFl6DXZjHR0HOTd734zmUyaBx98go0bN8+7XcaewvYsIoZ/7Y2tTTiOQ3okzXhgiPbYyl/67+e5Qsrjtg+qCosXS5JJ6O2F739f4fHHBRMTfhhcJgOWJdE0QbEIuRwUi4LpacnjjyscPQpveYvHRRdJnnpKoaXFz4A45o7pzagpj/2/40Bnp08It7WdZ3iw549xakoQi0na2jglHO9kHDkCX/1SnMyG9aQ2Pk6pp5LRUZ0lS0FZ0ku928Ai9/w8X7/+9f+kvr6Ra6+9meuvv5UtW37Ghz70dr733afJT+SpMauJECJDlgQVKKqClJKJ/CTNiSaSgQSd3Z14nqQ4XUTVVTTDr4GFECiaSm4sx2T3FPUr6xCqQChi1i7rGKQnUXWVmiXVBOJBOp7pxIwYs+IDPaBhxk3yvQVK6RKaqTHWOYFnuxhhnWCFH6jc8dxRItVh6lYcX3gfOzJObixHuVCmlC75XZdSkh3NsvuhfVz3kavnfL7NiIlq+MIV44T61CpYmBET7Qw1nFDEHDHEeAG+tF+hMwVSCqyihTdUwc11ZarD/lzPDBukBlL0vtJH/cq6OXYXnvTFYSd/+xTAm/mjCIXVVStYXTV/OmFHCr55SGEwC66EkCYIqpLRgmDnrieZHjzMhg98kTe1e9SdMOVRhiDwiIJ+WIAHTouk/AYPZ+lpL38ODhyALVsE9fV+eCL4wY579wpeeUWc1gMcAKdAaP9n0MdeQc4Eeutj29An95Bf+d9A++V0/L1euEAGX8D/cxgc7Kehoel1PWZYKlw8tBez+weo+QFA4cq2S9ndvoYqoWGYSUpagLwiiXkKjpBkhMcx1+JxxSXhQdtkD0Z+DE+P4CRWgBYgLBUutc/dd1JB0O4atLtnD0C0FGh2derRmFRcJL6CtsZVOazbdH/lQRbceC13VbZxRSFEUAq2uSUUmENIZ4VHSCrE5alqAw9f5fxaYSH5RiDNYc2m1fVVylPC4ymzQFwq3GSdeRlvgafz3mKcp8y8T5RLuN4Oc3M5fMZAttpafyV0bGyYBQuOL6/LQCXFlb9DuThGwMripgcZG9lJsThFMljJGxsu5cqmuW0wpVKJkcEhqhbXsNbewC59G1PKBAIFizIAN5VuJ8jx9zvhKUxqzpzjuDNtX+F5QvqSgVrMXJCckyamRwjrQYpOHl0N43kKul4ma5eI6jHiZgUTpRHKbpH1ySvI2VmCpsWlje10TPdhOXBx7SaWVi4g7Q4R1uope0Ust4wiVGyvTFiLEdTCdGcPY7klXOFgKAYJswpTCVL2Sriex6NdP+PIVM9sWnZVMMnblt3NRdW+WnVhdCkHUjvIO1kkHjknjZR+aImuGJS9IjEjQVALUS4XmbLGWZXYwEhxgLHiIGW3SMyoZEF4ESlrgoKTx1AMKs0aDPXsXoGKqrDq9pWouspwxxBW1sOMSJKXxTHWeUw6o3ieM6vcLrg5NKEzXhombU/PKl3HSoNMlkdZWbFhVrH8y0RLDD663qMzBUVHUBfWefenv8idd17FZz/7z/ze7/3RqTsJP9/8RISL0DQk6Y0r6NV+YTMxMk59TSNRo2J2O01o2J6vuMeDA48dZPTwGBUNcbSARilX5mtHvsORbBdVtVXUhao5MnWU7lQv7gqXS+pObcc9ZXiKINlaSXYsSzFVBAnBeBDHcqhckKBhVT1N65pINFecsl+iqYLEGTyAS5kSB588jJW3/LZARWAXbYb2DhOrj9J+5aLT7guwaPNCsmNZ/umrf8GKunVUZv37hR4yyE/kcR2P3ESOzHCWprXB2aJeURQi1WGm+6YpTBcJV/5y1QT9OwY4/JMjlLNlmFksql5czUV3rT5j0N/JuO66W1FVlaee+jHvetcHfoEjvoALuIDXG2qmk0DXd5FCwU34v8WiPEWg71Hc+CLsWr+GSQuXbwQyHNDLWICKLzR4eylGjfc6/865IE8qGf9o458yONDPH3z+A4QvreDmm+94fc85g2kxxY8C30UiWe74YaQd2iGeMZ9kl76NFnchKiptTjtFkafFXYhAECBIlVdDr9pNRmTIKCkSXpJ+rRtVatS69XRpHShSoJ0QoGZgkFEy6BiURZGgDKHhizdKooghDRa67aicn8jimvKN9Kpd7NV34OACkkqvik3lq/lW6Mt0aR1IIOAFGFdHUdHQpMZefSePBx7iq+4XuKZ8IxYWUsg5TgMSD4FyVpuJ00FKybe//VX+7M8+QktLK9/61qO0tCw8wx5zPwxtS32xxNF9HbQu/CVJXl8FPA8efVTw9NMK2az/AlZWCq67TrJ1q6CjQ1AqQTLpE79DQ/7vsJR+2JyigGmC6wpCIQlIHn1UcP/9cnY7gGDQJ7GOKYJN09/XsgTlsuSFFwTvfve5k8HZLHzrWwp79giKRYlpKixbJnnb2zySp2m6kxKeeEJhclKweeKN7Mr0MbXsMLmSZIct2VSq5k3e/aeEL54J+XyO73//m3zgA38wK2L4zGce4E1vuo73vPfN/NU7P4WR0VkdX8EW+QqWtAl4JtNamoRWwbXNmwloJsyQu57jIU4gMiV+jSjx/XoBYnVRYnUxUgMpEs0JhOIv3OfGclQvrSacDGPlLeySfcrCeTgZZqpnCqtgYc1uFVA6AAEAAElEQVT47ZpRk3K2TKgiNCNomGZo/8gcMricK1NIF7HyFsF4YJZszTk5Rg+Okh3LEas9rqI9UbgSq42iB3SK6SJ2waL9qrbTZmLMh0e7BYenBIsrJLoqSY+W2S01XjIbWSD7MGYEaoFYgMJUAbtoz1pTACyMSRIBhdGCpH6GqPUkTJQEVzZ6BKWNtCTCmJ+PyFnw9YMKQznBwphEU2CqJBkvwlVNHg98+l9ZuGw9f3//Ruojxz/DIgOhLylo3QK3ToIC+kGBOqqQ/10P9xyav7u7Bbbte2sfg2mCaUr27j0zGWyMvYw+vg0ntgi0mfm6U0Afexm9diN23RVnH8CvES6QwRfwfz88F31sK/rYy2SnR8nlsjRVnz0983ygpg4TOvhFsPN4oTq0qX3ctfXTDBVv5YWWiymaE1jRJhJmkmWezs4Zj96IVMgJD2kX+IOdP6BpaC/CLYJQcaILKS5/P27sF1fsJD2VhKfiCMly27/B20gOaGVSff0MPfs8H/mvB7jViswSj5dYQZ4wcwghiEtfQTyqOFxthWYnBRLJNr3ES3qRMcWl3tO4wgqy1jHPydJhPnRqFl2qzUJXIzBThCalSlF6bNFLXGuFz6pSWewatBd0MsJDl4LQORSzlZV+9TM9PTWHDD4GL1gDwRo2xhdxcdPlFJwiQS2APo8ytL+/Gykl0fYoYRlmo30Fw8ogOZGlJIrUeQ2stzfO2WejHeSwZjEuHKqkigP0KDYNMyrxkxHV4yyJr6Yzs5+8m6Y5VkNvZojR/DQR3UBKMBSdtvhCTDWArhikrAm6s4cZLQ5ieWUWVjTREq+n7BapDTQS1MKkch6GavrE6sylZawUGXuK9cnNVAfq2DHxInk3R5VRi6kF8KQfDDeUm+TgZBdLEm3oio6Uku5ML9/veITFFQsJ6SGqAnVcVHkZw8V+hvO9pO1pbLdM2StRliVCWoS47gcpnBjIF1Ij2J5FwcmRsdN0pPeiCJWgGkIIQUSLszyx7pysI4LxAOvuuYhFY23sGdpKLpQiWemrUYrFAilrAlMNkLVTmGqAhFHNeHmYmF4xqwR2PJux4hA1gSHqQ7+acB5DhRVJODaDa7z4Uj74wY/wT//0V9x88+0sWTJ3hV5UJNAWL8He9nNELI5QVZDQsK2X2MIakndcDfwd1oRDhVGFKma+51JSdovUh1pQhEJqMMVkzxTx+hh6UMeRDk8HX+BZbQtuxmGiPMWQNsxFtSspBko83fcz1tWsPiNpHoiZVC5IMLRvhPoVdZTzFq7tUMqUidZE2PR+v73v1WKye4r8VIHKBYlZVbEe1DFCBkN7hll0RdspSdBzXjtF4UhpDyPFAf71w//FgYeOgAArV6Zvx4D/fgR1nLLNeMc4RtiYLex9BQinqFB+0chPFTjyTCdISM4olR3LYfTwGL3b+llybfvZDzKDiooEl112JU8++cgFMvgCLuA3DPrkXoSVxk2smu3zlYEklMbRx7bNksEPmzm26yUWeDphqVDC44BW5ruBLB8qVKC8xgX/EWWIbcbPGVL6uezSS9nwg7UYNeHZWao6KfjUxs8yPjHJhz70Nr7xjR+zadPZg8lOxomZB/PhqHaEKWWCxc5ywPdA7dI6CMogKirtzlIcbPbqu7CFTaXnt3ALBEuc5aSVaUaVYbrVo4wpI9R5DejS8Bf+1UksyjieS9yrgBnbBgVBwksQ9qJMquOzoc9hGWaDdfm8RLCNzbgyiolJpVd1Sl0dlTF+O//7s4GBARmk3mnk26GvMKQOUO82AZLnjWewRJlN1lUc0Pf4XsZehFFlmBeN57BECYk3axPh4TGoDrDKvog6r+GUcZ0N6XSKP/7j3+Xhh7/Dffe9m7/5m38hHD4zQegLCwLknSxhLUosEadxYTOHdu3nbfe879dSFdzXB9/4huCRRxQCAVi8WBAMSvr6BJ/6lCAclrS3S7q6BMWiwLL8UDjP8xW4UvqErm2D6/pEcEMDdHSAbQuamiTd3b71REWFnFWTKorA8/zjGIZPJE9OSlz37MpeKf3jf+YzCrt3C5qaYNEiEEKyc6dA1xU++EFvXveYVAp6evxAu0iujkt++keMNm8jGxlmsjvGHVeuZd2y8+sceuih71Ao5Ln//vfMPhaJRPnqV3/I7bdfyf/+7p/x+9f8BSsnF2NWmOxx95MuZWiONnDvxW/iisZLAahqraTvlX60gEYxVUQPaD4xDOgBHdVQic7UZKqmsvSGJex9aB+T3ZM+WSx9knjpdYsRikAP6YQrw6SG/IDkY9BNjVAiRDFVxCnP5EhIiCRDs2pc1VApZ8pzrjNUGaKcKaEFtVkiWEqJUBWEEEz3p+aQwYqqsOq2FaiawnjXJPnJPEbEpP3qdhZceu5zjnQZ9k0KakISfeazoRsqNbLImBtnSA3SSgHwVceBeAAtMLdWrw7BjS0eD3cpHJ4GQ5EUHUG7mOTm3U9S+NYrSNdFW7Ea/bobUU8S4h2cEgzmYFHcJ4IBkkHI2bDlhZ+w/+Wf8NnPfo36yNwPnb5boHWDs1jO/kY4MYl2UKBvFbjNZ6+pldNM/6X0vbfPBHX6IKAcJ4Jh1h5Cmz54gQy+gAv4tYKUBI5+m0DvjwFJesICoHHqpyj52/DCr09bqzHyEqI8jZNYiZrtRi0MEdRj/M7ex2ksFsgFYwSlRufKd3IkWsFa22Spa3BYs5kUDjd1/YTQ0HPsirSwUG0l4thomU6Ch75M7uI/B/XVkx1nQlQqXGEFeTiQw1McQlKwUysxrLrw1e9iVldx6I2b+DIp3leoIITCbeUwjpBs10v0KDZhqXCVFeKu8vEfq2eNAt8zc0ghiUqFA2qZIyGL+4sxNp2HyvlE5ISHI8A8qegNSYW84lESEv1kOck8EAji5+FbdWxF2nXds2wJmqLN8UE6GV1dnQAsWriUHq2LNmcxbe5iCiJPt3qUa8o3EWBue8mldoAxxeEFo8hhxUJB0ORq3FOKnfY66kMtJIwqpq0JXOmQMPbRn+/B8gpoqkJIDeNRxJMuykxY3HCxH8ezqDCqZotrQwkwXhqhJtjok1YzlhPH4EkHQw0ghKDSrOHiqit9da+dIe9mAQiqEY5OHqAmVDVLkAshaI420ZXu4Wi6Z7Z9KG5UEjcqWRpbQ9ZOcTR7kJ7sYWJGgqgeRxEqnvRwpE3CqKbo5DmU3knRKRA1EkyXxik4eXRVp8JMElTDpK1JDqf2sKHqStRzUOoKIYjXxrgosYEDqR2+dzBgqAZN4TZqgg2E1DAJs5qBXBcCMccS4ti/U+WJXxgZLKVkKD/CaGGcsBairWLBvIsPJ+K///e/4MknH+EP//CDPPzwz+a8j0IIjDe8EW90GO/IQdB0pGOjJCqpvvbN1C++mGAwhJwWFNwsjrRQhEbZLWCqQZpCvqrHKtq4ZWdWMbFHHmSrvR3btomWwmhoTFkpXuneyZr6lYypE6TK6dmU4fkghGDRVYvIjueZ7J1C1VU8x8OMmiy+pv2ciODCdIHJ7inKM+rfiqb47PW7tjt7HonE8Wy/fVAXuLaL9OS8ZLDruHQ+18WuZ3fwd1//Uz5y818yfnASz3VnjuWrToTwj6MZGq7jMtUzRbgyhKIp5CbyVLUlXzdVcG48x3R/CiklieYEkerwvBPlqZ4pSukSyYWVs89rhkYgYjKyf5jFVy86IwF+Mm666Xb+9m//hHw+d9aJ/QVcwAX8GsGdISZOuE84OAihIJwcANPCZa9eptbTZkUBARSaPJ0jqsWA4tDivXpbpKPqEb4U+gxDWj8WFj+/9Tne0/EuNh+4hoRRibAFUpc4twg+fdMDvPNdd/Ke97yZ7373KVavPntnCUDByTGQ72a8NIwAqoONNIfbCKjBmQ42DxV1log9hil1gjIlQvJ4f3KQEEmviiF1gCF1EM3VGVGHGFdGKFCg3V7G3aW3USWriXpxPhP+R3JKBkUquMJhXBljWpmi3VmK5ulUedUIFJqcBbP1oIONK1yutK6bPa+LS5d2hK36i+zRd1ASRXQMVthreGPpLVR7tXPGfnJg4JPmIwyo/SxzVqLMdKMZGJQocEDfS07kqPL8fsVJMUG1V8uEMgrAQW0fGioOLk1uM288hwC6k/HKK1v43d99F+n0NJ/97Ne47Y43cWqD+akIa1EWRpfSlT3IdHncrwnWtNO1t4um8JkUxb8a9PXB5z+vsHWrH0ql6/DCCwJVFUSjMDEh0XVBICDJZHzC1zSZJXGP+ZcCM3W3oK/vuPq3XPZ44xslX/2qwqFDAteVBAIS2/YJX03zz+k4/r8dBz73OYWqKsm6dZLFi+e3A3/xRcHXv66wfbtA0yQ9PX6o3bp1ksZGycGDgsFBaJqnsVbXQVUlzkwTo1mO0dJ5HZYFRq+gbtPZ508nQkrJ17/+H1xzzU00NS2Y81x9fSMPPPBD7rrrWr6+6zN86LpPsGi0mTa9mUhLmLU3ryFWddxOpXpJNU3rmuh6oYuclSM9lEHVVQLxAJ7r0byqiYqG4x7cydZKLnnnBsYOj1HK+GG/NUtrZsN+FVVhwaUtpB/aR2ogRSAewCk5FDMllt24lIrmOEd/1kUxUyS5IEm8KY6q++HbdtE6pYOtdlkNZsSkmCqiaipCCOyCjRHSfRsKeSqxGawIsu7etWTHctgFi1Bl+LzDiB0Jricwdf/4vnraBc8jly6RJo+XlJQyJeyiQ/vVjfPmaNy4QFIfcdk5JkiXBe1mjg0/+jzmkd3IRBJUBfvZn+Ae7SD4O3+AUnP8PpV3/M+3psy9RgOLn3zxo1y6cTNvfOM9p5xTGQc/1OWE/QTIsETrP7fQxCVLJMEgTE1B5UymYT7vf1/WrDnL/op2mnPImed+s/CbN+ILuIDzgJrtxhx4Gs9M4gWrKOUmAAjao5gDT1Fc+p7X5zy5AaQeBiFQc/0gNKQWImTn2Dg9wh4tTOPkAY7W7yEavoa7S3HCUmFAnUZ3bNYMvEJ/KEpf2GC/zHGxEmBRbBFG5iha6iBO8qLXZZzz4SYrjAa8ZBTpUm2mFY81ZZ0XH/gea+6/l3YRZI9qsUMvsdkOEUThraUY15dDTCsecalQe0KbYE54PG0UMBE0zoTW1QC9is1PjDzrbHNW2Xs+qPY0glKQFR6xE0jQacVlkaMTPgci+NVAmVk+9I4Zcs2DEiW6tQ5cXFrdRUTk/ITwxMQYAO+N/Q7fcL9Mp3YYkKhobLAv48bybafsoyK4sxxlox1kUHEwELQ7+hmtLQACWoh6rYWx4hCK6rKicgXp8hRpexpTDVBwcuScLFEtBhLKbomgFppDHumKjsQjoAYIqL71RESLY3sWOSeD6zlUm8fbnRJmFRcnNzNeHsFySwTUIIYS4WH35xjq3MmiKgRhQ2WyPMJkqYqEWTVrtSCEIGYkWJO4FAXBaHGQvJNFoFD2SlToldSHmpkoj5K3syTMaiQeBTdHQAvieg45O+OrSPQEWTtFypo656A3gJiRYH1yMxOz1xIiGahFV46Tj0Oil9MVBOIMAWmu5wc27J+EsguLE7C+RhI+h/l0ySnz/c5HeGVkJzkrj65otFW0cv+yu2mMnD4ALBgM8j//56e4775bePzxh7j11rvmPK82LyD44T/E2bkNb3AAkahEW7setdkvxJcuXcFE1ySLY6sZKfTjSJvaYBPN4UVUmL46KlwZwoiYFNMlzAqTvd5B1KKCjobQFQJmwG+LdbL0jPSxrGIxQe3si0Px+hiXvH09w/tHyI7lCMQC1C2vpaLx9CE64Be3R5/vYu+P95MaSOPZHkZIZ8GlLVx8/3rClX64nR7QSKdTFM0sZbfk+22PKyzZuBRFm/997H25jwOPH2T3oW1kS2n+9uGP+eEtgIdHd+oIWwd/yp9f9u84RZdwMoSqq+Qm8owfnUQzFEKJEO1XnVl5fC6QUtK9pYejL3T7tg+AGTVpu7yVtisWnkIIS0/OOwcXisBzz1+lfNVV1/MXf2Gxa9c2rrjimldzCRdwARfwK4AbXQBCBadIVrfp0jqYZJgmkWKwpoo2ZZwSFZTxF/ZPRFAKRhRJUZz/PeMYPDweDfyQPq2bvMgzpUzg1rn8zf/4G258cSsf3fUJoqE49lqJswpMxeRLX/oO9957M2972+185ztPsGzZmQPESm6RfdOvMG1NElB88rcrc5Apa4xsXYGfBX5Kj34U0wuwyF0MwLA6QKPbgucnL1AQeVqdRegzbVEKKm3OYnSp8xPzUXIii4JKQPrhqI5wuLp0I18L/icChcutq+nQDjGg9lESRZyZ7IWUMs3tpbegSpXnAk9RwvetNzC5qnwjF9kXAzCpjPP14H/ysvEih7WDCARNzgIWuYt50XiGKWWC38v9MQH8oNtRZZicyFLt1RKXFQCMKMPo6LMkroqGLg2kIpgUfm3aq3Xj4tthecIlIqNcU76Jaq+GKWWSaq+WtfYGqrxzr6Vc1+VTn/p7/vmf/4a1ay/hq9/6AV7CYsvYTwBBTbCelnD7KRkIJ6I5vIi4UclEaRRH2ly76Wb+6uFPUMyVMM6TBPtF47nnBCMjgkQCXJ9bw3H8vyMRSaEgyOcle/YIwmEoFsHzJKp6XNWr6/7fxxTCxaJgbEyiKPD88wof/rDH7/++x44dPlFsmvDSS8dUpf55ATTNP9/+/RLbVtiyxfcdvuIKSankq4dVFdJp+PGPBbYtiUYhFPL3nZwUdHb65Nj4uE+WzYdIxN/mpz9ViMclhuGPv68PGhsli87stnUKtm59gZ07X+ErX/nBvM+vWLGGL3zhW7zznW+kuW0Bf/qRv8cIGbMhwydC1VRWvWE5NUuq6Huln4nuSTzbJVYfp3ldI83rm06pwcKVIRZuaj3t+OpX1SGlpPeVPvKTBXRTY+n1S1i4aQF6QKd5XRM7vrWT0SPjTHZNUJguUs5ZxOqjJFoq5hwrEA2w4g3L2fPQPjzHAyEIV4UIxALoQZ1ESwLXdvEcDy2gzQm2O1ExfCacLOQBSJiwICbZNymIqh4TnWNkRnNMYxAqFXA6uumLqCTbkizavJAFl8wvbhECVlfB6ipfwW5v3Unp6D6U9iUIw+84llU1uIcPYG99CfOON83uWxeSGKqvBI7ox8YKO3/0aTLDR/ifX/navIIGGcOfep1kWCxKAjd5+rn6iVi8GK6/3uMnP1EY9de80DTJxo2wYcOZf9Oc5EWYg88iyimkWeGfuzyFVHTsyjXndP5fJ1wggy/g/2qomS6EncWN+Dex8owKTA8l0CZ2wRIPzjHV/kxwww1o0/tASoRnIYUK0m9DaRZRQk4IXJ03FDQq8pUkpMqXAilywiPiWthuEVsL+ipX4bFbLyMxWOE5CKf4msd3JugIbrYiXGWF+GowjakLQj/ZQrZ/kFXvvB8TgQYc1iw228cVbNVSo3qexd5hxWFKcWlx5zJb1Z7KmOoyrrg0e+f/mi9wNVbYBk+aeUwEkZlQNRPB1Vb4Nbcpng7HSL3TkcH7tF18P/hNhtR+PDyqvVreUHoTl1tXn9K653keiqKwzF3FH+b+nIP6XvIiR61bz1Jn5exEYz7UeRp1r8Kbb9qawJMeumIQ0WPknAy2V0YiydkZXM8mqlf44V9Odu54pX/NUb2CaLyCjvQ+BvJdFN08EohoUfrzXYT1GFHdJ+Y0xQAkU+VxHM+m0qyhraKJneP7SQajs+1sDtM0x2PkvDF2Tr1IlVnL8or1mOrxwl5VNJYn1hM3kowWB3ClS2OolcZwK0EtjF0sz4TjCVzPD8tThIIUCo7ne4ApQsXDw5VzfZfPBYZq0hBacNrnk4EaBos9lN3S7LjLbglFKCQDtfPu40n4fqfgp/0CxxMoSF4ahh2jgvet8oieRej6TP8L/LTveepCNTSG6ym5ZQ5NdfCNQ9/lI+s/eEaF8JVXXsfmzdfxD//wF9x00x2oJ/UNKpVJjOtvnnffiy7awEsvPcvC6FIWRBbjSQ9VqHMKtXAyTONF9XS92EPRLpIN5zAzJqFwkFLEIkAAFQVP80iXsizVFhPWz00VG0qEWLT5/CxzRg6OsuehfaSH0+hBHb1Cp5gpcfSFLoQquPz9l/n+a6sS7H9pL57ioRs6bt5FJCTFJZNYbvkUz2nXdunZ2kd2LMvC2FL+6Lp/ACA9lMH1XJ4Ye5CEUsXltTchPNBMlapFVZgRk/HOcRJNcWqX1dCwup5Y3dkDgc6GqZ4pOp7pRDVUkgt9iUNhqkDHc0eJ1ceoXjQ3jbqiMY4RMiimirOBep7rUcyUWLR54XmT0+3tywiFwhfI4Au4gN8w2FXrsKvWI8dfoDfUS17J0VgSDFZW8LW2Lhr1f+f9+Y+RlCqTikv4hNptQnGJS4Ua79WHBk8q43RpHWREmillgoRXiYZOqaLEN+74Bvb1Gp/I/9WcWioSifLAAw9z330386Y3XceXv/w9LrvsytOeY6w4SMqaJGEcX3A2tSDb3Bd5RW6n2zxKSSliY7OXnVR51ZgyQEZkKIsSBVEg6VXRNkMUOzhklBR3Fu+lJErs1XfR7i0lJCPUenVYWLxoPsMGeyPdWgdxr4IGr4l6t4m0Mu1fs9rJCns195XezWp7HQLBamctndphJJJFzhKWOatm1coPBh9gl76NIkVCMkRUxhhXR4gSZYm9gg7tEAf1vSx02vl+4JvsNXZSEkXiXgWbrWu5pXQnSVmFg41EIhCoqDR6zQyqfTjCoUyJAEFsYaFIhS61g0qvikavmWvL89cFZ8KA2sv3B7/Nf/3eZxjdMsxdH72Pv/rdf+JIZjfZQoqAGgIkPbkOMlaKiyovO22+gxBitnMMQN8c5C/lx9mxYyvXXnv+Y/tFQUo4fFiQSEg0DYaHBbmcwDB8QrhY9O0iNA3Gx/1wtkjEJ1p9YtYPkKuslKRSUCqBpgkURVJRAUuXSjo6YMcOQUODpL9f0NXlewovWiQZHPSJ4GPn03W47DJ/X5D09cF//ZfghRcEU1MQi8HmzXJGsQytrTA87BPUsRiEwz4hPDoqiUYl1dWnv/Zbb5UMD/vj8zzfXqGuTvKWt3gEz7Mh9FOf+gdWrFjNDTe84bTbXHPNjfzLv3yRj3zkt/Bw+ad/+sJpLUMUTaGyJUE04GBcFoNoHK2m5rxqHSklnuuhzNg3NK5poHpxNZNdk2imSqKlctavVw/orL5zNenPv8TQ7iGk8H13kXD46Q7CyfCcILcl1y4mP5lnvGPCFyCpoGgqTeuaGNo7xMiBUVzbJdFUQe3yWpyyg1N2iNZESbZVzqvYdcoOA7sGGdwzjF20qGpL0nJxM7F6v+ZUBNzc6jGUV9jdV6Y87iDDcXRVcGW8wCKlklK2zPKbltK09ty7qN2hQb+jzjj+XRaKggiFcY92zNl2cQWsrZb8fERQYUoCKvQPDdP50F9x11t/hxUr5idW7TUS86cS7Sg4zYAC6jDIENiXzE/kynIZVBUx0+2rKHDnnZJly1wOHxY4jm+LsnKlv5hxJthV6yk33YA5+DTk+/0H1QDlppt+oeK9XxQukMEX8H83ZuX6/vKR4/rkliokKDrn0qJ0IiSSKeFrBZJSnSUg7brLMUa3omaO4hlx1OIEjrQYiDfwxJLNqAjWeTnaA4twZ1StvZqNLgV7giabYnUsHz9KKZDARqAA09Y4ZSOKGz5/X64T4SA5oFl0qGUk0O4arHTMU/x1gyhUev41tV5/Df89PTL7nAfnZMEAEJACQwrKQmKcsM+x/w+c4Tg54dGhWpSFpMnVaPS02UnApHCZUjwsASOKg4uk2lN5f7GC9c65Bx6dL2zbtxYxTv51cMtMFLfxjeTnmBIOLU4bCipDSj/fdj5DRfcky8Ob57TEnLg6G5cVXGadfgJzOnjSY6w4yFhpaJZsrQs1E1Dnr7Z8FchMCIAWojpQx7Q1Sd7J4ngWlaEFtMdWkrMz7Jn6OUOFXlzPRRX+CnTSrCFp1mCoAYpunpQ1QUyvpMJIogmNaWuCg6kdrE9uRhEqh9K7GCr0ogkNRaj05I5QHwtSkNWUvQk0oaKo/mehKdJCXbAB27MYKw4R0MIsi8/9ITUUk9boElqjS05Z3Q6pESTgShdNaBiqScHOg5BEZsjZopvHVAJE9NdOup2MqkA9TaGFDBZ6Zoh0iSo0msJtJM35yeDOFDw3oFBpShIB/30pu7BnQrB1RHBDy+lXpMuuxZbhV4gZURKBCgCCWoCFsQV0pXrpTHWzvPLMac2f+MRfc9ttV/D973+De+4591T2iy66mK9+9fPkclkikejspPpkLLl2CUbIZGDXAAkrzmRgkjaxgCFtjBw5HBwcXNrtVq6vOf/P//mg8/kuJnum/NY3/II0lAhSnC4ycnCMyZ4pahZXE7lCI2aayKM6btEjtMIktipEMZ5lojxMQ6h1znHtkk16KI1ru1RUVZIQvs1F3MuRH89jqCaVlVU017SiB3Qkvi9cKV2icU0Dl//WZWjm61d+jR0Zxy47xE9odQwnw0x0TzJ2ZPwUMjhaF2XBJc10vdhNKVNC0VTskk1FU8Vp1R9ngqqqrFmznt27t73ma7mAC7iAXyK0IIWVv8O+sRRTE53UOHUcam6gY0EbdbrBEe0gB/XtXFfeyLcCWXZrB8iJl5lWB9BlHXcXryEhz10lejIEgoLIM61MEpPx2XA1XWoEZYhO/RCTyvgpStRksorvf/+n/NZv3ctb33or//SpL3D5XVcTkuFZJewxpKxJFKHO/mZlRIrtxlYmrTGGlH5G1WFqvHoavGYmlXGEVKjwElxqXYGKQpvTzoD6/7P33mGSXeW192+fWDl0zmFST86jnANKIJFFMggZMMY2DoD9OdyL7etr+9r4Gl8DNmAwYIKEBAgEEhISiiiONDnHzrm7qiuetPf3x+npmdbMSDNCODFLzzzSlKrO2SfUqXevvd61ehnRh5hU45RFmcXeUi5xr+TzsX+gO1hIe9A1t78YcUYY5LB+kBpZR795lChRbBWhVtaTkTUESG5y3sxab+Pc55b5q+ZC607EoN7HXmMnbUEHo+YwtopgKZs4SYb1QRb5S1FIxrVRnok9wfPWUzT7bdSrUM37g8hdRFSUte5GHrN+wmHjAK1+RxiFqwS1sp5RbQgEeLjEZIJ62cCYNkJSplnhnT25ccDby+989lfZ9qkXiDREueT+q3Avdfm3mS+wdHIxtVbDnNAiokeZdicYrw7Reoa2D93di6itref555/6T0UGQ6iqnZmB1lYYGAhtIXQ9JJuKRUVHR0iSPvwwVKsC04T2dkU0qjh0CBxHsWhRGOR2+HBI7Pb0hK+lUrB/P7zwAvzwhxoTE4LGRoWUkMmE221qCq0n9u4VtLYeI4JDCAHbtgmKRUVXF4yNhd7GK1eGdadhQGenYvduQT4fqntdV5HPwxvfqOba6U+F2lr4jd+Q7NwpGB9XxOOwapU6bejc6bBt2ws8+uiD/NM/nVoVeiLe+tb3YBgmv/Vbt1GtVvnMZ76Kac4XRDgFh4M/3cvgA8/ij40Tp0xHg0PzZWuwb3kLIvryggSlFKN7x+jb3E9xokg0HaV9fRtmxOTAYwcpjIZ2OsnGBEuuXEzDkpAxr85UkH5A5/kdWHEr7DKTMHFkkr4XBlh+/dK5fUTTETa8Yz0ju0fIDeQxIgZ1C2rpf3GA4V0jRFMRNEPnyNNH2XnfHpL1CayYidA1Gpc2sOoNK+ZZpimp2H3/HvpeHMC0DXRT5+izvUwcmmT929fOEcJLa+DX10i+dWSMQ9KnyfBYKoosFEVEW4aJw5NhaPNZQIuFizwvna8p10Gk5nfz6Rq8e5mkKR7OfxxfMXj375KIRfnL//HJ0+5DNkD5PZLId7XQFkKCrFdUb5T4S+e/N+g9gvvTnxDs34swTYxNF2BdeQ0ikUTTYNkyWLbsLLtbNIPKkvfi1a/HyO0HwM/04GdXgPbqF0f/o3CODD6H/xYQ1Um06jjKyiBjx1ul/exyZKQOvdhHkOhEn10FVE4Br/GiUxsnnQb9mseP7BIHDReFoiswudFJsDCw8LPLKC/7VSJHvofml3ANk2fqu/jOypsZj9cjkTzSfAvXRjq5wQtdyaZFwGazyowmuXfhpTTn+mmbPMB4LEuL4xDxSoy1vp5k/AxiMU8DH8WdkQI/MytURUBJhH0Vy32by9wYSaWzNLBIzLb/LQtsHlMVpkVAdpa0nhEBGrDyDAnXVmmwMDDZbrgsDEwsBFUkw5rPhW6UutP43O4yHO6KFBjW/FB1qgQXuVHe6CTRgR/aRQ4ZLhd7UQTgIBnQA/YZLr7Dy2hqfz647slksDGxhcihu3gq/SxT9mGWjXUQZLMEKknzrhH2xQ7wzOYBOp59BPP8i7BvfgvCtueUwa8WSin2z+xgoHgIEGhCY9wZYbw6xKrseUSMk4uarF1HX8nACSrYepS4mZoNjbNZklrNgtTSsK1R+gQqoODlj+0NQ1h0xRdj6RG8wGV/fgfloIQhTCYcj7SZJW1mybvTTDnjGJrBaGWAhJGaU3jowmCiOkJ7qp6qp5hx81iGJGrEaEuECx2mZhExYoxVBlmQWPqy6pATURtpJGvVMeWMEzPiRPU4BS+PUOF+8+4USkm6k0uJGa+9l6kmNJakV1MXaWbamcAJymhCJ64nqQZlYkYCpRSTzhjDlT7KfoHefBpEF9nIcYmFrUPUUGwde3kyuOpXKXsVYi+xVrB1C1/6FN3yK4553bpN3HDDLXzqU/+LW2659eRFjtNg7doNKKXYsWPLywb3GJbOoksX0HVeB/GhFJ95/AtMT+XpoIUcM8xQoLvQzrsib6at++db6DoGt+wy1TuN9CXplhTx2jjjhybofa4Pt+yiWzpu2cMte0TSoce19AKqM1UAimqGzOoEyQ2ZedutOAXK/sl9kVbUCr2LPTnfViVqopka0g8wbB3d0KkWqsRr4hTGCsRrYvRcveQ1JYIBvKqPdgqFi6ZreBXvpNeFECy5cjHp5hQje8aoFqoYtk6yMUVhrIidsM96jGvWbOS++07d0nkO53AO/3mhrCSP9GTZs2Ij3cHx8MhjNdWg3s/bK1dwxHiRr8W+QEHMEFNJovSx09rHs77L+d4lr2rftbKeFr+N582nyMiQaVIoZrQ8aZXBVhZlcere9FQqzde+/n1+5WM381sfvo1lfatY9/+dx8bgAm6qvomkCgkPU7NQs11OHh7bzBcZ00cwlUHZLqOhMa1NYGERVTE84aKAHn85V7jX4uPzovkc28zNlEWJHn8557mXUKNqMTGRzO8aUyf84+Gyy9jOLrYTJ0F90ERMRWkO2l6RZJVI8iLHuDaKI6pEVIyYijOujYECE5MyDhUR/u6XKbPL2E6r30FAwKQ2QUzFcZXLk9YjXOpczXvKH+T7kTsZNPpQKFqddlaPreZgsJfhxBBDiSGUAVWtSlKlaAlaaZCnt586FR599Cf81p/cxlTfBBs/eiEXfOISzLhFLpjmeesp6iJZ6tTxbR7LrSj6M2e8DyEEmzZdyHPPPXVWY/tFQwg4/3zFt74lCIKw3bxQEAwNQTQaErodHSF529EBqZSiuzv05x0ZCdW8mgZ794bbSiTgvPMU7bPTQKVgYkJw332CfD4ke1MpaGmBdDokk6+9VrF6teLP/1ybFxoXBHDkSKjYbW+Hutk14vFxOHRII5VSDAyE6mBdPzYmQVeX5F3vUlxxxSsTZrFYON6fB//4j/+H7u5FvP71bzmj97/xjbdi2zYf/vC7+dCH3sE///M3se1wDhH4Adt/sJOhh17AmujHjEfIq1r2jDuIHz1OkxBEbn3Py25/aPswO364K8yqSNjkB/OMHRhHegF2KkKqIbRqmBktsOPenWx69wZSTSnywzP4TkCm9YR6XQttIcYPjgPzWUs7btG5qYPO0OKbsQPjjO0fJ9OaxoyY+K6PU3bxyi6aLqjtrsWregztGCbVlGTx5cef3dP90wztHCZZn8BOhOciXhdn8sgkfS/0s/L1K+be25WCa41R1pUnydRkXnL0oRr6bKCvWIX4SQ2yvxetrQOEQI2PIXQDY93Gk94fN+HmhYrruhRf+co/880n7+af/unrpNMvHct8+MuhuFBiHAV8CDpBvWSaFwz0Ufni55CjQ2g19chSEeeeuwj6e4l+4COIM5z/nBKajl+75r+kEvilOEcGn8N/bQQOkcN3Yw0/ieYWUEYMt2Ej1UXvQFkpZLSB6qJbiR74Jsb0TsxCSHS5mWU4bVef8W6mRMCXo3kGdJ9mqSMQ7DQcxrSAj5SzNEuDauN5jNevIlYc4Gk1wh0pjSXT/aSrVfLJVobSHTwgqqwOYvTpPqNagEKhAdtaVvE57d1ceehJluVHqERr2bzkJl5X/0aSP0dS7g7D4WdWGUsJhjVJTkimtIAtpsMjVpnFgUVbYPKOaoqewGKZb3GlG+NRu8TwrG+YBVzixlj9MmRwgKIqFBEl0BG8tZqiEs1zWPeQKHQEK3ybW5zESdYJEBLjd0RmmBIBCwMTHZgSkofsMs3SYLlvs9t0aZIG1uznTXS6AkG/7nFU91gc/GJC9qrVWQ+32ZYXvXCU2O4vonkzTLcmQI+iO3m08RfwRiLI0TzRhQmmFiYRL1q4D97HTK3G3uvr2Wa8gBKKKTFJjTrL5XJChctQ6ShRIzFnSyCVZMqZYLDcy8LUsuPjDsoMlfqYcEbwpc+UP46l2WhCQwiNjsQiupNLQlsFpRgoHcLSI7Sb3eTdaTzlAoKB8hG6kj0cLe5nyhlHFzq6MPCky4QzQp1oAhSedKgEJQIVYGoWnnTRhEbZLyKEwtRMFtQuoOwXGSr1IQSUggKWEZ5XQxi40sFXHhazPlNKUfByTDpj+NIjaaapizTNhbSZmsXK7EaOFPYx4YxgahZdiSXoQsdTHlE9RnOsg6ZoWEkXvBwjlUHKfoGYnqAx2krKyr6q++IYNKFRazeQdydDf2EZLh7YhQgLU8sQaOzPbydQPprQKQUDLKzbgkkdfrCIIOhGzXpMv1LHWsKM0xir50i+D010MO1kEChMrZeYGaMhdmb31O///p9y1VXr+eY3v8xtt3147vVABuTdGSzNImHN9+9bvHgZ0WiMrVs3n1GKu2EbbOpey4f993PnI/cwVBomKiOscpZyQWQDK65ZPhc09/NgdN8Yex/cR3GiBEphJ2w6NrYz3Z9DNzR06/hsyKt6uGUXwzbQI2FgGkBUjzPtjM/bbpg6r7C0k/0INUOjZVUzY/vHqc5UMaMm0pcEbugN/D8u+nui6QhC05BBgJSSaCpC90XdNC5tQEnF6L4xRnaP4pYcsh1ZWla3vOoguXRLir7NisAP5toFpS+RfnBaX2WhCZqWN5FoSLL9+zsZ3TvO6J4xEIJsR4bVt6wiUXd6D8eXYs2a9Xz+83/P5OQ4tbUv00t6DudwDv/pkJZZXOHOe03NRqvFVQIPj379fhb5ina5FlOFfXFH9UPcF7mH1d4Gopx9OLBA8Nbqu3ncfpgRfZCoiiORxFSMOtlIvWyiLjh1lw3Ai4lnqf1qAytWrmHXn22jtKvI2BdHKETz/Gr5N9HQqI80M1TupewXmTanyYlpaqt1TOuTuKaLoUyUkMyIPCnSRFQoODhWqxoYnOddxHneRSftf713HnuNXVQoY2EzoY1x0NiHQ5Uf2z9gSO+nPehkUpugJApMGuM0ymYW+j18K/avLPVXsNG9kJSa/5zea+zkAfuH9BlHqFJht7GTvfouFFAQeRASTRgYymRYG2Chv4QG1UheTDNiDDGlj+PjY2GRDrKAoiSKrPbX0VNcTp9xGF96zIxNctDdQyKaoF10kKqm8S2PVLyOklFgYdBzxtdyaGiAP/uzT3Dvvd+h6dIWbrzzjfT0HCed0iqDr/mMWaMsdY77PB/7rTXF2dXw5513MX/zN3+K53knqUH/I3HxxaEdw+bNGo4Dzc0h+SsEDA3BkSMauq44//yQNH32WcHYWEj8dnbCmjWhErevTxCLhSTysQyxrVuhv/94YNvYWEgOe16o9IXQWziZDNWOTz6pkcmEthSVSqgEzmQge0LZW1sbqo2vvVbx/PMae/eG3ql1dbBmjeRDH5J0/GKykE/C/v27ue++e/jUpz5/ko3Zy+GGG97Il798Nx/4wNu5/fa38i//8m2i0ShTR6YY3zNEsjyCkbYQsSi2glwxwojWSt3mZ5HXXI9WW3fK7QZewJGnj4CCmo7Zk1YTY3DbIPmRAouvqJtbOM/OKmlH9oyRakqhzdZiJwVv+wGG/cq1XnG8hAzkXJ1cnq7gV30i6cickMGMmNgJm+Edwyy8ZAGaPtv9MFIgcIM5IhjCBZRIMsLEocmTxlS3sI6RPaEVhW6G43bLLpqhk2nLnMklmIPe1kHkre/A+f7dBAf2hvaZqTTWjTdjrDl94Ofu7c/zl3/+Md7//o9wyy1vP7Od2eC/zCPK+9kTyOFB9GUr545XZWsItm8l2LMTY836szm0/7Y4Rwafw39pRI7+gMiR7yMj9fjJLoRXwB54EFRAZfmHQQjclsvxU92Yk9tRxiHgUXJtt6CsM28b32pWGdA9lgQW+myBmAo09ugum80KXYHFQ1aJQd3HSKUZ0BJklcZwcjH7DJeiFqYVe1S5zy6S0yQNSqfWj7LZqDClSZ5uXsHTzctpdzySwuYNbpb66s+XMr/XcHFQDOo+BU0CCikUOjCjSaxAMKL53BGZ4ePlGuJK401OghW+xSHDo4qiWeqs9yIYpyBxJYpnzCpPWGWmtIAaqXOpG+MCL8JvlbLsNVzymqRG6vT4FjaCCeHzolllVAuoURprvQi9s+T4ksCcs96oVTozSJ4zqywKLAIUxktC0wwEPuCdEGAiqpOYE1vRvBmCaCNe3Vo4hWL2TDEyMgRAU9OsinX0WbTqOH52JQ3+EZSm40azWLlB9KIkyLRRiU/RfrAGra6evtQ436r7B4YiLfTqR5FC8unEX/K+8q+xMHj5lv6XYsabxlc+qRN8dTWhYek2E84wCwnJ4Gk5wR3el9ma3EyQkXQWu1g0vYhGkaIl1kGN3UBdpHFOkeFJl7w3jaVZTLuTVGcVrgrJpFNg29TTOIFDTA9VJrqmo6PjBFWmnUlSZoaoEafoFagGFYbKvXjSnfXy9VEK9NnWGUuzMXUTJ6jOhagAVIIyKTOLJ1325bcz405TlRVK3gyC0KNLIKiLNLEis2FOPRw14izPrscNHCQBthad8yU+sdiZrI6yO/cilaCELgwC6TNc6WNZZh31keazvS3mYbw6zNHifizNJjv7XCn5Bfbnd4TnAElEjzHpjmPoZQwtQKlxTMNH18bIlS8g5yTpyb78Cryu6Vzadgk/G9rHi2NLECJJoAKggWs7q7QnThHzfAr09KzgrW99N5/+9F/yrnfdjmVZ7JjYzU96H2OgMIypG6ytX8UN3VeRscNJqq7rrFq1jq1bz9wKwKt6LIst4Y+v/z3GRscIcgHJRIKGJfXEa8+caDwdSlNldt23B6fgkG3PIDRBearM/p8eIAgkNV01uBWPwmgBGUiEJlCBQnoB0g0oz7a/NUXbGKsMUvDyxI0kSkkKXp6YkaDuNN7PK29czsjuEcYPTSJ9iWZo2LEw7OO8X9lIsinF7vt2M7hjmMALKE9WKE2WccsezkyVg48fRkmJZuiM7h9naNcICy7qRgUSM2pS210zr+3v5dC0rJHB7cNMHpkkkoyAgOpMldquGpqXn17VpZRi70/2MXlkkpqOLLqpE3gBU0en2PuTfWx4x7pXbNM8hrVrQ8XH9u0v/qdr2z2HcziHl8c6byPPWk8wog3RIJtQKAb1ftIqyypvLaP6EMP6IE2yGeuEILmmoJWj4gDPVh+l3e0kZWWptRvmaoszwQp/DR8u/S7fiH0JF5e0yhBRYRDa8lPYJhyDj89j1kPoQue6j72BhT1LuP+D9zB9/iTuPztctfZ6kipFPpaj1m8hVxij6OaJBjZRLca+un34eoAvfEwMqqKMrSLUywbSKsuil7AMORH6/aZUmnoZ/i5c6F7GfmMPT1qPcFQ/xLg2ii98YjJOv95LnDgrvDWscNeQFzn2GDuZ0MfYY+wgqVK8YD7DFvN5PlD6rTl7i8P6Ab4U+xw5bYq6oJ795h7GtREkkhpVSyACRvRhoirGQrmEld5a3lZ9Dx4eo/oQBVGgVtZjYuJQpdc4TMSPzgUb29gs9pcxVO5lwDmAnjB5seEFcuY0lrIQgSDOODWino3eBa94/RzH4V//9XP83d/9L2KxOP/4ma+w873bmNLH4YRcEYnE1iPYyqbkFWa7tRRFfwZbi1AXOTsF8qZNF1GtVti27QU2bnzlcf57IRKB975XcfHFAYODoQ3EXXcpnngiJIejUUVdXajitSyB44T2DJoWhrlFIiFZWy5DW5sinxfs2xdaP/T1hYSx58HBgwJdD//7mWdCZXAYUheO4/rrFUNDigMHBLquqFYFliVob5/viVqthgF069crLrooYNu20CKitVWxdu18m4lfND772U/R3NzGW9/67rP+7NVX38DXvvZ9brvtzbz97a/jS1+6i/KUg6w46NJFGikIwvNsmzAjE8hSPyqfg9OQweXpCqWpCrHs/IUuzdBRgcQre5h2SNYKIdBNncp0qNQ3bYNKvsKhJw8Tr4uTakxhREJLrpaVr3yvm9GQojvWUapmc2ukL9FPIHl1Q8P35Oyiyuxrlo5SoV3Eib7IgS+JZk5eOGlZ1czYvjHGDoyjWwZKKpRUtK1toW7h2QuXzAsuRu9ZRnBgH/g+WvdCtKbm09aTk5MTfPCD72DVqnV88pN/c9b7Ox2Cg/sQ6cy8/YpoDAIfOTwE58hg4BwZfA7/hSHcAtbw40i7BhkLCzOl1xIgsMZfwCkNImfJEZnowEl0kM7kgE8wPjV9Vvsa0QJ0xBwRDKFqIIrGFsPhMatCSUjqpU5BSPYZLgYhWVkVEkMJdKAsFI9YZWqUTkbq1CqdPKFSVwqFRFA2o1hK5wwteudhWgRsMx1yIiCtwrEUhaQgJBkp6NfDsWizvsTDms/5XoQjus8+3WW9H0FD0BmY7DNcXjSrVIXkUavCFW6MC73IPGXvI1aZ70QKGAhSUmNQ9/lGNE9FSK5246zz56vqjuoeX4nmGdB8TMBD8YRVYZEfViYvDYGLKEFek9RJnZbA4LDhkQzMuTGMzhLQbUH4KDOmdhHd80X00tCsBYgILTxW/Doy+up87YaGBkgmUySTs6b7lTGUZoMQrMu1sLB4hH3JCZorHkHMYaBxmvqZJBuPdiCF5EdX9NOXnWRZ+VI8M2C3u4NB1c/3onfwu8U/RufMJ03HjvskLyYl0Wa34+HxT8b/5enoYyRECl3pvFj3AsOJIa4ZeR2rI+edVHRrIgzjK3gzVIMyET02R6gGMmC8OowCDKHj+lV86RHRokjpU5ZFkmYaX/pIFVD2C0gVENVDhU81CJOzG7WwQjU0k6SRpuKX8KRL2S/iBlV0zSRj17F96jkqQQkBjFdHEAjqI81k7Fp86TNWHSJdqmFB6niL1ac+9ef83//7F/OOaeHCJTzxxE4gVE8fLuzFCapkrfq5Y8t7Uxye2UON3YB+ismrUgpfeejCOK1HLoRksFJyXhp23EgyUh6gEpQwNIPxoIojKxjCImbEKbqSqUqKiYrOnskqukry6IDADeC6LkXsNEIXU1uLrXeTtgeQahBDM4ibCyg4TfQVBV1nuMb1kY98jLvu+joPPngviy5azVd23UHBLZC2ElT9Mg/2/pTxygS/vua2uVC6devO4/vfv/OUycQvPW/9Lw5w5OmjVHIVNEOnrruGnmuWvCYk8DGMHxinPFWmtqtmruCN18Yp5ypUchWS9QmaVzRSyZXxq6E9j2Zo1HTWkGpJ0f/iAG3r2qixG1icXkVvcT95dwqBIGmmWJRadVp7kUg6wmUfuYTdD+5l8tAkaIJMa5pFly4k25Gld3M//S8OkmiIY8ftUOU+WmDXD3ehpMJORuaUwKVcmQOPHuTAIweJZiJEUxFqF9Sy8vUrqO16GZO+WdgJm3VvWU3v832M7h1DKUX7uoV0bOrATp6+o6M4XmLq6BSpxuScGkQ3dZINSaaOTlEcL5JsOLO06s7OBaTTGXbs2HKODD6Hc/gvhhX+Gm6uvo0HIz9kv7EHAdTJBm6uvo3OYAHD2iA6OgHHF3ADBAcDxaBo5kl/itWlCrGioDnaztL0WnTtzKeYb66+kybZwpPWIwzp/YzqIxjK4HH7YXYbO7jGuZFL3avm1Z8lUWRSHycjQ8Xeotf38J4nP8gDH7mXx69/iF//8HtY+ucr8eIuyUSKjeULaCl2syW6hRrToD7SQp0apo8jTIkptNlFZ1+0cE31RlpkOH9wcbkvcg9PWY9S0GaIqhhr3U28qXorSZXinaX3s9vYTp9+mLiKUxc0IkVAn34UIQXbzS1EVISkSlPVypjKoFmG9gvHbCSeth7jeucWAJ6yHmNam6DHX8GYNkJBm6HLX8S4PkJExUioFEUKXORewUdKH6MrWICOzmH9wGwdqAiEj6Y0POGhKwMNDVc4WOo4C1jw8vgEPJt9GnSoc+vD6xsIJs1J2tVCNrknq6GPwXVd7rzzq/zDP/w1IyODvO99H+b3f/9PSaczJLwk3zb+jaIokFBJJJI+/QitspNL9WvJiXGm3QkE4YL+guSys+7SWrNmA9lsLQ8+eC8b11wQhpv84uJDzgq6DkuWwJIlip/+FDZvNrDt0D/X86BQEMzMhKRvezsMDoafO3gQXFdw/vkKIWDTJsWqVZKDBwX9/TA9rREEcPRoSCLbdqgSnpmBRx4RXHCBYtWqkBRsaYHf/E3Jli2hTUU6fVyxXCyGpLHjhArkZcsU3d0hKd3e/vPZPLxa9Pcf5bvf/Raf/OTfnLF92Utx6aVX8d3vPsxtt72ZG2+8mE/9yRcININJL0F5TAfDwLYkuoA6q4yWiCOyp6+xDNtANzUCN4ATNEWaqYVJRPqJ87BQaBCvizN2YJzdP94DSuE7AZOHppg6Ok2mJU3nBR0kGxMUx4vE6+KnraXrF9aRqIuTG8iTaUljxS2kUuDKubpMKUVhokhdVw3jBybItmWwkzZ13bXEamLkh2dIN6cQmsApuSERvfo4KSsVHMrBYMnGunQtCxYOUe6dQOg6jT31NC1vOmU43ZlAy9agnXfhK74vCIJZz+cKn//8t171tT8VRCoNgwPzXjtGqp91quF/Y5wjg8/hvyyEm0d4RWRkfkuqstKIyiiaO41kvlIulUoTiUQZHR0+q31lpYY/2zR3YjHqIBnTFJ6AJYHJuBZwUPcJhGJcSJQAW0EUgSdAA4oExNGY1AL6hMdOwwUBJgJNwSbPplbp7DNcBjWfNnlm7U9HdI+vRfMMaB4CgZq1ZygIiS8UQmnI2SAxJSAeaDhCIQgVvhURPiAVirsjBR63ymSVTlJpjOo+34zMIIALvfABWhKSR6wyUaXRKsNHSU2gM6j5PGqVucCLEj9BQaJQ/NAuMqT5LJ1VACsUh3WPHYaDUFBGEptV/yoUOSFZ49noCK5zE3xNz7NP90gpjRISA8Hr3TgppYNfJXLg6+iVMfzschAaBA7G1A7sw9+hsuwDs6GBZ4fBwX5aW4/7Nst4C0I6oBQZP8KvHd3E95t2s8fcTTVmsPxQlusOrqcll2E4nedIdpTWUj2GFSPRnArbjYZqOdp+mAG9l85gwRmPJWvXYWk25aBI3AiLAV96+MqjMRqmve42t7PVeJ7GahMpkQHCVqe+yFF2x3ex1jv/JDLY0EzqIy30Fw+ihMCVDoYw8FVI+rqBg6MqWFoETeh40sULHAIVoGsGvvTYPvUsnnKJ6gl85eJKBxChX59UVGUZJzhOBDZEW0kYaQLlURdpoiXWxWDpCNWgRNaqo+Dn0YWOhs6MN03CTGNoBpZmM1odoDvZM6+I6ulZzp13/nju77p+/Oet5M+Eqk8zOfcZIQQJI0XRn6Ho5edSqit+CSeoUvYLjFQHKHqhbUJLrIvWePcpSeNQBT2fLBZCECiPSlAiSkiuoyDAx9KL1ESiTJVrOTDdQlwPWFGrcAO497BGzpXctlyd0tJ827ggbdewsTGDE7gYWmjbsWdasHtS0pU6s0K+p2cFGzZcwDe/+a9c0fF2xsqjZKMxSnIaEERMwZaxreydOsCquuUAXHPNDXz+83/Pjh1bWL16/oq6E7gczfcRqABrwOTg/QcRmka8NkHgBQzuGMYpe2x694a5xOWfF17VB8FJidB23MIpOUwemcSIWURSEaxWGydfJZKK0LGpg6DqU8lVqM5USdTFaYt30xBpYcbLoQlB2qx5RTIj1Zzi/F/ZRGkybOeL18XnCufhXSPophYSwSgQkGxMMrBlACkhO9tyWJ2p0v98P86Mg2YIrJhJaaqC744hhODC28/HjL7ycyuaibL02h56rg67Dc4kJTtwfQJfzhHBx6CbOoEv8d3glJ+TgaQ4VkQpRaIhgW7oCCHo7FxAX9/RV9zvOZzDObw2eGlN+mohELzOeT3rvE1zpOIiv4esCn8Xm2QLC/0etpqbifkJAqJs1jo4ZEBUJtlW28VISnLl1DSq1EfWrqcl1jm3fQ+PZ6wneNZ6kryYZpG/jEvdK1kQLAZAR+dS9yrWu+fz6cT/piiKtAUd2MpmXB/lzthXian4PKVqVMVIyhRT2iTZIBxndmENr7//zfzgC3ex+39up+/BI1z7zzehLlT8JPEjbtFvpdXoZof5Io2yma6gOwxPQ9EgG0nKDKYy8IU3d15/Yv+IH0a+Q1bW0O53UtQKPGo/iI/H+yu/zmHzAI5waAiaMXSLhEoyqo3g4uJqLiYWz1lP0xQ0U6VKnAQRFdbRJhZxFWeHuWWODD5iHCSp0ggEOW2aiightYAASVPQzAp/DWPaKDWyhoWz5w+gLEo0yRZqaWBcH8ERM9gqwlJ/JWmVoSSKc+pgCK25xu0xRqwhOp1uDGVS1kr4gUu920gkEiGvTZ8U3ud5Hnfd9XU+/em/ZHCwj5tvfhu/93t/zOLFx23KLnWupl8/ygvWswzQC4SLC2+rvofV8U1U7BJ5dwqEIGPVzgtAltNTeE8/ib9jK8KyMNZuxLzgopOCvgzD4Por3sCPv/19/rL4l2hS4K1QOFcpZOvZ3P2/OEgJDzygUakoOjuPx9SMjITK32xW4DiKqamQ1JVScPSownUFHR2weHFo/5BOK158MfQVrlQE1Wq4Lc8LvYCFEASBYt06aDqhvM9k4Morj9eEhQIIodi5UzA4CJqmWLxYceutEuM/mBH63Of+L+l0lje89XbKPsRe5XjWrNnAffc9xW23vZlf/dhbePvqD7DAW4DtFdBQzLgmQkm60pMY512I9jJkcDQdoWFxPX2b+zEiJmbEIPACVCBJ1MUpTZbnrBmK4yUS9QkaljSw80e7cMsebeva8Moe5VyFwngRUOQH8zz/jRfxdJND9a0UF3VRXxPhui5J9wluMZFUhBU3LmfPj/cwPZBDSUksE0V6oSVZYaxAbiCPU3RAQX54C9FMlCVXLKJtXSvLr+th9wP7mDw6BSKs69rXtdG+LuRFKj58c6/Gi6MCJwAhbJri3bzr+k6WvbIG4ZRQSlEYKTAzUkC3dGo6s/OsKl4KKSUf+9iv8fjjD/H1r987b679WsDceD7Bru3IyQlETS1Iiew7iqitx1h++q6TXzacI4PP4b8slJ1BWSmEm0OZx9Vbx/4u7ZNbG4QQNDW1zLX+nynW+BEeU2UO6x7tgYkAhnSfpNJwUWSUhoNip+4wpUl0pZAaKMAl9AKNKIGtIK9LlrsaOwyXcc1HorAUuELNrt4rMlJnRHOZ0iRtZ+DdLlH8wC4wqPlzVhYSxT7dm7VmULP7gopQ1EiBEIJMoFMUkqjSaJkldAe10MahWRpkZsPeUoHOUd3jMavMRi+CiWBM88lpAS3B8ceIO0uY9+o+/ZrH0uD4j8CECDiiuzRLfU4BLBC0BSZ9ukdbYNCre6SVjqUEE5pPvTS42AsLwFW+zYfKGZ4yK/TrHoulxXlehLWzXsbGzAGMQi9+sjskggFUgHALxHZ9HnNiG17jBeQ6X8dYvJ6oEtQq/RUnUoODfbS2HjfMchvOxxp6DCO3lyDeSpsLvzWcZDx9HROHm4g/fAA9G6BiM7jlPqSQmM1doGkkW8JCvDJQRnZI/BNUNmeChJGmK7GEI8V9TFXHQiIMjcZoG82xcIz9+lGkUFjS5pjoWEcn4tv0Ro+giidvVypJoAJ8FeAFDo6oIoCIHiNlZSk5RWwtTIi1tQhKKcqyiI5OrdVIfaQZV1Y5WhwiY9XSYDdT8csoJJYWYcabIm6k8GYJ4oZoCwuSy0iaGUI9vIYjK+zJ5YgaiVniNCxgLc3CkVVcWcXQEmgIpDr5S6HrBg0Np269ErNX+cQ2Kjg+kRaEBPihmd2MVYYo+nkK3gwRPUqt3UAlKLM3v41KUGZJatVJK/kZq5ax6iBSyTkFcaB8XOnMqWqPqY40NDzpkDZT5CqNxA1Fd9qlZlZEHzUUW8Y0rm4P6DiFyrfoCSw9VOZGjBP8wIDq2d1OvOtdt/Pxj/8aqTd3IxMukggRPYZC4QYOOWeKvkL/HBl83nkXk05neOCBe+eRwXum9vOdAz9ksDBMoAJkn2SZvoiLGjchhMCMGBi2znTvFJNHJqlfVEduIEdpqowVs6jtrj1jgrhaqDK0Y5iJQ5OUc2Wq+Spe1ZvzVZNSkhvIo1A4BYfqQB6v4uFVPJINSRp66tF1jWrVQ7eMeUSrpdvU6af3pzwVhCZI1J+sHvYrHlIqJg5PUJoohd//hnh4WysFKrz/pvqmqRaqaKaGaRtEM1HcsodX9cgN5pk8OkXTsjMf05mQwMcQr4sTy0YpTZVJNx+/2UpTZWKZ6Ck9g6f7ptn30wPkB/MoBYmGBIsvX0hjTwNtbR0MDPSd8f7P4RzO4dXhsH6Ax62HOWjsJanSXOBeykXu5Zg/Z5RuvWycs0A4EQLBm6q3khNTHDT2MaCtZ5goSWeSlU470SBgyDJ4Ipvh+kqeieoIMgnPm08zqPfTpx9mUB8gI7NEVJTH7YfYY27nV0u/yeLgeJfPAXMPfcZRFvk92LMyz/agi4PGPp60HplHBltYXORezrejX2NMG6FW1lMWJfbZu6n97Xouuupynvrw43z3um+x/jfOp+MPu3k2+SQfLv4u2UgNO6wtHNYPklIZNroX0BqERMSYNsLj9sNc5F5OTMV5yn6UlEzTKEM7qRppoymd7dYLDDr9FEUBxfHf/qJWYEbkiKgIjnDQlIZQ0K/3hUFwQZSd5lY0NGqDOqSQ6CdMx2tlPYN6Hz4+ffoRprRJTGXiCY/DxkESKomprHnELkCDbArtIZRFj78MV7jEVJwRbZA6WT8X0De3n0gjBAJHVTGUgYFBzI8RKIuElqFCFZfjPtK+7/Pd736Tv//7v6S39zCvf/2b+drXvsfSpSt5KWLEuK3861zkXs6g3k9ERVnur6JWhuKdqBGf10l1DDI3TfWLn8PftxuRSIKU+Lt3EhzYS+S2D80LfBJleKN6I98a+wr7xvewLLsc+xEN47Ci9BGJ/E9gXT89DVNTgng89PU9NvxIBHw/fK1chkRC4LphaSClYGQkDIbbv1/w2GPwzDOC3bsFmgaep7BtgWWFPsCWBZ2dipoayGReXgyQTMKHPiQ5dAjGxwXJpKKnJ1QY/0diYKCPb33rX7nknX/Mp7Yn0QSsqVdc16mofRUCzubmVr73vZ/ymx+6ja/+9P9xxcIbuabuGrSKiylddFOglq/Ffv2bXnFbi69cTLXgMHF4EhUo0KBhSQPtG9oY2j5MfijMIqrrrmHxlYsRmqAwViRRG6p+rbiFFbcQAga3D2HGLfzmer5ZaWFk0sIvVnDqIty5X+f6LklNRJCyFKvrFUsX1nHB7ecz1TtNMKs6nhmZYXjnKDMjMygUmbYMNZ2huKA4XmT3A3uJ18VpWt5EuiXNxOFJAi8g2ZAg25GdI68f6xc8NSRoSyiSFvgSjs7AHXs1PrFRkjhLga70JXsf3sfAi4O4ZQ+hCeK1cZZd10Njz8mduVJK/uAPfoO77vo3/vEf/5Urrrj27HZ4BjA2XYA5NID35GPIfWMIAaKhEfst70Srf3Xdwv8dcY4MPof/slBmAqf5CqKH7gA0pJ1F8wpolVGctuuQsVP7gDY2Np+1MrhFGryrkub7doFe3WVaSBSKnsDG0wLyQuJpinEtoCqOZwtrhISwQtEiDUwFQ3pAgzTIKh9X6VS1gABFjTSIKcGQHtAoJTGlkTpBWRvukXlWFccwrPkc0T0apU5JSEwVWli0SwNLKToCg92Gh60UBSFxAQuFrWBEk1zqRukKwknEuBZQUi4d5RIIA2VnQGhkpcaEFlAUkqzSiSoNS4nZ4Djo1zwOGC45IQlQ/Gs0z7uqKdbMWkWoUBh5EsTsn9c7CQZ0j+fNKp5QbPKiXOXG6A6OT24WBxaLA4sykl1mGOD3rFllpW+TCTyQARxT8wUu1tjz6KUBEDoEVZ529nC/n2Ykuh5Li7LCs7jFSZJVpyeihoYGWLdu0/HrkGijvOLDRA59B71wBAC/ZhXRhW+leWU7jvlD/M3PIvM5mus6aWsyGGwPSASKZFtIuPQP9rI+OI/W4OxSGYQQdCYWk7FrmXLGCaRPyspSZzfOqRhNZWHpFgo56yerqPhl8lqehBtnoHSYmBGnKdo+R2qOV4cZrvRRG2kk50ygCyNUlguDUlBEE2Hr56Qzxow3hSe92bsRKkGRaXccpcCXLhPVEaJGnKSZQggtVIoGEZZn1pEww2XvqH68NUrMMtYa+qx9Q/jtsfUoutCP+worqPoVZrwcHYnjqbnHcOTIQdat68S2I2zYcD5/+Id/QVtbeH5jRpKUlWWyOkpG1IX+W0pR9GbIWLXEjRR78lsYKh/FFjHKXolqUKHql3GDKhmrFkMzGC730RrrImHOZ2mbom2MVgaYdieIaFEUimpQxtQsMmYdZb9AxS8jlUQiQ2KYLJNVnYRZJW4cX7hKWTBcVoxVBB2nUPkuzii2jmsEUjFb0+EEoAtF65l19M/h5pvfyic/+TF6H99J9HVNNGkNs9dEYAiTQAV4qjL3ftM0ueqq63nggXv5xCc+CcBEZZJv7LmbicoUHclWNDS2VLfzbOpFWmmmi3CCbVjGnFXC4NZBRveHacxi1l5h5RtWkGp6eY+LSr7Klru3MnVkCiNiEng+lXyFvs39NC5tQDN0Jo9MMjNWINWQpHl5E0EgGd41glt2SbekiKajOEWHcq7CwksXYMd/McGTqZYUh548jALMiAEqYHTfOLqpk23PMDNaIFYTpZKrhBIfqWYnDKE6uJKr4JZcfOcsGf6zgBkx6b6wi93372Gqb3pWUR0mVXdf2HVSwF95usz27++kOBHaRwghmBmeYccPdmG/y6atrZOHHvrRL2y853AO5wAH9L38S/wfmdTGycgacto0h2L7GNEHeXvlva+JUvhUaAs6+e3SH/K8uZV/iUVIui6ZYoakHv7wNHk+I5bBqG1TtvbxXPzfGNdGkUh2mluJqTjNspUm2UKTbGGfsZtH7AdYVO6ZG3NOTBEQYBN2VFRE6L+ZlCnG9GECgnnWWlc4r6MoijxlP8ohYz+2irDQX4KpG3QuWUDHg91s/sdnePqvHmfn17fS8/EVfOhdH+U29WEOOwf429SfkQ1q5wX61skGDhh7GND7aApaKIoCWTlfXJJSaUbFMDltigbZiIlFUqYY0Hqpiio+PgYGujJQSHRMfMoEwievTTMhxnFEBUyIyTgL/Z65YzvPvZgdxlZ2mlspiDy2iuDhEpdJojLKNuNFWmUb76zcNm9MDbKJ851LeDDyQ1IyRVwlGdYG8PG5wnndHLl+DGmzhk32xfzUf5ARfZhatw5d6KStWqataTqDbuplI8Vige9+91t84Quf5vDhg9xwwy186UvfZvny1S97vxgYLPdXs9x/+fedCP/5Z/D370FfshRhhL8/qlzG37IZf9OFmOs2zL3X3Ca4tnINCTPBD3L30NOzHFmnMPYKrOcE1Zv+Y+wOToRhhKredBryeUEqpTDNY2re8P/7fmjNUCyGyl/DAMMQDAzAX/+1hhChnYSmMWdx5jgKEAQBJJOK9esV/f2C1GwJVSzCk08KXnwxfM+aNYqLLw5tKk60sPjPgv/xv/4EPZZFv+CjGAICBQ/1agwWFb+xRp7WNu3lEIvF+aNf+2tS5Trufu4rDDt9fPDKj9KarcfxNMp1KUTk5IDglyKajrDhneuYOjJFOV/FTljUdddi2AZta1rD8GIgURdHaILSVBlNE8jguGhFKUV+eAYUJBuS3OE30G+maNRKmKUpnKzNvkKcr+zSuKhZoWkaTw7CLQsl13Ra8wQBmZY0Hevb2fuTfTgFh7oFJ8wfmlJMHJ5kdO8o2fYM0UyU9vUnZ4kEEp4ZESTNkAgGMDToSsGhPOydFmxsPLv7Y3D7EEef7iWaiZJsTKKkIj80w+7795BqTBLNHGf1lVL88R//Nt/85pf5+7//Im9+87vOal9nCmEY2G++FfO8C5F9vWBZ6EuWoqUzv5D9/VfFOTL4HP5Lw+l6PUJJrOFHMcpDKCOG0/F6qgvfyil7rHl1ZDDAat9moW/wpWie56wqEaUxqQeMaD45IamXGiUh0REoERKtAnAF+AqKQuIIRUQJOgKTg4bHctdmv+FyWPdIKg1DhcFuR3WXy9w4HYFBQUiesMpsNqq4QrHCt7nCjdEsj399PRTDus8+ETq6GQiapEGHb6Bpgg+Vshw2XJ4zq4xrAUUCTAXt0uRSL8YlbjQsxpUiO/QU8XSFoDBOTEqkXYNXu4pSNElcCSKzZsaNUme5b/OMWaUkJbsNBw+FrqBDmhS1gG9FZ6gvGbRIgzqp0xWY7DIcUoE2Z2UxqPs0BgZLfYt1foTrnQQ+isicnnM+hjWfr0XzHNY9jtHLHYHJezPtrIjUopWHkfE29PIQWmUcpZnIWDNPdZ7HV9sXYzrTNM0MU0ov5EmrQkGT/Ho5e8qAPKUUg4N9vP71b573up9dTnF9D1o5VJjLWAtoIU0fecutqOtuRJXLiEyWN8R38DW+wH5jN9G6OFadReVAmRurbyLCKxciL4WYbavLWKc29V/ur6LRbiFnT2AenUCMjSPNClabxurSlQT47M1vwxQWQmgMVXrpLeynKivU202h6tcvIJXElZUwTdxIEjMSzHjTWFoES4tQCYpE9TgKxXClH0vYCKHhSZeRch8Zq56MVUPBDy0YauwGjJex6bB0mzq7iaPFA1SDCq6sAhqloIApTMadYap+GUMzGKsMspPn6UmvxtIjrF9/Hp/+9L+wcOESxsZG+Lu/+wve9KareOSRLSQSSaacMZygSs6bYtIZC1UpeoyEmWJhajmVoMh4dZiYniTnTVH087MktCLvuVSDMlE9gW1EKPuFk8hgW4/SnliIX/CZcaex9QiLkiuYdifIu1M0RzsoB0WKbp6iXwi3LUpYWhXIzpHkANUATE2QNE/dEnBek+KFMcW+aUEmopAKZlzBmjrF6rqzK97i8QRvfOOt3PvAd1h6dSNT1TwZO4UvfUZKE9TFMnSm5qutr7vuDXzve3cwMNBLW1sn28d3M1Iaoye7CE1o4cJXpImDxaPslQfp0kMy2HfD2c/4oQkmDk2Sbk5hxSx8N2CqL8eu+/dy/q9sRDNO7808sHWAySNT1HbWzL0vkowwcXgSr+zhu2FIm/QklZkq5e1DxGpidG3qoH/rIPnhGaQvMSIm7evbWHTpmVu0nC10Uzse3qHCZ4mmCXRDo21tKxOHJ5nunZ5t8VNhAN0xf1/F3DiTDaf2LH6t0L6+DStq0r9lgOJ4ifpFtbSva6PxFGrk0b1jFMaK8zyas+0ZJo9MMbxzmKamFkZHR36h4z2Hc/hlhkLxiP0Ak7OessdqpCkxyVPW41zkXk570PUL239KpVnnXUJrMImQZSoMhXZRQsdQ4KMo43Og5gWmtUmW+isZ1PuIqhgCjf3GbuqDRnR06mQ9h4z9VKgQmzXkzKgadHRGxQhHjUPktKm5477EufKkjAUTkzdVb+Uy9ypGtWHiKnxe/t/EXzAjcqSNLOf97kUsu3UlD/ztD9j+P1/k6s9s4PWfeAu33/oRUjKNL7x5SgUPFx0DW9mkZIakTDMj8vOUuHmRI6ESZGUtTbKFpd5Kvhe5g7yWpygKSBGgK5OGoIHF/jLagy4etx+CAGa0PK5wMJWJP2tAt8/YxbPWk1zkXs567zzGqsN8OvFXVEWVmIpTEQJfeAzrg7jCxcTkBfM5lvor5ym5b6m+naRK8pT1OCWtSGPQzBXu67jIvfykaymEYFlsLe/gV/m2/VVKdom0qGFMHyOpUizdtpJPfunj3H331ymXS1x33Rv43Oe+werV616z++ml8PfsQkRjc0QwgIjFUJ6P7DsCJ5DB2iBE9Civ676Bew/cwyfO/yPQQMUU+iHBqeUn/75Ip2HVKsXoqCAaVUxPC4pFcJzQniGRgMOHQ6LX90OFbiymiEZD1a9S4VS2WAxtJDIZRaUi8H2IxxVSCpqbYWhI0Noa+gVXKvCv/6qxdasgHldoGtxzj8aePYpf+zX57xoIdyZ44YVneeDeO1j5/i+wsiUxN3XP2Ir907BjQnB+86u7llbM4poVN7NqxTr+33f+gj+587d5/42/xfrGS4gJmOqdIl4bf1krAwDd0KlffLLUXGjipBotlo1S01XD0I5hrLiFpmuoQFGaLBNJRahEoxwqJUniYWoCoRTlqo9hC3wJuqZYklUMl+DHRzXW1AfUnyID3Sm6p6yXNUOjWnBe9nh8FXYS2i/RQekClBJU/bM/34M7hhG6RjQdkr5CD4Uek0enGD80QceGcC6glOKTn/w4X/3q5/nUpz7P29/+3rPe19lACIHe3one3vnKb/4lxTky+Bz+a0MzqS58C077tWjVCaSVRkVePvmyubmVHTu2vKrdDekBB02PBYE5Z6HQGhg8ZVXIzZK9Foq40oiiURQBQoEvwrAzXWm0S40u3yRhaZSFYqVvYynBgO4zoQWYSnCRG+Xt1SQe8LVonq1GNSSLETxsldivu3y4kqFRhgrO7YbDgPApa5KICoPY8rNE9cVulEWBxYrA5ionzk+tEputCiNawCHDJacFjGo+m7woy4e3sHLXV1m6/HK21HXRXcyRqIwxk99JPrKeK70s0VlPX4HgzdUkZSH5iV1iUpOklUZToLPCt4kg2KO7bDeqtLhhe/9NToJxLWCP7hJB4KDIKJ03OIm57ZoIzNMoWxSKH9hFDuguiwMLE4GP4qDu8Z20RXfnTcQP3YmR24tWGkK4eWSsCS/Tw6O1zShN0FkuIJnEjncRLw9wUM4weHA7S+xu3JbLUfbxEIvBwX6mp6fmeaHNQdORiVP7G4lEMmxxA9Z4G/j14u/xnPUzBvV+Wha207S7mU3eKxvrn4hABVT8EoYwiBinqAxm0RZ08sby2/nu8F8xXj2In/SIeAZX/CjNtQfHKb9LZzLqsm9mO6508KWHE1RxggrjaoQau4GkmcaVVYp+gQa7hUpQohKUcYIqth5FqoBqoCGEhlQSX3rEZu0dYkaSQHpMOaMgFPV2Mz2Z1S9LBB9DU7SNAzM7w8/OQkfH1Gw86ZG168lYdSgkw5U+NKGxPLOBq666Hk+6TFZHibRb/M0X/pFbrriaH/zgbl735hvYOb0ZX7rU280U/RncoErMSrI8swFdGOTdaXzphqv63kx4DQFNGCglkSg86RD4HsFLLCqUUhyc2UV/6RC+9NA1PQzew6c9voCCl6MclIjpCexIFN21sHWbBcllqK467j+aZrISkLIC3ECnv2iwolaxMHPqc1QbhQ+tkjw2INg2LjA0uKZDclmbIvoqftHf9a7b+frX/4WGURunU9FfGEYXGs2JOja2LKX+JcGLV155HaZp8uCDP+L22z9CwS3Onqvjz4V0cwr7gMlYcRw/6hO4ATOjBTJtGUoTJWKZKFYslCMYlk66JUVuIEduMEdN5+nNysYPTGDFrHkFcLw2TiVfoX1jOyO7R7ATJQLPx6u4+E4QhsnNVEk2JGjsaWDBRd3EslFSzamXDcH7eVGeqlC3oBZFqKjVdJ1kQwK34hJJRTj/VzYyfnCCw08dYfzgJNIPcIoOumXgFByEJug8r51U88urpX9eCCFoWt5E0/Kmk5KnX4pKvhK22Z3wHiEEhm1QnChRV1dPqVSkXC4Ti53+GXUO53AOrw5Vqhwy9lMr6+ctlmdVDWNihAG976zJYImkXz9KVVRoClpJq8zLvj+tNFoDg/2mTdpIUfDyCCHIGQaW51JjBeyK5GgO2hAIdGUgECRkkoJWIK/lqJG1uLjEVXyetcVSbyVNQQsP2PciEKRkmopWoiTKjOhDTGrjc1YDJ6JW1s+9rlCs9jbwpPUIvvSJywR9HUdQ/wwrfm81+T/LccfHvsKPPvtdLv3Dq/Bv9UmSIkoMH59e4zCd/kIW+EuwsbnYvYLvRr/JCBoZmaWoFRnTRrjMuZoWGR5jc9CCjU190IipmxQI/XozsoZV/jqmtUk84VEX1DOj5YmoKLayScgUSih8An5mPcoF7qU8bz7NDnMrgQjQMVjgL0ZXBvvMXaRVGp+ADr+bZ60nqIgSv1/8szmS3MbmBueNXOlcT0kUSan0K1qHXBe8gSanhWesJxjyBjDuybP3Szu586mvUl/fyAc+8Ju8+90feM39PE+EcKbRC0ex7CmqwamILAXG/ONQcUDCGxa9ifff9y76ZnrpSHUiXIFMnYG/3r8TbrpJMTam2L8/tHTwfUVHB3zgA5LpafjUpzQKhZDM7esTsyriMN/KdQWxmCKfDwnhsTGB54XbCW0lQrJ3wQLFW94iqamBZ58VbN8O3d2KY8JXz1Ps3y/YvFlwzTX/8ST5MSil+NM//Tj13Wvpuep98zRclg5SCUbKr3689YvqSdTF0XKdfOrXv8zXHvws/3TP37C0/gF+5ZLfYGa0SCQVoXNTBwsu6jorm63TQQjBkisXUclVmeqbRqjQii+WjWInI/joYdA9AUiJEoIyBqYOfqDwZbiQ0RiD/dOKQ3lBfezkc5BqStL3YoAM5Jz1g5QS6ctX7LKzNFiQgedGBHXR4/kkeRdiJrQmzv6cu0UHw5o/CRGaQAjmOtwcx+ETn/gwd9/9Df76rz/Du971/rPezzm89jhHBp/DfwsoK0VgndmkuadnBV/4wj9QKMyQTJ7dRPuQ7lFB0XWCrUAEjYW+RUQqfAF5EZKiEkVOC+0RDBWG0GWUTlbq/DRSZq1n86hVJlChYjaqBBOazg1OnPdUUwgELxpVdhgO3YFJZJYsbUBnr+7yrFlhlR/hR1aBuyMFJjQfX0BJCMK9CypKscK3sGcnDU9aZX4UKSFR9OoeeSHZb3gc1D02m1X+ePRhFgUe7xmdQFlp9iXSOPE06coE106OcbU9P5WhRul8pJxlWgTsNTy6A5Mapc15AusIctrxomxhYPEb5SwvmFWGNJ86qbPOj8xZVLwSxrSAfYYTWm7M7sNA0CYNjugeh7qvZVG0AXPkZ1hDjyOiZbzGC6jE6hm1I6Q9F6EClGZijr9ApDTAYKaFXJAneuDrmJPbKK367dAaA9i8+WkANm06O+L2pVgU9LCo0gPA+OJRnn32yTP+rFKKkUo/fcWDlP0imtCpizSyMLn8lKSwQHDxzjaav9XI9p4oOduha6qOjsEEkUP9yBd2ol+6nNHKABmrlqxdB4DveEglmfGmaIl1YasoIOhO9jDtTjBYOjKr1hXoQidmJFFKUZUVlFK4yiFmxGmItiIQTFZH6YwvZmlm7RxR+EoYqfQjkaTMLAhmlccwVR2lIdoyL21aoRivjlAJwhatXdMvkHMn5+wrGtsb2HNwO0tLi/GlR2b2OJNWmopfZtqZYMvkU4BCoij5RTQqHFeTiOMewAp8fExlkXMnSFmZuQC/SWeMvtJBbD0yNz4nqNJfOkwmU8uS9Gp6iwco+jNoaDTH2lmcWknCTNEYDRguD/P0iKQ4rWHrsKzG5NaeNIZ2euuShhi8bYniLYvDAMhjhZwbVBmvDlMJyliaTY1dj6VFMDTztNdgzZoNtLS2Eewv8YZrrqLoepi6QSYSpdauoz4y324nmUxx0UVX8OMff5/bb/8ItdEaQOBLH2PWqiReH8cqWDSNN1AYLaKbGs0rmug8r4Mtd23Djs//vhtWGMrhvYLpsdBDte2JUEqBEpSnK1QLDrFslOn+HLqpY0YMfDegkqsQuD7LruuhecWpfaVfa5gRE83Q5pHbSikmjkxiWDqp5hSp5hRNK5rY9t3tjO4ZpTRVxi042DGLZTcsZdUbVr6mhLVTchnZPUKuP4cRNWlYXE/dwtrjli2vMBGKpqMoqeaRxkopfMcnXhejzg4VapOTY8RiXa/ZuM/hHM4hhImJrSIUxcy81wMCNASWOjvzz2FtkLui/8YBYx+ecMjIGq50ruNa5yY0Tv2boSO42o0zEPWYjtdg+VGmZZUAxeuqES5I1POUuHfO8qlW1pGUKfLaNAYhMVwWJXLaFFdUXzePrIwQocdfzuP2Q2joeJpLUmVY5W2gKsq8aD7Htc5NL3tMAsHbK7+CrWy2WpsZNPoY1PtpClq4qP1y9H/RGfudEX7yv37EfR+4h8T/SHL0fYdova2DWEeMtqCTd1TeN2ercI1zI4HwedJ6hGFjkKiKcY1zAzdX34ZAUKHCVvMFlnurqVeNlCmxy9zGiDZMXp9mt7F9Lh9iRB/GFQ62ilARZTzNw8ZGEjChjfGM+QTfjH8ZqRRtQQe7jR306YcpiwoaAkc4VEWFAaMPgeCocQhT2Xyo/FFS6niHUYQIEXWGnWcKtBc0pn44wffv/Dbj46NceOFl/PM/f4Prr78Fy/r5rZSOWX4YysTihO0phd3/Y+zeH6FVx4llcjhNIxRKFn58IUop1NgoIpHEWLYCCIURgfQQKywiD2lcX74eS7e498A9/Fbbb6NMhbf+Pw/hWVcHv/Ebkl27BBMToRp45UpFdrac/YM/UHz724KjR6FUCsfd0ACpFBw8GIbQ5XKCchmkVGHkgArVxIsXw5/8ScDSpaFyGKCvL1R3RiLHz4Fpgm2HhPCrIYNHRmDnzlDV3NgIq1cr4idbPp817rnnTl544Vne/39+wrg0OFHNfSziI/4qLCKOIZqOsOKm5ex5YC+FsSJvX/NBOkUPd+34Mn95/8f44E2/y7roRex7aB9W3JwLV/t5kWxIsuk9GxjfP04lX8FK2KhAsvfBfajJKerNBgZEnFghh2dUKOaKVPrGEMURxncN87OgROB7TJY8vnS/yz22h+d5eJ6LpmlEozEMzSJ3MIfYoZPJZonbSXTPpLO7m9pF2ZcdnxBwZbvk4LTGvmlBNqJwfCgHcEWbpOtVaBCynVl6n+0jUX/cBtCreghdI1GfYHJynNtvfxvbt7/AZz/7Nd70pne8mlN7SqhyCVUsIlLpM7L+OIf5OEcGn8MvHdav34RSim3bXuCSS6582fcqFB7hF0VDoBG2wY1oYWGXkVpI0gpoUCZvr9j8cJZsrQiFgSCYtTyoUQarPZu00unVPa514kSUxvNWhVE9ICF1XleN8zonMaf4GNLD0LfICUW5hiChNF40q7xgVjmqueQ1iSaYLV0VQgkcoSgKxXftIhLFtW6cJ60KcSUY1iSugDZpUBYKKcBBMewM0GElaHSr/O6h3RyOJygaJi2j28i2x3E7N/BSGAjW+xEGdJ9apc2NPZgl2Rrl/MdMkzS4yXl1rc8eigAw1XzSwlQCX1N4AryGTXgNm3A6byKx5f8g/BJWkKXWdei3dBqFDpqJXuilHK1DM2IkzVr8zDKMqZ1Yo0/hdNwIwObNz9DdvYi6utfOaP7CCy/j61//F0ZHh2lsPLWv9YkYrw6zJ7cVUESNOIHyGSgfxQmqrKm9EF2cTBoGRw7RNKgTS62m35zE0iLIiI+MR7H2HKJ6URdKKWJGeB2SZjr0tQ3KVPwSOXcCgPpICw3RFhpjbdRGGtk59Tw5d4Iau4G4kaDkF+kvHcbQDGrsBtJWFlOzUEphzZKjZ0oE550p9uS2UA2qsypiRSB9IlqcalBmygnHFDeS6JqBqVlUg9DTd6jcx5QzTsauRRc65VKZkYFhZNJjxs0RMeYnUFT9MjPeFKZmkbVr8QKXnHSpBhU0oaOhE+qBJSBQSKRS+NLj4MwuRiuDNMXaWZRcwZQzRqACIvpxYt7WQzuJSWeM5dn1NEZbKfkFdGGQMI4rUofL++ip30NrKk3RS2FoZWqiU1TlUiAMbQtUwJQzRtGbwdAMau3Guet2IndX9GbYNb2ZvDeNUopKUMSXPikzS9auoy3ePc8n+hiEEFx6ydVsefE5FqWXkHPDttwau57u5FJs/eT0jltueTsf+9iH6O09zJrmFXSlO9ifO0xzrAFNaIyWx+loaefdV76NGieLYesk6hOoQJGoi5MfnpnXllfJVYgk7FMGlp2Ixp56hnYM4VY8TNsglo3iOR5WwiJRF2NsH6FKwgg9oWUg5/6t6Rqadmb34muBxqUNDO8aDo8tHRanxYkSdsyiblHd3PviNTE2vms9o/vGKE4UMW2Thp4GUo1nZwDdOwM7JwSlakB2apKG0jQxW6OmM0tNZw3VgsPW72xj8sgUmq4hpaT/hX4WXbaQhZcuOCPSuXFpA33P9zHVN02yITEXlBJJ2bSsbKE6WgCgUJh5hS2dwzmcw6uBgcEm7yLuidxBWmWJqwQBAUeNQzQHrfT4y894Ww4O/xb7IvuMXbQFnURUlAltjO9F7yCpUqe0FjiG1b7NB8oZnrAq9Okm3SLDBV6M80UEgaLbX8hOcxtL/CSWsllZWctz5s/wTI8RbRATi03eRVzlXHfStouiwDJvFfWykUD4xGUSE5MDxl6GtcEzOrakSvGeyge4wbmFPcZO/i32RTJBlkG9H4Dsqhre+O1bObBjH8nPJ/nJZ37Igf+zl/VXn8fr3/MWOq9aMDdDNjF5ffUtXOZcw6Q2Tkql56mTq6JCRSsTk3FMZZImwwbvfPr1XvYYO2iSLSz2lzKiD1LQZrCUjYFOUVSYEXmiKsZecxfCE/zE/iFKKRYEi3ClQ0EU2KvvpKDlsbBQhEpuTei0Bu1M6w5PWY+SUik+WP7oGftFSyl58cXn+NGPvsd9932P/v6jZLM13Hzz27nttl+jp2fFGW3nTHDA2MND9v0c0Q9iKYuN3oVc49wYBuGNv0Dk4B0ozcJPL4WEg1V8jNTIc0wcyCN9C5FMYd3wBlRXF0cL+xku9+FKh1g0Qfc7FtJ9dztX1lzND7d/j4+2/A7VmyT+qtds+K8JYjHYtOnUJOymTYqWloDNmwV33KEhhGLVqtAmYnBQ0N8P1aoiGg0TVjwvJHbr6qC2NvQYNk8gTC0rJJBfCt8XRCJnr5jetk3wzW8KxsfFrG+xYtEiwa/+qqSu7pU/fzpUKhX+9//+Y66//mbedt0VfHGnYqQUih2kgr4C1EUVq87S/uylqF9UR6b1fKb6ppk8MomSiouvuJwvPfAPfPo7f86Gnot48/r3kXkxQ9uaVir5SihisMJ8B908s3Djl8KOW7Sta0UpxfDwIHv37uG53GNsefYFjo73knPzHHVz8JJuw52JLFYkgdRMdMNkX8IgaluYpolhmEgpqVYrVCplSsUiM/k85Wpp3jbEPwkaG5tpbe2gvb2TtrYO2to6WbJkGStWrCGZTLE4Ax9aLXlsQONQDjJxuKBZcUmr4tVoEDrWtzF+YILJI1NEM1GkH1AtOLSsaqYvf4hfe8e78DyXu+9+iA0bzn9V5/SlUI6D+5P78Z55ElUqoaUzGJddiXX51Qj91NdNSti7F3bvDr9LCxaEixvRVxFU+N8F58jgc/ilw8KFPSQSSbZsef5lyeDdepUf2SV2GA6BgFW+TX2gcUT32C9cTBWSwxkV+mReHcS41I1R1RRbjAp7jYBAKJJK0CgNykKy33DZ6EXwgUAo3uIkudqNMTMbypZU88mKiAp9dRVqXpHnosgJybQIaJI6W1GYShBBMC0kxmyoW1HAc2aF560qn/fzpJRGj28yZQTEVejJG1GQ1yQppdGbbmVVfhsJ2tBRLC4VIHAwKkVKkdNH8270ojxnVtmnuzRJA0loi9EZmKz1XruY2gZp0Ch1hvWA7uD4uRqbVRm3BMcfaUFqEZVF7yRy+DtY03u46qjDl3ouordxNfVOmbJpcjRRw4pijqWFPGgmSo9iTO06gQx+mo0bLzhpHD8Pjt1zP/vZI69omq+UYrB8BKkCMnZof2JiYQiTKXecaWecush8pWPZL9KfmuHojS0UmhMEvolW9Yg6ihYnj42HoRnEzSRytggxNJOGaCtT1XGm3XHy7hQxI4mpWfjSI2LEaIq2kWxMs2t6Mzl3kryXAxR1dhOSgLRVg6mZYTCbP0NEj1Jjn1mc8zGrBVe5WLqNrUdQSjHj5pghR0Aw62PsUzRmqI+04EkHS7PRhM7f//VfsfGy82hrk0yNT/LNz30VXTdYf+1GpArwJRyzGpQqoODn0IRG3ExgaGZ4/LQyUunHky5CCITSiBpxdKFT8csIIbCNKHV2E77y6SsewJwNWTvVBEwIjYAgvGaadZLHsxNUGSr3YutRsikLCD2Sy36EoXIvbfGQoNs9/QITzghShVGUET1OT3o1jdHjKn2lFEcKe8l7U2SsOnLOBE7gEEifAtNoQqPg5VBIWk6h2Lz00iu5886v0iIX0NOwBoHA0iKnJQhvvvlt/OmffpxvfONL/NEf/W/ev+Kd/OjIT9g7dQCpJEtrF3ND19UsrOmef04MQdcFnez4wU6m+qaJJCN4VQ/f8Vl4STfx2tOTwb4bkB/MU52pMt2XQ0mFbmqkW9OsffNqsh1Z+l4YoDQZ2lAopXBKLkoqUo0pkg1xpD+/6PYdH4TAsF5dsf9yaFzaQPcFXfS+0E9pugwK7KTN4isXkWlNz3uvFbN+LkXKU0OCuw9ozJQDarbuId43SEoP6EwpYjGTrgs6QcDI3lF0XaM8VUbTNYyIwcEnDp8x+RzLxlh1yyr2/3Q/uaEZUIpUc4rFly0k05pGjP3ibDfO4RzOIcRVznUMawNssZ7HwwUETUELt1beN8/X9pWwz9jFYWP/nB0CQLNs5Yg4yJPWI1zoXnbSb1u/fpRnzZ/RZxyhNmjgYu98frWyCn2eilhwc/VtTGkTHA72Uz9ZQ6JicYO6ngazhfpUK53mQnr8FRgnTEMVigltDB2DqqiQUEnE7MK/QuHjkVWntoErl2F6GpJJ5kK0ILSPaA86mdTG2W/sIZhV6NpEaPPbya7J8vt/86d86k/+mXvu+Tbf+Ma/8Ou3vYfm5lbe/OZ3cs01N7JhwwUYhkFKpUkF6ZP2nVJpGoNmevXDZIJQkWerCLVBPevUefx28Q952n6cZtlKXCU4ahxiWkyFIb0YmFgkVJKCNsM260VWeGHgmqVsFvlLGNB7KVPCVlF0NBQQiICcNk1cJegMutlhbuWofoju4ORw3WPwfZ/Nm5/mhz/8Lvfffw/Dw4PU1TVw441v5MYb38SFF16Gaf4cMsxT4JC+ny/G/pEpbZI62UBFVPhB5C6G9AE+WPoosZEnEdIjSC0MP6DFUKuuxc48R2rFWqrx9eiLe9A6uzkws5Ojxf2YmoWpWcx402xr3Yz3uwG31N7KRz59G1vfvofu85e8psfwWqJUgr17Q5VvU5Ni4cJQ4dvaCq2tipoayV13aRw+DNGoIh4/3qkGoadwc3OoLp6ZgUJBkcsJxscVuVwYNLd8ueLhh0M1b+OsnfT0NOi6Yu3asx/vd74jKBRg2bKQJPS88Bh+/GPBe97z6onaz3/+7xkfH+F//I+/pqtecfMCeKhPY/+0QhPQFIc3LwrtEk6JwEUv9ob/meyCl7GiM6MmjT0NuOXQZzebreXj7/hznl55FV+5/zP8zzs/wjVrb8as/Rj5A0WqBQdN18i0pFh+wzIybZkzPq6pqUmeeeZxnnrqcXbs2MK+fbuYmckDYJtRWtJtLGpagG03EOhZSk1LcNduIlHTTCLbiGXbBEqQNOFNiyWXt73yOS7PlCkWZyhWCwwO9jMw0Ed//1EGB/sYGOhj8+ZnGB4eIAjCOUlX10JWrVrLypXhn5uWr6WxofEkEtgN4HAeqr6gOXH8WlT9MLg6aR0XpaRb0qx72xp6n+1lqncaK2bRsamdJw48wF+89Q9ZtWodn//8t2hpeW3U1wDOD76D++B9aOksWjqDzOdwv/2NsOPg6pMXGpWCe+8VPPighuOECulHHlGsXSu47TbJL6u72Tky+Bx+6aDrOmvXbmTLludO+54fWgX+X2yaAd0HAZYS7DIcNKBWhkXvlOZTFYp+oDEweMqssCCw+Eg5y+ejkNNCgreKJKE0Ckh6dQ8PRbM0aJwlLjNKJ6N0PBTPmhW2GQ5VoejxTdpnvYkHNJ9WGUacTQmJKxQTwmdIDxhUAg+Qs+01vgBNQVWAFKCp0H+oV/fRAY2wrDj28+KhMBRYCHZ0XMSNQwfR8weQ0SaQLnp5CL9mJV7t6ROBm6XB+ytp7rdLHNE9BHCeF+FGJ05WvXZES6TYz00Te/m32hQHrTgxK0tJDy0jrq3GiZ04IRECt+1q/NpVGNN7WY9k1OrmkUSMXq2XqBtjY36SdwwexpolRYXyULNKyHK5xK5d23j3u3/1NRs/QH19I0uXruDJJx99ZTIYRckrYOnzCXVDM1FKUg3K814v+0W2Tz3DeF2FGTOBEhLNsDE8hYfD4NIsDYlOVmY2MVLpZ8IZJaPVoolQ0T3jTeMFLoGUOEGVvDvJcLmXCxquIWmmiRtJ1tZcxHh1mLJfwNIjZMxajpb2M14dniWXFbYWZWFy+ZyC9ZVQ9GeY8aZJGzUU/NycT68kIJD+LOkb3rklrwBqiIgRY0FyKZZmMzk6wf/7w7+jkC+QzqZZvn4lf/Nv/0Aka1NjNzBWHcQJLCzNxpUuFb9M1IjPU/NGjTgZq4aokWDanaTsz+BLn0D5KCRRLU6t3YCpW5hYSBUwXOmnM7EYAF96c77IgfSRSlJjnV42UQ3KuNIhYbwkjE6LUPRnqAZlJqujjFaHSJvZ2WuuKHg5DszsIG3VEJm9Vx1ZYcoZJ2YkCJRPwctjaAYRPYITONh6BE+69BUP0xhtP0lNfvHFxxYoHuWNb7z1Fa9XLBbjbW/7Fb71ra/wsY/9T1oSTXxg5XuYdnL4MqAuWnNaRXjziiY0XaP3+X6K4wXitTHa17XRtr71lO8/huGdwwztHKFlVQuB41OZqeKVXMy4TW13DenmNG1rWsj15XArHtFUBBBY9SZNyxpDkrgmvN6FsSJHnjnKxMEJENDY00D3hV3Esq9NJaikQtM1eq5dQtOKJnIDOYQmqOmseVWBcJVchdF9Y5SnykTSURp76ueI88kqfP+QIJCKpflhtOEBgtoEY1ikM4qsUeHIM0eRbkBxrIgMFIalo6RC5iTFyRKTRybPWIlc05nlvPduojBWRElFsiHxqpUz53AO53D2iKsEt5d/gwPuHob10LZgmbeKjHr51uCXIq+FC63HiOBjSKgUU9oEHt68lv79xm7+NfZPjGojJFSCPcYOXrCe5q2V93CZe/W8bSwIFvPhmd/hiakHKLozRI049aoRipJoNU5nTTeGeXwKOqD1cW/0bg4Ye8mLafr1XjzhstxbjQIG9F5qZB3rvI3z9uN58PDDgieeEMzMhIFZ552nuOGG40qvkigyLSYpixItsz7GRVFgh7mNq5zraAna0eM673737bz73bezffsWvvGNf+GOO77KZz/7KdLpDJdffi3XXHMDV155HbW18xe5dXSudK7ja7HPc1g/QFbWUhEl8lqeq5zraJXtCAUxFWeZt5qkSrHFej4MCkWSlCk2ehcglWSbuZkZkSc9ey2LooCpTBpkIxoaeS2HpWw8PPLaNAu8xTTLNvYbu5nWpugOjo+rWq2ybdtmnnnmCZ599mc8//xTlEpFmptbufHGN3HTTW9i06aL0HSNg/o+7jO/R0VU6Ay6WeNtIKZ+fh+AJ61HmHpJ2GFaZdhuvsg+YxcXVMZQL7U70wxEMo2xaCnWghuAsLYdLvcS1WNEjXBcET3KjDtNn36Y1/3WLdR8rY6v3PcF/uz8T/3c4/5F4MAB+OY3NQYHQUpBJALr10ve8Q41R0JdcomipiZUCU9MCDZskDz+uMaBAwrPg/r6UAmsVHjvSyl4+mnF3XfrlEoQj8PGjZLXvU7y8MMae/eGhFcsBtdcI1mz5uzI20OHYHg4VE8eIwpNExoaFDt2CEqlV2cXMTIyxGc+87fcfvtv0N0dLmBc16XY0BjQOxNmYSzKqNNaRBgTW4gcugujOACAn+yguvBt+LVrXna/0XQU3dDwqh5mxOTClVewoedCvn3fV7lvy1387Hcf4i0Xv4+bLnsLKMH0QI4d9+7i/Pdtmsu5eClmZvI8++yT/Oxnj/Kznz3K7t3bUUrR2bmAdes2cc01N7B06UqsqTi57UXqu+vmPH6dokM5V+G8mxdgttayZUzQO6NImopV9YqFJ689nRJ2IkafH+OI24zesYSL1ig6kswjd33f5+DBvezcuZUdO7ayc+dWPvOZv53r5Gpr6+SSS67k0kuv4tJLr6JkNXDHPo0jeYEvFWlbY0OjxNbgxXGB4wtakopr2iWrZx+J2bYM2bYMvhtwtPcQf/D/fYSnn36c2277dT75yb/Btl87cZgcH8N79mm0uga0unAAejJFMNCP//hPsS66FBGd/2w5dAgeekgjlVLUzq4rVirwwguwdKngqqv+89jL/HviHBl8Dr+UWLt2I3ff/Y1T/r9depXPxqbpnyWCBQpHgKYUrgg9e7OBRsGQpKSOj6JbGhSF5K7IDJ8o1eKLkCDWhGCP7nBQ9wiEwkVR1T0MBCO6R4cMf+kkirsjBR63yggFhhDsMKosCixe58R52C6xX/cAiCowlKCghTYMUaVhIygKhafCxvYAkIClIIlGoEInVXc2bK0jMJjWw3C7oqZoC3SmtIC62lWUln2Ig9PP4rtTtFcUDenLcNuuwcjtQxnRcAVfO/nRERLhJlNCzimmz7Rl7UxgjjxNbO+Xubw8TH22jceae+itXUhPdj3nU88q/9Q/1DLagDsbgnUtcH4xYHL6MLW7H6bV11CxJhAC4UwDAq9+PQBbt24mCILXXBkMIfn2wAP3opR62fZsgSBixMjPtu4fQ6ACmFVvnoiB0hHyXg4rlkZ389jjeQIh8U2N2qKk2tpGuudKWuKdJK0M3rRL3p0EoOgVqMoKESMWhsEhcGY9aPflt7Gh9lKEEFi6TWu8a95+V1qbmHLGKHg5dGFQazeQMM+wigGUkigUKSuDj0fFL+MrD195SCTW7H1UDUKFriOrrExuojO5GA2NP/m7/8VkdYyMddz/tOjNoAudxamVmJrJaGVoVl2siBhRkmZ6HmHpSgdTt1lVcz5lv8BIuZ+Cl8cNquS9HA2R5nnktqlZuLJK2qyhIdLMaHVolmQVBMqjLtJMQ/T0BKel2RjCxJUu0RO+T650MYSJIUxGKgPYs56/EFo6JM000+4k0844zbEOIAynCLsHNDzpEigfWzvW8xQWNxE9RiUo4QSVk0j6xsZmlixZxpNPPnJGZDDAe9/7Qb70pc9w//338MY33ooQgprIK5MRQgialjXSuLSBwA3QDG2uMH45jOwZRdO1WZKX0HZi1oN36ug0mdYMy65bimEZbPnONtySS7IxSaI+TmmyTLo5TWNPA+Vcha13byM3lA/JX6k4/NRR8kMzbHjnegxbZ7ovh1/1iNXGQzuEM+iZU1IxtGOYgS0DlHMV0s0p2je0z7Yonvl34aXIDeTYds8OZkYLaEKgFPQ938eqm1dQ213LwWnBZBWWZEB7cQwE6FGLqAcjZUFPW5TiRIn8UB637JFqSs4dj+/6FCdKFEaLZzUmTddIv0ywnVK/nAX1OZzDvxcMDJb5q1j2c/TD18hadAwqlIlyfNI8o+Xo8ZbP8/KVSH5s/4AJbZxl/sq52q5f7+XHke+z1ts4z7MWICh7ZKs1LLAWI0Rop6YsxZQ7zkiln0VmaEWQFzn+NfY5jhqHaApaSZIkr+UZ0PpRJiRUgqaglTdWb6UtmJ8I/9BDgu99T5BIQH29olCAH/1Iw3Ek73xn+BzaY+ykXjZiE2FCG0cj7LgzlRkSwcxfzFq9eh2rV3+Wv/qrf2Tbthd4+OH7efjh+/noR29HCMHatRu5+OIrWLlyLStWrKG7exGbvAsRZcEj9gOM6sMkZJJrKjdxlXM9AsESfwUPqx/jCodW2cFg0E9cJSiIGdZ759EctJIT09SoOqa0KRIqRUZlcXEpixJdwUKaglaeth/DEVWEEmRlHSu8NZREEVtG8Id9Ht35IM8++yTPPPMkW7c+j+M4JJMpzjvvYj760f+Piy++grVrN86zTHrIvo97I3dTFAXEbOLHcm8Nt5c/8ophgsDcIvWUM44kIGlmqLUb0ITOAWMvaZmdNxeIqTg+PiP6EEFqAUb+YMhuijCnoVAdA3ecfZVe5PTztMUXhEHH0iH7kgX2iB6l4hfBVLzznbfxb//2Rf7gD/6MWOw1MLR9DVEuw7e+pTE4KOjuVhiGYnAwvFcLBclll0Emo+jshBUrYMWKsBNMSpieDgPkJiZCRXEsFobJOY7ANBU7d2o0NiqSSTh4UPD5z2ssWwY33iiJREJCcMECRXc3Z93+7/uz+S8vWe/VdfA8QRCc4kNngL/6qz/BtiP8zu/80bzX66KhNcTLQS/0Etv9RTQvTxAPQw2NmcPEdn+R4rr/D5k4vfK0piNL7YJaxvaOEa+LY9gGlWmHG9e/jctWXcfdP/sKX3vks/x05w9517UfYsOii8gP5pk4NEnLquO2fvl8jvvv/z733HMnP/vZIwRBQEtLOxdffDkf/OBvcdFFV9DW1jFv30/+81NEE9F59a6dsCmMFckPzbCgu/aMVMAvhSfh2/sETw1pVGevR9KCG7okr+s8bvlgGAZLl65k6dKVvPWt7wFCu5i+viPs2LGF559/mieeeJg77vgKALVdq0ktvZrlG69iwarLyKs4X96pETNhcUYR0RX7p6B/RudXVwasnP1q+r7P57/4af7u7/6cxsYW7rjjx1x22VVnfVyvBDkxjirMoC2Y3w2hZbPIyQnk9DT6S8jggwfD71DnCT8j0Wj4Z+vWc2TwOZzDLxXWrTuPz3zmbxkc7J+XkKtQ/NguMaUFCKHwCakUJcJwOAHkhMTXFPXSIKk0pkXYDN4hTQ7rHluMCgd0l22mQxzBDBJPKGwFAo3FgUFrYPADu8RS3yaldA7qHk9ZZRqkTnpWSeuj2K+7rPAtPlGsZb/h4qOoovhutMAq32abWaUqFC3SoFfzcIXCVKH6VxMQk6HytyIkttJIqLDUS6AzhWRcD0hLDUMJ4kpjjWfz992LGVrYiQockkpw2cgR3rH9M1jVSdAs/PRiKj3vJUh2nnReBYJAKJ41q/RpHhmls86LsCQwfy5iWDjTJLb8DXrhIAiLVZUJludHQT2Ks2CKSs9tZ7QdrTRE0+G7aZ/ciT5zBM2ZQkZqkXYG9AhO27V4DecB8NBD91NbW8+SJWfuwXemuOSSK/nSlz5Db+9huroWnvZ9QghaY13k3ElK3sysZ3BAwcuRsepOsmGYrI5iaxHKsogWT6JbSbRKGV/zUDVdRFIpAjMsRJJmmnV1FzNZHaUaVNiT24LpW8SM4+b/th7B813GKkM4sjqnRH0pdKFTH2k+KWzsTBE3ksSMBCW/QGOklZJfJOdMUPVL6OjEjAS6ZuDN+vqmzDRdySVzZG5XYglFb4Zpd2LO2kIXOt2JHuJmkuWZDbTFF1DyC5iaRd6Z4khxL2W/iK1H8aRL2SvQHOsgbWbJWDW0xDqRSpJzJ9k29Uw4eTrhFnZkhYgeI24kWJ7dQE2lkfHKEApFfaSZplg7pnb64JWoEac+0sJA+RCaCIl9R1apBEXa4wuJGjECFaCUnAsOtLVQ7QrMWXxAGLSXsjJMVkeJ6nGE0JBIAulhahaWHsEJKujCwBCnllpccslVPPzw/Wd8zRYvXsaFF17G1772hTMmkE+EEALDPvMSxHd8NGM+aSxEaHUTzNo/6KbO0tf1ULeojoOPH2JmeCYkn5c3suSKRURSEQ4+cZj8UJ7a7pq5CXE0HWWqd5ojTx9luj9Hrn+awJNYcYuWlc0sfV3PK1pJHPrZEQ789AAIgRUzGd49ysSRKVbfvIKm5S8fWqeUojxVpjpTJZKKzKl+lVTsf+QgxbEidV21CE2gpGK6f5p9Dx/g/NsySBWeQwGhGdqxMDiOB7AIIeaOVUmF0GfvIV+iaWLu7z8vXsuwu3M4h3P4xWKJv5xl3kq2mS/SJJuxVWTWpkHnUvfqeTXbtDbJUeMwjUHzvNebg1b2+gP8YGSc+kqGuigsr1VEjVDNGQacHn9uCyEwhEnRO+4rvs18gaPGYRb7y+ZsI853L2ansZW13kaud26m019IjPkT+3IZnnwyJIJbWsLXYjEwDMXmzRpXXx3Q0AAlUSKlMixzVzGiDVEWJWIqTpUKCXX6Tg1N01i3bhPr1m3i4x//n4yPj/LIIw/w8MP3853vfIvPfOZvAYjHEyxfvpqVK9eyfOVqLlx2GR2N3bTWt2MY4fEs91dxqXs1j1sPU9JmqIoqHh7dwSJagnAOMqGPscG9kOaglS3Wc4yKITzhUScbaAna6A4W4Zc9Xux9lur+KtYukx/v/T7jB8ao7q/wvfwdANTW1nPBBZfwx3/8l5x//iUsX74a/TT+mUPaAPdHvo+hTJYF4cKCg8MOcwtP2o9wU/VNp7+BCH+7+ooHOVLciyud8LwJnaZIG0sza8moLNPa5LzPHLPPiqk4bvNlWOMvYOT34kebyFeHUMV+ZlLdTKe7KZV7mXYm6UouQRcGvvIwxfG6ylc+hmZiahbvfe+H+Nzn/o7vfe9O3v3u21923K8IF8wtAnMXIAX+UoW7XsFZNg8FAWzeLPjBDwSPPy5YsAByOTh6VDA2diyYTecHP1AsXhx6l77znZJMBo4cgf37wyC4ZFLD90NriNFRgVKKJUsk1Wro41suw549gslJQRAotmyBajUMi3vHO9RJZO6ZorNTkc0KRkaOf8eUCsewaVNIQJ8tHn30Qe666+v87d/+M+l05qw/b449h1Ydx8+unKt3/HQPxvROrLHnqL4MGawZGqvesJJ9sf2MH5zAKblE0xG6zuvk0FNH+LUbP84tV72Dr97/Gf7PN/6I9oYurll2Cz0ziymXyzz00I+45547+elPf4zneVx44WX8xV98mssvv4bOzpfPXtAtHRnMtypTKgzkPZZv8WpqqBdGBY8PCBpiirQdXp+xCtx3RGNRJmBh5vSf1TSNrq6FdHUt5A1veCsAo6PDfP2+R7jz/p8y9vy3OfLjv0e3IrStvYFizztoWXcjTS1RhIBsBPZPw6MDGitqJTt3buHjH/8wu3Zt44q3fpSOm/+Mx+w4pSOhF3Hy58+inINIJhHRKKpURJxwH6lSERGNIhInP9tPp1MQ4tQ+278sOEcGn8MvJc4//xJ0Xeehh+7jfe/7tbnXS0IxrPvYSjBBOKE2AaFCkjMAqkIRVQKbWR8zAWmloQMekm9GZjikuwQiXLFz9VlfVqWRQdARmDRJg17d56Dusd7XOap7ODBHBEMYzJZUGjsNl9c7SS70QiLuEatMSUiUFgbUTYuACiGZG0fR4OuM6AE5TVIVECBRcwpnxcLA4vdKNVSFZED38VFklE63b3JHdIZxEdAtbQxhk6sM8JCVp722lUuLdYigijG1g+juL1Dc8MfwkvauPs3jy7EcA5pPFA1n1vriLdUkl3ivvgU7evBbGFNbkVYWZUQR0sPIH0RGajFHn6ey8FYwXt79XXhFYrv+CSO3lyDWgqxdhTFzECF9nNYr8OvPw092gQjDp/5/9s47zK6rvPq/fdrtbXrTNGlGvbpI7jZu2KbZGBtsY7opcUxJCCZACiaULwkhoYYOpptiTMcY3LslWXXUp/eZ28up+/vjjEYaa9SMSUKi9Tw8yHfu3Wffc8+9591rr3etn/3sh7zkJdcctYj+Y3DQm+23v/05t9zyzmM+tyG0ANOtMFDcT85KowiVmkAD3YlVs4rRg1AVFc/zMJSAH98XDCOCIYRbRAlU4eIQO0yxayiBWXXp/txOFOEHVMxi5p++rvxPt2OqKhodscXszGwma6fRFR1VURFCIaRG0FT/feqKQcUr4W87HJpnVaCO1VUbGCn1k7PTBNUwDaEFs+S0EIKEUUXCqJp9vhQwUuojb2XQFJ3mSDtd8RUI4Rfao+UBBor7KdkFik4eR1qkjBp0JYDplXGly4JIJ6qioQILIp0siHSe1PtelFiGxJ2x3SigKwbN4XYWxpcBYsaKoh9V6ChCIaiGiGhxdMUgbiRnxxFC0B5dTNEuULTzCARFO4ehBKgOVON6DhW3RFu0e9ZyxPYs0uYEjnSIanHWrDmdr371sxQKeaLRE6vwb775Ft7+9pvYvXvHn2TT5HDULKxmcv/UbBgcgFWyUDWFRNNclWpNZzVVbSlK6TJCERgRA2WG8MwOZVED2hxl1EGSedd9u1E1hWRzEtVQqeQq9D7RRzARZNF5R/9sy9kKfU/2oYd1ojV+ARqpjjA9kGbz/duoS40TC0aps6vJ9GXxHI9EU5zUghR2xabnd7sZ2zmGXbbRwwYNy+pZckk3lZxJZjBDtNYPawMQiiBWHyM3miM3mqcjlSRhKIyXJQ2NNYi+UaTjUnJUOhISt2KjqMJXw+wZp5KrzF7jQhGEqyJEa59foOcpnMIp/PlCR+e1pVuIB3/ANmMzeZGn1qvnEvNK1tlnznmuIlUUFDzhgoRpZYpJZZzpqWp6t7yMwlQdSamgIFmcErx+uUdA8b3bn0tyuNIhdFjtOKVMIGCOf7BAkJBJpJBHVT+n05DLCWpr59YmiYRPpE1PQ10dtLkdPICLIQMsdH0/WQeHPdpOOt2uEz5ftbX1XHfdzVx33c3+vKcm2LbtWbZt81uuH3zwd3z965+f7YwQQlBdXUtdXQP19Y3U1tURagzhRG1qjBoyepopMc6j4gHKWomAGmSB3UY5VyCaj1LKFZFZDzkl+f3Ub6hM/ARrwkK6/vj5eJZ4d5KOroVceOnlrFq0lu7upXR0LDphUmmz/jQDSi+L3MV+MB0KAQLEZZxn9Me5svKKYwo58naWA4VdCBRSRi1CCGzPYqTcTzJQzYbgeewO72RKmaDKq8HBoU/bT4PbxDJ7FW4yRXH52wj0/Rw304Np5cjXn8FEy4sIBFMYUpK2JsmaU6SMaibNUeJ6ClVoWJ5JxS3REV2CrhgsWNDOJZdcyde//nluuOENz39z0obQdwWBR2deL8B4XKBvERRf750wISwl3H234N57BVNTUKn4ysTt232rBSkFnud7BpfL/vX89NMCTVNoapLce69CYaZpxzShpsZX9+q6JJWCPXsEPT2CcNj38LUs6Ojwr41CwffOfvRRhdNOc1my5PmdiupquOwyyd13K+zeDcGgpFgUNDRILrvMO2mlcaGQ573vfQfnnvsibrjhDc9rTkppFKkE5sqchQDFQJTHj/v6UCLI6qtXUs6UcSoO4aowWkBjqneake2jLGxfzIff/Gl29m3hR3+4k6898O98/8kvYzs2tm2xdu0Z/O3f/hMvfem1NDYe297scDQub2Cqd9qv80I6ruMy2jNGaarMngf3Md2Xpu2MVuoW157Utbt5QiAEJAKHTkV9GHZMQ8+0YGHy5NZu9fWNnHnZjexsfi2LUx7poV3se/LnbHvgLtJPXk/6+xHMM65kyfIX07HoXGriNWzbOcTbvvARfv6zu+havIJrP/EIudozKSuSYgnu2qOwJyN5y0qP0Akwj1JK5PQU0qyg1NQhjCNZZKV5AdqyFdhPPAqKgojGkJk03tQkxhUvQ4kf2ZHX2enbB2UykEz6j1kWlEqCVav+77LBp8jgU/g/iaqqas455yJ+8YufzCGDdSkIS2UmuO0QDvrxqhIqSCw8xhUXHUG1p9LgqexUTZ7VTHKKb5OgISgpYAMOYCkeZSl4yChT5anUeioHf3pUmDcozhSSgnBJC3fWe7cgXParNgoSAwV95vkpT+M8K8Qbywm+EErzzXAOEwgwE0QnJZYQIKHb1YlK1Z/cDB7Ty4woLl2ujooAKalP76dowCNNSzlv71akEsFJdKPl9qFPb5tV0TIz/98GigwqLktcY6bZDAYVm18Giqx0AnPI7hOGa6GPPg4IZCDp/78aQAoVpTyBiOQQ0jsuValNbkbL7sZJLpkNGrBrTkOb3IQ+vhF9egfCs3FirTycXsDQUD8ve9mrTn6+J4B4PMHll7+U7373a7zlLbcd2ypCCNpj3TSGWyk6OVShEdOT83qy1odayGSfJaRGCKjBwzyFBaZXpipQR12oad7jNIbbGDeHMd0KwRli3XFtpPSoDtQdZjvwp0F9qAVDCTBSGqDo5gkovmJXAhWn5LeYSklIiWAoAVzpoolDt7DDyd7jwbePWM6CSAdlp4ShBgirh+wARsp97MxsBiCghggTIWOZ5O0sET1OUA3THO6YN4ztZGAoAZYlT6Po5DHdMgE1hEAwWRlhojzq226gzdhoCLLWNCWnyKqq9US1uYVOVaCWNdUbGCkNMG2OkzGn8PCwpY10JY2hVjpiiwFImxP0ZJ6l4OQAiSZ0VN9Jhf3797Bq1boTmv8VV7yCuroGPve5T/KpT335eZ0DKSU5O03ZKaIrAVKBahRx5O9Ey+pmxndNMN07jR42kK6HY7u0rG6iuuPIz11RFTzHpffxPib3TyEUhcbl9SiagmfP7W30w+ZMHNMh1ZJkcv8UdtnGiBqomsrQ5iE6NrQd1Ru3MJ6nkquQaj1kk2F7Nv1ykMld0ww9NkbFNYmOhNkwdRoJL4YWUGlZ04znSvqf6idSEyFSFcEsmPQ90Y+qKrSsa5klbQ+HEMIPoPYkDRG4uNXjFwcEu5NN1NaMow+NUxXSqBEe+YKkeVUjiaYEZsFEqMK355gh1MOpELWd84cynSy8GWnFKYXwKZzCnweqZDVvKL+dKXOCMuUZO4UjfR2TMsVSeyWPGPczqUywX9tN2fUY3/lWyEWRNdtZ6J6B5xhsnxb8/IDg2q5GBor7yNlpolocIQSFmU3K+uAhEiUhk0jkLBl5EGVRotY9eldFLAaRiG8NcXjwT6Hgt/0eDJJbZ5/Jk84j7NS3knSrEFJQsvMsyy6jsdJATk8T05Mn/btVXV3LBRdcwgUXXDL7WKlUZO/e3YyPjzA2Nsr4+Ajj46OMjg6zZ3cPYw+NUqmUcT0X27MY8gZ8taAnwJU8K58mEokSiyVIJBLEYgnaaxbS1b4Ep94hVBdmZccaNnSeT6AhQIw4DV7TEYRtwc4xZY5hexYRLUZNsGFOp5KHx32BX/Hd8NfYq+1iQh0j5VWz3FlNwksiOBSAeyykrQkst0IqcIjA0hUDRaiMlYfYEDmPUWWYRwL3s0vbgYJCo9vMq8uvm/W4dqpX41StZGT6GXbndxCJHgqeFUIQVENkrCnWVJ+NzPrksJQeqtBpCrfRHjsUGPf617+NG298Kc8888TztnjTt/tEsNsIs8LxCugbwVgpsM47MXJtcBAeekghlZLU1PibEyCZmBDE4z4J5bo+eZfN+v67q1dLHn1UoKqCmhpJS8vBv/vevcuWSSYnBb//vSAcliSTYBg+mWWaUCj41hG67vsLHzgABw4Ilix5/mKOiy+W1NX5PsbptKCjw2P9eknzifOgs/joRz/I9PQkP/rRvc+7TvAiTQjPnLUWAfx/exZe+NhdWAchhDgiI2LBaS1MHZhiuj9NKBmilC3hmv53wPVcHMcmmazi4ouv4KqrrjkpIhigZU0z6f4MIztGcR2X/KhfNyaaEwQjASb3T5IZzLDq6pU0LPGT/0wXChaEdY5KopoOzNfcJfAFaZ6EA1mYqggShmRhEp7TZIfpwt6MHxTXHJXUhSSGCiVHUNWyhHjTEmoveS8PbtlLYvM3ST/9Pe555C50NUgomCRXHKO5qYWP3vEfKK1X8/OJOIusHPFYGFVXqTiSbZOwZVKwvuHY16I3OYF5z49wdmwD20apb8C49Aq009fPuWaEEARedQNIibNjG97wICIaw7jgYgJXvnTesbu64LzzPO6/XzA+LlAU34t7xQrJhg3/Ny0i4BQZfAp/bphRhOJZuNG2GXLw+eElL7mG97//L5mampgNhAggOMMOskmrEJICS0hM/DpNkxCXCg4SU5F4AkwpKQiPR/UKE4pLBYkChKXAwidIXcCesZhQ8K0n+hWbrPCIe/4PW5djEJcKY4pL/QxJvFM16dFsmj2Nj0WnWGcHeYkZYZ9qo0hQhULU8xXJ44pDRnE5wwlSIzXeVa5is26yS7Owhc/56ii0Oyo1nsp+1WGVM5fYKIuZVuvZolIinBIhLUxaN5DMCEVVA/AQVnbO6/PCY7dmUe+ps0Qw+OFye1SbA6rNGufkyWBh53yHNz2OsAtIfUa1qAYQTgE30nRkAMU8UMvjfsEwR03roZZG0LJ7sJouwFND6NPb+Pl3fkB9bQ1nnnnOSc/3RHHDDW/ihhuuYuPGJznttPXHfX5ADRJQg8d8TnO4naw1zXhlGEMJYHs2nnSJ60kWRBfSHu2eE5p2ONpjXYyUehktD2HbfruflJJUoIalibX/JQRPKlBLasb6YqoyTtHOo6k6llvBlS4BNYQjbeJG6ogQtJOBlBLLqyBQ5vgMg+/H3F/Yj0AQN/zFSlANzdpPrEydSdKoRhUqaWuC6coELi5Jo4qaQAPqPH7ax4IQgqgeJ6LFGCju40B+F6ZbIW1N4EnPnx8KplciqIZ8b97A/MqBmJ4kNtMu5UmXjDWF5ZqEtAhxPYUQAssz2ZnZTMnJkzBSCBT/XNRlANi7d9cJk8GGYXDrrX/DP/zDX/OXf/k3LFw4N8Xb9aDs+EXsfLbAtmexK7uF8fIwjrRRZj6PJck1RPW5at9QMsTa69YwtHmIib2TqIZKw9J6mlc1oWpHXguFySKbfvgsudE84VQYz3LY88A+IjURFE2hMFEgUh1BIimMF9AMFbNoMbZ7HOmBqimUs2WYUdE6pnNUMlg1NBRNxbU8lKD/RvdlehnKjBLRw7SEmti/p5deZQCjxeAa9Srsgs2+Rw4gPUmiIU4oEZp9n9KTDG8fpW19G7H6GJmBDKnW1KyiNz9RIFobITYT+nZFu6QlKtk0oZGtXk3j+DB1mQmiAYW6xbU0LvN90fNjeYa2jMwSB8F4gMWXdB9VGZwbyzO2c4xytkKkJkLjsvpjhuxNTIwBHBGwdAqncAr/s1HtHfs7KxBcWXkFe7Sd3Bf4td+bM70cLd1NQ9xiQhtmgD46ZRf1Icm2SYWXL4yxNLmWvbnt5O0sIAlpUTpjS0gGDnm/rrLXcV/gV+zTdtPitKGhMqIOEZFRzrCPTujF43DmmZJf/EJB0ySJhE8EDw8Lzj3Xo3HGtSom47yx+Bc8ELiXjdqThNIGK9PLqHFqGJD7GFUGaIt00RFbPO99tVfdxyPG/ezVdpH0qlhvn8MZ1tlHeA0DhMMRVq1aC6w9ofPu4c0G9Z20ldpRhGxj5UF2ZbfMiAH8UVOBWlYkTyc4Uys/rT/Oj4PfI+SFScoqFBQmlXGe1Z7hDPssMkqa88wXHXdOnvStiZ573hSh+Bv2aLyycgNnWeczqPYRIEi3s/TIcDqhQKgeqzJASHpzxA6O5xDRYkS0GGurz563rjmICy64lPb2hXz9659/3mSwuhdwBTJ6GDkUAFGB0A8U1D4PdwHYayXy6Nb59PcLJiYgEBDk8/5jk5PgOAc9f/0lia77HrymKdi711cKNzXB0qWHxorHfdXwD34giEYl09OCSsUnkyuVQy3uU1O+Mr67Gw5mdf2xzY1CwKpVvoXFH9Mh+NhjD/L1r3+eD3/4k7S2dhz/BUeBVbceY/h+v8sz0gJI1OIgXqQJu/74a6mjoXZhDctfsozvfuXr/Pjub3NgbDdtTZ184qOf47rXvJaRkUE+//lP8tnP/gv/+q93cP75l3D99a/j7DUXYqVtVEOluqPqqEFzWkBj1dUraF7dxGjPGHvu30t1ezXxRj/HIZQMMd2fpu/xPmoW1fHgsMJDQ4KMKYjqknOaJC9q9Unaw7GkSvLshILtSWZcACnZoCmSmhB8cYvCtilBxZUYikJ3SnLTUo+aGX3Pvgx8b5dCf94PIo4HFM5u9FheLXlqTFBxYLIMOUsgou2sTV5KcG09j++7i729D+OUxgFJNJhgeOMofWMTmCHJ5FCRfCxA/eI6gtEAnhQM5CXrj8HXS9Ok8s2v4GzfgtLQhIgl8IaHqHz764RCIbQVcwMClVQVwVtuxevvReayiOpalMamo65XFQVe+UrfkmXrVollCbq6JKefLpnHVeL/DE6RwafwZwM1s5vQnm+h5Q6AZ+MFazBbr8RsveK4rvjlchnTrCClnFEvSc4442yklPzgB3fy6le/nlgsjqZpvMiKsE0zmVBdbE9iC4kqBYscjXHFI4TC+VaInPAYUR0GVJspxSXkKYQEFIWHB9jCw/Y3+5H4Pr5C+kWTMRMm96xu0m0GWOBpvNiM8MtAkR7VYlRxGFVcGjyNFbaBKeB3RpGMcBlUbU5zggyoPgHsApEZNXON598lPKDTM+gwdXKKf6wqqVDjqfSqDu48N/R6z2/SK+ER9meLG0iQ0VzOy+VmS0LhlEBoeMG5i4eDjftHjFxJo8lpwvt+RdCLYdVvOKbB/3MhtShusAYl3IBSHkdUJkBo4JSRegxrweWUheQZvcx2zScxlzsBTrODhA5TmXjGjJJSujBDJCrlCd97Kr5odkfZ1BL85PFf8IoLz/iTWER40iNtTtK2rpXG5ia+8a0vnBAZfCLQFJ0VqdOZMsfJ21lUVOJGkogWn7UHOBrCWpSz6i9lX24Hw6V+pPSoCzXRnVh5UmFwLxRSgRpqQvVMVEaI6DFUoVF2S2hoNEc6njc5nbMy9BZ2kTYnAV9R2xFbPPseTbdMxS0eQZoH1TBpdxKQqEJlX34H/YW9M4FtLp50qQ02srb6nOOe6/mQtabYl9+JgiBpVJO3Mzg4FJwc9aFmajRfKZA2J7A967jjKUKlKlB3xOPT5gRFJ0/CqJpdcAXUEIlEkmgixuBg/0nN+6ab3sznP/9J/vVf7+Bzn7sT8JUIjw4LHhwSpCuQCsB5LZKzG+UcUri3sIehYi9RPU5MSeBKl2lrgl3ZZ1lbfc4R6vdwMkTXhYvounBuYMR8GNoyTH40T01H9ayyNpgIkRlKU7+knuxQjqm+aQQQTIZYeN5CnvneJj95e4bwlFKSG81RyVXmEMGO5eKaDkbEQCiCZHOCRFOCdN80qQUpXMVlKD1KuBzCWGEwXcmimSrNiUaGGGGUcZpjDWSHoDBRoHbh3FAcI2JQmCxgl226LljElp9uZfLAFHpAxzEdArEAXRcsnPVcFgJW1cKqWolf1rXO/G8uVrx0OU2rmsiN5FA0hZrO6qMSwWO7xtn2s+2Us2UUVcV1XAY3DrL66pUkW5LzvmZ0dBhVVampOfK6O4VTOIU/bzR6zZxjXkiPto1qr46y2U2fU09CpMljMaIM0UkXugIVG2wXasMNJI0acnYakMT05BFe+jVeHTeV3swPgncypPUBUOs2cKX5ChY7y485pyuukJimx9NPKxw44CuCzz3X49pr5ZxlQbWs4ZrKazi3cgFbpp8goAQJ6v7vfNkt0VvYRcJIUR2snzP+XnUXX458mkllnLiXYFQbZqe+hRFliFdUrv+jQ5IP2jI8F1PKBDu1bVjCZIHbxkJn8RzV9NFguRX25LbjePasbYMrXabNcfqKe1mcWAXAY8aDICRdzhI84bJP3YMUMKwOsBGV9dY5XGBdcpyjMWPZoGK55mzd40kP27Woifg1tUDQ7C2g2VtwrKGoCtQR1qLk7Yyv1MbfvHalTWO4dSYjYP665iAUReHmm2/h4x//EB/84MdoaDisE85z0TI9KKURpBbBqVqBNOaxxJpnIaP2g7ZH4EVAmAJccB4XlN7k4dUcOQTA0BDs2+eTva7rt6aXywLb9v9bVf2/hcO+TYRh+PfyYhG057Az6TSMjYFhCIpFQbkM0ag/jq+Al+TzAs+TdHfDokWSiQlfPd/d/d+veMznc7zznW9iw4bzeMMb3v5HjeVFWygtexvB/T9CzR8ABE7Vciqd1+KFn19uCcDjjz/E3/3dX7Ft22bOWn8+f/fRj3PZi6+aXWu0ty/kE5/4LB/60Me5554f8v3vfZ13vOMmIoEoZ3Sez7lLLmHZklUsv3LZETXdQaiaSl13La7t0v/UwCwR7JgO2ZEc2aEs073TbI818Wi4hUhQkAhI8hb8cI+g5MA1XRK7bDO0ZZixnnEUV1Cf7GaXkyAeFrgeVFxY3+ATvU+OCRZEfb/eki3ZMiH4garw9lUeJQe+tVNhqCBoi0sMBaYqkt/2Ca5ZJOnPwQNDAlVAlcjhPPWf/OHBrzCV3U0oXM3ZZ72Ti067ktyBh/jt0z/l0z/+J4Lhz1Nz+uuoPe1VVHJVM+F7TXhA8DhLanfnNtw9PagLuxABX/ykRqM4e3qwHn7gCDIYfEGN2nbimwuaBuvWSdatgz+l/eGfE06RwafwZwFhZQnv/BJqYQAn1gGKjlIaJbj3e4yVFLZNBdi9eyejo0NMTk4wOTnO1NQEU1OTTE6OUyoVjzr2HXfczh133A74u/rxeIJUVTXUJrHqEoTr66nt7MDs6kB2tbCosZ0gCkGpUOdoLHMMfhooEAAUBK4UTCiurwSWvioYfGWxAsQ9hYhUGRceBzQbTL9YusSKsNA12KKZ3BXMU+NoLHWNGe9gUD3YpplIASlPZYMd9MPshCToCcZUlzySh/USpvCIuwq9mj3HsmFYcUhJhXb3yBCpbsdgpR3gGb1CSqoYUjBVs5DayW28qPcJhKMh3ApqaRSr7nScqrlFekwqLLUNHjbKJF0FDYEoDDFaPkBTJcOS/ocJmjmMkQcpLX87TmrpEXOYF1oQq+kC1OIQbiCJYhcRVhahBTGbX0Su+Xy+GcrxjF6etcx4Rq+wXbN4XTlOcKaAtmvW4kYXoGV2+d7Aio6a2w8I3MQhcumxnkFGMxVedVrN3DakFwCOZ9OT3cxYeQhXumx4ydn87Bs/5Lbb38ui+pP3XHU8m8nKKEUnj6bo1AQaiOix5x3mFtairKw6k5VVZx7/ycdBySnMWDAYRLXESZO3ilBYmlyLkQswWRnDkhYhLUxrZNGcFtODsDyTjDmJK/0064gWO+KYJafAtvRTFJwsYS2KlDBS7qfo5FhTfTZBNYym6KhCx5E2xmGLNEfaaEJDU3Qy1hT9hX2oQsekQskp4EibTG6aklvgjJoLKdhZxisjuJ5NVbCOhlALgaME8AFMVsawPdNf6Egw1CCuU8SbCZALa1Fsz571Ua645RmfYf2kzq/j2UjkEUSrKjSq6qoZGRk8oXEOIhgM8q53vZ/bb7+V2257H0uWrOD3/YIf7hFoCsQNGCnCt3cKSjZc3u4XX7ZnMVYeIKiFZhXvmtCI60ky1hRZa5pU4CirrMNQyVUY3jZKuj+NETGo666lamGSwf39WIZJ2SsSUsIIFD8ETgri9XGWX7GU9EAGoQiqWlPkJwroQQ27bGMVLRRNwTEdtICOHtSp5E0CUrL/kQPsf+QA5WyZUDJM90WL6Dyng+VXLGHrPdtJD6YxHQsyErdBMhmfZmp/mkq+QiQQwQrZlGUFBOghHaEIzLxJuOrQ5oNZMDHCBqF4kGRzgtNvOI2RrSPkJwpEqsM0rmgkdRRC9lhQVJ8ArjmOLYRjOuy+bw9Wyaa6o3rGlkIy3TvNngf2cfoN6+a93kZHh6ira/yTbKKdwimcwn8/AgSp8epY4qygEg0yHbIoFmOIRBoPzw+XKkFXSlI108ikKdoRYbeHo1fdx6PGg+SVPIrUWOqs4NryjTR6x2/DDoXgNa+RXHyxy/S0T4w1Nh69bJs2J5DSI6QdUqaGtQjTZomhUi85O4PlVQhrUWqCDfwu8ksmlXEWO8tnid8JZZwHA/ex3jqXJu/EhQ0niqf1x7krdCdTygQgCBLkDPMcXl1+3bzE8eFIW1OUnSIJo2r2N1oVKkE1zHh5mIWxZaiKyoQySsSLIhAsdpaT9KoYVYcZVgY4x7qQNxf/ctbG4SAkkglljIooU+c2EiRIKlBDY6iVoVIvJbeAgootTVKBGprCR25IHgsBNcjixGp2ZZ8la00hAU3otIQ7aA4fGVh9NNxwwxv59Kc/wb//+8f42Mc+DYCwi4R6voIx/hS4Fghwo62Ul7zxiLWI0y2Rv5OIDMgkiJJvHYEEZxW4LYAN2i5B4PeC8nVHEkquC7t2+eFuUkI+79tCHFTwKor/b9f11bwHFcK2DamUf7bLZf/6BhgY8K0ikknfGsXz/Db3YBBSKcmLXiT5wx98glhKP3guGoUrrvBoO/FT9yfDhz70HtLpKX70o3tfkPrAqVpOIbkEpTQEgBduBuX5jdvff4CPfOT9/PznP2bt2jO4554Hj6kqj0Zj3HDDGzin62J+d+dveHLgQR7afi/37/wltfEG1t17Fje+4/Wcd9GLCATm/75qAQ1FU3BtFyEEozvHKE2X8DyJo+v8Yb+HSE7RtaIKVVeJGzBegsdGFM6tM+n/5VZGd4yhBTRfDND3BOHWbvY3tVHxVBZXSZbXeNy9V6UudCi4LaxDU1SycUzwxAggBIMFWJiQs9YRYQ0GbPhlr0CRku78oww+9DV2PvIDHLNMa9061p7xUa5YezY1YY2oATk20FTpQHuJ5Mfbf8eTT3yJoYf/g5ZlL6FrwztwmmqJBw2WVh3HImJqCum6s0TwQSjxJN5g//MO2TuFY+MUGXwKfxbQJ5+FXB9PTSV5+rHt7OyfoKd/gp6+USbzfoKuYRg0NDRTU1NLdXUtixcvp6amlpqaOqqrawgEgiiK4u8uC///f//7X/Od73yVf/mXL8zcsLNksxmmp6eYmJrgwMgYw5t2Mtj7DaTjALA/ECDV2U5qYSephZ3Url2FcvZS9JYmTCGJeIJpv27AxSeEVSAqBToKdZ6Ki69aSHqHpSwj6HQNQlLhfqNEtVTRDlMdxKXCuOLQ6uj0ag4JV6FKqpjSY6NukvUqfN7dR41ZQA9UMxFsJC9gh2oRkwoVIQkguLISnfUfnnOOEby2kqDJ03hGr2AKyRqlgcucPJ1sQTGHkapBpfXFVDqueY7dgo/LrQi7NItNmknY8zDcUaqdCtdM5QlH2nDCEjWzE/3AT3CSi/3WMMBCsl0zOaDaqPjE9OKDJLaU2NWr0Ka3oU/vQGphvGANTvUKyt03s9mw2aRXaHP1WSVwGY+NeoU1ToD1M8F7MlhFadlbCO35zoy63EEaMdxYK17wEOn05V8+Q2ddlNNXLaf0At90hkt9DJf6iepxDCXAS155DXd/6cd884df5Pa3foSwduJ9KhW3xPb0M/7iZibgrU/dw5LEaupCz8PMawaWW2GsMkTanEAVOjXBBmqDjfN6FM8Hx3PYl9/BaGkAyzPRFI3qQD3diVUEj0GGzoegGmZ56nTKThFHOn6Y3Dw2DJOVMXZnt1B08kgkhhKYDWJThELFLZOz0gyX+sjZaaoD9bMFRVANkrYmGS8P0xpdhKEEaAi1cKDQgyq0GX9ih7ydoSbYSFxP0VfYjeNZOMImZ6XRFYOQEqFCifHyMA+P/hpH2njSQxUqY5UhxsvDrKw646g2HbZnIQ4qfwTE9SQVt4TtWViuSdkpUnKK1AYbSFuTbM88g+VWUIVKKlBDd2LVCV0/US2GJrQ5Sh4pJaZbpr6hgdHR4ZP6jACuv/51fOYz/8y//MuH+ffP/4A/DApCGjTPTKcqCMMFeGBQcHaTX5g6no3jOUeoxFSh4Ur3hNTPxekSm3/4LNP9aTRDw3Ndejf1oq6xyZGjXLSplHOEtAi1wUZUNJASPaQRToXnWB6U0mUSzQkc06WcLuE6HoFogHAqhB4xEAps/dkOdvxqB3bJRgjBdF+G0Z1j5MbyrH3lata/7gwm9k1SypfYsq+HnZndJMfifoFvuoxOjaOmVJLRGPmJApnhDMF4kOmBDJ7rEYgFMAsmlbxJ14WLCMT8zydSFSaYCJIeyDC5fxohBHpQJ1oTOdqp+aOQHc6SnygQb4jPfk+EIojURkkPZChNl4hUH3nskZFhGhvn9yQ/hVM4hT9/dLgLCcoQGTFNMlhFR8cudvasJDddQ0o0s6ssSATgsnZ3Xmug52JQ6edL4U8zqg5R49UTkiE26U/i4vK24rsJcmxrLICKW8aNThCLuET1JJBkThDuYXA8e141r+Wa9OZ3EVTDCKH4hLERoT+8nxq1fs5rarxadmk7GFD7TpoMzoksFVEm5VWjc2QNPa6M8v3QNyiLEt3OMhQUciLDw4H7aHM7uNC69Jjj+/kC8oj3KHyzNZj52wK3nWeMx2f9hhu8JlJeFREtyiXmlUcQwRPKGD8Jfp+d+lZsLGq9ei41r+Is63wWJ1eTDFTN2D05VBl1NEVaj1rrHAs1wXri+vlMHwy31eMk9KqTIoDi8QS33vo3fOxjH+SWW95JR8ciAv2/whh+CDfW7quBPQctu4fQrq+TP/3v54RhO8vAulBi3C8QowJlGkQGnCXgNs6QWTp4VRJ9s6B8teS5H+XYGExOClatgqef9j2BwSeBdR1CId/r2vMEqupbOgSD/iaGELBoEfT2+o8pih88JwQ0NclZpbEfoOgTxsPDgksvlZx3niSX84+xeLFk0aIXVM/yvPCrX/2UH/zgm/zrv36RBQvaX7iBFRUveuIbDumBDGM9Y1RyFRACy61w1+++wV2//iapqir+4z++yjXX3DAnXPhokFIy9OwwzXXtvGHtam6+6u08u+9pntrxME/teIjfvPEnRCJRLrjgEi655CouvvjF1NYe6jqoakuRbIqTHsigaArFdAkjrPtZFZ0NOIkowVyewkRgNiA5FYS+HPTsTJPvGSPZnJjtDsvbOj0ZBS9qkqoJMZgXfGO7Ss6CpYdFakyVYee0YKgg+NyzKlFDkrMEmiLxJOzJwP7RLOPb7qXU82usnl/j5keJ17VxxjXvZfmaVxB9eoiSEaI6rBEz/HNRzlbQAjoLGpt5T8df8PuL/4p7N/6Gvoc/w8D2n9G64wbe+VcfojNxbAWvSPjCFmnbCP3Ql0oW8qhLlp0igv9EOEUGn8L/WEgp2b17Bw8/fD+P3ftdHn16E5mSjaYqdDVXs7S1lguWLGNpexMLXvpxOjoWoT23t2Z2MA81tx9h5fBCNXiRBSAEp5++gbvuupOxsRFuu+32o87FdmyGBvv5+sCzPNTXg76nj9y+A+z80U946t8/C4DaVE9gw1q09auRZ61GXbMcPRAgIQVZ4WEJfx4lPKZVj2pP4RLzyAV1XCpEpUJeeETkoZvSwf++woryS1Fgt+qTJftUm5JXpDHbj1HOMKUadI33ccZIkJ91XcwiTyciFVKeymlOkGXO/H5GAFGp8HIzxovNKI6QhKVApM6kcPpalMokUg0iA6l5XzshHO4OFGbeq0ekPMZl+x/jvIpOu2XjIni8uo6HO7qY0jUatAHOEQ0sdHS+HczxtFHBnSljg4ESl5hhXlZUCO/+Lsb4EwgrB0LgxlopL3oNTs0aEAp71CxCMscSIoSCImG3as2SwQBucgmF0z6EmtuH8Gw8o4rI1k+hZffgxDvZP1bg7kd28m+vW4PTfMFRz9PzxWh5EE3oGIpP8tQ21HH6uWfyq+/ew5teeytt8eO3vx9EX2Evk5UxkoEqVKHNBnHtyW0jadQ8L6sC0y2zNf0U0+Y4ilCRUjJS7mdBZCHd8ZUndCPuK+ymr7CbkBohYVRhexaj5QEkklWp9c/rZn64iue5qLglerKbMd2yr4ZBUHFL9BV2++pfJAfyPVTcElkrjePZBNXwrCetEAoChYKdmx2zPdaN6VWYKA9TdPKoQqU6UM+SxGoUocyG3ZTsIqriq4XBt2bwZs6ZoQTQFAMBGEqQcTnMcLGfzvj8sc5xI4ksebjSRRUqES1GdaCe8crw7DFbIwsJaCH25XZgqAHiRhLHcxgvD+NKl7XVZ88bvnY4EkY1daFmhku9GF4AVWhU3BIBNURNsp7cdPaYr58PhmHwnvd8kHe/+8286ulNZCqn0xiZqwCoDsFQQTBegpgxE8w30xJ6uBd2xS1jKAEi2jztm4fBtV12/GoHoztGqe2uxQgZSCR9w/uoPF2mfn0t7oEc5KAYy6NKjUAmQjARonbRkYrjRHOcVEuS9ECGlrXNIP32s/RgmpqOaqySTe9jB2bTp1Xd/37kxwvs+HUPdV21BKIBUgtSNMYb6OhrZbO9jWwiT4woFcWiVKiwYttiJsoT2AUbPWIQToYoTZcZfHYIhMAI6bSd2UrHel/O4zke23+5g4FNQ2iGhqor7HtoPxP7plh33Zo/GSEMRy4ij/fVHR0dntuWewqncAr/q7DQWcx51ov4Q+C3TDCG2rWPqthWansvYmGuge4qj/OaJF3zl4lH4HHjIUbUIZY6K2YJzCqvmh36Frbpmzn9GJ7BAGPlIfbktlJ2ikhAFzpNkXa64svnvRcmjWqGS32z91nwN2ILTpaIFpsNQpPSY8oaJz4dZaJhes4YLi4KAoOj19LPRVZk+EXwJ2zSn8IWFvVuI5eYV3K6vWGWqN2ib+SboS/ySOB+Gt1m3xLLbSUuk0zLKZ4yHj0uGRw3UgSUIGW3OLs5LKVkgjEy1Tl2RfcQlXEa3WZCMsJ+dQ+1Xj22sBhVhllur2apvXLOmCYm3wx/ke3aFhrdZgJUMaGM8Z3Q1wjJMGvtM2gKt//RgboHYahBGsLHtpQ4Hl7/+rfz5S9/mv/3//6Bz3/mq+ijj+AFUodsIRQNJ77QD8NO78SuPe3Qi1UoXyuxl0q03QJtNxiKgr1qRtlzEAftJOYROyqKf7+sq5MkEoLpaZ+0VVVfEdzcDLt3gxCSJUv81nXPg0zGD4Bbv14SDsPmzQLHgfFxP7WlUvGfW1cn0TTB8LCkpkby8pdLzj5bUvs/zK5/cLCPv/7rt3HZZS/h1a9+3X/fPDYNseM3PZgFk+J0ie17N/KjvV+jaOe5fPXVvOE1f8EZLz39hIhgAKTfwXWQjFVVjXXdG1jXvYFXrno9wRUqO8Y2ce+9v+Sv/uoWpJS0ty9k7dozWLduPevWnUnX5V3s/vUe+p7qxy5ZqJpCvDFOsCFMUEgsRfUD5mbI4JLt2yy44xmkBNMIMCk1ItLhKaWWrBakq5KlORECJAcyMFkWjJWgMwEFGzZPCKYrgpghaY5KevPQN54nOLiJ0Z5H2fvUryn3PgqeS7BxOaF1N1Gz+gquuuA8AroCnqQ44mHsH8CeMiiFNMqZMuFU2A/dmy6TbDa4JFRh5TmX07PofHbsuodNG7/J+67/Hs9cdzPvetf7aWmZX66uLVuB0taJu2836oI2MAJ442MgBPrZ570AV8IpzIdTZPAp/I+Cbds8+ODvuPsn3+PBB37DxNQ0uqZyxrIO/uKyhZx/xlpOW9KOoasgJVpmB2bThZS75idYAERlivCur6NNbUU4JaQRx6o7k0rXjVRX13LttTfx1a9+jre+9d1HbenQNZ329oXc1tFOLHQBuzUbCw9DtSmNjZN8fBtTTz7D1BMbKf3Dv0O5AoZOYsPp1F5+CeGXns/Q4gU4AtKKR62r8sZygk7vUDEpZyqKiFTYYAe5J1BEURySnkpBeIwqDudaIdY6ATpcnc26yaN6iQlZ4PShZxFOAfQ4tufSk2pi1Z5HWTfWhFm7lneWquZ9X0dDAEFAHrbyV/RjejE5SL4TyrFNM2nxdNptnUCpwoqRZ1HCnSBi/LK+mZ80tqG6JlGryKaApEfNsMoO8KRRpsXVicwQulPC5b5AifN3/JbU4G9ww03ISDPCzqPmD2CMPIRT4wd0qAi8eUgKTxwehncYFB03eeh6KS19M6FdX0fLH+Bzdz1DVSzAtTfdhtVw7kmdsxOB49lHKGyvu+VG3nvTX/Lrn93DW298zwmO4xOAIS2MKg76hgpiepKsNUXGmnxe6uDhUj/T5jiJmYA08Mm5oeIB6oJNx23btz2LkfIAASU0S+AG1CACwXRlnLydmQ1le6EwVRmn5ORn/fHAJ48tz+RAYRe2ZyJmfHhd6TJVGWOqMoauGATUIFL61O7hhKSuGCxPnkY+spCyW0RXAiSN6tnPLqFXo6DgeBbajLJVSg/Xc/DwCV1jJvBPSknJKVDx/DbUo4XU1IaaqCoNMGWOE1SDgMCWFu3RbroTK4loUXQlwFMT96Mq2ixZaqgqcZEibU2SsaaO6acH/nWyJLGaiBZjtDSAI23qgs0siC5EF99GnKAC/Ll45Stv4NOf/gSf++f30fmXv6PsQPCwCqPsQED129TAtwFpjSxkR2YjGWuKoBrG9iwcz6I12kVIiRy1JWy6b5odv+lh34P7cR0Xq2yTXJAk2GDgRm2UrIYWVKneECe9KY87BNOkaaqL0rSykeJUCaEoc4hUVVNZculitt6zjcxgFmZCP1MLUnRftIjsSI7CVAnVUGf9g4UQGBGd3EiOR7/8OJGqMEY0QNOKBpoG6rnUOJ+dyh6yMk+9qKG9r5nQSJCi9FUgoUSQaG2U4lQJu+xQ1ZYklAyTHsjQc+8uVr5sBVN90wxvHSFeF8OI+NdatCbK5IEpBp4ZYOnl/m+ZlJL8aJ6p3mk/lK45QVVratYv+WQQb4wTrYmQH8uTaE7M2kQUJgvULqo9aojc6OgQ5577opM+3imcwin8eUBB4ZXlG+l0unlWf4ayKHF1ajlnRE4jKQ1O1oNxr7aLmIzPUbIGCOLhMqoO+enHR0HJKbA7uwXbs0ga1YDA9Cr0F/YS0+PzkpN1oSZGywNMm+PoSgCQFG1/w7cmeKhjSAiFqJagsdjIfu8AURHDFv5kptQJGtxmup0TszpzcLgz/CU2Gk9Q69aT8BIMqH3cGf4iRslgtX0aT+mP8a3wl+lX9yOlJKOkySjTVESFRc5iDBmgKAqMKENsNJ5kXBml1qtnnXXmHHVyWIvSGl3EgXwPaXMSVWhMqhPc33QfhWiRyMz7CMoQy+wVDKtDjCkjRIhynnkxLzOvPcKKokffxh5tJwudLgIzSu02t5O9Wg8PG79nrX3GCZ2H/0qEQiHe854P8d73vo2/uOUdnO2a8NwAZkUH6YFTPnIA1beEcFZJlHFQ/x8oE+AdXAa5oEwKzEs95tsTqKuDzk7Jli2+oldVD3kDB4P+vzVNoOv+Y57n1zr19T4JbJpw1VWSiy7yv0//+q8KnifJZARTU/4xhJAsWAC33iq57LL/ed6n5XKZN77xVUSjMT75yS/9t6k6zbzJ7vv3Ij1JIGnwnd9/kQd7f0Vbsov3vPgOFq9YyvTuDMPbRmg97cQ2IYQiqGpLMbBxiEh1ePa9WSULVddYc8ZpXNJ5ObfddjuTk+M89NDv2bjxSTZtepJf/OLHWJaFYRisWL6G+sACQpUoHY1dBFJtVFGhS+R5VERIKRpS+kTuUFGwoVGSMj3+EGyi322hjIIiJRMEqXWm0Q67xhfEYbAIaRN6c5AuS0bHxtAmtsH0Rh4f2cTYvk1khvfQDyhGhGDXi6h75adJrLiSDUtaOJAV7MlAf0GyKAk5WzC8dBnntYSJTg7hWA713bW0rW/DLtts+/l2pnqnMMIB9NI0aw2VG9/5dqoXf4A77/win/nMP3PXXXdy441v5r3v/XtSqbnchAhHCN78Rsy7vou7fy/SsVFS1ehXvQzttD/evvAU5scpMvgU/tshpWTz5qf50Y++w09/+gOmpibobmvi5rPquGDJEs7uriasKwingFRzeM40UhqopVGkkcBuOoaCU0rCu76JPvY4brQNqccQZprAwL1ILUyl+ybe8pbb+Na3vszdd3+f66+/+ZhzrZIqby+l2KKb3GcUmVY8zq1qp+qKhXDFy5nGYa8soTy7m51PP0Hu3gd55sOfQH7gDkKL2ll02WVcfNYKXtcgaHFLuLEO9rVs4IFYmF2aSUQqnGkHOd+M4ACPGWUGVJuQVLjACvMKM4ZAkJIqF1lhhhWH4XKG2nKa/kgVYQQ6EqkY9EWrWDz6LI/XrZm3bexo5ws46b6ivarFbs2i/TCrBmKdpGPNhHO9TCbXcV9tEzHbojk7gBtuIkWMfcLmV4EiYQlTisuEcEl4ClVSIW+mYexhvGANMuh7XEojgRtpRZ96FrXQRybeioXHsOJQwaPD04lLlZxw0YClx1BBH4SbXEzh9H8gs/9xvvnwVbzzHbchlt14Uu//RFEdrKMvvwd5mJ9tx/IOTrvwDL766c/zhlfdimEcf86SY7UCziQ8nyQy5iQ7M5soOnlczyGiJwipYYJqiJKTPyEPV9uzsD2LgDK36NYVg4KTw/LMk57X8eBIGzgyzVoVGllrGkMxSM34FUb1OAU7R8UtUbRz/rzsLAEldITHshCCuJEizpHkdSpQQ3OkkylzjJLlq4MFgrAWpWjnUYSCiq/WLjsFLM/E8WyGS31szzxNd3zVEcptQwmwoup0Bgr7Ga8MA5KO8GJaIh2zCh/LMzG9CsZzrBU0RUdKD9OtnNA50xSdjthi2qJdeNJFFdqMGur5+3FpmsYdd3ySG298Ka3n38nIspsxVElE9xUNwwXBhiZJw2E8Yn2oxfcsK+6naOcJKiFi2RaKj3vc3/8QRkineXUTC9a1zCowKrkKW+/ZTn6igBE2cB0Pz/WY2jdFUosjQ3LWBqjmrCSx7gj54QLlTBl1SKXviT72P3yAQNSgeU0ziy/uQtV8creqLcWZN5/B+J4JrIJJKBmirrsWI2xQypT9ZDztEFkupaScLuM5HnpYJ9mSpJwps/+RXlzbZXH1IlbHllPyykz3TWO5FpWw304XrYlQzlYY2TaK53kEowFUQ6OqNYVZMBneNkLzmmbyo3k8x5slggGkJzGCOhP7Jlk6M48Dj/Wy98H9WEX/O6YFNFrWtrD08sWz7+9EoQd1ui9axLZf7GDqwDSqruJYDrHaKF0XLJyXYC6VSuzbt5s3venWkzrWKZzCKfx5QUPjDPsszrDPmvfvZcqYokJMxlE59m9PUqboFXvnPCZnpBFhefSuBwuLMXOQslucsxEcVEOYbpnR8uC8ZLChBFiZOoOhYh8TFd8SqdqoZ6Q8gCLmLokFgia3hRqvlvsiv6QsyggEtV49ryhfT0SemK3XHm0n2/VnaXcWzr6nmJtgr9bDA8a9LLVXcm/gF7g4LHZWkDWyRLwIlrDoVffR5C4go6Rptdv4j+jHGVdG0TGwsHjE+AM3l25hqXNIzdse7SaixRivDGG6FbbVbKMQK7HMXY02s+zvUbfz09BdtLmdqKg0261cal5FtXektHRamcTFnSWCDyLuJRlRh3Bxj/s5/3fg+utv5gtf+CQf+5ePcvd7L0CfeAYvWD27vhHmNFKP4kWPTQB6dVC53CN4j4K2C6QmEZbAWSgxL56fhFUUeNnLPKamFPbvF7NEbigEwaBkYECgKJL6ejjzTEmlIggGPZJJ2LsXqp6j3znjDElPj6CpSWKa4Dh+0Fxzsx+G9T8NUkr+5m/ezt69u7jnngeoltVozwifZF8okcdu/Do4iE/WP08v4INID2Yop0vYsQr/fOeH6J/cx0tXvobzWl+McASKpqCoChO7J06YDAZoPb2Vyf1TTPVOE06GcG2XSt6kZXUTVW2H1g01NXVcffWrufrqVwNgmiY7dmzhmWeeYNOmJ3l200YGBnqxN/vdvkIIkpEa9EQrkwsWszleQzhWTWt9CqMzwrdHVJ4YN4im9xNWbDKOx4CjkTFziLTHWJ+CVcqSmxxibGQIozTExolhClPDSM+3u9SDUeo619Bx2otxr3w/NZ3rGAwuwRUG9WFJZ0JSG4KoLpksC6YqAjctieiCc9pVXrW4k6Bow7M9tKA2+/urh3SGnh0iN1agurOKltXN1HbVIITgrW99F6997Vv46lc/x6c//QnuuecuPvShj/OqV900R5GttrQSuu2v8fp7kZaF0tiEEv/jQsyzIzlGto9SSpeI1kSp667FLllYJZtwKkSyJfm8hBP/W3CKDD6F/zZMTU1y551f5Ic//Bb79++lvr6Ra6+9kesu3cD68k+RgeQsCeg4JbR0D260FcU1EW4ZJ9FNpf2lOKmjB2+phT606a24kQVIw2+1kMEqPM9CG30U2X4VXV1LuOSSK/niFz/Fdde99rhkSBiFDXaIjVqFBZ5O1WH+u1VoqLqBffpyOs9Yhvn211MqFSnf/zihnz3A0A9+yD9/7ov8Z0jjqnXNXHrxEp64wWMosYakiJIVDt8N5hlUHG6sxLnACjOtuMSkQp135NfVkOBJF20mZ9hFzihlBaoEz86zxDGOSwQLK4cx+HuM8cfAs7Fr1mG1XHzCyaxZxcNhrlWDqwXYvORqolu+hWmPksFkcWYML5DAqVoGQlDraWwxTBwkQVzAD9pb4OnUlIpodglpzJ2D1GOI0jBpJ83nwzH2qBaOkOzQLHZj0eLq1Hka51phVjonaJWgBvjS3fcjFI2b3/zXJ/aa54HmcAdTlXGmzQkCahBXunjS4W3vehe3XH0T3/ve17nhpjcwbU5geSZhLULSqDlCTawJnZRRzUi5f8bjzv98y26RgBI8afXteHmYnZlN5K0MljQx3QpZK01dqJmYnmA+snU+GEqQgBL0CcvDyM6DBObz8ZA7HiJaDEUoOJ49a9cgpcTyKuiKMadVNKiGqQ7WM1oeoODkEEIQ1mIsjC87qXOmCIXqQC0hNYrpVmaOrWF5FTzpoisGEo+yW8H0KgipzBwrwlCpFwWVZal1R4wbVMN0JVawML5s9jgArnQZKw8yUR4ha00jkdQHm1Fn/JNtz0IR2kmfX0Uoc64tKb0Tb5ObBxdddDkvf/l1/P7L7+Xmz15JX6kGy5MYimBNneSVXd6cfSYhBA2hFuqCTTieTW6owJZfbqWULhNKBP12vl/tJD+WZ+XLViAUwfieCfLjBara/c9r6sA0oUQQM29iTlrIqICwJNTiX3+Bap1SwMO5G8pTZZLNSVRDpZypcOCRA4STIdrXH2pZCyWCtJ1+5IKgqq2KVFuS0R3jGCEdRVOo5Eysko0RMYhWRRBCEKmO4NoupbRDabpEMB5EL2vYeRuET9Kqur/4MMI6udEckSpfBY3nL+oC0QD58QL5sfyMHYV/TTsVh/RAhuJUEbNgkmhJkB3J4Tkeex/Yh6Kps4FvlXyF/qf6SS1I0rzq5K0bGpY1EEyEGN0xSjlbIVoboXF541FtKbZu3Yjruqxd+z9PJXYKp3AKf3qURJHfBn7OU8ajVESFZreFi8wXs8Y+fd4atCSKeHjs0LaxW+uhzelkgdvKlDJJjVfLcvvI5PgxZYT7Ar9mq76RfChLSk1yQfliku6h+7cqVCz36J7zATVEZ3zJrGWT7VnknSxFOzdbB/gdPXlkSDBqjBKVCWq8euKu3ynxmPEg661zqZLHDuMEmFQmsLGPILcTXophdZBRZYhxdYQar46IjNLktjCg9qJKjZJSZKu2kcXOMiaUCaaUSZbMWGpIJHu1Hn4avItFhSWzHsRCCOpCTdSFmnBw+FH8LupkwywRnBUZBrU+JpRxFrjt1Lr1bNc38zWR57bi7SRkcs48k14VAgULc06obl7Jsdhe9j+SCAZ/g/r22z/MW97yan7/2ldyWSiJltmBF6hGOCWEW8ZsvQL3BHxnzUskbrOL/qxA5AVuh4d1mkQeo+myvR1uu81j0SLBnXcqZLM+6Tsx4X8XDMMPlevthSVLJJ4H+/YJ2toka9bMJXg3bJD0DUiefFzFj6+RdHTAtddKao6fs/tfjq985bP86Eff4TOf+Qanja4l8A2BkvH/5tYLKldL7NOOQmJLD2P0EYzh+1HKE5TDrWwNvYje4DpSQcHyakn0xB1aANg68AxfeeCTBLUwbzv9b1ncvgK7bM925AoBrnNyIprUgiRrr11D7xN9pAcyGGGDBevbmG5r5z+3aRRsWJySnNUoqT2sNA8EAqxde8ZsrSQ9ye7797D595sYGh1gIj/CdGWCopZjonKAzN5NjOam2ZOd5tf20Vsl8sDBxA89GCVQ1UK8uol1SxfRfPEFjKnN7HFbWLF4CVQtwhMKMR36C4KXL/TozwueGZMsrZIczot2pSRXL/JoCAuqQx4t0YP7KeoRQoPaRTXz2rAdRDgc4dZb38urXnUTd9xxO+9+95v5zne+ysc+9h8sW7Zq9nlCVVE7Fp7gJ3FsjO0aZ9vPtlPOllF1jb78AM98dyPBRBAjpKMaGnXdtax4yTKM8EleWP9LcIoMPoX/cgwNDfCf//kpvv3tryCl5GUvu5aPfvTTnHPOhaiqSvDAT1D2lHCCh/0QaGGkEceLNFJYegvCs/zQr3nCpACElUVL96BmdiEq08jIocX9hGHwn50X8kB1PeVYmpXS4pJ3vIPbr3kJDzzwOy688NieXAcRQODJI29macVFkZKL7IhfAOsRzEtfSv+lL+KdN3ZiPLWRn2/NcdeD2/nuh39D6CsbWXv1xTS8+d00dLSREy5PGRXOskMscg1S7tELraVugIfUMHkjTNyVZFQoawbSc2mfPkC56RzOs44T2uVUCG//AvrEU0g9jlRUggfuRktvo7TqPXihY7edA1R7KgZQEB7Rw3yOtzYsQwbfxWWjA6iBJMW6RgKhRqTuF8XTwqEiPJCQ9AQqgiIeW7QK1ZEwk6E4qdIkQf2QAkMxp5FGgvviUfaoFt2uwRLXYEJx2K3a6MDNlRin26H5bSLmQT6f4+tf/zw33vimI9pWXkhE9TirqtYzXOpjyvStChpCLTQ2tnLNNa/hXz95B+0XLsBSy0gkyoxX7dLk2jk2BkII2qJd5OwMaWsCXTFwPAdFKHTGlp5UEJ0nXXoLuym6eaQA27VQhIrtmQyX+2iiDV0xZtowjw1N0WiJdLA7u8VX3KohbM+i4pRoiSw8rg/s80EqUEvNjLduQAnNBMaVCKlRUkYNw6XeOYpXBQUNDV0YRLQY7dFu6oMnZ6lhexb78z3E9ARxI0XezmC5Jra0CKoRgmoA06tQdkoIwMPFUIJUB+vxpMtEZZiS033Uz+lwgtaTHj2ZzQyX+vzwSwQ5O43lmjSEWpD47bINoWYSxh937b4QSb3/+I//wvnnr2T07vfxrr/7IumKIBnwWJTkqIFCilAw1ACDT/VQSpep7jgUGGMWTIa3j7JgXQup1hRW0U8CVxSFZFOCSrZCKV3GMW0Kox7R7ijK6SaVaB7XMbDcCtaghzLl+/ketHgIp0LYFZvBzUO0nr4A5ThpR5qhcuZNp3P/fzxEfryAUPzFgxC+knZszwR6f5p4Q5xAxCCUkERrImSGM1TyJuaM0jjRnCA3nPNTpBU/LcaxHBRVJTRjvyBnSGFVV0m1pgjGg2SGshTGC5QyZRTNt22wSzabf/gs1R1VmCWL2s5DRXgwFqSULjG2a5ymlY1IVyLUE9vUOYhkc4JobZT8WB4EhJJHD3N65pknCIXCLFmy4oTHP4VTOIX/HfDw+E7oazxmPEDSqyLohdit9dCv9qIW/4JVztzNTwuLO8Nf4mn9cWq8GsaUUTbrT7FH28l661xeXX499d5cIcC0mOJL4U+zT9tNjVcLimBT8mky4QzXTr6aiBf1N4Jdk8bw/J6U4BPKjxkPskfrIeYlWGefSXtsMXuyW5k2J1CFiisdQlqEe+p/ylZjIyEZpiQKM8FuSxlS+9mibzyuhy8wo5A+kkwtigItbhtRGfeVvsIkJuOssNeQ8JL0avtxpM251kWcbV3AN8NfpNldMEus+8rlBQyq/QyrA7S5nfMeX0gFKQ6tVYbUAYqiQERGicgoKVlFzImzV+ths/40Z1fOZ7IySsUtE1CDdAa6WBjoYre2kxa3FUMGmVDHEFLhHOui477/EiUGtV4UqdLmds4bnPenwpVXXs3q1adxx2e+wllf/zzBkT+gZffgRluwG8/DbLroxDohhR8s5yw7iknwUVBdDTfdJGlo8PjUpxT27PED4GprYcUKSToN09OCsTHfU3jNGsnLX+4rhMFXyW/XLB4OlRl4q03VtSpNe8OsyAVZtgRSL6zr2guCxx57kH/8x/fy1re+i+u6XkPwPxWkIXG6AA/UAQh9T+A2SLx5Su9A388J7f0+UigUiDEwtBHX2cn22C30hM6iPS543TKPlhNcTvz+6V/ymd9+hJWtp/H2q95Hdm8Rx3SwKzbxRl8kZpsOdd0nZ7hsV2wKkwUQkGpNULuolkeMJv6wT0MIMATsnFLYPCG5ZaVHw1EaHYQi6L6oi4alDaT70wCkWlPEG2Jz6jUpJXunLP7fIxWq1AJeLodV8hCajh2O82wlQWPCIBnScFGoDkpuXCJZU+dfryNF+PDjCo9NCuSIBNNGuh5t1Spr6hS6UpL9GZV9WagNgeX6nsNr6yQXt4KunLwC3XVchreOMLJtFKtoUtVezYJ1zdTXN/KZz3yD17zmDfzt397G5Zev501vupW//uu/Ixp94daJjuWy5/69mEWL6g5/DTv47BDFGcu3hiX12GWboS3DBBMBll1+YtY//9twigw+hf8y7N27i89+9l/48Y+/Qzgc4a1vfRdvetOtVFc/ZxdpHoLVh0B4LjJYdcxbsT7+JME930EtjSCcClp2L8KzsOvWU1JVbluxgc3xJEHPQ6gK9yklnnnxYhasXMW/ffrjXHDBJSe0YF7hBHhar5ATHvEZAnRSuFSEpMPVnuOBJpBOgVFNct6a07htjcL7XnM+r69E2fnDe3n6a3fz6Ge+R8s5Z7HixuvhlS9mUI2xyD32LtUyx+AirZGHouOU3SKmooJ0uLT3SZa5EULJi2nwjl146VOb0Sc34sQXgeYTx16oES2zHWPkESqdVx/3XHS6OsvtIE/rZeo9jaAUTCsunoRVgUW0NK2gJTzNHs1moeuXgmU8elXfEzkpFSYUD4lHWniUhAeGwa87zkbZ/iOai/tJ6TUIO49ipsm1XcmTiQS1ErSZ81zv6VR7GvtUG0MqJ0wEA3zyk/9EpVLhbW979wm/5vkiqsfpTqzEcruZNsdxpEPeyfLu93yQu3/6fX78ne/zmje9DlWo2J7FeHmIoBZmSWKuSiZupFhdtYGRUj9Ze5qAGqI+2HyE3cHxUHIK5KwMFaeMAAJKyLdekIKKUyJdmWBtzTnE9ROrOlsinX7SbqmXilOasSRYclSv3D8WqlBZljyNaDHBWHkIV7rUh1pojS7CUALkZwjzkBql7BSYMEdRZlQ1w6V+hot9NIRbWRRfRm2wcVZteyxkrWmKTp6EkUIRKjE9gSsdTNek7BZQ0RCuQsX120o1Rac20IChBGaeV/GV3xyftJ82xxkt9xPRYhhqAKlXEVDDTFVGSFtTxPUkCyKdLIwvO0JBfrLwPO+P/ozq6hr4wAc+yvve9xdcd91NnHXW+Sd2bMdjuj9DKBGcM4dZlex4gVRrinAq7Ks4bBctoNG4vIHCZIGpA9PUdtVy2vVrKNfkGCn3U3FK1AQb0PQovWJolgg+CD2oYxYsPMc7LhkMUL+kniv/4cXsum83U73TVLJlxnaNAxJFEVRyJrnRQRRNpaazmnXXr8EuO2SGMuy5fx96UCPZksRzPPLjBeyShWaoOBWHeFOEaG0E6UmyIznCqRDVndWEkyGWXNrNM9/bRGY4ixHSAYWqthQ1C2uY7k/jR8sc+bkJVSEzkOWZ72wkN54nlAixYG0LzaubTqglbqxnnN3376UwUUAIiNXHWXxxFzWdR24Mbdr0JGvWnH70ENdTOIVTeEExWhxnb+YAEklnop2mSP2f5B57Itiv7mGz/hQtbisx6bf0ppwq9mg93B/4LSudtXN+o3bqW9moP0m720nYWUle5Egr0wyqvZxpnT2vD+0zxuPs1/aw2FmGhoYUEt1W2avvYouxkdWFdVTcEhE9TvNRyOAhZYAvRv6dAbWPqIxiaSabjae4TH0JF6mXMl4ZpuwUieoJRuKjPJV4jIAMzdon5EWOHm07utT5QeibPGk8whJnORus86jzGuY95mJ7Oe3OQvZpu2l1OwjIIJPKOLawOMe6kCpZzUprHQ8E7iXsRQkRosVtwxImS+0Xc2vxbxhS++cdW8ymmM0PDY019un8MvgTarw6dHRySgYHhzgJqrzq2ecBjDnDbJp6hKzlh+ZJIK4neaW8gV8l7mGP1oMtLKq8al5eedVxA/6e0B/hl8GfMKaOoKCwwGnjmsprWOwsP+brXigIIfj7v/9/XHPNxXztF4/yutfdBp4DQj1pO7zni2IRenoE6bRP+AYC/jLXsmDlStizR3LVVX5oXG3t3Glt1Ey+FcpSEZK4VMjUWkzW2HSYDinrxEUf/1UYGhrglltew1lnnc8HPvBR9G8LhAVu+8wTFP/f+k6BvlVgNs+9dn0bx9/i6VHcSDNbRwWjNLFY380V8mc48dPZkzX48V6FW9d4HK+M+fznP8kdd9zOyy67jpd334xTclB1hcJkES2o4Tkek/umaFhWR+OKRjzHY2zXOGM94zimQ1V7FU0rGwjG5m6E22WbZ3+yhdGecQDK2QrP/nIXQ+EqGjesJNrdAKqKKyU904IHBgXXLz7691QIQaIxTmKGnK7kKxx4rI/p/mmMoE7d4jrqumupjQVIpkJAivrDfm6myrDGFlzS6lFyIGl4rKqTtBx2iYQ10AQoZZPw2DiaaWLpBrlSkK2bJFeeV83rl7v8rl9hMC/QVcnl7R6Xt0v057G0kFLSc+9u+p7oQygKmqGy7+H9TOyZYO21q4k3xjnnnAu5996n+eIX/51/+7d/4mc/+xGf+tSXOe+8FyZ7Ij+WJz9eIF7vE+vlbBkzZxKuCmGVbKySRSAaIJwKM7JtjEXnLfw/qQ4+VbWfwp8cY2MjfOQj7+fHP/4udXUN3H77Hbz2tW856u6Pk+wCNYgw08jADAHlmgi3hF17ZGv14VCKw4R6vo6wCziJxf4N3y6iZXYi1QA/X3o5W6MxGksZgoFqSqhUsOnXHGJ33MpTr7iFj9/7Pd532atRjkMmrrOD7FEtHjPKDM0UY2GpsMI2sJ9TnHlI8org3sZufhtIoErBmtwUjdEE0bVtXP0XF7LzkT62/Owxfv2Od6F/MEXsjbew8nW3UV199N1KBcHLnRpWitX0lTahZHfTlZ+mM9iBs/QyPKP+mO8BQM33gXRniWB/YBWpRdEyO4Hjk8EKgtdUYkSlYItuUhAeVVLlQjPEejuIguC6SpxvB9L0euPglFCFSqcbI22EWeoaDKk2z6omioB6T6He0ym3Xsp9QuHM3odZn59GNWJUWl5Fvv0qPFFAleI58/BxMvuXe/bs5Ctf+TR//dd/R1NTy/Ff8AJgqjLGruyzFJ08EtCERjya4sJXXMzPv3o3L37JS6lpqEVXDIJamPHyMJ3RJUf4zEb1OF2JP06JJ4SCIy0sr0JQDRNUxaz3r+JZRIwYCyILT3iRqQiFtlgXzZF2Km4ZQwkcMe8XGoYaYFF8OR2xJUjpzXrgAqyoOp3e/G6mzQnydhZVKCioZO1pHM/Ck5JivkDBztAUaWdZct2s3cTRIGcjpA+GzQg0oeNImxARuuIrGC0PYHoVVKFSHagjPKOKNl3fviJ0gpYOWWt6JpAuMHuspFGF7VlE9Rhn1FwwG9b3xyKdnqa+/uQ2E+bDjTe+iR/84E7e976/4N57nz5qMOfhEIpAD+mU06U5j3uuT1Crhk/k1nbVkGqtYurAFNHqCIqm4Foudd21nHb9WlKtKaCKpnDbjLpeYbowzaAxilWy5hR6lVyZmkU1s2OfCGJ1UU5/zTpcx+Wxrzzhh79VHBzTwcxXZloPoZQp8+xPtrLiqmUse/FSIjVRdv66h/RAhnAqhGM6GGGdhiX1gKSYLpPuzwC+annp5UsIJ/3f5JY1zUzun8IsmMTqYgSiBqFkCCEEelDHtRyEIrArNnpQnz1vhfECRVGkkisTiAXJDmVJD2SoFEwWnTe/iuwgMkNZtv5sG3bZIVbnK+4ygxme/vYzLDy/k3hDnOr2qlkv540bn+Saa15zwufxFE7hFJ4fpJTc1/8Qv+69j4yZBQRxI8olbRdwRfvF/y2E8Lg6SkVUZongg6jyqhlU+ylTJsyhe96g2oeLc8hDV8aJuXFA0qcdOGL8PWoPPwp+h15tL1J4NLsLSHpVNAYXMM44U8FplIJCS7iTlkgnUX1+j8k/BH7DgNrLEmfFjLkaTCkTPBi4jzPCZ7E4cKhV+XeR3xDywhSVwiyRHZNx9qt7sIVNiDAaGj3adjbrT3NL8Z00eEda8oQI8dryW/h+6Jvs1/ZgYZL0qnhJ5ZWcbfmZJy+tvJK0MkWPtg1XOAgUFjrdXF+5GRWVJncBLW4b+7TddDlLZm0ihtQBOpxFNLlH9zp9kXU5+7U97NZ2oqKSFVkcYbPIXjzreyyReFIi0i4Za2omMFfFkx5Zawo9Y/B29T2MakOUKdPoNRM9jvHrbm0H3wt/DRubVqcdF5debT/fDH2Jdxf/lhrv+F2HLwQ2bDiPm256M//0T3/LJZdcSXPzifvCvhB47DHB5s2CaJTZ/5VKviVEba3fjRWNSuqeczpsJPcGitgCumaEQfXAiOJwv1FmvR0iIf/nWHRUKhXe8pbrCQZDfP7z30LTNJSMQBrPWZEJkAJE8cgx1MIASmUKJ9FFwYLpiiBmSArUE7dHSLkTNEeb2JcRDBeZQ3YeDiklH/nI+/n85z/Jbbe9j/e978NkBrOM9YxRSpfIDOaY7J2ikqsQSobQAjpOxWbfQ/vofaIfKSWqqjCyY5TRnaOsvXYNocQhQnh4+yijPePE62NM7Z+inClTqXgEJocJVcqQXoRcvwxVVUkFJVsnBdd1yxPafyhlymz+4bNM9U6jBXzCemjLMB1nd7D44i7WN0p+dcBPOE4YkLdgvCy4uNXjFYuOvvrdPiXIF12W5oexFBe9JkBAg/6Kx2+eybFhgWBVRzXLqz0yFgQUTtqO43Bkh3MMbhoilAzPnrtorR9+3PvUAKte5m8IGYbBrbe+l1e84jre855buP76F3PLLe/k9tvvIBg8ejfaCcFvvvNt2PBFJ57r+efVdQ8u4dACKmbBwi7bp8jgUziFFxKO4/D1r3+Bf/7nf0DXDT760f/g+utfd9wvt5Nchtl8MYHBe6E0CkI55GPbcO4xX6tPbUapTOCkVsxur9qN5yA8C8XMsCMaxVU1goEanECCccWlIsCQAu3FF9J02Yv4yt9/kPMvejHnHEcJqSN4TSXOOifIAdVGAbocg4xw+Voox5jiUOupeECPajKmhwhFqmktTOEYCX5b20TYLlE2y1SiIVa+9ByWv2QDWyeL9N75e37+2U/zs8/8O9df/3re+tZ30t4+v3+OgmCRVsvCqksxUy9CR2CJYxcIFTzKQhKTClKb+TyknLMlLTwL7yRIpoRUuamSIG26lIRHlace8hD2XBaNPMHf7/wiOwMemVCKWgdqbIcvr34Jhfr1NLoae1WbqCsoKZJaTwVFY6LtMv6z9SwCaUG3UoPUI4SRLLEtHjUqVLnKLHE/qrikpEK7e2JtaFJKPvjB99DS0sZb33riquCKW8J0KwTU4En7tFpuhV3ZZym7RRJGNYpQMN0KI+UBrrzlKjY98Ayf/fC/8Xef/acZklHzA8ikPafF8IVCWI0S1eJMVkZACSMUgaEYeNIloMUJqREkLpxAa5+UEtMrowgVQwkQPQ6pehAlp8BkZRTbswhpEWqDjejKyd+QVaH6G0CHjWl5JlWBOhrCC9g4+TBZK42Hh+e5aIov0XCkhSNdRsuDVAfqaI50zI5pexaj5UH//EioCTWQMmoxlACTlVHCWoSAGkITOiWnQH2ohcZwK02RNuJ6FQcKO5GALS1sx8T0TDpjSwmoR9q3TJZhy4Qgb0NdCFbV+mFoh+9uFO08GWuSgpOn7BboyW5mYWw5cSN50ufruThwYC8bNpz3R43hSY+0Nck7/+GvecPVr+aOj7+PO/7u345LUAhF0LyqiR2/2olZMAlEA3iuR2Y4S6QmMqtG1YM6q69eyd4H9jGxdwJ7Rr2x8JyOGSJ4ZrwZSw2A1IIUdd11DG8ZJpgIoQVUyukyWkCj7YzW50WeHLSnaFjWQH4sz/gefy5aWEcIQSBikB7I8uxPtpFoSRBOhWg9fQHZkRxmoUJDKkzNwmoWrFtAOBUi3Z8mN+MRXLOwZpYIPohYXZRQMkSyJTFnvq7t0rC0Hs9xGd42ihbQUFRBJW/iuR7R6ghVbTP2IVVhChMF+p/sp2V1E8H40e/Fo9tHqWRNqjt9yw7XcbFKJtO9aTIjWWJ1MZJNCVa8ZBl5L8vIyBDr1q0/6fN4CqfwvwElSuzVejCFSYvbSuN8/c8vEPZmDvCz/b9GV3SWpLoAmChP8sv999Iaa2FFzZI/2bGPhqgXQ0U9wgqhJIrUeHUECJAW0+zQt1ASRYaVQRzcI4Jw5/PWfVp/nO+Ev8oBbS95kWOfupshdYDV1unUew0EtRCrImdytnrZMbtjHBx26Fuo9mpniWCAKq+GXdp2etX9LJiVMPoq4AaviSFlgGllipiMUaZEXslS49Wzzj4TpEBRxnhGf4K7g9/nraV3zdul0eK28c7C++lV91EWJZrcBVTLQ12RVbKadxT/il3adqaUSeIywVJ7JSH8+4CGxssq1/LN8BfZqW0jIANYwqTWq+dllWuPab1Q7dXyjuJfsVF/kj71AKvFOp4JPIEqVWxsPFwGtF6aKk1EyxEiWnw2a0ERClE9QdaapmTnaRFHt994Lp7SHyMv8iw5TAW80Olml7adZ/VnuNi84oTH+mPxwQ9+jN/97pe8//1/yTe+8ZP/0g2TTZsE4bCkvl7Q1+cTv6GQTwgPDUEkAk1NR5J4k4rLmOJQ5z3Hl9VT2a/aDCsOiWPYCJ40HND2gsgJvCqJ2wmcoCLUcRxuvfVmenq2cffdD8yKmNwOif6s4uchHBzLBoRkPiG9VIOg6AjXxJUangRVgOaZuMLAVoJogOOB7c4/Fz+87h18+9tf4cMf/iRvfrMfaptakCS1IMnEngkm9z1LvDZKuCqMa7sMbxshN5qjnKsQToZmayPP8Zg6MM3gpkG6Llw0e4zJvZOomko5WyE/4auMXcvFxkMWTMSmfYiaKvTuZlwPnqs3yJpwICsQAjoTkthhy53+pweY6p2mur1qtmOtlCnT/1Q/jcvquaojgefBk6MKA3kI6/hE8MJjy6Dylm+7ZlRs4snwLBEaCymkczojO8ao7qhGVaD6j+RgAXIjOd+Ko+HQppFQBOFkiKn9k35H3mGBzC0tbXzve7/iy1/+NB/96Ad46KH7+MIXvkNX1/O/n8UbYsTqYmRHsqQWpNDDBqquUs6WidXFZonfcqZCtC5CMPECvPE/Q5wig0/hT4JnnnmC22+/lR07tnDTTW/m9tvvOHEvVkWl3H0TTmop2tRmhGvhpJZh169H6sduixF2kdmtoNkHVdzEQtxIM1rD+UjdxVU1ikJSEZKQVMjjYQAXffTDfG3DhXzmS//GOe/48LHnKT308gQrhMKyYM3sMT0k46bLHwIldqsWCgJTSKqlznKlFsMeQVT6qSro7A6H6MiNM1q7DFMLYisCWeXy0tsMmm97J1u/dz93ffnz3HnnF7nqqmt4xzv+itWrTztiKrtUi/uNEr2qRUgqrLeDnG+F5wS6gU8C3xco8YRepix8wvVFTSt5UW8KtTiAO+OtrJhTgMCuO/N4n9bse84Kj4AUpKRK6vDdaukR3Ps9wju/jJrfzwY9Cqi4kSacYDXXbL2HT6Y6MYMpSsLDFpJaT6Nlxt5CAJYWxIykkDOBcALBxVaEPs2hR7UIo2AiCUrBlWZ0TqjfsfCrX93NQw/dxze/efcJqRcdz2Z/fiej5UFsz5r1/O2MLT2umvQgps2JGYuB6tmFi08qhzAjJd78gbfz/971EX5/z2+5+OWXU3ZLxPXUnyR8DZi1VRgs7qfo5P0WfOmiohHQQ0T1BLpy/HMzVRmjr7CHvJ1BESq1oSbao90E1RAVp4TlmUS1xBEBZZOVUXqymyk5h9Q3SaOG5anTTsr7+GhjHqx4oloc063gYiOkAkKgCMVX+UqB7ZkEtRCTlbFZMtjxbHakn2GsMuwTzcCEOUJIjVByi6TNSabNcVShEVRD1IWaaY92zy40OuOL0RSNoVIvpuuH6LVGu2iNLjpizjum4Fs7FcZKM78j0qYlVuZli3RUoVF2iggEE5URXOmgCpWYlmSiMorpVlhbfc4cX+mTRbFYYGxshM7OI+d2ovCky67MFobLfXgNDq9826v46mc+x5LTlnLjVbccdwHWeloL+bE8I9tHyU/410OkJsLyK5cSiB66BiNVYVZfvZJKroLreIQSwWPaPAhFsOIlywinQgxvG8WuOCQXJOnY0HbSPnEHcTAITnqSVGuK/FjeT5R2XEpTJTKDGRRVITOU4Z73/5JQIkA5U8bMW0gpiVSFyQxnGdw8TNvpC1h0wcJZT7P5UNtVS+ixPrLDuVkvueJUEVVXaFrVSKolSaotxeiOMVzHpXF5A/2bBolVz/0OhavCpAcy5CcKxySDi9NFtIA6+5llBrPkxwqohko4ESLZlCAzmGH7L3awzXsKXddP2BLkFE7hfxN2adv5fuibDKn9uHjEZYJzzYt4WeVVs+33LyS2Te6kaJdYWtU9+1hduJae9B62Tu74byGDu51ltDkd7Nf20O4s8slfZYqiKPJS63x6tG18N/Q1RtURACxMJtVxIjJCu7sQgSAj0rjC5TT70KZShQq/CP4Ykwpr7DN42ngURaqYwmS3tgPL9X12Vzlrj2uTJBDoUqeslOc87uFRFEUG1X6mlIlZS4jFznJ26ttYaa1ln7abvJIjJzJoUmetfTpZkWG7/iwFJU9ZlPlO+GtUe7VcXXn1vJ+7hsYid/FR52dgsNJZe9S/L3VW8peF9/GM8QTjygh1XiPrrDNp9o6vdI3JOBdYlwC+Cvhx5yF+GfgJvdo+FBRanDauLL2cjDc+h6ABP2fBw8OTR2HfjoJxdYyQnLup6ZPwgqzInNRYfyzi8QQf+9inecMbXsn3v/9NXv3q1/2XHdvz/OVhe7tkchImJwWhkKRUgslJOP98j0XzlF1BKdAQmMhZQ7FcDoYzkumgYGRasLgZ/ojM31koExD6loK+W4AtkUEFZ4WkdIOHjB/v/Xm85z1v4be//Tlf+cpdrFp16Bq2zpDoT0m0XQKvToIHyrjAWSKxVx9JXrrxhdjJLvSpbURjXUT0AJZZJs4Iu+OXUdaqGM9BXVge1YP33/7tn/j2t7/Cpz71Za677uYj/j6wcRC74lDdfoiT0EM6w1tHUDSFqgWHRAWKphCIGIztGp9DBgtF+CGT0yWQklK6hDA9FFSKmoFeNik/24veVE/e0rmi3ZtRqMIjw4JfHBBMzvwM1YUFL18oObNBIqVkfNc4wWhgTk0bSgSZmi6RHsjQ3pTgusWSi9tcpiuQCkDNc7QllguDBf94LTGwpwuIrePUbZlGMW0Uow4nHKYgNSZFgGVyinJ67u/iH4uDvyPPzSJxbQ89pM9rVaYoCrfc8k7OPfci3va2G7niirP4+Mc/zbXX3vS85qBqKt0Xd7H1p9uY3D+Fqikomh/obUQNKrkKlZyJUAQd69uPCMT7v4JTZPApvKAolYr84z/+DXfe+SVWrlzLz3/+8PNLF1c07Pr12PUnpzRyI02+ktg14WBruvQQdh4ntZwLZR0/YYxRxZ3VLph4eEC7q1PVtYglb38Tj3zy3xl+xS1HtQ3Q0jsJHLgbLbffDwBKLqXSeQ1urA0FwZVWlDPsIP2qgwb8IlBgUnGRsVYsI4ZaHEFxKwilwLqyzdIDu9gUr+be2kaKqs7uRA1bY1FSH3orX/mLW+n5zg/4whf+jSuuOIsrrng5f/u3/8TChf5CYLdq8ZVwhqzwqPZUMorLD4MFRhSHmyuJWdWsRPLjYIH7jRJxqRCWggHV4RvVYZTVN3PR1m+jpbf7z9WjVNquOiEyeKtmcp9RZEh10CScZge5zIoQnyFktfQOggO/QdgFZLAWz0iAZ6MWh/BCtSwtpHnd8AC/aK9l1HOp4LHWCRCZ8WEentkZf67at9XTeXspyVN6mT7FISlV1joBljonpigtlUr8wz/8DZdcciWXXHLlCb1mf34nvYXdBNWITy56Jr2F3YCgO7HyhMZwpANwxMIloAYJqRGWn7Occ6+6gC994nN0rOuktqGW1ujCP9oPdj5MVkbZm9tO3sqiKQZlOwOAKjRc4VBy8gBHqHeei7Q5ybb009ieSUiL4EqXvvwesuYUlmcyVh7E8Rwieozu+Cq64itQFAXHs9mT3YbpVkgZtQgh8KTLtDlBb343y1LHtoWZD45nsyd35JhpcwoPF9d10FQDkCDlDLGqIVBmWy8PYrwyzHhlmJiWxPIqlJwCFbfMaHmQhFZFS6Rj5rESQvgtqoeHuClCpT3WTUukA9OrYCiBeRXPFQd+uEdhugLdKY+sNUHazLJpIoIli7yozcZ2bAp2lopbxFCDxLQkyYBPHmasKcbLwzSEW9CE/rxULwcO7AWgo6PrpF97EOPlYYZKBwhpUQJqkBve/Ab2bN7Dh9/7t6xbsZ5l7WuO+XotoLHq5StYsK6F/EQBzfC9dw8ngg/HscjM58IIGyy5dDELz+vEMV0CUWNOsW260J8HJCyIQfA41VEgGqBhaT37H+0lLMIziwMoThZRDY1oTRTP9Shny2QG0lRyQTxXUs6UkY7EsVzqYkGEItj70D6EIkg0JwglgkRro3MC9DKDWSSSrgsXsv+RA6RnfIKDsSDdL+qidlENQgg6NrTTsaEdgEquwtiucRzb5fArzrVcVF2ZtXeQUuKYDoqqzPFUjtZGGdk2ipQS6UnyM6plJOghX12RaE6QGc5x9yPf44ILLiWZ/B+YZnMKp/AnRFZk+Hboq4ypw7Q7C9HQmVIm+HXwHuq9Rs6xLnzBj1l0yrObk4dDFxoFq/CCH+9EECTITeU3893Q1zig7cPBJiYTXGpeyWnWBj4V/Scm1Qk6nSVIoeBQIqOkZ+0lBBCUYS40L+N066zZcYfUfsbUEZqcFgIEWWwvZ6+2CwebIbWfZm8B15dex0K3++iTm4GKymn2Wdwd/D7V1BAkRF7keNS4n5JS5NfBn/KM8TjnmS/iqso1nGWdx2b9KQ6o++hwFlFQ8gyqfUSI0eA08WTgUcqiTMqrRlWyqFLl3uAvaPJa/iSfO0CT10JT5Y+zM8uKDGkxjYpGzIuzzFnJy8rXkRJVPK0/SMHOzqljSm6BkBohoh+HFXwOmp0FbNM2zakfXXxC+SDh/l+Jyy9/Kddf/zo+9KF3c9ZZ59HWdmyrpBcCUkIyKfntbxXCYQiHIRSSZDK+Ivi66ySvepVEnYeDSkmVFXaAh4wyYUcw2qvQsw+maxxifQF+9NMgY2dLrr3WD6V73vAg9AMFfavA7ZDIEIi8RH9SEIwJyjceXXEqpeQDH3gnP/rRd/jc5+7k0kuvmjt0I5Te7BH4rYK2G1DAvMTDvEwi59N6KCqV7psRO75IMLeXtaqk19XZrJ/OQ8a1jGYEQRUub/cIzVOj3XXXnfzLv3yY22//8LxEsJSS3Gj+iJpSM/zBXNs9gryUnl+v7f7DXtIDaQKRAKqh4rkeruNimw6u6WBoKmFXkg8FMSsK445OZcTknGUa57X453BvBr7bo1CwJXUhSARgrATf36VQH3ZpjR0imo+AYA6BWh2cX8G7fQp+uk9hKC+QQEthku4dW9ALRZqnPDJ5BzefZ9eiFfSnUqgSoiLCcOqFVcVWd1QTToXJjeRJNMURwrcyM4smnee0HzO3YtmyVfzqV4/xgQ+8k9tueyNPP/04H/nIp55XJkXtwhrOfO3pjO4co5Qp05UI4touE3smsYoWqQUJWs9opXH5/J7v/xdwigw+hRcMPT3beOtbb2BwsJ+PfORTvO51b0Wd7w53svBc9PEnMMYeR5gZnORirKbz8aJH7obbNWuxq1ZgTG7GDdaAoqGUx3AjLViN57HGCXJzOcGdoSxTwqOEhyEEba7GyhnVadvtt9L/vR/xkY+8n8997s4jjqEUBghv/xxKeQI33AhSoo89ilIepbDmfcigX0TVSo1ax/+KPa1XGFB8ElAGUjiBFB4SN7+NhG2yuGRxf3UDlqqyKjeNXhnHMhawNyz5Vcrlb173Fm666c385Cff5eMf/3suumgNr33tLbz7PX/L/S0GWeHS7Rp+wSVVcsJjo2Fynm2zcMZralhxZgLeDil3k67KAdXm3palrAl8iGC6BzzHV1LHOo8bsLBTNflGKEsJjxqpYgrJrwJFxlWXW0pJNARaZhe4JVA0pPRmTqIOQkMpjSECSc60NFYXq+lVbb4eyjKoOATxsPCISYWrzChReSQZWu9pvMR8fsmjn/jE3zE+PsL3v/+rE3p+xS0xWh4kpEZmPVrDM2Fjo+UBWqMLT0i9G9ViqELDcisYM0pOKSUVt0RrdBGpQC1vv/02bn3izXz7E9/gS1/73kmHwp0Iik6enZnNWF6FeCCJIhRKTgGJJKiGiOhxAmqIjDVJ2pygOnh0/+nhUi+mVyFl1MwWUToB9uS24UqXgBJCVTSydpqNUw+jCIWuxAoy1jQFJ0fcSM2+ThEqYS3CpDmK5Zon7TectaYp2PONGcWTLqZawfZsPOlhSwtVaGiKTkAN4UqXmsPeZ2ZGIZ+302TtaaSU2NLGck1KIk+d2kxE96+/tDlJzknPOydN0Y+pHN+fhaGCoC0mKTpZMtYUuqJTH4bRQh0lp5dUwL/+dcUgGagmrEYQQsGTLkU7z7bMUxwo9BDTkyyILJzzPk4Eh8jg568MnjRHAWYVyoqi8Ff/9H7+8lVv4T1/+VZ+/pNHjlvMCUVQ1Zaiqu2FJxZdx2V89wTju/xwkOrOappWNrLHDPrFc0GAhMao5KWdkrV1x267W3T+Qio5k7Hd43iuR2m6DIogXh8DAaV0GRAoukIlUyFcHUbRFBRD4Nku2eEsoVSI9ECWp779DInGuK9EiQVItqSw8iaZkQyu5SIlRFJh2te3EUoG8TxJsjlBODX/b04wHqRucR19T/ajB3X0oIZru2RHctQurCbRGGe6L03v472kBzOoukbTigbaN7RhhA0alzcw9Oww0/1pQokQdsXGtVyC8SDRGv/3T9VVJjNjbN72NP9xy1df6I/rFE7hfzx26FsYVgfpcpag4tdVNV4dOZHjCeOhkyYFJZI+dT9jyihhGabbWUbgOfZQbfEW7pcutmujq/59xfEcTM+mM9n+Qryt54VWt4N3Fz7IXq2HsijT6DbT6DWzWX+aQXUQQQePGia2kMQ8hSpvESlP4SWVa0BAm9NJp9s1x8JBRUWRCq5wEVLQ6XZR7zUyqPRTVAr8Rf6vWe6uPsas5uJC81J61X1s0zfjYLNH24Upyqyz1tPhLmJKmeAXwR8TlwkuMi/nluI7eTBwH1u1TaTcKi42X8zj+kPs1ndSFAWqvVpc4eBgs9hdipAKTxgP/8nI4D8WeZHjK5HPsE17lriMI5E8bNxPQRR4c/EvaY92szOzibQ5ga4EcDwLRWh0xLpP2rprvX0OTxmPsVfrocFtxsVlRB2ize1gtX1kh+N/BT784X/l0Ucf4Lbb3siPf3zfC7NGPRwOiArIIKDBww8LNm1S8DwYG/NVwpomaG/3uPZayStfKY+p7H2pGSWjuGw0LXYVBaJesqCks6ovjpeA++8XdHbC+vUnk5QyF+oQaLsE7v9n77zD5LjKrP+7lTr3dE/OmlHOyRnZlpyTnDDOARywsYlewi6wwO4SluhlYQkGG+NsDDjgCM45yJYlWzmOJufp7ulY6X5/1GikkUbSjCRgl0/HD49xd+XqqXrvuec9p84jggFkBNxyibFCIX+mgxylHJNS8u1vf4U777yVH/7wVs4996JRt+9M8AhhkQEUkPsYJjmRCaQP+yp63wf4C0lUs4zmzBy0rMERYckxVS6zRmmiev31l/jCFz7BpZdezac//c+jblsIQbA4SO+WPmCHtNixHc8yQEC2P0uoxPvOLthkEznMrMX6rkGMgO4RxoAR1Mkn85gZE9dx0RVBUdRHWM0yqEkCPpPJoo3zZ0/Gp3oTAw9uECzvBkMVbEtB1IBpcUl/AVb2CCZEoWpWJev+sh7btIdJ6kxvBl/Yt8+6uD0Nd69RSBSgJizBccm+uJGN/XkWzClhQbXDW6sH6U1ZlG5rIh0OU1JI4wb8PK/FmJuExp3s1lMmrO0T5GyoCEmmxmAMWcuAp2aeceo01jy9lr6tXiCloilUz6mi/vB9dzOEQmF+/OPbOeKIRXz5y5+iubmJW2+9j0hkfJNS4IkbJpeNnH2YumQKtmmj+0dXKf//hENk8CEcFPzxj/fyxS/eREPDRJ5++g2mTJlxcDYsJf4tf8Tf9Kj3n6ofbWANes8ysnM+ixNtHLm8FiQ760ac5qcwut8G18asXkKh/nTcsDebfm0uxqJCgGd8GV4xcpjCZYZtUBDQopjEozE++bVv8f3PfIIzzzyPpUsvGLELo/N1lEwndvEOX2LbKEJLrEHveQez7tTdTuMwy88KrUDnkMrVAZoVizK1iPl9LaSMOGsjMapyGbRCP1IPIwPl1DkaW1WLrZrFLHx85CNXcNZZF3D77f/DT3/6Pf7wh3uY8vmbmHHTtYidbA6iUqEdm42qSVgqlLgq3YpDWkhq3JFP8rir0KM4JEMVKMHdwy/2hleMHIPCZaqjDxHREEFhlVZgg2Yy095+TAI3UIGa2ozUQ3hTnCDsLG64FifSgEDQ6Bh8MhvnXS1Pi2pR7KossP3DhPbBwl/+8ji//vVP+OY3/2vM5FfByXuhXdrIF5FP8ZG2UxSc/JjI4CKjhIpADW3ZJnQ3jyo08k6OgBqiLjyJmFFCzcQGfviDX3Ht1Rfy4mPPjjrDfaDoyXeQc9LD6tmCm8OvBpHSJaLHKAt4BHR/oYf+fZDBSXMAn+IfMZueddMUnDwBLUxQ9worn+pn0EqwIfU+kyIzkXiTA7uGNQqh4Lr2kGZ/fJBD/+yqZBZCEFCDlAeqactsI+sMYrkFb/9DdhHl/ioqAjtUN6qibAfCFwABAABJREFUYrkFsnbaI41VHewsBaFguRaDZoJif9nQshoFZ2SbVd7OkrZTXkCgUbxHdbctGfZGG7QSCBQ0RUcREolGRCvGcruJG8WkrCQhLeIpHMwEXblWck6akBNFFz56nU6SZj9z4kfs9Z7tii1bNhKLxSku3rNVwb7gSGe3CaRovIgbv/1pvvuJb/HDH/47//Iv39zv7R8IpCtZ++f1NC9rBuER1Z3rulm1ZoCXGxeQBmpDXsBHWxruXatQ7HeYsJe60xf2seDCeQy0JOje0M2qx9eSaE1g5S3MjOn5bwd1rLzt+eVJz7REqJ69hGM79GzooZDK44/5CZWFaFvZTnYgRyDagZk1UVSF8unlqIpC79Y+BloTHH31kVTP3vcE0ZTFk8kn8/Rt6cN1vF7VeH2MGWfMINmRYsUfV5JN5AjGglh5iw3Pb2SwO838j8wlWhll7nlz2PjiJlIdKYSqoBqCiukV6AGPgMqn8rzX9iaG4eO00845KPfpEA7h/xKyQylI24ng7QjKIP2if59dNTsjR44HA3fxrvEmWZFFRaXBnsTluWuod3bUugvK5vB28XJW960n7isCAQP5BFPjkzisYuzE6F8DBgYz7bkjPiuQp0U1yePgR0GXgj7FoUvAVFvhGOv43XyCt6PWmcAEZyIbtLXU2Q10qm30KF0MKH0cZh3FFGfkWGOruoll+huemtip5XDraCY4OxSgUVnE9ZnPslpfydvGawwo/UyzZlIqvfSucreSvMjxqv4CxxVOotKt5qLclVyI16YsENQ5Dfw0/D0GlRTgWR9Uu7XUO430iO4xWyBIJKu0FazQ32VQSTLJnsIR5iKK5e7v4FZ1G6u19ymIPLXOBGZZ83abJBgL3tXfZI32AVPt6ehDPSN58nygL+d9412O5jh0Rac920zaShLylVMVrKfEN77JZYAJzkQ+lr2BJ/2P0KpuQ0FlgXUE5+YvJDYau/g3QCQS5Sc/uYMLLjiJ73znq3zta989OBt2wHhNYLwiUBMCJy7pP0Ly9IsCv19y6qnQ3i7o64OBAUlNDZxzzt6JYIBiqXJjNs5v3jIZeFcyuVRQ1uFDtxSIeTYT770nDogMFhkQhSECeyfIAIheEFlGJYN/8pPv8vOf/4h///cfctllV+9jJ4yuBN4TtOBwZ/AE4FqAvYwHNm/ewLXXXsgxxyzmu9/9n712x9UuqKGvqZ/B7kGCxUFcyyXZmaJkYjHlU8toerOZns29Xu6EItB8OtJ1KJtYNkwaZvqzOKbNvPPnsPz3K+jfNoDm98bBeqFAY3UUoSSYHC/GN/Rq2DAAr7YpONKzdnAlDFrwQZ+gPCBJesMR6hbW0r9tgJ5NvcOdWUbIYMriSYTL9n4R3+sWdOcEM+JDYXWpNKFMiv5QmM6MYHqxRm19hP7mAg2D3UQTrWjVJcTqImxzDZZ1uTQWeb+ldf1w3zqFjgxIBIYC88skl89wCY1RiV41q5JoZYTezX3YBZtIRYSSicXjsmO4/PJrqK9v4OMfv5jzzlvCnXc+Qm1t/ZjX3xMUTcHQ/v8LixsNh8jgQzgg2LbNt771ZX71q//moouu5Dvf+SnB4Bi8Te08WnIDSAcn0oD0xRFmCr37bbTEBqQewiqdj2vE8LU+g+uL4waGolalizawGqPlaXKzbhzepCgkULPtSC1Ifspl5Cd+BCEdpBbcjaCY7vqYnvNxWd7hcV+a1XqBBA4Njs4phRDzLriatc88wxe/eCPz5x9Obe2O0AQ13YzUd9mmogIKarZz1NOdZ/s4pxDiWV+WjaqFAKodjQ+LqUQrlpDueRPMBKptIbUAdsk8pB4ablrf+RUYCAT41Ke+yKWXXs0t//Ut7vzWD1l3212c+M1vMO38cxBCkMKhWbX4nX+QP8sM1a7GNMvAwFO4htPtiEISVD+5omoCehF+OfrLs0WxWKHnGRAu1a7GAstPiVRxkWxTLWJSGTHYCaJgI+lWHGYCdtEUUP24RhFKvgcl1w1CR5gJ8MUxq5fgRHYMdMpdjTPM/fOKHQva21u5+ebrOPXUpVxzzU1jXs+n+tEVA9MtEFB2PDoLbgFdMcbs1yqEYFrRPMJ6ER3ZZizXoipYT11oIjGjZHiZM047lwsvvIIvf/kzzJo1j1mzDu4gr+DkATFcNMkhpkoRKs6QlQVsd9zde6Hp1wJkCyPbU7NWComLvosiVhcGGWsQ080T0WP41AAZO014qAVRSknWTlPur8KnjL9tKaLHCGohsvYgfjXo+R8LjZydJm6UMrPocCoCNXRmW0hZCRRUokaMymAt5f4aNEXHlS6ZofVtaWO6FuGhoEXJkPegopN1BimmzGu1d83h++dKl6bBDbRmt1BwvEC9Ir2YqUVzRw16mxCB0oCkIytxsT1yWsJA3sek2CBRn0vSlESNEnJOlqQ5gOUU6Dd7yNkZNKGCkCStPsp8VRTcPC2ZzRT7ysdsGfHaay+xYMHYPML3hGJfOV251mHrDQDLtZg+fwY33XwzP/3R9zn66ONYsmT3CbP9QX4wj2Pt2zMYoL95gNb3WgkWh/BHvEG0a7s80yVoDuY5Ykpo+JHeGIV1A/Bul2BCdO+/fUVVKGkopqShmEh5hNdvfwtFFQSifjSfRvfGHgoZEz2go+gqCIFjOegBHelK8oN5hCIIl4UZaE5gmw6h4qDnY6YKhKbQtqIdfci3wspbvHnH2xz/yWOHQ/X2hECRn8MuXUD/1n6yiRy+sI/SiSVoPo33H11FdiBHSWPx8G/EH/bRtb6b/q39lE0po3RiCcX1cdJ9GXo397HxpU3kElmvbbJgYeYsVrS+wYknnrZfao1DOIT/6yh3KtGkRlZkhglNiSSh9LOocMKYiWCA5/xP8bLvOaqdGupkAyYmm7X13Bv4DZ9Pfw1jiLwLGyGunn0ZL7W8zns97yOl5JjGI1hS9yFivqJ97OXvgRrSIkhAJogMhab5pMI20UuOI/HJ3cNUt0ND4/zcJdwa/DHP+B8nLVIoqARkkHa1jYcDD3Bh7goUFFbo73BP8DYGRB8BGWSFvow3jVf5aPZ6Ztvzh7fpx89h1lFYwuRd/a1hIng7wjJCSklSEHm0IQZr5/u4yFxCPp3jp+HvU+TGKZPllDkVKCgk1QEWFvb8Hk2LQbqVTgIyyHL9bZ4MPIyJiS513tXfZJn+Jh/PfpoKd8dk36vGCzwceICE8NR1KhpzrYV8LPsJwnJ8nXEbtLUY0hgmgrdfDxBsVTdxNMdR7Cun2Fe+542MA7PseUxPz6ZL6UBFpdytHNffxF8DRx21iG984/t84xtfYOLEqVx++TUHvE3fs4LAwwKpgxuVqB3Qe4dgICuoO8qzcWhokDQ0wOAgJBIeMVw9Bv2NgaCq00/5coXq6SPrEV33gugOBE65d8xKP7g7cf5KH7glEneUMuO2237K9773Db74xW/w8Y9/5sAO4ABh2zaf+czVlJaW86tf3Y++D8+MqlmVFNIFtr6xjWRbEkXz7MhmnjadSGWEskll9G7pxbFcImUh1j23AYEYJoKlhEwsRlfrIHXhECd/4QRevfUN8qk8gSI/gVgA13YRqkLF9B0X9J0ugSrApwqEkGgCYgb05qFfEZ6Slx0ig+71PSTbk6g+jbJJpcTrY/us53vzoCtyZGwSnq90xhaAxNY0ghGI6EH8s6vQarwb7E9KurPeMhkLHliv0JWFSUWgKZK0BW92CKrCgrMnjn3yIVQSGlZa7y+OO+5E/vSnl7jyyvNYuvRY7rrrEebOHb+N4CGMjkNk8CHsN/L5PNdffwkvvPBnvvWtH3P11TeOiXjQ+t4nsPE+1HQzuA5uoIxC1WL0gbVoA6tA0cB18LU+hx2bijCTOPEdabQIBddfjt6/mpydB0XH1/wEvpa/oBQGkKqBHZ9JbuoVuMG9q6dKpMpH80UMFBxMISlxVbQh1er3v/8LTjnlCG666Soeeui54fZm11+GsHcxWpceZev6Rp/tFghOMcMcZgXYplpoCCbbOgEU8lMux6haxAR/P8tDfvxKDEX1iuMOxaZsFL9cgJKSUr79rR8z44Zr+NE3v8rjV9/A2j88xPG3fJc36yMUhgLiQig0qRYdik2skKM1s5lJA80EXIcB3UdaSXOSVUogtLuH13tanvsDKfqFiw7YSF4zclydLaLO1Yi7Cts0e8Q69pASJjykQLbjsyjUnOSR+v4SBBLFTGLHZ5CZ81nM2pP2aUdxsGDbNp/85FX4/UFuueXXe/y9SiQ23gNye+HqV4NUBmppSm9A4imCC26BvJOhITxtXAFvmqIzITyF+tBkJO5wcvOu+M///B/Wrl3FNddcyJNPvk5JSemoy+0PQloEkLjSRREKATXMoJkCITGGAuMs10QIhSJj76RTVaCe/kIPWTuNTxmyljD7kEiyVhopJX7V7xFh0savBtEUA03RaAhPYVNqNQOFXjRFw3JMgnp4RBDbeOBT/dQEG1nR9wY5ZxvgeQZrQsN1Jcv7X6YyUMe8kmNGbXvsL3SzObWWQSsJUuK6LrY0yTmZIUG7QlALYzkmlmuRtTPknAwRrYiqoDdb3ZlrYUt6rWfpYJTgSId+s4e1ieUcVnrcsGWE7VqAoMincdoEyR82CrpzxUjSuG6IIr/JUdW9ONJEESoVgWriRgkbUh/QnWvFkQ6aohPRY6hCpeDkSFr9FOmegtiRNprY9xR+Oj3IW2+9wje+8YNxX++dURGooSfXTm++E03RkUgc6VAZqOWLn1nK6nc/4MYbr+CRR15g2rRZ+97gHpAdyLLxxc30bOrBdSTRijATj51I+ZQ9+xAmWhPYBWeYCAZPGZDzB3HSeYTYUawKAT5V0pvzCuOxonpOFdNPmkLL8lZUQ0OoXlCFoisIKQGvSLdNB92v4TqeX2+4LEyoJETX2i6MgI6iqTh2Fk1VcQoOhcE8tqWhqgqO7ZLsSLH2z+s45tqj0XaNqd4FqqZSNsp1GWgewB/xjfgb0wM6ruOS7s0Mr6NoCtGKCJHyMKGSINuWNTPYNUigKIA+GdbftprPfulfxnyNDuEQ/pEw3Z7NHHsB7+hvUuKWYmDQq3QTd0s4zjxxzNspUOAt/VWK3BhFQzI8Hz4a7clsU7ewUVvLLHvHhHCxP8b5U87k3MmnA7tnEPxvgqCSmLOIrPIcA0oKTQbIiwRRWUrEPYGcgPBeHrOTnWkstI9knb6aSe5UwjJKpVtFjhyvGM9zmHkUdU4Dj/n+QJYM0+3Zw/7/m7UNPOF/mOnp2buFupU4ZfjxkxaDhGWEtBikT+mhTW1hhjUb365SyZ1wvHky6wtreEd/08s5UHrpU3oodytHve8ODs/7nuYF359JKAM42LSqzUyypzHNmTy8zDptNS/6/sLFOS/grFvp5FH/gzjYw+eVI8d7+ttMMqZwRuG8cd0LvwziCHu3z13cvZ7vgUBFpdo9MJ/jg43rrvs0mzdv4Mtf/hT19Q0cd9zY/1Z3hRgE30sCNwhuNSDBVUDvBKMD7CzoO83R2LZH4L7xhqCoCCZPlkyYsPdhUH09qKokl4OAH4QFloBMRjB9+kjFrEiB8Y5A3SiQAYk9F6y5EvbwiJDFYB4n8T8uEAVwI6AkABcKJ0jY6WchpeQXv7iFb33ry9x44z/xuc99ZX8v20HDrbf+mJUr3+XRR18iGt33ZJgQAt/sBnpDdazptIj5BIsmG4QrBUIwwqbMsR02vbwFu+D9zSSlxotuOdvcIOkim5WtEc4oDjL/koVseXEjmf4sZsYkEA8yZckk4vWx4f325QRVIU9005cThHRP7JU2BbVhiabAU02CkAazSnVq5lVTM2983boVAbBcgSsligCKwrjxCEpbglBZDBAEVImWSkNVFK3SOz4pIecIqkPeb2nDgKA97VlGbM+TDOsQ80mWdQpOb5Dof+NXztSpM3n88Ve4+uoLOP/8E7n11vvGnPdzCHvHITL4EPYLuVyOa6+9kDfffJm77350zEovJddDcN3tKLke7Eij5+mbaSe0+udIRcUqPxpUj6BRsl3o3W/jBT25sDNhJi2kGgRFxeh4mcCmB3D1CHakEeHk0LvfRth50gv+eXh7e0NcqruN+YuKYvzsZ3fx4Q+fyC23fJMvfenfAbAqjsLofBU1tQUnXOcpldPbcAMVWGV7n6kqlirF9i6DdyFwI42cotbQYiRZp9gEsCggCUnBmYUwkVH8crfjI3WzkXfdwyNPPsS7N3+Vu49aTMl/foHTT11AjZnBCVQSjtSzwacxsXc91ekm1pdOxlJUwo7FyR3rOLvtGezDpyGNHS/SHC5/8qfJCcmMIRsIF8l61eJJX4YbckUcYwXYpKXoUWxKXRUTaFIt6hyNGdutHRSV3NQrsItnovWuRLgWdnwGVvmRQ5YR+4blmvTmOyk4OQzVT6mvctxesuClzC5b9jp//ONzo7bDO0je0HO8buRICIcaV+d4M8hs2/NjnhjxWhI7c62k7RS6YtAQnsbEyP6ldwshEOyZzAkGg/zmN7/n9NOP4ROfuIz77ntin7Pee4OUkr5CF83pzfQUOkkUekkU+ogbZfjUAIqi4AzZMyQKHplbFazfp/9sZaCOnJ2lNbuFtmwTWTuNXwkgcbFdi6w9OEw6O9KhPjwZbUhdXReaRFAL05lrIe/kiAbjVAXriej7p3CSUjJoJdAUjbAowhyyebCwkEJiuzabB9eQtdPMih8+YhCdtlKsSSwnZ2cJ656fXt7NoTk6fjVIUAvjVwMIFLrzbfjVIAKoDTZQH548bN/Qnt2GQAwR7qAChuKjM9fKhuQHVAXqaM8101/oBgTlgSqOrJxMSSDES60aawfylAa6mFeeoSQ4yKCVoyJQS8woRREKLpK0lUQXPvrNHlShIoRAU3RMp4CpmYSU8B4nGXbFK688h2VZnHTS6ft1zbfDUHzMjh9BZ66FnnwHilAo81dRGahDU3R+8Yt7OP/8E7j88nN4/PFXqKwcX6ELnofbyoc/oHdLH6GSELpfob95gPQjH7DwogV79FQTijKUGTgyHKTIyeMIBVfCdtswKSHvCKrCOwZZmb4MbSvbSbQm8EV8VM6opHxq2QivMUVVmHnmTIqqi2hf1YGVs5h7/hzMTIHNL28lM5DFCPnwR/1IKQmVhnAKDpHKCL7QjveUnbfQ/TqO45BP5nFd6U1S4qmZnYLtqUbaEpQ07p+thy/iI5cYOanput756v7dnzFCCCqmlVM+tQy7YKNqKv/0hY9TVVXDmWeev1/HcAiH8H8dOjpXZa+nylfDu8abmMJkrnUYJxfOYLIzbczbKYg8OSVLwB2pkjXwYQuLrBhd/rc/JHCXYvOOnqdVsSj5K1lx7YyQVKlzzkFzaulS36IgklQ58/G5x1DlTsLYQ1faztiibmKiM5k6p2H4Mz8BOkU7W7SNKCh0au1U2bXDE/gCQZVTQ6u6jQ61dcS6AJOcqcyxFvCW/io2Ni1aE4PCs37wST9/DNzLBbnLdyORYcd9r/VNYJn+GqYwOdo8jhMKp42wpdiO142X+GPgXoJuiBq7ji3aRtqVVgJqkFqnHgUFFZUSt5SV+rvD+12vrWFA6WOaPWv4vAIECMsoy4w3OL1w7riUtnPtBbxhvES/6CMuvXyTXqWbgAwy0x5bCPKukEg+0N7jHf0tEmofjfZkjjKP/V9HAO8MIQTf/OZ/sW3bVq6//hL+9KeXmTJl/+p4pRuUhMCukZAHfbVA7YYpecHkJKx+GRpPkSh+ME1YvtwLm33iCQVF8YLkTj7Z5ayz9mwbMWuWZP48wfI/CyJJ0GwYAKbNlRx15I7Bq+iH0G0K2nqBNCTJvMK2pyUcB9XXSvQ9/Jnnz5TICBivCpSk5x9sHi8xj96xbcdx+Nd/vZk77/wln/nMP/PP//wf+yXYOJjYuHEtP/zhv3PDDZ/jsMPGFjjfMgi/el+hLaMS1nU6TFi/BtqzknMmjVTVqpqKNaWOF1YMYlpxthEmjUZ5LkmVJtFCZdy3TnBaQw2nXVmC7EmAhHhtDF9k5Bi1NiJZ2auwoEyyOekRwq6UxPySiCG5Z63AlZ4AoSIkuGz66N7Ie8PCCskr7ZKNCUF1SAIK/ROnUTW4knBvPwNphUDBwR8J0TRxKsWOinChIwOlfsnhFd79LjheR7K2y+01VC/w2nL5m5PBAGVlFfz+98/wyU9eybXXXshvfvMHTjrpjL/9gfyD4RAZfAjjRjab5eqrL2DZste5885H9j2jKiVKphU104bW+x5KuhW7eA4MFbFuuA6l63WkERtB3LqBcpRMGwiBOtiEEx0KNLPzKPl+8hNPBKFitD0ProVSSKCmmpBGBCdYjZpYhzawBrt0/n6f6xFHHMMXvvB1vv/9f2PRohNYtGgJdnwG2Wkfw7/lIbTkJhAKTriW3ORLcEM1+72vRsfgpmycd/QcrYpNXKostPxMGUUVvDP8KFyRj3LsKVew4eUT+PE/f5yVN36dVz40kzP/9XJiFd2o6RbisXrSuRa+9cFzrK1Jk/YFqMjnqcum0DPtZBIbsMqPGN5uk2rRqdg0bPcDxvN2rXRVNmkm/cLlKCtAr+LwqpFjg2qhAA2OziX5CKGdCWxFwyo/Eqt8/G3oaSvF6oF3SFleOJcEivQYM+OHEdFjY97Os88+yY9//B0u+eev4h67kG7Hptwd+Qh80pfmSV8GHUFIClapBTYFTK7MFbHQ9qMpOlOL5lIfnkzByeNT/eNSBO8Pamsn8OtfP8DFF5/Of/zHP/PNb96yX9uRUrIptZoNqfdJmP3DYX5SSnoKHRQZxUyMTCesRTHdPKBQ6q+gwl+zT1JRCMHE6HSKfWW81fMCYT1K3CghbQ3Slm2i4OTIOp7tQl1oEnOKjxqxbqm/klL/wUlyHbQS9Oa7KPGVYyh+unKtuNJBCIW8k6XYV4buGnTnO6g1+4n7dqitu3NtZO0dPsoAZb4qTCc/pDRyyTtZQNAYmcb0ogXou4TDSSR5OzusOrZdm+5cKxknjeUUWJdcybrECnyaf+j3K2lKbyRlJphXfDRzSsP05EM0pztIW4OoQqUxPJ0J4SnDA39D8WGofkJalIwzSMHJ4VMDSClxcbBdi6pI/W5EgeWa9Bd6sFyToBYaJpefffYpJk+eNqZ0bce1SVr9ONIhrEWHwxS3w1B91IcnUx/e3Ys7Gi3i7rv/xNlnH88VV5zDww8/P257gZ5NvfRvGyBeH0M6XuEaq4vR3zRA64q2PZLBJQ1xjLAxIhzEyltMyAzSOXkyGxKCqqA3EOjMCCqDksOGAuRSnSne+8NKUp1egIhtOXSs6mTykslMPm7kNdMMlQlH1jPhyJGeZrPOnEnn2m5yyRy6XydeHyNaEWHza1vZ/MoWcskciq6SaEmAlPgiPqTjeooUxSPBpesiFAXbtEl1D2Jbzriu3c6omVtNf1M/uUQOf5Ef6UgS7UnCpSFKJ+15BCKEQPfrtLZu46GH7udrX/suhnHId+0Q/v9FREb5cP5SzsyfjyVMwjIy7lb4sIxQ7dSwQVtH3N7x95cQA4RkhEr34ITIblUtfhtI0KbY+FEwkbxh5LgoH+Uoa892DQeCKbbBRCfAZvVIDjMXYQBJ4dKhOBxj+jHGcK1U1N0yBDw/Zs+zV0FBSIEU7ghhh4vrfSt3r2EUFC7LXoMdtHggcCe2sKl2aml0JhNyIzzve5oJzkSONo8b9ZjCMsI5+Y9wRv5cbGz8+Ee97w4OrxjPoUmdardueN2gDNGr9DCg9FHilu04XqkOb8dhdxUvgCpVTMx9XrddMcdawMmFM3nR9wxdogMxdCxn5M9lmr1/3TrP+p7kkcDvsLDwSR+rtJW8o7/JddlP0ejsfyAteD7aH+jv0aY245d+ZtpzqXcaD4rVhK7r3HrrfZxzzvFcddV5PP74q/vVfSdDIH2et67WJFBbwY2CakgucwS/ysHmVwVOLaRSkkxGMmNGL9FoC4VCgu7uFL/6VZKVKwcIBpMMDqZIpweRcscPWVVV1FYDtcVHk+lD04uZESxjYUsZvY+WYZ1VTnFxKdFXfWjrBNZkyV9a4ekO6EsK9E0KNdtcLvi4y6RJo5yE5qmAC8dKLwAvCDvrVLLZDDfeeAXPP/803//+z7niiuvGf8EPMmzb5nOfu47a2gl84QvfGPN6zzYL2tKC6cVyWADQk4UXWxWOqHSo3smlcFmn4HG9geayFJmsTasWJuyalCoKkfoYTXmNlkFo/kCwrDNAY1GAeWWSelsyaSeBAcCRlZJlXZLOrGBSkaQm7Nky2K4ka3nH49ckrvRCpX+/QaHxCJfgOJi68iB8bJbLY5sVtqUEEpgwrZhTjjiMYHsnmb4MweIQU+sqeCZVRFNS4EqYWCRZOtGlfqgcrwlLwprCQEFSPKQMlxL68oKF5ZLAQc5dHA8CgQC33no/119/CddddxG//e3DLF588t/vgP4BcIgMPoRxIZvNcNVV57FixTvce+9jHHPM8XtfwbXwb3oAX/vLCCuJkulEKfThhutwfcU7lhM6ws7vvr5qYFYdh7bdQgIBQsEqO4xC/engFNASG9B6V6CYKZAOAnCNGE60AcVMHPA5f+pTX+LVV1/ghhsu409/eomJE6dgVR2LXTofNbUFhIodnQTagbdYVbkaZxfG5wEGDIWv6Uwt9FBz4+F8/fwTWP3ln/Krj3yTk794EYedXEtes6lKNBHofZ95+T6sssORenh4C2Pf1w5oCM4tRDjGCniDCymY6Bj4DpInmEdgriJp9RMzSlCEiitdEmYfG5OrWFCyaEwz08veeYPrbriU2rNOJfXla7lDTRKTCqcXQpxgBhEI+oTDK0aOIqkMk8RlwGbV5Flfhrm2b1id51eDo5LArnTozLXQmW3FdAvEfWXUBCcQ3k+V63Ycc8zx/Md/3MJXvuL5B19yyUfHvY0Bs5fmzCbydg5DMfCpAVzpkLdzBPQwYS3CnPiRY/Y+HhXCC12L6EUIoRAxipiizSZh9pC1M8wrPoaG8FSUfaVmHADyThZbWuhKERKXgptHVbwJDdu1cKWDofrI2CkyVoqQFkYRGpqikXHSKEMq2+FTUgRhvYgio5i4rwxH2kT1GGX+qhEk8HYoQiGix+jOtxNQQ7Rnmkha/d7FQZKzMzjSRhEqAX8QIRT8aoABs5eefDs1oUbK/FVE9Tjd+XaQkiKjeISlRcwoIaIVkbISlPjK6S90k7Mz2NIkosWYEJpEbWgkSZk0+1mXWEHKSgASRaiU+iqYXrSA559/mvPOu3if1zZh9rE++T6DZgKJi08NUBucSENk6pgVatXVtdx7758477wTuO66i7n77kfHRSZm+7NYOZPu9T3kkp6y1R/14wv7SLYn97hetCrKpGMb2fTKFno39w29SgTTZlZw+CIfT7dLtiS9EMwZJZKlje7woGDrG9tIdQ5S2liyI0CkL0PTG01UzawYkx9aoChA49ETdvt8yuLJ6IbG1je3kU/mcF0XVVNwbIfCoDfYF1IgpUTz66i6ipUzMdMFfMH9I2Ed2yFWF6PusDo6VneSaepHCEG4LMTM02fgj+77GfCLX/wXkUgRl19+7X4dwyEcwj8a/Pjx72ervYLCCYXT2aY2sUlbT7FbQk5kSYkUJxROpdbZ/dkxXkgkTxtp2hWb6Y4xHNy6TbF4wpdmtu0bOYl/kOBDcGkuyv2BFNtUCxtJSCosMYMsNsc2mX6YdRQbtbXkyBHAI627lU4iMspUewbVTh11TgObtQ1MtqehoODi0q62MNOaS5U7ulAjIqNMtqfTaE+m0ZlMUIaGwwD76WW5/vYeyeDt0If+2RNyIsuA0k/UjQ1/VuyWEpYR+pU+ckOqbwuThNrPsbkTho9hgjORoAwzIPqHg+UcHAaUfo4uHLdHUlQiKVDAwEDZyR9AReXD+UuZbx3OFm0jAsFkexoTnIn7RbD2KT38xfc4fjdAo+sRvy4uG7Q1PON7ko9nP73fxO2gSHFH8Od8oK/AxUUiKZJPcH7uEo43T9qvbe6KaLSIu+56hLPOOpZrr72Q++57Ymy5NzvBrQBrpsR4QaB2eDYLQoHu/m62xldQqW2mtWcLbWxlW9tmEomtbNo0uNt23n8/QDxeRDRaRDgcQVGUYULYNm2cNgvLMcmTJ5Ee4NWuQX69FXgX+Jq3jbgvTm2gntI10+nKzqDUP53G2Ayi6alsWWtw990K//RPLtHR5uBtPCI4wAgiuLe3m6uuOo8NG9by298+9L9GiXnvvbezYsU7PProSwQCY5vIKjiwrl8Q0CStae+XWeyXlAZgfUKyLSWoHvKsydrw+FaBo6ocPbuILR0FkgkFRfGTCgcZlBr9OYgakLUFK3rg9XZ4uU1QG4YjKlwuniYZinugNgJXz3J5cqtH1CoCjqiUrOuHgLZjOUXAhChsTcKmAZhbBoV0gVTXIJquEq2O7jWAbXIRnDfJ5b0ez/biqApJTSQMU0dOzMyUrhcOJ6EytMMOAqA2DMdUuzzbLEiZENBgIC+I+yUn1bsI4dWR3et7vKA7x6W4sZjKGRWjdpYdbOi6zi9/eR/XXXcRV199Affc8yc+9KHFf/X9/qPiEBl8CGOGbdtcc82FrFz5Lvfe+xhHz5uByHbg+ss8n99RYLS9gH/b47j+CpxQLariR003o3cvw6w5Eanoni+nHkK4FjjmDpuIXBfSiFJoOJv8pI+g965E2BmcUC1WyTyPfLXzqKnNKPkeUIOgBZGug5LvRdg5XPXATMvBm5G99db7OffcxVx22VKeuOcOap1NKNlOnHAtVsUxB4UIPhhQk5uZ29/KUYsuovT5X9H0jZ/x5L/fxbq3F7Lwm3V8qL8biUDJdaMl1mOVHYaaacENlHlBbzthgqNT4aq0qzb1jjZsE9Gh2Cyw/RTvNGgod7XdVLYHAxl7kAGzD4wYyyIhNgd0VAkNGQ13cIC0ndynOnjjxrVc+dHziS6Yw4d+8z/U4AcHuhSHP/nS1Do6Ux2DdtUmIRym7tIuWeaqdCned6Vyz+copWRjahXN6c0oQkEVKtsGN9Cb72Ru8ZHjUjGPho9+9AZWr17Jv/zLJ6mrm8CiRUvGtX7C7KPg5JG4aEPEoiJUFEVF4AV9ZezBAyKDDcWHJnQs10JVvWulKipBLYJfDVEdrP+rEsEAhupHExqWNBHSI4AtaaGg4Ff9QxMKDgWnwNb0OjYPrkVVNCoCtRiKD1c6I6wEtqttS3wVTIyOrY2wJtRAv9lDe3bbMBHshc4Z2NICJAU3T97JEdBCw8rrtO21qPblu1mfXEnGHgQkmqJTFahnStEcVKGiKRpTi+ayNvEeGTs15MHseQfPLFpIib9iBKHtSIf1yfdJWQMUGcUoQsVyTbrybWxcsZGuro59em+ZToG1A++RtlNE9RiKUMk5Gbak1xLQgsN+yWPB9Omzuf3233PZZWfxxS9+gh//+PYxtxu6rkuiLYlQBL6Q14aX6c2Q7hqkpKF4j+sJIZi4qJHi+ji9W/pwbJeiqihlU8rQDJXJ5S7dWU9UVhHcoeiwCza9W/sIxoMjLCGCxUH6mvpJtCUPKBxDM1QmL56EVbBI96WpPayWnnU9pPszqIaCXQAU0Hwa0pWYGRPpSlxNMtCaIFYbG/O+pJR0rOpk6xtNZPqzKJqgpKGY4oY4gaIAxRPiGGMgmHt6urj//t/w6U//M8Hggb9jD+EQDgEmZaZzdvpilkfeoMffSdSNcZp5NksKpx0UJeSAcNmsWVS52jARDFDjamxRLZpUi1n2+O23xoJ6V+fmTDGbNJOMcKl0NGpdbczn9SFzMeu1NazU30UOUYNhGebM/PnUOQ0IBB/OX8JvA7eyTluNOkQG1zh1nJu/aAQhuitMUUDHICJHMmSGNMiI9B7WGjsCMkixW0qb2kyx4xG6IRmm3mkkoQzQrrSREWkkklnWPJYUdtjuNTiTOLZwAs/4n6CPHnzSy/lotCdxfGF3QlQiWa6/zUvGs3Sp7cTdYhaZJ/Ahc/EwwSwQTHKmMsmZesDn1qRuIaH0M8WeMfyZgkKZW8FGbS1pMbjbdR0rXvY9xwr9HSbaU/HjRyJpV1t4LPAHptuzKHcPTjdZfX0jd9zxRy6++HQuu+ws7rzzYYqKYmPfgIDe01NsfHsFKzuW8bbzNu8UltFsN8M20BSN+sAE6iobmTjxKDKZy5g4cSLRaB0+Xxyfr4jm5iKWLNG54orRzbPVTRD5kYpdK2HoTzRn5egd6KG/tYfmpZ10Kd2kHuymubuJ53rX0ZF5jjVOLy+3gkAl1jMRX8s8LOtDXHzxImbNmouqquCA8brAeFmgDgic2JBFxCLJ5qYNXHHFOeRyWR566Hnmzl1w4Bf8IMA0TX760+9z3nkXc/jhR49r3ZZB2JZSUAWAJKApTI5JkJKdc4hbUtCVEdRHJKqqUFIeIOQoqIqk1wLhQLEPkiZkLIj7IOrDs4nwSV5pF9RH4UPVkrztee5OjcPkmEtfzgt10wR86y0VXRl531UBrvTI661vNnmChP4cJgpF1UUcefY04qPUfo4Lj24WvNSqkLa8z15vg6WTXBbXjLTAUATU7CGrXQj48BRJRcgLjUsV4EPVLsfVSqbEPLuyNU+to+XdFqT0lm9d0U7Xum7mnT/nb0II+3w+fv3r3/Gxj32Yq646j3vvfZyjjlr0V9/vPyIOkcGHMGZ897tf47XXXuB3v72XJaH3Md66G1wbJ1xLoeEcrLLDRq4gXYz2F5FqADfgtd444XqcyAS0wSbUxHqcUDVqthM7PhOpR9CSGz1vYGmDFiDXcB5O2HPWL9Tv3ianpptR7CwCBSkUJN6gXyo6uCZqtmMPTVb7hrDS6D3vomQ7qNJCPHDb7Sy96DyuuOrD/OWfjyEciaL3LMPoeJXsrBuwS+bte6N/dUhig21c98HT3DfpcELfvZHQUZNZ/vXfUjj7i3z5ihKUkizSdNGcgnfNfHGskvnovctxfXHs+CxQDYIoLM1HeCCQYp1qYiAoIKlxNc4ohP8micCudMkKeK40RnPAj991kcBWX5QtBiyQNnvTUXd0tHHppUsJVVYw53e3UefbsXSVq7FONflAKzDVMfBLMXyOgZ3OrSAkOgLfXgYT4NkTtGe2EdBC+IcCAKUmGTB7aE1vYUb8wJJPhRB8+9v/TWtrM1dddR733ffEOF982wlOT2W4/RQFIIf8fNVR7CBydoa+Qje2axLUIpT4ylH3MPkTUEOU+atoy25FIDAUH6abJ+dkqA9Nxq8duKWGZ3XQPWR1EB62OtiOIr2YEl85zZnN5J0splug4Ho2D6qiYrkFEoU+ck4awzEIahEc12bL4FqKjVL8apCk2T/kGQwZK0VADVMeGLu/bYmvghlFC3iz+xkkElWoQ5YiATJWmrybw5EW7k5WHSDRhUHBybMuuYKcnaHIKEYgKLh5WjKbCevRYcVv3FfK4aXH0VvownYtAlqIYl/5qPcwafaRMgeI6vFh4llXDPxqkJ/95idMmjx1n10efYUu0naSIqNk+HoHtTCmWaAj2zwuMhhg0aIl/Nd/3canPvVRSkrK+drX/nNMhLBjS5AghIKiKghFoOoq+ZyF5tt7SSOEIF4fJ16/u5WEIjx1xCgrIYTAdXZpUXa9FuWdCWIA13Hp29JH79Y+pCOJ18cpn1qGqu+9ry7ZMUiwKECuP4uVtwiXhFA0he71PV6IYcH2ro/wQt0A1j27geo51SP8hveGrnXdfPCnVdgFG9u0yQ7kaF/VSc2cao7/5KIxF/C33vpjVFXj6qtvGtPyh3AIh7Bn5OwcT259lrc73yNn5ykyIiyuO5eTa5fgVw6e0ECwvT9lJORO3+0My7XozyfwqQYx34F1NwEYCGbuJ9kckmGuy3ya9/XlbNO2YEgfM+3ZTLanD9eiU+wZfC7zZVbq79Kn9FLqljHfOnzYgmFPqHUmoKGRE1kC0qtRHBzSyiDTCvsfdLodKirHmSdyT/A22tQWSpxSciKHjcUZ+fOYbx1OXmSpcxqYbx1OSO5gaNara+gRndjYpESCclnBWbkPc7x5ImXu7lkObxqvcG/gdmxhE3PjtKrN3BP8NSmR5KzCwfd29whmMWzHsR0OjueDvBcBxd4gkSzTXycqY/iHEswEgmqnjvXaatZrayg3Dw4ZDHD44Ufzu989zVVXncsFF5zEffc9QXn5nrdvmRbvPPYaz73wFC++/yzrtqzGdV2CapCF0cM5v+EjLGw4knkVC2hIT0ALaQx+xeWFZYK77xY0NMD2hqhCwft3Y6OkqwveekvQ1CSIxSQLF0pmzfJsG6QPRM77N0BAD1Cv19NQOYEpJzu45eArEgR+r/ClCCRtiPt76R9YS19uHV3Fa9javpy77/4X7rjDJByOcNhhR/OhkmNZ3LmII8qPRsT9qF0QvFfw4jvP8vHbr6C0tIwHH3yaurqGA7rGdsGmbWU77R90YOUtSieVULewjkj5HhjJveD3v7+bjo5WPvvZ8YXXrusXDJqeNUNJEFQFBguS5V2CwyolU+M7+S+Lkc/LuA/Kgp562HYlmvDUwznb88+N+SFneZ66IR0MBe5dK3i+WZC1BRVByQl1ni9v2dBQSEpoKJKs6BbEfUMBwy5sTEDBEfRs7qXr+Q105VXalTim5eB/P8HG7tVc8KkjqCoZWfut6BE8s00Q80lqhy5rRwYe3aTQEHVoGMe8jK7AklrJ4hov6G7nUrd3Sx+t77USKgnhC3s/SLtg07m2i/IpZdQfXjeu+7K/8Pv9/OY3f+CjHz2PK688hz/96SWmT5/9N9n3PxLG/ZR+8skneeihhyguLuaaa65h+vQdSqmBgQEuuOACnn/++YN6kIfw98djj/2Bn//8R3zja9/m1KIP0No+wA1VI/UwWnIT6ppbycz5LHbxToWTa6OYKeTOnpKqgVV+FMLKIpw8ip3FKplPoeFsnHA9evfbqKlNoAaxSucNeQvvhSCQnj+Y4y9FuBbCNUEoSF9saP+J/TpfJddNcNUv0BJrvKc1khlGnEc+ezinfft5LvnlGn7/9UswNAUtuR7/lodIx2ftUSH9t4DI92J0vYk6uI1p6Rb+teVVtsSqsWNprE9FuO7uJMf9dzO/urSWS2YUkFJiVi5CzbRhtD2PaLU9y4vYdLIzr8cNVnK47ackq/KelqdfcahxNBbafir+CirgTsVmvWZiI6lzdCY5OiEtTEu0mC0+hQkFa7gRb9At0ByKsNXxU7EH28xEYoDLL18KwFUP3s/6eAx2WVYD0sIjeRodnQZbZ4NmMcnR0RFkcekRDiebob2G+AGkrASWtAgrOwZNQgj8apC+QvdwgNqBwDAMbr/998MvvgceeIqFC8fmw1ykF6MLA0MxyDlZVKEikcOEZFSP76Ze7s13si65gqydATwLhBJfBbNiCzFGURALIZgSnYUrHXoLnWTsQXTFoCbYwMTojN2WHy+SZj9rEstJmZ5/tCo0ygJVzChaMBwo6PkXz2BbehOWa6ELA6EqSFdScPJ0ZdvQVYOQFt3hDayC4fpIWQkaw9PpLXQwaKUQQNSIMykyk7A+9ipKCEFFoIZiXwW2tHGkg4JK2hrEdi0caZPf7kMsXdJ2Cp/ip9RfSX+hm6w9OIJ09asBTCdPe7aZmmDjMGlqqH6qg/tuH7aHQgF39X7ubu7inRff4tvf/fE+FduWaw4VhCOX0xWDnJPZLZhtLPjwhy+lv7+Pr3/9n5BS8vWvf3ef2zDTBWI1Mcy8SWGwgJSg+zSiVVFU48CMzFwJ73ULlnUJBvKej9qiapXKGRVsfnULgSI/qq56IYVdgwTjwREexdKVrH92A01vNeNYDkLA1je3UT2nijlnz9orWW0EdcycxWBXGt2vo/t1XNdFD+jYBQvXkWgBDc3QMII6CEG2L0vn2i4mjKHwllKybVkLZtYin8qTS3iWFGbaZP2z6xnY1s+RHz2C2nk1w2TzaNi4cS2//vVP+NSnvkQsNro/8/9vOFQXH8L+QkrJHzc8zgutr1LqL6bUX0yikOSxDc8QIMhJ9fuwYhsHYlJhqm3wtp4n6iioCCSSVtWm0tVotPXhY1re/T7PbHuRzkw3uqozp3QGSyeeRrE/BkBWZNiorcPEpM6ZQKU7/jDQ8cKPnyOtD3Gk9aE9LlPmVnByYXwJ87Osecy3Ducd/Q1CMowmNRLKAI3WZD5kjv36i8IAet8H4ORxw7XYRdNA8d5JHzIXkxc5XvD9mQ6tDUP6WGQu4cO5y4btH3aG6eR5yXqGuyK/Ii1TlObKqdArKWgmaZEaleA2MXnO9xQAk20vvLCUcjqVdl7yPcMx5vGj7utAMNmeRoVTRbPaRKMzCYHAwqRX6eakwpkE2X8BgI2Nsoe629m1mD8IOPzwo3n44ee59NKzOPfcJTzwwJMjchR6e7t5/vmnee7pp3jphWdIFVJU+qo4peo0PnH2p5h97eHM3jqT8J913CjICChJz0s4d7pEhuCwwyTvvivoeEewoCApzgs6JdQcLSktlfzsZwqtrYJgUFIoKLz9Npx3nsvJJ0nPiuJNgWNIZBBEBtQOQeFYl+0/B3ORRNsgmdgieLkPakNlhH1lVM8+jkn1kuomuPrqHIryDm+//Spvv/46P3/8Fv7T/AYhPcQpDadzWuNZvLflHX712M9ZfOwp/OJX9xzwu951XFY/uZaW91rRDA1VV9j86lZ6NvWy8KIF4yKELcvipz/9PmeddT5Tp84c13G81w2lAfCp0JPzhvcSb2g4JSaJ7TRXNSEK1RFJSxomRT0qYmaJpD/nBahlLIEiJPUR6M56xHHBhRK/p+xtTwu6shDRJUFd0pSCO9coSFyOrPQoZiHglHqX5pTC+gEvUG5lryBtQkSHnzfrlCkNlKppoopDkU/B8sfoax/gDy/0cdOHq0aomVf2eMFvJTu5ZlSFYO0ArOkTNERHV57vDdtJ8Z0x0DKAY7nDRDB43WuqptKzqfdvRgaDF7T+298+xDnnHM8111zIE0+8Rjw+skvQNh0GmgewCzaRigjh0oPX0SalJN2TJtU5iGqolEwoRg/sLqwopAskO1IIRRCvje1TvPK3xLiO5L777uOqq67i9NNPZ/369fz0pz/ltttu4/LLLwc82f5LL730VznQQ/j7Yd26Vdx888c599yL+OT5x6J98N/YsWkwRL7YehQtsQa94+WRZLCi40Qnone/jRso30HqSokTn0Fm9k3YRZORvuLhMDmz9iRg7F5QTqQBJ1iBmm7GCU9ASAtQEGYCNAU7uu9ApNHg2/YEWv8H2LHpnm2FlCi9K5gYa+PbXzmDf/r3J7jip49w++fOJRqs8RTK2Xbc8PjUcQcT/q1/Qkm3YpUuRBvcgu4UmN65CqXQC2GVVz8R5RMPp7nizhbePT7Mt86rQJgp1EwLdnQKaAGw82j9HxDYeC+Zuf8EwvMibtxHiN2B4nU9xyP+QRLCAQQ+4CgzwEX5KOloDbrsx7azSKHiSAdDKAS0CM2Ky9Gj1ISJxABXXnkOXV0dPPLIC7RWTWAVSWzksO+vjcRCUu9656YhuCgf5d5Aiq2qhcR7QC60/ZxR2PeLY4cic7vOxoMjHXTF2H8ltWuhDjaBdHDCE4ZffJddtpTLLjuLBx/8y5hat+K+MmpDjTSlN5BzcgxaSUBiqH5K/VVMLZozgugz3QIbkh+Qd3LEjVKE8CwXuvNthNNRphSNPvtqqH5mx48gbScpOHn8aoCQFj3g1GHHtXm//226cq2AR7gaio+2zFaCanjE8eTsLAEtSLGvnO02C650SVkDKKgYig9FUUYck64Yw37Ch5UeT9ry/GcjetE+A/RGgxCCIqOYlJUgY6dI26khlYFEGbKN6Mg1EzNKCOkRJkZmEDXiQ7YSu5OuqqJhuoXh/x72p861Yjomxb4yqoMTRiWtw3oUn+In72RHBL49es8ficaKuOgj+/ag9uwsFGzXGvZKllJiOnmKA3X7fX+vu+5TCCH42tduxnUd/u3ffrDXbfmjfjSfStnUagppzzLBCOok21MEYwcWgPTUVsHjW71ADb8KGxMKK3skV86aQFlnir4tfd7gQUoCRX6mnTwVf2THpEjv1j6a3m4mUOQf9t21chbtH3R4KpgFe05Xr5xRQcu7rZhZi0CRHykl1tD/z6W8sLtgPIBmaNimQ7gstJNP8r4L73RPmv7mAcycSbovg2M55JP5YVV637Z+3r1/Odn+HNNPGb192HVdvvQlz6bm05/+53Fd239UHKqLD+FA0JHpYnn3+1QFK4gPEa0hPUjrYDsvt77Jouqj8GsHx7pBIDijEKJTsdmgmmgIbCAuFc7JhwkMKTtX963j7jUPYrk2ZYESTMfkpdbX6c8nuGneNWzyreXBwN20q624uERlEYsLJ7M0f8GwFcHfAj3CplW10RFMsvXh498b+kUfG7W1OMJhgj2RarcWA4Orstcz0ZjCO/obmMLkQ/klHGeeSKlbPqZj0XrfI7D+t6jZTkCC4qdQcTS56VeDFkBB4eTCmRxjHk+30klQhih3K0cPnJMOqxLv8lT0YQbVJHX5CdjSQjMVIoEylhlvcKx5wm42D31KDz1K127HXOqWs0XbSKfaRrFdgpQSW1oIFLQDFLCEZYQL8pdxf+AO1mqrhhTmgun2bE4rLN3v7QoEc+2FPOV7lAq3aoePs9JHSEaYeIDBdHvCtGmzePTRF7nkkjM599wl3Hbbg2zYsJbf/e5O3nnnDQAW1hzOZ+v+iVPnnMmcCfNR0wpqq6Cw1iV3kSQXlPheFyhJgRuVmGdJCid4JFwkAjcudim8q2B2CRwNToxAUT/8/HcKbW2C6dMl3ty8pL0d/vxnwYIFEuUCF5FT0NcLMCUYAmuhJH/+jk4/GYXMx12OqhIsv09hrS0pb5QUwtC5UTBzpmT+fB+BwCKOOmoR6lII/UCw0nifv7Q/xYNr7+eRjX8EYEp4KmcceTaFwih5PuPEQPMA7as6iJSHh+29wqVherf20bK8hZmnj10s8vDD99PcvJXbb39w3McxaAqCmmRa3FPfdma2/wV6vsE7w6d63rv3rlVYNyBQhcSRgkU1krMbXZ5qUtiQ8LbXnRX05jzf3fqoJGVCWwYmhKFmqCE17octSckLLYLDyndYUkyNw43zXJ7ZJrj1fYVUAeJ+QUiXFNpyrNFLaVQNjnR6vOPSFVQNWntMtqZgcmzHMactgb7LY3A7mZvf3zbpcWDnrtO/JUKhMHfc8UfOOOMYbrrpSu6550+eBQow0JpgzZNrSbYnvfFCyKBuQS1TT5yyV+HDWODaLhue30jz8lbMrIkQgkh5mJlnzKB0ojfxJqWk+d1WLyg6kUUoCpHyMNNPmUbZ5PEHVv41MK63wA9+8ANuueUWPvOZzwDw4IMPcs0115DP57n22kMhIv+ISKcHufbai2homMiPfnQravdzgBwmggEQAlcvQkttHbmyEBRqT0IbWIeWXI/jL/PUwPk+zKrjsMqOGJ41329ofnLTriL83vdQM61IPQiOBbiY1YuxS/fD38jOove86xHYQ/7FUgiebTiCkuYENbUxPvml0/jv7z7FZUJyzyeWUIwCYyhER4PI92J0L0NJtyGcPHbRJOyyw7z9j3UbVhq9913cYCVuoBwnVIOa70EZbEIp9IFiEFQFd14c5YjqQb7wdJqNiVbuuuk9lOJJHhEMoPlxQnVo/WtQMi1/E3K7Q7F52D+Ig2Sa45GmqaEwtwZHp1SNEVEh4qQx3QIBJURELyKv6hjO7m+dnp4uLr30TDo62rjvvieYMmUG5ZbDW7rBBs2k2N1eVDpMsQ0WWjt+y/WuzucycdZpJoPCpcxVmeIYwwTy3hD3lRHUQgxano+xEALLtTCdAg3hqftFlmkDa/BvegAt1QS4OMFq8hM/TKjiaO65509ccskZXHrpmfzhD39hxow5e92WIhSmFs0l7iujO9fGoJXEp/op99dQEagZVtZuR6LQS8Ye9KwKho5dU3T8SpDufBsTI9P3aBchhCCix4gcxDmEzlwrbRnvGbP9WLO2F/jWnm2iMTJtmKTcnjruU/0jrntIRpHSRVcNcnZ2xPZd6U1E6IqOKlSKjD37z44VNaFGBsweMlbK82gUAoEkopdR7q8mafVRH55EY2TGsLVISIsMe/puD42T0lM11wQneDY4o/hTbx3sH/an3jWwMKiFqQ410DS4Hss10RSdvr4eXnnsRa79xCcJBvat3in2lVPsK6cn10FAC6IKjaydxlD81IQaD+g6XXvtJ1FVla985TPk8zm+852fDBdyu6JyRjnN77aQ6hwkWuFV2YPdafwRH5Uzdm+b3RscyyGfyqP5dZLC4PkWhbAuqRhu4ZOsGxC8MhDiYxctoGdjD4Pdg+gBnbIpZcP7347+pn5s0x4RwKYHdIQi6F7fs08yeOKiRpb/7j0yfVk0n4rm06iYUUHf1n5SHclhW4xYbRElDcUkWpPo/r2XcflUng0vbKJrfTe9m3rI9GVwHYlrO57XmyKQDji2i+tIWpa3UDu/mnDZDqVOLpEj1TXIw0/dz1tvvcrvf/8X/P7/HT75f28cqosP4UDQnx9g0MpQFRr57CryRUkUkiTNFH5t7zYH40Gtq/OpbJz39DwdikNMCuZZfmrdHS/rV9reJGfnmRqfNPxZSA+xrn8jy1LLeLL+QfrUHhrtyWho9CrdPOV/hAq3arewtT7h8LqRZaVWQENwmOXnQ1bggILqXCRPGxle8GVJChcVqHE0PpKPMN3ZM3H+lv4qDwXup0/pBSQRWcQJhVNZmr+AkAxzWuFsTiucPe7jEYUEgfV3ouT6PAGJUBFmCl/7i7iRegoTdpCiIRmmcR9EZl++i26zjYHgAHG3BF0x0PE6cIIW5I0c7WrrbmRwQAYx8JEXecJyx7spL3IY0iAggyTMPprTm0iYfShCodxfw4TwZHzq/k+kLrCOoNKp5gP9PTIiTaVbzVxr4Qi7i/3B4sLJrFc9S4igDGIJC0UqnFI4izqnAQCZHsR67x3crVsgEECbNRd1xqwDEh/U1k7g61//LjfffD3nnOMpw5csOZVbbvk1J889nYm/qsINSuSQWFZGwa2QGCsUCmc6FIbIX2UQ3Ciw86V1oPxFBS0ssM+Twz4E+fWwebWgbOF2IthDZSVs3AhbtwpKDpdkPumibQaREMi4iz2JHWFvFmhbQGQFk46TXDHN4c9/8QhmzYLjj3c5+2zJzllrMgTCpzAnMJc3jdfZmtjMpNgUzq07n3e73uGr/30zX/6vz7JkyalcccV1nHLKWWja+CcQUp2DOJYzTASDV3v4Iz4v0HeMSBTgJ7+6lUlHnMlrzgLSnZKF5XI3AnRPmBKTvNetsCUJTUmB5XpK2qzlWTWcO0kS3sl5YU4pfHahy4puwUABKoKS+eWSEj9Mirs8tlmwokchbEhSQ98jPRWuJmB68UglbrEfurNeKFt8pxKqIepZS0gJU+LgV4eCAwMauUSOzlCEgtuPTzpgO2iKIOsPkCyMNP6ZEpOs6FE8G4uha5K3QRVymJTeFZm+DAPNCRAQr4uNKQOjeEIxqt5EfjA/LIiw8hbSkZRPOXjvq/Ggvr6RX/7yPi699Ey+852v8rWvfRcrZ7HqsdWkOlMUVcdQdYVcIs/m17YQKA6Oqatub2j7oJ0trzfhj/qJlIdxHZdke5JVj6/h6KuPwB/x07upl3V/XodQFeJ1caQrSXakWPXYao786BGEig/cPvFAMa6/6I0bN3L22TtelBdddBFlZWWcc845WJbF+ecffD+iQ/j74pZbvk1HRxvPPfcuwWAIaQyRDK49whJBsdNY8Wm7rW+XzCMz+0Z8zU+hDTYjNT/5SRdSmHDWgRPBQ8hN+xgA/k2/Q8n3In0lmOVHkpt9E1IffyuAcB3PfmInNWCHL8CjDTO5oPcdJqbaCR03hyAK3/v+k3w0OcD9//px3ND42+TU1BaCq3+O1r8GNd2KsFJI1YddMpf8pIvIN354bNfJtRCug6v6QAikvxjbX4yRbkWqPs82QzHAtfjkyXEmVSa4+O4+zr5lGfd/bRKxnV5KUgsg8t0IOzfu89kfrNUKJITLdEcfVklEpUovLu/qeY41g7xt+DGCIcqlZ23QJ1wCwmW6PdIvqbl5K5dddhaZTIaHHnqOadM8pXqRVLk2W8SLRpYVegEBnJYPscQMUiRHXt8ACgvs8RMdfjXAlOgcNiTfZ8Ds8bSfQqEyWEvtfpBlSraL4JpbUbLdOJEJSBTUTCvBtb8h7YsTiU3j3nsf58ILT+Xii8/gwQef3qdXkiIUKgIe+bsvONJBIndTrihCwZEuLu7fUAMEPfkObGkR0WLDPq2q0MjYg6TtQRxpow0ZiUT1GIbi9wZPmjcgkVKSczJUBmqJGaWsT64gZ2fxqwFcHFJmgoheNKQmPjgo9VcwM7aQ/kIvckihHNKjxI0SNEUn72aHwvV2VOcxo5RyfzUd2WYM1YciNApOloAaHCZdU9bAbv7UIU3Sb/bQktnKjNj83Y5lUmQGATVIR3YbeSfPa398DYHgpuu+MLyM7cK73YL3ugUZE6YWS46p8hQTqlCZFVvIVm0DPbl2LNdTI08IT6HYd+DF38c+9gkCgSCf//z1pFJJfvKTO9D13WcTopVRZi+dyYbnNpLsSIGUhEpDTD1hypjD1KSUtK1op+mtJrIDOVRDpX/yRAaMBmaU7vi9CwHlAcnGBJiaTs28fT/jR1N6bSfw9wZFVZi9dCaOZbPx+U3oQYNoZQTpSvxRH6oRJ1ZdRKg0hGZoZHoz6AGd8ql7/r26tssHj62mc20X4ZIQ8fo4yY4UruWiqAJlyD5F0QXSlZ7SO2OS6hokXBZGupLNr22l6a1tdHd28MM//BtL5p/OjNr/Df74/ztwqC4+hANB1IgQ0PykrQxRY8doPW1lCOlBIvtRw+4Lcalyojn6dl3p0jrYTpFvZIeJX/PhSpflyjt0qe1MtWcOe8SWuRWkRJK39NdGkMEDwuG2YIINqkmRVHCBB/0pNqkmV+eK8O+ngOIdPc8TvjRhFKY6OjawTbG4L5Di5kwxcbl7ZdKqNPP74D2YFJhqz0AgPBLb9yi1zgQWWmOz2xoNWv8q1EzHMBEMII0oUg9jdLxKof6svdvd7YKsnQZXEJBBkkpi+PPt9Y6AYW/jnRGTceaZh/G8/2mCMkhQhiiQp1XdxixrPsX5Yj4YeJusnSGgBnGkw9bB9aTtFHPjR+2mEpZIUiKJRFIkY3vtbqtya6gq7LuuHA8q3Cpuyn6et/TX2KitJSQjzLcOZ4F1hBdonRgg/5tfYq9djVBVpONgvfwCxpnnYJy+dNyEcDKZ4O67f80DD9zBli2bqK2dQDweZ+vWzUyaNJVzzrmQcFsAkQe5i6DPDYGWAjEIlABBcLffogJoH4CxWoGURFshcBp2UlAKcKtBXSMQA5K6LESaBeYgtPpBVUHLD9UPGngOICPrCaUVgg8oaJsFWBIZUTjqaJdZn3LpGwRdh/goTg9uGbQ1dvLZ26/nqZ4n+Pi8m/j24d8j3BbEvFDSen4PTz39KPfddzvXXnshlZXVXHLJx7jssmuorR27WEjRVZBDWQs7mc86lou2jwnt7ejPw4+ea2HLmmUcftM9LO/26tXNtXDJNIkyhtt9RJXkhRbJcy2CgCrxa5B3oC4C3TnB6x2CUyeMvLZVIahq3L1+i+gwswQGCpLwkEdwzgYEHFPt8n6vwq5OAFkbApokMMopbxjwdPU+RWK7YLqgxaOoyV6cvIVt2visPCKVIV9egqguo9g/8riOrJK82y3ZMOD5BrsSUqZgfrlkbunIZaWUNL25jc2vbiGf8roO/VEfk46bSMNRE/b691PSWEz9EXU0L2sm3ZvxfspCUDW7kqpZB8/Le7w47rgT+frXv8e//dsXmT17PkdPXUKqM0W8Nj6sAg7GA5hZk9b32qg/rPaAJo7aVrajqIJg3BuHqZpKrDZG/7YBejf1Ubugho7VndimQ0nDEH+mQrw2Rt/WPro3dNN4dMOBnvYBY1xkcDQapauri8bGHcTGCSecwOOPP87SpUtpbW096Ad4CH8/rF+/mttu+wlf/OI3aGz0ZrKt0vk44Xq05AbsSAMoBmq2A6nomFXHjbodu3QBdsk8hJlCqn7QDrKiSAhy068mP/EC1HQLUvPjhBv2m2yWehg7Ph2j4xVcfwkIhS2hCAOqYFPNkcS6NlLVu46LZiqU37yAL/33Cs75ztPccdenx+erJCX+zX9ATW5BWF5isROuRykMoPWtIphpR01uIjf1Ctzw3mevpBHDLpqE3vMOtq/YKzylRJgJpF6E1ALYeoT2UDEZIZmzUOXuxsO47nsvcOZX7+Oh//golcXeQETNdeP6S3CDf30POAATEKOQjoaEnJDMs30sMYO8YmTpxEYiCEnBKYUgU50dZPD77y/nyivPJRQK8/DDz9PQMGnE9sqkxoWFKOcVvBei/lfoZakI1BDRi+gvdGO7NmE9SrGvbL9sBvTed1Ey7V6g35BlgBOdhDawGqPzDXKxacRicR544Ckuvvh0zjvvBG677UGOPfaEg3IuET2GofhGWAt4hGqWqkAdmhi/7FdKLwd8tJCzfcGRNopQsbHQ2XHft2/P2CloJ6iFqQ9PZsvgWhKFXlRFw3JMQnqECeEpnrLcydCebSZh9iKEQlSPMS02b1iNe7BQHqhhYnganblW4r7SYTW149qYTp6uXBt5J0exr4wSXwWKUJgem09Ii9KZa8GRNhWBWupDk4gZXtvR4B79qQP0FbpG9e9VhEJ1cAJxo5SWbU388Y77ufbaT1FS4hG5UsLDmwTPNXvr6Qqs7lNY0SO5fo5LRRB8aoDpRfOYGJmO6zoYqv+AfbB3xsUXX0UkEuHGG68glUryy1/eSySyu+1F5fQKShtLSLR5Vh5F1dFxpRe3f9DBqidWe9esKOAFX6xsY7CuGKe4CG2nn6ctvcRndQynGauNoWgKhYw5HOpmmzaO5YypHUwIwZyzZxMsDtH2XhuFrIkR0Jl1xgwUTWHbshaSbSkA/BEfU0+YPMKzeFf0Nw/Qu7mXWHURekDHH/XTvb6bwZ40ji0RUqIZKqqu4lie/76Zt9jy2lY2vbQZK2+RbEsSrgjzx5V3omoa5865gg8eW8XRHz0SX+TgtK//X8ahuvgQDgR1kRpmFU/j7a7l1ISqCOshBgpJEoUk50w6naD+t1UNKUKhNFDMpsRWYMckn+VaCDxNgUCMCAsDj5xMKP0jPntbz7FJNZm2U3dVHpeVeoHVlslhY5x0Nx2LlJkipIcIaH6W6V7b+vbsCgOY6OpsVC3WaAUWWbtfs9X6SvqVPmZYs4drzTK3goQ2wLv6WwdEBgsnj2cNMbKukaoPYWdAOiDGPtzWFB0FhVnpOTxf/BfSTpqQG8KSFonAANOduUy3Rw+2W5q/gIQywBr9fSxMFBSm2jO5OHcVndkWsnZ6R1YC4FMC9OW76C90Ub6TSKBNaeEp/yOs01cDnjfwGflzmeDsn/Xe/qLELePMwnlQOG+376xXXsRe/QHqlGkI3Xvfut1dmM88hTZnHuoYycq+vh5+/eufcMcdv8A0CyxdegHf+97Ph0N1f/Obn/Htb3+FV199np99906Oji5AGQB3J85L6Qe3SOLu8ppX2iD0CwXfK8IjiqWCyEvsfoF1tOf9q/RD0SbBzSmJ+bIgJCHvClAlmwE3ArNfE7BQMqoFswnBexW0jQJ7ggQ/KP0S3zMCNw7qqbsTmc3N8NprJo8++j+8/dZ/4hcGfzj6Uc4sPgv6BNZ8Se7DLsXFJVx56jVcXXstqzas4I5lv+bXv/4JP/nJdznxxNO55ppPsnjxyfsk1EobiwnE/KS6vI4uoQgKGRPbtKmevXs4/Gh4o13w+nMPo+o+jjr+THxBSBbgtTaFwyscpu6hFDId738h3fPzPbJSsqJHoAzZJ9SEoSEq6c/D8q7dyeDRICU8tkXw522erZiuQNYWNEYk181xKQ3Cfy+XrO33fHr9KiRNSBYEJ9S5jMZ/F/skqiJpywgKDjguKCKIXVJGRbIPmUjj6ILshFraJk3l8GqVhujIYOMSP1w/x+XlVsHKXk+dfMoEl+Nqdyeg+7b2s+H5jai6SkljMUhI92XY8PwmopVRShr23B2pqAozTp1G6cQS+rb04TouxROKKZ9WjnaA2R0Hio9//DOsWrWCz3/+Bu7+7ye8R/MudhB6QKeQziMdidD2nwvIDxZ28/7dnr9i5S0AsgO53ZYRigBFYGas/d73wcS4yOAjjzySp556iqOPPnrE54sXL+axxx5j6dL99wc6hP9dkFLy1a9+jvr6Bq6//nM7PjeKyM68nsCGe9BSW8C1cQOlFOo/7Nk+7AnbQ93+msdsREd6Fu8vhKAw4SzU1Fa0/tVIPYSuZRBuDW11S0hOPI/arhUEcwMU5sY5d7HgmSs/y7nnLubeex+jtnbfgU4ASq4TLbkBqfpQzASuEUG4NsLKoFhJFCuNsvFetFQT2WlXYlUdu49jXoo6uA1tYBVSjyKcHFIL4eghspEGtsh+shSwVJ13Go7i1fOu49szTuPbH/8Kp37xNh756lKmlABIChM/jDT20FMyVkiJsAaRir7DhmIU1LkaGoKMcIdbBx0kSeFyvO0NIj6SjzDP8rNFNVEQTBnyMd5e1D/zzBPceOMVTJs2k7vuemSY4BoNfw0SeGcEtfCwGvVAIAoDgDJMBHsfCqQWRMl2Dn9UUlLKww8/z/XXX8rlly/lhz/8JRdeeOUB7z+kRYY9hgtmHk3omG6eoBamLjxpXLOptmvTlt1KR7YZyzWJGSXUhiYS943dLymix7wgNbeA5ZqoQsOVDkIKKgK7z+42hKcS0sJ05lopOHmKgsVUB+uHLRSmROdQHWwgY6dQhU7MKDlg/7yd4UqXpNlHxk7j04Joik7WSRMkjCNtOrIt2NJioNBD0uyjJbOZmsCEYUJ6YnQ6DZGpuNIju3c+vz1NLrjSxbeHc+jOtbEtvYm0leS7X/wm8dJiPvnZzw9/35SCV9oUSvxyuH3NcT2bhJdbBRdO3VEgG4pvf11x9okzzzyfu+/+E9dffwlLlx7LHXf8kYkTp+y2nObThj25xgPXcdm2rBkkFNV4vwVfyGCKz2ZZMsmmrgBTfTkUy6YQDtNX8HN6gzuqkmNXlE0qpXZ+Da3vtZLu9chd13GpmF4+ZrWEqqtMXTKZhiPryQ8W8IWM4ZCO6tlVDLQkAIjXx/cZgpFP5nFtdzjMQiiC4gnFFDImTsEBIYe9s/WARi6VQyBQdZVAUYDudV3k0yYvN/2ZN1a/wOcv+XfqptTT3zxAz6ZeahccXCXY/0UcqosP4UAghOCiaechhGB133q6sj2EjRCnTFjCqRMOzsTueHFM9RFsHNhCR6aLskAppmOybbCVxqI6Dg8exvu8QY4cgaH+d4kkqSRYWBhJqG7QLAIoI2y2/Ci4SFpUa59ksOM6vNr+Fi+1vE6ikCSkBzmm+gh6ps8koI58IKtD/fZpMTqRkxNZhBSjiA4MBpXkWC/N6McZrkNqQUQhsWOcIyVKvpdCzYnjDpYu8ZUT1EJMHJhIn76QdaHVdNOJxGUKM7k097ERNhA7IybjfCJzM5u0dfQqPUTdGNPsmfjw8Za5AUPxjaglvJpHkrHTw58NiH5+E/oZTepmyt0qhBQs01+nXWnhU5kvUe7+/ZR/2yGlxF7xDiIaGyaCAURZOXL9WpxNG/ZJBnd1dfDLX/4Xd931K4QQfPSjN3DDDZ+jvHzk+V133ac59tgT+NSnPsqZFy/iq2d/k8/nbka1FdwoKKmhkLhTJCOcMWwI3K/ge1WALXDqJDgSbQvoW0DGvM/09wRKLzSqgnwG0jZ0+6BVFUxyJLNjULoRsu8LzKN3/31rG0DbCnajhKH5WbfEOybjDeH5Fe80V75uneQb3/gj77zzFfL5Furrb2DunK/DiSVkGuUI+wl9mSDwe4HSLziShRyp/4Jvf+773B+8n7se+BWXXXYWhx12FJ///NdYvPiUPY4LQiUhpp8yjXXPrKevqR+EV+vULaildv4YOhVdWNkj6Hn3D0xYcCpZJUo6D1EDOjKwJSmYGh95bbI2PN8seLtTkLOhNgwn1rtEfVAb8SwdtiYFHRnoygoMFYp8YwtYa03Di60KRcYOr2FHStb1C15uE1w8TXLZdJd71ylsHBAkCxJDFRxR6XJS3ej7OLoafrceunISQxHoKuRtScEI4DRUsa4hilQ1ysoCLK50OWeSO2rDQXkQPjJVcsGUHSF1o6F7QzdWwaaoekhUIiBcGqJ7Yw8dqzr2SgaDRwhXTCunYtrB66Y8GBBC8J//+VPeeus1vn/r17jhiH/BzFkYgR1ZJ/lUnqqZFQj1wPiA4roYze+2EioNDf/2rbyFoiqEhurzouoovZv7Roh0XNsj8YPFB5ZzcrAwrjfUzTffzOuvvz7qd0uWLOGxxx7jrrvuOigHdgh/XzzyyO94/fWXuO++x/H5Rqp/nKIppA/7V9TUZoRjei3s2+0j/kHgRCeRmf8FjI5X0ZKbmBiopCg8meZAnBpXo2XSSQwIhw7F5oKaCJ9+9CWuuOIczjrrWH7+87tZtGjJvnfiJREhrCwiP4BWGPCsGZwCUvXh+uJILQhOnsCm+7Hj05H+PZNndtFUcpMuxOh6E1Hoxw1W4Uz8CL7W53g5rPJQ/WLmdG1ACMFA6SxkuI6XTp3AHfeV8+mPf4aTv/4oj37zSqYdewlm5aIDun7awFp8zU+iJTchhYpVfiT5CUuR/t1fLtNsg8NNP28aeQJSoCHoU2z8rkKXYvOob5BZto+pjs40Z6Rq07Isvv/9b/Czn/2Q008/h//5n7sIBv/+/jsHA66/DK+vytlhWSIlws7g7KIUj0Si3HXXI/zLv3yKz372WlpatnHzzV89oPYXIQQTIzMIaRE6cy0UnDyVgVqqgw1EjdiYtyOlZH1yJW3ZpiE/Xo3OXAsDZi9z4keOmRAuMryZa8sxcaSLEBJd8VHir2BSZPfwCSEE5YGaEWqXXb8P69FRA9cOFJZrsi6xgu58O450vL91BIpUGLSTmE4BF4cKfy1hw9u/6RRoy22j2F8xbOOhCGVU5W2xUUZADTJoJXbypzax9uBP3ZvvZE1iOba0eefPb/PBWyu5+cdfpMncQJGMowiVppQga0HdToMZVYG4T/J+r+AjU+R4Ol0PCMcffxKPP/4qV199AWedtYhf/vI+Fi8+ebflpIS1/bCqV5C1obEIFpZLInsRd1t5m2x/Fl9kJBERDSgcuWUTXd2tZFNphOPiBHzMnjeBk4+bwFhSMRRNYdaZXnBE94YeXMeldGIJVbMqR00X3huMoIERHHki4bLwCC/ffcEXNhCqgm3aaIZX7kUqwvRv8yEioBoqZtb01BGKQFFVArEAZZO8wMi+rf1s7fyA36/4DWcfczGL5uwIdzVz5rjO5x8Vh+riQzhQxP1FfHzOlbSlO0iZg5QGSigP/v2CZY6sXEiikOLFltfYnNyKoehML57MRVPPpUKWMcuazwp9GcVuKToGPUoXZW45i8wlI7YTlAJrF3sciUQCxhiepy+1vs6DGx7Fp/qI+aKkzQwPb3yCMH1k5i2maqeOsjwuKoJyd/SJ0mqnDhWFAgV8Q4yZi0tGZJhs725xNx44RVMwKz+Er+05ZKEfqfpRCv24wUrM2lPGvb2AFmJa0Tw2JD/gqM6jafA1MOgbpNqo5yTfUqLsfbyloTHd3t0yzKcEGHRHEt9SevdDV3a8n94z3qZJ3cxUe+ZwcFvMjrNOW807+pueUvd/A/bA28k9fTGEvr5ebrnlW9x33+34fH5uuOFzXHfdpyku3vPk8vTps3niidf53ve+wb/d+i/8eerjfGf69zli8EjcIom5VFJYMkS+9YG2SaA2gfGOAFcg49Lz9lXBLQelW6Ct8+wdlEFwi0EzIaR6VhPTFCgulpQHIeqAkxOo2yQcvfuxiYwAG9il7pEhEGkQJsih27ts2ZvceOMXaW9/i4kTl3L88Y9TXDydlhZ4/H2Yc5ZLZGieQemDwB8EIgf2VOlpU9JQ/F6Yay6+jsuevoaXXnqWH/3oP7jssqUsXHgkn//811iy5NRRxx41c6uJ1RTRt7Yf2esQqYsQmxffa4hXXx5ebPGsy95c30b/xjeJfPRO3uhQcKVnj2ZosCun50p4YJ3gtXaPsPVrsKYfmlIqp07w8hLe6vA8g4MaWK5HKleHPKuHvU3+Zy34c5PC1iRMjXn7UoR3DCFd8pdtHrFsKHBkpUtPViFpChwp2ZpU+NNml/OnSHy7PKqmxSWVIUnaVrAcz+tXEYKwDhlHRcbCCEVQZEhOnrD3Ohf27UxjZj3ScjvSvRkSrQlSXYOYGRPNpzFxUeNudejBgMzlsFetxO1sRwSCqDPnoFYfPHFBKBTmBz/4BZdccgZHTnqNudbRBIoCaIZKdiCLEdSpO3z/g6+3o+6wOno299Lf1E8gHsS1HHKpPFWzKj21Nd7vvmN1J/3bBgiVBJGOJN2XoXhC/H8NkT4uMnjx4sUsXrx4j9+fcMIJnHDC32cm+xAOHgqFAt/85r9w1lnns2TJqbt9r2TaMNpfRhtYB4qGVTKXQt1pnv2DlIhCPyCQvvi4fLJwLYzO19C73kJYaez4LMyaxbjBsbWQ7Hm7DrgFUP0jVZajQbpoiXXeuSGx4zPIT/wIAUXlbJHj96R4Vs8yoLo4QNRVeF8vMH9aA4899jI33XQlF198Ojff/FU+97mv7DEECcANVGCH6wl0voZiZ3C1IMK1QToI1wInhxuuwYk2oiXWog+s3aMVh5rcRGDj/R5B71pI1YcTacQunoXI9/NOqA9XqKTC5UjVj3DyVPStZ1XFDDqOOJM/PrWEyy9byrnffJRHHvkcE/dwnfqEw1tGjvWqSUgqLLB9LLD8I5QfamozwVU/Q8n14AYrwLXwN/0JJdNGZu7Nu9mEaAguzUdpdHWW6XkSOOhCkFUky/UCLnleNLKcXQiP8Llra2vhxhuvYMWKZXzta9/lhhs+N9ye8Y8Aq+wwnNZn0AbW4oRqQaio2TZcfxlW5THDyxWcPGkrhaqofP8HP6e+voHvfe8bNDc38YMf/GJU39WxQhEKVcF6qoL7HySYtPrpyrUR0iL4VO/eB9QQCbOX1syWMZHBUko6cy2oioZfC+K4tqcKFoIKfzVRYxz2LH8DtKQ3055tJmIUYSie12LK7MenBphWNJeWzGZ6ch3DRDB4oXgZO0V/vpuKQA1SSnoLnXRmW8g5GcJaEdWhCcSMEvxakClF2/2pewHvXlUG60b1p27LNGG5Flre4M4f3s5xpy/huCUn0pfvYqDQS4m/AlWMPrZyhlrg/taYPHkajz32Kld9/Eouu3wpi6/+Aedf8WmOrvb83aSEp5sET2xVhgMyXmmDtzsF1812R4Rz7AzNp2KEfOSTOQJFfqQrSXUNMrBtALUtQV00iNpYhVYUQs/miGxZT3a9TvEYVbCqrlI9p4rqOQf43hojChmTTG8Gza8RKQ+PKG6LG4qJ18fp3dxLtDKK7tNwXUmkLIwe0tEMFTNjoeoKVXOqGNg2gC+8QzmWop/7V/2CKSWzueDIqwDP9kIogmD8H2PS7UBxqC4+hIMBIQS1kb+NNde+oAiF0xtO5KjKw2jPdOJTDRqidcOdMx/N3kC1r5Z3jGXkcVhoHcUphTN2C0aba/t4V8+TEA6xobyHLsUhLJXd8h52Rc7O81Lr6wRU//B1iRoR9JzOQMsqApPnszFURJmrYglJr3CYa/uYaY9uXTPHWsAMay4f6MuJucWoqPQpPdQ5DRxl7qXrbiwQCrlpH8UN16N3vopiDlKoOQmz9iScor2Hxe0JZf4qivRiBsxeXOkQ0WOEtMhefXv3hcpALb2FTrJ2moAa8jyBrQGCWogS344Aw3alFRVtmAgGUFDwSz8tatN+7/9gQgiBtuBwCo/8HmlVDKuDZW83IhJFnbR7N5HjONx332/47ne/huM4fO5zX+Hqq28iGt1Brvfl4J0uQfOg57k6v1wyucgbyvp8Pr7+9e9y4omn8dWvfpYljx7DaUuW8oWrvsGsBZ6PvvGmwP+wQOkDJemRwlKAG9txHG4IKJa4ERAu2LXgVEmMFQLFxCP5hCQaABkAkoAFcg81jVsmkQEFMSiRO2kblH6wpoLrl6x45x1++YtbeOypPxKLzeeMM/7CjBknDi9bVQVbtgi2bYPZQ/MI2jrvPOypDHeCyTDIIOhvCwonC5YsOYXFi08eIoW/yeWXn82CBUfwpS/9++4T+A4ULw9T9WIEJSGQPjDfd8mfL5ExdsOgCbd/oLC2XxD3S8xNLwAwUL+UuOaFxvVkgYJAV0ZWr1uTsLxboSYkiQ49Eor9sGEANiUUSnySFQVBWBcUXInjCuojkpwDq/sEh1eMPqHQlIJ71ios74KWQUjkBeUhqAtLWgYF6/oFpitpSQnKgh7BHNThqErPKmKgIHm2WRD3w2kN3j6k9AjoQRNqIoLqsEveFhRcaE55RLPtCuqiUOyXbEgI/twkWFIraUoJdMXL9ygZpwNnrKaIluWtuI5LLpGja10XjuWpjXW/zqaXNpMdyDH/grkjSOMDhZsYIP/bX+OsXYV0Xa+LorgE30WXox9+1EHbz/HHn8Sll17NnX/6Gbd/6yTsdrALNiUTS2g8uoGySQc+4RqvizH/gnk0vbmNRGsCzacxZclkGo9pQB3ym4tWRZn34blsfmULyfYkQhHULaxl8vGT/ipE+/7ggHtizzrrLG677Taqqv42A59D+Ovj0Ud/R2dnO1/60r/v9p06uI3gBz9BTWxAzfeh5HsJILHW30luyuVo6W1oifWAwCqeSaHhXJzIGGwTpEtgwz34Wv6MFBqoPrTEOvTe5WTmfhY3tB8zRq6D0fESRtvzKPl+3EAFhdqTsCoXjU5SSxf/pt/ha/mz5/MFoPop1JxIbuqVHGMFaFMsNgcsJtkKldk0C1rfIpzYQq/tMCVyFL/77b38+NZbueWWb/HWW6/y05/+loqKPfxtKCpOyRzYdD9SMVDsrEfkChUpBIpTwIw0Ap7/L6496mZEYYDgmltR0824/lLU/tWomVaM9pextz2OsLOoh38EN1yHo3p+wsLKoqVbEKWNSCKUlJRx731PcP75J3DJJWfy6KMvUlU18pp3Kza3BZJsGSKCLSQr9DzbFIsLCjuKVKPtJZRcF3Z89vB1do04et/76P3vY5Xv7s0WQOEEM8QJZohHfYM8pqapdzQ6VJsexWEbFt2KzURbp8E1ePbZJ/nsZ68hEAjx0EPPc/jho0yX/x+EdF2czRtx21oQ/gDp2ssJ9j6NltgISOyiaeQnnocTnYiUkub0Jpozm8k7WRShUKTHufrGT1BbW88//dP1dHS0ceut943Py/ogI2OlcKSFT40NfyaEwKcGSJh9ONLZp4dwyhqgL99Nmb8KVajkXS/c0HZtsk4G27UPqsXDgcCRDp25Fnyq37NTwBtcR404SXMAicSnBBCjTLgIxLCqpS27lQ3JD3Ckg67oJM1+evIdzIwtQBEaeTtDZcBTiBuKQVgvGtWf2pUug1YCQ/Fx6/d+hm1bXPvFG9EUHYlLzvGeddOKJcV+aMtATcj70/UKVMHpDaO3o+0KKb31e7KCsC5pLIK9iD72iRXpOPU3PEZ/9Mu8ePvn2bj+fRbf8FPOrewik+jiruUZZCFFwBkkl0uRywzyyMAgq4ocppeqaJqOrusEgyFCoTCBQJCysgrccsi0ptEMjWwiR9/WPgrpAgKBT5Vond1UxMoJTwiTaE3QvLyVmvnVB6wiOJiQrmTrm01se7uZfCqPqqsUNxQz49Rpw2nQqq4y95xZrHl6Hf1N/WQsB3/Ez4KL5lE5s4Jke8qzjqj31Dmv/OJ1nKH2tVwhy69f/QEhX4QLJl+LU3DJ9mfJDGQpn1pG6UEopP9RcaguPoR/BMT9RcT9u6tQNaL45Dmo7kkowkF1g54McRcssPw0KRavGjk6FBuBICoVziqEaXT2Pkk9kE8wUEhSuks3WdwXYyDZxOl9Npt9PrapFpqEU60QpxRC+PZAlgYIcHX2Rl7w/Zl39bdwhM2JhdNZUjiVCvcg/J2qPgr1p3vCGOS+hSdjgKH6xhT2O1aUB2potNO0ZrYwYPYiEIS0CFOL5gznQgBEZQwHe7cAYVMUiLvjt2b6a0E/djHO+rXY69cgNA3pOAjDh3HG2Si7WES8994yvvzlT/P++8u5+OKP8tWvfpvS0pGqvNZBuH2VwrZBL8TLchVea4ePTHE5tmYHOXjssSfw/PPv8cgjD/CjH32LU846grPPvoAvXfF1Fj4yGyyJPRVEDpR2ULok6jaQMY/cFXlAB2uBRNiABLcUZEgiskNGtg6eEjc7NPwrBWvW6ASl0wDWfInxmsAtlsiAp+pNiyz35O7jt8fdyvtN71EfmsBPFt9Ov3ol28pH/j4dBxQFtJ3KaGECCNiFaJW6d27Dy4kdpPDLLz/HD3/4H1x66ZmcceaH+eZ//JDq6loAjNcEgT8KZADsSonIgf8VBSUnydzgsmsi9fJuwfoBmBr3iF+3cyVK8UQcX5yujCSoQ0CHoCZpHfRsYrajKyvI2xDdpfGv2C9pTUPEB7NKPBW57UKJX1Id9mwjOjOjX2fLhQfXK2xLCWaVSDKW50XcnBRsS3mEbdaWxH2Qc7zAu6wFUgryjufZW+z3PnujQ3BivWRTAp5vUWhKerVyf14S83n1eEdGsC0JPg0U4a2vKVDik/xps8JbnVCwASQlAcGHp0iOqhybzQVA5cwK2t5vp3drH5neDGbGRNFVwqVhSieV4NouXeu7GWhJ7NMyYjwwn38G+4MVqJOmInw+L0umuYnCww+iTJpC1jJwLIdwaWg3r93x4utf/x4vvPBnfv3Ef3H7r36PYzn4Qr4RIYYHipKGYoonxLFyFoqmjuqZXNJQTHF9nFwqj6IK/JFxMvd/ZRzw6Pnll18ml8vte8FD+D8BKSW33fY/nHDCaUyZMn23742Wv6CmtqBYGYSV8gLWnAJ630pPwRipx47NACS+9pdRM22k538J6d97AaEmN2K0v4QTrPIUxQDSQetfja/1eXLTxu+B6tv2OIHNv0MqOlIvQk1tIrhmMzmngFl70m7La/0f4G9+CtcXw4l6IQmikMDX+ix2fAZmxVFs1iwabJVJXauYu+ERZrYvR3EdEr4wmvkq4bJFfOETn+Hoo4/jk5+8ilNOOYKf/vS3o7Y4A7h6GDs2BSE077rmuhBOAamFkUYMN1COku9BGtHhY9oVeu97qOlm7KLpGD1vo+R7cYPViEICYQ2ipls4a9PrfKtyBmYhjyFdpBYg4WYI2HkmDRXlJSWl3H//k5x77hIuvfRMHnro+RFtU6/oObZoJtNsY8ifzUuLftXIcZgdGC7uteQGpBEbSbhrfpAOSrZjr/fMxVMDGwje1wukhINfeh5z6zWLX4ouAv/xP9z6y//ilFPO4sc/vp14/OC9pP6ekPk8+Qfu/n/svXecXGXd/v++T5k+u7O9Z3ez2fQOaQQSQgu9KV0QaeqjAoKKosJX8BFRbJSfKIhIkybSixA6BJIA6T1bsr3O7k4/7f79cTabbBoJRdCHi1deJDPn3HPOmZkzn/u6r891Yb23BJlOgwCjsBjztLPwzDwPpO2q5AdJz450C5tja9CERrYnF0faRI1u1va9zwmnfJmSknIuuuh0Fi6cxZ/+9ABTpx74mZyXpuiAwJHOMMsDW1r41eAuQTS7g+FksKSJrmQjhCCouH1shp3GcAxMJ/O5IYMT5gDRTDe2tDGdDIFBRbRAGSw8TXK9BbQkXbXutvZM0zGRQK63AMNO0xDfOEQig3tv7st0s6z7DXTFg4MDSLyKj5qs8eT7du/j5xKcfh66+15effolvvuLH5JbkIcjbUCgDxLWRQE4scbhn5sV1kVdIYgiJNOLJIeUfXiBmbbgH5sESzsUYobEqyqMiri+acV7t7bdLepaOvnLPz+gp2Uz2ZpBfvUUWt68hwfevIcHdqNhVnQvqi8L4Q3Trqqs0UykY5HOZEink6SSiV32CfmyCGsRIt5cckP55HtLqc0ZS8TIJ9rURzA3iCfoId2fclOvP+NgjB3RsqKVDS9uRPNpZBVnuSF4azqwMhYzzjkAVXePNZgX5MCzpjPQEcNKmwTzg0OFaCh/uOVE8bhC6t5qQPgkNz38U6Lxbq484n/dYEPN/fxWza5k1CE1u1yLVH+a3sZepCOJlEc+1M/4vxlf1MVf4L8VFpL7fQMs0VNEpIpXqrynp6nXTC5JRhi5g5WXhuBLmTDTLR/1qokKjLY8lDrahypcQ54gftVHwkwSHAzRSwmHDnsAW9cZr2ZzdDLCgHDQpSCwD3VEROZwSvpMTkh/GQcHz8599Z8EhGBfLIU+CyhCoSZrHMWBcmJGH4pQiXjzhhatt2GaeSCve1+iQa2j3B6BQNCqNhOWWUwz95IN80nBsVAyUaTqQ3rCKIlW9J6VCDuFHazAzJsMqgclJxff17+N9f5S7LotCH8AbfIU1HEThxZue3t7+MUvfszf//5Xxo+fzJNPvr5b8YiU8EKjwtaYYEyOHLQdkDTF4Jl6waR8SfYOl0lVVb70pXM48cTTeeSR+/jd7/6X+U9P45SiL3H6rHM4XB6Fx+dBBiTCBBEF4ri5H16JOREyx0uUFvA9p+BkSawa0OMg+gdtxTpdawarFtKnOtg1uxy2CwVSZzk4uQLPUsGmtg38ufV2Hth0D7HkAEcVHM01Bz3OEaOOQY+p1G0S/L0f4nMlmgaOA01NUFYmySqBN1sEaRsqIpLpPlfhLLetCTmgRAWZA5xdPuZCCMqnHMGx1x+J99kHeeXe73HQwZO49Ls/5TsXfYfQ616kDvbg+ob0g+WR6GsEah3YO4m5Gwdc64VtnWkDTavwl00izycJ6lATcf16YxnoTA0ngwODBKppg75DqZKyIM8P+T5oSUhqI8M/AxI3aG53qO+H+gFXQezXXMJ2XY8gbrqKYr/mHlfpYFnVnnQPyXJccjphuoF3Kcv1M17dBfevVxkwXDI6Y0FfRtCeAE1IDAfSNjhAZRaEBo+rKwUtcajOllSF3WNuisGjG91jK9nH0ssX9jH1S1Oof7ueDx5dgebTiZRlEynPdm3FPDDQESPRk/hIZHB/xlXZb4gKAppkcgFMzspgv78UJ5JHj+3FSUC2V+AvH0FsXR1r73yDPiuIYzv4IwFGzq2ifGrZRxZiZGdHuOGGW/ja177Eolef4+ijT/xI43wYhBAfqvIViiAQ+Xx4BO+Mz8fs+Qt8brBkyVusXr2c++9/atcnHRO9dyUIBSXTg+PNc4kpLYDI9CKMKLaoHvKFNb0RtOhaPJ1LyIw4Zq+vqw3UI6wkckfCU6g43ly0ng9AfmW/LCdcEvdFHD28XVXsz0eN1eNteh6j+KBdQs20ntVgp3F28OWV3ggk29C6l2MUzSImHALJTnK7PqC2cz1pbw5xX4RuXaWwt5Wc7vfwNL/EQQedw4svLuU73/kaZ599HF/96je46qqfkZ0dGfaa0pMNWhAzaxRm/jSUdBd65xKUZAdS1V2VtVBIV56AHdp9q76S6XXTMq04SqLVjXuWDlLVEVIi9TDjO1Yxs2srS/NH4JEOtrTQLJV5KZ2RO3iFlZVV8OCDz3LKKYdx7rkn8tBDzxMKhZFIVukZIo46RAQDRKRCh2LToJpDZLDjy0dNNA0/SOmqzeQ+BKtJJJ3CYkDY5DvqoFpSEH3hFR6+/DqsplauueZGvv71yz9XSr2PC/Ot1zDfeg2ltBw1Kxtp2zgNdWQe+TvK965G2Und25ZsBCA46HmrCIUsPYeudBsre96lcEI+Dz71LD+89FJOPvlQrr3215x//jf+7dcsx1tAUAszYETJ8uSgCIWMncZ0TKpD++bZ5FP96IqO4WSGrCYAMk4anxrYZTLzWWHAiLIqupSEHSNjpYmZfaiKSp63GJ8aQFc8bpCcGUMTGl2pFryqf1DNKynyl5PvK6Hf6CVlJV2f5EEI4aqGo0YX5cGRBLQQUkoSVowtA2vJ9uQR1ndVcQkhaFzWyN//cC8nfPUUDj3ucCzHJGb2kaXnkOfdro45pExSlWWzpkdg2G7K8sT8Xb3NdoeXtgpeaRIUBSTloX4c0UJ3JsFDm0KcP76U7L0EUjqOw8aNa1m27B2WLn2bZcveob5+MwCq7iW7eCSR4pHkVYxj68pXScd6OOK0b2NM+x/GlWbR6YRZ1+/FGkyNRsDkfFdhkbKFm8osbXwkURPtFJlN5JlbqX9/DavfWUlcDtDQv5HFW1/G2eigCJXiUBljNk+gNFTJlCkH7qKO+SwhHcnW95sQiiBc6F5XVVdRdZXexii9Db0U1G4P0RSKILvkw72xaw4eSbStl+v+/D02tq7m2wt/yvjJk5hw3HiCuQGMpEEymmKgbQBVV4aK35YVraxftJF0XwopwRv2Uj27kpqDR36iCowv8AW+wKcLG8lW1SKDQ4mjkS2H3/w3qQYr9DSVjj4U+JtnK2xQTV7Xk4y0PThI4sLBKxW8CGpsDzX2/hGvWZ4wBxZP4bmGl5GaStTnp96JEU21UVA2kScLA5yesSly9n8aq/0fn/oGtTBBzf3dsJHEhINPiqFw5Qq7ijOT5/O47yHqtE0AFDiFHJ/+ErX2riKhTwxSoncuwbv1WdREK1L14HjzUBKtKOaASz4KFbPgQJLjLkF6wgiP162NR9ag5OS6asPBmvK1117i8ssvJJ1O8b//+wfOPffiPVr3xU1Y3wsFfjnMf7Y0CJv73YCyaYW71gC6rnP22V/jlFPP4S/fvYuHX7ydfzxxMlmeCCeWnswZxmkcnncEHksDRSBNwAeZOQ7GVImYAGq7RF8DWGDnOzhhgRNxcCJgTZCkj5M4exGIm6bJ+6uW8MaWRby24iXe++AdcnPzOe+sS/hG9yVUBarZlvln50OJKTm8SXDLBjBUgZRQVCSZuEBy6yqFziSAwKPArOmSi98Ffy9ILyh9Artckpm367VoGlRWd6YEtYecRdn0Y1nywP/jNzdezZOP3MPNVf8fYtTBvKMLehUY7cAhYcmoZokSFdg7LfIHdbDldpI31rSSwJyv41EEFWHJ6ByXwO1ICspCzrB9R+dKRmQJ6gYE1VmusrgvAwkTjquW5PphVY+gMwkFftcObWsM8v2SCXm7r/XSNpg2Q/XwiIBNtgovmhqqIcjzu/tt64ZThTtuxoEtfa63MAjiBuT4JPeuU+hJQ3nIJZfz/BDySFZ1u17GluO+Vq4PJuS6mR22hMYB1ze4aNCpSwAjwrA+KljbIygJ7nr8ZtrEyth4Q55hlg/B3AATjh1PX1Mf8Z4kkbLtcwjbtPeJ5NwdetNw5yqFDVG3285yFN5tkxxeojAjYVEf1ehIu589nwaVQYW+viJi9TGyx+Wi6iqJ3iRrnl2H7tcpHlv04S+6ByxceAJz5x7KTTddx1FHHf9fZSf5SeFj/yJWVlZ+LE/KL/D5wp133kJNzWjmz99d6IGCVHSEmcBlH7d9fCQ4JlLxIoyB7ZsLFYSGEm/50NeV2whJKYeRvkKaOKpv/7yHATXZipKJYoW3+2cKM46wMmg9q9F7VmEWDbcrENLc/esIBWEbCASjLJ13rX7y4p34rAwdwUIMRUERKmEjAWo2etdS0rVnkZ9fyP33P8Vf/vw7fv2b63nmyb9zzZVXcOp530MMJiGbeZOwsmrQ+jdih0YgFR9W1ihUxYudXYuZNwmzcJZrrbCHa+D48kFaaN3L3QJK0QAFKQRW3hSkN4K3dzUXbXibickM6/1eAskuJopcaquqUXZa3h01agx/v+seTj3rVC456xAe/MPPcUpnoQcF9m6TmiXqDg8bxQehdS9HSbbj+AtdhXesDidQgpk/Za/vm4JgiunjZT2JfzD5OdPcxqYfXEf/48+Tc+hcrrr/Ec6rnLrXcf7TIKXEfPdtRCCIMuhfJlQVpWok9uYN2OvXosweHuqXtBKDqtvtY0Qz3fSbvVjSpNfoQvWr/OruP/Dgzffz4x9fxltvvcKvf337v1VN7VG8jI1MYX3fCvqNXkCiCo2yQBXlwd2r3XdGSMumwFdKS7IeR9poioeMncJ2LMqzqlE/B6pgKSV1sXUkrBgRPY92q4mMYyAdScqqI6xnUxkew8b+lSSsGHIwVM6UJnl6LjnePEr9lahCHQqOc6Q9TE2dsGIIlCHyWwi3zTNqdBHNdO2WDN60aT0/vvwK5hxyCGd9+ytEjW5UoZKt51ESqGDzwBpSdpIsPUKxv4KKcJiK8P4Rn2kL3mkTZHkgL9COrr+PIhJ4dZWMbfNGez0HFR1Arnc7QRmN9vLii0/z7LOP8847bzAw0I+qqkyYMIUFCxZy9jev5R11DmOrRuDdwbS4sz/Nsr//hBcf/B2FK5cT+vpfaFbzQUKOD3rSgrKg5IMuQdjjerWt6hI0xVUi3jDjckN02rVMKpBcMKOH1eElhAvDKLpC4/Kt1DVuoCXRSEusgc1NG3g9+i/uf/N2fnH/95k37wgWLFjIggVH7ZI4viOMpEFvYxTbtMkuzd6jSjaTMOip68HMWITyAuSMyNknbzbbtEn1pfEEhy+CaF4Nx3ZIxzIfOsbu4Gg2t714A5s61vCrH/+Zg2bNp2BUPp6Qh/rFDdS93UBmIA1CEMp308H92T7WvbAe23LIrcwFAcneJJte20K4MEzR2M9HQMa/E1/UxV/g34k2xeJvvj6WeNIAzDb8nJvOpmQ/idJ6xeQvgSiNqoVHCoocjUMNP0cYwaE6sV21MJFDRDC43ScRqbBFM1mupXjVk6JVtfBJwUzTx+GZIP59UO7uCImkePQhmEo3izrWEBuIE1T91BRPoHb8MazUM1higG8nIvj6N6P1u6SlFRmDnVWz33OGTwuWY2E5Nl7V87kSLzhI3tXTvO5J0qPYZDkKc00/BxsBdAQHmrMZZ02kTt2ERFJtjyIsP3xB8eNA736fwNo/gWPi+ApQjD78TX/H8eaSqTgaFBWsJHrHO3jDI0kG55C+5y/YdZuQtoNQFdRRo1HOPJcb77yVP/3pd8ybdwS///2dFBfv3Y97T3puucPze4Ij4ZmtXuSk/+GvLd/incAa3mp/iDcbH+a+zN3kKXkcV3oiB0QOZGpiCpPiEwm+GEbtk6S+7JD4uoO2HtROgQyCOd5BhmFPXxnHcdi0aR2vv76IN954mcWLXyeRiBOJ5DB37qHceuHfOO64Uwk2egn9VsXaidz0lsOBCly0ULJVSMJhGDFa8tdGhb4MjM5xVbVxA14vhfIT4KQNbnhcZpZD5uDdk9Nvtwo6koKxOS5xmefPpvBbv2PlvPPofOTbHP3SoVR3fJ3JC29ifHcAdSs0DQhyLAj0O7uMNzlf8noztCYgy+ggM9BJfvlkkrYkoEtihqu+zfVJZpUMP8eABmePdXhgvUJjDGwHgrpgQYXD/AqXHG6vhleaFDZE3eMtCsBpo93QPnD3WdML63pccrYgAGGPpKcrSWFjI2ZjB6oJwbLxkJ/HqIjG+qjAkRKBq2gO6tCakBi2K5QwbFekUBSQvNyk4NckTTEFXZGUhVxLjDwfnFQjGZ/nsKJb8K8GhS39ri9yxhEEPFC6U2SDGHQXSe/kJmkkDererKdtTTuWaRMuCFI1q5KicUVD9yOhCCoOqGD1M2tJRlP4Iz5s06a/ZYDs0qyhILT9wZstgvVRQW1EDiq7Jb1p+Ferl/bIQUyue4KcSAFCVUiaUL95ANvOpri2CD3k1rXZJVn0NPbS/EHLxyKDAb73vWs55ZQFPPvsPzn++C99rLH+G/GxZ9CrV6/+JI7jC3wO0NHRxnPPPcHPf/773a+cKCpm4Wz0zqUIYwDVSoOiu+nAWgAcA6nvoPwc9LmVvg+/kVi5E1xFabwRO1Q56GubQJhxzOKD9vtcpBZAql6EnUIqOurAFrS+9SiZfsAhuPpWUqnTyFQeP1Q0Wtmj8fK8q1BWPAg77RI2joWVOwGAeWaAukyShlAeUxSVAU0nrWqMSCXIzySR/vAwrzAt2cx3p/dxzi8WcPXfV/CdH1/DAw/cxf/+5m+MnTwHtACpsRcSWnat61Vsp3E8WaSrTiIx7UegfviE0syfBo6N2r8RKRRAgGOgSBthJXFCI7CyawlbGY7a9BJHagGs3Akkx544uP1waNF1zDGe4pFvT+P4mxbzy5//gOsvOpaZM7/OQ7kh0jj4BlveWxU3GGT0DqoPs2g2mWQ73qYX0PrWAip2qJzU6K8gd1Bd7wnzDT/3+1WanBTdt9xL589vRg0HOeCvt1D45eOpzOTAxwiyN5E0qCaGkJTaGjnyc9D6LSUykUB4h/sICVUFCTK9a8txlh6hPdU8pPBIWnH6zV5UoZHjKSCoh8nYaTrMJi778Q84aM48vve9b3DEEQdwyy13c9BBew49+qSR6y3kwIJ59Ga6sB2ToJ5Ftp67z5MjIQSjsyehKTqdqRZSVhyv6qc6NIaywK6BaZ8FUnaCPqMXn+Knx+hAV7xo0oPpGEjcQrcv040AcjwFCCGwHIuWZD3NyTq60200Jeoo8Y+gJms8YT2bfiNKxJOLEAq2tDHsNF7Vjya23xfEYFuqLe1djika7eX880+lpKSMv/75MXS/RtwaQBUaCWuATQNrsBwDVWh0pVtpTzUxMWfGMEXyPp27BUlLENAtdG0tQqRxnAIEgoQhSVk91A2sw3BMXvzXMzzzzD95++1XsW2bAw+cwze/eQUzZhzE1KkHEggEh8bsWabQlICqsKuSEAK6bT/nfvfXjPrqMXzz0ot4/odTCZ54MwWzvkJvWlDglyiKu7CkDBagAyZUhKDfAF11VSWru2FaXoTssgjRxl4i5RHKx5fj1T0Ut4xg/pijKZlYQtmBxXTKDh5/4UXefv0FnnrqYqSUTJgwhcMOW8jChScybdqMoc9y58Yu1r6wnkR3Aikl3pCXygMrGDW/ZhjR29PQy5qn1xDrSrgBGrpK8dgiJh4/Ht2/9/u+qqsEc/1Em/oJ5GzvcDHTFoqq4Mveux+ZYzv0NkaJdcRQdZX8mjwMkeG8805i7ZpV/OXPj3LYUQuHtu/Y0MnGlzeheTRyK3ORUtLfNsDqZ9ZSNK6Q9ECavJF5Q9cgmBck3dhL+7qO/5Nk8Bd18Rf4d6FTWFwR7mCjZuCV7vfvIf8AH+hpbukvIn9wmieRdCk2SSEptNVdbBXWqml+FO6mVbHwIfBL6FJstqgGXYrNyekQQVT80q0xbeSwLrG0kDgS7vEPkBaSPEclJSRPeON0CZvz09n7FX72rp7mYb9BeNqx5MSnINJRvN4QWeERFDh+wrbDFpFmoP4+8htfGRSpgNTDZCqPJV19yifi2/tRkTCTvNL0Jkva3ydjG4yKVLOg4mBGRT4f9cpbeooHfTEUARFHoUexecgXIyYcTsy4NWVQhphkTfv3HJCUeJpfRNhprMig+tjoRwodYWdQMr04/gLQAkhPNlr7W6SXN2BvWIsystb1Hs2k2fr+Uq548C7WdLZx7bW/4uKLL90nJWDIA+PyJG80K+R4JariTmFb465ydGRkzwvkG6MuqVg4RhKrg4VbJjCn4jpyjOvo6/uAf3of5pn+Z7i/+R5s3DqtOjaSSY2TGL9oItnHF6KVe/F6PXiSXjyLvfh8rrVYT3snHdF2OjvbadxcR8PmzTS01ZE20ng8HmbOnMull/6QefMOZ+LEqcOUzzLgqnlFyvX53QaRAi0EB8yTTB90AnynTdCZgtqIG1i27ZqEvPBWBA477sNzI+r6BWFdDtvOq0J4xHSO+83r/ONPd/D+E98nVf8apxfczyRtKh2aIOZIcp5VcQoczKnbr/OoiGtd9ly9wsqNbqdYbc0Y8kslCoK+DIzKlhxT7VC1m3WKkdnwvQMcNvZByhKUBBwqwm4dKSXMKJZUhG0SpsCvQW1EEhqcyjoS/rlZsKhJYDnunUsi8dgWnsWr6GvrZkD3YzqSsrotbJQq9Z4cwh6VzqTAdCS6CiMCEke6oXKOhIgPqrMkvSnXXkIIt2bN2LC2V9CacI+vLQEHl8OxVZJR2TbLuwT9GUFllkNHAl5tVnCkHHqvUoMhyqU7UDCO7bD66bW0rmzFl+1H92lEt/Yx0B5jiqpQNGZ7bVY+vYxkNEnz8hZ6G3oRqkJORTbjjx2P7tOJGTBgQMS7ZxuNHbGyW5ClyyGLj23v6uY+6Ko4jImt75HftAbDHyZiZmhJZ9OTVUtFZLigxRv0Eu+MI6X8WItps2bNZf78I7nppus45piT99gh8H8V+0UGd3d3k0gkqKzcHgi2Zs0abrrpJhKJBCeffDJnn332J36QX+Dfg1de+RdSSk444ct73MbMn+JGChj9COk63EvFgx0sRdiKqxZ2XEd8NdaI9OVhFkwHKVH7N6F3ved6DYerMApnDvkDO4Fi0qPOwL/p72jRNYCrFjaKD8Yo3f8kbjs0AitnHHrnkqHwMqQNQsXKHo2jB/HVPep6HOdNGTy36ZgFM/A2Po2S7kbYBlJIzIIDXY8qYKTt4aI+yXuGScITpCjRRVj4qI71upYMSMyCmW4R6lj4N/wNrW8DhSPGc+fV0zjv/XVc+acXOPLYBXztgm9x6aVXUd7/PsLOYEXGIge9dfW+9Xib/0Wm8rjdn6B0EGbC3R6B9GZjBytQE82ubQQCKXS06Fqk6iUx+Qqs3HGoqS4cT7brP7ytUJYOWvdy9K6lKOkoevdycDLMnXso13cHuPqul5hZ+w6HBwvYNPtiVuoG9mDMVbZUOD4dpnhHBYpQSI88FaP4INRYg/v5iIxB6tvVcXHhsFLL0KFYBKTCJMtL6eAYOZZg0j0vsfx3N5LeVM+Ir5/H7B9/H5kdxseHp1DvDfWqySO+AbaqJiaQIxUOywSGKV8+CwhFQR09FvPNVxCF21dsnYF+hM+HWr6rRUhpoIqeTCd9Rg8BNUS/0YvhGEQ8uUNhIF7VR9KK0Zvp5NhjT2Hq1BlceunXOO20o/jGN77LlVdeQyAQ2GXsTwMexUuxv/xj7T82ewpVodGYjjFoHfHpJ7EmrTjtySb6jB48qpdCXxkFvpI9FCaStJPCsDMEtBBCuF7Jhp3Go/iIZroYEazd7meX6WTA6HPvG4pJ0o7Tb/QicajNmsS6vg+IGt0wOI3O8RZgSQsHZ8hr2XAygxYhkWFH0tcX5bzzTiIa7eFPj9xHo7WRYCpMkb8MTWis6VsGg2PCoCex0U1DbAOTc2fvV+EV9kChX9KR7EdR+nGcLMAN8FClw4bXPuD2x55i/ftrURSFOXPmc/31v+Poo0/cY8CmX4OTRjrc9J7Ck3VuQe5RJFMKJEdXSUomLODNV97jgu9+l3ceOh9tzUMc8NXfMqaolmUdAl11JyV9afc8dBUQgpQl8aigCaiLK3zpmLGsenINfS39ICW+bD/jJxRTe2gtkfJsYlLjmXW1DBx0CGPnXEdNrBOl7kXE5ue4//67uOWWX1FTM5rTTz+XY484la3Pt5KOZcipiCAUQSqaYvMbdQTzg5RNdtVJZspkzTNriXcnyamIoKgKRtKgeUULoYIgtYfuPYF+m5Ij2tzPQPsA/ogf27CJdcUpGlNIbuWeAyOtjMWaZ9fSurod27RBQr/s5bbnf05PXzffO+F/cVZ4WJlcxaj5NQRyArStbse2HCJl7kxDIIiUZdNT30tvQ6+7eLvT50XVVDLxj6ZQ/k/CF3XxF/gs8ZgvxibNpMzW8A7+JmRw2KSZ/MMf4+upHHqFzT+9MdbqGQwhyXVUFhgB5hkBFAQDwua2QB9tikXJoBVYi2oRFwZIWKcZ3Ovv5/BMkMONIMWOSr1iUuXoqEBUOGRw0IRCCkntNnGAhKAQLNczNJgm1ftoFWEiecWTRCCodHQaIkUU4SrDWnGoMh1CUqGyaxXZjc8h9QLscLW7qJbuwtfwJFZ2LdZg3f7vhuVY3LfuEZa0v0/Ek42u6rzb/j5b+hq4ZPJ5jMzeh1DtTxFpHF7xJNGBEYP2bjlSpVOxeMuT4mAjQO6/WyRhZ1DjTTjeHRaipeOqgaWNsOLAYK0iNJzOKPbGGKKsAuF1lYRvtbVw+duLCKgq/7jzEQ488tj9OoSFlZLmuGRjn0ATElMKIl44qcYhay8f3c19rsduMBfeOl4ydpmgcgPEI1CpTOPag6dyfeMNmC1p1qnrWdO+klVyFatSK/lr35303tyLKc09jh/2hCkMFVPprWSeZz4XjL6IMbXjmfyt2XjH77l+t8vAGiPRlwnsajdYTsRB7RCkD3WQO0T5ZGxAugvoO8KjumpTRzLMPmN3yPFCw8Dwx5xBH96krVBwwje4sOJQXvnTV/hq/Wy+MeI3HF7yLdblQHlS4n1euCF5g2SjEHD4CMnEfJt/9CR4B/je3ADTRjl0plzlbmFg7yHFPg0m58OOfsLNcXhyi8KmqMCWUBqUHFO9nQgG2BR1CddcryTH5+6bsmBto0kIQV8kFwOFsA4Bn06zkWZrv8TjEwR1SZ7f7Uo7ukryfAP0pF0StTspqOsXNMXBo7jXO21BzHCtFTqTrgL51SZBb0ZwwQSHMbmuP/G2c2hLwJZ+yfpeQa5PYkvozwimF0nG57nEcl8GzNY+Ojd2klWchSfonpwv7KO3sZfGpVspHF0wVLepmsq4hWOpmF5OvCuOrWl4SnJQvSqPbRIsaRckTEGWV3JwqeSIyu1E7+6gANu03obtWnK0xwXtSUmHWsS9B3+Xw9vepLB9I5lQhE3+cYiWNLZpD+VdABiJDJGywo9FBFsZC+lIrrzyp5x44jyeeupRTj75jI883n8j9osM/s53vkNpaSm/+c1vAOjs7OSQQw6htLSUmpoazj//fGzb5txz9z/s6wt89njllReYOvVA8vL2rN70tL+N9OWRKT8SvW89wkq6xCSS9KizEJkoWv9GQGAHS0nXnI4drsLT8gr+Tfe7NhJCA/kSeuvrJCd92w3FAozS+VhZNei9q9zV4XAVVu5EUD5Cu6VQSI0+F5HpxVf3D9RkB6heHE8Ex1eAEyhB612D3vXBEBmM5sPMGYe36Tmk5sPxRpB6FsJK4Wt4ktTYC0AIKnNmMa55MSKWwJtqR7ENkBLHn4dRfDCZiqMAUAe2oPdtcotU1S1W5k0fx+JfZ3PrM6u48e93cd99d3LBghouO24CJSO2u+er8Sa8zS9ilM7fRW2td76Lt+lfKMk2pB7GzJkAUmLlTnLfD4T7nkgL7AwIBbPgAKQ/H2dn32Ep8TY8ia/+MdcKwzbQepbj+Auxs2r41kmzeHd9M1+/8wPeqCzmolFnsTq7kObBNsJxlocRe2hFdALFOIFdW6m7hcVfA/1sUg1A4CB5Wap8ORag/uHHuPnmG6mr28TYI4+g9C83E5g6kSSQKxVOTYcpsiRadKWrFPBEsHLGg/rhk4yYcLjP10+zalJp6+gIOhWbJ7wJcqTKDHPfjd0b1TqW6otpU1soskuYYc6h2t47gfNh8Mw/DHvDOuz1a1By8pBGBplMoB98KMrIXcfO8xUyPjKdxvgm4tYADg5+NUi+r2SYtQAIHMf9WS4tLeehh57nj3/8Lb/5zXU888w/ueGGm1mwYOEu439e4VP9+NR/jwl/3BxgVXQJA0YfuqJjS5uOVAvVoTFUh8cOK1D8apAsTy5N8S3AoGJXgulk8ChevKqPhBVD2daJ4Fj0ZDqR0kFVNPxaAIkkbSepj29gZNY4pucfTE+6HcPJ4FMDBLWwSxBnuvGoXhxp40iHksCIIVIXoKurg7POOpaW1iZ+cMuPSUX6yaTiOI5Na7KB0kAlKStBtmf7bEAIgV8L0Wf0knFS+NR9XyTQFFhQIXlwgyBuuqHQfb1RVjz3DJv+9RQD3d2MnT6eX/zq95xwzOnDgin3hsaYQAhX1asMKs8MG9YP+qJFIhHuu+Ov/M+tp/LmXVey6EeT6T3+2wQO+ylxM5sR4cGJwuCEBLnd783BlYcszkR4b/JBxCtSjNGTHFwBI2tzUAZnGE+sESzvElSF3fTqTFYBdaFzmHLY2dxwk8nSd17lucfv4Xe/+wW//OU1jCudwlFzTmRW0Ty8qo9AboB0PEPb6vYhMrinsZdYZ5xIefaQWtgT8OALe2ld1cbIg6tRtb1PxksnlWBlLBrebSTRk0DVVSqmlTHm8NF73bd5eQtNH7QQLgzjDXp4c8VL3PbYLwl7Ilxx1PWMrZmAbTk0Lmsi0ZtkxjkHkB5Iu4EiO2DbZ1/36a6X3Q5FvHQkVtokpyKyT+/zfzK+qIu/wGeJFVoGAUNEMIN/F9is1DJYSO7z97NSMyh1VPIcQbdi84g3jk8qzDb9rNUMWlV3Yd6LQrdikRYOFhJbgJDQL2ye9sZpVy0OywRYpmfYorqhp0EpONgIsEpP70IihqVCq2LTrthU79q8slv0C5suxSLXUdEGx48qzqCC1SEhHAQwsWMNXtvGCQ/OWYTA8heSSbezpX8pncW1jLO8hOW/VyG8IbqF5Z2rqQxXDIXf5ftyWR/dxJst73zmZHBUcehVbPJ2eq9yHZU61aRDsci1/81ksOrB8URQEy3gdxWL0hsZtIZII7flQkgHJdODEZwO5maEx338iXUruer5fzKnvJJfT5pB2W4C0D8MpSH49hSHDzoFzXHI9kimFMrdqk53hLMDf5oKwQeHSlYcAm1tcO5iwbwuUNtAy/iYLqcy3TMVp9hV7irdAqdAYpY5xOanyagZ7C0ZnBUGlmqTl1dAVnMQrU5g1YI5RYIB2laB/bAkfuWgpQSAhcvAbZsOCUid7kBGQd8owJTgFRgHStInDSd9t/nWDhgMBeVJCb1pwbxyh31wr+LAYsmKbkHHjj68A1AYcP193+uEypqx3DvlbW5sv4pbGy7lfedNvnPAn3GKQ+itAqUDnJ00I0UBqPa7yv/K3MCQpcNHQcyAu9co1PcLSoOuArxhAO5Zq/DNKTajIu52m/sEKYth771fA5E26PBHKJFR8n2QVHTeDZRhm4LiTD9Odh7FIRibI7l0mkOe3yV4/75eYbPhehZLCf1pV4GtK7A1LkgOrgUowvU4HpXjqmtfaxacWLP9vcokDOxNXRyXMlhjBdjkFKB7NQ4f4TCvTPJum3CJ5LQguylNWa/DuJLh82Nflo9YRxzbsNG8w+s6T26I5fEwb7UKEq2CzqQkbroq7QK/pM+A+9cLVnULDiiSVGdLqrN2deU5oEjy8EaFtCXZ3Cdojgl0Icn1CSxHskEpZWD86cxc4J7b+k6LOcoSok1RwoWhIc9gRVMpn74Xw+y9INmXov7tejrWdyKlJK8ql3lzj+APf7iBk046/XNl2/NZY7/I4HfeeYe777576N/33HMPubm5LF++HE3TuOmmm7jttts+1aL3tttu49e//jXt7e1MmTKFW265hZkzZ+5x+0ceeYSf/vSnNDQ0UFtby4033sixx25fLZRScu2113LHHXfQ19fH3Llz+eMf/0htbe0ex/xvhGVZvPHGIr72tf/Z80a2gd79/iDJV4KdMx6R6UM4BkqyHaP8cMy8qagDmwGBnV2L1IOIdA++ukcHCcuJ7liOhRZdi7fxOVLjLhh6CSdUTib00dWDO8Lx5iDVAAgPjicbx5ePkDZa7yqkHnT9j43+YefnbXsNO1iBXTJv6GGR7sHT8Q6ZioU4oXKkL5fkpMvw5k5FaV2EkurBDpWRKT8Ss3gucjAoSVhJcDLInYLqdF+QK08Yx2lX/oW7/vpH7rz7Tu54cS3nHDqe7x1bzch8L1LxoKS7UVJd2DuQwXr7WwTW3QGOjePLQ0n34Gt8CmHFEbaJkDZ29qhB5XDcDZITGnr3+xiDJPWOUJKt+Lc8hEj1DFpSCKTqRxj9qP1bkAXTuO3SEzj0ijs55w+v8/QRCaYHfUy3dhlqn/Evb5INqkGt7UFHYBkZ3njoAb5y023EGhpZuPAEbrvtHiZNmc5m1aQ5baJLwVjLQ2Gqj8DaO9B7V4O0kELDyhlLatzFuyWed8RaNU2v1cNYK4jU3GqnxNHYohgs1lP7TAav0j7gnsAd9Cpd+GWA5foylnjf4tzkxUwxD/jI10WtGon/km9hvPYy9sb1KDk56LPmos+dh9hDm1uhv5R8XzEpO0FXqo1NA6uG6ZtN28B0jCHiUBEqqqry7W9/n+OOO4Wrrvo255xzAieddDo/+9lNe/VB/b+IpvgWYkYfud58xCDBnrTibE1sodBfRkjfXikKIRgZHks000XM7ENaEoFAEzoRTz5pJ0VIzyJhxwgrEVJWAtM2UBQFTdFRhOKGNUgvCTNG0oxR4C+lLDi8rXRS7kxak1vpzXSgCo0ifxnF/hFDCwDNzVs588yjiSfiXHfnL8mtyiVLz3ED6AbVv82JBuQ2dnQHbGvFEvvp7wgws1giZZgnFnex5PFb2PjaYhRVYf6xhzH/tEOZMvEApucdvNNCxZ7Rm4Y3WgRFAddfbeg9icErzYLZpe7kJaDBFeecQO7Eo3jv8d+z/OkbUBbdT95xP8d71Plk+dzCvy0hyPVKCv2SnpRLLm/uE7zV6io5bF+QV60QXQnJ1x2HLFw1x+puhSK/SwSDSyZHvJJnGwRrezwEPUcx4eIj+ebVfbx4x+089vgD/P7R6wg8HeLomSdz/NzT0b066Vh66BzsjI105C7+wKpHwzIsHNP5UDJYCEHljBGUTiohGU2heTUCOf4PLXBbVrahezWER/KnJ27ihSWPc2DVXBYWnk5lWQ3eQb82b9BDb2OUrs3dRMqy6a7rHtaq54aLQOnEEhRF0LmpG3+2D6EIkn0pskuzKJ28d5/G/wZ8HuriL/B/Fz7EzrdxwCUcfAg2qQYbNINqWxvy7S13FOpVkzf1JDNNH3Hh4JECVYCBQ0y4npfOYHZTtlQocjT6FZtWxaZBs7gymcMm1SQjJGW2RrGj0qCaRBUbdiAZTdzFweB+ELJ+FHxSISUcsqTGCFsnKjL0CjdiKoFDj2JzrGETQmcbx2wIySotg65bfCD6+Je/n1Jb4+x01na18j6gQ2mjVW3GL/2MtEbjYf86kNoTHVjSGiKCwb1fR7zZbIrWfeyW548LvxR4EaSQ7OhmnxISrxQE/s3kOQBCwSg7lMC6v6AkO3D8BTiqF6n6EI6NkulDOCbC7McOjcAedQrKG3/F6e7kbx3N/OLVF/jShKn8bNxUvDl5KEUfrZbN8cFhI/YvM6Em4nYbDWQga5BITQG9Aej/mkOqDfwPgLpJgB9EwiWCsQFF4uSC9z0FvcGPkxdA2wAyBMbBEhkEmgRSgDKA+133g1Uj0TaDvkZgjpF4XxHo7wmEA+YESeYwiVMKTj4kvuOgbQbRL5C5DtZI3C/lDqjOhlklDq81C6IZt87pywiKg5JDy/ftekwvlHRUw8tNChv73HtISRC+XOswMhtebRZs7QTV6+U7U3/PmJK5/H7pxfz4tblMPeppRqgj2NNXLZVKAuD3f7xOxpXdgoZ+1xJim6K4Jhs2ROHdNsGoQTuQPZ2xqinYctBjWkCrHmJA9VIQ6ybtj4AXZhZJNkQF73cKjqyUzC2TPLAeOhIu0W5Jga66ZHC/IQhoEscBVXED1bpSrlI74pG81yE4YeRgl1tzH6ueXMNAuyu/LhSC0SNymHLyJEJ5Ad5sETywwQ3+y/FKTF1nc08vj957Cw1N72KYaYrzyjl/3qVMHD91SPCwI57YInihURDWQVUkdf0KCElF2K232xNQ3y/Y3Adre9xcjnnlDqeOksMWDOaWSjZGJe91wLoorg2MDuNzHXrSbj3eGpc0BiBtCQqzNQ45fSKxxRvcvA3LIZDjZ+RB1cPsLPYVRtJgxWMr6a7rJhAJIBRB8/IW5pYfyQ1vXcWKFe8xdeqB+z3ufyv2iwxub2+nqqpq6N8vv/wyp556KprmDnPiiSdyww03fKIHuCMeeughrrjiCm6//XZmzZrF73//exYuXMiGDRsoLNz1w/L2229z1llnccMNN3D88cfzwAMPcPLJJ/P+++8zcaJLSv7qV7/i5ptv5m9/+xvV1dX89Kc/ZeHChaxduxafb+++e593iEwfwkq4AWOqd6/bLl++jL6+6L4pBOXgbVIoSF8uUjoo6R4kAukJY+UP95jS+jaipLuxIuO2P6hoOP5C9O73SVlngfbJK/303tXofesw86ag965EerKRCJR0t0t06kHsyHbSX8lEUdI9ODt5HEtvLiLZhpLqxBkkqqUvj3TNqaRrTh3arlfYrNMypEWCUltjTLAM6clx99uBqFRSXdjhSrKLa7jysu9z5fQ+7nxhNTc/t457X17N6dPCXDQrxNxxJcN/kBwTb9MLICV2ZDQAti8PJdWJ2r8ZJdWBcCywDYRjuOrq7FpQdJR0126vkbfxWfTOZUjVO6jYtlDMARyhoybbsORksvwe/v7tGcz/2Sv84Oe/4g83/3W3Y4lMFG/LK2hdSwGBWTATo3wB0rPdAyiFwyotTb5USdQ1svr+B1n9wEPEW9soOflYbrjrAU4dO2No+9G2Z7sfsZT4Nz+E3vU+dnaN61NtpVwLkE33k5h8xR5DQ+yeFWS1Pc7Z8c1kCT+txdNZV3M0aW8WQVy/tH2BickzvseIKf2MtSYiEEgkdeomnvL+g3HmpP2eOOwItWok/qqRSMfZbev17qAIhaAWxhv00W/00JFuRREKhp0hbvahKjoNsY30ZjoZGR5Pns+9V1ZXj+Khh57jscce4Nprv8/8+ZP58Y9/wdlnX/BF2irgSJueTAc+LTBEBIOrAI4a3QyYvcPIYICIJ4+ZBQt4r/sNutJteBUvAS0LQ6YJ6WEKfWNpSdQTzXRhOiZSOCA1fOp2Em/bRFEVu/95DmghRmWNB8bv8tyWLRs544xj0DSNex95nM7AVoJa1vagCCEIalmk7SQ+1U/M7B8iih3pkLLjlAYqhwLq9geNjVu4+5fX8OSTj5BfUsip3/gyC045gkDYT0APUxMet89EMEBXEmKGoDJreFme54OulBhMYXYfm1wA35/jZeWoH7L1zK/wr7uu5q0HL2HZ0j8x5oybyK2YR8yQZHldtXFQh5psycaoS+y2xAXRNCCgMyGYkCs4dqTbvpexXRuMbUhZsL5X0JkUVIYd+tLw8AbB03U5HD/3G3xTHIiudbNo+TM8+84/ePrth5kz6nAu/Oq3h8YIF4bwBHTSAxn8g/6+UkpSfSmKxhai+fa9NNN9Otkl+9Y9I6Wr2O1KdHD7P39JU2cDXz/xe4xRppHsSQ6TOKm6ipSSZG+SsqmltK/roKe+l2BuAMeRJKNJ8kfmUTKxmMIxhTS+20jb2g6k41A5s5Lq2SMI5v57LGg+S3zWdfEX+L+NeUaAxXqaXmGTK1Ukkqhw0AQcYgToU1yF784BblmOQrdqk0FS4KhEpILpuL7CBg6OlFgCvEC2VFEBiSAsBVtVE4FgmjV8jjTL9PGoL05U2ESkgoFrzVVt64y2dDoUi9VahoSQFDkqk0zvLt7F4BLHB5o+nvXG8QtBiaOSsjys0jP4pYIPwWzDz+TgZOLKCpKqIGJDvWrQ5SSokQI1ezQ1tk69avKQb4ArE7kfGmJnYvKU71He8r5Kv4iioVNt1XBm6nwq7X0LuwXwa36QYEsbVWxn3dJWhqJAwWeuSItIlammj0WeBD5HEJIKKRyaFJPplo+K/QweBJCJOOayd7HWrkZoGtrEKWjTZwxZOOwLjNIFKOlevC0vu3kjipdM+VFYeZNQYw0IYwAr53C3YzJYhrbgSP73Z9/nzo1ruGTCNK4YNR7hOOhHHIX4mKTh/mBsDhxc6vBGi6A9KVCExHJgSgFMr5FkxrrkbfCPCp7XBUpcoPQCFjj5ErUVRBqcAoFTLKFeQaQk2kaBOVUikkAIRFKgJCRONi57IwVKlyTwtoJntcDOkaCA92UFbYsk8T8OToG7rTUWdqY4lRbwLBWoza46+eypkvJxsKRNEDNheqHDIWWSEfuYG6gIOG6kZEaxTeOAQFegNkcOecx+ebTDw6bChghUNsPU0tO4beEk/t/rx3L4Ewfzj/OfobJg17oWtpPBe+JlpHT9aN/vFPRlBCPCkgOLJAU7fQx60wACTdl+LYSAoC5pjrurX5ZhkxuPIxMBujWV/Cz3+5C2gKCPWjUOsRhpPcSAz4tqGKAqJAMhqgLuddAUSYsrZsajQlHQ/b/pgK64IXWbo4K0I8lSQFHAccCvQ3dKsLYHykKuBYQEHMtm7QsbGOgYGAoatk2b3voeNr+2mfEnTeLVZgVdwIhBpXg6X+HexT+kLGcCl51+I2V5uTRurcMrfJRPLUNRFWKGazvhVcGnwjttCvk+Sb4felLuMSsCGgZc0cTGPoFXBccR5PscdBUWbRVUZbkezNsQ8sBFkxxqIoLbV7jh0hVhSa4P0rYk1Od6bactV2F8RKVDbSSErJpOvCuOZdiE8oPovo8WxNu5oYvehl5yR+QOdaz5s/3YW0aRn1PIww/f+wUZvAP2646flZVFX1/fkDfakiVLuPDCC4eeF0KQyXx6HnG//e1vufjii/na174GwO23384zzzzDXXfdxQ9/+MNdtv/DH/7A0Ucfzfe//30Arr/+el588UVuvfVWbr/9dqSU/P73v+cnP/kJJ510EuCqOoqKinj88cc588wzP7Vz+TQhjH58dY+5QW92GsdfQGbEMRgl8/dIlr366r+IRHKYNm3Gbp8HQPVgFhyIt+EpHH8hQtpIoaIm23B8ediRMXs5qt2ts4k9PP7JQEm2uwra7FE4qTbUVCeOFgBpo8a2kK45DSNvKlr3coRjYPvykVoAYcaHEZjCjLuBdJ7wHl9rhZbmYV+MrkFSUQemeXxcUHE4uVsec0Pp9BBKJopUPGRGHAOKjhMsI1g8nh/MWsx3Dqjmrvdt/vBSC39/r43qvB5O/+BHnHLJDYwYUY2SiaIm211yfwc4vgKUVAemdxJ69/soVgKperByxmJlj0br3zjkzTwMdgZv22vgWDjBctdDWEqEbaAYAziZXtSBOoSdZtzICm786dV85yc/49jjTmXhwhOGDSWMAYKrbkXrWTF47STapvvQ+taQnHTZkNVFLJVg01OPUnfvQ7S/uRhPVphxXzqFKZd8jd5JNVQlI26r0+7ez1QnevcK7GCZSwQDaH7sYAVa71qURNOuNhhAy8BK0ht+hzCidIdySVtJqrY8TTjRwRvTv06/Khmzj6rgNrWZZrWJErtsKAxFICh1ymnTmmlRt35suwhgj0rgvUFTdCbkHEheqpm2xFZazAb8WpBcbxGKUOg3oqzte49peQcR0t3PtxCCL33pHBYsOJqf//xH/OAH/8PDD9/LL37xByZOnPqxz+M/GwJFKNjO7j+Qe1LPhvVsDi5aSHOinvbUVkzHIsebT0VwJFl6DnneIjpSTfSbvcSMPkxpIpHY0saRNoZMk+PJJ9u7b1YK27B06WIuvPA0cnJyefDB5/Dn+ejs3rr7YxcKlaFaGhOb6M10DU5OJdl6LlWhMfs1We3p6eJ3v/sF9977Z/LyCrjppj9x7Ckn0Wt2kLIT+NUQRYGyoaDDfUVQdwvUlAX6DmRs0nJ94II7VS8lQSgJSqgq4+KD/8bSpd/gxz/5Lm/9YgFTDjiYCy6+ioIJC0G46o93WgUfdCk0xSBtC/yaW3h3pQQPbVQ4qsom3w/5fkl3antoRltC0JUU5PslcUvQPACWhN644Ak7wsjyiRzUsIovzfgqx049jWff/gcvr3uaN3/yL05aejrf+tb3GDt2ImVTSml4dytGIoPm1UgNpPGFvVTNqvzUyIJ0Os3za//BA0/fSV52ATd8/XZGlo5m6/tNSCnxhLZP3B3bwbEcEtEkyWiKcceMo+WDZnq39iFUQfXsSkbOrcYTcN+csUeNofawUUgHNM//nXCOz7ou/gL/t3FCJsh7eoqXPUnqFNcOyifhiEyA4zNBNmomKoIUzjAyNKY4lNsaXgTjLC8TLC/LNDc0eQCblABVQpGjEZSCNBKPBA2BVwp0ues9ar4RpEdxWKKn6FAsNAQ1ts6ZqSw2aAYP+gfoFq6tnEAwWtc5PxXZxa4AYKERpF/YLNcztCoSDTg+HWKhEaRy0Of2H1XTWJMvsY0+ytJJajrWMLGrno7CaTQVT0VDUGnrNKommzSTydbeicm3PK/yvO8Jcpx8xtgTyJBhs7aBe/13cEX8pwTYN4JxfN5oioKF1A9spSpcgSpUopk+DMdgVslH7x77JHFcJkhcOKzSMzRjoQMTLS+npcL7nZ8hE3FSd92OtXK5a9vg2FjLlqCtX4vvK19D6PtI6Cga6VFnYJQeipJoAc03JGjZGbZt84PH/84jG9fw48OO5asjalCKitEPno82fS/z2E8BqgJnjJGMyYU1PRLTFozJlRxQuL2jyB4BydMd9HdURAykz1X/ioxAaQAnIpHZEqkBOkgNlC6XJJbZQA8QBKmA1uDuo/SAVxMo/WBOlkOqWidfom0QeJYI0sftfo6tbYLAXQpKp0D6JGK5gucdOPJMh0NnObvdZ19RGHCtIXbG5Hwom+lQrwsijwhqugRZ3rHMn/kGpyw/gaMfOJS/LvwHs2cfssu+6XQaXdf3KFR5u03wyEaFuAEeVfJuu8I7bZILJzrDyGzX+9mt9QD6My6RHDMERQGH6NYoa59fT7RtgCJvGetDxTTmBgkXBHEkzKzUOLwoj9efidLZ2ouS68PIC9Kfk0cox09llkRKsBy3Ew1cr2WfKikNMkROW46rAE4mBI50/y0BwwHThk1RQWcSLpjooAiItg7Q39pPVnHWUEeZqquE8oN0b+6muydDTypIxLv9ui998reES6qYM/f/EaEPfcBhfNlUyqaWUTa9nEVbBYu2upYSugIhj6QjCeNzXRGER3VtLJBuHd4aZ1Ag4VpovN+lEPJIPIpkRZcYRgaDa6tx+AjJsna3hs7zb3+8OCDJ8wkum25TGd5OTQkhCBfu31xhd4h3x5GD12gbhCLwBnwcMvlInnjiIa699ld492Ox6r8Z+0UGz549m5tvvpk77riDxx57jFgsxmGHHTb0/MaNG6moqPjEDxLAMAzee+89fvSjHw09pigKRxxxBIsXL97tPosXL+aKK64Y9tjChQt5/PHHAaivr6e9vZ0jjjhi6Pns7GxmzZrF4sWL90gGZzKZYcX9wMDAbrf7TCAdAuv+gt7+tmvn4M1DSXbgX/9XpOLBLD5ot7utW7eKyZMP+NCExUzF0ejti/E1PIGwUiAEdqCUxKRL3aTX3cCK1OJ481ETLdihwc+HY6OkO8hULPxUVMEA0pPl2udK6QbcGQMomShICzP/QDIVRxNcfQtarBEcC+nNwdGCqJk2pOpzFcFmHDXegFk4AzurZtj4It2Np/VNrL7V9AWgpHQKeUUzQdGJC4d39RQVY47iBE8OntZXUTJRzNyJGOVHYBYMrkgJgZk3BZ8WxK/Ct2bbfHP2KN5o9XPvu73c9sBT3HjXY8yefQinn3oaX861yfN242g+pOqukAorCaqfVO1ZoHoRZhwrXAWKhharxwmWYubvWoCqsQawDRxvLooZw/Fku++nLw/NTmMWzcbKHY8drMAsnsOph1Tzz5ff5eqrL2Pu3EMJhbbfsPXOd9F6V7kpwNv8e+0MevdKzKY3eb0Bnn32cZ588hHi8Rh58w/i6D/fwpgTjkMPBGhTLHIkVNl7LhqFlUQ4GdB2Um5rfkSqc9AveTjiwmFz+/OMyERJ5Y4F1aZVSKK+LMZ2rcSKriZcOJm5+0gGK1JFQeAwvFhycBBSGQr1+qygKTrlwWpMxyBqdJHj3a5Cyfbk0mt00ZFqGSKDtyE3N4/f/vbPnHbaV7jqqm9x1FEzOfnkM/j+96+luvrjk9v/iVCEQoG/jPqBdfjUAKqiIaUkbvXjVwPkePbsra4pOlXh0VSGapE4KDuog/J8hUPq7GxPPhv6VmA4GWwsJJKgGmZizoxhiqK9QUrJnXfewvXX/5Dp02fxl788Ql5ePrZjEdLCxK2BYTYRCWuAPG8RI0KjyPMV0Z1uJ+OkCWphCnwleNV964hJpVL8+c+/57bbbkIIwfe+dw0XXvidoUDCiD93t/tF025hmeff7t+7O5SFYGyuZGnHdr/emAGdScHhI9xE5d+8p7Cyy02CXlAh+co4ZygEZMaMOTz/3Nv8619Pc8stN/L9b5zAxIlTufTSq6g85mQ+0DQ6k2DaLrG7DUlT0pFwVQvj8+CISskD6xS29EmyvdDQDzau3URjvyBhuYV/xoZeQ+CNFBGd6lC2dQMBJcjF513GT6f9L8+/8Th//ONveeyxv/PlL5/DD77/Mybmj6d5eQuZhEHZpFJGzKggr2r31+3jQErJc889zs9+dhXtbS0sPPAUjhx9ChEth76WfhRVIRAJYGUslwS2HdrXdZAZyNDwbiNN7zUTzA0w6tAaJhw/AUUR6P5d79UfZm3x34jPsi7+Al9AQ+G6eD5H6ine1V0rmlmmj0NMPwoKtZaH0ZaH1VqGMkfDJ91OKBvJIYMBch7g3FQ2BV6NFXqagKNSrxn0C4duxSKOgxeFalsjg8MMM4hnN4ShF8GZ6TCHGH7aFZsAghrLgyUkf/X1EROSsbaOQGAgWasbPO8MMM1qokltxIOHseZESpwyglLh/HQ2DaZFt2IRchRGDVqLGUj+FIiyQrMp0Cfii7ewNjDAkkgBB5QfhFpwIOZgYLGOe7/OiL0TXA4Ob3tewyt9FDju77MPHyOtWuq1LWzQ1zDN3DeSMeLN5qyxp/LwhsfZ0t+AlJKgHuDwEYcwp/SzU6NJJFsViy7FJiQF56WyaDEsuhWbrMHrq32EIGVz2btYK5ejVtcgvIOdLvE41pLF2NMOQJu6ewJcSsnWWDN9mQEi3ixGhMvdLqVAEU6gaM/nISU/+cl3+cc/7ufWW//GKaecCbYNqvqZqa41BQ4skhxYBHsSOal9ArsUpFeixEAGQSRB6QZZBHYhoIOTJ1G2CndVxwa7UKJtVHCERFsnULeCMMHOHySGo2BXS5xt+gEVZECibtmD4MoB7zMKSrfAGjvoeYBEbQDfMwJzkmQf1z32G3l+yDtIIsZL9NUCox8iuSU8WvUSF152OmeeeQy33vo3jj/+S8P2y8rKwjRN0un0Lurg/gw8XefWt2MHyydHulYNLzQqXDTRGSIbJ+dLSkOCDzoFMQPiJqRMl6A8rMRi1VNr3DyH0iyOUxNUDzSypTlAcV4ZMydlM61AEtArKBudxzvL+3C6dFqdHPBoZHuhLS6wpCTHC1ML3Gsf1GFSgeTlrQqRwcC3vrSrxI14JTMKJUs6BHFrm/2EQFXc7bbNKC3TxrGcYeQmgKKpmCkTj2MT0iUJc7vn85YlT1M25UheWP07Htv0GsUFxXzl7Is5cuHhLGkXPLpJ4FNhRFhi2LCmR1DX756DqrjjBHRJW0IQ1CS241qIZGzI9UFAk8QNiJuCDb27/8x7VVgwQvL39YL6foh43WuetODYaudD/bg/KnS/jnTkLpY8tmmz8JCT+Odr9/Pyy89xzDEnfzoH8B+G/SKDr7/+eg4//HDuu+8+LMvi6quvJidnu+LwwQcfZP78+Z/4QYKb2GzbNkVFw38gioqKWL9+/W73aW9v3+327e3tQ89ve2xP2+wON9xwAz/72c/2+xz+HVD7N6J3L8cOVw8pWW1PGLV/E97mFzGLZrsK0J3Q0LCFmTPnfuj4woiCY7h+vL58dyyhoPVtJOOYw1ZwhRlHSbQiNT/p6pPxb34QrXeN69XrZLCyR5EZsX9pr0OQg8mae2k7NvOmYAdK8Ta/gLDSrurVSgM2jicL35ZHUVMdWFkjQfG4dguJFuzwSJRMLyLZhtQCmIUzSI35GiBQEi2DpKOKf/2daH0b6fL6KY0lGNO2nA1VTbw/4XRCKISlwlKPyZHlh2OUHQa24dp17FysaH7s7FpsfzHCSSPVALNG5jDngA5+faHNw31zefjRB7jyqsv4HpIDyj0cPjaLw6ePZvqUafiNZsy8yRjlRyI92fjqH0NLtIFQsLJGkh515h4KKwGqFytnLHrfRpRUh/uYtHD8hcSnXAmaF22gDrV/M1LxcMMNN3PooVO58cZruf76324/hb4N7nuvepBSsqmlhxff28Kid1fwxoanyJgWFRVVXHzxpRx85tk8Py5Ci2LRKhUywsCD4KR0aK8JxnagCNuXj5LqxA5XDT2upDpxfLk4gV39KdepaYL9m8GbiyYUih2Brtj06n7S0mBcrJejwlmMtfdtdbDEKaPKqmGdvopaaxwKCg4OLepWaq1xlNm7KpM/DUgpcRxnj4s3KSsx5EG7DUIINKGRtOJ7HHfOnHksWvQ+Dz98D7/5zfXMnz+Zs8++gMsvv5ri4v9+/8+dMSI4kpgRdYPecO85XsVPTdZ4fNqHV8uu/+6eP9NjsibhU/w0J+vJ2CmCWojK0GhKA/sWMBON9vKDH3yTZ575J1//+ne5+uqfow+qcFRFoyZrAmv73idqdKEIFcexCephRg6G34X0rF2sLizHDWnza3tsJOH995dw2WUXsHVrPeef/00uvfSHew0eBbdgf6pOsKJLIWNDvk9yeKXkoBK529cRAk4f7WA6Cht6BRlb4tMEs0sktRHJFa+ptCbc47QkbFolWNWj8Lt5DtuyzhRF4eijT2ThwhN4661XufnmG7nkkrMYObKWU8//PinP2ahe/5DzUXpQDRH2QHtCMD7PPT6v6vB6sxuMUhKSeFS3iO7ZIQREEWA7gq6MwsaiUr5xXAE4DppPQwjBBbX/w7nnXsxDD/2NG2+8lmeffZxLL72Kiy++DK/Hu4t/8CeFDRvW8NOfXsmbb77M4YcfwwMPPE1BqISm95rorutB9wcYe+RoLMNi69Imok1RrIxFeiBNVnEW+dXuDDPWEWPd8+sJnhMkpzzyqRzrfyI+y7r4C3wBAAWF+WaQ+WZwl+d0BF9JZfEPX5wNWoZuxSbHUTk6E2aC5WGxnqJPsYk4KsdnQpyQCfGeluJufz+bNJMoNgkhsbAxhMICI8xhxq6vsw0CQbmjU+5snwusVzO0KzYjB4lgAA+CXAce9S3nNe7BIYEEcny5nJo6i4OM+QgE1bZO9U4igQ2awVrNYKSt41O8kFVLEIfnPEnagaOM4BCl2SscwlKhbIcxpJRs7qtnRdcaYkaMiqxyJheOYyCrj4Acfm46Hhwc4iK2X+/JhLwxfP/Ab7MhupmMbVARLqU8VPqZkZUpHB72xfhAT5MUDjqCkZaHs9NZzDQ/urUZ4FpD6J4hIhhAhEJIy8Kq27xbMnjAiPHQhsdZ1b2OpJkkoAeYmDeWM8acTLZ37wzRrbf+mr/97XZ+/es/cuqpZ7kPavtvbfHvhtIAhCTmKFAbBGqnqw52wiC9uCsXgDlO4u0EHFCbBOiQPtxBmOB9W0HqErsUrGqJMgBqp0CrExh5O1gfGAIntPsFEKUbtEaBXbyNCHZhl7rBdNrWbbYSg2P1uSplmQVOIXyE9YJdICOuJ/I2hMnmvvue4vLLL+TrXz+b3/72Ds4447yh53Ny3DokGu2hpGR4oFjDgKA7BaN20LgoAgr9ko1RMSwUL8cHJ450+OnbKt0p8KuCXJ8k4oXXNlnM6IGJFZGhemxyjk1FUxO5HXFmHjUD03HdtLLzAiw8PMBcA9JLFN5ohrW9AolLeH5twnBF8sJKSUtMsrhN0J5wFcCaEPhUyeYBl3wt8EGfIYh4JZPyJF4NtvS7JHdWURh/xE+iJzGknJVSkuhJECnPJjffz+yk5J+bBd6Ue5797XX0tf2JWadezjU/v4pVK9/jmmuuIJwbonXU+SBd0QUw5J8cM1xP40K/pDPhLiV4FPf6DBiuKjjHC6VBOWiHAX2G+8d22G3Q4MGlEl2B15rdrrpcn+TEMsm8ffSi/igoqC3Av7iR/tYBsorDCCEGg5YVZhwxl8lPTueRR+77ggwexH7dPSdPnsy6det46623KC4uZtasWcOeP/PMMxk/fvd+L/9N+NGPfjRMcTwwMPC5UX6oqS6wM7tYGkhvDkqyY8iuYEc4jkN9/RbOOOOrHzq+p+UVFDOOUbpgO1NgJdF7V6D1rnb9gqWDp+lFvE3PoaZ7kIoHK2c8yTHno8YbUTL92FnVGEWzkb79a4UWZhxP80t4OhYjrDRm/lQy5UfghIZffyXR4hKUqg9hxt3gAcMt5qTmw9f0L2xvLpmqE4aUrE6gCGEOIPUgscmXoqQ6kXoYO2skSrqHwOpb0HtWIuwMIt2NMGMYJQuIeaBZyzAiOUBt05s0ls2gO6cGHUFKSBxclSHa7hV3drgK6clCIF27BnATc9NdeMoO40tHnsdZc0cQfSvAi6s6eHl5A3e83cMvX3yboOcdDpkykoOOnMT4zFuMH5FPYemhqIlW7FAlRtmhe/SLtsNVWKEKtFgjRvFclHQnwsogjH6M4jl4ut/D0/r6kOJWenOoqT6V73/vGq7/+Y849dSzmDZtBolEnJXr21m3bCMftG3i1RX1bO3sx6urHDImj59e8mUOOf1HjBo1dqgQrkpaLNNT1KsmEUdluuVjgvUhBakWIFNxNP6N96D2b0J6IgijHyFtUtUnu0rwnZBQwPHnUJZsII7b4ui2PRpEpMJkuwisfVNCAqionJQ+najSywZtDQoqDjbFdhknp09H279bKuD+oPf2dtPQUEdzcyOtrc20tjbT3t5CLBYjmUyQTCZIpZJDf08mE9i2jcfjIRAI4vcHCQaDBALuHzwST0SjsmokJRWlFFeUUlRWjO2zCGju99/p6cZa/h5OezsiJwdt6gGopWXous4551zIqaeezd13/5FbbrmRhx++lwsv/Bbf+tb3iUR2YznyXwqv6mdy7iy60+3EzH50RSfPV0RYj3zssQ07A0iqw2MYEarBckw8qneYingbLMddjfdrbpEL8NZbr3LppReQSiW4886HOPbYU3bZL99XzLS8uXSmWknZCQJ7sWzI2PBak2BxmyBhusnaCyokk/O3k7WZTIbf/vZ6brvtJiZPns5LLy2jtnbcLmPtDNuB+9crLGsXFAZc37CuFDyw3m1Pm1m8a1EoJfRmYGS2JKC5qtypBQ41Ebj6TYXWBFRmuS144BaxS9sUXm1xOGonLl0IwcEHL+DggxfwwQdLueWWG7npmkvQAj/AO+UMUjPPx1c5A68qqAq7wRnb2juFcBU/BxRKMrYboHHrcpWVXYOtdIrbMqcKyPJIYiZ0pgS6V2XnlBZd1/nKVy7ixBNP43e/+wU33XQd999/F9dc80uOPfaUT4wokFLy7rtv8uc//4EXXniK6uoa7rnncY44Yvvi64Rjd63VyqeWMdAeY+uyrbStbie/Jn97Z0FpNt11PXSs6/iCDN4Bn3Vd/EWo8hf4MORLjUtS2bQrNinhUOho9AmbWwN9NKgG2yzbKm2ds1NZvOtJE0Dh5EzQDcJSbDoUixxH5bR0eL/C4MDNyXJgl76pLqWVNrWdsUYxedKPRNKsNvJP/4OMtGopdna/AN2lWDiAb4cRFRRG2jpbVJP1aoZcqZESEgPJ4ZkAJc72e/ETbW/xwKaniJsJgoqHQOu7jG6rJHtmAfU56ylwtosnEiKOBw+Fzu4EFXtHyBPkgKIp+73fp4EXvQne8CYpszUqHI0MkvWawYO+Ab6TzPlIiuBtELq+PUtmGCQou18Mf2Lzc7zTtozyYCkjQmXE0wM0LnuJt5au54jaI9HGT0TJ37Xb9JFH7uWGG37C5WddwCkDcRLXXY0yogr9oHloo8fu5pU+P5BhwBbIsMQaJ3HyQRig2QJU17rBCYISE5hTIHOwg1MMTq7EqgXPuwJtkwOmqwb29AmkDxyv6//LFEAfJG41iTV9DweisHuXRrnD8wAm+J4TeN4UKDH3tYxJDukvSde64hOGx+Ph1lv/RigU5sorLyE7O8LRR58IQEGB+/3r6urchQx2KVixh9PZVWwwYAhKgjC1UCKQhD3ugv77myWbvTlMVhPDttf9GqujKouWCHrSgjwfHFTqBsO93uzmRxxeIUnbrkVE3HRVtj1pSd7g9LIwAPPKJe93CEqDUBiU5HklTQloGhDYDvg9UJktGRWRhHRoTbj3TQl4Q16q51Sy4cWN9G7tRfd7MOIGnqDOyLnVKJrCEZWSuAnvtits6nO5ndLRB/CnG64n3w9TJk9j/fo13HPPHUz+4flDXXTgeil3pyHHJwh7JLYUOFKStmFSvuTHsxyWtAn+v5XuhyNmuLVxxnZJbJ8qiO+gSh72/giYXSKZUSRJWO48Rv+Um2izisKMP2YsG17aSHRrFAn4wj5GH1ZLQW0+xx57MrfddhO2bX9oR/z/Bew3c5Gfnz/kr7szjjvuuI99QHt7XVVV6ejoGPZ4R0cHxcW7Tw0tLi7e6/bb/t/R0UFJScmwbaZOnbrHY/F6vZ9bnxHHk4VUdbBSw+wXhBHDDhYPWQvsiI6ONtLpFFVVNbs8tzO0/o043pzhkjEtAI7tBo4xDb1jMf5N97mq01AFwkqjd76DsBLEp131oWF2e4RtEFj7Z/SOxUg9C6noeLc+gxZdQ2Lyd3GCZSAl3q3PuTYWmSha/xaElQEBjr8A6c1DKjpq/0bUVPuwUDgAqQURmShOoNQdD8Cx8K+7A737fexgBdJXiK9nFcJKoCZbyNIr0BH0+iNUJTrI76unK2ckUaufBW2bidR9gB0swyiei529a8u9HRpBpuwwfI1PoxhRNz3X6Hf3qVgIjoWn6UVK88J85cSpfOUEiUz1smr9ehatbOFfzdn8/KZfYRg/B6A44mViRTYTKvIYO3k25XO+SkFRCfn5hS5RuA2qh3TN6QTW3YmSbAFFRyoKVt5ErJxx+BqfwvYXIbNGkkobdLVuoWPD/0d57lEUFhZz1lnHkpdXQEPDFqSUaKpgbFkOx8yo5YjpNcyr8RPSMiQmfgc1sxX9vYfBNrDyp1JWeigle7AV2RuM8iOQegBv8yKUVIe7qFB2GEbJrh5TAEW2yqvlsxjbtZlQoot4IB/VMSnor0cGKxB5U/fbtbrGHs3liR+xXF9Gt9JFrpPHVPPA7RMIKVHjW1H7NwJgRcbgBCtACOLxGKtWfcDy5ctYufJ96uo20dCwhVhsu9VMMBiitLSc4uIysrOzKS4uwe8PEggECASCBIMhAoEguu4ZRhDv+PeBeB91mzfy3qtLSQxsL2yCWSGqq0YxYUQN02JxpqMwIpwNjo35xiv4zjkfbaI7efH7/Xzzm1dw9tkXcPvtv+XPf76Ze++9g0suuYyvfvUbH6oE3RmmY9CdbidpxdEVD/m+4iFi+vMMTdEpDlRQzCez4JcwYzTEN9CT6QQgx5NPZWg0WZ7ILttaDrzZInijVTCQgUI/TMvq4ZV7fsbdd/+Rgw6azx/+cBelpeW77LsNYT2bsL73yl1KeGyTYFGTIKhBQIP1vVDfr3D+BIdphZJVqz7g8ssvZPPmDfzgB/+P//mf7w2FZH0YtvTDmm43DG6b925Ih7p+eL1ZcGCRHCK5wVVePLlFsGirQmKwdc6rguk4VGe7/mQBbTsRDK6atzsl+aBT4ajKPbcET5s2g7vuepQtWzZx9W338vZz95JY/Cf0ovGUH3we5iFfoby8mHG5w+8MQrhexVXZcNZYh3W9AsMRGA7owg0A6UoJFAFhfe8tyVlZ2Vx77Y2cc84FXHfdVVx88ZnMnXsov/71H/fpd3hPMAyDJ598mDvuuIVVqz5g9Ohx3HjjbZx22rn7VLP4wj58YR9tq9tRPdou5LSiKaRjX/jf7ozPqi7+IlT5C+wrBIKSwWAwB8nd/hj1qkntoDWAhWSzavKAf4AuxabQ0VBQCAJBR6HE0ahXTdoUi4i9b5PnrYrJWs2gW7GwkLQq1pBiWCKpV/uJOFFypX/oGMvtStZra1ivrabY2D0Z7JLREgs5jMT0Iphr+qmydOo0kzJHZY7hZ47pH1Ikv2a187utz5DSLAK5I0gCEVuyrrOeSRurCcwMslnbQL5dSEak6Va6mGkexCjr30s0pnBYrmfYrBroCMZZHiZaXtSPQNqmcFiip8l1VLIHu+98CKpsjS2aQZ1qbg9q/gjQJkzGWvoOMh5HhAbFBtFehNe3W4K2O9XD8q7VFAcKyfKGUTMmMxatJrJ2C9JYTX/uZvyFZXhPOxv9gO3WHK+++iJXXvl1Tp9/FN9IW9grPoBgCPvtN7BWr8R/7gVoU/bEgH72sCZL5OugbgK1XaD0gUgBQmDMcLBqQOmHzEQH8yDpqnOtwT+a6xGstgtkAJwswAElBsIZ9BLegtvcmSXIHOu4PsK426lbXf9huxicPLBGSfT3BFZYMpgOidYEVpnEGlxM974s8D0lcCJgVUhEArxvKghLkrzYrXHURtDqBFIBq1bilPKxlMOKonDDDbfQ19fLN795Dg888Axz5syjrMytv1taGpk8eXhI/chsl2xtjjPkP2s7bj02t9QZ9Anejn7DFVXk7fRzFvQpDKg+rEw/mte9V1qO5O2on1dKa4hvVgjorvJ3S78raFjZJbAcSddg2FqB3/UF3hiFjb2COaXb68g1PYKID0bnbH8s4nOP1bBdu4bSwSmR5UA0LTioxPUMztiQM7WSSWEfbStaSUaTFIzKp3xqGfkjXVGdV3W9qw+tsOlMwrLCEg6ZOo78HVwQa2vH8uyz/+ToEKzqhqLBBse46XoV+zTJlAIAh8YBQVvCVV6/2y6oynZ9sR1H0pMW2NIVZOiKK/Dwf8h0QFXY5b34NFE6sYS8qlyiW6M4jiRSlk0gxz3hmTPn8stfXsP69auZMOHzsWD3WWK/yWDHcbj77rt57LHHaGhoQAhBdXU1X/7ylzn33HM/tRYYj8fDAQccwKJFizj55JOHjmXRokV8+9vf3u0+c+bMYdGiRVx++eVDj7344ovMmTMHgOrqaoqLi1m0aNEQ+TswMMC7777LN7/5zU/lPD5tWDnjsbNHo/Wuwg5VIbUASroLYadcNa+y61teX78ZYJ+8QaU3DyXVPfxBx3Z/gLSQS8a2vAy4JCeAVH1Yqg+tbz1a7xqsgumITNT1rFV0NyBgHwhivXcVetcy7PBI5KAfmBMoRutdjaf1NdK1Z6P1rcdX9yhS0bFzJiDMBFqqA8VKYfkKkdv8bDU/GCZqrGE7GSwlihHFyJs0jOzW+tah967Fzho1FFzmeCOoVhI1Vk8kVEmZotGoGCSFQ7cKG60OKjpXs3Dty6jpNFr3cjzti0mMvwSrYKdiRQjSNWfghCrQOxYjMn1YpYdilM7DCY1wr1W62yXhB7cXgTwmTzuI6VVr+Pboc5Hty2h+535Wt2VY2e1jVSc8vrSZxmf/Cvx16KV8Pj8FOWEKwl7yc8IEIyUongCa0edad+Aho1k4fa8S6+uiPebQEY3Tn9iRAHiOYDBEIhEf9OD8IRPGT2KSupasjkUIMw4yhXBiWL4aAmv+hJpqdYPvhIa++WG0nhUkJl/uWo3sD4TALJ6LWTQH7Myg7caelxhrbQ9vFM3npdFdzGp8i9ze9aSFoC+rgsKaC4cFBe4P8pwCDs8cs+sTUuKtfxzf1mcwElFWNg3w3tY0Szr9vL+5g02b1iOlJBAIMnHiVKZMOYCTTjqd6uoaKitHUlY2gqys7E/kPtqb6WTLwDrae5rpaO5goG0Ao9Oio7mDFa/8i4c725BAYTDMAWUVHNBUz4xUgum/+D1qYLsFQnZ2hKuuuo4LLvgWf/jDL7n11l9x662/4stf/goXX/ydfVKGpqwEq6PL6DO6B+0WwB8PMjYylQJfyYfs/e+DIx260+10p9uxpEWuN58CXym6oiNQPvb7krFTrI4upc/sJaAGAUFbqomY2c/UvDm7kOPP1guernMtCQKqxcuP3clt/7gWVRpcc82NXHTRdz6RVe2WBCxpVyj0u0UduN5um/skL2+Ftx/9LTf+8ieMHj2OZ599e7+Lp56UwHC2E8HbkO2BzqQbShHY4bn1vfBio0KWR1I5KPjvz7jE8egct+i1d1rF2SZM8u3j5egNjib/uP9l0tzr6Fi9iL5376b+iWup/+ePmTHnCP5xzDEcdtjCYeSsYbtE9dQCychswZY+NwhJCPcnw3TcFOlJBfu2xDRq1BjuuedxXnnlBX70o0s58sgZXHfdbznzzK/u82dNSpeof+65x3nwwb/R0dHGoYcexQMPPM38+Ud+pM9suCiMY9o4jjMU2CIdiWPahIs+frjHfxs+q7r4i1DlL/BR0K7YbFFNyhxtiEzVEJQ5Gs2K5d7LdlJ6mrghbrvzCt4d3tCTPOGL0ydsBK66uEVYDNgOIamQEA4+GaNc1iPYfk8Rg/8Zwtjj2OMtL2WDKuAqW0cHuhQbBzguE2K26cdGDgog3eNN47BSy3DbwBpiqT6qcqtRpIqNpFe1UYLZRDsTnDVwAW8EX6JVbcYrvRyXPoWFmRNQ92L39EkjIRz+5u9nhZZB4JL3b+gpDjX8fCmz/wFvKSFJC0lwp9A/HwJj8PViYoClnrdZq61Ekx6mWNM5wJiNhw9nb7TpM9A2rMN69y2k5QbuCq8PfcGRqGN37YqIGQnSVobcwTlNyaotFK+up78gQo9qUF1Uia+zj8yjD6BWj0TJzWNrYx2XXHw6h86YznUlRQg8qGXub7OUpTh1mzD+9RzqhMmIz6llhFUL6eMdQjcprno3CDIisMolIimwKxwS33W/dyIBvqcE+lKBMMAaAUqbRG5T9CqANhhC1w/pYyWZYyTCBqvaVRQD6EshcI+C2iqQXrArJJn5kvSxDkqXgrZRuGPZ4BRK0qc44AVS4HlTuDYW23QuHjBUyaZNgg1LBf46wbQlguL+weezIX2MQ+YI+bEIYVVVueWWv3HeeSdz/vmn8uijLzJx4lR8Pj9NTY27bB/ywMk1kgc3KKyLgiZcZWtVtuToql1rMZcElsNsDaQE2+elJk8juqWPQE4AVVfY1JRiadZY0sEQxUG3Boym3Y6wV7YKNva5Klmv5hK4uoBREYnlCExn+Gs3xSBuQHPcDZcL6C4p7VddgrgzKdgQdcnVjC0YnSOZVSJ5uk7wbpsgZQmKg6UsOLyEQwp3b68GLsFbFIA5s+ZQt2XjsOfq6jZRVjaCQ8okG3oVGgYkBX63Bk+Yrtghxyv5oMtVPDvSfZ1/NSjURCRVWZL6fsH0QklAc63SulOuUnrH3OCelEt+Jy3XUmJsLnwWucLekJfi8bsKRqdMORBd11myZP/nM/+N2K87ppSSE088kWeffZYpU6YwadIkpJSsW7eO888/n8cee2wonO3TwBVXXMFXv/pVDjzwQGbOnMnvf/97EonEUCF83nnnUVZWxg033ADAZZddxvz58/nNb37Dcccdx4MPPsiyZcv485//DLito5dffjk///nPqa2tHVJBlJaWDhHO/3FQdJLjLsK/4W9ofRsQdhrHm0N65Jdc39rdoKOjDWCX1ovdwSg5hEB0LUqyA8dfCI6JFqvDDpZj5k0Gx0RJdiB38qFE84G0UdK9eLc+h7fxGZR0FwgNK1xJevRXsHL23kqpxre6QW/6DupWoSA92WjRte7L9KxAmHHsXFfl4gSKIboGLAdhxZGeMMLOILUAUvGipNpQkm1IxYua7kB6cna5Tko6CtIcIoIBnGA5SqIVYQyAtBhveihKt9HlidATqWVh/Tss2LqcCr0E2yNASrT+jfjq/0k8b9Ku6biKilFyCEbJIYh0N4rRP3QNpRbE8YRR0lFs7/ZwIWElQSh4G57C072MiZEEEwoCnIGN4yvAKDmRRG8Tm40RNOcfQ097A7E1j9PTVk9XzKQ7FiXa1I3lycHyFeA4OpqmousGPtOkMDvApNHFFEWCFOWGKM4JUSq3kl99AFlVB/KNG+7izQ/Wc/LJZ+D1epFyKvHy2Wg9q/E2PY+aaEXrXok2sAnpyUZ687CyKrEdC61vHd7WN0iP3LW1fZ8glH0KHtQQnG3k8NLIs3mqbC5ZsSbyRJCJwSlUiE++z2nrB0/z5kM38sLKTt5Y107asNBVhUkjIhw083C+8Y0rmDr1AGprx33qrSm53kIi+fnEs/thJIT0LNc3tquThNAZ0HSWJwZY2tzIspZGbty8HnPVMspee4GTv3wOp556FuPGTRoar6CgiJ///Hd897s/5r777uCvf/0j9913JwsWLOSSSy5j3rzD90h6NMQ30Wt0EfHkoQrVTSw3o2zqX03Ek4eufHrLxYaToSvVRszsQ1M85PuKyNZzdzlWKSWbBlbTlKhDShsQNMQ3IBBk6RHCeoSyYBWFvrKPTO50ptvoN3vJ8eS71jGAT/UTzXTRnmxmZNZ2FU1PGt5oUYh4JenNr/DsnVfS3biKqnnnM/+86zn/qEI+qY9QR0IQM9204x2RraR49MZLaHzr73zzm1dw1VXX4fHs/3uV5ZWoQpC2XHXtNsRNKA7uGiS3vleQtqF6h694thc6koKV3ZL55ZJ71gmSpksiSwntSQjqgkMr7A89HtuBV5rcQndKoUL77CPJzDgKJRWl970Hkev/wc9+9n1+8pPLqa4exUHzFhIat5Bk+aEIPUBZSBI3BZXZEMtst+5w/eQENdn712+wYMFCXnppGddccyVXXnkJzz39ON+/+DryCwrJrcrFGxx+zS3L4t133+T555/k+eefpKVlK5FIDscf/yUuuujbjB798WwJiscX0by8hZ76XkJ5QRCQ6EmQVZJFyfj9b5f+b8ZnVRd/Ear8BfaGtJ0iacXQhE5Yjwz7zTKRWAJ2bmDQpWuRM8LysFEzCNkKnkHV8FbVZJTloXIvIb/b0KFYPOVz09zHDSpOI47Fas1Al1DuaIy0PPQoBou99dhMGCJbY6IfDx5G2FV7HD8sFb6SzuJhX4ytqokFRKTCcZkQM0x3NXNHBW29avB3X4xVWpp1AxkywqFVsSiRCiqCAAq9qo3lSCab0zkwPot+0YcP3y4ewtsgkWxUTVZqaWKKQ5WtM830kbOX3It9xRI9zXItQ6WtYgmBjcRE8ro3xWTLx5j9VPFmSYV8R6VFtcjeQdUdFQ5hKQjJJHcE/sgafTle6ccRNu973mGjuo5zUhd+qP2Z8HrxnXM+9rQDMDdvhFQKtaISbdZBCGVXsUa+P5csb5hopo8StZDCtQ1YPg8xXeIVHvzeIMqICPbGddjr1iCnjueyCxeSHxD85TA/1qLFqCNrsOxCNzRbCERhMXZrM7KnG1G0+27hzxwC7CqJXQ5WDeAFJ9dBBkBpd20gMkdIZAgC9ynoSwQyJFG6wPOaQBlQkB43VUzpwV2B1sDJl9hjJeasHeoOCd4nBaGbFZRekH4BHlcdrEQhmQXx7zh4VgiUDnAiYE6ROIPaDCUOSlzgZG0fMw3cmyt4NylIrnYJ7WdK4YxyyRwblA7wPa1gVdvYHzN32uv18pe/PMzppy/knHNO4IknXmXs2AmsWvXBbrefUSwpCtgs73I9gstCkqkFkpzdNLNMKZBUZgk29glKghJVuPVjXkBwylHFWCsStK/rJJW0aMwupL+0lJxsD7ri2hsIAX0ZQW9GkrJAEe4cP24IUpakIyXI97kkMbj16ctNglXdgoZ+aBgAIRSKAi65ipAsrJQEPQ4ruwQJU1ATcZheIHmqXvB6s0K2R+LXJFv6YGtMQQiH6YXD60wpXWuJjO3W1ZdcchknnjiPH/3iRuYe9WWa1y/hnnvv5MKr/0hIl5w91uGlrS7p69ehJuKG3bUlBF1J8GuuNdq4XEl5CDb2CY4a4RDUYUufW89neeGoKocFFduPZWUXPLhBoSPpCiVUIZmUD18d7xD+NyqD9wa/38/kyQewZMlbfO1r/5niz08S+0UG33333bz++ussWrSIBQsWDHvu5Zdf5uSTT+aee+7hvPPO28MIHw9nnHEGXV1dXHPNNbS3tzN16lSef/75oQC4rVu3DqlYAA466CAeeOABfvKTn3D11VdTW1vL448/PtQOB/CDH/yARCLBJZdcQl9fHwcffDDPP//8f3Q7nBMsJTHtKtSBOoSVxA6W7lWB6Tju5FlVP/zjYBQfjJJsx9uyCK1vDQgVOzSC1OjzkN4ISIkdLEHrXQc7hpZZKRAqSqoDb/O/kIoXK3ssSBMt1oB/7R3ED/gp0rfnJHXX4kK6d7wdyRg7M0ScCjMJKCjpHoSdwfGEsQNlKJlelEzfoDJawQ5XggQrqxohbRQ7hZk7hUzlMVg5w5WOji8HhO76LQ8SwnZWFVr/BoSVRouuQ0NQ4o0QqTyD78VDhNe9ghUs336cQmAHy1DjTYMhdVW7nJ8wE/i2PIKn4x2ElcTxZGGUzidddRJGyaH4N93rkvC+PISVRI034niy0XtXIdJRhJMBS4B0UK2tqP1bCAdzmFwcZuSBh+NteBJ/xIeVffKQT7KrOu4hPu0qrNwJQ8firXsc/+b7sSLjhtTkSqIVT1s7Tmo5Tl0jP56nMP2Fdh6661ec982fuueYVYPWvxkl3YsVGYMa34pMtSNVL1p0HY6/AMeTjdSCaL2r4KOSwfuBkFQ4ORMmzXjMrPGEpBhSi3xcpFIp3nnndRYtep5XXnmB+vrNeDSFuRMqueYrhzJ7fAWTqosIxtaTrjqG9Oiv7PdryGQCe8smpGWjVlWj5Oz5O7IzFKGQ5dnJ49dxQEqy/X4WFBWzYORo91yScd5b8T4v5uXwwAN3cdttNzF27AROOeVMTj75DCoqqgDIy8vnsst+xDe+cQVPPvkwf/7zzZx11rGMHTuBiy66lJNPPn2YHYlrD9GGXw2gDvrhCiEI69n0G1H6jJ5PTR2ctpOs7l1Gr9E1ZJHWlNjCqKwJVARHDtu2z+imJVGPT/XjU/30ZXpJ20kMO4OCiiUtokY3Y7NNyoLVH+l44uYAAmWICIZtycEaMatv2LYdCWipX0/zkz9l8+LHKBk7m7NvWkyo8kCiGUFv2qZkzzk++wW/JtGEMhjSNnisvW08c90p9Dev4eZb7+PLp56+y37SNLGWvoP5wTJkIo46ZjyeOQejFA4nDGsjMCoC63oF5SGJf1BRkLHh4DK5S+iE6ew+vE4VEtMWnD/BYXWPyopu96cFIKQJzh7nDLa47R0xE1rjgvYkrI9uVxkHtBxCB32Dqy79On5ngLfeepXlb7/AU889zcDfbkPRveTXzMBTNg2zcCqjx08lmDeBfsuDlBD2uP5zoz+CrXYoFOamX93OuPwp/PrOazntnSM4f953mD3tUMrnFtOWbGLFivdYufI93nrrVaLRXkpKyjn66BM5+ugTmT37kKHwwI+KeFeceHcCzasx6cTxNCzeSk9DLyApm1xGzcHVQ212X8DFZ1UXfxGq/AV2B0c6NMY30ZTYQsZOowqViDefMVmTCequArfY0SiyVTpUmyp7+823Q7UptHXOSod50Bdji2aSEDZR4ap5Sx2NfuEQkQqrtQzrNQMDSa3tYYrpxT9oNrpRNegTNmNsD2kkq3Q3uC6OQ0ZIxqU9jLM8eOVBbNLWsFSPki29eGUnhkhxSOYwaq29dxyNtD1ckchli2pgCEm5rZO3GyI2icP9vgGaVAtdCpy8MsxgmLZ4B6msYsptDWnbpBJRxpVNwz+Y7ZEr955n8oonyZPeOEkh0YHFeoolepoLUtkUOh9PmbpKSyORrNYMehQbR0BAKugObNYyw8jghHBYoqdYOaginmT5mGX4COzgp6whWGAEuM8/QJ1qkusoJIQkJhwOMwI0am+yRl9OjTVmSAkcEwMs9rzBNHMmk61pOx/iLhC6jlI+At5firVmlVsXvPkqniOORps5Z9hiRNgT4pCy2Ty++Vls22JqMk5MZkjbMCZnFL6hTlGBzGT4488vYum6Rp6//kuEcgrp07tR+hrRNC9WwWA4nWm6IXIfYbH63wGRdMle7/MCbQtY48EukEgVtA2gbRCIuIAkWOMl+gcCe4RE6QC1xVXokhgMhgtInAjYI6RLFrcJREKgvwkyyyWb1W7wP6IgkmBXAJp0ieBekF6B500wDpJkFux+4doJg5MlUfrBHhTuv6nCa8AIIJQEUoKGfMmDCEZmJEXFoK0Dfa3AHvXxw8FCoTD33vskJ500j4svPoPZs+fx4ovP7HH7EVkwIuvDXzd7MODtiTqFzVGBI6E2Ijmm2mFMnhfKxlN76Ciaem2eWhfE2y+wdhjWp0K3ASkLxuVJmmPQmhBoAnRF/P/svXe8XVWd/v9ea+996j2395JbctM7JQQSIHQQkCYKKjZ0rIz6dSyjM2OZcUb92btYBkSlKMUCIiMlEAg1Cenlpt7e26m7rPX7Y53c9JCEMDqa5/XKK8m9u6y99zl7f/azns/z4Cljt7CsQ3BqlWY0B7/dZjrvBjOC/oxREfemBW0jML0UWosVFTGYW26CqgF2j8FLvZKauAlwAxMM1zaieaJdML9ir71aVxLua5NsHTb7L49qGgsXsvRjv+G+X/4LP//hf2CXNlN33dfZOemtfHMlnFmr+OipipGsEWkMZ+HOzZIV3YJxzyh5W4qgIWEs2SKWsYf4x/mKHWPQOQ4pT1AUMfV8TdwQ4PdskYzkYFqJGV/Gh5W9gkkJwesnv3ahcceKM85YzP3334XW+i8W7PnXgmN6Yt155518+tOfPqjgBTj//PP51Kc+xS9/+cvXjAwG+NCHPnRYW4gnnnjioJ9df/31XH/99YfdnhCCL3zhC3zhC184UUN87eBncYbXI9xRVLRiP6LuIAh5SH/aQyEI9pDBRzGjLS2yrW/CrTnbEH1WGL94+l6VphC4tedjj2zGGt9JEK1CBBmsZAde+TwTYqc8gsI9rbcWftEU7OGNOAOrcOsvOOyuvbK5hCPlZruJRkAgcsMIHeBVLQKMfYM9sglGNhjS2AoTRMpQkUqQFipUio6UoQUERa2k5v4/E6inXGMZcAjLAb94Bl7pLJz+lwgK6tFWFJntxytsxas9FxUpNeehdDZBogVrfAdaWAgd7O9HqwOjZD5EQBRaE9n6K8Idj6CiVQTxeqQ7TGTbbwBJtvn1CC9JuHsZ9uhWtBXBq1wIfoZw5+NoJ4HO2yZoBMIbxRrfgZIOuRJD8joDK9F2fIIIBhMKR6oDa3TrfmSwV7MYp/957JEN6FAxBD7O4Eq0FcatOgNkiJaSgDeesYXv/OBbvOkdHyMcNSSB0/sc2o6Y85o/Vm0XILL9yEw/KlRkFN5Hoew9kYggiZyA51Amk+GRR37Pvff+iuXLHyObzVJf38j551/CF99xDhfWDBOpnrX/SsJC7GGtjgH+2tXk7rsH1d0JgUKUluJc/DpC51983A8vUVGJ1dSCv34tIlGIECatNtTXx+LZ87nok//KFy2bZcv+h/vvv4tvfOM/+a//+leWLDmPW275JEuWnIcQgnA4zPXX38Qb3vBWVqx4kltv/RYf//j7+OxnP8brXnc11177ZpYsOQ+NQqP3I0ABBBKNRusj+6u+GrQntzOY66U4XD5BRCe9MXaMb6IsXLmfLcOoO4SvfQqtKIH2GfOGkViErSgBPkWhUsa9EXYl26iK1mMfqO4/CtjCJqeyZP00YSs6cQ0DHRCxzPdBa82KFU/y9e98g2eWPUS8tI7L/t/PmX7ujQgh8t5kpkXrRKG1GJqLNFuHBc1FmrGO9dz/+SvxgoBP/mAZb7hs/kHraKXI/eZOvGWPmpCYUIhgyyaCNSuJvudDyOq9BH/IgptmKu7ebIpVNzBq4de3aBbXHvylbCkCiVFd7PEhcwNDEk8rNWqP75wf8KedsG5AErVhaUPAaUcpWo1YMJzTdCYFFVGjTE57phgeyAh+vRnSQTHZoqvJXnQ1DYs1s/QmulY/TO+W5+jf+DAjj36HXkDaIUomzaa0cR7ZslYq6hvoKanDqa2hrKyCRP47tgeBF5AZyWBHbCIJQzwopRgc7GfNspcZahvhqkU38tjah/j2n/6dHz/+dTJfMr7f8XgBc+Ys4Kab3sOll17FvHmnnpAiNvADNj+6lc7VneRSLtKSFNcVMfN1M5h5qVGrhwpCf/cF86Hw11AX/6Xx1xyq/PeG7vRuto2tx7HCFIVKCbTPQLabQPmcUrYYS9qEEVzixrkzMsZmy6VAS5JCEdPm5w0qxIfSJdwbGefBcJIwkoSWPBFK02a5VAc2axwXTygsLVgeyjDfDvP2TBExJIEAE+ikWW+79Eg/71WrGRWKO6Kj3BsZJ4REcBOSIQblEBUqxY2ZYl6XW3xEWwaZ6cca3YIDzCqaijpE/kQWxUbb5UUny1o7R31gs83xEU6C6LSzyK5fxmj/dnzh4ASKacWNvK7h3KM6x/3C5+FwCgfB9LxS2kezxXJ5PJTmTdmDw4yPBR6a7ZZLIIwK2lKCtFD0WIqd0p9YLo3iv/N2EtF8gNY622WjneOd6aIJch5goWfi9paF0vTKgISWXJyLsdSN88P4GiI6up8lREIX0iF2s8PeelRksHZdsnf8FH/ty8jqWkRhEaqnm+yvbiMaiRzk5Xtx41JC0uGpzuforCti2ppxqptm01hsDGvV6AgiEmHN4E6+fMcf+adrTmXR3BloP8CuqcBr78EJdyBKpqOUjerpwlly7jGJJf7XkIXY7RLnRUPzCRfslQLZDbhg7zCKXR3WhNYInI3mOeu3gLVLoB0TPqdcjRwUJpdvHPyIxt4mECMQuRdiwxIhwG+EoF4jR4AQE0yPjhj7CZE26mBc4HC6twi4SzTRXwtkF6gSeC4kiHmCeJUGH6TUTNKwUcJ6CVWmoQ6RPXGnrqysnFtvvYsrrlhCRUU17e076erqOGJOxh4MZOCFHsGOMUFhyCiFZ5UZoUF9Aj4wV00Qs1Ux9hMlhGIhyqQJv3QDGM4Zr+CSCOR8yPgm5K2hwNSOY65GCoElTOjb3HIYzArWDQqGs2aZ5iLYMmKsJBw77wMcAceCZ3sEV7bsXwv3pAVpX9NwQLRKWcSIGZKe8eAdycG3Vkl2jgomF5t3g51j8OddkqYpV/DGb1zBcz2CcdcEvZVGFCELnmgXTEoYQQZAeRQ+fpriZ2sFj7ZLZpdrEs5eYYavBAUh42HckRQ8uMMcG0BR2GR1lEagNw2Ti/Z2zEUsCFuGHL+0SR/WLsJXsG0ERl1BedSokXeNQ8aDqvhef+MThQULTuf73/8aQ0MDlJUde4bR3xKO6XVyzZo1fOUrXzns7y+77DK+/e1vv+pBncTBkMl2Yht/jD2yFbQC6eCVzyU94z2G0HsVUHlfG3mIdp7DrhOvRcUPHe7gVS0i46cI7/4jdqoTbYVwa84m23gliRf+FTm+GydnCO0gXm/ISSGQ3pFbC1W8jsyUtxBtuxN7ZCNojXbi5CZdilt9FmiFPbQOdIDQHsouQAQuzvBG3PL5ZFuuwxlci1AeXsUCcvUXoSbUy0eQ2EmbzIx3o524WT87RBApw225jlzDJQcRyEHBJILCZuzhjYYoFxJ0gJXqwCubvzeYbt9dpLsJ9T1PEKtFR4wiQdk1oBWh7mXkJl1CdsoNuPUXGFsLJ0GQaKTgpS+A9lHhUoTKIdwxhLAQSiGzA7iTLtsbriZsDoqP1SaFFSGRyd04A6sQXtKc6+k34/S9gDO4CvwsKlptVNN7WvqFxT/dsJR7PvIL7vn5N7npvZ/On4Bcfl8QRCuxrTDCGzf70gEiN4LQviGz/49AKcXzzz/Nr3/9C/7wh3sZHx/j1FPP4BOf+Dznn38JU6bMQAhBqP0RYht/gq+8vVYggQs6IChsOfJODtxnXy/ZO3+OHhlBNk0Gy0L3duPe/2usikrsua9coB8KQkpCr3s9qq+XYNN6RCQK2QwUlxC68hpENEYIuOiiy7noostJpZI8/PBvufXWb/OmN13KqaeewYc//M9ccMFlpj1PCM4661zOOutcdu/ewb33/op77/0lv/71L6iqquHqq9/E3IvmEm+MEpZ7CdC0nyRiRSl0Xt3963BQWtGf7SJiRSeIYIC4nWDY7WfEHTjAo3efNlrl4muPkAzjqb3ehVErTtpPkvZThwx8OxJ6M510p3cx7o4w5g4TtxKUhsvxCXBkiBKrgvvuu5Mf/eibrF27imnTZ3H5R3+GP+tGmkodhDB+Y/1pwYWN6pCpvceLkAU3TlP8cpNk5UsreO6rVxCvaOR9//U73rXk0Pd5tWMb3oqnEBVVyGJzDXUQEGzegLv8CSJvuHG/5ati8KH5ivZxSPvm/6WHeRGZU645pVLwYq8Zm1EXCGaXa06r1IzkoDMJs8vhyhaFfYzJxGHLKB2UNoV+f9p4m2V98LVmebfgwkmmEH6xV9CZEmTCM4ifMoOShTC/UDM8Ok7n9rWInlWM7VpN7/aXyT7/AJtTw/z5m/uc21CIkpIybNsh8AL8nI9WRolgORItNYND/ROTsnuQiBVRVVJL73A3tcUNfP4z3+CyG684puf00aL9pQ52PL2DaHGMsqYCAi9geNcw6/+wgTPefvpEmMpJHIy/VF18MlT5JA6E1pqu9E6ksIjbRs4nRYgip5RRb5Bhd4DyiLnGp3tRCpTkWSdDt+UzOwixyIsyPa86DYRmrdNNoYYpfjkSSYDmJTvLs6EMp3kRSpS55lkUq5wcM4Ms57oxGgOHmBZ0SJ8B6VOoLSyt6bcUoAkQjMgACBAIGoMqzso10mcF7LTCWIfzqdWaUOefiWy/H5kdBEBFysi2XINbd+EEa9EvfH4eHWWr7dInA3ZJj52WiysghiDVPA+nsJygZytBNsvkgno+U7SYGufoMiy22R4jQjF1H8sMG0GZtlhj57gGfVhvZS8ffBfh8B1qRdpiTGpqAotwntANa9NyPbAPGbzKybLGztIcOETyy2VRvGzneNnJscjbJ0QcwelelFO8CEmhiGhJOL9/BwfFoSbl9VF7JQebNxBs2ojVMgWR76y14gUEWzfjLl92EBlsS5sLG89lSd0ikpWX4tz2c2RPN2R7CNwcOpfFX7SYD3/tyyxoKuGTNxqiXtgWBefPYvyhLH5nH356A4QKsGfMJnzF8XUa6iDAf/E5vOeeQQ8PYTVPxllyLlbLq/Q6yMNZI3BWgd8ERMHTYG/V2FsAH4Tax5s3MApekQGrC2QO1J46yYKgTqMjRi1stQNJ0BGNHBPGg1iBtVtgdWKYTAfwzN8iDXJUIIeM6tfeBv4e3Yo2IXP2ZgGBsbPInWNI3/BTArtHkJ4EVo3Gn6GxOgSyWyCVRkjICSAHiL0BdCcKM2fO5Qtf+Dqf+MQHAHj++ae5+uo3HXGdriT8ZJ1k55ggYmk8JXm2G17forg47yMshAmeOxxW9gl60oKRnCEqu1OCvowmbBnV67QS6MuYt+qSMBSFNUnPvEmURDSjriDjG7WsLTX9aUMA1xUYO55R16huK6KaF3oElzUZJa0bmBo17mhsIcgF+9urpX3ThRaxYP0g/PBlyTPdggIb0r7xGralIBsY7+LhnCbrQ3UMhnLQkxJMK9UMZOC7qyWbhkzOxSmVpmvvshbN1lHzvpHI27ANZMwxzK8wwc8PtEkEmqkl5nj7Mprfb5ecVaOMDi9/axvMwNYRQU/KnO/vvyy5plVNZIHsQV8afrFRsnXEeC1rLcn4Jmg60EbNfWaN4qrJhyeTjxV7ul07OnafJIOPZeGhoaGD2sf2RVVVFcPDw696UCdxAFRAdPPPsYc3TYStCS9FqPcFVLiUzIx3v7rNq3wq6IkyoBQCt/5C3OqzsNLdRnXY/xJFT74fZ2gNQrkEkXKs5G5kqguvbB4gCKKvLOvyapYQFE/DHl4PyiNINBuVsRBYI1twxraRqz0PK9OLTHWirTBBtBIVrSLX8gayU28yQXGZXkLdT2GPbkWFivGqFuJVnHbYMDIVrSA9+xZkuttYOMSqjfL1UJA2mdYbiK3/0YSXMWiCRBPZ1jcech8yN4jwkujY/u3yOlRkAgBzw2inwNgs7KOC8Atb0VYY6Y6gopUIGUbkBhFC4icmk5r9wQnC26s4DXtwjemrzqtyZbbfHIefJbHyvxDZAfaQYn7JDNKzP0h2yg1YY9spePHzeauOvZjaUMEbz5zEt370Q970zn8iFArhl8/HabubQPnoUDFeyUycwZcRfhqRHcQSFrn6C3Crznyly/0Xx7ZtW7j33l9y77130t6+k4aGJt797lu47ro309Iy5aDlvaoz8HuewRlalw/80wh3BL903jGT3/7al1F9vVjTZ00QqKKmDn/rJrwXnz9uMhjAnjKN6Ac/iv/S8wSd7ciKSpwFp2O1TD5o2Xi8gOuuewvXXvtmHn/8T3zzm//F2952NbNnz+fDH/4Ul1129QRBNWlSMx/96Gf4yEc+zerVL/Kb3xhS+Ec/GmDSlCbOuGQRpy9dRNWkKqS0aS2YScR+7drOTVjdoV64xETg2B4Uh8twhEPGT2EJC4kg0D5KBxMv1r72kcLCPlxHxmEw4g6yaWQVvvapjNYx4vYz7o+SVkmsoRBblrXx8Ts/QldXO+eccyG/+tWDnHvuhfRlBL/YYAokX2vCUnB6lWaerVm+XOA4MHWqpuQE8OmTCuEi5yV++o3LmTJ9Lt+89QFm1RZOzO4fiGDXTnQmg9W4d5JDWBaiuIRg3cvo6244SEkqBQcVgYdCxIa3zVJMLxOs7jPXak6F4vQqzTNdgsfaBUNZgS2hqVDzxmmKpmMQY2lM4nNt3CQlZ3xDOidCMOYaJUJ/WlBYbIronC8Y0qY4H3NhzYCgKFTIteedxbzKMxnIGIXG3ApNVCXp6uqgt7ebwcF+BgcHGBoaYKx3jJ7NfaA1TthGKYWb8SmqLGTu0nlUVdfQ9VQfcZGgrqkBxzZEw7rtK/nKLz7DZ7/6UaYunEJFtIbR7jGkLSlrKn3Vtg0qUHSs7sSOOMRKzHPBDtkUNxQz0jXK4I4hqqZXvqp9/C3jL1UXnwxVPokDEeiAbJA5yIPfkjZaa3LB/pK9GUGYGcHBJP42awv/HfsNT4Q3UKhtxuU0pvqXU6jrsREMC0VU761jIxhv4bV2jnPdGE2BzdlujPsj4wwLRZGGtDQ9QjYCQwlDHAla0yN92m2fKb7DVtulzXKZfsC4FJpd6U1khpdTmCimNVaFrTVWqpPo1rtQ8Xr8khloNHdER/mfUAqJwBearFQYAzlMiJoQyPI6yksnUaIsrs0WMjd79BkSh+uPOFLjWRrFY+EULzpZskLT5Ic4z40d0v+3NrAoUpJxqUnqAC1AamhQNr7QaNP/x1bLxUJMEMF7roXUsNVy9yOD98BC5FXaezHXO4VVzgukRJK4Nu81A7KfmI4zzT86/3k1OIBWaoII3gNRVITq6kAHAeIQ75gRO0ykdS7B+z+M99QyI1CI12AvXMR3n19OZ08P9335KsL+CCqc7z6sLaH0qsnkdleRqn8joroRe+YcRPQQ3YZ7Cr0jdLW4D/0O96HfmWWiMYKnHsNfv4bIu96HPe2VA5JfCfZ2QAmImrH40zQ6DOEnBMITRp1rG7JWxwQIE8AmdwA5sEZFnq3ReLNAFWlUBbjzFeEXJGIwrx4uMNsQo6DDRqEbFGEUwhgSWKRAxzUyKYj/2CJ9o8JbqAk/Iog8LBF7NFlhyJ2lyLxJ456tkYMweUCwdVBS4QC1GtkNqRFBJKZp7gd7QODN13hzTowVwFDWWIvlfDjtspu5cvnj/OH39/Loo398RTL4z7sFO0cN6WmISU13Ch7ZJVlQGVDxCmVTXxoe2iFpKtQUhwWdSaOsTfswu1zzjXMVfWm4bb3Fbh/GPENaCgHTSkytawlNdQzitgmDHsyYzrOka2wkLCEoDmtC0ggRnuw0QXHdKUMunz9J01QIbSOCpkJDQo/kTI16caMhc+/YINk5DkKbToyBDGR9QVkEbCFIeprSPYrt/JjGPXipV9A+bjrvXuiFF3oFbXVwwzRNcyFc3ap4cLtk87B5h0qE4LJmxawyzT2bjSJ4zj5zZ1Ux2DxsrlkiJBjKGtJ2db/J9gi0oDqmWTsgGM5J/nGBygf5GVHG3Zsl6wYFzYVmvWUdhtCfWQoLKjWDWfjTTkFxGC5qPDGfr7q6SQB0dOxi3rxTT8g2/6/imN5ogyDAPkJKp2VZ+L5/2N+fxPHBGt+OM7rF+MzmvZS0EyeIVeH0v0S25bpXpQ5+zdq07RhBopnYuu8R3vkH7OROgnAZdrYXmR1CxWuwk7sQQQa37kK88vlHtVkVrcCNLj3o58JLgp9BJ1rwo+WQD6QT3hjSHTVkqxPHSu4ktvbbWMl2tF2AHWRx+p4j13wN2ZZrD79jIVDxWr7znS/z0EMP0Na2mUgkymmnLeIzn/lPWlunTSwaFE8jdco/4wysRGSHUJEyvIrTDuuJrMKlaKcA4Y7tt4xwx9BOgbFqOAS86jPxi2diJXebMDs0OlSMkmlwYsTXfRe39jxDvtacjT28HqfvBfZ4L2s7Sq7ufMLdy8FP45fMNk8M5WEPrSe860Ey099JEK8niNdiJTsIClvNMlpjpbr4yPXncNdH7+DRR//IZZddhVu7FHvwZeyRjcaWQgUEBY34Rc24tecRFE0ximn5F4gWPQoEQcAjj/yeH/3oWzz//NMkEoVceeUbuP76t3L66WcdUZmnQ0Wk5txCuONRnL7nAfAaLiFXdwE6lDjseofcVmochDyIUBPhCHpo8NgP7ABYtXVYtUevpBBCcP75l3LeeZfwzDPL+Na3/ov3vOcG5s07la9+9Yf7JbIKIViw4HQWLDidz33u/+OJJx7hrl/fxm9/fD+//u5dVNXVcO7SC3jdRRYVS2r38xg+UZBCUh6pYWdyC1Edn7CpyAQpQtK00O6LIqeUhoJWdiW34isPDWT9FIVOCQmnCF/5pLxxamOTiFrHNt6eTAc5laMkVI4QguxQlucffoHlf1rGjvXbiUSivP71b+Af/uHDzJw5d2K9qhj84ymKLcMw7gqKHcXqRwW3PmORSgFoKisFb3iD5pRTXl2BtGnTOt7+1tcxbco07rrrARKJV2BXbRPRc5Dflu9DKPyqLQViNiyt1yyt33tcz/cI7t8miNrQUqTxlFEd/HyD5P+doig4SrtAKUxBv2NUkHCMv1rIMiRwyhPEHU170nigJX0TGqIxf8cd0wY3kIFTqjRnHmRzUcCUKdOZMmVvGKDWmufveInBgkFKJ+19VnsZj+RAktPPPY3yljLW+uvZ+dwu7H38+6fVzuEz136dn6z4/7jidWfzwYs/Q0up2Xa0OML0C6dRN+/Q6u2jgfIVuaSLE9m/trMcC600bsY9zJonAX/ZuvhkqPJJ7AsrrwgedgeI7tPt5ikPKSyiRzHx2i07+Vn8e2y1urB0CRaCDvs5krKXM3K3AHk7owPWE/v9W3B1roAiJfhWfASFpkxJdlqarNBIlMm+UmbZrNBstF16pUdKwMPhFLUZm8I8aTkmAu6MjLORUcannY4fLmJKcpSPbNtAtWjAHlqH3b8Sv2QGm6XLQ+EUGaFJaEEWTRaMNRyGVFUaJJLpvjGqaFYHWz6l8zYTnZZHVAvOcKMU5lWyk32HIm3I7xzmmAq1xEdzdS5xkCo4QPOr6BjPORmKtEVIC1Y5WXbYLu9JF9N6ACFcrRyj9tWCMamQQIWyGSOgWjkTimIHOaHn1Wjapc8uy6NH+nn/4DDz/PArZmSc5p7JZnsDz4eexscDIKrjXJR7Ha3+9COuO3HNC4sRUqBdF7GPb68eH8eaMg1eoaPFqmvAumFvpsbAQB8/ePf1vPOdH6DxtIXI7b9GaB8VKkLmRhB2Bu/itxJqvurQ48kOEu58LF+HC7zKheTqLzjoXVn19eIuewxRWDSRdaBragm2bML988NYU6e/6lpGH1iXWBBMAl0gwAPhYxTCY+aPdkCVamRKIAdAZDVIgSoWyFHz89wijSrLuyHmQNlgdQtjA+GCzuRTMlxQcXC2AxnANspiciC7NZGHTUhc5CETThdMxxCHYxB+UhC0grtIExTAkjLN+jWaTcOC4pDGm6rJ9cJ5fTAlAZmlCvdsc2dwXhTIYRNM58/S6GOcs17dJ7hni6AvY4jOkCWZ984f8vhjf+L3v/8NX/varYcNM876sGHQWA1Y+1y6qhhsGdZsHxVUxI5cL7eNCEZczbRiaEhoWoqMbdloznS0lUaMqvi9cwMe3CF4cLvEU9BUaEQDbSOCuRWa2eWazcOGxB3KmgkpT0EmK0iENCVhTVdKUBTS/HyDoCclSLkmQPmxds3lzYopxbBjTOArTdwRnNegWFqvuWOj4JkuwUjWkNER39hUjLumnvW1sYwoDBulrrFaM9sZzkLIErQWa6YUm+N6uktyalXA9FJTe88sDdg6YoKWm4s04zn43mrJY+1mnGhoLdETVm6ONIrkJXWKR3YJupOmTo5YhvSeWaYpDMGWYSPyuGBS3ht53PysIZ8n0p+BtGeu355skfKo8R5+pkuwtEHjnIAGudLSMmzbpre3h97NfYx2jSJti7KmUorri/6ubNGOiQzWWvOOd7zjsO1g+6YJn8SJg/DTJiTN2n/WU1sRZG4Y4WdeFRm856V/dHSE4uIT27JtjW4h1PcC2o6iZQgdrcR34ljJdhPohkBkBpGZbqJbf4lbd/4+fsLHBhWrMkra3CAqUj4xEyyzg6hYTV6pCaHdD2Ml2/FLZk2odGW6h3D7w7hViw5rf7EHK1Y8xTve8X7mzz8V3/f50pf+jRtvvJxly17ej9RS0QpjI3FUY6/FrTiVcMejKK1QoQQyN4zMDZOdfP1hicQg0UJ22k1Ett+HyA4gcyPITC86XEqQaEZm+olu/m9EboTslBtIzfogTtVK7NE2tBXCL52N8JLI3Q/hF03bO3suHVS0Eqf/JTKtN4AdJdd8bd6qZAPajiP8FNopYvLSm5kzZx2//vUdXHbZVahoBam5HyHc9RT20Fq0HcWrXGiUwNZRsjV/AaTTKe6663Z+8pPvsHPnNhYuXMwPfvALLr74SqKHUhwcBjpSRrb1jWQn573Kj/OBIiurDefuuQjHnDetNTqVRDYfm+XEiYQQgsWLl7J48VKee245n/rUh7jssjP5wAc+xkc+8pmDwjcdx5mwnEin06xYsYzHHvsTjz32MPf88heEw2EWLTqH88+/hKVLL6a1ddoJewg3xFsYyQ0w4g5gCRulA6SwaUlMn1D77ntckxMzKAmVM5jtJROkGHL78ZTLqDuEEJLySBWthbOOeXxpb5zR3hFWPPMMTz38BBtWrsV2HOacNY9//erNvO2q9xOPH7rTwJEwqwxA88wzgscfE5SVaerrTRbg7t1w992ChgZNxXF2Om3btoU3vekyamrq+OUv//DKRDBgT5uJW1KK6mxH1jUY7+l0Gp1O4py+6PgGchgkB1IMtw+zYqvECpdQW2fus7aEyUWabaOwfkhwRvXRE+Ln1mmWtWu2BobgdQPTvlkQMoWmGwhGXU2gTIta1jeKDHSzJLQAAQAASURBVICQNB50la/wQrEHgadIDiSJJPavnZyoQ+Ar0sNpoIxJp9bT3zbA4I4hYsVRAj8gN55j1mlz+OFVd3PL/3s7X/3dv/CPb/gMi2dfwFjvOBsf2URhTSGJysN0qrwCrJBFoqqAgbaB/VTGbsrFDlnES08Gxh0Jf8m6+GSo8knsCyEEdfFmRtxBxtxhonYcX/mk/SSV0VqKQ69sg/BC6Bm6ZRfz/FmknCw5oSlTRQzJLXRbq/A5kyItye6jDs6iyKKZ7e/9DlgILvQKGM1o/hRO5nt0fHJoQgLiShBgSNocmkAo4nmy9XkngwTeky4miuQP4RTPOhk8VzAUKyYdirOpoJC2eCHfX7OCWulM2Mw9F8owJhXVeYuFrFREtcBF4wnICU1CWwit2W35LHajhJXgl5ExRkVAk3Ko823ui4zzfCjLiDDK3KrA5n3pYi5145Rrm3Jls9zJ4qOxgXagUls0+gcTy1stl9VOlgblUJA/Z2WBZLPl8aSTPogMnuGHaA1CtFkuzUGIkIZ+GRASkrPcvbXoTD/E006afuGzyXbZmrfCAM0aJ8f3xTA3ZAs53z3y5HWECG9L/wOneYvYbm3FxmaqP4Mp/oyjDlu2Z8wyWRTbtmLVT4JwBNXfB2ics84+5prpG9/4TyzL4pZbPkm2uBBtRQh1PY50x1CREtzaaw/7fiXcMeLrvmveP/JCmkjbXdjDG0nN/ch+71NB+y4YG0FM3asAFkIgKypRO7ejx8cQhUevGj8U/OkaHgU5CCqfSyjyk/lI0LZR/uq8OliOC4I6jUqAN1NjdxoSWKTB3iZIX6vIvFHhbBBgaZQtsPoFImN8gdGgivKkcJkmqDG+xMTyimFfYHVr9IgAB8LPCUSSCSIYQBcCfeCsEriLTJ1TWwDvnat4qlOwcUgQjcPC2YozazSZPJskeyB+u8TehvlyC43fLEi/TaEOdkg8JIaycM8WY88wtRik0Iy78NJoEW/4wH9y21c+xBe/+Gk+//mvHnJ9ISb0SvtBY45v3DPCAgFMLtaHtSvbd8Yr7pg/vtp7jgCmlMBHSjTXtgb8ebdg87DpWLuiRXHBJENubhwU1MShLm4Ci8dcY38gMF1mrcWapAs9SUHKz5PEEnoz8Medks+fpbhysiLlCarjivoCeKxdcH+bpDtF3q7PkKYFNmQCQV9aUxQyE19aG7uH3ePG6mE4a2zXisLGrxhMjduVgh2jguml5sArY0zUuNtH4WfrJSM5ozruSWnaRiHpm25FMIrklmLF+Q2a2jh8Z7UkmjN5JJMSesLazhKantTec5j2zNj35KC4gSGgIxZk8lkhkfw1SHpGKe6cACpBCEFJSRmbVmykun0Vyjc9K04sxOQlLUxe0vx3QwgfExn8tre97RVPzN9ySMZfCkG8HhUuRWb7UPvYCMhMP0FBvSE+XwWqqsw2+/q6TzwZnOoGlYN9FAnaKSAoaEBmTMssdgy0Jtz+CM7gGlJz/pGg6OAW/FeCitXiVp9FePcfIcii7QJkbgi0T67hIkNE+lnTwh+pzHv5KhASFa3CHt6APbYN9xXI4F/96g/7/f+b3/wJc+bUsWbNShYtOvuYxw2AEGSnvAWETaj/BezkEMopJNt8DdmmK4+8XvM1JoSvfyXRbfegwiX4laej8561Mt1LuONPJrDNChMU1JGZ8uaJ8EGnZwWgcaXN9ngCvBR1mXGK0KDVRBCeV3k6yVCCUPfTWKkOgng9XvVZ+CUzuP76m/j3f/8kg4MDlJWVoyPlZFuugZbj8/D630RPTxf//d/f5447fsz4+BiXX34t3/vez1mw4PRXt+FX8RBRA/3mH6VlBJs3QkHCfFdS48jaepwzznp1YztBOOOMJfzpT8/zne98mW9/+0s8+OD9fPWrP+SMM5YccvlYLMYFF1zGBRdchtaa7du38thjD/PYY3/ii1/8NJ/97D9RUlI2oSo+5ZSFzJ9/OiXHGQoSswuYV7aI3kwno+4QjgxREamhLFx1yGfZsNtPZ3oHY+4wYSvK5MQMIlYMV+WIWFFKwhX7+Q8fDlprdu/ewbPPLmfFiid56plH6e7oxLIt5i86lQ//+yc447wzccNZZhQvOCwRfCBeeEEgJZTmT4dlQWMjbN4sWL9esHTpsauDOzp28cY3XkpxcQl33fXQUT8DZFU14auuI3v/b0xrpxBg2Tinn4mz+JxjHsehoLVm+zM72f70DpLjHr3jEeKhEMyugxmNIEzhrbVg3D26Yx93jZrYV3D1ZEV3yiIXGJuH6XHNmCvYOASlEY1WhgS2pebMWlOsjuSgO6UZysGP1kqW1mmWTjpyoJ9lSyKJCOO948T2Ob2+GyClIFxgquSi2iIWXD+Pnc/uYmj3ME4sROPCRprOmMSaB9bxkdd9jjtf/BHfvMcE3i6ecwGDOwYZ2DZwWDI4M5IhOZDCidgU1RYhDvD9EELQePokRtpHGNo9TKw4iu8GZEcz1M6tpaThtfH0/lvBX7ou/rsOVT6Jg1AZqSUons/uZBsZP4UUFg3xFloKZxwU4noo7LZ24uhChkUFxUrQYY0wKEZIC2izOznDDVHt2qyzc/RJF4mxcFjgR1joHsyqXJkroEALlofS7FKSUUsRVRYJLemxfLJ5xiWuBVpoWoIQ03yHDbbLettlcuCw2smSE4qeWIJEqo+qXJqk5bApUcQ3m6bx6d4XGCpqoAhNnwwoUJK0NPVrGoUShpwuVIIyZTEsA0aFBhRDMuCnsREkghCCl8jSH/UZEUZzW6bM877b8vhOfJhyZTFJOfRLn/lemIxQZIWmTEvSwConx0I/sh+J2msFeDBBBGs0AyJgVAQ8Ek4x1w8z149MKIqjSN6WKeL+yDhbLRdPasqVxYXZOPP3Idzn+mHOc2P8PDrCVttDYUiTQmVjAZ3S539CKU7xIhTnVdZJofDQFGmJ3GeMDg7zvFOZ5x1fu7SIRgm/7Wa451cE29vQnossKcO57ArsY5wc3rGjjTvuuJWPf/xzlJYa9jTXdAW5+guR3hjKKQT78JNTTt9z2EPr8yHreXI+Wo09tBan/wXcuvP3jjsUMqpl39ufYfI8sB2Ec+xBwQfCnw7ZixXhP0vsvvwPwxDUaEREYI1ryApEYL4LKgo6BrpKo+rBbTFEsEiB1QHeqRpdbLyCRVZidYAc12AbteYeSwm/2ZC6wSQ9keYl8kGDwjXEs4ruQ5oe8BjTNgxmYfugIeIaEoYQftM0zSFNUTRE75fYWwT+ZA0hDR7YbYLovZLUhxQchaJz85CgLw1TS/aGkCVCZgzRM/6BePxT3H77D/nIRz59yHeDsAXzKzR/2ikpjZjJfa1NzkTOF/x+G6TyzTqlYcEVLZqz6/R+r2yTizVFYUlvWlOdn0vxFQxlBZc2KTRGvbxzzIgSppVo3jlLkw2MGnlfX9udY2bDiTAsrlVkA0FvGrpTMLVEc1mT4kdrLdI+lIT1RAZGRcR4Ei9rF/zbIo0Q5pwPZuDhnSKvejbdbUrnLc58w8Fr4JYFipQH6wZNsFzUhl1jAqVNLRuzNVuGjbhhUmKvj/Kh8EyXYDgrmFZiOvJGcoLetKBjfE/InNnHaVVm/GfWajqSiod3SqaX7D23WpsgutJ90tyrYqb+Hsoa+7aYo7GlZMw1BPIe5fFw1lyX2Kv/Sk4gHknQs6ubgvlxQjHz/U8Npdm2fDtlzaWU1BefuJ39FeOYyODbbrvtNRrGSRwJOlJGrv4iott/jfDS6LxqVEuH3KTLXrXacg8Z3NPTzdSpR+cPdbTQThwQqHARSAfhp9F2DOGnEH4GFS7CL5uFitehYrXYw+sJtz9C+jjIYIQg03ojOlRIqPtJpDdOEK/Brb8Yt/bc/DISLS2sVA/W+E5kbsj4Chc0AAotjj0oZ2xsFODwJIrWxsYhO4gOFxMkmg95x9WhBJmZ7yaXvhLhjqIi5Ye1ldj/uCV+2VxUtIJw91OocMkEEWw2HBDqWYFMdqHDxWA5uJULyUy/2diNFE1mc1krD1YUM6l/A3Uj7ezyXUq9DLUli4zVQx5B8XQyxQe3jV1zzZv4whc+wQMP3M3NN3/wlYfsJXF6nzWBf9LBK5+PV3H6/6pyeOvWTXz3u1/hgQfuJhyO8OY3v4ubb/7ghKn8q4HWGtWxm2DHNoRlYU2ZPtGCdsT1ggD3od/hPfkYemQElc0Y5UIua0L+yspxlpyHrD7+tvATjVAoxMc+9q9cccW1/NM/vY9rrjmft7/9fXz2s185oppMCMHkyVOZPHkq73nPP5JOp3juueW89NJzrFr1Aj/72ff42tf+HYDm5tY8QbyQqVNn0NzcSm1t/VGFaUWsGI0Fr3w/Gcj2sH74RVzlErYijHkjjLiDNCem0Vo467Draa3p7+9l69ZNbN26iRdfXMGKFU/R3d2BEIIZM+Zw4YWXUTGrjMnzp1BdUYvWiqQ/TsIuoiJy6LCnQ2F8HCZOqe+DlEhpEqQzmaPezASy2Szvetf1OI7D3Xc/THn5sXnDOovPRTa1EGxcj87lsOobsGbM3q9N9NVgYPsgW59oY0e0jJcn1bEtGyKX8aluTzKtaJSiumJygQnjKD8K4eLqPsFvtppCXGuTgDytRDOUNW2DMdu8qNcnTFuaQlBXAL6G2gLjH7yzXzCUNe2AXgD3tkm60pp3zlT7pVHvCyEFDafUse7360kOpIiVGMJ1tHuUssZSyprLJpYtqS+m+LoiAjdAWhKZfzNw0y6RaIQPXWeCOr/5639HCsnUgnn4bnDQPpWv2PrkNtpf6iCXzGGFLEonlTDzshkUlO+vFKucWsHcq+ew89mdjPensB2L1nMn07K4+SDy+CT2x8m6+CT+miCEoDbWSFWkjkyQxpYOEevoO5uUbmSDVY5NIxqBxiOse7HUds7JVfGedAkl2mKdnWOD7eKhmRqEmO+FiR6C6QkhuMQt4Fw3Rpf0+X5smBWhLONCEVMSTyqEhiJl0RI4tAYhHAQBPl2WT62ySaMYlAFhESWKjfBSxFSYEQvWRAT3NM7khZZWam3jzV2uLHbZHl1S4aNxAQQ0BBYJJRkTigSCFt/hBSdLTEuWejHiWjIofFY6WTSapvxYACqVzbBQPBJOcXEuzmBeMdwrAwI0Oa0pUxbbbZdk3qJiD6L5f/toLGCz5bLd9hgTiqgS/CQ2wululLdkCyf8f2uUzfvTxXRJH1doqgP7oPNrIbjQjfFgOEm3DohqSUJLFJoxoeixPISANsulJQjxcDjFGidLADQENhfn4gd5M78aWLX1RG/5GKp9FzqXQ9bUIo9DVfuVr3yOiooq3v3uW/b/hR1BHYEEnlhsZKshgfd9D7JCICys0a2wDxlstU5F1tajdu5AtrQiLAudzaIG+wlddiUiegI6YyRkX6/xZgXYWwVCg9+sid4psXo0gSuwejRoCKryORch2MeW25DDUaMuBrDXQOwXFtqBoBHEeoFwARtUsSaoB2+Gxt5l1hGBQFt7LSu0EMg0yKzGnwfh53Tes9j83vPg3qjgsUoYXmmZ0LRSzQ3TFOWHuZ3IHhNAF9RqJjIgHQjqNXabwOqCoP6VT1cuABBIsT/hHJKQCSRvfet7+NGPvsF//de/8JWvfP+Q27hgkmbnmGbrsCHA9wh6vcAQpjELLGlI4fvaJLUFAa3Fe9evisEljYrfb5dsGjKCAE8JWks0Z9Vqbt8geaEHAmW2HbfhkibF5c37k8qdSVg7INg0ZLx0BVAWhbnlxov48mZNY+FektSWe4/ZVxDJ+wS7ypDcANtHBcM5k/OxbRSygbGBsIRZLmFr6guMFdqUYsj4JnT526sktXGNq2DtgFEDp33YMWq2XeAYcvpQ2DFqFMsiT3QvqNTsHIMNg0a5+/rJiqUN+6usT6nUrOiGnWOGfB5zTZhdfUIzr2LvfsqisLhO8dAOwUDWWOIN54wQozKcIZPKMRAksKTFufXqsDkmh4LSsHUYtowIAmXs5WaWMUG45zJZ7DJngggGiJVEGdw+yNCOoZNk8KHwrne96xWXEULw05/+9LgHdBKHRq7p9ehwsWmTyQ7hlc7Grb/QhJ7tC61NyJnyCOK1+z8MD4Pa2gYsy2L37h0nfNxe6WyCRBPW2A78RCP22E5EbgjpjoHABMDFG8zCQqAiFdjDGyFwj48YtCNkW64jO+l1SC+JChcfVBCoaDWRnQ+i7XBePTyKlWwnKKhHZvsJdT6OXzwVFat9RXWnUorPfvafOP30s5g+ffZBvxfuOJGtvyDU9yLCG0c7cbyy+WSmvd0Qs4faZqwKYq9MHB4IbcfQVtjYhoRMm7cIcjgDK0H7+MVT0dEKhDdOqPspgvgkci1XMxwr4+ezLmbxSz9gyuAOxqKljIRjBEITz7QTHl6HXzrniPsuK6vgggsu4ze/+cUrksHCSxJb9z2c/pVo6SBQhLqXk6s7j8z0d00oll8rdHV18PWv/wd33XUbVVW1fOpT/85b3nIzha+yFWwPtFLkfn8f3hOPQnIMjUCWlBK64mqcs887SEW2r+eq/+zTuA/9FlFYhGidin5+BYyPQWkZzsIzIefiP78Cr3kyofMvOiHjPVGYNm0WDzzwBLfd9kP+4z8+xZo1L/GTn9xDTc3R9YbFYnHOO+8SzjvPtP5prdm5cxurVj3PypUvsGrV8/zhD/fiuqZXPxwO09jYQlPTZJqbp9DcPJmmpsnU1TVQVlZBUVHxUbf4aK3ZnWzDUx7FobKJ9dJ+is7UTkpEBZnRLENDg/T0dLJ16yba2jbT1mb+Hh0dAcC2bWbNmsfrX389ixYt4YwzlkxMEg1ke9gxvplxbxQhBOWRaloTM4lYR/+yMWWKZtu6DBV9G2FoACEl2fJGLLuZurpj9+D+13/9KG1tm/jd756k+jgnGKy6Bqy6huNa95XQt7mP3UGUpxNNeAjqHJduV9KpC0h2aWYUwXBOMLtcM6PsyMrg3jTcudmEgEwuMoVzX8YkOs8pVwxkBWOuoD6hedtMzdxyTdo3he7dWyRbhgVbh2HUNWnNc8s1ERsKXc2qXsE5ddBabEIvcgHUFLCfWrh+fh1u0mXXi7sZbh/Bciwqp1Qw89IZ2AfEIwshsMP73wdLm0oZ3DlEggQfuu7TaK35+t2f470XfILTqvZPagdoX9lB27I2IokIJQ3F+Dmfvi19BL5i4U2nYtl79ymEoHpGFZVTK8glXeywhRM5gfKLv2GcrItP4q8RlrQpkMeQqglkUOyy5uGJrYR0O0W6gKy26ZfFtAZn8fbMYiq0uS/N9yPM94/eOiSCpEWF+EqygsdDGV6wM7gCuqTHiFDMD/YqY3U++jWiBaVKUqAlKaEoxSEoqEVmhxnTGezApT9exq/mXkM6GmMNKRp9G40hV2IIAjRSmAm9fqEYsjQxBJOUTZW26SHAF4pO6TE1CKMx4sWM2F/EaCGw9V7yt8PyyQlFQluEkCSlol/6TPFDDOIRxcHOb2GGH6ZG2eyQHgkt2W67CC0oQDI7CFMRWDwfyjAtCLHE21sPCAR1h/Az3hdZATEtKVEWDpASihGh0EKTQ9Mhfe6LJCnSki2WS6WyiCLYYLt0Wj7vSxfTfIgQu+OFsCyspuO3Mevp6eIPf7iXz3/+a0dtzxZ0thNs2gi+h2xoRMkIqEP5tKv9xC0AIhoj/Ma3kPvVbQRbNwECYUnseQsIXXjpcR/HQRAQtELQurdO8U7TWPdL/CkafzbGNaLbNK26MzXh5RJVqQ1T44HdZsLgrC4IPy4RI5qgFfyIIYrtbcYf2J+u0QVg9YJOCHQCdFSjpUCOYvyJ1V5FsPY1fr3A3pYngy14xBbc3wJFVZrJhYY0XNknCJTkQ/MPPfEt3Lz/8QEfJ+3kf36Ujkl1CVNbjeWM3y2YsQ7nBEsbFGe//R/40Y++wS9/+VPe+c73M2PGwe+n5VH4wDzF6n5BZ1ITt2HXGPxhu2QgK3CVIYcLHIg58HK/sWvYFxc1ahoKA9b0C8ZdQXOR4tQqzZp+wXPdUF9g/IEB+tMmnG566V5SWWm4Z4vEV1AcBtBELehOCsZcWFStmVNhbHMqokalm/VNgLIXGKK6IGSCjkP7nG9LmrFXRDSVMRNMN+6aADuApAebhwX/stxiWqnigklQFNaM5IwCWAij+O5JkVf5QlVccE2rovkwj4yyqCG29yBqG+JYAzfN0JzXcHD9PbkYrmtVfHOlYOeYRGmIOlAW1QznjMp8D65o0QykBXduFiQ9QaHlkgjGGBjK0ZXsYXp8hHOnlnNq1eGFOQdCafhtm+DRdkkmfztwpObMGs2N042vcs7NEnYOCL3M+4yo4DXK0/orxDErgxsbG1mwYAH6QDOWk3htIS3cuvOMwlV5IEMHEZUyuZto293YI5tNaFe8llzz1XiVR253D4VC1Nc3smNH26sbo1Y4vc8ZVW6mhyDRglu3lPSMm4lu+hn22A6CWCUicPGLpiH9JF75KfuRvkLlUKFiOIpW7CPCjqLswyTLBjmjWNba+DGbX2CN7ya24VZDqoaLyTZeQa7xyoMJ4cDFHt2M8LN87Cs/YNOm9TzwwOOH3Fds3feI7Po9KlxKEK9D21HCXcsQ7hjZ1hsIEo0nTA2rQ0V4VYsI7/qd8WgOFSKTHchML35hKzpvJ6KdBDpURKhnObmmK9kQdnFyY7SmxhkonYpG49hROosnUzy4k/qup16RDAa4/vq38u53v4nt27fS0nJ4JabT8wxO/0v4Ra1gmZuwcMcIdT+JV3k6fvmCE3I+DsTw8BDf/e5X+O///j7RaJzPfvYr3HTTPxCJRNBKoT0PbPtVewT5q1fi/ekhRFExIk+Sqa5Ocg/8BmtS00ShHHS24z31BMGGdRCNYZ9+Bv5Lz4NlIyurUUOD6NFhRE0dJJPoZBKrtJwgm8Fb/gTOknNflQIzlzMf7RMk4gRMWNLNN3+Q005bxLvedT2XXXYmP/vZbzjllIXHvC0hBM3NrTQ3t3LttW8GwPM82tt3snPnNrZvb2Pnzm3s3LmNRx75Pbt37yAI9iokbdumtLSckpJSEokiCguLKChITPh673mGaa0JlE9/thtTsWvGR8cYGx5jbGSU0eFR3Oz+VWw8bkLCWluncfHFV9Daav7d1DQZ5zBtheWRakrDlaT8cQSCuJ045s/ama2dvNS/i01DYUoTCfxAMtY5ximzVjCt+XTg6FU+99zzc375y5/yta/dyuzZ849pHP9bcNMebZEyMlg0iDTYYMcUg4M5eoIYvWnBhY2KK1v0hGricFg3IBjIiP1a1qpiprUuERK8b54i7UFxhIlwinj+u3HLfMXmIfjaS5LJRZrJxfu3L+5Rfzy8U9CWt6Aoj2ouadKcVWP2Jy1J67mTqZtfR3IgiR22KaopRB5OTnwAGhbU0be5j4Htg0SLIrx9yS1kxjLc+tj/x4Lr53PF9L3hp8pX7F7ZgR12iJeZz3soFqK4rpiR3cMM7RymovVgeylpSaJFJ71hjwUn6+KT+FvBZtslJYo43atjuzXGsBwCIKFrKQ8upVIdhazvFSCRXODGuSDvY/uSneVn0RHGhKJMSzTG3qBEW8zyQ0SQXJKL86yTZUD4lEqHXLyCHuHhopFSUhfYhBAkRcAmW1GpLCwBcW0BkkolKFUWWy0XG5jnR6gLbAZlABocIRiSCgJIaElESVJWgI8iJYy1QkpoHKBICUJa4AqN1IKoFggEvoaddsBAKMM7Snpo8B3enC3kUjdOQktuzBRyd2SMl5wsw0JTpgVNgUNDYBuiGRNIty8ZfDQoUZIqbdOhPQakIiUDHG1awQMkNYHFJisHAk73ooTzhHtRINlkeTzjZF+ZDNYKa2wbwkuiYrVGsPIa4Z5f/hTbtrn2iiOEee8D98nHyP3uPvTIMAiBCIUQp9YTbnCQ6V5U1HQ7yUwv2oril887aBv29JnIj36KYMNadCqFrKrGmj4LcRgf+BOF3Lkaa5vG2YDp7VfG1iFzpcKfo7F6NPYWgZYap00gxkFVQeQ+ibMZdBysdlAloCo0akAYy4hkfibDNvvw5mvUHwRyPL9jJ08E+yD7ofALFqoasDQ6DtlSeLRBEy2HimKzSiIETQnN5mHBtlFj4XAggioIKjSyz6iV98Dqg6DceBcfDSYXwenVxpt4OGeUqCM5QXVcc26dZlLhZBYuPIs1a1by2c9+nHvuefiQ20mE4Oy6vc/kTy+X9GeMF27CMVYKoy4M54xY4EAIATNKYUbp/rYYL/eLiTDhPSiNwAu98OvNksV1mlnlxud424hgRqlmNGfsyVIeCKHR2hzj91dLVvYJMp5RAm8fg+KQOeaYDbVxzdn1B1hYFGnKI4KOlPl9f9pYRggwXru2wJKa9YOweVjydBfMKdekPKMiLgwZG42+AugaF3jaBOGdXnV4/dvCas3aAUFPypw/X8GucUNU76vyPRBp35ynRdUm4DnuQEcK7tos+fhpisL8rUdp45E8pVgzKZYl3v8Ulhhmg55GXSjNpwruINYPqeGP4JceLLw7FLYMw6PtkoSjacqT3OMuPN0lmFYKi2o0buBiYxP4wYRAwk25SFtSXHdiBGL/F3BMZPD73/9+7rzzTnbs2ME73/lO3vrWt1K6x7zwJP53ICRYBz+ghDtGbP0PsEe3EcTrQdpY47uIbfwxKacAv2TGITaWhwqYXF9J28pHiK2rxy+ejld5xmFDyw6H8O4/Emm7C4FC2QlCnY8R3v1H3NpzcesuJNtofLSCgnq0U0jBi5/HSu4mKGoFIRFeEpEbxZ10BcijJ4Nlqgt7aB1CuQSJprxP1MHry2QHTs8zhHuexk80o8PFCD+DCDJYY9sRQQ4Vrc77GfcQ2X4vQaIRv2xv8WCNthHdfDv22HY+evuLPLqqlwe+/wXs2nKSQk14gqE1kc13ENtyO1oFWO4YVrIdFS5BBBliWzfgDK7BL55CruU6vIrj8+k6ENnmqxG5YZyBlYhUOzI7hHaKDMG6z11eyzAiyIL2GROKwswQwgrhJ/Yq/GyhGA0X0JTcdVT7PuecC7Esi2eeWXZkMnhwDdoKTxDBADpUiEjuwh7ZcsLJ4HQ6zU9/+l2+//2v4nke73//x3jf+z5KIlGI9jzcJ/6M98xT6OQ4VmMzzjnnY884ePZRDQ+hx0YRJaVHbH3zX16JDgKs8r1pXrK2jmDjevwN67CaWgi6Osje+j2Cjt3IkjL06Ai5u+5ADQ1i5c+dzmbB9xGhEEFyHFa9SBA26RBqoA81OoJVcWxt/QAdHfDoo4KNGwWWBQsWaM4/X3Mib+Xz5p3Kww+v4Oab38gb3nARP/jBL7jkkiN4Xx8lHMehpWUKLS1TOP/8/X/neR4dHbvo6elicLCfwcEBBgb6GBkZZnx8lLGxUQYH+2lv35kPXBBA/m8B494IACEnRKKokKq6GhLFCUKJELPrT6WxuoXS0nIqKqqorq49rkkDKSQJ5/gLjMptj/OOyhdYXnMZWwariFs+501t4yzrMWRbFBYc3X1kw4Y1fOpTt3DDDe/gxhvfcdzjea1RXF/E4A5FWPsTfnZxSyPCAXaZzRWTFTdMOzoCLp3fxIGXbU8b3p6AkEPBljCzzCgdelPs16bmKwi05vF2QdYX1BUYj7q+DNy5UVJgB8zb52saLYocF+EaL4uz4Pr57HphNwNtA4QKQnz5i9/na7/4LB/44FspLnmQJUvOM2NyfdxkjtAB5mp22EYFCjflHvP+T+LQOFkXn8TfCrJCo4CmYBJ1QTUjctjU7LoIKWx89IRtwquFi2aT7TIgfRoDm922zwA+ICjTkquyiQlV7FIvxtasy12RMUalqbOlkCihqFSSYi0RGGJ2l+URaMmcvG2F0Mb3N44kLVT++GwEghIliWvJgPSpyT9GLASl2iKnFNstn0DoCRpIa8mAVHTZAbWBzbjQDEqFQtEjFT7mpVpq2GS7fDk+hK0FF3lxpgUh/ilVyo9jI/xPKEVrEKJaWVj58ykR+MdxaiNIlrpRdguPXpkjkw/KU0CZkizwo2yxXYZEMEEEg1EdF2rJTuvIzwKZ7ia66TbskU2IIIsOFZOrPZdsyxtOqK2bzmTIPfIgv7r121xaXY/zg2+TO/s8Queej7AO/T4YdLaT+919oLQhb4VAJ8dJPbudUOUUCpx27OH1ZvuhIrLNV+OXHJpIkiWlyMXnnrDj2Rdu2qV9ZQfdG3rRvqJiagWTTq0nVhIj9V5FaI3AatfoiAmM00WAD6n3KEKrBNFfS7QF3hJDqoaeEYg0EAgo1li9IMfyRrEW6GITVKeK8xYRKfDmaMLLBNpmggyWGSAGwjNWDrLXjHfgBsXILou4vX9tFbXBDUyuwiE9gyOQu0QT/ZXE3gKqUCPHQTuC3EUKjnKeQwq4YZpmUgKe6xYkPVhQqTinTjMpT+rdcssnuemmq1i+/DHWrVt9VIKGkRyAIGIZclVgSOGRHCh1mGM6BHy1fw2YCwxBvH3UBLO1jQqqYpoza5SxerANMV0V14zlDCHbmxbcscFi26hR/VoSSsKQDoyFdX0CJiU0F07SLDwgGLk3bbaxcUiS9jSZAGyhzXc6DIWOsT4Dozj2tWA4a9S4alQwo8Qor0vCMJKFJXXqiEQwGMuH/hZ4aIfgqU5IecY2YkGF6aA7VMWjNDzbY8Y0aR86qSlh7C02DYmJY+tKGsV0fUITy/Rg50ZQ0XImqSR9fiHdsbm0pp/G6V5+1GTwpiFBxmeCCAZzHXrTsKZfGDLYzVJSVcrwrmHsiIMOFEpp6ufX7Wff9reOYyKDv/e97/H1r3+d++67j5/97Gf88z//M5dffjk333wzF1988d9N6t5fI5yBVdhj2/GLp0+02QdFU7CHN5gvz+HIYK2JbLuHJdVJvvXHzVhdzxDqehKv91nSsz90WCuDAyFyI4R3/xFtRwni9ch0L1a6F5nuxkruNkrQkhmkZ31gYlY5M+1txDbdhj2y0dyDpYNbswS34ejb30Ndy4i23YXI5o2UrDC56iVkpr9jP9Lc6X2O6ObbkZleZHI3llIEiQa8ytOxxnaA1nmbBce0B8RqsIfW4/SvmiCDhZciuumnWKPb+PDdO/nDqn7u+NxlyOAJfj9US1vjOZzqRrnMjVM4spXI7j+YVNdYdd4vOYU9vB7tFKCtGCpcnFcj/5jk/CJDir9K6FAh6Tm3YI1uRWb6Ed440ba792+Z0hqZ7cetORusMBVKsyVSTIBAKh+V//zk0JTm0gQVR9fmX1CQYM6cBTz77FO89a3vPvyCQh4c8zqBo1PJHQ2UUtx11+189aufZ3Cwn7e+9T185CP/TEWF+fxprcndfw/enx+GSAwRi+K99AL+1s1E3/kP2LPmmuUyaXJ/eAD/hWfR6TQiUYhz5hJCl15xSGWuTo4f9HMhBEiBzpjpZ++pZQQdu00Bm/e91clxgvZdqM7dWDW1iGgUbIeguwuS4xCLI6IR1OAgZDvxli/DuubwQUCHQl8f/OQnkvZ2QVmZxvfhoYcku3Zp3v9+RewE2KPtQUVFFXff/TD/+I/v4Oabr+dLX/rukT8XrxKO40woiY8HW0bXsiO5iUKnFEc6KK0YdYcodEo4rfxsrNfYvuRoEGzbSkN1jrfWr8QLJFJoLKkJNiVR3Z1HRQan02n+4R9uoKVlCl/84rf+F0Z9/KiZVU3dmh5WpgKK8y+tfs4nXBSjoChC/WFy94az0J40RXZLkVFZVMeMIiMX7PVe09qkE7cUvXI7mBBwZo3mV5sEw1nT9udr44cWskxr3rQSQwSP5GA4K2gbge7nLN47V3F2nZ4IwjheJCoLmH35TLTSIMx95fuL7+Cmm67ive+9kYceeobGxhaciEOsNMZIxyjRor0dMm7axQpZRIuP3kP0JI6Mk3XxSfytoFrZxLRgVCiKdZhKVY1Gs9XymOs7RI6TCHZx8YRLTMcRCMaF4o7oKOvsHD7GFiKuJWd60XyAXIhSvY+NDYJ3Z4qZ5of4cyjNuAwYdRQBmkplT4S1GTJWEFcSBUwK9v4ui6IkT75usTyc/H5jWmCZ/C42Wy4aWOxFKVRxfhIdIScgpAXFymJqYOMKxVaZo1hbTPUtRqVih+XSIwPiGuJYlGqLUg27LJ/fREe5wIshEQzIgF7pMygVYzJLsZZM9kNUKossitn+8ZGrZ7sxbC24NRhg1XAv0b5hSjI+TWlNylOMqSRjXpZNaQvluYz5Hl48wnhRjHkFFWxx6uksjbG9NIwfDdEahDjVi1AcaKIbf4YzuJog0YS248jsAJGdv0U7heSarjiu8R4KuQd+zVP33MHu8VH+a+mlqJFh3F//EoDwYSzRgk0b0CPDE0QwgChIIEIRxtsicPO/Yo1sNssWTyMomHRExktrjerqQPX2IhIJrObJCPvVPbR9N2DNb9fRs6EXJ+ogpWDrY20MvzjIaUsXEJoawV2kYRHIXoj8XuJsFOBDUKvJnqdQZdqofyvB2gkyBUGlwBoA4QtUqUZ2mJ/7reCeoydepawOcNYIspcqnDVmlkAIIAtKQtAAMiUQviZoAXujoGSjoKxU05XaY29gMO5B1BaURQ5fL7lnaLQbEHlMIobBmwPuEoU379i6ZsIWnNdg7Ae0PviynX/+pSxYcDrr1r3Mj3/8Hb71rSPbMGltJvuLIpqBrCCWJ7rTviDuaBoKj358s8o1awclXqBxLOPh2z4OcVswq8x4Ku8YheVdkgJH0582lgiONHYL20ZMWFrag6IQlETM+AZzUBeB0jC8dWbAohpjE7EvulNw23pJyhPMLVes6BK4AXgILEBoY4WmMPVv2AbX1ZRFjbdxSVjTkYRAGzXxvArNVa1GNbx+yKxbEYXppZqdo/B8jwmKqyvQlETMhRjIGIJ+OCv41SbBugG4cYZice3+51Bpc4yRA+Zy9oQ+Z/29y0thLCsGM6CzHiVag5AE5N9zhEY7CaxUx1FfJ3WYS7pnX77v43ouU86awowpMxhoG8AOWVROr6RmVs1EXsffA475LhcOh7nxxhu58cYb2bVrF7fddhsf+MAH8H2f9evXU1BwdKnoJ3FiIbMD+am+fS6pEGinACu5+7DrWaNbCXc8wllzWvjCvetYPVzMvKZSnIHVhLqeINd89VHt30ruQmYH8YumgPKxh9Yi/DQqVotQOYJ4HfbQesI7HiAz670A+OULGD9tEqGe5VjjuwjideTqLwEn/gp7yx9zqoto210Q5PBLZpkWIXeMcOdjBEUtuPWmgBDuGJG2OxFeCr90LggHe2itURSPbAXlIfw0QUEDKrK3dVZLB+GNT/zfHlqHPbaDW+7axW+e2si3//U6tlUUUDHWTtPqR9lYcRp/igeMiYD3D28A5RslsJ9BhxyEn0OoAFSAisTRkXKUFcEeXo/Tu+KEkMHmgKUpfIqnmfC6dA/h9ofR7jDaiiJzQ6hoJbkG4806ww+xonQB24v/TOPQFoYKGxi3LBpSfZTKuCGNjxKLFp3Db397z34+uAfCK1+A0/c8wkuiHXO/ENkhsKL4pQeH0x0PNm1ax8c//gFeeulZrrnmBj7xic/R2Li/j5nq7sR79mlEeSWyzFx3UV6JatuM++gjJghLSnL3/xr3sUeQ5ZXImlr0yAi5P9wPQhC+8pqD9m21TsVfbdTBexQNOpdDCIlVPwmAYPMGRHHJBBEMpoiVJaUmRKJtC5RXGA+Hnb3ghNCei+7pQTgOsqkJ//lnUOdfhCwqPurz8txzgt27BdOna/bsurRUs2kTvPyy4MwzT2ybczQa5Yc//BX/9m8f4xOf+AAFBQmuvvpNJ3QfJwpNBVNI+0kGsj0YfRQUOEVMK577V0EEA4iiImg393PHMmM0rekacTRMvobvfulLdHa08+jvXjpqT76/FCKJCNdeVE370x59Y5ISnSNSX8R4YREtpYI55fu/lCgNf94l+PNuwXDOFLuNhZrrpyrjK1wqWDcgKIuaxOf+jGk9XFR7dJ/7JXWa7hQ81yPpzWgEpk2usVCzolviSNMOuLLXFMu2hIE03LNZ0D4ueMcRQuaOBfuGujmOww9+8Asuv3wx73jHtfz+909RUJCg8fRJjHatZbRrlGhxFD/nkxpKUzunhpKG4uPar9aarlQPSTdFRayc0sjxbedvDSfr4pP4W0BjYHO6F2VZKM24VkS1ZEgGJLTkvFxsglg9WqRFikfDD/Ocs5ycyDIpaOL83KW0WS2stLM0Bw5RTODZDumx1fa4Opcgrg++SUoE53pxFnkxRmXAHZFR7o6OMyoCYtrCF5q00MSVZJEXpdcK2GR5lGmJD4wIxel+FFsL7oqOMo7Od5toygKLsAZbwyIvylW5AlY6OU7xI9QoiwBBgRZEkHRLn0FL0eDbrHRyFGtJNl8xSCEoDOTEeYppwW4ZkBQKgeCO6Ci9MqAxsOmRAX1C0e9kqFc2070wES3pF/6EL/OByGazbN26kS1bNtDZ2T7xp7u7g76+XoaGBiasatqBNQes33WIbW4A7tz3PIdCOCVFlLU0s6imhgX2diY3N9PakKW5Jko4WonwM4S6nyTXcPEJUQernm68F5/j3v4emkrKWDhlBkIIgo7d+E8+RuissxGHCiP2vH26vPaB46AzGYJEo7HiOwroTIbcfXfjvfgcJJMQCmFNmUrkhrchq/f3NxDuOPbwBkSQJSiYRJBoOizJ3L+1n95NfRTXFWGHbcQIlPQWMLhmiMHVPUyd1ELuPI17tiZ2mzQBbDXGJ9jeAfFOC7IanRcpilTeV7tUo0cEYgSkKxBZ83n25+r9NDUqBjIt8OeAtwDIaIiBtcOoi0Vezq7DmO+DBaExOHee5o6Ngt3jxgIh40F/VnBWjd5Pabn/9YDI7wXhZyRiNL/NQJvjeRVzooc6tUIIPv7xz/HmN1/O/fffyb/8y39SUVFF1oeX+gTrBwQamFmmOa3KTMTPLtd0pQQSTX8+cLm2wAQHH+gXvAdZ33SVFYbAT2bp3dRHyWCO2lwVm9xCYhETMKc0NBVryqOGbGzMh7udWql5ecCohgscY0uR9QUxG/xAELL0xDEmnD370iTCgoLQwWN6qdeQs61Fmj/skCQ9CAlwtcbDBLBZ+fPlhDRagS0FMUcRcQUXNWpaiox/cGlEM7UEOpLwvQ2S9nFAC6TUxB1Bxhf4AUQdzbpBya5RE6RnC0Fzoal/B7OwexweaBNMKdZU7vMaYkuTo7GiS1AR3Wt1MZYzaunaAnN8njLHtXMMBjOSEquGWi/NjNAYnX4Rs6K91DljiNQ4Qfzou6ibi4xHcMrb2/XnBkbJPatMT1ijNrdOZvLiZiYvbj7qbf+t4VW94ZoUc2E8F/fxajyJ/32ocIm5m6hgP4sE4adMkNxhYI9uRXgpTpk1g7DzIMvX7WLe5Gp0qBCn/8WjJoO1FQZpI5SLcMeR7igqVIJQuby1RQQVq8EZXE3WHUWHikArQj3PEN79J2RuEISF0/8i2ak34ZfMzG9YGZVrdggVKSUommK2BzhDaxHZwQkiGIwyVlsRQr3PTpDB9shmrHQPftFUEAK/qBXhjWOPbcceWk8Qr0ZbEdN6sCdsTvmGxN6HoDXEsOanD68C4O2f+tV+5+B132yi8Q1LWSdhaHwTdYBf2IwztA7cUfDToANQHkGiEZ33NNZ2FCt1qDLtBEAIMlPeQlBQT6h7OcIdJVdxIW7d+RPHFs8MctPwZp6qWohAU5HspUIJSiO12C1X45bNP+rdLVq0hB/+8Ovs3r3jIPJ1D9yqM7GH1hHqeQa0AjRYYbKTLjPX8lUgnU7zzW9+kR/+8Bs0NU3mvvseZdGiQ5PZqqsTPT6GrN3rgyeEQJRVoDp2o1NJdCaDt/IFZGX1XsK4OgpovBXLCZ13IaJgfzsVZ+GZ+CtfINi0HllajlYBenQEe+4C7LnzzTbicXR3J0HHbnQqZXzOyiuRiULsxeegBwcJujqQFRWoHdtAKfTwEFgWJAohGkOPjaEHB+EYyODt2wWx2F4iGAzfrLWgu/u18bu0LIv/+I9vkEyO8eEPv4uSkjLOPffC12RfrwYhK8Lc0jMYzvWTDlI4MkRZuBJHnkBT5VcJ59QzCNauQQ30I8rKQSnU7p2IskrsmUf29Raj0PHjrXz/Z1/nY1M+yfw7p5Pr0OQu1q+yEnhtMb85wgeiUR7ablrrhCWYX6S5tlVRdIBj0ku9gvu3iYni3gtMCvL3V0suazYJxmUR43vnK6P0vWCSOqTC2FdGhQGG8LWkUQC/ebpmSV1AZ1IQsY0aeMOgYEWXpi8N6wYFnSkociCnBE2FmroCM7bFtTD9NXAQKCkp5bbb7uWKK87mllvewU9/+mtqZlcT+AE7n91FeiSD7Vi0nNVE6zmT9yOTjxZD2RF+s+V3bBjcTDbIURgq4KzahVzechHOUQTV/r3gZF18Ev9XIRC8IZugSlk862RICcV8L8K5bpQZwbH5pyoUv4r+NytCyyhSJYSJsNZZxTZ7G55+CyV6BtE8ayURNCqHbZbHFstlQT6Yrl/4PB3KsM7OEUJwmhfhTDdKpbK5LptgjZOjQ/pkhMIGSpWkOrC5wI0T05LHQym22C5xBOe6cab7Ib4VH6Y6sGnWgqyA3dJlWCqqAhsl4CUny5QghJ3n1Iq1RQbNmFSktcZDYSFJSc2wDGiXHuMoPKBSCYr2IbJdNFVaENaSlU6WXZbHtMDE5HVLn778+kkUfVbAf0dHKdKSs3IRTt81xpaN61i/fg0vb1rDlg3r2NG2eeKeUlJSRl1dA3V1DSxadA6VlVWUlVfSVlvI5poEOhbGDoUQIYeZsoAzKeTnRSnWxDS2bZFOp5Gj44jRJONjI8RG0lQMpakeyZAeGKR9xzZe2riF/9m6lfHsWnOdpGBSRRHzmko5e3olC2IvMm3OIqR8dTOcarCfYGyUZZ27uGnBGRPkriwuIejtwXviz4iCAmT9JGRj897fT2o0Qonk+EQdroMAPTaKtfTYakz30T/hPv4/yOpak/WRzRCsW0P2rp8T/dDHJhTC9tBaoptuw0p1glZopwC35hwyU958SGJ8rGccrbQJhHXBeVkgxwR23KYnMchU1ULkdwLZYwLi/Cka8o9TvwDszYAWiH5QpRpCGpESiGHDr+oQ6AKNioCwQR+gwrSGwJuqCRrBPU0RflISFGtENdjrAFcQTNboKBAAynj/nlmj8RU80S4YzBprhUsaFZc368OKq8NPCiJ/NErloBVExqiS8SWpf1QTx3WicO65F3LKKQtZtepFfv7zW/nQR/6Vn66TPNZuCEZHwNOdgnW1gnfMMt1ZGweNMnh2mcZTMO4JFlZpikLGksKSxrc2bMGfdwue7RakfUFVZoSmtWuIjZoA6LnsoKyyjvHpLbSHC4yatkxP2EdYwujz5lbAgkrN012C/gzMLoPGQsVDOyTZQJPLB8btga/AsQQl4UOrr3vTEJKaXeMmtC5sgS/A9c0UlMZ0rNkCPCUYzEFzkSZimc640rDxe95DQnsK7tksaR8XTC7S2FKTdOFPuwQlETg/HwzXk9Ks7RcoBKVhzR7RbNwx5Gp/WrB5WFAZ2/898tx6xZZhyeZhQz5nA0h5cE6doiXvmPf4bsEfdxqCWQoYzcZY5zfTMTLK0sQOrit8AWe8De0k8GqWHPXnY1aZ5vRqwbPd5npIAbnABE6fVqV57GFzXztUAOHfG475FTCXy020wy1fvpwrrriC7373u1x66aWv+oFwEscPr3wBQcEk7NFNBAWNaGljpbrQdhyvevErrh8J2Zw+rY5n1u/mg1edkW/jP/oXxqCwFb9wMvbIRpRTlF8/QHjjBIUthvgMMkg/ZwLwAKfveSLb7kHbMePzqzzs8R1EN/6E5Cn/AkIQ2/QznME14GfBCuOVzSE9/WZ0pBSCvNfVgU8my0F4qb3/14EhHcWevpkQXuXpqHAxwhsnM+M9OD3P4oxuBm08y2R2AL9kBm7VoonNqFgVSIfxez9CNhTnyVAaWwsaB7fSUdLEitpq6FtNT1EVQ+ObmDS+A696MV7pXKzxHUhGAfCLpu617ciH2AXxIwRzaI1MdSL8FCpWbYj0Y4EVwq2/yJDjB/TbWCNbiG34IYXJdt6CZNhx8EMVRJquJKg9F9c+Nt+AM85YghCCFSueOiwZjB0hPfO9eFVnYA9vQUsLv3SmCakTx38PeeKJR/jnf/5Heno6+ehHP8MHPvBPhI8QACEiEaPc9VwI7bNcLoeIRhChMEFXJzqZRDbvH5ghCotR/b2okWGsA8hgWVZO9N0fwH3yMYI1qxGWhX3eRTjnnIeImvMpp0wj+P194Adg2+a6aI01fSbhy69GlJahR4bJ3XsXwaYNiMIiRDgCjgPpNMGWTchZcxGFx5YWXliocV3Jvt5Y+V3zWopEhRB89as/YnBwgHe/+43ce++fmTv3lNduh8cJKSRlkSr+Wp2i7NPOwOnuxHvycdTmDQghkZVVhK67AVl5hFAXBdE7Bf98+0eojdXxkaWfgDGIPiAgDLkL/rqDr06v1sytgO6Uxham5e5QfOZzPQKtoSbfXCIsU+w+1SXYMQrlMRN+cdVkzZxyoxY51IvNxiH43TZJx7j55aRCzVWTFVPzc66NhUYNvActRZqkJ3mhVzCS0wRKMJI15HEiZFKe3QA6k4Lppa/NuZ46dSbf/e7tvPOd1/HVr36BT3ziczQsqKdmVg3Z0QxOxCGcOL5AHKUVd266l5V9a6iL11DtVDGcHeHBHf9DzI5ycdN5J/ho/m/hZF18Ev+XobWmP9tNX7aTXJClKVTKwmgDIaeAMOKYFcEA26wtrA69QH3QSEKbOqVUlbHR3kCftYw57v7WdRamKnGFuT8OioCfxEbZZrkktCApNC/bOTZZLu/OFNOkQrw/XcJvw+N0SR8JVGqbS3JxpgchBIJ3ZIvJobEwbdP/FR/kRTtLJH9E4/mcj4gWZNHMD0Lskh6PhFK8I1NIkbZ41s4wLhW5/LiU1rQGISwkF7ox0kLTJ32eDGVMMBwKB8GIVATAeW6cMMZ6A4yNBUC9cqhWNr0hn1EC7NWbGHryGVY/uZxfP/8S7tAwAOFEAYWzZlB89mlc/L53ctWUU5g3bTbPlEpWhXLYGk71IlziFhDXkgDNettlg53DRTMlCDE/75/8XGyQwMmRRdNVGKIyUcboJEVGepQFNiCo9yNUKZtO6VOcHePLD/87fbkYW/tc2rqG2No5yIvr2/j0L3fh3r6UkpJSFi5czKJFZ3Pmmecwa9Y8rMN4/O4L1d+H+8yTJrQt57K5u5PRXJZFk/aq84JdOwl27SD7618ipAXxOM4Ziwm/4UaE42BNnYF9+iK8FU8hQmFwHNTgALKyCrt16hE7FPeFzmTwn3saWVxiuvMAojFkYwtB2xaC7W3YU6cjciNEN/0MK92bFxdZiNww4fY/EsRrcRsuPmjblmNs8bTWWP0COWYC34K0wnFsVDVY2yD0gjAhcs4+9YEAndCG4HWMhYMc0chRQAuCKkMEyz7jEZw7R2FvlaiUIXflEOiQwF2qwILstRrha5y1AjJmmzqiCao1YhCsHoEu1FhtUNAmuXiyZtE8xVDYhJkdOPm+HzwIPS3QMVD5uBRdAH6TxtkqDNF9hOii44EQgk996t954xsv4dZbv830q/6ZuzeHCXTeigBj37WsQzO/QnBmrebmOYrHd0u2jULMgQsmKdwAvrHSYjRn1K4VMWMdsWNUUBiCmK0Yf34rWzvHmTazlIZCiVaagvYOCneMUbXwTJ7vs/YzORzMmmDixkJFXYEJYFPaiAqMx7BRJ6dcYew3LGMdEZIm3G3yYV7zK6LgKkFvyvgG2xq8AMAodgXmXhe1wVXGYiPlwu+3C6IW3L5R8Od2E2zcUqx5JN9JVxvXZH1jS+Eqw/64viAbGCJZIwhbMJoT+PsolqWAQJl9+ofgr1uL4T1zFMs6JG0jUBGGK6s1Z9fpPDlrAt3iDtQXQF2BpictGUnaBCnFG+OPM8PfQhCrJtt81VGF2e9ByIK3TFdMLxWs6Rd4CuaUK06v0hSEYOPGtVRX11JWdnCY8t8bjokM/sAHPsBdd91FQ0MD73rXu7jzzjspLz95Ev8aoMMlpGe+l+jWO03qq/YJotXkXuHL4xdPQzsFyGw/i2c38uMHX0R5WWw/iVd5+tEPQNpkpt1EbMOtWCNbQOWw0j0mgK1k5oRdgVc+Hx02D9tQ93KEVgTxvCetFcYvmoI9vBFnYDXWyCZCPSvwC5vRTgLhjeP0PUfUjpGe9QHT/mOFEO4YOpQnxYIc1tgOgkgFdv9K/NJZBIUtqEgZMt2Niu/1vxXKxa2/iFzjFbjVZxJufwSn7wVAk22+Brf+InS4ZJ9zNQOv/BSc3mcIRyooDjSRdD+uFWZHQRmgSMdqsJ04ungGdK3DGViFn2gmSDSh7DgyUo6KlhmyWlhY6Q50pByvei/pbC6owhptwx7ZRKjrSWR2AKGN7USu/iJyky4/ppC9vQedL4xUgD28gfjqr2Ald+OVn4IOFZNAY49swu97nmT9wYXNK6GoqJipU2fw8ssvccMNbz/8glYIr3IhXuXCYz+GA9Df38tnP/tPPPDA3SxevJRf/OJ3TJ489RXXs1qnIRsaCXbuwGqZjLAddHIcPTKEffY1iHAYWVSMcBz8tavR42OGmK+qQcRiiHgB4jCqXFlVTeT6N6Ovu8HYlxxQkOrBAQiF0dJHKIUGROAjbNt4nlkWFJeg2ncja+qMKjgcBinRtgP9vciKSuQ+IXWHgxoewl+zCj02ylyriReYR2+vQ2UlKAWdnVBcDLNnv7aEoOM43HrrnbzxjZfwlrdcye9+t+y4/X3/XiEsi/BVb8BZeCZq105wHKyp048YaAhg7YYHH7mfPw/+D3df/QDReBQVNy2CoeWC3BINr21w9qtG2OLw7Yl5DGRMgb8HXSlDAlsCquLQXGgSkO9rE7QUaWKHUKp0JOH29ZLhnElq1sDWYcHtGyS3zFdUH8LFaEW3QAij8NA5gRQmBCRimYJ6/QCURvV+CpDXAhdffAWf/OTn+dKX/o3TTlvE+edfih2yKKg4tE1B3nr4iMEhADtGd7NpaCuTCuopCJkTUBkrx1MeT3c9zzn1ZxGx/8o/QK8RTtbFJ/F/HTuTW9g+vhGlFZawGcz10pvpZE7JQiKh4uPaZo/VRZbsBBEMRnlcocoZkO30ywzlQcEE0TwoFAVaENKCdumx2smyzXKpVTZbLJchGeAKzZ1RjwItuSlbyEIvTKHqYZudokTVMMMvpuwAaeSe0LT/CSVZ6WSwEJQri5TQ9MgAgaJYW3h5srdSWfRYAUoIWnyH5dE0IAiTnzhHsN3yOduLEkYS1lASWGQ9zSo7R6cMEMKE2V3oxnhHxhx/ST7kzkNjaxjetp2XnnqCLU89RbDsOXYODCPDIaoXLaT1fe+kfvZsKmbNYKilhhrtIIEe6fO4ljwgs2y0cvh59eFjoTRPhTL8S7KcMm0x1w8z1z/4fpwVmgpls8PyiCLYa2hhnlkKzZhQVAEeml3RGP925lvwM93MSSa5eLCPd43sgmubGGx5O891WaxY8STPPvsUX/7yv5HNZqmsrOb1r7+ea6+9kXnzTj0kGav6+8jc+h2C7dsQhUVo1+X5zt04QjA3nkArherpJti0DlFRhTVtlql9R0fwnvgzsr6B0NnnIWybyI1vw5rcivf8CvwN69C5HHp4iMxPf4A9Zx7ha96ILC45aAwTYxkdIejYTTA+hiws3v+X0SjkXHQyCRirQCvZlc/myVvARUrR7jCh7qeM4OaA4y1vKSNUECY5kKIoFwctyPhZQFBvV2G3CcQgoDS6UBt17j4fYZESeKdpcpcpQssF0bskQQ2GBVQCkROoEhM6l7lcE1qlCC8XMCLwp2pyFym8BeazrQshfbPC6gAxAmIMIo9KnBcEchS0BAoEYlxASMMKifO8JvJuNWFTYU4ayD5MJk6lGa/IghwXqIID3iOigK8RY3t0qycWS5acx+LFS3n66Se49d4/Ml53NZMKDOmqNAznoCspWDuoObMWphRDa5EyRKqEtQOCH68VJBzNtBKzzuZh2DJscVqVorYAGEtjjQ0zWlTArnGLuoRGSkGiqoDx3nEWRsbZGSti07CgKKTJ+IYcvaRRUbtHmCD22jeELbiqVTGWk2QDGM7AYGCEA5c0ad56BDuxU6o0T3ZoXuwVpn7Ln1KBqUElxnP4rDrFiz2CtG9qWlcJfKVpGzEapPWDEqVMnTqYFWQ8E3o8v8IQ4pY0CuNAgSfI55NILAluINBo0EblWxw23sdN+wgktIb+jBFjNBbCzbPVRPCeFMZLeM0AjGSNB3HU1vSkBLbUNBZqmgujbB6eRHbKuxkvTRrBnH3swcsRGxbX6oP8jAHWr19zUhWcxzG9mvzwhz9k0qRJtLS0sGzZMpYtW3bI5e67774TMriTODYERa0kT/k0VnKnsSIomASvoOwMCieTm3QZkV1/4JLJ8OXxDM++8CxnLrkIt3bpse2/cDLJU/4Fe2gN4Y5HcfqeR9uxfJDZmPGpbbxyQv0pM/2oA8cnLEBgpToIDazGj9ehnXz7j5MgiNXjDKxGZnrwS2aRqzqLcPcydDYCgYcz9DJosIfXU/DyTrySWWRm/gO5SZcT3XYPcngD2ork7TPqyTVebryVI+Vkp7yZ7OS8n+mhiFZpk57xbsLRKkJ9z9KQS/J8eSvbqueT88dJRsvYGS1gcd82po/24pXMQqoMftk8hPbwyuYRRKsJt/8Re2wbaI2faCbbch1B4eS9+/EzRDffRqjnWZzBlxHuMCpahVtxGiJwibbdibZjuPWHaYPyM4R6V+AMrAat8crn4VWdid7jxaw8optvJ7L7j9gDqwyh3r0cv3g6QVErQcEkrLEdWMndBIXH7qHT0jKFnTu3HfN6x4MHH7yPj3/8/Qgh+eY3f8L119901IE9IhIhcuPbyP7yNtT2NvP0CoWxF55J+MJLzUKFhajkOMH6tVBYaJQHa1cjQiEi770FmTgyOyUOoQpT42MEmzZgz5mPHhtFj40iCgoQtQ3o/l6C7VsnwusArMYWVEEhqq8bkilwHER5Bc7ic17xGIO2LWTv+BlBVzsgmazhInU5T41fxebhOEJAeTlcdZWioeGoTturQiwW5/bbH+Dqq5dy002v55FHXiAWOzqP8JMwEEJg1dZj1R6hm+AAuL05Prn6Y1zWcgWXtewNfVGFIEcFIpn3d/s/jsZCzdOdkpqYaWXsSpriPmKZdjZLGkJ587Bg/ZBgaezgAnFlr6A/I5hesrcdckqxZuOwYFWf4LLm/ddxA9NeWBLWFIbM2/moCyEpCLTxLOtMCWoL9GumCt4Xt9zySZ555kk+/vH38/jjqyk8xETBrjF4okOweUgQc+CMas2SIwTcjbnjZPwscWf/53WBE2fcTZH203+3ZPDJuvgk/i8j7SfZnWzDkSFitpk00loz7PazO9XG7NBpx7XdPWFxHh7OPv3hGZFmil9BSEXYZHkUaUlWKEaFIqYFt0VHAEG75RNXsM7OMSwDEsoirqHT8vltZJxaNcBG517a7E34wqc8qETkruAs99yDlMw+mmedLGXKIik1HqZjPaQhI0xrdJNnan5XGLLW0TAqFbP9MCEEPlCoJVprngpnSApDIu/BFD9ERAtOcyNEkMz1wswNjK5aowl7Ab0rnuSZ+35P+n+eJNPZDZaFXDiXknffSMnSs2DRfEpDMVp8h0ErYEjD9CCMlT+ewkDwYCjFsFCUa4tiZejcIeHzvJPlD+Ekb88efmJ4RhDiESuFrTUqf4psbYKnfDQCiQWkUWywc0S1IFExi/B4AQ8WjrMhUcyHdlmUVJ2DXXMui5sFixcvBUx3xOrVL/DQQw/w29/ew09+8h1aWlq5+uobuPbaG2lpmTIxDvfpZQTbt2FNmzmRq/HSS08zL1pAqLODoL/PhDUXFmMvWjyxjCguIRgZxn9+BaGzTTeKiEQInXM+OptFbdmEaGxGlJSg02m8p58EzyPyng8eLMhIp8g9+Fv8F59Dp1Ootq2oWAxn0ZK9oc4jw1BQgKysNPvy02b29ID3Q23HkO4ohuzcfz9FdUVMOa+VtmXb6B8dwvbAzjlMlY00bajFcgUiqVHFhtyzN0PQrCEJzmaBSIHIK2qzV2pCK8ELaXQByBFDyOlioy6OPCywuyV4+VneIG8tse+QhAmNo8GE0Yk0qCIIKjWhDQKdAVEE3gzA1zibBeEnBdlrTO1i7TAhd/Z2YfJNGjXZyxV+KwQVGmsnBPtw72IcdESgyg8hGz1B+O53b+eUU5pYe/dnqPz4NVjSjFUKQ1J2pQQjuX3GJJiodV7Kk6p7vG4tASVhQco3YcOAKSI1RBxByjOK3Jidf7/Tmsaox/vmKp7uErQNCxoLjRJ4YfXhLTXmlsOHT1Ws6hN0JQ1BfGqVZnbZkSfn6wvg7HrFim6L0Sxk8ypejSFeBcYWojJmjn9yscYNTFhbYQiGcjDimvOR9WFpg2bcNaRvyjPCh9OqTKaG0prOpKAjCRnPdL3ZwuyjfdzsNWRBQcjUj3vEGt0p+G2bsYYINNTENZc2aRZUmuuyph/ubZN0p4yqed0AeEpSGNJIISkOG0I45giKy6oJjq0B9qigtWbDhrVcffUbT/zG/w/imMjgt73tbSeTkf/aIa39icVXghBkW67DL57GvLqXaf7vTdy+Psa89//jXvLwGKBDCWONUHUW9sAqQj3PILN9BIXn4NacTVC41zrAL2ol3PE/KF2/9+4XGI9hZccRQRYd2V9ho50YIjeI8JIQqyEz/V0ERa2Eep4h1PWE8W+qXISOlIGfxRlYjW67xyiJC+pwep/Dyg3hF7bi1izeTym85/wd8XQFWVRBPbnwZcTjtYxUN9KmdpNK7aAqm+KDL9zHou6NRAOTShwUtpCa+xGw9phAZZHZAWRuBBFk8UtnHzSG8O4/Ee58HG3HQAhUvN6ooofW49aegwhyhDofM2T9gcFWQY7YhlsJ9T6DFmafTt/zeAOrSM3+INgxQj0rCHc8igqVoEPFaDsOQRZnZCMqWgZWBKGVsdc4DjQ1TebBB+8/rnWPFplMhs997p+4444fc/nl1/ClL33vuFo9rJZWYh/7Z/yNG9DpJLKqBmvylIniM3h5lSHfZs5GDfSD50FhEVgSWXqcZgK+jxocJNi1w2xPK7RSiL5erJIy8H3AqECteQsI/vh7rNlzsTKtkM2ix8fQ0Sj2nPmosVGCDevQyXFkeYUJvctbY2jPI3ffPQQ9XVhTZyKkRPs+F7T9gfktSXrOfjeWJZgyRVNyeOHECUdZWTm3334/F154Gl/84mf44he/+b+3879T3PPcL+nMdvDbUx/e7+dyzLws6L+RfKsltZp1A9A2CpVRowrJ+DCp0LTXgSmQBSYQ5VDoTQvCeT+1kZxpfUuEDKnblzl4eTeAbCBwpGkDLI0Y0nksB7lAM+YZj7cLGzVlxy5sOGYYS5Yfcv75C/j85z/B1772o/1+v2sMfrRG0ps2BHbSg7s2mzTst8/Uh1SklEaKiTsxxt0kheG9tjij7hjl0TIKnL+RD9Bx4GRdfBL/lzHmDuOqLMWhvfWTEIKoFWc414+vPOzj8ASf4c+mIWhkh72VJr8VB4cRMURapLkxex6T/VKecTJstVzKtIVnuyg0pXkV7Ebhstv2iSKpUBZWnlSNaYGvM/wo/iMK9S7qgkmEdIg+q4e7ordRoBPM8/YPGXLRJKWiStl4AXRYPrYGC0EKRYUSNCjn/2fvvcPsusrr/8/ep9w+vVf1bkmWLPfeC25gY4NtbGzTEkINhC8QQgpJCGBC+CVACM2ADaYZ4wI27k22Zav3MhqNpveZW0/Z+/fHvhpppJEtN0JA63l4kO8995x99p17znvWXu9a+Gi6ZMAyP0qFkozKkGplU3UA6eujcbSgV4Y0qf3z0m+FNIcO78qXTaiRwzDkmWcf57/u+wkr772HwuAQzvRmolddRO1ZJ2KfshwnlaJa2bgIFJpBGdJuCSqL3sPWAUyeB6SFJhRQqvbresu0RbcIecbN8fZ8asKL+WCc7MV4zsnRKX3SQuFjyKrm0GJcaEIUYygGbMOcLfWjlGNBah6VYZ5tZQUeqr2YK/xDC8ZIJMIJJ5zKCSecyuc+90Uee/pRfvKrO/jWf3+N2277J5YsWc4tt/wlV1xxLeGmDcb6rFhnK614ob+Haxtbcc+9AHvpcoJNG/AeegDpTl5kFJGI6dI7ADqfJ3j6CUimkLV1Zjs3ApZFsHEdqr0Na9r+Z0+tNYVf/BTv8d+bYOiaetTICOHWzfg8jb3wGMhmUCPDuGedh2w0SgmVaADpIvzxCZESWiPzgxQazpzS5k4IwfQTWqmaXsHQjmHcX2tqdpVT3VlutE/ShMMFC4zdg46AvUkYslVogiZwNgisHkn2eoUq0ch+Q7jus2MQOZAjAvdFCKdpwgWAp7F3C2I/lmQ+ptBT6MLcxwWyXxAs0VjtoB3QKbC6BWGLRlWCKtU4awT5yzTWXoj/j8TqExOhcPYWQbxfkv5LReEMTbxdYLWDqiiqhfsE3kma8NXrio4YtbX1HLviVF56/kkKm++lcOwlRIo/2UCZ73v6YQjFkQIT2+5DxNZIjFcwaEjF0eUpVNcwTm05TvFrTveniVckKKkrMf7AVZoT6gwpeiRBwQ0JCKo0M0qhNg5VR2jV15oyAXJo6MwIQmXIYI1RBmcDeL5H4FgwsxTW9BvlrRAmxG4gC0obFa5SpmNuy7AhhLsysHFQMLPM2Fas6jUKakcagjkiTZebKzWBMh12F0zTrCgS3xkfvr9RsmNEUJ8w3sK7x+CrL0lW1ChSkWInHea4XWmBF0Im0CQcQyz3ZAV9ObhunqY19bJT8aqhtbHw2Lh5E11dHRx//CvbqP454FWRwd///vffpGEcxf8qhCSoXExQuZi3vbOdb3zjNv7RZ8oW2iPfpyCoXkZQfXhfUK/hdNyBl4zPcawWoQJk0UrCqz+NSPfjyPwAKrlfASfzA6hIGSpW9Me0o3jN5xOWzsIab0dFK/dbRthRwkQjztA6ZGGAoOpYgqpj9w8g9HD6nn9l39qwgDXejtP3PG73U0hvGLQm6pbwzpHzOb1hBdnNzzKt61nKs8NopwTluCZgINeHM7QOv3o5qIDYlu8S6X4CLV2QNrGdP8ceXEd28UdQsRoIPdyeJ/b7AqsQbUXRwkF6w+b83VKswjAiyO4/1yKc/hdxe58lTE7bT+YHOZy+Vbh9z+M1nGmsMIQkTDZhje1AeiMotwKR78fK9qGFJEi1GGX5a0BTUytdXR0opd4Uv8QtWzbwgQ9cT3v7Lr74xf/k+utvBQEFNC68an87EU/gLJ/aEiVs3w22jbNwsQmUKxQQsRiqu8uQua8Fto3q60H39aDjcRgehkIBvWc3Khan8NADWAuOQTgO7pnnonZsJ9i5FeG46PE0OvBw5p1G2NGOd/89hJ0d5jwsC2vuAqI33oosr0B1tBPu2Y3V1DqhdBC2jaxrpKrnJVpn9EwUzn9ozJgxm89+9l/47Gc/woUXXsZpp5192G11LkeweSN6dARZWWnUJC/jBX0Uk6GU4hs//yqXzLuc+ePzCIc0OmH85EQOvFP++C0ijhSzy+HGBSE/3SrZVgyJizuwuHJ/4EUuAEtq0/43BWrjmpGCZGU3DBeMaiRiaRwLKqOHKnvjjvE52zQISUczWjBEtCs1uRAaE5rWUjinZfJnx/vSDLUPoZWmtLGUssbSN4xUbGpq4e/+7t/4xCc+wKWXvo0zz9xv+fNEp0mjPlD5PO7Bi72SUxpC5k4RcNeSamJx1QKe6X6BalVFwo4zVBimEHqc3nQSrvV6ioX/2zhaFx/F/2WYa44hWg+snRQKS1ivyS8YjDL4ndmb+Uns+7TbOwlRJHSSswsXclrhHFwcpoXmuvGIm2GX7TE3jCCLx1sQODzs+oQo6rDRaNJCE9GCqNhKh7WTZfmFOJjQrpZwOtvtLTztPnYIGRxF0BDabLU8jgkilGmLLhmgi23Slcpib9F3eFbockUhRRRJfWizxfImkcGe0DQoGxvYLj1SSMaFIqIF53kJ7FDx7PNPc889P+O++37FwEAfidZmZt1wDcdecQU1S49hQCpWOjmqlIUbSrqsgLgWWBqyQlEqFMu9OM87OXqlz14ZMiYUEQ15lLH2Ofh7BDRFxe8UDSid0ueX0XHGhDI+n0BBaOqURYWycBHMDFxalEO3DFhnFwwRvG//VoSsrfmhk2VNEDIrcDjVj098h/ug0bwY9Xj2oqXELzmGi3OfQzzwJF2338WHPnQzX/7yP3LrnIVckSxjHz/ZMz7GcC7L0rJKZEMz9qIlICX+Yw+jM2lEYr9iXY8OYy+bXK/r9DhqfAxxUKeeSKbQnR3o0ZFJr6vuLvzVq5B1jRMewfbSZcbbd3gQNTqCVVJC5Mxzcc+5YOK+HJTNx6tZgdv9pBHSWBFkvh8VrcJrOufQST8AqZoUqZoUYh4kvyKx2o0/r0pBMFOj6oAQwjrQMQ2WJphf7NjSJlwu+ltJ7oSAr3zhH/jJ/XfQm++hPtrADTU38qmqz6DrQO1b04lAMENjtxnC1l920B+FNspjVbbPK6r4smvUvHJMoKMakQGZg8TXJO4LAms3BIuKgXMOBEmNvVngrhLkLzcWCZFHBHJAoCOawoWK/CWaw6xPvGH4wHs/xHuef5LBn76H5MzNiGgZYBbkW0s1Z7dM8aMAZpZqNg9KlN4f/hazjQBg3DP1YtSSjM6diR5YT/PIIFnbxi8ERBIuM06fyf17HZ7slIwUjOfvzDLNNXONV/Dh0J+Fn26TbB0S5EMoceGkBsVlM4za9uXQnNKGYykS0OM+pD0YzAlCNHvGoTFhSFzXAkuYMEDX2h8HFSiICXAsmFGmidqC7cMw5sGSGs05zYqfbJW40ngfx2xNbdyoosc8wakNiq3D5t9PdZrr6PG1mg0Dgl2jJsDZkUatPJoXbBuB7rQkahvl8NIqsIWmM20EFLZnts0Hpo62JCyqPLyy+rVgKA937xRsHJCs+dndOLEU9uzzD45S+rPEH3GG+FH8b+Dqq6/ny1/+B+6//1dcddX1b+qxwrK5ZBZ+gEj7vdjju9HCptByIfnpV6CjVRSaziO2407EWBvKLUX6o4ggT272Ww4hQUWYRygPbR0kvbIi4I0igoMkXUGe+Jb/we1+er8Cds/95FsuIj/r2glC2Ol7geiuX2CPbMMe3oiyk/i1J6JiNch8P5H2e6hPteKKUuJje9F2AhHmEL6HSjQRRitxux7Dr16OPbyRSM8zhImmifGHKsQZ3ojb9Tj5mVcjlIcI8mgrNtGOJJRnVL5aIZQ/ETinp7AAsUe2AnqyqtuOgbCwhzbiNZxpSGTpmEWA8gW4/S8i8/2IIIOVbieoOIbC9LdOmYx7JKitrSMIAoaHB6msfGVP2yOF1pof/vDbfP7zf820aTN54IFnmTN3AS84eZ52cvTJgEplcaof53g/OvFQ8bL7VAo8DyKRqYmYaBShTREhEsn9RWkQIF+jvUGwehVqaBDtFQwRrEJzJ5IWBAH5u+5AVlQSu/n9yKpqou//K/yXXsD77X2onh6E4+CteRHv3rshkcQ+8WSk7Rh1xPo1ePf/huh1N6KDwOzbnlxZCNtCqxAdBoeMbVwoNtsmYKRe2cwMnSOax9eCm256P7/97a/52Mfey8MPvzhlO3vYtZfC7d8l3L0TrTRCCkN433DzEfklHwX8/vf3s2PHVm67478p7Fa46yRixCg+cldoCme+vHWB1tAxDjtGBBpT6Lam/jiLp0AZVcNIwagiqmImffnFPsH8CpMePeIJltVo5h/GsmFBpWYgZ/yHa+KmXW4gZ5QeU52yFHB2i6J9zMK1NIGS7E0Xk5ujxqv4kummRQ/Mdaz9+T3seHwn+fECCHCiNi3Lm5l7zhyk/cY8Nb3znTdzzz0/5+Mffz+PPrqakpJStIatQ4KyyOQiO+VCZ9oE3M2dYl6EEFwz90riToLVfWvpzw1QGinhkunnc2rDCW/IeI/iKI7iD49yt4qYnSDtj5JyyhBCEOqQQpijNTkH6+Dus1eBWeFcPp7+HNvtzeREjgbVSFPYegjB3C/DiUC3EWHIznJlUaNsumVArwywEES0YG7o0iFHiGoxQQTvQ1Kl6Jadh4xDIjjDi7M75tNu+VQqi5h2GBKSFbkUS8MIY0JTriwWBu6EsvY0L87OmE+b5VOlLPJC0S9CzizEOMGP8ZyTp8cKmBlEWdCVZuUPv8YHf/g/dHbuoaGhmbe97Tqq33YJL500iwVq/7NJjZaUaMmwVJzrxYhpwR7LZ9AKyRaVx2VKEgh4wskR0cabuF8qVLGzZVwoSrSFRjMqzTPMsX6UuD70LpVFcXtsjDbLp1k5zAhdeqXPiFSc4sWZG7jMCV3qlPmuH3YzbLQLEwsEGs1my2O77VOuLDJC8bibY5PtcUuulJnh/u9ho+1xZ3SMUEBNaOFFE3S/7VxWXH4xn3qhg//++pf4u9/8nK+7EW459gTeseIUusdHAWisqceaZzwRrDnzsZcuw3/+WUSqFOE6qKEBZE09zqlnTDo/kSpBlpSiRkdM514RemwUEklE+eQVTj00YLrp6g/IkEFgz56LGhok9pcfxWpuRRycqiwtcvNuQSWacXqeQoR5vNqTjSCpbO4h8z4VdAV4p2usvZpwRpFU3XfLt4yXr8wL/HkHLNILCOvB6hB8NftvfLvjW3y3/HssjC/kxXAVt+65hRJdwvvn/dXkgzkYX+H0ofd02WWyJKx2gS4zoXY4IDIYVfFGsNdI5JhGu2ANalAgQrC3CkQW/KWGSNYxjew2SmbvFI23QpvwurjxKf5D4MwzzsG2HVRuhLG7P8j0m27HV4LmCNy86PBdWSc1aFb3abYOC6qimlAbD92zWzQRC9rHBF6oSVbVsOSty1mW6aQwkCZeEadhUT3rrQru3WxqqlmlmsJwlu07An6Uj/ORE61DVMdgFLh3bJGsHRA0J012xVAeftsmSDnGN/jlUBmDueXGXmHMM9YWGV/gWuY8yyKwpFrzeKdg/aAJVN45KpDCdIJVRKEva2rbEsdcT+oTmnFPcME0zQeXKgZyUAhN4HHqgMC4iAUv9kJPRlCfwATtjcHuTUahPFowvupO0aqjMy3oyhi7jrgN1XFDhLeNCaoTZvHAtcyYUw4cU23I8D3jgjfSWCRQ8KPNkjX9grq4pueFn1O/7FJ+0RYlGTOWHn/OOEoGH8UkNDdP46STTueuu370ppPBgFEkVyxCFIZAupNI3kLLxWg7htv5MFZ+CBWrpdB0Dl6DURGK/CBWphNtxwhj9ahoJVaulzC53/xU5npR8TrC+GQFpNv7LG73U4TJFnSxxVXkB4nu/g0iyCBCH+GN4vSvQlsxtJBoJEL5OAOr8epPRcVqsPODOP2rCCqXEJTMAitirCSiVYTJFoQ3ikx3mAC9sTZQ3mQiW1ootwR7cB3MvBptJ1CxOpzeZ0zoXLQSK9uDkq5JrvXHQdjGL3iqIl0cbklRo4vb+xXHYA+uBRWg4nV4dScjR3dgZfZSaL6A3Ox3Epa+9mCvmpp6AHp7u98wMnh4eIhPfOL93H//3dx44/v43Of+jVgsxuNOhp9F0yA0Jcqi3Q5os8bICsXZ3uHJWq0Uwcqn8Z96vFhg1uKcdhb2cSdMIoXthcfgP/YQqqcbUVTR6sEBhG1hL16K1ppww1r8F55DDfRhtUzDOfGUSS1pB8Nf+QzkslBaAdks2MXvUUpIJEAICg/cQ/Tt1yGSKWRZOVZdA+Sz2DNnI2rrUP19+Js3GkuJsTGoqEREo8iaWoJ1L6EuuxKrqRlZXYvq7sZqnWbGrjWqpxtr+gxkzeTfxGarwE9j43TLAI0JXlnmRbg2X3LYtsPXAyklt93235x99jI+//lPcNtt/z3pfa0UhZ/dSbBzG9aM2QjXNYT3hrUU7vkF0Xe/72h79hHgm9/8KscddxLHnXkSOa0p9IeItGkx1K/QgqU13N8meGiPZNwzryUdOLtZcenM/UqKPxS0hp6sKTCjlmZWGZNC2V7sFTy2V1Ad00wvNdvvGIG9aZPYXBmDM5sVZzUfXn0x7hn7BNcSZItWEvVJEFrzs23GZ7c+aYrHmWXm/cVVcMuikEc7JOsHFEN5QdLRHFsDpzZqltXsLzRHu8bY9ugOhBBUTjcPqfmxAm3P7qa0sZSGRfVvyFwJIfjKV77J2Wcv41/+5bP8y798faJNcLQwedtQA7x8wF3STfCOeVdy4bSzyfgZKmPlxOwj7Gs8iqM4ij9KuFaU2SUL2Tq6juFCvwm8BSoiNbQmX3/Aa4wYi4PDdwgClCtJpwzYbfnFIDdBVENSCxYFEQpCU6ktakOLrNDEdTmOsvHxJhHCaTnObH/elMdYEkS4IVfKo26GHiskogWXeknOKySIH6a+WRpEuD5fwsNulj4Z4CI430twYSFBibZY5kdZtWolP/jBN/mXe3+BlJLLL7+G6667heXLTS15XyTNapE+ZN9V2qYgfPpkQJOy6ZI+AZpyLSnXFg9FsgyIEFeZcDdfQJW2aA5tOq2AQRkyrBUIjUSy1I9wSSF5CNEOsMn2aLd8ZoYOTvH9FuXi4eEJzen+ZGHJ/KJ6eq8MaFQ24yJkl+XhasHCwKVe2dSh2WJ5POpmmZFzJo77jJMjLzSziwRxXEM8lOy0fc5ZMo9vfvPH/PWHPsXX/+aDfPm5J/jGi89yVk2DGdPV75jIQhC2TfT6dyObWwmefxZdyOOccibumedgtUybNF4RiWCfdibeXXegujsR5RXoTAbV24Nz8qnI5tbJ25eWIeJxdHoccSB5PD6OSKWwmpoPJYL3beMkyM+4kvy0S0H5YEVf9cp4OE1DEgjYTwQHJoDNP0lhrREIfYDAe58HgIBVL67kLQ2Xcf7Ci1Gl8Ba3lbMf+gkvjryA1SZQB9QaIgO4THoNDBGc+LpEDAlEARgFa0SArbH6TLAcQoBdDJUTIIcEYQ3oiAmJs3ogGDF+xSIv0K4i8rDxOA5rwT9GwxTWFAdCDIK9V6BtTTATeB02WolEkhUrTmIknWPzc3dQ1rqMWRd/lBPrNcdWH57oq0/Ae45R/H6PYOuwICLhkumKc1o0KRd2jULaF9TEFI3JFELsv74oDU+/IIhYUBNmkU9txe0eJO6HjLkxnsm1cOY5zYc8p+waNbkV01L7Q4yrY1AI4ZkuwZnNekoSecyD1X3G0suRmtqEIGYZqwvX0tTGIK8EpVFNRQzmlGvaxwSlrulu680a8UJS7VfubhkWOJbGV4KmpOYtM5SpE12j0B33jFhgH/pzMFoQTC/RNBefHyKWCd37yVbBJdM1aE2ojICiJ2P+X2uT25FwNBFbUAhhOC8oi0DnuCBA05SE0ogJl7OEEWO8Udg2DJsHzbiz3ZsZ7tjIaTf8A0rBU52CFbVvrAr5/xqOksFHAYDM9eH0rsQab+edp8/mQ//2XdradjB9+usvBl8RQh7iDWwGZeE1nYvXcAbCzxi1q3RAhUR2/YrI3oeQhWGwXPzSOfjVx+HufQhrZBvaLUF6I2jpUmh9i1EIHwBnYDUIyxDBOjTKYekU02L3EKZascbakLle/KrjMHdDGxWtQub7kdluwtLZaMtFeGPGniJaUSSE9185rUwHfulMrOEtOP0vIbM9yEQjKnKA55YK0MWHaqf3GazRrdhjO7FHtqCdFBqJLAbwhbF6vObz8Romr4wT5BFhDr98Pu7ehxCFIXTEEA3CGzUtJZVLAPAaTsUZWIUzvAnlloIOwI6Rm3sT2YUfeM2K4H2IFYsoz/Ne1372YcOGNdx889Wk02N85zt3cdFFVwCQQ/FIJIsLNBULz6rQolMGPOJmOd6PkdRTF/mFe+/Gu+cXEI0hUyWEO3cQ7tpJJJ+bCKYAsGbNwb3kCrzf3oveusm0kySTOOdfjLX4WPwnHqHwi5+gPQ8Ri6O2byVYvYroTe/Fnr/wkOPqIEB1dkA0Br5nCOBI1NwpvQLCtiGeQA8No4YGsZLmbhusW43O57FaDckstEZEougwQPX17Pcvdlx0Jg2eh6iswr3oUvI/+SHB1s2IeBwyaURZOe5Fl5pjFZEWip/GxukTATNDBxvBuFA84+ZpUDYXeG+OJ2hTUyt///df4eMffy9XXXUdJ5+8/+9adbQT7tyO1dSCcM33K6JRZH2jSY0eHEAcVQe/LNasWcXKlU/yne/cZV4QxfTnmiP7/OYheGC3JGFrGouXrME8/K5dMr0sZPGrt+l+zQgU/Hqn4KlOybivsYSgOQXXzlXMKjPbrO43j6Rlxcu9EDCrzKQiXzhNc/H0/XYRh8OoByURwbJaPUGAZ3zBql4TeJF0FJuGJC/2wg0LFEuKDxqLqmBhpUmpduXhPeMGdg3ipQtUzqiceEiIlUbJjWTp3dz7hpHBYH5fH/vYZ/nCFz7Ne97zIWbMmM3xdZo7tgpGC6bwDrXxEa6Jc0QBd+XRUsqjhw8pOoqjOIr/W6iNNZGwSxgs9OIrj4SdoipahyNfXy14pJBaMCYVCqhUphepT4Y4QvCJTBl7ZcA22yMtFdXK5srsiayMrGS7vYXGsBmn6Bns6ggnF86Y8hgCwbIgypIgwphQRLV4xUVugWCFH2OpH2VYhsS0JKUl2WyWH//qJ3z/+99g48a11E6bzhl/+2mWXfcOVqTqWORHJsjR1tDB0YK0UBP1qI9Gas0lhSS9MuRFu0C/VNSHDvPCCA3KplcGbLAKLPciRgEsoEwZhqhEe8wKHTpkgBZwnB/lkkJyQtl7MEZkQAF9CE2cRNIrJ3eIGU9kuKiQ4HeRDFstjyEZkpWaBUUieN/cVCubnbZHHk2suPdOyyd1UN3tYrqKhooK5lkLjuHff/UwH3vyEf6/b3+dHz/2O6SQdNbVc2BJIeIJIhddinvBJRCGCOfwdkTuGeeA7+M/+RiqpxuiUdyzz8e97MpDyDjZ1IK9cDH+yqehoQmRTKJHhlGjw0TOvApxJF1/0p5akHMECGZB4WRF5HEB/carV2QE/ixN/lKNHNTYu4zvrtUukMOG2PWWaY5vOokfbvgfNp++jdkVc1jfv5ZnB5/mi81fRo6D8wLoJGgLhCfwT9AE+7L7NDirBYn/EjhrJapco0pABICvEQMY/xBLoG3AAgrF1/LGA1gnNTKDIa+HgWGjFo6sEvC0MOS20ATzBJmbFHqqaBUFkYcEkd+bc8OSBE2a/NsVwZEJrKfEnDkLePjJJ5n7lk+w9WefpGraIlZyHn1Zya3HKGoPQ063lBj1cC4watkDhQJzyosTNwW80CysJ2SIfGY9orOfbEU5A+WljI95rH1iD3NqHBoXT67pxjyjNj44sDfpGOI56x/qYzyQg//ZINkxbAjdQmjsJZyiLUV+UOAp89/NKTPehoSxvDivVZHx4aU+QceYWVxyLIEtoCWlaUpp6hOa5bV6wrc4ZhvV9C+3C5wslEWNH/CuUUHM0bSU7BdbtI0J0p75NxiBxo5RQcrRdKcFg3mIOTCrTFETM8rktlFjqWYJk8WhtDmvMV+QcODEel2c+zcGwwVjqxGzYfWTd+HEkrQeez6jynQP5kMOG6D854A/41M/in2wxtqIb/xPrPF2tIzwzplZ/qk0yn9+8WN8+Zv3/G8PD6SDjpRN/Kfb/QSxXT9DOSUEpbMRYR5ncC1BmCc772bc3mewsr34VcfiNZxl/HoPhgrQQmCN7cIe22nIYC+N9IbwSmYSlM1D5gfR3ij2+E5D8gqBuSsKkyqrQ0SQISybh1+5mKB0NvbIFqM2li5WrgeNwB5YQ/nW28HPIIM0VqYLv+YEgvK5CG8MoQr4NSdgje0kvvUHIB0KjWdjje0yvsPSJTP/fRSmXYpKNk+2gAhyRPc8gNP9BDLIEcbqCFPTscd2Qca0y2krQqHx7Il50NEqssd8GLfrceyB1YZMrzmBQsPpr5sIBuNRCrwhfsH33fdLPvShm5k1ay6//OXDNDXt9zHukyFDMqQunHwZq1IWey2juEiGk89HDQ6Qv/tnFH70fXTgGxVCqgRrxizCjnb8hx/EOe7ECVWAEAL33AuxFx5DuG0LaLBmzES2TEOPj+H97j6wHayW6ejREbTtEHZ1UfjtvVhz50949U4gDCEWR9bUovbsRmsNQVF+KAQkkpDPIeoaJvmf6XQaYe8vhEVJCSIaRQ0PooeH0OlxdDyB7u/Dmj0XUWbuovYJJxMrLSN4YaUhjRubcU44GWvG5EWerZZHt9xPBAOktCSlBc87ec71EpOCTN5IXHvtjfzgB9/kK1/5x0lksC4UTMDeQf7Awo2gx0bR+SnSvI5iEu6664c0NDRz/vmXvqbPbxwU5AMmUoLBWC8M5mHDgGBx1ZvUWqVBDoIWpq0SAU93CX63W1AR1ZS5Juji2S7BnjHJP5ysqEsYf7eDyd59z4GaQ9+bCpVRjSU0XmhI5VDDugHIBSaEpLXE7G3XKNy3S1AT03SkzaP/rDJN+SuoW0IvMOq7gx9QbQs/f6h1y+vFjTe+n29/+z/40pf+nm9840ec0qjpSMMLPYbcRhhFydvnGluLoziKo/jzQ9IpIen8gXq6D0K77dMaOuSEYkwqNFCjLFwkjpa8P1dGjwwpCE1taBFDMjd8P7+I3cEOewu+8KkJ6zivcAnHBMe+7LEsBOX6cB10U8NBUKNsBgcH+OdvfZUf/vDbjI2NctY5F7H4859i7IKTcKVkK7CJUTbZUW7IlRJBMC9wOcGP8KybxyoeP4tifhDhXblSJPB3yQGqlWRuGMEt1llJJUHDuNTMDOwJPiotFAkk78iXHuLXOxU2WwUecXNstQv0FFXIM0KXSHHBf2ZgLvoazQtOnkeKKugIgrm+S7NyaLd8HnOzzAvcyYF2QpPQYqJmBKgNbTbYk1tP/OLgSw8giYVt03rW+XzprPNJf+B6Hnjg11xyySnccssH+cQn/o5kcn/bkpDSCCdeBsK2iVz4FtxTz0ANDhjriMOEPAshiLz9OpCSYMM6VH8fMpXCveAS3PMuesU5fd2wIPd2TTjDkLMiB/58hXeCCZPLX6FIfE0SeaQoCXc0OgJWP3yy+W/IN4xz3PcXYkmLUIV87tR/5NrkO2EvWH0C0QY4UDhWk7tSsc/62VkniP9AYu8AXcw/EAVQlRA2aZyXBIwWG0yjgACRE4hxTRgDCuAv1tgbBVYPyGFBMEObIklAOL/oP+yBvUEQfUiQu/bQGtFZLYj+WqBjEMwBAo3dDrEfStIfV+gjJQE9EFnQCXO+DdPm0fWj7/DWv3uefOdaVn/jnVzzpZXs0DN5eI/gnfNevl59tWRgxIK6BOzePAS9w3Q2tLAxUUdauoRxTZ/Kk30hxycWahxr/2+kKqaJO5JxT1NywOPNSAEaEnqSEncfHt4j2DYkmF2uiwF2Rj3cmRYUAkGojTXDwio9EZY8mDfq4Mtnalb3CZ7uhNnlmvLiMfty0JcTXDNPMbts8vEKoRnL/ArYNSoZ9QzJe1ydZvuQ8QXuygj2jhsSO2mben2f7UPE0jzbLRnzjBhDCk3bqOmYm1mqyQfGtmLPuKAiavbtK0FnWrOoUnP1bDUR1Hc4jBRg+7Ag1NBaoql/mTWcEtfU9ePpDGvv/wbzz3gnTiTG+KhZDJhKif3nhKNk8J87tCay+x6s8T0E5QtBSBzgY2/p5tN3/I4P7VhHy6zF/9uj3A+tcLseQ0vHJLsCWiYNETu2k8L0K8gs+4zxSpWH/3X7VUuJ7LkfmR8Ey0VbMaTfiQgKSN8k1SqnBImE0EMLCxWrQ2Y6EaqACPLYQxsJS2fh1Z8CdpzsgvcR234H9sgWhPIJYzVYmU6cnmfRbgk6Wo3Og/RGcHqeRHiDxhu54Uy8+lOJtt2NKAwTlC8CIVDxBnwdYA9vRiUbCcsPan/Tmti2HxHZ+xDKLUPZcazxXYAk33I+yCigCcrmmGC8A1axVaya/MyrYOZVb/Q39KrJYB2GhFs3E7btBCmx58xDTJ/Jv//7P/PlL/8Dl112Nbfd9m3i8cnLulEErhYUhCZ2wD0+LxSuFkQPUifoXI78D76N//yzaC+PKClD9fagx0awjzsRWVmN6usxdg8HtJUJIbAamiba1/Yh7GhHDQ4g65sIXnwe1d83Qez6o8Ooq66dtB8otrPNX4jq7cKqPM4kIGez4LgQixi1sFK4Z507EWwBYE2fQbDyKXQQGEWvG0HbNgwNEeTzqJ4eiMWw5i/APf+iiZRmIQT2/IVTqpQPRF4oNJqDfzERBFmhCOCQ994oCCH46Ec/w7vf/TaeffYJTjrpdABkfQOiohLV14fVuH/u1UAfsroGWV37Jo3oTwNKKR544G4uu+xqLOu1fXu5wBRxB8MSmjeBtzT73gnR+2UxTRuCOZrcxYpne8WEYuO5XqNE0EBXr+CfnpP8v+MVM0s1a/okgZocGGeL/S1tr4S55TC3Atb3C2rjmkJo/NGSjqa1ZH8rWV28qLQYl/jKkM4VUcFlMzWnNEz90DGYh6F4CXkl8AsBTsRck1WoCPI+FdPeQClEEbFYjI997LN84hMf4C//8q9ZtGgpN8zXnNIQsjctiFowt/yVSeyjOIqjOIo3A2mhKNGKUt2FLwYBSZOqJEcdeaEQiAlF6j7Uq0b+MvPXdMtOPFGgNmwgxptjWzM0NMi3vvVVvvvd/0Jrzbve9T4uffetrJxTye8iGWaEFg2hjSwSvaucPEv9KMcFUWwE1+ZLmRW6POJm6bB8KpTFzNAhBMq0RWvoENhMEMEAMcyC/KBQ7JYeNoKUlnTLgMbQpkN6rLHz9IuQHiugXFus8KMc50cnCNvtlsc34sPssHyyQjFkh3TrgD4R0KyNYrkxtNlsFRgUIXfFxtGYQL28UDzj5jjBh2vyKTosn13SxxWCIaEI0fhobsiXTFhPAJzkx9jsFNgrfaqVjS80HTJgRuiw0D98Uu3y5Sdw3nmX8KUv/T333vtLvvCFf+fCCy874u9IK0W4cztqbwfCdbFeoeaVpWVEb34/qrsTPT6OrKpGVv4BW50c8E7SeCcdWisEcyFshbATVLlGJ0zAnMjB3at+xk867+A75/+I+bULWNe/lk899jGaUw3c0HQj+TM0QgM5sLoF7jOC/GUmwM19VCAKGlUtsPZgiFsFcgD8eUAIIhRoxwTJIYAkiFGQYxDOAFUCqhaCeZrsTQqREST+RxBMZ3+ogguqWuOsEeQu1xz8s3SfEwhlfJD3bR/MAHs7OBsE3mmvIDLwIfKYOTc5JgjLNd4ZmmjtfFTok8i0c8knfswdHz+Je//lrZzz90+xvj+JX66J95hgu2CGOe7rgRBwRpOm+6UCA6HDukQ9BSwShSwpF2Ku5vl8ipV74bQDHgVbUsbT9+lOQVWoidswVDCc+hnNh3aveSGs6TdCiAMJ0lllEGrN22Zpdo6aelRj/IeH8hARmtMrPQgt1g5YhNrsYx9q47BpyNgnzC7b/3rbKNy5RdI+LgiVsVRbWKm4bKamIgLve9hic7/AV0bh7CkYF4KlNZrZZbBlyPgXzyhT1MaMmngobwL5nu81+R3Xz1fkAnhir5gIisuHGlVUG9+1TVAZM3X20ppDhQov9Ah+tUPQnzMK5ZKI5OxmxcXTp7avm1tu5uu+H/83+cwIS6/8JF0ZM+enNv7hLe/+2HCUDP4zh/BGcIY3o+J1E6FpADddeia3/WYD/3Hb3/Pl//rF/+IID4LyjWLXOejJ3oqACo0lArwsEQxMqGRFkEULC6HGTRqvm0J6Y8b6IdmEHm9H5vsQyiMom4MTZNDKI0w04tefTKHpPFTM9FyrZBOZpZ9AZjoQYQHhjVP6xF+gncTENqGbRGc6ETokqFxCbvZ1BOULQNqIwrCxwdjHNggBwkFbUWR+6JBzsNLtuL0rCRMNE5YQYbQCa3Q79mgb6eWffcV5mAStkbk+UAVUrG5CJRyi6ZMhEqhW1isGir0aMlj7PoW7foz/zJNQyKOFIB2J8tm9u/j180/xyU9+ng9/+P9N6Q1boyzmBxFWOjmioSCKJI+iSwYs92PUq8nnHmxcR7h1M3L6LNTgoCFVKyvRgwOEHXuwmpqNCjUSJezai06nDel4UPjEBCwbhCTcvN54CpeUgluKGh5G93TjPfwgsZvec8jH3LPORe3aTti5F+uEkwnXvgSZtPn9xRJEzj6P2K0fmPQZe9kK/OdXEm7dhKysJty7B93ViaitR1RVQSZjfKmra7EWHPOK834wGpRNTEvGhKK0qJrRaAZFyPFB7PXWTIdABwHhts2o3l5EIsG5J53OwoVL+OpXv7CfDE6V4JxzPt4vfkK4czsilUKPjkLExT3vIkTk8A8WbypCwGNCOfHHipdeep7e3m4uvviK17yPGaXwRKcpRvcRsX5oUn9nlL0hw5wE2Q2J70pkryCsM6Z5zguCQqdkz2LBLqsYUKGN15u5LAjaRuGfnzPJx11p6BgXTC81RXY2EBxXq1lYeWQqZteCdy1Q/GanYMOAUUTEbc3cCj2pzXAgb7yIK2OmnU1jjv3zbZL6RMiMA5wUfAX37hI80yUZz9RSE62lamMP02ocYq4gP1agvKWcxmMa3sjpnMA119zIN77xFb74xb/jhz/8NbJonzGr7Mjm5CiO4iiO4s1Ccxhyd3QnodiKo21As87pJKpy1Kglh/2cQNCgmg77/uvF0NAg//3f/853vvOfaK25+ea/4H3v+yhttUl+GhvjRXucYakYl4q+MOSYIEIciQa22x7HFVW3kWKhMCxDNIKMVNwXybLe9rg5V8pxfpTNtseoCCnVFgrNXhlSqSwGZcizTh5tBKJUKBMa9434KO2W0dzOCVwSOmCDnWeHFeMthRSl2uJBN8Nax0OjqFQ2o1KRFiHbHZ9Sz6IUi/uj4yigU4bEEJzgRxEISrQkLiTrnAJneXEuKST5h8QA3VZohAHaENkjQhOiJwjopUGEq3MlPBzJsNfysbVgUeDytnzqsN7MSoU4jsv73/9RLrnkSj796Q9z881XceON7+Pzn/8ykVeo9XShQOGnP8J/YSU6nwc0sqqayFXvxFm+4rCf2yf0+GODyJjOqGCRRlUYEtjaI5C98Jktf8NfT/8k1+prUb7mmMpj6KrZw791f5FrLnkXRIoi8ijQo4l/X+I+a+wg7G0mK8JqBzFcVABLEIHAfano8ZvVaNeolYkYUlhIYfyN8wp7OwSzNbnrFOE0cFYBGoQPZDGBeA5GPRIYC4mDqww5VNzuQFgAB4TdKbD2GuVvWGe8ifcher8geq9AJzD+xb0Q/7Fg+mlGMDW0dwuVTXO4/DO/5I5PnMwTX7mK86/5Nal7okSHNdiSoFWTu1YRTn9939Wx1ZqRBS6/3FVKWltUqjypiCEvg3SefCrKi4OS01oP8HIWxt4s5QpW9QiGCya87sxmzUn1h6nJ9NTW1AKoiME5rZrHOuDZbkGmoJjXv5fm7g5GXsrxTGmM7sZF2JFD7b2kgMIBwo6sDz/cLOkYF0wr0bgShvKadQOCY6pgtzACkeaUYNcI+z3KhaY8YkjVgoKRgukcTLpGCd2ZNnX6YB7q4pr5lZr7dkkqY3pC9B+zjWXDtmHBUF7QmNSEWvBUl+bmhYqmIu3TlTZkcS6AWaXmHPpzmvvaBA1JJmV07INrwdUzMnzrwS/TeuoNjMSmU25pLmh9mTn/M8JRMvjPHgf00B6AWMThYxfP4dN33c9f/c0uWlsPH4j1B4V0CRNNOEPrJwhWAOFnQDqoqbyHp4BQAWGqFRWrRvhpEBYqVoPMdEGQQwRZVLyOoGQGjjZXShFkybdeQn7GVYQlM6cmWoVAJY2Vgbv39xDmD/Er1m4pwhsmKF9IULlfda1SLdDpgw73B8FphVAFwlQLB0NmexD+ODo5+T0VqcTKdpn3DrDXeDnIbDfRHXfhDG0AFRAmGihMu5QNDcu4P5Jmj+UjgBmhy1sKyZdtTwtD4wt2JGrEYPUq/CcfRdTWI0tKGc5l+cDPb2fdQC//9fkvc8V7P3TYzwoEV+ZTZIRiq+UTigALWBBEeOsUQRqqrxeURlZVISurJoImhBtBD/ShLIl97HEUfnUX4ZZN6HwOWVKKc8oZxmP3IL8ya/pMZFkZ3upViOpqcBz00BC6vxfhuhTu+QX2cSfgLJqsrLdaphF934fwn32KcPtW3ONPRqRSiNo67DnzsVqnHdpCXl5B7Jb34z32e/yXXkAP9iOnTcdZtgIRMwyVHh9D9feh2tuwps98xbk/ENNChxV+lCfcHKNaEcWoPyq0xZlefMpQktcKNT5G/kffI1y/Bu0HIMBqbObD73g37/3sR3juuac54YRTAHDPPBeZKsF/+glUfy/WMUtwTzkda8nLB9K8KfAg8oRRI4gMhE1QOEMR/BE1ThyI+++/m+rqWo477qTXvI9jazQv9ArWDxj/LzABawsrNcunKLheL9xVAtktCPa1GwJjcU3PKkG1DU/NgLwvsC1w81BaLD7zATzfIzmhTrGwUrNtWNAxbkIhLp+lOLn+0ECOfACbh4yyoMSFBZWakuKqR2UUrp+vWTcQMl6AJ/ZK9qRFMV3dqAm2j+wPsdv362hKwpZhoyqeUWqsJjK+sbO4v80E002rtMieegy7NpSjh7tZkghpXtZE8/JmYmWvTdmmQkW6P41WmmR1EsuZfLK2bfOJT3yeD3zg+km/r6M4iqP400KWLINWH3GVpFK/sUpHrTXpYJQRbwiBoMytJGGnXneQq816fHYj9EwcfBQCrW2UWI/CBl77Pexw6JYBOy0PDcwIHRqUPVHnDA8P8a1vfZXvfOc/UVpz6q23MP9D76W5oo7tgc2vo2PkhKZe2YQiIKkle62AUi2ZGbqHdFmNiJD7IhkA5ocueRR5odlleTzkZnhnvpR2K+A5N0c3QXFOBA6CBYGLgyAvNHuFT5cd0BLaKBHiALYW9Fo+M0OX3ZbPxrjHc06eE/wYz7hZCigalTFySIWCgpB0yZARGZIIJZWhTZ/lG+IWoxRuVqbWTWpJJwH9MiQjFJXKMrW/MO/ZGla5OU70o8wLzXOOQHC6H2d5EKVbBrha0KTsVxCR7H+vuXkat99+N3fc8V0+85kPs27dS3zrW3dOWMTpTHq/DURRqOE/+yTeU48h6xuxSkrRSqH27Kbw8zuwpk0/RPGrPY9w1w7wCsjG5j+sIvhw2BcSJ5lQ5oq8IYKdFwXWoEA7mqzKIn1pfIGjGBVwg0T1T7ZXkH1g75CIPATTDWFqtwloN8dRVQKZBnzzPzkiyJ+nifxegKVBm3A56YOOaMJaoMR0a+nE/tpPVWvkkMTeCrgCHdWELUABvBO1sXA4CME0TWR3MVVs31dfAKRGVYPsgdjPJPY2gfBAlWkKZ2gKF2jEKLhPCVR5Mf8CCMvA6oBTtjVgR5Psad/B7BOhsmUBl376bu7+h0t58ZtXoU/7OcGcCHgae5cgvs+W4ggsog8HIeCMFeWs3TzCzjGfpmiAYwuCnPkdl1XGSHuCg0mWhANvn6O5cJom60N59PBWBa4Fi6o0v98jqYrqiTyK3iyURQQzSxURCy6Ypjm7RbPt8V3s3rINO2JjJ1zSfWnk0A6Gps2nuSQ+oS7OByDRtBzAEW8ZNtYPM0r3q5ArY5D24ZluE0aXcmBxlXl/z5igKqbJ+MbvuBAaO4aEI4rBxOBYxnYi7QsCBTtHJf+9XuOHTFLkBsqolAMF00s000qN8nnbsODeXZL3LTbhdhsHDVk8r3x/p15NHEZHTIj0VGQwwO9/9V1yo/185TOfoLIxpC7BRM3/546jZPCfAUR+CGdoHSLIESabCMrmT1gGaLcUv2IhbvcTqGi5ISG1xkq3c9MlJ/Llh3r52tf+ldtu++/XfHyZ7cYe2Q5AUDbHqJBf88kIvKZzsEe2YI3uNGRumMfK9uDVHG9UtkcA7STRkTK0FUVVFJWUYR63+ymszF6sdEfRQiJC+thP4dccj5YWKtE4SUH9ssewY+hICSI/NIngFUEBLV2CksmEnVdxDJFIBXb/i6jkdJDCBNWlpuHXnjjFORQD9cI8HJDqLoIMyklNhNK9IoIs8Y3fxB7eSBhvAiGwhzbij23nkdQH2JqYT72yUWjW2QUGZMhfZcqpPIzvWqGQB8BxXvkqG6xbDQhkSSkdo8Pc/IsfMpLPcfuJZ3J83Suv2Fdqi7/IlrPN9hgWIWXaYk7gTmq32weRTKJRoDT2gkUEgY8aGoSxMaiowFq8FJ3LEq7ZjGxoQtbWo4cG8e69G+JxIudeOHl/kQjOGefgP/UYanQExschl4NoFJEqRe3dQ/47/4X8yN8Yj+H+PrTvIatrsRoasd52zSue3z6Eu3fhP/8sqqsTa9osdH8fsqllgggGIJmCzr2okZFXbekgEFydL6FB2ax08mSE4iQ/yul+nJnhG3u39B58gGDVc1jTZiDiCaMSbtvBGZbF/LkL+fd//2fuvPM+My4pcVaciLPiRLRSh3ow/6GgIfYLQeRho0bQMXA2gL3LInuTwl/yx7WyrLXm/vt/xYUXXnbEFhGBgo5xE+TQmDQeXgkHbl6oeLpT8FK/aUE7v1VxSuPUvmavF3KveZg48OfbnhHklWCF0jziQt43GSUDOfCUoCKiGfEg6Wqq4sa/rDGp2TwsWFKtObfl0O9mMA8/2CjZOkSReNA0pQQ3zFfMLIPdY/CzbZLdowIvNCp5P9RsGRLI4jNM3DZpxwdeaYQwFhojHjzaIXhir1F9bB6Ccte00EkBJSkX67gZbMvO5OLl4SF+ba8GI3tH2Pr7bYx0jaIVJKsTzDpjJnXzJtuoXHrpVXzta//Ct7711aNk8FEcxZ8YFIrH3Yd4NPIgQ3KQCBGWeMdxef5qSg+U1b1GaK3ZNb6ZjsxOPGWSNSMyQmtyDq3J2a+LEO6x1jM73ILAoU9WINFMV13kxbPstRPgv3oyOI9io+3RaQVEtSFVm5SDRvOwm+V3kQwjwggXSrTk3EKCs9MO3/3Of/LVr36BMAy5/Jb3wcfezXhNKd1asktkuT8SUhBwoh/FkYK9MkBr03HeJQMqlfE5nhfsF4G0WT6DMqSl6KfbLQMCNBr4fSTDlYUU78inON6P0mH5OFrQbvk87mZpUc4ESd3vhKAFaakZlZqklkS1oFMGvOTkiSmzYJkVmkfdrFEOC42HZlgqssJ07eWEYkAELNQR1rh5xoUiQJMWiuecHCWepFRb5FFYGLuKlU6OMixa1WRRRC8F2mx/ggzeh4SWzDrC2tG2bYJ92RkYxe51193CokVLufXWa7j44pP5zpe+wcJt2wjWrwEpkakS7GOPI3LZWwmefxYRjSFLDKslpES2TCPctplw8wbkqWdO7DvctYPCXT8m3LMb/ABRXo5z5rm4F1wyYa/2h4RIg/ukwH1BgGfUwIUzNP4KTfQeCYMgB40VghyFiyvewhcH/oWmzmamXbyANWo1X//1v3Nj67uRQ8b/Fw32LoHIGGJWNQIS5Cg4z5kchrBBozNm36pOE9aDf7xG9oO9U0BMo4NiEN0Y6EpDKqONlYMclmQ+qHAfFvtD5goamQWrG7xjNIVz1aHdcwpUlUYOQuRBgWoEVaERI4JgocafrUl+W2JvFoRNGh01KunYrwU6BapGI8cEwbTJdZ2qhLp+SWmqHD87zpZhgSU0QdNZXPm2u/nNXZfxruffwe1v+SlO1CGYYUL67E0Cf8Xrq98tx+LkM5p49gmPwlgeXfCxIzZlLeX0xOPMfZlg3hL3yAjJc1o0bWOabSMCV2p8JYjZcPlMRfUBj4EqW6B39R4iCZdktQn+jpZEmd83RsfgAFtLmimLCRSQ9gTH1uhJ2R9pD1TRf/hAxG0YK5ixakwdu6DCENmZQOApEwi3c1SwtFpjSc26fkHC0YwUYOeIwFcmeG5ZjSGPNw2a33pPRlMTh/4c9GRNLkltkaC3hFESrxsw9XRtQjNWDHY++JYTKXYOToWRkWG+/vUvcuWV13LSollTb/RnjKNk8J847IHVxLd8H5ntMRdl6eDVHE9u3i2GTBSCwrTLsNId2MObDLmoAnSkHDn3Oj704QV87nMf4/rrb2XZsuNf3cG1JrLnAaK770F4w6BBRyvIT7ucQvMFU/c8vBJCD798Idl5txDp+C1WthusCPmWC8nPeNsRp7tqJ4FXewrRXT8HK4KKVCJCDxWtIKhYgF95LNhx/KrFxm/3lQhgFZptDjinoGIRfsUxRLqfROb60XYUEXoIfwyv6Xz86mUT8+R2P0Gk/TfI/CBWfgirMEyYmoFXfxqFaZdNUkFP7L9sHkHZbOyhTYZYtqKIwjDSGyXfctEhiuTDwRlchz2ylaBkLlauG2tkK9LPYAdjvOXpfyVy1hcZKTWr8qlQssXyWOvkOdubejm1vb0NIQSNjYeqmQ+G9jywLHYPD3L9Xd8nYtv8/J3vobGne3+w2iuNH8HC4JXP1V60BKuuAbVrB7JlGtZxJyK2b0Gnx4m8/XrsRYvJ/X+3YbVMQyTMTVTU1hH6HsHTT+CefjZISbh9K3p0BFFRib1sBfay4wk7O1CFApSVI8sr0JkMIpVEj4xQuPduhOMQbNkEYYCsrce94BKc5VP/nnQ2Q7B5I3psDFlVhQ4CCnfejh4ahHgSlR5D7d6F9jzk0uWowQFUdydqaAgsifYPczec6lhBgB4fQ8TiRKJRzvYSnOXFCWFSKMgbBZ3LEbz0ArKiaiKxWdg2VusMVPsubr30Kj7+5b+nu7uT+vrGSZ/9XyOCAdkJ7nMSVWPa9gBUFdjbNe7DAv8Y48n2x4IdO7awZ0/bhOfe9hF4rlvQmTY+uCfUa+aV779cbRuGX+0wrWFKQ31Cc8kMzbIaTWkELp6huXjGm094q0qN8CQHqih601CrNXal5rhaeHwv5HyBFoaMrYqYArI+bgpWMOdV4mh2jk5WZPRl4alOwU+3STrTsKDcJDGD8TW7a5vkvccobt8kaS/6nO0eE2QDgS01J9SZtrLqmCGj798tCbVJoAZDqIca+rImrCNmg2tpxj3JuAdlUaMkBjPWgtKMFQ5VjRwpcqN51t69gfG+cUpqUghLMN6bZsM9G4kkIpQ3l01sK6XkXe96H3/7tx+lp6eLuro3x5LiKI7iKP7weNZ9grtiPyRChBpVS07keCz6O9JyjPdnPmryL14HBgo97E5vw5URyl0TNJcN07SNb6HUraA88trVlbZ2ccQIc4I25odtgHlc2WwXsPWrf0wdEyG3x0bZYHuERdK1XFu8NZ+iUlncG0njIphXJCv7Zcj3nn2Az3/s7+ncuYMbb3w/H/7o/+POFofNtse80BCyGs0Tjs9eK6AhtKlWkmZls1cGZIUiIzSDUnGaF2dhMJnh0Wg22x57LZ+EliS0ZESGtFkBK50cF3hJZocus4tj6rTGkIhJXVkeGhsTyCYx/BtAVmgiWlCqLUaEolJblCjJNtsjpxV7rYAQjY2ggCYAcgI22gWyQlOtLBwp6BQBI0KxxS6wyI+wxwqZEzjMCVwedjOow8y30K+vVkwkkmSzmUNeX7JkOQ/c/zQ3v+08rr75av6prIqLU+XI0jJobsV7+EF0PocaH0e4k+d7X72oC/vrYTU+Rv6H3yXs6sRqnQaOi+7vw/vNr5AVlTgn/oEXSQsQv13ivCjQSY22IfKgxN6qyd6gsHo1sZ8KRM600qsUfGnF1/iHzX/HR9b9Ff1/0UdtQwM3vOtW/qblb5GPGosJ7YDswNSkgSGbUUZJjADyAjmgQQrCZk2wUCM7jFdw9r2a2M9NzQsCOWY+463QUHzMCko09hZB5EGBu1biL9WIAlhdApEHQqPaDRsPOl9dtHh4QBrlcwj2RqPyzb1NkbtKY3eAvaNIPBd9YlWdUUm7Twty7zQEsciZ89wHkQWiUJFKsKhknFNbFeM+tCY1Zzx5Dted/nOuffJt3HL/9Xz3kh9jO7Y5v/QbU9cumxHlrHSMZ/ekiFohbsymN7BoTGhOPkyGxKtBXQL+YonixV5B26igJKJYUm3q+AORGcxSSBcoqZscBlpZ4nDW0HbCmkq2BwksAcunK05p0JOC82rjxiM47RthxT4MF4wn8DGVmjV9gr4sWNJ01O0YMTVveVRxTovmvFZNNoAxT7J9BLrTghFP05CA+RWGzO3PQsY3Hsam084Q0dGiCtooiAV+CN0ZGM4L0p4g4UhcS5MPTJ7HPjW10obcnlk69VXq85//a/L5HJ/+9Bde93fxp4ijZPCfMIQ3Smzr7Yj8IEH5PBAWMtdHtP1+tJ0kN+/dIARhqpX0sZ/E7Xsemd6Ddivwa5YTlszkxhuP5667bueTn/wADzywEsc5vD3AwbCHNxLd+TO05RKWLQA0MttFdOddhKmWI1bx7juXSPsDOH0rjcI5NYPcrGvQ0Uq0HUdHXn3wTn7apYggjdvzLHZ2E9qO4dedQm7uTahY9RHtwxrfjbv39ziD69B2DK9qOWHpLLRbQpiaRm7h+0FrnMG1yMIIWC75aZeTXv65CTWvM/ASsS3fAwRB6SzCZAv2+C7CRD3ZebeAc5geFumQnXcL8S3fxR7ZBtpH2ykKzeeRb7n4iOdB5gcAjfSGcAbWoBEot5SsVDQNt3Hyuh/w8AkfpeAmkZjk4F4ZHnZ/bW3baWxseUWfLwB73kK2PfEwNz34K1KRKD+8+iaqpYWyLKxpr87q4JUgKyqJvPMmCj+/k7B9F0IZXzHnmutxz7uI4KVVUMhDfPJ8i2QJanyMsKMd7ze/Ity2BR34xZCKRdjHnWBUBsKoFXQ6DWGANWcuwong3Xs3sqER2dCEcBxUVyf5H30PEYtjL1g06Vjhnt3kf/hd1J7daGWKNT04gCgrx5q3ECEEUmv8MUMI+0oRdncaRXIQIEpLKfzq58jySuzZcw87F1prgmefwnv8YROCl0hgn3gq7tnnm5C7N3TmDziu74FXgMhBaQCOg1aK85afiOM43Hffr7j11g++SaN49bB6jJfZwcVtWAV2l0CMgz7Ujut/DWvWrAJg+fITWdsvuH2T8b5N2JodI5LVffCOeYoT6zX9WfjBJkl/VtCUNLYLXRn48WZJqRsys+zNGWOgoG3M+BE3JqEsAv4yTeRZ42kXNgAaantgtAT2zjbbndqgWTdgkoSjlvHutaVgZpma1HKWD80+lYa1/YInO+GRDknONwoHKY2lQzowSoaWlEk2frhD0DFukpa3Dgsk4ErIhqZNbnEVXD1HMZiDTUOarUOC6pghHPpzgtYSTU8GUq7xNVbaeKb1Z01LXUvKFNvjPsQsQWXscI/Yr4y+rX2M945TMa18wqO9rKmUwbYhutZ3TyKDAd761nfwj//4KX7yk+/zkY98+jUf9yiO4ijeXAQqYNQbQhGScsqIWofv9AoJecJ9GAuLptAkFcV1goiOsNFZy05rG7PDeYf9/JGgP9eD0iExe399lLBTDBX6GMz3vi4y+JhgKc9HnmRMjFBSVDEPyQGiRFkQvPoMhMfdLGtsjxmhTRSJRrNXBvw6Os4CP0JGaFqKVmejezp49jOfZ/s99zHjpBN58BvPs2DBYvpkQLs1SK2yEAhCNBvtAkNSMSYUK50cNcpmfuhQoyw2WgUWBFFuypcyP3AnPHTB2FBEtWSjnaNCWUSRqOICYIWSPO/kOctLTOpoawxtNIb43RfQVq4keyxNuZYkQ8kO20NqM7aYlowKRUpJKpSFg6A+tMlZijGhcTELlQ6CuDa2E71WQH2R6C5RghEkWijarYCUslgYRLgmX4KLYEkQZaPtkUMRKy4sDIiQuJbMfhnbuCNBIpEknR6f8r3SPXv43twlfLq/j08O95EpK+ftYYDq3IO1cDHBujVYs+cSrl6Frq3fTwKnxxGRCLKxeWJf4aYNhJ0dWDPnmMwQioKP9Dj+yqf+4GSws0ngrDW2CftC1lSNxt4qcNYLMrcqRK/EXVMMlKuGhJvii7W38ZXar5K7WlG4yPwdKQ9ytdrYmI0UbSayAqsHxDiIAIwdtwatCWaDqjbKXWe9QPYL9G8E3qmazC0Kq0/grAP3foGVEURWQthcHKsEHI2126iPdRNoAarOjEWMgRw3NhTqgMdAay9EHpaopEZNx6yBj2ucNoGqNepjuUVAKCA6mUDVKeM1HFaZ8DrnBUHYqtFxo66WfYLCuYrI2igxclw1Z//n482CC/su5va3/JTrf3M1tz7wLr59xvexrQhh9RtDBjsS3rVQ01oieb7Xwgvh2HrFmU2axuQbcggqonBe6z4/kcOMI+ZguTZ+PphkFxYUfMoczSkzNfHyw9ecs8pgabVmZbchaiOW6aRLOHBGk6IyCqGWPLbXXE9sAdVxuGVRyDVz93cMVgAfPlaxrl9w/26wB4xtW3/OWEHkQ8gGkA8F8yo0V87SRG3Nr7ZLdo8JBvOCfGBs1oYKmoqI6awTQrNrVJDxoaNjkON2PELjrhfJhpK6hSs4cfHpQNmkc/r97+/nrrt+yG23ffsQkdFRGBwlg/+EYQ9txMp2EZSaItAeXIed3oMoDJNY/zUIsuTnXG/sEqJVFKYgEG3b5ktf+iYXX3wS3/rWv/PBD37iiI/v9K9GBBnCkn0O7QKVaMIe2oDdv/rIyeCwQHzjt3B6n0H4aWR+gMjeh4ns/jWZZZ+iMO3IE2cnwY6Rm/tuCs0XIHN9aCdFWDLjiG0gZHoP8XVfw8rsRbkV2KM7iLT9Gu2kCMrnE5bOJDf7OtInfhF7eCMizBMmmozf8AEKYrfr0WJAnfmetB3Htxdhj+3AGdqAX3vCYcegks2kl30ae2QLws+g4nWEydZXpbpWbhkA1lgb6BBd9F12tKYj1UDFyG4a+jbQ1nQiGhMWUaEOP0e7d+9k+vQja8PYWVrCu1Y9TYWUfO/kc6js7yMs5HFWnIi98NU/BBwM7Xkg5UThZ89fiPXXnyFs2wGeh2yZhqyoBEBWVEA0ZorI1P5VVT02gqiuofDbewnXr8GaPhMRi6MzGYKXXsA++XTcM8+l8LM7UPk8MhYzyuNp0wnWvITOZJCz5yAdUxVZ05ME2zbjr3xqEhmsg4DCXXcQ7m7DmjnbEMf9vXgvrcKynYk2TCEE9pJl+KueQ3W0g+8jS0sRdQ1Ys+eh2tvwHviN2cdh1LTBM0+Qv+MHIC1EeQUqnabwy5+ix8eIvv261z3vh4NIppAtrQQb1iPKyifOSQ8OIEpKqZi7gDPOOI/f/Obnf1RksI4Vqx6PCXUEGLWCimr0/1KW3eGwfv0apk+fRSxZyv1bBFlfM7ds32VB0z4GD7QJFldr1g4IejKCueX7E3Wnl8DWEVjVKyaUs68FYx5sGhSkfaiJaeZVGP+z3WNw11ZT9AUKKqKas5s1587UZK9VRO8V2LuNP12sUfOjuQJVYaa+OQWZQNOfNanETSnNmn5zjApl2sr6c0YAs7xG85udgt+2CzrGBN0Zk3Kc9o3SImqbMIrGpGlNC5RpM8sFgt1jAluYIhhACkE+1Ny3W3DDQuOj9s65ih9skqwbEFgCjq/TnNms+MEmi6akLn4OppXASM4U1QM546HWnxOcXK9pTR12+l4RudGcOcYBv3MhBE7UJt2fPmT7kpJSLr/87fz4x9/lr/7qb47YQuQojuIo/nAYKvSxfXQ94/4oGk3EitOamEVLctaUdgxZkWHIGqBUlU16PalT7GUPQ3Jwv4z0ZRCqAA3YU3TYBdpDTFEbCwSBfuUurlyQoSu7h6FCH7Z0qI01UBtrxhIWx/orOK1wLk9HHqWTDjSGzD43fzHzgv010qgIGZKKUiWpOIxNmULzolOgVEuiRdJSIGhUNtssn92Wb7Kt8nle+Po3eO4r/0GktITjvvsfXHbFNSzIG2GJpQUSMaGG7bCCIkkqyQiBDQxYAS8IRUtosyKI8d5sGY3qUGK0VFus8KM87+QYkyHZYh1dpixmhS4jMmSn5dFhBYyJkGptMzNwmB06bLE8KrWhlgsCqpTFHhngC8Uoin7LhLdlpaJW2RMew+OY0Ofm0GEvflFlbCwfZoQOa+0CoyIkKgIEEk8oKrVklh9lSIQsDiKklORFJ0fgRznBi7LV8ljt5FEYOiqK4LxCnBmvkwyuq2ugs7MDpdQhwdPBi8/hhAH/0jST0kiUf2jbSmzWAi71fXQuh/AKWHPmoXt7UFs3Icor0J6HzmZwTj4d6wBRhC4SzvueB/ZBJJLowUG01q/b//rVwOrEEJ+xA2osC3TSEMJcbnxy7U6BSmC8SLTx1NUlhhSdgAuFczSFMzWiB8o+bCF3a1OzWgIVBzkGqsRYcVk7AQX2VuMNHMzU6JQha60ujbdUY68DoTD2Dz3muCIj8Bdp8AWqVqG7pSGEDyA8RdoEu6kD7AsArJ0CMQb71qXGfPAdKC3XOKsF+cs1qlKDY5S++oDPixEIZwFRyL1dgS9xtgooaHRM4J2kyV+qET8wtl8HwjtN4WyxuCx7Kd8/605ufvQ6ruy/mNvf9TPcuW+cikNpiDlQGwfb0syvMIKAPySS1QmqZlTSua4Ly5Y4MQcv65EeyNB6XDPx8vjLft6S8M55iqqYYFWvIB8K5lRozm0OWVAB31on0RqWVGtyvglxk0KbQGeMPdr6AYEUxld4RZ1RHneMwY4RYxfhSCiPmO1nl8OuMcHCnObK2TCS1/zjc4JcoEk6RjQhMM8wL/YJjq/TzC7TtHeOcPUz/0mibRO5eBl1rqJh7U9IyS3o9/zFRHfvyMgwn/zkX3DWWRdwzTXvetPn//8qjpLBf8IQYcHcsYXEHtmMPbIN7cTRbhlCB0S6HkGgyS764MuSh4sXH8t73vNhbrvtn3jLW97KtCNUbAp/zNhOHPKGjfCnXgWeCs7gepyB1Qg/i5XtQdtxwlgNVraLxNqvECabCaqOPeL9TR6LQCUajRfwq0Sk81GszF6C8oXIXB9WrhcsFxHm0UJiD28ivulbpJd9ZkrPXwCZ3ou79xFkfgDhjREmmlDxerBc0BpZGJy0vTWyFbfzUewx45fs1Z2KX3uisbJ4JSgft/tJ3J6nEd4YQfkCvMazCCqXEJRMJ9r2a2MtoUOknyYqbIZLW4kWRnEKw+RR7JUB1cpmcRA97GHa2nYcUWjVxo1ruebGK6lraeVHH/wUJbvbwLKwly7DOf5kxBEoiw+HcM9uvEceIty6GeE4JsjtzHOQJaWIWAx7waHzJVunYx+zBH/l01BTh4gnjKewV8BeeAz+449M9unVCp3P4/3657iXvw172Qp0LoucORvpRlDjY+ixUWRFxQQRPHGsVAmqY8+k11R7G+HuXSZArqjAF7E4IhZD9fWi8zlE1MgHBCBLS9HxOPb0mch4fP+46uoJd+8yBGv1ofYi2vPwHv09WI5plQMoLSN0XbwHH0AkU1jTpmPNnndIaN7rhZAS95wLCPe0E27bjCyrQGczEPg451+CrKvn0kvfxoc/fMuUVhH/WwhmQdBqfNiC6cYgUIyDHBbkr1AT7Wx/LFi/fjWLFi2lNwtdaUFdQk+6xNclYG9a0JmGoZwRehyoqhUCopamN/vaLQx2jMCPNkv2jgMILGmK46tmGxuGjnGjknWkUeH+aqegLArHn6AJFmmsdkBApBHiOyQbBjFdC9oEWLzjWMW5xYTmtf3ws22CnaNmtCWu4OIZitqE5o6tFqWuplsam4akY0jqgZwhaZUWjHmgtElDnlUG9+7S5AMxyRc51Eb1MJQTfG+DwLEEG/oFaV9TEzOpx+3jhvyOWiaVeV8LW3NKM5o376d9QanUnNeieMsM/ZrckvZhIJLi+VgDMiilRnrMFmmS+PiFgGT11E8hN9zwHn7yk+/z2GMPcs45F732gx/FURzFG458mGXzyBpyQZqUW4ZEkg3S7BzfRMxOUBM71N4lpuOUhKX0W72Uh5UTr2dFBheXslfwDM4GafZkdtKf6wY0VZE6WpKzSDj7V6rK3Ep6sh0oHSKL+RehDorX21fe//qh5xjxhnEtl1CHDOR7GC0MM69sKbawuTZ3I8f6x7HL3o5AMDuYx6xgHhKJh+Z3kTTPOnnGZUhUS5b5US4rJEnoyeShBoKDAtxgn3WppkZZrH78ER7/0GcY39vJ8r94Lys++VHay1xm5vdf8Cu0ZE7g8oKTJxVKOmWApaEgNM2hS0to020F9MqQeYHLTfkyGtTkR+oAjY8miuBUP8ajYYagSDiWaEldaDMkFQU034+NMiTDCQJ6WmhzRT7FNLvAGqeABi4oJNhueTzv5skLKNUSqaFUWSCgNXQo15JNVoFNtoejIaEFDpK5oUtKGXsKTyhKlCRtKXoshYXC0YJSZdS+WalYaxeIIPCAJ90c78iVcFOulOV+lF22h6sFcwOXuaH7ugOGZ82aSy6Xpaurg6am1snf5+gIJJLIaIS/qW4kB3x6xyYi9a1cOD6GKC/Hnj0Xe/5C/CceJdy8EVFRiXP8STgnnz7JB1hW1yCknFRLa61RoyM4806aRATrIDB2cNHoBLH0RkNH2B8cd2D95TFBrvrLNIWdEHlGgvl5okshf4kinDbFTi2w+wQ6pQnrBM42Y/8gCoZc1SnwlmjsLgGBsY7wj9emE0uCKtfYm8HaIcy4FMicQCtTFzqrQOQ1QYumcL5GeBpnlSBsNGFxctiQzrnz96udD0YugI1DgoGcEQM0DGtKpaZCAbPAX6BxXxKEtUXP4AEQUlA4TZkxVkLmLxXWLpBjAlVRnAthFsMPJoODBZC9SRF5SHBl11upPvd3vOPpqzjn16fwo2vuobV1xuv5GgEjIvjOBsnGQdNJFmrJiz2aM5pMUJw1tS7nDYcQgvnnzyX0QgbaBlF+iOVa1C+sY845s49oH0kX3jpbc9F0Y8VQ4prng/YxE95WX3yesOOmJu7OwJOdgi3Dgk2D++veNf2CjYOCpKMZzAvax4waOCoFmQBaUprWlGYgBy/1Cy6bafbXmNC4tmDXKATKkMdaCLYOaywhWF6rmbF7FTN6N1F5/FywHWwJ2isQbFpPsOYlnFNOR2vN3/7tR8lk0vzbv/3XH3Sh5/8ajpLBf8IIUy1oJ4HM92GNt6PtKNqKI/1+wtQ0wmQrzsAarPQewlTry+7rE5/4HPfd90s+9am/4s477zuiH1VYMhO6HgcV7PfyVT7owChwjxAy24kojGDl+42KteiFq6NVyPwgkY4HCSqXvjYP4pc9gQLCT6OdlCFnD4I9tBHtloGQWOk9oAJUtBqZ60OqgKB0HvbIZpyB1XiNZx3yeWtsF/H1X8fKdiG8NCIsIDOdhGXzCUpmGKI6UrH/eIPrSGz8BiI/hHJLcbJd2IPryOf6KUy//OXPRWti239MZM9v0dJFW1Eie+7HGVxD5pgPk5v/PuzBjTjDm5AatBNHlM6hyalnvDBGW6KELiugOXS4vJCkXk196QjDkLa2nVx99Q0vO5x1617i2msvoqVlOnfeeT/l5RUvu/2rQdi1l/z/fIOwqwNZWYPKZCj8+ueEe3YTe89fHpZkFlISufYGRDxJsGYVqr8HWVaBfekVyIYm/IceQMRMdaMG+gnWvoQeG0X7Pv4jDyISSaPm3bUDhTB2C8ceh+rsQAfBJDWCTqeRs+aYf3seKIUu5I2NgnvA+BJJREUlqrsL7XuIaAytFOHePVj1DajREWQyNfmclDI98IdR/emRYZPGXLF/znU+R7hrB2rXTvT4KLKiCmv2XKLXvxtZUzvlfl4r7AXHEHvPX+I/+Shh206smuk4J56CfeKpAJx//qVFq4hfcuutf/WGHvs1IwK5dyhiP5bY7UAo0FHwTtYUzv3jCo9TSrFhwxo+/OH/hy3MSn9wUFdYUFTQ2sIoYhVG1bCPENbaqGMbEq/NwqAQwk+3SrrSglllGltq8gGs6xdkfGPDMLNUYxcL5IYE7ByFZ7vMyr9OmAIeIAW8Z7Fifb9gz5gmYhufspkHCDqWVGtmlppwDV9Ba0pRl4An9grSHjSWGzWu8sx5V0RguACDOWMn0ZvVyARcMVNzYr1mWolgXT+Me0bJDGAJAdqoip/vkfgK2kahKiZoSmlq45qhPLzUK2lKmbE4UpN0jReaJeG6+YqLp2sSjrGweD1Y2S24K1/P7vIYOuMjo6WsI8npA9upTkZoOGZqT+ClS49jwYLF3Hnn946SwUdxFH9kGMj3kgnGKXerJurshFPCSGGAnlzHlGSwjc1p3tncEf8ePbKLSlVFTmTptDpY7C9jZnB4y6hCmGfD8CqGCwPEbLOg3JHZyZg/xJLKk4ha5rXaWBO9ub0MFQaIWFFA46kClZFaqqMHjEmFWGPbkd44KlZDmGyhM7ObEX+Y8kgVsqguLoR5unJ7qIs3UR6pxsJiQbCYBcHiQ8b4ezfDbyJpyrVFfWiTFppH3AwBmuvzJZPISKuYIfGwm6EGC1l8b1AoIpkC6//fP/Pcd79F5Rknc9HPf0Dp3FnsFiGzQ5dj/f2rugLBWwpJ+qTPJqtAt/Tx0ZRqixmBTbN2ma5ctlgFTgxik4jgPIrH3SwrnTw5qWgOHc7wYqzwYqxy8jSFNgktGZYhIyIABBrFvCKxGqDZZnk86+a4JVfK5YUUGthgF3jGzXG6F2MfBRsC260CswKXYal4wSnQIX3KtGSFHyWH5qlIjnbpM0+5rLHzDMiArDAewqUKXASuFvTJkEEZMj10mB+6yKJP8m4r4J5ImnmBy3FBlONeRgzyWjB7tpGKbt++5RAyWM6cA1s2I5pakJs38vnaZrKBzye624mVlHHeKadDWTlq53asmbNxzjoPWVc/5TOqNW8h1oJFBGtXI6trwI2g+3oRJSU4p5wBFC3UVj2H//CDhH09iGgUe/nxRC64BJF8HW08U8Cfr4mWG/uEsBFDZo4AGrzlxbrSgdy1Gn9FiLVLgA3BXBP4Vgz3OBTSfC6Yq7EGDek84a8rgRiETaBKQZdrwgOzuh0QnkAOGVsJmYagUSOHgYJAZEH0C3KfVIQtkL1WEbMlzkaB6DOK5fzFisJZh9bFwSyNKoHd2wWdEUGJqykFEhl4oAoaOgTnT9Nkr1OolMBdL43KuFpTOFtNDnqzIJwN4UFihZGRYUpLyw6d6yUaf5FGjsDiyCnc0/cE73rX5Vxyyal873u/YMWKVx9SeSBWdhvic2apLtaMJjjtqU7JspqQuW/cI+4rIlYWY/k7jmW4Y4T8WJ5YaYyyplLkq2SkYzaT/ISH86bmDbVRBFsSKqPQmtK0jwk606b7bx8ZnPXh9+2mc21RpSblCDYOmnUPW8K8CjNXUkCozNqDryDpCsoimj1jgooo5ANTtyMEfVljE3FGz1bcWATb3S9YEm4EISRhexvOKafzX//1FX7xizv4+te/R+MBdjFHcSiOksF/wtgXQBZtuxuZH0Q7CUShH+0kCUpmot1SRLYbURiGVyCD4/EE//qvX+f66y/j+9//Ju9+9wde8fhe7Qk4PU9hD29CRasAjcwPmoC2miMPo9N2HBHmDal8YCiaDtBuqSFiwwLYb1CBonwie36H2/UY0htDRcspNJ2HV38G9tgOrNFtgDDEdpAHMKQxAlEYQvjjiMIQ6GmAQBaGpzgpTaT9PqzMXvzq43H6VxkyW2OC/IJxgsql+JXFwlgrIu33IrwRY68hjHpAZrqIdPwWr/6UCXuHqWCN78LteoIwVoeOmruS0o3YwxtwOx4kt/D9pFf8A8nV/4JQBcLUDDTQNLyDfNkCLk6cykWZBC2hM8nX7GCsXfsiuVyWY489/Pe7fv1q3v72C5g1ay4//vG9U964Xw/8Z58yvmDzFu73D6uoJNywlmDTBpxjlx/2szJVQvS6G1EXX4rOpJGVVYhYHDU6gigtRw8OQE2d8Q3OZdGxGLK8AuuYpej23cjaOpxzzocgRDY2IapryP1/txHu2IbV1AKug+rtAcfBXrSY/C9+arzOwgDZ3GrI5O4uEMLYVTiOSUmuqkZ17EFJC6EVVn0j7rXX4/3yLsKOdmRdPXp0BDU2gu7pxV6+4hDv430QiYQhtbNZKNphhNu2oPZ2IBIJ5LQZCCnx162Gn7nEPvCR1x3epoYGjWq6stoU13PmYc+ZN2VbXmlpGWeccR4PPPDrPx4yGAinQfpjCmdbMaW5RhPO4I8mOC4oFlN72naQyaRZvHgZtXGYVaZZ3WdSfW1pfL72pgVzyjXNKSiJaB7v1GwfETQUV/x7Msa6YUXdayO6d41Cx7jxz91H+EZtqI5pNg0KXMsEsh2IhG3sHbQ+dF0vbsMJ9ZoT6qc+nhfCpiHB5iHzebSgPLrP9sL4+dYnTIBcIYSIDTWW8RyWgQmmO79Vc1ytJhdAc9KoO9KB2bdracojMFSAUlezolbzTLegImLmc9uwoCqqqYhCbxZml2tKIrC6T5AZ0ZS4ghV1mrfPMYF8rxejBbhnpwDbYsWcBINtg2THc3RYSTZXtXD2GfFD/IL3QQjBZZddxde//m94nofrHrrQeRRHcRT/O/CVCbw6+L5oS4d8mD3s5071ziYjMjwZeZh2u42IjnKcfxJX567HfplHvf58FyPewCSiNmrFGfEG6M110po0arKIFWVR+Qr2ZnfTn+tCCEFLYhaNiek40lxDZLaX2Jbv4gxvhjCPdlJ4tScwUtZIREYm9r9vf5lgjDF/hPLI4TM6MkLxjJujVFvUFgnXqAZLwRonz7le4hBxwplenB2WxxbLI6klBTSjq9ey+eaPMrp3L5/+p6/Q8L53sTbiozWc7MU43YtRdpD1RFC0cghsjzyKrNRElGCL4zMWahqVjYugITxgoR/Nz6LjPOnmSGpBVEvW2wXaLJ+rcilsBJvsAn0yJKUly/0Ya50CzcqZILVtBPXKZqvtMSIU5cVxtVs+Ck0MyagI6ZUBBaHJCI3jC/4mW8lXE0PEpGBxGEEiKENznBflBSfPs24OF0GpkriAqwWh0JQqSVZoUloyKhV1yp4g0QWCxtCmvWixMT984z2xmppaiUZjbN++hbPOumDSe86JpxCueZFwbweyrh66u/jnSJJ8yufDOzdyu1Yc++UvoPt6TT1ZWoZ75rm4F192SN0qXJfoDbfgVd9rxByZNNacebjnXYg9z6w+By+tIv/D74JSiMoqyOXwHvgNeniI6M3vf0ODjFWTCU6L/kpgbwMQ6DiG+Fx+QH0kIZgNwWyNyID7uCD+PwLhGULZO1MTHsB1BTM1YY1ADkBYAbJouSCHBOE0jRwQ+Es0OqaxuicH9qLN8bSjsXoFOmFI4TAGMqchK1ANoEvMZ3QFZN+jkJ2GOJbDYK+XpP5RoKrAO0kbEleCaoTdpymyd1osGNFIC6SC7tmwfSm0dQvOLNXE+8E7S5N/S2is2Co5ou47pRQ9PZ3U1zdNvYFV3BcwIzmbe+55kltuuZq3v/18vvrV/+GKK6454u/uYKwbEMRtPSEeALPg350x4cRzK/6wohFpSSqnvbEM9J5xQX/O1PJlLgTaPCsM5gQlrhGWRA44/7gDg3lB3IGaOMRszVBeIITpvBvKGxV3V0Zw6QyFLWFaieneM2HWgsqopistyAamBg+KYXNvq0yQHAkOHaQKIRrjN7/5OV/4wqf58Ic/xdve9uZZH/6p4CgZ/KcMIcjNvo4wVkPqxX9CBBmC1AzCkunoSAUiP4h2Ukcclnb22Rdyyy0f5POf/2uWLFnOsmUvT+jqSDnZRX9FpON3OP2r0EJQmHY5hZYL0JGyIz6NoHIJKl6DlW6H0AfLQQQZ0BoVKUO5JVPbUbxGRHf9imjbL8zcOClkro/45u8Q2fNbrPHdCK9of6FDhJ9GRSsAYRS+KkRLy2wXZFBuWZEInwwRZLGHN6NitahYDegAe2Qbwk8jgjQq2Up2wfugqNQQhWHs8d2EsdpJTImK12GPbMIeb8d/WTK43RDWyQNIfyFQ0Wqc4Y3klE9QtZTM4o8Q3X0PMtuDkDZ+7Yl4s65lpiw/Is+5Rx/9HaWlZYf92+jo2M0NN1zOjBmzufPO+0kd4M37RiHcvg1RUjqpaBPRKIQhqmsvvAwZvA+yvAIOUCvL0jKcM87Gu/tnqOEhVH8vWghEqLCmzUA6LrqhCdXXg9VsPJsLv/wpwZbNCEua1N7uvQgpkVU1OKefhf/CSsIN64xnsWURvPQCOpsl3LvHBMJZFvg+xOPEbrwVa9Yc1NAQsqwce/FSRCJJOG8B/tOP4z/zJGQzoBUkU4iyUvLf/k+iN96KLJnsiSUSSezjTsS7/24T5BZxCTo7IAwhGiVYtwZ8HyHAGx/FveAt2EUV86uFGh7C+82v8NevgUIBWVmFe8752KecgRDisB0GJ554Grfd9k+EYfiG+ZpqraFQANs+xDPuiBEzCoM/Jgzk4JE9grUDAq3B2bkLMK2XQsAVsxTDBcmOETBr8prGlOats0zxVRmFmxYofr1T0jZqbBgq85r6Hs3vtwvq6mDZMk39YYjYqeCFgkAbC4gD4Vgm7M1Xmp0jglCbwrImrhnzjDfZq23w8EL40WbBym5hzk9onumC4+sEF05TlEcl3RlNQ0IzUhC0jwnGCsbjbEElXDZTcXrT/u/04T2CTcPms093S4bz4IfG49eSkHI16wdNuIUoegqPe8Z6Yp/a15aQcrRJoNcCW2iaU3rCf/j1YteoKcpnlYIlozQuacDLeCTygny0itSsl/8bPfPM8/nXf/0cL764kpNOOv2NGdRRHMVRvG7ErIQJLNMhVtGOQWuNpzxqnbLDfs7G5pLClZzinUmf1U1cJWlUza/Ywj/mjSKQk4haKSRSWIx6k4UMMTvB7JKFzEoZ4mzS/VsrIlu/R27wBXaWteLbMWpy49TsfYiG/AK21i2btK99rdzyZTI6FIqt9h66pU9TmOTAR9YSLemXISMipP6gR9l6ZfO+XBnPOTm26hyrv/J1nvzSbSxYsJi7fvccs2fPBx+u8ItjmGKOemXAd+MjdMoQH40WRrHbL0PCEIbskC4VcHU+xexw/4JauxXwopOnTlkT5HJlKNlu+axy8/xFtpRuGZIWmmpl0WMFrHMKh6wpC4z/8YHKRwfD1XXJgI12gRwaCYwLhXThnQWNRFCnJ5O5M5TLLuXjCc2xQQRLC1a5eVJa0i0DeiwTBOehyQjFZsujRtkTQXFvdnO1lJJZ06exa9W9JFbkUfE6vNoTCcvmYjU0Er3l/XiPPEi4dTNWcyuR6TP45qln8u7PfoS//LfP8etzLqVm9jyQEt3fh3f/Pcj6RpzlKw49VnkF0Xe8C33plWjPQ5SWTVhJaKXwHvs9emwUUVEJuRyishIrHidYuxrVthNr5pG12h8pvJM0wWyNvUVAoAlb9YTlwSHwIX67xHlBoEs02oHIYxJ7mybzfoUquqrpJOTfqondKSBilLz2iEaVaFAQ1mryFytkWuCs0chuULVACFYHhA2asFITv10iRvTENUQLCKdrdMyMZQLCENvWWkHsrqKat1RjbzWexPkhTNCdgN2nwL2jilP6Ba4Pg3WajtlG+TnjKYG8SxLNCKwIBHM0uauP3IZtaGgAz/OO2F6uoqKSn/zkAf76r9/HX/zFDbz00vN85jP/fETh5wfDEocaqu1zq/hTcCfQGrYMCcqjkPEFBaVxpekuHPFgeqmeMm4pUBApvp50zHbbRwRDec3KbnMPKXFh77igN2sEKqc3Kb6/UZL2zTNE1NE4liGXCyEsqICTZy1GdjyOGuhDVBoOS/f1QjzOGh3yoQ/dwpVXXssnP/n3f8BZ+r+Lo2TwnzosF6/1EtJaEdv2I5RbCjKCzPYic30Ums9HxaduJ50Kf/u3/8qaNat473vfwe9+9xyVlS+fIKziteTmvovc7HeYF14DaatiNWSO+RAlz3wcK7MHbSfAihHG69F2Ar/+DJBvDGEk8oO4XY+hIpWoeB0AYaQcp/d5ot13ot1SY3khHJSbROgAa3QXcrwNghxa2KhoDdpJYY+34ZcvxJ/Cz1gLC4QF2qxshalphIlGZGEEK91Obs4NqOQBq5vSRQsbEfqTbzjKB2GjX2FetRVhX3nJAW5qQnkop9yMRQj8upPxq5djZbrQVsT4F7+KO9mjj/6O008/B3sKsm1kZJjrr7+MWCzO7bff/aYQwQCipAT27J702r4Hjwlf3dcA97yLEIkE3v2/IdyyCVlWjjV7LiJVaiwWRofRY6Pkf/ET/Id+i+rvBcdBuBF0MoWzdBmxa2/AmjmbYON61JZNJtW4WHiIymr8Rx40/25sQvg+OhpD5LP4jzyEbGrBPfs8ZFk5wYa15H/5U/xnnzZK4nwOolFT2AoBlk2w5kW85haiV1x9yLlELrwEPT5GsHoVenc/pNPgOOhCAek4kIijs3l0+268B+9/TWSwDgLyP/4+wepVyNp6REkpaqCf/E9+SNSN4Bx/+LasRYuWks1maGvbwaxZh29xPVIE27fiP/IgYdsuRDSCffzJuGed+7r+Hv4YMObB/2yQbBsyyb8AL+40PuNW0lybm5Im1Xf9gCnASl04pkpTfkCBPb0UPnSsoicDG9fD7x6QrB6SuK6mUBA884zmppsUs1/uOSgLkZUC5yXBsiycHxVsXqBJmMsoWhvlb0vKhNZtHICIZVQClhTMK4dTG169LcW6AcFzPYKGJCQdMwcZH57thoqYYEWt4ukuybYRk/hcn1AsroILp2mW1epJAR9eCM/3CMpcTUMSrkoq2sZg+7CgO7Nf9bF7VBAo4xMcsYvXFmEC4uI2bB0SbB4W1MVMcTtSVPJGrH1p0K8PE3soXpqFEESSESKWUSof7Jl3MBYtWkplZTWPPvq7o2TwURzFHxGqonWUu1UMeX3ErARSSLJBhqgVoyH+8h18AGW6nLKg/IiP51pusXdiMrRWRKypSZGpFnHF2A56RtextaKejOsAAbtSMeaqFA2jbWytnI1vJ3Cki9aaTDBGRMYod6cWo3TKDn4Zu5PN9k622WfQZqdYHDTSHLYiEIwJRUKLQ9S8+1CjbBZs6uZbH76ZNWtW8aEPfYqPfvQzOAfkIExFAu/D806OvTKgJbS4PxrgC40LFIBhK6RESZTQtFk+X0wMckwQ4WQvTk/RgqH5gJBlgaBSWey1fAowKWTODQXVyqJLBrQUX9doemXIosCdFJQ3P4jwazfNE06GEEOI2wiSRWuJ37lpWgKb590ctVgTJF4BjQAqtUW9cvDRxJVkTCjSQhHRgiolGRPgK82ADGmzPBaEUTSaTiugTtlMe51BcYeDPbSeJTUBL61ZjT3cgOh/EbfnabLzbsavPRGrdTrRm94L+TxYFqLYzfK1mz7ARR97D5/a8CLfmTsfKQSippZwbITgpeenJIP3QSRTh3z7enQE/5kn0QN9sHsXCIkoKcFetASdz6P6+95wMhgwCtpTX7kusDeDsxbCaXoiXE0nNe6LAvEfEv84E2asExDM0KQ/onHWC5yV4GyWCE+jyqBwuiKcaXIQ8pdD5EGBvR0QENZB7iqFHBaIH4MYL3oOByC0hnbjKxwe7B4Xmv2InOZAVxrZA5FHBN4JGl0BFTFNf6vk8bmakuIaSqjBeUyw5HHYmpLkKzStNsxcJYiPS9IfV5OCmw+Hrq69ADTUN2HtBHu3QFtGUa0Oww9HIhH+4z++x9KlK/jHf/wbnn/+ab75zR+/YjaS1ppRf4hskMaRLsdU1bJp0CYX7LdWGMwbscDs1xHC/McCT8FQHhZWagZz0J0xoc1RR5Nw4fg6eLHPPJPs+16H81AaMXYS+zr+ZpdBxjOK4rht5qYiBpuHBT/YKPmrYxWXz9Qorfj2eokUsKQUGpPGv7g3Czcs0JTULcY7/xK8Rx9CbdlkaIqSUjqXreDWv/s4S5eu4Lbbvn3UJ/gIcZQM/jNBoeUis3Lf9Qgy14u24+SnX0Zh+ltfFdnnui7f+tYdnH/+8Xzwgzfyox/dc2TKvdep3PXrTmH0jP8mvu7fsdN7i0RlHYWG0yk0nfO69n0grFwv0hshKDE3ApkfQGb7sIfWI71RglgtOlJm/H3zQ2grgrZdpArALQEVYOUGUaFHmGgxqmA9RSuDHcWvWUGk/R5UpNJ4EgvbBLuVzCAon2+I6d6VWKM70G4JYbwWZ3CdIfTtKKgQe7yNINVKUPbyhFlQvgAVr8MaazN+zUIaFbI3itf6FiYt6VkRwpLpr3ruhoYGWb36Ba677tZD3svn89x881UMDPRzzz2PU1V1aLDZGwXnuBMI169FDfSbNi+lUB3tiMoq7IVHELR3GAjLwj3tLOzjTyb3lX8mbG9D///svXe8XVWd/v9ea5fTbu8tyb3pPYEUIPSOIFWlqQyKFcWCjm1GnVFnvo6/URmdsSEgYqGD9Bp6SyAhIb3f3vs9bZe1fn+sm5vc5KZRHGfmPq8XL+Dcvddee59z9vnsZz2f51GaYMVLJom4rxeEINy4AdCISXWIeBzd3o7u6sRva0U4DrFPfg7V1oJWapTXr04OEra1QKiQRUWIwmLo6UIPDRG0taJ+8VO8px4j8oEr8B64B9XeahS8+YVoLwtSGhWwVuiOduS0GYRvrESfe+FI8TxyLvEE0auuQZ16BqqlmfTvbyJYu9r4D+cMJ1dYHuTmGguJ3h6jlj4ChNu2EG5cj1U7GTFsWWFNTBDu2Ib/wrPYi485YMvd3LkLAVi37s13TAaH27aQ+e0vUL29yOJi1MAA2XvvQLU2E736U6MCRv6nYVWHYGuvsSXYrcJtVN1Ybpz1fTHKh1v58lw4vurgBakUUOzAq09JkkMwc6ZR6Wqt2bJF8OCDki9+UY1tRe1B/A8S9zXQMcix4II2qNwuePAcTVgEA54g1wGEeQiYVQRdGU2ojE9YfkQzY5jD0NoE0G3qFfgh1OabIjQyxrE394DWYoQIBlOQbu4VNK8VVMbBkprJeZqphVCXB6VhEz/54bf42jOPk06nqK2dwk9/+lumzV40bAthxrGl8TPe0SfIdYz1g6+gOKrpyghUaIrahGOC+mI2LCpTvNklqUnssYSI2eaB58VmwYnVmug7rLzq8jTFUUHrENQM2xiG2qiXj69S5BzC+UFKySmnnMmzzz7Jt771L+9sMuMYxzjeNdjSYU7hInYObqYr24avfIqj5dTmTCPfffeNJ0uiFTQldzDo95NjmwX6VDCILRxKo4ffDlIf9qB0EmFPoGQ41M1D0xCNUBB6FLoxOoI+hDbe6xErypS82eQ4+4sCUiLJ7+O/Zpu9mZpwIlODITbYebxub0Nol7gup12GnOjFqFBj/37fccfv+da3vkB5RSV3/eUZliw6FvsINK71MiCOpMVS9AtFTIOLRUpoYsoEyXWIkBYrIF9L/hIZYr3tcbwXRQIBRsm7G0MiRGhBg+VTG7pEhueSoyXnZBP8PtbPG3aGAi3JAhXK4n3ZnFGEta2hV4b0SoUEkmhsYEbgMjV02eh4XJbOY6Pjsc3yKFQ2HppeaTyRB4QiQOMgmBw6rHDSZNDkaUmvUAgB84MoOy2fjbaHi8RHU6At3p/JGVEKv6tQIdEd93H2nEL++NxWdmVLmFCShzWwg+iOe/GLF4AdM6RObHQiWYkb4YcLj+ETK57n5jde4ROLjzd/iERNPX6E8N9Yie5oQwuJLDHPDrqvl+DN15FVNXtq4/8mWM0CAoGODyvae8BZI5AdgkiDIV21A+EkUKWQPVWhisDqkogAZL/AahK4ayTpNxTJzyqyZ2q8ozV2vUDbmnCqCZVL3CDwZyqsZonVwfDCs0D2g9UK0UcF6Sv0iLZI9oDVIgjLRteZqhTsbQKrGYIimJQL80uNzVZ5TBO3YVW74NK14NiQKdfIAchsF6RSkL8FcAWpj+kRi4cDYTcZXPfGBHJWW4g0owL3sqfpMRXXQgiuueZzLFlyHJ/5zIc588wlfP/7P+Wyy64ak0z0lcfm/jV0pFsItfH8dp0S5pQsZWN3HIXp0ovZcPYkxeT8/Y/5Pw2uNFYPW3thbolmaoGxVAu1ETscU6mIO4KXmiXNQ2afmG06E3f2m3C54qgmUNCcgqocOGvinmC94qg2NX+P4KgyzSXTNJZQPN0oSQfG2s6RmhOqYVG5NmHk51+MvfBowu3bQMDG0OejX/4kxcUl3Hzz3QdUeLcljTAlz4UJuaPDs/+vYpwM/r8CaZOtuxCv5nRkphvl5qEjh68e2BtVVTX88pd/4IorzuUnP/kBf//3332XJwuEHvbAdlA+YW4d2s0lKJ7PwEm/wu7fjAjShIlqo2p+F1d+lJOLtmIIfwgr3YHdtwmCFDLThdDKeAi7eWgrCo5CplpxQg9txwjjNRhf5A5EkELZUUO6qmAM3QVkJp2LNViP3bsO84ulUdFSMtOuRARJ4m/9HLt/K1pGEMpHC4m2c7AHdwLGIDTMqSYz/aOjvZTHgI4Ukp7+UWJbbjWexEKAsPErjserOfNduXbPPvsEWmtOPfWs0ddUKb74xY/z5psrueOOx5ky5e1ZDhwu7EVLcZob8V94FrXZnKssLSdyyWXI8orDGkP19RJuWIdOp5EVFVjTZyGGFSUyEiHygctJ/+pn+K+8aELfhoYgEgHHge4uQKCbGo2yNzSLAdrLErz2ChkhsebMB61HPHN1OkWw6nUYGoRIFNXTg962zVhMVNWg+3oIu7pQrcsJ3lgBhUXYM2Yb9YLrgO2A76N6ukzLTDZjClnfN/YPY0AIgTWxFmtiLaqpgeCNFahMGimlCbULA+Tk6eD76N6eUbYZhwPd0w2+N0IEjxw3v8CoprMZOIAyt6iomKqqCaxb9+Y78vEC8J5fjurpwZoxa6Sw0/kFBKvfIDxxK/b0me9o/P9O1A+YNq297Ri8oW7cnGJ2Dhz5eI2N0NoqqK7eY9cgBFRVaerrBe3tUDVGI4mzQeCuMg8huxUrxZWaE9YK0i3wQjnMKVbMLNLctcWiLs8QpbuLSa2Nt1jjkHlYeHSX4NGdkmQwHDYhNIvKBR+ZrYjvU7UMetA0CC1JgSWMEqFlCNK+wEaza0DQ7xlP4TOUZl6im8svOYVly07mD394kOLiEnbs2EZ+fgEx26ikV3WYwlUI6PcEg74hsGcXaTb3CpqGIFDmrj6nWLOwTDMpz/y3F8JLrcZ3eG/ku4YQH/R4x2RwYRTOm6y5Z4tkUw84UpNVgkm5mrNqD0+FcsopZ3LPPX+io6ONsrLDuy+OYxzjeO8RsxPMLjwaL8ygtCJixd4zhVO+U8S0vLlsH9xEn9cFQMSKMSV35gFVu2NhY24R1ZFcijO9JGOGtXERqEwrL+crHpnyMLmZIman5nJUsJTySDW5ztgsyXp7LTvtbUwNZuDgMjPswCbCettlvdPFAr+MU704F2Zz9rPB8DyP73znen7/+99w7hVXseBH/8QDBS5PqG6O8aOc7MUPi9QsGPYaHhAhEvYcR5uQupTQFGiYHDrEkCOhb7OFy8TQYYflMzl0sIHtls9bdpZSZfHzRC9VocMF2RzmBhFaZcBqJ4MP9MuQpFIc68f4aCZvlIJYo3ksksJCUKYsoggTuATGxsJMjamhw/syCW6J9bPeTSGAWX6ES9N5vBBJscXyKNEWMS3I0ZJBrcjVkgJlMVE5FCpjFzEgQ2b5ESYom4VB9D1RBWutkclmrMFdnHrMUVi/WsnjK7fxiXMXEeZUYw82YA/uIiicNeb+srySE8ur+MRRx/LjF57mmJpa5pZVogcHRwKaD3suShGseBlZVW1yPdJpU5/m5KJamrGmz8KaPvY8/lrQMRh5owF7k0AMGWsskRWonOE/ZIGsJvpnAbkgshrZZz4vqkwjuyH6sEDHJMnrFLoY/OK96oYUWG0Q1gG2Rvab4DrtAloTTNBEXpb4i0KC4UuiXdC2Rnj72CV4GKZp+BFVhPDRmKLaErzgSZoykErD1ABiRWBnoXaHwBmEwSjkheC+YKSlyWsVevhxQqbacZuX43StRksHv+I4Nm9YTW48j+rXSlElGj1xeNs2iD4oCepCk/NxAMyffzSPP/4a3/nOV7j++k/y1FMP82//9ov9OqDrh7bRnKwn18nHtSKEOmTA62RJ1QqOqzyB+gEL14JZRZrphf87bCKEgBOrNdv7BTv7GQmCHvRhUh4ESnD5dM3C0pAd/WJYBWzOv3FQs7xRsLlHkONAZVxTHoe98+xcCzSmexEMQXvhVM2s4pDNPcZSbnI+zCnZI3zZ+xn2xRef4eOfuoypU2dw220PjBlKnwngvm2ClW2SQd9khswq1lw2XVEc22/z/1MYJ4P/j0E7OYTOO1/dPPHE0/j7v/8uP/rRPzFjxmwuuGD/VvS3C7t3A9Gtf8Ie3AUqRMXLyNReiFd1KlguQdHbV3ceCipRg188n0jjY8YqQUbAioOwUVKMqGl1pAgtLESQIYyWIJw40u834XF+EhFmcLreJAg9RKYTxvBl1tEShhZ+BadzFVaqxVhelCxEJaqJbfoddt8WExYnzddUJlsQyic17XJDMLu5+MULDpvU98sWE+ZOwu5eiwjThIkagqK5I+O/U9x+++9YtOhYKipGs0U/+MG3eOihe7jxxtvfcWLr4UBYFpGLPoSz9DhU/S5wHKxpM5AFh3edgg1vkf3zbYTtrWY828aefxTRj3xsJE3YmjwV56TTCLduQSUH0YCIRA0BqgGhjYdvNoMuLAYrhCBA1EwkbGpATp2OLCpGNexC1kwkaGogbGo0v7jZjAmqy2TQtm0sL5Qa9v7VqJZmGBiAiXUIx0Vns8YL1/fQvodOphB5eYRDA0SOWgTRQxtu2aeeif3Q/aYI1gqRm4s1cRIkcsDzEXljP7Qd9H3IzwfLRmcyxrN5GHpoEFlVbfyKD4K5cxewbt2aIz7u3tBhSLh9K7KwaNTDtMjJRTc3olqb4X8wGZzjmGTfvcvv9EAPTk4xOe/g2e0QLgP7waoH/D2KFQBhQ0GR5hIPzlpm7B+aBo2H2O5Qud3tdNmQEYXwrgF4fJckbhuCFUwq8Yo2mF4oOGXCnmN0peGtbkFbyqzyWxLqB4wnsSugN2sC8spjRi28ukOw6k//TkVlDTfc8NuRcSZO3NMJcfpExY5+yeZek2jcntR4oaCywBSPGlDaWEo4EhZVaK5dsEe1XD8w7CPs72mXA3P8HEcfUrV7IPjKqKWHPEF5XHN8paYiHrKqQ9CfFUzKUywq15QcZlF70klnAPDqqy+8q7/f4xjHON4duNa7FIp8EAghqE7UURwpp88zFkP5bhExe+wA2gNhKKeC9dVLOG37M9hBFs9JoLON9IhBnp18FHFL0J8zwGN5jxBmBZenZx9wrH7Zi0bjYG6WFooZYT1R3QMU8vWhEyjR+9etra3NfOpTl/PWW6v56r//nPZPX8w6EVKsBP0y5J7oIK0y4O8y+Qe1iAA4OjCha83SqII9IIvCBkIEIZqivXx1bQQJJDttn8szufw5OshOyycpFDstn3wtWehHsBE0S58/Rgf4ZCqfu2KDbLM8JiqHqaFDpwxptn06ZDiKDO4Rim22R11ok5RG4ZunLUI0fTKk3go4w4sTonnVTRNBsNiPYmnok5ono0kuTeey2snylp1BCzg/k8MGxyNPC8qVQ5P0edFJ0yFDypRFhxWw9D0ggnUqiffCswQrX8Hye0hM2kF+zXSWzZnI469v5RPnLjL5F0KgD+Ipbc9dgDVrLl/wPF4tKOJLD9zBPcedSv7EOpxjjj+ySYWhIZEn1SFy8wgbG6C709hS5ObinHzafh12f20EszWqWGPVG7Wr7BWQAdltDGtln4AQZJcJShODAqE0YRmgQLgC2YlRzIYQfRqyZ0Cw7yN1BFQeWB1mW1Vi7CUIQA6ALgTRDfYOQTBrOEgu3+RpRJZLQ0pHzfZ2gyCYqgnqwNoKsfsl+fWCKzVcUKpZs0zz0yKJVQWxJsgZgFgSBgogCCCwQE8x/sPOGoG3TCMyXcTf+g/s/i1otwB0iL35VlY8vZ7jqo9DItnbKUdVDFtsbBSEkw9e3Obm5vHTn97IGWe8j6997VpOOmke//AP/8rll/8dUkoCFdCebiRqxXCHRViWsMhzChn0uzmquItjK9+7ztf/TuwOWP75m4Id/aYerkqYuva2DRJ7rmJBqWZuyehrPCkPPjZH44UaS8Av1kjWd48eOxtA2tf0Z429RGHUEMIzi2DmIcL3Hnzwbq677mqOO+5kfvvbO0gkxua4nqwXPN0gKI1pKhLGTu71doHSkmsXqP/TCuFxMngcbxvXXfd1tm3bzHXXXU1eXj6nnHLWoXc6BESmi9iG32Cl2glya0HaWMkWYptvQ0WKCEoWvuNj7A051IjbsQKZaieMV+GXLyU9/SM4PWux+zaDrUBYqGgBqBAZpJHpDjQC6Q+iI3lGnRwpwG19HuENoK0YGoG242gnh9iW2xha/E9jq3ftOH7lCaO8+FE+TtcqYzGxF1Gr4pXYfRvATpCtens+jypWivcu2mrsxsaNb/Hii8/wi1/cNur1W2/9Nb/61U/43vd+wrnnXvyuH/dAEEJgVU/Aqp5w6I33gk4Okb3zj6iuTqypMxCWhU4l8V9/DVlVg3P8SSbIYs1qE/QW+Ii8AkQYogb6jTLYskwhq5RR5WazoAJEbh5WRSW6vw/d10vk0g+Tve8u1LbNqI0bDHmcSBi2KZOBMADPA8dGVE+ATBrV32fGy2YJm+oRlVXoN1aYBFWlDEuVSaNViLRs3NPOOixFkVVcgnvO+/GefBRRUoooLIKhIVRbC+6pZyFLDl8hNDLmtJlY02YQbngLWTMRojF0dyf4Hu4JJx8ylXnOnAXcdtuNR3zcUZASkchFtbWOelkHAYJ35iH9t4D5JZrnm4wStiJhVLS9vd1EcotZUHrkPmUTJhgVcFMTTJli1iaUgpYWwezZmooDCEiNYmT4n70+biIwLYe7UZ6AqhxNw4BZ5d/90WxLmhC56hx4vsl4kdXsVc/FHZNSvLpjNBn8Sqsg6QlmFUHzkCFM/VCTDAQZAbYQBBr6hSksJzma1199iDNPO5NPfepyXnnlBSoqqrj66s/w4Q9fA8D0QvjUfMULTYJtfYKZxSYFOdSwrlvgKahMQF9WUJOjWdspuGmdYGqBSTueXaSZW6x5tU1QGTehcb1ZQw6fU6tHCPAjQfMQ/GmTZHuf8SpOOJKjyxWXTtdMK9xLKnQEKC0tp6SkjC1bNh75hMYxjnH8r0LUjlNhv/3fwymhy82zLiLmFjC78TUcf5ANeREenXI8orKACbqDPF1AhCgr3Jc4JXsmFWrsvJICVYRA4OHhsoeA82QHR/mTxiSCV61awcc//kEsy+Lee5ezetl0Nok0M0LXqHq1xaBQrHKznOD7o0LfxsKswOWibA63xPpp1YEhgYUgoSUhmqgWTA9Gj+FrTUxLJocu1ycL2WJ7POEmUcBRgYscJo4nK4fVdpZ/S3Sz0fYo1pIYkprQZoJy2CY9XnLTHBXsvxgQQTItcNlgZ+mSIQLNgFDMVRZnZRO86WTZZfnMDF0sBApNaajZYvnUWz5XZvJIk4MG4kgeCYd4NJLkDTvDLsvHF4rK0DbHbmzkzbc2UdUrqSifYIKL40e2SLAvtO+T+cMt+CteMYvyjk1mxwCxwRc5a241/3LPClKZLHmZeoK8KQS5daiGXYRbN0MYIuumYE2ZhpASEYuZkOTlT/LT3Bzef98f+Z2f4evXfAar6vBCxEZg21i1dfhvrMSaOQdrYi16aBDtGZGFPfsQIqQgjdO1Gmuw3tgAFs8nzJ387naulkH6Uk3sHoG1A2S3UQTr3Y+WLogeEAqCSpAJjegTWC2g8wyBu7tGExpkE0QfkQzN2yerwQL/eI39Z4FIYaJmfJD9hmRWRSC7Qe9TwmfO1chOjbMJCM1Bwgma9KUK2Q/xWyVWhyCs1mgJiRY46mHJtGNhzRxNZYuguEmgBcgs5Kc1ahLocmNxIVtMneO2vLifWMrPDPLa+of41vFnsd/tQQASo5g+TJx33iUsXXo83//+N/jqVz/Nn/98C//v//2MabNmEqgAax8RlRQWGkWgvcM/yP8wCAET8zRlMUl1wuSOxG3z+rY+zTONgnkl+oCk6m77tWVVmk09ksZBTVnc2Ey83m52emSn4NVWzUnVmnPq9IhwZCxorbnppv/ku9/9KhdeeCk33HATruvSn4VtfSYMuy7fCCRSPrzaJsiPMKICznVhQo5mU4+gfsB0Bf5fxTgZPI63DSklP/nJjfT393HNNZdyxx2PsXjxse9oTKdrNVayxdzkh1eEw9xJ2L0bcNpfeVfJYLvrTeIbf4tMt4N0QXlEWp8hNfvTZCZdgEy2EuZMQNsxrGQrdu86w3cICy0swkQVXsVJ2ANbCGNlKCd3TyuZlPglRxMUzcEe3Indt5mgeP5hz00LMdwENurV4R/yv73lq5tu+i8qK6s577xLRl5bvXol3/nO9Xz845/jE5/4/H/j7EAN9BOuXY3q6kLk5mHPW4As2zcBAYLNm1CtLcjJ00a8ZEU8gcgvxH/5eYL1bxFu24wsKkFEY6juLrRtGdI2DI3aNZ0yhZAe7n8fGoBYHFFShurpRg8N4uQX4Cw+BmvqdMJtW0j//MeowQGsyVPB8wj7+6C9zYzlOOB7qN4hw8xFo5BOGz/eRceAZRubCNuGaMwcKycXUVmFnHT43s+RCy6BIMBf/Tpq1w5EPIF70mlELvrA27rmwnWJfuRjZO76E+GWTWgviywoxL3wg9jHnXjI/Ssrq+nu7kQphTwEcXzAOQiBc9wJZP58K6qvF5FfAGGA2rkDWVmNNWvOfvtozyPctB7V2YnIzcWePXdEEf63hqkFcOEUxaO7JFt6h/3QdEhFrj3iv3skiETgwgsVt90m2bhR4Loa3xdUVGje/37Fgd6GYIZG54FsH06lFiD6AQX+UXvuY46E99Vqfr/RBLolbEPcxm04p9ZYQBwogdkSEOxzS9zUI8hzNdW5hgzvSglCBUODGqUFsag5ph9C0jfKhFTHDh6689d85tNf5Lrrvs6aNW/w7W9/GcdxuPTSqwATcjGtQKO0Cd7Z2AM/eUPSloKEIxjyNZU5mtp8zWutgq19MLPQhOFNzhdcMlVhW4I1HZJdA8bGYVmVHkVkHwqBgh390JMRPLRD0JoU1OUZBXK/p3m+SVIUVZx/CKXLwTBt2ky2bt30tvcfxzjGMQ6AOUGEo+x8HplxNk9PPhkR9rIy3kAZQywNdo1sV6iK2GJvpEO2H5AMnu3PZ0ownS32RqrCGlwidMg2XB1htncKG60suVpSqSw6peLRe+/ge1/+DHPnHsXNN99FYWkZd9tdFOk9IWoAuVrSTECrDA5JBgsEp3oJpgYu/xnvZaflkacslIQhFENSYSPQaASCPhGiBSwIDDMXQ7IgiLLCyVCo5QgRDDAkNA2WT2CZvYeE5i2ZoV84zA8i5CJpk8HI2ABFWjI5dFhtZ5geOiT8GC2WT5sImKMifCVZyETl8LxMYSMIgO1WlhYrJBwOkHvLznKON9r79xwvQbWyuSXaT6sMmB/EqApt6l5+nSm334vX201KO2S0gzVrLtGrP3nE+RF7I9y0nmD1G1iT6hDxBDoMGeqeg2h8jnPjGb7tBTyxfDmXnLaE9JTL8Z5+Cu+xh9AD/SAEIhrDOf4kIh+8AmHbyMIioh+4jNnnX8w1RYXc/Iff8olYjCPVZgohcE4+nWDrFsKtm5HFJehQoYcGcI494aDBccIbIL7+Fzhdb4IOAU2k/hHSUy/DqznjbV+rseAv1gSTNc46Qe4PQHYIdBHIDgzZqYxdgwwBW6AqNLIRZI9Ax8zivEiDyjVkrrNZILsNwYuG3R8Nb6HGfUbgvGmUxzIHggkaf75G9g2H1E0bXXvoIkh+TmFvAqtToHIgmKPRORB5SmC1CIKZeuQY4WSIb4LT2zS/mC1wTtZceD8UNwu0hOwE4Kjh7TUjFhFO3wa0kxglllq1q5+UF3L80RWI9cPF4u4/ZwCpCY9MG0RpaTk/+9ktXH751XzrW1/gnHOO5WMfu5azrjmLjJsiau1pw8qqNI50SdjvTTD63wrak4JsqKnNG12jF0WNaCHlc8jut6PLNEMzFE/VCxoGBdv7jI5pcZmmJA7daXhghyAnAqfUjF3fDgz08/d//xkefPAePv3pL/Htb/8QKSUr2gT3bxN0ps12BRHJWZMUC0o0SV+Q744eL8cx8x7838vhHxbGyeBxvCM4jsOvf/1nrrzyPK666kLuvfdpZs6c+7bHk16/+Y99WoO0HcdKtb2TqQ4PpHG6VuE2PUV0x72gA4KSRYQ5NaA1dt8motvvIT35ElSs1Kh8I0UEbj4acLrfRCUm4BfPw6s6hWzVKSQ2/Aq37WWEDtDDFhwqVmJ8rqRjVKHhESxJSge/dAnRnfehYmUj4XtWshkVLTpkWNxfG93dXdx775/40pe+NZLU3NfXy2c+82HmzTuK73zn3/5b5xe2NJP53W8Id243L2iNXF5B9MMfw569z2fVy6KVYt+ULOG6hM2N0NGGPWM2wnbQ5RVY6TTh2lXowDceY2EA8RzDaKkQMmnQGJuF5JDx+7VtON+opGVBIXLxMXgzZxNueMtsH41hlZahtUK1tJj2tb4+sG1EJIIsr0ANDhoFcHsrIhpBTJ6CdF3UwMCwcjhDuPZNVFOjsXs4DIh4gsiHr8Y945yRwDhRXvGOvAplWTmxz34R1dyITqWQFZXI/ILD2jc3JxetNYP9feS/g4cP5/iTUK3N+CteRrW1IqRAVlYTufyjyNzRhZvq7SFz202EG9ajVWgEBTUTiX7k41iT908XVgo6O0FKKCn566/TCAGnTdTMKwnZ1mcOfmOZw2BvFpEGqwdUDPQhgjf2xrx58IUvKFavFnR1QUWF5uijNeX7r52MIKwzAR3RRyT2JgwpHYHsyQpv0ejia2GZJuGEvNIqaB4SlCcUx1ZoZg2/xbX5moilSQVd5EVbEfj4YQHpsIZ5xaPbVRM2+NoQxQURaByEjC8ItUYLU+DluUax5Vow4Au0VsyYvYhvfvMHw+d7FGvWr+dXN9/I3NOvYnL+Hk/f3QqH2cVw0VRNd8ZYNOQ4UBLTvNEhSAaCwohmeqFRD2/vEzzZILlsmqJ1SNOTMct6a7skN6/TXDlTjQTLHQgdKfjjJsnWXkF3WrNzQFKbZ8I2xPC5pgLNq62CMye+/UC6adNmsnLly29v53GMYxzjGIaD4MpMPrPCCOvtKGk7Rh9PUqAacdjTWZQUQ0R1jFx94AXWOHGuSn2a+2K3s8XeSCB6KAorSagP8GC0iiHRi48moxXN3/8pm3/0M+Ze/iF+9KNfUWrnotHkaEmHGB3eHAyTovF9JY0HwQTl8A/JYl5zMqx1ssgQFgQuGTTLI2k2Wf7wz53gJC/O0f5oNW+lsngNPYrY3WV5JIViWuDQIY1nbyA0zVZATegwgGJeGEEM21G87mR43cnQKM1+b9pZCrSFowVzVZTL0nlMUoaBydfG83eNk6ZNhkS0oaE7RMibToZWGVCp9vxgSAQLgijzwwy+1EwJXdzePurueRDpeXTMmYGrHGpSgmDdGrzlTxD9wOWHff32hWpugsCHWBzV042/aiW6u4uMHaG4RrOsIo+fP9bKmdd/g6A9iffIAwg3gpw5ByEEqq8P/7mnsaZMw1myR3wkXJfPf+lb/PGOW/npT/+V//f/fnbEc7Nnzib28U/hLX/SWLgl4tinnI57+lkHDRqOND2N0/EGQf40E+6tNVayidj2uwmK5qDihx/EeDjQReCdpPGPEkSe18Y42hKIAVN6eZbg1RismaFJ5MH7n4DSNm3I9JBh5kejSoEURO8UWJ0CPAjmarLHaWIPSqwmgTdfY283SmDZbcLgVCFkz1KE+5fE4EIw33zX9obsxYTN7f3VE6BjmmOBtTWKVRFJ+/nwgUfAytWUz9RoF6wGc87BPDOmsuPYalQ/LS+9VU9O1GLO6dMJshpni0DlawjBHxBsmqZ5NQa5DYIFpYdvpwWwbNnJPPnk69x448/48Y+/z18euINLrr2U485ZRjySQ6B9fOUzMWcqCftvUzjydpAJTDdce9J0580p1sQdjSUFvtqj9AWT/5EXgchh1KJCwMk1miXlmheb4Y+bLKYU6JE8kIqEsWF7sVlwQtX+6uBVq1bw+c//Hd3dnfzmN3/m/e83gqWmQbhzsyQbaqYOdx62pzR/2S4pjoYURjTdGaMI3o2+rPn/cc/gcYzjHSIWi3HrrffxgQ+cwRVXnMf99z/DpEkHcWk/CFS0FBCg/BESFK0R/hBhbu07nqvb+gKxTbcgs11Irx8tLJzOlaA8wrzJhIlqrMGdaCeBV306kcbHIdNh/IFRZCZ/kPS0jxDmTYLhlrrUnM8SFMwg8dYNyHQnQeFcwrw6sGPIVBs6UkSYM/GI5ulNOBu7fyt270YQFugA7eSRmfwBuuPF9EmPAmVRqA9coIw5LprtlkdaaCqVTYUarZw4EvSLkLV2lj/8+T8JgXM+cjVgWjeuv/6TDAz0cc89T+L+N/psaa3xHvkLasc2rGkzEbaN1hq1fSvZ++4w7WZ7JY7Kmgmmda2nGzEcGqC1RnV3Gt9bIRC2+VwKIbDnzjekrJdFNdajwwBRVIJVPQFZXon/8vOQHDKKXSEQBYWIvDyCN1ehzj4PmZfPkFB4ixYjnluOznqQTJrJxHOQk6egentgcACRm4fIyzcWB4mE8R0uLUf3dkMsTrhpvTlGbg6quxv6+8nc+Qfin//KKM/eg0EIgaioRFa8e8WrkBJrwuER0jAc5PHKC0QeexiAjv/v+8TPvRD7uBMPWpAf8PiuS+SKqwwp3NwEkSj2zFmIMXylvMceIlizGmvyVEQ0hg4Cwh1byd75R2LXf2OUZ9zWrfDww5L6evP9mTJJc9F0zVSM+sKfo41K9q+A0jiUDvv13h516enIkvtDiewREAVvviJzoUYfZhtUTQ3UHGBFfkwIyJ6hCWaG2JsFBBDWaYKpjKRN741phRzQ3mB6ASyt2kpLegNZ7QECJWBeeQNHlS8G9rQyH12uWdMp6MvC1j5By5BASkOOOlIz6Al6s8NFqzbFYqKwkrkzTeqJ1rC8UbDNnsXOhvv42WqL6lzNB6epEXJ6N2YUGhuL4qgm1zUewD1pgS3Mg4UctqKoSmi29ghu3iDZ3i+YVqCJOzDkaVa2CRKO4KrZB762SsPtmyXrugS1eZqoZQI4WoYEua4JJQHTnpcOBOng7QfSTZ06kzvuuJUwDLHexndrHOMYxzh2I4LgWD/Gsb55sr5PTeSh6Ep6tUWBLiQlkjRauzjaX8qkgyU5AZWqms8mr6dNtuCJLGusAh6KehQrQZmWvCKH2PHFb5O6+U6W/PO3KL/+0/xJZ/h8Kka5sjnWj3JLrJ8eGWJhCOAsmhrlMOMQquBOEbDNNmTTlNChTNmc6SU409tjkaDRzA+ibLU9AqA2dJgSOvt5ES/yY7zqZNhi+cMkrGar5ZOvLGYEEXzHo1uG5CqBJxTbbY8yZbPMj6HRPBgZ4olIEqHN9bW1QAtNbeAwNXRZGESp28vTd74f5b7IEDutgIrQwkYwKBTFWhIKeNVJc3F2f8KqOrR52QGFJn/zNqJd3fROn4wSkKclwnWRRcWEq99An38JwnXZbnm87KRpsHxKlM1SP8rCIHLwZ4pIFK01wbYthGvfRHd3gOMQKsVQpIoPz5rL5555hNeXv8xcy0EPDWLN2lM/yoICws52gjWrRpHBAAUFhVx33df54Q+/zac+9QXq6qYe9H0eC/bseViz5pq63Y0gXBetFKrLrPqLffIn0Bqn/RW0m2eIYAAhCBM12L3rsXs34r3LZPBueCdq7J0CFQU5qJFdEHYKblyoeXYW6ALQFqw4Fb7zNJT7xlZCZIGswEpqZCCwdknCqcZbOPKExH1eI5IQTNUQg3CqRraboOCgTpP+qCKYBkfy6KiKMXYTij2EsAaRFtgTFB+brdnQHXK7LXliEI59E/RqQToClZM1wSWKsMbs5pctwe1Ygcj0oKNFoDWPvbae42dWoCcvIPUphfuiwFkr6AvhL1M0y2sFmRaBboFnGjV/N0cxreDw5+84Dtde+xUuvPBS/umf/p7f/PN/8fCtD3DxJz/EyeecxpT82VQn6t6zoM+/Nnoz8LsNko3doLQRNJTGNJdM00zKhZ0Dpj51JfR7ph4+e5IeFWZ9KMQdyI8IHEvvFwyd40J/1hDSu5XGvu9zww3/ys9+9kPmzz+aP/3pIWpr96xIvNVt6v2Ze4X2VSZgc6/52ykTNH/eJGgYhMKI8QzuzQpOqVFUvTP3m//xGCeDx/GuIC8vnz/96SEuuuhUPvjBs/jznx9m6tQjV7D6JUcTFEzD7ttEGK8ynsGpFlS0GK/y0K3luyEyPQjlD/vuDj/kBhki9Q8BmiB3CtZQE8rJRQZJ7P6tqJzdPSQahCQ97UqCguk4nW8ggjRB0Wy88mXoSMGoY2k3j2zdRYS5tSTW/xLh9SGyfchkM0KHZCZ/ABU/MlZIxUpJLvgKTsfKYXI6l4HShTxQVslKt5ukUCS0ZIkX47xs4rDSkeulz+2xAeotnwDI1YJjvRgXZnNxj5AQbpY+v4v1s6m9iWd/9h9M+NgV/H6ixUfSWV741a957LEHuOWWe6ipOXwS8L2A7usl2LQRUV6JsM3tTgiBnDAJ1VhPuHM79sw9QSayegLOshPxn3qccKAfEYuh+nqQJWXIibWEq1aOGl9IicjPI3L6+5A1NWTv+KNpZ3NsdH8vsrgEsfBohOeh+/sQsTiipBTV0Ubvto08vmw2a50MkRMnc/YrC6nd2khONG6kpp6PLC9HVNfg3XcX2rJMCF1ODtbU6ejkEPbMWehUkux9d4HvI4pL0Mkkwo1gzZmL2raFcNN67IWL/qrX/Z3AW/4E3j13kDM4CEBfUyOZP/4ON50mcub73taYQgisSXVYB7HNUIMDBG++gSwtR0TNw6ywbayJdYQNu8xnZYYhEVtb4Xe/k3R0CCorTbrMur8IBj3BVydDqQuRYk36Mo2/6AhI1XcBeYO5pNqTyAGTHC3SEHlWIpOa5KcVh3GreHsQEE6A8AisEMZCVg0xqWATiYikN11GoKE4GpLjdjDg76CcPWr+ReWanf3wxC7Jll5whSFPi6JQEoHmpKbfEzBczOa6Gj1tGWs2bQFgbZfg3q2S3uYt5JdNxJGa9V0w5Em+tkRRvNcaypQCOLpM8US9IBMIkj60Jo3n2aS8vawwLFMYD/UKahKGCNYahnxTqN6+WZLnKk6doMdUCO8agC29ggm5xl/Yd03RHChN8xBMyTfkdk/GHDfvHay1TZs2k2w2S0PDzrf14DyOcYzj/y4CAiSjLRD2xtmZCxgUg6xyX6NdtBAlxlH+Ui5PXz1qn+xQlsGOIWzXIq8yDzkcMy8QVKpq0ijeiHWTpyWl2mazGqL+E18ldffDVN/4I+Zf9mHylWST5bHaznCOl0NCSZJC0WwFKMw9uFxZXJ3OJ/cAymCN5lk3xaORJH3C+KgWaMk52QSnevFRJKdAUKMcaryDh6sVKsmxXoynI0kapEeellSEFvnaIg/JrMBli+XRJ0MyAvKV5PJ0HguCCK0y5AU3RYGyKBkWflRjs8nyKVHWmKTuJOWwMIiw2fYYFEaRHNeS6aGDRrPF9tBZzWbL4y07y6BU1IYO0wOXiaEZO658PK0YyKYoztpU7A4TlBY6DEAp1ttZbo310yeMsrlZZlhnZ7kom8Pp3oGZFXv2XLAswjWr0F4W3KixQsukEZkUp1VOoCaW4Le3/JKfXvWZsdutHAedTo85/sc+di033fRf/Nu/fZdf/eqPB31vDgQhBAxbg4Xbt5J95AHCXTtACOxpM3DPuxCrZi+Bjw7Q8gCLqWq0Oh0PCIF3QYnoLdU4r2usBkEwXSMmwupd8PxkqI5AjgdiSBCxNC/OgTPWQZ4NQhuyVyaHr60Aq1UQlmuCmZrIs+b13UpcAuNDrHM1IgVBLUdcR/oLNO6zGnurIKwy1g9WK6gijb9Yo4HnmiXNKYF9sublRRCvh3ZPMP0YzUV71dF+2TFkJ2zDbX4GkWphU8sgr27p4Mbvf23YpxkyF2lSF2p+vlqyvttkOljCLLZv6xPct1Vy/SJ1UE/asVBdPYEbb7ydtWtX8e///j3+8x9+whO3PspXvvJtas6bfEQE+d8ynm4QvNUpmFJgOvW0hp0D8MB2wZUzFX/ZbizQQmWs006ZoI7IBm03iqKaiJQM+XpU6HV/Fibm7QmZfuGF5Xz3u19h69ZNfPnL/8AXvvANbHs0hZn0zeXf95YRsTT9WcGJ1Qqt4bkmQU9GEHc0769TnF2n/xbdN/+qGCeDx/GuobS0nLvueoIrrzyPiy46lT/84QEWLlx8RGNoN5fUnM8S3XEPTs96CEKCwtlkai8gzD/0w6pMtRPZeS9u1xrQAWHuJDKTzicoWYiVakWm21GxCrQVRbl5SK8f5eQh/QFEth+Z6SQonIWKV4O08MuPxS8/PB/koGQhyflfxG1+Bqt/K2HuBPyKE/EqjjDVduRa5I0Ke3sgMsjjkSQlSlKtHAZEyOORITSaD2UP7lOURvGn2AA7LZ/a0CaCoEconookKdYWpx2keNtvXmgeiiTZZQU0fvNficRinPuNr9MsQn65/gXu/8E3+fSnv8TZZ5//ts77XUUQGLuGfVVvlmXsIFQ46mUhBJGLPoRVWYX/2svovj7cE0/DOeEUUCHJdWsJdmxFTqg1KuP2VkQ0jj1/IfasOVi1kwk3rENnM4jiUjL33I5uaiTs6gR/+JeqoR7t2Dymu1nuJilTFqq0lIc/cznH3P0YS7e2klACa+p03PMuROTmoep3oQcHEaWliNx86O+FtIV99FLkpFq8Z59GdXVBcggRT2DVTUFOnES4eROqt/evdrnfKXRyCP/ZpyAWp6DISDNTxWZBx3/uadzjTnjv/Hs9b1h1vY9i2HVNW2M2M/LSypWCtjbBzJmmiLA3C0p8wTo0r+VqzqkDqx5i9wiCKRpd8N5MeT8EUNhdQL/fN6Ki0HHQEY2z3oSOhH/jnF+f14WvMtTmllA3TLIGSrJrIMYTDa3cn53L3GLBsZUmwOLoMs2LLZpASSK2pihmnlM60+ApcCUUxqDA1Syp0Ow454s8/70T+ekNPyQ781J2vLaS7ct/S8Vlv2JNl3ncbxyEhaWCK2buKW53Jxs/vsv4kYWm65J0MLr+70hBfsR4Ie8uZDf1mvRlLxT4SnPHZuPx/Il5iqJ9RPtJ36ghdislch2T2Lytz+zfldZkQpBCcOoEhfUOyP3aWqPOa2ysHyeDxzGOcRwWdlnbecZ9gi3OBmI6zlJvGSdlzyS+V9cGGLuHj6Y/wWnZs+m2OslRudSFUxkSg7TLVgqCIppfbWHXaw1k+tNI26JwYgGzzp5JXvme3/mU0AwJRa62CDIZXvnYZxh88jlm/OHncMnZZH2N1IIIgjYZkBSKR6JDVCubOYHLkDD2ED0iZKvlcYI/dlDeesvj9sgAMQx5CtAuQx6IDFEa2kwNnYMKL9Io1tsenVZAjpKUKIsHo0PstHxCACGoCB1O9eLcFR3iVSfDkFCARmiY57t8e7CYGm1W+Bosn0GhqVJ7jikQlCjJFtsjjRpzPnODCKuDLKVaAoICJYki2S498pRkuZvigcgQaaFxgFecNFNClwszObzuZBi0JTlNLRy9cQuJSA52UQnhlKnorg7ck09HRSM84fYyiGJG6IyQ5C0y4Gk3xSI/SsEBuhZleQWyegK8vsIEJQcBWNKEFEsJne18pHYqP379Zf7lsgWUVm4hDH2y9lRCnYMOfHQ6NUrEsTdisRhf/ep3+MpXPsX11/8D06ePvd3hIGxpJn3Lr1Ed7cjyKtAKf+WrqI42Yp//ivFOFgK/5GiiO+9HxSpHxEcy04V2ckeeW0UvRJ4WuKtN55Q/U+OdrgkPp2k0a8LbdI6p53ZDlUHyE4rI0wJno0DnwJvnafxAkNsncDJwVL2megjCXggyAqlB+AJtGw9ekQFcAaHG3iXwKjQ6AbIN0CB7wF4rkIMgk+Y8Er+QpK5W6CPIpVDFkLpaEf2LxN4pQEFQq8mcqwhroWkANnYLJuSY0N1kofmnK6XpSgpO8/Za+JY26ekfxSs/DntgGzc9+XsKCwo44yPfHsUEtiZhZ7+gKmGIYDB1XHWOpmFQ0DQEtW/T4nf+/KP5/e/vZ9WqFfz7v3+PT3/6SmbPnsf11/8jZ599wf/oTqtsCKs6hCFqh09DCJiYC9v7IRsKrl+k2NILqUBQGVdMyH17NnmT82FOieb1dkF5zHT1dWeMgPzkGk1D/Tb++Z+/xhNPPMSSJct4+OGXmT//qDHHqkwAaHzFiEJZaUj5grp8hRRwygTNcVWavqzxC04cfC3v/wzGyeBxvKuoqqrhvvue4aqrLuKDHzyTm266i5NPPjIDfZWoJjX3OkS2e1jdW7ZH3bsXhJ/E6ViB3bsRbbkEBTOIND6J3bsBFa9Cyzh270YSQ80MLfgy2skd9vD1wEkQFM7B6VqFTHcglI/TuRIVK8d/ByF1QeFsE36n9btqINorQla6aUqUUUYA5t8KXncznOElDmoZscX2aLB8JofOiAq4WFuktOIVJ83JXhzrMJc0u0XIVttDP/MKW+65n/f96mfECwoo7e7hT1d/imnzFo74cf53QxSXYE2qI9jwFiIvf6SFR7U2I0vLsCbWjtpeDQzgv/QcZNK4H7jchFwIgRocIPvog+jBAdTO7YRr1yDKypB1U4hccAnWcGFqVU/AqjYKc601/nNPkX1uOaKkFFlQYI7R20t2qJ+uwW6mhe7I+6Enz+Chr01E7Rrg/EwCWVk9omaOXnIZ2QfuQXW0Q0c75ObhnnMe9sKjEVLinHwG4dpVxuYinmOIai8LwxYX3jNPIfLysGfNecdp0O8lVEc7uq8XWVlDbmjaNAeyGWT1JFRrE6qjA+s9IoNFQSFWVQ3hts3ovT4rurMDUVCI3EsJ0tIiiESGV5M1WC1ATGN70Dq0O0UZ7G2GKPaPeXfUwUpzwKReAJGCorCIXr8HrfWec8gBmjSyx3gQ/k+C0vBWl6A9LRBa0z4AG3oka7s0509W3LZRMuQJio2LC73DnmDlcU1rUmIJyHc1s4s1BRGYMXcJ3pfu4d77v8Wunf+CW1xH8cU/oXjph4nZhuRtGjKk7wVT9EixmArg8XpBSUyzqNwYXDQPCl5phZeaBTOKNAOeSSu+cJriyQZT1EYs2NUviFoaS2gKLJhVrNnUK3ipRewXAFceh/yIUf6WGHcZZhdrhnxDFGtMkNwpExSLyw//vdRaM9SZpLfBLA4VTiwgL68AgIGBvnfhnRrHOMbxvx0N1k5+k/gP2mUrRaqEbtnFXbE/0GjV87HUtdj7PFYaBe1EatREBkQ/d8ZuY5XzGlmRpeL1GmqWT6PcqSS/Op/QV3Ru6yLIruOYq5ZgDxtQ5mpJgbJoTw3w+uWfpGfFSqrv/hWJs07G0xDTJsgti6lrd1g+7TJkcuhgIygcvk3GhGST4zGQDcnbq15WaF5y0twY62Oz7VGiLJJCMTV0yVeSl500P8jpYnLoMCuIcLoXp1qNZhG6RcitsX42254JmkbTLgPi2gTJRRCkUGyzPBJK4KHZJX3c4RA6LSERWuTu5avkaEOzjti8Aj6adhkigXrLZ3ro7mdNMSuIUKYtMmgmKFPZdw8H3NWFDo9HkrgIJg0T3gGaLZbHZsfh6sYsA3c+g8JFKolOJgl7e1ANu3BOPAXn9LPoESFNlk/ZPgF95cpim+XTZAUUBPs/j+hsluyD9xKsfh20MgvtSplurPx89HDA8kfn5/GfO+DXjz3KvxyXC72vEU1vprdvLkGPwp4xC3vJgUU6H/jAlfzgB9/kjjt+z7e//cMDbncoBG+sQLW1Yg37FYPJ/wi3biR4cxXuqebZ1qs5A7t3A3bfBrQdRygfjSQ76TzC3DpIQfwWibtOEBZpsCD6gsTeoUleq1AHcpFQ4D4viDwnkL0CHdd4x2oyZ+oRZbGqgfTfadIZo7b1dwr8ekE2ppjznGByP/SWQotrasfiLuOjG04A4YHVYmwjVA7IVrA3gw41Okdg7QKrQSCThoRWEoKZ4L4lUI8L0pcfWS0ZTobkFxWyxSiNw0pg+Gs06BsScl+7gLhjQnSTPqO7oIQkLJhOOl7L7Y99kg9+6CoikdGtVlqPXTPvVgiH6oimPxoaxAAsmrmUP/3pIVY+/zI//sH3+MQnLmNC4SQ+dtFnuPTLV1NUcviBHaJ7ONyvBDg8Z7/3BKEyAcb7qqYtYa6pr0xdO68ExrJ6OxJYEj48U5HrCNZ0SobSUBzTHFPQx+M3/is33/Rzysoq+eUv/8AFF3zooDYcC0o00wsFm3oExTGNFNCZMl12Syv2zDNimTp7HHswTgaP411HYWERd9zxGJ/+9BVcddWF/Md/3MxFF112ZIMIgY6WHPA2I7zB4fTWVRhTSkV0+91Ir49s1elgmx+FwM3H7tuA2/I86VmfMARw+8sEdhwVr8ArXYrb+pxp8xESrUNi2+/CGmokNfNjI77AR4x3ueegT4YkhdqvAM3TFs3Sp0+GFIYHJoOTQhOi97ODiGnJkFR4aGKHSQaHAjwvy8tf/UeqjzuG2Zd/CICX/uGf8foH+Md7H3/XfYI1mm2Wz2bbI0AzKXSYE0QOaW8hpMQ95/2otlbCTesRiRx0JoWIJYi87/2jVKbe00+QuuFHqPYWCBUiNxfn3AuJfelreH/+Pf5rLyNLy2DYR1Y31GPNnY89Z/6YP1BCCGRFlWmBGxxApVPmD5ZNpqKcwvomwn3aDnOkw6baci5Kji4gnONPwpoxi3DLJlAhsm4Ksqpm5LjuCSeR2boZ3d+PcKOoVBK1azs6mcR7/GEQEiFATqwletU1R+Th+9eEiCeMp1wmjRgOI9RaozNp498Wf+9+wYVl4Z55DumWJsKtm5B5BehUEpTCPf9iZNGe96SkRJPNSkO4DvugaWECjItjw3ctCWiBCN45+bquC15sljQMGoJwWZUpbvYtcnUMJpbVMhQM0ZnqoCxh7GlECnAFOv+dVL9/HeQ7xURkhHSYJG7n0JmCliFFQSyFDmcyMU/iK83mHkEmkLSnjPKjJwNtSRMq15UGpYw1RMyBTCDY0G3Gj9uaiYvezy8++z6erBf8co3EtcxDB5i3LWHDoC/Y2CNGCNeGAZOkPDF3z3WfmGdI2jWdgnXdgrgNCVeTDmBZpebhnYLe4QeZ3Q7JtXnGHy3XMX7H+5LBZXE4tlLx+C5BJjTqhd6MCdW4dLriqDLTTnckimCtNTte2smOl3aSHTKxyZEcl9rjJiGEoL+//22/X+MYxzj+7+B592naZSszg7kjROCQGGSVu4ITvFOZFcwbc7+QkD/Fb2aF8xKlqpyCsJDUGx677B3EK2PkhblYtkVhTQF9Tf10be+iYnYFAC6CRV0eX7vywwxs2MwZ9/2R+lPm0y5CpgYuroYdlk8wfcZ1AAEAAElEQVSxlhzlR+mWZslz36pMsMe2dG+84qT5c3SQXhkSQaAFbLU9BoWiSwa0ypBiZVGiLJ53U+y0fD6bLqBsryC2xyJJNjhZMx8E3SJgrZ2lRO2xzY8jKVM2L7lpoghO82IMSY2FoFBJWmTIm06Wkz1T50wLHQqV5HUnQ0QLsmi6ZECfCKnWDv+V6ONoL8LlmbxRCuFKZfPBTC73RgbZYvmAJqEl57T7lK9+kQmpFooLSuheOB8/Pw8bQZG2WGNnOXfVSqzmZpwTT4PODsK2FnQ6jU6lsBYtxaqegEOIhWAfAwR8NDaGxB4L3lOP4T3+MLKgyORyRGOoliZUeysM9sPAAFJlKTs6xvUXzONf713L3533caYUDuJ0riEn1kfq5E/gHH8SsuDAslTXdbn44su5554/8c1v/mC/dvLDRdhYj4jGRtX3wrIQ0jZzHoaKV5BccD1u64vYfZvQdg5+2RL80sUgBM46gbMJ/CkahvlKVayxNwncVwWZi8euEd3nBfHbBdoFVWisHaJ/EYghSF+5zz7D5OGMQuOJm81C7Q4YLICBOJARFAOqCKxmTHhbwvybJFj9RtlgrxOIAPz5xhLCagYdN68FkzVhrUZ3grNakDnfqIiPCNIQ2PuiPG7I3p4slO5lodGTMVYChQcI3X3yyYfo7u7kiiuu3n/MhFEB7xow9lpimMxsSUJlwmRAvB3Ym423sl0v0JbGn6k5te0Ezpz4BG8Uvs6vd/6Sf7v1n/i32/6JCy+5jI9//Frmzz/6gOOJPvO+um9JyJrPRvZ0jXeCftdsJwIFDYPgh1Cds8eHdyzEbJPr8VqroDi6x0KhM208fiflvbvPEfkR+OhszXmZkKb2Dh6+/Zd89fe/IpNJ86UvfYvPfOZ6YrFD+6rkuPDxuYqnGgRvdgiUhhOqFWdO0uPk7yEwTgaP4z1BPB7n5pvv5itf+RSf+9xVdHV1cs01n3vXzNXdtpdwOt8gyJ8KlvkVdDpfR6bbkX4/yi4zGwqBdvKwB4zfU3ra5QivH7tvs1mZVj7YUYL8qQRF8wxp5g3gtjxHmDORbO3fgNUBUJjNcFTP6+SnunDdQprL5pKOFjIoFPFh1cTBUK4sIkgGh/29dqNXhMwOXaJH8ItToiya/t/P6d+2gwue/xVCCBqef5GNf7yDE3/2I5ZWjRUx+/ah0TwSSfJgZIheEWJjSPDFXpQPZ/KIHsK8yp4xi9i1X8Rf8QqqoR5ZUoq9+JgRNS9A0FhP6kffQ/V0I8orwXaguxP/ntsRrovavhVZM4Fw6xZUS6OxahgaJHj2adLSIv6ZLyDLK8Y4uIOIuKaYHhpExOLY0+tQeTGk56PQo1QdaTRFY7yXOpVEtTZDLI5VW2fa0/Y+zNFLiCSH8J9+HNXaZOZvOxCNYdVOQUSjaN8n3LGF7F1/IvbFr72tMLb3GqKsHHvOPPyXX6A/z/Rv5WKU3M5xJyDGusZvEzqdRieHTDDfsKLAnn8UsU9+Dv/FZ1H1u5DV1TjHnoC99LhR+y5apHnlFdi1C6qrQRVA02YoK4Wjh6coe4wiN5z0zsjgVR2C32+QpHxjP7CtD7b2mtC0c2r3GduBKWfMhMdh87YNlM0uR6TBahb48zXBu/vVfE+QcHKZlDOdHYMb6cl20p21iNghUpfgDYcOORLijmZ9l2DQhw3dkiHP2EIMeIAGx9JMytWEGgqjMOQZUr0iITixRlMag2MqNTe+ZdrIorZGaUj6gpK4aUlM7hVYvftbuvcVVxo60wIp4LhKTXEMutPw4A7BpdM1V87U/HEj1A8Kch2YlK+ZkLNnnAOpvM+b4JOD4NUuhyHfKBtOrtEcW/n2vM26d/aw9bntOBGb4rph+5WeFNtf2Ek0GiOZHDzyQQ8CNdBP8MZKwp3bEPEc7PkLsGbN/V8TrjKOcfxfxWZ7AwWqaJQiNEfn0kQ9zVbjAcngHfYW3nJWMzGsI0fnQgiyO8FAoo8GaydVYQ0CgeVYoPXIohVAf38fP/3ghXgtDZz/4J04i+bhBIoMmjiSFiukMrS5IJtLjXLI1caioVUG1AwLKNSwonaJHyV/rxrYR/Ocm8IB6pTLWpkxSmMtecvJ4qGRQEZqttoeMwKXRsvndSfDuVlzMx8UirecDGWhPSJQUEAMQVIqekU40tEXRTAgFFXKplw7lO/lVNZGyDbpIR0YEoqolnhommRASigGhCIUkKugMrAoUJKXImnKtM152dHM1hI/xtTAHRFQ1G5vouCW39HVvJP5tk+OkgzVTmTzNR9hqNZ0PQlMrSXcCNKyYK8w4XDbFkiZcOMCbTHXj/C8myI3lLiYjqMGK6A2dEYF2u2GTqfwX3kRmV+AKClFJwdR7W2IgkJ0ZwdkM4hoDCsnyuCqHq5eMo/fl+ziG7c+wz3fvRxRmU9CK9RxZ6CdQ7N4l156FTff/F8899yTnH7628uakMWlhHvZg8GwOCEMEPkFo1+PlpCtu4jsGONYbUAo0LZH2N8CQmDlVKNzLOxtw+1l+yILkecMEbzbSkLnAi64r0uyp4WoMcrhuSWaE6phwzpBalAwmAMZT1NTqKnsAJEELIHs0eiYqV1FL4gAVL4wgXE5Gtkl8OdoZC+ENRpdqFHDAcTaAZkRxv/4XWo0LInBsVWKx3YKvBBybOgbNAKLU6boMUNytdb86lc3sGjRscycOXe/v0d64JJGze+aJVttiA1bT+TF4Nw69baCd60dYP3Gp6W9jeZYC+2DbfQ920VvqpfOmm6GSBIpjvGh2BVs6drEww/ey513/p7S0jLmzTuapUuXMWFCLWVlFZSWllFSVE7Nn0uIrJaE5RqdB7ILYn827/270VVYPwB3b5HsHBAEyghWzpqkOal67HpSCDh9omLXgGRjryDX2W1NZq7b4RKrftqne2c3fiYgpyRBQU0B4gBF79atm/jNb/6Du+/+A5Zlc/nlV3PttV+hqmqMlYODoCQGl8/QXDjF+BzH978VjWMMjJPB43jP4DgON9xwEyUl5XznO9ezceNb/Mu//AfR6Dvvf3C6VqGt2AgRDKCixaAVMtOJipWNvC6CJGF8jtkmXsnQUV/H6XkLmelGDtYTaXqSoGguCFMgajcP7eTgtr1IdtL733WV75FCptqoWf9LLhtYR4f0iWjBjNxqnpj/ETpL6zgre3CLCDCtYQu9CK+4aYq0RVQLumRIVAtO9hIHT//dB088+gCv/vsNHP3db9C9YCrJ9CCPffnvKTluCV+47JPEw3c3oWqL5XFzrI8eEZoiCkgqzXORFNNDlxMP4P+2N6wJkw6ohtVak73pV4T1uyCRA729yPx8RHkFqn4X3tOPY1VUIdJpVFsLIr8Q4Tho10EHIeGObXivvED0og+NHtfzCNetQQ8MIopLkGVRdCpJ0FhPQXExmSvPZ4f0maQcLKBDGrXFEn/PZ1qjadi8hrYXnyLR0MiEtl6sgkKcs87FPe2sPa1rQuCedBrO4mNQbW3obJrUTb/CciOI4e+bcBysCbWEO7aj6ndiTX7vPEK1UgSr38Bf8TK6qxNrUp1RN0+ZdtD9hBBELr4UnUwy8MrzAOQMDmIfczyRiy89JJkUhjA4CLEYRA6gJNCeh/f04wSvvIgaGkQWFGCfcCruyachLAt75mzsmbNH2Szsi7o6uOIKxYMPChoaBNKFSSVwaQ5M6gPRAWjInKkIqw91tQ6MQMETuwTZQDN9LyFMaxKWNwiOqTC+uXuj6pLJ2F+32Ty4iVPrT0NHwF+sSX9I/c394isNW3thx4BAAtMLNbV5MClnGrlOPp3ZNvpSWTqGSqiMVSPEHnVAqCCrYGe/uUVbwrR/pX3wlWZqgbFzWNMl6MsY5fZQ1lguXDRFIYRRi5w2QbO8EbxAYElNbZ6mKmEsH5TWbO4xqtyJeVCVo2kaND5nQkBnynjSRSzY0A3FMRPq5il4pVXwjaWKSXmaH78uiNh62NPMeAInfcGistEKi6GuJDtf2UXn1k4iwMVTSylbXEtZReKIg072RseWToJsQEFV/shrieIEXTu6saWN7/sH2fvIoHq6yfz2lwRbNyJsFx0G+C89h3vuBbjnvH+cEB7HOP4HI1fn0Su7R72mUIAgqg+s3uqSnXhkDREMRi5bHhLdHGeoeJBA+DjaJcgGCCmJFZix0uk0V199CS0tjTxwz9NMmj6b3qQioQVRLWmwzL1rYugQGa5j87XFudkEd0cH2WR5RBCkUVQrm7P2qXcHhaJbhhRoSUQJmqRFtwxJC0V6mAiOaolG02YF9EijRt4p99wzAzQBjHJMztWSmBb0DRO4u/m+LhlSPEaAnUbTL0KeiyR5TRsCslX6DErNAt9llZNFCohq0EKwwfZJh1CqLFY6ac7MJvbrlCvUFsf6MXQYkr7nPoLmRnKmzaIv5tEXhkzYtJ26ux9g9fWfpdsOOSeTwC0owvOyo22mtEb7HrJwT3fUudkEnTJgi+0Pzx+qlM0HMmMHUuuhIXRyyJDBjou9cBFhQz3hlo1opbDmzsdZtBQr7MHa9izBmga+d/4xXPXbx3ns9W2cN8eYkmohCbZsIli1krC5EVlRhXPSqdj71Pfz5i1k5sw53HnnbaPI4J5MH02DzdjSZnJ+LVH7AIUi4Cxagv/aS4TDwgCURjUOC0oWHFjpud+5xyDbv5aBV+4lGGoCBE5BHXmRD8C86WPuI/tA9gpU4WgyUBWAvQVkJ2OSwbY0ZNiGXCh4SVHZK3DKoSyuUVHQq8RwjgSgQeVpZABBFahCQ1zLARBJgewEXFAJ0Lv1JxqsTkMU6/z9j/9OcMFkTY4Nr2wUpOsFE/vg9BQsawbvPI2qGr39Y4/9hTfeeJXbb390v7FkF8R/JTlmh6C4SPNKRNDcBiWTNAsuUkw9DPeGVCrFtm2b2LRpHZs3b2DLlo1sWbWBxt76UdsJBIWikMJsEYnYnoUKHWhqiyczIPrp6elm+fLHWL78sf2OE5ERZhbNZl75AuaWzmd+6QIWeAvIfaEAf4keHdYXGnsKHQEOg5Qd9ODWDZLGQUFNjsaV0J6Cu7ZI8l3FwrKxyebJ+fDZ+YpXWgXb+gSFEVhcoTn6ANvvi97GPtY9tJ6BtkG01tiuTcWsMuacNxsnOmxPEwS8+OIz3Hzzf/HUU49QXl7J9dd/m49+9JMUHET5fziI/Y096/ytY/xyjeM9hZSS73znh8ycOZuvf/1zbNz4FjfeeAfVw76qbxcmDmKfH8lYBdqKIZOtkDsFhIUc3Amhh1+6dM+Gdgy/zPy/2/QUNC9n32hUbUUQQdqoh8V/o4JSayI77sXuXU9J/kx6ItAkspT2bufYjXdTlfgW5waHXiWXCK7I5FGiLV53MqSFZlrgcqoXZ15w+JYOW7du4otf/DjnnnsR3/7MP7Iim+H2H91Apr6J/7rlLpaF734vxoPRJPWWT5myiSlBIKBPKgZ1yConc1hk8MEQvPQc3vPPgO8hVIge7CdMDmGVV6CjEcTgAKJmAmF3Fyi1x77A8xDxBCK/gGDDetQJpyCi0RHriXDzBlRPD9a0Gai2VnTgmwWHgX7s8gpOWHAKrQp2Wj4KKNSSC7IJFgamOE2huMtv5nXRxNCps4jbC5je1s8lj7xKwf13Y5WWYe9jpC/iCazJUwhbWxBBgMjdJx3BjaB9H50ZrXZ4t+E99RjeX+4BpSAeJ2zYRbBuDdGrP4k9e2zl0G7IwiJi136JbFEhPPsopZ/8HLETTjmokllrWLFC8Oyzgo4OQSKhWbZMc+qpej9SOPvQ/XiPPYjIzUfk5aN6evDu/AOokMgZ54xsdyjSavFizezZmsZGQwzW2pC3ShBuEeh88BablOR30ubVnYG2pKAsvo+VQAy29Quah9iPDHZiDnVTprJu6kaGPhWi4qCqec9Tjo/UJj1QcOcWwUvNksywOirHgbNrFe+rheJoOcXRclzgpSaLlqQm5Qtakia0zZFgC42vJFGbkaALZWuyniAVQF5Ec2yFpiNtCNjOtODCKZqy4VuGEPChGYq+rEVvVlESM+fROGjUvvdstQgUFESMMvf9kzV/3iTY1Cuw0KzvEWRCQ8gHGuoHBF1pmFZg0ovTgQkoef8UxSM7JRt7zDEFmgWlmmVVe97XdH+GN+9ZQ29DH/GiOGhN44p6km0DFF1xFHbu219E9dM+cgxFhrQktnTwfW+Mvd4evOeeJti8AWv6TIQ9rMrraMN76jHsBUdhHaHSYxzjGMffDo7xTmBrfCN9ood8XYhCUW/toEyVM8s/8G97rsrD0jYZMkSHe9r1Up9gZ0C0IUGYqwn8NENdScqmlVJcV0QYhnz+81exZs0b3HXXEyPqv9hegWrTwrHr12V+jFJls9rJ0CNCJimHRX6UcjX6sTemBTlakhSafG1xtB9lh+XxupPBwnDWoVD4SGwtGBKazbbH/L0W7fO1ZGJos8H2yA8lAoGDwNKCrNTUSw+tISkUjhZckM3lJTdNk/SpVDYaExbXLUOqQ5vpoYNE0CoD0oRsHSZc4whiWpAZnlerFZCnJGmh8YXGPYA9g2ppIty1Hat6AnHpMC3QbLY1DRMqSOzYTkd7E9MrJ3KqF8dZuAj/xedR9TtN0Bsa1dSIKCzCXriHAC3RNtemCllve3TJkFwtmBNEDhgcJ3LzkAWFhL09WLEYOC72tBmo9lZEKoU9/yhjGxEW4xQWwa4OTi+YzKkL6/jGjY9z1veOhynnkV3+LJk/3YpubkKnk+ggIHv7bcSu+wqRs87dczwhuPTSq/jhD79NX18v+fkFPFX/HE81PEdPtg9LWNTkVvHBaeczs2hskYI1dTrRyz6C9/BfCHdsBymwKquJXPRBrKrDX+XPFOzE67wRnRnAKqsGFH7zBvrtXiJTvgqU7LePzgEdM9YQu9dPwCh7dWz0a/vCljC/RuNcDOr30L9L0JQrKAo0BRM0/oUK73QNHrgrJZFnBP5cjfuqQGQNAS2VRvYa7zO3A9Q2gRomjHUxZM9UyE5Duupc40H8TmtM14JzHc0FLwnS3ZAo0bgK5KsCu12SvE6NENBBEPCv//qPnHTSGZx00un7j/WywNkh8GdoJlkwCY0YBOsNwdAyCMYgg5ua6nnpped45ZXnWLHiZerrd6C1qdEmTKhlRt1sLk1cxkx7OlWxaipyqyiZXkFpSxFOo4W/WBNM3VPT2RsF6Q8psueY19raWnjwwXu4//7bWb16JY7jMG/SQmalZ6LyYX3nW9y16c9kQ6Mvn/jcJJZsP57TzjqHk08+k/L6EiJPC6xWgY5ovMWa7FnaZIIcAOu7BQ0DMLVAjwgKanJha5/m1VZxQDJ493Yfyt1tcHb4CLIB6x/ewEDbIAU1+Vi2RTaZpXF1M7HCGAO53dx//x08+ODddHV1MHPmHG644bdceOFl+/k+j+Ovg3EyeBx/FVx66VXMnDmXa665lLPPPoaf//wWTj317CMaQ6Zacdpew0o2IL1+ZKaLMFEN9rAaIUgRFs4kjFVg92/BGtyFCDOoeBXRXfcjdIBXeeIoxiLMmWgM/70+dGR4JUprZKaLbPUZYwbX/TUhsr243WtR8SpsK8LsACYLl2x8GnO7m/E6OwgK9y8kxkIcyYXZXM7KJsgKTZ6W+wVPHAxDQ4Ncc82HqKys5oYbbiJHu2Tf2sZXb/hPrvvc33PWlPlv9zQPiBDNRiuLpQWJYTWFqw1x2moFtMl9XcuODDo5hPfEo4icHHActLQQbgSdSRN2dyF0iFx8HNbkaYQvPov2fZTSiHQK/ABZMxHd0U7Y2kzyX7+LcF3so5cYn+LuLgDsoxajWptR7W2GHK2sRlZUMiNSyFeTgh22j49mYuhQtFch/WgkyXPZHsp6+6mK5pCKCN6sLcV633F89JYH8F9fsR8ZvBuypBRZXoFqbhoVuKY72xE5OajuLvzXX8OaVGd8kN9FqN4e/KcfR8TjxjMZ0JXVqG2b8Z541IRxyIPLHIVlMZRr5l101OJDWlq8+qrgD38wn+XCQs3gINx1l6CvDy7fK+RC9XQTvPICsqgYWWr8dMnNI2xuJHj+GRK5J+OuTSB7IKw1YR3hQeyV43GYMWPP/2cmH3nhdDBELHAsE6yxd+3vDaflRg7wCz59+iy2NGwkGFt08q5iax+80CTY0S8ojMKxlZpjKvQhlayrOgTPNQnKYpr8iCFhO9Pw6E7JtIKQacO347o8OHOS4j/flLQnzYOOJU0KcI8niNnmWNnQvP9RC3wL+j2jPHYs45HWnTbXc9o+SptZRfCxuSFPN0iaBo06OGZrBn0xQjr3ZeH+7YKPztJ88SjFm52CLb3QnDS+ZPmueZDJcTRdacHWfjixyngDCwFnT9JMyQ9Z3y3IhoYgnl+qR6kX2ja00dfUT3FdEXLYEDhWEKOnvoe2De0waxKvtQoaBwWlceMZPbPw8Aj4gpp8Glc1EQYhlm2+S2EQokJFoANs+93ppdNaE775BqKgcIQIBhCl5ejNGwi3bRkng8cxjv/BOM47iSargVfdF2gTLYCgXFVwafoqivWBa9HpwWwmh9PYbK9nYlhHVMfontOG98GApU+dTLYzg2VbTFw0gWmnTkXakm984/M88cRD3HLLPSxadMwRzVMgmB66TD8AWbwbMSTHeDHujw4RESGFWjJBOWzUHkVK0GUFCC12W72OyEb6ZUCI8fuVCM7KJmi2AjZZPlEt2GhlGZTKBN/JkH6ZZZkX4/xsDgsCl3Jl81gkyTbLRwOOhiJlMTOMjNTlFhBB0CnNvDwtCNBoBK4W+ELRagWc5iWIH4AIBiAITCuNZX5wJoUOedqiU/hIf4CLh6LMTRWQry2onUz0io/iPXAv4a7tZh7llbgXfGC/wOUYksXBwRcptdboPhNaKuqmEL7wLMGqlch4AlFYhOrtQRYVI3J3K8aj+EVzoL4Le6iRf790Csd8+2n+6aE2vry4h9Rtv0Z1thlLv6JiKCpBtTaT/tm/Y02djr1Xx9vFF1/O9773dZ5++hHqTpzH/dsfpTgZsiTIIRV32TTYwh823s31iz5LUbRgzPk7xyzDnjufsH4nCIlVOxlxGP6leyPb8BpBaRfR5BzkoAANMmcGWXc9OnwDl/2fhXUCvGM00Qcl2tXoQhBDYDUK/MV6xDriQFAa7qiAjqMEc96E/HbBzhioEzRHf0RjD79tOlfhrLGwGkD2gyrQiHCY5I1qVIUJmlM5IDvAn6tJXaFw1wjclRIxaMjpYLYmdanaoyAeA6IXnI0CkYGwWhNMBfYp7d2VgmirwJ1lhBQKo162t4KzRuCdZOq322//Hdu3b+EXv7htzGPZ6wQqV48aX+cCTWDtEgSzNK2tzbz00jO8/PLzvPzyczQ07EQIwezZ8znjjHOZPXs+M2bMZvr0WSScHBL/JYm1CbBAFYNMApshLNEQ6lGVv+w2oXt7k8MVFVV88pPX8clPXkdTUwMPPXQPD9x+J39svg27xWZpzrF8tfBrTIlNBUuzOncNy7ct556//AkpJYsLl3J2yTmcMeMcjuJoog9ZyC5N6hq133Xcjf4sIAS2HF33JhyjEH4v0L2rh/62AQqqDREcqpDG3p08u+YxVtz9PF0DHVRWVvOBD1zJRRddxvz5R493jP03Y5wMHsdfDfPnH83jj7/GF7/4cT784fP5whe+zle/+t3DMve3+rcSX/cLrGQTWkaQQQqZ7cPtWImKlYBWaCtCevKHyEx6P3krv43w+gnzp6KdPGS6ndimm9F2DL9syci4Yf5UvMoTiDQ9ic72oK0oMtuDSlThTTjjvbwchwWhvOFwuz3q16gWRIliqxBPHXl7bwxJ7Aj5Kt/3ue66q2lvb+Xhh18iJycXpRR///fXMmFCLV/4wjeOeB6HAw+NhQkQyaJH2gAF4ANV6p2RGGFLM6qrA+uoxaj2NnRvD3q3jUlfL5SVEbv0w1iz5qDSKbzHH0Y3N0BBEda06egwINy5HWtSnVEEZ7N4jz+M6u7CWXqc8UdSapRNRbh9K1Z1DUhJDKOm2BcDIuR1N03xUIb8oTQymktONqCqN8WmqgJaq8uY2NtzwPMSjoN75vvI/OEWgt1haEODqL4esB3SP/v/hslqgb14KbFrPos9Y9Y7upa7oRrr0b09yGkz98xHCERpBeHw30TxoRcwBgcHEEKQk3MQCQTg+7B8uTDq3FrzWkEBRKOwYoXk5JNDKofTmlVnB2pwEKtu8qgxZGERrO0g0tSDY+egYxp7i8RZrUl9XL2npGqgjGo17uzvIVsQgXklimebJDmO8U3zFTQMCqYXauryxh5z+vTZ3Hrrr1FKIQ9BvL8TbOyBm9dZ9GaMenZ7H2zpFXQk4eJpY99k2pLwRrvgnq2S9pSmZPi5SggTnraxBzb2iBHSVgioTkBJTFOVMGRwYcQUs3dvNZ7BebbAsSDP1SRcQywXRmBTjyDX1WQVoA2pXDOGimJeCcwpVgx6sLkXfvCaxWBW0GYsEolYghxX80KzCe+rydU83yRY3wW5DjQnzXwcCUpr+rNwfPUeQlwIE8gxtUDTnISOlFFrTCnYk9jc39KPtOUIEQxGuSstyZuNAS9lJT0ZQcLRbO6VrGqHy2aoUeriA6FidgUtb7XStaObaK6532QGshTUFjCUHKCwcAyZzDvBmFM6Qun4OMYxjr85uLhcmf4Yy7yTaLTqiegIM4I5FOiDt/W6uHw09Uluj93KdnszWZGlQBdy6qwzOKfmQrxeD9u1R+whfvzj73PbbTfyk5/cyBln7FF8BtmAoc4hLNcipyTngB6UR4LTvQRDQrHCzbBV+rgaqkObFitAa2MqkUYTYjiXWb5DIKBfqJEF/JlhhM+kCnjFyfBIZBAt4Bg/yiTl4KPZavnENcwLDNl7kh9ncujwQGSIjXaWNJoBoUiJEHfYX7hSWXTIAIFGaMhD0ilDJBqFZgiYqCXLvChv2lmyQlMd2tQoe5QVhqysNuKAthasuimmrT2U5Dd1I6snES+ZgthLiOAsWoo9aw7hrp0AWHWTEbEj78AL63eSffRBwm1b0QN9hK0t4DgINGpwANHfi6yZhEzkQNYb8fYKKUSXzkEvOY7JdYX847UT+aef38yc7ZplQwOmw8620f39yGgMyqtQne34Tz8xigwuL69kypTpvP76q/TUJFnw7HrmNaZx0ln8qEvdjAk8Ni/Nuq6NnFRz3IFOA5HIOWRH28Gg2lrQlTG8SpDdGjTofIFqsVHdnQfcL3O2RiQV7usS0WFIV3+RJnWZ2reZdT9s7IGnmyT5CzWpxRBJa7oEtAYmLG5R1PxIh3XgHaeI3WeIXWEbiwr0sJ9w0vy3P0cjk6DzwdkqiDwlUGWasMKQ1M4KQVxLkp9RYyqE7bUQv0Mi2wEEuOAt0qSuVLAXt241CHR8n446GxAC2WHmnEol+fGPv8/FF1/OvHljC2KIwn4Jhxlo6W3mjifu5N5f3MUbb7wGwKxZcznzzPNYtuxkjj32RAoL92e07bWYEMA54GwA4ZvrI7tAtgvCGhApjb3FMNjCh2CSxn1NEDaDf5TxA96NmpqJfOYzX+Yz13yZ7i/u4tlnnuBp/2n+q/fn9HX1kSNyOLHiZK484ypmzZ9F86oGlnc+wX/s/Anf3/xP1OVP4erp1/B3Kz5G/NRiggM48BVFQaLxQiNY2I1BTzCn6N0TruwNP+PT1LWLV5q3s27HKjbsepOh9CA5sTyWTjuRT3/jOo4/6ZSRZ5NAGZ2U+9+rvfs/jXEyeBx/VRQVFXPrrffxy1/+hB/+8Nu88MJyfvKT3zBjxpwD76Q10Z33Y6WaCQrngJCEWmPZcYQOyFaeCE4ufvE8gqL52N1rkJlO/NIl7F7+DJ3J2H2bcJufGUUGIyTp6VcR5kzEbXsJ4Q2QLVuCV306YV7de3w1Dg0VLSXMmYjdt4nAzR95mLZSLahYKWFu7Xs+B8/zuPbaj7B8+WPcdNNdTJ1qpJB33nkbK1e+zN13P3nEPtAy2YLT+QYy20+YKMcvXbxHmQ20yoA37QydMsDShgAfEookpkbICE2hkpzgHdkK/b4Qtg2WjbRs3DPOxn/lJVRnu1FS5OYS++wXcU8/C4DEP3wPe/ZcQwhns2gNasd2ZHEx9pJjEMMqcpHIIVy3FmfZiVi1Uwi2b8GqmQiRCKqzE9A4y0486EpoUmjSaArsqPFJUyFCWiSyAe15UQYJkLUH/3zai5YSi0bxXnwO1diArKoyiue+Xug1Sg1CH//Zp9BdncSv/wb23AVjjhW2NBFuXI/2PKyJk7BmzDbXbiw4rlGg+P5o497AM/s4h0fg9/R0kZeXf0gys78furoERfsUNkVFsHmzpqNDUFk5TCzm5JgwvWRyVACI7k3idMShPIdgdyibNoVd5HFJMG3sAvedIFDwQrPgxWbBgGcSlE+uMYrPvT8a75+s6c4Y79pQCwSaSXmaS6erA6pvjz/+FH76039h3bo1zD+AevydQmt4ql7Sl4UZhaMTh19okRxXFVKxT7jI9j64eb2kdUjQMAi9GcmKNphbrKnOGX6PMMrn3WhNwr3bBPUDguockwJdFDVewFobBVhGgaUhEwoSvqYopvnsfOMLvLVPkOvConLN4rLR1zYVmCI0MUzE5w8TyC1DUBLVI+ETSd8QuDv7TVG925ZCSs3sInAtQVvKbGcJcz2O34ekzQRwz1bBynbJkKdxLMnUfM0VMxVVOeAmXDKeYucApHzjeVaRgCBUvKoKGfBg5sh11jQMwqM7BQtKNYlDfKUiCZeFH1hA/coG2je2AzBh0QRyp8TR39UUFR1EznMEEEJgHb2E8IF70WVlCMeo8nR7G+QXYk35K0jVxzGOcbynEAjqwqnUhUeWO1CpqvlC8uvUWztIiSSVqppiVQoORMr21Aq33XYjP/7x9/nmN7/P5Zf/HWAUpi1rW9n+0g5SPSmEJSmaWMiMM6aTV37wBeNDIYLg0mweJ/txWmXAM26KQaHQCLJCY2mwEeRpwWw/Som2sLQYESfsRl3oUqws1jgZJoYuJcMEq4tgcuiwyw7YZflMDV08NA9GhljlZEa69PpkyEtuhpO8OLlaUhPabLZ8QmBQKiJakK8ljjLzqlE2F2RyeCiSpMUKUEBCC471Y1y8l3eviEZxz72AzB9/R7B5AyKeQKeSiNw83HPOR7j7q6dFPIE9e/9QrsOF6uwgc8tvjK9veSXhjk50cxOyegL2MccBAjwP1d2FLC8n3LEV4Q63CCmFffxZhGdcTdpx+NgHjmP5nY/z9a0buKeonFIhENEYOpNG9/chSkrBcQmbGvabx5Ily1i58hUW5fdT+VYzYUkp6Ypi3FSGCW9sYWGqkKG5ybd9nocDWVFJuPp1tKtRVcM+zEqhgwBZfJCF2Bikr9RkTw2NUjdvOEzuMNb3Nw53IRVFjbo2nWuy3lSvYF23yVMAQED6QxqVUOT8lzQkcJkJiLM6QXga7UJklSAs1uhccLsFqtCoY2F4XhM09kaB1cB+3XSiH2J3SkQPhrS0jGWD+zKENYLs2Xt175VoxDrJqBVlDShGyNTvf/8b9Pf38vWv//MBz987WhPfIBBD0Ba08cAr93Lvzjt5KfMi7gqX0+afzc9vuIVTTj+L4uLSQ15Pq1uAEoS1GhEI7J0C0a2NKikKA/8UIj2BtV1jNQjsrQKrSQyHB0LwiiB1jUKVgtUAzmqB7IawGmrLJ/Opys/yafFZwiBklV7FU/opnmldzj//4Zt42sPBYY4zlwsLLyZekcO2/q3866p/5gfqu5zbexFXfukajj/+lP2el+aUaKYUCDb3CirjGseCjpSxZFtWPTqzQmuNqt9JuGsHSIk9fdZIgORYCALYskWzY0cLPT3rGBxcz9q1r/PSi8/S09uFbTlMnzCHc4/7IHPrjqKUGqpmVrL4ZKME7s+a7JM3OgShgpnFmtMnaGre2e18HG8D42TwOP7qkFLyuc99lWOPPZEvf/kTnHXWUr70pW/x+c9/DWcMokhmOrH7thLGq0ZC3hCCMK8Oe2A7fuWJBMXz99q+y3j92qMJSuUWYCWbQIWj7R8sF2/CWXgTzjpy48uxEGZxut9CplrRTgK/eAE6+jaVV9IiU3sBifUt2L3r0W4ewk+CdMhM/iDafZcd/PdBNpvl05++gmeffYIbb7xjRKGRzWb593//Huef/0GWLTv5iMa0u94kvukmZKoNU9VowrynSc69FpUzkQ12lj9EB+iUAQ6CLqHICk2RsrARZFEktOS0bJz5Y6hqjwRyYi1W3WSCTRuwpkwnct6FhN1dhNu34px0KtGPfHxkW2FZRD94Be5Jp+FvXIdOZ/AfuAdy80aIYACRSIDvweAAkas+Dnf9mXD71pEADud978decuxB51WgJAVa0l9RQkVRMaqrExI59ObFyWnvpNTJwTn2+IOOIYTAnrsAe+4CtFL4zzxJsHkzpFKIiLvH27irE9XciPfkY1iz5u5nyeC/+CyZ++9G9/UaAtt2sJccQ/SKvxuzXc6aPBVZM4GwYSfW5GkIy0Jns6jODtxTzkDmHd5ndseObdTWTjnkdvE4RKOaVAry9lp5T6chEhEkEnsKHllVgz1nHv6rLxmripxcdH8/NHYQi52HmLCXuklAWKaxdwlELwdtf3s7eGSn4KEdgqhtCrNdA7Brg8BXcEL1nkK4KAqfW6DY1AtdKUNszinWB03JXbz4OBKJHJ577on3jAwe9I1HbmlsNMFaEoXNfZqmIUFFYs95aA2P7JS0pwQzi4zK+a0u8EPY0mtUwYECKUyIHEDjINz4lmR1pyAdCNqSmrakoDwBHUnIixhFcioQJANNoIyFxCVTNedO3m3DsL/6oSsNT9QL1nYKQg3TCqAmV7O9T/DITsFAVowosdOBmdegB164x5t4ZpGmPC5oTZmCe1oIfRnozggunb6/TcbTDYLljYKKuKY6AZlQs7FH8KdNki8erQiqy9mWaiG5fQg/N8f4SDYMUVqYR388j4r46J+myjjUDwoaB2HmYXw2Y/lRZp4xnRmnGwmJEIJt2zYDvKvKYPek01BbtxjSwbLQYYiIxXHffxGysurQA4xjHOP4XwsLi8nhgYNkn3rqEb75zev4+Mc/x+c//7WR1zu3dbHu4Q2gNYniHFQQ0r6pHS/pseQji3Djh599cSCUK5s2GbDV9pisXCZnXZ6NJOkSITaCGWGEKmWx3vaYGjg84yaZE0SpDfcocTNC46OJ7cPYuQj84b8DbLSzrHWy1IXOyLZ+oFhpZ3ndzjA9dBkQioVBhIVelNfcNE1WQExLcjSUKZvzszk8HknRLgLqQgcb6BOK5W6KcmVzirdHzessWorIzSN47WVUW4uphZYehz1tBu8F/DdWEDY2YM2cbVZ4sxlEaRl6oA/d2TkSYKy6OnCWnYSIxgjWrkI4EeyjFmEfvcQENWezBC8+y7/kF/OB3i6+1tPOb90EVjQGlo3OpKGlGR0GBG+9SfbB+3BOPm2kzly8+DjuvPP3VE0opSfHIZpvAgSzeQmUCpm0vZPS9HsrSXQWLSV49SXUjm3IKhNEFzY3IiurDh1EJ0xQnEiD7BZm31oOSQgHmjEFDFJovGBP5o4YAHuzQBdD+hKF+7pAtoFIC3BAxwRhuVHq2jsk/pwQq0eiCkbXVToBosWMty/sTYYUNUTw8Pa5xhfZfU2QPXNPSJq/SOOuAKsRwkpAgd0Iqkzjz9c8+eTD3Hrrr/m3T/2M6W9NRu001hV6nxImvSTgyUce56b7fsHyzqeQSM6MncWv59/MeVMvoMgrIF2gyRYfnjpW5QJCQwDBNE1YrZH9Atmq8RZAsAhAI2ZC7o8EOlfv8VH2zTWOPCkIZkL8TxLRi/GGyRoCPZiuUaUgfMm8nMUs7FzMt174Jv0npVmdv5r1r7zJmr7VrO5fzfqudfjDXcHFbglPrnqYv1x2F4WFxZx33sVcdNFlTJxYR2VlNXHb4uo5ir9sl2zqNp10NbmasycpZu1VN+owJPuXe/CfX27EMiiyBUW4515IeMwyurs7aG9vo7OzjZaWZtau3cDLL6+nvX09QdAPgOMkmDNnPh/+yDVU23UkeovIycnBidqk+zO4CZfaYyYhhLFgu2W9ZE2noDCqsQU81yTZ0af57AK1n5BkHO8txsngcfy3YdGiY3jiiZXccMO/8JOf/ICHH76Pn/zkxjHIC8EYeXGGXYD9yFujMJUQemDtKRCFN0BQNGsPoTwW3iERLLJ9xDf8Cqd7LegQtCZMVJGe+fFRhPWRoKd0HmsXXUt+8/NU9DeRE51LUHECfumidzTXQyGTyfCpT13OCy88zU033TUqkfePf7yJtrZmvvrV7xzZoEGG2LY/I7M9BIVzzfVWIXbfRmLb76F3/hd5IDJEnwiZGbpGgYJihYBAa2qURQSHo/wo52YTWO9Qriksi8gll6Fvu4lw+xbE8GKAe9QiIpdctp96Vw304694heCN19B+gBoaQvgelJWPbKM9D6RE5ORiVdUQu+4rxjohk0FWVo0iQ8PWFsJNG8DLIidMxJo+C2HbxJCc5MW5OxrSuvQocrfvYnCoDzkwwNlv7qJi2hxA4K96HdXcgIhEsWbPNQrksc5TSnRyCLJpyGRgLzWCcJwRVYXu6x1l4RC2NJP5yz0QKuP1KwR6aAj/1Zew6qbinjraSkWn0wTr1yLLKgh3biNcvxYRiYCQ2LPn4r7v/MN+b7Zt28S0vawmDoR4HJYu1Tz4oCQW0+TlmVOsrxfMnaup20tALYQg8sErIAwJNq6D5iZIJIgcfQo52y5G7e4D3b19CFpqeHcsVUfQnTGq4IIII2FmRVGoHzAr5Usq9AjpCKZ9an4JHK4fseu6HH/8KTz77JNcd93XD2ufQBlStiUpiFowq1hTfBDBvyPBtYxSdm/4yqhsXcvMVSnzNe/OwI5+Q4ZKAVUJYxnRkYLOlGBDtznnxeUwr8Tsu7xR0DwkWFiqWREaf15LQEM/ZIbb3uYUQ0Vc0ZWB/qyxhfjwLHXANOGkDzevk2zqEZTENJaAR3cJutKCyfmmKBVC0zQE7UljPxJqc149WXMeJTGjIr50huKOTZKtvUYt51hwQo3mxJrR71M2hFdbBfnunsC/mA2T8gwBvbUXlnvFtM6cwYQd22DAWMD02zGa6qYQSbgEo0UcBNpcZ+swVEJ7Y+97Wu+w1cxYbZFvF7KwiOinP0/w5huonTsgnsCeOx9rxqxxX7hxjGMcB0Rj4y6+8IWPccYZ5/K97/141P2i6c1mAi+geNLue9X/z955h9dRnVv/t/fMnCbpqHfJRe6925hibGN6L4GQEAIhkISSBMhNvcm96eQmubmp5CaQBEILhB5a6MUFjDHuVS6yZPVyVE6bmb2/P7YsW7Zccdp3tZ4H/GjO9CPNvHvt9a5lY4dsOmpjNG9toXzy8ZloqrZMhkNmb0bFyekIa+wUdZZHjfSolR4CaJGKJ4LdvBqIc0Eyk7F+EEcLcpUkX1mst9OUKptsLcnQkhbpk6MlZb55Ma21U9RKly6hiGhBqbIZ4QfoQZMUmnwlme6HONENMdwPcFUqynbLpdbyCGrBWC/AZjtNvfQY4TvYvbVwrrbo1IplToJT0+F+dhH26LHYow9fU+0PnUrhb1yHampEZGRiTZiE3KezaiCoul2IYNDUnmgIBCHVBZaF6upE9nTjbd+K2ryJZCqFzC8AIRDhMKKoqJdEFiQfvJf0S8+TG0/wg8Jyrqvbxq88xS2pVN94TQdCiNxcRDSb1BOP4G/fSvj6mxHhMLNmnYBSiqZYCrsoi9ZkO5lOBF8rUlaCYTrEWOvIclcOvDEamWhApLtQ4SJ0cOB7Yg2rInTVtaSeeRK1uw6kwB45isCFlyH3GTsMBNEOkQclznoBSY0OSbxe316dffDtqrKNPUDCo68WSvmmzhvT20VnrzaKXasRY10RFvgVGtEqjM2CMH69SBBxAZZGlYJQGtkB/r7Bdh2G3FUD3EqRwuxsP99aHTAkN4o+MtgbA4kPKULPCextZuzvl2sSFykarUZu+/wNnDn8HG6u/Qyi1iiZ/VJN4mMKbwzEYh089NAf+MMffs3OnduYNmImv4jeyYXll5A9OhfVKzTQdRB4U5A6WcMROKB44zXeUIFdLfCHGLW0RqOyIT1/73XZ1QLZgvFD3vOn54Aq1DjvCez1QFzjje39XGnsarPf1AjNHutv2WyuPZwTZk7ZXE6cMRdnrUB1+zRmtfGO8y472rayOWMTu4bvZsu2DdTU7OC+++7ivvvuMvuQktzcfLKzs4lGcwhlZBMMRUhnRngwI4OHLQvXdXHdNOmmRlJbN+PZNq6UJD2Pls4OWh7+PV37BQwHAgFyc8ci5QRmzDiHkpIJZGRMorV1KCefLPjEJxS+67NrxS5q368jnXApHlvE0NlDKBxhfkHWtgjWtRrrtD1jnYKwZmO74O16wYUjj2ysM4jjg0EyeBD/UIRCIb785W9zzjmXcNtt13PuuSfymc/cxm23fb3PekCFCvByxuI0LsUL5hgyV2us7p34GeV40f7qQTdvIl7OKOz2jSYgzgphJRpBCNJl8/sTvgchlI8VwZ3P4DS/ixcdZYLttMKObSG8+Y90z/xPtHN0013r7RR/CnXSkFWELruMsBbMckN8KBk9oEXteKKzM8anPvUR3n77TX7/+8eYP//0vs/i8Tg/+9kdXHrpR46IrNsXdmc1VnctXtYwVMrDa+gAKdA5RdgdG9jtNVFnGb+2PUWshWSiF6RV+FyTyGa475igi+MEa1gV4c99EX/dGtNylpePPXEyItL/u9LJJMl77sJ7fwUiO8eo3mLtqLY2REYGsnwIpFJGETtsBNaY8YAhYkVePmr9WrzaGmRBIdbYCXgr3iH1+MOo9jZTBDsB7JmzCV35cXRnjLnvLaegpYY1JZlsmTGR6Wu2MOuviyntSZP21pH8/W8QoRAiNw+0RuTkErr4cpyT5g14nbK4FCwLUz35xnNNa0inEXkFxsJhv3ZBf9MGdHtbHxEMe6wWwngr3ulHBqu2VpJ/+C3+JqPe0a4LwsIaMx5nxmycOScecfCG1pqtWzdxxhnnHdH6Z56p6ehQrFwpqa/XOI5g/HjNlVcq9s+ek7l5hG64GVWzw3zf+QXIvEr0jyzsGvCqMIWpa7zAUgvUIZObjwWNPYa4rMruX/Dkh6ElKWhLQukHnBmfP/8MvvGN2+jq6iQr6yDmwr2Iu3DfBsnKJqNMBk1RRHDFGM2UwoGLsrBtrBee2SbJDhoVrq+MwjlowVtbBX94RtC9A4oVZI/W7IhAJCgYkqXJC8P0Is32GGyNmZC3M4ZqTigzxWHah42tgvyQJicIo3M0a1qg0zOErK8Nubq906iUJxVoQramMGyU1gfDqt7wt1E5pmXOU+a/uCsISBiabfbfkjA2EpkBiNjaPI204LGtkusnGguKyQVQOVOxoU2Q9IzVxcicAwnahAdxTxBx+t/LkAWu1uzsFGzvlORMGoIaU4hoNjYudm4uDSJCeYYhjbMCRnGsNNR2w7CoZugH+N1s7fUrPN6ewTIrSuCUBXDKguO630EMYhD/fyKVSnHDDVcSjebw05/+DuF5uO8tx1vzPjqVhG2CcG5/RbFlW6A1qa7UcTsPsc//AXK0xQlumPd10rTQC5jiBQlhSM61VorvZ7ZS5QfI0JJcJdlmuVTbaTbrNBEtyNcWxcrm/GQmedpig5Xir4Eedls+caXwEdRaHpO9IJlIZrtBbor392CWCEb4AUbsE4iX6FUZB1JpArFOvIwMvIwIIQRdQqE4aK7UEUN1tJO89y789WvRSpmxV1kFwY9ec0hiWeTmQzqF1trYB1UMwV290tiHCUF6+TL07lpEJAN/2xb8Ne8jyyuxRo0h/exTqNpdOCedgvf2EqxRY8H1mLVzGzeHsvhZsosyaXOpFmbslp2NPXMO1vARkE7hr1uDt+Z9nNlzGTFiDNnZOWxMpLgqNIotdjexdCcCwQiZz9DyUsJF5Ud9X0Sqg9DWBwk0v4fwE6hANumyBSSHX2iSTuI9EAj2WXDYU6ZjjZuIqtsFUiLLKw9utbYHGsKPSZx3ewnITBBdmsA7AhUSJK45OGE2uUAzrUjwbmOvpZWAhCeYVKCZUawRrRB5SCI69ta8IqaxdmOOVahBC6zdgG8IWZRRKfsjNeE/Cazavb7Csk2QWqhQJQeei1+h0WGjGu4jsDXIdkHqJNWfiRKQPlnjTtFYOwALvOEmzO72j38KkYZfD7sLf4QwyloFdjXs+N+N/ML6BY88dh+e53L++Zfxy1/ey5zUbLJ+YKEiQBtox5yLyjbHlzHMZ4eBjkDiakX4YWlIahdUniZ5kcadtc/30JtPccDwXJjrlx0Cb8Q+nsjSEMfOOpC7QFUA6T0BfoZEBmMPkmpMYjdAOB1kZsEMRpaNZsioiRSdV8qYhebZuHTpG3z3u1/lvffeoaJiCCedtICcnFxisQ66umIkEglamhvYVRPH8zwCgQC27SCbm7A8DyccJiwtcsMRJhaXkt/TQ+nJ8ylbdDZFRcUUFZWQSOTzox85ZGVpsveZkAgGYe1aQXMzFBVZDJ87jKGzh6B8jeXIfpN79T2gtCBo7b13UpgQ5s0dA6n/BvG3xCAZPIh/CkyePI3nnlvKr371I37yk+/y1FN/5stf/hYXXng5QgiSwy9Cxndjt60DywHfRYcLSI64HOz9nuR2mPi4GwhvuQ+7YxPCT6NCBaQqzzCew4BIdxGoe5lA41JQLm7BdNIVp6EiB/fHOSy8BIGmt1GBXKxUG6IngbZDeJmV2F07sTo24RUepiVoH8SEz0OhTlqFzwjfwQI6heKNQIJyZbMg/bfpo9i6dRPXXnspzc2N3Hvvk5x8cv8B/b33/i9tbS3ceuvXjn7n2getSG5sIL50G357NwB2boCsE0rRc01S8gDvUSSmNe54EsEaTZ306MoNkX/SCRSpgz8SvbWr8NeuNtYHvRMVdn4h7huvoOp3QzwOTgB71FiCH76qj/j0t1WTvO93fX5mwrKQ5ZXGm1ha+yhuu3CXvoUIhfE3rMWv381wJ8AQN433p+exEkmcIcMQFYV4WzcZlUY0ijN+EiKajarbReqpR7FGj0UWFh1w/vbkqdjTZpKufRzV2GA8c5MJCIfBtrAnT0PuTxp6pnA/QNG3pz1vH6RfeAZv3WqskaON99vuWrz3lpPeuQ1VW4O/YxvB8y82CpDDoK6uhlisg7Fjj8yzLhKBa67RLFzo09wsyMpSjBgBB6u1hTSp0Psicakicr/E3tTbiSA03jhN6uzjX5REHFOgJ3vJxr5z8CBo6b2q1j2HPoZ5nwULzsDzPN5661XOPvvCAdfR2lg13LVWsrxBUBwxoWbFEUPqPrxZMCyqyT6IG8vpQzX1PZq1reApgdaaLpN5yXtrJPE4BDJANEPyTwI1A8Rw2N0tqcrRjMnVBG1YUKn50qz+al4pwLZMrszubtjRZersHk/ga01eSJMZgLAF9d2m7awkojlxhD6kWraxx9xQp7cIjXuGDM90NO0pmJ6t2R4SNCcMOZwV0FjCKJpH5Gg2tAqaEuYegVH6Hi7ELcuBoohmZ6cJBdyDWBoybEF+SKF6lb5khtGZvZMmHugemF+hUFqypcMokDVQkqG5dJT6QKEbW7ZsIBrNprDw0OqkQQxiEIP4W+I///Pf2LhxLU8++QbRzCxSD96L+9ZrRvxhWRRsrUc6a0nnfxQVNjNgyjNtJ8GsD2YXti9G+QECOk5M+H21ZhpNVEs0kK0sQr0yxnahqLc8WqViqK9Ba/4S7CGoNXPSYZqkR6Pl0YHiPDfEmekMPDR/CXYjERQqiQ/kaEGH0KyyUwzxbaa7R5bBUewKxixdwoSXFhNtaceLRGg4cRZbzzuVmVbOB+6cA0i/8CzeqpXIoVWI7i5UrANv7Wr0vXeR8e/f6auF94c9dhypRx/Ce/l5RE4OMq8AkZWF7uhA7d6NbmtBlpYjwhFUUwNUDkV3xkBKrKpR+BvWAhrtprGi2TBhEn7NDj4ZjNCgPL6VjpNdWM4iy0FkZiKLS0ydGgyhfYVfuwtn9lyklEydOosN7W1Eezxm5paRzhuO6O7BjscInLjABAgfDbQmvOkeAg1v4UfK0eEiZLKV0LZHSG2pJ74paZTR4Qj2nBMJzjkdZ3sE0RNElYwwqtEjeG/LRnDWCvxSQwSDsVfwSzSBNZJUiz+gEhdMrsHHJyjG5Qnebzb5CpMLFbNLTMaAs14gdwFSEHwDdK8dhRYakQY8gTdKG2sHZdS9Vr3xA3YnatAQeF1g1RjiLj1FkTpZI7o5QDzhD4P0HEXwNYHuAB00RKwq0v1Utf1ucRZ4+2T2/fYXP+Oll57lz/OepHBIEXuywze2r+c76/6Dp7Y9TlFeCTfeeDsf+9j1FBWVYO2E8M8kVp0RduCAlQXuFA1p0BF9VEIPvxLab1GsWC1Y0SrojsDYUpib3Nvh54/QqFxjtaH2NCv4IFsE7miFs+3Av0m/1FhOCCWwN2uwBe44jdUKJDB2HULTotoI5Fm0zOimo7ybroIE6U7FrhW7qJhaTkZehLlz5/GXv7zFG2+8zPe//+88+ODvOf/8S/ne935O/iHGXvGf/xh/88YDxkXexnUEps0hdOpe4U91NaRSULif1XIoBB0dZii8ByYE+cDjhXpr/f2dOVM+ZH9wx59BHCUGyeBB/NPAcRw+97mvcM45F/Od73yFG2/8GL/97c/4xjd+wJw5J9Mz9Us4Te8ge+rQwTzcopkHDVBTmRX0TP0iVtdOrNgmnKb3CNW+TLDuVdzc8ViJJuyODWgnipYWoe1P4LStpWfKbajwgUTakUAoF5GOGQLaS7CHybECeehgFOEfnXpho52mQXqM8gN9RV22tohpxTInyfx0ZADa9IPh2Wcf59Zbr6ekpIxnn11CVVV/JUZ3dxe/+MUPueKKjx+Rn+v+8KNVJDvC9Dy/HCUj2CU5oBV+bS2xt2xKzvAoy41QZ7lU+U4v8aGplx6jPIdidfyI4Hbh8+dQF+vtFAmhydKSmW6IC5OZfcX+vlB1u4z/5T7Fr3Qc7JGjkUXFBC+4BEJhk9bc632tXZfW+3/Hf7/wJC+1NNKa6GF8QTFfyS1iou3gnH7OPorbLAiFST32J0RegQlpkxKpNbz0PDoRR0yZDgJ0fR1kZ4ObRjc3IrNzkGUV+Js34G/eMCAZLMIRwp+8ERGKkH7qUXSsAzKzsAqLsWfMJnDOBQdsIyuHIoIhVGesz95C+z66swNr3vy+9XS8xwwYCooQgSCquRFvzaq+e6ATcdw3X0W3txG+8fPGOuIQWLNmJQCTJx/55IkQMHQoDB16bOStNwG6b1PYawWyC/wi8CZp9NGHaB8WQ7JgVK7m/WbB8Kghf7vTxi5h0VBFbhKCLwic9wQocKdq0vP0QQv/gTB0aBWjR4/jqaceOSgZ/HaD4P4NkpVNhojsSMHKJsGEfBiapdkag43tgjklA9/TaAA+NVmxqQ0a4yZ47fVaQbJdIFsFw/I17b5gd56maDSorSAroNPSrGwywXnDopqLRugDbB1sCTOLNPdtlLT2zjtEHWiOG3uIaAAidm8IHJrWhODMoYpTygc+17RvnsgRp1e80VuE2gKkhLQnCNmagGX8gOu6BWnfHGdo1IT2aW2sLZL7p1QfBpY0hPe96yXbYiYAL+4ZdfipFYqpRfBKrWZHDEZkm/PS2qgnSjM004tgYoFidbOgJWHI+ckFmvwPlp/JmjUrmThx6qB9wyAGMYh/GB5//CHuuefX/OAHv2Ty5Gl469fiLn0TUVzaV3eEI/lkLH2P5HvL8GYuwPd8uhq7yKnIpXDkMbb5D4BxXoB5bpg3nAT10u8TIsxMh9hmu/j7PCprLZeU0GRoCGtJWoCDNrZYCGZ6IbQH22SaJBoLQa00Vg/DlUOelmyw07RJjY8mgWKKm8msIySDh72xjIUPP0ZTUNCal0e4O07BE09zUneM6R+6gWbhEUb2WV4cLXS8B2/lu4jsHPz1a9BNDWilQfmoht2kZswmdPHlA26XfvUlIxjo7ka1NKP0FuSw4YT/7Wbc115GNTdhT5yEu2yJsZNwHJTS6M4YorQcfB/d2dk3whFSIrKzsXJy+Vp3J51tjXyxeTe/zshmdiKBt+IdnJlzIBwBdL8utJKSMjbH2gmefzHu0sUEWjsQmZnY515I4Kwjty7bA6trO07r+6bztDe3RWWUkd7WTOLle/AzxiEKilFdXaTv/ROBe5qJZH0SgYQApKdqEh9V6MPoeUQ3iCTo/X69VQTsmPmcQ/zqR2yYX6mZX3lgTSSbwd5uhA86bOxw7S2gQ4L0JI0errE3CnSGBh+EC+k54I7XYEF6tsbaIAzR2g6RJyThZ8Abo0nP1STP1ug94nbZG1JXDs7bpr5OzVOkT+31Pz4YNFhb4a8/eIJvPvtFPj/0di5oOg8vC6qtar6/9Fv8acP9DIkO5deT7+K8730YOb2XSVQQelz2KnHBqtOoDJCdAuc98Ms0yQv3kuxHAqXh4WrB6+0Cy4KAB5u2SVa1aD41WVEcMaF6qbM0oSck9iajRBZJgT9Uk7hMI+/VWLWGWLYajaWEbAR3pqb7JoXVLNAhhTcEwo8LAm8JRJ3AdxV2m6RhQgf1U9vYo4mK5IRp3dlGd1M3GXl7Byvz5p3GKacs5PHHH+LrX7+VBQum8sMf3smZZw78+26NGoP3/ntoz+tTrOtkEiEtrMr+9oPFxZCbq2lthbJ93HlaWyEvT1N0BBTKhHxTB9d0QUWWeca2JMw4ZEbxoCr4741BMngQ/3QYNWos99zzOEuWvM63vvUlLr54IWeddQFf/ep3GTny3CPfkZBoaROqeQ4Zr8cPlyK0T2j7Y1iJZlIVp/d5PKlwKU77OgL1b5GsuuSYzlvbmYhUhzlW5jCQNmjfkNcqhZ81sJ/rwZAURiO7/+x+EEH3cWoB6ztWMsm3vvUl/vCHOznvvEv48Y9/M2Br+e9+90u6uzv53Oe+ckzH0U4m3R2VqMQygmVptB8zJHpFIYmObIJrVnPekEXcH46x0UoTRJBCU6Rszk1lHRelAxhF8MOhLpY7CSqUTZmSdAjFS4E4ES04P3XgdLEIhc2WvdYiuqkRv2E3ancdtuMgK4Yg8/q3W/vbq/nKUw+zJd7Dj865hKKMLJ7csJpPvPMWTxYPYUg6BcF9Cn/XQ7W24EycYgLO6PX4jETQHW2mUM7NA18hpYXWAu32MlN7/NNc96DXLfPyiXz2doJXfNQU966LLCzGHjdhQIWHNWoMzuy5uG+9jt/SDI6D7urEGj6CwEmn7r2frgu+Z3zhAH9XDbhpRF4+tLYgMqPIvDz8TevxN6zFnnpov+s1a1ZSVFRCcfEHUOofIeRuCCwWOBsFOgPSMzXJMzX8DWenpYArxihcJdnSLvC0JmQJZpdozi/RRH4ncdYIdFSjJYT+IrE3a3o+rdA5R36cj3zkE3z3u1+ltbX5gMTklA8v7jQq26yA8ePNcKAzramOGZsKrQVp/9CFmSNhYgFMRPPARqMij7dDIKAREtIJozpJ50BcQyAlsEKahK+JOppPT1KUZg6c3blgiOapbVDdAZmOoN0z9644bHx8K7OM/3BbEjrTMDZX4+w37m1JmPC2PeqYiixN2Nbs6ITKLKPQDlnQ6hu1MRglb8jWFEXg9CF7A/J2dUF+WFN4DBMEs4o1oHh1l6AxLgjbmgWVikVDDAF97nDFveslG9vNZ0nf2GCcW6UI2cbC7+SDEN3HijVr3uessw6cBBrEIAYxiL8HtmzZwL/922e45JIrueqqTwKmbiKV6pevECnORg8rwevexY7mbqQtKR5bzJhFo484PK5d+LRJnywtD9oFZiG4LJnFOC/IJiuFEjDSCzDJC/J4qIsXAz0UYmEhiPXW4SEtydUWjdJD9taocaEQvU6+YSzapTHXF+xt9ClXDnlpixbpk0CTlpqz0xl9/r+Hgk6n8V97maEyApVl7JYeXdEsCp0QOS0xHvcbaImGCSKYng5xdjrzqElhnU6jPQ/V2ICqr0Pk5CIdB6UUetdO0i88Q+C0Mw8IBfbeW4636j2c6bNBSnRHOzoRRyfiWJVDUWPHQTKBcAKIUAjV2oxGoNNJs74yvfayaiSquQnV0oyIRhGWbZTCWvO93CJuSqX4bE+M32XmMr6xAVauwKqoRGTnYI/f21VWVFTMkiWvE7zgUpz5i/DrduGvW4O/djWJVe9hTZ5OZEopweQ6rJ5d+BkVuCUn4eVNGtBCUKbaEW4POnPY3nulNT3r2iGRxJ40FKUddKwLZ2uIVPxVEnMnExgzCxGXBJYJVIkgecGh3+eqCFS2RraB2qcUlm2gco5OHHDANbSCiJv96l5tho6AVQNEoed6ReBtgbNWoJ1eQcKc3vwMHyK/FwTeEKhcjegS4JnQObsaZLdANml6blR76+ggpBZoUgv0wDYKe5ACe7Mhba2NsOLexXxyy8e4PHw5dzh3UNdRx/df/S6/S9xNfqSAHy38GdfmX0cgHKCrSvU108l6Q3b75cbfFymwmjA+zzFB4nxl6vyjQHUMltZLiiO93XI+0ACbdgne3iy5dLLGnahJLdT4pT7O+wIRE/jDFe5M830lL9BE7pME/woyBihDxls1EHxFkLhS9zFziStMaJ5VDW7KZd17O4gV9BCx9hagvutj2RI7dODzTAjBJZdcycknL+ALX/g01157KZdffjXf+taPie73N+vMnov33nL8zRsQObmgFLqrE3vKdOyJU/qtm5kJCxZoHn1UsGMHZGVBZ6fJB7ngAk3kCOrj8kz40GjF41tNZgZAVkBw1lDFtKJBMvjvjUEyeBD/tDjxxFN59tklPPHEn/j+97/OggVTueqqT3LjjbdTWTnsiPYRqH8L2VOHl2te6hqQ8QZEZzUy3YG/x/BfWign09hQHCMZLBMNIB1UMA+ZbkPLEEJ7IB20HUaoo5OTlfo2AaBLKLJ6iziNpk0oTnJDx40YXbr0Db7ylVvYuXMb3//+z7n66hsGVIolk0n+939/ykc+ch0VBwkqOxKk0zmo4ol42QLhdaMDOfiZlejkblR7O5O9IDk9ubznJGmWPiXKYoYbpuwQFg5HixrpscFOUamcvnubry1crXnbSXJaKoPIfupga9wERE6uCVqLx1HVm9HJJHguaud2Enf+lPAnb0QW7zXNSnbF+OvuGu684ApmVwwD4HMnLuCVDav5U0+M2zvasXoJT+376K6YeRHvB5GTBzU70b5CIpBFxXhbNiIsGxE1pL1uN97F+7f5DASruARZVIxubEC1taL2tOzt970LyyJ4xVVYVSNwVyxHx+NYC04ncOIp/dTHIpqNHD7C+Cnn5hkv3mAQenoQoTAyJwcRDKGVQrW2HPb83nlnCVOmHF1Aok6nzXEjEUTGkU33yzrI+I3E2iXQ2RoaILJBYO2ExEf1YRObPwiKI/DZaYqtHdCZEuSFFSOyIbRU4KwHb+ReQloVaewtgsC7gtQiU0xb2zHeZZh1/aEcUGBfdtlVfO97X+Phh//IZz5zW7/PmuJGzVsS0cTSsKvTKEkyHHM+u3s0EQcqMo+8MNtjwR4IGlvqPcuEgM6wWZYjNXkZUNct2NAGP3hXkhOEkTmwcIhiZM7e/UUDMC5Pk/AEmY4hfrfHTHia60NNp8DVxjNYa3h0q0VaKc4ZbgjcrjTcvVayqU2QFzLbv9ckidiakAPbYkbtMSyqGZJlCPJN7QIpNFMKNCllPImzAtCZMgri04ZoIkfxKPKUUQFHbJhdopleZKw0Qjb91NCTCuCmqYp3GwS7uwXFGYqZxZoROQfd9QdCR0c7O3duY9KkqX+bAwxiEIMYxCGQSCS4/voPU1ExhB/84Jd7u6Qs6wDHSIEgnB2ksmo4pZfPxgpYZBZkIuTh6+A0mmeD3SwLJOgSirCWTHaDXJzK6qv/9oWFYLIXZLLXv4NpmhvklUAPzwa6UUC79FHAJD9IhoaM3n15aBJCscFKodF0CsU01wgMSpTNEN9hs51mlO8QRlKhBFulyxgvQIV/ZGm1OtaBam/DycljtBdkBAEUsHpCBX+ZWISlEhToTJJC8UIwTodUXJfI7iOrjwQimo2sqMRb9hZEMvo63kQijs7Ng0QCf/NG5Mw5hjBuNgFz3vq1CNvuExiI3pA0b9N6/K2bcKbPwn//PfzmRlNLtzSb4sCy8HfuQKfTWCWlBE4/G5kVJf3S8+jmRjSgO9ohGMKxbH46firXrXmXT/V0cGd2DpNqtiNzcgldez1yyLC+6ygsLKa5ucH4FzsO7kvP461aiciKgpQ4r99NuLYVZ8QQdEYedqyaQNO7xMdeS3XGMN5pWEl9TwMF4QJmlUxhTDAP7WQi0rE+QZFOefiNbYjsTHxP4K1+F7EziewcQ5qdpNa9ivRD2BMmo3LAeUcYMvIQTXI6Cql5mvATAuGBDnVhN7UiepKkz6jBSpfi6fHHlnnjCnQ+iJhAhDTaMmSuDmlURBN4V2BvEagccCdr3OmGpJSNEPmDJPykAF/ANtPG5A8B4WtEQuAXKJwNYG8EbzLggr0VRKdA5Wn8EQxIBlu1EP6jxN5hgvM2b9jAZW0XMTfjRH469ld8s+Y/+UnLj4kQ4Ru5/8lVJ36CApWP7dkkTu8vlBAK+hRTIXCnabzuXt/emFHvcmQC/D7UdAqSHmRHAR+c1UYZne8I1vpw9VJInQSJD2u88SZ0bn/vW3eGJlWjsDdLvHzQ+Rq/1JxrcLHAm2SIdwBtaWpH1rG7dAc9Xjc9Q+L0vONjJ0oJhAP4rk+sLkbusDxyKrIPON89KCoq4Z57Huehh+7hP/7jdt5661V+/ev7mTnzhL51ZF4+4etvIv3ma/irVoDtYJ9+Ns4p8wfMejntNEP6vvmmoK0NhgyBU07RnHDCkY8XZpdoRuUYj2BfwdCooizjuEU4DeIoMEgGD+KfGlJKLrnkSs4552LuvvsX/OpXP+K+++7i/PMv4zOfuY1Jk6Ydcnu7azvazuz/dLGCCG18g/eFUGlU4NjTeISfRDsZuEWzkfFGZKoVZUdQ4WJDCvvJo9rfCN9hejrEkkCSTC0IImgVikJlMc/94L3rTU0NfPvbX+bRRx9gxowTeOaZxYwfP/mA9TSaBunzx78+THt7K5dd9+kPdFyrcgjeyndx8/eGkmnfNyrqXiJ1iHIYkjqyovhY0CUVcaGo2I9gztSSduHTJRURtR8ZPGQYoYs/RPLBP+Kteg+kQGRFsYYOR44cjb95I+nXXiZ0xUf7tlHFpfhaE+ju7lumtSYoLVYqZcLjer2GdWcH1uixCCnxd9UgRmeZIDbfA6GRxcWoHdvQtbvQyTi4HlpYJul562ZA45x2JnLo8ENeu2pvI/3yC6Qe+xOquQkiGViVQ3FmzCJ42ZUH+AaLYBDn5Pk4J88/6D6FEAROPxu1qwa1cR3ac6GtFbKiWFWjUI0N+E0N0N5uyHTX7RtY7I/OzhjvvLOYb3/7J4e8jr77qRTekjdIv/oSqq2116dtLsEzzkGED/13EnxTYO0SeGP3Er8iBoG3JekTfPxRh9z8A8ORMC4P9i0YrZ0AAgL7FFU26KDG2iJgoSb0lCD4ikT0mFV1BqQWKZLn9iew8/LyOffcS7j//rv49Kdv7Uf2Byxz/LSC4VFNe0LQmgRLGPKyPWXUqsMOnT3XD6Nz4fVaTW6hoKEBEnFwHPAECA8yPEF2jsbXkPY1KR9quw1R+06jCVH79GTF0H2OOTSq2dAmGJVjCF5HCtY0Q0caul3IsCEgYEQuRAOa53ZIqnJ8xuUZy4vN7caSY49iOD+k2dQhWDTEHMfX5vqzArCxHdoSguwgjM7VrG0RvFknaEnAqFyjzJ11hC1svoK3dpvtO5KCnJBmXrnmpDJN7kEGIcOihpj+e4RnrFtnbFwO9w4dxCAGMYi/BX7yk++wY0c1f/3rO2TsM4FrjR6LyMxENTf1TTrreA86nSI4cxaZFTlHdZyXAj08G+wmT1tU+g7dQvFGII6P5ppk9oB2a0kU6+00uy2PsBYUKou/BHuIoegWii6pkdp07+20PDK1pMK3kFrQKXw2WWm09klrRQiLZuGTRhNAcH4qk3tljA1WmgCCdG/32wXJTJwjJGtFZiYiEjFBZdFsLARCwOLh+SjHYYQfQGpJVEvCQrHGSbE97fYLoDvsMaQkMH8R6SceQXd1ooXo7TzTWKPGgNLo7m6Sf7oP952l6K5Oo/Tt6kSIAWbStQbLxp4+C6dmB8kH78XftdMEF/s+IhQ2pK+UBG++DauoGHn+xVhjx+Nv3ojT02Xq7BefR6RTZMQt/nfKCdy8cyPXNuzkv0dO4KxzLjDhpfugsLCYZDJJd3cXofVr8d5dDpkZ4PvIzDBZBQl0dzvdXaNJ5uQQCBaQlainZ8Mf+K1VTn0qRtgOs7p5Pcsb3uPKsZdwasE0ArtfRykP5WRgpVqQjiZtF6Lq61GN9TiZwxHdEiFDyHAuqmYnqrgUGSpCpIz1gj6M3XXqDA1hCP21jUDNZgg145+0EatiBRmrIiTGXE267NRD72QA6GyNyhQIT2M1CrSt8cs0OgTOJoGzyRDDwsOIELZB4hJN+H6Js0qgA8ZCQtYIhGsIVpULogtTg/oCq0mgmjTh+yTOZmFm8YMSd6Im/lGF3re2dCH8gMSuFnjDNQ0dtZzbfjaVVHJT1i2cuHY2teld3Jp9O5cXX8U2ezdLOlYRzgsz9NQhlM4v7/d37JcYKwh7J3i95LPONMFs/nCNf/SZgdjSVGZag91giGCdBemgJqDNfEbwLYE3cS+hOxBkuwnV8/YfX9QZz2B3au+P8R1sjq1GoQjIILJco2Z107JmN4HtWQghyKnMYcI540yY5iEghODKK6/hlFMWcNNNH+eyyxbx4x//L5deune8KguLCF1yOfriD/Vtc9BrkHDSSYb8TaWMX7A8BvFMboiD2tAN4u+HQTJ4EP8SCIVC3HTTF7j22s/w4IN/4De/+SlnnjmHefMWceONt3PKKQsHfHD5oXzs/UhYP1SElg7S7eyTsolUGwBu0axjPkc/UoYKFyMTTf2C4qyunfiBfFRGxVHtz0Lw4WSUMmWz3EkSF5oT3RCnuhGqjqKgO+A8fZ977vlffvCDb2DbDj/+8W+44oqrkQM8yRWa54I9vBqI8/wff0v+KXN5ZEo+6VSCE9xjM6y0Z87BfXupURSUlILWqIY6rMph2FOP3CP2gyBfWWRpSUwocvcJpOvoDQ3JVgO/1ZyTTkV1dKB21yLKK5G5eYjs3sFEXgHe6pXoS6/o81yKVgxh+ogx/GrVO1Q5QQpz83h6wxreb29hWGkF4auvw3v3HXS8B2vefAInzkN3dZK4607cN15Gd3Sg08ZuwZ4yFX/TRtTO7SAEsrgEa+JkZE4eIisLZ/os7OmzDvkCVx3tJH/7K9Kv/NUQp6EwpFIoDenuLpAWoauvOyYPUXvUGMKfvgV36Vuw5E38TesRhcX4bS3opgZIJhHhCO6bryFsh+AVVx2Qpqy15tU/34/neZxaOQydSh3WX9hb+ibJB+4BJ2AsNHp6SD/9OHR2Erzq2oNfiwZ7nUDl9CdQdTaIerB3CfxRf/8iRYcxiob9IFyBzlDYayH4V6Nk9ivNZ7IFQs9LvCofb0L/7T72set5/PGHWLLkdU46aX7f8qKw8cZ9u0EwMtskTG+PCapjxn7hhkmaE8v0Uc3STy7UzC4VvK01GUOhuUmSdjWBOKSCgnCZJm5BT8IUjnlBIxApCBuSdmO74K06wdDo3vs+u0TzToPxLy6JQHZQkxMSxH2j6o0GjedYVbbGErCxDda2CMblaWq7Dbm9r3WEJSFkaep7BBeN7H+jJxdAwjNBbzVdMKlQM7tE4/au1hg3Pr4lGb1hb4fAizWCx7cKgr0hdI098MBGoyw5Y9g/vvh9++23yMzMoqpq9D/6VAYxiEH8H8P69au5887/5vbbv87o0eP7fSaHjyBwxrmkX/gL/oa1aCEQto0zey7OrLlHdZw4iqWBBNna6rOGyNMWUsFqJ8XutEe56j8x3Sl87gnHWGen8entyJM+EkGmEmRgMcSTdAtFXGgsDWvtFD4BJngB1ltJuv0eUigEkNaCJxyX0a7kXC+HUX6Am+O5vG8naZI+Bcpiqhei9Ci630Q4gjP7RFJPPQqBICI3j7ibpNHyybVDiJycvnWztGQ3Hs3SZ4R/VLcPZ+p0gudcSPql59COY/IpKioN+5NI4NfswH3zVWRhMbKkDN3TjdpVA52diLx8dGenyadAQyCINXoMwrIInHuRqQUDQUP4B4ImsNj30cl430S+EAJ79Fjs0WMBM/mvEb1hziMozM3n3hNP4dZnHuHmTWv43sbVXM2H+13DnoDUpqYGSl5/GW/zhr660slRWLM62GnbVHesIW3VYgmLslAWTk89dhTGFkzrqyO3x2p4ZvuLTJpyLblWiEDzu9g9HahgNnL+hXhvVKNqdqCljQokcb2dOOFKnJxR0NaBbmtDimLcSfqwnsEA2JCa72Nn/BZRtwmvZBjYGtE5AxmrJbTtMdyC6egjEDIJtxsrttWIo/xRWHVRtNT4pdoExLUaP2JlCdxpvZYQgOiA4FsSlevjbBG4ozWys5f4DQGuQHRqhGN+1iEA49EbfljirDUEL2EQ3RpnuSAUFab7bs9lbjO2Dt5QzabuDVz0ztkIYIQ1gg81XMK8rPk8MfZpippLqE7W4A/V2CcG6eyOs2rDOvxVUDl9nzG2A8lzFRn3Wsa7N6IRcYGOQvJsddSqYDD1cl4I6rphWKNZ1h2AHgFzvN4wujqBvWkvoTsg9CGKx95b4imXnd1bDOHrGPvBiJ2JPdRBF8Lw2BgiGRkUVOVjB4/8uVFRMZSHH36BL33pJm655Vqqq7fwb//2H/3GSUcz/rMsjsgWYhD/3BgkgwfxL4VIJIPrrruJj3/8U/zlL4/yq1/9mA9/+GwmTpzK9dffwrnnXkIksvcN6xbNIdCwFKt7F35GGWiFTLXg5k1AB7Kx29cBoJ0MkkPOwS2ac+wnZ4dIDT2P8MbfYXdsRDlRQzgLm9TQc9HOkbz5+yOM5Mx0JqenM/DhiFUDA8H3ff7yl0f52c/uYMOGtXz0o9fxla98h7z9fG73xWo7xXOBHryNW2lb/Dbn3H0nSaF5NNRFuW9TqY5evWuVVRC+9gZSzz6Fv2MbCIE9Yw7Bcy88+lTfY0SJspjmhng9ECetdZ8iOCE056XCAwbI7YHMzUPkF2ANGdbn6wumSJWWdUCPy8/veYzbPvUR5j3/KJYQTCir5MLTzmbNjmoCpyw4QMWgi4qRQ4ai160ybXrRbEglcN94DXvseOTckwEBLU3ozk6CH/k49oQDFd0DwVvxDu661WitkPkFiIwMtOehYx3I4hJDZjc2IEqOzavXGjoca+hwgpd/lPQrfyV1z93oHdvMdQwpxRo/CaTAXfIG9rQZ2OP3xgXrRILUIw/w4l2/YFRWlPyHHyTxztsEP3I11j4tf/3uleuSfu1lsAN718mKQjCI+95ynIWnY5VXDnyyvcEZMrbfcgWg0X87Yfoh4U7QBF81nmeq13FEtpggCneqxlkrEK7Az99bSKtC4wHnrBN4E/oTjXPmnMzIkWP4/e9/1Y8MFgIuGqmIpSRbO4w/cMTRXDACrh6vKD+KYI09CFpw1VjFhDzBulJBW5siKw6u0rwQlwSDRl2RF4SmBCgEWb0KaCEgO6Cp7hDsq4wdGoVrJiie3SbZ1S2Qwlg1xD3NpjbB6Fyzzz2QQpPodeTJdMDTwrSH7vNn6SpBdvBAxn1lk+DJakFDj7EUKolozh+h6UjCfRskdd3G2mFqkeaTExVDDqKa7kqbIL0MB8p6H/u5IUzAXp3gpHKT6P2PxHPPPclpp52NZR2/UM5BDGIQgzgcfN/nC1/4NCNHjuHGG79wwOdCCAJnn489eizexvVo18UaVoU9YRIicHRCiE5plLz7TvoDRLWkUfp0SEX5fq+C1wJxVtkpqnyHEBIXxTO28artkRZhLZAIsrQkLXwmegFapOLMVCagWB2I4aOJaEHasui2JG1C8J3MZhIJi3NTGRQrmzPTA79kNZo66bGll4we5jtU+c4BFg+B089Gd3fhrngHtWkDTkaYaOaJxMaM7kfoJFFYmgEtMY4Ewcs/gursQDfUQzQHUkmI92DPW4i/fi0imoPIyMDfvBHVsBtSSVRnB+nXXkYoBbZlvFrLK9EdRnhDKgm2gywuQbe3oZobQVqI0jKE66HjPQOei5CS4DkXkmxsMGIBpQi6af5n7GS+F47w5V/9iOZwmNtu+3dIp1FNDewZVTRv3kjBe++C5yHK9khDYyS7Y7Rk2riykEwnA0951MZqCLmd5BTl4+1zL8syS6jtqmNXqovQ+OtJJS5EuF2oUCFiZoCAfJDEfb+Drk6U1lAwmqi6HKuzCJXU2A3Z6NGQWqgO7pu7H2SyBTu+Bb8oimjNwV4yHllbiNAanbeBQLiW1Ixxh9yH07CYUPWfseL1RrXbXI6adAVi11xEsteWwAaRAlWwlwgGjP1CPdhbBLgaq1kgukwAGgrjF6wEOqzxRmqseoFfqdE5GnuzxK80RDAYda4q0gTelyTP8ftC5kTcKIffblvKh564gLAI00M3b/pv8rvQ7/nI8I+RFGliyRh+piY8IoKfBYGsAB11MXa+W0PZ5NJ+CllvMnTf6BN4W5hzKlGkZ+lj7vgrjsCFIxRPbNF0sYnSrO0EZICR3iRO9Yp7L4TDNnZ54zWBZQIR7/ULrhNY24xi2Btp/INjJV0k/TgZdn+SP+Jk0pPRSf7QHLIDe8fLylPEOxJYtiSUHTokoRsMBvnJT37LyJFj+N73vkZTUwN33PELbHuQEvy/isFvfhD/krBtm4suuoILL7ycN998hTvv/G8+97nr+OpXP8f551/GFVd8nNmzT8TLn0Ji5JWEdj6N3bEREPgZ5SQnfx4/owy7fQMoDz9ahR8d8YHNatKlp6CdDAK7X8Xq2oWbPZV02XzcwmNXHAPGJ/YYt02lUvz5z/fxq1/9mO3bt3LKKafx9NN3MmPG4Ynv9+0kntDU/f5Bwvl5jD7/HCxls9FKs85OU5k+NkbDGjma8M23oVtbQEpEXv7fNdFeILg0mUWGlix3krQKnxwtOTcV4dS0meZUHe24b72O9/4KAOypM3BOPhVr1Bhkbh6qvg5ZVoEQwvjVdrRhnXIqopdc0VqjGxsYYtk89tQbJLSiq6uT4uJSPvWpjzB06IHevjreg/v+e3irV2JPmYHVS457G9bCzh3o7i6TqoyAohK8zevxVr9/5GTw5o1Gkun7EOpVXti2Odd0GhGPozpjRrH9Qe6vlAQXnYW3YjlagFU5FJGT20eee3W78Ldt7UcGp199keTrL/N6w24umTgNOWQYXvUWeOBewrd+aUCFsO6MoVpbELn9fZZFTi66sR7d3AQHI4MBd5bG/pNAdJsiFW18y1QeeOOOTr2p2tvQ3V2GZI8c/cTPHvgjIXm+IvScxN5oluksSJ2l8CZC4F1ADnBuUsMATjRCCD7zmdu5/fYbWL16JZMnT0Nr44W7LSaYVKCZWAABqckJwfj8o/PE3R8hG+aWaeaW7T3HuAfxlZot7YKyDE1THHZ2CaIBo0Leg860eQS/sENQEDbnErZhQj6MyVU0xo3StzhiSNXN7Xt9igHSvWrhql77tIkFmld3GSVHWe+4uzFuSOsphf3v4a4uo9ztcWFIlkYAtd3w4xWSHTHjJ5zpQI8Lz22XNMbhv05RJkhkPzTFIZYSlO/jt+wpY0mxrUOwvg1mFfffJu5BLGV8kv/WRPHOndtYt24Vn/3slw74zFMeWzq20Z6MEQ1kMTp3BAHrH8xcD2IQg/j/Br///Z2sWrWCJ598ncBByF0hBNbI0Vgjj75zobOxi4Z1DXQ1dePkhcmeHaSzMqNfgFqnUGRoQa7qTxJ7aFY4SXK01ScKEAgytKRDeIAmuB+LZyPI0haVymGV6KRDKqIeJG2bHkviaI2PURw/HYghgEsHCCkGQwS/EojzXLCHmDDK4hCCk9NhLklm9QuXE+EwwSuvxllwOrqlCZGZxbxRxTwc6qbZd8nt6CYpYFdOiDF+iFHesXUUWsOqCH/qFtzFb+Bv2YSIZuPMOgE5cjTxFe9AMIS3coXxDA6FekMCOiESQU6cigwGkQUFqESc9PPPYk+dicjMQmZmkn7lryAEIhxBux7+yncRUpL80wN4K9/FmXUC1uRp/cYH9vSZBGIduC+/gL9jG8JxCI6dwPe/9m0qn3qYO+74BjveW85/jJpAsK2FeLexBFRrV5lzy8mB7m7IzMIlSnNMUBFJ4mXkgrQJCEmp8HkDh2pfMHSfe6G0QgjZdz4qXAThot7fEwh+9BoIBkk+9EesyqHookoS9c0Etwgsx8KdLfE+5OONPfL7r4UFQkIsgvOXOcimHFR+Jxofa/soQg+V4FaAKh54eyu2hcjGe0Cl8bJHIWPgeDWoSb8jPawY0TIcBKgMTXCxQHQYz14dwpC4vWWMygHRJbA3ACFjxSBbDYGsLVD5Zrk3TJO4XCHc3s/2ayLVYRC9AXZ7yGC/RPNc53N89LnLCTpB6pO7ubrwGv5L/4B8CqEdrAS0hmJ0Tkrg7+OGF4qGSHQkSfe4hLP7/z37VZCoOn7WWyeXJJnVdA9EFiO7koSVJssqQKmPoGLzQGq8wzyy0tM09ixBYLkJsLYaAAF+KdibJBl3ahLXBrGyLHztY+/DzPvaQwoLS+wt0ps2N1P91ja6GrsQlqSgKp/RC0aSkX/wcYgQgptv/jeKi0u47bYbiMXa+fWvHxgUB/wfxSAZPIh/aQghmDfvNObNO42amu088sh9PPzwH3nooT8wbNgILr/8Y1x22VVUzpqF3bkNLS387NF9Kt105IORXgOcEG7hDNzCGXuTk/5B6Orq5IEHfsf//u//0NhYz9lnX8gvf3kvU6fOPOJ9dEqFnUiy7oGHmXT1R7B7CTkBJMQAvexHASElYp8Qsr83wkguTmWxKJ1Bd6yNzKXLsNatJRUIYI8Zi/v+SvyN6xC9qaupxx/G37yB8A03Ezj/YlKPP4K/cZ0p0gTYE6cQOPU0AFRTI6nHH8bbtAFcF1lQSGDRWRSdeAodHe28/vqL/Pu/f7/vXLRSpF97Ge+1l/C2bMLfvhVr5BhkOGx81FwXEQiiO2PguuCYol5YDrqn+8CLOwhEOIywLEQwBMkEOL0DEm2IaDIzTajGcYLIiCCzosiB1Of7eMrpdBrv7SWs9z1ak3EWjBiNCAaxhlfh79yGv3XTgIS3iGQY4rWnG/ZNx433QCh82GtJnayxdgoCKwTUAhpUniZx6ZEnNauuTtJPPY73/rvoVBKZk4d96kIC8xf1TQwcFQSkFmncCT72ZgEa/BEav8J85o2AwGIgzd6k5hSgTOE7ED70oau4884fc8cdX+eP9/2FJ6sFr9VKelzzeVYAzhiqmFF0dLYQR4qIDR8fr3hiq2RTu0ABuUHIDSnyQ/SR0xvbBUVhzSNbJBLNqFzBNeMVhRGj/t1XrTyjSLO8QbChVRANaDTQ5Qom5uu+NOLhUbh0lOKpakMc+xqyHMH5VYrx+zUhrGwStCUFY3P33oNhUViyCRKu8Xbes7wjpVnVLFhWLzhzAMuHDMcQzgnP/NuaNNYVLQnjJfy7tRZ1XYrzqzQKeLnG2GN0pQUZjuaEUs2ZwzTBv1Fd/txzTxIKhVi48Kx+y9uSHdy/4RE2tG3B9V1saTMqdzhXjbucosgHiC4fxCAGMQigtraGO+74Oldf/al+AUb7Q6fTeGvex9+0AYTAGj0Oe/LUg2YN7EHr9lZWPbGWeFsPdsDGS/sMXSN5/0OVNEwoIEdJuoWmWXqcnI5Quh8ZrACX/oNjG0GRsmiyPUqVoE1qIlrTIzQhLXCBHC0Z6TtsEz5KgEaQkBJbmeBSCQSUIqwUywMJTktHyNEHPuB3WC5/CXbjIBjrOwgEHcLntUCc4b7DrP3s2YQQWKVlUFoGwDxP07Gznrc6trNRuwRcn1GdaS7PqCIw/uBdgIeDVTkU68Mf67dMuy4yLx9v7WpUS5MRdVgWursbLAvhBLCiUayqkeZcPQ+/ejNqxzbsSVMhmo1OxE2dZlvQlYDuLrQQ+PW16M52vPffw7n4Q9jllaSXLUbV7UJmRXFmzCJ825fRzU3GamLoMIRl8dnPfplSIfnyf/0na1Ys4+fnXEqqd9xiLV+GyMjCHjMef8smaG3CFbDNDVOVF6DE7oFkHDS0Rkp5QQq64/VUZJdjCQulFbXduxkarWRodGDbPyEEwfMuQre24K1cgb9jE11soLsqiDP/TIKXncxAdsqHgg7m4eZNIPR2AtmYgxraBFKbXJphUURLLs4KQeqcgQlPp2k5It2BlzfR7C8A2huJCK5BZi3HCw8z594r2nY2CexaQwb75aByjQVCerYm9EQvwRsCkQR8k2fhF0H8ah9vInhVQMDsT0U1srU/US1bQeVrQx734oHX7+ELb30KG5uQCHPfGQ9zmjgN2S5IFykIQyw/zfrYTpwRAZx9JqjdRJpAJIAT+ttTWoH6N8lpeRl/SAV2PAerTiOCO0A/gNs8itQJJbgTD0M8hyB+tcIvE2T8WuIPNQS6KgYk2BsFOa9lkntpIY2JWmxhY0kbX3n0uJ0Uhyv6FMNtO9tZ/eQa0j0uGQURlKepfb+ORHuCOQtmEFQOfhH9wvX2xYc+9DGi0Rw++cnL+epXP8sdd/zi7yrMGsQ/B/5lyOC2tjZuueUWnn76aaSUXHrppfz0pz8lM/PgvazJZJLbb7+dhx56iFQqxZlnnsmvfvUriovNU2nVqlXccccdvPXWW7S0tDBs2DA+/elP87nPfe7vdVmDOI4YMmQ4t9/+dW699WssW/YmDz98Lz//+X/xwx9+k1mzTmTRonM4/fRzGJ0f+QBmC0eBf8ADNZ1O89prf+Wxxx7kr399Gs/zuOSSj3DTTbczatSh24gGQpXv8NwTz5Ds6GDyNaYQTGNCM8oG8DeT8QYCda9it60GO0K6+ATSJaeAfQwGTX8nZLTHsO76X7yN6/EjJlTCffWv6K5u7FMXIkOm+NZuGm/DOmM/cMoCrIoheOvWoBNxrPJK43ccCKJ6ekjeezfexnXI0greaqzFX7+K4Tu30bBpHd974G5GjhzDFVd8vO8cvGWLST/6IASCyKIi/F07UTu347ku9sw5iOwcdCqJToVQnZ0Q7zEpzK3NBA6hfN0f9uTpeO8sQ+Tm4e+qAa0RnouKtSN6usFzSf7mF9jzFhI4dWE/G4xjgT15Gt7q99HxuAk7AaPkjURMAEkvdCqJTsR5vamerGCIaaW91xQIGhuLnoO0DIbDOCecROqJRyAY6vMM9nftwJ4yDTnsIOzoHoQhfo0iPResWoEOGkXwwRQW+0NrTeqhP+IuW4wsKkFm56DaWkn/+UGE4xCYt/DIdjQAVCmkSw8sKt0ZGneFwFkr0L3KU9EtcCf1Jj4PANu2+eIX/5MbbriSB55/kyXBU8kNaip7X5/NCaN2HZHtM+Zv5NRSmgGfnqxoiBsFb0sCnqyWbIuZXJGaTqMUnlkMAUuT9mFDq+DpbYJrJ+wlaJWGbTFoSwrmVyhG5whWtZjwnDMKFSfvY8EghAl9q8rRPLxJsq7VEMLvNwtyQzCreO9+21Jgy/5keNKHuCsIWP2XRwPQnhJsaYczhx14rcURmJCvWVIvUFqzulkQSxmf4apcyLI1z+0QFEWMGvjxrYJMB3JDmq40PFUtSCu47G/kWf3MM49z6qmn9wttAniy+jlWNq9leNYQIk6YpJdiXesmHtn8JDdO+cTgIGEQgxjEMUNrzVe/+lmys3P46le/c/D10mmSD9yD9/ZitG9EB+KNV7BPOJnQRz5+ACGsMcSs9hVbXq8mGUuQP9x0m2mtEbUdTH++iU0j8qgPK8JasjCdwQWpzAPC4wIIxnkBXg/EKcDqs2bIVRa5ysLRElv71EgPCyhTNgEE5yUzKVI2c1QmeW4r7Y5NSgrs3ke4oxSZvqJQO3QIRUyoAcngjXaaLqEY5wf6zi1HWzRrn1V26gAyeH9YLS2c9dPfMi3VRcvY0YQ9n8o1G7GCYfwbs7GqRhxy+6OBcBwCpy7EXfJmH6mrEwno6UZkZEIggGpr7SOD0cp0pu2ZJPdcrLETIB5HdXeh492QkYkIR7AiGVijxuJVbyHx33dATzc63oOI5iCLivHXrcaZt5DglVcfUKeelxGl6pTT+fzq5Vzy6B+5eprpggwmEmghsatGIQuL0K0tCN/D2vU+z4bLySkaScSLE7cjbHEKEN31DAlG2dxejUCgUJRkFHPxyHNw5MEnJUQ4QuiaG/BmvI9fvQVsB3vceKxxE4+tphaC5PCLCSRWYKl2ZKoJY2WWgVswEeFJrFo4mPpVJlvQcq8qXGeAXwh2iwOBPbYdGFGENvZkIgYiJnCaNP4QQc9nFDrbkLqeBHsTyASokEbngtUlCL4hSS1QfSIFnQfpeZrQ08LYT2SC7Og93AINIdO5+o1v3Mof/3gXACdPms9d0/9AkVeEWwbp+Qp3ijYKJBUi84EsGjc3kV0cxQk7JGIJUj1php84/Ki8c48VgcalaBlCR7JxJ2tUkUA2DUOKtaTnrCYxr3ivSONQCIEuAFWi8cbQzzJEFWjsasHowERcnaYj3dJrdSbJCxYxKntiXy1Wu6qOZGeK/OF5fcuC0qHz9Q66lrWS75Sjopr0KZrk2XpA1u/MM8/nhz/8Nbfddj1FRSXcfvvX+6/ggb0B7J0CbYE3WhvhyWA5+P8N/mXI4I9+9KPU19fz4osv4rou1157LTfccAMPPPDAQbe59dZbeeaZZ3jkkUfIzs7m5ptv5pJLLmHx4sUArFixgqKiIu677z4qKytZsmQJN9xwA5ZlcfPNN/+9Lm0QxxlSSk488VROPPFUvvOd/+GZZx7n+eef5Cc/+S7f+97XGDJkOIsWncOiRecwd+48gocJqPpnh9aaFSve5tFHH+Cppx6hvb2VceMmcvvt3+Diiz9MWdnRBdfti9lumO8+9iy5c2eRHlVJPR7twmeCF2SS2/++yXgDGav/Byu2FR3IBuURaVuH1bGFxPgbQP5zPm7ct5fgbVyPNXJM3yBDtTSh2tugMwa9ZLBwAgjLxt9eTWDeQqzhI7CGm8Lar99N6slH8dauRre14tfWYE+fiYzm0NVYy49Wv0tDV4ycN1/k3A9dxZe//C2c3mNp38d941WQFlbFELTnIXfuQLW2oJoa8Ot24W+vNoRoTw/pJx4B20bYNiIrird8Kc60mUdk7WBPmYZz2hm4r71sLBZamlHdXSaQrmok9tjxqPY20g/fj7DkByIzAZxZc/E3rsd7bzlaKUM+B0M4p5/Vr/1TZGRCaSlPPHYfi0aMwdljtRHrQGRkHvLaAovOMr55y5ehNm1AhELYU2cQuuKqI1Pm2uBN4ACv3SOB2rENb+0qZMUQ4+0MWOURE6ryxqs4c085rJLpaKEzIX6dIrBY4LxvqjF3miJ9kkYfIsjh3HMvYfLk6fzyv7/GpC++Rd4+8zNFERO8tr5NMCavv7WBFIcPSztSJH3ICxm17NCosX7Y0iHYHoOntwmGRzWB3q8sYEFJhmZdq6Q95ZMXMsTpAxsl61oFSU8TsCSjczU3TlEUHGSMrDW8WiNY0wLZAYg4JiDu3vUSjepLMg5bUN9jht85QW0Uy4AldO+gfB+PZg2gyTrIq0MIuGSUIulL3qwV1PeYYxdGTAhJ2IbumPEV7kpDZmCvt3CmA7aAZbslCyp88o8tp/OgqKvbxYoVy/jpT+/ut7w10cbalg2URoqIOOagITtIRWYZm9u3UdddT0VW2fE9mUEMYhD/Z/Dqqy/w0kvPcvfdD5N1iK4db/VKvGVvIUsrEL2CH93dhbf0TbyJU3Bm7LVdq5Mufw30sMlJIxvjVDa1MKwws48YEUKQXZyFbOhiQbWNGp5NVEnyBiBi92BBOkK1nWaj5ZKlBSnMZOC1iWxKlU21dGmXPhEtGKocJnpBhvnmPT9SZnFWj8WzkTReUGJpTUCb2meI7+DaFhnaKIkHQgpjUbQ/Se0IQVwcvkbxVr2HX19H8ZjxlLQb3yg9tAq1aT3uu8uOCxmstUbV74ZEHGvSVOx5C3D/8gQ6lUTYDtaYceieHvzNG6HXh1RrjdpVg1Va3lc3i6yoIUcryrESSTzXRYbDxsLBttGui6qvQ23fCqEIsqwc4j1GUJBfgLtsMfbsudj7CAsAVF0to0sreGzSNL75yrPc+c6b5t6Ew8iyCvyd28yxgyGs9laioyezfnQYX2aRHx1K0kvS0FHL3IwRXDL1CtZ3bacl0Up2MJsphRMO2iUjE00E6hdjt61FO5mkK2bhzrjyuIx9VGYlycn5yE0JVE4m2EH8SCnazsJOCVT+wTs1/cwhBOrfNGR8ryzZG+NhuS5yRwV2h/HrRYM7yQQTW40CEdPGyiHTWKrpCOhsDe0CnSVwy7UhPj0goJFNpssudcbe39Pk2RqVBYG3BDIG/lBIn6pJz9Hs2FHNJz5xGZs2rUdKyde+9l0+9albkUrSlVbGpkKAtRWc1QLRZTE9bwIrhwpaG9vwm7oIZAapOmkYw+YMHfDajzeE2w17VMk2+BWma89uA39k8siI4F5oB9My4NOfjfOMlUY4mMm0jBNpSzeT8pMErRB5gUKsfX6fOhu6CGYE9k7UawiutxFt0DM0jjdEI9sg9LRAZ0Bq4cDPkA9/+OM0Nzfw/e9/naKiEj72sevNBykIPyAIvCMQaXMMnQnJMxWps/XfjxBOg9VkfK1VMYNE9HHGPyc7sx82bNjA888/z/Lly5k507S4//znP+ecc87hRz/6EWVlBw5QYrEYd999Nw888AALFxpC4/e//z3jxo1j2bJlnHDCCXziE5/ot01VVRVLly7lscceGySD/z9BZmYWV1xxNVdccTXJZJIlS17jxRef5YUXnuZ3v/slGRmZnHDCKcyYMYcZM05g6tSZhyxS/1mwa9cO3nzzVRYvfpXFi1+jqamB0tJyrrzyGi655ErGjz8yD9nDIRr3aHxtMQu/+AUsLQgA56YzmZ+OENnPxThQ9xpW51a8vAkgTKEt0p0EG5fglp6Ml398zul4w1+/1tgN7EPaiUgmKB/V0Y4sKulbrpWPCPVn3FRLM8m7fom/YzsyrwAV60DtrsUPhREzZ3PumImcO2YiqrEBHIeMb/9Xf3VAMoFqa+mzoxC2jT1xCt7aVajaGrxli0H5iLETTGhcSzN4HnJYFfbUGaianaSefozQJ280BYFWOE3v4DQuQybb8HLGkC6bh8qsRFgWwUuuwJk2E397NX7DbtLPP4MoLcMu7iVcs3MMmfn6KzhzThrQq/dIIcJhQh+/Hm/GHPxtW8GxsceMxxozrt89EFLyTnYOu+Ld/Fd+MaqtFZ2Io7s6CcxbiKw8eKEnQiFCH/4YzqmnGd+8jCzksOEfWNV8JFDt7ZBIICr7PzNEdg6qo914PP8NQhF1NqTO0QdtCxwIQgi+/OVv85GPnEvJ+08zZuH5+30Oyd7gtV1d8MouwcY2QdAyCtr5lcceelbbDS/vFKxvMwFwUws1i4Zo8sMwrcjYIbywwxC3td3GSiErYEjRHmWUxGAUs8sbBUMyNZkBiLtGdfuIJfn0ZDVgM0ZdDyxvlJRENLm9BHhOELbFNK/tEswo0rzbaGwamuPm2iO2pDRDkxWAIVnQlNC0JY0iWGmojwsKQpoFFQe//3kh+MxkRVZA8ORWydg8TU5wL7Eeto1/sesLSjL67ycnBDs7jb3E8SaD7777l0Sj2Zx99kX9lse9BGnfJSeQ3W952ArR5DeT8AYwpB7EIAYxiCOA1pof/vCbzJw5l7POuvCQ6/ob1hklXC8R7AvBjuFlxDIUpXXVVM2YiUDQIjzuDsfYZXkUKqPibbR8uu0ks0SAUC/hqhUgBFlY5PqHf4lVKIdPxXNZ5iTYaqeJKovpXpDpbggLwaGmyAWCz+gRBHuqeYYUTQEHS8EQz6HEitImFOekMsg+CBk9xHewEejmJoYufpecjZtJZ4SJz5nEmEknH/bcVVubyeHYt74SAsIRU4MeBLqnG397NWA8gkXmwJ7GqrWF1KMP4W1YB+kUIicXa9QY1NQZCKUQFZUIJ4C/czuiYTckk8YuTfnI/AKCF13W6w/sojva8TetR0sJlo1ub0NbFiIQNGKIeA+qpQWERGZlIUMhdDCIbm1Bx9ohGELV7ID9yGBZUopfu4uMsgr+66yLyQtFuGvFEq5b/ibfnH4rZ51xDv67b6NTSZyFZ1B1ynxO86t5vXYJu2O1jFtXz9nV3Yy2k4Te+CUnnrIAZ965hxQXyHgDkTU/xY5tQduZCOXiNC8n1VlNYvTHOGpviAHgzorgLclExAqNbRhg7TQWAAfrCgNwi08gUP8Gdvt6/EgZoLFS9aSmDCN5wixEl0IkIfykwC8BLPDLNJQBabB3CUS7UQvrANhbMQRwPhAH2Ql+BagcjbVjvwQ1G9LzNemTNSJpSE4tNY888ke+9KWbcd00RUUl3H33I0yfPrv3ZhrSDyDwuiD8uER0AbYm6GUyv2om9ed1kIqkieRFDumNe7zh5k0mtP1xyKjo+05FugusAH7W0RHS3miNXySwdmI8kCWQANkmSF5gFNYWNoWhg4thMnLDxOr2pmDLdhAtCh3RBLKD4BjyVKQMIZ86pX844L64+eYv0tjYwFe+cgvFxaWcccZ5BN4VBJcI/DLQWWbCQDZD6AWJN8bHP36NBgeF854g+JzAahBGvDNKk7hIoQa1CccN/xJk8NKlS8nJyekjggEWLVqElJK3336biy+++IBtVqxYgeu6LFq0qG/Z2LFjGTJkCEuXLuWEEwb2qorFYuTlHXrwnkqlSKVSfT93dnYe7SUN4h+APR6JCxeexfe+91M2blzLSy89y9Klb/LrX/+Ezs4YQgjGjp3AjBknMH36HKZMmc6wYSMJh4/ziPwokEwm2bp1Ixs3rmXp0jdZvPg1amq2I4Rg8uTpfOhDV7FgwZnMmXPycTd/X7bsDZKJOF849SKquguQ0C/AYl/YbavRTnYfEQygA1HorsHq2nFEZLBRHNShdtWAE8AePeagRelxQyAAntdvkSgqBinRXV3o3oQq3dKMCASxJ07qt677zhL8HduxxozvLRY1qm4XfmMDsrEBq9fGQXd3YY0dfyBJGQojcvOM0qLXW1fm5mFPnY6nNfgKa9w4E9KxdDFi9DhIxM15ZmZBSRnepg2m5a2gkND2xwlufxyhFdoKYbevx2leTnzSLfjREcZjrmokVtVIvNUrcV97GWsfwhtMAJtqb0N3xj6wr7MIBnFmzOqn5hkIDyx5jTFDq5h5wino+jpkVhT79LMJLDj9iNrT9/XN+3tBZmdDMAQ9xm95D3RXJzI75wMFyf0tcOqpi5g4exGr//g5ps0+lYxMQ2KnfADNsKgJW/vNasnuHkFeSJPw4LGtkpouzXUTVZ9y90jRFIe71khqugSFIY2n4fkdkppOzWemKDIDUJFlWn1frt2rwRXanFdRBvxhvSRoad6slYRt47MLEHGgIlOzsU1Q1208hWu6YFO7wNcwPKrpTgu605ry/b6KvBA0xgXVMXh0i7GZWFip2dohaIwb9fApFZpPTVLcvU7ybqOgKWHOLsPSlGRofr1aMj5fs6BS9/kZx11Y0ypoSxo18JhcTVHEqIH3EMFam4C5acWKHTHoThtyeA/2/Jx1bHk/B0VnZ4z77vstH//4p8jc77laEM4nL5RLS7KNIU553/KWZBs5oRyKMwqP78kMYhCD+D+DF154mlWrVvDwwy8cod2MWac5M8SfZw1na3GUZHIoWdF8ZoU6uSyZxQonSY3lMtYPGDuH/EzS5VHat7Tw2nCLIhzyfEGwqZvC0myipUcu9ChVNhensowf/1EiWwS5lfFc0NPFk14nOwIC7UgcLTknFeas1MHrgglekNm7YkR/8xuKt+7EzcogmE5zxso1jJjXgb7so4e8fzK/AKEUWqm+WlNrDfEe5EHqI/fdt0k//Rh+Qz0AVlExgfMvwZk9t9962vNI3v97vFXvI8srEKEwqrXFKHQnTMav2YHathWBRkRzCN10K7KgEN3chIxmY02ailVm3i3usrfwt1cjho+ElmZ0Ig7xOPgeFBWjGuvRPT0mEyMUAtswWEIICAbRHTFkcRAG6LxyTjgJb+0q/JodyOISJuaYMf344jJu/Nn3mT//DL797f+mavjIvnt0JkM5sWw2bY/fT+i9rYQzc0H03pvXX8aet4DI9TdhHaTTMlD3CnbHFrzcCSB7xTDJNgJ1r5Iunoufc/RBiPvDr4DERzWhJwR2TW+eRLEmeb7qF6i2P1SkhPjEmwhtfxK7YxMAbvFcklUX42cVABrRAcFXNLIT1D5dY7ITdASclYLgYono6F3eDrLTkHM6ALpbI5QgHT2IQtk2itKGht184+u38ZdnHkMgWDj6DH7z1fuIjM0+wORCtELoGQFC4+1xOfQ0ziZJ8docEh/929hoHQrp8vk4rSux29aigrkI5SK8BOmyeXi5449qXzoLEpcrIg9Jkw8CYBm7t9RpR3ZtZZPLaNzcTGdDF5kFGfidio5EF9GiLEqie2s2lWW+LxE3YpKBIITgW9/6Mbt37+Lzn7+Ol19+j5ErK9G2OVezEqiiXtuIzQJ/xN/2O7A3QeSPElIaVazBM+SwaJf0fF6h/7mGWP+y+JcggxsaGigq6k9I2LZNXl4eDQ0Dz3Q2NDQQCATIycnpt7y4uPig2yxZsoQ//elPPPPMM4c8n+9///t885vfPPILGMQ/HYQQjBs3iXHjJnHLLV9CKUV19SbeffdtVqxYxrvvLuX+++/u9ekRlJVVUlU1iqqqkVRVjWL48JEMGzaC4uJSMjOzPrCXYjwep7W1iZaWZnbsqGbz5vVs2rSezZvXs2PHNpQyL9gxY8azaNE5nHTSfObOnUdOTu7xuB0HxUsvPUd5+RDGjJlwQNvaAbAjoNz+y3Rviqs8PKuhPY/UU4/hvvUauqsTISSytIzgh67Enjjl2C/iMHCmTsdftdKoODOzTOGsFXLocEQkA3/jegBEVhaBs87DGjex3/b+1s2IjMw+1YAoKETmF+Dv3I5qbEAWFKKam0BKnJPmHXB8YVk4J59K6oF7UPV1iPwCdCKBqqvFHjMOv60VWVyGbm0BrRCWNMVwKgVuGuE46O4udDqN7KkjuOsFtJONH+k1vtUau30twZrniU+4sZ+XtciMIsJhE8C2LznU023U0ofwZN8Xqr0Nf91qVGcnsqAQe+LkoyJCGxvreeGFp/jmN39M5JpPo3u6EcEQ4iBJ4/8skMNHYI0dj/fecmR5JSKSgW5rhZ4e7HMv/ECq6oHgeR4dHW20t7fS3t5Oe3srPT1dBAJBQqEwoVCYYDBEKBQiHI5QUlLWj/QTQvA/P/4VZ58+jWd/82VOuP5XaKDbFUwt1Ewp0vylWlDXY4LU9pCXCU+zqtlYSUw+Sk5weYOgplMwNm/v/vKCmk3tsKpFcFKZRvUSvwnXhM3ZEnbHBWlfE3XgxR2ClqQg5UNuUNCcgPF5mooskztT0wU/XylpTRov4UhvgFvIgmFRRY9rSN+coCYvZEjZuAdhW7Ozs39w3MxiQ4DXdEJpBIozoCAMFZnQmdK0p0xXnyPBVfBKjWRLh7GqUBruWS+p7gCtzSCmPFNQngnbY4ZcD1rQFDc3IiDNtVZ3CCypyQ5CV9ooj08uV5REoDUB61oFCQ9KMzXj8syxjwX33383qVSS6647sPMpbIc4bcgp/GnTE2yL7SA7EKXL7cZVLhcNPZdo4G88KTeIQQziXwZa9056b9kMWmNVjTA10wC1sFKKH/3om5x44qmcfPKCw+7bGjsesfh1vHicRxeMZW15LpWN7YRa2+iZM5w3AnGiWtIkPRwEsd4wY2nBprOLyenqJndbBzWWYLuvkAVhis8tJjfsMcEbeDazTfg0So8wkiG+3ecVfMC19FJWB/t8X4wQWdyWzqLd9emQilwlB/QJ3hdBBJe8uIKWzXVsnzAGz7IoURaVLV0E3noTNXNun83CQLCnTsd94xX8LRsNcSktU1fm5ePMNN65OpXCW7cGv2YHuqMdd+mbEAj0evsKVN0ukn+6D1lQ1M9Wwt+6GW/dGkR2Nvg+2DZWWTn+zu3o7m4it34ZtX0rWimsEaOQpeUHHRt57yw1lmGjx6E7Y3jr16KSCXQ8bhSXWqPdtAlgHj0O2lvR6TQiEECnXbBsRG4+9pgDCThr4hRCl19F+q/Poep20dbajCMt7nnwGV7bsoGvf/02TjttOjfd9G/cfPMX+8Q+oa3bybn/EfyOdjx/h/EoDkdACLwXniHe00Pkhpv7WZztgdOyEh3M7SOCAXQwFxHfjd257biQwQDuVI07RmPvwJDBwzikPdge+NER9Ey+FZFqBQQ6mNdvLKBzID1HE3pKIBsN0Sw8c4z0SYrgEokOaPxJ4BdB+C8gEuAXCFSBCYmTHQKVOTA56Loud9/9C370o2+hXNPqdcvwz/PdUXdgPWzhboT4JxR6nyGHXS2QbeDte+ts46nrrBUkksZ3+JjhJbBjW0D7+NEqY294GKiMcnomfZ5g3avYratQdgS35ERSZacekx2INxm6yxX2WkPUqjJwx+kB7SasnRB4U2BXC1QU3DmaolmFjDtzLNsWb6e9rgMrISkM5zCpYCzhwN6bIzvBL9GHJU+llPz4x7/htNOm8/nPf5Kn5zwH1sAFp3AHXHxc4SwViG6Mr3IvvIjG3gbOGkH6hL//hMD/j/iHksFf/vKX+cEPfnDIdTZs2PB3OZe1a9dy4YUX8h//8R+cccYZh1z3K1/5Crfddlvfz52dnVRWHnmI0yD++SClZNSocYwaNY4rr7wGMCqqDRvWsG3bVrZv38K2bVt4553F/OlP95BM7m2ZDQQC5OcXkpubTzSaTVZWlGg0G8uyAW2KZqX6/dvT001bWwstLc20tDQRj/cPyCoqKmH06PEsXHg2Y8aMZ/To8YwePY7s7Jy/2z3RWvPyy89x2mlnIYQwrTDaQwdyBgzHSxfPIdK6BpHuNIpgrbF6dvUm4U488AD7wXtnKe5fn0Hk5iPLKsD3UTU7SD74RyK3VyDzjj0J+VCwZ56AvWUz3jtL0Lt2AkZxG7ruM9jjJ6K2VZtrGTkKWTn0gOJWZEYNMbvnZ9vBmjwN1d6OTqdQdbXI/Hyc087Enj6wOtY5cR66p8cU8du3gQB7ylSck+eTvOe3hhyPRk1h2tNtVB9ZUbQTQNfVYg0bjiwqxmpehkjF8PedoRYCFS7Gbl8PfsKQ9r2Qw4ZjjeklMyuHIjIy0e1tqFgHwQWnm+MdBv7WzST/+Dv83bVmwkAIrJGjCV1zPbLoyJLYHnroDzhOgEsv/QhCSpMu/S8AISWhj3yclOPgbVhnkq2jUQLnXUTg1NOOeb89Pd1s2LCWdetW9f23bdsWYrGOo95XdnYO5eVDGD58BCNHjmHEiNFc+4lb+M2v7mDq/EsYOmUR04sUJ5VrIjZsbhdEA7qfT3DYBk8Zknhy4dEVX9UxQcTpvz/HAhDs7jb72tgmcCScVAabO2B3t0BryA8Z0lZIQ8zWdAkEGl+ZbXKCxuKhOWHOcXtM4mtNaQTGlGhiKXhmuyTpGbI5y5EUZWiGRTWxlAmgM2ey95EmhFEcZwXNsR/bImlJGDuJ5Y2ChrghZlc1C2YWa0bnaN5pEnxnmaShRxBLw/QiTUFY4ylz/cOzNWcNU7zfLEj6Rl2c8ATL6iWeMkrg9a2CwrA59kmlmstGaVa3CB7aJGiKm6k4W2omF8LV44yi+miQTqe5666fc/HFV1JSMrBC7JTyEwjZQd6oXUpzvJXyzFJOLj+BuaUzB1x/EIMYxP89aK1J//VZ0i88g+7qNMRRZibO/NMIXnDpAd1PzzzzGOvXr+Hxx189ov3bk6dhz57L1l2b2BKGiuoawikXWV5BXl4xrta84yTJVII1dhIQ+ELjaxDDA0Q/OYr8NR0EW5O42QHaJ+VQnB+mXsf4VDyHkf7eh6eH5tlgN28FEnQKRVALRvkOH0pGKd4nJLlN+LwWiPO+Y2r/qW6IU9MR8g9D7gLkaotc/8haarTW2KtXURQtpNTPMjOPmKAuf+cuvG1bD0kGy7x8Qld/ktRTj+Lv2AZKYQ0dTuC8C7GGVaETcZJ//J3JcfB9/N216OZmrBmzTBYFAmvocPwNa3HfX9FHBmutcZe8gb92NQSDJmcimo01doKpRTvakNnZWCed2u98VFcn3sp3UTt3QEYm9qQpWCNHo2MxRDBoxhbZOQjlI3LzEJEMZFkFIjsHicZf/T7Cc41auKnRdMUphTW8iuBFH0IWl6DTaXR7G0QiyKwoQgick+ZhT5+J2l3H7l/+kIpYK4HhIzhj+AhOPnkhP//5HfziF//FY489yL//+/c484R5JO+6E393HeTloRsbIZ00lhvFpZBKoupqST3/F8I33XrAOEBLB7Q/8Hd6vPNSwuxVyh4NhECHBvY7BkidqAk9I3HWAZ7YaxeRAtEF/liznvSMyhctkN1AAHQUVERj1Uugvzp48eLX+NrXPseWLRupLBlGXUMNP517J9fOvR4NeClwVgkCywSpRfvUlwcrNcUhPjtC2C0rCW95AKunDrRChYtIDr+IdNmCA8e3WoHyQDpmPJVZQWLMx4CPfbCT6IXKNz7Kh4JVDRl3SWSzQEU1diM4GwWyXjL0kkpKxhXT1diFlJKyZ3MIvWujbI0Og2wzxG163sABcvsjNzeP//mfu/nwh8/mVxU/5/Pxz4O7115CdIN2NN6wvz0Ra9fuDcrug4P53WsbJIKPF/6hZPDtt9/ONddcc8h1qqqqKCkpoampqd9yz/Noa2ujpKRkwO1KSkpIp9N0dHT0Uwc3NjYesM369es57bTTuOGGG/j3f//3w553MBj8lw8d+78Kq2MjgYYlWN11+JkVpEtOxM8ZM+C60Wg2c+aczJw5/X26lFLU19exY0c1LS1Gzdva2kx7extdXTE6O2PU1tbgeR5SSoQQB/wbCoUZM2YCJ55YQEFBEfn5BeTnF1FQUEh5+RBy/xY+o2hqpEeH9MlTFhXKRre342/dDL6PNayqX1BXdfVmdu7cxqJTTiS84bc4zSsR2sfLHklq6Hl4uf2rkXTpPKzYVoINS6C7xhwzmEdi5OWozMOH2LnLl4FlIwt6pYe2jRxWhb95I/76NciT5x+3e7EvRDBI6Kpr8WfONsWzZXxt5fAqU/ANPUT/FWBPm4m34m1UcxOioNAoGmIdWNNnEr78o8iCQmRJqQlJ64WO9xgPXd9HDqtCZucQOO1MdE8P6VdfhFQSf/duxM4d2OMm4i5/G1lYiCyvxFvzvvEMLilDbd2EiGYTOPNchG33FiuYolTs83hXLlih/svoJTOvvJqklPgb1xkFSVaUwOlnEzjtzMPeO51Ok/rzg/gN9VijxiIsC+2m8TatJ/XME4SuueGwqnnf97n//t9x4YWX/10nO44XZG4eoetvQtXXobu7kYVFyKP8+/U8j+XLl/D880/xyivPs23bFjMotG1Gjx7HhAlTOPvsi8ykUyiX3Jx8csvyyM3LJyMji3Q6RTKZIJVKkkwmSCaTxOM9NDTspra2hrq6GrZt28rDD/+R+vo6ACzL4sU7LubGG79A3rzTCJTNAIJkBYxVxL7QxiqM8DG40GQHNSlfsm/1rrUZLmT2FpdJD5QWtCYNAesps3ZXWhADhmRqNGab1qQg09FYEpY3QkMcJuZDyBJYwuxzZxe4ylg/xFLGhsLXsLsb6lugtkvwodGK04dqdnSasLqu9F5bBqWhIyUYnatY1yopz9CsbxPs7hZIoQnbhhBe2wrNCWNR0dQjUFrgac2SesG4PM3wqLGx2N0tuHKM4vwRmhWNgvvWG4I4p7eMaEvA7jicW6WYWQLlGdDlwiObBbGUsZqQwlhQvNsgqMwSnF91dIXwk0/+ifr6Oj7zmVsPuo4Qgtkl05lZPJWUnyJoBZHHwetwEIMYxP8/8LdsIv3sk4hgGNmrzNRtrbgvPoc9fAT2lOl71/V9fvSjbzF//hnMmXPSIfcbJ84aZyUNoToyPlGCtamKdHGUTKdnbx1lWWRoRZ102S4V7VKhAFtDt9RIQOQ7JOYXU6gsLCApFdmuJCYUi51EPzL4zUCcZ4I95GrJCN8hITSr7BRuqJOb47k4CDqFz92RDjZa6b7guWeC3VTbaT4VzyF6BITwUSEYMOHFgE4k8LZsRDU2oNtbST/5KFZBYb97vD+sqhGEP/sFVH0dKIUsKevLxHCXLsZd/AaisAgrJxedTKLa21HVW9CFRYjs3m7DQBDd3tq3T3/zRty3Xkf7vukasy1UrAO95n1kWTl21ai+sOU9UG2tJO++E2/zRtM55/u4b7xM4IJLscaMI/3qi4iSsr01ouuB7RhxQ2Gx6dLr6kRGc9BSmH1YNvaM2QQv/yhWWTnukjdJv/oiqqUZEQxiT59F8JwLEJlZiHAEa8QoatpaGDZsL4EeiUT40pe+xaWXfpRvfON2rr/+w4wdMpwbs/NZkJOL9Hy0VpAVRScSEGtHZkaRZeUmzLmtFZHfn1R1i0/A3vxH8OJGcKE1Vk8tOph3wFjpuEBrRLoDhI0+Tl07wbcEQkPiDI1QGoIgWyCwQqIt+kK7RKLX+zdDo23wJoHK04h2sHbv3V9t7U6++92v8eSTDzN16kzGjZvEjq1beXj645w+95x9Dgw6rHFW9SeD/ZEalSuQDez1h/VANgtSi9Qxq4Jlz24iG+5GpjvwsqpASKyeOsKb70OFC/Hyeq0AlUuw7hWc3a8j05340SpSFYv6Wx76KayuHSAkftYwMwY7UiiwdoGIg18C+mCNvhqCL0tkk8Abuze0TbZC8C1Jeo5PsCJAsMoIplJXgYgqAu8br2WVq0lfoEmfdOQ147x5p/HJT97CN+/9KqddsYhJWyaiI6ZwFy6k5upjm5A4SvilYNXsx/57ABqV87c//v8V/EPJ4MLCQgoLD99zOnfuXDo6OlixYgUzZswA4JVXXkEpxZw5cwbcZsaMGTiOw8svv8yll14KwKZNm6ipqWHu3L0+SOvWrWPhwoV8/OMf57vf/e5xuKpB/LPCaVhCxqr/Riaa0E4GdnOAQOMy4uM+iVt0aD/TfSGlpLy8kvLyfx01eIfweTjUxTo7RVwoMrVk/hurmPfIc1gtLaawyM4hsPAMAmedh5CSV199gWAwyOnRdQR3bUKFS1AygNP8LlbXTnqm3I4f3YcotYIkxt2AW3KSeTnKAG7exCMiggETCLFfMdnneRaPH69bMSD2hLYdix2FPWUagbPONwXpJmMpIfMLCJ1/Cc6cEw9Y31v1HqnHH8av343QGlFQSOCs81BdXbgv/AUZzUYUl6C7unCffRJn3kICZ5yN995yCIdwZs429g0ZWViVQ3Hmnow92kzZe7lj8UIl+BtXk2zPAA1OcYRwVgtd/kwSf7gbHetAjhhNYM6JyJJSZF4+4U/dgtq1E93dhSwoQhYPPMm2P/wd2/B37cQaMmyvTYYTQJaU4a1fi+5oP2yA2ksvPUtt7c696bX/ghBCHNRL7mBIJBK8/vqLPP/8U7z44jO0t7dSUlLGokXncNNN/8aECZMZPXp838SjrDWhDfZ7Znt3nCZ1pkYVmsENHJllTHd3F2vWrOSFF57mrrt+wc9+9n3++7+/TSgUYtq02Yw98UJ6Ki6jLVSOI6HbNUFmRWHN+Pyjn4mfVqR5px4aeqAoYkq6ui7IDcLEArO/skxNjwu7uiEvCEnHkLOe1vhK0JYyQWu+gpANcVeg0OQFoSIDJuRrNrUJutMQS0Pag+1GsGbsHHxDBgctjS0grWBbh2Rzu2JivmZmsWLJbkHINuu3JAQZjjm3jpSxc2hJCDIDmnSSXpUuoGFrhyBsawojhmyOu4L2lKY9Ianr0ozONQphVxn18o5O8DGq5j3IC0Nz0qiGK3pVEJvaBI1xGJm912s44hhyfXmD4Oxh2pzDEX7nd9zxDc4++0LGjJlw2PWlkITtf5xP/iAGMYh/Xnjr10A8jhyyt/4T+QV4rS14q9/vR1Q+9dQjbNmykf/5n7sPuc820cofIneywVmDQqNDmsis4fji43RVjOgjYc26Pkk0TZZHhpa4KHwEEo0HpAWEtDZBbL0kgoUgqgU11t7eZg/NYidBRAuKelXAmVowzHfYaqfZaqUZ5wd5z0mx2XIZ4wf6MjMKsdhsubznpJifPoI+/SOEEAJ7xhxSf34A1d2Nv24VXbvr+FlrIy/3dNL2WDXjXn6ab97+dWZe8+mDBuUKKfvyKvZAp1IkH7wHv3oz1NaA4yBsBywLnUwaK7LsXLRS6FQKWV5pyOj1a0j9+SETqFxeiW5pQofCaKVQ26vR3d0Ez7vogIC19Gsv421cb4QCvWS0qt9N+rmnCX34Y8h1a/A3rUfmFaBtB90ZQ44cjcg3vIBua0UWFRO+8VYjNEinkRWVyN7OMfedpSQfuBekQObloxMJ0i88i+6MEfrE3nuzY0c1J510oD3JyJFjeOCBv/D224v50Rdv5LNr3mV0RhafCWex0FdIrc3MdXc3YuQYRCCITqdggHueLl+IHduC0/QuaJNBogO5JEZchsooP2D9DwKrYzOhnU9jd2xBC4lbMJXUsAtRvdZwRvS0DJlows+sJF16EipzyKF3mobA+wI/X0P2XurNLwfZqJG+QHQaBbAOY1ZwBf5YjV/e+zdWK0iP1VRXb+YXv/ghjz56Pzk5eXz3u//DfffdTWNjPU/824uc/N4JeANJe/dbpPIhea4m/LjE3oiZ8XEF3ogj99QdCE7Le8hEI17uxD4VsJ81FLttHU7D24YM1prwlgcJ1jyDtsJoOwOn6R3s9o30TPwMXsE0nKZ3CW37s1EXI/CyhpAcecVeMvkQkA0QflhibxGINKhsTXqeJnm2hv3nlhJgV4Mq3EsEA6g8sDdp7FpBujfMWLW3oXu6iV+ST/LsDGQc/HzgGB5RX/nKd3jzzZe5/r1refm2pQTXW+iAsSpJzzp4EN3xRHq2wlllYdWAX2ysS6xagT9E404cVAYfL/xLeAaPGzeOs846i+uvv55f//rXuK7LzTffzIc//GHKysx0UV1dHaeddhr33nsvs2fPJjs7m+uuu47bbruNvLw8otEot9xyC3Pnzu0Lj1u7di0LFy7kzDPP5LbbbuvzErYs64hI6kH860Ak28hc/h/YndvQVgRS7Wg7A6Vcgtsfx82fAtY/tz/psUKjeTzYxTtOggrlMETZ6F27cB55mNqkomrkGBOW1txI+tknscrKsafOYMWKt5kydhTZya142WP77o8XzMVuX0ug/k0S0f1Us9LCy598RGFx+8MaMQb12ovo0r1KAZ2II2yrn2L5nw1CSgLnXYQ9YxZqezVIC2v0WGT+ge1Y/u46kg/ei+7qwho+EqRE1e8m9af70Eojs3OQvS3cIpIBloW3eiUZX/4PAmedh+7pQeblI0IDT4krO4uObaV4z76GjscRQNKx6MwpIm2vQ8gABIN4a1bhvvAMzoJF2KPHYo2dgHUYBfSA8Fy054G936vEtiHeA+6hTaWUUvz4x99m7tx5TJt25BMy/8pobKzn97//Fffe+xs6OtoZNWosV111HWeddSFTpsxADjDQkK2QcbfEqhGoIiPTDS6W2LWa7psU+igcVDIzs5g7dx5z586jqmoUX/7yzXzpS98kEsngzTdf4f6ff4V0+nYyR5yMPely7ImXEM0rpThybIXXpHy4YITixZ2SzR0agfHgvWiEorJX0DIiGzIDkPIgYRmLsrQyoWsWmo6UIGQZMrUiS+P5RiFclaNJ+aY+9oGErwlZAscynsEJz/zXljIWGGUZhhTuSgu6XMXT24yC9yNjNcOzjb9xbbchoXtceKdesiMGO2JGmZAdhM6UUQVnOGAJs//sAEQDsMU1quZM2wTYxVJmn3NK9wbMpRXIAQZCArPtHqR71dH72muAIabTveT2kRZwP/nJd2lvb+M///OHR/PVDWIQgxjEgUgm0QN0DAjLMkFg++D3v7+TU0457bDv95dCz7Im8D6j3DEECKLRbLe2ERdvUWedQ1IFydCCDqnwNOQqSbUNRb6FxqJV+ri9dhEJFGEsfBTdQhPWgjxlsVt6VKm9DEZSaDqlEUfsizASD+iUCnzYKV0c+ocn2wgczGfHG4FT5uNvr8Z9/WX8bVv5+v9j773D7CjO7P9PVXffODlHSaOcM4iMyMlkY7AxYIIzDjiu7V2v7Z+/jrsO67DrtbENBkw2mJxFEEGgHGc0Go2kyTnd1KHq90eNRhplAY4753nmQcx07r593zp13nP6u6j3Pb4/ZzHFns+fd2zl6q9/nidaWphw3Yexqscf0Xbdpx4lqN0MQhgrLtdF9fUYFWg6jR4YQPX2oNpbsarHYdVMIvWLHxNsrTUhxcmEqWuzs03IcyYDKkD7Pu7LLyKrJ+AsMIItHQQEq99C5heMEMEAoqwcVbsZ0mmiH/4E7rJnCepqsadMQxeXoNNJVH2dsUILh3DOOg9ryrT9bRmCAG/Zc6AV1vjJZtvZJgPDX7ca1diANXEymUyGbdvquOGGTx70uixZciJ3fPn/49Xf/IJfNG3nlqZGJls2N6SSnO2EiZaUYk2ait65Hfv4kxAHyGrRThaJ2TfjdK/FGtyBtsL4BXOMWvRdhBzaSXzDz5GpNoJoOUIHhHc9jZVoJjHvC9g964ht+T3C7UfbUZzOFYTaXyU582P4BYeYCNaYImrfj7U0lhDeOIXdLKBjmLNVgNSoXA0psDpgQ3oD31v2HR761n0UFZXwta99h4suuoLrrruUjo42HnzwWWaKmejNIHr3UsJmQKQE3vzR9hLCG8JbkkaV52Ovs43v7QSNu1AfXEV7BBDuAAxb2o26BHYUmTZd6DLRRKj1JVSkFBU1YzkVLcHu30J455NoJ4vY5t+An8CPVyHQ2APbiG36NUMLv4KKHWLM6kLsDom9WRBUa3TE1PiRPwtU9gEsI2zQjlEQj8Lw/TIBfoNkHn3I2L+kU8i8fOyTlhI645z9JmkOCw1WE2QPRPnBl3/BxTecxgPe/Vz0+SuObjvvAvzZkLpKEX5SGMsIC7zpmvTlCv2P4Sb4D4F/CDIY4M477+Tmm2/mjDPOQErJ5Zdfzn/913+N/N3zPGpra0nupSD88Y9/PLJsJpPhnHPO4Ze//OXI3++//346Ozu54447uOOOO0Z+P378eBobG/8q5zWGvw4i2+7F7q9HxUrRdhzQyEwvMtWBPbAdK9FEkDPxb32YfxG0yYANToYKZZMzXPSOW7+Fgu4+GmZPo8ITRLVAlJThb92Ct2Yl9vxFbNiwmjMXTjIz43sT5UKgnWysgW3v6nE6J52Cv3GtUQoUlYDnonp7sBcegzX98Gq2vyV2K0MPpw4NNqxFdXUan7XhQsSqqsZf8xaqtxfruNFtlCI3D7WjAdXdZdS/h7FRCGo3k3qzHjnhBOywi1AuXp9L5q2N2DMqsWZPQw0OmNTndavxt23FnjgJa8o0ItfciCwuOeT294WsGocsLDaDh2EVitYa3dGONXnqfq10++KJJx5iw4Y1/OlPz7/9EMb0cOJx1pEFafytUFu7kf/5n5/wpz/9EccJcfXVN3LRRTdRWTmNwsL9+fS94awUWDsF/tQ9qgGVr7HrBKHV+/isHQWuuebDrFz5Oj/96Xf5059e4MMf/jT9/X384I5HefjP99H7yOfQD38Ga8bJrD7+Om53ruSWY0MHy5M4IISAcyZoFpQEbB8QSGBKvrFI6ExC05AgZGlmFGh608ab2JYaJ8tYPfSkNUprfC3IsoxVghAwKc+wpXHbWEUoZQhgVxmbh7ijUdp4DGcCKIkYdW5PBiKWpjobWoeMxUNNLpxWrVlSpvnBW5JMAOOy99hltCQMGewroy72lQl863dBCs3EXEPQxh1jeZHyDYmrgXQgmJCjiQ+Ph6fmwavNZrnI8D1P+WbSbnLenus2LluTZUt6Myb0DsyruCstOLFCET7C+r6ubhO//vV/8fnP/xvV1ROO/MbtDQ2yHYRr1BmMuWSNYQz/Z2GNrzGa20xmJCRVex7ay2BN3mO7Vle3ibfeeo1f/equQ24vTZrVzgqKgmJCwy8XgWBcMJ6M/TLzvWPplFUkhKYssDnVjbHJSvNaOE2ApksqMkITBjJARkA/irSlKVAWU3yHfqkQCJZ4URTGCmKlnaLB8gjQLPAjI6TwkFCEERQp85KNa2E6k/eBD8T0OwuPPhBEPIvoDR8DyyY12M+zrdv5xfGns7C/HwYH+EQ4zrLkEH+4+zY+39FB7Ns/xMo5dPiVGhzAe205sqLK2EdIgYjFkEIQ9PcZa6toFJ1J4yxeQujc9xhl7+YNWJOnYnkufn0dKpkEN2O608orEckEcso01PatpH78PfjkZ7EXHAOWhfkGPND1McHSVs0kojWT0K5rls+k8VevJNi2FcIR7NlzsGbMPmBtqJMJVFfHfp1nIjsH3dJk6uyJk6mv30IQBEyffujcEnv+Qo6ZMp3flVWyasnJ/PylZ/hqZxvfz0guGYxz5eZ1TJmzgNB5Fx28VrXCeCXH4pUce8h9vROEWl9BJltHVK0aUOF87N4tOG3LCTc9DcrF353TojV232bCjQ8Zu4qD2T6FTbdZ+CWJKtIjpLDsAJ0nSN6osHeBvVYgMuCeqrB2CUSTZvn2F/ll0895pPEhKivH8e1v/4SrrvoQrpvhqqvOo7l5F/ff/zRTp87EV5A5TRF+ViDaBUhjPeDN0yNhYMLtJ7L9YZyONxCBR5BVRWbpBaSLF70r11DFhjsflb8n9E1rhD9EkG1EMdZQE8IdJMjfS10vBEGkBHtwB07zMkSme9R98HOnY/duwOl4k8yEiw66f7sOrHrwazRCGqWr7AXZD5E/gXvCPqrbEHjHaCIPS3S+8QFGmUC5oAy8KYr0fXfhLX8JWVyKLC5D9fbgPngP2A7h08864msjeiF6n8TZIBApODP7FM6ecT4/+N6/c955l+A4fwU58KgDMtfDm6+RzYADQTX7q6fH8I7wD0MGFxQUcNddBy8oJkyYYPyF9kIkEuEXv/gFv/jFLw64zje+8Q2+8Y1vvJuHOYa/R2iF07UarBB6xDNVoML5yGSrmSUU/7xvliGhSAlNsdpTBNjJFFJKfAEumt0NwSIURvf1MTDQT0NDPfOuuRDYbgz09yoihJ9CRd5d9bw1voboTZ/Afe4pgm1bEdEYoZNPI3T6WYjQP4dqWw0OoAOFatqJ6ulGWJbxRotlQU8PemhwVGibGugHpdGJIePVts8M7+7gut0DMr92EzqTRtRM2p07gkrsBN9Huxm01gSbN6B7uxFFxQjLQlRU4m/aQObh+4nc+PGjImVlTi7OWefhPngP/tYtiFjcnENuHqFzzj/kjHQQBPzwh9/k1FPP2s+X+4gQQHiZIPSSQPYLdNwUk+mz9d8VWdXQsJVvfOOLPPvs45SXV/LFL36D88+/iZdeyueuuyS+D2VlmkWLNDNmaEpKYN/H3doFOPu0j1nmd3LX2z82IQTf//4vh5Uz7+Xxx18lt7AcOf9azp51Lfm6h21v/Jnal+5mw603svW+r9DzwY9wy0c/SnHxkYUD7kZJDEqG1cWBgkcbBMt2SfoyGktKlNY40gS1OZYZQjYNmRC1QEFllmLANc9meVyTEzJk6qlViicbJW1JQ8Bmh4zVQ1EUyuKaxn5D1LYlYcgXKK3JDQnWdEJpzKh7d6O2V9AyTA7X9gga+o2fcF5Y05sx/sCTcjXHlCn6MpDwjUdx1IH+hCA7ZI6rPWksHaqzwVeacTl7apOFpZrVHWb/EcsMizMBLCqFeXuF81VlwYmVimd2CPoyZtl+V1Aa05xWfWTkv9aar33ts1RXT+BjH/vc4Vc4AGQbRB+S2LUCPFAlmvQ5Gu9YfeBx/hjGMIZR6E3382bbKmp764naUeYVz2ZByWzsdztY6q8Ee/5CrFlz8devQWRlg5DogT7s6TOxF+1RAN911+8oKCji7LMvPOT2lAgICLD2GeFbWAhcjvWSzPUKSQlNtpY4CIoCwW3RARpsDxBENSgBWQjiSpKtJUXKJgSkhCaq4eJMnAV+mHvDg9wTHaBXBrhohoSmRwYscSPYSDqkz7FelJrAEB+z/TAvh1K0SZ/SYYK4XQZEtWC2/+4UG6qtFW/1W+jOdkRRMfb8xTjzF6Jff5lAa0LppPGq9TzwPCJSsjo1hPfGctw/3Uf0upsOuX3d34dODGJNngKuS9A1nMOTSkIqhXzPpcQ+9y9Ix0Hk5aP7+wg2rkOWliNCYWPP0NpMMDAIvT2I4hJEOgWWhdrRiM6kIbmd5E9+SOi0M4lccwPWvIW4jz9ilh2e7dYd7SYcbuKUkWMbqe9jcZwTT8E58ZTDXi8RiSKyslE93bAXIazTKYRtj4QQb968HoDphxGUWBVVRK7+EJmH7mdBazO3Hn8aO1TAPbsaeXDLOm5rbuS4zhaunTKF8y664m+WG2QPNKDtrNGqVukACrtnA1ayzfjg7oYQBLEK7IHtyFT7IRWrmTM19naNs0WgYhrhCrSjSb9HoarArdK4x5vao7l5F/feczv3vHo7O1u2M2niVH70o19z2WXvJxQKMTQ0yNVXX8j27fXce+9TzJgxbJ0gIX2Jxp+icTYIyEAwBbyF2og5lE9s0//idLyBipSgnDh2Xy3W0E4Scz6FX3j0Vn77witehJ87Gbt3EypWgZYSK9GKilXglhtBjrYjIC1QLlh77rUI0ig7ipXpRlvR0fdBCBA2Mt297y5HQQ4IxHBHm/OWQHaZ60JGEO4RRP6sSV82ur7KnK6xdmmcDYAfIGJroGoz/vEKsS0Pf81KZEUVclgwZMViBLt24L/yAqETTxkZIx4S2hDBodcFQZUmk+ci+uCbed/i+M2Lueee2/jgBw/9njkSyGQ7MtONCuejomUHDKTf79Bi5jkZw18G/5iVyBjGcDTQCqRNEMpHugOoSKEhNrU2pGasjCDrH8f/92hRpCyytaRPBiO+aMnyMjwB0bRHVMYBjE9YYghr0hQ2bNkIwPTF56JSf8Ie2IqfVQPSRiZbQDp4ZccfdJ9vF9bEyURqJkE6BZb9d0cCa60JNq3He+0VVGsLsrIK57iTsGceWnWwGzInxyh9g8D4jmkIdjRCOIw1fyGqswOkhcjNI9ixnWD9WkRODunf/xprfA3hiy/HmjSFoLUF7/mn8TesAyGw5y0kdMbZ4Afsx9BI05SuPRc10GdI6OxccF3TLhiOIssr8TdvRHd2IEqOjuQLLT0Dq6AAb8VrqM52rAWLcU44GWvSob+5H374XurqNvPjH//mqPa3G+EXBNH7BDoCKk8jhiDykIAUpN/3t/eSSqfT/PznP+AXv/ghJSXl/PSnt3LxxVeidYhf/EKyYYOgtFSjNTz7rODPf5ZMnqyZOlVz3nmGHN4NlQd4B4hQ9gQ6752dayQS4Tc/v5fzLjiej1xyJXd94lkKdRRRo4lGC5h95oeYfeaH6Gmq44UHfs4ff/sj7vrND7jkkiu56aZPMXv2/KPe55vtgkcbBNmOpjACHSlN25Ago2BLryAnpFHaWCFcNlmzrd+QsxlfEKCp75NoNB+aqTh7vGZWYcAjDYIntksm5ioqh8dKRqFr1L/dKUMUF0YEMUfTPGR8fwv3clwxQXZGfdw0JIjZmkFPkPaNPYNA05GCqmzBhFzNqZWairjmj3WS7QNmHzkhmJxnvJJtAQ0DhnTejZgNN8xWrGgTrOsyn9V5RZpjy4wtxm4IAZdO1lRkwYo2wUAGFpcpTqrQjDvC9rgHH7yL5cuXceedj7y9wWsKYrcZIjio0BAySqHYnZJENMA/ejegMYzh/xR60r38ev0fqOvZRtSO4mufle1rWVp9AldMvfgfMqBRRGNErv8I3isv4q9+E5TCPu1MnJOXQm4u/SJAp13uu+8O3ve+aw/77onqGNP8WbwaWkaBKkIOyxI7ZBu5Op+aYBJRJNHhr7oMmtdCabK1oBkIhMYTENGCQmUx1w/jonlvOofxgY0noCqwydcWW6wMv431MSgUOVpiA77Q9ArFJttlVhDmTDfOBZks5HAtZQE5WvKmk0YABcqiQtm8JxNnevDOa1S/bgvp23+DamtFOA7a8/BeeZHw5VeRUzOJ+fmF/HftBsbbUQoth8cDj7VehnGWBUrhvfoSkQ9eb2w6hmvLfSf1RW4eIp4Nroe18Bj0W28QbK+HZBK0Jlj2LKlMmtgXv4bMLzBKb98fESfI3HzseYvQG9ehOtvQvocsLkH39Zr9FZVAbw+ypBTvzdexJk0ldOoZqPo6/K1bQFqoni4IFM6JJ6NTSbxXlqG6uhA5udhz5+8Jj94HOpUylnHZOSOWE8JxsE88BfeeO0x4c2ERJJOoXY1YM+dgTZ4KwPLlLzJ9+ixyDqOcBrDnLsCaMt0ESWvNtNZmvvzEI3ymrIqn25q5d1cDn/jMDeT9++c5++z3cO65F3PqqWcRjf71/PWDSCF2z4bRvxwWwpkgOWnGvKOgMOOCQ79rVCUkPqkIvSGwGgQqR+Et1PjDPG46nebppx/h7rtv48UXnyESiXLhhe/lp+//Lccee8LIM5dMJrjmmoupq9vEPfc8yZw5C0bvyAJ/Lvhz969d7d5NOF1rCbInoR0zNjXWhJsJNz33rpDBOpRLcvYniWx/CKd7PUIpvOJFpGsuGvFW9vNm4GePxx7Yhp87BaSDcAeRmR4ypecj093IdJfpKN79DtcKtI+KHrrDUhVqcCRWnQnDU4VGiS17NUEIwi9JvGMCgr1snnU2JD6isDf7ROtvx/aeR2e5OAGE3xzCautHV507aj8iNw/V14ceHECEDy/esna4hNY5pAsUHTu7SPWl0EpT5JdxWcUV/Og/vs3ll1/99p93P0m0/h5C7a8jvEG0nYVbcgzpKe9HO1mHX38MfzGMkcFj+OeHtPEK5mINNKBcG5nuGjaZdNFODqkpHzh468w/ENIoNtsurZZPlpdmuh+mRGaTry1OcKM8Hh7Ck6aobV84HXv2VBatqsPOL0VJC9XVYQLJlpxA/bOPI4SgZtbxJIfyiW69E3twG2iFCheSmnwlXtHB04zfCYQQEP3r9ftr3yeo24JqbUaEw1gzZh/Q7xfAf3056Xv+gE6nEfEsgqad+OvXEvnAdTjHHHf4faXT4PlorZBxw1ip3h6E7xE563xUWwveqjcJNq4laNyOzIpjL1yMsB2j+r29l/DV15O5906C7dtM8azBfeox1I4G7ONPRlgSnUwYla7W6MSgKZK31aMHB81PJIJIp4wXm2WZ5Oi+XnQ6ddTXTwyT0YdKt97vOvo+//mf/x9nnXXB2/MKTkLoRYGOQjDszKGzAQfCb0jc0wLUOxSu9/dDOg35+fsrdQ+HZcue4Wtf+wxNTTv4+Mc/x6c//ZXhkDdYs0ZQWwsTJ2osC15/XeC6huhNJKClBe64QxKPB0w3uYB48zXhVzAhCpUYT68WE+bhzXtnZLBIwKRHq7h35gOc/dppfPO/PsnNE2/llXnQchZ7EqSLprLkhp/xke/+Oy8+8jt++9tfcu+9f+Ckk07jX/7l/2PhwsO3RvoKtvXBA1sFCdeEwm0fML/X2oTVTcg1dglhC+YWaxaXav7jLcHylDT2EdJ4+koBPWlz3aqy4YbZGltqVrTBtv7h/Wk4o1qztQ9WtguEMPvqzwhKopq4o6nvFyOK3MosTdyRtCXADTQegt60sXDID0OWDb2uoCSm+NIiRXg4cO5zCxXP7RTcUyvozQg8pVnbKfAULC7VzCkafY/ijrGlOJzC15ZwYoXmxIqjv8f19bX8y798iksvvYrTTjvnqNcHcDYJ7G3gTzZEMEAwwbQ5hl+V+HP3HXSOYQxj2BuvNL9Bbc82puVPGlEC96b7eaV5BYtK5zM572149f8dQObkEj7/IsLn72mH3mRneDbUyy7Lp+mJP9Pb283FV3/osNsSCM7MnEejVc96ux6LcnwSZOsEF6Yvo0SNDrR9y0nxaijNEj+KK6Bd+mgBthZM90OMD2y2Wh5xLZgejCainw4l6JaK8YGFNUyOZQWaDumTryRfGCqgWO8ZGq+y09wVHWBAKMb7Np2WQgLnpuOc4cXf/gUchg4C3Ef+hOrsGLEP01qj6uvwnnmS8FXX8J+pJF++5/ec0d+BBcyIxDi/qIxNQwMQCqGTSfx1a/DXrCSor0XEs3CWnIhz0qkjikCZnYNz/IlkHvkTDPSheruNt5IAkV+AiMXwXnqBpO8R/9YPEAWFWJXVBPV16OwchBDI4hJDsnqesZUoLDRq5ewcdG8PqICgox0SCdwnHsE5/SwiH/s07gvPkrn3LkgkEDm5+KtX4j3zJCIvD5GTi9Aa9/kKIh+8Hnv6zD3XJp3Gfe4p/NdfQSWTyMIiQqeebupcKQmdcjp6YADv1ZdQW7cgwhGsufOJvO+DhlTXmhdffIaLL37fEd8PEY1iz5iF6uwgfduvIQiIzZrLpbPncUk6Td36VTyWFeOZ1W9y771/IBqNcdppZ3P++Zdw2pKTidfV4tdtRoTD2HPmY89bOKKKfjfglR5HqP115NAuVLzCeCYPbEdFS8lUn4M12Ig9uAM/d6qZUVYBVqIFr3TJYUlKAFUM6fcYGw+A1tZmnr/rSZ555jFefvl5Uqkkixcfzw9/+D9cdNF7ycrKHrV+EAR8/OMfZMOGNfzxj48zf/7iw+5TDEBoucBZKbDtDqxyj2BifJRVggrnYw1uH23t8A6g4pUkZ30C4faBDtDhwtEKVTtCatr1xLbcit2/dcQuUYXycTrewEq0Yg02IpMteCXHoZ24sZuMVx82kN6fDN4sTexOgZYafBDDQ69ghkYMCux6E5I2CiEQ5WuQnc/hR0vR4Tzz+8EmLLWdoGMblO2x6dFDg8isbET80ESrcAcI73ic8Bu7sHadTYd2SbhZhHKyEVKQ7s/wifCn+XP7n7jzzlu56aabj/Aqj0ak4QHCOx9HRcsIciaZ/e56GhCkZr5zxfEY3j7GyOAx/J+AW30WTt8m5EAjOpSL8IZASNITL8etPP1vfXjvGAMi4LZoP82ZJhbVPkdZ+3q6Nci8RZRUXcx5VBLRghdDSRqkSygrgnvjhykpexWxahU6CAidegbO6WfhRNM0rHmW8ZXlRC0fv2g+g3lTsfvqEMrHz6lBR/ZJrFIBdu9G7N5aQOPnTTGJqn/nbZA6lSR95+/xV79llBBKIYtKiF71Qez5i/ZZNoX71GOgwJ4yfeT3QWMD7pOPYs+Zf9Bgt5Flt9ZhTZ9hkps7O9BaY42bYEI4EkNEPnAdoTPPJX3PHaA01vyFI8oha/I0gtpNZB6+n2B7A9a0mSM2DKKwEH9rLdYxS7AXL8Fb8RrCdlDdnQQN9ci8fLRtozva0IMDkExgTZuJVTMJANXZgSwuRpaUHezQ31Xcd98dbN9ef1gvwYNB9oHsH55R3wsqD+wGkF28bTK4vx8ee0ywerUhaYuLNWedpTn2WH3YbqZkMsG//MvN3H//nZx44lJ+//sHmDJlxqhlurtBKUEkomluFvT1QUGBJmm6Nampgbo6ePVVwfTpw7YKkyD1PkXkEYFdP3yuxZC+SBG8DavzIdf44YYsmLpC4KwWLJh3LD/L/l8+8uR12NFcPhj8iMbxkKmBhGcC22pyFSv6C4ie8nl+8p7P0L7qIX7xX/+P97znJN7znsv48pe/xaRJUw+4z44k3LFZUt8nWN0JXmCI2ZIY5A2P14OEoDsFx1cojis3596XMfYIx5QpAgWuMsrhmA07Bk3gW3W2IY+vmaGYWSjY0GU83GYVamYVar7/puSYMo0bQNI36tzSuGbngGDI2/MMVWfDknLFQ/WCpG+C4pTWhG1BXlgTaCiManYNCn6+VjLgmlC7Y0o1tgRbCtoSxifYlkYRnPah3zW2EX8tJJMJPvzhKykvr+T73z+wTdaRQPYAWkBon89ZzrB/2xjGMIZDYn3XJnJD2aMsIfIjubQl29nev/MflgzeF7WWy++i/QyhKNIWtbffRf4Jx/LanDJmp/So8LUDoSqoYYb3WbbKZpqtFBFtM84vYIE3bb9l19gZbCBbW8wIwgRCEw8kfVLRLn3qLQ8L2GZ5zPQDCvQe+4kmy8fWhoDeDQeBApQQ5O21bAbN4+Eh0kIzLXAQCKYqTYPl8WYozSleDOdteOVo30ft2gFBYHK7djZiVVSNKCuFEMjKaoLmXQjbYfo3f8Ddk6bS87MfkYhFKS0u43P1G6iyLEQ0hozFSN/1e3RfLzK/ENXeRvru21HNuwh/8HrEcCht6Jz3oANF5t47DHnr+4iSMmRxCcKyCJTC37wRf+UKQmeeS+ic80m1thDUbTa2C4kEwraIXHsjassmvDUr0Ykhcx6DgxAOQXsrOpHAG+jDfeIRQuddCIMDiFgUe97p4Dh4b72B6u5COs6Iv7DatpXMg/dgfeFriFAIrTWZh+7Dfe4pZG4eIp6FamslfedtRADnxFMRjkPk0isInbwU1d6KiMaRE2pGzreubhNtbS2ceuqRe6buRlC3GdXTgzV1+p77EokwubyaW/Ly+Nqv76a+vpYnn3yYJ554mJtv/hC2lMzPK2BxURmLC4pY8MqL5J95LuErPnD0IV4HgV84n/Tk9xNu/DN23xYQkiBWQWrK1ajsCaQnX0Vs82+wezdglMCaIKeG9MTLj6gd33Vd1q1bxfPPP8Gzzz7Bhg1rkFKyePHxfPazX+Xccy9iyl7jn33x/e9/neeee4Lbb3+IxYsPL5AhCbHfSpx1Ap2tEbFspJI4/T7eIhs9XBsKL0GQM+GILR3l0E5Cba8hE82oaCle6RKC3MmjFxICHT54El2QN5WhRV/H7tmA8IawEi2Edj6BDmXhFc5FhQtwOt8i1PEGXuE8vMK5pMdfhMj0YnlDBFnjjdXEvrAg+UFF6BULeyfIFKiY8RAOKsCuBYQJlbNrBfgmOC+oBrt3A0L7e4hgwKqqJFydTWJHHTpSiojH0b096MEB7LPOQxxKyas8Ypt+hdPxBjo+iSHtkezUJHJaSLj5xMJ5FMo8iFdw7PSTufvu33HjjZ886owXke4h1Paqsf6ImsGZjhSitCbUuYJM8kJU7Oi6Usfw7uHvm6kZwxjeJQQ5E0nMvYVQ8wvYvZvQoRzc0hNwK045oi/Iv3c8H0pS73dw01u/p7ynnqFYEQMCvJansPt3EJ73JaZZubyu00gMUfJ6cZihq8/lAxddRk4AwpFE6u8mXP8iOza+zNQCTdab3yA17Vr8wrn4RfMPvHMVEKm/i8iupyFIA6CtMG7FUlLTrhv2s/r7hPvi83hvLEeWVKC72tFtrfi1mxnaVkfWt36APWOPz5hqbUF1dSDLR4fEydIyVHubCVEbf/iBnYjGsKfPQmXS6JZmgpZmVMsuMg/cDaEQoaVngpTI/IJRLaRCSpAWQUM9IhIZVVwK20FIC93WRuRqo65w31pBsKwJa+IU7PkL0ZkMurMdv3aLae3LykYP9KOadoFjEzrzvMOS2e8G+vv7+P73v86FF773bVkMwHBYXEQjEqD3EueIBOjosEr4bSAI4M47JW+9ZUjgvDxNayv84Q8Sx1EsXHhwheauXY1cf/17aWzcxk9/eivvfe8HD1gwZWcDaDzPkL9aC6Q0/5+TY15HWVmalpbRthDu8RpvlsZuMNv0J+oDpunu2gXr1gkGB6GiAubP1+QML6c1vNgseGaHoDtlCMuPPgELHE0kAlfNvJohb5DPPXczTsZiQdcPWFYpCVua3JAhT5sGBUJolrc4HFNzBX9+/BIe//NdfP/732Dp0nlcffWN3HLL1ygt3eNNpzTcXSvZ0C2oydH0pQUbuwUJX5PlGd9dX0HI0kRtzbpOMUIGJzxI+8Yr14Swmd/7CnrS5u+7EbH3V9JqDZVZsLEbpuxlqTHomuV3+xiDufbvnaIpisKPVgq29UFuGIqjRomccKEqS1PfJ8gExj94xwA83SgZ9MzQy5KQLUzgkK80Df2COzZLFpVoNDAxVzMh5y/3taO15l/+5WZ27mzk8ceX76fcORqoPAANHqNUOnIQvH8ODmsMY/iLQgoLtU/b9u58E+sAHWkpP43Sipgdffuhqn8DvBxKMiACpgYhhppbaVv2Cqf/90/Y4GTY6rrMCA5tFfG6k+KVUJhxajILfIu00DRJj7siQ3wqmU9oL9LVFRp7OLitOrDpEw5Nlk+XDOgVAUXaYlzg8HIoSYfl85FkHrnDJG9VYONoMWITIRD4KDyhmRI4o8jdFsuj3fIpD+wR8lggKA9sWiyfVukzTh1dfRs01JN58B5jD6YCRCRm6sqCfbrRBCPt/8JxiF57E7kb1hF/6w16e7tY3t/L56snIktK0ZEo9PUagcDwM6P6+/DeegPnpFOxJhoSTITDRC69Ap0YQrW1QhAgikvBzRhLCMsyHrMNZsbZnj2P6Edvxnv1ZdTORsTkqThLTsSaMw+1cztUVuE+eC/a9yHqI8vKTZdlECBKSsk8eC+qtxv3+afBtvE3rkP39aHaWiAeN1kYw9YSsno8qnkXwY7t2FOmGQ/lN19HlpQhC4bFJ7l5BDsacV94FvuY40ds5GRR8QEtJpYte4ZIJPK2Mil0EJiR0r52G7YFvg9aM2XKdKZMmc6nPvVlGv94G0/e+ktezyS5d9d2/qduA1IIpi9/jmNffpbjz72QuXMXUlU1HuudEMNCkBl3Lm7JscY/WFoEuVP3WCoUzmNowVdwulYh070EsVK84sXoSAFkwNkskJ2mhnZnKhq661mz5k1Wr36LNWveZMOGNWQyGfLzC1i69Gw+9rFbOO20c8jfJ6jvQHjggTv5+c9/yNe//n1OP/3cwy4PEForcDaCP8lkfQTMRTg1WIk6VNMk/IkOMtWJUC5uxalHVDjZPRuJbfofZLINbUUQQZpw60skp9+AV7rkwCtpjUy2IDP9qGjxHsLSiZt1tCZr5f8H0kbFzRgwyKkhiJXh9G4iPel9qFA2sbrbsJLtaGER5EwiNeUqgtz9LfN0LqQ+qIjcJQnGD9fy9nBgXw6IQcj6gUR2Awh0HDKnKJi2/xhESEH2ydX4G6OkugdQXe3I7BxC511E6PSzD3OtNmB3rSHImcxQbogVRVup3rkAZXmkvCFkECIlQrTP6OXE0Jn88L6vsX79GubOXXDI7e4L6fYhvQR+vHLU71UoB3uwB+H2whgZ/DfDGBk8hv8zCLInkJp+/d/6MN51eGhWOWkW7VhHec82enKqibgJsqVkW34N5V31iPbXuGfmCbRYPtODEA6CJIqVTpqiuMUVmRxCTc8R2fEYKlpCT8ZhfEkOVrKV6JbfMbT430fNRO4Nu2cd4V1PE4QLTcGBSYMNNz+PXzAbr/QIZof/BtBK4b/5OkTjBPW1qLYWQ4bG4qht9aR++gPi3/ieKXABHBssG3wP2EOaas8D2wbn8F4C9rwF+JvWozNpdFcn/paNJvAtFAbbJnP/H426o7SMYPVbaK1HinutFAQBsrQU1dE2+ly0Rgc+IisbEY3inLQUOb4G1bAVWVyGCIXNPrJzEFXjCNauwqoeB4GPNXM2zklLsef/ZWw/9sV3vvM1kskE3/jGD9/2NnQOuMdoIk9KtKPReSCGwGoWuMdpY6XwNlBfDxs2CMaP18SHSebsbKiv17z4omDBggOrg1955QU++tEPkJOTyyOPvLQnLOMAmDVLM26coKFBYFnm3g0MmL9VD9sGJBKCmTP3L/p0jrGM2Bfd3VBbK1i3DlatMqF0xk5Q8+qrghtuUJSUwJpOwX21EsfSjM/WuAq6k4L6fsHUQBOy4KZ5H0NpxRee/zSfXCP56k3fYWu/5A+bBFVZkDWsEk168FY7zC22ueKKa7jwwiv4/e//m//6r+9x33138LGP3cLNN3+JaDTKjgGo6xWMyza+uBNyDaE66An6MxCxNBklKItr8sLg7cWdFEQgP6LpSTNMBhv0pCEnrCk+jKOMELC0StHQZ1HfZ4jetG8sJo6v0Ezax0owZMHZ4zU1OQGffcGiI2XO1ZJGOTzoGvuHnJBma59g56BgwNW4gUANZ35Eh+0julICN9DsGJCs6zTXN8sxx3PxZEMwv9u4885buf/+O/n5z29j2rRDh+YcDv4sjT9BYG8TBJUaHQKrA3AE7gljFhFjGMPhsLh0Pnf3/YmUnyZqm7qhPdlJTiibqfmTRpbrSffyzI5lrOnYgNKKyfkTOWv8Uibk/P3nWSg0jZZHnrYQCBqfW4aQkhnnnsMONG1WwIzg0Ou/EkphIygbzrVwtKAmcNhmudTZ7qigtpl+iI22i49RHM/zw/hAlwiY64eYHoSJa4mPps5yWeVkOM01XxTH+VGWqxSdVkCXMO+wDJoiZXNpenQ7tdQCqQVqH7v+QIC1T57rEV2n3h7St/2GoK0Vq6oaLMuIATra8QF7wTF7bCKam5DjJiCrxrFs2dNorZlw1dXUJQf5/gtPUpOVzeULlhCatwC/vs4IBPYqTmRuHkFbC0HzrhEyWA0OoDs7TAdYPMt0pzXvMvZgWoNSWJVVI+FrAPaUadhTjDpbBwHe8hdJ/fDb6L5eRH4Bzomn4D79OGjTlo7rQm4uCIm/dhVBSxOqscHMfFuWqaNdF4aGDCGshh8My0IHyhCtgO5sRw8MIMtHF3OioADV3YXu70MUH9r2YNmyZ1iy5OS35XFqTZhorlFPt/EjHj5/1dNN6OwLRtTHu1HS0szVM+dz7XgTZr+9t5u3mnewYuNaXlj+Ar//0x8Bk9EwceIUJk+ePkImT5kynXHjaojF4kc8AaQjBXiRAxO0Kl5BJl4x8v+DgwPsXLWG1tsb2bW1ge2J7TQk6lk9tIreTC8AEydOZv78Y7j44itZuPBY5s5diH0U9harVq3gC1/4GO9737V89KOfPeL1rEYAAeHdH7A4nv9hhPc7rL4G6HXR4QLSNZfilh8+WBAVENn+IDLRCsLCHtgOOoBkK9Ha2/EK54E9WvQiMr3E3/o24baXke4gKlpIetwFJObesmdZ7SMTzYggg0y0oML5YEfBjqLtCDLZQmT766A8/Hg1QvvYvRuIbfoVQwu/dkAFcuZEjVWrcTYBXYAyGSjuEkX4JQmuxp8KCI3sgcgzgkz8WLCeRniJkQkA/CRWlk30+g8jxGRjD1FcYqxcDnf9Ey1GaWzHeLW/lT/N2sG5bSEmDlWQ5Ubpj3s8U/Ea5NmcOOtUbn+pnHvuue2IyWA38NBoIpFCVCgH6fah7D2fR+n2oZ0sVOTA1oxj+OtgjAwewxj+waEwhHBJ306yEh3kDzTh+Gm0EOSFsxBOAY1+Jzssj/GBPaJ8iCEp1BarnDTnu1lktb6ClmFUpIj+RJrcrDL8nMnY/VtwutcZFfUB4HRvQASZESIYjEE/iRbsrjV/t2QwSpkieGgQ1dGOyC8wxWqg0FlZqNYW3NdfIXLJFQDIymqsiZPxN6zFmjwNYdto30O1NGHPW4gsrzjMDsFZcgL+pg2mSN5ai04lkdk5yJpJWNNnoXu68Va8RviqaxAFhaiGemRFJShlivqKCpxzLyRzzx9QrS2IUmProFqaELl52LP3SnSybHQqRdDWglU9DrGbrM5kkGXlRD/ySWR55VG3r2mtjZluKHTU665Y8Sp/+MOv+fa3f0J5+dtkbIeRuUAjUorQaoloN4pg7xhN6gq1X4bekaKrS+C6jBDBu5GXB21t5rT3HldorfnNb37Gt771ZU488TT++7/vOKyCIh6Ha69V3HOPpL5eGK/cIaip0XR1CdauBceBM87QBIF5JA+FN94QPPigoK3NEMJg7CWmT9coBXV1gqefFnzwg5rXWgW+1tQMj3kdC4ZmafRTgq4BQUW+Kcg/Ov4T6LmKLz71WcRPJTWX/T98JUeIYDC2B0LApm44rtwMcj72sVu46qoPjQTnPfzwvfznf/6K+KSTyATGngEMwTun2ByPp4wTweQ8TXW2CXabuZf9R9SG06o099YJdgwYpe6QCwkfzq/RowLgwNhR1PcZYnZirqY8DnOK4LpZxte3NSEIW5oLJyrOnnBwQnZKPnzxGMWv1pltlcTADWB1hwlkXN5sAu+kMOruQBm+ICRN8F3cwpDtaUFBBCblGmVzTxqe2SmYmMeIV/G7hVdeeYF//dfPct11H+Wyy97/jrenY5C8ThG9X2I3CIQHQZEmc5Y64KTEGMYwhtE4oeIYtvY1sLZjA4EO0GiynSzeM/FsKrPMRHPSS/K7DX9kY/cWiqKFWMLijdaV7BjYxSfm3UBF1l/HvuntQgC5WrJLGiJv+3MvULZwPk5BHgKPLH3oL+QMml4ZkKX2EGwDImCX9Km3XX5PP5ensznGi2AjONaLss7JUGu5xLUkENArfSqUzXw/gjVcANgIQggaLJfTMGTwQi/CZZlsngkl6JQBATBOS65M5TDXH/1lUqVsxgc2tbbHlMBBYgJMm6XPbD9EuTq6IbS/bjVBSxPW1BkjZKJdMwnd3QnpNMGWjYhQGO1mkIVFhC+8BOE4DAz0893v/hutrU3k5eZz3mln84X3XkNezSTEhImo734D1d01al96mFQVkajxJX7mCbyXXkD394LtIBwb1dMNaAhHTFsUoAYHkRWju992w33mCdyH7oNQBJGTg2ppRnsusrgE0ilELI4oKjb16tZaE7YghCF4d4cmKwWeB56H9lx0Mmn229qMVVKCNW68Oe6sHCPOSCQgay+SPpFARGOI2KFngVOpFG+88TJf/vK3juoe7YasHo9zyul4zz5B0NNtvJkTQ1gTJhI69QDWgnJPcJsQgokFRdTkF3KZEyHy3vfTO38hmzevp76+lq1bt7B16xaWL19Gd3fnyCYikShFRSUUFRUP/9f8Ozc3n3A4jOOECIXCWNaez4lSmqGhAQYG+kd+Bgf7GRgYoL+/l6amnfT07Hk2sp1sanInUWPX8OnoZzmm/FhmXr+Q2MX5qMNbCh8Qra3N3HjjFcyZs5Dvf/8XBye0tR4OdN9T1OoI++Uiaz2JoPnf8cZtwZ2fIsiqPmILAZlqw+qvR6Y6kJkesKJoIRDpPkKtL+F0rcQrO3HUMWW//hXCu55EWzGUE0ekOohtuRW0JrHwKyAETudq7N7NWIkmtJOFtmP4edMIouVoYWMNNiK8Qfx8MwGvAT9vOnbvZpzOVbhVZ+x/OfIg+VGFs0pgNYCOafzZGmuXQPSCP52RsYwqBNkLsmEG7sknEGpbDkjz+dI+XslxeKXHYtlHN/Ghnbg5WB2wKtmDX55k/ewV1LbFiMRCDBbl093XwwSqGbd4PJdf/gHuuuu3/Nu/fY/IITpJO5NdPLfrZdZ1bkRrmFM0g8sK51PW9DRojQ7nIdx+ZLqb9ISL0GNk8N8UY2TwGMbwD44wgml+CCfVSd5gM0OxYoaihfg6IDvTR95QL6608GFUqx1AWAuSQpPRCuH2ooe/SAYSGXLj4ZEvbeEnDn4A2j9w644QCOW/vZNSAdbANkSQJsiqPqSv09uFsG2sqTPw164y6oTBQfRAHzqTASnRRcUEdVv2LC8l4cvehx4aJNhWh9CmhcyqmUT40iuOaEZfZGUTvfFjuC+9QOqXP0GUV2CPq0EUl5j1CwrRtZuRjkPkg9eTefQhVGsLCKPQCF38XqzJU00S89OPE9RtBiGQBYWEL7xsxAPYr91M+oF7UE27UJ3tBNu2Yk+fiSguQTftxFqwGFlZfVRtqFpr/DWr8F563oTt5eTinHAyzklLjyggw3VdvvSlj7Nw4bFcd91Hj3i/Bz2eGKSu0WTOCJDdpu0qGMfbJoIBsrM1lqXJZGDvAPShIWO5sPfvtNZ873v/xs9+9gM+9rHP8dWvfntESaE1bN0Kq1cLenuN0njxYk3xcCfj+PHwuc8pGhuhsRGefVayfLkgkRDEszXhcZo7Vwu2IPjgOYqJuQf+iLW2wv33CzIZKCgwY69YTNPYCHl5gqoqTXGxZv16QTKpaU8aZereaJwD0S1QuR3sbow6wdLceMMnSfR6fOObX2Rph0/0vB8c+OLu86u8vHz+9V+/y5VXXsvnPvdRLr30dK68+mNEz/ge3eksiodr1an5hvjtz8D4HE3UguYhwewizTGlo0cHp1ZrbAtebBL0pgW5Ec355ZrT9grY2G2B8ViDpDetAUFOCM4crzhvgmZBiWZukWbAhbC9h5g+FE6t0oQteGGXoC0p2DkIIanJBAIpMTNx2gS9uXvNQQTaqJszPnhak/CNLUZNrqYkBl1pwdpOzbx3GHK4N1aseJUPfegyjj/+VL7xjf9417arqiDxaYXVDGRAVZjP3hjGMIbDI+7EuGHWB9hQtpmdA02ErTDTC6YwMXf8yPfv+q7N1PbWMyVvEiHLvKALInnU9tbzRutKLp1ywd/yFA4LgeB4N8q26AAdymXnspeZ+9EbaLA8KgObGf6hLSLCCEoCi222R1Fg0ScCVjlp+kSAQtAufW6P9tMifS7LZJGvLW5K5vGGk2Kj4xLSgkrfot72Rojg3fCBmN5DntkIrkhns8iLsH3YW3iqH6JS7bGC2A0LwSWZbG6X/dRaLgKBRlMVOFyUyUYeZbGh+/vM9dpHVSpLyxG5eTiLl6Da25AlpdgLF2NVjQPgoouu4KKLrjjodu0lJ5C5907U4AAyOwcdBATDHWjuYw+Tvv+PqB3bkZXVyMpx6FQK7boQixlbBzcNaJAWOpXEfe0VnONPGrFhAFAD/XgvPo/IytkjfMgvIGjahYjG0JaFnDQFkV9I8PorxioiHIZkwnTNBb5RBPvSEKeBIYX9N1+HxCAir4DQuReOhF3JCTVYU6fjr1mJNX4ixGLofhN8F7rw0sOGYr388nOk02mWLj16v2AwhG744suxxk8wmSKDg1hTp+EsOfGAlhT2/MX4mzagU6kRj1bd3YWIx7GmTKO0tJzS0nKWLh3dtt/T083WrVtoadlFd3cnXV0ddHWZ/9bVbeLVVzsZGOgjk8ngeS6+v/+YKhKJkpOTO/KTnZ1LXl4+48ZN4NxzL2JC2URmPD2JmuxJFJQWElopkR0Cna+RSfCfAq9Dk/i4Qh9eTDoKnufx4Q9fiWXZ3HrrvYTDB/isa43T/jqhlhewkm0E8Urc8pPxC+bgz4yjn5fIdkbIaNkDWjq482fjlRztpLNAZnqRqQ5DIIvhmtwKYw014XSuHkUGW93rCbW+jArljhCSQliIwUbiG3+O8BL4JYsINT1jSGAnBxAIP43TsRIZr8CtXIrwk2h7n2dy2N9Yur0HPVodB/dkDSfv+Z1djykm93m96BCIhE1ixofxC+dhd68DrfCL5uKWLDFK5aOEVzCXIF6O3V9PoAJwAuSsbXiiiEyqBj0UEEQVOSflUjqthCuvvI5f/vI/efbZx3jPey4/4Db7MwPcuuFOtvY2UBg1D9SzO19kZ041Xxj3Hgq63jQWHk4W6ZrLSNdcctTHPYZ3F2Nk8BjG8E+A09w4/Zkkacsm0D4poUAIIiKMrdKM83xytaRbBhTvpWbokgGTfYdcLPy8aYSbX0DFyo0yOBYZDtpzCOIHV3G6edPp61yOFgGFergU99OgAvyCo29TtgYbidT9AadvKygPFSkkU30OmfEXmML1XURo6Rm4zz+F3rYNzXCPNxIRdVDNu1B9o7/ErerxxD7zRfwN60ba5OzZcw9bmO4NEYsTOvV0vJdfANdDlu6l+kmnTAhHPAt7yjREaRnuE48SbN+GjsfRPd0QBITPPBdn3kLj7SYE1pRpIy1BqquTzB2/RXd2Yi9agr95A6p5J95rL2NPnoa9cDGR977/qP0I/ZUrSP/ht+C6iLx8VHsbmbtuR/X2Ern04AOV3fjv//5PGhq28uSTr78zz7S9IUBVmp93A9OmwcSJRk1bWanp7RU0NhpF8OzZxtc3HB5NBP/7v/9gv7a4V14R3H+/JJGAUEizYoXk9dc1N92kqB7u+rVtmDzZ/OzapdmxQ1A1XtFcImgNC3oy8OguaFpucdlMxbkT9reo2LxZ0NNjlMBdu2B+GsZJaEtB506oqhoOlFaGLK3OgjfaBOXxPQX2QBT+fI5msoK8DtBhhT8XvLmaj1ifwXYc/u3fbqFs+07KPvt78rNNwZnwzDZnHWTwMGXKDB566AV+//v/4bvf/VfCzzzGpA/+kszi84jbJhhufI5mQbHxFPY0zC5UHFumydrHcUUKOLlSc1y5Zsg1dhGhfR6hhn54qF4i0UzNB4GmMwWPNUiqswPmFBmrh/yIUfiu7zJWEXlhmF5gSN99IQQcX6FZXKZ5oxVu2yTJcmBtJ0QsgSsM8atHFMLmXJQ2JLfSGoEgbkFnCvoykvklCltoUv4+vcfvAGvXruSaay5i3rxF3HrrfQcekL0TSAj+/rvVxzCGv0tE7DCLS+ezuHT+Af/eluxEaTVCBANIIYnbMRr6d/yVjvKd4TgvSqcMeHjtctJ9fYTOPJFxgcP70tlk6UPXbRLByV6M7dYAO6VHm/TpEYqQNrYR8/wwvUKxPJRkiRehSjnka4tz3SzOdc02ai2XX1q9tEmfUmVe5p0yIATM3YeMlggmByEmB4e39poYhPh0ooC1ToZ+EZCvLeZ6YfL10dcwu71vdRCMdFVprSExhL3kBMIXXnrU2wQInXwaqrkJf9Wb+C1N6MFBdEc7oqiIoLeXYM1b6EwGUVCECIcR4bAhjcMR7Nlz8eu2gOshcnLQQ4N4zz9FpmYS4fd9YI8HcXMTQWMDOA6qtxtZWGT8hpUJw9OAatxuCOZkAsIRrPJKgpYmiIQNGex5pu3JcYxSWGt0YhAdKKIfuA77mD1dhEJKIlddQ1orgrpayGQgK4vQKacTPvv8keW065o62M0gq8aNXOO77vots2fPP6xVkq98VndsYF3nBlJ+hmkFkzmmbD554VyEZeEsOhZn0bGHvQfOkuMJajfhr15pLN20QkRjhM4+H1kz6aDrFRQUsmTJiQf9+74IgsDYwu32kxbisHYOohtyVliomEa2CxOwXKBBGi1PUK2xtwlCbwoy5xxhTaLB3go//eH3WbtmJY9990WKsw6s3g01PUO07g4ECmVnEd71FNGtd+JnT8QvXoR/5pnYy040YWkYgjRztsKbu8+xKG9YJJQhyBp3QJGQipWhnSxEkAYshJ9EeEmEP4i2wsh0z6jl7b4tCD+BypoAgHQHkMlWc/98H7t7DaH2V0F7uJVnoYZ2YvdvRXgJhEqgYiUkZ36EyPaHsPu3miJwd6E+LIZS4X0C1w+DoAKQGtLscSRUIIYE/lQFdgS34pSDduseDXSkgNT064nW/oGFQ5vYlO7Di/lYpxcTiDgDqSH6wkPMPtb4kU+ZMp2pU2fw4ovPHpQMXt2xnvq+7UzNnzQSnFoYKaCubxvLKpdw1rGXIDM9qHC+6SIew98cY2TwGMbwT4DxykH4EdJ505DpTkqSvUS1IGzFCbKqyXGKOMmN8kQ4QcLyiGlBn1DEtOB0N46FwK08A6d7PaJnI4Mplzw7iTXQgFd2In7+zAPut9ZyeXLCJJqLPoidbGf6QBeX7NrMuMEOvJLFuAcz6z8Y/CTRTb82s5TZE8yXd6qdSP096HAebvnJh9/GUcAaX0PkmhsZWrsaMhnTmpaTA04I3dMFQ4Noz0M4ewZpIisb57jDF2+qrxfV1oqIRpHV40epQUQ0hn3s8biPPAjRKCInFzJp1I7tWDNmY9VMQnV1kr71fwi2bUXEs9Ad7aTXrcFp3E74ivcbT6gDeKb569ag2lqQU2cipMQ57kR09zT8rZux5s4n+qkvjFJ87A2tNcHaVSbtuacHa0INzvEnIcsr8Z59Cnwfa/LU4aWLUZ0deK++ROikUw94LLuxbVsdP/nJd/joRz/LzJlz9/u775vQsw0bzL+nToWFCzWH6QR81xEOwzXXKO64Q/Lss4KuLkEoZNS1K1eC40iuuUbx3//9vYMSwX198PjjJmRt+nDoslKa2lrBU09JbrxRjSJ1Mxlj71BdrcmUQrMDWQrybejsBjcJTzRKpuQHTM4bfbxpk9dIdhLO2C6wB0D0G4qx3YOdM2BTh+CkkxTxOJxQodjYbdHQb2wPvABak4Lp5ZqSBYrkASqCG274BGXlVXz8E9fw0NfPZOGnHiSSX4YUmiXlMP8Qyg3Lsrjxxk9y9tkX8IUvfIKXf3Qh/UuvYd61P6OqIIulFZqTkho5IFAFmqAKk8J2EDjDZO6BsKFLMODCzL3I6ZIY1PbC2k7BnCJznN0puH2zpLYHAi2QQjMpV3DtTEVZ/MDbdiSACY2Twmwn7ZvPi9JgC0FRBHrTIKXGGeZ5bQsygVEi21KQDoxXcn4YpuS9O567mzat4/3vP5+pU2dw221/IvbX/tCMYQxjeEeI2VHTqbtXRgBAOkiTFz5ASujfIWwEl2ayWf/ECmLZOfzrrNOZmogSOdQLfS8s9iK4aJ4JJVnlpAlh6tppfgiJoEBLamXATsun6gChbVMDhwsycZ4OJ9liGYY4R0vOS2cx0z886XsoFGhrxHP4cHADj7rebQy4g+RHcpmSN3GEELHmzMcaN4Fg6xZkRRXCskyYWl4+zpIT3vbxiWiUyHU3EZx0KkHzLrwnH0VFoliz5oDroqIxkBK1vR5dUWlsFnLzwfcJWpoRjoMoLceEhXqIwmK811/BOfFkrKpxaNcl89RjBNu3gWUhHIdg+zZkcSlBeyv4Ps4pS9H9A6gd21FKI4uKkNNmoLo6IRxB9/UZz6ucXCN6GJ5ZF5EYqrUZ7/mnsWomjfIBlsUlRD/5OVRjA3pwAFFUPKqjLdi2lcx9dxHsbATfNx7Gp51Fz9z5PPfcE3z72z855HVTWvHA1kdYtutVNBpLWKzuXMfqjnXcNOcaCiJ5R34PYnEiH/oI/qI1I6S5PX0W1rQZ72oQ5NsRUuh88Ks0Ti2IDMaXy9KIIWPToPNBZwwhnDmSCWoN4ScFG+9YyX8s+398adJXOeWl4/D6IHm9Qu+ljRHeEOGdT4B08LPHY/dsQKa7EEHGWC5YEciqJ3N9gO4+GZQJkwvGM0oZaw1sI1r7B+yBbcMioSIy1eeSGX/+aJGQkGQqTsHuWIndX4vwjR+2thyQEayhRjN7P7yOjpWCDCH8BDqUjcgMW6cIC2U5+PkzcLpWYSc7ECpDkD2eIKsK4Q1hJZrxC2ajI0W45ScR6liB1b8VFa8A5WMlmvBzJ+IVH10eizdL480ROKsFOlejLZA9gmCcxj3u3bfn8gvnMXTMJBZ1rGTl1idZn+whZGUR6O2IuODUqhOZkj9xZPnjjz+FV1554aDb2znYjCWskfcegC0twtKhsX8netwpBKF/jO+1/ysYI4PHMIZ/EkRyppHbv4OgdJ7xSkLjOllYyTZUvJwLMlkUKotXQyn6hGK+H+FkL8qsYdVEkDeV5JxPkdz4IPAQuTk5pCdfRab6HJD7vyqapMdt0X56pKIsZxrYubyWXUFb/kRuHggRLzoO7KMjJpzu9dgD2/Bzp4BlCngVr8Tq30qoZdk7JoNVfx/+2lWo9nZkTo4pzkvLsCdPRQ0NmSLV90FKrMnTTdtcX+9hgyr2hg4C3KcfN3YKvb2IcAhr8lQiV1y9J4wOCJ91Hrq3F3/1W8Z2wXGwps8k/P5rEbZN5pVlBNu2Gn+54Zl/1deLt3wZzuJjsSbtn1ALoAf70VqMkM9CSkRxCVYqaYr+gxDBAO6zT+I+/CDadxGRKEHdZvw1Kwlf+j6CjjbjB7cXRGERqm4LqrX5oGSw7/t88Ysfp7S0gltu+df9/h4EcO+9gpdeEihlSNRXX4U1awTXX6/28+/9S6OiAo4/XrNmDcyYoSkqMj7BiQSsWAGtrb/ml7/8d774xX8/YFBGY6Ogq8sofndDSigp0NSthaFOyN7rUlmWUQknk9AuBWI4S0Mb4QYFIej3TADb5LzRhWBlJdiWZtYGwbg+qCuF1h6Bn4I5aY18WbDrVM3ZZ5v1ZhbCB2conmo0tge2gOPLNRdNUkQPUQ2cf95FPPjAc1x3/eW89f+W8KFv3c8Zxy1ibpHeT6F7IFRXT+Duux/jnntu5+tf/xz+zjf4zQ/vYfGDc3C2mKAMHZF4czWp9yt09uG3uS+SPlgHGHM5UjPoGnZWa6MeXt9pLBuitiYTQG2v4L46ySfnq4N6CCd9zba+3QQy+BpClsDSGkdqNMbPWAO2gGQAjhAoaVTB3WmjSBtwNVdM0SzaywojE0B9H6R9QUWWpix2RKHZbN68nquuOp+qqvHccccjZGW9jQs3hjGM4W+K2UXTKdqRT+PATqqzq5BC0JHswrYcFh1ETfz3ipXLnufUk05jLkf3xS0QnOjFmO+FGZQKF0WN2sumAADN/jTwnvXPcuPM8SNsHSaDJwUOFQewf/hLoSPZxR2b72Vr73Z85eNYDjMKpvDBGe8jP5KLzM0j8qEPk3n4AYJtW9EqwBpXQ+j8C0dC3g4EnU7jb96I7uowIWbJJHR1IPLysRcsxpo8FSEl9pRpyOIS3EcfRlZWmTA6x0FEo2jfQ6dSqP4+rGgMkZsLkQi6uxNRUASBjx7oR0SjWJOnoduaCZqbwPfJPP5nvGXPmiC1oUFEbh4ohb91C8L3sOYtxCoph5Jy9OSp+CtXgBDoXTuQ2dkE3V3DfsEBDA1AxoVQCFlVDb6PrBqPv7UW9/lniFx59ahzF5Z1wFpXDfSTvuN3BC3NWOMnGAFHRzvunx/k7ucex3FCXHrpVYe8X9v6Gnml+Q2KogXkhY1C0VM+tb31vNbyJhdMPDqLCRGJ4BxzHM5eCue/C0ijtLVbLKwmECnjPwvgT9XoGAhPoGIKAg6bjGjtAv14hhtXXsfckvl84YKv4nvgrBaEpo5WF8tEM1a6Ez9rHMJLYA82Gm/eUB7SG0LFy5HpHuzUEwydugTk/p9w4Q4S2/S/WION+Nk1IMPIVBuRbXejIgV4ZaMnUvyCuQjlItI9gERbIbSdZcRFyTbs7nX4RfMBcEuPx8ufgdO9Bq08hD9smaJcguypEMpBxSthYDsi3YOOV4Cw0E42eAlAYvVvJcidSnLGhwlvfwgr0QzCwiteTHry+45e/RqB5IcU4RqB86aHJTYSzO0hc0w+qmgm8M4mtw4E7WQRrTyVG4oXsbJ9LVt6txK2wswpmsH84tnIvQj34447mdtu+xUdHW2UlOzvZ5/txAn0/omhnvbJCe2ZKRDpLkKty7H7alGhHPzixYY4f5c7gMdweIyRwWMYwz8J3IpTcTpXYQ01EkRLEcrDHtqFW7wAr2AuNoKTvBgnetGR7/t9i2Q/fwZDMz4KfJP0tGtIT3rvQfe3wknTKX2mByEz8x0fTxxNXa7Hm/k5nHqESoq9Idx+QI8QwbuhnWxkqnPUjO6RQLW3obq7jNpXWqR//7+mpWw4yEI+/zT24iUgpAmkiIQRufnIcRNQbS0ETTtJ33sX9oxZ2AsXI/NMW5Lq7UHt2G5I40lTRtlE+K+9jPvnBxBZOVg1E00wyNrVpDMu0U/vUeWKWJzIdTehTjsT1dmBiGeZon5YhRysX4PIzRvlxyvz8vHbWggaGw5KBsvCIvA9/G1bR9r1REkJJIaQwx50B7xWPd14zz6JiESwys0ssNaaoG4z7vKXjCFtOm2UHbuRSSPCIYge/F7/6EffZsWK5dx339MHVC1u2QKvvCIpLtbk5g6rLtOwZg28+aZg6dK/flDV1q2Qnc2IpQOY4LeOjud59NGbuf76T/DZz371oOunQrDDMkRvvg/52wVOLciMIPs/IXyqJnOmhpAhghct0jzyiCRdNWzTraF/AGJxKCzUDCRN0Nq+mDFDc8JUQc49gqaIhvCwDWAcKITjQrD4YkXuXnkwi0o1c4s1HUkIW1AYOTLicdHCxTz71GvceOMV/OqWpdR87+csuuIajtSkWQjBVVddx+LFx/GRj1zFhVedyM9m/DfvO+FqMxgZ1IReF+i4IHX10d/z6mzQaLzA+PV6ypxf2hdMGlbhdqVgY4+xydhNfoctqMrSbO0TNA+Z7ewLpWFjl8SRIJSgOKrpzRiCWCIoiBhiOeYIHAkDLmwfgLTWVMWNlcSQDwkXHAGXT1XkDncub++Hu2slOwYEvtJkhyQnVCgumayHFckHxksvPceHP3wl48bV8Mc/Pk5ubt5RX7MxjGEMf3uUx0u5avplPLD1Ubb1b0ejyQvlctHEc5hXfPRWW38ruK7L6tUr3pFneRyLpW6UP4eHSKGIIlFodlo+Jcpm6iFUvgJBubKPOtjt3YDWmvvqHmZjdy0TcyYQscMkvRSrOzeQHcrmQ7MMMWlVjyf6yVtQba0Q+MYv2DkYxQ2qu4v07bcS1G5CDQ2hGhsAjZw0FRkK4b32CpH3XoVz4qkAZsLftoyoAcDNoG0b3d6ODnyC5l0mXLinm9BpZ+K9tQI90A++h8jOwZ42ExGLEUgL/603yGytxd+03liUZecgpIUeHDTEbjptLM2m73lGhRDIvHysqdOMf/DmDRCLowf6jYI3kzEBdtXjzTZCYazKKnBd/HWr0Je975DXYzeCTRtMqPKkqSM1sigrx6uv4+5HH+Cii95LIm+IBmsrUR1loj8VZ5+phO0DO0n5aSbk7KmLHWmT7WSxtnPjUZPBf8/wZ0PiowHhhwTRRyXaAX+GJqg05K5sg9AbArte4M3XuEs16iCZXvZWwb+99RV2JXbw8qVv4lgOWKCzNM6qfawmrAhahhBBxqh0/TQ6WmSsBIUEaaOiRVipdmS654BBcXbPOkME504bESapeCV2fx2hlpdGk8FeivjaH5ux5HBpKlQGke7AK1qAdrJwutePkMFYIQaXfJuc17+K3bvJeP9KmyBrHF6FER+pcAE6lIM1tAOkjdYQ6nwTEaQRba9g927Ez59JatqHGDrmG1iJZrR0ULFhb+29rSOOEDob3NPbsMr+F6t3MxYBsWYLJzmL1MwPo6JvM+1PBdi9G7EHtqGFjV8wkyB74sjxZYeyWFp9IkurD979etxx5rq8/vrLB/Qyn1M8gxebXqV5qJXyuLmfbYl2qrTHiVaA3bsJ5WQT2/gL7P5taCuG0C6htuVkJlxMeuLlR329xvDOMEYGj2EM/yQIcqeQnP1Jwjsewx5oQEuL9IT3kB5/Idh7eqsF4pAf/JxhUmFgKHnI/TVZPjEtRxHK1vD/dYm3FxynosXGdN9PjTLDl27fUc0Y6lSKzMP34731BnpwACJRSA6hMy72rLkI2zZEZ+0m0r/7FaqzwxS4joOIZ6FaW1DdXciSErzXl+MtexZ7xiwin/o8wcZ1uE8+iu7pAQGyrILQJVfgLFiEVgr3lRfBCe1RAWc5yJrJBNvqCOo2Y8+eN3KcQgis8TVY42v2PwknNJLwPHJeWg/bGh986l6UlqO6OlGtqyESAzRCaawZsw7ZihjsaET39iKnTBt1fLK4FNW8C2vadNxHHoLaTYhQGFFQCJk09uy5I8F1++LFF5/lpz/9Ll/60jc4/vgD+1s1NJgAtNy9OOZIxHDP69b9bchgMez/ujcSiXbWrLmWSZNO45vf/M8Dtv5pDY1h2DlJkAwgFoJIWjC5T+MNwOnjNTkeyAcFIg3py8xOTj9ds3On5vkm6C0UZFKCrIhmxnQILLClZsIBuqpCIbjyEoX/mqBxyHjiTp+umTBBkx0Bu1EwlGVCdPaGI6HyyG2uR1BWVsEDDzzHV77yKT772Zt49tnH+d73fkFBwZF7ok2ePI0n/nc5X7vmU9y47jqWs5zvLf0RkewIqkQTWiNJnx+gjzIzckGJ5oVdgmd3CdK+QGkAzexh9fJtmwTNQ4IdAzB9n22HLeMjnD7Ia6sjCTsGBceWmdC79oQYIXNdZbY/4Eqk0Cys0ERsuGeLpNfVJHwoj0FWCAZsyHE03SmjVE76cOcWQwRPyNGEJPSkNU/vEBRHYWn1gZ/9P/7x93z5y5/g5JPP4Fe/umtMETyGMfyDY0HJHCbn1VDftx1fBUzIqaI49o+VsF5fvwXf95k1a97hFz4ETnPj7LJ81tsZApPmQJGyuDydTe7b8Or9a6B5qJW63m1UZ1USsc2XQ8yJUh4rYUPXZrpTPSNhSkIIrN0hbIeB+9jD+BvWIidOhk0bTLixlDA4gDz+JFRLM5nH/ow1ex4yNw8Rz8KeuwD3uacgEiFYv5ago22EGAvqamFwkNBFl2NPmEjQ0U6weiU4YWMBVlhA0NCAiEbxVryGiMYgL9/kW0Rj6HTaiBaysvDWr0FIC8J7xhdaKbTrYs2YTfjc96B9HywLPdCP98qLZO7/I/76NYhM2gggpkxDFhQacvwooIcGzbXcxy/39cE+dvX3cumHKvlh9jcZEH04hJjoT+H9qQ9RFYwfWXa32nFfexalFfYhaux/VPhTwP+CxjtGEX1MInrB3iiwm004mY4Y1XDkcYldr0l8TKHz9tqA1th9W3h1xR/55Y6f8YPTPs60wr0UEwKjLN4LQVY1ft50nM43UeEikBbCTyO8QeP7a2chU+1oK4w+SCepdIe9z/bpUFXD6+6NcPMzON2rTdCbFTIBcloj/CQy029Uvioz+hgL5tB31h9xWpcT2XoX1uBOvJLF4GRD4GIlW0lXn4u2ozjda7FSHYggg1c4jyBnIsJP4HS+CVqRmP8lguwJiHQ3kW334nSsADRe8TFkqs8aCanbD/sSxloT3XonTs9a/JypZgzvp3C6VqO3/pHknE8fPWEauERrf0+49SUIXECjnWwylWeQmXAxOnTwQYHWmsH2Qfqa+hFSMGH8JF577cBk8KTcGi6bcgGPNjxDXd82nMDn/MwuTrMCKhvugR1h8JPgp/GLF4+MZ2WynfCup3BLj0VlHVy4NIZ3H2Nk8BjG8E8Ev2AWfv5MhNsHMoR2jr7H3rZt4vEsBgb69vtbEoUnNDlaUqwsNtqjyQqNRqHfdsHu58/CK5iN07kKFSsf9gzuQFsRMpVnHPF2Mk8/hvvsk8iSMuSkqajuLrzXlyPLKoyRJ6Yg155nlMJZWYZZc110Zwe6vRXGT0REIqjGetTgIP76NbgrliOLy4xf7+SpoBVq104yd9+OKClBZFyCxu1GZbwXRCQCQYDu7z+i49epFCIWx9+wFtXchKyoRJRXQHcX5OZhTZt+0HW9V19G5ORixbNQfT1G1oiGSNQQuAeBsC2TghUERjm9G8NBH6qrCz00iE4MmSTohq1YEycTuuS9+xXkAG1tLdx883WcfPIZfOpTXz6i897vmP5Gk8OzZsFrr8HQkHk0fF9x//0fwvcV48b9gZ//3OG00zRz544OdavthedaJTXjoKVOkG6FoRS8WSlYKjUXTNKobMCG0GuCzFKNLjBE+Ec/qpi3RnDnNmjVUJavSUdgcAiWlMPMwgMTg5HxkLVIU9kEfs2ea2Y1gyo0yo93gkTCKLQ3bjQWFnPmRPnOd37N6aefw5e//EnOOGMhP/7xb44qtTsexPj1nN9x3MQT+eJLn2F1+0pue8/d1MRqEJ0gEhw1GRyxTKicIwFLY0mI2rBrQPCLNcanV6BpTUBPSnD6uD3q4K4UFET0QT2D1bBtR9Q2pHPS0+wcFGzpMXYR47I1dX2Q8ARrO2FJmSY/qkkHgv4MOJYmLGFcNoSkIBOYe7mlR7BzACbm7lEBF0ZhyINXWwSnVOlRthWe5/Gtb32ZW2/9Oddc82G+/e2f4ByBimoM/3zo6enhU5/6FI888ghSSi6//HJ++tOfkpV18AFdOp3m85//PHfffTeZTIZzzjmHX/7yl5SW7lFjHWiS649//CNXXXXolusxvHNkh7JYUDLnb30YbxtbtmwAYPr0d6ZmztKSm5J5bLYztMqAKIKZXohi/fc7ZE36KdzAJWKNDquLWBEG3CFSfvqot6n6eg0RXFJm6tXuLkR2NsIJofr7zOR9eSWqvg61fRty/iIAQue+B9XWgvfSCwQ7dyCzshDV47GmzzT2DT1daN8n86d7ENJCVlaiWlvxXl6GNaEGa/JUgp07Ceo2I+JxtOehU0l0To7pzvNcZG4+dlU1hCIEdVuQw+S2am3BqqzCWXgMsIesFbl5hC+4GGv8BIb+7UuQSSOrxyOLStC+j+rpInT2BUekCgaQRcUIy0Kn06a+xhBW925eR3llHltP3UKJLmVaMIs0aWrtjdwZvZXPDn2NMOYeTc2bSHYoi45UF6UxY4GW8tMk/RQLS9/ZhMbfLQS4SzX+zAB7q8BeA2I5eLMZcR9QxRp7qyC0SpA5fU/dGd75BLLuHj53zyOcUlnEpxe1gf1jXPdm8PIRgwL/zH1a2IQkNfVqhD+E3VsHWiGTLQRZ4/ELZiG8QWS6g/SEi9GhA09qq0iJIQyDNFh7Jh6k24e7T0i53bsRlIcOFyBSbWghEcoDAmRiFyJSDMIm1PQcOpSDVzAb7CjaycIddw5eySJim/4Xp2cDJAJA4mePByeGM9BgguXS3WgnPixekmgnmyB7Ek7vZuz+OoJ4FfENP8fpXm9C0oBowwPYfVtIzL0FHc4zB6s1dtdqwi3LkEM7UbFy3IqleCXHIhPN2D0bCeLjwI4gM31Ygw3IoV3EBhsJYmVkai7dr4v2UAi1v0a4+XmCeCU6lIs1uBO7ey2httcI73qKzLjzyEy4EO2MriG00tQtq2fHip24SRc0VMUnsuLVV/ebSAFTQ5xSdQKzCqdT37ed8pZlTO/YiZU9ET9cgPCThBsfQoVyRzUWqmiJUS331+OOkcF/Vfz9frOOYQxjeHsQ4oApq0cCrTUD7iBZ2dkMDOwhLvtEwDOhBKucNL6A8YHDZN8hrgU7pEeFsgnQNEmfUmUzZ5/05iOGFTLJrA0PEupchfATBFnVZMa/B79w/pGdw9Ag3muvIEJh06cPyJwcRCyO6u1BDwyYwDYwVg9+gMwvRGTnQipJ0NMNHe2QSqLSKXQqCUqBALVlC6qlhdDpZw+nQVvICRMJ1q0m9ZPvgx8Q1G1Bp1PoWXOxpkwzBWsyYVTHhYdX+uhMhvQfbsVfuwosm2DHdoLt9YjcPKzZ8whfcgVWRdWB1x0aJNi0Hmt8jdnX8LHrUBjduA21bevIgGG/Sz95qhlY7GxE1kxCSIl2XVRnO9b4GoKGepylZ0Eqac4H0IMD6I4OmDR11LZ83+eTn7wW27b5+c9/j5QHV3RPnqyJRKC3F/KHH9tUClwX5u6bJvxXwvz5mhNPhNdeE3ieYPXq/6S7+xmmTHmCyspStmyB7dslH/qQYsGCPce4vkuQ8mHOBM24fE3HdoG/SdBbqJlfCru1QCof7B0Cqwv8YRbo5wABAABJREFU4cCzSASWHqeZv0DzeotgQ7fxpq3O1iws0Qf0wwXAgcw5mtgfJM5WUNkamQBtQeZCfUD/3f5+WLVKsG2bIBbTzJmjmTVr9BwAGB/j3/5WsmaNCdfTGlavFmzeLLjuuveyePHx3HLLh/nABy7ghhs+yde+9h2i0ej+O9wHQTHoXLgh9ybmXbWA6x69itPuOp4HT/gzC2uORR1d+DJgPHd3DAhOKDfKXDB2Dc/sECjg2FJD3NtS81qr5I1WzaxCGPSMH+X5NZrsg9TVpTEYn6PZ0iPIcjRRxxDIrjJ/ywsbEjrb0fSkjT9wVRYMZCBia+YWafLCxtO4Ky2oyjLPTMIDpQWOHP2cx2xz7L5ixJO5uXkXn/nMDaxYsZzvfe/nXHvtR47+Io3hnwZXX301ra2tPPPMM3iex/XXX89HPvIR7rrrroOuc8stt/DYY49x3333kZuby80338xll13G8uXLRy33u9/9jnPPPXfk//Py8v5SpzGGfyJs2bKRiorqI7as0WhaZUBCKEqVRc5eIoIQgnl+hH8USq4sXkJeOJeudA+lsWKGvCRCQG+6j6JowYgq+KjgeWjfN+rc3ayJxhBjShmf30wKnU6iU6mR1WRBIdGPf5agrRWkhayqRhaXjogU3LZW1FOPYlWNw5o4BWvyVHRvD0F9HaK0DFFQiHrrDQiFEPkFxm5scMBkWyBQLc2IcBjnlDNwjjsR96nHCHZsB8CeNYfwhZciS0oJWlvwV71pAo2LS5EVlbhPPTosLuhEtbcRbFyLrByHM38hodOPfELZmjEba9pM/PVrkCWl4ITYvr2ep9uaWPLvi8kii0JlCN4oUWr8yTTY9dTZm5jjLwCgOruScyeczuPbn2VzTx0CoxZeWDqP48sXA5DJZOjoaKWzs4NkMkEqlSSVSpLJpPH9gCDw8X0fx3GIxeLE41l7/cSJxbLIycklO/vvKzBLlYBborG2gc4So9kgG3A0VoOAYTJYJpqJND7ML55vYFtXgjs+9QHkUB4ivIZQ37OojvfhT9dkTty/ZlfxSoYWfBWnZz1W31ZCrS8i3H6swR1oO4JbdjKZCZcc9Fi9wjl4+TNxutaiYmVoK4RMtqOdbNzKpaP3FcoDBMqOIawwMm0C4YQKQATYA1uJbvktKl4OSPyciaRm3ESQY7ozdaSIxLwvYPduRKa70KE8rJ4NRHY8RpA9AWIV2EO7EN4QTtcak2MjpFE1B0bx7HSswO7ZhJc3fU/2TawMu3czoY43TA4PEGp7heiW3yKCDMrJweleh9OzkeQ0o5oWykVbEWSmF6f9dYQ3hJYOwhsgVnc70kuQmn79EatmnI63jN9xKBcr0YTTtQq0QqOR6U4iDQ9g927GrTjVfEZDWehwAc0tWTS8sp1wVojskgLQULyhlHXr3qS/ZYC8ygN7IhdGCygMxcluuBMRLUFFTGGvnZi5rpleZLp7L8uL4WdnzDP4r44xMngMYxgDAE2DLTzR+Bx1PdtI2x6vNrxO02ALxdnl3B7tZ52doUhbRLRgg51hl+VxkhtlrZOh0fKQQFXgcGk6i9J34NmmI0WkZn6EdLobEaTNF8UBQgUOBm/jevxVbxk20bYQWdmG6CwpJdhWZ8jdYTJY9/eZdOR4FkIKiMcRnovuaIfBYdNWrRGxGDrjgueD5xLU1yHLyhGO+aIPmpugswNn8XFYc+bhv/UG/tpV6MDHKqtAtbdiLzRBH4eDv241/so3sSZMwpo2A9XRgerugO5uQkvPIHTamQe/dlqPtBsJIczxg1El6/2tD/aGiMYIX34l6btuR9VuQg9vw54xG6JRREsTMhYzprTDpHZQt4Wgvg7n+JNGbetHP/o2b7zxCvfd9zRFRYf2tpo2DZYuVTz/vKCjwxQ1QmgWLYJjjvnbkMGhEHzgA5q5c+HBB1/jiSf+lUmTvsQFF5yFlFBcDPX1mhdeEMybp0dI1KQPUphjzs2FvOmaUCfUOaBtwDPLiYRpyVMHEPHlheHcGk1pHB7bLnixSfJyM9Tkai6drA5oF+Et0iSiAaHlEmsXeBPBPV7jLdj/+nV3w29+I6mrEziOJggky5fDeecpLrhgtNJ55UpB53K4WgqKe2AgDpsLNSvehIULBYsXV3LXXY/y29/+ku9856u89NKzfOc7/8VJJ512yOurC8A9WRN5VLA4uohlF7zOlU9ezHnLzuS3C+/hlOg5R3KbRqE3I3ADTWyvV0VfZrhO1kbB6wiYkAMDrqIvY8IKJ+bCSZWaY8sO/qxZEi6oUXSmJFt6DXnbmjBq5Kn5moKIIXBTviF30wFUZmlqeyFiG0XxoAsJH06sUEzOM9stjWnCFgy5xkZi73OZV2LUwlpr7rvvD3z9658nHs/i3nufGvFsG8P/TWzevJknn3ySN998k8WLDWnxs5/9jPPPP5//+I//oKJi/xb0/v5+br31Vu666y5OP/10wJC+M2bM4PXXX+e44/aEHuXl5VFWtn8wzBjGcCg0Nm5j4iGC0PZGl/B5IDLEFjtDRmjylMUpbpSz3DjWXynw7XDQiSH8LZshnUJWVCInTDygch4gJ5TNqdUncvume1jVsQ5PeXiBR1Yoi4/MuZboXlZtRwpRUIhVUUmwrR4xaQqiuAS1sxE8DxGO4Le1ohvqQQjSD9yD6u4idPb5iFDIBMFNmAhDQyM2ZFprgsYGU9+l0+hEAtnXhz19JrKwCBEOE+xsJNiyCWviJIItm0FrZFY2qqQUkXHBsbGPWUL4wsuwFyxGhEJY02aYmhkQJaUIKfFrN5O+7TeojjZEKIzOZMy/ozHs40+C/n6C1mZ0SxOioJDwjR8/aAjxAa9NOEzkuptwH3/EeA0nhvhFyw6KCosYd/Mswnr09Q4TISAgIYf2bEMITio+lszOAZave5mO1laC/gwv99fyQPvPaGtrpbe3+5DHIaXEsiw8zzvkcjk5uVRVjaeqahzV1ROort7z7ylTZhCJHP3z8Y7hgbVTYG8SJhQuF/zxGlUM+AKdtUfla/duobe7ne88sJZrz5zPjNNL8NoFVmseOvd1UmdcjnuM2cYBYUfwSo7BKzmG9KT3YvduQnqDBNESgtwphyYArTDJWR8zIqGu1Qg3RZA7icz4Cwli5UQaHsTuXoO2IgTxalTEeBAbHz8HlI+WoCLFIATSG8LLmoCWDnZ/HdHa2xha9DWQDjLRgtO1CpnpI4iVEcTKidbehooWo0NGGa9COUjlIzM9xuc4WoRw+9BOFipSTKhrrTmfvVW70gHpYPVvhepzIMgQ3vEoaIWfZzo9FWAN7iC84zESC76MihRipTuQqQ6EN4SKliDcPoJoCX72BEKtL+NWnEKQe2TvXBGk0dIGNFb/NnMukULTfWvHEW4f0fo/Et75BMIbAGET5E6hv7uKGMcSzh/uSBVQWTmOgTf6aNrUdFAyGDD2HH4CZe/ddicIssYjk63gDUG0BLTGSjShIoX4eTOO6HzG8O5hjAwewxjGQHeqh1s33MnOwWbKYsWE41F2de7itxvuZOmx17HZVkwMHCKYL+y8QFJreSSE5kuJQnZKDwvB+MAhvFchr7SiPdVER6oZT3vkh4ooj40jZh/esFRHCjlaKlD19uA+8idIDKFDYWQ8Cz04QLB+LaK8ApGVg25pJkil0BkTfoHrGTW1BtyMCbgQwihqfc8UskojAg8di4OXQQ0NogcHEAVFqO5OVH8vVtV4gh0NEAphT5mOV7eJYMM6AEKnnkH44ssPaKewL4L6OtN6M6zisMorsMorCLbWovv6TLug75s06oF+ZEHhiJJXZGVjTZthlNH5BYhhllK1NiMKi0yg3SFgz55H7JYv469fi04OIYtLsefMI/PQ/fgHYJK1VrBPW9+yZU8f1id4b0gJl12mmTZNs3mzwPdh8mSYN88ohv9WcByYO9fn5ps/SW7uAi644JujlLOFhdDcLBgYgN3CuZoceLkJvAAcyxC+mTLw22BKygS8iUGwWwSZEzVq2FY6CKC11Yh9ysthRwLu2CxJepryuCbQsKlb0J+RfGaBIv8A18WfCf7MA6TM7YOXXhLU1gqmTNHDt07T2QnPPSeZPz+gai/Ref8LgisaBWUhSDtQ2gPj2gSDOYYMX7zYDIZuuulmTjnldL7whY/zvvedw3nnXczXv/59xo8/+POWPs+kWIeWC4oHinj4vU9z3coPcPWPLuE/Kn7FlVdee9hz2Ru5YY0jJSl/j/0DmCC5iA327jARYULzJudp/uUYRcg6MmHFtAK4eb7i1RZB86AYDqrTlA/XuNMLNGs7BUOepiOpSTiwtEozLkfTPCjIj8CScs1JFcbCAmBSLswr1rzeakLowpZRHMcdOLVS0dnZxpe+9AmefvpRrrjig3zrWz8aC4obA6+99hp5eXkjRDDAmWeeiZSSN954g0svvXS/dVauXInneZx55p7JxOnTpzNu3Dhee+21UWTwJz/5SW666SYmTpzIxz72Ma6//vqDkmBg1HOZzB4fxoGBgXd6imP4B0RjYwPz5h2482hv+Gjuig6w3s5QoWyiStIjAx6ODBHTklO8Q4cPazRpNDYC5y9EHPu1m8nc/QdUS9OwR1AM55jjCF/xAUT4wJ1v0/Mn40gHHfjEtI0TySbkRNjQtYUzxp181OpgYVmEzj6f1O23GiuG7BwUAt3bA9EIen07IjsLe/Z8hIDMnx8AIPyeSwBw5i8iWLsKPTSIyMpGNe0kWP2WCUzOzgEpUbsa8d0M9jHHoTMZU4RojZw0Bd3dg+poM51lrgeZNOFzLyf26S8g9vKpF1IidmdkADoIcB99CNXThTV9FkII1NAgwaZ1kFeAEBJRWIQsLEJPnEKwqxHv6cfwLAdZWIg9b6FR+x4GMr+AyNXXoS66lLrNG3jk7t/w/77zU3qcburkJgr3SkAboA+/3mfjm+t4ecPzbNmygS1bNtLYuG2k1b2srJKysnJKS8tZsuTk4X9XUFpaTnFxCVlZ2USjMaLRGOFwBNu2R7relFKk0ykSiaG9fhIkk0P09vbQ3LyLpqYdNDXt5KWXnqWpaSeplMllsW2b6dNnM2/eIubNW8TcuYuYPn0WoVCIQAU09O9gwB2kOFpIdXblId/FRwwNkYcFzkYQPpAAkRCEugT+OIWOgTeqM0/x3Ye34PuKr119KggIyjQ6S6BDAZnjDx+SJtI9hDpex+rbaiwaihYQ5E49ogJsRCSU6TUioUgxwu0jvu4n2H1b0E42qACnay1+wRzsng3YA/VmXTtKEK8EpHn2gzTC7UXHq/BzJmEP1GP31YHyiG25FZlsM8uiCWJlyHQPQWx4clRIVLwCa3AnwhvA7ngDlVUFCDJVZxBkTzAqYR3sfxLKRw+Pfa1kKzLZjoqOnnQNYmXYgzsQbh+Z6nOJ1P3BEMjCHj4u8HOnoSMliNRGrMEdR0wGewWzsbvXILykURnbMeMdLCyEO4iVbIPARbp9qFAuIsggUx3E04PMzO5hu65BCfPuK8k3x93SsovZHNwWSDs5BLEyrIEGgsie95+KFqGiJVjpLmSQBK3RoTzSE997wBDBMfxlMUYGj2EMY2Bl+1p2DjYxPX8KUkiy83LxEi47Bpp4Y3A7Qf74ESIYTAhdrpY02B7xtGRGsH9xrLVm68B6dg1tM0FkwqI73UFnuoU5BUuI2+9+6JG/dhWqvQU5dwFqy0bTOheLozo7EALC7/sAVtU4gp2NyPxCQkvPJPWbX6JaWtDppCGDld5DcCaTaMcxpGosC2wbkZSQSKAGBkApgk3rIZ1Gd3eiB/rAdVFDg8MzwxJ8z6Q1H0qWuzcs64DLaq0Rto3q7CB95+8Jtm5BDytE7NlzCb//WmROLqGzz0ft2kmwZRMiEkG7GUQ8i9D5FyLzDm8fIotL9mvXs2fOxntlGaqvd2Qbqq8XYTvYM2ePLLdx41o+8pH3c9pp5xyVT7Blwdy5fztbiIPh3nv/wPbt6zjppOX4vsPeXH46DZHIaMJ6YYlmRZtgU48gN2TC/npLYU6gWbJRYA+CjoJ7rCZ1ubEeaWiAhx6SNDYKlIKKCo19jKZfw7T8PXXyZEdT1ytY2yVYWvX2rpPWsGaNIDdXj+Lwi4pgyxaorxdU7d62BzM3Q+BBazkjXaqFfXB8G7TtwztPnTqThx9exkMP3cO3v/1VTj11Lh/96Gf51Ke+fOCAMwcyZ2oyp2hECnQ8wq+5l6985VPccstNdHS0cvPNXzrigc+UPJhaoFnfJaiMG0LYV4YEzgnvuY6ZAAZcwVnjFeGjqIC60/Bik2BtpyBQguKYpiUBOwehKGLUvyVRmJavObFCU5UDC4o1ueFhxT77j3ksCR+YriiOCt5sF6QDwbRCzRlVPg2v3s+VX/k0lmVx6633cd55Fx/5wY7hnxptbW2UlIxW0dm2TUFBAW1tbQddJxQK7Wf5UFpaOmqdb33rW5x++unEYjGefvppPvGJTzA0NMSnP/3pgx7Pd7/7Xb75zW++/RMawz8Fdu7cfsBAoX1Rb7nU2i4TAofocF1Zpmwy0uOVUIoTvCj2QUjeOsvlhVCS7bZLREsWe2FOz8SJcQhl4VFCDw2S+eNtqLY2E9xm2ej+ftwXn0OWVxA6Y3Tnig4CSKdYu30F81Y3ccPOAOmmGKyI0rRwHG8kWljTvo7TsmaZTrS8/CP6XtNKIcsqiLz3/fib1qN2NBJ6zyUIyyLz0vNYVTGsydOQI59pgffqSzinno7MzkEUFKFtB/f5p9GWZUIQLIm9YDGquxPd1QnZOcOBw83o/j7seQsJtm+DdBp7wSKCXTtMuFt/H9bsuUQ+cvMoIni/Y/Z9vOUv476xHJmXb2rfnFxD9kSikEygB/qN/QSghgYINm8kM9CPyMmDIEC++DyRa2/EnnrwbIy9IbNz+PGtP6OychwfeP8NbHDXsN3ayrrtq0gtS9L08g5aX27Bbc/wNI+QW5DPzBlzOPPM85k+Yxa6JExzpJs+NUBVdgUnVS5hduGMoyJdpZTEYnFisTjFxYcns7TW9PR00djYwIYNa1i7diWrV6/g7rt/TxAEhEIhpkyfSfa0EpxpeWTVFJIViTO/ZA5XTr2YmHPoCZPDHm8bhJdLgnEala+x6wUirRE9YEnB0C0Kfy+BZm23xa+e386/XrGY0vxhMY/ykW4P6crTDkvoymQ7sQ0/w+6rRcswQnuEW5aRqrmMzISLjtjqQA978AKEW17G7t2Cnz9zJFxOeIPIRAtDcz9HfPOvEYGLnzcZFSkk3PISWlqgBOx+X1hhUC7C7SfS+DAy04OfP3tYEORj925EeAmktAnCeQhvCDnUDMpDILBSnUhvgEzlmaSnXANC4BfNQzc9i0y0oGJmkkSm2o06uthYlGgrPKxazgB7VLMiyKAtB2SYTPXZKDtG9utfwh7caSwenCjWYKMJWtfD2zlCuOUn43Stwu7ZgAhSCD+NtiME8SpEpg8tHaTyhm0cctEqQLq9iOxq4oMtZAdb6bfNeK8gbuqPQdV36J1Ki0z12cQ3/S9Wfz0qUjgc5tdDaup1+EXzsIZ2ou0YXtE8guxDC5bG8JfBGBk8hjGMgeahVkIyNJKuWzyhgnVPvUbICjEw1AmMJ0CPat9LCkV1cPBXyIDXS3NiBxE7TsQyPqLaVvRkumhONDI1990PSlG9vYDAnjSZQErUzu3odArhhLDG1RC9/qOIvTxNdRCg0hnS//szY1RrWRCLISoqEYMDqN4ew6DlF5giXsphf+Fsow5RCllcimraBdk5iGjMKEkyaTMDPb4Ga+IU/DUryeTkEr3upsOegz19Jv8/e+8dp0d1X/+/752Zp2/vvar3ApJAgOhCphow1YBxxyU2tuPydUnsJLbj2E7sYDs2bmB6770LCYR6X7WVdrW97z59Zu79/THLSiutCgIncX57Xi+9YOeZuVOeMp8593zOsV99CdXfP1Lkq0EvwVVOnU7ygbtxNm/AqK5FBEOeR/KqlYhIJoHrbsSoqCJwy5dIv/QcamcDorAY68xzjruwHgvGjNlYS87xAknaWrxrJySisIjUs0+Sfu1luioq+Og/fo3q6jp+85u7juoT/LeAWCzKv/7r97j44quoqVnAzp2C2lqNzweDg9DXJ7j0UjWKDI744OPTFa/vF6ztFGgHTi9TnHaGxuiDaK/XRudWAsKzbPjTnyRtbYLSUo1hQFMTNApJ2fTRlg2G8PjY/veeQzMKR6q5hRj9muyASiFYHQTT9qwzADosyBsUFGYdTkgLIbjssqs5//yLuO22f+PXv/4p999/J1//+ve5/PJrxw4783lJ1gAmJv/6r7+iqKiEH/7wO7S1tfKDH/wMwzh2IKUp4YYpigd2SLb3CrqTkBPQXF6v2R8VbOv1AvAEmpkFmtPeA6Eed+CPmySrOwSWofFJja08mwlTQmdC4DfgvBrFpXWebcTo63LksSM+uGyCZmmNJuXCnq2r+cEt32TFite48MIP88Mf/pK8vILjPtZx/O3iG9/4Bj/+8Y+Pus62bdv+qsfwne98Z+T/58yZQywW4yc/+clRyeBvfvOb3HrrrSN/Dw4OUlFRccT1x/F/D67rMjg4cFy/VQNS4cAIEfwuMrRkQLokhSaiD//R3GWk+X2on37hkqdMokLxqD9Km3S5OZH1gdlLONu3olpbkXUTDgSgZWejhwaw31qOdea5XqaC6+KsfIP0G6+ienuo3f4W5bFB7LJSXJ9J0bZ9ZLd0MzQli8gbfyYWD4GUmJOn4lt2CUbJ4XYu78Lds5vUU496xCxg1NYTuPGTGLV1ODsbcHZuR2TloLs7cdpbPH/fSCZ6aAjd34fT3ETyT7+FVBKjbgKqswPV14usmIAxcRJysBhnw3r0QB96cBDV3IS1+Az8H72Z9CP3Y69YjiwoxCgpRwSCkEziv/YGSCVJ3vsX3O2bEeEMzJMWYC06DeH3Y69dTfLPv8N+ZyW6uwsVCCLyCzBq65ETJiMjmaiOtpHJUa21Z+mmNcbMuUi/H60U7u4dpB99EOPWbxxXN92WLRt44omH+N73fsLDD9/Dmyte5bUVL9Dd2oWQgpxZeZQtnkBeVSXZVUX4s4JUZJZzw9Sr2NW3h4d3PQlxiPgirO/czPbeXVw/5UoqMkrpiHcRtoJUZ1Ziyg+OMhFCkJdXQF5eAfPmLRhZHo/H2bp1I+s3rObeF+9mzbOvYz+QxB8OUjqzju3T1sCHHD624KNjjpuKpWnb3Eb3nh4MU1I4qZDiqUVY7QZWgwAH3CqNGBSIIY07CSgAVaiRvV7nGkFB+hQ9Ktzrn37xa0oK8vjCedWYfVvRwkS4KZzcqaTLzsLo34HVvR5hR3EzqrALT/JsFYbh2/8CZt82nJxpI8StjLcR2PcUdsG8YXXte4PZu9FTBB/0vmgrA6FshOkjMemj+Pc9iRsuB2mirAhGtAk3VIwKeKpxmehC+3JA2RjR/TgZ1QcKNmnihss9pa6yMfsbEMlujFgz2p9NKmsiblYdIjWIdOLIZCeuL4KTM41k7YcJ7HvCC7QDtC+LRM1lOLkzAVDBYuzc6fjal+OYYY+UdtMY0SZPMZ1RBUJiFy3EzajFjLXgBovQZgjhxLA6V+Lkz8XJPbIq91DoQC6xGV/E1/oGgd0PYPZtRmXUYGdNItDyomfZIE2UNTzZIyRoRTg7hNNnkuhoI+qrRmuNHDSRQhITx+4CsotOIQb4m57FSHSgzSDJ8qtIVi0D8/1Naozjg8E4GTyOcfyNQ0ab8bW9iTG0FxUswC5a4N1w38OsdrY/C1sd8LwqrCkj3j/EQP8gVVaYpFCsNBNMcf3kaEm3dNHAAvvIYVGDdh+OtsmQB/yEBBKf4acr2caEzOkfTLvTQZA5OYAGpTFr69GVVah4HN20F99Z544igr0DEsicHITW6GDQU/76/AjXRZaUIssrUF1dnpLBH0Dm5WNMn03gozcj8/NBKeI/+WdkWQWquwvd34vu7/OKE8dGZGUjMzKhuBRn0wZUTzfyGCFyxrSZmAtPJf3CMzh2GhGOIP0BrMVLkHn5uA3bkOWVw6EiICIZyMJinA1rUMsuQgQC2C88i7N2FToaRfT1Ivw+jNIyrzXwBCAMA/8V12DOnOPZUwwNYq95G93WiooOMRiPc+N//gjDMLjjTw+PrQT9H8JgGnb0CVwFlZkH2vqPhV/96qf09/fy7W//E0op7rpLsnev10Xp9wtOPVVzzjmHE4o5Abi0QHPVarDWCaQjcco1qXMVzszR627YIGhpEUyadMB3uK4OdvdAa5dgSvEBQtjVXrxC9vuwzhAC5s7VPPqopKBAjxC8nZ2QmemF+cVtLxgt24ScAqiwYVc3DA56BxIGCks0WZOOvJ9QKMzXvvY9rrnmJn7wg29w662f5Gc/+yc++9lbufrqm44aMieE4Ktf/S7FxaV84xufJ5lM8G//9pvjmlzIC8KnZyraYh6BWxSCiAW7B2BLt+flW5UJMws0ofdQ/WzoFLzU7G3vKo+VD5kQMgXnVCmWlCuCJuQfOzvviOjYv5sf/vA7PPHEg0yaNJU77niUs8++4AP/jRzH/1585Stf4aabbjrqOrW1tRQXF9PZ2TlqueM49Pb2HtHrt7i4mHQ6TX9//yh1cEdHx1H9gRcsWMAPfvADUqkU/iO0x/v9/iO+No7/fyCZ9ALMjsf7NFcZ+ICYUIT1gd/1Qakoc02CYxDBAG9YcfqEYpLrQyBAQ6aQbLCS7EwHmTxGh9qJQCfigD6MiBT+ADoW83ydpCT9wjOkH3sQLB/acchp7WEIm8Fil2R2DsmsCPk7mjl9z16yy+sRtTNhmEBWnR0EP3+rVx8eAtXZQfJPv8Vtb0UWe4Sxs2EduquD4Oe/gsjIRPf14WzZDMkEOhb1ChPLh1FZiYrFsF94BjUwMGLToKco7BeeRne0QjKJzMzGWnQq7v5mVFsLgRtuxnf+hQifj8BV1yMiGV74W08nMjsP86KzkFU1JH/7n7j7mxHZudDTg7NzO6ppH8bc+ST+65c4m9aD5YNwBjo6hO7vh107EZlZiIwIMpmNbm/FjUXRvd2o6BDWjFnI4d8PISVGWYWnSN7f5PkeHwU9PV383d/dTCAQ5Ac/+Dpaa6ZPn83lF13PwlMXM2nBNJ5sfJl17ZuYkFWLZVg4ymXXQCN3bXuAwXSUoBGkNOL9BhaFCtjV38gv1v2O/EAOUSeGT/qoz67mqkmXjaz310IoFGL+/IXkTShlQ00Lp1qXEGvqoXHNNu/f7ZvZdPsr3DvrNq668gYuu+xqcoZV1qloivUPbqBzVzem30QrRduWDgae72NBy3SMweHvWgCcSuWJY9OAH3QE3IinGManRyboAXbu3M4zzzzGT3/6G9z5c0h1rfFI3+yJpIsWYXWtIbjrXkR6YFixqnDa3iA+/XOoYIFn39C12iNgDyJuVbAYs28L5sBO0pHyYZuHtYhUP25mDW5m7VG9hLURBO0cslADGi19pKsuxIg2YfZt9ZZLA20E0FYEmepDOFGEmyJZcxnanw1aHb4/IVGBfJK1l2N1rSbQ+BDKl4WTO8MLnRMG2gxj9m3BGNw9fMyCVNWF2PlzMPsbAI2TPQkVLj/wXC4EyfqPIFN9mP3bR7JenKwJJCZeP3IcZt8WBA52zhSMRCci1Te8vYUK5KN92aPOXcb2e8F3/hzcSNVhPIAO5JGqvZRU1TICu+/D3/YGZnQPWgBmCCdUikz3e6S6mwTDhwwGyK7MI790KoONJtIQVJ1cRfjBCLFElGNCCOziU7ELFyDSA549hfk+iuVxfOAYJ4PHMY6/YRj92wlv/hUy3ubNGHavw9e+gsSkj5IuXXLc48wqnM4bLW+zb3A/ZZEScmu8gqdJ52JWV2EDXYZLuxGjRFnUuhbLUmHm20cu/MVBqg+lFc1DLewfasXRKUJmhExZwYyC4zeK1/GYp5LQ2lPFjkE4mrPmYrz6Eu6uBoyyCi91uasTkVeAdfKiUeuqwQFS995J8sF7UN2dEAwjgj5kQSE6HkN1dWDOmkfkhz/H2bYF0mlkbi7G9FkjVgnatkEr76HBHraDSCbBMr3xfD5vbH8AhgaHHzKOco5a47z1Js6qFaimvajBAWRGFtall+O7/GrU3j3oVAp5CJkmgiFUTyc6HiP94rOkX3keWVSCLCrxiNvlr4M0CNzw8RMml8SwosWcPJXUE4+ge3s9f2IBX3joL3SkU9yz+Fzy+/uh9L3P8v81sK5T8OBOQadnSUWmT7KkQnFhrUYe5TK0tu7n17/+GZ/85BcpL68C4MtfVuzYAbGYoLBQUVMDY/KTaQjdIfGtFbgFGhXUWNvB3G8Q+5SLcxCJ2tPj1WoHjyMElNqajqSgcRBKwuAqaIkJyjM0M/Pfn5XG6adrdu7UbNvm7VcpQTAIZ16geGNIsG6PwFGCqgzNxRM1s4YEOZWa3gGQGspjEJyjiVUfe1/l5VX813/dwxe/uIHbbvs3vvOdL/Ozn/0Tn/zkF7jxxs8c1f/2+us/QTAY5ItfvBnTNPnRj/7zuAhhIaD0EEvy+mzPI/hEsaJV0BH3yOWAzyPlB9Ne8FtDr+CjU0587O7uTv7933/IHXf8FwUFRfzsZ7/jyiuvPy419Dj+b6GgoICCgmMrKxctWkR/fz9r1qxh3jzPo/Xll19GKcWCBQvG3GbevHlYlsVLL73E5ZdfDkBDQwNNTU0sWrRozG0A1q9fT05OzjjZO46j4gAZfOyH/FrXYorjZ62ZpFh55G+HdOhDcZoTxAEO/fVTaHabNjlaekTwMCJash9Nu+EyeQybzhOBUVIG/gBqaHCErNVao/p6sE5aiLAs1OAA9usve5P1JWW4e/fgC2einCH8+1sZzM/ANSSp5BB5Aykyz5uD9Hs1q87Mwt3VgLtxHfLUMw7bv71mFW7LfozJU0dyH0RGJm7DVuw1q7zAtngMooPodNrraDMMSMRR/f2k7/uLpwIuLB6p94SUyMnTcFe8gbtrB8aEyehkAuIxfIuX4Lvg4gMq6EgGgauuR53/IXQ85nXDBYMk7/4zqqXZI5jf9codHMB+5y2PvO1oQ1g+RH4+hCOotlZ0bAitXJz1a7EWLMJ306dRvd24O7bhxuMINKppH46QGJXViEDAK0y0J+oYCz09XTzzzGM8/viDrFjxKkopJk2axic+8XmWLr2EvIMEF13xbvb0NlESKsIyvM4kUxpURkpp6N2FQjM9b/RzSNJJ0dC3k5zi+UzKrifhJNnS08Dd2x/i7+Z+Cus9BFqfKGJ2jKSbIpIRJnNSBiWTqjnl2gvo7Gqn4Z2NBHc6fO97X+H73/97zj//Yq655ibKjDo6d3eTW5mDYXnfoHR7itblLXQUF1M4uQAEiEEwdwq0BeZegVOrwQKZaMRkHc70AXw9ZcPq3ixuv/2XFBQU8eEPX4fj9+Pkzxk5TpnoJLDnIUDg5A53eyobs28zvubnSE68/kDb2ZGs8oTA7FpHZM0/YvVtB+Wg/LmkKs4hNvtraP/Y9nZ24UlY3WsQ6cHhcDeNjLeifVk4udNQwQJis77qBcLF29BmGC0trJ4NntdupJJ06RmkS5cgnARuqAgjth83wwtcRGuMeBt23mxSleeTqjgPker1wuUya8Y4j4N+tYRARcpJR8oxBnbib3kFGW/DDZdjFy/CzaxFhUqIzvkGVu9GZLIH5c/Bzps5Si0rk72AwC46BTfZjUwPoKWF90ZKUDYYPoQdJbDjL/i6ViPsIbQZxs6fTaryAqzu9Zg9G9FmELtwAemSxWD4SU78KOmysz3/4mgzgX1PIaPNkOzCiDajpYEKl2HEO7AL51A5+3wqpPf7LqRASDGi8j8uSBMdyDv+9cfx34ZxMngc4/hbhdYEGh9DJjoOeBwBxtBeAo2PYOfPQ/uOT6FZm1XFVZMu4Yk9z7F7oBHt15iREHZHikkyk6AjmeX4aDDTGFrw8VgWk9XRHw5z/QUEjCBRZ5D9gx3sHtiLKQ1ClkVjfzs7uu/ixqlXM7tw+lHHAbDXrib92IO47W2AxigswnfhZVgLThm1nszJJXDDJ0g99iDu3j2ejUN5Bb4LLsY4JOU6/eSj2G+v8Ahdnx/8PnQsiuoSyJIyVPNeb8zKavxVY9z4AWFZiHAG7r69iIJCRFERat9eiEUhFkXt2oHa3YRwwKqfi2EePS3Z3baZxO/+E2fXTpASmVeAHhwg9fD9iKIS/MsuRmZloXt6EEUHFAqqtxuZkweGibP6bWR+ATLXu+mK7BzQGmfjOnRH+6igjxOFs2WTpzI2DL7xzMOsaW3ijitupC4a9dKupx77Pf1royMO9zYIomkvqMsQ0J3QPNMoKAnDScVHLmJuv/0/CQQCo3yPAwHP15hjxBqa28HaAnathuHnYicLzB3gWy5xJh0w283NHX7mUQcIYa3B7BecmaVIhwXtMYEUMCVXc2m9OsyC4F3s3w+bNgliMSgu9gL4Msb4+mdnw6c/rVi/XrB3LwSDmolTNS8lJBv2CQqCmoCh2dwjaK6BWwc0U/bAuw2t7kRN/HJ13NWD0pAqmM3Mz9xFxtIfsOupf+NnP/tn/vM/f8JHP/pJrr32ZurqJo657eWXX4fjuNx66ycxTYt//ud//x9RyjYNeT7pAdPzgRZAlg8aByFqj4g63hM2blzHH/5wG489dh9+f4Cvfe0f+MQnvnBU1fQ4xgEwZcoUli5dyic/+Ul+85vfYNs2n//857n66qspLfW+qS0tLZx99tnccccdnHzyyWRlZfHxj3+cW2+9ldzcXDIzM/nCF77AokWLRsLjnnjiCTo6Oli4cCGBQIAXXniBf/mXf+GrX/3q/+TpjuNvAImERwYfz++XgeDaRCZhv2SzlaLRsOmRLhEteNUfZ6dpc2kqwlTnQI0p8GwkWsVoJaCD95t8sJpYo0cRxocdK4rNVoou6RLWkum2nzx9gMiRtfWYc0/CfvM1dEYmwh/waqzsHKwlXgCj7uxA9/cjKyq9jUwLS1pkZxYSHewiOdRPKuynNqEJZ+ahhWBXfyNxO07IClHipvB3dY15fKq9FeH3jxCuMBzS5vOh2lq9/8/Ng4F+6OwAKRGGgc7KQhQU4O7b63FFh7wXsrAYt6ICEQjg7t8HrudJbM6e6+VlHKKEltk58K7wQWvcbVsQ2XmjjktmZuG07sfZ2YAIBNH0effoUAhZUYnbuh8RCmPUTyT4ha9glJaj+npJ7t0Dju1ZR/T3oeNxdF8Pxpz56Jb9yLJyRG4uzvo16GQSUVzCW/v38ec//4Znn30crTWLFp1Bfn4hRUUlPP30ijEnUNPKwdWOF+yHpj3WSWu0nagdJe4kyPHnkHbTWMOK1f7UIJt7thGz47TG2rAMk5JwETWZVezub2Rn3x6m5h2lLeoDQmGogExfhN5kP/kHBQ8m/Q7zzjuNv/9/X2Cwt4+HHrqbe+/9E9deeyF5WYUsqj+TJRnLSEiboXSU8vZigikfPaEBCoU30agzQXeBKgaExmwEGXoTo+jP6PpejBxJaKvCaX2VppJreOCBO/niF78x5oSg2d+ATPbi5BxEqEsL5c/D6lpNsv5qkCZ2wckE9zyEChaC4cmOZbwF7c9F+bI9X9z+BlSgAC19yFQfwT0Poc0IsbnfHLPAShefgtG/A3/7GxDd552bleEpcns3YwzuwcmbRbrktNHblZ/rkajSHFHgal8GqZrLCDbcgdm3BW0EEE4cFSomVXOJt54Au+R0AjvvBicJZsAjjGP70f7c0ddgGFbnO4S23Y5I9aHNIFbXanztbxKf+imc/Nmej3DhyUf8HKhArkcyu2lUsNC7foDRv8MjpIcnJgK7H8Lf8hJuqAwdLkekB/HvfxF/09NghFH+LFCOR4QP7CQx5ROeJUS4FBUuhYJ5uJm1+JtfwOpYiYy3o62I59EsJEZ0Lxlvfwu7cCGpyqVof7bXcfBeyOAjQMSAFOhs+ACt38fxHjBOBo9jHH+jkMluzIHduKGSUTdKN1yOObADY3C3d7M5TiwomcfUvEk0DuxDA2tnr0Fv2TPi6+ZDMs3xs8Ow6TFcUEcfL2RGqMuYyqbe1XQm28j0B/AbflzXoCiQw67+vbzU9Doz8qdgyCOr4NzmfaTu+TM6kcCorQMEqmU/yfvvQuYXYNRNGLW+UVtH8ItfxW3dj2puAjsNjo0a6EcOKxFVXy/O+jWIwiJEexsEg5BOow0DHYuie7oAgTncYndUCBCZmZCIe+oMIQ+we1EDmVZoO460o4R/kiJ9fRB7ztg3UPvtFbhN+zySOTffm0zPzES3tZJ+5nH85yzFWryE9JOP4KZTiEgGeqAfXBfrzHPBTqPjcWTRaMJXRDLQPd2e0vgDIIOF34+2bX7yxos8vm0j/3Hhlcwvq8TdvvWwB4r/KWzpFnQnBJNzDlgtFIRgIA1rOsQRyeBEIsF99/2Jq6/+GBknYKthdAlwBQQPGl+AytIYjXhc8vDxzJ6tefVVza5dBzyDW1shJweuXKQpq9K0Rj0/3NKwFzg2FlavFtx3n6Sn511SWVNfL/j4xxVjCQ0jEVi8WLN4sff3+k7Bto1Ql6UJDL992X5NQ5/g+fM1VQmQvRqVBfYMjX4Poegv7BM8tlvgaghm1pJ9+a+55MLvYr3zH9x99+/49a9/xvz5i/jIRz7KRRddcZha+KqrbsBxbL72tc+SmZnJN77xgyPvTIO5CXyrBbJL4FZr0gs1btXxH+9YCFsQsjT9KY8EdhS0xwRRW7OjD27fLDmvSlF1jI+Lbds89dTD/OEPv2L16pWUlVXyla98l+uu+/hIq+c4xnE8uOuuu/j85z/P2WefjZSSyy+/nF/84hcjr9u2TUNDA/H4gW6Un//85yPrplIpzj//fH71q1+NvG5ZFrfddhtf/vKX0VpTX1/Pz372Mz75yU/+t57bOP728F6UwQDZ2uDGZBarnQS3hwbIciWVroVCs1/a/CUwyBfjORSrYbUqgoV2kLsDg/QJl2wtcYFGaVOiLCY4PpZbcZZbcZpMhyLX4Nx0mEV2cBQx3C0c/hwcYIdpM9xQTrHP5NpE5kgQspCSwNUfRZaU4ry9Ap1IYM1fgLXkHMwJw0RgKAQ+HzqRQPj8yPwCRCSDUH8fgVABkdI5CMchGEmT9Bmsal9L1I4hhYFSDvG+BEUixlgyA5Gbj06nvNDg4eJFa41Op5F5+WjHQZomKpKBME2EPwCmiU7EkdIA00AWFnnEcUaWV6+5Lnp/E775Cwl84hZSD9+Ps3Etbmc7qXvuwH7tZfxXXjsqEPgwBINe99tB0Mp7EJC5eai2VpAS7Tieytg0EeEMZH4+vmUXYQx3jDlrVnnq5ElTkYVFXvBzdAh3byM4DrJuAubMOST+86cM7d3DY02N3NW0m12D/Uyon8Q//uNPufjiK7jjjt/y9ttvcM89Tx2xk6YwlEdxuIjmwRaIw/a+nWitSLk2GoWBwdbeBmYXTCdmx1nZupreZB9hK0RPoo/eZB/R7DomZNfiKJeoHTvGJ/uDQWEon4Ul83h+36uk3BRhK0x/qh9XuyypOBW/4aOgoIjPfObLfPrTX2Ldunf45Q/+jRfXPs7T6x6kfsJM5sw+A3/UJN/Opi/VP3oHlhdmHPu8wto0QGjfvShfEqd0Gsh3w9O2ctcj3wEEN9zw6bEPVKvhevbQ5ySB0Ip3xROpivMwB3Zg9m4Z9qJ10VYGybqPYPZswhxsRAWL0ZbX1qWMAEa8xQuZm3TD2J7Chp/ElI9jFy/EGGwEZWN1r8fXsRLal4PWqFARiUk3YRfMO+jQxAghfTDSJaejAvlYnW8j4524GVWkS05FRSpH1kmVn40xsAOrex1orxVB+7JJ1F0xEhY3Ajflqaad+AHrRqWwut4isuq7pMvOxM2eiBssBDOIG6k6TMDl5EzDzp6M1bsRN1yBNgIYiU4QgnTZWSAEItmLr/MtVLAIHfDqyHdtL8z+nSSrL/H+Bo8kbl+OXXwKTt5o3zonb6an7lZpAMyejQS3/x4j2YuW2QgnQWDPgxjRvcRmfOl9CzNEHwSeFVjrBcIWOBXDdnr/81qi/9/hf8dT+zjGMY73DC2GicdDZ+be9T06itfSkZDhizCzYBoaTf68Oey89yG0Ugda1YbXs4/zHlAWrqZ5qIO2wVWUR0pJuxKlDECQH8yjNdbOQHqI3ED2IaegcDasw9mwBvudt1BNezFPPgVherOgRlU17vYt2OvXHEYGewN4dgv2m6+jY1GEEMjCYvxXXI05ex46EUenksi8QoyCQpyBfu86xqKQTgMCo7Ye66xzj32SroMxbSYCUP29XnFsmehoCplQGJklGLmV6FQUu3kTwftPx6nU6DG6ZVRzE9pOIzOzD1hLSYn2+2FgAHd/M9ZZ56G6OrFXrUDHohg19fiWnI258FSIxzw/uf6+0UF5/X2ISOSYfsXHC2POPH788N38bvc2vrXkfJZNnIZub0NkZWFMOn7rj78m4o73eT20XvEb0J868nZPPPEAfX293HDDiREgKgKgwWHUHVbEQZUyqmbOy4Mbb1Q8+qhk3z6BUlBWprnwQkXtsFVezQHL7TExNASPPCJIJDRTpnjna9vQ0CB4/nnBddcde+a+M+F54QbMA+sKAZl+zZ60IL34GDM/R0BPAl5sEgRNRryatdbs6C9h1od/yNpvfocXXniC+++/k2984/N897u3cv75F3PVVTewePFZmMMTC9dd93GGhgb5/ve/TlZWDp/97K1j7s/3miD4oESkQQc15g6JtVYT/5jCOfH8RGYVaHb0CRIO9CShJymwXY8kLgvDW22C5iHJ52Yrig7Jw9Bas3HjWp566mEeeOAvdHS0ceqpS/j97+/n3HMvHDnHcYzjvSA3N5e77777iK9XV1cfptoJBALcdttt3HbbbWNus3TpUpYuXfqBHuc4/v+Bo5HBMaHolC5BLShSxihyttGwcYGJ7rvkjKBOWWwz0mw0UxSnD/w+LkoHaZMOb1sJOqSLBEpck6uSGbzqi/NQYIhW6ZASmrSleckf54pEBp9N5GAN7/NZf4xtVpp6x4cPgUKzR9o8FBjiKzFrRPwggkH8Sy/Ed+4F3g3V7x9FfsiSMi/s9523ED4fIhjCqJ+I885KDMMko3MAEQ7BuUvZt/Y5fO2dFJSUIRVEenrpyDBZE2rjM8o9TAxhzZmHs/xVVONuZHmFF4PR0ozMzcOcMw89NAg+P8LnQ8fjiFAIrVyvds3NQyCwlpyDu3EdbsN2UF7Gh1FSiv+Ka1C7duBuWOPZP+Tlg+ug9jaSuvcO5K3fHLFBOxhCCKwFp5C89050dMgTGCiFat6HzC/AOvNcUg/dhxjoR/f2oC0LHYshLBNjyjR8CxePjOXsbAB/wCOyC4owT16EatmP27gbWT+R4E2fYvvtt3Hn22/wSMs+4naac6rq+PaU2Zxxy5fxn3sBO3du5xe/+BG33PIVpkw5chi1JS3Orz6T32+6izUdG7CkiSlNgmaAqbkT6Uv1k3BT7OnfR+PgPnqTfeQFcgmYAXIDOcTsOI2DTWT6IgTNAPnB/74294vrLiDDymBF2ypidpyScDFLKk5lYcm8UesJIZg792S++81/5b4/nMTK/a+wZdtb7NyxgZkVJ3Oh73IqY6U4yvFC8FyQUUFqikKHQNc0wGA7TtZkRvzTpEncyOP2R+/lqiuvJTczhEj2on1Znv3eMJzsiWhfNjLe5ilMAZSDTHaTqr5wRLmqA3nEZt6K1fUOxuAetBXByZuFkz2ZyJp/BOUg7OiwDYIP7ctASx8i1YOv7XVUsBA3UoGbNWH0s600vXHyZhHY/SBm/w7crDrPl1YrjMHdBHbciROpQKYHQRi4kcpR53DQhcTJnXbUUDbtyyQ244tY3eswhprA9GPnzcTNONzb2hjahxFrwXnXK1hrzP6tGNH9CCeBdBPI7b8HYeJk1aMiZSSrLyFdeiYAVufb+FpexYw2g2tjDjWizQhusIBU5QXYRZ69k0wPgBM/jIz2PJwlQjkj/YzalwnRJoyhxsPI4HevAYY3KWb2N2Ake0Z1Hit/Hlb3Bqzejd54J6oMftdOb8OwnV7oIDu9T7s4YzcLjuOvhPGnkHGM428U2p+DnTsVX9sbOL4sr+VFa4zoXtxIBU7WGCTpcUIgOPP8C9n0H7+m6a23qTrFu+n0C0VIC6rc4/fMyvLlEEtpYpYmcBDxkXJT+AwfgUNmaLXWpJ98lPRzT6IdF9XiBVw4q9/GmnvSAa9gnx/d0z3mPp23V5B+5glEVg5ywmSPqN3XSPL+uwmVVyJz8pA5eV7rX1UNsqcLNTAA4bBXpFZW4T//Q2MTzYfAqJ/kFcSTpmGIOvTgINowMNKaQMZJyJIyBIJ0z1acnEFkF1jbBOnFh99EZWUVuMpTVrx7PZQ7nFoWQA/2k7ztZ553suN47YGZmRjTZniEfSQDa/EZpB99EFdrZFY2emgQ1d+L7/wPHUYGa629MBTDOO5ZXq01P3zpaX63exvfnD6XG8NZuNu3IDKz8F14KbK88tiD/DegOAxSaFKuRwCDx/cPpQWnlR2Z3PzTn37DmWeeT3V13Qnt15mqccsE5h6BUzXsxdYNQkF64eHveX2950fc0uK9FWVljAS74Xoksg4AR/jK7dol6Oz0gufefQstCwoKNBs3Ci67TBM6RmBvxALQuNqz03gXCQeqj6F2TbnQl/TGiBwitmgaEvSnYEL2gWVCQGFQs3dAkJIBLr74Si6++Era21t5+OF7uP/+O7j22gvJzMzi9NPP5swzl3Lmmefxmc98mf7+Xn7wg29QUFDIFVdcP2pfYgACzwmwNE718EKtMXcI/M9KnInqhFvQTi3TbO7WNA0JTAmDaU2WDyozPD9iITTbewWr2gUX1Wpc12X16rd4+ulHePrpR2lpaSInJ5cLL7ycm276zFEfXscxjnGM428N6l2F6EEki0Lzqi/OK744fdLFrwVTbT+XpTLIHbZl6JIuwUNUhQKBgaBPjjYB9iG4KpnBqekg+w2HgBZMcnz0S8WrvhidwwRxsTIQaNql4vFAlFmunzPSYYaEZw9R6Jr4hvcpEVQqi32GTaNpj7KmAC88lzFUp0II/Fdcg04mcXdsRzs2wufD9+GrvTrV50OWlbNfDbCtfxUTdyn8Ld0gJdGCHPaeOokdxgCtsXYqMspGjW1UVuO/9ibSjz+EatoHAmRxKf6LP4xRUYV2HMwFp6CefgwVj+EmkwjTgEgGuqMNnZcPyQT+y65C9XajuzoRkQjGtJnInFziTz8OhonMH24bMi1kTR3uzu04mzcigiHsd1aie3swqmqxTlmMUVOHdcrpuPsacda8g97f5F2/vHwvXHjOfFCK9PNPe2TvYD8yNw/fBRfhv/yqUR1pwh/A7e5Cp1IgBDI/3/MhBlonTeEX//xNHn3lOXKCIT4652SumTmf0sxs3OZ9OG+9ibnkHP7+72+hrKySv/u7bx3zszm3cCb7qvazo283AcNPxBemPFJKUaiArFQmUTvGsppzuGPb/VRnVhK2Qmzq3kZfaoCQ6acr0c/ugb18qPZccgPZrO5Yj6tcqjIrKA4f3frt/cBv+FhacxZnVi4mbifI8IU9MvcIMOosolVpFpvnc2r5uWzcu5JVO1/hXxJ/z1k951GY/mdmF85GDgrsOk36lOF6VOsxva4efquR7sEEnz+nmsy3vo5wU7jhMo+ILJjv+eKGSkhWXUhgz4OYfVvR0kI4SZzsiaQqzh81nvZleGrWsrNGL5d+hD2ITA+iDT8CDak+Tz0sLILbfw8ygArkky45lcSkG0cIyxEoB6vjTZQ/2yOCAYTEzajB1/EWmSu+igC0kLiZdSTqr8LNfm+M4y9/+WOefvpRdu1qIBAIMn/+Qv7f//sX6jOP8KwgpGdYo5XXEJjqwxzYCW4K4cQxercMB9AFAIlIDxFsuBPlz0HGOwnuuguhXZSViTDDaO2QrFpGqvoStHUgCdsLkstCpnpRwkQ4MTD8CGWjhYGyDnoAGA7Y4zh8r83+7Wgrc/TnwgyAdj1C+33YRJjbwdo6bKc3bH33rp2etVzgTHz/9hPjOH6Mk8HjGMffKoQgWfNhZLzdSyP1KE9UsIhE/dXvO63zo7PP4PelJax67DHE4vnYaJSAs1Ihqt3j/+moy6qhKrOc3QP7qMuqwpIWMTtOd7KXZdXnELJGM1WqdT/pV19EZGRhFBTiWhZ6cBDV14u7rxFz2ky0UuhkApFfgI7HEKGDboy9PcRvvw23Ybunht3XiFFbj6yuxd2xDWfrJnynn+UpGe670yt46yfBnt3o3m6MmTMIXP8xrEWnjfJGOxKsU0/H2bwRt2ErMq/AKwD6ejH8UzDCJYBAO0kEEiNSBAMCkRz7Ruc7/SxSTz6KatyDkhJhSPTgkFcwT5mGvXoV7s4GjJp6r/UvHsNZt5p0Th6B6270xjj3ApASe/lrqJ4uRDiC/6LL8J33oZH9aNf1lNMr30D19iDLKvCdfibG9FlHJYW11nznO7fyhz/cxj//4Gd8dMFpnl+yz4cxaQqyoup/xNN1LEzP00zNE2zsFuT4NaaE7oSgJKJZVDr29V+/fjXr16/mT396+D3tS2tobobWVi+MbeoVipzHJGazAEejswXJZYr0grH3axhQeTCHrsFaLfC/KjA6BSqiSS/SpJZoOIhw1Rq6uqC/3/uXm3vAe1hKj1xWxyHqnZqnKY0I9gxAVQbD18o7jgVHsNNQGt5oEbzaLOhNCoKm5qRizQXVmtBwnWlKjRQCV40WYjgKDKmxDvp6FReXcsstX+Gzn72VTZvW8eKLT/PKK8/x1a9+Gq01U6fO4Mwzz2fJkvP4ylc+TU3NBCbOWEDjoPfLN7kZZLfAqR9tz+EWacx9AtkH6gRFPeUR+NRMxcvNgid3SyImTMvXVGd61h1aa5yuBh7fsJJX2pfz8svP0tnZTlFRCUuXXsKyZZeyaNHp4yrgcYxjHP8nkTucUdDbe8AH920ryUOBIQJaUuqaJIVmpS9JTCpuiedgIihVJmtJjfL5TWuHQTtGsn+QfqHJ9h9ojxEIKpRFhTpAZmw2UrRJF0docpVEDju7Z2sYEIo3rASnp0M4aFxG3UIB70HYBexj5AEcCplfQPDzt+LubEAP9CNycjHqJ3r+vY5D+unH8L38FHX7mjFMP8msTPYtnEbX1GoGhY1O9ByRTLHmzMOcPBW3aS/gEcTvdnsJ0yRwzQ0Y9RNIPfIgbsNWb5xYFJ0EMrJIPfIg9huv4r/qo/iWXjhqbK9zbHTdLQwDhPBqwr2NXvEQCqEad+NsWkfgY5/GnDSFwI2fxF10Gqp1P/h8mFOmj5DK/qUXYi04BdXW4hHMldXIwOiQA51MovY3oRt34RgmwufD3WPSFgrxX72dPPzcw+RnZvPd6fP4yNnL8B90zxTBIDoW4567fs/bby/ngQeeP26P/QnZNdRmV1GTUTkSJAfgKBe/4Wd+8Rxe27+StJumKFSAEILGgSai6RhSSE4tXcDk3In82+rb6Ix3eYHE/gzOqljMBTXnIA/pxNRao9GHLT8R+A0f/jFsDQ6FL2AROymBr8YiMhBm7vwzmJF3KhvXrGTNg6+x5PVF/N2cr/KlG76BeWZwpB5ysupQgXxkrAUVqfAWKocHX9vIqfWZTE+9jGtVoayQpxYd2kds+udwCuYCkKq6EDejEqtrLSI9gJs1gXTRwgNhYcrB174Cq2MlIj2AkzOVdOkZ3r6Ui0x2ghFAO0lvfWGBPYBw02h/Nka8G6SJsKP4U/2oSAWpygtGn7x2Ea4NcvR1kqk+jKE9aCFwsicj00NYXe8gUn1E530LHTj+bsmVK9/gpps+y+zZ83Achx/96Ltcc82HeO21DYQOegZFa8ye9fj3P485sAPRvR67YC64aWSiE5wECBOB8kRctouMNZPOuwCzdzOBnfdhxJpBgJPl2dKoUAnG4B6s7g0kaz48+tR9GaRKlxDe+B/I1Mrhhcoj032ZiOHOALRGxvYPC8mO7cWg/TmIwcZDFnpksmsEGRoaPCErPQCjY9hOL3CInV6mxmwUHCubZRwfLMafTMYxjr9hqEg5sdl/j9W1GiPejvJlYRfMRYXLjr3xMVCGj0s+9GGeeuRBSv7ln8gUPuY6AebZgaOGcxwKn2Fx9eQP85dtD9A4sA+FxpIWJxfN4fzqMw8/p8bdMDSImDQV8BQRomkvurPDIx5LStH7GtHJJParL+KsWok5fRa+cy9AZOeQvOP3qB0NYFle8MdAH2rjOqy5870dJL2CwzptCcKySL/+MqqnG2v+yVinnjGy/HhhVNUQ/MQtpF95HnfnDozqGi/8oz+Cjg6iTBs33oGvYCa+4AxIgFt2BFKwtp7QV79F4uc/xt2315tNjkSw5pyE7+zzSD10H0ZFNWI4yEGEwsOea2tQF16CzMpGWJbX3nj6maj+fmRm5gE19TDSzz1F+omHwTAR4Qjupg0kdjYQ+OjNWPPGDjNQSvGtb32RO+74Lf/6r7/i+us/AYA5fdZxX6v/TgRMuGma4qUmwdpOgaPglFLN2ZWKssjY29x99x8oK6vk7LMPFJr9/bBmjaCxETIyYOZMzeTJBybLUyl46CHB229LYjFNUMO8oOBDp2rKFip0jsCtUKgxvHuPBGuVIHSnBKVRORrZB8H7BaIfkh/RI/t99FHBa69Jmpo8MrqsDKZP14TD0NUlWLTI+/9jIdsP109R3LdDsm/II3qzfIIP1aojeiu/2Sq4d7vAMiDXr4k58HSjZCituHGq59Ncl+15He8bgtosrwMx7UJXQnB2pSI8xtdMCMHMmXOZOXMut976bXp7e3j99Rd55ZXnuO++O+ju7gTgkkuWUDZ3GeG6xWRVz+X08Gz+zs4l02VUFL0Y/luP0RX4XlCRATdO1eT4XO5b201w/zbWN7xF67YVtG5/i+RQD0IIpkyZzmWXXc2yZZcxb94C5HFMKI1jHOMYx98y8vOLAOjsbAfARfO6L46BoGzY9zegwecKGsw0u4w0k10/J9kBVllJdho2xcqkPzXIumQr9HfyyuZ32CZDnFN5OmdVnnZEYs2vBUpoFAwTwQwfA/g0RIVCAVlaUuVabDZTZLlypI7tkC652vMsfq8Qpok55fC2cnv5q6SfeoxQVhbJqnJ6h/qo6B+ieOteOqZW0xprpyarktJI8RijDo8dDGIewXZL+Hz4Tj0D36lnoGNRErf/GvvtNzFmzEb6/GitUXv3kH70fowJE5EHkTZG3QTSb7yMKC454EmciIPj4u7YjszOQRZ7Lf9aa9yd20k//zTGhEkIw8CYPBVj8tQxJ/5lTi7yKP73zppVuPubkFOmodpa6YrH+G17Mw8M9ZMRDvPtb/+Q6047G/3rXyDiMcjMGjkO1dtDT/0E/ulH3+Gqq27k1FOXHHE/h6Iuu5rScDH7ovupzaxCCknatelKdHN25enkBrKZUziDpxtfJCeQRXmklOJQIbv69pAbzOHiuqXcvulOYnaC+qxapJB0JXp4qvEFisNFzCvy6uDB9BCvNC3nrbbVgGBu0UzOKD+FwtAHY9F2NBSG8qnLq2a92szE2joMaSC1QSRSynVLv4q1cohf/uqn3PuTu/lB5s84//yLANCBfJLVlxDcdS9m7xY0MNiyhZe39fKzizIxoi2e81nBHJzsSZj92/Hvfx4nf45XCAsxYtVwGLQmsOseAk1PozHADGD278DqWkt85hfRwkCmBkiVLMHq3YhMdiHcBHqkjcsY/tuHTCYQ9hBWy6ukKpaOFOHCjmEM7UH5szF7t3oha8Ovmf3bh60TDfwdK8FNgTAxovtJlywmVfvhw4/5CLj77idH/f3v/347M2aUsXHjWhYuPBBSZ3W9Q2jLfyGcOE5GDVbPRnytr6KFibCH0FYW2oog0v1oI4Sw+5HpQUR6AGNwD2bPRoSbQvtzEa7jBdMJiQoWIRMdGIl23Izq0ZfZDI28FyDR0kQbAdxwKUas1Us6RqP9OSRqrxjlg3wkpIsWYXWtRSY6UYECz3ZjqBEVLKTLrMZxHAoLi477+o063gw4kp2eW3FCQ47jfWCcDB7HOP7Gof3ZpMvP+auMfdWFH+Hu393GGa82jLrZvVdUZ1bwlXmfZVvvTqLpGIWhfCZk144dHCeNkVlMhPAK4jnzsdesgngM3d+Pjsch4EeEM0Br0q+8gNvchO+MM3G3b0HW1KGa94HPj/T7UT3duLt2IgsKkCVekSukxDrlNMyTF3nq4mDoPZHAB8OorSNQ8xmPaDZNr93ugWdQrzQh+ixCxecRybsIsy1AepE+qh+Sb+FizD/Nw1m1AtXbiywtw5w6HdW8D51OwSFKCxEIovp6IZGAg8K3RCiMETqcCVR9vdivvYTIyEIWDT+IFBbh7tmF/cKzmLPmeuEfB8F1Xb72tc9y331/5qc//S3XXHPTCV0nc6eneJVdArdcY5+sccfIhfigkO2Hyydor3VfQ8A43EP4XWitefHFp7nkko+MhJF0dcHvfifZvVtgWRrHESxfDhddpDjvPI8kXb5c8PLLgqIizZwQnLxR4GsHZ53Amg3qFIV9ZAuyw+GA/yXPX8wdtiFzczyrCbVcsj5H0WvA9u3wzjvefmfMEGzbJti9W9PXJ6ishMpKzbnnqiOe76GYmANfm6fY1Q+2EpRHFAVHsJewFby237NMqByeZ4j4wG9o1nVKzq50qciAkAlXTFTcvU3S0CeGPZw10/I0S2uOb+Y/NzePSy+9iksvvQqlFDt2bOWxF1/jtp99h9ZNLyM2vYyTivE28MdAOTWryqkoqKAso5wKu5Lq7gpKyyuIPJ2HdUqYUEkEn8932IOs67pEo0PD/waJRqMMDQ3Q0tJMY+Mu9u3bQ2Pjbvbu3UM8HgXACmZQMmkBdefeQs6EU/japfM5qeoYJs/jGMc4xvG/HIPCZZ2VolGmCSKZ4fiZ4viOKAQIBAJkZWXT1eVN1qXQ9EmXDDWawA0hcYA+qcCFMmVxYyKLZ/xRdosEW4f2YvbsZ15PnLxgKZ2JLh7e9RRZ/ixOKp49aqyoPUBPqhO/tpngalqygiQRBJDYaBLCJUOZTHB8GMPHfW4qTIt0aDDSRLQkgcZEcEkqQs77nTHEy7twd+8k9cA96GQCs24CE1M+NqrNNMoo+Xv30b9lNdmTJ3Jh7flHbfs/bkiJam/FqKhC+oaFAkIgK6pQe3Z5te+cAz6z1uIlOFs34e7YhswvBDvtdYiVlKI62hGFBwhqIQSyoAh3XyPu3j04G9fhrl8DCMy5J2GdtuSo5O+hsDetR5gmbv0Mbu9q4zf7GjCF5JbaiXzyS98i94pr0EqRnHsS9puvoYcGPVFHbw9OOMKXXniScDjCd77zo/d0iYJmkCsmXszd2x6koW8XQggEgql5E0dEKedUnU5ztIXtPTtwhxWQecFcrpp8KfuG9tOT7GVyzsSR2qEwlM9A/yBrOjYwr2gWcTvBP674CW+3r0EDPmmxsXsLW3sa+Pzsj5MX/IBDYhWYu0G2CnQQnCmSi+uW0pvsY0f/7pHVisOFXDn1UiYuruPKK67n29/+Mh/72OWcc84y/umffk5lZQ3pivNQkVKsjlX49z3F41vTuBounVuICuRgxFugx8QuPBnlz8OINoObPGYHqjHUiL/lFVSgAPWuCjdcjtm3BV/z86Qql3mnEi4hmVWLTPYgnBS+/c8j3QRCp8FWCFujjQCotOehO5zEbLW/SWD3QxiJds9zONaGsIdwM6oRbhKZ6EIbIYyhZrQV9ghWlUZG9xPY+zipmsvGfihwU5h92xBOHBUu83yGD1lvcDhMMftgj23l4N/3FLgpj8QFVKQCs28LxkAj2gyjQoWAQKT7vTdxuJPB6lqDTHThZlQjk93gxDF7N6PNIG5mLagUSMu7DqOONY2/9RXczBrS4dMRbtKz0XDiyGQ38dorEdoBw4eTMw034/gSle3CBSSrm/HvfxGzfytocMPFJCdcT0ef13L47iTge4U9lp1eFwgN6UXjquD/boyTweMYxziOiHnzFlBSUsbjjz/4vshg8IqxuYVjGNYfBG3bXn+7Uri7diDrJyKlRPgDyMIifOcsRZgmqeeewpw87UCwXU4u7s7t2KEQWimMunp0Xy+6pxOCYbTjoFqbMc86F2PyaGZOmCYi8+gEjrt3D87mjeihQWRZOebsechDthFCwHDLmjl1BsZ3piEv68P3lg//3ixUEBILNKnFepRycSzIYBDfGWePXlhc6nkAd3chig4U66q7C1lSisg9vh541bof1d+HUTeakRYFhbgdbejeHsRBs72pVIpbb/0kjz12P7/4xR+4/PLrjms/h8J6WxC6VyKGQAc01gaJ7x1N/CaFM+mEhjxu+I7jGW/r1o20t7dy5pkHfM5eflmwa5dg4kSNx49r2tvhuecks2a5FBTAypWCUAiKIrBwhSAnCp2l0DwAOTYUvyJQRZA65/gKHNkPRpdA5Y1ev9UPv18B29oldoZHBhuGFyA3OAhSeirgdBoWLFBcdpmm+MiiozERMGF6vneeR4SC2HYY2CPIM0GYGj1MGmf5oD2m6e4X1LRqEDCtEm6dp9jUI4iloTAE0/M1wROoPqSUTCmdztZ5M1jwtdN4+8enUbvgIhZe9W06d6+nc+12ihub6enZz4Z969ifbiZFCtqAdw6MYxgGlmVhGCamaZJOp0kk4kfcZ1lZJdXVdcybt4DLL78WJ7uO7UwgnTsFpEFuAC6o0cw/guJ/HOMYxzj+VtAnXH4f7KfBtLHwxFvLfQmWJcMsTYePSAgXFBTR1dUBgB9BjjJoMRzy3AM34AQKE8g+iCSe5Pqoj+fwfNdeBra/xFR/EZY0QRqUhovZ1d/IW23vjCKDW2J72TW4hZRKAIKTo5p0PJM3S2qR0sRAENCSCa7FaXZo1L4+ncjmLSvBPsMmVxmcZAeYdYhX8IlAx2Mk770Te/XbOGvXgCHQg0Pkz5zFySXzaI22Ywzu4szsWUyYfS3lGaXve5/AAT+og/2a9XDws1agRnsvG7V1BD7+WdIvPYdq3I3wB7y8h7IKkn/4jZdH4Tuo3d5xQLmk7v4zbuNuRFYOoEk99iDurgaCn/r8Yd1nR4TjsLqrg++9+ASN0UFuyC3kU8UVZBomAdc7TiElgas/iiwuwXn7TXQigTlzNj/fuYW12zby0EMvjdiSvBdMy5vEl+d9hs3d24naMYpCBUzPn0xwmNDM9mfxmZk3saVnO+2xToJmgOn5UygKFfDEnucAcdgkst/w058aBOCehod5vWUlEStEyAxjK5uhdJQVras4uXguF9ScfeghvSf0JvvY3rsLW9lUWGVMfbKKwFoDUoDQuCWC2mvK+cKcT7Gxews9iV6y/VnMLJg6EnxXU1PPX/7yOM888yjf+c5XWLJkNv/4j//G9dd/Aid3BiqQj6/jLR7Y7HLalBKKsyyUMFBWJkaiAyfVP2y7IAnseQQnfyZOzrQjqiyMob0IO4obOYiAFBIVyMfq3URi4o04mbVYvZtxsqegQsXIeBfSHvJC0o0gWlqAQjhxhKNwQ8UgJEb/dkLb/wTKxsmoBTSmMBD2kBcslzkVFSwisOdB0Ax76fpQVibaDCDj7RhDe3Eza0Yf88Augtv/iDnUCMpBWxmkSxaTmHDtiFexUorvfe+rnHTSKUyefMByQSZ7MGItnjp5GNqKYBechEgPoU0fQilQaUAjU33eeSobY2ivF/Dm2p5S2ImhhYnVsQqUixFrIl1yBiow2qda2EPIVB/Klw3SRMvh1kcrExFtRocKSReO3fF5VEiDZN1HSBef6oXXSQsnZwral0XL5mcBKC09MTWPzoT49Yrg/cN2eq5GZwmSFyrsk8br6P9ujJPB4xjHOI4IKSWXX34tf/rTb/ja175HzntQABwK1d2F6upEZGYiS8sPV+a17id1zx24e3ah+3pxOzsQ+/YgS8uRpok1cw7+ZReTvOcOZDA0ys9XmN6Dg47HvBn/UBhz7km4e/ege7oRQmDOnk/oho+/Z/WvvXI5yQfv8TzhDBPtOpgrlxO4+TPIgiOHRwgp0TPzSM2ElDscXvU+7HRlbh7W6WeRfvIR3ETcS3Me6AfTwHf2+QjfsT3FwAvvEJbP8xk4OFkslfSujf/AQ1F/fx8f//iVrFnzFr/61Z1cfPGVJ3TsIg6BJz3/XGfy8EKtMXcK/E9LnAknHu51MMbIwDgikklv3XdP9+WXnyMUCrNggZd67TiwcaMgN/ddIthDURE0NGh27xbk5GiiUW8OoLgbcgehM3vYkkAI4gHtKURWClJn6eM6Rx3yyHKR8Aqmd8/r/k2CbTGortK42bBnj6S1VdPbC+XlnoVFNOqRwVVVvGci+LiQhuADAv9bkqwCiBqQt1vgTPGsT+IOBAcFxX+CSIt3sm6pxrxUkTfzfRZ4KQg8LfC9LYj6BSWZc1h25R95/K6rKayeyclXfgNrNpwT1tywHYKPSJxiRUdRF/tTzfRGe0g2xembP8RgXQzHcXBdB9u28fl8hMMZZGRkEIlkEIlkEolEiEQyKSwsxjfGdyuaht0DAldrajI1OYHDD3kc4xjHOP7W8IYvznYzzQTXhzVctHRKhxf9MWY6fsrU2DVUQUHRiE2EgeC0dIi7ggO0SIc8JUkKTat0mOn4meCO/k01EJjxIfzJONYhM4VhK0RXomfk75gzxK7BLSgUOb4CBqXG0mkW9vVRaQ6yIa8EC5jhBDgnFWLiIfuqdi2qT8AS4lhIvfAM9orXEaUVGOUVqO5OVF8PbNpAxsLFTPKVoAok02ZfhPlBEcEA4QjGhEmk33gVZ+d2z/PXsb3lEydhVNcetok5YRJG/USIx8C0vAyKRAK7rALVtBdZW+/VsKkUqqsDWVaBu28PxsTJCNO7drqwCGfbFpz1a7AWLznmYfb39/GDla9yz5svMtMf5IGJM5icnYeOxdA9Xai9B9SsIhjEf8FF+M5ZCnaa515/kf/656/z3e/+mJNPPuWEL1V+MI8lFaeO+VratdFaMa/w8OyM4lARUkhSbnrEv1dpRdSOMiG7hrRr89r+FaAhN+A9JwXwYzgGA6kB1nZueF9k8OqO9Ty44wl6Er0ARDqDLNk5n6vyL8HMsMABcy8E75Xkfi2LM8qPfI2EECxbdhlnnHEu3//+1/n61z/H66+/yE9+8hvyRIze/gFe2bSff/3EuSh/FJnsQVmZ4KbxtS9HJrtwM6oI7H0Uvf95UtUXkzySwlZaw8sVB6tghLJRZhgMi1Tt5RjJLsy+zSB9yGgT2rC8sDKVRmgXhPQ8hIUkNRxA5+t42/MgPsj/1smbjdW7mVTFUlI1lxDY9jtC6SE0CowgODHMZDduqBRtBIbVuQfBSRDc/nvMwT04mfUgfYhUL/7mZ1GhohEl87e+9UW2b9/Co4++AsrG1/YGvrblGAM7MHu3oUIlnk/wu8p/N4kKFkCoBJQNWmH12QgniVApZKoHEGgrjHSHcEIlGMluRKoHI7oXme5DBfMxB3YQ3PFnEhOug+HPobYiKF8WMtmL6x9WKWuNTHaBtDyS+EQhBCpSTjoymvTdt68Ry7IoKTlxS0p3AkS/qjB3g0gL3HKF+uu7qYxjDIyTweMYxziOik996u/4/e9v43e/+wV///f/8J6316kUqccfwl610mv5CgQxp83Af8U1I+1lOp0mddefcXZsw6iuRdbUI5r3ovbsRlZWEbjgYsyZsxHBEDI7ByedGr0Prb2Z04mTQbm4u3diVFZjTp+F6miDZILATZ88fvXCMFRfL6knHgbX9dKOhUA7Ns6O7aRffJbANTcc30An0H2oHQfVtBftOBjlFYhQGN8FFyGzs7GXv+6peydNwTrtTMyDWgCPBVldi1FTi9OwFaN2AsLyoRNxVGcHvrPOR2Rk4u7eyd7tW7jpB1+nZ3CA++57jgULxi6gjwdGExidAqf6kHCv4uFwr25QJxjKrDVs2gRvvCFpaYHCQjjlFM1JJ3m+tVrD+vWCFSsEbW0eaaqUpr9fICXMnq05+2zNK688x6mnLsHv9x82/pFgWVBd7XkKWwENGpThEbKGAZGIRrsgooANHIf4SIcgfZIm8KREBzU6A9p6YHuzoLRYYxZ6HychNK4r8PnAMPQIqd3ZCS0tJ3YtjwXf2wL/awK3UHNqJtxnCvyDkLsNBsLQHBUsaIAJbeBUetfD2A+hOyWxv1PvyxIk8JQg8JRA5UJFWLNcCKYHr6Tr7M0s/8t3KJy4AKfiLHLLNbrH+zypSYICCimgEIq898TNhugNx5GqdwxEfDCrYFzBMI5xjOP/DjSadWaKLG2MEMEABcqgwbDZZdqUpY9MBre3t478vdAOkBCKV31xWg0HnxYstINclszAHGNmPD+Yi0SSclP4jQM3y4H0IJNzJ4z83ZfqIqUShHz5rLNSdEkXW2hCWpAb7eCnvln4EYS0eE/5FicKrTXurh2kn3gEXIUMBqGqBtXfC66L29WJ2LMTbBvrpIUYtfVHHy+RwG3eB0J4wXH+oxcOQgjMM84m8affoVtbGJm9HhrEdR2c3Tvw5R3OsgghIHwgQEEEg/ivuIbU3X/C3bHNu3ZSYk6bAaaJ7u4cIYIBhOVDSAO3cc9RyWCtNY8+eh/f+95XSSUTfKeijiswMA0LPTjgEVdVtaj2dlRH+wH7MkBYFvtam/nSlz7B0qUX8+lPf+mo1+JEELPjvNT0Ou+0ryPtpqnOquTsytOZmFM3ss70/MlMyZvIpq5t5AVyMKRBd6KH0nAxC0rmMZAaIGEnsAyTdy0MAIJGgE6367CgwMH0ECtb32Fj91YkklmF01lUMp+wdbg3V2e8mwcaHifuJJiYU4fQgvjmIV7IW0G5Wc4Z7kIwwakGcw+Y2wX2/GPXJuFwhB//+DbOOOMcvvrVz3DeeSdz+2/+yL4tAyitufjUqTghB7Nnnecb7CYQ0sLJneERncJAxjvw73sSO28GbtaEw/Zh50xDBQs9BW5GrWf958QR6QHsyg+BkDg5U4jO/jpWxyqMRBsy1gZaIewY0omBSiFcFy1NnMwa0hVe955nAXGITYUQaGkgU72gbMyBXSh/tmedACAsFBZaK7Q/64B1xTCs3i2Yg3txMiccIFsDeWh7CF/ra6TKz+db376VF154mkceeYnSkjKCDX/Cv/cJjGjTsPdvH+bQHmSyg3TJ6Wgj6JHLuTNIl55BcMed+FpfQya6QBhoKwvly0Sm+5DxTpysiWAGcA0LMz0AUuCGykhVXoCwh/A3P4cbqSRdPjy5YPhJly4huONOZLwN4aYx+rZhJDpxQ0X42paTDBai/dnH/EwcL5qaGikvrxqx1PMuFJgNYG4SnvdvNdhz9bA/8BHgB2fq8Mbj+B/DOBk8jnGM46jIzy/khhs+ze9//5986lN/R3Z2DtpxcNa+g7NhLToRx5g4BWvBKWN6h6VfeIb0808j8wuRNfXoWBT77RWgFIFPfR4hBO6uHbh7dmFU1yGGPXHNyhpcaSIRmHPmjyhfzdnzsN96E7dlv+f/qxSqpRmRnYt10kKs+QtI3X837r49aNv2FLVLL8Scv2DkmFR3F862zZBOI0vLPbWDcThj6+7Zhe7uQtZP8ohgNJgmMr8AZ+N69GUfGTneDxLu7p2kHrrPS5J2XWRBIb6lF2IuWoy1eAnmKad7jKPfP2aIx9EgTBP/R65D3/lH1N49aKURpoE5ex7mqaeT+PV/8PbyV/jCylfI8Pl48GOfZ/KUYyfPHhVy+J8LHPwc6R702gli1SrBXXdJUilNZiY0NMCOHYKhITj7bM0bbwjuv19i2xqfD159VZBMSqZNUxQWwrPPShoa+nnnnRX88z//x8i4pukRxU8/LcnP17wrKG9rg+xsQX2958e7ZIli506DDa2C2S44PTCgBJWVmpxskLsE9hx9WIS5GARro0D2gsoEe6ZGD399UudrZJ/GWi8QrRonJUhnaOR0z3pBAFlZnuAinfZUzrYNiYQgP1+TSLy3NF6lvKA8v5+jBs753hFoE3Q2nOfAEPBmJuwYElhdcLKrualJQD0jKni31ns4sdZ5PtEnAtHnqatVnkfynixguQVbs2Ha1O/S2PoGT//8Y1zz7+uYU5gFxhG+Ey7oD14QNo5xjGMc/+ehj3FPmTZtJi+//Cyu62IYBhLB2ekwC+wgXdIhqCVFyjgiQTsldyKTcuvY0t1AUagAn+GjI95FhhVhcdlB9Zv2JvO2W2laDIdMLclUEkdIWkSaZ/0xrk2eWMr9saCV8ibpUynPnisYIvXoA6RfeRFn8waEYaK6uzCmTMecMRu3cTe6rQVsG9+5F+BbdvGYtea7sNeuJv3kI6j2NhAgS8vxX3L5MYN61Z5dXiFQUOhdXcuCUBi6O0nd9Wd8x6mmNadMQ375GzibN6CjUWRhEea0maQefQBn88Yxdux6+zkC+vv7+NrXPsNTTz3CRRddznc/+xUyf3sb2nXQg4MgDWRhEaKgCNW0F9XfN4oMTiaTfOpT15CTk8fPf377e653jwVHOfxl6wOs6lhLti8Ln+FjXedGGgea+NTMG6jP9iwEgmaAm6Zew0tNr7OucxOOdjil9CTOrjyd0kgxMTtOUbiQ9lgnQ+koGT6PZB+0hzClwezCGSP7jKZj/H7TX9jS00DIDKHRbOvdQUPvTm6efh1Bc/QzxbbeHcN+xRO881eQa2fRa/bxtrGeM9yFDKWjdMd7yYwG6elKUKiKj9uPetmyy5g5cx6f/ORVXHL5BcyZXMu08ixKjQ6UzsYNl3sBZVqjzTBO3oFzUcFCzL7NmH3bxySDdSCXVPFiwlt+jdXxtmfTECwiVX4eqYOyblS4lFTtpQDIaDNGrA2R6ECne5F2HAUgBImpnwXDK+LcjCqszrdHtwRqF7SLCpci420Y8U7s/DmY/Tu8Xy8z6D1TpbtxMutQ4dEKBeHEPGsVY3TBrs0wpIf41v/7Is8+9xQPPvgClZU1GAO78LW9jrCjCDeFGyxChIox+ndgDO3D776Akz0FJ6uOxKQbcTNrMHs24mt9BRUsQAUL0UbAI6+RCDeGTPejRQ4y0QlC4IbKvXOWBjqQi0714utYcYAMBlIV5yGcBIHdD2B1rwXAyajCzagm0PQ0MtlNbNaXPaX2B4BNm9Yx+WDLRQ3+5wWBJyUiAUgNr4OzShD7uBp5thnH/06Mk8HjGMdfEbay2dG3h45YJxFfmCm5E0eKhL8lfPazX+bPf/41t9/+S75y67dJPXQf9qsvAAJME2fDetz1awh88nPIgxQIOh7Dfms5MisHmV8AgMjK9tJnt25GNe/DqKxGx6Joxx5lUQAgwmHvtVRyhAw2Jk/F/+GPkH7mCdTO7d5Mc0Eh/osvx6isBiD45a97RGc65dlMHGTwb69ZReqBe1A9Xd62lg9z3kkErrkRETxkllkpNKDtNO6eJlRbCyiFCIWQJWVe0fABQ/X1krzj97jtrRjlVWjXwdm6ifTKN7DmnYzv3GVYC09BhE/8c2RUVBH68t/jbNuKHhpAFhQi6yeRuuN27nn8fr6/ZR1zSiv55VkXkLVrJ+nHHiLw0ZtPeH9OFThlGnM/OHV4RKELRrunYFDv3f4N8IjQF14QuK5mwnAdWlTkKWNfekkwfbrmhRcEhqGproZduzzPt8xMTW+vYMoUTW6uZsWKN3BdlzPOGB3EeNZZmj17NA0Nnm2EEILsbLjwQo9IBpg8GT7+cZdXXpLsjAlmdEFdmaa4VGPtFOhMSC1RoyxC5H4I/VFi7gO094JbponfoHDrQYchfrPC2AuyU5DhU2Q/Iunsh3czgIuLYfduzysYvHOcNEnjupCXd/yk6+bN8Pzzkv37vZC8OXM0S5dqsrMPX1fEGFE3+4FrHc3ZLnS3gjVDUdsm8EuNe/DzmgBMjeg97kM6DLIPZEzgDHvyFmn4TFrzpBDswGThDX/i2X+aTcv9nyf3nDuxJ2r8YS+QQhUMH0YUhAP27HEFwjjGMY5xjAWBYK7j5zF/lEKMg2wiXDK1pN45Mpkwf/4iotEhGhq2MHXqTJxh+jiiJRH32DZWAdPPjVOv5ok9z7OleztxJ0FNViXnVZ05ShmcaWXjSJNuFSciA/i11wLkc9KYGaWss5IsTYXJ/QAC4Q6G27rfExrs2ekJDXJyEUUluFs3IfLyPSuFri5EZweqdT+yuBRCYYypMwh9/buYEycfffy9e0je82eIx5HlFV6nUUszybv+RPALX8EoLcdRDus6N7OxazNJN82k3HpOKpqNuXGd18FWVTOqW18lErhbN6OUQsqjz7qrrk5UZzsikoG1eMkoKzZz5mzsN19HdXUg8r3iR3d2QCiEOX3sLJBVq1bwuc/dQDQ6yO9+dy8f+tCH0dEhYtk5CNtBTpx6YN+9PYhweFStrrXmu9+9lR07tvL446+TdVBA8geFHX27Wd+5iQwrwmB6CK01Wf5MuhM9vLF/5QgZDJATyOKKiRdxUd35KO0SMAIj5HTYCnFWxWL2D7XQnxqiK9GDqxRplWZe4SzOrzpzZJw1nRvY0tNAcaiQnmQfCSeBKS1Wta1lftFsFpSM7vRLOklG+RUboHIg0OVn0IrSPNTCtt6dmAOCSDrMg70vULG1gmsmX07APD4v7PLySh555BW++91b+ctfbmfahFqccAVGqgc3o5rE5JvxtbziBccdDOGFn3k2EIfD7N2Mv/VVlJWFzgoj7DhaGrjhUrRvbMmoCpeTrLmYQOPDKGmhfTZaSOzCk0jWXDKyXrpoIb62NzD7t+KGSkFrjHgrbkYN6YL5CJUGaeCGy8EIYAztQThJtPBC3VI1Hz7M2sINFaPNACI9gPYdyIWRqW6+cG8zD7++lbv/4SaK9vyegc5qQGMO9hBx+j3C2PChATejEmHHUMEiEnVXkKr6EJghhB3DHNjhWTcI0yPZAeXP8RTTbsqzxnCTw69pjwT2ZYLwvo+evcXg6IsmLZJ1V2D0b0fYUc/b18rwlNJOHKtnA2bfVpy8o08qHQ8cx2Hdunf46le/e2D3rRB4TqIDGvdde2gbrG0C/xuC5CXjdff/ZoyTweMYx18Jg+kh7tx6P5u7t+EoFwSURUq4fsoV1Gcf7uH1vxmFhcV89KOf4vbbf8nHz/kQ5puvIvILRwo37Tg4O7Zhv/k6/os/PLKdjkbRsdioAg9AZGSi2lq8FjFAFhQiQmH04IBHFr+7fW8PRt2EUcSnEALfGWdjzpyDu2c3SIlRVz8q0E2YpueHdghUT7eX8hyPYUyc4nmiRYewVy7HqKrFd/Z5o9Y3ausRGZk4b76GjsXA7/dsA1qawbTQySQieHhb1/uBs2k9but+jAnDlhcb16E629HJBM6mDaieLpztmwne/NnDyev3ABEMYc2dP/J3smkv/3Dnb7lz51aunTWf75y5DMswUNLE3rgOX0/3KKL/PcEPycsUob9IzG3CUwJrcKs1yYtGE6XvBZ2d0NEhKCwcXWgUFsK+fYKNG6G7W1A+rEjt7wfT9ILWYjHvX1YWRKNbCASyqKwcHSSRmwvz53v+wJ2dgkAAZs1STJmiaWnxto1EYOpUmDJFkbwWMt4UBFcLRBLsWZrUWQpnykGDagg+JjH3CpwJGkwNLpi7BcGHJdFblXdnFuDWgFujsYBz45p77pHs3OkpoONxTSQCJSWaadM81XNXF4Bg3rzjK7waGuCPfzSIRjUFBZp02iOGOzs1n/mM4lCrXHuyJvCMhJID/sfFUSizIVrtqZblSomrDvJHVoAjUMXHWQy6YG7z1MTCAafem0jQIZBDoIafbWo0fLFL068gdlMZ52T8gs9//kYefngZH77sGpLnKwLPScxtw5fd0qQWadLj4RTjGMc4xnFELE6H2GHYNJhpDLwGngCCDyXDlKojPzbOmjUf0zR5ffUKdsypYa2VxAWmOn7OTIeOuu27yAvmctO0q+lLDpBWafICOYcpHLN8eUTC5cjkDgJ2CiEMUGm0PwuZUU5SKIaE+kDJYJ1IkLrjDzi7d2CUV3m1YHcn6ccf8tSzE6egq1K4exvR0SHQCleDQGNMmYbIGFuprNNpr8tu03rsdWvQLc0YC05B+D11qKytx92+BWf9WkRJKQ/ufIJXm98EwBAGm5vWMLDzTpa8sduzhWjeh5Gbh4h4NbN2bGQo76hEsE6lSD3xMPZbK9BDAwh/AGPSFAJXXT8i4jCmzsC37GLSLz6LbtjqbZiZhe9Dl3r2bAePpzW/+c3P+Zd/+X/MnbuA2277M+XlHkMkIhlYCxd79mtCILKz0UNDpDta2D2jiuca78Ldo5iZP41dT63mL3+5nZ/85DfMmDHnxN+8o6At2kHTUAtJN4WjHACkkESsMNt7d6G1PkyNbAhJb7IPn0yQ48/2ckqEYGn1WQymo6xsfYfORBemMJmWN4XPzroJyzgwibKjdxcJJ8Xark1E01GkkCitcJXLW21rDiODyyIlWIZJzI6P2EjYVS590QEWNs2mraONglQ2WW4mO+Y2oaslK1rfoSar6oj+yGMhEAjw7W//kL/85Xa27NzD5x7s4Uc/+BHSFwFpINKDmH1bwU2PKGdlvAPhptDSB8o54JELoDX+fZ4qVYeKwImjgl74m7/tNdJlZ6LCY3hnC0Gq6kLcrDrMno0IO46bVYtdMB9tHXgWVJEKYtNvIbDnUczB3YDALlpIsvbDnrWD1jg5U7E6VuJkT8bNrEGkh5DRJpz8OTg5Uw7btZs1AbvwZHytr6KtDLThR6YG0FaY3z+9CoALv/qfo7b57fW1fGxm2jt/w+cRt4D256D8eZ5iepj0RdkI5aL8eRjJDrT2CFuERAsDFSzADZXgZtYhVBpf+wpA42bUjFxTme4jVbzo8OumXM8aIqNq5BiAYVW3g4x3wAkKbw7G1q2bSCTizJu3cGSZuVsgBsA9+KfAApWlsdYIkhfr95WZM46/LsbJ4HGM46+E5/e+ypqOjdRkVhKygjjKZc9AI/c2PMpX533uuGds/7fgllu+wp13/pZ/+/d/4etWEKPqAKEtTBORmYWzcd0oMlhkZiGzsj1/24P8enV/HyKSgcj1yEVZVYM5Zz728lchEYdACN3bDaaBteRscF3sFa8P+w4PYUyYhLX4DKx5J72nc3C3b0H1dHu2EMPFsYhkIEJhUi88gxrsR/d0I0vLsOaejCwuwaifiL38Va84d12E4yCGE1Sd1W/jO/eCE72kY0IPDnhFsmHgtjR5SozcPEQsivD7MarrcTesx9mwFmvhifv4HoympkZuuflKNuzaxj+ccT7Xzz/QUihCYXTHIDoWhRMlgwFnGkS/pLA2COQAuAUeWapzjr3tWIjHoaPDs0dIpUbbG6RSns1DJOJ1Sw47auD3g1KeklhKz0NWaxgc3ExZ2bTDiv61awWPPCLx+TTz5mkSCXjxRckrr3hewZGIZuFCT0nr90MwD5yLNUMXaIQNOshhBZDsBHOXwC3VB+7ABrjlGrNJYDR51gqH4pRTNOGw4s03BS0tMH8+fOhDii1bBO3tAqWgoECzbJli0qTju4bLl0sGB2HixAMCiYwMzdatsH07zDxE8JM+VWNt0pjbBSrHO0cRg/RCLxhQ5Wt8K7xgQHeY/DXaBG6Zxp57HCSsgsAjgsDLEtLetfO/oknPF6RnKvyvSRAalQly0FNNR85R+AfhmpxreXnxM3zrm1/k5JNPpXxZJc4kF2uHAAecau2R8uM2EeMYxzjGcUTkaINPJ7JZZyZpNGxCWjDN8TPZ9R3VgzcUCjF1+mweWvc6Vf5LyNEGEnjFF2OXkeaziWwKj4MQBk+FeSQIIZiWOZOXM0zcWCcZjosbrERllNLr95GpxAeuCna2b8Xdu9vLWRieJRUlZbBqJbq3D41G+IaD2KSAWAyZkYExdQYkE57g4MMfGTWmtm2Sd/8ZZ+VykNKzJevthvVrEXPmIXyeDZgwLXRvD7v6G1ne8jYFwXyy/ZmgFBNffYPM9VvpyiylwO+Hvh7cVBKjtAytNKRTXg19FKRfecGzcssrQNZNhEQcZ+07JJUieMuXEIbhiTAuuAhz5mzc3Ts9P+P6iciSslF109DQILfe+kmeeuoRPve5r/L1r38f0xz9nvvO/xBaKZy3lqNa9+MG/KyYlMGTdUMEEj6kEPzmzl+w6faX+fTnvsx11514V9qx0JvqozPRTV4gh2y/95lLuzbtsQ6qsioOqwnXd27muX2vsG+gme5EDwpNTVYli0rmc1rZQm6cehVnV55GV6KHDCtCbVYVhhz9WTSlyf6hFqQQFATz8KYMNPsGm9nUvRVXuaO2mZRbz5yCGbzdvpYMK4wvbtHX109FVgn1/TUkVAIKQ7wzfSs7ZzUTscIEUn5Wd6x/T2QwwNq1HuH5zW/+Ez/+8XcZHBrkF7/4Iz6fQbr0DKyejZi9m9DShxFvQ8Zb0b4cQjvvwde5isSE63CzPE9sYXvksRFrQfTv4F3rMm1lovyZGNGmsclg8LpHc6bi5Ewd+/VhuNmTic35e89/V9nIRCcy1gLaxc2oJVH3EUSyB3Ngh7eBBjerjsTEa0GO8RshJMmqCzF7t2B1rEQoGzejhmTVUmL3WmitDxyzVpjtK7D6tiPSURDe1JmIDYEwsXOnogI5uAeFr2lfFk5WHb5YM9oIDSuttefHLCTxumtws2rxda8H7eBk1iK0451bvA0j0YUbKSNdduaYx678eRiDu0cvVzYgRhHE7wdr1ryFZVnMnDn3wMIDNtmjoMcJ4L8JjJPB4xjHXwEJJ8Hazo3kBXIIWZ5605QG1ZmV7Bvaz67+RqbnH71l7H8biopK+MY3fsA//MPXWLJgCafoqaMLJVchrNFSQhEIYJ5+Jun778JtaUZm56JjUVRPF74zzvY8f/GK+8BV1yFzc7FXvYVOxDCqqrHOOg9jznxS99+N/coLHrPnD6Befh5n6yaCn7hlzKTkg6FTKTAMhGmiU2lAj2p/A9ADA7gb13kBGT4/+q03cVYuJ/CxTyMKCj0v41DIUwJn52CUlqE62nEatp0wGayjQ9hvr8TZtB4Ac+YcrAWLkMMEuXYcVE8PSAlSotNpRGYWIhBAC4G7e9cHQgY/9tj9/P3f30J2Zhb3XPBhZucUjHpd9fYgc3JOXBV88FhFkDrv/SkztfZ8f196SdDTI2hqgoYGwaJFmtxcjxxubvYsIk46Cd55R7Nhg6C+XlNcrNm3T9Dd7ZG5gQDs3w+JxBYWLFhw2H5ee03gOJr64byXtjbBwAC4LtTWamwbHn9ckE7DlVcedF7WUbxpHTyp1SF1qB6WYAl37M2E8DyMZ8/WoyzS4nFNYyO4NtRpyFYC1QRuJUedidcaGhshK0uP6pQLBsFxBF1dh/sOq1KIfUrhf11gbhWoHLBP1qQWe8S2KobYxxSBpzzlM3iEf/JCNWLXcEQo8D8PobskOgRuhWcfIhLgWwXxqyB1tsK3xrPX0GFIna4QA4LIf3jk8S+Dv2SBmsuXPnEz9z31HNQbuPUn/nnTjoO7swHV0gx+P+aU6SNKqXGMYxzj+L+KiJacZoc4zX5v21WefBIvv/g0Z7k+/MM3oAIMthtp3rYSXJR6byG+R0IePuYE6ng6q4gMbZChJYNCEROKS1IRMvT7CCIYA3qw38tXOKRdRuQXoNrb0ErB4CDC50PmF6AHBzBPXoRRVILb0oy7ZSMcQga7WzfhvP0msrQMEclAKxcnHkV1tKFaWzCqa9FKeere4hIaB/aRdJJkZ3qkTlZLN0W72+gsyiGVGaT05EXYa1ZBNIrbuAeRnY150iICN3/6yOeVTOKsfAMRyTxwb4tkYFTV4jZsQ+3dg1HnWXQIITDKKzHKK8ccq6Wlmeuuu5C2thZ+//sHuOCCS8ZcT/j9BC69ArXkbHRfL2uSe3ls/5PUZFbhN3w0rtnK5j+8SvnpUzjl+mXH9f4cD3oSvWzu2U7cTlAYymdanvcMZkmTlJsmaAaRQuBqBy00/kN8Yxt6d3HH1vuJpqN0xDvpTPTgKIeB1ABd8W4a+nbx6Zk3UZFRRkVGGdt7d3LntgdojbZRHC6kPFJKzI6zrXcHfakBCoK5gEBrTcJJErZCOMqhPd5JWaRkZL+WtLh+yhVUZpSzdsU67OY0C/tP45yBUzFSBo/UvUDnsgG0qUdtk3CS7/karV69kpycPD7/+a9RVzeBW275KDfffAW//e29hELZxGb+Hb6OlfiansMY3I2bOQE7ezpGu8LfsA3/G7cTL/kWqcWZqGIfMtqMjHd6hKjw1Bcy2YlhD6DfT1jIwRAS4cQJbv+DpxBWLtoKkS5cSGLSDcTmfhOrex0y2Y3y52DnzT5imJpIDxHa/gdkohO7YAGgkakeAvueASeKmz9v1H6lVgidRks/0hkCNzlcNmvMgZ24WfWecnpkG0/1bAw2IqPNCHvQe11IVKgYFcwnMekmknVxhJtAGyF8ba/ja38T4SZJVpxHuvwc3IzqMQ5ekC47g9BAAzLejgoWgpvCHNqDk1mLkzvj8G0AlIuwB70wPvMoGThuGqFs1qx5i+nTZxM4KC/HqfOC4mTXQWHgNhj9gsRpJ979OY7/HoyTweMYx18Badcm7aYJGKPVv5a0cJVLyk39Dx3Z+8MnPvEFnnnsfr61eTVPVNWQUV7lBaslEuhEFHMMpa7vjLNBKZzXX0H1dCKCIXxLL8S/7OJRZLIIhvBffDm+c5ehkwlERibCNHH3NWK/9QaioHAkoE6XlOI2bCH96ksEbxqbDHb37iH9ygu4O7Z7vsAnLURWViECQdTgwIithEqlcHduR+TmYUz21KFaKdyd20k99SiyqhaRkYF5aIhaUhDorcP/gkBlgTPda2U/Huh4jMQff4uzcS0i4G3kbN6As20TgSuvw6iuxd25HVIpdDoNfb2IQOBAEa5d8B/bg+9oiMdjfOc7t3LPPX/k4ouv5F//9Vf4X3+F9BMP49ppREYWerAf0inMZRe9L4/iDxLvvCN44AGBzwdlZZ7lw6pVgjffFNTVgWFoams1V16pME24/HJFLCZpbPRUwVlZXhicz6fZvRuys22i0e3Mnz9afZJKecrjd71z02lobvYUyKmUR6YWF3vq4lWrJGed5ZJ3HC1YqgjcUo259yD/ZMBoB7dA45Qde4yDydtQCGbkQ/AvEmu3gLRGhyT2LE3iaoU+QraLEJCZCfv2eaR4ZqY37+B5I3sWFGMefzkkrtXeBYDDijy3HmJfUJibwNwhwAeyV+CWaDhSnakg8JAg/AeJbAGdJTxFcZXGnqLRJlg7BLFbFKlzXWQ/qGyw1giCD3jBdDoDMtwcfhf/I8vePJc7/uu/+Ngttxz7Yh4BOpEgec+fcdas8r6DaGReIf4rr33PHQnjGMc4xvH/BxQvmE/8t78m3dqOv9QjtCSCsJbsMt4js3wMXJCK4NeClb4EA8LzNF6airAkFcLZvgVn7WpUXw9GZTXmSQsxSo6gQjwOyNw8hCE9QcBBJIgMRyAvH7WjAdJJrxZWCqOsAlkwzIrYNmKMkDWncQ/acRDDXXOypAy5vxnV2YHbvA+Rk4tqa8Uoq8CcPQ8Z8+wZ3rUuCPYNYdgOqUCQgBCY02chS8txNq1HJ+JYi89EFheTfuUFrDnzMapqDjsGnYijotERW4kRhEKQSqEGBg6dtx4TDQ1buPbaizBNk6eeepP6+mO3J8nsHMjOobFhIwKB3/DR1rCXx3/0J2rmT2HGzeewu7+Rc6vOOI4jODo2d2/jnu0P0xHvQgiQSKbkTaIoVEBFRhlxJ0Ffqh+tNT7DR0m4iPKMklFjrGh7hyF7iLAVZiA1RGEwHyEE/akBsnxZ7Opv5J32tZxffRZrOjZw17YHGbKjZFgR1nRsoC3WSVE4n5ARwnEd2uNdJN00pjCwDIvqzAoyrMhIQOLBCFkhLkydxUdWnoMbdhH5BuTBQNsACzfP4I369fROHQLA1S4DqcFRoYvHiw0b1jB37skIIVi27DLuuOMxbr75Cm688VL+8pcn8PszSVWcj9G3DSPehpMzBXOrwNwDiEmIcAPBtzZgbjqDxMdSns+tkF6+ijAYERhoPbYy90TgpjwiuL8BJ2sCSB8i3Y+/5UV0sIBk7YdJl5x2XENZ3Wsx+7Z7FhLDYWsqXIKvbTnCTeBoNeLdCxpjcBfCTaMC+WhlI5yo94r0oYKFGLE2Mt76Bumys3Ej5djD9hTJiqWYPetxM0IofyZuuBLtz8XXvQan8y3SpUtGCORUzSWkqi/yruExQgHTxachE934W17yLD0MH072ZBKTbkBbh/wGaY3VuQp/07MY8VaUGcQuXkyyatkBWwtA2FH8Tc9gta8AJ8nqN5/lgvNGT9KoMkieNywE2Y5nf2d71nKp08et2f63Y5wMHsc4/grI8EWoyChja28D2f6sEdKzO9lLpj9j1Kzv3xKklPz8tjs458w5/Gj1Cr4fjaJdF5IJjPpJGNMOn3kUhoH/nKX4Tj0d1deLiGSM8vc9bP1gcJQXrmreB/E4orzqwDpCIHPzcXdsQ9s2whotw3Sb95G4/Veo9jZkfgEqFiX12IOYc+d7dhRvLUf39oDP740vJcaM2SPvk5ASWVyK27gHa+HiEY9jUew9TMj9Nnm7LiUwsBjZ7BngOtWC+I0KNTqcdkw469bgbFqPUV0/8mChk0mcDetw55xE4KZPknr8Yey33gTHRmRmeYV+Ti6qpxvhC2BOnX6MvRwZW7Zs4LOfvZ6WlmZ++tPfcvXVN3ok+NILEaEw9vJXPcK8sAjrtCVYp77/YvyDgNawfLlAaygfvs7FxXDOOZotWwSnnqqYNQumTtWEQh6xGY/DGWdoZs2CdFrT3u75CCeTUF+vmTx5B3fdlWbKIWS/z+d5Are2Qn4+JBKQSgkCAT1iOwEeWbxvH/T2clxkMCYklynCdxiYDaDDGhEX6BCklil4LxbUDvheF4Rvk5jN4BaDWy9QYY3vTYGOCBJXjV2IvfOOYPdu2LbN+29REdTVaQYHBWVlmqlTj1HAHWWm33pHEHpAIvqGFxgae7ogfpNCj9Gp5n8CIrdJxABILz8D5dMYe0HlCRB6JJ9EFQyHwinwvS3QEY8I9vYDp85bwo1NN/OvP/8HLr7qSvLyTkzJa7/5mtfaW145rNryUuRTD92LUVOLzP0AzNfGMY5xjOP/EOYsORvp97PtwUc4+YsHJuPSaLLUB6vW9SFYmo5wRjrEkFRkKEkQSXr5KyQfuhcdjyP8AZz1a3BWv0XgY5/GqKk7oX0Zk6ZiTJqKs3kDsqgE4fejujoRubkEr7oO1daKs2kDIhz2hAUzZiGkgRoaRKeSmPNPPmxMYcgDk6qAzMjEnD0X+6030ek0OjqEOXMW/gsvQ+YXMMFXS4YvQleim8JQAXbQh4NGpVOU5BZ5Y+TmIfMKUG0tqOa96I42nHQKZ8UbBK69EXP2aD9aEclA5uahOjrg4LyOwQEIh5H5R+4I01qj9jex6vknufnH36W0tIK773uGoqL39nwTMAIorejd38HD3/8dBTWlXPi1G9kTbyJonXguxruI23Ee2PE4fakBJuXUI4Uk5abY2LWFmflTyQvmMMFfS8pN42iHgBGgM97FzPxpo8ZpHmwh08qgPzWAFnrEy1prTVqlCBoBdvTt4cyK03h270uk3DSTcyaQdJNs692JRuEol6mFkxhMD9E0tJ8MK0xtVhU5/my6k71UZJZREi4a8zzMLQKZEKjqAyRqZkkm+S15ZGwPsKZsM5a0GEwPUZVVwaknQAY3Nu7inHMOEH2nn342d931JFdfvZSvfe0z/Md//MFTiMfbUFYEOQBms0CHNDpoIiWoqn6MzQLfG2lUZSmum0amB4YJYVBWJtqfdRCp+v5g9m/HHNyNk1UPwwIs7c9B2VGstuUkqz40svxYkLFWvNC2g54phUSFipCDjRiDjZ4NhhDIeBsy7SlqhXa97YT33gjw/IFTPfh61mEM7UOFinHDZSSmfALpRFHhcpzcQ57j4gZm90bSpUtGL3+XVD8ChB0bsYdIVi0jXXoGMtoMhh8ne+Lo8xmG1fUOoa2/AddGBfIRTpzA7vsQyW4SUz/tKUaUQ2jb7Vjtb6J8OWzeH6Wpo4/zSrsw+rfjZg93OAtILdW4lS7WZoGIgVML9lw9Zs0/jv9dGCeDxzGOvwKkkJxTdQbNQy009O8ix59N3I5jK4el1WdTHC489iD/S1FdXcf3vv9Tvv71z3HeyaewuLMb7SpUWwuJX/wU39IPYZ1+1mFeWyIYwjiRsDWf35shVcqTYQ5Dp21kdtiTMx4C+83X0e1tI0pfAJ2Ti7tpA4GP34JRV4+z6i10bAjjlNOw169BRg5pX9R4N/yqGnwXXUb66cfRDVvRCrJaLicQmIuanY3yezOg5m5B8CFJ7AuKY3U/Obt2IKQcpTARgQBCSNxdO7BOOY3gp7+A/5IrSD35CM76tajeHlRfDyIUwjp3KcbkaUfZw9hQSvHHP/6aH/zg69TXT+bZZ99iwoQDdiUCk8Dkc/FPOgM3GEVkZBxGtL8faK3p7GynsXEXe/bsoqurnf7+fgYG+ob/9dPf38fQ0BC27aK1RkqNYRj4fBam6aO3N4zfX8Cu/4+9sw6v4zq0/W8PHBQzk2VLZqY4hjAzcxpsm6QpN+0r0+0tJu1rmqSQNOQmaRxmZjNbliWLLGYdSQcH9vtjZNmKZccO3Nf2nt/3+fNn+czMnjlH0p41a6+1OwufLxOvNxOfL4toNIuEhFJmzy5BVVU6O+GhhxR27xbEYhKXSzA05HyEfD6JZUF3t6C62skS2/86gPOxOvpoyYMPCjo7GXHKSnp7IS9vn/A7POxEKyQdwYTHnAHDX7BwrRaorQIr28ZY4OTuHgmepwXeVQKtFezEkRzdLQJjBtiZI8UNpxw4GaupgYcfVtA0yfTpTtleU5Okq0tw7LGSyy+3SfyYq3lFL3hXOQ5lsxJnVhwBfbPA/eZIkcR+qE3gv19FDDoN2SIKYhAUwxHIlVbHcW9M+5A4bYGIgPzwHF+FH036GU+sfpyf//y7/OY3dx/xOUgpMda8j/AnjLq2hKKgFJVg1ezEqq5COerwnCZx4sSJ87+F+f5MCk8/ic0P/4O5t3weRQh6hIWCYLZ5iCXInwAvCt4RodkODBB74RkQKlqFkzcqbRurZiexl5/Hc8PNB8xNDwfhcuG58lqiT6/C3L4VOTyEkpOL6/iT0RaMlDlFIhjrVxN75glkw25MBMLtRj9qGeqkSqy2VqcseWROpZZXILw+J4pr5OGicHtQJ0zEfeZ56PMXITKzRmPNihMLOLH4GF5ofI2qvl24Ei2SUjQmBgT5RenY0Qh2WxtmXQ1Kesa+lW5SYjfUEX1mlfO1/eeduo5rxXFEHvgbxrrVyEgIwhHQdVynnolSWMx4SMsi+swqXnr4Pr78/uvMSEnjrjmLSauvgyMUg6dmVPDs5ud59Md34ktO4OzvXsegPYymaMzMOPJ57oepHWigI9hJaXIJyoig5lbdZHjT6Qx1MzdrJus6NjuOYaEwFBtmWuZkFuTMGbOfLF8GbcEOVKGCdOYJEqcYy626CZthElx+OoKddAS7yfE5D6L7IgOEjBCZ3nSGjRBhM8zk9EkMxoYYiAWwkXSGu8nwpnN62YkHFCbuRUQ44N5CCEFhah5LUhbQnNJD2IywrGAxR+UvINt3ZA/CbdumpaWJwsKSMV9fuHAJv/3tn7nppispKZnAV7/6XczEEtxDjcghATFGHsjHAJBkINMlWk0m1pQJCDuKmToZYQZBcSGlBbofK2H8z9YRISUi0ufkpCljJ4NS86GYIYQVQX6EGCyiA2gD1ajDe8AMMSaLzYqiDDUBNlr/drRADZY3GyU2gO1KBmkjjJHMYEVDSBuJghruQsYGka5kpJ6ImToFLVCDp/YhR6AdfyR8OJ7to9A7PsBT/xhqsB2EwPLnEZlwEUbWIVawSRt380sIK4aZ4rj4JWlIzY+raw2xwhOwkiagDexE716PlViK1BP45/o3SEnwcNxED7LldcIp+920CKcbxpwadwL/uxEXg+PE+YyYml7B9dOv4J3W1TQEmihOKmRx3nwWfagp9t+Ryy+/jheeepSvr3qYh5eexMS5852npx1tRFc9gpKZ/Ymcq/ujVkxGzcrBbmpAKSlDKAoyGEQOBdBOOBmhHrjUyKrdBUkpB8RQYFrI3m5cJ53mxFfgLAeXv/opdsselLLy0ZgIu6MNbdYclMws3MeeiFY5FWv3LtQeHd/zc5G5CbB3fqGDlS/R6gRqK1iFhz4noetOuccIEokYyQ7D5exUCIGam4f32i9g1dVi71/aMTLOI2HXrh1885s3sW7d+1xzzU1897v/NSbzSd8icL8oUNsEqF6MSg+RM2zsj2li7+7uZP361WzduoG6uhrq63fT2FhHKBQcPb/U1HRSUlJISUkjOTmVrKwc0tMn09GRzNCQhqIIUlMl5eUmPp9BLGawdm2Q7u5u+vp20tLyNuFwN7GYszxu7Vq47TY3paUTkXIyljWFkpJK8vNnsGdPBTt2CGbPlpSNJIsEArBhQy9CCNLS9nPASFBb4VgDQqXwRhO0Dgj8ficvuKjIee/6+50c4eXLbbKO8PmONQHCEyRHOvHbi9IN7vcUbJ9Eup3JuFRA6ZeoDWDMBKXfeUL/YTF47VrB8DAjRXOS0lLJwICgpQWWLt2Xkfxx0GsESi+Ykxh1DwsVpEuirxZETpf7bmgs8K5UUOtBRATCCzJBIoISMQwiBtIDkbPAmP+h66SDWQ6uDwR25r6mYhGAjJRMvn3jj7ntt1/i8suvY/bsI4x1kBIikQMz0BUFEMjYv2fMT5w4ceJ8lhTYOldccBU/vfg81m3dQMqcmSRKwckxHzPNz7402W6sR/Z0o+wXUyAUBSUrB3N3DTIwgEj5eK21Slo63quvx+7rRUbCKBlZYzOEvV5cS49Bm1iJWb3DiYdISMTYsZXQb/4LTAMlOxfXiaeiz12AWjEZ/ZgTMF5/GbPTEXKEpqEvWoLrhFMQ7rHXSwjBySXHUp5SSnVfLTE7RtnnT6Xkxfdh3VqMrk6kZTo9Gbl5YMSc1W9C0leQgdLWjLu5CW3i2AgHdd4ixHNPYe/YijQt0DSEPwHZ2+OUPY/Es+2PuWk9j/z5D3x38xqOm1DJb085F72znciqR1CKilHzP2ISvP/x+23W/NeTSCQzv3Iie8w2/MLPCUUrmJF56AKxwyFmGdhINPGhIjehYUqDc8pPZ1rGZLb17CRqxZiSPol52bNIdI2NzliUO4/tPTuJYKIpGgOxAKZtkuRKRFVUVEVlVuY0dFVHFQqmPbYAwpYSBYEiFLJ9mUxLn0xXpIcpaZMoTCxgfs4sChIPHmVilQFvSoiy797DANVQKF5QwlfmfuETXafOznZisRhFRSUH/N8551xMU1M9v/zlDykpKePCFctwdW9ENWtAyQUMFKUF256CZc1CNcD2qkRKzsBf3YoS68d2JaMYQaekrfAUpOfAz9VhIyV693pcLa+i9+9EC+wGaWGmzxh10KqRHozUyWBbaP1VSM2HlVC0z2ErbdShJvSutbha30SJ9iGsEOpQI4oRJJZzNEgTd/PzKOFejPSZSHcq2nAjKCqx/GPRejahDdYhQkGEtBG2iRQq0uVDWDGk5gMkUnODUDATS9CGmjAyZiMVHRELIF3OSllhhpzSuIyZY85T692Mq+N9lHAnVmIZsdyjR0v61MBufLvuAyviOKOlRB3eg6/6bwx7M8fPFsaJflCDbViesc5/6UpBBNtQQh1YSRNQg21gxZB6AlJKnnxvJ6cvrEDzZ8DALsL7i+Zx/m2Ji8Fx4nyGVKSVU5FWPprx9Z+CEII7Lrue87du4tq1b/LYhInkJCah5hdi1lRhblz3qYnBSnIK7gsuIfLIQ1g1O53j6zr6gsW4lh87/viSU6CjfczXpG07T/G9Y5edCa8X91nnEXn471jVOxyhNmagFhbhPu2s0fdNzct3/uwB8bqK1MeKU9IFwsCZqH0E2tTpGO++ibmrCjnQjwwGQdNQEhJQJ491QghFcSbvH5rAHy7hcJg77vg5d975G4qKSvnnP1/hqKPGxj5oteC7X0EEJVa2dOIH1giUXoXglw6eO7sXy7KoqtrKhg1rWL/+AzZsWENTUz0AWVk5TJxYyZw5CzjvvEspLS2nrKycoqKyMWI0OHEL//f/OhO1WbMcTa6tTeB2S266ySYtzYk3uO8+gaY58Q3RKDQ3Rykp6WDZsl00NFSzfn0177+/i+HhN9m9u9s5Ry0Tl2sJ27YtIzV5Cbm5M0lO1giHB/B6U1D2OswluF8WeF5UEINwMXCcDk0zbGJflmzYKNi0SaG+3skPXrrU5txz5f/4fEhpd4RPqwBko1O0Jv04btqAQN8M0i1xvS8wFkqs/eJLOjsFXu++z6/P57ilBwcFodAnHJgJMBLtYIFWL1CbQQQE0iNxvyCcEkENPM8I3C8LhA3CBqUfcAnsDInoA1wQPkcS/pw9bh53dIWNVqugVQvsVOm4isPO1y+7+HoeeOmvfOc7X+K5597b9/4eBkJRUCunYLzxCjIre9SZZQcGxmZ3x4kTJ06cMdy45FTuyckl8b6nuXryMkosnVxbRfxPtAgpiiNOSBtpgeztRoYjIz0UieOuJNuLGbMc8cV96Fvj8SKC7P4+rOoqZCyKWliMvuxYiMUI/9/fYu7cjpKbj9CTsFuaiTx4L8LrRZsyHffZ56NNnopZUw2WiVZWjjp1xkFXZAkhmJhaxsTUfV0Z0W6byPbtiEmViOQUzE3rkS3NmLqb2jmlvKa30qIFUPNCTGt5gTPz08ny7ROB7JqdyIF+9BUnIjQVdBeoKlbNTowP3sV96pkHjOOZB//Cdzat5sLpc/nx8aejKgqyoAiregfWjm2HLQbv2rWDiy8+lURfIveufJJhXxRb2pQlF1OUWPCp3DcVJxWQ7EoajdcAx9XbHelhTtYMUj3JLMydy8KPMOrMzJzKqaUn8GLDa/h0Hz3hXnRFI9GVgGVbnFB0DDMzpyIQlKeWsblrGz7dS6o7BbfqpivcQ1FivvN6aRGxIpxeeiJXTb1o3OP1hvvoDveS6Eogz59DbCbo0wT6FiceCwFiSGBOkRhzP7kbs6urA4Ds7PEF6Vtv/TaNjXV89as3kP/Iixw95QY8ytO4W1pRgxqmdymmdSEi7EcEBMaxNmb2bIKuW9Hb3kIbrMNInkgsdylG1oGxKUeC3rka3857nLxedxrS5Ufv2YgSG8BMm4YS6UUqLlC9JK77Pkq0H6m6MVMnE550BdgxvDUPofdsRu/dglRUjLTZmOlzkYobrb8KV9tbqOF2lHA3tjsFNdKD5UommnsM2sBOLF8eqrcFO9qPsA1sVNRIF0gb252GGuxwHMt6ErZv5JoKHaSFlVhKLP8Y3K2vQ7B19GeWkb2YWNa+eA9X62t4ax503M2aH62/Gr1rLaGpn8dMn4HetRYR7cNMnTYqylpJE9D6t6N3rT+oGCxVD7buR4kOYLHfzzM7CoqK1J0HIbbmx8n2sNje1MPu1j7++7qTEFYYy5cdF4L/Q4iLwXHi/A/wnyQE7yXRtvnzguVcsu5trln1ACsvuoZkjxfh9mL39X6qx9JmzsFXWIxVtQ0ZDqPk5aNWTEFo4/8I0+ctxKra5uTrpqWDbTvO4swstCkH5hprM+fgTc/A3LwR2deLyMlFnzN/X7vyflhZYGdJlC6wSvZ9Xe0CK0NiH0ZHiTptJiInD+O5pxyrqaY5eVq5+cjg8OFelo/k7bdf47bbbqatrZlbb/02N9/8TdzuA905+vsCEQCzglGHpZkg0epB2+6IiR8mHA7zzjuv8eKLT/PKK8/R29uNpmlMmzaLE044jblzFzJv3mLyj8Ahsm6doK9PUFm5T1z1+yU1NYKtWwUrVkjmzZOEw/Dqq4KODqdIbvFiN+edV0hmZiFwPPPmCUxTUFEBkUgvnZ0bWLPmPfp73qG97dv849EoPiWR8rSjGLBsvN59mQhaLXieVZAeiTWyAiq9DzKqBKFlMOUKyYknWvT1OZnCubn/f+ZD0gu4AMVxomu7QZoQC4GnD9QB5/PpeU7BtVoSvkxizHLex7w8yc6dyshDKmd/lgUgSfsEZg0Aq0QiE0DpA6VboNU4D0qwna97nxCIMMQWSNxvKcgUsA0QHhw3cAgUKZCqJLZEEr56fCEYHHd18EYb11sCvUZgpYOxSBJdIlE1lZ/97A7OPnsFK1fex2WXXTP+Tg6CvuwYp019VxUkp0IsioxGcC07FqXsE1in48SJE+c/GFVVOf/cS1m58l7u/N5vcX+EuPqpHrus3Ol7qK3BHuiDvl6kZSGHh1GnTINIBD7UWRHqD9HwQSOdu7pAQkZ5JqWLiknMOrzSXGPjeqKPr8TudrYXXi/6oiWo5ZOc1WQTJo26fNWERMzaaoz33kabMt152D95KtrkjxeHICMRzNXvoeTkohYUOdEF7W1Yrc00BFt4UIQYFpAxHMXyJ/B+pI6+HSu5eda1+HTnF6vV1AiGgbK3LXcE4U/E2rkdPiQGv/vuG3zl8Qc5rbCMn5xw+mj0gnOfI5DRyGGNfevWjVxyyWnk5uaxcuXzZGaOn5X7ScnyZXBM4RKeq3+FwdgwXs3DYGyITF8GJxQvH3N/JqWkLdjBrr7dmNKiJKmQ8pRSTNvilaY3eb9tLYY0yU/IZmn+ImZlTsWluihLLqYwMX90X+eUn0ogOsjugQYAfLqXmG3gUlw0BPYQs2OUJBdx/DjleDHL4Jn6l1jdtp4hYxiP6qYwMZ9ZmdNIPTOZqRMnkL45CWwwT7SJHrVfb8InYK8YnJU1/vsghOCXv/wTe/Y08oUvXM7rr28i5ejvE3N14n7ajbI7G1UIpyNiniR6jDPfNFOnYKZ+cof3KLaJe8/zYJujEQfRvGPRuzeghlohNoiRNgXpTsfV9ja2KxEzsRRhhXF1vI+IDgA2WqDOeWgkVKTqRRvYgXT5HHextFFDHUjVi+XNwvbnI8zgiMPYg9T9KNEeIiVn4au6B4b3gOLGdqUgbAOsiHMvJcBInYLtce4l1VAbtjcTK7kcM2MmZtp0tL5tjqs5dSpG5jzQHJOMiA3iaXwahLrv+kmJFqjG3fQMZupUlEgPUnGPvQkRAhQdET3EfbjqIpazFG/tQ4hIL9KdBlbEyV5OrcRMmey8d+nTsRIK0AZreeKdVlISPBxbkYAweg+7lC/Ovz5xMThOnDgfCzU7hxyvl7+dfQmXPPZ3bnzyYe4993L0UBCluORTP56Slo5y9IrDeq22YDF6eyvGu29h7+pyfjdm5+A+/5JxBV4AtaDo8Bx/HoicJPE9pKDVgJ0oUYZB6oLoiQcXrvZHhoLISARt2sxRB4hIS8fq7iR89x+Ivfgswu1Cm7MA/ejlKIlHlsDf09PFj370TR5//GEWL17G3//+xAGZuPujtghk4r6l9gDoAAK1V7K3AzwQGODFF5/mpZee5q23XiUcDlFWNpELL7yC448/lVmz5uP1fvzCj7Y2gc831mWrqs7cptsx+CIELFvmiMJdXeDxQI4G7q0CpRvsNMjOliQmKvT3S9LS0ikpORHFOIn2NwQTXBEWpq9jW+hd1gdeoyv6BhLJOeccy2mnncPZ4mzKwyVY+8WZ2emg9Qq0LRJjplO2lv3Z3LccNlYpGBMk+k6BUSSpHoKhXYKCfoFPkwyWQMIcWNMh2bhBYFQJJt1gs2iZZMECyYYNUFfniNmBgBN3UV4umTHjkzlMrAKILrPxPCvQdgKKkwNsp4OdC2q9IOEPAmOCRO0GY4rENeTcQEiPQBkAYo7TZfgrh+FKL4VwiSTaItFrhJPf3ehESCxYcBTnn38ZP//5/+HMM88n8Qi+j9TCYjw33ITx7lvOigF/LtqCReiLl446hePEiRMnzoFcdNGV3Hnnb1i1aiWXXHL1/9hxhT8B/azzMX54G3bLHvAnIlQFkZePkJLIqkfw3njLqGgXDcbY/PhWehv78KV4QQia1jYRaBlg7qVznK8dArunm+ijDyGDw6gTK0EIZCCA8eZr2N1dSNs+IO5BSUzGbt7zqZyvHBrEHgw4q+EYiRebVIkVHGKtq42+vggTBxWUhET0KVNIT8ujtr+e7b3Vo5m4QtcdEfnDKxhNY19T7ghbt27kc587j8UV0/h5UTlCMjpvlJEIQlNR8j66RXnNmne54oqzmDRpMg8++AwpHzO643A5pfR4snxZrO/cRH9kgPk5czgqbx7FSfvMClJKXm9+h+fqX2UwNgiAR/NwVO58/LqXZxteIdmVRJ4/hyFjmJr+3ZQmF3LBpLMOOF5+Qi63zr6BbT1V9EcDJLkS8eledvfXE4gNUZxUyLzsmaR5DjzvV/e8xYsNr5LuSacksZAdvbt4v20dzze8woSUErKSMznvujOYnXWgseWT0NvbA3DI0l2Xy8Uf/nAfxx47mx/84GvcccffiC4qwJgA2g6JCEus/JG+CNdBd7OPMTEI3VhJZcRyl2IllR50EyXahxrqGBVYnS9qGFnzkf0+wpXXYmQvImHDj5Gqju3PRw22oQ7WISJ9eHu3YrvTiBYcjzbUAEJBupJRor1O0Zu/AGGbCGMYM7USrX+n8xo9EWH3oQ41YnuzkXoS0eLTMZMm4K+6G613CygatuZFSIgmlSGiAyhWGHV4jxMDIRRiOUsQVhjpSsbIXoiRPX7RnzrUgBLuwkzeL19YCCxvLtpgA0qkByuhCFf7OyPlfPviL7AMbP+hvw+jRSejRHpwdX6ACLeD0DFTpxCu/ByozpsnXcmEKz+He+d9rHr7Bc6ak42LKNEip6Quzn8GcTE4Tpw4Hwt1xmzUsomU7t7F3cecylUvP8Utj9zLH045F//8xf9fxyY0Dfe5F6EvPAp7TxO4XKiTKlE+5Aj5uBgLJEGvhft9BaUVjAkQO8rGmHl4Qprd3gb9fagTJ43mkspwGNneht3ZjvB6ER4vscf/gVVXi/faLyAOQ2SNRqPcf//d3H77zwH43e/+woUXXvGRznQ7C7Q9HyousAApsRIlGzas4YEH/szTTz9GNBphzpwFfOUr/4eTTjrjkCLzkZKZKdm6daxj1badP6kjc+ZwGFpbHTN1QQG4W8B3r4LaIpwsWhsmZUsWFNi8WiWIRJw4B6sbkhVJSYKbZG0pSxKXcnzqt7mr/Sz2JO4mISGRn/zkNr5vfI15KQu4MnYNF0y+BL/uqJFSlYjwkZc7fGZoEL7YRjygsG49/KVRIUWFqxOhPxV6u6DqJUG1AFUBrVuw9UGFbbskN9xgc+WVFo89pvD++4LBQUeEz8yEjRsdB/bH1jsFRM5yHiz4m50SODsTpAZ6rQDDiY9QA04us0wTGNMlWq1AGXbK4uxcGP62jXXw+4F9SHC/IfA8qyACzvHxQHSxTfhCybe//VOefvox7r33Tr70pduO6FTUgiLUi6/4OFchTpw4cf5jCWGzRzVQERRZOu4PRUBMnDiZM844j9/85iecc87FB0RCfZZoefkoefkoqamg6YiEBJTsXGQk4qz2aG9FHREsO3d20tfUT3pxGorm/NLzpnjoa+ijbVs75UvLDnUozJ3bsXu7USdNHn1IKFJSsPp7sfY0OF0QpoHQ9sU+yOGhT80wIRISURISsYcGYeRhp5KahpqWTrO3nYSojaI7195qakAfibjoDu1zDaoVkxFJycjOdsjOdXozhgbBNNFnzxt9XX19LZdddgYVFVP4yx8fQPz9L1jVO1DSMhz3dWAAbdYctKkzDjnmN998mWuuuYA5cxbw3dt/Q0OshazhCHn+HAKxQUJGmHRvGm71cNTEw0MRCvNzZjE/Z9ZBX9M01MKz9S8TsxwHb9gMMxwa5q033iOtOZlimYevyIecCL5UL4pQWNu+iWMLl5LuPXBJVYLLz+K8sX0Fc7IOfW3CZoQP2taR5Eoi05dOW7CD1mA7iXoCSEm6J52BSIB/VD9BXkLOEZfEHQrLWR6G/hGl0fn5hfzoR7/hq1+9nlNPPYeTTjoDOxNiK8afGwsjiDZQDVYMK7EY279v+aS7+SU8u1ci7Jjjzu3f4cQgTPviQd3EUvUiVZcTnbD/cawIqB6kNwOk4URF6EmowRb07g0gLaTqRVgR1GALWqAW6U4diWiwwHbcwkp0EBHpAs2H7cuFgd2owTZszeuUWod7sb3ZGFnzHHE2bQqDS36HGqhBG6h1soFTK7ESy1AHd+Nqext1sN4RhmMDuPe8hHvPCxhp0wlPuQHbd5DCEaE5f2wT9isVFNIEoSEVDSN7Ea62t9H6q7D8eU7fSagVK6n4o6M4VDfhymuIFRyPEmpH6n7MlEpQxr7/Zto0XhhcRH3X7/m///0LhueePJK9/J+34vl/K3ExOE6cOB8LJSkZ7zU3En3xWWZXbeMPy07glnde5XMb3+c+j4eDrTaXpond3ASWhZJfeFgipwyHMbdvwW5vRXh9qFOnj07mD4YQ4vDdvkeKAHMGmDNsAKzmJsxtm5G7hlDy8tFmzjmk8CzcbtB1iMacfDbAam1G9vVCStpo3poVChF76Vkn1/jCyw7qELYsi8cff4hf/erHdHS0cvHFV3PbbT8+5BP+/YkttNG3qqjNYOU42cfD9YOsDD7EX75/D1W7tlFQUMytt97GhRdeSW5u/pFcrcNm/nzJunXQ2Og4VqWE5mZBbq7jWF2zRvD884KuLoGqQnGR5IogVLYJzAo5KgZrdXCFT5B4pmTNekEwKCh1S06YAqUlgkBAoqqQkSGxnw0xJXUqdz7wMIODAd686wWeevgRbn31C3z37W9y6dSruG7KjUyJTcaaaH8m5/1RGDZU90FvRJDkgslpEn+LE7mg1oCvQzA9WWJPkYgdgqIwZA1AclCwPFmyJwMavTBcLKmuFqxbJ1i+XJKUBGlpMHWqTWoq9PXBY48JfD5YtOgTiN4qxI6SuN8B2yuRyU7RG7Yj9mKDMRlEP2i7BOaJkthREqXHcamHz7cxD3PVrLoHPM8oSDES6yGcLGX32wJrAuQuyueSS67h7rtv55prbiIh4VNYTxknTpw4/0tZq4d53j1Mp2KhAPmWzjmRBCZbY12k3/zmj1ixYib33fcnPv/5r3zkfqWU2HsasXbtRBoGakkpyoRJyNZmZDSKkl+AMuKAPeR+ImGEpqNMmozwjJ1fyt4eJypihMHOIYQiRoVgAEVRUN0agdbARx6LaNQpfvvw01O323mon5aBtbsWtbAYXC7srk5QFfTFR3/0vg8D4fWiHbWU6KpHsDs7EKlpWA27MdevI32um5ZsFUwdkZyC7OslVl+LzPePKUdTiktxnXomseefxq7e4cj6bjfakmVo8xYB0NvbzaWXnk5aWjr33/8UCWnpWNd9EeOt1zC3b0XRdbRjT0BfdgziEML/qlUP85WvXM+io5cz7+ZTuGfXgxiWgVt1oSgqmtCwpEmaJ5Vji5ZydN7C/7GYveq+GtqGOxmODTNkBFEA2QvuXh2ChaS5UhBVQBvI5ZKUpCSahprpiwyMKwZ/HIJGkKARGn1/2oc7kdIm1ZNKT6QPU5qUJBVR3V/L9p6dZBd9emLwXg6n4+aii67k+eef4Fvfuon5848ibZwcbQCtdwveXQ+gBltASqQ7mWj+8UTKzkMYg7ibngXFhZk0Ye/B0QZ24m58xhEmxYGuBOlKxMhaiLvpaaTuR+qJYEVRB+sx06ZhJk90Cs292aiBWtRQ20iOb/q+MjQrhjZYTzRvufO6oUZEpBcUDWXvqccGUXu3gYwiIj1o0gJpYXsyiBSejJG5n9AvFKyUSqyUseYYK3ki4eSJ6O3v4ttxF0q4FyXWjzCG0LvXo3etZnDZXdgJB97PmsnlmIlFaEONjjtYKGAbKKE2YnnHIN1pSCEITfsinoYnRoRoMLIXEyk9G9t7GJ8NIbASi7ESiw/5srv+chdz5y5i1kk3Yh3ylXH+HYmLwXHixPnYKNk5eK+6DntokFMsi3/W13LVVedw5pnLuP/+Jykrmzjm9VZdLdFVj2DtaQLbQsnMxnXqmegLDu4ktgf6idz3Z6yd25G2DVKipKXjvuBS9PmLPutT/EiMdauJPPoQsr8PFBWkjTbhHTzX3IhykOwtpbAYtawcc8dW1AkTEboL2d2JjMVQy3KwQ0GsrZuRw0PI4SGijzsTfc/VN6Dm7RNipZS88spz/OIX36O6egennXYO3/zms0fs1jWnQ/hCG/dLgr6abm6v+zX3NP6JqIxy4omn83++/3OWLTseVVU/emefgPJyuPRSm+eeE7S2ChQFysok55xj09sLK1cqWJakoEBimlC7TfC3ZvjGAknq3jmj4kQVJLQLzrrU5viTJKEQZL8k8L+mYOZJ8vbLdZYShO5snJSUzJk3X8wlrktpW93EX7vu4b6qv3HXpj+wrGAFV/Z+gZPts46ojOyT0heB+6sUqvvAkgKBZEmv4Kb7Bd4OgaVIJgwICl2STakCTwzyeqBGEQSAclMwv00isiVV6eANS6qqHIG9rg6mTpXsvXfLz4f6enjnHcHChZ+sFM/OgtgMG/fbCnZMogw5OcAiCGYZyBQwZktc74G+Q2AngYhKzImOMHy4aFUCMcioEAwgk4Fu0DcJYoskN9/8DR5++K/cd99d3HzzNz7+ScWJEyfO/2Jq1RiPeAYxgWJLx0bSohg86B3kS6FUsu19t5UTJkzissuu5fe//wWXXPI5kg8h5Eopib32ErHnn0YODSIAGYsiLQuRmAS2RElLQz/uJFzHnnjIqB4lOwclPRO7u8sRYUewu7tR0tNRsnNGv+by6ti2fYAAZhsW7oQD+xUOOFZePkLTkcFhhD8BaZrOuPv70Oefhn7U0UT/+Q+shjqkYaCkZ6CffhbanPmH3K+UktbhduoCjUgpKU8pJT8hd1yRznXsiRCNYrz/NlbNTsyq7YBkrplJFTHa7EGyu6JYqam09NeTN+EYpmVMHt1eCIHruJPQJlU6QrxpohaXok6qRGgatm1z663XEgwO889/vjIq/Kl5+aiXXIm0rPEF8f2wbZv//u/v84c//JILLrycwkvmUB3YTUlSIW7FzXvta2gaaqEydSKVaRPpi/SzsnoVuqKxKHfeQff7aRIxo3QEOxFCkOFJQwSBfkmHr4vduY3kR3NJTElAdAC7YWj6MF7NR5I7kdbhdtZ1bKIj2EWmL5152bMoTiokasXY2l1FbX8diqJQmTqRaRmVaMr48kuiK5FkdxL9kQGSXIlErAiq0IhYMVyKjk/zjmYzh8zwp3r+ez9bhyMGO/nBd3LssbP53ve+wh//eP+Br4n04q2+FzXSPSLQaijhLjyNT2H7c7E1P0qkGzO5cv8dY/ly0AbrENE+pCfjgP0CRErPRon0ovdsAmsPKCpmSgWhyqsdQTfUjlRUtL6tqOFubHcawgw60Q/+IoRtog3XoYY6MVMqnQgJwPJmYyWWYCZNwNXxHnrvZmxPBlZCIUp0AJBY/nzshHznfu9wkDbu1tdQIj0okU4QqhNxYYVx9W0nYdN/M3jUb0ajGUZR3UQmXoZ355/R+qvYW8xspkwmUnrOvsK45HKCM7+OEnFiPmxPxqfq2t26dSMffPA299yz8lPbZ5x/LeJicJw4cT4xex2rc+Ys4Jln3uaKK87i1FOP4s47H+DYY08GwO7rJXL/X7Ha21ALi0BVsTvaiay8H5GcglYxedx9x954BXPb5tESjr3ukegTj6FOrED5jLPGDoU9GCD61D8hGkWtnOosrzMNzJpqYi8/j+fyz427nVAU3BdcApEwVkO9k9c2NIRISkItn4S5eQMyFIL0TKeDIK8Au343seeexHPdFxFCsHbt+/zsZ99h3br3WbJkBc899x6zZx/6BuOgCGif0s1dr/6Ge9/6E4qicv0XvsRVn/s8OTmH0Yg3Qm8vtLeD1wslJU7e75Eyb55k6lRJS4uzfX4+7NkDf/+7QlMTzJnjxNi53VBeIqnfKdjcB8fst9JK7k1zsJxMYY8H5CyJfB+UDrBHNHqlE9y6m7Bvv0m1D0LX2GRVFPH9DT/jNuP7rLIe58+b7uK6Wy9iyt3T+frXf8BJJ53xP+JYeaZesLVHUJok8WqSmAkL7xTYTWCVSmwNAjFIiMCcXTDsg6BXYIYgJiEgQVcEEzTJLsUpinO7YWBAYFkCj2es8JqUBD09juHpE63sFRA5VyIsib5RODdWCljFYGVLJyJCOqWFxkzbEXWlQO2AhD8qRE6UxJZLiIHaCVIHO4cxudZKu+M41upAxARWnsQeMUNI3SmjA2dZ48UXX81dd/2Oz33uC/j9h1cMFCdOnDhx9rFBjzAkJJXWXuFCMMHW2anG2KZFyY6Nva38ylf+D4899iB//OOv+M53fnbQ/dp7Gok9/zRC1RDFpciOdsydO2BoEG3hEtQJE5HdncSeeBQlJRV93vg5mwDC60M/7gSijz6MVVfrRCAMDQISbcU5iP1+/mdNyqRp3R6GOodIzHJWjQR7g6hujezJB1m+vR/qpMloc+YR++Ad5NAw9HZjDw8581JVQckvxHvrN7CbGpGxEXfzR2TXSyl5ofE1Xm16i8HYEAJBgsvP8UXLObX0+APmHcLlwn3WeejLjiH6z5XY3d1Ij5dpRhJndkV5IzNMgxZClYMUmRlcXHEeaZ6UsfsQArWoBCW/EHPdamKvvoh89CHUsnL+umc3r7/+Ig8//CwF46y0Ex8x0QsGh7nllqt56aVn+N73fsGxF53JHZvuoSSpEK/mpS/Sz1AsSKKeQH8kgEd1U5RYQEOgibdaPmB+9mzUwxXePgEe1UPYjJDuSUUIgQhLsMErvVhem55IH6ql4fG4iO0x6JjQxfKCo+iL9HP/jkfoDvfiVt1ErSir2zdwwcQz2dKzg/Udm5CARPJW8/ssK1jMhZPOGvec3KqLpfmLeLTmSVqH2/FrPtqGO4laUYqTC0l0JRC1YihCkOP7dIsr9heDD4ecnDy+//1f8tWvXs/113+JWbPGivZ67xbUYJsT9zDi8LV92YhYAL39PaLFp4BQnYgG9l0LYVtIoToRCQdBupIITv8SWmAXSrgLW0/CTJsKqhtluJmELb9B66tC6gkQbHeK2+wMzJRKjLRpCCOIsIYdp22kF+lKIZIxx3H2joiyVnI56mA9ihGE2CAoGmZSKbY3E1fH+8QKjncGY5sIM4zUfOMLxFYUJdSJiAUQEmz3yKpRJRF7JEJD696IYg6jd290yuQyZhHLORozbSrDc7+L3rMJJRbA9mRhZM5xzmvsm+c4gaXtCNtmCMtfgPR8csf63XffTlFRKaeccvYn3lecf03iYnCcOHE+VUpKJvDcc+9xyy1Xc8UVZ/HlL3+HW2/9NmzbjNXWjDppyqiDQC0uxdxVhbn2g3HFYGkYWBvXoaSmj5ZwCCFQCoqwdu/Cqt2F8v/RHWzV7UZ2d6GUTRydSAlNR8nMwty+BRkKInzjN2CpIzcJZtUOZGAAu7sT441XsLs6kYMBSEpGBIeRbjdqTi4ARnUVa59/knse/htvvPES06bN4uGHn2P58gNvEA6X/v4+7rrrt/ztb3cCcN0Nt3DjjV8hNfXwJxGmCc89J3jnHYVAwBEby8okF19sj3HhHi5eL0yc6AiS//iHYO1aha1bndWd4bBg6lTIzZUoiYAPBjsFVIwU4ElQ28DKkVj7ukEwKyFyho3nJQVtl/M1mQx6gYchfXjM8WUSRE+RRE+RgM7pXMzpXMzate/zq1/9kGuuOZ+ZM+fyzW/+kBUrTvzMROH+CGzrVsj2Srwjv60zAlDRKgh4AVXiUcCfCoFOQXYQLBV25kqMdoEQEEwCT5LEB5g9jgA7a5YkMVGiqpJw2LneexkchLKyA3pjPhYyyRHW1ZPAs0rgeleBMLjXC4iBCAusfInaIhCKwJgiQXfEX+8/FZR2G61OoHYI0MAsl4TPsbHzQW0A318UtGpHaFbrBWqbwJgssQokIigwJu+L9bjllm/yj3/cx9//fjdf/OLXPvnJxYkTJ87/MroVC++H8oEFAhXBgDhwAXF2di433ngrd911O5dffh1FReMHwVu7djrznvQMrNXvOeVrA/0gBGbVNtSyCSi5+Vh1tRgfvHtIMRhAX3oMwuvDeO8t7M5O1AkT0Y9ejvah+WJKQQqVJ1ZQ88Zuepv6APAkeqg4diIZZeMvfR9z7pqG57KrsTo7MF56FoSCWlyGSEnGeO0lRGIS7pNPRy2b8JH72svOvhpeaHgVn+alMtVZXdcT7uWFxtcoSS5iiq/Q6Z3QdSdqbWQ+raSmgW2j5OZimwYMD7NUJDEz4KY5EkPXoOLcc0nKHt94ARB77iliLzzjOH19PtY8/Ri/+OANvnjpNaxYceJhn8NeGhp2c+21F9Dc3MR9963ihBNOY2PXVgzLwKM6T5vbhzvpCjkNwX30s7Wnislpk0hxJ9MT7iVkhsfEWnxWlKeUkuZJYTA2jGEb6LaOZqn4VA+610W+mUd0OMJwxACvYFnBUZxZdjJ/3vYAfdEBKlOd+wApJfWBJu6tWolhGRQnFuDTnWbpQHSQd1pXMy2jkukZYzNxw2aEtR0b2dpThSZ0eiP9qIqKV/PgVl1k+7LoDHbTG+lnesZkpmV8en0dH5cLLricO+/8Nb/+9Y958MGnx/yfMIKOQ/VDUQ9S86FE+zCTJ2ElFKINNYxGOzgxCO1EC05AulMOfmAzjDZQjTDDWImlYzJsfdv+iGvPC6C4nbgITzoiNoRUXJgpTtyCGu4gWnQqocnXIYxh/FvvcI6/nztXWhEEElvzIN0pSKGhRPtRjGFsXw7YFq62N3C1vo4S7cf2ZBArOJ5YztGOwK1oI/t0Y7tTUaIDSNd+8YG2CYqOBLy1D6CGe5CqCxDoPZvQejYTmn4L0pNBrOCEj3wvlGAr3l33ow3sQlhRbHcq0YITiJacefgu5g/R2trM008/xg9+8MvPfGVonP9/xMXgOHHifOokJSVz772Pc/vtP+f223/O888/wX+dfQlTUA5YSiZ8fuzOjvF3ZNtI0zrQYqooI7ZC8zM6g8NkJLbigLYtoYDtFBIcCuH1oc913LzSskDTiT6zChkIOE3OXj/qpMkYSYk8s2kd925Zx66Vf2bq1Jn86U8PcsYZ53/syAIpJY8++gA//vG3iEYjXHPNF7nxxq+Qnj7+sqxD8e67gueeE6SkSCZOdEreduwQPPigwpe/bOP6mD0g778veOcdQV6epLRU0NjoCMQ7dkBKihO7bOVIUj2g7RRIn1PyJlMgcpoN+ztbBURPlBjTLbQ6Z9JoTpDoP3MTbe85rPEsWHAUjz32Mu+++wa//OUPueyyM5g3bzHf//5/M2/ep/9QImI5ecEJ+/U5uCLOudiAPWLgSE2V2BGB7ILhqGQ4JPCWSyYlQmurIDQoGFAkrQHB0mU2c+c6G06aBNu3O9fX7Xac3VLC0qWfLCJiDMKJ7QhdJ9FqwPUBSBfgAitPIgxwbRBETnaEYAArH/T14H9AwSyV2NkSTCf2QQQUgrc6or7aKTDmSdAFaptEhEDfDMqAIyzHFu5zuBQUFHPhhVfypz/9lquv/jy+gzykiRMnTpw445NvaWzVIkgkYkQUtpDYSDLk+LeUX/jC13j88ZXcdNOVrFr1+rgFVdI0wTSxqrYjQyGE348MBQGBHBjAaqxHm1jpfL2r8yPHKYRAn78Ibd5CMAzQ9YM+tC2cXUBGWQb9e/qRUpJamIIv1XfY10TGYjA8hLZwCUp2DmJE/LLaWjHefRPX0csRR5BVv72nmrAZpSRpnws305dBb18NWz54kpJ1A9g9XaBpqBPKcZ970Wg3hshwVpMp5ZMcgb2nmwShUBkYRpszmYTjTjnoce3ODmJvv45ITkbJzGYgHOJr2zYwIyWdW4rKDys+YH9effV5br75KjIysnj22XeoqHCKALK8Gfh1HwPRQWxpUxdoJGYbKCi4VRd7hlqIWQbZ/iyyfRl4tPGfTNvSpj7QRFeohwTdx6TU8nFfa9gGW7urqOrdhSVtKlInMCtrOl5t7NKn4uQCjspfwOaubc5qvWSJL+BlWAmSq2SxJG0+Q2qQQGSAiuMrKCotYGPXVnb07aI4oWCfIUQI8hJyeKd1NZne9FEhGCDZnURbsIOa/voxYnDUivFA1aOs69iES3WhCIGQUJpSzOdnXE1V3y529dWhKSqnlh7PcUXLDhj/J0XXne/faDSKz7ff5z8GWg0oAYGdJjEnMqoeqarK1772Pb7whctZv371mHmw7c9z7oXMMGgjjgMpUWIDRLMXgOYlPPFSfDv/ita/AycGAcy0KURLzxp/kGYYrWejk5EbbAVpI/VEYrlLCZdfgjZQjWfPc4CC7UkDJErEBtWFMIfRezZh+3IwUysJV16DneA4RoycxXgan8F2JTtjtS204RZAYnmzR8cvNR/qYB1S0XE3PYu37h9IxY3tSkYd2oN/86/w+B4GVcd2JRHLW0Es/xhiBSfibnoOJdqPpbkRtomIDTpjFAJtsA4zfZbjLgawoug9m9A7V+9zIB8KK4qv6s9o/TuwEkuQqhcl0o23/lGkK4lYwXGH9Rn4MHfe+Rv8/gQuvvjqj7V9nH8P4mJwnDhxxiViRtnQtYVtPVVYts20jErmZ88aM7E5FIqi8NWvfpeTTz6Tr33tRs775fe5oricrxaVkDAy0ZBSQnAYpaBw3H0Itxu1cgrGO284k9wR4VP2dCGSklFKDt30/Fmjlk1ApKZhd7SNFtpJ28bu7kQ/aikcwXJ0oaq4z70QrXIyoV/9DBmNMFRcyiP1u3jgpVV0BYdZUTaJH9/9EEcffewncqPW1lZz220388EHb3PuuZfwgx/8kszMj7fkzLIcMdjthqyRVZV+v+MMrquDXbtg+vSPN861awUejxNdUFAgaW8XGIbjXm1sdM6/ZLqk4nybSJXjDLWzbGLzJNZBjDh2LsRy94mEbreHaDQy/osPwtFHH8P06Su4++5XeOih73PmmctZvvx6/vu/f0pRUcrHO9lxSPdAlk/SNgwJI4L6UCq0ZUqKmgVuE9BAEZCjSGJ54J0kWdYJeomEHChNksQaBHULJDdeYDNp0r5nK1deabNqlcLOnYL+fkhLk5x1lmTBgk9QHncwbJAJEmM+SK9AuiR2miPi69sEYkgi9zOjiyGcLOBSwABlAKQicW0A402BViewsxwB2Zjp7EttEyg9EJsvCV9mIz9k7Lrllm/y8MN/44knHuGyy645ouEHDajqFQzFIMMrqUwDV9woESdOnP9FzDc8rNcj1KoGObaGjaRdsSiydGYa44t2iYlJ3HnnA5x99gp+85sfc9ttPzngNWpxCTISwe7vQ8nKHhGCAWkj/AnYrS3ICZOwBwPokw7fESmE4HCeRnuTPXin5x72fvdH9nQjBwMoBUWjQjCAkpaO3d6K3dONegRicNiKoIkDf7kog0MEttdhD+QhMjKQfb0Y776NHOjH97XvIPwJ6LPnY3zwLkQiaLPmYHd2ILs6UabOwPv1/zMmL/nDWC17nPOYNBkpJd95+WmCRozfnXwuanOTk+V8iGLkvZimye9+9zN+97ufceKJp/P7399L0n7b5SfkMjd7Jm+2vEdXqIeYZeBWXETsGCmeZBL1BJqHWlGE4NzyU9GVAx8eBI0QK6tXsaV7OxEriioUipOKuHzy+RQm7uvVMG2TR3Y9xbutq5HSRgiF99vWMre7iiunXjRGUNUVnXPKT2U4NkxfZABPgocBZQB3h4vU7mSqjBpylWwmLZhEc04bK9etoj3YRW1/PT2hXuZkzSDZvTcCxAmGOPg8fewca1tPFRu6tlCUWIBPd4THmGVQF2ggYkX53NRLiVgRFKHiVl2EzTBtwx0kuhI+sWu6eaiV7T3V1Fh7AKhv3s20ihkAKF3gfUBBqwVhCme+NVkSutwena+dccb53HHHf/GrX/2IRx55YXS/Rtp0jPRZ6N3rsN0ZoLhQwp3Y3ixieSuc9yd95mgMgogNYvtyMDJmHxiDYBu497yIu/llXO3vgB3FSJuBlVKBiAVw73ke25eDMtwKtuGUyo18D9neTBRpITUvkdJzieUucaIr1H0/ryIlZ6IE29D7toJ0TDy2KxnLX4BiDCHtGAgVYYac0jrNh7vlFWw9CdvvfN6kbaB1rUUdrCOWsww12Iav+q8o4U4i5ZcSmXSZk6EcbENqfmxPOlJPRI445EeFYHDGpuhofds/Wgy2onjqH8fV/hZWQhFScYOiYftyEWYIV9ubxPKPGbeM71DU1lZz//13861v/ThevPwfTlwMjhMnzgHELIMHdz7G2o6NqEJFEYJNXVvZ1lPFNVMvPWxBGGDKlBk888w7/OWPv+KXv/kJr973B3607ESWF5cjO9shJRV9wVEH3d513InYDXVYu6qcrLdoFBQF16lnouR8vMn7p4WSmoZ+8hnEnnwUc1cVwuVGRsKoBUW4TjjliAVbIQTa1Bk0nXgK99/1W1a9+zKWhLPzi7j6+DOY9qVvok2e+rHHG4lE+P3vf8Ef//grCgqKWbnyeZYvP4ynzocgGnXE2YT95m6hEHR1CdraYNMmhYqKj+cODgb33celpcHMmZLaWkFTk2DPHsns2U65nH8yRCY7k+8jxe32EIkcmRgcCsHf/qZSXX0Ky5adQF3dn3j33e9y3HFP87Of/ZYLLjjvU4mOcKlwfLHkoZ0KtQOSZBcMG/DqUZLrXgFXr0AKEKZ0ItcqJdlugRYB5QMFO0GiVkLsUpvJ50gYW6xORgZcf71NZ6fj5s7OBt/hf2tj207h3J49Al2HykpJ5kEKjEUYRFRgZUlk0r73SSaDsJ3/H/2qBCUAdiqIIOibBUrfyH6GwPcPsBMl7L3v0sEqA6tQotVD9BgbexyDe1FRKStWnMw99/wFw7gW0xRUVkrmzJGHPO+mQXhgp0LTIEgEqnDE4Kum2KR9uuacOHHixPmXpdDWuTKcxAvuIHtUAwWYY7g5PZpAqjz407G5cxfyzW/+kF/84vscffSxHH30MWP+X62YglpRibV7F3JgwIkpYOR3gtuNDIewdtc4gueS5Z/hGR45wp8Abo/jaHbtE5hkcBg8XqcA7wgoTSrknZYPMCwDXXWEUMM2iPZ1URJ0btvNdWuQoaDTodHeijqxAs8lV6GWTcBzyZXEnnkCu7MDJSERZVIl7rPOQ6uccqjDIlwux3BhGry+p4GXd+/k96dfSJ7bA5qG0D96EldfX8uXvvQ5Nm9ezze+8QNuvfXbNA218Mbu9wkaIQoSc5mVNZ3zJ51JqieFOzbejaqoFCbmY0kLwzYZMoYByfSMKSwrGL9c+uXGN/mgfR2FCfkkuhKIWTHqBxpZWb2KL8/5PK6R61bVu4v32taQ48saFU3DZoT1XZuZmlHJ0flj40amZ0zhplnXsq5jM+3BTtrdnXT7eqgfbqZJtjEhrYRYkckrzW+S5EpiesZkBmNDNA+1onarLM6bjyZUWobbKU8pI2JFCJuRUdF5KDaMruiUp4w1sjQM7MG27VEhGMCl6uiKztbuHSwvOAqv5sW0TV6sf40n656ndbgDXdFYnDeP66ZfQeqHcqAPhzXtG3is5mn6owOEjSEA7nr7L/yo6Ieke9LwPqag7xCYZRI8EhF25mOeBEH4c040m6IofP3r3+e66y5i9ep3WLRoqbNz1UVoyo24mwpwda0B28DIXki06BSs5PLRMdjeLKKFJx1ynJ6Gp/HUP4owo2BHkaoPfWAnQgjM1CnI2CB621tIV7IjCke6kbYXFB2kRFgRbH8e4YkXj1tKJ92pBGd+Fb1vO0qoA6knog7U4t7zHKgelGAzwjaxkkqcFavuNNT+aqRQ0IYaneiLUCdyJGpC6n5sdz5KuAd329vE8lYQnPk1zKSJeBqfQhhDSFcKRvp0UFy42t448KSlhVQPndcmIj34qu7G3foGaqABJdyLOtiAkTkX6U5F6kko0T6wovvc2YfJT35yG3l5hVx33S1HtF2cfz/iYnCcOHEOYGvPDtZ1bqIgIQ//iPAbMaNs6drBhsytLM0/siXxmqbx+Vu/zYkLj+abX72R6196kjnpWdx09HEcf/nnDpmnphYU4f38l4iteQ97pAxEm7MAbdac/5ECr4/CteI41OwczE0bsAf6UEvL0OctQsk6MqdtV1cHTz75CI899iA7dmwhPSWV61eczGWTppBZMQV94VGoxePn7R0O1dXb+fznL6OhYTc33fR1brnlNrzeI5scjIfXCzk5UFMD6enQ2SnYvh0CAUE0Ci++KAkEFK66yibpyO6JqKyUvPqqQk6ORFEgMxOampxUDr/fiTV4+GGF8893BL2PN34vob0upMNk0ybBjh0wYYLE7VYpLr6Z2bPP5vnnb+XLX76UZ589lZ///Pfjlq0cQMQpOpNJjPsbeUG2xKPavNck6BiErCSYdIWEoyTRZ0fc0CkS2w+yXbAnCq02pEUkBTHQkm2MiyUcxBQghPP+HSmGAY8+KvjgA4W9WnpamuTccyWLFh34XtgpYGVI1HawRj4HwgDRB1KT6FUCw5LIFFC7QKaATABtl+P2tdNwbjxsECGBQCJCzrmj42RFN4OVy0Fd4aYJRUXX88Yb5/LCC1tISZnF6tWCzZsF11xjjysImzY8WqPQNCgoS5boiiRiwrZuwTP1gqumfAYu6jhx4sT5F6XScjMx5KJHsVAkZEh1NDLiUHzxi1/nnXde55ZbrubVVzeMiaQSmobnimuxanYhh4cRLh2KirFDIWRTAyQmoZZNwHXiaWhTpn2Wp3fEiOwctOkzMd59C6Fp4E9ADg1id3bgWnEcyhFGb83Jmsm6js3s6K0m2Z2EQDAQG2TSkM7MgAuzYZsjlqdlIKSN3dJM9Nmn0FecgJqbhz53AdqUaVh7mmCkGE4cpA1WSknD4B5q+ndj6CGyixMorKvlp+++wpLiCZxcVIrdUIfrlDMQ480XbQMl0oetenjg0Uf54Q+/QXZ2Lk8++Sbz5i3ivba1PF7zDIHYIJrQsKTFB+3ruWbqpZxediLVfbvZ2VtDZVo5AkEgNkTEjNAV6uK4oqVoyoGTorAZYX3nJtLcqaMCr0t1UZJURENgD3WBBianTQKgdqCBmGWMcc96NQ+a0Kjq3XWAGAxQnFRIcVIhb7d8wPaenaQnpDIpcwJRK0bjYDMbd24hz59Lts958j0tfTIxy6BluJWNXW4SXQlk+zK4sOIsVrdtYEPXFhQU9jqFj8pbwNT0ijHHdKk69n5mBktaNA22sL13J23DHUhgReES2oc6uGPTPfRHA2iKcz1X7nqC3QON3H7MT/FqXsKBCLFgFE+yF7f/4AL+QDTAU3UvYFgGk1MnYSdavCkeoqZxJ6/ueYtL/eeg1QisAjkauSa9TjSba7tCtNcafeh+8slnUVExhXvvvXOfGAxIdwqRSZcRKTsPYRuO4/dITTLRAVxtb2C7UhFqFFXoTp6wMYQ61IiZVOaIsbFBTE86IjqAiA2jhbuRmhc0HwhBpOiUcYXgUVQ3RuZcsGJOAZs3E1fXapAWsdxjABs11AZCxfTl4g3UOLm/mg/sKGq4B8uVjNR9jggN2J50tP4dqMMt2AmFREtOw8icizCHRoTrPLS+rbg6P0BEepEeZzmbiPQ7JXe2hd7xPmbaNKTrwBsoT/0q9O5NmImlThGe5keJDqD3biWWuxQl2oeRNgXUI3MtvP32a7z66vPcdddDeD5Rk3ScfwfiYnCcOHFGsaVNd7iXTV3bsG1rVAgG8GhuNEWjurf2iMXgvZQtWsqj727ntVUruePPv+fap1Yyo2EXt976bU466YyD5t8qObl4zjr/Yx3zs0YIgTZl2se6QQmHw7z00tP8858P8dZbr6CqKieccBpf//r3OeaYk3B93LDd/ZBS8vDDf+N73/sKJSUTePnltaPZbUdKNOrEPgwOCtLSJJMmgabB8uWS+nqFmhpJfb3j6NU0Jz+4ogI2bhQUFQnOOuvIhLNlyyQ7d0qqqwWpqZKWFkFdHZSUSBaO5MHu2QOPPCIoKpJkfGieJ6UjUm/dKhgehpISmDNHkrzfSsecnDw6OtqOKA+vocERpPcvWUtNLWDhwsfx+5/kzTe/zPHHz+U3v7mb0047d/ydRMH9hsD1nkAJCqwMSWy5JLZ4rHCrDMPCD+DotUAMrIkQPU5irADjaImIgBgG789VtnVDU4vAbcCQgE4Dclap5Ey1UC75lETLKCj9sHab4K23BFlZkpQUxyXc0gKPP65QXGyR+2HTvg6x4yS+BxTUeon0gr7FcfzaKSBMcG9UMEskxixJ5GQL9wsqrq1gJ4KwQAwJZIrEnCJR2p1rptXB3hBlO0MSOUciDxIHvHMn9PWdis+XTyDwZ+bP/yORCGza5ESSrFhx4DVqHISGgKAoUaKPvC8ezYnv2N6jMBC1SPkUyvbixIkT598FFUG2fWS3kKqq8vvf38vxx8/jlluu5u9/f2JMfrA6YSLus87DeP0VcLsRHi9ioA9x1FI8F12ONneBI7b+iyGEwH3OhRCNYlZth+Y94PehL1yM66zzPnJ7aVnYLXvAslHyC0hw+7lm2mW80/oBm7q2AY4QOL9xB97mp5CmgZKeObKt40yW4TDWts2ouU5jr/D6xi1lHnNcKXml6U2eb3iVYcN5IK5WGgyt30b74AB3z16EbGlGmzUX1wmnfHhj9I73cO95ge72Rr7459W8tKmZKy69iu//6Hf4/Qn0RQZ4uu5FbCmZnDoJIQSGbVLTV8drze9wccU5LM1fSN1AA93hXjK96Xg1D12hLkqSi5l6kIK0iBklYkXxfcjp6FZdmLZJ2By70su2LcJmBAG0BjvoCnbTF+0nweUnaITG3Ofsf23eb1uLS3WR6XMmll7NQ0lSIVW9u8j07ptspnlSOCpvHus7tjAlbRKLcucxK2saOf4sKlInMjNzKtX9u1GEwtT0CmZmTj1A5K5IK8e35y16wn2ke1PZ2VvDrv7dWNIm05tGdV8NtQP19IZ6GYgFyPCm4R5xjQ7FhtjcvZ2Xdr3JxKZi2nd0YEZNXH43BbPzmXB0Kap2oGu/tt+57hNTyhBCoOoaCalJiIDFlu4dXCBPR0RV5Ie1QA8w6Kzm2ouiKFx44ZX88pc/YHAwMCYWBADNg+QwRUXbQMQCSM0PmpN9q8QCmAmFKMYQKCrCijq5uMYgIhZEifZipkxC79uOsEKgurBVNyI2CFISzT+OcMXVBxxKCXehd61FCXYg3ckII4jWvx0lNoTlzcJIm44WqEUbrAEktjeLSOl56N3rkIqOAGw9AUQySqgbNdxNLKkMqY9MQu0YKBpSdeNqfQNXyyuokR5sPYlY3nKihZmYadOJFJ2Cu/lFRKgNpIUabAeh4mp/C1fHO1j+fMIVV2Gmzxwdu4j2o7evwQxp2FGBqqWixbqwVQ9KuBOtdwu4kojlH3dEArxlWfzoR99g3rzFnHHGv+Z9d5xPl3+936px4sT5/0LdQCPPNbxCQ6CJxkAzA9EAWb4s0vZbeiSRaOon+7GhKAonnH8Zx593Ke+++wZ33PFfXHvtBVRWTuULX/gqp556Dv4jyNr9dyMYHObdd9/gpZee4bnnVjE0NMi8eYv52c/u4Iwzzic1Ne2jd3KYhMNhvv3tm3n00Qe44orr+eEPf/2x3cAdHfDAAwq7d4NpCkwTKiokN95oM2eOxDRtHnxQEAg45W75+TBxolNMlpoqWbdOcOqpknG6Yw5KQQHceKPNW28Jdu50nMalpZL588FvwIQWOKlT0N0PvSsVMq6zx0QhvPaa4PHHFQYHweORvPuuYM0ayXXX2aPCcW5uPpFImIGB/gOvvQWutQJ9jUAZALNcEjvaOSfbHl3Euu/llmDOnLP4/veX8Y1vfJ7rr7+Yq676PD/84a9wu8cqht4nBe5XBHYS2H6J2gbeBwVIiB09st8Y+P6uoG/c54B1rRFoDQrBz9tYRY57VumE/jZo7RBkSFCScLo4TBgehN2PKVQut7DzDv/aH4ANrncE7jcFSp9gW60TY5FaKpE44nhhIVRXw86dgtzcA4XV2EKJVGzcbwlcqwVKCGJzJVa5M151twQfBD83kken2ejVCiIkwAI7U2JOltg+p6g5cqGNCAuUDolMBGO6POQ51tUJTFNnxozPsXHjHSxb9t94PAm43U7h4XhicNRy3MGuDz2ncqsQiDr/HydOnDhxPpqcnDzuvPMBrrjiTG6++SruvPOB0ZZ6IQTucy9Cyc3DXPM+9uAg+tErcC095pCrx/4VUFJS8dxwM3ZTA/bAAEpqKkpx6Uc+YLbqdxN94jGspnqwJUpOLq5TziR17nzOnHAyZ5Q5y+eFEJiRPIYefwIZjiINAywLOTSEkpYGySnYQ4NHNObGwWaeb3gVl+qiMiEPIQQd0Vae31DLSScew5RLP4+SnYNaORXxIWOC3rUG386/8NTaPdx873o0RfD4lxdy/MlTCY4ssakbaKAv0s+klAmj10FXNNK9aWzp2sE55acxP2c2PeE+3mx5j5r+OlRFpSipgAsnnU2Sa/yc0iRXArn+bOoHGklx7xMd+yIDJLoSyPE55RWDsSGaBpupGaijPtBE1IoiAY/qJmSG2d1fz307VnLNtMsOKGMzbJNAdBC/5huJrzDQFR2P5sajuemPBsYOSjqlcRdWnMWU/Vy/Xs3D4rz5LM6bf8j3YlLqBI4vWs5re95mz1ALtQP1uBSdSSkTmJRWjipUtvXuZHegEZeijQrBAAkuP32RAXa+vBOtD3ypXhIyEogMRah9vRZFVShfemC/isTJxt3f1Z+YlUqwJ4AtbWJZFrZfolcL7DSwk515ltIDdrrE+pD54uyzL+KnP/02zz//JBdffNUhz3dcpMTV/jau5pdRw93Yug8jdymxzPlIzYtiDGO7M7D8eaiDjYAEoaIG9yC9WdiqD80YJpZ/Imr/DrShBgQ2mGGUaB+uzg+IFp4MivPzRg3sxrfjTtThZhA66lADItqHmTIFK6kYdXgP6lADkbILnDI8KbH8+Ugh8O5eiZE5H3WoATXcjTDDOI6EKOpQE9KdiplQhDbchOXNRuvZhrv1JVBc2O5UlNgA3poHEcYwkYmXECm/CDNjJlp/NVrPBhQjiJEyxXFASwttqA7vrvsYnvdDpMv5zFtb1mJtXY81ZGHbOpZXx5OXjOaNIswI0p1GuOIqjKwD3e+H4h//uI+dO7fz3HPv/Uusvo3z2RMXg+PEiUNXqIf7dqykI9RFri+bXH82zcOtrOlYz7L8o/DrPoZiw0gpmZZ++OUdh0IIwdKlx7J06bGsWfMed9zxX9x667XcdtstnHTSGZxzzsUsX37Cp+KO/f+JlJLdu6t5/fWXeP31l1iz5h1isRhlZeVcd90tnHfepZSVTRyzjQg6xVpiGOwsiTmJI/5p3drazDXXnE9tbTV/+MO9nHfeZR/7HGwbHntMobpakJAgaWqC/n5BVRXU1qr85CcWCxdKDAP6+6GyEvZfWaTrTqyAaXJEYjA4gvBll0mklPzoR46wm2DBso2C3B6IaeAZEpS8DD63QuhqG1xOnMRddyl0dTnHdLkEeXmOy/iNNwQXXOAIf7m5TvFfW1vzWDFYgudpgedFgVSc5XHuNxT07ZLpK2ze8kBPK2RqgIAeCboumT5dkpycwt13r+T+++/hBz/4Gls3b+BvP3mE3PwC7BynlMO1RsHOlNgjJWdWMqhNjls4Nlui7QFtk8D1ARjlEnVYIPoBHdRGgetdQfhS5xzsNOiNQXLYWQ0mR+ZvCTb0uSR7BmBarSCW9xHu4BqL3/7kJzy29mE6wx3kZORxweVX8OWvfAf3ewrefzgFInaqJBgT+AYE2g4wZjn73TtvjEYPsn8BxgKJMV2S1KVgFjNGvLUmgLYbtHqBkS6JnCTRNkn0GolVAvaIyK22Ojcj5iSQiYfveN47vmnTrmHNmp+za9cjTJ9+LVIe3DiR54cUt6QrDLn7OY67QlCcJEmPr6CLEydOnMNm2bLjuOuuh7jhhkv42tdu5Le/vWd0VZhwuXAtPw7X8uOOaLXOvwJCUbCLi4jkZ+PTvB85druvl8jf/4LV0YaaXwiqit3eRmTl31GSk1HLJ43ZhzplOq4TTiH25D+Rw8OgKii5eagTK7A72o44mqymfzdDRpDJI0IwwLoHXkHzuqi47lRcS04Zf0NpE9j2BF+6+z1WvtfEGYsquOOmU8n02qjdG1EDNVgplciDdDg4IU/OnE4RChVp5ewZamGnWUO6N40TilZQnnLwSDRVUTm2aCnNQ23sHqgn1Z1C0AwTMkKcULycXH82pm3y4M5/srW7imxfFk2DzfRHA7gUDcWTSklSIWnuFF5peotsXxbnTjwNZb+CLV3RyPXn8GbLu5h9JhErhld1k+XLItuXiVt1Ux9oJMObTtSK0RXqYU7WdCamHrzU2rANLNvGox24lEgRCmdOOJmpGRW82PA6geggU9MryfCmjY4r25vJFnv7AdvGLIPEkB9Xq05CoR9PkjMpSXAnIG1o2dhC8bxCdO/YyXdJUhHJriS6Qj3k+B0BPS0/iz01dUxOqyBxjxelC7Sqkbfdp2CnSKwCSfS4AzsocnPzWbLkGFatevhjicGu9rfw7vwrCAXbnY4wh/HsfgQRCxDLmIu75ZWRcjcNYYZRjQFsVwqWv4Dw5Gvx1P8TqSdhezNRB92gebE8mQgjiGKG8NY+hFQ9xAqOA2njqf8narAFM3UqwgqjBWoB4biM9elY7jTUoQZcHe8RLr8Yd9trqIMNSCTqYANm0gSMnCXYgd3oPZuQrhQUVIQVQe94H821HduTAZaBu+0NpNAwshch3alY7lSUUCfutreIFRyH7c3CTJ2CmTKZhO71WP48pCfVuTBCw0wsRwtUo/Vtx8hZgtXeRnjVc3j8Gq5UBUNmIIPDhBujeKbkIXKnM7jwZ0jfkWXAtbW18NOffpvzz7+M2bMP/QAjzn8OcTE4Tpw4bOjcQluwk8rUchShkOhKIGyG2dZTxdrOjeT4stBVnSX5C5iZ+enntS1cuISHH36WPXsaeOqpR1m1aiVPPvkIqalpnH76eZx11oXMm7f430YY7unpYsOGNbz55su8/vpLNDc34na7OeqoFXz3u7/g2GNPOkAA3otaD74HFdQWwB5p750+0t77EZm7dl8vdnMT9Z3tXPadL4EQPP30W0ybNusTnU9LC9TWCrxeyebNCoYhSU2VaJrjqPzTnxRuu82mtFSSmakwPCxHxWApoadHsHix5JNETwnhZAi//LJCcViS2wNdqRCxYciGklKJawMY8wTGHMkDDyjU1TklaarqiNG7dwuysyWbNwuOPdaJnQgGSwDYs6eBqVP3LcFS2sH9toKdKkdz0excibZLMKdB4fQiyWvPCHYPAwJ8iXDKeTbTpu0dr+CqK29krjaXa350ESdcsJBHlqxi3lGLMSpsxCBYk8aeo50Gaosg4TcKWptAaZWorQK1UYAyolhK56K63tknBstUaJskSasC3RSENfBY4LUkb3ogdSRT17advOONGwWDg45ze+FCSXY2aDvgzq//hnur7ub2GX8jPzyVjcENfO+Oa9GUZL49/CXQwBqJQZ5cDNsCkmi1YDgK7kxISpLouiQ//zAEWoUDc4ydWD1GDCugQ/R0iXq/gtIBBCVKENAE4TNt5BEWHO91qltWESUlJ7Nt21+YMOFaDAOmTx9/zKkeOLZI8tRuQd0AJLggEBX4dDih2EY7soLmOHHixPm3wkDSrVi4JKQfZj7wR3HKKWdz++1/4Utfuga/P4Gf/vR3B4inRyoES9vGqql2SuhMC61sAuqU6f8j0RKmbfJO62rebV3DYGyIDE8aywoWszB37hiRccw2WzdhtTajVkxxitsApaQMu2YnxrrVqOVjJwhCCDyXXoXd24Pd3ISSmQMeD7K9FXXCRLSZc45wzBaCfde5p6mdmnc3M+/6kxDu8csADcPg73/7Pb/+5f9FUxX+dOsZXHrsDIQQjvQb3IMa6sJKqaQsuZgUdzIdoS7y/I4oZdkWPZFejilcikdzs72nmr/v+Af90QBJrgTag53cv/MRhoxhjitadtCxz86cjjpN5c3m92gb7iDTm8aiCSexLH8xQghq++vZ3l3FhORiPJqHYCxIyAhhSpuQGaY71EPHcBcBY4g/bv4rPeFerp56CZqiUh9oImSECZthWobaUIRCgiuR3nA/zcNtLMtfzCWV5/JG87t0hrrRFZ0TipdzSulx6MqBbof+SIA3mt9hY9dWTNuiMm0ixxUtpTAx/4D3tzyljOOKTXb0VpPkShzz2bGkRUFCHm3BDgLRAEmuRGK2QX9kgGw7kwyRhith7D2SO8FFeDBCdDh6gBic5cvgxOIVPF33Irv6d+NWXYgSP0Ov97HQnol3pUAmQGyRMw9V+iXKEETmd2NPbUMdSsRKKB7zJP3ccy/ha1+7gfb2VnJzx57fIbENXM0vg1CwkpxVANKdglS9uDo+YHjmVxFWBG/tw6ihdmw9gVjiTKQ7BcUMj7w+FTHouHvVcBeWOx1U92h5nETgan2NWN5ylEg3WmA3lq8AhIIwhsGOYnuyUGKDKNEBbG8GtjsDbbCOhK23gx3D9mQhrDAi0oMr3E2k+DSUWACEAnoCli8bI30GSqgDvXcbUqjYnnQI1CLsCHr3Boyco7DdqdieDLTALpRQB7bXEeORNooxjPxwxq+iOkV4Zsj5LOzYit3dSyh7IbqyHhfdWIkexNAAdr+OMff6IxaCpZR89as34PX6+NGPfnNE28b59yYuBseJE4eOYCcuRR+deDjZVpVELYNsXwbLChZTnlrK5LRJmLaTJ9UfGSDVk0Jl2kTc6qcj0hYVlXLLLd/illu+xc6d23jiiX/wxBOP8MADf8bj8TJnzgIWLFjCwoVHM2/eon+JOImhoUG2bt3I5s3r2bJlPZs3b6ClpQmA4uIyjjvuFI477mQWL16Ob7yGqv2JgvcRBbV5pL1Xl4gQ6OsF7ixB5PzxBStp28Refh7jjVfY2VjHNWvfJtnnZ+XdKyn8hEIwQCTixEQ0NioMDIDbLYjFICVF4vdLGhudXN6jjpIsXWrz8stOXITH4xTJ5eZKjjvOPtLeiAM4+mjJtm0S9T3BkAH9QYjFBPn5krQCYJdAbZC0F0F1tUBK6OtzIi0UBVwuSXu7wO2W/PKXCr29IEQOup7Ae+/Vc8p+Rhi1WSCGJNb+sQPCyaV1bYQLNcGCSZKdumNWqAxDaasg2CWx987BtkLFCwt5qHgdX2q+gNPePJGH+//JiU0nIbWR4rj9PsIiCEoLaBGBWS5RdVD3gNoBdrbAynHef7UF1CbhlK+NmJldl9i894HKvG5IMiCiSdZ7YYuAW3LALJM884zgxRcFts1INILChg2S66+1qXxJYXX7+yzLOxN3+AzaYjDDLmWW9g+efXQ9X5spcGfs+/yVWJKufsHWiMTVJ7DcoCbC6WdKJh86qhC8YEyWuN9RsNP3ZSQrnSCiAvcb4H5TYFY4GcrBGy1c7ymozWCUQ2yxPepGPhIqKmDFCps33xQkJ19KQ8Pl1NY2s3RpIfPmHXx/JxZLUt3wXrugJwRzsiRH59tMO7JeoDhx4sT5t2KDFuEVd5AOxURDMNlwcUYsgawjzAsej/POu4xwOMw3v/lFfD4f3/nOzz62E1jaNtGnH8d47WWIhJAIDE1HW7QEzyVXHhBz8EmpDzTxQds6moZayPJmEDYjbOupwqt5SdT9NA+18cDOxzBsk2UFi8cf88CAI0Tt15UhhACvD7ujfdxt1LwCfNd9keiLz2I11CEsE3XJMlwnn4Hy4ZzWj6A4qRCXojMUGybRlcD6J9/An55M2sJipo6zAvC9997ku9/9MjU1O7n2+Ap+cGYxyYVT9omBZgSEij1SdJXhTeek4mN5uu4Fqvtqcak6EStKaVIRxxUtxbItXmp8nSFjmMrU8tH3vm24g1eb3mJe9iyS3eM7IIQQzMycyoyMKUSsCC7FharsE7C7Qj0YtolP99Ed6qUvOoAlbTShEogOEjbCuDU3UTNKb6Sfv1f9g45gF4kuP3WBJoJGiPqBRpLdKfh1LyEjRKIrgSwtA5eqMy1jMnOzZ9IXGcCjuQ8aaRE2w9xXtZJt3VWkeVJRhco7rR9QH2jkizOvGXXkgpOFXB9oJGxGyPZn0zC4hwnJxbhUF0OxYfojA1w2+XxWt29gdcf60RK5JFcCyyYuJrMvg1gwhidxn5AYC8Zw+Vy4DlIkd0LxCnITstnYtY3B6CBzT5zCd/76Bm0v16H0TMWslCDAKpFgGrgDj+ONvYFr4wBS82CmTSM86YpRMfO0087htttu4vnnn+Taa2862EfvAJTogBMN4U4f83XpTkOE2lHMELH8Y3C3vomZOhnbnebEJQiB1r8Td+trxLIWoXdvRB1uBdsARUeJ9iF1P5Y3G6wYaqTPEX6lZMySMMUFQkfYMUd0NYYRQnFK3MLdSK/ETJs5+npD0XB1vIeray3qUCPYFrYnDTNtOrYv14mNkAa2Lwfbm+EU2QHCDKEM7wFpooQ6R8Td/X7mKSpGSiXutjexvTmjxxOxAGhebL+zklEGh0EIYnYhgZiOV61HV/qJmunEgrNRik877Gu/l/vuu4u3336Vhx9+9lONK4zzr09cDI4TJw7p3jQM2zhgWZ5P93BM0dGcOeFkADpD3dxf9Qi7+xuQ2ChCoTyljCunXESW79NVRiZPns7kydO57bafsHXrRlavfoc1a97l73+/m9tv/zmqqjJt2izmzFlIaWk5JSVlFBeXUVRUekA+6ydFSsnAQD9NTfU0NOymsbGO+vpatmzZQF1dDVJKfD4/M2bM4bTTzmXWrLnMmjWPoqKPzozbH60OtCaBWeLkwwJIn7Mk3rVBED1VIsfRk82N64g9vYpNoWFuWPs2hUkp/HnOYtJeeRF72kyUlNRPdP7Dw44YPDjoiIgulyMQt7U5xW0ul6C3VyIEnHWWJC8P1q1zBOGFC51M4a4uwbZt4Pc7LsyMDGhthVAIsrOdnOGPYm+G8FC3grZd4PVCebmkuFgyej+lQ1+fM9ZIRCAEeL1gWY4wbdvOv9PTnYI72xb4fBN5++3dtLY6Y1mzRrD+SUFop6A8DMtLJWV7x2eA6HdyeYumQNHewUlQdwr0rYJojsSyoOZPCq6d0JKQxVeyXuAO82Iu2HwWf7b+zoXTL0LdI7BKnDI1EXBcwahglTpL8Ox0Z79ScYRiYQIxJ7ICt0SvEcQWOSLm1Nmw+wabv/9NQemDIQkuS3BSrmTSOTb1Crz5pkJSkiTT6Z7Btp3YjLeeE0xtFszJXcyfq/7C8qJdVGZOYvfwFna1vcc85dc09Egme0EmA93w/mZBtg35LkGnR6JJx7UszcP7TMWOl2gNEq1aIP0SERUobYAi0RpBukDbpaBvkQRvsAlda3/ULj8SVYXzzpNMnizZsOEktm7VmDDhaa666gsc6keGImBhrmRhrjxkpEScOHHi/KdQpUV5yBsgJiDLVjGQfOAKM6BYfDGUiveApR1HzuWXX0coFOSHP/wGQ0OD/OQnvxtTKne4WLt2Yrz6EiI5BaXEWaovh4cwP3gHs2Iy+qIln3ise6nq3cV9O1bSFwmQ6PJT21fHrv46ypKLqEgtByDVk8KeoRbeaH6XBTlzxo0GEKmpgI20LMRIbrKUEkJBlNyDh9+rEybi/eKXkQP9oCiIpOSPJaJXppWzMHce77WtwWyLsPOtDUw8dz5TMitYlDtv9HVtbS38+Mff4umnH2Pe7Nm8+bvrWeCtRRvcgWxqxUifie3NQh2qx0ydipk6habBZrZ2VzEUG2ZBzhwM2yBmG5QkFTIvexbp3jS6Qz20DLeR7c0cM/4sXyZ1gQaah9oOKgaPXkMh8GoH9mAk6H6EEEStKLUDdWhCxa26iFkGCgJLWgzFhvFrPtLcKQwbQf5Z+wyVaeXMypxGyHAyhSNmhLLkIgoSctEUDUvadAQ76Qn3UpxU+JH3Pdt6drKzt4bylLJR00yGN42qvhoeq3mayemTSNB9aIrGi42v0zLUhiUtdEVHEYLGwWZsbDyqhyX5CzhzwkmcP+kM3m75gI1dW3GrLuZnz2Zm5jR2BWpo2diCtCUun4vIUJTIcJSKBUW4fOOLwUIIpmdMYXrGFMD5/P0h94d8sO1tztXO3KdTClC9L6Foq7Cj6ZjJExFmEL3jA7ANgjO/AYpKYmIS06fPYcOG1eOKwf0RWNcp2D0g8GmSmZkwI0OC7kfqPoQZdHJy9x7WDILqwdYTUIebkaoLK2lsFIftTkUdbMSYejORkrPw1P/TiYaQFtKdgpk2A6knoEYasb2ZSN2PdCVhJpeh927D1JOw3SnYnnTUgWpQNPTeLWBFwDaQuh87dfKYiZ/ty8VMmoCVUIIIdaBaA854owPgSR8RnAHVDaoHK6EAbaAGpI3etwM5UINiDGK50/BW30d4yrWYqc57ECs4Hr1/B1r/DmxPBsKKIowhonkrMFOcPGolKwcUgTRixPQcYnYOUtpYTVW4TluB5yCrEQ7G7t27+MlPbuOqqz7PihUnHtG2cf79iYvBceLEYXbWdN5tXU3DYBP5CbkAtA63k+ZJYXbWdMCZJDxe+yzVfbVMSC7FrbqIWlF29tWwqvZZbpxx1WeS76YoCrNmzWPWrHl8/vNfGc3gXbPmPdaseZd33nmNhx/+K9GRoFIhBLm5BRQXl1JYWExCQhI+nw+fz4/P58fvT8Drdf5t2xahUJBQKDTyd3C/fw/T3t5GW1sz7e2thELB0TGlpWVQWjqBJUtW8MUvfp1Zs+YyceLk0SKUj4uICrAkfGjeJt2ghAVEgXHEYGPt+6zp7uDGNW8xLTuPe865lARNx6qtxtqxDWXJwZfbHQ67dgmSkmBwUBIKCfaepmE44q6iSJJHTCmqCosWSRaNiJS9vfC3vynU1ICUjls3MVHi98PQkOMwTk6WLFsmOflkyUddwqIi0K+V+P4KsXRoDwtqagSJIcj0AhUSn88RsL1eJ6c4GnXmcZblRCX4/Y6wvHe8ubmVtLdvY8sWQSgEzz8vGOoHKwTvb4R32wTfWiSZ5AOl3ynTwP0hJ6kAFBBDzj+rq2F4hyA9EdLTJODlh8n/5Fc113Pt1svpr+jnuhmfR68REJNIv8CYYaPVKo7YiyP6ylQQloSIQPQAPjAnjBzQ3DcGRYGzr5TMnGnR+IqC2g4TC20Kj5XEpkPTB4KhIUnFvm4TFAXcbslzbwiSTZjmvo15viG+UjcFpV7FlhY3ZfwU/5RLeX8IKvsdA0VHjaA2KJiggqZDolvg0cAtBZ010NwMZQePzgPAKoDgF22nEK9OIE0bV0jBLNrndsZyxGL324LwRUfuBB4PVYXp02H69GReeGE51dXP4HZ/4bC3jwvBceLE+d/A+3qYoJBUWPsmJH5LoVYzqNZizDY/ncD0G264Fb8/gW9/+xYaG+u5++6HSU5OOaJ9mDXVyEgYtXRf0ZxISAShYG7f8qmJwba0eanpDQKxoVE3q1t1U9VXQ3e4j4gVxTNS7pXuSaMn0kdvpJ/8hAOXbGszZqMWFGPt3oWSV4BQVOyONkhJRV9w1CHHIYRAfEL3nqZoXFx5DuWppdz+65+h6hq3XPN1jpm0nFRPMqFQiL/+9Q/cfvt/keD1cfttP+DKGX14Yi2Y/qlIzYc+UI2r/W3M1CnEshcRrriK1V1beazmaQLRQVShYkmLCSmlXDvtsjHiqaboaELDsMc+QTZtE02o6MrHlygmp02kMDGfnX219EcCpHlSsZGEzD5sJNK2kVJiSZPeSD9RK0rUijEYG8areTGlhU/3EbNjtA53MCG5BFVR6Qn3IoTANU4cxHi0B7uwpT1m9WTMNmgbbqcxsIfy7lJiVoy2YCfJ7iQSNB/9sQCGZeDTfSzMmUPUiqErGgKFN1veJ9ObwaLcuRxbtHTsOZ9UiaIqdFZ3EhmM4PK5KF86gbIlB89f/jBCCBYvXsp7296GSgkRwAMQQ1XfgOFE7KwcUCTSlYyVVIbeV4U6WIuV4rjJ585dyIsvPn3AvnvC8OdtCrsHBG5VYtoKazokJxbB2eU+YtlLnNxf1etEPpgh1KEGjIw5WEnlKNEBZwI24vodHbMZxEosBtVFZMIFxLIWkrD5F+i9W7ESirA1L0qwFWGGieYfN7pttPRc1FAnWv92pOpBqm5AQVhRpBEEzTNa9qYONWD788GKoUS6EEYYJdLrGCA8adh2FGFF0fq3I6J9CNtAah4sj+O8MFMmI6wYevd6kAbSm4ORXIGZNh013I63+j6G5/3AcTGnTCI4/Uu4m19GG6jB9mYQKz2baMHxThwFoM2YhTaxArO6CiUjy8kb7+5EyclDX3hkP+tM0+TWW68hLy+f733vv45o2zj/GcTF4Dhx4lCYmM+llefzdP2LtAy1AZCTkM2ZZSeP5lq1Bzup6dtNvj93dGLjVt3k+3PZ1b/bKZ/zH1mBxcdBCMHEiZOZOHEyl19+HQC2bdPR0UZTUz2NjfU0NdXR2FhPXV0tweDQAWLvh1EUZVyxODs7h8rKk8nLKyAvr4DiYsd9fKQ3KoeLlSeRSQpKj8QecW8iQe0Go0I6rsxxqKmp5qa1bzMnr5C7zr4Er+68P0II5PDQJx5XV5egpESSng6bNjnRDD6f4wgOhWDqVJgxY3yx7oknBG+9JfD5HHE2I0OycaMgEoFjjpEkJjqC8dNPCxISYPnyjxb9jDmSwFboWKkQ6nd02HZV8GKxJLdVMH2GIyrHYhLbdtzAquqI0I4zeOwxcnIWUFPzOA0NUbZt89LaCuEw2BpEDWisFxgD8Lc5EjlHYqVKPK8pI8vMRnZiAnLf+1Zf7+Q9l1gQGnmJW9e5OedvFCupfO2xmwkvDPLlgq9hVUiix9vYhZD4C1D6cPajglkE+iDILIkxTWKngoiBiAisYklXF2zcKOjsdPKRZ82SHPuNfS5aa+TvvbGJ+ztbm5udeA/bhqd80LbnUapDD/Ol0geZ7J9Kd/dmftnzVWZ051JRfiWhWZKEDQqiRWDaMIRzr4ANg2HwRqGvzRHdP4wYcD7Hth/sXJzIjWyInCkBietNgb7LEb9HUcFOk2jbBFy437X+lDjppDP40Y++weBggKQjXGIbJ06cOP/JtKgmyXKsw8yFwAZ6lXF+yH8CLrvsWoqLy7j++os47bQl/PWvj1JRMfXwd2CZ4z+pUxSkYRzRWDqCXWzq2kZPuJdMbwazs6eT7XN+sfdF+mkebB3jZnUpOgm6j2EjyGB0EM/Ia8NmGI/qwq8f6FwFUFLT8Fx1HdGnHsdqqEPaFkphEe5Tz0QtmzDuNntpHW5nR081YStKnj+HaRmVeLUjF+fdqovZKdOoemktV112A+fOOJNwOMw999zBH//4a/r6erhy1kJuKiwhefNqhjc1YS6bj3taMjJzNlbKRLS+bRgZswjM/Dpvta3hjo33EDbClCYXU5CQi0t1sat/Ny83vcnlk88fPXaqJ5kpGRW827KaRFcCLlXHkhZ7hpopSS6mNLn4iM9nLz7dx+WTL+De7Q9R219HxIyQ7Eoi25dJfaCJsBnGljYKyohjOObMI4c7qRtoIGSGiVpRAtFBkE75W+1APVV9NaR7Urln2wMsyVvI8sLF4+YE78WreUZqHvatuqwbaKQ73Ed5cgmVaROpDzTR3b2D7nAviS4/HtWDaZs0DjbTNNhMRWo5zcOtDMeCpLiTKUkqpDzVWZG5/0MGt9/FjLOmETy6lOhwDF+qd7RM7khYvHg5Tz75CB2n9pCzKxM7RYI7iOofwvYkYhXumz9LzQ92BCUaGJ1rzp49n3vuuYPu7k4yM/fdE77dIqgdEExKkSNdC5LeMLzZojAn26K45AxELICraw0i1AaqByNjNuHKz4GiYqZNw0wqRQvUYCZOANWFEu5GWDFiuctHv/+lOwkroRi9ZzN61zp0RcVMLCY0+Xpi+ceOjsdMnczwrG/i6lyN1rcd2woDEsuXjUBF6j5sdxp6x/uowWbsgVrU4SaU6EjUhGWgePuJFp6KNrgbdagJjCBaoBYja4HjBh5uwkosRgoF25UyUniXh5U2Fdud5jys0n1ogd1o/TsxshxHvpVSQSilwhG+hToqAu9F+BPwfO5GYi8/j7l1M5gG+vxFuE44BTXvCLKagV//+kds2bKBp556C5/P/9EbxPmPIy4Gx4kTB4BZWdOoTJvInqEWAIoSC8YsbYtYUQzbxK2OXe7mUl3EIgYRM/o/Ot79URRlVLBdvPjQLlgpJeFwmFBoGEVR8fn8uN3uf4nWajsboktsPC8KxDBIPygDAumH6PHWgYVbQFdXB9e9/BS5Hi9/OOPCUSFYRiPOEsLMTy7Q5+dLtm5VmDpVkpwsqa2FYND5U1YmufJKm9Rxkij27IFHH1UIBBzx2LYFtbWOEOzzSQwDdN2JZujthYcecsTl8nLJhAmgHGylkw7PZMKmdJhbIlFd0JEu2RoC5XkFr8/CMBwnsjKi2VqWk2GcmgqmKbDtfdESWVkLse0Y7e1baGhYRCjkxGH0DoHhgogFrwYEP0iQ3HSxTUoEXDucMjkrRyIsUDoF5gQ5mmUrBGxNgzn9kBaAQT+oNhQNKdyQ8FvyUxP47ppvkU02lwWvQEQloS/Yzvv/vECEwU4AVIlMENjJgAJqlzPmyIk2uyJw710K7e0CXZcYhuCddyRXXGEz9UP30RMnStLTBS0tUFjoxHxUVQmiUcGsWZLKIslbf/0WWeq3aOm8GMUL3oTpzJjUxObt/81Z512BdZlk6ASLVEUh6XnBFhPKfE4UBzZ0hyEWhcT94/MM8LwocL0jUAYF0gPGVEn4PHufAxhGZiPSWVq337eiMJ3IiM+CE088ne9+98u8/vqLnH32RZ/NQeLEiRPn35BMW2WH+iHXJhIBJMlPHhHxYY4++hiee+49rr/+Ik49dQm//vVdnHPOxYe1rVpajtA0ZHAYMdIlIaNRZCyKNvnwReXqvlrur3rEKQYTOoY0eLdtNVdNuZiJqWVoioYqVEx7nxie5EokxZNCY2APMcu5XkOxYTpDPRxfvIwU98EfNKolZXhv+ZrjCDZNlNx8xEfEZKxp38BjNU/THw04C5JGOj6unnrxuLEK0jCwdm7Hamrg/7H33nF2lQX+//s57fY2vZdMJpNJgxQSkhB67wiiCAiC6Cqou+jacP2ubvu56+ra0EXBBQRRRJr03gKB9DaTOpPpfe7M7feec57fH2cyyaSRUHZX975fL168Zub0cyfznM/5PJ8Pmo7W1IxS3zA55n300d8zNjbKNdd8ml/+8sf87GffZ3h4kCsuuZKb3D6qxmKopRWoiQzW7izxF7eiBLwYdcVI3Y/lq0JaGR7a9SQP7XiCnngfISNA6+gO+pODLCw9jiJPIW/1vEOxpxCXajA9XE+lv5wL689iKDnMjuhupFNBR4WvjI/OuAhDPfa4kP2pD9Vw25Ivoyk6G4e2MLtgJn7Dhy1tNg+3IG2JbhikrQwu1UBik7WyvN23Dp/uRSBIWxmG0yO81r2KkcwoBS6nJ2U0Pcbvtz9C0kxOxugdijmFM3luIjKkKlCBZVu0jbXjVl2U+0pJ5lLkLOdlRSqXpNRThN/wkzSTmLZJMpcknkuAhHJvKbFcHEM12BVt46Edj3PL8TceVFDoK/ThK3zvot65517EN75xC/f57ufmi76AvkYgcgHM0iJkaRd2MDy5rMiNI1Uftmef43vhwiUArF37NueccxHgjME3DApChpxSulvghoFR2D0mqA16SDV/mkzNOajJfmwjgBVsdMrTAKn7SDV/Gs+2u9HGdoFtIl1h0vWXkq04hb078my7B2NwNdkS5ziU9NBEBrAKB7rNFRU11oYa70BNdKPG2pFGGDPcyN6BqBmagbCzaCObULJRpB5wsntz4yi5BNr4TnKFx2EFahGZMdR4B6npH8eKNOHe+QDaeDtIG9td5OQd+yqx3fvFiwgNsBFW6uCbcYQXDUpRMe5PXIe89AonasYfOObn2Ece+R0//vH3+OY3/2nyvuX5v0deDM6TJ88kbs3FjMihHQll3mIK3GEGU8NUB/blmQ2lhinyFFDmKz7kev/bEEJMxEa8S5nb/xDpix13qbFSoEQdF2zmZBtz1sHLJpMJrrvuMixN45ennY+vcw92cSnjCY3WNje56gupN+Yx3T6CsHoUnHCCZNUqya5dUF4O8+dLdu8WlJfbfOMbNiUlh17v1VcF0aggEnFiIcDJDo7HHWHWspxB4q5dsHOnExmRzToO4RUrbC6/XHKoInApYfU6wXiVpGW/l+DlYdi2DZ5/XsHtdpzAhuHsS0pHcF64UOL3O9EXpaWOUzganY+qushmVzE+fiIej7OsaQrcXmf8mMnAW+2C5tcFl14qSXzKxv20gtYmQIHsckn6fBs58SzW2Ch5uhReCEhOHBBEYpCVsEODiiK4bel36H21l8+982lKLinjrN1noa8SpC90HODGG454as6SpK60EWnQdghkALILJZkFksd+ojAwIJg5U06I3s49evRRhcZGm/07cwoK4CMfkTz4oEJrq5OrPDwM9fWS+nqJdEFWJBkzBC+ZUGoLYilJdFjDtm0qKhy3tKcERs+ULH1B0C0F7SmJRwhkRuLyCIxSGBiAsgnDiutlgftxR8w2ayUiAcabApFTSPyVPfmCw2yS2AUCtduJkECASIGIC7Ln2R+4KxigqqqGOXOO55lnHs+LwXny5MmzH0uzHlo9WbqUHIW2iomkW7WotTRmmR9sL8Nepk1r5PHHX+NrX7uZm2/+JM899wTf+c73pzgMD4U2ey7aCSdivrUSNBUUFZlOoc09Hn3R0YkcOTvHo7ueYjg9SnNkBkIIbGmzM7qbx3Y9zV8v+Cxh14SbtfstgoYfXdWRSIK6j+pAJSkrRevIDlyaiyVlC7hw2sH5m1JKMnYKELgUN0JRUCuqjuoYR9JRHt75BDkrR3OkcTIXd8PgFl7ufINLpp83ZXmZTpO+79eYq1chzRxIyPoDGOddhHH2+QghePzxP1BTU8+VV57jiMBXXMOXvvR1Kru7Sf3XHajTmxCaBoTRS72kB7Kkt3Rh1DljfiUXo8VbyxvdbxN2BfEZXkLuEFJKhtMj7BnvJGmm6Ir1EM8l0RSVoBHg7NrTOKfuNG6ZfyObh7YxlBomYPiZUzTziAL6seDSDG6cezW/3vJb2sc6EAlBgTtM2AgSyyWR0sY3kdmr5jTGMuNkrAyl3hJSVpoKX9mkKD2vaBYzCxoRE4OR/uQgr3StRErJ9tFdRDNRyrylLKlYyIKSeWiKRoW/jCtnXMID2x5mdf96LNsibWbxam62jeykhe3YUpI0kwgU5MQ4J5lLYUuJqqj0JwcpcEVwaS6SVoqR9CjNBTPYFW2jJ95HVaDioL6X90NhYTFnnXUBDzx6Nzc+dwvpC9kQGX8AAQAASURBVCWYCsbIGXha7kSNtTtZtrkESnqAbPnJWIF9uWCVlTWUlJSxdu2qSTEYQJ0wZhyI80Jj7xcC21+D7a85eEHACjYQn38bRu8rqMl+zHAjuZLFk85ZNdaOPrwBM1DrFMwBliuENrYTo+MpMlVnI42J1mYri3frL9FGNmH5a5CKC3W8HW1oPVJ1OdETgJIbxwxOR9X7MY15oPuw3EUYfSuRVtaJoAg3Yxth0AOIXBzpLsSMzCK+8O9Qx9sQ0sL01+Lbejv6wGpHDN5bDpcZRWrOS5X3gvD63tPweMOGNdx6601cfvknuPnmr7ynfef5yyAvBufJk+eo8Opezqo9hd9tf5RdY+0EdD/juRgKCmfUnHzIEoc87wENsisk2ZMk2MBhMnRt2+bmmz/Jjh2tPPzwi9SrGtnnnmbLBpM/tK9gUKtDiZbj+rnBwoU2V10l8bzHW1RbC9dfb/PEEwpdXY7bdvlyycUXH1oItixH4H31VYHfL0mlwOt1xj5+v6S3V2BZTmncyAjs2CEwTUdonj1bEo3CSy8JGhpg0aKDR497nb6HG/u2tDjO11zOKbFTFEk4DMEgNDdLysvhnXcgHhe4XJJTTtHo65vPyMgqPB6IRp085L0ismk6xx8MwurVggsukNAIiek2YsS5RzLEFMGysRFOO93mxScV3hFQrILphdpmyQUpgfDAf5x5O72JHq554qM8t+IVZm2bR+Z8SeZ0SeZkiUhPlMVNfgb2XYu+HtizR1BRsc/hLARUVkJ3t6CzExoOeK+zZImkstJiyxbBhg0CRREsOk7iioMdh1DwQkZG/4WyshrCNbMZ2b2OROKHBAKf4v77BW++KbjmGpvIEkl0huTjHdCegl5L4imGzFybPULsE6EzYLwunBLEiWd5GQZLk2hbBWobWBPHaBdD+jKJ5yEFrQVHJFYluYWS7IoPJi/4UJx99oXceedPsW0b5f28McmTJ0+evyDmmy525Nz83hVjlZ5GQzDT1LksHcB/DM5gy7boiveQs3OU+8rw6Ud+Ee/1+vjxj3/NySefwd///Vc5+eS5fOtb/8JVV33qsP9GC8PA/YnrMZtmkdu0HkwTbfY89BOWONnBR0F3vI/OWA9VvvJJYU0RChW+cvbEuuhN9FMVqOCC+rMYTA6xM7obKUEiqQpU8tUTvogQgvFsjAJ3hGmh2oNcm2PZEdpj24hmRwAocBVTH5iJXz9yUdpedozuYjg9yozwPmevS3URcYVYPbCBC6adhbaf+zH39kpyq1aiVtUgfH6klMiBfrJP/4lEeQX3vfIsL7/8LEIIPvrRa/nU524mGTDZbfZgd22lECaEYLC9ZdiuAjSlG2tgyBEDk71I3c8Obw3J6DrqglV0jHcRzyYIGH7cqpu2sQ7GsjGq/RXMKXRyZQdSQzzZ9hx1oWpmFjRyQtnxR3X+UkqG20bobx0gE08TqghRPqccb/jwg9tKfzlfnH8TGwa3MJwaIWgE6Ir1ck/L71BwBH+EQEFhLBNFVTQyVpqg4ac+VEPGzLBxaCuV/vJJIRgg5ArycucbdI53k7WyDKVGSJhJ/rDzT5xZczLXzvooRe5CopkxLGlj2TYgyNkmg+lhR2hWDGLZODnbQggLW9qkrTTxXAJVKLhVN7a0SZgJhtMjJM0kIGiSNhkry8qed9gz3slYNsb0cD0rKk+kIVx3VNfySHzsY9dx/fUfYdOmdcydOx90HPetncPV9SxKehCpesjUXEh62kemDMaFEEyf3kRHR/t+34OFJZKHdihkLIlrYkzbl4SQCxrDRzfGE9kY7u33YgyuRuTiGN1ezL6VJJuuR7oLENkoIpdA+h0hV413oEW3IzKjCNvE2vQfpJpvwvaWoo1uQYu2YgYbQXMj9YATQxHdhjbaguUpcVzFdpZc6RKUnpec8rqJ32nLX4OS6EHkEhNxDgJtfDeWr4JcodO1g6JjhWdMHn+m+ly0kRb0vteRmgdQQNHJ1J6PFah7z/frWOnv7+WGG66guXku//Zvv/hfMTM2z/8ceTE4T54/M/aMd7K2fyP9yUHKfCUsKj2eqsDhm4c/SFZULsWre3m9exUDyUFmRhpZXrmYhSXH/bfs//8UgsMKwQB33fUznnnmce6++2FnsAYkKmbz6D+ZjCkaTY0aqgqxmGTlSkFlJZxzznsX1WbPhqYmm/5+J3+3pOTQbuPhYfjtbxVaWgSbNoEr40QyDJlgTBS6aZrE5XKcpl1djmAbCEB9vTNojERgcBA2boRF+0qtSSYdoTYQgOOOkzz5pEJJyT738NCQc0yDg477uLJSks3C2BgYhnPcr76q4PNJhHCK6y66yCmva29fzLPP/olLL7X57W8VMhmnbM4pWYPCQigocKItTNOJt0CALDz09VLTcFUGFg9LtowLsm6Y4ZIcD7gtp/vNSOrcN+sBzh4+nctfvYhnm94gwsTvsgZywsBgms5x7H+9D+Ww2P9ne38uxsB4W6C1CKQLph0nqTpdMneupH+jSuJlQcAW5CxYav2E19x/x/DwLfT2DqAoFRQXf4Zw+O+IRKC9XfDb3yrceqtN6nzJ488KFhRJat3QF4K2dkFtjZwsjxMJUGICOzD1YKUPRJdEGRNY+wnc2aUSs8ZC3yoQGcchnJt1cKHiB8mSJSfxgx/8I7t2baOxsfnD21GePHny/BnRrZhs1rIEUSnOaUghMYEXXUkakgauo/CjdcV6eHDHY7RF92DaJoWeAs6qPZUVlSceUYDYK06efvp5/MM/fJ2//dvP8eCDv+Ff//VnzJhxiGlSgHC70ZefjP4eC3PlxB/Og45LAFJOxhiUeIu45fgb2TzcymByr5u1mQJ3+IjbT5gxNo++Q9KM49H8gKQ31UHCjHF84TLc6ru/rTelNXFIU49RVVQs20QMS9xvC7RWgfSAvWsQoXknhWAsk622yX0rX+Txh+8lNxHw//vfP0OoqZQHtj1M/55BJNA0NMQ5453UZKdhGB5QDHLFi5DdMbwRFSXVj+2rJF13McKU0LMWt+pmengaraM7GEoNk7YypM00AcPPcSWzJ69tqbeYlpHtbBlqZWZB47ue91463umk9flt5NImqqbSs6mPno29HH/FPAIlhxf9g0aAFZUnTn49ko6ye7ydnaO7GU2PMZoZI55LoCgabtUg7AqxsPQ4DNWgI9aFpqikzLQjJG/exTsPv0j3jnYy0QRzbz6DTKNBwPBT5ClkIDXIs+0vsap3DcWeQjrj3VT5K1lcPp+h5DC7xtrIWib9yUEM1UARwulhEQIpJTnbpNRbjJQSr+YhYSbpSwygKRo5O8d4dpw3ulcRcYd5pv1FPJoHt+bi9e5VtAxv59Nzr6Ex8i4NvhNIKemO97J7bA+KUJgRaaDEW8Tpp59LSUkZv/vdPZPPFwiFbPXZZMtXoKSHIOZHxCOIKMgDTCGFhcUMDw9O+d6KKsnOqGTzMEgEtoSADhdNs6nwH9Xh4t79EK6el5xoEn8tIhdD738Tj6KTnHMLtrsIaQQQmVEUO4M+uA6QIFRs3YU+uBYh/5P4/K+hZEZBWrA3a1so5IrmI6w0SnoILd6B7SkiVXcJueKF6EMbUFID2F5n2psVqEUb246wMqixNhAKlq+KVNMnka5D5OYBUnU7Lx4yoyjxDqTiIlt6Iunai/7b2onT6TQ33OBkd9911x9wuz+YItA8f7782YjBIyMjfOELX+Dxxx9HURQuv/xyfvSjH+H3H/5fkHQ6zZe//GUeeOABMpkM55xzDrfffjulpQdPNxoeHua4446ju7ub0dFRwuHwh3g2efK8NzYObuHelgcZTUdxqS5W96/nrd41fHLWlcwqbPrQ9y+EYFHp8SwsOc4pXxDK/703ihkwVgn0tQKRBnOWJLtMYhcdenGlC4x1AmUIrDIn9mGvSxIbtF2gtgmnLKxJYlXyrlPid+xo4Z//+TZuuOFmzjrrgsnvb9uu0h/VaJjuCJ/gCKc+H6xaJTjrLPm+4iI0zXGeHg4p4aGHFNauFTSWSVYEBSW7IWYLtpqSXS7o80PVIliyxKajQ5kUaufMccTb/feVTApAYprwwguC114TjI87+cPNzZK6OsmOHY671zQFmiaprJQMDjqRE7mcc+5uN+ze7Yy1Zs+WVFc7Qm9nJ/zpT4IZMyQLFizml7/8MRdc0EdPTzkvvaSgCKdI2W9Bsy1JtgnmnSt5t7GT2gHeXym4nhcsyEK5C942YasQjO6ERS4o3gxqn8CVDPJ48HGWJU7kr37/SR745DOIGufmtbXByy8Ltm8XeDyOs3fFConX68Qw1NRItm1z3NdCONe/p8e5jjU1Tmmb75eK82DodrKNjbUCdSdUL5BckILH4rDVJVFtiKb8nOH7IaVn/4DXtzrubZfLyYY2DEltraS9XbBrF1x4oWRoSPLaNrATAoYlFRWSK6/cF08h/WBHJMogWPvN+hQxkB6BXWAfdO3sSshUfnhO4AM5/vhFCCFYs+btvBicJ0+ePBO8qafoU0zmmMak+JjGZouWoUXLcLx55D+EiVySu7c+QNtYJ9X+CnRVZyA5xO+3P0rA8DO/ZO67HkNhYRH/8R+/4sorr+WrX/08Z511Ap/5zJf43Oe+TEHBYd7EHsCkyPsug59KfzkV/nI64900BOucAl4p6U30My1UO6Ug2at7WVy24Kj2v5e+ZBcJM0bE2L98zk00O8Rgupdq37sLeHXBagK6n6H0CMUe5/xtaTOcGuGCwJmE7nCh7RJIn0TkINQ6HzWQYzi4liffeZ379+xkfTJOmabz2ZnzaMWmMxFjzvHz+P76/2QkHaUxMg1VqGSaCtm+fjeujSspb5yPqruw+oegciH25VcSb6jF8lWA6mJGop+wK0RPoo/aYBV+w0fHeBd9yQHqQzV4NQ9BY6pYqwqVpHmInNTDkIqm2PnaLhRVoah+4txtm5H2EdpWtjPv0nf/PO2lwB3mU7Ov4mfr7mRHdDc+zUuBJ8J4ZhwFlaHUMAOpIUq9JcSzCeaXzGUoNYJX85BNZwjXFGMfH6D9l2+RzKVxCS9e3QtIslaOlBkja+fImBnGsuPkbBNd0dAUDV3RScokaRMyVgZVUSn2FOLTvZT5SrGx0YSKR3OTNjOMZEaxpU3OyuIzfHhUN8OZKIqi0lQwnQK3IzyWeUvYFt3Jix2vMT1cP+XZLGNl2TzUQtvYHlRFZWZBI43haTyx+zle7nqDWDYBSCLuMBdNO5sVlUu5/PJP8MAD/8U3v/mPU4vFTA/GUzW43lacbhMPZI+3SX9EThoYCguL2bmzdco1Dxpw0zybjYOCjnGJW4NZhZKGo0wEEekRjIFV2J7SSbFVGkEsXxX68AaURDe2r4psyWJcnc+hJnvASiM1jxPTEG7GClSjRVvRhzdhuwudvF4zCdrEbAXNg+WtJFe6hOTMT2N7y5ATn9tM1Rm42/7oOI91H0pmFDM8k3T9ZdiuMKguzEgzUj+MLmVl8Wy7G5GLkak5D4SKMJOo47txdT5DesbVR3ch3geWZXHrrTfR0rKJhx9+idLS8g99n3n+9/NnIwZfffXV9Pb28txzz5HL5fjUpz7FZz7zGe6///7DrvM3f/M3PPHEEzz44IOEQiFuueUWPvKRj/DGG28ctOyNN97IvHnz6O7u/jBPI0+e90zGyvL47mdJZBPMnMgKk1Kyc6yNJ3Y/x4xIw5TpYR8mQghUcQTb6l8qFngeELheF0gd0EDfrqBtkiQ/ax8kCGubwXuvgjIowJCQFbhelySut7HqwfOQwHhNQUyMh2UQ0ufbZM6UhxWEc7kcX/ziDVRW1vDNb/7TlJ9lMk5pmqZNFdP2unAt6/1lB78bPT1OKVllheTUNsG0GOz2QTwJJ6UFl0Ulr5VA/adszjxTMjJi8dZbgoceElPiJgYGYPt2QSYj+dnPFECyfr0gEIDCQkk87jh8ly61OfFEyebNgpERR+ju6hKoqhOR0NEhGBraK5QKQiFHJAVHLK+thdZWwebNgmXLTkEIwZYtz/B3f/dJCr3w+uMCTxzKEZgxqDHg4jJwPylQuwVKHMwZkuyJ+70MMMHzOwV9qyPwby6EO/qgZwCUEYFpSF4FvjkiKMg4EQploQp+VX43560+k19+64d85u6v0L4H7rjDyQQuKJAMDztlfJ2dkk99ykZV4aKLbIaGHBe2yyXJZp1lL77YEWSNFx1HsNkoYaKHQoyDa6WC0mtzaRgq6yVregXRDMzSYfEItI/BK5ZAVSWxGJMRG4oCpilJJgWFhZKbb7bZskUwOCgJBGDOHEnogIG9FXGOQ98MVqUjDou4IHuixHrvZeEfGIFAkBkzmlm7dhUf//h1/9OHkydPnjz/K9ipZQlIZYoL1Y2ChaRXMTn+XdbfMtzKnvEupofr0SfGptWBCnZEd/NW7+qjEoP3smzZKbzwwlp+8pPvcfvt/86vf/1zrr32Jj772b+mrOzQM+Ps0RGyr7yAtW41SFCPX4hxyukohYd+c2+oOhc3nMM9W39P6+gOXKqLtJWh2FPIhdPOft/j63guiiq0KSKdIhQEConc+FFto8JXxilVy3hmz4tEM2O4FIOYmaAmWMm57Sej7RKYM+Tk0/3uoX7uXf8Cv17/B0asDCf6g/wwVMypmRwikWJF5w6ua2hi04M/o698mMZIA6pQkUhGDZOHFkfoWd3D/F1vUuqKUDZtHp7zLkGZvxhrv+Mq95VyccO5PLLzKVpHdyIQBF1+Tq5aSl2wmgd3PEbOzqFPFGJlrRw2NvWhox8EjHZFSY2lKawtcKIWUkMkckmEDsr2HprTM9HdR186VxusZlZRE53xHiSSaHpsMpIhZ1tsHd5OLBtnRkEDV864hOf2vMLmoRayDTp1jfOZJhTaf/kWpp3Dr+47r0QugU/34dHcZO0cpm0zkBhkJDVKwPAzmo6iCoUSbxFFngJM26I73kdDqI4vzv80vYkB3JqLmZHpPLzzKR7f9TQe3UPGygCCkCtAPJsglosTcYUnz0cIQZG7gN1je0hb6cnovpSZ5jdbf8/q/g3Y0pmL9cKEYLx9dCdBI0hlxBEFexJ9PLLzKWqC1Vx//V/xq1/9hDvv/Clf+MLXJvfjfkrgflpgF0msahBxcL2sgLRJfdJ5diksLGJ4eOiga+7V4MRyyYmH0yCljd6/CqP3VZRUH1agnmzFqZiF81By42CmkJ5i1EQPSrIXYaWdrF5poeTGsUUV6carkZoX//p/A2yk6sIMz8QKNTgRD9JCSQ+RqTqDXMEc9MG1Ey813CjpAVBV0nWXYoWnOtbT9R/Bdhdh9LyMkhkhV3ICmcrTMYuOP6rPmza2DW18N2awYbIYTup+bE8xxsBbZOovRervvfjv3bBtm69+9fM89tiD/OIX93HccQs/tH3l+fPiz0IMbmlp4emnn+add95h0cSc4Z/85Cecf/75fP/736ei4uCBwNjYGHfeeSf3338/p59+OgC//vWvaW5u5q233uLEE/dNF/n5z39ONBrl29/+Nk899dR/z0nlyXOMdMd76Yn3UeHfl2cmhKDcV0pnrJu+xMB/W1zE/1W0beBapWBVSOSEycEyJfp2gbFSkL54PxE2C+5HFZQxMJvl5FRDbbvA/bhCdpnE9aLAKpbIOkCC0g/uJxTMBgvrMCaRH/3oX9i8eT2PPfbqQSV4FRVOLvDYGJOinJQwMiJYulTyLiXV75tEwsnqneWG6j4YC4O7WBIcg9FxQVMJfKpKop7sXKeCAjj9dMdtumaN4/gdH4eWFoHbDR4PrFsHra0KFRWSpgnzu8/nRE1s3apw2mkWb78t2LjREStjMcnIiGDOHDjxRJtYTCAErFolKSiQJJOOU1hRHKewEI5QXlxcyoIFS3jmmcf42Mc+yT9Ol2zwCN6UMOKSzPTAqaag/m2wdwisRqd0TdusoK+TJG6ysctAbQetXWAVg90Pvx+GfgtmewWKhFwQ0sPQo4PvBInQQIYly8KncGvmb/nHF77NiW+czvrdCyfL4fY+O8bjkrVrBSedBDNnwowZ8IUv2KxZI+jvFxQV2Sxc6DifAfRNjkOI/e67DALdoE+4hxaWwcIy536MjsLgi4LNXYJs1rkXpaUwc6ZEVZ0IkEBAUFHhOHrdbqeQ75DY4Pm9wNgAMgJiCLStIIOC5OU2qasO/8LjSIikEz9hh/jA4iMWLFjC2rVvfzAby5MnT56/AIJSpQtzyvfsibAED+/+VjmaGUfCpBC8F7/uoy8xcMzH43K5+MpXvs2nPvU57rzzp9x11+38+te387GPXcfnP/9lamv3DZpkPEb6rv/E3LoJEY4AAuuJR7B3tOL+qy+ihMKH3Mecoma+MP/TrO7bwGBqiDJfCQtLj6PSf2wOOqunC6tlKzKbQa2uQW2ahUvzYqWtKctJKZHYGMrRTdUWQnBRwzlUBSpYN7CJscw4jZFpLClfSOnzEeygTVuincd3/JGHtj3Iuv7VBIWPj+gNfKRWMM3MIUdHobiYrbZJzMxx8sy5JLesw/YFUQqc+9qfGGT94CbiPou+02sY0GrIpBPMnzuXq+aecMhjW1F5IvWhGlqGt5Oxs1T5K5hV2ETGyrBpqIWWke2EXU6x3Fg2xtyimcf0QkAIgSUtOmJdbI/uImGmUIVAjal4DQ81ozXMKT+22T2WlPQk+iYiGbwE9ACWtDHtFLXBSq5p/ijHl8wlaAT49Nxr2DPexXB6hLAriILCH/h3JJA203g1D+PZOKZtYag6AqfcL5GL41bdCKE4Dl/bxMIx1MSyThZwLBunM96DruicX3/m5DNeY6SeqmDlZFngXlb3rydppjClhS72/X5lrRxe3TPlxcU7fet4u38dNf7KCfcyDKdGeLb9ZcLuIA2h+sllK3xltI7uYMtQKxdMO4trr/0MP/vZ97nmmpuIRAoQMWdmpF3g9DwAyAKwhMRYr5A5x8Iuc5zBIyNDTrEdoMb3oCR6kJoHM9y8L5rhAFwdT+He+QACia35MfpWog9vIjnrM5iRWUjNi9HzKmqyD2EmQDpjUctdhBLvhMgspO4jPeMa1OgOjJGN5CKzQZ0YLFpZQGArEYxVBmz6LNgPoEXWIyNjWIEiMjXnkys83il204OgTBifFJVs5WlkK0514iUO8XJI5BKo8Q6kamD56/atCwgz7WQLK1PLN6XqQjGTYKXhQxKDpZR885tf5IEH/osf/ehOLrzw8g9lP3n+PPmzEIPffPNNwuHwpBAMcOaZZ6IoCqtWreKyyy47aJ01a9aQy+U488wzJ783c+ZMampqePPNNyfF4K1bt/Ld736XVatWsXv37qM6nkwmQyaTmfx6fPzo3ujmyfN+EIhJN/D+7G2R/T8X1/A/gNohIMOkEAyABnZAom0WsJ8YrHaC1i0wK/cTvQRYFRJtj0BMPBPI8L6f2aWgtYDeKrCmHSyy7djRyo9+9C984QtfY/78gwfk9fVwWpNN/EmF6WnIeGCdAcWNklNOOXhK/gdNcTEEAhK7H1w5GAmCKpzIh/JySe1ciZEVjMedASQ4guJ119nMmOEIwm+9JSgpkSxZ4ojBsZjjNh4ehlRqXwleOOyU1N13n+CJJxR03XEGW5YjgG/d6ixTXCzZswc0TdDdrZBMyol8YklBgVMuVz7xnHfOORfxwx/+E+m+FOHVfla4YHkJMDF2EwmJ0uZk71pl0hFWLYnWKnC9Ikh9TCIyQA7sEkm3otCehGr3xEfABDUpKC2VJIcF0QKbYMG+6/fNBX/PC13Pc/PfXsuyk1cTDvumxIj5/dDdDT09jkgMUFHhvAQA6bifOkBsBLsIpIZTQrg/Ex8rqwjU3olsiYl9RIIQmi4ZWQi9aSYFeoCODkc0P+ssm7Kyd/8sqLvAeEvBKpOYTc6UVREHtV0giw/4HToakuB+VmCsEoi0wC6QZE6VZJdLjkKXOCILFizhd7+7m0Qijs93lOF1efLkyfMXzKKcmy1ahiFhUSgVbKBDNSmyVWbn3v1NXIE7jMARqAx13xvJWC5OU8H093xchYXFfPWr3+Gv/upW7r77P7njjh9x//13cfHFH+XjH7+eZctOwV6/BrN1C2pjE0J3jlWWlGDu2Ia55h2M08867ParA5VUB46Qh/Uu5N54lfQjDyJHR52/35qOtvAESq68mD6lg1guik8LApJYbgy36qXEc/RisyIUFpYex8LSfV0dHV3tfHf13/Nk62NsiW3EUF2cWnMGvz7/fs7dWE82/CeSobex+nsR4QKUyiq2t21DADNrp9HbtQV33Ik0CLmCtI3vIWeZGKpOZaCcSMlMRtNR3h7exMnxkw9pPBFCHPLauVSDG+dew+vdb7FuYBNCCM6oPZmTKpe8a5ng/gz4h9iW28lQ6zCD7hG8uocCI0I4F2SgcpiH9zxBQ3E9nsMIjYdCEwqJbIJibzHeCSetEIKsnWV20SxOrlo25brXh2qoDznTy/Y+i0VcYXrTo/TEe8naJrZtMZAcJuwOoiDwaB4yVg5D0VGEiiIUNKESy8UZzzol3IaqM5Aa4quvfodLG8/n6plX4Dd8zIhMJ6D7GEgNUeIpQghBPJvAo7mZFqplz3gn9cEaVEUlkUsSzYxxavVydEXHHh3BXL2KzKu/YZk9BvO8jNQ7TohCTwFJM4nrgN/jvc+SaSsNwJe+9HUeeOC/uP3273Pbbf+MGAORENjFBzyHBpwX/koU7DKIRAowTZP42DAlfY9j9L6OyMVBUTADdaRm3oAVmuq8FZlRXB1PITUPlq8KANtXgRrdjmvPn8gVzUfqAdT4HrBz2IoHBAgzhbDSeHb9AbPwuMlM32zNuWixNtRUH5an1Mn2jXdgBmagPTMP1yoBogip3AxyAHNOisQn/LiiLxF4+xsoZgrLW0Gm5hxyJUv2ZfoK4cRLTLkAEqP3FVxtj6GmBpCKihWcTmrGJ7CCTkuy5a9CuiIo6UFsz76pkGpqwImXOEzO8PtFSsm3v/1l7rnnDv793+/giiuu+VD2k+fPlz8LMbivr4+SAyrrNU2joKCAvr6+w65jGMZB2b+lpaWT62QyGa666ir+7d/+jZqamqMWg//lX/6F73znO8d+InnyvA+qAuVUBypoG+tgesjJg7KlTU+ij+aCxil5Znk+JHQcMU0y1dVo4oTLHgtpnKiJ/RET/+191yRB6QVtIlP4337+95SVVfKlL33jkJvUeuGaPsGYCj0SxCgc5we1ThJqOMbjew9EIrBihWTtbwVjGZBxGLcdgXbGDDAyjnAuD3j57fPBmWdK5s+X9PereL1Mir4ulyMwR6MwPr7v+7GYIzK//rqClJLiYmeMZprOf5mMExkxOurETui6s51EQpBMSvr7BeXlMGuWZGAAXnlFcMIJF5FK3cbrr77AFdlLnHuxv9CYBWxnppmwJ3RVFewCibpRED1b4gqD7pOMtAu2qpLxtKDAdjYjFZBByWCjJBqHxn5grxgswTPo4ldX3suKe05g06Zv09Dw71OukyN0HzqzWIyC93cK2haBSDmDc9snHSdtEuTEM5cy6GT5Zs61cT+qoO10sqyxQe0RWDMkcz9lMyfkFPi9/LJCd7cj9C9fLjnpJMnRvHfSOp1MbbnXoa47DmFSEy9OrjwGZ7AEzx8ErlcU7IjEDkqUAfDcJ0BA9qSpDyZiFLQOgVQlZgPwLr08CxacgG3bbNiwhmXLTjnKg8qTJ0+ev1wW5dz0KCavG0m2KRYCKLFVPpIOUCzf/fFxdmETDRNT0St8ZeiKkxnsUd0sLT+0u/RYCAZDfOELX+XGG2/ht7/9NXfe+VMefvgBSkvLuWDGLM5H5Tht3yBLaDrCMLB274QjiMHvB7uvl8yjfwDTRJ05yzFwJOLkVq3EV9/AjKXzaIu1MpYdBgQ+LcD04Gz8+lEGp05gWRZbtmzgtdde5JlnHmf16jdRVY3lgRX87fz/x8yKuQSMAIvt+XgLukmWxdDnngRrV2P3doGAbZk0tf4gXt2gJmOwSKtgZXKAodQw/clBLNsi5ApRF3SmGoVdIfqTg/S+h1mIBe4wFzecy0XTzgE4ZvPKcGqEB7seI9YQQ1+rUjZejFQkWTtLoiJF4PggG4a28LP1d1LpL2dGpIF5xbMmYykOh0/3UeQpnHDwJgGJpmhU+sqwLJOh1DBbh7eRMtOU+UppLpgx+WJj7zkcVzKblLuVeKzHGcILBbCxbJO4maHEU8RoJopHc+NWDUJGAI/mxqW50BWdtJlmNBPFUAx6Ev3cvfV3jGdifGbeJ6kLVnN+/Zk82fY8LaM7EDhO+xWVJzK/9Dge2v4YO8Yc7UIXGieUzee06pOwB/pJ/ep2rN07CSe7cKXGcbeNs+fE2XQsnQNC4Nd9ZOwslm2hTjhYM1YWJFT5nftbXFzKZz7zRX7xix9yww03UxGuRAYkYmzfmBJAGXOKge2J8axtOy4ET/+ruDqewvKWI/01YGfRxnfhbbmT2KJv78vqBdR4J0p6GPMAkdj2lqHGu1AmMoAtPYyaG3Pi3xQN21cJ0kaNtaENbyA7IQbnSk8knR3D1fkUWqwdqRjkiuZjZq/F844Pq5KJZxEB6VK09RJ/9S9QPS9gu8LYqhd1bAfeLe0kEeRKlxz2c6QNr8fTejcIMAO1CDuHNroZ79Y7iC/4JtIIYXvLyVSchrv9EUQuhtSc3GGp+8nUnO88WHzASCn57ne/zp13/pTvfe9nXHXV9R/4PvL8+fM/KgZ//etf53vf+94Rl2lpafnQ9v+Nb3yD5uZmrrnm2N6SfOMb3+DWW2+d/Hp8fJzqvfNy8+T5kNAVnUsazuOerb+nZXQ7mtAwpUmFr4yLGs5F+RD+kOSZSq5J4g47Aq1djvNWOg4iA9kFUwUpqwrMConWDeY0JmIiHDemOVOSmynR/qCAuS/fjRSgSqwawAbXUwL3CwpiXLJmdDV/evOP/Py0Owk/4sacAbk5UyMAXC8LjB5BZIUkojimT3UExDaFeKflbPdD5rzzJF4XDN4uqO4FPQJlTVDrl4gRyJ4rJ522B2IYoKqSXG7q90pLYXDQEYCLipz/9/QIqqslLS1OudreZwtNgbAuGE1KQkhiScHwsMDjkYT8UFIgGU86URqJBPT2Cv74R2flSGQWFRUzePrZx/lo4hKUIVDGBFapEwsiMiCR2GGw93O2jo/AjhHBL/5BxbQkzdvh1D7wC4FXwLYkzPWAa4YkN1eyaxDqj5OcrArUVpxyt5TALpNUX93IlyJf59/+7R8Ihz9HKDSdUMgpvNuzx7kWzc0HuMYleB5U0N8WWNVOiYeIgtolsP0StRswhWMe9kP6XJvscrDDNu6nFNQuAQrk5kvSl9qTAu68eTB3rk06vffeHP3nYFIrsJkiqAsTbA/HFBGhdIOx1nEZywnzhBUAdQ8YLwuyS+TkixrXiwLXswJlGFAVrEpJ6iM25pzDb3/GjFm43W62bNmQF4Pz5MmTB1ARXJrxszjnpkM10aVghqUTlEf3h8Cjebhu1sd4dOdTbBvdiWmblPlKOKfuNGZ/gIXHXq+XG2+8mRtu+Dzr1r3Dww8/wCO/v4e7YuPUb17DRTPncu6MWTQWliAtE3GE4vH3i7ltK3J0BKVp1r44N58f4fFhvbOKijPOochVxnhuFBCEjQK0dxEswRF1duxo4bXXXuKNN17izTdfZWwsisfjZcmyk1h288U0zJ3F5W+fS+32cpQRhWQ2SW+4D+MiG3PzEGLIRikuxu7agz04yA4zy8zScuzYOIqm87E5V1BflOONnrfZProbv8vL3KLZRNzOgCBrZ9EUDa/+Lm9Xj8B7ncG4ebiVvsQAtXOqWKtsJDwYxGN6GHIPo9UZiJxgV7QdkIRdIV7peoOl5Yv5RPNHGEwOs6p3LbvH2gm7giwsPZ7jimcjhCBo+Knwl1HkKWQ0HQUhKHCHGUlHSZopfrDm5wwkhxAIFEVhXtEsPjnrYwSMfZ+htrE9FJUVYts2YVeQjJWlK96LJSU5O0vCTDKvaDbTw/UYqs47fevpSw4Qy8bJWFnGszFUoaIpKqadI5lNsrp/PXN7ZnFu/emcVXsqjZEGto/uJGeb1AWrmVnQiKZo1AWraBneTtJMUeYrpWmiOyb98iNYu3agzmjGkwiwa6iFypRK1ZptDM2ooS+oUOotJugK0jq6kwJ3GFvajGXHmVs0i3nFsybPb68D/3vf+zb/8R93kl0mcT/ivIi3Q6DEQRkSdJ02SIvdgTqgMJ50Zkz7h99C6n6ke6LsUXVhhhrRxnaij2whV7LvpZBUXaBoCDuL3C+CQVhZUHSkUBHSBMOPpXuRms+JYRAqSqITJdaBb/PPMAbeJlt+Ctmy5WRqziVbthw10YVU3ViBWjx/UMGciFDbixuEtw2jbxXZOZWTLl3LXYA2vAnvpp+QHd6A7S4hV7IQ2z9V8zF6X0fYaScCY+JczFATWnQb+tB6shXOuDLdcIWTEdz7Ckp61MkdrjoDs/A4PmiklPzjP36D//zPH/IP//BDrr32pg98H3n+MvgfFYO//OUvc/311x9xmWnTplFWVsbAwNR8KdM0GRkZoeww81XLysrIZrNEo9Ep7uD+/v7JdV588UU2bdrEH/7wB2DflI+ioiJuu+22w7p/XS4XLtdh1Iw8eT5EZhY08qUFn2H9wCaGU6MUeQuZXzyHYu+hCzHyfLDYVZC62MbzmIK2tyhXh+xSp0RsCi5IX2zj/Y2C1iqQhkTJCKwySfpCG6sY9I1O3rAdkI7jNCnILZLk5kj0TQL3EwLpl1iN8Pf3fItZ2iw+tf2TyEcVZFCSWQqpT0wIYSboW5zp83vFNyGcN/XaoDM936o5TL7rB4iuw5nnSMx5FvpvFfy7BWoWpC2ccrzTDn8MoRRc6Id1awWaKkkXOG5Yw3AEUJdLsGsXeL2wbJlk2jTJhg1OhEQu53T0KX0CcxzSGUFqF5QVQdQlaRICTwd43VDQJNnog9ZuWLRIIiUMDwva2wU+5SKeeeFe7AUW0quhjIKWANsLqEBAYBfuu8bRPujbInitBNweR5x+pRv6iuHioOScEbh3SPCkS9JYIrF6nJK3mZ+0SRmgrxEowwKr2ia7WGJXwKc//UXuvvsOhodvo7Dw9ziTWSRlZXDllTaRA2aTqd3Ovbeq9mVZywjYOSc6InmVRBl0XjrkmiTWdBwHwxyIz7RRBpxzs0s4SKQVYp8b+1gwmyUyINHXCWTQcZHICady7oRj+xyqQwIRl8hKII3jOHaDHQF1WKCMg10I2ibwPKxgGxKzEbAl2h7w3q8Q/xt7MuPuoO2rKrW102hr23XsJ5onT548f6EIBJW2TqX93goHynwlfGbeJxlIDZGzchR7i3CpH1DY+wEIIViwYDELFizmtqtu4KXvfJ3Hu9q5c/VKfvzmy0Rcbk4oLGFZdTXLt2yguXkuygfdqGuazsSxA0VPTUNmnKn3huqiSD1y1lI8HqO1dQtbtmxg1arXeeONlxkc7EfXdRYuPJGbbvoiy5efxvz5J7B9fBc/WnsHRZESVl6wkbbZPRT0B4mJOK3lbdx4ztWEXjif3OsvY4+PgeGCZILWdIrrVB3Z34d+0im45i7gVE3j1OrlzCuaxSO7nkKZiKbL2jl2j3UwPVxHY/gwhRYfIvFsAjERuaCXGrQFOilwRYjl4khsEtEEYVeIeUWz8WgeYtk4K3veptATYVXvGnoT/fg0L9utDGsHNnFxwzmcU3c6c4qaKep4lWQuyfTwNBQh6E8OoiDoiveiCoWmyHQUoZAy06wd2Eh1oJKLG86dPLZELolP+NFVDV010FWDCn8Zpm3i0zwYmovqQAWaojKUHqHUV8xJlUt4bPczxLJxDEUn5AqiCpWMlSNtpbFsiw2Dmzmn7jR6E/3k7BwnlM2nwD118Bd2hVhaMdVlLy0La9N6lIJChKZR6S9nIDlItxykomec2PaN9M2q4LSaFZxefRKvd69i49BWNKFwWvVJnFy1dLJ8DhwH/m23/Qtf+cpnOeeciznv7EtAgrFSoPULLI/Nw6e9yJPlLxPdNIYAhre1o6gqhpVAagdEgSg6SNuJjdgPK9iAGWxAi7ZghpqcTF4rjZLsIVN1JtJbgRmeiT6wGmGlkUoYhEBJDaKmR7E1D1J1o0a34x1tQaSHyUy7DGkEMI19WdLyML/yQusFkUQa+0oNlcwo6vhulOwYwkqDELi6niU56ybMovmTy6mJHqQ29SWTsFIoqT6Mruew3QWY4VmgaGSrziBbebqTOyxUjmqq3TGSzWb5ylc+yx/+cB/f/e4PuPHGmz/wfeT5y+F/VAwuLi6muPgwT2f7sXTpUqLRKGvWrGHhQqf98MUXX8S2bZYsObRtf+HChei6zgsvvMDllztB2du2baOjo4OlS5cC8NBDD5FKpSbXeeedd7jhhht47bXXaGj4b5hTnSfPe6DUW8w5dad/aNuPDcQY2TOKtCXhyhChylA+j3g/sqdIrOkWWosAE6xqiTmTQ/5ras6DxBdt9LUCZRDsMkl2gXRcxUDiJhvXGwJto3CEukU22WWOc1ZbB8IUWCWS19a9xAujz/P7+odQVAUzILFKwbVSYM6B3EJHnJQ6EE+T6d+KnY2hekvRQ42Aus9BnAFjtUDb5OQW52ZLciccHN3wftHKQf6NTbLLcU/bJY5gd0gkuF4SuJ9UOK9fMisKfS8I3iyGt0slFZWSa66xCYf3lphBZSW0t0NZmRMLERsHhgUyDTEhCXqhsQREVFA8LinSwdYgkwbRLrBjEIlIensFbW1isvxOiV7KcO7febLkVc6bfiraVoHa4Rxm+hyJOcfGWOcI/ACJTtjshfiJkoAGySQUFsPLFsQboGyZpH5UEl0nKC6GhQttFi2S1NQ46SJm097ckX14vV6+/vXv8KUv3cjnP/86kcgyXC6nyG1vMeD+iHGcaIgDZm5KHyjDArPexl5+uBsF9ofQOymSQAq03TjxGgKkX5C6RJJZfmxisO2XoCvo60EZApEDaYDtA6tROkI9zueaLNh1Eyuqjitf2ybQNwkypx9+v3V1DbS358XgPHny5PkgEUJQ6n33Z70PElfzbE6/6RZOevpPfGdkiLWjw7wdH2d1Ns0//+rHZG//PuFwhMWLl7Nw4YlMnz6DadMaqa1twH2oHKajRKmpQ7g92GPRyZI6aVnIsVHU5SsOWt62bTo62mhp2cSWLRtpadnE1q2b2LPHmfqvqipz587nYx/7JMuXn8YJJyw7qDjYrbkwVIOMlUHRFboaB+hqHGAwOYRE4vH4cF32UfQlS7E7O5BAf283Yzdfw6xlJ+O+4hq0eccjtH2D2DNrT2YoPcK6/o30JvtRhEJDuI7Lpl/AO/3r2Bltw1AMZhfOYHbhTOysTXwwgaopBEoDCOWDfWYo8RYjhCBn52gI1RHLxhhOj5Ay0xOReRYzIg2TIqZP92LaJn/a9Sw5mWNmpHFy9mRfYoDn97zKgpJ5VPjL+PjMy/jjjifYPdaGLSURd4jjimezbmAj9eGGyfU8mpuwEWJl21vUJIvQJnJjU0MxPL1BUlaccEWIiYYXvJqH6kAlNcEqMlaGwdQwYVeIC6edzSlVy9gyso3h1LBzn4WKLW0saWKoBslcioyV5ddb7mfTUAspM03A8LO0fBEXTjtnSgb3QQgBQkHaznjHpRosKJlHT6yH3PBWmgoaOW3OZRxfMgdd0bmy6RKumHERwGFnmF511fU899yf+Nu//RwLX1xCyUVlZE6RKKOwztzCQ3uewqN6mOFtwJaS3dH16D4XHa4S6sZap2TkiuwYUnNj+Q7IyVY0Uk3X4t16B9rYtok4PoVc0XFkpl0OQpCpORd9aB1630rURAdIBZEdQQoVq2AOZmQWCAUl2Yur61myFSuQ7qlmKbNJwgvO7Lm9nS0iDtLyQ0gFKzNZcKeNbkXJRrE8ZZiR2SAUtPEdeHb+jlhkFqiOMdAM1OCK7YsaVWN70IY3oqQHMISGFttDpnQpqZk3ONs+VO7wB0Q8HuOmmz7OypUvc/vt93LppR/7UPaT5y+HP4vM4ObmZs4991xuuukmfvGLX5DL5bjlllv4+Mc/TkWF8xTb3d3NGWecwT333MPixYsJhULceOON3HrrrRQUFBAMBvnCF77A0qVLJ8vjDhR8h4aGJvd3YNZwnjx/6UgpaV+1h52v7iYTy4AA3a1Te0I1M05vRFHzMRSAUwJXBVbV0QlaR1pWFkL6YjmleG4vyrhA6s73f7bpRxynz+E8zwJkPA5ZLzIgoEegtUpyCwEFUvXtZF74L7LKbqRiIxQDt2s+gebryc3wQRa8v1Ew3gJU5w25vk6Q2yhI3mi/f0E4B/omgbbD+dJsmoiyeJe/NOpOcD+qIFWJMg+qZ0lC2wTTxmHp6ZLqCyR7/0kuKXGKzB57FLa+3IPeEUNGi3EZxfiArFfSNCGYej3QmJEwLNiuSUp0gW1DlyEhA4tseG0XZDKSZFKAJbGtZeiikW+9cidLPnEq4VMc17a2HdKfkOSOk2RPsZxs3gw8+oxgiwHFbpAT8Ra6DrmcIB6XUOYU2VVWSi65RLJ48dF9bi6//GruuOPH/OpXX+Wxx1494gsZe6KQTRl1iuP2ooyCHZbYx9hLEY87RXW6DjU1TjbzMWGB+yEFYQrSZzkPDCLrCPN4eNcM34M2Vw/SBH2LUzxo+52MOq0fzFn7tqcMCaT7gOu71yU/1YQyiYg7YnGDazpPbnns4DzwPHny5MnzZ4UQAteZ56LNnod713ZOk5IzGxpRyitJp9OsW/c2b775Km+99Ro//em/EouNT65XVVVLQ4MjDtfXT6egoJBQKEwgECIYDBIMhgkGQ3i9vil/l6WUWNW1ZOcvJPnay+S6O0krgqHhIUbDIcb6uxn6wT/S09NFd3cn3d0ddHd3kkolASgoKGLWrHmce+7FNDfPobl5LjNmzHrXmah1QafUrHVkJw2hOgxVJ5lLMpAe5qyaUwkaznQhtbIatdKZ3j66cS0A9Vddhz53/kHb9Ggerp/1cU6tWk5/cgCf7qXCV859rX9g0+BWFKFgI3mt+03m23OYvq2W9EgGRRNEqiPMPGsGwbLg+76Pe5lT1ERTZDpbh7dR7CmkMTyNbaO7MDSDYnchpm3SEKrDkhYdsW46Y930JfqRwPETkRB7KfEWsX10F+3jnRR7i1hQMo/G8DR2RtuwpEV9sIbto7tYN7gJ9QBxVFd0+na2cf4n9xXLtfz2DQAiS2pwXeXDo3uI5xJEXCFqg1V8acFnUIVG0kxS4A5PCtYLS+bRMrydaGaMWDaOpqh4NDeaopGzc4xlY7R3d1LpL6PUW0w0PcZT7S/gUl1cMO3wmddCUVAXLMJ6/GFkUTHCMDBUg+qkhqibz9yzb0Ypmdox824xg0IIvv/9X3D66Qu49dbPcO+9j0JQYAVhzZaN5GyT+mDJxLbAk9PRvAZrPDXUJrvRoi3Y7mIw0yjZUbIVp2KFZhy0HyvYQHzBt9BGNqJkx7DdxeQK5k6Ks1aokfiib+Pa9RCu7udR0iMoAsxwM2bRvMncXdtTijbaghrvxDxQDG6GzCk2rlcU6MUZ72mS9OKZqLXT0cZbMIMNCNtESfQiFR07UOtEUgCmvwYt1oE2vssRn4Fc+QqMwbWoYzuQehB9cA0iF8Py15ErWwpmGlfPK9ihBjLV5xzxWr8furr2cN11l9HZuYf77vsTJ5102oe2rzx/OfxZiMEA9913H7fccgtnnHEGiqJw+eWX8+Mf/3jy57lcjm3btpFMJie/98Mf/nBy2UwmwznnnMPtt9/+P3H4efL8ryfaNcaOl3aiqAqF9U4LQHosTdub7YQrQ5TNOvK0tjwfLOZ0ib5eoXdgF88MPcX3XDeS610HuSqSvbsxGs5Ao8DJZAVkLsd41z1g7MRlTUdIAzsdI51ciVlWhF74MfR1AuMdR6CeFH4zoG8A/R1B9tT3ESORdQq9XKuEk08LuF6SZE6C1MePLAjrmwQiDtbETC5Nh4I5Em2boEiBVHjfslLCw/dESf/+dyzJbMBNkqFskPWpk0i4P8LJc1wsrpL81yZB1zgsk/BRHe71wJ6k48Zt1OHcMvCMwfN+xxWsKKBrApcOBfZNbI9+iz/t/A+umVMIOUATSN252FYNk5EbY52CsS1QjCOeFhY62b5CSIyJ2bADAxAOC6ZNs4/6ciqKwm23/TOf+MQFrFz5CsuXn3rYZe1iyC62cT0rIOfkAitRJ04he6E8avFVSnjtNcEzzzg5y6oKdXWSK66wqa8/6kNH7QRt90R+sQ+ssPN9EZVoOwTKANjH0HcpooDLcTCLtECZ+Nqqk4gUTplJCKxaibZtIix777NfDlCkE4FxANpGJ2tZ7Ydpu2ro7enC9UfIXMKf0egoT548efIcCrW8ArV86tQXj8fDsmWnTObDSykZHh5k167t7Nq1g927t7N79w5ef/1FfvObX5LNZg+9bVXF6/Vhmia5XBbTNI94LOKZRyksLKayspqKiipOPfVsKiuraWqaRXPzXEpKyt7TLDxN0fh402Xc2/Ig7WMd2NJGV3UWlszj/PozDrnO6OgIAJFIwSF/Do4AWB9yhGaA5zteZcPgFhpCtbgmHJHdfT08vftFLkydRWNpPXbOZmD7INlklsXXLsLwHhwJkk1mHRexoRI8ShexR/PwqTlX8XTbi2wY2oKhGlzUcA5n157KWGacX226l1guzo7R3XTEutCEikDBtLNsHd5O0AhOlmzb0inB3VuaBhAw/MwvmTv5dY2Vwa/7GEmPUuhxrpGUkqH0MCefdAb/1XPX5LIvdrzGY7uepifRT3/ScWSHjCDLKxfzsabLKPI4U+IiTJ3WtbTiBNYPbGIgNURfYhAATVHJWllmFM8jZaao9lcQdDlifrG3iJxt8mbvO5xec9KUKIcDMU45A3v3TszWrTjhvhYiEMS4+CMHCcFHS2FhMT/4wR1ce+0l3HPPHVx33WcBGE1HcSlTX1jEhqO4wl5inlIScz6Hq+MZtPgebN1HuuYcMtXnHrYwTRoBcmWHm8oGVqCO5PFfJnn8lxHpEQJvfwsQyP2zt60MqPqkc3cKKqSukORmW2jbBdhgTYfcbB0lcyPelrvQxnYgcnGEncX012KGpu+3gYkCGLnvecksmENy1k242h51hGAzjhVqJFcwB6kYYBhIzY3e9+aHJgavXfs2n/rU5bjdHh5//FWammZ/KPvJ85fHn83jTkFBAffff/9hf15XVzeZ+bsXt9vNz372M372s58d1T5OPfXUg7aRJ8//FQZ3DZFJZClu2PcW1RP2kBpL0b9tIC8G/zeTWywx3rF54KE70FH5mH4lLruSbKCDeOJPuDdFcYU+jTnRw2Lt3onVsxvl1DpyMR1lXCI1P7YoIhNdhZa8EHWXH3IHFCe4nCn3+tb3Jwbr6wWutwRWOUi/sx0RA9cbAnM2xJqdYji//+CILJEE1IP3LTWJiDkDL9OEt98WPPcs+B++j+PsldhlVaT85RSkRzl14HH6VS8fjVyMEoZzp0l+vVHQNg5NiuT8APS4JBU1sHS2JLIL7t0uiEblRC6xwDSh2AfNqevYmfsWv916L9c0/zXaHoFZIzEPkR60ZIlk61ZBfz8UF0N1taS93dlWOi3Ztk3gcsHFF9sUHRjtbYK+VqCvFyhxZ/padpFEGQcRF5zWcCaNjTO5++5fHFEMBkhdIpFeMN4SKGMCOyLJrpBkjuGebtgguOsuhXhcEgpJdB1aWgT33qvw139tEzxKo4/IOUVx8sCZjBPZ1iJ3qLUOjzIKwoLMcomSlJB1XmZIDbRegTIKVsjJ7tbXS7TtTryKsEAZEJhNTnHflG0Og/cBBTHqREmUUUWmJUPi8RG8FYVkl+bHAnny5Mnzl44QgqKiEoqKSliy5KQpP5NSkkwmGB8fY3w8yvj4+MT/xxgfHyOZTKDrOrpuoOsGhqGjaTqG4XztcrkpKiqmuLiUgoIi1GNpYT0GqgIV/PWCz7JtZCexXJwiTwGN4WloyqEf86NRRwwOhY5+2tCGgc14Nc+kEAygDCtkZIbxopizLwMKaiKMdo4yuHOIynn7hHgpJR1rumhb2UYqmkJRFSI1EWae3USwNHCoXU6hwB3hE82Xc0nuPHJ2jpARRAhB2sxQ5i3h2T0vM5QanswWnhGZhlfzsnm4hR2juyn1FiMQdMa6KfGWHDH7uMJXxkmVS3huz8tEM+O4NRdj2XHKvCWcVj017uO06pOoCVayeaiVWDaOX/cxu2gmDaG6KYLzgcwrnsWKqmWs7Hkbr+5lPBNDIjmhbD7n1Z3BXZvvn1JUB45oPZYdJ5ZNHFEMViIFeD77Bcz1a7G6OhBeH9rsuSj17y8C84wzzuO66/6K//f/vsz06U0sX34q9cFatgy1Ykt70mE82NZDaFYppd5izKL5mIXHI3JxpGocWqB9j0h3AbniRbg7nsQ2gqB5wM6hxXZjhpswQ42HXlF1OjPMOVPHebZeQ3zhbWjRVkQ2hqvtEbRYu5PtCyAlarwTy1eBGZz6+cmVLCZXeDzunffj2f1HcsWLpu5TMRCmY1hUY+3o/W+jpAewvFXkShdj+95bXpuUknvuuYO///uvMHfuAu6660GKig7hfsiT5zD82YjBefLk+XCxstYhXQmKqpBLHaN6k+d9YxfB2EVd3PNf93JR8BSCgQBpaytp3y40q4DcnnWkrhokd9xEEG8q6bSoed3YPrAntHs55kYm4sh0Gg4oONiLkIcQ7o4RrQWQYlIIBie6YHyP4LH7YaUuyOUENTWSM8+0ad7X54BVDVg4Ls69x2GCyAqsBhsp4ZFHBM89J9D725mZ2ESnUYM5HKLcAOktQQ/mCMZeZXzkTAozXpb5wVMkedsSjCcFTYpk/iKoqpaowzCeg45KiTmkkM06zuCSEonHC5muYqqsj7C5/5eo27+EVQnpK22YGMNalrO8EE4B3eAgvPSSwo4dEkURnHSSpL5eks0KQiGb+fMlc/eZTiYuDrgfEbifF47JwABttYLnDic+BEUi/Rqfbv4stz35t/T19VBWdoTBohvSF0kyZ0gnoznofO9okRLuuUewdSu43YKBAeccy8qgrQ02bxYsW3aU0SiVYBVL1P6JezuB0g9WpcQ6xnGqDDsFdEp8au60Mgi2V2JPGG6sGkjeYON6WkFrF0hFkjnFJnPewZnY2hbHoWzOABSoCFQC0JXtYvbbRXkxOE+ePB8KOSRv6Sne1lPEFEmjabAi66HmPZa05fnwEELg8/nx+fyUl1f+Tx/OEfFobo4vmXNUy6bTTpmdx+N9lyWPTDaVRahTnxsUzREE0+PpKd/vb+mn5ekWFFUhUBrEzlkM7Bgkl8qx+NpF6J6j+/z79KnHvGusnZF0lAJXhHg2gWciUiBr55gVqmYoPUJfcoBNQy3oqk6RO8JHGi84SGgFSJlpNg5uYUe0DQVYUbmUwdQwsWycxeULWF6xmOrA1M+BEILp4WlMP4ZivYyVZdPQVjyai+aCGUghKfEUMbOgkblFsxhMDeHSXOyI7sanewkaAfyGj7HsOEEjMBn9cSSEz4++/OTJIbW0JbG+GGbWxFfow+V/b6Lsd77zfdrbd3HDDVfwxz++wIl1C1k7sIHW0R2UeIrIZDKM9gyy6LLTmFc84U4VAnkUx/xeSNdfipIeQB/eCLblRPkF6kk1XeeU1R0rio5Z4AzYbXcBvi0/RxvdjFTdTnGdq4D0tCsc4flAVAOzeBGy6wUnG9mYGJxKG5EZJVdxCvrgGjwtv0JJDyEVF4adwdX7EonZn8cKNx3ToY6NRfnKVz7LE088zPXXf45vf/t77yvzPM//TfJicJ48eQAIlgUQCphZE81w/mmwLRszY1JQd/ipZHk+PFa1v8auTD9/f24Zg7XPoPZLxCDYWQ+WOkbywjiqy1HHlPJKCIWQw0OIon1lLXJoEHVaAyIUxpwhwQ1iFOSEIUTEARvMue9T/LJxVOX9yFnwqw54s1sQOk5iGJKNGwUdHSqf+YxF48RL+9wCiblKoLcIrAJnG8qowJzuFO51d8PrrysUFEhKieJrSRJz15LNQjTqCJZxAvh9UeSl41g7vSgjcNxymLnCxsqA/zGBOghsgzEh+H0OWkOSujrJ7t2CXE4SjYJlCUSDpHT803RuO5Pnl7zK4itXIMOOKPryy4IdOwQeD5x4omTFCsnZZ0siEYu2NkFZmROpsG6dYOdO0HVBOg22LdnfFKS2g+tVBatAIguc66d3g7YHcgGw60HpgusHr+M74jbuv/8ubr31W0e8BbkcpC3wFsKxGpA6OpxjNgwn6kIIyGahtxdCIcGWLZLhYeehb9o0ycyZh9+H9ELmbInndwradqcATokJpB8y59pwjGXydhFkF9q4XlAAiR0AZdzJCE6fa09+lgHMRjCn24hRQAN5GDezSAEIUJzPW8Tt/Bs3pow5MRR58uTJ8wEjkfzBHeMVI4mBwCUFLxsJWrQMN6bC1Ft5QTjPh082m0FRFLRjKAQ4rmQOLSPbyViZSXdw2p1BRp2/oYNymCIKsC0nDssTmiqWda3rxrZswpVh5xuGSqQ6TLQryuCuISrmHFAodpS82fMOWZljXnEzKTNJwAggEEQzY8SzCZoiDZR5izm37nTC7jBzimZORkbsz0h6lHu3Psjmoa0oQsXCxlB0TqtezuWNFx02V3c8GyOaHiPoChB2HaLh9wBimTg/Xv9LNg+1YKgGuqLh1Tw0RaazoGQeQghGUqP0JwbYNdaOoRh4NTcRd4RCd5gL6s/CrR1eyG0d2cGbPe+wJ9ZFqbeYJeULmaFMo/XZbQy3j2LlLNxBN7Un1DBtWd0xl/0ZhsEvf/kAV1xxFldffRGPPfYKn557Dc/teYUdo7uIdQ2DlNxwxvUHCfdHg8hEUVL9SCOI7Sk7eCrhAUh3AYl5t6KNbkFNDWDrQczCuUj90OaXY8EKzyR+/Fcx+t5ESXRhe0rIlSzGmhIbMRUz3Eym7CRcPS8hU0Og6ohMFCtYT7Z0Gb6W/0RkY5iROc65SYkW3Yp790Mk5n/9sPEZB7JmzSo+97lriMXG+NWvfsf551/2vs83z/9N8mJwnjx5AChtKqGksYS+1n5cPgOhCNKxDAW1kfc8SMvz/nh183oiLjcn+LygS6wqoAqsriGUYAFKxT7RVyktQ1+6gtyzT2AlEgifDzk6Am43+mlnI1QVcyakT7dxv6hAH1gmjKdhoFlilUnK5eHHXaYJg4NgGFBQcPByVjPwpiOyyYlngNYuWDcGdUvANeFUjkSc6ITXXhM0NjoPETIAyRttjJecyASkIxpmTpPICOzZKhgYkJSXQ9pdiO3xo6eimEaEZBISCXAlohTMDKGdFyTusp1wYI3J7Nh4sxMfICx4cJXk2U0KTU1QN03idsPu3YJUSlJSIvGHJbOXn8Jgejq/Xn8HJ3xmBW1tcMcdCgMDgoICSTwODzwg2LQJhoYE69YJxsacsjWXyxGoCwokbW1O/EJ3N1x6qZy8blq7QCRBTjhnlSioo2BHQN8Fsk+BLBSkQ3zc+wnuu+tOvvjFrx/y4S2Xg1dfFbz+uiAWExQXS0491SmrO9oIwtZWgaKAogjEhKi/N/O4sxOef17BPzG2drngpJNsrrxSHrZcLnuSxA5ZGG86mbzZOTbZpRKz+dDLvxvpSyVgY6xR0DqdmIjM2TbpCydeQEhQepzPn13mFM0dCbsMJ5okBXjAvddNNJ7BbMy7gvPkyfPBs0c1WaWnKLFVwtJ5m1aGSqua4xU9Qf3egPU8eT5ELMtCURSklEedU3xi2UK2Dm+bLJDL2SZdrh5yWpYXEq/xlrGGWquKBf1zqawup3j61Fys+FDioAxhVVOR4JRWv0e64j0EdD8Rd5iwO8RwapSIOwzASDqKIuD8aWdxeeNFh1x/JD3Ks3te5sndz7N9dCfVgUqaItOJuMOMpsd4pXMlc4qaaS7YV3hmS5v2sQ6e3fMy20d3kbVzeDUPi8vmc8G0syfdyQfSHe/l/3v7x7zVuxq36sKn+6gLVeNSXTzb/hLNBTMIuQI8sO1hIu4wc7Vm+pKDJHIJehN9zCmaiWmbPLfnFaaH66kLVk+5f+sHNnPP1t8RyyUIGn76EgNsHmxhVnsTNe3lBMsCaIZGMppi2wvbMXw61fOrjvma+/0BfvObx7j44lP4xCcu4NFHX+HGOVeTNFM81HkfT4q7OGHu0mPbqJ3D3fYYRs9LKNkoUvWQLV5AevpVSFf4yOuqhhNHccxnchSH5ask3XDF0a+gqKRmXo8Vmoar42m06HakUJGKC1f/SqfUzl+37yFKCCxfFdrYLpRk37vGRdi2zc9//gO+971vc9xxC3nooeeorq57z+eXJ09eDM6TJw8Amktj3mVziawN07ulH2laVC+spmZhFe5gftrJ/wRvrXubJXOOR8THsTqyiEAQORYFy0I75UzEAVP8XBd/BKWwkNzK15DjY6iz52CcfDrqvIm2aAXSl0nMWRZ9LwneekNhi5Ts7gDvj1SWL7e59NKDBb716wVPPy3o7RVoGsyaJbnoIpuS/ab7Z4+XaIsE8i3YnID+DLSOQyIInipJ2QAEEpBxQdTnuHGl3CdW2oWQvkKSvkyCZPKv0/g4PPWU48bt7ARDr8brX8hM80X6UjnSih9fbISywgR1V12C2DtF6gCDlQxB7gRH5Nv6rEIw6Ozb5YKTT5bMnOm4lufOlVx4oWThQkkk8lf8wz98na9//bu88so0BgYEVVVO9nEwCIEAPPSQQiYDLpfA55P09QliMWfb8+Y5472hIXj1VYUTTrComhh3ywNdtRnAAiUnEONgh6Tjak3D9eb13NX3KzY+u5oF55940Ofk8ccFTz0l8HrB75fs2eNEPpgmLF9+lNEOFhQVwcgIjI6Czwe2DaOjAtt2HNTFE+8exsYc8XnmTFiw4DDbF2DOA3Pe0ZfmHQnphdQnJJmzLETUEXv3RkYog+D5g4K2TSAyYEckmdOd/ziMySLXLMnNFehrnHxlj+V8blKBNNmT8mJwnjx5Pni6lBxJYVNt7/sjKxAUSoXtWo4cEp1jc+rlyXMsDCSHGCKKaZr0jPRQWXh08Rd+w8en51zNusHN7Iy2sWlwKzF/jHJPCXafzXg6xhZ1G65pLs4976yDYh8CZQH6W/phP43YyllOxm/ovT9jlHlLWJ/YTLkoZU5hMxuHtjKSHiWeS5AyU5xes4Jzak8/5LopM8WvN/+WLcOtDKVHUBWNgeQQyVyKRWXHE3GH6Ev2szPaNikGDySHeHD7o7zQ8Rp7xjvx6z4awnV4VQ9Pt7+IaVt8fObBLs20meHerb9n46CTvVzgjpA0U2wb2cm8olkkzCQ7ortxqy76k4M0RaajCIVGM03GzLB2YCNv9q6mO96LBPy6l9OrV3BRwzkoQsG0TZ7Z8xJpM83MyD7n6q6eNlbGVtFY/TFchuMoDhT7iXZF6VzbRdVxlcfsDgYoKirh/vv/xMUXn8LHPnYu9933J0pLy9nespX6+ul4vUfpCrYthJnE6HkJ9+4HsY0wpr8WkUvg7noBxUyTmPfX7+oQ/h/DzqGNbEFJDyKNELmCOaB5sUKNIJ9Cqi6kEXJygofXo2SjmL7aqduQR3Di7Edb206+9rVbeP31F7n55q/w1a9+B13PzybJ8/7Ii8F58uSZxOUzmL6igekrGo7JMZDngyeVSrFu3dv83bf+BVfjLHKvv4Ici6JUVmGsOA1t6UkHrSN0HePUM9FPPt2Z4+9yHXwPFRgqh//oEQwFJLW1MF2DkRHJs88KysrgpP3EsG3b4O67FdJpxzVrmvDGG4LhYYVbbrGZHO+5oesim/taFVq7BJaEQQWGY3DJG4IlSVAsQEC1hK0rDuNa3U8klRL++EfBnj2CUMhxJ6ua4KH41VwY8dKYXEVzYT81c0KEzj0f1xlnHvJamiasWSNYvVoQj0Nfn+Mmrph4AS+EI4RWV0suu0xOCqhXX30jP/7x9/jxj/8V+E8GBqCjQyGXA5dLEgzC8DB4PILSUomUjjPY7RZ0d0uGhpxSucJC2LZNsmePIyaDUxYnw6D2gFUB0u+cuxgC6XHK4ABEGhZWLSY8FObVx587SAweGoKVK50Ijb1ibSQCe/bAiy8KTjhBYhiQTDpCbzDIZBGcGAfjDYG+TtA8CMVpKKyR9AxDLObcHE1ztlu8z4ROKAT9/YLNmyULFhzykn9o2MXAfsdCDjz3KuibBValRBY75XDuhwTSC9nD5RwbkLzOxlUl0FcL3EnnYXTslCRWzYd+Gnny5Pk/iIFA4KQq7f8+MIskLBU+nHqxPHmcoqln97zEc3teYVd0CwD/38s/4NrlV7O47Oj+kHt1L8srFjOvaBYtw9upD9VS6i3GLrfJJrPErDhjaoyYL0GQqRmx1fOrGNo1RLR7DF+BFytnER9MUFBfQFFD0WH2OPX4h9MjpMw0RZ7CSfft0ooT2DLcOlEMV8z0cD1bhjNUByr56wV/xYKSuYctcts01MK20R00hhuI55KkcikMxaA/OcCmwRZOKHOMFIlckmfaX2TL8Dbe6VtLIpckkUtR4A6jCJXdY3vw6z6KPUWs7l/PmbUnU+QpnLKv1pEdtI11EHaFSORSKELBr/uI2jk64z1E3CFsaZEyUwghJmMpPJqb8WyMkUwUv+5jZsTJVxuecDRPj0xjdmETQ6kReuN9lHiLp+w3TIgu0cOoPjblnhg+g1Q0jZWz0FzvTQ6qq2vgwQef4ROfuJCLLz6F++//Ey+99Awnnrji3VdO2xh9r2EMPI+S6kcf3oTtimCHZ4JQkKoLU1HRhjeixnZjBd9f+d2HgUiP4G25A21kM8LOAQIz2EBq1k0Ync+iJjoxC+ZMRj/IVD9qXxdatBWz6PiJmAgbNdlNrvB4bO+hi9qz2Sy33/59fvSjf6GkpJzf/vZJTjnl0M87efIcK3kxOE+ePIdkfxFRSkmsP8Z4bwxFVyisK3jP5QN5jo61a1eRzWZZuuwUjFnz0JefgkwlEV4f4l0CYYWiwIRDNp123LV+P5PC7datgr4+aGzcl/taWOg4PletElPE4DfecATUGTP2vbj2+SQ7djjbWbRo37JPvShYFxVMO9mJXqhIwuo/CFZug9pGUDyQjkPxMFwWBbIcMT92cBA2blSorpYUFQnefBP6+gSm6ecOcS1nLTmf735tnMKGAoTXaQiLxx1HayAwGcfFI48Inn/eOXiXy9nG4CB4PJL6eidmoaNDUFEhmT173/l4hY9bLv4y/3TPt/h4421ER+sIlEt8Pue6trU5/y8o2LeOEM41decE5bsk0+MQdcNOmymOa7sUUhfbeB5RnPI9nBI5VOn8ZU47QjAA9QqndZ3Bixuf5a/5uynXqK/PuW/TD4gwKyhw4isGBmDLFsGrrwrGxwUej2TJEsn5p0qK7lXQNwmkTzJHwslxeCkKZdMlRUWSdFpQXn7obGCBxO4E958EUnXEbasePnRTmw36JoG2FpSYAEOibwJzmoSJeBK7AtQ2MF4TZE88vDtYBpz4ifT5EjNtwAOQCqcPvXCePHnyvE+aTIMSW2OPmqPO0lEQJITNuGJzdsaHkncF5/mQ2DzcwuO7nsGjeZhR2cQqYGR4hN9ve5QKXxlVgSNPT9+f8WycpJmiaCJrX1EV3AE3ZBR6+3awc/Muwo0BfIX7mluLG4uYc+Fsdr/RRmIkiaIqVMwrp+mMxncVI0fSUR7d9RSbh1rIWjkK3BHOqFnBisoTmVc0iyubLuWZthd5q3c1Q6kRDFVHQeHB7Y/QNraHaeFaZkYa8Rs+slaOlpHtdMa6WT+wiUQuha5qRFwhWoa3IQHTNtkR3UXSTBJxh1nTv4GR1ChZO8fOaDuqUMlaWSoDFRiKjmWbdMS6WVw2n85YNyPp6EFi8Hg2hsSm3FfG1pHtWNJCFSqGYjCWGaPMV0J9sJa0lUERCikzPSl4DyQGyVgZmiINk89mRZ4ChlLDbBlqZXZhE4aqoyoqOXtq4bc0QBUqZOSUUuFMPEukJoxqvL9XUE1Ns3n00Ve4+uoLueCC5YyPj/Gd73z/sMsrw+B6XuDe9jyq715kSMUsN1Ayo4jsGLYrMlmkJvUgIt6Jkh7FOkz/wzFjph1xVj3G8opD4G77I8bAanKh6aB5wcqije3A0/prlPQQtrtoSgaw7S7B8pQAEm10MygG2FksXxXpaZcfMi/4rbde42tfu5m2tp189rN/zd/8zbeO3nWdJ89RkBeD8+TJc0Rs02b7izvoWNtFNpFFCPAW+Gg+t4mymQeXMOT5YFi16nUikQJmznTaoYWmIQJHPxoyTXjpJSebd3zciTBYtkxy5pmSZBJAoKpTXZMej1PItj+dnYJAYKqL1zBASsHQ0L71YzEnG7e4WO7VofFpsDgErRnBplFJZcKJU3DPltSYguRuMGce/hzicUdsLSx0RE9VBV3/GYnE97GsPp5/ex4Nz/4HX/5yNf398PTTgi1bBFI6ecRnn22jKPDaa45ztmAiR7aiQvLaa45QalkCVYW6OslHP2oTDjvLKP3gvVvhlt7P8VPl+4y1/X98xfVzHs/BiHtSa8dRP53rIIQjQpeOwadtycI9As8AxFNQVwozy6ZGJmRXSKw6C22LQGTBKgb3kwL3887XMgBWncQOwlm+s7ll9+eIrR8lMC8yKXD6fI7AnUoxmekLznVzuSTvvONESPj9UFzsZB0/+aSCuUXy2c4JEdXlnMXVpZIZq+HNICQqYdYsG12HBx8UpFLO5wMgnQTaBPNHBO425/SlH9Ln2WTOkR+qIOx6SuB+QiBMgTQkaqeCMgRWjWT/T7MMgDKCE79xiNLnKRigGRqqqpLJvPfswjx58uQ5EiGpckU6wO/dMbarjmijA0uyHk7O5h/w83x4rOnfSNbOUe+rJR4aAyBkehjNRNk83HJMYnDEFSRkBBjLjE+WhMWH4uze3U4qk6ZrUzfZVzPUL6mlYcU0hCIQQlA5r4LSmaUkhxOohoq3wPuuMxBN2+Q3LQ+yYXAzZd4SwkaIofQIv9v2CG7NxeKyBayoPJGcZdI23kG1v5JKfxmbhlt4qfMNVva8w4xIAzWBSi6Zfj5v961l3cAmTNtkIDlIX2KQAleYRC4JOHnAUkoEgqHUCCBwqQYzC2bQl+jHq3vwqB46Yl3EsuMUugsxVINELknL8HbGc3He6lmNpmhMC+2LAyj0FKAKlSJ3AcWeAoZSI6iKSiwbI2gEOaliCY2RaZi2xdzCZtYObKLAHUZXdLoSfbhVN9WBfZEetrRJmEnWDWym1FdMU2Q6swubeKP7bfy6D0M1MG2LIWWY2kAVrt0GqaLUZGawEFCzsPoDmQFaVVXDI4+8zPnnL2V8fIzu7q5DLifi4L1TQdueQml8GqQL0VGHNmZi1xSjWMNo47uxArWguhG5caTuw3a//xJzJd6Bq+Np9OGNIFSyJSeQqTkP6S5895UPdS6ZKPrgGixvmSMEA6gGVqAObXw3tqIhxIEym400IqQbPuocU3oQy1tBrnTJQa7gkZFh/vEfv8EDD/wXCxeeyDPPrKK5ee57OtY8eY5EXgzOkyfPEenZ3MvulW24Qx4CJX6kLRnrHWfrU60ESwN4I/kHmA+D3bt30NQ0G0U5umbZA3nhBcFDDzk5sqGQJBaDhx8WZDLQ2CjRNEcU3vuCWUqIRgXzDsh4LS6Gnp6p2zZNAElov+LkbBZyOUd0jsed7QUUCLoEvgjMmCVpDguCQRu/F5TtIDL7hNRDUVQEwaCkrw+6u8GyfsfIyJepqLgdn28JpvkjfvKTCzjjjM089VQZO3Y4YrSmSd5+W9DZqbBokSSRYDKrFxxReeZMJ9bh2mstPB6or99XmCZtyNytoKwW0Ozjhjm38oN1/49rvd/g7P4abk+DVCAcBiGc8x0Zca6lbkuutQS1AnZpzpTgUFBykg6etwSp2v3OV4BV4wiZe7GmSxAK6m6BXSqdyINVgrPVc7ClzTvfeZkLLv8IqSslGFBb69zPDRsE9fUSj8cR5gcHBaedZrN2rcDn2xeJ4fU60Q+rVwsuCktK9jP4u9xwahksmw3JTzufg3Qadu4UrFmz7/rkegVLLFjQJDEjzi1UBsD9lII5w8Kadthb+r5QesH9goL0S6ySifJBt8TVqaBtg9x+LnUxjnMcxzCBQdM0stnsB3zUefLkybOP40w31QmdFi1DSkgqLY1Gy0DLu4LzfIjEsjFcE25IT9B5c5waT6ASmRRCjxav7uWkyiU8tONPdMd78doe2na2M27HWOJbRH2who5oF1tXtjLLNYtlCxdPisaaoRIsP3pjw47oblqHt1MXqMGrO292a/RKdo+181rXmywqPR5FKKwb2EDYFWJaqJad0TZ64n0UewpJmikCup++5CA/WXcHtoSGUB1e3UOVv4IXOl/jnf51eDQv5b5SEmaKZC5JbbCamkAlG4e2IhDoioZH8zhuXlXHpRrEsgk8qodYLkE8F2coNUyJt5hXu99k/eBmLp9xEcsrFgMwIzKN5sIm1g9upj5YQ9gVojPWTbGnkGuar+TKpksmHMFxGsJ1tI130JvoI2QEWVQ6jz3jXXg15/wtabF+YBM7RttI5VIMpgYpcIc5tfokmgtnsGN0FzZO/0Z1sJKPnn0J2XfSDOwYJJPI4g17qDuxlvI5h44lSJsZUmaagOFDU45OKopECgiHHdH2G9+4hbfeepV//ucfE4nsE3L19QJtO1iNfWi+YWxZAUUSZUjDTtcjjRGUzDBKaghUN0qql2z5CqxA/VF/Xg6FkuzDt+nHqLF2bHcJ2CaetkfQxttIHHcrUve9+0YOQFhphJXFPqDcTmpusHNYRcej970F7kJHLJbS2b+3lEzl6cjDCNyJRJw77/wpP//5D5BS8r3v/Yyrr77xPT8L5snzbuTF4Dx58hyRnk29CEXBG3YGIUIVhCtCDLePMLhrmNpFf5lisDIEIgZ2keMy/O+mu7uTysrq97RuIuEUfPn9+0TAQAB0Hd58U2HZMot582D1akE47GTKDg0JiookK1ZMFWeXLpVs2aLQ3S0pKXEiFTo7BTU1kjlz9i0bDjvxEStXOg4QIRwRtCYrKQaOr4dCj2QsA89vgO1jAmWlZC5OaduhxjnBoJNffP/9gmhUMDT0H4TDn8br/RRNTZLq6tu5664nuf32u7HtrzFjxr7yu3BY0toqCIUkIA/qZ7AsZ/slJdDaKti1CyoroaRE8vxvFBY9opAAzHE4M/J5fq5+n3v4V74e/ClnTJNEpSA3Dt4iScoHq1sFQ0OCeUKyMCLxzoIKn0TXHUduMAmsFaQvlMgjjDvtEkh8zsb1slNupnYLrBJJyaIqKn9TxYb0ej7y8uVYVZA91bluH/+4jWkq7NwpyOUkbrfgxBMlS5dK3nlHobBw6j0NhaDNhOG0oORAMd4SSM++FwJuN1x3nc2sWYKNGwVSwgluyUkx8EQmFhLOcWstoG0TWNM+nAI2rV0gxiVW037XqxisEonWBmYdSB+oQ4CEzIrDR0QcSCaTIZPJEAgc4Zc9ixP2me/TzJMnz/ugQKosz/1ljp3y/O+kPlTL+oHN2NJG1VT8RWGGOnspW1BAue/QouCROKPmZASC17rforu7FzsjWRE8kUXKcbwsV7IluI3xZIw3WtawUr7Dx5ouZWZB4zHvZyQdJWebk0LwXoJGkIHUMBkri1t1MZKO4tO92NKmO96LoRp4NA8JM4UlLeqDNTzR9hzVgcrJbQUMP0vKFvBa15uMpEfw6V4CRoB5RbNoCNchpWTj0FayE9ELBe4wxZ5CeuJ9GIqLsDvAeC7OaDqKLSWF7jBCwHBqlMHkMHduuo9poVrKfaXois61zVcScYXYOLgVv+HnlOrlnFlzMovLFiCEoD85yF2b72PXRBSFobrQFZ2zak5l49BWNg+14Dd89CcG2R7dRZW/giVlC9EUja54Dy91vs7nj7uBkfQow6kRAoafOUXNBAw/slKSiqYwMybeiPeQ0RwZK8sLHa/yVs9qEmaSIk8hp1UvZ0nZwnd1EPf0dLFhwxp++tO7EUJw221f4vTT5/P97/+CM5ech9YqcL0sEAkBqg+JGyGSSLygSYg3YNWNo47vRM0MY7uLyFSdTXr6le+7PM7ofR1tvN0pd5uIYrDdRWijm9GH1pItP4qM4wOw3UVYvgrUWDuWsc8ZoyT7sV0FpOqvAGmjD60D2wQktruYVOMnDikEp9NpfvObX/LjH3+PsbFRrr32M3zpS1+nuDg/AzfPh0teDM6TJ88RycYzaK6pmVJCESDATOUOs9afLyIG7ocFxgYFkQY7KMmcJJ3p7/+N/2L29HRxwglL39O6o6MwNiYoKZkqyoXDTrHY+Dh88pM2FRVOqVo2C4sWSc44w2baAa7O44+XXHGFzXPPCdrbBZrmOFE/+lGb/XWzaNTZbzwu0DSJywV7OmBcwt9WQmmHYNAjuX2joGUctGrIrlN4Y6Pk7LPhlFMkb7whWLfOEZKPP94Rps89V5LJwE9+kiGVWkNZ2deYNcvJ+h0eVigvP4Ndu96ivt7Z714UBbxeiWkKCguhsxNqapwxZSrlXJ+6Opsf/lBhZERMrCOJxxUqTFihSmQQojkY6glwSfGt/K7vu1zn/huqBxpIjkA6CxdXCs4uhTWftNlVADVxmP+SQJnDFCFSmqAkhRNbYIDa7XzfqoQcMDDgxD0UFjqZt6lPSKxqUKJgzgBUaIhMZ2dqB9IDxluC7KnO+ZaWwhe/aLNzJ4yPC4qKbOrrndI4n89xhfv2E6DjcfCUQEgBZdB54QFOrILUJbm5Uz83Xq9zf045xfm+/wcK6m6w9l9IOP8J88ifzfeD3Hs9JfuiKFQwp0tkl0BJCUQcrIgkc4Ykd8LRi9JjY6MAk86WSY1cgBgB1wsCY70AC3LNzvbtqkNvK0+ePHny5PnfxOKyBazuX0/r6A6K3YUUNJSxe+s2TvnUJRxXPPuYt6cpGmfXncaKqhNZ+9IGejf3UlVQyXp7M2vkRsKECEg/ek6jN9HPb1v/yJcXfZ6gcWzuipARRFM00mYGt7Zvqk8sG6c6WIlLNRBCUBeq4e2+tRS4IuTsHKpQMe0cCgKf7kVKScpME88myFpZjAmXdLmvlJkFjWTtHLa0mVM0E+/EtP+B1BBV/goUoZDIJfHpXuYUNjOejdGXGEATERoj02gd2k7ONrGkTcd4N6Zt4lINOuJd/Mea/+S2E/+GoBEg4g5x7awrGU2PkbbSFLoLMFR98pye2/MKO0Z30xSZjqZoSCnpjPfwfOerfG7e9TQXzGDd4CY6Y93UBapZVDYffWL9Kn8FraM7aB/v4LTqQxRMC3HEmZxSSv644wle6HiFkBHEq3vpjvVyb8uDSClZWnHCEe/TH//4WwzD4IwzziMUCrN06cl8+cuf5dprL+H6mZ/me7X/hj4YRO0TSK0Ee8F8VOMFpHSDFUDqKaSqkWq8mnTDR5FGENtTcvQflCOgju3AnnD/CjOJFCqoLpASJX7oSIt3RdHI1F6Ad+sv0aKt2EYYkYsjpEmq4UrsYC2JuV9CH9mEGu9Aal5yhfOwveVTNmOaJr/73T388If/RF9fN1de+UluvfU2qqpqD7PjPHk+WPJicJ48eY5IpDbC6Mp2/MX+yTfDubSJoij4S/zvsvafGRI8v1MwVgrsMokVAWUUPI8K0CFz9ofjeDwQy7Lo7e2isrLmPa3v9zvlaInEvhgIcBzDbrfzc78fLr1Ucv75EtN08mAP9fJdCEcInDZNMjDgFJPV1k4tQwNYt06QTjvRBF1dgmjUETYtC4zTbbKG4JWXYbMFjUtBqXOyZYeH4bnnFNaulXR3C4JB5xo//LDC9u2Sz37W5vLLJX19A/zTP1k0NpZQXe3EMgwNCerqSujc2copnTB/UJDywJ5yyZ5yyGQEVVU2p50Gf/iDoLVVTBS8SWbOlOzZI8jlnMgIIZwoirVrwTtbkooIwnEww845XFL6RV4Y/k/+ffRWmuzHcBVLrpgmObMAXBsEy9eqLD7eJjdLogcEqS7JqMu5TpEwGEOQmyFRe8D9JwWtSyCBbk3ykAprco7QPmuW5KKLbEpLQeQARTilckBDuJE1fe8gDSd7DQmmxeR9bm6G/WM3/H5YskTypz852w6HHSG4p0ewbLmkqFwinlfQWp3lpQ8yZ9mYTkw12azz8iCXc4T0vZnEudkSbbMCOekEXuIcj9QlZu2H9ztiNkr+f/bOO0yK607X76mqzmG6JydmYAJhyBmECAKhiHLOsi1Ztq+0DvL1rnfX67Re57XXu85ry5KtYNlWzhISkkAIkeMwDDBMzrGnc1Wd+0cNAyORBQq+9T7PCKjuc+pUVY/61Fff+X5mlkBtAmMUliCcAjUqiN9sklokEXEre5mTNN0dFIMzvGFcrwkc6wTKAKTHSLR6gdYiMDItp7HrdQVtvyT6f0zM03OvYmNjY2Njc8bI9WZzx6SbWdnwJtU9NeRWFtP21zpuHX/tcITDqeDRPIzJL6WPXtIpnV3qHpw4COAjno7jD/kIBcPs7d9Pdfce5hbMPKn+x4bLqQyPYWd3DcX+QtzSTaR2gGCDj7LMYvYnDlA8tZCFRfPY3VNL3UA9btVFa7QDh6JR6M/HkCavNa0mno5TN9BAykwxLlzBqEARfckBgq4A540+h9caVtMUaSHDlUE0HcOQBldWrqA70cPG9q3ohk57vJP+5AB5vhxCriD9iQEi6RjZ7jAD6QgORcOrekiYSRQUavv28UbTWlaUnTd8TGF3BpAx4jjjeoIdXbvI8WQPRzMIISjy51Pbu5/OeDcXjFnKBWOW8p11/0lXvGc49uPge0GQMk4t6qot1sH6tk3keXPIdFvLvkKuIAcGGnitcQ2z8qfhUBxHbJtKpfj973/OlVfeSEZGCID8/EIe/OVT/PWO+/jntV/mlaYX+bfp3+IW3804qhVSweuhchBN3wa+eghrpLNnEp9wB6Yn55SO4WiYrhBKvB11sAmRGgChYngLABP5Pj776dy5RFUXruaVqJF6jGA5qaIlpPKHxHjVSTpnJumc937mI5EBHn30j/z+9z+nrm4vl156DV/+8r9RUTHuPe+1sTmT2GKwjY3NMRk1o5iO2i669/fgCXswdYNEJElBVR7ZZacWvP9RRW0ExzaBUSSRQ/M0swCECc7VguQieUaWiEspMffvRa/eiUwm6PL70XWdwsJTsx6GQpbT98UXFTRNEgpZObItLYIFCyQFhz2YdjoPZcEeiV274MUXFRobrYJzkydLQiFJ1rsufUeHJbI6HAxHPhiGlU+7HcGSu0zWtiv48iTKYYcVDsPmzbBnDyxYIIfdxoODktWrBaqqsGSJZPlyyXe+YxWua2y0cnCXLzfZvU7StxXOkRBRoNgFxW0C44AkkQHLU1AyAJVXm2yOCBJJKzpjYAD++EfB2LGHi+ACN5A+IOgIS5r7BIkB8Dlg9KCP70/+Ebdtuo5bJz7NZfMvJpgCx3qBGADSIHoF6jrB+naJs1uQMCDlFOQ5JAXjQZti4P2TgtIHRqGkuxs61wqmqtB7lqTTBW+9JejpUbj7bhOt0BJbRcSKKikPV/Do7ocQvZCYLHn9DatAYF+fFfdx9tmWm1o9zMh/0Fn9zjsKdXVgmpCZKfH5JWtyYdrnDPyNwhKWyyXGaEBAbS389a/WdTcMyM6WnH++1X9qjsSxVaLtFkifBMMSrlPzQJ9w0h/XE0ZmQuJyieevClo1lvNal5ghCTpoe4Tlaj6FuX3fUOXEvLfDeHYLpAekCzzPKqhtkFwkh4VfM1vi2C1wrhMkLvlgHhDZ2NjY2Pz9M5iK0pPoxe/0k+kOnda+iwOF3DbxOuJ6go3a21z7p1foONBM7qQcZDyGjEYRGSGE48ii39HIHZtDdnk2bXva6csfQFEU4rE4Tq+TQF4ATbEmJTE9ftJjdqoObp5wLX+tfYqarn14NyTJaAiQ68sioPqofnE3HTUdTL9mGrdPvJ5XGt7AkAY9iT68Dg8hVwbr2jYS1+OMy6wkZSRpi3XQlxygK9GDT/MyM28qlaEyMpwZVHfX0BBpoiyjhPmFc5ibPwNDmszOm87Gji28UPcqk7OrqMgYg6IovNO2GRODnlQf0pR4NDdCCAzdQKiCfF8eG9o2c9GYc1HEsXKr5NCip5GuDIG1ErM30c/rTW8xmIriUlz0xvso9OUP9zmYjuJUNIoPKzJ3MnTEuoikBykcigzRTUibEHKF6Yp3058cINtz5Hu+p556lLa2Fu666/Mjtjt2K9yRcQdLbl7G1976Rz795if4SfBHfMvzLS7dchlG4l7SeXtIz+siuSADPTweTjCj+ITR4wjTROvbixQKpjsHIdM4erZievLQwyfvih9GCPTs6ejZ060oCKEeN9Ji794a7rvvlzz66AMkEnEuuugKfv3rh5g0adqpj8PG5n1gi8E2NjbHJJgfZPrVUzmwrp6eAz04vE5K55RSOqcE1aEev4OPEaIPRBTku6J6zSAoAwJlEMzTLAZLKUm98gKpZ59EDkZACPriUQC8J1kwQMbjyFgUEQiyYoWTeNxkyxaF9nbLOTpvnuSqq8wTjt/atw/uu0+lv1+Sm2s5iFetUujslHzucyaewyLcMjOtiIKGBiuewOWy4hh6e618YWnVRUMepp0NDMCOHYIDB0BVFdats+IfwmHJtm2ClhZ47jlBdTVUVOSiqioXX9zK9OkG4bBVYO5LD3RQqOWTsQBaaqAtChlxWN4nuLIUctZY53CMDwqXmiQutdydb7xhuSiEsAYkJGREJWMjCnofPN5hRUSYgC4EVfmSf/qXK1hyz1J+VPMlrl6wDLXZjTKUK610AxnwOpL72wXTC2GaEzyDknUGdHgld/YJvJ0Cfbzliq6vETS5BJOQTBiw4iB8PkltLezaJZg1XZKaIXGuBemHCiqJpqO0BFp4Syngrw8JXC4IBCzX9sMPCxIJSwA+iNsN110nWbrU4NVXBa+9ptDdLXjlFcFrr0kmTRLcfrtJ8LB6Lj098MADCu3tglGjrBzm9nb4y18UwmGDyZMheqeJc53AsUMgnZCeLknNPuQUPlOk5kuMIgNtp0BtAOdmgdIP7lesD5dRKojdZmKc5Aq7g87g3N3ZGPkgQ9Z2swfUelCbBebB2BUFTK9E3X/sAog2NjY2NjYngm7qvFz/Oqub36Y/FcGjuZmRO5VLy89/X87dI+HR3MyYOgdN09i4bjWV+/eRfmctMhZDycrGueRctLMWHjcn9iCaS2PqFZMJrg1QWJtHNbUU5OUSHhXG5XMxmI7iUBwU+E4t/zTXm81np3yCXdt3szNSTXhsBoGg5RwwdZPuAz00b21m8qIqJmaNpy85QGOkmXfaNrGqcQ2mNJmdN4PRwVGkjBRNg61U99TiUpyUBIvY3VPL5o5teDQv03Mn8Y+z/4Gg61CchYrKtNxJdMQ6CboCVIbKhs+Nz+HBrbowpEHCTCIMa1YgMcnz5eDTvKRNnb5EPy7NddRr6dE8VGWO443mtWR6QqjCur9qi3YgJLxwYCV9yX4EgqSZoi/Zx9bOneR6s9FNnbieYF7hTMaFy0/pHPscXlyKk0g6TlvUS9OgIG2CKeOUBNx4VGvCn4qlaK/pINoVxel1kl2Rza9+9VPOOed8xo0bKayKOCAFo7PG8MdLHmVj23q+tfpfuabhKmZnzOWf5/47869YjJlzmtywegxHzy4wEpieQpytq3A1PIvWswuhD4LiRkn1gqJiurIwnQFEOnJ69n0MEds0TV577UV+97ufs2rVS2Rl5XDnnfdwyy2fpqDg1MR7G5vThS0G29jYHJdQUQbTrpyCnjJQFIGi/X1WNZUha6m8iIA8TBxTBsAMSMwzkIphNjeSeuEZhMOJOmFofX6bFShr7toByy48dvuBfozdu0i/8zZmwwGkoaOEwjiWLOPWWxazfLnlQA2FYNSok6vDsGaNoLcXxpWDIkE6IRiU1NTAzp2CWbMOCWHTpkkSCcshXFRk7ScSgWDQ6qOhAWbOlPzlLwrJpEQxYdsaQWuHwKuAyysxTaiuBlUV6LrA55MUFVnjrq52UVg4g61bX+Paay+zjj1m8sae17hr3OcoKJJk5Vi5xY5uQeFm0DOxhFcssdb9koJebqBPhpISidcL8XaYNwCjmwSOJsFLKcnjCFJOgcsNXRFoVaAmDr63Fb5y3U+55Kcz+MzzP+F2xz8zHchNAhoYQcmbuwWKEPRnSl7LAhGDlBP2DsDl2wVBpxzOu+3vB6dLYqQhELOERculLejulqBC7CYTY5TlQi2IWZbumqXtvLauEJ/vUIHAcBhaWiyR+3CH9UEUBTZuFDidkvJy6/okErBli2DVKsGllx66ltu3CzoaBedkSQJtEHeBLw921MH69VbBPxmG5AWS5AUfvBhqlIBRLPH9TAFdkB4nrQIkBmi1AvdjCtHPmydcPA6gt7cbgEwjPLwqAACnRDqEJfabWH0aoLQJNCnx/Fmgj7eiM+wZlY2Njc2HS7PSyGrXq1RrO/BLP7NSZ3FWajFOjrEE6iPAaw2reXzvswScfvK9uUTTUV6pX0XCSHB71fXD4uPevjrWt22hPdZOgS+P2fkzKMs4+XxRr9fLxIlTWf2XB7liVBlKKBMRCGK2t5F46H7cQuA468QLa7mDbiacP57bp9/E/+74I/3GAA7NRc9gH33JfuYWzKQyVHb8jo6CEAJXvwuv8AwLwQCKpuD0Oumo7aJiUTmKUMh0h8h0h5iaMxGf5uW1xtVUhMYAVtbx2HA5TtVBytCp628gx5NNrjebSGqQVU1rALil6tr3jCFt6gjECJE8x5ONU3XiVl14tSS6aaAqKqqiMi5cQX2kiQxngP9456doisrUnEmcP3rpEV3f55Yupj7SSE3vXtyqi5SZxqU4kUA0HWNcuAJFKKSMFDu6d5PjzSbDFcStuZmdN435hbOHIyZOljHBEspDZbxwoJrBVAlezY0UA/Ql+/BqF7Cl1cGkA2vZ+nQ1fX0mIhAAr5+6wT3s2rWNr33te+/p0yi0VlOKAeueamb+bJ686kXeWLOSrzX9C1d8YzmT/zadm2++gyuuuB6//9SrdWu9u/Ds/gPqYCOYOtpALUq0Ham6EOlBEAKhSkyHHz1zMqYnF3VgP2q8gzNR6kJKyaZN7/DUU3/hmWf+RmtrM1OmzOC//ut3XHLJNbjddiVim48G9q2LjY3NCaM5/76cwO/GGAXpyRLnWoGRL5E+KzNYDELqwjMTEWHsqUH296GMP/REXTitIhnpmupjtk2vf5vkk39F37QBs70VfAG0yrEY6TTmQw8gdYOic86l6BQfPDfsEYR6wN1iFc6SYVDKJKYp6OwcKQJmZkJeniQaFQwOWhNlv19SWSkZGIDGRkukrKmR7NwEAzWCxg4Ia1DghpZBgeKRJNMQiQjy8y1xODdX4nZbf0YiX+TBBz/B1KkzGD9+Nj/9z/9mMBXliuzbkdKKu8jLA63dEjtlmGHh9aB7t2O1YF2TVfAuNyiZ+aZgXATiCkQGIU+FPCkRpmB33BJRc13Qp1q5wxuLJjI68HmerP0PIoGbqTRKuCMHJoyT6CHoiYMfUA9YjlKkQBMSxRB05UhyOwT9HgiFJT4fDEZANaHHZR2vaVqTyLY2+J//sVzdxcWw4FITV70LnoXODQkGagTFQXB0W65hI9+K7mhuFnR28h4xuKZG0NPDiFgMtxsyMiQbNwpWrJDD8R7pFri2DiYeEChDl7krBAO5kvb20+uGVRvA8Y5AaxQYuZL0LGkVzDvOQwulBbT9VqTL8ExGBaNIotUJ1BYwTiJlpa5uH7nhfNyaC920+gIwcsHhAOJYYrAJzrcEaqvlGHe9LHC+JkkthPh1tiBsY2Nj82HRpDTwG99PaVIbCJlhOpUOary7aFQPcFP8Uygn84TwAySuJ3iz+W38Dt/wMn2P5kZTNLZ17qQl2kaRv4BNHdt4sPqv9CcjeDUPO7p2s75tC7dUXXtKReAuPPscfvqrnxCbOodgrrVfghkYB/aTev0VtNnzjhsZkTbTCMSwCDk+t4JPT7+VlQ1vcKC/EZ/TyzklZ3POqLNRlRO/hzClSV1/A63RdtyaiwmZY1FUgUSSMlI0R9toi7ZjmCbhSJDyvCMLzUWBAiQSY0ikBWuO1Z8cIGEkGRMsJdtjFY49mJW7uWMH540+hzzvyOza0uAonKqDwVQUv9MqSBZ2Z5DpDuFW3SSMJB2xTiSSHHcWzYNt9CR6cQ+JxWlT5+WG12mLtvOZqZ/Ao428qSjy5/N/pn6SDe1bODDQSMgVxOfw8dS+FygNFA9HQjhVJ6P8RUgk98783PBY3g+qonJu6ZU8f+AxYD8pM4VD9TEhcwEhOZ+u3/2e3TW1dA8GCTnjKAMaYvQYnlr7MCW5Zcyb9d4HB0Y5JGeYuNYIZD9INyjdgiWVy3j+B0t4ufEFHnjgN3z1q/fwzW9+hSuuuJ5bbrmTKVNmnNTYRWoAT/XvUGMt6BkVqJFGlMEWkGlMXwGKqiGS/WDqKOkoUnMjFQ0EmI7DJsumgda7E21gH1Ko6OEqjGD5CbtopJRs27aJJ5/8C08//VeamxvIzc3n4ouv5Morb2DGjDkn7La3sfmgsG9bbGxsbA4iIH6diXQKnFsVRB+YQUniMknynDPkgJSm9cT6sAmCS7Mm36lU8qjNjMZ6Eo8+iBzox0ynEbn5CNPAaDiAY9ZcZDyO/sarOOefjTiVJ9ApyK8TtB8AsgEVlBZhieOZ73Weqirk54PLJfH7rXN1MHogErFiI4JB+PSnTXb/RPBirSAWhGkFkiwXZDRBbY8goUnicUsULS+3CrvV1UEyCdnZ1/GlL3Xw3e9+i87ONoLBqdxY8hxd6/PZvQ8KyyA/T1LcaU06zayR12zzgOS3Lyi0ZVlZxhVdgrGD0J8PMgV5BnR6wdcOUgGPF7KdVoSE6YOuPqipEyw5719oe/ohDvB5AupjPCgF/1QhcUkYY8LGmCTfKTDyJCiSWBKiDYJH18GKBATqYG9IIRiU5PXAdhMe3qfQ02I5g7OyJGvWCBTFKtq2cb1gz7MqF2FZ011vJgk2C3QnqNkSpBWZ0FcmcbnAd4T7AsOAw2MxDr9u6fRh8R0SJu8SxAagqwhMJygG5PbC/D5B+yLz5D9LR0HbDd4/KCidAumVaLsUnOut38HU/GP/vgnDyvJ+9yxGatZrpE9uLDU1Oxk/YSJGniXkG6OxBGFhCcLSK9H2gtIjUNstJ7A+0XpdDIDzTSuvWJ9ycvu1sbGxsTk9vOFaSZPawHh90rDw2y/6WOdczbzUQiqN8R/yCI/MQCrCQCpCyDWyqFiGM0hbtIPeRB/Zniye2/8yCT3J+HAFQgiklOzrP8Cz+1+mKmvsUQt8HY5hGmzr2sXWzh04smMkDIPn2xu47qAYDIhwJmZXFzIygMg8ck5sa7Sd1xpWs6O7GkUozMidwpJRZ5PpDllF30JlJIwEmqKd0LgOJ6EneXTPE2xo20JcTyCEoNCXx4qs83B4HWzbt5NWpQNNVVHSKtG+KD2ufqYmpgwVZztEsa8Ap+rk7bYNjMkowav5aI+1ExoqEhdwjlzyF3D66Un0WkXi3iUGj8+sYHbedNa2bsARd6AJlUg6yuLisziv9Bw6Yp3s6z9Af3IAp+qkI9aFU9GYkj0RE5PWaDuR5CBP7XsRTXFw04SrhgXog2R5Mjl/9NLhf2/p2IGU8j1CuqaoJIwkafP0+VqlzKHY/2lyPXWkZRSvlk3QWUho0+uEtq2ly12FL9uB5vIjY1E2bHyZXU1b+Oy5/0RfUz95495VUVeB+A0Sswic64S1Um6OJHWOiVkpWDb2QpYtu5CWliYefvg+HnroPh588HdUVU1m+fKLWbr0QmbMmIOqHvshgtazAzXahJ4xDhQNNdpkCbjCiZLqw3SGUPUYGClkOkaqvw9N7cYIj0bPGpq0GSk8e+7H1fIGGAnrfDiCJEZfSnL0pUcVhLu6Onjrrdd5663Xef31V6iv309WVg4XX3wFl156DXPnnn3c8dvYfJjYYrCNjY3NYcggxG+TJDsMxFAerMw4frtTRR1TjvB4MXt7UMKWO8E9NOlIFY06ajt9+xZkbw8iKxuhpxGBIEIRyK4uzNYW1DEVmL3dmL09qAWFJz0ubTcsSku2+QXNEvJcoDsk9U3gFLBtG+zYoTB2rGTWLEkwCHPnSv72N4WsLMv1apqWkJuXB+PGWeKeF1gSEYSqJM37BH4nKCpUFkN+p2S9W9Lth7IySTwueOcd0HUYHITcXLjhhv/D5ZffTWOjIJ2WvLpVkBsVTBmA/laJyBAoWVBQAFIAccvdnUrAM/WC/mLJhAnWvG6SKaFO4AzCrCqJaw2kY0MCaRL8GRCJQZ+AvlZrmaLfLzEdQZae9zOefvoaRk+9nwN9t1O/A6oCcE6hpLpLYbcqyUlB0oCmCAwoko4YbJ8lmdYiCHRIIl1wQIEn3FAXl5hRcFimcMrKGHZ0F0mo3wKvDQnwWVIwzS15LQGaKvBmSGLtgrYIzLtFkpv77qsJo0dL/H7o7raylsESiLu7BcuXm8NF50QPjOkXbM2F9n5BIGAJ8vtNGG0KxpecpociBrieU1B6DmUoIyXadvD9UME1SWLmg5ErMQvBGCUtp+/QfNzIByNPorSCcZghSG2zXNJGwZF2enRqanaybNlFxK828TyioNVi7cwJiRWS+IUmWpPA/bTEDAj0SYfayiCIZoG2R6BPsTOEbWxsbD5oJJJd2jbCZtYIB3CGDNEqmmjS6j+yYnDQ6cfv9BFJD44QJwdSEbwOLxmuIC2DbbRGOyjw5Q2bBw6KpK3RdloH2ykJHns5jJSSJ/Y9x8qGNzBMg3xvjPFBLw9vWculEybj0YYKQcSiKF4vwnPkfNvueA+/2/4n9g/Uk+3OwpRpntn/MnX9DXxm6u34HF6EEIf6O0neaHqLN5rWUujLpyRQjCEN6gYaeJqXmD15Gt0v9RLWMyyBVAV9nElNeB/vtG0cFlKllLzWuJoXDqxkIBmhPxlhfdsWiv2FzCuYxeJR83mk5gn6kwMjHLr9yQF8Dt8RYxw0RePGCVdTES5jc8d2kkaSiZnjmVc464jv/866n6APibW7uvdQP9AIwir29uKBVxlMD3Ln5FuOWpgNoCRYRNgdoj3WOZy5LKWkI97FlOwqQq7gUdueLD4HeB0aHkcF2Yfp9+HaTaA6EA7HsCaadjl5YP8rTCmoYnrpPKR5lLmPB5LnS5LnWlFeR0prKSws5t57v8YXvvDPvPbaizz++CM88MBv+a//+h6hUJjFi5ezdOn5LF68nNzDHlocROhWnZXh3F6pW3830wjTQDoyME0dOdBCMhGnobOPBn0mg+W3MneqH48DnB3rcDW9iuErRDozQEqUeDvuA0+ih8djhKxc4+7uTt5++03WrHmdtWtfp6ZmFwBlZZUsXnwuF110BWedtRhNsyU2m48H9ifVxsbG5giYucARRLXTjTK6DMeic0ivfAmjqxM0DV88hkNV6cw6+gRRDkRAUREuN2gapFOW/VZVkckEMjaIcLsR/qMHHe/aBW+/LWhuFuTnw7x5JpMmWUKp2iqYHRBckyV5fp9gc7MgnYBUQqKkBJG3rUzf9esVNm6U3HGHyZIlkqYmyebNYshtKsnPh2uvPVSgTBggDEFVlqSqH7a0Q74PXCp0J2HMGMGFC01eeklQX2/FHQghyMyE7GyT++9X0HWJokh27hRE4oI/hGFsDApMyAlDogi+XCwpXCdQukCkLDF4sS4onCypGzp+0wEeF7R1CeLCEhsrd8FMEx43JE29goQEQ5MoDkilrCJ5iYSksvIKJk68jVW7Ps/CuYuIXD2aaAWUq5I7vyt4LgJNCXCqUBm2xNSxHkF3pmTlaIm/D159VRD3woIsKG0XqECrItncpDB16iEHrtIJ+S7BPt3apsYF15ZYbuNtfRDXwa1I5rrh6iXvKhBoWDEnxTmweLF1Xnt7rUiNaFQwerRkyZJDk3iRtGrAjZsqkU3Q1SUwTQhlSSo8EjWP05KvpnSB1mDFsRwUeNVm0JoEot8qNqisFIiUsITgEoXUfJP4lUNF6lyQuFDifVBBqwHpk4ioQPohcYE5ItLFMKzP9NHqMcbjcerq9jF+/CT0yTA4ysRRLRAJiVEo0SsAFdKFEsc2BbXjCJ2IIaeyjY2Njc2Hgkd6GFD6Rmwzsf7H7JAf3cxgj+bhrMI5PF77LJrQhlyrUVpi7ZxVMJtifyGNkWZUoWDKkV80hjRRhDihCIYDA4283rSWsCtEpjuMCJrMnbCRB9ZtZ1v9DuaUzUL29mD29eI6ZznCc2Qx9522zdQNNDA+XDlc6CzLncnunlq2du7krMLZp3wuDNNgXdtG/A4fGUNCpyY0xgRL2Nd/gD0FdbSd1UVlYgxSB5kNSoGCJ+qmuqd2WAze07uPJ/c9jyY0puZMZEp2FQ2RJnTT4NySRRQG8pmdN53n614BIMMVoC8ZYX9/HZOzJ9Id7yXsCr3nvLpUJwuL5rGwaN5xjyXTHaIp0kxPoo/GSDNezYtLc2KaJqXBYmp763irZT2Xll9wjD7CLC9ZzBP7nqe2bx8e1UMkPUi2J4vzRp9zWmMHRgehMiTZ1iUoDUg8GvSnIJnQyfaDy2fQ3qPicsAz+9+kOxXh85OuxRPyECo6jmtGZTh+i7jlFHZuEpACfZIkeZZEzVQ599yLOPfcizAMg61bN7Jy5fO8+uoLfP7znwIgNzefiROnUlU1mYkTpzJx4hTGhvJAcSLSEaQjgOnJQ0bqUYwEprCizQbiHkh42ZuYy0uJf6W2fyJdNU5akpIbb5RonRtBUSwhGOiPJdndkKamejvbn/gKu9pS1NTspKOjDYAxYyqYP38R99zzj5x11mLy80/edGNj81HAFoNtbGxsPkSEouC64lrUMeXoWzcjY1Fc4yZQWrOVA+2tR22nFBaCaYDXh5Kdg9nSjAxmgJ4GzYHZ3YnzgktQAkd2DaxfL3jwQYVoFAIBSWOjYPt2lRtuMJk/38pLFkJyQSkE+mBTv6TPDVtiAmlAX59ViMzplFRXC956S3DxxZJPftJkzx5oaRF4PDBhgiQz89B+pR/0Mol7o+BTUySP7RFs64BYBIrDsPg2k+nLrD4HBix9OxSSlJRIwmFYu9aKNdB1gZkWZGmWk3ebFzbpgnwhyddhW4GksEZYcRGZEHFDbjVMWCc40Ax7Rku6QhB1Q3YU9uwWdHQIcpOwQpXs8cGumER1CRwOgZSWqKjrkqYmwZgxkiVLfsKBA2+wY8+t5C5bSTqsQRymV0qmdUNXLrg0WHkAmtsFwj9UhFCBFic0mTC9W+DVweO3RFGzW2D2Q3/zoZgNTEiaEin6AfALP1lJ+LIKuwOSzkpJyANjFSt6wwSQ4NggcK0SqO0C0yu5Zq6k+GbJ5p2CSAQqK03mzRvpJDazLcdtoAVmzJDE41aWcaAPcEPkdM13VYaLsQGgg7rX+mzhE4gewGEJ9qQFplPiWikwCiC1yBKv07Mk0aCB422B2iIwCkzS8yT6UGHqtjZYtUqwbZtAVWH2bMnixZKMd92z1NbuRkrJ+KHcbhniqDEV+gSJc52wMoSH7pPFIKBJ9MrTdG5sbGxsbE4KgWBu+mwe0f7AoIjglwFMTBrUOrLMHCYcvpzjA8Ts6sTs6UbJCCFy844q3p1bspiUkeKtlvU0RJrwaG4WFs3jqsoVCCEo8hdQGiympncflaEyFKFgSIPmaCtVWeOGXaPHYn//AWLpGCV+a9mRVBWyb7sEZf0Ont+6hVlpLwSCOJeeh3PZ+cfopx6P6h4WggGcqmUlbYq0ADDQHqFrXxd6Qsef6ye3MgfNdXzZwZAm0XQclzpSvNcUDVNKpDRJZ+qYYUacS10auFXX8L+3de0ilo4zPnPoi1nA6GAJb7W+ww82/DfZniw8mpvS4CgiqQg7umuo7d1H2kjTFGlhXesGFhbN566ptx7TuXssZudPZ3vXLg4MNKCbOk6nRm+iD7/TR4Evj+54L9s6dx1TDAZYWDwfhKC6p4bBdIw5wZnML5zF6ODRVw+eCqoCN4w3MXcr1PYKUobE5xBkzphM4ZrNxDPjDMZ87O3s57Ha1zgndw75uRWUnV2GO3iCcXQp8P5JwbkOpAtQQdujoO2QRD9tIofuF1RVZcaMOcyYMYf/+3+/TldXB2+//Sa7dm1j585tPP74n/n5z38EgMvlIj/sI88nyQ35yc1wU6DFyHebBPxJhKwhEZd0pkrYIpYSk3sQopb+/ih/+lMvO3d2YbaspKO9ldbIm7T2DNLeOwiAIqCsuJ3KSXO58cZPMm5cFbNmzafoGCs3bWw+TthisI2Njc2HjFBVHDPn4Jg5Z3hb2YO/Zf/+2qO20abNRHvrTfTa3Sh5BcjBQcy2FoTThfD5cMxfiOuiS4/YNpmEF16wYhbGjTu4VVJfb22fOlUiqiRGniCxHaKtgslZkuo+MFWJsxB6ewUNDZbYGwhItmyxxGBNg6oqqKo6ypIxAYnlJmqDQl694DPZkh4HJCT4r5TI86x2oRBMmSIpfJf46HZDOi1QO2FmFPw6kIRWoCnDipRIpyGjTWBmgD5XQhqyNgk0Q+KICYrbITQIjbnwcgYs6pDkbYJyU4ImqCmEFge4mi0BOB63zNc+nySREOzaCZNckrJEBreNvp9f7VrCA3/4IZ//4letJXEXSDwPK+Q3gemXFPUIhIBYKahD37qaBsGEwGGCmSk5uKpVy5KEegRddYL8ConDAfEgtCYgM9wAQEm6BLVBICRUOQRyvyX2JpdLzKF7Fsd6gfcBBUyJGZYoEfA/Jli0VDL7ThOE5Th2viXQdghwQWqWJDXP6sf7oIK2B3wZEmXQKhmXuFgiT9OKRDML9LESx3qBHpCICChRLCXbIRFpS5hGH9ruBOkAY5VgjQpKHZT3QXEAjFJIXGaOiHPp6oLf/lZh/35BVpbETMFTv1Vo+iPcPV3imCFJzZfIDNi9ezsAY8dOOO64UzMkju0Cx0ZGFLlLLpCkJ9kRETY2NjYfFguS53BA3c9G59s0UY8Essxsro7fTJaZc9z2pxMZj5N8+jHS69+2cq68XhxTZ+C68lqEP/Ce9ztVB5dXXMSi4rPojvcQcPrJ8+YMC56qonJ5xcXcv+sRanr3DrcrDhRyeflFw8XFjsUR31OcS8nCiTy2aT//esdn8Y0ajVJQeEzHadDpJ2WODOaXUiKReB0emre1UP3ibhL9SWvVjCLIqchm6uWTcQVcR+nV6mNLxw6aIy3s6z/AqEARpcFicr05RJKDeDQ3M/On0Rhppj3WOXx+BpIRTNNkWu4hwT+SGhwuaneQxkgzjZEWcjxZjAoUMZiO0hbtoCprLNu6qjGlSYEvD1XR6E/288KBlfidPv5h+p2n5MCdkTuFzrJu/lT9FyJpS1wMugJMzBqHR/NgyG4cytGlGCklb7Wu59WGN+mK9+BSnUzPncLFZecSdL73M3Q6yPXCP0wz2d8PkbQgx2NSOGMeyYEtqDu3MdHh59f738CnOrlz6ZWM+9QicsYd/0HEQRzVAucGq2C3PFjfQpdoewTOdwTJC448j8rOzmXFiqtYseKq4W29vT3s2rWN3bt30t5aT8+BzXS1HmBTXQ/t/Wk6++PoRvSwXnqAz4/oV9MyaGvLJD+sUOgymV6exwWzKykvzGRiUYDxGVGMWV8mnXvqbncbm48ythhsY2Nj8xFkzJhKXnzxqaO+rgQzcH/yM6Reeg59x1bUCZNwLFyCY+oM1ImTUYpGHXXy2t4O7e2CvLyRk668PMvR29oK5eUQv9Gk80cKef2SDBP2mtDvA0cA3FFJRwdMmGAVHztafYREArZuFezebb1nwgRL5OWzJs61AnWfIDge3LMk6RmHxjN+vOTllxXy8+Xw8v5IBMJhWBw22VStYkgYlODQoUBCNApNhhUtoUo5/A2ntoOjA1z5glgrdKehQUDePvBPlNQPCIo16PNBwiXJSsGnDdidhA4DHA5L98sRAo8Lzo5Jrt6lkJ8jCWcvIFD5VX7842+zeOFyps2aRWqexPQbONcqqK0w4RxJRT3s6BJkd1hCcEeHYEpI0tUHER0CTivuoSECywokMgD79gMIhCmZOFbgamvELdxkp7KtiF235eAmCkoa0lPMYbet6zUBphzO0zXCILzgXK+QPNsAN3h/q6DtF8iAleXm3S3Q9glit5pE3QauNQpqI6THQeosSXrWaRQ7BcTPNdGqFZwbBBIQEYEZkpgFoNaLocp91ntRoScOu18XvLMZljUrxHRJaxhKi8G5RhL7lIk5FCf3zjuCujrB+PESVYJziyDdCzsSsF2DBXsVHDsl0TtN1qxZxYQJk/D5jh6pMowHoreZOKcKtGqr0KBeBelpQ/EVNjY2NjYfCh483B77DGelFtGsNuKULqr0yeSYJy5WnS6Szz9N6qXnUHLyEKVjkIMRUq+/CoDrlk8edX6W6Q4dMX8WoDw0mi/MuIutnTvpTfSR6Q4zJWfiUd//bipDZQSdAdpjneT7rCVBCT1JyYqpNKyu5k8b3+Zzcxcct59puZN4u3Uj7bFOcj3ZSCQtg20EnQHGOsvZ/coejLRJVlkmQgj0lE57TQcH1jcwbunRl9C82vgmf6t9BilAUxzs66ujebCN0cFRuDUXC4vms7BwLmk9xXMHXqG6txYBuFQXC4vnMzNv6nBfpcFi1rS8g24aaIqKbhrs6z8AQFloNCFXBiFXBp3xbl6pf4OueBd53lx8Disn2ak46Ex0s65tE82DrRQHTn5ZlCIULhyzjFGBQn644edoQqEiVI5TdRDX48T0GDPzpx21/fr2zfx+x0MMJCM4FA1VUWnZ/xIDyQHunHLLCT0AaIq0UN1TS8pIMSpQyIQTKDSoKla8mWUDAMjA/anPom94h8cf+j1ruvbxP1/6Gmf/w+cQzpOLX1HrAV0gfYfNJzWrSK9j19HF4CMRDmeyYMESFixYcmijkUKYaaTmxZSSRCJBLAbf+55A1yE391C15FjMw8CAg3vvNRid34tv28/QencgNR9IE2FESRUsIJE19Ui7t7H5u8AWg21sbGw+gpSVVdDYeIBUKoXzKJMtJTcP982fQMaiVrU2n/+E3AuaZv3o7wp/Tactwfbg7tJV8PJiyYv7BRlBSAYkg60CZ9zanaZZYm80Kpgx472BqfE4/PGPChs2HNwiWL1asmAB3HCDxLhOcmiyOZKFCyW7dkl27xaEw5JUCmIxWHCWyV2tggcKJX/pBGdaIE1oMC3zjTAEqZTkvgMCR1IwKV+i1Vo5tMEM8Pghmi8J+yA/CNeEoLMW9heCNuRSECaMaoZZSXhWtcTloiQURcCjw6cUQakOviCkJ0v+MfmvvNz+InffdRvPvfYWwWAG+mTQJx86J7d1w4svCrZuFaRSgpkzJRfNlrz+iMLmLmhOgCYEU3Ikn8oG5zKTjZNhYMCK2agaY/LDrzRQ3DQKCgTJIgnSyviVAQkxgUhY2WhiANQOYTmOD8cJ2kYIfEdBREHtFqTmSeSQBioGwblekJoD+hTQp5jW5Xm/kXQmiF5r/3LIzKJtB8/fFMQAYEhQBPpoCW4rpkJtEogkiAgYeZB0QHu1oN4Pl8YFroCk3QP7+gVJVVK1T+B6WRC/xTrm2lqB1ytRVVBbBEorODLB6IN6r2BuhUTbJdDekbz22ktce+0tJ348HitGIjX/fZ4XGxsbG5vTioZGlT6FKn3KhzYGs78P/Z23ULKyUbItR7IYKhCc3roJ53kXIfJPssrpEJnuMOeMOvuU2hb5C7hg9FKerXuZXT01KEJFIDmr6izyr3Pw85//iFtv/TT+IziXD2dS1gQuLlvOyoY32N1bi0CQ6Q5zSfn5+Lo8xPviZJVmDs9HNaeGO+CmdWcblYvLUdT3ipiR1CCvNryJW3VRnjOaEn8RBwYaqB9opifZy93j72BpyUJUReXc0sWMy6ygpncfhtQZHSyhMlQ2It93Zt5U3m7dSE3vXrI9mUTTMdpjneR4skZEamS6Q/QkezGlHI66AMuJrQqFaDpKNB07pfN9kIlZ47m16lqe2f8S+/rrkIBDUZmdN52zCmYdsY0pTR7b8yw1PXtRhYJAIJG4VRdrWzewrHQhFaGyI7Y9yBtNa3ly3/P0JQdQBKhCY2beVG6ecPVJF/dTAkHqCgv45spnueGGT3Dll792Uu0PIg+e4nfNLYUO8gSTJo6J6kQORYwoQuD1evF64eyzBU8/reD1SkIhyzzS0iKYPVtSUgJShIlOvgdn65s4ujcjFRfp3Fmk8s8G9aObN25j836xxWAbGxubjyBTpszAMAw2bVrHvHkLj/le4fUd8/UR7+2DIgdUlkk2bRV4PFYUga5Dc7Ng4kRJkRUnx5tvCl5dq7AjDsTB1SdQFEt0jcXA74f6epg2TXLWWe8VdTdtEqxfDyUlYNUhkUQisGaNFUUx5Rj3asXFcNddJm+8IaiuFuTkwJw5krOmSzzfFdw6T+LpFKyul2xsFSSSUOWCMbMklEo2r4eftMGPdghyDCsigkFQ3DDaLykJWxNFHRhwwGAaQkP7lgr0JQUTVXhRAz0qUeOCTuBiBfwKxDIh0CPQ6kBOdPCHGX9k4dr5fO5zt3D//Y+jvssqnZUFN94oufxyia4PuZebYepzsL8XOlTIUCUTekFxwsA8yZyJoLQORTm8pNC6v4ni4CjS4yTG6JHnS6s+VMBMesB0S0Tcyr8FIGFFR6gdYBSD2i5Q+gSOrVb2rnRZec6kQWsU6JOHruf7FIK1HeB6SUFrEkhNkp4iSc2ReB9SUPpAHwuooLRJ1F6BGZAovYAiUVrADIOZK0ltEbRLEKMkgX2C9jA4FHC7Jc3tMLZK4tghSMQk0mvlYKdSCiBR+gApkKpEAh7NcvFKl2Tn61vo6upg2bIL39+B2tjY2NjYADIygIxFUXLzR2wXgQBmdydmfx/KKYrB7wchBMtLl1AWKmVX1x4SRpLSYDFTcibS/YXzefyvD3Hffb/gnnv+8bj9XDTmXKbnTqauvwFFCCpDZWR5MmnsaDriQ2RFFZi6iZRHNgC0RtvpSfRSGrCyWLM8mWS6w1SGyulJ9DI1dyJuzTW8/5JgMSXB4qOOMeTK4M7Jt/Bq45ts69w5lA9cTKGvYES2cDydIOgI0CcHiOsJHE5LrTSlScpIEXD46U708ErDG/gdXqqyxp10RIMQgqWjFlIeGsPunj0kjTQlgSImZo0fIUAfTiQ1yObObZjSJNeTjRACKSU9yV4ODDTQGet+jxgc1+Osb9vCrp4aIqlBtnbsJNMdZkK4EiEE0XSMda0bKcsoZVnJopM6hlgsyl133Uhp6Ri+/e2fnFTbw9HHWSvflHYw87BiRAYAE9LTT3L1mZSogw2o/XtBKOjhCZje/CO+9fzzJdGoyYYNCh0d1j3J7NmS664zEVjmCunOJDnmMpJjLjvl47Ox+bhhi8E2NjY2H0EmT55OOJzF66+/fFwx+ERQOsH1jMC5UwEDPpsheSAoWbMfpLQcpaWlkquuMlEU6O6Gp5+2xOJ58wS7dwtiMatwXGYmzJgBc+aYjB1rxT4cqej0rl2WeHz4a4GAJTrv2SOsuIijMDBgxU+sWCG54QbJsOHZsARCbzPcPEkyuwC+vQaCGpSY0BGUrNssiCYFTTrsMCUTgVwEmhdEAhybhSV8CpBOyM6E6n7o7wev13JIuxMSLQSjwpDfBYvTkrRHMGnQqn3mz5BIA5Q2YDyUZ1Ry3+UPcfWjK/iP//gXvva17x3xuLzeQ3/XmgTCLakoEIyNWwcoNYl0g0gL1CaJ9zcKapMV5bCzYRtLfcvQagVGoYQhs4IYAJygjxk6n25Iz5W4n1SQHisXV623xGe91BJgRT8IXaJ0g9IqMEbLYZO2PE1xB2ot+P6gIiISI8fKAXY/peB5DIhbFaQPFpIzC61s4PR40GdIlDaJtg9Ep5WN3BGUPKlBVWio86HPg6pauc6mBOUwo/m0abB+vaSzE/KHEicaIxB2waSh6Eihw8sHXiAQCDJrlm3ztbGxsbF5/yihMMIfRPb3ITyHvvRlXx/CF0DJyn5PG2mamG0tkEqhFBQhXEfP1n0/CCGoCJW9R0gsLi7hxhs/xS9/+Z/ceOOnyDrCGN9NgS/vPYXrMgozcPqcxPvieMPWsUtTEuuLM3puKap25Ewxp+pEUxwkzRSOIYFUCIEhDZya8z0F5U6EXG8214+7gisrVgDw5L7neKHuVQaSEQJOP3E9QcNgE7Pyp7Gndx+NkWar0JvioC/Zj6IouFUXD+x6FMO0qt0W+vO5ecI1VIaP7cp9N0IIRgdHHbPoWyQ1yOrmdWzq2EZ3vIfOWA8+h2fYYS2EwKO66Un2kjRSI9rG9QT37XyEzR3bcAiN9lgndQMNjAtVUOQvQBMqPocXr+ZhQ9uWkxaD//Vfv0hjYz3PP/8W3sMnsieJMQYSF5m4n1fQdg9tdEFyoUlq5kmIwdLEve+vuJpeQqQGrE3uLOJlV5EqWgbvWiXp8ViGjKVLDbq6rALNowoSeNpew1G7GqFH0TMnkio6FyM45pSPz8bm44YtBtvY2Ngchb19dWxs30JHrJtifwGz86efUm7YqaAoCosWLWPVqpf5x3/81vvqS8TAe5+CVi0w8yXSA5mt8DmPYNaFJo0ea9nUpEmSwJDhYe9eQW8vjB0LiiIJhSRdXYLubgiFJN//vsmR5oNKN6h1AlRwJo48nqMYQwCruN1zzwnWrRMMDgr8fsncuZKLLpK4hioPp86WeB+0lv5neiGoQDAmSOVKqjsFiYQgP0Pi7hS0FUNak4zqEkweBJGyoiSkaeXoqt2SbB/M8cOufohFBZmGxJ8DHdkwo1Li2SCYn4A9Bgyq4PNKPBpWni1AGkRSsPjK5Xx9wg/4+te/zIQJk7jssptparKOt6gI3n1vp+2ynLvpGRKlz8rHlSFQ9wm0GmCbQG0W6OMlbbFWaqN7+Ndp30T0gvNtYcUvpEB6JImLTPRxh/pOLJeIXhPnJgXRJlEbBDID0lOtYnVmgUTtEGCA0j+U1NAKMgP0CaeeDax0W5EQMgzONQLRx/C41D0CtUOgtEukCkpCoI8CfaIlCku3VeguNe+w/acl6JDqgr4fq+yRktluyIhAXxBiMUFxgcTZI0guNIcLkkybJrngAli1SmHXIGhxSaaAK6dKxoQsMRwFXmp+gYULl+Jw2IG/NjY2NjbvH+EP4DhrIcmn/gYIREYGMhLB7OnEee6Fw9ERBzGaGkg+/ijGvr2g6yh5+TgvWIFj9rwPdNxf+MJXeeKJR/jXf/0Cv/zln06pj0Cun9LZo9i/po74QALNqZGKpQjmBymdU3LUdqP8hZSHRrOjq5qKjDE4VAdJI0lrtJ2zCmeT5c481cMadt9eNGY5kdQgWzp30hJrw6E4mJxdxY3jrmRXzx4e2PVn9vbVMZBOkuPNpjJURm+qn9H+Ejyam7Shs61rJz/c8HNunnA1U3KqcCoOavvqSJtpSgPF5HiPL6Ifibie4A87H2ZL5w4CDj+D6SgpM0kikcCtuQg4/KRNnf7UAAFngNJ3uaI3d2xnc8c2SgOj8GhuEIL2aCetsXYKYnkU+S0nuqZoJM3UkYZwVB599AEeeeQP/OQn/8vYsVWndHzDCEgul+jjDbQaAToYYyR6JZY54ARxdG7EVf800pmBEbYEdjXahGfvnzGCYzCC5e/dtYCCAusH08BTfR+u5lVIzYtUXbgaX8LRs4Po5C/YgrDN/zfYYrCNjY3NEVjftoWHdz/GQCqCR3OxtXMH77Rt4raJ1zM+8+gFME4nS5acx5e+dCfd3V0n5NI4Gtp2gVYL+thDRa70IDh3w+wYTLryveKfOSR0Hny4HgxCMCjx+Q5lDsfjsHatYMMGQTIB58RhegNkGOD1wdVAZx90hS0Hp9drib0Oh2Ts2EP7VDpA2y1QOmDTdlhfI9CLQNMkNTWWMLx5s+See0xycqxiZmIQXK8L8joEBWlYPSiJOwUNPeD3SwZMKHJICpwgfBDtlEhDWLm1gJFvCZCiTyCzJAW5gnwXpFISkQF980zSewW9zYKMSRIjJkgMwluZkiwNJrZApilQiyTafoFeZcUf3LHsHnbt2s69936W1avHoevzkBIKCiSXXCKZOvUIQqsKZta7thlW1WUz0xJvVze9AcCCiYswN4CIg0gPuXgV69wpXZLhOjkeiN8qSZ1joLQLnKslju3KcGavUQRKL2i7BEqnRNstkCGIX2JiHH3l5dGJg+cpgXO9lUcsfaA0SYxcCQKULtD2CavoXcA6PtMNWgPILDAKrQJy+ph3ZU87rJ+iYliwwOTllwUvBSTntAh83RD0SCoBo1SSXH7YZ0qBSy+VzJxpULdf4FkvmLxHkBcTUA24oXFGKxufWcf3P/WLUzhgGxsbGxubI+M87yIA0m+9gdnRhvB4cV5wCa6LRi5BNyMDJO7/X4wD+1CKShCahtneRuKh+xE+P1rVpA9szLm5+fz7v/+Uu+++jYsvvoIVK6466T6EEFQuqSCYH6R1VxupwRSZpWGKphbiyzp6pJmqqFwz9lJi6Rh1A/WYSFShUJU1jssqLjyhehjHQkqJbupcXXkpS0sW0RXvJugMUJZRiqZo5PpymJk3lQP9DRjSYFSwmB+u/x9y1Ww8mhvd1NnRs5vWaDsDvXvpT/aT6Q5bcWOmgYlJhjPIOaPO5sIxy06ouNvhbOvcyfauXZQFR+PWXOimQVu0nbr+BgZTUdKmjoKCR/NwVuEcRgWKRrSv6d079LoVvBtyBXFpThJ6kp54L0X+AgzToD85wNlFc094XKtWvcSXv/wZrr/+9pOrrXAsBBglYJScuvFA69yAMHUMT+7wNsNXjNa7A0fX1iOKwSPa99fgaluL4S9GOjMAML0FaL07cDavJB6845THZmPzccIWg21sbGzeRVyP82zdS6SMFBOGhF8pJbV9+3mu7pX3FKo4UyxefC5SSl5//SWuvPLGU+5H7QIQ4Dhs4iWswmNqvRUR8W7KyyWhkEJ7uyR/KILLMKCrS7B8uRUl8cADCm+/DW43BPcJ1D2CDSoYuZCdJSn1SLQmwVsHICYEDockN1dy1VWSqiFzgXO1wP03gWOHgB6Y0y+o8Er+3AN/TUFCQioFL7wgSKUU/s//MRk1CpIXSlJnSapXChp6FVolmClJKiXo6IBUhmB6gaQgAS1uCKcAJNItkBrEfWAAvohASMnAvQbaXsspa5RLXMXwqSbJSy8pVFfDA37oH5Q4k3C/AZUxWOCTTJ8kUZZIqxBbEASCL33pf3j11RqefPIqrrlmHYFAEc3N8Kc/KYRCBqWl1rHrE8C51nJuyyGXtYgAmkQfC1oNKEPu6tVNrzM2czx5Mg+1E9KVEn3a0MXSQdsjcK0UxG8ceY0PTrilH7T9VqyFmQcoYGRL5CRILTIxxkB6nMQceX9xwrifEbheUjBzJMYoUAZAaROIAUmqBJQuywEig1j5xE5QBgWkJUqdQETAzJOk5x/55kAIuOIKSUEBvLNO8OoByVwFJuSBMkMSnSUxs9/bprgYioslLJSodRCvtRzVRpnkvhd+j9Pp4uKLrzi1g7axsbGxsTkCwunEteJyHIuXIvt6EcEMlIzQe95n7NiGUV+HWjEeoVm35eroMvQ91aTXrflAxWCAK664nmeffYx/+qd7mDdvIdnZucdv9C4UVaFgYj4FE4+c33o0ivwFfHHmZ9jZXcNAMmLl3WaNPaWIiMNpGGjihQOvUtu7HyFgQuZYzh+9lEL/yPEFnH4m51iT075kP7qpD+cLN0SaaRxoIuD0Y0qTLHcmWzt3IgScW7IEn8NDZ7ybZ+teItebw+z8aSc5xmYkYjgXWVNUJmSOoyfRh6Y4KAkUoUudIn8h1469DE0ZKeGowqqRcJBMd5jiQBE7uqrpjHfTEGkmkhqkLFTK2UUn5jjftOkd7rjjOpYsOY/vf//n71uQP50oqQhSedfnQgjrx4gft7062AhmclgIPtjedGWi9eywlvR9hI7XxuZMYYvBNjY2f7dIKdnWtYt1rRvpindTEhzFvIKZVISOvfynMdJCe7RzxJN3IQQFvjwaBproiHe9JyftTJCfX8js2WfxyCP3vy8x2AxgTWxM4DCzgogJzJwji2+5uXDeeSZPPaVQU2O5eRMJQVmZZOlSye7dsGGDoLhY0t8vKG0Fr0PSqAjCEjp7BX9ugc4UTCuBTr8kFgOXS1BUJNE0UJvA87iCWgciIUgEJb0xMJOwe78gJwCxMRLDgP5+QV0dvPSSwic/YeLYCeoGwUvPKqRSsPwsSVOvYO9eSXe3wOWWdI+V9O8TZDRaxeFUBLoOnQr0NltREUUJSe+gINgoUBuw4goyBEaBZNQo+OQnTbZtg18NqhSNl2THBSSgPwD/GYMLZkiWzpJkZRyql7Jrl5tJk/7G+vVzefHFS7nmmpWUlYWGz1lpqXXOU9Ml2myBcz1wML7CI0meLUlPlagt4H5CgUzJm42vs7B4MdoegRRDhdcOooGZJXFsF8STEo4QNaiPh8QlJu4XFbQaa/8yQxC/xSS5TL6vQnGiF5zvCMzsQ4KsmW0Jro5qgVpvRT1gWG5kMwjpaRKt0coVVlKQmCZJnG9iHD1OD02Ds8+WnH32yM9s8oQGCUaZNSYAwzB44I+/5fLLryMUCp/ScdvY2NjY2BwLJRCEQPCor5t9vQjJsBB8EOELYLY2n+nhvQchBN///s9ZsmQaX/3qPfzmN4+cMREwbaZRUEaYKzyah1l5007bPjpiXfxux4M0DbaQ781FSsnqlndojrZx97RPEXJlHLFd0BmgJFDMzu7dZDiDNEVacCgODNPArblIyzSqUBBC0J/sJ+D0kefNIdIXYUP75pMWg12qE1MaI7bl+3Ipyygl05PJ2HA5Bb5cZudNP2LhvKqscbzV8g6R1CABpx+BoNhfQEyPURkqJ8MVYELmQuYXzDqhKIva2t3ccstlVFVN4Ve/eugjF6Wlh8bi6FwPpgEHPz9GEqTA8Jcet71U3UN/MeEwF7cwkpienKO0srH5+8MWg21sbP5uWdW4hr/tfYa0kcbn8FI30MDWjh3cNvF6JmWPP2o7RQiEEJhy5JJ1U5oIIU56+df74bbbPs3dd99Obe1uKiuPPuZjkZ4kMfIF2l6BXipBA7UNcFiFxo7G8uWSoiKDrVsFkYhgzBiTmTMlWVmwebMglQKfD3bsgLkGSIdAE1YUBFmSPW2CKj+EcyUlQzm0DQ3w1luChQslgZ0CpROi/dAnJaaAhAMOxKHXlIzWBfuT0GOA0wmFhZYInX5WEHpRITIIbc2QawoyDciYISkrgy1bYP9+eKtN0JEtOSsbxihg7ITuQeiICNIahJEkBLQ0gOsHCv4hTdC1SpJcCPHrJEKDtjZBKg0ZEyA95LyI9EPzO4Jf/1ph7VooK5NcdJFJeTl0dYHPl8eVVz7Dn/98Dk88cSlXXfUCbreXjo7DnNhuSJxnotUpaNstx6o5BvQKQIPkORK1XrJrzRZqe2v4zpjvIZ1gFklwn+SHQEDyPEl6soG2T4AAvdzEPDnjzhFR+kGJCfSCkZ8lvRyUnqGc5kEQCSvGQp8ikWFI+yRSCOJXmyRXvD9B+mR55ZXnaGlp5PbbP/PB7dTGxsbGxuYwlFAYKUDq+ghBWEYjKJOmfChjys7O5bvf/Rl33XUjDz10Hzfd9MnT2n9jpJnXGlezu6cWh+JgVt40loxaQMDpP637AdjQtoWmwRbGhyuH5+4hVwY1ffvY3LGdc0adfcR2ilA4t3QRjZFmavr20p8cIGWmcKkuKsNlKEJBKAIk6IeJuG7NTW+i76THWZU9lpWNb9IW7SDPa4mRvck+gq4At0+8nhm5x/4sTM2ZyNlF81nTso6mwRbAKsp3ZcUlXDP2kvc4iY9Fc3Mj111zAUFPiC+u+AZN61oomlJwzJiPD5p0/gIcHevQendiurMQ0kQke9G1hajr5uDaq2D6rZoc6ZnyPXnE6cxJmJ481IH9GMEyEAoi1Y/Q46Tzz7ZdwTb/32CLwTY2Nn+XDKQivNywCqfiYEzQKlohpWRvfx0vHljJhMzKo0Y9lARGUeQvoDHSTEXGmOGKxi3RNqblTibXc+r5vSfLxRdfxb997Uv84Zv/yL/MPAvhC+CYMBd1znSE68REaZkJ8VtN3H9R0OoFot8q1pVaYJKuPLoYLARMnAgTJ0oOX362Zw+sXCnYtQs6O61Cc00emB61llUpCgwYkNBBS0FvGnymleMaDEJfnyAaBX8CWltgS5sgIayH86oOKV1gSokwwIhCzLQcyU4niAR4XhVIl8QRBH+toD8N2R0CdQ+4ZksmT5aoquC880wmTICqKokShdc+r/DqO4J8U+JKCfZIQYZbsjQGa5sExX5JdjbkucH5piA9WaJPOTQnPLhqLB63xPDubkFursTnk2zdKmhrU7jnHpPcXNB1QWbmJK688hn++tfzePrpaxg37gny8w997YoYeB9WUDoE6elWnrPSDt5HFaIhA30iRD9t8rsd95EbzGPRPeczWGLg/b2C2mrl/gKgWzEMyfPNI7qCD8csgFTB0a/5qWCGwPRJlAErB/ggygAYlRD5jInSi1U9er9VvE2NgIgL0lMlqSUfrBAMcP/9v2b69NlMmTLjg92xjY2NjY3NEOqkKailozH21qAWjQKHA7Pdyhh2zFvwoY3rkkuuZs2aVXz1q3dTUjKahQuXnpZ+Wwbb+O22P9IcbSXLnUlCT/LE3udoiDRxx+Rb3nckxLtpjDTjVt0jTByqoqIJlZbBtmO2rcoax6en3Mrq5rfpS/TTlehmSnYVxYFC2mId6KaOU3EQHBKxpZREUoPMyZ/5nr4M06Cmdy+1vfsxpUl5aDRVWeOGRdp8bx6VoTG83rSWvX11BF0BQq4gy0oWMTV74nGP06E4uH7c5UzNqWJvbx0AleHyY97rHImWliauvnw56ViaL13yHZIdOjUNe2jd0cq0q6eSUXB0l/sHienJITb5H3A1voLWtREpNHT3ZTiePx9PoxvTL9FS4NwiSDRA4qqR80zpziI+9lY8ex5A660GIUH1kCxaSrJwyYd2XDY2HzS2GGxjY/N3SWOkhe54L2UZh5YLCSHI8+TQNNhKd6KX3KMslXKqDq6ouIg/Vf+V6t5aVBQMTEYFCrmk7LwPNDdL6+7k6tIKHn7jFb6izyK7vQgtEUNU1KHfXE5qCccVAMGKFYjeaeL/uYIWsZzPzg2gdAritx55eb6U0NMDiQRkZsL27YIXXhCsWSMwDNB1y4GbSAgeVaDUKRmXlOgBSLQKginYp0LzAQhGBVOmSAYGIDvbKkS3IwJqD7gUCKpgOKEvDnlIAorgQAqiCMaMkZSXS+rqBOeWSQIHrIxd/zbB4hg8OCjpQRDeLYiMljR0C2bMkNx8s6SvD7Zts8Tn3/RCrSkIqOAVEFPgygR0JAVNElL7BfX1klGjBNM1cNQI9CmSykqJ3285fnNyoLVV0NMj0DSJpknq6wV+v/XnxlWCBR5Yk5TsWysoGD+X8857jOefvwTDuJ6vf/0hDlbx03YItDrQKw4V9jPGgLYHnG8p6BNNEjLBX998iBtv/iTm+RrROLxTLml5XsFdA5NCknFugV4pSS49vSLviSJDkJoncT9r/V6YQVAiVtG4xPkmZrmVUDI40cS5TuDYKhA6pKeYpObK4aJ2HxT799eyatVL/PSn//vB7tjG5jTR09PDPffcw9NPP42iKFx11VX813/9F37/0Z11v/nNb3jooYfYtGkTkUiE3t5eQqHQ++7Xxsbm1FECQdy33kHy8Ucx9u8F3UDJzcV5/grUCccXAc8k//7vP6WxsZ4777yOp556nbFjq953n2tb19McbR3h1M10h9netYtd3TVMz538vvdxOCF3kJSRGrFNSokhDTJcxxc2K8NlVIbLWFy8gP/d8Uc6Yl10xLpI6kkcigNV0UgZabrjvXTGO8nxZnNW4awRfRimwWN7n2FV41skjCQCcKgOziqYzfXjr6A73sv9ux5hb18dLs1JKpVCEYIrKi5mcfFZJ3zPoSkak7OrmJx9ateptraa66+7mFQ0yVeu/C4V44ZqppiS7gPd7F9Tx7SrpnxksoNNbwHxcbfA2JsA8DyqojQopMcfcgIrPeB6UyE928B4V3pEOncWenAMjp4dCCOB4S9BD40bERthY/P3ji0G29jYfOxI6EnWt29me+cudKkzMWs8c/JnjFhi5lA0VEVFN40Ry6N0qaMK9bhLpqqyxvH5GZ9ma+dO+hJ95HizmZoziUx36Ewd1hFJv/YK14ay+K2u81yNzp2h8eiubsS+LjwPFKP2uolfd2LOSvcrAq1RoE+QVsGyNGj7BJ5HFAa/aI74RujqgqeeEuzcaWXy9vVJEgno6REMDIBhWH+aplVYrsWA7xjwySyYFRV4YqBlSA4Ewem2BNRYDEaNkixcaLl8X+0WjA7D7F5JMiow01CIpNYB83JM3g4IZLYVEVFfDxUVkvPnSqgVaLutWIXzSqyxru2B1ohE2SEYt0Ryww0m69cLnnjCci739Ql2NAhSisQdFEQ1iOlgJgWGaRXBy86WJJNWlEV5zqEkhtGj4dxzTV58UaGrCxobIRKxco8Png/TFJCE2gcUbiqAz+nw1x7Yt1bgKT+XFSv+wnPPXc13vnMzv/jFn3A4HCi9gPmuwn6A6ZeoQ1GBL774FH19vVx33W0MDsJ99yls3waKCwr6YWtcMHO5ZMnnTCsY+UMicZF1DM63BVqrwBQSvcSKiHC9KkhNlcgsSC0ZcgJ/iPzoR98iL6+ASy655kMdh43NqXLTTTfR2trKyy+/TDqd5hOf+ASf/vSneeihh47aJhaLccEFF3DBBRfw1a9+9bT1a2Nj8/5QR5XiuftezJYmSKdRCooQ7pPNgjr9aJrGr371IJdfvoRbbrmcZ555k5yc91cvo7Z3PwFHYIRT16O50U2DlsG20y4GT8+dwtqWDTRGWij05SGRNA22EHJlMOUkRNOyUCl3Tr6VN5rWsqd3LyF3BstLz2EwHWFH1250mWZm3jTOLV1EaXCku6K6Zw+vNa4hyx0eziiOpAZZ07KOceEKtnXtYk/vPipDZQihkDbSNESaWNe2kQVFc3CIM5/Xu3HjOm699TIyM7L5wjnfpKLyUGEKoQg8IS8t21vRXCp60iCYH6BgUgHekOeMj+24CAVM0LYLzMyRkRBmGLQOUOsERul7557SnUWqcPEHOFgbm48WthhsY2PzsSJlpPlT9V9Y17YRh3CgCMHWzl1s69rFnZNuwe+0Mq1GB0sYFSjiQH895aExqEIlbaRpi3VwduFcwkcpGnE4ed4czitdcoaP6MjIeAx953ZSK1+kOJDJtVnL+V7Xr7my5GyCqpd0dxuKsxfnuiKSiwzMwmP3JwbAsUVg5AwJwQAOMEolWp1ArbOW9IOV+Xv//Qo7dwry8yVSwp49CpomUVXIyLAE0WTSEmpdLkilIOmCJwPwtCbJKYOySRK9TtDRAYoiGRyERYssMRis/N4DEyQyDWXV4OiHAw7Bc0Ky9Hz4vzcabNstiESgsBCmT5dkCJAPCZQeMEaBS8Dt2XCeCc1ecI2W5N9j0t4Fjz2moOuSsWOhpQW2brUmtf0JK+s4kYTtwAIBGUNzbZcL9F5Bf1SiDZ0PIWDFCkllpcHOnYK1awWdnYJg0HJMC2HVsGjYBXUGpBdLylS4NwUdOwSpCknGly9k5RWPcOed13H33bfys5/9AUfIbS1NSzPsDAZQBgXpsdY5+tOf/pfZs8+isnI8L74o2LJFMC9DMr8TsgQk49D7oqBvoiB06QcftzCMGxJXSpLLJI5N4H5OQWsQaE2WaO98XRK72QQPSBWQ4NghEBEwc6yCcvIDWH24ceM6nnjiz/z4x7/B4/kI3MTY2Jwk1dXVvPDCC6xfv55ZsywH2n//939z0UUX8aMf/YjCwiN/GXzhC18AYNWqVae1Xxsbm/ePUBTU4pIPexjvIRAIcv/9T7BixdncfvuV/OUvL+H1nnp2bMDppyEysjCelNZ8x6OdfgG8MlTG1WMv5bm6l9nbb8Un5HpzuKz8wiMWYjsWYzJKGJNRYtUPQQw7ZON6At3U8Tt8I1yzUkp299Zy346Hqe6uZWy4DIfiwOfwEnD6aY218077Zur668lyZ7G/v57mwVYM08Dr8BJLx6gfaKQiVHb6TsgRWLnyee6883qmTJnBD/75V9Q+W4eUEnHYhHKwc5C+pj4M3UB1KjRtkTRva2XaVVMI5n3Ay7uOhAAcVn2KEcih/9iKl43NEbF/NWxsbD5W7OzezYb2zYzyF+FzWKpmykixs2s3G9q3sGSUlbHmVB1cXXkJf6x+lD29+wCBImBcuIKLP+Coh5PF2L+XxEN/wGhowKipBkPjS9pinjRX8d8tD/Mvoz4FgBk00CIStV1gFh7bbSkSIFICMzjyfdIFpK3XD7J7t2DPHigvl7hccOCAwDUURRGLQSwmSCRA06wc4EAAolHw+QSZWVakQlmZxOOBadMk0ajVrqHBEoa/8x2FUMiaKLdHFXZUSXZWSNAhGZF0tYFYIPGF4eKL5Yg6DhJInwXOHaB2CqRibS3Kl+QXW5EDAyrs2iXo64Px4y2xNhiUeL2CSEQihCCdtmIu1gmY45RcIsDTY2mz/UlJU4UkOPHQuRLC6mv8eElmpmT1ast6YJrWa5FuCAlB2i3pSUGWB1QnFJZJlA5BpBfOP/8Sfv3rh/nsZ2/illsu5fc/+wuu0hDaPoFRJJFOUNtBOiF5lsnq1a+xZs0qfve7RwHYtEmQr0mW1AiCg9ATtCIZnM0C54MK2lgDfcLJf95OJ9IHzncUlH6BPk6CApjgWC/I+IqKUWwVnBPdIAMg/YCU6G8KYp80MQvO4Nik5Bvf+L9UVU3h2mtvOXM7srE5g6xdu5ZQKDQs2AKce+65KIrCunXruOKKKz7QfpPJJMlkcvjfAwMDp7R/GxubjybFxSU88MATXHXVuVx//UX88Y9PkpEROqW+ZuVNY3tXNd3xXjLdIUxMGiMtZLnDVGWNO70Dx4qHO7toLpOzJ3BgoBEBlGWMHjaOnCjd8R4ODDSiKRoVoTHD9x9wdBH79aa3eGzvs9T27mMwHWF3zx6aB9uYkTsZTdFoHWynK95DNBXDMHUSZgqP5kYTKl3xblJGmpqefWdUDH700Qe49967WLbsQn75ywdRTJWWte0MtEXIKAwihCAVTdF9oId0SGe/7wDRdByf20PW/kyCawJMu/LDKXQ4AgGp2RLPXxWIS/AAEtRmyx2sj/twV6TZ2HxUscVgGxubjxX7++vRTWPERMypOnGpTnZ21wyLwWBlfX1xxmfZ3rWLwXSUbE8mk7LG4z2s7UcNGY+TePgBjMZG1LJypDQxtu8iM1HNpwOL+EXLo9zuXUKhpwDhz4aUQHrNo/cnLRdvd4cg3yUZ2wkc5sBUOsEMSYzDTF/d3VbsgctlTZ6UIcHV7YZ0Grq7JaYphrZbDmGvd8gha0JenqSrC0aNsrb5/dDUBG1tsGWLQna2pK0NBgcFqRRUV1uF2HQd6ust5feJJwRPPAFOpyQzE3JzYepUyYwZksQFJs53VMSA5XKWfjCzJWqdIDXPRG0F8x2B2gpqpuU81TRwuSTd3QJdt8alaZCWkqeLIWeypKRfEE/B9jzJRedK3M9bzlWjCNLTJXLITJ6bCyUlMDgI/f3WeP0uSYkfAu/Ob1YBA8v9C1x44WU8/PBzfOITV3H5zUt55IdPU7qqCG2/laNrZEuS55ukJ0m+e8m/Mn36bC644DLAiuMo74NQBNqyQA6J5F1eqEiA4x0rAuTDRG0EtVFgFA8JwQwVjGsDpVcg+iRKjwBDYpRCepLl2ND2CNzPKcQ+aZ4xd/Mzz/yNjRvf5s9/fgFVPfGCKjY2HyXa2trIzc0dsU3TNDIzM2lrO3ZBpDPR73e/+12++c1vnvJ+bWxsPvpMmTKDRx99kZtvvoSrrjqXhx9+9pQiI2blTaNpsIU3m96mo7cLISDHk8UVFSvI9+Uev4NTJMMVZGrOyWcwSyl5uX4VLze8Tl+iH0Uo5PtyuapyBVOO0V9Poo/n61aiCZWqrHFsbN9KwOFnIBWhumcPg6kYbfEOxoXKGUhHaI92kuvJIuDJAgRpM41u6uzvO3DqB30MUqkU3/72P/G73/0PN974Sb73vf9B0yxZaOyySnY9X03X/m4URRAfSDDoiNIR7kbEBS7VRXeyj34Zge1QdcF4nN7TW/jvVEgtkmj7JY5tgGFNJGUIEleYmDkf6tBsbD6y2GKwjY3NxwpNqBxJ7jKliVN5b65W2J3BouL5Z35gpwmjdjdmYz3qmDKEw4k2phz6+zCrY9wZL+Ih4eE7nQ/x6yW/x9HmRR8n0Y9iGohG4dFHBVu2KAwOSrwxwZRuuD0uycqzllMJHRKXWHmuBwkGQQhJOg0Oh+WqVVVBT48loJqmwDQtEdgwrMiFnBxJRwcUF0suvFDy5z8L9u+3IiX6+y2RNxCAyZMPOX27uqyfceMknZ1Wf1JCIGC5irdtE7S1CUIhy2m8caPV57XXSBIXm7ieFwhDgGlFXeillrvW/xOV8npwtoEeETiLYUcaHA5BICAxDDHk6JXk50N2seQ13SpKJ3ySa/IlM19VUCOAKjF1QWSlJHWrSaDSyhCeWCEZ7AA1bMU8BF3Q8AaM1SDzoElEWk5ffYzEPOyeaf78RTz++KvcdNMlXPyZxTz4p6cZ5xoHKTALrXG88PyTbN68nkcffXHYxT5liqRnjYJpHhKCEwnrGnmyJWqHgCP+dpxm0gxnGhtFjIi4wARx2PgAtP0CpROEAUoclARIRaA1SozRVtE8M0/iqBaIfmvyfrqJx+N85zv/wvLlF5+2qug2NqeTf/qnf+L73//+Md9TXV39AY3mxPnqV7/Kl770peF/DwwMMGrUESqS2th8xJFSsqVzB++0baIr3kNpcBTzC2ZRHhr9YQ/tI8H06bN57LGVXH/9RVx++Tn8+c/PU1xcevyGh6EqKldWrGBO/gzqB5pwKBpjwxWE3cePbvsw2Nq5kyf3v4BX9TA2XI4hTRoGmni45nHyfXlHLURd119PT6KXseFyTCSF/nxaBttImzo1PXsRisLYUBnTcifhVB10xbrpTfbhVJ0oQqAIlYpQGS3RdtJmGscR7m9OldbWZu6660a2bt3Ad77zX9x++2dGrJYsnFSAP9tHZ20XyWiKwegg+1YewKFpBN1WJITP4SESi9I82EZcj+PkwxeDpd8qlO3YIVCbJdINepXEOLk0EBub/6+wxWAbG5uPFeMyK/A0vE53vJcsTxiwCjGYUjIl5/1XOv6wkbEY0jBAsyZ+wuVGmzkHw7cHz17Jvxhf5IsHvsFNbbtYMreY2I3mSDHuMJ59VvDmmwpFRZLiYojFJG9vE4i04ItCYpZAaoEkNX+kgFhVJSktFezZIzAMaG+3CrD19VmZwV6vJahKaQmqhmG5ZAMBK9Zh0SLLRfzmm1YsREGB9XpBwcjIh6ws6Oy0MoQnT5Y89phg1SrB+PFQWyuIRgUFBZbI3NkpyMiQvPSSwsyZBpUrrAmeY4tE6RckK0z0EonvfhUpJFWzYZpD8E4jiN3QJAXSI6mogEmTTNJp6OuDcNjKA963D1RVMnkULHxRoJiSgXJ4oQ5eb4LIFgXvdkHVxSbXBuDLXdCwXaEzJtmhCrZqkmI/XJEBjj0C6ZWIqLBcCRca1rWMgPQAGkyYMJmnnnqDm25awWWXL+GPf3ySGTPmAGAYBt/73r+xaNG5nH32OcPn6+yzJatekkTXCPq7QUegKJIxoyVBIUgWH90hflqQoG0A9wsKWrtAAkaxJHGZORxPYRSDUSBRWy2RF2EJxyIlMDOtWAsxCNIBShSUDoExRiIVS0TGOMb+DVD3g9IjkCGJXs4Jz2K+9a2v0NHRyp/+9NT7OgU2NmeKe++9l9tvv/2Y7ykrKyM/P5+Ojo4R23Vdp6enh/z8/FPe/6n263K5cLnevSTCxubjx6uNb/L43mfRTQOv5qGuv4GtnTu4feINTDwDEQYfR8aNm8gTT7zG9ddfxGWXLeGRR56jsvLk8qmEEIwKFDEqUHSGRnn62NC+Bd3QyQ9armVFKJRllLK7t5YdXdUsLVl4xHaKUBBCYEqJpqhMzZ5Ijieb/f11RNMxJoQrmZY7CYfiIMeTTbY7zKAeRxEKhb58igIFxNMJgi4/mjh9cs3LLz/LF75wB263m8cee5WZM+ce8X3B/CDBfGsZ4Z7mfcTfSuIf9IILa/WWCd64m57SPnpkPxl8RMR8F6RnStIzP+yB2Nh8PLDFYBsbm48VY8PlnFuyiJUNb9Ie7wAELtXBgqI5TM/9CORWvU+UgkKEz4fs70OELLEbVQOnCtdO5boLP8+T/7CGO/Z8kpW/2ERW/pFdCQMDsGGDIDtbkjE0R/P5oHiSZFuPYOedJiVlHHFJvtcLt91m8s1vKmzfLtA0CIUkui4QQlJQALm5cjjmIZWCjAzJeefB4sWW4Dt3rmT2bEkiYcVMfPvbConEyP0YhvWaS0CoRtD5ppWFqyQsAVoISXu7lfMrpSVIp1Lw2muCykpJeoYkPQMOumFdKy1nqTHe6vOOqZKKMDy9WcAAjJsGo0dbmcYHjzMSEUycaLJ4qJiwY7PA0QNbMuCXrwlWN0FCh6AKhfUw9RGFgRSMqZIE3ZKBAcEkCYuKJOVhQb5fkh4vESmBUWySmiVRWwX+7wnUPoGRKUktlKQWSIqLS3jiiVXcdtsVXH31cn760//l0kuv4de//il799bws5/dN+J8ZWbC8n8ySX1TIbRXEA1LsvMgz5TIoCA178y5grVt4HlCwfWKAAP0SuthglYH3gcUBv9hKO/XBYmLJd4HFbTdID0S0QcoEjMLcGMV+dCtyyZ0OeSgFlYRucwj71/0gfchBcdOAUkJToX0OEn8ZtPq9xg8++xj3H//r/n+939ORYV9Q2/z0SQnJ4ecnOOvZZ0/fz59fX1s3LiRmTOtO95XX30V0zSZO/fIN/Ynwpnq18bm40Bfsp+X61/HpbgYE7QefhRKSW3/fl6qf40JmZUoQvnAxpPQk+imjs/h/cjVuCgtLeOJJ17jhhsu4pJLFvGzn93Heeet+LCHdUboSfS9JxP44PUYTEeP2q48NJocTzbNgy2UBIrRFI1CXx6RVAS/w0eeN2/Y7ZvtySLTk0Ui2sqErLGUBUvpTfbRayaYVzDriNe/N9FPe6wDr+ahOFB43M9mMpnkP/7jX/jtb3/GuedexE9+8r9kZR35/uHdBEJ+EpNTeLa5UNvU4VoQepZOaoKOW7MfBtrYfFyxxWAbG5uPFYpQuLT8AqqyxrGndx+mNCnLKGV8ZiWa8vH/X5pSMhrH7PmkVr2CHIwg3B7Mni6EP4Bz6blQofKT+/6XpUtn8OWv3sXvf//XI04Uo1FIJASZmSMFQq8XWlthMMkxs1n9fiviYfZsS0yWEtavFwhh5e3Om2dFOzQ3W67fG26QXHWV5HCDmKJY+wOYNUvy1FMKoZAVAWGaUF8PhZkw7XWBdy/k1giqe0B2CoIqHOgXw1EVoZA1nvp6eOcdwa23SjwJULpBBq1c4IO5vAePy++EFRUwU5d8cx+4ig4JwQC9vZaoHTwsQxkTelJw/07Bzi5QBYzJEMQSYEYFU53QqEjc/YIyCRnjJGIAKjRBarJE1AiMUkniCsul63pB4HlcIF1gBizHrPdBgUhC8lxJOJzJn//8Al/60p185jM38eKLT/Pss49x111fZOrU91obAsWgfMPE85SCtkeAablxExcalhP3DKBtA999KupeaWVU+6WVcWxAerLEsVvg2CxIFliftfQMyWDIwLleoLQLRLfEsUdA2ipYaAZBbQU55ATWqgVmviRx/tHzgt1PCJwbhqJAfCBiEsdWAS6F6F1Hb9fQUMe9997FJZdcxc0333FmTpCNzQfIhAkTuOCCC7jzzjv51a9+RTqd5u677+b666+nsNAKf29ubmbZsmU88MADzJljrThoa2ujra2NvXv3ArB9+3YCgQAlJSVkZmaeUL82Nh9FpLS+e96PaNoUaaE70UtlxqEvUiEEeZ4cGgea6Un0ku05zpPH00AkNcgr9a+zoX0raTPF6IwSlo1axLjMijO+75MhL6+AJ55Yxec//0luv/1KvvCFr3Lvvf92Snn8UkraE820xRqIGzEyHJkU+koJOc/8+T4eYzJKqOmtRUo5/PlKGWkUIY6ZcRx0Bri84kL+XPME1b21qELBlCaV4XJGB0fxSv3rxPUEHs2NQ9HI9WaTMOLE0nFqevfid/o4t2QRZxeNfBCnmzrP161kdfPb9KciOFUn48LlXDP2sqNGVrz99pt85Sufo6Ghjm9968d86lN3n9TvSr43lzHTS9ni3s7ovlE40hqpYJr9oUZmlk4j33vmsp5tbGzOLB9/5cTGxub/OxShMDZczthw+Yc9lNOOEALX1TcgcnLR161BRqNoU6bjPOdctKrJgDUJ//GPf80nP3k1Dz74uyOKXOEwhMOS3l5GCKA9PVYmcO5x5m49PRCLCYqLLYE3nQa323L6Hsz2rayUBAKgqnDJJXJY+D0Sy5ZJmpsl27eDYVjZtnl5cFOWJGOrwg4dhCnoT8LOeshwQDINOCzx2OeDWAxCIUEqCW2/F0w5IFAiAumF1FQTfaIEF4gBSyAGQIdRhmDOLMmLbYJEwhKEe3utY1qyROI8LOpMHyPZIgVtXZYZ261ZArhfQo4DlKikPyyI90tAgGLlkikxEEmQXonaaB2fGADX6wLTj+WaBYywFZvgXCVIzbeETY/Hwy9+8UdmzJjL179+L26355jCpVkE0c+YKF3W8Zm5WIXqzgQSXK8qiJhEBgQMSmQQZALUFtBLQTokSufIZkYZxMskIHHOEPh/CmJAoAzlMBt5VmRGahbok01ScyTmUVaMKt3g3K5g5FvnC6xcZaNIou0WKC0csW06neZzn7uFjIwwP/jBLz9y7iobm1PlwQcf5O6772bZsmUoisJVV13Fz372s+HX0+k0NTU1xGKx4W2/+tWvRhR6W7RoEQD33XffcDzF8fq1sfko0ZPo482mtWzu2AbA9NwpLCyeT6Y7dNJ9aYqGJlR0qaMe9oWqmzqqop7WzNajkTbTPLDrz2zs2EaWK4xDcbClYwcH+hv59JRbqQidoSe+p0gwmMHvfvcXfv7zH/L973+ddevW8N///QcKC08upPXA4B72R6oBiSocNKXr6Eq2MTE0iyz3hys0zi+YxZaO7ezu20ueJxvdNOiKd1OVNZbJ2ceOx5idP518Xy7bOncxmB6kwJfHtNzJqEKlN9nP1o4dGNJAYtU3uaXqGrLcYRJGkiJ/AYW+/PfMW1Y3r+OZ/S+S4cpgTLCEuJ5gU8c2Ukaau6d/asTntKenm3//96/yyCN/YNas+fzmNw8zfvykkz4HQgiurLyYwXSU2r79mNJAESrjQpVcWXmxPbeysfkYY4vBNjY2Nh8xhMuF67yLcC4738pgcLvfM9m64IJLufnmO/i3f7uXKVNmMmXK9BGvu91wzjlWIbf6estZOzgIkQhccIFJ9nFWhwWD4PFYbVwuy51bWgpbtljO4GQSGhstgfayyySh0PH7+/SnTXbvhtZWgdcLVWWSMf+t0JCA2lYo90lWjILVndA9IPBIiDvA6RT09VnjKC+XONrA8bqAUtCLJUoUXKsUSJsk55q41ghkJ1YcwaBAr5RcdpuJa4Ng3TrBwIAgJ0eyZIlkwYKRzmmZCb1TgWoIpAR9CWkVqXNB0iVJ9AtcJji9wODQ9dKtDFzpAJEUmNmWK1jpAKVPoBcf2odIAQmBow5czwqS50lkyJpsd3d3oGkagUAGl1yykJ///AGWLFl+5BMq+ECqI4uoJV6bWaCYDFVotkR3BrHEXV1g5h49oiI1RxK/DFxrhBUZgSWOxy8zSS05frSFiALJ9xaWkx4QnZYQ/+60ZCklX//6l9m2bRNPPvk6GRnvamxj8zEmMzOThx566Kivjx49etgteZBvfOMbfOMb33hf/drYfFSIpAb5/Y4H2dW9h5DLevr75L4XqO3bz11TbiPg9B+nh5GMySilOFBIQ6SJsozRqEIlZaRpi3WyuPgsMlzB43fyPqnp2cuOrt2UBUvwaB4AMt0hdvfu5c3mtz9yYjCAoijcc88/MmvWfO6++zaWL5/Fj370Ky688PITah/XozRG96EpDnyaVZxMSklfqpv6aC2ZrpwPVWwsDhTyyUk38Ur96+ztr0MVCueWLua80iXD1+hYHC0b+VOTbmJXdw1Ngy24VBdVmWMpDhx7BYZu6qxpWYdbc5PntSaAAaefMcFSanv3sbevjgmZY5FS8re/Pcg3vvEVdD3ND37wC2688ZMoyqnHnOR4s7ln+p3U9NbSm+gn7M5gXLgSp3rmH5LY2NicOWwx2MbGxuYjilBVS5F9F7oOO3YIpkz5MW+8sYUbbriMp59+g7Ky0SPet2iRRFHgjTcEPT0Cv19y/vmSpUuPL8BlZ8OMGSYrVyooiuUADoclOTkCvx9M04qgWLFCUlEh2bnTyrTNz4ejzdudTpgyBaZMGVrSGQEjBa1dltDr9cI0YKwPUq2SP5jQkgOFhRJNg6wsSaQHCuKC0rIhRyxgukAKiXOHQuQLBkYFODYJRAL0KpPUPIkrCy4rkixfLolGrcgJ51GKH2cvk8iNkDUIzQ2CPhd4syW7+wT7XZK5JuSMkshBUNqHrsnYoegDn1W8Aqy/SxeI+NCfEXBsESjtlgva/bTAsVMQu9XkteaV/M///JCvfOUb3HzznfzDP3yCm25awRe/+C984Qv/jKZ9OF/X0mW5cJWBocJwjaD0WNswQWkTGBWS9PRjfKacEL9Rkp5toNYLcEB63NGdwO/GyAYzLFG6GVEVWukGMyQxjmAc+ulP/4M//OGX/OAHv2D69Nkndcw2NjY2Nh9tNnVsY3dPLWPDZcNuyFxvNrt7atncsY1FxWedVH8u1ck1Yy/lT7v+wp7efQghEAgmZFZy0ZhlZ+IQ3kNbrBPd1EeIjEIIQq4g+/sOfCBjOFXmz1/Eyy9v4MtfvotPfepazj//Er797f+kuLj0mO0G9QGSRpyQ85BDQQiBV/MRSfWSNOO41WMsO/sAKA+NpiyjlEh6EE2oeB3vfzwu1cn03MlMz538nteklNT07mV7VzUxPc6Y4Cim505BIOhPDuB3+Ea836O5SUudgWSEmpqdfO1r97J69atcfvl1fOMbPyQ399QLix6OU3UwOfvjX6jbxsbmELYYbGNjY/MxIp2GRx4RrFkj0HU/lZVPs3btAq644lJeeWUVOTmHKnApiiUIz5sniUSsqAW3+xidv4vLL5fousmWLQpdXZZYe/XVJpdcYhV0Mwx4/HGFZ55RiMWsOIpZs0yuuOLYkREHkX6IFYFvNUQOi4bLS4ISAndAMirLipVQFElzsyDLA1fmgifzYNm4ob6CQy7RKKQWWAXajoTXy3HHNqFKUjVPsHkz5PhM2toEDa0Cp1Oyc57kwgzw9wvMDImatPKAUcAMQPJiE32s1Y+ZD+kqifNtgeGUOGoESqcATaKXSvSJoO0T1P1mF3c+cR2LFy/nc5/7Mpqm8cADT/Bf//Vdfvzjb/Pyy8/y4x//mkmTph3/pJ5uHJCaJ/H8VYAP0lMlWrVAbbGuX3qaJHGViXm8ew0VjAIrRsMMgwycxBi8kDpH4v6LQD0AZoblSBYJiF8uke8qYv373/+CH/7wm0PCup0TbGNjY/P3Rl1/PZrQRiyLdygONKGxv7/+pMVgsAoUf3HmZ9nRXU0kNUiOJ4tJ2eNPyAF6OvAN7ccwDVTlUFRFXE9Q6D89gt6ZJDMzi9/97i8888zf+MY3/i+LFk3hi1/8Z+6664s4j/L0XUFFIDAxUA+TJQxpIoSKIs5MBlZcj2NKiVfznJDzWAhB0HkyE5fj05PoY0P7Fur66wk4/UzNmcSEcCWvNLzOM/tfIqYnUIXCm01readtM7dXXU+2J4v6gSYy3eHhfgbTUdKdMX7x9f/glWefoaRkDA899AxLlpx3Wsf7btqiHaxr3ciBgUYyXEFm5k1hUtYEOzbCxuZjhC0G29jY2HyM2L7dEoLz8iAQkEAOWVnP8vjjZ3PDDVfxzDPP436X4ut0QtYp1OHw+eDWWyXnn2/Q12dFTeTlWc5fKeFXv1JYuVIQDFquXVWFV19V8HhMrvx/7N13eFTV1sfx75mZ9JCeECChl9ARkCIqvSNFFBsKYlfsvV3LtXflVbg2VAS5gmLBAigWQEBAqnQInZBAek9m9vvHmLmEBAiQkEB+n+eZRzmzzzn77BmYNWv2Wfvi/yVjN26EhQttOJ3QubOLNm3ciWosMINd5M+xUzMRrFDwdrqTvEtqG7wiLK6+3Mm8eTZWrLCw2aBmmCG3AEwqcNjkCCvdPRPXFVKGC8vFvRryUWYGe3vDmDEuate2+Osvi8hICA52cd55Lnr2BF8HZG4DK8PCFezCKsS9kFs9d4L0f52CnJEurFwbXqss7NvBeBuctaEgzoAD9obt49KZQ6lbpz7/+c80zwxgm83G3Xc/Sq9eA7jnnhsYOLArt912H3fd9WiJ17e8ZGfDoUPuZPnh75e8HgZbInivcNcOdtU0FLaA3GEu8jvhrlds3DOfMf/Uaz78u0AO+H5n4f2nhZVtYWoY8s8z5PY3R30NjpTXw2B8wPt3C3uyhTPKkH++If+C4kn/yZMn8thjd3HTTXdx550Pn9qAiIhIleTv8MNpnCW2O40T/1NI3ob6BnNBnS6n0rUSClwFbE7ZTkpuKiE+wTQLa1RqDeIW4c2IDqzJ9vSd1A+KxWE5OJSbTKGrkM61Si4oW6TQVYjTuPCxl/EDtQJZlsVFF11Cz579efXVZ3jppSf5/PMpPPfcW1xwQa8S7YO9w6jhFUJGfirB3mFYlg2nq5BcZxaxAY3xtvmUcpaTl5h9kLk7f2XdwfUYY2gW1pi+9XqUWsqhIh3ITuL9tZ+yPXUHvg4f8l0FLN63jG61O7HswCp87D7UC4oFoMBZwPpDm1mwdykXxnTlk/WfsytjD+G+YSQlHGDpf+eyd+EmoiJr8vzzE7j88rFHTb6Xl53pu/lg7VT2Zu0nwOFPrjOP5QdWMbzRQPrU616h5xaR8mOZI4uKyQlLT08nODiYTZsOUqNGxdeUEpHqa+pUi19/tdGsWfF/upcuXcrSpb3p06cfkyZNq/BAcMUKeOABO7m54PhnkbXISEPt2u4F3x55xEVgIPzf/1nMnGkjM9OdQA4IgP79DQ884MLnnxh/9SyLre/ZqJMFzhD4O8Sw1GZxYQ8Xfn4wd65FjRp4Fn4zuy2utxm6N3Inf22ZYEu0yOvuIucaUzwReRj7LvD+ycJrk4VxuMs55PUyJWrRHi4nx12WIzDw6OUvjssJXn9A4EQ7ziiDqzZgg8z8TAb9txcHMhL4YcpCorqVvuhKfn4+77zzCm+88RyxsfV59dV36dTpxGc9HY3LBb/8YvHrrxYpKRa+vtCmjYvhww1BRR9pBuw7wXbAwvgZ9+znf3LStn3g86OF1wYLy1gUNDXkDXDhrOt+3m+6hc88G65wg6sG2NLAlgq5wwy5Fx0jBDHuMhxWHu5SEH6AE6xsd73gw3/ONsbwzjuv8uyzj3DjjXfyxBMvaXZKNZORkU6zZhGkpaURFKRYrLIoJpbTYf2hTUxcPRl/hx+Rfu4SA0k5B8kuzOGWttfSIrxZJffQ7VBOMlM2zGBT8hYKjQuHZadpaENGN7+USP+SizdsTN7Cfzd9xb7MBFzGSQ3vIM6v04mhjQbgsBWfw5Wen8EvuxayPHE1hc4C4sKa0qvu+ac9sXksGzas5ZFH7mTp0oX07TuYu+9+lHbtOhZrk5KXxIbUVWQWpv8TvlmE+UTSMrRDuZaIyMjP5J3VH7IpeRtRfuFYlkViziFiAmtza7trPXV4T4fpm2Yxb8evNAtt7JkFnpCVyP6sA1iWRduIlsVimH2ZCQR4B/Bkl/tZuG8ps9d8z+LP5xI/fw1+gQHcfvsD3DD2dvxKKS1X3owxfLBuKov3LyMutAk2y12LeH/WAWyWxYPn3kG4X9hxjiIiFamsMbFmBouInEGO9vNdaGhnrrxyOtOnj2LMmBF88MHn+PsHlN74FDmd8MUXNpKTLWJiDF5ehsJCSEhwJ4Wjo90L1a1YYTF9ujtIrPdP2biDB2H2bIu4OIsePQyOfGjf0OB9mYs5Wyx2Zln4+cFFnVycc47hrbfsREQYz4J3wcGwyxh+yIQuFvjttzB+kNfHRe6woyeCbfvA/z0b9n0WrgiDlQu+s23YdxmybnK5E42lKJe42g4FXaBgkcGxA1wW5BTkMGb25WxN2cycIb9S85wYjpYW9fb25q67HmHQoOHce+9NjBjRkzFjbua++/5FWNhJTPk+wqJFFjNmuJPANWsasrPh119tZGcbbrzR5ZnF7awPzvrFe2mlgP8HNhw73IvIGcvgvdTCvtdG1m0usIH3MhuuKIPrn666/hlT7z8s94zfUu68tCWC35c2HJssrHxwRhjy+rrLfxzZPjs7m/vvv5lZs6Zz2WUP0azZ00ybZtG0qbs+tU/5TiwSEZFK1jysKYMa9GXezl/ZmLIFgCDvGgxq0JfmYU0ruXduxhi+3Podaw+up0FQPfwcvuQW5rHu0Ea+2DKbm9qMKfGjZVxYEx44dzxbUraT58wnpkZtov2jSrTLLczj47+nszJpHaHewThsDn7fu5htqfHc3HZslSkr0bx5a7788mdmzZrO668/y6BB59GjRz/uvPNhOnfuBkCoTyQdIs7nUN4B8p35+DsCCfOJKpH8PlWrktaxNSWepqGN8Prn2GG+oWxM3sLyhFUMbniUBXvLWYGrgDVJ6wnzDS1WDqSmfyRb07ZztEDWGMPy5UuY/tFEvvvuS3z9/Ljjroe47aZ7CQg4sQUTT0V2YQ6bU7YR5RfpSQSDu/+bU7YRn75LyWCRM4SSwSIiZ5CmTeH3390Ju6Lat9nZ7v+OGDGYIUO+5dprR3LZZQP55JOvCA0t/4Bs505ITLQICTEUFICXFzgcEBBg2LfPol49F6Gh7hm9ubnuRHDR95jISNi2zeL1120snQT9kqCJDzSvZzi3Hhzq5sI50OAfAGvXQno6NGlS/PyhEZDoZbH7Jie17OAKAnOcnKj3Ygv7XovCOOMuEQG4wgxe68Hrb4uCjhV8k4wX5A002D+xkfN3BpeuHMayQ3/y326zaDa6Lfn+xz9/06Yt+OqrX/nww7d56aUn+fLLadx22/1cd914/MtSpLkUBQXw228WXl4Q88/EZD8/8PY2rFtnER8PjRodfX/vlRaOHRaFTY27XATuRd0cGy28Vlg4GxmsLPcicIczwe5ZxrZUcB6ZDM4F/49tODZYOOsYjDfYk8B/ug3j76Kg/f/Gas+enYwbdynbtm1m9OhpZGSMYv5895vtt98MnTpZXH21SwlhEZGziGVZDKjfizaRLdiWGg9Ao5AG1A6IrjJ3hSTmHGTDoc3U8o/Gz+G+lcbX4UOdgFpsTN7C/qwDpSZt/Rx+tIlsecxj/31oI+sObqRRUH18He4PuAi/MDambGHJ/uVc3GRI+V/QSbIsi4svvoJhw0Yxe/YXvPnm84wY0ZOuXS/kzjsf5oILeuFj96O2f/0K7cf+zAQATyIYwGbZ8HP4EZ++q0LPfTgLC5tlYY6YAmAwBDgCsCz3LPeof2Yq5+TksHbeEtIW7uKDLf+iQYPGPProc1x22RiCg0NOW7+L2Cx3/13GVWy7yxgsy8JeQXWeRaT82Y7fREREqoq2bQ3nngt79sCWLe7H7t3QsaP7ufPP78mMGXPZvn0LI0f25sCB/eXeh9xcd83f2Fh3IjozE/Lz3dvz8w0dO7oXkEtLc7c7/HtZbq47wZu6D/rvsQjNgaXJFl9ttpFyCKJ+sQje4t4hMBB8fP6X7C6SnQ2+vgb/aHA2KCURXAAUFt/k2GZhAk3xTz0fwGVhL/8hKlVBW8POyw8wYH1vVqet4otx39PlX73I71r2RLTdbueGG+5g8eKNXHLJaF55+Sku6NSSGY9MxlrqhHRwrHPX1nWsAfKPfbysLEhJsQg+YhG2GjUgN9eQknLsL9W2fYD9f4lg90Ywvgb7LgtXMBh/sDKL72dl/LOYXCl3LnlttHBshcJG/ywO54e75EShwXvR//rzxx+/MWBAV9LSUnn11d9ITx9FZCQ0a2Zo1sxQpw78+Sf89VfVSAyIiEj5sSyLOoG1uDDmPC6MOY86gbWqTCIY3Au/5TvzPcnaIkU1YnMKc0/62AlZiTiNs9ixbZaNGl6BbE7ZdtLHrUh2u51hw0bx008r+OCDGWRlZXL55QPp27cj778/gUOHDlbo+QO9AnHh4sgKmfmufEJ9go+yV/lz2By0jWxFcl4qBa4Cz/b9WQeoFVCTIQ37k12Yw7K1S/l60hTeG/ck6z76jQYxDZk69VsWLFjHjTfeWSmJYHD/WNE2shUHcw55+m+MYXfGXqL8I2kc0qBS+iUiJ07JYBGRM4iPD1x9tYtrrzV07uxODI8bZ7jmGhdF64q1a9eRr76aT1paGsOG9WDDhrXl2odatSAkxBAebmja1L1wXFFt3TZtDBf9Uwe2RQuDy2WRl/e/fZOTLQoKDM19INZhSAq2CAyFrBxYts+CAguvZe4vc/XquRN7u3ZZZGe7S2SkpsKhQxbnnmsIPOKuONsBd33aoCds1HjChu9MC+uQ+zlXkLv2bDHG/TDlV5LumPbv38vw+3uzK2cXM2bPo93T3ShsyVFLWxxLREQUL4x+neXD13G+z/nc+dFN9BzdnoXDZuP/ioX/FBuBb9sIfMuGLfHox/H3h6AgQ0ZG8e1ZWeDjYxEcfOxEtQkGnBZH1riw8i1MiMEVBfntXNj3W1jJQAHYDoLtEOR3+SfZewQrFXctjSNm87qCwL4fUpNTeOih8Vx6aT9atGjDDz8sprCwHQUFFoeXxQoIcP8Y8fffVSc5ICIi1UNN/wjCfEM5mJNcbPvBnGTCfEOIDog66WMXJYGPTGzmOfMJ9qnatbptNhsDBw7jhx8WM23abOrXb8TTTz9I+/b1uOGGy/j55x8oLCw8/oFOUOvI5oT5hLIzYw+FLicu42JfZgK+dh/aRbUu9/MdS++6FxAX2pitqfFsTtnGhuTNGAztbc3Y+906Vjz5Db8/+hl7F2yk3/BhzPttOdOnfk/Pnv2x2So/fdO3XneahTVmW+oONiRvZkPyZvy9fBnReBA1vE9fyQoROTUqEyEicobx9YWuXQ1du0KJLNw/mjRpztdf/8rYsRczZMgFvPrqfxg+/LJyOX9oKPToYfjmGwsfH2je3JCcDP7+FmPGGE/5iuHDDfPnG+Lj3UlHy4LERIPdbtE2xGCywFjuXKjDAampFnk2F45/vjfZbHBFfxe2jTY2/2qRB/jVhAt6uxg48IjatcnumsCObRaucINlwPd7G47thqxbXBR0NHittrAlgSsCcIF9N7jCDQUtK34d1bVrV3LddaNwuVzMmjWfxo1PcXGbXPD73EaTvEZ8cNlUxifdw5OzH2HUpouJ292cW867g8saXEXQ+gD8ZtrIusVVatLZ2xsuuMAwfbpFQgKEh7tnXu/da3HOOYaGDY/djYK2Bp/f3IvLOWMAC+z7wATiLudgQe7FBnDhvcqGlex+Lq+fi9yBpY+7CQYs457VfNg6iFaa4VPXVB7t8QC5uTk8/fRrjB17M3a7HWNKX+DPmKPX2RYREakofg4/etW9gM83f832tJ0EeQeSnp+JMS4GNehLgNfJ/xLdMjyOcN9QdmbsJrZGHWzYOJSbDBg61mxXbtdQkSzLokePfvTo0Y9Dh5L48svPmD79I66+ehjR0bUZMeJy+vW7iA4dOuNwnHrKIrZGHUY1G8ZXW39gW1o8xhjCfEMY3LAvLU5znekw31BuaXstq5LWsWLdn2xY+BfLfv+J/255icDAGvTvfxFPPvYS3bv3xeewOlc5hbnsTN+NwVA/KBY/R8UvGFeaCL9wbm07jjUH15OQdYBArwBaRjSnThWpVS0iZWOZI39SlBOmlZNFpKo6fHGtceNu4/HHXygWWJ4spxOWLLFYuNAiJcW9aFz37oZ27UyxpNzmzfDBBzb++svC9U95saQkGB4BVyTCLh+LQpt7wbnQELi4icE1xEXOZQbrEAS8Z8PaZLHZZcjIhSgviO5nyL2qeGkCn7kWftNt7tq1Rd8ZCsCxFbLHuhce851t4fOrDSvNXS7CFQU5FxsKOlTsx+DMmZ/ywAO30qRJcz78cCZ16sSe8jEdf0PgW3YK6xnwBisLvBdYLMz4nQlpb/FNxtcE+4QwrtkN3BRzK0FP1MIVU/qxCgvhxx8tFiywSEtzLyTXooVh5EgX4WVYn85rmYXv1xb2A+4/uyIhd7Ah/7ziC/rZDoAtHVyh/yTkjyYXAt9yLx5XVDN449Z13PfXnfx+8DeGDRvFk0++TM2atTy7rFplMWmSRc2a7hIX4J6tvmsXjB1r6NZNoc7ZrqwrJ0vFUkws8j/GGJYdWMmCPYtJyjlEuG8Y59fpTOdaHYotvnUylh9YxZdbviMp5yDGGGp4B3JhnfMY2qh/sYXJziTGGNas+Yvp0z/m229nkpx8kODgEC68sA+9ew+gZ8/+REbWPKVzpOdnsC11B07jpGFwPcJ8Q09ofysZHFssLCcUNjC4ojmhO7z27dvDH3/8yqJFv7F48e/s2hVPQEAg/ftfxEUXXUL37n3xLbrV7zCrk/7mq63fsz/LHWzV9I9kSMP+nBvd7oT6LyJnv7LGxEoGlwMFviJSlRljmDx5Ik8//QBNm7Zg0qSpNGzY5Pg7lunYeBaRO1apvuxs90zf9evh7rvtkA0PGGiUDQkuizwndKhjaNDFkH2bC2cs+H5r4TureILXSgf7QYvM250UNv/f8f3/Y8NrNTiPmMnq2Ax53Q05VxkwYNsPjl0Wxg6FTUsvU1BesrOz+de/7mHatA8ZNeoann9+An5+5TOLw2uVRcDbFoVNABvY0sB7kQ2Xwz0relO77by78W0+Wfsh2QXZXNR7JFfcOI6uXS/Ebi/9S2JqKiQmussr1K597NfzSFYm2LeDZSwKGxjMKX4U2hLAd4bF4j9+582Nr/Ljge9pGN2EZ19/k+7d+5RoX1AA06ZZ/PGHhctlYbMZjIH27WHMGBflNOxShSkZXDUoJhYpyRhDnjMfb7vXKSeBD5eal8aWlO0UuAqpFxRTpRbQO1VOp5M1a/7i559/YP78H1m9egXGGNq27UC3bj1o164jbdt2ICam3mm7Zu8lFr6zLGz/lCAzQRZ5vVzkDjKlFt90uVzs2bOTFSuW8scfv/HHH78RH78VgObNW3HeeT244IJeXHhhn1ITwEX2ZOzjrZXvkVWQRUxgbSws9mbtx9vuzfh219MguG5FXK6InKGUDD6NFPiKyJlg7dqV3HLLVezbt5d77nmMm266Cy8vr9PaB2NgyhSLyZNt2JMtBuRBexfUijI0HWLwHunC+U+eOvAFG/Z94KwFtlTAAleIe0ZGziUu8g4rM+A31T3rt7BZ8Y80xwaL3KEucoed3o+69evXcNtt17BzZzz//vfrXHnlteX6ZcU6BDVesIELXLWAAvBZ4C6DUVgfCjq5Z+Vm78hgyt6PmXjg/9i+YysREVEMHnwxQ4deQqdO3Y6aGK5MBQUFzJ79BZMmvc7atStp3rAVN99yN8MuvQxvb+9j7OeeIbx+vXvmevPmcM45hmN8v5KziJLBVYNiYhGpCAcPJvLrr3P5+ecfWbZsMfv27QYgNDSctm070LZte9q06UDz5q2oXTv2mPHCybDvhsA37FBgPGWxbEnuH8OzbzQk1U9m48Z1rF+/lo0b17Fhwzo2blxHVpZ7Bd0mTeI477wedOvWna5dLyQ8PLLM5569fS6ztn5H89CmnljSGMPGlC0MbNCbkU0uKtdrFZEzm5LBp5ECXxE5U2RlZfLyy0/x/vsTiItryUsvTaR9+04ndSxjID4etmxxl4CoX9/QtCmUJb+4dSv89ptFbi60bgDt2xgC61DsVrvA12x4LQUr08KW5d7mqgHGB7JvcpHX938fX46/IWCiHeNrcNUEjHuxMYx7FrGz0Uld4gnLzs7m9def4T//eYPGjZsxceKnNGvWskLO5TPHXZ7BcroXwbNvt7AlgDPKYKLB2AAb5F5iyO3lYvXqFXzzzUy+/XYme/fuombNWgwefDEDBw6jffvO5TZr+WTk5+ezePHv/PjjN/z449ccOLCfCy/sw80330337n3OmplOUnGUDK4aFBOLyOmQlHSANWv+YvXqFZ5HYmIC4K5HHB1dmzp16hIbW4+YmLrExtanVq06BAbWwN8/gICAQM/D3z+gxMJsLpeL3NwcsrOzyMnJJveHdJJnJ7A3dB/7MveSkLWf/Zn7SDi0n73OPSRmuss3eHl50aRJHHFxrYiLa0WLFq1p1aodUVEnX093yvrPWbT3T5qEFr/9bXvaDtpFtuKGNtec9LFF5OyjZPBppMBXRM40a9as5IEHbmHt2pWMGXMzDz/87xP698sY+OEHizlzbGS6Jz3g4wPdurkYNcpQHhOOfada1HjDhvF3zwjGuGdh4IC0150UdDy8Q+66wb5z/6kJjLs2be4QQ/6F5oTquZ2s+fN/5OGH7yAxcT933vkwt9xyb7nUZz4qF3ittPBe+s8ti5ng2G5hTwTywRUOOaNd5IwqfvuiMYa//vqTb76ZwezZX7B//168vLxo06YDnTt3o3Pn8+nYsSuhoWEV13fcX+SWLFnAjz9+w88//0B6ehoxMfUYMGAol112DS1btq3Q88vZRcngqkExsYhUBmMMCQn72Lp1E7t372TPnh3s2bOL3bt3snv3ThIS9uIqWryiFH5+7gX9nM5CCgsLj9rWwiLSP4pagbWJDqhFbVObmnVqEXtlE5o3b0WjRs3K/a67eTt/Y/rGL4kLa+IpM+IyLjYmb+HiJoMZ3LBfuZ5PRM5sSgafRgp8ReRMVFhYyOTJ7/Dii08QGFiDO+54iKuuuq5MCcwtW+Ctt+z4+xsiItz1ZdPS3DVnx40zdO586h8tflMsAj6ygRNw/ZPN9QKXnyHrtuJlIorYEtwJUSwobGKOvVBZOdm/fy9PPnkf3377BRdc0JsXXphAgwaNK+ZkBsjFXUP5sO8aXiss/N+3MAHuhfHIB0c8UADORu7Z1AVt3Yu6HV4n2eVysWHDWpYuXcTSpQv5889FHDiwH4C4uJbExbWiceNmNGrUlPr1GxETU5ewsIgyz9Q1xpCZmUFCwj42bFjL33+vZt261fz992rPDJ6WLdsyYMBQBgwYSosWbTQLWE6KksFVg2JiEamKCgoKSEo6QFZWJjk52WRlZR72yCI7OxOwcDgc2O127HYHfn5++PsH4OfnT+iOGjSYW4fwZtF4+f0TgLnAsdEi92IXuUMqLqVyKCeZCaveZ0/GPmoF1AQsErIPUNM/kvHtric6IOqkj+0yLlYlrWPlgTWk52fSKKQ+XWp1JMr/NATQIlIhlAw+jRT4isiZbM+eXbz88pN88cU0oqPrcPfdjzJq1NXHnNnw7bcWs2bZaNGi+EfIli3QsSPccMPRZ1+UVcDbNhx/gwkAW5oFNnBFGGwH3At25FxRuR9fiYkJ/N//vcynn75HYGAQTz75MiNGXF5hyUz7dvCZb8OxBYw3FJxryOtlMIEQMNG9gF5hUQ66ELxXWNi3/pMUj3aX2yhsaci60YWpUfo5jDHs2hXP0qULWbZsMVu2bGTr1k0kJx/0tPHz8yc8PBJfX198ff3+ebj/32azkZqaQkrKIVJSkklNTaawsNCzb61aMbRs2YZWrdrSsmVb2rbtSEyMFj6RU6dkcNWgmFhEzkZWNvhPsuG9zsIZ6r7jynbIwlnPkHWrC1fZSwCflJ3pu/ku/ie2psQDhobB9RjYoA+NQuqf9DGNMXwXP4/v43/C6XLiY/cmsyCbukF1uKH1NdQJPPnSFiJSecoaEztOY59ERKQKiompy5tvfsj48Q/w6qv/5v77b+b//u8l7rnnUUaMuAKHo+RHRUGBezbwkex2yM0tn345Yw1ef9lwNjC4av6T+HWCbT+4KjE+TUo6wNtvv8Inn/wHb28fxo9/gOuvv52goODj73yS7Dsh4D0btiQLV7jBygLfr2zY9xiybnBhJYM5rOSvPcHCluhOpJtQcNYFCgyO9e5ZxPk9Sk+kW5ZFvXoNqVevIaNG/a8GXXLyIXbv3sHevbvYs2cXyckHyc3NJS8vl5ycHHJzc8jLy8XpdFK3bn3atGlPaGgYoaHhhIaGERlZk2bNWhIerpkmIiIicmYx/pB9rQvnLxZeKywsF+T1cpHX01R4IhigXlAst7QZy6HcZIwxRPiFn/Lkg/1ZB5i/awFBXoFE/jMTuKj8xPzdC7i6+aXl0XURqaKUDBYREcC90vGkSVO5444HeeWVp7nzzut46aWnGD36Oq68chyRkTU9bevXB4fDkJ0N/u4yaxQUQF6eRfPmpz4rGCC/g8F7icGx2cIZbcDlTnI66xry25bDrGAX2PeAlQfOWmACj918587tfPTRf/j440l4eXlx6633ccMNdxAcHHLqfTkO74UWtkSLwrj/1T82oQavNeDYAM4GBke8zV3M2QJbKuC0wG5wBfxzEC/3jGLH5qMng48mLCycsDD3it0iIiIi1Y0JhdyLDbkXGXfZLu/Te37LsojwCy+34+1I301GfibNQv9X2sxm2Qj3C2P9wU3kOwvwtpdv/WMRqTqUDBYRkWJatGjDhx/OZO3alXz00STefPMFXnvtGQYNGsHYsTfTqVM3WraEDh0s/vzTvXCc3Q6ZmRbNmxvOPbd8yje46kDWtS58v7fh2OGuA1zQ0ZA7xIU5xbXNbHvAb6YNxzYLqwBc4YbcPsadJD1sokVBQQFz537LlCnv8/vvPxEcHMLNN9/NjTfeSUhI6Kl14gQ4tlq4gov3zfgBhRb2/Rb55xm8Vv+TOI8ykAW2TENhI4rNWLGcFsanfJL1IiIiItXOWZIftVs2wGAwWIcFmE7jxNvujU1rOIic1ZQMFhGpIvLyYM0aiy1b3MnV5s0NLVu6/78ytG59Dq+++h8ef/wFZsyYwscfT2LEiF7ExbVkxIjL6dVrBE2bNmPlSovCQmjd2kWXLobgI6olOJ2Qnw++vqWXlgDIzoZlyyzWrbOwLGjVyp1U9msCWbe7sB3CXTM4jGIJ0ZOSDf5TbDi2WjhjDcYb7IngN9PC1HAnnOPjtzJ9+kdMn/4xSUkH6NChC2+88T5DhlyCf9FU6NPIFQSOA0duBDAYf3DWh+zrXPjMdSfOTaihMMaiMMZ4PumtFDB2Q2Gb09t3EREREalamoQ2ItwvjD0Z+4itUQfLsshz5pOSl8b5tTvjsClVJHI20wJy5UCLZYjIqcrNhSlTbCxbBsZYGANeXoYePQyXXGIqLSF8OJfLxcKFvzB16vv89NMP5ORkExfXkkGDRjB48Aji4loVq19WUAALFlgsWmSRkQF16kCPHoY2bUyxpHB2Nnz4oY1Vq8Db213poKDAvRDd2LEufH3L9zq8llsEvGtR2ADP7A6XcbF6+XK+KfyG2cnfsGnTeoKCghk58ipGj76O5s1bl28nTpD3Hxb+H1u4wv5JiDvddYRNKGTc68IU3TVowEoDHODzq4XPPBtW5j9P+UFeDxe5IwxUgfeTSHnSAnJVg2JiEZEzx9L9K5ix+RtS8tKwAJtl0Ty8Gde2vIIQn4pbC0NEKs5Zt4BccnIyt99+O99++y02m42RI0fy5ptvEhh49CKPubm53HvvvUyfPp28vDz69+/PO++8Q82aNYu1++ijj3jttdfYvHkzQUFBXHrppbz99tsVfUkiIh4rVrhLLsTGgr+/+ze6tDT49VcbLVs6adWqkjsI2Gw2LrywNxde2Jvs7Gx++20u3303i/fee4vXXnuGhg0bc8EFfejcuRudO5/PwoUxzJ1r4efnriu8YQNs325jzBgX7dv/73fIVassVq921yEuSvzm5MBff8E551h07ly+v1na0gFjcagwiT93LWZu/I98v+1b9mftI8wnjN4XDeL++5+gR4/+lTILuDT5nQy2feCz0IZjk7tchDMaci49LBEMYIEJcf9v7mBDQUsnji0WGChsaHA2AmyVcAEiIiIiUkJ6fgarEteyJ2M/NbwDaR3ZgvpBsafl3J1rdaBOYC3+PrSRnMJcagdG0zqiOX4Ov+PvLCJntDMmGXzVVVexf/9+5s2bR0FBAddeey033ngj06ZNO+o+d999N9999x0zZswgODiY8ePHc/HFF7No0SJPm9dee41XX32Vl19+mc6dO5OVlcWOHTtOwxWJiPzP2rUWdvv/FmMDCA6Gfftg61aLVq2q1k0c/v7+DBw4nIEDh5OXl8eiRb/www9fs2DBz3z88SQAAgIaEh19Po0aXYCfX0fq12/Kzp0+zJ9v0bbt/2Y7b9zoLh9x+AxgPz/3DOktWwydO596f51OJ5s2/c3y5Uv4a94S/lqylK1ZWwCoH9yQi5tdylCfoXTs2Y288VUwW+qA3JGGgi5O7LstjBcUNjOYGsfYxwJnA/ficiIiIiJStSRmH+SDdVPZlhqP3bLjNE5+3bOIS5sOpUutjqelDzE1ahNTo/ZpOZeIVB1nRDJ4w4YN/PjjjyxbtoyOHd3/KE6YMIFBgwbxyiuvULt2yX+80tLS+OCDD5g2bRq9evUCYPLkyTRv3pwlS5bQpUsXUlJSeOyxx/j222/p3bu3Z982bVRQUUROL2NKr6drWeCq4ut9+fj40KvXAHr1GgBAYmIC//3vH3z22SLS0xcwd+4UwGBZNmrUaMjy5c1JS2tG69ZxNGrUjPT0aJzOaKD4LARjwHECn1Iul4tDh5LYvn0r8fFbiY/fQnz8VrZv30J8/DZycrKx2+20bN6WXk368ZjrCTo27ULd8HrYkyywWWRd6Cy/gSlvFjhjwBlzlOSuC2wJYLnAWZOzZoETERERkbPRTzt/Y2vKdpqGNsJhc2CMYXfmXr7Z9iNxYU1UqkFEKswZkQxevHgxISEhnkQwQJ8+fbDZbCxdupQRI0aU2GfFihUUFBTQp08fz7a4uDjq1q3L4sWL6dKlC/PmzcPlcrF3716aN29ORkYG5513Hq+++iqxsUe/NSMvL4+8vDzPn9PT08vpSkWkumrZ0rBihUVOjntWLEBGBjgchsaNz6yZnVFR0fTtezF//30pMTEGy0rj4MG1HDq0kd27N5CZuZEffvicDz7YVWy/efNAKcKRAAEAAElEQVT88fePws8vAm/vKFyuIPLzHSxf7o3D4Q6QjTG4XC4yMtJJS0slLS2F1NQU0tJSSE9Pw3VY5rx27VgaNmxMhw5duOSS0bRt24G2bTvg7x+ALQl8Z9nw2mjBQXDVNOT2c1FYuaWBT5p9B/h+ZcMRb4ELnHUMuYNcWixOREREpArKKcxlzcH1RPiFexZrsyyLOoG12Jq6nW2pO+hQs20l91JEzlZnRDI4ISGBqKioYtscDgdhYWEkJCQcdR9vb29CQkKKba9Zs6Znn+3bt+NyuXjuued48803CQ4O5rHHHqNv376sWbMGb2/vUo/9/PPP89RTT536hYmI/KNjR8O6dRYrV4LN5i6RYLMZunUztGhR2b07cY0aQcOGhs2bLRo2DKZOnfMJCjofh8NiyBAXF19syMrKZMeObRw4kMR33yWxdu0hsrMTyc9PoqAgkcDABNLSCjh0KJ+CggJsNhuWZWFZFoGBQYSEhBAbW4/g4BBCQsIICQklPDySBg0aUa9eI/z8jl7vzBUJ2Te4sB0EKw+ckYBP8TYHDsCePRY+PobGjSn3hezKi5UM/h/ZsO+xcNYxYAPHDgiYYifzVifOBpXdQxEREREpzmAwWBS/NdDCwgAuc2ZNBhGRM0ulJoMfeughXnzxxWO22bBhQ4Wd3+VyUVBQwFtvvUW/fv0A+Oyzz4iOjuaXX36hf//+pe738MMPc88993j+nJ6efsyZxCIix+PvD2PHumjXzmLjRrDb3Ungdu3MCZVKqCq8vOCKK1xMmWJj505wOi18faFrV0P//u7gNiAgkJYt29KyJXTv7l5gbutWC5sNGjc2NGuGp65whbDcSeEjFRbC7NkWv/9uIz3dYLdbxMbCZZe5aNKkAvtzOANeKyy8F1vYDoAzFvLPc1HYyt3vw3mtsbDvtihsauCf8SpsBI6N4P2nRY5qBouIiIhUKX4OP+LCmrBg7xLC/EKwW+4gLiErkTCfEBqF1KvkHorI2axSUwz33nsvY8eOPWabhg0bEh0dTWJiYrHthYWFJCcnEx0dXep+0dHR5Ofnk5qaWmx28IEDBzz71KpVC4AWh027i4yMJCIigl27it++fDgfHx98fHyO+ryIyMnw94fzzjOcd15l96R8xMbCPfe42LwZMjMtIiMNjRq5Zz4fyW6HVq2oEgvlLVli8f33FqGh7oR0QQHs2GExdaqNe+91UeNYi7aVE59fLHxnWlgucAWC9woLr/V2ske7KDi3+BjZDuFOEB+eOLfA+Bts+y2g8sdURERERIrrV68Hu9P3sCllK752X/Kd+fh5+TG4fl/CfEMru3sichar1GRwZGQkkZGlTMs6QteuXUlNTWXFihV06NABgPnz5+Nyueh8lGXmO3TogJeXFz///DMjR44EYNOmTezatYuuXbsC0K1bN8/2mJgYAJKTkzl48CD16umXOBGRU+XrC+41Oc+chOSSJRZeXlD08eTt7S55sW0brF9v0blzxV6LlQE+P1ngA4V13Ntc0Qb7dvCZa1HQ1sBhVYxMKOD651GUaDdg5Vi4albx1QdFREREqqk6gbW4td04liesZmf6boJ8gmgX1ZK40NN1K5qIVFdnxM3HzZs3Z8CAAdxwww1MmjSJgoICxo8fz+WXX07t2rUB2Lt3L7179+aTTz6hU6dOBAcHc91113HPPfcQFhZGUFAQt99+O127dqVLly4ANG3alGHDhnHnnXfy7rvvEhQUxMMPP0xcXBw9e/aszEsWEZFKYAwkJ7tnaR/OXarDIiur4pPa9n1gS7YorFf8XK4osB+wsCWBq87/the0NXj/YnBsddcMNnb3MUwQ5J975iThRURERKqbCL9wBjToVdndqJYSEuDPPy327LEIDze0b+9eJ8Syjr+vyJnujEgGA0ydOpXx48fTu3dvbDYbI0eO5K233vI8X1BQwKZNm8jOzvZse/311z1t8/Ly6N+/P++8806x437yySfcfffdDB48GJvNRvfu3fnxxx/x8vI6bdcmIiJVg2VBw4awdKlFVJTxBIPZ2e5SFjVrVnxy1XiDcYCVD+afj6KCAsjYD1YuZGbC4TcOusIh+xoXfrNs2He5S0s4axlyB7lwNq7w7oqIiIiInFHi4+HDD23s3Wvh52fIy7OxeDGMGuXivPM0mULOfpYxWqbyVKWnpxMcHMymTQepUSOosrsjIiKnYNMmePddOxkZhshIyM+HQ4cs2rc33HSTiwr/rdAJAW/a8FpvUdjIkJQKm9daBO2HNWGw8Bzo2cvFgAGmeP3lQrDvce/vrAP4VnA/RaqQjIx0mjWLIC0tjaAgxWKVRTGxiIhUdcbAxIk2VqywaNbsf/H0rl1QowY89NDpWSNEpCKUNSYuZRkfERGR6qtZM7j2WictWkBuroXDAYMGubjmmtOQCAawQ+6lLgrrG1wb4NDvFoEHIK0e7D3fgGX49luLv/464h42Bzjrg7MRSgSLiIiIiJQiJQW2bbOIji4+saJ2bUhMtIiPr7y+iZwuZ0yZCBERkdOlVStoEecidx34Zlh4RxkK/Y+/X3lxxkLWPS7WfWqxIMUipIkhIQoKHVCzBmRkuGucdeyom3tERESqI/s28F5k4dhp4QqH/M6GgvYGVO9U5JhsNndpuCPvkTfGvd2mKZNSDSgZLCIicgQrAwI/sxG6xsLKNeBto6CpIWe0C1fE6emD8YddsbC8JsTVLv6cn597VoOIiIhUP471EDDZjpUCpobBvhcc6yxyD0FeP/1QLHIswcEQF2dYvNgiONhgt7sTwbt3Q3S0oWHDyu6hSMVTMlhEROQIvt9aeC+xcMYaTA2wsg1eayz43EbWLa7TNuumViC0TjE02AD5QbA3CvIdkJFhce65rtPTCREREak6XOAzx4aVZihshicmse0Hn58s8s81mNBjHuG0SctL5+9Dm8jIzyDCL5wW4c3wc6iWlVQuy4KBA13s329j82YLu91QWGgRHm4YMcLgfxrvBhSpLEoGi4iIHMZKAa+VFq4odyIY3LN0nbEGx0YL+x53GYeKZt8FXX6yqHPQIm07ePtAYgjMiITw2kYrHYuIiFRDVgo4dls4o4qXhHBFgWOLhX03FFaBZPDW1HimrP+cfZkJYIENG01DGzG25eWE+4VVdvekmqtTB8aPd7F6tcX+/RAcbGjTxhATU9k9Ezk9lAwWERE5jJUNVq6FK6J4stX4gZUAVtZp6IQTfGfa8NprEd3DkLXT4sA+iE6GyyMgYJyL+vVPQz9ERESkavECYzdYhVAsUinE/e3+dCx2exz5zgJmbP6GhKwDNAlpiN1mJ8+Zz/pDm/gufh7XtLissrsoQmgo9OihyRVSPSkZLCIichhXGLjCDLZD4Az433ZbMriCDM6oiu+DfTc4trvLVPgFQKtWhoJmQAq0KrDICAUViRAREal+TBAUtDH4/GLDVcOAD+AExy4ojDUUVoF6p/FpO9mdvoe6NWKx2+wA+Ni9ifKPZN3BDaTlpRPsE1TJvRQRqb60TqKIiMjh/CCvl4F8sO9w345p3w22VMg/32BOw52NVgHuGT+Hze7x8gLvAKAQrPyK74OIiIhUTXmDDIUtDY6d4Nho4dgCzpqQO8rlTg5XsgJXAQXGiZe9+NwzL5uDQpeTfKcCGRGRyqSZwSIiIkfIv8CAA7x/t7AfsnCFG/LPN+RdcHpuJXPWBmeEwX6geH1i2wFw1j49s5NFRESkanKFQ+atLrz+trAlGUwgFLSqOgvHxdSoTbhvKAeyk6gdEA2AMYaknIM0CW1EmG8V6aiISDWlZLCIiMiRbJDfzZDf2WBlu+sFn84afCYA8voZ/D634dgMrkCDLcNybx/gAu/T1xcRERGpgvygoGPVrHca4hNMn7oX8uXW79iauh1/hz/p+RkE+wTRr14PT+kIERGpHEoGi4iIHI3DXZuvMuRfYDDBLrwWW9gPQH5LQ35XF4UtKqc/IiIiImXVq+4FhPqGsGT/cg7mJNM6sgXdaneiSWgVKGosIlLNKRksIiJSFVlQ0NZQ0LZqzvoRERERORqbZaNDzbZ0qNm2srsiIiJH0AJyIiIiIiIiIiIiItWAksEiIiIiIiIiIiIi1YCSwSIiIiIiIiIiIiLVgJLBIiIiIiIiIiIiItWAksEiIiIiIiIiIiIi1YCSwSIiIiIiIiIiIiLVgJLBIiIiIiIiIiIiItWAksEiIiIiIiIiIiIi1YCSwSIiIiIiIiIiIiLVgJLBIiIiIiIiIiIiItWAksEiIiIiIiIiIiIi1YCSwSIiIiIiIiIiIiLVgJLBIiIiIiIiIiIiItWAksEiIiIiIiIiIiIi1YCSwSIiIiIiIiIiIiLVgJLBIiIiIiIiIiIiItWAksEiIiIiIiIiIiIi1YCSwSIiIiIiIiIiIiLVgKOyO3A2MMYAkJmZUck9EREREal+imKwophMKodiYhEREZHKU9aYWMngcpCR4R7sDh0aVHJPRERERKqvjIwMgoODK7sb1ZZiYhEREZHKd7yY2DKaQnHKXC4X+/bto0aNGliWVeL59PR0YmNj2b17N0FBQZXQw7OLxrP8aCzLl8az/Ggsy5fGs/xoLMtXeY2nMYaMjAxq166NzaYqaJXl8Jg4IyNDf1fOEPp37cyh1+rModfqzKHX6syh1+r4yhoTa2ZwObDZbMTExBy3XVBQkN6w5UjjWX40luVL41l+NJblS+NZfjSW5as8xlMzgivf4TFx0QQJ/V05c+i1OnPotTpz6LU6c+i1OnPotTq2ssTEmjohIiIiIiIiIiIiUg0oGSwiIiIiIiIiIiJSDSgZfBr4+PjwxBNP4OPjU9ldOStoPMuPxrJ8aTzLj8ayfGk8y4/GsnxpPM9eem3PHHqtzhx6rc4ceq3OHHqtzhx6rcqPFpATERERERERERERqQY0M1hERERERERERESkGlAyWERERERERERERKQaUDJYREREREREREREpBpQMlhERERERERERESkGlAyuJwkJydz1VVXERQUREhICNdddx2ZmZnH3Cc3N5fbbruN8PBwAgMDGTlyJAcOHCjWZtmyZfTu3ZuQkBBCQ0Pp378/q1evrshLqXQVNZYAH330EW3atMHX15eoqChuu+22irqMKqMixxPg0KFDxMTEYFkWqampFXAFVUdFjOXq1au54ooriI2Nxc/Pj+bNm/Pmm29W9KVUirfffpv69evj6+tL586d+fPPP4/ZfsaMGcTFxeHr60vr1q35/vvviz1vjOFf//oXtWrVws/Pjz59+rBly5aKvIQqozzHsqCggAcffJDWrVsTEBBA7dq1ueaaa9i3b19FX0aVUd7vzcPdfPPNWJbFG2+8Uc69rpoqYiw3bNjA0KFDCQ4OJiAggHPPPZddu3ZV1CVIGZ3MZ+K7775Ljx49CAoKOmrcUL9+fSzLKvZ44YUXKugqqoeKeq1O5rhybBUVtx/5d8qyLKZPn16Rl3JWUix75ijv12rs2LEl/g4NGDCgIi+h2jiR1+rvv/9m5MiRnljhaPH1ib7+1ZKRcjFgwADTtm1bs2TJErNgwQLTuHFjc8UVVxxzn5tvvtnExsaan3/+2Sxfvtx06dLFnHfeeZ7nMzIyTFhYmBk7dqzZuHGjWbdunRk5cqSpWbOmyc/Pr+hLqjQVMZbGGPPqq6+a2rVrm6lTp5qtW7ea1atXm6+//roiL6VKqKjxLDJs2DAzcOBAA5iUlJQKuIKqoyLG8oMPPjB33HGH+fXXX822bdvMlClTjJ+fn5kwYUJFX85pNX36dOPt7W0+/PBD8/fff5sbbrjBhISEmAMHDpTaftGiRcZut5uXXnrJrF+/3jz22GPGy8vLrF271tPmhRdeMMHBwearr74yq1evNkOHDjUNGjQwOTk5p+uyKkV5j2Vqaqrp06eP+e9//2s2btxoFi9ebDp16mQ6dOhwOi+r0lTEe7PIl19+adq2bWtq165tXn/99Qq+kspXEWO5detWExYWZu6//37z119/ma1bt5qvv/76qMeU0+dkPhNff/118/zzz5vnn3/+qHFDvXr1zNNPP23279/veWRmZlbQVVQPFfVancxx5dgqKm4HzOTJk4v9vTrb46Xyplj2zFERr9WYMWPMgAEDiv0dSk5OPl2XdNY60dfqzz//NPfdd5/57LPPTHR0dKnx9Ykes7pSMrgcrF+/3gBm2bJlnm0//PCDsSzL7N27t9R9UlNTjZeXl5kxY4Zn24YNGwxgFi9ebIwxZtmyZQYwu3bt8rRZs2aNAcyWLVsq6GoqV0WNZXJysvHz8zM//fRTxV5AFVNR41nknXfeMd27dzc///zzWZ8MruixPNytt95qevbsWX6drwI6depkbrvtNs+fnU6nqV27tnn++edLbT9q1CgzePDgYts6d+5sbrrpJmOMMS6Xy0RHR5uXX37Z83xqaqrx8fExn332WQVcQdVR3mNZmj///NMAZufOneXT6SqsosZzz549pk6dOmbdunWmXr161SIZXBFjedlll5nRo0dXTIflpJ3MZ+Lhfvnll2Mmg6vD35fTpaJeq1M9rpRUkbEmYGbNmlVhfa8OFMueOSoiHhkzZowZNmxYhfS3OjvR1+pwR4sXTuWY1YnKRJSDxYsXExISQseOHT3b+vTpg81mY+nSpaXus2LFCgoKCujTp49nW1xcHHXr1mXx4sUANGvWjPDwcD744APy8/PJycnhgw8+oHnz5tSvX79Cr6myVNRYzps3D5fLxd69e2nevDkxMTGMGjWK3bt3V+wFVbKKGk+A9evX8/TTT/PJJ59gs539/5RU5FgeKS0tjbCwsPLrfCXLz89nxYoVxcbBZrPRp0+fo47D4sWLi7UH6N+/v6d9fHw8CQkJxdoEBwfTuXPnY47tma4ixrI0aWlpWJZFSEhIufS7qqqo8XS5XFx99dXcf//9tGzZsmI6X8VUxFi6XC6+++47mjZtSv/+/YmKiqJz58589dVXFXYdUjYn85l4Il544QXCw8M555xzePnllyksLDzlY1ZXFfVaVfR7oDqq6FjztttuIyIigk6dOvHhhx9ijKmYCzkLKZY9c1RkrPzrr78SFRVFs2bNuOWWWzh06FD5X0A1cjKvVWUc82x19mdwToOEhASioqKKbXM4HISFhZGQkHDUfby9vUt80a5Zs6Znnxo1avDrr7/y6aef4ufnR2BgID/++CM//PADDoejQq6lslXUWG7fvh2Xy8Vzzz3HG2+8wcyZM0lOTqZv377k5+dXyLVUBRU1nnl5eVxxxRW8/PLL1K1bt0L6XtVU1Fge6Y8//uC///0vN954Y7n0uyo4ePAgTqeTmjVrFtt+rHFISEg4Zvui/57IMc8GFTGWR8rNzeXBBx/kiiuuICgoqHw6XkVV1Hi++OKLOBwO7rjjjvLvdBVVEWOZmJhIZmYmL7zwAgMGDGDu3LmMGDGCiy++mN9++61iLkTK5GQ+E8vqjjvuYPr06fzyyy/cdNNNPPfcczzwwAOndMzqrKJeq4p8D1RXFRlrPv3003z++efMmzePkSNHcuuttzJhwoRyv4azlWLZM0dFxXYDBgzgk08+4eeff+bFF1/kt99+Y+DAgTidzvK/iGriZF6ryjjm2UrJ4GN46KGHSi22f/hj48aNFXb+nJwcrrvuOrp168aSJUtYtGgRrVq1YvDgweTk5FTYeStCZY+ly+WioKCAt956i/79+9OlSxc+++wztmzZwi+//FJh560olT2eDz/8MM2bN2f06NEVdo7TpbLH8nDr1q1j2LBhPPHEE/Tr1++0nFPkcAUFBYwaNQpjDBMnTqzs7pyRVqxYwZtvvslHH32EZVmV3Z0zmsvlAmDYsGHcfffdtGvXjoceeoghQ4YwadKkSu7d2akqfCbec8899OjRgzZt2nDzzTfz6quvMmHCBPLy8ir0vGeaqvBaSdlUhdfq8ccfp1u3bpxzzjk8+OCDPPDAA7z88ssVek6Rs8nll1/O0KFDad26NcOHD2f27NksW7aMX3/9tbK7JnJSzs7ppeXk3nvvZezYscds07BhQ6Kjo0lMTCy2vbCwkOTkZKKjo0vdLzo6mvz8fFJTU4v9knvgwAHPPtOmTWPHjh0sXrzYcxv+tGnTCA0N5euvv+byyy8/+Ys7zSp7LGvVqgVAixYtPM9HRkYSERFxRq5IXtnjOX/+fNauXcvMmTMBPLeZRURE8Oijj/LUU0+d5JWdfpU9lkXWr19P7969ufHGG3nsscdO6lqqqoiICOx2e4mVrUsbhyLR0dHHbF/03wMHDnj+fhf9uV27duXY+6qlIsaySFEieOfOncyfP/+snxUMFTOeCxYsIDExsdhdE06nk3vvvZc33niDHTt2lO9FVBEVMZYRERE4HI5in90AzZs3Z+HCheXYeylSkZ+JJ6tz584UFhayY8cOmjVrVq7HPpNV9mt1Ot8DZ7qqEmsernPnzvz73/8mLy8PHx+fMl9LdaVY9sxRkbHy4Ro2bEhERARbt26ld+/ep97xauhkXqvKOOZZq5JrFp8Vior9L1++3LNtzpw5ZSr2P3PmTM+2jRs3Fiv2/9Zbb5no6Gjjcrk8bQoKCkxAQICZOnVqBV1N5aqosdy0aZMBii0gd+jQIWOz2cycOXMq6GoqX0WN59atW83atWs9jw8//NAA5o8//jhrV+msqLE0xph169aZqKgoc//991fcBVSyTp06mfHjx3v+7HQ6TZ06dY65kMOQIUOKbevatWuJRTdeeeUVz/NpaWnVYtGN8h5LY4zJz883w4cPNy1btjSJiYkV0/EqqrzH8+DBg8X+fVy7dq2pXbu2efDBB83GjRsr7kKqgIp4b3bt2rXEAnLDhw83V1xxRTn2XE7UyXwmHu5YC8gd6dNPPzU2m02rtp+kinqtTvW4UlJFxppHeuaZZ0xoaGj5db4aUCx75qiIeORIu3fvNpZlma+//rp8Ol1NnehrdbhjLSB3ssesTpQMLicDBgww55xzjlm6dKlZuHChadKkSbEvKnv27DHNmjUzS5cu9Wy7+eabTd26dc38+fPN8uXLTdeuXU3Xrl09z2/YsMH4+PiYW265xaxfv96sW7fOjB492gQHB5t9+/ad1us7nSpiLI0xZtiwYaZly5Zm0aJFZu3atWbIkCGmRYsWJj8//7RdW2WoqPE83Il8qTuTVcRYrl271kRGRprRo0eb/fv3ex5nW0Ju+vTpxsfHx3z00Udm/fr15sYbbzQhISEmISHBGGPM1VdfbR566CFP+0WLFhmHw2FeeeUVs2HDBvPEE08YLy8vs3btWk+bF154wYSEhJivv/7arFmzxgwbNsw0aNDA5OTknPbrO53Keyzz8/PN0KFDTUxMjFm1alWx92FeXl6lXOPpVBHvzSMdLVg921TEWH755ZfGy8vLvPvuu2bLli1mwoQJxm63mwULFpz265PiTuYzcf/+/WblypXmvffeM4D5/fffzcqVK82hQ4eMMcb88ccf5vXXXzerVq0y27ZtM59++qmJjIw011xzzWm/vrNJRbxWZTmunLiKiDW/+eYb895775m1a9eaLVu2mHfeecf4+/ubf/3rX6f12s50imXPHOX9WmVkZJj77rvPLF682MTHx5uffvrJtG/f3jRp0sTk5uZWyjWeLU70tcrLyzMrV640K1euNLVq1TL33XefWblypdmyZUuZjyluSgaXk0OHDpkrrrjCBAYGmqCgIHPttdeajIwMz/Px8fEGML/88otnW05Ojrn11ltNaGio8ff3NyNGjDD79+8vdty5c+eabt26meDgYBMaGmp69ep1zF95zwYVNZZpaWlm3LhxJiQkxISFhZkRI0aYXbt2na7LqjQVNZ6Hqy7J4IoYyyeeeMIAJR716tU7jVd2ekyYMMHUrVvXeHt7m06dOpklS5Z4nuvevbsZM2ZMsfaff/65adq0qfH29jYtW7Y03333XbHnXS6Xefzxx03NmjWNj4+P6d27t9m0adPpuJRKV55jWfS+Le1x+Hv5bFbe780jVZdksDEVM5YffPCBady4sfH19TVt27Y1X331VUVfhpTByXwmHu0zb/LkycYYY1asWGE6d+5sgoODja+vr2nevLl57rnn9GX7FFXEa1WW48qJq4hY84cffjDt2rUzgYGBJiAgwLRt29ZMmjTJOJ3O03lpZwXFsmeO8nytsrOzTb9+/UxkZKTx8vIy9erVMzfccIOSi+XkRF6ro31v6d69e5mPKW6WMf8U+xQRERERERERERGRs5atsjsgIiIiIiIiIiIiIhVPyWARERERERERERGRakDJYBEREREREREREZFqQMlgERERERERERERkWpAyWARERERERERERGRakDJYBEREREREREREZFqQMlgERERERERERERkWpAyWARERERERERERGRakDJYBEREREREREREZFqQMlgEZEzzNixY7Esi5tvvrnEc7fddhuWZTF27NhibS3LwsvLi5o1a9K3b18+/PBDXC5XsX1Xr17N0KFDiYqKwtfXl/r163PZZZeRmJh41L7k5uYyduxYWrdujcPhYPjw4eV5qSIiIiIipVJMLCJycpQMFhE5A8XGxjJ9+nRycnI823Jzc5k2bRp169Yt1nbAgAHs37+fHTt28MMPP9CzZ0/uvPNOhgwZQmFhIQBJSUn07t2bsLAw5syZw4YNG5g8eTK1a9cmKyvrqP1wOp34+flxxx130KdPn4q5WBERERGRUigmFhE5cY7K7oCIiJy49u3bs23bNr788kuuuuoqAL788kvq1q1LgwYNirX18fEhOjoagDp16tC+fXu6dOlC7969+eijj7j++utZtGgRaWlpvP/++zgc7o+GBg0a0LNnz2P2IyAggIkTJwKwaNEiUlNTy/lKRURERERKp5hYROTEaWawiMgZaty4cUyePNnz5w8//JBrr722TPv26tWLtm3b8uWXXwIQHR1NYWEhs2bNwhhTIf0VERERESlviolFRE6MksEiImeo0aNHs3DhQnbu3MnOnTtZtGgRo0ePLvP+cXFx7NixA4AuXbrwyCOPcOWVVxIREcHAgQN5+eWXOXDgQAX1XkRERETk1CkmFhE5MUoGi4icoSIjIxk8eDAfffQRkydPZvDgwURERJR5f2MMlmV5/vzss8+SkJDApEmTaNmyJZMmTSIuLo61a9cC0LJlSwIDAwkMDGTgwIHlfj0iIiIiIidKMbGIyIlRzWARkTPYuHHjGD9+PABvv/32Ce27YcOGErXUwsPDufTSS7n00kt57rnnOOecc3jllVf4+OOP+f777ykoKADAz8+vfC5AREREROQUKSYWESk7JYNFRM5gAwYMID8/H8uy6N+/f5n3mz9/PmvXruXuu+8+ahtvb28aNWrkWTm5Xr16p9xfEREREZHypphYRKTslAwWETmD2e12NmzY4Pn/0uTl5ZGQkIDT6eTAgQP8+OOPPP/88wwZMoRrrrkGgNmzZzN9+nQuv/xymjZtijGGb7/9lu+//77YghylWb9+Pfn5+SQnJ5ORkcGqVasAaNeuXbldp4iIiIjI0SgmFhEpOyWDRUTOcEFBQcd8/scff6RWrVo4HA5CQ0Np27Ytb731FmPGjMFmc5eOb9GiBf7+/tx7773s3r0bHx8fmjRpwvvvv8/VV199zOMPGjSInTt3ev58zjnnAGgFZhERERE5bRQTi4iUjWX0L5OIiIiIiIiIiIjIWc9W2R0QERERERERERERkYqnZLCIiIiIiIiIiIhINaBksIiIiIiIiIiIiEg1oGSwiIiIiIiIiIiISDWgZLCIiIiIiIiIiIhINaBksIiIiIiIiIiIiEg1oGSwiIiIiIiIiIiISDWgZLCIiIiIiIiIiIhINaBksIiIiIiIiIiIiEg1oGSwiIiIiIiIiIiISDWgZLCIiIiIiIiIiIhINaBksIiIiIiIiIiIiEg1oGSwiIiIiIiIiIiISDWgZLCIiIiIiIiIiIhINaBksIiIiIiIiIiIiEg1oGSwiIiIiIiIiIiISDWgZLCIiIiIiIiIiIhINaBksIiIiIiIiIiIiEg1oGSwiIiIiIiIiIiISDWgZLCIiIiIiIiIiIhINaBksIiIiIiIiIiIiEg1oGSwiIiIiIiIiIiISDWgZLCIyEno0aMHPXr0qOxuHNVHH32EZVns2LGjsrtSYQYNGsQNN9xQ2d04qh07dmBZFh999FGlnL+goIDY2FjeeeedSjm/iIiInJ0qO8Ypi6oeq5+q3bt34+vry6JFiyq7K0f15JNPYllWpZ3/xx9/JDAwkKSkpErrg0hVpWSwiBxXUWLR19eXvXv3lni+R48etGrVqti2+vXrY1kWffr0KfWY7733HpZlYVkWy5cv92wvChpKe0yaNOmY/ezRo0ex9n5+frRp04Y33ngDl8t1Eld+9jtyvP39/albty4XXXQRkydPJi8v76SP/f333/Pkk0+WX2cPs2jRIubOncuDDz5YbPuzzz7L0KFDqVmzJpZlHfP8e/fuZdSoUYSEhBAUFMSwYcPYvn17hfS3Mnh5eXHPPffw7LPPkpubW9ndEREROSsUxcVFD19fX5o2bcr48eM5cOBAifYHDhzgvvvuIy4uDn9/fwICAujQoQPPPPMMqamppZ6jU6dOWJbFxIkTy9yvogRt0cNmsxEWFsbAgQNZvHjxyV7uWa/oO0vRmIWEhNC6dWtuvPFGli5dekrHfu655/jqq6/Kp6NHePrpp+ncuTPdunXzbNu0aRN333035513Hr6+vsedGPLNN9/Qvn17fH19qVu3Lk888QSFhYUV0t/KMGDAABo3bszzzz9f2V0RqXIcld0BETlz5OXl8cILLzBhwoQytff19eWXX34hISGB6OjoYs9NnToVX1/foyapJk6cSGBgYLFtnTt3Pu45Y2JiPB/4Bw8eZNq0adx9990kJSXx7LPPlqnf1VHReOfl5bF3717mzJnDuHHjeOONN5g9ezaxsbEnfMzvv/+et99+u0ISwi+//DK9e/emcePGxbY/9thjREdHc8455zBnzpyj7p+ZmUnPnj1JS0vjkUcewcvLi9dff53u3buzatUqwsPDy73PleHaa6/loYceYtq0aYwbN66yuyMiInLWePrpp2nQoAG5ubksXLiQiRMn8v3337Nu3Tr8/f0BWLZsGYMGDSIzM5PRo0fToUMHAJYvX84LL7zA77//zty5c4sdd8uWLSxbtoz69eszdepUbrnllhPq1xVXXMGgQYNwOp1s3ryZd955h549e7Js2TJat25dPhd/lmnXrh333nsvABkZGWzYsIEZM2bw3nvvcffdd/Paa6+d1HGfe+45LrnkEoYPH16OvYWkpCQ+/vhjPv7442LbFy9ezFtvvUWLFi1o3rw5q1atOuoxfvjhB4YPH06PHj2YMGECa9eu5ZlnniExMfGEfoSo6m666Sbuu+8+nnrqKWrUqFHZ3RGpMpQMFpEya9euHe+99x4PP/wwtWvXPm77bt26sWzZMv773/9y5513erbv2bOHBQsWMGLECL744otS973kkkuIiIg44T4GBwczevRoz59vvvlm4uLimDBhAk8//TR2u/2Ej1kdHDne//rXv5g6dSrXXHMNl156KUuWLKnE3hWXmJjId999V+pM8fj4eOrXr8/BgweJjIw86jHeeecdtmzZwp9//sm5554LwMCBA2nVqhWvvvoqzz33XIX1/3QKCQmhX79+fPTRR0oGi4iIlKOBAwfSsWNHAK6//nrCw8N57bXX+Prrr7niiitITU1lxIgR2O12Vq5cSVxcXLH9n332Wd57770Sx/3000+Jiori1Vdf5ZJLLmHHjh3Ur1+/zP1q3759sVj4ggsuYODAgUycOFGlo46iTp06xcYM4MUXX+TKK6/k9ddfp0mTJieclK9In376KQ6Hg4suuqjY9qFDh5KamkqNGjV45ZVXjpkMvu+++2jTpg1z587F4XCnhYKCgnjuuee48847S7xfz1QjR47k9ttvZ8aMGYqFRQ6jMhEiUmaPPPIITqeTF154oUztfX19ufjii5k2bVqx7Z999hmhoaH079+/IrpZog/nnnsuGRkZJCYmFnvu008/pUOHDvj5+REWFsbll1/O7t27Sxzj3XffpVGjRvj5+dGpUycWLFhQos3RavT++uuvWJbFr7/+Wmz70qVLGTRoEKGhoQQEBNCmTRvefPPNYm02btzIJZdcQlhYGL6+vnTs2JFvvvmmxLn//vtvevXqhZ+fHzExMTzzzDPlUhbjqquu4vrrr2fp0qXMmzfPs33BggVceuml1K1bFx8fH2JjY7n77rvJycnxtBk7dixvv/02QLFbFou88sornHfeeYSHh+Pn50eHDh2YOXNmmfr13XffUVhYWGoJkrJ+WZo5cybnnnuuJxEMEBcXR+/evfn888/LdIzU1FTuvvtu6tevj4+PDzExMVxzzTUcPHjwqPusWbOGsWPH0rBhQ3x9fYmOjmbcuHEcOnSoWLuMjAzuuusuz7GjoqLo27cvf/31l6fNli1bGDlyJNHR0fj6+hITE8Pll19OWlpasWP17duXhQsXkpycXKbrEhERkRPXq1cvwP3DNMB//vMf9u7dy2uvvVZqYq1mzZo89thjJbZPmzaNSy65hCFDhhAcHFwijj5RF1xwAQDbtm0rtj01NZW77rqL2NhYfHx8aNy4MS+++GKJGDI1NZWxY8cSHBxMSEgIY8aMKbW8xdFq9I4dO7ZEfOZyuXjzzTdp3bo1vr6+REZGMmDAgGKl46B8Y/UT5efnx5QpUwgLC+PZZ5/FGON5rixxrGVZZGVl8fHHH3vi4LFjxwKwc+dObr31Vpo1a4afnx/h4eFceumlZV7r46uvvqJz584l7qIMCwsr0+zX9evXs379em688UZPIhjg1ltvxRhT5pi8LN9njjR58mR69epFVFQUPj4+tGjRotSZyMuXL6d///5ERETg5+dHgwYNSiRzp0+fTocOHahRowZBQUG0bt26xPmjoqJo06YNX3/9dZmuSaS60MxgESmzBg0acM011/Dee+/x0EMPlWl28JVXXkm/fv3Ytm0bjRo1Av4X5Hp5eR11vyMTV3a7ndDQ0JPqd1ENtZCQEM+2Z599lscff5xRo0Zx/fXXk5SUxIQJE7jwwgtZuXKlp+0HH3zATTfdxHnnncddd93F9u3bGTp0KGFhYSdVOgFg3rx5DBkyhFq1anHnnXcSHR3Nhg0bmD17tmcG9d9//023bt2oU6cODz30EAEBAXz++ecMHz6cL774ghEjRgCQkJBAz549KSws9LR799138fPzO6m+Henqq6/m3XffZe7cufTt2xeAGTNmkJ2dzS233EJ4eDh//vknEyZMYM+ePcyYMQNw35K1b98+5s2bx5QpU0oc980332To0KFcddVV5OfnM336dC699FJmz57N4MGDj9mnP/74g/DwcOrVq3dS1+RyuVizZk2pswM6derE3LlzycjIOGYwnZmZyQUXXMCGDRsYN24c7du35+DBg3zzzTfs2bPnqLPa582bx/bt27n22muJjo7m77//5t133+Xvv/9myZIlnoT5zTffzMyZMxk/fjwtWrTg0KFDLFy4kA0bNtC+fXvy8/Pp378/eXl53H777URHR7N3715mz55NamoqwcHBnnN26NABYwx//PEHQ4YMOakxExERkWMrSrYWlZr65ptv8PPz45JLLinzMZYuXcrWrVuZPHky3t7eXHzxxUydOpVHHnnkpPtVlGA8PI7Ozs6me/fu7N27l5tuuom6devyxx9/8PDDD7N//37eeOMNAIwxDBs2jIULF3LzzTfTvHlzZs2axZgxY066PwDXXXcdH330EQMHDuT666+nsLCQBQsWsGTJEs9s68qM1YsEBgYyYsQIPvjgA9avX0/Lli2BssWxU6ZM4frrr6dTp07ceOONAJ7vQsuWLeOPP/7g8ssvJyYmhh07djBx4kR69OjB+vXrPWVGSlNQUMCyZctOaabyypUrATxjXaR27drExMR4nj+WsnyfKc3EiRNp2bIlQ4cOxeFw8O2333Lrrbficrm47bbbAPddgP369SMyMpKHHnqIkJAQduzYwZdfflns/FdccQW9e/fmxRdfBGDDhg0sWrSoxPk7dOhQYbWbRc5YRkTkOCZPnmwAs2zZMrNt2zbjcDjMHXfc4Xm+e/fupmXLlsX2qVevnhk8eLApLCw00dHR5t///rcxxpj169cbwPz222/FjlvkiSeeMECJR7169Y7bz+7du5u4uDiTlJRkkpKSzMaNG839999vADN48GBPux07dhi73W6effbZYvuvXbvWOBwOz/b8/HwTFRVl2rVrZ/Ly8jzt3n33XQOY7t27lxij+Pj4Ysf85ZdfDGB++eUXY4wxhYWFpkGDBqZevXomJSWlWFuXy+X5/969e5vWrVub3NzcYs+fd955pkmTJp5td911lwHM0qVLPdsSExNNcHBwqf05UtF4JyUllfp8SkqKAcyIESM827Kzs0u0e/75541lWWbnzp2ebbfddps52sfMkcfIz883rVq1Mr169Tpmf40x5vzzzzcdOnQ4ZpukpCQDmCeeeOKozz399NMlnnv77bcNYDZu3HjM4//rX/8ygPnyyy9LPFf0OsbHxxvATJ482fNcaWP32WefGcD8/vvvnm3BwcHmtttuO+r5V65caQAzY8aMY/bTGGP27dtnAPPiiy8et62IiIgcW1HM99NPP5mkpCSze/duM336dBMeHm78/PzMnj17jDHGhIaGmrZt257QscePH29iY2M9scTcuXMNYFauXHncfYvijqeeesokJSWZhIQEs2DBAnPuueeWiBn+/e9/m4CAALN58+Zix3jooYeM3W43u3btMsYY89VXXxnAvPTSS542hYWF5oILLigR43Tv3r1YbFxkzJgxxeL4+fPnG6DYd4kiRdddEbH60RR9Zzma119/3QDm66+/9mwraxwbEBBgxowZU+KYpcWDixcvNoD55JNPjtnfrVu3GsBMmDDhmO1efvnlo34XKHqu6HU+3Lnnnmu6dOlyzGOX9ftM0feMw5V27f379zcNGzb0/HnWrFklviMe6c477zRBQUGmsLDwmH01xpjnnnvOAObAgQPHbStSXahMhIickIYNG3pmi+7fv/+47e12O6NGjeKzzz4D3AvHxcbGem5ZO5ovvviCefPmeR5Tp04tU/82btxIZGQkkZGRxMXF8fLLLzN06FA++ugjT5svv/wSl8vFqFGjOHjwoOcRHR1NkyZN+OWXXwD37UmJiYncfPPNeHt7e/YvulXuZKxcuZL4+HjuuuuuYjOVAc+s0OTkZObPn8+oUaPIyMjw9O/QoUP079+fLVu2sHfvXsC9SFuXLl3o1KmT5ziRkZFcddVVJ9W/IxXdfpaRkeHZdvis46ysLA4ePMh5552HMaZMMwmOPEZKSgppaWlccMEFxcogHM2hQ4dOepY44Cln4ePjU+I5X1/fYm2O5osvvqBt27aeGdqHO7wcxpEOv+7c3FwOHjxIly5dAIpde0hICEuXLmXfvn2lHqfo/Tdnzhyys7OP2deisTpW+QoRERE5MX369CEyMpLY2Fguv/xyAgMDmTVrFnXq1AEgPT39hBasKiws5L///S+XXXaZJ5Youp2+rHEwwBNPPEFkZCTR0dGeu5iK6g8XmTFjBhdccAGhoaHFYuE+ffrgdDr5/fffAXec6XA4is1Ctdvt3H777WXuz5G++OILLMviiSeeKPFc0XVXZqx+pOPFwicaxx65f0FBAYcOHaJx48aEhIQc9xhFpcUqMhY+Xhxclu8zR3P4taelpXHw4EG6d+/O9u3bPaXOio45e/ZsCgoKSj1OSEgIWVlZxUrZHY1iYZGSVCZCRE7YY489xpQpU3jhhReOWxcK3KUi3nrrLVavXs20adO4/PLLjxsoXHjhhSe1gFz9+vV57733cLlcbNu2jWeffZakpCRPkg/ctVaNMTRp0qTUYxSVr9i5cydAiXZeXl40bNjwhPsG/7uFsFWrVkdts3XrVowxPP744zz++OOltklMTKROnTrs3LmTzp07l3i+WbNmJ9W/I2VmZgIU+zKza9cu/vWvf/HNN9+QkpJSrP2R9WqPZvbs2TzzzDOsWrWKvLw8z/bjvS+KmMPqtp2ooiD08PMWyc3NLdYmISGh2PPBwcH4+fmxbds2Ro4cecLnTk5O5qmnnmL69OklalgfPnYvvfQSY8aMITY2lg4dOjBo0CCuueYaz/uuQYMG3HPPPbz22mtMnTqVCy64gKFDhzJ69OgSX36KxqqsYysiIiLH9/bbb9O0aVMcDgc1a9akWbNm2Gz/m2sVFBRULIF4PHPnziUpKYlOnTqxdetWz/aePXvy2Wef8eKLLxY7/tHceOONXHrppeTm5jJ//nzeeustnE5nsTZbtmxhzZo1R11styhG2blzJ7Vq1SpRm/ZU4sxt27ZRu3ZtwsLCjtqmMmP1I5UWC59qHJuTk8Pzzz/P5MmT2bt3b7G4tqyxdEXGwkXP5+TklOhPdHR0mb7PHM2iRYt44oknWLx4cYkJDWlpaQQHB9O9e3dGjhzJU089xeuvv06PHj0YPnw4V155pSeBfeutt/L5558zcOBA6tSpQ79+/Rg1ahQDBgwocU7FwiIlKRksIiesYcOGjB49mnfffZeHHnrouO07d+5Mo0aNuOuuu4iPj+fKK6+ssL4FBAQUW1isW7dutG/fnkceeYS33noLcNeMtSyLH374AbvdXuIYRwa8ZXG04OLI4LssihbuuO+++466yF7jxo1P+LgnY926dcXO53Q66du3L8nJyTz44IPExcUREBDA3r17GTt2bJkWrluwYAFDhw7lwgsv5J133qFWrVp4eXkxefLkMi2SEh4eXiIJfSLCwsLw8fEpdWZ70baieti1atUq9vzkyZM9i3+cjFGjRvHHH39w//33065dOwIDA3G5XAwYMKDY2I0aNYoLLriAWbNmMXfuXF5++WVefPFFvvzySwYOHAjAq6++ytixY/n666+ZO3cud9xxB88//zxLliwhJibGc6yisTqZH1dERESkdJ06dSpRc/VwcXFxrFq1ivz8/GKzVo+maPbvqFGjSn3+t99+o2fPnsc9TpMmTTyx8JAhQ7Db7Tz00EP07NnT01+Xy0Xfvn154IEHSj1G06ZNj3ueI1mWVWqC8mRj4fKO1U/WkbHwqcaxALfffjuTJ0/mrrvuomvXrgQHB2NZFpdffvlxY+mimtSnEgsXxbf79+8vUVd5//79njsO//vf/3LttdcWe/5UktDbtm2jd+/exMXF8dprrxEbG4u3tzfff/89r7/+uufaLcti5syZLFmyhG+//ZY5c+Ywbtw4Xn31VZYsWUJgYCBRUVGsWrWKOXPm8MMPP/DDDz8wefJkrrnmGj7++ONi51UsLFKSksEiclIee+wxPv30U0/B/uO54ooreOaZZ2jevDnt2rWr2M4dpk2bNowePZr//Oc/3HfffdStW5dGjRphjKFBgwbHDHaLFijbsmWLZ4VocN/OFR8fT9u2bT3bim4/OnJ15aIZC0WKFo5Yt25dsaT14YpmMnh5eR21zeF93LJlS4ntmzZtOuZ+ZVW0+FtRUnrt2rVs3ryZjz/+mGuuucbTrrRbtI6WIP/iiy/w9fVlzpw5xW5Pmzx5cpn6FBcXxxdffFHmaziSzWajdevWJVasBvfCLQ0bNvTM/jjyuooWDmnUqJHny0FZpaSk8PPPP/PUU0/xr3/9y7O9tNcP3IH6rbfeyq233kpiYiLt27fn2Wef9SSDAVq3bk3r1q157LHH+OOPP+jWrRuTJk3imWee8bQpWtW8efPmJ9RfEREROXkXXXQRixcv5osvvuCKK644ZtusrCy+/vprLrvsslIXnLvjjjuYOnVqmZLBR3r00Ud57733eOyxx/jxxx8BdxyTmZlZpjjz559/JjMzs1gCtrQ4MzQ0lO3bt5fYXlosPGfOHJKTk486O7giYvWTkZmZyaxZs4iNjfXEUScSxx4tFp45cyZjxozh1Vdf9WzLzc0t8T2iNHXr1sXPz88T352Mou9iy5cvL1Zqbt++fezZs8ez4F3//v1LjfHL8n2mNN9++y15eXl888031K1b17O9qOzHkbp06UKXLl149tlnmTZtGldddRXTp0/n+uuvB8Db25uLLrqIiy66CJfLxa233sp//vMfHn/88WITZ+Lj44mIiDjqTHiR6kg1g0XkpDRq1MiTZD3yVvrSXH/99TzxxBPFgp7T5YEHHqCgoIDXXnsNgIsvvhi73c5TTz1V4tdtY4ynFlfHjh2JjIxk0qRJ5Ofne9p89NFHJYK1oqCoqMYauGdCvPvuu8XatW/fngYNGvDGG2+UOEZRX6KioujRowf/+c9/Sp29mpSU5Pn/QYMGsWTJEv78889iz59IbbmjmTZtGu+//z5du3ald+/eAJ7ZGYePmzGm1HIhAQEBQMkEud1ux7KsYjNFduzYUeZVfrt27UpKSkqpXzjK6pJLLmHZsmXFEsKbNm1i/vz5XHrppZ5tffr0KfYomkkxcuRIVq9ezaxZs0oc+2gzJkobO8CzYncRp9NZ4pa8qKgoateu7bmdLz09ncLCwmJtWrdujc1mK3HL34oVK7Asi65du5baLxERESl/N998M7Vq1eLee+9l8+bNJZ5PTEz0/Hg7a9YssrKyuO2227jkkktKPIYMGcIXX3xR6m39xxMSEsJNN93EnDlzWLVqFeCefbx48WLmzJlTon1qaqonxhg0aBCFhYVMnDjR87zT6WTChAkl9mvUqBEbN24sFqeuXr2aRYsWFWs3cuRIjDE89dRTJY5RFCNVRKx+onJycrj66qtJTk7m0Ucf9SR2TySODQgIKLUfdru9xHVNmDChTLOovby86NixY6mTGsqqZcuWxMXF8e677xY758SJE7Esy/ODRK1atUrEwlC27zOlKS0WTktLK5FIT0lJKXGcogR20d+BovdAEZvNRps2bYq1KbJixQrFwSJH0MxgETlpjz76KFOmTGHTpk2eGZNHU69ePZ588snT07EjtGjRgkGDBvH+++/z+OOP06hRI5555hkefvhhduzYwfDhw6lRowbx8fHMmjWLG2+8kfvuuw8vLy+eeeYZbrrpJnr16sVll11GfHw8kydPLlGHrGXLlnTp0oWHH37YM9Nh+vTpJRJ2NpuNiRMnctFFF9GuXTuuvfZaatWqxcaNG/n77789Qfnbb7/N+eefT+vWrbnhhhto2LAhBw4cYPHixezZs4fVq1cD7kT3lClTGDBgAHfeeScBAQG8++671KtXjzVr1pR5jGbOnElgYCD5+fns3buXOXPmsGjRItq2bcuMGTM87eLi4mjUqBH33Xcfe/fuJSgoiC+++KLUW9U6dOgAuGez9O/fH7vdzuWXX87gwYN57bXXGDBgAFdeeSWJiYm8/fbbNG7cuEx9Hjx4MA6Hg59++skzc6HIlClT2Llzp6cG2e+//+75onX11Vd7ZpDceuutvPfeewwePNjzWr/22mvUrFmTe++997h9uP/++5k5cyaXXnop48aNo0OHDiQnJ/PNN98wadKkUmeiBAUFceGFF/LSSy9RUFBAnTp1mDt3bomZHRkZGcTExHDJJZfQtm1bAgMD+emnn1i2bJnnx5T58+czfvx4Lr30Upo2bUphYSFTpkzBbreXqGU8b948unXr5rmtUERERCpeaGgos2bNYtCgQbRr147Ro0d7YqO//vqLzz77zJOgmjp1KuHh4Zx33nmlHmvo0KG89957fPfdd1x88cUn3Jc777yTN954gxdeeIHp06dz//3388033zBkyBDGjh1Lhw4dyMrKYu3atcycOZMdO3YQERHBRRddRLdu3XjooYfYsWMHLVq04Msvvyy1ru24ceN47bXX6N+/P9dddx2JiYlMmjSJli1bkp6e7mnXs2dPrr76at566y22bNniKZW1YMECevbsyfjx4yskVj+WvXv38umnnwLu2cDr169nxowZJCQkcO+993LTTTd52p5IHNuhQwd++uknXnvtNWrXrk2DBg3o3LkzQ4YMYcqUKQQHB9OiRQsWL17MTz/9VOZYbdiwYTz66KOkp6cTFBTk2Z6WluZJ1Bcl4f/v//6PkJAQQkJCGD9+vKdt0SLb/fr14/LLL2fdunX83//9H9dff/1x7yYr6/eZI/Xr188zm/emm24iMzOT9957j6ioqGITYD7++GPeeecdRowYQaNGjcjIyOC9994jKCiIQYMGAe6JRsnJyfTq1YuYmBh27tzJhAkTaNeuXbH+JyYmsmbNGm677bYyja1ItWFERI5j8uTJBjDLli0r8dyYMWMMYFq2bFlse7169czgwYNP+LhPPPGEAUxSUtIJ97N79+4l+lHk119/NYB54oknPNu++OILc/7555uAgAATEBBg4uLizG233WY2bdpUbN933nnHNGjQwPj4+JiOHTua33//3XTv3t107969WLtt27aZPn36GB8fH1OzZk3zyCOPmHnz5hnA/PLLL8XaLly40PTt29fUqFHDBAQEmDZt2pgJEyaUON4111xjoqOjjZeXl6lTp44ZMmSImTlzZrF2a9asMd27dze+vr6mTp065t///rf54IMPDGDi4+OPOWZF41308PX1NTExMWbIkCHmww8/NLm5uSX2Wb9+venTp48JDAw0ERER5oYbbjCrV682gJk8ebKnXWFhobn99ttNZGSksSzLHP6R88EHH5gmTZoYHx8fExcXZyZPnuzpS1kMHTrU9O7du8T27t27F7uewx9Hvga7d+82l1xyiQkKCjKBgYFmyJAhZsuWLWU6vzHGHDp0yIwfP97UqVPHeHt7m5iYGDNmzBhz8OBBY4wx8fHxJcZkz549ZsSIESYkJMQEBwebSy+91Ozbt6/YezMvL8/cf//9pm3btp73R9u2bc0777zjOc727dvNuHHjTKNGjYyvr68JCwszPXv2ND/99FOxPqamphpvb2/z/vvvl/m6RERE5OiOFReXZt++febuu+82TZs2Nb6+vsbf39906NDBPPvssyYtLc0cOHDAOBwOc/XVVx/1GNnZ2cbf39+MGDHiqG2K4o6XX3651OfHjh1r7Ha72bp1qzHGmIyMDPPwww+bxo0bG29vbxMREWHOO+8888orr5j8/HzPfocOHTJXX321CQoKMsHBwebqq682K1euLBHjGGPMp59+aho2bGi8vb1Nu3btzJw5c8yYMWNMvXr1irUrLCw0L7/8somLizPe3t4mMjLSDBw40KxYsaJYu/KO1UtTr149T6xoWZYJCgoyLVu2NDfccINZunRpqfuUNY7duHGjufDCC42fn58BzJgxY4wxxqSkpJhrr73WREREmMDAQNO/f3+zceNGU69ePU+bYyl6z0yZMqXY9qL3QGmPI18DY4yZNWuWadeunfHx8TExMTHmscceK/baH8/xvs+UNibffPONadOmjfH19TX169c3L774ovnwww+LfW/566+/zBVXXGHq1q1rfHx8TFRUlBkyZIhZvny55zgzZ840/fr1M1FRUcbb29vUrVvX3HTTTWb//v3Fzjdx4kTj7+9v0tPTy3xdItWBZcwpVAAXERGpBAsWLKBHjx5s3LjxqCtNi7sExUsvvcS2bds8K0OLiIiIyJntuuuuY/PmzSxYsKCyu1KlnXPOOfTo0YPXX3+9srsiUqUoGSwiImekgQMHEhMTw3vvvVfZXamSCgoKaNSoEQ899BC33nprZXdHRERERMrJrl27aNq0KT///DPdunWr7O5UST/++COXXHIJ27dvJyoqqrK7I1KlKBksIiIiIiIiIiIiUg3YKrsDIiIiIiIiIiIiIlLxlAwWERERERERERERqQaUDBYRERERERERERGpBpQMFhEREREREREREakGHJXdgbOBy+Vi37591KhRA8uyKrs7IiIiItWKMYaMjAxq166Nzaa5DpVFMbGIiIhI5SlrTKxkcDnYt28fsbGxld0NERERkWpt9+7dxMTEVHY3qi3FxCIiIiKV73gxsZLB5aBGjRoArFgRT2BgjUrujYiIiEj1kpmZQYcODTwxmVQOxcQiIiIilaesMbGSweWg6Da4wMAa1KgRVMm9EREREamezsTSBL///jsvv/wyK1asYP/+/cyaNYvhw4cDUFBQwGOPPcb333/P9u3bCQ4Opk+fPrzwwgvUrl3bc4zk5GRuv/12vv32W2w2GyNHjuTNN98kMDDQ02bNmjXcdtttLFu2jMjISG6//XYeeOCBYn2ZMWMGjz/+ODt27KBJkya8+OKLDBo0qMzXophYREREpPIdLyZWUTURERERkUqSlZVF27Ztefvtt0s8l52dzV9//cXjjz/OX3/9xZdffsmmTZsYOnRosXZXXXUVf//9N/PmzWP27Nn8/vvv3HjjjZ7n09PT6devH/Xq1WPFihW8/PLLPPnkk7z77rueNn/88QdXXHEF1113HStXrmT48OEMHz6cdevWVdzFi4iIiMhpZxljTGV34kyXnp5OcHAwmzYd1CwIERERkdMsIyOdZs0iSEtLIyjozI3FLMsqNjO4NMuWLaNTp07s3LmTunXrsmHDBlq0aMGyZcvo2LEjAD/++CODBg1iz5491K5dm4kTJ/Loo4+SkJCAt7c3AA899BBfffUVGzduBOCyyy4jKyuL2bNne87VpUsX2rVrx6RJk0rtS15eHnl5eZ4/p6enExsbq5hYREREpBKUNSbWzGARERERkTNEWloalmUREhICwOLFiwkJCfEkggH69OmDzWZj6dKlnjYXXnihJxEM0L9/fzZt2kRKSoqnTZ8+fYqdq3///ixevPiofXn++ecJDg72PLR4nIiIiEjVp2SwiIiIiMgZIDc3lwcffJArrrjCM9sjISGBqKioYu0cDgdhYWEkJCR42tSsWbNYm6I/H69N0fOlefjhh0lLS/M8du/efWoXKCIiIiIVTgvIiYiIiIhUcQUFBYwaNQpjDBMnTqzs7gDg4+ODj49PZXdDRERERE6AksEiIiIiIlVYUSJ4586dzJ8/v1gNuOjoaBITE4u1LywsJDk5mejoaE+bAwcOFGtT9OfjtSl6XkRERETODioTISIiIiJSRRUlgrds2cJPP/1EeHh4see7du1KamoqK1as8GybP38+LpeLzp07e9r8/vvvFBQUeNrMmzePZs2aERoa6mnz888/Fzv2vHnz6Nq1a0VdmoiIiIhUAiWDRUREREQqSWZmJqtWrWLVqlUAxMfHs2rVKnbt2kVBQQGXXHIJy5cvZ+rUqTidThISEkhISCA/Px+A5s2bM2DAAG644Qb+/PNPFi1axPjx47n88supXbs2AFdeeSXe3t5cd911/P333/z3v//lzTff5J577vH048477+THH3/k1VdfZePGjTz55JMsX76c8ePHn/YxEREREZGKYxljTGV34kyXnp5OcHAwmzYdpEaNoOPvICIiIiLlJiMjnWbNIkhLSytWQuFM8Ouvv9KzZ88S28eMGcOTTz5JgwYNSt3vl19+oUePHgAkJyczfvx4vv32W2w2GyNHjuStt94iMDDQ037NmjXcdtttLFu2jIiICG6//XYefPDBYsecMWMGjz32GDt27KBJkya89NJLDBo0qMzXophYREREpPKUNSZWMrgcKPAVERERqTxncjL4bKKYWERERKTylDUmVpkIERERERERERERkWpAyWARERERERERERGRakDJYBEREREREREREZFqQMlgERERERERERERkWpAyWARERERERERERGRakDJYBEREREREREREZFqwFHZHRARqcpyC/PYmLyF5NwUgnxq0CKsKf5e/pXdLREREZFqy2VcpOYfJLMgA4fNQZhPFL52v8ruloiIyBlByWARkaNIyj7IJ+s/Z3PKNlwYLKB+UCxjWl5OncBald09ERERkWqn0FXAhtSVJObuw2mcAAQ4AmkW3JYI3+hK7p2IiEjVpzIRIiKlMMbw9bYfWX9oE/WCYogLbUzDoHpsT9vJ55u/xulyVnYXRURERKqd3Vnb2Z+zCz9HAGE+kYR6h5PrzGFT2hrynbmV3T0REZEqTzODRURKcSg3mQ3Jm6kVUBMfuw8AXnYvYmvUIT51J3sy91EvKLaSeylVhTGG/Px8cnNzKCwswBiDy+XCGIOvrx81agRhs+n3VxERkVPhMi4SsnfjZfPB2+aOzyzLRg2vENLyD5Gcl0S0v+IzERGRY1EyWESkFHnOfAqcBQR7BxXb7mPzpsBVQJ4zHwCXceIyLuyWA8uyKqOrUoGys7PZunUT+/fvITExgQMH9hf7b2JiAikpyeTl5WKMOeaxAgNrUKNGMEFBQQQFhRAUFExISCh16zagfv2G1K/fmAYNGhEeHqn3koiISCkMhkJTgN2yF9tuYWHAUzZCREREjk7JYBGpsowxZBSkkVaQjIVFiHc4gV5Bx9+xHET6RVAzIJL9mYk0CK7r2Z6Yc5Aw31Ai/ELZlr6BAzm7cRonId7hxAY2IsQ7/LT0T8qX0+lkx45tbNy4jg0b1rFhw1o2bvybHTu2eZK8NpuNyMiaREVFU7NmLVq0aEPPnv0IC4vA19cPX19ffHx8cTgc2Gx2LMvCsixyc3NIT08nPT2VjIw00tPT//lvGrt27WDhwl84cGC/py8BAYHUr9+IBg0a0bJlWzp06MI555xLQEBgZQ2PiIhIlWC37IT6RLAveyd+9gDPj6f5rly8LK/TFieKiIicyZQMFpEqyRjDtowN7M7aRoErHzD42PyoH9iUuoGNK3zmpLfdi771ejB1w0w2p2wj2CeIjIJMbNgY3KAPu7M2kZC7Fx+bL3bLzv6c3aTmJ9M2rDNB3qEV2jc5dXl5eaxc+ScLF/7CokW/snr1CnJzcwCIiIgiLq4VffoMIi6uFXFxLalTJ5bw8EjsdvtxjnxysrOz2LlzOzt2bCM+fhs7d25n+/YtvPPOq2RkpGOz2YiLa0XHjl3o0KEzHTp0oUGDiv97ICIiUtXEBjQiNf8QKflJ+Nj9cLoKcZpCYgIaEeSlGExEROR4lAwWkSopKXc/OzM3423zIfCfUg3ZhZlsz9xIsE/YaZmBe27Nc/Bz+LJw71L2ZOyjeWhTutY+l/rBNVmdsoQgr1C8bF4A+Nr9SclPYm/WDiWDq6DCwkLWrl3pSf7++ecicnNzCAkJpWvXC3nwwado3rw1zZu3IjKy5mnvn79/wD/nb11su8vlYsuWDSxfvpQVK5awePHvfPLJuwDUrh1Lv35DGDBgKF27XoiXl9dp77eIiMjpFuwdRuvQTuzN2kFKfhL+XgFE+9eltn89/UgqIiJSBkoGi0iVdDB3Py7jws8R4NkW4FWD5LwkDuYeOC3JYMuyaB3RgtYRLTDGeL5g7Mzcgsu4PIngorY+Nl9S8g9WeL+kbJxOJ0uWLODrrz/nu+9mkZJyCH//ALp0uYD773+Cbt160rJlmwqb7Xs0+dn5FOQU4FPDF4f3sc9ts9loWK8pATkhtA3twk2DHHhFOYhP2sQvv8xlzpxv+eijiQQFBdO790D697+Inj37U6OGbpMVEZGzV7B3GMHeYcXiMxERESkbJYNFpEoqcOVjs2wltluA0xSe9v4c/kXDYXlhQYkvIIWmkEB78Gnvm/yPy+VixYqlfPPNDL79diaJiQnExtbnyiuvpW/fIZxzzrmVNoO2ILeAbQvi2bduP4W5BfiF+FH33LrUbR+DZSv9i2xeZh6rZ60laUsSAMaAl6+Dxt2b8e9/D+Df/36NdetWMWfOt/z44zfMmjUdb29v+vUbwtVX38h5XbpzYFMiiZsSKcwrJKx+OHVa18Knhs/pvHQREZEKoUSwiIjIiVMyWESqpBCfCA7k7sVlXJ6ksNPlTgLX8KrchGu4bxR+jkDSC1Ko4RWChUWuMweXcRHtF1upfauu9u7dzSef/Icvv5zO3r27qFmzFsOGjWLYsFGcc865lf5l0RjDhjmb2LV8N37BvvjW8CU7JYf136/HsqBuh9LfN7tX7iFxUyIhsaGeWcRZh7LYtjCeyEYRBNUKonXrc2jd+hzuu+9f7N69gx9++Jpp0z7ksssGEFOzHuc16E3H2AvxtfyIX7yTPaui6DS6I75BvqdzCERERERERKQKUDJYRKqkmn4xHMjZQ2r+QbxtvoAh35VHhE80kb61KrVvvnZ/4oLbsjltDWn5yRhceNt8qBfYhFr+dSu1b9WJMYbly5fw/vtv8f33X+HvH8Dw4ZcxfPhldOrU7bSXfziWjMRMEtYnEBgR4EnCegd4k7YvjZ1/7qJOm9rYvUr2N2H9AbwDvIuVk/AP8+dQfDLJu1IIqlW8HERsbH1uvPFObrjhDn765gfeefU1vvjzY778cwptap5L59o9yUhqgG+QL51Gd6zYixYRkWolsyCdlPwkXMZFDa9gQrwjSr3LS0RERCqXksEiUiX52v1oHdqJPVnxJOXux7Is6gY0pk5AA7xs3pXdPSJ8ownyDiUl7yBOU0gNrxACHUGVPgP1bJafnc+h+GRysrL5Y+0vTJ35PqtXr6Bhw8Y89dSrjBp1NYGBNSq7m6XKTskmP7uAGjWL9883yJectFzysvLxD/ErsZ9xGdzFUUoyxhz1fJZl0SiiOVe0uYW+dS5hVeIfLN41n7/2/0Hj0JYMLLyUloNaEBDmf0rXJSIiArAnK55t6evJc+UCYLfs1PKrS7PgNtht+sopIiJSleiTWUSqLD9HAE2CW9E4qCVQ9erCedt8qOlXp7K7US0kbTvIqq9X8e3vnzNv3dekZafQvkUXPp48i959B2KzlW3mUXpCOvvW7ifjQAb+Yf7UahlNWL2wCu49ePl5UZhfyIFNiTi8HfiF+OEf4kd+dgHefl54+5Vex7hmXE02/bwZZ6E/dod7dnBuWi5e/l6ExoQc+6SWRW56LjV8gujXagR9Wg5jzd5lzF4znQl/PMmqcQv517PP07Jl23K+WhERqU4yC9LYlr4eg4tQ7wgsyyLfmcve7HiCvUOpE9CgsrsoIiIihzmr7tv5/fffueiii6hduzaWZfHVV18dd59ff/2V9u3b4+PjQ+PGjfnoo48qvJ8icmIsy6pyiWA5fbJTs3n7+de5a+I1zFo+hXObd+Olce9zy3mP0DS09XETwYX5TpK2JLFh7ib+eH8pW37bxqEdKWxfvIMlHy8jfsmOY86yPVWuQhf71+4nMymTpK0HObjtIPvW7GPv2n3kZuRSp21tHD6l/zYb2z6G8AbhJO9MIWV3Kod2JJObkUe9c+sSXOfYtbPD64di2SxcTpd7gxMa+7bixqYPM7LxODZv2UDfvudy001XsmXLhvK+bBERqSaS85LIc+UScNgdUt52X2yWnQO5eyu5dyIiInKks2pmcFZWFm3btmXcuHFcfPHFx20fHx/P4MGDufnmm5k6dSo///wz119/PbVq1aJ///6nocciInI0xhjmzp3N0088RPyuLZzXqidX9r2R2hHuxdbS9qWxZ/U+6p4be9QfC9L2p/P37L9J2ZvGofhkCnMLCKsfhn+oH1mHssg4kMGh7YdI3plCs95NCYwIKPfrSNycyK4Ve4huEU36vjSyU3MoyC0g40AmdVrXpkHX+kfd1y/Ylw6j2rF/fQLJO5Nx+HpRs2kUUU0jj/sDSVCtIGLOiWH7wngyD2aRl5GLs8CFw9eLc2p15YJ2vdmYt4pvlk+nZ89zuP7623nggSfx9y//MRARkbOX0ziBkndw2Swbhf8s/isiIiJVx1mVDB44cCADBw4sc/tJkybRoEEDXn31VQCaN2/OwoULef3115UMFhGpRMuWLeaZZx5m2bI/6NC6K1cOu4kOnboUa2P3dpCflQdHKatbmO90J4J3peIf7k+Kw4ZPoA8pu1NJ3Z2KzcuGd4A3+Vn5bF8YT/LOFM65pA3hDcKPmmjNSMxgz8q9HIo/hHeAD7VaRVO7dS1PCYfSJG07hHEZAsMDCAjzJy8zD2eBk8ykLGrUqoHNbmPfuv0c2HCAgtxCwuqHUadNbfyC3QvN+dTwoX7netTvXO+ExtCyLNpf1o78zFwSNiWC5U4u27xsBEUHEdUkEr9dAVz6ytX8suE7XnnlaX744WtefPH/6NGj3wmdS0REqq8aXsHYsFHgyves6+AyLvKd+cQGRFVy70RERORIZ1WZiBO1ePFi+vTpU2xb//79Wbx48TH3y8vLIz09vdhDREROXXp6Gg88cCvDhnUnJyebadO+48N3vqBhrTjys/M97Ywx5KbnElYvDMtWeuI2eWcyqXvTCY4J4f/Zu+84u+o6/+OvU29vc6f3kt4DgRCKgKCAiogoi7o2UFcXXcu6uqyruy77s6xrL2tfF8VesIAFQURagJDeZzKZXm/vp/7+uGHCkISEZFLA7/Px4BHm3lO+59wp93zu57y/qq4iSaBoCo5lU86V8Ya9KJqCZdgUM0UGHh3k/i8/yMYfb6KULh06tvEsT/xoE70P7KOULpPYn2TLHdvY9Yfdzxgz4T4Z00C1QOsNeQnUBFA9Ko7psOuPu9n0sy2MbR8nOZBi5+938cSPNlJMFU/gTFZFGsOsu+k8mhY3oft1/HE/dfPqqF9Qh6IpaB6VwliRv//7f+Tee5+go6Ob1772ZfzDP7yZRGL6hPcvCIIgnBjbtTGcykmNMzpRNZ56Gn2t5MwMWSNF3syQMqaJ6jGa/e2ne3iCIAiCIDzNX3UxeHx8nIaGhlmPNTQ0kM1mKZUOLQQ86eMf/ziRSGTmv7a2tpM9VEEQ/go5rnP0hZ6DjKJBOVvGdWZf2P7xj3dx6aWr+MUvfsjHP/5FfvXz+1m7+iJq2mM0Lm4gM5YlO5GjmCyS2J/EH/XRvubIv3/NkonrOCiajOpV8UV8GEUDxzqwXxfyU3msioXm0dB8GrIiMbJ1jG137jhkfAOPDZGbzFHbGSfUECLWGiVQ42d40wiZkcwRx1HTWQOui1U5eKusWbYAFz2gM7hhGH/UR01HDdGWCPHOGpKDKQY3DD/7k3sY4cYwnes6qO2ppe3sVmraYzOdzLZloweqXVydnT386Ee/5TOf+QZ//ONdXHzxCn7xix/OyRgEQRCEZ8d0DPqyO3lk8h4embyHzcmHSVYmT/ewDkuWZBZFV7E4uoqwHsOrBOgOLmJ5zVr8avB0D08QBEEQhKd5XsVEnCq33HIL73vf+2a+zmazoiAsCGcQx3XYnx1isjiFX/WxIDYPr+o53cM6Jq7rsnlqOw+MrmcsP069v47zm89lTcPK5/wkeuVcmb4H+hnfOYFjOYQbw3Sf34lSI/GRj7yPn//8B1xyyYu59SOfpdRn8uDXHsaxXcKNYdrPbiXUEGR0yyiWYdO8vInOczuItUWPuL9gbQDVq1HJV/CGvMQ7azDLJoVEEddxKaZL2KaDL+JF82lYZQtPyEugxk+iP0F6JDOzfcd2mO6bxhv2zepE9oQ85KcLZMZzRFsPP5bGxQ2ML25gfOdEtQjruti2Q/PSRlSPglW28DYfnAxOVmS8QQ+Te6ZYdPmCuTj11M2rZf8jAxSmCgRrgyBBMVVCVhUaFh68hVeSJG644Y288IVX8OEPv5ebb34D999/D//v/30ev98/J2MRBEEQnpnjOuxMb2S8NIRH9iJLKlPlcTJGiuU151LjqTvdQzyEKmu0BXpoC/Sc7qEIgiAIgnAUf9XF4MbGRiYmJmY9NjExQTgcxufzHXE9j8eDx/PcKCwJwl+bklXih7vvYOPEFkpWGUWWaQ+18rrFr6Ij/Mwf2silSbSJ9cjFcVxvHKN+DU7w1N7e+MjY4/xg188xHJOwHmJ3qpc9qT7yRp5L2y88pWOZS7Zps+WX25jYNYk/5kfzqEzvm+aev9zJ9x/6GrZj8bnPfZNrr3ktG3+0kYk9UweWU5jeN01uIstZ16+m+/wuHNtB0ZVjmkCtaWkjg48PYZZMVI+GP+bHdVysio0nWO0EVr0q5WwZza8RqPFXC8MTNpV8ZWZbkiyhaApm6WkT4bhU4yfUI99oo3pUVl67nPr5dUztnQJJom5eLU3LGhndMlbdjOvOOh7HcZGfYZvPVrQlwsLLF7D3T70kB5K4LugBnXkXdVO/4NCiQn19I1/72g+47LLb+Od/fhdbtmzg61//IT09c1OcFgRBEI4sbSSYKo8R1CLocvWaw6v4SBvTjBT6z8hisCAIgiAIzx1/1cXgdevWcdddd8167O6772bdunWnaUSCIJyoewf/wgMj62kNNNEeCmI6Jvuyg3x/189539nvwKPoh11PyfTi3/4VlPwQSBq4JvrwHykueStW7epTMvayVeHugT8DMD/aDUADdQznR7ln6H7OaVxNUA+ckrHMtel9CaZ7E8Rao6geFcOs8JM/fYs/PPpL1q24hK/83//R0NDE+K4JpvcliLVVc36h2n2b2J9k6Ilhajpix1wklSSJJVcuwhvy0PeXfaRHMnjDHpZdvZRA3M++B/YztHGYUrqML+wlEA9glExs00bzaviivlnbql9Ux9Y7tpOfzqP7NHwxP0bRwBPyYhs2I5tHCTUECTWEDilUa16N9jVth8Ra1HTGZrqLg7UBJEnCqliYJZOmJbNjjJ7KdVxsqxqBcSwd45Ik0bGmjdquagSF67jE2qIE64LPuP7117+B5ctX89a33sBLX3oBX/nKd3nhC6886v4EQRCE41e0ctiuPVMIhurvcY/iI2MkcVwHWfqrTvsTBEEQBOEEPK+Kwfl8nt7e3pmv+/v72bRpEzU1NbS3t3PLLbcwMjLCbbfdBsDb3/52vvSlL/GBD3yAG2+8kXvvvZcf//jH3HnnnafrEARBOAEV22D92BNE9TBhTwgAXdHpCrczkB1ib2ofy2oXHbqi6+Ld93OUwjBWbClIMrguSmYvvt4fkYsuBtV70sc/VZpmqpSg3hef9XjcE2NbYiff2vY9mgKNLI7PZ0nNQhRZOeljmiuFRBHHcVA9KpOpMT71/X9lcLKfN1/2bi4/52oaGpoAKCarEQ5PFoLhwKRrQQ/pkfQh281PF0gNpgAI1PoppcsYRQN/zE9tdxxZkakUDBzHxRP0IMkyg48O0rKyhXU3nkv8TzVs/fV2KvkKRtFguj+BLEksuHQe4cbQzH4q+QqpgTSVfIVyvgKOiyRLBOsChBrCMxnDul+jZVULi160YCaX95mE6kPMe0E3e+/rI9GfACQkWaJxSQOtq1vITeZIDVWPu6Y9hi/qY3DDEMObRjGKBpGmMB3ntFM3r/aYXodAPEAg/uw+UFi8eDm//e3D3HzzG3jDG17Bhz70Md7+9vc+52NLBEEQzlSqpCHBIUVfyzEJamEkxO9fQRAEQRCO3/OqGPz4449z6aWXznz9ZK7vG9/4Rr7zne8wNjbG4ODgzPNdXV3ceeedvPe97+Xzn/88ra2tfPOb3+SKK6445WMXBOHEGbZBySrje1rhVpc1bMeiZB1+Yki5NIGa2YPtb6kWggEkCTvUjpobRM3tw4otOdnDx6N40GSVim3i16qPWY7FxqmtDOVHUWUVr+rjvuEHuaT1fK6bf/VzpiDsOTBJ2aY9j/GZH/8bAW+Aj73tf6hxGvBHD2bR6j4dl2pGr6wcvAA2yiaR5vDM167rsv+RAfr+so9yroJZMsknCugHoh5kRaG2J07j4gaGnxgmWBfAE6h2WBlFg+FNI9TNqyXaGsUT0KuFTbda4JVV5UBx2JwZ99DGYRL7EnSc245RMKgUDCrZMpmJHN6QQd28OiRFopwts/+RAYK1ATrOObaIkc61HcRao0zvS2AZNpGmMLU9cQYeG6L/oX4qeaN6DkM6mlcjP5VH8+loXpXJ3ZOkBlOsfOUK6uefvNuGQ6Ew//u/P+OTn/wIt976z4yMDHHrrZ8RBWFBEISToMZbT1CNkDGShPUYMjIVp4TtWjT5O8TvXkEQBEEQTsjzqhh8ySWX4LruEZ//zne+c9h1Nm7ceBJHJQjCiXJdl/HiJAWzQJ2vlognfNjlApqf5lAje5J9xLzRmcfTlQwBLUBjoP6w68GTvzeefnElVZ97ht8rc6nOF2dxfAGPjD2OX/PiUTwM5kbozw7SHmplVf1yJCTSlSz3DT/E0trFLI0vPCVjO1Hx7hoe2P97vnfPV1nefTbv+5uPIperRdfWlc0zy9XNqyVUHyQ9nCbcFEFRZYqpIhLQvOLgcsmBFHv+1IuiysQ6ooxsGsUsGeC61HXXogd0xndNkBxK4TjOTCEYQPdXC7xTfdMUEgXCjWHCDSHMiomiKsiqTGowxXTfNC0H9jm+YwI9UC3Gal6NQDxAZjxLYn8Kza/PRFf4Ij4qeYORzaO0r2k75giHaGt01gR0U73T9P65F82rEe+qASA1lGZ40yiNi+qJNFV/BvwxP4mBJPsfGaCup3bW5HZPV8qUMcsm/qgP1XPkP/9WxWJi9yTp4TSKplLbEyfeVYOiKPzLv/w/2to6+eAHb8Y0DT7+8S8iy+JWZUEQhLmkyx4WRVexO7OZrJHCxUWXddqD82jxd5zu4QmCIAiC8Bz3vCoGC4Lw/JMqZ/jZ3l+zPbGLilUh7AlzYfNarux6Iao8+1eYLMm8sO0iBrPD7E3vo8YTpWSVyZl5Lm27kNZg82H34fgasMI9aIlNWHqkOiOY66Lkh7ADLVjh7mMeb9ZIMVQYYNv0DiYKKWo8taysW35MsQ6SJPGKnqvIVLL0pvdhuy79mQHCWpCVdctmbguNesKMFcbZm+p7ThSDHcfhox/7J27749d42XnXc/Wy11CZMPGGZeZfMo+mZY0zy3pCHpZfvZQdv91FdjyLYzt4Q17mXzKP5mVNM8tN7Z3CLJlEuuMU0yUq+QrBeJByrkx6NIuqK2THs5RzZQLxANGWKIr2lPMvVXN3K7kKmldFVmU86uyJQa3ywcniXMfl6R8WOJYDuMgHCrC2ac8UXAvJYvUzhuNs3prcM4lt2ERbojOPaV4Vu2JhVexZy/qjPrLjOab3JUj0Jygki8iqTLgxXI2WiHjZ++c+JnZNYBs2vqiPzrUdtJ3Vekjx2CgabLljKxO7JqvH7brsXz9Az4VdzLu4B0mSeP3r34qm6fzjP74NwzD47//+Kory3OhQFwRBeK6IeWpZU/sCUsY0tmMR0MIE1bDoChYEQRAE4YSJYrAgCGcsx3X4/q6fsWFyMy2BRup9dSTLKX6173d4VS+Xd7zgkHVW1i3lzUtfw31DDzGSHyXmjXBV12Vc3HbBkS+gJJly97UopXG05DYc1Ytkl3E9cco9rwLVd/j1nmayNMLmxGP8vv8BetPDOK6DIqncN/wQL26/9JhiHer8tbxr9VvZmdhDqpLmnoH7mSolCOvBQ5Z9pjshzhSWZfG+972Vn//8B3zqU1/lb171BlJDaWzTJtQQIlDjP2Sdmo4azrtxLamhFLZpE24M44/Ofg2MkomsVF9P13ZwHRdJkXBdl/RIClVTkQ48n5/KM7ptjJaVzciyjFk2wYW6nlpcx2Vkyyj+Gv/M94dZMpFVmUDtwWzdUEOIwceHSOxPoHk0Qo0hHMtG1hQUXSE3mSPRn8QomhgFg3BzmKGNw9WC63FcuBtFE0mZ3XErq9XJ4izDmvW4ZdiYJYNNP9tMKV2mkMhTTJcACX/MBy5ofo2ajhiegIdiusT2u3aiaAoNixtIDaawKhbBugBTvdOM75wg2hKd6R4uporse3j/TKwGwA03vBFNU3n3u2/CNA0+97lvoariLYUgCMJcUmWNOm/T0RcUBEEQBEF4FsSVmyAIZ6z+zCC7knvoCLUS1KqFucZAPaZj8eDoel7Qug5d0Q5Zb0XdUpbXLqFkldAV/ZAO4sOxo4vIr/oA+sR65PwQjq8es+Fc7HDPMY3Vckz6sjvZldzH/swELYFGPKpO2S7iOC73DR17rINH0VlVvwwARZL57o6fULYqeA90ruaMPJqiMS/WdUxjO10Mw+Dmm1/P73//a7785du45prrAY5psjNVV6jrOfJykeYIg48NYVs2etCD6lExCgZG0USWJHz1PsrZMuHGMIqqkBpJI6sKvrAH14WmpY00LKrHG/GS6E+QHEjij/mxDZtytkzz8iZq2mMApIbTTO2dwjZtKrkKRUqkR9MEa4M0LW0kO5kjP5HHdauR076YF1/Ey64/7CYQDxDvrHnW5y7WFmVk8yiO5cxEUOgBHdWnYlVMbMtGURUqBYNSupqFLSHh4mKULGRNoZwuU8lXsE0bT8CDN+wl3ukj0hQmPZxmz5/2sv+R/WTGczi2g+7XKWfLaD5tVoyEL+qj2J8kOZiaFWVx3XWvQ9N0br75DViWxZe/fJvoEBYEQRAEQRAEQTjDiWKwIAhnrHQlQ9mqzBSCnxTSg2SNHAWziK5EDruuJEn4tUO7Tp+JE2ih3P3K4xpr1kxTsHKM5VMokjJTuNVkD45kk68UjyvWYU3DKrZO72TT5DYUWcF1XSQJLmg5j8U1C45rrKdCqVTibW+7gb/85R6+8Y0fccUVV8/p9hsXNzC6ZZTpfQm8IS/ekIfkYArHdFADGqVMGUWVqemI4Y/5cVyHcGOIup7amYnlVI9KvLOGVdetZP/D+0mPZVE9KvMvnUfXus6ZIuzA+gEqeYOu8zspTBcoZcrYpo3m1VjzmtXs+P1usmM59ICGL+wj2hrBH/UztW+awccG0bwq/pj/GXN6n65pSSOjW8dI9CfxRrzgupSzFdrPbkNSJNLDGVzXRdUV4l1x0iNpfDEfqa1pZEWikjOQNRnHclA0Bcd2SBw4V8HaAJpfZ2TLGJHGMLGOaoRGOVtmfHuaQG2A2FOKvk92NlejMmZ7+ctfjaqqvPWtN9De3sm//Mv/O7EXVhAEQRAEQRAEQTipRDFYEIQzVo03hk/1kjPyhJ4Sk5A1stT54gT1Z1fsPRVcxzlCTKx0XLEOfs3PjcteyxOTW9mV2IMiqyyNL2JV/dJj6ng+HQqFPG960yvZsGE9//d/d3DxxZfP+T48AZ1Vr1zBwGODjO+cINZeQ+PiRka2jlJIFAnVBwk3hQnE/Liuiy/sZd5F3XSt6zxkW7Xd1QnSqvEQCqp+sLvVMmySAyl8UR+aRyPaEiXaUo3oSO5PUs5ViHfEyE3kiLVFq1EOSJhlk/xEnl1/3MvEnin8MR9d6zppXdVy2NiIcq6MYzn4Ij4kWcIT8rDquhUMPj7E+M4JJEmi7ew2Os5pR/UoJPqTmCWTQDyAJMH62x7DNm0c28E2q7EZiq7g2u5MXIZjOxQSBYK1AXKTOWzDItYeRdWr30e+iA9fjZ/cZB7LsGYeL2fLqF6VWFvssK/FS15yLR/+8Cf4j//4IAsXLuG66153oi+vIAjCaWc5FlkzieO6hLUIuuI93UMSBEEQBEGYE2dmJUEQBAHoCLeytHYR68efoNFXj1/zkSynqNgVLmpdhyYfGhFxuoS1GEEtREMoyp7UAIZtoikqplPBdVW8ive4Yx18qo8Lms/lguZz53jUc880TW688dVs3ryBH/zgTtauvfCk7csX9bHoRQtZeNkCXNdFVmRGNo+y6edb8Md8eMNeXMclO5HDG/ZS+wyxE5Ikofv1Qx6XFQlFVzAKxqzHXccFSULRFEL1IXBdJElCQsJxHCZ2TVKYLhCfFycQ81NMFdl+5w5UXaVp6cEJ8wrJIr1/7mOqdxrXcQg3hum+sIu6nlr8Mf/M8QGzJntrXNww8/+2Vc1fTg2mUXSFUrqEWbEwy1Z10jypWryWqOYN56cLWGULf9R3SLdytDmMWTJJDaZQdRXHcZEViY617cTaokc8f3/3d+9h167tvP/9b6eraz5nnXXmf68KgiAcSbIyyd7MVnJmFhcHnxqgM7iQFn+nmMBNEARBEITnPFEMFgThjCVLMq9Z9Er8qo/NU9vJFnNEPWGu6ryMC5vXnu7hzaLKKj3hpWSNPP3pEfZnhpEkGRWViCfGJW0XnNGxDifCKBpkRrMgwce/eAuPPHI/P/jBXSe1EPxUklwtwkI1CzgzlmHoiREKiSIAvqiXRZcvJFR/6CR8RyMrMs3Lm9h19x68IS+aT8N1XDKj2Zk8YNd1GdoYJbE/SaDGTzlbJj2SwV/rJ95Zg+bTiPgiJAdTDG4YpnFJA5IkYZZMNv9iK4n+BIF4AFlRme5PkJvKc/YNq2eiGp5aBD4cRVVYeNkCtvxyG+nRzIGc4GrWsOu4uLjofh3bdrBNG0mG7gu7mNg5QaVg4AkcLIIbJZOudR00LWkkOZBC9ajUz6+lfmH9M45DkiQ++ckv09e3hxtvfBV33fUQzc2tz/p8C4IgnG4lq8CO9BOUrRIhLYIkyRSsHHsyW/Eqfmq9DUffiCAIgiAIwhlMFIMFQTijhfUQr19yPS8pJcmbBWp9cQLPMgv4VKnzNrGu4VJa/J1sntrGSG6KqKeGVXUrz+hYh+Plui7Dm0bou38fhWSR327+KT9/9Db+818+z/nnX3xaxiSrMouvWETz8mYyoxlkVSbeWYM/dvzfMx3ntJMbzzG+axLHdsCFQNzPkisX4QlWs6FXvXIFfQ/0M903jVEy8Ua8NC9vRvcdLLR6gx4K0/mZHN/JvVOkBpLUtMeqHbyAJ6iT6E8wvGlkVm7v4dimTXY8B65LtDXKOX97Ng989SHK6RLlXAXHtnEcCUmpdgS3rmhm9d+swhv0YJYsSqkSiYEk/ki1Q7iYKqF5NXou6KZ+QR2dazue1XnyeDx8+9s/4aqrzufGG1/Fr351P7p+aLe1IAjCXLAcE8OpoMk6mjx3v2umyuMUrQIxvXamCzikRUhVppgoDYtisCAIgiAIz3nPr8qEIAjPW3FfDXFfzekexlGFtAhLa1axtGbV6R7KSZfoT7Lzd7twXdiR3sDPH72Nq8+6gU5zCYVEgUA8cNRtmGWTkc2jjO2YwLEc6ufX0rq6FV/k+LMZJUki2hIh2nL4yQWfLd2vs+q6lST2J8lN5tG8KrU9tbPGGKwLsvLa5VQKBlN7p9h8x9aZzN0nVYoGsdbIzMR0pXQJF2YKwU+OXQ94qp3Wz2Cqd5rd9+4lN5EDF4L1QTrXtlPOlVE9Kl4ZKgUDq2yBCZpHo5Qts+2X25FkCbNiIckSyoHuYcuwCNUH0INe9ty7h74H9hFtjRBrixGI+wnWBY/p1ui6uga+/e2f8LKXXcgXvvAJ3v/+jzyLMy0IgnB0juswmO9lpLgfw6mgyzqNvjY6gwtQ5uBDV9OpAO4hv/NUWaNkF054+4IgCIIgCKebKAYLgnBaGYaBaRq4rovjOLP+dd3qvx6Pl2AwJHL6zjBj28YwSyaT8ghf+sXHuXjVFbzx2r8nuT/FxO4pus9/5mKwbdps/eU2RreNo3pUJFkiOZBkqneas65fhTd85kzWI6sydfNqqZt35NxhqE5s17ikkcENwyT3Jwk1hFB1hWKqBK5L21ltM9/Hul8Ht5o//NQIBqts4Y/5jriP3GSerb/aRjlXJlQfRpIgN5Fj00+3MLlnCqtk4krV8ytJEkjVqAnXheHNozQsqKN2Xi1WxarGXdQGWHjZfLb/dheTeybRvBqpoRSl35fwhDzUdtfSuLiBxVcsPGyu8tOtWHEW73znB/jCFz7BS17yCpYsWXGMZ1kQBOHoBvO97M1uQ5M1PIoX0zboy+3AcW3mR5af8PZ9agCQcFwbWap+WOe6LqZjEtKiJ7x9QRAEQRCE000UgwVBmHOu6zI1NUFf3x7279/H1NQ4k5MTTE1NMD09STqdJJ1Ok04nKZWKx7RNRVGIRmuIRmNEozXEYgf/v6mphe7u+fT0LKCjoxtNO3Mmlns+K6ZLFOwc//WDDzG/dTF/f+0HkeVq16tRqBx1/aneacZ3ThBpDqN5q6+ZYzkk9ycZ2TJKz4XdJ3X8AJV8hfFdk+TGs+h+nfqF9SfcUazqCiuuWcbO3+8iOZDENh18ES89F3XRvLxpZrm6+XUE64KkhtKEm0LIikwhUcAyTIyiwRM/2US4IUTT0sZZXdYTuyYoJIvUdsdnCsuqV2V4yyiVXKU6uR2AC8jVYrNVsajkysiqRDFTopgsomgK4aYQmdEs+x7eT2Y0Q7yrhqneaYyiiSfowTEdHMtmcMMQsiKx/OXLjukcvPvdt3DXXb/gfe97G7/5zQOoqni7IQjCiTMdg5FiP5qsE9TCAOiyB8mSGS0O0hroPlDMPX613kaieg3pSgK/FkRCpmjl8Sl+mnxtc3EYgiAIgiAIp5W4OhME4biVy2X6+nbT17dn5r99+/bS17eHXO7gbe41NbXU1dVTV9dIQ0MTixcvJxKJEo3GiESi6LoHWZaRJGnmX0l68l+JSqVMOp0inU6SSqUOFJOTDA0NsGXLE4yMDFEo5AFQVZX29i56ehbQ3b2AefMWctZZ57Bw4dKZQqUwN8INIb79lc/j4vL+19yKpurVTF04akZvbjLP3vt6yY5n0bwasiqjqAqyKiNrCgOPDuIJeAjUBoi2RI46idrxKCQKbPr5FlKD6WrnrOMw8PgQS65YRMvK5uPaplE0KOcqeIIe1rzmLHITOcyKRbAuOGuiNgBfxMvyly9l5+93kx3P4toOjuNiGTZTvdOomsro5lFGNo2w4pUrZjKEi+kSiirPFIJtyyaxP1Ut/krVqIknC8Ku44J0oKutbOKYDpmRDJVcBVmV8UV9aLpKYl8ST8CDbdgUpgrofg3Nq1FMFXFdCMYDjO+apOei4jHlL3s8Hj7zmW/w8pe/gK997XPcfPP7j+t8CoIgPFXZLlGxy/jU2ROCehUvWSNNyS6ecDFYlz0sja2hP7eLRHkSF4u4t57O4ALCeuyEti0IgiAIgnAmEMVgQRCO2ejoMBs2PMKGDet5/PGH2bZtE4ZhABCLxenpWcDChUt5yUuupadnAfPmLaC9vRuPx3NSx+W6LhMTY7OK0n19e/j973/FN77Rj+M4RKMx1qxZx9q1F3LeeRexfPnqY5rcSi5OoOSHcBUdK7oQlGd/LHmjgOGYRD1hZOn5U5B+sO+PbBl8jJtf/CG8boBSpkwhUSDaGqFhYf0R1xvbPs723+5kum+a3FQBs2jii/lpXNyAVbGY6p1G0RRK6RKKR6VpSQNLrlo80z08V/Y9vJ/kQIp4Zw2yIuO6LtnxHLvv3Uu8uwZv6MgxFdmxLNP7EtimTaghRKw9xsCjAwxvGsUoGug+jZZVLfRc2I2iyaQGU+x/ZBqzYhFtitCwqB7NpxHvrOG8N59LejiNUTTY+YfdmEWTSHN4pqibGEiy50+9tJ/TSmogTXIgRX66QLAuiO7XKecqGIUKmk+jnC/jOi6yJuNY1cI8crVAbJVtzJJJIO7HF/XhWA7ZsRyeoE5zc4RypoRl2Ni2g8er47rVgrIsS2h+jfJEhUrBOObJ+M4661ze+tZ389///VGuvPLl9PQsOOHXTBCEv266rKPKGqZjoMkH/yaYjoEqq+jy3LzfCKghlkbXUHHKuK6DV/H/1URVGRiMKyMorkqT04LM8+d9iyAIgiAIVaIYLAjCYTmOw9atG3n00Qd5/PFHePzxRxgbGwagra2Ts89ey7XXvobly1czb95Camrix72vil0mVZnCci2CWpiIVvOsLrokSaKxsZnGxmYuuOCSWc8Vi0U2bnyU9esfYP36B/j0Z2+lXCyh+lQ61vTwokteypte+ne0t3c97QTYePt/gWf4j0iVJMgqVqiT0sI3YkcXHtO4UuUMv9t/L5umtmI5Fq2hZl7UfgnLahcd87Gdqfr7e/nEZz7MdVe/jhdd+FIyoxkkSaJ5RRPzL56HJ3T4C/JKrsKuu3djlS3qF9RRyRtYhk16NFMtAGdLmCWThgNxDZV8hcENwwRqAsy7uGfOxm9VLMa3jeNYDqmhNKquEIgHCDUESQ2kSA2maVraeNh1BzcMsfuevZRzZSQkJEVCVmTMsok/6iMQ81MpGOy9rxfHdvAEdPbe14dZMpFkiQHXpbanlpWvXI435EXVFWq740z1TVPOlqud0Ae+/yVZQvUo7P7jHnb/cQ9GwUCSJBzLppAo0rikHkmWMUrVfXtDXsrp8sHBuoANjuRgFI0DHdYStmHjWA6SBLIiUzcvzv5HBnAdB1VTMMsmuKDqKr6Yn0qugieg448cOcv4cP7pn/6NO+/8Of/1X//O1772/eN8tQRBEKo8io9GXyv783uQJRmP7MVwKuTNHC2BDgJqaM72JUkSXuXZ/c57rtuoPcadnl9Ui8EodFnzeWX5BtrtrqOvLAiCIAjCc4YoBguCMCOXy3L//X/kj3/8Lffc81umpyfxeDysWHE2r3jF9axZcx5nn30e9fWHL5I9G8lyir8MP8K25Fb8Hoe4L0LcV4NP8dLoa2NhZMWczAru9/u54IJLuOCCS3hA/xM/VL/DxOYxcg/mGH9whG/89+f56q2fYdWqNVx99au4+urraG3tQB9/EG//L3D0CHZsCTgmarYP/85vkj/7w7h6+Bn3a9gmt+34EZuntlPni+NXfexO9jKSG+PvVryR+bGTn4d7sliWxT/8w5upq2vgE5/5In5fgFKmhCTLeMOeZyzkJwdTFFMlIi1hEv1JbMOikqtg2w6jW0dRvRoNC+sIN1Uv6D1BD96iwciWUbrO70TRlDk5hvRwhok9U5hFA0VXARdP0EP9gnpcmOmKfbpCosCee/fiOi61XdXM3lK2zP6H9xNpjRCsrd66rPm0auH3kQGQQPWoRJqqH5jYps3k3ikGHx9mwaXzZrYtSRISVAu4B1iGxfS+BMV0EY9fJ1QfxDZtKoUKuC5Te6ep6aohGA/gi/pQPSpW2aoWcwHkA9tzq8ek+3TMkoEkgTfspXZeLa7tEGuPYVVsRreOgQSlZAnNr1HbE8coGhh5g/mX9ByxyH8kfr+fd7/7n/nAB/6e3bu3s3Dh0me1viAIwtN1hRZhORaT5RFKVh5V1mj2tzM/vPyvpnv3ZNir7OK7/m9QpkSj04yNxTZtI1k5zbvztxBxo6d7iIIgCIIgzBFRDBaEv3L9/b3cc89vufvuu3jkkfsxTZMFCxZz/fWv5/LLX8JZZ609pjiFZyNTyfLNrd9jb7qPnpoYlgO9qSGylRLL44sYLvYT1CK0B+euEzQv5fit55fIssLa1RfCanDf6bK7vB3ulHB/4vCpT/07t976z5x11rlctzrKa1b7qG0/UPhWPFiRBaiZ3aiJLZhNFz7j/nYm97AjuZueSCdetVpAi+hhdqd7eWDkked0Mfh//uczbNz4GHfccR+BQLX4eazRAY7t4Lou+akCmdEsvpgff42fUqZEJW8gKxLR1uisfGfVo2JWLGzTnpNisOu49D24D0kGSZXxRb3gVrN4BzcM4q/xM7lnCkVVqJtXi6weHEuiP0k5WyHedbB7XaJabzXyxqz9eEMeUkMpFFWhcUnDzOOKpuDx6ww+PkjjknpCdSEkWSLSHCZYFyQ7kZvpDi5MFyilSqiaiu7XUTQFRVOwTRtfxIvq1Vj8ooV4w162/3Yn5Vy5OiAJFE81g9kxHVSvimu7aF4N3a8hSRJNyxpxrGqWcKg+SKQ5glEwqOQruK6Loim4gOZR6Tqvg651ncd1vl/96tfz+c9/gs9+9mN89au3H9c2BEEQnqTJOoujq2m35lG2S3gUD0E18pwuBDuuTaqSoOKU8Cl+Inr8lMdKrdcfICtlWGgt4cBHk8y3guxVd7FFe4KLjBee0vEIgiAIgnDyiBAoQfgrNDw8wOc+9zFe8ILlXHDBEv7zP29BURT+7d8+xSOP7Oa++zbzr//6cc4776I5LwQDbJjYzJ5UHwtrOgh5fHiVADXeKFOlBMlKFlXWGCsOHLE783gMKwNMK5M02QcnBpOQaPG1Ebg+yP/79ufZsmWEL3/5NurrG/mP2+5h8Xt/w81f/DU7ByarK8gquCCbuaPub7I4jeM6M4VgqHZ+RvQw/dnBOTuuU21qaoIvfOETvOUt72LNmvOe9frRlgieoIfUYApJktA8arXAqSrUdlWze7Pj2VnrFNMlwo0hNN/cZAbnJnOkh9PUL6zHF/ZSSpUo5cqUsyXyUwWsssXwphE2/Ggj23+382D2LtViNk+rNyi6iqLKWBUL9yltvUbBQNGUWcVkgEKyyFTfNKNbxnjom+t59LuPkxpKo3k1FrxwPppPI9GfIDmQJD2SQfWqeIIeZOXgdmRVwazYqHr1Oc2nofs0JEWqRkvoKv6oH1mS0Twq4foQuk/DsWwAzJJJeiRDfjpP05IG9IDO1l9tY3znBIG4n2hLFMd0KGcqNK9oouPc9uMuxOu6zrvf/c/8+tc/Zc+eHce1DUF4Prv//vu5+uqraW5uRpIk7rjjjlnPu67LRz7yEZqamvD5fFx++eXs3bt31jLJZJLXve51hMNhotEoN910E/l8ftYyW7Zs4aKLLsLr9dLW1sZ//dd/HTKWn/zkJyxatAiv18vy5cu566675vx454IkSQS1MLXeBkJa9KQVgl3XJWukmSgNk6xM4bjO0Vd6lopWno2Jh9iYeJDtqcd5IvEgW5KPULFLc76vZzKmjBBwAzOFYAAFBXBJytOndCyCIAiCIJxcohgsCH8lCoU8P/7xbbz61S/m3HPn86UvfYpVq9bwrW/9hO3bx/n+93/DjTf+/aHZuSdBX7ofXdZQZZkn2xgVSUFCIl3JoEoqpmMcbTPPiuKqKChYWLMet7BRUFBRCQZDXHvtDXz72z/lie/ewrte0sydj+5k7bu+zhUf+ia/W78ZV5Kx/UePyQhqfnDBcuxZjxfNIjXe5+5s5J/+9K1omsZ73vMvx7V+IB6ga20HZtnCLJmUcxVK6RK6X6e2uxZf1EcpXSY7nqWYKpLYn0T3aXSu7Zizi33bcjCKJlbFItoaId5dg+ZRkWQZX9RH45JGarviBGr8DG0YZrJ3ambdSEsEzaNSyVVmHlM0GdWrIasK5UwZx3ZmJtNrWdF84JiqF/WlbInhzSMUpgsEG4P4Ij6m902z5Y6tFFNFGhc3cO7rzmbBCxfQelYr8y/poaajBn+ND7Ns4rourutiGxaKpqAHdBzLZvMvtlJMFqmfV0e8K47iUfAEdXxRH8G6IJpPQ/GqRFqiaF4N23KQZZkFl85j8RWLmNozTWJfgpqOGLbpkBpMYZQM0kMpNv50Mxt/snnWMT9br37162lubuOzn/3Y8b9wgvA8VSgUWLlyJV/+8pcP+/x//dd/8YUvfIGvfvWrrF+/nkAgwBVXXEG5fDAf/HWvex3bt2/n7rvv5je/+Q33338/b3vb22aez2azvPjFL6ajo4MNGzbwqU99in//93/n61//+swyDz30EK95zWu46aab2LhxI694xSt4xStewbZt207ewZ/BLMdkR/oJNkzfz5bkejYmHmRj4kEK1tE/ED5WruuyN7OVRHmCoBYm5qnDrwaZKI3Sl905Z/s5Fg12M0WpMOsxGxsXiDo1p3QsgiAIgiCcXCImQhCexxzH4aGH/sxPfvJd7rzzFxSLBS644BI+97lv8tKXvnLmFv9Tzaf5sVwb133y8ygXkHBdF1VSKNslWvydc9rp02n30G51sU/dyzxrITIyJibjyghrjQuJO3Uzy+aNAr+RTC67MMDV67r49dYKP/vTGNf/v1+ztKued7z3pbzilStnRRk83dLaRTQHG9mX6acj3I4mq0yVErjAuqY1sxe2DdTMXnBM7HAXrh6Zs+OeS3v37uL227/Fhz70MaLR4y9o91zUzXR/gt77+/AEPfgiXsIN1aiEcHOE5mWNFKYKGCWThgV1dJzbTt38uqNv+Bg4lsPYljHSw2mMgoHu1/EEPaieandvMB7AE6h2w3uCHvLTeRL7EjQuqsY8RFsitJ3dyv5HBiimSyiaglE0aFnVTKg2SHIgSSldQvNptJ/TzsLL5zPw6BB9D+wjPZImuT9FOVtB86lUMhWMokFNew2J/gQTuybpWtdJuClMuKmaSW0ZdrVTefMokiyRn8rjOiDJoPs0WlY2M9WXwChUqOmoRlfEO2PV2I2cQaDWTzlTxrYddK9G/fxajIJBuCnMeW86l1hbFIDsRA7XreYZJweSIEn4Y34qagVZlZnYPcnA40OzMo6fjSe7gz/4wZv553/+Dzo6nrsxKYIw16666iquuuqqwz7nui6f+9zn+Nd//VeuueYaAG677TYaGhq44447uOGGG9i5cye/+93veOyxx1izpvr35Ytf/CIveclL+O///m+am5u5/fbbMQyDb3/72+i6ztKlS9m0aROf+cxnZorGn//857nyyiv5p3/6JwBuvfVW7r77br70pS/x1a9+9bDjq1QqVCoHPyjKZrOHXe65aH9+DyOFfgJamKAcwXYtEpUJdqc3syp+/pzEOOStDMnKFEEtgipX737RZB2/GmSqPEbZKuJVjy2G6USdZ17ARv1R+pU+Gu1mbMliWBmkze5ghXnWKRmDIAiCIAinhigGC8LzUDab4fvf/zbf/vZXGB4eoKtrHu985z/xqle9jtbWjtM9PFbULubh0ceYLKRplAOoqkXJquBTNfy6hk8J0BqY2w5lDY3rSq/lu/6vs1vdMXMbZI+1kJeVr5t1W+TGqa3cX8pDy5WsyO3mVeclefnaRm7fU2L94wrv/Icb+fZ3vsqtt36W1avPOez+wnqI1y95NT/a/UsGc8PYjk3UG+Fl3S/inMbVM8upqZ1493wPNTcAroXjq6PS/lIqbVfAGZZ/+LGPfYjm5jbe/Oa/P6HtSLLEspctxSgYZMayeEIeStkyZtGgeUUzK6+tTgLkWA6KrszphwLDm0boXz9AtDVGdiyDZVjkpvNYZQvVoxLriM2KY3BdcBwXo2ggyRKaV2PR5QuJNEcY3zGBWTKJd9XQsrIZf8xPIVGglCnjDXsJxP1IksT8i3vQ/RqP374BF/BFvQTiAayKxeSeKbTlKpIiU0wVDxmvqissv2Y5/niAoSeGyE0WwHWJNIfxhryMbRtjYtcknpAH26zGRviiPhoW1DG6dRzHrN7S7FRsfHVB8lN5ZFVh/sU9M4VgAEVXsC2bYqqIVbHwRX3V18Bx0H063qCH8R3jzL+k57hfj1e+8rV89KMf4Be/+OFxd5YLwl+b/v5+xsfHufzyy2cei0QirF27locffpgbbriBhx9+mGg0OlMIBrj88suRZZn169dz7bXX8vDDD/OCF7xgVvTTFVdcwSc/+UlSqRSxWIyHH36Y973vfbP2f8UVVxwSW/FUH//4x/noRz86dwd8hjAdg/HSEB7Vh0fxAqBKGiEtSsqYJmskiXpqT3g/lmNiu/YhE+aqskrJMjBdE+8J7+XYLLCW8NrijfzWewfj6giyK7PEXM615RuIuaIzWBAEQRCeT0QxWBCeR0ZGhvjmN7/E7bd/k0qlzLXX3sDrXvcW1qw574yaWGVZ7WJe3HEJfxp6gG1TCSJenbg/QEe4jYWRJbQFe4joc3/hMd9exHvyH2Lrgdmx404dK8yzCLqhWcsNZUeQJZnJYCf3BNrx2iVsSWUiOsY1L1vKhwtdfPjD7+OlL72A669/Pbfc8p80NDQdsr950W7ev+Zm+tL9GLZJa6iJWl985nmpnMS38xsohTGsUBfIKkpxDF/vD3B8dZh1Z8/5OThe69c/wO9//2u+/OXb8Hg8R1/hKIK1AVa/ehWDG4aY7p1G0RWaL+mh7axWFLWaTfvUouxccF2X4c0jKJpCvDNCIO4nN5Gjkq9QTJXwhjx4QwePzSyZ2IZNsj/JX/7nQWzTJlgbpHVVM41LGmlZ0XzIPgLxAIF4YNZjkixhmzbeiJf6oE6iL1mdpA1wigaZ8SyKUo2oOBxfxMuyly5hwaXzsE0Hs2Sy8SebyE7k8Mf8SLJEejhTnRRuaSOyIhOqDxHvMmg/p41oc4RyrkxuIo8n6KFxcQN186tFDMd2GN40wvCmEdJDaWzLwbVdvBEXs2QiyzKB2gCu41bzkqtN/MfF7/dz1VXX8LOffZ93v/uWM+p3kiCcqcbHxwFoaGiY9XhDQ8PMc+Pj49TX1896XlVVampqZi3T1dV1yDaefC4WizE+Pv6M+zmcW265ZVYBOZvN0tbW9mwO8YxkOSaWY6HJs+dNUCUN27UxXXNO9uNXQ3gUL2W7SEA9+F6kbBfxKX58yqnpCn7SOeY6lpurGVWGUFFpsdsP5AYLgiAIgvB8IorBgvA8sGXLRr72tc/yq1/9hGAwxJve9HZuvPFmGhsPLVadCWRJ5uU9V7K6fjl9mf1ISMyLdtEUaECRT+5FR40b52Lj8mdcxq/5sN1q1q8ryZTUanHPdE2CWoB1617A73+/nttv/xaf/OS/ceedv+A97/kX3vKWdx1SKPUoOkviCw+7H216I0p+GCu2GKTqcdvBNtTUTrSxB05JMdh1XQpWDss1CaihQy58n/TpT9/K8uWrueaa6+ds36H6IEuvWjwzUeDJLg66jkslV0HzqriOi2s7KJqMP+ZDVmWizRGyY1kkWQbcmVzjzFgWx3bJjmWpFIbovX8fraubWXLV4pn4iKMpZctIkoRVsqgUKri2i6zKOLbD1J4pui/spmFh/TNuQ/dXX5uBRwfITeSo7YojyRK13bWM7xwnM5Yl1BjCH/UxuWeq2nm8a5LMcIaWlc2sftVKVM/sP/v71w+w6w+7kVWFmo4YU73TlLIlbMsmEA9Q0xHDF/WS3J+idWU3kiyRHc8yvGmE5GAab9BD07LGmSL00bzyla/hpz+9na1bN7JihbjtWBCe6zwez5x8QHim8ShefGqAgpmd6QwGKNsldNmDX5mbmC2P4qU10E1fdjtZx0JXPFTsahZ0W6B7JjriVPLipduef8r3KwiCIAjCqSOKwYLwHOW6Ln/+8918+cv/zYMP3kd7exf//u//zQ03vPG0ZQE/G5Ik0R5upT3cerqHcohltYu4b+hBRgvjNPmrxb6p0jQeWWdl3TIAFEXhDW94Gy9/+av59Kdv5ROf+DA//vFtfPnLt7Fs2apj2o9sHshWlGYXwF0tgFyenLPjOZKilWdvZhtJYwrHtfEqPtoCPbQFZkcB7N27kwce+BNf+tL/PWNO8vGSJAmrYmGbNnpAP2lFYVmRibREGN0ySn46T3Y8j+s4OLaDbTo0L29iwWXzSfQnkSSJcrbE1N5pfDE/4zvHUXSFcDhEOV0mNZRm+507CMYDBOuO/vMWqPFTypYppooEawMYBQPTsHBNFxeX5qWNszqKLcMG1z2keAuQHEhVz5NcPU+h+iBmJc7ErkmS+5MUgh6KqSKBeADNq2EUDXbdvZtCosiKa5bNrGcUDQYeG0LzaYTqqx1pkZYIQ08MU06X8df4kBWZ5P4UsbYo7WvaSA+n2fizLRSmCuhBnexYlsm9U+SnCyy4dN5RX7sLL3whtbX1/PznPxDFYEE4Bo2N1QlLJyYmaGo6eAfKxMQEq1atmllmcnL23wzLskgmkzPrNzY2MjExMWuZJ78+2jJPPv/XRJYU2gLd7EpvImMk8So+TMfEcMq0B+fjV+fufVZHcD6arDNS3E/FLhHWYrQGumj0Pfc7rAVBEARBODPN/VW9IAgn3RNPPMqrXvUiXvval1EsFvja177PAw9s56abbn5OFILPdD2RLl7ecyUyMrtSe9mV2ovp2Lyk+0UsfVqXbzQa49ZbP8Pddz+Grnt46Usv4Ktf/SyO4xx1P7avvpoLbBsHH3RdJCOLHZrbzORD9u3a7ExvZKI0jC57CKphTMdgb3Yb46WhWcvedts3iMfreOlLXznn46jkK+z6w27+8j8P8pf/eZDHv/8E0/sSc76fJ3WsacOxHKb6EkgyqLqKJMuEGoKkhzJ4gh5Wv2olq65bgawqaH6dwnQB13LxBHQURalmB/s0iqkSk3unjmm/jYsakBUZI2+g+TQCNQG8QQ+RpjC13XUYpeotx8V0iR2/28n9X36A+7/8AFt+uY3c5OyZ6z1BD5ZhzXwtyRI17THiHTUseOE8ajpiRFuiNC5uwBf1EaoPEaoPMbZjnPRIZma9QrJIOVueFU+h+3Taz24j1llD06IG4t1xFl+xiLP/ZjX+Gj/9jwxQmC4Q764h3BCipj2GN+hh8LFB8lOzZ6E/HFVVueaa6/nlL3+MbdvHdO4E4a9ZV1cXjY2N3HPPPTOPZbNZ1q9fz7p16wBYt24d6XSaDRs2zCxz77334jgOa9eunVnm/vvvxzQPxhvcfffdLFy4kFgsNrPMU/fz5DJP7uevTZOvncXR1YS0KJZjocse5oeXMz+0dE4/tJQlmdZAF+fUXsy6+stZU/sCmvztIkrnOezJO54EQRAE4UwlOoMF4Tlk795dfPKTH+Guu+5g8eJlfPe7v+SFL7xSXDDMMUmSeGH7RSytXURvuh/XdemOdNAUaDjiuV60aBm/+c0DfOITH+E//uODPPDAn/jiF79DLHbk7GMrvgorugg1tR3H14wrqyilMVxvHKPpBSfr8ABIVaZIGdOE9RrUAxPXBOUIGSPJSHE/Dd5WCtMFkpMpfvSj/+NNb3rHnN8KbFs2W3+1jbEdE/giPhRNYXLPJJnRLGddv4qajtic7g8g3hUn3h0nM55FVmQkWSLeGSPWFiM1nCaxLzET1+ANe7FNC6tiIqnV1911XVy3WkR2LAejYDzT7mb4oj5aV7dQSBSwjWoRNFgbpKajhlK2hHtgkrpNP9tCoj+BL+JDkiUGHx8kPZLh7BtWE6ipZkc2LW1kcvckxVQRX9SH67pkx3IE6wN0n9/Nhh9uxB/zzfpe1QM6+ak8+en8zMRxmldD0ZTq5Hn6wbcDtmETqguw/Jpls7uVKxbJgdQh2/bFfCT2J8mOZwnVH/3DqJe//NV861tfYsuWJ444AaMg/DXJ5/P09vbOfN3f38+mTZuoqamhvb2d97znPfznf/4n8+fPp6uriw9/+MM0Nzfzile8AoDFixdz5ZVX8ta3vpWvfvWrmKbJO9/5Tm644Qaam6txUa997Wv56Ec/yk033cQHP/hBtm3bxuc//3k++9nPzuz33e9+NxdffDGf/vSneelLX8oPf/hDHn/8cb7+9a+f0vNxppAkiSZ/Ow2+Vkyngipph0z0NpdkSUaWDh/VJJz5XNdlsjzCSHGAopkjoIVo8XdS520W79MFQRCEM44oBgvCc8DY2Aif+cx/8sMffofGxhY+//lv8cpXvhZFEZN6nEwN/joa/HXHvLzH4+Hf/u2TXHzxZdx88xu44oq1fPObPzri7fCuFqC49B149v0cPbEFyS5ixZZQ7nw5dnTBXB3GYVXsEq7rzBSCn6TLHvLZHJvv38LUngT3bPwNhXyec5svxiyZaL7Z+YWuW83hdRwXX9g7E0FwLBL9SSb3ThNtiaB5q9v1hr0k+5MMPTF0UorBAMF4gJqOGiJNYSRZopKrkJ8uUMlVZnXcNi5pYHTrKC5gm9U4CaNgoHlVfFEvhUSRQG3gyDt6mtZVLUzumcIf8aF6VDSfhmVYlDJQ2x1nYvckyYEkNR2xmUn0fFEvif4kY9vGmPeCnplx5Sa7GHhsiOT+JAD+Gj+LXrSQcGMI3adRypRm7duxHBzHpZQukxnNEGoIEYj7qeuprU6qp1c7nouJIrlEga61HfhrZk9cJMkSsiJjP+UcQTWLWeLYJ/xbufJsPB4PGzY8IorBggA8/vjjXHrppTNfPzkh2xvf+Ea+853v8IEPfIBCocDb3vY20uk0F154Ib/73e/weg9m2d5+++28853v5LLLLkOWZa677jq+8IUvzDwfiUT4wx/+wM0338zZZ59NbW0tH/nIR3jb2942s8z555/P97//ff71X/+Vf/mXf2H+/PnccccdLFu27BSchTOXLMl4lMNP8CkITxou9LM3uxUXB032kChPkqpMszCykpZA5+keniAIgiDMIrniPpYTls1miUQi7N49TSgUPt3DEZ5H8vkcX/jCJ/jmN7+Izxfg3e/+Z97whr+bdQEonJmGhwd461tvYNeubXz96z/kRS966TMuL5WTSI6J462FkzyJHsB0eYJNyYcIquFZE9SkKwlKfwJ2eQjWBvjX7/49MX+ct19yC90XdrLkisXYlk1hqkAhVWRs+zjJ/UlcxyXSEmHeRd3UdBy5G/qp+h/Zz/a7dlLbFZ/1eH46j+pRufhdF52UbpqhjcNs+cU2ArV+kgMpConigQ5gm7azWlh341rCjWFc16X/4f3svmcvEzsnsAwLb8hLpCUCQLyzhrNfcxaewLF1clmGzdZfb2N0yxiSDLgSruPQtKyJFdcsY8+f9tL/0H7iTzsf6ZEM8e4457z24IcKruuSn8qTGa12ONd0xvCGqr8X9j3Uz47f7iJYG8AT8mCbNuM7JjBKJpHmMIqmEGuJsPBFC/EEdLb+ejsDB2IeHMvGG/bSvqaNFdcsI9IcmTWWXXfvZu99fcTaoqie6kR86dEMvoiXdTedd8zn4uUvv5jm5la++tXbj2l5QTiaXC7LwoW1ZDIZwmHxXux0Ee+Jhb9GpmPw6NR9GE6FkHbw72bWTOOT/ZxTd/FpmQxQEARB+OtzrO+JRWewIJyh7r77Tm655R9IJqf5u797D+94xz8SDkeOvqJwRmht7eAXv/gTN9/8em666dV8+cu3cfXVrzri8q63hlP5yVyNp5aYXst0eYKgFkKRVEp2ESvt4AxohGuDpMxJ+sf28jevuxFf1MfY1nHCDWEGHhsiM5om0Z/CdR3q59fhDXuZ2jNFfqrAmteeRbghdNQx6L5q4dCxnZmuUqNkkB3PEaoPUclV8Ibn/oOPpiWNTOyaZO+feikki2heFVlViDWEMAom23+7i7WvX4OsynSf30XjkkaGNw4zumWMSsFA9SjUzauj58KuYy5+Aqi6wvKXLaWup5bpvmlcF+p6amlc0jDTKew6Lq7rziqC26aNJ1jdTzFVJDuWRdYUatpjMxO/PVX7mnYKiSKj28bITxcwSgalTIlIS4RIUxjHcpjel8D45TbWvPYsmpY2MrZtjFh7lGBdAN1fnRhuyy+3sfaN56D7Dx5j13mdZCdyTPVO49rV71h/jZ9FL174rM7F2Wefx29+87NjXl4QBEEQ5tqTPVEn+sFzwcpRtosE1dkX3X4lQNHKU7TyhPWTc7eTIAiCIBwPUQwWhDPMxMQYH/nI+/j1r3/GJZe8mE984ou0t5/cycSEk8Pr9fK1r/2A97znJt7xjr+lVCpy/fVvON3DAqozpS+JnkVvdjuJyiRlt4xX8VEvtzNgjaP7NZ547BFURWPFvDUojkZ6OMXWX23DdcG1XWzDQlZkMiMZAvEANZ01JPoTjG4ZJfyihUcdQ21PnFBdkPRwhnBTiMxYlkRfAqNkYps2D31rPQsvm0/LiuYjbsN1XTIjGdIjGSRJoqbz8AVSgEwFBnOgySo9ly9iZPMIkirj8ev4a/yE6oPYpk16OE16JDMTU+GP+lhw6XzmXzIPI28gKRKqrpIaTpObyhOMBwjWHczKreQrjO+aJDeRQw/oNCyom+mwVT0qrataaF3Vcsj46hfUs3/9YLUY3hBEQqKQLKBoMg0L6+m9v4/96weo5CpIskSoPsTiKxdR2z27k1jVFZa9bAntZ7eSnyqw7+F+NK9GXU9tdQEdws0RhjcNkxhIkBnNYlsOTUsaCdQEkCSJWHuM1GCKqd7pWeffE/Jw1vWrme6dIj9dQPNq1M6rnckzPlZr1qzla1/7LGNjIzQ1HXouBEEQhLlTpkyfuhtTMmmzO4g7xx6B9XyUrEwxUugnYyTxKD6a/O00+zuQpeObW12VVGRJxnYtlKdcXtuujSzJJzVrWhAEQRCOh/jLJAhnCMdx+MEP/pdbb/1nNE3nS1/6P6699oYzYtIJG5tedRcT8jgBN8hiaxl+9+hZqRPyGLvU7ViSSbvdSY+1ELU4iZraCa6FHe7BDnXBGXCMJ4uqqnzhC/+L3x/gPe95C8VikTe96e2ne1gA+NQAy2LnULILWI6JXw1ieizG/EkquQob96xnadcqvLqP/FQeo2TiOlA3v5bpfQlkVcYX9VFKlyhMF4i2RtG8Gtmx7DHt3xP0sOzqpez47U4m90yRGkyhelUaFtVT0xEjP1Vg5+93EaoPEm489BYXx3bYc89eBh4fxCyZgIQn5GHeRd10ntcx87PjuvCnIYm7ByWmSxKKBO3IzPf5aWqJ4gkenBhPkiRs08aqWIfsT5Kq289O5Nh+1ybSQyls08ET1GlZ2czCyxZQzpbZ9PMtpAbTSHI1BmLg0UGWXLGIlpVHLmoDRJrCLH7xQvbcu5fUQAoX8IY8zL90PpZhsfe+XnS/Tk1HDY7tkBnLsO3X21n7pnPxRWZ3UEuSRKQ5QqQ5wv71A7OO0XVcpnqnSA9nyE3ksSoWru0y8OggpXm1NC1rmunUruQPnSBP1RUalzQ+47EczdlnnwfAhg2P8LKXXXdC2xIEQRCObLe6nR/5vsuIMoCDQ9St4dLyFVxZeTkyx1f8fC5LlCfZlnoMwynjUXzkzDTpdIKSVWBeeOlxve8OqGFiei2T5VEisooiqdiORcHM0uBvxa8cfXJVQRAEQTiVRDFYEM4Ae/fu4oMfvJlHHvkL11//Bj7ykU9SUxM/+oqnQF7Kcbv/W2zWNmBgICHRZnfyt8Wb6LbnH3G9B/X7uMP3I5JSAgnw4uP63npesiOLWk4B4GpBKq2XU+75m1OSk3u6FJQ8r/v0TRSCef7lX/4ByzJ5y1vedbqHBVSLhn714EWKGtVoXtHErvt2sW3fRm649CZyU3mMfAV/zI8kSUhStTP2KVvBLFeLp1bFwhc79i7ReGcN625cyyPfeRTHcamfVzsTSxBuDDG9L8HU3unDFoMndk3S/8h+vGHvzPOF6QJ77+sl2hol1hYFYOs0/LxXRpNd5kVcLMNiojeDnLBxc2naFtfPTHyXncxRSBZ45H/X41It0Had30XTkgZ0v45t2mz79XaSgykiTRFUj0IpU6b/oeo4CskiyYEU8c4aZEXGdV2y4zl237uX2u44npDnkON4qtZVLdR2x0kOpHBdl2hLhEA8wOPffwLXhUC8+iGMIivEWmMk9ieZ2jtF+5q2Q7ZlWzaJ/iRGwSA7msEf86HqKqVMmexoFtd20CM+VK+KUTBwHZfEQIpQYwhf1A+ShD96cvLJGxubicfr6O3dfVK2LwiCIEBaSnG771tMKON0WD2oqEzJE/za+1NqnXrONc8/3UM8pVzXZbCwF8OpENVrZwq/JavASHE/zf4OAtrRY66eTpIk5oeXYzoGaSMJuEiSTI2nnvnhZWdEY4cgCIIgPJUoBgvCaeQ4Dt/85hf52Mc+REtLOz/+8e+58MJLj77iKfQHz29Yrz1Au91F0A1hYrJP3csPfN/h/fmP4OHQ4taIPMQvfD/ExGSRtRQJCS07QGzvHUyzgNrYOYCEXE7gHbgTO9SF2bju1B/cKbBee4Bf+X7CpDyO+2mXZd6V/Nu/vZ+urnlcdtlVp3t4h7Xg0vls2PEQpm2wsH4Fqkela10n5WyJ/Y8M4rougVo/6RGNUqaE67gomkJ2PIvq02ha+uy6RlWPiu7XCcYDs/JpJUlCkiXMsnnY9SZ2TeI64IscnOU9WBdkat80033TM8Xg9eMyhg2dYSCZRXtkGx3TaUrpMtOFLEYiT8PCeoySycSuCcySSUaRsEoWQ48PsefevXSe18myly1B86ikRzJEWyIzBXF/1IdVNhl8fAjLsPHH/DOdtb+4/3t87w9f47KlV7P4xQuP6dx4w16alzfNeqycKaF5Z//JlmQJJA50Rc9WKRhs/eVWJvdOU86WyU7kKKSKNC1ppJI3KGXLyKqCP+bDMmyssoXtVDuik4Mp/LkKdfNqqX0yWuIkaGhoZHJy4qRtXxAE4XgkpGkqUplapx6dY89CPxNt1zYzogyzwFqMQvVD9waniZyaZb3+wDMWg03HIG9mkSWZkBY97giFM4npVMgaaXyqf1aB1qv4SRnT5KzMcRWDAQJaiNXxC0hWpqg4JTyyjxpPPaqIiBAEQRDOQOKvkyCcJtPTk7znPW/h3nt/x9ve9m4++MH/wOfzHX3FU6hEicf1h6lxagm61TfHGhqdVg+Daj+96i6WWisPWW+ntpWUlJwpBAMsmCwTLln0xQ1qzeoFheOrRa5Mo00++rwsBu9T9vIj/21YmPRY1Qxd361+knsSvOPmv+W3dz5MT8+CI65vOgYlq4gu63jVZ5fJeiJUXWFvajstzW286p+uxxf2ovk0UsNpxndMkB7OEKwLUtMRY3zHBK4LZsXAF4vQcU4btmExvmuCSHMYT8AzUxh9JsG6IP2P7KeQKGCbDnpQJ1QbwHXdWXm8T2WWTWTl0G4bCQnLsGe+ni6BX3XBdpAf34k0mcKti1EIKzRYAZyxSdKjGTwhL7Zpowc9mCUT1asiKRJGwWBi5wSSLNF+diuO5WCVLfLTBWRFxh/1ofk0jKIJEqha9YJ77/BO/vDYr+ho6AGqHUlPnxzuWEVao6QeHSBYF5xZ3zKsamf3YfJ6+x/qZ2zHBNGWCDXtMYJ1Qca2jzO+a5JgXQDNq6LoCrIio/tkgvEAuak8tmVjGzZtZ7Wy4NJ5qJ6T9zahrq6Rycmxk7Z9QRCEZyMhTfMr70/Ypm/CxKTebuBFlZdxrnn+zHuZ55qclEVCmikEP8nvBEjK04ddx3VdxkqD9Od2U7ILyMiE9Rjzw8uI6DUnbayO61CwstUPnNXQScnZlSUVRVax3dlRUA4OsiShSie2T1XWqPc9cySUIAiCIJwJRDFYEE6D+++/h3e96024rsvtt/+aSy+94nQP6bAqUpmKVMHrzC5S6+jYWJSl8mHXMzGQYNbFk2ZbSJKMIc3uYnRlHdnMzfnYzwSbtMfJSCkWW8tnHutwu8l8O822F2zizW++jt/85gHC4cis9RzXYSjfx3Cxn4pdQpFU6rzNzAsvRldOzm37T7dx42Oct+4iwg0HO2RirVGWXb2U3vv6yE3lkWSJeS/ooXFJA+GGEKVsmX0P7ic3maOQLGJXLEL1QTrO7aDj3HaiLZHD7quSq5DoT5CfKlDOHviecl1kTaFpWSPxrsNffNZ0xJjYOYFjOzMFZ8uwkORqvMOTOsIufWkZdzqDNJXGjUdwFAXJhGhLhFi9imO54LjIqoKsSLi2i+pVkCQJWVEwyibldInsZI78dIHEQGLm+1vzaXiCHpqXN+ENexl6YhjX6/C5H3+Ud7ziA/zwD9/GdVz6H+xn9x/3EG4M03ZWK3Xza4+5MNy2uoXJPVMkB1L4Y34c26GYLFK/sI66ebO7d62Kxdj2cbxhD+VshaneaayyRaghiGPaLH/pUvb8uZfx7ePYho2syciqjDfsQfNqrH7VSha9eOFJv621vr6B/v6+k7oPQRCEY2FgcJv/a2zVNtHoNBFyw4wpo9zu/ybeopeV5tmne4jHpcFpQkGmTBkv1fcPLi5ZOc3KyprDrpOoTLI7vRkkCKkRHBxSlSl2pJ7grNoL8ZyE9yEZI0lvdjtZI4WDS0AN0hVaRINvbicYVWWVBm8L/fld6LIHTdZxXIeskSKsRYnqJ+9uGEEQBEE4kzz37/cRhOcQ27b59Kdv5TWveQmLFy/nj398/IwtBAOE3QitVjsJZXLW4wl5mpAbodluPex6LXYHqqtRkPIH1wkFqcg2jUb04IKujWzmsWKLT8bwT7u0nEI7zC2m/nCAG77/RiYnx3nnO9+I4ziznh8tDrA3uxXTMfCrQWRJYajYy67MZlzXPenjdl2X3t5dzJ+/6JDnGhc1cN6Naznvzedy3pvOZd2Na+m5sBtP2MueP/VSShcxyxaVXAWjZJIcTLHr7t089M1HmNw7eZi9wfDmEdJDaXwRL7pPQ/NpKF4VRVOwShYjW0YPu17z8mZiHTES/UlyEzmy41lSQ2nqF9RTv+DgTOnrmlxiXpehlIVp2JRQSJShxgcNflB1Ddd2QJbAdXFsByRmFUNlSUJWZXLjOWzDwjFdFF1B82uU0iWy4zkaFtXTfUEXkeYwX/7hJ1nWcjYtSje2YWEZFqnhDLZlM7Frgk0/28zY9vFjfk2irVFWvXI59QvqsE0LJOi+oJMV1yw/pHvXthxs06EwXWBi5wSF6QJmxSQ3niM9nCE9lmHVK1cQagyRm6yet1KmhOpRaV/TRte6zlOSb1hX18DUlIiJEATh9Nulbme3tp1ua97M3VBddg8VqcJf9HtP9/CO22JzOYvMZexT9zAlT5CSkuxVdxJz4lxoXHLYdcZKg1iuRUiLosgqmqwT0ePkrAzT5WP/u3WsynaJHaknSFYm8ag+AmqQgpVjZ3ojqcrhu5dPREdwPg3eVgpWjlRlioyRIKiGWRhZISIdBEEQhL8a4i+eIJwiicQU73znm7j//j/yj//4Yd797ltQlDNr0rSClGe9/gCbtQ0ArDDP4nzjYgbV/exRdxJzaihKBSpSmReXr6bJOXzHxmJrGWeba1mvP4DfDaC6Knuak3QMd7Fi2EbRBnBlFbmcwIrMw2i66FQe5inTZnfwAH/Cxp65RdPGxsBgVdc5/M//fI/Xv/4avvjFT/Lud99Sfd61GS7sQ5FVglq1u1WVNRRJYbo8QdZMndTbNKEaYZLJpA9bDIZqjESsNTrrsak9U5TSJfwxP9P9SbwhD67rkhnJYpZNchN5/pwocNb1q2g7qxVFV1DU6jmZ3DOFYzvYpk2kJVItREpQSpeQZImRTaN0ndeJos3+efFFvKx+1UoGNwwzuXsSWZXpvrCLtrPaZhVIuyLw5qUOvyNI8Qkv5Eu0N/hZEAVNhnSqSKgxROPCBoY3DmMbTrUo7Li4toPruvhr/TiOSzlbJtYaxXFccpN5HMuuRjfIMrIiE24IMR7pZ6IyzD+99ZtgKTi2i1WxyI5lMUpeatpjlDNl9j20n4ZF9TPn4WjiXXFqOmuo5CsoqoLm0w67nO7XCMR8DG4YwhPU8Qa8VPIVyrkyZtli5+93Uc5WOO/Na5naM8lU7zSqR6P97FbazmrFGz413ecNDU1MTo4fd3SGIAjCXEnKU9jYeJl9J1TEiTKqDM36O/5c4sXLG4tv5/feX7NJe4yyXGKZuYoXVV5Kj334mKqSlUeTZ/99eTIvuOIc/o6wEzFdHidnZYjpcaQD+wlrMdLGNOOlIWKeue3W1RUPy2vOJVWZpmjn0CSdGk89uvLME7wKgiAIwvOJKAYLwimwZ88O/vZvr6FUKvL979/JxRdffrqHdIgiRf7X/xU2ao/jdasXQ9vUzaw0z+ZvizfxqP4QA8o+muxWzjcu5oIjdJRANVf4dcW30GXP4zHtYcpSiXXWxXQvOAfHuxNn4iFwTCqdF1BpvRzHV3+KjvJQJavEQ6OPsWFiM4Ztsrx2MRe0nEutL37C2z7LWMtD+p/Zo+6g3qlOHDYpj9Nhd3G2uZb4pXW84x3v43Of+xjXXHM9nZ09mE6Fil1Cl2cX5DRZJ29lKdslDh+2MHd6e3cDMG/ewmNep1KoIMsSZtHAtR0kRSI/Xs2gVb0evCEP+akCD397Pdvv2kGkKULLymY6zm0HScK2XVzHRZIlJEma6YBWNAWzYmGb9iHFYAB/zM+iyxew6PIjZy8DLInDoos8bCq3MvRALx7DRMppJHNlFF2l67xOGhbXM7R5hN77e7EMG7NsIckS3ogX3acTbgxhVSwcyyFUHyLWFsWxHVRdJTmQxDZtRkaG+M9P3sIPf3gXXc3zuf8rD+JYDoqqICky+ck8RsGgbl4thUSBUqp0xEzkw5EkCW/omYu1kiRRcyBawyyZGAWDUqZcLWpHfciyTHoohVUyOfcNazj7hrNmrV/OVovGvqgPVT95xY9AIESpVBTFYEEQTruwG0VCxsCYNWlcXsoxz16A/By+mbLGjfOa0pt4eflVGBhE3OgzHk9QDZM2krN+NzuujQT4lLmfv6BilwBmCsHV/5dQZY2CdXJixGRJJu6tJ87pe/8pCIIgCKeTKAYLwkn20EN/5sYbX0VLSxu/+MW9tLS0ne4hHdZm/XE2axvosubN5MpVKLNFe4KzzbW8vfBeLCxU1GOaSMWPn8srL+HyyksOPqhCpXshla5rABdO88zUhm3y3R0/4dHxJ/CrPhRJ4Zd9v2NbYhdvX/FG4r4T68CNu7XcVHgnv/P+it3qdpBgnfECrqy8nLhTjTF473s/xB13/JgPf/h93HbbHWiSjq54KdulWbl8pmugSupJyep7ut7e3SiKQkdHzzGvE6wNVgu4By4cjaKJWbGQFRnNp2JVLMyyiW3ZKJqKL+Jj5x92k5/K07CwjvHt40iyhG3YqB4Vq2whH+iYDdWH0LyH74J9NmQJVl3WTV2tl6GNw5QzZerm19J+djv1C+uQJImL3nE+LSua6bu/j+RQGkWVCTeGqF9QT89FXQxvGqH/4epEboqmVIvVJRNJrk5a9/Cv7mN6epIrrlgLbjVyw3EdBrJ7eXDgbj79yu9RTlfITeSq29AP/TNsmzZTfdPkJ/OoHpW6ebUE4oEjHpdRNBjZMsrErmoMR8PCeoK1ATwhD+V0iXK2guM4eEIeNL+OrCnE2mOkhtJM7J6i+/zqtsu5Mnvv62Ni1yS2aeOP+eg6r5OWVc0npVjrONVJ/mT5uVtkEQTh+WGRuYwuq4c+dQ9tdie662FKnsDF5YLKpc/ZCeSeKuAGOfJfkoOa/O1MlsfImEkCaujAxG45IlqMuLdhzsflUXyAi+s6MwVh13UxHZOAGnrmlQVBEARBOC6iGCwIJ9HPf/593vvet3LeeS/gG9/44SEThZ1J9iq7kZBnCsEAHrzIyOxRdnEBl6AxuyDn4LBf6WNQ6UdFY6G1hDrnGC4UpOr0cqfbjsRuNkxupiPUil+rdrs0Oja70708MraBl3a/6IT30eq0c1PxZnJSFoCQG551Uen3B7j11s9w442v4ne/+yVXXfUKmn0d7MlupWjl8Sg+LMekYGWp9zYT0U5uRARUi8EdHd3o+qF5x0fSsKie2BMxpnqnkWSJUrqEbVjofh1FUyhkiii6jDdcvegL1gXRAzrjOydoWtZE61kt7PnTXvJTeWRFRtEUvBEvvpiPznPbkeS5+X6RFZm2s1ppXd2CYznIqjyr0KmoCvMv7mH+xT24rks5W6GUKTGxa4LNP9+KUTKwKhYTeyYJ1QaxTZtiugSuS+99vXjLYT56/ZeoaYsiKTLTfdN8+97PEfc08OJl16LI1QJ3fqpA17oufJHZxX2jaLDll9uY3D2JY1e7o31RL0uuXEzT0sZDjscsm2z+xVYmdk2geqo/n9N9CTxBHatkIsnVieHMioVjOhQTBRoXN6LqKrIsUclVb/l1LIetv9rO+M4JAjUBdL9GMVlk253bUXTlsPs+UY7jnHFROYIg/HXy4+f1xbfxE9936VP3YEomMaeGK8sv5xzz/NM9vFMq5qljSfQs9uf3kDezyJJMg7eFnvASdHnuoxRqvY2E1ChpI0FADSNJMkUrh0f20OQ7vgYK13Up2QVc18WnBmZiLgRBEARBqBLFYEE4CVzX5Ytf/CSf+MRHuP76N/CpT/0PmnbinY0nk46Gg3PI4y7OrFsmn2Ri8jPf7Tyo/5mSVACgxqnlleXXcJ5xkjKAXRe5PA12pRotoRx7sfJwhvIj2I49UwgGUGWFoBpgV3LvnBSDASQkwu6RPwi44oqrufzyl/DhD/8jF1/8ItqC3ViuyUhxgJyRRpVVmnztzI8sOyW304+Pj9DS0v6s1vEEPay6bgV9f9nH4IYhpnqnsUoyiqbi2NXJ1rxhL7Zp44sEkCQJT8BDbjJPJV9h9XUraVhYT99f9pEezaJ5Vep6auk8r4OGhXN/G6ckSYeNnXj6MpIMu/6wm8T+JIomYxRNyrkK3qAHFxdv2ItLNVoh1BCixltDXamRzGgGb9iLHg0QCAYJ6RFqpHpKqSJmySS+uIH5l847ZJ8Djw4yum2MWGsMzatWc5dHs+z8w25ibdFD8nwndk4ysXuSaGsU9UCXsVkx6X9oAE+wetGem8zPxGxIkoQ37MF5Mgu5pvq9nxxMMd2XINocmcki1v06qcEUg48N0rikYc6/9wyjgqad2M+wIAjCXGl12vmHwj8zqPRTkco02a1E3OjpHtZpUe9rptbbQNHKI0sKPiVw0t5/eBUfS2Nn05vdTsZI4uISVMN0hRYSPY684JyZpi+7oxp1gUtYi9IVWkiNR0RCCIIgCMKTRDFYEOaYZVnccsu7uP32b/H+93+E9773Q2dUHmZKSvKIfj87tK14XC+rzXM4xzifJeYK/qzfQ0pKEHPjM8uqrsYya+Uh23lMf4h7Pb+nzm6g3e3ExWVYGeSnvttpt7podlrndNxyaRJP30/RE5vBMbH9TVQ6XobZcN5MNMGzpcsaLu4hmaWmY+JTT80kWlAtOv7nf36WSy5ZyRe/+Ek++MH/oCe8hNZAF0WrgCbrBNTQKfs+yuVyhELP/tbMQDzAilcsZ9GLF1JIFnjs9idIDaYJNwRJDqWp5Cp4AjqRpurEeI7lIEmgeTVUj0r72W20n92GbdnYho3m1easI/hIrIrF9L4EpUwJb8hLbU98ViTF2PZxEvuTSBKkhjLYhnWgY7hM57oOus/vYv13Hq0WUQ+sp/s0grUBiukSqqc6gVwgHqBhUT25yTyRVo3zbjwXf/TgREWO7TC8aYRHvvMopXSJ6d4EoYYgtd1xwk0hkgMpEvuTtKxonjX+5FCqmq34lLgJRVWwTQtZ89C0tInMSJqpvsRMJrNRNEkOpIg0RWYK7aVMCduyD5mUzhPykk8UZ+I75lIulyUUCs/pNgVBEE6EgkKXfegHdc+W7dq4rosqn9xLLdd1cXGRkOb8PYIsKQS1U3NHW1iPsTp+AXkri+s6BLQwivTs7xwp2yW2pR4nZ2aq75uQSJQnSVYmaQ/OI6zFqPHUn5LILUEQBEE4k4lisCDMoUqlwlvf+jfcd98f+Nznvsn117/hdA9plqSU4OuBz7Nb3UHQDWJhsUXbQK+ymxtKb+KyypXc5/kD49IoLtXbJi8tX8Eyc9Uh29qgrUdxFWJuNbZAQqLN7mCXuo2d2laaK3NYDLYr+HZ8DS2xGdvfAnoYJT+Ef+c3KaherNrVsxZ3XIet0zvZOLmVnJGjO9LJuY2rqfPP7jBZVDOfiB5mrDhBk7/a+ZipZHFch9X1y+du/Megvb2LN7/57/nOd77Ku971Qfz+AB7FdyBL7+RzXIdkOYUiqeTzOerrjz8WQPfr6H6di/7ufPbe18vk3im8IQ9W2STaEsEX82FbNpmRDKH6EPGu2dEXiqqgqCc/PqCQKLDll9tIDqTABSSItkZYcc0yQvXVYnhqKI1ZNCkkiyi6gi/qAxeyEzl6/9xHXU8cy7AJPK1QqnpUFFVh3gu6+ffI58hPFzDLJvHOGuZfMo/artkTFO57oJ8nfrKJ7EQOCbBNh0q+TDFVonVlM7jVLOGnU1QZDky29yRJllA9arWg7lGp7a7FH/Mz3Zcgn6jeNtu0pJEFl8yb6TTWfTp2xSI9mkb3e/BFvEiSRDFVneBtw4824g15aVzSQP2CujkpOuTzx/ehgyAIwpmqbBUZLPQxWR7FdR3ingbagz1zXlR1XIfR4gCjxQEMu0xIj9IW6H5Od79KkkToBM/TVHmMnJkmptciSTK2a2E4ZdJGgpyZIaRFCKghFkVXnZJzVaSIIVUIu5Hn9CSEgiAIwvOPKAYLwhwxTZO3v/21/OUv93DbbXdwySUvPuFtWlis1x9gvf4XUlKKedZCLjQupcdecFzbe0T/C3vUHSywFs/k/+akLI94/sI55jpeWX4NK6zV9Kq7AZhnLWSeteiwb2DzUg6d2dlx1SxciYpUPq7xHYmW2IqW2okVWQAHujnsSAg1tRPPyH2HFIN/t/9e7uy/G9M28Sg6m6a2sWFiEzctfz0twYNFzvZQKy/rfjF39t/NrtQeQMKreLi49XzWNK6a02M4Fm9+8zv42tc+x09+8j3e+Ma/O2X73ZPq47f99zCQHUKWZEbSI3SqR+6MMmyTodwILi5toRY8R4jr8EV9rHjFckqZMpVChYHHBhnfMUFqIAWSRKg+yLKXLUH3n/qoANd12X3vXqb3JYi1xVB1Bdu0SQ2m2Pn73ZzzurORZAnNp1FMVTOB9Se7ZiVQPQpWxaKYKuMNeSilSwTrgjPbL6VL+CJeOs5tp31NG5mRDC4Qbal2EOcm87iuSzAeoJyv0PfAPkqZMt5gNd/3yWJuOVtmbMc4mk9j5x92s/e+Xmq74nRf1E2oLkjdvDoGNwxTypTwRaofHFRyFQJxP7KskBnL4o/5kRUZX9RL09JGVl63glB9cKagW8lVGNwwSGIgRSlTQpZlPGEP4foQ+ekC/piP1GAa27QZ2zbGvEvmMe+i7hN+DSYnx4nHn7uFC0EQhKcyHYNtqcdJGpN4FB8SEsPFfWSMJCvj5+FXg0ffyDHqy+5gf34PiqSgyhqTpRHSlWmWxtZQ6537jPfnipJVAKSZiegylRQFK4cme9BlDxE9TtZIsTu9hTV1L0CTT877j6yU4Q+e37BBfwRDMmi12rm88hKWW6uPvrIgCIIgnAKiGCwIc8CyLN75zjdw772/49vf/umcFIJdXH7l/Sm/9/4SxVXxuX7+4rmH7dpm3lJ8JwusJc96m9u0TQTd0KyJ4EJumGEG2a/2scRawQJryTFte6G1hL3qLhycmWJxUSqgotJqdRxxvRIlXFz8+I+4zNPJ5Slw7ZlC8JMcPYKS21/tjDxQ2BovTHLP4P0EVD8N4Tqgervm7mQv9w79hdcvfvXM+pIk8cL2i1hYM4/dyV4s16Yz3Ma8aNdpmWyktbWDq666hm9960u8/vVvRZZP3hiejMYYyo3wv9u+T7Kcot5fj+Pa5CsFdqf7yBl5Qvrsi9ftid3c0XsXo/lxXFyaAw1c3XMlK+uWHnFfvogXX8RL5OXL6DynndxkHtWjEu+sOSSW4FQpJosk9iUI1QVBgmK6hKxIhBpCpAZTZCdyRJrCNCysx3Vd7AMZuwBmyUTRVDxBD5IE7ee0s+eePWRGM+gBnUrewLUdOs5tn4mOqJtfh2M7TPdNs++h/WTGsuC6hBvD1HTWkJ8uANU4ifx0HqtiAWBXLDKjWfwxH6VMGatsse/B/ez+Uy9rXruajnPa6Vrbwf7HBikkqtndqkdj0YsW4q8JMLB+gPRIGlmWaFrWxJIrF+GPHfzZc12X3ffsoe+B/mpkh0fFPjDRXClVJFgfonVVC7JS/V4sJArsf3g/TUsaCMSPZV76I+vt3c2SJStOaBuCIAhniqnyGEljiogen4k48CkBUpUpxovDdIcXzcl+CmaOkeJ+vIoPnxqY2U/GSDBY6CXumfuM9+eKavyDOxOfUbCzKLKK7dioso4syYT1KBkjRaoyTb2v+ajbfLYsLL7n/yaPaw8Td+rwOj52adsYUgd4W+EfWGQtm/N9CoIgCMKzJYrBgnCCbNvmPe+5ibvuuoOvf/2HXHbZVXOy3TF5hL947iHmxKl1qt1zjU4ze9Qd3O25i/nW4gOduMdOdz1YzL7V3MUFXBT32f06ON+4mC3aE+xUtxJxYjiSTUHKs8Zcd9g3ulPyBHd77mKLtgEXl8Xmci6vvIRW5+gTlTl6FJDAMUE+WDyUrTxmqHNWZnB/ZoCskWNh9GBnqyIp1Ppq2DG9m4ptHNLJ2hJsoiXY9KyO/2R5y1vexbXXvpD777+HSy6ZmwnsnmokP8b9ww+zI7Ebv+bDsC3GC5MsjS+auXjUJY2cmWfz1HYubFk7s+5YYYLv7fgx6UqGlmAzEhIj+TG+t/MnRD1hOsLPPOu3JElEmiNEmo/9NlDXdXEsB1mV5/Ti1jZsbMuhnMiTn6gWXyVZwhPQ0YMebKNajK3rqaXtrBb6HuinmCpWJ57TVaKtYSQkArUBGhc3oPs0Bp8YopytEG4M0X52Gy0rqxeZju0wvHGEvof6GX5iGNdxiXfWEKwPkhxMkRpM4TjVyRtlVSZYF6KSr1DOlUGSUHQFRVdwTAdwsUyL6b5pHvn2o/hCXhZevoD6hXUkB9MA1LRHibXFKCQKTOyaIDeZw3VdCskCyYEUvqhv5lwWUyVGt49jliw8AQ/BuiC2YWMUTXJTuUPOub/GT2J/kvRI5oSKwa7r0tu7m5e//NVHX1gQBOE5oGBmAXdW1q0kSaiyRsZIzNl+8lYGw6kQ0w9GX0mShFcNkDOqz/21ZuLWeZsYKvSRMZL41AC2Y2O5JqqkEVSrsUQSMi4utmudlDHsVrezRXuCTrsHv1v9Oxm1YuxRd3K/fo8oBguCIAhnBFEMFoQT4DgO//RP7+COO37EV77yXa666po52/awMkhOytBsH+y4lJCodRrYr/SRcdNYFQMXh5AWwasc7PabkifYp+xFQmaetZCaAxPCrTbPYbu2mYKUJ+AGcXGZkMcIuREWPstO4waniQsqlzDsH2CzvoGAE+CyylVcV3wdw8oAOjrNdhsyMjkpy7f8X2a3uoM6px4Jifs9f2RA3cfNhffPFLuPxIqvwAp3o6b3YIc6cBUPcnEcFwmj+eJZyyqyAi4zE6o8yXYdNEVDPsO7Zc499wKWLl3Jt771pTkvBo/kx/na5v9jpDBGzBMlW8mxaXobAdXPkvjCmfOl6hq2YTFdmn3xunFiK5PFaRbXLJgpEnZHOtiZ2sMTE1uOWgx+NlzHZWTLKENPDFNKlwjEA7SvaaNxydx0PPnjAVzHZWr3NJ6gjifowXVcshN5fBVrJktXkiXOumE1RskkPZTGE/Sg+XVsw6JuQS318+uQFZn2NW20rm7BOhDx8GQnLcC+h/az5549lNKlmeeTgykUXaGmPcZ07zS6X6eYKFHOlvFGvKgeBXKA66J6VIyiiXMgM1jVVayKRXYix8afbebKxQ3UdNRQ03Ewe9ksmzN5yKH6ELKqUEwW2H7XDnS/Tv2Cate8WTQoZ8s4toMnpFcLFx4VSZYoJGTMkolZNvEEqpEwrutWw2BOcGK/8fFRCoU88+YtPKHtCIIgnCnUA5EDT5+U1nbtOS3OKpKGLMk4ro0iHbyUs10LRVaOa+K1Y1GyCuTMDIqkEtVrUI5xcrwn76o5Fd3KfjXIkuhZ7M1sI2dm4EDDQ9zTgFetvk8u2yV0WT/hfOIjmVTGsbBmCsFQff8edWLsV/tm3VEnCIIgCKeLKAYLwnFyXZcPfejd/OhH/8cXvvDtOe9w8+BBRsHCmhXrYFAhVAyybepRSmb1tnCP4qUjMJ+2YA/3eH/L772/Ji2lkICwEyXsRJhQxyhSQAL6lD1o6Li4hN0wLylfS4fdTVEq8Jj2MNu1zciuwnJrFWcb5+Hl0IuYx7SH+bXvp6ionGucT17K8aDnPjZqjxEkhOqqdFsLuK78GvqVPvaqu2ZlFdc4texWd/CY9hBXVV7xjOfC1QKUlrwN757voWb2IjkmjjdOuetazIa1WI6J7Vrospd50W7ivhjD+THags1IkoRhmyTLaV7SdTmafHpiCY6VJEm86U3v4AMfeAeJxBTxeB1FK0/RKqDLHkJaZOaCyjJsjEIF3a+jeg7/69xyLEzHxKt4eWDkYUYKYyyKzZ+JwRgrTLAvO0CinKLOV/3QwBsJUMjk8amzX/dkOYUma7Mu6CRJwqt4mSxNH/cx5ybzDG8cZnpfAt2v07SsEaNosPfeXiRZQg/oJPYnSQ2lMMuLCdT4GdsxQSlVItwUonl508yEb09yLIeRraOMbh3HKFSo6YjRdlYr4cYwAKquoHlVwMWxHayKhW3a6D4NVVcPRDNULxzD9SHWvn4N/Y8MkOhPIisSTUs66FzXMeu8y4p8SP5xJVdh8LFBdL9eLQTrKr6Ij0q+QnokQ6ghhObTCNQF8EW8jG4ZJzmQwq7YIIOsKhgFA6tiISsysiojKw6SJKF5VTIjWdLD6VmFYIDpvgSpoTSxtiiKVi0MRJojJAeSDG0cnikG+2J+vCEvaTuNa7s8eX1qGzaqV63eautUL+Rd1yU3kccX81PTHjvu1xuqERGAKAYLgvC8UettYDDfS97KEFTDgETJLiBLMvW+ljnbT1SPE1KjZIwkQS2EIms4rk3ZKlLnbWZL8lFSxhSqpNHka6c12IUue3Bw0CT9WRdlXdelP7eb4eI+KnYZCQmv5seplUn5U/jdAMvMVTQ5s48xZ2YYzu8jYUyiSRqN/jZa/J2oJ/l9WI2nnjV1F5MzUqSNJPvzezCcMgUrh+VYOK5NR3A+ATV8UvYfcEJIVOMi1KdcahelAh1297O+q08QBEEQTgZRDBaE4/SVr3ya//u/r/GpT32V66573TGvl5KSPOi5j83qBhQUzjbXcr5xMQF3djbrfGsRzXYr+9U+uq35KCiUpCJZO8PK8WWUzSJhPYaMTNHK0ZfbwZh3lF96f4LH9bDAXoyJya98P2VaniDohtDdagG4zerk6sqraLJbWGQtpdPuoSQV+bb/K2zSHkdzNVzJZYP+MDvVrbyh+Hfo6EzLkzymPUSfuoeHtfuRkFlpnY2EhCu7jMojILu8sHwFiqSyTdtIXsrSancgI88qaisoeF0f/WofVI5+3uxQJ4XVt6Dk+pHsMnawjYrqZSC7g4nyMLZjE9TCtAfncU3PVfx076/Zmdpb7WKUJJbGF/LC9ouO+XU6nS6//Cpc1+XP99/NkhcuZbw4hOlUUCWNGm8984PLmdg4zdDjQ5RzFXS/RuuqFrrWdc4U/iq2wZ+HHuSRsccpmCVaQ03sSfUR1SOz8pC7ox30ZwfZlx4gpAXYm+4noWYxpwrcP/wIIT3EuqY1SJJEvb8WyzVxXGdmG67rUrbLNAeOb8Ka7ESOjT/eRGo4jaqrIMHknknKBYNIY5hwQ7XI64/5yYxl2fqr7SiajGXYqLrKxK4JRreOsfLaFdR0xGbGtOue3ex/eABJllE9Cvse2s/U3mlWvWol0ZYIrusiqzLxzhpyU3lK6RK6X6OmowbXdankZ39TRpojrLx2ObZhI8nSzHk+mkKyQDlXIdIUxigauE41y1D1qtWu25KJWbHQvRqB7gC2YTOxewqtQaWup45SusTIllFc28XBAQlsw0VRZeSgjqIrFJMlAvEKiqbMFKdLmTK47iHj1P06+cn8zNeegE7PRd1M9U6Rny7gi3hxHRezYhGo8eOv8ZOfLpCfLuA6Lr6Il4WXzZ/pnD5evb270XWdtrbOE9qOIAjCmSKkRVkQWU5fbgdpI4ELeGQvPaElxD0Nc7YfVVap8zUyVhpkujIGSHhlP2E9xnChj5yZmbk7aqw4wO7MJgJaGFXWiOm1tAfnEdFrjrqfJ42XhtiX34kue4jqcYpugc3u4yRz0wyFx7BUm7injr8pvYE15nkA5M0MW5Lr/z977x0nx1Wn6z+nUuc0OWmCNJpRDpYsOedswDawYJNzWMKyicvu3cDeDWzg8mMJF1iWYLMmB4NtDDbOUZJtWTmNNDnP9HQOlc7vjx6NPCjbsg24Hn8G7JqqU6eqe7rrvOc975e8ncGnBrDcMvvS28iaKZYl1r3kdRlUoRL31cz+VDOc72XGnCaoh2kKttIYbHvJnMrL7JW0OG0c0vbTZi+afX6exBIW55oXeWKwh4eHh8fvBJ4Y7OHxArj//nv4l3/533z84/+Lt771PUf9vkyZkigSlhFUjogxaZHi66EvsFfbSURGcXE5oO1hn7ab9+U/Ns+BG5Qh3lx8B7cHvskBbS8AmtRYk1pLTbmWmFE19yAb0qPMlKfYU9pGWZRodxYC0KPvZUaZRpUacTdBtVtLRqQZ0gZImyneU/7jufM9o2/iOf1p2u1Fc/3IixxbjCdYa22g3mnkG6Ev0a/2IoD9+l4CMkBUxljoLGZIHUBDQwCmYlLnVtFpL6FH20eQEA72UdENljCJufFTv/GKihOrZAG70mXvzBZGi4P41QCqojFTniRrpVhRtYFPnPVBdk7tpWAXiEaDKI0mm/SHqXXrWW6tJkDg1M/7MlNf38jSpSv45f0/I7zBIKiFCWkRLLfMeHGIqadTlB4H3a/hi/gw8xZ7f7Mfu2yz5MpupJT8eP8veHDwMSJ6mIDmZ9f0Pg6m+qgP1tIUPiLcVvkTtIQbCeg+7h98jFQphS8WxOnJkjGz3L73xyhCcE7jetbWreSx4U3sTx2kKdRQGWjmx6kP1nJW/eoXdK29T/RVxE4pcW2JqiuohkpuMkdVS3zevr6wwdieMarbq6lZWHExSylJ9iU5+NhBEgvWIRRBZizL0LPDBOIBArHK6xyuDTPdO03/5gHiN61ECIEvaDAwlEZRBXrQQErJzGCKQMyPP3a02Hk4QuF00Pw6mqFil23CtWHSo1lK6RKKpiBlRQw3CyZjeyfQfBrlXBmrZBFMBAhE/bi2g6IpIEA6EqlIhCJwXTnnFD7wcA97f7MPVVdpXN7AwvM78Ed9FeHYdlC1I59BZtEi8Vuu3kXnd2CXLLbdsYPCTBFFVQjXhFhwVgtLr+4mM5YlN5lD9+vULq6Zc1e/GJ5++km6u5ejad5jiIeHxx8OjcFWEr7aihgsXWJGFUEtfPIDfwsp4eBBOHBA4LrQ3i5ZsgRUFaZK4wzkDs49G9iuSdkpM2NO4roOPjWAofpwXYeUlaRYLuDiUuWrZ7Q4QMaaYXXVuYT1U/ssHy0OAMxdR7/ey7g6Rk2hlnA2gR2RHFL28wPj23SYi6gWtQwX+shbGRK+2rlnVcs1GS8N01huo9p/4niwM0ncqCZuVB8V3/FSEZYR3lZ4H98P3MqA1ouDTVTGuaZ0A+eaF73k5/fw8PDw8DgVvFGYh8dp0tOzj4985B1cfvm1fPKT/zDvdyVKPOj7FU8aj1BQCtQ7jVxSvor11jkIBFuMJ9ir7WLx8+ISShTZpj/Ddv1ZNljnzWtvqb2Sv8j9Hbv17RREnnqnEX9Bp5d9Rz3QaopO2SmiyyPu2wGlDwAdDWe2cFxUxsjKNM8YT2HmTQwqy9r3ajvR0OYJ0iEZxsWlR9vHTu05+tVDdNvLsYXFkDpAQRQ4qO2nwW0ip2RQZaXI1+E+HF4eV+PUEVerGFT7aHZaEQjGlBH80s9a6+wX9DqkzWkmS6NE9Tj6bE6fXw0wU55iKH+Q1VXnsiDSzAFtD98J/DfD6gCWa+K4NgvFYt5f/Dit6sIXdO6Xgwsuuoyf/Ox23qa8cy4P2lD9BEou/c+Mk/DVzYlyvpAPJakw9NwIrWe3Mq0m2TK2lYZgPQl/JROvOlDFRGGK4dwoC2NtxHxRXOkykBkmbITxK34yZoawESZQ38zudC9tkRb6MgM8NPg46+vXUBus4Z3Lb+bOQ7+mLz2ABLqrOrm+40qaw6fvDJZS0vPIQYrpEsF4AD2sYpsOhZkiVsmmMJsVfJjCTBHHconUHxlYC1Ep4pYaSlPMVNrJjGWwihaR+si8/QLxINO90zhWRWC1LQfHslF1AyNoIF1JbjqP5tfOiOAJEKkLU91RzdD2YeJNMeq7a5k4MEluMk+oOkgwEUQ6LpG6CMFEgMx4luxElmK6RHo0g5QVN66qqZQypYr711BBCBzLppwzKSQLBKtCOJbNgYcPkk8WWHH9MuLNcWYGZojUR1A1lXyygKIptKyZv5RX0RSWXr2EhRd0ML5nAsd2CFeHqOqoQtXUM3YvDuM4Dg89dB9vf/v7zmi7Hh4eHr8L+NUADYGWF3y8lHDXXYL77lPIV9LAMAw47zyXN7/ZZTjfi+WaVPlq545JlicZKwygCZ2AUvnetKWNlC6qULFcG5/qqzwnmZOMFgZYHDu1QmYlu4CQgqJdQCiCMXWYAEFUFGzHZqo0jmJL9vt284vc97hcXM90aQJD9c97VtUVA0c65Ow01bx8YvBhXg4h+DCLnC7+Ivd39Gh7KYsyzc4C6t3fjULFHh4eHh4e4InBHh5AJddrj7aDPu0gmtRYbC9lkdN11FKudDrFu971eurrG/nyl29DUY4sc5NIfhr4Lvf7fkVUxgi6IXrVHvqDvciCZIN1Hvu03filf15cgp8AEpd+7dBRYjBAXCY4zzxSJG1MHUQgcKQzVyRESonllqnR6ymJp3FwUFGRuAhZKZ/hwzfXhitcVKnOcy2rUkfiHnX+SsVli93GdmrdelRUVKnS4DZxUNtPjiwzSpKAG2RQG2Ch00lMVpyHFhYC6LaX0eUs5ef+H9Iz63KOu1VcX3ozS+2Vp/FKHaFg53ClMycEH8anBshaaVzpYAuHH/r/h1FliHg2TtHOYwqTZ/2b+Lr7ef64/Bc0Bltf0Plfas698CK+/rUvMN43TnjxETFO5FWsnI3eMH/5fyDqJzWcojhTYMQ/RtrMUBusmhfp0BVfyN6ZA0wUphjJjyOAvFXAciuubUPR8ak6U9o4ju1QyhVI+ONMFqfJWXnivhiL4u18dNV76R0eQCjQ3tSKpr6wr5J8skAxVUTzqeiByt+EEVCQrkspUyKfzJNYEEczNKySTTFVxB/xHRV9IN2K2+fwQK/ihBWV7eqRv2HXdjBCBkIRlLNlzIJFXXc9xWQes2ghFIWq1sScMzkYf/Hu8fx0AQQUkgVm+mYwQgbh2jDNqxpZfOliCskiu+/ZQyBemYTxR33ofh2rYJGfyhGuCwMCI6QTqgkiXXBMG6tko/k1wjUhqtqPrBIwQj4m9k2SPTvHqhtXsPfe/SQHZnAdl2AiwMLzOqjrrj1mX30hH63rz1wRwOOxffuzzMxMc+mlV7/k5/Lw8PD4fePAAbj3XoVgUNI6+4iSycCjjwoWd4HdljqqIJ02W0jOlXJuFZYjKyYABLNPs5XvSV0xSFvJU+qL5ZoUnQLjpSE0oSOEguaqSJ+LFJKkO0XGnEFTdRRFpSSLHMhuRxEq7mzhuMMcLiT3/KJ3f8j48LHcfmGrpjw8PDw8PF5qXh3fxh4eJ6BMme8FvsUm4zFMYQKSkIxwdem1XFe+cU4QdhyHj3zkHUxPT3L33Y8Ticx3yw0rg2w2nqDBaSQhK0vYE04Vh9QDPOj7NeusjQTcII5wjuqDRGJI31Hbj0WNv4GEUcO0OUFQDSGEQtHOEVBDdPvWssfZy15tJxJJQeQoigI+fOhupf28yOHicq554TwxeIW9mk2+R8iKDBFZubaUSGJIH932crYbWyuZpbMstpeSFzl61R4G1V40aZBwq4i6ccqihIXFsDrIQruT5fZqwjLCEmsFPdo+XFwWOYupdiuilINDmTJ+/KdcYVlXfPBbojiA7VqEtDCKUOnV9jKo9ZEoJsjaKXyKH78IIB3JoUAPz6U2ETeqCWih45/oFeKCjZeiGzrPPLGZRYsXz223fSaaT0WWgeetPDWLFppPw/HZDBcOkTanGci5BLUgcaOKkB7Fcm1W1izjliWvZyw/gSMd7un9DYpQ0RWdkdwYQS1Auaby+o/3D6G3RwkbIQJaRRid7Jmi55GDZMazCCFItaXourTzBblHzVwZI2RQTBVxLAdVV5FS4lguvrCPmvZqMqOZuUiE5lWNFFMlsuNZ4i1xhBC4jktuKk/z6qZKNAJQ3V5FsCpAZjRDrCmGUARWyaKULdO+sQ1FrbzHhIBwdZDqtgRWyZqLXUgNpSu5wXkTRRFzQvXpUsqUeO4n25gZTFG7qJZyrkQ+WSQQD7DulnUE4wH6nx6cd4wRMIjURxjbPYZZMCkVyuSnCxhBg/oldQQTQXKTecxCea4Y3fPdTkZAZzpbYu+9+wjVhIgviLHg7AX4QgbhmhC6/5UvoPjgg78mGo2xbt05r3RXPDw8PH7n2LNHUChAW9uRbdEoTEzA9u0KaxcGyFipeccYig9VaAihYLplfIofQeX5ElcQ8kfmJoYd18avntpk58HsHkp2AVXolZgFIFwIkrNylKMmKbUiTKf1NBE3wkJ3MaDgShdHOpSdEj7VXylAaqUIqEGqfS+9K7ho50mbSYQQxI1qfKd4vR4eHh4eHq8WPDHY41XP08aTPOF7iEanZU4EnVTG+bX/F3Tby+h0KtXu//3f/56HHrqX//mfO1m4sCLO2diMqIO4uEwo4+REhmY531lX5dYwro6SEWlW2Wex2fc4MyJJQlYhkUwoY4RkmGX2qS3X0xSd5Yl19GX3M1kexXVtavwNtIW7qNJqeU/+I3w2/H/YZDyKRBCVcQoix0FtHyEZQkVjjXk2txTnZx0vt1bTZi/kSeMRHOEQkRGq3TouLl/JGvts9ll7uM93Fwm3Gh0dn/TR6DRT7dRyQ+lNNLrN5MnxoP9XjKujqFJjjbWem4o3E5aV5fpxmZgrLgIVEfgJ42EeNR4gpSSpc+u5sHwFG6zzTlpgo8pXS1SPkzGTRPUEilApOQUcadEUakcIgYmFjU3ZLKIKFWVWNFaliqKq5N0syfIkzachBo/kxtid3E/ZLtMUbmBZdTc+1Tj5gSchWUrxzPg2BjJDRH0R1tSuoGvpUvr3HSJrpfGpfizXxPQXaVzWSGmbQ0kvVTKDCybZiSyNqxro0/bg0xwaQzWM5qepDUpMt0zIKpI2M1zWeiHLqrtZVt1Nb3qAsmPRFKrGUHWq/AkmCpOEW6oQhsq+7btobVrBVW2X4lMNUsNptt+xg3KuTKgmjHRdxnaPU0wVOftt6/BHTq+omD/qJ9YcAwnlfBnpSpgdvtYuquHc959DbiJHOVcmEAtQ1Z5g+lCSnXftYvrQNEIRSAnxljiLL140J4r6Ij6WXrOE3ffsZboviRAgVIWmFY20bWid26eqrYqRHSP4Ij58IR9SStIjGTSfxsDmAXaM7kKogvruOhae33HaTuHR3WPMDKWobq+ay+FNLHBJ9s8wsW+C9o1tVLUm8EV85KfylbiL4TTpkTSO5eA6EnvawQjpgGR42yiR2hCx5hhdly4mOZCaVwwOID2aZmYwhWM6hKpDOKZNpD7C6ptWnlAIdh234jb2qfMyhl8KHnzw11x44WVeXrCHh4fHMXCO9gwAoChglgVNwVZSqWkKs0YARzoU7Bw1vgYsaZKxUpSdIi4SgYJP9RHR40gpKdg5FKFSfwoxFiW7wERxmKiRIGIkSJvTmE6JoIxQEmW21ewgQwqhKQipcF7mQqrtGkpqEce1qPM3MVkaJW9nEUBAC7E4uvIlnYCXUjKQ66E/f4CSU5w776LIst/ZlWAeHh4eHh6vBN5IzONVzzb9GVSpzQnBALVuPbu1HezXd9PpdLNp0+N86Uv/wac+9Y9ccsmVAOzXdvNz/4/oV3tBSPxugLzIU6I0rzhZURQIuEH8MsAaaz2Xlq/mUeMBxsQIIInKGNeVbqTTXnLKfQ5oIZYm1rLIWYaLW3GAzAphjrBxhcsF5mVUu7WYosx+bQ+7te00Os28qfgOXl98C+Hn2UrLlPlB4Fb6tIP4pJ+cyFKixDJrJa8v3oKKypXl6xhQe+nR9iEQuDjEZIK3Ft/LOeaFc22dbZ3HmDqMLg0a3eYTirr3+u7ijsAP0KVOREY5pPZwKHgQs1jmQvOyo/YvU2ZA7QWglQ6WxteyN/0cGTOFi4uh+GgLd9EcrNhpFjhtVDs1DOt9JMxK5WyJZEafobXURtiO4HKcUdcx2DT6DD8+cCfJUgoBaIrKypplvHPZzYSNFz64Gc2P840d/0NvegCfamC6No8PbyJaX0N+ModP8VfyoBWDjshSmq/t4IB6kPG94+Sm8uh+jaaVjdReEmef1UdNsI5rFl7Evb2PM16YpuyUiBpFLltwGZe1XjB33rAexK/5KNgF/FqClTVL2TkFY/lx9JYw0z0jvKf1I1w6e8zIjhGK6SLVHdVHYgmCBsn+Gcb3TdJ2mhED5bwJQDFdRPNp+BM+FE1FqIKuSzsJJYKEEsF5x9R11bLh7euZ2D9JKVsmVB2kfkndUUJ0w5J6ovURJg9OY5csInURqhdWzQmdQgg6zm1jdPcYBx85hJSgBzVC1SEUVWF83yShqiCuK+l9spfseJb1t5x1Wi7h9EgGVVPnnMhAxX2sKqRHMkAlU7jzwoUceKiH0V1jzAymEKIiVoPAFzJwbJeaRdWUUkWEIlh90yrql9Qx8MwQO+/cSWGmSCDuxypajO4aRzNUGpbVoxka0q0U2Ot59BDrbl57VGailJKRHaMMPD1YcSCHDBac1Uzr+gUviSicTE6zdesW/v3fv3LG2/bw8PD4Q6C9HTRNUihAcPYr0LKgXBYsXerSGGyj6BQZzvcyY06hoBD31dAdXYUtLQbzh0iXkxiaj4AaImulKNg58mTxqwEWhpdS4zt5zn/ZLWG5JiEtiqZoBNUQtrRwcYnbVcSK9WxWHiVermZZcQWLixXzhOmWCalhlsXXkbVTZK00qlCpMmrxa8GTnPXFMV2e4GB2N6rQSBg1gCRnZ9if3kFIixI14kClCHHaTGJLi5AWeUFF/jw8PDw8PH6f8cRgj1c9pijPi0s4jKCSeVso5PmzP3s/69adwx//8Z8DMK6Mcmvwa0wqEzQ7CxAIhtVBksoUu/XtLLdW4cNPWqRIKUluKL15TiD+o+LbWGdu5JB2AAWFLnspC5z2kzphj4WhHh0t0aceIi9ytMwWagvIIKutddQ7jRgYvLX43qOu9xnjKZ70PUytU89C2YWGxrQyxV5tF4e0/XTby6l16/lI/i/Yqm9hRB0kLKOstNbQ5lSKsKXEDE8aD7Nd34qOzlrrbOLlKoIc+8E/LVI87LuPsBuh0a0UtKqmlgG1l/t993C2ed68Ynbbta383P8DBrV+ciJDtVvLHxXewfnaRaRmH+jDWpSQfqRoWEJWcYV5HbdpX2WAfqIiRkHNE3EirE2vwxAVt8ypMF1M8tOeuzEdk6WJxQghKNolnp3YTke0jesWXnFK7RyL+/ofojc9QHeiE1WpvDbDuVEygRIzu0bZUHsJZbeErhgYSuU1X3XjCrLjbRTTJXxhH7HGKAOFHkhLVKHRGK7j5mXXM5AZJV1KURus4vULb54nBtYEqllZs4xHhp5EV3TCeoilVV0oQiW6Zi2Dj+3mzd03zh2TGc+hB4x5bSiqAgJK6eJpXfNkzxTb7thBOVsmEA+Qm8pTzpWpaq9iyeXdLLpo0XGPDdeGCdeefOAWTARpW3/s9590JUPPjeCYDv64H6fsIKXEzFdiOOq76+eu0x/xVdy8ByZpXtV0ytfoCxm4ztE53K7j4gsfcZO3n9NGvDnGtp/voJQtUdWaYLoviaIo6AG9UlAvb9KwtIFkf3IuI7llTROF6TxDzw0z3ZfEKlgIRdC8uhnNqHy9C6VSYG9mMEUpXSLwW+7m4W0j7LxrV0UM92tM904zvnec1GCKNW9cfcYL7vz0p99FURSuuOLaM9quh4eHxx8KK1ZI1q8XbNoEhiHRgwUKeY1ly3ycfbZEEQqd0WU0BVvJWRk0RSNmVM9FZlX76+e1Z7plUuVpJC5RPXHKzly/GkBXDEy3hKaEQYAmdAp2jqgS5zJuYGVyJZOlMcJ6FKlI8k4Wx7VpjrajKipxo5q4UX3G79HxmCyO4EibqJGY3SIIazFmzEmmSqNEjTg5K8O+9DZS5jSudDAUH43BNhZFl82LHXs5GFYG2a1vpySKNDutLLdWz6vx4eHh4eHh8VLhicEer3qWWavYrm3FwkSnItDkRBYdnQ67k8985m8ZHR3mttvuQFUrD4nP6VsYVUZYaq+YE3E77W5yIoNP+hnSBrCxCckQF5Yv54rSdVhYFEWBkAzT6XTPxU+cabTZP+vDBUQO/3tKmcHG4mf+72MLi5iboN5tZJm1ivuMX3JQPUC/2ouKSpPTwkK7iwltlH3abrrt5QBEZJSLzMuPOmdapPh66Avs0XYSlmEcXHZp29mv7eFd+Q8f88F2XB0hpSRps+eLfjVuHZPKOFPqOC1OxeE7pAzwP8GvM6QOMKNMkxNZ9mm7eU5/hg/lP8EtyruPmzV8Rfk6giLInc4PmVTGaTWXsTS7jPpSPU2hVmJ61Snd130zB0mWZuiKH4kjCGh+okaELeNbubbj8hcknBXtIrum91EbqJ4TggEaQ/XsShiMjQ4jHeaJ3FBxtkYbovOyen2KHxC40kERKj7VYHGijVQ5TI2/4aj+CSG4qfM6ynaZXdN7Gc6N4tf8XLrgfOquuYKP/vTtDA8P0NJSeR1CVUGmDk7Na0O6EiklvrAPx3U4lO4nVU6T8MfoiLbNu6bDuI7LwccOUc6Vqe2sQQiBVbZJD6fxRXx0nNuGZry0A7LkwAzD24aJNUbxhX1z/Trw8EEC8fkV0FVdRbqS/FQes2BSTBXRg8ZJYyPql9Qx8OwQ2Yks4ZqKeJ2dzGEEdeq6jmQmCiFItCao6agmP1UgUh8hOZDicOkdoQgcy5krviOUI4XyllzVTcvaFnKTOTLjWfY/eAB/5NQGko7l0PdUPyDQfCqTPVNYRRu7bPHcT7aDIlh948p5zuYXg5SSb3/7q1x77Y3U1Z3clebh4eHxasQw4G1vc2ldNkFP8gD4UyQSgu6merRgN4cLBgS18Ck5Wg3FR13g1CcyD+NTAzQF2ziU3YtE4lP8FO08JbfAosgyglqIpfG1qJkdJMuTFO08PtVPZ3Q5TcH20z7fmaDsluYiwQ5T+T4XWNLEkQ57UluZMaeI6nFUoVFyCvTn9uNXA7SGO1+2vj5lPMqPA7czI6YBgYbKSuss3lX40Fy8moeHh4eHx0uFJwZ7vOrZaF7ANv0Z9mo7CcggDg62sDnHvJD0oym+8Y0v8Q//8FkWLeqaO2ZKmURDm+fmFQhibhXLrJVcbl5DTuSoc+tpdFp4xPcbHjceIqtkqHFqudi8knPMC1+QG7hEiV36NqaUCSIyynJrNVEZIy1SSCRd9lKq3GqG1H4WOO0A7NZ2sEPfStSN8X/D/4gpTGrcOlqdNmrdBrboT5BRUtS69Tg47Nf2kBM5grP342RsMZ5gr7aTxfYSdCrL6IuiwLP6Jtbr57LO2njUMQEZxJB+CiKPjo6GjiENiqKIgY+Ae8S58qyxmVF1iBllmqIoUu3WUE0tI+ogPwjcxlJ7JWdZG47ZN4HgfHkpa5z1jGT7SZoT6IpBfbyFxuCCUxZwbddCSo56zTShYTomrnRfsKPkWD2QSAI1UVzXZWRkkLa2hSdtp9pfT1SPkzKTRPQYilAo2HmEUGg6TlZezBfl/aveTn9miJlyirgvRnt0Acnmiui7efMTc2Jw08pGRneNkR5JE64N4zqS7HiGSE0Yvc3gK9u/xZ7pA5iOiaEaLKvu4i1L3kjCH5t3zmK6RGYsS7g6NHf/dZ9GVVuCmcEUmbEstZ0vrTMmPZLBNp05IRgqLmdf2KA4M9/lLGVF8J46NM3w9hHKuUrxtvoldXRd3oUvdOzM6ERrgiVXdNHzyMGKoxcIxPwsvriTRGv8SPuuJDWcppQtU5gpEK4LEaoOkhpKo+kq0nXxR/1kx7MEq4IkFhw5VghBpC5MpC5MzaJqxnaPkx3PEmuOVQrsuZUCe00rGvDHKk77zGiGkZ2jTPfPMLJzlHBNiORAErvsVLKDTRu7ZPPsD56jkCyw9OolxJtiKNqLE4Ufe+xBDh06wH/8hxcR4eHh4XEiTDVJYNHTdLWXCGphXOkyZfeyaybH2urz0JSXpxhoR7gbEAzlDzFaGMCSJn4lyEx5moniMHWBZlYlNpK3M1iuSVCL4FOPrh/gSpeUOU3ZKeJT/cSNmrmCdmeSkBZhINczm5Xsx6f4Z+PAJGEtykx5irSVJKYnUJXKMDigVeIvRgp9tIQWnrF+2a5F3s6iCpWQFp33vDmlTPBT//cwMVkyaywpUuRZfTMdvk6uL910Rvrg4eHh4eFxPDwx2ONVT0zG+UD+T9hkPMYufRuudAnJMMFMgI/9+btYt/Ec3vvej847psatxcbCwZmLXJBIyqJIs7uApfbKuX1/5v8Bv/T/lKAME3YjDKoD3Bb4Ojb2MXNxT8S0Msm3g19ln7Ybl8ry86gbo86tZ0qdRCLptLs517yIx30PsVfbSVZk6dUOUOPWoqLi4hB3ExRFEZ8M8JT+CEVRnBVnfaio+KSPYXWAFqeNhc7ik/Zrj7YTvwzMCcFQEXttHPrVQ8cUg5ucBfilnwd9v0LHwCf9VDm1aELl8tJ1VMuaI9ctpsiLPHmRo9qtnRNkgzJMURTZoj9xXDH4MCE9wuLYqRXpOxZt0QWE9CAz5RRV/sryQ1e6JMszXNF68TEdsKdCQAuwvGYpDw4+SsKfQJttZyQ3TlNLpcDLwEDfKYnBumKwLLGOA+kdpMwkrnQIaCHawoup9R/fFaQIhY5YKx0cEYxraupYsmwFt/30G9jLg1QFEqyuW8by65bS88hB0qMZhCKINcfovqKLOybvYevEDtoilfuUtwo8M7GdoBbgXctvOSpaQlHEUREKruNWXLC2g206L6k7WNUUkBWh9/l980f8lHNlRnaM4DoSRVWQgFBg6uAUgUSQcE0Yq2jRv3kA23RY84ZVx5xUEELQdnYrdYtrmRlMAZBYEJ8X1eBYDrt/tZfh7SOYOZNCqsihR3uJt8RQDZX0WAYjqGMWTEJVIbov75onYD8f3a/TfVknO+/ew/ShaRRNwXUk0YYIiy6qONonD06x446dFFIFhKqQm8qTGc8ihECoAjNvoigCIQSldIntd+xkYt8k9UvrWXp1N4mW+Au+57fd9jW6upZyzjkXnnxnDw8Pj1cxI4V+Sk6RhFFzJKNf9ZEyp5kqjdEQPL2M/heKqmgsjHSTKk+St7Ik9FoMxSBrp9idehZFaNT46wnrMYp2ntHCAHk7g08NUudvJGokKDkF9qSeI1memFu5lPDVsjS25owVk3OlQ7I8xWhxkIKTI23NoAudoBbCUANU++qo9TcxXR5HSndOCD5MJQ7DxJE2inhxRYGllIwWB+jL7qfo5GcznatZHF1BWK9Mju/TdpNUpumyl8490wYIEJUxtuhPcF3pxhdkGPHw8PDw8DhVPDHYw4OKIHxV+TUss1bx7eBX2KPvZNdntjE9Mckld1xFj76XnJJjt7YdC4uUkmREHWKPvpNGu5kWpxVTMal2a1lvnTPX7rQyyeO+B6lya6h1KxluCaeKAbWXB32/ZoN5/mllg93l+xm7tG0ssrvx4SMvctznuxuA881L0KTGM/pTNCotvD3/fibUMR41HsARNh32Yp42niDmxkEIMqSZVMdwhYuKSrVbS1KZQkVDIimIPO32IpZZq07aL7/04wj7qO0SicGxH6qf1TeTVKYwpB9TlCkqBSaUMVZYa3hN6fXz9m1wGyiJilvz+dEXDg41Msq0MnVU+y+GMWWEzcYT9KuHqHKrOcvaQHdkORc0b+T+gYdJllP4FIOsmWNBtJmLWs57Uee7svViBjJD7E8dRBcatrSJGGGuW3I1d/D/KBYLp9xWRI+xpvo8clYaRzqE9Si6cvoDm0PpfqLrmnj6e/eT2NONGtR5KPQY71x2M+e97xwyY1kURRBtijJZnmZ3/z4agw2E9EpGb0gP0hSsZ8fUXiaL09QFj4j7/qiPmkXVDG4dxggZqFolhmGyZwrHctjxi13oPo2G5Q0sPK8dI/jiBmbHorqjCn/UR24iR7guXImqKFqgQCAeIDWcQboVsVrVVfxRP6HWOJHZrGLNp6FoChP7J8mMZYk1Ro97rkA8cFRW72GGtg4zsGWQUE2IeFOM+IIYo7vGyE8XqF1cS7AqQKg6TKQuRH1XHdETnAegYVkDwUSQwdkcYV/IR+v6FqINEVzHpefhgxSzJao7KhmOZq7M2J4JXNtF82uomopdtpGOi9Aqor1ZMpnpT7Lj5zvZ+I6zZ4vbnR6jo8P86le/4P/8n8+d8RxiDw8Pjz80slYKQ5mf0a8KFYmk6ORf1r4ky5OkrCTV/ro5R7Kh+pkpTzGc76XGX0/GnGHnzNPk7DSKUHGly3C+l+7YaqbKo0yURojqcXTFwHYtpkqjHBAaKxMbXtR3QtkpMpA7yGhxgIniMIpQqfc3U3QK5OwMOTtLu7+RFYn1GKqPgBZCFRqmU55Xd+Ow8K6JF++4niqPsTe1DQSEtCiudJgsjWI6JmfVnF+5B9jwvDi3w2hSwxTmvKg3Dw8PDw+PlwJPDPbwmEUiuSPwA3q1g9TsqWXgv3o5/x8uJduV5p/4awIEkEhGlGFG1SFCboSQG2ZQ62NEG+Jc8yLeUfjAXDQDVArNTYhRfCLAqDJMkCANTjNVbg1TyiRJZYpGt5lxZZQ92k4sYdLqtNNpLzmqyFtapNipb6XObZwTkCfUMSQSHR2BQkJWE7MT7NV2MaQNcH3pJtIixYg6CEgcXGaUJFklQ0EUKIoCPunDFZKV1lpm1GkmlDGQgipRzWtKr5/n9j0eq6x1PG08SVqkiMk4EsmkMk5Yhum2lh+1v4vLw7770KXB1eXXklSmKIki7qzAm1KSVDtHxMOzrI00O608oz9FkDAKgpQyg18G0V2DDufEGW+OdMiYM7jSJWLE5oqwHYs+9SDfCH6ZYXWQgAxiihKbjMd4Q/Gt3NR5Ha2RZp4Z30bWynFx/HzObVpPQ6juuO2dCg2hOj6y5j08O76dwdwIUT3MqtrlhEoVEVRRTm/JoiKU5xVPOX1c6fLzg/cQOasR939c5J4s3VdsoCd1iJ/13M2frfsw1e1HspbzuQIlp0xNYH7+ckALMFNOk7fmi9lCCBZf0klhpsjMwAxSglUwyU3nCVeH0PSKIHngoR5ykznO+qM1Lzqi4LcJ14bpunQx+x44wHRvJa9P0RSCsQCKUKjvqsMsWpWoBcdheNsIcRmf14YRMshO5CimiycUg5+PYzlMHphkZiiNogqGtg6jGspczq8RMGg9awFTvdMsvaqb1vWn7/4qZkpMHpgkP1UgQ4bU4AzTh6ZpOauZzFiWSE0Yx3KYPjRNbrqAEGCbNq7tYoQNJBKEQNouUhcUU0VqFtUwM5hifN/EC+rTrbd+FZ/Pzxvf+NbTOs5yYTwPmgL1QfB0ZA8Pj1cDfjVIxpyZt+1wbrx+gmeYl4Kik0dK96hoCp/qJ2ulcF2X3uw+cnaaiBan7JaQuJTdIvsz27Fdm5AWmZuY1hSdkBYlWZ6gYOeOqolwqtiuxa6ZZ5gqjeFKF9M1UVBIm0nqgy0VkdpKowoVv1qZqI7pVdT6GxkpDhCQwbnMYAWVltDCMzJZOVoYwJE2CePwc6xGXKkmbc4wXRqnIbiAdmchIRlmRpmmyq3s5+IyrUxxZfn649bB8PDw8PDwOFN4YrCHxyzjyig92j6anBYe+cf7CTdGOOsDGxiS/ewwHuc88xKq3Gr6tEPE3SpsYbHUWkHcraJX66HBaZoXDwEVh+kh7SCusNGlDxeXAbWPFrsVUNit7eAR9X4260+QUpIIBD58bCifz83Fd81zDVvCxBY2fveIwzArMnNOXnc221dBwS/9DCp9ALQ5C1FQMKSPsigyrUwhEDg4+IWftEjjn3XnttkLaWIBfdpB6u0GfPjZpD9GlVvDIqfruA+nZ1kb6ClfyRO+hxhhEBBEZIRrSjccs1BeQeQZV0ZJuNWoqHOuaYC92i6mlAkWOUcymuvdRv4k+1d8OvoX9Gj7sIQJCArk0VCpco9fBG6mPMmBzE6yVhpXugS1EO3hLpqC7Uc99Esk9/ruYkQdZIm9fO56h9UB7vHfwWprHRsbKz9nmrgvxmWt85fPDw8PAswVLny5GM2P05ceYNGCxRxY2cneh59h5ZXn0BxuYjA7zFBulPboEUGwLlhD3BdjujRDU+hIYbDpYpKg7UcZExRlcZ47NlQdYv1bzmLywBSFmQJDz42gGCp1i2vnXhdf2MfE/kmm+5LUdh6ZHDhTtK5fQHxBnKmD07i2Q7Qhyv4HD8yd+/mF5YQQFKbyxJuO5B+bBRPdr+GPHJ2PeCzsss2Ou3YxumMU15FI6ZLsnyFUFSLWdCTjt5QpY+ZNCslTd4QfppgusfuePZQyZRKtcYQiKGfL9G8ZRPNrCAGu65I8OENmNIMRMog2RUkNpLAtp/J728W1XRRVwbFd8pMFDj3Wi6IKdt2zh1BNaN5kwMkYGxvh61//Iu95zx8TiZyaaA7w3ITgl32C0ZxAVaAzLrmx06Xl5PWSPDw8PH6vaQgsYKo0Rs7KEJrNDM5aKUJahBp//ckbOINUJtCPFKc9jOWaxIwqym6RlDmFBEaLA1iuBYCKSlak8atBAmqQnJUmZ2VxcTAUHwIFS5onPLeUEsstAxURfF7ubmmM6fIEMaOKvJ1DU1T8SoiikydnpUn4KpEWpmsicRGoCCFYEl+DXw0yXhrCck0iepzW0CLqThCndTrkrexRpoPKfZOkzGlMt4wrHdaKs3ky9ChJbRpDGuREljang0vKV56Rfnh4eHh4eJwITwz28JjFFpUM4OQzUxy4Yw9Xf+W1aH6NjEjj4BCUQbJKhhJFqmUtMyLJlDJBm7OQDnsR4+poxdHq1lbaw2aL8SSG1DGBiIxgYzOpjNNv9NLuLuTbwa/Qo+8j7Ia5wLyMkAyTFWke9T1Am7OQS8wjD4QJt5oFTiv7tD1E7RgCgV8GKIoCVW41Efd5IpUok5CVZeArrbWssc7mceMhkmIakzICgYJKXuQIyAC1bgNZkWFam0RBJeFW4wiHb4S+hI2NHz8rrDW8pfAeonJ+MTAAHZ2bi+9kvbWRQ2oPKiqL7SW0O4uOuczNLwNEZZQpZZIqp3pue4kiGtoxqyivcFbzL+kv8sn4hxlS+4nIKDG3ipAMcZ//l3TZy45yCJfsArtTWynaOcJ6HEUI8naOfekd+NTgUQOqrMhwQN9Lnds4T/hucJrp0fbSq/Ww1jr7RG+jM4qUlZgC5QXmEb9QXOkipUQRCksuXse9X/oB2ekUWsyPKyWunJ/1GzHCXNxyLj/ruZv+zBBRI0wyl2JqeIp1yVXsfGIXvrCPBWub6bxkEapWuR4jaNC8ugkpJcPbRghVBecN9PSAjnRcclO5FyUGmwWT6d4kZtEiVBWkqi2BolZe32h9hGj9kffbwccO4Tpy3vFCEQTiAWzLIT+dxx/1YxUtspM5mlc2Ems6NYFzdNcYI9tHidZH0AMVh1U5a5IeyZBoi6PpGhMHJikkC1glmwOPHARg8WWdc/fsZEwdmiKfLFDdVoVQKvfSH/VTypZJ9s0QXxBnZPsoual8JaLDUDHTFon2KgrJAqV0ESQomoLm07BLNkKpZAkDDG8b4cHphznvvedQv6SOif2TJPuTCEWhuqOK2kU1R7m4P/vZ/4PfH+BjH/tfp3QNAAdS8J09CgVL0hCS2C5snRDMlBX+ZK1L9Mwnh3h4eHj8zlDrb6Qzupz+XA8pcxqBQkSPszi2cs7l+nJR5asjqsdJm0kiehxVqBScPBJJU7ANgcB0TdLlaRShEJjtn+mWKTp5/GqQidIIZadYyadHkDVT+LQAyOOfN2ul6MvuZ8acmu1HLe3hbsJ65Ts3Z2eQVPJ/dcVAoOBgowiVklNESknZKVIfaJknYuuKweLYCtojXdiuhU8NnNFidiE9Qq6YIcSRZwvXdSjYeXqz+2ZrTAiasnVcVnUFU1VJ8kqObnsZ55gXUu82nrG+eHh4eHh4HA9PDPbwmKXeaaLRaeb7n/421Utr6bx5CXmRI6lM45N+Im6MtDKDgoozW5n4MGVRRpc6PnnEITiiDjKo9nO2dR5b9S30qj1YwsLCRAJ1VgMhGUaRgpIosVvbwXrrHCIyhk9O8bTxxDwxWEXlytJrGA4NsU/bRdSNU6KAhkbIDaOhYWMzog4SkVHOMisF1fz4eVfhQ0yIMTbrjxN14yhCQZMqPlmJvojJOG8vvB8NHV0a3O/7Jbv07XTYnQQIMKIO8VP/93lCf5iLzCs42zqXVdZZ84ReFZVueznd9tGxEL+NhsZ55iV8P/BtJpUJqt0aSqLIgNpLt72cxfbSYx43rPdT7dZytnUukkpWsUSyV9vJZuMJOorzxeDJ8hh5OzuvAEtEjzFTnmK8OHiUGKygIKRAMl/srGS3KajypRdlpZSM5Mc5lO5jcmS00q/TjIl4Po7rcCB1iL7MAAoKixOLaI8uOOFSyIZQHc2RRvrSgyw+dxX3f/XH7PzNJuqv6aYp3EBz+OiByhWtFxPQ/Dw6/BQzxTTKMKwfWc2GxFqMhEExXaLn0UMYYYOOc9rnHSuEwBfxURouzdvuOi6SSnTCC2VmYIadd+0mM56ZFTlV6rtrWfHa5cfMIm5c3sB0bxKrZKH7daSU5KcLxBfEqe+uJdk7Q2Ysg2poLDirhSVXds3dS8equPNV/djvk4l9EyiKmBOCAWoW1ZCdyDKxd3LWFVxC0zWqO6oIJgIcfLwXf8xP+8a2U7peu2yDZE4IPoxmqJgFk6XXLCHZN8PkwSmMkIHIgx7Uqe+qxXVd+p7qx7FcXNvBMR1QKm1ZJQvN0PBHfMwMpnjqW5tpWtXIdF8SXIkE+jf1s2D9ApZds2RObN+3bxff//63+fSn/4No9OiJpOOxaVSQNqE7fiQaIqRLelKwY0pwftMJFAQPDw+P33OEELSGO6kPNJO10ihCIaZXHVX47OVAVwyWxteyL72djDWDKx18aoCF4aU0BBYgEGhCo+yWiGoJEJVnGUklWsJQfKTLSRShYAgfjnTQVQMVlfHSEHFf9VHnLNp5diafJmunCKiV5SAjhX5yVoY11efiV4Noio6cLQTrVwOEtChZK4Xj2hiKj5Q5haH4WRA6dgFeXTFeUE2Fk9EUbGO6NE7GnCGohXGkQ9qcxnLLRPQ4Eb2yEsh0SohpwQXOZTSH2s94Pzw8PDw8PE6EJwZ7eMyio9Pw6yamHppg0Y+7+HXoFxRFESEFYRkhL3Ik3GriboIpZRyBSq3bQJECU+oEV5VeM8/R6uICEpMyrnCJyypUV2NCGcMWFoPKAG1uOyoaETfKlDpB1k4TlXEMdIri6CXiK+21fCj/CR7XH6ZfO0S7tYjV1noOaHvZq+3AwE+dW89rSm9gsbNk7rigDJGQVdS4tcRkgqA8Ur15VB3CxKTT6abWradPPUifdpBWp50AAZLKFLu17aTEDHkti4rKVmMzNxVv5orydS/4fl9UvoKUmOEJ38Ps1/bgw8cyazW3FN913KJz02IKAfNEd4EgKEOMKkNMKuMURJ5ap4EgQUynIi7+tvCpKRoFJ4+JiYuLn0p7YRlhubWah3z3EXer0GYjOAbVfuqcBhY6i1/w9Z4KrnS5p/c33D/wKBkzR36skhe4Y3oPF8hLTzvLznItfrz/Th4b3kTZqSyzDOshrm6/jGvaLztue7qic33Hldy2+4ccKg3SdvFyttzxADdevpTrOq7Epx79+qiKykUt53Fe0waGeobZ+cguYrUxdH9F+AxVBXFMm8Fnh2ldv+Aop2vz6iZmBmYopIoEYn5cxyU9nCZSG6Zm4dEDxVPBLtvs+uUeMuNZEi0JFE3BLJiM7BglWB1iyRVdRx3TvLqJZF+SsT3juE5lUsAIGiy+eBEd57ZTzpUpzhTRgwah6oqTOT+dp/epfib2TwJQ311L+8Y29IBOaiiN67rEm2K4xM2qRQABAABJREFUjjxKpPVHfMSb42iGynRfklBViGhjlFhTdLaYm8PQ1so9OyywnohIXQRVrwi/h8VuKSXFbIm2JW0kWuKsf8tZFFIFpCMJJgKEakIYgUr+cWJBZSBvhAymD06TTxZwLAdVVwnGA/ijfiQwM5SinCvTvKYZ47DLOVdm8JlBajtrqO+u5Gj/0z/9Na2tHbzjHR/ElXAwBQdSAkdCR1SypKqSB/zbDOcEIU3Oywiu7CdIljwh2MPD48UjpSRrpUhbMygIEr5agtrvVg6NTw3gU49dgPTlJGokWFdzAWlzBkfahLUofu2IQ7nKV8dYaYiSW0BIBSklmtAI6hH02YxgcLFlRaiNGnGEVJgqjdMVlUc9j4wXh8naKRLG86KjVD8pc4qJ4git4U5qfPX0qwGydpqIFqXaV4crXfJ2Br8apM7fxILwIhK+2pfzVlHtq2dJfA19uf0U7BxCKARm31eHhWCoFOErOgUmSiOeGOzh4eHh8bLjicEeHrNIKfnhP95G6znt+F8TQEqXeqeRkBtmRBtiu/4M9W4jIRliCjGb75XBVMusNTdwTemGee01O600OQt4xLifMmXqnUZKosQ4I0TcKGWliCUtVFSkcHGwsYSFK11SIsX51qXH7OdieymL7G4G1T6+H/g2fdohShRR0VhpreXthfeTkEfnecbcBHFZTV5kK9nEsznBJYp02IuocevIiSxJZYqyKBOQlYf8Hm0/RVGgzq2jqBRpdTrIigz3+u5inXnOMc91KujovKH0Fi40L2NMGSUog3Q4nUcVzns+VbJ6Nh/ZnYtxkEhSSooedR+fifwtpihT5dRwWflqOrWKePv8nDspJTNihp1Vu7gj8lNsYbPEWsEV5rUscNq5pvw6htVBDmh7URA4uFTLGm4q3XzM+Iozya7pvfyy734iWogliU5GJ/sB2JTcytXpfhbF20+rva0TO3h46HHqArXEfFGklEwWp7mn734WJzrojB/bLQOwomYpH1nzHraMPcfCtzXz1Yc/TdUOwdprVh73GKgI7UbZAFvMCcGHMYIGZr6MXbJRw/Nf55bVzeSn8gxuHWa6L4kQgkhdmGXXLsUXeWHFcpIDFRdvvDk2F11gBA38UT+jO0fpvHAhmm/+16Du11n9+lU09UyRHsmg6grVC6uJN1cGcP6If15GcClbYutPtjMzMENwNhP50BN9DG0bQTM0SpkSUkoCsQDhmhBW2cY2HTSjcv3lvImiCnxxP6pPI9IQJlwbmhPLdb9OOV+ey/A9GVVtCRqW1TO8bRgjaKDqKsV0iVBViLb1LQBUd1Sx+OJO+jcPYIQqGYy5yRxmwaRtYxuT+yYIJIKousrwc8PYJRtFVzFmM5TlrKhdzpfRA0funy/sIzeRI9mXpL67jscee5D777+Hr33tu2i6wS8OCu4fUMjbIABDhfObXN7cLdF/69Lqg5KelMLzV2BU0jskMS8iwsPD40XiSpeezC6G871YspJx61cDLIospTnU8Qr37uVFSslYcZDR4gBFu0BUj9Mc6qDqt0RURagkfMeObKoNNBIvVKMIFUfaFSFYC1Owc4T1GKY0ievVSCErq7CEIDtb3O1YZO0UilDnicSKUBAo5Kw0AGE9RldsJQfSO5ksjSNxCGhBlsRW0xZejKboJ51EF+UUarYPVAM72gnHmOw+XYQQNAZbqfU3UbCzKEJlvDjMwezuo/qjCAV79v3n4eHh4eHxcuKJwR4es2za9Bjbtz/LlXdeT61dT4N7pJBElawho6Q4p3wROgZtTgea1DCVMrVOPd32cnTmC186Oq8rvZGHffdRFHlmlCQWJioaqtRxcTGkjyZnAQe0vehSJ0uacW2UNqeD881LjurjLm0bjxkPclDbxz5tNyoaq811pNUUfepB7vL/BAODd+Q/SJD5mXIr7DU85jxIRkkxoyQpiiQuLg1uM1eUr+Wbwf/HXm0XZUqMK2P4VB/1biMpJUlIhimKAkE3TEAGCcoQPdpeBrReEtbRYvCtt36N2277GoODFTGzu3sZf/qn/5vLLrvmqH3r3Abq3CNFxySSjEgjEEflE6+21vGgcS8HtL20OK0oUmFYHWRSGcOkRKe9hISsYkqd5AeB7/A25z3E81WkzGmCWgQFQcpJ8nDjAyRjM9TRiIbGo/776dUO8OH8n9PoNvPR/F/ynP40Y2pFuF9praXFbT35m+hF8tzETizHojZaGWxlJ1MAuHGVXVN7T1sM3j65GxDEfJV8PSEEdcEadif3sTfZc0IxGKAtuoC26ALoei25twzyw29/m7/44/9NKHRi51QgEUDzaZTzJr7QkYFVKVsm8ry83OejaApLruqmZW0zmbEsmqFR1ZY45r6nil22cR2JoilYRQuzaKFqCoqu4lgOjuUcJQZDJeahYWk9DUtPXqRndNcYqcEU1e1Vc2KtaqgceqKPaEOEpuWNoEB+Ks90X5JwTYjUUApVV5HSpThTxJ7ti1Uwmdw/RXY8R8OS+tms3xI1C6tRjVOLKFFUhRWvWUakPsLI9hFs02bBWc20nd1KbLb4nRCCJVd0oRoKozvGyE8X8IUNus9ZTNvGVvbff4CBp4dmc4IFju3ij/nR/RrlXBlFqywJRsqjMsGlACmhUMjzqU99hHXrzuE1r3kDe2fgNwMKEV3SNhuxnDHh0WFBVwI2NMx3+25slGydgP4MNITAdmEoJ2iOSFbVes5gDw+PF8dEcZiBfA9+NUBYqXw25u0MPZldRI0EET3+ynbwZaQ3u49DuT1AJTphrDhIsjzJ8sQ6avwNJzm6Qq2/kVp/I9PlCUJ6BIEg7+SIGnE6IksopHLknQxhrTKxarsWplOmPdx1TMHWpwTm6iYcphI9ITGe55Su8tURNRLk7Szu876TVEU9sRAsJb7BX+Eb+CVKcRKEhh1tp9j1Npz4kuMfdxpoikbUSABQcgooQsFyzbloCle6WI5JdajujJzPw8PDw8PjdPDEYA+PWW677b9YsKid4GUhEs58gbParaGkFLnUvOq0ogJW2Gt4bekN/NJ/Bz7XT1Kdxi8DpNWKEBuWERbaXTQ7LdS4ddTIepaXVnOBeSmNbvO8tp7Tn+bW4NfIiywWFsPqIEEZ4infYxRFAYlLXuS5PfANLEzeU/gIIXlEtFtjrefS8tU87nsQQxqYWERklAvKl7LJeJwhrZ86p5EAAUxKPKc/Tbe9HFOWyYksfumnw1mEhoaFecIM3cbGZv76r/+Zjo5OpJT86Eff4d3vfgP33ruZ7u7jZwr3q4f4te9O9ut7EFKw1FrJNeXX0eTOOhrdWt5Z+CA/C3yffrUXFwezaGMndSKFRpRqH2pUAyR96kH+K/IFPuT8KXXJZtLlJDYu0/EZUtEMS9yV+PDNtbtX28VTxqPcVLqZqIxxkXn5Kb/OZ4qslUNXjoifmckkesCHPxSk6BRPu72iXUI7hutGIDCdE1fw/m0++tFP8t3vfpNvfesrfPSjf3nCfeNNMeoW1zK8fYRQIojm1yimS0hX0nb28eMOKm7gCJG64zuwpZRM7JtkdNcoxVSJeEuM5tVNRBuOLuIWqasIz6M7xyhlStimjVAVkJKOc9qPmRl8umRGMiiaMu+aiqkirl1x/x52JEfqIkz3JYm1xFh4fgdTB6eRjsvYvgmCulpxHisKmfEMhZkCEwcmCdeFUXWV1rNPnPH82+h+nc4LF7Lo/A5c1z1m8Tk9oLPs6qUsPK8DM2fij/nn7sfSq5dQu7iWZN8MtZ01HHj4II5pU8yUMAI6kbowxVQRKcEq2ej+yqOEWTBRFEFVW4J//MdPMTIyzLe//TOEEOxNCoo2tD/vZYoaMF6A7ZPiKDF4SQJuWeJyT69gKCdQBXQlJG9Y7BJ/YUZxDw8PjzkmSiMgmVeMLaRFmTEnmS5NvGrE4KKdZ6hwCEPxzUVkBNUwKXOa/twBqnx1p1RcTVcMViTWM5A/yGRpBFe6tAYXsiDcSViP0hldzoH0TmbMSpySIlQaAs20HMeFXRdoYqTQR9ZKzUZMQM7K4FP81PkrdQuklOxLb2OiOEzEiKMrOmWnRG9uL5qi0x45Ogpqrr+TT+Pv+T5SMbBj3eBaaOmDBHd/ndy6v0H6Eqd1H09Gla+WBn8LI8UBNKEhhILplIkbVTQGX3qzgYeHh4eHx2/jicEeHsD09CS//OXP+Phff4phBskpOXzukaXgOSVHQAaPcqqeCteUbuCgdoCt+hYsTGrdWlQUSpRIKpO0ig7+JPfXXGBeinacP0kbm1/77qQg8nTZy+hXD80JvX1aDwvsdsIygl8EcHHZqm9hs/E4l5avnmtDQ+Pm4jtZa63noLa/UkzMXsJ+dS+PqvezxFoxF71woXk5m4xHUVCIySqmxQTL7dUscNpxcenXeml2FrDI7j5mf6+66jXz/vtTn/pHbrvtv3jmmSNicFqk6NMOIqTCImcxOZHlG8EvMawOUu824iJ5zPcAI+oQH83/JXGZwHJN/HmdKyauJCWSbE8d4PHB50i6DqYc4VBgFHFOCqu2gIXJjDrFt2Jf4Ur/9Vyfu7Fy3tAv0VQdn31EUVJQCMswPdq+0359zySd8YU8M7YN23XQFJXU6BTR+ioQ0BJpPu5xtm0zOTnOxMQYY2Mjc/+/9dBWdvXtYpOjIWaLrDiOgyVsxqqe5c7It4hEojQ1tdDS0kZLSyvNzQtobm4lGAzNO0dz8wJuueXdfOUrn+Od7/wgkcjR4uthhCJYfv0yfGGDsd3jFNMlAnE/7RvaaF7VdNzjToXeJ/vY/8ABHMtF82lM9U0ztmecNa9fRaJ1/uAtXBvCH/Mx+OwQmk9FDxpYRQvXdCrRC647W9X7hWOEjLls4cM4lgNSoBrz/541Q8UuWLSd3Urb2a1MHphkfP8ksYZoxbXdVYvu10gNpynMFGlYWs/iSzrn8ndPF6GI416flBLpSHxh37zYC6i4i+sW11K3uLJEuG39Arb9fCeldAk9oKMaGosuWohjOkzsm0IolTAHATSvbmbH4NPceuvX+Jd/+QKdnZXPCMeFY8nZqgDTPXq7EHBOY8UFPJwDXYEFYTiFpAwPDw+Pk2K55lx81GGEECDBkfbcNgeHlJjBwCAij/+99/tKzs5QdorEjSPxD0IIglqYrJXGdErzsoFPhF8L0hVbyaLoMpByXrG7pmAbUT3BdHl8NnM4RrW//rgxEXGjmq7YKg5l95A2k4AkqIVZFFk257bNWimmS+OE9SiGWvkeC2phXOkyXOijJdSBphx7dZE+9jjCtXCiiyobFBU73oU2swd96jnM5mNHtb1QFKGyJL6GmK+a8eIQjnRoDS2iKdhGQAudvAEPDw8PD48zjCcGe3gAP/jBbQgheNcbP8xd5k950PdrVKkSlTEyIs2YMsKl5aupcU9flOl0urmwfCnP6E9hYOAIh3Z7EV32MpLKNA1uw3GFYInkGX0T9/ru5B7/L4i5MUJKiIAMoaBQxsTGnlsWVxYl6p1G/DLAdm3rPDEYQEVlmb2KZfaquW33G7/CL4NzQjCAgUGd28hF5cu5wLyUHwRuo0fbyz5tFxJJvdvIG4pvIcDJi5o4jsOdd/6YQiHP+vUbkUgeNR7gHv8dTCoTKAganGZq3XqGtEGWWMvn+hJ3ExzQ9vKcvoXzS5ewc2YLk6UxVKEyVZymlE6xJNLMlDFCyAkyFRkj6Q7RVliAFtSIOjFq3Foe9t3HavssuuxlBAkhZ/95/hL3sigTd19Y/vGZYn39GraMbWX/TA8Jf5zRvgGMhhDLqrtYU7sCqIh4vb09PPfcFrZufZrnntvCzp3PUS6X59pRVZXa2npq6uqI+MIUVRNd1StqnHSo0WPUBeqwLZv+/l6eeOIRxsaGcd0jqlxVVQ1tbR2sXbuBs88+l7PPPo+Pf/xT/PjHt/PP//zX/Ou/fumE1+ILGSy/bhmdFy7CLFoEYv5jRjKcDoVUkd4n+tAMjXhzeO5+TPclOfREH2ctiM9z0MpKyCxVbQls08YxHWINEUI1Ico5k2T/DLWLjp1/eKrUddUy8MwQ2Ykc4ZrKgM4qOwhVzGUIH+6nWbSINj1vQkmIStX12VxcVVepWVRDIB7EKpqsu+UsQlWnNgg/HSZ7phh4epDUSArpQKI1Tuu6FmJNMXKTOYQQRBujqHplkN6ytoWq9iqmDk7jWA6R+ghVbQlc22Vs9ziTB6dQVEHNohqMepV3XHUdl112De985wfnztkRA0VIChYEZ8fmpgNlB5ZVHT/2IajB4vgLv1YpJfl8jnQ6RTabJp1Ok8mkyGRS5HI5VFVF1w10XccwDHTdwDAMwuHKJEl9fSOa5j0qeXj8oZEwapgqjeNKd875ars2QiiE9Yrou13bym/8dzGkDqJJjTXW2VxbuuEF10r4XUQVKgIFVzqo4shnnSNtFKEeJZifapvHmv0L69G5e3sqNAXbqPHVk7YqxXRjehWGemQiv+yWsKQ1F/NxGEPxYbolTLd8XDFYLYwjf1uEFSoIgTDTp9zH00FTdBaEFrIgdOKILg8PDw8Pj5cDb4Tj8arHdV1uv/0bXH/966mqquam0puxsdhuPMO4GCEkw1xYvoybSm8GmCuyFnFjpzwgaHZa6bSX0OwsmHWhVvLUpHTJiRxlUUKTR+ewPmY8yPeD36ZMGQFMK1M8pz/NEnsFdU4j+7SdOLhYWCSVaTR02pyF5ETulK8/JhOYojxvW6VIm0NcVtHuLOJj+U+yQ9vKhDpGWEZYaa09qTC+Z88OXvvaiyiXS4RCYb7xjR/R1bWMvdpOfhK4HYFgsb0EF5dBtZ9njKeodRrmidIaGioKI+ow48UhJktjxPQEqqLRMzOA6brU+eO0yxZ61F5KdWlcE9KFLIGgRpvTQZWsYVyMclDdT5e9jOXWaqp9NfSrh1jgtKOgMKmMo0iFddbGU75vZ5KiXeRgqg9Hury560a2Tu7guYmdZIamufK8mzhXWc3Xvvg5tmx5km3bniaVqgyMOjo6WbNmPa973R/R0dFJfX0j9fWNVFfXoqqVAdxMKc1To0+zY2o3mqKxtm4l5zSuI6DNF/Ity2JsbJjh4UGGhvoZHh7k4MH9PPDAr/jmN78MQHNzK52d3dx223+xevV6br75nSeNL/BFfC+4ANxvkxlJU8qUqGo78ncnhCBUFWRmcAYzZ847l23aWAWbxII4/pgf3IpbFmCqd3o2E/fFUdVeRdflizn46CGm+5MAhKqC+MI+8jMFhCIQiiA/XSBSG57njI43xwhXh8iOZYnNFqhzXZdiukjTigaCiTNfQX583wTb79hBMVUiP52nkC7St6mf3ffswRfxEYwHUA2NaH2ErssXz4nlwUSQ1vXzhWlFVWhZ20zL2oprXUrJ+973Jmzb5v/+36/Ne2+sqJGsrxdsGa+4fAVQcsTs9heXAew4DoODffT07KOnZx+9vT0MDPQxONjL0NAApnns11lVVRzHOWHbqqpSX99EU1MLTU0ttLa2s3r1Otau3UBTU8uL6reHh8crR2OwlYnSCDPlKfyqHxeJ5Zap8zdR7Wtgn7aLW0NfJSey1Dn1mMLkN/67mVDG+OP8n2Pwh1HJMmZUEzXipM0ZYkZVpaCZa1O0C7SFO+eJr68EhuqnVm085u98SgBd0THdMj71yAoX0y1hKH4MxX/M4wDs6EJ8mYMgWyoTswBOJSffDXgZvh4eHh4ef/h4YrDHq57HHnuQ3t4ePve5/wIgIqO8u/hhhs0BZkSShFtNs7sAG5tf+u7gMd8DZESagAyyzjqH15XeSFCeeIlXtVtLcNZ9+/xlhjMiySKni4A82v1XpMhvfL9ElRpLnE4sYXJIO4CUkn71EGvMDYwrI5RFGVtY1Lh1dNidRN0YSW2KVfZaoCLs7tN28ay+maQyTZuzkA3medS7lYfrtdZ6njIeYVgdpMFpQiIZVgeIyypWWZU2gjLERusCOI2Cx4sWdXPffVvIZjPcdddP+JM/eS8//elveHb1FvIizxK7EhehotLhLKJH28uUMj6vDYnExiEm40yXJlCEMrfs0HZtkAoCwRK3i4Kw2SNGcISNIhWW2itocdqOtCUqglOT28Kbiu/gZ4Hv0aPtRQJRGeP68utZY60/9Qs8Q+yY2s1PDtzFaG4cF5dqfxVXtV7C+ZllfCtXZMvdD3LXN79LNBpj48YL+MAH/oQ1a85m9ep1JBInn4xI+GNc23E513acOANZ13UWLGhnwYJ24MJ5v5uYGOPpp59ky5Yn2bz5CYQQ/Pmff4AvfvHfuPHGN3PjjW+iq2vZi7gLx0dKyXQpSdEuVRy0s4Lp8+MPXEciFAWhzhemdb9OqDpIajhNIBbg8DxDOW+i+TSCiRfvuhVC0LGxjfquWmYGUwAkWhNIx+XgY71M9kziWi5NKxroOL+DSN2RSR89oNN9+WJ23r2b6UPTKJqC60iijRE6L1p0WjnBp4J0Jb1P9mEWLRzLxixahGvCmHmT3GSOUqaEZmjUNsVIj6TZ8YtdbHzHekLVp7aE9Zvf/H/cc8/P+e///gH19fMH7z4V3r7MpbtKsH1S4EhYUeOysUESPk1NZXx8lGef3cQzz1R+tm17hlKpkqkdCATp6Oikra2DK698DS0tbVRX1xCLxYlG40SjsdmfOH5/RShwHAfTNLGswz8WqVSSkZGh2Z9BhocHGRkZYsuWJ/jSl/4DgIaGJtauPZu1azewYcP5rFu3cW4SxsPD43ebgBZiVdVGhvK9TJZGUYRKW2gxLeEONEXjUeMB0iJFt72ssopIQsSNskffzl5tJ6vss17pSzgjqEKlK7aKPTNbSZvTSASKgNpAI+2RY0eBnQkORzxYrklIj1Lrb5wrrHaqRPQYNb4GRosDuNJFVwzKThHLtegIL0FTjj/MNZsuwph8Bi29DyfQgHBtlMIIdtUKrJo1L/LqPDw8PDw8fvfxxGCPVz2/+MWPWLiwkw0bzp/bJhC0OG20cERMvN93D3cEvk/EjVHvNJJTctzru4syZd5RfP+8yIHfZqGzmFXWWTxlPEaNW0tABphSplCFysXlK+e5YQ8zro4wpU7QYFechIvtJRREnjFllDFlhEP6Pi4zryHqxNiub0UIQVbJkFFSrLHOZqN5AQCPGPfzk8B3KYo8hvTzrL6ZzfrjvK/wUdqchSyxV/D64lv4pf9n9Gh7AUGdW8/rim+i1Tl2YY9TwTAMOjo6AVi16iyee+4Z/vu/v0TrlzvwyfluDYGgyq2mIAoMqv00OS1IJINqP1WymlXWWmbEBDzPQFjtr2a8MIWLi0/6WW+dgzrhsqNlE2u1s1hoV5bhzYgkAYJ0Pi/f+GzrXBbbSzig7cURNm32wqMK9p0JHBxG1SEkkkan5agokPHCJN/d8xPS5QwdsVamekfY9Jtf8ePN/0lhMgPAhRdexo03vpkLLrgMw3hlnEh1dQ1cd91NXHfdTQDs3r2da689F5/Pxze/+WU+//l/Ydmyldxww5t43ev+iLa2M7MEMllK8fOD97Bzag+mY1GlxOkOdCBGBYmWSiSEYzkUZgp0nNN2VEE4oQha1y8gPZKuCMLxAHbZpjBToHlVE/Hm088APx7BRPAocXnVDSswCybSkRhh45jibsOyBoKJIGN7JyhlS0RqwzQsrScQP/Ou4FK2THYiV8klHkzhCxuomoJj20i3kh9cTJdAVgTtqUPTjO+dYOH5J/8cuO++u/n7v/9zPvjBT8y9T36bgAYXt0gubjk9J3Bvbw8PPPBrtmx5gmef3czQUD9QKVS5bt1G/vIv/55ly1bR2dlNY2MzinJ6wcKaps1GQRx5/Robm1m6dOUx9x8bG2Hr1s08++xmtm7dwn/+52fI53PU1tZz3XU38prXvIGNGy/w4iU8PH7HCWphumIrWRytxDA9/zO6Tz1E3E3Me7bzU6nLMKGOgX1Uc7+3xI1q1tVcyHS5Is4GtBBVvrrj5vm+WEYLA+xP76DsFufub8JXy4rE+nkF/U6GEILu2Go0oTNRGiFvZ/GpPjrDy1kQXnTCY514N/nlH8Tfdxdqrh+ERrnlcsodr4dTzEj28PDw8PD4fcYbqXi8qpFS8vDDv+Haa284oQuvQIHHjAcJu5E50TDgBtGkxnPGZq4sX0eTe/wlwwoKbym+h7hbxTPGU6SVNM1OC5eZ17DeOueYx/hlAEMalEWZgAzilwHWm+fSrx5iWp3k7YUPcrF5OX4ZYJe2jd36dkwsFjvdrDbXEyRISsxwj/8OFBS6Z524Li77tF382ncn7y98HIHgEvNKVlln0av1oKCwyO56QcXyToSULqZZptXp4Fl9My7unAhuYxOSES4yL2dQ7Z8r5FbvNHJj6c0scNrR/ArjxSFMt4yh+GiJNDFRGidbzpIqSPK2iZIPsapmHaWaPPvFHiQumtS5tHQ1nfaSef2JywRnW+ee0Wt8PgfUvdwZ+BF96iEkkhanjdeUXs9ye/XcPjum9jCRn8Q4aPHTn3+VoZ0HCcYj1K5tQ06UqQ/W8oUvfOsl66PpWNiuRUALnJYLddmyVXzyk//AZz7zN/z4x/eRSs3w85//kM9//jN85jN/y/r15/LBD/4J11xzwwt2Stquze17fsTWyZ00BuuIGzGmS0m2tGxnY98a3F63MoRUBDULq48rWDatbEQ6kr5N/RRmCqiGyqILOirOW+XMOm+PxW8L1Mci2hgl2vjSFybSDBVVVymmTVzHreRIA47pIgQouoJ0XBzbRRcCRVUq4vBJ2L59Kx/60Fu5+urX8rd/+69z28sO7JwS9GfAUKG7StIZO7Ii93hYlsWWLU9w332/5De/uZuDB/djGAarVq3j+utvYt26jZx11sZXLKahoaGJa6+9kWuvvRGoOIu3bt3C3Xf/jLvv/im33vo1qqpquPbaG3jLW97D2rVnvyL99PDwODWO9f1X49ayT9s9b5s9qwBH3D+8QnKG6qMx2PqSn6fkFOnJ7MLFIWHUViZ1pcN0aYKB3EG6YseehDsehupjaWIt7XZXpciwGjzlaAu7Zi25qlUopSmkoiP9fzhZ0B4eHh4eHifDE4M9XtX09OxjeHiAiy++4oT7pZUZskqahFs9b3tMxpkQYySVqROKwQBhGeFNpbdzXflGxpVRDmj7eEbfxG51B6vss1hjrZ/nHK13G1lir2CT/hgBO4APPymRZFDtY6G9mDanHZ/0o6Kyyj7rmEsW+7SDTCuT84RQBYV6t5ED+l6yIjMn+lbJaqqs6qPaOB0kkj71IJ/753/mgssvYUPDBZRyBX72s+/zxBMP893v3k23uYwt+uPs13ZT5zTiCpcJZYxF9mLeWnjfXL+FFCxyugjNZinX+ZtoCrYyWhwkJzOAZHG8g1S5xIPVjzFa10ssHGG1byVN5gIsLAx8rLBWs9xefUz39UvFpDLOrcGvMq6O0eS0oKBwSDvAbcH/4mP5T9LitFEoFPjlj37MI7d/n/xYisbuNl77v95F5zkrGcgMc+eH/x9v/shbXpL+Zc0c9w88wtPj27Ack4Wxdi5rvZDFiVN39H7wg5/gnnvu4CMfeSf33PME11zzOgqFPPfddzff+c7Xef/7b2bhwsV8+MN/xhvf+DZ8vtPLHexJ9bIneYCOSCtBveKSDerNHGrsZ7BxjDcEr8cu2YSqQ9QtrkUPHLtIjBCClrXNNK5oqMQg+HV8oTPnsM5P5xnbM04+WSAYD1C/pI5StsxUzxS26RBvidGwpP64/Xs50QM6jcvq2f9gD4qqYJVsjICO67gIVUG6Et2nowc0pCtxXZdQ9YkdUsPDg7zznTfS3b2ML37x1jlXbsGCW3crPDcJtiuQQFiH6ztcrmqTRwnChUKeX/3qF9x771089NC9ZDJp6uoauPzya/nrv/5nLrrockKho3PVXykcFyy3En+hqirr15/D+vXn8Hd/969s3/4sd931E+688yfcfvs3OPfci/jwh/+Myy675rRdyx4eHq8M55oXsU/fxbgySq1bj4nJgHaIZqd1XhFej9MjVZ6i6BRIGNVzIrwqVPxqgMnSCIuiy1CFipQSRzqVAnenMFkd0EIEOLVIo3koKm6w/vSP8/Dw8PDw+D3HE4M9XtU8/PBvMAyDc8+96IT7RWWMsIyQFRnCMjK3PSvSBGWIuHvqbgITk58EvstebRd+6ccRDpt9j3Np+Wr+qPi2OdFSIHh98RZyIst+bTcTyjgj6hCGNJhSpvhS+D/YUD6fW4rvxs+xi2QoUgFEJWv1eVhYlCnjcOLiSadDXuT4fuBWntOfZlPqUX75sZ9hjpWJRuIsX7qa73737oro7sJ7Ch/h1/476dH2IaTgwvJlXFN+HTEZB2C1te6o9lVFY2l8LXWBZlLlaYQQxI0afhn/OY5/hoVyAQEZ4ICyj1E5wnvzH5nnwn052apvYVQdpttePvd6dtrd7NV28nD+Nwx/YZBbb/0aqfQM9We1c90n3kbrskqkhpSSkf29mIUSl1xy1Rnv21B2hC8+998cSPbQHGkirId5evw5ejMDfGDVO4gaYVLlDAlfjOrA8d/Xmqbx9a//gOuuO4/3vveP+NGP7iMYDHHDDW/ihhvexLPPbubLX/4PPvnJP+azn/0/vO99H+Ptb38/0eipOc6TpRSWY80JwYeJGRGmZJLGsxuOKoJ3IlRdPeXs21NlZmCGbT/bQW4yh6IpOJbL9l/sQlEFmqEiJYhNMNpdx+qbVuILv/hCPNKVL8rRvPD8DvLJAr1P9JEezVDKlPCHfZglCzNnEWuKY5dschM5ovVR6ruPX0gnm83wjnfcgK4b3HrrzwgGjwjHT44Knh6H9igEtMrnz3gBftWnsLTKoTVaKd65adNj/OAHt3H33T8ln8+xevU63v/+j3PlldezYsWa3znxtOzAw4OCp8YEeQvaonDpApels38qQghWr17H6tXr+NSn/pF7772TL3/5s7zjHRXB/EMf+jNuuunmVyzyxeP3E8dx+PSnP83//M//MDY2RlNTE+9617v4m7/5mzmhTErJ3//93/P1r3+dVCrF+eefz1e+8hUWL148104ymeRjH/sYd955J4qi8IY3vIH//M//JBw+MtGyfft2PvKRj7BlyxZqa2v52Mc+xic/+cmX/Zpfac62zmO6OMWDvl+zX9uDhkaH3cmbiu+YV/vB4/Q48jw6/3tMCIErXaTrMloeZijfS8kp4FeDtIQ6aAgsOOM5+h4eHh4eHq9mPDHY41XNI4/8hg0bzicYPLFIFJJhzjEv5Of+H6GhEXcT5ESOUXWY882LaXYXnPI5n/A9zF5tJ4vtpehU3IIzIsmjxgOsMzfS6RzJtq116/lo7pM8atzPN0P/jyX2MpZYK9HRyYoMj/seosPp5BLzymOea6GzmHqnkUG1n3ZnIS4uB7S97NK2UePW8uXQf3CJeRXnmRef0Dk7poywX9uDi0O7s4g2Z+FRGcn3+H7O48ZDtDit/NGX3k6JIoe0HhbanVxZvp6gDFG0iwQI0OF08sH8J0iLFArKKUdSKEKl1t9Irb9SnKpPPcjTvqdodJqIy4oaU+3WckDbwwO+X1Pl1rBV38KYOkyNW89Z1tnzisq9VEwqE6ho8+6pdCQj3x7m7z7951CEW255N29553u5M/UA+2cOMlWcRhUq48VJivumicZirFp1tNu7YBXYPLaVbZM7caVkZe0yNjacRcQ4sWvSlS6/6nuAH+//BdsmdxHQ/BTtMkuqFtOd6GRXci+fe+Yr5M0Cw7kRHOmytLqbD6x8G6tqlx+zzcbGZr75zR/zhjdczv/6X3/M5z//jbnB2llnbeAb3/gRPT37+MpXPse///vf84Uv/Csf+tCf8uEP//lc8a7jEfNF0VSNkl3Grx0RUbNWjuZwI75XuMK567jsf+AA+WSe6o5qhCIoZkr0PtGLalSEZzNvgoDcVI5Yc5Tuy7pe8PkmD0wy8OwQmdEMgXiABWtbaFrZeNrCsObT6Lq0k4aldYzuHifZl0RRFRRVwbEdpCsp503qltSx+JLO42YX5/M53v3uNzA8PMgvfvEwtbXznVXPTggCWiUn+DB1Adg7A4/u7GX0ie/wox/9D4ODfbS1LeSP//jPeeMb3zpbwPB3EynhR/sFDw0KQjr4NXhuQnAwpfLeFQ7LfmthhaqqXHvtjVxzzQ1s3vw4X/7yZ/nTP30fX/rSv/Nv//Zlzjvv4lfmQjx+7/i3f/s3vvKVr3DrrbeyfPlynn76ad797ncTi8X4+Mc/DsC///u/84UvfIFbb72Vjo4O/vZv/5arr76a3bt3z33evvWtb2V0dJT77rsPy7J497vfzQc+8AG++93vApDJZLjqqqu44oor+OpXv8qOHTt4z3veQzwe5wMf+MArdv2vBAoK15VvZKN1PkPqAD7pY6HdhYE3kfNiiBoJfIqfopMnqFWeW6SUlJwCLcGFjBT7OZDZCYCh+MhYM+xOJbFck9Zw57y2pJQky5OMFgco2FlCWpTGYCtVvtqX/bo8PDw8PDx+3/DEYI9XLeVymccff4g//dP/fUr7X1V6LRYWTxqP0K/1EpBBLixfxhtKbzlh8bjfZpv2DBEZnROCARKyijExwiHtwDwxGMCHDyEEARlgib1i7lwRGcUnfTxrbDquGByWEW4q3cz3g99mj7aTEXWIcWWEKreaxfZSxtVRbg98AxeXi8zLjzpeInnYuI+7Aj8lJZJARRi/qHwFN5bejEolDzYnsmwxnqDGrZ0Tdg18lEWJuwM/Y7e+g6iM0uy0ckvxXSy2lyIQxGXilO/bsRhTRyiIPAvkEYFXIKh2a9mpb2VQ7WNamUTHwMTkMeMB3l54PyvttS/qvCejxq3Bci0GBscZGZpm8pkRRr+1l9yBFBtuPp+v/a/vUl9fEbRrCg3c03s/u6b3Yrk2a2pXsH/nA1x80ZWoqorlWjw7vp3nJneSM/MMZIcZzo2hCwVd1dk+tZtdU3t538q3EdKPv6R/x9Qe7u69j7JjEtKCVAeqyJhZtk/touSU6ZnpY6o0RUgLE9T9IAVPjGxmJDvK3537F6ypW3HMdteuPZvPfvZrfPSj72TJkhV8+MN/Nu/3nZ3d/N//+zX+8i//nq9+9f/j85//F374w+/wD//wWa666jU4rkPJKeNXfajKkXzhrsRCFsc72DW9j5ZwE37Vz1RpGsu1uKD5HBTxyjpG89N5UqMZwrWROUG2nCkhpSSfLMwVZHMdl9xMkV1376HrksUvyNU7tnuM7b/YhVW08Ed8zAykmOmfoZgp0XnhqUV7SCkZ2TFK7xN95GcKqJpCXVcdF3zofIyAjubTkFJSSBYQiiBYFTyuCyudTvG2t72Offt28Z3v/IKurmVH7WO58PxLla7Lwc13semnX+CevQ8RDkd47WvfyJve9HY2bDj/d8rx5biwKwkHZgRSQmccltdIhnOweUyhISSJz85F1PglB1KCBwYVlla5x8xDFkKwceMFbNx4Abt3b+ev/urjvPGNV/KGN76NP/rIv5HR69AUWFolaY2cPFPZ49XHE088wQ033MD1118PQHt7O9/73vfYvHkzUPn7/vznP8/f/M3fcMMNNwBw2223UV9fzx133MHNN9/Mnj17+NWvfsWWLVtYv349AF/84he57rrr+OxnP0tTUxO33347pmnyzW9+E8MwWL58Oc899xyf+9znjisGl8tlyuXy3H9nMpmX8la87FS7tVS7vx/iouWajBYGmCiN4EqHGl8jTaHW0yrK9lIT0iK0hjvpze5lplwppGy5FlE9TlOwlV2pZ1GFSlivPEsGCJGz0gzmD9EYbEVXjojxY8VB9qa3YbsWuqKTNpNMlkZZGl9LfeDMFwX28PDw8PD4Q8ITgz1etWzf/izFYoGLLjpaBD0WPny8vnQLF5evJKlOEnFj1LuNpyUEA6iouLjztlWWzcnjunMtLCoy5/xz6egUKZzwfOusjdRnG3nE9xtuDX6NGreOarcWS5g0us2MKSM85LuXjeYF+JjvtuxTD/LzwA8RUtDtVNyhSWWa3/jvpt1ZxDprIwAFkacsSsTcI+LukNrPkDqAkNDitFLlVtOrHeT2wDf5i9zfzYvbeKEEZBAFBRsL/XlunaIoMqIMYePMCegSyUFtPz/3/4ju3PKX1N2zxjqbbx/6Lluf2kTh9lFyT02iLwrR8q9r+MQNn6Y+1ji3b12whncufzMZM4vjuvTt2c8/793D3/3vf8VxHX607xc8MvQkQgimi0m2T+5CU3Xivhi6ohHzRXh6bCtr61ZyUcvxC+JtndiB5VjUBqoZzA4zU06RLmXIWFkGsyNYrlVZoinBp+nUB2sJWn7GCuPc0XM3y2u60ZVj596+/vW3sH//bv7pn/6Kjo5OrrnmdUft09DQxKc//R+87W3v42/+5k9517tez1kXnMuKt16Em1Cp8ie4oPkczms6G0Uo6IrO25a+iR8fuJN9yR5MZ4q4P8ZVbZdwbuP6E95/13GxyzaqoVJKl7BKNqGq4BnN7ZWy8j/PF+6klNilirvWF/Gj+ypfsVbRYmYwxZbvPkvNwirqu+tOObLCtV0OPdmHYzpUt1fc76Hqitu4f3M/zauaCMRO7LIGGN83wc47dyElBBMBbNOhf8sApUyJdbesRSiVz5dw7Ykd5tPTU9xyy3UMDfXzne/9mkXL1lO05zuAAVbXSHpmFArFPAce+g7P/uI/mRneT9Xi8/jrf/0W73njjSddkfFKYLvw/X2Cu3sFmbJACIgbkitaYVECCpZkwfNukRBQ5ZcMZKDoQPAkT1XLlq3iZz97gNtu/zb/+E9/xS/u+SXdb/pXWi56DzGfwmsWuly24OhMZY9XN+eddx7/9V//xf79++nq6mLbtm089thjfO5znwOgt7eXsbExrrjiSP2DWCzGxo0befLJJ7n55pt58sknicfjc0IwwBVXXIGiKGzatImbbrqJJ598kosuumhejMnVV1/Nv/3bvzEzM0MicfQE7mc+8xn+4R/+4SW8eo9TwXFtdqeeZbw4hCZ0hBDMmFNMl8dZVbUBn3rqsUovNe3hLsJ6lIniCGWnRNyooiG4AMs1KTkFQtr8GA6/GiJvZyjYOWJG5XvQdi36svuR0iXhq5nbN20m6cvuo8bfgCpeWAFbDw8PDw+PVwOeGOzxqmXfvl2oqkp397GXwANkRJpt+jNMKuNEZJRV1lnUu41U2zXHPea3sbA4qO2jIAo0OE2cZW3ggLaHIpXIBIAJZYyojNFlLz1mG61OOwYGWZGZy6pzcMiINBfbx3YFP58Wt5WiKJBUpvDLAKPqMCPqIEPuAF32UqaVKVJKknq3EROTfdouskqG3dp2UmKGZc6qOSG62q0hqUyxVd8yJwbH3Spq3DrGlVEiThSJZFDtx8EmQiVvWcdgob2Yg9o+dms72GCdd8r38Hh0Wctotdvp1XrosDvR0MmINJPKOAY+WpzWuX4LBM12KyPaIANq71EO7DOJkxaMfX6EyR/tRAlrNP5FN02XdhKYjrOrt4eLV190lBMyalTE8X/8ztdpbm7lkkuuoifVy+Mjm6kL1hDzRTmY7sPBRUgHVShEjQgzpTQZM8ee6f0nFIOzZhafalATqMZ0LGZKKTRFxXJtHFxcXHShE9QCZMpZglqQoBZAU4oMZkeZLEzTFG44bvuf/OQ/cOjQAT74wVv42te+x2VXXkt/ZhBHOrRGmgnOupY7O7v53vfu5l9v+2f++7P/H899bDMrX3ce7devoT8zSMkuc0VbJcO7LljDh1e9i+HcKEW7RH2odu4+AdiuTckuE9D8qIqKdCVD24YZeHqQ3GSe/HQeRRUYYR/BeIC2Da20b2h7UZm7hwlXh4g2REn2z1DVlkAIgRE0sM2KCK3plUGoVbQopksA9G8ZYGLvOP1bBln1uuVUd5y8YGMxXSQ3mSdYNX8gH6wKMjMwQ24ie1IxWErJwNODOLZDVWtlIG0EQffrTB2aJtmbpHbxyZ1v4+OjvPnN15BMTvPnX/wNvyyvJrNJENYlFzRLLmuV6LPzWV3GODO/+ir33/0VrPwMjetv4vz3fJOLz93Ite0S5Xd0pfVzE/Cd3QoZEwo2lOzKNN3+Gcmbu1wUURGM9edpDGUHoj7mrv1kKIpC+yXv5bzA6xj4ySfZ+a0PUN5/L6vf99/cdShCV8JhwYufK/P4A+JTn/oUmUyGJUuWoKoqjuPwz//8z7z1rW8FYGxsDID6+vlxLfX19XO/Gxsbo65ufga4pmlUVVXN26ejo+OoNg7/7lhi8F/91V/xZ392ZEVIJpNhwYJTj87yODNMlceZKI4Q1RNosxO3rnSYMScZKw7RFl58khbmU3aKpM3KirCYUY1PPfmk46kihJgX+XWYvJ1FFSqOtOcVVHakjSpUNHFkQjdnZyg4uaOE46AWJm9nyVtZokb8jPXZw8PDw8PjDw1PDPZ41bJv327a2xfh8x07e3RUGeabwS/Tqx2cc+4+4PsVby28lxX2mlM6x6Dax/cC3+aQdgAbi4iMsdY8m1XWOnbqzyFxkUhCMsz1pZtY4LQfs50uexkbyufzhO9hDGmgz4qeHXYnG80L2KQ/zjb9aYqiQLe9jA3mBVTJI0LTuDLKZuMJNKkTc+MY+HBxmVInUKVKp9NNSIYZVYb5TvDrHNL24+AwroySFRk67W58zytSp0udgsjh4LBH28FebRdIwaQygY1F3K1mWpnEwqTeaSQoKw5ADQ0JFETulO7fyQgQ4Jbiu7k9+E16tYO4uARkgLPN8+jR9pyRc5wuExNjvO9Df8S+pzax9nUXseJt56IHfUSdGGl/hr7MIDkrf8yM30wmzc9//kM+8tG/YDg/ypaxrRTtIjFfG6ZjkSlXBkqGolOwi9QEqon5oozmx8hYJ76nC2PtPDe5E8u1MFQdn2qQNXO40kGIWVcr1px0nrcKqEJFFSpB3Y+hnthVqygKX/7yd/joR9/B+z9wM1d+4s34V9fiSpe6QA3XdlzOOY3rEaLSdq5T4frPvp+he3ay5af30/f4bjZ85DoeCjzGuU3r5yIvhBC0RJrmnct2bR4f2cxjw0+RKmeo9ie4qOU86vuq2X/fAVAE6ZE0uak8mqFRs7AKq2Cx99596D6dlrUvfvmooiksvqST7T/fyXTvNKqhYZVMfBEfSFkRgAUUU0WEEPhjfhItMULVIWYGUuy7/wAb3xVH1U7sXNIMrVKcznR4fqF0x3RQNBXVOPnXuOu45CZz+MLzB/O6X0M6ksKsWH0ihoYGePObr6FUKvKnX3iAJ60lBE2IGZKMCT8+ICg7sCE0zH/+52f4/ve/jaKoXPKad7Po6j/BV7uQiQLsTQr2TENDSHJNu+TsBnnSc79YXBdcQDsFsfZXfQojebAcQdGpOMAFMGYL7u5TWF4t6c0IOmIV4TtThowpuKrNPWUxGCqZytFEHa/9829z4Nwb+NV/vpvpvzuPFR+7g30LO1gQOfq+jObhsWHBnqQgqMH6Bsm5jRKfZ377g+eHP/wht99+O9/97nfnohs+8YlP0NTUxDvf+c5XtG8+n++4z1EeLx8ZcwaJOycEQ6XOgip0kqWJUxaDpZSMFPo5lN1LyckDENBCLIwspSn40tZcCKphqnx1jBUHK+KvomO7FjkrQ2NwwVzGMICCikBB/tZKO1e6CKG84lFSHh4eHh4ev+t4YrDHq5YDB/ayePGSY/5OIrnb/1MOaQdYbC+dFTErMQM/DXyPRdnuOVfv8ShT5n8C3+Cgtp92eyE+/CSVaR7x3c/ri7dwrnkR/dohDOljmb2CTnvJUTEQNjYFkScgg9xSfDcdTifPGpsoUuBi+0rONS/iId+9POi7FwFoUmeb/ixb9S18oPAnRN04KSXJdv1ZbCwWOG2MqsPE3AQ6OoqrMKwO8ObiOwnIIF8PfpF92i467MX48aOqGpuMx9it7WCtffbz+lRgkd3Fz/zf5wH/rzApY2EzpUwwpUxQ49YhkZRFmVF1mILIs9BZTNiNYGBQ7x5xg1hYZJQUATdEkNPPtVvkdPEX2b9jv76bgsjT4DTR6nTwhdC/skffSddsPrFEMqwN0GK30ep0nLzhF8BDD93Lxz/+nv+fvfOOs6uq1/ezdju9Te8lkzKpkE7vHREQlSKCCIIFFb1iuV57Qa8Ne8dCUbAhAlKlE0ogIX2STJKZyfRyzszpZ5f1+2NPJplk0gC9/vA8+eSTzJ591l57n7bWu9/1frGlzVE3ns+yY45DV3V2zVUKtivE7i9u4U9/uoN8IU9iDnxj5Q/pTffTnxmkwldOQPdhKDqqomA5Nh7Vfa3Yjo1EUh+smbLNXSyrWshL/atZM7SBgl1AU9zXtCo0okaYtJkh5+QZHXcQ5+08aVMlaPhZUD6XUm/JQc9f13U+/43vsD6+mQe+dQenf/hiWk9cTF+6nzvb7ibmjdJaMoOB7BCJ3Cg1kSoaL29gzilLuf/bt/LoF25nztuPo2/OZbTEmvZ7nAd3PMY97Q/gVT2EjCA7k738Zu3vmbt5BrO9M1ANFTNrEaoIYuVtkoNpYvUxkgMpOlZ2UbOgGkV97RPFsmmlLH3HInrX9ZEaSuEvCdB0VBNb/rEVkDiOJFPI0hPtJ1WWpi8yxDQaqKqsYKxvjLG+JLG66AGP4Ql5qJhZQccLHRh+A82jYVs2Y71jlDSVEK09ePFFRVXwRXyMdo9Cye73mG3aIMATPLBN98UXV3DNNW/H5/Nz213/4NbeFoICasbF6ZABnQNxvv+N/6X9oR/g8/n4yEc+zTvfeS2xWAmJPNz8siua1gYkqgJ9abhjk0LEYzPztUWH75e+NPxyrcJTPQLTgQVlkivmOCys2P9j2kchYwlsBzThxkBYDlgSBjJwVDWUqpLto+BIgVeD42odTqo/PFE7Z+8Wp2cccyEl9a3c89WLWPGlYzj2m3/itIbJLv+eFPx0rULXmCDikfTb0BYXdIzB5a3uNS3yxuXGG2/kk5/8JJdccgkA8+fPp6Ojg5tuuokrr7ySqip31UZ/fz/V1bu/X/v7+znyyCMBqKqqYmBgYFK7lmUxMjIy8fiqqir6+/sn7bPr5137FPn3RFVUmOJjSEoHVTn06d5oYZgtY2uRQNQoRQJpa4zNo2sJaKGJmIZ/BkIIZoTnYTkm8cIgjnRQhEKZt5IZ4XmTVlQF9TARI8ZIfoCIUYoiFBzpkLaSlHurCWjF5RVFihQpUqTIgSiKwUX+Y+nq2sEZZ5w35e8SIs4mfT2VTvXEUjWBoMFqolPbzg5tK7Ot+RP7W1i8YDzDC/ozjCmjTLdaKbMr2DEeX7Ari7fUKSMtUqw0VvDfya/sNyrBweFZ4wmeNB5hRB0m4kQ5Pn8KxxVOmVQsbou6iaeNxym3K4jKkom+tOkb+I3vp+RFngG1l2ExxIDax3RrJn1KL13adhSpokudOruRs/JvpkPdRrvWRoPdjHfcBdxkt9Dp7GCLvomwjOKTPuLKMNOtWcScMu4P/IKYXYqC4BX9JbIiQ0ZkGFNGcaTEj580KTJKij61h2qrltML5zLDmo1EssJ4kn8YDzKsDuKXfpYXjuP03JsOKrTvjR8/R5qTc2TPy1/EsDrIRm0dBgYmBUqdcs7Pve11zwsuFAp87Wuf5Sc/+TYnnXQGX//WD/l1553ErT6qPWUgNdIFi+FcnHOnnYZX29dFJaXklt/8iLrFMxhQE9T4qjAUna5kN8/1reSEmqMo8UZJWWnSZsZ1dmeHsaRNXbCW42uXH7CP5f4yrp7/Tu5su5s72/5Mzs5T4i0hb+fRVI2ACCBNiSMdsnYOXdEJe4IcW7OcN7ecNWkStiW+jae7n2PbaAclvhhHVS1mWdUiVEVlQ2Izs959LF7Dy8PfvRNFCuadupxNI1tY2bea1pIZ+DUfHtUgZ+fwah5iNeVcctOHeOTXf2bd757kU73v48ffv5XS0n3jWBL5UZ7cuYKwEaIq4Kp6MW+U9r7tvGStZV6kFSthIx0HVVMRQpBPFTCzJp6gQW40S2Y4Q3IwhZSSaF0Uf/TVZymGKkKETtkjuqJgIxD0rOtlJBFnRf1K+kuG8IW97BDdvGKvZ4k4gtnODHCmFhD7M4M8tfM51g1vxKN6OGLGXKKJCGPbkzi2W6RMq9HZuaCPR1d+B4nkyPL5HFd7FDHvvuKwEIL6hXVurMRgCn+JH6tgM9Y3Rkl9lLIDxFX87ne/5lOfup4jj1zKz3/+ezKeSkZ3CKoCbt/NfIZV9/6AF//4v1hmgXe8+wb++4aPEg7v7seaQcHOJMyI7hYtmyOwaQSe6xXMjL3+7uCxAtz4pMraITfPWBXw2E7BhhGVb51gM28/ST+OdAvITfxfuu5giRsb0TEq+NrxNkM5QcaSVPklzZHJxfIOhXmlbqay5Ug0BUrrZ3Pul5/m71+7iG9+8AzmBf/IySefObH/U92CrqSgtUROHGusAC/0Khxd/c8T1Iv8e5DJZFCUyYq/qqo4jvtibW5upqqqikcffXRC/B0bG+P555/nfe97HwBHH300iUSCl156icWLFwPwj3/8A8dxWL58+cQ+n/70pzFNE113b1o+/PDDzJo1a8qIiCL/PpR4KtDVLWSsFD414H732TlAUuGdfLNYSknGSiGR+LXgJBftQK6XglOgxONGBwkgpEcZyQ8ykOv9p4rB4LqQjyw9mnh+iLyTxaP4iHnKUPbK/1WEwozwPDYkXiJRGJ7YHtFjTA/P/bcqSlqkSJEiRYr8O1IUg4v8R+I4Dt3dndTVNUz9e+GmqCpyr8EnKjYONvbENonkbu+dPOy9F1VqeKWXRz33I4GcyOxTlC0gA6REkoLIo8mp34KPex7iD75bUaVGxIkxqPRzh/9XpESKN+XfMrHfDq2dnMgQldMmtmlo2Fj81fcH5ppHUOaUYyomq5WV7PR0EHNKCToR8uSwhcPJ+TMIyTCdYjt5kccrfZPaOtJcTJu2kbAMo0jBcflTOC1/DquMF8mRJSKjPK8/TUKJU+5UMqD2kRJJgjJEiV2GT/oZUxIkGaXCqeSyzLvR0HjWeILbfL9AQaHEKSUt0vzV+wdGxSiXZ68+7MJ8ezPDms0HU5/gZf0F+tQeypwKFhWWUedM/Zy/Wjo7t/Pe976DdetW85nPfI3rrruB4UI/R+Tr2TZmkbVGUQBHkSyqmsPpjSdN2c6KFU/SvrmNJR97E9MjzQgh8Os+FpXPZ+XAK7w0uAZDNVCFSlWggppAFaZtoqkqF04/l9rQgZ3BALXBKj688D30JHt4tOspaoIV9KT6iecS2NLBo3jwGR6WVB7J6Q0n0hKbxryyWfi03a+JDcNt/Grd74jnR4l6wmyNb6NtZCuD2WHe3HIWyUIKRVE560OXomoaD373d6TjSapOn8VAdgiASn85c0pnsaLnRQzFwK/7yGNS/5YjOGLpUu7/zm2cccZSfvSjW1m+/LhJ5zCQGWK0MEZTeHImZWmghAFtiOFCnFIjhlAEtmUjbYmiChRNIRPPIgS8cPtKsvEsEvCGvbQc20zTUY1TTh6tvEVqMIWiq4TKgwfNG9YMlXlvmkPDknr+tuEBRjaOUp2tIqKH3II+ToLn8i/RVF5PqCq8z+MHMkP8dM1v6BjrImpEiDsJ2hPbWbZgEecuOx1ztIDtc/hL5j42jGwhrIcQwF/a76ctvpXrFlw5ZQRJzYJqcqk8nS90Et+ZQNVUylvKmHN2K5pn38+hfD7P5z//MX7zm59y+eXX8OUv34xhGAznXHE1lbPY8vSvWPH7L5EdHWDmadcy8/xP85EzygnvZfBP5AEhUJXJom9Al/SlBVPa2V4j928XbBhx3cve8dMrk7BjDH63SeErxzlTPm5eKTzTI8lZAgko471TBSgIOpOSggMLK15bn4+tlawflmyOCwK6xHLAVkr4yHf+zuPffjvXXHMxd931IIsXuyLdhmFB1JCTROew4TqGu5L/HEG9yL8P5513Hl/5yldoaGhg7ty5rFq1im9/+9u8+93vBtwbPjfccANf/vKXmTFjBs3NzXzmM5+hpqaGCy64AIDZs2dz1lln8Z73vIef/OQnmKbJ9ddfzyWXXEJNjfv9cdlll/GFL3yBq6++mk984hOsW7eO7373u3znO9/5vzr1NyRSuu/X11OwjOglNAdb2ZFqI54fBAGq0KkLTKPCt3t8MFoYoT250c0DlpKQHmVauJUSj3tzNe/kpoxYUBCY9sEjhV4PFKFS6q086H5hI8ai0uMYyve5N5dVH2WeKgy1GFtSpEiRIkWKHIyiGFzkP5Lh4UEKhQK1tVMXOYk5pTRZ01irryZiRSeEyV61m3Kngiq7llGRICTDdKudPOS9l1GRoKDk8UgvNU4d3Uono2KUpBglJHe75OLKCK3mXHxy6kiEDBkeMx7CI73U2W4+W8wuoU/p4WnPPziucDJR6Tp0VNTxNGM5STztUXYCkmn2dAB8th+/9DOoDBAUJl7pQ0EgpCCpjOHgUGXXEpUxhpVBKpzdy0HHxCgLCkcy31rESmMFa4yX2K5tRUFhWBnkaeNxtmtbCDsRbGFjYyGRRJ0SckqWxfnleKSHEWUEDRVDejAxecx4CAWFJrsFgLCM4pU+XjKe4+T8Ga9ZtM2Ro0/twS8DLCkcxSxr7j7C/GtlzZpVXH75efj9Af761ydYuHAplmPRPrYev+FlcfkShnIjFKwCqmozLTyTkL6vUCel5KabPkP1jEYisyrpSfdjqDpRT4SWWDNJM0N1sIJjq5eRstLsTPYynB0hYPg5pnopJ9UfN0XvpkZVVC6aeR6rBtfRmx4gZaVRhIqhum5pn+bl0tYLmR5rocxbMkkIdqTDIx1PMlZI0hqbPjGRHcwM8eTOFRxVvYQSbwyJxEZy+vvfRiAa4unf3kv15jaWf/5zgDsBfsuMc8lYWTaNbMF0THRF54iKeVx+4lv5yDkf5gMfuIKLLjqNT3ziC1x//ccRQiClxJYFLJmnP9NLqa8Ur+pHILA0i1AoiLXNQqvQ8Mf8JPuTSCkJV4fJJXPkU/mJSXiswS36lhpO0/aPLQTLApOKqEkp6VnTS/sz20gPZ1A0hZKGGLNOm0m48sDLT4UQRKrDdHX1UNNQjd6hkk1kUTQFw9IZ8RWQ813heG+e7XmBHWNdtMamT1RCT5sZVg2t5egjlzC/dQ5Pdz9PW187MyLTJrKcK22TTSNbeHlgDSfW7bviQFEVph8/jboja0gNptEMlXB1eMq4jK6uHVx77aVs3LiW//3fH3H55ddM/K7UC7HBZ/jNN64ntXMtrSdcyoK3f54xfwvLah0qfPuKkjGvez1thwlnsJSQtgQ1walF2ddK24jAcXYLweC6d70arBvevwBzeqPDvdvV8RgINyZCFaAKiVcDjwrdKUFtUJKx3IiMrOUeL29DXUjSGHIfdyBKvXDtAofnetwMYK/mCsxLKw0u+ckdXHbZuVxxxfn85S//YObMOfg0GNlLh9llLD+crOIi/3/y/e9/n8985jO8//3vZ2BggJqaGq677jo++9nPTuzz8Y9/nHQ6zbXXXksikeC4447jgQcewOvdnRV+++23c/3113PqqaeiKAoXXXQR3/ve9yZ+H4lEeOihh/jABz7A4sWLKSsr47Of/SzXXnvtv/R836hYjkl3Zgd9mS5MaVJqVFAXbCakR19z20IIGoMzKPFUEC8MIqUkYsSIGmUT39VZK836+EukrTH8WgiBIFEYYn38JY4sPYaQHiGkReiVHcjx7F1wv/sd6RDUDx5L9K/GUL3U+Jv+r7vxb42Ukuyu/Odx13iRIkWKFClSFIOL/EeSzWYACAT2FeYAFBTOzp9Pr9rNRm0dARkgJ3IY0iDqlHJz8CvklBw1di1IwSrtRfIihyUsQLJZbqTZasGDQYe6nTKnAq/0MawM4pEeTiqcsV/n67A6QFwZodKpIkuGAbWPvMihS4OcyDGo9hG1XDF4pjmHiDdKn9JDlVODQJAkSVIZZYY1e6LNUSWOFx+lThkVdhUhGSIqSwg6IQbUfgaVfiqdao7Pn8K93j+RFRmCMkRCxFGFihc/93vvJuJEiTmljIkELxnPM6QM4HdCFESBuDpM1sngz/upNCvRpKDgkTiOgyE8FESOmF2Njk5cjDCkDhBzJi9Pj8oY/aKHIXXgNYnBfUoPv/X/jHatDRsHFYWZ5hyuyF5LuXNwt8mh8OSTj3L11W9j+vRZ3HrrXykrc101Y+YIKTNJyIigCo163XXk5O0sY2acnJ3BpwUmtfXAg/fw0kvPMeP6E3iu/2U0oeLXfVQHKplTMguPqnNy3XGcM+00wM0JzlhZvJpnv/nDB2Jp1UJOrDuav2y9j6DmRxUqjpTI8VKJ33rpJ0wLNxD1Rji+7mhOazgBTdEYKyTpTO6k3Fc6aTJR5iulLb6VnakejiifS1OkgS2JdqoDlSx4+wmYFQqrf/YIv/joTRz3yyU0Nk6jxBvj/UdcxdbEdhL5UWKeKC3RJjRFg5oYf/jDQ3zrW1/kpps+Q2fnDr761e/RndtOX24LQY/G1ng7KStBma8CjxKkPzPE8fOPYro2jaFtw2heDd1vIAR4/B50r46/JUB8xwjh6vBE/0PlQYa3j9C3sX+SGDy0dYh1928ARxIqD2JbNv2b+smnCix752IM/8GjRhwp8YV8VMwvJTmQpJA20XwqeZ9JtGHqJdebRrYQ1oMTQjBAQPdjOiZdyR7ml81h+2gHilAmFfXTx7Ootya2TykG78Ib8uINTV0VXkrJn/98B5/5zEcJhSLcc8+TLFiwaOL3iUScm276H2677RfUzFjMUV96Hr1uMY4OR5VKLpohpxRBF5RJGsOCLQlXRFUF9GUgrMOySsnWBKwbEqRMqA/BogpJ6DUmufg119GbNaHguOKsX3Pzf0MHeMssKIeLZzn8aJVCynI/pYWQaEJQH5KUemFFr+CRTkGqILClJFnYFRMh8OtwVLXDW2dIptD6J1HqhXOnSc6dNllAN/x+fv3rP3PRRady6aVv4q9/fZylVY1s3SRIFSBouGL6ziSUeCWzS4qu4Dc6oVCIm2++mZtvvnm/+wgh+OIXv8gXv/jF/e5TUlLCHXfcccBjLViwgKeeeurVdrXIfnCkw6bEanqyHeiKgSJUujLtjBQGWVCynNDrILQKIQgbUcJGdMrfD+Z6SVmjxIzyie9AXTGIFwbpy3QRikSo8tXRl+0kXhjCp7pjlaydJmLEqPBNLr5qORZJMwG4URLaYWQTF/nXkCgMsy25ibHCCAARo4Rpodn/9LiPIkWKFCny70/xW7vIfyS7cvZUdf+z9RnWbD6Q/hjP68/Qqe2g1C6jW+lik76WqFOCz/HTpm1kvbaaIXUAn/ShS1fBSIsUG/V1nJw/g5PzZ/Ky8TwZxS2idkr+LI4wF+/3uH4niAcPvUo3Hep2ksooAkGBAl58JElO7Fvr1HNO7gLu9f6ZTdo6QKCiUGc1EXZ2TywEAgeJR3qZZc2lctz5mxAjFEQBBdf9cU7uQsJOlKeNxxhTEsywZ3FkYSn3++6m1CmbEFJNYVIQBQzpwYOBKhVsbLxpD2XZUjR0Bj0DRJNRoqMhhquGyIoMx5gnoqDglwH8ToCMSBOR0Yl+ZkUGD14CztQi/aEgkfzRdztt2nqmWTPx4CFHjvX6K/xF3sl7Mh98zREUd999Jx/+8Ls59tiT+fnPfz/FTQUJ+xxDTPRvTyzL4rNf+hix2dVkGxW0gopEkjGzdCV7GMnFWVa1iIWVuzOqVUWdMgoA3CJ1A5lBNEXDcix2pnowVIOZ0RaChjux0xSNpVUL+dOWe8nZOaQEdbz4imIreFSDUl8paSvNX7bci6HonNJwPB7VQFM0Co456ZimY6IqKoZiEPGEuWrupdzT/gBb4ttxpM3JZ57Ne068mps+9gnOPHM53/3uLZx55nloikZrydQVzjVN4xOf+CKNjS3ceON76Rvs5vLPXYHP5+fcaafy0I5n2DG6k4FMG2XeCpZULeKi1vMIHREk3hUnN5bHEzIw/Aa26RAo9bP1yXZGuxL7uGJUQyWXzE/atnN1N1beorTRnTBpHg29QSfeFWdw6xC1C/Yfy+HYDr3r+whu9PFCbgdqCEpqSvBM87B1aDuJvjHW3bUe08jRMn8aDYvr8IZdgdY/LvzuiZTuq2aXe9ureXHY11FrSQu/9uryj3t6dvLJT17PI4/cz/nnv52bbvo+0Whs4vh/+cvv+fznbySXy/KlL32HK664jt6sSjxnE/FAwwHcsBEPXDnH4S9bFbaPCmwJNUHJOc0OvRnBX7YoJE3XfWtLN0f4mnkOpa8+ypkT6hxu3aixZRQ8qvuek7hF4U5t2L8bWRFw1VxJT0rycKe7v0+DprBDhc91Fa8agOoA6IrkxV5B1oKllZJpEUkiD491CWqCcFLdqxdpo9EYt99+L+effxLveteF/Omvz7KjxsfKPsGWUVfUrvK7Anz54dfdLFKkyL+YRGGI/lw3QT2CobirlPxqkHhhkJ7MDmZFjvin9yFrpxGISd+BQghUoZO23LGlV/MzL7aMjtRmhvMDgKDW30xTcCZedfeH8mCul/ax9aTGHxfUwrSE51Durea1UrBzmNLEq/gOq/jda8V0CowW4gBEjBi68vrWl/hXk7aSrI+vJGOl8I8X1BvK9ZGx0iwsPQa/9urH2kWKFClS5P9/imJwkf8YLCxyIotP+rFtN/N374Ise1NnN05ENexQ2/l28MvU2vVExmMaglaIZ40n3Hxh1PHCZAJFKiSUETSpc6S5hLATQUFhobmMUrmfykXjlMoy5hUW8svA95FIyp1KbGEzIoYQwAueZ1hoLXUH9AhOzZ9NizWTjfpaChRosJsZFoP8wX8rg0o/pU45hjSwsdDQKHXc49vY9Kk9LDSXUea4rlYNjZMKp3N84RTy5PHipV1rIyWSNNrNE30cVeIoUiFKjOlWK91qJ4NOH4pUSHnSCE0wpo1haiaP8TClVjln2hdwXP4UAHz4OMo8nru9d+KVXqKyhIxI06luZ565kGn21ALhodCtdLFF20id3TARC+HFS41dx0Z9LQNKH5XOq5+s/Pzn3+Nzn/sYb33rO/jWt36GrutkTNdp7tf9hPQofi1E2hwjpEcn4g0yVpIyb9WE02YXv7vz13Rv76D146fiaDql3hhDuREyZpaCncev+1hQPpfqwMEdzS8PrOGB7f+gJ9VLd6qXnJ2n1Fsy4TJ+28zzmVfWCsBYIYWmaNT5StEVDQdJb6qPgm3iSAe/7iXiCdHldPNU93McW7scn+ZjceUR/H3Ho4SMIDkrz3B2mL7MANOj02iJNAFQG6zmvQvexVB2GFs6lPtKURWVEx84kY985Bquuuoirrrq/XzqU18iGDxw5MIll1xJaWkZ1153KTuv38Hnvv81YuEwb515BjuT/fRlemkOt3BWw5smcg5L91MQLVQRQgK2ZaNq7o0g6UisnEW0brIrKzmYxvBNnggqmuJmgY8dODdxyxPttD+1jRqlktpwFZty7XgSXaTCGbLDGZp66xjJjPCymqKvY4Dh7TNYdPFCPAGDxZVHsH5oE4ncKAERQCjQlxsg5okwu2QmAPPKWnly57MMZIYo97nnOpQbwVAM5pfNPlDX9kFKye23/5IvfemT+P0BfvWrP3HmmbsLa27btoVPfepDPPXUo5x33kV84QvfoqrKFcLrQ+7fQ6ExDB9a6NCTdgu0VQfcAmhff1FBCMnscZOS6biRC//oErxt5qsXU01HUOmXdCUFeWv8RoyAsoDk9MYDt+vT4P1HuEukO8cEMY/EdGBn2hW8G8NQ7nOzegVudm9XStAYlsS8MFqA53vFaxKDAaqqarjllj9wzjnH8KPvfYlrPvxl+lIKrwwJcjYMZtzCcuV+SXP44NEURYoU+b8jaY5iS3tCCAZXiDUUL8O5AfgXJDB4Vb+7AkjKCUHYjV8y8e+xYimoh5kbW4LpFAD2EUWTZoKNiVUUnDwhze140hplY2IV3lL/q3Y5F+w821KbGMh2Y0sLnxqgIdhCtW/qTP/Xk75MF1vHNpAoDFNw8ngULy3h2cyMLJgyQ/nfBSklY2aclDmGpmjEPOUTr7H+TDdpM0nMs9sJbige4oVBBrI9NIVm/l92vUiRIkWK/B9TFIOLvOGxsHja+AdPG48xqiQodyqZprhio6IcZB3vHvQrvWREhnrZNLFNIFCkQBMaKioZkRnfDhoGfUovNwdvIieyADxmP8TF2StZYC084LEWmkvx4SdPnhFlGBWVGqeORquZzdpGhpSBCZeuQNBsT6fCqWKztpECeWZaczgrdwHPeh5ns7YRDx6Oy59MRsmwWduIioqDTYPdzHm5i/Zxyqqo+HHtZiEngk/6SYkUMekqNkIqWFgEZJAGq5mZ1myGk/10yK0MBoewFItKs4aYFSWjpRlVEyAl2h4fOafnzmVMJFhpPEe/6MWDlwXmIi7NXoXKoT8ve5MT2XHX8uSl8B68JER84rk4XKSUfPWrn+aHP/wm73vfR/n0p7/KQHaIh7c+wbqhjSChtXQGZzSeTHNoFuvjLzOcH0BXdGxpEdBCNIdaJ01ostks3/7Wl6hfPotQUxkpM41f91Ove8lbBeL5UWqDVYT34wLek7aRrdy24Q/k7TyWYzOQHcZxbAzFYFashd7MAL9v+zP/FfwAMW8E0ym4DlPp4Nf9pM00jnSQSAzVmIifCBshRvNJ0mYaj2pwRuNJ9Kb7ebjjcfrTA1jSxqt6iXoiPLDjUc6ffjaKUBBCUO6ffOMjEonyy1/+gV/+8gfcdNNneOihe/na177PqaeefcBzO/30c/n2LT/m4++9nv9+90f5/I9voqS8lMZIDVGflxJP2SFN1ipmVVDSGGN4+wj+qA+hCNLxDOGqMDXzJt8gCJUHSA4kJ21zLAcBEy7eqUgNpelc2YU36KGkJMZF8k20Oe2s7l+LNqQwb2wmteEqRFghVUjRaXcT2OynZn01jcsaWFJxJKs3ruWpDSvIFLIIRVARLePyo95GbdB19LfGZnBW06k80vkEm+JbAAjqAc5qOoU5pbMOeh12sWNHOzfe+D6eeeZxLrnkXXzuc/9LJBIF3Nf7bbf9gs997mNUVFRx2233cMopZx1y21OhCKjb46W8NSGI52DmHokZugIxr2T1oOCC6XJSHm7ehoGMm9tb7juw+PlCn2BGTLKkUrJjTGBJ10mbMQXto4K60IGF2pqgKwg/3S3YNOLGP5T7JE/uFJSMazmWAyDwa5Ks5WYHhwzwqpAsuFEOr1W/mDv3CD760f/hG9/4Aqlp59MbO4oSr2THqKA9LdgUhxf6VN420+HC6RLt31ezKFLkPxpVuOOfPYVYAEfa/zIHarm3mq50O6OF4fH8X0HaGsOj+Kj01e2z//761Z/tIWdnJsVNRPQSRsZFxlcjBksp2TS6mr5sF141gEf1kbHSbEq8gkCh2v/6Ff+VUpIoDJM0EyhCQRM6mxKrSRSGKMjC+A38FKuGnyFjJjmy7BgECmkrOf4YlZinbJKw/3+B5VhsHl1DX3YnljQRCAJaiNboEZR4KkjbSRRF3ccJrgiVjJU8QMtFihQpUuQ/gaIYXOQNz9+9d/M37x/xSh9BJ8wOdRtrjFUA9Ok93OW9lSF1gBq7jqWFY6h1pi4q55cBVFQK5PHgikEaGgEZIkWaWruevMhhY5Mnj6qkSCgjzDRbicgoNjY71Hbu9P2ahlTTRBG4qfDhZZo1g7ATxRImXumlxCkjJZIkxAgmk5eRb9DWcKfvt/SqO3GQhGSIY/Mn87HkZxlQ+vFJH832dIaUAV7RX5rIJD6isIQSObWLcheVTjVHFBbzpOdRhC0IyTCa1JBC4nP8ePEiEJQUSoiMLmCbtoO4J059vhGBIGdnsXF43vcMxxdOod5uAly37mXZd3Ny/kwG1X4CTpBme/ohC8G2Y5PIj6KrOmFjtz2x2qmlxCllUO2nznYnD6ZTYKfSQUmhjDKrct8Eh0Pg61//LD/84Tf53Of+l+uuu4FEfpRfrrud9tHtVHjLQAie2rmCF3pfpspfQUFmiPl8tEQbmFc6jxp/IwF9so3yF7/4HkODA5z/yYvoUxOYeXN8oqigCAWvaqAqKmW+Az9HACt6V5I0U7TGZrCiZyUe1SDiCzOSjzOcixMxQqweWMfP1/6WMxpPRhM6daFqUoU0w9kRTMfElg66qlPu2y3iJgspIt4wAd11DUU8YY6vPYrne18iZkQp9cUo95eSNjM80vkk02PNzC+bM6lv3ak+Xux7me5UH+W+Ek5665s444w38YlPXM8733k+F1xwMV/60rcpLS1nfxyz/EQ+/fMv8K0PfY2Pv/PDfPGnX6OqoRpbWpR49v+4PfEEDI58ywK2PbuDgbYBpJQ0LKqn+ahG/CV+hne42cG50fEbBhIS3aMESgM4lk1yIEm0Lkb59P27+8d6x8in8pQ2uTdOAsLPInU+ZqZApiONN+qb+OYNGkGGssOknBTxrjiNyxqIb4kz7fl6vNJgLJKEPJS3lRA2gsgmiVDcJb7nNJ/GgvI5bE1sR0rJ9Ggz9aHaQ3JPJZNj/PjH3+InP7mZsrIKfve7+znxxNMmfj88PMTHPnYdDz74N6644lo++9n/xe9//bMI5HiaylSBKlJO3u/5PsFDOwQDWYGuwOxSyYUtzn4jEpIFV5Qt9UGpzy1eN5KD7hR0jh2aUFsdYNyd7Hamcwxe7GOicFzIkKiKIGO5Irahuu3G84ITyp3Xzan7gQ/cyD33/40/fP0q3vT1lWwcC5C33f4lC+55PbRDUBuEY2qK+cFFirxexMUIr+grGVQHiDhRjjAXv+qVRaWeCnyqn6SZmFg1lLdz2NKi2j/1uPP1xq8FmRNdxNax9STNUQACWvCwM2RzdgZF7CsyqkIlN16o7HAZNUcYyvUR1CIYqiuyGoaH0cIIO9PbqfTVvS4OXUfatI2upSe9g4ydxnTybjTT+JeCrhhoqoZPujELHZltVGcbSZljdGe2U3AK46JrkJmRBZR5qw5+0H8S3Znt7MxsJ6CFCCkRHGkzmO/l+cHHmBmeDwgcx97HCe5IG49azBcqUqRIkf90imJwkTc0I2KYJ41/EHFiEwP4EruUtc7LANwV+C0erxev9PKS/hzPG09zVeZ9zLTm7NPWTGsOTVYL7dpmmqwWPHhIiBHK7SpMYTIshjAwkIw7a50gtU7dRCauikqT3cIWbRMb9bUcXThhv/2usxupsCsZVUZpGBdPJZJ+tZdp1gwqnN2Dz4SIc4fvFobUQZqtGWhojCjDPOS9lyqnhuMKJ0/sW+XUUJXff9bpVAgEb8ldii1s1uovMyB6CcgQZ+fOJ6GMsFFfh5CCUCjAtGQjWTVLxIq5OcXSBiSVooYe0U2PsnNCDN7Vdo1TR42zryPlQKwb2sRDHY+xM9mDpqjML5vDOc2nUeorISCDnJY7hz/4bmOrugkn7zAk+8GCecNzaSuspjVyxGFlpf3wh9/ke9/7Op/97Ne57robAFg9sI7tox3Mis5AG3eYp8w0K3pX0hCsZU7pTLoSCbYM96PLEma0zJvU5ubNG/jWt77Ee97zYY5cfBJ3tt2NpmgM50bwqAY5J4OqKES8HqQySnd6BzX+/S+V7E71EtLdc8rbOTShuRMnCdtGO8hYWYZzcZ7qfp5tox3UBauJGhEagnXknQIFu8CWkXYGc8N4VJ2CXWAklyBtpTm35nQ86m6HUFt8K37dR2tsd5yHR/UwkB1m3dCmSWLwlvg2frX+DvrTg/g0H6vsPM/1vsSlrW/hjjvu5U9/up3Pfe5jnHDCAj7/+f/lrW+9fMpzLPVUsXDOMv77F5/jWx/8Gp++5mN86hefYUbDHKqmcDTtD3/Mz7xz52CdNhPpSHSf64DuWrWTjQ9sopA10XQVM2ehqALNUEgPpxCqQmVrJbNOm3nA4nGKriAESFsitN3nYZuOG0C7h1a367eOLdG8OlJKOl7sRJqSuQ27Hb55vUD/pgESOxPExgvPCSGoD9VSH5pc0GcXWSvHYGYIn+albLzgXz6f59Zbf8bNN99EJpPi6quv54Yb/ntS5vVTT/2DD33oKgqFArfc8kfOOuvNh3xtD0QiD6kClHjBP17AbVpUEjUU+jOSqvEVypYDwznBmU3OhCt4zZDg9o0KEkmFT5K33Vzh0bzCB4908E4xkplVInmoQ6FaSlKm28ZQxhVyH+5QsKTD8bWSnUmBBKZF5AFzjwHqQq4IvbJf0BiSxDwQ9Ui2j0JTGMbybt/LfZITaiWdY26Mw+a4IGTA0irJMTWT3c67kBLSpiso7114TtM0PvTFX/GBty/l5bu+iHPa1yn1un31alCw3Uz45/tEUQwuUuR1YqfawS3+H9Ghbh+vu+DwmOdB3pl5D3OsBYfdnk8LMDOygC1ja4kXhgDQhEadv5lqf+Pr3f39UuKpYElZKWNmAqQk+CoKv/nV4H5Fxl3ZtIdLzspgS2tCCN6FR/WStdNY0iRrpulJd5Awh/GqPqp89YctEvdlu+hKtZN3suTsDI50yFppTGniUby7c5EFqIqGIy02pV4hLoeIKqXEtDIkkqSZYNPoapZoJ+DV/vXCqpSSvkwnuqLjUb3Y0mYw10vaTGLKAuudAn41iC1txsw4QS0MuHEeXtVPhe/w5gJFihQpUuSNR1EMLvKGpl/tYUyJ02xNzqAtibpOwsRQnBOt0xAIJJIt2kbu9fyFG6zWiaJqu/Dg4fLs1dzhu4XtWjsWJkEZ5q25y/A6Pu7x/5FhZRBNqtTbTaRFmpgz2dHpul4leQ6cORqQQc7Kn8+dvt/Spm3ALwOkRJKYLOGs3PmT4hY26GvoU3uZYbVOuGpLnTLGRILnjKcmicGvlrCM8O7M++lWuhhV4pQ4ZVQ5NXSrnazX1lAQeWqdBlQdXrZXk1LG8Ng6IAiLGAHLh8fU8fIaKkKNszWxjV+v/x0pM02FrwzTMXm86xkGs0O8/4ir8WoeTiqcQUCGuJc/sM1po9FqZnFmKdOy0xkye9koHRaWHntIE4jbb7+Fr3zlv7nhhk/x3vd+ZGL7zmQPqlAnhGDLsegY60ITKj7NS4k3Rok3Rn9mkKe6V3Bs7XJiXnfppGVZfPjDV9PQ0MyNN34OoSukzSyPdj5Je2I7eSdLyPAyq6SZExqWIoXJxtFVWI5JY2jqPOUqfzmdyZ0A+PUAA9lOAPJ2gcHsMEHNT8QIUeaN0ZceYM3geupCNWSsLLqiMZAZIm1l8age2hM7GMwOM6dkJm+edjYn1R876Vh5u7DP+wNARSFv7y7E5kiH+7Y/zFB2hNklMycmjTvGuvjbtgeZV9bKW996OSeddAaf+9zH+PCHr+ZXv/oxH//45znxxNMnicKaojEntoiYp4ySX5TzyStv4Lsf/DZ//PPDeNTDf11pnt3voXwqz9bH2wEoG88blo5keMcI0dooLSe2oOoqofIgQjmw3bOkIUawPEiid5RYbRShCKy8RcgM0N84SOWYB5EUEHQL7+kZjYDPT8WMcqycRWowhW+vGApPwCA1kCQ9kpkQg/eHlJKne57n0c4nGc6OoCsGc0pmoK3P8uObv0l3dyeXXPIu/uu/PkN19W4huVAo8PWvf5af/OQ7HHvsyXzve7dMZAO/FjIm/G2b4NFOQV9aoCqSo6vhqrkOlX44o9Hhnm0Km0bcgmx5RzA9Kjm5freo+XSPoOBIZkTdn/06+HXJ1jhsGhEcWbGvAHpcjWTtkGTDiKA7BfEcGKpgRlRSF5L8aYvCve0QHb/UQR1Ornd4c4tkz6fYdqB9FHpSAo8KZzU6WI7C5rggZ0lqAtASkXhUsCUcXSM5qc51Bf9kjUJ/RhDxSIay0BZ3+3LpLDlJdF47BP/oVNiZct3My6olpzZI/HuM0I6YPYuZ5/wXm//+bRqP/hjC536H5Wzwa5Kg7orRRYoUee1IJPd4/0iHup2Z1mxU3MKu7Vobf/b+npbUrImaBIdDpa+WiBFjJD+ILW1CeoSIXvJPz8PdG0WoRI2DrzjaH1X+OnqzHSQKwxM31jNWkoAWptI39Q3Kg6EpOo6UmHYefQ9B2HQK+NQAycIoGxIvkbXTGIqXtJVkOD9A2koxPezegC44eYbGBVFd9VDmqSKohycdpz/bQ8HJkbXT6MJAVTUEglFzBNMpYEkTXRggwaTAhpKttEe3UlALlMgyjkwt4sjUYsJ6jHhhiOF8P7VaM/9qJA4FpzARPzJWiJO2kng0H8IRBLQwihDYUsVQPK4TXOwu9Pdqc52LFClSpMgbh6IYXOQNjV8GMKSXrMgQkrsHhJmaNBhgbPNM5OUKBFV2LZ3aNgaUPqqcfYWQeruJj6T+h63aJjIiQ5VTTa3dgEBwnHky27QtKCjMsFq5z/MXnvQ8QpVTM3GMpBjDg/eQnLDHFE4k6sR4Xn+WQbWXOruRowrH02JPLviQHc8p3jtewSt9jIr44V2wAzCo9LNZ30BapEjZKSJObFKBPQAZlpzJedztuQtdeKlN1ODNekjLFDPEdITuYIVMtPE82lfD090vMJofY1Zs+sQEKmSEaBtpZ/3wJhZXHoFAsCi/jMJgkrxzEmE96j4HCoT0GInCMKOFYWJ7xAsMKv1s0NaSE1lq7XparXk88ejDfOIT7+fKK9/LjTd+flI/QobruNjljMlYObJWDl3R8Wq7JzKl3hjbRjvozwxMiME/+tE3Wbt2Fffc8yQ+nytkXtp6ISfXH0tvupctY6/g1TxUB6omzjFljrEzs43qQMOUOXXLqhbxQv9qntj5DIn8GKP5MQYzwxiKhlfzoSsahqKzY6wTcAuhDWaGmRZpJKD76ZeDzC2dRVOknpFcgt5UP02RBs6ddvo+E9WWSBNP73yOvF2YcAwX7AK2tGmJ7p4UDWVH6BjrospfMamNmkAVHckuOsZ20loyg7KyCn74w99y2WXv5mtf+yyXXfYmli8/jk984gscddTxE4/TFYOG4HQa5kyn9Y9HcsEFJ3PV5W/lz39+lHD41U9sEt2jZOJZYg3RiW1CEQRK/SR6RvFFvHhD+88J3hPDbzDnzFbW3beB4R0jIEBRBDMWtDDQFKdzXQ9l7TFEAmxpUxYp5YhT5lM+vQzpSAy/QSaexRfdLXBbBRuhKAd0JO9iZf8r3Nn2F1ShUmaUsuWFNdx3589Jdg1z5llv5rbb/sqMGbOREnpSsG1UMNCzgx9++mK2tq3l05/+Kqde/BFeGlWx2qE5LJldypRu1gPhSNgah1+uV1jRI8jZAl2AJeH3bbA5rvDlYx1Ob5TUhWxeGRQkC4LmiMOSSrcQG7iO2e6kW6RtTzwq2AhGclM7YetCcN18h9+1CdriCqU+aApLGsOSsQKM5ASG4mYKawoM5+ChDkFzBOaVum5iCfx5i+ClfoW8AyCp9AveNsPhnGYYKwjKvA71Ifd8bbnb1fur9YL+jKA1tlv4jefguV6FY2psmsa/jtYNwS3rVDKWpNTruoP/slVhMOPwrrm7henGELz58g/xzQduZuSx7xK+8MtYjitWN4QgZQqWVzuH9yQVKVJkSoaUAbZom6i2aybGVgJBndVEt9ZJh9Y+5QqyQ8Gr+qn5FzqB/xn4tSBzY0vYltzEWGEEgFJvFdNCrYe16grcG5i92Q46U+2kzFFG8gPEjDJinnLyThbLMakJNbIzvY3sXjnFGStFd3ob1f56BIL18ZXEC8OABAmd2lZaI0dOcsFajjleGE+gjjuiDdWDamnY0iZnZRCagukUWFe2jnXla/HbAQJ2gDF9lEdiD+AIh6XJowAwpbn3Kf1L2CXo92W78CkB0uYYqtCQ0kGg4FE9eBQfCWeIpuDMCVE8pMcO2wlepEiRIkXemBS/DYq8oam3m5hpzWa1/iJNVgs+/KREkrg+jNHgIds5OdtM4g6ipnI97sLAmHKJYLVTS3VhtyPixMJpbNLXsVZbTcyJ4QhJRqQ5Nn8SLdbBizwJBHOtI5hrHXHA/SrtajQ0MiKNXwbGz0OSUOIsyC866HEOhTXaKn7nv4UBpQ8x/meWNYeTc2cxpibQpM4saw4VThXny0tIyyzbM5tw0iZjehafCDI9P4u+XAcGBq2RA5/Tgegc6yJshCaJix7VwMFhKDs8sc2SJrZj4lV8kwrkacId8BfGq2QDvKQ/z12+3zKkDCKAbCEPz6g8856HWXz8sfz3576yjyC6oHwuT+xcQVeqm9pANYpQyFl5BFDlr5jYL2vl8KgGfs0V9zZuXMu3vvUl3ve+j7Jo0bJJbVYFKvDpGiPWdgJaeNIxfaqflDVG1kpjGJPF4I6xLp7rXcnOZA9b4u1oio5P8+LXvOTtAnk7R2NwNn3ZAQxFI6gHSORHiRphVKGwbbSDGbEW6kPuhMkf9BPWQ2xNbKcr2U1DePLNi0UV83mp/xXWDG0goPkRQMpKM79sDosqdr83EvlRUoU0xl7iv/s+E/s4s4899iTuuecJHnvsQb7+9c/xlrecygknnMbHP/75fa5VY+M0fve7+7jwwlO45pqLue22ezCMV1eER4jx4Nq9dEU301YctmurfEY5R71rGYNbhzBzJoGyAGXTyphrzeap2ufZuLUNz7DO9Og0jj5iCRX1rlguFEHdwlo23L+JTCKLL+LFLtgkukcpaYhS0nTgTEfXFfwc+VSW1Au9PHTf04z2D1Mzt5kjP3sqH3vbZ5hROgsp4cEOwUMdCttW/YNVP7oEwx/m0z97iurpi/nBKwppy70kuiJZViV5x2zX/Xoo2A78Zavg/u0KqwcFyQJoClT4oNIHo3lYNyx4YLvgXfMkc0phTunubN49EQIq/bAxDpV7bDdt95ZG5ADmvIYwnNYAm+OSWTEmhNXBjEA6oOuuOK0LKPO52+9qk9ynK4zkBINZyWAWFpa74rQtYcco/Hytwol1Eq/mnleNdP/ddXlMBzbHBaXeyQ7gqAf6M9CVFDSFJVLC4zsV0ubkInrBguTlAYUT62xaoruvw9VLY6x483U887cf0Hf8fxGOxqgNuIXrqgKS44oREUWKvC44uMVU9x4LKihIHBzeoDde7Dz60CrUsXYQBlbpXKzo7Cnzc6JGKQtLjiFjpwA3OuLVOJy7MztoG12NQKHUW8lwvp/hfD9ZO02pt5LmUCsVvhp2pDbjUwN7jYsCxAtDJM1R4rkBRvJDxDylKEKdKBK3IfEyQT08IVKXeMrZkWxD7vV941MDFMbzg4WdoWCYbIu1U0k1JZSRtBJEZIwhY5BVwZXMTs5FEQqBVxmLsTdy/JWloOxT1Hl/1AWmES8METeHKDgFHGyccce5R/GNtwtCKJPMD0WKFClSpAgUxeAib3AUFC7OXoFJgS36JkxMvHg5Pn8q2xu20t/Zh0RO5MH1qt0caS6l3Kk8eOMHwZAeSuwy1mtr6NDaKbcreUvuUi7MXnrIRdIOhVnWXI4wF/OivoKoE8PAw5AyQJlTzvGFUwA3++5l/UWGxRA1Tg0LzWWTcoenIiHiPON5nBe0Z3jeeBoPXpaaR+OVPnJkedBzH08YD1Ph1CCAmFPGW3KXcHThBC5LXsWjI3eTVbL4ZIAyuxxdNcjKNP2ZnTQGpuPTAq/qfEt9JfSm+ydtsx0bpFuQaxeG4sGnBUmao3jU3a7OgpNHV3R8qnv8ETHMH323kRYpZllz2J7oYOPWdjreswFfU5DSy2bziw238+65lxHx7HaXN0caeNvM8/jbtgfZMroNJFT4y7CljTHuls1aObpS3SysmE9dqAbTNLnhhmtobp7Of/3XZ6c8P10xUIWGJc1JcSCWNMdjKSYLq73pfn6x9jZ6Un1YtolX9ZK1s0gcynwlhAkTzyewsSjYBUq9JTjSxnRMKoMVBHQ/60fa8KiT2w0ZQbrTvSTyozQwWQz2637ePe8yVvSuZPXAWgCOKJ/L0TVLCeh+UoU0d7f/nVUDa+hIdrF2eCPzSluZGWtBQaEr1UNtqJrG8L5Fc4QQnHLKWZx88pn8/e93841vfIE3vek4jj32JK644lrOPPPNE6Jva+s8fvWrP3HJJWdz443v5eabf/mqJqPRugjBsgBj/UkiNa4I79gOmZE0dQvr8AQPbzlwPuWu169fVDcpVqLcKOMtM8+Fmefu97ENi+vJjmbpXt3D8PYRVF2htLmEuefMRts7THYv2to3cs/3b6X9iVdwLJtZxy3kTR+/kqoZDWyKb2Uk564U2DACf2sXdD78XV6+7Ubq55/Ekg/cwQrKkFugMiBpHH+pp0xY0SuYGYPjag9NbFw7JHiky3XeakJMFFcbyY9n4iqQF/DygOBS++Ai87G1ks0Jwc6UKyjnbdiZcuMkZpceuE+VAUnIUBjN73YbWw4UHDcaYtexe9PwyiA826sS9UjqgjCQFaRN6Ey62cCqgIIjeGUQutOCUi/oquToKsmlrXLCFawKt93EXoYxR4IQu/fL2dCVhBLv5HMIG65ruy8jaInu/l3IgO9/+gaW/fWHzNv2A+rf9D84EmbE4JR6h4bJq6GLFCnyKil3KmmwmtikrydkRSbEuV51JxV2FQ32vz4W4J+OlSGw/qfoAy/AeL0H2Rkg13Q++eYLphSEhRCvSQy1HIuudDuKUAnpUQACWojRQhxbWsyPLSXmKcdyLBShYEt70uN33ViW0mEw349/fFyZNpMkzVFyVoa8zPFM/0O0hObQFJpBtb+BqKeM/uxOhOXe7HWkjVf1EdIjVPnqURSFnZ6deHx+mpzpJK0EWTtN0hrFNh36PXG2FjbSqswjrB84uulgSCQr9ed4xniMfrWXKruW4wons8hcdlBROOYpY35sGV3pdvJWhrSVIuYpJ+KJIYQgZ2fQFZ3w+LUtUqRIkSJF9qQoBhd5w1PhVPHB9CfYqm1iTIxS4pTRYs9kTd1LPLv6STZq69BQsbGpsxs5L3fRId+V3x9xMcIv/T9gh9bO3HEX8YDST5u2gZQyhs957dm5u9DReWfmPdR66nlOf4ourcPNMybEi8azbHO28KDnXkaUQXQMChR42nicqzLvY5o9OX9WInlZf4HHjYf5h+fvZJQMUTvGiDqELj2s0leyxFzOoDJAUoxiCR/HFGaiotGtdvIn3x00WtMIFPz47QDlohrN2f0xYygeUtYYeSeHj1cnBi+vXsz64TZ60/1U+MsmsnprglXMK22d2E8RCnWBZjYmVjFWiONVfZjSJG9lqQ00T+SltenrGVQGmGnNJlVIs2V4OwMf70RRFab/eh7Te1tYN7SRp7uf59xpp0/qy9E1S5lTOoutie0AlPlKuX/7w6wf2kR3uhdNaMwtncXbZ16AIhS+9o3PsH7DGq69+Sl+tN7PtIhkebWkeo9L4VMDlHoq6Ml2ogoVXTEwHZOUlaTG14BfnbwE88W+VfSk+5gRnUZbfCumNAnoAUzbRFd0HOl6nAazI2SsLCIXx8Gh3FdGbbCaRG4Uj+ohbWYntZsspPBrPqKeqeMXQkaQMxpP4ozGkyZtl1Lyh81/5eme56n0lbOwYgEr+1bx8sAaBjJDlPtLqfCVcUHLOZOK0u2NEIJzzrmQM898M/fe+yd+9asfc911l1FeXslll13FO95xNXV1jRx11PF85zu/4AMfuIL6+iY+9rHJInvBNrGljVf17FcoNvwGM0+Zwfr7NzK8fRjbcsiN5fCGvXjDXnLJ3CHFRKRHMrQ/2c7A1iGkI4nUhGk5tpnS5kPPZlR1lTlnzqZhcQOpwRS6VyNaH0XVplZMs9ks//jH37nzzt/yyCP344sEaTlrISddcA7BEve5y1l5VKEQHb+ZsarXYuUvPkTXE79g8fk3cNyVX0NRNR7f6bpf55Xtbj+ou3XvVg+KwxCDwXEElX6Jquy63ebmB3cmBapwRVTrEM11iyskyTw80iXoSgo0BRaUSd4605mUq7s3UoICNIUcVg0KMhb4NTeKQQhJbdCNYdgchxU9ComC6xLOaYKupCsYhwxJT9otEGdJ2DEq0ISkPugK5skCPNPjiuVHjztzFQHLqyR/3KKQNiUB3RWCO5JQ4YfWmLufoUBAh+HJbz3X9SwkgSnOraKiiksvfRd///tP+OWXP4kUyiE7tosUKXJoKCicm7+QAbWPTdo6fNJPXuQIyjDn5t4ysQrrjYSn9yn0/hXY4WnI8YJoSqYfb8ffsErnYUemrldwqFiOyXB+wF3dpBqUeqqwHJOcncGr7i7AJoQgbEQZLYzgSPdLQlM0Kry17Ei24VG8aIpbcDVpJghq4XFBVpK1Mwzn+0mZY+N5uiqa4q4Ga0+uRyJpCc9mefnJPD/4GMP5fnBcJ3jGTuGRPtLWGHWBFipC9TyqPsTO3A5yuTSmY+LgkFKSSEti5Qqk9DHWjDzP3Njiw47H2MUTxsPc5bsNKRxCTpiN+lq26BvJZjKHVPMj5inDo3gRKGweXctIYYC8k8OjehBCoSEwfUJoL1KkSJEiRfakKAYX+Y9AR2e2NX/StgW1i3n8L4/w1vRlJLQElU4VRxaWUirL9tPKobPaeJEd2jZmWnMmXMAxp5RN2npW6is4O3/Baz7GngRliDflLmJEDNOrdhOSYRQE93v+Sq/aTbVdwzzryAkH9GZtI/d6/8T16Y9PWgb5uPEQf/TdTp/aw6A6gEd66dd6Abco3aDST6/STa/ag4KCLt14Bh2FOruRjdo6NuprOdY8CV0xKDg5NGX3ADnv5NEVY3e15lfBoooFjLTEebTzSbbEt6EpGo3het428/xJzl2Aal8DAF2pdrJ2Gk3RaQ7Ppjm4u5hZgQKMy1UjuQS9P9lO+uVRmm+fAxUCfUAnpIdYNbBmHzEYIOIJs7hyd+zFtfOvYNtoB8O5EcJGiBmxaeiKzl//ehc/+ME3mHvxV+mOLMcYlawfVnipX/Ke+btdfUIIZoTnYUmLkfwAjrRRhEq5t5oZ4Xn7CJrbRzvwa35UoWI5Fo6UGIqO5VjYjk3ICJIqpDix9mjWDG9kKDtEa2wW9aFakDCcj3N09RIGs8P0pQeIesLsGOtiS2IbUU+Ev217kONqj2JB2ZxDct32pPt4ZXA9NYHqCfHx5PrjWT+8CQfJ+S1ns7RqIdWBQ3Pfq6rK+ee/nfPPfzubNq3j1lt/wS23/Ijvfe/rnHrq2bzjHVdzzjkX8vGPf55vfOMLLFlyNCeddDpjhSSPdjzFSwOvYDom06PNnNpwAtMiU2c1Vs+tIlDqZ+tT29j+7A43DEXA1ifaGdwyyBEXzidctX/rpZk1eeUvaxnePkygJICiCwa3DJHqT7H40oVEag4v0zhYFiBYNrXgUCgUePLJR/jrX+/igQfuIZ1OsWDBIr797Z9TvbyFO9v/ypiWxbB95Ow8O1M9zC6ZyczYdBKJOD/++CXsXPs0p33wFwSXvYtn+wUFG0ZybgGzfZ4DhUMWbgHytkARkrABtUEYzkoSlsCWrrlM4Lp74wVXmD2YmCkEnNwgWVol6c2AR3EzgQ9Uy28oC3/eorBhRJC1wHYk8RwoXsGiSkljBIYzgk0jkuf7BElToisCvwYF1xSHLSFrCryamyGcNscFZd116YL7b38GXhkSE2IwwIl1kq6kZNWAwBzPG67ww9tn7o62UBU4pkby+02CeM6NkTAd2D4maAxLZpZMLb5fcMHF/OY3P2XN6hdYsuSoQ31aihQpchjMsuZyffpGXtCfZafaQblTxRJzOTOs2f/XXfunoA+8iFR9E0IwgOOrQIuvR4tvwgpPZ8yMMzqeERw1ygjpkUMaF2StNOsTLxHPD03EMwS1MNPDc1GFhukU0JXdN4ctx0QV2qRtjcEZpK0xhvMDSOnGeAS0EDMj8/FrQTyql670NjSh4UhnPBLMwnIkfi2AxM0mrg9MI2zEOKn6TfRmOlgz8iJZO0VELyVsxCg4ObYlN9KqHkGzPp0HlLsJigAePBS0PGkjzayhWeimBgbEC4NsGVvHgtjyw16ZlBFpHvX8HQOdOssdm1Q4VXSq23nYcx+LC0fhO0jh5ZydYV3iReL5YcJGlIyZJG0l0YTG/NIlVPvq/+UFCosUKVKkyP8fFMXgIv+xLF68nHQySf3LTZwzf+Hr2na30oU6/mcXCgpe6aVT3fGa25fjf/YUcrepW1hpPEed3UBEusvWdAzW6qsos8sn3M4KCtV2Ldu1rQwq/VQ61QCkRJJHvPejY+CVPvzST4lTxpAySEpJkhUZBIK4MkKBPKYoELNLMcYrau/KEs6LHF7VR7Wvge2pNgAMxUvByZGxUzQFZ05ygRwuilA4o+lkllYtYmeqG13RmRZpwlCnKEonQA0a1ARaiJpRvIpv0uQCoN5uxCcDjIo4g8/2Ev95L7UfaEE9UaV8oMbNbxNgy0NTw1RFZXq0GW/Kw3A2Tleyh1TnMB/5yHtoPPZSFpx/I9VBdzLkSElbXPBwp8K75zoTqzC9mp8jSo4iURgmb2fxqF6iRtk+GbsAUW+UvJ1HCIFP85IspMlaOezxKIixQpKIJ8Ixtcu4dPZF3LbxD+xM9bBjtBNVUZhfNpt3tF7Ei32rebr7OVYNrKUn3UfEE6YpXM/aoY20jWzl0ta3cHTN0oOefzyXIG1lqQ1WT2zzaV5mxaaTyI+yvHoxlf5Xl13X2jqPr3zlZv77v7/M3XffxW9/+1OuuuoiAoEgJ598BrNmzeX666/ggYee5+6Bh3h5YC0lniiaovF870tsH+3gvQvetU8G8i4CZQGy41m9sfoYQnHjIkY64mx+bCuLL1m430nVwJZB4p1xShpiqLr7vveEPAxvH2bnKz2HLQbvTaFQ4LnnnuKvf72Lv//9bhKJODNmtPL+9/8Xb37z22hpcQtLOtLBVGwe63qarmQ3umqwqGIBb535Zob7B7jkkrMZHBhk6ccexJp+IuuG3OJwQrjCbMFxC7bVjq/8NW3IWoK5pYeuBs+ISVb0uiLokeWS3rTrtHXGtU2vBnVBUBG8PCA4reHQHMdBA2YcQiy06cCtGxXWDglqA5JynysOZ0w4q9nhlHpX3H26W3DHJoW8LSj3gelIHCnwqJCx3OuiKJKUKRjLS4ZzkLMl06MQ2yM5RBXjAjLQl4YX+wU7k4KoR3JcrUN3yn3fLq6SzIpNPtfjayUDGXihV6E/I1EVaAxLLmvdv+t5yZKjKS0t5+9/v7soBhcp8k9k7yK5b2iktW8UhHDD9KVjsXVsPTvT2zClW3zNUAwagzNp2uPm+v7YnmxjOD9AVC9BVVyxdrQwwvZkG+XeGjrTW1GFhqF4sKRJ0hyl0lczydHqUb0siC1nOD9AxkqhKzql3sqJ8aRX8aOguDfCpRsroaCiCo28nSOkR8hYKXJ2FkP1oCsGAS2MV/VR5q2ciODSlCCWY9GT7uBkeRrr1ZV0B3Yypoyh2SotienMH1qAxGGsEKfG30g8P0jGTh12ZEav0s2wOkStNTkyq9yupF/rZUDtpdGeduA2Ml0kCsPEPO4YMeYpw3QKpK0kutCnHDcWKVKkSJEi8AYVg3/4wx/yjW98g76+Po444gi+//3vs2zZsin3/fWvf81VV101aZvH4yGXy/0rulrk/5DFi4/C7w/wxBOPMH8vMdjBYUQZQpM6UXn4eWBhGcXGYvcCaZeCyBOVBy4CdSBSIsmTxqO8aDxLXuSZZx7JifnTqHXq2al2kBMZInL3wFFBQcdgWBncpy9706N2MawM0WA1kxAjOOPujYiMkJUZciJLWqQZUxIUKKBIlWZr+kSbGZFGR5twNzSH3MiG3mwn/XY3/Xof/SUDrIg9zxK7nVPyZxKQr25ZHUDMGyHm3b/AtlPt4G7vXbRrbdjY1NoNnJ27gAXW5Oe6yW7h6MIJPDh2Dy986Am8ywKI/1IJZaM0Dc7CdExG80lOrj/ukPqVNjPc1XY3qwfXkzEzkHZ46gt3UtU4g1lX/ozKwO7nQBFQ7pO0jbhFoPx76NmKUCg5hIIfi8djGHoz/VQHqkhbObKFLIpQ8Ot+qvzl+HQf1cFKWqJNfHTx+9gw3EbSTFPuK6W1ZAYe1eDcaaezqHIBN73wXcr8pcyI7n4ddYx18XDHEyysWIBXO3B+btQTIaD5SBZSk5zayUKKkBEkbLz653wXgUCQd7zj3bzjHe9m06Z1PPjg33jggXvYtGkdAKecspDG845k4fFHUxFzi7OVeUvYFN/CMz0v7lcMHusZY6wvSagyPJH1q6gKwfIAIx1xMiMZAqVTu3UzIxmkIyeEYHBd3obfYLR79LDP0bZt1q1bzdNPP8YzzzzO888/TTabobFxGldccS3nn/92Wlv3dYorQuG0xhNZXr2Y/swAXtVLbbCajo5tXHzx2TiOw113P8UdA7N4tFMQMtyohJztZvCOFmD9sCBruxm5rhDsFpGbioINm0bcfFufBnNLJYsrJC8PCNYOCQKaJDjupHWdwpKqgFvwrCvpZua+3myJQ9uIoDks8Y2PcmqDsH0UnuoWzCpxo1nOnSbZGpf0pt0iO44DQzmJ5QgcB6QCJV6IeRzK/BA0JAhoDu/WTAq2e+3mljpsH4Vb1il0p1w3cV9aoT8jKPdLyrywNQEbhgTvmutMOIs9Klw6S3J8rU1PWuDTJDOjrmC+P1RV5cwzz+OBB+7hf/7npqLrq0iRIq8Zs3Qh2vA6sAswHuEk8nFQfQx5o3Smt+JRvQQVd9yVtdNsT7YRMWKUeCr2227ezjGU78OvBlAV94NNEQohPUrSTNAcmoUtTQazvaStJKpQKfdVMytyxD6fbaqiUeGrmfI4QghKPOU40mEo14eiqHhVH7ZjYUmTglNAU3Q86u4xTMHJI3H2qcVgqAZ5J0eZXcNxfcczqA9gGiZ6QackX4KUriHDkTYIBcdx3NoVB8HCYru6lbzIUWs34JFedGmQF3k8cncUVV7k0aU+adv+GMkPoO0l+uqKgSNtkmZiv9erSJEiRYoUecOJwXfeeScf/ehH+clPfsLy5cu5+eabOfPMM2lra6OiYurBSjgcpq2tbeLn4sTqPwOPx8Mxx5zIk08+wvXX3zixfYO2hgc8f6NL24GGxpzCAt6Uf8thFZU70lzMk55H6FC3UWc3IhD0qF0EZZhF5sHdlVORJ89v/T9jpf4cYRlGkzqPeO6jTVvP+9IfRceA8RiIXY7hiBPDKz1klexehfJ2Ums30Katp1PuoNWai0d60aROQeSpcKroYDsZkUYgCMsINXYtA0ofM8zZtFrz2KSto1/tpeDksYRFWiRZZh47EcehKRozIvMQEY2HjYfoN/qIKaUURIG/6L9np9rBNekPojOFo/c1MioS/Mr3Y3Zo26ixa1HR2K5t5beBn/L+1H9NykpWULgofRm3vfvnKJbK4u8dS6ZbEu2tZigzRt4ZprVkOsfVHpoD72/tD/J09/PUBqup8VRw59d/SC6XZfkN7yRleHHk5OXttnQzWQ+05N110QyTHnfDlHgqJhzOc0tbOb/lHB7qeAwpJQoQ8gSYHptGiSdKspBiSeWRE+JuyAiyvHrxlMdJm2ks26QhNFksLfeVMZAdZCAzuF8hdRe1wWoWlM/hmZ4XsKVNQA+QyI8yZiY5rfFEfNq+Sx5tGywLPIdXpw1w3cKtrfP48Ic/RU/PTn7yk+/wi198n/V3PMX6258iWBKhurWJmllN6I1BNnnboHXqthxHIh2Jok5+MoSiuJM/Z/8OVsPvPh/SkZOKxpl5C1/04LEomUyGzZs3sHLlczzzzGOsWPEkY2Oj+Hx+li8/jo9+9H84/vhTmT//yEP6jgoZQULjwvvmzRu4+OKzCQSC3HnnA9TW1tOhS9YMuUKwABrCkqawJJ6HRE6woAxMCXNLHZZVuZEPe5MswG82KKwbAstxHWSVfsGlrQ5Xz3NY0SNYM+QWYTM0t9DanjXwLEcQMQ7NFXw4JPICW+4WgsEthNeZFLwyKBjMCOpDkrOaJRUBScQQxMeL20UMGCtAzpH4BBxbI7lyjkNN0I3K+O0Ghed6YTDLhIg+t1SypFJy+yaFnrSgtUSSs6E9AbaUFCyYFpE4ElYNCJoigvOm7T5vIaA+BPWhQ78WZ599PnfccQttbetpbZ33Ol69IkWK/CdSqDkBfXgN2sgapOpFSAsQ5OtOZ6c3iJONT1rV5deCjOQHGM4NHFAMdqSNI519VmWpQkHioAiFudElJAMJsnYGQ/EQMUoO29Ea1Nzir2WGWxx5zIyDFBPGhpyVpik0E88eMWVe1Yci1H1iKvJ2jrAeo9pfR1ALY+VM9IKBJc2JHGNFCLyan7yVwa8HD5oZ3Klu507fb9mubcXEJOaUcFL+DKZbM1mtr8RjzcSDhzw5etQujiocP7Fy70BoQsdh8sodV6wGVfzfBMormV70wZdRcnFsfwVm+RKk99UbYYoUKVKkyD+HN5wY/O1vf5v3vOc9E27fn/zkJ9x3333ccsstfPKTn5zyMUIIqqqq/pXdLPJvwgknnMaXv/xJMpk0fn+AbeoWfuX/MaNKgkq7CktYPOV5lEG1n+tTH8fPocUbNNjNXJy5kr9676Rd2wxAmVPOm3IXMd3ajxJ1EDbqa3lFf4lmqwXfeD8qnCo2aetYYTzJCYVTKXXK6FS302A3o6CQE1nKnEoMDNq09RjSQ0ZkSIoxLNXit/6fI4Bqu463ZS+nxZrBev0VplkzmW7NYqvWRlwMU+KUUeKU8/bslZybuxAFhV6lmyc9j7BOewWP42WpeSHHF07ZR9x9zvc0PZ5uWq15CMcVsWKihFf0l2jT1jPHXDCez3p4N2GGlUHWa2vIigzVdi2t1jwM3MH8Gv1ldmjbmGG1oo1/zLVYM2nT1vOc8TTTspMLofz65z9m9aMruf32v3Fi7emsG9rImthGssEM02PTWFSxYJ884qlI5Ed5eeAVyn1lRDxhHvnRXQxs6eKcz72LQnAAv9LLzlQNjSFXACrY7vL1Mxvlfp2AplNgU2I1A7ke14WCIKiHmR1dSNQoRQjBaY0nsKhyPttHO+lK9rB9tIPedD+GanBKw/Gc2nACmnLwj3uP6kFTdQp2YVJxt4JTQFd0PHu4gnNWnhf7V7F6YB2mYzKvtJXl1YuJeMK8beb5aIrGK4PriedHCRshzpt2Jqc1njDpeJkMPP644PnnBfk8zJgBJ53k0NJy0K5OSU1NHV/84rcYVVL84ee/4ujLzsLKm/Ru2sEzt9+PVTBRNJWXFtzLwoXLaGmZSVPTNJqaWqirayRcFSJQ6ic1mJqIdZBSkh5KEWuI4S/Z//u/fGY5wbIA8Z1xItURhCpID6VRNYWaebsndLZt09GxjY0b17Jx4zo2bVrHxo3r2LGjHSklhmGwePFRXHvthzn22JNZuHAphnEI2Qj7Yc2aVVx66TlUVVXz+9//nfJy96ZWhQ/qgm7kgaowsWZgMAstUcn7jnD2WTG8N490CF4aEEwLS3yaK3ZuH4O7Nit8fInDmU2SM5skWxPww9Uq/RmoGTdWd6ddp/CRFfsKoBnTfV8EdIh4YCADmgLlvikL2u9DzCvRhELWcgVh04HVA4KelKDCL6n0S3am4NYNCmc32zSEQY5JkqbbuKZI6oLw4UU2b57misQZC57pFuwcj7uwHEF9WDK/zMFy4DsvK6zoEVQH3N8PZwVZGyr9rjidyLuZwRGPZGWf4NxmecAbQAfjuONOwePx8PTTjxfF4CJFirxmpCdGesGHMPqeRRtZj1S9mOWLMCuWYiVWTTlGEyjY0gLc7Fo3E9ghrMcIjIuzHtVHSI8Qzw9hKLsLuWasFF7VT3A8dzhsxAhz+CvxdlHpq6M320nCHCKoh7EckzErgSpUDOGhJtDItNDkvOeIUUqZp5K+bDd+LYCm6GTtDAD1gWn49SBHlhzDswMPk7fdSp8O7jhMx4smdIRQaAzOOOAYKyPS/Nb3M3Zo7TTa0zCkh0Gln7/5/sCbsm8lK7K0a23jZg6VOdYCLshdfEjFrMt91QzkesjbOTyqFyklGSuFR/EcUKT/Z6GNrMW/4ecomV7cEq4O9s5HyMx9H3a4+V/enyJFihQpsn/eUGJwoVDgpZde4lOf+tTENkVROO2001ixYsV+H5dKpWhsbMRxHBYtWsRXv/pV5s6du9/98/k8+Xx+4uexsbHX5wSK/Ms58cTT+Oxn3SzOU045ixXGk4wow7Rac91BmISQE2GLtpF1+mqWmccccttLzaOZbc1jm7oFKSRNVgsRGX3Vfe1WO7GxJoRgcF2tIRlms7aBC3OX8LbsO/mD71Y2axsANzP4rNx5nJg/jQ36WoaUQYaUAdbpq6m3m4jIKBYWO7R27vL9lssyV5MRabZrW5BIauw6ZjvzODV/NkvMo2iwmycGp9VOLRdnr+Tt464LgWCsEKc914np5AnqYcq9NWzRNhFxYpMGtX4ZQMsqbIyvJpHuw1A9VPsbqPE3HZKTYY32Mr/3/5oBpQ8QqKjMNxdyZeY6QjLMiDIEMCEE7+qfXwbpVjsntbVzZyf/+7+f55prPsjJJ58JwILyuSwon/ozwJEOtrTGJwGTB+ppM0POylPuK2XF7x/klQee5fTrL6Zl/my2j3Vyal2KFb2wKS5wpEXWTlMZGCLm76B9rIK6QNMk1wpAZ2orvdlOgnoEQ/HgSJvRQpy2xCssLjt+YnljiTdGiTfG4sojxicDWXRFx1B1bNNmePswtukQrgrhDU+99LA2WM30aDOvDK5neqQZQ9XJ2wV60n0sr1pMhc8trmg6Jnds+iPP9b6EQJCxMjzT8zwreldyw6LriHjCXDHnYgYzQ4wVUpT6YkQ9kyM9LAtuu03h+ecF4bDEMGDFCsHWrQrXXefQ1HSAF8BB+PTHvszTzz7Oy/c9yRXf/TjHX/EmBlODdGzdxsx0DcNbenjssQf5zW9/imWagLv0vr6+iarSWvyFMEE9RCAQwoOXSDTK3NnzWLd+NaFQmEDAdQA5jjP+1yabzaLMtNnw6Fp6NnYzloljijzemMYfvvFzBgb66O/vY2Cgl0KhAEBpaTmzZ8/ntNPOobV1HrNnz2PWrLn4fK++wOKevPDCs7zznW9m+vRZ3Hbb34jFdjtzWktcF29nEhrDbu7tWMEVYpdXy4OKrgUbXhoQlHh2O3AVAY0haB+FzQnBonGhd3oU3jrT4d5tgq3jiRnlPjhvmsO0PV4WtgOPdQke3ylI5F1HccF2RWOvBjNjkje3ONQeJGlkRtQ9v1cGBdUBSSIv6Em7MQ8zY5Kg4eYPt8WhOyV4x2yH+7YLOscgZ0FNUPKuuQ7zx2uJWg7csVFhRa8rUMe8kMhDqgAr+wW9KYGhSsYKbhG+eN7NIEa6bmnYLXirwhWnpYSp5vkZEzqT49cyvP/ieh6Ph5aWmWzevOHAF6NIkSJFDhFpRMg3nE2+4exJ22OeUvqynRMFbQFsxwIkYSNGX6aLLWPryI0LqbrioS4wjWmhVhSh0BicScocI15wBWHLMRFC0BKc+ZoKCu9JQA8xN7aEbclNjBVGCBlRKv11VPnqKPNWTencVYRCa3QhhuJlMN9Lzsrg0/w0BFqo9LmroFrCs/Epfp4dfIikOYqQAoGCpmho6JR4y/GrQaSU+zU1bNDW0qltp8WaNWGYqHJq2Ca20Kav40OpT7JJX0dCjBCTpbSa8/BwaEulqnx1jBbi9GQ6SFtu7pKheGgOtk7KXD4UpJTEC0MMjovLYT1Gpa8WnzZ1PNY+2AW8W36HkhvCis1z795KGy2+Ee+2P5I+4mOHdke3SJEiRYr8S3hDicFDQ0PYtk1l5eTl/JWVlWzatGnKx8yaNYtbbrmFBQsWMDo6yje/+U2OOeYY1q9fT13d1Muhb7rpJr7whS+87v0v8q9n+vRZ1Nc3cd99f+GUU85ih7qNkAxPEi4NDCSSIbUfzMNrPyhDLLAWvS599UgvznjhuD37lxd5gtJ1rS4xj6LRbmaDtnY8k6yeWdZcNDRm2nNwcPhK8L8pcUonhGkNjSarhXatjaQyxkfSn2a9toZRJU6JU8YccwFe9p9btqsvvZlONo+uJe9kEbjL/6JGB1F/hEHvwOQHZR2qBiuw8lkc4ZAyk2xKrCZtJqfMiduTpBjjLt+txMUIM605KChkRYaX9edp8DTx5tzbiDiuu2TPyAyArEhTaU/OT/vCF24kFApz442fO+D1d6TNzvQOejI7KDh5glqYukAz5d6aif6WeKNEPGGe+eODrPndExx3+bksOONoBjNDhIwgJ9WXclS1w6pBk02JdnS1n5mxDB7NYmtygERhiAUlyyeWK9qORV92Jx7Fi6G4EwNFqISNGEkzTqIwTJl331UNQggCunvTIN6VYOMDGxntHcOxHLwRL03LG5l2TPOkOAO3bYW3zjiPjJlh+2gHzniRwtklM7lg+jkT57lheDMv9K3Cq3rYPtZJspDCdmzuTz6CoRp8YukHUYRCub+Mcn/ZlNdz82ZYtUrQ0CAJjM8zysokmzYJnnpK0NT06uMDKoLlfOu7P+PdF5zP/T/7HUdceyphI8TFJ13Mm1vOIp5P8LdtD7FucCPJwQS+MYXqfIzCUJYdO7bSvmULw4ODpLJJCub4jb+/HX4/jKCPUFmUWfWzaGmZxTHHnEhlZQ3Tp89i9ux5Ey7dqZCOJNEzSn4shzfsJVIT2ef5OhAvvPAsl156DgsXLuXXv/4zweDkwjYxL1w8y+HONoUtcUAIPAocW+twXM3Br73luEKtvtdKXlW4Qqe5V3zisTWSeaWSbaPuOUyLSCJ7zXWf6RH8cYubPexRJGtHFZIFSUPIzSJe2S8Yzil8eOHuzN2p0BS4fI5DYIvC+mFBT8rt17xS1/G7i6Au6U4KrprrMK9MsnNchG0IMSnOYnMcXup3oyWC4wsfKv2SJ3a6RfJOqXdd0ZuGXUdwIu9mDZu2K/yWet2Cc46E4bzgjAYHdYoV0C/0Ce7dJhjIuJ+qNSHJhS0O86Z+CzFz5hza2opicJEi/wmYmLysv8Aa/WXyIsdsax5LC8cQlq+tOOmhUOmtpd/oZrjQj6F4AYnpFCj3VuNXg7wy8hw2NlHD/bDK2Rl2pDYT0sNU+uoo81ZyRMlyujMdjJlxYkYpVf4GKryvb55t1ChlYckxZO00AD41gBACW9oM5/oxnQJ+LUhIj06MZzyql9mxhTTbrViOiVf17+PyzcscYaOEcm8tqlAYyg+QMhOMWnFERpDIDzEtNIeG4NTLmpLKKBK5z8q5gAwypAxiYHCkueRVnbMiVFojR1Dlq2XUjKOgEPOUT8RmHA7dme1sGV2HJU0UodKX7aI328n82DKC+sFXx6nJbWipLqxg427RV6jYgVq0xGaUbD+Ov7gSt0iRIkX+XXhDicGvhqOPPpqjjz564udjjjmG2bNn89Of/pQvfelLUz7mU5/6FB/96Ecnfh4bG6O+vn7KfYv8eyOE4LLLruK7372JT3/6q5T7K+jUtk/aZ1cWV9A5+EDocLGxadfaGFaGCDsRZlizJ6IO9maOuYBSbxld6o6JHOK4GEEiWWwun9iv3KnkxMLUIpODQ0ZJ45WTnRgaGhLIigx+GWCpefSUj98feTtHe3IDNjYxoxwhBI50iOeHaBmawcb69YyIYWKyBFvaZJIJYlaEWq0Jv3CVwJydpTfbSY2/kbCx/6WCbdoG+tUeWqxZE0KvT/qJOiW8oD/rFokzF1Fr17FV20Sd3YgqNfrUbgIyyLI9zu3xxx/mvvv+wg9+8BtCoQM/v+1jG9mRakNTdDRhMFIYJFEYYU7Uocrvvv99mo/cM/2s+d0TzL7waGaffxTdqV6ShRTnNJ9Gidc9L1t0ohvriBqlKOPn75Nu/t5AtofaQJO7n7SxpTVRdGUXCm6GreUc+O5EPpVn7T3rSA6kiNSEUTSFzHCGzf/Ygi/io2b+vnl0NcEqPrzoWjYMb2YoO8Jgdoislef+7Q8zu2QmR1bMZ8dYFxkzS1eum4yZJeaJIIRCf3qARzqe4LSGE1ladeQB+9bbKzBNJoRgcOcO0aikrU2Mu2wO2MQBOWn2iXz2M1/nvz/+Qa678oOcfcybqQlUkrPz/Hr979kwvJkqfznBmlr6Y4MM6Q7vPeJapkcnV+42TZOxsVGSyVHGxsZIJkdJp1OkBlN0PN9FbjSHqqpk9TwvVqyhvKGCxvpG/NEQUpFsjrdz7rTTecuMNx1y33PJHOvv28hg+xB23kL1aFRML2PuOXPwhA7uFtq4cS1XXnkBRx65hFtvvWe/TuMF5VAfctg0IsjbktqgpCV64PzqXfg01/H7Qr+g1Lv7uRrOQcgQ1IWcfR4T8cDCKWIhwBVNn+wW6Ipb7G3tkEBKN1ZirAA5GzyqZEUvVPgULm09sCBc6oWr5zkMZN14h3vaBfWhyaaktCWYE3D749dg5n4+dnamXNE3OKnAo9vnvOWKz2uHBDnHLQCXttzCewVbUnAEDSGH3jSkLEFTSHJi3b7XYGsCfrdJwXQkDSE377ErBbdtVPjwIofqKYxZra1zeeyxBw/oSCtSpMj//9jY3OW7lSc9DwMCVaqs0l9ktb6Sa9IffE2rzg4FQ/Uyr2QpPekOBnM9IAQV3hpq/U30ZjvJ21linvKJzyGfFiCfz9Kf7Z5w2EY9ZUQ9+7mztR+klBScHAIFVajY0kLfI2piKoQQk1zAKXOUjYnVjJojONJBEzoVvhpmRRZMygn2qj6YwqVsOxa9mU4MxSCgB4nnh8g7WfxaCEua+NQADg7bk5so8ZRPKZqW2hWoqGTJ4mP3MUaVBIvMZZNMC68GIQQxTzmxQyg6vD9ydoZtyTaEUIgZbjtSOsQLQ3SktjA3NnWtiUn9cGyQDoznPecKGl2DEQxHpTGcdH9XpEiRIkX+bXhDicFlZWWoqkp/f/+k7f39/YecCazrOgsXLmTr1q373cfj8eB5NZWOivxbcvnl13DzzV/l9tt/yfEfPYVXjJfoUbqodGqwMOnUdlBl1zLPPPJ1Pe6oSHC7/5es01djUkCVGtPtWbwz854pi0bUOHW8NfMO/uz7HZu1jYAkIIOcljuHJYVDE281NKZbrawwnqDcqZxw9Y6JUbzSS4194OJg+z2XwjBZK01kPMMWXJepT/PjTUtOyZzFCt9TDIg+FEcwKzuDZmZMCMEAHsVL1kqRssYOKAabouAWxmDy+mkdA1MUsLAokaVckbmOP/t+R6eyHSOrU5upYaG1lEq1EmlICoUC//M/N3D00Sdw4YWXHPD8MlaKnkwHXtU/sVzOh5/Rwgid6XYqfLUoQuG3v/0Zt9/8I85756XUnD+P0cIYQT3AGY0nc0rD8RPtJQrDKEKdWG4Juwp9uFEbu8RgXTEI6RGGcv14FN/Etc07WXTFOKhTY6h9mGR/klhDDGXcihgsDzLSFaf7le4pxWBwRe35ZXO4beNdPN/7MgjXAb6i50WWVi2izFfKWGGMVCFNqa9k4nVkqAaKEKzsX3VQMdjrBZDYNqh7PJW5HFRVvT4rCa+47D385Q+/49Zv/oArznwXQgjWD29ic7ydGdFmjPFc5JARpC2+lWd7XtxHDNZ1ndLSMkpLd09iHcvhud+8gCcWpeSIGEIRvGKvh9RmokoZwdLIxHMV9UZYNbCWC6afc0gFcaSUbHqojZ51vUSqwhgBg3y6QM/aXjSvzoLzD5wP29W1g8suexN1dY386ld/OmjkRMwLRx+CE3hvhIBTGhy2jSq0xQVhwy2aZks4s9GZyAaeiqEsPNsj2Dgi8KqwqFIyKyaJ5wQRj9uX4RyARFMga7mRFI50i9b9tR0GsgrvmutMcvqaDmxLQMoUVAYktQE3s/e0Rsm6YUH7qKAuKNEV6MuAR4Hl1QefnHpUN+hh7+KPcvznrAW9aUHMIyn3Qk8aKvySsOGKusur3X1nxByOqnazg/fmpX5BsgCte9TYaQm7kTK74i72ZubMOYyOJujv76WqqlgxvkiR1xMbmzZtPdu1rQgEM8zZTLdnHVKO6+vNZm0Dz3geo9KuJjwu/JoUWK+t4TnjSc7Mv/mf3gev6mNauJVp4cl1L3bdlN5boFUUjYKT59UyWhhhR3Iz8cIgKTOJg0NADRL1lFIfaKHcW33Qm2COtNmUeIV4YYiwHkMVKgUnT096B17Vz/TwnIP2w5IWplNAGxeOM1YSBQVNaJhOAYlDQAsRLwyRKAxNOS5rtebSas5njf4SFU4VHulhQOnHL/0clz/51V2g15nRwgg5O0PM2D3WEULBpwYYzg/sU2RvKqxwM7a/GjW9k2d2nsw9K2bTFw+hW3Gam07g3KYqml9lTYgiRYoUKfL684YSg93CO4t59NFHueCCCwA30/HRRx/l+uuvP6Q2bNtm7dq1nHPOOf/Enhb5d6K0tJwLL7yUX//6J1z33ht4i3IpD3vuY6u2CRWNBquJt+Uup0SWHlJ7EsmQMkCBAhVO1T7LwnbxN+8fWWmsoNGaRkAGyZFlo7aWO32/5fr0jVM6BZabxzHdnkWbtgETk0a7mUZ72mFNTk7Mn0abtp42bT0lThl5kSMlUhyXP5kWe+YhtzP5nN2/e/dj189vyV3KMfaJdKkd6I5Oxo67qpGyZxsOINDEgT+W6q0mQjLEiDJEqTPuXkAyrAxwVOH4iUiL6fYsPjz2SVaM/YNEdhCP40Vgs0qsoDE4nft/cy8dHdv4xS/uPOiEIm0lKTi5iSWQu/CqfjJWiryd5e4//IFPfvJ6rr76er74xW9hOhbJQpKAHsC7R+E1KSWa0JFTOCQkciIDOFlIsTWxnTHTxpGSRGEIj+rDcixsadIQnE5AO7AYnE+72bTKXmvSDa9OJp494GNfGVzP870vUxusnoicyJgZXuxbxZlNJ6MJjbydn3iOC3YBiaTSX85wNn7AtgHmzJFUVgp27IDGRlcQTiSgUIBly159RMSeKIrCTTd9jzPPXM7PfvZdrr/+Roayw26htj0K5LnFa0J0jO08pHbH+sYY6x0jXBlCKIIe2c9quZ4B7xAyI9ETBpWxXc4aeVhV0dPDGQa2DBEqD2IE3D56AgZOWYD+tgEy8Qz+2NSF7IaGBrj00nPw+fzccce9hMPu8mHTKTCU6yNnZ/CoXso8VRjq/qNfDpXpUXjvEQ7PdAu2JAT1HjiqSrK0arKru2Dn6M12MZjrJWsJVnTXsXm4Eb+uYTmwblhwdBWEDUlfBjrG3BzelOlm8+ZttyhcpR+QgsaQZEtc8NM1Cm+d7tAcdfN779ik0D4qMG1JUFeYW+Ywt9QVbM9ucnhyp8L2MYHtQJlPcmaTw/xSN6u4fdTN+o0au4vq7WJOiaTMJ+lMunEPvWlBf8YtKhc0JENZV4j26q6DOeaFRRUSvw7dKbhwukPzQVZyD2YFHnXy614It++J/egpLS1uIcwdO9qLYnCRIq8jJiZ3+W7lGc9j5MkjkQQ8QU7Ln815ube+Zifn4bJd20qe3IQQDO4N8IAMsFZf/S8Rg/dHQA8jhILt7F7F5K5cKuwzZjpUUuYY6+IvkraS5KwsKWsUCZhOHhuLMTPO3OgSKnwH/txLFEZImMOE9ehE9INH9WI5Jv3ZLpqCMybGXPtDVwz8WpAxM45X9U1EtdnSQhXKhEgM4Mjdn+F7rtgwMLgicy33ev/MWv1lRpVRGu1pnJ4/l3nWkYd9fRzpkDQTSCRBLXJIBYIPjhgfz00OtN91vgedZzgmQkpyzRey/ZGH+e2907AcqI9sxVJCrBlcytCtGjfc4BCNvg7dLVKkSJEir5k3lBgM8NGPfpQrr7ySJUuWsGzZMm6++WbS6TRXXXUVAFdccQW1tbXcdNNNAHzxi1/kqKOOYvr06SQSCb7xjW/Q0dHBNddc8395GkX+xVx99Qf4/e9/zYMP3MN5572VxYWj6FJ3oKPTbM045EIO/Uovf/X+gU36OixMquwazsy/eVKMA0BcjPCK8RIVdjUB6VrbvPiosxvZom2iS91Bo+06FKWUDJp9bJEbKKgmDXozRxdOOCwBOEeObrUTFZUmu4X3pD/EE56H2aq1UeqUc07hQk7In/aqJzgRI4ZX9ZO2khOuCCklWTtNjb8JQ/HQYDfTYLuVhLd5N7E1uY6CncdQPTjSoc/ayah3jI7on6mhnsXm8ikd0rVOPcflT+Eh799IiDg+6WNUSVBhV7EksYx16ZXk7SxhPYaCgpnOUanXouvugD1rpXlp6wpuvvkrXHPNB2ltPbDTEkATOsr4EkVN7J442NJCUzT+dNfv+MSNH+Cd73wPX/zitxBCYKg6pb7dNr/to508uXMFWxLbiHp91IQDjOZymLZNQPcT8nrQhE6pt5KXB9bw5y33MZAZRCKp8EdZWDmdyqCfoB6i2t9Irb/xoCK2P+YDRWAVLDRj9yQtnypQNt2dpCXyo4zlk8S8UUJGkIJtIpG0xbe6E2B9t/DoH/9/spDixPpjuHXDH+jPDKIKBUUo1Adr0FWd5kjDQa9pLAaXXOJw550K7e0gpSAQgFNOcTjmmNdHDAaYM2cB11zzQb7zna9wwQVvJ6i77zdb2pOKFaatDDN80/bXzCSkdP8KRbDV2c5DzhOMEMcSFl1qD/lhk4XGfEq8MRKFMU6sO+aQBWEzU8AuWGh7Cb66VyeXzFPImPinMM6nUkkuv/zNJJNJ7rnniYk84rSZZH1iJaOFEbfvQEiLMCe2iIhRsm9Dh0lTGJrCk4uk7UnBybM2/iLD+X40odOVhJFCBlW1ieemEzIUSjywckBhWZXDk90KY3lBQJdkLEHKlDiOQMHN4g0ZEqHAzhRsHBHsGFWoDbru3JQpaApLvCpsjkt+uU6hzAdVfknYo3BircOFMxwGMmDZgpDhunj/slVh47Ag70gMRaG1RPKO2Q6l43p5uR/eNlPyy7WCRzoFOcvNFA4boCDoGIO0KcmYEDJgRlQS9cBg1s0ljk2hu0sJWdt1J6sK1AUlqwaUSfEotnTdyJVTa/8EAm4OdCaTfo3PYpEiRfZklf4iT3oeodKumhBgh5VBHvbexyxrDq3WwccNrydCut8fe9eMcHDQ5IHFzD2RSHqUnYwoQ8RkCbV2w2t2Opd5Kin1VjKY68Gj+FCEQs7OENKi1PgPPhaYir5sFykrSUANMfb/2HvvOE2qOt//fSo9OXXOeXpyYIacQQQUERUEMWJg191V77rRDder7u7Vu+FuDq4YVoyoIKIoOachzDC5Z7pnOufuJ6dK5/dH9fRMM6mH4PXnPm9evKCrq06dqnq6+5xPfc7nayYJqKGFdr1VUY7rMJo/eEp3sO2auNJFfYXJQFN0bNfElhbaCQwbh1GEQmuoi92pl0ibSXTho+DmUFCJGQl8ip+SW0AXOjEjwVxpmrHCITJWiqAWojHQRkOglSqq+WDxVlKlJGVRotqtXVLkeLkky7P0Z3YvisEhLUJXZBV1gebTbuto4kYVAS1Izs4Q1mKLcW8lu0BrqPvEorldwjd6P8bEEwgrjxNu46WZ8+itPURX7RhJuhk1zqNbjXLgAOzYIbj44tdvjFehQoUKFV49v3Zi8E033cTMzAyf/exnmZycZNOmTfziF79YLCo3PDyMohyZlCeTSW699VYmJydJJBJs2bKFp59+mjVrTr10qMKvD2vXbuS88y7my1/+B972tuuJkyBunziq4HgUKPCN4H/Qp+2m0WlBR2dUHeZbwa8QzAdZba9f3LcoCpQpUS2X5nsFZBBTjFEQ3uTedMs8l3mU7eZWCuRwhctTfpfGeCc3Ox8hyPFVAguLZ40neN54mgG1j6SSxCd9hGSYdqeLdxbfw8cKn8TEREN7zS4XvxqkK7KSA+ldJMszi8JpRIvTHl5xzP6t4S7ydobp0jh5O0NKSXIwMMBI3SiOX/KMeJynjEf5cOG36HFWLjlWILiudCNNbgtb9adIKUnOLJ9P7+wK5tITSOmiKhrzpWkKTh5D9S1Z2hbQQvzg375DMBzi937vz5d1fTGjiphRxXx5hpieQFU0TKdMwc7x2Dce47/+7St84AO38sUv/vNxJyYDqUFu23k7M8U5Er44A8l5nhgbwXYtFKHgUw3aY018cPV7KJo239t3FwWrSHesk7yVZ1/yAC9M7uLqjsu5rvuttIbal9Xvmu4aajqrmT4wQ6gqiKqr5OcKGGGD2g01/HD/T9g6uY2CXUQXGgHNj1wQ9XJWnqJdOqZNied0/di6DzBdnOOlqR1E9BAJXwxL2tQEqjm/6exl9W/9emhvd+nrE5impKVF0tb2+heb/v3f/5/cffcdfPazv8///fev0hRuYCB1iLZIK7qieYK2onJ24/KKPUbqwvhr/AxNjfBozdMUtRLdogO9pDEZmCFppXh28kV6452sq1nFxS3nL7uvwaogvrCPUqZIuOZIBkIxXcIf8XkC/yuwLIuPfOTdHDrUz49+9CDt7UdeJPVnd5My5xbyqVVc6ZI25ziQ3sXmmgtPy7X8apgqjjFXniJmVKMKlV0zMV6aasCREiFNHDdAzOeJqLaEqA5IiSMh7pMUbUiXvX9XJCRtUcm+eWXB/esVexvLwd55hQuaXAKaFyUxnBUgPddvTxySJckvBgVDWRjKKKRNiZCQLCtIYEONJKRDwZJsnxb4NYVb17mLn8UtdZJH4oLJAjSFvOJ3CR+MZMF0vXiH3XNe3nFDEKYKnqP3mi5PGD6MlLBtRvDoqGAyB1EfXNgk2VwneW5SciAlaAh66yQm84K2iGRj7fEnzj6f91nI5E/u8q9QocLpsUvfBsglTtxqt5ZpZZJ92u5fuhjca68mJCPMKjPUunUAFESesiizyTp1litAXuS4w387LxsvkhdZgjLMBmsz7y6+/zUVodMUnbXxLYzkE0wXR3GlS2uwm9Zw15Ls3tMhYybRhY4tLRzpYijaolnVdMqE9Ag5O4MlTQxxYrNGUItgKAZlt4hfPTJeLjkFonpioRjeqakPtCCRDOcHkNLF5wYQCHTFR8qcBSFoC/VQsovsTW/Dck0M1cd8eYb58ixlp0RHxFt5F5eJE707PSVFO8+e1EsU7CxhPYZAkLez7E1tw1D9xI3lrWAEr06HI20CahBFqPjUAF2R1exP7yRZnkGIhULQvmraI8eO4wGQksCBb+EbuR9pRJFqEN/YQ5xRegLadPxhA1fuYtQd4Wnz40CMdLoiBFeoUKHCrwq/dmIwwCc+8YkTxkI8+uijS77++7//e/7+7//+l9CrCr/q/O7v/ik33XQ1P/rRt7nhhvef9vG79ZcZ0ProsVctFoELOxH6tD08bTy2RAyudmupdmuZVaYJOUcGy3PKDDE3QYPjveEfyO7l5dJW5gNzREUc1VVxCzZDoo8Hqn/KdeUbj+mHi8sPAt/iUd99FClxUO+jSIlat4711mYOaHv5ZvDL/I/cn1ItjyzhmxdzPGs8wW59O4b0cYZ1NmebFyzGLpyK5mAnQS3MdGli0ZlbH2g+7mRAVwzWJs6k2ZwjZc/x/dA3GQuN0y56EI7AxeWAtpef+u/kU/k/Pkas1tA4z7yY88yLASjZBbZmH0UTOiHDc8tJTZLKzuG4NtJ3ZLne5OgEz9z3FJ/84z84ZdE4V7rMl2fIWPME1TBFJb/oxpC25Dtf/BYP/uQX/Mmf/CWf+MQfntCh8sjIk8wU51mVWLGYWzudT6ErGp2xZmzXpX9+gseGX2RlVYa50jyrE73Ml1Nsn965KMzee+hBxnKT3LzqXWyp33jKZ6IZKhuuW8eBxw8ys3+act6kqj1O5/mdPGw/xYPDj1Hrr6Lal+DZyReZLsyyItFJZ7SNsdwkM8VZGkP11Ae9lxY5M4+LxK8F2J8a4JY172Ft9Uq2T+/Edh06Ym1c2X7pspzBh4lG4ayz3tjJQTgc4fOf/1t+8zffyweefpYPnPFufrD/HkYyo9jSodqf4C2dV3BG7fpTtuVKl6cnn+dHxk85oA0wYc5Ql6wiYPtoTNTT2dTOrJokWUpxTddVXNVx2RJ39anwhX20bWml7+EDuHYGI+SjnCtjl226LliJETw2s+8v/uIzPPvs43zvez9n3bpNi9uLTp5keZaQFl3Mp1aEQliPkbGSZK3U6+IOPhmHXw6pQqVoq/TN12G5GvXBWZAalhNgrgR5C0xbUB2QbKn3IhgUPEH16QlwXMGGWslkXlC0PDdt1PDE1KIjMF3JfEnQFJbMFD2ncMIHjhTYUlIbhIMZuOegwhm1kpVxSJuwc84rHnc4CzioQ1NYsnfOE34PF25LlT1xdk3VUqdvcxj603BDr8t5WcEzE4KZkiBiSN7eLbmqY+lne+uU4Ft7vEJxQQ3GsvCdfYK3dMKH17rce0hhKON1ZnO95Noul6rj/Arum4d7D3id+95uE3ul4M3tnkhdoUKF10aZMopUj9kuULDEyQu3vhF0OSu4svw27vP9hL3aFMIrqcbZ5vmcbV64rDbu9t/BE76HaXKaaZItZEWap4xHUKXKh4q/+Zocwj7VT090DV2RVYBcUg/h1eBXgzhyCk0xEEJ4MWLS65+qaNiuhU8JnDJWLKRFaAi2MZzrx3ItdEWn5BRRUGkNdy95GSqlJG3OkbUzqEKlyle7KCALIWgMtlEfaKbslLClxWxpimR5Bl0xqA00UeNrYNvcU9jSInFUoby8lWU4P0BDsNUrUvcamClNkLczi4WaAaJ6gqQ5y2RxdFlicMkpcDCzj5nSBC4uQS1Me3gF9f5mmoLthLQIs6VJTKdM2IhR52/Cd4JYKTU7iDH5NE6wCemvQlh5hJkl4tfZPrWZqkANuijSpj7HlOjhZd5F4vR8NhUqVKhQ4Q3k11IMrlDh1XDRRZfz9re/my984TNcccU1xOOnN2KZV2ZxkYtC8GEibpRRdXjJNh8+3lR+C98NfIOD6gFiMkFOZDBFmWtLN5CQVViuSX95N2kjTVTEvOVkCqg+nWgxykvOc1zJtUsqEwMMqgM8YzxGndPAiDqEKjXa3A7mlVlm1EnWWhvp03azw3iRy8pXeX0Xc/xn6B/Zr+0hLCPY2OzSt9Ov9vHB4m8saymbEIIqXx1Vvrpl3S9HOMwF5+jX+hgIDNBpdy9ORhQUGp1mBrUBZpSp48ZFHE3GSlJ2ikvy6YQQRPQ4yfLMoksD4Edf/y6hSIj3vu+jJ++fdNifepmx4hCudLBsm4n8PAE1RK2o49/+5K/Z/uLz/Mu//Bdvu+4GRnPjaIpGQ7BuiShcdkz6U4eo8ScQQlB2yoznpvCrPqYKsxTtMkEtgC1t7h74OdcWC2A7IGB/coCCXaQ2UE26nCVqRCnZJe45eB9rqlcS0E4t1AfiATa8fS3lXA+O5eCP+pkrz/Pi89upD9RQHahiJDtG2SlRHagia+aJ+WKc3XAGDw0/wa7ZvQwaQ4uZcUIoPDj0KPcPPUzCF+ey1gv583N+D2chA7nklDEdC0Nd/tLVNwopJWO5CYazY9Sf2c3mM8/h7/7uL7jnnif4/S2/zaH0EJZr0RJpIu5bnjPqybHn+MrT3ySfKRAJRJjUZ5mOzKOUVNZFVtPc0ETUjDLv9+7h6QjBh+m6oBPVpzLy0ijlnEkwEaB1cwttZ7Yes+9dd32P2277Z/7iL/6e88+/ZMn3XOkgpXuM+1cRCq50cX8Jlb1VoSIXchTHs0Fs10dY95ysEoEiPMOX6Qq64w6751Qc6UU9vDwrmCt6juGSDQ8PC0I65G1JdQB64hJVeFEMhqKQLHvnsRcuq+R67mLfwuVnTIHlsKSAW0CFsu1lAHtxFxDQvAJ2haM0H2Uhv9d9xXsLL+ncK4R3XY/k8jZJusyi2/loLBceGhakypKSLThoChThicIPDwsuanb41Bkus0XvXNX+47vkB1Jw2y6FZNH73V8uFbl3UGEiL/nNDS7Ga9NhKlT4b89qex0vGM9iUsZYiAkriDwKCp12zy+9PwLBNaV30muvYq+2GxuLDqebddamZcWYzYlZXjCeBSSHtH5cXGrcemrcOnYYLzFdnjzlOGs5vF4rTeoDzUwWR3FcC0P4KNpFhBBoQkcVGqZr0h7uPaXoLIRgRWQtfiXARGEYS5rEDa8AXZ3/SN6w49r0pXcwWRzBkTYSCGoheqMbluQSK0JdLCIc0eN0Ro6sXCvYOfJ2hqC6tHpqQAuRNufIWZnXLAaXnCIglowxvfuiUbCypzzekQ57ki8xW54koIbRhUHOSrM39RJaQqPG37C4Em45KIUJhJVHhr3VakppBmHniSXq8c8WSc67hKMBMiJOVe4p2tvewfpTv3OvUKFChQq/JCpicIUKR/G5z/0NF1+8nv/zf/4XX/ziP53WsTEZRwA29hLxNK/k6LVWH7P/psIWCuUsWwNPkzbSNDrNXGBexgXmpYBXodmUJo5iox79oypAdVUsaVIWJQJy6eByWB2kKAokqOaQ2k9SmccUZVSpMaVMso5NCAQpcaTI13PGE+zX9rLCXr1Y8C4nsmw1nuYs6zzW22ec1r04FQfUffww8G1GtEFSYp4h7SCaVOlxjlSpXlrC4uQIoQCee0RwZHIQUIMUtaC35FA1SM7O8/DdD/D+3/oIzfGTRy1MF8cYLQwS0iJkzSIPDm5lODtGYTbLwL9txUoW+fJ/fZ+61W387Qv/ymR+CkUo9MQ7eUfPW2mJeBMITaj4NR+ZsjdQNx0Ly7VIlTPYrk3cFyNqRNBSKdJTw5RfuBO3XiM1mqIQyhIOhpASLNci4Y/RFG5kJDvGSHaM3sTyyzL7wkcmjPOlFDkrT0fUc/BmzRxSQkjzk7MKlJwSUSNCzAgzW0wikTiuS9Ep0hiqpz3qxStMF2a55+Av8KkGI9lxds/txXQsagLVvKntIs5tPPOUuca2azOem8RF0hxuQD9FMZflYrueuP7k2HPkzDxCCGquWsH9f/UtHn/8IS655ApWVvVgmw7lbAnTMY/ruj2asmPy0KHHMZMmjWodWkAnLbPMMU/WyJPKZaguVDFWnmBNVS+NofpX1XdFU+g8t4O2La2YBQsjqKPqx0569+7dyR/8wcd517tu5iMf+e1jvh/UwoT0CBkrRUyvWnwWBTtHQAsdt+r5602Nv4GJ4ghlp4QkQkADTTEpOwqZcgjb8frUFpacWe9l/W6b9vKBpwrgVyFuQHOV5/gtu1Dlh821ntsXIKx7WcK262UASzxncUgX1AUlWctz/+ZM778Daaj2eS7ggAY52xNqDzNXgoRPLhGNYz5YXeXy5LhCzCfRFC/yYSQLjSFJV9zbL2IcKwIfJlWCgZRgNAsugpAmcSVMFwRzJRhMQ01gqVh9PJ4cF8yXBKuq4D7NIEiRzqhk1xzsm4cNtSc/vkKFCidni3UuL1nPs0vfTlAGcZGYouyNh6zXdzy0XASCXnsNvfbpx9gllTn2aDvJiSwaKiAYU0epWVillhUZ6nntYvDrRZWvjt7YOg5l92NoPky3hIu7+FK/NdhFa2h54x9V0eiI9NIW7sZ2bfQFt/HRjBeHGCscIqhF8Kl+pJRkrCT7MzuI6nH82qlf6qpCXYhIc5Zsd6WzuDrmtRLQgiAlUroL496FYn3SXtbf82R5hnlzhqieWMwANlQfqfIsY/lD1PgbTqs/UguAooJrguoD6b0erQmnOKN7jD2ZFrI5gV/XaKg1ueVtDrFY5W1lhQoVKvyqUBGDK1Q4ioaGJv7wD/8Xn/vcH3LzzbewYcPyMkQB1lobaXU6GND6aHHa0aXBtDqBjs655kWL+zmuTX92DxOFYYRrcY44m6AvwvroWUT1+OJ+PjVAXK0mYAUo+YoEFvKBhQ1FrUi1VkukfOzgz8CgIAq8oD9DWklTpowrXGzFQroSB2+gmnCPLCfbre8gJEOLQjDgOYTFEEPawddVDE6KeW4PfoVJdYxWu5NGWhhXx3jR2Eq4HKPBbcTFZUIdY4N1BrXuqQW1uFHtibZWmqjuOXAd6VCWZXqj64kaCebKU/z4e3diGD5+/zf+1ykdLLOlSQQCTdF5ZOhRhjPjBGYE2/7ucVRV4+w/vY7tgYNM7XmWkl2mIVSH4zpsn9lFyszwqTNuJWpEvCza+s38qP8eYlYU/4KbN2vmCBthQnoQI1fENziGz7ZpSjuU42H2lwcIzdmkOpvIiBxRI0RLuMkrMAWvaUln3B8jqAXJmlmq/An0BRdvyS5jqDp+1cd0YZah7BiNwXoubDmH4cwoz068SLqcxXItfKpBbbCa/vGD/OmTf4kEqvwJOqNtTBWm+fbeH6IpGmc1nPizsz85wN39P2c4O4ZcEIOv7bqKdTXHvjxZDmXHZPv0TnbP7eNgaoh9yX66Yu2sTPTgSpfBlSNUdzfx13/3OS668HJGt40x9PwwxVQRzafRtL6R7gu70ANLBeliusTYjnH6Dw1woDSAntVRoyqKELTTgilN5tQkQ9Yw1pxNV3071/W8FVVZOukxHXh5RrA/6Tk+V1fBuhqJfoKPoqqrBE4wcUqnU3z0ozfS2dnNX//1vx9XdFeESkd4JXtSL5Ey5xD4yJQhqCv0VvUuydJ+o6j1N9ES6mIsf4iAPkxQj1G2fbSEo1j+IEJIMiZc3Cyp8sN7V7vYUuGH+xWkBF2V9MShO+7lB4/lvAiG2ZLnqhUCpgqCsxska6ole+cFjiuoC8JsUbJrVrBrISoiXYYJRzCUEeiq5wSuD0mmil7OcLIEGdPLAL6qXR4j6r6lUzKRlxxIeT9/LlDjl7xrhefuPRV+DeaKUHAEzaEjFmNFkUwXYCwvOHMZYZIHU4KYT+LYZRzbxAiECWheJMZkQbDh1QZSVqhQAYCIjPKxwifYajzFTn0bilTZaG3hHPPCZRcUPh0kkj5tNy/rL5ISSTqcLs60zqPafX3e7Eyrk2REEh8BEq7n+rSxGVNGCMrw63ae1wshBC2hLmr9jaRN74U0UiKFF/0Q1qKnfNH8ShShYqjH/3s6URhBFdpiHIIQYiF+YYa58jTNWscp2/epAWp8DYwUDqIrPjRFw5UOWStF3Kh5XSKZav2NjOqHSJlzhBbuQd7K4lcDNASOXTn0SopOASndY4rBGaqfrJXGPc5KopNhJ9ZgRzrRMv3YkW6k0BB2DuFYNDS1EF8ryeVsQvkZWHENpbaKEFyhQoUKv0pUxOAKFV7Bhz/823z/+//FH/3Rb/OTnzyOYSxPMInIKB8q/CY/DHybQ2o/trCpdmu4sngtG48q8DGSP8hw7gB+NUTIiGBLi2wpxQG5kzOqz1+S7bkmeAZDuQGmyuPk1QyG68NyTYqJMle5N6By7MBqpbWWosgzpU7Q5DYzro5iYyGlpEyJnfpL9NprlvTJJ/2LIvFh5MI/p1Opejns1Lcxro6wwl692P+zzPN4wvcQL+nP0eusxsSk2WnhbaXrT1nczsRkh+8lXm72lvjVFWrpzPegS40qo5bu6BoCWoiIVcXP77iHj33sE1Qlak7aJnhuDiEEM4Ukw5kJCs9O8tztTxFtq+LKz9xMOF7F0+NbifmibKhZszgxCepB+lMH2Tm7lwsWCqmdUbeOXXP76E8dREqJsrDML6QFsF0Ha24SC4tVxAnrBjcpa3lGm+bJ9C6SySQtbavpjncS1kMMZAZpjjTSFm0B4EDyIM9OvMBYboL6UB1nN2xmTVXvSSdKdYEazqhbx6MjTwFQ5YsjgflyivU1qzEUg75kPwCrqlegLhQeC+lBinaR6cIsoViQvXP7GcqOUrAKtIabKdkl9qcG2Fi7lpJd4rHRp9lSv/G4k4upwgzf3PN9ZgqzNIebEAiGs2N8c88dfGLTRxevb7mYjsV39v6QZydeQAiF/ckBMmYWXdE8wVvR6Iy1MfK2Tbz4j/fyk9vvxDcUQdFVAlE/VsniwGMDmHmT9detO+KiTRbY9oOXmR9OYgccpN8lV8zjs3Qi9REiIkynbMdwdNY7a7hixSWc23MmVf6lMTNlB761V+G5CS8eAeDxUckFzXDzSs9pulxc1+WTn7yFZHKO73znpwSDJ3Yt1QWaUITOL4ZSPDPuJ2+GqPYHsU0/1e2eQPlaOZCCp8YEwxlBTRDOafAKognh/R7rja6nzt9I2pzH7dF5cKiOouXDp0LJgRVxyVs6vWJt1X64cYXLnllBSPfcv76FX3OG6onob+926U8p7JsXSNc731UdLu1RKNmSyTx8eafC9mmB5Xru25kslB2BXwNVeIXl9s1DsiS4aEGIzpqChpBkS52kOSTZn4SOKIuxC40h+OQZLtunBVMFSdSA9TWSpmXWSIoYENDBdSWm47Vru5CzIKSBtkxtI+GH6QIUC9MABOP1OBKQXhG8ChUqvHaiMsYV5bdyRfmtb/i5HjMe5M7AdymIPIY0eM54iueMp/ho/hM0u6cW+U7FhDJGwq0mp2TJijQ+6acsytjCIu7GSMjjCJVSek5P5f+dgOdTA9QFXlu0wqmQUmK5JurR+cPy8At3gSPtZbfVFV1FySkwb04vxiNF9DgrYxtelwgNvxpkbWIL/dk9pM15pJTEjCo6IyuJGqeItpMuBhreNTmLTmXNyhGd30NYD6PE5iFw6vHxIqqP4uqPEthzG8bUU6i5MYRdAOmgJXcTsLL49CgEDezcQULb/xarZiNW/flIPXTq9itUqFChwhtKRQyuUOEVaJrG3/7tl3nHOy7ls5/9Pb70pX9Z9rHtThe/m/tThtVDmKJMs9NGWEYWv+9Ih/HCELpieMu9AF0YiwUgUubckszdhkALV4p38lT5QcadEcp6mUAowhX+d7HZOvu4fYjIKFVuDXPKLHmRw+f6sBWLICEE0OA0c0vh40sG/5usM9mhv0hWZIjIKBLJlDJBRMZYZa89zTt4crJKGmCJkF3vNrLe2oyNzTnli2h0G9lsnUOde/IlayYm3w58lWd9TyBxsUM2A84AuUKR9+Q+RKO/DUdxsbD46lf/Bdd1+djHPnnkeKfMbNkrlOHXgtT46hcdE1X+OqZKY6SzOfbc9iTTzw7S/eb1rHnfOVRFaxBSI2vmqAvWLhFetYWJ03wpScEq8NOD9/PC1MsUrAKaotIRbeP6nrdx+74fMJ6fpOyUqc2abJj302ALor4wzWqcG6ni0tEMD8SjPKv5mS7MMJ6boDZYwzu634JPNdg+vYvb995BppwlrIc4mB5i+/Qu3rPyHZzXdNZin9LlDKZjUuVPoCoqQgje2XMNAsG2mZ0UrCKd0VYKdhGBoC/ZT9Eu0R5poW5hYhDUgyhCYLkupmOSM/OMZscB8Gs+oj7PpZ4spTiUHmZlopvpwgxFu3Tc3Nzt0zuZyE+zeqGoHkB3tIN9yQO8OPXyaYvBO2f38NzkizSFGwnrISZykyBhPD9JfbCW1kgzilCo29RO64ou/u0//5bfv+YviDV6/TZCBqquMrl3io5z2okubB9+cZT5oSTVnVUoqsImZz2P8jTJ+TRaVocwzNtJVma7+WDvu9m4dsNxhfjt04LnJryCYyHdmyRmTU9EXV8Nm+qW7+a87bZ/5sEH7+Wb3/wxHR2nXiq7d66OJ0fr0QW0hSFvw10DkLUkN696bS7SnbPwX7tVUmWIGpLxPOyaFcz3wJvbvbYVoSzmiXdGYH01bJuWJEuC1ojLlvqlkQy1QeiIScZzR4RggJmCF9+wqRYubnbJmF4kRMw4kq3r12A4K5gpwKUt3vkH0zCWU/BrEPd5+5ZscC2BJSW/v8WlKeQVsXtxWvDgsGCmoKAq0BKWvLvXpXdhnh014OKWV3/PzqyXzBQFBdt7/kJ4he5C+vKLv53TINk3LxibnALAF63nYBoaQrC2uuIKrlDh/0/MiVnu9d+Firo43nJw2K/t4QHfz7il+PHXfA4FhTq3kVangxF1kJJSxJA+mp1W1lqblu7sOhgTj2OMP45SnsWJdFFuuRy7+tRFa49H0c5Tdkv41cBiQbZfJlJKZsuTTBe92ghRo4rGQCsh3Rube/UuahnJD+BzAmTtFAU7i+XaaEI7rRU0fjXIxqpzmTdnKNp5DMVHtb/+dV2FEzUSnFF1Pnk7i0QS0iInFZqFlcMYfRBj8mkCTomx6k7mgjnCgSZq5/ZRP/IAvtI8UT2OMbmDUtc7MZuvOH5g/XFwol2Uum9AT+7Cqq7CDbUi7Dxaqg+lNIerh8EuoWYOgRDo01uxZreTX/c7sIz4jQoVKlSo8MZREYMrVDgOmzadyf/+3//MH/zBb7J27UY+8IFbl32sikqnc/wCI7ZrYbkm2isGhqrQcKWL6ZpLtgshaAl0cKP/oxRlAVuxCRNBsU888FNQaHJaEQ4U7QIlt4DfDRAWUTLBNDcU30+707XkmLPNC+hX+3jO9ySjDAOSiIzx1tI7jtn3tVLj1CEQWFiLsRSeB9nl8vLVfKD4sWW3tVPfxrO+J2h0mhdF97zIMRDt5wXjBeaUexnQ9qOWVX74X9/mhve+j5oaT2zPmEn2pF4kY6UXfJqCuFHN2sQWglqYen8Lzw0+wd/+4f9ibmKG1beex8pLNxDWogS0MKOZcWqCVcfkwzmug0QS1SP88MA9PDb6DLX+ahpCdSTLafpThzijdj2/seGDfL/vLgp2kcsyeVqGxsm2RumOd6IIBem6VKlB3rXunXSuqmIqP03CF2dT3Tqawg1YrsXPBx+iaBVZmehZFCCHM6P8YvBhNtWto2AV+dmhB9g5uxfbtWkJN3Jlx2Wsr1lD2Ajx/jXv5srCZaTLGar8cRSh0p86iCMdds/28fTE1sXrqg1UE/fHGcyMYLkWk/lp5sspwloIRzrYro2maAT0ADkrT7KcpiXciE89/iRorphEE+oxhVB8qo+pwsyyPwOH6U8dxJUu4QW3SZU/QaqcQSCYKc7RGmmmaJfQVZ333XIrX/qzP2Eo08+GpqMc8hEfudk8hVRxUQye2T+DP+JDUb2fuXOUzeQjebaXdzMqxzCKPjpo5Ybea1lz1eoTOrJ3z3tzq6OdmxHDiwboS0o2La/uIgcPHuBLX/qffOxjn+SKK07tWDMdeHREYAhoW0iVifrAUOD5SYVLWxwal+lsfSWOCw8MKWQtycrEkbnjeN4rlHZWgyR+HIGzJ+4Vf+MEcQaG6gnJ396rcCDlOXBzpldM/uqOI6LpicTT+ZLnvlaVBdHdFtiuxKcIXAmtYYkjPTG27HhF5VQFhrJw5wEFRUg6YxLHheEsfHOPwqe3uFSful7jKbmoWXIg6d27wy7lnA0NweULuWc3eLES39npicGzaj3dIbhppXvc+12hQoVfXQ5p/SSVOVbYR+KRVFRq3Dr26jsplPIE5WtzUfbaqwkQIOFW0+50URYlbGym1Qk22Wcu2dd/6E78h+5ECgOph9Cnn0NL7qGw9uNYtVtOcIZjsVyTgcwepopj2NJCVwwaA610RlajKb+86edQ7gAHs3sXs3tny5NMFUdZnzibqBEHoCXUyWxpkuFCP5ZjemMwXFB8DOf6Sfhqli1kq4pGrf+NzV8WQiwv89+1CO75CvrU00g9iq4abBl/nh2JTorBeRoO/RxdSpSqjWi+akR+jMCB7+IGG7Gr1i27P1pyD1L1H3VMDU6kHWPicdTiFGbTpcjDwq9dRJ95AWN6K2bTpad97RUqVKhQ4fWjIgZXqHAC3vveD7Nr13b+7M/+B729qznnnAtfc5u6YhDUwmSs5JKqwqZbRhf6MVWIDyOEICiWNxnQ0FhX3MgO/Xlqi3VUixqkdBnXR4nnE3SXe3hl8oIPHx8o3spZ1nkLxdx0VtpraHe6XlM27fFYZ2+i217Jfm0PjU4zGhpT6gRxWcV5R2UrL4d92m5c3CXu65AMkxM5vhX4T6plHTVuLf0P7Cc3lyV8awQbG9Mu88Ls42SsJFE9seAQESTLMwxk9rA2fibf/PpX+Mu//AztXZ28619uYZcYpmi6RFSDQ+lhFATv6HkrWydeYjAzQkOwDkc6jOTGaIk0UR+q5ccD99IYrCPhjwMQ0AKMyQmeGHuWz5z9P0j4Ymyd3EZ5vZ+aMYeVNBL3VyNNE3d4EKW+icSWC7mi+thle9OFWSbyU9SH6pYIkPWhOkazYwykhnhg6BF2ze6jLlhLUA9wIHWI8d1T/OaGDy0Wn6sL1lAXPNL+4YzflnATB9OD9KX6qQ/UYkuHkBZiXfUqqvwJ0uUMMV+UddWrGctNMFucI2pEsRwTy7WxXIvzm84+4aSvJlCFLe0lGXVSSspOmfrgMpXRoxAoHFUDjNZIM9OFWSYL0+StPOO5SVLlFJvrN/Khc9/Dt//hy9z1xLfYsOrI5NYq2Wg+FV/oiIAtNAXXPSLS+YWPK5VLiTsxfN1+1nT2sqV5I9GaU0zMTqR9yhNJosfiOA6f/vStNDQ085nPfGFZx6TKMFcSJPxHziKBoi3YNiP43DMKm+vhwmaXtQsx4q6EF6cEz04IpovQHvG+v+oVK4mTZRjJCuoCcomJqD4A/WnBSJZXLU6eVS/xaw5PjCmMZWFFFVzQJDmr3hNpCzYLxeiOPfbwOR0XxvIwnAHL9URfoXiRHT7Va+PoQnHPTwrKDosuYF2B7hj0JQU7Z8Wi01hK2DEreHEKUmVBV0xyTqOkcRm/os+sl8wV4eERhVRJoiiC9qjkxhUuiWWKzaoC1/VIJhKTPAH87oXVrKpzl5VbXKFChV8tDo+x5Cv+Ekik9z352sdgq+x1XFq6kkf99zOFt6JHReMc8yI2m+cs7qcUpvCNPoRrJHCD3sosN9iAlurDN/QzrOpNp4yNkEgOqf08bP+MaX2cdrrotLqxHZtDuT4QCiuir++KsxNRsHMM5Q6gKhpRzfvFLqUkac4wnD/AWt0rchvR4zQF25kujqOoAXRFI6zHCGlR0tY8k4VROiK9v5Q+v55o87vQZl7AiXQtxjKEZD0XzO4iF0jiFz5kzUbUhRVxTrgNLbkbffqF0xKDhZ2H4xTIE1YeXPuIEAygBQAVbX53RQyuUKFChf/HVKYOFSqchM9//m/p69vNrbe+h5///Bmam19bdpsiFFpDXexOvUTa9ARhW1qU7CLNoQ4iRxWQey30JntpD3QwHplgXiQBSdSOsnn6LCy9DMcRLTQ01tmbWGdvOuZ7Dg479W28rL1IQeTpdVZzpnkeMXn6/Q3JMLcUPs5P/T9ir74LB5seeyVXld5Oj7NycT/LNUmWZ3GlszAojyy7YMisMo1Ecn75UgSCp7/9GDUbaxnfOMq2uefIzcwxURhGVTRMp0zWSlPrbySoRzgwvIcvfurzPPrwA3zsY5/kT//0rzB8Bs9NvMiTY88xV0qyItHFRc3nsrl2A92xDn4++BBj+QlUobIqsYJ3rXgbeatAzirQFFoadREzoiTLKTJmlp54J7Z0yF/SjRZfT+TxF3EP9CGFQG1pwXf9zSjHEYIBNEVDFQqOu9SZbLs2qqIymBlm33w/3fEOfAsVuCNGmL5UP0+NPbcoBp+IlkgTH173Xu4fepSDqUFUoXBV52Vc3X4Zfs1PwS5w245v8fzUyzjSpuSUyebGcaXLikQX13ZdxflNZ9E3389kYZqA5md1VS8Rw7Ohbqpbz+Njz9CfOkhTuBGBYCI/SbU/wZkNp78cdVVVD4+MPrkoUsd8UVZW9WC5FlX+BEEjyCWt53NZ64WEjRA3v/Oj/M1/fpaRoSFaWtuwihbpyQz1q+qINccW221cU8/e+/qwyzaaT2PeTXFf/hHGEpNEtQiD6REyoRxvrboCXTlxYOvqKnh2AgoWBBd2y5bBdCR5S/DYqOeWbQqdeHXm1772r7zwwjPceedDBIPLezkU0iGgSfIWiwXRDqYEu+agbHviw4tTgr3zKh9c47K5TvLgkOCuAYHEy7J9blKwe07lQ2vcJXEWuuKJsZa79JyW6+XfGq8hIlEI2FADG2pcXAmK8ETqZycEj48K5kpeEbULmyQXNi/NXF5fK2kekbw4LZjKg4DFfOKSDaM5QUCT2K5XFC68cF9mit72o1GEd3z2qEUb9w8JfnJQYLkCnyLZNavw4pTkY+u93OKToQjvnGc3OIxkBbrq0h17ddnN+blRamrq2NxUCQquUOH/r3TbvdS4dYypI7Q67QgENjYzyjRvKl9NkNe+lF5F5frSe1ljr6dP24MrXLrtXtZZmzDwfgEmxTwH+AmyZi8N6lpW5VzUBeeAE6hDzY0izCTSf+JMWYnk5767+ZnvTkb0QyhhhR3iZdbnNvKm5NWAZLIwTHuoG0N9HZZanIKMlaLslkgYR/oshCCghpgvz3iOZeFdvysdIkZ8cd/D401V6CTNWTo4Iga70iVjzlN2Sri42K6NIgQJXy1B7VUutXkDUHMjCGkvzecVCsKIE8sMIY0I9ivGLVIxEOX50zqPE+2G0YfAMeHwajDXAumeIBtYIk8yXqpQoUKFCr8cKmJwhQonQdd1/vM/v8vVV5/Hhz70Dr7//V9QXf3aqi7XB1qQSIbzAxTtPJqi0RVZRUfk5EW/Tge3bHNN/h3MlmeZ1WfwSR+dxW5EXlKM5E+rLYnkJ/4fcL/vpzjCQZMaLxjP8IL+LLfmP0WVrD7t/jW4TXy08AnmxRyWMKl165dkCM+Xp+lL7SBnZ5BIDMVHc7CD7uiaJdloq+y1PO57kJzILrqDcyJLTmTpcVYiEBTnChz8xQEu/sKbcFybQ+l9hOwgmjAW3NmSkltgrjjF1p9s5dv//F9EghG++c0fL1mGf17TWZzbeCaWa6Mr2uKzOqdxCxtq1zKWm0BXNFrCTaiKykh2jKAWIGvmiPmOKERZK0fICDFTnOMrO7/JSHYcicQX8LHp+nXcqK4j6A+jdq1AnKRwSl2ghhWJbl6cepmQHkRTtEVn8spED9pC0bfDQjAsVMg2IgxlR5f1nHrinXTHOkibGTShETaODOr9mo+A5idZSlKwiyDBxaU92sLnzvtDOmPtfGvvD9k2vRPTMUFAU6iBm1e9i1VVK6gL1vChNe/h7oGfM5wZRQLt0Vbe1nUVrZHmZfXvaNZUr+Ti5vN4fOwZxgtTCAmqonDz6uu5oedaArp/iUv5lk/9Jv/0jb/isZ338Rb3elSfRsPqeta+dfViJARA6+YWkiMppvZNY7s2P48+zFhgkp7GLhrq6pkvJfnZoQeIGGEubzuxs/2MOsnOWcELU57Q6bheETIpBU+Nw9PjgpgBV3e4vLldHiMIHzx4gC9+8X/ykY/8zmmtUgjpXsbsPQcVfKrEr8H+lCeKdsS8yAYhJAMpuH9Q0BaRPDwiCGjQtPC4G0OS/pQngq6rOSK8xnywrtrlsTGFqCExVBaiFQSdMUln7Ph9Ol2UhXvx+Kjge/sFKhD3LUQl7BPkTHhb9xERt9oPH1zj8udPqRRsCBueezdjQrosyJqeyP2eXsnH1x9RslsjkgNJBSmP3H/L9e5PzYJuMVPwojECqqQz6p3TlZK+pODBYYWPrHWXFbVYHYDqwGvL9929+2VWrVq+e6tChQq/esRlgreX3s0PAt9ir7YLDQ0HhxX2Kt5cvuZ1O4+KesIX/ju0bXw/+A2mA33o2jCakuTMdCu3DG8m6OgIp4xUdTiFgNuv9vEL/93ojkZzsQW/GqCg5Nke3kZLuZ2VuVUU7Bwlp7RsMThrpRkvDJIsz2GoPhoCLTQEWpdVkE1Z8F0vuqwXOPz10dtURfeWfcCSsbgrnSUvegt2jn3pl0mWZkhbScpOEV0xCOsxAmqQruhqWoKdR9qQEjU3jCjPI30JnHD7svN4XytSCxwpBHj0/XLKOMF6FCsHrg2Hx0bSRTglnOjpxcNZdWdjTz6FPrcT15dAAmo5iVWzCVFOLly7t7RImGkQ4qQZ1FJK5sszTBfHKLlFYnqChmDrr5TQXqFChQq/DlTE4AoVTkF1dS3f+tbd3Hjj1bzrXW/i+9//BQ0NTUv2mVImGFGHMKSPHnvlSZ0cQggag23UB5opOyU0RX9di0sABNQgShlWFlezsujl0EkpSTKDoZzeuu1BdYBHfPcTl1VUO55jwsKiT9/Nk76HeXvp3a+qjwJBtaw5Zo182SmyN7Wdop0nZiQQKJScIoO5/QS1MM2hjsV911tncHbpfJ7QH8YVLrqiowqNDqebkOsNGvvu3IN0JStuXM1ceRppOsSNakynhOWaGKqPmYMzfOdLf8/g7kNcdcM1/N8v3EY4HmWr/jS7tJdxhc1Key1bzHMJHic3LqD56Yl3LtnWEm5iXc0qnp14AVdKwkaQVClDupzhLU1XcPfAzxnNjtMVa0dXdLJmjmdSe2jo6eBtXeef+v4JwXXdbyFZStGfOghCIKWkJdLEO3reyvbpncyXUkzmp6kNVi9Wji5YRTpjbct/TkIQ9x2r6u2d28+B1CEuab2Asl3CdC18qsFMYZax3CSDmVGemXiB1nATESOM7Tocygzx/b4f8wdn/g4hPciKRBef3vxxxvOTuNKlKdxwUnftydAUjXf3vp211Svpmx9A4rIi0c26mlXHbTNeFeft77iRZ55+nD//33+FP+wn1hhFKEsnaUbQYNO7NjDTP8v2oV3k00U21awjloghENQFayg5ZZ4a38pFLecuOZeUkp2ze3hpeidZM0druJPrV5zJRD7OeF6SsxRaIpL6Bc1/qgA/PaTQEXMWowoAXNfl93//N6mvb+RP/uQvTvveXNkhyZguL017GbzpsqA9IllffUT0rAvCZF6wZw7mS4Lu2NIfzLogjOcF8yWWFHu7pksyW5T0JcGV3rS7OSy5odfFeB2L0BdteGRE4FOgdSEVJuGHyTw8MS64sGVpPnFPHHoTXt5wTcD7r6ZAsiTZOy+4eZXLu3uXXuN5jZJt05L+tBd1MVkQ9KchokN/StIa8e5BqiyXPB9FQG1A0jfv9TP4SzI87d69g2uuedcv52QVKlR4wzjPvJhGp5kd+ktkRJoWp50zrLNe1eqr0yUj0twR+C/mxRw94kwCxTJ5Z5InqwZpLcZ4+0grSmmacvu1SP3kYlyfvpucyLHCXcm4GMJxbUKESTLPgUAfnZlOdMXAv0whOGMm2ZncSt7KYqg+cnaa+dIUOSvDiug6hBA40mG2NEnGnEcIhSpfLQnDK+wbN6oJamFyVpqIHl/cv+gU6AivWCwYDFDtq2NY9VGwswQ1749MySkihEKt3xvzSynpS+9gtjiBKjRs11x4EW8jEEgkA5ndRPU4MaMKYWYI7L8dfeYlhJVD6iGsms0UV34QaSwj8/c1Yletxw3UomYP4UQ6AMUTZ12LUs9N6FPPoqX24PrrQQiU4hROpB2r/rzTOo80ouTXfQLf6IPo088BUGx+E+Xmy/CP3Idv9AHIjwECFINy85uwajefsL2R/EH6M7txpI0iVKaL40wWR1mXOGsx57lChQoVKrx2KmJwhQrLYOXKtdx550PceOPVvPOdl3PHHb+gtbUDB4ef+e/kMd+DpEUSBYVmp40bix9gtb3+pG0qQiWgvbaiICeiPtDCZHGMnJUmqEWQyIX/D1Pjbzh1A0dxUDtAQeRoddoXt+noRN042/UXXrUYfCLmytPk7SwJoxqx4GQIaEFMt8REcXiJGJwpJemd6cbUc4wEhlEVnXPEhQQDUb4X+jrzyix7vruD9jd3MdU4QWOugYgbQ9N1YkYVk5kx7vnG3Tz07Qeoaanlz77yed59+QeJBGJ8O/A1nvY9gsQryves8SQv6y/ykcJvL6uYixCCG3uvQxMqO2b3MFeaI2yEuarzctoizdw/9DAd0fZF8TBihIkbUbZOvMSV7ZdhqKdWlJrDDXzqjFvZObuX+VKSmC9KR7SVew8+yEvTO5gsTDGUHaEl1EhbrJXR7Bg5q8Dqql6mC7NLsoJPBykl+5L7KToFokYYYRzJbM5bRXbO7iVjZokZkcVYCE1R6Yi2eTnEyX42120AQFXUV+UEPh6aorGhdi0bapeXR3jzzbdwxx3f5ODcPs5fecmJ2/VpNK5tYKR6HP8OP/FEfMn3w1qQufQ889NJ6uprFx1Bvxh8mJ8duh/T8YTy7TM7aY+8xEfXf4BHRho5lPYKhx2mIQT75mHXrKA3cUSo/PGPv89zzz3JD35w/7LjIY4mqMEHVksub3PYNi34QZ+kOy6XiJam44mlwYUcXtOFwFFGIsvxYiFeKfDWBOC3N7nsnhPMFiURA9ZVyxMWd3u1zBU9kbr2FW7a6gAMZbw4iFfmE7dEPIG76qi85LDhOXKPF+fQGYNb1rrcN6jw7LhgJOc5q+uCkkdGFfbOS85t9AT0w9EVh3GkVwzul2T4Ym5ulpGRQdatO/1IlQoVKvzq0eF00+GcPL7p9URKSdZK85jyAIdkPyvNNaiqgV2zifDMi8QL4zwX3M7bC2DVnUOp47pTtmlhIfDqY4S0CGnTixsQrqAgc5ScAl2RNct2BY/kB7zxoO/I39WiXWCsMEhjsJWAGmJvahuTxdGFzGXJcK6ftvAKOsMrydlZqny1jOWHmC9NIxQFAVQbdbSFlhZ6juoJuiKrOZTtI2nOAqArOu2hnsWCcBkrSbI8Q9iIkV6IUvCpASzXJG9niBkJMmaS2dIUMaMKf/8dGOOP4oRakOE2hJnCGH8Uqfoorll+YepXixtsoNj7QQIHvoWW3IfnvhC4/hq0me24Rhw7sRalOAvSxWy6lHL723CD9ad9LumvptRzE6XuG70Nh5/Xivdh1WxCS/WBlNjxXi+P+AT1JEpOgcHcfhShEF2I7PBynmcZyu1nXeKs120VZYUKFSr8d6ciBleosEy6u3v54Y8f4KabruLad17M97/3C5Jr5rjXfxcxN8FKZy02FkPaQb4d+Cq/n/ssCVl16obfAKp8dfTG1jOY6yNtzgFe5eEV0XUnXGYlpSRvZ7Fdk6AWwVB9uNLFKpaIliOoCFyv7oO3Py6KPKIMJZnnq+F/4VHjAcqiyAZrM+8v3MoZ9lnHPx8SExMNbUlEhO1aAItC8GE0oVN2SgtLuMXCUr1tmE6JzZzDmda55O0crnRYVbWaN2lv5b7Be5h8YZzN3z6HeqeBdzrvJavOUrCzDG4b4l/+4u+YnZjhyluu4rpbbuCC5jdT429gh/YSzxiP0+A0E5GealSixHb9BZ7Xn+ES84plPYeYL8ota29msjBN1sxRE6iiyp9g6+RL2K6DftRg2JUuqqJQtEuYjrksMRg8Efn8piP3+Dv7fsTWqZdoC7dQG6hm+8xuBtKD9CUHiPmiNIUa2D6zk7lSko+su5n26OnlYGfMJP3ZPQzm+pgvzzJeGCJh1Cx+rhzXRlM0Sk4ZQ9GZKc4xnpukYBeIGlFKVolDqSEc1yXmi9AVa3/DK4vPFucYSA0ihKA71kF1wPu5PPvsC+js7OF73/sG559/YjH4MNX+Kvyaj6yZWxS5C8kCA4OHiOUjbNv2MtWtVfRevoJitMSDw48R1IJ0Rr1oGUc69M3388jIkxSsG3FcuVgI7fDURhGSon3knKVSiS996bNcffXbueCCS1/1PRACWsLQEJTsm5fsT3rRCbriRUZMFQQXt7hsrIP2EUl/ynMHawsF16YKgkta3eMWhAtoXmG0N5KgDk56jPv/4zOMv/wLrHKBeGMPF/7WbRitZxF6xY9LsgSakIxkBeN5QU9MEtZhoiDoiUvWVR+/v2urodbvcjCtUBeE9qh376SU9KUEAylv4PTIiEABQoYn4uctL+Ij8EsaVb3wwjMAnHXW6Tm4KlSoUEFKyWBuP0O5/QyEdpMJJZkqjRIzqkn4apBNl6BYe8g6DplNv49IbIBlrNppd7rQ0CiIPFU+r7htypqnIPK0ljrojKymM7y8QmyOdJgvz+BXg0vEP78aIGnmyVop0maSyeIoYT26uMKuaBc4mNnHVHGUklPAkQ6Oa2MoPhoD7cT9VdT6G49ZkSeEoC3cQ7WvnqQ5i8QlplctOorBq2XhSBtdGDjSWdyuLNRvkHjjU1taKMVpjJnncQONSJ+3lKSkBbCMIMr4g6QazyUaX4NynMJrrydW/TnYsR705B6UzCF84w+jFKYQdh5hF8B1sOMrseO9OIk1OMHG13bCVwq1iopdvQG7esOyDs+YSUpO4bg5z0lzFkuaGOJ1fttcoUKFCv9NqYjBFSosk33aLu5e/QM2PLiZJ659hGuuv4ArfvQWSmeWCAmbOWWGKreGTnsFB7R97NZf5kLzsv8nfRVC0BLqpNbfSNZKIRDEjKolS+KOpmjn2Z/ZyXx5Bkfa+NUADf5WcnYGyyzQpDYgMfHpPqwaSTFQIiuyXG29HfCE0j+If5yX9Ofw4UeVKg/5f8FOfTt/k/53NtpblpyvT9vNI777GVQHCMoQ55gXckn5zfjxE9QiKChYrrXompVSUnZL1PgbFgffs6UpCnZucTkgQFiPkirPMl+c4kb/B9h1+zZCsTB/fvH/Zn3uDEIyzOMTD/B3X/oSLz3+AivPWM2n/vb36OzuYW1iy6JrekDbjy3MRSEYwI8fXers1l9ethh8+Fk0huppDB1xWjSG6gnrIVLlNGEjzGBmmNHsOHOleTpj7QxlRlhbs2rZ5zhMqpxm+/RO6gI1i/m+5zScQdpMUzALXNx8LvWhOlzpsi95gPsGH+HW9R84ocuiaBfZNr2LA6mD6EKjJ9GOyTwFJ0tXrJXtU31MFaaxHJPGYCum42BLhy31G/HNGtw/9AiZcg57QSAeyY5TsAvYfTYxfxRd0VmZ6OF9q6+nJlCNlJLZ4hxlx6Q2WINPPf34FCklo7lx9s4fwHIspgsz7J7rI21mAEj44ryt681c1HweQghuuumD/MM/fJG//Mt/IBo9echtZ6yNdTWreW7iJeqCNahlhQP7BzAti0v1C/Arfqb2TVNMFxFXqGTKWVYmjriPVKFSE6hi68Qouuqya05lKCNI+CUr4p6r1pVelu9hvv71f2NiYpTvfOenp30vjoemwI29Lt/cq3AoDRKBIiTraiTXdnvi8I29LrfvVRhIAwgUvO+/rfONFXxPhlJK8ugXLiK04lKu/rOfUl1Vy9ToAZKiiourJM1HveOaLsBtuxT6k4KIAWM5eKag0B6VXNIqeWe3e9Ioh9G8oOzAiviRea0QUBeQ7JgVlCyYKwlcKaEIh9JwVoPkivbTuz8FG3bOCCbynti9rlrStMxIxBdeeIaGhiZaWtpPvXOFChUqHEXSnOVQts+rWSF72Sq2UtSKYM7hVwMEtBAzIYXzzcsR1VtO3eAC66xNbDHPZavxND7Fh6IpFClxjnkR7/ffSq2yfKFRIFCFhumWl2z38n5BQWWqNIIilCXCrl8NMFEYIm+nqfE3kDFT5O0sllvGlhb1wYtPGs0W0iOE9MhxvxfUwhiKj5JTwK8GyNsZpJTYru21ufAnIKrHEWYGYRdwQl5djbQ574nMTpFwcZb9M08QpMSq2Blv+Atx6a/GbLiQ0PTzCKuAXe2tXNRSfejJHejJnViJNTB6P+XasxnuupaMLKMKlSpf3RJB/I1HLEZuLM15do/Jea5QoUKFCq+NihhcocIyGFdG+a/gl5lTZmmpb+ed997E3TfcwU/e/EMS/1RF9YdrUVFJuNWsszYBkBe5/7edBnyqH5968lgIV7rsTW9ntjRBSIuiKSFKdoGdyedRhEJdoImCXuSAuhezVMactxhrnWSTtYULzcsBuN93Dy/rL1LnNhCSnprhui6DWj/fDnyNjdkjk4k+bTdfCf0zaZGkyq1hTpnljsDtjIkRrp6/hoKdx1B8pMqzBLUwilApOXn8amBJRITllgFxzABVVTSKTgGB4KVHtnLFpW/lXPUixkZH+PO//TQ/+MHtNDa38Jn/8znOvvJcYr4EjcE24saRQniHB6KvRCJRyoL58jR+Nfiqi1m0hJs4p3ELDw0/zsTMTuaKSSQSv+pDui5f2/0dPrrufaypXnla7RasImXHpMp/JNA0a+VRUAgZYRTFc6AoQqEhWMeB5EFS5TTj+SmeHX+eifwUTeEGzm08k45oK1/f/V22z+xCQUEiuW/4QdqiVVzT/SYShsp5TZt4bnwHg5kxZgoZEr5qzms6i7MaziCsh/h+313krALV/gS261C2y9iuTckpc3aih6JVYsfMbnRV590rruXugV+wZ24/tmtRG6zhze2XcE7DltOahDw68hT3HLyftJkhZ+YYzIxQF6jm/Kaz0RSNifwUdx74GU3hBnriXdxww/v567/+HD/5yQ94//s/dtK2FaHw3lXXEzWibJveweTUFMFigDfFL2aDshohBEZIZ34wiX7QAOXYwjUlW6cvdS61AUHCD+myJJsVTBU85+5ZDZJNtd5nL5mc55/+6Uu8//0fo6fn9D4LJ6M9Cr+32Yt2yJqSaj/E/ZLRrCBZknQe9f2MKakJwOoqr/jccrFcz3Ec1EA9dZ2fU/Kv//o3dLe28NY/+yq75wQTtkRf0cU5CcmNvUuLtj0yIjiQFKxMeM5m24XBtERRBNf3uDScImlDE94zc14RBWE5nvALENQlRQt8qhc7EdTgdC4zWYKv71bYO+9lLUug2i+5sVdyVsOpReVHH32Ac865sLJctkKFCqfNXGkKW5pEtTgBK8Ta/Aa2hZ8nraYoYeKokhq7lktKy3/xDWBg8IHCb9Brr2GbsRUbi/XWGZxrXkxcSZy6gaNQhEJ9oIX+7G78bgBN0ReiLVIEtDAJXy3jhSF4hThoumUs1ySkRUmWZyg6BXTFQOAVJ96RfI7N1RcSM068ci9vZZkrT2FLm7AWpdpfjypUglqYhmAbQ7kDqEJFFzpZK40qFEJamKydosbXQK2/EVcr4BoxlPI8RVFF0pxFIIg4DvirUYJNTBRGiBs1tIQ6T9iX1wXHRJvbiT79PI6/DoRAlObR0vtxjSjCKSEDDdh6mNLIT5mWSUZr1jHjn2ZKnaZBb+Fs7UK67ZUop/WX7vTxcp5DS3KeXelQtAu0hbpe9xorFSpUqPDfmYoYXOG/BVJKDqaH6E8dxJWSrlg7KxJdy6pGDPCi8RxTyiSr7XUIBOHqCBfefzn3f+Yekr85j/a0TsvftzMbnmabvpUGt4kGt+nUDf8KkDJnSZZniOqJRedwQAtjFkdRhYYmdLqdXuIywbQ2hVUqcVHyKs5VLyFIECklLyjPUHaLOKZFXmQwFD+6YhCUYXYZ2xbPJZE87LuPtEjSa69ZFMlm5TQPcg9GQVJXbkBKFySLy/Dq/M20hbuXCLZBLYzA2+dwcTQpJZZrEdUTpFJJXn75Rd75zpv5i7/4DF/72r8SDkf5whf+L+9//8cwjBMPKFfYq/DhJyXmiS9EfRSdPNUzcRKZCC/aT2IoPhqCrXRH1py2q0MIwfUrrsWVLrft/BYhPUhtsIaOSCs1gSoOpAZ4dPRpVlf1npbYUx2ootqfYK6YJKwfUbzKrklUDy/ZJqUEAdtmdnJ3/y8o2kUiepjh7Bg7ZvawuqqXbdM76Yy249e8JXmD2YPsnj3Ixrp1dMSaObdpE+2xZvbNHyCghris+UpWVfWgKRqGqtMUbsSRDplyFr+mEdSDVKkJinaJglUkpAdpiTSxZ3Yf/5KfZjQ7TmOoHkONMl2Y5Tt7f0RAC7BxmRnAo9lxfnrofgSwOrGCPfOe8ylj5RjPT9ERbaU53Mje+f3smNlDT7yLpqYWzj//Uu6998enFIPBi+W4edU7eUvn5TwzuBW7ZFOrHvlcKoqCEFBlxamOJxjNTdAabkIIgelYDGZiCLpYVy2wXMlgRjCZl8yXoCMm+cg6dzHy4J/+6UvYts3v/d6fL+v6T4eQDmc3SEo23NUv2DqpkrUkPkVhRUJy8yqXcxpP3wlsufD4qOCpcUHW9DJ+L2mVnF0vj1k9ejrcf/9PufTSK3npX27kqWeeIFHbxLtu/jif/NhH0Y7ONnbh5RlBtV8ubtcU6I5DX0pyMC1oCJ38unri0BiC/fMQ83mCcFiHoawgXYaADgkfRHXIWVCwBJMFz4GcWIjCLNheEfegdvwc4YeGBTtnBSviEkOVSAlDWe9Z9CZOnrk8NHSQ3btf5lOf+uPTuocVKlSoAOBIm8MiqkBweerN1Fl1PO97lpJapD3bydrMOubcccKhIPWBlmWPRQIEuNR8M5eab37N/WwNd5GzUsyUvSKzIAmoIXqj6/Gpfmr8DcyWJnGkjSq8cVjZKXq1HoRC3i4uxkwIoYArKNoFxgtDJxSDJwsj7M/spOQUAIEiFGp9DaxObEYXhpcFrARImXNoikFCC6ALg5AWoyHYQkuo0xOujRhm02X4D96BayZRZJGwK9HtAuMtl4C/CsWcZ6o4+oaKwdrcDvwDd6ClB9Bnt6HqEazaLQgrB04ZacQRVh4JZLApY9OYGeGhNTrPRZ4hKzIIBE8Yj3KZdRXXF9+L9gbKB4bqozu6lr7UyyTNGQ5/ThO+GtojveBaqJlDCGnjhNuR+htTe6VChQoV/jtQEYMr/NrjSpd7Bu7joZEnKFgFAPyan4ubz+VdK962LCFvWpnAJ32L4qWDw0RolKp/q6V8TpHZT06TeTZN0zdbGNs8wgZrC6usdW/odb0eSCkpOyVc6SyJkJDSW47lSmdhaZZKtVtLlawhac+yxlxPMOBVvposjpCWc9gBx8tmkw6WaxHQQtiqtaTYWpkyg+oAVW7tErekXTLJalnKQZMqanGlS9qcJ6RF2FR9HobiO2YiUuNvIG7UMF+eIaiFEEKhaOcIqCEaA208+NOf47ouf/M3n8NxHH7nd/6Qj3/804TDx1/+dzSr7HVcUn4zjxkPMCnGEVLQMFNLe7qNRtGM3whSdooM5Q6gCY3u6JrTvveGqtMRa6M71kFPvAtNURFCkClnyVlFnhh9hjPrN7Gpdt2y84N9qsHlbRfz3X13cjA9RNwXJXfYGawH8auewuS4DlPFWc6u38QTo88ipbskzmAwM8wvhh6mPlC7KASDF7FwKDPEaHaSjljzQgRGLX5d0B7uYWXsSLSFKlSiRoTWSBO6opO1cjw95rnNEaAsPM+AFmC2NM98OcW66lX4FvoY0oMcSB3kibFnly0G70v2kypnWJ1YgRCCsmNhKDqKUJjIT9KxkI+sCY2slV887k1vupovfel/UigUCAaDJ2p+CXFfjLaaFg71Dy7ZLqVEupLq6mqu63gLPzxwD3vn9y9MRgV1wWsoudVoiidSrq6SrErAcMYrxnY4k3d0dJivf/3f+N3f/VNqa0+/mMtyeWhY8OCwoD4oaQpBwfaiEOQ+hU9tck/b1XvPgODng4KgBmFdMpiBoT0Cx4Xzm46IsFMFeHpMsC8pCOmwpV5ydoMXU/FK5kswOHSIr3/jy1xx0+/yj7f9MVP9L/LZz36a1rjOjTd+cHFfwULG7yvakHji7HLkjIAGTWGvaFzW9Lb5VW+boggiuucIBkgoMF2EdNnbNlOA+4eEdw8l9CbgynaXtqMK1pkObJv2BOvDBfmEgNYw9Kdhf1Kc1B38s5/dhd/v5/LLr17G1VSoUOFXlSJFxtRhdHRanPYl9RNeD0qUeNZ4nOeMpyiIPGvs9VxYvpyokUDkvYx/VdHQpc6GzEZi+RC64qPGV48qNHJOhj2pbShCpS7wxhocChTIKCmiMrY4bjQUH+uqzma+PE3eyqApOlW+usVVWQ2BVmZLk8yWJ1GF5kU2SIuIHsORtlcubWGsYS9Ej/nUAFkrdfz7ZRc4kNmF7dqLEWS2azFVGiOSi+MiGcrtx5WuN8Z1Lap9taxLnENACx4zTi11vh2pGtiDd+LLz+H4q5lqOp/phnOAhaxh6ZzwnqipfRgTT6LmhnFCzVgN52NXnbw49dEo+TGCe7+CUprDjrQjyim01F70mRdwAvUIM4NWnAahoif34vgjCKFxKJLk2ejT+Fwf3dYKSk4RQxg86ruPHnslW6xzlt2HV0NDoIWQFmG2NInplglrEWr9TQSygwQOfActcwikgxOop9x5HWbjxce8dc3bWZLlWSSSqB4nqicqK2kqVKhQ4RVUxOAKv/b0Jft5YPgxInqY9kgL4GWrPjLyFD2JLjbXnbqoQa3bQFmUF5d829iYwsSQPla9by3ZMzPsuWUHgxcOUP/ZRi76yJvw/YoWOHCkw0RhmInCMKZbwqcEsF2bnJVBFRo+1YciVBSheoNUKRYVlJJTAAUOBvoZ1odptzoYyh1gY3kTT1c/SdJIUm3VIKXLvJzFxuay8lWL59bRCcggKWV+cZvlmuSdDLqu4ZeeEKcIhbAeI2dnKDp5fEdVnk6587xQeopUaRYffiJqBFvaKFKhxt9IqBjn3//+//Lv//73ANxww/v53d/9k9MS1FRUbii+jzXWevq03ZhmGSMN9aKZgOr1MaCFcKXLRHGYtnDPq1q6FtQCqIoCwis6MpwZZe/8AZLlNIaqc9vO29lSt5EPrHk3QX15IuUFTWejKxqPjz7DTHGWplA9m9auo2++n31Jb2mjIx06o61sqF3Hjr0/WJJnDFAfrGXffD8xY6lwHtTC6EIjb+ewXBOBQt7O4FN81Ae8ny1XutiuQ3u0leZIIyPZMbpiHQS1ID7NYLowy4pEF37Ne6bzpSSa0FAVdVEIPkzUiDCR8xxBy3HxO67tJcotDPjjviij2TEUoWC73oTLdm1saS/+LgC4/PKr+Pzn/4hnn318icBmFkym989QmC9ghAxqe2oIVR95udG0roHxneOkxtKEa8MgJZnJDKHqEPUra+mq7qA53MjuuT4KdoGmUAPzpY3c2W/gSrkYQSAElFxBTcBdbPurX/1XgsEQv/Eb/+OU1/1qKdnw7IQgZkDVwo9YSIeOiKQ/KTiYhhWnsbJ3tghPjytU+SS1Cx/XhB+GMvDwsODMek/8nMzDl3coDGUFEV1iOrBrVjCYhptXySXRDGM5+NouBdtxiXduQVzxv7mvDO9582beu283t9/+lSVisKbAplrJzw8pVPklM0UYzwlmi57QHtBcTsXOOdgxo7ChWuJKQdmFou05gMMaFGyBT/WcxxIwHYFfldQG4D92KvTNe45oRcAzE4LhrMLvbHKpX7gnjgRbgvrKOjvCE6zdUxiy7733Li699EpCoVcXU1OhQoX/t0gkzxlP8nPf3cyoU6hSpcPu5vrSe+lwul+Xczg4fD/wXzzhexi/9GNIH/f5fspu7WU+Kj9BdbGBmdIEhmIAgpyZBimo8zXi0wKA59JMlecYKxyi1t/4hohpNjYP+X7Ok8bDZJQ0IRnmPPNirixdiw8fqlCp9TdS61+aN1ywc8yWJglqYepEE47rYKg+avwNZM0U++eeQSvPIdQSJT0AAqJ6FY60CajHd5POm16sRMKoWbxWTdExFB8j+YM40sFQfAQ073hXOiTLc4wVB1kRPc5La0Wn3HEtyZoN7J1+BNVfg2ZEF451sRyTmtDxx6b69AsE9/4nwkwjtTBaqg9jaiuFVR/GarxwWfdWn34epTCJnVgHQmBXr0fYBbTsIZTiFEo5hVT9uMFGhJUhXhijZITZ3lRLSUnTYB6+55KoE2OKKV7WXnrDxWCAiB4joh8poCBKcwT3fBk1P+45ghUNNT9OoO+/cH2JxQJ1UkrGCoMMZPZQdkuARBcGzaFOeqJrl70itEKFChX+O1ARgyv82rNnto+SXaIz2ra4Le6LMV2YYefs3mWJwVvMc3jaeJR+rY9mpxWJxBQmCoJeezXhrghnP3AeT/7lI+z+3A4++7VPo/wvwZvffM0v5U30rDLNgLofgG6nlxq37rj7SSk5kNnFSK4fVWioQiNZHiFlzuGWXXThw1AMAmqQgBpCEQppazyBQU4AAQAASURBVA6fGsB2LabFJP3V/QzFhpECQnaItlwL5xUu4pq567i3+m6mfONICapUObd4ER/Kf3zx/Coq55kXcUfgW6SF5wCxMJn0TVJr1dFe6ljcVxEKUroLSwM9+sU+7s5/B7tQxlEdhFBIiCrWqWcQnI7zzdtu4667vouuG/j9fq699nr+6q/+4VXdUxWV9fYZrLfPYLY0xTbnSfxGYMk+umpQtoukynMoQsWn+glpkWU/85WJbloiTRxKD1EbqGHf/AHKbpmA6mNdzSoaQw1snXqJWF2Q+o4oDg4ddjddzooT5rYJITincQtn1m+iaJfwaz40RWM4M8rLM7tJm1maQvWcUbeBvJVHU1Rs117ShuXaVPsTWK5NyS4vuoPzVomaQD298W6Kdh6JJKRF6IysIqRFeWTkSZ4ef56cmaMt2sKm2rUUrCJ9yX4EAk1oBPUAATVAqpQhZ+Uo2AXOajiDPXP7MB1riQs6Z+XpTXQve/DeFm3BUHWyZo6IEaYxVM9Idozh7Bg1gSqmCjPMFudZkejkjLoj7pqenlU0N7fx8MP3LYrB+fkCL9+1g/mh5KLNNFgVZP3b1lC7ohaARFuCtdes4cCjA2QmMwhFEGuKsfKK3kXRuC3aQlv0iPA8U4CnxiUH09Aa8QTBibznRj2r3jtRLpflu9/9Gh/4wK0Eg2/cEsiCDXlbENKXqo8BDcqOJGsJjvXYnpjJPKRNL2bhaKr8MFMUJMtQH4THRgVDGcHKKrkoiKbK8MyEwjmNzuLxUsLPDykMZgThqkaaO9ewKiEZysKP+wVtXau49967junHZa2SgZTk8VHBVNG7Ar8isHXJHfsVoj6XrpPUCtw2LTBdWJlg8fqlhMfHBP4Fx/NsyYt2AIGhSi5tddk1J+hPQm/iiMO5yi/ZNy94bkLw9m6vLUPxtj89rmBLSV3Q+xzMFCHmE3TETixYj4+P8tJLW/nnf/76KZ5GhQoVflXZq+3ku4Fv4OLQZLdiC4t9+i6+ofwHn879GTEZf83n6Nf2sdX3FM1OCxHp/cJrcJvYp+3iGf/jvCv+HsYKQ0yXxkG6hINR5spTi0LwYQzVT87K4OK+7s5lgAd8P+OuwHcJyQgJt5qsyHC3/weUKXND6X3HPWauNMXe9DYKdo7DboWYnmBVZBNhBPrBe4ildzGgOJRUjZgaRFRvxMZbtdQQbD1uu0ePN4/GW3mWR6AsESgPj/tmiuP0RNaccOwXD7UST6xnvDCIYs4jhILtmMR91TQG2449wLXwHboLrAJ2fM2i61XNDOAf/DFW7RZ4xXM6HkpxFoS+eLzUwwtCsouSn8CJrQCnBAuraXxOiYIMMB+tRzAF4JktUPGpATSpURbFU573jUCf3YaaG8VOrIGFMaET7URL7kGffHpRDM5aKfozuwFIGDUAlN0iw/l+okaChkDL8U9QoUKFCv8NqYjBFX7tMV1riZg0V5xnNDfBcHYUieTchi2srOo5SQvQ7LbygcKt3OP/IaPaMEjYaG4mqc4zo0zhuA7FQIHWL3Zw2U1Xs/2Pn+eWW97FpZdeyec//zesWLH6hG0XKDCljmNIH01uy2lVypVIHjXu517/j0kuuG2jTpSzkufQmexCQaE+0ExTsB1dMchaKSYKQwS0MH7VcwTPlaaRUmIoBqqiUXZL2NJibfxMmoLtjBUOkTbncX2SXdW7yUSz9DirEAjGxDDPVj1Ng9PE2+bfwbr8Rl6MPEeRAq25Nj7i+xRhfal77eLym5lQxnnReJYJMYZA0JBt5NyZ8/G5/kUXcsHOEtBCiwNvE5O7nO/iFEv4fSFURcWUZV54YSvfve1bHHh6P42NzfzRH32eSy65gje/+Sze+tZ3LPtenoz5YpqR7CR7zH58aoCmUAONoXpKdoGCnWNn8nkcaaMrOrX+RlZE12Mc5XJ1pMNsaZKUOYeCQpWv1lvqqAe5edX1fHffnWyb2sFMcY6EP05zvJH2WBuqUMi0TfGV6n+gI9CMAAIyyIXm5afMbVMVlbBxREh8pSgJEPNF6Ip1sHN2LyvinWiKhuXajOUmObthMz7Nx8szuxYKeLhoisYVbZfwjs63UXCySCRhLYYqVH6w/24eHH4cv+onqPnZPr2T/tQh3tH9FkzXImvmSPjjmI7JC1PbmSsmqfIneHvz1ZzdcAb/sv2r9KcO0RJuwqf6mCnOAp7Tebn0xrs5t3ELT449h17U0BSdiB5mbfVK6oI1aIrKlR2XcnnrRcR8R9btCyG4/PKrePTR+wDvpcmux/YwMThFQ1sdqqYiXUlyLMXe+/uIt8bR/Z5o3bSukboVtaQnMiiKINoYRdVPPGGuDcL7V7v8qF9hJCtwJdQGJVd3uKxeiDC8447byedz3HLLby372l8NUQNq/JLxPEsyalNlCOuCusCpXbRHE1yITyjaXsbuYUo2+FSvyJqUsHtOEPfJJc7YuM8Tk4czgp64J5pmTOhLCuoCkqbV55Mc60MIaA57hdyS+w7Q3OxNpKWUTJXGmCgMUbQLrKtv5IWp1bSpBlUBqA+6JHxeew8NK3Suc1+5qnSRdNlz/h6NEF5OcMmxQeSoD+UxHQXLCdIW9fPWTo2XpgUg0JUjxyrCKzY3mPGE9YwJ396rsGdOMFOAkawg7vOuKWJIruk84iA+Hvfe+2N0XeeKK645nUdToUKFXyGe1b3YhpX2QsSU9NNjr+KAto+d+jYuNC87rfbSIoUlTBJu9aJgO6oOU6a0KAQDKCjE3Sr2aDt4j3oLnZGVdEa84qTTxXFS5tyS/F3wivVG9cQbUjgsL3I86XuYsBulyfXGJ2EZQUPjOd+TXFa+impZs+QY27U5kNlFyS4uRjm40iVpznIwu5ezZg/gH3+UnlArDUJnLjdKMLuDZG6SgZU30x5ZTY3v+IWVo3ocXdEpu17WMHh/W0pOkaieIGdnjnOUPKUBQBEKq2IbiRvVTJfGwEzTVpinrlBEK5lYNZtxA7WL+6v5cdT8GG6oaUn8gRNsRsuPeLER8VMXlXWD9SAtkO6igCoVAxQdGayn3Pwm1PwYSmECIW3sYBOWcIkUIjhxm4ybxpA6MaMKVVUpixIrnSOxaAU7x3hhmKQ5gy4M6gPN1Ada3hD3rWKmvP95RdtSC6EWJha/nitPY7qlxc8GgF8NUrKLTBfHK2JwhQoVKhxFRQyu8GtPd7yDR0aepGSXmS8n2Tmzh5JTpmSXmCnO8x87vsF7V13PloaNDKj7GdWGvPgHey3V7pHB2Vp7I725NYypwwA0Oa28rL/II777mFTHCcgA15Zu4MrOtxH4XpD77ruHz3/+j3jTm7bw4Q//Np/+9J8Rjx9Zcy2RPGU8yv2+nzKnzqBJnV57Ne8q3kyj27ysazug7eXHge+jSo1eezWOtNlrv8ydge9yXfZ6mootCwXiZlmXOIucncZyLcKaNzko2DksWSaohQCx6JbIWRkQkip/LVX+WqSU/NR/JxOBCVYtFNEDaJUdjGsj7PbvoMdcSUe5k7ZSGylz3sv0DVUf02fVUbhi7iraRRspI0mj0UrJKrI18DhDcpAWq4WmfDMGfjrCKxbjFwa0PtK5OepkLcV0mT0/3cXLP9rG9L4panvr+IMv/RmfeM+fYBgG3/72V1EUhfPPv/S0Py+vpD91iK/vvgNEjupQkJyZZ7YwQ9pMEjJ0BAqG6sNQolhumbHCIAKFNYnNgDdx2Zt6icmi9/IBoD+zm6AaIqCFCekRPrL+3dwbrOPngw+ytnoVMV8UgSAZmmGsfT8+12CVvdbbJuZ5xHcfnXYPZ1vnv6ZrU4TCu1ZcQ9bK0Z8+tLi9M9bGTaveQZU/wfbpnRxIHURXdNZUr2Rd9SpURSWmHim+Mpab5JmJF6jxV1Md8D7jNYFq+tOHeGl6J58842NLJgeXtV5IwS4S0PzoCxXCr2y/jHQ5w3B2DEPVqfYnuKbrzZxZv2nZ16MqKjetfCedsXa2Te+kZJe4ou1izm86i7DhvZTwqcfGeUgpWXP2Zm6//St875k7mNNzPD32HEqDwmqxgjPlRqJKhFhDlNRYmtRIatEdDKD5NKo7TlyZ/JWsqYaumMtgBmwX2qMQWeiW67p87Wv/wlvf+k6am4/vXnq90BTPRXv7XsFgxnPwFixIlgUXt7g0n2YKQUcUVsS9zOGOqCSgQdaE6ZLg6nZ38Rp9KqReYTiW0nMmHV0MbjHnV4Et1/0PvvdHF/HcHV+k+4J3M/ry8+z94W381Zf+jWfGBXuTk2TsETpiKRI+GMzMY8k5NteFCR31QqomIOlPQdHxirsdj+6YZPuMsiTKw3JAUx221O7nUEYyX4yjq5LqwATnNCVpCK0mbERxD/f5KG2g7AjiPk9Yv/eg4PlJQVdM0hmFwYzgUNqLhvjIWsnm+hM7saWUfOtbX+GKK95KLBZfziOpUKHCryCT6hghufQX7GER9+gYrVMxq0zzM99d7DK2Y2PT6rRxZela1tmb0KX3C9fFXSLkWsIk6B674qTKV0fMqCJlzhHRY6hCo+h4K4CaQ+1vyCq3eWWWtEhR7y6NgIi7VQxpB5lTp6m2l4rBGWuenJUhYsQW+6QIhZAWJpMfRpl8Etdfg/TFCQHByApMo4p4cYp6ow0l1HXC/kT0OM3BToZzByg7JRShYLomES1Gd3QN+1LbKDg5QpoXoeVIG9Mt0xbqOeX9URWN5lAHrVIl2P9vaOmDLPyVwwg1U1zzG57rFXCFypzuZ1ZTQBMkXEmVC4q0kUKFZRYtturOxhh7GC21FyfUAgjU/BhOsAHFLoJr4UTacSLtXh9zw0T1MJeq1zFUnuNAaD9xqshpBSbFFGvtDZxpngdA3sqyI/kcWSuFrhi40mW2NEHGStEbXf+6f15cf433h9W1j1y/lAgrixM+4q72iiNyzPm9uDDzde1ThQoVKvz/nYoYXOHXnvU1a9hYu44XprZzKD1EyS5hqAZt0Va21G1gLDfJTw/dz87WZ3kp+CylhSVQ1W4t7y5+gDOtcxfb0tGX5LmdZZ3HZuts0iJFQAYJsLBsS8DVV7+dSy+9kq985R/5x3/8Et/5zte4+eYP87GPfYK2tk626y/wvcA3UFBosJuxRJmX9OfIiQyfyv3JkbZOwg5tG3mRY5XjFavLWjlC5RC5YI6J8ASrnHXYrsVMaWKxwIZALGYfO9Je+NoT0g4XajPdMgX7SIEtIQR5JYuCeoxzuUqrQfogYybxBraCmJ5gRXTdMYOxjJlix/yzTBVHKbteBvN9dc8z0jhOWSlRcorsdF5mY+lMPlL6LRp9RwZ4WSvLwQcGeOz7D3PwiYMgJU2XN3PtZ95Bz8YeLvVfgWF4k5/nn3+GtWs3vmbBRErJQ0OPM1OcY1W8Gx0Lv8+m7JSZyE2xoqqdal8tftV7Vj41gEQyXRqnw+4lqIWZKo4yWRwlrEfRFYO8lWWqPMp8eZpqfwNJc4bhfD/tiTjd6RZYeDYA0+FRCmqOVZy1uC0hq5hlipf1F1+zGAzQGmnmU5tu5aXpHWTMLHXBGtZVr150FZ/XdBbnNZ110jbGc5PkzDzNiSMTOiEENf4qRrJjbJveyWR+GvBezvQmuoku5BEXrAI/PHAP26Z3ki3nAElTqIFb1t5MW3R5L0WOxqcaXNR8Lhc1n3vqnYGcmed7fXfxon8PQhX81bf/ksSF7TTIWgxhsFVuY8ad4zrlai/CQkpcZ/nRCSfCr8Gq4+jHDz/8Cw4e7Ocf/uFrr/kcr+SVIiXAuY3eK4qHRwRzRUFAk1zb5XJlhzyhc/ZEKAJuWuliS4UDSYHtSvya4LxGyTWdR+7Z2Q2S7/UpFCxJ0LuljOYgakhCumQiDw1BiBle5MJzk4KVPWfx9j/9IU9888955vt/SaSuk0/+yd8x2PF+HtzpMm+qSNlNfbCDt3SNETdUwGW+PEdQDy3+/Fiud++1E1ybuVDLJ1mEnyYFzWFPQM5bgpZIllW1+7i4LcpcMYMrBTX+IjlnmolimPU1a3h4GIay0BL27sdkwRO/N9dJsia8NO3lCYcWnNPraiRdMZgqCOL+pXnJr+Txxx9i//69fPGL/3x6D6ZChQq/UjQ7bRzSDiyOxcDLzgVJlVtz8oMXKFLkG8H/YI+2gzq3Eb8M0KftZTw0xsdzn2aNvZ5qt5YRdZBWpwMFhazIUBRFzrYuOKY9TdFYEz+DvvQO0uY8jnTwqX66I2toCBwnyuB1IOxGCcggOZElII8siTj8dcQ9Ns/HlXLhvi11iAoUVKeEYheQ/iP3UAgFn68arTDt1Qw4SX+EEPRE1xLRY0wVRzFdkxajhqZgO0EtTDGc51B2H/PlaVh4cjW+BppDncu7YCkJDPwQLd1PNrGaCU0nKSSBwiS1A9/Fv+nPkarOgJNkoqoNp5zCVUHXFDosh3XJIZzEOpxwx7JO5wbrKaz9LfwHf7AoPtuJ1ZQ6rsU/eDfa/B7saBeoAZTyHMLM4na8nVXxM/lzsYpn7MfZpj+P49hstLZwnnkJ0QWn+WjhIBkzRZWvBrHwsr/kFBnPD9IQaCFmLP8F+XKwajZjR7sXhO1mEBpqYQLpq8JsvGhxv4geQ6Bgu/ZigXBXutjSIm4s72erQoUKFf67UBGDK/zaE9D83LL2PVT7E9y283bqgjW0RJppCTdiqAYNoTpeLr7MgXKGbn8nMRnHxWVYPcQPArfT7nRS6564+JiKSpU81gEL4Pf7+eQn/5ibbvoQX//6v/HNb/4nX/vav3LNNe+k6ndrsc6zWGGv8vopA/jtIAPaAfboO5ZVoCGjpNE4sh676BRQUNDQKKiemKspOhJJ1kzSHu4lpIXJWimiehxN0XGli8QlplUhhPCqMS84IY6m2WnDxcXCQl84p4ODpdhcYLyJDVVnU3KK+BQ/1f76YwqqudJlX3o7Y4VBpJT41ACT/nG2xp4lko+wMXAOumKQ07MM+wc5WDhIY7mNHTte4o47bueuH3+X5Pw8gQ0Bav6yhugNUdQajRFziLbpNlp8RwbjfX27WbPm1FnQpyJvFRhIH6LOX+1lvNk+bNsA/AxnD9EZa1sSBwGgOwrF1DT5sWfxNa1ixjeBEAJdMZBSkjLnQAjvuUiHsjTJWRkUVFbV1DOcmeZQJoUqNIajo4S0EO2BpQ5RXRoURJ7Xg6HMCA8NP05fcgBNaJxRt55VVStOq42A5kNRFCzXwjjKeVuwi4znJrlt57ewXAsAn+rj4pZzuX7FtWiKxr2HHuSxkadpDDXQHGokZ+UZyo7wi8GHuHX9B97wzO17Dz3AM+NbaYk3k1jRSLFvjvhF7WQDOVpTTcT1KMOMcVAO0TrbSCAeINYUPXXDr5Kvf/3f2bTpTLZsOfLzP5KFrZOCsZygJiA5q14uu7CblLBzFp4YVxjLQl0QLmiSnFnvib1CwPlNkrMaJOmy55YN6idvM2PCyzOCqQJEdFhfI2laMLk1hOBTZ7j0pyBjCmr8Lp0xloicFzRLDqUlL02D7XpRGaYDAV3ytV0qugqrEpJ3rnC5qsNlNKewLynw976Nc75wLREdbljhsntesHtC0BIp4i/PoysBxnNhHhlu4LoVw9QEbEZzOnUBC59qULIhWRK8rcvFOE6Sh+PCHfsFj48KIj5J2REcTAvmS/DulS4ba8dI2ja6otIQKi0ep7k6GXOenhq4sdflxwOCgbR37xN+eGePy9pqLxPYdAUxY+nLBL8G5kKRupNx223/zNq1Gzn33ItOvmOFChV+pTnPupDtxvMcUvupdxuxsRlXR+m0u1lvnbGsNnbrL7Nf20OPvRIDbxwStWP0abt5yniMDxV/g+uL7+OHgW+xX9sDgIGP1dY6ZsQ0/xn8RxrcZraY59DsemOMsB7jjOoLyFopbNcmpEcWX3a/VnJWmonCCFkrhV8NUh9oocpXy5nWedzvuwdN6sRknKxIM6GOcmH5chrcpmPaiepxAlqIvJ1djBGTUlKws9SEWiDYhFIYxzGOiscozSF9cdzg8eMhjkYRCo3BtqVZvk4ZfeYFVuZGqFUVJoONWHqIqJGg1t+47ALCSmkGLbmXTLiVF306SUWgIJCReoatAm2zTxGIr2Y4P0Ag2kt8fidKYYaiojKkqNTEOomseA8oy89uduK95M/4E5T8OCBxg02gqBT8NQT3fR0tfQBcE6lHKbe9hXLrVeA6hESIK8pv5YryW49pU0rJbGkKvxpYFIIBfIqfgp0jY6WOEYPny9OMFYbImWmCWojGYBu1/qZlj/GkEaWw9rcIDPwALbkXpIMd66HU+Q6ceO/iftW+Bmr9DUyXxtEVHwJB2S0R16toPEFWdIUKFSr8d6UiBlf4b0FID3JRy3k8M/ECCV+MiHFkeZ7t2sxrs7QqkcWiHQoKbU4nfdoe9mg7ucQ8sRi8HOrqGvjjP/4Cn/zkH3PHHbfzn1/5RwbfNEDteXXI35J0Xb0Cza9hYCBxl71MsMPp4ikewcZGQ/OWQWFjC4cG8+hBtEQRKobqoze2kb70dpLmHK50UYTiOSdUP6ZTpuDkMBT/MYOmTdaZ9Nqr6NP2UO3WoqAwq0zT7nRyrn0RNYHjF607TM7KMF0cw5WSgBbEcW3GA+PYqo2R95FW56jxNxK0Q6SfT/GPP/si//Onn6avbw91dQ28+8YPsOsj2xlbO0Rdth5NqtgFm2Rgntm6eWpMb5Dvui59fXt4xztuOq1ndDxURUUVGrZ0jtoqsFyJ4wivgJxbQlM89cydmyXfvxOlkIcnHqegBbGuW4/o8T4/lmtiumUMxaBkF8laaQACaghHWnTFWggaAZLBErmSoDNez7bYYyjyyGDZxqYgCqy0T5xDvVzGcpN8ZeftTOSnqA3UYDom9x56kNHcOL+54UMENP+y2umJd9EWaeZQZpiuWDu6opMz8wxnxzAdkx5/Jwl/HIBUOcNjI0/Tm+ihO97B85PbqQlUk/B7E7eIEaYl1MTuuT7GchO0RI6dDL5eZMwsL07toDZQQ9QXIdCRIPPMAHEjSjaYwyrZiLTA9JsM50dpsuvouaQbf3R59+V0SSbneeKJh/jCF/7v4gRp3zx8Y7fCdFEQ1CQ7ZhWen4T3rHI5p+HUDuWtU4Jv71UoO5KoAX1J2J8UZEx4U9uR43UFapYx558qwFd3KRxMAQhc4KFhyXtXSTbVycW2vPzj4/cvqMGH17mcOw+jWcFIFp6bUAhoUBuQmI7X76yl8MlNLp/Y5PLilPDydf0uZ9RJqnxwZ79CQ1BiqAKxsMahLlhkuhAgUza4uHWYh4dbGUhpKEKgKl4fD1/3wTQ8My4YygpqA1AXkDw95rUZ9Xn9L9mS/pSgPgi1IYVkSiLl0oxIz0Hn3byzGiSrqrxjHAmdMUn1wscl4Ye6oGQseyQWBGC26LmgG09SK7C/v4+HHvo5//APt/1SipJWqFDhjaPXXsMHCrfyc9/dTKpjqKicYZ3FO4o3EZHLe9k4o0zh4i4KweB5VaNujGFtAICzrfPpdLrZq+3CFGXyIsfjvofYre/AL/2UxJM8YzzGLYWPs9JeC3hi6NFCnonJPm0Xc+oMUTfOams9QU4SbH4cUuVZdqVeoGDn0ISBI6eYKo7SG1vPtcr1lCix3djKtJgkKIOcb17CDaX3HbeGhqH66Az3sj+9k2R5BlXRsR2TgBamI7oWs00Q2PtV1HQ/rq8Kxc4hzAyljmtxg43H6Z03DzgccaAIlWpfHXGjGiEEwkwT3P3l/4+9/w6T7CzMtPH7PblyVXd17p4Ok6NmNKMcQFggIzIYDMYGHJBz3F2HXfPtmmttr/fzrrH9+WcvGLPGBmMwAoMBkSWE4kgaaXLu6Zy7ctXJ7++P01MzPUkzQsKEunXNNZrqOrFCv+c5z/s86Iv7gIA+KemJddLY+G687OA1nQdCH2TAqGGyrAjaQyJ/s4S6DBizp0nX4oQywIh14nXfjlKfQffrlIVkInsdGzPXdrMeQKnPotankVqMMNYFqNixTqY3v4uwcoaklKQTI2jOEomDf4VaOUNo5XF7X4bbffsq8TmUIUv2PDWvjBd6mKrVHAM3t3e+azsMWKyOcrB6GE96GIpJzS6yXD3NuvhaBvI3XTxl6XKnLzlAbcdvojTmIPQjcf+CyIzI4b6bTL2ducYkoQzojQ/Snxgmpr10hbwtWrRo8f1ISwxu8UNDT6KTtdkhDiwcZl12BE1RCcKAqdoMiR6DXCq5SrtQUBAIXOG8aPsQjyd4z3t+gZ/6qffymw//HN/4qy/zb+/6NEbaZO29Gxh503rCV0my4cXTq6aVSQ7pz9EQdXqCfrZ5O7nevYnH9W9zTDtMPuygYdaZ0iZZYw+xsb4ZKSWNoIYuDNrNSKzNW10k9TtZdubxV8r1onKzZRzZIKO3MZTaQNZcPZ0qJdP8bP1X+Kr5RZ7Tn0IiudN5JXc7ryYfXlkIBggJIneolNS8Mr70scM6gQxwSzbHHjjCUw89yehXTtFYqhNri3PvXW/kfe/7H9x5592Mmaf509T7aZPtLCTn8QKXuEzSpfQj4woLwRxdYQ/j46PYdoONG7d+x69XTLPY1bmdB858nayZxlRNQhkyXpmkM97JhswWpuqnogI+X6Fxcj8N2SDl2YyPOMSXKliPuizl9hB0ZzkrWfmhDyISkSwtjgACKTBUk/7EAL3xgJs678KRNrXGDM8ae0mQJKYksDWHDf4mbnRv/46P77HpvcxU59jUtr6Z6ZuzMhxZOsahpaNXnddraSZv3/hmPn70XxgtjRFKiaEadMbbafh2UwgGyJpp5urzHFo6Rle8g0Zgk7dWv9/jepzZ+jwVr3p1B+KBdgzUBUGYAH+rRF7FmL/m1bEDhzYz2r/sYAdTDxxCVn0wIbc+i17Qma8s0Zvv5vrdu+hY/9JNM/zKV/6NIAh49avfAERO1S+OKizbgs25s7ENkrEyfOG0YHteXjb3FiK37VfPCIJQsj46RLqAqSp8fVxwY7dcJUpeDV8aFZwoCjZmJZoikRJGy3D/ScGGnHxeV/FZdAV25GFHXvIX+xQMVbImSg4hpoGlSU4W4OiyYGen5NXDUbbiWebq0flR9SiexVQt7KCBKuIEUuAFId2pae7bnsQL1tDwQroTsKVdYqpwaCkS2ZdtQVKXHFgQLDaiicfD502KsDTImJIDi4I7+trRVYOSu0zayCEQ1P0KqlDpip2LNEkZsKvzYiFcVyIB/h8PK5wsSbIG1DxwAnjNcHhFMf7DH/4r8vlO3vCG7/wmV4sWLf79ud67kW3eTubUaTSp0x32XlOBcEqmkUQztM7mDQPUlBrrvXM3izvCLjrcLmxs/t/Uf6NOjc0r3Q8SyQntKF+w7mddddOq9QAsiUX+Pv5/OKYfIiRAIBj01/Luxn30B1cnhEopGa0eo+HXVpV6VbwSo9VjdFg9vKvxXl7p3MuyskhW5ugL1lzxXPTGh7DUBHONSRpBjbSVYI1bJz3/DKGWpLH+nRiz30ZtzBOaWdyhN0SO10vghS6HCk+xYM+u9DpIJqonGU5tYjC5Hmv8y+gLT+Kn14MWAylRy6eIHf8YTnotNTUyYsTV5PPeqAtjXXipQeaJouWUlWNU3DJxLcacaqD6Fc42KUvVJEgNRcs6iwQXzER7XkIP69SnMKcfRDhFUHT89AhTa9/EIX+J2kohniJUuotPsmf0K+huldDMoZZOEi8eRdjLOCNvap6rI8V9zDemqflVqn6JRlCjw+ohriWp+iUsNUbOzEPoY0x9A2PqGzjlo2zR41R67qBsptBKx6mENjOL+xme+hbhuncQJq/StSvE8zq8DdVkOLWRoeSGlUVaN1BbtGjR4lK0xOAWPzQoQuGNa19NySlxsngaiErc1qT62Ll+iKfVh+nx+5t3tKuigo5OX/DiTytSVZWffuUvEb4hpHq0wvK/LHHy/qMc+cQBjIxJ7J44xdctc8cdP4JhGDypP8qn4v/AslhckRMVtnrX8dP1X+Tn6r/C180H2K8/TVLNcHf1XtYs9ON6DVwaGIrJcGoTGeNclIWlxuiNnxvI98WHaQQ1QhkS15KXbAJ2Qwe7VmPH0ja2ia20WZ30mmuuepCV1NJYaoyiu0joSxZOLzD52UnGnjzDkScOI31JfmsnW96zA/01Or+w7Td4lf+65vKhCJBIBoO1bGALoYhczRVRpsgyAZF798iRgwBs2vSdi8EArxx8OdO1WY4sHSMkRErojHfw1g2vZ1N2AzEtxnT9DI3CBGGlhCtnmC6eROghslvSc0ZBH81Tao/c1BKJEzRI6VmcoIEioylslhbHUEw86eL7LmW3yOnKEbYH2zDSGifix/AUm9t5OW8Ubycjs6siO14Ip0tnSBmrX29TNQmkZKY2d03rWpsd4j/s/iWOLJ+g5tXpTnTy+MzTPDL1xEXPVVDwApeclSVnZig4RRL6OadR0SmSMpJ0xC4dv3I+ogTxjypoh0AEApAEA4L6T4YEl++JAaDNytFmZVm2CySNBCMbN3GIB5kdnaBtSx+pZIolbZkNvet43e5X05nouPIKv0O+8IX72bPnFrq6IvfSQgPGK4LuxOr83p5E9Ph4+dK5w2eZb0RZtJ3x1cJkZwzGKoKZGtckBldcOLQk6IrJZtGbEDCQgtESnCxFAu+1EEqYrUH6gv0wVQgRFJxLu4vzFvSnJCeKkNIF7WYXC/YM01WIaSUsY5a82cXmzFqmapJDi4LDS1D1BDvaJV8+o1B2oT8p2b8gWGgICjY4IWTmIjFXXzlGiUPcOM7B4gROYNPw69hBA0M1sdQYa5MbaTevbvbIjV0SXQn51mR0/nsScGuf5Pbey7u8l5YW+eQnP8ov/dJ/wDSvUQxo8QPD1NQUv/M7v8OXvvQl6vU669at4yMf+Qh79uwBItHtv/7X/8qHPvQhisUit912G3/913/N+vXnnITLy8v86q/+Kp///OdRFIW3vOUt/Pmf/znJ5LnZUvv37+eXf/mX2bt3Lx0dHfzqr/4qv/3bv/1dP94fBgwMBoKhF7TsNm8nfcEAp7TjDPrDaOjMKTOoUuOmS9wsnlTHmFGn6PMHmkKrQNAT9DGmjjKrTDfjIs7yOetTHNSfZcRfh0UMD5dT2nE+af0Dv177vYvE40vhBA3KboG4tlosTWgpSu4SZa9IXu2mN+ynN+xftawbOjT8GrpiEFMTzeWFELRbnbRbnZFz9/AH0RefhZXysCA5SH3jewgTvUgtDpcojj3LdH2MeXuatN7WzJiteRWOl55DELJu/nE8PU0ND+F7mGocUsMsNMY4NPMlymYaRQiyRjvr0ltJ6hfnHDdRVOyh18H4JxFOIRKDQz8SaTMbQFFJ6mmqfpkg9FFX9icIozzprPn8Y6LzMaYfxDrzOUIrT5DbCoGDLJ/g5OS/Um/fRsbsQBEKXmAzt/wEpzWN9YlzNxKU2jTm5Fdxe+9AWnkmqqeZrU+QMrKkjCwLjWlKXoHp+hhZs524mmRdeitxLYl18pOYo5/GESqODMjV58mf+Gdmkt0Ukr3E9RRFBRpLB8gdKlG7/veQ+jW21j4PLRG4RYsWLa5MSwxu8UPFQLqPt+1+Lc8tHkQ0dLrNLnbkt1CJFZkNxjiqHSQXtuMJl7qocbN7Jxv8LS/Jvuz09vD2xnv46oYvkHhfkpHfX0f2QBvyk5Jv/OsD3P/Jj5NIJLluzx4aL6uRui3Ddbt2o1s6Djb79af5pvll3mj/OO9ovIe3NH4CiEruSpklSl4BgSBr5KNChSsMioQQxLXLD8KcwOZgYS9LznyUb4Zkvj5FI1ljbWrz8w64Fhfneeqpx/nytx/giaceYfrYFJ7joRkabbe0kf3DLPl7Olmzbi2+4rHVu46b63euWkefP0hX0MOMOslQsLYprM6q06zzN9IZRk6Bo0cPkcnmsBNVxqonyBrtpPXcCx4U5qwMv7DjPRxaOspcfYGEHmdr+0byK0LlcGoj/YlhKke+wtijf8/sxgpVmaUoJDogY8t0Hvo2a3/kPdTcEjkzT9Fdwgkc6n6Fql8hoSVpNzsRQtDw6iS1FPMrg+weo5/exhruaryKqlfGFz4P93+dpxNP4ovoXN3l3ENv0E9w5BD+vqcIC8vIgQEm1ndTzMVos3KszQyhXpAzlzHTnC6NrXpMysgZE9euPScwrsfZ3XVd89/LdoFvTz1Ow7ebkRNO4BDIgPW5tcQ0izv6buFfTvwrE5UpsmaGilel7FS4Z+iu5jm+EtYDAuMZgT8ikTEJPminBLF/Vqj+hxCuIHaaqsFdA7fziWP3c6Y8Tr6vE9XUqE8U6Ny2hpn6HJ3xDt6y/rX0JL6zqJjno1Ip861vfY3//J//sPmYKiJ/UHiBThiuFMGpF76lHUCF0Ro8Mi04uCg4XoC6L9iUOycoOyFoCpfMzb0SgQQpBYqyeocUEXl2wzAS468FRUQ5xkcLcP78AicABUn2MtqnqsCPDoXMHVY5VoCEHqPqDZE2bH50eJmbO/fQZnby8KTOZ08pVD1QhSSU0J8SzNcFeUvy2IxgsiKamcZ+CEeXIoF5c7uk7oWkrWfoSEwQyjhJPYMiFEIZMpzcyEBiBEu7+inTQkRlcrs6JHYQid5XKo0D+LM/+0M0TeM97/nFq95Oix8sCoUCt912G3fddRdf+tKX6Ojo4MSJE+Ry58LD/+f//J/8xV/8BX//93/P8PAw73vf+7jnnns4fPgwlhV9/77zne9kZmaGr371q3iex0//9E9z33338fGPfxyAcrnMq171Ku6++27+5m/+hgMHDvAzP/MzZLNZ7rvvvn+XY29xabIyx081fo5PWv/IhHaGAJ/2sINX229gl3dx6etZAVhe8B0tV4p/L3TiLoslDhnP0R32YK0UGusr4vVp7QRj6mlGguePLIjGXoJQXrzds+aGCwllyHj1JJP1UZyggSo08lYX69LbLsowNs98Hn3uCYL0CCJooDQW0eefJB40qNzyp1cUggHmGlNowmgKwXbQoOguUfFKNPwak6ZK3vfINqYQgCZ0iHVyLNGOHdaJad2EMmTensYJbHa133ZRl8SqY8vvIucvc6b0HJZTR9GTBMl+yqqFqSgMJTcQSJ+FxkwzfsGXHh1WLx3WNcRmyRBj+iGkahGejXHTLOayG6gGDbK+B6ZAsZewnBI4JSYTeUZ8mhJ/GO9CKx5FrU7gmVHsgqGazYzkzlgfCT1NyV2iJ7aGdektJPUMsj6Le+YzFEKbqpGkgUpDz9NROkV76TTLuc0EiopQQKaG0QrH0Jaew+u+uNiwRYsWLVq8dLTE4BY/NCwoc/xL7GMc0w7hdDjkwjbucu4h5STJhGl+vv7rPGR8nSPaAdrCdm70buN25xVoL9HHRCC43b2L692bmFOnMaVFz5o+xH8U/Lf/8P9y5MgBvv71B/jK3s9z4AP78N/v84jxTbqu76H/tjVYt8Z5cNNXuDfzJgzFwDiregnImvmLYh6+E6ZqZ1hy5sgY7agiGiY2/BoTtZN0Wj2kjeiC1PM8xsdHOX36BKdOHefQoed46qnHGRuLnNidXd30bull+89tp39LP/0bBzBjJkvtRc7ER+lx13C9vIkb3FubjcUAfugRBC53117N/alPcFQ7REzGqIsabWEH99pvRENDSsnThx6jZ6SH4+UDhDIgkD5ZPc9AcoQOq+cFZYZZmrlK5LwQXTFQOtuYV4vs9QzGzdrKZENJmyUYHHTpyTrs8KNSMDewWbBnmaidYsGewVRjBDKg6CyhCpW+xDCjlaPE1HiznENKiaFaHJZPclQeRxM6KhrfMB/ghHaU937tJjIf/xrSruMqgvFv/gtTKfj2KzdT7c6zpX0D79z8Y2TNc+d1d9d1PLdwkPn6Ih2xdgIZMlGZpN1qY1v+6jOJpZScKp3hZPE0UsJIZpD1uRGu69jGzs7tPDP3HJZqAoJG0GBnxzau79wOwF0Dt6EIwcNTj1N0SiT0OK8YuJ27B1/+vNsVNdCfEQQdEnn2+lADf0iijQm0U/B80cq3992Epmg8NPkIS40C+eFe+hs9/N7N/5GYZrIuO7wqY/yl4mtf+yKu63LvvW9sPpaPwfqc5Ok5QUqXqEpUSjZRhTUpyeBKtKR6CsxvKmgnoSDhqQ54dKPATEaX+ftWitq25yVeABMVwbb8uViG5yOUcKoEYyWBEJLxsiBryqaIOVuDdguGMldwtzaiErzjBUFCj5y3OzuiY7q9T3KiGGUHd8SieIupmmBjTrK57fLr3JaHX7wu4LFpwb55gaYoZIw4QRDD9iTLEr4wqqAKuZJhDF4Ah5YFDQ9KDoyXBeqKMB6s5DfageDAkiSQgmxsgS0dMwymslha9B1rKhZFd4lGULsmIfh8hIjiMJ6P06dP8NGP/h/+03/6b7S3t5rQf1j5kz/5EwYGBvjIRz7SfGx4+FxpqpSSD3zgA/z+7/8+b3hDFDPz0Y9+lK6uLj772c/y9re/nSNHjvDAAw+wd+/eppv4L//yL7n33nv50z/9U3p7e/nYxz6G67r83d/9HYZhsHXrVp599ln+9//+35cVgx3HwXHOxWmVy+WX4hS0uATr/c38p+p/ZVQ7gYvLQDBEVq5uF/VCl7nGJMvODDoKJ2JH2Cp3ogqVkJAZdZKt3nWrytpCQo5qBxlTT5MOsxjSJCmjXxiGNPGEiyNsrgZDsWi3Opmqj2GqFopQkFJS9Uok9TQZ4+I21On6GCfLB9EUg4SWxpce0/Ux/NBjR9vNzdlMwqthzD9BaLWjlY6jVsaaubzW+Jfxc1tpbPn5K2bSSs5lwAcr2cFu4KAKFV0xKWo6DSuD7ofkfRs3dBn3C9SNDjJWN6wIo7piUHKXWHRm6I0PXfGc9OVvpqBqLLiLK3EdIbqQDCU3kjZybM3tYdacYN6eBqDT6qU7PnDVRXUAhD6KU0ReMOb1VQ0pQXVKiMoZbLeAg8QLGhihhy8zTUeyCJzo+FQLSYgvfRRx7i6yIhSSWhovdOmwupuu6Jn5R2mvT1BLrUEVKgJBza9gqQYpt4rm11m0UrSFkiwaSFDsxas/thYtWrRo8aLQEoNb/FDg4fHR2Ac5pD9HX7CGDhljSZnnfuufSMoUt7h30h8M8s7GzzRdE9eS3XY+fuix5MxjB3VMNUa72XnFAVycOMPBulWPCSHYsmUHW7bsYLdxEx82/z/anssz/egkU4+Nc/AfnqP+v2oA/Kv1KUaG17F27QZGRtZHf6/dgDaoEbaFdGidz5u/9nwsODOR4IlCtVyluFRgeWGJU2eO8cXZL7IwPs+pU8cZGzuN70fT9GKxOBs3buHuu+9lz56b2b37Zjp7uti7+BB1vwZC4voOuqrTXu5mT+NmblRevqqIQkrJRO0UE7XTTXfIq9I/ynzbAsv6Mn3+ADd5tzG4kgew5Mxz7Ohhtu7eRlJNM29P0wiqFJxFlt152swONmd30WY+f8bxtRJsXMuD2+JMLNbpdXU0RUWre0wnVb6VV7i7UeJsooOhWvQlhuiNDzLTGGe6Pobt12m3uuhPDJPV2xmtHAUglAFlt0DVL1ORZRylzkBjABGLVtYRdnFU7uPRqWO8Wh1A3bCZQ3PPMoPKyLzLy4/W2Tuc5+m5/SSNJO/eci5z9PrOHcwOz/PNiW9zrHASIaIIjDevfy1d8auLRAhlyOdOPcA3xh+m7jcAsDSLO/tu5i3rX8d7tvw4m9vWs3/hEBLYkd/CTT27ia/EQqiKyivW3MFtfTdRdsokjQSxq3Ul2yA8QXheDEIoQ5b8AkZFYWquSPeGXgz18lEailC4tfcGbuzeRdWrod20zGOPPsStvXuubh9eJL761S+wffsu+vvPxbcIAa8bCTldUvjGhKDuR7mz67KSN64NMVRQz0DibxWUBUHYLpmfFew5BGuK8MjrIGdJHp0WHF2OcqljmmQkI3nrhvB5HakQCbOfPC54fEbB9qHqReJv1Rf0JyVOILBUeMO68LIu3vk6fPCAwumiIK5LvBCenhO8chDetE6yu1NS8+BrY4LpmkBXYFObZG1G8vnTgqwJ13VIOi+hu45kYKICj88IpAQ7kHxlTGHffOS+LTqSjedpDboalbidKcHpksAPo6gMKcFFkrUiV1TOglcOhvSnK9gyaArB0esiUIXFqVKR5ZqkMy7YkKMZnfFi8j/+x/vo6Ojm537uV1/8lbf4vuFzn/sc99xzD29961t56KGH6Ovr45d+6Zd473vfC8Do6Cizs7PcfffdzWUymQw33XQTjz32GG9/+9t57LHHyGazTSEY4O6770ZRFJ544gne9KY38dhjj3HnnXdiGOfe7/fccw9/8id/QqFQWOVEPssf//Ef8wd/8Acv4dG3uBIGRrP87UL80ONQ4Wnm7WlUoXLd4i6+nv8Ke61v06Z2EIiA3qCf19k/1oxI8/D4VOwf+bbxDWaUKU6rJ5jQzrDR28pgMMyCMocpY1hhvOnuvRJCCIaTm6j71Whs7NfwpIelWvQnRlaJixD9Dp+qjaIIjaQe3fHU0FCFypKzQNldPmd2CBxE4CLsZdTyaaSeQqpWlOtbm8AafwB34B6C9PCFu9Wkw+zmpHOIUAtoBLUVIVhHCIWQEM9qRw9cyt4iA/VFkqHL8ewAvpEBI9tcjyIUJJGz+PmIaQmua7uZeXuasltEV3Q6rO5mnJuhmKxJrmNNcvW1QSgDlp1F7KCOoZi0mR0Xlbed2yGdIDWIvvA0YfzczKaMW8PQwa5PUhEBFStNIBT8ME7SKTNTm2QwPoQIPdTKKH5uM35mPYpQaTPzTNXPrIrsaPhV3MBmvHaKJWeOlJ5lyV2gXY0RkxCoOik9Q9kt4iKpKypLmk46lGzxJMpKh4Y0r5B51aJFixYtXhJaYnCLHwqOaYc4oR1lxF/fnO7WGw5wWj3Jw8Y3uMm9vTkQ/k5E05pf4XDhaYruMqxMvUvrObbmrr9yjtgVGPRHSMeyKDtVdu24gV2/cAOBDNg39SRrD25k6MhI04n7qU/9IzMzU6uW19I6yWyKvswAbdk82WyOXK6NWCyOlBIpJWEYrvpbyhDP8yiVCpRKRaYXJyiXSpSXS3iu11y3EIKevj42rt/Ky1/+Ktau3dD809PTd8lohjWJtTyz9Ag1vwJA6EbZv+1mB6pY/ZU00xjjePkAmtCIa0l86aEUJDvt69jRdvNF619yZlmaW6RvYICit4gTNohrKeyggS4MGn6NE6WD7Mnf2XQ+vFhklTxHN/XTGDuDNqug+JJGd4r6OhVbKDiLIVxQZC2EoDc+SE9szbkpkyvHlLe6GaucpOIVqXkVBAqOsFEDFa2s4cYl0oryd+NlOJmdROm+iYpbZamxTNpM47b5ZCbnybkKdryTAwuHWbYLtFnRBb0iFF478ir2dO1krDyBpmisz42QNlZbRt3QYdGeoepV0BWDvNVFSs8CcGT5BF8de4iMkWIwHeUNFp0SD048wvrcWnZ1bueugdu5a+DKhXematARvzbno8xC0C/RjoGfgUbQ4LmFwwSzHiIQfGz5C3Q928lPbn4bnc+zbk3RyJoZdmy/nn/8hw9Rr9eJx1+Y6/OF8OSTj/K61/3YRY83/MixaqqRk1QSibozNcHWvMR4SKDMC/xNEk/CmTrETBg8ASfHIRyGl/VLnp6H23rhhm7JlvYrF8+dzxOzgm9NCrrikkw6Ek2PF6DoCHrisCYdsrvrnPP2UnxrUnC6GDl91RXBdKkBD00q7O4KGExH+3hDl2S2DosN+NKowhdGo3odCTw4KfnJzeFF2ym78MAZgSokG1Z0Kikjp/Hjs5cOrRBEruvpKgRAyTlbGwSaDm4IvQnJj2+ULDoGB5YFoQyaokXJgUPLHsv1JFNlFU1Er8VPbQ7JvIiRvk899Tj/9m/384EP/C2x2LXHtrT4weH06dP89V//Nb/1W7/Ff/7P/5m9e/fya7/2axiGwbvf/W5mZ2cB6OpaHWfT1dXV/Nns7CydnatvhGqaRltb26rnnO84Pn+ds7OzlxSDf+/3fo/f+q3fav67XC4zMPDidy20uHbm7WkW7BnSeg5N0dgRtNG52M0B41lSmRzr1S1c791IR3juffOU8RgPml+hM+jmBu9W9uvPUKLAPv1JptRxCsoS+aCL/y/1P9nobeV19lvoCaMCzSWxyKPmQxzWnsOQFtd7N3KTezsJPcWmzE72Lj5IA4mlxjAUk4naKQSC9eltzbGPH7rNTPbz0RWDUJZWia3SzOKn1mAt7AUpIiEYEH6d0MgiZYC2fJAgPYwXuthBHV0xsNRzv9t7E4MsOfMU3AXsoIEbOhiqIKPn8KWPZ6SoqyYLegrHC/H0JJVMP9V4B+3nDUFDGQLRzJGrISoMHobzjLt20GgKvRdGt9lBncPFfRSc+ZVtCTJGG1uyuy59fSEETv8r0YrH0IrHCGIdCN8m6yzR176Rg6qkpCcwZRT+FhM66cDjpAYdpaNkwhA/PUJjw7thZbw8kFhL0V2i4C5gKBZe6FBwl1BRqXkVan6F8eppHE2wLjVEtnSSerwbX4+RFTqG71CMd9HlVFlLQCbwUKvj+Jm1eO07L3meGn6N6foYS84cilDpivXTExu4vAjeokWLFi2umpYY3OKHgpJSJBB+Uwg+S1pmWFIWcHGxuLoB3OWQUnKyfIiCu9iMUwhlQNFd4kT5IDvbbn1BubW9YT8vd17JA+bnKGpLmNKiKqpsHNjKe9t/lf7bVzc6fyj4Sx4Y/xzxsThKUaFYKLBYWCC5mCK7lKVQWGZ09CSNRh0hBIqirPpbiOhvTdPIZHK0t3fQuaYLz7Lp6uihvSNPtj1HLGuR7W7jlt5XNGMi3MBh0ZnFDerMNSbJW90XDdjiWhJd0bHUOFKGGIqJqcYoe0UWnVk6rEgxDWXIZO0MCkpzoKuhowqNJWeekrtETEsyb09RdgvoislSdR671iCZTVLzKgRhQDUs4UsfQ9HpMtZQ8UuUvQI588UtA5NS0sswh4YrPLfVwfQ1HDNEkxpd852Y4eXfX0JcnNc3mFzPfGOaidopNKEhhERVNCrxKtkgh1YBz4qkLlf1SDZ0kBIv9AjCAE3XEKEHQiFUFWKaSbVRpe41mmLwWboTnXQnLu2Wbvg1DhaeougurghrkvHqSTZmdtAdH+DI0jGcwKU9dk6ly5oZ5usLHFw8wq6VOIiXBBXsu0MSkyrqcclEOIlWgDaljUO3jZLuzXBo6Rj3n/g3fn7Hu6/q87dly3bCMOTkyaPs2HH9S7fv5zE1NcH09AQ33njLqselhC+fUah6gjv6zmX+TlfhK2OC67skgycFYVaCACGj/Nm6CaoPmSWYGQYEtFlwS49kZ+e1Zfo+NSfQFJoipxCwIRdl/N7SK3nFmiuvT0p4bjGKlVDPc862WTBfgNGSYDAdrSOuw3AavjiqMFERbMhJVHEupuIzJxXW7g5XZR2Pl2GxIVh7XkRF5HCXLNQj1/J8Q9K1cu3vBVEJnqmu5PUiaQQCVUBMjcR3N5BU3Cheoy/ZSUrPUHILpPUsoHJ4uUHNC0lqa9iYg7oneWZe0BET/PjGazu/lz9vkve//3fYsmUHb3nLO1+Udbb4/iUMQ/bs2cMf/dEfAbBr1y4OHjzI3/zN3/Dud7/733XfTNNsFRt+j1Jwoqn32nk3v7uDXsyiQZ87xJbcxb/jntafQJUqWZkjE2TRpMaYeppT2glmlSl6wwE2+VsJCXjCeJhFZZ5fq/0ODg4fTHyAE9pRkjJNgM9hfT8ntWO8q34fc/Y0fhisuIGjXwZ2UGeqNkpXrI+M0bayrwaWGqPmV1flA3uhiyJUzPMzg4WCs+ZerNOfRfglhGchQhdkiJ/dCIAMHMYqJ1blD3fGelmb2rJSAhpnR9tNzDUmGa+exAsdckYHKT1L2StQ9Uo4ioae3cDRnrsIZYjXmMJEUHYLJLQUISFVr0RKy5C3uq/5dQpCn9PVY8zUx/BCF03odFi9rMtswVCiz9bp8lEWm8K+HkWLuUscK+1nV/ttlyx+9vM7qW/5BcyJL6FWJ5GqiTP8JkasTibmH6ChxdAEWCFkpCCuZVjS4kx3bkdLbcRv24bUz6nVaSPHjrabmKqdoeAs4oceKgo5M4+lxtEVA02UqTrzLJspuuxlUqXTBKpOyUgyml3LdN9tSLdEya2x3a3Qlt+Fvf4dSOPi3KqGX+NA4UkKziKGahJKybKzQMlZYkvu+otc5d8pSmMB4SwhjSxh/NpfxxYtWrT4fqMlBrf4oSAb5lDRaFAnxjlHQFkUGQnWn8vb/Q6oB1UKzgIJLd3M1VWESlLLUHSWqPqlppvyclwqokIgeK39FvqCNTyjP0FJKbLO38gt7p2rMt4gykU+mjnIxtxm2rafc0IuKYu4wuF3y39AzI2OP64lLzl4vBRuYHOw8BRLzhysFMipQmM4uaF5TGW3wOHiM5S9YnO/s0YbW3K7SWjnBnlLzjyaYrAm1nfWPA1EFy2LjXNisB+6NPwahrpaRNUVg2KwzP7lvczb0/jSJaGmMFST+dk5AKyMRT2oIWWIEAqB9KkHdYruApowLioyeTHQFI1b2m6mMFHGshQqeolYECdRTWMqcQZT/c+/kvOIa0kGEiOU3CUMxUJRVLI6zFpzVNwyiUYKKSVL6gJqMs31i3nCiTHiA32YmknDqZEv1pjeuR4/ZrJYmSJn5VaJtlfDeO0ky+4COaMdRahIKal4JU5WDpEz83ihj3IJkVURKk7gXtO2Xgj+Dqi9N8D9Rp3FJ5ex+12O7pzg9NYpLNWkL9HD0eUTTNfm6EueG9xLKSnPVqgt1dAtnbbBHKqu0tMTOZxmZ6e/a2LwM888AcDu3TeverzowJmyoDMmV0UediXgRDHK/l2TBmU5elxToDcJJ5YEgZR4RiSkjlWgJwEbctf+vq97YFzwNXF+Ed3VoImLS/Ag+uhfGFUx34BTRUFPQjYL8hQB/UmYrAiOFqISOyeA/mSUW6xeYv1BCEkdbukNeXBS4VgBNCGZawgaPmzKSdQMNHzBfEMipECIKDM4oQtGS/BHT6qMZGLs6d7FYPY5Kl6BsiupeDEINrBgD6EKyJqQtyJB+DXDkuR3/uuE++//OE899Rgf//gXUNUX94K3xfcfPT09bNmyusx28+bNfPrTnwaguzv6bpubm6On59wUlLm5OXbu3Nl8zvz8/Kp1+L7P8vJyc/nu7m7m5uZWPefsv88+p8X3D9GMt0t978uLCmXPUhc19JVMK4GgK+yhK+yhoBQwpM5N7u3NMWrST3NKO85+/RkKyjIntKNs8Lc0uzaqosJe/TFu1G6l2ljCVM1V405TidHwa5Tc5aYYrAiFvsQwR4v7qHplLDWOLz3qfoVOq7f5vLP4+V00NryT2LGPAoLQzBGkBgnNdtT6NLO6zonyATTFIK4l8UKXidop/NBjW+4GhBCYqsWa5Dp640M8u/woy858NKNsJeLND30MxaDuV7H9Onmzm57EGqZqZ6j6ZQSCnNnB+vS21WL1VXKmepzRylEsNUZSjzJ4J+unkIRsyV6PEzZYsGeIqcmmwUIV6sqNymUqXvGi83IWr3MPXn4Xwi0hNQu0OGr5NMk56PJd0sJoXnEogY20cjidN+KlN11yfSk9y6bsTqpeiW/OfB4ntFlwZlGJ9idndLBl/gBdC4eoJLrRYp1gz+MgKed3kMzvQQYupcY0z2gJ9vS8Bl29tFlipj5B0VkiZ3agIFADG1cGzNqTdDsDL0h4vyS+jXX6U5gzjyC8ClJP4HbciL3+7Uj9pe+MaNGiRYt/L1picIsfCjb4W9jobeWA/gw9QT+WtFhUFwC4w3lFMyLiO2GeWcaVUYpGCVMx6Qn66Qn7miUdgQwuu2xFlHnY+AZPGY/h47HD283L3LubU/dUVPZ4N7PHOycW+fgc1J5lTDuNJnU2+lsoigJLygKD3siq9SfCJLZX47mlJ1CdaNiX1nOMpDaTu4qiOUO12N52I/ONaUruEqqikze7aDM7EUIQypDj5QNUvCJZox1FKAQyoOAucqp8mO25G88VdMjgnNR9nhAkhMAJbSZro5TcZVShIJF4gbPKHVLzqizY08xKn0AGKEKJBudWD345yisO4j6hDJBSogqBpcaw1DhFd5muWB8p44VFdlwKKSX7Fg7w6PReThfP0Gi4eJMKPYlhPOkhpeSOwZsZSPVd87otLY6lJcgZ7c0iuS3edk6ER1gyF5nUZkiR5h7/TezZPoR/+OPoJ06yqSGZqc4z3tPG4Z19TJXGkEheMXA7mqIyWhpHIpk6eJoP/Z8/58CBfczNzfDhD3+KV7/6Dc3t+6HPfGOGmBpvOjCEECT1NEV3iaK7xEhmkG9OPIztO1ha5GBxAxcv9NiQG7n4oF4C/M0w0bfEhzbeT1eyg5h27sLC0iwW7WUa/rmppb7jc/jLR5k5NIvX8FBUhWxfhm2v3UK+oxNFUZifn31J9nW+DscLglDCcEbSn4RDh56ju7uXzs7VFzaaAoqQBJcQOlUEqghxb4T4CYEoRLEZIwmJMQqTSXgoK6gWJF0JeNvGkPgLmFW5qU1yqqgQynOFcTUPdFXSn3x+cVkI2NMl+fQJBduXWFrkFp6uRSLqhQJ1KKM/F4rEiojE8Q8fVGh4UZaxFyqsSUkCKTlZhE1t0fO8EOYagtt6JW9eJ9nUFrB/UVB1BaeKkrQu6E9F+5GPQcWLsoNj2tlzHv1JaKArkq+P5XmLfic39i5yZNnn49M5Kk4KOxB4IViqpDcR5QzbAXynl47z87O8732/xRve8DZe/vJXfodra/GDwG233caxY8dWPXb8+HEGB6OZQcPDw3R3d/P1r3+9Kf6Wy2WeeOIJfvEXfxGAW265hWKxyNNPP83u3bsB+MY3vkEYhtx0003N5/yX//Jf8DwPXY++ML761a+ycePGS0ZEtPjept3qYqpxBiewMVcENyewUYRy2e6ETf42juoHCYIAlej3fl3UcIVNd9izyqwQicaSRWWeY9phkjK1qnQ5KVP4Yowx7TQdItc0PJxPVOC2egzeGx/EDz0m66PU/DKq0OiND7IuvfWSJgZ7/TtRGvNoxWNIPQMyQK2O43TfynHDRJXuufxhRUcRKov2LBWv2JzZFv1MY2t2N6crR1i05wgJ6U+MoAkdN4wK8wYSaxlMrSeuJemLD1H1SpHxQ89ctcFCacxjTD+EtvgsgaIRWibxtk1YK9EQqqoBggV7hppfQRBFFenq6juNqtAIVoqSr7xBFWmdE4uD1DDtsX4KzjRIB6HoCL+OLRREvJv08+T3BqHPkeI+7KCOIjQsJUYgfUruMim3wlBxDN9qp64a4NcJrTS5RpEtc89xpOdOUA1SiQFKboGCu0xnrPeS21l259FUg1Rtms6Zx0lVJggVlclkH3WjF14kMdga/SzWmc8TxjoJUsMoXhlz4suApLHlvS/KNlq0aNHie5GWGNzihwIdnXfV7+MzsX/ikL6fkijQFuR5hXMPN3q3fcfrn1Ym+fvMB6HmEndjFGJLzKrTlPwN9Df6sdT4Knfs+TRo8JH4X/Os/hRpmUaRKl+0PsMx7RC/UPst2uXFYq2Dw8diH2av8SiucPHxKSkFDGkwqY1zRjnFxmArI/56VFQqfone2S5c2yarRoO85ZWSu53tt152385HVwz6EkP0JYYu+lnFK1J2C6sGw6pQiWsplp0FGkGtmX+WMXJM1qLB5Nnc3kAG+KHLsjPPvD290q4scQKbYKW92NLi+KHHdP0MvvRQ0dBUDSFVfOmy5MxSLUaCX649R0yL4wYOilDQFAM/9JCE5Ix8c9rdtVDxSszWJ6h4RWJqgq54HzmjgwcnHuHTJz9PEAYk9SQpM0HFqSIE7Gjfwu6u69jded1FEQVSSgIZRE3Ll4kvaDc7SWhJSl6BtJ5DIMh7nai+gplOEGukGfLX0hcOIK4XBN19BAeeo79UpKIV2ZutUNQchmLt3NF/Myk9yZ8+9VdMVWYA8I6V6Bju44/e8R5+9mffdok9OJukunr/zl4MSinZnt/Cpra1PD33HKZmYqombuCzPb+F6zuvu+bz/ELpjOfJxbMsNpYZSJ27sFhsLJGzsnTHz134nnl8jPG9EyTzCdJdKQIvYHmswMF/O8xN776Bjo4u5uZmXtT9kxIenhJ8/rSgYAMIkga8vD/kwMHn2Lr14nOVMmB7XvLNCYWMIdHVSCgdr0BfSrI2C+6tEnUGjMcUxFzkfh0ZkdTukbx6TRS9sLVd0raij9e8SIx2Q1iTkvQkLtrsKm7rlRxYlBxZFuRMiR9C3YeberhiTnDZjUTZnAl39ktOlSQHF6O8YwlkDHjd2pDuC7bfGYOBlOR4MXL2nv1ojJVhoREdT1cM9i8K5htwZFmQj0lCGRXsJfRo7sL6rOQ1wyGKAtvysC0fvZf/Yp9C1VuZgSGgIw4VT+IFgs6EpOIKkrqk5ETCcD4GTgCPzujctaaLhieYrCiogC+h6sJCKJiowPqcpOhEy7xQpJT8zu/8Mpqm89//+wde+Ipa/EDxm7/5m9x666380R/9EW9729t48skn+eAHP8gHP/hBILpJ9xu/8Rv89//+31m/fj3Dw8O8733vo7e3lze+8Y1A5CT+0R/9Ud773vfyN3/zN3iex6/8yq/w9re/nd7e6DvzJ37iJ/iDP/gDfvZnf5bf+Z3f4eDBg/z5n/85f/Znf/bvdegtvgPyVjf9iRGmapGoCpGA2J8Yod3suuQyt7h38Jz2FEe1g2RlGwE+NVFjrbeJC4cqISESSIdZLBnDZ7UoKVf+06VBZ6yPE+UD+KGPpmhIKan7VUzFImesHucqQmEotYHexCANv4auGKtKywICGqKOKS10dMJYB/Udv4Ex9U30xWeRqoHXdQvlzj3YhccxLsjxNRSTml/BDhqkWX2TI6Yl2Jrbgx00CGWApcZRhIIfRn0Z50efaYp+rszuKlEaC8T3fwC9eJzQyCCDBkMzE2SqM0ysfRNyZVxsKCYNv4Yb2mSMduJakqpfwTDOjV8bfg1LjV1xDC/sRdTqBIpTQDhFROAQJvvpXPMm5qf/jSVnFiPw8a0MQbyb3rbdZI0rH1PBXaLkFcgZHSw780jCZnSFXzpNLHBIGB1o5ZP4hHh+g7hbRXGfZXbxAMudOxFEho8rCdmq0IjVZhg+8zXMxhKOlUMEPoNze0FqkL0OtEu7ikMZIKE5U/Oy58ctYcw+TGjlCWPRODFU80gExvxenKHXEsZ7rriOFi1atPh+pSUGt/ihoV3m+dn6r7CgzNEQdTqCbuK8OAVRD5pfZVwfZWt6O+aiSryWxNYazIYTZMlwfXJ7c7rZhRzQ93FQ37dSbhcNajrDbo5rR9hrPMKPOm+4aJknjG/zmPkQfcEakjLFfv0ZptUJDGnS569hXDvNPmUvNg3awzxGWaGj3kGb1oGmRh97XclTcBaYq08xcpnpYJei4deoeCVUoZIx2tEUjVAGhDK8KL9LFSqedJrFGgAdVg/tVjeL9kzznHihiyp0nLBBm9nVHLzVvAplt0AgfSpuEQWVQPqRC4FgJQZCoqHjSof5xUjA6+kYwLKiLOKqV8IJbTR0QoJmDMW1UHAWOFR8mrpXRVN0FuUcs/Yka+Lr+drEt9AVneF05NDqTnQyWhqjM57nZ7f9JIaqE8qQIPSb52fenl65OKtgqjH64oP0xAcvcpWYaoyNmes4VtpPyV2KCq6Eztr4JjboO1Dd1V/ham8/am8/BrAb2BkG2IFDTLOYrc3zl/s+RNEp0ZeMzsHUhhBtW4rtu2685HH7SsBiYpHJ2ii27tIhO1kTDKN6CqZikdKzTDdG2dzZTahUGC1OIwh4Rd9NvHb4tSSN51EaX0RiWowfGbiDfz7+WU6XzpA2UpTdKlKG3Dv8yua++G7A1P5pzKSBlY4+b5qhke3PUJwqsTxeoLOz+0UXg8cqUeYtREVnAsmSDV8eExw+dow3v/5Nl1zu3mHJbE1yohjFI0igKy55y/qQ2MrL3/hxiXtLgDopkAb4myQjKbjQl31wET51XGGmBiGQ1hXu7A953cjqPN/z6U7Az+8IeXhSRFm7GtzYLbm1V6JdYpmlBnxhVHBgURCEgjVpyT1DIe/dHnJgUTBVkZgabGmXDKYi93nVLzWzwVVF4TUjIQuHFI4UBHFNYvtRtEPKgPVZeGZeUHAEOUNS9SCuC9I6JA3J3Wsk3QnYkb90XMP6jM/+xQA/1NEUhc645ExZIIQka0LRjgRvVYGOeCQaJ3Wo+ZHYfHRZ0BGTTFYFNQ8sFUwhccPIXfzpEwq/tTtEf4GTTT772X/my1/+PH/7t/9Me/u1iQwtfnC54YYb+MxnPsPv/d7v8f73v5/h4WE+8IEP8M53nsuT/u3f/m1qtRr33XcfxWKR22+/nQceeADLOieWfOxjH+NXfuVX+JEf+REUReEtb3kLf/EXf9H8eSaT4Stf+Qq//Mu/zO7du8nn8/w//8//w3333fddPd4Wz0/ZLTBdH6PkLWMpMbrjA3RaFxT3ChhOb6LD7KHoRvnBWSNPzsxf1sXaEXbx8/Xf4GHjGxzQ92FKixu8W8gEOT6a+CAT6hjdQS8BPhPaGD1BL9u9XWho7NefpiLKpGQaiWRWmSYtM2zyt9KT6KPoLrFoz0YOYSnRVZOR5Kama/dCDMVcJX5KJE/qj/It82vMq7MkwxS3u3dxp3M3eqwTe92PY6/78ebz1dDHUEzc8JwzGljJ5dVWPXYh1gVxDy9WYZkx8y304nG83BZQNKQMaOCRXNiHk+xmId1PTEtiKAa6YmCqMVShsia5jiPFZyk6SxiqiRe6SCTrklsvHU0RBlijn8GY+jpa8ThqbRKpGASpQaSewMhtY+vmn2bGXWDZnkHV03TG++mKDTyvw9kLHaQMyRjtuKFD1S8jZTQDsiFULC1JqnQSgSQ0stSEghv6WIFLfuFplju2YwcOhmKQ0jPR3XLpg1DhvG13Wr00Fj+BUptmNtkT3XpQVIxEL0PlMdyl5/C6blq1b7ZfZ7x2igV7mlBK8lY3axJrSeiXFswVp4DwahdlBEsjjbAXUJxCSwxu0aLFDywtMbjFDxUCQWf44ubeBQQc1PfRFuaRSYGrhqhlgelazCVmMY0UverQZZefVMcICFYV2GloxGSc49qRS4rB+/S96NIgKVPURJUZdYq2ME9d1GgL22n38hzRDjKqnmTd3AgDc/3onsa0eoaUniVrtK+UxqnUg+pVHaeUktHKMSZqp3BCGwVBSs+yIbODhJ4mpsWp+9VoYLdC3a+S1DPEtXOioK4YbMvuYap+hnl7GqSkI9HLdP0MntRX3cWPa0mc0GYotYGc0YGKxpen5/ACNyrxCB0ESuQkFpJioYBhGqzPb2G0eoyYUGi3upBSUvXLqEKlzbr01MgrHnf1OA2/Rs7saF5oVbwiR0r7KdrL9CdXN6d3xjuYqy0wU5shFA1m6xP40iNrtGOqFpO1UUJCTMWi6pU4UtyHEziXFOXzVjcpPUvBWcCXPkk9TUZvu6oyNFVRSSjRDY998/uZbyyyObehuezazBBHlo/zzPz+i5YNCPin2EfYG3uEvpkeTNtgSh2jGCyyjk1sS+6hHlQZqx4nqSd4Wf+tvKwfGkENP/QJcFadw9n6PHO1BRJ6nOHMmlWFNi8Wd/bfQky3eHjyMRYaSwylB7i97yZu6tl97rhcH8/20K3VF3aqriKDEK/h0dXV86KLwYeXBCUXNufOuV3zsUg8XV6cpavr0hcb+Rj8ys5ISJ1vSJJ65BZe5T4VEAxCMHj52IYlG/7pqMKyA8OZKMd30ZY8cEbQnYCbey6/bE8C3rbxrEv88tR9+LtDCkeWBJ1xialLDi8JpqoKv3hdyI3dEs77+p1tTDJaOUrdryIQpPUca9Nb6U+2c9/2kGcXBFNVQUcsxAvh4UmFhg/jZUHdg5IjVpzKklcMSOxAsKszZPgSKTChDJionsayxtje5bLUSBK4wzj+GixVYCiRA7juR0e5IRe5lCGKp+hOSFJGlGk8nIZlG0IpEQhiGiSQrElJzpQEp4pRZMW1srAwx+///m/w+te/lXvvvfTNgRY/vLz2ta/lta997WV/LoTg/e9/P+9///sv+5y2tjY+/vGPX3E7O3bs4OGHH37B+9nipafgLHKwsJdGUMNQTCphiUVnjpFUjeHURmxsHja/zuP6w9SVGuv8TbzMuZt1wcarWn9n2M1b7J/gLfZPNB+TSOxGgwesf+WMdhIFlUF/hB+zf4J2mecG91ZOqcd53HyYScYBSVpmeY39ZtYEwwhFsD13QzOeQVV02swOMvrVfVlKKXnM+BYfS3wYKSXZsI1lZZFPxP4vJVHkzfY7LlpGU6J4iRPlA5ELWY3hhx41v0yX1Uda/+5Hn2hLBwiNFKyMgYRQ8DQL6dfRqmPY8Tw1v4KCwqbsLuJqNKuuOzaAKjSm6qNUvQoZvY3exCA9sTUIrwpSRvm2KwMMY+YhrNFPI5UYwqsiVQtkiPCq+JkNGJNfpat0lLaOm/A7duHmboSVGIqaX6HmVdAUrVmIfT4xLYEqNPzQo8PqIRmksf0G9aBKsmOIeGkeZfkIQSIaG1tEzupCvB29NodTGcM20wwm15OpTGFO/l+00klCI4nXcydO3ytANemOD1CuL1ARkkbYQEgBAlQ1gfRt1NoU3nn75YbOSr/JPJYaQwjBRPUEJXeJ69puJqZdbFAIzTakkUI4RaR2ziCkOEWkniI021+0175FixYtvtdoicEtWlwFISHj6igNUac76CMnzw1eBQJd6jSUBggI4xDGJSEhs9oiqXoW4V5euDOJXA8SuSqLzRceSXnpO9musJu5bA1Rx8MlIduoixpSSNb7m+gIujjhH2a4NEKWHFVRIgxDCs4CAkHGaCOUATF19eCoIso0RJ1c2N4sEQGYbUxwunoEQzHJGXlCAspukSPFfezO38FgcgPHS/spOotoioEXOuiKyVByw0WOYUM1GU5tZDh17sJk3p7ECzwuha4YzWzjNrOTsepxkAJFqJHrFh8hBbVinVxbG8OpjThBg9nGJHJlGqOpWKxLb7mqSIzzsYM6ZbdAXEutEmATWpqSUyaum7ihi6GeO1du4KIpGuO1EzTCArpiogqV6cY4FbdAUsuQj0WqWIwENb/CZP00vYnBi9woAKZq0R0fuOjxa2GhsYShGKuOISpOMVmoL130/JPaUZ4yH6Mj6MLqSqFWBLpjMmNMkzcGGGQ9R4vPEspw1QA7riVZdhZYsudoMztwApfPnPgCT84+Q9mtYKom67JD/PjGN9GbfHFvzAghuLH7em7o2oUTRK/JhQ4XI26QzCcojBebzmAAp+Kgx3SS7Qk6O7s5dOi5F3XfbD8qJ7tQw1e8Cp7TIJ+//E2KuA43XUGsvRoOLwnm6pHIeTaPtyMGZQcemxZsbpPEdV6woxXg0KLgeAHWZSXGykc+Y0iOFgSPTQtGMueOoeAscrT4LIEMSGhpJCHPLfr805EiQdBG1VfRRZTDm9AFnfEQVZFMVqDgRKVxpgI+Ej8UHFmORGv/MqV2p8pHOFM9hqYYbG0zmKwVKNgFkrrklYODDKQkszXB1ycE4+UoEqPqQcGO4iBePiDRFViTgm9XBHFNkDEkhioJQqh4kDag7EZO5ucTzi9ESsnv/u6voCgqf/iHH3hhL0CLFi1+4JFSMlY9jh3UyRnnblDX/SoTtVN0xnr5l9TH+Zb5dRIyiSUtHjMe4rh2mPfWfu2qBeELEQjucF/BLu8GxtVRdHQG/bXNAmYTk3c2fpYbvFsYU0+jEXVZDARDzbGtpuh0xwfo5urHM27gMFkbZcYeZ6/+CLlkmlyyE6lDW9DOgjLHI+aD3OG+otmzcT5rkmvxpc90/QwVt4imaPTE1rA+sx1PutTdCqqik9TSV3WT/TtFajEIz0Uj2H6dgqJSad/EVKITO6yhKyaaMDDVeHOfhBB0xnrpsHqQhAgU1MYs5pEPoS8+C4Cf24Iz9FqC5BqMqW8iFQMUFeE3CGN5CAMUewlj7lGUxiKKvYgIQ4y5R1F7D1Lb9NOcrJ5guj6GGzooKGSMHBsz163KVs7obXRavUw3xjDVGJrQURWVtJJjfW4nzoiJOfsoilsEIVCERphej7AyiKBB0mxnKLuNAbtK8uBfINzCSuHfLNqx/4taOEKQ3YjtFvFkQBqFQMsgAA0FxVnGbSyhXlAGvdCYYdldIGueE7BjapyCu8hsY5LhxFrU6gQQRkK1aiCNNG7Py7BOfQqA0MyhuGUUex5nzWsI45eOU2nRokWLHwRaYnCL7zpVr8xMfZyCu4ChmHTF+umK9V918cJ3m2llkk/G/oGT2jE84ZAJc7zceRX3OK9DRUVBYbd3C5+1PkE7eSxiSCST6jhtYTsb/M1XXP8WbwdfM7/IjDJJT9gPwLKyhCIVrvP2XHKZrd5OjmgH8fExpYWGTlVUUFDJhtGArSJL6J5Gl+gjbsZxwij/DCkouctIJJYabxY3VEWFL5qf5WnjcRzh0BF08SPujzabo2caEwDN7F8VjYzRRtFdYsmZoy8+hKlaTNfGqAdV8lYXvfHBy5aUXEiH1cfJykECmWwO4iLXi0HWOHdnvjvWz0TtJAHhiidYoKCiCwMrjJOMp9EUnS253XTH11D1iihCo83suOxURIgKUg5o+1hU50mFGbZ5O2mTkYP6bIbx+UgZktDj9KV6OLhwinXZYXRFww1cZupz7OjYjBtWSGoZDDUS/DV0loIZvAumJsbUBCV3mZpXuaQY/GLQFe/EDV2klM2LCyklTujQbXUAoCwBPqDBpDqOg01KZpA6+G3R8btKyDF5BFEReNK75MWTAPyVHLgHJ77N18e/RUcsT2+uGzuwObR0jI8f/TS/fv196C/S1MtV2xeiWWZ30c8UwdBNg5RmKiyPF4hlYviOh1N1WXPDAKnuFJ2d3Xzzm19+UfepLwlCSJwAzBWhNAihuDQPQEfHS3vBUfNYuSg79z4OZeR6/cqYYLKqkjUld/RL7ui9fGzElVhoEOXsqee2IYG0EUUxnC+QzjYm8EKHnBm998ZKCR4aH6Dk+GjCZaoaxw0igXe+DklDJR+TPDItkCslcw4SUxV0xSVzdehNSPouca+n7leZro9hqvFoloIOWSu2cpPnJDd09K1850ju7Jd86Yxg76yg6Ag64pKX9UtuWRHjb+8LObSoMhZK6n4kAFe9SFhXgKwp6EteRpG+Ah/+8F/xpS/960o8RMc1L9+iRYsfDtzQoeQWiGmJVb9/Y2qCgrvIIfax13iUvqCftMwCUfTDMe0QD5lfY139hYnBZ0nKFFv8HZf8mYbGFn/HZX9+rfihx6HiU8zbM4RqiBM6dCznUWoCt1siNWgPOziuHWFWmb6kGKwIlXXpLfQnhpv5w3E1yWT9NBO1UzSCOioqWTPPhvT2y8YJvFh4nTeiL+5DuCWkkWEWlzEria8ZCKsTSzWQSFRFo7QS7RGdDBu1PoPUYoSxLoRbIn7wr9CKRwmtLhAKxsxDqJUz1Lb/GopTQOophN9YqX0QoGgIv4bwa4RmGygafmY9wm9gzjzMVKqXMdXHUuPkjDyBDCi6S03Tx9moDCEEG7PXYWpx5hoTeKFLWs8xkFxLh9WL29OBPfCj6Ev7CePdhEYaqSVpLx/D6b+X63qjGQ6xk3+GcIv42S3NO+Vq8Rjxox8mSA5STHQwrxt0uFWyTplQMUmXT2HVFwiFghz/POgJnIF7QAiqfilax3kGFCGUyMW8+AzJo59ArYwCkiA5gD3yFvz8Luyh1wMyKvWrTxNqCeyhN2CPvPklfS+0aNGixb83LTG4xXeVilfiwPKTVP0SumJSlsWVKWMl1qe3fVfuyl8LDg7/EP8QJ7Qj9AeDWDLGojLP52KfJC0z3O7eBcBdzqs4o57ioP4sIQESSU6282b7HbSHV76wHwrW8obG2/i32P0c0w4BkJBJXum8hp2XEYNvdu9gv/4Mx1fam1WpMq/MMuyvIxPmWFTmWWKB9eUNZMkhVEGH1UPBWcAOGjihR0xLsClzHSk9Q0jIx2Mf4XHjYfJhB5kww4w6yT/G/xZVatzg3YLt19HF6hBORSgIovw1IaJtXJjJK6VkyZlnwZ7GDR0yehsZPU/Va5DQ47RZkXjdlxii4C6w7CygIAiRqEJjOLmRpHZu3rehGNFgM3BoBDVUoZHRc2iKgZR7URSluW95q4u89fwi25wyw/+N/zUntWNN0bcn6OddjfeyTm6izexgpjGOoZgoQkFKScUrkdIzvHHtHmrupzldGkUCCoLNbRu4ve965p1RUkq2uR1VUVEVnYZfXyXKBtJHFepLEp1wll2d2/n29BOcKJ6iJ9GNQDBdm+W6wmbuffp2AGL3KyRnFOzXhhjXRWJqSIjCOWXQEy6JMHICZ412ZhvjhDJour8D6a8UhOVwA49Hp58iZSRpj0Wvc0yLMZRew6niGU4WR9nctuElO+bL0d3dhboDRk+MU3arGHGD4VuGGLppECEE+XwnS0sLL+o2t3dItrYLnlsUZA2JImDZFrSHswB0dFxbfAlEn62Cu8CyvYAkJGO00W51X7IwpScBqpA0fJpZw6eKcKwg6EtCTJMs1OGfjgocH+4ZunYnclIHZJS5e7womChHrloh4I4+iVz5fz+EZ+fheGEthppgIFXj0GKGuqfRHitzutBBWgcjBjVPkLdCCm4U29CXgLonqXjnROGJKoShIGdJTCVk2VnEDuoYK8VE9ZUSngtLcSw1TiOo4QSN5k2uuA5vWS+5Z1BS8yFrnhPvISrNe/fWEOWw4PEZhWUbuuOS9pik7MG9wyEd1xhF/8QTj/D+9/82P//zv9GKh2jRosUVUYSCIpRVXQyw8rtaCOa0WWzRaArBELl628I8J7Wj+PjNmWXf6yzasyzac2T0NkIlQBrQ0G1SjSRqBfycpCHqWFjE5ZU7Ciw11rzZPluf4ET5IKpQSekZgjBgwZ7GDz12td/2ko7F3O7bUEsnMGceRlYnmE9kcPQEutWBYuaIiShLvxHUCGQAUmLMPIQ59kWUxhwoBl77dsJ4D1rxeCSkruxvaLWjFQ6hzz9NkOxHX9pPkOgD1YyiIhQDEbiEehIROPiJvii32EhB1aO+8BRq7w3N2V6a0EgbbZTdAsvOQtM8AtGMvfXprQwnN+BLD0Oxzpl6VAN703sQRz6EWp1E9esgVpzLI29aGQjUUUsnCa38uSlToY9WGUf4dcJYNzI9xIIGc40CnbV5Mo15lMClYaQpZobpRCV24uOEVh6vcw+aMAC5anwNYDSWGD7zDbQwIEiuAQRaZZT44Q9S2/nbBOlh7LVvxRl4FYq9TGhmkeZ3P0KkRYsWLb7bfH+MBlr8wDBZPUXVL62a2mYHdabqZ+iODZA2sv++O3gBx7RDjGonGPbXN+McusNeRkWDR4wHudV9GQoKKZnmvtqvc0h/jil1HEvG2OLtoD9c87zbEAhe7r6KTf42jmtHCAkYDtZFGWtcWhxvk+3cV/t1Hjce5pD+LAP+ECWlQFkpM6GNkpRpXll/DV3ldlzhYKoWcS2JpcabMQ43tN+JqUUD41H1JPv1pxkIBknJyD2bCjKcUo/zkPlVdns3kTFyTNXHiMtk87XzQx8hlIuiJs5nrHqC05UjK43Bgq+c+QYnlidRRZyElmBnxzZev+5HSRspduRuYt6epuQuoykaebObNrNz9aBOtdAVnU6rd9Xjy84CqtCaYvDVIpH8m3U/x7QjrPc3Rc3UhJzUjvFJ6x/5T/5/ZTi1ibpfpegucdbdGNeSrE9vo93q4jev/3kOLR2j6JTJWiliumSqfprlFfE9Y7aR0jKoioapWNhBAy90MVSTIPSpeCXyVvdLml/XnejkPVvezudOPcB4ZZJAhgw1ennzg3fjV1wAwjRoJyH+9wo7fu062jd1MK6OsiYYRkGhIko0RIMbvdsA6Ir1MdeYZNlZwFRNJFGxSN7spsPqoeHb1L06cW2129lSTfzQo+rWXvDxzNUXOLh4hLrfoDvexbb8JmKXaZVuEoD5ZYH5oEK22Mc6rZfqGofgdQoMnVP8TNPCdd2LLii+E+Ia/PTWkIcmBU/PCQIJPzoUEtRn+SzX7gyWUnKycpjx6kkC6QECRSh0WX1szl5/0cXs5jbJtnbBvvlINEXCc4uChA7XdUTlaVkTpqvw0KTgtt5LF7Cdj+3DySI4gaA/JdmWl7TFBPefFBTtSKxFgKpIji4JnpkXXNch+cQxwRfPrKXi1TEUg6dn2ynaBr2pKg1fww1VsmYUZ1EOo/K2zphkoSG4sTvE0BQUQvbNK9h+JNb6QjJecfnM6b3k4nOEMkAIQUZvoz8xEjmDpLfqhpYvPVShoYmL3elJg8se/65Oyfa85JGpkL1zCksNyFhwa09UrHeWqldmuj5G0V3CXJkF0xnrWzULZm5uhp//+Xdwww238l/+yx8938veokWLH3J0xaAz1stY9QSmYqEqWnSD2i2S0NPENR2BuEj0tYVNR9C56ubu9zoVr4RErvw+0+gJ+jmlHcMUJqqt0aDBuDrKNm8XQ8Haq1qnlJLp+hgSSXKl40JVNTIimum27MyvEj1fdFSDxqafxeu+FUonWfKmaEiXupGi+StHRjn3CS2FvrCX2NGPAEpUZBbYGDMPQxhEIvD5v+uFglRjaNXTuH13oxWPodTnCBJ9qIVjKEEDKTSE3yBIdRBk1q06Lx4B+gW/D8/OmvFC95KHoyk6Ghf/DvVzm6le//voi/tQ3BJBrBM/vwupr1wvKDpSi6G4peYyirOMcItILYFUdXIhpBEc674eMX8AM/SpJnsp6xYpsxOsbigeRZ/9Nl7nHjqsbiZqp6j6ZZJadC1TD6p0lc+Qdsv4+d3Ngjo/vQGtcBB97jGC9HB0DowMgXGJ0oGzp9etoJZPAhCk10UieosWLVp8H9MSg1t81whluBLqH18lsJhKjLpfo+IVvufE4JJSJCBoCsFnScoUy8oiHl7zZxYWu72b2O1FzbYh1zZVuDvspdvtxQ897KCOozSw1MtbzNpkO/c6b+Re541AJGrOKTOUlSJtYZ72oIODxl5mGuNIQnRh4IQ2QgiGUxubQjDAojKPLeymEHyWXNjOnDJDXdToiw+xaM9RdJeIaQlCGdDw63TEemi/TBRE3a8yVj2BqmiktRzPzR9l78wxBAG9iRSGovONyW9T8+q8d8dPYagm/Ylh+hPDlz3uDquHydppSu4yST3KeKt5FQzFJKbEr1m8K4hljugH6A57mhnJCgpr/CEmtTHOaCfZwBZ2tt/Kgj1Dw69iqBYdVs95bsI4N3TvQkrJoeJTTNXHMJQYphqj7lfxpAtmlP1raXGyZjuNoEbNLxPIEBUVpORU5QidVs+qbLbLsWwXeGz6KQ4tHcVUDXZ1bufG7t2XjUcAWJ8b4Teu/3nOlMf5zMkvknhE5dDx/Xy1M4oqOFDZz5qOQTrG2ul6qp+3Dv4Un4r9A8e1wwCYWNzu3MWtzsujf6sW29tuYLJ6hjl7EkUoDCbW05cYQlcMFE2lM57nTHmcnJVt7kfZrRDT43TE8xfuYpOSU+ZU6QxSSoYzg7Sdt/yz8wf5p2P3s9RYjoJCROTGfs/Wd5CzLj+QN54QxD4ncNM+C9cv4GKTGEuQ/USe+q8DKx8Jw4jeB77vo+svXoxFxoTXr5W8diQSDBUBnz8eAKBp17adgrvIRPUkpmpiqdH7xQtdZhuTtJmd9CWGVj3fUCNHa18yEqMXbUidJwSfJWXAWFlwugQ7rjCp4UQB/vm4wkQlirvImAp39oUkNEnRji62TC0S03UhWLYlXx2LPpuPTAkGkhb1YIFAlvHDGGfKcURVpT9lYCga4YqmKoREV8ELQVMkN/dITpXgwKIgpkm64lFMQ86CdbnDTNSmycczZEyDIPQpuIsoQiNrtLPozJLWc6hCwwujmQXDyY3NGJcrEcooq9hQonOkKfCyAbizP8SXUSHf+V89ZbfIgcKTVL0yhmpSkQUWnFkG/TLrUlsQQuC6Lvfd9w5UVeVv/uZjaFprSNaiRYvnZzC5nqpXZtlZgJX5THEtyYb0dvTAojvo5Yx2iiF/LRoaZVGkJqq8znvLiyoG29gsqnPEZPx5Z8G9EFRF43yn53p/E45oUGSJBX2BZa3EZn8772i8JxpHXQUSSd2vYiirv/fPbssN7Rf9OC5CUfHbthHmtqDPf4OYs0AjqNMIarBiA4lpCTqtXozj/4QIPfzsSsGwFsfX4pEgrFo0p9ysIAKH0GzD69hNffN7Mce/BLVpZPs2QjUOArTSKfy2HcgV4VPYS6DFCXJbcILGqh4IN3RQhLqqCLp5GI15FHuR0GwjjF/cASGtNtz+H7nMOdBxu+8gdvJjCLeMNNIQuAivSpDoJbQ60IEtvuSAIimrOjXdpGSmsNR4Mz5OanHU+hwAaSPH+vQ2TlWOUFyJ2DAUiwERx9CS+OfHEQqBVGOo9asrC9ZnH8U69UnUejSbK4h3Y699K173bVe1fIsWLVp8L9K68mjxXUMQFX754aVLwi4sGfteIB92oKFTF7VVU9BKSoFN3tZmccZZJJKn9Md51HiIOXWa3mCA2927uM7bfVmXb3NZKZmsnWaidho7aKAKlU6rl7XpzRjqxY7Hsijh4ZGTbSgr6bndYS/d4YqjQcDGzA4UobJgz+BIG0MxGUltZjC5btW6kjKFJnVsGlicE4mrSoWOsANLxtDMFNtyNzBWO0HFLUbCX2o9Q8kNK4Poiym6S83p2UEY8OzcUTRFJWel8aRNrzWAoRocXDrKaGmctdmhK54jiC54Nmd3caJ0iKpfQkpJXEsyktrEN40HCcNrE+F94RHgo8nVwruGToCPt9JVbKrWFUVqgIpXZKExQ1JLY6oWmqKyaM9R9yvM29N0mD0MJtaxLr2Nil9k0Z5jonYKP/RYdhdYdOaYqo2yKbuTrljfZbezbBf4P/s/yoniaVJ6Aj8MOLh0lFPFM/zUlretcoWGMuTg4hH2Lxym5tdZlx2m7JQ5unyCnZMD/PSRt8OR6Ll//Ngf8MeP/QHvHHgXfzXzd+zxbmYoGOGIdhBXOPQHa1jnb1p10VX1KlT9IoEMUFemr2oi2r6qqLx84Db+/vAko6Ux2qwcDd9m2S5we99NDKb6L3l8j888xedOfZnFxiJSQpuV496Ru7mz7xaqXo1/OfE5JDbbO6Ppfo4fHePXxh/irRtef+mTJsF4RFBJVXjm9qcpJJeQQqKsU+g808WGo7sQOy1OleBoKfpcTxZdhjte/Exj5byvAsuKPm+23SCTyV71OoruIr70SKvnbhzoKwWBC/bMRWIwRPm2b14vec2IZLoKf7FPbUYghBJOl+BEQVD3BR86oHBnf/Tc+AUf77IL/3hEYaYmGEpHxWqLDclnTgkKtiBnQtaUSAGWKvElVF3BaAmemZf4UtARs6j7PRTcRQQ2KcOm4SfJGBoZU7BsAwiypiRtSMbLgrvWhNzYA2U3ZP+ighDghtCdgM1tNvnUFEUnSd03SBrRxX1Kz1Jyl9iS3Y1EUnSXCGWAKnR6Y4MMpZ4/P/PwEnxlTGG8LFAVyXUdklcPSdpj0TW4LqLzd6oIszVBTANFPUnNL9NmnpsF0/BrTNVG6YkNkNTTvP/9v8Ozz+7l/vu/8ZJnRrdo0eIHB0uNc13bLSw5c9T9KrpikDe7sLQ4SHhH46f5p9hHOKUdQwKWjHGn8yPc4VxGmLtGJJJHjAf5uvklFpV5DEy2u7t4vf1W2mT786/gKmk3OxlXTGp+JXLJorO5sY0luUBa7aC72sd6f/OqsuPnQyCI6ymW7fnmDX04N9PtSiaMFxtFKPTE11Dxi7TrXQQyIAh9POmSNfJ0mJ2otWnCC40yqtUUctXKaBR9IARqbQqpJ/A6bwAh8Lpvxeu8AcVeQmqxaJnAIX7kbzFmH0FxFqLJbqqFvebVZHtehlZ8OjJ9qHF86WMHdXpia8ic192B3yB28pMYc49G8RN6ArfjBuz170DqSa4WZ82rUOtTGPNPQnUCpE9otRPGukGNxmEdboO75p/ADR0st0LSC9ASfSiKBlIivAp+503NdfYlhmg3Oym4i0gkWaOdnCth/hmQYdMZjJSIoEEQf34XuFo6SfzY30Ng42eiaDO1Ok782N9TjXWtcli3aNGixfcTLTG4xXcNIQTdsQFOVg7ihx6aoq9krxaJqXFy5uVdgv9erPc3s8XbwT7jSbqCHkxpsajMo0mNO9wfiYrFpFwRwlQeNL/Cp2IfQ4qQZJjigL6Po9oh3tn4GW5x77zitmYa4xwv70cRGjEtjh/6TNRO4UqHbbkbGNdGWVQWCPA5rB3kiL6fQASs8Ye4x3kdm/3tq9bnBDYFZ4GM0Ube6sZQTOJaAvMSBWUb/C2sCzZwRDvAmmCYmIyzpCzQEDVudd8KoWSqMcqSM4+KxmByPV2xfmLalQfNUfFaRN2zWbILIAJqfhWBYM6eot3sxPZtlu0iVzfJD9rMTvZ0tFNxC4RI0noWTdHRdR3Pu/TNhsvRHnYwEAxyTDtCys80Rfs5dXrlZ0NXva5GUMeXHkklGqTHtAS9iUHK7jJe6LGj7UbyVjdCCHJKBxPVUwTSb0ZhSCkpewVOlQ+TM/JU/RJ20MBSY2SNfHN6+WPTT3GicJqNubVN4bfq1nhybh83dO9iWz4qLZRS8vlTX+bLY9/ED300ofHkzDPMNxbpT/awbttW9s+fYGZoESkkS40Cuzq2MzTVh90Zier5sJM73Fdc8ngX7VkOFp7CC10sNUYjaHC8vJ+GX2NTdidCCPZ07SSUId+c+Dbz9UUszeK1I/dwz9DLL+niPlOe4JPHPkcgfdZlRhBCMFOb4/4TX6A73knFq4BSYSCdQxFnXbUwnGtn3/wBXjdyz6Xd0T6IZcmBnftZSi+QrbahhRqe6jLTNYV0TfYd3c1jMwqjc9FFyJ/tDXnbdYLb+649P/dqOSsGO861uZGkvPQ+RZ+5K98QMVUYSsO2fFTIpquSuVrktnWDSOCNafDAGQXbD/nJzXKV6/XIkmCqCuuyEm3luqojDrN1WLIjR7CugqqAKkDIyNl7NmJlqQFPOAI/TNFuJelOOHRYKiVH55m5KFPYCSMhOaHDTDXa13uHo+Vv6Jbs6gA3iATZpA6K4iLxQcbPie0S6q7GbCOg4RoMJW/DEksUHYeclWBtKouhXPkm3akifOSQSsmVdMYkXgjfGFeYq0l+aWdITIO6D584KnhmXsEOQMVjU+ci67NxhHlu/dZKq3nZK/DZf/4kf/d3f8Uf//Ffsnv3TZffgRYtWrS4BJqiXfam8RZ/B/+p+t84qh3CFg16g35GgvUvmiv4Kf1x/in+EVSp0hF2YYsG3zK/Rlkp8ku1//iiZRKn9RwjqS2MVo5SWHF66orO1vj1rNW2oPjXfjxCCPrjQxSdRcpekZiaIJA+Na9C3upqlppeERlC4IJqrp4S8gLoTwxT88vMNaYIpI9AkjPa2ZTdia5aBPEutOUjcL7zNnCQegqn/270pf1opeOAJLTy2MNvxM9tPfdcRV/t2lVN6lvuw+u8CbV0HISG37YZP7eVvFDYIkMmivuo2UuoRoaR5GYGU+tXxRtZo/djjn+BMNZFkB5BuGXMia8AgsaWn7v6g9fi1Lf+Im7fK1Bq00gtjrAXiY3ej1Y4hERFXz6ACF2M9DpUt0Js/kl8p4DXth21MYs0cri9q6+vLC1Oj3Yups/ruAFj8mtoxaMEyQFAQa1NElodeF03P+9u6vNPIZxl/Ny25usdpIajmIn5vS0xuEWLFt+3tMTgFt9V+pPDlL0Ci84sUoZIJJYaZ31m+3f1bvzVoqHxk/WfIxPm2G88TU3U6A57+RHn1exyb2DenmayPkrNKyNUhW/nv4ZhGfQH0SCkK+xhTD3NV8x/Y5d7IxaXzjQNZchkbRSBQmolw0xXDFShMmtP8qT6BPuT+6iKCqPaKQICrvduJBWmOazvZ0qd4Bdrv8VIsB6ABXuG46X91P0qkmjw3BsfZH162yW3r6PzzvrP8onY/+WUfhwbm4zM8mr7jdzaeBmHik8z15iKBoNSMm9PUvGKbMlef1lXMEQFY5Yao+aXCQkRSkDNaWDEkhiKSd2vUPfq6KpB1kxfdj2XQhVR+/O5cxggNHBc+5qyXlVU7rFfz0ximqPaIVIyTUPU0KXBG+wfJy0vHztwIbrQcUOXmcY4oQwxVYuUnkEVGqEIGa0eY6x2YiViIs3J8kkc36Pu+nTG8xiqQVLPUHQW2bv4EHZQX8k+VWgzOtic3UVMS3B4+RgpPbHKAZw0EnhVj7HyZFMMHq9M8s2Jb5MxUuRjkavDCx3m7CncsMb4xhk2PLuGzsk2ljvKGK5OfMwg7JR4u68sgEopGa+dwg/dVTdy7KDBrD1BnzdM2sgihOCmnt3s7rqOklMmplnE9ct/1vcvHKLkltic29B8DfuSPRwtnODZhQN0JtIkTB2kQijPqX5xU8ENAkIZXHrFGiyvLbIcXyBdz6CF0bkzHIOkl+awNcMj0w3arThuQrKPqPzsMycVhjIB/VdvdrkmTDP6TrDtxjUtlzHaUIWKGzjNmIMg9AmkT966eLrmhQgBb14X0vAVDi0Knl0AiWAkG+XhWiroiuSZeYW7BwN6zpshWvWiFWjK6vdIzgKtDHUPFhpgKAJLA01IAhk5asfLMFaOMpQNDRYbCicKFlUPBtMwkpEs2ZFsfEu3ZHsH5GOwpV02XcwZE67rCHlwUqFXlSuldHGqjSS5WIWcmQUJp0uC0XIDN4xxajHDeEXH0rrpTYAiJCNZeNfmkN4rvLaPTEcu5U25c9f8aUNyrACHlwS7uyQPjgsemVboT0pSBnihoOqqnCwGtMdAP2tEQiKAb375K/zu7/4qP/Mzv8y73nXf1bzcLVq0aHFNpGWGG71bAaiKCo8aDzGrTJOQSbZ7u66q0+JSSCQPm18jlCHDQSSCxWUCS8Y4oh/kuHaYLf6OF+UYhBCsSa6l3eqk4EROz4yeI6Vnv6M8/w6rl03Z6xirnqThV1GESm98DevSWy9ZwNok9DGmH8SYfhDFKRAkenH7X4nXccMLFoU1RWdLdjcDmDgzD2GVTpOXAtFVwhl4FW7vXWiFo6jVcYJYFyKwUavj+LnNNNa/E3vtj6HN70Wtz+BnNuJ3XP/8+6LoeJ178DpXl1SrlTGGT/4Ta4vHccMAjRDFyCKNFEFmPW7vnYRmG8bMI4RWnjAWCefSaidEYiw8iVN/7SUjIy6LUPBzmyEXjVuRkjA1iD73GOb0w0gtgdv1ckKrA9/birbwNFp1Ammk8dqvwx56HUH2yjN8wngX9a2/QOzkJ1HLo0CInx7BHnlzMy/4iqfLLYBirD6vQoBioDjLV3+sLVq0aPE9RksMbvFdxVBMtrfdyJI9R80vowqddquThPa9G8Kfk228q/FeCvZbaIg6+bATA4NZe5IjhWcIZICpWiz58xiLCv1eL7SdW74j7GJOmeEJ42Fc4aJLnY3+VnrCc44OP3Rp+LWL4iAM1WRKTnBAPEdb2I6qaPi4gGBameTG4FYyfpaj2iEeM77FSGM9tl/nWOk5nMAmY7QjEDihzXj1FEktTd9log56wj5+rfa7nFFPUV8RvTvCLqbtMeYbU033LZzLJu2I9dIdu/RUf4icsWtTmzlRPsSyM8dwtounZ05Qd11S8QxO4DFTm+a23lsYyQy+4NdovjHNmepx5rwpanaVg4W9jKQ3X/X7aqt/Hb9Q/U0eNx5mXB0lH3Zxk3sb2/1dq55XcBaYqU+slFOk6I6voe08F0nFK9Hwa9hBHUM1cYIGJbeAlCGWGkNXDCQwXRvnTGmWqlfGl5IgCEmbKXbkt5A2UlS9Em7okre60RUdP/RYdGY5UT7I9tyNmKqJL/1V+yalRCLRzxOIT5fGqHo1+pO9gERVPUzTZ0/vWhzfxY4v8u03PcXur24nMWPRFbSjbjWovtljtmOOeqWCdv700/PwpUfVK130uKlY1P0qNb+8KgNcUzTaY208H2W3iia0iy70DEWn6JQZzLahKzo1r0GiKSoLnMClP91LTLvY+b7yFBo3egTjAfqShogRuYVrAqVPZUG10RSPjAmzKy3tfUmF007khO1PvjTu4HMxEdfmDG4zO+mJDTJVP0Pdr4KIbojkrR66rvCZPJ+cBb90XciTs1B5ViVvhfSlaAbaZEyYb0RO3vPF4M64RCAp2pBeKXqTK1EQaQOW7ejfdiCpeiClYG025NkFwXMLCoqQOCEoIQgkUzXImXBjd+Q0XgvM1GCuIfjprpDcJe6hvWZEMt+QHC9EIjZSZSi7jsHUM1T9ZRq+yZmKh6YEGGzGDuLU/cihnG6DvBVFYnz6hMIv7wy5nEF4rCzIGKud0aYKoRTMNyKn8OOzgrQRCcEAuqJiKH044WEW6zF6kisFT16JU/tO8z9+8w94/evfyvvf/79etILCFi1a/ODxYpSYLihz/F38rzihHQMkIZJvWA/w4/V3s8d7flfkhTg4zCmzZGR21eNxmcDHY1lZ+o7291IktNSLep0ghKA3PkSn1U/dr6IpGjE18bzn2hr9DNbp+5GqidRT6IXDaMUT1Le89zvKjlXr0/Qd+QhqdYLQykPoo5z6Z9TyaWrbfo3G+p/EmvgiWmUMBASpEeyRHwNFxxz7N4ypr0eipPJV/Mx6GhvfRZC6tjG1cCvEDv8ftNJJguQAscoY+tKzSAR++w600gn0haexh96A8GsE8Z5Vy4d6Gs1eQnEK1yYGX7Qj0fb89h2o9RmkohFaK6KznsTrfRly4Wnswddhr30bWuk4+uxjBMk+wsTAZYXwILuJ6vX/BbU2ATKMHMLK1cWLBIl+CF2QAZy9WSBDCN0Vp3GLFi1afH/SEoNbfNdRhbrS1PsStvW+BORkGzkZiVmhDBmvniQkJGtGjsuU8JBCopYhTIFcGWPUqTOunuGj8Q82YxNyso03Nn6c2927gMgZYKoWjaCGdV6MQy2sUlCXSCsZUjLDrJhBQSMdpllWFyj5BbKyjbRMc0Y9BbCSIVcjZ+SbA1tLjeEEDWYbk5cVgyFyya4NNqx6rOAsAKIpBEPkWgZJwVm8ohgM0BsfIqlneGTuK1zfuZWkmuXY8gTz9QKGqrOhbQ33Dr8cVXlhmdHLzgKHi8/gS49MJke1VGW6PoYd1NnZfuvKvj4/I/56uuwu7KCBoUSO3vMvDOYaUxwp7sMNHXTFoOQuM29Psym7i+5YP05gM1E/RdZsx/ZjNII6UgZ40gGg01pPgEfJXeZY4RR1v0ZMjZPWYgShQsEpcXjpKBvahgkIyRiR6AnR+yOhpVl25qn5FXZ1bufg4hEqbpWUkURKyWx9nrSRYmPbuelq50/rU5QAXfeQEqTUsP0GFb9Mo7fBh197lNRckpu6dtN/21qeazzO0vLcSi3NSk5zZiftVtd561bRhI4bOpzf2xISoghx1ef9QvqTPQQywA8DtJX3RCAD7MBhMDVA2khGpXSlGRy7iKaoOIFLzkqypW3DFS/mzC1JNGLU1DrppTSoEAxLKiM1vHICQaR4no1gUFQFAbiXMRtfDX4IBxcFRwuRQLo+BzvyEuOsyzUTOc+Xl6/tAloRChuz15Ez8yw6c4RhQLvVSYfVx8mCybcmBY0ArstHImvyvJdjyQbbjxy3pgrb87AmJXGDc0IwQMWNHLyZ81I3bB/OlATzdcGz84KcJVmTkiuZ8NFz93TBXB2KTvSN1/AlbigoOZFwbGng+FG8Q0yDihtlA2vnzfjtjMHJUhRHcSkxOB+DX94ZcmhRsNiIjm9rWz8eClO1UWaqFapempw+RMkeYrEBbWYU6TBXg94E9CclJ4qC6Sr0X0ZnaI/BVHX1Y4GM3HEpHbwgOifWBV9fYbCOalCiHsyy7ISAZOb4LP/rt/6YW255GR/4wIdRlBevyKlFixY/ONT8CpO1UZbsOVSh0hXrbxazPh9SSoKTx/H370OWy+xbd4yx206yNr0DHR2JZEw9zWesT7DR33JRcfDzYWDQFuYZV8+Q51x5sI2NikomzF7r4f67oSnaVRdXK40FzKlvEJq5ptgZxjpQSycxx76I13njVYuLF2JMP7Ti9t3WzLQN/Tz64rPohQO4a+7B67qR2LF/QF98GrU2TeLg/49QT6FWzxBaefzsZoTfQFs+QOzIh6he//ugXXo24qXQl55FK5/Cz25EBC5qdZzQbEMENiJw8DpuQCseRp9/HKklUZwi4XlmAMUtIvVUJGa/WIgoF3gVUoJqIkKX5DN/iFYZhTBAGinc7ttprP+JZt7wRSgqQWromnfD67oZY+ZbaIXDhPEeJCJyYqcGWc6ux5/+OrpQiLVdhzCf3/TQokWLFt8rtMTgFi1eAE7QoO5XVkVbpGSGtJql7BcRromq6zjYHNH30xA1+oIB0jKLRDKljvOZ2CcY9tfRFw4ghIKRinGqeBQRTtOm5EkFaUr+MvVMA82MBjaGNAGJioaPjye8KBdT1GkPo0H5WcfohaKYKjSc4NpbksUVMuaUSwhvfuizaM9Q8Yqoikab2UlGb6MvPsiCPcvdQyPc3Ftn2S5hqjq6KulMdF5i7RdTFzX26Xs5pZ7AwmKrtwNZcPFCJyrb6Owk8H2Uuk5RLLNkz9Edf/679l7ocrx0gHl7Gi900YVOu9XFxswOTDVGEPqMVo6u5PuecwKX3AJnKsfIm90rruA6GaONrN6OE9oEMqBgL1ALyrhhgyVnHidwKDlV4rpOw7eRSJJ6gjYrSSOoU3GrjJUXeXziJF7oM5TpY0fHBtJGAltGmcQ3dl/PqeIZnpx5msnqNBLIGCleM/wq1pxXyrYuO0zaSDNXX2Agm155faJm7m35zaiKQslZZk1Hhus338jNPXs4Uz3KQmOGjNGGpmgrOcbLHCvt5wbjZc2LUVWozQxwJzAwFJOQkLJbIK1nyRov7IJgV+d2Hp3ey/HiSTqsdhCChcYig6l+9nRfhy/r9CS6SBtZ5muL2IFDv5miLZZiw/NMFTQ1i77hEU62HcJ2lzA0A1uzESj0xNbxrKcTSEnouwDYoYamSAZSL8wV7Ifwz8cED08JfBmlUT84KbmxW/CTm0NMFfr//+z9d5hl2V2eDd9r7XjyqVM5dFV1zj09OSdNkkYSkgAhISGEEQgwNsKAvw/82ThhY1/4fYH3sw0YCYRACSRAEpJmRpM0SZOnp6dDde7K8eS443r/2NXVXZ17RpIlfO6+5rp6Tp+99tr7pLWe9aznNzSCbdscOXKQu+6674ra14RGf3yY/vjp7b6f3i/4zAFJ2REIofiyBjf3K37rxhABfOWYYN+SxAmgO6a4Z0Sxq1MxmFJ8eyr6PHfFoiiImZrgpgG1EpGhFPztUcFjE4I1qShKYqoG+/OSWwdCru5RPDkp2ZxTbM5Fmb6tAL49GQnFOTty/HbaUHSi+IRd3YqZmjjHmeuG0b9bF1kjiutRfvBpBDBIjz3AsULATMkknYviPkIVZRhLcSq/GEwt+nvrImL/DX2KN5YEs3XojUfPn6jCYBK2d0bZysNp2LsoyNmnHcQlx6bu3cSWzAwpq8rsxDS/9yu/wqaNW/nkJ7+Iab65xZI2bdr846bh13ij8AJlt4Sl2ShCDlf2UvYK7Oi4/uIxBoD3zJM4X/4i1Gv4hiT5wiu884U0J3/BodlvIBAMBSMc0w9zTD/Mbu+6i7Z3NhLJbe7d/GX8fzErp+kKu2mJFlPaOFv8HWz2t1+6kR9CZH0a4RTPiSQIYz1ojTlka4nwLLcsQF4uUhc1usKeVYWoIRLum0EdI7+HppHClxJLEY269RioAK02iddzA+bMU5jzzxKaHYSmhVabxJ58mNDK4eeimiHKSOBnNqKXj2EU9p0TA3ExxKm4A2kgmgsIvxUJu0oh3QoIQRDrQ69N4/behDXxjej6rSzSrSBbSzgj71qJjvhu4PVcj57fi/AbqGXhWTYXUHqMIP8qEyJgvmsjupL0OxWGJr6JFe/BGX7wu9YHiET/xo5fxjrxFYziQQTQ6LuFY3aCxCv/jnhjDqFCnFgP5safRo78yFvOkm7Tpk2b7wdtMbhNmzeBJnU0oROcsVVfINjkbeMgb3DEPEZTbyLRMJTFSNhDenlLnUAwGAxzUN/HmLGPAWeIb1lf55vpr6CZimw5zZQ/TrfoYYd9DXu79lHQ5kkGKXrCPpIqxYKcJa6S2CrGjJxCVzo3etEWtYSeRiIjUXNZuAtUQJkia60tV3ytObuHmcbJVdmkTtCKcmyt1SKuGzocKL7CYmsucpUqhaEdZV1yC32xYfLOQlQwUE/Sl8hR88tkzU66rN7znXoVVVHhU/H/wT5jDxJJSMi3rW9x88JNDMlhhBDkuiOXdnmpRCaRohU0Lusaj1UPMt04QUJPkdTTeKHDbGMSISQ7stdR96s0/Brxs7YpJvQkdb+6HHkikUISqhApJdZy5EdFFiCAmlcmVAGGtEBFQp6rYKZSYjAZw9QMFutFWq15Xl/ahyYltmbzbHWe46VJ7hu9ka54jriexJQmP7X1x7m+72rGK5PoUmdLxwbWpAZXLQIMJPp4x+jb+NqJR8i38li6hhME9MS72NKxAUu3KDiLrE9tZX16G37osdCaJqbHV/KIhRCkjCxlt0jJzdNtn57sDCfX0whqzDUmKYcFpJB0mF1sye5elWd8JWSsNB/b+WEeGX+SfUsHQSluG7yJ+0fuoivWSRBm6I8PM9eYIJEdWO6jZCA2cllZucPJDZiayXQ9co/n9G4GE6OIzCBjecWhgmBucQndTjLVsrixD7a+yeLo+/OCp2cEPTFIW5FoWffghVnY3im4qV+haRobNmxhbGz/mzpHyYkKv1Xd6O+f2ifxAhhIKBBQceHbU5L12cidu2dR0BuLIg0WGvCHr0oylsIJBHM1OFGKisGtz8AtA4r3bwpX5jQzdXhpTtIbV+RsGE0rdodwvKzIxWAkBRVXMVmJHLVxI+qTE0QxEN1xhV2O+hrTonvR9CBhRk7plh+5hv0QJqqCjVnF6OVHdq8ghGAkbSBE1GbCiPpScyFQgs5Y9FosNKDLVgwkLtzWNT2K/Hp4bEJwuAS6iITwn9h0Or7ibWtCTpQ1DpcEOUvRDKLc5LvXSHZ1DjI9Pc6v/ewv0ZHN8ZnPfIVE4nsUQN2mTZsfemYaE1S8EjmrC7HsEvVCj8XWLAVnYdVv8NmEhTzu178CUqJt2U4gHJa0o3QfqBA8fIzDP3MVwHIhOUXAm9v2cpN7O1VR4UnrEcb145jKYrd3Pe9v/hQm3/+FLjd0aHhVNGmQ1NPfk/gdpcdBmgi/hTJO/2gIvxnFRpwVmVUSRf7e/iJvmK/h0KIjzHGXez93Ow8gkThBi6OV/cw1p0jKFq24Tah7dAqTdb6iL1CAitr1G5gzTxLqKUTQwlh6DeHXEV4F3a0QLL6K33V1JEBqFqgQ4VWu7PpOOVpDL4pCECJqJ3QIzGiMLkIPJTVaoz+CMhKRo7kxi9ITtNa+j9ba976VW3wOTv/t6IUDGAsvRhENKJSeoJHdwiveHEt2CtMrowKXBU2jYllsmXocZ+gBeJO7DS9EkBqlsfNXEG4JgPHSflJ7/gspr0ErOUyAQGvOYBz4Y7TEGoLua76r52/Tpk2b7wVtMbhNmzeBKS16YgOcrB3GkCaGNAmUT+gGXG/ewi3BfVSaZXJhF1+1/oYlbXHV8WL5j4vDMe0wX7f/FkOZ9Gb6EQlBMVjiNfNbHJbHEUjm5RwePrmwk96gn4peJkaMaW2SzrCLd7Tes+LuyFld9MQGmG1MYmomJ+MneTn5PBW7wqA5yrQ/zV3OA8S4QLbqWfTY/QzER5lpjlPzK4BCopE1c5ScPGW3QIfZRYfVzUx9nIXmDOllVylE2x1P1A5xbedtbM5cxXjtCHW/ghSSbnuAjekdqyIoLsSz5hPsNV5lvb8Ji0iUXpILHLePkGwlSZKmoysazOYXl8iMpM7JYD4fraDJQnMaW0tgLUd0mJpNEkW+NUfdryKFRAiBWs6ShcjR4Yc+QgmkkCSNDCkjQ9ktkDU7EUISqAAFWDJOM2iiSR1dGCTNOE7g4PoCLwyouwELbpOK41FinrXpUVphtDc9YyUZr8zwxtJhPrLlJzFldO261NneuZntnRd2wwohuHfkTkYya3h58TmaYZEOs5v+RA+GZhCoAEEUAwFR/EmowhU3uB/6NP3WclyFIjirOJsUGgktdVoEFxpJI0NMv4i6dhn0xrv5yNb30/AaKDgjGzhaiNmavZpuu4/CcoRJzuqmy+plpjZPxa3SFeukJ35+Z7IUkoH4KP2xEQIVoAltZeL48V0hT08J/qyxSDzTzU9uUdwyoFYKgF0phwvgh2JFCIZImBQCDuThpuU5/ebN2zh8+MAVt3+0BJ85IJmpA0pwpKiYrcPmLBjL86CcBdO+4hsnJAMJGE0p4ssft6YJL82DqQlSJoSAp6IYhR2divduCFdFRCw0BFV3tXhqSOiLR/m7hSZM1wVHSpELeENGIUXUbm8C0iZs7lCMFQWLTdCkYqkJ71wbRVQcL0MQChCRG/v9m8I3fe93dSmu6ha8tiBIGIq0oZitSZKmwhCKo6XIKXzvyOn7cT6kgAdGFdf3Kaaq0X1dl1ntWN7WCT+3M+DJScl4JRLTb+5X3D6oOH78MB/4wDvQdZ3Pf/4bdHZ+F7fQtmnT5h8dRWcRQ5orQjBERYBDFVL1yhcVg4PjRwkLebSN0cK/oUxyood8b5nc3gW0pkcQM5iTM2TDTkb99W+qjxoab3d+hJvdO5jXZrBVjKFgZFlk/v6hlGKidpSpxnGaQQMNjQ6ri42ZnasyhkuiSFVGY/KEenOLcUF6PV52M0b+dfzMxiiqwKshW/M4w+9CmadXLkNCPhf/M142n6cv6CerOsjLJb4U+yssZXOrcxdj5T3M1idphQ3KqUE6KifBKTBvd1IxDK6rT9Njd+N17kI6JaRbQSEwCvtQQGj3IPwWtJbwK4eZSA/QSPSTcxv0acYVO3S9zt346fXopUME8T5CPYZsTIGRifKHAwfZWsAZejsi9HDWvB1nzQPIVp7Q6kBZ2dUN+g3Mue+gF/YCAr/ratyeG1dFV4hWHqPwBsJvECSG8Du2wZlGAj1Ofcc/xVi6Ga1yDISJ37md4+X95POLdFcm0UIPpE5LaEwYBgPlMbTAAXmZRcmVQqueQC/sh8AlyKzDz+04f+SHECirAz/0cReeJuWUaWQ2gIgixfzEGoLyMfzpbyHaYnCbNm1+CGiLwW3+UdD06yw5c7iBQ0xP0GX3rYhm3yvWJjfTCposteYIVQAIsmYnW7NXk/IzsGwaPq4d4Wv6lwgI0JbDVeuihqFMhoIRxox9VEWVbUG0zatltDgY389J7RiFcImRYD2B8KmJCnEZZzRYz/ubH2FNMIxCsCYYWckyhkig25q9mqSe4Rke55Gub4AmGJTDOLrDl4zPsSAX+OnmzyO4tHtCCo0t2d10x/opOXlCFVLxipS8AgV3CVBoQmcosZaSk8fQzFWu0LiWpOguUXTzDCfX02330fDr6FIncQUOjj3GyyRVckUIBugMu5lMH6derVH1yqQ7o8H4zNwEu/Tdl+U49kIXP/SI6asnCIY0afqNlQiKjNnJUmuOjMhFg3ensFJIrujkSegpNqV3cqD0GkU3v3xnBb2xIRJaggPl12gFDQxh0JPo4MDiOCdLi+iaRtOFmB5jfXaUo6UT9CcGqXllKl4JP/TIWGnqTshIcuNl3aszEUKwqWM9/clOXi88T8OvE+Dj+y4Nvx4JqcuOWkOaZIwc880pZmuLTFSnaAUOlqbTHc9xdW71Z2qqfpyj1f0Y0qTL7sULXaYbJ1CEbM9e95bdOXHj/IP5UxmKpwqllZwyf7b/8+xbGqPpN0mZSa7vvZr3bngQWz//94AQAl2s/gnsjcOPb1J8x15EDHRx7/BbKxoXAlygiVCJlX/ctGkbjzzyD5csFqRU5LI1ZNT2l45I5uqRg1YTipm6wK8KCg7062r5OiNBs+JCT5xVwudMLTpXxY0CFrrsKKt3riE4kIdvnhR8ZOuZQrbC1CStIIpHOEXBgfEK6BJuHVAcyAsWGvDSgmBzB+zsUgRK0PSjbGIpFMfLgut6Fe/bELK1M4pxGCsIFpuRa3l7bnXO8ZVi6/Az20I2dQhemRd02XDTQEArEJRaUUG8WwYU1/Rc3mucs6P/LsTWHGzpCFfiLaSAAwf28sEPPkhHR44vfOGb9PcPXriBNm3atCESfs9eeI1y7NUlIyLORiBYH2xiTM1QlAXGtRPUdR9L2byv9T461QUWp5RCz+/BmH8BrZXHT6/D7b+N8KxiWRmVJeNnr6hPl4sbOpx0j3BSHichElyl3UBSrt6dNdec5Gh1H5owSBkZgjBgoTWDH/pc3XkLjubwNfvLvGx8h6ZokFFZbnPexn3OO9GvdAosNZqbfxpx8E/Ry0cgDEEz8XpvPscRe0w7zH7jdYb9UZIq6vNQMMxJ7RhPmY+xtbadfGsBUzOp+EWMWA8EDnZ1Cr02TcPOcTLeQXLgRwgTgwivjjKTaIUDEDgouxuEIDQSLOk6gYBaY4pjyRya5rGmZwcjmc1XJM0rM0Vz2y9gH/08eukwyu5BLWfxyuYislUgtDrRSmOkXnwepVl4PTfQWvs+lHlW7rTfILH/jzDmX0RJA4HCnP8Oen4vjW0fB81CX3qN+NinkY3Z5ftr4vZcT3Prz61yXqNZeL034fWeLnY4XzuA5TXQghbKiO6vBVSlRklKeupTBNnV9U/Of9EKc+Kb5Ke+waQGDU0ns/gkQ8mNxLZ8/IKZy77ykG41WrA5szaHkLiaidWYu9DQr02bNm1+oGiLwW1+6Ck4CxwsvUbdr64UaMuaObZ3XHfF1YcbhLxuOExqHjEl2O5brA2M84qmpmazs+MGSm6epl/H1Cw6zO5ztsff6t7FG8ZrHNL3k1FZfHwaosHN7u1s8XdwRB9DntH+lHaSRTlPWmXIhB1s8rdSEkUK2hIfqv8sO/2rL7kNz5Amo+lN/H3iS6T1HBuD5XiIABIiySvm89zl3stIsO6i7SilKLpL5Fvz+MonY2YBwWTjGDEtsRKF4AQtpuqRi/lMN8sZLVHzyrxReJGSW8CQBn2xNdiJOLq4vIIby9LWOY/XE02SHR2YRZNm2CCZSdIqemzruOaynMG2FsPSbJyguVKwDSLHsKlZKxWmN6S24wZN5lvTVN0iSihsGcPS4hwqv44TNNmY2cG1Xbex1JrHDR1iWpwuuw9N6OjS4GB5D4Y06Y+PkJRdSLGXUrPO9twObhm4iYpb4UjpOChIGVmSRoZQhTjecYRQPL/4GKa06IutoT++BnkFE8OUkWVH9jpO1g5TdosIIRhOrGM0tXklTkQIwXByA2OlMU5WTwAaKdPCDwP2L5wE/zE+uu0DCCEIQp/pxkl0aZA00gShjy5N4kgWW7PU/DIpI3vZ/XuzKKX460Nf4fnZVxhK9jOY6KPklHl04tvEdJv3bHjHFbeZzy+Sy711B+emDnhiUlFzWRE2m34kfG7rPD1V2LZtJ9VqhaNHD7Fx4/mjXA4V4PFJyclKVHRtJK04UYoiC7Tlj0VPTHFEQtkRdMcicTYIo6iEnV2RS9cJTrtaG35UAC0IowJup3J7dRGJxnsXBZX1ivRy39dlYH1WcTAvGElHmcFlFyarAluDjdlIBL11QJFvwYkKXNUNH9ka8vlDkmMlgRsokqbgA5tDfnyjwj7j6/Kq7rc+fVIKputwvBTlEF/Vrbh/5Mx2FaHinIzi7wZCnL63r776Ih/+8LtYs2aUz3/+63R2fvdyFNu0afOPl57YIIutOZygiaXFUEpR8ytYMkbnJRa4tfUbkblOwplptKFIuE27KXbMDHH8gUHWGYNknU6u8a5nu3/VBduxJh/CPvoFROCgNBs9vwdz4XnqO/4ZQeb8i9IODnPaNKay6AsHLstscCEqXpHP+n/Ks8knqegVNDQG/TX8nP8Jdovrgei3f7pxEhAkjUiM1DSdjMhR8pbIuwt8s+MfeMr6Fl1hD71hP0VR4O9in0dD437nXZfVF6UUdb9KyV1CCUVm5y+TLI+z1JphSZOoeB+dQZVulVoR6wsyj4OzIgSfIhN2UNCWKIcFfOUtL0grNKlTTa2lbuXQvQpxq5sluxu378Yo0MNI4AzcTXLueUTgACHCd6hLSSE2QKZVpMOp0uX7NBNDHEv0k3CXlot1X9ZFohf2Ys48hWwuESTX0Bp9D27XtRjlMaRXRTglzOnH0fwmYawb4TexTn4N2crTXPd+zMUXkdVxlN2FEgJj/kX89Loo+xgQXh1z/jm8nuvxO7YSO/QZRCsfuYGFRHg1zLlnCJIjOOvee9HuCitHKPWVGAvQUKGLMjrATKDVpy9LDNZq40zOPMxYIg16DEMJppTPkjPO1umHyI6cvx+mtCDeR6BCROijlud9fuiRChy4wGekTZs2bX7QaIvBbX6oCUKfI+V9NP06HWY3QghCFVBy8xyvHGRHx/WX7U4siYA/j5UZ0x1AEKJ4wmzwHifJne75t71LIaOCYtaFJ/o9YR+/UP9VnjYf53nzaSxl8w73PdztvB0DgzXBKBJJkyYxYszLOXQMAnw6w6jdrOpgjmkWtDlM//Lscg1RZ1abXmnjFBmVZU7MMCdnLyoGK6UYrx3heHUMX3kIYLLOSlSCZZ4WWi3NXi6oZ9Pw64R6iFwWhZ2wRagUM81xQhViazGaQYPDlb3U/Qrbstde1mu027uWI/oYLu6KGF6QeRIk2Bm7jiF9mJpXpq93EL1mkTEvr6KvIU0G42s5UtlH1StjSRs3dPBCh9HkZuzlHLi0meWq3C18Z+FbKBWSNrLE9SSa1Gn6dWYa4wwmRonrSQYTo+ecZ2NmJ5rUma6fpBk0yMUT/Pimd7A5c9VKX2dqc2StDNP1WQYT/QghKDklFlvzbOruxQlaNPwaBWeRml9mU3rXFblvs1YXV5mduGGU+Xw+93xcT3N0aQFXKbricZQSaMrAlpI9C29w38idDCb7cUMHJ2ihLYu/Db+GUgpTWgghaQXNNyUG+6HHTGOcueYUoQrotPsYjI+sRFmczVRthv35Q6xJDpC2oolXZyyHF/q8MPcK9wzfQdK8stiKfH6J9evf+mB+R5fipn74zqxANQAVLWpc1wtXn+FIvfnmO7HtGI888g/nFYMPFeBP39Aou5CzFaUWHMhHDteRMww5a9NwsABLTZhvCEypqPuRI/jjO0OemJIcKgiGkpEIG6ooFsLWT4ujoQJQJI0o4sI9w6CmS/jQlpDPHZQcLQu8QJEwBJuyinzzdBE4XS4XWwui9gaS8ImrQ46XoeYJeuIhg4nvfn2VUME3Tggem5BUoxqAZC14cG3I3WtOF3f7XgjBZ/Lcc9/mox99H1u37uQv//IrZDLZ7+0J27Rp84+GvtgQFa/ETH08+l0lWrTekN62InpeCNmRw3zne3D+9q8JxvaDrqM8j/j6nVx/xy9yY/3CEROnEK081smvoaRFkF6OkVAKvbgf++Q/UN/1q6u+vBWKF41nedj+GgvL49dN/lbe1/wg/eGV74ZQSvFN92/5Vsc3iYsEa70N+MJnSpvgj+X/zX/y/v900kVISNOvR3UYiOYDjaBGEAY0gwZTwTh7zBfpCwZXds/FVJxpMcHT5uPc5ryNOBePEjgVQ3GovJeqXyJQAaawsLUYUpfRgnxzitnmFP3OCFuzu5FCo0N1YGLSEPVVReOqskxP0EdG5tCFsbyrMBpXCyFp6Ta6mcTQs6TN7KoCzs7wgxhLe4gd+RyylY8es9No0kI3MpQH7sAdvA2pWShnkbyzcNlisDH/HeIHP4Xw64RmGq0+jVYdR5lp3KF7QSkSr/0uQoX4ywX0lJlZzg1+Cn1pDyJoorQYImih1aejrGP9dDFBZSSi91FpLIpmaMzgZzavOGuVkUSZGczZpyMXsBBR1MV5DCY9sSGO2Z24vosZuigVUrU7MIw0naoSnTv0EG41Oq92/h1iXv51ThgahhYjGUbv6QQ6JWkwWd5HWv3IylzmTKSQJAfuozL7HJnyUdxYD76ARHMJLdaPGLyv7Qxu06bNDwVtMbjNDzVlr0jVixyIp0QxKTTiepKCs4gTNrG1y8uNespssF932BCYmMuOhhnp83Wrzhbfojd8ax+XBW0OT3jUZY1nrSexiXGXcz87vavZ7V3PK8bzxFWCqihTESXWBKMMBJGzQy3/ORUzcTmYyiKuElRkGYLTFbBcXDQ04uri96XuVzhZO4wuddJ6FoBA+UzWjl0g41eQMTuwtRgFdwld6JFwLASmNPFDjw6re+V1coMW881pBuOjZK1LuzBvce/ioL6P/cZedHRCAnRlclv9TrQyzDFJ1uykv3eQhfnZy75PEBUVEwimGydxwxaGNBlObGAkdbYgqJBCo8vuW3FFA9hanJK7RN2vXlC0lEKyIb2dgfgINa+CLnUyZueqbZ8DyT7es+EdfOXoNxkrHgYkzaDKhtwQN/Vdh6VHIngraDJTH6c/Nkza7LiiaxVCrGQjn4+qWyXfrJIyk7ju6fdIxsww11hkqVlgMNmPIS00obPQnI4cLtJACo1GEInCjt+8on5BVOjwQOlV5pqT6MJECsGJ6kEKrXl25W48bxZxxa3R9JsMJVdPcpNmgkKrRNWrXbEYXCwukcvdcsX9PxtDwoe2KLZ1RhnBIbC1A3b3rI5ZiMfj3HXXfTz00Ff45V/+jVVtKAVPTErKLmzKnhY0bR2eqcGxchRRAJGbd0NWkbWic/sqEqQ/uj3k+j5Ykwr568OSI0WBG8JAMnIVT1QFJQfieiTWZiyFQLEmFRV+O5P+BPzKNSHHSsvCbixkti741D5wAzC10/2ueoKbM9HikS4jp/QFczO+C+xbiqItkrpi8/LHYrYBXz0mGU4HbMh+z069wsMPf41f+qUPc911t/DpT3+ZePyt5We3adPm/yyk0Nic3hWJwm5pxXRwobHF2Ri33YXWP4i3dw+qWkZbM4p+9bXIjstbINcrx5FOHj+7FYWiLEsE+GQSnVilQwivsiofd5++h8/F/4yAgJ6wDw+PV4znKYsSn6j/5iox9HKoemVeMV9ASEGPGzmhNaWxxhthwjrJK+F3uD94NxJJQk9RdJdwAslicxYndFAqJMCn6rxOiSL9amhV+5mwg6IsUJEl4uHFx8Flr8D+0stU3BIQ1Yeo+CUK7gID8VFyVjS2dgOH2eYEPbEBuu1+1vub2eJtZ4/xMoPBGmIqQV4uojkV3jV/Iz3BEl1mjunmDJrQaQUNJDqhCojrKUJC+uMjqxf7NZP67t8At0Rs4psQBmSqZZKBSzM1TDm3DbUsegpW17i4KIGDffKrEPqRSxcIE6BVT2Cd/Bpezw0ooaHVpwmt1eNNpSeRtXGElcXtv3NFuNXqU8j6FAQuaGcYWFQIaJGbV6moUN2qvrgYpRdJvfivQQj89Fqcte9d6dcpBhJrqcVHWPQbBHoMhI4JbC5PkbR78Vp5Ei/9W/TWEqGRxh24E2fNA6v7AlTDJi2p0aFO32cBJMKQih7iBM0L1sDoTm8hv+tfUj7yl8RLh7EA0XUt2qafIcy2ncFt2rT54aAtBrf5oUapEIU6J5pAIFH4hJc5GApRvGI4ZJW2IgQD9IcahzSPo5r7psVgB4fPxP8XB/V9DAVrsJTNkrbA39ifJRGmuNG7lY82foGN5hZeMV6gJVqYapJt3q6VIm9LcoGkSrPRP/8W8vNhYnKTextfjn2OkkiQUR24uJzUj7LO38gmf9tFjy+5edywRYd52lmsCR1Lxih7BeabM9iavSqKo8ceJGt1Md+couAsYkiDTquXo5X96NJYNbA1NZu6X6XmVy9LDM6oLB9vfIJXjBc4ph/CCm0y5TSxvMEh9ToAhrToXtPDGy/tASKnSN5ZoOHXor7YfdjnEUKlkIykNjKYWIsbtjCldV7BW5cGutQJlL/q8UD5SKGdk0F7PuJ68qKTutsHb2JdZpSxwmGcwKXgTjCQ6kLTZfReR2BJm6Zfo+KVrlgMvhQJI0HciFFz66TN069tzatja/bKY7rUSRkZxmuHsbTYaZeLiu5T2S0wxMVjSM4m35pnoTVNyuhYiewoOxWOlo8wXc2zJbuLzR0b0M6oEt1pd5A0EpScMjn79L0oOWUyVpqsdUZRFxXy6sJeXp3fS8mpsDG7lhv7r2Ug2be6H/ml71qhL1ODG/oUN/Rd/HnveMd7+MQnPsbc3Ax9fafdPG4IJyuQs9QqJ21XDDpjUHUFh4tRnm/VE/Ql4DeuD+iJRXOtvkQkxEL093+2O2SyGsVV9CdgvgF/+Cq8siBo+dAZU6QtyNqC+0YCtPOkvhgStuTglLDbFVds7hDszwu6YwpNRsXmumOKm/svLv4q9d1zCL++KHADQXf69DkHEpFbev+SYEP2eydEK6X44z/+fX7nd36Ld7zjPfz3//4ZbPvSMTVt2rRpczZCRDUosmbnpZ98nmO1DZvQNlxGZup5UEIDBBVRYL95kKLMExLS58IQwyTPGm9/x3yapmisGlPG/Tgn9CPs1/dyvXfzyuM+PnL5z4UICSgZJWLh6rGahoYCyqK0cp2DiVGK7hIz9QkC5WNIEx+PlJah6tcIHI+qUSGjsivtVEWFpEqSVhkuxXxzmpJbQGoaMRlHIPCVh+u3KLv5qC6FAFOzqPkVSk6ebrsfDY0PNz+Ghc1B/Q0WmeeOYzXecThgtP4EQj7Djck1HOi/jiNGlpKTJ8DD1uPE9QSDibUMxEfOfW30BFidkSirQkIVUBYKTWp0L77KRPKd+KGHAjouY1wN4JSPIstj1IwEojVLUk8T0xME8X702gSyPkWQ2UhoZtHq0xDrWTlWNGbRWkv4dhfCr6/k9/qZzZgzT6LVJgkykbtcOEWUZuJ17lh2FSejx+zlRQqvgbH4MugxPCNJiMKbf5Za/hWObf4gydxu+uPDK+PzzSMfYPDwn9OojKH5Dl2+Q9zq4Vh2LXML38LTdLoSOUabJbKH/wrh1Wht/MnVF58YQpZfIwwctFPuYRUShh7CyqFdZEwvhKCr61rC3G6C1hxaCDLRjzpvVF6bNm3a/GDSFoPb/FCTMrLE9AR1v0rKiAZ2Sikafo1Oq4eYdvmOhEhoO/+/XOb6+nk5pO/nqD7Gen8jFpE4MBgMc1w7wrPW49zg3UJCJbnPeSd3Nu9l1pvii6nPcCh2gAUxi0IRV0ne3voRRoMrq/x8t/N2FuUir5jfYU7MoqGxzt/Ih5o/u6oQ2/k4X0ZvK2jS8Gv4yqfoLACgC4OUmWUosZYuux9d6owkN64UOzu1za4R1Fa1dUqovxwB9RRJleJO917udO9lsTXL3sILmNJeEXibQYPcuizHvnCYUi3PcWeMfGueKCc0WjiIaQlMGcVIdNq9ZM3cilNWlzq6vLBQa0iTHnuQE9VDKBW5hKWQ1P0qObObzJuYuJ2PwWQfg8k+fOXz543/hy8nHqNltuhyu9ldv4b1jY2AuOJiMpdDTLe5uf96/u7o1zGbBh1WlrrfYKo6w7V9uxlJn3bZZMwOEssuFidoIYUkbWaxtTgVr3TF5675ZUKlVoTgicoUh4pHUXg4/gSPnnyJ6/uu5kNbfgxr2eHRG+/m2t6reGLyGbzQXxGGG16Td669j9gZBUC+ceJRvnHiW4RKYWkWh4pHeHVhLz+/8yMML19XrValXC7R23sJ9fa7zL33vhNN03j44a/x0Y/+wsrjp/J782cZrb0QeuKKe4cVFTcq2HZ1T8jNA4qN2QufR4rV0RIZC/7w7pAnJgWvLggaHoxmFLcOqGUn7+pzHilCxRV02op1GdBk5Cj+mR0hD50Q7F2KBNndPYoHRkKGL7Cr+UgJnp4SHC0JOiy4aSASjvW3MI+qeQJDniv4SqJ85O8Vruvyr/7Vr/C5z/0Z/+yf/Ut+8zf/I1K2J4Rt2rT530dAwKR2Ek+4DARrSKjLcxYH2U04iR5m6k+yaCnSKovhQ6Ixw6PrlhiOH+Qa74aV509rE6TC1cKqgYlCUZBLAMzIKZ6wHmG/sQdDGVzr3cxdzn3nFWSTepohbw0v2y/QTe9K9nBTNdGFzhCnRdIee5ChRCEq5CxDfOGjLAExg0F3hP7aABOdJxgKR0iqFCVZoCxL3N36wGU5lvfLPTw58DiVeJVYGGNTZTNr8muQSNzQWc79PX/di86wm4/XP8GMnEJbeJ51e7+MLlMEmX4IPezCAa7J72Vbeh2OmabatZt61w0krc4LGga02gRa9QRu7y0oI4FoLhI6CzS9CrGl12l078Ax4vTEBum2Lx0JUnYLHK28zha/hifAFSE1r0LO6iGLjhIGSBOExB28i/jBTyEb84SxbrTyIcz5FxBeE1kZx3LL+JmN+NnNBPE+wlgPorWEHiwPXjQTZ+g+/NyuqL3+O7AmH0I5RZRuo5fGEMrH6b2FwMow35qhYSbJ1aax51/gkCYpOAvs6LgeQ5poRoLMtn9Krnw0ygjWbF4RLgvzT2DoFkJPclzAopngOmGSnfk27tA9hGeI2cmuG0jmX6LSmqHDqSGR+MqjGutiqOMqzAvES5yJlBoy3i4O26ZNmx9O2mJwmx8IaqKKI1pkw9yVRSFoFqPJTRwuv0HRWUKXOl7oYmtxRlObzslT9fE5qL/BCf0oGhob/S1s8LcgkezyLB6y63SHGvry4HNJBqSUZF1weUXOzkdJFgkJV4TgU6RUmgU5j4eHicl8c5qjlf00/Brb81voSGUI0mDbcXrDPnZ616w6/lRRi2ZQx5QWaaPjnOu1sflI8+e4072HOTlLXMXZ5G+7pBAMkDFyGNKkFTSI6QmUUuSbc7TCJkmZRtM1wjAkUAExPcHWzNXnFM+DaPW8Pz7MWPl1nCBy3SoUVa9IQk9FmctvgqXWHKEKiOmnt/nF9QRDm9YQhiFPvPoQ8XXmShTDQnOGoreIjklMT3CidghNaHTb/axJrGNtassF4i9W0x8f5kR1jOnGSUIVoEmdDrObDZkd580WOx9Nv8W+pYPM1OawdJNtuc0rYuSZfMv+Ot9OPkHDrZJSGY5YhzhijHFD80Z2hbuJXea20Svl3pE7afgNXph9laPlE8R0m+v7ruYnNr9n1TVaWoyEkSappwlVgBQaprQoe/lVr8vlItEAFRXM8eocKh4lVIqsnSLQdUTg89z0i6zLjHDnUBTjIITgRze+i5hu89Lca+RbBdJmigfX3svda25baXu2Ps8Tk8+QNlJ0xyPHTKhCxopHeWziKf7Jjg8BcPjwQQA2bdrO95OOjhy3334Pn/vcn/HTP/3xlc+yJuGmPsVfHxaUHUibUfzDyQoMJuFd6xQJ4605XnUJ940o7hu5cDsLDfjLg1HEhK8UlpTs6FJ8aEtIxoJOGz68VfFeT+GHUT8v5Pg9VIBP7pMUW1HhugN5eG5Wcl2v4mM7QvreZLLChqzi5XmJH54Wld0gWujricMj44JX5wUhsLtLcctgFKnxVlhcnOfjH/9JXn31BX7/9z/JBz7w02+twTZt2rR5i4xrx/mS/VlO6EfxhU9X0MMDzru5zb37koXdlJHk9S27qR95iB0LBkKUQAjmc8N8Z2MH0+ZTq8Tg/nCQ14yXONM14eEBkA1zLMg5/jTx/zCuHScXdtGUTf7O/gIntaN8vP6r2GeNjXVpcH/wbg75BzlqHEJJyBtLtLQm2/xdDDrDK88VQlBKlXkh9iIL9hwL1gKhFpLzu+hz+thR3MVoYiOH7THyYpGUSnNv80Fuqd+BL/3zjllPcUDfyzd6vsaEd5JskKVklHim+2muD65nzdLQ8nglwglaaEKj46zxrEAwGK4hMf03GKHET0fRb8JvIJuLyPoUNgLT6iBdPobj1mhu/ugF+yS8GiJoIQIHY+lVpFOiTwW4KBzdplcmSGavpjc2dMnxrFKKk7XD5HUDJ7uZXPEAVTODQ0ClNU/Wc/C7ryFIjUDgoPQEQbwPozgGxQNotWlCswM/OYLWWkQp0IsHCI00wq/h9t9Gc+2PoddOAhK/Ywt+bics7+xqbvowQXIIc/ZZhFfGz2xAGSnCWDd1r0TTr2HrcYSRotOps2R0sNSaY7E1w0B8dPmGSILsJoLsJgrOIvPzj5Lx6piajVKQVJCXgvFEJx1LB5GNuVVisKaZrFv7QQ7PPsRScxahAjCzdKQ3M5K77qL3r02bNm3+MdAWg9v8b6UkinzD/nv2GC/hCY/BYA33t97FLv+aSx+8zGB8FFuLM9+cpBnUSRnZ82apurh8PvZpnjefwhUOAHGV4F7nQd7d+nHu8OIc0T2OaC42EheFoQRvd+MMvIW84FMCd4smNqe3vVVEmY3+VgwMal6ZQ+XX8UKPjJlDAFrD4GnrSYqJEr7l87D9D+xyr+H9rZ8iFsQ4Ut7HfGsaN3TQhU7O6mZz5qpz8q0EgpFg3UWLxZ2PlJFhOLGek7XDtJwGvgooelHRCg+PMIwiASzNJgwDnLB5wVX0wfgoNa/CXHOKhl8FIKGn2JzZjam9ua3UgfLPiQcBGFy3BqlpvLb3Re7d9AC61Gn4dep+lYSWpO7XqHkehjTwQ5+6V+VE7RABIVsyF66uDdHg+XglEgv7YkMoFIHyCZWi6CySvYyidRW3yqf3f4F9SwdX3MpZK8P7NryT2wZvXHleURR4wnqYHtWHFvZTdJZIqgRL9iL7snvZ3NzKodIeduZuuKwswYZfo+IWEULSYXZe9L5bmsn7N72Hu4ZuZbGZJ2UmGUoOnLPY0Gn1ktTT1P0qaSOLFBp1vwoI+uLD52/8IuSsHkwZxYcsNoq0/BZdsRygCEOdlGljaSavzL++IgZD5Gb+0Y3v4r6Ru6i6NTrsDDF99RbT8cokFafK5o4NK49JIem2OzlUPErTbxHTbQ4e3IeU8ryF3L7X/NIv/Rof+MDbefLJR7j77gdWHr9jSDHbgJfnJLN1EEIxlISf3BKSePPrVJdNqOCLhyQH8oLRdJR3XPMUL80L4obg9sFIAB5Kcsn+KAWPTkpKTlR47nBJMFkT1Fz4+6pgqir44JaQOwbVFcdHXNereGlecago6LAVSkHZFWzuUOxZEBwoCBK6AgF/U5IcLCh+fmdIwYFjJYEiEpRHUpcXXfH666/wsz/7foLA50tfepTrr7/50ge1adOmzfeQqqjwF/E/YVI7yVAwgqEM5rVZvhj7DJkwe1nj6/H+Xh7t3sKdM2ksz6WSSDLV249vLzGnZlY992b3DvbprzOhnaAn6McTLtPaBGv9Dezwr+Ix65uMayfY4u9YiYfopJt9xh72GXu4zrvpnPPfqN9Jy23xf6V/hwnzBCYmPaoPpcFfxv+UX2z8Kp1hN0f0g3wx/pcc8sdYMObwNB+pJAKJo5rUuxv8B/f/RnoaRfLUqxWcWo3Xw+ewZIyhxFqGEuvOWchXKB6zHkLpMFwdwQ0dktKgptd4PbeHvlIfaWFS9UooQBMaQ4l1FzQ3yMY84Rljc71yfCV7WZkZgsxGhFPAnHkKt+9Wguz5Iz6CeD9Kj2POPQfKI7RyIHXMxhym12CnSOAm1l7y9QVwQ4eSkydupJgZvgfDq5GsTZFUCle5NHJXw8YPI/wG8QN/EkU4hD5KM5BOidBM44y8E+GW0WYeQ6tPQOBgOWW8vltobfhJ/J7r8LlA/QVp4A7dGxWoA6yTXyN2+DOgFK1lN7EQEt1v4Jjx5cgGQckpnBaDz6DmlQmEwESDwANpIQBLwaLwUVoMZZw7Vs7G+tk98iHyzjxe6BLTE+Ssnu/Jzrs2bdq0+UGjLQa3+d+Gi8tn4n/CHvNluoNekmGSI/oYM4kpfr7+z9nq77ysdoQQdNm9dNm9F33eq+YLPGs9QX8wSEpFe5fzcpFvWV9ni7+dzf52frGR5WWjxVHNJakkV/k2O3zzkk6Ki7HF384mbxv7jD0MBENYymZRW0Aiud19Gx4eT/AQ3+l4kpieZMRZy8bmJl7LvcrriVdZ461lPZupijLPWE+goXHr/G1MNo6hGQZz6XkK2hK4imq1ym3Z+88R7d4MQgjWpbaSMXMsNmdZbM0hkdhaHHtZaAtVSCNoIIWGUoopOcEbxqtUZZW+YIDd3nWkVQZN6mzNXs1AYoS6V0ETeiT8XcYWrAuRNTuZaYwTqGBl0BaEPqZlsHbDek4cOrYywHeDFpHjFJygiRQavvJX3NVpM8d8Y4rhxPqLCqsVr0jeWcDW4su5wYK4nsILXGYa46xJrLukG+Pbk8/x+uI+1qVHsXULpRTTtVm+euwhNnesX3GtzmpTlGSRdf5GfNOl6pXRMegLTaqxChWtQqs6RtOvs7vrlgtmCyqlOFE9xGT9GE7YQiCI6wk2pnfQE7v41rbueNdKf86Hpdlsy17NofIbVL0SISG2FmN9chu99pVvm0sZGdantnK8OoajasQMAxEq/MMG4biG8BXpjiStTa3zH28mSZnnf/10qSOEIFThqkG+r3xszVp57NCh/YyOricWu3CRve8Vt912N9dccwN/8Ae/y113nf4c2zr89FbFHYMBM3VBTIctHYr490EIBpiqwuGiYCh5uvBd0gBbU3z2oOSZadAEdMcUb1+ruKVfUXHhmWnBa4vRNVzdrbh1UGFIOFmGLlsx3xQcK0ft9Ccg34Kqp/j7o5KRdMDoBSImLkSHDT+/I+TbU4I9iwIp4K6hkLgBf31YsDatsJf77wWK/QX4n3slszVBPTKykTDgbWtC3r1eIS/wNaqU4jOf+V/8u3/3G2zbtotPfvKv6e9vbxNt06bN/372GXuY0E6ywd+MvjzNWxOMclg/yHfMpy5LDM6FXRRTcfasH0EgyWuLeMyyKBe4w7ln1XN3edfwweZHecT6B2b0SXSls9O7mh9rfYiEisb1SZVclRNsYxMQMK1NnFcMjjKTu+jXh9ju7yYRpogRw8fnsH6A581neGfrfXzbfJSKLJOVOebEDL2NHjKNDChFt99LtaPBXvkqb2+9h1J5gWJ9DluLY2kxnKDF4fJeICoifCZ1UWNCO0Gv6sdIGMw2JnBDh5hnU01U0Losrhd3Lo+pogJ/OasnGnP6LUTQRBnpFResn16LNX2SUEWLkbI5h9IsROgRLu+iUmYHoj6DVj15QTFYWR0oJLI2DkJHOiWUZqGMJIHdjTn/Au6aB1YfFPoYCy9iLL6C8Bv4ue24vbcgzARCCJRStGJdHN3yIdLlYxhOiaJQDI3+BJ2pUazxf8CYfx4/vR70OCiFOftttOoEwquhVU9CGBIaKeTyXElpJqF9aXPEmXjd12JNPYJeOYzUYwivQbJ0lDDwaLhljIUXEMm+VTUjzkQTGkroBPFujPJxQqmjNItQBdhOCT+3jSA1et5jTc2i/00YGNq0adPmh522GNzmvEzLSZ6xHuegvo+kSnKdewu3uHdiYl764MtkTN/PAWMva70NxIgGQ2k/y2H9IE+bT1y2GHy57NVfRSJWhGCIMr0W9DnG9ANs9rfToTTucxPcx3evAryJyU83P87fqi9w0HiDkiiSC7q413mQXd41fDb2Kb6V+Bp1v4opLV5Pvsb65kZmrSmyXo4EcTRDI6tyhIHiNeNFOr00gRXyZM83mLImCJf/7HX2kPI72C1uuGS/TmX2XizaIBLa++iy+zArB5lpnoxihBXRgBZJqAJCFXIoNsZfJz9DXiwhiYqdPeM/wccav0xv2P+WCqKcjx57kDlrioKzgCGj96UXunTZ/Vy141r2H9lD069jyRhCSBRQ96uEKHShoQkdX7k4YYtWUF+u6Ny8qBjsBC2qXgkvdAmInNFSSOLL2dRe6F5UDA5VyCvze8iYaWzdWrnHA8k+DhWPcrh0fEV8tVUMU5k4ooUfuAghiMkEVTFL4HuoIEBgMNuaRBZeYGfH9edsUQRYaM1wojaGIU06zK7liI4SY+W9JIz0qgKAb4as1cV1XbdT9goEKiBlZLC1K4+IOHUv1iTX02F1o/Ma+2cnEa9KzOMGQoASCuuYQX+tB+c6Byt5+YsJG7Pr6I51MVGdZjS9BiEETuBQaBV517r7MbXodRsb28eWLd/fiIhTCCH4xCd+i49+9H08//zT3HzzHWf8G6zNwNrM964I2oVo+FHcQuyMEUPLj9y0xRZs7lBIIZiswufHJKYMeHpasi8vSC1HWHzpqGSsqPgn20JMLTp+rh45hWM6BCrKM+6NQ9FhxYV8pXTH4cc3KX50Y3SsFPD5MQEIbP10e8ZyH56YiPKNh5Y/9vkWPDwuWZcN2HmedZB8folf//WP88gj/8BHP/oL/Nt/+3vtQnFt2rT5geFUgTX9rCleMkwxp82c54hz2ebtYoO/hVeNFymIJeqyTks0MdDZ5u2iqqrUW2WKzhIC2GRtYrf975jXZzExGQyGV8TfVJjG0Z1V7avlqhS2uvCi66R2koCQrjDa1u/jo6GRUEnG9H28nR/huH6EjjBHw6jR3xpg2+xWbNfGkx6ZMIvjesx1zNDwa8w3p4nryZXxiSktql6Z6cZJBuKjqyIjTGVhKZuGqJMzurBSm2j4NVqqSWgKdmdvZjTcvLrDfhN74psYs08h/SZBfABnzf14vTfhDdyJufQaeukQQSLKDJZOkSC1ljB+qj7BcjVV7cLzLHPuGbTKMdAslNQRgYsIfcLEIEFmA7K1BCqEU+N6FRI78lmsyYejuy4MjPkXMKceRWz/ZbqsPibrx7E0G/QYhdx2Kl6RmJYgnYiymY355yNH7anoLyEIkiNo5WNo5cNRXq+ZQWg2tBbxczuRbgVz6lGa2z5+/gtRCq0WiclBYgBldRAmBmhs+Rj2sS+QLexD1sdpSZNC9y5q8R6cxjRGUKer4/rzNtnVqpAuHKDmVcj6TTS/TstIEZgJ+ux+mps+evq+tGnTpk0boC0GtzkPU3KC/5X4A6a0CbJhB4tygUPxA0xqJ/lw82MXrQJ8KQICJBKBYEmbxydYEYIhijTIhFlO6seWC7p9l8rMA57wkOrcFWWBwF/ONzuFQlERZQxlEufNCVtn0hX28PONf86CnKMlmnQHfcSJ84rxAs9bTzHQGqLeqmJrMRzZ4kDiDQJ8+p0BLOP0YDmpUiyKecqixFjmICftEww5azCUQUDASeMYf2t9nu3NqzE4vyjZ9OtM1o+z2IomBd32AGsS686JlzgbKSQJPY0btGgGdaTQopxYJEkrw1fiX6RBg63+juV7Gjk4HrK+xkebFxgQvgVMzWJnx/VM18dZdGYB6LEHGIyPsGvnNXzj63+HgUnRXUSi4YduFKmBtiyAK4SQmNKi4pXIWT3RgPgiBMqn4dfQhLZStC5QPmWvgKXHMOUlivIpha+CcwT46H0uCMJg5bGRYB3r/E0cMF6nS3QDggZ1lvRFtpe2k1NdOLKFIU2coMlE7ShZs+scV/h8cwqlwhWRWyBIGx0U3UXyrXkSybcmBgNoMnJ6Xy5KRYJ0wVlYFpCzdNq9K+7cpJHmxt5bObhnnOnDMzRTLsIWOIFLNpMhvZhg5o1Z1t48etnnzFhpfnTjO/nrQ3/PWPEwINGEYGf3Nt42fPvK88bG9vPTP/3df79eLvfe+yDbtu3iv/23/8CXvvStN+3yb/rwwmxUFM4LYWen4uYBRcdl6pYtHyaXaz522JC2FPlWJNZClCGcb4Ep4WBB4gXR5ylQir84IGn5sCGjMJe/ct1AcTAP+/KC63sVXz0mqXmRWBsqKDuQNKErBiUnOv/loBTsz8OrC4KSEwnIN/SpVbnDphad42yKLUGooPsMPaIrFl3X/rxgZ9fqg5566jE+8YmfxXVd/vzPv8wDD7z78jrZpk2bNt8ncmEXAvBwMc4wcFRlhR3O7stqI0aMn2z8E17KfIeSViShEnQGXfQHA5yQR/h67Yt0VXIrou5U4wSDsRE2Z69CnrW9/hrvRl4zX2RJLtIZRgvSU9oEWZVju3fhaC5DmYBiQc4xrp+gLIoYmOhKZ6O/FYkkHWaY0iZIBmm6FrMk/CS1RI2mbJL0MlhNnTgWDb2GGzrnLH5b0qYVNHHD1qriwSYmN3i38vf2F0mrDEmZQpiSBX2BjcF2doRXr+6sUsQO/xXW1LcIzSyhHkerHCV+4AQNIfB6b6K+/RexT34NrTZBaHeBCvGzW0EakThaPUlod+F3XGAxOvQxJx9G6QnCeC+hngShIQIH4TXQGrM4Q/esEjy18mHM6ccJ7B6UnUOrnEBrzmPk92AUD7Jz7XtodYyw5BYABQpsPc6G9PYVk4UIPTjrNQ2Tg1QSvRzXBfO92xDSZKg2y4jsRksNI9wyRmEfzTBYcUefQjbmiR35K/TCfkTQIrQ6cAbvxRl9N37XbmrZLdgH/hfVhac41LWV5rJxwlAxNpYn6C8ew02vjvEyZr5N7oV/BSJkb/cW8kYCJSVST9DXeQO5gQcJdQtUiFY+jF4dj1zDVhd6cR+yuYCf3oDXdwvKyp577/1mdH/eRC2MNm3atPlBpi0GtzmHp6zHmNImV+V7lUWJF8xnuMm9nY3BlWdpTskJnrQe4YCxF0vZ3ODeQixMIohW+890MDREnYFg8LsqBANs83fyqvEiLg7mcgG1uqihobMu2LjyvIP6GzxqfYNx/QS60rnGvZEHnHeTUdm3dH6BoDdcXd33kH6AEEW31keg+bT8Bro0EbqkrtVxTZekftrJXJZFMmSIyQTHYkfIeZ0YKhJ9lQrpdfuZTc1y0jvKRn/rOX1wghb7ii9RcJewZaSAnKwdouwWuCp340VzZLNmjoSeJKEn8UIXL3TRhEZIiJ61WNDmWOdvWnnddHS6wz72G3uot2qXXcn6SrC0GOvSW1jH6vfkjh27abVaJPI5etfEKHkFuhmIhNFQ4YUuEg1Ls1fE1A6zk7h28T62gia6NAhCH1/5aEKu5P4a0kK7SDESAE1q7OjayiMnn6An3rUifuZbRZJGnLWZ09vUNDR+ovkR/lK0OGIdpCAWUWHImsoari/cuOzIDkjqKQzN5ogYQ9firFGjqyp0O2HrnH6dEhh9tXoR5PuBUorJ+jGOVw/ihA4CkEKjxx5gm3UNeswAGcU63Gxez0vxVyhmyvihz3B6iOHUEN6Cx8KRpXPE4CD0KbiLNP2oqGKn3bsyoQG4tvcqBpP97MuP0fSb9MV72dG1hZgeve+XlhZYWlq4bGewKIFxQCAaEPQq/C1wgTWY8+IG4IeRM/aU5iuE4F//69/lQx96J3/1V5/kIx/5+ZXnhwpOlGGuIYjrsDmniJ/nLecG8JcHJC/OgaVFRegOFSX78oqP7wovWTTt9UXBV48JZupRp3JWFJlwrCQoO4quGIxXoRVEGwV0oUjHIFCK2brgpTnY3nlaCIZIkNUEHCvD+zcqZuuKh08Kik50D5JmdEyoQBOKwctco3h8UvB3RyVOAJam2LMoeWlO8XM7Q0aWvzq3dyqemITFRiT2CgGFVrTBocM+VyWWKBpniNGO4/Bf/stv8yd/8vvcfvs9/OEffoq+voHL62CbNm3afB/Z4V/Fen8zh/WDDASDGJjMy1mSKsnN3u2XbmCZkizQoTrY3HonctmRq6FRrC8y25hgyFizMo50A4fp5jiddh89sdXfjVd713N/691823qUQ/p+QJALO3lf64MMhRfemr/V30FIyHPWt5FKElcJyqJITVYpiqh+xU3uHXw2/kmyjSx2SyMfWyKQIYYyaGkt0nqadD0FGdCFgRe6q+LJvNDFkMaqccIp7nXewbycYY/5MlNMIBEMBsP8ZONniLHa0azVxjHnn192uUbxCIGdQysfwZp4GK/7evyuq6mlN0RxB34Lo7AXvbAfvbUAKEK7m+bGDxHGLpA77BTRGvME6bWI0EGrT6P0BEpoSCcPqWHcwbetOkYvH0X4TVR6PVptAiO/B4REGSlEq0Bq+nFu9m7k+IYfoxHUMTSLLquPpHF63uF1Xk3s+JcI4gOwPJZseHVeHrqVgpUkWZsk1Ez29exgUdhcHRjYzQVCM32uEzf0iY19CmPpNYLkMEqPI5uLxI5+HoTCGXkP6DYSxaiIkQwNCn70G50LFd3NOm59evV9acyRfO2/otemGEgMkc2Ps6hr+JpNSsaxuu/D1y0IXGKH/wJz9pmoCF9zCb1yNFpRlgZKGvidV1G56b8SpkeX257HmvgGxuIrgMLL7cQZeZAw2Y6UaNOmzT8O2mJwm1UoFAf0vXSEuVUO4IzKMiummNLHr1gMnpFTK07jXNhFQ9b5Uuyz7PSuYSBYwzH9MCP+OkxMFuU8oQi42b3zu31pXOfezB79ZfYZr2OrGCEBvvC40b19xZ1wVDvEn8X/JyVZpCfoxRUu34x9hTlthl+s/4s3HZOhUExoJ5iTM8RUgk3+1lUVlDWh02MPUHLzNPw6GpL1/iZCW7HIAhmVpSLKlGWJB1vvZZ29Hh8fFQQEy6KgrzwyRgclWcbFPW8/FlrTFN0lOsyuFXdqTMUpuosstGYYSly4yFzW7GIosY7JepTFa2kxFIo+u48gLs/r5JZIfIIoUuH7yDXX3Ihtx3jluRf5xV/8F0BUQO07C4/iBi1aQQsvjLYt+qFHysyyKbPrki7McNnFClD3K3ihhy500noHiUsIyae4a+hWjhSPMVY4StKI44YuEsl9I3czlFw9iRoKh/nV2r/ikLGfI84BisV5rJKOFNCiiS4MZrVZnut8iqXEErnYI+RUF29z3s49zjuQSLJGJ0VnEaXUyvUFoY9AEn+LERFvhppf4Xh1LMrZM7sRCMJZj6XCFKW93YzWNuDepnBvVViGSVesky39q/PzCkGBpqpyvHoQXRh0WpGr+EDpVQruAqFSCCBlZNmWvWZVMcm+RA99ifO7mMfG9gOwefOlxWD9AMQ+J9HmAARKV/i7oPGREHWJ21r34LEJwYtzAjeI4h/etiZk83LE3l133ceHP/wx/v2///9wxx33MDKyjoYPXxgTvLoQuW6lUKxJCT68NWRdZnX7+/OCVxdgOMVKrrAXKsYK0Tlv7lfMNyCuQ39idbG0ySr81cGooNtgUjFdiwTXQEGnrSi2BFUXhlOKmTrEtdPnkESCbMuHsnMqT+Y0gRLEdEXShJ/fGXJVD/zBK5KTFYHvwEtzgqQBdw8rdi27cqtu9F+HvTqmApYjHU4KTKlW8oVDFRWQe2Rc8nM7QoSAzR3w9tGQb41LxorR8+I63D6oOF6KxPPTDmbwFaxfvqdjY/v4lV/5WQ4d2s9v//Z/5eMf/wRStreatmnT5geTuErwM41f5O9jX+SQfgCfMkPBMA84P3JF8WtN0SREkVEdq8Z2yXqCUK7e4WRoJuWgyBPqIZJ2lo3+Fjb4W5BINDTe2/oA13s3c0I7ho7OJn8rneH5Rc9TrAlGyfgdhCJAAAUtTygCOsIcE/pJJrQT3Orexbw2y6s8j6lMlIjGoUmVoivsZn2wkZiKEdeTdNo9zDenSJLGkBatoIkTNlmf2HZeMTiuEvxs45c55h5iTs4SVwm2+jvOa2yQjTmEV0WdIRKGKsQ14sjqMQKngNWYIXb4r9DqU6BCQjOL13MDQWoE9Bhe5y7CeP85ba+0ZyRQRhzht/C6rkUZSbTaNNJvoqwOGpt/Cj+3Y/VBpxy9YYhWOR791cwinGKUM5xcS6ywj2H/Rwiy539vuENvQy/uQy8dQOkJROgxE0tTyGwik96GFXwb4bew9DTzUrDo1Rj2qrhr33tOJVa9NIZRPECQXo/S4xD6SLeMXjxI8uXfwVh8FWf4QUKrExk45MJIBAZAKYTyVsT2UxgLLyLrUwR2J0qPynQP+yGyehJlpqk35/EBc/IR7GNfjtzXyVHshVcRThG0BEF8ABG6GAsvkHj996je9t8RXpX4/v+BXjhAGOsBBNb0o+iV49R3/8byY23atGnzw01bDG5zDjEVoyJLqx4LifJlo21bV8Zz5reZ0iZWOY0bos6Y8QY/0vwJXjdeYVw/jo9Ph8rxrtaPc4N761u+jrNJqTQ/1/jnvGg+yz79dTQ0rvKu5Xr3lhWR9xnzCYqywGZ/WzT4VZAOs+w3XmdM33fBwht+6BESYgjzHEGxRYsvxj7DK+bzNEQk9I746/hw82Ns9rfxbetb1ESVpEzRbfdTpUxTa/FPGv+UilPmGetx8nKJpEryntb7ub/1bjRLY4vYyUFzL7FWHF3o5Ixu6rEmXWE3Q8HIeftZdgtIoa0axEuhIZBU3CIXi0oWQrA+vQ3LjpNvzuPi0GsMMhQbpaZq5MIu5rQZBoM1QPSeWZCz3Ojeviqn+fuBbdvccsudPP74wyticFxP0h8bYapxjC6jFykkDb+Orzw2pneiS4PjlYPL8RmCbnuAocToKrd00sigCY2UkaHD7CRQAVJolN08ApPxyiR9iV6si2S+9SV6+MVdP8OLc69xpHSMpJHk6p6d7O7ecV4xOkaM3d51XCWupZBY5KXGkzT8KgEhLRo82vktZmLT9Dl9ZOnAsR3+1v48KZXmJvd2+uPDy4sAi8S0BCEhLb8RZUFbvStF9BQhCT11zhbP7zZFZxE3dOhYjrTQpgT2Xgs3bTDTO8X6IxuI/5VAViC3M4dh6zSKDeId0fa8VqNJobmE06fhVqKtjbYWx5QWZa9IxsihS51QBZTdAofKe7m267bLuq6XXnqOVCrN2rUbLvo80YDYX0u0JfA3AppCNMB4SWD1C1rvu3DWrR/CXx2UvDAnyFoKS0YRB8fLko/vCtmYBW0Cfnf77/HMVx/jX37w43z5D7/F4znJszOSwaQibYIXwPGK4HNjkt+4NlwpjAYwUQUvFMSNMzJyJcR0xVePCp6YFJRaAkuDLTnFj28K6Vo2Or22ICi0BFs6FGUXjpYiF7ITCAaTkDYVDV/wwc0hh4oa5eVIBymg5kXF7jJmNAc85cQFWGpFLuUdnVGfdAlxTZCzBVUvaiMII3dxdywq9vilw4KX5gVNP7pXdw4p7hpSaMtfXyfLUWbxxtNaP1JAT0xxuBiJ7snlvrxzrWJHV8CRokAB6zKK3jh8cp/kjUVB0owWEKqeYEenYluqzn/6T7/Dn/zJHzA6up6vfe0Zdu06a2twmzZt2vwA0hcO8Av1X2VRzuPi0hP2XbGZYSAcJKmSlESBDnW61kNd1kiHAxjL45yQkP366yyEsxT0Iot2gbhKcI/zdt7T+omVaLihYOSCY9PzMdU6wUCxm3u4l9nYFDW9QS1ep5Qqs8/Yw1PWY3yk8fP8RPMj3MDNvMozbKtCwkhhYJAM01TdIikrS0xPsDmzC4BCa2GlRsdwciMjyfMXa4Noh9Ymfxub2HbRviojEcU9BC3QY9T9CgVnCaM+g6/HOLz0NDumnyHZmMdPbwChIRtzGIuv4PXdgtdz/hzcVehx3N5bsY//DUqP4+d24SdH0CvH8Lqvj1y1RHF8DVHHVjG0jq0oM4NWn0R6dZQWg9BDhB5+cghlpqA+gWwtEbA6A1m4VfTiAUTg0Fr3Y2jVCfTiAZQeZzbdjTDjYMTxO69Cz+/FbC6CYVPz6zgDdzDbczWFyn5CFZIxO+iy+jCcEoReJAQrhZ7fi145BgiECtHLx9AO/AnOmndE/a4cJ0gOAwqtOo6yOvG6r8UPfRZa0xScBczmSYaSgwzUZ1eHGAoN/CbKTGEsvETytd9Fa8yh6jMYS6+htRZRRgaUjwhdQqsDGbpYc89Ra8xhFA+gF8fwO5ajPIAw1o1e3I8x9xzO2vde+jVr06ZNmx9w2mJwm1UIBDd6t/EF/dORQKlShIRMaCfoDLvZ6u+4dCNncUjfT1plVjmN4yqBh48Afr32bziuH8bFZTBcc0m3wFshpdLc47yDe5x34IYOc40pDjqvIBB02f0cTxwmE2ZXuSBiRC7ieW0WzsqxbPp1xmtHWGzNolB0mF2MJDeuciI+YT3E09aj9AWDrFEjeLic0I/y2din+JXab3GLcyfPWd9GES7XZRPc7N7Jdd7NWFjc6d5LSRTJqOxpR4KAHwt+ir+w/oiiWSBDliWZR1cG72792AUjLQxprhSOOxOFQj+PM+JM9uqv8aj1dV40nyUvF8mqHJu8bdzt3s9dzv28o/Vevhz7HIf0/ZjKpikaDAZreMB51xVFfpTdAvPNaZpBnYSeoi82RNLIXPrAs7j77gf4j//x/0u9XiORiO7bxsx2pBDMN6dxQwdLsxmNb2YwPsy+wkvk3XlMaQOKI9V9lNwlduZuWHGNdFm9dNt9zDdnMDUTgaTslpipLvFIfg9uENAT7+aB0bu5vvfqCzqNu+NdvHPdfcB9l309Qgg67R5u6LmL5+a/RdHNM5eaYzG+wJAzTEIkabhVBvQRJo1xnjGf4Eb3NpJGmp0dNzBRP0rRWUITOmtTWxhObqDh1zhaPUDZzaNUlNW7LrWFLrvv0h16k5xaWBJCgA/6cQAFCUGgBwQjIOfBfEqQ3RYyMmpw4mCZeqGBENAIa+gbQ3q392FaJkopym6eueYkPdbAShEYKTRSRpaKV6TiFsla56kGdhZPPfUYt956F7p+8Z9G/bBAmwF/LbCsMas4hDmF8bKg9aCCC0QxHCnBnkXBSEqRWHbU5uzIzfr0lGDLoiLxZ5LUQoY/uvZPefDJ+/jTf/UHdDz462SuiYRgiAqgrU0rTlbhcAl2nXF5hjzbkxsx14gcu7u6FUMpRcODh8YFT05p7O5WbOtUnCyDKRVCRLnArQC6bCg6ilYAW5JwqAgnKoJ71ygeHo8yiUMga8FQUuErwVXdIYcKp524SQMeGA3ZtqwpBCE8Nhm5h+9ZE+3UhMjt+9Ks5I1FxYvzAgmkTFjUYbYe/f/dw9GTtShqm3C5+NwpTv3/mY8JAaNpzilK97PbQ56dFuxZjETiB7pDvENf513/9FdZWJjl137tX/NLv/TrWNblFyts06ZNm//dCAQ94Zv/LR8MhrnBvY3HrYeoqipxFacg83TFuugt9uILH0MazMkZpsQ4SZL0WYN0+4MU5BKP2t9gk7+V7f6Fc4EvRM2rcLx0EFMaOPEWVhCjrzGIVpYcEycYSx/kWfMJ3tv8ACmVZi0bMeIah8tvELYCpJSUwwK2FmM0uQkpJLYWZ1fHjVS9Mm7oENcT2FocN2xByKoCcleKn92Cn92IXjhAIzHAkruE7laIBx5TA7dRb82zVwc7s5H08lwoTAxETtm55y5PDAZao+9COEXMhRcQ9WnQTLye62hu+VlCKXjBfIqnzMdY0hZIhxluM+/ivtF3kzj+d+BVkIEbOYITQwTJEYTfiCISzNVjbH1pD7HDn0Grz4BSKCOJO3An9d2/AdJAK7xI2JwCIEgMEFpZRH2eIKzh921kf2KQk6VXl6PIRPRejA1wlZUFaSG8KqgQvT6FMpLL2cFZ/OwWtMox9NJBGht/Cvvk36GXDwOCINFPa/0HcJJrOFB6hfnmFCDQNMlsz2aGK0muLp5E6klAIdwyQXYzQayX+NifI90KoZlFmWm02jgidM8YJ0V/U5qN9BtIt4xWn4xiLs4oCh0Iia8nkJWjQDQHrPkVNKGRMXKXjIpr06ZNmx802t9abc7hVuduTmrHecV8ninGUUBn2MWPN3/qTQm1SZVimslVj0VFJxQ2MUxMtrwJkfmt4IUu+4svs9iaRRPRx2CxNUu36mKhf445OYMnXJJhipTKoOCcrWFRG6+QdxeIaXEEktnmBBWvyFW5m0kaaVxcvmM+TUqlyapIIDaxGPU3MK6f4IR+hJ9s/hO2+1cxpu8DYLO/nau8a1dcHHGVIK5OW3ZbQYPJ2nFqrUXeJu5hPjnPXG6OTq2HG93b2O1dd8Hr7rb7ma6PU/erK/m4Db+GIYyLCoB79df488T/5JB2gIJcQiKpixoto8mCNktIyH3OO+kJe3nNeImCzDMSrOMG9xb6wsvP1lxoTnOwtCfKuRUa82qKueYk27LXXFGBMoC7776ff/Nv/gXPPvsk99//LiASw7dkdzOS3LgsBsewtRiT9eMU3AWy5ukc3yD0WXLmWWzNMBAfBaJCads6riVjdrLQnMIJHI4W5jiSn44mFYHLvqWDTFdniOtxdnRtQSnFTH2eulenJ95F1rpyYftMsmYXaTOLJnXy8RKmZtOhRwpbM2jQCpqk9DRLcgEfHwODtNnBduM6AuUjEGhSpxU02F98hZpfJqGnEUJQ9YocKL3KVbmbyJi5S/TkzZE2OtCEjhO0sJs2oi4IEgGO4bJ2IdoiGWRdWs99hebvfpt+rUKcLNXujYjrbmaqawp9OIZpLxc3EYK4nmShNYN3VgbyqSKHvrp0TEm9XuOVV57n3//7/yuqE3IAzOcl2gwEA+DeFOJvIwrJdYjUz7N/QU0QLggf1AW0w7l6JJ4mzsgWFgKyluJoUWC+IZCLAn+L4jZxN5+o/Tr/5uXf5LflKD2bfgzO+BoyJIQhNP3VkQybOxQpA+Yb0LOckVtqwUIjcvcOJCLx9XBNsNSAViDwAsXjk5IgjCITBpMKN1hexhFRxENy2WlsSEXFFbxrfchMXaPoRIXpNBEVZdvVrbiuB1JmSKklGE4rrupWDKdO7xqtuDBTE3TaCieIIiYMDXIWPDKnWGpJdKFwQpioCgwp6LAVXzoCtwwqLA3WZxW9ccFkNRJ6hQAvhIWm4J7hcCW+4mJkLHhwneLBdYrZ2Wl++7d/ja9//e+44457+cIXvnFJl3ibNm3a/GNEIPix5ofoCXv5jvkUNVFll3cNd4i3IayAueYUApjXpjE0HZk0cOMhAkEu7GJBznFQ3/emxOCl1hwtv0EmkeOgPIiJiWs5JBpxrLpBb7IfF4+j+iGu9iIhdTC+FkuLMdeYpBk0SBkZBuIjq8YyQgjSZhalFHPNSfbVX6bp19ClQX9shOHkhjcnCkuDxpaPER/7FMHiSyTdItLMstR7PUv9t5AtH6UodeakRto/Q4LUYsjW0uWfR4/T3PZx3OEHkI15lJHEz24GqfOM+Rifj30aiVgp/v35+Kepbv5R3pf9reUCd48SJPrwM1sQQQutNo7XeRWhkUYrH4liKgKX2KFPo7WW8DObIhezk8ea+AZBcgh38G30xAaZb83Q9OvYWhylxajFc+h0oqU3cay8F0OapPVo3uOHHvPNaSbTO0l0XYU59x2UkOA3EFpkwAhS60AIQrsTrTGL37mDWvc1aJWjgCDIbEQZCRYbE8w3p0kZWXRpIIQJtWkmUwP0tcqsqUwjgibKytLY8ctRlEZrkSCzEa18GCUEod2NbMwj3CrKiKOkBUoh3TJBrD/KMy4eABWAUgRCcEITTGngpzsxTQ2WnqHqlXHCFhJJysiyObuLrNl50ZewTZs2bX6QaIvBbc4hRoyfafwit7h3MKVNYCmbbf5OusPeN9Xe9d4t7Df2UhQFsqoDhWJSO0ku7Gabt+u73PvLY745zWJrbtVKrhd6pGoJFpxZ9qRfxsRComEqk+vcG8+perzYmqXoLpI1O1cERFuLUXAXmW1OstHYjiscmqJBTK2uQGtiLm/lamBicp13E9d5N12y327osK/4MvnWPLYWIyVSGAWdHbWdXNV5E7Z28Uq3HWY361JbGK8doehGA1BL2qxLbaXDPL9zMiTkceshSqJAIH2yqoO4StCiSUM2EIHgKfMxbnPvZpu/i23+m3tN/dDnWPUgvvJWIgSUUpTcPCeqh8iekXN8Oaxdu4GRkXU88cQjK2LwKWJ6gtgZmRgldwkptJXXESLhVyAoO8UVMRjAlBZrU5sZTW7ixblXGVt8BD8MOFYaww19QDFemeJzY1/mN677p3zp8Nc4kD+EEzikzRS3Dd7EO9be85ZcKJrQiWtJBsQQprBwhIOlrBX/dVmW2OLtWFWYUQiBLk6rY4utOap+mQ6zE7F8X9NGjqK7yFxz6nsmBneYXQzER5iqH6cl6lgdOq7h0lntZE0+2kLaPPII9YWvwEgOfXgduUad7MxzaDMe+es3ouRqx7UuTDQRCdxn0vTrWFp8VTGUC/Gd7zyF7/vcccc9GC8K4p+TiAaEKYU5KTD2ajQ+FOLdqAiGFColkXlFeOpjo0AuCrxrFeoiH8O4DgJFELISdwCRA3eNAuO4IOxRnHox//3tv8t0dZr/vP8jvPepLGt+4p6VY0oOJAzBQGK1239dBh5cG/LNk6eduZqAzthpZ2zRifKB0yY0GorpusQQUPGivj0yIRhNKfxQsNQUpExFf0IRqEg8XpuOXL4/uyPg0QnJVE1gSMXtQyHzdcEn9wn8UFAPFMnFKHM4ZytSy85mS4emrziYF/gqEoP7EpEonm9JlDq9ESNlQnM5h3jPouDAUsjVvdHj79ug+MIhwVjx1P4Dxeac4oGRC0d1nI3jOPzFX/wx/+2//QdisTj/4398hve+9wOXzBBv06ZNm3/MWFjc6zzIzc6dvG68woI2x7QxxZbO7Wyv91F0FjlmHuNEZpxyokpZL5EIE6wJR1GAL/xLnuMURVFgQj8BCl6MP8XXO/+OpdgSVVGms9HFaHUtUush7aXp9oZYkvP4+NS8CnlnHj/0SBhptmavRpcXXwmca05yoPQqALYWp6nqPBL+PUYYY4t1FTv83at22IUqZKk1R741T4BP1uyiNza4Kms4TAzhDN3PuJen7FcxzTT17AZCIdHMFBrQVD6ntxMphFchyNxx2fcIACEIUqMEqdGVh1q0eMJ6GAOd4WAtAB1BJ/NylqetJ7mt4x5y1/02fudOzOkn0KsnULqNl4vqI6Re+Y+RO9fOEcT60erT+B3bVjKHQ7sL4ZQwZ5/BHXwb3XY/I8mNTNWPr8wlbC3G+tS2qMC08khqp40PujTQhM6CM8/wlo8RGmmsqUejeAbNRpk5ZHMe4ddRQiOMdUdREnoMv2t1PFPBWYwK1y6/xspMI7uuISgfZCGzngFlEMR7aW75GF7/rcTG/hyEhp8eRjbnkc0FlB5DaSbCd1DEIGihOXmUkDQ3fhCMOF7XbqzJh9BqJ9mfHeW4ITC9BqaQzJpxSuU95KweuqxeQhVQ8QocLL7GtV23rypS2KZNmzY/yLTF4DbnRUd/S8LemVzv3sKkdpJnzSeZF1Eea1fYzY82P0RveOFiCW8GpRStoIEQEkvaF5zMF5xFpJCrtvQIKaioIj3NHirpCi4uHi6ucBkNNpwTvdDwayhYJSAKITCkScUtAJGrdzBYw5ixj1x4WmwtiyJxlaDvCq9/sTlL0Vkka512sMa0BCV3ifnmNN12P6XlgVnG7CRxVoEwIQSjqU3Lz8tHjkSzi7h+4eJnDVFnWpsgpmI4tMiqSCS0sKlTQ0OnLIsU5BLx4CKhw2eQF0u8ar7ApDZONuxgt3cdOaeDhl9bcame6m9CT1HxijT82mWJemde6913P8Djjz+0qnja+dCFgTpvfEZ4wW1fQghKTpmiU6bQLGLrNikjut/z4SLPTb/IH+kxjpfHGUz2E9d7KDglvnb8IWJ6jHtHrnACsIwUki4/xfGFPfQd9VizM83xnnHSQQemZrFgzhEGPtuqO6j6JVJG9rzX3vLry9dxWpGMBGOTuld5U327HIQQbM7sJGvmWGjOQMxj4MU++lmDbcQJa02cw09BZxqxrhcEiEwWISXhwQN037KOiS6XmJZYuS4nbC4v7GgUnSVMzcILXQSC9clt2FrsEr2Cp59+nMHBYdYNbMT+rIBA4S9H6IUotJNgPyTwrlKEA+DcFmI/IhAVUDGQJUHYqXDuDVeE3POxtVPRnxAcr0RuVl1EwqwbwLXDKvpVPmP+LIXkj+/5M5aWCnzlr3+MGzY8zrqt19D0o0zcu9eEDJ318RUC7h9RbM4FHC4KghDWpBVfOyaZqkY5viUnKpQWKGh4grSpyNlgu1HMghdG4mtMB08phpKKugfjFcFoRnFDXyS27uqG7Z0hZTfKBP77o4LDJcFwUnGsDPN1wWEHDhUFL88rPrwlEpEXG7DUhNl6JFILEUVoeGEkKocK6j4kdIEQUdtRcbcoUuPq3uj81/Yq+hKKN5aiwnb9Cbiq+7TofDGCIOBv//Zz/N7v/QdmZib5qZ/6OX7rt36HTCZ76YPbtGnT5v8AaqLKp+N/zF7jVQICFIqMneV9xge5w72H5+LP8kriZWIqTkzFyGtLLGhzZMMc6/0L5/GeQqF41PoG37K+TkHmmdEmGZfHCcKAeJjAFR4T6XF83WekNkK3NsAsc2RVjmQ1zivFp3GCJhCNZ7rsXrZlr8U6o97DmYQqZLIeFVPLmDkqWpmHct/guHWUgICnzScZVuv4SPPn2OhvRSnF0cp+JutHV4rTzjQmWGjNsCN73YrwZyy8SPzgJzFMk/nMMN1uE724DxE2cbPb8c00mcJxJDGU1NGac4SxXtz+NzcWPJO8XCQvF8/ZvdkZdnNCO8y8nCanOmmtfz/O4NvQGjMoLYZ99IsYS68RJIdQdjdaaxEr/0h08Fl1FpQeQ7glIBqXbEhtoy82SNktIoVYmUucqB5afi1WD4QEAoVCWVma236e1uiPkH7hNzFnn0H5rZXMZYGivu3joJ9/3HZuaVoI4734EpzOG6nEl4Xy5dclSAxGhepakdgrAw/htwjNLGG2H4RA+A2C1CjN0ffQ3Ppz0XHpdTQ3fQT/xJeYCSokAkVMGATZrYS6CX4JN2gBkXkkY3ZSdvPknXn648O0adOmzQ8DbTG4zfccA4OfaP40N7i3MqGdxMBgs7edTnXpDM8roeAscLJ6mIpXQiDotHtZl9pyXqFTE/J0SOUyeblEM2yy0d9MvzNCRZbRlIZDiyltHBd3VQGOyBGgzhEaw9DH1mLR4wje5rydk/pxjupj5MIumqJBVVS43bl3ZQX/QjRokNcWiKk4XWEPVa+03P/TgzQpJBo6J6qHOFk9jBNGgxNL2owkNzKS3HjOoCxhpEgYq4XiC2Eqi5iKURUVdAxc4WIrm5AAgcAXHskwTTK8PKF2Sk7wqcR/Z1w7gYmJh8fT1uO8M3zPirPvTBTRfXwzLr27776fT3/6jzh27DAbNmwmVCHzzSh6ohW0yBo5BhOjdNl9zDTGV7a8ATSDOrow6LIv7IhPGgmKrRIBIbEzBq660PCUzysLe9ndvYO4EcMNXHJ2B27g8ezMC9wxdDOmdhn72M8iOH6Mri98jaX+FuUuixtfGEBsbTI9XIO4wGiY7CzuRFUdXhFP0xMbZHNm1zlOGWv5Os98/yql8JVH/BLvjVbQYLYxScFZQBcG3bF++mJDl118TgqN/vgw/fFhxLUQPygxDgjwIPBrBIkq4c7USqSAUoqw2SQ4epiuJ/ewePcohewcphEjCAMQsCGznazRxUzzJBWvRMrKMBAboTc2dFl9euqpR7n99rehzwu0eYHft/p9GPSCPifQZiFYC633KMI+MF8QiCI4u0Lc2xXBuoufJ23Ch7aEfPGQ5EQ5El6TZhRrcOM6hXc12A9Jwsxy7nAI8WmTL7z7b7j7pft49fffSfo3v87w5mt41zrFHUPq7ILdwPkzciuO4i8PCqaqUSE7N4CaK9AldFhRX5wwKgC3PqvQRZSp+/qSYO+SQCm4ezjknjWKzjPmaVUvKjx3MC94fFLQFVMsNOFkJRK747qg5sLriwqlJL95Q8izM4KYLtjcEcU6uCEIERWO64krKo6g7ApCItewF0bfDL0xKDmrp4ODySjW4nJRSvHoo9/gd3/3XzM2tp93vOM9fPazX2Xjxq2X3UabNm3a/J/A0+ZjvGa8yKi/gRgxFIoZbZKvxv6Gdf5GprVJMiqLh7viBG6KFhkUI/4lfhBDn+PVLzHt/inbpM1C9xBjuX20ZAtTmSS9BGmRZklfYMlc5Ej2CJlUJwjBO2vvZak0Q0hAh9WNEAI/9FlszTJVP8769PmLvrlhi4ZfWxnrPZ9+lmOxoww6QwS+Tzf9zNkzfNH+DP+y9u9ouBWm6scxKy2MyVlUqUSQjLOwpsCM0c1oehOEAdbkwxC49NqjjAvBkm2S9JqI+gylWI5kxy46tT5Yeg0ZOHjd1+GMvIsgffF5wOUQVwms5VodpyLtNN9nZOoAd04fZVvt97E7bsYdupcgvQ7f7kQv7MMoHSRIr48K4AFBcg2iOY9eOYlwayhzef6kFNIp4nSfjqETQpAysqSM7Kq+ZMwONKHhBK0VQT5QAZ5y6bJOx9Epu4vQykUOYAQQgJEklAaytQihtyqv9xQ5q4eZxjhu6GDKSPB1AwchdbIdOwliq6PpvO7rIPwfmDNPoPQEoWYh3TLKzlG96b8SpEeQboUg3oeyV89L3f7bWUr0Ul/8NjkZx7U7CfUYfuM4hjDxQhdfeRjCRAqJIooQbNOmTZsfFtpicJvvCwLB2mADa4PvTf5ixS2yr/gyTtAiridRhMzUT9L0a1zVefPKgOEUnXYfs83JVYOVlt8gECF6TCOhkiSCaBBUkEs0ZRNXOJjqtBjcZfUxrh2h4hUj9yWChl+j5bu8sXCCvz30JF7os7NrG+8d/RB7Op5nWpskHWa5330Xdztvv2BhtZCQp8xHedx6mIJcItlMsqGwge5qF47fQJcmyTMctK2wgeu55KzulbiHRlDjeHWMtJm94rzdU7hBi8XWLCPhKEfSB0mJNIvGPGGYpiFqxFQcF4/rvJtWMpEvhkLxsP01JrQTbPa3oS1vl5vQTvBY8mHuM++n5lTImLmVmIiGX6XL7lvJOL4Sbr31LuLxBF/96t/wa7/2rzlRHeNE7VCUmyt0Jr1jLDlzbM9ex0hyI5P1YxTdReBUHMQWOsxzc7KXmnken3ial+b3kG+VcH2XmGZjaRZ1r4EhdWLSxgkcGn6TffmDlJ0qmpBkrDShCmj4DcwzttFdDioMcb76JYyT02yPbSYvBdVYis1fDtByKcbfdRXKUXTpfWimhhs6TNdPYGkxNpw1Meq2+5ispyi5SySNDAJB3a9iSYu+iwioLb/B3uILlNw8ujAICVlozbDYmKUvsQZDGmTNzssWhlUW6r8Uoh8FuSQI7CTBVzOE5QIaGRSK4PBB/IMHwGlivPQ6mybGWbx1G42bdmHZKXrjQ/TYA5FrOtZ7SSf42czPz3Lo0AE+8YnfQhmgdBBe5Pg9hfBBaaAMQIHxhsB4QyBq4K9XeDdfWgg+xZYc/MvrQg4XBU4Ag8mQwWQk4LbuU8hphTEGhAIUhL0K+aEYf/MbX+GnfurdPPkf7+Z//tFnue+mBy/7GgFu6le0fHhiUlD1BLoQGIbC1gQlR1BwoOVDECqMimBbp2JrJ2zrUvxoEAm19lmjhsUGfHKf5Ggp+rxOVQXzdYGUioYvcINIZHYDWGwIXpyDvYuCE2VBzlYMJKDgKOqewJRRkTsAoXyKVRen5hJ4DoHvMphQJByDlCcpl3V0XUfXDQzDQMrLi5B54YVn+c//+f/HSy89xy233Mk//MMzXHPNDUC0Phiq1fEdbdq0afN/KiEhD9tfY0Gbpy5qZFUHA8EaBoI1HNL385L5HAWZ50b3NsqyyLycRSDYEmzHEy4FbZEe/wIL6n6L+NinyCz9BXeoBVIqxUsD43zlxiKWtEFCYIQYnknGz1IxKuRzBe72HuTaxk30VLvZF7y8EisGURE4U9rMN6dYm9py3mgxXRgY0sAPXQIj5EjsEB1+B3qoowhAKHJhJyf0YxzTDxGvWrjFRezXjxK4LsKykAtLSKfEfENj9IZNCK+CVp8mtLtIKrjaVRzWoaTHkG6RPkxGum9HDHZQ9WoQ+lHRtu9SFFGHyrHbu47HrG9ihzYZJ+Dqfa+xZuY4cb2PZGiiTT2KUdhHfdcnCNLrkU4RQm9FCF55zZPDhK0CeuUIgd21nBlcIIz14A7efcm+ZM0uBuOjTDVO0PRrCCEJlE+n1bPKMavVp5BOAWfwHkTownJxO6SBXp9Fq44TZM6dM3bHBuh3hpltTlJTFQSRI3wwNnre2ifCr4EeJ0itjQrmAX5iAITEyL+G138bYfIiOwftTkSsB0dPYEgTARjCpBqW0dCoemVSegYhBFJIYvrl7ZBs06ZNmx8E2mJwmx9o3KCFr3wsLbbKDXs2M80JWkGDDrN7ZVBoSouSm2epNcdAfGTV83vsfgZjo8w0x6n7leXnGzS7XOrxIh2czkvNyyW2ebvOKSCXMFJsyVzFkcp+ym4BUAh0Xps9ycnyHGkziRSSh048xtriCL+w6+PE4zamsjA4vdrdLDWZOzhPbbGGnbbp3dLD/uE9fDH2GUwsBmuDmAuSxWAG12iSdTPMNibotHrJWf8ve+8dZtdZnnv/3lV3L9N70ah3ybItWe4FNzDgQu8hCS0JcEgCX3IS8qWQHE4KCR8kIbSYYowNBgPGuDfZlmyrd2mKppfd+2rv98cejTRWG9kmOOfMz5cvXbNn7bXetfbes591v/d7P/WU3AK2Z6Ep+iy3b1ALk6pMMlkee0VicN7Osjf1Ahk7RVOmng67g97IUUxMkvokujRpd7u4tHIVbyzfOrd9ihwHtD00eE0zQjBAi9vOUe0QWo0PZdIlVZlECAWJR0iLsiC87BU5gwOBILfccgd3330nv/2J36u6OxRzxi0uZZiUNcVg4Sir4hfR4GshbSUAQcyoJaxHTzluppLl63u+y8HkEWp9NXRF2tiXOMRwfowaX4ywEaYj3ErGylCwi2wdfQkPj4Dmx5Ueh9O9WK71ilzB3ugwbn8vaksrmqfQPAXNCGQhTvnIAKll3ehdJxqgmKpvunHHIF2hRbPcwX4tyIrYeg5n95Kz03jSQ5te6pa2EggEET1+yvkPFwdIVRLEzWqGs5SSidII+zIvMlTqxVT9RPU4S6JriBjnniAAQKUaybBEAiZa5iqsu7+DOzwECjj79yIcB2XxcrS16wmVy/gf2I4hW1GiLrI8itvchlixCuE7czzMmXj00QcRQnDppVfh1VTFXX2XwAmciG1QhwX26mpEhPG4IHCPAo7EC4J5TMHYDYX3u8w1WSc0ARtfBGVE4NWDfYHE7QJZUxXH9b0CdULiBcBZJfFqoYZa7rnnYT7xiffxoQ/eymc/+5d8/OOfOeP5SlmNoHC9ahSDIuDqDsnGZslkqSrK/qxP4ekhSdGtNqSLGNVIht4MLIyduFc1z/Dn9/EhwaGUYElcogrIWpLBHKSLVXHYb6fw8hPo+QmEM85gYoIvPzdOPj3J6MQESnESq5THrhSwywUqpTxepYDnndr079D0/08Af/iy3wkh0HUdVdUIBkPE4zXEYjXEYnFisRry+Rz79u1iYKCX9vYuPve5v+L6699ER0c3JQeeHBJsHReUbFhSI7myTdI591SaeeaZZ57/43jMfJAX9a1URJmgCDHMEMPqIGutatM2RarVelZCt7OQbqriXUHkmVTGMc7USRUwRx7HGHmSyRqNhK+OiBshWBknViiR1jVQVHTVICKqTWcRCputq/mt4icAGJb9p92vIhQ86XFqmECVarO4Do7k9uJ6Ele4GK5B2StS1IscCRzFEhYlijxi/pIbvBuQE2NgOyi1J5yjUhRxe4/iLU5RDnvsacgwqaTQUGktR+hwYVm5hFmYxOtahjtdD0n9/I0Nc+FN5dsplI+wz3kQLTFJ67EsQo3RJlYgg604gRa01B6MwYcpLetClCZRC6MIu4wbasXzN4BQEHYOq3ETaCbG0CMoTgEn1IXVchVupOec41CEwuLoamJmHVPlUVzPIW420ORvxTgpukMeLy6EWj32NMIunFUkV4XKstg66n0t0+YNQdyoo87XdHrxPzcA0sVqvhK8CiBB9aOUJ9HSB8EtgXbmRg8RPU7cqGWqMkZEj+N4DgU7j+1W8BSdRHmMtJjCrwVpC3a/YvPNPPPMM89vgnkxeJ7XJZZbpjd/kInSMK50CahB2kM9NPs7Tit85Kx0dcb2pN8pQkVSbSL1chShsiS2hgZ/y3R2riCi15D2F3max8grWfwESCpTBGWYqyrXn9bF2+BvJWbWka4kkHgcSQ4ylHuCBdFOfFq1CG4M1HMwfYRtozu4acG1s56fHc2y40e7yI7lUFSB53r0vdjPUx9/AMWv0O50YqQFiiPApzClTNJCG5VikURlDE+6BLQgNUYD5enMtJMRQsH2Kud7+ZFS0pc7QMZOzjRuuy37Tg7n9lP2V2iNdVMnG2n2Wmnx2s7ocD5fomYNC2sXM1EaoeKWCGgh6n3Nc5ppl1JWm58Vhym5ecJ6lCZ/O+94xwe4665v8fjTv8JcosxqlCeEwK8GydhJHGwiRvycAub2id0cTvWyJF4VXSNmCMdzGcwNETejtEdaKdolNjZt4KXJXRzLDdMYqEdTNGynTEDzI4SgN32MlXVLz+8CeV7VuvhyF6Si4HkunutWb5pOQpt2wDjSRmO2AB0z67ig7jKyVore/H6S5UkmSsNMlIfRFYPO4GK6w0tmfa4SlTEM1ZgpunN2moKbRQKG8BHSoqStBPvTO7ig7tJzNnI5HcYVV4Pn4jz5KM6+PWDbqMtWoC5eWh2L34/M5yn/x1dRWtpAEQhVRV2xGt/7P4wSOT/H9b33fpfNm6+irq5axJdu9RBZBe0oIAUIidMlKd/qIfLg+5VAGpLjKS9es0Q7Wo13yC/3Tvlm9WRVXE1XBHFTsmgMQt9WUCYE0pSIioL5LBTf7WGvk+AHe4PEPs1YA4EAX/vaD/jiFz/P3/zNn7Bjxza++MV/JR6f3fBvrAA/61U4kKw2b+uISG7s8lhaAwEdOnVoDkr6Mx5PDylIKXA9SdkRGKqkKQgFWzBWqDZ2Ox2OK3muN4U3fITDe3pJjx5hfKiXxOBRilP9UJgg750IQE4DqDovxhporK/HNRrx1XZRGwqjmQEKIkg4GOSGxQE0w8ehnMnhnI8KJiGfQXNQckG9TZPPxnFsHMeZ/ted9XM+nyOdTpFITLJ//26eeupRyuUSqqqhKAqDg/184Qt/yhe+8KcAhGuaUWu6iDR2E27o4oVYF4+3d/G7l3eyYXE7mjb3UknkAAkyzFmzo+eZZ555fpM4OBzW9pNQpoh6MZY4K2ZFoSWUSX5p3k+dW8+UOkHUq66ASyiT7NF30O52sspZS8Ka5GnzUQJOEB8+bGwG1QGW2SvpPMtyGX38WaTmx6c3YXEYhCBMPcunJhmIWFiqh4aOjUVeyRH2Ymy2r5h5fsSIYygGZbc4UyNKKSm5RToCPWddndQRWkjFLTNaPka4GGY4MEhIhBkKDWHiQ0Ggo7NV30KXHcHIlyjXhDi+WMhVwAv4qNkzzPDYc3y79UmK7ZJoKohCgbDhsaAQZmE5xYrwAkrxc0cQedIlbSVwPJuAFiaohec2se2UMCa20Tz+HJ8Z3MreMOSVWnqSLiHPwNB34bXEqxm5Zi16cg8c+s9qAzcrg5brR80exgl3IwNNCM8BPYiWPogbXYSjBVCsLOaxn+EGm7GbLpm51mlripydQRUqNWbDzOugCIUmf9tZV5l5gVbccDdaaj+OvrQqAEuJWhjEiSzADXee8bmKUGnwt9DwskiI0yEVY1pc9mbnEHsWUguCOP33u7ByqLk+UDSWhJcjkSQrk6StKRxpUWNiZvQQAAEAAElEQVQ2ogoFy7OwPQtVqCyJrDmrcWmeeeaZ5/XGvBg8z+sOT3rsz+xgrDSIXw1hKj4KTo4D6R0oQj1tceHXgtOOzhNIKUHKWTPRJ6MIhVpfI7W+RrJWir7cQVpTjVyhXsZIcIRkTZrV2gVcWbmOlc7aM47XUMyZgmRLcSeu9GaEYABVUQloAQ6lj3ATJ8RgKSVHnjxKdixHTVccRam6LMemxuib7KWtphU8UCoCdDCEQVa4YCq0ix4my2O0h3roDi0hZU2yL/0SnnRnCmBPenjSJWrMFonmQsUtkaxMEtDCM6KfIhQWekvIpTOs1y6hxjw1PuFchGSYpc5KthiPE/NqZtzBI+og9V4D3c5CglqY7vCS8973aOkYBzO7ZlzSicoE46Vhlq9ez4IFi/jpPffwtj95OxIPcZIr2cNFFSoKcyvgBnJDaEKbcd8GtAAXNq0DJIqiUuevZUPXWi5r3cjg8yN0hNoouWUKdhG/ZrI4toCKZzFenGAl5ykGNzZhNcax+w8hFnTj10OoaHgjQ+gtHdDSRMUrzWThQTXfN6LHMZQzfw7KbpFkZRK/FsRUfTM3VP35Q8TNWuInvdaKUKufLarv4ZydQUileg0VpSqQGzXkrBTJyuScivWXI1QV89obMC65jPK9P8B++JdoK9fM/F6WS3iDx8DnQ122ohopUi7j7HgRu2sB5pveOudjDQ72s2XLE/zzP39j5jGvDQqf9ND3CERKIuNgr5TIEGj7QEkKnK6TXEeimimsjgiUKfBOWqmYLMP3DlRF2bIr8SkKF+yDD6YhtFROd0ORqH3gu19gL5PgA8qgZEGGQL7MtKKqKp/97F+ydu2FfPKTv8Xll6/i85//X9x667sQoprP+829CofTgqZA1bG7LyEYKyh8bI0343h9bFDw/LggZgoag5KSDQVH4tdgU7NkKC+YKkGD36O39zB79uzg4MG99PUdpb//KAMDvWQy6Zlx+aP1xJp7aGrrYWzB1dj+JsxoPeF4E/5IPTLYQDAc462L4HdXe/yyX/DoMYVMpZp73BKCOxZ7rKg9ca6erArbEmgKzC3CIZVKcued/8599/2AiYkx3vCGN/KRj3yKiy++FIBcLksyOcXIyBDP7+/npy8O4Cb7KE31M3DgCfLJEfZIyYOAYRgsWbKClSvXsmLFGlauXMvy5asIhWbnaisjYD4oqtnXVJ3ules93PZzj3eeeeaZ57+SlEhyZ+Br7Nd342CjoLLQWcJ7i79Nk1f9zu5Vj5BWEqx21rNdbCWhTKJLgwplRpRBbiu9i253IdFKjLSS5JC+D3e6l0S308Pbyu+dtQLsZJIiwe7oYbxAjlqaCHgjJJRJgjLEHYeC7GxSOBZxGVdGURWVereRN5fuYFPlhBgc0iK0BRfQnz9MpVJGUVQczyKixWgPnd3Bqik6S2NraXMWoBPgO8Y32O57AVP6sKgaKJY7qwl7EV6KvsSbh0OMd2pUfCfm+GqnHOrGK9wbeYBJK0tPcRmqksD1EowFJgg4MYxwF8HmG2k8x6R43s5wIL2DtJ3Ekx6GYtAU6GBReMUZmxgD4BQJ7v0q+sRWlNwAZI6wLG1gmVE016Sk+aCSRGR7EXXrEV4FPAtj6GFcfwNux41oyT2ouUH09AFsI0S56y2Yw4/jmXG8QLWY8YItqJkjmMcewG64EBfBwcwuRkuDuNIBJD41yOLISpoCc/zSU1TKPbcT2Puv6Mk9SNUAz8LzN1LuueO0ecEvx8IiqUzhl4FTmnzPXKL4ctxgC2quFzeysOp+dooolRTl1mtBfVm3WSkxRh7H138fSnEChIov3EFo4TsYjrezM/E8PtNPWI9VY8ykgys9Sm6eilciwK/H+T3PPPPM8+tgXgye53VHxkoyVR4nrMdmsn4NtRr5MFzoo9HXespseZO/jYnSMHk7Q0ALI6VHzs4Q1MPUmWduAAZQdPLsTm2j4GQJaGE6ZDe16ToChTAbai47r/wnn1YV0l6eWWp7NqGXLQ0rZ8okBlKE6oIzeZdCCGrjtZgJH4lKgjqzAalIhBTY0wW7T/pQFY2gHqTJ30ZQD6MrOqPGIClrElOpznyXvRJxo44GX+ucx38cD2+mcdvJCDHdDVh6571PqGZHX19+E8PqMQ5p+9AxsLGIyBi3lO8gIufu6LTcMlOVcSy3jKYY9OUOIJEzInXVtZCgP3+It7/j/fzD3/8Vb/v0O8mJNBG9ZqbZSMHOETNqOZrbR0ALUu9rniWmvpywHsSRzqzHQnqQ5lAT13ZcztsWv2XmtW8M1lOwizQHG7Bcm4DmRxEKh9O9BPUzH+N02J7F/vwO8pe0UDO6H23PNgqBIBHXh1nbTPCNd9AYVTlWOIrj2WiKQcUtIRC0BxecdvnccSYrYyDlrO7bmUqe0cIQuyaO0hlczMXNF9ASaqLR30qyMjnthNBwPHtaUNfwT183Vah4yFfkSj8ZEQiiX7gR5/kteOk0SiwGgDs6giwV0FatmbnWwudDxOI4257DuOkWhDo3cf/ee79HIBDkxhvfMutxGQJr46nLTKUOqIADs4zWdvVxedJjUsKPDiu8NCHoCEtCOuRTkmcrgkAX/Nbxj5cAtwW0YYE6ANqQwHhSoGQEMiixNkrKb5huKHcSN9xwC088sYs///PP8Hu/90F+8IM7+cIX/plx/xKOpmFRTKJPv+wRQ3IgJXhuVNAZkVgubBkVxAyImBJXQnMIKpZF8thetu3aTubYDj7zTy9x5OAuisXqCoumpha6uxeyYsUabr75VsbNHvaziNWLFxAMVVXm0QIkS4KcI5GeoOBUIyga/BJPwopaiSLgpm7JRU0uA1mBrkoWxiDwsqpEmRaJz4XneTz77JP88Id3cv/99yKlxx13vJff/u3fZ+HC2ZNLkUiUSCRKV1cPpbYr2duhsDRefa2VceCAxZGJYziZXi4tH2SHuYNdu17innu+g23bCCHo7u6ZEYdXd6/niq2bMYfCeA1Vgd94VqAOKhQ+4eGd/7zZPPPMM8+rwsXlkLaPPu0IQgoWOktZ6C5BIPiJ72526NvocnsIyCBlyhzQ9nC3/04+UfhDlOn/oNqcbIO9icPqAY5qhygpRUzpY0qZYEDtpcvt4eOFP+SAvoeEMknYi7LCWU1Anr52fs54ih/77iK9vBelMEpEjLJpspGeRBjbniBW9vH2zBvY4ktxTO1DAEvtVVxpXYePEzWKEIIF4WWE9SjjpWEsr0LMqKMl0HHaxtEvp9oALcplXEfFsuk3elGlSlCGafM6aHHbKFNivG6U+mA38ScPk1nRiTR1whmX2Eu9FHpq2L14ks5EN4rU8MKNKE6EAFlG6gT1xUVMqnC2uxBXuuxPbydlJYjoMVShUfFKHMsfwaf46QovPuNzjbFn0cefB89FFEerQq90MZwSjh7EL8p4gFuexGdlEHYBN9SOWhhCGjGElcENd+OEF6DlerGaLsWJL8XXdx9ufBk21VLHBIS/HrU4ilJOMCSLDBf7CGphjGkDQc7OcCi7m4gRn9P1h6pQm1/3WfTxrajFEbxAI1bDRXihswvKEslW/RkeMn/BpDqOIQ3W2ht4Y/m2U0RhaYQpLX4fgQPfREvtrz6oqNgNF1HpuOGUfWvJXfgP/ScgcKILwXPRcv2EDnyD+JpP4dcC6IoJonpPowkdIV1wquOaZ5555vnvxLwYPM/rjpJbwJPOKU3fTMVPwcnNdG49mVqzkcXRVfTnD5GxEggUwnqURdGV5xRzx4pDFOzsTDfi6rF8pKwpJsujdITm3vRuee1iomaEkcIYLcHqjHqqkkYgWNuwcta2Eqpq0ctQUVl6YA17N2xjVBumJdiEloaslqLObSTmVl2XIT1KzKha6AzVx8r4BgYLvUyWRwDoDiyh1mwgVZlEEQoxs46klmCHvo2EkqDRa2KtvYE679R8K78aJGLESJTHMRRz5roUnBwBLUjYiM35mrycNq+Dj+f/kJeM5xlUB4h5cdbaG1jgLprzPjJWkn3pl8jbmenuvRUKdo6WYBcAFddi7+Rh9k4dpuxW2LB+PbZtceTJo6y+eSUpawoBWJ6F5ZbJ2EkKTrbaAMvoY0VsA5EznOOquuU8OfQsI/kxmoIN2J7DodRRSk6JoFYVinVRVQQ3Nm/gUOooebtIna8G23PozfbTEmpiee35uZ8H872MFgeJrFhBOd6MsX0v7ugwhYYGFl31XvSeZSzyHBSh0ps7QKE8iU/z0RVaQr2v+az7dj0HcZJYfCTdx+F0L6YmKFQkuyd+xUsTu/jwqvfQGuogVZliojyC6zm4OLjSoc5smnEfH18y55/jDcHZUBcvRb/0SuwnHsGZGEOoKnJsGBGJorR1zNpWqBrScapxGnMQg6WU3HPPd7j55rcSDM5trG4XOJ0S7YjAWSirwrAF6pjA2iyRJxnxx4qwNyFoCVaFYICwDi2OYLspmfSg/vifAElVRHxeYD4tkAHworIaS3GfwCvC/uslgzmBpsDSGkmdHxobm/nXf/0u73jH+/nc536fa65Zz9Xv+B84F38KvebEYISAoC45lhOApORAvgJKuhdv+xMM7N2CM7yD8ugepGszIAR1bUtYdcEabrn5FlauXMvKlWupqTnJtgskyvCN3QoHUqCmwJWCkA7vWOoxUoBnR6CGaf1cwgVxuDgt0fLgdEKdH+r8r/wGqr//KHfffSf33PNdhoYG6Orq4fd+7494z3s+PBP7cTaqWchVkVpLg7FLgYqJ2byIRXWL+P0D1+O0SQr/y6Pitzh8eD979uxk796d7Nmzgy9/+Yvkclk0obGh6SIu67iSK9qv4qKeTYSO+NFfFFRumL9BnGeeef7rcHC4238nT5uPUqEMQECGuLZyI5srV7LH2EGj1zIj2Prw0eZ2clg7wJA6QIfbTY+zmDqvgWH1GPVuIxklhYKK3wvQ7nWxR99BQpniE4U/pNFrZo19wTnHNaj280P/d7CxWahuxiw9z4QyxuN1OVZNLGRVcgHjbWs4WjtJSRQJyCAZJc3z5lMMaL18PvtF1jkXzuxPEQqN/jYazxJHMBdW2etY4azB5/mpkSe+43JKloAMErz5bRiTP6Tm6f1Ir2qGUNs7GXvbxVS076Cd1Fxaan4UBLao4Amm84vPTKoyRcZOEdXjMy5gnxrA9mxGi8foCJ0aeSHKCcyRJ/Hv/Spq/hhCOjhCBUXDU/1oThHFtUALYDpFSlYapTRJpeVKQEHNHMYYfhSlkgShIFVfNU5hevyW5uOQ4jBsmDgIQlLSY3s0KwZSCzCWPYQqtJmVl0IIGqwSamI7TB7CqF2HXX/BnPKRvWALlQVvOed2J7Nd38Z3Av+Bh6TOq6csyjzie4CUkuQjhU+hvUzecOrWkdvQjp7YVRXEg604NStPdQUD+tizCKeEE59uuqzoONHFaKl9RJP7iQTiJCon7o2klBTsLAEtRESfY5+M1xFq+gDG+FaU8iRuqBOraRNe8PxNRMeRUpIbz2EVbYI1Afwx/7mfNM888/zGmBeD53ndYSg+hFBwPGdmKT6ALS2CauiUTFSoFiJtwQU0+FrI2hkUIYjqNWdfXjVN3smiKtosJ68QCgKFvJ09r7F3hNt4y8Ibuf/orziQOowAAnqQazsvZ139qlnb+qM+4u0xxg5MYIZMhCKmv0TzXFC5mG63na08Q29tL41OA035FtqcDnKkCGoRFkdXn9IQbHF0FQsjK5BSMpA/xJ7UNiyvAggmQ5Nsbd5K1siioeHi8KTxMB8ofvQUIVYIQVdoCQU7T6oyiaYYVRFe0ekKLcFyK1TcMsGTYiTOh1pZx3WVm8/7eVAtrA9n9pC3M0SNWhShULRzpCoJUpUp6sxmHux9mn2Jo6iKQCB42n6J9nWLue+Hd/PB93+URHmciltmIH8YQxjEzLrpos4jZSXoy+1ndc3G0+a1LYx18+aeG3mg/2F2Tu1hIDuM7do0Bur4We+vOJrp573L30aNL8bFTeuZKEzx9MhzHEwfQREKbaEW3r7kLUSM2cvMy06FvYkDjBUm8Gs+VtQtpTFQP3POY6VBTNWHrhg4bU04bU140iNjJWmoCeMDbGmTsZIIAREjhgBGigPVjOzo6jO+VjVmfVXclS5lp0J/9himohPSqw0Pw/EGDqaO8MixJ/nginexIr6BpvJ4NS/YzjJWGkIiqXhlXOlQdos0+ttmJiuq51ek7BYxVN+cXSMAQlEwb38H6qIlOHt2QqWMqH0D1mMPQz4HvurNiPQ8ZHIK/arrEPrZlxcmSpCx4Ni+5+ntPcIXvvDlOY8HHUq3ewTuVNAOAwhQJM5SSflN3qyc2IINZQfqT6qFZRD8YUkmI8gbknoACdowOE0S7YBABsGdrsVlGGwd7j6g8GgEitPiZY1PcNsiycXNVaHxyivfwKOPbudLX/ob/vXf/gn3ri8xdd2HuOAtnyTa2AVAyQaRPMx3v/sEzz77JL964knyiaFqVmP7Gsy29fgv/BB66zresnklv39xgPjp00VmqPXBR9Z4bJ8Q9GergvPquqrL1/ZgeQ3snqrmFq+agkt/AZFRteqGbpKU3yKx155ZLPWmkzRO/igmEpP88pf388Mf3snWrc8QCoW55ZY7eNvb3seFF246rwaCy2olDf7q2BcOCShDsg5KCmySEmcRaEdA3yWQlxmsWLGGFStOiiyRkqF/OMSzDz7B45XH+PrOf+OLz/8NpmqyMX4Jl1au5KLaK1m7dgP6Od6X88wzzzyvBTv0F3jSfIh6t5GorApUCWWSh3w/J+rFqFAhLGd3yPRJP5aoUBJFAGIyzpvLb+Nu/51sM7YwqA4QkmGavVZW2evwywAH9L1sM56dcxPhPdpOUiLJMmclQhfYjZuoy/aT0vaxtU1jWf3HeaJrkn7tBVJKkqLIE/RC+IWfAbWPfw79Lf+Q+Rpxef7xZwB4DsKtILXArC+VBq+JVZW1PKT/nKyTJCKj2LpDUklyY/nNxGp6kB/7JM7+vcjEJDIcoriyk0gIYt4DJAIJmrL14AIqFESBmBtHkzq152goZnsVPOmdIvjqio4tLRzpYJz0O1FOEtz9JbTkPhQrg1JJITwbzxdHKhqaV0GiIABXNclFu5lsu5q2nvfgRBYT2PdVjImt1eugmtVrIT0UexzhlnHCPRyoXcSQm8WUOjoqGVx2GgpufB1RI4LtWTPj1ewCNZM7aR7dgiwnCOlRAqPP4tQsp7Dy40hfHa8lEskTxkPYwmahUzVVRN0Q6waSNI58H5nbhz+6Gat1dsM76avDar36nPtXS+PVa3IyQqmWenaW7vAFFN08KWsSVei40+alBeGlr6hHxm8SffRpAge/jbAySNWHPv4cxthTFFZ8DDd2/nF9pXSJ/Q8eYLI3gVtxMIIm7etaWXhlD6o2n6U8zzyvR+bF4Hled8TNOmJ6DUlrkrAeQxMaJbeI6zm0RLrOKj4aqo+6M2QEnwmf6p/OvDqBlBIPD1M9vxlNIQSXt13CkppFHEwewZMeXdEOOsNtpwgUQgh6Lu+hMFVgqi+BZmi4tsP4ohHG3tpPOpxAIFjlreeK8LXUqrWUnAK6YlBrNs5a0n8c13OYqowxmO+dXsIVIabXYSkWj0fvIuFNcYG1CUMx8fA4pO3np757+P3CH88sCTxOjVnP2tqNjBYHydlp/GqQgB5irDjIoewupISwHmVBeCm1vrNHcbyW5Ow0GbvqjD7+XvBrIfxqgJydIl2qcDDZR0OgBoRLUItQazYxdckxtv/LgxzYvY+1azeQqkzRlz9I2IifiBoQCkEtTMpKUHILpxUthRBc1XEpy2uX8Hfb/oWCXWJFzVLiviglp8zOqb3Eex/kfcvfjqZovHXRTWxq2cBQfgRTNVgYW4Bfm/3aZSpZvrX3LvYlDuJOd8Gu9dfwjiVvZV3DKjzp4UrnVHcIYjqzzAVgpDBA0pogatRwNHCEg4H9ZESK+lI9t7nvZZm2htPR5G9jojxCojJOppJHCoeQEcJ1VTxPRRGCOn8tB1NHKbtl/Jp/pnmHlJKmcgfHCkfI21k0RaMrtISu0GKU6Umd3tw+RouD2NJCFwYN/hYWRlagK8Z005QkrnQIauHTX3NNQ7/gQvQLqo4gKatN9OxfPYCbToFpInNZ1PZO9CuvOeN7p2DDT44KXhxXKDmw99vfJd7QzoaNV57xOafD7YH8p6YzhbMSrw7sFRJedv9Q74eoKUmWT2rCJmCiQxI9BM0HBJqQ4Aq8eol1hYf/Rype3Wxx9Jk6eMiBWkfSXlc1EQ/l4J7D1ciH4/v2+/189rN/ya3v+X0++cV/Ze8DX2HnA1+lYcE68NeQHtzNz9OjKIrCmsb1XN96B2NXXknd2ktpWBwlYcNYQbCpWfKp9d4pfQrPRNiAy9skl7/scUOFi5urgrXaD6Fvq1CuNuNDgjoE/u8KvDqJ+zJjV18GnhgSHEoJApqktbCL5O6f8/gjv+Cll54H4LLLruHLX/42N9zwZgKB84tdOU6tD962WHL3IcGhAoiQxCfgagcud5mukgRK8vSCtRCCRYuWsmrfcj609CN4eOyd2s0Txx7j6f2P88/P/j25x/6ceLyGG254M2960+1s3nzlvDA8zzzz/NrYpW3Hw5sRggFqvXomlHHGlVHqvPpqRq974vt2Spkg7tXQ5J5wBW60LqPZbeUrwX/AEhaLnKU0ea2Ysrpyz+f5GVT65zyukiiiIGYiyKQexqldhVADTESWYhWuZdT4ZwpKjrzIUefVV7eVEJVRhtRj7DC2cVXl+vO7IK6FOfQQxuiTCCuHG2rDansDdv0FIARFJ8+C4U46gu0U5D78+QxREWJd7E3crFWFbuHzoa+7gN3adh4xH2BQG8CQBqY0GQuNIMKSWC6C7VgE8NPsNNPoa6HBf3aXZUALoQkdy6vMquvLbjXq7eSVkFmRYSTxJeqzP2GqsZEuXw1thSEADKdE1ohBJY2OhYdG2QixY/HtNHXehhPsQkvswhh6BKSHnF7BplgZ5HR8hFIcI2snGYr3EEodwl9OAR4BoTIVbORIvIv1UlJjNjCS3E7b0DPUTu0mntqPKxSmot24NatwhI42tQP//q9T6XkbbqgDlFcmBir5IbT0AZAebrSHQriFMXWEmDf93paStQf2sqL3EGklhyOmMAcfRJ/aQa77zSTcPFNukVyohVp/K82BjtPePx3HDXehJfdUV24ev2/zHEDg+RuIm/WsrdnEWGmIvJ3FrwVp9LfOMj/8d0DYeXy994JnV13SAFKipffh6/sJhbV/OHsW/hx4rseen+9jbP84kcYwen2IUqbMkad60fw6PZu7f01nMs8887wa5sXgeV53qEJlWWw9BzM7SVuJ6VlXH93hJbQGul6z47jSxfYs6nxNjBQHyFppQnoEqGZf+RQ/Df6zL68/E42B+hlXZ9bKMV6cJO6LYb5sSVK8LcaGd1/AyO5RchN5xjuH2HHJM5TDJeq9BipU2GpsQQjBh73fO2MzDqguzd+XfomJ0ghpa4qKW8b2LAAKoRJZX4ZYOUrZLGIoJgoKLW4b/doRxpVRmr1TC9awHiMcjQFQcgpsT2yh4GQJahGEEGSsJHvTL7K25hIiRgwLC4nEfHm46WuIK13ky1wUQgjiZj2T5RGOZvopOSUQYUzFR41Zh65o9Fy4gv7W7fzjP/413/72j6ezj+Uptc70Lco5s78KThHLs1hXv2om/9ev+Wjy17N7cj+pcoa4r5qB3BRsoCl4ZnfIo8eeYufUXnoiXfg0Eykl/dlj3Hv4fnpiXYT1EDGjltHSIH41MCNeV7wyuqIT0qoOn+NL156PPsuWyFNI4aF7Okd9h5kkwWfsP6fd7Trl+IbqY1X8QkaKx9id2EnFdqigIz1j5oq4nouh6qcK0kLQ4G+hzteE5ZZRFW1G5J0qj3Mou4vJ0igRI0ZYj2G5FQYLRwFoDXRxMLOTjJ1CSg9D8dEa7GZBeOlZJ32EEJi33Iba1oH94lbIZVEWLcHYeClK0+k/s1LCvYcFTwwpNPglYa3AL579Ae1X/S4PH1N5U8/5LeWXUbA2n+E5DogMxPxwWavkJ0cFlgcRA7IVqPjhxus81NVQmQCvpuqOlUHwPSARhaqD+DjPewJNEUSDEimqr0h7GA6kBPsSgqbgiXG4rktm9AhLIyX6wrVk8gnGj7wAgBmMcfmm2/gj8+NcFbwcrwaeVuChIcGogPAiyVVLPW7oknMWgueK/pJApMFZyox72u0Gbb9Af0ngtlVF86INWRu+/kKOQzueIbv3Fwy99HNKiUEMX5DLLr+WD33u34itvInGxkYW1kt8r3IV4toGSWdE0ndE4PUptLZKejyq02PVuRm8s8SZ22sk5hOgHgNaFVbVrGFteQ1/0PgpMr9b4aXSS/zylz/hZz/7Ed///jfnheF55pnn14otKqjyNKvoAITg2spNfC/wDY6qh4jKODmRxRU2by69/ZTM1U53AZdb1zCpjNPhds/qJWGLyqxYhXPR4rUhEFhYGFTrYReXoijS41adiPVeIxmRQUObdSxPSHyen3FldO4XAkBK/Efuwhz4OVIPIrUQenIvWuYIxRUfwaq/kL7cASrFAh8/1kR8shfFtpCei2o+R3DlJtyWauO6A9oevhn8KgWRo85tpCLKjKojNHpNhGtryPtzNOYb6XYWsiKwjkZ/K7pyahTByUT0OA3+FkYK/dP9HnTKbhEFlfbggpl6Lysy/EfgX1iZ/TGKUWJMm2A85mAmdBrSJRTPIqjolI0IGSOIFBoHu99IpPV6mgPVSC199GkUp4DnbwDFRDj56WbbblUMtguUy1M4qgFNl2KXp8CtIPUgmh6gIG0cadPqa6N2+MtEEnsQKEjPRgqVxtwgSnABwrPRsn3o6QPoyT04saWUF70LN3IegqCUmIO/xNd3H6KSqj6kh9DarqVmTYxBfYg6GojnMiwa6CXrMxgNhWmz2nCMWvTBB5FT2wiqGgHFIBNq41D75UxFl7A6ftEZm4tbzZeijz+PljmA628G6aIWR3Cii7Hr1lcnfAN7eC72FFPKBN3OQi61ryTm/PcSg9XsUdTiWDUX+ThC4AZa0DKHUcqT1ffJHMkMZ0j0JYi2RDH81ZomWBPAtV2Gtg/ReWEHmjHvDp5nntcb82LwPK9LgnqYtbWXkLPT2J5NUAudVyO3s+FJj8H8UUZKA1huGVP1EzfryNkZMlayenwtzMLIcsJ67BUfJ28V+EXfQ7w4vpOya1Hri3NVx6Vc2nLxLJdwsDbIoiurX8b/HniSslFksbN8pgiOyCg79Rc5rO1nqbPytMcCGC0eY7w0TESPUXTyVe+FUEhbCeS0giRQZlykUHWWelRd0OdisjxKzslQY5zIVo4aNaQqkxwp7+dI9Ag79RdwcVlpr+Wy0tU0uk2Yiv+8lm2fi5Aewa8FKDp5wnpVoak6uV3agguwKmH2Tg1RZzYR1CNo07EiDh6XvuNG7v/7b7Nnzw6WLl9BQAtRsHNEjPjMfgpOnlqzgYB69iiDslPGdu1TBH5TNSk4JcpuGTh3QzzLtXlxYic1ZgyfVhXRhRC0h9s4ku7lUOooGxrX0h7qIW0lSFlTmKoPy7VIlZOUHYVE9imW11UbwyS0KV4MbyXgBYg71aWUYTfMRHiMh8UDfLD00dOOw1T9dIeXUGu2sHX4CL35cboi7QgBlmuRKKe4sfuaU873OIpQ8E0vrbM9i/3p7YwWB0lUxgGqzmZUgno1HmOsOESiMk7JKRDW46hCpeQW6MsdwK/6aQ2e/aZBqCr6hRvRL9x4zmsMMF6E7RMKTQFJ3Acv/fTr2KUcK677MFtGBVe1S0Jnv2c7NxL0bQLzMYE6JpA+yVs2SCIrJY+nBOmKoCEguaxNcmmLpLL61F1YF0p8v1SQukTGQOSgKEBvlZy8qvf4R6riQrFY5KmnHuHBB+/n4Yd/wdTUBLW19Vx3zU34l/8NQ7VXkOrdytSLP+K5bffxxtK9dES6ePOit3JV57V8wroI9tYgr/bwd02fioRjOejLCISAxXFJ86v4E6wkBNKQs2I0EIAuSU95fPmB/Tz93PNMHHqe8cPPUxrdB1ISbexm6SVvoXHNjcjOK6kLGwyXBQlXcmBI8Oyo5MYuyc3dp07snA9xH9RvlAR3AH3gNoFwQR0SuK0Se9WZJwvcTii+zcP3U4HWS1U8roPyzR4s1VgvLmL9+ov43Of+ij17dnD//fecIgzfdtu72LTp8tf0b+U888zzfydLnOVsM7ZgUcGYnpwvUURBYYGziA32Rgxp8pT5MOPKGO1uJ5daV7HJevn6jipr7PU8bv6KfvUobW4nAsGoOkRQhlhvXzTnca2217PUXskeYyc1bi0qKlPKBJ3uAi62NgNwgX0xtbKOfuUoYRFGAjmRIegFCcsIUS92XtdCKQxjjD6F52/E81cjCzx/HWr6EOaxB8jFl3PA28No4HH21z9HTTTMqnQn3dkQojCEfuAbyEg3XqiDJ41HyIksi51lM47liIwyqg7zPut3WMYqlND5zaQKIVgaXYNPDTBeGsTxLCJ6nI5QD/W+lpntthlb2KvvZJNoIuJOUPZqsRSL5zpdrnF9hHJTCD2CHukhKC2KNSvpXPQhAoHpCXK3gpbah1RNUHQ8zQ9m9VoqpUmUShqrZgWqEUMUFTwhcIMnJtdtO4NP9aMKlVhhGF8pQyq2FKc4BoqB6qvHdAqQ3I1wSuBaeHoIz1eHntiJYmfJr/8TpDE7nuRMqJnD+I7eg1Q03PiK6jjLU/iO/YKbazfxzwt6yVn9XLe7l66RY6R8EM/VUR/0oeUP4Tg5snoML9yDLl0ac0MYI1t5wYwyVhqmI9Rz2uO6kR6KKz6C2f9TtFw/CBWr+TLKC25DGhF+ad7H/b57gGpzxWfNJ9hr7OQDhY+wyll3Xq/9bxShVAvJl/eukbKaI32eEYCVfAXXctF9s6UlI2BgFW2csj0vBs8zz+uQeTF4ntcdUkrGi5NU3AqNgQaixmvrMu3PHeRobj+aomMoJiW3QN7O0BVaQsysFopRI37KbH5OZOnVDuPh0e0sJHbS8ruX40mPuw7+mGdHtlLvr6PGjJEoJfn+gR+hCZVNLRee8hwXl37tCDGvZpYbIihDWFiMKSMspSoGSymxvGpTkGrGsmC8NIwmNDRFx6f6KbslfMKkLEtESnFiVpwJY4w2pSqySSSj6hCLneU0eS2njOfllNwCCsrLspUFUoGnlIfZae6kxqvD8zx+ovyAJ5QHuWXyLbQpXXSGFlFzjty0uWIoJp2hxRzK7CJVmUJXDGyvgq6YLIqspN3vsX38AAXLIqJXC49sJYcrXd59xwfZfe/TfOlLX+BrX/sBC8JLOZDZSbIyiSY0HGkT0EJ0hZfMnKfj2aStBJ50CeuxmUmJllATcV+MyVKC5uCJmIzJ0hQtoWZqfXPLtKs61J0Z0fo46nQh5njVCJOYUcvqmosZLvQzWR7jaGqQI6lhsmUbT3o8PvQMl3WsY6JhhJySo6tSfZ1tz0ITOk2ylQP6HkqlEn7ObKWMGGHeuvBm7j54H/unc68VobCqbinXdJz+RvHlDBX6GCsN4lP96IqBLnRsaZOojGOqPgzVJGMlUVxBra8JddptHNBCOJ7NSPEYLYGu11QYS1eqrtPGALi2xQs//nuWXfEuWtq6mSwJMhavWgzWXxIE7lTAlXi1ElGC4M8Vrk9LLn6fR9GDoA76WWrsys0SUfIwtiuIcZABWLoc9rVAgwRl+pKk0mlGn/oxX/vWT/j4c49QLpfo6VnM2972Xt7whjdxwQUXM1pU+YcXVRqRrO24HmXz9VQe/yovTjxO5OC93LX/u/zLi/8IQFdoMQuHLmTdVRdx+UUXcVBZzPOpGAW7GksRM+Gmbo+r21+Z6Oq2SvQtgrHcKEczhzmUPMihxAH2HdvDloe2USlnEUIh3rESvfMSApd9mg0bNrF88aKZ98EjxwQJGy5pkWgKgGSyCI8cU1jb4NL2KvsVOkuh+E4P3y8E2pAABZweSelWj3OZ3+yLJc5yidZbFQicbol82VyQEIJVq9axatW6GWH4Zz+7l/vvv5fvf/+bLF68jA9+8GPcfvu759zQcJ555pnn5WywN7Hd3sZufQcBGUAiKYsyG+yNrLLXIRBcaG9ig72RChUMjFOiwk6mze3kncUPcp/vLvq0w0gk9V4jN5dvZaGzdM7jCsggHyp+nEfdX/KSvhVPuFxduYGrKtdT71XrqHa3iw8VPsbfh/+KcWUUvwwS9qKEZYQ2t4PVc2hUdzJqcQRhZXDjs7OIXH8tSmGYw2zlp40/wrUOEjOLFHwOzzcXuLWvh5Vj9dRVkuhTOymGWunXjhLz4rNq9IAM4mAzqYyzgtNHcZ0LTdFZGFlOV2hRNSNYMU9ZHbVX24VP+hlpirJgbBxfuQw+H1lDZbS+li6jCTfcheerwWvYiGi7loCvBqREH9+Cr/+nGJMvohQnkKqJ6pTwjBhSURFOAZQmrLZrifsaiepxUlaCiB5DFRplt4TtWXSHlqAIFaU4gQbEg10Iow6jOAXSBTWAUhxFakFQFLxgM9KM4+hhtPRB9KkdWC1zqyP1xG6EncWtOdFvxfPXo5Wn2DAKb61/E+Fd/0zr8ADSswm6ETpSEiX3PMKzyKkmlubHUDVcdArBZiK5QaLFKZL+iTOKwQBO7Wqc+AqU8iRS0ZBmLQhBQkzxmPkgQRmauW9q8lo4oh3kIfPnLHdWn3UF568bKSWJviRj+8Yp58pEWyK0rGwmWHvqTL4TWYgbbEPN9eNGF00Lwy5qcRi78ZLqOZ8H/pgfzadhFSzM0In79kquTLA2iB6YXwE1zzyvR+bF4HleV4wXJ7nv8C84kDqM4znU+Wt5Q+eVbGze8JoIQxW3zHCxH0M1CWpVh6Kp+sjbWcbLQ3SGF512Sdc2/Vl+4vsBE+oYEkmtV89N5bdymXX1rKLwOAPZIXZN7qU93EbYqN7Uh4wgfZljPDG0hQub1s1qjgegoBDxqploJ+Pg4OFREWWKooBrOfTnDpKsTALVjOWu0BI8vJlrFNKjFJwcZa+IK11cx2HN1Fqeb3qOY2Y/fgKURYl6r5E3lm+dU/HiUwO4ONONLqpFqpSSpJhixBxikbMUVWqMlQapceOMBUY5HD6ML1G9vmtqNs44cF8trYEuTNXHaPEYRSdPva+J5kAnNWY90id504Lr+VlftYkfVN26l7ZtZGPrhXziE3/EZz7zuxw4sIelS1fi0wKMl4YoOgVCepRmfxuhacfxVHmcw5ndFNwsUkpM1U9HcCGdoUWE9CBrG1byUP9jlJwyIT1IxspiqAbXdl6Ooc6t8PGpJkviPTwzspU6f83MazhVShI2wnSET9zARI0aokYNjx17mhdGeumKdNDsry51S5SSbBnaybL6elzPpugWUKRAVVRieh1D9iipVIb/tf1f6Ax1sLnlIhbFF5x2TBc0rqEl1MSeqQOUnBJNwQZW1S3Dr517Pb6UkvHSILpizrhIXFxMxUfJLVJyi2hCQxEKilBmhODjVJdIlpB4iNewqI6ZENAhZ8HQk98hnxzhwtv/aFoElkRfrSvYA/MJAY7Enb7HkFGQAYm+Q+C/CozTX+5ZyACU3iupXOuiJKr7WF8DW3cr7E94lA/+it4n/pPhF36C51ps2LCJz3zmz3jDG97IwoWzG34cSAkyRVjmA+GAVCGsqbS3XsPCBddw99VfZnv6MP/f2FZ2DG1l28BWHv3ru/miawOg+yNEGjoI17WhxtrZHW9n+/I4LREDw9DRdQPDMNB1A03TyOdzZLNZstk02WyaTCZDLpchmUww2NfH4LGBacd8dYJhgb+H1oalLLzij+hcdRELll2I7g/z1LBgvAjJYHXSSgCOVxX0e2LHheAqdX44mILetKAtdH5RH6fDvrjqAtaGq9fL7WDOlZIMVyMj5sLJwvBnP/uXbNnyBN/85lf4kz/5A/7mb/6Et73tfbz//b97ymv6WiMK1exmqVUdzvNV4Tzz/PcnJMP8VvETPG88zS79JVSpsda+gIusS/FxYnm8QMz6+Wysty9iibOcXrUqBne5PURePuM1B+KyhtvK7+KW8h2kRAJXuNR69bO2ublyKxEZ44f+O0koUwRliBa3jVtL76TVaz/jvj08ypTx4ZsRt6XmB0UHtwyan1Ezx2P1fewMHMHwdPprcxTsPKvGfAQcgV0JcyyU45etfSxJrkGzTTyn6qqOenGOqf2zjungIBAEZfg0I5obY8oI+/RdlEWJFred5fbqmRiN4+jouMJloLmV+lSChcf6ieezhEQOtGYKa36fSvuNoChV1+c0WmIngf3fAOlgx1dg2EWEnQbFQDhFFCeP62+gsOJjWM2XowqFZbF1HEjvIG0n8aQ3bcJYSPu0gFp19yrgWkg9hBNdhJ7ah7CyCKcIVF3FTmQ6gkDRqqsTK8k5XxPhlk6bWSsVHdUucsvwWvSRZlI1SwhYezBtF2nGUEvj4JSRZgRPCzATdab6UD0LzSnOqbk4iooXaJr10JA2QEpJsthZdmKcCOrdRoa0Y6RFilr52jbMOx+ObRvkwEMHcSoOqq4ytneMkd2jrLttDZHmlzmyNT+lhe8gsP8/0FJ7QKjVuJDIAsoLbj2vvGCASHOEhsUNDO0YJmi76D6dUqaM63h0bGifbyA3zzyvU+bL/nleN5ScMt/eexcHU0doCTZjqjoTxSm+f+BHBPQAa+pXvPpjuAUqXnkmXuA4PtVPwclXG7QZswuwQbWfHwS+TZkSC5zF08vjhrnX/10avMbTRjdMlZKUnBIhfbYTIWZGmSolKdhFoubsL2aBYJN1Bd8N/AcJZYoar5YKFbYZWyiJIj/x3cPTyqMsnVpCbbl2Jid2rDRI3s4SN+rIVBJ4moup+mjwt5Isj1N08vjVANcoN3N15Y3sFjuZVMZp9lrZYG2izes453UbUHt5NPYQU/Ywuq1RrzTT4XZRcUqU9TKFcJF6DPJOlrJbJKCG8Msgk75JYkYdKWuKkeLAayYGCyGo9zVT7zs1H/Z4g7fFNT0cSB7G9Vw6Im0sii1AVVRuv/3d/NM//Q1f+tLf8tWvfoeYUXvaxg8lp8CBzHYqbomIHkegUHTzHM3toy8zzLbRPQznx7A9h0olg67orKxdymVtm1hdt/y8zuXqjsvpzQywP3mIsBGm4pYBwQ1dV89yHR9n19RefKo5qxFdrb+GieQUdZluelqWkAwmaHXaCaghBosj7HZ30zS4gEwpxzOZ59k9tY/3LX/7GT9XzcHG0x77XEgkjudUhV5FI6RHSVUmQVQd82WniKqoNPhbSVtT2J41awKm4pZp8DUjzuJSeiU0BmBdg8djxyTP3fO/WHDxW3Bql5GpwJsXvPqICFEEZRS8l73FZRjEiESZFLgL5ihWCvBaqv8DZA/upfzAnTz74++TTYwSb1/Obb/zeT75gXfS3XYGV38Fgg8KVh2ATkthvElS7ga3GfQBQVmpNkfZWruEcmQJ1656L76LJYlymSdf2o2X6KVTHiM3NURuapBc3wsc2/pj9v04jec6pz/mNOFwhHA4SjQaJRKJEYvFufzq6+gMdrFwZAGL84tYEO5BXWzw1GbJVyYES08y0reFZdWtXRHYXlX87c9WXdWxM7xOr2m6QgCcRa/h/s6BEILNm69k8+YrGRo6xne+8zW+852v8/Wvf5krrriOD37wo1x77U0or2WQswTjaYH5S4GaEKCC0yEp3e7NTGbMM888/32JyCjXVW7musrNr9k+gzL0miyFz4g09/vuYafxIhUq1Hv1XFO5iU3W5QinjD71EtflxtlkvIEjTRFkoIMud+EZVzW5uDxrPMnTxqMklQQNXhOXVa7hIvsSnNhSnOgitNR+hhua+PKC7fT5x6kpuAzXNvKCuZUmtYmKGcNfGcDVfNQXDIZDBXIRUEoabqh9uka/nKOBQ0wqE9R59VhY9GtHaXM7WHaWGLezsVXfwg/93yGpTCEAFZU19gbel/8Q8cnDaIk9gMfV9Sa7uyyyWpFtK9Yw0NyGke1DWDluzlxczblN7sZuuBir+VJQq+5MY/QphFvEiVUFTFtRUVN7UQujuMFm7PoNFJe8Hy+2eGZMIT3K+rpLSVtJHK+6Yi6ohWfMCnbNSpzIArTMYdxIN25kIcKzUXP9SKUFkFV3qT69wsWrTjB7vrkLpU64CxNRFfGP5/t6DsIp4cSWoOYGMaRGrdaDUhOEqR0o5QQ4FYRbJqDUIfTITA6zYWUpqz5KZowWs+nsBz8DhjTQ0LCxME+aRLGFhSa1UwT8/0qqzdqOomgKtS3VexrpSRL9CXq39LHm1tWnmKqc+vXk/Z9Dn3wRpZLCC7Zg1W9A+s4//1gIwYqblmEEdEb3jVNMFfFFfSy6cCHt69vOvYN55pnnN8K8GDzP64b9yUMcTfezMNqNMZ1L2hlp53C6l2dGnn9NxGBdMdCEjuPZqOqJt78jbTShndYVvEvfTkokWOqsnHEBt7kdHNT2sl1/4bRicMQIoasGZbc8y02ZtwvEfdEzOiwvsa5gXBnlWfNJDmp7mVTGSShTLHGWUe81IAsOg24fbsCjzq0WM6biJ2VNUudrJG5WhVdV6Ejp4ddCLIysYHFkNdq0U3VJ+fwK1iF1gH8PfokxZYQm0UwgYVKwjpAjzWptA5HaOkq+MtKR2NJCUhW2HWETdIMIITAUg6ydOq/jvhpKlJiID6HWVGj06ulxumbcz4Zh8IlP/CGf+9zv8elP/ymLFp1+ieNUpSqkx0/KSA5qYfZM7eOJgV/iU8M0BuoJ6QFGC+O0hBr50Mp3E9DPv5tVV6Sdj675IFtGtnE03UfUjLChcS3rG04t3gBc6SFOk+clkfjdEO+2f5sf+L/NmDGG67kccnupTTWzKX8l/lAQKSVHM/082P8oK2qXnOJSfzUoQqHWbGCw2EtADc0I7RkrgcRDVww6QgvpCi2ebng4jE8LoAqNkltAV3Rag93ntRLA9RyS1iQVt4xP9RM3609xHAsBty2S7H78bnJjR7jgY99DADd3S67vevWOUmmCDIGSBPfkhJBK1XUpg+d3jGQywX33/YAf/vBOdu58kXi8htve8g7uuOO9rF69HkU5y/VxIPx5hcseF7QYYAhJ4Si8NAbHNkC6QXL9AIwnBbvbodUPwRUSVwPD9GF2XES+6SJWLpD4TzK4H0gJru/wuG2Ri23b2LZFqWLRm7QoWTY9DWFaaiKo6lkcIBKUBFiAV+sRSoORUCjakuOrCDvCMJCFnCU5mhEoQtIYgBW1HtsnFSxXcjx6brRYdX0vir361/D1QFtbB5/97F/yyU/+CT/72T1885tf5QMfuJXly1fzh3/457zhDW98TVbJaHvAf7cCisTplAgHtKOCwH8q5D/l8bIeUvPMM888QLXOGFIHyIg0tV49TV7LaVfInQkXlzsDX+Ml43ka3GZqZIgJZZzvBb5BoGyzefcL6FM7QLr4kNT2xSktfDtW26oz7vMh8+fc578LTeqEZYRe9TC9gSNUSmUut66htPQD+Pd/nS3Bx+jXB1mRqIFgKwHfYsLyUZJqklL9GuKVPAErDaaJrTk8UbuHl1oW0N6YYb2X5xLrCqaUSZ42H+WgthcVjS6nh3eU3k/oFTiDE2KKH/m/R1kUWeqsQCAoUeQFfQuLEr3csSsN0gEEFw55fGxK4d8uGGTQ8NjbJGkNhvjE8zqtyf1IPQqejZ7YhZo9Smnpb4GiouYH8bQTY3ODrbiBJvSplyh330ppyQdOO5uqCJUas/6Ux4Gqq3TJ+wnu/CLG6NMzDegKa/4QJ76I4O4vo+SOIX01SKGhlsZwYotx6tbO+drY9eux69ahT7yINCJIoaBUkjjxZVhNmzHGnq5uKCVeoAmr5QqU0jhqthc32IqKoMnKk1SKVQe0XWSk8QJqajfQ4D93NN7pWOAspt3pYkA7ygJnMRoaZUpMKuNcU7mJsJxbHvKvg/RQmnKmTE3niQJUKIJAPEiiL4ldsjECp97jeqF2KqEzu+3PByNgsOKm5fRctgCraOOP+tB98/EQ88zzemZeDJ7ndUOqnK4uR3pZg6qwHmIkP46U8lXfBAfUELW+RkaLAwihoAsD27Mo2nnaggtO26QuK9IoqKcUu7o0SSmJ0x6nJ9bFovgC9kztpyPUhl/3kSylKTgF3thy3RkjBHR07ii/h83WlRzVDvLdwDfodHro8Lqqv68IHCoMa8fodnvwE0AIgSJUKl6ZVTUXM14aIlmeQFN06n3NNPhbT8keOx+eNh5jVBlhmbMSYQpohopVYr9ykE32tVwgunlGPsmQOjDd7EOS1BKYrklPfhFSShzPxqcGXvEYzoch5Rh3Bv6dPu0oEg9VaixzVvLe4u/MdL1++9vfz5e+9Ld84Qt/yje+cc9p92N7FiBOec8dTAyQs/OsqFl1UixHkKPpfvYnD3FB45kz46SU7Jrax7ax7UyVEnRGOrikZQOdkXZaQ83csfiWOZ3jqrrl7E0cxHKtmc9LppLFp/roiXWx1F5Ei9vGXn0nhwtHKO71sdpZh19W399CCBoD9Yzkx5gqJWkKvjZ5zsdpCy0gaU2RqkxiqD5UoRLW4zT6WlkaW4t/utHc8tg6/FqQidIwtmcR1WvoDC2izjd310bRybMv9eLMckZFKMSNOpbH1p/yeQ5okpfu/Vs2X/YG/vqOtcR9HrHXKpJcB2uTxH+3QEmAVwOUQRsQOIskzuJz7gGAgwf38i9f/Rfuv++7eJ7LNVffwH/8xw+45pqbMM25DdZ8pNrEzjBB1guOAi0pyZKj8GwLdF0iWb/ZY3JMkE0qxFokJQ32TVbjGdLlaobySxNwYVNVeK24IJB0REBRFEzTZLRi8sM+hf6MwPGgJiW5tkOePVdYVJurHacnCktrJDsmBW3BqvicLENLUHL5Ekl7GHxadRspwfIk+xPHm19CSIc39ng0vdLmdh6IMkiD11VF5PP5uP3293D77e/h+eef5otf/As++MHbWLPmAv74j/9frrzyOiwLxsdB06Cp6fzc0cZWBVFh5n0p9Wo+snYI9N0C67L/M8T1eeaZ57UjLVLc7f9P9uo7KYoCIRlhvXUxt5XeRYC51XhHtAPs03fR6SwgKKdj1NwwR9VDTI59HX3SxokurrpBpUQtDOHvvRenZgVe4NTVYBmR5nHzVwS9MM1eKx4eruIyoPXyXf9/UOvWYdVYBC++nZfEGAE1ihtYiOerwQ80uE0c0vZT8EnUlqtRsod5KfwiGd1iW2cALaDhKN/iBfslfqv4CW4tv4NLrMsZVgfxSR8LnaWYvLJC4pC+jyll8kRDOsBPgHjJ4wX1Cd4SeAPCiAEg7AKbB/uIxq9je2cUFZXNh/voSmzBia8EpTpDKqwM5uhT2E2X4NSsxA22YuQH8QDhFBFuGanoSC2Aczwrdtq5izJH4c4pYw7+EsXK4unhal4wEqSNG+rEDTRjTj2AMpVD6gGspksoLvvdE07hMyClJGMlSFpTeNIluvBWmiNL8E8+B55DufVqrLZrkL5anNo1SPMXqPkB3FAHUvUhVT9uqJ3iyt9DuGX8Q7+iKT9I0agh23AB0a5b6PC3veJ7IhOTt5fex3cC/8ER7WC1pwYqq+z13Fh+8yva52uCW0HP7EctDqOkpyDUhDRONNhGnHov8+vEF/bhC88tfmaeeeb5zfI6uvWZ5/92omYEIQS2a6OfJJbm7Tyd0fbX5ItMCMGiyEo8zyFRmSAvs9XmWoF2FkZOv7S/2W3Dw8PBQZv+yHh4lEWJTvf0IaCaovHupbdz98H7OJQ6SrlQIWpEuL7rStZ3rJy1r1PGiKDVa8dzXXRpUC9PCHVSBcMzyZOnqBTwe9Xi25MuPiWAT/XTGVpEZ+j81zhX3BJFp4ChmAS00Mz1PqjtIypjJ8RwAabpp6AVGWOUNZUN3FZ6Fz/1/ZAR3xAZkrQmW1maWYZegQEO4VeDNPp+/cuEXFzu9t/JUe0QC5zFGBiUKLJTf5GY717eX/odAEzT5M/+7G/56Effw8MP/4Jrr73plH0F1CCO5zCQHcR2Hfyaj3p/LWPFKWJmbNb70VANJJJE6ex5aI8ce4r7jv58en9+jqT72Dm5hw+ueCdLahbO+Tw3Nl/A3sQB9kztx1D0aaew4Iq2S1gUq74nW7w2WiptdOf72Ds1gggqs/7iO56Dqqjor5ErOGulSeQGsYRD0IyxLLaWyfIoycokhmLS5G+jOdCBcpJj11B9LImupju8BNdzMFX/eRXpUkoOZXaRtCaJ6jWoiobjOUyVxzmS3cvK+IWzXqcHHriP/fv38KMffYnu8486PCeVKyVKCoznFbRDEnSBs1RSfKfH2VYPSil54omH+bd//2eeePxB/PEWem75M3qu+hDrFtRz8UKP4CHQdwlESeD2SKz1pzYoO47xlEC4Ai8mWeNJahQYjQlapiS/dQhab/OILwbZIwlthbQLI1nBsRyEdYiZEtsTHM2AJwXdUUm2IrjSk1yQkCgGZMPwn/sUBnOC9pBEV2GiCD86IoiZcEHj3MREVYF3L/XQFYUDScFECSKG5JYeyY3ds/OBAT6y2mPHpOBYVuLXYGWdpOeVvJYS9O0C43GBOlaN87AukVQul/A6M7JcfPGl3HPPQzzzzOP83d/9Oe96182sXXstixf/HY6zFk2DhQslb3mLR9sc/8wq49U861mogCJQsvNC8Oudv/3bv+Vzn/scf/AHf8A//dM/AVAul/kf/+N/cNddd1GpVLj++uv5yle+QmPjibifY8eO8dGPfpTHHnuMUCjE+9//fr7whS+gaSe+Bx5//HE+/elPs3fvXtrb2/nTP/1TPvCBD/wXn+E8rzckknv83+E582nanHZaZQcZkeJx80F80scd5ffMaT9TyiQ21owQfJyYF6NudAuOseZELIAQuME2tPQ+tNR+rNOIwRPqKGklRafTTVmU2aW/yIQyjkWFXdp29uq7aHe7iMsaEkziJ0CrUzUGCGCRs4xBdYAxdQQCkA1aTCkRVjirWaQsBw8sr8JufQdbjMe5ofJmmryWszZeLlOmoOQIe9GzRgfY2IA8xWyilXNUFBvXjKDJ6vE9RSGAwpKJMu2NHwYgPPE5pBmfEYKBqgiYH0TN9uHUrMRquRxjchvm0EMIuwCehfAs7PhKPCOCf//X0RM7q+OpX0+l/YZTsnJfjjG+BWPsGdxQFzI+bTIoJ9AHfoY7/iwiP4hdfyECF1FJo1g51OIwXvjMDlQpJf35Q/TnDk6bMUBRVMbirSzr+ks0RZ814+mGuygtehe+o3ejpfYDEmlEqXS9GbthAwgFq+mSao6xFiCqvTbi5EJ3CZ/O/yn79F3kRJYGr4ml9spXPCHwahFWjsD+f8eX2M4h2UGhVyVWdxi3diV2oINiqkjnRZ3o/nMXNnmR4xnjMV7St+HisMbZwObKlTNGmnnmmef/PObF4HleN6yoXcKCaCeH0320h1owpjODVaFyScuFr9lxfKqf1TUbydjJmWXlET1+RrF5nX0hW5zHOaTto95rQkFhQhmj3e3kQmvTGY/TEKjjY2s/xLHcMDk7z7GaQ+wMb+V59ZdE3TiXW9ew2bryjM3bQl4Ev/STFzn8sir6ukGJm7Xxl/3owsCTHnkni6H4qPefWiTPBU+69OUOMlzsx3IraIpGra+JxZGVmKqfsBdlXBmd/Rw8QOKT1UiEzdaVLLNXcUjdz9DkYUrpLKqrYouq28BQXDzcVzS+82FA7aVXO0S72zVTgPsJ0Og1s9t4iWQ5MVPU3HLLHXzve9/kT//0U2zefBV+/+x4h4LtsH+qD4cStushkcTMMBEjTKEy+1wcz0VKOdMs8HSkyhkeGngcUzHpjnQC1eL3cPoovxp4jEXxBXMWQsNGiA+vfA8vTuzkYPIIhmqwsm4pa+tXoiqz30+dkTY6I20cSfexMNqNqqjYrs1ocZxNzRdS43v1Oc7jux5n6ld3ow4M4fp9DK9djHL5Zaxo2syS6Lm7axuKCcr5F9IFJ0vKmiKkRWYagmiKRlAPk6hMUHILBLTqa1IsFvn85/+Iq6++gYsvvvS8jzUnDCi9XVK53EUdF8iAh7OAM37Tlstlfvzju/ja177EgQN76Vi8ljW/821WXnEHdSGDog0vjAnEPoVPPynQbVA0Cc8q6FslhQ97HK/RXQ8UUb1XEmXABGGB6ocuD7qQKBVYXIZ0LUig1gebmj1+fETQmwG/CpYHHlVHcMGuCrz1Rfj0dsnSMYFhqcgIDC6TjLZAT+0JwbY1BEfS8NyomLMYDFDrr4q8w3ko2NAQgPgZ7tuCOmxukWyewypPZQq0AwIccDskbjfHe8mgv1CNRBC2xIuDMgn+uwQiCeU7Xp9i6ObNV/KTnzzO17/+U774xT9hx44NLFnyfjZs+Du2b68jk1H4gz/wCJ3dfAWA2ybRehWQcuaaYANIvPn7vtc127Zt49/+7d9YvXr1rMc/9alP8fOf/5wf/vCHRKNRPvGJT3DrrbfyzDPPAOC6LjfffDNNTU1s2bKF0dFR3ve+96HrOn/zN38DQF9fHzfffDMf+chH+O53v8sjjzzChz/8YZqbm7n++uv/y891ntcPI8oQe/QdtDptRKZzZOKyFtuz2WZs4Q2VNxKdQ75MxIuioFKhPCt3NS+yBFwDReh4L3+SlNPu01PxywCGNCmLEgNaH6PqMHG3hkllAg+XChVKosBSZwUj+hDjyigtbhvx6S/PnJJlvX0xV1SuRQpJv3oUTdNZ6J5oEmZgEpIhXtK3cUPlzA5QC4tHzQd4xniCvJIj7tVweeUaLrOuOW2t3+l2E5QhUiI5U5t6eCT1PDcmI0jXYbwyQcktIqVHvDKBW5nAmF4pKTXfCVfvydcKCdOrxpyaNbj+RrSJFxAIpGri6T7UzBHiD96BwKtGR4Q7MPt/hpo+RGHNZ5C+mtn7dSsY48+hTW3HGH4MYReQkarxRCJJCYmRPYxI7WYwvhhd8ag1G9GCbajpQ5hDD2E3XDSrud3JZO0U/fmDqIpGeNoNbXsWY6VB4mY9bcHuU693yxXYNSvQU/tBujiRHrxg2wnRWNFOPY/XgIiMstG67DXf7yvBGH4Efew5RH0PyzaG2LnNZHK8hJg8gh0zqOlupGdz1zn3U6LENwNfZbu+jZAMIVD4sfZ99mu7+d3CJ+f02Z5nnnn++zEvBs/zusGv+Xnf8rdz7+H7OZLuw/Ycan1xruu8knX1Z84KeyUIIU7bNOx0RGWMDxU/zoPm/ezVd+LgstG6lOsrb6LBO/vsuSIUuiLtPGz+gvv9d6NJjagXY1wd4buBr1MUBW6onD4aIC5ruMDeyK/Mn6FJnaiMkfNlyTQm6U50slN5EUdxaHc6ucy47ozn40mPjJWk7BYxFJO4WTfLnXksf5Te3P6q8GvEcDyLkcIAnnRZHb+Yi+xLOKDvJiUSxGQNHh7H1D7qvAZWOCduRmtkLStKqyiVU2j+6nWRSAzFpODkGCr00fgqlmfNhbIoYQkLU85WkkzpoygKVESZUrrM5OFJrJLNpz70P3nH776Bf/mXv+OP/ujzM9u7nst9hx/gSGaSxTUtBE2Jh8d4PkVIi+E4eUbyYzQE6rA9h4HcIG3hFlbWLeNMDOaGSFZSM85dmG6E56+nPztIupKlxheb87mGjCBXtF3CFW2XnHU7XdG5bdGbuHP/3RxO91Y7OgOL4wt5U8/1r9pxX9q/k/zX/hlfJotSU4vI2YR+uY3EZIqB99SyrHbDq9r/2XCkgyc9VDH7q0wTGpYs43gnGp19+ct/x8TEKD/4wQO/9uVyXjN4zWcWFFOpJN/85lf41rf+lampCa677mb+7C/+iYfFlRQdQeO0mBc2IFyWbDuqcJ8rqQ1CW1jQFZD49wp8DwsO3ih5bEhwMCnwaXBRo+SNyyX6LonICRASaYAoVMVhe/0JARng5gWSVBl6MwIXQUCVLI5DV0RiuTCWE3xmBzT2Ctx2ieMHJQV1jwjWr4HMNbPPM6jDePH8r5kQ0PbKm7GfgvGcwPcjgZIEEMgAWJs9SrdWxU/zEQGuxDluyK+tisfmswrW5S7e+fdO/PXigdoLSlqhPvEWLt/8JkrW13jmmf/J4OADXHXVV+nvv4XduwWbNp1bzLY2SYydoB0FtxFwQB0TOAsl9qrXpxg+D+Tzed797nfzta99jb/6q7+aeTyTyfD1r3+d733ve1x99dUAfPOb32TZsmU899xzbNy4kV/96lfs27ePhx9+mMbGRtauXctf/uVf8sd//Md8/vOfxzAM/vVf/5Xu7m7+/u//HoBly5bx9NNP84//+I9nFIMrlQqVSmXm52w2+2u8AvP8psgpGUqiSJNsnfV4SIaZVCbIieycBKMlzgp6nMUc1PbS7nbhk34SyiQVxcZXdxXK4cN4/uYZt6tSnkLqYdzI6VdPtbodLHaWsdXYwohyjKAXwsUlrSQJyhDNXisZJU1ZlFhrXchW82mGtGNMyDEA4l4tbym/nc3WlUA1f/igtu+0xzolG9mzUbO9CM/BDXfxs/BP+YXvPiJehLAXZkqZ5K7At3CEw7WVU1egdbjdXFq5mod9vyDJFKb0kRMZOljINaN50qWj5IXEUE1010V6DocNk5rSMVoCndiNG9GS+xB2DqmHp2M1BpFGDXZkEXguSnEUxcpQab0GFA0hXbTEHrTCIXDLuOFulPIU4GHXX4SWOYIx8TyVjhtPDNS1COz/D4zRp0CoaIUhRCUFiopdfyE5r0DKmqTZtdAkSD1E3skg8Wj0tyHNOEphtNoMTjt9nEiqMont2YSME0t9dMVAESoTpeHTisEA0leH1fz6EGb/y5ESY/xZpBEBLUB7t0M46jE6qOAkRzFXr6J283r0oEbZLaIJ44w9QnbpL7JLf5EFziJ805M0NjYHtb28YDzLNZUbT/u8eeaZ578382LwPK8rWkJNfHztbzGSH6PsVmgJNhLQ/2uyZs+EJz2ClSC3Vt7OW/W3gxCEZWTODTMKIs/jxkP4pZ9WtwOAuFvLiDLEE+ZDbLauPGPTgVvKt1OhzA5jGxNiFL8Mopsmu5p2kVUyaELnqNpLxZa8o/T+U5YpWW6Z/ekdTFXG8KSLEAoxvZZl8XUEtTCO5zBSHEBXjBkHpar6ESgkyhNk7RQXi0sZVo+xxXiCcTGGABq8Jm4vvYc6b3bWbNHJY3sWISMyS3DzqQGKTh7Lq+BTz7/B2lxpcduJezVMKuM0eyduWCaVcVrcdjii8vzPtpKfLFRFUSF48+Z38pX/739z++3vZsGCqsvhWG6Y/uwxmgPNeK6filt1WgjPpWAVuLr9Ml6Y2ElvZgBN0VgUW8Dti285qzNYVTQUFFzPm9Vgy5UOqpgd1+B6LpZn41PN10S47Il18an1H2FP4gDZSo46fw0rapec9bMlpSRRGWesNETZLRLR4zQH2gnrsVnb5B/5GTKdRi5aiJwW+pVcnuiefiaP7MSOrz5tY8bXgoAWwqf6KbkFQsqJG4iSW8CnBghMZwb39R3hK1/5ez72sf9Bd/fsG0rXhZERcBxobgbfrzHmLJfL8rWv/TP/9m//hG3bvP3t7+PDH/49enoWkyjDT55TCOknhLjxIkwNK3iuZCIGoSIcG4RIStCRhInvKHw9LxluEMSjkkIJfnhYodAs+cByibEPRF6gWIArsdZKCr8zW+gzVbhtsWRvUmK7ktYQM07fdAUWJiXxXkGuRZJVBZojideBkoHV++GJzaCcdM2ylmBZzW9WTFRGwH+vApXprGYhEWkwHxW4HeAslqjjAq929ji9WtAOStQxgXcezuZfNyINge8r6HsFlCUXHBaE/Qr7N3+UhQvfzMMPf5Sf//xWWlreyeDgP7Jp07mdUO4iKLzfxfeAgjoiQAXrYkn5TR7yleYvz/Nr5+Mf/zg333wz11577Swx+MUXX8S2ba699tqZx5YuXUpHRwfPPvssGzdu5Nlnn2XVqlWzYiOuv/56PvrRj7J3717WrVvHs88+O2sfx7f55Cc/ecYxfeELX+Av/uIvXruTnOd1SZ3XQFhGSSvJWbVfSkkS9WJzXkpuYPDe4m9zt/8/OawdxBIVol6cW8q309G0EWfyy2ipvUg9iHAtpKJS6XgjbrjrtPsTCN5Wei8pkeSgfy+qVNGEhin91HkNqKi4uDg41BCk2W3lTaU7iBJFlRpLnRWzjB09zmKCMkRCmaLOqzZQs6iQF3lW2+sZUgdACtoTOYJHfoCW7QPpMhaN8PymUWq9NuqnZxMjbowh9RhPGA9zSeXKU3KVBYK3lt9Bu9fJNv1ZciLL5c41XOJsJhS6ByfxE+qEhhAKQkKyfi1T8UWUCv00+zuoNF+BmjmCMbYFpqMVQMH1NxDa9Q9IzY8bbEdUMsia5SBUlGwvSiWBpwVRPAs0P54SRSlPopbGQNFRM0dmjVNP7JiOheio5v4qGtrENtT8EG6gmawqMJ0yUg/gALrnIRQfJadAxS0RtHO4wZazrgDzmK6zX1bzChQ8PNTMYYzhx9Eyh/DMOHbTpVhNm2dFZJwvavoAxtizqMVR3GArVtNm3OjcI9teF7gW8qT7h1iNRyzuoqXGKSw16BMjDE/2UZo2BLUEOmkP9ZzSZLlf60XCjBAM1T42pvSxX9nNsuLyGVNRna/plD4w5VyZ8f0T5KbymEGDxiUNRJp+cw315plnnrkxLwbP87pDEQpt4bl1evWkS9ktoQoNU33tVZy0leBodh9ZK4WHhyZ0GvwtNPrbiBm1c3K5TirjpNQETc5sN0WtV8+QNsC4MkrYPf0XZkAGeV/pd7i2chNJMUVf8RB3Bb+FJSzqSw0oCDAEj/kfIFDwcVFxE2E9NjO2o7n9jJeGCBsxdMXA8RyS1gSHMrtYW3MJtlfB8iroLyvQdMUg72SpuGWi1PC20vvYaF3GMbUfQ5oscZYTk6fGC+iKgSpUXOmgiRP5VLZnoSvGrMd+HcRknKsq1/MT/w/oFYcJyTAZJY0pTa7O3sCRB49STBap7apBKALHcrm8chNPRR/mT/7kk3zvez9DCIHjOTiei6ZoOJ5LspzC8RwUoeB4DhtbNnBt5xUM50cxVJ3OSDv6ORpvLIh20hZu5lhukAXRLhShYLk248Uprmy/hLARwvEcnhnZytPDz5Ot5GgKNXBF2yWsq1/1qkXhqBlhc8tFc95+sHCUI9m9eNJFVTSS5QkmysOsiG0gbk53ALMsGBjAiYXQThqfFwqijo6jTySqzSt+TRiKSXuwh8PZPaStBIZizjT+6wwtRFN0pJT8z//5aRoamvnEJ/541vP7++FHP1Lo6xN4HjQ1SW64QXLhha/tmIvFIt/85lf4ylf+N8Vigfe//yN8/OOfob7+hCgTMaDWJ5koQnT649iXEdhS0uQJGpHUudDeLzDzYJuSxxphbEKwKg/OBpABKNiShwuCC97psuIFBW2frGYeXiQp33r6nOGYCZe1Sn7eO53Zq0PWgpwNt/kkkynBTlWh7ElUoRA3JSsjkuaiYHhcEGyU6Eo1UiKkw+bWUxb5/pei76/GPThLmYlAkHGQyWo8hL1GIn2y2jju5OtRBgyB9P9mxz8LCb4fC4xtAqdDIkPgpmHBUQjsFzy9roU3v/k+9uz5Lo899kk+//lHCYe/zI03nruZjbMa8ss9lAnAqIrhc5zjnOc3wF133cVLL73Etm3bTvnd2NgYhmEQi8VmPd7Y2MjY2NjMNicLwcd/f/x3Z9smm81SKpVOiVMC+NznPsenP/3pmZ+z2Szt7a9Nd/p5Xj/UeQ1cVNnMg777sbEJywhpJUVRFLjRejOB85hFavZa+b3CH3NM7aMoCjS5rVUxOQDF1Z9GH3sGJb2XUTPPkeYa8g0RlrljNHqnj0Nr8Jr4w/yfk1My9GqHWeAs4pC2n4ySokgBPwEiMkpWpAnKMOucDWfs+dHtLuSayg38yvczppRxFFQ8PJrcFrbrW3nQ91PCxQofPDLAkkwMf2gVCI0pdS+F4l46ZDOc9DGp8WqZVCZIKlMEvI5TjqehsdG67JTYgcnGiyhn91NXzmCZURJ1a0nXLEWVNmW3iCtdNM1HaeG78IwoWm4AKRS01D7U8jieWY9STqKlDiAqKUSgBemvRbHSIJRqC1ahVIVEIUCoiHIKaYSqTtOTx5g6BJ4z0wDODbWjFEdR04dRk7sJTPd8GW+6BF8lQTg3QCHQTAUXiiMI18Jqveqswm1Uj6MKDcutYKjVIsiTLra0aLAtgvv/CaU0iWfG0Aqj6Mk9KMVRygvffsZ9ng19/HkC+7+OsNJILYA2tQNj7FkKy38Hp379K9rnfzlC4NStxey/v5rzPC3wikoSqQfpNwMcyOxAQcVUfVhemUPZ3diexaLoylm7MqWJPDWghbIsM5UfY0/qhelHJEEtwrLoOmp81cmS/GSeHT/aRXoog1AE0vUY2DrIipuW0bxi7g2h55lnnv965sXgef5bIqVkojzMQP4IBSeHKlTqzWa6I0tfM+dpySmwN/UiRSePqfrIVCYpOFmGi/3UmPU0+dtYGlt3zuMFZQhT+iiLIn55YtuSKGJK3ylNNF7O8YZy/oqPR637KcYKdFcWIlSBJz0K5SwZkjzKL4hmgqhCo8XfQWd4MZPlUfxacMaZqSkaIT1KqpIgZ6cJamFM1UfZLc4S0y2vgi70mXMTCDrdBWcsno8TM2qJGbUkKuNEpgu7ilfG8sp0BBeecXnSy5FSkqxMkKpMIZFEjRrqfI2z4i3OxPHcumeMx0kok6y013K5dQ3NRzt4YfxFoi1RhFJVPDRDJVYf512X/i7/cN+f8d17vsklb7iGgBag3l/L0XQfiXKSTCWHJz0qboWFsQXEzChBPUDUnPust1/zcduiN/G9A/dyMHUEECgCltUs4sauawD4We+v+EXfw/hUH0E9wKHUUfrSA7jLXS5sWjfnY71aym6JgfxhFKESMaqiv9QkaWuKgfwhYkZtVZzWNIxQHC03iuVZ1exfANfBFS7BUMOvzRV8nPZgD4ZiMlzsp+gUiJt1tAa6aPBVJ18efPB+Hn30l3z96z8kEDjhZEin4VvfUhgeFrS2SlQVxsbge99TiEZdFi9+9WNzXZe7776T//2//4LJyXHe/e7f4vd//7M0N7eesq2uwOVtku8fEAzmIGRUxVWCcEVJsmgCAvmqO3gqCFEL9rZCLABqArwxgdspCepge3C4SdD1MQ9RAKkB55gru7lbYiiwZVSQqgjChuS6TokYhwEXQkVJMAauJ5kqCYbT0L5IsrYH9mQEBRs6I5I3dHkse+0j+s4LUaEqar5c2DRA5KuiubVB4vu5ggxIZBiogNYvcJbJas7z6wRlCozdCk5TVQgGqF8gOTCuEO2TmJ2QMAW6/l7e+c5rGBr6CL/1W3dw223v4u/+7iuz3vOnRYOz9EGa53XC4OAgf/AHf8BDDz2E79e5fOEVYJompvmbaZ40z38tby6/DR8+njOemnEE32y9lasq55cn7eGREkniXi1dsmf27/z1pBZcx52BfrZr+7DFUWAbNe7PeGv+bay1N+BTA6eYMXz4eFfpQ3w78G+klSQxL86IOkRO5FjqrCAtUqSVJJdWrqbDPX3kAFTr3TeVb6fHWcxefSclSsS8OE+bjzGlVleZLRvrp1QZ5M6FOZa7tazMtRHUF+C3d1EWRwn4T/xRLYg8PuknJOeYgyQl5uCDtB35HrncUTTFQLcLlALNJOtWYNl5okYNqlDRErvwH/w2amEYpIeaHwLpUmm/Hqbrbc+MYYxtQcscwGUJEgWcYnUllx6uZv/qYZAuwi3hqQ3Y9RfMHpKivOxno5r/67l4Zpx0tI3xUBNWw0WYlTTtA78kkO0n7pTRwj2UF9yG1XLVWU87btbT4u9gqNhP0ckjhIIrbWqNerpHtqGUp3DiK2bygJXiOObwI1jNl+EFW8ApolgZpB5B6ueYmHDK+Pp+BG75xD6lRMscwtf3Y/K1q+AcBo/XGtdxSfanyE/l0X069T11mOFz/12ttF2Llto37aYPIzwLpKTQfj0DlFGFRlivznqbqo+iU2CkOEBbsBu/duI6LXdW8Yj8JePK6IxTPiWSlOw8jbkGYkYNQipkDxYY2DnKYG6Knq7FdKzvYHTvKKnBNLVdNSiqgpSSzEiWg48cpra7BiPw670PmGeeeV4582LwPP8tSVTG2Zfejidd/GoQVzocKxyl7BVZU7PxFNFQSlmNMJAWATWIMQcX8UR5hIKTJa7XM14eouKWCKhhLK+MJz3GSyMYisny+AVn3U+d18BKax1Pm49iuCZBGaIoCoyoQ2y0LjtrR+KTSVtTlEXVBX08okLiYXkWwgV0hRqznopbZrjYh6H6cD0H38vyuTSh48octmejKhqtgS4OZnaRt7P4VD+2Z1FyCzT7O2ZFAswFRSgsja7lQGYHaSuBK10MxaA92ENHaG5LrzzpcSS7l8HCUdzphiGKUGj2t7M0unamUdgZx4DCJutyNlqX4eKioiIQjDnjeJ5EUWcXtaqmsLJzPWsu3cif/j+f5Er3XTS0NBMxQhzN9JGzCsTMCK6UmKpJxa3w7OgLXNtx+XldG4DltUv41PqPsHtqP3m7QL2/llV1ywjoAaZKCZ4efp4aM0Z9oOq8rfPX0JsZ4OFjT7K2YeU53cdnYqqUYMvINvYlDuHXfKxvXM1FTesx1dMXaDk7TdktzuRQp8oZhvLDZK0sfm0Izw2wrn4NQlXxbbqC8PcPkcrmKYcchCvRB0cRzc00r732157PK4SgKdBezaXDQ6DMHLNYLPLnf/4Zrrrqem64YXY2965dgqEhWLSoKgQDdHfDwYPw3HOCxYtfnTv4kUce4K//+v/hwIG93HLLHXz2s/8vXV09Z33Opa0ST8ITQ4KpkkBTIBqH7k6JMSRoGAKtAhEbXBUiScGUB0oJlJzEBTxZbQ5nqoCAc8w1zWCo1fzgq9olWQsiJvhU+NtxhTUL4KLDkJdQ8UFtVlIuw/CF8IE1HskKWC7U+aui9m8atxVQJaII0g9KEpQRgXoMvIhEGYfK9RIlKdF3CsSIBKUaH1F8h/ebr4xs0PZXxWklWRWE3ZMmJxoawFktyeyEzLAgEYXlyyW33dZIR8ePuPfe7/LHf/wJjhw5yLe+9SMaG19Zc9FXew5KEqQJ831nXj0vvvgiExMTrF9/wrHmui5PPvkkX/7yl3nwwQexLIt0Oj3LHTw+Pk5TU/XGvqmpia1bt87a7/j4+Mzvjv97/LGTt4lEIqd1Bc/zfxcmJreU7+Dayk3kRJaIF8PP+b0vDmn7+KX5U/q1XlRUlluruanyllmu36eMR9iqP0Onu6DqOLYk+UyKLcWHyNqT1GtNdIUWU+ub7WJfa29AL+g8ZT7KgNpL3KrBwiYvshzRDhCUYfJKlt3adlY5684Y96agsNJZy0pnLQA/9d1DSkmw1FmJQDCpJfnG6jK9sSEiXo7uchO3jC5hZaaeZ1pGaRE5gjJEVqSZVCZ4Q+WNp11NdzrU7FF8vfcgFZOp2FJSbp6ga1E/+iyTvgjEF9Ea6EKxMvgPfBO1NIETXQwI1MxhhF1AzR/DjVRnNaURxQs040S6Uew8wimgOEWkVJBGoJoXXJ4CoeD56in33FEVSE/CiS+HYw8gyskTDdmcMp6/geLKj2NHWhnP7EBzC9hmlN09b0bN9tKox1lYfxlqYRhf/0+Qegi7di1e4NRQfkUoLI6tIWbWMVkexfUcanwNNKtxgpm78HyNJxrDAZ6/oeqCzh5Fn3wBY/gxFCuLNEJUmq+k0nnzTBO9U65x/hhqYQTn5GZzQuAGW1Hzg6iF4TPGkgCouX708edRSuN4wVashovxQm1zen1Ph1W02P3TvYwfnEC61cCMcH2IFTcvp27B2eNXvGArhdWfwhh9Ci25F2mEsRouJlWzgkriqVPuAX2qn4yVpOQWZonBi5xl3FR+C7/y3c8BbS8ApmeyIrWCFZXVKKpK8qUsk0+m8RDYRoX/n733DrPrqs/9P2vtcvqZM2d604zKqFc3uXdsgysYYwLYBgwJEEjACUnIJeRe8sslNwQICSSmhFCMYzAYG2Nj4967LatLI2mk0fR6zpy66/r9sUcjjUaSR7JsYmc+z6PHnn3O3nvtfdpa73rX++3d0stwxwh22SVRHZ8cXwkhSNYnGNubYWxvhrpFUyMFZ5lllv8+vCFDnnvvvZc77riDdDrNRz/6URYvXjz52NjYGFdffTUPP/zwG3HqWf4HoJSip9CJ5zuk9i1XJ4QhTUatQUatYaoP6CCWvSId2Y2MWAO4yiUkIzTH5tIabz9izEPZKyKQOMqayEkKo0kNqSS+8kgYKYbK/ZTd4rQf2wMJMsGupSjzbDE2YGFhYrLaOYmrSx+YcfawUopqpwZdGZRkkYgfxfEdPOWigAXZdgpuLshxEoKMPYxjuIx6e0lpaSomOqL7XMCxieVezbF5+Mqnp9hJ0c2jCZ2W2HzmJ5Yek5AXMxKsqTqDrD2C4ztE9RgxPTnjY41ZQ+wt7CSkRSadybZv0VvsIh2qpSE6fZmdQuHgMFrM8FzvS+zIdJIw45xQt5ITalcGHZOGJOFkmMJokXh1bPKeFkYK9KWHqPmDZezctpVX//0BLvjbD7EusxEpJAtS8yh7ZZJmguZ4IyW3xDO9z3NW41qGrF6Gyn34yqM6XE99pOU140qqImnObTlj2va+wgBZa5yFlVMFw+pImsHiEJlydlIkPhqGisN8d8NP2JnZTdKM4/gum0a2siuzhw8teS/aIZbtCSQg8FGMFkdYN7SRslsmYpjknTw/2vQzcu1Fzmk+HfPs80n296E//xjlkVF8oZDN7VR86A+JVx5753gfJbdEzi6QNBOE9cO7JIQQiIOqdf/v//3nDA3181//dc+0918mE1yppk0VfaNRRX+/IJBUj56+vh6++MXP8tvf3sVpp53Nvfc+zerVMyuiJwWc26I4tUExZsEzvYJ7OwUvrYJcveKsOwW1WxSZmKBag9Nzis0VMFYWxMfAU9A1DjURxbKqY2t/1Aj+AZRdyDrw/IWKWFrQthUS41BIwINrYOVqRbOAqv9eRkWcJQpntcB4USCKCq1LBA7pBOg7BbFvSYof8yne6KPtAm1I4Mcn8oV/3wbHMkR/KjFfABwRFHbrBFES2Gv3v6bNYUX9afDBD/poaWhqYmJSQ/De936IhQuX8eEPv5t3vesMfvSjO1i+fPWb034FxsuC0AMCrV+AAfYKn/JlCvV7dowfD35f/dsLLriADRs2TNn2kY98hMWLF/OXf/mXtLS0YBgGDz30EFdffTUA27Zto6uri9NOOw2A0047jb//+79ncHCQ2tpgcP7AAw+QTCZZunTp5HPuvffeKed54IEHJo8xyywQRJgdTSzEPvZqu/lB9N8YkcPUefV4wuOJ0EMMaL18uvAXxFUCheI582niKhGcw4PQoCRcrmI0PEpe5dAtjYKbY1X6VCrMqV8sy9xVzHMX0q/1EFYRdujb+Enke1T7taT8NBuNV9mub+H9pQ9PFo17LQZlH6YKIRCMiVG21vVQNebTnDNJEKZo2vyseSMf25DCqpjL+oohusUeYirOmdZ5XF6+esb3yBjdgLCzeJXLqVEuo9YgRVnAKI9Sk91NqvU91EdaMPqfRCv24lYsDuIXlEKZSeRBYjC+i9IMyvPegx+qJrr+6wgnj3TyQUE35aC0GOUF76e47BP4semTh256BeWWiwnvvR+KPYAAqWM1notTewqN0sBTHnsLuybHEenaM2gx64hu+QH62CZQQQRBKFpHcfFHcaunr3bThEZDdM7Ufr5bDly6nj31ycoFITEGnsccegFlJPBDaYSTJbLjvxCeRbn9/Ye+yVJDCYlQ3tSenvKC6IwjrELUh18huvl7yPIQSgsjvCcwex+luPQTuOllh93vSHQ+s5vejX2kGiswIga+55PpzrD5t1s47aNrMSJHNoL40XrK86+B+ddMbjO8MprUJ2P69uH6NrrQp63aEwgusa5gmbuSDn0rPj4NViODY3vRpYFb8Bh7OYc0BeHqEJZXJhlNUNxrkesfJ159qHGwOtau9CyzzPImcdzF4FtvvZXrr7+eSy65hG3btvGv//qvfP/73+eDH/wgALZt89hjjx3v087yPwiFIudkp7l7dWngK5+yV5jc5iufLZl1DJZ7ietJIjJG2S2xY3wTujRoiR1+PXBYi6Lw8XwPXykMGSx98VEYMoQudUqujaOc11qBTaVK88nCTezStzMqRqlUlcx3F6EdIF7ZvkXJLWBIk4gWmyZeVZhVtORbac+1syW5mZAfwnNcIqUQJ/aeQG25igHVjSFDCF3yRPoxbM0hPhTB8HVSMk2rPQ/pS+YlFk+G/0shaUsspCnWRsktYkrziOL2TJBCUhmqOaZ9R60hPOVNid8wZYgieYbL/VM6iQrFC8YzPGk+TGdhN7s3j6GPRWjRWrB9h3WDG+hp6+OK+ZcQTUVoW9tKxyM7GO0axQgbWAUbERPsaNpNc3UTV/zlR7jtr/6Fbb94lvTlC+gp9LGyZilhbb9CNFwaJe8U2ZR5iRGrHykkAsGINcBQuY+VlafMyHl+MBE9jKmZlF2LqLH/2suuNfGaHJvi9nTvC+zM7mZx5YJJ4Tdn53mu/yVOaVjDkvT0PISUmSauJxm3x9iZ7cT2bKojlUjp43kGw2KE+3c/wpraFSTDCcLXfRTzzHPx+7ohHEVfvAQRm6El9TDYnsODXY/xdO/zFJwiSTPJWU2ncm7L6TOKG/n1r2/nllu+z1e/+u+ThQEPpLIymAzwPCadwUpBsShoaDj6zFjP8/jxj7/LV77yRSKRKDff/FMuv/y9Rz2hIvIQ3wtxHSqbFDkHXhqQPJ5U9JypuK5b0mhBnymY1wvnCHi+Fro9cIegJgnXLFRUHQcTX0iDxhhsseDFCxQb10KoDINh6PUEF8Z/v718kQFjk0AUwatTQUawGfwrXu8TqhPE/l0Grt/V4LYqVAz0bYLQfZLiH/p4C8Bb8N9gtFIEY7vAeBZCj4PbDioRtEvYgYitxwPXsxwHUQbv3Yq5qw99uJUr13DPPU/xkY9czVVXnce3vvWjae74NwJ9I0R/IsFW+DUKYUHoUYkcVRQ+5Qevz1uU32f/NpFIsHz51IzHWCxGVVXV5PYbb7yRm266iXQ6TTKZ5DOf+QynnXYap556KgAXXXQRS5cu5brrruMf//Ef6e/v54tf/CJ//Md/PBnz8IlPfIJvfetb/MVf/AUf/ehHefjhh/n5z3/OPffc84Zc1yz/s3jWeJJBOcCSCYetUgpDmWzSN/Cq8RJn2OeiUNiU0VUggGlFgbQEXkThaR4oQYVZxZg9RF9p7xQxWKF4ynyUB0L3MKINIZSkW+ui2qtmibsieJIPXVonvwv9hhPstTNyNtf69djCQqEY0PoYMSTzhxMY5SxhHVJ2hM2xbnZWpfgj9/+wMy8Yl1mq/GqavdYppg8fnx6tCxubRq9l+vndiZwjISbqlDTh+Daa45EKNVCKB4YB4RaDTsu+CX0h8OKtaIVehJUNHlMe+vhOvFgjbno55sCzaOUR3PRKhJtHeBZKjyPLQzi1Jx5SCAZAapQX/AHKTGEOPI0C7IazsJsvBGkggDnxBTRGWyl5BQwRjCMiW/8TfeRV3NSiQND1HbTcbiLbbyFfsfC14xwANBM33kp4z6/xvBJ+vAUlNPTxXYE7eLwD36zAjwXxW8pMgNAx+x7Harlov5P5ALx4K15iHnpmC25qMQgJykPL78WpXjN5rOk72oR33o60s7iVy/fHS2S2Et71S/KpxUdd0M61Pfo29hNJhidFX6lJKpoqyHRnGd0zRt3io3fWhrQwdZFmdue3owkdU4ZwlUPeHacu3Excn15AQiCY482djFHxlU9JHydrj2KMRHFyLuG6ELZvoUsDU4aQtTrZvnFy/XmiFdHJGL78UJ5IKkqq6RCFKmaZZZb/Nhx3MfirX/0qX//61/mTP/kTAH7+85/z0Y9+lHK5zI033ni8TzfL/0AEgpAWYdwZm7I9iBQQU4qhZe1RRq0hkkZqchY0ZiTwHY+eQidN0dbD5tDWhBvoLuwi74wjhcD2LJRQ6EInbiQoe0XCWoSINjPhVEOj3V0ybbuvfLryO+gudmJNFMOrCdezILmM0AGCaDpUQ0tkHqcPOlSWKtme2IZWkswbnkutaiApUiAUlm/RRSe7IjtoM9qJqCTeuM2A14cIa1xiXkVTdHpemiFNDPONG6n7yme43M9QuQ/Xt0mFqqmLNB8ic1kd2istmFbc4HHzQX4W+QkKn/7OAr3jQyTrQzS5tbR7CxgqjfBY99OcVL+Gpng988+YSywdpXd9L6Vsifpl9XjzfLK9OVrNZoyFac6+4XIe/Y87OXtBFX69YqQ0SlN8fwd51BpjZc0CRqwB4kZy8n3lKY/R8iD9pe4ZR2IcSFtyDvNTrWwe6WBeRSshzSTvFBgqjXBx27kkzGMTVzeNbCNpJKY4gBNmnJ58H13j3YcUg3Vp0F6xgleGn6HsFUiFI0ip8H0N1zWoj9ayK7uHvblellUtQgiBNm8+Yw0pslaOKt0jdUyt3c89u37HvbsfJGkkqDCTZO1xbt9+F55yubjt/CPuu3v3Tj7/+U9yxRXX8IEPfPSQz1m5UtHaKujomJoZnEjAqacenTi4efN6/uIvPsXLLz/Pddd9nL/+67+noiJ1VMdAgfmkIHyfQA4LkBCeo/jo1T7nnujRXxAYFYrkHYJSURAaUxjARQVok8H+XKBoX6KoPE5OXSHgnGbFzqxkV1ZRFQZLwlBZcEqdYv4b2cdXIIdAlMCrgYOKsKNvgchPJVo/gABd4SwPRGCVBBUDbz54bQp3PlN6O16dwugQiOx/jwgDbWfgBta6Qe8IXMxIcJYpMMBZDaJIYMYqCfwahXWWwj7zyO/ThoYm7rjjYf70Tz/CjTdew9/8zT/wiU987g29ltCTElEkcFkTuLH9mMLYCvrWoGjdW5X/7v3bb3zjG0gpufrqq7Esi4svvph/+7d/m3xc0zR+85vf8MlPfpLTTjuNWCzGDTfcwJe//OXJ58ydO5d77rmHz33uc3zzm9+kubmZ73//+1x88dFlws4yy6HYo+8iruIIBBkxRoe+lVE5TEaO8dPIf9DgNTHPa2eJu5KHQr+lzm9AdzQUYIlAIK7wUwghMKRJzslMOf4rxgvcFv0hUkmqS7UMOP3sTe7G1Rzme4vQJ34Iar16evVu+rRu5nnTJ4sP5gTnFJ40H2Gnvp2cyOJKycaGCPOGoTpvIlQZXUvRP+dElFjIPA/wph+nS+vkl+Fb2aXvwBMudeV6zsidzTJnNZWhKsJaFC/RGoiTbgn0oH9sINGVQym9YvJYXqwRtBDCyQWZvwQip5/ZAtJAH9s8sa2Z0qIbUGYF2vB69GwHZHcAPggdL9qAb8TRxjsPfwN8j/CO2wj1PIhwAsONVh4BoWE3v2MyakGXBgmZAkA4BYyhF/FDVWi5PWj5PQjPxjfiyPIwWmbbaxdqc0tEtv0QY+glhJXFzD+O0iL4iVac1CKslouJ7rgNNzo1bs8PpdFzu5HlQbxDiMFInVL7+4lu+k7gWp4YdXiJVsoL3hfc/0Og5Xaj57tx4y3T4yVyu5HFHvz49NWLR8J3PTzHQxpTx6L7sndd2535sRTszMKujEAImJtcTFPEZqjcS8HNoQudmnAjCytmVpRaCsncxGI2Z15inAyedClZLnpYJxWqCgr+OQ4VjUnC8RDDnSMYIR3X8TAjBgvOnjej3ONZZpnl98dxF4M7Ojq4/PLLJ/9+3/veR01NDVdccQWO4/Dud7/7eJ9ylv9hCCFoirWRzYxScHMTmcEeOSdDhZGmKrR/BtXyy3jKnbYcxpDBEhfHdwhphxaDo3qcpakT2DG+Gcsrk3MyGDJEwkhR9koAzInNR3+dRQZ6i3vYMb4RXZrE9CSuctib78T2yqxKn46cKNwghWRRaiWVoWrmWPN5x6jPqDXEsNeHp3xsYSEQlCliS5t5pQUkjEr8OIiYiaXybNa3cnk+gvTf3GBPpRQ7c1voynfgKw8pNAbKPQyUelhRefKU3KoKswoh5JSlTa7v4iufdGh//EeRIg+GfouBQaPdwu7BddRE0njCplPfQbM/h+pwmm2ZHewe76IpXo+QgoZl9VOq2w6XRggNmOwZ30silGDJu9bStaGDZ2++h8V/dSEjpVEEgogeZqQ8SlSPsLRqPg65Ke8rTWhoUmfEGjgmMViXOtcueje3bPkFu7K78X0fUzM5pX4N75x74bHcdgBCuonrT+1MKqVQqCM6bKvDdSyrPIUn9r5KSFfE9BCeFwTRur6DJiTGxP55u8CdO+7llaENlNwyCSPGaY0n866578DUjv7zMVoe45m+F6gKVU5GYyTMOL35fp7oeZYzm04lZhx6Esa2bT75yQ9RVVXDV7/674ft8FZUwIc/7HPHHZLOToHnQWOj4l3v8ml/7bEhAKVSiW984//j5pu/wbx57fzqV4+wdu30CJCZoG+EyM8lSIXbpsADvVMQ/Ymk7XM+rQ2KlwYFu1PQPVexIwElDaqA9n44NQ+ROQr/OEc2rKpRXL/U56EuQX9BYGqKd7b5vLNNob1BXyNiBCJ3ysD1a4OXVlhnK5xTgiJqogyRn0m04cBBi6YQpYl4gkZB+T2BSKqEmnDuHHR8NfHYm/E1aDMR4nyYx4sQvVWidQvcuQo5DNIFrSvIe3YXABr4NVC+TGGd56OiwAw/VtFolO9857/4f//vS3z5y3+Jpml8/ON/clwubRo+aHvBrzjohkcAT6CNCNy38JrR/27920cffXTK3+FwmG9/+9t8+9vfPuw+ra2t02IgDubcc8/llVdeOR5NnGWWScqUiXsVFPUiRVFgnfEC4zJLzI+jK41urYv/jP4bny78BedYF7JN38RWfSMNRgMVxCiKEq3efFITeTOu7xDVpk6SP2k+jOM7pHfVMtQ3gu141EabybRlGE4MUR8KJvZtYWMoA1PNTKhq9lq5rvhx7g7/giFzgJIokDZaiNWswE/GKeJQiHTRVDoDrEMfIyfG+VHkO+zRO2l25xDNhPGzFq94T5PxhqgTDcxPLqGxeg1OzYkYA89PuFw1hD2Gm1qM3bC/f+GmlmDXnozZ+wQqlEJpocDhW3My5bnvBqmjtDBuenlwHN/DHFmHsDKB4CwN8Cy0/B6kHsNuvfSw128MPk+46168cDUq3gaALHQT2Xk7XsV8vOQh6iEoF+F7aLldyOIASjNAGmjl4UAoHtnwmmJwaO/vCPU8ghdrxqpoRxZ70bMd+GaKwqo/R+kRVOddSCeHr+83lggnFzxmHn7G2kstorD685h9jyHsPF5iDk7NSYd0Eu8/cNCfPHyQ2NFH6xkRg4qmCga2DBCpCE/2WUvZMmbUJFk/s8KDng937BA81i0pTXT540aEC+ecxNktGSyvgKmFSRqVR4xIPJjqcB2r0qfSa+zBbuygvMelpq2WmJHAtV0KwwWa1zSx4Jz59G3sJ9s/TiQZpn5JHem2t0E21CyzvM057mJwMplkYGCAuXP3Ow/PO+88fvOb33DZZZfR3d19vE85y/9A6iMtWF6ZvYWdZO0RpNBImzUsSq2cIs6GtQi60LF9C/MAx7DtWcT0+DSR+GAqQzWcWH0W43aGgXI3Y9YQtm8R0WI0x+ZSH2l5XdfhK4/uQidS6sSNJJ5yKTp5Ct4427LDOMplUcXKyWVwUmjUR1uojwbnfW7oYdKhOhSKgjOOj0/MrEDT+oh5BzhuBURFnGE5RE6MU8/My8crpVDZTHCYitQx5QjnnAzdhZ2YMkxkIoLCVz5j1jDdxd20J/fnbFWH66iPtNBX7Aq6VULgK5+acAN1kf1Ltwa1Pka1YerdJoQAKQWOq4j6MXIyS17kqCDISdaPkP81Ws4wVBph62gHpjSIm3EaP7CK3i/vYfy/tvGJb32UF4fXY7kWi9LtnN9yJvGwZHt2IyW3jCEN9AnXrVIKTRz712pTvIHPnvBHbB/bwbidpyZSxfyKtkPm+h6Icl28jm2ozBgiVYnWvgihB+04sXYVW0c7yDsF4kYMpRR9hQEqQkkWp4+sejZEG2hLLOCp3udpT8UxZPBadOW6aatooS05B6UUt3f8mid7nqUuUktVPE3GynLPrt9hSINL573jqO/DcGmUcTtPW3Lq56syXMFAcZjR8thhxeD/+3//F5s3r+fXv36cRCJ5xPO0ReEv5ioGR8E2oPYsBStmJla9+upLfPKTH6Kvr5ubbvoin/rUn2O+Dme9+YJAWPsdlRjgLlDo2wTGeoF9ruJVCXuWQo8S5MMKoQl8X7HTBeoU5xy5zsgUbBssC2IxkEcYFwgBJ9UpVtcoshaEdYi9kUW2XYjeIjFeFXgNCpUEYyOEHpF488BZofCaFVoPuPNgX9KOCgNKEfmZQJTBXQpum8KrA60bvDaCcZoLclBgnRE4iIGgYFsHyIzAr1SBwPw6e0dyBEIPCox1gRjtLFNYFygOrhdqdAi0veDODVzAfh3IIYEyFLJbwDwVOIX14P2gjsGNLaXkC1/4//B9n7/92z+nqqqa97znA6/vAg+FAL8atB0HbXcAofBnNqb9b8ts/3aWWY6ejBjjwdC9vGw+x6DsZ6+2hwHZS1ZmqPSrGJdZUirNCc4pdGm7ecl4jndZV/GJwud42nyMbZFNRI04c/JtzNEWoIRP3s2hC4P6yP66BD4+/VovYkBjdPcomqkRi8WI+TGyo6OUtpRxznQoyDzdWhcnOmtp9Gde12CFu4ZF+WVs1l/lluh/MCwHg2JyoSJDsp9Wbx4nOUG2tj62BaP/KbRCL168Bbv+DDbW9rNH38UCdzFmUccckyBCDEeGGPVGqCrVsD27kVhVEmfxRyiHkmj9TyGUhWo8m3Dbe5DhA+pGSJ3S4hvxY0F+sPDK2HWnY825GC+1eFr7tdwuhFdGhSuRTh7frAgKrCmFcPK4lRP7TMQe6EMvIe0MXnIe+vB6wEeF93cy/Fgz+thG9JENeMn5KKWmjA+UkcSL1GB2P4gfqUHti59TPnhltOy24P8PJ0z6Lmb/kygjgTJiQdG3Uj9KhpClQbTxTpzGs7Dr1hLacy9K6KhQCmFn0Yq9WE0X4EfqD31swBh8kdCe36AVulHSAKFwao5cENyLt+Im5qCP78KtWDQx2eyjFbpx0svxozMfW+1DCMHcU1vJ9mQZ6RwlnAzhll0812fe6W3Ea2a2KnD9sODhLkE6rGhLBikhwyW4f49Ge2WKxenUUbdtHxVmmoqqNE3vmc+GX28i05PFUqMIKaheUM3C89uJVkZpP/fojTCzzDLL75fjLgafcsop/Pa3v53MKNvHOeecw913381ll112vE85y/9AgqUri2iItpB3cuhSP+RsZ4WRpipcR3+xm5gRRxcGZa+Ej0dTbO6MZkelkKRCaVKhNL7y8ZSLLoxjEkUPxvEdLK9ESIZQEzEKeWccTej4+AyUurH9Misr15I0p1cirjCryNqjVJo1JMwKxuQIBT+P5muMhrKk2Z/bOyZHSPoVVPszz57yevZi3/cbvK2bUYC+YCHmJZehtU6PmTgSWWcUx7eJHzBLL4UkpIUZKvWy4IBidVJoLKlYTTpUw3B5AKV8qsJ1VIfqGbfymJpJwowTVhFMZWKJMhEZoaGhiu3b9iKjoEkNzdfpzvVSFU7TXnkI1wIwVs5yy5bbA7dveiEDxWFyTp6CKHLVX97IL7/4bzx285185avfwvEdYkYUgeCJnie4d8dTjJVzJMw4y6rbWV49H4WiJnyYzLUZEtJMVlQvnfHz/dERyrf8AG/bFpTjIHQDbdFiwh/8CLK6hlMbTmJXdjcv9K/D8R0UkAoluWL+JVPiLw6FEIJL513EYHGYjrGd+Cg0odEYq+fq9ssxNYPefD/rhzbREK2nMhy8vnXRGnzl83TfC5zbcsZhhdvDETdiRPQwBadIKrT/PZN3ikT0EHHj0J3jBx64h+9+95t8+ctfY9WqI3fq5RBEvyMxdgla4wocED8VWN1Q+oPDu0aVUvznf/47X/7yX7B06Up+8pO7mD9/etTG0SIHBSpykBAd1PFD5oI/iy480CZYNASLRgWaC5YheLlRwQo4ZwbnKZfh0UcFzzwjJvKRFeeeq1izRnGkrzRdclxyiF8LvQOMrQJvrkJFQN8k0AaD3Fw5GAiNxksSYSnYd9t9MDYLtO0gPEHod4rQ4wrrNChfqoj+UqJvBTQFnsBtU1iXBPdaDkPkJxJjmwgq8OkSZ7Gi+CEfdRTi+oGIPET/Q6JvFfjp4L0UekSidyoKf+zjH3BcUQrav6835jUp5ADIHoEoKbSOYLxpneEHmcivg7/+679nZGSYz372Y6RSac4//5LXd8CDEWCfroh2CGQf+LUgLNC6gnvuLn3ruoJhtn87yyxHi4XFj6I3s858iSqvmhq/jiE5SJfWiY+PJjUSqoKlzgriKoGpTDpFByW3QIPWxHvLQR73WGyI7e5G8m4WX/lE9Rht8UWkD1gJKJHU2HVsy22jTm/EjAaTsymzkoFQH+UOh4eW3M94yyhVfg01Vh0+PvIoloiYmKx2T6Y238D9obvZYmxAAWdY53GRdRlVfg3GwLNEt/wHwhlH6VH8zHq80ceBpdAGOjpaQYAPKgIGJgWZJ64nGbOGGCh1U3BzDCWr0CreAwQxZPV2D0v9+ikrupQRozzv3ZTbLgffBS3E4X7IhVNASQOn+gT0sS1IayQ4hhbGj9bhxQJh3Ox5iEjHfyGcXOAe7n0MWRrGjxxUwFgIFIJ+N0PnyFMUnRwxI0EqVo2KShIkMdPLiEgNYecQvh20UUi8ikVo5dHAwXs4967vItwiShqYQy8iCz2ABN8PisTt/DlO7clB4TTPwhx6EVHqQ+lx7IazKbf/wWHvhT66gejm7yDcIl6kDuHbhPbejywNUVj1Z8F9PBSaSXn+NUQ3fw99bONkDrIXa6I8/71HnRe8j6q5VZzwvtV0vdTNWNcYsaoYTasaaV7VNOOx5vph8JSgMjxRa0BATRSGR2HTiGBx+vX//ibrk5xy/UkM7xzByltEKiJUzatCN4/tumeZZZbfP8ddDP7c5z7H008/fcjHzj33XO6++25+/OMfH+/TzvI/lLAWnSyEdiiEECyuWIUuDIbL/djKIqRFmBtdRGO09ajPJ4VEiuOXq2tIg5AWpuQV8JRH0c0Tmig+plCkjCqKbp6e4p5DisGNkVaGy330OnvYE9pNjhy6JxiNZ9hcsZW5/nwq/TTjcpy8GOfS8tVUzrCkuz82SvkHN+Pt2Y2sa0AAzovP4ff2EPn0TfTWlXkq9Cg7tK0kVYqTndM4yT5tMpPtQILiGdM7NAoVFGA7qLOjSZ3GaOvka/Tq0CZ+tuUH9OT7MaTB6toVXDL3PJaEV/Cs+SRhN0JbWz0jmQy7h3uo9NJ0e/2kQ5W8e8G7SIdTh7zGjSNb6M33s7ByPprQKLklyp5Fb36AhfXL+PrXv8tnPvMRmpvn8Gd/9jcAvDy4nl923EfGKiGFYLg8wv27+xgo9nPtwndTGzlM4Yk3AKUU1q9+jrt+HVrrPKQsIga34T/yPAw/jvHxz0DTeVy/9FpOqT+RvbkedKmzJN1Oc2JmDoaEGeH8tjWkopKslSMdTnNe0/ksSAUCe8bKUnCKNMTqpuyXNBOMlEcZt3NHLQY3xOpYXrWYp3tfQApJwoiTtccZKg7zjtZzJ0XnA9myZQOf/vQNXHTRZdx446df8xzmEwJ9l8BZpCbdpSILoWckzile4A49iPHxLJ///Ce4++5fcuONn+Zv/uYfXpcb+EC8OQp9hwzsHPs+DvsclVVACWJSUNYUA3PBaQTDhpwGYxpkxcSuRxg3KAW//KXg4YcFySREo4rt22H3bolSPieeeIyDBRW4b+WAQEVVEG1wDLdFZMB8ViKHwGsMRFW9O8j/VTHADxy+YrtCGxTIIYVfGwj72k7ABXeOwl2mEHkIPSVwlyjyf+phvBpkBHuNCme1CrKCFYTvkBgbgogGIiBKCuNVQSQmKd7oH3rVpwrapnQ4VO0h4xWBvg3cdjUZ5+BXTbi8XxCTQjSAV69QUYnIBm1SYXDWKAwvEArc1cHf9onqdffYhBD84z/+G6Ojw3zsY9fy85/fz0knnfraOx4F9ikKkYHQwwJ9pwgc7osVpfdNxFu8hZnt384yy9Gx2VjPJmM9852FhCdKLZ9pn8eDZomiLHCKfQZpvxodnaKbZ9DtZaCvmkf776U6Xs+S+lXEjSSVoRpOqjmbcXsMhU/cqJiy2m8fJ4+dwRPOY4zWDpG2anB0m+HkAI0jzdTsbESNuZh17ejoPBy6j0o/zcXW5dOOs48RMcwuvQOABe6iyf5zo9/MR0qfJF/OoVAk9i0zccuEd90BnkWhahEP1Ozgiao+Sv4Gov46sqqSMmXKvg2aT0mWsYVNtVODEAIhJMPlAfLuOBVGGm1C+HV9h4FSD7WRpilu6EmkHvw7An6sEWWmUELDbjoftzRIwS+jWSO4sRZKmkmiPBy0Xwjc9ETBSt8jVHgQLbcbN7UkOI9SiGI/e6TPencYtxRB06P8LvQbNsU2IsIaYSIsb67kD3e3UOtE0ZwcSosGBeCUF8Q4HKnYshbCS84ntOceZGkQX48jnXGEnUX4DsbAM5h9j2G3XExp6R9hFS5FlodRoTRefM4RO0NmzyMIJ4dbGRgvFOAbCYzRDRijG7FjwcyriE4vcOdWrSK/5q8wBl9ElgfwI/U4dafgR1+fEaRyTiWVcyqnOaxnStkVaPhQKAfLvSLB50MKcI6+JvJhMcLGlKi9WWaZ5a3NcReDzznnHM455/D+pPPOO4/zzjvveJ92llkOS0iLsKzyREpuAce3ieix14yHmAlFfDbrNhnpkfI1lrom0aMMoZRCozk2ly2ZdRS9PJ7y0JTC8S1ieoKwHsVzPTLW8CH3T5op5lcu5zn7u5TsAnESqAoJ6QKakBRFAU1qxP0kl9iXc771zhm3zX35Rfw9u9EWLkFM5CqLVCXe1s3s3HYXP5y3iT6th6SqoJsuNhrr6JF7eU/5D6ZUTgZImdWEZIiimye2r9CF72L7Fq2xI0cVbB7Zxo823UbJLVMTqcL2bB7Y8yjDpWGuXnM1OTFOh7EFL+5RtVZnXt8prB4+jTqtnmVVi2mKH77TkrcLCATaRIxERI8Q1sJk9Cw9uV7+6D1foKdnL//wD1+isbGF9117HY90PYHjOaysXonllSh5RbLWOMOFMhVGw+Sx3gzUQD/upg3IhiY0WcAYeB7hFnHTYZwde0g+++/oK/soLbqB5dWLWV59dNZCx7fZNPYiGXuY5TULkUiKbo6eUgfVkTSpUDWpUAUxI8q4lZ8i0mbtceJmnArzgLXhJdB3Awq81gmB7xAIIbh64eV4ymfjyFZ68/3EjChnNp/K5fOnFzLq6+vhQx+6kjlz5vKtb/1oRh1pY6NAVewXgoFgCX4faLtFIOQdwIYNr/BHf/QBRkaG+N73buPSS9/zmuc4GuxTFeYroO8Arw7wQOsT+FUK40VB+C7Be2wYjsMjCwVdJhAJHLv1pqJ6Bq7dnh544QVJfb2icmJuqbISdu5UPPKIYPXqoJDeUVGGyC8E5gsSUQicte48RekDPt7MV+BiPi0I3y3QdoHWCWJM4NcDVpCdK7PgVwAaeE0gM4FTWGQDV7DsBUICNarQdsFEMWz0TVA6Bbzm6UK37A/29RrVpKirIuA1KIxNAjkYxDYciL4dQg9I9N1BlIO9RmFdNDW+QeshGIEZB5xTAxVW6LsE1gFpg14r2Cf6hB4XqHwgBssRgTcfCjd6uPsTdI4ZkQPjVYHWB37c4Dtf/Cnv//ylXH/9ldx//3O0tLTtf7IdPF/FOXzO8ZGQYF2isNcqtF5QoYmIjuPe23zzme3fzvJ2pEiRrcZGcmKcar+Ghe5SjJmGkr8G/bIHH29SCIbAHNDqz2Wn7CAvcqRIk/dybLBexC/6VK2rZXR8jJHkMMP9g5y3+l2EjQia0KgMVR/hbLBWnsG56y/hpfnPMFw9gO7ptPW1E345RsJIUh1dHoSrAr2ym8dDD3KmfR4xNXW1kULxmPkA94TvZEwOoxTE3TgX5d/F+d47J+tcxNXU7Bst34VW7MOJNfGzpg38rnYHSTdEzE4yGBolq8rcF/41zfFGFhTnMSyGEUKQk+NYqoxC4SkXKeSkEAxMxN8psvbIocXgGeBHarGaziPceQeWM8awX0KWR0FItqeaGM+8yEpPo8Iaxk0dsDpNaripRejD64McY6mhFXpx3XF2tZ6FXugjXRrluXnwYs0LCFsQGjcpGA73V3RiLbW46WVFrHYtmhYJYhwKeynPueTwDlwAIbBaLiLUeVewjzWG8MrB62MkkKUhopu/i91wFuiByOzHZxDdpxR6die+mZq6XY/gDhco3fITygMTm5avwrzwEmT9fqHX812yZhTRfC5xI3lU+bsz4VhXnbYWR+h8cheikEVoEtVUTXnJfCDG3CMnps0yyyz/g3lTuueXXnop3//+92loeH2zZjPl29/+Nl/96lfp7+9n1apV/Ou//iunnHLKYZ9/++238zd/8zfs3r2b9vZ2/t//+3+8613velPaOsubR0SPEeEw6tNR0iddfhTJsltz8AlWc7d5BjeUKmjwp3+sfOXTX+qir7gX27NImVU0xdpImpU0RttwfZet2XV4vouHS8JIkQ7VIoTAUy7mETpMfbFettVsZ669AFdIkFBDHQUvT6Wd5urh9xNVMWrNBnRDn3F9A7+3G3RjUggGEFIiwmEeiT1Ev+axxF0+KfyOyCGeCD3MWucMmr2pruu4kaQtvohd+a2MWkOTTagJN9Aca5vyXMd32Da6k6HSMHEjzrN9L5B3Ciyq3J9FFTfjbB7ZznnDZ/IZ/pJt+ibG5CgpP83i5DLM5MzE/ppoFUIIbM/G1EzG7Rw7xnaxI7ubhlgd39vwE6768Ifo7u7i85//BEbMpK9ikHQ4jUBMOtNTRhVbxzoYKA7N2HF7PFClItgWoqoaLbsO4RXxIzUIzcMr5XBFJaG+x7Ebz8FLHl20B8BIeYCMPUIqVIWcELkNaTJmD9Nb7CIVqqYhVseqmuU80f0MnvKIGzGy9jjjdo7zW84iOuEK1tdD5FcSrS/IUPVqFeXLFM7aQ7tRU6EKPr7iOvbmesja46TDlTTG6qd1lHO5ca677kqEEPzkJ3cRj88smFSZwMFFmlXwTx0wDlZKccst3+dLX7qJ9vYl3Hrrb2hrO3TsyOvBmweFGzzC90mMzQIxRCAI7xVoneDNV1S78P5XYVFJce87wJeQMBWjZcFJ9UeOeQDo7xfk84qmg8zr6TT09QlyOUiljq7doUcEoUdE4HBtBiyFvl0Q+akk/zl/Rg5hbRdEbpfgK5xVBBm+e0EUBEIETlMU+I0gR4N/bruifLEi+mOJzAAhgdekQAUCb2DfBXEER4woBfET6iAhXUUCl7IoHdTOziD+QY4K/JogWiT8W4nWpyj8kT8pnu5zMaOY8n0rHIGfPKhBAkrXKvx6MJ8RiCI4q4JCce4ykAPB5ARGcM3qKHN35RBE/01irgc8gQpBuCXOT//0V5zz+RO56aY/5Gc/uw+pJKHHBOYTIshOTirsMxXWuWrGxeoORFWCO30xy9uON7t/O8ssx5O92m5+HPkee/Rd+Pjo6CxzVnFd8eOk1Ov/AEdVPHBdHhTHINFYa5+JqUy69F1k3FG0rM6J609mjj0XERXYBZshb4CtWzaxeuVJMzqfETK4NPlumm+fR6FpnEQ4gT+g2JTZiHGCBo3O5HMrVZoB2c+oHCbmTRWDt+ubuSNyG7rSmGO1MVIeoE/v4kfmd8gPZjhFP4u2+MLpwp3UUUKjN5zlmfRe6qw4VXYU4VlUlSrpr4nSL3YxnAzRMN5AY6EJUzcpUWS308FCcxmmjDBY7pl2bcHPyesTHsvz3oNnJsns/Cm+XcJLzmeo/iTsyqVYzhgddoHmQ63ikyG8UBLPL6KP70WzMowl28hH64lrUVx7hPXmCxhunIgVw1UOFX4VcS/JupYeXunWWTuygZAeR2kRrMbzKM/Zb05RKHJiHIHY77IG3PRyrOYLiGz9EZozhkKCHkLpMVAOWqEbY/gVnPqjKNwrBF60Dn1085TN3tg4uQf3UC7b0LIElMJ+5AG8rt1EPvVZZKqSwVIvnbmt5N0cAkHSTLEguYyUeYyZUseJbG8WnniVulGLETOK4Sn0TXsp9BdYeeWJrKx5G8zGzjLLLG8Ib8q3w+OPP06pVHrtJx4Hfvazn3HTTTdx8803s3btWv75n/+Ziy++mG3btlFbOz0r9emnn+YP/uAP+MpXvsJll13GrbfeylVXXcXLL7/M8uXL35Q2z/LWQqH4VTjHTs1mgWdiIHBQ7NAcfhnO8cfF1BRnrFKKHeOb2JPvCGb7hc7e4k5G7AGWV55MyqyiLbGQdKiGF4Yfx/VtKs1gyVjZK+Ir/4iF6nJiHF8oDG2/6qJQuJbNbqeD3mwnutLpEbtoic9n/gH5vEdCVKRQrjNtyZLtF+loyVDlz5tynWm/mm36Jrq03dPEYIA58QVUhNKMlAdwlUvSSFETbphS8C9jZbll8+1sGtmGpzyUUuzJ7WV+xVQhM6KHcX2XodIIy1nCCnfNa17PoVhWtZhFlQvYPLKNpJlg8+g2hkqjJIw4TfFGnut/md5CP3/2pf9NNjvGn//JH3LeX15L1dJmYL8V0PJsDKkTNd6EUNUDkLV1iFQaf6gfqUaDDjICL1tES4SRdY2I8g60fNcxicElrxCc5wC3s6scbK/Mnvx24kYFteEGrll4BaZm8PLAenoL/STMOJfPu5h3tJ4b7N8TFAUTuWAZPwK0XojeJslXeXiHqTkhhGBO8vAumFKpxA03vJvu7j3ceecj1NfPXIh3TlLoOwLxTUUJog56QaXAXRgI1L7v86Uv/Rk/+MG3ueGGP+Jv//arhMNHWNb4OnGXQ9nx0XZqiMqgCJkcAeGCqhOE5ipiJiztFLzao9jbosjasKJacfYhnK8HE4spdF1iWYoDL6NUCiIjjvrS7EDAVMlA/AMgHBRu0zsFegczcrYarwrEOLhLJu7DKgWGQN8J+CAVePWBaKyPS0Q5EI1VzEelwV4L+g4F4Qkhfxy0XQK/SU0e81D4teBXKrThwG28D20YvMogguJAzCcEcljgLt4f5eEnFcamIOfYWRW8Bs4KReiRicJ1E8eVg4FL1ll9iNcpDNbFQYE5bAKXsoLwbwShR4IICQR4dYLye9Whj3EYIv8pCP82EJMRgB44f+v0Sr7x99/lfR++hB/+8GY+2fTHRH4pUJHgmmQWIrcHRQ3Ll721s37fSN7M/u0ssxxPXFxui/yITn0H8912DExKosjL5vOk/Wo+WPro6z7HcncVdX49ndoO5nhz0dEnCq/BlaX3scJZwx59J891PIbb4VJFLUILvlzNsEmZAv1d/bBy5udsWzsHz/Hofrkbq2Bjhyyc0wrYFzlUiNTk8/IiT0RFiPvTbZPrjBcpigLt7iJ6y104vk2T18Ke8G52RXZQPVpNRItOFnMuigLrjBfZW9NJelEZPb+TnF6muRRU8hJ2Fj/WQEgLEyLMif6pRGoiJMcSGEWNcZllNJ1huX4y484YQ1YvlleejI0re0U0oU3JSD4QYWWQpX6UHsePNR0+IkEajDWcwYtamaiSyFAFCIkgMG2M+w6ZWB3p/F68xEQ/3nfJFPaAm6MQrcXR5xN1CoSEhm5l8MMGVixGXssTtmJ4ykUTOlIIon6UobDOuvbzCVckWJVYioo14SXnTxaO69I6uS90Nx3GFlCCJc5yLrGumCzuZ7dcTHTbj4L1NFowXpDlQUDghmvRcruPTgwG7Iaz0Uc3Iwu9+NE68GycV17EHgVx8skIMzAxiMoqvI6tuK+8RPGMNWzJvIKrHGJ6AoVizBpm89jLnFB1BmH9jc9BUkrh93bj7dqBEAJtwSJkfQPdr/bijhdZvbKK7rxgoCAQSZOG8VHO0gaJ6G+eSWWWWWZ5a/G2myr6+te/zsc//nE+8pGPAHDzzTdzzz338IMf/IC/+qu/mvb8b37zm1xyySV8/vOfB+Dv/u7veOCBB/jWt77FzTff/Ka2fZa3Bn3So0OzafINjAlFwEDQ5Ovs1Gz6pEfjAe7gvDtOb3E3YS1KZKKzoFSCMXuYvfmdVFSmEUKQNCtZlV7Ltux6ss4oCoUhTVrjC2iIzjlse2r9OgwMiqJAdGLdfckt0K96aXcWUmM2IBCU3CJ78h2kzGqqw3WHPd4+9NUnYD/xCP6eTmRzCyDwe7uRsQpC6QjFg2yVPj4Cga4ObSMTQpAyq444g/7bzod5eXAD8ypaiehhHN+lY2wnW0c7mFsxZzKCwfFdhOCwhcRmSkQP8+Fl7+fezge5b/fDjFlZ2pIttFfOozZSja98to11sG54I//6rz/iIx+5mke++nNWf/YSYidGSZhxbN9h93gX7ZXzWJA6esH19SBicYzz34H9i1uxR4toIfAcB3yf8OntyLAESxw5l+0ImDIUmGWVjxCSkltgqNxH0c0T1iJszb5CT6GTZZUn8oHFV3Nx63mM23nS4RQVof0DLOPVCRFtyX4RzWsFfSuYLwlKC45ebLJtmz/8w/fz6qsvcdttv2Xx4qObvLNOV2i7BOZLgBs0SlVA6QofvzE4/mc/eyN33fVz/uEfvsX11//hUbfxqHEh/IBEKIW7PHDd+tUEQvWuIEd3ToOidhjeaSq2NStak7CmVhGbgXtz/nyYN0+xfbugrU0RCsH4OGSzgvPO86eKwQpEYSIX9zBvH1EGWRT40YNevxDgKkRhwqH7GohxQN//PBULxHoVUXhNQT5v6GkJKFRc4TYGh43+RCLz4CxUiJJA654YA7ugFcCuFYR+B8ZLAvsUhXPSQbEgcbDOU0R+GbivVXKiLR7YV6hpGbd6p8CvUFPcvkQATyD7FawKNnltUHqvT/gugb594lwpQfkK/8jiuM5kr8x4SRC6R6ASCm9R0CatCyL/FcRazKQOqOyH8H0SBEHutACsoCChsRXOu/YCbrjhE/z93/81l7/rEubHF+BPjBm9imB/80mBddbUGIxZZpnl+DDeN87eV7oZ3TNGKB6icUUDjcsbkPrxXXp+KDq1HXTqO2j15mJMLOGIqCi1Xj3rjBe5vHw1ydf5wa/ya/iD4ke4PXILnXoHPj5JleKd5as4yTkVDY0l7goGR/vYQwdCHmCkQCE1gT/uHdU5NV1j4bkLaD2phWKmhBk1GG7ew1Ohx/Bcj6Is0C97GBfjvNO68pAO6KzIYCiDopvH9i0iWgQQaErDM4JlH/2lvdRHWxgTo/xn9N8mi8mxcByZG2RcjGPZPlFPQ4VSuJXLsNiCpjRSKo2hG3g14Pk+vdoQ81WKSD5GSIvQFJ1Lb3E3BXccAF0YzIktoOpgMdh3CXfehdn7CNIaQ+kRnKqVlNo/iAofrq+tUELDNSKYB0UcKM2k3HIx7PwV+ugGkAYjUmNYE6T0EF6kBj2/h7xuYqNTVRqlN5Qi5kaIOyEGwkVCrokpw0ihUZJFQp5OS2aMWC4DZgMqOW9SCB6U/fxH9Nt0a13U+vUoFE+GHqZX6+bThc+TUpV48RaU1JHKD+6v8kHogYjt5uEIdWMOh1N/OuXyCKG996NntoI0sAsJvOpaNHP/8YSuI3QDf+8e+ks1WH6ZSrN60iCTMqsYs4cYsvpo0Y//irEDUb6P/du7sR+6H3LjgVM8VYn5zsvJ9iYxoyYRXdCegvaUAiQju0DlCm9ou2aZZZa3Nm+KGNza2ophHJ/8qSNh2zYvvfQSX/jCFya3SSm58MILeeaZZw65zzPPPMNNN900ZdvFF1/MnXfeedjzWJaFZVmTf4+Pj7++hs/ylsISClcozINW+5oKXKmwxFTxI+dksH2LSnO/OCaEIKJFydgjeMpFF8HnozpcT9KoZMwewlMeCaOCuF5xRCdvu7uEFc4aXjSeodqvJeSH6HH2UF1Os6Zw0uRS5YgepWQVGLEGZiQGa3PaCL//Ouy7fhHMQgOipo74ZVdxSrSXO+XPqPTTRIji49OldVLr17PIXfqaxz4UOTvPusEN1EaqiOiB+mRInaVVi3hhYB07M7tZkJqL7TnsznUxJ9HM0qqFx3SuA6mKpLlu6fsouiVCmsnSqkWTjmcpJKY06c71YZom3/vez7j+hit5/mv3oP2xIr2mBSkkTfEGzm46nYJTJBV6c1UT89wLkeEI/t03Q9cr6FUJIie0E1rSiD6+Ay/WjJs+tuDR6nA9cT1Jxh4lpicYsQYoe0VMGaY20kREizFmD7Mzt5nV6dOpiqSpikwvUCgzBGLfQW9jZSrkyMwEwwOxbZvPfOYGnnjiIX70ozs5+eTTZrajR1CULQREofhhH2etQNurUCa4SxReMxSLBT72sWt5+ulH+c53buWyy64+qvYdK3IEtF6BVxfcKxUKxEmVBDkmEOMKlYa4qTh9geKkJUd330wTPvABn5/+VNLZCa4L0ajgrLN8Lrhg/7H0bRB6UKLtBkywT1RY75geUaDiQUE2rQO8A8bTIgsqJvDrZ1a1xJ8DPC7APaBImg9IgX2Oj9Yp0AYUfoPCjwbubXwwXg3+K8rgrFT4NUHOrxwE3w4ek1nQ+sHYJCgPQPmKqffMOj8Qfc0nBHIEvDlgnx1k3k5rZwr0/oM2egBqWv61fbrCWRy4z1HgzfVnJODuw3hRIHzw9u2jBznI+laBsVFgnf/ar72+RSALE1nL+8b8YVDlIJcYT/HFL/5fHn3wd3zikRu595qH0Q5Qy/006HtFUNBvVgw+JG9W/3aWtx+Z7gyv/HI9+aE84USI/FCB4Z3D5AfzLHrHISIIjjNlUcLBxlRTY8hCKsS4zFIWpdctBgOscNcwL99Oh74VG5s5Xhv1/lSnYkO8ha7BXZTNAiEvgkJhh8owolGXOLaivKF4iFA8uLarSx+kQIG7I79kRA6ioZH0U2zTN/Go+TvOsy/GVx7jTgalfJrNOTxrPomt7Ilui8DDwxMetU4dmjAoeUUAHg39jo3GOha4izExUULRk6iiS77ERjPKEmshRrSVrF4Goaj3G8iL3GQxurIsU9KKrCqdCAT9zkUVK6kNN5J1RoCg7saBIuTkNXY/SHjX7fhmCjcxF+EWMPueQJaGcOrWIjwbL96CU7USV9NwcYnocRJmijFrGMM0EUKglKLg5ojrScL1Z5NPLMAYehnhZOkMx0l0/hrNV0ihoWlRItYIRcNgYWY3VrSBfj1M03g7u1IdOKE8ERUjp40zqvVxxh6HszY8TlRGiY90oLrup7joepz603nReJa92h4Wu8smY0RSfiUd+lZeMZ/nPOtitNwevH1ZwEqBDKH0CMItIjwbt+IwS8uOhJBYc6+kWH8q+ey2QGwe3Y7ofXDaU5XrQLKCopNDF8aU1yD4f0HZLR59G44Sb9N67N/+BhGPIRZNFL7r78X+9R2El7yXMWuqQUf5Ch+FGT1+Rc9nmWWWtx9vihi8cePGN+M0DA8P43kedXVTha66ujq2bt16yH36+/sP+fz+/oNHfPv5yle+wv/5P//n9Td4lrck9Z5Gja8zID1a/f2z6oPSo8rXqfemVmDShI5AolBTYhU85WFKc1rxAVMLUXcUBSJ0dD5U/BhV4Rpekc9jDAuWZheTKCcwlGRQ66EqXIcuDQQC35+5y8I48RT0xUvxOneCUsi2echEkvOscbq03WwwXsbDQwHVfg3vLX2ICpWa8fEPxPZsHN8hbkxVVVqTLXTlenCVy/bMTgyhsyA1l/ctvIqY8dqOgH2d3KKbQ5cGKbNqSuzBPuqjQUbzwTmftu+QCgdCfjQa5ae3/IbPfObD3Psvv+Kaz32MwkqDx3ue4eG9T1IfreHitvO5euFlb5ooLKTEOP0sOHE14fXfIZRZhyAHuW14sUZKi29AHaODOqRFWFp5Ah3ZDQxbAxScHGEtSmWohqgWBwExPUHWHqXo5Ynphw409esBLxCfJrUmBcISeM1HV+Y4n8/xsY9dy7PPPs6///tPOeecC197pzJBHupzAlEIBD/rPB93aZDP6qza/9TR0RGuv/5Ktm3bzE9+8mvOOuv8o2rf60EZBMv4HVAiKHpmbBBQJHhPKtA7g8gE5yiF4H00N8PnPufT0QH5vKCuzqe1df+qUm0HRL8vg9zYagVlCN8t0foVhY/7U/NjJVjnKKK7JdpOhZ8OcnZlVmCd7ePNoJYLgL1GYTyt0LcJ/NrguuSQwJursE9UxF4U+PVqSpQDGqioQMUUerfAbVJ4DQoVAa1foKrBXb1/AkIOQegxiX2KF7wfD7gG+3SFfWpwrYThcLGM9qkKY0sgjvrVgAv6nonXY9n010OlwTllBq9TKShoqA2BHwNnuUJmgmJyUwjGnYijGHf6CRDl4D7tuxfCBnRwWxWxWJxv/v33efeHL+An63/Ah0/7+P7TFYI2qNe3AONtzZvVv53l7cfu5/ZQGC5QPa9qUmAqZkp0vdxN46pGknVHGRB+lDR6LaT8NMNykDp/f+b1sBxkjtdG2j9ysbajIabirHYOn/s7b2473dv20mvtxkvmEELCoEZ6sIl573p9jkurYGP3utRWNVDVVsVSVlChUiRUkj7Zw28id9BQbCQ3NjIhBitkzqO2uYY9od0Iz6Mgi4wbWVqsVtoLi7H9MrXhRnx8XjaeJ+WnMSfc1QJBo1jAkJYlFalmT9TGZSdRFeeS0pWEVIgnQ48yIPqQSBQ+q52TWGufOdlmKSRV4VqqwkeYQfRszJ6HUXoUPxaI60ozUaUhIjtvx+x/Ct+sIB/SeWBxmCcXpChrPgvcRZzgn0xkOMqYPYQmdDzlEtaizEsuQZM6XmoRXmoRAJnhx6FiLnW9zyJLgwhnHOGW0XyPcDlLQ2GAATPMouxSrNE6tlRspt/oI0yE0/ZGuO5lCysxj2S0DVeG0XKdRHbchlu5lD3RXYRVeEqetI6OhkavnMhNlhoqlMY1U+i53eDZCL8EQuBWLMBNHtv7Y7g8wI7cRvLeOMpThFeGqd9dRV33XrSGRsCHgV3IVBxj5RpiRp5hq39KdJ5SCpR6UyIi3FdfQTkWWs28yW2ioQlvy0ZqxBCDejW5wRyx6hjKU2T7xolXxahdWPOGt22WWWZ563LcxeDh4WEKhQKtrfszQzdt2sQ//dM/USgUuOqqq/jABz5wvE/7pvKFL3xhipt4fHyclpYZjnpnecsTQfIOK8Zt4XE6pE0CSQ4fE8HFVowIEh/FuPAJKUGlWU1MjzPujFFhBJEQju9g+WVaYvMQSLL2KAU3hy50KkM1GPLoZnKTqoJrS9ezOLOQvYWd6Mog442iaRp5dxwsQZVZCygqQtOdm0dCxOLoy1dN2ZZQSf6w8KdsMTbQK/cSUVGWuauo8V/bcXw4UqEKGmJ17MrumRIxMFIeY0nVQv5wxfWU3BIhLcS8VCuGfG03lue7dOQ20V/ci+1bSCFJGWkWp1YTN6aKtatql/N4zzN05bppijcAgt58H0kzweqaFZPPC4VC/Pu/38Kf/MWN/Oxr3yVxcRs1F7Vj6gZduW5+uvV2Cm6BT636KLp87a/YETFMWZSo8mumVNw+akIJyid+DjezBVnoRelR3KoVKPP1idIps4oTq8+iO7+LjeolUmbVlIKGAoFCBZ3iw2CvVphPBIXFvPqJzOCBQOCzT565qDk0NMB1111JZ+cObr31Hk4//ZzX3kkF2aehRwUqHhQHM14V6Ls0Cjd6HGhk7+nZywc+cCmjoyP88pcPsnLlCTNu2/FApcFe6hN6UuLHFXYz5Ichtl1ARCFGgwza0rX+61q2b5qwbBkcypFtPiGQYwJ30X7xUFUojPUCfVuQa3wgzgmKovIJ/VagDQr8lKJ8vk/5gulOcICMBS8OCHaMCWKGYlWNYnk1FG/0CT0QuF4BrHP8wI1cFYji+i4ZuIL2HdMJ2l++XKHtCpy/YiAQL/0KcBdMPb9fDfo2hdYVvO+mIYHXGM85JynKA4GorG8LJja8Bii9z+dYay2JEYj9UKJvJZgBUODVK7xahZ476JotQCq8+iMc8AD8JoXfDLJnwoGvK3AFoqywLlSoiTHiKRedyZUrruafX/0nrl/yUWSFhsiD3iewzlBM+1pXQWE74YBXx4yKBL4d+J/Qv53lzcG1XEb2jBFNRaY4DSMVYUZ3jzLeN/6Gi8FVqppz7Hfwm/AvKIoCMRUnI8eIqCgXWpeiv4lJgrqpc8Z559DxTCPdu7rwHUV1RS3zLppH5ZxjL2TXva6HHY/tJD+a576rfwNljcrKKuI1wQxXg9/EJu1VHrbupd1uJ2FUBNHqw1v4YJdPKRrm2cpBXq3xWFVczUnjp+KWLcJahMZYKwo1GZF2IAJBhAjvLv3BhBM4T61fxxxvLj4+K9w1bDRexcGm3VvMavtkogf9ACkUrxgv8LT5GP2yl2Z/Dqdb57LCXY1AINwC0s7iH9DHE24JY+RVRHkIDfDcPD9YUuDpdJZ4cQlaYhHPmU+ys3I718mP0ZALUXBzRPQYdeEmkmZq2j2Magn6KufT0vccybFt2EYc4SsMN0PWiLE1NYd4rI3K5AKaLIM1fSczog3RIGo4c93tRPQUoehcwkJDFntAeWjZDvTRjaRTVdjCmnI+hcLDnTSWuKlFeLFGZHkUq+FspDUKykNYY1hzr4KDTSHKR8tuRysO4psJ3MqlcFAR7qKbZ2v2FSyvRMKsQCApJMbpescyIvdvo67vKWIVfeiNCtHajue/SIN+Ov0yQtYJVsoFRpNx4kYFNaE3voCoKhYQ+vRxj5Ia1QmHRasW0vlUJ2N7MggJiboEiy9aRLTyjReqZ5lllrcux/2X/jOf+QyNjY187WtfA2BwcJCzzjqLxsZG5s+fz4c//GE8z+O666473qemuroaTdMYGBiYsn1gYID6+kOPnurr64/q+RCIQaFQ6LCPz/L251QnTExJnjFK9Gku8z2T05wIK1yTjbrFw2aBvZqLqQQnOmFOTC1nb2Y9Y/ZQ0IkTkvpwEw3RVrZkXqG/1I2rHASCuJFkccVqKkNH58ooe0UypREqtDQhLYzj2xTdPAoYt0cRCBqic6gNH59CAiYmq5wTWcWJx+V4mtR4R+u5/Hjzz9g+toOKUAUFp4inPC6fdxHtlfNe+yAHsbewi735HUT0GDE9gadcRu1htmRe4YTqsyYziAHaki28f9G7uWvnfezM7gagNlrNZfMuYn6qbWpbNY3V15/P49kXGLm3g2jZpPbaE4kbcTJWluf6Xuai1vNYnG4/bNsyYoy7wrezwXwZC4tqv4YLrHdyhn3utIHFjJEabno5pI9v8UspNBpjbfSU9lB08xhy/9LCopsjFaomqh/ePqjSUPyoT/geid4hwA+W9Zff6ePPsA+9e/dOPvCByyiVitxxx0MsW7bqtXcCtD1gviAD12gq2OZXKfQOQehhibvEBxE4gq+99hJs2+bOOx9h/vzXH0FyLFiXK7QRhbMFusYFRQty7YqtyyC+GM69yCeaOr7nlMOgbwyK6ZnPCvzEVCFVRQlyeAcE7vKpQqq2Iygip40KkEGBQPskFWTpHsRwCb6/QdKREYQ0heNLnulTvHOu4rK5itJ1ilJRBSboA8Yv9mkK81XQd06Ij27g/nXnK+zTFOoChdYTRGr4cYjfLINCbAfiEgi+5tRtxnqBvgGEI3Dbg1zhgyMf9l9sEDNhr/XQ9gqUqXAXMEVEFhmQo0H+tD+DAuPh+wT6JhGI16YCH/RdIGyBXzPhlq5WwTWPCuwV6pAu5EPhzgfrrKCYnSgGrm20IL6i8LGpjvw/+tJNvPOa07ln8128O/0eVERgn6woXe1PeS/IXojcJdG3C3DBq1NYl0zkMb/N+X32b2d5eyE0gWZoOEVnynblB58jzZi+eumN4J3lK6n00zxlPsqYHGGlcwJnWxeywl39ppz/QEKJEMsvWs5iazGe42HGzGOKyihS5EXzGdb3rMN7UKPSTdMypxUjaVC0fAY7hjEiBqF4CEuUKfoFin6elFmFAOr6nqah5wl8J0tYT3CenmYgVsfGljOwjCiJcIrWWPtk/YuV7hruC91NrV8/GbMzLAdJqAoWukto8qeahTQ0VlrLOKEPpDWCMmM4aTltRP64+RA/j/4YD5e4n+Rl43k26+v5YPFG1jpnoow4XjiNVujHCwWCuT62Ba3QA0Li6XG2VJV5ubKPeWMRIqEiVrSGar+GrfomXow+zw3itWsh1EebGcqsZ0PNEtpCCeLFESxDko8vIhNO4cUaiExEkQmgXmsiZIdYFl9CQ/ipYM7ZLQaxE3YGfIXw8kR2/Iw19dfzjPk4e7U9NHrNKBTd2h4qVRUrnaAwtDIrKC94P5FtP0Ir7A3OIgRuzclYcy6d0lZhjxPd+gOMoZfBt0BouBULKS75GH58/4rL4XI/RTc/WawbCKIzalzy746xdP12UE34Va1IzcfY+QvqyiMsnX8Vu/LbKDjjCATpUC0LkssO6QxWSuH4FlLoMzKGvBba/Hbc559BuS5CD46nbBtQ6HPamLu2lYaldWR7x5G6pLI5hR564yZ0Alc0U3K+Z5lllrcex/1b4tlnn+WHP/zh5N8//vGPSafTrFu3Dl3X+ad/+ie+/e1vvyGdZdM0OfHEE3nooYe46qqrgKAa/EMPPcSnP/3pQ+5z2mmn8dBDD/HZz352ctsDDzzAaafNMINylmNGoRgVPj6KKqUhj1UA+z0gEKx0Q6x0p04KbNEsfhjJUsCnWmlYQnFvqMBQKsoHtbPIWoO4yiWmJ0iHaujK76S72ElMT5CQFSh8svYYW7PrOKn67KNyCDu+jadcQlocKTRqw43k3XFyThbbt2iNt9NesRx9Bo7a3xera5djaAZPdj/Lnlw38yrmcFrjKaytP3qHpq88+kpd6NIkPFFgQhcGFUYlWWeMMWt4WnbyyfVrWJJeSOf4HpRSzK1oJWFOFzl95bMju5v0O+YTScbp/tk6nFyZBdefihCCglNkuDR62La5uNwS/R4vm89T5zWSUElG5BD/FfkhpgpxinP6UV/vsWB5NjsznTi+S0uikXT48A4cXRrMSyxha2bdxNJCA085RLQY8xKLp8WdHIzXAmMfK6LGPEJEoFoe0jl6KNavf5kPfegKkskKfv3rx2hpaZvxNWp9AlFUqAPHYwL8tELrAkpQEiU+/OH3kM1muPvux2lre2MLgRwJvwayn/K5807J2F5IVCuG2mHIgL05QXFQ8N7U8RPe9PUQ/S+JHBQgQN8BaGBVKyajJPfl4h403tG6IfYDiRwWeLUK4UPoGYk2rCh82p+WMfxYt2B7RrAwpQjqIymGSvBwl2RNjUdzAohO9yt77VC83id0n0DrFaCBfYqifIU/Kdx6ByTr2CcowvdK/JQKYh980LsCF6/bPnF0HyK/DBzjeAKkwnxW4LwiKN54UNsVgSvXCO6NXwd+3UGttCB8j8B8ViDzAhUF+wSf8lXT79s+RD5wqfs1wbFEJjiH2wJ6l6D0Hg+tS6J3ACaU3uVjXXBoof2QaFD8gI/XLDCfCwRhdwFY5/p4B8UsrjrjJE477Wz+KftV3vHFq1CVQZHHAyMzRAGiP5LoOwKHv8iC+YLAfFWQ/4SHfT4z/ky/Ffl99m9neXuh6RqNy+rZ9nAH4WQYI6yj/GBpd6w6Rrrt6FZwHXM70DjDPpfT7XNwcdHRX3MyWqEYlP2URYlar4HIjL+QZoYe0o9ZxCpT5sex7/Ci8SzVmxuJFysZaO8h448wd7Cd5xY/jjVSZmBkgMF0L72ym7IqUh1Ns8JbQ/O4R33fM3iaSS7SRlmGiYYaaM5sJWW75JvOIySnurnPtS6iQ9vGNn0zYRXBETaGMnmXdRWN/vTIN1kcILr5u+iZzaB8FJKhdBXPrjyZUjzNfHch9X4jD4TuQVcGcye+rOv9RnZrO/ld+G5WOycTkiHspguIbv0BWn4vXrgKOb4TfBcvWo8yk3Qnx7B1k3jZwZcFmIirS5cUnaW7CO2M4lcswqlaBdqhxxvpUA3LlckeGWJT41qk75FSsNgVDJLHsDJTni9E8A5ypI6TXk6o+0FkaRhhZ/BDVQi3CJqOzHWyYsd23rvsQ9wTvoMd+jZAUec1cGX5WuZ4+wsyO3Wn4sWaMIbXIZw8fqwRp+bEaRFo4c47MfqfxIvPRZkJ8MroY5uJbPshhTVfABmI9ZZXnmzrgWhCwy3vQqQq8FKBIcAHlB7FHHwBo34tUS1G2S0Q1mM0ReeSNKb3m4fKfezN7yTnZNGlTn2khTnxBUe96vNA9BNOxnn+WbztW5CVaZRSqMwY+rIV6KuDMVI4ESa86NhXGLq2x8iuEQqjRUIxk+oF1YRi5rTndL/STfe6XpySTbo1zZyTW6hsTh3zeWeZZZbfH8ddDO7v76etrW3y74cffpj3vOc96BOzWFdccQVf+cpXjvdpJ7npppu44YYbOOmkkzjllFP453/+ZwqFAh/5yEcAuP7662lqappsw5/+6Z9yzjnn8LWvfY1LL72U2267jRdffJHvfve7b1gbZ4G90uGeUIEduo1C0eoZvNOK0+69ddebKhSPmyVywmehF+TzoiCBz3qjzNmhCEv0/Z0bX/n0l7owpElIC368BRoVZiVZe4wxa4jayMwLZ4S1KCEtTNkrEZcGmtSpMNOTnY/5yaWY8vg52ofkABkxRtqvpkodv2y5ZVWLWFa1CF/5Ey7qY1MXXOXi+Pa0OAkpNJTycXzrkPvFzRgrqo9cBE8gSIdTSClJnNbEolSCjv98mvX/97dUXb+c2vZqKkKHX+K5Q9/KZmMDbe58ohNqVsyLs1PbzuOhBznJOXVKhtobwfaxndy+/S725nrxlEdlKMUFc87iHa3nHlbYrYs0EdLCDJZ6KLp5YnqS+mgLiYMiNw6m7BXpzG1jqNyHr3ziRpJWq52a8OFtwY5v01vcw133/Jx//l//yNwF8/nRj39Fc23rYfc5FCpCIGi5TMm7FeWgIJineXz6U9ezceM6fvnLB49aCC65sHFY0FeAiA7LqhSNE+MTkQd8AmHxKN7Guxx4uF5QN08RnWhzAqiKKF4cEFzSpogfh69KkYPI7RKRJYiFkKB0MJ8X6K8KnJMDR6q+G7xGcJZOFUCNZwVyQOAumSh4B/hJhb4zcNzaZ+x/vlLw6pAgZe4TggOqw7B1DHZkBc2Jw4vczqrAESsHAWMis/cw99S6UKH1KIzNBEIv4NcqSu/dLx7r2yH0uMSrUqh94roFxoYgW9q6MNimb4LQoxKtC1QscClbZ6ugCOEBhO8ThO8R+FXgNilkLijCh+9T+tBhrssJHMliOMiGFmVQGvi1oAyFXyewLvIRpWD7weecERGwLg4KAOJyxEiHT37yJq6//iqetJ/g1LlnTXtc3yjQd4HbpjC2CrQewBeInCL+dY2CmhCr36aC8O+7fzvL24vWta2M948zuH0Y5fsoBdF0lCUXLZ4mwLzRCAQGRzYKdMsu7gvfxW9Dv2ZcZqlUlSxylnKRdTln2ucd+4qm48irxku8bDxHqzeX2GgaDA3TN+iXfczvaqe5ppWdzdspRQvktAxhFaHebWRPbDe/Me7k072L0J08uUQbnl/E1MKgmahQisjQK7jz378/YH+COr+BTxZu4kXzaXbqHcT8BGuck1g+EecwBaUI77gNfXQ9bkU7SguxXazDzd5LcecT3L52IVHiLHVWMKwN0uK2Tdm91q9nQA4wqPXR4rVhNZxLrjSI3v1btLENuPhEQymEmQQhiLgSEPjKQRkxEAJ9dCOu9wrp8TCRzl+D1LHrz6C45GPT4hT20WBWMyfbx5iWQiJJBt0FqpwCo+EU5gE5uo7vIIQkbiSx57wTc+BZ9KGX8MwU0hpFCYmbWoJvJgkNPMeZc/+BFc4aOvUdSCWZ57UTP3gmGfDjLVjxw0cyCnscY/A5/HBtIAQDaGG8xFyMzHa08R2TOchxJ4+W34s+ug2hR/FizXjhWlyvRKqcwQ9PXdajzAr6nVHWj6/DCqUwZYicnWGz8xK2X2ZOfP/s6nB5gE1jL+H6DmE9iuu77MxtpujmWV558jGPaWRlmsjHPon9+MN4615GSIF29vmYZ5+HiL3+YP9StsyGuzYwvGsE3w9WaCXqEqy4fNlkVIvyFZvv20rXi10YYQPd1Nj7cjcjnSOsuWY1lS2p192OWWaZ5c3luIvByWSSTCYzman2/PPPc+ONN04+LoTAsg4twhwPrr32WoaGhvjSl75Ef38/q1ev5r777pssEtfV1YWU+0eip59+Orfeeitf/OIX+eu//mva29u58847Wb78+C6znmU/Y8Ljh5EsXZpLg68hEGzSbQZllk8VK2n037ycsuOJB3RpDpVKTukAxpC4wKDmseSA2m2BIOmgi6nXu6/YnKuciecpck6GklfAkKGJAmjTxTpDmjRF57JjfCM5J4Mpwzi+hePbzE0sIawdH/dGQeT5Vfg2XjFfoCDyxFWCU6wzuKJ8zevLvD2I13KavhaGMInpCcbs4UlnMIDtW2jCOGKswWshhOC0xpN5pvcFBkrDJJdVsuyv3sG27z1Jzz8/x7yP1tL+zsOLiqNyBAd7UgjeR0pVMij7sbCmuG1Kbpkto9vJlLOkw5UsTrcT1o9d2M9YWW7ZcjsDhSFaky0YUmewOMxdO39LOpzm5PrVh903ZVZNLo+cCZ7vsjnzCsPlPiJaDEOaZKxhCs44K9KnkA5NL5DiKY91A8/wza/8Iw//4gFOPv9Ubvzff0gPO6lyq4noh1vHPx1nocJrFuidAneuCgq0jQdCqHWxz9/+3Z9z//1384Mf/II1a06e8XEhyL/9z02SLSPgK4EPVIUV769RnPn8RI6tEjjtCusiH69tZsctOWB5ED7oqzCiQ84WlFyOixisdwi0AXDnMekA9eaBOwRaP6gtgBEIwYfKKda7BCp+kPhnAAjk4HQBVHKolOJg9xl92nXwZ5Byo1JQ+ISPsVkg+wNnrrtcTYlt0HcJKMOUepchUJEgH9m6UKFvhNgPgvxcP62QIxD5mUAOQukDBxRky03Ea1QGQi6AHyJwG78ssd7h4deCNiGSi3xQxNBZpVBCYW4UQaxEAoQbCNVercBtCiIaDucsPioOjsg4BOeffwmLFi3lP/7jW5x66nQxWI4CKnjPaN3BJIcyFSIURFCE75G4i7wZFw58q/H77t/O8vYiFDNZc81qhneNkB/Ko4cNahZUE00dX6ft8WCvtpt/j36DJ0MPUxRFwipESRQoGUVGtGEiKspJzqlvyLlHxQivmM/TLbtIqUpWOSfS5h26f7VD34ZCEVFRVKOPfFVH93WkFNhlmyuefj/3R37DxoteoMldRr3XRI1XS5/VRWdoJ11GiAXKp+yV0KVBQt9Xv0KAOnyx2ypVzcXWFcEKkiMgS/0YoxvxYk2ghRmU/ewwd1OZrGT5EPSMtbAj5fJs6El8PGxhEVL7+3o2FgYGpgqhlKKzsIPdsRjMu4SIPY6f2026MMDJAxsJWyOs7Fc0trjsSAuaY3MQpUGy5U1YUZ3TSifhpuchnDxm3+O46WXYjecest1ueiXhcDU1mU68ZBsgkfku5hZ76Y83kBnvIBSpwxdBH7su0kzarILyLtzEPAz9RZSRwA+n8KJN+NEGhDWG9EoIz6JCrzxigcGZINzgWP5BdTKUHgHPChzJBFEabdtuZdCAESNG3BpBFnsZr5hHNN5Kk9KQbhaP/R0G37PoiFbiCknKrJ4UdPPOOF2FHdRHmjG1MEopugs7cX2b1L6oPw0Mz2So3EfWHtm//RiQNbWEr34/6t3vC65ZHj/TyM4ndzGwfWgyXsL3fMb2Zth8/1ZO/fApaIZGpjdL74Ze4tVxwongfRlNRxntHGXP83uOSQxWviLTk6U4WsSIGqRb0+jmmxOTM8sss7wBYvCpp57Kv/zLv/C9732PO+64g1wux/nn76/Gvn379je82NqnP/3pw8ZCPProo9O2XXPNNVxzzTVvaJtm2c86w6JLc1jomWgTo+mkJ9miObxglLjSemOLZrxRaEBSSXqlO2W7OyF/xP2pP9pSaCSNSgbLPYS16GTnwvYtdGEQ05M4vs227HqGSr04ykETGimziiWpNYcUM+fEFyCFpKe4G8srY8owbfFFzIkfv2Xvd4Rv45HQ/dT7DVR5NWRlhvvDd6Oj857yHxy387xehBC0xOYxbo+RtUcJa1Fc36HslWiKtR1yaddM6cn3sX10B5rUKDtlxq0cRtSg5bOnYjyU4Ynv3sWf9N7AV796MxUVqWn7J/0KNDTKlKcI6DkxTpM3h9ABFsDefD8/2fJzdmX2ECwoFLRXzuP6pddSGz22TuWmkW305Qdor5w3mZtcH6tlR6aT5/tfOqIYfLSM2kOMWgMkjfRkbpopQ2TsYXoKuw8pBr+w8Sn+9I9vpL+rj0/8rz/hne+7HIUiYw3TV9zLvOTimTcgGiyVj94q0TsBX6AiYJ3p8y87vsEPfvBt/uEfvsVFF1121Nf2cJdgw7BgfoUipCmUgq5R+PXvJCdshIq0QmkK8zmBtldS+GMffwbFv+pjUBGC0TJUH6ALjJSgKa5IHa85F4dgFuvAfrcEb4HCr4fi+xUkwWlXhyyu5lUp9C0HSbyK/W7ofX87IHQ4oU5x5w5JTUSxr6/fXwyutb3yIJlYgewPBEg/NSECH8JQI7Kg7wxsye5chdq3ujoEzprDO43V4cw5auI8CkKPSERe4bbvP7cYA/N5iX2Whzdn4pZlQeQEfu3U8/kJ0EdBZkDfJojcIRE5QAuygb2Hg6J8oiiQBZAhUGGFCgftl1nw3sRC4FJKLr/8vXz3u9/E8zw0beqALBDO1WRUh5oQl4UdTBiIcdC3CryWt2d+8H+H/u0sby80Q6NuUS11i6b/Dv534lHzAbYbmxEIGr1mdHTKlCjKAjk1zhPmQ5zorD3u7uAeuZcfxL7Nbm0nOgYuDo+FHuTa4vWsdc6c9vwDi96p+S5KmIjndcy6CCousIc9Wtvm4sYLzHHagicKqI+00Ecv29JwhmZS4VpEki2EtAj4DtIaw2p+xzRX8NEi3DL4NmiB0Dik9ePjockkmp9Fdz2q/GqGZD8KjR6ti/nuIgwMbCx6tW5Occ6g1q8n52TpKnSgS5NorAVigAzTKxTbWMayTC9pt8hHtjXynyfobK9yobiOCs3iouFVnDkSTGopIw5CRx9+5bBisB+to7ToBiLbb0HPbEWWhtGKfRhGnFMGN9MZ7WMg2YJKr2ROYjkt0TnEOm7F7HkEaY0iPAtpZ3CSc/GjwYowrTyIk172uoscT7YxnMaL1AWRGQccU5aGUKFK/FgTKJ/Q7rswioOsrFxCh5QM6zFw8zRku2isv4RwnYfYfguyNBw4hL0SVqGL8dolhCMNU5y9UT1O1h4l54xTpYXxlEfOyQbvmwMwtRB5d5yCm39dYvA+jqcIDGAVbAa2DhCrjE5GtEhNUtGYJNs7ztjeDNXzqsgN5HAtl1DD/pllIQThVITRrjE810PTZy7k2kWbTfduYWDrIJ7tIqQk1VzB8suXveEFNGeZZZaA4y4G/93f/R0XXHABt9xyC67r8td//ddUVu4XXW677TbOOWcGFeBnedsyKF0kYlIIhmCJWhRBz0FC6lsJgeA0O8ItkRG2a1sxGUNTSXJiPm1ejMUHRWAIIWiJzyNjj5CZcK8G0QYWTdFArNwxvone4m5iepK4rMBTLiPWAFuzr7I6fdo092zZK6JQpM0aDGlSH5lD1Ji5i/K1GJT9vGI+T73XSFoFndkavxaFz7PmE1xgvXOyAvB/B2rCjSytVHTld1J0c+jSYH5sCa3x9mNeqjVSGuU/NtzCnlw37al5NMYb2DPeRWW4kk+t+ignfWAV9/32Lv7sz/6Iiy46hZtv/uk0x+lCdykL3MVs1jfQ4rUSVhFG5BCOcDjTPm8yIsJXPnd0/IaOsV3Mr5iLqRlYns2W0e3cueNePr7iumO6jrydB8GUAnoAUT3CyBGyjo+FklvAV0wpoCGEwNTCjDsZ1AHLC5VS3Hrrf/LFv/ksVQ1VfO3Wb9O2MCgcKBDo0mTUHmQeRyEGA94CyP25j7EtyA/2GhSP9z7Kl9/3l3z605/n+utfu4jKwTg+vDQoqAwpQtq+64LWvGBXETYtUuzzSfmVCn2rwHxeUL7itYWy2iic1uhz3+7ABRwzYKwMmhRcMMfHOHgc4IA2MBExUM+Ml+l7LQpVIZHDCn+f6KhAjgjstQrnzCMv+XdOVJgvBi5Rr4HJbF6/TuGsCJy1oUclWjf4SbhwrWJHpWJLFphwUscNuHyeT8MBX1OiCOFfCMxXJKIQOGPtlT7la9SULF/jOUHkLoEcCv72KwXldynsc147qsBbEDiG5Sj4+wTkEggLnNUKUQRnLxSqISb2H06lQAwoZJ/AmxO8ln4FqIRCjE918crc/r/DvxEgFO6SiQddCP1WoA0FBeKEJxAFEJbAXaxQIYUcE3iH9FLPEDXh5rWCLOrXWAUOwBlnnMs//dOX2bTpVVaunJrV7ixXeHMCARsRXIMoADp4zQo5GhSVe7sy27+d5S2NW8bsfxJz4DnwyrhVq7Abz8GPHHnGycNji7GBsAqj2C+4hgiTJw8KBrV+XNzXjJo4GhSK+0N306ntYJG7DA0NhaJL6+TXkdtZ6q4koZJT9lnirOBR8wHGd+dI/bIOSuAVFZHNCYxYmNqzahi7spdSqjjlu8qQJgk9RUvthYTn2aT23o/M7QlmMX0HJ70cq/l8Xi9etB4/UossDuAl5+LiAoJEsUA+EmE4HkahkGgss1eSl3k69Q4UCoFksbucd5fej0CQsUewfZtKMzl5v3bWubzQ0MF/aXtZmbU5Z7CBtdZF3CSvYUsxh+r9Le3bX6AxtGqKcK+ERPhH/vJ2ak/BTS7A7H+KyNb/wA9V4lYuJyol870S7X3rEOGFOA2LMfqfIrT3frxwLV5yHkqGMEbWYQ4+h618hHJRRhJrzrvgda4CnEQaWHPeSXTL99Ez2/BDaYSTR3glym1XBfe9NISe3YUXrSeBYI2jKKNQyiSR2U6hnMFquQhZHsXsfxJG11NSNgOxekaj1YTdAmktOtkh8JWHFBJtIotYCokpTYpeYerrrrwgiuV1ZAYf1a0oDSJLA/hmBX6s5TUnMTzHw3d9jMjU9mm6hvJ8PCdY1qqbejBX7iuEtv+Ynu0SqYhMWXk9E3Y91Un3uh6S9QlCsRCu7TK6Z4xN925h7XUnIfXj897wPR/XctFDOlJ7YyP4ZpnlrcZxF4NXrlzJli1beOqpp6ivr2ft2rVTHn//+9/P0qVHzuOc5e1NytfwUBOdm/0/JmUU1eqtvTRkiVtEiP9knbEeWzhIpdHoL+DjpU8RO0SubjpUy4r0yezN72LcGSMsI8yNL6I5NhdXOQyUuyezgCEogJYwUmSsYcadsSnL9YfL/WzNrqPo5ie2CIbKfSyvPJmYcXxmWDNylKIoUKOmFl5L+EkGtD7GZYYKL3VcznU8EEJQF2mmJtyI7ZfRhfG6C+i9OLCOPbluFlUuQBMaNUBzvJGOzE4KTgFNalx66XtYsWINn/zkh7jiirP58Ic/yZ//+ZcmXcImJtcVP87PIz9hu74FW1ik/EquLL2P0+39YkJvvp/tmV00xxsxtaDdIc2kMVbP1tEOBkvD1EWP3jpYE61GIrE8i9BERpxSijE3w+KG+dM+m68HQ5oIFL7yp0xeOL5NIlQxKQSPjo7whS98hrvv/gVXvO+9XPqZS6lPTi2+ovAwxDF2pqP7naLj41k+e+3HOP30c/irv/q7Yzqc5wf/tINuk54PXKeuxkThNYIs3qhC2z1hOZ0BV85XVIbgqV5Bzob5KTin2eekgwqXGa+IQFTsC9ya7gJF+Sp/SkG1gxEjExEWJXDbfIyNAjkOKgwiK/DrFdYF/msKqu4yKL3XJ3yfCArPSYHXoChd7aP1C6I/DMRcv1Kh9UHjzwR/epHiudNhz7gKMparFQsOMgaFfy0IPSbx6hWqPohhCD0pQfqUbgiEXm0PRH8uwVG4Cwi29UHkDolf7+G+xnyBOx+s833CD8ogh1gAvsJZDqOrFb/rErxaJ7A9mGPCxa5ihU/gptaCOIl9qESQJRz+tQAZiMMyB3JIYJ3vIzICOQruwv3nl3kQtkDJoFCfP5FbLDPB66PaBH7qEMuSFYH4XQqiGYQPXhPTIjzkIIR/LTE2BwKt16CwLlY4Jxz5/bdmzSmEwxGefPKRaWKwSkDxwz5aj8R4USKFwo+DO18F2dwmeK1vT1cwzPZv/6fiOR6jXWM4JYd4VYxEfeKYJ5N/b/gu0a3/gdn3BEqaIHWMsS3oI+sorPwsKnx4t6JEElYRJEGhZw8XbWL4KICyKFHj1aGjY2GxXd9MTo5T7dcw312ExrH16/MixxZjA3V+w+QxBIImbw679O3s0jtY5Zw4ZZ9l7krOzb+DTfduJTeUw1peRJMaTZlWqruqCSdCnFy1lhfVU/RoXdR7Tfj47NX2kCqnqXysnsdeVKTEecyp66NmTgijZSV23anHx8GqR7DmvIvoth+iZ7bRGJG4XpaNaZ07VyToSjyGoUw0ND7kfoyTnFPZpK8nK4P6HEucFQdEiKkpP9Fbo5u5L/0biqpAhVfP9grBljZBj7uCd9lLOdEBHYNYqQNf5FATIjJeGeFZQRG5CUqUeM58gmfMJ1D4nOyczqn2WSTC6SCPVwvhppYwpEl26IKsjKEZbdRkX6bReQfVgy8ELQwHM61uehlKj2IMv4S0M9hNF2A1X4BbtfJ131KlFKPWEGPWEH4kScOC91I39DJ6oR8vVo/TeC5WUyDkK6mDkAjfn1wEFAHwfYSQIDTQQpQWXc9I3cnsHniIPB752Bxsd5RMppt8uUiFmSKUDFEycqTM6smVhlJIGqKtbM2+StkrEpIRfOUx7oyRNCpJh97gpT6eRXjnzwn1PYmwx1F6FKd6FaWF16FCh18NGU6ESNQlGN09SihuTn6/FUaLhJLhSZdu1dw0sZo4md4sqcYKpCaxCjZ20WH+WfMQcubfi07JoXdjP5GKMFJKnLKDHtKpaKwgszfDWHeGqtdZSFP5iu5Xe+h6sZtStkQ4GWbOCc00r2maFYVnmWWCNySctbq6miuvvPKQj1166aVvxClneQux0g3xmK+zSzo0+wYS6JUuCSU4wTl+mbNvBp7vsWV0O5tHtuP6LtsXvESZrZxrLcARYXxsMnIXz5r/xcnOXx6yKFg6VEulWYOnXKTQJgWzopvH9R1MOfWe6MLAVS6u70xuc32HHeMbsbwylWYNQgh85TNmD9OZ38qy1EnHZfCS9quJqTjjMkOVv79Tk5UZEipJyn99P9xvFFLIKbnBr4euXA8hGZriqtWlhi50uvN9k9vmzJnLr371CN///r/y9a//f9x118/5X//r/3LNNR9CSkmd38AfF/6cbm0PRVGg3msipaZ21sqeheu7mAdVeTalScbPYrnHlk+5NL2IxekFbBzeSk2kGkyPjemXKSwexqgZxdIGucC6hCXuimM6/oFUheqIGxVk7VESRgpNaBS9YMKiITIH13X50Y++w9e+9mV83+c737mVcy6+kJdHnqTg5ojpwcDb8koopaiNzCA09jX427/9c7LZMb7xjQeP2smwj7AOS9KKx3sk1RFFPAcVIzCUgZQtmKdPFcWEJfDTh88cPBjDhnfugsteBL8ArFA4cxT+AR9jvQOiP5FQUvj1QYEw4xWBGJMUPutziBos6OshetuEAIoAqfArgzgCaYG71sc+Q80s91WAfY7CWa3Q9hCI0fMAE+LfFIiSmiqADkP6acmZZ3qcfhixVoyB8VLglt33cVAp8JTCXC+xBj38uons3TECp+3EPfGaQN8W3AN38cT998F4QWA+K5DD4LWBfaaPuwjKVyrchR7GSwLjVYHMgOyCnV/XeGU+WLUQ7RRsMBS7TcGnioqVuwXunInoiAMoXxzcf/NZgd4jUFGwLvQpX6nQN0z/7hVFAb5CVQQ5weSCYodKgDYE1vkKb97UfWQ/RO6UGC8J9J3BtXk1gcvZukDtL95Wguj/z957h8lxllnfv6dS5zQ5B41ysiRLcs45B2ywwQabuLALLLvLLmx4dz/gXfbdRN4lY6IBg3FOOMlBsiXZVo4zmqDJsaenY6Xn+6NGI43iKDgAc3zNdcnd1U+Frq666zznPucexbNsqJSgg7rXO1cyfgf7KHylYRisXHkOq1ev4hOf+OtD3nfqYPSLLuH/9nyX3RLpKdN7POsVe86Rx/5jwHR9+6eFVG+KLY9sI9k1inRctIBO5YIK5l8xd6Kd+p0GKSWO6aCoyoSyThvejN63BidcixwXBziujZbchq/7RfIzbjrieALBSvNsWtVmwm6EYWWYiIySJY2LS9SNc455ET1KFz8Lfp8WbRcODj58LLBO447sh4kePFs1lf2YmDidfP0UCOSk9/dDReXi5quR7X5ylRkUCQmrmJJAGfmyAgPNQ5wzcibvUt7H4/4HaNZ2IBCUFSqY8+ASRl/IEIj5GVZm0LmlkthwjNPnLiVknJr6EcCsuhCphzA6n6Uk285vZ3TzozldpANgUCCn5Im6MYaUAYIyxArrrEmfd3FpV/fQFewgm87ic7IYmo91kVewsanIV1BklJHQSuhVunlee5ozrQsoksXYxYswK8/H1/0cZBV6/AX69EES4SjlfasJpPeSKVvCf9b9mqcDT1AQeQSCZ/yPc0H+cv4q8w8UuTZIyYgi2KArFIQk6HrfTIeqMJJczzl2dnJ4s1BwYk2eSrfxJvIzDw3iO15IKRnId7N5ZD2D+V40oeLXQrSrPioaLmd+eB6qHgahoOQHwCng+suwihdhdD+P64uBooOUaOlWnHAtdswrWCSwhyzdkUoSRikRF9LrJaNWlqHIAGPKKOqgSmlJBbPmLJwkdKgONZJ1MvRmO8jaGRShEDWKmBs77aTFKMeCv/0R/G0P4wbKcGKzxv2gX0RIl8yiTx/xmCuqwoyzGxnrTzPcNoIvbGDlvGfMWRfNJDDuY+4L+1hw5Vy2Pr6DkY4Rr1PAUKlZWk39irrj2la7YJMdyZIZzGKb3roCsQBFdQkc28HKW8cY4dhoW9fBjid3IBQFX9ggM5BmyyNbsXIeeT2NaUzjTSKDXdflnnvu4f7776etrQ0hBI2Njdxyyy3ceeeJtTVP448Hla7Ge/NRHvCNsVe1kECZq3J1IcxM561NUD4ZuNLlgZbHeLbjJQqOie3Ps71mDeUDpcyL+8fb4jVisp5mbSd71TbqD366H4cQAk1MLhJ8agC/GiJrj2EckPDrzTb7JnkGj5ojpK0UESMx8ftShEJICzOU76fg5k9JgFyJW8Zy8yye9j2Ki0vYjZJSkiSVEa7P33JIy97JImfnGMwNE9JDFPnjp3TsE0XcF8N0JxcpUkpsaRMzJrNvhmHwiU/8NTfddBtf/OLn+MxnPszPfvZ9/vVfv8aiRUtRUKhzGo+4rspQOcX+BAPZQWoi+0nQgdwQpYFiyk5AFQzg13x8YMFtPNb6DBsHN7Ot/lXSlYM0+ZooMorYpLxGm9bCRzOfYvbRmKMpwFB9zI8vY+foJlLWCK508asBmiLz2b1+N+/95+vZtWs7t99+N5/73BcoKSlDSsnM6HxaUtsZMQcB0IRGbbiJ8sBRJK9TwJNPPsyvfvVj/uu/vkttbcNJjXVxnWTPsKTkScHK7RBIgy0g7krqSsaVoGI8jM3PMVWZE7AhcK/At1p4vqw6iEcUjK2SzMfciZAyfY1ApPAIuPHbqh2SaM0eWWqecxAhPQaB+xSPRJ0NKBKRA7VVYJ7tkrnBW763F4a2QiwG1dXHfmaTMbAPEPiIEVC7BE7JQR66xaDt9Dxn3YrDHwtlDJScwD5IAS3DnmJWSYFb7u0L2qF2EFKXiOR+BbbvKYH/wXEbkqBH1upbVTJ3OdiLwZ4JvmcEyrBARiWjNkQ2wh174eUbJKlKKOoVbNckqzKCBTWS3O0uHHw59UH+ZknhYulZT8Rg3EkHp0niJgRK7/4APKlJFEtgNYKMe6pxZcyzqbDrIXunO8nLWWQ9glffLlB6gLwATaIOg9sH/vsFbhys5RJ9m6fUtmfKidA4J+wR5cZqBXv+0SclzjnnQr72tS9j2zaadmiZKMsg/VkX41WBvlV45/ZSiblScoIiwD8YTNe3fzpwLIctj2xjuH2EeE0MVVcpjBXoWLeXYDzAzPNPXRbDqcJQ6xBtaztIdo6i+VSqF1dRv7IOf6oV4ZgTRDAAiobUI2hDG+EgMnhsDPJ5iMdB1+F881I61DZW+14gQ5oBpRcNnYXWadyev5vTrTP4Ruj/sUvbRqM9Ex9+MiLNen0NCX8xt+fuOu59icgoc+z5rPatIuEWTYgputS9lLplzDh4Rm4c0pZErAh11KHY+0k6VVewTRvXklxgXspp1um0aS0oUiXwRphdLzcTr4+j6t5FLJAIMNQ6TPfmbmZdMPO4t/+IEAKrbCVW6QoKMsfO2N+T0LcSc71Wogq3ipzI8svAjxkTKebbi1lkLcWHj7QY497Aj9ikv06OHKWymJqhGmIySq/SRbAQIqCGiBpxAErdclq0nfSonRTZxaDo5ObeTaFoDo+qP2GrvpPzdg1CHlJ2BzOSNWT7f0Fa24XdFKHYLcFFMiZGecr/MMutM7kuugiph+l0s+REhGLXo+cVK40WnMmQOUx3rJ6Zg2+Aa8M+izA7C0LFiTVNmQiWUjKwe5Cerb3kUnniVVGqT6siUhZhb6aFrSOvMWIOoAoNRzrknSwBNURvvosifzm1bgF/y33oQxvBtXCCFVhlZ2LHZqMld8H4tIIbrCA383bQPPGNI21GCgME1BBCCFK7M6TW5QjGizDLMsTCCZSkDm8EESUaHKBRUIXKnOhiaoKNpO0UmtBJGMWoyps7eSSsDEbPC7i+BG7AKxKlL44jBNrQJtSxNpzokZ83ymaXsuzW09j7eifJ7hSRiig1S6qoWlg5abnSWaWcVRFlsHUIO28TLguTqI0ft9LWytuM9Y2RHc4SKvZ8wtL9abIjOYobiwiXnHjIN3jK445X21ENbULZHIgFSA+kaV+/l5ql1fjCJx7EPY1p/LHglF+ZpJRcf/31PPbYY5x22mksWrQIKSXbt2/nrrvu4v777+eBBx441audxh8YFto+Zto67aqNi6Te0QlOLU/+HYPdI3t4bu/LJHwxEv44Kf8IbX4fQ6lROrUeGqKetM4n/ZjCJCeyxzW+KlTqwjPYntwwHoAWwHJNTNekITxrEhkscQ/b2u+VaBJ5lBTk48UNuXejSY21xmr6tG7CboTr8u/iyvzh1VInAle6PLf3JZ7f+zLJQgq/6mNJ2UKub7qSiHFyBcLJYknpQl7uepXOsW4qw+UgoSvTQ9SIsrh0wWE/U1lZzf/8z0+5444P8w//8Gmuuuos7rzzI3zmM/9AWdmRE8VCepBL6s7nvt0P0TzaSkQPkzLHMBSdS+svxK+deCFT5E9wx7xbmCfr+J/oDmpY6JH5LhS5xezUtrHKePqkyWCAmFHE6SXnMWoO40qHke5hvvQP/8jjjz/IihVn8/jjaya1pAshqAnNIGGUMmwOIKVL1EgQ04tOimwZGhrgs5/9OJdeejW33faBk96v2gh8NicR2wQDBphNUKtD9W5QejyPWBSPBM3f4O73iz0GtF3gW6vgVB3gketItJ0C42VB/iaP6FS7BDJ8ECGqAQiU4UPJVm2XQO31LBL2XW5lwLMo0NcJhi+UPPCoYP16hXQaAgFYtEhy660useMQdkkfSEMiCgeZYhQATSBVF5H11n0wmesmwI1IlCS4BxCuStJ73dlHsFbh2XDY7K9kXBB5gdPgXe/EsEf0yhATwX1uhUeW+3+vkF7gom8XGOuAPKjdAr8lUA2ID0matgnWXi5RkpLoiGCHDsnLXNSjXIJkHA52ynFLoHCVxP+AgrZjnLDOC+w6kAo4FWBXSdRuz4M3d4fkICcetM2eGtiNgtqGF1angDIESkEgDYnxisBaLlGG8CTGxkFkfFSi7h0/nj2gb/OsQpxKsBZI9mVZzpo1l2w2w+joCMXFh59wksVQuFpSuPqP1xbiYEzXt39aGO4YIdk1SrwmhmZ4Fxl/1I+Vt+ja2E3DmQ3vqNT74fZh3vjtJgqpPIF4ADNtsv2pnaQH0qxcqgESpJxMxLk2Utt/oU2n4dFHBa+/LigUBGVlkksukaxcGeLu7Cc427yALrWDtEhTa9ezwDmNkAyzR91Ni76LWqcB3/iFJCTDlLrlvK6v5er8jcedJyEQXFm4ni51Lzu0rfikD1OYRGWUazI3Y+2x6Ux2YYQNihuKJkjcaEWEQDxAZjhLpNS7WEspyQxlKGooIhDzti8uEyyxlgOwo3cnEibGAC9QU/frjHQkj++LmPIOCgbVIVLKKIutpYSkt627tO20q3sYFUke9Uteks+xzFrJ+zMf4xH//awxXqTWqSMso+RiWbb7d9CYbiSixgjoYcpENaYwARNXuOgY+OUBN1PVYFW9ywOBLLeuDzMjU6CruIgRNUm/HCM4NsClu1L0VTSSCozbk0kfe9U2Vvl+z9WRGzCrLiSVeh1fwUWVEpwC0hdHRptAFhiLz8KOz0Ef2Ybri4PrIOw0VvmZk+wojoW2V9vZ+fRuHMtB82kM7h6gd1sf826cQ7uvGVtaqEIjoIWQUlJw82TsFH41yFCmgzkdL6ANbcQJVYMRR013o2YfJDv3bkzXRsn1IY0oVsky3OD+WlyM/+eMe32N7coCEiPkw005hK0iQmqEoeQQgy1DxKomF0hCCMJ6lLB+agUyR4M9NsxQt4USTBANeFbMwkp7YX9jrWj9a3HCtfvJ+cOguLGY4sbiI76/D76Ij+rFJ9el17ejD82nYYSNCYsIRVfIjeYIFgUIl5xc3k12JEsulSdUPLlgC8QDpHpTZIay02TwNKbBm0AG33PPPbzwwgs888wzXHTRRZPee/bZZ7nxxhv5yU9+wvvf//5Tvepp/IHBj8KcPyAl8MFoTraSs3M0Rr3WmKAZIWrG6Qv20JcdmCCDB5V+Em4RlUcz8TwItmsxXOjHdEzK/dWM2SlMJ4+h+GkIz6E2PFmREtXjBLQQGXuMiO4VJVJKsnaaEn/FKbNIAAgS5Nb8HVxauJqUkiThFp9QC+DR8FLXq9y36yECmp+yQAlZO8czHS+StXN8eOEdp0x9lTYzdKV70FWdukj1pJCzI2FmvJF3zb6Ox/b8nt3JPQigJFDC9U1X0Bg7epvU2WdfwFNPreNHP/of/uu/vsgvf3kPt956Jx//+F/R2Hh45ckFNWcTNkKs7lpHX7afhSXzOLfqDE47AvF8vBjzjSJUiNj7i1aBIOEW0ao14+CcsOffgVCFSqY/w/e//01+8pPvUFRUwje/+WNuuum2I36fIT1yyvyuAf7lXz6L4zj8539++9ScQy5UvCZQiyTlDftftqKg7YH81V6rv92w3/JgKlA7BJhMtnlQQUYl2jYB42SwUyHR9ihMolwdAIkbP3RcYY0vevDXqXvvPfWY4JlnFMrLJeXlHjGwerVACIUPftCdeldnEMzlEv+jCjIkkWHA8shogOC9CrieOrlwqYvTsP+jMgKFcySBB4XHXcRApEAZhfxVEjnuRGMtldirBdpO4RGjwvPodeok1vJxsrwTlBGBPfuA4yPAKQO1U6CMgLYD1BbhkdQhiSKgqh+ET1DV5o3rJiCtSmrCoJzg80nhIolT5aBvFIgxgVPvYtdL/E8qaLsEIu/ta/5yl8JFhxKsyjAghWcpMW7vASAN7/g4FYxbf3iqZJCTiXI8n2JrhmebEfi1p4ZGAIrEWijI3uUio0x4m4+OJo9IBv8pYrq+/dOClbNwHXcSQQig+3WsvIVj2u8oMrhtbQf5VJ7ihv2TpkbYR+/2Pobm1xIw4ijZbtxgFQiBMFMI18QqWwmA68IvfqGwZo2gtFQSi0m6uuCnP1VQVZfly3UW2ktYaC85ZN05kaVAYTLpCPhlgKQyQl7kTihcuMap588zf8Nr+ivsVduJmQlmtc+n8LTN2o7XcC0HoSokauMsum4B4dIw/oifhjPr2fnMbobbh9EDOoWMiT/iY8Y5jYdVMGp+3bPtOSDQ1hE27eXNdM4XOEaWedaiQ6y8jgcSSVJ4xyLuFhEgQEAGMfCRF3lCMsyIMkSLtgtFKiQoYpY9Dx8+1uqrqfbV8Zr+KqVuGZHxmjtIiGJ/Ge3BDhbbK1jjW0WP7GJMSeHg4OJybuGiSV2JEslqYxVhU2HmYJ5UMIwidBJuMSPKEP1Rk7JhqB3OsbXaI4MVFCSSAnkQgtys96J3GJiZVlzbxI3MwInU4+phpJlH95eSXfQpjK7n0AdfRyoGVvkZmFUXgTo1Ai43mmfPy62oukq8ev9zzVDrMDtf2kn+gjQ+xU8WL6xtX4dlYfxZKTzajJbcjh2fDeO5K04sjJbcgdG/jsxpf3XEdauKRmmgiraxXfjVIE7BRWgC08mjKzoBNYhiZ1AyXeg7NxOM+Ty1d9kZoLy11wQpJR2vddL2cjNmSz0aBYqq/CxbuId4YQPCHEG4DsHmX6Fmexksey99rRnMrEmoKETZnFKM4Fv/LD7WnyZUEiJREyfZPYqZMdEDOkbIR7w6ftLja34d1dCwCxa6f38hZOVtVF1FD7wzbX6mMY23Gqf8l3Dvvffy93//94cUygAXX3wxn/vc5/j5z38+XSz/iSOTSdPX18PoaJJkcphkcmT8b5jR0SSO46BpGoqioKrq+J+GqqqEwxGKioopLi6lqKiE8vJKSkrKUNW3+AZ8kBJXczVm9C+gr6KHoWAvA0o/aZHCETY35m6bciGcszNsTb5GsjCIO07yhLUo8+PLKPKVHrbVyFD9NIbnsGt0MyOFQVShYUuToBamITznTWldTcgiEs6p9wi2XIsXu9bgUwwqQ2WYboGw4cdQK9k8sI221N5jkq7HgpSSF7vW8FT78wzmhtEUjYZoLbfMvn6CxD8ShBCcV30mC4vn0ZpqB6Ap1kDMNzUFgK7rfPSjn+Y97/kAP/nJd/je977Bz3/+A6699mb+/M8/e0hokxCC5eVLWF6+ZNKDyqlCUIYQgI09kRIOXihMkVN8WJ/r48Ubb6zj29/+Co8+ej/RaIxPfOJv+PM//xuCwZOb+T8e7N69nfvvv5d//devH1WNfVwoeIpVebBS1AcIzwphX2jdcWFfXb4v4WQfLCbZE1hnSIwNXpiaUw7CAXWvwKmRWIsOXa9dI5ERBWVY4u4Tf0hQBgWpJZI1bwiKiyVF4z/rWMz7rWzeLOju9iwjporCFRJ1QKJvEtAJSIkYHd/+cZLSWCOgRfL/qf/Cb35/LwMDvZSXV/Hud93J3930D/hfVlCGPPVz/gZJ/vIDQttikP2Qi+8pgb5FgAuFc1zMyyUTduY+QAdMJlSvAML0bBqkAWqzQMmA3egpbX1+yQCCRK9EzXlCuuE8mA6cVSVP3OZQgD2XcS/j/fuRmemidniKYLfcUxEfDnKc4JW+ccW55VlKKEOe8lztFp5nMGAvkDi1Aq3FOxekAWo/SA2shZLA78bDA+eOq8rzoG8Q+J4T5G+QRKPeg3cqNXqCO/vHien69k8LoeKQRySOFfBH919AcskcifrE20KgHAmu7ZLsHCUQ9U+qEXwhg7H+MYbTxZTMuBn/nvvRRrbg+RcZFKouxqw4G4A9e2DTJkFdnSQyPhEZiUBLi+SFFwTLlkmOZLG/L/NgSBmgzN1/fx1SBqhwqki4x1YbHgklbhlXFK6nd1sve9a0sfP1PWQGM8Rq4pTPKgUBQ63DbHtiB8vfuwxFVWg8q4FgPEDXxm6yyRzx+VH0FRp2VQHpHtpFVzazhNY1flK9Y0TLI+SNHI/O+Q3NVTuI1Id4JfgcFU4Vt+fuYoE9dWXrPuxRd/OT4Hd5xXiRjEhT6pRzXeEWbsm9j8Xm6Tzvf5KADDCkDJInhyJUKpwqHGGzXd1Nu7qHb4X+k7CMMNeeLAQIyCA9oou51gKe9T3OXq0DQ/oQCFQUsmqaEWWIUtdrN3FxGVVGCNohpBAocjy8FAUJhN0QkkFySh7w6to8OQDmOuPrVjRKKy6gdQSGlRwBLUjcMRgzB8gYGV6MP49fDTAvspA59rtPKJB4tHuU/Gieovr9zxlCCELFQcb2ptGyAiNioAiB4zqoisqE7QMOxVIB6UwQwfvgGjHUsfZDVfIHoT48kzEr6QXTVRTIN+cJFfsp8pWh2VnoXIueLZAwhjD6hzH615GvbyM/8/aT9kM+HvRt72P7E9tRVIVIbTXKwDaG9xbIGJuIl6dA1bGL5mHHZjL4xkbWtcUYs8pAeMczUZfgtJsWTVg1vFUIxPy4lkuoJkSoJITruAghGG4bJhg/eVvDYCJA2axSOl7bi2Zo6AFvEi/dP0bVokrCpW9vl+k0pvFOwSkngzdt2sS///u/H/H9q666iq9//eunerXTeAdCSklPTxfNzTsn/bW07KSnp+uQ5XVdJxZLEIvF0TQNx3Em/bmug23bjKXHyOcmWy6oqkpZWQXV1XXMnj2PuXMXMHv2fObMmU9ZWcWbQobOiNXjU32MFlITRGBpfw01I/OpXhxDADV2HfPHFlI+UsIm8QrFvgrKAzVHVaDuGdvOcKGfmF6Eqmi40mXUHKItvYtif9kRP1cVbCCghejNdpJ3skT0OBXB2gml8JuNPHl2a9sxhUmNU0e5W3nsDx0GaTPLcC6Jokq6sm1YroVAYCg+Rs0sw/mRkyaDNw5s5de7HkIXGg2RWizXYudICz/Z9is+s+zPpmRFkfDHSPhPPAk5FovzyU/+HR/+8Ke4776f8u1vf4UrrzyTc8+9mD/7s7/k/PMvPcSv8804j+fbi6hyamnVmmmwZ6ChM6IMkRc5zjIvOKEiHsBxHJ566hG+852vsnbtyzQ0NPHFL36F97zn/W8pCbwPX/3ql6moqOa22+46dYP6PRJW3zWZxBMZwAC3WCIyYKwVXoiYAtYiiblCwlHE+tYciT8OapcXiobwPHJFAczT9xOJ9jzI3e7ie3w8tEwFe54kd7PL4eae3Gov5Mv/lECMej7GyqinrO0/3SW7S6W0dDKJHA7DwACkUsdHBsswZD7iojWD0i9QO8D3zLhFxbgwyC2SfPWx/+DHXd/lq//7A+bMmc/Gja/xmc98hMjfxfjIP/wFSpunaJUJDlE0u+WQu1OSz0pPRXzQaWU3gl0n0VoF9gzpVTx5T0FsrnDxPyjwvSg8i4hWcCtBDUCFkNgKdAZh5whEdMFNIZcLUhKlnwnP5lMChUnK6CPBWihx6oRnERECrVVAwUuTFzYoaUFBut5xCEP2Ay6B3yjecrZ3rPPXS6TPUxDbs9g/0eAftwpZL8hfIycpg6exH9P17Z8WohURKhdU0LFuL1beQvfr5JI5FF2hfkUdQnnn+EMLVWAEdTJDk2vjfQSL7tcp1F6JnZiHNrzV80+NzsBOzJ9oGx8cFOTz+4ngfYjHoafH8xAOHuG+VSxLOK9wMY/4f0tOZAnLCCPKMJrUuNS8GoOTI877dw+w6aGtmBnTUxD6ddL9aXAllQsriFZEGG4fIdWTIl4TRwhBxfwKyueXs9pYxSO+3zGo9qNLndn2fG7K3Ualu/+GFq2MMu/yOex81lMTr1v2MjsaNzPTmEONvwbHdmjVmvl14Cd8Nv0vhA+XznoEdCl7+c/wF1hvrEGTGj7pZ6/Wzg/UbzIqhrkj+xFSygjb9M10KR3kRJYGp4lqt4bX9bXjFnMSU+TpVpLkRJYLzcsmarNhZZCYjJNRxihyS5ltzycncvikjyK3hBZtF6/pr3BlwbNyU1Gpcxp5zf8qe8vKmb+nhbzPIKuBKgVLRorYGEqysSRPXulDIsmLHDOcWdyQew/giQdeiD3HBt8rGEkVwzLwE8ANQWtZK9lgDonkGfk4VxSu49r8u467lhSKACE88v6A35p0JD7NT9DvI+MmCalR0vYopgOWLOBXA5QHaoipDl5rj+UFxY1DscawEguPSdj61SCnFZ3JYL6X4eVDtHV2Y/XYyJhgbGQ3znCBqlkBSmeXYWtlKLlBfJ3PYJWfdVRv3lONjtc7KYyZBOJ+MrKScJmkvvA8FNL0jJQQKi/FiM/Fcn1s2u7HzPZTvGQuQig4tsNQ2zAtL7Wy+IaFb9k2A1QsqKBzYzej3SkiZd4z12hPikA8QPm88mN8+tgQQjDnklmYWZPBPUM4poOqK5TNKWPuFXOn/f2nMY1xnHIyeHh4mPLyI/+Iy8vLGRkZOdWrncY7AFJK9uzZzZo1L7B69SpeeeVFenu7AS9Iq7FxJjNnzuHd776TpqbZVFXVEovFiceLiMcTBIOhY16cX9azPOhPM5jLYA2P4A4MUtc5yuyOEQZ6uunoaGXjxte4//5fkM/nAYjHE8yePZ958xayYsVZnHnm+VRVnVwIFcCcxEzOrT6TFzpX05ft926s0uGCkku4270dNSnYObKBgXwvQ6IPgL5cN8OFfubHlx1W4Zt3cgzm+wio4Yn3FaEQ0eOkrBFS5ghx3+GlY0IIinxlFPlOJVPhoSPVyfq+DfRlB6gIlnF6+WnURfcfw93adn7p/zGdWjsODjGZ4LzCxVyXv+W4LQZCehBdU2hLdVEaTOBX/UgpGTVHyTsFgifhk7sPr/Ssx3YtGuMeqayrOjNjjTQn97BlcDtnVa046XVMFYFAgPe//6O8730f4tFH7+db3/pP7rjjekpKyrj22ndxww3vZsWKs1COJMk5SYRlhDtyH+LewD20ai1eMKCMcGnhGs4xLzyusaSUbN26kUcfvZ8HH7yPtrYWVq48hx/84D4uv/zat1y9vw/NzTt58MFf86UvfRWf7xR6hAkwL/CsGtQ2iVsCIg9qv8BcJnGqIfhDBX2j184PngJT3ybI3O1OUqseCLcacje6BB70PGYRgDGufD1zsuWBeabEPE2idgEaOLUcOcRLQP4GiVsJ+lqBMgqF013McyXBUog9JkkmJz/wJ5MeIVxyBMXqUaGOh9vNkfgfFqAI8B2w/Qq8klrNNQ3Xc+mlVwNQW9vAAw/8ig1vrMNXIfCt8gLvMDz1bu49Ls5Bc0HySMS6Abl3uwR/qqC1jB8ARWLPkSi9Am3vuJ9wWKBkJWq7ZwnhD4FbA7MvdPlEEyx8RlK6XfF8jkNgLnfJ3Xh0Qv9UQ0Yh+36XwG8VjGEBtgRd4EbBKZe4xRJ9q4La5uA0glMP6U+Pq45Nb1JBRsBYPX6PPfhyonmkMi5Eo3EARkena7UDMV3f/mlBCMH8K+YSiPrp3tyDlbdI1MWpX1lPxfyTJytOJYQQ1CypZuuj28in8vgiPqQjGelKEi4NUdJUDELgRBpwIg2HHSMclmiaIJ8H/wH3pkwGSkvBOAafe3X+JqJunJeN5xhVksy25nG+eSmnW2ec9P7tfa0TO28RrYiQ7Br12r6F8HxBkzkCsQCO5WAV7Emf26i/xr2Be1AQVNjVmKLAa/orjIlRPpX+PIEDWm1qllRT3FhMf0c/z8x6gMZYIzV6Da4jyQxkCYyE2Bbfygt9z3FZ9VXo4366Dg57tF0MiSF0NOZYCwmzX1DwqvESO7St+GWAYrcUgSDmJOhX+njZt4rLC9fxZ5m/olnbwev6Wh7x30+D3USb1kJOZEm4xQwrQ8y252MKky36G2zRNlDr1DOmjJEVaa7O30xSjODDR8JOoJqCnJNhUPRgB0w6lLZJx+X8wiW0iC10+vayPJtkwWA3OR1cfyll4ZnsbbyJ2cp6Otw2BIIZ9kw+nP3kBIH+svE8TwYeoshXQjRYjFOwWWusoyPYzkXWldSN244NKv383vcY863FNDmzvZVLFyXTjXAtz8tXPfyJlaiNEyoJsqfQjFNr4reDVPRVkxnJUr+yntryMrYnNzAmkwS0EAU3T0gpYV58CbXhJoRjY0eb0JI7cSL1SMWHmutDIjCrLpjSeacrBpXBOiqDdTS8bw57X9tL/85+/OkBapdlqV+ksk+z4fqL0UZ6UVMtbxkZbBVsOtbvJT2QYbTHI8+NgB9RVENxYDPJ4QqMQgD/yAAyUklqzEdRcQ57/Flb1VRCRUEGmgcoZEx8obeu2yFRE2fB1fPY/Vwzo92jIAThkhCzL5k1Efh2NEgpyacKSNclEAscdnLOH/Vz+m1LGdmb9K6LYR9FdQkU7Q8ro2ga03gzccrJ4H3t/UeCqqrYtn3E96fxh4X+sSS/fPJ3rHv292xe8zL9fT2oqsrixcu46abbWLHibObMmU9tbcNRz4upoEexecCfxkGywBdFVMYYq6qje6nNzFyUu639T+aO49DevoedO7exa9c2du7cxurVq/jxj78DQF1dI2eeeR5nnnkeZ511HnV1jcc9S6gqKrfOvo45RU1sHdyB6VrMTjRxetlignqQrkwbA/leonpiQglsuSZ9uU7KAlWUBw4lpF3peXzpYvINWREqrnRxpHO8h+3QdeCSEWn8MoCOfszlNw9u46fbfs1wPolf9fGas5FXetZz5/z3sLBkLikxys8C36dH7abBbkJHZ1Dp53H/g5S65cdNKBqqTlO8iu3D20mbBaKGTt4ukMxnqI2WEvGffLHSnx0gpE2WEWqKCgJS5thJj38iUFWV66+/leuuu4XNm9/ggQd+zYMP/pp77vlfKitruP76W7nppvewaNHSUz6jPcuex9+M/TO79e3kyVPt1FLt1k5JySGlZMOG9Tz66P08+ujvaG/fQzye4PLLr+Nb3/oJS5e+dcT6kfD1r/8b5eWV3H773ad8bGuZJGe6GM8I1AEvyCt/iUv+Wom+UaBvBLuB/cRvDvQ3QF8msM44soWEeZ7EbnLQd3j+wU6d9IjVwxG9AXCmGnaugXm2xDx78rqDwHnnSX77W0FXl6cGGxvzyODLLnMpPUnrWBlkXwfnJOuLM+Nn8YPO79PSsoumptls3bqRtWtX88Xb/wP/wwI3KnFmAwXQdgoCP1VIf8adMhHrNEH6r130rQIx5llIiCQEfyawZ3lBdcaowI156mu3zFN0y4Sg6nxJw7MC3ysKTqUX6qakwPeMgtRc8u9+a8PTnAZI/6VLZExBRvZbW8ggIEDb4XkoO43j26XBAVaRgKeUlhEFZUjuV7O7oAwJCue7YIBme/cr27beql37g8B0ffunB82nMevCmTSe3YhTsNGD+mE9Z98KuI5L/64B+nf2Y+YtiuqKqFpUgT/i3Vxql9WQHkjTtbmH9GAGoQgiZWEWXD1vSpYWs2fDjBmwa5dnFeH3w9AQ5HJwzjmSY5XwGhoXmpdxnnmx5x+M/5TYTLmOS6o3hS/s8wKnAjpmzsQf8SNdiZWzQebwRXyESyZ3db1sPI8lTGbZcwEIyAABO0iLtptt+iZOt85gVCRpV/egoDAjNpuyRaXoUQ0dHWlL+nf1M9Y3hgRyWo7tr+ygTKnmtJsWkwmm+UXwh6wxXqBT6SCv5ChySrg1/z5uyt1OTMZp01owRYGQDE/UVAoKGioZkaZf6WU+i5lrL2SWPQ8JrPI9RavaDMCI6KM8ozOjvwOfCJCsiBJUAuSUHEVuMdeaN3Nh4XIe8/+OrJuhN9eJ5ZpoQseSFiPOILlMGldxUYT3fcy3FvG3OxqhYz098SDBoJ/KtELUqaBQdw3zi2/k31IpOtRWNDQa7Jn4xlt6JJI1xioM6aPELQMVtKBOxsiSU7OknRRRxyODi91SBrSt7NZ20OTMRh1rw9/8a7TkTpAOTqiKQuONE77Vk753I8OeWx/gRedlUqqJY8Uo663l2ugtNJ3bSMgXZFnJuQzlezHdAgE1RIm/Am1cBSwVH7n5H8W/62doo7sRronrL6Ew42as8jOP+zwMFQWZe9kc5l46m8iaB1HMJI7vQNJy3HZiCtkjE/touxNdBydCUHa+0UluNA9SEogHQUKqN0XbsE7TQj+xuIvrM8il8mSGRsBO4IYqOLAIE4qCa9lI99QFjU8VVQsrKW0qIdk9ihCCeHUMzXfs45ceSLN7VQtDe4aQUhKritF03gyKGw61LlRU5bCvT2Ma0/BwyslgKSV33XXXEdVXhULhVK9yGm8xCoUCzz77BD948Oe8+tQTOPk8ieVLmHH7u/jrlRdz4+nnE4mc+gTVHVqBpHCZ6+gTBVVEKhgIXtPznHcAGayqKjNmzGLGjFlcddUNE68PDQ3w6qsv8corL7FmzQvcd99PkVJSWVnDpZdexdVX38TZZ1+Arh+bJAXQFI1lZYtZVnaoXcCwOYCCmGQJoSuGFyJhDh2WDParQcJalFFrGF0xJki/rJ323jsJyweJ5HV9Lat8T9GjdhN0Q5xtns+FhSsmiryDYToWD7c8xZiVYW5iFkIIpJS0jLbyyJ4nmVPUxDb/JrrUTmbacyY8Z0vdcsZEileNF4+bDAaYV9LIUP40dg130JsZwFB1FpTMZGlFE5Z78gRFbaSazvT6Q/ZVICjyv71FgxCCxYuXsXjxMv7xH/+VdevW8OCDv+a3v/053/nOV6isrGHlyrNZseJsVqw4i3nzFp30RAt4wYCnWacfczkpJW1tLbzxxjpee+1VnnrqEbq6OiguLuXKK6/ny1/+Buecc+GUf0NvBlIp7y8Wg8HBZu6//16+8IX/PrWq4H0QYJ4lMU+XKEMgA0xYNGi78MjbAxXAAUAKtGbJsQRTbhUUqt46wvGSSzxP3BdfFAwNCUIhybXXSq688uS3wVog8RVL1Lb96mVlAP569t8xtDDF+ecvQlVVHMfhc3/3Bd6Xex9S86wgAAiA3STRWkHfIbCWTX2bZIRJimr//QKkAN0jh615Em23Z8UrkuAsgPxNLtIAY4OCU7E/ANAtAhyJsU5QuOz4ggH3QYyB1uL5HNsN+0PxpgTVUye7cU8tfMjYx5gvdKuhcL5nFaIkQfrwAu1qJObF3jEaGPA6WUpLT5G39h8JpuvbP11ohvq2hsVJKdn17G5a17ThOhJFU+jZ0kvvtl6W3HIawXgAVVdZcM18apfVMNaXRvOpFDUUTdnb2DDgjjtcfvlLhZYWQaHgTQpedZXLeedN/XqrohI8hW0TQhEEYn6SnaMe4VsaYqitQDaZxbFccqk8rq0z84ImArH9N1uJpEfpJOJOvlAaGEhcRpQhXjCe4XH/Awwq/QgUqpwabsrdRpM9m7XGy/iHAoz1jWGEfGSjY0S1GI1aE73b+inb1suz5z7KKt/vGVD6cXEIuxEGlF5+HPguKTHKn2X+isR49oKFOWk7HFw0qRM8IHRAReXduTuZac/mP8NfZEwO8rdXxgABAABJREFUMHfAoiZp4RdZbFJUDI3ykaE6ZlT9LQHCExYcS6zlPMx9dOod1Nj1AIwYg8TcOKXJYpLhIYp83qyuku1mZmc7rn4euWAENaShx3VEchf64EYK9dcRUaKH9Ud2cBhVkgQP8mXynsvEpP30XpNIXIQ5SmDbt9FG9+CEa0FoqOm9BLd9n4wewU7Mm/ickulmzfBnWFf+CvVjPiJjCmlNYffpvWw9dz1XFq4CwK8GqA7tV+EWnDz92S5Mp0BAC1EcqsJZ+jnUdBvCzuOEa5HGST6fCoFVcTb+3feCPwdaAKRETXfg+oo965VjwHVc9r7Rxd71e8mP5fFH/NQur6V2afWUJ5ukK+na0E2kLEJ6YIxCuoDm13Ash8FsEV3Z+Syo2IMUWQIRnVCqm07/+aSsqgndupSSzHCGinnl+MJvQn08jsxQhuxIDiOoE62ITlLx6gGd0qapt54V0gU23L+ZZOcIoeIQQlEYaB4kPZBm+e3LiFaeev5hGtP4Y8YpJ4Pf//73H1O1Nh2u8YeJN95Yxy9+8UMeeeS3jI4miS+az4J/+GtOv/kmgjVVdKo2u10VMxOaFG5/qlAABIcGP+gScmJqM5rFxaVcffVNXH31TYDnibh27cu8/PLzPPHEQ/z0p98jFotz+eXXcvXVN3H++ZcSCJyYkf2RFBGeMO7w7ylCoSEym60jr5E0BzEUP5ZrIoSgMTwHn3qEvvIp4DX9VX4c/A42Fgm3mFElyX2BnzOsDHNb7gOHVYF2Z3rpzvRSFdzvuyyEoDJUQVe6l550H5lAGgGTwsfACyYbFkOHBO1NBQlfEcsq57CycgnJwhgBzUfCH2XMShJQT/4h46yqFWwe3E5Lso3yYCmWa9GT7WNOYiYLS+ac9PinCoqicMYZ53DGGefwhS/8Fy+//DyrVj3NunWrefSx32FbFv5QgNNOP51zll/IokVLqa2tp6amfiIE6mRg2zb9/b1s3bqRN95Yx4YN69mwYR3JpNcK3dDQxGWXXcM119zEGWece0pI6ZNBPg+PPSZYu1aQTgsiEcnQ0L2EQhHe+94PvrkrNzzP2QMhVTzS0ZUow3gBagJEljfh7nvy0DS4/HLJuedKRke98KDwKcrYcCsgd5vEf79A2wO4IGOCe8t/zW8euZdvfesnzJkzn61bN/LP/+dvaFhQzfsaD6oVdMD1FL4nAxlikkrZaZC4FaBtFFhLJJlPucgIqLu970oeqIougBgAvVUQ+XeFwnkS8xw5HvB2bOjrBf7fCdT+8eOSEOSvkpgXSqZ6mbQWSrTtCliSfc0dYgzPSqPhGMdGQP56z8LEeE0gUmDPcjHPkhPnb3//PjL41FsO/SFjur6dxtuF0e4U7ev24o/4CYyHK7m2y1DbMHtf72TOxbMArz6LVcWIVZ3Y/b+qCj79aZfWVshmobKSk+4KOVkIISidVcaeNW307RpA1RVA4ORtjLBBcX2C+jPqqV0y2dReIKh0a9ikv06FWzXxuomJQDAqkqzyP42CYKY9FxeHDrWNXwR/yA25d9Oi7WK7sgViCkpCYGkmpzefTWWuhhE1SXP3bjYbb2BJGxtrwgLCwCArMqzXX2G7vpnl1pk87jxAu7oHHQO/DJBUhnGExRx7PvPtRQDkyNGptqOgsMw6g49mP8V9zpcoHctg+KoxhWRPaISqTAmnN3egBPqwE/tnEuvsRs5JnsuL8VX0+LqQQLFdzDmjF1CSLyXtG50gg9VMN8IcRSYW4Jf7r2luoBQ124Uwk0j/4UP/9nkOv6GvnRQWGHMTdKitGHI/qZgUI/gJMMOZhT74BtpoK3Z87oR61onORBvZht7z0n4yWErUlp+yunYjEYop0hOQcCjODxHIO+wpaqHZ2cF8e7IIJ2kOsX3kDcbs0YnvP2GUMD+xjGC0aSqn2pRRqLkUdbQZffANkA5CSlx/EfmZ78ENHPu+2bqmnZ1P70Q1NHwhg8xwlq2PbMUp2Mw4Z2oWE67rYuZMwqUhgnE/I3uTmBkT15GoPp1u42rCaidFcjOakqbXmUli+Vn0tsNQ2xCaT8fKmYSKQzSdc/zdsVOBbTrsem4XXRt7MDMFNEOjeEYx86+ad8Ihcf27Bkh2JimqL5ogzn1hg6HWYbo2dU+TwdOYxnHilD+O3nPPPad6yGm8jZBS8uKLz/LNb/47L730HDU19XzgAx9De8+17DqtnrnOfsVBk6OzUzXZpBe4yDz1Zop1roaOIC1cwtK7AThIRoXL+faJrS8Wi3PZZddw2WXX8M///O9s3bqRxx57gMcff4D77vsZwWCISy65kne9631cfPGVxyS7bCQKoCAo9pfTk23HdAoYqlccFZwcqlAnCrLDodRfyeKilXRl2xkzk8SMBJXBOsr8x5HedBAcHJ71PYGDPeHbVeQUM6wMstZ4mfMLl1Dt1h7yOW+eXyAPYPellAznRmhP7eVXO39HLBnAboKcP0tg3LxTIhlVRphfOLEAsqpgPYP5XixZoCJUhItDyhwm4Suh2H/yXn1zi2Zxx/xbebLtOfaOdZEqpCkJFjG3aBZ52ySgnXyS7amGpmlccMGlnHvBRTzg/xXPyifpeaOTkTVDdK5p57v3fJ30cGpi+UgkSk1NHdXV3l9FRSU+nx+fz49h+FAUBdd1kdLFsiwGBvro7++lv7+X3t5u+vt7GRzsxx1vHSsuLmXp0hV85COfYunSFSxefDpFRSeeEH4w3JFhrDUv4WzbAn4f+tLlaMvPRByHmvfhhwVPPCEoKoLyco/QfOGFh5kz5yr8/hOfSDlR2AvB97JEXydQhwDb828FIM0hlgnvFASDRw4KOhlYyyT2LIm2W4AFToPL/7n6c/zFX3yWG2/0gmnmzVtE594O/vMH/487E++fFNa2j0R3S06ODLYWSXzPSNRWPP9hBcSwp0LO3+ARweCpgGUElCS4pYDleT5r7SB1UEbxQtq2S7If3f+5I0HthOAvFchL7DpAgjICgfsV3HIH+9iCIsBTOeubJdoOgQzKCa/fwjmuZyVyLKhgrZRYKw9/HDs69gBQXX1yQZ1/bJiub6fxdiHZmcTKmUQr9l9kFE3BH/bRt71vggw+FVBVmDlV26HDQEqJdOUps9OwTYeB5gEURUEIsPM2ju3ii/g44wMrmHFO4xHXdbZ5Adu1zXSorZQ5FZjCpEvtYJY9j1ExQk5kmTN+4VVRmeHMYoe2hTElxccyf8mve3/OZmsjxaMlzO9YwsKOpV5NKyUFf56CyJMVaXzSP1HralL3OoaESZ/Sw2WFa/hw9pN8N/h1OrQ92NgEZJCV5jl8LPOXhGWE1/RXecT/W3rVbgSCGruea/I3cnFnlLXxYfb6BhFAVS7Knd1LiOb7yY21TlLTCiFYkD+N8q4K0uEMAJVmNX7HzwgDqGL/84vUAqAY4OQ9Zeu+MewcUvV77x8BAsEFhUvZre1gt7aDcrMYxRpDUGC2mMeIMkRWZHCEg4LgwsLlzLLnoeR3eAMcaKMgBFIPo6b3TrykZLuxxraS9vsJOuN1m1CRRoxANolth8iIzKRtcqXD7tHNpO0UcaMYRSg40mao0E/r2A4WJJYfcX9OBNKIkln0KfShjajpDqTqxy5ejBOpP+ZnCxmTjvUd6AFjIjjNH/Uz1p+mff1eapZWT0nNr2oq8eo4PVt7KKovIlwaxsyYdG/pIZfM4S+KMaBUMMByCukCeTfPyguXU5W36dnSS240R7wqRtVpVVPy6N0HJdON0bsaNd2O6yvBKl/pEfyHIZPb1rSx56U2gokgiboEVs6id2sv0pUsv33ZCYVwZke8kMwDf/NCCPSAzmjv22PzN41p/CHjlJPBH/zgsdVXQgh+8IMfnOpVT+MUwnVdnnzyIb7xjX9nw4b1LFq0lO9+916uuupGVFXlK8FhgnJyu74yXgqljtWneoKYbRssN/2sMfIEEGhSMKo4NNg6Z1snT94JIVi4cAkLFy7hb//2X2hu3snjjz/Iww//hrvuupnKyhpuv/0ubr/9bqqrJxOnXYrF80aObVoBQwqW2z7OpYyiUD392Q6U8ZlqVWjUhGYcM+TtVAfBpcQovWo3Re7kVpyEW0y/tpU+teewZHB1uJK6SDXNyT3MjM9AEQrtqb282vs6Ad3PntF2CiMmyX7YunI7teEqDGkwoPZ5IXLWRSe0vQmfN5vfNraLtJ1CQaEyWEdTdD66cmoCDpaVLaYkUMS3N/6YjJUhY2Z4qOUJXuvbyJ3z383M+FuXBnw0ZESa1cYq1uuvYAuLkBtms76BSreKhpUzYCV0/1UnUkru3PNhaBd0drbT2dlBV9deOjvbWbv2Jfr7+ygU8phmAdM8oI1PCFRVpaSkjPLySsrKKli6dAVlZRWUl1dSXl7JvHkLqa6ue9PSd93hIfLf+xb2rh2IUARsC2fTRrTm3fjfdxdiCorjoSFYu1ahpEROKJksq51UagPB4N+RSkH0LRYMWKdJ7FoIvC6QugDds5FwEhJjs4LZ4eAc+9nhjwoywiSLh3w+e0g4oqqpOH4HkRL4Hwc3NG6JoIC5Qk6N8DwK3CrI3X6ASll6wXH5a13sBQdsazEUVrr4n/AsHcQYE0SwvQDsGYAp0beD/prw1L1HgbZZIPoBIfA1Ay64MW+/9NcF9vypkdwyAZmPuhivemGE0gfWEom5QKIMesfqiKF6U0Bz804qK6vfFLunP2RM17fTeLvg3XsPvf9KKRFvk4fxwbDyFh3r99K9uQe7YFPcWEz9yjpiJ6nUG2odYrhthJol1UgpMbMmiqqQGc6S6hubIIX2iRYOFCAssZZzW+4unvI9Qo/WhSZ1lloreVfuvfw68BN8cvJksxj/b0ykmOHM4oPmn7P+t68TjAbwRz1i0sx69dOsqtkknCJc1cUW1kRHZE5k8Uk/PnwTPsGXF65luXkWrxmvMiQGaHJms8haih8/LeoufhH8IXly1Nh1uLi0ay38KvgTPtc3j4v3hmgvjRJ0dOaNlRG1DKAfqRxqxVUZrGU0OURNpgZjPHw5ZSUJqKFJIhQ7Pgc75qlyrdhM+oxhhq0WwvYAg/WXU6LnKDrKTWS+vZi7xj5KR///ULH3JWI5m6hWj7/qPbzeWMJOfReG9LPYXsoSazkKCq4vgXezdUDZb7ki7DROaL9yWzgFIqakshBldyRF0fjznRQqo3qBkOOjzJ0sChk1R0hZSSJ6bMIXWRUaQS3MYL6PvJPDr55ikYfmxyo/A6v8+AISsyNZ8qkCkfLJbVeBuEcIZ4ezU7Z2qV9Zx3D7CMPtIwRifhzTQQ8aCFWQS+YQQmAVbPKpPHWn1xKviSOEoGzWicn91dFmglu+iZrpQqoBhJPH6H2R3Ow7Dwnlsws2nRu78IV9BBPesfeFfFAJgy1DjOxNUlR/dJ8tKSWZwQyO5RAqCaMZqndspPSufQc8k9h5e2I905jGNKaON0UZXF9fz9KlS5HyrQ1XmcapwapVT/PP//zX7Nq1nbPOOp9f/OJRLrjg0kkX3RpHY7tWmGQB4IzL3Irc4/NVSwuXNtUjlhsdnZA8fGGrIbg9H6XBNXhNz5FHco4Z4BwrQJk7+VTOCJfdqomNpyg++P2pYObMOXzyk3/LJz/5t2za9AY///n3+c53vspXv/qvXHzxldxxx4e5+OIrGTDge8FROhWLIqmSF5J7/WP8xjdGcbiKQDZAfTrNYsdPk15Owlc6Uay8VfDLAAEZICeyxA8wucyTR8cgeISiT1M0bph5FT/Z9it2jOzGdSXbh3cS0PycW3UGRf4EjnTYMmyT2OFHW25RUAostJZwaeFqZtnzDjvuVFDqr6TYV07OyaCi4lMDp5SMlFLy+J5n6M8OMK9oDrqi4UiH5mQr9+9+hL9c9jG6M30k86MU+ePURqrfNDL0SChQ4MfB7/Ca/gohGUZB4Tnfk9jYzCjMmvjtVTk17NC20F/dy1UlN3L66UcvTvddm9/q/TkSrFdewt61E3X2vAniV46lsF9djbPiDLT5i445xvAwpNNQe8CcRnPzQyiKTjR6JSMjbz0ZjAYyIbBnS/bZArpFErfIC0PTtguc+kPvk2qbRy4qQwK3QmIulxzQ5XpEiCQoY+OK1tAxFz9uKD2gdgowwJolpxzidjRcdtk1fP3r/0Z1dS1z5sxny5YNfOc7X+POeXeBJZHOuKVCr8BaIMnd7B69crFA2w7aXoE0wJ4ncQ61Z8daKrFnS9QWELY4ondv/jrPisF4RXj7ro4TwU3j35sBaKA1H5sMVkZAbQfhjpO1Kqj9nlpca/a8FacKGYfCFZLCFRIs8D3t2VYoaYEMSwpnSwqXSTiBubPt27fQ1PTOsct5p2C6vp3G24VEfQJf2CAzlJkISbNNGzNjTrmt/M2Ea7tseWQbXRu6MEIGqq7Svr6D4bZhlr17yUm1bmdHskhXTgRL6X6PBHUsl9HuFP2ilxd9z7JJfw0VjeXmWZxnXkxERhEIzjUvYpl5Br1qFz7pp8qtQSCocxrZqL8+6TnGxgbEhP1ByYxiGlbU0b52L+nBDAjPvqv6tGoaZzdyoXk5O/SttCktXocJCjYWcbeYWruBBdZ+z90iWcxlhasP2b/1xiskxQhz7QUT2zHTnst2bQuvNS7kulc7yYSgPZplVOvgtF6HKl8RdmLBIWNVBepJmUn6cp1kbE8lGdBCzI4uInBgaLKik5t7N4Ht36M//TwD2S4KusLmuhIenb+bGuUrfCT7SUrdI3firWjt47zdOUx1ARhFGNksYvtjlBdu4aKmzxyyvFWyFCdSjza6AydchxQaarYbqYWxKs/xFnIKKOlOtFySK3dkaFtq0hwaotgMkndHGA5IznYvoM6ZfM670sGVLoqY/PypCgVHWrgnGL6tDICxxqvVZBCs0yXmiv32TCcCI6Cj+VTsvI1m7C9mvP9X0QNTH7y4oYiltyym7dUOkl2jGGGDJTcvIhAL0PlGJ+mBDLpfo37FbBrPqvfI4ZyF67gYIeP4ngGkxN/2IGqm2zv39pHuY234W+/HKlk6yY/ZzFmYWQtfyCtCJJLMYJZkd5Kx3jHW3/s6cy+dTc2S6sOG56UH0ux4ehfD7SO4tkuoOMiMcxopnVVCqDjEyN4k0YooiipID6TR/BqVC/7wcg5sbFTUE+qincY0TgVOORn88Y9/nHvvvZfW1lbuvvtu7rjjDoqKplMc/xDQ39/Lv/zLZ3nggV9x5pnn8eCDq1ix4qzDLrvCDrDeyNOsWFRKDRtJj+JQ72gstqfe1v2qnuMRX5p+xUEAZa7K9fkIy+3Dt3T7UbjIDB7VhmKzVuC3/jF6FBuJJCZVLioEudIMoZzgxXbx4qUsXvwt/umf/o0HHvgVP/3p9ybUwmf+2UfI/NmtLPQnUBBkcdmhOvSrDstsP75gKa+EE/Q4On+eTaAcgex+MxEgwErzHB7y30dQhojKGAUKtGktzLUX0HQUqd3sRBOfWvpRNgxs4fW+jQwVhllaupio4T2QqEKlKlCJ7HX5q6E/xzA0IjJ6SlKkFaEQ0qbevnQ8GMwNsXOkmcpQOfp425oqVGrD1eweaeU/1n+Lvkw/WTtHSA+yqGQ+7559A/nRDG1te+jo2MPg4ADp9NjE39hYikwmTTo9RiaTxufzEQpFCIVChMORSf8uLS1n5sy5zJw5h+Liw4cnbNE3sFFfT4PdRGCceetQ22hX99CldjBz/HvbF9xREFMLMHqnkMD74GzZjAiHJymARSQKXXtx2lqnRAbH4561QToN+245LS0PUVFxEbFYhGj0rU9KBhAFcIs5LOkrDpOFqL8uCP5CQQyD9EtEXsF4RZK928U+QiewyILvEYGxTiByAhmTFM4dJwNPxV3eBv/DAt+LCiIlQQGnSpB7j8tJzPcA8KUvfZV///d/4fOf/xRDQ/2Ul1fxgcs+wj+n/wm7BsSYROkQiLxE6ZPo6wWF6iMQcVkI/kzBeB2wPTWvjEPuRhfz/EM/I0Pg2Q4ehdjzQ/5GSeESSeBegbFawZ590PKmQKRdlO5x3+gj/bwKHlnvVDFB0krfOEGcPmA5y1MhyxAcIdtz8iY+KfA/KHAj4CYkYgwCvxMIE/I3HR9pmc/neeml5/jkJ//2uD73p4Dp+nYabxciZWGazpvB7lUtDLQMIhThKfzmllG77DCzXW8xhjtG6N3WS7QyOqFqDBYFGWodouP1ThZeM0UPnMPAF/KB8AjnA0kjK2ch612+H/omu7XtJNwiHOHym8DPadZ28JHMpwngKQWDBJnhTL6BrjTPYZ2xmm3aJiqcahCSPqWXmfZsllierYCiKsy9bA6lTSUMtY0gpSRRF6d0ZgmqpnJR4Qp0qfOj4P/SrO9ESkmpW85i63Ruy71/kvjiSBhQ+gjIwCQySCBQUdlbUsL/rBCs97+AJRykgKLSMDfaH2Zl6NAZYlXRmB9fRlWwnjFrFE1RKTLK8GuHPjM5kXq2r7yT+/NbiBbKUQI1DCaKqBMOu7RtvGA8w7vy7z3sNgsrg9H5NGhh1LA3A+/4QMn2YHQ9R6H6EuRBYczSlyC74M/w774XbbQZIR2cYCX5xhuwixaBnSW07bvo/a8i7AxntwwjchaPz/HTE80QcHzcmL+FC5QPHUKchfUYAS1I1k4T1j1CUkpJ1k4TM4rxn0DWiNIHoe8qaK0CN+xNuuqbBWqr11l0oo84waIgpTNL2PtGFz49R8Lfh5l3GRmMULl0FsGi49vW4sZiihqKcEwHRVUmfiNViyqxshaqoaL5NHLJHDuf2U3fjn6k65KoTTDj7AYSdVNLwRWFYbTkLpxg1QQRDOCEatBGd6GmWrBLlk687gsZBGJ+siM5fGEf6YEMfTv7sXMWiqpQGCuw5ZFt5FL5Q2xurJzFxgc2M9KRJFIWRtEUMkNZtjy6jaW3nMbC6xaw4/c7SfWmkK4kWBSk6dxGSmacOuu6Nxu7te2sMp6mRdtF2I1wpnUe5xYuPmKg+zSm8WbhlJPB3/rWt/jv//5v7r//fn74wx/y+c9/nmuuuYYPfehDXH755e84EmIa4DgOP/3p9/i3f/snNE3na1/7AbfccsdRv6sGR+f9uRiPG2m6VBsFWGb5uK4QJianpgxuVU3u849hIWlyvJnQTtXmV4EUpRmVevf4p14HhM0v/SmSikOTo6MCA4rDo74M5a7G6UcgmaeKcDjCHXd8mDvu+DCbNr3Oj370v9z3xS+ife2rZD/xMZZ+9IP0FPnJKJLQeExcsVSJOwq7VJPX9TyXmm+CZG8KuCx/LUNikA3GOnpEF5rUmG3P4/bs3egHTHP3ZvppGW0DoCnWQEWojLJgCZfXX0hJoIiW0TZCB/uJSUAIQgSPqDJ+p8FybRzpoh6iJFDpGNtLf28PiWEfma4kO9s7eaL9R3xx4ONY+f2EaygUJhKJEg5Hxv+ihMNh6usbCQZDFAoFMpk0mUyajo62iX+n02MMDw9O+PEWFZUwc+YcZs2ay6xZc1mw4DSWLz+Lvf42z1/uAAlmqVtGu9rKgNLHTDwyOE8OFYWaP1TPAZ8BzmTlhtzXBqZP7TpQWgpLl7o8+6wCSAKBHJ2dLzBv3ldZsUKSmFq9e8phzZNoWxWw9xOznvet9PxqD/QNznmkK1npkawCRFaivyEIfU0h87Fx8vXAO7cE/28EvucV3FKJWy4RIxC4X4DiqUdPFsZagf9xgVQkMgRSA7ULAj9XSP+Ni4yf+NjhcIQvfOG/+MIX/mviNf9vBMYTCrJXou8U4HhhfEpSELpHYJ3u4B6G//CtERhrwakFGZIgQe2GwEMK9izHI2qzHkEvx+0ZpgoZAfNcibHJUwu5JYAAbZtA2w0ir6C1gT1TknuXe0iYIIAcV2wro8LbPgEi45H3CMAFY7XAeF6gDnsPoObZksLFR1b4ihQYLwvcmBfSB3gqdNVTNBUulEyBj5jAyy8/Rzab4corr5/6h/5EMF3fTuPtghCCxrMaiFfHGWgZxCnYxKpjlM0unVDKvp0Y6xvDsd1J7e1CCHwRP0OtQ1Mexy7YOJaDETQm/ERLmoqJVkQ8JWBlFNVQyA5lAUn/eR00azuZYy+YCDHOk2eLvoFN+uucYZ1z2PVIJN1qJ0Iq9Ku9dGhtVDrVXJC/jGsLNxM9IBFUURVKZ5VSepjWegWF881LOdu8kHZ1D4PKAAEZYKY9l+AxWmdcXAoUqHSqeUNfO0mh7OLi4jBojLC1Huoz5xPNm0ih0B63uE/bRX26h/LD3GiEECR8JSR8hxcZHIgWo4NNcWOSKllDI+4WsVFfz8352ydeP9CGQ8kPoBRGcIOTlZiuvwQttQc114vtP3SizIk2kVn6ebSRbWhDm5Cqd74IK43etwa9dw1OpAE7Pg8lO4czh7ew8sUhBhvOQ6m+AVF1JYe7cftUP3WhmTSntpIsDKIpBqZbwKf4aYjMPqFuTONFgdqew13wLKqxBsgj00sw1l2KtbLsiJPzx4IQgrmXzSGWWUt8+DH85jBCUZg1owr/ko+e0H1ECDGhnJeupJApIIQg1TfGaPcoriPp3tzNWP8YwUQIVVfp2dbLaE+K5bctnZpyX3jBjQJ30vS560j6+kIMbR5FqxmguKEIVVdRdZX6FXVsfXQbo92jjHQmPXJaU4hVxShpKiY7kmPva3upXVpNMLH/9zLQPEiyM0miNo6qe89o8eoYQ+3DdG7oYtmtS0jUxkl2jSIdl2hVbEKB/IeAXdo2vhf8BiPKEEVuCX1qD/dq99CjdPG+3KGTHdOYxpuJNyXP3Ofzcfvtt3P77bfT3t7OPffcwyc+8Qls22br1q2ET1U8+TROGi0tu/jUp+7mjTfW8b73fYi///v/SyIxNaXLAtvHHNtgQHFQgVL3+Noc3tAKpITDXGf/LFi9o7FDNdmo56kvHH+Bu1U36Vds5jjGhAq4zNVIKSbr9NxJk8EHYvHiZXzlK9+j9O8/yaPf/Dpr/uMrrPvG/1D58bvQP3kHorgIbXwbVAQ6gi7FPmXrP14ECXJ37uO0m1fQp/QSlmFm2fMwxlkGKSXPdLzAE23Pkix4HsdxX4wrGy7mkrrzEULQFGugNFBMV6aH2rBnm2C7Dv35AS6sOYeg/odBBAOUBUuoClfQNtpBSAuS6h+mc0sLW19/g95t7djDOQB8oQDFdRVUzq4ndEGCu8+7kwUzF1FX10godOLXskKhQGvrbnbv3kFz8052797Bxo2v8dvf/oJ8PkcgEGTWeXOxrrAov7CSxMxihBBUO3Vs0zYzIoboVjpxhMOYSLHUWsFCa8kpOjpvLbSly7G3bkam04hw2COC+3oR0Rjq7LlTHuemmySu67Jhg0JrazNSOlx88WKuvfbta+k2z5DoGyT6LoEbkeB4KlC3BHwPC/y/EdgLJIXzJEoG1F6BXeORg2qvQNvq2T+onQoiLTDPkeTe5054wiq9YLyh4FTuJ/1kAOj2HmYK5528nYOxSqC2CoQDOHgqrQSQk+jbBObZp/j4akAetA6B1Jggm6Xjhbb5nhHkPnDoOvV1Ahk4wCJDeCpcbQfobwjEajDWC4QpsKslhUtcZAy0TQKRA6cJrIUSjnCbsOdC/goX39MK2g5vW9ROgVMmseZIhDO+njGF9KfdQ467WwJOg0dSK4OAC06NROoSp9b7vgK/FF5IXlyijEHgNwKRhvwtk/dXbfFIYH2DwNgksOYAUk5MLLhxzypDGQLnOMjgJ554iIaGJmbPPnEl3x8zpuvbabxdEEJQVJ84ps/m2wHVUEF6RNSBoVCO6WCUHFsAYeUs9qxpo2dzD7ZpEymP0nhmPWWzSzGCBouuX8i2J3Yw2jWK6zj4o35mXzSLR5s2EJDBCSIYwI8fF5e9atshZHCqb4zerb2s9r/AU4seROJSnq4hEx1DxiTlaiWV7vGHNWtoNDmzJwKajwYHh5eN53nJeI5RZYSA9MQVu7TtVDk1SFy61U5qnHqSIkmQEEH/DPY9vlQj2aFsZYe2lXLzUDJY5vM4LbuQpoVaV49yhM4z8Mjswz21ucJBld4xzZHjZd9zvKq/RE5kmWcv4mLrdOaqfoSVQR6gOhZWGqkFcPUjd/Sp6XYCu3+GmmqbuF/ZsVng2kjVh9S988UNVmJqQYyu5ygZaMfNP4A9uJV8ww048UOPc22oCb8apCfbQdbJUOqvpCpYPyVS/HDQt9iodd9F+F9CyiCgo0R/h2puQmv5DPasEw+zDrltnFb7CmZRiJxoRDNUQkofYu+9pEvrcSINJzTuQPMgrWvaSHaNMto1imM7BONBChmTsb4UZbPLCBZ5KnR/1M9Q6zCdG7uYPwUyWBpxrKIFGD0v4BoJUFSyadjwvEnf0GwKsTSK9jqJujiLb1hIqDhE7bIaXNdlz4ut5Ebz6H6NeHWMRF0CIQTBeIDB1iHaXmlH0RRUXaW4sZhcKudN5uuTxTq+oMFYn9dGpfm0Pygl8D5IJM/6nmREGWbOAZMwSTHMWmM155oX0eA0vc1bOY0/JbwpZPCB8NJfBVJKHOfEPHum8ebgoYfu46//+mOUl1fywAPPs3Ll2cc9hoag8gT8eAGSioN+UBkixknTpDixlu6scL1Z64PG9SNIKm9Om/glVbPY+5UvccbffJrd3/guG77+PdxvfJ+Sj91B9C8/AzGvcLeQRN8Gi4gDIRA0OE2HvdHsHGnmoZYnMFSDuQlvyrsvO8BDLU9QE6libtEsYr4o1824gvt2Pcz2kV1owvPZbYo1cnn9iYXFvV3o6+mB15JsfOZpntzeTn44DQIiNcWULqljyfIzqJvXRKgoihCCnJ2nN9vPiuXnUR89NGzveOHz+Zg7dyFz5y6c9Lrrumzfvpnnn/89T6x6kC2f28B2azPRhjgNl8yg8qYaZlw0i/nOYtJiDF3qXGlex7nmxfiPxGKdQjiuQ292AJBUBMtQlePzCD8c9JVn4zTvwl73KtiWpwiORDGuuQGlrmHK44RC8P73S664wuF3v9vGqlXwZ382m+CRyFDpqTxFHpxS4E3InpDFkP2Yi/GyQNvsEX3KsNeCKApeGJnvKQVtmyR/uTuxXSKPRwTnPeJVMSVumcR4ReDUCgpXeuSgMuyRy/IgqzQZBWVEoIyCNEHb7dkmOI0S9zizKfU3BEoSnHI8haoNyhCIEYEYOwwR7IzbHPg4oWNqz5EoNigpcMafdcV45qFdB/o2QS4nDxlbmBxa1YzfCvy/F4ikwC2RuAGJvhN8L6uIlKeuFXhhboULJJlPuxMez5OgQP5aibXQQWsW+B/zfImtpR4JKwE7INFaQd8qsFZMPjbWAondKFD7JOYsQPXWrYyBuVTif9YLGXTqxg9jzDs/fa8omBc4uOPCNG0HBH+goIwIXJ9EpEF/Y9z7eNa4eivtTQrI47DqdF2X3//+UW666fZplesUMF3fTmMaHkqaSggWBRntHiVa5dVMhXQBx3KoWnx0w3vpSrY8so3OjV0Eon5UQ2NozyCpnlGWvOs0SmeWEK+OccYHlnsEl+kQKY/gj/oJE8bad3PYNx4SF3fCImIfBloG2fzgFkZTozx7wxMkW5MkuktR4jpxrYTRRJLHqh/k3MBFFMk3j2R63P8AD/l/g0/6CMkIHWo7/UovERmhU20nLKMstpZxff5W7gl+G11OFsaIcfro4P0GsHdup3Dfz3E7O8FxEIkE+kWXYVxxDUI59Pljlj2XmEzQo3RRNd5uU6BASqS41LoGF5dfBH/IamMVIRlClwbP+p5ge9Fm/rFiBhXt65CKjjRiCCuNmunErDwPN3QE6xLXwb/7F6ipVuz4XFA0lFwfvs6nEWYK11+GGyxH6hGwc+j961DyQ7jBKlw1iD6wDiXTRWbJZ3FDk0l7IQRlgSrKAlMIWJgCRHAzCq/iuA2AVxBItwrNtxXNfAG49YTHNvrWolhjaBUL2UebuzKMNrwFfeC1EyKDh9uH2fi7TRQyJtJ2SQ94Htd6QMcf8zHWLxjtHiVUEiIQ9SOEwAjqJDtHp7YCIcg33ogcbsdpfQ3LhM07a+kaqCE6u4lIURm2aTO4Z4htT+xg+e3LEIqgYWU9pbNKMfMmiqoSLd8/UVDImqR6Uux8drfnleyCHmojURdHcqg1jJk1SbwDJ8OOBwUKtKrNFLulkwR0MZmgV/TQrXZOk8HTeEvxppDBhUJhoo3upZde4tprr+Wb3/wmV1555SGp4dN462HbNl/60uf57ne/xg03vJv/+I//JRx+c3xZj4ZqR2O1LnGRE+Stg8RCUnWCBHO5o6EABSS+A1qbxoTLmc6b00p3uuWnU7F5qUpQ+W9/j/9vPsq2//k+w9/6Mb/88W846x//jqK730NMaiw5Dj/ltxqbB7eTtfOTiM6KUBnbh3ezeXA7c4s8gvisqhVUhMrZNLCVlDlGTaSKpWWLiPtiRxr6HYPe3m4eeeR+HnroPtavX4OiKMyeP5+Vl5xPdFY5p51+Osvql/G75kcBCAf379NIPknMF6X4MK1vpxKKorBgwWksWHAan/jzv+ZR635+uu77dDy9h+andrLpB69TNruCc+64iI/d+pfEYvG3rKVo98geHt7zJO2pvQDURaq5rulKZidOrnARfj/+Oz+Es+JMnPY2hK6jzpmHUt94QqRUeTlYViuxWJyiosM/1IkhCDygoG/1vFWdIs8X1jx/v7ryVMEthvz1Eq6XaNsh/HUVp+GAULkKibZDoHUI7CqJ1gnSJxAZLwxOGfaIWLcUREFirBUULvc869yYF0Ym0uPWB/v2bwxkUKJuFgR+75G5SJBxhfxlrvf5KeynSI6T1ooAY5zc1Lx1KkNMDtKSoG8Q+B4TaHsE0u9ZHBSukhyPe4w9BworXILNCsqAZ3eB6vkuu0XjXn2HKSWsBRL/owpUSBifoxCjHkks+seD/MZFaq4LvucFUoBTK5EqiDFB4FFwZgpy7zmC2lmA0+iR6saLnnXGpOOoA1KgJA/jURyH3B0u/l8paJ14nsYxQf4aF2eGRPmN4u3fAXCLQGvxwvuUJCj93vFVhgX2XG/dVhr07aDtEDiVEmxPVV64wD0u4v/119fS39/LlVdeN/UP/Ylhur6dxh8bbNPBtR30gH7Ck0DBeID5V85l+5M7GG4fATzlXt3yWmpOOzo5N7J3hL6dfcQqohjjrd6BmJ+htmHa13VQ0uR1RamaSlH95PrrNGs56/Q1DCmDFLnevb5H6SIq48yz9mcNOLbD7ueayafyaAsUxsJJwr1RVE3BNR1CxUHIxOlNddEu91AUeHPI4GExxCrjGWJunHK3kl6lm71qG31qN7r0scBazHxrIXdlP4EPHwus03jM/wAVbvVEDkdKJPHhp85pmDS2OzJM4ec/wu3vR6lvBE1DDvRjPvI7lJJS9BVnHrI9lW411+Rv4iH/fWzTNqOMU82LrWWcX7iE3dp2XjNeocapIzI+s1juVrJD28qj8+Zwp3k++uDriEwnUgtglZ9Jbtb74AjnkZpuRxttxonUg6KhpjvRh94AM4ViZRDmKMLNYZWdiSgkUXL9SF8cJ9qA9Bdh++JoI1sxeteQb7rl1H45B8Gd0wbbLIQVRup44oEx1SO+I5s4GTJYFIaR6kHPg0KAoiEKIyc0Zsf6vRTGChQ1FNGztRfNr2GEDLLDWVTNs22wCza5ZI5A1BOO2AUHf2zqIpLh0Sg7tlyMP5lAtUfYvqcC11+EXy0lAGiGRrQiwnD7CGN9YxP2E6FEkNqltexZ3YoV9aMHdBzLoW9HP47pUNxYjDEenJcZyjDcOkwgFmB47wiRsgiq7nkGK7pKzZLjV+6/k6ChEZBBksrk79nG9hTb8k1QpUxjGkfBKSeDP/GJT/DLX/6S2tpaPvjBD3LvvfdSUnJiLRrTOPUYHU3y8Y/fwYsvPsMXv/gVPvjBT7xpCqAcLv2KQwBxWAuJZZafNUaenapFues9ufcpDnWOzjLrxBSO822DebbBJq1AiVTRpGBAsSlzNc4yj/8Cu1M1Wa1n2avalLoqZ1oBlti+SfuiIbi5EGaF5adDtVBDccb+9l95/EMf5uUvfJlnP/N3JH5wD3/5xX+n8YwrTmi/3gpkrAzaYVSemqKSsTKTXmuM1dEYq3urNu2kMDjYzyOP3M/DD/+GV155EU3TuOCCy/jGN37EZZddSzR6KIk9kBvk4T1PYrs2ET3MqDlGzs5xWcOFhI23zvNZILhGv5kl565g90Xbsb5kkXpplKd/+Bhf+sLn+H9f/j/cdNNtfOD293J6uYma6cLVY9ilXmrzCa/XHEPJ9SH1CG6gDISgLzvAj7f9kv7sIFUhT4a6a6SFe7b+kr9Y8iGqwieX4it0HW3RErRFS05qnH0YGOijvPwwxq0AFgR/qqBvGifPEl7bfuBXAukH64w3z1ZC3SvAYrLyVAEZk2i7BbmbXYI/V9C2g0gLFClxo3ihZQKk4amFcb3PudVgLZYYLwsc1yOYlSSofeCGIfYlBRxwZoA9QyIyksBDCk6lMx6ednSInEdIijGJMiSQwXGri6xARiTuAc/5+kZB+N8U1L3jn3UF+iaB1ixJf9adukevAtm7JVqz9PajCNwiiRsCrUVQuNo9bLCaeZ5E2+b5DO8LfBEO2I0StVvst48A1Bbh+TiHhPddCJABibpX4HtSkHv3sclyt8pT5E7qObEAIXHjh/+MPRvSn3XRmkFYAqfWxS3Zp6SWiOzkc0NkQSrgf0RB6xKQ9vbPKQJR6y1rz5EIW6DtluhbBU41FM51yd98fOfxD37wTaqr61i+/PDBsX/qmK5vp/HHhELGpHVNGz1benFth3hNnMaz6g8hXKeKinnlnq9n6zC2aROtiBKvjk2yjTgcMkNZbNOZIIL3IRDzk+pJ4VgumnH4DqQl1nIuK1zDKt/v6dd6EXgKu+vzt9DozJxYLj2QIdU3RrgsQi6XgYzACTuoroptOTimAzGJkdLxv6DhR+BWQvY0l3WJPOv1PFnhMsc2OMsKUH4ighUp6TdfJ+VvocmaRdY/xmpjFcOK56mcV/K0ac1klQzz7MWcb17CeebFbNM3sUPbQlTGMLGwRIHzC5cy0/YstAaVftrUPQR2biDRtxNl1izCQvMsIMorcJpTWGtXoy9dDIru/R2AiwpXUG/PYJu+mbzIUWc3sMhahsDPBq2bMTQaD2gxUVCIuwk2hZrJLvxP1LE9nnrXl8CJNk0KFzsEromQNlIxwLXQRraB6+D6y0BN4eoR1MxejJ4XkaqBcArYiXm4/vG2GKEg1QBquv34j/9xwppn4OuTiB6JcBWv9ccPdm0BN3Zy3ltOuAGj92WQ7v7j5Tog7SOrqo8CKSXJzlF8ER9CCFzbRSigagrSlaiGihHUyQxlcSwb6UoywxmEKqhaeIQ6eXxcu2AjFAVVU9j17G6G+gSJ+kuwshamrxPbdBjcM0TNEs86UDM0HCuLVZhsizjzvBnkRnMM7B7Asd0J7qGoPjFBBMO+4Mlhak8vJzOYYWTvCK4tCRYFmHFOI2WzD/Xu/kOChsYZ5jn8JvALxkSKiIxiY9OmtVDl1DDHnrbomsZbi1NOBn/729+mrq6OGTNmsGrVKlatWnXY5e6///5TveppHANDQ4O8+92X093dyc9//gjnn3/Jm7IeieQFI8uzRpZhxcGQgvmWjxsLEYoPCJcrlRofzMZ43JehWfPanVZafq4qhCiaYgjdwQig8P5cjKeNDBv0AraQrLACXGIGqT3OQLqNWp6fBlKkhEtMKvQoNts0k5sKYS4+KAROIKhzdeoOWMeZ0fm0fuWH7LzzE9zzj5/nn2+6jlevvpF/+qd/o75+xgnt38nClS5Jc5CcncFQ/SSMUjTFuww0ROt4qetVLNdGH3/Ncm1s16Yh+odB/O6DZVk88shvuffee1i9+nkUReG88y7hv/7ru1x55fXE40dvM7qq8RL8qo+Xu9cyZqZJ+GNcV305F9Qcv5XKyUIgqHHrqCnUeSrMJRbv+t/3MjDQxy9+8UN+9pPv8Itf/JCzZxfzr7ctYuWMGLLzSbJz7sIqP+P4VuY6+Doex9f1e5T8CFLzY5YsIz/zPbzet4meTB/zErMniriw3sT2kd280b/ppMngU43+/l5KSw/v6abtAn2HwG7cbzXg1IK6B3wvCqyVp14dvA/Sh/dQcWBwHIDped3aiyD9SRffU4LAbxTcYokz01PiIkEZFhTOd/ffvQXkbnWRQmBsVhBDnvWEBNR2IO8pUtU2gUjhWReMetYP9uJjk4VuZDyMTRMgJSLjbadTIXGrJc4+kYYE/70Cbc+4WjkMOJ6q2f+AQAqBYgrcCFjLJeYZ8qgViCyC7B2S8Pe89G59kwDbU1krvQK1VeI0HrStZZD9MxdjjUDbLpBBsJZ5qt/QD/GI2vFLtJIBXOEpfvZ9DwLQJMrwMb58C7Tt3v6JIYFmS+xGEJbnIWzPlNgLjnJs/WAvHD9o+/Y3AuZyif8JBemTyKhHBKt7PWW0tlvg1Euo8cL71CGQW8fPVQOs+RKpCnLvcrBO9yYJjucc3r17Bw89dB9f/vI3UNWTt375Y8R0fTuNPxY4tsPmBzfTs62PQNSPoqv0busl2ZVk2XuWkqiJn9C4/qif6mMogQ/GPkWyYzuo2v5rj5W3CRUHUbUjk4sqKjflb2OZtZJWrRlFqsy25x3i+ztxKZSScCFC7e4mdizfiGEamIpJLpGm4Etz1u/PZsn98/FHFFzF5de3p3nyqgyKBoYU7PKZbNYKfDgXPz6LPDtPYPfPKck8SWj+btxCFz1RyUigD4MgCgpSShRUutS9PON7jPPNS6hwq/hY5i952XieHdoWgjLM6dYZnGmeB8ATvod42v8YHUordbKZ840MqWArFU4lc+wFFLulGPE8odzjBFc3I7UAZsV5FOouh3GvX4GY5HfsInlRz/Kcb4gdWjmt6uWYskCj20yR9EhmUxQIuVUgBE60ySOBjwE11YLesxplbC9GuhMnXIewxnB9CRRzFNcXxyw7C210F2q2BzvahKoFsYoWT1IaCyeP63/zJ+GsqkXYsxKIknbI14MmcGMjKIqFVX6oyvq4xq44E6P3BbSRrTjBKoSUKLkenGgTZtnK4x5PuhLHdhjcM0x2OIsQnl+3a3tT1b6wDz2gY2ZMrKzNcPswvqif2RfPonzu/vahbDLHSIenWFVUQc+2PkY6RlBUhXhNjMHWIcJlERRFQQ/o6AEd15UU0gUK6QL+iJ9sMudZuBzkFe6L+Fh26xKGO0bIjmQxQga7n2sml8xNWs57vpAEon7mXzGXsf4xHMslXBp6R4RlngpcULicbrWL141X6WIvAqhyarg9dxehw3qUTWMabx5OORn8/ve/f9pr7h2IkZFh3vOeKxkY6Oehh1Yxa9a8N21da/U89/nSGECFo1EQkjVGjrTi8olsYpJPcJ2r89FcjKRwx2f0lcO2vNtI8kISkAL1GE+4CalyayHKGZZJj+qQcBXqjtMiwkbylC/jKQEcfWKbuhWbp40syy0/0WMQ1lGpcpqtctric7n1wRf43e9+yf/9v//ABRcs5tOf/jx/8Rd/i66/uTc2d2QYOTKMiMawEhG2J99gqNCHK70UqJhRxPz4UsJ6jGXli3m19zV2DjdT5PfI0uH8CHOKZrKsfAoSwncAhoYG+OlPv8dPfvJdenu7OfvsC/h//+9bXHXVjUe0CzgcdEXn8oaLOL/mbDJWhrARxqe+fUm1Ukrsda9gvbQKd6AfpaycxLkX8OlPf57PXhzlmUfv5f8+3M6FX3ied1+wkC/eJKlSf4mdmI80pm4BY3Q/S6D5F7haGDtUjbCz+DufQbGz9GkVGIox6fouhMCn6PRmBk5q/+zdO7Feeh63dQ+ipBT9zHPQlp9xWI+7qUJKiaoe/hanjAgvCO2gZgEZ9TxaKXDEELGThT1P4hZL1A6PgEYZ96w1PTIQPCIvd5cEn4vvOYHS55HIyqjArZCYF0rUFs+SQRkRODWSwnWSwlUOyhhomwX+xxTcco/0lGHPMkIZFqi9ngJVGfUK7qOiAIFfC9QezxICKZA+kJpEJjxbjX3WiiIHxgYBeVAdASP7iWS1BfxPKNinSZRuz09X7eTo6lsLtD2ALRFJzzJD+j0fXH2TQB1QSH/cxT1IROOW7rfk2AeRBafGs66wGzzydN/lW/oPOAauZ4lhzzjKdmUh+DMF43XAFgjbUxkrKS+szloiyd3sHpctxsThvkYi0i7GBgXRI8EnsOaM24YE9yuG3RpQdgrUfomd9M5brQOcmZLCBXjn9XGWYV/72pepqKjmPe/5wPFv+J8IpuvbafyxYKh1mP7dg8SrYxMESyDmBUrtfa3zhMngE0FxYxHRygjJziSxqjiqrpBP5bHztqc2PIay+GhZGPsQLg0Tq4wy3D5CUX2Cs3ZcyFCsn+3nvEE+lvWCq3qLMdDJrbTQkTTHLV4uzVHRohKZ490wXCQ7VJMXjSzvzk/dkN3f8Si+vU9SFyxnptnExlgnebufYL4A/ih5USDuJihyS9irttOmtk58tsKt4l359x4y5hv6Oh7y/waJy6iSZKRCQwrIF0bp9wnyIsf51lyKQ6+jxRMUFIuk3YG651Uy+TWE5v4LhnJox+RaPc+v/d4zXKXjY6c2xutGgJ3SoNF9kUqnAlOYrDQPFUdoI9vRe15ETXfgBKuwKs/BLlqMNvgGoe3fQ+QHkVrAI3zH2gEXRTqg+rFjc0AP4kTqkP5iMgv/nOC276KO7cEJ14NQUNN7kUYU83iFDicAN1xLbvZtBFp+jchvAUDRAhQqL8WsOOcYnz7G2MEKsgv/An/rg2jJnSAEZtUF5BtuQB6nFZ10JTue3kmyc5TMQJrcSBZFU3Ash9GeUYywDytngoQF186n+rQqhIRoVYxg3Pv+pZR0rN9L86oWcqk8Vt5irC+NP+yjuKkYx3Joe6WD7EiWUMIrbjyCOE7/zn4KaZPcaJ78aB7Xlcw4uxFf+ND2LUVTxkPfvMJxrHeMbY9vp5AxyY/mEYrACBle0FyN11UQrTiO4IM/EAQJcnf245xvXky30kVABplnL5ywYpnGNN5KnHIy+J577jnVQ07jJJFMjnDbbVfR29vNb3/7+zeVCJZIXtazIKBmnIANSPA5gp2aSbNqMs+ZfIMQCBJHIFZtJC8aOV7Ws4wpLiWuymLLh4EgLyTljsZ828B/QP+xheQRX5rVRo5R4WIAjbbBbfkI1VNUBw8pDt2KTbmrTSKny1yVPapFl2ITdaaunhJCcPPNt3Plldfzta99mf/+7y/x5JMP87Wv/YA5cxZMeZypQhYKFB59EPvVl5HpNASCDM6rYPC8ekLxCnTFwJE2SXOInaObWFp8DlEjwocX3sFze19mw4BX+FzdeBkX1XrvvZOxZcsGvv/9b/Lgg79CCIVbbnkfH/zgJw4JZzte+DUffu3t93k2n38G8zf3AiAiUZzdO3FaduEfGyCmbePac5dyxcWX8bNnNvKFnz7PkjU7+MzVM/lIzS346qdYtLoWvs5nkIoPN+wxbFILYCsG2tBGykqLMF3TC3cbJ0SklJiuRWnwxP317K2byP3oe8hUEiUWx92xDWfHVoyhQXxXnbh3aSwWZ+/etsO+58YkqBLyTCJ9xRg49XghaW8S3DLI3SIJ/Fag7QIQ4IfCeS7mWQcQk8JbzqkBY61AjEFhuYt5nkTpFgR/qSBSIA2JsVrBWCPJfsjFjYDvWQVtp7cfIst+RawiESOAT+A0eYoRpc8LZKMATo3EnsOEetZ4XeB7RWAtBKdOoraBMuIFu+VukeSvPmB7x7yxlOy47cJ4OB9SIFyJW7pfRSxGwHhZwTzD4SDbwwnomwW+lwUyJCAMdg2e0jgFdpNE6REYawT5Ww8ltJVBMF7wLCqk6imRcze7+B9W0No9ewi3zNtfkfeWlyooaXCjkLvpyGGjvtUCY61H5MuQ9BKv2z3Lh8xdDvZpTN0O4yDIEOTulph7HZRBgRvxSOXIv6vIwP79tGdKRMpbr7Z73K4j7J0L0S8roHqBdIVLJHIKz5UtLbt44IFf8aUvfRWf7+2/3r1TMV3fTuOPBemBDK7jTlLaCSHwR3wTysC3CrpfZ9G1C9j62HZGu1NI10UPGjScVU/tsuNvmT8cFE1h9sWz2PTgFoZahwi4QQzTIJiMUNlfS0mqmMr+Uraeu4VHdz3I7a/fSVuZRTbm0tDhozBDgg7K+PPKVs1EIqeW12DnMHpe8BSw/lLe1+nDrHN4Lj6IJRxwM4SVBMVuKRKJhUWJe2grfIECljAJyTACwXp9DY6wcXHJiQzpeeWkZ1nM2JDCLdXJBk1Uux81DoMNM9kY20papAkULIqGfsPrps3VgX8iJuMT63CRvGRkUQRUOJJXja0YZPHJNFlRzR7Vplddx7uy7+Ms84JJ26f3rye4/buIwijSiKCNNmMMrCc3670YXc8izBR2wqvJnUgD+sB6lFw/rhrELluBGywH6aBmurDKz8AuPo3c3A/ib/kV2tgekC5OsIJC48048bk4OLRoOxlWhoi5cWbZ89BOMa1hVl+EHZ+DNrIV4Vo4kUbs+JyjW2FMEU50BpnFf4kwk4BAGrEjei0fDUNtw7Sv3UtRfQJfxEeqO4WZM3Esl2hllGhFFM1QKZ1VypxLZxGIHjoBMNKRZOfTuxCKoLi+iIHmAeycRUERCCEIlYQxwgZjq8fo2z1AMB4gN5IDAYoiCBUF0f06waIAtctqqDltsjI/N+oRzMF4AM23/zsqaSqhkDEZ7U6hGirSleBKKhdVnjAJXMiYDLcN41gO0YoIkfLIO3ISV0Fhlj2PWbx5nMw0pjEVvCkBctN45yCVGuW9772Gzs52fv3rJ98U4vFAFJAMqA5Rd/KNMoCCAwwrjqfImyIe96V51JfBLwVhqbBRK/CwkaZEqpS4XljcPNvg/bnYBKH8kpHjCV+GYldljquTR7JDM/l5IMWnM0UTwXJHgyEFGgLrIOWchUSDSerm40EwGOLzn/8SV111I5/+9Ae54ooz+Nu//f/42Mf+8pS25ppPPoL5+EMoRSUo1bU4YyPw3LOUFZZgvvsGAFShEdHjJM0hUtYIcaOY4kARt8y+jptmXu0tcxgP4ZOB092FteZFnJ3bEeEI+ukr0VaehTgBhbTrujzxxIN873vf4NVXX6Kqqpa/+Zt/5vbb7z4uFfAhkBI11YKS7UXqIezEfDg46OEthMxmsJ97CgwDtWbcrqO0DKezA/uFp+F8E4wgqqrwgcuXctO58/mvX7/Ifz60lh+tfjf/8sWvcd11xw7aEFYapTCCe1AQoDQiiEw7yyNlvJAsoWW0jepwBSDoyvRQ7E+wrOzElOPSdTF//zikx1Bnz5so2Ny+Xqznfo++8iyU4hNrB4xGY6RSycO+Z88BexZoWz1VrfR7hKBwPd/ZEyXzpgprpcSZIdG2C4QlcWoldhOHrtcA8/zxULtxiDEIfdsjNO19NaQr0XYK/L9WPGXwbhAFgatJlBxobeBUADmBMuRZNJgrJPqrgsBvxH5bBB3M0yXZ97kQ9BTGCM/CwImAU+X58Gq7hRdWd0AFYbzuqYbJSBACaeAFmQ2C6wP3AMcOGQfRB2qHwGk4vDpZ2wE43vqRwiPvVTzydVDgFkm0lkPVzWIYgt9V0JoFMibBBe0+BWupJP0JF60LRF7gVLlozYLgDwVqu6doliFvH32rFJScxDz30OA7fb1ABtjvPyy8CQRth6fcRjkOn17LU4TLAPu/ewFOnUe+A1AAt0Si9IMzrgyWfs8H2Y0J8le7/P/snXecXWWB/r/ve8qt03tLZtJ7ISGFllBDEwQUwRUVAQGxYNt1d9V1dV3Lrrr2im0VUBGk9x5IIQnpPZmU6X3uzG2nvO/vjzOZZFInEFzxNw+ffELuPfe0O3Pve57zvN9HhyC0VGDukKhiDQ5EHpOY+zTJ20+cUv7ud79GaWk51133weHv+4hGNKK3raxI8OGtlBpSfOhmPeJlf/2b//nV+cz/4Dy693bhZj1ySuPES+Kn1MQpqi3k9H+YQ8uWFnZ5O/DnZznNOZ2oGSPi2RQQRzoGK+qWcdmmK7FUkLRVWg8Bw3toQnr4+yXdfoSbQlnBea3K5PLZnWcxJc/gL6Ur2VgVwbJyyIoMSdFPWIc52zlv8PVJ0c+zocdZab1CVmQZ5ddxfvZiumQnYR2mTbZi+5KarmYaLkkjIh41G3rRjkSMi+JXn86Gsr2kRJIiVYywBLleCx2Z13g6/1HelfmHwW1l0HTI4BquVTbRJTup9KtRKFpklFp1FoJVlKlyQoeC+5VLaM+D4KaCMfPA+2Yk6onsuBvhZ/HjNYOPq1gljn0eZvdWlJ2HzLQhs12gPfyc0WTqrgEhcEvn4hZOwezdGZjBuWPRdg7doovfR3/BZmsDDg4WFhPcKdyQvpnik2lNPYaEm8RufhmrbSVoH7d4Nk7lInTo+Ii5k9+QeNPr7N7Xje/4RPIiRPIi5Jbn4PQ7JFoTGLaFkIJs0qFxXRN9rX1Mu2wKBaOGbrNtextOyh1I7UKmL0soJ4Tv+iQ7k0TzI9hhm3BemL7WPnr29yAEOCkXgPyafKpmVjLunDFDbjBl+7Jse34Hbdva8ByfaH6E0fNGMWpODUIKOvd0YcdtqmZUkO7NYpiCUE4I5Wk667uGICyGo7bt7Wx+YivJjn4ArKhNzewqJp4/AXkc3MyIRvT/s0bM4L9j+b7PzTdfS339Tv7whyeZOnXmW75NG0GRMthreBQfkpzNoDA05J8EC7hb+Cy10+RpSZkycdAkpMKVGs/XjPMtfGC9meVZO8m7srn4aF61UkS0oHhgWxEEY3yLesNlu+kw3TuxsZevJVNcm1fsNHFfYiPw0OwzPMb5FqNPEjtxuGbNmsuTT67km9/8El/96r/wxBMP8T//8wvGjBn/ptYLoPoSuMteQeYXIksDB0ZbhTiZfGIbd+Gf24VfEsTFTGGitI+n3CHrONUmMIDfuJ/Mz36I37APkZsPjQ34mzdgNewndO17hz3o11rzzDOP8fWvf4EtWzYyf/5Z/Oxn93DxxVdimm/yI81LEdn2G+zWlQg/BcLAyx1LevJN+Dm1b2iVPdleOtPd5NhxSqMnZ2z2iQSZti3YXR2YFTVDnpPFJXjNjbiyklBqKypUCEKSGw3xH++awIfOreOzD/dw663v5dVXX+Tf/u2/CIePzT7QVhwVykem2/FDB+OEwu0DI0JF/lhuyBnLQ7sep6GvGYDqeCVXjr2Y6pyTYwQObrOnG3//PkRJ2VD8REkpavtWVMO+N2wG5+Xl09vbe/QnbUjdoIjcJzG3CUQ7qAJN+pIBlu1fQap4wHg+ikQKRE9gwurDrsuNepBtBObxAUnwyzShVwUqBu5sjb1aBNy4apBtAtGrIQSZCxTp6wLDO3K/CLAIkwIsgkiCvRz80YLsRRrhBeVlB3eMIDUtOeKmnrlD4E3QiC0S0R/gFgC0CErt/EM7ShSAPn4C+8CpOfyjdsD/FRmOWtJmvyYwdwb7MjjCyWis9WBtG2DsDqzcKde4UzXWKkHkfoFMCPwyMNrA/IPE3KVJ3qSG7KfIcOTISQR/hMfwlIXQCwL7VREgLCrBOVfjzjwKniIE2fM10d9LjF06MNKTwX5krlJkrtCEHxLIDhG8jwe6aAo15tYAqeEsOPbP9OrVK7j//nv48pe/fdzPhxGNaER/PyoZV0y8OEZPQy95lblIQ5LuSaN9RdWMYxdKvZU6kF58KxUvjjHu7LFkzD5CcZsqrwoDA5X2SXem0b0eraVdNEb2M6l5IsXtBvvGeJTbQQl2CkW/UFzsho6ZCtZoNpkOq80M3dJntG1xbkEtozu344fyAQgpk0tbaqlqb+E/SsO0WT4ZkSakwyxyzubC7GUA+Pj8PnIXy+2l5KsCwoTZYK1hj7mL0e4Y+swEUR0jmuomJ+nQWRZh/3uhOlFOq+xgzushknaKPpGgQBUiEEilkBjYRhFr7BVclrmayAAzK4QgXxk0Gx5ZERhqBgYKm5iOUOuVkJVV7DP2DDlmmWrG6G9AxSqHpFv9WCVOz0qSIkmvTBOXlRSpkuDcaY2KFJGccitGsgWZ7UTFKnBK56EPZQKbUbyig4EDjeYv4T+wxnqN0X4dMR0nLVJssNZwH7/n1uSdw0tsH0tehujmn2K1LkebwZ1as2sTVucGkjM+HiR4/4ak9cExDYAdsbEjNt37e0h29DJqbg12zEb5ip6GXjY+upkFN84batomHaRxcLBnhkyy/VmEFPhOMLDRSuO7PuGcMIYlSTQnAhZx1MTpd9jy5FaU5zP10qAATfmKDY9sonlTC7GiGLFYiFRPms2Pb8W0TapmVtKxuxMrFLCH7WhgNpu2ScfuTnqbe0/KDE73pNn02GYyiSwFowoQUpDpzVC/bA/x0jg1s0/NLIMRjejvTSNm8N+xvvWtL/Pqqy/yxz8+yYwZs/8q25QIznSi1Id7aZQexcogIxSN0mOqF2K8N/z51y3So1coxgwYr13Sp18oin2DjNCkhSZHS4q0wetWlsuzCg30SUVMD70DGEKggH5x7Om/7cKjS6oB89ng8mycLumzw3TRAxPCqn2Td2Vy3nAy+FCFw2G++MWvs2TJ5Xzizpu44IK5fOlL/8UNN9zyptIQurcHnexHHlKeZUoTmZuP3rsP2ds3aAan/SQhI0LMfOuTIO4Lz6Ea92FMmjrIglXdXbivvoQ1bwFG3YkLKJYvf5n//M/Ps2rVMhYuPIcHH3yR009feMr2Mbz3MUKNz+HHatB2LSgHs2c7ka2/pP+0z8NJcIMd3+WJPc/ySuNK+tx+ImaYmSVTuWrcZeTYxy8I6BU9PBb+C69bKwkX93BJZDflnqKUCYODXJ3Jgh0iU3MZZksSq2sTyowg/CzailEx//3cdfV5/O53v+CLX/wUa9as5Kc/vZva2mOcZ2mRrTqf6NZfIZONqHAxwk1ipBpwS+fj541nqpCMzx9DQ38TWmtqcqqwjTd+Y0TYdpAKd52hT7gumCbYbzyRnZubTyLRMwRrcahUKSRvV8jGgHeryo80Xt+wFIFZanJy7FYPQs8K7JcDY1JHNc48TeYSPZRvfJR1CkD2gl+hUSXgTtJBQjgDAo03RpP8sMI5NzAc7aUC2QnehIPr07Hgj7VSkL1Q407RWKsDhMSBEJDoB0zN4XhGHQOikF2ssLaIAEdhgIiBXxCUq2mLINm7Pzj/7sRjm5TeBAg/rqFbIBIaMxGsBzXALJYDZXiHydwmAqTCoaObMKAERoPGnRcwmu1XBdbagQN3QPYL3BkD6WNApDTW2gCh4c46uB13uib8qITyQ5btDbbhjx7GjQQNkfsEoedEYPZHwdoG5h5J6oMKd/aR63AWarSpCL0U8KP9UnDOCpLLAOYuESArDv3as4Njls3H3qdUKsUnPnEjs2bN5f3v//CJ931EIxrR34XCOWGmXjaFLU9spaehF601oZjNmDPrqJr+xm7u/l8rRYpN1jo6ZTv5uoCp7sxjMjjLVAW5Oo9u2UmeLmBd3irEJIXVZlHYWEDvjnbKuoq4JpbL3acl2Go6Qb8ogtPdCGc7x55u8byd4i/hfrJowlqw0dSsnXUZH13exrjEblSoEOEliWd7GTv6fVykc9jibkQgOM2dxwWZywZLpHaaW1lrr2KUX0t8YIBSoIrYbm4mI1OUq0qa1HbKEw7bCl2iriZCjH2lPsXZSgqVAZkO4ukM0hZI5VPc201vTi6tJWW4wsUT7sD0FDAQnOVEuDuSIEkMhSSNSb+IU6q6KdS97BJJStVhhcHSAmnAYcGSermF3uheukOCglQTW4p2U63qmOpMx07uxyuYildyOl7Z8AMonbKdjfZayv3KwfMU0VGq/FFsMTfSJBuoUjUnWMuxZXW8jtW2Ei93zGDJHr6D2bUeu2UZ2VEXv+F1vxUqqM7HsAyy/dlBTq+bcUl2Jcktzx18zDAN8qvy6GnspXNPF+WTgmtEJ+WQ7cvSta8LJ+0QL44TL47R35HEy3rY8RDKV/Q2JVCeIr8mn/62fmJFscF1p7pTSMugaUMzo+eNHrjR1EP7zg7yKvOwI8F1Ql7Eoqehh72r9lE6sYTufV20bmvDtIP334pYFNUVBjM0zZMLJbXv6iDZmaKotnCQMx7Jj5Dpz9K0vnnEDB7RiI6hETP471QvvPA03/3u1/nHf/wSZ5yx6MQvOEklhaJBepjAKN8aYo7Od8OkhOJ5O0WD4WLrYPB0dSaOfRLOSFRLQjpgA8e1wB+wZH0RDFisgWtcSws8ETwe1YIK32S74VB0SAq5XyhsBCXqyC+XNIoHw/2stjL0C0VUC2a5Ya7K5vCRVAGbTSeYNqUlUz37hMVxJ6NdPXtYG97Fwn+/htd//xyf+9xHWbr8ef7nv39BNBo78QqOIpGXj4jH0YleRPRAW7AkL2PSGwnTHfUwvCSucvC1z5icSUTMN7at4Ur7Pt6WDYiC4iGlYCK/AN3agr9/73HN4I0b1/L1r3+R5557gunTZ3P33Y+waNGFp5YD5WWwWpaiQgXogfQGRggvdyxm7y7Mnq1D0gkn0rP7XuLh3U9SEMqnOl5J0k3ywv5X8ZTHjVOPnYT28Pjf6M9YY6+kxC+D8jJ2Ta1HLV+BGB2m1B6NzmRQzQ1Y8xaixswnWTEau+VVjMRuVLgIt3QeXsFUhBDccMMtzJo1l1tvfS9LlsznO9/5OZdeetVRt+1UnYfw0oQan8Hs34c2wjgVi0mPf+8gI802LMbkjT6pU3ssiXgO5szTcJ55ApGTiwiH0Z6Hv3c3xugxGGPGveF1l5aW4bouzc2NVFYeYxAoOKKA7E3JBfsVgf2KQPYH+IDsInUQ53AChZ4JEqoqDqpI4/TC9nsEXVsh/3pNbS34YwIz0GgY4BsLApZua5BqPSC/VgcFcp0CYx+k/kHjnHcIbsIlePFhWANtDaRfCcxWd73AWgfaDjAaaMguCBK1aJCNAbbBLwrwENiQPUcH6eaOgAnvV2qM/QFXFwLsQfoajT7O7Egd04g+GbyOwIQ2EwK/UCMygVEeekkge8GZpwfL1XTsKAndA8GZUJCqjd4lsTYIdDw4dmtNgH4QUw4plosC3oCBPOvgqpyzNNZmjbVNoOIBNkP4kD1HDU1rH0NGI9ivSfzSgWS0EaS8zV2a0PMiSAcfPptRBGgRd27AONYhBo1oCBjYZlYyBJmhDzmOY+irX/0Xmpoa+fWvH3jzsypGNKIRva1UMraY/A/Np3NvF77jk1ueS7wk9jfJ1zyRWmUzv47+hF3mNtTAh98ov5b3pz581GK5ElXGguzZPBl+mHq5k32yHntsiFhxjAUvnUl3XS8PXPgXFs24hDtlBVsyWbJCU+VbTPbsYwZCuoXPU6EkthbUDnSUaDRbc8p46LQb+PiaP2OmO9BWlEz1tajaRZzrJjjPuYRSVT6Y0D2gZtmEgzNoBMNAz4oqolt2cWPyI7wiHqB4906er0nSFQ8hVA7jk8Vc0ziJfNlGZ1E1edknifY3YBGhJyeXFdNnsj/cziz39CHrBljoRkgKxVN2MXuMajpkHzV+H5O9PTQbe7G1zXxnaBeFipTjFUzBal2GZ8VBWnTRSrJ/HR1FxWwfP5czNrzOuLZOUsZakm4vRnQm6XHXBibySSglkmTJkM/QAURER+gQbaRE8qTWd7jM3l2g1UEjGMCw0UYYs3vT35wZXDSmiJrTqtm3aj/9HUmEEPiuRzgnTM5hyBdpStAaLxMMkrJ9Wdb+eR2t29pQnqJjZwc9+3uIl8QwQwZSCry0R/e+bmLFcUrsEtyUg5f1Btm/QTIZQnGbbNIh1Z0iXhwj3ZtBuf6gEXxAdk6Ijp0dPPftF2lY24SbdokWRIgWRMn2Z2ne1ELxmOLAFB5Yv+/4CCkwrGP0C2U9Gtc30bWnK8BaFETJq8zFjtqYdpByHtGIRnR0jYz+/w7V3NzIRz/6ARYtupCPfeyfhjzXLXy2mQ4OmhplUesPLUg7kTSa5VaGJ0L9tEkfA6jxLa7O5DDeD1KTEsH5Toz5boRW6RHRkgplnPS0nRplMt63WWdmqPUt8rSB1IJuoZjkhwgj0WjapMc8N0JUCwSCc5wouyMuuw2XYiVJC02nUMx3w4Mp40P1aCjJs3aSUmVSpyz6hOJFOw3ADZk85nhvzdTZxv5mfrnpblpT7ZRFi1lw06VE6gp44jcPc+mWM/nlXX96Q9gImZOLteAssg/fD0Ig8vLR/X2EO/sRZ11AX9UEkm6CuJVHZXQ0ldFTY+wdf6ckIhRCJQ8bpOmBzLV59HRpff1OvvnNL/Hgg39kzJjx/PSnd3PZZVcPYdydKgk/g/TStGuT1b3NtHkZiswQsyMFVGkX4aWGva60l+bVppXkWjmURYNpjyHDxhAG69s309jffEy0wnZzMxutddR6Y4kOgEl7330ODclniWxZR6GTRBgG5pTphK66FiEEKlpBZsw1x9yf6dNn88QTy/n0pz/MzTe/hw9/+BN84QtfP5JTLU2ydVfiVC1GploDdES04g2VWgxX9sWXo1pb8LdtRvsBe8CoqiF87XsRb6LMat684GJl2bIXueaafzjB0qdAOsAuhJ4R6PBA4nO1wNxhkLzRxztBl6FIgf3ygBFcCfsT8Kt6we5WUHsFZj3MWqi47jqNeYUm+gcZcHVNDZ7AH6Vxp2nCT4igWC43YMuiAh7x4SlaryZgJYteAv4vgALZLciergJWcAxSNyms1wTWZoE2wZuucU7T4EHkDwJ7lUQkNVgCrQL2smwdOCUFkL5M4ZwZIAuMpuDceFOCArfjncvQcxJVDH5dUEQnPCCpkQlggOFovSoIPyjwyyH1foVzjsadpbFXCWQXqIFrRdkcnA93ssZaI7A2BiVsB/APshHMepDN4iCr98Bfh00GUGWQvE1hLxOYWwQ6Cu5pQYJ7OKxp2SyQjRrDE8i+AIesigPMh9EkEP3Bvh79xUc3d905YK3WyNYgcY0KbhboQnCnHT0Z/NJLz/KrX/2Ir3zlO4wbN/HEOz6iEY3o705WxBpMCL5dpdE8GP4T28xNjPMmYWPj4bHL3MafI3dzZ/+/YHCkiXRl5lpydA7fi30DhCBfFTA6NobkRQ7LL1rDVnMjRelqLsjWUOEM73J5j+HSJRTjD7nWEAjKlMm2wlE0zfsSBZkEnhXm2dhLvBD6D3pkNyEdZpozm6sz15F/yF3SoCwuCAkcWo6WFikq/WrG+5MYrz+H5WrevWI1+ypGIYSgJp2HnWxFh4uxJn+OBnMcm7IP4ZoG7YXltIfbKFTFXJi99IhrMwPBEifOQjfCSqef50IP0GVsosX0KVYlXJR5B9O9w2abCkF67LsRmQ7Mnu0A+LKB5kKLrZMX0llQyDPzz6KmtZn+7E7225M4L/efUZGT5/sW+2UUqCI6ZTvV/sHrl07ZQb4qoEy9OcyJNmwO7yIAENpDGUeWr/1fSxqSKRdPpqiuiPadHShPUVhbQPOGZjrqO9HFB2/wZPuzmGGTeEkcstD6TAuJjQmKJhSRX5NPT0Mvvc29JDtTTF4yidr5o3AzHoYpKawtpHFdExse3gSA7/pIU+IkHcyQiRUyEUJgR4NBUygeQhgSN+NhhQ/+7PY29NLX3ocZSmLaBkIKUt1p3JSHHbfwsj75NXnkVeXR25ygftkeOuu7kIagfHIZdQtrCecevC5XnmLDw5toWt+M53iItCDTlyXVnaJ8ShnZ/iwVU9/8Z5zWmr6WProbe5FSUDi6gFjRWxukGtGI/hoaMYPfpmqSHqutNE3So0SZnOaFqfUtlFLcccf7CYVCfP/7vxpimr1uZrgv3Ee7DAyXqBbMdyMnhT3Yajj8MZxAA6N9Cx9NveHyv5Fe7kwWUnhIajauJXF/+NPqD5dEcG0mBzes2WE6+AQsX1MLTAUN0qVPKEqVyflOdHBAM8sLcUM6j+dCSVqlR0hLLnFiLHFiyMOOs0f4rLLTFGtjMElcoA20gnVWloscjzL11vyaLG9eRUt/K5MLJwx+US+65GLyR5ey42evcNllZ/LjH/+OxYsvOul120suQyuFt3wpqrkREYlgX3gJOe+4iuJIFKV9pDD+agkQIQTm3AVk/3wvOpVCRKNorVH79yKLSjAmTRmyfDab5bs//CY//N43yC0u4V++9UNuffcHsY5hGp8KaSuHHWYed+1/jT3CxiBIoz8r93BzbiE1seFPnUw4/fQ5/eQfVsaWY8dpTrbSk+09phncJlvx8AaNYIB0WYy1d86m47VuJndch1VeHRSu2cP//crNzeNnP7uXu+76AV/60mdpbW3me9/7FdZAeV/C6WNz5zb63RSlkSImFU54UwiI4UoWFBK5/RP4Wzeh2tsQ8RyMKdOQuW+Oy1ZUVMLkydN45ZW/jhksm8BeLlElGjWAXFbFAaoh9JzEm6qOi4wQ3SD7BKpI4/rwmw2C7Z0wphAifYKOkGLpUklhoeKd79T0l/lYawMcg6rWOLODdKxIQmiFhKaBfSjQZK7RHD6r0x8DzkJF6EWB7gRsEAmBX6PJHsIy1jFwFmucxUMvjsIPCULPS/xyja4KsArGHoE3WuOepdEWeBM0B67LvBngzRgej1n0gFkvgnUXBNxjAGO/wHgZ/GIwugRGG6CDwrzYzwJWcOqDiswZEH5JIpsAIyisy7xD4deB/SLByOeQXx1Vo2GPwGgMytvQQYJX5wXG9eFSJZC5QsMVJ8+Xll0ac7+BtjUq5+C2ZJvAmauD1O9Jyp2uyVwO4Wck5rYAA6JKIP1Ohao6cvne3h4++clbOOusc7nxxttPfoMjGtGIRvQ3ov09+9j3agPjts4kbIfRMzzM06A6Mpp6cyf7jHrq/CNnGdnYXJy9khXWqzQbDdT54w4zjQX+yTReExipR9aago9GAoa0UJESltrP8ufI74mqGBVeFWmRYmn4WTIyxa3JOwf3Y7I3jSp/FLvNHdR6Y7Gw6JadZESahc45wdaEgFFXYyeamdDYiLbzEF4bWggyddegYuUs4naiOTNZYS0lJLtYlD2DM53FjPGPHTjJ1QYXOJM4x/k0e81duLhU+6PJ1Ucfm6l4DcnZ/4zV8Toy08G6nDXcW7ORGjMYEPXH4mwZM57dhmCKV8fi5BsreosS5bzsEv4Q+V/qjV3k6Tz6RAJHOFyVvo5cnUeHbGONtZIW2UyRLmamM4dqNWpY6/cKp6L3PYZMtQZmtRCIbDcg8EpOe0P7/FZLmpKKqeVUTD040IvkRUi09dO1t5tIXhgv6+GkXEbNqaF0cx6R+yW1G4ooFTF6EykaZ3Rgj7cpHlNEZ30XxWOLKJ0w9D2qnl1F9/4etr+wk77WPkzbwIra5FXkkunNUD6ljLyK4G52wah8imoLadvRTm55DmbIJNWdpq+tj1hhFIRAeYp4PEQmkcHNeBTUFOC7PgU1BfS3J1n7p7X0tfcTzY/iuz47X95NoqWPOdfNHkwmd9Z30ryphaIxRQgp6G/rx7Alya4UTRuaKZtURs1pb24KoPIVO17Yyd6V+3DSLmgI54YZf+44Rs2pflvOphjRiA5oxAx+G2q74fCbSC+t0iOMJEuGZXaa96Zz2XXvfSxf/jJ/+tNTFBUdLGLoEB5/DCfol5rxvoUEeoXiRTtFjW9ylnuCuvEBvWZlSAnNxEGTVzDOt9hmOKy3siw+DkfrjahIGVyQjVKkDDyhmemG8IE1VoYeqVjgWyx0ItSooXfh53phZnkheoUijDiCIXxAfUKREpoyf2hqIEdLuqRLQijeqsxEfe8+4vbQKXmWYRGrLuQzP/kGj/73b3nf+67g85//GrfeeudJfdmIUIjwO9+FWnw+ursLkZePLCwafN4QJtrzUF2dEAoh8/JP5aEdVfY556L21uOtWwOeB1ojiooJXfOeIfu2YsVS7vzH29lfv4uxd97GuH/6OLvCEe5z01ydMQmdAl7zAXWmu+jK9JAXyqU4UsgfVZj9ymea4SKsGMrPsiPbx72qio9Hyo/osjqWcu0ccuw4CadvCB844fQTs6IUhPOP/Vqdh0Tg4mANOFbFa1oY/cw6qvYZ+MZziNPmYdSOgZMwgyEw5W+++WOUl1dyxx3vJ51O8ZOf3E1DponfbbmPxoFiOEMaTC6cwAemvucIQxsAN5hab60TiExQQObMC8qt3ohEKIQ589QPss84YzFPPfXIKV/v0WQ0CkSfxj/U4xeBcWnuH0h8HodJrHNBRzWiH3akYVcP1OVDxANhQW4RFIQ0r70mWLJEExl9dEZt+n0ad4GPsU8EhuzkY6RwJaTfrfFHgb1KIJLgnhWkeE8YrEmBvUKg8g+iHnQsQFOY7YJ0reIo197DlwXaGCiwO3SXOwjIFimB0TRwziyQEnRIYK3RxHolso+A22xqnFmQeac6iAMZ4Acfuma/IkjnCgfMrQL0QKr5SjVoRJ8qyRaBtnQQCTYD7rDWIFtAl2o4GTPYBWutwNwAwhdkzlHo/OC98MZrjnbNrpTi05/+MP39Cb797Z+/JbMsRjSiEY3or6FUd4pNf9xKpDkXM8cETyJ2hRD7JPa7HDzDxRHHnyJ+mjePB8xdKNSgCdsjuokQYcxJfpGN9S0qlMk+w6NuYOali6Zd+ix2osS1xMPjJftZLG1TOcC2DesIlmez2VrPbmMH4/1JAMR1Dv+Quol7or9mr7kLH0WOzuGC7GWc6Zw7uF2vYDLJmZ/EbnoJs2c7KlyEU34mbtl8ICiBW+CczQLn7JM6HghM8/HDZF1pOwen8hwALKuKdHgXaT9FZGBKi4tDVqSZfKKpUifQIudCQoR52X6ODtlOhV/FWc55nOkspt7Yya+iP6LB2I+FhYfLi/bTvC91CzMOTzQfRV7BFLK1VxDa+yhmd5CCxQiTrbkIt2Tum9rvv6ZKxhUz65oZ7F25j96mBOHcMGPPHsPk/bXkfstApCGaCRFSFgWrcrCTFtvPbUBIMcjcPSDP82htbaa7u5NMVS9yTpa25XvobOvC6cvgd3gQUeRk4ty/8beDr3MzLt37usi8lkX7GmlItCPIT+UjPInb45GTl0fYjCCzBl66ijBRwgU2jesa6Wvrp6iuaHB/wrkROnZ30ra9ncrpwUA10dKH8hTheIiyiaWE4iH62vrwsj52zGb2u2eSV/nmwiVt29rYvbSe0CHojf62frY9s528ilzyq/62SgVHNKKT0YgZ/DaTj+bRUD+d0meybyMQaDR7DI8H/DYe+doXuOyyq5hx1jm8aqbplj55StIvFJ3SZ4JvD6Zj87VBN4rXrMywzeB2wyeqh35JyIE74YnjlLO9EaVQ3B1JsNbK4KABwR7D5bpMLreljwObHJCJGMINPprytUFcSXqlIqIOXhT3iqCErvAojOFTpcJwPjt6dg95TGuN1oqSghJ+/ev7+cY3vsiXv/xP7N27m69+9bsnfeEu8wsg/8hz5b6+Gvfpx/CbmxCWhTnzNOxLr0AWFL6pYzqeRCxO+Kbb8bduwm/YjwhHMKdMQ5YFd7J7err5j//4Z+6++5eUz5vL2b97jJmTpmMBPTpgUJcogwucNz8tJ+1leGjXE7zW8jpJN0XUilCXO4qtTpay0rnodBPC6UNYUSpyx1JvxNnf3zxsVm7EDHNW1Xz+vOMRWpJt5IfySLpJWtPtnFU5n8pY+TFfO8mdxiivjl3mdkZ7Y6hcl2D0Xa+iM2mKC+ZA0sV57EF0Zzvhm25HHI56GIYuv/waotEYN998LTffci0Tbz2b5nQL4/LHYEqDtJdhXfsmntjzHNdNPIwvrCDyJ0HoRRGgBAyw1kms1zXJWxT6rfsROmmdeeZi7rrrB+zbV8+oUXVv6bZ0WIMhwGVI6lRkQYX1EbiBI16fE7Bvw48GaBvXg4gLoj9AF+gciPiQSgnSaYgca7aiDMrXvAnDSK3a4Jytcc4++rKiJyh7IwReHRy4GyJTIFIClRNwgwcL6KJARiOTQar+jUrHg7Rr6DmJytfBdhWIbo0OC4QPcMBUHSjJ6wWjIcBpOPM1frVGdoO1CbzZAqdaI3pASxB9GtkY4DgAZHeQlE5f4YM9gMOYeGSa+lTIaBZ4E3WA0+gmOH9WkAj3TobYM4DpCL0cmNdIwBO4MwK0hz7Gx+S///s/8sQTD/GLX/yR6urhJaVGNKIRjehvUQ1rm3D3u9gTTBJWLwWqEFLAepPuOT0UTSyhyj/+59yZ2cVsNtexzdxMRMfwhIsAFmcvYqx3cgidmJa8MxPn3kiCLYYzSA4a79tcNDB2TYkkPUYXuSp/yGvjOocG9tEtuzg0kDzen8Rn+/6NHeYWMiJDpV9Nlao5Au/g544lnTsMcP1fSdPcWZzmzuM1axlhHcFA0i/6mOxN53TnjDe1bonkDGcR852zyIj0wPoNPOVyr/1rdugtjEmNJ2bmYEmb3cYOHoz8gQl9kwlzAvSfEGTqrsItmoHZvQVQ+Hnj8fInDXZnHEu+59PfnkQIiJfEkcaJr9kyfRkyiSzh3BDhnFOLJSwZW0zxmCJ8J0A6GAlJ/OsGpMGvAZWAbLtDOpNk95bNLJMrqc/uobO3DXb4dHy5jZaWJtrbWwfZwIcrGokRi8XJyc0l3BHmwIDw0ACT9lRwfetDb3sPmcYMjpcl46Tx1SElD2sG/v4DxCM55EYKKMovoSCniPycQgpyirDSYbwVSRbVnEd+fgHGQPmc1hrDMiiqLaRwVAHdDT0U1RVSUJ3/ps9jy5Y2tNZECw4OvOOlcTp3d9Kxq2PEDB7R21ojZvDbTG3SZ5/hUqEOsn4Fgkrf5JkffZeOjjZu+sKX+X60mz3GwVZXH01G6CMwCSEt6DsJE7fKN9lqOAHndWBd3oAbUKRObcLoRTvFCitNjbKIa4mPZo90+UO4j88kLXKOkfY9XC6aPYaLh6ZaDX1djpac4YZ5KNQPEvKUJCEVXUJxYTZ6QjP5zWhO2SzWtK2nOdlKWbQEXyv29zVQGilmWvFkDMPgX/7lq9TVjeezn72Nnp5uvvvdX2KfZBr0cHmbN5D537sgk0GUlILj4Dz3FKqjncjtnzgp9MDJSlgW5vRZmNNnDT6mtebBB//Iv/3bZ8hk0tzxjW+z7/arGaNDg/iSAm3QrxXLrDTnOlGMN5kOfrz+GZ7e+wIlkSJG51TT5/bzatNrJJw+Sspm4+aPDVqRhYH2PVSyGaVPbrrgeaPOxvU9XmleSVOyhYgZ5ryas7ly7CXHTXlHifL+9Ie5N/Ib9hg7qX1hG1bKJz5uHpXeJGSORMTjeOvX4u/agTlh0hs6B+eddzG//OWf+cAH3smW3h1c9bmbMQeKPCJmmJJIEa+3beAdY5YQsw7eLDJ3QuhViV9ySBLY1ZjbBKFlgsxlb9wEPNVasOBsDMPgyScf5pZbPv6WbsubAF4NmHsEXl1gYIr+gMGbeacaVuIzc4lGZBRVL0nysoIuoGCUHkQVdHVBba0m7/BxpwZjH4i+AA+g3ux0BhWU2YWeEchuASZ4ozTpaxX+WFB5oEyNtXrAiLTBr9aoGOgcgV98yHeKBmMnWBuCdLQ/KmDsHpOLS5AA9qsPYjaQwXb8ao3K1whnYLvewUS24QbnWuVrMANz3c8BYxfYLwTLhx8VyHaQCYGxPzBgVXFgPmcuVmSXcESh3huWD9Z6gbkeREbgT9A4czV+qcbaKIOUeA9ggF+p0aZGlZxgnYfI3AKhVyR+2SHnMgvWOrBeE0dgPQB+9rPv8vOff4+vfe37XHzxFafgIEf019LXvvY17r//frZu3UokEuGMM87gG9/4BhMnHjSrMpkMn/70p7n33nvJZrMsWbKEH/3oR5SVHfxA2LdvH7fffjvPP/888XicD3zgA3zta18bUiD4wgsv8KlPfYpNmzZRU1PD5z//eT74wQ/+NQ93RCMaljp2dRCKhhnHRNazhk7Zjh0LIZtNRJPJRaMvP6Ig7XAV6iJuTX6SlfYrbDU3EtExZrpzOM2dhxwOCP4QpXvS1CYdbi+Ksz1XkxxA2c3wQkidoU8EKdl8v4A2o4UC/+Dd86ToJ0SIgqM0q0aIMMN76xAFCsV2czNbzI14wqPOG8d0dzYhQri4bDc30y7byNV5THKnEeXEAaIQId6fvJWJoSm8br2Gj8d0dzYLnHOOiZo4WRkYxAbaY13lsLTvKdaGVhBKRejy2kjIbgpDJVQzigZzH/vM3UzwppxgrYAQ+Hnj8POGnwxv29HOjhd20dfWhxCQV5nHxPPGUzDq6OElL+ux86XdNK5vxE27WGGLyhkVjF80bhCBcCokhBhcX+LVTjY1bGaTsYUtezazNb2FzcnNtPkDRQ8tEA/nUlZazuiSOqZNm8UFF1xKRUUV5eWVFBYWE4/nDP6JxeLDDil5WQ/f8dmzci/bn99JTknwvrXsaqO9oRWiiqqFFcRGh+lNdbH22ddpqN9PSvTT0dvGjobNdPd1knHS/OIF4ItQUFDE6Jo6Ym4epRsrqB09lsriUZREy9FKD8FmvBm5aQdpDvUDhAgQLV728MbiEY3o7aURM/htJgkDPKqhF3r9LS1s//aPeNfNt7FqUin1RpbxvoU5wD1dY2Zokz59+OQMTIPSaHqFz+n+8O9EznfDrLEy7JIuZdrER9Msfep8i+neGy97Olw+mpV2hlxtEB8wbw0Eo5XFLsNlu+EMq9htp+FwX7iP/YaLDxQrgwudGOc4kUEz+6JsHIHgVStNq+ETV5JLnRgXZ99aMPyM4im8c+ylPLX3Bbb37EL6ilIV4eqieZTaBwcP11//QfLy8vjIR26gry/Bz352L9HoG8dxuEtfRCf7MSccnPYl4jn4Wzbhb9s8xKh9q9Xc3MhnP3s7zz33BJdddhVf+cp3qK8p4NeyF8sfaphGtCQpFQ6ayAnM4P19jSxtXMG27p3k2HFOL5vNwsq5WNKiN5tgRfMaisIFFEcCPEWhUYDSilWt66hP7GVa0WSEtNFa05RsoTxWSnX8KPDN48iSFpePvYhzahbSlekhx4pRFBlebHaUX8cn+/+VPf5m7N3/TTweI3JIbFDEc6BxP7qtBd6gGQywePGF/Mt/f5Mvf+rTvPCj+7n4E+8dNKptwyLlpcn6zhAz2KgXkGEoEsICHdeY6wT8DZnB+fkFXHHFu/n5z7/PjTd+ZIjhccoVhvR1iujdErOeAEUQAmeBJnv+MM9JBNLv1eSc7zPn94KnVgu6iyGWhK79YFmC885THBoGF10EZXJbBCI9kDA+XZG+SnNoAMbYE6AdjH0Cv0Tjnq7xpnBUjrG1RhB+IChH88ZpcAOGb/R/Jf2fUhiNYO0IUrjaFuhoUH6m8iD9boWxX2DuBa9WY24XRB6QiH7ACIrfvOWC1M0KVXzYhhWEnhSEn5OInmBZHQJ3psKfGBTA2UsFkb8IRFJjdIHMBigObNC9wY1Ja1WQ7DVaBaIdrA3BMakKjTc+SE2b2zWyV+Ccrcgu0vh1Q8+FbIDQy0ERnT8KnDl6MEl8QikIPyAIPSsQvkAbGnulxFqjccerwLhPBca1cMHcCP4YgTt1+DdmzXoBzmFlc6HgfFnrjzSDH3roT3zpS5/lox/9LB/4wK3D3s6I/jb04osvcscdd3D66afjeR7/8i//wkUXXcTmzZuJxYKxyic/+UkeffRR/vSnP5GXl8dHP/pRrr76al555RUAfN/nsssuo7y8nFdffZXm5mbe//73Y1kW//mf/wlAfX09l112Gbfddhu///3vefbZZ7n55pupqKhgyZIl/2fHP6IRHU1m2ER5igq/ClOb7Df20id6iagczvDP4Bzn/GGtJ18XcFH2ci7KXn7Ecx4eO8wttMoWPFwq/Gpq1FB2rpNy2PbcDlq3tOJlPULxEHWnVTPmjDp6rG4eDf8xMESFzxhvPGP88ewz97DD3IpGkxUZMqQ5yzmPMd6EU3Z+hiON5uHwfTwdfpQ0KQQSIySZ4yzgivS7+VP0d2yy1uHhIRGM9sZyQ/pmRvknnm0VJcq52SWcm33rPzsakvW0Z5oxpUVER4iYMRyVpSvbTrFRjh74761Qb3OCDQ9uJJt0yCmJo7Wms76T9X/JcPoNc4kWHHnNtvOlXex8aTeRvDDx4jjZpMOul+vRWjNlyfCwHMeTUoodO7awatVyXnvtVVatWsbu3TsBsLAYH5nApMgkbi67hQnuRCZ5k3BuDZN3WRHFh6AZ3qzcjEv9sr00rW/CczyiBVEKavJJdiZx0x65RXFqplcx9ZLJ5B+S4j1n2kW8ft86pGUgjSDK5qRdlOVSclYhLd0N1NfvpL5+J1s3bGb9+lX0regFQAhJTXktc9rnMWPVaUybNotp02aRNwwkYqIlQeO6JnoaegjnhqmYVkH+qAJatrahfIU05AB/2UFrPYiNGNGI3q4aMYPfZipRBnW+xQYzS9yXyAFMxAv/9W2sUIib7/wcvzAcqpSBOXBlayCY5Nv0ygzbTYcyZWIh6BKKcmVypjP8dtQ63+YD6Twet/tpNDwkMNcN845snNxTmKL1gAwK+7DvbQMGBk5HKfVBk0ETRiARdAuf30UStEiPUb6JiaBFevw53EeBMpgxYF7bCC7LxlnkROkRPrlantJjOZaEEFwwehGzS6eza9nj6JVLqW3uISruIz3qNUJXvwdjbFDucOmlV/Hb3z7Ihz70Lq6//lJ++9u/DOtL7XBprfH37UHmDb1TLcJhtFKo9vbDXqAwuzZidm1CKA8vbxxuyWlgvHnj/9lnH+cTn7gJ27b51a/+zJIl7wAgpRxCCBJCkXtIirtb+Ez1Q4RPYATvTezn5xv+l+ZkG/l2Lh2pTrZ17aSxv5nrJl5Fwukj5aUpiw51ovJCuZREigibYbZ0bydqRkh7GfJCuVxWdyFh840dc66dQ6598oMFC4txYjqp2BhUSwscsrvacYLSkNibH4Rcc/l1PL39eV794UNUTBjNrEvPQmtNe7qTiYXjyA8dFuE88KtxCB4ACBitb33fHKqzA93bgygoHBbW5PbbP8UDD8zj4Yfv46qrrntL980fB/2fUYExm9Soco03Fo5SZH5c6TJ450c1ec/DsmWCZBImTIDFixWzZx/y2acgeq/EWhUUv+nqAD0QekagYwMlZ4C5GaK/ksgugY5pzO0Sew2kr1U4Zx35WWovEwgdsHQh2H9vjMbcCfargujvJMZe0FGBSIFwBxK5CqxVAnvlwHHYEqMDvCqNf+DaxgVzuyD0nCB97dBtW6sF4YcDc9kfD3hg7g3wD+lrAwRJ5mqNN9Un8ltJ5DGJHwfM4AaFkEAGzF0Cc59AxQYYvEkwE+DmiCD5K8CbBObWAG3hjxl6/KGHIP5jA9kB2gySw/Y0TerDiuGgE43dEHpRogo1umDgGF2NuVEgOiSqAERMI9KABFUR7IfRAd7hBvmxdKyPQc0RI7vly1/m4x+/kauvvp7Pfe4rw9zAiP6W9MQTTwz5969//WtKS0tZvXo155xzDr29vdx1113cfffdnHfeeQD86le/YvLkySxfvpwFCxbw1FNPsXnzZp555hnKysqYNWsWX/nKV/inf/onvvSlL2HbNj/5yU+oq6vjW9/6FgCTJ09m6dKlfOc73zmmGZzNZslmD3JZE4nEW3QWRjSioaqYUk77jg6y/VlK4mUU+6X0t/dDVHD6qLlHoBROVgnRy++iv2C1tYK9xm4SspewDjPdPY3LMldxUfZyDG2w+Ymt7F/TQKwoRjQnzJ6wx2tNDYze5bHrtAfZYa+kxC/F0harreUUqhIK/EJeDb1IUiQxkBSpUvIOQ0cMVxkyrLaXs8F8HV/4THVncLp7xmBq9lC1So+tpoODpto3kezmmdBj5KhcalWAmUiJJCvtV2kyGthn1lPnjSOio7g47DZ38IfIb/lk/79i/o3YCFpr2tINFKkSqpwadkd2Es3EsGWIlJ9kj9xFrT+OUd6YE6/sDah5QzPp3gxFdYWDgYrCUYV01nfSurWNuoW1Q5bP9GVoXN9EJC9MrCi4mWeGTISApvUt1M2vJZI//GtzCM7Bli0bePbZx1mx4hVWr15Ob28PUkqmTJnBokUX8dnrv8AZj89lXPdYzKgVoL38oMTWnaXp+bAaEiLo2N1Jw+sNJFqC0reqWVWUTSoddoeNVppNj26mYW0T4ZwQZsikp7EXwzYYf844IgUR7KhFQU0B0hyaMC6dWEpOWQ67ltbjpl1AE8mLctq1M5l8zpEhmGRnkvr19ezdv4um3v3sbtrGxo3rePyJB8lk0gCMGlXHtGkzmTlzLgsWnM3MmXOGzLbt3t/D2j+vI9mRwo5ZdO/vpWVrG6Pn1ZBfnU/7zg6ySYd0dwov6xEviZHuTQ+axCMa0dtRfxuf4iMatiSCy7M5dEqfbUbw4did7GP/vfdz7a0fJTcvH0UXxmFcX0sLRvkWC50wew2PrNCc40ZY5ESpVifn4Ez1Qkz0bNqlj6mhWBtDBlwHGMabzKBsrtq3mOmFjlnidjTZwFjfZqWVptg/uP6eAZZvlX/wR1ehWWFleNlO0SV9CpTB2U4UD02T9BjvW4NYgWplsd1weM1KD5rBBxTXcjCF/NdU3t4WJj+8FHwfWVkHvo+3cwf6t3cRufMfBw2vc845nz/+8Une9753cM01F3DPPY9SUnJy88GFEMjiEvwd24c8rr2AkSZyDzH+tCa860+E9z4KfgaEIITELZtPcsqHwXxj6WTHcfja177AT3/6Hc4//xL+539+MaTssNa3mO2EeCWUJl8ZhLWgU/hEESx2oicc3L+w/1Wak21MLhg/OGDpzvSyrHkVCyrmUhwpJG7H6M32ETEPDrZ6s31Uxst53+R3s6u3nqb+Vsqixcwpm8XY/No3dKxvVkJKrDPOJnvPb1Ed7YiiYshm8ffWY4yuxZz05tMDxZEibrz+wzRv28Pzv3gAszqGWRMnz87lwlGLkYcx0rwJAb9Wth3EEYh0wG11T3vrUsE6nSL70P14q1agUylELIY5byGhy69ChI89S2DatFmcc84F/PjH3+ad73zPW976q6Pgznnz58G2YckSzXnnabJZiEaDorRDZewDc8uAETxwX0AVAQ7YywXZCzQ6BOHHJTIRFP0Fvz4aYx+EHhe4s/URfFnZAerwX28TQGA/B+YOgSrW6JzAAJadQSJV9oLsAXcOIMHcpDF2CfyqQwrbLFCFGmutCNLLh3z92CsFQoF/YGafDd4YMHcEmAlVqZFNAh2G5G0Koy0oMNRhkI0g+wlYiypgN8segRYaLAFewA/2K/Ug11rbGtEztEzO3ALx7xvIroGfbxVwm63XIfyApH+8OuHIydwdmOT6UAawBTonOG5vcoCnkImAYazzgnNqNIjgPRqGvPFB8lt0M1jiFxjzAW/5gNasWcmNN17DvHlnjhTG/R2ptzdIQBUWBj/Mq1evxnVdLrjggsFlJk2axKhRo1i2bBkLFixg2bJlTJ8+fQg2YsmSJdx+++1s2rSJ2bNns2zZsiHrOLDMnXfeecx9+drXvsa///u/n8KjG9HfgzSafUY9m80NOCJLtT+aqe7MEzNbT0KV0yvo3t9D4/om+tr6AQjnhBh/3nhyy4/DIhqmngg9xGvWq/TIHrIiS6Eqol/0sd3cRDaSIUSI0/eeSdu2NnJK48j8MM/NCLGtyiLlebxg9ZGwKpnrnUGpDvavQBWxylpGSqSY4ywgqqPYhMiS5VX7JaZ7s5ntnj7sfXRw+F30F6ywX8bAxPRMtrKeDXINN/p3EOdgaGCllebP4T66hI9AYAERvZqE6Gf0IXdFozqG0LDCfoUZ7uzBAjgLm1F+HfXmTuqNnYz3J+Hjs8paxnJ7Kd2ykzHeBM5yFjPGH/+mz//JyNc+JiZnJM6m2+xib7geS9mk6KdKjeYd7ruIcHIG63DV35EcMHMPji+FFAhDkupOHbF8JpHFSbnklA4160PxEImWBJlEZlhmcDabZdmyl3j66Ud5+ulHaWjYSywWZ968M/nwhz/B6aefwezZpxOLDWzHh6glMR4SiB6Csl1P4Fdqkh8fagS3bG5h/UObcNMOdixEf0eS9l0dTF4yidGnD69voKcxMFNzSuOE4sE1dzg3TNfeLjrqOzn9zDnHHJO37Winv72f0vHFg4gGN+3SsqWV0fNGES2I4mU92nd2kOpKYcdsxs0Zx7Rzh5YT+r7P7t3b2bDhdTZsWMfGja/zve99na997fOEwxHmzl3A/PlnsWDB2cjdIZJdKYrGHDT1+zv6aVrfwsyrp7P6njV07e3Gipjk1+RjR222P7sDK2Izem7NsM7JiEb0t6YRM/htqDrf4n2pXL4f62aV6ZD481/wUmlCN76L/oG0b6PhUudbg8ZZq/SpUCbXZnIJIdBwTO6qRtMoPdJCU6aMo6ZkTQQV6ug/Pq9YaR4I99ErFMZAcnmSF+LGdB4Fw0zcCgTnOlF2Gy7bDJd8LcmKIPm72Iky6pBtv2inuC/UjxSQqySNhsfvIr2M8gOX4fDjjGpBm/zbYfx4ry1Hp/oxJxzkWBnjJuBv24y3/nXsReeTIShQazpzAp985BF+eM27eM97Lub++58j/ygFcceTteAs/O1bUS3Ng8xgf98ejNGjMSdPHVzO7NlKaN/jKDsfFQniasJLYbUswy6cilN94Ukf6969u7n99vexadM6/u3fvsktt3z8CHNCIrguk0uJNlhpZcgIzWTf5lwnxrRDDHzt+yDlkIGEpzy2de+gKFww5PH8UC6t6Tb29zVSlzeKMypO56FdQdIqL5RLn9NPZ6aLC0YtYlbpNGaVvrmm41Mp68xFqM5O3FdfQm/bAqaJOXYcoetuQESObchr38ffvhV/904QAmPcBIxxExBHMYMurj2fvK98n0984P288t0H+cSP/4MLJ5/PhIIjy0j8moCvGn5MYm6GlJFmW2wXqTkuJTMrKOUkwKcnoexf7sN59klkSRmyqgad6MV58lGQBuGrrz3ua2+//VNcf/2lLF36PGeffd5bsn9vlSwr+HM0iURgxB8e/tFxkL0Bo1f0gbFf4JfpIWlSvyLgGxv7OCLt6o3WhJZJlD7kNVlAaYw2DhbHQZBsLQzSuyKrURUMJqF1HqBBtoA/DMqK7AJ9+PWPAfiC8MMgMxIygND4ZQK/NEBFqPzAgPVqwGgBmQ5MVlwgHLCHjTYB3WBtEkFhnj/A8h09FM0QeizgJPuVB41qHdLIzuC1RlOAjTiuDpyzw9PzA3xl4QR/D2IyVPDkiUoGD5U3ATLnKcLPSmgd2I7QOPPAmRu8OS+88BQ33XQt06bN4he/+OOb5s2P6G9DSinuvPNOzjzzTKZNC76rWlpasG2b/Pz8IcuWlZXR0tIyuMyhRvCB5w88d7xlEokE6XSayFGaK//5n/+ZT33qU4P/TiQS1NSMXBj//yQXzQ7ToVcoipXBGN/iZfsZHoz8kV7Rg0BgYDDTncMHUreekOM7XBmWwbTLp1A5vYJEcwJpSorqCskpffPrT4p+VtvLCekwvbKbPJWHTQhbh+iTvXi4LLWfZ1LfDJy0S05ZDmtGW2wcbZGb7CGWSdCfVnTUxthhTaLYXU8YB4HAFQ7dspNqdfDLJE4OnaKN9eaakzKDN1iv85r9CtXeaHITOZg9oF1Fn9HJk8b9XBi7gly7gHbhcX+4jyyaSQMF5Anh8rJdjyubydExilQJOQPsIY3GEy4hPdS8D+kwLg4ZEaQtHw3fz2PhvyC0IKKjvBR6mo3W69yc+ujw+LynQEIIisLl7OnbTlWmmne1X8+W6CZajGbiTpQrQ+9lunzrmMuxoigtW4KStQPXHlpplK+OauqGc0JYEQun38EsPHg9m+13sCIWodxj3zBxHIennnqYhx76E88//xTJZD9VVaO48MLLuOiiy1m48BxCoWPMZDQg9T6FP0pgvyyQneCNU2TeqfEPwSP7ns+uV+rxHZ+i2qLBxxOtfex+dQ8VU8uxowfHE+meNO07O3AzLrHiGMVjijFtg2RnEn8Am3LgnKR703hZj9ZtbaR700Tzj34d07yhGeVpCkcfvMbVStO5p4v2HR2UTihh/V820FHfFZTb6aDUbfrlUyiqO7jPhmEwfvxkxo+fzNVXvxcAz/PYtGkdy5e/zLJlL3HXXT/gW9/6CoY0GVsxkRnj5zJ30hmMq5pMrDBG194uehp6EFJQt3A0oXho8H3ubU6w77V9VM+sxLDe+lnFIxrRqdaIGfw2VJfw+Umsh2V2BlND/11/JHbR2awbU4jpJTg/G+OhcJAcjmtBUmiiWnBxNkbkBGUIbdLjz6E+tpsOWaHJVwaLnQgXOLEjyueOpg7h8Ui4Hw1MHhhsuGg2m1met1NcnT1ygNYtfF63MtQbLnEtmeGGmOTbjPVtbknn8ZKVZofpUKQMFrgRFh7C+00NmKQhoHrA/C3yDZqkxy7DwUfjoLAHjluj6Rea2QPLajTt0scjQHBYwzjGUy3V2oyIDI3mCSlBSHRvD7ukwxdy2tllBtxj4/QSRj32W7Zf8B4+8IGruOeex06KIWzOW4jd0437/NP4u7YjTAtz/ARC1/4DInbQVTJ7tiK8JG7uGByhsbTAMKNow8ZqX33SZvBDD/2Jz372dgoKinjwwReZNWvuMZeNILk8m8OF2TiO0MR0gP7QWuOvW4PzykuopgZkSSnWGedgzp2PkBIpJCEjRNrtHbI+hQINISMYvCypPQ+NZlnTa7Sm2oiYES6uPY/Lxlx0Usd0LPn79uCtXYPu6UZWVmHOnossGu7876ESlkX4mvdgn3kOflMjIhLBGDv+uEV/2vPI3ncP7tIXIJMJPKlIFOvcCwm9811HGMKGNDhr9EIe/N0zLFkyn+U/fpTbf3Pz0HWmU7ivLcdbv5aU1tjzZrLHLOEPztM02E24Ri+5L4VZNHYxl0+/GkOeukGR6mjHXf0asrR88DyKklLQCm/lq6jzL0IeB5tyzjnnM23aLL7xjS9yxhmLMIy/jwGbKgkYwaLnYDoUgnSuKtSoXJBJAtj84f2HPmQ9WLtR0N0BdXWaqqqAPuKepbE3BWWBfllgXso2gTdGY3QIfE9j7hPoAVMTMWBKh8E/5F6AKgTCQXJ4UB7ITkF2iRqSChadoC2NbAxK0QZHJ1mQnRqZELjTBhLQHph7QBugQwJzR5CKlQQMXd/QiKQI1m8HuAgRG0guNwuMfRrRL/BHB+zkQ2U0CrCOxicSiMxQQ/1Y8sbrwJDv4GApXB+ILkH2DI21IzDydS6ggoS3KmKwKHBYkpC5SuNN9DG3CoQH3riBVHAIHnjgXj7xiQ+xePFF/OQnd78pzvyI/rZ0xx13sHHjRpYuXfp/vSsAhEKhY5sPI/q7V4v0+H04wS7DxRcaG6j2szSaj2BqmOxPQyBIk2a1tYKx9nguzl457PU7OGy21rPH2IWJyURvCuO8SYPXAdKQFI8ponhM0QnWdHLKkMEVLhqFh4s90AJrYKBQhInQK3tw84Lyr2zSYV0tdMUfpyV/Ha5KI1QE3yqnX1xOp8ynSrUBkCaNrY/8nTG0SVocmSQ9nnabO/BR5CTj2B0iuBEaMvDx6ci0sNldw2lFZ7E17NMlfCYMXJv5ONSb99JtLKVfNuCJTuIql0neVMr9SnzhM9adQIdsI9c/yEdul63kq0Iq/RpaZTMvhp4hV+VRqoIpPRWqiu3mFp4KPcp4b/LwUB1ag/ZAmMEg5A2oOlZHd7aNrmwHtmczPTWNaXoqNfFxTAhPf0PrHK4qplXQuL6Z7v095JTG0Rr6WvvIKYlTNrH0iOXDuWEqp1ewe+luEEEiONvvkOpOUbdwNNGjGMjbt2/mnnt+zZ/+9Du6ujqYOXMOH/3oZ7nwwsuYPHn68Ge9RSF7sSZ7UXBT/Gh4t1RXmv72JLGiYNzgez7JzhSZRJqe/T00bmimdt4ohBC072hn46ObSXamECJIRJeMK2HGldOwIhZCCnzXD1Ae29tJdqZwUg6mbbD6nrXMuHIqeZVHFgqmezOYocNK2wY4xk7KYefS3bTt7KCgJh/TNlFK0bO/ly1PbmPBjfOOW8JnmiYzZ85h5sw53HrrnSil2LR+Hb//79+ypWE9T6x4gPte+A15sQJOG7+AicUzKe0swc145JbnDjnX4ZwQmUQGJ+USyfv7uLYY0f9fGjGD34Z6xU6x1swS05LI2m3sWb2eqj/+hIxQ7DBcLgA+nMpnhZWmyfAo8w3muxEm+cdPBTlofh9OsNnMUqUswkrQKX3+Eu4nrg3OcE88ZWWn6dIlFBMOSSVbCHK15Bk7SZVvMkpZlKsA/dAmPX4Z6WWnEXBiXeAVO807MjEudOLU+TZ1vo1GH3VA0Wb4dEmPIt/EQWMPLGNrQYPp4qDZbrrU+SajfJtu4ZOvJWe4EZqlx19Cfay1MvRKRYlvcE0mh8XuW1scd7hkVQ3+ti1D7yh7HqChsIivxzrZZrqU+wZRJGkUjdNGMfX+X7L20vdy223v5a67/oR1rPjgYRJSErr4cqz5Z6CaGsAOYdSOQRz2eqUVndJnvZ0iIzT2AGpkrJAIdbizdGyl02m+9KXP8L//+3Pe8Y538V//9WNyc4fXJBxCEDoEeeKteJXMPb+BrIPIy8Pfvg1/+zbsvgSh85cghWRe+Wnct+Nhkm6KmBVFacXeRAOl0RImFga3vm3D4oqxF3NuzVl0Z3rIDeWQH3rz7cba93GeepTsQ/dDNosIhcB1cV99ifCNt2LUjD7xSo4hWV6BLK848YKAt+513BefQ5SUDpqkqqsT97knMSdOwpw646ivq64ezQ9+8Bve974r+MEPvsnHP/654LgyGTK/vQt31QqEHQIhaHJX8ouZmu6yPKr2d2P09tIpHR6u30Lh7mbOuvKjR00hvxHpnm5I9SOKa4c8LnLzUC3NwfPHMYOFEHz1q9/lqqvO5cc//jYf/ehnT8l+/V9LlYMzVxF6TqKyAwZkd4DsyJ4TYARUCNxJGnuFwMsZMFkV9KyF13vhrscFnhTk5ARM4ne8Q+NNgOQHfMJPycAcNSG7SJG9TBO5R2IlBH41GM1AItieytFBmvaQj2kdAb8QMMHcKtCWRjgCb5zGmasxt4C2gudCLwqMBjD3g9EscGZqiIJsD1boVxxEYWCCVwvmbsicrzC3CcJtAhUHfyzIdrBXEFzouGA2DtxIKgx+FkQasucoshfoAKtxiPzRoFcH0ydVAcHxKBBpjV+lD3KUjyN/NGQuUoQfl5jtwf7IziCxrdrALwDZIRBNAAEyIv1ufWSh3okkwZsG3rShJvJdd/2QL3zhk1x77Q3813/9ZNjfDSP629dHP/pRHnnkEV566SWqq6sHHy8vL8dxHHp6eoakg1tbWykvLx9cZuXKlUPW19raOvjcgb8PPHboMrm5uUdNBY/o/28pNH8KJ9hqZqnzLcJIkkKxzGolI6s430kPjt0jRIjrHFZZK4ZtBqdJ87voz1ltL8fFRQMxHefizBVcmn3nm2YCQxAK2WyuZ7n9Mo3Gfir8ahY4ZzHZm06lX81aazUmFlmyhAiRFilChNEo8lQ+FSUVdI/vZe/6BnaUvEJXfBWx/gKsbD4Up0hYq3BUMRmKUSjaZSs5OpcYPhkyg9gMFxdHZJngnxz+y9BBm4rRF3zPaXvgO1MqRNggkeyhM9uKM1CYfCDY02isZK/5EjmqDlsbWDTQI7tZab/KGG88c52FzHUX8OfI3Ww3t5CvCkiKfjzh8Y7MuyjSxawyl9MruinTFTQa+7B1iEJVTIkqZa+5mz6RGFK0d+TJ15idawk1PIPRtw8VLsSpXIRTsQhOMlAQM3OYUbiA5tQ+upx2LGFTFqmiLFL9luPBcsri1JxWxe5X99C9vwc7alE4uoCJ548fZAIfrgmLx4HSNG1sJtGSwIpY1C0czYRzD+I1UqkkDz74J+6++5esXr2cwsJi3v3u93H99R+ksnA0Hbs68DqClO2BNC4w5HrymJIDf44iw5JIQ6A8hed4tG5tI9mZRPkK3/HZ9MhmDENSPqWMzU9sJZ3IUFRbiJACL+vRsrWVnLI4484ZS15lHj0NPXiOR6K1DytkYoVNCmsLSTT3sumxLcz/4OkY5tD3O68qj669XUOOxXd9hAArYrFv9X5iBVFMO7CypJTkVebS29RLd0MPJWOHP6CSUjJ91mzed32I+mV7yKvOZVfLVlZtfYUVG17m+bWP84sXvsWE8qnMnXwmZ8w8l9KC4DvTSTrYcRsrMnSc1dfWR9OGZhKt/UTzI1RMLaNw9PBKxEc0or+mRszgt6HWmVkEgeHZ+r/3YZWXUn7x+XQKSAlFQirOdm0mnMD8PVw7TIcdhjM4oAOoUCZ7DJeldooFbviE6eADl6SHLtUtfDaaWVJC83PRQ56WLHAivDObwwt2ip2mwwTPHiy8a5EeT4VSTPfClA/gIIK72JpdhkuH9MnRkgmeTYv02C09NpsOYS0pVQZFvsE6K0NCKGZ4YVqkxw4jMKkXOxEucXIoUybfi3ax1E6TQuEKzTbD4TUrw01pl1vS+Sd17t6MrPln4K55DbVrB7K8Au37qOYGjNoxbJ8zja1WkiIliQ68JxEkRcqgdeFU/vVXv+NL77uWz3zmVr7znV+cFA9SnqCAa1lxNbkNEu30Ytp5ZIRmm+gjRyXIK5k9rG10dLRx443XsGnTOr75zR/xD/9w0xselOlsFueZJ0CDMX5i8GBpOX5TA+6zT2HNW4jMyWVR9UL29zXyetsGPOWh0ZRGi3n3hCuOMHxz7Dg59pEFG29E/s7tZP58D85jj6CzGWRFJebEyYjCYvztW3Aef5jwLXe85YNSAH/TerTyMQ4xSGVhEV57C96Wzcc0gwHOPXcJd9zxGb797f/giiveTW3tWLz1r+OtWYUxum4QTbFd7aOldx1j2zoxhETk5VMiDfqddlase4wFlXOx5p9xSo5H5BdANI7u7UUUH4ye6kQvIh4Pnj+BTj99Ibfeeif//d//zoUXXsrEiVNP+JpTIh3wlWVvkP483Hx8UxKQvlqjIwr7tQBvoPI1ziKNc44eXCZzuUK2S8ydwQOpJGzugBdqNWMmgRCazk54/HFBTQ2cdprGmwH9U1WAbrAHkA9AdonCaJTIZoEaYEfrEKQvC4xme5PAL9RgBIane5ome5HCaBSIvgDLIPog9guJ7BbIXhCt4E/SuLMD09d6XWBvEjizNNnzFPYacSRCwQCUQJVq+q9U6BwZLBcNTF98AZ5GhwJ+sJCgcsAdr+n7jEIdA2fonKGxVgZoDdkRnD/RHxjDqRtOzAs+cM6zl2r8sT6RuyVGk8AfA15tUBpndIEzR+FPDs6tO1EjsmC/EDCTvTqNX8uwUsiHSmvNf/3Xv/M///Of3Hbbp/jCF772V/m8GdFbL601H/vYx3jggQd44YUXqKurG/L8nDlzsCyLZ599lmuuuQaAbdu2sW/fPhYuXAjAwoUL+epXv0pbWxulpUFa7emnnyY3N5cpU6YMLvPYY48NWffTTz89uI4RjehQ7TM8dhouNergdUNMS/K0R4uoJsNeIriDy5vawBXOMQMeh2ul/Qor7Feo8UcPlqG1y1aeCj/MJG8qY/0JQ5Z30ayyMrxuZkhKxSTPZoEToUQf+4P7NWsZv4/eRVokiatcGu19bLDWcH36Ri7IXsp+Yy/NsoF2oxVLB5GTMr8SjeYc53xCIsSUSybRVtCMKzeCW07YiRHKCRGJVOIqnz6xgW5ZjCs7yNeFXJ/6EPvNetZaq4nrOAJJn+hlsjeduc7J/a5N9KbwDI/heBkiRnDDJksW0JSpCgSQVRlqfGtIOXOTsQqBhSKXSd5MCnQVrbKZZqOB052F3Jz6GHGdQ74q5KXQMzQa+6j1x3KGs4gFztkAKHz2GvXsMrfjo5AIClQRFX4V+boA6wTsI6v9NaKbforwUyi7ECOxh2jPdmSmk8zYd5/UeQCImnHG5k7hSMDZW6f+jiQbH9lE195ufDcIyuSU5TLrXTOJHAf3YIZMplwymdqFtWQTGUK54cFEcE9PN7/+9Y/5+c+/T09PF4sWXcjPfnYPF130DmzbpmljMyt+vZJ0TxqEQBqCkvHFVE6vpGVLK30tfcSKY1TPqqJ8StlJjwMi+RGKxhTRtK45mFHbkSScE6SXYxUx7IjF9ud34ns+/R1JCqrzB1O7xoAhvfmJrXhZn7LJZXiOz+5X60EFM5fyq/MpHFWA8hS9Tb307O8ZgnYAqJ5ZSeuWVrr2dBEriqF8RbIrRfGYIopqC9nx4i6sw5LD0pAopVHuUPzXcDX2zDr62/vp3N1Jia7ikvHXcs38D5I/O8baXSv48+/u5e5nf8pvn/4hk0bNYMGExcwsn8fcs08fNOIBuvZ2s+6B9SQ7k5ghi1bHo3F9E1MvmUzVzMo3tG8jGtFbpREz+G0oE0FEC/qkoue5pRRech6YwZQgARQqgy7h0yo94lpSrcxhDbp6hcIXenBABwGGoR/FZjPLdsNh4sD0omOp1rfI1ZI26VOmTFw068wsncKnTlkUaYmn4ZlQikJtsN7MUqiMQSMYoEwZbDNc6g130AxOCJ+7w31stDK4aCSCIt+gT/p4aDKARrHO9MiYGldoKn2TSmUw2bfpxqfJ8LnQiTPNC7HcSvO6lSUlFJYWpITCF4pOAT+IdiE0fCCT91fBRhhjxhG54UM4TzyC39gAhoE5ay6hK68hkRvFFf3kq6Emb0gLElIx9vxz+e53f8lHP/oBiopK+eIXv37S23cGLHz7kGNNoXiofDRza85i3p6lCNpQ0sDzs6wun8r0ioWcqJpjx44t3HDDO8lk0tx//3PHxUIMR6qjDdXeiiweOuVKlpTi761HNTUiJ+YStaJ8aNp72da9i6b+FsJmiCmFEyiKvHV3ZFVbK5nf/Bxvx3ZQPqK4BN3Tg7f+dczTFyDLKvC2b0P3dCOOY8CfKmnXQRw1VSHByR7l8aG6885/5f777+XLX/4nfvnL+/B37Qjuzh/CKE5bEjIeorsHMWbcYAo4Eo7TnUniLn/llJnBsrgE6/T5OE8/DmhEPBed6EV1dWJfeuVxERGH6rOf/RLPPPMYn/jETTz88MtvbWIyExSEhR4X2CskRgtgQ/Z0RfLDGk4NMhEikLlak10clML5paAPG2uqSui6VbHjXknDGtjWq1kqBBOqIX/go6W4GLq74fXXBacdKAI0DsEcDMibAMkbFTnfMZC7B9KuhWDtBuc0TfpSjbVOIHzInqvInqtR1RDcKtRYKwTR+wU6Epie9jKBTAh0E4hRGlUJ2VKNuRUyV2qcxRqREdgrgtK6Ax9TIjGQPK4MjOfUPyhUXGBvkBh7g6SyIPhbRwERJHRVsUaN45hy52jS74fwQ2DsGcAvjNUkb1GcBMYRBPjVA4VuUwdYyoDOD86rWS9IX6fQBRB6RhB+XCIG6DY6HiSxM1fqQf7yiZRM9vOP//gRHnjgXj7/+a/xkY98+iR2dkR/67rjjju4++67efDBB8nJyRlk/Obl5RGJRMjLy+Omm27iU5/6FIWFheTm5vKxj32MhQsXsmDBAgAuuugipkyZwg033MA3v/lNWlpa+PznP88dd9wxiHm47bbb+MEPfsA//uM/8qEPfYjnnnuOP/7xjzz66KP/Z8c+or9dpVFkhSasho6Vi/08dpgROkU/1QM4BB+fHtnNmZlzh53oXWutwtb2oBEMUKxK2WpuZLu5ZYgZrND8OdzHi3YKSVCcvS3ksM7Mcks6n7Kj9JxkyfJk+EE8vIN8WwV7jd08EXqIz/V9hZuTH+Wp0CO8HHqOfpGgQBcx1p/I4uyFnJ09Hwim+ZdcXEhV2KSLErJ5EktKEmgMXUqdSnF78l3EiTHKr6VEldHv9PGK/TxrrNfw8bnQu4wzs4uPn6Q9iiZ70zkvczEbwivw+hzSMoOhDWq8Wkr8MpIkiBhRanyLeU4kKNtG0ib76BGSMi0Y7YfI0TVU+TVs0VEmeVMH2cEzvNlM92bh42MwtDB8j7GLftGPRFCiylAo2mQLHbKNO/o/e/zCNuUR2vso+Fm8/ANp6DJkqoVQw7M4lYtQkSMRC39L0kqz+fEtdOzqIL8qHzNkkk06dO3tYvcr9Uy95MQp72h+ZNAE7u3t4ac//Q6/+MUP8DyX66//ELfddic1NbWDy6d7M2x9ejte1qNoTBFCCNyMx57l+9izYh+xwih2LET7zg466zvJJidSO+/kZiUKIZh43ngyiQzbn9uBn/VIK0UkL0LJ2GJCOSE66ztp2tBMf0c/QkIkP0oobtOxu5POPV1oX7N7WT2mZVA8poj8ygCvkFOeS7QgEszSEgLfVbhZj47dnbRua8NJOuRX51ExtZyZV8+g/pV6ehp7EYagdv5oxp5VRyQvQl5FLu07OwjlHOT3JruSRHLD5Ja/sQF2JD/CnOtm07a9nVRXCitiUTKumFhRjNMWz+X6d93E6gdX88yzj/Lq1uf47bM/QErJ4pYlXNNxPRdeeDmRcISdL+8i1ZWiqK7oIFu4qZedL+6kZHzxEN7yiEb0f60RM/htqNluiPVmhv62DtJbdlD+2dvpEgoHzSTPZp90eCDeR59QhLRgkhfi3Zkcik5Q3lagJCaCFIookkbpscXM0iF9Ylry42gPZ7oRrszE0QRT+A8MCnqEz3ozS6t0sRXsMR26hE9KaPZLFxPokD6dUhHWgqgSLLNSRx0OHo2a+FgoySorzWhlEdOSLJqX7BQZFOc6EdZZDjsMl6zQOOhg0pWAtabDXC9MoTZpw6dZeswCuqRPt/ARGnqlIiMgrA0kCgd4NNzPeGWzyDkxZ1GjBxjFQfphrG9ROUwD/oDMGbMxpkwP+MGGiSgrD76MXY9cJemRivJDDOFeqchRkkl+iLKrrqO7u4vPf/5O6urGcsMNtwxrmx3C47lQinVmYA5O80Kc50QpUyYd0qfbFGyc/B6cwslUta7DVC4NxZN4qXompW6M3OOQIl5++TluueU9VFZWc999T1NdPbzm2eNJhMJg2Wgni4gdMu0qm0VYNiJ88A68KU2mFk1katHEN73d4chduxq/qQlZMzp4D20bQmF0R1tgUpcND+9wqmRMmIS7/BV0NhOcN0Cn0wgRlBOeSNFolC984Wvcfvv7eOmlZ5lnmgHT7RCV6AhSabIGxKQMrL50kl66mdZt4XfVn9JjCl1xNUiJ99pyVHMjIhbDvuQKQpe8Y9jrCIfDfPe7v+Qd7zib73//G3zqU58/pfsIILog/LTAel1gbhEYbQNlZiIwBqPbJeYuTeK/FPpUEGk02MsFoWdEgFSwwJmuyFyh0QP3HZSCe/8oWPqkIJaBxpRgRw/0rxbMmavJG7j2tG1IJE68SaNDgK/JXAQHrvVEH9hrBcnbNJl3qsGytMNlvxJ8LqoDhrUHqkAjewIkxAFzV2QE1uuB4eucrjB3SMxtAlWoEZmAEZxdpPAHApK6ANIf1GQ7fGI/kPjlIF0RJJIdwBwovrPAWinwJuiDnOWB9DY+qFLILtE4p2uMhuAYvDFHP5YTSXaC7BN4ZUN/d1Q+mPuC8ha6IPyIRFsaf9LA67og9LTArwN39ok5wlu2bODDH76elpYmfvzj33HllccvVBzR208//vGPAVi8ePGQx3/1q1/xwQ9+EIDvfOc7SCm55ppryGazLFmyhB/96EeDyxqGwSOPPMLtt9/OwoULicVifOADH+DLX/7y4DJ1dXU8+uijfPKTn+S73/0u1dXV/OIXv2DJkiVv+TGO6O2ncmWSpyWdAwGQA/JEnLFeEUljHzvwsbVNUiQZ403gbGf4Ja6ecDEOuyN2YGytxNBBaL3h8qqdpkQZ5A9c7/hotpkOS6001xzWWZIUimftXay1GijzK3DRgwGQMr+CJrOBJmM/073ZTPNm8fHk59hn7OFV+wX2mLt43XoNheIMZxH5uoA8nU+JkUMVgnZt06l9IgiKtMNYr4T57hmDSAiAuM5hSfYKlmSvGPb5OJokkqsz11Nr1rJZr0ElFYWyhAJVQL/bQ0GohKJQGQaCazO5VCqTe8O9JBmHI7aTJehYmeSH8EliaZtqf6h5KBCYh1kGSZK8Ym+j0rucLtlIh9yGxEEisbTNOP/4Y02Z7cJINh1h+KpIKWb3ZmR/w9+8GdzT1EvX3m5yy3MHGbWhmE20IErL5hbGnT1msDzteMpms/zsZ//Dj370LbLZLDfeeDu33fZJSkrKjli2a08Xqe4URaMLB41Gw5Jk+rJo36d6VtXA4zESrX3sWbaXymkVJ21AxopijDtrDDtf2o2bdpEqMImVr9Bak2jtJ9WdJtmRJNmZwo7aRPLDATtYa/Kr8ykZW0ymL8vOl3fjpFx81yPVkyanLIei2kIyvRnCOSG69nazb9V+vKyHNCSN65poWt/MrGtmMOe9p5HtyyJNOeQY6s6oJdHaR+eeLkIxGzfjIoRgwnnjj1raN1xZYYuqGUdP70bywpx5wxnMWDKDj6U+TUoleeqFh/nLX+7l9tvfRywWZ8kF72Bq6HQm1c0YksiOl8bpaeyltzlxUgiLEY3ordaIGfw21JlulF2my59fXg1AZtFcpNCc7USY7YZ4diB1W+dbpIRmlZXGE4rbUwUYxzEox/s2kz2btWaWHC0H0A6KHCWY6dmEtODecILnrRT5WjLat1nkRIhowQ9iPWw1s/SiyEqN1FCuoMCXGIYggiBfBUO6tNC0GD67tMfF2RhPhpLEEISRWAhaZMD1HTtQ8pYQPmutDCXKIKYDQzSEwNLQKzWuEFT5Js3So1RJWgyf6AAyokMqmqTLeN9GD2wDIE9JPKHx0GSFJqKD4aVGYKORWrDMSnO2EzkuGkOjedDu48lwigwaE8jVkguzMZY4sZMyhIVpYlQNbeAuxuSSbIx7w300GC5RJUlJha/himx8cPD9oQ99hB07tvCFL3ySGTNOY+bMOcfdVkL43BXtZYfpUKiCn4pn7SR7DJfbUvnEtSSkBf2myd7K09lbGUTieoQPQhFzjo2juOeeX/NP//QRzjzzXH72s3vIyTlRhnh4EkXFmFOm4b7yMiISRYTDaMfBb9iHOW0m8k3weN+ItOMEzGXDQLW2IiwTWViEiMXRiQQyPx9tWqi+wF2z5s4fFs7gVMg67XS8tavx1r0eFBF2d6FS/ZhTpiOHacxfccW7+dWvfsy//dunefx/7kK8+Cyqu2sQLTKhGyZnI6wvzFDs92H2Jmj3EuRnNHPXZPByBNnnnsY+94JTMlVdRKKE3/1e1AUXDyas5Rs4n7NmzeVjH/snvv3t/2DSpKlceulVb3rfBpWC6K8l9gaBimpkO4hOgTDBr9OoEIhesFcLQo8JMu8+icKwY8haI4j8XoLQqOLAKA29JJEJTfL2oKStfhWs+YNkdFaQF9XYLrQ5gkSTZs8ewcyZGt+HZFIwfvyJp9eZW0TAmjtkvK1zgEaBuUPjzjrGCxUYHUHydfB1+SAbCAraHA0ugZFeD3gCazP4FYLsBQpjf4C68EvBXajJnnVkoZsqDhLHoUaJO1Xj1WlEMsA+mNvBWh2wiVWJIH21RlVowg9KzJ0CFPhVmsylKmDxvskQv8oJzGyZBHXIuRLJ4HGdA/ZKgeiHQzGRqgjMToG5NkBnHEtaa+699zf8679+grq6sTz++DLGjfvr3AAb0V9XWp/4syIcDvPDH/6QH/7wh8dcZvTo0UdgIA7X4sWLef311096H0f0/58KtMHZToRHQkkyMiiB7pU+Qgs+nDqNELez1nqNpOxnojuV+e6ZFB0+3eQ4mubOZJO5HhcHa+COXJ/oxSZErTcUBrBXZrESjZT0dSF8BxUuRORUki/DbDSzQ8zgRuny20iCLWaW/VLTIvppMixmuSHiWuIKF1Ob2APbbJY+T9td/DnyOzqMtVT4OZRri+3mFrZYG7gl+XHq/HFM8qaxxlpJrV/DFC9Oj+ykU6ZY5Fw1xAg+1ZJI5ppnMiqnlj39O0i6CZTwqYiOYmzuFEwZXE+FBkq9BYIz3EXs0tvplDvYZubQYWiqfI+FztlM8qYdd3sOmj+Ee1ltzQUdxUBiqxaq1CpKVYYu2YFxAotBG2G0tBB+dmgIyM+CYYP51p2vUyUvHRicZnjosVohk0xfBjfjndAMXrFiKZ/97O3s2bOL97//Vm675VMY/RaZBpeebA95VXlDxtC+N3AT5JCxjzuwH1bYCm7EDzwXK4zS25ygv73/pHm1yc4km5/cihU28R2LSF6YTH+W1m1twf8nMlRNryRaEKV7fzdexqVjdxI05JbnkF8d7HeiOUGqO00kN4xpG2T7HTJ9HaR70sSKYlTPrqJpXROmZZA/UCSnfEXnni72vraPKRdPJnwU3EbJ2GLmvGc2+19voLexl4KaAqpmVlI++UgD/VRKCEFuWfBZUkQRN429g5tuuoM9e3bxwAP3cu+9v+H+/fdQVz6BS8+4hrNmXEDICoFmMA09ohH9LWnEDH4bKq4lH0rl8frz62irreWTBZOYnAgxxbP5RrybmJaUDJiEuVow2rfYZjrUGy7jjsMRNhH8QzqPeKiPp0MpeqWiTBmM8WyqlMkG06HB8Eijme2FWWtlWGulaZIeDdIjLTSKYPazhaRH+NgStNDkKmPwjntUQ6fUpIWiUAm6pc9mM4uJIE9L6nyLazI5lA4cQ0ZosmjyD0sH5GhJs/BJCcVewyUlFAoBOkgDpIXC1NAtFPukR5GWTPGC45/mhRjtWayxMmg0GokzsN6oMijUkl7h4wLH+hp30fwm3MP/RhNoDXECU1oAj4WTjPYtJvtvvmH7Y6kCcrTBo6F+uqVPmR8YxDelh04l+9KX/pt161Zzyy3X8eSTKyg4Bo5A+z6runaxo8hnnLKxcvMRQlCEwQ7DYY2V4Xwnxiw3zPN2ElsJ4lqQFJpG6bHQjVChjkyZK6X4xje+yPe//01uuOEWvvrV72Kab/wjxt+5HXfFMlRrM7KiEmveQkJXvgvd2xsU7vk+wpCYYycQevf1xy0r076P99py3JXL0F2dGGPGYZ21GGPMGyOLeWtXk33sIVRzE0gBnofq6UGOszAmTMLbtB7V3oZOJhEhG2PiFOyLL/+rDQJELE7kxlvJ3P9Hsn+6G93fh8gvQHd2kPnZDwh/8BaM2jHHX4cQfPnL3+KSSxZyz+oVXH/uhbjPP4Pf0oQGrFCIG+a9h2cS61jbso5sJsX0TIhFjYJaOwejrALnkQcwx4w94bZORrKgEN4kauMzn/kiu3fv4CMfuYHf//4Rzjxz8SnZN2ujwNoacGllAoQPWDowOvsEOhSUopES2MsFmauHjwE4qjTYLwuEp/EG0Ac6BjqqsTYLzB3gTYGWpyTZBOSM1igJZblQ7kF9r2DfTk1pKXR1CerqNPPnD9OgPsaPsj7ej7gM0AnWWoEaSMv6NRrZLJC9Gu1rrPUCazt4NeDODZYx94D9iqT/HxU6RHDOjrMdd47GXgVGI0FCuD0wgnUM3NMCbISxHyK/ExARBxPJMiili/5GkvzIwdTxG5UuBOc0RegZiTY0Oi9IUBvNguzZClUGInv0Y9GmRqaCW5RHUyqV5J//+WP86U+/473v/RBf+cp3Rgq+RjSiEf3VdXE2To42eMVK0yt9xngWZ7tR5rphBGcwz33juKj5ztmst15nq7mRsI7gCx+F4qzsuUz0DnL/tdb0d2+lOLUDqQ2QBkamB5lqg/KphKz8wWUP4CR2Gy5TvRocOYkGYzWdRNlqCma5Jo3GPqa5s6nyR9EsPX4W7WG9uZxWYx0xXUu7DGErk6luFZvNDay2l3NudgnXp27EjJpsMTfSJprJ1flcmrmKc7MXv5lTPEQaTav0yaIpU8YQtF9ppIricDkpL4khDMJGdMi4M41imZ0mVxtUqkqK1G3sM5bSYGzAFTYXZhZzZeZ8LI6P0HrFTrHchhxlkxUN5OliekQVCZFPOc+TrxVlJ2hb1XYubul8QvseRltxtBUD38FM7MIrmIKXdwyw/9+QYsUxQvEQ6Z4MscKDM0lT3SmEFOxZsRdpSAprCygdV4I0D75XPT3d/Md//DN33/1L5sxZwFNPraQkVMGmR7bQ19aPVhorYlI5vZLJSyYNMmnzKvOwIxaZRIZI3oGpWQFuIV4SGmT3QlC4Jk2JYZ38QLNlSyv97f1UTq+gZUsbmUQGaQiSnSkyiQzRggi5FTnBODRm09fah7OnCzNkUDG1nHBOGCft0N/Rj2kbRIui5Jbl0NvUS39HEi/rMfGC8dgRm/2rGyiqOziul4YkkhemdWsbEy+YcES5HAQGeKK1DzflEiuKUTq+hJJxxYPHf4Df/EaO/Y2otnYsn/zkv/Lxj3+Ou77yE+577Hf86IGv85vHf8j5cy5jwajzGDNmHPlVb76sfEQjOpUaMYPfpgojqX/1VS6Yv4j3ZfKBoKgtKRRxPdQUiyLIAglx4sRXgTb4QCaPiJY8HO5nuhfCQtAjfJqkS0yLoMBMS6TSPGkn6ZOaiAJHaLSALiCsFTEt6NUCUwv6hcInuIZPoYkM8MUeCicpVSZFyqBL+mRRVPkm5zgHL2oLlEGpMmkyPHJ8iY+m58D6NKw2M3RLjz4BPVIjdHD53Cx90GBKwRhfcHU2ZzBJm6cNPp4q4J9z2qk3XHyhsbQgAoxSJkJDjbKOOzP4GTvJA+F+HDSl2sQRmh2mx1hPYCDYaGZPiRlsIrk5nc/707kkUOQisY9SARsKhfjZT3/PkiUL+fjHb+Q3v3ngiEI51dZK5u7fsLs2AlNGoVt78corMadOw7RsbAR7BnAX78jGSAnFRjNLo9SEEMzxwlyTyTki8ZzJZLjzzpt4+OH7+OIXv8Gtt955QuNTa43aW4+/awcQsJNl7ZiAf7X6NTJ3/xrdl0BEovhbNuKufo3I+z5I5I5P4m/dhOrqROTmY06eijiBCeI8+hecxx8BIRDRKM7Lz+FtWk/4Q7dhTjy55mZ/907Sv/s1pJLIiipQfsDU7WzH27wes3Yc5vTZ+Ns2I/ILCL37ekJLLkeW/JWnu4Uj6JZmZGU1xrgJCCnRvo+/cxvZB/9M5GOfPq6BDjBjxmlcd90H+e9vfZmrlm4mPnUG/s7toBXG2PHEJ07hvYleLvrnj5PZuoFQyCKTGyM1cTz5JTX4Wzfhb918ysxgnU7jbd6I7mhDxHMwpk5/Q+lgKSXf+96veP/738mNN17Dn//8DNOnD68U8XgyWgAlwNboCKAC3AEEU/91HpAFbWuECJ5/U2awG2xTHTa21BHAB9kpQGtiewAr2DUJmBJOq9b4WUG/CPAQF16oOPdcTckwQlveNB0kWpPghqC1VZBqhpwkJEKaCg3H+vXPnqUwtxkYuwMkAzIoZ1OFgCUwWgMj2JmnB0coXm1g0ppbBe6cE5vV3lRIX6MIPyEwd4KxG3Q4WKceCIf5o8FeKhAasmceNOW9eLAde6UgXXeSyW0Nxt4gOS088EZrMpdoRDpNaGU/Yp9A5YZxFkbJXBOkmr1REBIashy88+gFPzfehKN/Z2/btolbb30v+/fv5Xvf+yXvetf7Tm4/RzSiEY3oFMlEsMiJcpYTIYsmjBgyo05rTX97P/3tSayIRcGo/KOaO0dTvi7gw8lP8Jr9KpvN9dg6zCxvDqc584dgC/rcHsy+JoxQhF4zTI6WCK1RmU6sxH7m5JYPLtskPXYbLtXKxEIyyb2KjOihXdazy9CY2Exyx3JN5nokkmVWkgbDJZcGurSkQEfxCBB4vmXSKwUPhNYyyT2fClXMrck7aTT20S/6KPHLKdKnbkp4q/T4S6ifbWYWV0CJb3ChE2OBGx4cl0thELeOPiOvXyj6hCJvAKMR12VM8a5hsnc12wyHKV4hkWNGYAIpNMusDFFtMMWrYZ3VRq9oJ6zTtMo4FpJ3Zc6iQlWd8HgydVciM21YHetAu4DAyxtLeuIHQL6FnQ6nSNGCKNWzqtm1dDde1sOOWqR7M/Q292KFLfas2AvAnhV7qZ5dzdRLJ2GYBo8//hc+97mPkcmk+drXvs8NN9yCl/FY9ssVBwvZDEG2P8ve1/YRL4lRt6AWCFK3NadVU79sD+lEBtMyyPZnyS3PwTANfNfHsAx8zyfR0kfp+GJyy4f+PChf0bK5lebNLTj9DgWjC6ieVUW8+CC7LNmVQhqSUCxE5bRy+tr6SHam8Bwf7WvcjEeipY+ckji5ZTmDaVkn5SCN4NrCy/qD6IdoQZRYUYxoYRQn5ZDqSVM6roREa1+wwUMSzQf+LYQ44noTAiN43QPrad3ahjQNtNY0b2qhfXcHY88aw/7V+9m7qoFkZ5JQzKJqRhV1C0efdDr6jcgwDK677QYml85ix7atvLj1CZ5e+RAPLb2Xc864AHvOvzB//plv+X6MaETD1YgZ/DaV1pqdO7dx/fUfGnwsriUFStIq/UFeFkCfUES1oPCQxxSaDIHBdzg6QiCY5Nu8oCXeAEMrIRQJ4eMKiOLz/9h7zzg5rjrd/3tOhY4z3ZOzNMpZloMky7Jly5azjbHBgMFgm2RY8i7sZS/shd39s7B3We6FBZYlGWNjTDDBEQPO2bIsWTlLk3Pq3BXO+b+o9kijLCMv+O48+swLVVdXnTrV3XXOc57f87xgFeiRLqNS4QooyEAgGdZi3H4hI6BSBLYJjb7JkOHjAzXawCgRtr7QzHmVMPWDtvYYPm2Gx/SSitlCcKET5a5wig1mgQHpMSIDj+QiikEZcCoeYGqIaQji5AKl8c25ci5x4hOuH+BML8L3xur5X/FBtpkOZVrQpEzCWhJHssqJHtXmISsUz9h5TARRLbER2FogUXQZHo2+SU786SXgB8NGUn0EEjglfHrybVR3PMrM/s384KOreMuXfss3/s8/8Mm/+Yfx/bTvU/jZHXibXyE++yL8RAIxUkS178MPhTDnLcAlUFwDlGuD9+UTtBkeI8IfV20fapuRy+V43/uu54UXnuL73/8Zl1/+5uNei1YK58F7cf74O3QmE/ipxmJYqy/GvuRKnIfuhWIRY/Y8hBABcbx3N8WH7iM6fxHm4hMn7lRfL86TjyESCWTJf0vUN+Lv3I7zx99hzJ57Uopd98XnYGwEOWf+Ab+uRUvQThEZjqEG+0CDdc55hK56M+aiJSd87FMJ1dGG39GG0dwyTvoKw0A2NOHv24Pq7cFoPP5g/W//9ov88pd38otf3skHPvBxzLnzJ7wuKiqx5szn8USKjVU+Q1EI08PpvsslhiLkOkc58okjl4P9Lw8Sue97lA9sxzZ8NGA0NhO68RbMWSdfGm/bNt///s9429su5V3vuprf/vZxpk07RrLY0eCDsQ9kRiByBD9GGuSoACfwq9WA0GDsE+i4xq8EZ0HgX/snwQqIVKMdOJjELQJSoxLBb9D8WqgLwb5RmJYMfqsdH6ptzYev16z6mOJkRPzO6RpzuUA+C517Bakx8AQ8WQXP/s7gMlNx2WX6iISwtwhyNypCfxAYfYHHceFqReEKjXAh/lUZ+PMevL5jAEogCieuWnYu0LhLNGabIHJ7oMBVdRP3EQ4BO25M3K5jGqPzgCpXpMF+TmC9ElyQu0TjzdBY2wSyU6AqNO7pGnO/IPyARJTmNlgab14XYtHXIZpGp+OQBLV4MTr+dsDEXaRxFwmsDQJdFqiT5ZjAna1xzpp4vYVCgW984yt861v/yrRpM3nooWeZPXvi93ESk5jEJP4cMBBEDxkfeo7Pjj/uoGtjD27OQRiSZHOShVfOO4ygOhoSOsma4hWsKV5x1H1S7iim8pipE+zAZVAGqsCQFaI1k2JFKDRONDlC4wmNVVprK9dNnF38FB3GRnqMQd6am8Ia5yzipZXDnaZDuZKkpI0SgR++IzRDUpEXLkIU2W4K/j06wnvy5cz1Q4d57p4K5FH8ODzGTtOhUZnYStAvfe4Op4hqwWne8W0VyrSkXBuMCZ8yLdGlZ9yYUMS0pFIdWyAAwVwrLXwiWlCtmxCuoM3cQ1qksXSIc4tXcG1+5QnZ5OlQkuziTwUewfn+QC1cuRDM4+e1/KVg1uoZhMtDdLzcSTHjYIVM7IhN9YwqQrFgflvMOnS+3EHVtArufPA7fP3rX+HSS6/mn//5GzQ0BOPwwb1DZAayVLQkx8nUcFmYYsaha0M3rcumImRgMzDnotmU15fTs6WHYtZlylktVEytZO/TexluG0ETBOhWtCSZe+ncCWphrTU7H93F3mf3AxrDMhncN0Tf9j5Ov37JOKkbTUbwvcAf2ApbJBoS5EbyeMXA+qKYLtK7rY/cSI66ObUoXxGK2VRPqyLdn0EI8Fwf31XEKqPEawKiWQiBm/cIxUKEysNUhEzC5WEyg1nKagMPMeUp8mMFZq6aPq6m9l0fN+9iRSx6tvbSu72fZFMC0w4Gr07OoW1te0nRnCU3nMV3FWO+YqRjjIHdA5x27WIaFhxYGHq9UF5fztIbz2TK9haWD54DoS/ywu4nuOPu73LttatZsWIVH//4Z1m16qJJ24hJ/NkxSQa/QTE8PIjjODQ2No9vsxCscqL8NJKiQ7pUaoMcigGpWOGEmeqbaDTrzCJP2ln6DJ+EkpzjBiv6ZunB3Sc9dhlBANwO26FemfRLl0EZBNL1SJ+89Iio0jw6sHkEHZDMQoMPWGh8NHO9EAWhme+GMIGUDAZTBaGpOCTdt0xLuvDolx51ysTQgQp6mRsmj+ZrsSFSQlHpG2SFIisVBsEH2aSUVy8EHhBVgmZlcb4bPYwIfhVTlMVf5SvGB1jDQrHAN3lHoZyF3oHV8VdV15XKIIpkRPikhaJeGewwFb7WGAjCWjAkffJCM823GBM+I1KRUJKK4wT4nSx0aXX+aXcP52/5NqGxDjaFKzlrQZTPvmk2X/naV1i+fBUrzg3SjlXbfvydOzCmtLJgMMezRY/eugpqfQ+vu5P+2dOJSJPFbnDdKp3C372LZt9jaut0ZPXhksFsNsNNN13L+vVrueOOe0+43N7fvgXnd/cjysrGPWz18BDuHx5ChMOo3h5kfeP4Q1IIgWxoRHV3oXq6kFNaUV0dqK5ORCgUELrRI6dx+R1t6NQYcvYBBbAQAllTi793d0CY1jcGoW8nANXbDZHYhAe4kBJRVo514RqspWeDUsimlhM+5sHQxWLgiRuLIeKvLRE3aKgCrUAeEr4iDbRWoI6RAHgQ6uoauOKKa7n99u/wvvd99DC1OcCjLYoHi0NUhKuo12EywuUR0YZXY/OeP9HLedMmuOceScvz9zGrfzPp6tlMm2MzpSVQZBd/8VOMv/k7ROjkVfjxeBl33PFb3vzm1bzjHVfw058+wPTpRy9N1LlsEMRXnkBYFnIQIndJrJ0C8hpMgRwGczsY3aATGuWBSItgIloAQuAt1jgXnoLFIgHOuZroHQLZE/jlimLgj+vN1nhzgn3KztXcuENwRxq2DwVvDTuwqhlWvvnoRLAYAmMo8L5V9RxQbYQg927F+qJgZ4egah4MNWi6asEegN//XrJggc+UI9lTC3CXBkStHAQdYjzoTmvw5gU+uqr6gB+wGAMdDfx8TwY6CW5SY6yE8AMSVEC2AlAgsO0wAq/gg9fZRF7g1wZMgchB9AcSa6NAByuNWOskIhe0W0U1wpGE/xDs6zeUguAEyFSB8ONDKFWJs9IGYSALg4Tb70eVNeE0XgBRyN2isJ8WWC8JhB8E4znnHRRwBzz77BP87d/+FR0d+/nIRz7Dxz/+WcLhv3xPxUlMYhL/fdG2tp19z7cRq4xSVhvHd3yG9w+z+b4tLLtp2Xjp+58KKYIf8BZlkXSDEGQfje061MkosYNW/Bp8k2pl0C99ppTIT5sYBmdxumNwWbESE8F2o8ioVORF8Fej5rNPP8KQ3M2IqMITNhGVx9Y2i73TGRE+94cyzMzZ4/Opk0Gf9NhtOLQZLmmhsBBM923OcEOUa4PtpsMe02WGH1TxAUxVkt3S4Vkrf0JkcBjJeU6E2yO97DW6KYgeNBHCtHBVvpkmdeTBgNZ63PvVippMmWvySsih2jdoVE1UOVV0yCEy0uaSYjOh46iLJ0CaeFWLT3z/vzAYpkHr8qm0nNmM7/hs/8NOnLw7TgRDECo30JXjY595Dy9seor/+T+/xEc+8ukJ8wiv4KG1HieCX4UZMnHyDkopjNJ4XpqSptMaaTptYtBZ1dQKBvcMkh8rECoLUTOjGisyUXWQ7kvTvq6TcFmYaEWw6q6VZmj/EG0vtrPo6sB+pX5eHe3rOhlpH6GsroxUb5rRzjEiiTCNCxsYbhshM5BhpH0EIYJ5UEVLkoVXzyc/WmBg1yBaKZJNSUY7Rymmitgxm2KmSCFdYPYFMwnFbEIxmxmrprPz0V0M7h1CGgLla6qmVzJ12RSUr+h4uZP2lzoopIuE4jZO1kEIMU4E58fyjHaOMbAnOGcoFkJYMiCXdfB6ZjDL7if3UDOr5pT97hwL4fIwU5cdGAQvuGgBN3/gQzz88H184xtf4YYbruDMM5fz6U9/YZIUnsSfFZNk8BsU3d2dABPIYIBz3Aie0Dxu5xkSPiEEFztRrizGEQiet/L8JJJCoUmWBkN3h1OkhM+bimX0So/vRUZpM1xqlYErFLsNh6JQVKkg4K0gNKYO1MCaQImbEQEBXIRgEgx4ClJCs9SJoIRmu+WQR9PsW1zgRPm9nWFEKqoOIkmLJa/fh+0cvw5nsLTgdDfMGieKEFCrDM5xI3QZHpvMIkkMRglIrYSWFIQmqiVCw3zPJowkcwyF7vNWgbvDKZQIfIRTQpEXmlJlN2mhuD+UYb1VoCA0SSU534lyuhsigsDUkmplMCh9wlpQFIo8mlmeRbd0+V0sS1oGgXZnOWGuKsaJHKLuzaPYYTpkhaJGmczwrWMG/b2K3YbLL8Mpzt7/HNNHO+mpns+YhEHgQ+++jCe2DfKZT3+APzy2hUgkgs6m0YUCRGNMH0hz9YZ2frewmd1Ta8FxqPI83qSSzPFt3HVrcX7zc1R/XzA4SVZgXXw59prLxh9Y6XSKd73rarZv38xddz1wUmUv3pZNaKeAUX3As1dUVeMNDgQ2BFKCfwhZ6ftgBEqG4s/vwn3+6cALV0pkUwuht994RMsHYduBMtZzwQrIWe0HRKIaHCD/ta8gq2uwzr8Qa+X5x7VOkPWN+Fs2obUe7wutAjmorKrBmPrajEa1UrhPP477+COBBUYkinnWckKXX3VUovuY7WxqQdbVB+R5yX5Da43q7caYNh1Zf+S03CPh5ps/zLXXrubJJx/hggsunvBa2smwNlkgGa2kZjALIY8KrUG7vDIlwVBrA0eMc9AKI7UX4aZR0XpU9HB/ub4+uOsuSX5gjDVqA7q2HkeF2LYNolGDmqmtqM42/H17DlMsnyiqqmr46U8f5IYbruCqq87l+9//Oeecc/7EpuayFB9+AG/tC+hCHllTi33BxSTWnou1QeBPDXxoxahGDgpkv0D2lwjMRtBRBb5A5IPQsNy7FKdKOOScrRFpCD0uMPcLtA3uYk3+bWrcdsA5T3Pabk3ry7BlTFDwobkaprzrgC+uGAVzR6Bk9usD797Q84HKVUfAWaQovCXwvQXAht+NCXrm6gmkb20tbN8Ou3YJpkw5BnlrgTr0lgtwVivMXRJzp0AlNaIYkKzFC157nzkrNNYrGnOHQFVp8ECOCJwVGjGqMXcL/BaNNsDoKXkLLysRvy8LrM3gzdBBf2qQPYHnsNMK/hQAjf2CQHaAO58DJLboRYgx6FwG8kkAVKQG4Yxi9zwdkMEEn4ni5Zri5Yf318jIMP/0T5/l7rt/xNKl5/DDH/5iUg08iUlM4i8evufTtaELO2KPe5uaIZNkc4LRrhTDbcPUzjrxMLljodKuIWJESbujlFlJyrWFq1wGfJeBRA0/j6SpVQZL3DAV2uCSYpyfhVPsMhziWpIWiogWXOLEyAvNTyKjbDcdXGBU+gwJnzleJRrollvIiyQCn7RMcJrzZkJqEb4Q7DVceqRHi5pIwI0Kn52mg1uaA01R5rhyVqN53M7xUCjDNsOhz/CRGhqVQaUyecm0eV8+yYgMKqLsQ+YHcSTdhldSgx5/7jDDH0BzH/2yEiGSCAqgH8aVCRQfwDjEu8rNu2x5aBt92/tw8x7aVnhLYeTNtbwYihCmQJvRTUZAlW7jV5FejMI7J3g6vwoPD1n695cG3/XJDmaRpiRWHTtpcs4wjcD+5Ahv6xnq5Mu//QxjhRFuv/3XrFlzuMo9XhMPAtayxXEiWWtNfixP06LGE7JWMUMm9fPrxwNHj3QNg3uHyI/kqJ55wL5ESEEkEWFg9yC+52OYBtHKKC1nNLHz0d307xogN5LDjlrUz68nWhHFjtmkeqOl1wpEkmHS/WnW3b2Burm1zL98LnbUxi247HpsN92be8kMZrAjNrPOn8mM8w7Yx7Uum0Kivoz+nQM4eZdEQzn18+oIxUPsfWYf2/+wA8M2CcVDFMYKDOwZwrANqlorKaQL9G7twy14KD8Qm+VG84TiNpRHxtXUQgoyg1ky/WmSzcnj9uXrASkll19+DZdd9iYef/z3fPWr/8gNN1zB8uXn8tnP/iPLl5/7Z2nXJP57Y5IMfoOip6cLYLy85FVIBBc4MZY7EUZkYA/xqmWEg+ZRO4sAZpQsGKq0Qb/0eNrOs9KJ8pSVo81wmePbGAimKZunzSxdhmaZG2G/4bLXDDxlNRDXgc2EqxVO6ZmjCEqihRBUKcnToTyLPJtPZiuQCCpLYXI5ofhZOMWg8KnSkgKarWagSLYNh3pl4QjNQ6EM3YZLs28hS365xVIJTLwU9Ka1KNlECDw0VdogrCXlBGTtkZBH8YdQFoBZBwXrtUmXh0NZlrg2Pw+lec7OU6tMapVkRCruCacxEZzlRHg4lKHVM6mQgnYjCJxb6YSZ41s8GspToSUNvklaKH4fyuILeEehfMK57oqkaDNcFEF19GI3zDsL5Yd5Px+KDWaBjNDMH9yNY5chhCSpYUD4DEZDfPPmJZz9hSf4+3//J879+7/DbI6zKBmjYngQs6qGlbv6mNs9ym6VQVRWMa9lNXVmBL+7i+LP70Tncsjps0BKdH8fzm/vwairx1x8OumxEd75rqvZtXsHd9/9EGecsex4H9mJKBQQ4vD7IgwJ4ShG6zS87dsCr1vDCMjb7k7MBYtR+/biPvIwoqYW2dQShLft30vxp7cj/+Z/Issmlh4as+YgG5tRbfuR02YgDANv8yv4O7cjp01HlCdQA/0U7/ox2vMJrV5zzKZby1bgvvQiau/uA57BXZ0YjS2Yp71231nv2Scp3n0nWBaysgqdzeI89Ft0Jk34Pe876YGpCIexr7yWwl0/Qu3YCpEoOpdFVlVjX3kN4iR8AZYtO4f58xdx223fPowMHiumyEmf6oXLMHr6UP19CMOgon4O7QnBqModRgbLfD+R7T/CHNmK8PJoO4FTv5L8rHeAcUBNsWmToL9fsGSKg9Hu4RoRymIwNATd3VBTZaE9D/5EK4rm5incd99TfPCDN/COd1zOV77yLd75zluAYDBe+NmdeM88iaioQpSVo7q7KH73R/gFC3/O2egSV68rwJumkSOgbYFq1KhqSq9rxAjIvMA/cR7++DCgeJnGWaEDIjMCfgsTlK66DLIfUEQ2CZZ3aIiAO0/jtwavWxsEkV8EBDZaIEcI/IAXafypIDIQeloilCb3PjU+2VHqcG9gcdBrrwXeLMh+UBF6UmLuBj8J7god+Pq+RuGEaoTcBxT2owJru0CHoHCBoniBxuiD8D0SsyNY0fTrNIUrVKCqBoxdBDYSr34sCyBTgQexHHmVDAaVBHN3EISnSvbgQrkgNXgTiQFtRhDFkaN0QAFreDMUhrjnsXV8/t++heM4/Mu/fIt3vet9R1TmT2ISk5jEXxp8x8fJu1iRiWMNwzJQvsLNu6fsXGEzyqzyhexMbWLEGQAEGanZmahgb3UFwsih0Dxp5bk5n2CFGyauJc9beXqkxxxls8KNMM+zuD2cYr1ZZJpvEUWS9X2esfO8YL+CJzPE/QtA1hPTGQzdQJtxNt2Gg4NGaMGLVp7m4gGyd71Z4J5wmn7pARDTkhVuhGsLZVgIdhsu94YyFIQaF96AIC01M7xAEfy0naPRD2K4HYL5Vl4oQlqSxmeqCp8QEQzwrP0YlniaK5wlOGIAW7u4YpDN9mZ2Oecy11s4Yf89T+2l4+XOwD6gMccr/isUXiySDNlsvi7EqBkl6cc5zSvQ7PfTYe7jjsj3+FT2c1SpgOxvN/bxmP0HtlubCOkwy52VnO9cTFSfvNDh9UDPll72PL2XzGAWKQWVUyuYfdHsccuEk0FVayUd6zpxCy5W2KJ3uIvPf/cj2EaY2//9N6xcs+qI76toSdKwoJ6OdZ0U4w6mbZAfzZcUpi0ndO5i1qFjXQc9W3pRnqJmdjUVUyoxDIkZMujZ1seeJ/cyuG+YQrpIsjlJeX0ZQgiUr7BDZmDjUHDZ/MBWerf0onyFkBLDMrETNvGq4J6ZtknllArSvSmU0pTXlREqC+PmHdpebEd5itOuXYQVtph/+TymnTONYqZIpDxMqGyiclwIQeXUysM8fZ2cQ9tLHVgRi7La4F6Ey0LkUwVG2kbIjeTJDKSDvo5YmDkjCNMr+nh5D7fgYkftccW1lOIw5fVhfZgu0r9rgEIqUFfXza49rL1/KoQQrF59KRdccAmPPPIQ//qv/8C1117I1Ve/lb//+6/Q3HyksrpJTOL1wSQZ/AZFT08XpmlSXX3kUKoIksgh3k8j0mdA+lQdQo5WKYPdhkuP4bLZckhqY4IyNYxECwFCsNKN4AsYFj4GghmeCUKw0Szio6hQshQgIalTBkvdCAaCTabDaabL+c4BH6hznShDwucFu8AO6WIDthZUacl8PxQMbDQkkWw1HRLKAAI/4hASDZQpQUhIXBR5AT6auJYklcQTmnOKkaPaM/QaPgPSo8Gf+DWoUwY9hsfLZpFNVpFmZVJeOkZUSdqly1NWjo9lK3CFZp2VxwOm+SZLnTCXOXG+FR0lqeV4YF1YS6QSrLMKrClGqdYmDpqfRVLsM1ym+xY2QdDei1aBamVwXfHYg5Bh6WMjKNpxDP8AGSaBglYsaYzzlvdew0+/+X/peMfFJBbOpXvVYlbc/wRTCgWMWBnJ/cOcpRThd16AZQbKDX/LRtTQIMbcBQdsGuob8HZux1v7NFru4KaP/w92tQ3ym//9URbMqOTEDAcOwJg+A+fJR9HF4nh5v3YctOdhzpqN0TwVffv38HduG2eXZNMUzFWrcX/zS3Q4hFFZFRzMspDTZqD27MTftgW5bMWEc4lIlND176T409vxd21HOw7+rh3IxiasM5chQmFIJPE72vCeeAT77JXHDKQzps8kcuPNFB+8F9XTDVJgzltA6Jq3Il9t00lCuy7OE4+CaWFMaQ3aHS+DUAhv/Uuoiy7BeA2DA+uMs5DJJO7aF1D9vcE1n7X8pNXLQghuueWvxsvUW1pax19LhMqJWVEyWhGbMx/mBKrF4cIIUa9IMnxIupnyiWy/Dat/LX7ZNLQVQxaGCbU/gLLLKU6/dnzXdMl7tRitIlM+hYqhbTihBKYpyOcFur8PWVk1bjUCgAvmzsB3VVVqvJmc0JMumazgJz+5j89//pN8+tO3snv3Dj73uS9BRxvehnWIxmZkIhnsXJ5Ard9NrvuPmKctRRykpNEx0IXg3CIbqIOD6wajLwhA05WHnf5Phk6AlwCcwKZC5AV+vUY1EpCokUDt6h6ybiOHIPIzgRgDbyaM5TTpHYLKPJQVBViBXYEvNdYmgdEFfqkgZfFiza9/LXFdjVXiO0dGIBqF6dNfuw2GPwtys1RQbiJ5zSTwhGO2QP4mTd4phcW9GhhXDpm/URjtIHzwmoGD7QpDgH/AP/hAW0TgkVSCSmi0EYTqjW8TZVBIoaZuPbBRa2RxlGLt0sPaKLPdRLd+l00bnudzP9vE41sHuHblPL74rz+mpvW0P7kPJjGJSUzivwpW2KKsJs7Q/uFxZTCAk3UwwybRylPrC1sfbaHMTjJU6KeoXX5dBh2xMuYQRvgCH81Ow+X+cIa/yiVZ7IVY7E0kefqlx2arSKMyiZZWVGMYzPUcngmZLHRPo1Kb7JeLGSPBqDQYlB7TPIUnBTbweCjHLD849qDw+Hk4RUZqZvlB9POoUDxq52j2Lc5xI2wyi2SEIoLEFYJyJREIBoXPkOFToQzWm0UuKkZp9k0es/O4IrDh84EyLVjtRNltONT5Blsth81mEQ/NPC/EmV6Y2EHikm3mJpKqkjCKsA4eWGFdThftdBrtE8hgJ+fQvbmHaDKCHbdZb73IaGiIyoYa5r5gUjjzWTbM2EITM5nnLwUsYt5sdphb2WSu5wLnEjqNNv4z+nV6jS4qVTV5OcwvIz+h3dzPe7MfwfqTwxP+NAztG2LTfVvwXZ94VSwIV9vWRyFdZOmNZxGKnZzdW93cOhoXNtC9uYex/DD/8tvPYkmbf/v0D1h+8dErKIUULLhiPrHqON0bu3GLHg0L6pm6/MSCz7yix8bfbKJ3Wx+hmI1Smpfv3oBWmvKGcvKjeXzHo3JaFXbMJjucw8m7CCGIVkQopItMXTYVaUj2Pd9G14ZuyuvLAjJVaXq29pLqSZEdyRKrCAjhzFCWfLpIVWslsXGSOIIQgv6dA2QGMuMkbiQRJpI4OWur/GieYrpArCqGW/CQhsCwDCqnVpAbypHqTTHaNYby/HGbitxogYJfwM07eEUPrUGYErQm2ZQgXvIlPhLGelJs/M0mxnpS40O+8oZyFl+zkGRT4qjve60QQrBmzRVceOFl3HPPT/jSlz7HqlWL+OhHP8OHP/w3RI4Tjj6JSZwKTJLBb1AMDvZTWV3D86EirtBM8S1affOYK8MRLQghKKAoO0g2VhAaG0FMS8JaMHaIrUKVNtiJi681NpJFXoi1Zp4RqcgJTVJL5rk25VoyJhUdhsscP0RraVUdAsXrdqPI+QfNsm0E1xfLOc+N0is9olpyd3iUdiOwQBBAhTKo1DIgeZUgoQyetHJEtUBr6JQe1cqgybfZb7rkgJm+xRzf5hwnwoXO0VedQ1pgaoEjIHzQJTsiUBhnS9fXcgipntCSUakoCM3ZTpgu6TFqBjK4ManYZThkpKLOn0hCl2tJm3QZlopqH/YaDvsNl1bfHC/7imtJlZasswpcVoyN99+RMMW3WGsV2F9/Bs19rxDND5MNVwCK+lQnI5Ekkb97P4knNrHrr/4H7/zjA/Rfdw2PJsq58rEN1KWzyKZm7FUXYq44UJqis1kgKKtBOcjiKFpIhG1S3Pp7PnDnS2xpT/Hb/3k5Z5ftRW36OtnFn8Ivn37EdmrXhWJgT/GqBYN5+lmYa1/A2/wKIhYHIdDpNObCxVhnLEXEy4h88m/xXnkZPTaK9jz89v0U7vgh/strEbEYqjyJrAgGSMI0A7/oXPaIbTDnLUB+6rP4Wzfj7diKUyxgnHbmBE9fWVmFGh5EjQxjRI4drGYuORNj/iJUdycYBrKxGWG8dg8qnU4F1hDJ5ITtIpFE9/agBgZeExkMAXltTH8NwWiH4Npr38E//dNnuf32/+Tzn//y+PYyO87ZDWdx/97fIxAkQuVknAx9+QFWN59LXXRiGaiR2o01shW/fDraKoVFRKrBL2D3PElxymVQWpiorQUhNK4n6Zh+JbF0F8mhbTj5chrCWXReYr/5reOfAzkAkTtKHr6+BlPiztPkblQnRMBalsVXvvJNZs2ayxe/+Bn27t3JV2/6MHY+jzyIAAcQtZV4bb2IgQw0HBgkyhFQtZrCZZrILwTmtoBQxRP4zZrCleqUkJtHgtEOkZ9KzH0CPI0ukzhnK/LX6uBH+AgwtwS2FrmZmgd2w1N7BZkBQUzDspc1b2qAmBWoi0UviBGgRAavXKnZtk2zY4cgFNJ4nsAwNBdeqJj22txSDrmgU3CMQ3GkfrDAn3GE7YC7UGM/rRHDJW/jUKC+lkMav6r04NCBlYU3TSHyYOwBTBCZOvyZbTDlj8isBdJGFvpRkVqcxtUTT6Q1fU/9O//83Z/xs+c7mdNSzc8/dy1XzVK4Y4+S1YsPl2FPYhKTmMRfEBSawZKdQQ0GU5dNYbRrjJH2ESLJCF7Ro5Au0nx60+tCrsTMMmLxMnYbDu2xYZoOmhcZCBqVwR7DoV/642KNg5ETGkdoqtShwdoOaEGLX0mVzhJR7TxnLSYvEhjkGJWKhLY4zQ0zLH1etPIs9kJsNR0Gpc8c3x4PX67QBiMo1lp5znEjZIUa9xg+ePYldVDR+WrIW4RAYOKi8bXGFIHP/qBQ/Cg6xmN+jlGh0ARzJylgnVVko1fglnxynBCO6zJG5NCE61MEc5iQPoisUy5m+2PMlfcQDjsMqkYUWcp1LTIswAGVU5RrkxHZQ1ZkiOn4uAVESo4B8LT9GD1GF/O8heP3IisyrLfWssPcwkJvyUne5VOLrle6cXMOVdMOiDmsiMVI+wgDuwZoXnL8oGWAQqpA3/Z+cqN5yuriNMRr+f8+/ymUVPzg/97DGReceVyvWjNkMvO86Uw/pxXlKQzbOOGqwP5dA/TvHKCiOYkRMujb1ofn+mhfo5SimC2ifI2bd6mbUxt4C4/m6dnSS9X0Surn1jF1aQtaa7o39WBHLexoMGASUlA7t5bCWIHRjjHyI/mAZBUQjoeIVkQm2JSE4iGyw1kKqeI4GfxaYEVtvIJH1yvd+K6PV/QQUmDYBlbUZs6a2ex9dh+ZgQzV06uJJMOM9aQY2DWAk3PIj+SxYoGaubK1kjlrZh9VGayVZscfdzLWk6JySgXSkChfMdI+wo5HdrLsxrMmBPGdSkgpuf76d3P55W/m61//Ml//+pe5++7b+V//6ytceeV1k37Ck3hdMUkGv0HRbSnSKH4UCR62US1Y6US4tliGiSCPYp8ZWA+0+hbxUoLsEjfMH+0sESWJa0kBRbt0WeiFmOJbLHXD/CycIociSilpVgvqlEFGKPYZLgUUxVIeUqfh0ofPPNfmf2SrWG8VeDCUZYE/cbVdaY4aqFCvTOqViYemy/DZbBaJlJS/lmY8+G6r5TAifYpCMyR9PAKCu1EZxJAscoOQvCnKZokbYo5vH5Mcb1AGM32bDWaRiG9hIXDRdEmP07wQ032LMIKs0MT1geMMCZ9RqfjX2DCbrAIgWOAFZPhO06VL+iitSUs1QZ2dFoqoliRLA7K80HhA6JA2hrQgJzRFoYkeQ1x3phfmBZXn9y2nEU5fyLy2p4nleqjVkqTVwJPzrqG/eiqXf+Pf+Omlb2L9d3/ImR/+ADuuXEP5+Wu4adhGlJUfZhcg6+pBCuTwbszsHqSbQQNuR5a/6h1g/WCeX3/xnZw1vwVPa8yRzdhdj5I/hAzWhQLOY3/Ae+HZwJ6gqQX7goswFp+OiMYIv/dW3GeexNuwDgDzkiuwVq4aD02TFZXYF6xBDQ+R/+bX8Dv2I+ubUJXVqLZ9uC+vxVp+DjJehs5kEJZ9TB9cWVGJXLkK0dyC++zTgUo4FEYkkoiqanQ2iwhHEPGjrxofDGHbGK1HJsAPht++H/fltejBQWRDI+YZSzEaJrZTRGPIWByVzcKr6lOAXBbCYWT5iaVuv56IRmNcd907+c1vfs7nPvfPEwYnl027CF/7PN+zjs5MN1EzwkVTVnHNjMsPO4500uDl0ebEhRptxZFuGulmUCUy+LTTNDNnCnbuFIzVLmZ41sdI7HyKFnsfFefOInzxOZhnnFU6AER+IbE2C7xpgRWCyGmsDYJwXJC/5cRsBoQQvP/9H6O1dQYf/ehNrFn3PP8wcwFrpk6D8IFVeqWz0JzEGI6gNeh4QATjQvECjbtMoxo11nqBGAHVqHFP16jXJh4/PgoBEW7uF3hTA39bOawJ/UGgqqF40YEfk3weOjqCgfycNCDgof1wz05BhQWNYcjk4cEhKGwT3LxII8eCa1QHkeqVlXDrrYqXXhLs2CEIhxVLlgT37f8VNwNvARTXaEKPSURfsE1Vgy7XyBGBTGvwBboSMjcphE9wzwuBf7B7xlzs9OVYfc8h/CJO3UqKUy7FTxxYoOnv7+WbX/t7fnzXHVSWRfjGR67gxjVLMA2JXxzBHNmCzPWgYqfSX2QSk5jEJE4d9hkuD4Qy7DWCSrWEMpi5JEwkORfzmR6MvWnskMmss1qYvqL1dSU4VOnv0DmARKBLrx0Jtb5BRSkHpPmg8XtRRIhqH1d0g05Qo0eZ5ncxKiGmNQu8EI0qRJmWFLWmr2QJURABQSYPaUdYC1IiaMVU3+Ip8lQpQUgHhHQkeKwQ15KUUKz2oowKxW7TYbkbJkTw/q2mg9YKh4AY3mu6lCGZ7dsklUERzUbT4SWrMF6ZudxZya7odlJilHKdRKFoM/ZRrWqZ7y0KGqgVkZ13Yrf9Di8yhlsQTLX2sMbxeN5cTWY0BGWaUJWNj4eHiS880OASiHmqS35JO8ytJFVywr2I6TgeLl1Gx5+dDE71Z8ZJz1chjWAOmh8rnNAxxnpSvPLrTaR6xgCB73v8nz/8L8Zyw/z23seZNevwPJNjQRry+HYGmSK92/tJ96UDpe9gFq00ZsikmHXIDuUIx0N4jkd+JI8QglDMIjOQoXJKBU2nNTHaOYpX9Fh09QKaFjdihkyUr/AKLvIQj2LDkJTVlzH1rBbCiTCZgSwj7cMM7R+mY30XiYZyqlorsaNBSJwVsQmXhyhmHXq39ZHqTWFHLGpn1ZBsSZ7Q97+YKpAbzZMeyCClwMm7KE8hpKCsNk7/zn5mr57Frsd3ByF2QpCoK8creCQaEtTPryNaEaG8IUHd7BrC5UdXJqcHMox0jlJWGx/ve2lIymrLGO0cJd2Xprzh9Z2LxeNlfO5z/8wNN9zCF7/4GT74wRtYtWoN//Zv/0lT04lZhUxiEieLSTL4DYge6bEt7KNcl9klD6lRoXg0lKNFWYS14LehDL2GhyIIXbuiGGeFG+GKYowxodhsFegiqHKd49m8rVCOQUAo7zNc1psFPBGs8yW15G+ylQhgi+mw0ShQqwwWuRHCSHygvVR6dVExyuOhHMPCp7JkrZAWgRJuoXdsz52NZpHRUoJuRAuiWpBDsdl0mOFZtBkejcpknm+TR5OSijHhs6YYY4cZJPDuNl124vKMnWOKZ+ILgQKWeCFWOBMtIwSC6wplpCMByR0MHGGGb3FdoYx6ZTDXs3nZLNCkTKJaMiA9tpoOcS0QEtJCY6LZbbic5YWZ5VtsNxyqlcGQVAjhkdAGaaHokx6rnRi1JTVCozIp15Ihoag+qF1D0meGZ1F+HM/gWmVySy7J70NZnpj/FrY2LWXpYCdnqHLc5CI6k2EgS9PypZz23vfwzD//bxa843pEdRQnHEJWVBzxuObiJdgtVei1v8MvD+GH4njDGb7Y1ctTfVl+9fdv4ez5pYeSEKhQJebI1gnH0FpT/MVdOE88gihPICJR/O1bye/fS+TmD2CedgayPEHo8qsJXX71Ma/TW78O1b4fY/Y8hGFgzp2Pm8ug+3rwd2xDNzTC2CjmshUYs+Yc81haa/y9u/G7O9Gd7fhSog0DWV2DrG8gdNW1yPJTp1bxNqyjcNftqOFhhG2jnSLuc08RufmDGDNnj+8nwmHMFedS/NXPULaNqKyCXBa/fT/mkjORJ0A6/1fg4ouv4Ec/+g927do2IcQqZNhcN+sqVrecx3BhhESojOrIkVlPFalBW2WI4gg6fIBZlMVh/FgDKpQc3xaPwy23KB58ULB1q6AnNJfwm+Yw41JFw6yJx5VdQQCa3xwQwVAKcKvX2JslxSEfVc0JY82aK3jssQ185tMf4sOPPcw1nfv5/CXXkKyoRA8PobNpeNeVFLGwngOZFqg6TfF8jbMyIF79ZoL2HAUiB+YmgTEAKg7eotdOFps7wWwPPItfVb+qqsD7135WULwgsEd4+WXBffcJenuDgfhyW3N9SvPUiCBpQ0MZSE8TywpkRPBSp+aKBDRlBcXzVWA7cRASCbjoIs1FFx1j5eqNDAMKb9a4i33M3UGfeTM1qgLsVwSyG3RS456mA69mwDnn4L4oo1D3NgrT3gzaAyMyrvAdGhrk29/+Krfd9h+EbIu/e/MCPvSW1cRiByYc2gghisMI/8QmpZOYxCQm8V+NQeFxe2SUTuliIthhOPSVrMzmzrFpnDWFC8dsVntxrNDrbwvQ4pvUKZMew2NqSR2s0YE/sGdTe5QskSiSC5wo94TS7DWC0Ot2w6UgJLVK0GnsR1FFXJfhih2EdBmnu43M9INBh0aTForTSoKYRmViasYFNq/uMyZ8lvoBMXW6F+YFL89206FSGXQYLn1SU1ay3VvkhTjPiY4HXNcpgwiSPulTEIpqJUnJYE4UJshP6ZEeSd8gRGBdseWgyswVzvm0GftZaz9Dt+hEIwjr2UzxruNFK8I0r8i8oX3Y3U+g4o2YTS0M7xxAZSqpj2ykNb2DzaMrUasc6irr2coruLj4WjEihug1upnhzWGRG2RolOly+mTvhH5+VYkc1n/+Mvh4dYyxrtEJ25Rfal/58b1itdbsfHQXqZ4Ula2VSCn55WO3s6t7G//03n9n+rTZxz3GySI7lGXDrzYy0j6KkAKtFMWsi3J9tNa4BRev6GGGTbSvMaIGvusjpMB3fHzXJxQPEUlECJWFaF7ShGEF3wlpSKqmVdH2Ujuxqug4aVvMOFgRi5YzWxBS0PHyyxQyDsmmBMPto4y0j1DMFKmaXkVxrMCUs1qQlsG6u19meP9IqZ2atrUdzFkzm6lnHZ3cVL4iP5pnzzN7CZeF0WiG9wc5C6ZtIKSkZkY1qd40lVMLzDh3Gu0vdTC0fxghBOV1Zcy/bC41JxFQqX2F9tVhJLw0JcrX45+J/wpMnz6LH//4NzzyyEP87d9+hIsuOoMvfen/ct1175xUCU/ilGOSDH4DYqtZpGibaNedUHY0jM/jdo5R6ZMRitYSUdwtPX4RTlGtDGb5Nu/PJ9jrRBmUPuVaMsuzx20KIkhuzic424zQabiEtGCeF6KhRGAuccO0x12qPZPkQTW8Lcpit+lyVVFyYTHGo6EcvXiAwNaCc5wIp7vH9grabBZJaoOkJ2kzPIakj0BgIoiVlMKvhuFFEUSVJC0VL1kFhqXPNN8ijMRB8aSdZ61ZYLEbIo7kVyGHbUaRD+ST4/6/AA3K5OPZCraageo4qSUL3ND4oO2GfDlWWLDVLNJfGnjFtGB5KUwvRBCSNygU3dJlth8iqoPQujPdCC/aeTqkS1RLLnRiXFM8oDqtUybnOBF+H8qS1YqYlowIn7AWrHZiE3ybj4apyuL9+QQpoRBmNWV1ZyEICPDpfgGTHBmhOOezn2bLXT/n+W99h/ov/DXzj0HMi1ic8ota8Jwq8t2Bz9L3fZ+7e7L84G1JLpppTfAIFl7hMMWa6mjDXfcisqEJmSyRzpVV+Lt34jz2R4xFS8YtI44H1dsDhjluwyCrqrFOX4q77kV0OoWcORvzgjXYF158XKsG1dmOc9+vEZaFEgIKBfB91NgYIhrFXn3xMd9/MtCFAsX7foXOZjHmzkcIEZDRO7dTvP83RD7+6Ql9YF94CTqdwn3xOdSu7YhwBPOMpYTffuOfZEFxKnH22asIh8M89tjvJ5DBr6IinKDiUI/gQ+DHp+DULSfU+QeUXwgUwYUh0B5O8yUgJ04U6+rglls0qZTG86Ci4sjV8iIPohgEe01ABEgFxOvJoqGhiTvuvJeffe/f+cKXP8+zd32Pf1x0JhfOmoe95jLsi9dQCGkKF2lkDlQZR7VjOBRyAKK3ScxdgA780VV9YGnhNxOEwYVL/rwn8FWRmSAA7dDz6yiINFCE/f1w112SXE7T3KzRGta2Q9mYYHRAUJ8MfI7xBapGUx6C/rRgKK+pvkRRuPq1h7i9oSHBnwn+zImEd/HCkyDADZtXb05nZzs//OG3ufPO76G15kMf+hQfeu8HaNrxVaQzgs8BMtjI9+GHa/CjDafiSiYxiUlM4pRjg1WkQ3oIYFMpCNokqIDrNj1qXIM/JB2acj7TlCSh5QkHnr0WRJBcWYhzdyTFDsMlXKqYrFEmlxePPb6+wIkS0ZIfREbZbhYJack038LUZ5GVaQriZbRIMdMPU+k3URCNjAgfE0Gf9EiUAuIgENuc5oV5ycpTrg0sYEgo6pXJSifYp0xL3ptP8oSdY71VoFIZxLSg1bdZ6NlYSH4byjAsfUaEB1LTqmxyQiERFEWQt2JrEcyZSuriV6GYWJkZIsS78+/nHGcVHWYbL5tJ9hgNvGzarDfThEOCd3Zt5TIviy6fTnl9oLgc7RrDc2poCnfz8FV7YKVP2hhlljePKlVNVmYwMFjunMfVhbdQpoPn2DJnJdujmxkRwyR1BQpFu7GPKlXDAm/xCd1P4aSRuS4wIvjxFhCnrvSoaXEjfTv6GeseI1YdQ3mKdF+aRH05tTOPTyZmh3KMdJQUpVLS1reXnz92G29a8Q4a7KmketJUtCRPWXsB9j23n+G2EapaKwMVs9YM7h1ieH+ajpc7cfMuudE8+bECVsSidk4t6f40Y90pwmUhDMsgN5LDzTvMWDV9nAh+FVOXTWF4/zBD+4YIlYUDAtnxmbK0hWRTgq0Pbyc/WqBqWiVaa0zbYqxnjMxglnB5mBnnTWfOhbPY89RehvYNUzm1AqOkNE71ptj2u23EKqNUTas8jNwc3DvE7if3kOpN07e9HytsEKuOkyvLYcdspGng5hy00kQSYQb3DHL+x86jcVEjqd7UuKfwoWrv4yFWHSdeHSczkCFeW0YxHSzAFzMOZXVx4jUnVjV6KnHRRZfz6KMv87nPfYKPfewWHn74Pr785W9SVXUSypZJTOI4mCSD34AoCo0wTZTrTdhua8Fe6aAEzDvIImGKCtSqL1sFZvk2BoJZvs2so6R+WQgWeSEWHYEwLAiNgyZ+CDsR1gIHTRHNNcU48zyb3SWbimm+xTzPPqpNxKtwhcYEZvghmpTFWCmkbkj4xLUkKw6feAsB+6VLozIJl9rUJ31yQhFCEBaSZt/CRbPddHjZKnKBMzG0IorkLO/IRHWFNnh/PkFPiWBfa+V53M4RQRIulZsFfQbDpZKvvNDUK4u3Fsu40IkyIn3KlaRGH/51u7oYp1IbPGflSQmfBX6I853oEfv+aBAIEkcIyVvghVjqhHnBLiAbEky79T2s/4/v8bEPfZAzI0cOHnwVtp0hcuEcwnYTv3hyC//yuxf5u7et4D3Le/HT+/HLWkEYiOIIQhXJeTMp/OIuVE83sq4BQjY6m5kY7AWIyipUdyc6m0GUHV5u43d14K59HtXRjqyqxjxrOSSTaN9Daz0+aJDVNRhTpgYk8IpzUZ3tuC+vxZw1F1FXjx4eAtdF1NROIFL9HdtQ3V2oTBrZ1BIQtL6HTqfRo6N4WzZin3/RCff9q9Ceh79zO6q/FxGLY85bgN/TjertGT8PBANq2dCI37YP1dc7wS5ChEKE3/auwBpjoA8RiyOntJ4waf5fgUgkwtlnr+Kxx37Prbd+8rUdRAjys29EW+XYfc8giyOoSC3FlktwGs8/6tuO55Sh6kBVaOQgE9SrchBUpcY/cYHAIc0VvOODH2fVFW/mf3zivXz4uSe5tqaav1+1mvpS+CERUCcpcAk9KDC3C7xZOvDDUWDugfhXJao6CMDTVqBCzV9/kCLXB/sFgf28QAyDN13jnKfxazU6LBFpjT7Iok2OgLsgaOO6dYKREZg79wCh3joLHs6CUJqUBzEJ/lSN36oZdsAeBfuvFfm5r63/JnEA69ev5bvf/Tr3338P8XgZN9/8IW699VPjA/ti/nIiu+7EHNuJssqRzhhIk+LUK8d9tCcxiUlM4i8NvdLDE5pe6eGjsQREtURqRUooRqTPoFR8JT7EFGUxxTO52Ikx1z/xse7J4iwvTEVO8rJZYED6NCqTs9wwzepwZfLBfqcSwTQ/qM47x4lSp42SvUSIXVzBLO9SbiyYJFQFw1JyfyjDdtPBJxDhXFqMUaUMXrTyKOASJ8oU3+RFq0BRaM5zI5zvRCe0o0oHodHXFONoAvJWo7k/lOFhO4MnNBaCUaFpN4v4HuOKYyUEMz2bGmXQgUteBKFyAFmh8Dm8MlMimeXPxWcavwmNUqHkeIXimPB5JeSzQijiJWPYsroy4jVxxGiBWLnJrPNm0iO7mOPM5xxnFXO9hQzJAUxtUamrJhD9y51z6TTaeNZ+kj7Rg0BQo2p5a/7GcSuJo0JrQh0PE+p4CJkfQhsWXnIe+dnvQsWaUCi6ZSdFUaBBNRHVR8+JORqqZ1Sx8Ir57H5qL+m+DMIQVE+vYu4lcwiVnYAyWOnx+Ynve3zzl1+ioaqZ61ffTHGoiFaHK0pHOkfpK4XUldXGaVhQT7TixAIVvaJH/84BohXRcRWrEILK1gqG9w0x1j2GYZkYlsTJuQgZWEpEKyIUxgrYMZux7jFC8RDTz51G67LD80gSDeWc8bYltK/rYGjfMHZVjMbFDTQvaUJIQaonhRWxECLIl6maVkmiqZyB3YPMOHc6i65agO/59G7rJ5IIjxPB2aEso91jpPsyFNLPMuWMZmatnkVZKdRtrHuMjb/eRD5dIF4Vw45apPvTuAUPYciA4NXgAtIyUJ7GjhrjthFlxwiHOx5M22DGedN54fYX6dnai1Ya5WvsqEXz6YGFxp8DiUSSb37zdi699Go++9mPctFFZ/Bv//afXHTR4TZ8k5jEa8EkGfwGRKNvYloWynXHtxVR9EufKl+W/HwP8aFFMCyOwv6eBCqVQbUyGDB84v4BkmqwpKqtVyYSwVw/dNKDvFmezQtWISCb9QFP45ShON0N84ydn1BqVUDhAUllTPDjGpXBg1dMIGsFNoJdhsMFnFyCsUDQWFJG7zOCPtdoan2TuOEyLBS+0JgI2qVLTAvOdINrr9QGlf7RVZ0WggucKKucCC6Bbuxk1RL7DIdnrQJ7DYdKbbDMDXOmG8ZG8M5Cgrl+iC1mkVkf/QT//r2fkPvaD4l+7p+PeUw/PhVzeAvPtXXzkf94iBtWL+KzN6zCH3gBZZVjju0ArdFWnHRhAanfP4ceHYVIFH/TBrRS6EwGHAdCBz4HOp9HlpUhQoeT796uHRRu+25AqEZjeJs24K59HuvCi5FV1ai9u5EtU0DIILitPAG5LPlvfg0yGUBTsEKIeAx8H5TCaJ6CfdmVmAsC9YF2XXQmjfA8ZGngJQDl++D5eBs3nDQZrNIpij/5Ed7GDWjXRQiQTS1Y515Quugjv+9opT6ytg5ZW3dSbThVUKMjeC+9gLdjGyIWw1x0OuaSMyYQ6qtXX8KXvvQ/yeWyRKMnP/AGwIxSmPUOiq1XIdwMKlQBxp82KdTlgVdv5NcCsb8UeDYG6JKC80/k0hqbp/DjX/6Be+75CV/4wmf43cr53HrrJ/mrv/ob4vGTC8gQKbA2C1StZjxIW4K2wF4ncBdpvFkaUQRro0DkJZlPKIhA+D5B+CGBNoIgs9CzEnsbZG/2cZdo7OcEKqnRYTCGQEcExQt8EDA4GAS9HfzREwKIacqXQN8w+JWQqAi+Ur2DglWrFHXHdl+ZxDHg+z4PP3wv//mfX2ft2mdpbZ3BP/zDv/H2t7+HWGzixMVpvhhtlWF3P4aR68WtWoTTdCFuzdI/U+snMYlJTOL4qNQGKaEoCo3BgXGsFoKwEmwzHUBja5t90uGZSI7fhjO8rVDG9YXyCRZupxIzfJsZ/tEVgu3GPp60H2G7uZm4LmOZu5KVxdV0SU1GKOaoA8IagaBaGfQYkpCuwsagXsH78gmGhI8roEYZvGgV+Nf4EEPCBwQJLVlTjPK5bCUagXXIGH+vsYvn7adpN/ZRo2pZ6qxkkbeELunxmJ2jTAtqSsRxo2/wrJVnRPoktEG5loS0oEEZGAgiysCRPlk0240iBoKznTBnuEceX203AyuM1oP6P6EN2qrm0BtNMifTjh+fAkIg/RwGBSoa3sOHslcddqw6deTqFQuLt+dv4mznPNqN/YR0iNnefCpOINXX6nuO8K6fgBHCK5uK8AtYA2sRXobdZ97ML8vuYZe5DVe4VPk1rClewQxvFWmhqdTGEUMCD4UQgqbTGqmbW0u6L420jAm+scdDrDJKeV0Zw+0jvNjzOPt6dvHlW7+DM+ISrYxRXj9RydC1sZutD22jmHGQpsR3fbpe6WbJdYtPyJM2IJ8Pr5DzCj6+p6iZWU0kEUH5imLWYax7jLHOUZrPbGbF++eQbE7gOz6xyiixqqOP4csbyll41YIjvhatiDLcNjJhm2EaWGFrAiF7sIgnP5anb0c/nuNhWBLTNuje3Et+rMDSG8/Ejtp0bewmN5ofVwxXTq3AyToUsw6GJSmkisECQczGjtlkBjJMX3lq/MdHO0fZ90Ibw+2Bj3IobpOsLcOwDfp2DDDaNXbKQy81uvQ7ESwIHWv+f/XVb2XZspX8zd/cyrvffQ2f/OTf8elPfwH5FyQYmsQbE5Nk8BsQ870Q02MVbPQ89mZHGE2E2Wd42AgcDFLSp9k3iJVsHBSaPIrmE3goHg8hBBc5Me6KpNhjOCSUQVYqHDRXF+LHHdDlUKy1C2w2igAs8kIsdcNEkJzphdngFdhkFokiUUARzeluiDcV4hSFZq2V59VsYAdY7IWo8Q3+EMpSh4FZKpFygLiGZCkA4ruLziLV3smvgS8c1J6bbvoQX/7yNw5rp0KzzirwvFVgSPq0eiYr3SgLPJs/aoMO6dGkTBa6NuutIimhGZY+ZVrw1kI5c44x+DwSJILXQoXtMBxui44yJBQJLenFY6tRZED4XOHECSFY4UaCkrVokuL7PsL3vvfvfOSDn6Cm5uiEo9NwLvvWP8w7v3Q3K+Y28M0PnIM1th2n5ixyCz6MkelA+EW8cCOZ792NzuaQcw6yQtizE53L4u3ZiTljNtg2emwMnRrFXHMpwp7YP1opnN/dhxocwJi7YPzB7rfvx3/xOUJvvh7nDw/ht+8HrTHq6jFmz8N95skgAK6pBV0s4j/+B9TYGObZK5GVVfi7dlDo6yHy4U9gTJuBMWUqGCba89C+DzKwwcBxEJXVcIQV/OPB+ePvcNe+gDF1GiIWC1TCe3ejn34CUVuH6upATp8Z9I1SqJ4uzAWLEX8mwvdoUMNDFL73LfxdOyAcQXsu3ovPY110CaHr3j5+T1avvpQvfOHTPPvsE6xZc8WfdE5txdHWqSu9Kq7R6BjYTwvkMPjToLgqCHMjB/ZLAmtzYMngLtS4SzX6JNaGhBC89a03cvHFV/HNb/5v/uM//o2f/OQHfPzjn+XGG99PKHSC32IXhC9QoYNWCjQY3SW7iGrABm2DN0Nj7gVru8Bv1ISelKikRpWUzqpeY+4UhB6V5N6rUNUCe61AFgTebE1xtcJbFBDjU0bhpb0Co2RJoWPBR973BatXK/J5eP55QUeHIBKBCy5QXHedPmzSUShAVxeYJjQ3w1+Ii8nrBqUCcjwUmrC2dUxkMmnuvvt2vv/9f6e9fR/Ll5/LD37wCy655CqMo3WYELj1K3DrV4DyQf4/3rGTmMQk/p/AEjdMMizpwiOKQKHIozF1MF4vAAkNGaFQUpPQkhGh+HUoQ0ZoPpRLjgs9/qvQbuzjP2P/l17ZTVJVMiZHucu8jQ7ZzhL33ciS/+7BOmJHaGwtsA4KlRYIqrUJGtqky6/CaXw0s/3AgK9P+twfztCkrMMUulvMV/hR9DuMyCFiuoy95i7WWy9xff5GpD6HjFA0qQPj5RCSuX4ISws+kksyKnweCeVKQhXNhU6UeZ5NQWh8NDN8mwVeaNwK8FB46JIJ30Sk47W8Mvda5my+H3NkMyBAmrj15+A0rj7pvhYIWv0ZtPozTvxNWmN3PwZaB4Q0gYe+l5iNGN3OY9l/Zn3lGE3+FGwdotvs5cvxH1Dre0T1EuJacqYb5s2FOJFDPlupvjR92/rIjeSJVUdpmF9PrCpGxZQjZ6kcC9KUzFw1gw2/eYV7n/wZZ04/hwqvDiMmmXX+jAmKUjc1xt7HtqA8qJ4eBERopRnaP8Tup/dy+ltPOy6xaUUsqqdX0b6ug3AiPE4GpnpSCCmonFo5wSIh0ZQgN5Tj9LcuOWVkZuOiBnq39ZLqTRGvjqOUItWbJl4do3ZOoPY2TIPa2TXse24/kWSEVF8ar+hh2AamZVLekMC0DEY6RujfOUDzkiZSvelxxTFAvCZO1TSX3m39GKZRCraTmBGTVPcY8boyIolw4I/8Jyh3x7rHePkXr9C3PUgJjiTCODmXwliB2tk15McK9G7rO6Vk8H7D5cFQhr2Gi0Yz3be5ohhnmn90T/W6ugbuuOO3fOtbX+XLX/48O3Zs5RvfuO0wYcEkJnEymCSD34CwEdxQP4/fAMMdHXRWTqNWSeZ6ITzgeenxuJ3nbDeMgaBXejQok7PcU1NmerYbxtaCJ+0c/YZHgx94X51znOPnUfwoOsYGs1gamGhesYpsMx1uypcT05Jb8kletPJsNItIYLEXZrkTJorkhnwZ1cpgp+EQ04KFfpilTpicUOwzXXYaLlEEOaFRQFJLEjootbry8QcZVS435stZ4IfYvn0L73jH5Vx99VuO2Nbf21nuC2UBTRTJs3aBLZbDLfkEby2U8dtQht2Ggw+EdRAsUa6CgeOTdo56ZZ6U1cNrgUbzh1CWEaGY61vjK4p90uPxUI6lbvgwa4pbb/0Ut932H3z721/jC1/4l6Meu88t581fe466ygR3fXwlYQq4dSvIz7geFW9BxQPjf7+jDb+7C1nfOMEKwWiaAoUCoroG1b4f7XuIWAx71YVH9OXVw0P4+/ch6xomDIRkYxP+3j2IRILoZz6Pv39vQAa3TqPwi7vQvo9RHbBiaqAP7boBW1MsIsvK0fEy1I6tuC88izFtBnL2PIzWafg7tuIPD4FlIewQsr4BEY9jLjzt5O5BoYD30ovIyipELFhhF6aJ0Tod1bYX+9IrcV94Fn/7FoRloR0Xo6mZ0NXX/kXZPwC4Tz+Bt3M7xuy5CDMYjKjhIdwnH8M6fSnG9GAQP2PGbFpaWnnssd//yWTwKYcBzrk6CPAqAGECv90CxH4ssV4K1LcIsNYL3M2C7C2KkywWIJFI8rnP/TM33fQh/vVfv8gXvvA3fPvb/8YnP/l3vP3tN2HbNhRKHsZlHOb5qyvAa9VYmwReouTDqwNLCx0HffB40wa0QIxpDCUQKY1/sFJXgF+rMdsCz+DCmzWFS/WBcxtBuF70Nsl5W+HFUdj9PDRUCty5mm5H0NSkWbZMU10Nq1drhoeDYLiaI1hrrFsnePBBQU+PQEqYOlVz7bWKmTNPrg/fKNi4ER59VNLZKQiHg35as0YTPcJnRinFs88+wS9+cQcPPPBrisUCV1/9Vr7znZ+wZMlZJ3fiSSJ4EpOYxBsEjcrkvbkk/xQfDDIsNPgCYkqQERrQhLWBh6ZWBQo4Hwgj2GE6bLSKnH2K5ignisftP9Iru5nrLRwfP6fEKGvtZzjTWUWzX81+6TJdWRgEgdbDJaHFoeTiq9hsFRgTPnMPsuqrVyY7DIf1ZoEm32Sf4WAhmO0ZPBy+l5QcZY63YHz/Tm+q3EkAAQAASURBVKONh8P3srqwhCOZ9CuCeWCDMmnCYl4uRJ8M1IW1JYXwodBoOqXHHjOobpzpWTQpk1bfRpAjjxq/plct/+yGC0mHTsMa3hSIP+JT8aoWHZbr8LpBK4xcL9o+RC1rhEiLUUa9AWZ4K7FLXvwp6hmQO7D1syxyljImFI/aWUJacF3xQPVW/64BNt27mfxIvmQ14NP5chenXbvoNZHBADUzq3Gmpegb6+ITN3ye1jOn0riwgcqpwfFktotQ24NYe55noT9ArmYR/XolRVGNkIJYVYzh/cMUM0XCZcfO1wGYdk4rYz0phvcNY4RMfDeo/ipvKEfrieS+cnwiiTDRilP3/aqeUcW8S+ey9+l9jHSOIg1BeX05cy+eTTQZnMf3fKqnVdK/o5+h/cOMdafwCh7SkFRMq8COBJ8jDeRH8wBEK6MM7R0aP48QgoqWCty8x5SlLVROraCYLtK5vov0QJrcUI4Nv95Eor6NBVfMO+n753s+Xt5j/9p2ssOB33EhXaSYcfCKHsV0kWI2CM7LDmZPTecBA8LjR5ExuqRHgwpqKV4xC/RLj4/kKsaD5o8EIQQf/ehnmD17Hh/5yHu45prz+dGPfkVz89RT1r5J/PfCJBn8BsWs5mkAxPd1s2TBXFoPUqKu8CJsMosMSZ+4NjjdDXOpExsPgftTIRCc6YU5wwtRQBNCTLBpOBo2WEVeMQu0loLeICCI11sFlnghlrsRyrTkIifGRc7E0pVtRpH7wxk6SwEVU32LZt8kiiSqJbfmAhJ5p+lymhacjWKv6bCjZOsQq6ngKifKqmgcA8E3v/mvtLbOYMWKVYe1c1j4PBbKEUNQXyrNqkezW7r8wc7ysVwFsz2b3abDS2aB56w805RFQhv4aPYbLj8Lp2jOVr5upW8AKaFoM9zxgfWrqFEGOw2XTsOjxpt4zysqKnnPez7InXd+n7/92y8SiRw+OPA8j1tvfSd5x+eX9z2NURUiLUxUpPbwuiQpQYpAxXYwlI8oKyPy/r+CYgGdzSLrGwIP3NIxtOeh2vahiwWIxoJj60OUuUqBFGhf4W3fir9nFxgS0Kh0CmEdGJTqXJAQJgwDvOC+CyEgFkd1tAHgr38J5RaRtXWooUFwHLTvo50i9tLlWMtXnHD/A2inCE4RDrW9sCy07yNbphJZtgJv/Tr08BCirh7r9LP+bDYQx4K3cT2iPDlOBAOIikpUfy/+vt3jZLAQgrPOOpvNmzf8mVp6ApBMIHitVwTWy+BN5YBdRAGsDWBvEAF5/BrQ3DyFr3/9h3z0o3/L1772//HZz36Ur//fr/D+5R/ilsh7qRW1eI0a5yKNe9pB55BQvFRhdEnM7QId14icQNug43piCF4BkBpVWSKNpWDcU6YEUQRta8RIQHILH7wWjV8OaAg/IDH3CeoXa97Xorn3ZUFbh0D0Cs5YrrjiCkV1KY+isjL4OxhKQS4HHR3wk59ICgVNQ4NGKdi1S/DjH0s+8QlFVdVr6sa/WGzeDLfdZpDLaaqrNbkc/OY3kv5+zXvfq3h1PWfv3l388pd38otf/ISurnamTZvJRz7yad72tvfQ1HT0xOzXCnN4M3bX4xipPahILU7jKty6Fac0VGcSk5jEJE4G57lRPpep5ieRMYalIociIxQ1WhICwkqQlge8eV0BcSVRBF7D/5VQKLZbm6lQE/1ty3WSbtHJoNHO2wrTuSuSYpfhIBAYWnCGF2aNc/TV46zQBNF4E8fKFvCsleO+UIYuI5jH1PlFbDHALL9hwv71fhN7zV2EdTdJXU+39GksjfMdNGPC5wInMj7vkiVi+MC1afYZLgPSp0xLZngWj4RyPGJnyQiNBsq1YE0xxulugQZ/N1tMQYVuxdQmWaFY4IU4yw2j4i0U46f+GXZCEBI/1oQ1tAkVrT+w3S9QlC5jEWucCE4LRZ/hU67L8WQfksC6xNOatXaeNU6Ucm3gOT47H91FMeNQNb2qVLGnGW4fZufju1l241kI+dosB37669tYuHAJN/39eyeIWkRhkOjmf8cc241HAoGmgecoUz3sku/CFSdnMwZQXlfGWTecTvfmXsa6xgiXh6idU0v72na6N/dSXi+woxaFVJHcaJ5Z58846UC1Y0EIwZQzW6ifV8dYTwppSBKNCUw7mPP27xxg1xN7SPenUZ7CjljEq2MU0kXq59QSKRHTSinQEIoH4qnGhQ30bullrCdFvCYI80v1pUk0ljPnollEK6Lse34/+bE85fUJwuUhlKcY7Rpj8wNbOfvmZViR4y9WKE/Rvq6Djpc7KWaKDOwaJJyMYIZNiqkCwpBYETMgr01BYTRPbuQ1pFAfBS9bBbqkxxzfGv8el/syyHcyC1zmHF/pe8klV3HffU9y003Xcfnl5/CDH/yCZcvOOWVtnMR/H0ySwW9Q1NbWE43G6N6zh9PVRKVlvTIZ8X3eVihniRum4nVK7RUIIsc5rlcKbuswXJ60chTQ40QwML4SvdtwWX4UVUCndPlxJMWo8GnQJprA52pUjvHRbAU12qRCG1zqxLnUCd6j0bRLjz2mgwam+xatJfWs4zjcc89d3HrrJ45YjtNhuIwIxeyDSjUEghpt0GF4jApFpTY4yw3ziJ2jQhvjAW4GglbfYpfhst10xhOFX4VPoFo+1DPstcDSgSWGK/QEX1ofcFA8Yee4P5QhqgVneRHOdiKEENxwwy1861tf5aGHfsN1190Q9Jfn4W9+BW/7Vv7Pg7/ihRee5uc/uZ/mKdM5lnGCbGjCaJ2Bt/4ldCQKuQyEIohCHvO0MzCmzUCYh//M+O37Kf78Lvz9e9Gui6ysDErBeroQ5QmEYaC1DoLk6hpwn30Sf8M6tOcBGveRhxFVNahcDul5CNNEhMPg+2gpEeUHpJU6n0PU1qFdF+eRh5HSxLz6OlRvD6q/F51KQTyOufh0ig8/CNkMsnUa5ulnIY8QcncwRFk5cspU/C2b0MmKA0T30CCiPIFsasFoaMR4HQihQ6GzGfzODoRlBaT7Efr9mDCMw8n4V3GISnHWrLk8/vjvJ/iBnQhUOgWAiJedEo+vE4W5C9ACIgd9UcKAAGM78CeOn2bNmst//MedfOJjn+UHn/kG/3LvP/Iv/CNvmf52PtzwV5zRvpTse30ODs725kD2wwr7OYHRJlBVmsHlGh6SOE+AroW6pCY6InAXary5gA9+M5ht4E0HDBB5kENBEF38mxI5UvoMhsE5R1G4SGPtEPj1GkxY2C9Y7EBvpUY6UJUSqAckuUaFf0iOiNbw0kuCJ54Q9PVBZydks3DOOYFFBMDMmZodOwQbNwpWr35tpPpfIrSGxx+XZLMwe3awraICyso0GzYIXnlllC1bfsnPf34HL730HGVl5bzpTddz/fXvZunSFa/b59sceJnY1u8gnDGUXYE5vAVzeAuF/CDFade8LuecxCQmMYkTwflulOm+xVbToSAUTb5FrZJ8JzrGequAK0BpTbbkLdygDIaFIqL/axeyBIKoipExUhO2+/h4hOiTcZp9+Gg2yW7TJScUtcpkjmcfc/ze7FtowEWP7+eXVLk90kUiKC9d634DivJ0EnoD5QfpKTxcDAzqtMlVhTi/CafZbjjjpNEiL8R5RyGkM0Lx03CKjVaRIkEod1wJhqWiRhs0+cGDu196/CC6g0r9W3yxE0dKevUU5rtv56ribJa5Ycr+i+/JYRACp+lCzJFtGKm9qEgd+EWMbCdO7Rzaq4dppohNiKLQeGgUacr8xeNz3riSDEiftFCUa4N0b4p0f4byugNjUCEF8Zo4Y11jZAazrymEbGhogEcf/R3/+q/fOezZb/e9gDm6B69iAaYPXjsMZh2q4+1U6K306qVkh7I0LW4aJ0VPBNGKKDPPmz5hW7w6jtYwtHeIdH8aO2rTumwKMw7Z77VAa01uOIdX9IhVxTBDJnbUpmZGNVprUr1pUr0pcsM59r/Qju/6xGviaKXI9GeIVEQJl4Vxix4hT6H8gOgtq41TOzsoQ6uaVsm8S+ey6b7N7Htu//i5pp8zjUgyglaazg3dmGGLSCJQTRiWQUVzkpHOUYb2DVE/v/5YlwHA7qf3suux3Zghk1DMxnN9RvYPU95YDq9a+nkSpTRewSOUCFNMF/Ecf5zw/lPQK30smCCkC+wiBd3GiS+MzZ27kAcffJYPfODtvOMdl/PjH/+Gc889eRuXSfz3xiQZ/AaFlJKZs+aS27aTtFATFKg5FCEEzb5F5euoTD0e8ijuDqdYaxXxhKLN8EgJn6Q2mHpQiq4qDViOhpesAoPSm1B2Ncu32G64vGIVWeMc/m6BYKqymOocvkL4u9/9llRqlLe97T1HPJ+tBQaB+O7gx7IrgnYe7L2VEUFfHwyjVPNdEAeIkRyKJ+0cL1oFCkIzy7O4wI0d0xvoeIgiOd0L83s7SzmSMBIfzTZZpF/6vGIWqdQGA0KzwxyjTbq8q1DO9OmzWLFiFXff/SOuu+4GtO9T/MVduE8+xjO9XXzzhcf5xJzFnL5jO3rZSsQR1MOvQkiJedYynIfuQw30B0phpRDJJPb17zoiIamzGQp3/BB//z6MKa2Bn/BAP3p4CBmL4e/cFhzHV8jaOsxZc3CffBTZ1IIo+SKpkWFUTxeyohJ/5zZEsjIgin0fYduIZBLtuqjebkQ4gjF3Ae7zT+Pt2oFsaEJIidHYhNHYFPgVP/04hdu+G3gZGwb6qccwnn8Ge83l6NQowrIw5sxHVk+smxdCYF90Kfm2Nvyd25HJCnQ+B46DdckVyPojB2qcSmit8Z55AufhB1CDg2AYGFNbCb3lHRjTT7x23zz9LIq/vAtdKATEOqD7ehHlCYxZsyfsO2vWXEZGhhkaGqC6+jhp0IDf3YXz8AP427cENh9z5mFfeiVG8+Epxq8LTI4S5CfAOnUE5gJrId+b9gP+v4Vf4Y79t/H9V77DXbt/zNLkMj6Q/wgXf/06QuEDvyp+K+RbgxLa7dvhjh9Lqn1YPgzJHsFwHJqu0ETfo8aVwPl3KCJ3yoDgRqBNjTdTY/QKEBpvTqAgFqMQelygkgR1pRLkGJgdAh3V1Nsg+8ELa6zNgtCDgtyHJvbFiy8K7rhD4vuaiooggG50FHbuFMyfH+wrZSDqHx09Zd34F4FiMVBCV1Ye6JN8fpD29gfYsOE+7rvvd/i+w/nnX8y3v30Hl176piNWWpxSKJ9w2/0IN4OXnD9eqSGz3YQ6HsZpOBcd/n9Mnj2JSUziDYUWZdFyyNj7hkI5KeGz1irQLTVJLZjtWYwIxaj0edTOstUscpYbZrkbOarH7amCQHC2ey4/NX9EWqQo0+X4KNaZimF5DX+wm3nKHqbZt7i2UMbZ/on9ti/2QszxLLaZQaCzBAZFQEYqAQ2+OU4SR3SEnbKVzcZLTPVdLCwUig6zjRZvGlE1jRZh8M68we/Cj7PLWEcMTbNajuZC4PAAtofsDC9YBaYoczyE+ykrT1Fo5joH5lAuPbQZXSi/muWewBFF2o19hPg5K93PEz3K3DGPYqvpMCp9KpTBPM8+qmXGqYBbs5T83FsItT2IzPeCtHEaVxGacQ2t3MZ2cwtNfjNSWziiA4HJFH/l+PtHpaJcSyrUQdfzOqxZr1+/FoDzzjuciDNSe9FGCKSBIaGqtZKBPYMUMj7a3cNwagbljeXMOG/an7yIHEmEOfPtpzPWk8LJOkSSkXGFbc+WXob2DYEQVE+rpGZ2DYYZCG/SfWmG9g+jfU2iKUHllIoJCuncSI7tf9zJ4J4hlOcTSUaYdnYrLWc2o33Njkd20vFyJ07OJd2fppAq0nx647gVhDXVYmjfMA3z60kPpBkr+RtXNCeZd8kcwuXBnEMIgTQkQgTWGXbURgjY/0Ib4USYltObcXMOVskfuJAuku5Pl0Lmigy1jRyXDC6kC3Su6yQUDxGvDqqQa2dW07mxm3R/hlDcRmtNIVsACxLNCcqq4yAFyvPhBMhgrTQjHaOkelMYlkHV9CqiJTJ7tGuMaHEYUe2jGyrGLQM1GgdNpTo53qaqqpq77nqA9773rbznPW/mzjvv5Zxzzj+pY0zivzcmyeA3MObMnse6LVspiKDEqlIZ5IWiS3qc5oWYcZJEo0Kz2XR4xSyQE4oZvs2Zbvg1Wx08bxd4zi7QrEziyqJcS5618myyClQ6BmVaMip8LC2Zdwx/3V7pEWaiulkiMIF+6R/1fUfDT3/6I1avvpT6+sYjvj7dt5niW7QZLjP8wCusgGJA+FzoxIiXVssFgjmezZN2njoOlL6lSwRxg2+w33DZaTj80c7SabjUKYNQyYN4t+nywVxyAjF+LBTR7C4l/9YrgyZlcnExRo/02G46KDQaQVFoyrRkgW+PrzqmhWKtnWe5G2GOb3PDDTfz8Y+/l/3799CcyeA+/TgDsSif2bSWc6bO4MOrL8N96QXMBYuwVh79oaK1xtu4ASqrkGVlqNERRDiKiMXwXn4RffFlh5HJ3rYtqLb9GDNnjVsSiIZGdC6LbJmKdfZKVH8/MpnEXLSE4r33BMrUSBS/uwvV24XOF1CFPKErz8BoWIW/YR0kEli3fBC/uwvd34fyfWR1DaK+geJvfoHu68XfsRXd0Y44a/k4sauHhtCDA1DXgDFnHgCqWMB95Pe4zz2FrA4sHWRVFaFr34a1PJCRFrwi24Z3MhgbpvrN5zNtcyeyoxOjeirmipVYK877L1G/+ls2UvjFXSAkckoreB7e7p3oO35I5BOfQSZPzEPLPvd8/N078TdtQCsNWiHiZdhXXoM8RNk8e3bQTzt3bjsuGaxGhinc9h3Uvr2ImrogJOu5p1CdHUQ+8qnDCPbXA948sJ/QiFHQyWCbSAFS4x05LPk1wegXUICq1mo+WfMZPnbmX/Pwvgf57ovf4oP33UTVM5/mzde+nauuegtLl64YD/5wXfj1ryWDQ5BcqXnBh3hGs6NdMCUEH608YDvszYbM3yisrQKR1fh1GqNLYP5c4M1j3GJQJ0EPgblb4E3VWFsFOgQ4gS+x0Q6iIDD3gchC7LsSkVY4l4C7QONqeOSRIBByRinzpakJUilBR4dm6lSIxcD3AxVtxWuz2vuLhWVBPB5YQOzffy979txLd/dzaK2pqFjOu9/9RT760Xcc9TlyMPJ52Lo1INKTSZg/X/NaeGNZHMLIdOBH6idY9qhofaCeyrTjTZLBk5jEJP7CsMgL8S/pGu6MpHjWzpMWig7pkRKKamXgCdhruqVKQo93FMpOyH7uT8HK4mra5X5eCj3PdlGkVyxkVC6kwa+iUUUIa0GbdLkzMsYns5VUncBcKF7KPnnUzvKKVcQHznUjpEKKvNATVMUWkrhuRIgZ7DGfB4KMkwp/OoZ+N/8nliIv8nQZt6HFRqb6SSSSB8J3s8fczK3ZT5J4dUADjAqHh8JPMiDXM2oUqFKzafbPJqHj7DNcRoUipgWugDajrdTeGgxGiego073Z7DV3ssXcyFL3cMu0bunx48gY+w0XTTDUmO5bvCefoP4U2RAeBiFwGi/AqT0bI9eDNiOoSB22ENyUu5VfRX7KDnMrrnCY4tdS1OcDC8kKxZgIbEouLsbHwwnL6suJ18ZJ96VJtiTHQ68zA1mqp1WOk4Mni/Xr11JZWU1LS+thr2k7gVDO+P/L6sqwIha6u5e00cT8qfOpn1837rX7pyA3kqNvez+ZoSyRRJhIMoLyFJvv30LXK91oDWhN+9p2mk9vZv4Vc+l4qZPdT+6hmCkCAjNk0LykiXmXzcUwDXzXZ9O9W+jfNUBZTRzDjpAbybH1oW2YYRPlqfGQuLK6MgpjefI6z+DuIcLxcBAIh6CQKtC7vY9IIkyioYzGRY1MWdqCYR74XnmOz95n9yGkpOEgUjfVl2b/8200Lqwn0Zigd1sfWmv6dvTjFTw04BVc9j23n8opFTQsODohnBvOUcgUSTQcqPwsqy+jcrSCobZhfDxGGgYZaxrGSpjkY2PU7m5i3vS5J2RB4Tk+W3+3ne6NXfiOj9YQqYgwY+U0BvcO0bt3kELRoSGuGZmdoGxhLTLlMBCByplJTj/UdvAEEA6H+eEPf8ktt7yFd7/7Gu68894j2mBOYhJHwiQZ/AbGGWcs59e/vpu/HtC8VCVoN1xCWrDUjfDWQtkRQwSOBo3mwVCG39k53NKA5SWrwEtWgfflEocFkZ0IXjbzhBHj5Gm9Mpnt22wyizxp5UiWVmsvLcZY4B3dy6hWmayjUKI6xXh7M8KnTTrcFU5RrwyWuOHjKqE7O9t46qlH+P73f37UfUIIri+UcWckxa6S57BBMJi9vDhxoHCeG2W76bDdcKnUEoeg/O1sJ8Qu0+FRO0dPKbQhriUxLWnyDWow2G64PGXnmFo4fjppm3T5aSRFu+HioYlpyTI3wnWFMm7NJdlmOvRLn5gWPBjK4IiJ5SdlWtJFYIExx7e54orr+NznPsnPfnY7n5i9EK9Y4NMvPYMhJF+74jqMaAzfNHE3bzw2GTw0iLf5FRgZRhfywQpnJoXOZ3E3Ovj792LOm8i26VQquJcHedNqzwNDooeHsFZdOIFE1Z6HFgJ/13b8XTvQmSy6mIdcjmIuR/wr/4fQtdcDwaqydpwgtM510alRCnf+CCwbY+4CtO/hb96I9/KLWOetBinxdm4NrBVmHlC/6v4+1NgoQinkijkIIVCd7RTvuRs5pZXRZJjbt97NjuE9qJKRxpQlTbz7HR9jamLKf6kFgvvic+hiEXPW3GCDbWPMmB3015aNyGPcv4Mh4mVE3vdhvE0b8Nv2I8JhzHkLkDNmBf2ay+Lv2wNKMaWpBdM02bVr+3FXoL3161D79yJnzwv8nAm8iP3tW3HXvUjo0iv/pOs/EbgLNc4FEHpCQk9pow3F8xTu4lMnE9ExQGpwguMb0uCKGVdztXU128zt/Gfsu9z3wC/5wQ++SX19I1dccS1XX/1WKitX0Nlp0NysEQJ8E8aSEJWavfsCi4aGg0TmOgHOigPtNjsIZmaHfuzsgOgtvEVh9ErMHQKR1xgdwXZVo0EFVhPCh+g9EmubpngRDFyoGRgQVFUdOE9Tk6arSzA0JBgYCDyDu7oELS2a0047vB+zWWhroxQ0x1EJUJEGYzeMjA2Trs1SjBRxnCLFYvBnGAbRaJRIJEp5eZLy8gSGcfTf+kIhIKmj0cNtznO5QNlcKATX09w8cR+lFC+//CIPP3wvv/3t/XR3b8cwwkyduoYLLvgOhnEVs2fX8td/rY4YIncoenvh9tsle/aA1gIhNDNmCN7zHjXhnp4ItBFCSwuhnIniJr8YhPrIU+cJOIlJTGISpxIxDFY7MfYZLjnDZdDwyKFpxaROGZgIRoXPC1YQgD3df31/zyJEuCl/KzaruS9UQIkQlg5TECYbzCKneyFmKIsdRhBwt/oYXsEHo1IbvLVYzjVFXQp7gy2Ww37DRaHHx+YOGkmIs4tv4S3OUgZkHxGVYK05jz2mQYMSjMhN7DM3ENJTqfPLmePbuLjsMLeyzn6eC4uXAcGc6J7wT9li3YuFIKRt+o1N9BrrqfU+hEuILWaBgghsKwZkFQKXhO4bb7dFMCZPy7HDrkmh+VU4zR7DZaZvYZX8i3caDr8OpflQPvm62BGOwwzjl0+bsKlONfCh7Kfold0URYGkX88TIcULVoEh4RPVitP9fsJsY5dZz3RvNqZtMHv1TDbft4WhvUOYFoRUP9VVcWZd8Nr9gjdseIkzzlh2xLG/W3MmdvfjwUJurAnQxEQfNDfRuOQa/GTrazrnoRjtHOWVX28i3ZdGGBLta9rXdlA/r47ODd2U1cbHfYOdrEPn+k4iiTD7nm9DSkHVtGAheaRjlI2/3Uzvtj6mnNlCqCzE0P5hKpqTmCVFbqKhnJH2ETrWdYIMqkQjiWCAZ8dDGCM5nIJLdjhHsinBcFsQICcEhMtCjHaNkRstEKuKjVtEeI5PqnuM7HCOWOXE71qsMspYT4rsYJapy6YwtH+Yro3dKE9hRkyUq6horcAwJbue2EPNzOrxth4KI2QgDYFbcDGsYBwppSTRWI4VNdkxZxO5nRnifhlm3iDXV2Bf5S6mndt8QnO7rle66Hipg3hNjFA8hFaasZ4xnv/xWtJlBoPTYxRiEfyMg/lYJ94fO1F1EWqUYEp5HPsSG45BZh8NrxLCN998HTfe+CZ+8pP7OPvs8076OJP474dJMvgNjPPOuxDP8yh78mX+xyWXlchAScMhgWIngi7p8aido0wLakpKVQ/NDsPhSTvPW4pljAifV6wio8KnQhssdkOHqYZzKMZKZTlFoTEPmrEKoFyVlHBonNK2hJbHLDI60w3zvJ1nr+HS6AeewVvMwApBmA5tePhonrHy3JxP0HIMpe3dd99OdXUta9Zcccz+mOnbfCpbwVbTIVNSLsz3QodZQrT6Fu/PJ3nSyrHTdCjXkivcCNW+wQ9jY8SVoFpLerUgpgT7DJdKbVCnTJJassks8ridY0wEJVeLvRDJQ/q0gOKuSIp9hss038IGRoXiMTtHlZJc4sQ53TuwkvisnadLTPQcCiIjNLYO2h+NRrnuuhv4+c/v5OOf+zLf3rWVFzv38+Prb6Iq+qpf1ol9hlR3Fyo1hqxvGC93UWOj6J5u1MjwYfvLqiqElOhCAUI2/v59qPb9qP5eRF0D+a//78AOQmuM2XMxps8MPIN7uiGbgUI+aJttw9Ag+a9+CeOr38RoDTyxhG1jlIjd/A+/g3YczNJr5twFAVm8Zxfuuhcx6howmltQsdh42wFUTxcIiQiFAh/ioQFUNoPeswv30T9w32k2W4Z2MCPRSsgI4SmP3WP7+Pmue/nrMz+MKV7fn1atVODxvGkjzsMPBSF7xQKitKIsDCMgsMcODOq17+Pv3B6E6ZkWxtx5GI3NwfWmxtCpMUQiibVsBdayiaoQb8M6ir/5BX5PD+jAvmNqXSO7dm07bltVZzsY1jgRDMHAUYRCqI72U9Edx4cB+bdq3EU+5m4BCryZOlDSnkInHXeOxpsqMPcJvFYNNsgREDmYdsMc/uGir/KFf/jfvPTS89x33y954IFf8cMffovq6kbKyq7jtNPeQmvrOciSR7MQQXibOpZxN+A1a7A0Igf61TG0DyIt8C5UeLMh+xGF/Zgg8lOJ0QNuBWRcQXQw+KbrOrBMDYbAfgqS08G2NYUClJcEFBUVMHeuYtMmQSYjMAxYuFDz5jerw5TBL7wguP9+QX+/QAhobNRcc41PRcV+XnllHdu2baKrq4Oe7Z30tHXSlemkoAon3NcVFZW0tLQydep06uqm09IygylTptPbu5gdO6pwXZgxA9asUcwsuaVs2wbf/a6kuzvwPK6ulqxYmmVh4z289MIjPLNhB89u3MnI2BiVldVcdNGVxONfYmzsYgqFKIYBU6bAO995YkSw1nDPPZKdOwUzZmhsW+M4ARl9zz2Sj3xEHUZWH/N4dgK35gxCHb9DWWVgRkB5mOl9eIlZeMnZxz/IJCYxiUn8GfCqn+2YUJzlhXhSKiyhaDd8yrTLdN8moSU90qfd8F53MhggJWCzWUWLDwqXMeFTriWD0mev4bLEC0b9KeGXqu84YaHNwSrgNxVirLXyQbCzb6CEZkQoypTk6mIVZ7tTgSAs+1fhEVp9g27DZ5O5m4JQFIXJi3Ye4cIszyakQ+wwtnIhARm8z9jNevtxkroajyRJLVHaZ0huR8qnEHoN+wyPhJYYCIoiiWYUeVAqSIE8EoNq//Bqrx7psdtwaFYHbC5sBE3KZJfp0Cv9UxZSfjIQCBpU0/j/rynCRU6M7eZ+7g/9iJftXay1FSFCnOacyTvz76VuTi1Ra5Tcc/dQnnqWsnCaUE0tDO+iUHE9fuLE7dVexfbtW7j++ncd8TWvYj75WTcQ3vtrzNGtgECFqylMfyv+KXpma6XZ+fhuMgMZqqZVIWSgeB7pGGXHIzsRhiQzkKGYcTBCBvGqOMpXdKzvwskUx8P0httHGN4/TCFTpH/nAPmxAkKAV/QPI1ftuE12KIsZMib46JbVxkn3ZyiM5fEKLrmRPH07BwjFbSqmVJAfyZMbzTPSPsoGrVn5gbPpeKmTnq29ODmH0Y5RAJKRA0Ip3/ExTIlhm1Q2lDN79UwGdg8iDB/TNilrLiPZnEArTbovQ7ovTcWUiYNSr+jRtradro3djHWnGNg9SN2cWsrry3GyDrmhHKFlFp3n7aGmvInwngiYYJ5p0HbOLl5qfoaV2VXH5Ve6N/Vg2Ma4/7OQgkgywp5NXQzXJCBuEdOCjBCkq0NUjnksrK2kygyR60mz7eHtJJsSRF6DUjwSiXDbbfdw883Xcsstb+Ghh56jtXXGSR9nEv+9MEkGv4ExbdpMmpqm8NRTj3LxxVeS8F87q7HXcMkIRZM6MPgyEVRqg1esAqd5NndG0nRJt+SIC82WxU2FBK2+hYPmETs7Xv4V1xKDgLRsQGMgGBGKzWaRkJac40ao1gb90ueBUJYW32Kef2SriKnK4l35ch4IZekxXHyCQWWjMlnihxAIfDQ7DZcHQ1k+mE8c8cdaKcXPfvZjrr/+RswTCNdKaOOwALgjYZpvMc1P4KORBIOTe0IpimgatUGbCPrWkIEPcZ/0qFMmQ8JnwPAZlmMo9P/P3nuH2XXV5/6ftXY5/UzvMxrNaNS7ZEnulptwxQUbgwnFgQQIJbk3QMq94abekAv55ZKQdgkQsOnggh2wce+2JFu9jNr03uf0Xdb6/bFHI426XICQeXn0AGfOXnuvtfc557ve9a73JS+gxjf4eLaEecfdhwOmS6fh0uxb0z5qJdogo4MV8KucGOZx/V3nRjgUniQ9dR80mi7pUaINFh1XXF933S1885v/yo8O7eefDu7l02sv5sKGYOVd53Joz8VauvzMnY8npvaJq2l5ndagfRWQf/7JRvjGoqUYi5bg7d6Jdlz8tkPguYhIFPIF8vd9A1lZjaytp/AfDyKsEH4mhe7rDc5lmSANRGkZ2rbx2g6T/+F3iP7+H88gdAHU0CDiONZGZ7OIcBQsG1lShn3bHZiLl5P9+y+iBwcQVcFqrMrnwfcQZRX4O7eh+gMSVKcmydz/XfKDlVStXUDICJ5ZU5o0JurpmOymfbKLluKZCoa3ElprnEcewPn5T9GOi06Nowb60YUC1tp1iHAE7QaKdlkarPTrQoHC9+/D3fwSulAArRHFJdjX3YROpfA2v4TOpBHxBNZFl2G/48bAPxnwe7vJf/db6HQao2keCIHq66EZwYEdr5/9gouK0Z57ctic6yCKi9/q4Tk9DPCWgLfkrVMCy3Q3dt9zmGP70HYRTtWF5N57EZHvW5gdAjyNTggK1yoKlx312JWsX38x69dfzJ/92ZfYuvUVHnzwx/zwh/fz4INfIRQqor7+CurrNwJXsnr1UqrPIhLwFoOzBuxXBTqm0SbIMYHfpKcVxH4D5D6g8ZYoIp+WZIcEhhN8XidMmExDXURghDQiL0h2atat0/z0p5JIRJNIBIrbXE5w002aO+5QWFagWD7hY8fBg/C970kcx8E0X6Sz8wlefvk1vv7113CcYIGoqqqGhspGGicaWFO1mtpVDdTG60ikY4SJoO6wMBaECIVslFJks1my2QyTk+OMj48zMjLI7t1t7NhxhOHhzWSzXRw1A0wkFlBZuYHt2zfw+uvr+cM/XIoQFn/wBwa9vT5S7sR1n8Fzn+aRh54i5+QImZL180r42FVzuHTTu1lx4+9jmCZaQ08P9PVpIhHN/PkQOseMl74+OHhQUFurmfo4YduBIvngQUFvb2C9cT7IN92GzA1gje4B5YMAP95IbuEHAnXwLGYxi1n8CqLVcOg1vCn7tSAE2SXI6OiWHk1T4WsCCOlfzO6qtqnA6Pm+xZj0GZIeaIhpyYj0yaFxgQOGw5/HR/DQLPVCbHSipyU/+2Uvr1ub6Td6qVBVrHbWcanbwCczJfx7ZIJhw0foYLfknbkEF7vHatQxqfDQZIXmoOFgEMLWwW5NDzhoOBQrA0+4RPSx4zrMI+RFhsVePbtMhzHhE9YSjyK6jQ6qfYNKZTIqA/K3yjc5bE6wx5QUuRM4wmFQ9rHcXcMib9lJfSoIjSc01gkL07YWuDJQOf+qIKR9ngx9k25zP83eAmxs0iLFK/bzlKkK7upZRn3732LzEiT8YMdNPo3qSyPzI6TX/CE6XH5e5/Q8l3D4dFufBE79tbjlqzHHDwbvL16IDp/s+fxGkRnNMt49QbwiPq1uFkKQrErQ3j6Kk3UwQ+aUYlgx2ZciFLOJFEXQBErh1GCaoUNDGJZBKGZjx2zKm8ro2dWLk3HwHA/TNvE9PyBTDw9jRyxKG0vIjuWIV8YRQhApilA6t4TBVg+34DHZP4lpG1TML2fo4DCFjINpG3iuT/fr3Tz5xadBCqLFEcyQiVvw6N7WPe23G4ra+J5PzdIaklUJAMqbyyifV4YVtggnQ8cs1/IuQgInKLy11ux9dD+dW7uwoxYlDcUMtA7Rs7OP9HCGaEmUykWVdOXaKfq3asLZBMIE3eijN7rEaiL00k2OHFHOrARwcy6GNbModjyfgtYYphGEZWqNnHCwDEkhJJGGxJYSqybJSPsow20jNKyuf0PPQjQa5atf/T433HAxH/nIu/nJT54nei7qhVn8l8UsGfyfGEIILr/8ap5//sk33dbplLmKYNfzQ6E0fdJj4ZQPrYtiu+nwlegoH8gVcdhweTSUJqkNyrTBpFAMCh8xpS5OaEmb4TIpNEt9m3JtIBFUK5P9hsMOq3BaMhhgpRdmsReix3A5LF1+GJmk6bhAOQNBtTI4aDqMCnVKb6/nnnuSnp5O3vOeD73p8ToVjlcL5AUUhGKzlQ8KSqFJCw9TC0qFZFR4tBkuJdqgQhkcMF3Gpc9B06HNdPmTdDmrptS+GaHwgROn+REtyUiFg55BBl/khGmXDq/ZeboB0JRog9vyCSqPK17Xr78E27b5y3/7By6ev5iP1s3BO7AvGFOtsdasw1y7/ox9FkJgNDahMxn08BDaMAMCOBRGlpVPK1VnHBMKEf7ARyg8+EPy3/53UAqjug5RVo7fdhhRVIJ2CqiBPtT4GOTziEQSrRSgIVkcELyFAgwOgOvi/OR+sMNE3vt+ZGXVsXvS2IQ61IrWGj04gLdzGzqbAddBT4zjPP4YsrIGe9MNOI88gNe6B2FaCNeBcAhMA9XRgygqDhqUEj8Ro2XLATJN9RA9VsxZ0sZVLgXf4e2E6mjDeepxRKIIo7wCVV6Bu+UVVGcbXiKOMacJ3deD0bIAc/lKALwtL+O++Cyipg4jkURrjertIffPX0ZEIhi1DcjKavTERODRrDWhd94eHLv9ddTIMMaipdNkrtHQSKVpsbW3+6zXa61cjfv806jODmRdsCdf9XZDPIG1au3bN1BvM4xUB9FdX8ZId6KtIkSqDWv4dfKNnaT/23sxDwtETuDXKFQtpxTaH08M33bbl/jKV7bQ3v4kY2NP0db2RyhVYMeOCg4fvoJLLtnIJZdspLl5/slb1SzI/YbCbxJYWwSiAPmLFM5lGnWCyKewRvPqHGia0CQioBxBNg46CxNpSEQ1VkGAhuuu04yNaXbsEPT0aEwTFi7U3H23ovY0VrljY6N89auP8dxzP2V09DEKhXGi0Uqqq9dTVPRpbrppDffcs5qKiipCPxNEfiTxFukZ42PuE/h7NWoIcMFbqnHWafQxizf6+mBgQLJsmaCqStPf77B5cxux2Gskk68yNPQqbW3fZc8ej5//PEok0kQ6baLUEbROIUQY21rHovKbuWVdhI/cVc/geAVGfpCm2BEK2U78ZDNCQH091Nef/2S3UAj8oO0TBG62HbxeKJx3k+hwGZmVn8Ea3Y3MDaCsJF7ZCrSdPPvBs5jFLGbxS0JOKDRM16t1ymSP6WDpIKDZQ9Nr+JQpycIzWMe9lTj6s6OBOt+kV3qMCjU9JzpoOGSEYo9ZoEybGMCTdoZDhsNHc8UzamqAA+Zevhn9V3qNPmxt4eHwov00v5H9CHcUVnFtIcZ2K/jiX+jbJ3ntFmuJqSU90sMVUKaWkJPPUmCImK7ER9NmDFKmTFZ6x+onY8rKr843MbSgw3BJS4WtFdWqlDIVCG60r6f6HSWmPboNGJNbCWNwRWETN+ffhc3JY1/jm5QrgyHp06COzRg7jBSaEXZam0n7c1joLcX8JVMLh81W2s3DzPXmTfclrhOUqQq2GS/wvsM7MCcOoc0wKlwBAmR+GOEXMFLt2INbKcy57rzOeZLY4VTvCZfjVp8fyXweFxD8O/ESBPiuj5v3iJZGMe3g3uTGc6SH08y7vJmR9lHGeyZwsg6FyQKGbSCkoLQxCJErriti8MAwox1jJKrijLaPMt4ziZSCaF0R2dEc2dEs/fsHSVYGimM/77F400KaL20iNZBm36P7yI3lcDIO0ZIIQgiEIclP5hg6PMzcDY3ESgMbxmRNgp7+FKNtY1hRC+0r4hVxGtY2TBPd3/juP/HX//A/uXrpzXz8PZ8NhkAFQXjF9cUkqxMzhmG8Z4K+3X3EK+KEYgG53HRxI0MHhimuL2Ll7SsY2D9I/nkX1eDh1zgYjoE4bCB/EiLzkTQVsooQZ1cClDWX0fZiO/FyPX29BTTKltjHxRxppYlMOGRLQ2RKQzARqIgRgRL6zaCoqJivfe0H3HjjpXz2sx/jK1/55i/UvnAW/7kwSwb/J8dll13Fd7/7Dfr7e88pyOZ0aPFtirQxY6uPg2ZC+CzzI2y3CtQpE4mggGaX6dBnuLQZDv3CZ0T6zFPW9LExLTFkYHFwoROhwwyCIkwEy7zQDD9biyDg7GywETT5Nvkpz61T/Oahp/5zKmzceC29vW8vUXcUTVNFZV5oKpVBTEsGpEtKaHoMj1JlktCC+Z7JLtMhI4PtYqaANunyp/Fh3pVPcKUTpVoZRLQgJRTJ40juUeGz2LeJnDASYSQfyBdxoRuh2/CwESzybKpOKDojkQilpeUMDQ3yf+99iNjQEP7eXWjlYy5YjLlq7UnhbydChEJYl1yOHh9DR6OQzQQKXyGQJaXTdg0nQpaWEbrhFtytryLjSUR5Bart0JQatxzV0Y5Op5G1dejJSUQ8hsiVoocGEYkihJMPLCiUgmQRJIvwd20jrxWRT/3+tCWBdeEleNu24rXuRXd3oVOTaMPAqG/EuGA9ursL56cPEfn9P8aon4O3awc6k8K66h14r23GffHZwCYimwWngKxrILR4BcWbe5GHD6Pqj63cDuaGKAuXUB8/TyPQ84R/5HAwNvVzgrEsK8datRZ329aAcC2vxFx/EfbNtwd2G4D7+lYwLWQiIIyEEIiSEtRLXRjzFyGnFNEiEqweuy+/gLXxamSyCD05GSixTygk4qEwmfGRs16vMbeZ8J13U3j4ftSRg6BBFlcQm3MrsYcXghB4KzXOeh147v4ngd398yCwq2QZgRQBZG6YUPeTuNUX4y2dy/nEVq9bJ/j859ezdesGBgf/BxUVWUKhlzlw4GleeOEZ/sf/+F1836eqqobly1ezbNlKli5dybJlq5gzpwmigsK1msK1mml51SkwNASPxeG2aghNQMTRRPPB92mPBaoA5VLjzQ8C1D78YUVbGwwPC+LxQBl7IrmplOL555/ivvu+ymOPPYzneRQXr2X16k8xb95NVFauRghJa6tgzhxFRcWUSjoFU9spjmsMjF4w2gT+Ao02wNohMXdosh9R04Twli2Cvj7B4sWBz/LwsEU0alIomBQKUcLhEmy7mHx+GMfJ4jj7AR+QmOZKTPNu4uatDGWS/GzXKPvHCyhlIISmMXGQW0QP865uPuf7dypUV0N5uWZoKLCXOIrBweD1sym+TwsjhFvxn3chZRazmMV/PdQokzCCSRFYyc3xLSaEzwHToUgZHDJcyrTBHfnkWfM/3irM8y0qlEG39JijTFZ5IQ4YBboMnyrfIKolKalo8iwSU55SFRi0Gg6vWjluLhwjnXx87os8ylazEUO/A0v41KhBRuTz/CT8Ixakl1CEzRXuqVV6Dppmz2KBb/GzUAEfjaXnEVWbGJdPgDhABkjLBO/J3cgKd830sQu8xRTrUnqNLmr9BqqVSZosnWaBDc5SdpiBx6993I9tjAqucKr4UG41ER2hWJ8+BTaC5JpCjB9EUhyUDgkkbbKfPrOdCvUCD0basbG5wLmIu7O/SYQ3H4b2RpETOVwcQswUo4R0mGSqD5EeQRuhYCfN0R2NVhLpjKOtGDI3dN7nVEr9Usm2aGmUopokw22j2FF7OhhvoncSaRkU1SZxsi5ONtg1qLXGjtmEi8IBWZx1EFIgJChPIY2ArIVgvlDWXEJJfQl9e/sZ75kknAhTOreE4roi0NC3r59ocRhEQHLWLKthwZUtJKoSlNQX072jh4FnDmPHgmtTnsLNuYSTYTIjWbxCQH46WYfMYIZoaQQzZFHeVIq0JLmJPMOHh6lZUsX27Vu579v/xsL5S7GiFqPtY4ip88Yr4iy8ZsGMUDqAzFAGN++BLDB4cGhavRuKh/Bdn0gyTN+efsqTFfTFihiVwxSHSjHrwOlwUV2aS2o2YpyDr1zDmnqGDw0z3DZCJBnG9xRe3sFbXYHOOBh9GYhZhNMeeccnvbSERF4CikK6gGmbJKvf/OL+okXL+Nu//Vd+53fez+rV6/nIRz75ptucxa8nZsng/+S47LIgbOuppx7j7rvvecPtVCuTGwoxfhJKs88oIKdSbZd6IdZ6YV638xhTfO1BM9jqFVMSS2qKteSA4RBBMNe3poneImWQMhTrvQjvKdg8Z2W5NzI5gx7x0Thomvxz3946R1lUKoNe6dE45Q+s0fRLjxVe+JwSf99uzPEsDC1whGJcKAwgpg1KlSCpJdc6UV6ws4xLRUr6lCsDHxgVQeJwu+Hy3fAkO60Ct+XjrHJDvGTnSGtNRAtGhE8EwUYndkpLDAPBYj90RrX1K688T39/L5ZlU1lThzVnLtbadefdV/vqd+C3t+EfOYCIJ8FzIZ7AvunWaZuCU0HE48jiEvD8KfI2UCPiOGjXQU75+Go0wgphrV2P8/jP0N0d6KlVcBGLQziMUVOHKCvFO7Af/8ghzPkLg5X6SBT76ndQeOYJ3L27EJEoRk0d5vyFCDuErqlF9fWi+3owlyzDXHJse5x/8WX43Z2org5ELIqcvwBjzlyEaVMVKycsTLaNHSJpJ8h4WQSC61uupij05n7Ej6qYdT6HrKyaJmiPDdzJ91tW12AsWYaMRYn+9z9GlFfMDOHLZcE64TOWz4PnwwnqbVFcjOrvQ4+NQrIIWV0N2kf7/jTJrpUiCmS8k21ATgVrw8UYS5ahDh+EPCSen09oezF66vG0dgvMPYLMbyrOsgPrrBBjYO0SyAlQZeAu1+jE2Y87LygXa2TnlKrkOK/pcBnmWD/GZBt+Yu55NztvHsybF/h7Qxi4EriSP/gDSKdTbN78Iq+++gJ79uzkO9/5BoOD/QAkEslpYnjp0pUsWbKcuXPnkUic+lkciMPjyzTrBgQrD0I8p5kMQ0FAaBAK12rc5UdtLY6/rpkYGRnm+9//Jvfd92+0tx9m4cIlfP7z/wfbfhfPPlvPokV6+nH1/WDtpuqYcB+/GlAaOQpIUHGQQyAHwF2q8Y5a9zkaa4/A3iwY3jBBW9thfv7zNjo7D9HVdZixsVYGB3fheSkAxsfrqatbxapVH8f3V9PYuIqBgQa6u/sZG3uETOYR8vnPk+d/EDGvIet+iPH85bxj3WEMqTjSXc2/P1DNJ5cdu16t4fBh2LFDMDkZBOKtXq1P8ko+HpEIbNqk+e53JQcPBlYbqRRYlmDTpnPzHZ7FLGYxi18HNPkWq50QL9l5JrUgrCURLVnphrnEidKkLJa49hsKrH6jSGqDWwpxfhBOsd9wkUCpMilVJpaCLtOlT/q4tmaBZ1OnLAwEMS05NBUwfRSvmz08Y9WiKCOBT06Y7DeaqVQ2XeYTdBsdNPvzgWDOkhUZTG3Ra0iesTIcNB1CWrLIs1nuhnjezjEpNaX+JhZ5ywnRRq90eU9uBbfnl8+o/atVLe/M3cmDke+z39wNgIHJBe6F3Jm7iImoy2HDZY5vEkIwLH0KaC51YtSoc/shusSNENeSl60ch40JcnILzf5hlnkxJMtIixQv28/R7LdwZeEdb80NegOo8etI6mJG5BDlx22NGpFDtPhV2HoUZcYwc0PBXEJMpe8GHneoN2DfIKXEO8d6+O2ANCQtG1vIjO1ipG0E0zbxHB87agX+s0VhlK/JjWVBCCJFYZysQ3Y4QygeQpqS7EjwNytkYkUt8hN5/Gqf7HiO5kuaWHLdIvY8shff8alaWDkdvoaAeHk8IFeLonS1TdC5dYA9rRMsvngOF1wdHNu1tYv8ZAHf8RBCEC8PAtayIzm00kz2TzLcPkp6KIO0JfHyOMX1xQAYpsHQoSEmxib4xCc+wJ/+0d/yj//yRYrrilhy/SIKqQLhZJjKhZUnhc8BmCGD/GSe0c4xpBSYIRPf8RntGAuUuL7CdzzC0RArvNXsMrczLkdRUUXMKeKiiY1cVnr1Od2LZFWC1e9eRefWLoYPDxMJWyxY3oJel+D5PZ2Ubx4mnHJRdXG6Wioo1gahPROMuwrf8Wm4oIGShuK35Lm49da72L59K3/+55/jiiuuYf7RoPFZzOI4zJLB/8lRVlbBpZdexQ9+8K03RQYDbHSizPEt9pgFckIzx7dY6YYwEFT5Jv2GT40PXdJFac2YVJQpg0plkFSSIekzIdR0qFxaKKJakpjaUrTKC/GyHyTzlunAVGFE+DT7Nmvdk+0EToeYltw4XbwVCCHJo6YJ7bc10fYcoNFss/LkhUKhGZsK9lvk2dRPjeMSN8Qhw2WrlZtSOQvGhcekVCS0oEgbJLTAQ/OTcJpPpUuo0AabrTx5oVng21zpRFnhnUz2poRim5mn3XCJa8kyL8R835oxLvl8ns9+9uMsXryMfft28/rrr7Jhw6VvqL+ysorI7/wu3pQylUQCa8VqZPOZQxhEPIG57iKcnz4YEJUlJSBADQ8GJK9pBd63noesrkE2NmEsWoLO5VD9vWDbgV2ElPgdbej2wwjHwd26GaOmlsL9P8Dd8TrksmhAlpRhLF2OUXmcHM9XAdtlnLyAYNTWBYrWh36EXLAIaQZkqhofo7iomqsvvwMrNERHqpvGZD0X1qxjbdWKNzSGR6FGhik8+EO8vbuhUECWlWFduQnriquOWTS0zEckk+ihAcRUX7RTgEwa67qbkBUnh3+Yi5dROLB/BqGL54FlIU5YQdepSUQ0Om2NYa5ai/His6iD+xCVNYHNw0A/ibJyMm0HzrlvMpFErlqL/aIg1Crw5sK0cCMP1g6wd4hpj9s3AuMwRL8lMbqZKvA1XpMg+yGFOk9v1jNDoqWN9E4MPAu8s/Xb4N0ajye46qrruOqqY9sXBwf72bNnB7t3b2f37h089dTP+Ld/+4fpvx8NWZszZ+7UfzdRXz+XZLKZbd1zya0JsXkF1PeB3SpIFMPcj2v8DUH43ekwOTnBv/zL3/HVr/49nudy003v4u/+7t9Yv/5ihAi8cFtbNQcOBBYOWkN/v6CxUbNq1bH7q0o1clRi7QBtC7yox2huhEExSG94gN69fbRNHObI+GHa+g9x5NkjDGePqXZMs5iyshaKi1uYM+cmBgbWkMmsYu3achYtguFhmJyEa6/VPP64JJutZXj4o8TjH8U0R8mnvkbe+Sa57HtIF2p4ftf1XLH0ahbW2ewYX8aOHYJNm4LrffFFwY9+JEmlwDQ1L7wgeOklzUc+os6o8L34Yk0spnjxxWBc5s6FSy5RM8ZhFrOYxSx+3SERvCefpFaZvDJVI1/gRtjoRFn4CwiLOx3WuRGqfZNdVmF6l+ILVhZDCOqUxaRQ5IRij+kQcyXF2sBBE9czDfa2WD55kaBSjRAmBBocTAZlJSFdMf2+A+Zengw9yhHjEA7lDIrrCNNAlQoxKRSPhzKsdEPECwatZoFyZWAzlwnRwGUFm5sKxaec51zuXM1cv5m95i4KIk+938gydxUhQvxGzuUH4RRHTAeHwI7ipkKMi88hF+UoBIJVXphVXphn7K2MmY+xyFs6fS1xnSCsw2yxXvqlksHVqpZLCht5NPwQGZEmpuOMyVHCOsKq0J348Z9iOpNoI4wsjKHsBKIwCsLET87DrbjgvM85Z85cOjqOvA29OXeUzS1l3fvW0Le7n/RQmmhJlOolVXS+1k3H5k7KmkpJViXQWjPZnyJWGsPNe4z3TGDaBmbIxFYar+ChMw7p4TShWIjy5jKaLpobELiVceyojTRnPvtuzmW0J8ugyDMUKcK3DczRLN0PHWLcCHPtVbWsetdK9vzHXiIlEcLJMJHiMGOd44TiNsNHRlCej1fwcQsuFAKVsO/6GJaB1hopDP7H//w0q5rWI/dFSA+mGVYjDOwfZNlNS0hUnqz6yIxkGDo0TGY0S3Ysi5tzSVQngvmUACkDlbL2FNHSKKn+FMXxYi50LmNMjpAdzxEOh7kqcRXWSWaNp0eyKsGyG5cElny+opB2uMaTeOvnsWV9NZmcQyhqsy4lWfvsKPn4CFbEonZ5DfWr6qbtJd4K/OEf/gWPPfYwn//87/Od7zwyaxcxi5MwSwb/GuC97/0Qv/M77+fgwf1vatVHIJjn28w7RVF2QyHOtyMTvGDl6DI81JRPrY1gVCrqlMEe02VE+iR9SUooBqTPRicyvcqf1AYfzhbxtJ3lWTvLoPQp0pIa3yB1HIl8FCmh2Gzl2GUWkMByL8wGJ0wUyXo3QrkyeN0MPHnrlMlaN/JLSbM9Ea9aeR4NZbCncnpjSpATmpwIiOFKZdCibK4vxNlrFugRLlIohqTG0lCmDPJCk9AGdcrkgOHSaXrcVEhwbSFOYSoYTp6iGBwVPl+LjHPQdDEINkU/Z2e5uRDnaufYHvy///u/prOzjUcffZXbb7+KV155/g2TwQCyqBh74zXT/197Hv6hA4GdQVUVsqbulD9AoetugmwW9/XNqMkJRFkZZDII00L19YDjIOsbkHX16MEBZNM8wh/6LQpf+2f8vl7IZNDZDCIUCnyAHQf3mcdRXR34rfuQ1TWIikrU6Cg6k8LfswtZXhkojpXC7+3GXLAIWddwyn5Zl16Bv2833oH9qGgUUSigpcTeeC3z11zLghOTs94EtOuSv/freLt2IGvrEKVlqOEhCj/8DiIaxVp/UTDW9XOwr70e52cP4+3fg5AGWivM5auwLr3i1P24+DK83TvwW/cikkXgeeh8DnPVGigUUKMjAcE8MYEaHsJ+x42BahuQxSWE7/kozs8ext+/F6015poLKFYezsvP4DgO9om+AWeAcYDAGuD49Z8wIMA4CFz0hoYPPIj8WGL0CrwFGgwNHpgHBZGfSDIfU6e1TjhvSAO36iLCh74P4XIwI6A1RqodFanEK1nyFp3ozKisrKaysporrwwmXuk0dHekGHt5L8M72tnb1s5Bt53u9jZ27nyQ3t7OGeqVJ58sw7ZLMc1SYrFSFlWW8OQTZRRvLaG4uJSSkuBfKBTGNC08z+OnP32AH/zgWxQKBe6443188IO/TXl5FZZlMTjYj+MUcByHyy4r8OyzLu3teXw/Q0VFmsrKFA89lGVycpzhniEmXh5iaGSQocIgg4VBRvxh1NF08/+Y6mO0iubiecyPL+TaFddT++5mmppa8P1m7ruvAt/X1NYGop5du6CvT+A4mtZWSCYF11+vuP56zciIYmhIEokE6yCuW0ok/t9oiLyXbG47Y9kfsKfju+zpuI+V82+jqGYdo6OBzcrYGDz8cLC4sHhxcF2ep2ltFTz+uOD97z89sSsErFoVkODTIqRZzGIWs/gviAiSdzhBPeqhCU2JId5KFNIFCukCoUSYUOzcapMGZdFQCMieH4dSFIRmkW8zKXzaDYnSmpzw6ZcungryqdacIGIZMGKUaJOMTBFSIQRg4ZEWmqg3j3q/kcPGAb4a/Qd6jE6iOk63rGJADLDQH6NMrcfAoBjJPsvhg7ki1nohtpkFNHCFF+EyJzrDLu5EzPGbmOOfHGBcpyw+nS2h3XDJC02Nb74pKw5XOAR62pn3zsImJ3KnPU6jyYkstg69rd7C78zfSaku40XrWVJygmXuSq4oXMtytZp8c5RoYQzhpTGygxjpLjAjFGouIbv0Y6ho1dlPcAIWLFjCnj073oaenB8SlQkSV80kRc2wxWR/itGOESJmBq1cjGgF8y5rYu+jrWhfY8dtTMskXBQmPZRGeYo5axtouXwelQsqsKPB56iipZxIcZjJvsnAykBAIVUgP5lnsiDoLyuhOGpgSiAWJ9s3xivP9bD6whrmXdYckLMHh8hP5smP54gURyi9vJkDTx0CQJoCwzYwbRMn55IaTJOojJMbz9MZamXb1q185qq/IhSzscImdsxipG2E3f+xj/XvX4s0JAP7BujZ1cfw4WEm+1MYtoFhGBQyDmjIjeUQUiBNScmcEqQd2FDM3dDIrp/sZqxrnHAyjJ2LQM6g+eK5xMuD+bPWGt9VGKY8K2GrtaZney/tWzrIjeWwwhYrl9dw2aUNTEQkSUdSbRmIa6rgmjM29aYQDof50z/9Ivfc8y4ee+xhrrvunW/fyWbxnxK/fOZsFm8a1113CyUlpXzve//On/zJF96Wc6zxwgwWPFqjDhEtArWwMvHQ7DMLNHoWDb6J1HDYcIlqwaVOmFsLM3+UyrVJk2/zMjkSWpIRivsik3w/kuLGfJw7CglqlElGKL4RGWeXWSCCRAO7TYf9RoF7ckWEkTT7Ns2/RDXBqeCjedbOIoXgAjfMditPTmgEgoOGQ0SHuCsfJ6YlF3hhfi9Tyt/FRumXHhE0lpZkhSamJbXqaMyGRk2Za4QQhM5QwD1jZ9lvOizwbaypo/ukx6OhDEu9UBDYt383X/nKF/n0p/+QxYuXsXDhUlpb9751Y9DXS+F79+IfOgCOEyiFL9iAfeudqJ4u9GA/ROOYixYjIlFCd38A66pr0SNDEIujczn8Xdtxnn2SVF87Y+4w5lMPEHU0sbomrC2vYF2yEfXgD/BHhhHllYE1RT6PMX8hOpvBff5pzJVrkCXBdi+jqhq9cCnq0AH8PTvBshFaYdTVE7r1jmNq2RMgS8sI/dYn4Nv/jvv8M6hCHqO5Bdkw55Tvf1PjdnA//oF9GM3zEFOpxEb9HPwjh3CffwZz3YWB168Q2NfdhNE0D2/PLsjnMOY2Y65ei4ie2nRXllcQ+a1P4Lz0HP7unYhwBHPtOszlq3F++hDu9tdQo8OIWBz7musI3XTrjOON2nrCv/kx9MR4YM9RXELyZw8CkOrppChfQEQiyDlzEWcjyG1AB8TaDGgB9ptQBXeC2SHwGzTTtl4m+DUa84BADnFSmNqbQaFhE0aqDWt4GygP0KhwBbmWu9/SlOhzgevC448LXnxesHJ3MWv7L6bUvZj5BoQUdCZg1/Wa2z7ioHUPnZ3t7N7dzq5dgwwMjKDUKKY5QibTyfPPb2dsbJSxsRFc1z3jeb/zna/zne98/byu1TRNEokiKmNVVGUrKK+oospYQvdYJY5XgShUUUQV65oq+MjFVZRHY4h04COc/YDGufTYM+K6iocfFhw5IhACFizQfOpTikgk+OqZM+eYavfd79Ykk4qvf10yMACVlTBvnkEmVcGu7VfTXLuGDUs/wK7un7J19zdxWu9H6fdz4YWfZXi4hdFRaGk5vh9QUaHZvVuQzepzsnyYJYJnMYtZ/DoiJRTd0sWYsouzz0LwmogZwcdvBbyCx+Hnj9Czsxcn52JHbepX1TLv0uZjW9rPAWPSn66fk9pgsWfTajqMCegwfOLa4NpCjFUn7MxLKoMav4khY5hhOYihDTyhsGngUucSbGweCz3MVutlhBB4eIzKUSzVR5+xjBG/iUpVTQyJRzCmNxcS3FQIFiXNicNYg1uQhRH8WANu9UWoyLkXNcaU4OetwBx/LiHC9MpuMjJNnhwxHWdSTHCJu/GUx+w0t/FM6DG6jU6iOsZFzmVcUdhEmHPfGXqusLC4svAOrihci4NDaGrZAcCrWEMm9DmsgVeRmR4wLNzy1bhVF4F8Y7TIypVr+dGP7iObzRL9FfN/ipfH2HBbLf6WJ7FGdmJaYFS3MGGHUJ5PcX2SzEgOz/SmRDuCUDzE8puXUtY00+ovVhZj8aZF7Hu8lZH2UQCssEn5vDKO7E1j21NE8BQiMZuJyRz7R+HSepvVd65i+PAwqcE0VtiktLGUQy8cJlEZJ1GdAB2ojMd7JshNBOFyvuNBmce/fP1LfPaWvyJelCScCPyJhSEpritmvGuMsc5xJvsnOfDkQVzHZ6xjDDfnYMdsIiURQOM5PrGyKGVNpYRiIYQpyY1lsSIWFfPLEVLQsbmTzEiGcDLEvMubmbt+Dlpr+vcO0PlaF+nhDJGiMA2r66lbWYs0Tj3n6dney65H9iCmbDncnMuBpw7SmC6w7Oalv1CF7qZNN7Fx4yb+9E8/yxVXXEvkLHlAs/ivhVky+NcA4XCY22+/mx/+8D7+8A//AutEb9C3CIEC2GKBb7PLKpBHE9IwbPiEDMmHc0nWu1HGpE+xMqhRxkmrxjkU/xFO44pgQt9v+IBmAs2PwpMMGR4fzRZz2HDZbTo0+zahqTbyKHZYBXZ6Bdafx9amXyQyQjEsfYqVpEQbrHPDdEuPSeGTkoJ3FGJceJyaYIMX4X+nK/hqZJzH7QxD0ieqBXEdqIpHplTAc8/BU9lHs93KU6qN6UIWoFoZtBouhw2HClfwmc98jKamFj71qT8AYOHCJbz22qsntae1DhS2+/eiHQejYQ7G4mWIM6hAtedR+O638PftQjY2QziCHhvFefxnuFtfAccFpwBSIhsaCd/9QYymeRg1tVBzLADRXLyUx1cWs/O5H7HspVYahrMMRS2ENcHCV54jVFyObGxG9XaD5wThaM0tGM0tqPYjqIlxxAl+qbK6Bu3ksa69AWHbyJIyzJWrkWVnTvf1dmzD378HWVGJSBahU5MUvv0NdCFP6KpNZ70vM8Ynl8Xbuxs9MoxIFmEsXT4d6qbHRsHzpongoxDJItTQYKDgnRjHP9SKVgqjuYXQ7e8+54JCVlQSvuUOuOWOGa+H3/+b2JtuQI2PIUvLTmkzAVOhc8XHDFJj4aDgHfriX2L5GhGyMeYvJHzn+5DVpw/R85aC/bxGjIMunmp7DLSpcZe8cTJYeIAPJ9kNmsHrnJnXPG9oO0lm+aexRnZipLvQZgS3bCUq9saDPN8onnhC8MADgoXAhZNwJC/ozgWEZ22lZvEQHH5F8JNKm499rJH6+kYuvvjUKvKj0FqTy2VpazvMl7705zz22E9Yv/5iPv7x36eiogrXdfE8F8/z8DwX13UxTRPLsgmFQth2CNu2CYXCxGJxotEY0WhsWkUeflgQfkiSmqf5yxcF42mojQviBShoza5hePUVuKUGMDXOBnDWzXw+LrxQs2yZpr09cHuZO5fTkrLRKNx+u6alxefrX5eMjwcBK3nHxoqCXVaNX1nN4oqriJf/KWNj/8KuXX/H5Zd/ixtv/CSu+ycIUTSjzVlydxazmMV/ZWg0L1g5HgtlGJY+BtDgW9yeT7DgFyzWOPjsIQ4/30akKEysNEohXeDA04dAw4Kr5p9zOw2+yStWIMKQCBqURbEj2WEVuNKJcls+QZ0yT5rfXOBFOGAmWeReTFr2kRYpsqKUOV45NxTmoFD8PPQIaZGmSlVjYZMmSlruQ+scabGeSpgWf0R00L5AYPW/SLT1m4jCGFqGsNWz2P3Pk136CfzkyUrgtxst3iJqvQZ+EvkhHi4GBg4ONaqOed7JwdE7zW18I/ZPZEWaUlXOuBzlB5H7GJKDvC/34bfN3k8iT0k2+8mmNz9uyscaeBl74CWuCB/E8zy2vfAQl2x675tr962Gl6Wk4xuY5m5Ucy1aWhjZdoyub1BkXkq8ZTGx0gypoQzaV8TKYthxm3Dy1CR97fIaShqKGWkbxfd8kjVJChmHV1q3YXge2MeKcJEr4JRW4iEBjWkbVC+uonpxFSPto+z+j710b+smNZDCd/0pK4tSYmVRBg8MkahMsPzmpWzrfIWR0SH++N8/NtWwQCmfve07+Nkr9/PPH/ohmeEMbS+3Y4RMzJDJmACzKMp43yTjw1lkyEK7DhO9kyRrkkRLooz3TFC1sJKi2iRCCGqX1VC9uAo372GGjOkguu7tPex+ZC9KKcLxEJN9KXZ176GQLtBy+byTxsj3fNq3dCCECEL2gFA8hBky6ds7QOOGRpJVb3WYyekhhODP//xvufrqNXzrW//KRz/6e7+wc8/iVx+zZPCvCd773g/xta99hSee+A+uv/7Wt+UcKRGsltcpE8MVtEuXjFRElGSNG+bGQgITcUarhm7DY1B6JJSk1coT1YKINiigyQnFEenwjJ0jJxQWTBPBAOEphfARw/2VJYMjUx7J49KnRBsUaYMi3yCHotfwWOaFTip6TC3whKbFsxk2fPJCMSJ9nrWyLPRt3lmIU6tMRoTPEdNBakGzb51kq3FmBMXlvfd+lddf38wDDzxNKBSoGlpaFvLDH96LUgp5nKrTeeZJnIfvRw30QaGAsEOYl20k8qHfOjnUbAr+4YP4hw8gG+chplYeRWkZXute9IF9WFdfhywqRrsO/pHD5L93L9H//keBzcNx6E738lT/K5TUVlPht5GtLCWbDDMqC4TLwiwcy0AkgmhZiCwpRfV0o/t78fr70K4DkQh6cgJRWoZWCtXZjr93N9r38HfvwLriaqzLNiLOsnCisxm8px8HO4RRP6UGLq/A7+nCfepx7A0XB/7G5wA10E/+m/+Gf/ggaI1GYzQ0Ev7AhzEamwKi1TDQ+TwifKwI06lJZONcnBefxXn0EfT4OAINiST21e/Avv7ms6txzwJZVY2sOoPx6SkgptTkrtIYzS2Qz+Hv2EbecYh86jOnXTRwl2ucKyH0nIS+qRcjkL9a4S09t3PrdAqdTiOKS6bHyq8Hv0Jj9IN/VLitwegHr0mjzn/n39lhhHAr1+FWnn/wIhDYWOwF84BAaPBawF2mOQ9rMrJZeOEFQSIBi/NguTAqAvIzk4G8B4UIrFHw762C/n6oOT1XPw0hBC+88DSf+9zvkM/n+Lu/+zfe/e73n3HxYXAQ9u8XDA4G1xWJQHk5LF+uSZ6QZaeKAa3Z2QM7OqBYBYQwDowUCwqNmmejmqtu0JhxkMMQ/1uJLgXnAo27RoOEeByWLTvFxZwGK1bAZz6jePVVQWen4IILFGVlmn37JN3dgcJ49eo473znf6eq6uN89atf5stf/gJaf4fR0b/ksss+hBAS34ehIcGVV/4KBsF5eYxsD1raqFjdjJDDWcxiFrN4q7DHdPhROIUBNPsWHpoOw+W+yAT/LVN6nnXqG0duPEfPjj6ixRGiUwFSVtgCBN3be2hcP4dQ/PSBysdjjRvmFTtPq+FSpQw0MCA91rhhfiNXdNo+XeRE6JYemy2B1s3ENDQpg3flE5Rpgx7ZRdpIESKETXAtJRqyupSs6MEniz81fpXKYPGU8lg4KcKHfwi+g1e6PDiZVphjewm1P0R2+e/+wlcmCxTIixwNXiNKKDzhUeaX4wiHLeJZVky0IBNFCClRKJ4KPUpWZJjvLZ5uY0yMssV+icudq09pbfFWwlUOnnIJGWGkeAueSa0JH/4h4Y6HQQiWV4SYWxHloX//31y+YR1+0ZkzU36RsIa3Y47vxyteBEZQl3tWktDIbhrLDrJreA7FdUUU1xejtWa0Y5TypjKiJacvbCLFEepXHwvi8D2f0qYyevcPoctiQRZJKosjTbz5c2hMzFzIz4xk2PXQbjKjWYpqi8hP5smOZfEKHrUrArVtrCzG8ncupX5VHcXzN/HE46+x86FdpAbTJKuTfOXH/5v6ikauX30HoXgE5StyE3lKG0vIDGXIupr0ZAHhabSUuOE4tjAJZzMM7B9E+5rKBRUsvWHxDHWvNOQMexnP8Wl7uR2A0oZAEBMtgfRwms4tXdSvrguUysfBSTvkxnJEima+HkqESA9nyI5mf6FkMARz/RtvvJ377vs3fvu3f3fWO3gW05glg39NsGTJCtasWc//+39/z3XX3fK2fMjn+hZbrAIKqFEm1cogN1W4XO1Ez2nLlySw7RwXCldAUh01QtBIoEQb7DMLzPcs/FMcrwFb/+p+gVkILnYj/DCcYkh6lCqDnNB0SZelXoiWUygltlp5xoVinR8mqzQ90mVY+ExIxTo3zI2FGM/YWR4NZRgTPgJBmTK4uRDnouNIcQPBSjfMT0NpKjCm78eg9Elqg6qJAh/727/grrs+yIYNl0wf19Awl3w+z/DwIJVTgWR+bzfOQz/GbzuEymQQnht47H73XmRZOeF3v++U/dfpFNpx4Hgy0/PQ4+NgmIhQ8LqwbIy5zQFJe6gVc+kKtOsGPsFCcER3kXLSLDswhtXRzWjSgpzAtqDfOcjChktgYhyjqATv1ZfRWiHiCcil0bkcsqYOv7sDfB9/ZAi1Zxfa8zAWLUXnchR+9F10Nkv41jtO2Y+jUIMDqNERZM3M9DFZVoHq7UYN9AdE6Fmgtabwkx/jte7FaFmIsO3AV/nwAQo//h6R3/0cxvxF2AvqkO0vIyqqcY0avKE0KIXR1ILzyIMgDYyFQTGthwdxfvYwxpxGzOWrznoNbyW07zO5bSsAiZo6hGlCPIFsasE/dDC4p0uWn/pgA3J3atxVPuahqW17LRpvPsEXxJnOm8tRePQRvC0vo7NZZHEJ5uVXYV9xFUQNCtdrIt+TmK2gYxqRFugiyF/v/+r92nkQ+YHAfk4g3CDMIvSkpnAR5N575gC34zE+DqmUoKxMo/NTgdgKLDuwSvA8EBowAzuJQuHsbfq+z9/8zef5yle+yDXX3MDf/M0/UlNz5gS+l18W3H+/oKtL0NkZnLu0FOrrNU1NgnvuUdTXH3u/u0LjNwqGHhc42YDMjvqgItBnaSZHwSiG5ydh5U8E5b7AKNeITjB3CPKDULjhjSnJ6+uD6zrequT66xUDA4HCuLIy+G+I8OlP/yF33vl+fvd3/5gXXvhtDhz4V1au/Arx+HqamjTXXPMrFASnNVb/i4Tbf4KR7UdLE694Ebn570HF33prm1nMYhb/tbHFzJEXejoAzkQwbyooepdZ4HL3F7NSlp/M4+acwMf0OIQTIdLDafKT+XMmgyu0yT3ZIn4eynDAdAC4xImwyYmdkdy2Ebw3n+AiN0yX4WFrwSLPnj4mI1MUqWIKskBOZInoKDE9SRjIYTAmwhyaIqDvyCenPX2NyUMY2X68ouMUt0LiR2uwxvYFauFfsDVVm3mQQaOPte4GrKPFiusw1LONPWP30XtfK2UlLdhXbSK/soUeo5MyNXMXXrEuYUD00Sd73zYy2FUO7akDDOS78ZRLxIzTEGumJjLnTc2TZaabUM+TqFDJtFXH+zet40s/fIG/2v8A1vrP/MpsHZK5QQJlxHFFpRCIcDENc3LsT5uMtI1iWAae45GoiDP/innnFWJmmAZXv2cZ9337AKNtg1jKxytOkl7YxIVry5h7ghigf98A6eH0tA2Fm3UZ6xknM5Klb1cfRbVF1K+qpXpJMCeNxxMsWbqcYl3Ozod2ox2NLW1ChCmVFdQsqyZeGUcaEuVp3HCIlG8g83kMQ4IhEQZ4CsyGcorCgnmXNbHomoUnheGdiNx4juxYjmjJTBFatCTKWPc46aHMSWSwFbGwwhZuzp3xvePlPQzbmPZg/kXjfe/7MHfeuYlXX32BCy+87JdyDbP41cOv2vR4Fm8Cv/d7f8wHPnArzz33JFdc8da7ka9zI2y18rQaDmU6WC0fET6LfJtV3rl5Ps3xLep9i9fNPHpqIq7QpKWmyjcwCcjepV6IV+08E8KnaKogGhU+IWDRr5hP8Im4womSFoqX7ByHDZeQFqz0wrw7HyinFZojhsuQ9IlrSbd0iSARCGJasMAPsQBoNRyKtcEBw+XBUBoLwQLfRgO90uPH4RTVyqTpOAuJK5wIh0yHg4aDjcAFwghuzMd48J/+P9LpST7zmc/PuN6GhkYAuro6jpHB+/fhH25FTUwgk0UQKkb4Hqq3m8J3v0Xo1jtPUn5qrREVlYhYHD0xfsxSwPfQuQyyqASOP8a20b6Pzufx9uzEefhB/N4uEAK3KYKqGMLp6UEJMKwQvtBoXUCPjaLMXozyCuS8BXivb0aYQYCcSBZjrFiDTqcw5s1Hj42gWveBZWEuX4kxbwHCMFCD/XgvP4+64qppX+FTQYQjwXXmczPVuoV80JfjLB207wdhdpFoQI5OjYkeHsLv68HbsysI0psaA2GaGPWN+G1HUB1tRMVuipZ0oSMj6HQ7XsEmXbwS/5IPoNMpSE1iLD4mgRQVVXhjo3jbX/+Fk8EU8qTHxwCIH6fqFuEw2vfQExNnPl6CtxC8hedOpGmtKdz/A5xnfo4sLgsU4eNjOD/8NgChq67FuUijinysVwXGoMCvVzgXavxfHZHGNKw9ARGsKkEng3EQaQi9KPAWg7v+3MYmmYRYTJNKwUAZuFGoSUNPAQwDIkDIhR0lmvJyqDqLQjqdTvHxj/8GTz/9GH/yJ1/gYx/7b2edNPX2wo9/LHEcjeOAZQmKivS0Ori9XfDgg5JPfEJNz490EYzfrRh73qBIBm4vhQgMlGp0BPoOgFMQjB0R9I/DzgpYVCZoaNHIAQg/JXHX+aiKgAAfGICJCSgpCcjc84VhQO1pHD5qaur4/ve/yQMPfJQvfOH3ePHFS3nnO/+Yj370j6is/NUpo8yRHUT3fx20jxerRSgXa2grwhkns+aP0da57WKYxSxmMYtzwZDhEztBoCGnQuEmpTrndrTWHBpvY9/IAQq+Q2NRPcvLlxAxz21uYcdDmGELJ+sQKTpWlzkZBzNsTRMyaaHYYxYYE4oSLVnqhYjrk8mgOcriw7kiJkXQh6SW52RlIBGnzTMp8itJqiZGRYQxWWBCj2FRoFhLVrkreH92HkniLPTs04TEnaomECe8Q9NheHRJFxvBQs+m+G1QZ6t8DkUK8hNQFJC83q4d6PE2dJGBjoTw9+8j19mB8ZEPE9kQIS1SM9pwcTCQRPTbs2CgtaZ1Yge92Q7CRhRbhsi4k+wb345EUh09dXD0ucBMtSPcSfzYsbDg37hmFX/1nee4/5GfctfaTwbBwr8C0HbRlErAh+NU0cLLEGlYyrrVa+nbO0BuPEe8PEbNsprpwLRzPofW6LY+FosUu6uK6ZMxYkUhrl0Y4h0LTubFc+M5hJTTtWVZcynR0gjDh4dJVCdZc+cqylvKpm0ajqJmaTBH7djcidYaaRos2rSQuesbQQiSVQkm+yYZLyoiW1REcjINjgfRMEYujxu2yZSVU29mKKotOisRDGCGTAxL4jk+9nGPqlfwMCwDM3RyDWiGTOpW1tL65EEM2yScDOHlPcZ7J6haUEFxfdGUCnuM/n0DFCbzJGuT1CytIVb69i2gXXTR5cydO4/vfOfrs2TwLKbxqzOLmcWbxtVXX8+aNev50pf+jMsvv/otVweXaoPfzBXztJ1lt1lAANc5Ma5yYiROUUydChaCW/NxRiI+XYZLl/SIIChSBo2+xYj0udYLs8oLc7nj8qKdpRcPgKiWXF2Iscg7NRms0YwLhQCKzrFweztgIbi1kOAyJ8qA4RHVkjm+iUSQForvhSfZYRUooDGAAgpHQCPHPMgUGh8oUwY7rAI5oWk8jvStVyb7DYfdZn4GGVyuTT6WLeZ1K0+b4RLXkuVuiPKBcT75r/+Xe+75BHV1MwugowTw4GD/9Gs6n0OPjiLiiWMkqGkiEknU6Aj+oQOYS5ZNvTeP+9LzuK++hM6kwfMCK4TaeohGUSPDiEgsaOs4OwM9OhKoeZUi/62voScnAwWu1sw9eIT4RCetMcX8RIhE3wgjMUkhIpg7rFDj+wldewN6Ygxj6YrA99f3EYkkwrbxD+zHbG7BWPVuchMTyLr6gIyegigpw+84ghoaPDMZXFWNuXgp7isvIcJhRDiCzudQPV1Y6y9C1tSilcJ75UXc555GjQ5DLI5RWwfRGP6u7ahUCgoF/MMHMBYtRZSUHHs2bQvtuRgD2whnf4KOxNFr34lOjWOnOiiNR0hfuIzsw0+j5ckFvTAtVCZ9hqfxZOhsBu/1LXgH9gcK7aUrMFesmiawzwnhCJlwGIkgYh57/nQ6jbBDiLP4ML8RqP4+3Nc3IytrkKWBmsCIJ/C7O/Gefxr74ssQ4TDeUvCWzlR9ni+0fvtFHeZ+EJ6YJoIB9BRXZ+4Fd/25tROPB965P/mJ4ICEymZN07ggMqoxQ5Acg9fjsKtMcOu1Qbja6dDX18P7338LXV3t3Hffw2zceO05XcO+fYKxMaiuhvFxQTKpsSzI52FwULBokebQoZMtKgq18PJKjWvBrjEQkYCUHekLVMwtVbA0A14V+F5wnqIiTbICzAMao0MwGdH8+MeC7dsl2WwwHmvXKm699dxC3c4VQsDtt1/MzTe/yJe//Nd8+cv/m46OR/mHf/h3mpvP3Y/y7YTd9zzCy+KVBBNUDXjFUcyJA5jD23FrLv3lXuAsZjGLXys0+BYHDReNnq5rXILf33J1biSk1prHOp7mp21PkHWzQT4BgpUVS/ngkvcQt89OTMVKo1QvqqRjcycAoViIQrpAdjxH8yVzCSfD9EiXb0UmaT/ueuf6Fh/IJalTJ3szCcS0GOXNIoviobDJsFjHhPwPoASLGkr0BDW+zV25u7jarTjlsX7RfPxoLUa6Ez/RHPwYaR8j24tTfSk6FNS2LpofhVO8auXIiuAeVCqTO/KJMwp28l4BQ0oseXZ/Kq013kvPU/nsA5Rc10FX7BANbiOyphZvoJfBFsHK3gbKrFrEfIF3cD88/RwbVl/G/ZHvENMJkroIF5c28xBzvRYWHGcd8VZi0h1jMNdHzEwSMoL+20aYCWeUrswRKiN1yDdooaSPjpX2QQS1c01ZgutW1/ONJ1t59/+Sv6QZ6Mlwy1fhxxswx1vxEk0gLYxsHwgTt/pSiiqLKKotOntDZ8DggSEOPn2QkpDJdfUmWk8w2Z9CvWTTVX8BtXUxQgYoT6F8RaQ4gvYVWmmEDIKxI1MWL00XzaVq0emzS476+v7szpcCf2D72Gd08XWL2PWTPfR1jAXjH4+gs3lIxqAohhNLYOQdwvURShvPrqZXniI3niNSEmHo4DBW2MQKW3iOz2R/isoFFRTVJE95bNNFc8mnC/Tt7iczksEMGVQtqGDpjUuQhqTztS72/7wVJ+ciDUnX6910vdbD2rtWkTxNmyfCUx6b+7expX8bk06KBSXNXFK7gfrEqZUNUkre+957+Lu/+0v+6q++TCJxbueZxa83ZsngXyMIIfjMZ/4Xd999I0899ShXX339W36OGmVydz5JAY2AsyYGnwqL/BCfzZbSrCyesjO4QLEymJSKtW6Yy6csJ+7MJ1jlhjhiuggNLb7NPN/iVD+xbYbDo3YmeC+wyLW53omf0b/4zSAtFEemCsq5vnXKgrFMG5R5M1//uZ3hVStHg7IwNWSE5ojhMy59DhuSOt9ECeiRHjXKYKUb4v5w6qRxFggMBClxMuFVpA2udGJcedxr//P/fgHDMPjUpz530vttO1BMeN6xhC1ZVY0GhDpm1qGVAteDZBKdzUy/lv/+fbgvPBMQviF7mpzUngMZhdEwB/uSy3G2vIJ3cD+ypBSdzaJzWexrrsPr6kCNDAdE6RQDVxNdxrW72/hpXYGfzRNc5ApKsz4L0oK6MQ9dksfccDHeqy+B6yCPCzbTWqN9D5FIYtTWB0Sx683os85mEOEIIn5mpZwQgtBt70ZnMvgH9gV77g0Tc/lKQrffhRAC59mnKPzg2wGLFQ7jvfgszuhIQChqhSgtx1i0FITA27YVmUggqgJGTA30I8sqCMtOhPLwo8HrIlmCShRjju3GGt6OMacR0GjHmVYWa8+DQh6z5eSwjtNBp1PkvvGveLt2IAwDrRTipeexrriK0J13I4xzm/gIKcnVNxCzTFRnO7KsIiDJB/sxL9iAMe+tJ8f0yFDgn1w9s8iRxSWo8bFAjR4+te+xnvJGONGbGgj8n194Dq91H7uzC9nsX0a/2URtneDiizWrV+u3hxhWnCjqCSBAnLugCoB3vCNQ5L76quQ/JMxbpVnsamKGYIvUOMvgfRs1q1adniAfHOznzjuvJZfL89BDz7Bo0bkb8TrO1PxUzyTSDSP4m2mC7wf/jkckAvMXwtAItMzR9PQIXBeUgqIiWLBUo7YKDB0ooIeHYXhYkIwEfsFYcP/9gmefldTUaCorYXISnnxSYpqKd7/7rbdwsCyLz3zm81x11XV86lMf4tpr1/Fnf/Yl3ve+D7/hBdjJSXj9dUF3d0BmL1+uaW4+/wUJI92Nsk4o7qUFGmRh9A1d2yxmMYtZnA4XumG2W3kOSZcqbeKhGZAeLb7Ncu/cbBm6Uj081v4UYSNEYyLwEsp7BV4f3MX8knlsatx4Tu0svGZB4O+7b4DcRB4rYjF3QyPzN7ag0NwfTnHEcJnvW5gIPDSHDJcHwml+J1t8yrnFW4WX7BxbrBwXODfToBJ0Gi8wKicYE3Us968hwyUcMhzm+dZJQhZtxcnNu4Pcwa+TmdhG3jRJeh6x5Dxk0y3TPxSvWDmetbNUKoMGJXHQ7DEdvhQb5V35BBe6kRlzoo7JLp7qfJ7WscNY0mRN5UqubryMpD3Ty7TNcNhq5hmWPlXtPSx74iFqRrNcd+gC7l+3g/3WAULDR8jXOdRkG9i0e/F0H2RJKaq7kytT9zAo+3nd3kyf6MHAoNFr5u7cPacMeHsrkPMy+NrFljOJzpARIeul8ZSDbbyxc3slS/CjNRipNvzkvMCX38vxyavrueGLL/Pgww9w223veSu68aahQyVkl/w2kQP3YqbaQfmoSDn55nfhVqx9S87Rv7cf31MU1wVzKk9L9lXP5bXxEOGXBbV1sCA1QH3bIfycQ6QojGEbjHaOkaiMI4QgNZQiUhyhdunZ80ukIQklTv5+KZtbyoUfWof1+hCte3xkRBLt7oWh8WAsxtIkbA90nJ0P7aK8uYy6lbUn2TwATPZPsudn+xnvHsfNueTGc/Tu7iNWFkNKQencUpZctwghBW7ORWs9w/7BDJksu3EJc9fPITOaxY7YFNcXIQ1JPpXn0LOH0QSWEhPdEzg5h9GOMZysw5X/7XLsyJl3QWutefDQz3ii41mkkITNED/veIZdw/v4yPL3Mzd5auX7DTfcyl//9f/ktddeYePG8wtBn8WvJ2bJ4F8zXHHFNaxffwlf/OKfcdVV170t3sFDwuMpO8uA4dHgW1zoRihXBkPSx9JQro2zqnIrlcnHsyXclk+wzyyQF5oa32SJF5omPg0Ei/wQi/wzF5R90uPrkQkGpEeVMtEEhVef4fGJbMlbvkXqNTPPQ+EUAzJgNsqVwQ2FGBe7kTP2O4tiq5UnoSXthkuf9PDRWFogdZAc3G94CAQtnsUthQQV2pzyas7jozGm2vfQKKDeP/tHuKPjCPfe+//4zGc+T8kpVLDWVIia6x4jg40VqzFaFuDv3Y3y/WlWR8SiyIbG6bAx//BBvK2vImvrAzsJQFTV4O/fg7liDaFb70AkixCmiblyNc4zT6K6OpFV1VgXXop16RXkvvbPATF73LMqpOSybCm1m9tprbHJNs+hbtClYQJsslBRgeo4grlqDe7Lz+N3dyFrApJQdXchikswV65GlpRirr4A54nHwLIQySLIpFG93egLL+KFxhL2mGNYCJZ5Ida4YUIItNZkvBQZbxIzblH08U/B4UPosVFEcQnGgsUIy0LncrjPPAG2jdHQiLd7Z2DKGo7A2AjMbYFcFtXRhrliNe4rL+K+vgVr1dpARW1a2Ndeh2m8iD6xKBUCEAgvg7nyCszFy/B270AUlSCkQI2OYsxfiLn2HCWkgLv5Fbyd2zCaWhChMBqN6u2hcP8PQGvsq9+BrDy3pLVscQmJZDGyohI1MoSwQ9hXvQP7plvOmVQ+H4hEEhGJoDNpxHGr2TqdDqw5jiP2teuiujpRQwN4+/biHwjC7sxlK4M+Hn1+O9vJf/Uf8ft6eTW3gR8fXIirhimao9g91sL+/ZK77lJcdtlbTyp68yH0jEZkQB8VPuUABd6i82srHIY779RcdZXPyEhAnFZVBY/QFeqo/+3pMTIyzF13XUc2m+WBB56isbH5vM5fV6cxTY1pBpYVmUxwDYUCNDZCfz80NOiTLCqEgGuuUbS3S/r6BE1NmlwO8nlBcTE4Bmy3YX0vHP0a91wwO8Gvgb5izfbtBtXVmpKp9aCyMlBKs2WLYNMmTXHxeXXlnLFmzXoef3wLf/Znn+Nzn/sdtm59hS984SuEw+c3uRwagq99TXLwYPA16/uCZ5+F228//+fOj9djZzqZsZagXBCgQr9YT8lZzGIWv/5o8m0+mCviMTtDl+FiILjQiXCjEyd2wo5BF02r6dAvPSJasMQLUaINDo4fIeWkWVRybBE5bIaIm1G2Dew8ZzLYjtosv3kp8y5pIj+ZJ5wMEysLflx7pMsRw6VemdN5GiaCWmVy2HDolz61b5N4BIJskJiWJLBIeO+g1ruc180ROowwB4woI6Ecz9gFbijEuNaJnTSXeGTOCrZU/BZN/TsozY3TXVTLaNUF3CVqaZpaZN1i5bEQFGsDB81O02FAekwKxb2RCV62c9w+RQr3pPv56q576csMUhEuJevlePjIY3Sle/jt5R8gbAbzrtfMPN+NTDIpfEJItlgTvHzTOt6/d4QLuyYpL5Swva6DoYn91PUlWSc2UJM9tjNMZ7PIqmoiZpIP5T7GRudaBow+oirGAm/J20YEA9hGKAh71R6mOKZ6dpVDWEYwzkEJfTpoO0l+wfuJtP475lhQXyJNLtl4I9fvLOUv//KP2LTpJmLnGDD9dsMvXkh67ecxJw+DcvATcwP7iLcI+ckCphUUaRrNc/liXvFLMb0sRV6B9iMe20YEl/px1skxxrrG0RoiyTC58RwaKKotZsHGeeekitVak+pPMdo5htaakvpiiuqm5qCGZN2FtRyqNHmhR2DX1BLvG8QbnqBqqJ9SlcZJO4zlxhk6MMRA6xBr3r1yBiHsFTx2PbyHsc5ximqTmLZJtCRKaiBF9eIqGtbUU9ZURn4ix86HdjN0aAiv4JGoStJyRTOVLYHKXwhBojJBonLmAst49wS58RxmyGTw0DBCBOSx8hQ9O3rY/cheVt+x8owcTne6lxd6XqEsUkJpOCiAa3U1+8cO8nTXC9yz9L2nPK65eT6lpeVs3vzSLBk8C2CWDP61gxCCz372f3HnnZv42c8e5IYbbntL23/GyvB/Y2P0SC9Y0SIgQ5t8GwkYwCIvxM2FONXnUFhVK5Nq5809hlutHH3SY7FvTxdQSV9y0HDZYRW4wnnr9gp3SZfvRybJCz21gh+Q0T8Kp6hQJgvO4GecF5q88OkwPEakIqYFtpZkhWJCKO7Mh7jCjSE11B1XsF7gRtg85dVcMeXVPCx8WnybVeegvPg//+dPKS0t58Mf/tQp/25ObfOfoQy2LCK/89/IfflvUEODCDsExaWISBj70iuQ9UEYkeruQufzGMljRYUQAllShn/kIKKkdPrHzFy6AmPJcqZMRactI2RlNd5rmwPP4an3aqWQ8QTNowmaWlPIkjiIENrNIaprEMUl6MkJjMuvIvSuu3B++jDqUGsQilBeSeidt2M0BoEUoZtvQ+eyeDu3oft6IRJGr72A7/3G9bwWSWEDvtZs9UbZl1a8V1TQ6XXQl+3AVQ5CSJJWMYtaVpG0Z4aiqZGhIGCuohLtOqj+XkQ0hs7nwfOROpA46vExhB3CXLEaNTwI8SRGywKsDZdgrlqDd2gIc2RXkPx1dNuaXwAh8GN1iFic8Id+C+e5p/Ff3wxKY994KfblZ/Y8PhHeru2IcDQggrXGP9SKf+QwemSI/Le/ibdtK/ZNt2JdduVZF5LS6RSJ8gqin/sT1OgIIhKZodB+qyEbGjEXL8Pd+irUNSDiicA/emyE0DtvR0wV3d7BVpwHfoh36AD+gX3obBZKSpG2jbv9dbxd24l+7k+QJaW4zzyJ39eLO28lz7x4MTIZZZ7dB5ODWEvL6Zoo5vHHBWvXaqIy8Pk1+kBHwF2qUWcXMJwW7gqNs15gv8oxhbACdw04q98Y+VxWFvybMW5nIYInJsa5++4bGR4e4p//+UkOHZpHayvMnatpaTn78QCLF8OaNfDqq4JkEkZHBcPDGsOAvXshGhWsXKkpFGBq7YmREejqEpim5p57FK+9JjhyRFBUBIsWaZ58UvDii4JtOfDysOgw1FqaujFQCyB3h2LMgWywNjQD8TgMDAgmJnjbyGCAaDTG3/zNP7J+/cV85jMf4+DBfXz96z+iqqrm7AdP4cknBQcOCObP11Njo+nuhocfFixZok+6n2eCU3MZ1vA2jMkj+NEahHIw0l14RS14ZSvPu3+zmMUsZnE2LPFCLPRsRqSPqQUlp7BpSwvFveEJdpkFvKk/VSqD9+STKD3lmX9CzSGExNOnipI+PYQQxMpi0yTwUTgCPKGxTth1Y2nwpMY5xS67c0VGKApa0TXcyusDOxnNjdFc1Mj6mrXUxYMioYCeEbLdaxiMGUVE0dQpkybfZkB6PBbKsMQLUX+cbcWQ8HgqlIVQE4PJ+QRxYJrDhsMTbpaP5AI1cUooQlP+zR2GQ5/hUqwMtIAaFRDED4VTzPdsXu7dQl96gEWl86etEkpDJewZ3s/e0VbWVK4gh+KRcBpnKiBQICgc6eNQWZLHloVpfnqSlsEKWgYr8PbagABjCD03EWRtjI5ALot10WXT9X6T30LTLyjEodguo8QuZ6QwQMIqxhQWeT+Lqxya4gsxxMmiBTU4gLv1Ffz2tiCgePUFGIuWnLIedivW4sfqMUd3ItwMKlaPW7ac//Vnd7Jx40r+4R/+hj/8w7/4RXT13GDYeCVvjyVHcX0RQweHUJ6iozvNFqsSwxsnkstQ0HlCUhBPJDlo17JBupQWRxjtGCNRlWDhNfMBQbwidpJH8Kmgtebw80c48lI7TiYIeLSiFqWNJShfkepPY9oGa5dUM6epie2TIXJl9cxL2djPdlJaWUIoFszVfc9npG2E7u29tFx2TAQx3DbCeM8EJQ3FGFMkd7w8hlfwKKQdKudXUEgX2H7/TkbaR3EyDrmxHF2vd3PkhSMsvXEJy29eeko/YQBE0I/xngkETBPR2g9sMwb2D5IaTJOsSpz6eKAz1UPazVAfP7ZbUghBWbiUA6OHKPgOIeNkTkIIwcqVa9m7d+dZx3oW/zUwSwb/GuKSSzZy1VXX8Sd/8vtcfvk1xOOn/zI5H3RIh/8bG6XN8CjRkoSWpITPAdNhwPC5uhDFQrDZyjEqfT6VKSHKG/NjOh90SY8oM4tPE4EE+qV3+gPPASmh6JUeNoI5vskuKwieWHTcVq56ZbHfcNhm5c9IBie1JKQlvYZHpW9OK6A9BBaCI6bL+/PmtPr3KMq0wYdzxTxpZ9g75dV8lRvjGic6HTLRJz1esXIcNl2KlGStG2aVF2Lv7h088MD3+D//55+IHmegqZTC37EN1X4Ypvxd3ROsFKz1FyI+8z9xn3kC1deDiMYwL7wE+8prj5G2Y6P4PZ3o1CQiWYSsrUMmkmingCw5mRgUQsAJW/WtdRvwXn0RdeQQsqYOXcjjHz6ALC3Dvv6duE8/Mb0H3Wiej2yYg+rvRZaWI4TAvuxKzGUr8duOgBAY81qmVcoAIp4gfM9HUV0dqJERZFExLyyo4rVomibfwkql8fftIZWd4OXiOOX7HiY0J0K0ei4xO4k/Mcro6A52jm5mVbqOyJqLMJrnBW3H4ohQCJ3LBWpYrcCY+lqVIrDakDIw71Q+wjSxN15D9NOfQadTuJtfIff3X8IRkxjVGsvfgUrUgfIw8sM45atwy1cHzZWUEr7lXeibgwUecS4s3YmQMtjHD+jBfvyDrQHRH08ga+vQnk/hgR9i1DdO9/F0SKUmiccTiFAIo+Y0yVtvIYSUhO76DQC8fXsC4j2ewL7mOuxNNwKgRoYpfOtr+IMDoDU6m0Gn05CaRJeXg5C4zzxJYdFSwnd/EG//XmRpOYPZJCPZOJXxSYQZRmdS6IlxKiuL6e0VDByEZc9KrN2AEqAhVKbJvVvjXvAGJ5E2ZH9D4S0VmLuDNr0lGmeNDlLffgHwfZ/f+q276Opq5zOfeZz771/K+HjwcYtE4OKLFXfeqacJ3KNQKlD9hkLBI2VZ8Bu/oWhqErz6qiAeDzyCs1koL4eyskCp63mSe+5RvPCC4IknxNS5JPX1mjvuUNx1VzCWTz8teOghQT4vIKn5dxsaRgTzY5qK2xVik0aVQ+lIQPyOj88khCcmIB4/phY+V2SzsGWLYNcuge8Hdg0bNmhiZ7GsfNe73kdLyyLuuedd3Hjjpdx774MsXrz8zAcRjOGOHYKyspljXFsLBw7AoUPB384VXtlKcovuIdT+MGamBy1N3Io15ObfjbbfmjpgFrOYxSxOhIGg8gwCkKftDK9beRp9iygShaZduvwwPMm7ihuImhHG8uOUhIsBcJXLpDPJNY2XvyXXV+MbVPgmA9KnUR2rnQYNn0rfpNo//91M48Ln56EM28w8HUdeYXDf05R7mioRZd/oAbYO7uDDy95Hc1Ejy7wQPwunqVIGBoL+KUFNGEmRMtBoylQgYjloOtQ7x34QOg2PCaGYf1w+iJga74Omw7DIUqQtFvg2TxoZqpH0Sp+QlmgROColtUGZCgKpD5gOh8aPkLATMzxzw2YIXyv60gNQGZx3QHrMOW6+I5NFVA0M0FVbzVA8TFUqj3ZdhGFh3/hO/H178DrawAus2qxrr8e69Irzv2HnCq2wBjdjDbyCzI/iFS/Aqb0cFZ+DFAaLilfROr6DMWcEX3uEZIi58fk0xE+ub/3OdvJf/1f87k5EOILnFHBffYnQ7e/GvuLqU55eRatwojOzFebMaeLjH/99/vEfv8i73/2BX5lMgbcTdSvr6N87QM/OXjqyFrlmizJvknA8hPIVzqRLoijKpI4wiUWlCHyDU4MposVRrIiF7/p0bu2id3cfTtalrKmUhjX1JxGiI22jHHr+CFbYoqwpEMOMdY2x++G9JGsSlM4pwSt4tD17iLqxLL/7rhVIQ3Lw2QlatZomgpWvgjlkxGLo0NAMMtjJOGilp4ngo7CjFoVUHuUr+vcNMN49gVaa7HgOO2oRKgqRHcpy4KmDhBIhlrzj1Nv8ShpKCCfCDB0eJpycIoKVxs25JGuT+I5PdjR7RjLYlhZCCJRWMxY2XOWSsOMYZ/DDXrBgMT//+SOn/fss/mthlgz+NcVf//Xfs3HjKr7whc/zl3/5d2+6PYXmW5Fx9hsuQsCoUExoNU1bumjyQlOhTGK+5IjhstsqsN49O6uRForNVo7dZgGJYIUbYp0bJoJkUHocNBx8oNG3mKPMkxQHpdogf0JYlEbjoyk6xwCLE6HRPG1nedLOMip9LARNnoWlBQacdA0hBGPizAoGk2Bb3NN2lrRQhBG4aFwBc3yTvNBkhSahT16BrlUm788XkZvaABw5jmTvlC5fi47TIz0SWnJEarabBa53ojzwd39Fc3ML73nPh6bfr0aHyfyvP8LdthVyWbBDGELg9PXMOKcQAmvNBZir1qDTqcDKwT5GdrvbXsN55gn0+Bj+yAgiHEZ1dyLnL4RsBmvDJedkU2LMmUv4/b9J4ZEH8XZsQ/V2g5RBBEnBCewBioqQVTUBwdfXjdHUgrFi1XQbsqT0zEFwQmDMmYsxZy4A+6wxLMB2Pbyd21Ajw8QTSXpjUfbUx1nbfoSQUYQ206gdrxPLZ0mVRxnecZCyV14l/L4PYa1eG9hQrFiN8+yTyFAjorgUv6cr8H0tKg6sIJxADqkyWYQhsC65HJ3NBN69O7chwlF8rRk+4JFYZhFd6IIVItd0K4U514M5U9n+hkjgKZjLVwYK6XwO1d8XsHpTbJ4sK0eUlePv34O3d9dZyeD+/l4qKqpQoyOBfUZRMbL81AEobxVkSSnhj34K1dUR+AeXVSCrj6kwvR3bUH09GAuX4G5/DZ3LgWmB8gGBqKhEd3bgPP4zQu/9ACIUQuVy2FEP0/BxfYOI4QSO6IZxVMRO4jWBtV3gNWsIa9BgdEDkfoHXotHFb7BDIXAu1DgXvvU2FOeCv/3bv+Cll57ly19+jOefX41haBYvDsjgiQl45hlBc3MQUAfBOsLmzYLnnxcMDkJpKVx6qeaii4Kwtmuv1Vx7rebxxwXf+56gpeUYyZnPa3buDDx+X35ZBH7B88HzNJ2d8O1vS/77f1eUlUF7O8ydC4ahGRwM7BNYoNmVh1eL4YapXahlZbBhg+LRRwVKQSIRXPfEBNxyiyZ5HtkY+Tx861uSrVvBtoMx2LlTsHu34CMfUWcNo1u5ci2PPPICH/jArdxyy0buvfcnbNhwyRmPOdFj+U1DCJyay3AqLsDI9KANGxWrP7bbYBazmMUsfsFw0Wy18hRpY1ogIhHMURZHDBenrJrL6i/iqc7nGcqNYEqTvF9gUel8Lq49dxusMyGMZJMT4/vhSVoNZ0rMoohqwSYnRvg8hSsOmnsjE+wwC8QzafoPv0zWMjFKK2l2wzT4kv1jB3m8/Rl+e8UHuNSNsM8sTJ97VPgU0DT5FlmhaDUdMsInDxwwHC4kjI3E4OicA/Jo+gx32qJO6zyu7OILiQcIa8Fc7zJK1Qb2G5CSCg9FAUGdb1GmjBnztaJQko7J7hl90joInYuYATklp86rAlkDAEbDHHR6DCYnUZkM/ZNjZPu7mFjQgrr+ClZdfyPRQ4fQuRyypg5ZU/u22BUeRbjtQUJtDyC0jzYimOP7sIe2kFn2KfyiFmJmglVlFzPpjuEqh7hXoHh4F0b3i6hQGW7lBfjJZrTWOI//DL+7E2Phkuk62+/pxvnZw5grVp/XLrxPfOKz3H//d/nYx97HQw89SyQSYdIZYyjfh6McElYRleGaN+xZ/KuGeHmMVXes5JkvP0co7RASClEcJx438ByPfKpAKq8JxRTRqVB4v+Bhx0NIS6KVZt9j++nY0oVhSgzboO3FdoYPDbP6zpUkq48Vc8OHh/HyLsVToXcaTSFVwHc8TNskUhTwDnbMZqB1kLHOMcqaypAyEBXl03kmeibJjmZBgJSCeOVMO49oSRTDlLg5FytybBEmP5mnYn450pRMDqRQvk9mJIsdsbDCwfvMsInv+rS93E79yjqS1ScTuqGYzYKr59O7p5/sWA477KG1JlwUJlERx8m5M/yHT4UFJfOoilbQkepmbrIBKSQ5L8dEYZKr5lyGKU9P8VVWVjM8PHjG9mfxXwezZPCvKRoa5vK5z/0pf/7nf8Btt72HtWs3vKn29pkOW+w8gsDb1p4iMieFQhIIH/2p33trqmgYOQs5CsH2qq9HxtllFoggUcBOs0Cr6dDiWTwazjAmglIkrgWXO1HeWYjPUM+uccO8auXoki41U57BXYZHuTZYcY4BFifiNbPA/aE0oam0YUdo9pkOBlBA4R235UuhyaGYc4o04hOxzg3T7FvkhKYgNFEtafBNhIakkoRPQQQfj8gpCtYn7Qy90meRb08HYAxKj0d69vPooz/hb/7mHzHNYx/1zJf+GvfF5xClpVBZTSY1ia819s//A/V7f4Q0Z34tCCkDr93joPN5nEfuB9/Huvhy/H27Uek0eqAf7RQI3f0hrEvOXdFhLl+FNkz8tsMYsUUQT6COHMQ7chiUj6ysBtdFlJRiXnAh9s23Id9ECqqJQGtQw8OosdHAzsIw0aaBjoUxHYXf0Q6+gnwBs7QSYuA3x2HrYZz/eBBzyTJEKIT9zttRmTT+nl0Bq6M0wjKRi5ehOtshnUbU1CDLSrGuvg5zzTq8l57D27Vj2rsXQOVrGXu9jfzKuzDXXwLGmZ9d4WYQbhoVKoFTbAWahvKRuQGQFtb6i/D378XbthW/pztQzUqJ0TQPURYorZFyOiDwTOhoO8Q1LQvJfuHPgnaiMaw1FwQ+0dGzp3+/URwl9k8FNT6GFiJ4Zn0fXAcSSXBctOsghUCFQuh0Cj00gLnuQgr3f4+qkgHmlw2yraeekBzCjsVwkxX0dAtWr9TMPSxQxZppezsBfgOYh8A8IHDX/3LI3DeDZ555nC9/+a/5gz/4M6LRjaRSsGjRMWKyqCjws92+XUyTwc8/H5C8R//e3Q333itIp+G6646NwcGDglBopto1HAalBC+9JFBKUzPF4ds2NDdDa2tAvF5xhSaVEiSTmrlzYenSY23s3x+ojY/HzTcHdhSbNwuGhgJV8q23ajZtOr97snOn4PXXA4/jyNQaZqEAO3fCa6+Jc/Lvra2t58EHn+ZDH7qd973vJr797YfZsOHS074/HIZlyzRPPikpKwv6AYHHckkJzJv3Bp8rM4Jf9IvZijuLWcxiFmeCR2DDYJ9Q3xoEOaquENzeciPNRY3sHN5LzsuxoKSFdVWrKAq9dWn3G9wwCS155ThruQu9CEu8oH5ycZFIDM4uImk1HfaaDs2+xdjYACo3SW1ZI6NoOqVLmQpTFa3g0EQbKTdNlUjw0Wwxr9g59pkOrheix/AIacEOsxBwrRompM93w5O8YueY59lc5EZY7YYpU5Jn7SyuCHJGCsKlX6ZJ6DHm+5AXOV4OfZe5Xi+LnLuZFIpeQ7PEs6ds/ASjwiemBY2+RbhqFTuG9jCUG6E8XIrSis5UD2XhUpaWB2rGOb5FnW/RLV3mqUAdrMvLGVq1jKVbdpHpPEy/DYMXrmTPHTcyUu7zupfjN5cuJaHf/gVIme0j1PUY2krgR6c8u7TGHNtDqPNnZJd9EoRACkmxXYYx2UZ0zz9ipDpA2qBcQj1Pkl10D05sKf6B/ciKqhmCC1ldgzrUit925LzI4Gg0yle/+j3e+c7L+aM/+iSf/as/4eDkbhxVCMYRRa9dzrKSC4iavxq+wm8WRTVJSuYUsziRY6gc9oliYuQJhwWFcJi0Y3BpdIIYgdVCIePQdMlcDNNgrGuc7h29xMqiGJZBZjSLMAVDh4Zoe7mDlbcd22nlFbyZohgF+VQBI2Si/GM+MHbUJjWQIj2SpaypjNKmUoQp6X69B99TWBEL5fikJ/OkBtI4WWeagC2dU0LF/Ar69vQTK4li2AbZ8RyGbTJn7ZwgXDxm4xV8lOdjhYP5mu/6wftMSW4iz0v/9goLrmqh6cK5CDnz+2/O2gZW3rqMvY/txwpbxMpihBIhssNZKhdWUFx/Zk/nolCSO+a/k+8feJDWsUPB3EgYrK1excb6swkR9Nu6SDOL/1yYJYN/jfHhD3+SBx74Hp/97Md49NFXse0zrzKdCbvNApaWRKZIYIuA9BWAC5QgKJ7aeuWh0XBOwW2vmXl2mw7Nvk1oisjMoXjeyvKiLShVkgVT3ryjQvFEKMMc32Ktd2w1db5vcWc+yU9DaQ4bLgKo8U1uKcTfcCDES3YWjaZuiuC1tGCeb02t6hscMBwqlYkEBqRPvTJZ6559hbfFt1nvRthtFqhRJjEtGJeKcaG4xI1ME+nnihyKA6ZDuZIzkpArlMHz/+8bxIqLuf32u6dfV/19eK+8gEgkpv1dh6xgjCqHhvE2v4x98WVnPa/q7kT19yFrGxDhcJAYPDSITk2C1oQ2XY84zyAlf9cOBCBqavG2vIL2XER1LXp4EG0YiKISIr/1CYyFi0/5I6Y9D9V+BF3IB4F2Zyjclnkhtlp50l6esNIIw2QsahN2FS0DkzgJm/DIOGiNSCRwLTAURAsga+tRfd2o7k6MefORRcVEfusTqLbDgUo2k8bv7UF1tiPWX4TR2IScNx+zrh4xZdnite5HGMY0EQwgwhG0EHhtXZgXnYEI9nKE2x/G7n8B4WVR4TIK9Ztw6q48SQFoDm8n3P6TqeLXwC1Zinz3zThr1pF/6Md4r23GXLYCWR2oN7RTAMCY8oQ+7SV4Ht3dndQlSqBhHrKhEZ2axH3q5wCE7/5gcE+yGbyd21EDfYhYHGPpirfVUkJOGaxq30eUV4A0oOAEymA7hJqcBNNElpaBENhXXIXqbMfbsY0brPtJiWs54jQhKudg9IVZsEDzrlsU8p/lyb+WU6tg4s050fxS0N/fyyc/+UE2btzEJz/5OR5+OHj9xI+VaUIuF/zvXC7wtzVNmDP1eJSVQV9foCC++OJjStxwWON5R5cJj0HroJ0TfXylDM6dmVqDaGnRbN8u8f1jBKnjgBCa+iBsnkIhIE1NE269VXPNNZrJyaDt+BuYWx0+DFoLIpFj1xwKBe23tp4bGQwQjyf41rce5IMfvI33ve9m7r33IS666PQLY1dfrWlr07S2BgS66waq6RtvVJSXn/awWcxiFrP4T4EwgnmezatWjjL/mKXbqFDEtaDBNzGkwdqqlaytevu8zQWCpV6IpSeIRLplJ8+Efs4eawemtljnXsQVhWspOsOWn0Hp4RMojqU0QATBw2Ek49IPdicqhRRyegt3hTa5uZDg5gKMCZ9/io7x01Bmyi5C4EwRvTmh6JYecSn5fniSIemzwLN5xs5hAJ6AjMhiM0RUl+CyhHo1SIkqpct8iZsKG7g2tZqvR8bpNDz6pYeDRgm4rBDhNSvLq/NqGa94D309+7AHOsknEsjGdVxUupjRcJJKXxNCcEshzrfDk+wzHEwEPpqGZCUrLnwH31vYQrEZxqyspkIIkr5mt1lgi5XjKuftEwQchTHZhiiM4Zcct2IsBCpSiTm2D+Fl0dbUdWhNuO0BjFQHXslSQCNzwxipdiL7v4674i+m7ehmYGrrzolE3rlg2bJVfPGL/8ynPnUPyXkxrrzjGkrs8umt/WOFYTrSh1hcvOoNj8GvGkobS5nsa2ejMYTWknYdY1DbiASsEBMs6e1kRHlYIZOGNfXMXd8IwGT/JF7ewwuZ9O3px80FGTae47Pv5/tZcGULkeJglb64rpiOzV14jo9pGyBBGhLl+tOqYADlBcSwPaXsLa4roqg6Sf+efsywiZd3EVJQNrcUJ+9w5KV2tK9IDaaJlUWpX1lHpChM/74BChmHZHWSposaqVwY7ICsWljJkRfbgmsveJghk4neSXzXJxS3sSMB59L6xAHsqE39qrqTxmvpDUswQya9u/oopB2cdIHKhRUsvWEx0jj7gsqqymXUJ2rYO9JKzstTE6tiUekCbOPM4jTf8dBK0/ZyO5GiMOXzyk/vbzyLX3vM3vlfY5imyZe+9C9cf/1F/PM//y2/+7t/9IbbyqEomwogOGK4pITG0BqPYEG73DeIaEEWRafhUu9bLPXOTj63mg4WTBPBR+0mgm1QiuUqPl04lmmDUe2z3crPIIMFgovdCMu9EB2GiwSaPOuUKtpzgUYzJH1iJxxvTXn7Xu5ESQmf/ZaDB6x3w2wqxKg6B+LZRPC+XJIfRlK0Gg7DUpPUkhsKMS49RdCdniJTxHFE+UHTJScU1b5JtTIwERSEnsG75CZTdH3ze9z2mx+d6RU8PITO52coffudPABVSqPPdduIEFPM0dT1hSMYDY2oyQn05MQx39yzNeOmkZNH0BNp/PYDaNNC93YH11g+VTTZNrK0DJ2aQHV3Yi5aclI7fkcbhR98B7/jCNp1kSWlWBuvwd50wyltFVa7YfYbBV4pilKYWwnRGGEFl7f2ceHhCVrLHCar4oTGs/hhcCJQNQrJDIG1gpAzkrWEYWC0LDgHPcnU+y0LrdTJf1Cakwxaj4fWRA5+h1DXo6hQGcouQmYHiez/BgBO/TFfM2PiILG9/4pwJvCjNaB87P4XkPkhWPUHGPMXkvvnL+MfPogWAqUUenwMc9lKzFVrznj9Xft34ymfOdW1yPLKoE9l5YHK5/Ut2NdeD1KS/8b/C0LcpvbDy/IKQne9H2v12nMcqfODuWI1RsNT+Af3Q2UVsrwC1d8b+Az4PkiBLC3FWLEqIMClJPzhj+O37qWxp5tPGzEOiHomKKakRLN4sSYSAXeRJvycRJVrjn4tyGHQCYHXeIr7+CuOP/3Tz2KaJn//999ASsmcOWCammyWaTsEz4NMRrBoUdC/kREYGRFUVs6cLJWXQ0eHYGCAaTJ45UrYskUzNhYoXLWGwcGAwDUMzb59wcS5ujp43I8SvUd9f9ev12zdqtm/X1BervF9GBsTLF+uWbFCs3mz4LHHBAMDAsMIFLS3366mieI3Ass6eR4IgZrZNM/vHkejMb75zQf50Idu5/9n773j67rr+//n53PW3dLVnpYs2/LeK8tx9t4JEMIIgQQoBAqUltFCC21/tIVS9hcIkCYhCSEQSEL2nk484m1Zli3bkqy9pTvP+Pz+OLJs2bItB4fSolcefsS+99zP+Zxz7nif1+f1fr3e//6ruOeehznjjPF9E0tL4ROf8AP09u0TxGIeCxcqZo1vNTeJSUxiEv+rIBCclw3RqNns1GxylSQtFDaKCzNhyo9TO7ePWMU5Aqpdg2r3aKu4PwYdso07wt+jWdtHjipgQCbZpd/Pbm03n0h8lgDjixoiyl/sdFDE45VEI4UMDrSh4qVElY7jOnSmujlvyirCxtG1fVxpXJaJsNFII5QgiKBbuKSER47SSUiPPKXhKXjTSFHl6Mx0TcJKYAO79RaU6iLBNDbqM9mjKomqBK5I0aYdYIm9go+n4qwxUuzSs0Q9SY1r8JA1xGYjgwYEC4qQ8Tx65iwn1xGUaGH2ajo/Vv1cnYlwXjbMXMfiU8k4W/Q0fdKjyNNYZAdYY6bomlJOwWE5KRaCIJKteuZPQgYj9REC1wVx2HvIc0AzUYf5qIpML3p/PW6oHJEdxOjZhEz3gnLQB3YRjvyU9Ow5ZF9+ERHPYzim0RmH4cwgoeJKKqcUMHFd8CFcf/37eHXtC9z5zTuYPmsmcxbn+lMXkqAeojvdhuPNRZcn7iw9WaT6U/Q196OUIj4lTij3nQ+jqFxSQdeuLhL7Ojk/PkS7Z9Gf8JhaE+O8q2cw0JqLk3IIF4bJLc8ZFfZouobneb4FRMYhmBtECEFqIEV6IEPjmr3MvdS/9yueVUThjAI66zsxw35woRBgBA10U0Mphed49B8YIFocHfUVFkJgRkwKpxeiWxpKQSBqEYwHadvRwfY/bMeKBDCCOp0NXbRubWPuZXOYfvY0f045QaR+6L4vtyKX+VfNI3nXWvqa+v0OadshmBNAapJYaYzc8hz6W/ppfquF8oVHW6bols6cS2dTtXwKid4kZsgkpyw2ISL4IAqC+ZxdccaEtx9sH2T/hhZc22P7k3UIBHlVcRZcPe+o4M1J/GVgkgz+P4558xbx8Y9/lv/6r39l9eoLWbRo2dsaZ6pn8jppltgBYsr3BE4Ij6gSnJUJkYukWXPQEMx0TK7LxEbDzY4HQ/n8l0LRIh32aDZJ6dEhHSzPT8c9fBwTwbAY/8Y8qiTz3qYtxOEQCCpcg01GmpLDHs+gkMB012CRE2Uw46Lwg+FOVJy6KLbqGbboGdLCY6Zjck4miEBQ7OnkHXGuuoTDy2aKLUYGTcESx6LKNXjUStCs2bhAWAmW2UHm2xZPWQniaFgIFIrX7rsPN5Xmkzf/1ZhxZeUUZE4Onp+wBEDHiBq0KBpFm2DQgays8hWyzU3IaTN8Vann4bUdQJ+7AFlccvwBlMJsexntrXtJvbyFbPMwMungDOsoqwAMfWRMBZ7nh9JlMnh9vUcPlRgmdc8v8HbVQTQHEY6gMmmyjzzkk8Irj/6RtBC8L53DIiGp37sB1bKNGWlJbc8wdHZSm6/Tcc1ihsReZFcPVf1xKrp8CwjvQDPa1BpkZdUJj1Em2xBuBjdcNsb2Qc6cAy8/j9vbi5bnFypeXy/CNNBnzz3WiMjEAcyON3BDZaiAr4J1zRy0wb1YLU+TLV2FN5RA9fZgdjyBSPf4KoiRAsQxo+j9uzC630KVrSZ46yfIvvQc7pZNfovR2edhrj7vhDYPDVs3AjCtrHLM4yIWQzXtw+vvw1n/Jk7dNrTpMxGm6Rdo+xrJ/u4B9Bm1oyrpUwkZzyPwodvIPPIQ7p4GZM10hGWBpiGiORAIoNdMI3DV9aOLBMIw0OcthHkLMQH/G3IsK5g9X6HvUeg7BSqqEBlAQPpiD++dz86bEBwHbNu3Hjhe99eaNS/zyCMP8t3v/pz8kfDIuXMVS5YI1q3zLRKkhKEhQVWVb/Xw2muCcFhhmr637uH+uamUb/UQPOw+Y9Eixfnnw0svSdrbfbJ33z5/TKUE6bSitVVQVQUzZigGBgRz5yrmzfPPe0EB3Habxwsv+NYRgQCcdZbHuecq9u+H++6T2LaipEThOH4I2+Cg5K//2iP6Nt9Ws2Ypnn+eUQIbYHAQpFTMm3fy44VCIe6663d86EPXcfPN1/LIIy8dM1QuP58RW4v/fXYjk5jEJCZxItS4Jh9N5fKakaJBz1LuSlbYQVbYgXHrZ4XiVSPFo4Fh+oXv+R9SgrOyQa7JREdt2v5YvGG+QpO2l6iaTYPmkBAeHmF+H1hHtbOW6zLjd3XMcSzKXd/zuMoMMnPWBby56xkGhI0Qkq1eLwvypnNJ9bnH3Hehp1HpGuQoDUsJXjaThJXEFQqpfPFISAk6pEtKKgwFU0fI137p0qhFGRJRwipJRKXoEHES8nR2SYtzSVLshbgmE4WM37H5tUgXb5lp4p5GEEkSjx4DslhM0yxmOgFw/UDqp6wEC2yLAqVT6umUZse224xESB91TB4cFYL9TsHJnYUbKkMb2osbm+4XPm4aLd1Nauo1oI9D5HsuRp9PBHtWLiCRqXbMtleJzH0/A60z6R7Yx+6peWSCGnrEYrC6ir7MVmYlJSWhyqPHPA7SborbPv9J3tryBv/yqa/yz3f8B9NmH7zPemfOk1KK5g0tNLy0m/RABlAEYgGmraqhasWUd9QeIKc0xqJ3LWTfG/vp3d9HhemwfEkJ1SursMImwdj4iyv5U/PQdI1ET5JIURghBK7tojxFrCxKR10ntefOwAgYGEGDhdfOp3ljC+07OlCeomrFAtJDaTp2dtGzrxcpBTmlMeZeNnuM924gFkAzNfKqDlH7nueR6BzGilmU1eSNnp/+AwM0vLSHwhkFhCNhlALbA10cqrErFpUTr7iIrY9uY//6FoY6BglEA8RKosSn+IWkGTZJ9adwbc9XMh8BIQSRwgiRwnfeLsRzPeqerqero4NwMEJBdT5O1qW7sYddz+9m0Q0LJu0j/gIxSQb/BeDzn/9HXn/9JT760ffy5JNvkJeXf9JjLLEDrNd9r6uwkkSVxAVylEa50rkkHSaChgEk8HjeTPjBECh0/AC0BU6AFdnAGMXuPCfAWjPNXukn3IJPEIc8SUYoNuhpzrRDmCPtSQnhMcN9+3YXE8WZdpCdeoa9mk3hiCK6XTrMcSxmjSieJ0J2g1/UPmwN86yZxBMKDVhnpJnlmHw4lUv8iHH6hMvPQgPs1rLElYYnFL+1hhiQvjp7mmtiAAPC4yUzyaWZEAscix16Fg+/RX7Pj+/k7KuupbZkbLu/zMlFv/hK7HvvxG09gIjGaOvtIU9KwstPR5+3YELHJEwT65obSN9zJ+7O7b7fruuglVdiXXndCUPO9L7tWJt/Qf/jdWTabbS8HHSjH2+oj0zLECoQAtOCxDAiN44oKob9e5H5hwLKlOPgdXbgbNmE88areJkMomm/39IViSJycrHXvDIuGQy+0nuRjLFgwYVk9vwaZ1cd2DYyv4Cy8y+lct5ZpPfV47x2F6J5B0pquJ6LLCnFuPI63IZ6vNYDiICFNnuebz1w8DwnWgnufgC9bzt4Nl6whEz1FWQKTsN+5UWyr7zoW0rsbsDNyUEEg4hACOPSK9BmH5t5kqlORHYAFR4rgfQCeYjhLrK/vYfshi2o4SHs4TqylRahi2YgQyNEtDTw2+N8BbgsKibwrptQ171npBVuYqvRu9oOENYNSr0jbAAGBiAcQQRDOFs2IgsKR0MHhRDIyiq8xgbc3bvQF70z6mBtSjXBT3wGr6N9JDgOnK2bUT09yOJi9IVLkIVFJzWmWwmJv/Iw1wj03QIVg+wyhb1EjannRQKMdQJju0Bp4CxQZJcq+OPXqI6JVApeeknwxhuCVMoPXjv3XG9cZanjOHzlK59l6dKVXH/9+0YfN034wAc8pk8XbNggyGZh+nSPffsEDzzgE7gAPT2KREIwdaqiqsoXybe0CJYsUZQf1gGnaXD99YrFi1127hT85jeC4WFBNOpbSfT1+arjAwcU4TBcdZXHJZeoMYRyaSncdJPCthVC+HYNAL/7nSCRgJkzD207Y4Zi927Ytk1w+ulvj1CdMwfOP9/jhRd8Aht8m4hVqzwWLXp7YwaDQX7xiwe5+upz+OAHr+Xxx1+jsLD4bY01iUlMYhL/m+EreyemgGyWDr8PDAGCmSM1f5/weN5KUuUZLJ9AOPVE0KDvxCNCnW6P2tt5hGjRHB4KNrA6eyb549T6USV5XzrGg4EhmjSbnopqKH0v8WyKoANIkzKKyHGOrSct83RmuCYb9TRVroGmYFB4DEuXiKexT/rh2S2ag64UbTLJsGgmQhapQgwJC4VNgddHkCT9spWEiPFwIEWn/veszJ7GRZkrCKkwDZrvcRz0JDkjx6MjaQMkij55KOOl2POt8PbqNgX2+DTBdMckaPlBeAfFLCk8sigW2O9gwXMYlBkjPeN9BOvvRO/bhp/cLMkWLiYz5bKx21p5OLkzMZueQGb6/KwNqSMzfahAPl6ohKC9lfTH/472xsdw7D7yVAitqBSRk8Ngto+9Q/UUBEoY0Pvpld3kenkUeuP/nnvKZe9QPQeS+0g5CT70b7fw08/9hK989O/4lzu+ydSZNaTdBBWhmlOuCu5v7mfns7sAyKvyCcnh7mHqn2sgWhQhf+rJcwAng3hFLrnX5+BmXYQm0PQT3ysHc4NMWVZJZ30nmYHMyKUURIujxIqjeK4atX0AsCIW01dNY/qqQ0HXSimGzxhmqGMY3dLJq4ofZX1QMruY1i2tDHcPE84Po5Sid38fnucRnxIfQ4RGCiMMtg8y2D5ERzSPl1okTYMQteCMMsWZZQpDQrggzGm3rKRycQUbfrWRnLIcrOihz0B6OEN+VR6a8T8f5DvUMeT7Mw82Mr3Cv0nQTY1oYYTuxm6SfSnCeSdIS57E/zlMksF/AbAsi5/85D4uvngln/70Ldx99++REyR9DiKqJB9O5fK4OcwvQ4OkUcx2TSpcg2bp8JvgMJ9I5tIhXe4NDjAgPNqkQ5d00QVMcQy26hleNZLMGVHvVrkGsxyTs7Ih7g72+/5hI1YM8xyLQenQKF3e0lMIBF3SpdTTyHMlCnVKW8WOxFzH5KZ0jGfMJJ3SwUBwZjbIlZnISacO79VsnjMT2ML37FIK8j2NHXqGNWaKyzIRFAob/wO51kixR7OZ6ZqHQuokbJNZKlwdc+SxXKUxrDy26Vk+l4hTp9u0aQ51jz3B43v384Uf3j/ufEK3f5a0Jsk+/jDe4CD7XJvKgiLC//SNiR2Q52J2vE44/TKx0wdJtkbJapUwdSn6oqXIgsITDmG0v0G2sY1Ml4dRWYDQNSCCZSpc28UesqGvF1FegVZdg9fchFYxZdS+wNn8FtmnHsNtPYCzqw5vbyOyohKRXwjKtztQiWHcltITGuXLomKcm27mP/7p73jytWfo6etl7obX+Od//jaLFi3Du/3zOJvewuvuROYXoM2YRfbZJ3DeWj/qsXuQVNUXLQUnRWjHT9D7duCEysHTkck2gjt/QaJ1LZmXNkMohDZrDl7ddtym/YicHGTFFLw9Dbj1O9DnjK8gVGYUpQcRzjDKOCSBFPYww1t6yOx4FpFXhCyfgmo+QGpLI56xg+hli/xzoPxiyldEHILQJmpw4aNhfyMzKqpQ/b14QiBycn0bj+4uzAsvRebm+kzhkXYhUoJSKPfE4ZJ/DISUY7yJtfKTU3OMB68M0tcfW70pEhD6ucTYAsoE4YH5lkCvEyQ/4ME7sIblefDAA4JXXxVEIr4q+K23BHv2aNx6q3sUIXz//XeyY8dWHn/89aN+A0Ih37/2/PMVvb3wrW9JkkmYPh1aWxWbNgkGBgSmCevXC+rqfHuGefMU7363d5QaWQiYNg36+2FgQBAOCwoL/XNXXAy9vYpAwB//ppvUMdXMh7umDA76KmDbVtj2oed8oljQ1/f2lbVSwrXXKubNc2lo8AMma2p8u4aT/HiMQTgc4a67fsfll5/Jhz98A7/+9dMEg+98u+YkJjGJSfxvxQ49y4DwmO2ao7V+ntLoUS6b9MyEyeA0HhmhiCg5rmI15uXQLpPYqFHSVwBhBYPCZKuR4ZxxLNzAzwD5XCKPDXqKO0MD5MoYM4wCpAH9wuM16VCTSnGmPf7rJYLr0lESAY9G3WZYeLSNhMqBYq2RJiN8crZNa2efNoQjXAJqAJcgDjnkqnZS8gD9IsWwGCLsBYBS+pE8GvgNfaKXD6U+To/01dXaiLfxwXOqAVmh0JWgQzq0S9/+Li0gfZxOlemuwXmZEM9ZCTpx/bwPYJkdYPkx8lM8PMTIf6cKdtEy3EgFRvcmhJPADZdj5y8E/Yj3hxCkp16D3rkOfWCXn7GiXNCDOPG5KC2AsIfJBBySZXGisgz9sG6+sB6lx+3ibvOn7AhvZVgMEVJhlmZXck36PYTVWFVn0/AeGofqsLQgcasAlaf4yLdu5Y6/+Sn/cNvn+bsf/T3z5i5kSuTUB7121HeSTWQoqDkUOhAtitK9t4eO+q53nAwGX/xxsh60NWdUc2DzATJDGcyQiRmxCOYE6Gvqo3h28YglxPH3GS2KEi06dntY4fQCZpw7g72v76Vnn99pagQMcspzRz1+D0J5CiEFO4cMfrtPY9iGuKVoG4b76gTdSbih9tBnpGReCSVziunY1UXEU+gjoXNSCqYsq5yQ4tZzPVCMsaM4lXCyLnbWprFtF++Z8+HRx6Wh4Q1nSGdcUikI6RA69c4lk/gzxSQZ/BeCiooqfvCDu3j/+6/ie9/7Nz7zmS+f9BhxpVGsdMpdg1muMRpYlqckdVqWN4wUu3QbW/h+WimpKPU0+qRHh+aAq/GolWGjniZPaUgES+wAN6WirB1J981TGnmeRp6SZF2DQTNFi3QRQpHv6WhIfhUcwk7DqmMUWKcCAsFyO8hCO0C3dLGUIG8CdhDjYbuWoU7PcjBIWQGmsol7kk16mmrX4CUjSbNmE1Ua3cLBgjFtcAJQApJibHEWUJKk9JAIljkBcOC//99PWL78jGNagkhdJ/SpvyFw8224zfvZ8IErufLq9yBzcid0PNb+RwnueRAlJCo3Qm6wD2W4JOdegl1QiEKxQ8+yXk9zQNoYCGY7JiucIEUj3nBaqgM74Z8NMbpqLJBmAH1mMVilaMWleN1d4Djoc+djXXUdMi8fp6Ge1D2/gGQCWVzqG5HaWby+XrRI1Cc2c/PwmvYiDGNCP8Cf/+xt7Ny6kf98362UFJXy8O463vOeS3jxxc2UlpZjnn/R6LaZpx7DXvMqWmUVIhL17TH2NZL+zf2EqmuwMrvQ+nYy3BYlvW0nbm8CLTdMsCSLU7cXUbgQWVSCNzToeyPrGpgW2tRpeE37Sd/zC4Kf+hu0sqMNUN3YNJz4XIzOtbjRqSgjgsj0wmAXyb0CkZs/atEhymYh053YO3fjLq9CzwuiD+3zi+WCxRO61uCvtnsHmlE93YhYDrJqKjt3bmfuitMxz7kU583X8NoOIMJhzEuuwLr8GggE0KbXYq9dg4jnjSqOVWcHIjeOVl0z4f3/OcHraMfZshGvrxdZWIy+aMloUKGxTmBsBqeGUSWwSIC5DuylAvttqkuPh717YcMGSWmpImfEBjwvT7Fhg+BHP5JcfLGitlYxbRoo5fKjH/0nV131rhPaBe3YIejshNpa/+NVX+8Hx8Xj/p+pUxW7dwsWLFDcfrs6rs11T49PWh/OPQvhq5F9D2FoafGtKBoaBLEYLF+uWLFCjaqBlfKff/xxwY4dgp4eQVeX7+lcUuJbZIAaE0znurBxo2DjRsHQkB9Kd/rph3yJx4OUvuJ45sxTe63Kyyv57/9+iOuuO4/Pfe42fvSjeyZb8SYxiUlM4hjICjVCMI79njyeVdzhSOIHTq8z0qSFoszVOScbYpFjjRlzib2S+4Ov4YoelCoEFIOimaDKJ8ebw+Ax9tUtHDo0l5ASZKQ/11rXHL0viiuNPjzWG+ljksEAJZ7Op5N5PGe2sDvchKUAYdIrI2RFAAPI8RwScgsFyiSryqn0snj0s0vLUuvkUKiWs0PfQoAAIVWOJyTlXiEZ5bHRXMsC+yJ6ZTGWB6bwuwpz1MHQaYWDS6vWwC69CUe4oIoIqXxeNgwWONaokvhwSARXZSLMdEx26llsoZjmmMxzrNEMmINo0Ot4yXyWRr2BHC/OadlVnJFdjcH4hUOjluUVI8Ve3b9POmgnciz7CS9UQmbKJcc8xwfhxmpIzv80WqoThUAF8vDC5XhWHL2/HjtvLui+fZ86gghXKNblvsnu0B7KVSVV7lQG5SDPWU8CcGPqQ/hnVOB6Dq3J/RjSJKT7JHF+oBizxOKvv/MZfvTZH/Gfn/x37v3VI0SKYiec98kim7TH9ZyVmiSbyJzy/Z0qhPPD5J4+k1fW9ZBFUuxlKdzfSzQepOaM6gnVTEophtqH6G8dQGqS/Oq80eA58AnjaWdOpWRWEf0HBhBCEJ+Sy/Y/7KB9Zydm2EBKifIUQx1DRMtivJmOknAUtfFD++lOweutkjPLXUpHXPU0XWP+VfMwn9tF154eMokMoXiQqadVUzLn+B1hmaEM+9c10ba9Hc/1KJxeQNWKKqJFp9Y6IloUodftJGOnmVFxKH8n0ZukqaCMtXuj9GY1grpiRYniompFaJIp/D+PyUv8F4Rzz72Yz3zmy3zzm19j8eIVrF59wUmP0SlddBgteMAv1oJI9mhZOqRLkadRr2XJomjVXDJCkcEPhTOVIIJklmuRHCmUZrgm822LlKnGWEAc/Dku8zRWOgG0EUVui7R5wkow37HInaBVw9uFiaDsiHALhSKNQkOMqnSPhzojw4B0qXR1jJFjGBYeBzSXuOfwi+AAw8Ij7kk6pEOdnkVXUOMao0VrWAkk4BtBHJpHr3RZ4FgERrbbt28Pa9a8zA9/ePcJ5yVjMVqiEVrbWzn99FUTOh8i3Y3V/BSeGcMLlQLghUrR++ux9j+Gnb+IV6wMvw0M0SodOqVDQvipxPMck+vTUS7MhnGiVUjLAQXK83yy8KBqNQvGyqUEbvmYH/4lNWRJ6Sih6Kx5FTU4gFY72y8OAkGIxmBwEK+nB6cgn56AhpUfp3T6zGMey0EkOtp4/ImH+dHys1i0cyfU1fHxeB7PFBZz990/4Qtf+Pqhc64Uzto1vg3FiOetkBJZNRV3dz3uzh3IsgFSO7oZWrsPPIUMW9itfWTrBnAzLnKmH/LmtbWiEgkoLIZ0yr8m02bg7tyOs2HduGQwQpKa9SEA9L4diOH9KDOHVHwVttiOjB4KBvRCxVC5FLV9LaKtAd3Kw8mZRnrGTahAwdFjjwOVGCb94P04mzZAYhiCQVI109mxYwsf+MBtBK65Ae/cC1B9vYic3FFiFMC88FK8pn24O3cgIr6Ps9BNzKuuReZPbP/vJJRt4+6qw2tv8wMQ58wbM/8j4dRtJ33PL/A620Fq4LloLz9P4EO3oVVNxdghUCZjLCFUGHAE2h6FvejUH0N7u28NMWXEDcbzfKuEpiY/jGxgQBGNwgUXeASDT7B/f+OEvhtSKQCBlL5KOJmEvDxIJHz/39JSP/Cts1PgeccnTmMxCIUUg4P+XA+KYv3QOCgvV/z4x5K2NkFOjqK11SejW1t9qwkhYNcuePBBP7Bn4ULFW28JurognRYsWuR7DldVKebP9+eiFDzyiODppwWe5xPP27ZJ1qzxg+bmzIHwnzgnY9GiZXz3u7/gYx+7iTPOWM0HPnDbn3YCk5jEJCbxvwQVro6GII032o130Cpu5gms4jwUDwQGec1MEVd+uHWDnqVJs5GpHBYeFkC92F7O8uxlvGg9Q7fciQBCqoBa+1pSlJPvjSXVbBSPWcO8ZqYYFJ6f06H84Gt5xP2ApfzckxMhK4Z4NvA7emUlZWqYrArQKYvQiVLuxunXkggcirwYPcIlqlJMd5vpkS4tWpiZmSKkLrBULgkRosptJUAWqfJZp5XyvXAKSw3RrbmkUJgjnZbDIkNKJBB00aq1AQ6GCmExhMZLvGbVstR5L5dkxyekJILZrsVs99i2EHX6Vn4e/iH9oo+4l0ezto89oXo6ZBvvSr//KLK/Xsvyi1A/fSOEdbfuUK9n6ZQOV2f++JwJu3gl6ZrrMA+8iGfFUSi0/l0ozSJbcREhPUaumU9nug1TmgghUUrR4R1gb04jpZRT4Pk2Y/leAQmGeTD4S9Ybb5Cjcllun8HK5BlkvQyGPHRehBBE9VycfIc7f/kgt3/ow7zn+sv4wQ/u4uKLr/yjj+twxEqieK6H53oIKUD5vriu45JTlnPiAf6H8Ga74Lf6VFqnl5EeTCNshwWVNh890yKv8sTz9lyPXc830LS+mWzKBiCYE2TWhbWULzjUKZhN+paUJbOL0QyfQ5hx3gySA2l69/WO5tWECyOUr57J79s1CgJj69z8ANT3KVqHBaXhQ88Fc4MsvG4Bqf4Uvft6GepKMNA+iNqgKJ5djDWOutnJOGz+/VY66zuxohZCSva+sZ/epn6WvmfRKQ11M0Mme5LbMHWLPK+Y4e5hsoksDeEiNhVOJ5yW5AUUSRsebZQMZD0+OPvYnXuT+L+BSTL4Lwyf+9w/8NZba/nkJz/II4+8RM0EA8MOIt+TOKgxNg0+OepR7Fl0ai42ChfFgHTREVhK4Pq/R2SEGl1pDyEJINikp7k4G2abkWGfZlPkathCsV+zAcU01xolggFKPZ0GzWafZrPI8b/IHRR9wsVCTNjL9+1gr5blOTPJHj2LrgRL7QDnZUPH3Wev8AgqQVb4fsgAloIO6dIrHYSnU3uwuFWQdH3rhybpMGWEiE4JRYmr4QjBAekQUIJe4RJRknOyodFr8cgjDxIKhSdcXKxZ8wpCCFauPGtC22vDzch0L0589pjH3WAx2nAzw9lOnozppIXHkPQL5XxP0is9Wp0kDw91U7i5g4WZKOHqKsxNG8jubwMpUf0DuFkdVVSMNX8RQtfRKqYcNQe3pcknY0d+nWRBIWpwAE8p1i+ZwSsXnU5vUR6mB0umLeIq4R61aKCyWT9UTNNIv/Q8rvIIlJajT5/lK2Gb92MODfHmay+N3bnnodIphHlY8evZyFQ7JNvROrfgFtaQ2tyFkEH0Up9c1HLD2Kl+vB4HMdCPyC9AJYdBkwjXBU0fVTEL08Lr7jzmNfCCRSQWfR5tcA8iO4QXLMKVccST/4Q3OIAWOVi4CxyVBxUrSJ9+I9myCtzYtBHf4Ikh89jDOK++iCitQFRMgcQw655/Ctd1Oe00fwFB5uTCOKpyrbqGwF99BvvN1/D27kHk5mEsXY62YOKq5HcKaniI9C/vxNm8EeU6oBRaSSnWjR9An3u0b7bKZsn87teo3h60WXP9YtF1cRt2kvnD7wl+4jMoA4SCtAObOqChT2BIWDAE09+hQsoPi/ND1AwD2toETU1gGL5SeO5cRV8fPPOMZP/+H7JkyQqWLFlxzPEcx1cb9/crhoagvd0nVsEnbjMZQWmp/8CI48fo88fC3LmK2loxOubwsCCbVaTTsHjxwf0IZh9WcPb0wKuvSlaudKmshA0bBMnkIZ/ghQsVDQ3Q1gb79glWrfJJ3oPhcS0tfnhdTo6ioMBXINfVwbPPSrZuFSxcqFi92rfEOJ4FhD8PwebNAilh2TLFWWcpIm9DrOF5MGvWDVx44fN89at/y8qV51Jbe+pbRCcxiUlM4n875jgmCxyLjXp61OKhX7rUOCYrs8e3iNin2Ww0M1R6BlHl3zvkuhq7ZZaXzCQLDlMHa2h8PHkTSbGQdtlIvrLIcWfQJ2PUuAYLnLGWB6+YSZ6wEuR5khmeQUootmkZuqXLNFyi+D8oCsWgcFnpjm+ZcDjeMtbSpO0louYSUQKJIqMEfSJFSlgopZAjSmkAgYdEMdXdRrvMpUUTJCjBkRkq3R5muE0A1GmldIsiapRGtWsSUZJNRhrTE+R6iozWRI23jSHtCdopwFKV2KKbXAUxPAa1V3ndPJtLsicWVYwHheI560kGRB8znTmj57xHdrPGepkzs+dQ7lWO2f5ZK0G/8Jh5mBimSzi8YqZYaQcp8f5I2kJIUjM/hBcowGx/DemkcHNnkKm8BLtwKUIIaqJzSLlJ+rLdHJQluSGFYVlEvUOkZEIM02DspEO2UugWo4Tit4H7aJQNLOqZT9ZJY2mHrr/tZdCFQXF+GQ899Byf+tSH+PCHb+CLX/xnbr/9b09Zt1DJnGKaNx6geWMLTtrBdVyEkJTMLqJkzgnCvf+H0J2C3+4SZFxYVGUhhEXChv1DsNVTlEwgXHfns7vY9JvNKE8RjAeJFUdxMjZ1T+0kVhLFiljseaWR1u3tuBmHYG6Q6tOqqFhUTk5pjBXvX0rHzk6S/SkCUYuimUXIWAirS5E5wtku64EmBQH96MUeIQQ9e3vZ+Uw96eGMrzRWirzNB1h03QJC8bGdAp0NXXTt7ia3Mhfd9N/f4bwQ3Xt7OLCljdpzT12dmM1meeT5X3PlJe+iYnYFiZ4Esco4r+fOJmwGqR4RqsdMCOiKtzok51e6VJz6vO9J/Blhkgz+C4Omafzwh3dz9dWree97L+fhh1+kpKTsxC8cwSInwMteit3SptzzjQxaNYe40jgnGyIrYJ2RQgAuYClFVggMJXCEh0KQOczqQCpwhGK2Y/LedIynzQSdmoumYKEdIKJnj2oNUvi2CQdX4TfqaZ6zErSOePsutANcmgkfFcz2x6JZ2vw8OECHdCn0NNJC8QdrmBbN4bZk7lGtUQcRVpIy16BPunRJd6QxSxFVGlIICo6YZ61r0Cod+qRLUigEihylcXsyjoNirZkhJTzmuhbnZkOjHswADz/8ay666ApCoYmtJK5Z8xJz5iwgZ4IWEUjTJxPdzBhPLuFmUNKgydRHvMl8v7GCEWuNwp5+CjZuJRkNs6FpL9Of3Ui2Io/QOaeRue8F7LYhlDQgFEW4Lva6N9AXLEKMcxyyqBh3/77Rf2tTqlEd7WyeNYXffeQ6pG6Q1z2AXVHOy1V5DHqDfDyZi47AbdxD9sVncBt2IQIBtKUrsDZuYHFBET/csIbpJWUUhCI8OtTPpq4Oqo/ofxeahlY7G/uVFxBFxUh7EKNrA6q3Ay+TJTL4ON62KThJiRFN++dJagh7GKMoQmbAw21sQIRCiHAEshk8x0GvmY4IBFFKobIZZNEJCjYhcXMOLeQIwDhrNdmHHsBrl4jcOGp4CK+rA2PVuXizz0edZKHpDfTjbFiLKCj0PYABIlHWZtMUBoJUWye+ydHKytGufffE9+l5dHd30traQnt7K8lkgkwmQzqdwnHsMduGQmHy8grIzy8kP9//f05O7oQK6uwLz2KvewOtugYRCqNcF29fI5lf34f2t1NHVd+j82rah3egBVl5KIlZaBqytBx3TwOqswNnfinOG4KfvilY3+t/T6kMPOsJzjsAVxxhlXAqMGuWoqJCsHevH+rW0QG27VsvlJcrpIT8fGhsrOOtt57jBz+465hj7dsHDzwgWbdOcOCAYHAQtm2DoiJ/zAMHBPn5iqoqhedBT4/g7LM9Aid4G0SjcPPNHoGAZPNmQWur7/eblwfgWz+Ul48t9PPyoL5e0dQkqKxU9PYKTPPQNuXliuJi3z/47LM9PvYxNXpuk0l49lnB/v1+KNxBtXRLCwSDikzG3+a3v/X9j885Z/ybjL4+uOMOya5dvmLZdeFXv5K8/LKiosInkZctUyxZwglVE0NDcP/9ki1bBFJ+C017gRtvvIXf//4FpkyZLMMm8eeHH/7wh3zzm9+kvb2dhQsX8v3vf58VK469kDSJSZxKBJB8MJXDNMNgvZEmK+D0dJBVdnDcQLfD0SFd0qhRIvgg4kqjVXNIowgeVq/XuCafSM7hSXMKzZqDJwTLbJMrM5ExY9goXjNShJSgUI2QNsrvenvV9AO2yzwdA0Gv8CjxdM48AXENsF/bS0z1kibNoAiTo4aJqDR9IsqAcKl1A/RpgnYBGZGlS3RhaGkyspmbU4ppTh7PWxpvmM8y1RMEiNOPS4MepNDTmeoWoCGo8AyCWUG39Dgte4CXAvdQ5RbwlN6FSR8RWsioIdLCoMhbRa9sYFi0Am+PDE6IYfZrjRR6xWMUwHlePvX6Dg5ozWPI4OEREVChp43ZvkBp1MssLdL548lgQBlh0tPfQ7r6KoSTQpk5frfXCGJmLovzz6Q73UbKSWBpQaxgiLViHYOin6DnX9NmbT89opt8r5BKr4qQCpMixTZzI7XxWrROxbA9QEALYXtZUm6C0uAUokYuGPCtr91Bae4/841v/AN127fy7e/cQeBEBdXBY1AK5apRb1kn6+KkbcyQiRWxiBaGcW0PJ+0gNIlmSuyMw3DnEMGcie3jnUKiN0myN4kZNomV+MKeul5Bbxpq44fqqbDh/1nbLriw6vhkcOu2NjY/tIVUf4pAToDhrgTJ3iSF0wtID6bpbOhmqH2Q5o0HCOUGsaIBkr1Jtv1hB0IIKhaVE4gFqFpxtABpWbHiD42SqKkIG2B7sG9QUB1TTM89ei6p/hS7XtiN8hQFU/MRQuA6Lj17e9n7xn7mXjpWTDXUMYzy1CgRDH54nhk06G3qO+nzezz84Q+/obOznU99/vPU1s5BKUVfRpB8UyOujz3HOSa0JxSdKUFF9NTb3E3izweTdyF/gcjLy+f++x/n6qtX8773XcFDDz0/YTKw2NP5QCrGwwGfBFUoyl2dyzMRqj2TqzOSfunyupFEKEgK0JQipCS2EIQ8gRz5TrFRDAvFfNtfpV8x4tHbLh1MBCWexs8D/TwVSJJydXKVRkQJWjSHYk+jxjXYoWe4JzhARigKPY0s8LyZoFs6fDwZn5CNw0TxmpmiQzrMOhhooSCOZLueoU7PsMgZ/wd2oWOxT7OZavuEsCN8EtxUYCB9cvyw71kbQaVncH0qiiEEmvKDKg4WQRdmI2SFIqTGBjHU12+nrm4bf/d3X5vQ8biuyyuvPM8VV1w/4XPg5Nbi5NSg99fj5NT6xLCTQkt1kJ5yGcKIIekjha/yE0ogXJf8rXVYQ8O4hYUMTa1CTuknu6cBx6tCVSxCnxWDYAyRkwOewtmyEeetdRhnnXPUHIyVZ+Bs2YTbvN+3jzAtyMvnjavOxQsGqe4ZRpSUI2P5hHuHqcuD3VqIGXsOkPrZj/C6OpB5Bbi9vTgP3ovX389/nH4eX964hjN/8p9oQjK3qITLyyrZIY7+ATTPOR+3YSdu3Tak14I71IlLiOCSGYhpM9GbtyGsAK4WRLMTCDyUEcaO1CBnmsiyCry2A6hkAoREWgFkeSVqeBi3rQVZVIK++PieruPBPP9isG3s11/BazsAwSDmeRdhXXXd21IcqMQwKp1C5heNeXxtZxvL8wt9z4C3AaUU+/c3snHjOnbs2MKBA820tR0YIYAPYNv2Ua8xDANdP+T/rJQilUoetZ2u68Tj+ZSXVzJ79nxmz57PnDnzmTVrHnl5fmiGsm2cdW8gcvNGFxuEpiGrpuI1NuDU78RYunzsnD3PtzERR7C5Uvpqcc8ju0TxerFg3UaYGhSENCAEHYWKp+sls/e4zDi5RowTIhyGm27yuO8+yd69vpLXtqGmBqqrD23X3v5bAoHYMT/riQTcfbdPVnZ1+bYOgYBPiPb3g2VBbq4iLw96e31iuKrKV9ZOBFOnwt/8jcf27XDnnZL+fkFNjU+oNjYKdu8WlJQoiopgeBi6u31V7sCA//opUxQbN0o8T40h1EMhxezZh0j2hga47z7/OPbtg8FBQTzujxWL+dYUlgVlZdDcDC+/LDjjDIU5Ttfx+vW+h3Ftre9d7LrQ1ASvvy6wLIFhwN13w8UXK778ZW/U33g8PPGEYM0awZQpiurqCOHwf/PQQ6v5m7/5D371qy//UQF1k5jEqcYDDzzA5z73OX784x+zcuVKvvOd73DxxRdTX19PUVHRiQeYxCROASJKcnE2wkXZkd/pCdbzYSX9YLQRSwTXdbDtFEOWRqEIYIwzzhzHYqZj+qHXCvKVdtT+0kIxJD0iR5DMYTTKXYPFjkWf8APrzraDrM6GKPdO3IkVVmEEKWa6+9mu19AjcgGFwMEAAipMgtn0aC2Yag1Neh17RZIap5YV2VVM8QLMcc6izGtmrfka9XorDsVEVBGL7GloHEZ0jngZR8igk8bEIqh0+kWKATGAIwYRaHR7u3HQmOe8fU9bQ5mYyiItUmMed7DRkATU2HsmXYGhBNkj7okcfPHP4fdzg2KAN8xX2GpsRCJZZC9jZfYsQuokWur1IOrIoLkRBLQgFeGx2RanZ8/m0cBvAYh5uTRpe/GES4UzZXS/QYK4uGTCDgujC2lJ7iXpDKMJncrwNKZF5+DaHnVP76R1Sxunhy9EP8/ivx//PrsbdvGLu35NRUXVMafsuR4HtrTSsvEAqYE0kcIwuqkz1DlENmkTigcpnF5IZ0M3JbOLCUQtQqqVuNhFtqeD4dd2U1hxEwTjx9zHOwU77VD/Qj1tWzvIJrLolkbBtALmXDwL2w2BEMgj7rl0CWnX70A71m2Mk3XZ80ojmUQW3dIxAgZWWJIeytDX3E8oL8RQxxAd9Z3Ein2FMIAVNuk/MMD+dc2UzSs9ZmjbRVWKzqRiS7cg64FEURlVvHeWhzVO7dbb1Ed6IEVeVd7oPYumawRzgnTs7GTWBbWj9hQAuqX5/dZHhJ27jod1RIqb7cFg1g94C54kg6eU4o47vs+qVedTW+v7BQshCOkQ1hUJG3IOa3pNOWBqgqhxYqubSfzvxiQZ/BeKioop3H//Y1x77XncfPO13HffY4RCEwtkm+VaTEuYh5HBxqgqttjT+VQiztSAwX3WIGHlW0FoQK902SttXGCvZpPCY7ZjctphqcAWgqqR4mmnlqFBt+kSDo2mjakgz9OY41pcnY4SUZJXjBRJoQ7ZLABhV7BTz1KvZ5nvHNvL6kToEg57dBsFTHMN9mhZYkcUiAEkLop26R5znLOyIer0LLu1LBYSqRSaElyQDSFQ/MFKEBGSiJLYKPZpNtWuwSo7hIXAQVGvZ1lvpNGA6Y7J9MNaqA7i4YcfJBbL4ZxzLhp/IkfgxRefpq3tANdee+PET4o0SNV+kFDdHegDu/zHhEa2cCnpqddQ7RqUuTrb9AyM2IUE+/rRBgYgJxfbMqnuGkJYFrK4BGfzJmQshjZlbBuMMAycnTvGJYO1eQux3vVe7Kcfx92/F6HpeMtX0Ld6FbmDA6hEB153F56uY1gB0rOq6QpMpfrVl/A625HTa/H27fVD0VIp1EAf5a7Nfe/6ECnXYTiToSCd4q+ff5yqKUcXZVrVVIIfvR33md8gXtuJKCsmNH8agXmVCEODsqnooQ6Gdg0hpEQEAojSMpTjYaxYTODWT+A17kYNDqAGB7DfWofX0oQC9Om1mFdci1ZWPvFrctg5s668FmP1eXjdXcic3D/Km1fG85G5eX4w34jB6nA2w7aOVq5ffhaycGKkQFdXBxs3rmPTpvVs2rSezZvX09fnp/iWl0+hsrKK8vJKli07jbKySsrKKigtraC0tIxwOIplWWjjsGWO49Df30tPTxc9Pd309nbT09NNT08XTU172bZpHb998JdkRxTFxfF85ixYzML5SzitZR8LCksYM6qmoTwF2aMDNrTKKX7oX9sBZNVU3yZCKby2VvSZs5FFxaDB2gLQZinMEDgCvHxFTj607vSJxRkzTv3q+owZ8Ld/67Fzp+CNN+CVV2DWLEbJyWQSuroeY8mSCzHHYz2B7dt95ezBoLeDYXRKQW4u5OcrLrlEkUxCb69g2jTvmGFsXV0+KR0OQ1UVo0SnYUAq5ds9zJ59KBxu5kxYv94nhQcHFXv2CPr6fJ/fxx8X6LrHsmWKN99U1Nf7pLHnQWenr4ZevNg/p4kE3HuvpK0N5sxRDA0J0mmfwHVdiEQgmRSEw4o33vDn0dbmk8LTph19HPX1Ass6NM+mJsHOnYJMxj9H0Sg4juJ3vxMUFQk+9rHxfdWGh32bi8JCNWpjUV19OosX/x2vv/6vvPrqe1i9epwJTGIS/0P49re/zW233cYtt9wCwI9//GMee+wxfvGLX/DFL37xqO0zmQyZzKHvzcHBwT/ZXCfxfx8nG9xc6xhMcQ32yAz63q20Nq1nIDWAEwhzTcnpUHoeyKNvfzXEcZWnISUo8jT2aQ5x91D1MCQ8IkhfFOMaeIwNgD4RFjiLeVk9i2ITy+0kHTKPbpGkwOvlhvR1DIowScqpFkOAjiOmk+vNwlZL2KVHmJIFC4sbUzezOnMBHVobqDC/DuTjCjGGWB0QPpk9yynjNRVlWAxR4OXSpDXiV0oKiUWLvoFKZxFXp+dO+DiOhIXFcvt0Hgn8hpjKIawiODjs0xspd6cwwxmrkAwiWWIHeNwaJgdJcOT+ap+0KXN1Zjj+veGwGOLnoR+wzdhEUIVQKLbpm9mpb+eWxCcI8M4oXy9JXw3AGvNlOvRWDEyKvVJqnUNBXGrkvyBBqqIzKAtXkXaTGNIkoPn32PvW76dpbRPh/DCxkigXV11NcW4Z/+/Zf+f885byz//ybd71rg+MK+LY80ojDS/uRmoSI2TS+Eojyf4UBVPziZXFGOoYpmNnJ27WpWJxOQVqC1PUE5hqEDsKYngjwQ1NZBb/NV74OF3BSiETzWjJdpQexsmtPSmLucPhZBya32ph66Pb6dnbQzg/Qn5NHrqp0bq1DeUpKi9eTECTDGQOEZKegv6MYEWJhxA+ObmxU7CzDzQBc/NhQYHH7hcaqH+ugcxwGs9VpIbShOMhjKDBcFeCVH8aN+OQHspStqB0zNwCUYtUX5L0UAbPcRGaJBQPjjn3ERNum+/R0A8dSUHEgNl5itBxTsd4lb4Q/nk90lqtcHoBwdcDDLYPESuOgvDVxUIwauuhFLzRJnihWdCdEgQNxcoSxYVV6piksOd6JHuTCCkIxUPcd/8v2Lx5Aw888OTYc6DDaaWKh3YLAmmIW5B0YP+QYH6BoubP12Z6EqcIk2TwXzBqa+dw990P8+53X8zHP34TP//5gxjHi4U/DAaCqe742waRXJOO0is81hkpcpSGCTgeLPI0ZjomISWZ5ZosswPjJtUOCJf7g4MMCo/zsiG6NZd24ZIRHmdlgyx1AngomjWbnCNW6YP4vsbdxyFojweF4sURX7D+EX/j3BGiNiP8Mu8gvJGQu5A6duGXpzQ+lsxlg5Fml5YlpCQLHIsFjkVaKLqkxxYjTQsggUpX58Z0FAtBdiQI4w0zRXZkdiFTcn42xBWZyKhVhlKKRx55kEsvvQbLmhgBfu+9P2fOnAUsXLj0pM6PmzOd4aVfQe/ZgswO4oaKceJzQTMJAtdlogwJjw7psF+zydU9ApEwdmkhVb0Jlu7v9geSCpnuRapu9B4/EdgLFgHCZ6WOIbUTQmCuOhdjyXLclmaEYWB3dhDdvoNWE2IHWnwrjkAAuzKGGBgg+MyjODv3IXLiuNu34jXvR1gWwjRRjoPX1YWz/g2skjKsbIZ+2+bV7g7+4fbPjzsHbUo15vVXEq3ehBOpAu3QObfbU7h9CYRj4Dk2DCagvRttznysa9+NNAzkzENFsHHOBb6SVylkaTnieBLDcaCU8iWPuo63fx/ZV17AbdiJjOWgrzwD4/RViGOQgMeDCAbRV59H9sH7cJv3I3JyWd+wA1cpzrjoimMSzUoptm7dyJNPPsLTTz/Kjh1bASgoKGLRomV85CO3s2jRMhYuXEb+SZDVA8Jls56hR7rElGSBsCgsKKKg4GhS2utoJ/XT75OZUkuTFNT3drOzu5OG9nb+e/PP+E5/LzHd4MyaGZxdPYNV1dMpsm1EJIKsPLpNTARDmFdcQ/r+u/Hqd0AgiEolkYXFmJddhRhhPJUA8sCpProUPJG37kSQSsGbbwreekuQzcK8eYozzvAVu0uWKGprFZmMZNs2QSDgF51DQ13096/j2ms/esxxh4bA8/yAt8PfKqbp++0K4QfFeR50dPiEMMC55x4iOLNZP7RtzRrJ4KCvwJ0xQ3HjjR7FI0HK/f2glEA/rB1t6lRFS4uguRn27ZPoOhQUKObN8/2QH31UUlXl8pGPeDz1lGT3bt+/94wzFJde6o0S13V1fuhcTY3/1TF7tv/Y4KBPFJumT9Z2d/vHlUr58/nVrySf+IRH/DChjFK+b/KePZLubojHYetWQSIxKganuxscxw+o+8UvJK6ruOGGQ8d6+DXLZHyricNxxhlfpq7ul/zwh19m9eoHTuZtMIlJvGPIZrNs2LCBL33pS6OPSSm54IILWLNmzbiv+cY3vsHXvjaxjqRJTOKdRgDJ+1IxvtH2FOt3PomQOqFAlPxEgp07nuCJtOLKaRef9LgagrOzIfYHBtmv2eR5Gmnh0SU8VtoBql0DiWB8feGxMd2ZxdXpd/N44Pe0yXWAYqqKc1H6Ci7KzOVpM0m+spjtLAcOdS3tlzabjQwXHKacLvMqKPP88OED9hCPWsMoCTmeZEh69AqPizNh5jmFrMqcx1OBR+mTPehKgHBBaQRVhIiKk6eCPGM1Evem+GIFBNNdkypXnzBBf0HmMtq1VjYbG7CxEUCZW8mNqZsJcrQq94JsiDbNYYeewcUn1Uo8jXenowRHzux6Yw3bjc1Mc2oxsVAoukUnz1tPEHfzuSb9nneEELawuDr9bs7NXEy/7KVB28mDoXsYkgPEvXwUihatibjKZ47tZ08Y0sSQh4oqpRQtm1rRAwaBmD9HoQkWLVrOV8P/xVOtv+Uzn7mVp556lP/4jx+Rn39oxT3Zn2L/umasiEU4P0w2ZeO6CqFJMsksVtgiEA2QGUoz3DmMyPZTrj+HwGaAGrKOjaYpCoZ2w/4/kJpzjJrQSRNsuBezYw0iOwSagZNTS3LWLXiRyvFfcwwoT7HjyZ3sW7uf/uZ+pK6RHkzTsbOT0jnF5JTE6G7soSYxwGmlcV5qEfSkwdSgL+FRatisyPFIOhZ3bZe81cnIO0/weqtidrafnPs3kB5MIw2J8DyyiSxu1kXqEjfjEiuNIg2NRE+Ctq1tlC8swwj4/EU2ZeM5Lht/s4lEdwIhBXlVedSeN51o0SG7OE3CrDyYlXfiQj63PIdA1CLRkyBS6AdMeJ5Hsj9F9coqdHMs5xEriTHropnUP9dAz37fY84Mm9ScOZXiWf79zRttgl/u9D91cUuRdODhPYKBDLx/nIC3nn297H5xNwNtgyAETiTFP333b7nxxg+xatV5R835/CmKwSysbZd0phSWFCwo8NXP2im2t5vEnx8myeC/cCxdupKf/ewBbr75Wj7zmY/wne/8fMKE8PFgIHh/Oka5p7HeyJDB4yw7yDl2+Jgk8uHYoWdpky7TXR2JoMqVVGPSqNk0av76tUSQ72ns0ce2lTv4qb5R7+19gzVoNo9Yw2gIakfm2iFdOqWLBLqFS76SuECTZlPk6cxxjk+25SqN87NhzmdsC1NYCT6cymFPNkSndAgr6ZPlI0XPRiPNa2aKUk8f9S7rFi7PWklmOCa5SsNF0bV5G42NDfzLv3xnQsfY0dHGM888xte//u23ZyFgRLBLzhj3ubmOxeeTebxupHjTSNEZEOSYIeas38U57WkKhjMoO4nW8ApGbgKV8hAt2zFijTjx2Tiav3I7XpDX4RDhCPrM2XjJBPa3/43LMn1sLo0yHAmRyM1h0NJpNT1q+9JMXbsRrBCqpxuvrRURjSKsgK8EjeXwOh7CdZiWE6M5m+HfX3mW6bPmcuONHzrm/r1wOW6gEC3Zjhs9pCBOr9uB5+jol10N/f2oVNJP5hISkkdbKwhNGxOUp4aHcLZuxuvpQkSi6AsWI0csDo6EU1+H/eKzuHsbUXYGr6MDYZnIvELclmbcPXfhtR7AunF8lcGJYK4+H6Fp2C+/gNfXy7q+Hgpzcpn1kU/4dgtbNuLu2EYmm2WDneHZvbt4+rknaW1tJicnlwsuuIzbb/87li07nfLyyrcdkHFA2vx3cID9muOfI+AFM8n7UrFxk6yzr7+Mu28v1ux51GoatcAVw0N4ne2Y37uTrXt388z/+y9e3r+HL+/agQJqozlccuY5vE95jFfuGstWIuN52BvW4nV1IssrMJatHHPt5s9XbNsmyWQUB9dkBgZ8YrSm5o9jg20bfvlLyZtv+uNpGuzaJdm2TfGxj/lkZiQCt97qsXatYOtWnzQdGHiC555TXHTRsW9+CwsVmqYIBgW9vb6qF3yCt7AQMhnF888LHMf3DU4k4Pe/lzQ1KW67zcOy4MUXBU89JcjLU8yY4SuSN28WuK7k05/2SCRg/35fqes4grIyn8Q2TSgvh3jcD5OrqoKiIsXBRpWdO30i9oYbFB//uEd//1j18kGkUjA0JNi1y/97LAYLFni0tPihesEgJJOKWMz/OJomzJzpK5HXrhVcfPGh6/P004Lt230ieHjYt8VobfVJYl0/qHL2SWHw/YTXrxckk/6xHr4el5vrH8+BA4wS5wCDgyEWL/5XXn31Zt588zVWrjzzj3l7TGISpwTd3d24rkvxEasaxcXF7Ny5c9zXfOlLX+Jzn/vc6L8HBweprDw50mASkziVKMjY5O3eynxixMMlRJUkEhJ00s1rrWtZVXEaudbJS96W2wFcDtrR+aHVF2dCXJo9JM44WQgE52UuYZ69iN16PQrFNKeWEu/EeS7qOMFaF2UiSCV43UzRJV3CSnJ5NswlmTACwdXpd5PvFfLvka9SrirJVXFyvDiSQg7ICM1aPT8KbScrQkQ8SaVnkOtJLsiGuWxkjBMhoqLcmvgUDXod7VorIS/CHGc+UTW+/USO0vhoMpd6PUvHyD3R7JF7nYOo13f4FhRYuLjU6zto1vfRLTq5K/wTdus7uTH1Iaa7b8/r+ESIqRxibg5lbiV9spdXrOfo1NsBQZ6Xz3Xp947xQj4cylNkk9mjCEGpSUJWhK98+j+49sZ383d/90nOO28J3/rWj7nwwssBGO4cJj2UJr/KD6a2k1ncrEMgZpFNZLEzDmbQIFaWw2DHEKptB1Z5D8OiCtfxcDIOsao8VCSC0b2JtD2MMo5Ow7Wan8Rqfgo3VIoKV4KbQu/dRmjnnQwv+dJJKYT7DwzQuq2NUDzIUPsQmqmhGRqp/jT9BwYomV2M0zGEncjy7lpFVQzePKBoaexjansHtYkO9m712FpZzsboDKbEtRElrKI/6fLCNpulRoyKuIOdstEtHTtp49ounu0RLYlQc/pUNEMjm8jS3zJA7/4+imYUkhpIkehO+F1+rkc4P4JyPVq3t5EaSLH8fUtHLSVOBuH8MDVnTmXXC7vp2duDNDScjENueQ5TTxvfAqRiUTl51Xn07utFeYqc8hyiRRGEEDgevNDiK/yrR76ywsEMhgEbOgOcO8Wl4rDLONQ5xObfbSXVnyJSGEZ5iv+460tYMsgXPvv1cfcf0OHGmYpzKl06k4Kw4TE1xiQR/BeCSTJ4Epx77sX88Id388lPfpBEYpgf//i+CZvYHw9hJbkiE+XSTIQ+4WELRe4ECdqEcOkTLut1l4T0CHuSKZ5BUEHfYYrf0+wgDXqWdulQ5GlkUDRpDlNcndnuyashAbbqGYaFYvZhpHWpp9MvXAo8jYzwqB8hhos8jRvSMQrU2/8o+aSzOcbq4vC5SMWYEIsCpbFfZPlBqA8TgQc0Pn43sfx8zjrr3Ant84EH7sIwTK677r1ve97HQ5Gnc00myjWZKBkUmY56xONrwfNwQ2Fk23Y0MUzosjNx2oZJbdgLLcPQsg5VOB/j7IvRF51YsayUIv2rX2Kve4PqTJpAUKMvL4fWqeW8ecFpzFm7lavX7cGQQbS5i3D37oFUEnJyUZ5C9fchI1ESQYtvb1lHx/pXyM3N47LLruWLX/z6cRdGlBEhU3U5ofq70ft34hkxRKofu20YVT4TaVhQWDw6T7d+B+7eRuTUacckRb32NtL//VOcxt0jfUYe2nNPYb3/w+gzx7bUOfV1pH/2I7yBAUQ8D6e+DtXaipw7H624BCkEXl8v9puvYZyxCq1q6oSv30EITcNcfT7GaWehhod446bLOX31hQhNI/Ore6h74hHu27uLR1r2MeTYlOXEufSa93Dx5deycuVZp2RhSaF41EqwT3OY4Rrovgsze6TN7wPDTEuYR3mDuzu2IWK5o4pdABGJolqaEK0tLLvqehYvWMxHX/sDO7Y/ybP9TawdTPOTV57i+6c9wtlnX8BNN93CRRddOUZpr02bgTbt2Ma/K1cqtm1TbNvmk4aeJ5BSsXq1R23tH3ceduwQrF8PlZWHyFrHUezaJXjzTcEll/g3hdEonH/+IT/fL3xhDbNnz6OwsPhYQzNzpm8t0dUFti3o7j7Ct033LRfmzTukQIjH/eOsqxPMmaNYs0YQDjNqHRGJ+KrfhgbB2rXw0kuSnTv9cTZtgsZGSW2twjR9dXFNjaKpaazXMYCUvj0F+G128bhPjNfV+eRvQYGiuho6O2HvXoEQEAj4auNo1Cdj3/1uRWMjrFkjSSZ9P+TaWsW0adDcrKirO0QGd3TA009Lysp8cnz/fhge9glfpfzQOdv256Jp/vmKxxXTpyt274b6elhw2DqWYfjX4557JHv2KHJzfaVyOg3vfe+NJJPf42tf+1v+8IdXkac6YXASk/gTwLKsCXckTWISfwr0pPsYzAxQEyokeJj1Q56Vy/6hZrqSPW+LDBYIVtoBwp5gvZFCjQSzaaeg86fIK6Eoe3R48DTXIKQEvcIlb4QUzaBICo9F9rHv1UwEl2UjnG2HGBAuMaWNuZfQ0Tkrey7P22cwLAYp8yqxheJ1I0VKZDGUxMEg15WkpcJQioASPGUlqHGMcRfix4OOzmxnPrOd+RPa3kQw37GYz/jjG8rCFf59YIu2nz16PUEVIkKUIreEJn0v94Z+zueHv0pYHU12niro6Fyfvonl9uns0xoxMJjpzCHfG8c/awRSk+RNyeXA5lZCeaHRe4FMIotmakQKI1y+/DqWLz+Dz3/+49x887XccMP7+Pu///8wLAtN13CyLkZARzM0pCZHVbAHPW8926WgJp9IfAg7mSXppBFSI1oUJV6ZC45v0zZuu5qbwWx7Gc/MQQVGRCh6CCc2Db1/F3r/Tpy8E1/HpO17/Q50DOFkHKJFEZ+oTTvopo4e0EkPpEkPpDHDJsF4EFODVeWK/C11NG7fSyg3hJlvkhnO0PTaHnKmCswzZtIyDJ1JQTrp0a1MOnILqbUyJHoSOBkHTdfwHA+pC6qWTSE9mCbRkwABuqUz0DaIbmqYEYtIUZj0QIZ4ZXz0Wphhk979fXTu6qJyScXE3xCHYerp1USLorTv7CA7nCW3IofSeaWEco8dKBnKDRJadLRF4EAWupOCvIAiYbXTWPQ4HTlvAaBal1CfvYQKDn1vtG1rJ9mbIH8kvO7hV+9nx4FNfOqir5JsSTOu2gW/ps0PQNMgrGuXPKt8O4xlxX5w3p8azdJmrZGmSbPJ9zSWOQFmO+ZJ2wZN4sSYJIMnAcCVV95AOBzh1lvfw/vffyV33vlbotG3HxxwEEk8HrOG2WCmSaPIVZLV2RCnZ4M06jbt0iE4zspvq3Rp1mwCShBC0i1dWjTfv3e6a/KINcRKO8gKO0C/cHnRStKg2RjAdMfgXZkYYfX2bqwTwhv3g2EgmOGaXJqJsF+zMZX/7yMTi08lsijkEcRhGo8mzaFfeiy3g2gKnn7+BQouOZ8my6PmBO4YqVSKu+++g6uuumHCwYF/DCwE5rkX4eTkYb/5Gqqrk1AkSKh2PtrMKagZCrO6kGxTN9pwM94Z5+Kd88EJ2SV4ba04a17xf8UyGUr6EsS7+6nY38qiN7YS7x1ExnJg8VL0s8/DG+gn89CvUV0dvp9vJIo+dz6XZTJcsXApoS9/fQyBeCJky89HmTHM1heRiVac4mW45VHc1KHIDuV5uPv24NbXkb7vTpytGzFWnYe+ZNkYUlgpReax3+Pu3oU2YyZCN1Ceh7engcxvf4X2N19GjNxwK6WwX3wWb2AArXYWuC7C8yAeR7W1oqqmIuJ5iNw4qrMdr6X5bZHBByEsi+aOVjZv3citH/1rHvnxd7jzp99hbXcn+aEw71tyGpdWT6M2kSR0xQ0YZ579tvd1JHqEy249S6mnjfrwSQSVnk6LZrNfs5lxxEKKCAbBzo55TCnlF7+mfw6Hpsa5Y143u/QQud4ZzBKKwkQ32q8lXXd18LGP3UQ8ns+73vV+3vveDzFz5ljvvN5e314hP98nPsEnYm+7zWPDBkF9vcA0PebNg4UL1ZiQMBeXPXo93bKLiBdjpjMH6xg3Pgexf7+vqA2HDxXwug7hsGLHjkNk8JHYtavuqLkfjmwWWlpg1SqPWEzw4ouS5mb/I1VZCWee6ZO0icTYAI9g0J9PW5uipsYnZg+fG0AgALateOklwa5dgtmzfeK2vl7Q1KTYsQPOPx+uucZjcBD27BHYtuLgGoJt+36/BwlipXyy9YEHJHV1goEBX+E7f74infYJ6kTCJ2lNU9He7r/ukks89uzxSe7qal91fHC903EO/R18QnlgQDFzJpSUKEpLfV/hVEoyPOyfL8fxz8VBpXBtra8E9zxBf7/gSMe4FSsUuu7x0kuC9nafMD/rLMVZZwkqK7/Jtdeex5NPPsxll117zOs0iUn8KVBQUICmaXR0dIx5vKOjg5KSo4mqSUzizxFhI4SlWSTtFMHDQsKSTgpLswgZx89IyaLYbKTZofl1xEzXZJFtYSF43BrmKStJBoUA1pJmnmHyoVTuO3I/UOManJMN8ZyZpFM6uECPdIl7ki7psEfLUjNOjshBRJQ8KvTuIHR0lttn8LvAr8hVeQwKi0HhIEQLUITBDKJooDzaNJcZrkUPNnV6dsJk8KnGAmcxa61X6Be9NGv70ZRvW2FgUuqVkevlsUevZ4e+leX26e/oXASCKreGKXY1CDGh7rcpy6bQs7eX3v29BHOCOFmXzHCGysXlPlkLFBWVcNddv+NXv7qLf/3XL/PEEw/z6U99gQXFZzDYMkBuZS5W1MKMmAy0DlJQk4+m+6Fpyf4UM86exvTlCwm8/AZhO4PKqSSYG0Cg0AbbyJSfO64qWDgppJ3E048I4tMC4NmI7PBxj204C0/tF6zvEGRcQTSRR6EZJ09ATnkOXbu6yAxncB0PqQmGuoepXlFFtMifS7I/NaIkDhGK+59R3dIRvYJAUxtbp06jJeMXiJ6t0WOGacirYGVHB0ZJLgOugUhlsTq7CMUCDHUNMXBgcCSATuBkHYLxIAuvm0+8Ms6WR7bhOWOD26QmR317+9K+V3F+kHHD4o4FIQSF0wsonP72c1sOIqRD0FD0iT6ap36f3kgdwUwRroK+ysd4IriLJe7niStfMT7UOYxu6ggheOGtJ7jriR9y3dnvZ0HVMp8UPwZsD+7fKXit1RdVSGB9u2Bzl+Aj87w/KSG8S8vy38EBuka6A3bqWTYYaW5IR1llTyzfahITx/8pMri3t5dPfepTPProo0gpuf766/nud79LJHLslcFzzjmHl156acxjH/vYx/jxj3/8Tk/3zw7nnXcJ99//ODfffA3vetdF3Hvvo2P8ik4WCsWDgSFeMZMUKI18pdEnXO4LDPGEmWBYejgj25Z4Gu8daflO4tGgZ8lTGknh4Snl+3JJF10JdAWPWMNs0jPclsrl0myElXaQA5pDQAmqXWPctOCJYopn8ApJHNQo+eSgsFFMdQ1KPZ3S44RMnErMdk02GunRVGSAJumQForltklMSVK9fQxsq2Pqp27lTSNNzQkU0T/72ffo7Gzj058+OgjmnYIQAmPpcoyly0Epom98AZnpxQWEFJjVhZhVBeh9LsmZNWQn6Jvr7d0NnofIyUV1tIGmYdkOVibre/UaBgwPISsq0WfNQauqxt3TgLtjG8o0EbFcVDKJSqcwrrjmpIjgkQPDLlqBXbRiNO5Wdj2OevA+vMEBZCwHZ+d2vC0bIRRB5Obj7tqJ27iHgOtgrDhUqKr+Ppy6HYjiUoTu/+oKKZGVVXgtzbj7Gg+pg7NZ3H2NyDx/5VdJCZoOmgvZDGp4EOJ5PpOGGMt2vU3ce+/P0XWDf/7nL9DZ2cHSeAHfufwGLpoxG1Pzr5e7exdO3baJk8FuBmEPoYzoGN/lMZsI35tbHuHEp42o4t1x2iT1ZStxdu7AGxpERmN+G9iBZmQ8D22WH/axwXyDBr2OWmcO+shPYWGgmMYP7+KLN/4zRduKuf/+u3jwwXv46U+/y0UXXcHnP/9Vpk5dxCOPCN56S47YESjOOktx4YU+iRkOw9lnK84+e3xydlAMcG/o52wzNpElg0SjxpnB+5O3HrO1EBglSI9MVbbtY19epRS7du1g9eoLxn1+xw7f7uHAAYFSUFrq2z5UVyts2z+W4mL4wQ8kO3YcObb/JxhkRBHsk8aHe+8ODYFl+eF0BQV+GFs0CkuX+mTr7t1w8cWKpUsVAwOMhsTF44pUCjo6BNOnK+bOVXR0wCOPSH7zGzFCVvtKZE2Dhx/253H55YrWVt/WwbahosInXgsLwTD8wDvb9m020mloaPCD4XJzFa+9Jli27JDyWSnfjiIe9y0nWlt9wjce9wlj2/YJ52nTfLI6nQZNU8TjR193IfxjXrzYJ60P2nwArFx5Fqedtoo77vj+JBk8if9xmKbJ0qVLee6557jmmmsA3+/wueee4/bbb/+fndwkJjFB5AXiLCqcxwstr2JqJlEzQsJOcmC4jZWlSykLH7tTJovi3sAAb5ppxMjX+RozxTI9wFkjpGzUk0w9TKW7Rc/whpHiwmz4mOO+XUgEV2UiTHNNXjeSvGwmkfgClefNJOuMNNeno5z5NkmS8zIX06LtZ7OxgU6RYUhmKXFLyPFuYFj43kYa4AIePkmUFuPXNy6K/ZpNSijKXJ240tgvbTYYaTqkQ7Gns8wOMMV7+8zSInsZqzMX8Yr1LG2an7chEExzZpDvFSIQKCAhjk9cngp47W1kX3oed9tm0DX0ZSsxzj4PeRwxVV5VnMXvWsi+N5voa+knELWoObOaquVTfCJyBEII3vveD3HppVfz7W//C//xzX+ivLSSm+dcSbSthEwgRiBSil6dhxE06NnbixE0qFxSwbRVNehBA7n0fQQb7kM4jahBE+GmcWLVZKouH1tIjkAZUdxQCdrgHtxA3qG5ZPtRRhgvdOwFQdeDe3dK3mwX5AcUIV3RpoWpz5+O1tHAgqIISil69/Vip2zyquLUrp7OtFU1o2RseiCNnbKJlYw9fwV5JjvbNZoGIRbxPYXTukZyOEPStHg6WkM6FCapW5DOUlIyyJnZVnr2dhGIBjACBk7GwXNNpCZID2YI54cJ54Xp3ds39hx4CtuFV3otdq7VyLhQGFScP0Vxeun4QcHvJIIjAW93JN+kM1hPydBcXFdnICOoDhfSW7iDt1Jvcn7mUgBCcX+B4Y3tL/HD3/0bFy67kpsu/Ci9e/sI5Bz7XrCuB9a0CUrDEB2hETIubOmGdR2CcypOfQD2ePBQPGkN0yNdZrmHlMAt0uZJK8FCxyI2TtbUJN4+/k+Rwe973/toa2vjmWeewbZtbrnlFj760Y9y3333Hfd1t912G1//+iEflVDoL3fVYeXKM/ntb5/lve+9nGuuOZdf/eoJysvfnvfbAemwyUhT5umjqt+wkrxmJKnTXS7MhIjie97ulTYPBob4fMKgQ3Poky7LswFaNYd9ms2g9MhRkqDyfatiSrJTy/KymeQ96Rh5SiPPOTVfDottizcNk116ljzPH7NHusxwTBYfpyXrncAyO8gmPcMOPUMQiYfigLTJ9eQoId3y+hugFBVnnk6r5hx3vK6uDr73vX/nlls+QU3Nsdvd31EIgV20nMCeB8AtAc3/1ZGpDpQZw809SZ8vKZFTa/B21/smngcTnoQAwwTLwrz0KoSUOHXb/YhaIWBoCK+/Dy8QxLr8GoxV41tsqMQwzrYteD3dyJwctHkLkeMpqg+2GJ19Hl7rAZwNa3H2NeLuqkPGctGXn4Ys9MMA3L17yD73FPriZYiDLJ/jgOceHZyn6yjXBXfELzeVxG1rBeXhpTNIRkjj8krcHVt9BaxuoFwXb/9eZEmprx4e79hGGD1xnPb0np4uvve9f+dnP/s+UkouueRq3lM6hWnbtqLPmnPE1hMsFjwHq+UZzAMvIDP9eFYO2bJzyVRedFTKd4GnUeka1OtZooepX1pHrGGmjONBbqw8E3dvI866NTgHmhEoRG4e1jXvQiv1ffj2aLswlTVKBAP4hhOS/dpezqo9l3/8x3/nS1/6Zx555Nf813/9f1x00Qrmzr2W3Nx/ZNq0eRQX+yFkv/+9QNMY4zt7LDwW+B3rjNeZ4k4loqJkSLNLr+OB4N38deKLHNKUj8WsWb6dQkeHT9AKAYOD/lt98eLx99vT00VfXy+1tbOPeq6tDe65R9LbCxUVfoHb2uoTrrff7jJt2qFtly9XbN8u6OmBvDx/n01NPsE7Z46vel69WnH33b6tQl6e76nb3S1YsUKxb9/IusQIhPAJ5GDwEMmdk+Orqp9/XvDQQ5K2NgiFoKtL8P3vS5JJ2LZN0NIiyGR8MjWTgfJyf+4dHYKODpg+XVFVpQ5+xMlkfJVDTQ1cdpnHE09Itm6FvXt9X+PiYjhwAL7/fcHZZ8O73qXIzxccOOCTyWIkzzIvDwoK/HkHAormZn/OCxcqBgehtVUwb55Pch8LUsJ45cWtt97Orbe+hy1bNrJgweJjDzCJSfwJ8LnPfY6bb76ZZcuWsWLFCr7zne+QSCS45ZZb/qenNolJTBhXTb+EpJNiW3cdBxJtWNJkcfF8bqi98rgKzm16hrWmf+9yUFGbxGOdkSaDYlh4VBwmCLEQhJRko5F+R8hg8Be/FzgWm/U0ISVZ7Jhoh5Ekj1kJ5jgW8bdBkgRVmItSH6fK2UGXaOHpgEeJMxdb5rJFz+Aoj6TwKHUN3BGX4vGyXw5Im18HhmjUs2Txg7erHYPdeoYu4RFG4pDmTTPFB1I5zHPenrJYR+fdqQ+w2F7Gj8P/xR5tF/OcxeR7vgozIYYxMSnxSt/W+BOF191F6o4fjgozlOOSeejXuI17CN72ScRxRBh5VXnEp8QPWTwcx5w1NzfO17/+n9x40ZV89W8+xj8//X3OjBfx2Zr5zF96Bjmf/RhDKZ1sIksoHiRWGht9f2fLz8cLlWJ0rkNkenFjNdjFp+OFjrEYIjUylRcT2vETtIEGvEABwkkiM71kKi7EjVYfc557BmBzl6AiovAUSAEz8iXbU7nsUmVUNu8ABXlT4uRPzWfuFXMIx8cWRIGYhREwyCSyYywV8lQWN17MsKchbIGyQQpFbaFOh2Oxw6imrL+LaKIfEQowPLeWLaki5rY8i5aysVM2UpPEK3IwwxYdOzuZfvY0yuaX0rajnYG2QSIFYTxXMdA+SLOMsFUrIV8o8gLQmYR76ySG9Fhe8qchRQ/HhVMUTyb20uGa9CZ1dCkoCinm52u0YbJfaxzdtnRuCU89+ge+/ft/ZMWsVXzkks/Q3zJAKC9IyaxjL4Lt6Rc4niBqHjo+SwNTwvbuPx0Z3Cf87udiTxvT7VDi6ezRbJo0h3mniO+ZhI//M2RwXV0dTz75JOvWrWPZsmUAfP/73+eyyy7jW9/6FmVlxzbiD4VCk+1vh2Hu3IX8/vcvcOONl3HVVau5887fvq0b1B7pkhCKisN8gt2RQkrCaCGjIaj2DBo1m116lmJPx1ACBExzTYaER590iXgCA4GOQCLIUxrb9cyIavDQF0aHdHjVSLFNz2AhWGoHODMbHA1lOxFylMaHkzm8YCXZomdQwMWZMOdkQ2OsLE4FOqTDGiPFTj1LSPlzXW4HR1XAUSX5SCqHdUaabXoGDcFsZbHBSI+O0fzq68SmVEJVOUXZ48/vm9/8GoZh8JnPfPmUHsfJIlN+PnrfDvTe7T7557mgB0hVX4MbmXLiAUagTatF5MYRyQSisBjV1+tL+jQdFY0gAgH08ino02ag0mmyjz+CCAQwr7gG1d2Nsm1Ufy9KueOukrttrWTuugO3cQ8ohUKhVUwh8IGPoNVMG2dGIAIBAh/8CN6qc7BfexkvMYy+aBkyeKiwEQWFeJ0dqL5eRJH/4yzyC9Aqq3Drd6Cih4o5r70VWVCIqJhC9pUXsJ99Gq+nC6+r0w+ZMwxkcSmyohK3cTcinUb19uAODqAVl2Ld+P6jVApqeIjsyy/grH8TbBtt3kLM1echSw4Vz8PDQ/zkJ9/hxz/+L5RSKKX47nd/wXXXvRdny0ZS9XWj6mcAlUyA66HPObGfmLX/MYK7f4XSQ3hmDjLdS3DX3eCmydRcN/YaI7g0E6ZTuuzUsoSUJCUUYSW4NBMZ93MtLIvA+2/BXXkGXvN+MAz0WXPHHF9YRXDE0YsnHh4hdahANU2TG254P9dccyN33HEv3/72v5JILKKr612cfvpXKC2dg+fBq68KVq1S45J9BzEoBthorKXQKyaifMWNRYAqdyp7jHr2aXuY5o5vLlxdfYjMPJjjZFm+vcPSpeMXaY2NuwHGXfjZvFnQ2SmYNeuQ0mHqVN+Ld8MGQXX1oTGXL/cD0F55RVJf729fVATXX68o8tc3WLnS99V94QVBd7fAshSXXOJx+eWKhx8WPPOMJD9fja51tLf7Kttp0w7tJz8fSkogGlVMmeKTr9msP5+GBl8BnUj45KxSPpk7OOgrezs74ZVXYNMmn/wtKfHXVc4/XxGP+x/viy5SzJrlcv/9kp4ewcKFis5OQWurJJ2Gu++GbNZl4ULF009LenoOqq4Vy5crrrvOD6QbHPQJ5H37BMPDAsOA007znzffhlX9xRdfRUVFFT//+ff57nd/cfIDTGISpxDvec976Orq4qtf/Srt7e0sWrSIJ5988qhQuUlM4s8ZMTPKrfPfz77BZnrTfeRaMabGqtDk8evkXXoWD8ZYK4SQSAXNmo1vDjC2XhT4qtl3Eik8dhgZCpU2ev8EfqbJbs1mr2YTP0mSpEXa/DYwRKNm44gK8r0qZtuCZs1GoAgqQZPmElaCgBLs1WwWOhaL7LFEbhqPXwYH2aPZTHF1LAQd0uau4AAKnxjWEJS4Gn24PGYNM9MxJ9zFmRDDrDFfZpOxHoAF9hJOy67i44nPckf4+/TILqQSpEWaPtnDCvtMpjvjiyBOFew3X8Pd14g2c85oV6FKF+Ju24KzbTPGspXHfb0QAt2aGA2jPI/qjev5xfylPDdvMf/2+gu8a8NzXNHWwGdiOrM/96Vj7QQnby5O3rGtwo46ruLTSKKwmp9CS7bh6SHSFe8hfQw18UF0JAWdSWgZFgxn/TosbimKoybarCrmnWkibIdYUYTcilyEPHqsUDxEydwS9q7ZBwqsiO8ZnB1MMXdRLh2GJGr6NWhhEAoCBo9lc4lmbeYU56AZcWKlMVzdYOc+QXpGNZVmChQEohaBnACDbYOj5Hv+1DzmXjKbPa82MtA2iJACVZBLY+FsyosDZDzoTgl0Cams4pUDgqXFinGm/o4ioMN5xVH69AxlFpiaR9zyL4eDTdQ75H/+yPO/5j8f/irzpy7lljM/TaI7RawkyszzZhApPHanvBDjy3k89acNkpMjf478Pj3YlXAq/NknMRb/Z8jgNWvWkJubO0oEA1xwwQVIKXnzzTe59tpjt1/ee++9/PKXv6SkpIQrr7ySr3zlK8dVB2cyGTKZzOi/BwcHT81B/BmhpmYGDz/8Irfccj3XXHMO//Vfd3D11e8+qTFiShJAkBCKiBohtvDbiywlxoQ+aSPPZYSixNOodUxeNVMMCpcuzWVIeAxpgnzPbyEA37rBUGPze7uEwx3BfvbpNrmeRh+KXwcGadRsbknljNnnoHDplR45njxqNb1Q6bw7HePakX39MbYTx0K7HJmrZpOjJFkBO/Qs+zSH96ajowR3TGmcnw1z/ojioG/knOzWbCpcnaZXXye+aiURJVhpH9ucvq5uK/fd9wv+6Z++STyed8zt3iko18Xd04DX3ISwTLxpH8AqaUQb2A16ELtgEU7eguMWG0dClpRiXnIF2UceQkaiuMkESenSmKfxzMIo0jA5ffpczs2PYzS14HV2IEvLELqBGCEGVX4+XnsbXnsr2pTqQ/NViuyjD+Hu3oWcPhNh+Gpbd/cuMg/9iuBnvnBMX2Mh5WjQmL12DUId8bOWTvn+v4cTxFJiXnw56fZWvPodEI6gUv525sWX4e3aSeaB+0DXkKVlEMvB6+vB2bgBraoaoRsYp5+FccYqhGEigkG02XORufExu1bpNKm77sB5az0iloPQdLJPP4a7q47gxz9NNhrjnnt+yne/+28kEkPccssnEULwy1/eMdrGrs2Zj3HWOdivvojT1grCvz3SV5yGvmT5ca+ZyA5iHXgWz4zhhf2wBNfKRSYOYLW+QLbifJQ5NthllmvxV8lc1hlpWjSbQk9nqR1g5nEsUYSm+crlo9TLPubbS3jdeoku2UGB5zOa7bKVsIowz1l41Pa6rrNq1c2sX/8Bstn/Zu3af+WuuxayYMFtLFnyDQYHcxkcHF/5eRBpkSIrMuR4Y69JQAXJkiEpju3nJQRceKFi5kyXXbsEjgPV1YraWjiWu0kiMQRALJZ71HM9Pb6tweEft4Pha52dY31vNQ2uu06xcqVLU5NA12HmTEVOztjXnnmmT5r29fnnIerz3Zx/vmLvXj9MzjAUrisIBuGKKzwKDrNUUwpef90PaTsYROd78cLwsK/ENU2fIPbD23z1r1KQTv+QZPJb9PS0I8RCWlu/S2XlCubO9UaPUQioqvLVyFOnKjo6BO3tgpwcX3W9ezf88IeSUEgQCvmPLVyouPJK38oiHB5LXieTiq4unzAuKjqpr64x0DSNW275K/7t377CP/zDN44b9jeJSfwpcPvtt0/aQkzifz2kkNTkVFGTUzXh1/hf4+MzDoWeRkqoMWFuWRQJ4R1FkJ4s+oTLq2aSTXoGASxyLM46TIQyOqNjkCEny5EkhMc9wUH2ajYVno7pCTqlQ1YqTs8G6ZUuOSNiHgHkKY3FdoDT7SDBIxbh6/Us+zSbGtfAANpkK5uMHtq1YgyliLo6GkEadYcCV8MSNq3SoWoCdhFJktwZ+hEbjfUEVRCBoE7fynZ9Cx9NfppbEn/Fs9YTNOv7CagAV6Zv4MLM5WO6vt4JuLvqEeHI2KDiQMDvyjvQDCcgg08GXvN+3IZdaJXVXByJck7tXB7c9hY/WvMiF/3nP3Fdw3Y+/fmvMm3aH5lUDH73ZskZ2EUrEdkBlB7ExaR3Xx/ZRB/B3AC5FblHqZn70tA05Nsa5Ji+OrgzKehKweoKyZQFpRMiUWeeNwMhoG17O4PtgxhBk5qzqpm6sIidOyBm+mpdgMEMZJVgVpFJUYFfNGZd6EoKupVJpxmhxnQpKPILcyfjkE3ZFM8eEeEIQcXicopnFTHY7pPE28lhsM6gqx/aEgLHAyEUmvANSNKu7+P7p8YCZzEvW8/hxprIdctRQJs8QEhFWOgsIZvN8pWvfJZ77rmDD3/4k/z9F/8/kt0phBDESmPo5vEXimrjioDmX8f4yPlN2OAqmF/wp2Ngc5Vktm3yupki5kp0BApFk7Qpd/VxuxIm8cfh/wwZ3N7eTtFBidIIdF0nLy+P9oMpMuPgpptuoqqqirKyMrZs2cIXvvAF6uvreeihh475mm984xt87WtfO2Vz/3NFaWk5v/vdC3z+8x/jr/7q/WzfvpkvfOHraBP0Va1yDWY5Jm/pGSo8nbAS9AsXQwlCSoxphu4VHlElqBxpA78mE+F5K0m7dAl4AkNKNECg2KXZzHFgSHhcZIfHrNC/YabYp9nMPKyFKonHJiNNnR1goRMgi+Ipa5g1Rpoh6RJUkqV2gCvTR6sM3wkS+CBeNZLs12xmuYfmOiBc3jBTrLADR4ViHURcaXwwFeN31hANg110b9vBvL+6lfemY9Qe4zXpdJpPf/rD1NTM4IMf/Ng7dkzHgspkSP/qHpx1b+AlE6jBAZAa5lmrsa64Fjm15rhte8eDed5FaJVV2G+8RutLj/KY2cL2Ep2QESQbj/Hb8gTdDY9wY2gpSIlKZ/z9uy4iloPyPJDaUfYMqrsLp74OUVo+auUgNA2tcgruvr14TfuPqQ4+CFldgzZtBs6OrWhTpyMsCzU8jNfViXnRZUcpdvU58wh+/NPYb76O27QfWVCIseI05Jz5pL/3LfBctMoaALRAELH6fLydOzDPOR9tzgL0WbMR4eOnJzvbt+Bu3YxWM2O0jU0UFePW7+Cxn3yXrz/8AO3tB7jxxg/x2c/+PaWl5axevYBLL72awMHtdR3r3e9Dmz0Pd+d28Dy02lnoCxaPhtwd85ykOpGZAZzw2JReL1CAPtyETHbgHkEGg+/lPSVz6oqAec5CLk1fw3PWE9Tr2wHIUXGuTF9/TEVJbi5EIgax2K3MnfsBtmz5Ma+99o80NPyB00//PrHYlcfdZ9zLp9AtoVVrIeoeuvbdspMcFafMO35y8UEys6pqYoVZMumTy6HQ0a2rRUXgugLPUxx0CfFJVUFp6dEaJyF824SKE7SKmaZvvXA4iovhE5/weOstwb59vvJ34UKfyD4cngcDA0cT6umRRgjD8Anhjg7fVeUgGTww8ACe9zfEYv8PWEky+R0ymUsZGKhj+/YCzjxz7JxN099Pd7evKgZf5ds3Yh3nOD4JPTgIO3fC5Zf79hBHIhTyr8epwLvf/UH+9V+/zJNPPsIHPnDbqRl0EpOYxCQmcVKodUxeMpMMCW80EG5YeCgBq1uGGN6+mW3OAH1lxXQtmE3aMpnjWJx+HDHGiTAkPO4M9rNdz5Izss/fW8M0aDa3pXKJKEkIyRzb4iUzSZ57SB3cJh3ylKTmJEmSHXqG/ZrNdNcYzUep8AwaZJaUUHwumY9Cjd5jKRSd0qVDOjieNqZTckh4eICJYK+2m+36VrpkNSiFK7J0ax2UuKXkeBG6NIeIIybYqwmbzfVsNjYw1ZlOAL8GzZJhu7GZl801OJxGr/gImjvELCfKadk8IuqdpzdENOqvTB8GpRSgEIG3/14YDyqVQmWzowERlq7z/kUruGFqLfetf42fvfYiv330N1xxxfV8+tM8mGwnAAEAAElEQVRfYPqc+aMBiB6KWa6v5j6SxD8upIYK5DHcnWDroxvob+rDcz00U6NwRiHzLp+DFTlU73cnfUsBNVJuGRKCuqIr5at4J6qmNYIGcy+bw9TTq8kMZQjEAgRzg3gKzhvyeK5J0pH0FyikUEzLBXNELpqwYWOnT0CnXYOW3CK27u5hSm8POSNK2tI5xVQsKjtqn/lT8wGIdUF7EnrSUBBQWJp/TAcS0DwsyJ4kGayUomdvL61bWhnqShAtjlCxsIy8qpMTZU13ZnFt6kYeC/yOXfoOQBD38jh38FJ2bt7LbV94H3u31/ON//g+N7/fv88PRib+PpyVB+dN8c9v+8j51aRiZSksLfrTkcECwaXZCB0jojffmEZQ5Glcl4kSOJn38CQmhD97MviLX/wi//7v/37cberq6t72+B/96EdH/z5//nxKS0s5//zz2bNnD9OmjU/0fOlLX+Jzn/vc6L8HBweprHx7vrp/7ggGg/zgB3cxb94i/vVfv8ymTev50Y/uoaCg6ISvlQhuTMXQAkPU6RnapSKqJNemo+zTs9RrNrkjLd82igszYUpHPHptoNjVKPE0JIIB16VNc0gIj0Yti6UEK+wAZx0RmLBTzxIZaUc6iBASB2jRHBY68IyV4FFrmJCSZIFuadMQyDKEx63p3KOOQ6E4IB1aNAdLCWY65oQtJ46H7XqW3CPavXKURhsOzZp9TDIYoMY1+Uwyj3uff5lHlOLrSy9l+nEK0X/8x79hz556Hn30Fcy308d8kvCGBvGa9oHU0KZOw37jVbIvPosIR1B9fai+HrzEMKk9DWRfeQHzossJ3vTBExKZ40EIgV47C712Fi8sNNi85yVqZSEyGETk5TOYHWZd21ucsXAJhek02Rd+7bNOuo6I5UAshnXRZciSscWBsm1wnaPVv7oBrouyxxaB485N07De8z745Z24jX7YHaaFsfw0zEvHJw61qdPQpo797lG2jdfZjoiNJY9lIIgXDCGrpvrhfBOAd6AZ5bhj/Mx6M2m+vm0Djz3xGy644DLuv/8xpk/3zU+3b9/M7t31fO1r3xp7bLqOsXgpxuKlE9rv6LEYUZQeQDgJlH5oDsIeRmlBlBk9qfHeLiSSy9PXssheyh6tAYFghjOLUq/8mK8pL4f58xWvvy4oLbWYN++vicev5dVXb+f556/nr//6Wv7lX75DcfH4XnUGBhdkLuWe0M/YrdeT6+WREENkRJrL09eR7739wM7xcJAMDgaPlisvXqx45RXFrl2CsjJfIdzWJigqUixbduoLv3jcVwgfD5oGU6b4thCFhYdUy6YJmiZIpxWFhYrBQcHwsF+gOw44zneQ8lakvGUk9O3HdHc/Tn//nTzxxBf4yEfcMerpRYsUL74oyWR8dXNLi6C313/Osg5aUPhBdj09gmefhVWr1FEK7MFBWL9eUF/v22IsWOCPPcH8yzHIzy/gtNNWTZLBk5jEJCbxP4h5jsUZ2SCvmylaR4gIgDPWbKP4lw8zrbuXaVIxoAt6F8xB3PwRFgdzx9hKnCzeMtLs1G1q3UPWCVkUW/UMr+tJLhwRvlyQDbNfs6nXslgIbCCsBJenIyflF+z1dOPu3sB0BoiXVzMwo4aDq8JRJO2ab/IvDhOq/M4aYquRIS38e7kzs0EuyUQwEOR7GjrQT4o9egMSA0NF0QRIpePh0St7qHDDJIQiV0nKJhjE3ThSnx0kggFMLAQa9wc2Yqp55CoNSZT/n733jpPrqs//3+fcMn17L1pp1btkWdW23OQmdxvcMMYYTDChJISSH0nIF0gISQjBJHQw3Tbg3nu3JBf1vmqr3dX2vjvtlnN+f9zVSqsu2SYO2cevtaSZO+eeuTOz8znPeT7P87Lts9fo5ZOpPIreY0LYnHs63pq3UF2diIJC0BrV3ITIy8eYNuNdPZcsK0fm56M7OxAHrVWs3h4+uvgcPva5L/LHh37P97//bZYtO50JZ59NwSdvpvTi85GWySoynGaGuSWdc1KEsFaaLU9tpWt3J3mVeZghEyfl0LyxhVA8xIxLD3TedWYEkws03RlBTyawFwgZUBKFqvgJ1JTKw+yrQzj9qEgJ0bxxRIc8hQcdeKNVsLdfkBfWxEwYm6uZWgAZT/PbrZKGfk1rStCSFFiGZlwuTJlYRUtRFNXbyfJqj5IxuZROLsEMmWgNjQOwuy+wpZyYp0m78FyDpGEA0m4gmCiKBirnsBGQ23U9ggUn4RvcvKGFzU9swc14oDVNa5uoe76OSedPYuoFk7EiJ7aJIxCc41zATG8OO406QKM6TL73w++y8pdPIKRgwd9eSccUj850F0WRwhOeIwRk/dUTNFMKfLZ3C3wF4/NhRqHmOKLidx1lyuQvU/lsNLN0SI+ElszwQpSc4O+MUZwc3vdX9W/+5m+49dZbj3lMbW0tZWVltLe3j7jd8zy6u7tPyg944cKgrWPnzp1HJYNDoRCh46jf/pwghOCOOz7P7NnzuOOOm7noooX84Ae/ZeHCM4772AJt8Il0Lk3SY1AoipVBkTbZbTi8aqXZZTqU+QYL3AiL3Mhw4eEIjRZQ6VuEEIDFOKVoki7dUnFDJsE5TmzovgOIKol7iKFMsKuksbVgUChWWGkEgt2mQ79QCCAL3BPp5zQ3zGn+gYLDRfNQaICVdppBoZFAuW9yXSbBVP+dvQdCCHo40lzB1MffQjUQ7F6xgsrKMUwYUoseCQ8+eC+/+c1P+fd//yHTpx/e/v5uQmuNt+IVsk8+iupoD8Ldyirwt2xCNTehs1n0QD/kFwQyv+4u1I7tZBob8HdsJ/LxOzCPEnZ2PLjKZVf/XoqKajCjB3rPc+wELak2ep57hPxN64KdfCHA89DpFCKbxhxbe1iImiwpxSirxG+oRwiJbm9Fp1PowUGMMTUYVSe2AWRUVBH53Jfw67aiBwYQRcUY4yeOaC07LkwTWVKKX7cdDmoh19kMQorDrCCOBREKA3pIwQCPb1nP1154AuX7/PvcxVz3+X/AHDdh+Pg//OE3FBQUceaZ5534fI8BFS3FKZ5HqOlZfGmirRyEO4CZ3Eem6nxU5E/n3y4QVPk1VPknJu8UAq67TmEYgg0bAk/ZRGIMX/3qQ8Af+H//7685++xZ/MM/fIsbb/wo8gjBfAvcM7BSNi/Zz9Jq7KPMr+AM51zOdI4cYPhOkE6nkVIecQOoqAhuvVXxyCOS+nqB1jBpkuayyxRVxxYov6c4+2xFXZ3Brl2Bh7DjBORsSUng/dvXFxwXiw2pPMqz1NWtBr6M5zFkXSEJhZbhOKtoawssMUpKgj+3bxekUpCfH4TidXSA6wYL/v0fyf2hds6Q711rq8ZxRri60NsLP/+5ZMuWgAj2fcGbb8K55wYhdMfIZDwqLr74Sr7xjS/T399HTs7h6vhRjGIUoxjFuwcfzYBQhLUYVpxZCK7P5DDdC1FnOrRIj7b+Torvu58d2QGyM8cxUdmMS/qMeXMzdtnrhK+89pTO76Dpkj5bZAaTA92Ig0Kxy3DYaTj8MObTkPVYlo1SpE1uS+WyxXJoMFxylcFsL8T4g1TBB6t5jwR39Vtk77ubiu425hsuoVCEzkXz2fmhD6BCIfqFYoJnjxjvj+EBVllpypVJsZL0SJ/HQklsLbjIiTPRD9TRr1jt9ApBnsrDFxILjxAuPjkMCJ9W6RDWJsuysRFCmCMhjWKb6VBvaPqFfxAtH6BbeCSlwdlZe3gtWIzBdsPhTTvD8uzRxSUpkWSXUYcvfMZ44yjQxybOXDSOCDyU91v4mXPmYV1wCe7LL6A72tAIZEEBoSuuwah+l1qGhqAiCdITT0c//xhW/yBWQT66rxcMA/v8i7Bycrnllk9w440f5b8fv5uf/+pH7LzxdhLVlcz56EeY+JEbWV2qmeHZnOEew8vsEPQ199G9t4ecspxhf2M7ahMriNK2tY0JS2sJJ4I1c3lMU9cjWVSm6cmCqwIFbUsSCo8jUJWpViLb7sLq2QoqizZjOMXzyUy+hUHi/HyTZEOnIGwEq+Q2HwrCgqkFipABnlY8uUfQ1A4hQ1OdgCkFGtMQlI0vZE9fEdZcn8qhpaHS8OguwYuNkgE3uM2WMOhqLCnIs0Fo6Hcg5QnKY5op+RpfCzLeiRPBbsZl56u70EpjRSw6d3biZl3ctMfaP65jsHOQudfOIZJ74uH0haqYQlVM3a5tfPjT19K4fgeTz5rLuR+/BisnzJauOp7Y8xy3TLv+hMfcDylgeiFML/yfN+ZNaMmSd9BtMYoTx/ueDC4uLqa4+PhqqcWLF9Pb28vq1auZNy9Qqr3wwgsopYYJ3hPBunXrACgvf29TSP83YsmSs3n66Tf45Cc/xLXXns8nP/nXfOEL/zjcOn40CATVh/hC1fo2tb591KKlwjcpUgbt0ht+bEJLwkjmuRbnOrER/r/7cZoXZpOVpVf45GljWNWbpw2mejZ9wmdA+LRIlwGhKRpKq/RQNBk+D0cGmDsYGp7TKivN86EURcqgUkl8oF663Bvp5/PJAnJPMlBun3RZYaWpMx3apU+T4ZGvJYmhuTZJj3xtHNML9WBs2rSOOXOOrszcuXM7X/rSp7j66hu46abbTmqupwJ/2xYyf7wHAKN2Iigf782VeNu2QF4++F4g/+vqPKDOLShCew5qdx3Ze3+D8YWvII7Q1n48GMIgYkXoTveOnJNWWI5P3tMvgudjTJ6KTqXAc9FOFiENvB11HErtC9PEvvhSUj/4Lt7Tj6FdLwi6EwKRk4O3Yzu6ox1vyyawLKw58zBPX4g4AvEmQiHMmXNO+jkNP14IrCVnQv06aNmOzh8TEOvNTRhTpmNMPrIv7pFgTJuByMunded2vr5pNc/uruPCaIK/KyikOBIn89PvY513EaGrP8hgcpB77vkFt932KSzr1C0ahNMX7PaHC8GMkhl/PcLLYHWuQww2os0oTtkZZCbceOrGq38i5OTArbdq2tt9+vsDwrKgAOA6zj33fL7+9S/zxS/ewfPPP8mdd95F4hArEIHgNHcBc935ZMhgY2PwzrbdtYbmZobns98xyTBMlFIopY5o71NbC5/9rKKtLfg4lpUd3X/4vYLnwZ49gRK3rEwzdSrceqvPc89J9u0T2LbmhhsUDQ2CjRsDa4hQKPAjLi+H4uIO6up8oAzXDa5FOg2OU4JS2xgYEPzgB5KzztI8+6ygszNQQLe3B+MkkwKtAxWyGnLHCIeDt2EmA5alKSsb7s4cxqpVgs2bBRMn6mHyuLc3CNmbO9c/zALjRHDxxZfz1a9+nhdeeIqrrjr5Qn4UoxjFKEZxfGg0a8wsL4aStEmfsBYsdMOcn40RQWIhmOuFqfZN7oz1kFO3g6K2bnonjSclNZtklnA0Qm5eAf7qN9GXXnnUDImjnX+VleF5O0mX9Gkw3CBw2w/GWGdm6JYKjSCkBS/YKZ60k1Qqg5iWzPHCXJVJDPsWB88nwwo7Tav0qPRNlrgRZnmhEWss1dlB9o93o5OD5EyYhhvO0tvfQ+lLr9JZU8maC88gosUIEqZRemwys1Qpi5wh9XOpMvGkxwo7zVInSgTJh9I5eHTyB9MgI0wKdS8pHSGuk2SER0aYFKgyTnOjLHOOXePvMRzuDvfTaHj0ylp2m4IM+1joVpARmhbZSZ/U5PrTRoiCDAQxJDuMo3fubTDX8kDkbpqNJjSKfFXIhdnLOD97yWHr0SyaZ4xentXtDApFrYhxmS5hth8KOv+u+iDWvAX49bsRhokxaQqEQrivv4zq6EAkEpgz5yBLTj0HoKu+my1PbqW/JZ+cyGnkt2+jINNL7pzJ2EvPxZg1F2/7VnQyiSwpofr6q1lyy4Xkvb2NdT/7BSv/7Tus+Na3qbj2Mp657XaWTDvvhG353KyH7/iHBd2ZIZN0fwYv48FQM9+ics2adtg7AOUx8BQ0JwXVCc2s4mOQi1oR2f5LrM61+IlatBlFOH2Eml9E27m8FL2ZjZ1QmxtYNgCkPVjTDvNKBfPLNGdVaibkaroyBjFTUx47sJSQIsga8vWBLIxNnfD0XkGOrakc2jNYsQ+29wrKYpD1wRCCmoSm34VxuZrSGPRmofJEVM5DGGwfJNWdIpoXpXlzC0ppovlRVELhpBxaN7dRX1nP1ItOXATlOA4/+cmdfPs/vo6RsLn8729j0oJZw/eXRovZ1LmVvmw/uaGcY4w0ilEEeN+TwSeKqVOncvHFF3P77bfzox/9CNd1+fSnP80NN9xARUXQUrFv3z7OP/98fv3rX7NgwQJ27drF3XffzfLlyyksLGTDhg389V//NUuXLmXWrFnHOeP/TZSVVXD//c/xwx9+h29/+2s899wTfO97v2DWrNNOabyj7V5HkVyYjfGH8AA7DIeYlgwIRVQLLszGj0gEA5zuhtlruEFBhIcm8Ni9IhOnSlkMCoVC0C19ipQ5fH4XSGhBm/BolT7lQ60Iq+w0NmK44DKBccpip+Gy1XRYdBK7Vo3S5WfRXvZJj1xtkBWKAeHztpmlTAWOyPlaclUmQekJtkJs376Fj3zkyB7AHR1t3HbbBygrq+Df/u0Hp+zJezLwVr+JTicxJwXkpHYVKpsNGCbHQQiBNgxIpQJi1TTQloXwfURJGWpfI962rVinnX6cMx0OKSQLy07jj3WPMOAMkrDjeMqnfqCB8dkw+X1ZCIUQto2w7cDXysmiuzpRTQ1HHNOYOQdZVIxKJCAURsYTyIpKdGcHqX/6B0RuXkBc+x7++rVYO+sI3fSRk1oUnAjMrvXE5HPIhX2o1hYyfdsZTE9En76Q8NXXHdend8Rzqq7hlapKvvDdb2IqxXfySriovApj7HiMKdPQfb24Lz9HaGoNf3j+CTKZNB/5yCdPaGx/1w7cVStQLU3I0nLMeXOJW1uwW15HeGlUKA+nahnZ6otJzfwsxsBuZKYbFcrHz6kF8b/DC0qIwAf3UG/cgoJCvvvdn3HJJVfy2c9+lOXLl3DXXX9k4sSph4+BIMI73/Xu74f77guUyul0oJidP19x1VWaxFCC2+DgALm5eUd8vJQBqfpeIJWClSsFL70UqHHnztUsX66HyHPYtw/uvVeya5fAcTQ5OZLFixVXXqmZMUPR1xcQths3CnbsCFr2Uqng10lODvT2ClpbhwJuBPg+dHcHhDEENuCJhOaNNwQvvigYM0ZTUwN79wpiMU04DHl5muZmMWz9J2UwVjoNSmmqquCqq/RhexTr1wvi8QNEMASe0i0tsGuXYNKkk1dWVFXVMG3aTF566dlRMngUoxjFKN4jrDGz/DrShy80BUOhcA+FBtgpXS51YlT6FlEkG60srdLj7CwYSmNIgzwt6BR+IDQxzSFLMf+w3Injnf/uSD9SQ6E2GFA+u60sr1gpJvgm3UMEtSUCr8xdpkOrVIS9oAPyWTvJPulxRyqPCJJXrTR/DA/go0kg2Whm2WY63JDJGeFl7G/bjOrqwJg0FUNKZrthticKyUT7yHnzLUrPX8oFTozpBymDe6UiKRTVh6xLcrSkT/gMCkVES/K0wV+mxjEg/8h2czeTvCoajLHsNUpISYdSP8E5ToLrMznH9P3MoLg3PECD4VHrW5j+LEwuZKv5LK1GKz4aLSJE1Fl0iMn0CX+EMMdBk1BHHr9dtnJP9C56RQ/jvAkYGLTKZh6K/J4Sv4xZ3oG1rEbzQ7WbB7xmnHQf0nPYGkmwJtTB3xlTmKdiCCEwxowdDp/2W5rJ/PT7+Ht2BYMojSx9hvCHPoI5/eR5hcxAhk2PbibZlSKvKh9Zs5S27tNoGkgz87y5lJUIMv/1H/i76oLCJxbHu/ky9NI5lM2dzcXf/y5nf+OrbPzNPbz981/y03se4OVJU7nmmhu5+uobqK4ee8zzxwtj+IkYdf0GOXkhSslgCEj3pokWRAnnHnhvTcyHm6cqntgjaE0KDAnTCjVXT1DkHWOJYvTvxuzZip8Yi7aCTQIdykP5aey2ldTHr8KUiWEiGIKQOoVgV59m/lAzYVkMZhVr1rYLyg/qum1NQlEYanIO3LauQ+AqQVEkuC3jQUtKkPYEgw5ETU2bo2lOCmKWpmVQgNCcXaUYdxJNW8KQCClJ9aZw0y6RvEiwDtYaaUgieWFat7Yx6byJGNaxlRie53H//Xfzne/8E/v2NXD1zTczeEaY8WWTRxxnSpOsm8VT3olPdBT/p/FnQwYD/O53v+PTn/40559/PlJKrr32Wr73ve8N3++6Ltu3byeVSgFg2zbPPfcc3/3ud0kmk1RXV3Pttdfy93//9/9TT+F/BQzD4NOf/iLLll3CZz97G5deegaf/ezf8rnP/X/vqh/tEjdCQhusstK0SY9pvs1iN3JMewYLwXWZBPOHSGFTCyb5NmVDRUxcS2Z7Nm/YadJCIbXEQTMoFWW+iY3AH/oS0Wj6htrHRjz/oWTLtDg8ZOlYeMlOsU96TPHtoTYjk0JlsE96nOVEqVImU73QMBF9PHR1ddDV1cGUKdOPcF8n119/MQMD/dx337PETsGL91SgujoRoQPFgc5mwXUR8QQYBnpgAAb6wPUADa6L7myHnDxEQRG0t0I6dVLn9Bv34r72Mv72LSyKRXFKEzxb1sE+mkEIymKlXFa2BCtej9fWgfZcdDqD7usJFMLZLGiFdt3hoLjh57OvET04iLV4KSJ+4Bq6HR34e+uxL1iOLAzay/RAP+6q1zFPX4A5beapX8RDYPTtJLr5x0inB79mKrJ8HPGePYRz8kie+REInXhlopTi29/+Ot/97je5+LyL+cd4Pnk9PRiTpiJycxFCIPPjRPzXiaz+Gnf9fBXXLKxh3OArZPxrwDj6Z89bt5r0b3+B7utFxOJ427ehX7wbThcwYyIqUozM9hCp+y0gyNYsx88Zj59z7BC+/4246KLLeeKJFXzsYx9k+fIz+N737uKSS65618+jdUAEv/aapKIiULD29cFzz0lCIUVRUcC69vR0HZUMfq+QzcKPfiR59FFBKiXwfXjlFc3TT2v+/d8VBQXw299KduwQ1NQExGxXl+aZZwQFBYG/cH4+vPmm4M47Jb29gTq3tBS2bAmC6GpqNAUFhezebWCarYRCGikF0Shks204TildXdDeLnBdTTYraW3VJJOa0lJobBR4XkDo7l/LW5ampweUCsaprNT4/uHPzzAOKIn3Y39wyqlYROzH3LkLWLv2zVMfYBSjGMUo/oyRclO4yiPHTpySyMFH87KdxBOa8UNdeBJFnaH4Y6Sf9XaGat/igmyMHuEjECRrxuDmJAh3dpMpLsQCktpDdbVjnXnuCW3Ip700HakuwlaYlwskWRTl2qRZurQYPhLYY7q0Sg8pBCFtMMGzSQlNUmjylUABJcokD4NtZpYX7CRjlc1ToUEsYJwKnk8psFe6PGcnmeuGCCMZFIpBJ0UIMIe+pGJaMtcNkRI5+D0+lyYLD7Pfy1eSuJb0CzWCdO0TPjnaGA7ZAzAx+XD6Bn4V/QkN5lZgK+UqwWx3Nh/ILGSeWzAcVnc07DRdGgyXcb41ZJshmOtezQCTaDB3MM2zqPEn4utxvGJnWGmlOc+JYiHoEgqpA1X3kbDRWku7bGWKN2NYDFShqqiTW3nbWjWCDK7zB3jE3Yuf6qVcRDBEjNRgkkbl8RvbZK45a9gyAgKbPOeJh1G7d2BMnIowTbTWqN07yT7wB4zxk0ZkdZwIOnZ2MtAxSEFN/rDtWKwoQXfaY9/6feR3voC/dROyphbCYXRvD2OffhFjchl9ZWPI1QaRggKmf+4Ocj93G7OeWM2WP9zHnXd+i29966vMn7+Ea665kcsuu5bCwqIR59YaVvRHebJqDk2tGcxBqJBpFvQ1UKo1YxfVYB5iJjuvVDOjSNM8GATIVcSPHxwn3AGEn0GbI9Xi2owhsz1EGUDpHOCQDXYN5kFjCwEX1Sj2DUi29QiipibjC8IGXDl+JCGd9gSmOIgwTkHGB0OAbQS2FhELmgc1WQUlMc1NkzVnVOoTDsIDyClLkFeVS9PafcPWfFppnKRDvCiGtm1aBzWvNsKYQhibc/j1cl2XBx64h//6r39l9+4dLF9+Fb/61QOUjavmW29+j7ZkBxXxgBHXWtOe7mBqwSTyw3knPtFR/J/GnxUZXFBQwN13333U+8eOHTv8YQSorq7m5Zdf/lNM7c8SU6bM4PHHX+d73/sWd975LzzzzGPceefPmTbt3VFVCwSzvBCzvJPz5pUIxvv2cJF3KK5L5/CKlWaP6eIJhaUFNb5FSAsqlTVMHAsEEzyL1+0MpQfZWSSFwkJQ5p/4x8dDs810KNTGiOKhQBt04FPrW4eF4R0PdXVBcOLkQywCenq6uf76i+ns7OD++59j/PhT6Fc+RciqMXgb1w21XYugQDaD0s+YOg1vy2b04MDQt50Ay4bBQURhUXBbJIKsODzAa7+vWliLESEZ/t49pH/6fXRrC+TmI7u6OHtnhtkL5rDnwkVErAhTCiaSsOKk572J37AXv6kpIJy1BteBcATV3k720QcJX3PdyBMrFRxnjGR3dE9nwBwd1FMvEjmwrxF/z+7jksE6lcTbGlwLWVyKMWnKUdXEdsvryEwnXv70oNqxc/GjhVh927G71uJUnHPMc+1Hf38fn/70R3j++Sf58pe/xmc/+7ekv/Mv+HvrkXl5w8fFzY1Eclt4bKvPnvYkv/jsdMJ7HgQhyUw4slpROw7ZJx+FVApj8rTgtXf6EWvXMbA+Qd6cIoRl4ltxjMEG7H3Pk608D8yTK4yPCi+F8DLoUN77Rl08fvwkHnvsNT7/+dv52Meu4zOf+RJf+tLXjmjXcKpoaYGNGwMieP9LWFgIvh+oYa+7LigQ29vbGDv2T0u6b9ggePxxQTYbhMHZdqAU3rhR8OMfS669VrFnD4wbp9m/ji4qCo55/XXB2WcH39U/+lGgCo5GBalU4N3r+8FHM1AO24RC84jFXqC09CoGBkApn56eFzDNv0Tr/aphQTp9QF0cCsHAQBBOV1OjaWoKQul8P7D9GDtWM2eOpqsrUC8XFPhMnHjg+c2dq9m6NRhzv5dwZyfE4zBxYjB3xwl+otETJ4hnzpzLvff+kkwmc1wLplGMYhSj+L+C7kwvT9e/yPqOjXjKZ2zuGC6sOYdJ+Sf33ZYUihbDp2A4sFqzwczQJwNCNkcZDArFHyIDnOaE0EB/VTkt555F9RPPYvf24oVNynvTGBXjMM5bRo8I6tMjhXMprXip8XVebHyNnmwfhOOsX7ocGcpnsynpkz45SlLtW7RJL1C+IpjvhsnXBiutDLYWuIJhRW1WaLaaDlvj3fhoBoVmmmdTrgyMoWOKlUGb4fGcneRlO8VO02Xs5CjXxjT5qS5qogXBekRpwr29WKcvPowIBqhSJrPcEK/baTwVBNb1SEVKaJZnI4epfGv8Wj4/+HdsNtfTJ3spUiVMdWeecCdUBo2P5mBphgIyYhwJVcMMN0Zo6JzzXFhrZVlnZsnRAWl9kRNl9lHWjYNiADH038EI6zBdRgcAfnMT3pq3aWtcy8xySf/UCQyUBURl1IoQyaYZ3LGG3k1rCHd3I6vHYC1YgohE8LZtQZRWDNfzQghk9Rj8hnq8XTuQkUggmsnJDbJDjqMmd1NBm9Oh+RNW2CK1twW/tQ5ZU4sYKkJEfgGTW5tZ9Np63ry2nBbDQyCQwEInxofOvpzQ2VeQTA7y9NOP8MAD9/L3f/9X/MM//DXnnHMhV175Qc4992IKCgpZ0y54YKcgVJLDjDB0tw3S6MbwSyfyN6drqmYf2foiZHBS6lkVLUfbechMJypyIJxeZjpR0VLGlxbwfJemLwu5Qy9rdyY4z9RDvG0n5MGn5ijebAnC5goiinklmumHWEJPyNO82SrpyWh6s7CjVyBFsNTzFPhD4Xe2hIoEfGOJouoUsq2lIZmybBLJziT9rf0MtA9i2gbhRBhVUsDG+kG6amp4fqdNpB4WliuumxQEtnV3d3Hffb/lpz/9L/bta+DCCy/jBz/4zYhO7GVjzubBXY+zo3cXUTNKvzNAQTiPC2vOQb5P1kKjeP/jz4oMHsWfHpZl8Td/8w9ccMGlfO5zt3HRRQu55Za/4Atf+Cr5+QV/kjloNOvMLKusNO2GT7VnHlNBnIvBZ9P5/CrcR49UJJTAF4I8LbkkGx+xa32WG2Wr5VBnOBQqE0doeoXPAjfCxBP09QWQgK3FYWri4F9iODTiZLB7906klNTUHCiEe3t7uOGGS2htbeb++59l4sRTC2M7VVgLF+OtfgO/bhuytByUjzACKwitgZCNGDceujrQmUzAxESj6HQK1bCX0HkXIsceCMPTaN6yMjxnp2iXHhaCGW6IK7JBcrLz0vOo1haMKdOHFSKqt4f8DXVUnncVsqQKd+VrpFe/iepoDyweNnUFbX2mgSgsCoq4WAx3xavYZ52DLD6oGKmoQpZXoJoaEVXV6M4OdCaN7uxE2CFE4kB1oHUQyna8YDh/zy4yv/sFqrExON40MWbOJvyhW5FHCI0yBuvRVnykl640AY1Mtx92/JGwY8c2brvtA3R0tPGrXz3IsmXLgSAAw9u2OQiiC4UxxAAhbweejvPDVW0snFLFabNmoVIt2C2vkh1zMdo+fI6qrQXVsg9ZVjH8OggviZkrcfs9vI5+rMrg94Gy85DZHqTTgzLfmT+BcPoJ7X0Mu20Vws/iJcaQrb4Er/jUbGvebcTjCX7843v44Q+/wze/+Xds2rCeuz51LwkRR5VqvFp4J3bB/f0BeXqoXUU8Dh0dgmg02FjZt68BWDLimEwmUO8mEu9MyXo0rF0bkLVlZQERDAEpGo0Gat+lSwWeB4cKquLxgKTNZmHnTti2TZJIaPLygqI/mYSOjkAlXFwcHJ+X91esXv1RqqvnUVs7n/Xrvwckyc29FQjC8Vw3eM4FBZpkUtDcHNhA5OUFgXF5eZrCQujpgfPO0wy5SxGPw7ZtsGaNGCZ5ARYv1mzfLli7FrQOzhGJwMUXK8rL4cknBStXCtJpQUWF5txzNbNnH243cShmzpyL7/ts27aJOXNO3i5nFKMYxSj+3JDxsvxq871s7NpKcbiQsBFiQ8dmmgaa+eTsWxmbc2KBvgAhLYlpwYDQ5GvokD69QpGjJEmpiWpBsTbZIR26pc8Y32SH6ZG85mI6q8qIvfEWOf2DxBfNZOcZi3m2NodW2UVIC053w1zoxIgdpJZd1bKa++oeIWSEKIuWsL6yjHbDx3YHCYfjaCApNb34RLVkgmexycrSYHjkeAa2hgGpSGhJuW/iolllpWiTPgktiGtBRijWWVk0moVeICzJCkWzdPmPWDedUmEB7dOqqVo0g9NeXo0Z7qHSjKJ7upEVVVhnnX3E6yUQfCCbQwjJWitDv/TJVQbnZ+Oc4xxZxBLXCRa6Z57wa3IwypVBXEt6hBq26POBAeFTqIwRNoHjlMWgr1jqRJnk2Yz1LSoPsgA8FKUqqDldHCyCwkSjGRSDjPMm4G3ZSOY3v0B1tJGnBzhPpXBe38wrN1xIy4TgPTZr9U7Oe+AlhJuHH4nirV+D9/YbhK6+PtillocUddJAZ7M499+D6ugI6m3Lxpg4Oaj7D1pzHIpoYRQpBV7WG/bt1VqTHchSVGxAo3tYoIEVjnLlC2uZs/Qy6grCKGCiZzPTCw1fu1gszjXX3MQ119xEZ2c7jzxyHw8+eA+f+UwQfHz66YuJTb8UPfFS5k6fhsjJpaAyhzGOYk/SoLtUI4Q+dLqnBBUtI1t+FuH6RxB+BmXFkZkeQJGtvphZZTZL+zWv7ZM0JwN9cNSE88coph6BZqiKQ9XEIDj+aJhXqnlol+bZvRKNJulC2of8EOSGNH3Z4DrZpubsSn1KRPB+5FXlccZfLCZWGKNxdSMYEis3wva9SVI5uRTOHUtxvqbPgZcaND1bXmDzsz/nqaceRmvN5Zd/gE9/+iGmTJlx2NjnjzmLwkg+b7auoSvdzWmls1lSMZ/a3Hc3wHAUf94YJYNH8a5g1qzTeOqpN7jrru/zn//5zzz44D184Qv/yC23fALzXfRP9dCssTKsM7MkhWKKZ+MKzbN2Cg9NHMkbdprNVpab07lHbRWa70bIVQZvWxlapUeFMlnohhl3CME73rf5WCqPF+0Ue0yHqBac7cQ5z4kdt9XpYAiCAud1O8Uu7VKkDCqUQZ/UFCuDSd7J22vs3buLiorqYWuOpqYGbr31Glpamvj3+x9nxexK7jE7KVIGC90w89zwCFXyiUBrDZk0DTa8HfVpNjxKlcHpbpjaI5DhRtUYwh/9C5ynHsWv3wNSErr8arQdwlvxCgwmEfn5yFmnIUtKUM1NqPYgvcq+9EpCl18zou1vg5nl7nA/vghUDlk0r9op+qTPJ/sT+Nu3IguKRjxG5OahW5vxG+pxXn4Bd8UriFAYTBOtFESjmFOnI4tLEBVVSNtGK4Wq2xYQmgcVZsK2CV16JakffQ/vyUfR2cwBWWIohO7uRBQHTJzuaEfEExiTDveHHb6ejkP297/Db2jAqJ2AsKxAJfz2GzhFxYQ/eNNhj/EjpZg9Ww8ZKFAsKzvvuK/h008/ymc+cyvl5ZU88cQKamsPyButhUvwtmzC27gOISTS7kFWp1gvx/Lqtu386kvXBKezc5GpVmS2B/8IZDCGGRS8vjf8DtNGGI2FwBuhrBbuANqOo613GGygPKJbf4bVtgIVKkIZEayuzZj99SRnfgav8P3h+y6E4FN3/A1z0nO49T8/yI2rlvNw2aPkVuaTPVeT+rBCn+KlKCwMyMre3kBVux+9vZCToxkzJo/KyjFs2rSOq6++AQjI42efFbz5ZqDarajQnHdeoIJ9N+F5wcfk0NxBKYPbIxFNOCwZGNAkEsHxPT1QXw8zZgTEalOTQIiRBGrQbKDxPEFlpSY3FxKJ69C6g82b/x+O04phzKG4+Al8vwytg/NFo4FKN5kUWBZkMhrTDM7b1xcQ6rYdEOxKHQgaCc6paW8XtLYGj4lGYfPm4NqZpsA0A9uLqVM1EyfCb38rePllQU5O8Dy3b4e9eyW33qqOe52nTp2JaZps3Lh2lAwexShGMQpgc9c2tnXvYELuWEJDdlV5oVy29exgxb43T44MRrDIifBAeJAe4ZMWCh/NgIQCJYcJyASSPqm4PZXHI+FBdpkOvUvmUrVoPrOzcTrR/CrShytcQlqw3XR4xUrxUGiAT6TzWOhGIJ3mzc3PYnuaqrwKkiGb/pJKClMZuqMCpaKEZGBP1yl9Jno2E5RNj6+wtWC34eIDphYUK4MB4bHWcqkzXCRQpiwiWuAI6EKxw3SZ7gcdjzsNh0bpMSA1EvAQpEzJA7ddRXJsDQteW0dpnyZ83oVYS8/FqDz6NUxoyU2ZHC7OxhgQikIdELbvBSqUySI3wvN2kkGtiGpJl/TI0QY5Wo4gegeEIlcZnOvEGOcfP+R4pjuXid5UtpmbKVFlmNqgzWilTJWzMLUI55HfoXq6yEydipXpptlro2JfD/OeWsljd1Qisy7znn2DPCUIDVn0aa3x67birnoNWTMWf9PGYds1ANXajDPYi9q5HXv8ZEQ8gU6l8DatJ3PfPUQ++dmj2p0U1RZRNL6Itu3tRPOjGKYk2Z0inBOmelEp7IkH1mx5+cOP0T1dhMbUMNsuYE7m+OvvoqISbrvtU9x226dobW3m+eef4rnnHuf5u/8JL/sV1pSMpXb+pdTOX07l9KUIEaXPeXfrxcz469B2DnbzK0h3AD8xBqfqApzyszAFXD9JM6fYZ3df8OpPzNdMyDu+BcXR4PrBurwirnF9yIagOxOog8ckoCCs6MqAbQgurT0g4lI6UA2bAnqyQeNoQfj4udfhRJiFH5nPuMVjad3axo7mLG2F+ZTNqEDmxuhv38u2F37Dumd/yRMd9UycOIW//dtv8IEPfIiioqNvFgghmFsyk7kl75414Sj+72GUDB7Fu4ZQKMQdd3yeD3zgQ/zrv/4j//APf82vf/1jvva1/+Dss5e94/EVmgfCA7xopxA68AdePxTuMN6zmDjklVWOyS7D4ZlQkhle6Kiq20m+zaQTUPdO8m0mpi3SaEzEUcPrjoUVVpoNVhYNtBseTdJjK4LZns21mdwR1gcnir17dzN2SEX71lsr+djHPogZDvPhRx/k7rnlSFKUKpMOw2Or4dAtfC5yTtw72NuxHfeFZ+isr2NbDPYtnkvDBeewIR7lLSvDjekcTjsC2W5OnIwxfiK6pxukROTlI4TAO3cZqX/9BsIwMMYFamZZXoFftx1j/IQgCO2gjQON5jU7TRY9rMKOAzFfsM1wqLM9xoXDqJ7ukRNQCi0Eqq0V761VGBVVgYUDIAqLcfbsAqUwDlIgk8kgQqEgDO4QGDNmI4tL8eMJZGk5MicXUVCIt+5t3DVvI8fUBK5m8Tj2xZcjx9UeNsZ++Lt24DfswagZN+xPLKIxZGEx3uq30MuvQBzi7+yWLcFufxNjoB4/VgnKwxzcix+rxCuae9RzAfz2tz/jy1/+Sy6++AruvPMu4vGR29siniDy8Tvw1q7G37UDw8ogwjY/+f02qopyuHxxoCwXTh/aiqNC+Uc6DbKsHGP8hIBUjsURhoEyE7j9JpHCLFauD8pDZruR2R4yVdcNB0WcKszebVida4L0YSu4Zl64ALNnC6Gm5/AKZh6/QvsTwX5dcPkTF/JM/vNc1nsJF7Sez+P6SYofK0UVCNI3nlphXVwMCxYonnkm8OSNxwNic3AQLrhAE4vBzJlz2LhxHQCNjfDf/y3ZtElQUqIpK9PU1UFDg+S223zeaW6q1tDcDA0NgkgELEvQ3R0ExgkRKHMdJ7BlmDULZs/WrFwpME3N3r2Crq7guHAYHntMYJoB4d3XFyiCo9EDzi2Oo1m7VmKaIKUmJ+fTfPObn+IDH9D81V9JVq8WDAxoXFdQVAS5uZqWFoFhaGIxQXExCKEZOzZQRxcXw65dQXEdCukRz6mtTZDNanbuNPC8IKTONDWhUKAILiuDxYsVkydDQwOsXi0pLz9g3VFQADt3al54QTBrlj6mEjscDlNeXkVjY/07ezFGMYpRjOJ9gD7hs8bKsNNwiWnJLC/ENM8+KXFCe6oThRomgiH4XZ1jJ9jdt/eoj+sSPuutDN3Cp0AbzHbDFGqDc5wo3VLxtpWmXfikhaZSSWZ6IYyheQ0IxXgvTLWy+FQqjzbpD3n2GhjA96I9OASCjjVmlgEZWMjVWQ4/pIe929dxya8eYvaOV5kWi9AzN8OGs+fhmialg4Nks0mcSA5pYWJqsDRM9G0cNEXK5LZ0DtZQjslOw+F3kQG2W1myQqMI1kDdwqdMmxQrk7R0SKLZZGQp0AZCC7ICDA2xIWsEB01v1OaZS5bw1kVnss6JMMePcLobpugEypACbQyT5e8VBIKrMgmKlcEKK82gUMxxw1yUNXnFTrHLcChUBhmh6RE+S5wIY0/Qti+m49yW+hRPhR5ho7WWrPQ4zV3AhZnLqGgQtDbXs2NsET2hNNhhom0eHflhclva8foGiLZ3UN7cwbiJB7rPhBDIkjL8PbsIXf9hdGsL/rbNiGicVr+P13L6GaNa0TJBLB1mfGgs8WgcWTUGf/sWVFMDRvWRlZymbTDryhnsen0PrVvb8LIexROLGLd4LIW1hWTrFuC89Bw6lUJEY+ieoIiyzll2SkHWZWUVfOhDt/GhD93GD1c7PPXiSzjbHmfXm4+y7vHvI02bvNr5RM86C/v8szj99EWHrStOCYZNduwVZKsvQngptJUY6oAculvCtMIgkO7dwLYeQcqFJRWBB7DWsKtPs6FDsLUHJucL8kKBB/HkfEi58HKT4I1WQXcaUh4YUhAzYVyu5pJxigl5xz6nkILSKSWUTimho17T8PQ69jz2W3a/+Tgde9ZhhqJUL7qOaRfcxo9vXYg8Vab7HSDtwcZOQfMghE2YUfjOVNGj+N+BUTJ4FO86iotL+fa3f8RHPvIXfPWrf8ONNy7nggsu5ctf/toJ+wnXGQ5vWxk6pEelb7LAjeAKzetWmmJlkDdUjLRpl02mT4UYWZyUKpNW6dEp/RMOZDsWBILoKZDAAINC8WQoiaUFFzoxOqVPv/Bplz5VvsWck/RE3o+GhnqmT5/Nz3/+fb7+9S9Rc/rpTPvdD3i6PMGgcCjQkhwtqfUt2qTPi6EUC9zICRHP/u6dZH7+Q/zuLvaWJSCZ4cw/PEF7QwdbPnkre0Kax0ODTPPsw/zCtNaQzSByckcEspljawld/UGch+/D37UDEYuj+nsR8QShC5cfVrj4QLP0yD1k/DASX0CXqZm4YDHZ++5BJwcRsXig8G2oR5aUBuENTnaYCAZQsSiivALV0oQeHAh26LMZ/IY9mFNnjLCoGH5MQz26pxt78VmI2EEEpr0E3bIP65zzkQVFmJOnImsnHDvQJJsJwvMODVoMhdHpJNpxEIdwpF7BTNKTbyG05yHM/l0gDLycWjITb0JFio96qh/84D/4p3/6//joRz/FN77xncM8x/ZDRKJYS87CWnIWANuf6+PeVx/lnz60EBMHmepFZrrI1H7giBYRAEJKQld+AN3bg79jG8Geu8KYtAT7jASG14TItKPtHDJjLydTc9nRr9EJQqZawXeHieD9UKECjP49oH0Q74OvOAfCDwnkoGDe2NN5zn2JS7dexIVty3hGPkf+yhIyyzX6JDzWDsYVVwQ2DG+8IejqEiQSmgsv1Jx/flA0z5p1Gj/5yZ28/jr86lcGq1cLbDsgVzMZmDVLU1+veeklycyZ6pT5c98PCNyXXgrUvgCxmKazE9LpwGLb9wNy99prNdEo3HSTIhIR/PKXkoEBKC+H2tpAifv444JlyzTV1YFKuLk5IGW1DjyRi4ogHg/C3Xw/IJHLygLl8Q03aJLJ4Lz79gX39fYG8wmFIBrVGEagPN6zB+bN0zQ2wo4dAqWCazlxIpSWanbvFnR0QCwWWEls3izYuROqqgRLlgRK4d274Y9/lHzxi4qWFkEyqamqGnl9CgoYui8gn4+FkpIy2ttbT+2FGMUoRjGK9wm6hc/Por3sMBzsoE+IlXaKi7NxlmdjI1Se+0ObAXIPUYDGrGgQxqXVCC/MtJcmERlHneFgAmN8a7hrb5fh8OtIH83SQyJQaF6xU9ySzqXWt7kxk2CpE6FRejwcHqBjiOxNoWg3fEJasNgNs91w2GZmcdGM9W0KlMQBWgyPfG1Qb7gMSJ8iZSAQdEqw29pZkW5ldghI5CD7exj/4lpcJG9NHE/GEOQN9FKYkWyPQkZqhBbUS4ewkMzzwsz2wsNiln6hcNEktCAMOAJCWpOUmgEU+cogVxnEgYucGKe5Ye4O97NVO0ghAnUxYCMYANoMHy0E9ZbPTmuAlVaaq7MJxvnWiJC4Q9EjfF63U6w3swgEc90QZ7gRco7xmDSKzWaWDWaWvaZHVAlKh9ZxUS2Z4tlUH2LtYCM414lxthPFBfZXzGN9ixfsJG1G4NF8hRPnjGyEtWaWPumTrwymHGFdcjCKVSkfTt/OYGYAD49cnYdAsNeoY6/p0is1YS1RaOziGmJNu8jp72fs7h1MzEhmmAXkH9qVpxRCSsxJkzE+9Ve4b6ykq3E7v0tk6VWa2j3dpGyTvf2N9DsDnF46l3AkimptQSeTR50rQDgnzPRLpjLx7PEoTxGKhxBDRGHo2hsQefl4b7yOSicxxtRgnbMMc/6iY455IjhnnE3dwCVk5lzM/I9+j469m9i+5mW8+ld4/qGf88effwvDMJg5cy6LFi1l0aIzmTdvIYWFR1+XHBdGCH2MoOp3C54ChEAOWV0IwRCZq3EV3DpNMyFfUx4Ljv3tVskbrYKQ1NT1CrrSkBeCWcWa9R2C1pTkU7MVlcfQWw0M9LNy5Ss8++zjPPXME3R1tGDH8qg9/RLmX/tFxs67hD3ZXJZWK9K+ZiAdeCRH/kTLmL4s/GKzZEsX+FqggWdD8IGJisUV764SfBTvL7wPVsqj+HPFzJlzeeCB53n00fv45jf/nmXLTufCCy/jr/7qK8dsf33DSvP7cD9JoQlrwSYzy2ory2TPIi0UNeoAwWgjsTW0SZ8ZByW+u/tVvPp/XhnYYLh0yiAkzkBQqkxKMSlRPt3Sp0v4FOuT/yh2d3fy5puvc/fdd3HlJ+5A/MsXCFk2NlnKlIEnAlI9TxkUK4MdhkOT4ZHvHZ8Mdl55EdXVSWbKZNpDGSJaMpCbT9HaDeRv20lmxmSaDY99hsd430Z1d6FaW1DtrXjbtqDqdyNsG3P+YuzzLhhWu9rLLkbm5eOueh3d0Y41eRHWGWdjTp1+2BwMoFBJdpseBzfJOGgEkKMk9tJzUY178dauRnsuaI0sLiX8gRtR3YFiWGvNnuIcXptUyp7iHMILKpn39lbOqO/G2teIkAbmxMmEbrj5iDvp2nGCXnJ7ZAuaDIVRObnY510IQuK++iL+b36OCEcx5y/CWnLWcKjD8GMqqiA3D93VMWwvAaA72zAmTkEcwTMYIXAqzsEtPh1jYA8IEy9nPBhHVrVrrfn2t7/Of/7nP/O5z/0tX/rS104qcfvrf1hHaWEety+bhDnYiLISZMZdQ2bclcd8nDFmLJHPfhFvw1p0TzciLx9z5hzcvHwGko1Ipw8VLkFFjxw6cbLQZjQgfP0sHKwW8pL48TEg3lv1yolCdoPRIdAmaBOmWdN5etrzXLTlfC7adwFP1DxHOF14ymRwJAJXX61ZtkzT3w/5+YGCdj9mzpxLb28Pv/lNPQMDtcRigaWE68K+fYK8PMjP1zQ1BeRp9ORyLIexZo3gqacEOTmayZP3W0Ro4nFBOKzROrBVuPJKzTnn7CeLYeJEGDNGM2bMyKC1wcFAYXzWWYpf/1rS3y/wfY0a6tirqIA5c/R+xxb27Qu8iM86S7Nggaa5GV59VZLJaDo7D6iMHUczYUJgC7Fjh2DLFnjlFYGUgkgksMtobBRs3Ah9fYJ4XFNeLpgxI7hmAwNQUhLYSbS1Caqrg7nv3SvYtSsgnE1T4Dgj/ZDTaUgk9GEeyUdCUVExXV2dp/ZCjGIUoxjF+wQv2ynqDIdJvj1M0nZIj+ftJLPdEFVD9XyDdHk6lKTOdBDAJM/m4mxs+P4ZRVMoi5Wwu28vNYkqDGnQluqgSyq2jqtle6wbg4AMviaTYKxv8XBokFbpM9m3h8ngHYbLw6FBPpvKx0BQpSyqlEVtyuKh0CB1ZpZeCSW+wTInSqPh8nQoRXqo7pSkqPEtFjohMmg84dMpA69fgcBDI5Qit76RvniU1mmTqcgOstHcAv0uY97eTNn589hUU05JXhmpsI2BQuIjEew1PSa4JlenYsNEcJfw+WWkjzbpU6TlMM05KCCkFd1oBqQiIzSzXZvr0wniGDypkoQJhBUZADQKjSMgogXTPJuJvkW9dHk+lGKdlWWqbzPXDXNpNn6YDcSAUNwV6WPrUFibBh4IO9SZDren8ogeQsB6aF6wkzwRSrLWyuAS2OUNouiWgdVEpR+Qwldk45ztHN4tJhEc/JV5mhdmtheiXyjCQ0F2P431scdwUQTrhgmexYczuZQcRwgU1yN3Zd+qKURXlzN2dxP9E8eDkNjCxkyb5M8+k08t+QSVOgxv/wuqcS9y3HiEEGjPQ7e3Yp6xFJmbB7l5GFVj2NrwCo3bupgWnQVrBygYSGMWF9CV6aE12UZNJoTMzUWWlh1znvthRw+v+UUkQuiyq7CXXYxOpxCJnFNSBB8JUwvgw9MUz9QLWpKSaOUsPjRrJlfUfoqiiGbnzm2sWvUaq1a9ysMP/54f/eg7AJSXVzFjxmxmzpzLjBlzmDlzLhUVVSe1DnmvUZ3QhA1o63YQXX1kB7LIkMlArIDLZ1icVXWA/KzrgbXtgjEJTUcaHB9qcgJbid5soJ7d2i14q1VQOSF4nNaaxsZ63nprBW+/vYq33lrJtm2bUEpRWzuBa66+Hm/S5TTnn0lO2MA2YE9GUBjWZD345zckSVeQY2uWVmnOrdaY73Ee3MtNgg0dgvF5mpCh0RoaB+HBnYJJ+ZrCE8t/HMX/QoySwaN4TyGE4IorPsgll1zFgw/ew/e+968sX76Ec865kM997v9j4cIzRhyfQvFkaBAfmOwfMPffKV3WWgo19O/9O8hxLTERdEifDuFRpA1coFV6LHYiFLxHnlYnA0MHSa77d+b3wycodAxO7gtSa80DD9xNU1MDlmXzk5/cQ/cHlvG6nabEN9mGgxLBtemQHh3SI+pbWCdIjmutUbt2IPMKECJo5NOAF4shXI9ocytqxuQgodbzyT76AO5rL+Hv24e/d4gEnjUX7Xo4D9+Ham4i/LE7EKaJkBJrwWKsBYuPOw+BYLEbZZfZR4v0As9goWmQHuN9iymejYhKwh/9C/wztqGamxDhMMa0mciCQvzmfYi8fHaqFL8+ax69sRB5vYN05kZ55JPX0+NEuWFTG0Yi8PkVh6p1h2BUViEKC1FtbRhVBzzVVHsrsqoKfEXmrh/gN9Qj8gqguwfv3l/j1+8i/JHbRxRmsqQU66yzcZ98FD+ZRERjqN4eRCyGff6Fxwyf01Y8sD44zmv37//+Nb773W/yla/8M5/+9BePe50PxoYNa3jksQf5j//4Ce5ZlzGQ7UWFC9FHsYc4FDI3D/uscw+7XcXHoI5w/ClBK+yWVwk1PInZvwezdxtu4Wz8nAnIbBfCz+JUnP2+sYjQEVC5GsMUiBToGEyKTOapic9x0bbzuXT9hTxgvUiCd+ahnEgcWXE6b94ipDTYufMZZs/+JC0tgZLWsgKytrk5EKoXFh4e5nYiUCogSd9+O1DtFg+JQgwjUPk6TqAEnj9fkZNz+DkGB8EwAtL1YAgBzzwDSkkaGwOiNlANByRxd7emvT04BwQWGV1dwVz27g1I8XPOUYRCEA5rurrgkUcEhiHIywsK3bw8zdSpsH69oLJScfrpARk9bZqmvj74e34+tLVp+vqCPaH91w4EqVQwZ9MMwug8TzB1qqamBnbvFowdG6i2+/oC8viCC/RhTQFHQk5OLvX1u0/+xRjFKEYxivcJNJr1VpY8bYzI2ChSBtsMl12mS5Vj0Sk8fhHto0kGmRQAb1gZWqTHX6byA2uCcD43TbmWP9Y9wp6BBpRW6HAcNXkJoYrJlPsWHpqdhstvIv18MB1nr+FSqcxhOwqJoFKZ7DVcmqVH9UHCklJl8ol0Lu3Sx0FTokyaDZffhweIKcFYbeGiWW9meCiU4U0rjQC6pI/QgQ+xh6ZXKvLTPnldffTnVmD5iupEJa7yaNT1GG0djF2/GrvoMjrHjqHJUBQrSbljE0+naRloYY90+Zf6Z7nKrmFp1RLeiAV1fBxBQksyWpOnJe2GIikgLTW2VsSVRAt4OJzkukyC+V6IlcpiQHq4QpMSGgeN1AFhOtG3aZIeW4YI+LRQaA3P2ikGUdyWyR2h1l1jZdhmOkz07WGi2kGz2cyyzsqyxB3JFr1gJ3kgPEizDHyPc7Wg0fBwURT6Jp7QVCiTtIDHQkkmeiEqTqCT00CQrw0UmvvD/ew0XCYMrXGyaLaaDg+HBvl4euT8j4fGsCBz9XIqfv4H8rdsw7dDCCfLrvJ8Hr7hAihR5OkMF113OdN/dS/+ts0gJKAxaidgX3z5iPEa+puwpQ3hMPvmTmLi86vJbe0hYzk4qd0ouxRzxmyyD9+HHhzEGD8Ra8FiZGHRkSd4DIhwGBE+ckbOO8FpJZqZRZrONNgGFIT2l9aCiROnMnHiVD784dvRWtPQsIf161ezceNaNm1azy9/+SO6u4NN7fz8QmbMmMO0abOorZ3AuHHBT3l55VG7Ft9L1ObCLGuAJ7am0Y6HbRok05Li7hbGFgNTDqz1mpMCV0HMgvr+od8lAkJmcF2U7+K17+TZHVvZ425my5YNvPXWyuHurgkTJjN//hI+9rFPs3DhGcO5LSkPXm0SvNEa2DOcWalIubCyRZAfgvxQECr3x7qgtr5w7Kmrc5NuioyXIS+Ui3FowCHgK3irTZAX0oSG7hYiCOOr64G6HsHiyKg6+M8Vo2TwKP4ksCyL6667hWuv/RCPPXY/d975L1x99bksXryUz3zmSyxdugwpJY2GR7v0GXNQEIBAUKZNmoVLCEGL9ClXBkmhWWNmGBAKA3ghlCJHScb4FtM8m8uc+EkVAu8VxvoWVb5Fg3QZryzkUNHYKj0WuRHyT4Kw3rFjG1/5ymd5/fWXkFLy13/9FS677Fp+KHqwEIQI2q/qpYc9RJo7QtNgeIz1rRMKWBBCIHJzUb09xLQgTxm0Sx/bC74InFiEfYbHON+i5OXXcR5/KAgvEIFyQnseqqkRc+FiyM3DW78Wv24b5rTDk1CPh4VumH7h87KdZo/hYiKY7tl8MJMgMqRCEKYZjH3I+EZFJeGrPsBryZ10K4eJmxoRhoGsrCaZW8oaS7A0r+aw0MDDrkc8gb3sErL334u/YzsinkAN9COiESKLpuM/fzeqvg5j2lzEUFGjBwfxVr+Fv/hMzGkjCdzQpVdhFBbhrngV1duLOXce1lnnIAqK8DZvQOTkIqvGnPQu+n5F8He/+03+7u++yV/+5RdO6vEA//zPf8fEiVP44AdvRpsmfvjki9L3GqHGZwjv+C1g4BbOwOxcj922En9gL37BNDJjr8SpOOd/eprD0LngLNKYOzSiH4QH2oBp3VN4atyzLG05kzs+dzO//OUD72rY5n7k5eUzderZtLU9TFHRJ8nNDYLa8vKCYm9wMLCMuPzywDrhROF58PrrgtdfF/T1wc6dYkhVfKBgFCL4UeoASXwoSko0hqFJpwOVMwQq2tdeE2QyAfnrugEJ29AQWDrY9gEv39ra4Hx9fTB1Ktx7r+DNNyWZTDCXSCR4bFOToK4u+Ezt3Qs5OcFz8LyARM5mBf39ATFeXAzjxmnq64PQuJ07Jb4/tFGmAvI3Hj9AbLe1BdezujpQ/n7oQ4rf/U5SXx+cOxoVLF2qOPfcEyumI5Eo6XTqxF+MUYxiFKN4H0JoOJIGQaAZ6tBmtZWhUbpM8Q/4COf6kjrDYY2VYdmQYnRa4WS+NP/T7Oqtx1EuK4oSbMyLUDNUw9kIJvgWdYbLFisQRchDdqGlBiU54ua0GOrc248dhsug0FQN3bbHcGk2PMIITA2zvTArrDTd0qNfQEIH/sFTXYvW0nyKugeY2NaHFJIJeeOo8iNkZCmT5n2M8oKZfEt1k+c6jPdtspl+1rRtJOWl0QVltBmaP9Y9QvNgK10LLqRUmWSlS6v0SAo9JCYJvm3zfclCN0yVsnAFvGanmORbzHcjnOlmWWNmKECRFoFtHWjOcIMKeu9QEF1cSwygSBtElGSDlaXe9UasGXYaDjaMyGGxEUgEewx3BBmcQvGanSaqBb4IAuiiWtCAS0aCj4sWkjrtcrobYofhsd10qHBOvAbaJz12GS7VyhyeUwhBhTLZYKbZZIaZ7tnIY1hGHIxiZfDKrCnkf/5TFL29jkh7O1sq8nl28VTKSquZ6Bn0SJ9fLxzHzSWfZf7q7aj+PmRpGebs05CHdPbl2Ak87QLQMnsC2jQpX78Do30foqwQo3gC/tbNaOUj7BDe2tV4a94k8vG/PGG18LsOrTEGG5DpNpSdg58zEUsalB8n4kMIQU1NLTU1tVxxxQeHhtK0tOwbIofXsWnTOp5++hEaG+vx/aCVNxyOMHZs7TA5vJ8gLi4upbS0nMLCYoyTKUxPEEJrZtRvZaBX0VxSQVYYVNNPbU8Lybcd0nOKieQG5HrYAC87SFfTPgYa9jHQ2Ey6Zwf9+7bgtW9jfXsdyvcAyM8vYMqUGVx//S2cfvpi5s1bREFB4RHnEDXhorGaC2o0SkNPBr71lkFxRFM09FGK29A8CK/sE5xZqYkefwk/Av3OAE/teZ417RtwfJfyWCnLas4+LHBOExDCh9oUi6H/+3qUCP5zxigZPIo/KQzD4Morr+Pyyz/AM888xp13/gs33XQZ48ZN4KabbmPBjTcgYyYKDQcVHD6aOJKzMhFeDaXZajjsNlz6hWKiZzPDC9EiXfYZPpM9m0+l8ofJwqMhjWKjlaVN+sS1YLoXOm5b0akghODqTJzfRfrZbjjDv1wn+DaXZk+MsE4mB/n+97/N97//71RWjuFXv3qIj3zkKioqAmPKib7NaiuDh2aiZ5MyA7K5TyhCUjDXNflgJnHC4XfmwiX4O+rQ3d1MLswli4uo38O+imLenDWBImVwZTICr70M4QiiuBS9eSMiHkeEI6juTnRXF7K4BO25qJbmw8jaE4FEcJETZ6EboUUGRfiYIbuNE4E+YylNcgzFvb1YsZIg4bewiFwpaBUO+6R3XDIYwFywCNXbg7d+DXgu9rhyckpaiKpH6XxtA3YWRG8IL38aCImIx9FNWfSOdciaQlS0bEhBEJDX1pnnYJ5xdsAUuQ7Z+39PZs1bgfdxJIo5bQah6z6EPCgh+Hj42c/+i//8z3/mK1/555MmgrXn8eI9v+DVV5/np1/6GrK/D45SwPyPwkthNz5Ne6SAlsIJ5Lgu1YmxWD2bAcngrM/j50/5n57lYchcppHdmtBLAtkKwgd/LFR/Ygo/jtzDzTdfzje+8bd87Wvffk/Of8EFV3LnnX9Dd3cvM2fmsWmToKcnUKuOGQPLlmmWLj25Yu+JJwSPPSaGPHghmw1I1sJCQdVQi11/f0A8b98OUgaq2bFjA4LY9wMidvJkmD4d1q4VFBYGRO/bbwck9ZgxoHUQjhcOC/r7Nc3NQSDcnj2CaFQzOAgdHRAKCXJyFK++Kigr0+TmBud44QVBY2NA/lqWJpsNvHtbWgLSN1D1wuCgxrKCYL29e6GjIwi0831Bb2+gdLZtgetCKqVxXY3nadatC8jqCy7Q5AyJu8eMgc9/XlFXB6mUoKxMMWbMiYvVM5k04fBoT94oRjGK/70QCE7zQjwcGqQYY7j+bJM+OdpgwlDttc/wCA2RivthILAQNEqXLBp7aLyIGWFG0VQAnot1EdPeiHMaQxW1pQXlvsk+w6XWtxAINJoWw6Patw5ToPYIH0doipQxXF8GhHEgqnBRNBnOsJesEoIokrPcKFuMDLnaoFcG1gWtUYO8eB6XP/AoiSTonFz04ABmeyf5ZywlXDsLIQQVvkmD4RFDUtffSMpLURgupMu0iSeKYdwcnsq0M66niXBxKeW+yZqhIOr9s48gSCARQhBBEtHQpmGTkWW+G+H2VB6vDXn8Gggmehar7DQd0qdQSVJCY2lBUgZrKQNBQkOzVHQLn3EcYJ8iWuJxeJ2ggPAhN/dKRZ9QFCqJqQUZEVhDpAX4pEnJvSgt2Wy2EdbTsPWYI459LGSFxhUaSx1436RQvGWmaTB81litjPEtrs/kcFX2+ClYp7thVlsZto4pprzqEnqFz0t2ioSWTHJCRJHElKRRurwwJpf5hZcQPsZaZE7JdF7bt4qmgWbK46Xsm1HD29U2IWcSt1ddjvrlvYjcPIziwAhP+z7+9i04Lz1P+PoPndS1eFfgpYjU/Ra77Q2EOwBGGDd/KukpHw3WMCcJIQQVFVVUVFRx0UUHVNOO49DYWM+ePTvZs2cnu3cHfz766P00Ne0NcmeGIKWkqKiEkpIySkvLKS4upbCwiJycXBKJXBKJBPF4DtFojEgkSiQSIRyOjFAb60OITK01A90DrF29AlNnqezySGdT9GdTrMwk6e7o5r7Nd9Gb7qalpYmWlmYGBvpGjGEkyrDLplE2/VzyLv4UuRVT+dxFk1kwvvikhTxSBD9dGRh0YNwhlnF5IehIC7qznBQZ7CmP3269j7db11EcKSBuxdjTt5dfbfk9LWETu2gMJoLJnk05JtMLNc83SoqjGmPoKXRmIGHD2NxRMvjPGaNk8Cj+RyCl5OKLr+Ciiy7nzTdX8Nvf/pRvf/trqH/7R8ZedgmtH7uRhWcsxZAGPpoW6XOaG+IKJ84cP8IrVpLusM803x4OjJioQuTh0W34uEJzrI6GwIOrlzrTHS74ipXJjZkcZp5ioNuxMNUP8VfJfDZaWfqFokgZzPLCJI6jCu7p6eauu77PXXd9n2RykM985st8+tNfIpkcBCAeD9iHeW6Yt61M4BGsDcJAashTK6IEGaHZbTjDRfHxYC06E9XSjLvyNeLtrcyXgo6qavbc+AGuClcyKxmiaDBNsr8PEU8EauJQGJVJI00TrRTayaKHdn/FqRqRDiFPG+T5J787bALRSIKeWBTjoJZAb6jADx9y/bXWqKYG/O1bwfOQNeNQfb24Tz2G39aCEBJZXkZOcSPR3C78WC3kdaD7W7B6t6HNCH7OeEh3YPZsJ1rfS/Stl/FyxpOpvRY/b9LwuYQQYJpkHvojzkvPIcsqkOWV6MEB3DdXgmEQvu2TJ1RYPPvs4/y///dF7rjj8yOtIbQKJJRHaAsaPiSVJPXrn/Mvd36TufmFnLlpE6n/+Cbh62/GnDX3xC/2yAv5ntg0eKlWfl9exWvlU+i3I0R8nxn9PdxkSIq7d3DUt7byEE4vGBG0dRyJw3sAnQPJOxTZC0G2C3RY484AwnA2y/j617/D3/3d55g4cQo33/zxd/38N910Od/97ufYsuVJxoy5kerqwNd22jTNJz6hmDPn5F6uzk545RVJXp6mZMjQe948zfPPC95+G1IpqK8XNDQIpNSk05KNGwPLheXLFb4Pq1YJ0mkYPx7OOENRUiJYu1aQyQgsKyCY8/MhndZIGQS7SSno6AgC8np6gmNaWgLy+cILFa+8EoTj5Q4V044TqJ4dR2DbmlgsIJHV0MeioyMggoUIxnacgPSNxaCuLhgjEhHD6ub9x+bkBErgpqaAHE4kApuM3l7NTTcpKioC1fDMmcBJLnABUqkk0eif/n06ilGMYhTvJs5youw0XLaazrBdWkwLLs3GKB+yhMhTBu4hvyc9FM3S41U7TZ3pUKksznGizDioPq/yLXYa7gjruIBQ1JQqk3FZa1iEEUGSQpGvDZZn48NK0g7h8XhokM2Wg4+mwje50IkzywsxzrcwgHVmmi6haJI+YQ3mkJgDgmCzCJLLM3FiSAaFIoJkSu4Mcoo24rWsQ7fsg2gUa/4iQldfN1zTzfXCvG1laRUuXZlewmaE/miUtGWzt7AAUVTIgDNAOgJhoYgPderZaDKAKRQRbRDSglbpDc9JCnCGZNcF2uCKbIIrDiJDK5XFA+EBdhkuSaFwh9TP+1XAaTQ2gqRQPG8n8Qk6HKd7NiusNJ3Cp3Codu6UPmEE0w5ZN8V1ELqdkppKZbLezNIlPTzRixZJsjiYJHF4nLftl5nn3M5Y/+hZMkdCuTIpUAYd0qNKWSiC+TYaHiEtCGuDXYbLd4b8pC8fugb9oo96YxcAtf7EYe/gyb7N9ZkcngwNstdw6Rc+iZTDVW/UUb2zAWXb9EyfQv+MSfQaPgMi8D0+GsbnjuPaSZfz2O5n2NET2D4VRQq4dOqVVO9Okx3oR0yeNny8MAxkQSHexrXoD9xwTNu49wLhPY8QanoWP1aJjo9BeCmszjWwXZCc/cVjriNOBrZtM378JMaPn3TYfY7j0NHRRkdHG21tLbS3tw7/tLW1sG3bJrq7uxgY6GdgoA/P845whlNDyAoTCUWxjTClfhnVY8dw5pnnUVZWgRurZKNTSTpaSTZSRZeKETWhMg6lMc2l4zQLyvVJL3v2DbayqXMLA84ghizBFLMZdGPkHfRxGnQhYmpyTsBi7GDs7N3Dps6tjM2pJmYFa/B4OJeXCiy2RrupCeehEeTp4PfXudVRdvZqtncLYpbGGVIKX1SjqBwtR/+sMUoGj+J/FEIIFi48g4ULz+DrX/8O9933W37+25+w4rIbWTd+LGM+fB1lV13KtHETuTwbRyIZ60uSIsLrKkPNISrRiJb0CJ+U0OQcYw3+dCjJliHvq5RQtEqX9WaG1qjH1weKqNIn2YtxAijSJueeYAtUa2szP/7xnfzmNz9BKcWHPvQx/uIv/oqqqjEAtLU1AxCPB0VMvjb4eCqXV+00K6w0LdKnWlmc5oaIIGmTPo+EBylVFrNOgOwWlkXogzdhLTkL1dRIxA5RMHkKU2JxcIJjdDSGLC7Fr98NBYXIympUdyeqvw9tGGDZqD27MMrKMU5BFfxOoNrb8DZtQKcGmTdzDA9OK6VfGOQMKRvqpUu5Mod9qSEggp0XnsF5/GF0f1/A+jgOqq8XWVmNMWYcaIXetZ5k3S7kjedhmlFCUypw9nbipxXGQD2+XYDc8hIylMUcX42yolhd65DpdpJzvzxih1319+G9/QaysBiZXxBc+5xcUBpv0wZUyz6MIfX30bB583ruuONmLrrocv7u774ZjOH0E2p6Fqt1JUI5uIVzyFZfgIpXH/Z45+UXePSR+9jc180919+GWVGNqt9N9o93I2vGBYEYJwKtsTrewm5+CWNwH35iDNmKc/GK5r5rxPCzOSEeD02lyM0yIemSNExWFhSj/Qyf6WtAm4dULFpjtb9BqOFJjGQz2gzjlC4mW3M52j6+UuRdhQHeJGDS4b+YPvrRO6ir28pXvvJZxo4dz5lnHu67/E5QVVXF3LkLgN8xdeqNJJNw3nmKJUs0paeQ59fcDL29MOmgWj6RgPnzNW+/LVixQrA/IDs/X9DXF9gnSKn57ncl+fmaoqKAMH3zTcGOHZJPfEJx+eVBCN5//EfgE6x1YB2RlxcojF1X4ziwY0eg6s3JCXx9i4qgqkqTSokRnryOAwMDYij4LVAxG0bwdtQ6+LGsQEEcqIOhq0uQSgX/jsX0kLdycAwEPsOx2H4CGRYsCBTBmQxs2ya45x7J5z6neCeOH+l0mkhkVBk8ilGM4n838rTBrelcfh/u5y0rgwameyHmuuFhAneuF2KFnWavDDx+AVZYmSAwTQVa381Glt0Rh1vSuczxgvbthW6YtVaGXYZLmTLxhoQjtX7QLZjQkpyUwdtmmlbDp8w3ON2LHER6Kn4d6WOr6VCqTCwku02XXxt9fCKVxxjfRANbTRdTBx2KXVKTrwzyVUCGNkmPJsPjocggMS0p8w3G+RYb8ixKP/FRJu1pwewOAnVlzbgRm/szvRDLszFeCCUZzC0g62cJWQZSaxLZLLbjoDM9xOPVZISmSyiyQmNpSa4WFPkWjdJjQCh8oFf4RLTAASb7R67z0yiqfZObUznsM13eUhk2mQ5jfQsbwYBQNEqXqBY8GO5nUAREewjBYifM+U6EV+w026QHCBJacHEmxpRDOuxytMH8bJgnVAf5/UnMXIN0rgEI0DL4DhaNaKpIswPNy9T6i07qvRXTkvOdKPeHBtklHfqkT5PhEdZQoUxytUGeFjRKnz+GBrg0G+MN+zUeDd9Hh2wDBKV+GVdkrmOBuwSBYJEbYZYbosFwaU/30fzrexm3ZjOmEOArKp9/BX/5uTRefSnR42SwCCE4q3IRMwqnsqtvDwJBbe5Y8sO5uPWvBNsfhwgntFLI/UXKnxDCHcRuex0VKkSHgrWItmL4iXGYPVsx+neNELO8V7Btm8rKaiorD1+rHAqtNZlMhsHBftLp1NBPmnQ6NUINfPBnbv/fbTvEvjdb6dneS9m4cmKxgF/oaeohUZJg8W0LMUMji7i+LGzrETg+lER8PBVs9Y/NCewcThZr2jdw77YH6Er3IEUQyDjovkVv9kPU5hWRsKE3C10ZwfJx6qhkcMqD1W2CbV0Cw9BML4S5xZr2VCeucoeJYICW/Dz6KkqJJHuZmGMihaR5iB/4XI7FHbMtVrUIdvYKEnbgGT235ORJ7lH878IoGTyK9w3y8wu4/fbP8vGPf4Zn3nyFn/7uJ7z1r//Ftv/3b+ybMp3w8qtZvvwqpk6dSZlvkqsl3dKn+KB2ry7hU6IMCtTRdzBTKDaaGYq1Qaf02WRmyQiFADbILP8W7+ZLyYLhFOM/FXzfZ9WqV7nvvt/x4IP3EA5HuP32z/Dxj3+GwsKRhpuDgwMAJBIHQqeKtMnV2QRxLeiWHtP80HCxXa5M6gyH1Wb6hMhgCL40jaoxGEME9GH3S4l19nn4e/fg761H5OchiktQ9buRObnoni6MiqrA7iA3Dx+NhPfcx9nbsJbMvb9Bd7SjgflP2ey78WI2nLeQ5lAIAZQpk+vTCRJaoh0H1d6Kamsl++iDSMtCTpmOEAJn9RuoxgaMsbXDAXNGVQnemk2k39hKuMLGVJpwTQKnoQvd04pueRMpM0QvOAOjrCII37NzMHs2Y7W9QXbclcNz1QP96HQKWVQy8trG46iOVnR/P1Qc/bm2tbVwyy1XM378JP77v38VtEb5WaJbfoTV9hY6lIsWJqHGJzF7t5Cc9XlU7MCA2vcZXPEK/7ljM+fWTmJ+VQ0AsmYc/s7t+Nu3Ik8g7A/AbnqW6JafAT4qVITVsRqzaxPpqbfhlJ91QmMcC2kUK2I2CS9GaU8jys4jV2tEspfNsQi7KhdQEhtJnFsdbxPd8mNQHipcjPDThHc/iMx0kprx6WHrjvcDvvGN77B79w4+8YkbePzx1xk3bsK7Ov5HPvIJ/uqvPs6dd25lwoTJ72isSCTw1s1mD/j8QqDC3W+pEI8LPC9QCQ8OCjo7A8K4r08wZw7DVhJFRZq6OsFLL0mmTAnUxXv3CtJpwY4dMHaspqgoCGfzfZgyRdHfH6iKXRdycjSrVgk6OyVjxmjq6yVFRZreXlizJrCWcN2AnO7vP+BjvP95WFYwb8sK5l1drdm3LzjGNIO1musGxHUmA54XqIzb22HKlAMq5EgExozR7Nol2LMHJk489eubTA5SXl556gOMYhSjGMX7AB6ah0MDrLWyRJBYOgiH65KK21N5FGqDcX6QA/FYaJBdhksGRZ/0me5ZTBsiNYu0wU7D4UU7xSwvhEQw3rf5cDqXp0KDNEsPg6BL7opsfLjrbtwxsjK2mA47TJcJvj1sYZHwLbYbLiusFLNFGBAsciL0SZ8e6dMpPByh2WO49CrFeitLXEuKlKRHKB4Kp4FASWtrwewpuXw0PYacIyhIJYLlTpzTvDD391s837CKvvEz8RN5SM+jN9tHwooxw8xjtwrUtyvtNHEtqPEtBoRiu+HQLRU2Pg9Jj6iWLHWizHZH1vkKzct2ipfsFD1SEdKCWW6ITyfzec1Os8JOs8twCWnBWN9in3QJYzDFD8j4XuHzqp3m1nQun0tF2Glk2Sc9BAIDqDc8xvrmcH2vMxnOvfdB+sKDvD2umIJ4CKdM0lHVQcgYBNpwRBoQFKdyyG1bQf+qB0iUT8GYNGVE8PKhaJEedWag5B7jm9ySzmGlneYFI4WJoFpZxPT+0EBJXCuaDJcNxg7+EPkNPh7jvaAGajL28vvorygdLKPGD9Joo0im+CHGvbqGbW9tYsfEaqKhCBYC0dFJzTMvMn7qaUSqS446x4ORH87l9PCcEbcZk6Yg8wtQLfuQFVUIIdDZLLq3B2PpecP5I38qCDeJ8NIoe6RHgTZjCC+NcAf/pPM5EWil0VnIieZTXHzyqoYptWnWP7CB7r3d9HT2goZ4UYypF005jAgGyA3BwrJ3xy5h0Ely/7ZH6WzvojBbgGEa2Lk2e+ROIubz9Ds30JqCuAXnVisuGXfk86ZcuGuzZF0HmCIwtVnVDGdUwNTCGBKJ67tYRvA7sCU3F1955GiJRCIQVCiDrYbDVtPhwqjFFeOD7opR/N/BKBk8ivcdhBBctPBsLlp4NqlvpXj55Wd44omH+NnP/ovvfOefGDt2PJdcciWlly9j++LJpE1NXEt6pY/QgvOc2DG9cT2h8UUQbLHdyuIP+YQBaKlokR5PhZJ87CSTaCFIT26RQftQsTIoOEYLUY/wWWmmeGHL2+z444Psuv8Ruluaqamp5Qtf+Ec+8pG/GEH2HoxsNgsEu6iHIiX0kG/ayLmHtaDn0DSNdwhz/iLCrovzwjOork6MsbXY5yzDmDINmV+IOWkyDYkQr1i9NLU3kNOfZEpeJUsSVcf1dD4V6OQg2ft/j+7tRU6eFthXdHZw7T//gGV/fJq+885Fn346EyonEVcCd/UbOE8/HpDBrS2ozk7kWecM7x4LxwOt8ffuHg510zIM6XTgqWwUgtaEYprwHAMv7zSI5hKJ70OMPWhnW0iQNjLVMmK+Mr8AmZuH6u3BOKgtXPf2IBI5yKKjpG4BqVSKW2+9Bq0Vv/zlA8Nt5VbXeqyOtXi5E8AMmDoVLcPs2Yzd8iqZCdcfGEQp/vvN12hNJ/nZ0gsPmphEaA1O9oSuu9m5nvjqb2CkO9FWHN9N4uVPRWa7CdU/hlM8H8x3lnQ8IBVJqciNj8dP92Ok2sHxyRWSpuIJdIw/k5KDt6+1xm56BqEcvNyg8Nfkoc0Ydsdqsn11+HnvH39h0zT5yU/uYfnyJXzmM7fy0EMvvauBcldeeT3/9E9f4a67fsA3v3nnOxpr3DgYP16zdWsQ4Gbbgaq2rk6gtaC0VNPRERCp6XSgqg2FguLS82DfvkAtnJsbkK75+ZqVKwWrVwck87Rpmq4u2LULNm5kyDZCM20atLRIGhuD23p7BbEYlJQEamHTFLS2wltvSbJZcBw9rODNZjVKiWGrh0B8o/F9Maz63a9CbmoS5OZq4nFobw+IaCECUlgpPaxYLi8fWTBHIsE5k0nBOymmGxrqOf30k1NJjWIUoxjF+w11psNbdoZKZRIfImg9NHWGwwo7Ndy6v9CNMN0Lscdw2WZkeZIkUw9RmxYpg2YjyMLIH6qtZ3khpnk2HdLHIAgBO9G6vVP66CFLhIMh0ay00/RIRVYoJvthUBYaTZv0WW9m6JY+US0pUJIlbgSBYIPpYGoYkD57DQhpwQ4jSy8+/1+qEPsoNW+ZMjmvYAYvxRVNiRC+IekxEhSGLaaHqggbYQQOk3yLMWmTF0MpGqTHDtNBCUXuUMebApJCs9NweDw0yPWZnGE7jJVWmvvCA4S0oNQ3SImAHE4Jze3pXM5yorRLj5iWbLAy7A25lPkH6o88bdCpfTaYWT6SDvOaleJNO8N+k464TnJRNsZFTiwIrn7pOYznnuGD5ZXM6kiyIuqRzq7l6cs30TdpGdIIYSFQySTjN20h0pPFu/9+0iKCefoiwjfeggiPrBk1mlfsNI+FBukVPoKgG/QMN8KnUvkU+wbfifVg65Gik6wI1NzbrLcZEH1M8Q50Ktb4tWwzN7HeWj1MBu+Hv24NVaEc+u04ncIjicYuLmDyxm6qtzRC9ewTep8dCbK4BPvyq8k+dB/+ts1B1ogQmDPnYJ993oE5oFlvZllvZkgJzQTfYoEbGX7/v1tQoXxUuBiZasE/iBCWmS60nYeKnkIL2XuItu3t7Fmxh4H2QaQpKZ1SyoSzagnnnPg6I5oX4fSbTqNjRyfJ7hR21KJkYjGRvPe+K2tb2w7qduwitydOPwOARjZL4uUmiYotfGxGP56OURCGsujRheJvtQnWtQfq5PDQx3XAgZUtgikFkxiTU8mu/nrGJsZgGxZ9Oov2NdWJ6gPr3KH/XDFKAP9fxSgZPIr3NaLRKJdcchWXXHIVjuPw+usv8sQTD/GHP/yGrh9+h1A0SvH80yg8axGTFy/hA7POYJFx7C+DhJbUejYv2UkGhaJgqHgcFIqIFtT4JttNh96DCs4TQY/wuT88wGYzS0ZoElqyxImwPBsfUWym02leWrOCn7z5NJsefpRk3S5CRYWUX3s51157HV+Zfh6hIcViFs0u0yGDplyZlA3NNTrkwZtKHZ44vz8N2UUPF4IazaBQR1VInCqEEFhnLMU8fSGquwsRjY6wFWiQLr929lL52wdZvH4LMpVmMCfOK4vP4PwLb8K0j61STqPYbjokhaJEmdQeJzzO37UD1daCHDceIQSqvRV//Vro6yHv7T4K+5LI198i/OHb8KUk87tfgusiS8pQXV3onm78DWth/uLAO7i5Ed3fi7/bQ4TCmFNn4HUkESlNvERiFthobWGoPnQ6jTvzPELlIazd9+Ed3P6lNSgHHS4aef2iMcwzz8F54Pf4TY3IvDz04ACqtwf7wuVHJYO11vz1X3+MurqtPPjgiyNUhMZgA2h/mAgOTiTRVgKzZ+uIcbbUbeFnm9fylxOmUltwYG66twdiMeRRVOEHQyabiW34DsZgE360BDAwkvsQ3iBu4VxkuhUj1YKfM+64Yx0LOUqSpw16LUiULsbPdCG8NN12mHA4j/xU0ciIcD+NMbgPZReMGEfbOZBsxEi1v6/IYIDc3DzuvPMurrrqHH74w//gM5/58rs2digU4pZbPsGPfvSffPnLXyP3RO0/jgDThOuvV/zmN5L6+iBkzTQhHg+sIHw/+Htra2C3ECAgSC0ruK25OSBcISCMu7qgoiIIX+vvDx4RjQocR1NYCLataWsTdHUFnsLhcKBMDocDH+GOjkB9HItphAisISAIpRsYCBTKEHx2tA5IZMMIVMFSakwz+PvOnVBWppESpk/XbNkiqK8PFM9SBh7JCxYEwXX9/SMV0pkM5OQISktPfdNtYKCf5uZGJk2aespjjGIUoxjF+wF7pYuLHiaCIfDcTWjJFtPh8oP2m+NaMtMLYWp4MZTCAQ6uEDNCY2tB+JD2fBNB+SmEP+doiUbgEwgoFJqthsNm0yGuBW3Sp116FChJlbIRCMqUyaBnM9m3yVWSN+wMNpK2/XYNQpMSILTCRNItFfdGB9hneNyUzeVMJ4J5SA270czyD7FeWswKosonjU/CiGEmbDw/QlopTAKP5JleiFrf5oFQP3sNQZUfokX65OogoK9H+vgCVtjpwI7DC+OhedVOYyKGOx4jGkJKsMnKssdxGe/bFA7lcqwhc8TrZYogf2S1leE1O025MoPuOjQd0uepUJKJvk1tRuCteh2Rk4vMy6d6wKUqm0LpCqoaNtFbsZVU3kyE65PX1YYOp5jdN4ee0xexK2HhdbUwaeMqJs0/ewSp2yA9HgkNIBBM8e1hxfKLdooa3+KibIzfRvpplj4VKiA4+oQmK+B8J0a/7MTSIzcYfExSoprNhsfZIriOw1AaG4PT3BADwsIRmqiW2NrCehd4M+vMczCqa/A2b0Rn0hiV1Ziz5yIiwfpOo3k4NMjzdhI1dP3XWRnWWlk+nsqlSL97FE5aK1rzplHes4VYbx0iUoxwB5FOH9kxl6Ji759Opc7dXWx4eBNuyiVaEEW5PntW1pPqTjHvhrkY1omv262wRcXM8ndtblpphDz+ZlTr9jYyA2nKEsWYxtCaPePS29aPlROiPH50W4iDsbVbYMkDRDAEgW9Ng4KmwQg3T/0g925/iL19DbjaI6c3gpg8j4rQgeecYv/vl1FK8P8qRl/5UfyvgW3bnHvuRZx77kV861v/zfr1q1m16lVWrHqFt773U9b/07d5OBRi7twFLFx4BlOnzmTy5GnU1k7Csg6QoALBBU6UdVaGncLBFAJfgNSCyb5FBEnmJFVdCs0fwgO8ZaWpVCblStIjfZ4IDSKSaQpXbWbFipdZufIV1q59E8dxsAryGX/BeUz/l29Qc85SMqak2fDYmnKY44XZYzjcGx6g0XDxgIQWLHGiXJGND3sF7w+SOxgzvRCTPJttlhMkI2tBu/QoVyYL3fdmx1OEQhjlh/sZvGqlqP7lH5j1+lqSleW4pWVEe3qQTzxBg5lD7cXXHnXMBunyu0g/e40gWsQGZrlhbszkjFhYHAztegHTJI0gmbduG9rJIvILEUohJ0xCNzWSfewhZH4BOpXCnBQQgkZFJX5TA6qjHbZsRDc3BUyPZSMMid+wFz0wgM6kEVYpflkN0I6UGVyKGGyz8FtM5OwFgW9u3w78eECmGskGVLgYp3TBYXO2z7sQpMR79SVUTzciFiN0+TXYFy4/6rW5555f8uij9/PTn97LrENC3rQRAfRhXmTCzwZE6BB83+cLX/gk42tq+cSis1HbNyNy89HZLDhZrHOWIWuOT+DabW8g0x3oUD5CWGgjhDLCyEwHRrIJHS5CH00VrHzMvjpkuh1l5+DlTwPjyBsEYSRnORH+EB6gyfDJjxaSFIpO4XO2ExkOoxmGDKHtHGS6HTiIVPezICTq3fIM1hqZbMTq3gLaw8+pxcubcsoWFKefvog77vg83/7211m2bDlTp858d+YJ3HLLJ/iv//pXfv3rH79jormqCj78YcWTT0o6OzVVVZrt2wV1ddDWJohENIYhyGQOePSGwwGpG3j4Brf19QWq4nhck5cXkK5PPy1obxfsz0/ROgiBS6WCQDoICGEICNxEIgi1Ky/XxGKC/Pzgz54eSCYDojqRCM6dyQT2FdlsMI9YDAoLQetg7PJyuO02n1dfDXyLi4r0sA/xWWcpli7VTJgAK1YI/vVfJW+/LZAyeI7xuObmmxXl72BtsXPndgAmTZp+6oOMYhSjGMX7ABZiKNJNjyD2PBEoZ4+E8b5NjW+x23Cp9S2sIbFGj/BZ7sQJI9hmZFljZemUPtW+yQI3TOVJWrtN92wqfZOdhkuNb9IqPbaYWWwtmOOFiGnJC7bPW1aWHMcgoSXdQoGARW6EdunhDT03H3BFILqQaKQQ9AiFHPruqzMdfif6GBQ+lx0U5tYpPP472kOT4VHmG/jCoEl6DNrBxuVm4VDlm8xzw0zzQhgITvPCdEifdsMnKzRSKOyha2no4Cr7wDbTYa4XJiUUXdInR42sSeJa0oRHt/QZ7x+4fcyQ6CKNGu7g89CkUUz2bNabGSQMW3EIBCXKZKuRpc50qM1qdCo5TGrGtKDMN2mQUS54oZqumWH25e1Cqyw56Tbmto3DVOfw/fMnMRA20akUMcPjvNAgV2bjwwKQraZDn1BMHSKC4YBieb2ZZaEb4YvJAr4Z76LR2K8cFix1ItyeyuO10FhW2a+hUEgk7SKfreYYWqQiY49jQHRzcTbGWUNKb2PWHPztW8BxyLFDoEH1dKPDEeT4d+ADBbSnOlndtp6mwWYKJuUxt2Qxtbk1I46pNzxesVPk6wNdph6a7YbDa3aaq7LvTg27rn0Tj+x6ktbBVqZ5Ec7MtDLdS1OQGEN6zHKyNUdfi/xPoHF1I04yS0FNwQEP4JhN564uOnd3UTr5xOw7joSuNOxLQtiAcblgnUAZr7WmdUsbjWuaSHYliRfHqJ5bRenU0qMGgNv1JgmRoFf2UUQhAIZt0JPqY7o7mYQVP6H5HqsHzRBQk1PN5+d9kt29e8n4WcKJYu4PG0Nh8xoPTUpoFrhhpp6gheQo/vwwSgaP4n8lDMPgtNMWcNppC/jUp/4G3/fZsmUDb7zxGitXvsLdd/+Cjo42ACzLorZ2IpMmTWPy5OCnunosH6ksonesSacZJAhXKJNiJakzPE53w+QdhXA8EhoMj7W9LZh1u9m7fTcdO3bSumMH3Tt28Hh9E9r3yc8vZMmSpXz1q//G7vNm0zejlnH6wC/fKOAOjTXJU/wu3E+j4Q0FO0CXUDwbSlKkDWZGgy+KZHLgsLnEtOTWdC7PqBQbzQye0CxwIyxzYlScgnrinaB1327mbdjO4JhK3JyAiPRLSsh4Ds7KV9FnXzJcMB4MF829kX72DC0G7KHFwBtWmkJlcM1RiiBj7DhEXj66vQ3CEdRAP8QT0N+HqKhEhMOIikrUviZUezvyIBsOUVyCUVWNv3Uzft22wCrBtgNCNBpB9/UFVhiVVYhYnJR1GulsFilcfB3BG9yGbRr4OeNIT/044V1/wEw2AODFq8iMvx4VP1xpK0yT0LKLsc84G93Xi0gkIBpDtexDNTaAHcKcNBkRC17z+vpdfPWrn+fGGz/KpZdec/i1K5xFOFSIMViPH68BBDLTBYBTunD4uJ///L/ZsGENjzzyCrnFpbivvoy/fQuyvAJzwWKsRWcetZA5GHKwARUuQGgPmWxBhwtBBIWrMdhIpnwpKlJ22OOE009068+xOtcGBK2UeLkTSU29/YhBdwBLnSgKeNVO0SV8IgguzsZZno0d3hoqDZyKc4hs/0VANoeLwU9j9u/Gy5scEM/vFFoTanyK8O4HEU5vcJsZIVtxDulJN4M8NSX+F77wjzz33BN87nMf4/HHXx+xmfVOUFJSxoc//An++7//nZtuuu0wL/IThVLw3HNwzz2Svj5BIhFYOkCgti0qCkjdUEiTToNlCSZN0kyeDIahee01weAgbN8uiEbhjDMUW7YIdu2ClpaACN7/1lMqUA47ThAOZxiB1URf3wGS2fOCLoUJEzS7dwcKXdMM7s9kAiJYa4FpBuFvUmpaW/9/9t47zK6rvP7/7NNun5k7vWuKem+WbMlVttxxA4wxGGMMAQwhlDRCEgjhm4SEJD9IpRkwLhjbuOGKe5EtyZKs3kbSNE3vc+spe//+OFcjjTSSR7Ihbdbz6JF07zn77HNue8/a613rqD3E0JCgslKxcqWio0PgOILf/33JW28JWlsF8bhk8WI15gOsFDQ3+2pgTfNJZaVya0fv8qXat2937lzena/zFKYwhSn8V2OWa5GnNLo0b6yzLSEkWSRLnYkXiS0EN6XzuCdXA0p8hfAKJ8RaO8K6nOVBUihCSrDdyLDJzPDxdD7TvUnI6XLIUzq3ZPJ4MDhKq+6wR7cxECz0AlRI3//2LCfIm2aGrUaWEqn7oWXZCMudIJ2ay2tWmkO6S1xqCAUpzZ+Tg0/UCARR5Z9TSAles9KstsNjXYdvm1m6NJcowideFdRJg8Oai8yN8YFMjPOc8FiHYRqJpvyMlC7dZUQopKbIUxpZIamQJgOax2tmipSQzHAtIlIwrI3vdkwhsYD84xbSjwT8vWVmCCPQFIwKyUzP4iwnyB7DPkHdfAQOQDiCVlWDt2cXFBYhEMzxAgSGhhkZrWBO8mZqW5uY+erLLPp1G6GCWn7+0VqCnsfMrhRydJjhogKes1LUeyZL3CByZBjbGkUEtBNqPRNBUkhaNYctZpYGzySkXCwlWJuNcHsmDx2NZc7ZvGG9ynZjJ0kxjf36HGxsSpVinlNISkgeCo5SrHTmugHMc87F270Dd+d2hGGipEToGuaaS9HfBRncPNLGnTvu4XCii4BmYUubdR0buWnW9ZxVflTc0azbpISk5pj7NgNBgdLZZmTfEzK4fbSDe/c8SNJJUZNXw2heLT8ZbSOuCW6b+2mmxd/b7Ip3C6UUwx0jBKKBcfcnRsBASklq8MRu2cnAk/DkIcErhzUGM2DpUJenuGmWpHZit8YxtG5qZ/fTu5GeIhCx6G3qo//QIPOudKlZOnEAeJQIi1Jz2chm9tv9aLYilU1hDekEHJuHHrpnrPNX13V03UDXtdzf+thj9qhFd2cIrSBMLBzCsEKkRBghw9QEAygVxNRMZhUefR0/mXZ53Uyzw8gSQLDMCbLKDp3SXnMK/7sxRQZP4X8FdF1nwYIlLFiwhE9+8vcB6O/vZe/eXezdu4t9+/y/X3vtRQYH+4/uZxhYpcW0FxcSKCrELCokLxrDkiG+ppnouo5hGGiahq7rOI7L8PAgQ0MDDA0N5v49yMDQANm0HxyBEATqajBmNRC8ag3hWfUsXX4On6lbzmrP93X9z9AgA1rWX77PQeX0Eyb+in677tLomWNFV7HSSSrJG2aas6M+MXgkSO54FCuDmzN5pIjiCYiqEz2EfxcoGBpBT6Wwa4+SoApFJhbD7E+iRkcnJIMP6DbNXpKa9n50x0Pm5RGJF1IodDaZGS7PRghP4L+mFRVjXnI59mO/QnZ3oFJJSKfRCuLoDTMQCJT0FbNaaSmy5ZA/JylRg4OIeCFaSSkynUIEw+h1Deg1tRAMQTKJd3AfxqoL8DZvRCVGIRrDUwHk0CDCCmDM8lu7nZJlOPF5GKP++G5ew1HFq/QwezZg9r6FcBK48TnYFedCqBgRCqFcl+yvfonz+suo0RGEpqFVVBK48SMwcw5f+MInKC4u5a/+6jsTXnMZrSE186OEmu7D6N+BcEdReojstKtwSn0f0ra2Zr797a/z8Y9/lmXLfIJY/0jdO7+gSmL2bsbsWY+WHcTNnwnCQHhZnPgCTM9Gyw6AVGjuCHb8bNIzbprQ8Cp46BHM7nV4sTqUGQMvizG4h9Den5Jc8lXQTvx50hFcbEdYZYcY0iQRJSYMZjmCbPXFiGw/gY6XMQZ3gh7ALZxHetbHT6pAPh3oowcJHnwIJXS8+DwQApEdJND2LF7+9DMKztP6Ie+tIN+/6Cdc/INV/MvX/5Yv/7+/5L36+H7pS1/jwQfv5jvf+Wv+9m+/d9r7p9Nw992Ce+7RGBnx7SBsG+bMgb4+QTSqcgFzinAYYjFBRYVi7lzfv7ezE84+W7F2rSIvz1f0btrkk647d0I6nfMxy52vYQjfZUX6pK9h+BYQrut7EUt5NLyttdUfZ3DQt304sk0265O1/ni+T7Cm+YSxrvtjHDnGERQXw+WXTxyk0d0N27ZpzJ3r+x47jj/WwABs2SK44gpf5Xyqa7hvnx9qV1qqaGjwfY0BNm5cR2PjzDEroClMYQpT+J+KamlwdTbKrwMJ9ug2Ap/QOscOseIkZDDANGnypWScPYZPiJVIg0bPJCUUTwWSAMzOEb8KxT7d4elAks+lzNOqdRs8iy8lC2nWHb4fHiKDpEEeJZSrpclM12OxG2SZE2SaZ1IldbYbWbYaWUwlSAqXtObbzAl8dW5GU0h8W4uA0jCVRok06NBdejSPeM6SoUfzCCnBKAIXhYEgmPMiBnh/Osrltl/veyheslK8bCV5w0zTpnl4KKSAjJB0A2WewQAuXZpPGG8yM2ww0+RJnYSQdGkuRVInJSSHNZdFboDG4+zjAghuSecxwzPZbGZwgYtda4zEnuNabDcy46zokkgMBLWegdA0rAsvJt18kHTzAZrmTycpoGhYMm/hahp6yoj+5An0Qxm8Vp2nrgmQzCSoHsxAfgEik6EoXMCwUGxzB5lz329w395EYU0hXH8uibxSImWVCOFbfCSFZJpncHdohGbdoUaa1HkWPZrLdtNmm+uwxNUpkWV8IP1ZvhX9JU26S5YQUSWRqpEmQ2eJY9CWGWbbUII5wVq0vHxCn7wDZ/NGvH17EYEAxoJF6AsWn3HAm1KKJw89R0eim9nx6WhCQylFy2g7jx94hrlFswjrQdy3N+MMHsCdX4ab1dGra8bEKzL3PnkvsKVnO/3pQeYUzhwjV2vy69kzuJ/NfXv/25HBQghCBSEGWgbHPS49PwTeCk9+MehYvNEp+PUhQb6lmBmHrAf7hwR37db48jJJ+CRsmZN2aF53CFe5ZEMpmvs76BnspK29hR+/PEikMkQqnSSZTJBMJkgkEqRSCRKjCVzPmXDMt3kGwA8EB6Q8M9uxJ3N/x+NFlJaWUVpakfu7nNLScubn/i4tLccuLSeYlz8pAdARtIzAtl7BqANVUVhSqiZlbTGF/36YIoOn8L8WRUUlrFp1AatWXTD2mFKK/v5eDh9uo6urg66uDvb2tnNgsIeBwX68zgH05GGaPInneUjp4XkenufieRJd14nHCykoKKSyspq5cxeSn1+AKMznzWkxymfMYGRmNa0RgyKpMSokQSWIeyZPqBRzk0HiSmeRG+RtM8uIkDnfMkWP5hFVGrNci/acKuD4H/wwghEh0SwLy7JIJE6d8BrOKQ3+qzA7r4JkLII+NATxAhQwLCTFQyMUxEoRefkT7pfYt4uM2YO+vw0XwDDQKqsJLphPytTICkX4JOdlrb0CvbQMe/06SCRQ2Sz60rPQCuIoKfE62jBmzMZcs5bMT36A17QPr7sT2d8LiSQiLw+tohotL2/MQgJAGToiEsVceQ4iEMBdvw7ltgESYQUxL7wYffYxrd1GEDd+nO+nUgSb7iPY+hSgUJqJ2bcZs2cDqQV/gAyX4a5fh/ObJxHxIrTKavA8ZMshsvfdxY+iYTZvXs+vfvXCmFXIRHAqzgXlEdn1A4Q9CJqL2b8Vr/05stVr+ZM/+TwFBYV89at/PYlX8SgCzY8RPPgrhHRQegCjfyvSjIFmomX6sEtXomd60EdbcQOzGF3xTWT4xJ55YY9g9qxHBkt8IhhAD+DF6jGH9qOPNJ3SzzeERjibwuzZgD68H/QgTtEi3KKF4+0ZNJPMjI9gV61BTxxGmWGfwJ6AaD4TGAM7ENlhvML5Y+ylCsQh04PZs/G0yWDtMER+rKG3CM6ylvHHDX/G3//sb7hqzrXMuuXMw0qORVFRMV/84p/x//7fn/GJT3yWGTMm700rJTzwgOCRRwTZLFRV+WTq0BDs3i2YP1+RzQpuvdUjmz1iFQHPPKPR3OyTug0Nive9TzJ3rh/69uCDOo6jmD/fJ3NTqaOkbCDg2zjY9tEP+5HwN1331cLBoO8pHI3Czp1QUKAoLISREZ/09ZW6vtevYfj+xr29vmWFritM03/dEgnBnj1QXu7P8VQYGfEJ3dJSn8QN5NYVolHfImN0lJOSwS0tcPfdGq2t/lyCQVi8WHHzzZJQSPHCC09z7bU3Tvo1mcIUpjCF/64QCC60w0x3LfYYNq5Q1Homs1zrHQmtEBpL3CBpJK4ADWjVHfo0jwZvvPVbmdRp1h0GhKToNMO1TAQzPItVdognAwkkCu0YFW4IjYvsMIvcIArF44EEz1gpPOGToQJBgdL4WKKQh4IJXgmkSAiFUH54WVr3MD3YY2QZFpLXzBQBBHWeSZHUiCiNInn0HkBDMaxJpnkm5zpHFwVftFL8MjhCs27TrLuAryfxcr3iWQHtuovSoUrq1GUFxQPDZISktSjKHBmhT/No0R0CSnCWE+IDmdiEORxhNNbYEdbYkROeO8sJ8raRYa9hE1EaUvj5JityVhYA+sIl9H/m97jb7aAlrCFNg2B+IUsKarj2X36OMTiMtnwFBALYwQDCcZDdXYhsFq2wCL22Fl0qRrZswH7uabTSMuYO2czbcZC3Z9nkhwyMeBFDwqPRs4gpjRbdYfoxAppqadKk2bxhplni+gsPXVo5MXkri+00LQaUyAAeMJRJUPTYMyxa9xbxVJZUfhXWhZdgrDoP6/w1cP6aE67DESgUXZpHk24DMN2zxlTwx2MwO8SBoUOUh0vRcrWqEIKqaAWHhls4NNzKjPV7sZ98lLp4lFj5RXTaDuVdnRhLlmEX5DMqJJc5701L/0B2CFM3x5GAQggszaI/M3iKPf/rUL24ioHmARJ9SSKFYTzXY7hjmFhZjJLpxe88wHFQCl7vEBgCSnMft5ABDXmKQyOwu1+wqMiho6ONtrYWWlubaWs7RGtrMwebmmhuOsBQamBsPF3TKc4vIz9UiBmvpKiohJraeqxglLxYlLxolIAepG/3AM6ASzAYJqAHCAXCzFgxgwWXLSASjREI+OpnpRRS+pyE67p4nsdj+yUvtHrYjou0UwyMpAiTYUF+ktpwilIjhW2nSaVSDA72093dRU9PJ21tLWzatJ7u7k7S6fEq6mAwSGlpBSUlZZSVlVNSUk5ZWTmlpRU0Nvrd1fG4n8HyZqfg3j0ahxMgFZiaYlGJ4FML5Ng1nML/HEyRwVP4PwUhBMXFpRQXl7Jo0bL3bFyF4qfBYV61UrTrLpaChFC4AqZ5JjXSpEl3OKg7LHN1ljlBDug2b1ppOnKa4JjSuDwbod4zcfA9chNCjvnjKhSDQrLYDWIAkUhsQs/g/05YXjKd9YuWob3yEsOuTTYaoah/mIZRh+hla05ICwaQA/0UPvgQsauWMlhbTXHKRtlZZGszPdUlzCyqJu8UFh5CCIzFyzAWL8NdcymZn/0I2XEYr6cb5blo5ZVY174fffpMAqOjpP75b5HtbYhoHqK+EX1aHV7zQWRnB14ojFZcispmkF0dGPMXYcxbiDFvId6SZbgH9oOmYcycjT5rLkI/9U2IPtJE4PDzeMFi304BQLoYQ7uwDr9AZsaHcTa8AbpxNDzOMNDqGtj21hv84xsv8LnP/SErVqw6+fnbw5jdGwnvvROkh112DggdPdVBqOle7n9hEy+99Cw/+9nDpySUj4eW6iTY+hTKjOAdIXiVxBjciRur9wNTkm2AwClZRmb6h/AKJm51F24a4WWR1vjFAGWEwMsi3PQp5yKyQ4R3/Btm/zaf/FWSQPvzZOreR6bhAycokWW4YkJS+t1CeHauF3T88ZQwEe7pt6sFnhXozQJ3lgIdvlL3VX515wP89f/3Ve5535Oogvdm3rfddgc/+9n3+cY3/pi7737slGqA/n7fl1cpePRRjQcfFIyOCtJpnwgtLfVVsIODMDzsWzmUlfn+vUcwe7aks9Mfo6LCJ2UBNm70idPZs30CuKTkyIKdH/AGvqr3SEDbrFk+YRwK+b6/ZWWKefMUgYBPRkej/jiW5ROzsZivRi4t9T2EDx3yx/LVxIpUyj8PH4rubrjxRjlmB3EyHCGfh4b8OR/B0BDk5Sni8Yn3y2bh3nt9Yry+XmFZfrjdunWC0lJBff0Wuro6uPjiK049gSlMYQpT+B8CgaBGmtTYp+ehMyA8ng0kedtIo4Sg0bVo8Hyq73itnAQ8J8Nrra+yt2s7tuewoHgOq6tWUBwqOmHspJDs1m2aU520tW5iqPcQWEGYNp2dtXMp0UO4KEaEZNkxfprtmstLVop8pVEsx/u49uqSRs/iTZXGUMonaQFPQbvm0alLajyDN600e0ybm9J5LHKDvCL9HyFTCfo0j4SQFEmdT6YKmO35x00jedVK0SdcWnUPV/g38EeuQwBAMWZPMeQ5bBztpbGtg/rDfdBYTcwq5raKRfTpkojSqDgJYXk8RoVknZlii5lFAovdAB/MxNhr2Gw3fY/lRW6QFXZwrNXcFfDgklpa9XLqM4qAbpIwBG+4SSIlJu8rK0fTdMT8hdQFFK8EQ2Qtk3BFFeb8hXjRCNlkH/Vv7UCva0CEIwSBj+wboqGth62rFXJ5Mee6Uc61Q2w1syhOFNBE0ejRj7Zg7tNtQuhEVZQ2cvsoOP/ex5n+7BukiwoIxPKR3V1k7v0ZQSEwV518UV+heNZK8ptAihHhHydP6VyaDbPWnsC+bOz/xy845/7f34f94m8Q0RjV8XIuOzTEUwuq2eNm0Ee7MYrCLHaDrHbeG8atIlyK8jJUJw5RZA/iaQaHg+VkvQyVkRPt3X4n8LIYw02AxIvVo47zz61cUEF6OE3zhlYGWgbQDI14dQFzrphzRspgT8FQBiJmzoai+xCde9fTtW8Dzft38OrgIUb72pA5XzEhBOXlldTU1FFbU0dtYDoVpdVUlddQGq+kKK8YJ+VipxxW3b6SgyLKC60a7aOChKGYW6ZYO01hODadu7oZbBlAtwxKZ5RQMqMYTR9/fyuEGLOGsCyLA0OwflintEhRlIsBsj1oGhKcPVNyef3kFGCJxCg9PV0T/unu7qK5+XW6u7sYGOhD5VrrysoqaJwxj57oXEYL5mOWzcOqmIcezOM3LYryiOD2+f+FCrQpnBGmyOApTOE9gEBwYzYPA8Gd4SHSCgoQTHf9IAzwiwYp/C9JC8GHM3ksc4I06w5GTpkwzfN9yqZ7JgudIBvMNIVKJ6AEfcJXDVxg++EGsVgeo6PD79k5KM/z/bH27QGp0BtnYMxfiHgXJpgBobH62o/TYcbIbNqA0TFEXixO9Lo1WBdcPOE+3u4dFDc1s7J7Ni/MKyQdNAlnAwzEQ4S6u7koNmtCJcNEMGbMIvyFP8TduhnZ14tWXIKxaClaaRngewxrVdXo0xoQeQWIvDy//cswUV0diEgU2dsFVgBz1XkErvvg2PUwFi3FWLT0tK6HMXwA4SZR0bqjD2oGyopj9m0mM+PDqOFBRGh80J+D4k/eXs/Mqlq+8pW/nHhwpbA6XiR46GGM/u3oo814kWq0QBwZqcSL1tC6ZwN/+q2/5brrPsTatVed1tz14QOI7BDesV67QkMGS9HcJCNnfRMj1QEI3LxGME4eViiDhXjhCvTRFrxjCOEjQXQyfGIY4bGwOl7C7NuCF6lCzw6CclHSJtDyJE7JUry8xtM6tzOFF6sDzUQ4SZSZYy+li3CTOPH5pzWWSIK5WyBLfSIYwNIt/vL8b/GRJz7A6796gVWfOLk6BQAF+gEw9wlwwa1TuHOA4z7CgUCAb37zH7n11uu5776fcvPNt50w1OgoPPaYYMsWjVTKV7Qq5RO5xcU+cdrX5ytvCwtBSkFPj+Scc3zF7LHQdT9w7nh0dwvCOYm/pvlE8eAgY97AkYiit9dXFBcW+rYPug4zZ/qBdYW+SIGuLn9u8+f7nsBHyOOiIt+O4ZJLFLGY4s47BT09kEr5Y4XDR8hmhefBokWKG25QY5YNIyOwdaugrw/y8mDhQkVJiT/uihWSZ57xfYdjMd/DeHQUrrtOET1J9kdTE7S0COrqjobSxWIQjyvWrxfs2/cU0WiMFStWn/p1nsIUpjCF/wE40vUmgVKpT7p2SyP5h0g/G8wMAGGlcUCzafRM8qRGm+bQIH1drouiQ6Vh81M83r6bsBFCFzqPHnianf17+Myij1MYPLpC15ILJ96b6uDAtgdID3dTEsindliQ2dJEwUA3xuLLiQqDS53IOL/eg4bDqFBUHRPGZiCIK53XrTQ2kkJlEJQeKQFZ4ZEEHAFhBYucAOXKoEV3eTw4yh8nivhoOo/7QiMc0B0Klc4SJ8iHMjGWukdrqCFN0qd5tBounlLowicxHeETwi5+2RAGhASRzeB5Ls31FZRKA+W6eDs2EmsMEZ89+byENJKfhobYamRzqmX4VcBmt27xqXQBl9kn/th1ay5PWQleDqSo9AyUZSHQiCkokhpbZlVxyc7DRAChGyxydBYkFdsWzia/bgZ6XoARYTOjL8GSXc2I6oaxsSO2y5rDfaw58ByRxksQuR/SQumiATZqnPfpsJDMdI8ShGHlv18qpUlcOvRrktrmTho3bKWrqoxIQSHlThA9XILXfBD75ecxzjr7pPdBe3SbJ4JJQkowK2db0qN5PBFIUuOZzPHGK3jjgXxmFjSysXsL+YG8MZuI9tEOyiOl1PQ7qOEhtFn+a3TB3i7qD3XQ3XsYLZWk8OIraVh6HuEy//08Yo+yvW83w9kRCoMFzC+aQ9Q6Uc19MiwvbCSSaaGst4mwbiFQVCkIFy5jWfnku9Fc26PvYB+p/hRG0KBkegmh/JPbwJwMRv9Wgk33YYy2AxIvVE624fpxXXZCE0w/v5GqhZWMdI+imzrxmgJ08+RiHFfCngFoSwgCGszK9ygLSpKZBG+/vYnmpzawZ9sGRg6uJz3SB0C0fDqqbDHxJcupLqsjUlLHjLp67riwhvpC/3VVSrHpvs107e4mXhNHN3Vc2yPRk6RyQTmHiPCTHTopV1EcVGRcePSgRk9acft8i2nLa5i2fOKMlJPhwLAg6UDNMR89S4eopdjSKyZNBkejMaLRGA0Np1Y/ZLNZDh7cl7Pd3Mmb23bT8taTOP3/Mua/ZhXWopXO4z9q5xG4bDYL581n+vTZCCvMtl7BgWGwNJhTpJhTCNqUPfF/K0yRwVOYwnuEiNK4JZNPSkhesdLMcy2COV/bDs0lrnQajilK9FyowvHFwpHnbs7kUSJ9j9yMUMz2LNbYkbHta2qm0dx84D2Zu/I8sg/9Aufl51G2DQiEYWCsXEXw5lvHCq4zgR6JUfPBjyPXXotKjKLFC8fC0CacS9ZvtbpqWztFKZv1DaWMBk3mtvVz7r5uFk9bOyn/1CHhkRaKotJSrLUTK+1UMgmuRKutRhhHvw61cAQZjxP81OcQho4IhhDFJaflpzTh8TR9YtsO5aFyYWN640zsV15AlFeOHe+ejes4mBjlqX/4D6yTvBbG4E5C++4GBDJUipbuRXMSmH1bsM0oGRHmo//+FsV5Yb797X87/ckLPRddK8cC4o7MHS0AZhS3cMHkxtJMsrVXEN71Q4zhvUirEOEkEF6aTP31yHDZKXc3ezYivAyB7jdhTIGroTQds2/r74wMdooW4ZSuwOxahzLCIHQ0exgnPhu78lzwsmjpbtBDyGDxhN7JJ+C498fVjdeyomAlf/3TP+OJj68b8xGbaL/Ak4LgMxoiAQhQpsJeoUh/ROWkQ0exdu1V3HzzJ/jLv/wyq1adT13d0WumFNx/v8a6dYKyMkUwCP392lgAWzotKC1VHD7s++e6LmQyPlF6xRWSdxDIj6GyUrFjh38zJATU1yuGhgSplE+ojoz4l2zGDIVhCDo6BP39vpp4cNC3VwiHYWBAsHChryiur1djlzmb9QPe6ut9Ahnge98TufrVD6nzbR4EmYyvOv7RjzQuvVQSDMJPfqJx6JCvzJASXnhB8dGPSubMgWuu8QndN9/05xSLKS65xPdCPhnSaYHrHiWCjyAY9IPuHn30fi666LKTfsanMIUpTOF/Cto0h8eDCZp0GwVUeQZXZaMT1rzH41fBUV60UgTRCClBSkhSAnYLm4uzEbp1L+dB7H/Zxw63MdSxl/pYDWHTJ1HLpce+oSbe7NjElQ2XoFA4qJzVggMHt6IP91FV3MiwprA9i8aUTX/bbm4pOZfZ8cYTlJ26gomKOIkim/OvVSgKpEEpkBAarboLKPKUjhACoQRVnkGz7nDAsDmoOySFr/E1lSCqtBOyECJKkEGRRhFCkFXKt1DDL8s8fBuNAmkw6vo+v5ZuYBs6XfEIulLM2teKO7QR4zTI4LfNLDsMm0bPIpC7Fg6KPYbNJjPLRfZ4hepuPcvdoRH26Vk6hceI4dGpeyx2AhQonZAVZLi0lPRQH+FgGGFaBGyHD9/3GxovPItdc5fjSZ2L3DBndwmCaRtpZ/1aWGgIw0AlE36WxzEE7RzXotE12WvYVEgDE0Gv5hFWglXOUVJ9kesH4yWFZJEboEl3MHt7CY8mMSqnscANjl17ES9E9vXSnB1iazTIgOZR6RksdYOU5kLddhhZMijq5NG5lEmDPbrNDiN7wntdCMGVDZfQmepm72AThmbgSpd4oIBrp19BuC1DRmh++5Kuo/p6qdi2hbK+XoRhoPfbyNffwr31k7SXhblr1/20jRxG4ddKdXm1fHzeh6mKTk7VWzG4jXJDsTOvgUN2CpRimq74sJ5FyMwJCvyJkB3NsvXR7fQ19aGkQimIFkeYd/VcShonb9ugpToJ7/4RWmYANzYN0NCT7YT2/gwZLDoh9DlUECJUcHLRyRGkXbh3j8bGLkGiu43Rl+5jdO/LDPbvpmeoFaUUkVg+0boV1Fz0aernriRat4KtqRJ0DdbWKiwdHOmrb59oVXwuLhHCfz1nr52FnXIYbBvyi2dNUNRQyMyLZ/LTdp2kAzOP6RaLmoq3ewQHhmDGSbrIToWT3kWo9yxaZBwCgQBz5ixgzhz//u6hfYLWDRrlZhq3dw+pjh2kO3cx1LaDnrce5I+fbfbnKQT5ZfUEKuYRn7GKwtkXUtKwlMsbNK5pVJO6HZrC7wZTZPAUpvAe433ZGD2ax27dRuVW7YulxvXZ2Gl5mUWVv8/l2QhZ4ScFa8d81U+fPps333zlPZmzt3sHzssvIApL0HNmlyoxivvma7hz5mGuPLktwWShFcSh4J1/+bTqGkQgiDY8zHn7FKv3d2MLMHbuwFpz6TsSssPCX5XfamawURRLnTV2hLOd4AmFvVZWjpaXhxrsR5QcJSDlQB9aURF6ReWEVhYTQTkOsqvDt3gor5gwYMItmIMKxNGSh5GRKr96c9MIexSn7loAzHMvwN21HW/fbrSiEoYSI/zrhle4cckK5l953UmPb3avRzhJ3MJ56KMtIHSkVYCW7UdLdfG1+w+yq32IZ//ta8Ri7xCPOwHc+GxkqAw90YIXq/fn7mXRM32k6687IZBNZAcxBneDknj5jSfYNDhl55DSDKz236AnDuNFKrArL8Suegf1KyCcJPrIQTBCyJAvQxWejZZsxRjYBg03nPb5nRF0i+Tc3yNQMAuz+02QNtnay7ErLsAc3E2g5Qm0VDfoFnbRIrKNH0SGSiYcSkXAmacIvKwhC4+qg40OwTdX/i2XP7OGxx9/8KR+svoBCD6joUIKL5fXKBIQeEPgzQT73BNvYL/xjX/g9ddf4gtf+AQPP/wCeo7FbWuDbdsE1dV+yFtnp8CyFKZ5xIZBkUj4j6VSPpF5zjmKr3xFUlc3+cu3cqXirbcUBw74Xr1HVLarVyuWL1c895xGUZFkYEDQ0iKorFSYpmJwUNDWBiMjgsZGxaxZiiuvlPziFxp79wqKixWuC/39gnhc8cwzgl/+UiMU8sPtCgoYm7fn+YRwKOST2s8+K9i5U6O6Gg4dEjkiWiEl7NgB//qvGrfdJpk/31cBX3yxYmTEHzPyDoKc8nJFLKYxOHhU1Qx+iF4w+BwHDuzm7//+XyZ/AacwhSlM4b8hBoXHT0PDtOkuFTlF8AHd4WfhYT6bjDNNnrzjzM0FpXkCSryjNfOokAwLyYjm8YVUnO1GlhHhUSoNOnpH+I2UhM0QCsVwdoRRO0HWtdnYtwMx+2w2mRn6NI9DusN8x2Jb3wGCwRiW0IkoSbfu0hCKMZodoG20gzkThGdN9ywKlE6n5lGZIwSzOTuJFU6QLWYGXUmyQhFQGsGcmlZXAksJgmp8DbrDsHnFSlEgNZa6ARygWXe4NzjCl1KFxHKWaHlKZ7prst5M4wEhBcnc/YX/yy4JuJKYq/Ach2FdkAiY2IbOQDTAlVvbWNg6gNR7T+t1bNb9kKvAMbWzicBC0KTbXMRRMthG8WgwwYDwmO8FGNEUulKMapK9hs0KJ0if5lFXPo14VQNy/55c+7kir7ySq2at5trM0TpcNcwhGS/EfeFZ/xwNEy0SQeQVYK46b9y9QAiNWzP5PBZIsMewGUVS6Rlcmo2M+RgDLHIDXGxHeMVKkRKSoNKoCcWZrseoGVZo4aPvN5VMsm1+A4+UOAyZLhaCdabiDZnm4+kC6nOBhhMRKTqQEhMvDNfEqvj9xZ9kc892OpJdFAYKWFQyj9q8amRwGFFaimxrQdRMw9u72w+rDgTQZ85Gnz0Pb/9eMo88wCMXxGkbPcz0ggYMTceRLvuHDvLogSf57MLbJiVeMbvfRA+VsCSvnpTjCyvCRghjcCepwd3Y0XdWrR5cd4juPT3EqwswAgZKKgbbh9j99B4KPrESMzS57lKzdxNaqgs3fjSDw8trwBjcidm9/gQyeDJwXZefPbWBex5/mqGdT9N/6G1AUFxQT1nxAi6Zey0Lpi/gik9dRVu0hOdaBJ1JQdZV5HmwsNgnggFMDSojiqYhQVcKKnL1XrQkyopbltN3sJ/MSIZgfpDi+iI8w6BtN8SD498HMQsOJ6E7JZgRn/g9MmrDW92C3QMCS4MFJYqlpQpTg8YCRcSE/gwU57jwrAcJV7CkVJL1fCV02JhYf5JyIWH78widAQtYHAZDg4wWIlS1hHj1EooEmCkoDsK/nzdMX9tuHnljF89t3InTuZ0Dj32LPb/8KmYoj/WzzmX72gu45uILmTdv4dh9xxT+6zBFBk9hCu8xiqROlWew08jSp3mEETR4JnPcM1N7hdAITfB7MXPmHO6998e4rothvLuPsrt3D8rOjhHBACIaAwTuzu3vCRl8PLxcCXt826DeOANj+Uqc119GhSIIy8IcGkCrqMQ878J3HPOe0AibjQyl0iCe8wu7LzSCpQTL3PHErlZUjLH6ApwnH8XLZhHRGGpoEDwP86K1kyaC3bc3kX3qcWRnB+g6euMMAte+H71m2rjtZLSadP0NhA4+gDG4HdBA6DhlK8ZIUL2+kdDtn8V+/hm8Qwf4jz3bcTWdP/nXn5zSskPL9KIMf74yUoEMFaGlexHS4dHXd/GDJ3bwT59YzZwLPop30lFODhWIk55+E+F9P8cY3OE/KDScokVka8crr83O1wgduN9XxSqBChSQqb2SbN01R6sTIXBKV+CUnAVe2lcXa5MrClSwEOEk8YLHEKvSAT2ISPX4q/O/q2VnI0y29opx18DseYvQ7h8DChkqAy9L4PALaNlBkov/CPSJvwuylyr0VoWxL9cD6gpUHiz/vfO4xLuSb3/761x55fWYE7wPzH0CkWSMCAZQUVAGmG+LCcngaDTGd797JzfcsIbvfe/v+NKXvgb4SttUSlGTuw8IhRSmqaFpvuqjqMhX4SaTvjinqgpmzjzRHuKdUFcHt94qeeIJjcOHBZoGS5cqrr1WYtuwYYNPsu7ZI4hG/TC6oiIQQlFR4RO6l16quOoqRSwG0ajkuec09u/3rSZmzFAcPAi27QfLJZOQSEA8DhdfLGlpERw4IIhEFF1d0NvrW0c0N/vE8XnnqVzoHGzfLmhqEiQSipYWjSVL4OMflzQ2+gT2ZFBVBStXSp57TpBI+McaHIRQSNDc/C/MnbuAs88+vdDBKUxhClP474ZtZpZW3WGmZ43VeNOVyR7d5i0zw7TsyWuZ/pxvbkAJFGpsET+iBP26H+5WLg3K7aN17zNaAAU40mXPwD7aE5040iHhptlfEGGX1kmVlseokLTrDgIFRgAvPeIPoBsMlJbzWqSUEaeap8L5VBpZ5rvjF7nLpcFV2QiPBRLs1rNjAo0FboAPZGKkhaLDchkVPk2r8IPkPHybjPxcYHS77lImDVo1GwNBifLPxQIaPJMDusNuI8uKY1St12SjvBJIcVjzyOQ8gzWpyEobkc1gS0mr46AJnerDQ+QZYVxT57ZX9nH+ng60kVG0mlomAw/FBjPDi1aSXYZNSkhqPXNM0OLCCcR2u+7QrjtUewYhNKo9g4OGg0LRLVx26TYxpbHGKCV6xx/g7tiG7O1BxGIYCxajFY73dpaDA5DNoBwb5XrgJfAG+jEWl6AvPjHzpVQa3J7Op1942AKKpT7OMgL8+43rs1GWuEGac8rymRXLKWh4HXfHVkRtPYTDqMEBUukET191LhlTZ3bOwk95LoMHdrPp8DDVehn1Cxt4rchXS5vHKKc9GKcWPh5FoULWTrvghMe1vHwC13+I7C/vxnv7Lby2FggG0csr0Osbff/YympGD+6hvzaf6to6jFzdbGoGVZFy9g8epDvVS3nknQsyIZ2xujtsjld5C+VOtMs4uFmXrl3dhAtCGAH/PSw0QX5lHkOHhxloHaRs1uQKQy07hH9PdFwGhx707yUmiWw2y4svPsNjjz3ASy89y9DQIFa0iIZ5F3F22SVMa1hNOF5KXxoWFEvyBgdoe6udpTcWs7BYMZSFwwn4/jad8HG31qbmE63ucZJpI2BQPmd8R6OmfMK157j4EMcDDZ/QnQgjNvxou8bOfgjovqfxxm7B/ir48CxFfR5cXCN5tkWjLw2aUEgFs+KKgTR8800N24O6fLikRjIrJzywPXi+VfB6hyBhC2KW4rwqxZpahXHyGJ4TMCeuKAzCoeHc97MQBDT/fBaXKioLo9QUn8UL3kqWzIa6PPBch+79G2nb/hJ7Nr/MD7/7df79HzIUFMRZufJcVq++kFWrLmD27Pkn7348Dv1peLtXMJCGwhAsLjnqoTyF08MUGTyFKbzHeNFK8qaVodGzOMvVSKFo1R1+ERzl86n4OyYoTxYzZszGcRxaWg7S2Djz3Q0mvYnJMyH8594l3F07cF5/GdnWykBDLW9ctprt06sQAhY7QS60wxQeac3SdYI33YJeW4ezYR0kkxhLL8U878ITyNXj0aQ77DKyTPNMIjmLjqincUD31RdL3cAJ6uDAVdciolHcda8gR0fRqqqxzl+DcfbkfDu9A/tJ3/0TSKXQyitRnou7dTNqcIDQF/4QLb9g3PZ2zaV4+Y2YA9vBzeLl1eEULQbjKPGsT59JsHEGh3bv4O77fshXvvIXlNWfqFIZN49YPWbvJlAKpVk4JWdhDO6i9cAOPnNXG9eunsdHvvyveHn1kzqvieCUr2I0WoPZ9zbCTeFFqnCLlxz1ywX00RbC+34Ono1bMAfQ0NJdBA/9Ci9SiYzWonQLFcipxIUA4/TCMNzCeahAPlp20N9fSdAM3GjtCQplf4cUVtcbmH1bQEmc4sU45avHzfu9hHX4BYS0cQtm+w+YUVwjjDm40w/cK14y4X6yApKfl1hbBFobqDyFs0jhNcBXC/6aSy5Zzr333smtt376xJ1P9jHVQTgnn+uKFav44hf/jO9855vMnj2fK664lvx8RSikkUj4JGtBga9q3btXEAwqOjsFoZAiPx8WLoSSEsXWrfDGG4KLLx5POisF+/fDW28J+voEtbWKs85SVFX5z8+f7wfMdXf7fsBlZf5L2tbm79vXdzQUDnxiNxqFefMU+/YJOjrg5z/XqKnxx/3kJyXptK/y/e53NUIhmJb72igogERCsX27xsiIrxI2DOjJrR+UlkIgAK6r6O+Hzk4/IG7nTsHmzeB5imxWsGuXYMcOePhhwVln+aTxOeco5sw5+XUG/7xuuEFRXOxfq0TCP/8ZM/Zy++1P8p3vfP9dW9FMYQpTmMJ/NfqEi0CMW+wXCMJK47B+ih8kwFKCYqXTrTyGNEm+9CnXESHRFSx0TvyNn104g3wrxrbeHbSPdhKzogQ0i7Rpka5sZKRrH4vLl1GkmxzWHbo1SWXNXEa3PUPGSdE5bT5uXhHm6ChhoTOcX8BP9WFuT+Wf0Op/gR2m1jPZZWRJC0WNZ7LICRBC4+Z0HjrwkpmiMxdcVueZBJWGpTQO6A4OigKl875MhIeDCULHkapH7hGOWEccwRwvwI3pGM9YKUY0j4xQpJIDJNp2EAkWkC2twg6GITnMKElquka4amcXF73diuruRCsrx1xxzqRevycDCZ4KJMnk1K0HdIdezWWxE8BAw1CCBccR5R6+SvkIQT7bswjj198JIanzTK7ORlnsBhCR4DuKTJz1ryOHRzCvuBZGhsF1IBBEdXXi7dqBNsG5CATFyiDtZNg31IRUHrV51RQE8sdtU++Z1OfyXBAgb76VzC/uwtu/z28Zysuj67qr6auvpi5HBBujCWbcdT95b29F2VmGZZAF1bUs+9QNvDWjkoLcPcyQ8JjlWiyZ4H0KoNK+HYMIT1x/msvOQquswnnxN2QeuBetshq9dhrCODJfgVQeWjZL3fYWyg/1IJRioKESe3oZQ3g48tSfsbFrXLSE4KEHQVaCliNzMwMoI+xnf7wDPFfiuRLdHE/gabqGkgrPmfz9oxcuA5R/z3lEGKKUf68RPfW9n1KKDRvW8cADP+eJJ37F8PAQc+bM57bb7qC57EqM6uXUdnegvb4NFT9qXSERBPNCDLUP4TkehqlTHIKwCSUhRXcKph3TSNmVgsqoomyCW5aBDLSOCHRN0VjgK3NXVSju2ysYyEA8ALaE5hFBbUwxq3BiVfD6TsGOPsH0gqOq5BEbXu/QWFrqMbcI3teomB732NEnyHr+HLf0CH7TphEP+KKN51sE6w7r3DrP4+IaePKQ4NcHBXkW5AcUIzY8sF/gKrhykj7DAK0JgalBwFCg/CUvW0KxAdc2Ko7k3ymO2lbohknlnFVUzllF3iVf49q6NKUD63n99ZdYt+5lvvWtr2LbNoWFxZxzznmsWnUhq1dfwIwZcyasiZuG4Gc7NTqSAg2/Q+LldsWtcyWNBZM+lSnkMEUGT2EK7yGyKN600sSUNraCHkFQ55k05bzBZnpn7gfZK1x2mTYZFME5fqjC9qbddM6upk13iSjBfDdAvWdOKiX4CPTGmYgXnkUlE2N+viqTQXkuxpx5ZzxfAGfzW2TuvhOVTDJaXsLPGqLsz3ZQ1CXRK6t5MpDggG7zmXR8rCVOBINYa9ZirVmb8xOd3Ln0ax4OaowIPoJ8qdOju2RyfmvHQpgmgUsuxzp/DSqdQkSi4/yDAVASPdEKno0XrR5HXjpvrkMND6HPmuv7wQEiHMFr2ou3/W20cy8cP5YQePnT8fJPTe4KIfibf/prSkrK+dSn/uAdz90uX43VvQ5jaCdeqBKhJBlp8uFfusSLK/j2j17Ayz8Dg6rjIKM1ZE/ROmb0v43IDoxr9ZLhCsyudcQ2/bVv66CZ2MVLyNZff1LbhFPBKZyPU7gA4WUR0kYJAxkqQ8v04BYvHr+w4WaI7Pw+Zs+bKGGCEJi9b+H0byU5/3No2SG0dA/KyvcD4d4tGSc99GQb0ioY/7gRAuWhpU/dpqnikF1zYmE2Z84C3ve+D/DDH36XW2751Amr526dQpkKkfAVwQA4IFICZ96pnd++/OU/Z//+3Xz+87fyyCMvMm/eEubOVWzc6FszhMNQWuoTpFL6Ng1lZVBfDzU1vvdXMAibN59IBr/xhm/RkExCIKB4+22NDRsUn/iEZHruI2AYvmrW83zl7htvCF59VXD4MPT0CJRSZLPk/IqhsREOHBC0t4NlaUSjirfeOjpufb1P8Pb3CwqPK7inT4euLn+8/n4YGfEtIKqr/XNQChzH9xXesMEnn3ft8sllx/ED4zzPJ6gHBuCVVzQOH/bJ8Jtvlqxceeqi2rLgkksUF12ksG3/mF/4wrcoKirhuus+dMp9pzCFKUzhfwLiSkfhe+kea2+WFooSeepbz7jSOcsO0RX0SOIxqEk8FDaKxU6Ay+0TSbTaWDVX1V/K32z4Z9JeBuEIDM0gXjOHdOVcMgMd9KUHqIqWU++ZbDFshurnkj/URUuik6RlUtDfRQyTuUUzqRRR9gmHV600c9LHWWAhaPAsGiao5UuUwR2pOO/TYvRqLhqCamkgFGw2M3RpHnGlscgJMk2avO1mWW+lKUMfq9lTSHQY86U9Ag3BTZl8KqXJBjNDSkh6t28i3baTRfGZjLT30R+LMhIM0DfQzqq9KS55sw2kxFiwEOvy96FX+bWbUoruVC+ekpSFizG0o8fqFS6vWGnylEajZxJRvsVHnybZYGZZ4Aa4xA6fQAZXewblnkGH7lLnGegI6jwDW0kaPJMvpwoxmbwEUR5oQkSjaIYBx6iG3c7DviXbSbCjbzcP7f81HYkuFIqiYJzL6tZwQfWqk95LaKVlhD73ZWTzQVRiFFFShlVTDPogKsdn1jzzAmVvrKevroZULAJJA/3gQT5452OUf/X32ZKru1bbIc7Phgjv2k1m1w7IZtDrG32C97WXcXdsBUCfPQ9r7eVjr8mx0Csq0T54M15LM7K1GXT/9VFKITvaiVQ3sHjnTor3thAMxVBAUdNh2BVBu/4SysKTU+Pa1WswBrZjDO5CmRFfKQxkay7Dyz91sBiAFTYpqM6ne3c3wbzg2PVNDaYJRAPkV0zeks4pWY6X9zzG0G68cCUIDT3VgQyX45RPvHAwPDzE3Xf/iHvu+THNzQeoqanjtts+y3XXfYiZM31biYf2C544qOEZhp//4EoSUkMpyLPAHXIJF4bxhMaGDsHmHkHKhZil6Epq7BuEiKlIOIKoCZfXyTGSFvya8YU2wTPNgsGsf+9WGYEbZkjOrVL0puHNTo2elE+U1uUpbp4tT1AdH8GOPkHYVOOOkWf5auWDw4K5RQpNwLwimFekcvvAngGNuphi1BHs6IeE4//5p7d09gwomkegMAiludvXmAWdSXj1sOC8KkVsEtSEUvDKYUFFRDEtT9CWgIwLQd0n0CPW0fp3QbFi14CG7R09l8GMb00xu8Ri5szzOPvs8/jKV/6CdDrNpk1vsm7dS7z++st8/etfwXVdysoquPLK67nmmg9y1lnnoGkanoRHmjS6U4JZcf9aSAX7hwSPHtD4gyVyjJCewuQwRQZPYQrvIdJCkhSKyHEr/UEENoqEODUpcypsMTL8MjhCv+ZXJvo0i0BeHj8/tJmi0EoEfuH9kpXi+kyM85zJqy2N+Qsxzjobd/06f0VWgHJdjEVLMZaedcZzVo6D/ewTkM1izJzNthmlHJxRw/SDHZhdI5jxcooDAfYbDluMDOdPMOfTUcod8VXOosZ5nCU1SYVnjHvshONY1oRBefpoM8H992EM70NIFy9USrbuGuyK8/0V+o42n0A+Zp5HyGQ5OHDyyUoXY2gvwhlFhkqP+vDmsH79azz55CP867/+lFDonXtfZLSa5LzPEWx5DGNoPwiNP3lihB2tgzz26IvkvQdE8GQgnBS5ZIWxx7RML8ZoM9II4eZNR0iHQNuz6KluEov/aJwqejLw8mdgV19CoP1ZpBECzULL9OHlNZCtunjctmbfJsye9bix+qMkvpvG7F5PNNOPnhlAOCMoI4RbuJDUrI+hgkUTHHWyF0BDhkrRh/YBx4R4eDYI7agi+gxw222f5frr1/Dqqy9wwQWXjHvOnQP2SkXgDYHS8RXBKYE7R2G/A0GpaRr//M8/5v3vv5iPf/wGnnjidT784UosS2PHDkFfH+TlKe64w7dveOQRwYIFalxQnK6Dc5wYJZGAJ54QSKmYNct/TClfYfzUUxp33CE5cACamgS7dwu6u6G9Hbq6BDU1isWLFTt2CA4ehGRSkM36oXNHxsjP9713i4vBNP3Hnn5a4zOfkYTDvr1FKjXexiGd9pXCt93mexH/278JWlsFkYhf6La3C0ZGIBj0VcDbtilGRwWm6RPCR97aSoGWi0QeGBCMjEieflqwaJEfuPdO0HXfp3jDhnU89NC9/MM//OekPudTmMIUpvDfHQucAC9aKQ5oDtXSQEPQqbvElGC5885fkFdnowxoHjsNmxHhm4rN8Cw+lS7I+Q/bdGouQaUx27OIonFBzTk8evBJBtKD5Fl5FATy6K+axV7DRKJwc0RXo2fRLyQBYVKz8BrKEt00B2COBcWhIiK5dvm40mjRHTzUCXZmp4KGYJo0T/BFvsw+MTj5PCfMHtOmSXMoVjq2UPQLj6VukJkTWMuF0LjMjnKJHcEF/nk4SZfuB8TF02mCmSRKczk8uJ+3l65h/pqPsDhrEigsHsuxaB/t4JEDT3Fg6BCeklRFK7iy/mIWFPvkWbvuMiwkM3OikumeRYk0aNMcbAGfSOezcIIuuxAaV2Wj3BsaYa9uE0QjjaREGlyTjZ2SCFZKITsP4+3fC0qh1zdCfj6qKXPCdih10hDqnlQf9+55iKHsCPV5tWhCoyvVw0P7f01xqJD5xSdv3xE5i7cjqPMkZcNp2tJdNHSPUPzSq2QK4wzGQhRJgTBN9GkN0NrM1bs6uWbufH8cBfaTj5F++teoTMoPXP7N08jREUQ0gl7h54U4r72EbG0mdMcX0YpPFEUI0yRwzfVk7vox3p6diEAQlc2glZQSmDmXxb/exvbSPNKmwNJMvKykuqWfBSMlWPrkfHplqJTkoi9hdb6GObgLaYRxSs/CKV0J4p0ZNSEE9efUMdIxQv+hfgLRIG7GQQHTz2sgUjT57jsViJOa91mCB3+FMeTnjThFC8nWX4cXG68Mbm9v4Uc/+lfuuefHOI7NtdfeyD/+4/dZufLcE4QS51YpdvYr9maKKQnEcFuGGMwrIGIJDnRkqfIcpl9YyYNNGi+3+cHClg6jWY28oG/LkHRgQYlkVYViduG44dneDw83aViaYnqBT0y2jsJ9ezS+tFRy40zFuVUeHQlByFDMiPv2DyeDpvljnHB9AO0kX0PdKYGn/H139kHG8/2EQ4ZvM/HaYYHtwYry8QMXBKAr6SuXT0YGO9JXJocNX+nbnYR4EIpDiml5/lw1AXsHBf1pwREX83MqFbsGFDv6fHsNqQS6UFxYI5leMP4YoVCIc8+9iHPPvQiAVCrJhg3reOmlZ3n88Qf5yU/+nYqKat73vvez8pIbac6upDKqxq6HJqAqomgeEXQmoXqS1m1T8DFFBk9hCu8hokqjWOq06y4FxwRfDAtJJPfcmWBQeDwYHCWpKWZ6FhrCVw/MaWTv3p3c4pljflWHNZdfBxPMcS2KlM5uw+ZtI0uH5hBTGkudIEvd4Pj2Pcsi+JHbcOcu8FetpcSYOx9j6VmI0Om18B8L2deL7OpEFBfjHmziYKlE7wyi9/chdd1vASstRQdadAcm19l0Usw4JlG4RpoElaBf88iiWG2HxilUJgNhjxLa9X2M4QN40VqUZqGnOgnt+SnSysMtXoJWXonbtHfcfsrz/IL1OIuII9CSHYT23Ik5tAekjTKi2KVnkZn5sTHrgu9979vMnbuQ6667adLz9Qpmksz/CiI7wN0/+R4/ePTH/OMnVrM6eS/O/iaytVe8KzJyMpCRXP+/Z4954+ojh8BL4xUtRgUKfMWQlecHQwxswyldcXoHERrpmR/Fy6vH7H4T4SRwqy/BrrwAGakct6kxuCf3j2Pex0YIPdWNPnoIp2w1XqQS4Yxidq8jjCK58EtnrhAWgmzlRYSH9qIl2pGhUoSXRU+04BbMwimcf2bjAitWrGb27Hncddf3TyCDMSB9s8KbCeZWgbDBmSexVyhU/sTjHYtwOMxPf/orrrxyNR/72HU88MCzfOITBXR1+aRucbHvtbtvH7z4IoyO+rYL4JPAo6OCSy4Zv9jV3Ay9vYKGhqMFqBC+5cSBA4If/lCwfbtGU5Ov5A0EfJJVKejo0Cgtlaxeraiv920jFizwfX+3bBEMD/vk8Guv+XNbuFBRXq5oahIMDflzXb5c8eSTGsGgH4SXTkNrq2DxYsWSJaBpftDc3/6toDcn2E4mobBQYVm+zzDA1q0KKX1PY03zlcFHCGHL8sc1TUFPD3R0QEPD5F5Pz/P4i7/4EgsXLuWmm26d3E5TmMIUpvDfHCXK4KPpfB4OjnJYd/CAMmlwZTbCjEl0xxUqnc+m4uzJZW/kKY05bgBdwV3BYTZbWTIoBFAhDW5Kx5hNgIXF83izaxP1+dNIhELoCjzXxovkEzGjKBSDQlIidT6ZLmCRG2CzVcAPQ0NUWeY4G7eUUFRJ/TS0rJNHBslbZoatRhZLCSzh22BElcYVdpS1dnispj8WRzyUdQQ6MC2vhqahQyilyGjKD8mTWTJC0ZsX46dVOqvtADdn/O2HsyP8dOd9NI+0URkpRxc6zcOt3LXzl3x28W005E/Dyo3t4nsYCwQFSierFEIqQlLwgpXCAaZ5xjhf6KVukIKUxiYjQ6/mUSENznKCVJ/CP1cphf38M9hP/xqGB1FK+IrgukbQdGR3F6KkFKREtrX4WR/zFo67lrsMm27dZW/HFtpTvSyMzxwTaFRFK9g72MRb3W+fkgw+fk7aSy+wdvcbPHjOTJqEYu5gL4miOAkhERq8ZqUp1TRmeA6hdGbs/sJrOYj9m6cQ0Sh6nV8MuE17kNsOoJ+1Ei0XVi3iRXh7d+O8tZ7A5VdPOA9j3kJCn/8yzuaNqO4uRFk55rIVOK++RFGwgIU1Mzmc6CDhJIlFSqnzssR7suPOYzA7RNazKQkVjVOAj20TLCZbfx3Z+usmdW2OR1FdIUs/tJi2LYcZbBukoCqPyoWVVMwrf+edj4MXm0Zy4RfRMr2gpN9JeAwpvXfvTr773b/j8ccfJBbL4/bbP8dtt91BWVnFSccsC8OnF0ru32Pw3PR51O3ZybTMEAFHMZQyycyYxvS6Gtbt0SgJKwpygncvotg7KKiJSd4/4+SCire6faK1LieC1gU05MGeQcH2PsHFtYqqKFRFJ2fFsLhEsb1PkHJ8tS1A8zAMZ6F1BDZ2CRYUK4LHvJRhw/9u6EkJkq6vABb4RG48AAUBxf4hQcKBwmNoiKTjE8bRCb6SpYJ1HYKX2n2SN2wqVpb7CuKu5NHwOk34AXaaUOQFjp5jngWfmi/Z1CPYPyiwdMmCYj+U72Sk9tj5hCNceOFaLrxwLX/5l99m48Y3eOyxB3j44V/wgx98l3BJPbPOu5FFl3yMwupZY/OQyie/p3B6mCKDpzCF9xAGggvsMPcER2jRHeJSJyUkg8JjtR2iRhpkkLToLgrFNM8kNIlSc69h06t5zPDMsYIjiCCwajkj9z2CfkxgVoXU2ac7NBkOb4ssjwRHOag5DGuSjJDElMbF2QifTBdQcUwbmggEMM9ejTlJr9zJQAQCYJh4+/chOw4TnluCa5mobBYcB9ndiV5aigtE1LsvuQMIPpLJ54HgCE2Gg40iX2m8LxtllXP6qjuz/22MkYO+92uuiPLy6jGGdmN1vooTm4s+dz5i8wa85oNo5ZXguXjtbejVtRgLFp84qPQI7f0pZv9W35NLDyHsIQLtL6CsOJkZN7F//25efPEZvve9Oydtpj8GIXj++af5k//3d3z2knp+b+0chJsmdPBX6KOHSC78sm9ZcDpQCpTnKxzegSR1Spbhxhdg9m/FCxb7rV7DTUgrjhc7JrxEDwAKLTX5YIhx0EzsyguxKy98h+0MfEepY+DZiEwPMlSBDPn+YcrKx4vWYgxsRx9tftfeyhl7mEDb0xijB30P5+IlpGd+9PSv/TEQQnDrrZ/ha1/7Azo62qmsrB6/QQDs1Qp79ZlVQ2VlFdx118PceONl3HTTFdx335NUVIxfPJg+HVavlrz8sq8Y1nVIpwWzZilWH3dcLZcFIo+7/FL6XsBvvimIxxWep1FWphgYgO5uQTAIyaTvOLZ2raK83Cefb7hB8vDDGl1d5OwaBKkUtLcrlBLMn6/QNH/sV14R7N8v0HXFoUO+17Fl+dt86EOSIx+ryy9XHD7sq3qzWYHj+ERwIACzZ/sq364u/1wd5yhZfcTH+FiCOBj0A/Umi/vu+wnbt2/h0UdfnkpUnsIUpvC/CrM8i68kC2nNkcE1uWCxySKAYNFxob/PWAnWWRkqpU6e0nFRHNIc7g+O8pWUyQXVq9g52sJvSkJkSmuwdY20JgkXVtAZDNInHCJKcIkdYaEbQEMwx7Oo8gwO6A51nokF9AuJjWKVHToty7XJwEFxX3CEN60MBn6oWQpFo2dwe6qAUjX+1lyh2GXYvG6madMdSqTOKjvMUjfA2RXL2dKzjabhQyRj+XR5aYzRQWoK61hSPJesNFhvpVnuBJnjBdjWt4vmkTZmFjSi53xZG8069gzuZ33nZhryp9HomtR4Ji26Q4NnoiPIIOkTLrXS5PuRIUaERAAWgpV2iA9l8sbC2k5moXEyyAP7sX/9CMK0ELN8WzrV34fctwtj/iLsQ03sPryZvaEMXnWMmedcyPLiAkL4Qpm7QsPsNmwkcJheBg2HCt0bZ7MRMoL0pwcnP6f2VuxfP8xCBMWBPLZXFyDy88nr7MYpjBMNRpFA32A3oiDIgsoKjvz0ewf2o5IJtOpj6t10BsxcQMFMn5AWuo4IBn0biFNAr5l2Ql6KYxigoDRcTGn4qAeuO7gHYfrXvmnoED/Ydhfb+3ajIZger+eWOTeyvHzxpK/DZFFQXUBBdcF7M5gQPgl8DAYG+vnOd77Jz3/+Ayora/jGN77DTTfdSuQkCvHjURaGiCkobyyidsFKRNcAynbxwlE2a/moNkg7RwldAF3zvXW394lTksEDaUHQGP/8kdukxBkInFaUK/YOCN7q9tW0vWlf+RsPKDZ0aWzuUSwoFnx8nhxT884tUlREBDv7FJ7UQClSrl+rVscUluargDuSvjo3z4JhG3rSgktqJUUTNGus6xDcs0dgCJ9MTrrw6AFBbcwPwTtCCGdcaEv4lg1lIT8wryjoX7+oBRdUKy6oPnOGVtM0Vq5czcqVq/nmN/+RV19/hW/9+AF2PP19tvzq25TPXMHcNbcSXPoRGkqiVE7uLTGFYzBFBk9hCu8xVjpBFPCSlaJXcwkpjavsKJdlI2w3bB4NjNKpuyj8ZOKrM1GWuadum3NybRfHltEKiF56HoP//EN6d+yibEGuRSn3/KDweDGQYlB4pDVFgdIISI1ezeN1K01YCb6QKpxQffBeQcQL0evrcV59EZFfwLyWPtY7Hr0lBZT0jyL7euiQGSKafkJq85miQhp8LhWnVXfJICmXBnF1ZkSLyA7menPGf1W6MkTmuVdIPdiBSiV9aw3l4nW0IXQdY/YcAtfdiBYvPGFMfaQJc3APXqxhTK2qAnGkm8bqfp1s3fv44Q//hdLScq655sbTnvP27Vv49Bc+wxULy/ibOz6EMgxfiRssxuzfjtm35aT+WxPB6N9GoP159JGDyEABduUFPgE7gcIAQJkRkvM/S6D1aayejaA83ML5COWhzGN+pXPBhMqahGz1XcApXIDV/htEdgAV8F8PLd2LUBIZG+/VpswYItmOsIfe3UGFRnbaVdgV56In2lFGKOdH/O4XPN7//pv51re+yt13/4g//uNvvOvxjse8eYu4//6n+dCHLudDH7qcX/ziKeLHvI81DT74QUVjI2zdKkin/QC4FSsU8eNE5/X1UFGhaGvz1bJC+FYL3d1izGdYCIFt+3YOPT0C11WkUgJN8+0h9u1TVFRAOAzDw/D884JYzPcZdpwjylxBT4/i4EHB6tWKu+7SaG4WpNO+xYOUMGuWoqZG0dMj+NGPNFauVJx7rk/2fvSjipoaeOwxX8lcWup7IVuWT+5WVUEm44+VzYluhPAJYykhP99XB8+ffzQY752wf/8evvGNP+JDH7qVs86aXKjPFKYwhSn8T4KJoPFd5GQcCw/FBjNDVAnycjWdgaBemhzUHfbqNkvj9VSefRPbRT/mSC95GUlhfhVuqJR6L8D5Tph616RWGmMkb0RpfDSTxy+Co7TpDi6+5dil2QjnnIGI4J2wy8iywcpQLY0xEYSN4oBus8PMssYeX1ttNrLcHRomLRR5SmOvbrM35DCcjXJxfi23zvswzzS/wJPpg+iaYFrNEmbOuBDLCmMpOIyiWXeY4wUYyAwhYIwIPgKlFK+2r6MgkEd9fi03iGp+EUmyPxf2pwP1nkm35hHEt+YQCEaF5HUrTaNnnZHgAsDZuR0So2hz/HuYrFD0lhXAcBfZPIvNH1nNG/t/g6Ni6LE83rQ3sW1HltvmfZjfxFy2GVmmexYBBKFgKa8LxQ6R4jximPiZAwknybS8k2ddHA9vzy7UyDD6rLnUDKWoGUrx1MIFhF99jembd+FMb8RIpdHSGd66+kKM6iKWH8lKUxMQX1bAD9s67jnl2IgzsHAz5szDeel55ODA2H2GHBlGaAJ93gI6Et380cvfoHm0jYgRAgTrOjbSOtLOt879GvOKZr3jMWwUvuPYf12oreM43HXXD/jHf/wmnufxZ3/2/7j99s9jTWDp9044nICoqSBgkakuZ1e/oDsFAxnBQNZ/XRryIXDMx8+Tp7Z0AN8DeNeAlsu3yc1bghCK0jP4SIQMuHWeZFmZ4O1exVOHNOYUSubE/fo748KWXkFju+DKXNddfgA+Mlvyg+0abaOKrhRETf98qiKwb0hw2TSJoflq5c6k//y5lZJrGk58v9oevNjmE8FHAvTyA7438EBGcH61ZGuv4NCwwNShMV9h6Yp/eEvHVb5lw2X1koXFJwz9rqDrOheefxHxOWv46dvfZe/6x+ladxcvfP/3MUN/xvvefxsd5Z9h2rRJtudNAZgig6cwhfccAsE5TojlTpBh4RFWGmE0DmsO94aGGRWSGs+nYDt0l1+ERihM6UeTbSdAjTQJK8GQkGPEpgDyz17O4UiYg795YYwM7tE88pWvHx4QHqNCYik/xRkgpvyiep9h06TbJyQlv6fXQgjMZSvJPnQ/Kpth+oatXBoxeOmyc2haPo2srlHgZLjWK2XGKc7/dKHn0oLfLWSwyL/Q0kE4SbR0D0iHkef2kDwUQtaW4VomA53NjHpp+lYvo3zFGmYvuAjdnLhY0ZwEeBmUMd5+Q5kRNHuEwd52Hnzwbr7whT897YKnvb2Vj33sOmZWFfKT3z8f/dggPD3gB+El2yftxmH0bSGy4z8QzggyUIieaCW8+0do6R4yM24+6X4qWExm5kfJNN4ISmL2v014x7+hJduRoQqQNsboIbxoLU7xotM6x9OFW7QIu/pSAu3PQfKw/6AwcPOno8RxAS3ZAZSZhwyVvSfHVlY+buF7S3ZHozE+8IGPcO+9d/KlL30N83SkqJPE/PmL+eUvn+HGGy/nxhsv4/77n6bwmAAX04SVK9U7hqWFQr6a9957NXbvFui6H0BXV+cHuI2MgGEoXFfQ3e1X0bouxnx5XRd27PAJ2PPOUwwNCdJpQV6erwAeGPBJWNf1xyotVblUaY3BQRge1hDC37alRaOiApYu9Ynn++4T7NwJ8+b5IXEVFfCNb0i+8x3fJ3nnToHj5PzbcmTyoUM+Ye15vmo5lYJo1CfBZ89WvP/9RxXHp0IqleLTn/4wlZXVfOtb//xuXqopTGEKU/g/ARdICUnguEwOHT8vIyMkPZpHdyzG2TJGLFyHLjQ0odEnPRKaZJETmFAc0OBZfDkZ54DhkEVRKQ3K3yHo7kzRrDu4qHHdcBaCIBq7DJs1xwTkOSh+E0jiCMaFT3doLs9bKc5ygswrmsXs+HRS4iBtlmKmWTRmkaBQKBgTfeRbftiYVBJN+ARW09AhtvftoiCYz0P7HyegB1hduYLfn3UV+wKKlPB9f9s1h4eDo9R5R8PuYkqjB9hiZs6YDCaTxkXRMXqYw84wh/OiyGCYagsOZru5u7aCRWrR2D1Cxs2ypWc7jUUz2RKfRYnSx/JAKspmU1VQR/vgQQ4GKihVAbpTPZSGSzi7Ytmkp6Rs2w+DPqYTbvuyuZj5AZZs3Em+EKQqyug+92y2XbScOs+DHBmsN0xHhCOowQFErm4ShUV+UaPrvo2cEMjODkQojFZRQfapx5HNByEcRq+tx1i4GK3o5EyaPmc+5pq1OC8+h9fVgSLX3XneRRiLlvLAtjtpGW2jJlKJqR+5bhnaE508fuDpU5LBh3SHl8wk+w0bC40VTpAL7PB70r15Ojh4cD+f+cxH2LlzKx/+8G38yZ/8FSUlZ16bV0WhPeF72u4eELSOQsTwCeLyMOwbgvVdfpiaEJByIOkKlpedOuvn7ArF5h7F3iFBWUjhKuhNCWYVKhaWTE4Rm3J9+4ftfQIUzC9RrChTJB3BugDMjh9VGwcNiJmKt3oEV9QfJaBnF8Jfr5J8b4vgrW5BfgAKLJ8ILgsrLq9XTIsp2hMwmPWtJKoiEzd7Dmd90rcwOH7+8YCv/F1QrLiy3iedAR5v0tjWq1EWVoR1aBqGrp06n1noMeO34Ey4qETxhRUmG2rfT/dVH8Aabebgc//JEw/dycN3f5dLLrmS22//POedt+a0cof+r2KKDJ7CFH5LMBEUH9PutdXM0ic85uRW1MH329qj22wxMqckL6d5BqucEC9YKQbxi+ER4bFAxBi64Fx2P/8CZX/0WRwUFoKrMxEKlY5EYQuFeUzxrPDVFB4wqsmxAua3Ba26BmPeQpTrIjSNtcMaFU3DPJhXQEdhFNfQeVvPUi6NE1oCzxQqncI7sB9cF21a/YQK3cnALVqMmz8Dq/M1tOwAQtrYPSnsPWn0sulki/PZ0r+HnvAIZV0j9O5Yz8ONkoubHa6fftWEP0JeuAJl5aNl+scsCgA/AC1SzV0PPATALbd86rTmOjIyzMc+di2WFeD+b32cqL0D99gNlAIUypgg0OEYm5Gjj0kCrU8i3ARufO7ReaZ7CBx+CbvywhP8eU9AzjPYKV1BprGPQNtTGMN7QTNw82eQnnnLb10ZjKaTnvERnOLFGEO+t7ObPwM92UFo/93ooy3IQCHCGUXL9pOtveKdz+u/GLfc8il+9rPv89JLz7J27VW/lWPMm7eIBx98lg9+8DKuv/4ifv7zR6mtPX3rjIULoaREsnWryBG2sHix4pVXBI88olGeC7SwbZ9k1rSjYXRCwPCwYNYsyY03SjZuFGiaor39KEmrlL99QQHcfrvk29/W6emBTEbkgtwEiYRv45BKKTwPamrg4EG4/36dmhpFLAa6rpg3T1BUpBgYEHiev+/oKGSziuZmQTjs22GEQjAyonAcWLtWcvHFvmdx3iSDs7/2tT+gufkgTz21btJtjlOYwhSm8H8ZFj5pu9FMU3wMITkiJCGlUSkNRoUkLSTFmJjHGFNGlaBTk4wcI6g4HkE05r1HHWqnwsmUlh4K6ziiu0/z6NJcSo/LGymVOod0hw7NJc/T0TWd86wq7gmOkJJ+iLVC0Z3zWz4SRrewZC6VbeU0DR2kOlrJiD3K2707COgBlpYsoiRcxHB2hJcPr2N6vJ6zy5ZwONFJX3qAzsIIBIMn2GaYSpARZ94G7lSW0zzSRtPhVvqrasjgEOw/jDY8xHBVMYlQiM76WdQ0HUQDgkaAoB5gW+9OvLmzxl0zywqzdMn1BDY8xszXN1M6nGFpZR2L1lxGdWziuk65Lt6+3ci2VjAt9Nlz0GungWGikomxsLqikTRbZ07DXLocq74RaRh4msDTHWLuUaJUq2/EvOBinOefxu3rQeg6ynEwV50PuoY8kjESL0SrqCB9z0+Ru3b4XYaGiVZWjrFwCcGP3Y4xc/aEcxa6TuD6GzHmLcRr2ucLPRpnoM+ai9B1dvTtwdCMMSIYIGgE0RDsHWw66WvRqjn8KDREj+ZRpDRGkTwcGKVNc/lEOv+32kl6LB555H7+6I8+S1lZOU8+uY5FiyZP5J8M84slb3TqbOuDjgRYGqRcQTyomFuk0IXgwLBgW5+vBtY1xVllitWVp35vV8fgE/Mlz7RoHBoSaEJxUa3k8mmKiOkTve2jvqfttDw/TO1YpF342U6NTd1+cB3A272CnX2CxgLfF10p6Er5SmYvx00fb00BEDHhK8sU6zp8YnvUhqVlkvOq1JjCtybm/zkVIqY/ftIZHyyXdH0yOmr5IXLxoE9i7x+G6QVqbP4xE/YNwusdghnx346Jb30+1Of797VQC+f/Dd/40z/n4Yd/wZ13/is33XQFc+cu4Ctf+Usuv/yaKVL4FJgig6cwhd8R+oWHn817THAbggCCfs09xZ7+dtdnYlR5Bm+ZGUaFZKUXZLUdovH8q/jbP/8KM/uzFMf8QIy5rsWQkJTmwuzSuWJZokgLSZXnt6eVnGGg3elAq6rBmLcAZ+N6tGn1JOJ5PDuvmn5Lo8EzCRgRWjWHu0MjRFIa099lS6G7YyvZh+5Hdh4GqRCFhZhrr8Bac+kJPwYSRZfmYQtFhWeMqQuOQJkRMvU3EDj8AsKzUXoQxwnjakksI8Vw7xZ6MkkKg3HM4hDTkg57ZYiX2texqGQ+jQV1J8xPhiuwy88l0PoEwkshzShaZgAEJMrX8JOffoIbbriZoqIT04VPBsdx+NSnbqKz8zCPPfYy8bwR1I5daKnunPeWRB9tRgaLcYpySlzpYnW+itXxClp2ADd/Onb1xWPEr7CHMUZb8YLjvbtksARjcCd6om3ypKnQyNZdjV2+Cj3RAnoAN2/6GFn8W4em4xYtxC06GjriFi4AIbDan88pgsNkGj5Apu6a382c3gXmzFlAXV0jzz335G+NDD5ynEceeZGPfew6rr76PH7yk4dYtmzlaY9TUeHbRRyLVasUW7f6gW/RHB/quj4hHAxCYSGAb9Xw4Q9LCgogElE4jk/UaprvAZzN+mTt+ef7Fg29vUcIYv958Eljz/NtJl5/XaOuzvcnzmahqAgaGhSZDKxfL7BtwcqVinRasHOnQtMEUgqk9MnftjZBeTksWiTZv19w8KBGLOY/d/bZPlF8Kvzwh9/j/vt/xv/3//2IWTl/xClMYQpTmMKpIRBcaIdpMmz26jaFSieLIikU59khpnkmw7kQtiFtvGfskCaJSY3CM7QMey8x07UIWxp9wqM4N5+EkEhgwXFkdEAJTAQ2438/U57NSM9eXugYYKcymVU4neWF0zmgBXnLzOBoAIqY0rgqG6Umdy0Kg3E+NvdGfrX/CVpHD9M2chhNaCwvXUThSBb30DYiClTYZmPHJvYM7Gdz9zYSTpJMSRXdC1dTEq6hOCcqcFEkhGSOe+a13JYKjfYyg/quDFo4izaYITIwzP76UnbNn0YoOcpQfjmjoRD56fTYfhaCBtdii5km7mlj91ah9l4++dQhFrQ4WHoQs60Lvf1BvFsL0BtnjDu2ymTI3HcX7sY3UY7tBz8XFBK4+jqM5Stw3nwdEQyBYbDk5UF2fXgt3fU1lFsmDoo23aFS6uMWEYQQBK59P0bjdNwd21DpNPqMmRhLzwIp8Q40gVKoTIrsfXehOg6jNA1RW4dIp1CpFF7zAeyHf4n+5a8ijuv+Uq4LQuQs6eZizJ7L8QgZAT/bIKcAz+2JqzwKAicXYLxmpenR3DEbEIA4GlvNDPuc0G99scS2bf78z7/I3Xf/iOuu+xB///f/TjT6DszlOyDjwpOHBG90+OTo4YRgMCsoDkFZRDGn0CcxZ8T9LrI1NZKCINREfS9eaxJfGdMLoDFfMmyDIY4Gsq3vFDxxSNCTEmgCavMUN0yXzDxGLbutV7ClxyeKQ8bROb/dCxURPxzurR5BbwokAqEUQzaEDMGwzVjg3RFYOlxYo7iwRk2o8zkeUkHTEBwY9l/x6QWKxnxYWa54/KBGQPdD9VIutI4KFpeocd7KfRnwpL/4JJVPegsBMUvRNuorsc8USvnexp6CwsA7n0s4HOYjH/kEN998G+vWvcx3v/u33H77B1m4cCl/9EdfZ82ay6dI4QkwRQZPYQq/I1RIAxefgDwSAqdQZFDjgtxOBhPBaifMame8vcBVF1zGt9w/YMZzm7niiuvGHo8rnWuzeXTpQ+wysoziYClBVAl0JVjoBJj2HloznAxCCAIfvBnlOHh7d7O9QNCSH2SGoxOcNgOhNCKeyV7dYb2ZeVdksOzpJnPvz1DDw7kkYg3Z3YX9yIPopWXjAt2OtLwd1B1cAWWezmV2hLOOa3XTsgN4kWrsslUIJXFJQ2CT7/012oIVrEQXOkbGxg4HyIsV0Z44xIHh5gnJYIQgPeMmZKAAq/NlNCeJF6sjW3Mpj2/soru7k0996vcnf85S8kd/+BnefPMVHvr//pwF+i5crY7stGsItD+HMbTL3y5URnr6h5FRP3QseOBBgs2PojQTZYSxul7DHNhJcv5ncYsWofQgSg8gvMz4n3Jpg2agjNNXcatgIW7wzFTa7yk8G2NwF8qMkpp1KyoQR1n5KOvdFZ2/KwghWLv2Kn7964dyPmW/veJm+vRZPP74q3ziEx/g/e+/mL/7u3/jpptufdfjFhfDpz8tee01wbPPCjo7/cKvqEgQiSgMA9raYNEiRV2dv8/AgKC42CeIEwlfTaxpvk3DkiWKRAIiERgaOmrbp5RPMksJUvrXqa3NJ4bz8xWm6W/jOL5yuLVVMGuWP7bn+dYWlgW2LQiH/ccHB2HdOg3HAVAcOAC7dvmBdR//uORk7i733PNjvv71P+SOO77CjTd+7F1fwylMYQpT+L+EGZ7FJ1MFvGylOKjbFCmdq5wQ59ohNARxpXOOHeLJQBJPc4kpjREhGRWSa7JRYmfY6t6X7mf3wH5sz6YqWsGMgoYTfHcnfw4ml9hhnsvlioDAVIJVdohlzvi6Kq405joWr1tpIp5GAEHazbJu52O47TvZZPt3E8+3vcqF1au4ecbVLLdCtOkOhhLM8iymecY4Ecr0gga+vOyztI4e5pnm51nfsYny5h7ctpajP5zBLG8PDyJraqiIVlAdrWQ0m6arbQ/rawVz8usxc57B0z2Ls22/blZK0TLazu7+fdjSoTZWxdyiWQROsfC/PdHMgUsX4hwG1T1MwHbYu3I+65ZUE8mPYmYcstLByym9M26WjJdlUck8auwwrbrDHt2meChJ3p59LPvZA9T3J8mbuwQhBEpKvKa9ZB/7FaEv/CHimLBW583XcN54Fb2qBhGNoZRCHm4n+8QjhD/7RfSGGbibN+I4WcxzV1BWN4tdYZ12MsSVxjTP4gOZ2Alqc6HrGIuWYixaesL5akuX++dxz09RiQQqm0WL5fnzisZQvb2gm3htLcjW5jECW3Z1Yr/0HO72rQhdR1+6HOuCiyfsfLxk2gVs6tlGX7qfeLAAXej0pfuxdItLp1100tdiv2GTr/Rx75cwGi6+Nck8fntksOM4fPrTH+bFF5/hH/7hP7j55k+8J7Xtk4d8QrYo6NsblIbg1Q7/rW5q0Dbq2yi4Esojiuumq3Fq2MlCiPHE7N4BuG+vhicVtTGFp+DgkOCuXRpfWiopyt1q7hsCEISOUfoGDd+hcCgLswolb3RpmBoEdYWjoDoKGQ/e6PCtIk41p1PBk/BQk+CVdo10TpMWMeHiGskl0xQjtmRzt0Z3ShHQBYuKFR+eJTnSdCEVtIwI9g361zFk+IR3bcxXEc98F5brnUl48pDG7n6BBBryFZfXSaYXvPO+QghWr76Q1asv5PXXX+Lv//4b3HLLtSxbdjZ/+qffZPXqC898Yv8LMUUGT2EKvyMscYK8bqXZqztUSL9RrEtzqZAGy99FSMW0aQ1Mnz6LX//6V+PIYIBznBDlCZ1HAqNsMbNIoMIzONsJcbkdGSOlf9vQiooJ3fFFvINNJMNp9NoYwUDB2A+9wCepD+tnEL16DNztW5E93eiz542NrVdW4e7fg7Nx/RgZPCokPwsN06K7VEkDQ0K37nFfcISo1Mb5KAvpggBl5fneazVRjIoCnJZ2tDwXLImVSGGm0rStmIM0jbFzGoNSoFwQRs43LEC2/lqytZcj3BTKjIFm8Pivb2LhwqWTVgxKKfnTP/kcDzx4N3fesZpLIpuhaQtKt7DLVjG69E8xEu0ozcCNz0EF/OVoLdlBoOMFZLBoLLVXhiswhnYTaH3KV80aIeyycwge+pV/7mYUpIMxcgA3Og1hjxJofRJpxXGLFqLMCewn/htCS7QT3v0jjOF9ID2UEcQpWUZ61m3/1VM7LVxyyZX88IffY+fOrcyfv/i3eqyiomJ++ctn+NrX/oAvf/lTbN36Fn/xF98mHA6/886nQGkp3HCD4oorFP/8zxpPPy3IZHyVrm1DWRl89rOSI/duiQSUl0NhoaKzUyCEorLSV+O6riA/3yeOk0no7PS3B/8jp2l+6FxBgV849/WB4wjiccnu3YKWFp8gHh6G114T5OeD56kxQvgINM2fWyoFJSWKxkZFba3//82bYcUKweLFJxbnDz10D3/8x3dw22138LWv/c27um5TmMIUpvA/AR6Kt8wMG80Mw8Lzg8bsELXyzIUIMzyLGWkLG4UBJ9SxV2ajmAjeMNMMCo+Y0libjY7z4j0dbOreygP7HqUvPYAQAkuzWF62iA/Pfj9B4/TJMYHg6myUOW6AfYaNhx/ONtu1MI47F4HgfdkoQ5oc23agYzuZtu2sCNVQkVNNDmVHeKl9HXOLZrOweDYL30HBaWgGDfnTWFG+jM37XiPR3ko4moewAjhIUrIPb7CHysIK4sW+kjTPCHN21wD7U5soWlBOKJjHGtdilR2mUOkopXih7VV+ffBZRu2Eb0kndJaWLuSWuR8kbE5cLwggEwrQfVY9b8xoZCQUpCCVZjjdT5kVIT+ax8BgK12dO+nzJFJJFpfMZ0X5UiKexadTBeze/DKhRx6h6FArxbubCOQXIMMFaLV1CE1Dr6zGazmI7OpArzoaJOdu3ogIBBG56yiEQKuqxtu7C6/5INaatYiLLubh4CjrrBQOiojyQ+4aXIs7UnGinNkCg0omUJp2ok2bJsBzwZO+vzAg+/tI//jf8Q4eQCssRkqJ9/jDyEMHCX3684jw+Pf2xbXns71vN79peZGORBcSScSM8P7Gq7nsFGRwntToM8Z7B8qcHCSkfnv3i47jcMcdH+WFF57mzjsf5OKLrxh7ricFW3sFgxkoCft+sYWT1KIMZeGNTkE8AK6CPYOCtOOToO0J36LB1AV7BgQlIcXvLZBnRARPhPVdgqQNs47h6qcXKPYOCrb1CS6q8a+rqY3XzmZcf247+gWtI4KoqTAFFId8a4uioKIq6ityt/QeJYM96dtLbO2DjCuYGfdtLvJP8VWwrU/wYqvvDXxE7duZgPv3akg8rm9UrKn16EsJopakPg/0Y97uL7UJNnb5b9mEI3CkYluvoDupKI3AyoqTE9WnwogNd+7QOJDzO9YEvN0j6Exq3LFIUnUa7mqrV1/II4+8yMsv/4a///tv8MEPXsq1197IX/3VdygtLT+j+f1vwxQZPIUp/I5QpHQ+nsrniWCCA7oNwHw3wJXZ6LsOqrj55k/wd3/3F/zVX32H4uLxbf31nsWXUkU4KIaERxjttxoEoJRCDQ747VaFR4MshK5jzJhFoZlChEaQnh/8cQRJoSj13l0Ln0qOgtBOWE0WwRBqoH/s/zuMLC26w3TvaPFd5/kpzevN9Dgy2M1rQOlhRGYAFSxEmAaxtXNJPtaDMRiicLAPPVZAx+KZHF46k77MABEzwvSCelASq/M1rI4X0dJ9eNEq7KqLcUqWj5HCSvePlUqleP75p/niF/9scueqFF/72he55947+c9Pr+KD587EDVf47WPOKIHOl/EKZmFXX3zCvnqiDZEdwosfQzoLgRcqwxhpRtgjqEAB2WlXo6c6Mfu2gGf7oRfBYoSbJrLj3wCZu0aNpOd+Gi82bVJzP3pxM77y2MoD8TsIp5Auob0/xRjc5dtUGEGEPYLV8QoyUERm5kd++3N4j7By5blEozGee+7J3zoZDBAIBPiHf/gPFi5cyje+8Ye8/vpL/Ou//oyFC09UvkwGUkJLC4yO+lYNd9whmTFD8PzzgmTSD2z70IcUM47p6hwZgU2bwLY1NE0RCPjq3UgErr1WUloKK1dKhoc1TBP6+/2gOk0TWBYEApDN+gFwliXQNNi0SXDggL+NpkFZmWJ01PcKVsqfJ6gxVfCxNhVlZT45DRAO+8+1tCgWLx5/rr/61X188Yuf5MYbP8Zf//U/TbWpTWEKU/hfD4Xi14EEzwSSaAoCaDTrSbYbWW5P59PwLi3BrJOIGSwEV2WjXJQNM6JJ8qVG6AwJu/70AA/se4ykk2JWfDqa0Bi1E6zr2EhtXjUX155/RuMKBNM9a1KdcMXK4I5UnN1GlgHN4+XDHRR4QSqMo51MBYE8OpNd7Bs8wPziiX1mJ8K8aB3nN7tsTQ0zkG+BcEgKlzpRSLfXRXgwAceUdfl6kLzDB/lgaYb5xeMzBDqSXTxx8Dl0oTM7PgMhBGk3zcbuLTTG67nkJNdqfvEctvRuJzs6QMMhnR2zZtIVCiC1ApJ5hdRZMT6QDCMqJI50mBFvZGnpAkKGL6Kpbj5M4d2Pgu1A5XScth6QCnf3DsxIFFFcAghfxijHh4GpdBqM8QsTY7/Pri9O2WFked1KUSaNMWV5QkgOGQ57DJvlZ5h1ojfOQKxfh4hGUUNDYAVAerl2Jg+ttBy9phYAZ8MbeAcPjHkCA6jiYrzdO3C3bsE859xxY0fMMF9ZdgeX1J7P5p5taEJjZfkyFpXMO2X9cZYTZK9hMyg8CpSGpxQtukuJ1Jn7W7KIUErxxS/ezrPP/pof/vD+cUTwngH4+W6NrqRAw7/jeKldcds8Oc6q4GQYzEDCFozYvk+wBEZtQcr1PYIjpsLQfFVwyIDFZe+dv21vWhAyFY6EtlHfnsKT4EpoGTm63ZxC/5yGsv4cNvcIWkdgyBaMGrnONQkBXTCv+Chpq9K+JQX4b+1fNQleaBV4SmBoik3dGpu7FZ+YLxECAtpR+4oj2N4HnvJ9k5Xy57l/yPcm/uF2nU3diuunK5ZNcF1SLrzYJohZcG6VYtcADGcFWRd6M3DLXMniSQboHY+3ewQHh2FmXGHkvroLAoo9g4INnYKrGhRZz1cxa6copzOub4HhSI0FKy/liSfW8tBD9/CNb/wx55+/gK9+9Vvccsun0CaT/Py/GFNk8BSm8DvENGny2VQBfcJDCSiW+nuizv3Qh27l7//+69x775184Qt/OuE2JoIS9dv9yHsth8g+9bgfaICfqhu48hr0uoaxbea7AaqkQZPuUOMZ6Ai6NZewEqx8FwppAK2kzFfxOjbC9H/1lFKoxCha/dE5DGkeIMapMCKpXuakO8kaeaAvGVup9/KnY1ddRKDtacj2ooSFaSQwPrqWtHkRHU2v8rbWy3BxHl6imaAR4PK6NdTl1RBoeZxQ0/0ooaPMGGb/NozBPaTn3I5dcd64ub/00jOk0yned9HZCCfhK3FPAqUUf/mXX+FnP/tPvvvVz3LLrI4xIhhAmTGUZmH2bpyQDFZ6ADQdpDPOt1d4WZRujRHUyoqRXPAHGEO70VJdKCOC2fkaVu8G3PyZoAd8tfDQXoL77yG55E8nReoKJ0Gg+ddY3W8gvCxurJZs7RW4xUtOvaNSaCMHCHS9hsiOICOVOMWL8fIa37kfCtBHmjCH9uHF6iFnc6GsPGSoFKv7DbJ11/yPsYqwLIsLL1zLc889OekFhHcLIQQf+9jvcfbZ5/L5z3+cq68+l6985S/43Of+CMOY/HdLfz/cd5/Gnj2CTAZiMVi6VPL+9yuuv14hJSdYLezZA88+q5HJHFH6+iribNa3jigr81OVb7hBoZRk82aNjg4YGPDD3pYs8VXB6bRP+h46pGhqgr17/ftDXffHDYWgulqxf7/vMyylIBDwfY/7c+tJuq4oKlIsXXp0nke6a4+dt1KK733v7/j2t7/OjTfewne+85//54vOKUxhCv830KV5vGKlKJD6mDduJTp7dJsXrBT1afOEMLL3EmE0wvLdfd/uHthPf3qAmfHGMe/VmBUlZITY0LX5jMng00UAweIc6djkavRMdN2EQKrJJ0I7b63He+JRrtzQworBIdoqPfbNraCyqJ7FbjE/cgcYIE3BMfsMZobIs6KUhItPGG/vQBMj9jCz4zPHCMeQESJshNjUvfWkZPCyYD3b22w2Dz6PkpL8XbVoC5dT2ngWF1DMOakws6NliDkneuMCuNvfRg0NoM+eB1Ki5eWhRkbAcZFdHYiiYmTnYfSG6WgVVeP21efOx/v1Iyiv8ijJOjqCsAJoNT4LfkSRfazFSFRpKGCXkT1jMthYtgJj43qcTRtQnotsbwUpEdEooqQY64qrESFfTe0d2I8IR8ZZXAjL9wX2Drcxkc4+aARYWbGMlRXLUFIiJlF7rHRCdOgu6+QQHQM9MDRI6XCaG0YsCuaeDfkFZ3Sup8JDD93Dww//gv/4j7u59NKrxx53JDzSpNGXhtlxv77zFOwfFDx2QOPzi+UpiUCA/IC/z6ERKLAgYPiq04Au8BTUxhQN+b7P7sERaB0RVEfHE5iehE09gs09vufwrLji7ApF6Ts0xlVHFTv6NLoT0JHyVb0A/Rk/WO3yOkV5BOYXKS6qhpfbNdpGfULWk4KoCfV5fohby6hg1FE0DUF5BFB++N2SUn9x4+AwvNKuURhUxIMqd/0UG7oETcMaUUNg6orFJYor6tWYnYXt+YF3AAMZ2DXgX9Co6auFBzNw3x6N0rB3QuhcXwoGs75yN2TAqgrFUFaR8XxSeE6hmsxt2YToSfsLZoZ29LUQAixN8VyrYEuvIOUIKqOKi2r88zr+WHsH4P59GodHfcWyqzTiQUVj48f4m/uu4vmffJWvfvX3eeCBn/NP//QDZs6c+Pvl/wKmyOApTOF3DHGElP3/2Tvv8LiqO/1/zi3TNSONepcsy5Z7p/feWyAJJQkkgTTS66Zs2u4vIXWTbELKppAAgQUSlg6mNwPu3ZYlS1bvUzTtzi3n98eVZQvbYGrYrN7n0WNr5t57zj0zoznnPe/3fd/EgM2ioigXXfRe/vzn3/Hxj3/hNREzbxackWFyf/wtdm83SoUbLGatX4McHMB//edQSl3FcpFUeV82zN+943RqFoZjIsdjzBgYoD0DwZI51IVrXlcftIWL0WbOxtq5DaWsHFQNZ2gApawC/chjJo8rdFRAYiHx2iYLW++hqed5zHyCiBIgGFlKdtb7cfylIBSyzVdiBhvgmb9hbNmNLcOwaDaBE5dw0bKTaBjeyu7EHryqh3nFs2mJNqPk43i7H8HRQzhBdwLqBMpRk+149zxAvuzIfUSsleXBv/6UBfXFLBr6IzJxF0bVyRj154IydZrnOA5f+9qnuemm33DDDb/kfccWQ9ttB5KhQkPYuYOOk1XYglXQ4Fo+RJpB0RBmGiU3Qq7xYtD2I+UVFSs6H6LzUTIDBHb8ATtQ7RLBAIqOHapHi7eijndih2cctM19N2Dj3/57PP3P4viiOKoPfWwLWrKT9ILrp4S8TbkdM41/++/x7/oLSnYYhIbjLcSKziM7633ka0575XYBYWbAziG1qZsOUvOj5BMIO4vktZHBarwVfXgtSj6OXVBPvuwIpO/AhdJbgdNOO4fPfvZahocHKS0tf1vaBJg1ay733fcsP/nJd/nhD7/No48+yA9+8EvmzFnwquc6Dtx+u8K6dYLaWkko5Hr8PvmkQjDocMklB//D+NRTCsPDrhLXcVwrCFUVCAFFRRCLuUEVBQXwgQ9IzjzTJpGAUAhuukmhs1MQjYKuu97CnZ2C0lLI5SCbFRQWQkGBqwqORiVz50Jzs8PAgGDLFsHIiKssbm6WzJwp6etzlcI+n0sEDw66pPbs2W7/M5kMX/jCR7j77tv5/Oe/wec+9/VpRfA0pjGN/zPoUk3GhaRqgpC1kCSFgy4FOzWDHBL/22RV9nqRt93oNuVlm9weVSdjZt9yz/6DYU7xLFYPridnGZM2FSkzjSZUZha+yvxrAnZbK7m//hnyefSm2ZRuSFE2BMuzGfQjo3RpKYQj2a4n6ep+jgYriJJIkHQynFp+FGXOgQSoJV2RxcvHQxUqlnNwCzhpWYjbbuXdG0aZX9vAbn8eZXuSWetWseQ9R+Bbvi9pS2bS2J0d7jUbGietEWQyiVA1t11VRZvVgrVpA87IMPm+HrZGcmyqV8gtsGjZ8xhHVi6jLFBC2sywYUYAQhkKVj9MKFpBVCtAMU30Y05AneUqrN27OhBCvrFlnFJYhO/DH0OZ1YL59OM4Q4OIggL0ZUegn3DKlGA4URB2A+72H7uJ0qW9hPEBY+s4WGtexHz+aZzhYZTaOjzHnoA6f9Eh37M6gncNWCy64w660mN4fAGau4YJDQ6RW7AJ/3WfOMCS4o1gcLCfb3zjc1xyyeVceOG7pzzXlXR9aGtC+4g+VUBVULI7IRjKTBCjr4CoD8oCkuygIOxhUplrS1dVOm4KQh43+AwOfJ2lhLvbBSv3uM94VNg+prB+SHLtAoeqV7ArqC+QdI9DZxJ8qkvuqgrUhiQ5S/B8n+CSZomqwKXNkgUlNv+5QcGRrpI5pLv3HdQhoEmkFAxmXBuGiFeypExy9IQNQ0dCkLHcELq9SBiuGnowIzixRmLasHKPwnBW8tGFzmRo3qp+Qd6G/rQg77ikuelISvxQHYTtMTfkrrZg6rs9qINXhYzpKpoV4Y53wnDn6IGD7FDEci7pHPG6theHQoHufrZe7qDSnhBIYIEq8WuS1hjsSSpcPc9hSdm+/sVyrqJ8OAu1BZLtY4K2uKuCHkjBRk8J8678Lbde+n6++S8f5eyzj+GHP7yRSy65/NCd+ifGNBk8jWn8k+Dqqz/Gbbf9iZUr7+fssy9829s3176E3d2F2jJ3cgdahCPYO7Zhrn0J71n7dnxn2B4+nYmyyRzmv9seYbBvGx22Q5u0ebL7WS6bdQFHVi57zX0QwRC+a64j//D9rlrAyqEtXob3jHNQa+omj5tvueF5barJ2e2PMaftAYYCUVJFTdRlbfTBVeDYpBd9HhQVKVRSLw1iPp0ASsDrRT7yOOr2dnzXfpxjqldwTNWKKX1R070oxhhWuGnK446/DCU7iJIdmgxzE9tu4eHn1vDZCxbi+MsQ+Rj+tr8CYDReNHmubdt86Usf57bb/sSPfvQbrrjiGqyRDaB4XGsHz8RMwLEQVgozuvjgA6X5yM7+AIHtv0dL7AApQNEwy4/CaDjv4OfgKoenqIkn5JBS9SAc033+VaDFd+AZXoMdbnC9kgHLG3X9insedf2KDzJR9bXfib/tryhGAjtYi5A2Ip9ATXbg230XVnQBTuCVCVEnWIX0FqHkRia9kgGU7DB2QT2Ot+gVzj4Qnr6n8LfejMgnXNK+93E8fU+Tnv8JnFDtq1/gDeL4409FSsmaNS+87Z95j8fDV77yXU455Ww+97lrOf30FXzgAx/lC1/4V4oOEmiyF93dsH27SwQXTPDuRUVgGJLVqwVnnOESxPvDtl1LCdcWwg1+cxz37RePu8fsX/0phKvkrax0f7/8coebb1bo6HCVvh6P+1xxsesB3NoqKSlxz8tmJcPDri/xuefCggUOO3bA7t2CggKor3c9gu+4Q/D88wq9vW4b4TCcc45DYyPs2LGF66+/mo6ONm688eYDFjnTmMY0pvHPDo90db82MKpY7FTzjCsOWRzCjspWzWC59caqwd5q1BRU4VM9JPPjhCeqhhzpEMvFWVp3wj9kg29p2QIe7HiUx7ufwXIsgnqAqL+IU+tOYF7J7MO6hrnmRRhPos6ei7Rt5Ngodk8XcqCfjm3P8fsFklhlmNKiEnoG2xjNpmnJBDgvUcyJa7aS7fgVvg9+FCUcmbxmY7gOv+YjbiQp9BTgSeewpE3CSHJy3XEH7Ye9ayf29i0E6meyIhBkBUAE7JFdOM8+hVx2JEIIzLUvkb/nb9iD/QCo5ZV4LrgEfdkRKFXVSNtyiWVNQymvRF3uQa57iYeXl/J0kw5FUXzePNvbHmD90CaunHMpD3Q8ysbhrUSPr6FhB5R3DVAR9rD0zKvxHX3CpAp3luXhGU+GtHAmLfYyOEgBs9+A1Yk0DOz2XcjxJNqS5ahz5qEvXoY4SAKtsnAxqedWktuzBa20giJvIfT3IwoL0eYtcCsg4zEARGERQgjyjz1M/u47QIIIFWBvWEd2xzZ8V12DvvzIQ/bLXruaqhfXUdPcgrAVKC5HFhRibduMtWEd+jHHH/Lc14p//dfP4fF4+c53fnLAc87Ez8s/YWLC8cM+TCb+iErJ6gGB7QjGLfCpLnFZoMvJaw9koMgraCqcaiPSPe4qbou8cjLwzZGS7THBkz2CK1oO3omhDDy4R8GRoCluf8cMKPLC/FJJ1oTtY66AAVySeG4xNEdgT1KSMSFvQ7HPDZML6VARcsjkBUdWOBxXDQtLXUUu7LNK2J887UoKTAdK/fuC7UIeybZR2BGDhSWwrEyybkiweUTQl3ZtFeJSUBGUVAQkDpAxXQuIY6unBuQV+2FhqcNT3QpeVRLyuMRwb1pwRLmkaj+i3rDh/t2CVf2CtOkGzS0rd7iwSRI8CGm8sFTyRLdkd8INyzMdl1QfyQoqg5KBjKDQK6kJQU9K8kS3YFGpnByHraOCgQw0F8JoFvrS7jikLPdampA80CFIVh/Hz25Zxe9vuJ7rr/8Aq1ev4lvf+iFe71sXlPhOxDQZPI1pvM1wkAwpNhIoc1TUN0kZsXDhEpYtO4o//vHGfwgZ7PT3IjyeKaVIQlEQXg9Of+8Bx2sIdrY/x3DXRmYXNaErGlJKusd7+Z/2h5gdnUmhN3LAea8GpbQM31XXIFPj7uQwUnjAhL1AKrw/G+FebZj6nmcZ84awAmXMsz0UaRp2gY4e24qabMN0SrC2bia/8kGUomKUYlf1KR0HZ+dWzKceR73iAwf0Q2p+pOJxPXH3U/cKywDFM6lOVTKDPL3yLlI5iwtPOgKpByfD2Lx9T5CvOQ2p+hCDa/n8V77InY+9yC+/+zUueo/rb2tF52NUHou370lkRndVvlYKq2gu+apDT9rswhZSy76BNrYZJT+OHajAis47QIk85ZxABU6gAiXRgcBByfQCAhQPVuEs7MMgQJXMADj5SSLYHRSB4y1GTXZMBO29zMMt3Yev8x6UnKsIFkIgVT8gUawsaroPLb6d/KuRwYEKjKoT8XX+z0RwXwiRGwNFw6g98xXv/eUQRhzf7jtB2q5yGkDaaLFtePfcR3bexw77Wq8XFRVVFBeXsmXLhn/IZx7giCOO4fHH1/OHP/ySn/zk37j77tv58pe/zZVXfgh1/+S1CYyPu2rc4MsUHcEgxOOuX/DLyWBFgUjEfXx4WOA4EkVxCeB83j23qenQK4MZM+ALX3CD4tJp11Ji+3bBPfco1NRIBgZc5W8gAOm0QNMkxx8P8+e7vsSLFsGiRVOvf/nlkuXLbTo6BJrmKoIrKkx+/vMf8ZOf/BuNjTO5996nmTv34Er3aUxjGtP4Z0az7aHCUdmhGfQrFnkg4AjyisAL3OEfpyytvaEwubcaMwsbWVGxlGf7XmAsF8ej6sSNBFWhCk6oOfof0qene15gJBujyBcha2bJOSYeRefIiqXohzmHSfbuZig/SnfPc6hCpaqhjOrSEuTWLWwrVcjMrGVe3RJIxpk30ENHgYfyYIRT9CV4wjb2lk1YLz6P5/R9/q5NhQ0cU3UEG9Y/ROm6Lir64ljSomlmE8fOrDpoP+TYCJjmAWpTESnEGRgAw8Ae7Cd3218gl0NtcMUVTm83udv+glJSirZ4Gerzz2C3bp+wihPI4QEGj17CSys0ioNFFPvdDWpb2uyI7eIv2++gZ7yPGZF6vFEv2Ya5bMmnWZkZJDSvikX7kUELLS9H5H286MkxUVGPFLDM9LHYfH2kkTQMcn/5PdbqF9z+SrBWPYN95LH4rrx6CiEcNxLcItdDo03TxnYCXVsZ0UPU1c0jcvHloCjkfvufWLtcez5t1my0Y0/AfHwlwh/YZ41RXoHd0U5+5YNoi5Yi9IO/V6xdO8HrQ+xXYSo8XkBgd+1508jgoaEB7r//7/z7v/+MaLT4gOdrQlARkPSnoSEMWTtL7/gAu+JQFcowlHaoDM45QLX/cswvlsyOgiIcAhMevJtHBT0pKPJJdowJAjqc2+QcYP3QmRSkTLcve6EIKPZKtowIbEdOCVTbixcHBN1JwewiiekIwrpEAikTknnBaBYsR/JCv2BesaTA45Kd22OuQtd0BIm8G3AX9kg86kR4XNglT9cMCsZNWF4mSVkuQa0KGJxQS0vpBsw5iT76//5lfrX5IUwjQ2HlTFqu+T3Ds5YCLoH7ofkOL/QL7mwVbB8TzC+W1IYlyTxsHhH0ptxAutGsZE5UYkvYkxRUBmF+iSSdl2wbE/SkJB5VsLhUctksZ4qm56EOwQMdgqgPqkOSVB4e3SOI5ca5eGaK8kApQqi0J6A/5ZLFF85w+NN2hYf3CHK2IGdJcpYkqUHahJ5xQV8KZkQkfSlB2mQy/C9lAghUIYkZrlezRwVhTpDKOTdI8IluwWg2zPmf/yNHHnksX//6Z9i4cQ1/+tPf/k+Fy02TwdOYxtuITtXkXu84HaqJBGptnXOMIC32m7MLdc01H+X6669my5YNU0KlDCRbNYNe1cIrBXMsD7Vv8gRcFEWRpomUEkdAXHGwpEM4b6AfRCmYsww2DW+hxB9FV9w/RUIIqkOVtCV20x7vZFn5otffn1DBK9LstY7Ox5MetLSJ6SnBn/dPeghLLQj5NNYDt5NZP4zd2YHdvQd19hxEOIzQXdJbFJVgbd00qUjYH3aoAatwFvrIBteKQfWClUHJ9mNUn4r0uZMfJTfCI+s7mFkVZVbNPnsB6SlCyQ4iskPkWx/kQ1/7Pk9uG+Kmjy7nXbXbMbb/F9k5HwLVS7blg9iFLehDqxF2DrN4IfnK4yfbOBSkJ4xZcezhD6rqxag6iXD3wyjGGI4WQjh5hGNhF9TtM06dbECiZPrcY71RnEDVhBeycBXGU0jyNHaoBsTUcRT5JKHNv0AbWY8wxwGJamWxA+VILYCwMuCYKLkxvJ33oCZ3I72FmKXLsYrmHaAyzjVdhvRG8fQ9icgnsYtmY9Scjll2aKXEwaAldqFkh7Ai+4W1CBXHX44+uplcfvwt9x8WQjB//mK2bt34lrbzavB4PHz0o5/lkksu53vf+wZf+cr1/PGPv+JTn/oKF1xw2RRSuLTUtVOIxVxl7l7E41BUJIlM7P84DvT2QiYD5eVwzDGSnTvd30dH3Zc1lXKJ4LPOcmiaKsA/AIEALFu27/2p65KnnoJEAhYvluzZI+jqAo9HcsEFDu9/v8Q3UQlrGK638Pi4oLRU0tTkegy3tEDLhDJk27ZNnH/+tWzbtomPf/zzfPazX8fne31egtOYxjSm8b8dIalwaS7M94OjDCkOYSnIANW2xkLLS4dqsl7PUWe8c8lgRSi8t+Vi6sLVvDSwnrSZZnn5Yo6vPoqagoMTnPvDxiWBtDdJ9DGSHeXxrmco9IaZXeR+6Ukp2Rlr48me55gTnfWqauWhzAiPW+1UjHWTC5XjAFtyrcSD5cysLKd/ZoCqunkouo4VG0OzbOrVYvqVDL2kaMwrSKFgblw3hQxWhMIl0aNYsf4+cjtjOJZJJC8IDe3Bk/gl9r/+O2rxVAstEY641XdGDuHd930pU+OoNbXg8WCuX4OMuZ7Ae+9NqW/E3rEVc8NafBdeiv+aj5B/5AGsbZvBcdCPOYGBJZWkh5+k1rev4ksVKsXeKOsGN1ETqsSr7lt7hTxBrJRFW7yDRaX7wpU9CK7IRZhre9muGkigxfayxPTie53BhNaGtVirX0Cpqdtnd5FKYb34HNbCJejL9lUa3t32IGuHt9Bw4pEoi5fi7e6jPzdM9aIFvG/2XHK//Klrz1fulkKZq1dhbd2MHE+izp4zpV2ltAxncABnZBi18uDvXyUYwrIOtPUQjg1v4pzm73+/DU3TDlk55dfgvBmSW3YobBzJ0Z3sJGmaBLQslvMU/7V1mHMzp3NO4ytbxDWG4bQ6h0f2KAxm3LljqV8yqxBaiiVRr2RhqaS58MBzNQWEcD/D+3+qLAkh5dBRJR0JQUCXFPvAn4CcDWGPGwq3dgDyjiQXFvzXZqgLC943x+HBTgVLCuYVu4Sl5cB4HrKWoDEiyVquz/G6YTc47qUBhT9skUQ8rgI4bri2GkNZl/jMjscY+dVxNC0+ieO/eR+BcCkD3bvYI4q4p12wZlCwqERyTLXkjHrJolLJLzco9KYEIxlYPyyIG1AZhCWlrkr34U4FTXHtIRQBlUHBdQsdTm9wiOUEhV6HpghTCPLxPKzqFxT5mCTbc0qSoWwPt+7IsXH4IRrDEaS4lL5UBTnLHXNwCd66sEQVLvmelgIbKPW51h6jOQDBwlKJdz/tSVkAFNyQOYFkrwY8ZggcKYl6IQGUBsCjSh7eo/LFiz/MwoVLufrqS7j00tO5445HKJ/4TP2zY5oMnsY03iaMCZub/Al6FYsqR0UgaNXyjCg2n8gUUv0mkLPnn38Z//Ef3+Nb3/oid9zxCEIIUsLhz/4EmzUDCwBJoVS5KBfiWPNVHPBfA/TFyzCffYrxrja21peRwCHc04tSFsA+ooVTkFNU0BIHB4l42WRKCPePtSOdlzfx5sMTwe8tJZjpx9b3EdYiH8caGCX30jqINKKUV+D0dmN3tCOEQFuw2L0H20bRA6AoOKMjOIP97k58fSNCUcnOeh/CzqHFdwEOUmiYJUvJNV022ZbjLeTF9hhHz56qahXmOFIL0N+2lqs+8S16RrPc9c3LOXnxDGwzhbf/aazihZiVx4HqJV99Mvnqk9/yIRPSwfaXYPvLUMwUjl6AHaxCmCn0oZcmA+uEmcK361Y8Q6snAvGC5EuXYzRehF1Qj5bY5VpoKB6U3AjCzpKvPOGA2ZW3+2G02HYcXxRFKAgrBbbp2mz4SkA6SM2Pp+dRlNywq6J28nj6niLb9F7ydWdOvQFFx6g7C6PmNFe1rQUOK/TusCBtlNwoWBm00Y2YZSv2eSu/RZg3byF33/3fb2kbh4uysgp++tPf8f73X8ePfvQdPvGJ90/8+0UuvfRKPB4P5eWwYoXDo48K8nlX7RuLuQrfk05y3OEQ/wABAABJREFUCdjhYddXuLXVPSYclhx3nOTssyVPP+2Stuk0zJolufJKyTnnuErh14LGRrjkEod77xUMDoLPJ1m6VHDGGQ5nn73Po663F26+WWH3brBt12JiwQLJlVc6hMPQ29vNT3/679x++000N7dw333PsmjRa7e4mcY0pjGNfzYssLwcm/eTF5JSRyUkFUocFQ2BB8GocviBZ/8oeFUPJ9cex8m1B7c6OBgSwuZJLcVTIoaJzXJZyBl2hErnjS279yR7iBsJZhXt2/0UQlAWKKEj3sVAZpDRbBwhBDMidfi1A204nut9idU1GpeUlFE7nCUTjWDaArt1J7FZi+mfUYJ3Yv69l3x1kJSNZijYso58PI1MpZDJOPbuNtQZM/ddfP16SrtGcUwfZGzweJB5E3PVsxg3/5HAp784pS/qrDmoTTNdVW9NPfj8yJFhsEz0Y09AKAoyNuZWHe43NxRCgJSYTz+J07oDfH70pcvxnH+xq3gNhvD0r0GMMEEF7TvXlja6omEfYo2hiQMrmry4AddvNOR6L6wd20AoUxTRIhRCOhK7dfskGTyaHWPzyDYqg2UEPUEypZApLcQwkmzI93Pm8ysJvtyeL1KIte4lME0w8qDtW1tKwwBdx/ZoOI55UCW5tnAx5gvP4YwMIYpL3fMG+yEUQp//ypVOA+khNgxtYSQ7SmmgmCVlCyk7SNgguGTw6aefS2Hhoe3ZlpVLCr02/7l+E93j/Swu9VIbGiDiDTCYCfBY19MsKVtAZfDQVYFCuKRyc5HN9lHXF7cxAgtL9tksHAqziyRRr6sirg2518pZkDQEp9U5WA60xd0wt4qgpDroHhPxSgxbocDjqmm3jwlGczCSleiKYHGJa4UgcX1wb96uMJYTVAbccLfqkGQ4654zbsBJtQ7tcYWKgKRogo/fMOQGvs2LShaVuqTrzpigxOd6CY8/cANjJTUsu+73lPohY0FbdgaGBYYt6R2HnWMK28Yk1y1wKA+4KuGHOxWe7nWJ2JYoNBdK/LobYpfIQ4EuKPG7RGtnEv60VfCrUyXNhQevzksYkDYFpX73+ayVY/3wZuK5NFCHTy3jhX6FgUycY6oCNEQLMGx4qNNlB85skPSkXAI6qLm2DxHPPp/igQx8qMxVT+/F3KhkTrFrf6ELiZTQm3KD5AKaIJYDU0oqAlARgJ0xSXtCcOzCpdx116NceukZvOtdp3HHHY9Q+bLQyX9GTJPB05jG24QNeo4exWS27UGZmJiEbJ3tap7Veo7qN0Edoes63/nOj7niivN44IG/c+65l/CMnmGdlmOGreNDQSLpVSzu8aVotj2UvcHJ6V6o9Y2I917F9gf/itLZSY0U5CrK2fCus9neUkowm+OY/SZSfs1PS7SZZ3tfoNhfNFnqM5gZJuorpDFSd6im3jwoOkbNaQR2/AF1vBPHV4IwUyjJXsZ7BXa4HqWkDBkMISKFkEph9fWiNjW7aoZEDPWEk8nf+3fM557CScQRXi9q82x8774KymtJLfkq+tgWRD6O4ys5wIoh6RSwtTvOR0+uQ+TGkJ4wihFDyY2wyljEFR//OD7FZOUPrqGlbmJipruzEm1ss0sGv41QE7twfOXYhbOmPK7FtqOOd0z+7mv/b7w9K7ED1chAFSKfwNvzGCg6mTkfxr/jT2jjri2E1CPk6s87kMx2TPTB57H9ZUjNhxjZBEJFSBPFyCDsPFZkJo6vBCUziF1Qi5ruQzFiYA8Q2PkHzNKlSH/pgTeiaEjlFdIfXgVWpBnHX4aa7sYuaEBYGbThNWiJNhxfKcGtv8LqnU1m7nU4gbdud3n+/MX86lc/Zmxs9KCldv8ILFmygltuuZdNm9bx859/ny984SP85Cf/xoc/fD2XXXYVF19cSigEL7wgSCYFJSWSE05wf0wT/vIXhS1bXF9hn89VAt93n+CKKyRf/7pNb6/A65XMnOn6CL8eCAHHHy+ZO1eya5dbpjljhkP5fmsKy4LbblPYtUvQ2CjxeiXpNKxeLdD1Ybq7b+Cmm35NKBTmX//1Bt7//o/8n/Mam8Zbj87OTr773e/y+OOPMzAwQFVVFVdddRVf+9rX8OxXTrxp0yY+8YlPsHr1akpLS/nkJz/Jl770pSnXuuOOO/jGN75BZ2cnzc3N3HDDDZxzzjmTz0sp+eY3v8nvfvc74vE4xx57LDfeeCPNzc1v2/1O458LTbbOOkdhhq1Pzn0lkjySSvufbxmaxeFnShePG/2Y6ThIm/WBCC9oxXxLzKJcvv65vkfVEUJgS3tKebzlWIzkRvnZut8ylosjEFSGyrmo6RwWl82fco2dsV3YVZXsPLua+lVbCA3H8AhBW0OU/IWnURXIsWlkG8GiACJajKPrJGMDXLQmTkHSB/6Aa21gGOT+9Ds3ILrM/eJ0erpwRobAthElZZObqnZPN/mnn8D3wY+gFOxLuRJeL96rPohxxy3Yba3IfB6lsAjPBe9CO9q1I1CqapBGHuk4k4Snk0pht+5AFBaBZYJlYW/ZiH7siXivvBohBLOKZhD1FtGb6qcmVIUQgpxlkMgnOaZqBTtj7aTMNKEJW7ZYLo5X9dJcdHghfK8Gmc9jvvAs1uoXXaXz7Lnox52AWlM3QWwfwtpqP9I7bWYwLANNqPSm+tEVjaivCL/mZzg7itndifB6D7TnixQhMynsnk7UGc0I3YPM5TD6u9k1t5L7dvweR9rML57DSbXHUhXaVxKvLliM58xzyD/xKHLndrefkUI8512M0nTo74EdY7v487b/ZjAzhC50TGnxTO8LfGDuew86prt37+Kii97zquNYFzbx6w9yZIVNeWDfXL7MX8KO2C46El2vSAaDSxrOicKc6GuL/Cv2w8UzJXftUtgec9XBqnDD22YWSn66TqEjIbCkJKQprKhwuHSW+/yL/bBpRDBuuD61WQt0RbCi3GFeMeQdlyjVhaQ1JvCqENDcl7/EDyV+6XrlJgRlftgdZ5IIzlowlBUUaJJkXrArts8OIWMJTqx1+M9197J0yRk8/dP3MLjtafTCagJHf5TTL/7wZPBe3pZsHYW1Q27IXH0Yrl3gUOwT3NOhsKDYHa+xLAxn3f5rikvE+jV3PNoTgtYYLDvESxDxul7FybwbKjeYGSJpjBPyVOE4kqhPZXdiCY6Mk8wLqinAdty2spZk9aAbFjiac8dISjcgLq2CJQXVIYdT6qa+rj4NPjDX4eFOwfohQXkAelOSnOWOXVxAgUeQNuXkp3BvYWtj40zuumsll156Bpdeejp33/3E2xrQ/Y/AP9+38DSm8Q7FsGKjIiYnwwACQQCFfsV609o56aQzOO20c/jOd77CKaeezZpgjrBUJsuZBIIqR6NVNdml5l83GZwSDl2qiSKhYYJo3nnkIv52ZBkLd/WiIRhvrMcKBtAweV7PcLTpm7JDf0b9SexJdrMjtgu/6sNwTHyqlwubzqbE//YQW/nqk0HaeHseQcmNIFUfmeiJJAY3IsrdmnXhD6C2zMPauhHGRrC2bUGJRNCWLEfoOvl77kIURlGbZkE2i71xPTnTwv/JzyN0H2bZ8kO2v2HjWhxHsuTYs1GsPkRuCKkX8LeuSj78/37A/MZK7vjobIrrXkZoSuGWbb0SpIOabEfJjiC9EazIbFAOVD28/BxtbDPa6GaEnccubCZfuhwmFCbSE0bIgyRDS9tV2eIGsnkGX8LxVyB9ruJa+qJII4av4+84viiZuR9FMUYRdg47WIMTrD6w5sqxEbZrJ2EVzgYEaqINJTcGpDDLlpFa+Dn87Xe46uDBFxFmyiXbHRM13YO/7TYyCz75yvf8OiC9heRmXIq/9Wa00c2oqT2omQHsYDX5imORqgdtbAv+nX8hvfgLb576+GWYN8+1Utm6dSPHH3/KW9LG68XChUv5r//6b1pbt/GLX/yA73//G3zve1/nzDPP54orPshXvnIquZxKOLyP1N25E1pbXfLVP7F3VFHhWjU8+6zguONcz983C8XFUFx88Ot1drrBcXV1crJ/mcxGdu/+HY8++hd8PpXPfOarfPjDnyQUemstQabxfxc7duzAcRx+85vfMHPmTLZs2cK1115LOp3mRz/6EQDJZJIzzjiD0047jV//+tds3ryZD37wgxQWFnLdddcB8Pzzz3P55Zfzve99j/POO49bb72Viy66iHXr1jF/vksY/eAHP+DnP/85N910E42NjXzjG9/gzDPPZNu2bdO2J9N4XVhi+XjeybJLzU8oYwX9ikWFo7HE+ud7T71EnEdzXcjYIMVaAEWopMYGWVto8jd0PuaZ9eoXOQRmFjZSFapgz3gPM8L1KELBsPN0JLvJ23nCegFNkQY3fyPVx6077qI0UEx1aN+GdEAPYDomscYG4nXl+GPjOALWMcDldfVcUNjImBGjNdaOQGDVFnDs00PM6M9CwAu5LGpdPeqCxdi72zDXrd4XEK3pyPFxlNJ9RLCUEuHxII0cTlcnyryFSCmJxcaIxUbJ5bJkjz6ObHU12fEk+UCQfHac7G03uc+NDpMa6iHbupm8z0fOssgOD5LL5TDNDOZIH6qioNg26prn8DxwF1phEZqmMWbE6Er3sQoLRdfQPRoVhRVY1VHkeIxVmS0IRSK8Ov5giGNmHol/XGXckyQUKnjdAYHScTDu/Cvmk4+5kxuPF+fRB7G3bsR33fWoLXMxn38amU4hgq4owUkmIJ1C5g2MbZvYXaLRke6lPdFJPJfAo3lREES8BZQHyoj6CgmV1cCGbe4YT/RVSgmWhX7S6ciBfpyOdqQERzhsKLH5e2MOr5VFFSqPdT9NW6KDjy+6ZnLNJRQFz/mXoC1Zjt2+CxQFdeZs1KpDKyQtx+Ke9ocYzY0xp2jWRIWnQ1t8N/e0P8Rnln4Edb+1Ry6XI5Uap6TkIEKNl0EgUISC87J1x17Ft/IWhzgeXSWpDdtsGxXkLKgOwcxCya83KeyKCRrCrkVB3JA83i0o8cMZ9ZKGAsnf2gSmBF1xCWldcUPOdsVgy5ggnnOtH1QBs4okhi2IeN1jpISecSgPSionyNu9AXGG7dpIOMCumGBnzBU0SOn6Dj/UCV1dHXR1/YarPvhpFn/2y/zP02t4/HefYbTSQ8Wp75/0ME7lBS/1S06YWIIJAXVh8CqSvO1aThg22I7AclyFcUfC9fX1qxLTFiTzBx87R7r9XFomebhTIAQkDIO8XQAU0BjuxK/lsBwdj2ownk8Bbh9sRzKSdT2WA5pEBQxrX//KAzCclVzaLAkeJMcx6oPLWyQXNElax+Dn61U8CUjmJSU+18pidxIcKYj6YeZ+4YENDU387W+Pct55x/OpT32QW265F+W1lh/+L8I0GTyNabxNKHJULOSUkiWJJIdDifMqBN1rxDe/+QNOPnkxv/3tzzC/+qEDQur2/ma9ju9QieQFPccD3hTDio0CVNkaFxkFjCsOeZ+f8flzp5wTlIKY4uAA+99pdaiSTyz6IKsHNtCR7CLiKWBB+QIai5uxX2Yr8ZZBKORrzyBfeTxKbhTpKcDOSgh+G5lIIHwuI6XW1IJt44wM4T3/YtRZc8DvJ/Nv38AZ7HcTjB0bUV6J0tiE3bYTe9dOtLnzX7H5detepKAgTN1Z32Q8248wYvz6r//Dt773Xc4992J+9fVPUNJ6I7Y5vi90zcoANlbxgkPfVj6Jf8cf0UfWI6wsqB7Morlk53wIx1928JOkxNd+B74994NtuN+6PQ+jly4nM+9jSD2EWbIEb9+TKJl+HL+rJlAyfUg9hFWyZKLtBMLKYAdrJq7roI+sQxvbjjDHCeVGsaLzyc56H/mqEw49OJoPq2gOnr4ncfxlWEVzsCNNiFQvQjqkjvgOtr8M/+670MY7EGbavTcpEeY4Sm4EX9tt5MuPwipdfmiDr9eJfNWJ2IFKPL2PE9h1C2bJEqyiOZOkuB2qQ4vvQE11YRc0vKlt78WMGc34/YF3JBm8F7NmzeUXv/gT3/rWj/jb327lr3/9I1dccR5VVbW8612Xc8YZ57NkyQoURSGREFjWPiJ4LwoKXH9fwwDtbZq5ZLOufYWqZti69Q42bfot/f0vEghUMmvWZ7nxxutpbj7QD30a03gzcdZZZ3HWWWdN/j5jxgx27tzJjTfeOEkG33LLLeTzef7whz/g8XiYN28eGzZs4Cc/+ckkGfyzn/2Ms846iy9+0S3V/u53v8vKlSv5z//8T379618jpeQ//uM/+PrXv86FF7qBlH/+858pLy/n7rvv5r3vfe/bfOfT+GdAmaPxgWyE+7wpOlSX0JlreTjXCFHxJlWmvVaM51NsGdlO3EgS9RUyv2QOQf3NsU17KdNFWjFo8IQnCbpCb5jxfI5Vsu8NkcF+zc+7Zp7PrTvuZMfYLoRQUIWgQA9gaV7qwjWTxzaG69gea2XD0JYpZPDSsoVsHdlBwkgS8YZJF4fpTfVTpBQyJzqL6lAFn1pyHRuHtzCWixGZHWbBnvvxDq9DKSlFFBahlFciNA2pe5BDA5PXVhcuJqYqDA8PMBIMMpQ3GB4fZ8jKM5IYYfTTH2QomWBoqJ98/hAM0n7w+Xx4PV68CLy2jRfweTx4bRtfIIBP1wmpKlJKLEXBzGXJj43iOA6OY2NZFoG8SiqXxTBSGJbDrnw/W4wXyGVS5F7Wh1XczY/5F8DNQohGS4hGSyguLiUaLX7Ff4uKitEmJidO527MF55z1wOFhQDIyirX5/ipx/G++0q0I4/FevE5pCMhn8fp6QKPh+yTj7LnwZtpLVW594hiuu0+LMemWEQJ6QH6UoOM5uJ8ftnHidTMIvPSapdkr3Zfe6enG1FYhPeU01HKK7C2bUEmk2xxBrkj/yL10Rl4VJc9K/FH2RFr46X+9ZwzY5/3rhACtbYetbb+sN6XPal+usd7qQlW7vN1FgpVwUr2jPfQnx6c4rE9OjoMQDR6cAuJ/aEpGkvKFnD/7pUU+6J4VB0pJb2pAaK+IpoL3xwl9yuhJgQ1oX2igS0j0JGAxsg+r9oiH6QteL5fcESFJJ6HxWVyUkFb6IUne1ylqqJAOi/QFUCCJSVDGZc83T4m8Kpu8FzEA+c0OlQGJSu7XHVuWcBVEKtCMpgB23GvrSkuaZs24ebtKo7jsHDhMr77jX8DwKlYws6d21jzwG8Irng/fSlB3BDEDEnaVIj6Je+Z5RDywLwSSXORYNuooCLg2iyYjnt9j+KqmpMZMG1ByAtPdCmsH3JJ8qMqJWUBd4we7VLoHhcowvVPdvsXwWGExnA7c6JtqMKm0BtnMBsioHkZzMCOMeGGwtl7iWh3bEwHNEUymIYCXbKkDE6ue2VxSFCHnpRAKHBqrWT9iGAsC4qEZB4GNbi8xZlUS+9FXV0jP//5H7niinO58caf8IlPfOEV25ESWmOwbsgd1/oCyYoKSemb58b5lmGaDJ7GNN4mLLS8POVotCkmtY6GQNCnWkSkyvI3WR3R1DSLD33ok/zi5zfw5Ssv5sXGMGWok+RqTDgEpKDefu3laq2qyX/7kkhghq3jAN2Kya2+JGcbQVTcMjn/fl7AceGw2PIdlNwtDZRwzozTMJE848lwj55lXBmlxFE5IR/gyJepid8yaH6ckDuZUjygHXks+fvvBlwPLjmeQGZTeC+6FN+7r8TauZ3c736JvbsN4fXh9PfhDPShzJ6LPnMW0rKQ48lXbXbNmhdYsuQIFFUl5ynjm/9+Azfd9Buuv/6LfOUr30XBxhjfgrfvaXdGIQVgY5YdRb7siENe17f7b3gGnsUO1SE9EYSZRh9eC6qH9MLPHZQYVROteLsewvFEcPZaK1hZ9KHVeKLzMerOxipeRLbxEnx77keLbQVAeqPkmi7DKnTD1BxPIVKaaGObkYEKlOww+uCLbmicUBFGAm14LX47hxVuxAnVHvI+jNoz0RKtaLEtSE8ErBwCh1z9edihOhACKzLL7feEh7CSGUDJjbj3lOqmYO13yTZfRa7p0n0KXemgju9xlcmhWpfAtXOg+l6TitcunEVe9eIZfAHHXz5JBANI1Y+wB9yQu7cIqqpSV9dAb2/3W9bGm4Xi4hKuvfZTfPjDn2T9+tX89a9/5Oabf88vfvEDSkrKOPXUs5k//1xU9UzS6SDB/SZn8TjU17+p+SWviKGhAV544UE2bnyAhx9+FMtKU19/OueffyeKci51dToNDW+Dr/k0pnEQJBIJotF9GxGrVq3ihBNOmGIbceaZZ3LDDTcQi8UoKipi1apVfO5zn5tynTPPPJO7774bgI6ODgYGBjjttH2kQCQS4cgjj2TVqlWHJIMNw8AwjMnfk8lX/96bxv8tzLA9XJ8pYkixkUC5o06pkns70ZXs4aZtt9GV7AXhKg8bI3V8YO57p5TLv16YRhr8HDDHUjQvRjIBr6GIJJVP4+BQoLvq0U0j23imZxU52yCg+5kRaeDY6iN4oX8tO8fappwrhEATGglj6ufxiIqldCa7eaF/DX3pAUBQ5ItwYdPZVE/cf9RXOMUjObdomHxXL2rLPBK5LDv7e9gxPED77lZGu3Yx/N9/YnBwgKGhfkxzqoKzSNMo9fgoLyhl5pwFHFtVTXl5FeXlFUSjJfj9AbxeLz6ff8qP1+tF9naTu+m/sPd0uOXchuEKM3QdOTaG2rCPCJRSYu/Yhu/y9+E59WVZES+D8fgj5O+4FenxYoXDZONxMvFRUiuOIrVkOaOjw4yNjU7+OzY2zPDwIDt3bmV0dISxsRFs+8DKvMLCIsrKKqgLhamOxWhonEl9UTH1hVGqCiKoRcVY27fiVVV8V16NtXAx1o7tmKuehkgEbcFiWs1BOgbzNHabHONk6D+hnHErQ87OEdT9lAVL8CgeakOVqBVN+N5zFfl7/oa923391YpKPOdfMjk2+jJ3ndCx617sTnWSCAY3UC+o+WlPdBxwL68FUkpXkfxy4dGEt7N8mSVGwYRVSCIRP6zrn1x7PJ0Jt4oUCQ4OEU+EC2aeRbH/7d+QT5uuStajQMaEjqQb4ubXJJqArnGI5QS1BVODzWYXwmM9AscBXXWNQlQVij0CRZEEdTi+2iHvQH/KJSvvbhPMKoLFpQ7rhhS2jrrEoyKY7IPAJU0dKSkPwGAG9Egl45G5/GiNwopySdqETGEL8dV/46kegekIirySQg/UFEie6xMENcF7WyQBDa6e63B/h2DrqELShKAuJ9oU5C2XnLWBjCl5tEswOyrZMqKwfkhyUq3Dna0qOUtSFnDtLoZzgpYiySl1Ov/d2k3WHiBtgSJMxvOrsJ2TaU80s2NMuAQwoAC2dMfbo4JPc32ZQx5492zJMVWSgoOogl+OnOWOV9ADy8skAxn39QkZsLRccnr9wQnlk046neuv/yI33PCvHHPMiSxZsuKgxwE80yu4a5dC2gKPIlk9qPDioOTD8x1q3+GFg9Nk8DSm8TahwtG4Khvmbl+KXtVEAhW2xrlGiEb7MP6avUZ89rNf5a67bmHlZ77Agjt/z049T1AqmEgQcKoRpP51+LWt03OkhaRlvz7PcFzv47RwmGt52aDlKJUqHikYUWwCUnD8qwQv3O9N8aA3TUAKQlKhW7W42ZfERr6pQXeHC+/Z5wNgvfgcTn8vIhjEc+Z5eM+9yC0Bu+curF07kY6NHBlyQxpsC2dsFDJpRKgA5VVKoKSUrFv3Eh/4wEfYs2c3H/vYVWzdupEf/vDXXHnlByeOUsi2fAireCHa6CaEdLCiC8iXrZi0bng5hBFDH3rRJSc9rtWF1INucNvYFtTxTuxw4wHnafGdCCs19TnNj9T86ENrMOrOBiEwGi7ALF2GlmgFBFbhbJygu+sv8uMEdt2MmupFTXVBbLsbqoaN1II4vhLX4zc7hBbbij6yEeMVyGA7MpP0ws/i6XsKLbYNxxvFLD+afMUxk4sto+5MAq1/RjHGwHAQuTFQPTj+CR87PYS3636s6Dys6DyUVBf+1lvQ4q0IxwDbRKo+pB7CCVZiVJ+CWXHsYSuJbX85jr8UJTuEre8bOyU3hOOLYgff2gCCkpIyRkaGDuyXDYOD7m2Ul/OaQ9beKgghWLr0CJYuPYLvf/8/WbPmBVauvI+VK+/n9ttvQgiVgoJFVFYeSVXVEXg8R+LzNXPiie7E+c2GlJLe3i42blzLxo1refbZJ9iwYQ2KotDYeBQzZ36V+vpLKS9vIh4HTROccoqD/sZt3qcxjdeMtrY2fvGLX0yqggEGBgZobJz6N718wgB7YGCAoqIiBgYGJh/b/5iBgYHJ4/Y/72DHHAzf+973+Pa3v/36b2ga/yegIP5hSuC9sB2bu3bdx55kL82FM9AUFdMxaYt3cHf7g3xs4dWv2xpgL+ZbPu4xRkn4wkRy7iZJ1uPBcnLMMw7vS3ggPcRDnY+zbXQnEklz4QwqgmU82vU0eTtPxBMmb5u0xtqYUzyLGZF6Ng5vxZHOpJew7djY0qYiOLUSzKPqXNFyCUdWLGXPeA+6ojG7aOYBx5mmSXv7TrZt28y2F59ly4YXaH3ifgay7ua2rig0hgupqqqmua6R448/hbKySko9XgqffZLiPXsotmw8wQDazNn4rrkObe7Bq9k6k92sH9zE6EiM6lAly8oX4cNL7u47sPd0oM6c7SqRLQu7rRURCCBz2UmbBSklcqAPEYkgqmqwd7cjCgpQSqfek7RtrM0bMf70O2Q2i9o8G2+kCF9hlMhICNndTfCKaw5r7p5IxBkbG2F0dGQKeTww0EfHhjU8NdjHrZ1tmBNBdaoQVAeC1EVLaHIMGhqaaGhoon7mTIpXr8LfMg8nGKCvdwA1ECKrCip7u6hIllJQWkHcSDCvpIWqYAWtsXaytvve0lcchTZ3PnbnbredhhmT1hP7w6/5cRxniqUEQN4xKfC8/vwMcKs8q0KVdKd6aQo3IIRASklfeoAZkfoDPH3D4QhlZRW0tm47rOtHfYV8dNHVbBzeSu94P37dx/ySOTSED712eCtRHpQEdIVNI5LNIwrpif0PiWBmxCVT99or7E8GB3RJgS7I2y456dch7IGgJhk1wHQkLVHYPCLoHnctIxwJz/UpFHkl1UGHl1KC4Yz7+gkpkEJiSoGuSAp0t73BLHjqjyHWu5OOBKwdFKRNgR7bhRatx7AEEogbgkUlkhkRGM1K1g4Jzmp0Q+xKA3D1PEncsHm+T2BYCnEDdsRcda4iXELcrwlUxfXiXVwqWdUveKxLJWdDmd8lcevDkqAueapHsHU0immfS3+6n+7xQaRcg6oY1IaCDGSDJAxBgS7RhMQWrrWEPcHVaor7s7RcckqddJXVh4HaMAjc0DufBg1hV+ndGhccWyVfcan3xS9+i8cff4gbbvgmt932wEGPieXg/g6BEJKWiTxER0p2xgQPdyp8aL7zZhemvqmYJoOnMY23EXNsL01pD92qiQPU2NoUBe2biXA4ws9+9nuuuuoCFn//d1z4pU+xSzUJSYUllpelr1NxO6LY+A+wnRCoCFLC4X3ZMCVelQ16jpRwqLQ1oo7KA94UKz1pFls+jsz7ptz3qLB53pMlKhVKJxYLRbbKHsXkCU+G5aYf79usIhFeL76LLsU5+TRkPIYIR1CK3B1oq2sP5mOP4CTjbspUOuVu8Xo8kM9jrV+D5+TTUBrdxGcpJU5XJ/bO7UjTRK1vQJ09l+HYKGNjI2SzGc444wii0RLuuedpFi1aNrUzqgez4liXoDycvpsphJ3D8U2d0EotiEj3IazUIc48cGd/4opMCbsQAidUQz5Uc8CR3q4H0PufwyxdgROsRo1tR0n3gaLieF0iGKEgPRGU3DAiN/yq92MXNPCDR27ngQeepq1tJz6fn+XLj+JrX/13ZjbNxI7MIjPzvfg670FJ903cgUDJDCC9UZxQHWqyHW1sG1a4kcDW36AldmGH6lFTXeijG5CAVbIEYaUIJHaRtXPka0575Y7thebDqDuHwI4/osZbkd5CRD6JkDbZ+gsmCfm3CsXFpZNld3vR2gr33aewZ487Go2NkvPPd2hqOtgV/nFQVZUjjzyWI488lq9//Xt0drbz8MOPcd99L7Fr1xPs3HkjAMFgEd3ds2homEF9/YyJf5uoqqomEil6VX8/x3EYGRmir6+Hvr5u+vp66O3tYdeu7WzcuHZy/Corq1m+/Gg++MGPc/LJZxGJlPDss4LnnxckEjB7Npx4osOSJW+eb/E0/m/iK1/5CjfccMMrHrN9+3ZaWlomf+/t7eWss87isssu49prr32ru3hY+Jd/+ZcpiuNkMklt7T9mkT6NabwSelJ9dCT2UFtQhTbhYaorOlXBCnbF2hnMDB9Air5WnByZzcNdW1lX4pALFiJQsI0U9YP9XFp04quenzCS/H7LLexOdFLmL0ERCi/0r6E/PUhVqJK5UddmoowS+tODPNn9HB+cfwVVwXJ2xtup8JchcRjIDFMfrmFx2YEErCIUmotmTAZ7DQz08cRLD7N9+xa2b9/Mtm2b2LVrB5bl5plUVtUwp7qOi8qrmKXqzI4U0tjcQuiiy9AWLplybek4GP4Qxt//GxkbRUiXhJWZDKl8GsM2KPRGJj1k1wxu4K87/kbCSOJRPKzqX8OqvtV8oOwUytrbUKprQVVwhgdxRkeRuQwyk0JbtBSnYzdYpktwFoQRFZXkbvovSCXB60NbuATvRZeiRAqRloVx523kH7kfa9tmQGD3dKE0NKEtXY4ojOJ0tOEMD70qGSyEoLCwiMLCImbMODBUze7rYfwjHyDf282A49CVGac7n6fblvT6A7z00nPcfvufyeWyk+dUBILURArxeB2qwkFmFRZRbQnIZlBF1L1HFNJmBq/mpSK4r48iGEKbt/AV+zy/uIXHfE/Tk+qjOlSJQDCcHUUTGkvKXvncV4NH1bmg6Uz+vO2/2RHbhVf1YtgGJf5izptxBppyIN20bNmRvPDCs4fdRlAPcEzVoZWZbyfqC6C+wOGhTgXbgZDuWhnkHNeH9pcbFEoDkva4oCki8WkuWTqSE9SFJQnDXVVFvO7KKpGDVB4CqmDLiMUT3TuRcgtZy6DY10h9wWKe7AkjpQABhi1wpAQF8hYUelxfYVVAe8Jdkx998ad55JvHM/Tw98i1vJvd215i/Lnf0XTVrxlUwKuAmFAjK8INdxvJCtKmazuxF4Ve15rCr0uKfDCcg6AGhi0ZzoJXFQR1yVhOsDsBAymI5117iZwNW0bBdAR+TTKQdu005hUHmVc8g6d7qxjLLeTMOp1in4/1Q7Ar5noVS1xC3Xb1a+gKZEzBmCE5ptK1yOhICFQBzYWuHYMzYdWwfUxg2tAQgYUlkoWlkrnFgs3DLsEuBMRzgqZC18rhlaDrOtdf/yU+/vH3sWnTeha+7O9db6qfO1v3sGaghvqwQdJTQdhTgCKg1C/ZGXO9mQ9HwfyPwjQZPI1pvM3wIGh6C5TAB8PJJ5/JZz7zVX72w+/y16VH87k3wVO0xtbYqOWmeB9bSBxcb7iIVHlvLsx5RogBYXGXP8k6PUdIKjgCtml52tQ8789G8EycP6haJIVD08tsK4qlyqhiE1PsN6QoceIxZDqFUhRFBIKvfsJ+UCKFECmc8pi9ZSPOBEGMaSIDQbAt12DU40WtqgFFRcZjUBQl/8RK8vfdjUwmXMJK19GWH0lb00wAfv3rn3LBBZfxgx/8inD4jROHjq/UVd/mRrD1fTv+Sm4Ex1uIHag86Hl2pBk0PyI3Nhn8hm0gzDRm6dJXb9g20AdX4fiibmCdN4KjF6DkRlDMNFLz72fT4JbYOYHDS2ldteoZrr76YyxevAzLzHPDtz/JlZcex5ofX4qvdBZm8SLs2Ha02HY3WE+xkUIFaaGNbUVqPpAm+ugmtGQ7VmQWAoma6sLxFiIcE5FPYFUcizreibfrIfIVxx5Sff1y5KtOQqo+PL2PoWb6scON5KtPIV95/GGd/0ZQUlI2RWHR1wd/+pPCyIigstL1+9q6VTA6qvCpTzmUvnpmxz8MDQ1NfOQjTXzkI9cxNgYDAzF6e9ewdetqOjra6OzczXPPPcngYP+U81RVJRgMTZSc+jBNE8PIkc8bEz9TvQF9Ph9VVTU0NjbzgQ98hEWLlrFw4VLKyw/8bJx0kuSEEySG4dpUvJN3+Kfxvwef//znufrqq1/xmBkz9pVB9/X1cfLJJ3PMMcfw29/+dspxFRUVDA4OTnls7+8VFRWveMz+z+99rLKycsoxixcvPmQfvV4vXq/3kM9PYxrvFJiOhSVt9JeRU5qiYTk2pnOQcNzXiAJPiK9WnM6te1ayQXZjI2lwdC4tP4a50Zmvev7G4S10JPYwq7BpkkTThMbmke2U+ad6rJb6i9md2IPlWFwz/woe7HiU3Yk9CARHVizl3BmnE/UVHtDG2Ngozz33BM888zhPP/0YXV2uTUAwGGLmrBa89RHmrDiOUE0R4Zpimiubed/cd1OjF2N37wFArWtAHORzb2/egPn4I6iV1YhFS8FxiPft5q4Hf8i2kRlYPi+VwXJOqz+R2UVN3Nv+MIaVp6WoeTJ4bGesjQd6nub9Rg4xOICzphNnZAR0N4AQw0BdtAzftR/H6e9D6Dr26AjmEytRIoWIqhpkOo359ONg5PBd+wmsTRswn34MVA1pmjBhZ2FvWIPIGyhz5iJ8fkTB66/pdgYHsHZsxdq8EcdxUB2H6kSCaulwNAo4IISHgh/+Em3JcoaGBmh74H9o/fXP2ZOM0Z3JsiOVY033IHnbQQDe7n7U2lYCdUX0zg8zVhPh+MajmRFpeE19qwvXcEnzedzT9hCtsXYkkrAnzDmNp7KgZM5rupaUEjk6ApaFKC1DqCrzS+bwySUfZs3ARoayw1QEylhesXiKX/X+OOusC/j0pz/Ezp1bmT173mtq/x8NIcCvTZCoHokjBTYTvr2WYGUXHFcFXlXSNe4qhlXFJSWjXsldbQojGTcAbjwviOdcC4aQLrll+yOM5J6gxG+ioNGXWotf62E8f9lE0JqgPCBRBdhSEs8JRrKAcOf6adMlnOc2rsD31Tt5+qavE7vt31CKGqm4+Cdkmt4N6QyGBEvX6FagJaoRy0GRTxL1wXjeJVTdwDzJrEJJZVCwM+baUhR4wMq54WthDyDdPKGupBvMFtRdwjXscT2M9yRd0thwBP0pyWBaQVUE4/kQqpCk8q6ncEiXeDVB2nJJ4LDHJdFzE4F5AJUBScqEH69RGc+7913kFZzXKIkbsLJLITsRNKcgWVIm+MBchw/Oc3iqR7B20A3tO7rR4cQa935fDeed9y6+//1/5Ve/+hG//vUtk49vH2vlz1tvpzUeJJE/k12xfvpSfSwsnUdZoGRSXqW8w9cM02TwNKbxT47Pfe7rrF37Ih//+Pt45JGXqKx8YyXrR5g+1ug5WlWTCkfFAQYUmwZbY6G1b2IYkgrtep421WSW7UGbIH4zOKzVcyw3fSye8EoOSAUPgqyQhOS+v5pZ4eBFEJCv4S+pdNDiO9DiO5FGjsyGbrLre5DZHEphIdpxJ+E55QzEG0ihsvd0oESLkYk40jDckjWhQCKOKC5BO+EUnD27cQb6EOkU+fv/B6GoKC3z3PKpVIrtKx/gUz9dC8C3v/1jPvzh699weeIkNB9Gzen4W/+MmmzH8RShmOMIK0Wu8V1I38FDG6zCFozqU/F0P4zIDoBQwbEwS5ccFqkp7DzCNkDd9z5w/KU4gQrEeAeKMYr7fe6g5EawClvIV550WLd06633Tf7f134Hv7+8jPrrX2J9+yAnyIRL+CJwQrWI3CjSG0HqEUCiju/BLqjHjjS7XsJSgupB5EYQVhbHWwhWBiU/DlLi+EpRsgOo2cHDD34TArPiaMzyo8DJg6K/Ju/hN4KSklJGRvYpg9euFQwOClpa9pU/hUKSHTsE69YJzjzzf4eqNRqFaLSIuXNP5/TTT5/yXCaToatrN4ODAyQSMeLxGOl0ikwmjWHk0HXPBEnlw+Px4vF4KS4uobq6lqqqWqLR4tf0eVMUDgi1m8Y03ghKS0spPcydmd7eXk4++WSWLVvGH//4xwOSrY8++mi+9rWvYZom+oR/ycqVK5k9ezZFRUWTxzz22GN85jOfmTxv5cqVHH300QA0NjZSUVHBY489Nkn+JpNJXnzxRT72sY+9wbudxjT+8agOVVDqL2YwM0xdwb7KpsHMMFWhCsoDb0wVvK+dSr4w930MZIYwbZOKYDke9fB8hfrTgyhCmaKm1FUdXdEYy8WnHGvYeXRVx6f5mBGp5+OLPkjMiAOCIm9k8jvOsixWr36eJ554mKeffozNm9cjpaSpaRannnoWR9bOoCWXo0pKXtSGeaAkTlHjPPyaD9M2aU90cvvOu/nUkmtpLxZsG9mJsXsLjZE6FpfNx7/fprm5djXSslDL3c0lUxXcPsNgQ6af8rFCQvUz2Z3Yw5+33c7JtccxkB6iMVzn9tVxCI0mmWX4GMl3ke3txNPVi8xlQNUQ/gAE/CjFJTi7dsBJp+I990KkYWB+71uIYAilYsKyzOsDTcPauhmjYxf9q1ci4gMwnqRA110fLa8Xslms9lZU28J7zgWuEvkwIaXE6e/F6ezA2r4Fa9MGyKSxu/cgBwfdFFpNc9vRdTAM5EA/2T/8hoJfLKdkfJzA1q3Mq6lHDgcAyZjM0ukz6BUaL1SXsjo/zmh7HyMbh3jm79tRVIXu2avYvvhZFi9ezsKFy5gzZ/7k3/2Xw4nHcPZ0gKZxzIz5zDmimdZYO7Z0aAzXURksf01zIbuvh/y9f8dq3QG2jVpbj+fs89DmLqC2oJragsNbY1544Xv4/ve/yY03/oT/+I/fH3b77xRkbYFfhfowxA3JcEbgnSCIVQEFHolhC86dYRPUXcJ3R0xh/bCCV3VVr7GcS7wGNUFtWBL17WHj8DMYVhifGnUVx9JkdyLOeH4MISrQFcg7gmKfpMTv+hSnTUC6QXe1BZK6AkkqD8mG86n+4vkEczCSg5ydJzKeIuJAzhHYDsSzBjt0Db0oyAU1kt0JuG2nwkAapBT4NFhR4XDxTIe/7lDoSkJ3yr33sAeEkKRMQWVQMpR1q0gbCiRjhqsy9qkwZggGMhLTBktCSJPkHUgY7niZE/bblUHXb3kkszcQz5Wd+VUIedwwvqYieGSPayUxu8hVEPen4S/bFRwJZQFJQ3jiNbJgzSC0RAUn10ouaJKcP8N1sD5cgtZ0TFDgAx/4CD/4wTcxDAOv14vlWNzb/jAxI8HS0gqyJuTsBnL2HnbG2oh4owxnVU6scQi+wy3lpsngaUzjnxyqqvLLX97EGWccyUc/eiV33rnykJOGw0G1o3N1NsJD3hSdqkkeSbGj0GR56FMtQpYySfzu0PIE2Pc7QAAFC+hSLRa7FWjU2RrNls4mzaDB1vGjMC4choXNGfkgYXmYRqGOja/tNnw9j4CZwenegzY0ilYwk1RoGU4sQf5vt4Oq4n2VgIlXgvB4UMorcXQPxMaQluWamWoaSl0DwjQRXi/CH3CtIRIJlJa5kz5ad3a08p1nHyXo9eH3+99cIngC+ZrTQPXg6V6JmhvF8RWTr3oXRu3phz5JKGSbr8QqmoM2sgFhG1hFLZjlRyH3KoytHPrYZpTcMI4nghVdiPS4Sgqph7DDM9CH1+J4i92tWdWLHaxFMWI4ngjCzoJjYUdaSC37BrzGBG8lO4S35zG6LXfxUVhSjVVUhja2FW28g3zpCtTx3aiZAbBSCCkRZhIrMhOzeBH66EaXpLUNpKKDoiIcC2Eb2MGJPk8Eye0fBnfYmLjntxMlJWWTgSaqqtLfL/B6p/pgKQrouuRlwsD/tQgEArS0zKelZf4/uivTmMZbit7eXk466STq6+v50Y9+xPDwvo2fvWreK664gm9/+9t86EMf4stf/jJbtmzhZz/7GT/96U8nj/30pz/NiSeeyI9//GPOPfdcbrvtNtasWTOpMhZC8JnPfIZ/+7d/o7m5mcbGRr7xjW9QVVXFRRdd9Lbe8zSm8VbAr/k5o+Fkbt/xd1rj7YS0IONmiqAe4MyGkw+bsD0cCCEO8Eo9HBToBdjSnvR2daRD2kzjSIfh3Cj96SEqgqWYtkXXeA/ziluoL6hBSsme8R7a4h1IKakPVtOxdjv33nsnjz/+EPF4jJKSMo4//hSuuebjHHfcyVRX15J/5gmMO29DZrMYHhV/z0bOLiqg88JaEjU+dFWnrqCG3fE93LTtdjYObyVnGShC8ETPsywcnMs1864g5HGr7mQyjtgvyLJNSbBdjVNveAk5PjRvmIg3TGusjfVDmyfnvgV9IzQ+s5Fw/yh508AXH0clgtQ0dwNf05DJBOQNxOJliLyJtWUT+hFHI1PjOMmEW623/2tQEGZguIO/bfsz5bvXUpPoYFQzKasKMGdYQ8vmXFLYshE+P97Lrjjsubi0bfL3303+ycdwhgexd7UivF60ZUegVNdiD/SDkYNQGDGRhistGzQVe9sW5NgY5ovPI5NxtKOOQ/b14OzpJJpOgZkkceYyio5u4F2+CEdVLmNOpJme3XvYtnkjGzasYcOGNdx++03Yto3X62Xu3IUsWrScRYuWcfTRx1Nb20D+yccwH3kAOTqMFApqVTXhS97NUQuWv+b3JYCTTJD742+xO9pRKqtBVbF37SQ3NID/Y5+eEuj3avB4PFx33af493//Kp/73Nepqzswx+TNQn96kE3DW4kbCcoDZSwqnU+R741VYc6MOKiKRs6WZC2BItzAM9OGshCUB1x/XYmgrkByb7tCPA8VfklBkUsAj+ZAFQ6KECTyMDC2h6yZwXKqGcpAIAy2o5O1NCyZIKJVgHAtKYYygpDuBqhVBODCJofqAni8S7Bu2CV6sxYENDd33HYkKUcloHvQFXBslaBlouYNlKFxLj86wKJSyY/XKgxnoSniegMnDHimR1Adgq8e6XDvbsFDnQp5G/IO9KXAo0oUBXKWS1AvLoPuccmeccFwdl/QHZok6nWXSV7VDYIby01YXuASu7oiCehuwJ0mQFVc9W5VUGJLl1xOmYLSgHuOAKqC8GTMfV3mFruqYsN22/SpsGHIJYPBbftwPuHDmREe736GjcNu1WW02odhGGzatI4VK45mID1Ez3gflcFyfJpJS7SVLaNzSZuN9KaybBkxmF/i58xDhNO9kzBNBk9jGv8HUFxcym9+cyuXXHIKX/3qp/jBD371hsjHZttDU6aIJ/UM93rHGVEsHvdmeIYMS0wfV2bD+FHwSoH1shRZd09Oou/3sILg3bkwti/JLi2PCfgQHGX6Ods4/GADbWwT3u6HsL1RHFGKOdiF4osS9A1j5ZMYgRrsni6sZ57Ec+yJCN9h1IccrJ0Fi7FWv4A6f4H7xby7zZ1QAnJsBGvjWrQTT0Gpb8TeuR1wJ/W9yTjffPQ+nuzYxaWNs/CEQqwxsm86EQyAUMhXnUS+4jiEmXKJTfUw7EkUFbNsBWbZgd5cSnbI9duNbYMJja9d0Ehm7nXY4RluuFzdWWiJdrS4G/YmrAyoHjIt1yA9hSiZfuyCBvJVJ2IXzjp4H6REyfQjzHGcQMUUz10l1YPMDPLlmzZx1OwK5ta5Kmc7UImWaEWxUphlR+CkulGzAziOg/AXk535Htd7OboAq6gFbWwzdqAGx1OImuxAegqwCxoQZho104dRfcoBnsvvVBQXl+A4DvH4GMXFpZSWSgxDmVhMusdICZYlKC5+509MpjGNaezDypUraWtro62tjZqaqT7tcmIRFYlEeOSRR/jEJz7BsmXLKCkp4V//9V+57rrrJo895phjuPXWW/n617/OV7/6VZqbm7n77ruZP3/fhsqXvvQl0uk01113HfF4nOOOO46HHnoI3+v8rpzGNN5pOKZyBRFPAS/0r6U/Pcj8khaOqlrOnOgh5iNvMxaWzuWJnmfpTHZTFapgx1gru2K7MR0Lr+rh2d5VlAZKqApW0FTYyLtnX4gQgvs7VrKy80l6d3XS+9xOBl5qw0hmmd0yj6uv/iinn34eixYtm1JV4CTi5B+8DxQFbfYcDDPNAF1U9Y9T98I2Nr+rFITAq3pI5JM82/silcFyGsN1AOQsgw3DW3m+fzVn1J8EgDqjGWvLJqTjIBSFEZHFcmz8FjjJGObzT4OqUlASJKUlKQuUMNrXzjEP7yIwmiBdEiGZzLBwJI0aDqJUVWN3mS4DpiiuunbndpyCAqTp2j+JUAFKOIKTiMN+hLA1nuCuynF2moOU1tZS8PwOclqe3qCJ16Myy/YhfD6U2nrUWbMnc0EOB9aGteQfuAfHsnCSSbcvXg/Wts1ozS0IXUc6Dlh5IAiWCUK4imUB0shi93S5amZFgZo6lKoasC1K2ls5d975XHjcBShCmVwjlC8uZ9niI3jf+1y/+Ewmw9atGyfCb9fw7LOPc9NNv0ZKSUNVLcf4AxxbWcfRC5ZSoGs43V3k/vpnAmUVKBPK7dcCe9N67M7dqM0tk9WVMlSAs2Mr5ourXhMZDHDllR/id7/7BZ/61Ae5886VaG+gYvNQ2DyyjVu238VwdgRVqDjS4eneVXxw3hXUFFS97uue3gB37pJsHnFJ0b2ewR4V5hfvm3tbDrzQLxjNCVqK9j1e4JHc3yFIGq5tQUgX+FRBTt1HZo7lXL9Z2+knoKXw6ZKs6Qa2pS3IWK4Ng6oIfrtZIexx/X3HsjCag2KvwHIkEQ94cnn6LQVD1dCxqFWzVKo5+tBoJsmJNVW8OCAYSO8lgt1+RryQyMOqfsGptZKr5khOrbPZPOIqfxVcEtd04PEu2DKq8EyvIKhDsU/iUwULShyGM4L2hNsvv+b6++qKq/Ydygpi/ZKucQVbSiqCkLdd8rc+DLrqWmAsK4W8DQlj6jpGCPfHtF0f4a5xyFkCjyrxqlBXcPB1T9Zy+xPQmGIXkTCS/NeWW2iL7SbqcyurNiu9IASbdqxnxYqj9zU8cenqUD8hPc2eZITRnMlFM8s5q8FL5H+Bg9Y0GTyNabyDIZFkhESRvOGgueXLj+KHP/w1n/3shwkEgnzrWz98QyRkQjg85s1gK4IW24uCIC0cXtSz1NoaZ+ZDLLJ8bNANksIhLBUkkiHFJiQVWl7mm1zuaFyfKaJNzZNUHEoclQZbR3kNwXH66CaEk0f6ipHJAaSZR4ZLEAzjUQYwnGqUSCFOIu76975eMnjJcvTjT3YntVK63mOWiQgEkfE4jmmhlFUgFAWlrgFb1/njc4/z8zXPE/H5ufG8d3Oy7fDJvk6qq+teVx8OG4qG9Ba+KZfytt+JNvQidqgW6Y2CUNESO/G3/oXU0q+BomFFF5Be+Ck8PY+5IW0F9eRLV2BWHof0Fr1qGyI3hn/XreijGxF2FsdTiFFzGkb9eSAUPEOr+dzvn2F7Z4YnPtOEPvAcVnQBSj6OFCra6EaUdB9WpJl88VK08Q7MwllYJROm/5qPzNyP4Gu7HX1sixto55hI1YeSHYX8OGbpcnJN7/5fYw7r97sK5lwuB8DSpZLnn4fdu6G62n2L9vZCaalk8eJpMnga0/jfhKuvvvpVvYUBFi5cyDPPPPOKx1x22WVcdtllh3xeCMF3vvMdvvOd77zWbk5jGv8rIIRgfskc5r9Gn9S3CzUFVbx39sX8ve0B1g1tpDPRRcQbZkXxEqpCFbTH95DMJzlvxumcVn8Sfs3Hup4N/PK3P6bzsc0kekcIFhUw84TFlB01gy+f/wUWlu7zZJVSEjPimI4F7dvZktuNXVtNLeNUa35CnhCjBVmi/SN4xzMY4SDD2VFAIqWkxLePMPVpXoJagA1DmyfJYP2Io7HWvoS9cytKSTkBPQm+BHlT4OnvQ3p9ICXjiS4aEgZnnPNJXlr9C2RfDx3VxdgiQ0kwgj8iwTDAU+xaLVg5127BsnDSaRgdQc9kADfwWTvuRPJ33OrasxVFkek0nfEOOhd5aSifzWjPeobDOmVDOQIWGNLGShpoBRGc3m4sBMZdt6GffQFK4NWrwqxVz2K17QLbQo4nYXwcaduQySJn2Kiz5mAND0E2i9R0UFVXIew4qHWNKEVRlPIKV0wyAaEoSKkiJIhI4WTI3qHg9Xkpaq5gRcPJnPPeS6kMlpNMJnj++ad4/Dc/49nN67m1fSfqc4+ypKqWY+ubOH6wn2WbN+AtP+s1vCtdOKMjgJhisyeEgGAIp7frNV8vFCrgV7/6C+9616n85Cff5Utf+vZrvsYrIWcZ3N16P96OLs5MepCqw1h1CWtkN/fvXsl1C9//utfBAQ1+eILNf25QeHgPxHKCUr9kcZmkIeIGwumqpD4seaRTmfTR3YuEAUMZV+WqCZfY1ZWZCArQlRHCnlJaopK+lInpjDAvapA0oSclSZuCnO3aUUR9AoEkbQnSliTslTQXCeIDrvduUBdYEjRNQc85RDGYI8bRhSQlVTzSot5rToyXaw2hKVPXCT4VhjPwTK9AV6C5SHLGforXXXG4eZvC1jHBYNpVQ3sUiU8TNBfBB+c5/GKjgl8TCBuylhs6N7tIYkvBkjKHx7oUygOSlqikLOCOz86YSwbPiEiaC2FxmeTJbsGWUQXbkagTtEjedv2GRyzBhiHw665fc9aC3hS0FMEDu13FNMDiErfvLw0KYjnXCmNxqcP5TZKwB9YNbaI93sGson2+7SX+KE/4PWzZswWAymA59QU1tMbamKnPQBEKYU8Cv76B40vquKQ5gv72OAW+YUyTwdOYxjsUexSTRz1pWvU8ioSFlo/TjQAl8vV/bN/znveTzWb46lc/hc/n4ytf+e7r/iLcoeUZVmxm7UfYBqVCSCqs1nOckQ+yzPTRruZ5wZOlb0ITXCAVzjaCNNgHluNpuMQy9uu8QTvP3gIQ4fUhNB1pGEi/QAjXk0KmxhHB0BsKiRC6jve970NdsJjsz36AVBTUqhqER0d4fEgzj71pPc7Z57Mll+GLq59hW183VzW18JkFywkaOdSZs+nv3MGSmreYDH45rAz6yAbU7CCOHsYsWYz0Fb/qaUp8F4G22xBGDC3di1T9WOFG7GAdWqIdNdmOXTjbbaJoLlbRXJRkB96uB/B1PYiv60HMksUY9efiBA/hKSYdAjv+gD70AnagBukvQ8mN4G+7HTQ/jh7myzf8jAe2ZXns+lqqisMomQEUI4bIJ5GeCNIbRU33oKY6sYN15KtPJttyzVQf40AFmQWfQskOuvYQvmK0ZLtrZeErxiqcAwdJP37F8ckOo41uQMmPYwcqMUsWH3b43JuN+nq46iqHe+4R9PYKhIDaWsmFFzpUvzHL8GlMYxrTmMY0pvEWYln5ImYXzeTHa3+FpmgsKJmDT3XFCy3RmWwfa0UIhdx4hl//6cf86rc/IZ1MMevIeZz64XdRv3gWiqqyI7aLLSM7Jsngwcww97U/zPaxXQxlhhmK9RCoGiaiZ/ErOkdYZRxVWEfn6AgJM8VQLsaoHMGWNvNL5tAe7zxgzSAE2NKZ/F2pqMT34Y+Rf+wR7B1bmS2LqPOVs0cMUhcsxqtojIocti1ZvmuchcOCyqIVjBf044mUEtKDVAZK0YfXYvf1IG3bJUkdx63AU1VQFJSycpzBfndOHyrAc9JpkM9jPvskTl8P+Pzkly/DLO/Dm82RT46x7ZjZjLZ2Ex6IYeiCprE82uAA0uPFSafJ3PAd1JUPEfrBz1HC4Vd8jcwtm5CJOEpFpUsCp1KuatnIQWocdeES7M52ZCwGuo7weEHVUKJhPJe+F+EPoB9xDNaGddh7OlEqKsG2sHu6UGtq0eYvesX2BzPD/HXH32iL7SZnG4Q9BayoWMLFM8/ltPJqjhY6TnktPR4PL+DwvGnwh7Wr+JmRo2LbOs644FJOOe1sFixfSmGocIrv86EgwoUI6UyqvvdC5jIopYdniWJmTfq3DjCyewQhBFXN9Xzus9/gRz/+NkcccSwnnXTGYV3ncNA5tpuKh55iwa44umkjJOSDPrxL6tnqbSNmxCeVn/tjKDPCQHqQgO6nIVw3xb97f1QE4d+OdfjYQvjtZoWupEvs7k64StmjKyVzo7B+SLIztldD6wo0Xuh3A9rUiY+TIyFjVqEpp1DgeYS8s4tk3rVJKPLNpT48j4wtKfPDaE7SGhc0FkCx32HjsIKuuOrZzoTAq7qqWhxBodclTeOqhqNYpEzo1mPE5SZyTi/VlqBo5lKklFSFwKdBMu/6AQM4Dmwbdb0V/jyRU13kE5w/Q3J8tRvmdst2hU0jrjVFdYHrhZy3BRVBiVeV3Neh0DMu6E2DV3HJZr/qksYLSiXzi2HrKFOU0xEvlPrdsLr3z91HPB9RIVkzJNkZE0R9EkdCPC9YVCJZPeR6MJsTSm0bVym9elDQlXJ9lgF+t0UhmYc5UUlFQJKx4LEuhbQp+fAChz3JbjRFm/K6K1JgZQzyXpegUBWVC5rO4qZtt7Ejtgtd6JjSpDxQygVNZ6Er73Cj4P0wTQZPYxrvQPQrFn8IxOlVbMocFUdIHvOm6VFMPp4tIihffbtJIhkT7gQtKhXEBEl69dUfxTByfPvbX8IwDL75zR+8ZkJ4ULFYpWfpUUy8CCpsdVK5rCPICZf49SC4PBdmmemjUzXREMyyPNQ52mR/3kzYhbOh91GwsohIIUpZGU5vJ2gOeTuKMzyEk4jjPfl0N4jiDUCoKkppGaIgjKeuARHaRy5LyyLRup3vf/0z/Onu22mZPY//+eSXmZsch1wWdd4C9KOOY/TOP1FS8uYElhwOXJuHG9FirnUFUmIHq8jOuRYr+gppvo5NoPXPKOleHG8RUgsirAz62BZMocCE5+7UtoYJbr0RdbwDx+9OEr09j6COd5Je/MWDqoTVxC70sc3YBY1I3R1PJ1iNcEz0nsf4/O+f4761vTz4nXdTr+9GMWKARE11IT2FGDWnIrUAIp9EHe8EIci0XINTUH/gPQmBE9hXJmcVv/LE+5WgjWwgsOMPKJkBwK1XMqPzycz76CHD+t5qLFokaWmRdHW5i7W6OvAchkvINKYxjWlMYxrT+Mci5AkS9RVR4otOEsF7YeVMbvvP3/Cp/7kMx7JYOK+OwNJmFvqjKNYYJBNQFEUTGlnbrRjKmBn+tPU2WmNtRDxhelMDjDsZLL9gRtxADXl5RuunKtjIfFnMtjnFWAV+ZvijHFN1BEV5hV8OdzKaGaU44AoI8rZJKp9iccOpWFi0KlsYUUcIzyhkdu1VBLMQVBTe+7v/x52ZF+jWslhIItLDWTSyPD6O3bmbkqqZhHyl1BTPnSQZ7ZnN2H09kEiAokIwhLAtRFkF2qwWRLQYOTKMMzKMGipAaBrecy5AP+5E5MgIoqCA+qBCZPXPiSXGCFsWVmGY3iWzeD47QMuuMXxb+l1yuagIdB1HgnzpeYzb/4L/2k9MjrfcWwGo65PZHzKbAcd2vYFzWZeoNvPuvLqvFzQNz6lnoVRVY65+ETnQ5xLbVbWQzeAM9KPOmYfvPVeRf+g+nJ4uUFW0mbPwvuu9r2hZYTkWt+64iy0j22koqMWv+YkZcR7reprQSIKT/r4GLBuEQo3Hy6XJJO8uLsM57XzWbl7PU8VF3Pfovfz5z79F83moWdTEyaefzWev+AIlhYees2oLF2M+XoXTvgultg4U1VVi+4Noy4981fe0mTXZ+PdNDOwYQlUVJNC3ZYATFpzN2pNe4IMfvJQ//envnHDCqa96rf0xloOX+gU7Y4KADosiORb2vETg7j9z5DNbyVeVkaitQCoK/niK5tW76C8P4uy3iQFuWNg97Q/xfN9qksY4quKhqbCBK1oupjp0aFuN6gL4zFKHF/sFW0cFHtVVsR5RIdEUWFEuWTvohqMFNehPC7rGBX7NVbAatkAClhAITqWpsA5d2cFpdTlqC2r5y/YFPN4TQhUuWZqzwK+69hTP9ysYNoBAAVKmpCEsUIS7ylbEhHeuRxD0KjSlt9GTuw1DiVMkVbSozb3WEP4+L8dWHcWiUodnegSFXpcY7kgIxgxYUCyZWejaVrQn4L82K0R9NmlT0JtyPYAzpiBhuJR33ob2uCCWc1XFs4skIV3Qm3LJ1z3jcFSB5KoWhyd7XG9hVQgKPG4InCpAV1wrh/1R7Idr5zs81SPYOOxaZpxY49BcJOlJqVT4JQnTJaaLfW5Q3bpBlyyuCLmke3vcJbxVAQHd/fGqkk0jgo4EhPQQljNVlZZNpgEo2W/To7loBp9cci3rBzcznB2hxF/M4rL5r8sr/h+JaTJ4GtN4B+IlPUufYtOyV3UrochRadPybNIMjjZfeQe3UzV50JOiXXNLP2bYOmcbIRon1Lgf+chn8Hp9fPWrnyKVGueGG36Jqh5eSNt21eBmf5IONc+oYpNQchQrKkssLwVSYUzYnGQGJtXCKoI5tpc59ltvnJMvW4E+tBx9aLVb9l9XgMRHdkgn3ZdHBC08Z52H57TXXh51MAifz/UFM4xJMlhKyYPbNvG9Zx8lLh2+9rX/x7XXfuqgXli5XBaf7+1Tj3o77kYb3YxV2OL6B0sHLdGKb9fNpJZ/65DBZ1qiFW28E9tfgWKlkYqG9ITBiKGNbcWsOBo7NFXhrA+uQh3vwCqaC8J9bzneKFp8O56hlzBqDwzwU4wY2NlJIngvHE+Yz9/4EP/9/G5u+8JpBCOl9DiFCEaIeCwKzVVYhbMmA9+kJ4wVXYAW34aW2I2TT6CmupGqD6t4AY7/TSTgrQz+XbegGGMT9+qG0+mjG/B13uuqkt9i7PUNffmmjtcLzc1vefPTmMY0pjGNaUzjTcac4llsGN6Clc9RNJpGSofNazaz6taHyOVMTqst5V3BAF0VHh6LKOSFjmd4EJnNIJevwHRMZha6wVxbRnfQHt9Nc2QGfelBLMeiOlDBSLqTXnOMeTtGGCtSWGOPc+yc0zjtmus4tawMxsbI33c3+a0bOdLbw1Oh9QxoAlXTsQpCzGs8grmGxqO3XI7dvpN0ocqLxxbz0DFHcJXyUWqcOhq8ZXyir4LuUAUGDpUyQLH0YcktCF1HW7QU5cnHsNtaUatrXW/gTAZt0RLU2jryTz2GEi5Ea56NKCtHIFzLAp9/ihADQAlHJn2Dy4CjK5fz0PjDjAckwXySREBF6GGOG7dRGHSLCcdGkbZLDEohyN37d/zXfgLpOFgvPo/53FM4o6MoFZXox52EumQZSmkZVj4P6bS70+7xuP3OG8hcFv2Mc/GedCqivALh9WE+sRJ8PkQgSP6h+7C3bMJ33fXoRx+Htngpdk83QtNQauun2DAcDB2JLtpiHTQU1BHQ3TVE1FdEzjJYvfEhjhp18M5bgPPEI8huVxFgj40i4jGOOes8fJefykhnDdm+JLGNPXSs3sYf/+0n3PrjX3HpJVdw5ZUfZtGiZQfMKZXiErzv+xD5v92O3d2FdGzUsnI8Z56H2jL3Vd/P/dsGGNgxRGFVBM3r3mM+azK4ZZB//9Iv+NoPP8nVV1/MH/5wFyed9Aph1/thJOuqctvigoAmcfJ5PH/5I6Ge5ygfbsNOmxhdg3hyFiPNNWSKQui7Bpg3Kg5QBT/ds4qHOh/Hp0bJWjMZzBhsHGpl68idfP+4j1AROrTas8gHZzVKzmo80IptThQumOHwy42uQjZrSUzHtZoo8rlKX6TAo0DGEghmcvW8Js5plKwbEqiKoMzvksaOBKQknoP2hMBx9vn2Orgk50AagpoEsc9b2K9BXZFCUeVq1GSGEmYwJnwkdT9tyT7+tPVxBtML6EgUkHcEu5NQ7nfD2zQVZha6dhAdSYFhwbgJ33tJ4aRaCQjyNqQtiUdx/29Jdz+iL+22f3ItVIUk9WHX53g4CzUheGlAcPsOhfaE61cc0CRlAcGCEkkyLzihxjlgPCuC8J7ZkkubXXJcERA3IKBLFAEzCve9Bi/0u+MX9LiPmQ7kHYFXkYwZgvoJtXaBB3pTkrGcYGHpXJ7pXUVfaoCKoLte3LHdtYc4quWoKX0pD5RyVuMpr/gefadjmgyexjTegehUTQJSTPHL1XF3DgcU69AnAiPC4k/+BL2KRaXjknAbNINBxeL6dBGlEzYTV1/9UYLBIJ/97LV0dXXyy1/eROmrlPnkkdztSxETNkstL14UOtQ8A4rFGk1S4ahUOzon5t+Y6vZ1Q/OTmfdxPMXPog2vQ0iH/Nz3k2cG/qyDUlKKUnzoXe+ksFmr59ih5vEgWGh5WWz60A+iYpb5PE5/H6KwCGvbFkQgyPNDffz46ZVsGurnpBmz+N5f/of6xqZDtpfL5fD7XwMZLCXYOZdcPZwwuP0g8gn0kfU4gcp95woFq6ARbbwTLdGKFV1w8HONGDh57OL5KMNrUXLDSNWHsLPuGFeeeIAvsZrsQCreSSIYcK0XhIYyfnBvMccbRar+CcuHfSV6Sj7B7x7dAcDZ33kAeGDyuRuvP4MPzfCD8vLxcMCx8e65FzU3NGEhAo6/lOys92OWv7qK4XCgxXeiprqxIs0uEQygenF8ZejDa8g1vRupB9+Utg6FvV7BXu90yNM0pjGNaUxjGv8MWFG+mM6XHiF819/Q+sf5/a4u1ifSLA36ePeK2SxJQ6R/jLDPy9pak+3RBCUlIWRihGz3JhbOPZFlZW7V00h2DAnoqk4qnyZjZhCJOE4uQyLsw3C8jOrj9PuyROeqLGOAxbkCzD//HmvrZpTyCs7ZZVOXHGZbuYpZVUFTt82STTsZkQ/htftQiqKUdAoado6xuv8pbn+3j89kvoa2cAn62peoHs7Rr2bpzfcxnjQo8QbwzprNDiXG8KnzKX3sOUr69+BXvShVVXjOuQht8VKU6lrM555GBIIgwUklcQYH8Jx0KkpJKaZjsn20la7xXjyKTku0mdqCalJmmqMrVxDUgqwau5VEdxu1+RDHUU1jbzfYJjgvm987Dk7rDqz2Nuy2neT/505QVESoAHvnduy2Vry59yEtGzJpl0zOG+78z+9HlJWjzWhGX7YCpaISu6Mda8NalLqGSbWvdBzsHVsxn3oM9fL3I/wBtObZh/2+GDdT5O08fm3fnE9P51j+bBvRVeuxk37s9nawLAiG3PA62wLbxikt4cmRdXgVD81zF8PcxZx4+Xl09XSy7dHVPPrYg9xyyx+YO3chV131YS655HLC+4XyabNaUD/3LzhdnUjLQq2rd1+Xw8BI+yiKqkwSwQAev47jSNL9WX7/+zu49tr3cM01l/CrX/2Fs8++6FWv+UyPoC0mmFXkqnBLd6ynefcq2grriYbjePNpEiKHZ2gYp0BlJOqnXvOypKgFReyrsrUci1V9q1FFgM5kCQkDApqfoF7P5pE9/Hjtbr559GxCr6PCTgiX/CzwwBEVDkh4uk+QMSUBU1AekMRzLvnp1yTvmys5c8KT96UBgVeFk2olWctV1RoW3N0uMB3Xz3evEneiNWKGpDwAR1W6YyJxFcl7xkdJ009RST074xHSJngtMOwKVvV10BbvpcI/ByncADkhBFG/azPRn4ZdcZewLvaBLQX9aXi0C3Th9tWRgowFuDw0uuISrylTMpLbZ/tQ4HHD3jYNw/27VcbzkJ8Iw/Orrpp4PA/HVEmOqz50zom67+Wj0AtLSyX37FYYykiCmqtWThhuWF/BxOumK64KOCZBn/joJwzYMy4YzsCOMcm5jTO4sOlsHux8jNZYOwCdz26lqqGOM494c8Rk7yRMk8HTmMY7EIWOSk7LT3lMInHgVS0i1uo5ehWL2ft5+YZthR1qnnV6jjPzocljL7vsfVRW1vCJT7yfM844ghtvvJmjjjr+kNfuUk16VZMaW0NBYa7lIewo7FbzpIXkaDPA2UaQaucf55Uj9SBG7ZlTlKfKxM8rISFsfu9PsF0z8OKmu67Rc5ygmrw7V4C6HyFsd+7GuO1mrD27IZtlQ08n/7HmGV4cT7C4qJibL7mKE//l267C4RVgGLnDJvDU5G68XQ+ixbYhFQ2z7CiMurMPOxxO2HmEY+PoL2tP0cCx3J9DwPFFQfHieCLky49EHe9EMeJIPYRZNI9c48UHnCO9RQjHfNmDEqSF9EQOOB7AjszEKl6Ep+8ZrEA50lOIYowibIORl+7H8ZUQ3PwLcGycQDnYBmq6F1MsQJEW2IarbpYSdbwL4Rioyd3YkWakpwCkgzregX/XzViRpjfFwkE4JkhnKukNSEVHsQ14+Ri8BcjlsgCvbWNhGtOYxjSmMY1pvKVICJsdWp6skFTZGk22PmU++UoIjaW4eHWCVQOSL2/ZjWLb3BAuZH5RgGR5FaJzAJA0tg3z4YSPNQthdGYxju1liT2DE+dfQcjjEnUFHnfu35vqZ3eik2QuQdoysTwCG1jrE/QpGpVphbU9q9ngjXOirOGc1m1oTTOR6TRidISFvgoW7EkhMl7U2S3kn30MJTDC+Fm1BAhhAt7RLIuezvDACZvoLGpnxtIVxDevovuRO8jn0igIun0aTx05h7Gxh+jt7se0TcSxPurHA5xRdyLLl5wDHg9Ofx9qXQP27nbs7j3I0WGkLVGbZqIdcRRZK8vN2+9k7eBGDMvAkhZhvYDSQCkyPoaSTBIqqeb0E6/h/7P33mGSXPW5/+dU7Nw9Oe+Enc05a5UzEhJCSIicbMAS9jWXYGNjg3HA9nXCP2cMGJsMIgkQAgWU4wZt3p2dnZzzdO6ueH5/1GiDtApgsPG9/XmefWanu+vUqVPd06fees/7XXlknPEDjzBlLzJWY9A8BoqUQYE6IYKAVF9SxOa73/kDVvZMU0WI+qpWop6L0tSMPzuD9a2v4c9OQTi8FAMrwLGCuAgzhK/rQawE4I0MIYsFSFXhDfQhXQcRTyASKbzjR1+Qv/tyyHyOWhkmqkdIW1mqQkmE57HyvmeIHelB8TT0bD4osqeqKMvaEeEIfnoRrXsl1sljlJviVNWdG3vQ1tJO8SaHv/zY37FwbJwvf/lzfPzjH+SP//h3uOmm1/OWt7ybHTt2I4RAGAZq98pX3OfnEIoIrgNecFCgqALTNPnsZ7/B+9//Lt797jfw67/+YX73d//kvCsrn+PIvCBhBqInQHLsJKbwKRpRcvE6aucnaYxXYdnTNJQEteFWmqtdqtbsPKcdy7PJ2nlyVoSMFcQRBE2aWL5Hf6bAwVnxAnHSKTmkxzMAJFqSOKqOqXFO8bC0Bc/OCJqikrqlafpYQdKzIFiwJKoKugpJU3Jzt8/ruiV5G47MCfZMQdEOhNLxvGA4GxRgc/znEoiDfoa0wCFruUEsRFcyiGusCgVC8Xhe0JlUSZcV+tM+RRdqw4Fom7c9iq7KYFpjOCuQMuhPRAsKtyVMwVwpOHURDaaLgftYVQQTBagygo+PkOD6QZtiSVDWFEnZFRyeDQRtVQSvGc0LFsqBwC0EGBrgCUpLrmIJvHeDT/0r9JZJCeZS3nFfWuD6ENIEK1M+uhoIvc8db8oIxG1VkYxkBYfnBGlbUmXAQ6MKQ1nBr66/hA21a+jPDFEqFHj3vn/jgx/8GMpP8Vn9n0JFDK5Q4ZeQLW6IfUaJKcUNMoOBMdWlRqpscF86bmFK8dDhHFexgsBEMK6+UPC7+OIruO++Pfz6r7+d17/+Gn7nd/6Yd/yvD/G0WeaQbuEjaXd1Wn2dgvBxkafzflUE7b5OlVTJCI8brBhNvsaC8FhQPOJSod5XfyH5wD9vntZLnNAsuj3jtBM4IzyeMIpsccygsB0gS0XKX/kPvOEhBhJx/vbYs9zf10N3JMa/3Po2rv+VX0dfsw7xCoQ53/dRXqZaMICSHyVy5O9RC+P4oXqEWyY0+O0gf3fjB0F7CUFZStTMSbSFE+AV0QqjOHU7gww2QClM4odr8WLnydVdwkuswKnZgD7zDF6kFad6E0p5DsXNU1r5doRvoc0fDNzD8Q78aAtO/XaMiUdQc8N4sVaQLGX7VuHUbzvvfoSdxdei4GQITfbia2HcqtWUut+C3XwlCIXimndjDt+NWpwCxcBuuQKr9WpC/d9EXzgC0kfIwGXsGylQ9EAIBhAKXrwTbfEE+sIx7ObLXnbsXw4v3okM1aAUp/CjzWfGvDiF07DzRYXvnyeWFeQ1G8YvPoqlQoUKFSpUqPDyHNcsvh7KLq3oE5jAVjvEm8oJ0gWFgUwwO16RktSdR/RwD+zj4WNH+Mjhg6yorecf6lsJlbPkCovEp9OEF3PoJQupKNTkXK7dO0fdVB0iXkNo+wWY5pn5x4baNdSEqnl07El0RSclwqSxQQjS2KSFzTIZZ7MfJZGGrB7n4dG9rNUsVpgh/MmJwGmqachCAX/+eJDZOz2NmXDRfPW068KqDpGayhIbXKSUzCM1le9uijKjrGJjPgKqSqaxmv0LRxg+/H12NWwl1dgFisJYaII77UO0WhdQ9eMncB59CJnNIMsl/PExMEyUmjr8bIbyv32aJ6/fzJPWAaSUzJbmsTyb3txxLj68wDWzURKuQk7fz7EVe3js8t1MXBClbMHuYoJXHRNorkT3zuSDWrogq0vGpvtZP1nEKpRIWycQRgI9HEdpbERms4hwBLVrBd5QP5TLgXolBHJyHOIJRF0dQFDIemEeb2Q4EIuFCPKFhUBdvzGImXve9YL0PPzRYaRloTQ1BzEPYyM4Tz2Of/IEVUg2rIQn6uYoJxtZNpEn1NNHOaSzetGHUilwA7su/sgQ1NSh1tWhLGtHnZ4kaQtmneLpGwQABbdISDNJhpNsuWojV111PVNTE9x55xf56pc/x513fom1Hcv5zTs+yI1vffcrjhY8m/qVdUwencQu2hiRwKpZzpZRdYWajsA1bZomn/70V9m69e/45Cc/yrPP7uFf/uXLNDQ0nbdNTQQ5sKfHTiin9Wa3sRUlO4E5P4vhqSTcCGoG9O0XvaBAX1gL0Rit59mZQTSl+rSByPLyGIqJqdYynj933xNHJzn1UB+5uQKDRoreqhZm6xqxzRC1YckFTRJdgZ5FwbPTgs6kJGUGQvG2eokrYTgj0BVJlRk4ee/YJJkswH8cUxjIwEReMF2EsTx4MnDVJnSJqUDJWzJ8mUH+reMFecIxM3D19qdhpgh1EdhYJ7m1O853+lbw5OQ+qs04Ag0pfcbz4zh+I5J2VMlpwTZnB31/VbukdzGIrBzKCeZLgagaUcHywPIhpgfisM5SDq8WxFrkLVAUyUAmELG7UxJfCkxNEtWXRGYCR3BECyIldCVo1/gpdNfjC/DgqMK6GsnWeshYgoztE9YEm+t9euYVehaDfleF4NWdkqwFe6bB9yWrUrCuRmKq0JsW/HhI4VfX1VIXqeWP//h3kVJy661veeUd+h9ERQyuUOGXkA2uwU3lGA+YRU6pwd3lBl/l5nKcJv+lP7bVUsFBIs8SbSUSG0m1f/4v74aGJr7xjR/zN3/zx/z5n3+MO/c/RNdn/ppkTQ0jqsNdZp6IVOj0dGYUF0fAWtdAIJBIphSXVa5B0lf4jpnjGaNETvhEpMJGx+R1Vpz4Kyh699/JUd0mIpVzIiGSUmUCl0HNOS0GuyeOc+rYET43PcpdPUdojif5q+tv4YZIDK2+EbJpSp/+e2S5jLZuA/qFl6DU1v2n+mZMPIpaGMetWnc6isA3a9DnD6PPH3rxyAPpExr4NubwPQi3gLAW0XLDKKU53JoNCK+MVDTKna9Fhl68WAWKSnH1uwlpEYyZPYjSNH6knlLHTfhmktjeT6AWxkFKpJHErtuGW7MBu/EC9NkDaOkeQOCH6ykvvw0vcZ7oDM8mcvwz6LN78FKr8BLLUYuToIZwUytPi9dO40U4dTtQStOghvBDtSAEhU0fQp99FrUwhtQiODUbiR3628AtDAivDAikagLy9OMvimchPCvIL36JAot+uI7ysusJ9X8TLd0TxFw4WfxwA+X2G15y258Xc3MzJBLJl3RPVKhQoUKFChX+a8gKj2+EsswLjxWegYogJ3yeNMpMTenMHkqQtYL11FWmwg2dPpe1ynOmDI8++Qjvf/ZJXrVyLX913evQ9j1DqZxHKoLw3CKK6+MaOprjUTYUzEQSf3gQbf0G1BWrcJ7dB56L2t5Jqr6BXU1beWpiD4pQMDST6oKGrpnMKmUMqbLFqyVmlRhJOIwt9JIrjvK4W0SdT7FMUYPCaTPTyEwaYjGIxlEkhDMuoeFZ/M5lgIT5RUr5GfSBEqmHv8bs2mEGlWESq9YyasYJpfOsuvcZjqrDVJkO2vAenJohtG27aI0107N4imN7fsiuHz+BSKUQq9bgPrsPmc2i1NbhrVrJyZjN4sww9x44xfSyOAW/jKkE87vdB2fYfSJDqVYjVN9KvFCma28P4/lJqm9+HcmqTqKdgmLsGKJsk3IVFASuaVCSFsWISaOaoHZhHsX1WIipyJBCg1Dw+noR0TgiHkdd1o430AeIIFjVdUAJroy840dRL2tA1Dfgz88j7TLUN8HUOHJhASwLv1yi9Nd/Ruit70Tt6gbAGx/F+ubX8AZOIXM5ZCaDVFX8+TmEkKir16MkErzmmUliK8Mc2uIj5uaISo3GnEJUV5GtEeTIcCDeWxb4PtrGLVAqoSdSrO3exV2zT2KqBikzSdEtMZIbY0vdBjoSZ1Y0NjY2877dl/OuyRke7znC5/t7eN/v/i8+9Xf/hw/8/p9y02vfcFoUllIyWZjG8mwao/Xk7RBzZYjr0BILpsKNaxqY29TC+OEJfC8XmER0lfady6jpqjm9XyEEt9/+ATZv3sEdd7yFa6/dyV/8xT9x3XU3veBztq1BcmevQsmVhDVYXLaWmr0PkLAzVCcTaFt34J7qwR8aQFu1BvOGm9F3X4Qwg2s6ywtEz6iucGnrbn4yMsJ0cZiIVo3rlym6C7TGthPS2ometeg1PZbm2D0n8GyPuZY2nqCJcVsnN+ejx3yGswrPTAZF4lZVB4XMDs0JCg5srpNEdFhTJWmOSm5bIelMSrqSgbv3zl6FU2nBipSkMSLZMyUYyARO25gOGReWJWA4G/Q/bwf3YYpe8PejKwHtCcl0EQSC1y73uKINFCF4dec1fH9gnrQ1QMmDoiPJ2vVIbgJMvKWIB1UACCxP0pEIHnxkVJCzArE2dZb3RBUQNWBNSDK0VCQv60DJkVSFQVcEdWFJzoG8I3jnWp+j84ID0xJPKmjizKWSLwNRWxUwVoDqV7jg8chckGXcEoPeRcFEHlypUHIkjRHB+7d4DGaDnXSnJMvi8OQETBUVupJQZZ7pQ31Y0rMQ5CL3Hn6az3zm/+OjH/0kzc2tr6wz/8OoXDlWqPBLiEBwjR1jqxNmSHVQgG7PeEWC6iYnxGNGiSHVpdULPuLjqktKqmx2X9xBqmkav/M7f0zsgu385W+8l/EtV7Ly9z9I+fbbqEKnpEgMKQhJhVHFARWiKJTwqfM1rreiPGgW+LFZoEYqtHs6eeHzqFHER/LOcvKX2iGsBvn95xAsshEIKfB9n4ceupfP/vUf8+ih/dRFY3zsiut544ZtmJqGNz2F++xe/OFBRCgMmobVdxL36CHCv/a//lOCsJbuQeqJM5m0ELiBpRfEJLzYdovHMYfvBjUEbuF08TelPI8oL2C3XL4krm5/+fEpTqI4hUDwVTSc1FqcqlXEjvxDUDwtuRIQ6DNPE5t8BC/agh9pxA03YnW9Hj/eERR5exGnrLZwFH3+IG5iBWjBt78fbUVbPIYx8TCl1FlL0lQDP/b8CA4FqcfwYu14sRb8aBtOzQZCA99Gyw2iWItIBNJI4JvVePHzO6GFk8cc/iH69FMonoUbb8dquw63dnPwvJVGKc8hjSR+ODinVvuN+JFG9KknUcpzeMlLsZsuxUt0vuy4/jwYHx+ltXXZy7+wQoUKFSpUqPAL56RmM6V458RCxKWCWxbc61tsFz6rqpZqgRQkd/UptMY9ulPB9qOjQ/zvb/w7F9c18v+9+vVoqorX0oo+O43pSCb1Ak5coUHRieR9dKmQdFVENIqoqqH8hc/iT04gFQUlWYVx1bXUbaijO9VFfaQO37GJzB9BW8jzkzqBpfjEix7Dap7euInqlnCjEXIxj7lje2DZGlo0LXAI6zpKbV0gnkSiaI6g9lSBkx2zGBmbRP80s/Uq3eWVJPuzlI5+g+5VgtkLt4CUdD56kOqBCUrrVQzbQuSzePOL+PPzGJdfDUDp1DEQAqW2HmmVYWEeUVfPFAW+qTzLkKkiW32Oe7NkMwu0JJcRN2LIhXk2j5RYjOo4YY+YoeIaMRbzKqsHFpkqepTUMvPL6vCSSaKFmWCVnqohfQ9bE4y2pzCT1aAMIxWJruiUXAs/UoWwbURNCBGN4efzCBGYYrAt8GWQnxuL4e7fg3HZVci5WURtLWSz+Kd6IJ8LVCdFgXwe+9EHka5N9CMfB0XF+tLncftPoTQ04g0N4k9Pgu0gBYh4An9oAHXHBUS713N9zwmu23wB7vbLUZ/6B/yFCUQqhaJpeK6LnArOFeEwcnERaZUxbriZqzbcSLpXsH/mEFPFGUJaiM1163njqpvPydH1JsawvvlVRNnist2XcdlFV/DsiSP887NP8Rv/6538zac+yUc+8odsv/IS7uq/h96FfsqeJGNdgqrsJKLVEdYE62olt63wqQqprH/NWhrW1LM4vIhQFGo6qqjpqkFRX3h9u2vXRdx33x4+/OHb+dVffT2vetVr+JM/+dtz5rsXt0j60pJDs0GOrZLahFh/JRcPPUh4YAwPUCIRjF+9g9Cb34EwDGaKcxwfPcSzM3GGs8sQpIgagt2Nm3nTKp/PH3uCtDVDzDDpSl1NWLuKsKawofaMg3zqxDRWziLVUc0BWU3J1/AMg5DlUCUFWS3MrBOIwa4PG2vh0Cz0ZyBpQFiHvBM4VK/tOGNtni/ByQVBUyRwFSdN2FgrmS4qFF1wPMnyZBADMZyVPDYuAIEUMhjrGsmOxqCQWpUZFH07OKtQG/JRFFiebOBNK9/D9/uPEzXmODQbx2MdcOYa1ZNB7IIiwBTgIfjQVp/ZosK+aZAyiGFwpSSqB4J/yYHtLZKOJByaFQgbqkOBEJwwJB1JSckFyxOsrg5yiA/NKMT0QMwuuksuYy/IQc5Y8OSYYE21PCdy48UoOEHsw7F5wXBWENUkIdVnsezz8JjHpa2Cq5ad21DMEMSN4HycfRNOSknBmebI+BS/88F3s2nTNm6//QMv34n/oVTE4AoVfompkSo17k+3FGeZr/PmUoLvh/IMnXYVa7ymHKPDe/ks3+SrLuOKvfcz8Yef4vBvfwLzc19k+f/5GOp1F5NXfLa6IY5rFusdAxOFZl9juxMiJVW+pGdJSYW6JfdylVQRPhzWLSZtj+aXcTX/d7LZDXFcsynhE15aIDQvXEJzkxz7l7/gk1/7HgMjY2xctY6/3H4RN2zehZk8I2z6o0PIfB5l81aUWFD8TLouXu8JnCcfw7zplhfsU9d1LKv8sn2TZhUi2/+8B4PJg9Qip39Xs32ouRGkauJWr0NbOI5w8ijFaZQlx6w0kgi3iGKnsdpvxEt2v+z+1cwpIkf/CcVawAs3IHyH0Nj9GDN7EfZiUHhOKKi5IdTiVCC6Ci0oTpftR8zsJd923Zm4hrNQilPoc89ijD+CUphExDtO52AhBL6ZQkuffJn+9RHu+Xe03CD4HtKIYzddgpNcSaQ4jWLNI/UkQjpBnEPtpqCQ3lkIaxFj/GEiJz6LUhjDjy3DjbWjzx9Cyw5SWHc7WqYfY+IRFDuD1KLY9Tsod78xyE6u34lTf24GGVL+lziDx8dHaG5+6XzqChUqVKhQocJ/DdaSneD5M/hsUWArPnXRIGNTAM0xOLEAR+eCZdRSSm6//S0kUlX8zeXXI/t6mKuJUVRL+DKPwMdVBK7r0FNr4G9dyYXx1UTMJO5gP+6RQ0FmbchExOJI18P6wXdoTr2BuBFDEYLaRCP+Bh3n6EFCVhaET8ErM1oXQovGUYRKxIyjXLKVjLUPdWaIJmPpGkII/NkZhG6gdnSheh71M8PIAz658jx2PEzDil1snNyE2q4QGh9lU+8Ad64coc5soXpoinI8zOqhcZ5pUUA3QPGR83Ms7H8MY8MqWnMqPFdTQwZjIlWF7y9z6dMLLPeb0aXCnLPItG+Ts3LE9SiJkk/Y8llMaRhIPN9DUzQyBtSkXXqG9jFcrXHhgRmKio8WNtHKHprrU/Bc/lLxePLwKKWn+1lhmvxGQz1rC2WEFAg/j1LXiNq9Am3TNkr/9s/IfC7ov25AIokIR/FHR/CXnL54LmpVNV5tPQz0BXNCVV2aG0r8cgnv2FHcE8dAUfGGBlCXr8CfmkBmMygtbXgD/YEg3BhD5rJ446Noq9eBpmEMDxN/0zsoNH0Hv/ckMh4HVUWEo5BIgaEH7mBVxXjNLZivugGhmbx1zeu5YtklzJXmielROhJtqM+LrfOOHcFfmEddvQ6xNJfdtnYjnzFMjjc28k/9Pdxxx1tp27iCzjdtZ9WKtaRz6+lLd2CoA+xskMSMep6eFPi+wu0bfVRNpXF1A42rX7pg+XPU1TXwhS98l3vu+S4f//iHuPzyTfzWb/0B73nPb6JpGlEd3r0+cJqOZiWmprB655toG1+P19sDUqKtWIm6biNC19kz9Szf7v0BJxaaGM9fgBBD1IeTNMXa+U6fynUd2/jtbRu5ZzBP3jXRlSgpE27q8mk/U9eaUqaMqquUhM6Cb6AicVCI4OO7Po4anF9dgflyEEGgCMGzMzBbEqyLSq7rkFzRdm4GsSvB80E963K9PgLL4pLxfOCEblpK92hLwOUCrmx3WSgpPDkZuI6fQ4hAXP1Bv+DYnIKmQmMErmyLcWnrLh4ZE5TcIErSZynpZOmnTyASRvVg361xuLFL0rcoWLRACEm1EUQuFJxA8F5VLbmmXfK1HsGdvQphVVAVltguHJ4TuF4QbfH5YwrvWuuzrlYyUQj6WHIEth+4ilMhSUSDPdOCzhF4Vce5YzRZmGY8P0lINVme6iSshViehJ+MwFRBEDcknl9ksrBI3tHx/Qyf2p+lPhJjfe2Za97OpKQuHOQety1dnubtefZM/wDdPcrtf3InuYl53v0nvx3kXf9fyi+vMlOhQoWfmS1uiNV547QY3OHppwXOl8OUAqOhniv/8W+wfv1NTH7kTzl+87uIvuoyqv/s4xjd64hIhd1uhAucM+s3JhWXgvCpkedOJuJSYUbxyAiP5l/iPzm77BC9qs0BvYyHpNA3wPg//AOjX/8hdsnmdTta+Nc73sj6Gz9K8TsP4jzzJH6pFkIh5NwMeD5KdfVpIRhAaBoinsA9cfS8YnBNTR3z87Mv2ze7YTfa7LMoxWn8cH3gCM4N4YfrcGo3gWcT7v0yxuRjCLcAQsEPN+CHapbE4Cl8sxqUYHYhjRTCSgeO21cgBhsTj6CU55ZiKgIni2+kMKafQCrGaceymhsGETh0hVcG1cRNrkTLnERbOILTeCH4gSCLYqAUp4n0fA6lOIlSmkPNDYB0cOp3nXYQC6eIF+948c65RcI9/4aWHcRNLA/ateYxh+9Bi3fgRxrx4u0opVlQTbxIE8K30OcOYjdfGuzDShM98vfoU0+iZQeQSgg1NwJS4tRuQ8v2Ej38jwg3jx+qwY21odg5zJEfg+9QWnv7GdHXszEmHw3OhZ3BrVqL3XLlKxLdf1bGx0fYufPiX1j7FSpUqFChQoVXTouvEZaCrPBJLs2LJZKMKklkddTnLUVThaSwtMzriSce5uDBfdx5572YiTD7v/BX+P0nKDolhtphYdVa2o065GA/x5tNvKoUO6xGmMniT4zD4jyivhGhacj0Il4mjVJdQ+ORfi66fBc/GX6UtJXB1E3Sq5tYZ7WQkAYn5TxzxVEM3yKihFmZWo6aauXEm2vJD5ygPd9N7AtfQdoWQlEDd6tVRmloItrWzopdF1D60V0YjWtRi6kzx1ZbR1dujraywVC2lzXFDIplsStrM9lQQ18VxIs+ju5hp8e4aI/J8tgOZK4HKVshFEKprmF0cZjBpKSFGAYqucI8kXAIXZHMlxcIaSau4VEyFeKWxItoCCEoOiUiZZes5jOtOSTLGmtOznKiBoymOi4SbagFm0+eOspRp8j2972KjeF6pr94D789Msof7VjJlkQrRsMKvPl51JVr0LbthH/+/5aUMy+4+V/I4QsgX4BwULhPae+EWBz/8IEgRkLVgkgJ2wHXQ5RL+Nk0M+kJTk2fIDzXQyaSpiFj0yR8jFIpyBoul5BjI0F+8MQ4RKJ4x4/iLy4i5xfQd+7GO3kCf2YawmFEKISyZTu+VULRDUSqCm/v01jFIs4FO+kJFSm6ReojtbQnWl8gBENQIwUhTgvBpzFMNsSSfPGLd/Gv3/5n/upPPsH4H9xJ4TVXYl98JVWmwPYzTBYn2B6vRwjJ8QXBaA7a4vKF7b0MQghuuOEWLr30av7yL/+QT37yo3z96//Bb//2J7j++psJaQrbGyRbmcI9chCZz0NdPeZ1NyBiZ0wos8U5vt17N3nbw/UvwFCrKTo6fWmFubJNTSjCT0YEn7xIY0djioGsQEHSnZJUPW9hbbwhxtjBccK+hyF8MjKQUaWUqIYaqKoIhAhcuqoSRDdkij5X1lu8fr1KLPLCa+LaELTGJafSgeNWLEUoxPSgSFzRC8TXsgvTJcGOBskbVsKhWcnBWXD8M8Xr5krQsyBQhCSmB9nCcyX44aDgjo0+OVswlFVw1aDInOWfyV+WBEXkVlRJLlkqmndFm+SpiWA/tieIGZKyC4sWrK6G3U2ShgjcsVEykZdLBeIE01bgvrUUCGmS4azg/mGF96z3aYwIvtyjMJqTVOlBJnLcgA21EseHx8cFl7bKJYe1y/f7f8zj43vIOTkUodAaa+ZNq17H1oYuGvsFJ+YlccNhrpTB8XUShkVVKMdsyeXfj/6Aj+x8Gw2RwAWdMOCGLsk3exV6FkBTbI7P34ldOkzpS/eTG53n8o++gWfco6yY3MfFLS8Syfg/nF9eZaZChQr/KcIorPF++oJS6zyTRymSER4NG9ej3vtl7O/fz+hH/5wnd76K6Vtuou29b6d+4zXnbJf0FeJSISN8omfFWWSFT1QKquRPX2zgF4r0UcrzSEVDGinCQuGaviyjP7qTn3zv2wzt308ybvLr127gPa+5kJbqMFrmFG7/l/Df8GFEXT3uvmeQpSLqitVoW+M4zzyJlM+b6DhOEBtxHl6pGOzU78TqHMMcvR9t8TgI8CJNlFe8FT/ShDH2AObYvXiRJmS8A/BRswNoxUmEkwffOS0E43uAhx9te1nH7XNo6V58I3muy1ULgWIifBvcEmghhFtECh3hlfCibeCVUYvTS+7fgyAl5sg9qMUJkApKcQypR3GrN0LCwvDKqPkxUKPYjReilGYQSJzGFxc69YVjaNkh3EQ3qEYQ45AfRcv0oc/swU2tDJzLytLXnVtEnzuEungMmi4BITCmnkBbOIofqkcWJ/FDdeDZqIVxvFgbvh5Hn92HU7f9tKPYD4eQQsWY3Y9VGMePtYKUhE99FXP0R0g1hFRDmGMPoM8fprDh/XhnR138HKnERFSoUKFChQq/PLR7OjucEI8aJTLSx0SwIHyaULEnYrhKkAEKQUamL4McUIAvfOHTrFy5hgsvvIx/PfIF9l/TzHpnLYdnjzMctnEUaIvH2dC6k9RQH335eXpnDlNdqkaoKn4kgrq0ck2EI/iLi/jzc/jz87yu+w5aYk08ObGXU4v9aJpBoqqZHQ1b6S4vsnDim0S0CKurV5Aygw4VpEWxuQ7zmQwiGgVdx46GsISHMTOBkcsS/Z1PoFbX4AgjyBWOxhBLdQxkqUQ8Xsubtr6dZwqnCD05T2PvGDFhcPtYDU+FFuhRC4RdyeZMiJ3lRWg8DlLinTweRFIkk1hpD0cV6JZLnz/KUMTBTsRQhUbZLWP5FqH6FjLrPLr391E0fXLqIglLEikrPLy6lnRE0DVXIFx2WUgo6MJHKjpOSz0P7H+Mv9y2k+GOdvoiNpde0MUzd8/xzEyB61evQC5kELEY2s7dWN+9E6GpyNpayGSCmIhyCTJZUBXsB+/DvuIatB270Hbuxnno/uDkSj+wfxoGmCFkPs9ifYqv2fuwisNcIz3K+QzH/RzzoTwbpnMovhfEPQCUy/hjw/gzkwjdRKurxxsZwhsaQH/1TXgH9gWREKoaFKHLZfGTSfB9UFWyp45y7KEv8OOrOslURdEVnY21a3nbmtuIGdFz3sNKUwtCUZFWGbHk0pa+jyzkUbqD+ezyHeu59JNvIvvwCM986wHUx99M13v+D2ZzPVk7qLgWV33GpxaZ/re7qJ4/jtq1HP2SK9BWrPqpPlPxeII/+ZNPcdttb+PP/uxjvPe9b2Lduk389m9/gisaW7C+9qVADF9ycWrLVxL6lV9DaWgE4MTCKebLC7REN3FwroqcnUARPoaax/EyFJwIx+ZhMAs7G6EuIl+0L01rGxk/OEF+eI7u+jhTaiN4LmUjRDxkIC2JLoJYhBVRiW+59A5kKWVtxNE+nt3n03lBB61bWs65ZlQVuK7DZ/q4yslFiOqBoFoThgubJbNlmCsJTBWuaPW5aXkgNq+qlrTGBQMZQWcicCQfnhPMlYPidAfngm3a45KSI+hNC7Y1wmPjUHIljhc4fi1vyRUsYFujz0e2S+qXFqDWR+BNqyWeDFYyLJQBgtiHm7s9npkS/HAg6N9YXtCXDiIgUmYQA+FLWFMNtWFJz0LgBP6V9ZLV1R5/uVelJgyGGgjKUT3Ics7YgrwNYQ2entzPvUMPUR1K0RztxvFdhrIjfPnEN/mt7b/B29fG6M+oTBWKSCyaoi4pM0PZDVMbUpkvj3Ng+gjXdV55erwvbJLURzwOzAhOzg8wMnGIyS8+QHpoglv/8A5a1nYxlB3h8fGn2d20/bw3Tf6nUxGDK1SocA6rXYNrrSg/MYoAFFSJuPlqLrz2SrKf/TonP/Mf9F37Xd6zZj3veucd3HLLm4nF4kRQuNAOc1coz5TikvIV8kIyq7hcakdoeJHidf8daIvHMQe/j5YdYDJd5ltHLL6zZ5Q9+/dhGAZXXH4NH3//1dy0rQmz6ky2rJtYjpYbQHdHEa97A/K6G5HlMiKZwp8Yw+05jj85HkyghEDmsuA6aFvPn8lbW1vP3NzMy3dYUSl3vT5wCOcGkYqOW7XmtHvWmHwiEB/N54rAqXiJ5WgLR/DDDaiFsSAqAQXhO/iRBnwjgW9WvaLx8sO16MVx/LMflB6+EccP1aBlTuGHapCqgVocxo804psJjIlHUawFhFci1P91wie/iBtvx48vQxSn0dIn8WLLQHqghXHrtiGm96DmBtH1cFCEreO12I0XvWjfhJMH/NNCsDHzNMLOBc95FtpiD1IN4VZvQsv0oOaGUEozhAclQvqUVrwVbeEIUo2AtnTzRAbtYXso1iIgg6VTzxsvaSYRpSkUawE/1oqaG8SYfBQv3HS6IJ8faUZbPIY5ei/FX4AYnM1myGYztLRUYiIqVKhQoUKFXwYUBLeVE7T4Gk/rZQrC50InxBYvwg+FTu+ioDok8YF0WbCmRrKlLnAW3nff3XzkI3/EZHGakwt9tMRbcc0YM24/qq9gCo2JPPipV5NdXkO2MM7RlSoXxA3Ev/4dwnORrkc2JDiStBhtgOhCjuqGMvMnvsNEYYrh7Cg5O0fKTDGen+Lx8U8jhMCXPiO5MRShsLluPY50GC9McbXSRWTyGN72XUydeAZvZhLhufiKQCqC6OM/oi5r489MI08cwx8fRd2yHVEu4/b3cnJLO49OP8yMl2HtplZqRmdRphYxFzLsmp3mQtsjXNNAyBdoqzsQ0ViwxH/12iA6oa6e9p2/Rq2yl8HyLGOqhx6ppjZejSgtkLYy+L5EVRTGLt3C+to1bBxcRBSLiFSM+5Y3YO9aSaefwymN46uClrRHzvAo5IeR6VN4UpIq2lzRdTM9tQKrdY7o0x9mJO+gFy0wTRRNo/y5f8Hr64WaOpgYB88LxFZ5xlYpJ8Yo/dPfEP3QR9EvuAirdRmebYNtB87gUCQwZ9gWBzY2MKDkWLN2G3ZvidaBCTLRKItigaxVJBWKIuIJZDYTNJ7PQ7IKbcvGpf1JpG0jJ8cJ/+/fpvylz+Me2I90HWQmjZyZxu89CSGToimoDSns7mxm6OrNFJwie6cP0BCp43UrbjjnPayt34S6dj3u4QMoVdWwVMhObV2GsetCAFJmAt3U2XbblWxct5Jv/s2d9P3FO9i1+zpqX3M7APMnBwiPTZKaPQ6ahfP0E3gnjhH6ldvR1q5/2c+SlBJf+qeFuI0bt/L1r9/D008/xl/+5R/yrnfdwsb6Rn5z+Vouu+ASFEVBug5ubw/Wj+8m/M73AOD4DiAwNY+ya+L5KhGjhOVJhHCJ6oFzdiL/XFjCixOtibLplo30PdrPhtEp0obK8XgzU0aYybIgogVuXkVIHFeytyePyJa4QFugMyEppcscu+c4qqHSvP7c2Lr1tfC+TR5PTwpGsoL6iM+ORsn6Gih7QexERAsyeZ8josHb1vh8tUdhOAclF0azAlORNEWDAnNpK4iiqY9A1oKLmiXdVZLDswIPSUgLVn5qQnJ9l88nLwwygcdyMJwTaAJWVUl+f5fkxIJgoQwNYclkQfDQqELJDQrdZSxojEJ7HA7NQcaGVAhWpALXs+PBbAmKSyshWuPBjbCEIUme5WHL2hDXJTEj+P2pib3oikZtOCg2aKg6XckO+jIDHJ8/yY7GbVzTLvl2bwZdjFITClFyw1i+SVdyEIlkrrxwzlgLEfRrRUry6e/ez/E/+wq6qnPrH72PljVB3Ze4HmexnMbyLCJK5GXfr//TqIjBFSpUOAcFwY1WjPVuiFOqxYDqcEyzGAo5WO9/G7vf9w7W3fcsJz73RX7v997PJz/5UW699a286U3v5MoNm/GBJ4wSk6pHRAqusaLcaMV+aYrHiXQ/A/d8kicOneK7z87yxIkJNEVw5ZZO/v5T/8y1r76NZEiQePp38BXj3OmAYoDvI9xS0FY4gggHXwxKSxvGja/D/uFdeD3HAYkwTbTdl6DvvPC8famtrWNsbOTMA24Rc/wh9KknEV4Zp3YLdsuV+NHmIKMt1oode2E1U+FkkOrzXOBCAaFR7rgBBKi5IaSexI22ILUIipvHeQmR9WzsxovR5g6jFCfxww3gu2i5AbxYG4X178eYeRpj+hn8SBPCd/GNBPriiSBbV6h4yVVI30MtDSNibUg9jjBKSD2OYi2glqbxoq344Xrshl2ohTFKa96NU7ftBdm+p09FYRxj4lGMqcdRMwNBPK+VRVgZ/HADijWPl+hAWFn0dA/Cd1Hzw+C5+OE63NgyzPGHwHeRionAxQu3IY0kijUfxGogEU4BqZm48Q6EnUHqsTNDbAW/+0sivJofQTh5ZOys4nRC4IfqAhe2Z8Hzz9N/krGxYQBaW89fEK9ChQoVKlSo8F+PieAKO8rldgRJML9Gg+YNPg+NCg7PBTPji7p8Lm8LRI9CoYDjODQ3t1JyS9ieTUgzEQjqI7X0pQcJax3Mlq6j7K7AUIvYoS72hVZSpee4ua4BxXWZLc3x5S7BUMJHLTvMN2rMantp6J8gqkU4sdBLykzQEm9mobRIwSni+i4batcQUcMMZIbIWBlWVC9nW/1GXhXZAfIok36Gw806NQ1dhHwFKSDWO8jis0+Q2nkNocuuxDmwD29oEG/8uwjTZDGsUNo3wfKxHrhmJ4dWpBi9vJEtDy9QPz2M7jgUIyqOM0UTUVpiMZTaevzxMfRrrifU3AqqStQwuHbkMf5m/7+QLVjUmSEWy2k0ReOy1guZKc2xq3Ebt626meZrG5CLC8j0IuWoyalTX0R6DhujbfjxTkRslPqhWWRNCL1kESkU2KSo/EvfCT71uc9zwTvew92jeY6PjdHR3oW+4wKcRx8E1wXHRi7MQzQKxUIQ//Dcen6AkAmehzcxgfXD76Pf+FqU5SvAMPEnx5GFwlLBOR9R38jgpZuJGTaKadJz3S46Hz9M9eAkUVewmDCpkpGgaF9zCyIaw+s/BckE7sApsB2EooCi4M9Mo196JRQK6Dt34w724w0NBkK1AM9VMC0bs6Di7j/F0NW7iOoRqs0q9k0f5LrOqwhrZxRGEQ4Teud7sB9+AO/ZvUjHxbj0SvQrr0FpDObm62pW0ZXqIHd4H1c9Psru9g7+Reg88cTdXDp4Cu89KSYKES5lkubWagQgGhrxTp3E/smPUVevDfp/HmzP4fHxp3lqch8Fp0h3qoNLW3fTneoC4IILLuHb336Ah7/8Of7y//wB733qQZafPMI7tuzk5rWbCNc34h0/gp/NoCSStMVbCKkGljdPSLPIO3EcX8X1XSJ6grwT5M0aryxVkVRLkm1v2kI5U+ZKCVMixMlFyWRBUh2CphiYqqS3P0//kX66Ez6tpocQOkZYZ3FkkdF9ozSta3xBdEZXEiKa5MkJOLkoeGRMkLcl2xskLdHzlyPpSsJvbfPpTcOeScFkXlJ0BK4vmS2B5QrKHmRsyVhO0ByT/Np6ySeeCly8ngxiHBoiUBMSFB3J/cOCR8YV8jaApC4ieF33mazjgQzceUohaUh0RSIRtMYhYwlaY0Gxu9kSNEWDiBAIxOyUKWlYMqK3xoJYiCcnBd5SUbrFclBg77qOICJCSknayhDWzl1pqykqSCg4RRQBb17tc2I+zzNTJgvlKkzNZnmyn47EAKcyLvVLQvLZOI7D3/7tn/J3f/fn1K5r49Ufeju1ZxV9z9hZOhJthLTQC7b9v4GKGFyhQoUXIBB0ejqdnk6/ajOg2dT4KmEpmFM9HrphC9uuvYB7e/+aH3753/nKVz7PF77waWpq6rjssqvZdfnVrL/iCjpqm/7b4yE8z+P48cM8/fRjPPXUo+x58kEWsnl0TeHSDR384/+6kRt3LafOHaa4vhs7kURKPygetnAI16w+/a2rlOeRRhwv9kIXphAC84qr0VauCsRgx0FZ1oG6ag1CPf8Y1NbWc/DgvuAX3yFy/LMYU08gtShS0QgN3oW+cITCxg+8qCgK4Fatwxz9MX6k5XRfhZMDVcet3Uy+dhPhk19ELYwjfAeQlNtfg/0S8Qtn4zRcQLk4TWj0XrTFE8GkM1SLV7UGY3YPfqiW3JbfBSOOuthL9NBfo88dAKEiww14sdbAoasY6PMHEXYahB5cHLlFhFNcGgMXLduPm+zGbroUaSTO2x8lP0L08N+j5oaDfGI3jzl2H8L3kOpSXIVZjVOzGcXJB1nAC0dBNfAjjTjVG/AjjUgjjj5/EKvtOpjxwS3h1G5FmzsQFLOTLlIPY7W/BqmFCPV/O8hjNqtR7BxKcQKr9Wr8aAsAUg0F4y89EGe+XoVvLcVs/Py/co8fPwzA8uW/mAiKChUqVKhQocLPjnieHaI+Am9cJbl1RbDqSD1LfMpmM0CwJL4+UkcylGSuvEBztJH2RBtzpQX60u04fh3VoUGEgLU1y1gWj7C3GGbLqovpLN7LI90+x+OzNGQ9FNenvy2GowpKTpmoFiGmR/Gl5MT8KRzfJq5HsTybvFPg4tZdDGVHydl53r7mDexo3IxwPYrNLeQPPYBWYyAiCSwpSY1OE/EU+qoMYkqZZaFa9AsuAk3DP3EMVqyit6qEVS7QPm9T9WgPxm1X4BYO4dolanIumu0R8mCsVmNBOlT1nyRihkHXEKaJCJ8RgC5vvYg9T3+bk0ODIHPElQhNjd0Ybc1Ynk1tpJaWWBALQKoKf2QI5eF9bC6M8bA2iVPdgWaGOdIeZV6xWTucJzFbBjPEn9e38gfZeS797pdRv/tl1tbUcePq9RxbnMd9di9KsgqlvgFplfHHx/AX5gM3sKIEVb8UEcQ/JFOQTiMX5rDvvRt/fAS5uAC+h7ppC7JQQBbyCM/DvP41xJrjuHMnALCSMXpuuBAjk6f/6GNcPuCwylwZiKjJFF42i3f8KIyNIkNhRDyBqK5BlsvI2Wncfc+AVUKpWo7/xCOBeB0Og+fhKwJb0wiXXOIziwjPQ6oqpmpQ9ixszz5HDAZQUlWEbr4NeePrwPMQ5rmGhrAW5h0rb6X3648gMlnm2pO8ftmFtA+v5ZsH78X53B3cdMW7uaGhePozIIRAqavHGxlG5nOIRJLnI6XkW73f56HRx4loYUzN5MmJffQs9PHeDW9nRVXX6bYu2biNbRdexf5wmC8e2ssfPXgPf/34T3h99xreunw1qz0PgOXJDnY0buXxsaeJ6UdYLG+n4PqoogpdSVAXkUR1Sc35k/3O/9kWgnAq2KAL6Eq90FFcM5omUpiitv5cIdKMh8jPF/FsD8089/pgPA+fOaIwmg0E6pOLcM+AQtyAtTWSnY1Blu9zrtnnCGmwsRYm89AQhdlikNErCURuAShCcGJBsm9aENGCgnSdSUlYC7KRBdCbFny1R6FnMRBuW6rAkXB0Fv7sGYUr2nx2NcF8KcgwbovBzEJwhg0liHuYKwUC9XwJhjJB8bqsDbYPNy+XJIznxhDeuMpHVwWHZhXSVuASvqlLcuUyeXqcu5LtPDW1n4ZI3WnxvOgU0RSNhmh9MNYh+N0dcf5i730sWDbt8TAhtcBAdpKmaAOb6zecM14PP3wfn/jEb9Hf38tvf+QTKBfXcGj+OGpZJ6SFmCstIPG5pPUCFPEK7xL8D6MiBleoUOEleUQvkhaSGqlyTLMpCx8PeMAsYq0M8+Hf/Tgf/ODH2LfvKR5++D4efvh+vvOdrwGwfv1mLr/8GrZs2UF39yo6OrrRn8u9+gXgeR4jI4P09p6gt/c4e/c+xZ49T5DNZjBNk61bd/HeV2/h0i6DbVu2EzHP6ssCKMXp4P9CwVp2HVpuAC19At+sQbgFhFfCWnYDfvQ87tzyPMbM3sA921UV5Muex8V7NmdnBmsLRzFmnsGLd5x2n/rRZrSFYxjjj1Be8aYXbcduuRJ94TDa4lH8UC3CsxFOFrvpEtyqtaBo5Hf8EdrCcZTCONJM4VRvhFeafSQUrK7X4TReGMQsFKcwxn+CMfaTM2Mfb6e47g6EX0JxckjFwDdrEdINhGG3jFqeBwS6U0D4Qa1t4fuo6R5w82jp3mBiLQSxvX+A1fk67KZLX3AL3By9DzU/jFu9DjXbHxwvBPnFBBNzXzVQi1PgFvHNFAgNN7UKL77sTCE9PYHIj+LGO7Bar8SceAQ8Cz9UhwxVY9dfQGnFW/BjbUHusgRj4mG0/AhSi2Atu47y8jee7p9btRYv2oyWGwiK2QkV4eQRdgZn2Q2vfLx/Cvbvf4bu7lVUVVW//IsrVKhQoUKFCr8UaOfRFpLJKlRVZWJijIQR5/LWC/lu3z0MZUeI6zHqw7WM5DaSMFSaonU0xxppijagCBjPK8xc9jranXH2F+8hbPtITWGiIcZ0yMd0ymTKU4Tn00jVJhavJWNnAagyU5S9MqpQEQhao00M+2PUhFOBCKIrmDe8lmLPQzSOp9EjHqrt4Bkaxdokjqni+oHohgQKBQiFcGurKTkDhMMxsk0K8akFuh4+iLZvAKPk4UUjlGMSs1CmpaBwfFmY1OIM2cHDJC++hljzufNo78B+bnlgmNHZAnqqFrPsoB7u4VRB4cTqCMviLadfa9/7Q+wf3oVfLHLF7BSduVGyxhHSNXFOVQtmN6+ibn4UpcFBJBK0+z5fiCUpKoJ8ZpHGzdv50NH9tFg27r5nEPUNiL6TiLoGlNZl+Plc4Ap+bm6nGxCPB05c1wkyhOvrEQ1NCF8i02koFFDDYUhVoa5dj/nGt7GlPMChmWOkrSwpM4GUkiE1j1ixglVzJZjNQn0D3tgo7sF94C0FsKoa5LL4pSLFWJh8TJDpfZJ40SGWrydqW8GdBj8IeVOfuy0hJb6igGUx4i5wdL6HqlCKh8ee4KLmnaeX4Z+N0DTQzi8b1WccYnY1xbVtdEdCRPUIN3TpbEpp/P7Dd9Px8N9z6+veAmcLyZaFMAyEbpy3zeHcGM9M7acxWk/KDMTi+nAtJ9N9PDT6ON2pTspemcOzxxmjD3WZZFXe559ueiPj2TRfObiHbxzcy38cO8gV+UXe+MZ3cN3qG3nP/tt43bOX84i5wL91lilVh0hFIuQdjckCNEUFZU/iy9PRw/9pzLiJoii4totmnBlDu2iTaIyj6sH7x/WDKIeQCo+MCUZzQR5v2YXBjGC+LEhbQTG44ZxCf0by3g0+5nkuLRqjQd6wGoGJAqdvR4U0WF0t0VV4akKwoip4K7U/z3sT0yVPTQqqQpLacOAaPjEvGC8Egu5DY4Kj80ERtueKzhmqREpxOi0FoCMpmSwKQgrYvqAhGojYFzefK5rHDXjnWsls0SPnBKJu8nkLKS9tvZCehT560/3UhWuwPYd5a5HtDZtZVbX89OvaEs28b/O13D1wH+O5UQquYFVVN6/tvo76SC0A/f29/NEffYQHHriH3bsv5Z/+6YusX7+ZhXKaeN89HJ3vIW1nqQ5VcWXb9exs3PrTnPL/UVTE4AoVKrwoHpJ+zSEuBb2ajScktX4wnZhVPPo1h3uNPO/yk+zefSm7d1/KRz/6SWZnp3n00Qd4+OH7+frXv8A//uNfAaBpGu3tXaxYsZoVK1bT3b2K+vomYrE4sViMaDRGNBonFoufFo1936dYLJDP58jlshQKeXK5LPl8junpSYaHBxke7mdkZJCBgVOUy2UAEokkmzZt5447PsgFF1zC5s07CIVChI99GnPiYdyzhWDpA/IcJ6pbu5nCht/EHH0ANduPF23Bbr4Mu+WKF4iTanaQyLF/Qc0NglBBephjD1Bc827c2i0vOr7NzS2k04tkMmkackNBZMFZMQQIFWkk0BaPvfR5SnRS2PC/MUbvR188im8ksDtuwmq6GH3mGdTcEHgOWqYPtTCB8Mv4Rgq75QrKnTefKS4nJWp+OIg0kBI3tQIv3nX6eP1IA36oltizn0QtzQRCs1BAemjpHkK9X0Fx8vhakPeLUPH1KpTyHEp5Dnz39OulGgkiFdQQUi7FWJhV2DWbkaFq1MIY4ZNfxA/V4FaflSnmu+jzh/FDteA7GFNPBXEUajg4j1KC76Fl+vDKaQQOEoJ8Zd8+c6yAsNNIPY6MNFKq245Tvwst249EQRoJpKIHkRHhetBClLvfgNV2DUppFmmmgsfPQhoJSivfSfjkf6At9izdgtexGy/Garv2Jc/hz8r+/c+wbdsFv5C2K1SoUKFChQr/dUQiETZv3sFDD93LO97xa1zTfjlRPcrj40+zWE6zqrqbsLEJKZvoOEvA8WWgwYbqasj+ytsYuOsxVD+KG62hFFIxMiPYpRzSg3jRY9Eoky8WUZNVEAqxaGVRhTgtlqTtLDE9SpWZOr0PbeMWFt/5ZsYe/QHd5Qil6gQLnU2s/vaDdPVOU10YwGuWiLq6oGZGOIKWSKEtqDi+i6GFEb5PXe8IM/iEUJCGDpEwZUUQKpSZU8p8eoOJ21Am1r1A5+EvsK1xM52JNmqNFPb991BTVlgIJchnFyimkmiWQ+LpA2xY93q2LDn//KlJ7J/cG2Tzzs2iZXN0R1rIFxZw0gY7JsqY8yXKZYspWcAul6jKu0TQiYRjRJJV5CIRHp+b4kM1jfjjY0FBaCFgeAhRVY26rAPP8/DSC2BZgESx7cCNi4BYAq17FUosjrJiFR4SpXUZ01deyIheRG9qZVVIsi25icHMCE9O7GGyMAVA0kzw2g230t2ewPr3f8V59EFkehFZKkEkCuUyeC7SdXFKBYZjFiUjQpEieSdPeXgvGw1BRDeCbGLPQ/E1QkLF0RRG68M8vnCI4fwEhqrTqjXzvb4fc3Suh1/b8Hbqlt4Hr4iliIyYHkWYZ65hblu9Fj89x8cOPk3bg/fw3mteg9A0ZLGIvzCH8erXnuP6PpuJ/BRFt0Rb7Iy4L4SgJlTNQGaImdIsXz3xHY7PB4Ww3S5JfHSQmyYW2OXW86G6Zn7jTe/m3qZGvv7AD7n99reQMlLc2vAGXr/yjbw5eTGrTqh8diM84QkcX1AblsQMyddPCgoOvLrzpXODXyk1HdVULUsxPzhPoiGBaqqUFkv4nk/b1lYQgj1TgvuHBcfmBYvlILu4OQrFOIznBYuWoCkqWbAgokN9RHJ4Fo7NC7bWS4oOHJkXzJeCrOJVVZJ1NYL7hoP84qQhKXqCqCZZURUIzHNlWLN0Q2rJh3Ma97k6h0tC82wRRnNB21IK6sNB7MOJhWDb+XKw4iGSgQULXF+wPCmZzAfH8d71HstTgRh9vptgz1EXgboXeW5FVRe/sv7N/GTkUUay4xiqzo2d13BN++VoyrmS5rqaVayqWs5kYRqBoCnagKqo9PWd5DOf+Tu+8Y0v0NDQzGc+8zVuuOGW007j6lCKd617M3OleUpumbpIzQuiKf5voyIGV6hQ4UVRgKhUGFMd8kJS7SsIBP5SQa1aX+WEbpO1fJJnxUHU1TVw661v5dZb34qUkrm5GU6d6qGv7ySnTvVw6lQP3/7215iYGH3RfRuGga4bFIsFpDz/F7Ku67S1tbNsWRc7dlzIG9/4TlauXMvKlWtobGx+QQYTBJEHxvQzqPkRvEhzIGTmhvAjTTi15975c6s34FatB+kGS/zPF9IkJaHBb6PmhpbEUTUoepE9RbjvG+RSa+BFcobWrg2KPxw/fpj6FpOg6sTzvpF9J4hCsNKohVGkGgoE2ue5TL1EF6V1t1NaElyFkyd67J/R5w4uibWnEE4Op3oTbs06lPIcoYFvBQ7X9htASsyh7xMavhthB8sUpR7DansV5eW3BSIuoOaHUbODQeG355bMCBUv2oo+fwSQeMluPEBLn0R4FngOwg0ygvHKS9v5+EYCaaZAMxGuT7n1mtNtevFOtIVj6FNPnysGIwKR1i2h5UZQ7DRSiyCFDvhIoaKWF5bGx8UP1eJUr0db7AmEXj2ODFUjnDxKcRqr9cqlYxG4NRtxkysJn/oSxtD3EE4BFAU33kVpza/iJZYjzSq8lyi859ZuJh//BPr8YYRbxIu14qbWgPLz/7otFgucOHGEd7zj137ubVeoUKFChQoV/ut5zWtu5c/+7PcZHh6gvb2Li1t2cWHzDizPwlRNHh5V+UoP5OzAUSdlUOSpJiRZUy0ZzKaRVSnyThnNNJDSRdg2Fj6OJlCiMZqJcEzO4hdmkX6YsmdRH65FV3RmirPMlxd5VccV1ITPXXW0fcdN7DMXOVCYptnS2fGDxwiPT5Mqe4SGx3DHpyGRDGLAEgmMeBWNVgMDmSEiJRdX1/Bdh7KhYJoamiXxVBWSKdJigZFqjXIySrR9BQcL/Tx4dB93DfyIDTVruMJcwYX7nsadn6VW2FTZFv5UnsX6KlrDSXaZmzEfe5Ji7wn8qUm84UGUrhX4szOBY7dcJlp0oOgiq6tYGDtFyS0TKzioIYMHXAvVh25DMOtJ/uHRe+mKp3htNAWFMizOI1UtmKoPDeBPjDHXVovra5h5B831MbwyJipEImjrNqAs6zw9djIep3+qh8+XPfL5EiIN1WaK13a/mjeuupkdjZsZzIyiKxorq5bTHGvEn58D3UDt6MQvN+NPjAVmjVKRtOZSNFxUWyLKNklfML06Al2dVB08zoIsEVEE+EAsjkhVYRQL+IkER67cwEx5hvZ4K6uqu6kN1+D5Hj3pPh4f38PrVrz6Fb9flaYW1GXtuH29qN2rEELgeA6ZwWNcuG0NN7fV8g8/+jHXNrbSGokiVA1t6w6Ma6570TZN1UAg8KSHdlbEmuVZJM0Ej40+zZH5E6xIdmKoBn7VckYjx/lxbZrVuWZq29cQ3bWbt7W08bYPfJTRvzrJN7/3Fb4++xX+7SefoTHaxM3tr0dz3kjXFRewvCVw3AoBUwV4aFRhV6P3gsiInA0HZgSjOYjqsL42yMU93+Xhc6i6yoab1nPi3h4WhhbwXJ9Q3GTllSto2dTMwVnBl44r9GcCZ7D0IW1D1ha4ftC2ocjTMRuKgLAGnhSM5SRtMfj3Ywqn0oFQC5LmmODm5T6OJ/jWKQVfClpikq6kJGVAb1GwplqyuiqIa5goQJUZiL8lFxwfttVL+tICX0rmCx5+yUEpWSBCxFSNsKYS0yGsSXK2YM6DpCnJ5QSGJrF9iabADV0+G+teeoxeKWtrVrGmeiU5J4+u6C+INTkbTdFoi7cgpWTPnif59Kc/xX333U1tbT2/9Vuf4D3v+U3C57kZIYT46W6G/A+nIgZXqFDhRREILnBCHNLKOEgkS5WPhU/MV6j2VV6uLpwQgrq6BurqGrjwwsvOea5QyLOwMEehkCefz5PP5ygUcqd/dxybWCxONBojHk8sOYiDf8LUGLRHGStMEtUjrK9dzfJk53kF4LNxazZRWvlWQkPfQ8v0glDwYm1BHECk4XwHAOLFoy2U0gza4km8aHMgBC9t48aWoeWGgwzc6nUA+PNzOHuewuvrRcTjtK/fhGmaHDt2iIs23EzIrAlE6iVxUliLQfSBlMT3fAylPA+qgZPsprzy7XjxjvN0KPizbow9iD6zHze5AsUtQKYfaSRQi6N4ya6g2JtnY0w8hNV6NVqmj9Dgd5FaGK8q6K9SnsMcvhsv2Y1Tty1oX3oI30WUF4IMZS2CH6o569gV8B3cqrVIPYaaG0EpTiK1EG5qJUp5BqknCN44fjDrEWpQXO15eUxSi6CWpp53fCpOw25CfV9fyhoOlrwJaYFiLOX55oOibzWbAgFXj+IKHTU/AqqOUpyEJRG83HXrOTMUc+w+zNH78aLNQSE430bL9hE+8Xny2z7+osL+Of02q7CbL3vZ1/1nOXhwH57nsW3brl/4vipUqFChQoUKv3je9rb38NnP/gMf/ej7+cpXfhBkrArltEPtwmbJUBb2TSuM54NtqkOSW1dK6iIwklOpD1czZI9zKjOA49rYWPgCVOCgOo8pFSK+QcSCutp28ppktjTP3ukDbKnfwKs7r+bVnde8oG+t8WZu3/B2jt/1WRLf/SFNA1No8SSR+moUM4QsFqFYxLjyauTiIl5fL91V1SjlaUrz0xxb1YCGYPWJIo6p49nzaIs+hbDGohFk0rYmWtgfKmG5NtWhanzfx3JsHjr8LTJODyPtOk7YoC1v0L0g6R5JY4Rd1H/+NCVVoCSq8GdnkIMD+JYNjnM6p/e5yAQ7LSlqNuMXb2bZ4SF8z2V/ROXxw+Ok5+aJ6BoXd3bysc51GKf6kJoGjhO4ZATg+1jlHCOehtHRQN1UFrtc4qHlYaY6G+nKKaxpbmH5WYHQi/PjHE3kCOsRWuMtSCTj+Um+feoHNETqyFg5XN/FVHVMNYhPcI8cxJ+dRl23CTHYj5wcZyGioCoeRs7CjhkIz8dwBZPtSSYNm4mVddR1XAmHDlI1rhGbmA2cxLaFWtdAzdt/lfUXLuPEqe+zpvpMvQlVUUkacY7Nn/ipxGChaRg33Yr/hc/h9RyjLDxGF4aZikie7kpi72xFezLKH6an+NK7PoHa1IS6cg3iJWIDV1evoDnawFB2hI5EO5qikrPzZO08V7VdyjNT+6kykxhL46QIhbbWtfRG+xnbcAOtTduZLy0wNX+SiBJmw+Iq/nD3J/lY/R+zd/IZvn3yTr7T+y1mjv8DsUeWkbn89XTvei2NK3ZQHzE4tSgZzQlqwmfMSPNl+PwRhZOLgSDrScFDo/C67qAA5EsRq42y/c1byU7ncMsOsdoYZtxESnh4LIh/KLlKUIRNBA7enA3TRUHMkEgZxDNE1CBC4TmPlKnC3YOCEwuClSmJrko8Cf1pwY+GFN6/xceTkqPz0ByV6Ar0ZwKH70UtPi0x6ExI7uoX5OzAtVsXgdd0+lzbIfm3owonxossjk1TlAk836HBmaA6s4DctBlFRFlXAxe1eBybCwrUNcckCV2iq4L2RFBM7+eJEIKEEX/Z1xWLBe6557v8x398mmef3cOKFav5q7/6NLfc8mZCof87i8H9LFTE4AoVKrwkF9kRBlSHL4UzjCseERTivsIa12BB8dnphEjIny1UPYiFiL38C5/HQjnN549+hZ6FU6hCxZMeD48+wU3Lr+PKZZe89MZCYLdejVO/AzU7AIqGm1jxikS+8yOX/j1/DJSzngN/eorS5/4Jb2AAEYkgbRux52lWNbZw5MhB/Pf8JqUVbyLU943TsRBSC+MmlqMtHEZqMdxkN8Kz0OePIo5/hsLW30fq0fN0SaLPPh3EXmghhDUPvoMfqkMpz6KUZ/GMBL6RQLGzCKeA9pyTNdF1uhk/XIdWnkWbffa0GCzVEEphDD0/htTCoGh44XqknsBNrUTqMfS5A0v5vB140Va0+SMooRpABgKu0JCKifCKeLH2oOCbFgbfQS3NohQnEG4Z4RaxG3a+4PCstmtRcwOYg99HCoFwC4FjOtwQFG/Dxw/V4lZvOCP0CoGX6CS3/RNBlIWRQIael4vmOxiTjwUREeaSG0Y1cRPdaLlBtPSJl4z9+K9m//5niMXirFy59r+7KxUqVKhQoUKF/yS+hIyM8lt/8Hd88PbX8d3vfo1bbnnLOa8JaUG+5u5mj7GcwFBhTbWkPhI8v7JqOUKolLwSCT3GoptGlwIpBHUyxCo/RZ+SIYTKlbkoRmo1SlUNeadI72If13dcxdXtL35Du/HUBKk9Y3heCpkKCpvJbAbCEYzLrsQbGULtWoG+cTP2g/fD5DirWzaycG03oV3rqb33UZJPfws3X8LyFEShgJmFo2tiKB1dLHa3s1AapCZUhSJUZktzDGaHGVaGeHqnSbLs4+g+CQc2UyI6XmRZ2sYtFFDq6hCxJErbMrz5WfyxEQQgrXJQSM2yUKprcJwCuu0ys3Utw9dcwETPPsadcd5VpbJpwqPc3UlOsZksLZDIZYNVYm0t2K6NYtvodpm5qEk+bhCvrWK0sQ76+8gagu91w4qMz6P5vbzaX8Hlfhv+7DQLdpbB1c3ULrmtBYLWWDOH547zqf3/jOs5eEvXDA2ROt68+lZWFAoIIRCKgqhvwOnTsdJTiIiBVgZNqJR1j5+sT9C7qZblJQNlbprpHbsYaPTYsuFdNGcNvFMnQQi0HbtQ6xowRh8HgkJtZ5toXN/DVM3nn/KXRVu5mshvfhjrwF6eePa7nOhqQNm0lVR9LSHXYuINO3n8X+6nv7mZtWs3vGx7UT3Cm1bfwtd6vkNfZgBk4Ba+sHk7l7TuYu/g4+jPu+wSBIXSbM/he30/4vGJZ8hYWUzFYF3Nct4x8TrqRT27mnezq3k3v3fxX3PHyBP0z93J8Qe/xP7vfgrNjNC05iKiK66gT7+U9dVb0Zaykh8eFRxfEKyskkv7lkzk4YeDCutqPOoiL31MQhEkm84N5y17QbE3TwrmSoGzVxL89KQk7wR5vUIEqwDWV0vCWlBgLmlCa1xy/7BKYyTIAQZQBSyLS8Zygok8vHuDz90DgqNzClkbupKSa9t91lTDY+OC4wvQkQhE5JIDUgRRFO0JeO9ah/v2PcJjhVpmkkmWKQVWyXnE7DS5nlN4nZvZUCtZVwPrap4viP98YjZ+GjzP48knH+E73/kad9/9bQqFPBdffAVf/OJdXHnldSjK/51F4P4zVMTgChUqvCQmgneXklT7Kt8JZbGQVEuVecWj1dd5lRV9Xp3kXzyPjD7B8fleVqa60NXgzvJEfoofDf6E9bVrTmeevRTSSJ4j7EkpGctPMF2cJapH6E51oisvftf6OfxwPW6iC33+MK6xtFZIStTCKF6oAXwHbf4whYf24A30o65ai1CDb2x/bobVqs7Rg/sAsJsvx02tQls4hvAd3EQnof5vgVBOF6OTio6bWomWOYW2cASnIciLFXYGpbyAb1YhjSTCc087bQPR1ggiGxCnv58VK40faUQacYRX5nw2byk0hFt4bpAI939zKcs4HmQA+x56ugc31oG19XfxwnUIJxe4rpdwq1cjjRThk19EsRehPA+KimdUIxUdP9aGVE3M8Z8grHRwPJ6FFGogJBcn8SNNZ527BIUN/xsv2krk2L+i2BnAR/hOEKuhhfHOLvLnuyilGaxlr0aG6/DC50+kCgrvFZBqBHwXtTQV5BorBsLKBDnEyZXnF+D/G9i790m2bNmJqv78C9NVqFChQoUKFX7x+DJYmj6Sg6cmFIazAhm7idWXvYUPfuh26uoaueSSK8/ZRlVgTXUgAj8fTVGJ6hEiegTbs0ARhIWO7vqEVZ1lfpxJUaLgFfHjjYhECoCYHsFQQxyeK5G2BSEV1tZIOhPnLvF2nngUfImoqsbLZZgwbSbqXCx7iJpxjS4nRJWmoW3ehrpxCzKXRZghYqEQzb09lHqGkCtWoWQzaJkMslSEkMni5V0MLo8xXhxltjRPybOIaGEKdpGsnaeo+PgRlbqyQv2CQ96Ak/UaLe0m4cUIdZaKPzaKPz2FUlOHUJVAWCsUwLWDzkdjYBgIr0whZhKfmmdyeSP72wxcv42Hamqo2Zdh3VyZlF0iZ01zoFFhIuxhykXSEYHl2ewou4zUhwl5EnxJxs5Sivi0ZgUxR0BrK+mpKe7O97Nq0qIp0cTJdRuZ7gzRfta5EkIwkx5jOpdjdzqKqRnQ3MqInOdbvd/jwzUXgKIgLQslniDXvQzr8DTVGZeioTLflOLRlRG+u1KQEg4dZQPhe4znJ6iL1LG8ejlaYxRt5epz3iMrq7pJmgkmC9M0RRsQQlBwipTcEtsaNv1M72OlsYnZi7byo9DTVJtriBuB2SekmWy6ZBfHvvgo3/r+V/mDtX/+itpbXb2C39r+G5xY6KXklmmKNtA+msH79Ge4+dgBhu1ZrG2bmdy6Bs/UmSvNkzDizBTnuG/4IarMFN2pTkpumf31x3AzDr+Vux0troMPNaMqN7Rdxg9uuIwrqv6O7MgBRg4/TO+Bh5n6wZ/x/m/+Hr8Xi7Nr18VcsPsyjsSuINW2Gf2smL7GKPSmJafSgrrITy9+GgokTJidhbIbCL6qCP4m+ICuwIoqydpqGC8ICo7g5CJUhSSvXe5Tv1TcLfw8jVNXgsxfxw+cxO9cK0lbHrYH1aHAAWx5gcBtKNCdOrPtfAn2TytctcyjeWGIN5z6Fq+ta+EuZzfPaB2M6LXIqiQi57I7nmdL/cuo4L9gyuUyTz/9GPff/0PuvvvbzM5O097exfve9yFe//q3suysqJYKL6QiBleoUOFlEQhuseJsck0OaGUWFZ82T2ObE6JO/tf+GfF8j2dnDlMVSp4WggGaog2cXOzjVHrgFYnBZ1Nyy3yr9/vsnz5EwS2iCY3lqQ7evPpWWmKNL72xULA6X4danEJbPIpUw4GwKkERaWKHPgWegz7eT6G1lfJZS8ZETR2rw1G+03MIy7IwTRM/0oT9nPApJWppNsjaPRtFBySKlQbPIjR4F8bkYyhODl+LYjddjFO9gdDID8Bvwjer8aKNqOlToOhINYSaH0H4FlbrVaDogSNYyCCu4TlXgO8gfAsvFUwilcIo2sJRnJpNCOkFbTh5fBrxQ9V4sWX44ToKWz6KPn8QUZ5HhmrwIk1ED30Kt2rdkst2AOEWEdID1aC47n1IIUg++aFAwFZDeNEW3MQK1NIM5uj9lFa949wxUE3K3W8CoWIO37NUoM4GLYyVvAHFWkRbPBpkPUsPt2o1VvtLL3uTWgQvtgx9di/a4nGU8gxID6UcxHUI30afehK79Rqstle9ILf5v5JCIc/jjz/Ihz/8B/9tfahQoUKFChUq/Oz0p+H7Awon50VQjAnYVBcsre58x2fJZuZ557tu4etfu4edOy98RW2mrSymanJh03ZGcuP0+v1Um2FC84ukvRKFwgIhwyIbEihdK08bFMquZCgDi+UYtWEFH3hgGF6z3OeqZYHQJaXEn5lCxOIokQjjY8cYcHI4po4qJDMLY0jfpLWtmg4IHK3J1Om+uUcOQqGAtnZDUA/EKoOq4vWfor4APelTaEJDAHk7R9rKYCo6GhqOIpGmwpEWBdNWiRddTFTuSoRYvU+hrn8R1KX6HuEwFAsIQ0e0rkZOToCuI3Q9iDVYtQZ7ro95O0PJLQWiuVAoRHWO3nIZ+ckiE0NHGJEqVnUSOT7KfERgeIINExodMZXxiI+iWXQKl5yVJ+pARljMyjxlew61xsCJ++y54CLevO3tRKeeoNB3D570UIWK7dnMTA8yvzDOqpyBSTQoEHfiKC0tbYx2wMCaOCtWrwvGTVXxM7NkTYHbVkNfU4ip5Y2UqiPEmSQvHfq0LEpVilo1xM3d1xMzosE5Gx0ORPnqWpTGJlpijdy0/Dq+1/djTiyeQhBkrO5q2sbu5h0/8/u57Fk4nv0Cd3HEjFC7soUD+/f8VO3FjRg7G7eefu+U/uNzUCzSnGwlP5XGu/8JypPjPHl5N6qq86r2Kzgyd4KwFjp9LRjTo3R0tnOyPETviQE2jq8GJH49XH2tz5Ai6JlX8Wp3kLhiOze95rd4U7dFafRZnnzyYZ544hH+5q//iHL5d9EjSdrWX0Lbhstp23A5te0bQSo/swdWVeDiZsmPBgUK4HiACpYniWiBKLy9AX57u89wDoYyAlUJCsTVR8Dzg0JuJxchYZy5aTNVhNqwpO2sy8fU8wzfGQvmy4Kq0Lm9rw7ByUWYKUGtbSEdh7Ch8Ab3AOv8KU4q9XiORfdiLztaaglp//Vi8MjIIA8+eC8PPvhjnnjiYUqlIk1Nrdx88xu5+eY3snnz9peNjawQUBGDK1So8IpZ7hks94z/7m4gked1I0vkixabO287UuL3n+LAY99gYWIfncs6cFcsp4xHz8Ipvtbzbf731l97WYewW7WG/OaPYEw/hZofQaph9Nm9CLeMG+8AoaKoPcRjJ/GVVmy/+bkOsCaZwvU8Tp48zsaNz4sgEAIv3o4+s/ccZyxuUITND9cRGryL0MC38UM1uJFmFDtDaPAurObLcFOr0RZPILUwUg0jQ7VILYLwyvihauzOW7CbrwDAqduOU7MFffbZwPWLQDgZ3KoN2I27AVCcQuDY1SJI1QiyggHhFFBK00sO4jqkEcduOhPXYYw/iFKew61eC0LBrd+GsLOouWHcxHKcxt0Y4w/iRVrwGi4GIZFabGlW46HNPQsr3vpC8VUolJe/AbdmI1q6JxB9kytwq9aj5gbR5w4g7CxefBlO3Y6gWN1LIQTWsldhjtyDmh/BD9WjlGYQXglfNcHJo1hpwqe+jFR17NYX5un93PEdhJ1BahE4a8L14IM/plwuc+ONt/zi+1ChQoUKFSpU+LkyW4T/OKYwVRS4vsT2BbqAUxMltrnDdEsL582fYeirb+Ntb3sNf/d3n+f661/7su0mjTgxI4rlWqytWcViOY2PxGsIYWTmMHICDYWUFmbOL9IkPXzfZ+/0OCWvjt3Va6heykqdLATL4NdUezTHAierqG/AffIx5qsiDCYl9WkXXXpg28i6BAfW13AyvsC7z9M3mc+Bdq78UHbLzJRnmZrJYnamKLllLM/GlR6KUPA8DwBfCKQCwvcp61BIqMQcQRIDQ1HwfQ/FNBEEObbSMKFQwLjoUrz+U4h4MhCHY3GcUg47XGS4Jc54boyCW0IVCqurV1KfaOIp+zj9qkZMayNqRLDcPGk3R6IsGarV6ajWaFt0GagT9OdGSdiCUK7Ao2ujRGPVNETq8HyXASvDXZn9bLdfxa7GbRyYOUzPwinKrsVYYZJcehoLh4V4lJIMEZEa0rZgehKvVsMxFMy3/yreX/wxzpOPkfAdDjUbHFgeIRxNongWLJRpCsOqfISO2uXUbL6RTe07aI034y8uYH3zq7jHjyBLJUQsjr51J+Ytt3Fpy266ku2cWDiF4zm0xZtZbTbDI49SPPQsANqmLei7LkTEXj6jFQJjTlWoirnSPM1nmWlmSwvEk0ncvPOK2nnB+0ZK7IcfgGIRdcUq4sCamlqmJvuIjk5h0s6qDVfRnerkiYk9xJ63gi8UMim3O0xvyFAq+MgQOGsk8Wp4nys5sSCYKUpiRhB3kDJ1qN/Ftm27+M3f/B3svM2nvrKXe/Y+Snn4IR7/4u/jORZGrJr6NZfy0MVbyG1Yx6pVa1m2rPOnWrF3cYukOxUUbis6gWPXUAVhTWKqsLpaIgR0JII4h7NRBKyr8dk/rfLkJDRFJa4v0BV4TZdP8iUSP6JLxd8KTuBIfo6CE0TRxHVQmltQqmvwZ6bRWlrZ5E+wyZ/AG+5DaW0j3Fj/io/zZ8XzPPr6eti/fw8HDuzh6acfo7+/F03T2LnzIj784Y9z5ZWvYtWqdRUB+GegIgZXqPD/KBJJXkgUIPozZv7+d6AqKhtr13Hv0IPUhWvRlgTCmdIcSTPB8lTHK2pHSon9w+9Ruvdu1LFDbJcexpEFptctcurq7XQm2ulPD9GfHmJ19YqXbc+PtVKO3QaAMfYTjImHcavWnC6s5tetQRl+mpA6gE1zIERPjLN2WSfmfpOnnnrkhWIwYLVcgbZwDDXThx9uQPgWSmECp3YzbrSVcM/n8UM1p8ViXwuDUDDmj5Db9CH0TC/awnF8I45btw031oHilYKohLPERalHKa7/dYyxh9Bnn0FIibPseqyWK5BGEgAv0oRvVqGUZ/GjLae3Vcpz+KEavPD5JwXCW1qed1aBOGkk8ENVCN8KKiEIBYSC1CPnvE5IHymUFy9DKwRu1ZpgrM/CS3bjJbvPv81L4CWW40caQbooTjFwRocb8cO1KHYWVwuD62OO3o/ddBmoP+XNEbeMvngMYWeCXOOqtaeL/p2DlBiTj2GM3otamkHqEeyGiygvux7FLfDD73+d9es3097e9cJtK1SoUKFChQq/1Dw7IxgvCFZXSY7NCwxFUpWbYbboM1HM0l0eJxwqcuMNH+Pkvn/k3e++jfe970N89KOfPJ1jej4ieoQLm7ZzV9+P0IRKe6KN4/MnKZQy1JZcJjXBSjvGmiGVw9kT9JYzqFU1WF4TK1OvofqsKK3GCPQsQm9a0ByT+PNzyPExvMEB/BGLhGEjVIOppEG5oYn8pRcy1BRC5kawPPt0IbTnUNs7cR68H+fgfvzJCUpuicMJG7VUore9mpJbpuAUSRhxJJKSU6bol1BQ0BU9mB76EuF7SHzyOuRVkzElTb5BozpfRITC7Kma5WStQowYu5aH2Fl3IeqeZ/CyDvvKA/yktsj8ihr8hjgpPcLlrRcxkhujOpSi6JQYz03gS0ljrIG8ncdRBKvnfLKax7aBEiFU8jGVzrSPShFbFexvMzm6sYWGSB22bzOem8R2bXoX+/jTZz7FZa27eeua2/hqz3f48eAD6KrOhkKEOaEyGSpyxJ9nu1ePa2hMWkWilk97vBU5MgOlEvrFl2MmkrSG8zyjnGK8PI9SXYOqqOx0q3n71teR2nkpSjzIpZVSYn3zqzhPPYGoqUFpqQXLwn7oPgiHCd3yBtriLbTFg/m8LBUp/duncQ/tR4QDMdU9fgT3+BHC734fIvLyEWlxI8aVbRfznb67GcgMEddjZOwcmqKSlDESySA6Im1lmMhPEdJCtMdbUV9upV2piD82iqg+U+sjWo6w3NiA66tsju9Ar9+AL30aIrUMZIapDlWdfm3eLmBqJlWrq7FS5wqqIQ221L+4kUjrhdi3Qnxg8lK0tkvpXfcxtr69zEz6adInH0YOPcJXPv+3/HMmHbQXCrNixWpWrVrLqlWBQLx69TpaWpadV6zUFLhtpY/rK8QNSckVmCooQqIIcd4oGAgiIL7VK3hyQsEjEHFPpQW7GiWvXyHZVPfS5qioDjsaJD8cVDBVScqEogsjecGWOkl7AhSRRL/6Vdjf+UZQ/DwWx89lEOEI+jXXcyzTz9OT+5haihvZ3byDtTWrXnK/L8fc3AzPPruHZ599hmef3cvBg3vJ53MoisKqVWvZvftSPvrRP+GSS64iHk+8fIMVXpKKGFyhwv+DDKkO9xsF+jQbBVjvhLjWilAnNWwk/apNSUgafY0mX/0vzwR+Oa5ou4j+zCC96T5MxcSRDqZqckPnNTRGXtldSq+vF/u+e/DCJpNtVWhCI2FD45F+0m31TK3rxJUueadwehvpeXg9x/AG+kAI1OUrUVetQTwvkF6x04F4Kc5MbpSOTsgMoMyM4Y4fAykRyRTJm2/j4tkJHnjgHm6//QMv6Kdbs5nimvdgDt+NWpwExcBqvQqr6/UoThbFzuGeJcwC+EYKLTeEQGK134jVfuO5x/4iYyKNJFbXzVhdN5//eTOF1XoN4f6vI7IW0kgsZfz6QWzCkrgs7BzawmEUO4sXacSLNoMWRljpM+5c6aNYC1iNFwaCbmo1MlSNWhgP8n6FAM9CWIs4rdecIxD/p5ESNTeAUpxCatFAkH3ugsV3kGYKJ9KEcHIY03vww/WARLgF9LkDQZSGcgJj/AHstutfXKh+Hkp+jMiJz6Bl+oJCd0LHqdlAcc17kaHqc15rTD1OuOffQIIfqkG4BcInPkfo1FcoiCp+8sA9fPAtr0IpTgXidYUKFSpUqFDhfwwzRdCFRBGBQ8+3LPxsGs1MUkjUIaI2lkzSNHaID/zaB/j8BRfzp3/6exw4sJe///t/p7V12Yu2fXX75ViezdOT+1CEQneyA2Omn2Vpj5XJLnb49dQZIa4aOcm4HyP6jnfwjcHVFJwwUgaFrVQRZI8KAb4ftGv/8Hv4c7NoO3fjjp1gWk7TuegRNiKcesOrkYkEVn6SuBpGW5oDj+enODRzlAUrzfLsLN0jgyijowA4uqR1EUaa44zW6Hi+hwCydp6QZqIoCrqv4SMJqQaO7+IoHr4QIAWaD11pKJkKSenz5MoIR7qrycU0kkqEjGtxp3+cie3X8pat/4vjp57mrtLTkGhhWWM3nvQZzY8TN+K8uuMqDs8dZ7wwhSc9WmJNrK1eyejQEapH00jPZzqqUIiarFmwGa7S+Mm2KhJKiOko9FUJDFMlY2WYKy1g+Ta14Wo0RSOqRXhifA8xPYah6myuW09HchnuxKMslBawDYtBJUcWh7xwkDGbtVicSg+y9dQYlEuoncsB2ChDNDthjo3vx/JSdL7511nbsQ1d0fGnJnEOHwxOWiSC/ehD+PNzMDYMiopS34BSXYO79yn8a647LRwDuAf24x4+gNrRjQgFRbWlVcY9cgj3wH70iy4973tNSok/Poo/NgqGyRUrtxA3YjwxsYfZ4jxrqldyUctOPjRxD51bOvjx0IM8PPoEi+U0uqqzPNnObStfS2u8+cU/LLqBCEfwMxlwQTsB6oSAkoOwdSI/iuGuAqVa4dLWCxnMjDCUHaUmVEXZtZgpzbGrcStdyY5X9Nl8DrEA4S8qqDOC2mWS92uwfx5OZUKYF1/K+rdewtrqjwOS6elJTp48zsmTx07//NGPvkehkAeCoukrV645LRA/97OxsZkLm+HYvOTkoiCmS9wlHffSVp/lqfP37ZlJwUNjgrqQZFk8iJgZzYHtQXNMvuSlieVB7yLURWBDrc9gWmG6KDFVwaZayZtW+ShL2xtXXIOSTOE89ThyZhp95Sr0iy5lb6rM1498kZJbJq7HGMmNc2TuBG9d83p2NL50sW3f95mZmWJkZIjR0UFGRobo7T3BgQN7GRkZBKCuroGtW3fym7/5O2zdupNNm7YRe4UO9QqvnIoYXKHC/2NMKi7/Hk4zoXjU+yo+koeMAuOqw2vLMb4XyjOkOrhIYlJhpxPmlnIc45dIEK6L1HLHxl9h/8whBtNDxIwYG2rXsq5m1SteIuKdPAHFAuaytcQmJ0hbGcKRFCzkqOkfp6e7hqgWoSESOCSk4wR32J94BGnbCCSYYfQrrsa8+bbTmWvAkngI+M5Svi8Iw0TrbMVpX4mxYRMiHEFbux61o4urB3r5+Mc/SCaTJnlWtlqwocBp3I1Tvx2lNINUdPB9tNwQ2Dl8RUexs4EjeAnFziD1KNKs4qdFWGn0mT1B3IWewKnbeo7D1mq/EanHgugHaxEv0YndchV208UAqJlTRI5/FjU/vNSgilO9Hrt2K8b0U8jyHKgGwkrjJTqwWq4KhirSSKnrVsJ9X1/K+g3EX6d2C1bbNeAW0ecOoBYmkHoMp3bzudEZrxS3ROTI32NOPopwLfxQFW7VGkqr340XawvGUo+h5kbwIs2BSOzbKNY8ws4h1DBIHzyLcO+XARV72atefH/SR0ufRClOYw5+G7UwjptaA6qJcAoYM3vxzWpKa997ZhvfxRi9D6Q8M/bSQy1Ng1vkgfFOCpbLbatKRI79C4XNH/mlKWpXoUKFChUqVHh5akLg+AIpJY0RGHSKLCpRXMVA93MMKdUkpcOWUj/ecZc77vggW7bs5Nd//W1cdtlGPvCB3+P22z+AYbxwhZKpGtyy4kYub7uY+dIC0dk0sR/9M2pdI8Jbmi8IqK5vJzU2QcSKsbU+xFdPCoazkHNABeJm0M/OpMTPpHGPHkKpb0SpraOhuZHY6FNMVBdpKQoSmRLjYZWMnePqZZehKiqHZ4/xlRPfYq68QMSSxL67F6FkaEtGCUmVvCwgFSg11qB5ULKLeEIikShCQRGCkJkgY2WJG4EQZHk2hqpjuWXq9Co6k+1ktFmWOQNM1pQYjfnsoBolV0Spa6HYuIJ9s4e4cOsuDug1WBP1rKo6M69dkeqid7Gfazsu59Vd17JQXuT+4YfYM30ARag0ZhxmpUpPCgzXx/QkfbU6fTWCA7UuxALLjK4Y+L5PSAuhKRq1kRp0RUMgaI41kLPz7J16Fk/6JMwEilBQW9qoOrbA1lKC+yMWi6JMY0mhRdRgVjXy1RPfAqeZdb6PmJrETy9AsURifpZdC/OI2AJ69mt4V8zhKQrOfT9CZtKARKYXcU/2IOrqUKIx8Dz8kSFkNou6rB2Zy8FZYrDX14sQymkhGECYIYSi4PadPK8YLB0H67t3BkJhPodQFJSmFra94a3s3HoHrnTRhMaePU9w6lQPb/7AHXyv70dE9QhdyQ5sz+LYfC+l43fywW13ENZCL9gHgNB1tAsuwvrW11CmY6hjVfgRC0ftR6/uIjy8Fu8bCoU7fLY3bMbxXR4ceYy50jymanBN++Xc0HU1yk9pLNGPCNRJgbtKggLVwDUpuL4X7BEoXn26hzQ2NtPY2Mxll51+ECkl4+Oj5wjEx48f5q67vkG5XAIgmUzR0tJGVU0jIt5IJtpEqqaRdR2NtEUaGBpspL6+8QVC6N5pgSagKvRcD2BZHHoWBcfnBfUvUtBuKAtfPaEwnAsyh6O6oLvK54JGqAr7dCaCLOPTRyYE+rad6Nt2nn6s5Ja5d+/fIyXnfJYGMyPcO/QgG2rX8v+zd95xcl312f+ec8v0sr2vdrVa9S5Zkqtc5I4LppjeQkloIQVeCOElCQmEJISQFxISeq+OHcA2LmDLvcnqvWzvOzu70+e28/5x1yvJkm0ZjDEw389nPquduffcc8/MaM997nOeX34my+BgH/39PbOiby/9/b309/cwNNRPuVye26+6upb587u54oprWbt2A2vXbnhGJ3WFF5aKGFyhwh8YjxlFhqTDYtdEzgq8SVfjoFbm82GHslB0uDoBBGnh8UuzQI0nudSK/pZ7fjJVwQRb2i+A9tPfqX4ulOOgEGhC0hFvZ+fEHtLlaQI45HPTjBbG2Nx2Lq1R/061s2s79v33IOob0eJ+bIKXnsL+5Z3oi5agL1+Fsm3cg/uwBiZwh0OEkzuhoROkjlYYQYXrcFe9lWBy4Ul92bLlKj7ykfdx7713ct11r37GPhup3QQPfxt9ajcg8UINgEB4Fo6QeGYCaWWQxVHK7VceF6XPEFkYIbz78+gzh31Xs3IIDNxFqfN6yq0X+c5fqWG1XoLVfCHCLaK00PEsX9cidPAbaLl+nMQiP/7AKWJOPEmp7QoKS96BOfYQws5hN1+I1XwhXrR17vhWyyW4sQ6MyZ0Ipzib9bsO3BKRnZ/BmNrjR0ooRUgLUG7dgtV4Nm6i+8ycw8oj9sTfEDx2E0roIE2ElUbL9GCMb/MduG4J4RTQcgPIwjhKmui5AXALKCM+m7lcxKleCXqQ4MBt2E3nooxTvx/CyhA+8FWMiScR5RT69EG8UANurBOlBVBGBDfchDmxjVLpFXPuYGFl0Ipjc5nMAFq21xehZYCbH+tn2bx6OhetR58+gJ7agd147vN6rytUqFChQoUKvz1W1yvuG1IcnYGWKCySaZ4kiJqdmzd4Wa509jPPGgflu4A3bjyXrVt38ZnP/D3/9E8f54c//Cb/8A+f44ILLjntMaqDSaqDSdzCAAWpgfu0tWGuC1KCptEWUkyXYKoEYR0czy8ilaxXNEVApUp4E+OoYgF3oA+9qprltUvYO32YQmqY/skeJuONbGpcy4Vt51Byytxy9HayVp4lVQupOzxAa05wpEpSiAVZJZs4po0SypWITuUQbhRHOdjK87OCPZdEIEG1CBGbyhFWDmvqVpCob+NQrp9d43vorltIsGo+ue4lHGifz9Gee4iUimC4yHmd6N2LSIQjDE9NMpAdYiw/TmR2FZunPIpOCU34KyCnyzNUBRMkzRjXdV1FqjTN4emjqPIY5foktaUC6w9O0Zqy6K812V8XwBWgPGd2rKvI2XnKbhlDM1BKYXs2C5MLCOkhHM8lXZqmKphkopiiNlSNbJ+Hls1QGD+C5jhsyEZpM2vQl61E1jZwdLqXR40JFg8P4h0+6E+B0yn/fQuF0BYvA8+j/O2vgfLQmtuQi5b4x77zdigVEEohnrphYBh4YyNorW3IqqcZRswASnmnfIZmhM1BOUrh2B2Y0qBgFzk604cUgvN6HbrvfgSjrgnZ0gaug9fbQ/n73yT0F3+FkUiilOKLX/ws8+d3k2txkVlJU6QBAF2G6Up00JvpZ//UIdbWr3zG74t54RbU4Dh84zEsewQ0DSPZRXT5m1AygL5foA2A2y44p/ks1jesZqqUJqQHSQR+tTgBmcVXWZ92iaFCCjku4DlKxwkhCNe2U2PO4/xVV/KWpP/dcl2XgYFeDh7cx6FD+xgeHmJiYpSxscOMj9/Pk+Mj3H6CWAoQDkeor2+cfTTQ5zVDtIFCYxPhZD1mKIZuhsiVIwxEA0wEQwRnH4bhG5NKDnxnv6Q3I+iIK0wJ02XFzgnBwirFWXP1yxWWZZHLZSkUcuTzOfL5/NzvxyZ6eXT/Lwl7AQatXdilMlbRIpueZmp0gq+n/p5C4fjK2lgsTltbB+3tHVxyyZVz/25v76CtrYNI5KWlMfwhURGDK1T4A6NPc4goOScEAxgIckIxLm3OsUMEZl+rVhoF5fGIUeIiK4L+EnIH/7roHZ3Yuo7K52iK+pOS/tQxRHmaTFcLL19wFZe0b567K+nu3Y3y1JwQDCCrqnHGRnH270XrmE/x21/D3bkd5TqU7QKFQInEpmECHQmc5GJKHdfgPk0IBmhpaWPp0pXcffdtzygGB4/8gNDh76JnjqGEBkJDK47jhhv9yIVSClmeQukR7OoVYBeIbvt7nFgHduO5uPHO5xyTQN/P0GcO4iT9HFuZH8aYeJzY+CME+s7GatpMueMalBkHqaHkyX+89ZlD6JkenNj84zm4egg31IAx+SSlBTditZ7+ggXwC+adJus3dOzHGKmdOIlFCBTGxHa01A70iScI1K3Frl1LYfHbTolaeDrG6EME+n6G0gJ4wQbwLLRsD8Ito2eOovQQXrAeu2YlSo8g8/04sXa8QAIjfQCl+bffneQSnMQC8Gz0XB8yP4SbPDUjK9hzC8boA7jRToQZR8v2IawZjNROrMbz/IxkLYhw8gg752czSw2lh/H0CMLOz7m7ZXkahEbG8rht2wDvv+HsuWgLrTjOr1aSo0KFChUqVKjw26A5Cm9c6nHLEclwTuDGqrhg8BHWlybpDuRp9aYxizlc10Ffunxuv2g0xsc//mluvPGN/NVf/Smvec2VXHzxFXzgA3/F+vWbTnss2dSC1tGFs2832oJFCE1DeR7u8AD6oiXIljb2HBA0R2FBAqbKfkREddBfUn5gChY/+hBufx9qJg2xON5AH1W19WxqaiQ9r4ZLzn4N9c0L6E7OR5c6B6eOMJIbpT3WihACzXYRCiJakDEvz0F9BksoMrqF8EpktAAREWBaFUFB2AjRUTTZfP9hqjIWu+ZDOrWdiaFDyPZ5NEUbaY4cj8lKL2hlWDaCVWb1vIvQZnNvPeUBClMzaYk2cXS6l5HcGMcyfWSt3NyVjRwbo3jvF3GPHiERi/PODWvY3b2G3j6XxLHDrBs0MUbKTJUcoqUi1TNl7lpnsm+BSU0wias8koEEVZ5JzegEtbkc3YFGIt1hslUwVUpTH65jc+s5fO/g/zCYHaYmVE1pYScDkRmiRZ329nXotfWIgD/fTAbimAePoVB+9INdBteZM0Bo0RiyoRFn/15UIY+x1ndv2q6Nq/nOXjU9jTJNME0olcB2kAsWIkLH64YA6MuWY99/D96Un0UM0JcZ5Dst44yETNxD4/TM9OMoh8VVvsDdcvdDaAWd7uRidCFAN5CdXbhHDuLu24M8+zy++c3/5o47fsr/+/zX2WYdO6W4m6kZeMplIDNER7yNqkDytI5QEQwSuvqthB7egm0MQTyCWb0YoQdRHogREJmT222M/HoFzrwaAAU28FQtcQUiL3DbThXOAUTBj5fwonDXjOCuPkG6BEIKmiPwim6PFbUaHR1ddHR0cfnl15zShlKKTGaG8fHRZ3iMMTFwkNTkKLtzqVP2vx/4Pyf8rus6phlAaDqWp6FrGlLTEJqOlBouGg9qGmGKc+Kv4zjPOjZCk5ihAGYwgBEKYIYCGJEAdYtaedm6q1m+YAVtbb7Ym0xWVVy+L1EqYnCFCn9gJD1JST/5TqZCYeMXk3v6YrOQkuSkh4X6vRKDtWUr0Tdswn7kQYRu0KBp1BVjuOdcy7rXv5dgVe1J2yvbQsjTnL8Q4DhY99yN88SjaPPmI8JhlOdh9RwldShK5Kr3Qe384w7a03DppVfxzW/+N67rnlKFVuaHCAzfB56Lksac41eWUn7UQaiGcuvF2I3nok0fINj7U0R6L0oLo6d2Yo49QmHZH+NULz/doX2cAsbkDl8klTqyMIYxsQ3h2eDZyFKKYO8taMVR8iv+9LRFz4RbBmWfUlRNaSbSzoNXBp5n3pNTxBh/HC9YB1oAfeJJZH4AL1iHcHIooWGMP0JIC1BY/t65rGEt2+eLy7GOuagOc/QhhGvjBWtBCGR5BuG5CKVAubjBBhASffoQ5ZYL0TQTN7mQ7KK3EX/4gyhNxwu3oGYjOYRbQkkTtFOXtQkrizH+KF6wHmXGwNFRemRuLGV5Ci9Yi8wPItwykV2fRSjXj8VovRSr6QJCR76HKoVQgWqUNBHWNF/fFqJku7zp0tV+7rBSeGbilONXqFChQoUKFV7aLKuB7qTHQBY8J0J9fhj5yFaUp2azej2MtWehr91wyr6LFy/nppvu5ic/+RGf/ew/cO21F3D++ZfwZ3/2V2zadP5J2wopCVz3CtR0GvfwfkAgUGjNbXNRZz0zgrqQ7wI+kQNpwVj/BF333IU2f4EfNVAugRnA7T2G7jq0vefPWLD8ypP28/BQs0cCyNVXYYcDUCowqds43hRS0wgWSuxoNSkEJcFAlIDloms6BSvP+scm6Bq3mGhO8urQSgIyQe7gMWq0dv5nfYID6cPMi7US0kOkSlNEzSjKjFDQFDF8Ibg/O0RtqJql1QtpDNdz7+CDPDD8KKZmENQCZK0c0nHZtvWHbDgWR1RVow/PEPz+ERZfdD7h+WfR8ONHCEykMZtbMbQYg3KYmqzNZQctiktq0YMxCk6JmewEFwwHuHFXgH43TcjuQe0a4+Hz5uMsbOSi9vM4u2k9judwz8ADjObHMDWT9fM20jPTh5toQLdd3OFBALJmmY7+afTOBchIFHffbrxSCVFXD+USXmYG2dDoi8O2jatcemb6GMgM0uikqMZCSySISQ2KJYRhINvaMc6/6JTPk7Z0BcbFl2FvvRtnbBQPxU/apxhuSbKobQ2D+RGU8pAIMlaOJdXd1DsBxr1p4oUJWqK+rVRoGiiFKhZ5/PGH+b//989561vfzQ0vfy0jO7/Knsn91IePX2MNZoc5OtPHzUdu5b6hh1lc3c3VnZfSHD21HoaqEmitHRjTHbgn6LwyDSoGXt0pu/xa2CsVTpdAPyxwGxXooI2BqgLr7Ke5gh0I/FJg3ieQM4JJIF0D2tmK7mo/kqE/B989IPnztR514dMeEvAdxYlEkkQiSXf34tNuM5yDL+6S9E9ZhK1xSsU82UKJRbE8lzQXsEsFSqUipVKRYrGIZZU5lvZ4ZMihNuDieQ7Kc1GeS77s4Loul8wPkohFiUZjRCIRIpEokUhs9meEaDRGOBwlFA7zX3u/yZ7UAbqTnehSx/YcjkwfY3X9ct6z6o8q4u/vCBUxuEKFPzDWOkGeMEsMS4dGT8MDBjSHBqXjKkVWKOLq+H/gaeGy2DUJ/R4JweDnTwVf9xb0hYuxd24H2yawfCX6hrOR8VPFNX3REuxHH0KVSscLKxQLCCGQHfOxb/8pIpFEhGeLqEmJnNeJd/Qw9kAao/7ZK+VeeunL+Nzn/pFHH32Ac87ZfNJrWn4IYU37YvIJRemUEUY6WTxV5efLRloJ7/siAE7VstmNFPr0fgK9/+sXSnuGOAUxG78w5zbIHkN4ZbxADbKUQgVr8MwqjIknCPTfjlO1+CShFcCNtqMCNcji2PE8X6XQCmM41UtQ5q+QYaxcPx5B6H58Q2EYZcRmHbVZlBbEC9ZgpHYiC8PIwhjBYz/2ox0QOLF2Sl2vxqlZiXCKeEYU4RRQWgDhZFDSQHhlQIARRskAsjyBVppEGXFkYRwvVIfVuAlz+L655Zu4ZbRcP3b9BtxoO3p6H8bI/WiZHkChjBjazDF/jAClh3ATXejj25DlNObQvSg9DE4BFazxH0Ij0H8reno/+WXvQVppX8AujIBm4hhVfP6eXl5+7mKaEwb69CHcaCt2zernPa4VKlSoUKFChd8+psZskSgd9bo34CxbirtvD8p10RcvRV+9DhEKnXZfIQTXXfdqrrnmldx228382799khtuuIRNm87nj/7oPVx22TVzS8S1zi5Cf/pBnJ1P4k1NIauq0VetmXOB1gRhonBy+64HoAiNDaByObRFS5B19bgDfajpaahrQLa0Ylx82Sl964i30RCuZyg/wrxYG/naBP3L2jG39tGmBLWOwigWOJow2LEgQTBZz+LaBUyWUozmxjEmJmkaztAb1fBUmbJTYoHWAVXdqEMDvPayq/mh1Dg63UPeLhAzY9y46DoKTpEnx3czlBtBoagJVfOK7msYzI3w+Oh2RvPjlNwypmaggPZ4K21DGXZpA/zTCgNPDBFVBq05ONj3Q3JaK68zilQZNsHUIF48RqgqwlRU0JK2mT/h0B8ROK6NU8ixLBOjee3Z2JkBBnLDRIbG2bB7gguv/GMWVS9i/9Rh2sxq/pyzyA3sIRCMElm+ji+I+9h/9BGaB9IEihZThkMhpNOeq0LoOlpTC0II7GIJEYmiLAuUh1IKNA2CQY5MHuZQtpeADFCoq6ZqbIbBgEVsaRudshp3chxj4WKMZStO/SxpGoGXvwp95WrcI4cYsqcoD9/JljFJdGQ7xcA0kXrQownS5Wmydp58WyPRbWNMl9JzYrDK5xGmyS97D/O+T/01a9Zs4OMf/yeEEFzQsonD6aP0zPRRG6phsjjFE2PbCeth2qItKBSPjGxjvDDJ+9e8g5j5tPiAIFibFaEf+JEQXhJEDuQMlC/x8BrO8Et3hqgIFN7qEfyZxNgvoADOQkXpChd3/snbBu4VhH4s8KLg1SnGBwRnbYMWTfDwFQqpQWccDqYFuycFF7c/e8TEc9EchXeu8Ng6aLA31UpQhw0NigtaFWHj9Pv0zEBum0YyoEgEjj9/eNq/MfW+1d6Z1sTmFQtfRm5fniPTPTx1adSZmMfLu65G7xPIlMCLK3+cZi9dbc/mUPoYqWKKmBljcXX3M+ZEV3hxqIjBFSr8gbHMMbm+FOXOQJ7Dmo0A6j2N60pRdullHjSLVCmNkBKkpEtACS6wwnN39n+fEMEgxnkXYpx34XNuq689C33Hkzg7n/SXbymFsi2M9RvRly7H+t+bEMbTfNWa5k/SLOs521+9ej3z5y/g29/+8ilisNJDIA3fWTrrBvUtI37uMcrDi7aiZXuQhXE/wmDuJAVuuBk904MsjuOFT73TDqCMCHbNCkLHbkbNHMZI7UYJHc218MwEXqAGUU6jp3YRKU7ihZt8oXXBjTjV/qTSC9VRbr2U4LEf+7EHegRppVFGglL71c/qjH4mlB7BqVqMOfIAnmj2z9kI+a5gLYQKVPmCbnESffoAwaM3Ia0ZnGgbAtAzxwjv/xK51f8HJ7kIM1CFKKeRpSmE6/hOZuX5Gb4nfsaVh7SnsauWgTQodd2ILM+gT+/zxWkkTtUyit2vxxh9gMiuzyKLKb9tOwNoIBRarg+7eTNuuMmPflA24IFXRpZL4JYo163Hi7b5YxhuwJjaiznxOMVFb6XceilaYQzPiHDHT79P78Sn+Oa769HzAziJLooL3/Cc8RgVKlSoUKFChZc+wjAw1m/EWL/xee0npeRlL3sFV131cu6882d84Qv/zDve8Rrq6hp49avfyOte9zY6Oxcgq6oxL9xy2jY2NSn2TwnGClAX8jODezOC5ohiicwcj1OoqUXW+M5Od7AfWV2NkL6RYLo8w5Njuzg200fEiLCsZhGPjDzBgfRhAlqARxcJqq1GrpyM4+UchqtD3NlaItMQJGiE6K7qpNluYCAzTMCy0VwPM5QkHEzSlxkgGUjQGqghPzbAE30PM2ZMMFFK4bguESNM0SmxpX0z57VsYjA3gikNllR3s2tyHzcfvhXbc8hZeUxpYEiDpTWLaAvWMXjgdnrDNpaYYZGXYEIU+UUyRaRoc6m9FL2ukem6BtJujupILUnDIV2ahlSKVG6M3kweyy6yPGdyYWgxutTpSnbSkWjHqsqhjY7y2Fg/nx74BbnpCc7+5QG6B3O0RpsI6CHcBx/h9Uu6+PbkNMdCZezaAHEV5bIJk5bJEkpOoJrbELX1yNo6vOEBf84qBM6OJxDV1bjzOyjvf5zaWIiwrhMou6SWdFI0FPQexGpcRnDlGgKvft0z31yQEr17EdqChRj/+y06H9nP4SSMRgTmRIlV/Ro9GxZRMBWe8hhZtYDmgwdo7B3Bow5lWajpKb7pWnzqY3/G5Zdfw+c//425IocrapfyhiWv4q6+rYwVJhjJjxIzo6ysXe6/j55DVI9wdLqHnRN7Oa/l1O9BebNCCQjcJ5BpgQorStcqSpf9euLqM+E1QuGPPMQUCHc2OuLplzMlMO8TqDB4fqkZJmP+JUb7Idi/HqbrQApfN829QNlurTF4/RKFp/wrmOcScjvisLbB44EhSd5WBHU/IzykCy5sc89YCAZoi7XwgTXvYvfkPqbLGaqDSVYEl1D7gyjGLoEoKAhI7EWK4us9UpEZvrX/h+xPHcRVLgJJR6KdNy199dyNhAovPhUxuEKFPzAEgoutCGvsIL2ajQS6XJOokixxAtQqjceMEkWh6HIMLrIirHICz9nu7zsiHCH01ndiP/4Izp6dfpzAytUY6zdBMIjWvdCPnKitm1sao9JTiGgU2db+nO1LKXnTm97FP/zDXzEx8S/U1R2/ve0kFuEkutBTu/CMGLI0gZIBhJ1BBatxkwux6jeiFUZ8Z69yOR5uhf+70FCniXY4ETfWiSxNIIop8Byklwe34OcSu0WMiScQTgE33IwXaULPHCW877/JrfkIXsSf/ZQ6rsML1mKObEUWJ7Eaz8VqufjZIyqeDSEot1+FnjmKzPaBZyELI37xteQSlBFF5ofxAkm0mV5kadJ3RQuBmh07Y2oPxvgTWM3nY4w/ijG1G2HnoAzCLqBkCISOPnPIdx0bMYSdBy2A1XIJCIEXbiC35kPoU3uR5RTKrMKuXo6WGyD26F+h5YfAc5FODi9Q7Qv3KISdxxjeimo4G3PicQCslotwqpaiT+3HmHgcPXsMK9bmu76FhmdE0acP+MeNts0Jxf9xywOcte4sll7xfgrS9HOHjWdZZ1ahQoUKFSpU+INBSskVV1zLFVdcy/79u/nOd77Kt7/9Zb7whX/h3HMv5PWv/yOuvPJ6AoFT5/XrGxQTRbh3QHIordCkoD2uuHGhR3V+PoVYDDUxhqj3TQXKslCZGfRLLkcIwWQxxVf2fIdD6WOY0sDxHIJ6gPUNawjrIaZKaZyahTwSCHOgZgn6rEFAmzpIefIAumdguQ5PjO4gY2fQqhOUk4rITJ5cwCRshBnMDhErptntjfKT7HZ6rXGmyxmCuklAM3lo5AkOTR/lvOaNBLQA1eE6EIK7+rYS0AJ0JuYxVZpmspjCVS4juVFawvX0BIp4KOZ5UWpUECHKeHiUpWImojHTWkf9/j5mmquZxKYz3o4+mSYdFBxLCIpOkZA0qHUlExSYh7/CUBMaQTSejJW4Zex+QvEaNg16dPfN0F+jMxkts6lpJUZ6mvCdv+Qd4RCj61dTsB0aVAgjIdlT+zjVRZfaAzsIx6oRsThEopDL4ezYhgiGkG3zGK0NsyNYz5IJF0dojJ6znKE1CyngUBrsoXXdW6hassmPcXgOvJEhhh+9mzvmS6aCigRBCjGByOcJHj1GdNVSomaUY9UzDF2xlmWTDTCWYdhz+PuhHn6xaxvvfe8H+fCHP4GUx1ckCiE4q3ENq+uXM1mc4iu7v8Puib3smtyL5Vpz20ghGMqOnL5zGlgXKaxzFHIGVBTUc02F/djoU0XcM0WAqnnmcnFyBmRG4CWOb5EMwNGgoHkCIhnFdB3YLgihaHiBp+6nSzE8HULAaxYp6kIej40Kio5gcbXiojaPlbXPvf/TiZoRzm4+a+730PcF5kMCt1Wh2kAUFMYOAbrk1gvvYsf4HuYn2tFEnFQpxK6JPr5/4H/5wNp3oP0KhqEKvz4VMbhChT9QqpRGlXPyf7xhJNeUY2wpRygLRUxJtN9DR/CviohEMS/cclpXhXnRZbhHj+Ad3AvxKigVwXUwtlzhV9g9A171qjfyj//4Mb7//a/zvvedEP2vmRQXvYXQ/q9geB7KKyPtHG64gVLnyyl1vQoVrMYxIjixdvRsL05ioS8Mew5aYRir6XxUoOaZD648jMltOPH5qOQSX3wtjqK0INLKoacPopUmcCPteLE2lDRxEovR03swxh+n3HkdWrYPY/xxZHEMu2oF9qKz8KItz3eYT8FNLCC36i8xh+8j0Hcb+swhvEgjbqgeLduLcAoUO67DHH/Cd+YW/WgHhPRFYWkgS+N4kRYKK96P2X87xtQePKGhZ3tBC/jnWUohyylcPYIbbaM872rs+hNy+rQATt3a4797NpFdn0PLDeKGG9AKI7MifQ4lDZAmdu0a9JlDvlgM2PUbcJKL/b5pxuz4ZhBWZq5YnPDKeMbJ2cq7dm3nkUfu55sfvIbwkR+ihMQce5jightxk6fPE6tQoUKFChUq/GGyZMkK/v7vP8tHP/pJbr31Jr7zna/y7ne/kaqqGl71qjfw6le/kSVLVswZGDQJL5uv2NTkMpAVmJpHVwKCOqhkG+ZlV2Hd9r84B/YipIbyXPTlqzDO81ez3TvwEIemjrKwqgt91nwwVphg1+Re/mLdu2mONpKxsgzlRhnOj9Ie8+eHnfF59Mz0oQnJrsm9DOaGMaVJfbKVw6uTnP1QD8bQFDLuEHSKjJhhtm+oJxRNYk+O0BCuI28XGC2Ms7hqAfcOPMTeyYM0RRrQpU5dqIax/DhLavwCzm2xZlKlKZSnGCtMsC99mIGgTVVWUKs0MMHzXDTXxjEMCrEgg+sXExtJUTcwSS4oaCpWU/QMHlzeRGPbApaFqqgL1TJUeICbR/fxFjdJWVckHB19aIBtyw28UJDmaCONR/agQkHisSqmSmkmipO01DSh8jlKbony1BgS2BcR/DI6xVj7DKFIgsV2iPXTiqWxekRmGi816c8lzQAiHCEwOMy+RRqDl2ykOlqLmhV908UpnI4WoguWnpEQDGAfPsBWYxgVj9OUzeMpi7DQmUGS90oIz+Vw+ihhI8zZ57ychpbNfOk/PsO/ff6fqaqq4Stf+SFXXnn9qQ17YOwWhHaaJPJNVNfE6LUGSYTi1IaqAYHt2gzkhujPDj57JwPgPUd9ODEDga0CY5sAB5yVivJmhXf6RZK/MioGKqIQOf/fAG0xxcw4TAjBqOFHsEyUBEuqFCtqFXIc9CP+d8/pUi94xMUzEdL97/ll8xRlFyLGmYvJz4aYAeNJgVenUHH/ORUBt02RPZRhV8ce6qN1jOYXcWymk4ITRqmzuLU4xIbGEc5vbf31O1HheVMRgytUqHAKISSh38xqm99btPldhN71XuwH7sM9chDR2oax4Wz0jeeccYh+VVU11113I9/85pd497v/8qRCcm58Prl1f40xtdcXDs0oTrwbFTwhh1cLUOp+HeF9X0JP7+WpECcn0U2p84ZnXT8kytPo2X7cSNuc21hP70PL9CBL42huHs9MYtevmy1mlkErjiMLY+iT23EjzYQPfA1ZnPALyHkWgZF7KCz9Y5yqJb/SmJ6IF22jtPD1lBa8BnP4lwQGf4Esp/GCtThVSzFSOzDGH0PL96MVx3HDjdi16/y+KHsuHsONd1Jc/m7KmR5i2/4BK7kY4RSQpUnc+HzwPNxwI7k1H0GFnlk8l/khQvu+TGDg5wg7j1YcR3gOSuoIFNLK4IXq8CKtOAiKHdcQHLzLL/43m8vshRtBCyCcvP9eKYUsTaLQsBtOXhr35S/8Pe21Ua5bGsCNNIPy0NP7Ce/9b/JrPzxXVPDZkIUx9OmDADjJ7uO5zhUqVKhQoUKF30tCoRCvfOUbeOUr38Dhw/v57ne/xg9/+C3++78/R3t7J5dffg2XXfYyNm48D13XqQ1B7dMuAoQQmJdfjdYx318dVyyidc5HX7MeEYniei47JvZQFUzOCcEA9aFaDqQPc3Sml+ZoI3EzxtXzL+XHh37C/qnDBDSTklPinMhi1kcXcPP0Y2hSw3MVqVKaJ7uqKAW6ad/bR02mRGTBQh7tTpBZ0MZUdoCZ8gwz5Rk85WF5NunSNLrUiZkRFld3U7AL7E7tp+SUcT2/QHNzpJGsleXx0R3knQJZK0dJF7jRMM5YFiOTJSoVwZogM1GTMi5PhDPsu7CZln02K/MR6F7DA8E4YuE8zg4fn4eLzlU87D5AX+YedEeQcDTO75hPakE9EXN2xZ7noYRAzhoWSsUcypshrUpkMlkOTiqmAoqbEwWyStIoNcLVtYy2LeCrk33c8OQYZ+dzyHgCUVWDymZwe45Sv3Y9S8cP8ujEMbRgmJiMMmNlGS9OcHHb+VSfeL3wNJRtg6bNxX1k3AKDAYtWrZZANE7GymK5Nu0izlBUY3PHFuZXzaM93MrOXzzEpV9YR19fD29/+/v5y7/8GJFI9DQHgeDPBIGfC4QtUIaiLp6EeR6e7uHpCk+5ZO0sMTPKdDnzq3/oAQoQ/qrE2C1QCYWSELhDoh1SFN7t+XEPLxAq7BeUC94sQAevChIFWGfBo8sUg9UCDcXFbR5XzFNU3S8I3iYR0/73TCUkpcs9ypcqXggPlkz5WcperS/Ing5T8x8vFCIPoiTwap5WpD4E5ckytmVTthZwIL0ETbgkzWkcTzJcqOemI1HWNvjCdIUXl4oYXKFChQqzuEcOYT/6MN7IILKhCX3D2eiLzlzI1OZ1os3r/LX68OY3v4sf/OAb/PKXP+fSS68++UU9hF2//ln3d6pXkFv7UYzJJxHWDF6oHrtuHco8tSjeyZ0PzBVTUwBC4lQvxw03oU8fwKlehpbtwzOrfAfw1G6wCwg7izl8D+boQ6hglR8HIcTxwnXHbsJZ85FfKS/4tEgNq/VSrOYLEVYWpQWI7vhn9EwPdt1ahHIQVgY92wdIVCCBG2k52eELSCeHcPI4iW444cJF2Dk/KsMtoDj9TFGUpgjv+QLG+KN+ZpseQpbSfsyD1EAGEG4Jz4yDV0bpIaQ1g5Y5ijn+GE7VYpxEN54exQtWg1NEyw2AGESZccrzXoZdd3zZ1d69O7npZz/jX16/AlG9CDUr6jvJxejpvRgT2yi3X3navgKgFObQ3QSP/Q+yNOXrzmaS0rxrKM+7+rlDxipUqFChQoUKv/N0dy/h4x//Jz784U/w0EP3cuedP+NnP7uJL33p30kmq7j44iu47LKXsXnzpSQSyZP2FUL4Be0WLz2lXSEEQgi/TsbTcJXLwanDTBXThIwQy6oX8Z7Vb+PJsV2kRnto3HOYZb1pbovtoBCdoSEsmUoESZdnKDhFys113FtTS0u4gb87/yOMH7gZ13MYL0xScEpE9QiGZlByLTJWjrgZJaT7mbhhI0xjuJ7D08foywzQlexECknJKaNQdCfns7puBX3ZQfalDrB7QRVn6+3ouiSqRsiVJ3l8dAceLi4KY7nJSMtaGpdeQ/+eb1NzQq0QheKom6I36uHFk9R4AYpmgFsSYaqjETKlKZrCilRXC1X9Y3ilIiozQ6BvH7n0E9jFaZz6KlpSFj9dpRgKOkTLivFQiCkxSWs2SHwmy+PaOGfrOsIMIKRAJBJ4E+MwOcnLjCRKb+OANcNwfpSIEea85o1c13X6OaLbewxr6y9xD+5HBALoG87G3Hwxwc6F6I8HcMp5qgLVhPUQyvMol8fJ1tZzVs1qtt22lb/67z9idHSYK664li996QcsWXJqYbqn0AYh8EuJiincOv9z0iwaacs1UzIt0nIaiSARiFMdrMbUnkUZ9EDrAW1YoILgLFGop+nPxm6Bsc933TKbiuLVK/SDAuNRQfmqF9b1VLpUQRECj0r0Hl8ENTYr1r7CY34YdAFRE/TDEPxfidIU7kJAgBxTBH8qcdtcnF/BP1NyYLIIoTy03iEwd0lEGbyEonyhonyxOjkiwwXt2Gy0Ra3CbefXFqG9avCqFHIK3BMEaJmGmlAV9ck6fjlZi6cEVYEsAEU3S21QI11ayp6UYGPj76cTTSnI5fw6j+GXWLpfRQyuUKFCBcDZsY3it7+GmplGRKI4hw5gb3+C4GvfhHHWphetH6tXr2fVqnV8/etfPFUMPkO8cMMp4qAopTAmts26aWt8gThw3CWgjAhWwyaCPbegjDjKiPgRE8Ux7Lr1FBa/leiuf0NP7UDL9oFngxS40VbcSBuBsQexAmcdFxaFwI20omeOIQsjeNEXePmPNFDBavT0PvTMUZx4ly+W161DT+9HFkbQcn0U619BaeHrTymc5+lhhFPAGH8MZUTwgrV44UaEnUXpkWcVz83xR9FnDuNUrUKWMyjlgJCIcho8F+EU8YwInplElqZQQhAYuRc30oqw8+gTT6JNH8JNdGPXrafU9WrfHaxc3HgXbrR9bhyVUvzd332YruZq/mjLkpOFWyEBiSylnnWotJmDhA5/HyXkrEtbIAsjBI/9GDfWjlOz8ld9FypUqFChQoUKv2MEAgEuuuhyLrrocj75yX9n9+7t3HHHT7njjp/yP//zPTRNY82aDWzevIXNmy9l9er16PozywZSSNbWreCnx+6kLlyDIX0hry87yHBulK0DDxPQTRSKZCDB0uqFFGYmWfKju2idsplY0snepKB9JoSRS2GbOiIcI2fnSRVTNHsR3hLZyHIrwZq6Zdxy9OeU3DJBLYCLi+PamNKg7FpYrk1N6Hhh3aAepCPeRlUwycH0ESzX5tD0MZoiDWxqWk/ECBMLRCk5RYZyo+yO5IkYERYFF1KecJgopghoARoi9XQnOxkvpNg5vofGcD1DuRGiCV/5Spem2TWxF1u5ZEzICougJqhRJYJOkYge4uhML9aiBoJHksT2HKKz7FLrxsgrm0w8iB4M0tMQYCiUI+RJNFNHJJIkQkmGcyPoliJnAPEEangIgZ9JYBmSEW8KzYjxpnlXk1o0jxkrQ1UgSUu06bQrFN2BPopf/g+80RFkTS3edJHyTd/HG+gj+tZ3saZjE3f1byUyNU5Q6DiOxW7bInvPAG/8xysoFvPccMPrePe7/5zu7mdWMEUazEcFgdsF+m5wVgAuoMEC0UEHrQSmA7jdflZwxIhyNNPDytplp2+wBKEfCMwnJKIICIXbLCi+xjtJSNUGwa80d4LAqIEKK/QjgvJTCcAKRAGUyfFyKwq0AZAjAoJgdyt4LhEvAKVXKayLXeQkeNHZYnICkidspu8SiDy4swlvcgy0IYE2AOH/kOTf7+F2P8exnuqmgoeHBb88KhgvC669E/QeqJuvMBtAm4LQjwXKBGuzf75yEkLflRgHBZQVKiyxVymKN3qnCOrPiyCUL1KEfiDQ+sBLgMz5jmHvSo0tizdz+9YSZTdF3i5heTae8lhUvQBPGWTK3q9x8Jcux47BnXdKjh4VSKlYuVJx+eWK2l8ho/k3QUUMrlChwh88yrIo3/5TKBTQFi2dmzS5vcewbv8J+orViGDwRevP29/+Xt73vreybdujrFv3/CpKA8jiOLI0iWcm8cJNaDOHCO/7L7Tc8fwtN95JYekf48aPO5nLHdegFUYwJneAcvztovMoLnozXmIBhWXvIrLjX5HWLpSZxA1W41Qt9W8mCx0tP4z7qxaKOxGn6Mdh2Fm8UL2fsftMzmKnBG4ZNP+2vxeqxwrWIfODSDtPcdm78SJPi0PwbAIDdyMLI8jiGEoLgRbAC9TiBasozX8Fyow/Y/dkrh/h2WilMRAa0krjmTGkUighUWYMp3o5Tu1alB7CHL4XJ7EItABuvBMtN4SeOYJVt47CivedJMo/nV/+8ufcf/8v+O4n34fJAI5SxwVh5QEeXvDZZxTGxHaEncGtPu7Y8CLN6FN7MSaerIjBFSpUqFChwh8oQghWrlzLypVr+eAHP87gYB9bt97N1q138eUv/z8+85lPEI8nOPfcC9m8+VI2bTqPrq5FJ0WZAWxuO5ejM70cmDqMJjRc5TJRSGFIk6U1CzE1E9dzuWfwQR4efpyrp6oJDI2ysz5Mn3WMGaEYq1ZMliXF8gx5PYBU0JIq8sm9YTr0xyj8fD9bli1hd2cjh9JHCWoB8rNRWxEjhEIRMoJEdF+5U0oxlh9nbcMqblhwFUdn+ujLDGB7Nq3RZiaKk/RmisTMKMtqFxPSg7ys63KaIw386NBPGS2MEzdiKBTTpRlmyllqQ9XsnTrAtV1X8KNDP+Fg+ghxM8bOib0UnCLtsVZqQtUopcjZOcYKkySCcW5cdANPjG5nKDfCw+cvYEMux8KpAHo0yUzQokdOMy+vYWZzqNo6LFWgiCKuSwxp4CqPMdPh3JyB3tyKMz6Ol06zt8ng50uKjFWPo9UJGkr30tE/n6JbJCBNVtYtZ0398pPiOwDsB7aiRkfQFi87Xvg6X42zYxvG4QNcfc2fM7FVZ9veRxnc3sfwnhHyEzlqGxp44xvfzlve8ie0PEdNFJGCyJck+iGByPqF1owdAjENznLFfK+di3Kb+HnVfdhlhaHpjBYn6E52cn7r6a9/AvcLAvcL3CaFmgc4oPdC6PuS3F96c5m9hDheOO5EH4Ul8KK+8Kjv9d3KWr/v5LU2KcpnK0K3C8xHJSKvQILbKii89sxEWq+GUyMoFMhRkFO+yIz0hVmtT2DsFeCCsBXGfkH0ixr5N7s4zzU1V9Bzt6D8E8k10xAwoW4I9jUIYi6cFVTQAlq/P2bWOb47OPRDibFD4M5TqDCIrMJ8UKCiguKNv54z17pAgQ7mVoE25RfUs67y3cnrjdVc3D7O1sEiQgySDMRpi7VQH2qmL6uofvEus180hobgK1+RjI4K6usVnge/+IVkeFjx7nd7RJ4hwuPFpCIGV6hQ4Q8eb2wEb2QI2dh80t1z2diMNzKINzyINn/Bi9af669/Df/xH5/hk5/8KD/+8V1nnDmMUyJ09IeYow/NOlzDWLVr0PKDaPlhnKqls0XlXPTp/QSP/oD86g/NZdgqM0F+5QfQp/aiFcfwjBhO9QqU6c+snOoVlLpeiSxN4CQXo4yYX6BNuXhGDGlNgeeBlKAUWn4Qu3r5Kdm0wsqiTx8Az8aNd570upbtJbT/y+gzR8GzkdY0Sui4yYXYdesot1xyksvYi7ahAtV+0bin3L9CIJyin4sbqjtlmIyJJwmMbMWu34iW7UErjIJTQCsMYDWdTanz+mcdZpkfRZ/a44vIKFAe0sqC1HDq1pFf/UHsuvUgBKGD3549sdk1anoYN9ntC7pa8FmFYMdx+Lu/+z+ce+6FXPzyP0Ht/Az6zCHcSCsoFy0/gBttw3qO6BBh50Gc+udeSQNhzTzrvhUqVKhQoUKFPxxaW+fx+tf/Ea9//R/hui47d25j69a72Lr1bj760T/FdV0ikeisgLyO1avXsWrVOubNm8+7Vr6Z7eO76csMYEqDewcfImpEMDU/TmGqnKbolBBCUmVLwnqI6kgtB8ujDOCiCZ0aEQYPspFapqaGMF3B/PYVyFgSNTON8cijvCW4if6axXjKI2pGMKROQJocSB8mXc4wkh8nXZrm8PQxSm4ZXeqMFyc5t3kD13ZdwV19W7m7fytFp4RSCkMziJtRupPzuXzexdwzcL/v+jUiVAerEEKQt/Mcne5lcfUCQnqI1XXLSQYSPDT8OIO5YQKaSTKQIGr4Co8QgqgRpT83yGh+HENovHX5a3GVh7Z/P6ZIo53jG1DC+TEY20EurBOdsog7klI4ylRxChTk7QI5K09zsp3z4lV4h0fR2jvon+rhh3Up8mGDtrr5OAu7uW/kce4YuH9OgH98bAebW8/h1QuvQzvBWOEeOgCJqpOuMUQkgmvb7HroPu79eZo777ydXbueJBAKsuniC3nza9/JpZuvPOVGwDMReFBgHBLYCxWiBNqMRFkKfcB3zXoxwWsGrqFtRRuPNe4hbxdYVLWADU1rqQ4mT23QA/MRgQqDemoBnw5OB+hHQT8gsM/yBU17qSKQAG0I3FmHrpwEpSmcNaDvg8hXND9bt0YhsxC6SWJuVWgpcBsUqhVfbO4RhL87KzY/TwFPFCD4Y4G5XSJyIDJ+P9w6hX4UkAov7gvFzkK/CF3w55LcUm9OqRM5kNPgxZkrzmbcLwh/VdJaAq0KakagYQgKCcXRIqSKUB/295Fp4beR9cfIbVNz56Hi4NUpjG2C0hXq+Lj+KkiwzlNYG2eL6YWZi+gQCF61sIF0SWK5nTREBLYLRzN+Ub2lNS9SREQBjIMCUQS3UeF28oLkNJ+ORx4RjI4KFi9Wcz6eZFJx6BDs3i3YtOm3H4tREYMrVKhQQdNBaijXOfnvgeP4jtQznPS8YN3RND7ykb/nTW+6nnvuuYOLL77ijPYL9txCoO9neKF63Ph8hJUl2PczRDmN3XjenOiL1PwIh+nDyPzwyREO0sCpXY3zDMdwEl2+E1W5J0QWCLxgzWxO8F6QfgE5L9JMef4rTnL1GhPbCB7+Nlp+BJSHClRRbrvcF2CVS+jg19GnD+PE52Ok9yLzwwiniHDLyMIwemoX+ZUfwIv6bgQvVEe5dQvBnpsQdg5lRJHlNEoP+3m4TzkhlELL9iALIwQG7gTP9gu8hepwrBmEW0LLDeHGOmA2a+50iHIaPXsMP3xLobQASgaQxUm8cA2l9isBhXAKKCOCMsL4gvEJjl5AeJafKfwsfOc7X+HIkYN8/vPfwEssIL/kHQR7/sd3eAuJXb2C0oIbUc/hDHbjHTDozkZ7PFXAxEF4ZV+YrlChQoUKFSpUeBqaprF27QbWrt3An/3ZR8lmM+zcuW328QS33vo//Nd/fRbwiyCvXLmOVavWsmrVehYtX8Y2YxdytigZwEw5i+e56FKjEAuihEB3PKpkEEtlCKIjXAcZixNCI+IIjGCEcSNAkxKIZBU4Ng27DnHZjRu4J7WduBkjakSYLs/QFGngsnkX4aF4fHQ7Hh6Lq7tpDNczY2W4+cit7E8dYqIwSc7Oo0uDgDQouiVSxTQN4SxTpSm2j++mOdJAwS6Qs/PEzCgRI8xEIUV/dpDrFlxF2AiztGYRS2sWAfDxhz6NpzwydpaEEBiazlhhgnQxTSBf5qs//mtqZJTl89YTMIJ0FAYwBy2a6ruoD9fSEm0i23+EQkBjUpYRyqQ12kzICFGwCzRE6nn7mreyaEMX1h234R7Yy+42i0x9kiXta9Eamzia7sFV/viamkl3cj4z5QwPDj3K2vqVLKo+bmwRsRhMjAMwnJnhwb6jPNh3lId7DpO69YfEYnEuvPAy3vnOP+WKK64lHH7+NkZ9t8CL+W5RFQWn28/slSmFvlfgNSjsFZJVl65mZWL1czfozkY6PN1FqgNKIIrHxTV3HhRv8Aj+RKAfAhCoGJSv8LBXKiJf9MVZp/t40TYRVQTuEzhdCvWUV8MAp1Oh98yKzeueQcBTIId9UVclwG0DBAR+JgjcK30nc5Pvlg4MCQL3CoTjj49M+Zm7bjMI24+NkJO+wzhwhyDwkEBkBCqssDYpShcpzLsFeQWpVggb4EmoHRE0D0JvHIqO3ymZBa/aF3/FCIjSrEh7YtdDIKf8+AqVADkB5mMC7YhAxRT2GrBX+S7pM8Lg+PidwLIaeONSxZ29gtGCQJdwdpPiui6P0IugSmpHIPxd6UeIeKBCEmu9R/HVyneSv8D09goiEXVSwp9hgOcJRkd/+0IwVMTgChUqVEA2NqF1LcDZvQMRiSI0DeW6uEMD6IuXIFvbX/Q+XXLJlWzadD6f/ORHufDCy06aTJ8OYc1gjj6AF6zBC9UDoILVeOU6zJkj2G4JiB3fQeqgPIRyn1e/3MRCrKYLCAzeiSpNgjQR1gxO7RqKXa9Ez/Yji+O44Wbshg14kebjhyyMETr4dUR5Giex0I9YKI4R6LkZN9KMMuPoM0dx4p1IO4eWG8ALVCMMG+EWceML0TKHCAz9kuKiN8+1W+p8OV6wBnP4XmRpCrvedxA7tWv8DZwioYPfxBx/BGEX0HJ9CDuPG2lDBZIoM4Ei4Wfvnqb4yYno6QMIp4AbbkSfOQiu5Y+/cqHgEOy7FQbuwI11UFjyR9i1qwj03+67eCOtgPCjKaSJU3/WMx4nk5nhn//5b3n1q9/EihX+eTh1a8nVrJgTg91I6xkV5rPrz8IZuR99as9spIRAliZxkgux6l+8POwKFSpUqFChwu8usVic8867iPPOu2juuVRqYk4g3rHjCb7//W/w7//+aQCi1QnC7VV0LVlE08J52HWCsmeR1OOUF3QxvS9Fde8IkVpFQ1nDcTymDYUISUzHoqNoEguFsNzjc1URiaLGRrm+ZhPheA2PjW5nsjRFwoxxVecWLmw7l4yVZSg7Qlu0hbqwv2Y/YoQRwN39WxFCUBVMoBTYnk1UiyARCCHZPbkfIQQBLcCCZCcHpo6QKk7hoZgqp2mKNrK55exTxmZF7VKOTvcSNSNMlaaZLOZJFaYIW4oNk4IaI8j2wAT3991MV7CJlzWFaT64n8ncOEvb1tLtxOm3dR5dlEAFDUxp0BipJ6gHCOshLp13IRe3n48UktBb34nKZcns/x6RzFH0hD/XHitOYkoTV7kU7CIAiUCc4fwox2b6WFS9gGKxyOHD+9mdz7D9yYd4+O6f0JOZRgDLklGOkrQAAQAASURBVNW8cuFSLv7AR9i45SpM0zzlPJ8XBn5UwyxOp8KLK4wdAnu1onyFwl6tThEnn609p8t3B3t1J4i4GV8gdptPmMMLsM5XOIsV+iEBrsKdr3BbgLIfn+BVq5NcoSoKouTHNoA66bh4AlE4/TWCKEDwRwLtSUV5pkQ5WKbQXca6SBG5V0NEBQQFlAUiKtDWSAL7JUZGR0NHdhmoTj+fmCK+QmdA8A5B8H8FXtwvfidyELxVoI2CPiWwa6DsQdCD0SJEBbSMQSysCETB6PUF3vKFHpjgNfhOYDkF3gkLJ2XaHwuv2he0I1+SaH2++CwsifG4onwVlF52wnjZoI2B0sBr5IzdtesbFCtrFRNFCGhQEzzzOtb5PDz2mGDXLoEQsGKFR8vSYUbtXoSQLKzqoiF86opQAApPCcECp9O/QSEyvvjvNUD5ihdenK2qUhw8KDnxs6Rm/UEvhYgIqIjBFSpUqICQksB1r0RNp3EPHwAEKA+ttZ3A9a9GvMjOYPCXl330o5/kmmvO5+abv8crXvH6Z9/emkHYObxQw0nPu5EWlDTQsv04T0UmKIWWH8KJz8c9Qaw9s45JioveiJuYjzH2CMLO4bReitWyGS/chFO/4Rl3NVI7kIUxnKplc3/5vXAj+vQ0xtgjWE0XzOb/BhH5IfDc2XgFhXAtwMUzq9FTu05uWGpYLRdhNV8461jWTppZBPtvJzB0N26kFRXtQGlBzJGtmOOPUW65BKTmRykATvUyUApRnETPHEYrjM4WXluMm1gEykUWJ5HlNG60DaFAlKegNAnKxYnNBz2APnOY0IGvklv7fyl2v5bg0R+jp/cDCmUmKHdej/2UWH3i8No5tGwvf/PXf4tllfnQh/7maedqnJTzfCYoM0Fh+XsxB+7AmHgCoTzKHddQbrscFax+7gYqVKhQoUKFChVOQ01NHRdffMXcKjalFKOjw+zY8QT3P34vdz38c574yb3Y+TIAMqSTrk9gtfezoypOp+5RPzhDazIOzVVEW+bj1VQTVwYTI/dRXRQ0aMdte146jUgkiNS1cENkEZfOu5CslaMqmCA0u7IrVUyTs/PMi51cvDhuxslaeSJ6iKgeJRlM4HoumpCkymkUioJdZE3dCn567A66k/OJ6BF2Te5lMDeCFBIpBN87eDOvXngd9eFato3t5Oh0D47yqA1VMWPliBoRemb6CHuCFZkQdVXNWHhk9BkcTxKYyTJx3vkkQkn0Q4cY3fcYVijAtmU15M5dwSZdYyA3TNgIcXnHxayuW86CZOfJkQ7RGM3V83gsvQ+lFEIIJAIPD1e5hPUQM2MpJnqHObJ/N/81s4dP9X2AY8cO43m+QttR18DG2gb+tH0BZ3mKpFJo87oxZzLo02moP/ma4vlir/OdwKLou09RgA3OYii808N9em3p4uwS/qwvfjoL8BfinUB5s4d+UKIfFHjVClH2YxTK53u480/tg1cHVt3ThD4DioES6ZFJUtkU6bEUqXSKdD7FVDrF5I4U08MZym6ZslOiVC5hlcrk+4uUZZlyuUS57P8slUrYhTIlq4Sjnrau8jNnOFD7/BgFQxro6BiGgfZzg0DJJKrHiIVjxMwYUTNGTMWIH4gT02OoYIxJFYfpGFohRr0bY4kbx+yJkR+IMROLEq+LE3hAQ8U8ypcoyud4BG8TiLJf5E7OAA6UL1QQhMA9Aq1P4CxSs2OvkBN+trK11sVr8XOfA7cJtBEBGjgLFKXrT/N+PgOmBi3Ps1hdoQBf+5pkxw546h7Fzx+aRLXsofGiu9BMm6pAcu6G0NMjFo1DAm3QvyHxVKFAlQCV9W8ulLeoF1wZXb9esW0bjIxAQwO4LgwMQF2dYsWKijO4QoUKFV4yaO0dhN7/QdzdO/CmUohkFfqK1ciq355Ytm7dRq688jo+/em/4eqrX0HwWYrYqUA1yowjrGmUcfx2o7RzuIlulBFFT+9F6WGEnUcFqil1vvx4bMDzQRpYzRf64uvz4CnB9em3gJUWQpZSeJEWVKAKWZzwBd3ZeAXhFPACVf523iie0XRq40+1+/RsXLeMMfIAnlk1l8/rxjtwiiPo04cxJh5HBZKgXKzatYjyNNEn/gZzeCtaYRTPiOBFmvECtZTbr8RqOhdh58CzUUYdSnloxXGE0EEaaOUUrjkPJ96Fnj2GPnMAq+VinOrl6OkDvmCcWIAXaT15HJTCHLmPQM8t/PS+J/n+LQ/zn++/irbgDA4tz2ucT4cXqqO08A2UFtzoP/GrvO8VKlSoUKFChQonoPWD8YRAjgu8RoW1HppaW2hqauHKK6/jffkP8/DQE2w/tI1c/xTpoXGO9BxiYmSMvr2HeHBqdm64HTRTJ9ywk2RjDWZthFhAsGimzFj0CC0NjYhsFgp5jEuvQER8NSlmRomZJytL8UCMkB4iZ+ep0pJzzxfsIslAHE95FN0ySil0qeN4NigwhEFrrJnltYs5Mt3DwfQRZsoZJoop4maUFbVLaY+30pfp58u7v0XEjNCXGSQgDWzloJRiXqyVuBnDcW1qh6dZLOJIBDPCooxHSBpYrk1JWey/9jycoXlMTPSST0aZ176CRs2fnzVE6jgy3UNdqIbuqlNVzqJTojZUQ1gPsXd4P4EUzBwaoP/wIRiz6B+9F6vgC/BGOMCypSs5//xLeNe7PsCSJStYtGgp4XAEZ9d2Sl/8f3hjI8h5nQjTxLr757i9PYT+5P3I5DPXtngurLN9V66xA3Bmi9TFoHS1h9sCJafMWGEcQxq0phoIf1tD7wM8ASbYKxSFN5xQFA5wuyH/To/AvRL9qB+lYF2lKG9WWI7F0FA/fX09DAz0MjY2wvToFOnRSabyU6Tyk6TTU0xNTVIsFk7pr4ZGraylplxDtBQnGAkSEAGCXpBYY5z6xQGCdoBQKUjACGI0BjAjARL3hQjoQQLJIKYWIJQLEjpiQBmU4V/LeFLhNHg4bQoyCjxFYZONeMLFSznYro077eB4Dla1jR2wsVJlZpIZcipH1sqSs7L0l/vI5XJkRZZ8Mcu0k8HySqd/AzKgDWvUH2yg8fYmahc1UNvRQJNspHmwkUbRSH19I1WXNFC1voGwHcHYK/Bq1EkivFcL+kGF3itwC4rwtyQUFF6jAgeM7QKRluT/1JvLNH6h2b5dsHMndHRAMAij+XFy9j6sY/NYtOJcWpcNMZIf4yfHfs68RBvzE/NO2l8U8V3qT7tMVEHf2Y116mu/LitWwPXXe9x5p+DwYf+yr7ERbrhB0dj4wh7rV6UiBleoUKHCLDKRRJ534W+7Gyfx4Q9/gi1b1vGZz/wdH/3oJ59xO2VEKTdfROjo9/3fA1UIK4MsTVDqvBa7YRPG6MNo+WG/6FjjObjJhS/WaQD4LmQhwS2BNitsK4WwMzjJi/DCDZRbLibYczNqNmNXy/WjzDhuYgHCziGcEnbjOWd8TOEUkU4BTz9hDZrQsOs2IpSLU7UUN9qGtGYwpvYSPvgNhJ0Fz8UL1iFcC2FlINxEoP9WnKqleOEm9JlDyNIkIJBOHqWH8PSon8sLs7nJLsLxl+l5oXqs2fiO06FP7SZ08BuMThd479d3cO2mbt60Noi377/Jr/3oaQvh/UpUROAKFSpUqFChwguAvgvC35LIKYEKKMTjEvMRReFNHs5Sf5umSAM3LLyaGxZePbffUG6UA1OHsD2beqOWQEYw2N/LQ3seYvvBbYwMDDK1fYCeiTQ7XJd/4jEMKWmNJWif10n9bf9DzWMPUF1dQ01N3dzPmppaqqtrqYvVsKp+GVsHHwIEMTNKxsowUUqxZd5mDqePsntyP2OFcczZzOCQHmJ1wzJW1y8nbsb441VvYfv4Lr629/s0RhtYUbOEmlAVAkFXopOtgw9haAabmtZjzNanmCimmLEy/NGK17O4agE/PHwEz3HRAIlAoVBKEfMMv16JEEzXRDgiTGqCUQzt+BzNkAbYiif2P05oGMbGhhkfH2V0dJi9vXs5PHCI7NQ0xek8zqzrWmiScGOcaGs1ybULibXVUN/RwvVrr+Hq+ZedtiC1mpwA20LfdN7cSkhRXYt75ADO9m2YF235lT8fKgz5t3kYewVav/IF3sUKp0PxyMgT3Nl3LxPFFEHP5O23v5LVqSWwMACmH4lgPCYI1AlKr/RdlEopJifH6Zvpob/mGP2ZXvoGexj4Ui99f93DyMjgnOtZ0zRqo/XUilpq9VqqA9V0NXQRv6qKqvpaGh+ppeFQDfVTddSoamplLXEjjnUOyBxgC9x5ftauvcHP6Q3eJQjcIxFlwPFdpdZiD7ND4tXPxl0oMB8VyAgQE7jNHtqQ8F3RRYGteYgolC5XlF6lENNg7BIEbxHoxwTufIUXw4+CSAucRoWz/PiYijTIgiD3Tpfg7ZLhuwQjIzZuOEd/PEs6mKF+KMtelSVclSHeOMlYfpSJoTFGcqMcOLSH+8Z/wcTEKJblR91xs/8jEonSqDVSbzbQWN1EfbiReYkOuhILWJTrpt7rIPSoiZgBZxFz0RCOqTD2QuCXgtJ1J0dvvFAcOuSLqU/5osYKE0jDIqCbTA1U07Z8mKZIA/vTh9g7eeAUMdhtUqiwRMwoVHL2SQUyJfw85N9AZrAQcOmlirVrFb29Al2HBQvUSyYiAipicIUKFSq8pOnuXsxf/uX/5dOf/jiXX34t69c/c8ZrueNlgCIwfA+yMIrSw5Q6rqM0/wbQwzhVS1+8jp8Gu2YVds0KzIntuMFakAayOIoXacZuOh+A0vxXzOX/CuWgFUZQRgRRnkZzipRbLqTctPmMj6nMOG60BT19AOeESARhZ3GjHRRWvBd9cgfmke8hytP+i9JA2HmksnBDjcjSJK5rIZwCenoPpfbLCfWUUFoA4fpiL04RtADK8O0LspyaPfaZrZkyxh4GO8effG0Puib53HuvwY0F0dP70Ce3Y7VddsbnXKFChQoVKlSo8BvFguBPJTILzuJZAUgp9CP+87mF3jMqDS3RRlqiJ1jjWmDZkpVcfvm1J21n2zaDg330HtpPz8F99I6NMDA8wLGeI2x78hFSqUlmZqZPaV/XdaqqapBRgydDEj1qEknE6WjqINw9n4VmA+VcmmP5AQoiQ020mku6NnNl/Ra8gkPRLRINRrig9Rx+OfAA1cEktaHjc0gpJNPlDK2xpjkhGKA2WM3+qUMcnDjMktgC6gLN7B7dS7xYwEKR87JoBZtiOclhcxhr3wBD0yMYruTg1EF6i4+Rn5ohl86Qn5qhnC9xG/Cvs+0Hg0Gqamuxwh7BaICOxR0E66uxYoq2rvm8/7L3Mb96HvumDtKXGcAQBotruulOzj+tEAzg9h4DM3BSJJ7QdYRm4A30zT2n8jnsRx7EefIJlOeir1iNcc75z+0cDoC9VmGvPf7U7ol9fO/gzaAUjeF6ansTyGOKbS27WaevRlMaPU4Puws72Pml7ey8ZRd9Qz309/ee5Oitrq5l3rxO2to6WLduI+3tnf6jsYOure3Ebw2gkmDPV35htkGBnVRY6xWRnQLvfDAflmAosAUUFfoxP/ZAlH0HsjsfMGajEe4QqITy4xAUaMcgeLNEBRXCnC3MZs1mGGsgDF809eIKrV+gTSiEA6XrFKXLZgXuJLitCmFL7FVqzgXtxPz8Xv2oX2jPS/pxGNqwoHy2h9sF+fd6POEJ9j9gUNudZLQ6yXnbBV06GLqgtVGxapUCD/QdoJIC6xwPFQS702Oqbprx8ihj/aNMbRtj8tgoE8fGmBgbY6Qwwv7UPvr29FCcNbbIByTt0Xl0hRYwf6ib+ckuFtoLWJheSFd6PuFv6miDiuIrPT9H+AXEj4YQgEJ6sGjEZMtoJ6HpaqbjBbyZEPlEESk0is6pTml3HljrPAL3CVT2eNE8FYPyRd5vRMB+ipoaqKl5acRCPJ2KGFyhQoUKL3H+5E/+gp///Cd84ANv4847nyAcfoZKC9KgPP8GrNZLkeUUnpn0IxBeKughCkv/BLf/Nszxx8BzsJrOp9x+JW5s9g6u1LFat2C1XAyejSyl0dN7EW4JNzYPJ7nkjIqmzSEk5bbL0TI96NMHcYO1SCePsDKU2y7DC9QSGL4Hz0ygl2dAC6I8FyENhJVFBGoAgbSzKOFXubZat2BM7UWWU7ixDqQ5gjG5A09oftZ0tgfhFCi3X4UXaTujbmqFUb5y3zB3PnGEH37sRmris++xAGlNP69hrlChQoUKFSpU+E2iDYE+LHBbTnACCnCbfNFNGwH3zKZAz4hhGHR2LqCzcwEXXX7NabexbZt0OsXU1CSp1CSp1ARTU6nZn5MMjg2SmpogOzDD7t2PcO/3fopt2ye10Q/s4GenRLwGAgGUAE8odF0HKVACkODgMqqb7NFvx7HsuYddtrld/eezntcwAE8CIKQgEAyiRQNEqmMka2qondcEMR0tbvKqda9g48IN1Nc3EY8n+K/bP8njh7bSNeO3JaIhvO5uekNlcl6OsBFifcNq1jesPqMxFrE4yrZOfcGxIeYrk6pYpPj1L+FsfwIRjoAQWLsPwM/2EV/3bpiXwF6t8GrP6JA8NPw4ZafMwqouvw9TLo9OPcC9k/eRemSUQ6n9TM8aNBqCjSyJr+Sccy7kta/1hd9583zRNxqNndK2nIDw1yXBnwpEUaDSCjEtsFcq3E6FcUigkgqUgIAvEouUQDiALZAp0AYEKg6h70nK13i4zb6YKlyBW6vA8t2/eq+fb6xMgRdT2Gtnx6AMsgj2ct9Z7HYAEhBQulBR3qTQJsBL+LEZckIgiuqkOAwAe6nfX1EU6DNACUBh7hfo/yCwNinqzocfHVEsceCSRwVLeiE5LQibCtmGLwTvFhgHwEuCscN3NnvVksjaGuovrmb1/mUYhwEEohGkBzlT0RcV6MLDSA5zcN1hDkcO0/+TI/QdOsqDg/fxrT1fpeT6wquGRvvMPLqOLGD+rd3Me/lCVqxdw9KlKwiHf30r7NKlcP/9ipk0XDIkaDvSwlShgKd0zhmoIXvLGn7xssdAKtpip6mHI6B4o8Jr9DOCRQHsVYryRcdXMfwhUhGDK1SoUOEljq7r/Nu/fYXLL9/AP/7jx/i7v3v2igTKjOGap06QXgqoYLWfXTv/lQjloPTI6cvICglaAC/SiBX59W4v2zVrsRrOJnjsxwTS+3CDdZQ6X06x+w1ouX5kfhQ33IjSQ+A5fuaylQHPAc+eXdgnQEic5ALc5CIKy99DYODnaDNHcaOt2A0bwbP9GI5gE3bTBZRbLj7jErkHpoJ8+NvbeNvla7jirG7/Sc8BJfCCL1BERIUKFSpUqFChwguB4Cmj3sko/3n1G3TanYhhGNTX+9mnZ4JSilwuy/T0FKVSiVKpSLlcnv1ZmvtZLJYol0uM5cd5sP9RUsUpcuU8tm3hei66B4bSaI21EgmF0U2DnCowZk+RiMRJRpIIQ1IWNsIpE1YSQjpmsoqamkbWNq+is7aDNY0riRhh7uy7hzt67yVjZQGIm1G2zNvMlR2XzLl63b4ehnY9QAQbkagja+copfqx0n30LWpgV8NeNjaue0YX8OnQV63Ffug+3OFBZKMvonnDQxCLY6z0Cx07u3fg7tqO1rkAEQwiJ0E/aGPv24M3/BiR6ssxtyoKb/VwO2cdrX0CpYGzUKFOMA8rpdi9dweD2w9woOc+Rg72kkv5ynZDsJEVjWt4//q/YGX9atYU1lC3tJHcn3unFJI7/ZvrO3X13QJlgooolOlHARj7wNqgwFYoDRAKOeyLv8LC/8y6gA7CVVCG4O2C4P0a9goQ06A0/8NubhMYBwRK4h8nADLji4xeIwjlO0/dptm4iyf94mUqDOHvCCLflLht4NUqrI3KL2qmSyjOxhUo3wGs7/MdxtZyDxVUBB/WwPVzbmUGQj8WnHWuYst6xaIvS5ZNgFQC5UFzSaBtB7ug0HtA6SCnAQ3cGpBlUAOw7zOS/WX/xs2iBsXKOnjYhtuPCkYd0KKSeQ0tvOLyJjbMuwB9LUT+SwNHwaBibHSYI+4RDmuHOFR9hKOZIzxw7F6+9vf/je3YSCnp6lrIihVr5h4rV649rZD/bKxYobjoIjh8q6R+n2BCCzEYLKLX9OK1T9HaW0/84RBLr13Eqrplp28kCOXLlV8szvJ//006gn8XqIjBFSpUqPA7QHf3Yj784U/wN3/zQa688jrO3rAJlANa6IwFx5cUevCU64ffFMHeWwgM34sXrMULNyPsLEZqB2Lff6JPH0JP70Wb3ocXbgOp+eKvZiLtHLI47sdBeGWspvOwa2cnxtXLcKqWIpw8SpqgmaAUeJafyyvkGfdvairFaz7+ZVpro/zjK9oR1gx4LlphCCfRjV237jc1NBUqVKhQoUKFCs8btwWc1tll9d34oorni1jOEoX3DLV+fxOIFJg7BXISvGrf8ec9w310IQSxWJxY7MwrXT08/ASfeORfEOUMcTNKY7iehkgDD488jmaEqY014ykPZWVpdmo5p/ksTGmiUDw2sp2jM8fY0LCWrmQnM1aG4dwoRjjI+e1nzx3jvOaNxMwYI7lRakLVdFd10RxpOEnYtbc/QX3WY7gpRLqYZbKYQgQFoVwZJ51i6+DDrKlfxVmNq8/43LSFizGvexX2z3+Ke/gAALK6BvPq6yi3t/Jw333M3PM1WlOH0Gt12kULyQMRZMmEZIBS6ACBxZehHxYEb5a4XYrAVonI+rN8r05QuN7jicAT3Hrr/3DbbbfQ03MEzdRpXNDOks3raFrcwSWTF7J2+3Lm1bVRXVOFnAYVFxQueXYhWGTwhdbZmAZjv+9WlxmBnPFFWRVXyCmBNqYgILDXehgHBIHbQUnAAFF+KulE+TcyykACv/Cd56FNCsgIvIRC3y/AAcKAB6pKQQpkVuB2eLiNvjPe2C2QOT/n10v6lwn6oEDhZ2x7IQh9V1C6BOwFyu97u0I/IND3+CK126II3CuROVBxcJaAHBeIEiAh9Ljgded7BG1QugABehCk7sdK6AcEQhMIFOQFaAqtBF4IHh0R/Ncg2AZ4Cu4aFMxLwGAGTE/Q1qkoNSv2DQhy35D82Xs8qiw/RsPYAcaoRqtso7m5lXOWbMabFf31g4KZV5fY1biH3bt3sGfPDnbv3s5tt91CqVRECMHixctYu3Yja9duZN26DSxYsBgpn/naSdfhla9UpIoesZQk3SJZFI+Q0QOMFzWsuMOV05sxlyUJG8+wgnbuQ89vJCP4d5HfKzH4H/7hH7j11lvZsWMHpmkyPT39nPsopfj4xz/Ol770Jaanpzn33HP5z//8T7q7u3/zHa5QoUKF58Hb3/4+br/tJv7sPa/lgU9dS3VI4CQXUW6/Aje56LfbOaeIOfoQxuQOQGHXrsJuOMd32f4WkYVRAoN34ZkJvPCsa0QpzME7MccfxarfiJNYiDG1G5k5ghtq8AvvuWU8I4obrMWp30Cp8zqspvNACxxvXAiUET3p95NePwMsy+Id77iR6Wye27/9dYLONmS2zy9y13AOxa5XoszfUGneChUqVKhQoUKFXwUDStd7hL8p0Q/4IhOOwG1SFK85QyfnC4DW48cCaIPCX4rvgXmPovhmD+cFqpOsSUlztJGNTeswpEFAMwFYW7+SrJ3lvOZNRM0wuyf3M16YwJT+6zk7z3R5moAWwPYchBAkAwlsz+HJ8Z1c3nERVYEE9w89wl1995IqpTGkTleyk+6qrlMcvio9xaZiFfvIclhNEtckUtMZidl0OhF0I8Jdffeyun6ZX4DuDBBCELhoC8bK1bhHD/vFm7u66dWyfPrev2b35D5azQLnVxVJTh9mIj3JOdk1hOIhVMZF6CGQ4DYrzO0CtQu8RoW90OPRwYf4yYP/w09+dAsDhX6qqmq44opreetf/inbI0fRDZ36UB22Z3PXzGPoTUGWTSyBLNjLobzZxVnxDB1XfsGywG0C/YhAZn0xWAHeqtkc3mmBmPGdu6IIsl9gXahwloC1VmFuFciC75rF8wVlP+vEL2Kt4gqR8wVka5UieLcgeOfssQDp+C5dYYFwAenn8jor/JshWp/Ak0AIxAzok4ASCAX6Hr/fuGAchOJVHk6XwtgmMHf6lnsvLvys45QfJeHVK8yHBSIz20/8POCgFIQygA7KAGz/fJXw+6YS/vssHVCuLySXMopjIWjSBXETnFpF1oK7eqHKhG4Jx45CaVwQqFUcPQBHPiW5KAu4/hh5MYWKKqyzOe6wLQNCodeac07gp3BdlyNHDvDkk4/z5JOPsm3bo3z3u19FKUU8nuCcczazZcvVXHLJFTQ0nHo3SdOgtQPCjVDXrUAEgYUspRtdCdx6yAW8M/rcV/D5vRKDLcviVa96FWeffTZf+cpXzmiff/qnf+Lf//3f+cY3vkFnZycf+9jHuPzyy9m3bx/Bp8oVVqhQocJLAInLf//J+Vz03m286TO3cfNHriYw9iD6zGHyq/4MN9712+mYUyKy778wxh5BCb86sjH+OPbkDvLL3wP6c9yhfSFRClmaAM/GCzWgZfsQ5TRu1QlLhtwS0sog7DxaYQxl+MX1tGwPWmEYpYXwwnV4wXo/AqL+LKzmC3337wvaVcWHPvRutm17hB/+8E7aVp9DzrsCfWIbxvR+QEPPHMU2k6iXaOxHhQoVXjzK5TIbN25k586dbN++ndWrV8+9tmvXLt7znvfw+OOPU1dXx/ve9z4+9KEPnbT/j370Iz72sY/R29tLd3c3n/70p7nqqqvmXq8YJCpUqPB8cBZD7v0exo5ZV26dwl6j8OpfpA54EPxf6buRF6k5MVg/4rtUc3/xzEXsntdhlIdAENHDJwm0Ac0kpNdy4+LrCWgm6dI0I/mxudct18b2HDShI09YMRY1IowVxslZOfoyA/zw0P+iC43WaBOpYppbj97Jz3t+wQWtmzineSO1oWp2TuxlNHyIem2Gc1OtPJYPUzAMJB6rp3M0LG5iONLAWGGCyeIUTZGGueONFSbYM7mfvF2gIVzHitolp7gnZU0tssYP/e3LDPBPj/4/nhjbQTKQIFsluHdelmU5F21iigFzmK5yFUJKAvWr/AYEyEnoifXxr7v/lf89fBNj+VEaI01cW3s9V7725ax7z3nouo5SigeHH+OuvnsZzo+iS40VjUvYcP4mSmGNku2BC/ogaEfAbQee5rUwnhSEfiDQegUiKxCOQg4BRf/9d9tm93FAS4MyFNZZHuULFSi/cJuzAFRY+UJxYdbtq0BkBV4E8ICAwAt7YAIueHEQBYXwBF7AF4HFpPBvfujgzXpEvBrQ+maF4pzfD2ELX0W2ZtvG34dpCP1EULrEj5VQHhCevblh+sIujvLF5VrwqhWiqBDTIKfAKAFl3+UsZmbbliAcXwR2XYUoCv95w3/NKwtSliIeYc7VHzOhYEE0Dz2u72g2NMGUDgUFg5PgnK8gpMAFkVdoowKtR+E2+eeqDQmcxQp7yanrPzVNo61tGT09y4nF3samTfD6189QXf0Evb0Pcc89d/KhD/0JnuexatU6tmy5iksvvZrly1fPuYadhQqVBDmCv/pAgCgKRF5gr68Iwc+X3ysx+G//9m8B+PrXv35G2yul+Ld/+zf++q//muuuuw6Ab37zmzQ0NHDLLbfwmte85jfV1QoVKlR43uhTe+jWevj2/7mea//2Jv7yW0/yr398Bcb0Xsyheyj+lsRgY3IbxtgjuLEO1FPCr1PEmHgCc/wxX0h9EZCFEYJHf4QxtQc8BzfahpNY6Bec8+w5MVcWJ5CFUQC0mcMgJMqIYlcvx5zYhhtpwq7fCNJAlKcI9N+OF26k3H7FC9rfz3/+n/nhD7/J5z//dTZsOAcAY/xRwge/iSin8Gc4Aie5mMLy9+CFXqyrqwoVKrwU+dCHPkRzczM7d+486flMJsNll13Gli1b+OIXv8ju3bt529veRjKZ5J3vfCcADz30EK997Wv51Kc+xcte9jK++93vcv311/Pkk0+yfPlyoGKQqFChwvPHa4TyFS9W8NfJyFHQewRu86wQDL5LtVWhDwi0AXA7f/3jdMTbiQdijBcnaQj7+ROe8pgspriw/bw5p/DKumU8PradmXKGRCBOSAv68RF41IZq5tpLl6aJB+JUB6v4ybE7cD2HzmQ76fIM+6cOUXCKWJ7Nzol9PD66E0c51IdqCcYUuxqgeQjevOsGokoQLZjUOnU82jRNr7MTUxoEteP/X++c2Mv3DtzERDGFQCCFZGFVF29d9lpqQtUnnWfOyrN9Yjc/OHAzOyf3YQidhBlHBOKYBYfDKsWyfodyYQ+quILQ0ksxG9aBgqneCf5l9B/5r93/STyQ4Malr+O67hvY0Hw25kGNUqdHSfc/J0IIzmvZyLqGlQznxgjqAZoiDXOCubFbEPyZQBv1Yw+cVkXpeg/nBF+H+YhAToDI++5ZUZgtAucpRFbAoEIF8I2+NQpvHuj9Ev0/wW1XWCv8GwVuHeijAlUGmQcs5btrNb9Nt0OhQviO3GkQDX7RNzGl/GMzmxEswWsGr/X4d0EFQAmFTAuEmhVjZx8CwGZOuKUsCN3mFygUEr5gfYHPTP8Lo8OjrAys4nOBz7HB3oDSQesFOS38/QGjMFvdJOu3JQTg4ovemi90i/LscV1/n6KEXhdqQ/5NHJkSoCvqLTBLgpoARKN+f1qLgr05RW8tx+MVNMitAKuoCCsIjAqUAfZZiuINnp/H+zRsG771LcljjwmiUYVhwIMPJmltvYR3vesiXvWqj7J/f4rdu+9g//5b+dKX/p3PfOYTNDQ0cfHFV7Bly1VccMEl6NfECP2vRN/vv7/oCnudwjr3t/P/0O8yv1di8POlp6eH0dFRtmzZMvdcIpFg48aNPPzww88oBpfLZcrl8tzvmUzmN97XChUqVNDyQ6Bczlu9jM/+yZW87/O3sqi9jndf1IQ+feC31i89fRDguBAMoIcADX1q34siBgs7T3jvf6Kn9+GGW8HQ0WeOoOUGUHoEPXMEz4ghy2m0qX0It4gbapiLjpDlFMbkdlAuTs3KOeFYBWtQdgZjZCvltsueVxbws/Gzn93Epz7113zgA3/FDTe8zj+H8jShI98Ht4RTtdyfzXk2+tReAv23UVz0lhfk2BUqVPjd4/bbb+fOO+/kpptu4vbbbz/pte985ztYlsVXv/pVTNNk2bJl7Nixg3/913+dE4M/97nPccUVV/DBD34QgE984hPcddddfP7zn+eLX/xixSBRoUKF3znEU+La00pnqFmHMC+QUbAxUs+W9s38rOdODqaPENBMCk6R9ngrF7edP7fd6vrlbG49lweGHmU4P4bCozpYhe3ZlJwSWSvHjJWhYBe4tONCIkaY0fw40dnIsd6ZfvJ2gfpwLalimrgZ4+h0L3mnwMrapSQDCbSjLRwKH+Gu7vt50441iGQVE8kI7QdqcedvY8XqJVQFEwDk7QL/c/hnZK0cS6oWIoTAcm32pw5xZ9+9vHbxDXN9nypN87U932H/1CH2pQ6RtwuU3TLhcoiqYBVWazPTEclDUY1gcSHn9L4H0+kmf7TA5/b9K5/r+QwSyUcjf8172/6UUF0Up362UJdUuM2njmtID7HA6MDYJZBjoCLgJRThH0goKZw238WrD0D425LC6zzkjC+q6sdAlASi7LtelTbr0sUXMGVe4DkgPIUoCRypsDcq0MB8DAJ3aqD7bmYs5bt2XTVb+M1343p1Cnu+wnxIoO8H4frRDMISoJT/+XsKzy9Mp3QQhdkc44B/s8R4SrQ0/NfmcP3nVXjW1ZtTCE3wA/0H/GXuL/hC7D/Z6G3k38v/xlWZK9mb2E/DUD0i7wvQcPyzLp76EjwlLs8WeFRB//yVju9uLgOuoqgJUgaYdQrzbIU2AoURWDAGo5aizhOYGbA0mAaahGAyp3A9P1/4F72wtV+QnYBos+KCzR7nn6cQLTxjUbYDB2D7dpg3TxGevWSsr1fs3Sv4u7+TpFKCXK6eaPQNLF78Bm66qczMzEPcdddt3H33rXzve1/DNE0uv/xa3vWy97FJOxtp+y5we5k6xT1e4bn5gxaDR0d9Z1hDQ8NJzzc0NMy9djo+9alPzbmQK1SoUOHFQukRv/qA8njzZWs4ODDJh798J92J87l480W/vY5JjdPPuD3UGWaW/broqZ3o0wdxEovnhFzHiKGn92EnFmBm+wmMPQrKRVoZEDrCLSGcAkoLomQArTCGF6pDmYmT2lZaGGllZ93Fv/5M4/77f8n73/9Wrrvu1Xzwgx8/fg7TB5HFMf8cnlqGKA28UD3G+BMUu26cFdkrVKjwh8TY2BjveMc7uOWWWwiHT43defjhh7ngggswzeNRNpdffjmf/vSnSafTVFVV8fDDD/Pnf/7nJ+13+eWXc8sttwAVg0SFChV+93AbZ13APeB0MRejqg2D26RwW164Y13RcTEt0UaeHN9N1soyP9HBhsY11IVr57YxpMGrF17H2vqV9GT6kEg6Eu0cTh/j4ZHHmbEyxM0YV3deyoVt5yKEoCXayM6JPdSpGlKlNCE9hOt5s9G1Lo5y0IVGxsqS1BPohSjNifmM1kxy2GhGOSaKFB2DzZxjr+P8+efN9efIdA+j+TE6Ex1z8RamZlAbqmHnxB6uX3Alodl55daBh9ibOkR3spOJ4hRGaYapQhpv1OW6wxfSMtPArpoDHFk5SN2NN2JZC+j5+THe+MVXcmTyEH/S8l7+fPWHaOirRaQFYp+f2auqwF6pfLHuachJCH9Voh/Cf/M8P+ZACLA2MicqOl1g3i+I/72GF1Og/AJtctJ37yoBIznAhrSCJNCkQBOghEAUFPqkxBtReFW+U1dOCtxq5TuKcwoVUlirFSok8GoVzmqFvlsQuEeg983GQEg/BkHYgDru4vUSfj6vNiYI3uzHM2ALMBReUPgZu/j7ewbI8gmD4IDIz/5bCZSp+Gzxs7xdvp23Fd4KHnxRfZHbuI2vl77Gh53/4wu/krlYFHxt2v/4G/hRELPfBRWcdQX7l48IXaEiUBdUbNLhPgFiuy9Ee0JwTQR+lheYEjwPAi40GTBhgjlbZO+nh+EruwSZPHhKII7CnhkoVsHlJzijCwXI5SAW89/TBx8UjI8L6uuV318BUsLIiKK3V1JdrTBNyOcFqRS4boCPfGQz55yzmY9//NP09Bzhjjt+yre+9SVe9tPNrFq1jre//b1cs/xVJ81/Kpw5L3kx+MMf/jCf/vSnn3Wb/fv3s3jx4hepR/CRj3zkpAl1JpOhra3tRTt+hQoV/jCxa1bihZvQM0dwYvP5xJsv5nD/CG/+/EPcftYbeQFWwv1q/apegTl4F6I8hQr4S85EeRqE9F22LwKyOO7/48RcXyHwzDh6bgBlxrHrN4DyMNL78KSBLE8hrBmELIDQ8YJ1uJEWhJU5XrRNKWQ55e8rf/2JxgMP3MOb3/xyzj57M5/97JdPLg6iPPywsqfdUhcSlIdQHpUFUBUq/GGhlOItb3kLf/zHf8z69evp7e09ZZvR0VE6O0/+C/CU0WF0dJSqqipGR0ef1fxQMUhUqFDhdw4dSld7hL/hF7FToVkXZMJ//nRL1X9VhBCsrFvGyrplz7qdJjUWVS9gUfWCuecWVnVxcft5ZK08cTNGUD9uLDi3eQP7U4fozwwBipJTpOgUqQlVUxVIAr4oPJIfZzw7yaJyO5bmEAyHWde+EVVQlG2LunyS+YsWo4LHK/eJacXqnYtZXliEU7YYDk0wFZ0hUKVRarBxPD8zwFMe2yd2URVIYGomrdFmpkszrJ1cRjFXxMrYaNOSS0bP5hXFKlovnM8T04/zhv93DYloFQ9c+ThLqpfi1YHdoNB6QOv3s3rzN3iUrlXH4wVOIHCHQD8gcLoVGL4LOHCHX+BMzOb3ykk/nkEbFLjtCmc9gO/CNYclFOFWD/bZsBkYnG27RcH6Ihg6gF9IzrwfVEj4zlob9Bk/hkKFBDgKkRPY6xX6YQFZ/xgyjb9NgJP65bfqxzCgg1cFotd3/jrdCjnh76vNzG7v4ovdswsMFScYaN3jY+IUbJ5kGx8WH/bdvwrQJBebl/CI+7AvBKvj+4kT+uI3MKuCC79fuOBFFLIs/GJyGuBAMAdvW6PoiMGhI4KADmcZ0JEXPCJhOgpNfhkY3BLMlBSX1EB+r+C7u2EoAwEJehXkyzB5RPD1r8OmTYpQCO6+W/Dgg4JUSjAxochmBeUypNOQyQja2mDxYkWhAH19Es8DxwHLglAIikXYvRv27xds3KjwPHDdBSxd+ud85jMfYGLidr7znc/zvve9lU984iO86U3v4l3v+lMikRMKe1d4Tl7yYvBf/MVf8Ja3vOVZt5k/f/6v1HZjo788eGxsjKam4xULx8bGTirK8XQCgQCBQMWHXqFChRcXFayhsPithA59C33mELry+Pp7zuGSTz7ADe/7GD/+8dl0db1ApZOfB07NKqzWywgM3gX5IfwqEibllkuw69a9KH1QgeRsCWF31qnsI+2cP2eSOk7VUkAhrRlkcRRlJHCirXiJLmRpEi9YhxObhzn2KF6gCvQgsjSBMuOUW7ecKtI+Tx544B7e9Kbr2bTpfL7ylR+dksHpJrpQgRpkYQQvMmtnUR6yOEa5+SKUEfm1jl+hQoWXDmdqdrjzzjvJZrN85CMfeZF69vyoGCQqVKjw28RZDvn3ehiPC7RhgdfgYa1XuL+lmsrPREgPzblwT2RF7VJeu/iGuWJqk8Up2mItrKpdhqmZ2J7NdHkGgSCgB9Cj8P/Zu+/4Kqr0j+OfuTW9QBoBQiAgXUBQxAYoVXDFgl0Re1tldW372xV1Vaxrb7uu4LrYKOqKFQE7olKkCiKhQwghvd12fn9MciUkQKgB8n3zui9yz5yZOXMn994zT848J1gUpGdhV1K9yVhRFs41YFpAccff79Jz5ECPN9rRZJ6bUHmImJIo2ntaMb/jLyzNWEHvnB7EdY7GVH1cm+qhpUBGbHMq8ypIyotjRWw2+UmF+CODNE1MplNOFrmfFnDNvy+kefO2vPKX/9Hp7WSCcXZUMhQHoW4GfztwbbLwnWgw8TsetR00dS+0CCUbeyQr2KOhW4JnATh/tXDm2JOvWYUWjiJDKA17JKwTgkdBYJth/Qp4b6tFHOCwoKMFW0KwDmgGZFrGTsEQsHBUWpiy3/PmVqdSAHubzlx7tHEo3hAxy4Gx7NQV4bEage3qY4/CNdFgFYOr2M5ZHLQscBgcxWBV2qksCFUFbYP2I+QxOHw1rymqn21lK0GCpESmhieaM5GQ4k5ledFygnF2kL2u0SEGsIyd/9iePM/gCFjgsAi0NjgqLKwSO9dwSaVh6kb4pswBCYbOKZAWY2iWA+f4Ld6qgKUugwMLy2foFgEn3hLkl6UWK7534PRaRCdBsQNKCixKS2HuXItx46B1a8OPP1oYA9nZsH6dRbQP2nosmgehEEN2BURFQXExlJcbPB6Lykp7neJi+3jKy2HzZkNFBbz9tsWPPzqoqLD/MJOWNpy//vV0fvllGf/+9ws8+eQjvPTSv7jhhoe4+eYLcTj27ZqtsTjkg8HJyckkJycfkG23bt2atLQ0ZsyYEQ7+FhUVMWfOHK6//voDsk8RkX0RSOpOSVwWroJlWMFKrNhWvNnLw7kjBzFy5CAmT55OmzYHeeZ3h5PydhfjT+qOq2A5GEMg4SgCTbrWCMweSP6m3QnGZuAq/IVgTCbG6cFZuhHj9BCMboGreFVVTYtAYnvc/mIcZRvtlBHluRh3DBVZ5+BPPpZQbCbuzd9iBSvwN+1GZcsh+zzC+bPPpnHttRfSp09fXn11cp2TMYUiU6hoNZyI397Blb8U4/Ti8JcRiG2Fr9Xp+7R/ETm01Heww8yZM5k9e3atQQi9evXi4osv5rXXXiMtLY2cnJway6ufVw982Fmd7ZdXl2mAhIgcToKtINiqehjl4cWyLPqkH0uPlKNZXbSWaas+Y2V+NutLNmIwxHniKA9U4HQ4CYZCrE/dwlEVrThpUU+CXh8RzghCTQ3l59QMunqnW3jXROFtFcmKdSsJxoVIK0omfW0yrTNbMnx1fyK+clB+scFhOeiW3IWPsqeTEpmEy+Gis/MociI3kxaZwrD4gXRN7kCkKwITgDGvjSU3t5Cjj/6CVyYlc/16yHCDN+P3/TtKIRRjCCXs5MCDYIUsjKvmOQs2N4SWWLh/BhNhB5edhfakblYhuNZZBDKrAs+phgVJkD8TWvgs5jmhRwBSQxCLnTIiIxocJb9P3lYjRFi96xAQsiBkcBTYAWHnanu0L37AV5Xrd8eMeKYqxUMA8Fbl7zXgWmaPPrbzIFQFn11V9bBzD5uqDHvWTn5lrerJ5Sz7Zwv755CpCtBudwjWdv+HgC0OQ1nQItQMmjsMERvBudHCREOlx7Cl3GKNH4pWWxREgcNlMTnfMMVvcWIATvDADTGGVQEoNYZWsdCtq6FyCLz1M2z2WziCkLPJTiURGQkejz2qd/Zsiw8+sIiOhrIyC5/PkBCA3lhE+gEHtCyyWFUBy+aDJw5cLnC77ZQRpaUQDNqjhP1+i88/twPNX39tkZ5uiIuzl61eDY895sDt7kxm5vNkZt7ODz/cwaOPXs60af9m4sT/kprarPYLKzUc8sHgPbF27Vq2bdvG2rVrCQaDLFiwAIC2bdsSE2MPGe/QoQPjxo3jrLPOwrIsxowZwwMPPEC7du3CMyenp6czYsSIhjsQEZFdMJ5YO21BleQYmDTpM849dyDnnjuQt9/+mHbtOh7UNjnKNuDathhX4UqCEUlg2u63ydbqw3gTKOt0DZErJuIqWgUmQCgymYpWZ2AcXlxLnsfyl2Lc0YQiku3AdZ6DQJOu+FN64U89nkBiZ7AsKrLOpSLzDKygD+OO2ecRwVOnvsktt1zB4MFn8Pzzr+8ycFKZcTrB6Oa4t/yIo3Ibwfi2+NJODE90JyJHhvoOdnjmmWd44IEHws83btzI4MGDefvtt+nduzcAffr04f/+7//w+/243fYQq+nTp9O+fXsSExPDdWbMmMGYMWPC25o+fTp9+vQBNEBCRKQhRbi8dGjSjrYJrVme/xsbSzbjdrj432+f0DYhk2AoZE/o5o6iectUvk6cR1p0c45qnoW/c9Wo2Wrl4F5k575tW5ZJQnEM6xI3URFbSce8LDpuaU9KVBIsgfKgPalav5Yn8FthNivyV+F1uAkGgkS5PYxkGMdGdQuHH5duhZkbZ5De/CJatszA7zfMXGkxcL5FptvgaAKOIjugWjHEYJrUfbwmBvztDJ4fLEJNfh+VbJVDqKU9shkvOPwQSrVH3+IGx3ogEzvXbrmFv70h2BZMDmx0QZ4D0nJga1VKhKNjgbLtgq7VkdNwQ+xt4TRQAe45Fo4SwFg4ig34rDonKQTAb8Kvi/HbExo6t4EJWVihqpQM1btxV20iCDjBYKompNuOBUkmCSdOcoI5v0d3fbCxcgtNrFTyCixi7JciPKC6EtgKFAKfOmCZZZFpDJ1zINJvkRAyOABvKbiDFk4HeBwWKW6IBhZuhRKHhc8YfrIslvkM0RGQmQSRfthWCd9aMOsKB4sWWQQCVVPYVL2OduDWfr5ypT26t7DQ4HBAtGVRGoDfXJACRHrt1yDDDUmxkNE3xObNDvx+qJ5ywOGwtxUTY6eXmDzZokULOxAMdvC4WTP45BOLtm3tdBOQSVbWO/zww0zmzx/NkCF9GD9+Mt2796r7F1CAIywYfM899/Daa6+Fn/fo0QOAWbNm0a9fPwCWL19OYWFhuM4dd9xBaWkp11xzDQUFBZx00kl88skndY7aEhE5VKWkpDF58nQuuGAoZ511Gm+88SFHH93joOzbWfgr0Yufw1G6EeOOwVWwDG/O95S3PZ/KVsMOShsAgvHtKOn5V5xFq7BCPoIxrTCeWAhU4NvaB0/Od2A5qzqBISrank9Zp2vrnhTO6cXs42Rxxhiee+4xHn74b4wceQmPP/4yLtduvnYti0BSdwJJ3fdp3yJyZMjIyKjxvHpwQ1ZWFi1atADgoosu4r777uPKK6/kzjvvZPHixTz99NM8+eST4fVuueUW+vbtyxNPPMGwYcN46623+Omnn/jnP/8JoAESIiKHAJfDReem7enctD2BUIDP135JRcBBs7hUyvzlbK3IY61vI9ta5tPnmD60Sq8jXeb2gU+nRTNfMqkB+4+PDgM+Y7CCEIrFnoQMSIpsyrVHj2JezkJWFa4hukkUfZZ15ugNHQi1qhrhWgZfrglR5P+VXm1upOrriI0nGmbMtjhnk0Vqrp1moWKIoWL4LkZqW1A5MIRrjQPXMgsTY+d7xg2VJxrcSyyCLe31jQfc8yxca8ARBMdGcBRYBLIMLU4N4c12stVlaFoIFcbitzhYlm+4IK4qlUMUVSOR+T1/7/ZCdnoFR7H94pkIC5MAVmlV/uLtDsPY1QlgB9IcVXN9WEEwVV38cB7f7fZjlVf9UDVSuFYguOo8eIyHnvRkZmgGZ1kjwECQEF8yg6vNjZQCG4E0IA74FVgOlACLgewQXByCZlikBO1UzE7s1BDFHvsAYg20DhpWxFiU+mGbH+K94PFauByGgoDFvDzDlmKLUh9sCBkcGy22Bu1J87xee/Suz2ePDPb5tnt9qo49FLIIhezXyg/86rdfu6YBe50tTuiUB06nRVIS5OcbSkvt1yQYhMhIQ5s2kJ5uWLnSQUyMoaTEIhiExESDywWVlRYxMTV/xzp1OpX4+DmsWXM2Z599Gh9++A0dO3at6zdQOMKCwRMmTGDChAm7rGNMzV8Yy7K4//77uf/++w9gy0REDryUlDSmTJnBJZecwciRA5kwYSp9+pxyYHdqDBGrp+Eo3UQgsUt4FK2jdAPeNR/iS+2NiUjazUb2I4eLYMIOeZNdEZR1upZAUg9ceQsBCDQ9Gl/KcXUHgveD0tISbr31aj74YAq33HIXt99+Lw7HwRspLSKNR3x8PJ999hk33ngjPXv2JCkpiXvuuYdrrrkmXOeEE07gjTfe4K9//St/+ctfaNeuHe+99x5dunQJ19EACRGRQ4fL4aJ7clc+yv6cQDDA8oKVlPnLKQ9U4Ha4WJCziJ4p3WpMSAdAFPg7GbxfOghmGEyUhaMIOwduhP1wbLbwDa6ZNyHBG8+pGSdzKicD4MaCty1cy6tGv7oNaxKcxMUeRU7OvPB6lge+MNCmEoY4ASysgKmVtcOxCVzL7dy6gVZ2XueSG0J45li4si2CiSH8vQx4wL3SHrxhYu11/UcbHAUWxmvnGK4YGsLX19AmCU5aFWLW5xabveAuh3IndEwynJRiEXSChcFZBgZ733Ux1XNEB6rqFBuM08Kx3TH4LCiwBxATAeRh0QqIqg7+VgU8t4/F19rPdnmKa6R6qMrzYDCMYQyjGU1P04tjOY4neYoySjmT0cRYsNnApxb0MBAPNK1qzybgaCAZmAucZkGesUf/eow9yjpogQfIdVhEOQ3bghZuAx4flBkocVtsjjREBS3Kg5DrgKKgRVnldvPWhex8v35/3a/l9vxAFPao5WzsgLwPSApCfK7FzC/skcVpaXZQ2eMBr9ceVdymjf1/MGhYtMhBbKzBsmD1aouICHA67brb8/kgLq4Zr776GaNGncR1113Mxx/PJipK867U5YgKBouINHaJiU14++1PuOKKkZx//hDuvfdxRo++HmsfUx3sjOUvwlW4wk5jsN0+QlHNcBUsw1W0Cv/BDAbvjCsCX/op+NIPcHAcWLNmFVdccS5r1mTzyitvc/rpZx3wfYpI45CZmVlrYAPA0Ucfzddff73LdUeOHMnIkSN3ulwDJEREDi2nZpzE0rzlfJT9OcFQkEhXBAneOFrFtWR+7iK+2/gDp2acXGu9yoEG11qDM9vCxIFjjT0aNdjc4MwDfy+D75Rd51j2H2MIZhhcSyyscjufb5OvHKQXXMLSpWPp2vVK0tP7wDILRw5EdDP4j7LTRHg/dwAhyi80EICI/1l4pzuwqkaRmkjwdwoR6AzB5lDZL/R7Sokg+I4xeGZXjRjeBu7FFgQhmGGBMZhYCDWxUwqcc449inTh1xaBOdDFDX1ioIkXAhmG8pGGqIkWntlVKRyqdlOdZ9eiKkjrty9ljAMcZXZeX1OVViJgwWoLAkF7brZ4u5kUYAdXq4Nq2297+1e3unz70cLb7x9jB4KxLC4wF7CVrdzLWDazmU505z0+xksqRcZOCZFr7FHB3YBE7DYV2ofAS1U/t6vKndwUu/1+Cyot8HtgqQtclRbBeCiqANzg9sC6IBT7LWIrYa2x8y77QzWPJRiEkpJd/ur8/jtU9VoFqn5eBSQA7SyYZ9mB4JgY2LYN/H5DWZlFbCxkZdkB4p9/tnC5ID7eEApZREQYKishJwfS00MUF1v4/Qa3GyoqYPNmi5NOCtG8eRQvvTSRAQN68cYb47nqqpvq1+BGRsFgEZEjTExMLBMnfsADD9zNX/86hgULfuThh58nKipq/+/McmIsB4R2uOfKBAEHxtrJ10woiKtgKc6S9RhnBP6mXQ/uCOJ9ZPmKsQKlhLxNwOkJl3/xxXRuuOESEhOb8uGH39C+fecGbKWIiIiIHK6aRCTSJ/1Y5ucuIs4Tg9fpJSUqmURvPGuK1jNn87w6g8Gh9KpRt3MtnGvtydFMJJgmEGwB/o72CNzdCSWBr+/vocDjg4aFS/5Mbu40PvzwYv4w6HNKfsmiVVPo2g7w2OuAwTPXQTAjiPcjBxEzHWBBsJkh0MHgXG0RPcFhTxjXFEJJFuVnGfy97RzGZReFCKZbRE608MxzQNCeIM+RC94ZFp6vLCI+tCfO40TDsd0N/b+0cMXYkwmaSAhtM7jWgP84KHw4RNOznTi2GIzDDijbuYDtHLdUbHfQ1Zc0FuG0DqGQHVS1j8x+xFqwpirdcbPtlrHd6iF7E+F0CUWAx7LXdVTnFa6qWBSy2ALEOOAGcxM3WTeRDxSE7NG+KwEndmC30n6ZmIo9enebE1YHYWvIDgw7gI0OaB+CPCBooDwIsQ4oNeBzQEmlYXUhlDssIuIMQadFaZGhrNyiJFT7eLZXx9+k6xQE1vN70DEAlAOfG6jwQ0S2RdOmhtJSSE2F4mJ7xPGGDXYKCbfbkJ5ukZVlWLPGDhrHxkLTptCpE0RFGbKzwRgLh8PQpYvhzDPtEcTt2nWkT5++fPXV5woG74SCwSIiRyC328199z1Ot249+fOfr2PZssW8+uokWrbM3K/7Me4Y/Ek98a79iJA30Q6MGoOzeDXB6HSCCe1rrxQoI+qXV/HkzIGQD4whFJVG+VGX4U85tBP9W/4SvNnv48mZjRWsIBSRTGXGYMpTTuKFF//BI4/cQ79+g3juuddISEhs6OaKiIiIyGHMaTloGtGEjk3a1Sj3ON2U+cswxoTvACysLGJbRQHx3jiaNE2gctCOY1T3TbduhvPOc+J2v8FHHw1i0v+O59y0dxjdox9x22WrCMWBexFEveTAkQ/GMphEcGy18HwLBO1AqHFbBI4yONdD1DsOSuKDBLOAKAi0N1jGQSjOniDPUQTO9Rb4wHgN7rl2Tl/HVgh0NrhWWgQyjZ0qeWNVaguvPcLY19MQyDB48hz2ZHFVUdqgsfMQby+c87cqx7BxVI1KxQ6eBYBtwDZjBzbXYweDq1/p7TJBAHaO30VAEjATO8fvKSEYDnwDFBpYHTKsBUqx99PDCRdV5fw1QDF2MLoMKLOgrYFS4DPAacGSGCisMMSUW5xSVX9KCP4A9AdSq8q2huDXcoipNHzhtCiLMGRlGEIh+O03e9Tt/uRw2COL/VWviavqtQthB5QrK2HDBgun0w7wZmYaoqJg0yZISjJkZBi++86ivBw6d7ZzBVsWrFkDCQlw/fUhli2zKCoyNG1q6NDBTjVRzev1UlFRUVfTBAWDRUSOaGeffSHt23fiyivPY8iQ43nhhf/St++Avd6e5S/F8pf8HvgFKjOH4yxZh6tgadWfig2hyBQq2l2EcdfO0eRdPwPPxq9+n+DNhHAWrSJyxesE4tpgInYy9XBDM4bI5a/h2fgFoYhkQp4EHGWbWDvjKa6dOIafFi3jpptu54477sPpdO5+eyIiIiIiu9AiNp0Ip4diXwmxHnvWtpAJkV9RwDEZp2BZFpVBHx9nf87sjT9R4i8lyh1Jz5Ru/CFrMFHu/XdnoGVBv36GXr1acPHF3/DAvRfz9s+DSZx7I3eeeg8JEQngA89PFq5VFsZh5+O1fBDygGliB21NhCEUZ+fZtaOnFu454FjtZE6SIa81NPHACYUQH2Fh+QzO9RaWz07dYPksHPlglYD3OwsTEwKfwdpgsWQpzC+0c+C281oc1xoi37UgwsLEg1VuYYXsSxYrWPsYqwO6hMC4DCYO/JWGwnILN+AKwWZgOnAcBj9WnUFgsAccL8AOGrfADuh6DbwLfA3kGcNmYDWQgMUJgDMEH1bt4xbsEcBrsUf85gMRIShww6QAfG8MCW6Lwgrw+SzyHbDRCcl+WAL8q2o/p2FPOleMnU/4e2Pxixs8bott2wzBoKGycv/PbeJy2SN9TVVQe8c0w8GgHTB2OMDtNuTmWmRkGLp1M/zyi8W2bfYo4dxce6K5o4+2g8Xl5RbduoWIioKePev+Y0dpaQk//TSbUaOu3e/HdaRQMFhE5AjXuXM3Pv54NjfeeBkXXTSMK664kTvvvI+YmNj6byRQTsTq/+HZ/C1WoIxQRFMqWw7Gl96PUGQKpd3/jHvrPBxlm6pGC/cgFNWs9naMwbP5G4w7xg4EA1gOgnGtcRX8gjt/Cb5mtW93OxQ4i1fh3vITweiWGG8CgWCIZz5bxLg3v6RlcgJTp0yn9/EHPiexiIiIiDQOWfGZHJt2DN9s+J5IVwRup5uCykLSY9I4pUUfAD7JnsGHq6bTJCKB9Og0SvwlTF/7JSFCXNThnH1uw5YtsGmTRWSkoXVrO8/rccc1Yer/PuCVm57kiQ8f4u3sN/lr7/sYXXo5zvVeQhEG47UDrtY2e1RwKNmOClohO8AbTDI411q4F0NpMbxfCR+steAHMFHwVSXc4DFkbLHsCdo8hIeVmghw5lkQDBExDVzzLd4vtPgfUOE1OJ0WX28z/FwON0U4CLY2hJLA84OFIxdClp1OwU5s93taB1P13LgNltsi2MJg5YMvB3IDdkC2AvgeaI5FR6AEe6I22C4/MJCDHYBtjR0Qzq76fyP2COE/RFj8XAHllj1g+Tcgo2p7n2GPJi4GVgDzHOByAgFY54dSy06lUOwwBPzg8dh7/sUDKwvtNm4CFgLvAJlV+y6sar+j0g7SBwIHZl4ZsCd02xVj7IngqkfzRkQYNm+2yMuDwkI49liIjIRVq+ygcFGRRZs2drC4d+9dj3j/299upaKignPPvWQ/Hc2RR8FgEZFGIDGxCa+//j6vvPIsjz56Lx9//D4PP/wsAwacvvuVjSHy14l4131GyNuEkCceR9kWIn95FQBf81Mx7uj6BXFNCCtQjnHuMPMxVX+NDu7n+5P2I0fZFqxAGSY2k8XZOdz47DR+XrWZm4d356/ndMXf4+j9eCOeiIiIiDR2ToeT89uPoGVsOj9snkdZoJxeqd05ufnxtIhNp9hXwvebfiLBG09KVDIAES4vFg7m5vzMgIy+pETt3bwcfj988IHFt986KCw0uN0OWrc2XHp6iFYFFt5yNzddczvn97qEv4//C2O+uoFxzvu4qvm1XNnqWlpsSiPUFByl4Nhq7NQQBqwyQygdgs3AM8/Oybs2ACu9cFQ6uMohWAq/BOCdENxeXDWZWxB7xK7XIpRkcGw1uNc4wAXZ5YaPKyDOBW0tC+MFf6TFb0FD7ipo1gZCSYaKJIP3G4vQZguTD0Gnvd0dJ5YjZKeksPIhymOxNd0wa4NFihNiAnZgdVFVuoYs7GDw9qkiwA62NbebzGTgI+z1iqvqzffB1qoJ6iqMnRe4EDvg7AC+ApZhl4dC4HaCvzqNhQGnyxAy9jLLstMulJfXfS6zt39i7FG5vztwAeFdcbnA4zH4fBabNllYlh1AdruhSxdDbCx06mRo2tRi/Xo7Z/CAAYYRIwzRtW8+BSAQCPD3v9/FW29N4PHHX6ZNm3Z1VxT2/1hwERE5JDmdTq69dgyzZs2nXbsOXHbZCG644VK2bt2yy/Ucpevx5HxPMCqdUHQ6xhNPMK41WC6866ZDcDd/9q2xMSf+Jl1wVGytMfuA5SvAOCMJxmTs7eEdcMYTS4kfHvjv5/S97d9U+ALMeGw0D17QFW9MAsZ1ACboExEREZFGLcLl5dSMk7nruFu4r8+dXNjhbFrEpgN2nuASfxnx3pp3/MV5Yyn1l1FQWbjX+/32W4tPPrFwuQzt20N6uiHwg8Wvf3Qy62GL6U87WP24k2a+5jw1aTzfPfgzI9LP5smNj9P+20xG545iwZa5BJKgyGNRUQTlcYbKTnZeYUceOPIsystgiRP8yeB0gIkEl9OiWToss2Czq+qawW+nbgim23l/HflgBQ3BVoZfYqDYASkGrABgwJlmIAZWlYF/A2zcaPFrjsXaTCDKnmjMFfx9ZHB1IDfgABNl79O51cI4DB6XRYoD2jgMuW5o5gZ/JMxw23mDraqRxg5+Dwq7sEf1Pgd8iD1nXwJ2cLgA+Dpop7MoD9m5dLcCa7BHBvuBn4BV250P//Z5Fiw7NUR5uUUwaFFWtmOA99BmVc3jFwyCZdmpKnw+QyAAoZBFfPzv9dLSDMccY+cQ7tKFnQaCV6/+jYsuGs6rrz7PAw88xYUXXn7QjudwpJHBIiKNTEZGa954YxpTpkxk7Njb6dv3aP72t0c477xLcThq/43QWb4Fy1eEiW5ZozwU0RRHxVYcvgJCkSn13r+vxQDc+Utw5S8m5G2CFazAClZQ2WIgwfi2+3x8B4LP5+PVd2fx9BOfUVhSzq0jjuXPF/QlMliAVZ6PL/PMcA5lEREREZEDoXqyuGpx3lii3VEU+UqIdEWGy4t9xUS7o0jwxu/VfkIhOxjs9UKyPeCYeAviN8JrORaBphAZAd4iOHmqxXnNLLIGdOQfC57jr96/85+1r/LPuS8wcc1/6eDsTm/HcLKST2dTZi9S2jgYlWZIzbYIRRjyouHjQkiqik5ZAcAFVntDRYJFqccQ+BVw2hO7WUFwbLYnoQsl2TmIrQgwToOxLPDaAeX8oMWWfHjDaSid6cDlgEAcJIUM8WUQkWigxMLls3PzOoCgA0g2hKItSiMMEauANZDgNmT4YamxGO+AnCB4rapRwQ7IM3Ygtwxo4oDWBhIM/ICdXzgKOz1DGVUjgx3gA0JVcW4DVGIPfq46fAJV58LF7wHmag6HfY4ORdWB3h1Vt9nhsFND+Hz2z61b2yOay8stWrY0bN0K2dkW7dr9vpHcXEhMtGjRovZB5+Rs4qmnHmLixH+TlJTCW299zIkn9juAR3hkUDBYRKQRsiyLc8+9hP79BzN27J+59darefnlJ7nttr9x+uln1QgKh9yx4IrECpRi3DG/b8NfgnFHEdqurD6CcW0o7ToGz4aZuPKXEIpKw9fsJHzN+oJ1aN2wEgwGeffdN3nssfvZsGEtI886h78Oa0Ebz2askl8x3gQqMs+gsuXghm6qiIiIiDQycZ5Yjm/Wk2mrPsOBRZw3jhJfCTnlufRvcdJep4jw+aCoqOYoTM9amJ1nUe6CjrEW8fGGwkqYlQtZH1r0GG7wdzE0nZ3IzVm3ckPXW/jb9Gl8unoK74ReoHzjA0Tmp9Bk5VDmHTOMh+8/lfTP4omf7iCmEjaVQOtosAotgmmGjeXQupshanSIiv86cC8Dq8jCqrADwUSAVWrnIW6XCPF5Fjl+SAtAcQXkFcLPAUOhgTVBwwC/RUYBJDeFzS7YlAXHNzVUrADfZotAORS5wRVrEZFqyF0NzYxFyAWzXfCOBXMN5AftkbtBYweQPzGQih2sDQErQ7DVskc5R4Xs3L9+wGdBSVVaCKcDAoEdX/XfA8DbLwpSMxBsAQ7L3v+hxum00z+AnbYC7ICvszpFiAUpKXZZWRl4vXbe4qgoyMoytG9vWL4c1qyxWLHC/h0rLbWDyMOHh2ja9Pd9bd26hX/96xn+9a9niYiI4K67/s7o0TcQGRlZu2FSi4LBIiKNWNOmyTz33GuMHn0Djz9+P9dccyGdOnXlttvuYciQP2BZFsH4tvgTO+HO/YlgbCbGFYOjMg9HZT4VLc+HvUiPEIxrTXnclb/3Cg4xxhg++2wajzxyD7/8soShQ8/k9dff46ijOkEoSEnxKix/CaGoZoSi0hq6uSIiIiLSSA3JPI2gCTFn00+sL9lIlCuS0zJOYUTW0L3eptcL6emwbBkkVcWTN2+D4qChSYSFx2NHIuO9kOuCnzZZ9AhC+XkhjNPCs8iBP8dFeWgEJ/QaQWJPP5u2fM+qVR/y228f88EHr/Hxx25OOO4UTo8dRq/IYczY2JZfCiCyiaEwEuKiLYYODeJMgdLrQ7iXWDi3GEJR4FwHEZ86cBhwFEBGHJyeavjfRotFQUOBz2IVhrJIcDstfvHALyGgEs41MCoWthVb5HcJkWdZrPJDfA4Uuw1TndAmFy4qsrAiwe+Fr72GFeWQF7Tw8/tEc35gEnCMGzqEoChoB4Q9BrYZ+MICn4FcBwRc4AuBaycjZ+tiUTOfcfX/dQWSG4rLZad6sHMX2601BhwOEx4F7Pfb5VFREB8PXq+haVM7VUfLlobISHuyOLAnw+vf35CaCmvWQJs2cMIJ9qRxGzeu56OP3uPjj99jzpxv8HojuOaam7n++tuIj09ooFfg8KRgsIiI0LNnb95880PmzPmWJ564nyuvHEnXrj247ba/MXDgMMo7jAYsXPlLsQJrMJ54KjOGUpExbN92fIgFgouLi5g06b+89tpL/PrrL5x4Yj+mTfuGY4457vdKDifBeE1GICIiIiINL8Ll5Zx2wzm15clsq8gnwRtH08gm+7RNy4J+/QyrVjn47TdDUhJs9oMvZJHo/T1wB+D2WZTGG4gAEwHlow2VOUHy1kLJBCexSQaH20Xz5ifRvPlJnHjiOBYsWE2nTh+ybNlH3DPrLny+W0lrkklq0vHEN+nN0R2OY8SIbnTqVJWGLRL8vQzVaXMdG8D9i4FKC4cfnFvgrHJol26Y2cXwzjqwCqCpGwoK7ONxeqDUD6v9dooGpx82bYKFCy18FdAE2GhZ+EOGWTkWx1uG44IWBXGG/BLAWBggFgh5IeiCsnJ71OqyIMQAm7DTPfiBn6ueu6MNvqCF328HSatTJTid9v81cgHvwIE9iti/Q3aEQ2FQsNsNqal2PukNG+yRvmDn/XW7oWlTaNvWsGYNbNwIsbGQkWEIBCzi4mDYMMOPP1qsW2fRooXB54OcHPsPESNGhDj6aPu1WrduFR9++C4PPfQu8+b9gNvt5uSTT+PRR19kyJA/0KRJ0901VeqgYLCIiIT17n0i77zzKd999yWPP34/l19+Nq1bt+WCCy7nvJEXk96mzB4RG5lCKCr9kAvm7q1lyxbx2msvM3nyRCorKxg69EzGjXuWPn1OqZWbTURERETkUJMYEU9ixN7lCK7L0UcbLrssxOefW2zebGGlGFwphkjLHo1rnBCssCh2GtoP2C5aaUEoDWKTIHWmYe1aiIv7fXFeHjRr1pprrrmelJTrKS0t4euvZzJnzjf89NP3fD9nKl997eNf//LSpUt3unc/lqOP7kHXrj1o27YDLpeLUHMoHR0i4mMHrhUWjiIItDC0PDvEiI6w8H4H276ziIiwR6Zun7JgRQQsdUL3SsOWhQ5c+dDKZciJgKkhC1+uhVVp+NkN3byGvFiILYTCqqCtEwg47cugfAfkhSAQgh8wFGLhxA4ErwaMyx4tGx9vKCuDigr7usLpNDid9s+BwM5HCgcBQrWDv9WXJ9Xr7SxPb305ndvtM1izvPq5vU+D02mfz9at4dhj7dG/ERFw4YWG6Gj7/MbFQdeuhrg4+7VftQq+/95i/XqL1FTD8ccbjj7a0LGjYcoUB9nZFoEAJCcbTjmliKKiH/nHP77h44/fZ8mSn4mIiKBv34E8++x4BgwYplHA+4FlzL78yghAUVER8fHxLF++ldjYuN2vICJyGDDG8MMP3zJx4r+ZNm0Kfr+f004bykUXXcGppw7B5Tq8/55YXFzE9OnTeP31V5gz5xtSU5tx8cVXcvHFV9KsWfOGbp6I7IHi4iLat0+isLCQuDj1xRqK+sQiIkeeQAAKC+3A4OS3HfzwsUVUEbiCUOiGrJ6Gq++omc+12g8/WLz+ugO/35CYCKWl9mRhQ4cazj677lBUZWUlS5cuZO7cOcybN4cFC35i9erfAIiIiKBjx6507dqDLl26065tB9oktiU5ORUSrXBOhQ8+sHjmGQfFxRATA5s3W1RW2qNOo6IMvdvDyEgIfgtWwCI30fBdImS7ID/HwlFpSI+GO1MgYSP8vNXi5QpYXNXGYNWoXmPs1ycGSAGaAflOWBOCCgtcbjuY6nYb0tMt3G77+HNzIRCwjz8YtKr+r/k6OCyw7JTIuDAEsXA4wHJCsCr2HgrZaRpcLnuEsWXZ7aqstMvcbvvnHSebsyz7ERkJPp/BGLsNdg5fuy1OpyEUqtqnZXA4LFq2rD7PFikpdiC4vNyiRw/DlVeG2NN0vcFgkJUrf2XmzJ9YuHAOK1Z8z/LliwiFQsTExNK//2CGDTuL004bSnT0ns1T01jVt0+sYPB+oI6viBzpCgsLeO+9t3njjVdZtGg+qanNOOusCxkwYCi9evXB4/E0dBPrZdOmDXz22TQ+/fQDvv12Fn6/nz59TuHyy69jyJAzcbvdDd1EEdkLCgYfGtQnFhE5spWXw5w5FvPmWVRUQJcuhhNPNHUGgsEOls6fb/HllxabNtmpAk46yXDSSYY96XYXFRWyZMnPLF68gEWL5rNo0Xx+/fUXQlVRzujoGDIzs2jTpi2tW7elefO2LFnSlm++aU9xcQpOp4NQyBAVBT17Gq64wjB3Lsz40KIg18LTBJxV41xyc+2Aatu2Bk+lRcwySCqxWFIBXwUMPlPzrkHL+j2YCr+nf7As+2e32+B2W/TqZWjWzFBSYvHDD1BRYQeqfT6L/Hw7UO5y2esGAva6lmVP5pcQDQEDEVG/B6BDIYiNtdMydOkCTZrYz+fOtVi1CsAiOtoOEpeU/B5sjo6GUMgO5MbG2vssKLDCZVFRdgqHSy4JMWWKxerVDpo0MZx2WohTT7Xb+f33Fj//bAeKu3WzR/puP9lgXUpKivnttxUsXmyfx8WLF7B06ULKy8sAyMo6ip49j6dXr+Pp2bM3Rx3VCef2Q5alXhQMPojU8RWRxmTRovm8+eYEpk2bwtatW4iOjuGkk/rTv/9g+vUbSEZG64ZuYlggEGDZskXMnPkJn376AQsW/ITT6aRPn1MYNOgMBg8eTsuWmQ3dTBHZRwoGHxrUJxYRkboYYweSvd6aKQn2RUVFBWvW/MaqVStZvfo3srNXkp29klWrVrJp0/pwPY8nltjYtqSltaVz57b06ZNFRkYmP/zQjJ9+SqekJJatWy28Xrt+Xh706GEYOzbEN99YTJzoIDbWnswsN9cO3lYHVl0uSEkx5OfbaQ78/t8DutWTp8XE2MHygQPt0JvPB7NnW6xfb++neXP48UeLJUvstBbVo3wjIuzXrbAQEhPtoHJkpP1zMAilpRaxsYbiYsjKsgPBpaV2SorCQnsfFRUWlvX7KGQ7jy+kpxt69rRzQScm2sHh336zCIWgVy9Dx452+/eE3+9n48Z1rF27mrVrs1m7Nps1a7Krfl7Ntm1bAXA4HGRlHUWXLt1rPBIT9y3PtdgUDD6I1PEVkcYoFAqxZMnPfPHFZ8ya9Rk//vgdwWCQrKyj6NPnFDp27EqnTl3p0KHLQcnrZIxhzZpVLFjwEwsW/MT8+T+yaNF8KirKiY6OoX//QQwefAannjpUnQ2RI4yCwYcG9YlFRORQUF5ezpo1v4WDw9sHizdt2lCjrtMZiceThsPRDMtKJSoqjVNPTaFnzzQcjlQ+/DCdoqJmbNuWQkSEh8JCi9xcOyCblmY49VTDV19Z5OdblJZWb9MOfMfFGTp1skfvtmkD8fF2gHf5cqomTrMDxk2bGubNs9i4EZo1swPSpaX26OFAwB712707bN1qT4hXVmYHi487Dk46KURJCWzdak/EdvzxdkqON9+0eP99B1u32uufdpphyJAQ6ensdCR3XYwxlJQUs23bVvLzt5Gfn8e2bVvZsOH3wO+aNdls3LiOYFWU3LIs0tNbkpGRSUZGazIyMmnVqjWZmW3p0KELUVFR++dESy0KBh9E6viKiNi3b3377SxmzvyU+fN/4Ndff8FfNT1u8+YZdOzYhU6dupKZ2ZamTZNo2jSZJk2a0rRpMjExsfWaqK24uIiNG9exceN6Nm7cEP55w4Z1LFmygPz8bQBkZLSme/de2z2OJSIi4oAev4g0HAWDDw3qE4uIyKGurKyMDRvWsmXLFj79NIefftpCUdEmKis3Ewxuxu3eTEVFDlu3bmHHcJnL1RS3Ow2/vwmWFYvXG0NyciweTyy5uXGUl8cSGxsDxOFyxXL88TEMGxbNihXxLFoUTyAQi9MZQZMmcOaZhpYtDT4fNG9up1548EEH+fl2MLm83N5n796G1FTYsuX3lBIeDwwcGGLYMEPLljs/1kAgQGVlJZWVFTs8fFRUlFNUVBAO8u7s//z8vPA13fYSE5vQsqUd5LX/bxN+3rx5xmGTRvBIo2DwQaSOr4hIbT6fj99+W86yZYtYtmwxy5YtZunShWzevLFWXY/HQ5MmScTFxdcICvv9/nAHpqyslLKy0vAyy7JITW1GenoLmjVrTseOXenevRfduvWiadOkg3KMInJoUDD40KA+sYiIHE6MgbVr4bff7HQKbdsaWrSgKrVCgLy8XObO3cz99+dSXLwZy8ohGNxEKJRPIFCMMSVERhYBxZSVFRMIFBMMFu9ynw6Hk+joWLxeDy6XC5fLXfW/C7/fTVGRG7/fjdvtIDHRomlTC2Msysos/H4Ll8siJsaqmhyuEp+vOti74/8V4ZG6uxMdHUOTJkkkJjYJ/5+Y2JQmTZrW+X9iYlON7j1E1bdPfHhPBS8iIocsj8dDx45d6dixa41yn8/Htm1b2bZtK3l5W8nLyyUvz35eXFxUo67L5SYiIqLqEUlqajOaNWtBenoLUlObacI3ERERERHZK5YFrVpBq1a1x0i6XC5SU5tx+unNyMuzmDTJQdOm9qRvSUmGH3+0KCiw8/W63YaUFBg50tCtW5CyslJKSorreBRRUlJCaWkJgYAfv99PIOAnEAhs9/Dj8/kJhYKAwZi6HwAej5eIiAi83gi8Xi9eb8QOZRF4PJ4az3+v4yUuLoHExKZ4qxMmS6OhYLCIiBxUHo+HtLR00tLSG7opIiIiIiIiuzR8uCE317BsGZSVWaxda9Ghg52vt0kTOz9w+/aGhAQABzExscTExDZwq0V2TsFgERERERERERGROiQmwnXXhVi0yGLTJkNkJHTpYmjevKFbJrJ3FAwWERERERERERHZiagoezI3kSOBo6EbICIiIiIiIiIiIiIHnoLBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgKuhG3AkMMYAUFJS3MAtEREREWl8qvtg1X0yaRjqE4uIiIg0nPr2iRUM3g+Ki+0Xu2fP1g3cEhEREZHGq7i4mPj4+IZuRqOlPrGIiIhIw9tdn9gyGkKxz0KhEBs3biQ2NhbLshq6OYedoqIiWrZsybp164iLi2vo5sh+pvN7ZNP5PbLp/B75jpRzbIyhuLiY9PR0HA5lQWso6hMffEfKe1h0Lo8kOpdHDp3LI0djOZf17RNrZPB+4HA4aNGiRUM347AXFxd3RL8pGzud3yObzu+RTef3yHcknGONCG546hM3nCPhPSw2ncsjh87lkUPn8sjRGM5lffrEGjohIiIiIiIiIiIi0ggoGCwiIiIiIiIiIiLSCCgYLA3O6/UyduxYvF5vQzdFDgCd3yObzu+RTef3yKdzLHJ403v4yKFzeeTQuTxy6FweOXQua9IEciIiIiIiIiIiIiKNgEYGi4iIiIiIiIiIiDQCCgaLiIiIiIiIiIiINAIKBouIiIiIiIiIiIg0AgoGi4iIiIiIiIiIiDQCCgbLQffggw9ywgknEBUVRUJCQr3WMcZwzz330KxZMyIjIxkwYAC//vrrgW2o7LVt27Zx8cUXExcXR0JCAldeeSUlJSW7XKdfv35YllXjcd111x2kFsuuPP/882RmZhIREUHv3r354Ycfdll/0qRJdOjQgYiICLp27cpHH310kFoqe2NPzu+ECRNqvU8jIiIOYmtlT3z11VecccYZpKenY1kW77333m7X+eKLLzjmmGPwer20bduWCRMmHPB2ikhNq1ev5sorr6R169ZERkaSlZXF2LFj8fl8NeotXLiQk08+mYiICFq2bMmjjz5aa1u7+05WH/vAq8+1z9q1axk2bBhRUVGkpKRw++23EwgEatSpz+fznvbZ5MDQeTi07K4/VJ/Pwfpc39bnM1n2zbhx4zj22GOJjY0lJSWFESNGsHz58hp1KioquPHGG2natCkxMTGcc8455OTk1Kizvz5zD2cKBstB5/P5GDlyJNdff32913n00Ud55plneOmll5gzZw7R0dEMHjyYioqKA9hS2VsXX3wxS5YsYfr06UybNo2vvvqKa665ZrfrXX311WzatCn80Bdow3v77be59dZbGTt2LPPmzaNbt24MHjyYLVu21Fn/u+++48ILL+TKK69k/vz5jBgxghEjRrB48eKD3HKpjz09vwBxcXE13qdr1qw5iC2WPVFaWkq3bt14/vnn61U/OzubYcOG0b9/fxYsWMCYMWO46qqr+PTTTw9wS0Vke7/88guhUIiXX36ZJUuW8OSTT/LSSy/xl7/8JVynqKiIQYMG0apVK+bOnctjjz3Gvffeyz//+c9wnfp8J6uPfeDt7tonGAwybNgwfD4f3333Ha+99hoTJkzgnnvuCdepz+fz3nyny/6n83Do2V1/qD6fg7u7vq3PZ7Lsuy+//JIbb7yR77//nunTp+P3+xk0aBClpaXhOn/605/44IMPmDRpEl9++SUbN27k7LPPDi/fX5+5hz0j0kDGjx9v4uPjd1svFAqZtLQ089hjj4XLCgoKjNfrNW+++eYBbKHsjaVLlxrA/Pjjj+Gyjz/+2FiWZTZs2LDT9fr27WtuueWWg9BC2RPHHXecufHGG8PPg8GgSU9PN+PGjauz/nnnnWeGDRtWo6x3797m2muvPaDtlL2zp+e3vp/bcugBzLvvvrvLOnfccYfp3LlzjbLzzz/fDB48+AC2TETq49FHHzWtW7cOP3/hhRdMYmKiqaysDJfdeeedpn379uHnu/tOVh/74NrZd+hHH31kHA6H2bx5c7jsxRdfNHFxceHzW5/P5z39TpcDQ+fh0LZjf6g+n4P1ub6tz2ey7H9btmwxgPnyyy+NMfa5c7vdZtKkSeE6y5YtM4CZPXu2MWb/feYe7jQyWA552dnZbN68mQEDBoTL4uPj6d27N7Nnz27AlkldZs+eTUJCAr169QqXDRgwAIfDwZw5c3a57sSJE0lKSqJLly7cfffdlJWVHejmyi74fD7mzp1b473ncDgYMGDATt97s2fPrlEfYPDgwXqvHoL25vwClJSU0KpVK1q2bMmZZ57JkiVLDkZz5SDQ+1fk0FVYWEiTJk3Cz2fPns0pp5yCx+MJlw0ePJjly5eTn58frrOr97T62IeG2bNn07VrV1JTU8NlgwcPpqioKPwdu7tzubff6bJ/6TwcfurzOVif69v6fCbL/ldYWAgQ/n6cO3cufr+/xvns0KEDGRkZNc7nvn7mHgkUDJZD3ubNmwFqvFmrn1cvk0PH5s2bSUlJqVHmcrlo0qTJLs/XRRddxH//+19mzZrF3Xffzeuvv84ll1xyoJsru7B161aCweAevfc2b96s9+phYm/Ob/v27Xn11Vd5//33+e9//0soFOKEE05g/fr1B6PJcoDt7P1bVFREeXl5A7VKRFauXMmzzz7LtddeGy7b2fu1etmu6my/fPv16qojB96+nMvqz+e9+U6X/U/n4fBTn8/B+lzf1ud9LPtXKBRizJgxnHjiiXTp0gWwX2uPx1MrP/uO53NfP3OPBAoGy35x11131ZpUaMfHL7/80tDNlH1woM/xNddcw+DBg+natSsXX3wx//nPf3j33Xf57bff9uNRiMi+6NOnD5dddhndu3enb9++TJ06leTkZF5++eWGbpqIyCFvb/pSGzZsYMiQIYwcOZKrr766gVouO9K1j4hIw7rxxhtZvHgxb731VkM35bDkaugGyJHhtttu4/LLL99lnTZt2uzVttPS0gDIycmhWbNm4fKcnBy6d+++V9uUPVffc5yWllZrgoRAIMC2bdvC57I+evfuDdijYbKysva4vbLvkpKScDqdtWZfzcnJ2em5TEtL26P60nD25vzuyO1206NHD1auXHkgmigH2c7ev3FxcURGRjZQq0SOHHvaX964cSP9+/fnhBNOqDUJ0c7er9XLdlVn++XVZepj75n9ee2TlpbGDz/8UKOsvuey+vPZ6XTu83e67Lv90beSg6s+n4P1ub6tz2ey7D833XRTeCK/Fi1ahMvT0tLw+XwUFBTUGB2843ffvn7mHgk0Mlj2i+TkZDp06LDLx/b5c/ZE69atSUtLY8aMGeGyoqIi5syZQ58+ffbXIchu1Pcc9+nTh4KCAubOnRted+bMmYRCoXCAtz4WLFgAUONLWQ4uj8dDz549a7z3QqEQM2bM2Ol7r0+fPjXqA0yfPl3v1UPQ3pzfHQWDQRYtWqT36RFC71+RA2tP+ssbNmygX79+9OzZk/Hjx+Nw1Lxs69OnD1999RV+vz9cNn36dNq3b09iYmK4zq7e0+pj7739ee3Tp08fFi1aVCPYNH36dOLi4ujUqVO4zq7O5f74Tpd9p/Nw+KnP52B9rm/r85ks+84Yw0033cS7777LzJkzad26dY3lPXv2xO121zify5cvZ+3atTXO575+5h4RGnoGO2l81qxZY+bPn2/uu+8+ExMTY+bPn2/mz59viouLw3Xat29vpk6dGn7+8MMPm4SEBPP++++bhQsXmjPPPNO0bt3alJeXN8QhyG4MGTLE9OjRw8yZM8d88803pl27dubCCy8ML1+/fr1p3769mTNnjjHGmJUrV5r777/f/PTTTyY7O9u8//77pk2bNuaUU05pqEOQKm+99Zbxer1mwoQJZunSpeaaa64xCQkJ4dlXL730UnPXXXeF63/77bfG5XKZxx9/3CxbtsyMHTvWuN1us2jRooY6BNmFPT2/9913n/n000/Nb7/9ZubOnWsuuOACExERYZYsWdJQhyC7UFxcHP6OBcw//vEPM3/+fLNmzRpjjDF33XWXufTSS8P1V61aZaKiosztt99uli1bZp5//nnjdDrNJ5980lCHINIorV+/3rRt29acdtppZv369WbTpk3hR7WCggKTmppqLr30UrN48WLz1ltvmaioKPPyyy+H69TnO1l97ANvd9c+gUDAdOnSxQwaNMgsWLDAfPLJJyY5Odncfffd4W3U5/N5d9/pcnDoPBx6dtcfqs/n4O6ub+vzmSz77vrrrzfx8fHmiy++qPHdWFZWFq5z3XXXmYyMDDNz5kzz008/mT59+pg+ffqEl++vz9zDnYLBctCNGjXKALUes2bNCtcBzPjx48PPQ6GQ+dvf/mZSU1ON1+s1p512mlm+fPnBb7zUS15enrnwwgtNTEyMiYuLM6NHj64R7M/Ozq5xzteuXWtOOeUU06RJE+P1ek3btm3N7bffbgoLCxvoCGR7zz77rMnIyDAej8ccd9xx5vvvvw8v69u3rxk1alSN+u+884456qijjMfjMZ07dzYffvjhQW6x7Ik9Ob9jxowJ101NTTWnn366mTdvXgO0Wupj1qxZdX7fVp/TUaNGmb59+9Zap3v37sbj8Zg2bdrU+C4WkYNj/Pjxdb53dxzH8/PPP5uTTjrJeL1e07x5c/Pwww/X2tbuvpPVxz7w6nPts3r1ajN06FATGRlpkpKSzG233Wb8fn+N7dTn83lX3+ly8Og8HFp21x+qz+fg7q5vjanfZ7Lsm519N27/eVheXm5uuOEGk5iYaKKiosxZZ51V44+pxuy/z9zDmWWMMQd27LGIiIiIiIiIiIiINDTlDBYRERERERERERFpBBQMFhEREREREREREWkEFAwWERERERERERERaQQUDBYRERERERERERFpBBQMFhEREREREREREWkEFAwWERERERERERERaQQUDBYRERERERERERFpBBQMFhEREREREREREWkEFAwWERERERERERERaQQUDBYROcxcfvnlWJbFddddV2vZjTfeiGVZXH755TXqWpaF2+0mNTWVgQMH8uqrrxIKhWqs+/PPP/OHP/yBlJQUIiIiyMzM5Pzzz2fLli07bUtFRQWXX345Xbt2xeVyMWLEiP15qCIiIiIidVKfWERk7ygYLCJyGGrZsiVvvfUW5eXl4bKKigreeOMNMjIyatQdMmQImzZtYvXq1Xz88cf079+fW265heHDhxMIBADIzc3ltNNOo0mTJnz66acsW7aM8ePHk56eTmlp6U7bEQwGiYyM5Oabb2bAgAEH5mBFREREROqgPrGIyJ5zNXQDRERkzx1zzDH89ttvTJ06lYsvvhiAqVOnkpGRQevWrWvU9Xq9pKWlAdC8eXOOOeYYjj/+eE477TQmTJjAVVddxbfffkthYSGvvPIKLpf91dC6dWv69++/y3ZER0fz4osvAvDtt99SUFCwn49URERERKRu6hOLiOw5jQwWETlMXXHFFYwfPz78/NVXX2X06NH1WvfUU0+lW7duTJ06FYC0tDQCgQDvvvsuxpgD0l4RERERkf1NfWIRkT2jYLCIyGHqkksu4ZtvvmHNmjWsWbOGb7/9lksuuaTe63fo0IHVq1cDcPzxx/OXv/yFiy66iKSkJIYOHcpjjz1GTk7OAWq9iIiIiMi+U59YRGTPKBgsInKYSk5OZtiwYUyYMIHx48czbNgwkpKS6r2+MQbLssLPH3zwQTZv3sxLL71E586deemll+jQoQOLFi0CoHPnzsTExBATE8PQoUP3+/GIiIiIiOwp9YlFRPaMcgaLiBzGrrjiCm666SYAnn/++T1ad9myZbVyqTVt2pSRI0cycuRIHnroIXr06MHjjz/Oa6+9xkcffYTf7wcgMjJy/xyAiIiIiMg+Up9YRKT+FAwWETmMDRkyBJ/Ph2VZDB48uN7rzZw5k0WLFvGnP/1pp3U8Hg9ZWVnhmZNbtWq1z+0VEREREdnf1CcWEak/BYNFRA5jTqeTZcuWhX+uS2VlJZs3byYYDJKTk8Mnn3zCuHHjGD58OJdddhkA06ZN46233uKCCy7gqKOOwhjDBx98wEcffVRjQo66LF26FJ/Px7Zt2yguLmbBggUAdO/efb8dp4iIiIjIzqhPLCJSfwoGi4gc5uLi4na5/JNPPqFZs2a4XC4SExPp1q0bzzzzDKNGjcLhsFPHd+rUiaioKG677TbWrVuH1+ulXbt2vPLKK1x66aW73P7pp5/OmjVrws979OgBoBmYRUREROSgUZ9YRKR+LKNPJhEREREREREREZEjnqOhGyAiIiIiIiIiIiIiB56CwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiB9i9996LZVkN3YydWr16NZZlMWHChIZuygHz6KOP0qFDB0KhUEM3ZacyMzO5/PLLG2z/F1xwAeedd16D7V9EREQOHw3db9mdQ73/va9CoRBdunThwQcfbOim7NQXX3yBZVl88cUXDbL/vLw8oqOj+eijjxpk/yKHMgWDRRqZCRMmYFkWlmXxzTff1FpujKFly5ZYlsXw4cNrLS8tLeXvf/87Rx99NFFRUcTHx3PyySfzn//8B2NMrfrV+9rxkZaWVqNeQUEBERERWJbFsmXL9t8B70R1B7H64Xa7yczM5Oabb6agoOCA7/9wVN2hq354vV5SU1Pp168fDz30ELm5uXu97aVLl3LvvfeyevXq/dfgKkVFRTzyyCPceeedOBy/f+29/fbbXHLJJbRr1w7LsujXr99Ot1FZWcmdd95Jeno6kZGR9O7dm+nTp+/3tjakO++8kylTpvDzzz83dFNEREQOC9nZ2dx0000cddRRREVFERUVRadOnbjxxhtZuHBhjbo79j23f7z00ks16t5xxx1YlsX555+/R+3JzMyssd3o6GiOO+44/vOf/+zzsR6pLr/88hqvWUxMDG3atOHcc89lypQp+zSQ4I033uCpp57af43dzptvvsm6deu46aabwmUlJSWMHTuWIUOG0KRJk90O9li2bBlDhgwhJiaGJk2acOmll+5Tf/5Q07RpU6666ir+9re/NXRTRA45roZugIg0jIiICN544w1OOumkGuVffvkl69evx+v11lonJyeH0047jWXLlnHBBRdw0003UVFRwZQpUxg1ahQfffQREydOxOl01lhv4MCBXHbZZTXKIiMjazyfNGlSOEg8ceJEHnjggf10pLv24osvEhMTQ2lpKTNmzODZZ59l3rx5dQbKxXbzzTdz7LHHEgwGyc3N5bvvvmPs2LH84x//4J133uHUU0/d420uXbqU++67j379+pGZmblf2/vqq68SCAS48MILa5S/+OKLzJ07l2OPPZa8vLxdbuPyyy9n8uTJjBkzhnbt2jFhwgROP/10Zs2aVes9dLjq0aMHvXr14oknntBFo4iIyG5MmzaN888/H5fLxcUXX0y3bt1wOBz88ssvTJ06lRdffJHs7GxatWpVY73qvuf2evfuHf7ZGMObb75JZmYmH3zwAcXFxcTGxta7Xd27d+e2224DYNOmTbzyyiuMGjWKyspKrr766n044iOX1+vllVdeAaC8vJw1a9bwwQcfcO6559KvXz/ef/994uLi9ni7b7zxBosXL2bMmDH7ucXw2GOPccEFFxAfHx8u27p1K/fffz8ZGRl069ZtlyNy169fzymnnEJ8fDwPPfQQJSUlPP744yxatIgffvgBj8ez39vcEK677jqeeeYZZs6cuVfXKCJHLCMijcr48eMNYM4++2yTlJRk/H5/jeVXX3216dmzp2nVqpUZNmxYjWWDBw82DofDvP/++7W2++c//9kA5uGHH65RDpgbb7xxt+065ZRTzNlnn23+9Kc/mdatW9f7eAAzfvz4etevNnbsWAOY3NzcGuXnn3++AcycOXP2eJu729ehKjs7u16v46xZswxgJk2aVGvZggULTEpKiklISDAbN27c4zZMmjTJAGbWrFl7vO7uHH300eaSSy6pVb527VoTDAaNMcZ07tzZ9O3bt87158yZYwDz2GOPhcvKy8tNVlaW6dOnz35rZ6tWrcyoUaP22/b2xuOPP26io6NNcXFxg7ZDRETkULZy5UoTHR1tOnbsWGe/x+/3m6efftqsXbs2XLazvueOZs6caQAzc+ZM43a7zYQJE+rdrrr671u2bDExMTGmY8eO9d5OfffV0P2WXalv/3vUqFEmOjq6zmXjxo0zgDnvvPP2qg3Dhg0zrVq12qt1d2XevHkGMJ9//nmN8oqKCrNp0yZjjDE//vjjLvv3119/vYmMjDRr1qwJl02fPt0A5uWXX94v7ay+djgQ/fs90aVLF3PppZc2aBtEDjVKEyHSSF144YXk5eXVuNXd5/MxefJkLrroolr1v//+ez799FMuv/xy/vCHP9RaPm7cONq1a8cjjzxCeXn5HrVl7dq1fP3111xwwQVccMEFZGdn89133+35Qe0HJ598MgC//fZbjfI5c+YwZMgQ4uPjiYqKom/fvnz77be11v/mm2849thjiYiIICsri5dffrlWnV3l6LUsi3vvvbdG2YYNG7jyyitJT0/H6/XSunVrrr/+enw+X7hOQUEBY8aMoWXLlni9Xtq2bcsjjzxS69a2goICLr/8cuLj40lISGDUqFH7JS1Gt27deOqppygoKOC5554Ll69Zs4YbbriB9u3bExkZSdOmTRk5cmSNdBATJkxg5MiRAPTv3z98m171aIb333+fYcOGhY8/KyuLv//97wSDwd22Kzs7m4ULFzJgwIBay1q2bFkjbcTOTJ48GafTyTXXXBMui4iI4Morr2T27NmsW7dut9sIhUI8/fTTdO3alYiICJKTkxkyZAg//fTTTtfZtm0bf/7zn+natSsxMTHExcUxdOjQOtM4PPvss3Tu3JmoqCgSExPp1asXb7zxRnh5cXExY8aMITMzE6/XS0pKCgMHDmTevHk1tjNw4EBKS0uPuBQYIiIi+9Ojjz5KaWkp48ePp1mzZrWWu1wubr75Zlq2bLnH2544cSKdOnWif//+DBgwgIkTJ+5TW5OTk+nQoUOtvm0oFOKpp56ic+fOREREkJqayrXXXkt+fn6NesYYHnjgAVq0aEFUVBT9+/dnyZIltfazsxy91SnqdkwF9vHHH9O3b19iY2OJi4vj2GOPrdF3gf3b/94bd911F4MGDWLSpEmsWLEiXF6fvmm/fv348MMPWbNmTbhvW333m8/n45577qFnz57Ex8cTHR3NySefzKxZs+rVrvfeew+Px8Mpp5xSo9zr9dZKxbczU6ZMYfjw4WRkZITLBgwYwFFHHcU777xTr23U5xplR19//TUjR44kIyMDr9dLy5Yt+dOf/lTr+nHz5s2MHj2aFi1a4PV6adasGWeeeWaN36OffvqJwYMHk5SURGRkJK1bt+aKK66otc+BAwfywQcf1JnSUKSxUpoIkUYqMzOTPn368OabbzJ06FDA7pQVFhZywQUX8Mwzz9So/8EHHwDUSvdQzeVycdFFF3Hffffx7bff1gi+VVRUsHXr1hr1Y2Njw6ko3nzzTaKjoxk+fDiRkZFkZWUxceJETjjhhP12vPVV3cFITEwMl82cOZOhQ4fSs2dPxo4di8PhYPz48Zx66ql8/fXXHHfccQAsWrSIQYMGkZyczL333ksgEGDs2LGkpqbudXs2btzIcccdR0FBAddccw0dOnRgw4YNTJ48mbKyMjweD2VlZfTt25cNGzZw7bXXkpGRwXfffcfdd9/Npk2bwrnKjDGceeaZfPPNN1x33XV07NiRd999l1GjRu11+7Z37rnncuWVV/LZZ5+FJ7P48ccf+e6777jgggto0aIFq1ev5sUXX6Rfv34sXbqUqKgoTjnlFG6++WaeeeYZ/vKXv9CxY0eA8P8TJkwgJiaGW2+9lZiYGGbOnMk999xDUVERjz322C7bVP1HhWOOOWavj2v+/PkcddRRtW4PrD7vCxYs2O3F3pVXXsmECRMYOnQoV111FYFAgK+//prvv/+eXr161bnOqlWreO+99xg5ciStW7cmJyeHl19+mb59+7J06VLS09MB+Ne//sXNN9/Mueeeyy233EJFRQULFy5kzpw54T/sXHfddUyePJmbbrqJTp06kZeXxzfffMOyZctqvDadOnUiMjKSb7/9lrPOOmuvXzMREZEj2bRp02jbtm2N9A71tW3bthrPnU5nuN9ZWVnJlClTwmkeLrzwQkaPHs3mzZvrHeTbUSAQYP369TX6tgDXXnstEyZMYPTo0dx8881kZ2fz3HPPMX/+fL799lvcbjcA99xzDw888ACnn346p59+OvPmzWPQoEG7DPjtzoQJE7jiiivo3Lkzd999NwkJCcyfP59PPvkk3HdpyP739i699FI+++wzpk+fzlFHHRVu/+76pv/3f/9HYWEh69ev58knnwQIpwcpKirilVde4cILL+Tqq6+muLiYf//73wwePJgffviB7t2777JN3333HV26dAmfoz21YcMGtmzZUmcf9LjjjqvXhGv1uUapy6RJkygrK+P666+nadOm/PDDDzz77LOsX7+eSZMmheudc845LFmyhD/+8Y9kZmayZcsWpk+fztq1a8PPq8/7XXfdRUJCAqtXr2bq1Km19tmzZ0+efPJJlixZQpcuXfbglRI5gjXwyGQROciq00T8+OOP5rnnnjOxsbGmrKzMGGPMyJEjTf/+/Y0xtW8zGzFihAFMfn7+Trc9depUA5hnnnkmXAbU+dj+lqWuXbuaiy++OPz8L3/5S50pLOqy47bqq/rWseXLl5vc3FyzevVq8+qrr5rIyEiTnJxsSktLjTHGhEIh065dOzN48GATCoXC65eVlZnWrVubgQMHhstGjBhhIiIiatxutXTpUuN0OmvcprartAyAGTt2bPj5ZZddZhwOh/nxxx9r1a1uz9///ncTHR1tVqxYUWP5XXfdZZxOZ/gWxffee88A5tFHHw3XCQQC5uSTT97nNBHVunXrZhITE8PPq3+3tjd79mwDmP/85z/hsl2liahrG9dee62JiooyFRUVu2zzX//6VwPsNu3BrtJEdO7c2Zx66qm1ypcsWWIA89JLL+1y29W3e9588821lm3/O7Xj7ZYVFRXhNBbVsrOzjdfrNffff3+47MwzzzSdO3feZRvi4+Prla7FGGOOOuooM3To0HrVFRERaWwKCwsNYEaMGFFrWX5+vsnNzQ0/tu/DVPc9d3xsn0Zg8uTJBjC//vqrMcaYoqIiExERYZ588sl6ta1Vq1Zm0KBB4f0vWrTIXHrppbXStn399dcGMBMnTqyx/ieffFKjfMuWLcbj8Zhhw4bV6LP85S9/MUCNfsvO0jJUX3tkZ2cbY4wpKCgwsbGxpnfv3qa8vLxG3ep9HIj+987sKk2EMcbMnz/fAOZPf/pTjXbsqK6+6c7SRAQCAVNZWVmjLD8/36Smpporrrhit21u0aKFOeecc3ZZZ1dpIqqXbd8Xr3b77bcbYLd97Ppco9SVJqKu127cuHHGsqzwOczPz6+Vom1H7777bviadne+++47A5i33357t3VFGguliRBpxM477zzKy8uZNm0axcXFTJs2rc4UEWDfZg7scgKL6mVFRUU1ys8880ymT59e4zF48GAAFi5cyKJFi2pM7nXhhReydetWPv300xrbKSsrY+vWrTUeYM+cu33Zjre37Ur79u1JTk4mMzOTK664grZt2/Lxxx8TFRUF2KM+f/31Vy666CLy8vLC+ygtLeW0007jq6++IhQKEQwG+fTTTxkxYkSN2606duwYPtY9FQqFeO+99zjjjDPq/Mt99a14kyZN4uSTTyYxMbHG6zBgwACCwSBfffUVAB999BEul4vrr78+vA2n08kf//jHvWpfXWJiYsK/K1BzokC/309eXh5t27YlISGhVoqCndl+G8XFxWzdupWTTz6ZsrIyfvnll12um5eXh8vlqjVRy54oLy+vc0LFiIiI8PJdmTJlCpZlMXbs2FrL6rqdsprX6w2nsQgGg+Tl5RETE0P79u1rvHYJCQmsX7+eH3/8cafbSkhIYM6cOWzcuHGXbQXCv0ciIiJSW3U/t66+Rb9+/UhOTg4/nn/++Vp1pkyZUqNPvH0aiIkTJ9KrVy/atm0L2H3rYcOG7VGqiM8++yy8/65du/L6668zevToGndTTZo0ifj4eAYOHFij79izZ09iYmLC6Qo+//xzfD4ff/zjH2v0WfZlQrTp06dTXFzMXXfdFe5LVaveR0P2v3dUfZ531r/d074p2P3v6pGzoVCIbdu2EQgE6NWrV736x3l5ebVGeu+J6r7r3vZv63uNUpftX7vS0lK2bt3KCSecgDGG+fPnh+t4PB6++OKLnV7XJSQkAPYofb/fv9P9we93fKp/K/I7pYkQacSSk5MZMGAAb7zxBmVlZQSDQc4999w661YHeouLi8NfvjvaWcC4RYsWdeZsBfjvf/9LdHQ0bdq0YeXKlYDdCcnMzGTixIkMGzYsXPfRRx/lvvvuq7WNP/7xjzUCmq1ataqVl2xnpkyZQlxcHLm5uTzzzDNkZ2fX6KT8+uuvALtMpVBYWEhlZSXl5eW0a9eu1vL27dvX63arHeXm5lJUVLTb25l+/fVXFi5cSHJycp3Lt2zZAtj5e5s1a1br4qV9+/Z73LadKSkpqXH+y8vLGTduHOPHj2fDhg01cnUVFhbWa5tLlizhr3/9KzNnzqz1h4b6bmNfREZGUllZWau8oqIivBzs2z63v2UyMjKS+Ph4fvvtN9LT02nSpMke7bc6z/ALL7xAdnZ2jTx0TZs2Df9855138vnnn3PcccfRtm1bBg0axEUXXcSJJ54YrvPoo48yatQoWrZsSc+ePTn99NO57LLLaNOmTa39GmN22YkXERFpzKr7OSUlJbWWvfzyyxQXF5OTk8Mll1xS5/qnnHIKSUlJtcoLCgr46KOPuOmmm8J9YoATTzyRKVOmsGLFinCagl3p3bs3DzzwAMFgkMWLF/PAAw+Qn59f47b9X3/9lcLCQlJSUurcxvZ9R6BW/zY5OXmvg5HVuYt31b9tyP73jqrP8/b92/3RN33ttdd44okn+OWXX2oEM1u3bl2v9c0+5L+t7rvurn8bDAbJzc2tsbxJkybk5+fX6xqlLmvXruWee+7hf//7X61Ab/Vr5/V6eeSRR7jttttITU3l+OOPZ/jw4Vx22WXhdCl9+/blnHPO4b777uPJJ5+kX79+jBgxgosuuqhWkLv6tVL/VuR3CgaLNHIXXXQRV199NZs3b2bo0KE7DfR27NiR9957j4ULF9aarKDawoULATvvaH0YY3jzzTcpLS2tc50tW7ZQUlISDl5edtllnHTSSTXqDBw4kNtvv51BgwaFy7YP5u7O9h3yM844g65du3LxxRczd+5cHA5HeAK2xx57bKf5u2JiYursTO3Mzjoi9ZkQrS6hUIiBAwdyxx131Lm8PhcO+4Pf72fFihU1OoZ//OMfGT9+PGPGjKFPnz7Ex8djWRYXXHBBrcnt6lJQUEDfvn2Ji4vj/vvvJysri4iICObNm8edd9652200bdqUQCBAcXHxLke170qzZs3YsGFDrfJNmzYBhHP3nn322Xz55Zfh5aNGjapzksD6euihh/jb3/7GFVdcwd///neaNGmCw+FgzJgxNY67Y8eOLF++nGnTpvHJJ58wZcoUXnjhBe65557wH0/OO+88Tj75ZN59910+++wzHnvsMR555BGmTp0azhleLT8/v86LKhEREYH4+HiaNWvG4sWLay2rziFc30EJ25s0aRKVlZU88cQTPPHEE7WWT5w4sc5BETtKSkoKD8IYPHgwHTp0YPjw4Tz99NPceuutgN13TElJ2emI450NMNiV/dm/PRD9771VfZ6rR2vva98U7MEwl19+OSNGjOD2228nJSUFp9PJuHHjak30V5emTZvu0Z2QO6qe9LC6L7u9TZs20aRJE7xeL6tXr64VnJ41a1Z4Xo89FQwGGThwINu2bePOO++kQ4cOREdHs2HDBi6//PIar92YMWM444wzeO+99/j000/529/+xrhx45g5cyY9evTAsiwmT57M999/zwcffMCnn37KFVdcwRNPPMH3339fY/BL9WtV1x9hRBorBYNFGrmzzjqLa6+9lu+//5633357p/WGDx/OuHHj+M9//lNnMDgYDPLGG2+QmJhYY0Tirnz55ZesX7+e+++/v1anIj8/n2uuuYb33nsvPLKiTZs2dY5k7NSp005HHu+JmJgYxo4dy+jRo3nnnXe44IILyMrKAiAuLm6X+0hOTiYyMjI8kmF7y5cvr/G8eiRFQUFBjfLq0RfbbzMuLq7Oi43tZWVlUVJSstvXoFWrVsyYMaNGgL2u9u2tyZMnU15eXuO2vMmTJzNq1KgaFzUVFRW1jn1nFxBffPEFeXl5TJ06tcbvXXZ2dr3a1KFDh3D9o48+ur6HUkP37t2ZNWsWRUVFNSaRmzNnTng5wBNPPFGjY14dJM7KyuLTTz9l27ZtezQ6ePLkyfTv359///vfNcoLCgpqdWajo6M5//zzOf/88/H5fJx99tk8+OCD3H333eHb/Zo1a8YNN9zADTfcwJYtWzjmmGN48MEHawSDA4EA69at4w9/+EO92ykiItLYDBs2jFdeeYUffvghPJHZvpo4cSJdunSpM63Uyy+/zBtvvFGvYPCOhg0bRt++fXnooYe49tpriY6OJisri88//5wTTzxxl4MoWrVqBdgjdbfvg+fm5tYKRm7fv91+cMmO/dvqvvXixYvDAdYdHYj+9956/fXXsSyLgQMHAnvWN91Z/3by5Mm0adOGqVOn1qhT17mvS4cOHerdF65L8+bNSU5O5qeffqq1bPsJ7NLS0pg+fXqN5d26dSM+Pr5e1yg7WrRoEStWrOC1116rMSn5jvuolpWVxW233cZtt93Gr7/+Svfu3XniiSf473//G65z/PHHc/zxx/Pggw/yxhtvcPHFF/PWW29x1VVXhetUv1Z7G8QWORIpZ7BIIxcTE8OLL77IvffeyxlnnLHTeieccAIDBgxg/PjxTJs2rdby//u//2PFihXccccd9R6ZW50i4vbbb+fcc8+t8bj66qtp167dHuVI2x8uvvhiWrRowSOPPALYs89mZWXx+OOP13k7YPWtU06nk8GDB/Pee++xdu3a8PJly5bVyn0cFxdHUlJSOJdvtRdeeKHGc4fDwYgRI/jggw/q7KxV3/J03nnnMXv27Fr7AbtDHggEADj99NMJBAK8+OKL4eXBYJBnn3125y9IPf3888+MGTOGxMREbrzxxnC50+msdRvbs88+W2uUSHR0dLi923M6nUDNW+F8Pl+t12pn+vTpA1Dn61df5557LsFgkH/+85/hssrKSsaPH0/v3r1p2bIlYP+uDBgwIPyoHu1+zjnnYIyp8wJuV7f41fXaTZo0qdYo5by8vBrPPR4PnTp1whiD3+8nGAzWumUxJSWF9PT0WiNqli5dSkVFBSeccMJO2yUiItLY3XHHHURFRXHFFVeQk5NTa/me3sK/bt06vvrqK84777xafeJzzz2X0aNHs3LlyvAfovfUnXfeSV5eHv/6178Au+8YDAb5+9//XqtuIBAI98cGDBiA2+3m2WefrXFMTz31VK31qgO42/dvS0tLee2112rUGzRoELGxsYwbNy6ckqBa9T4ORP97bzz88MN89tlnnH/++eG7pvakbxodHV1n2oi6tjFnzhxmz55dr3b16dOHxYsX79PI6HPOOYdp06axbt26cNmMGTNYsWIFI0eOBOzUfdv3bQcMGEBiYmK9r1F2VNdxG2N4+umna9QrKyur9buRlZVFbGxs+Jjz8/Nr7ac6iL3j6zJ37lzi4+Pp3LnzTl8PkcZGI4NFZJf5uLb3n//8h9NOO40zzzyTiy66iJNPPpnKykqmTp3KF198wfnnn8/tt99er21VVlYyZcoUBg4cWGvyiGp/+MMfePrpp9myZctOc5rtb263m1tuuYXbb7+dTz75hCFDhvDKK68wdOhQOnfuzOjRo2nevDkbNmxg1qxZxMXF8cEHHwBw33338cknn3DyySdzww03EAgEePbZZ+ncuXM4hUa1q666iocffpirrrqKXr168dVXX7FixYpa7XnooYf47LPP6Nu3L9dccw0dO3Zk06ZNTJo0iW+++YaEhARuv/12/ve//zF8+HAuv/xyevbsSWlpKYsWLWLy5MmsXr2apKQkzjjjDE488UTuuusuVq9eTadOnZg6deoe5939+uuvqaioCE9q9u233/K///2P+Ph43n333XAuL7BHlL/++uvEx8fTqVMnZs+ezeeff14j5y3YnTen08kjjzxCYWEhXq+XU089lRNOOIHExERGjRrFzTffjGVZvP766/W+yGrTpg1dunTh888/54orrqix7KuvvgpfsOTm5lJaWsoDDzwA2OlDqkd79O7dm5EjR3L33XezZcsW2rZty2uvvcbq1atrjdqtS//+/bn00kt55pln+PXXXxkyZAihUIivv/6a/v37c9NNN9W53vDhw7n//vsZPXo0J5xwAosWLWLixIm1RscPGjSItLQ0TjzxRFJTU1m2bBnPPfccw4YNIzY2loKCAlq0aMG5555Lt27diImJ4fPPP+fHH3+sdRvq9OnTiYqKCo9+ERERkdratWvHG2+8wYUXXkj79u25+OKL6datG8YYsrOzeeONN3A4HLRo0aJe23vjjTcwxuz0zpzTTz8dl8vFxIkTw6ko9sTQoUPp0qUL//jHP7jxxhvp27cv1157LePGjWPBggUMGjQIt9vNr7/+yqRJk3j66ac599xzSU5O5s9//jPjxo1j+PDhnH766cyfP5+PP/641l1KgwYNIiMjgyuvvJLbb78dp9PJq6++SnJyco1AbVxcHE8++SRXXXUVxx57LBdddBGJiYn8/PPPlJWV8dprr+FwOA5I/3tnAoFAeLRpRUUFa9as4X//+x8LFy6kf//+NQYE7EnftGfPnrz99tvceuutHHvsscTExHDGGWcwfPhwpk6dyllnncWwYcPIzs7mpZdeolOnTnUGv3d05pln8ve//50vv/yyRqo8gOeee46CgoLwpMEffPAB69evB+z0bfHx8QD85S9/YdKkSfTv359bbrmFkpISHnvsMbp27cro0aN324b6XKPsqEOHDmRlZfHnP/+ZDRs2EBcXx5QpU2qNMl+xYgWnnXYa5513Hp06dcLlcvHuu++Sk5PDBRdcANg5l1944QXOOusssrKyKC4u5l//+hdxcXGcfvrpNbY3ffp0zjjjDOUMFtmeEZFGZfz48QYwP/744y7rtWrVygwbNqxWeXFxsbn33ntN586dTWRkpImNjTUnnniimTBhggmFQrXqA+bGG2+sVT5lyhQDmH//+987bcMXX3xhAPP000/vtA5gxo8fv8tjqcvYsWMNYHJzc2stKywsNPHx8aZv377hsvnz55uzzz7bNG3a1Hi9XtOqVStz3nnnmRkzZtRY98svvzQ9e/Y0Ho/HtGnTxrz00kvhfW2vrKzMXHnllSY+Pt7Exsaa8847z2zZssUAZuzYsTXqrlmzxlx22WUmOTnZeL1e06ZNG3PjjTeaysrKcJ3i4mJz9913m7Zt2xqPx2OSkpLMCSecYB5//HHj8/nC9fLy8syll15q4uLiTHx8vLn00kvN/Pnz6/U6zpo1ywDhh9vtNsnJyeaUU04xDz74oNmyZUutdfLz883o0aNNUlKSiYmJMYMHDza//PKLadWqlRk1alSNuv/6179MmzZtjNPpNICZNWuWMcaYb7/91hx//PEmMjLSpKenmzvuuMN8+umnNersyj/+8Q8TExNjysrKapRXn5e6Hjueg/LycvPnP//ZpKWlGa/Xa4499ljzySef7Hbf1QKBgHnsscdMhw4djMfjMcnJyWbo0KFm7ty54To7viYVFRXmtttuM82aNTORkZHmxBNPNLNnzzZ9+/at8bv58ssvm1NOOSX8u5mVlWVuv/12U1hYaIwxprKy0tx+++2mW7duJjY21kRHR5tu3bqZF154oVY7e/fubS655JJ6H5eIiEhjtnLlSnP99debtm3bmoiICBMZGWk6dOhgrrvuOrNgwYIadXfV9+zatavJyMjY5b769etnUlJSjN/v32mdnfXfjTFmwoQJtfp7//znP03Pnj3DffquXbuaO+64w2zcuDFcJxgMmvvuuy/cH+nXr59ZvHhxnX25uXPnmt69exuPx2MyMjLMP/7xj/C1R3Z2do26//vf/8wJJ5xgIiMjTVxcnDnuuOPMm2++WaPO/u5/12XUqFE1+oBRUVEmMzPTnHPOOWby5MkmGAzWWqe+fdOSkhJz0UUXmYSEBAOYVq1aGWOMCYVC5qGHHjKtWrUyXq/X9OjRw0ybNs2MGjUqXGd3jj76aHPllVfWKm/VqtVO+7c7noPFixebQYMGmaioKJOQkGAuvvhis3nz5nrt35jdX6NUXzts/5osXbrUDBgwwMTExJikpCRz9dVXm59//rnG7+bWrVvNjTfeaDp06GCio6NNfHy86d27t3nnnXfC25k3b5658MILTUZGhvF6vSYlJcUMHz7c/PTTTzXauGzZMgOYzz//vN7HJdIYWMbswzSUIiIih7jCwkLatGnDo48+ypVXXtnQzTlkLViwgGOOOYZ58+btdLIWEREREWl4r7/+OjfeeCNr167d6QTgYk9E99VXXzF37lyNDBbZjoLBIiJyxHvkkUcYP348S5cuxeFQuvy6XHDBBYRCId55552GboqIiIiI7EIoFOLoo4/mwgsv5P/+7/8aujmHpLy8PFq1asU777xTK3WESGOnYLCIiIiIiIiIiIhII6DhUSIiIiIiIiIiIiKNgILBIiIiIiIiIiIiIo2AgsEiIiIiIiIiIiIijYCCwSIiIiIiIiIiIiKNgKuhG3AkCIVCbNy4kdjYWCzLaujmiIiIiDQqxhiKi4tJT0/H4dBYh4aiPrGIiIhIw6lvn1jB4P1g48aNtGzZsqGbISIiItKorVu3jhYtWjR0Mxot9YlFREREGt7u+sQKBu8HsbGxAMydm01MTGwDt0ZERESkcSkpKaZnz9bhPpk0DPWJRURERBpOffvECgbvB9W3wcXExBIbG9fArRERERFpnJSaoGGpTywiIiLS8HbXJ1ZSNREREREREREREZFGQMFgERERERERERERkUZAwWARERERERERERGRRkDBYBEREREREREREZFGQMFgERERERERERERkUZAwWARERERERERERGRRkDBYBEREREREREREZFGQMFgERERERERERERkUZAwWARERERERERERGRRkDBYBEREREREREREZFGQMFgERERERERERERkUZAwWARERERERERERGRRkDBYBEREREREREREZFGQMFgERERERERERERkUZAwWARERERERERERGRRkDBYBEREREREREREZFGwNXQDRAROdL48fOLazG5zhyiQzF0ChxNrIlr6GaJiIiIiBz2rMoC3NsWYflLCUY3I5DYGRwKbYiI1Jc+MUVE9qNCq4D/RP2TJa6fCVgBLKB5MINLyq6ibbB9QzdPREREROSw5dq2mMhf/o2zdBNgwHLjTz6Gso5XYzyxDd08EZHDgtJEiIjsR59E/I/57h9oHmpJh0BnsgLt2ehcx1uRE6iksqGbJyIiIiJyWLL8pUQufw1nWQ6B+PYEEjsTjE7HvXk23nWfNHTzREQOGwoGi4jsJ2VWKfPdP9A0lEykiQLAhYtWgSzWudaw0vVLA7dQREREROTw5MpfhrNkHYG4LHA4ATDuGEIRibg3fwtBXwO3UETk8KBgsIjIflJJJX7Lj8d4a5S7cRMiiM9SB1VEREREZK8EK8AYsHbIdunwYgV9WCF/w7RLROQwo2CwiMh+Em8SaBloxVZnDgYTLs9z5BJnEmgebNmArRMREREROXwFYzMwnlgcFXm/FxqDo2ILwfi2GFdUwzVOROQwognkRET2EwcOBlSezjrXGpa7lpIQSqTMKiVoBTm9YgQpobSGbqKIiIiIyGEpFN2SyvS+RKz5EMtXiHFF4qjcRigiicqMIWBZDd1EEZHDgoLBIiL7UZdAd64pvZmvPTNZ5VpJs2BzjvedzPG+kxu6aSIiIiIihy/LoqLtBYSi0vFs+hpHZT6Vzfvja34qwYT2Dd06EZHDhoLBIiL7WftAZ9oHOmMwWGiEgoiIiIjIfuFw42txGr4Wp1XlD1ZfW0RkTylnsIjIAaJAsIiIiIjIAaJAsIjIXlEwWERERERERERERKQRUDBYRA4bFYFKtpRtpTxQ3tBNERERERERERE57ChnsIgc8gKhAF+s+5avN3xPYWURMZ5o+jQ7loGt+uFxuhu6eSIiIiIiIiIihwWNDBaRQ96MtV8xacX/KPIVk+CNpzxQwXu/fcS0VZ82dNNERERERERERA4bCgaLyCGt1F/G1xu+J8YdTYuYdGI9MaRHp9HUm8jsTT+xrSK/oZsoIiIiIiIiInJYUDBYRA5p2yryKawsJjEivkZ5QkQ8xb4StpZva6CWiYiIiIiIiIgcXhQMFpFDWqwnhkiXlxJ/WY3yUn8Zka4IYtzRDdQyERERERFpSP6Qn02lOWyrKGjopoiIHDYUDBaRQ1qCN55jUrqRW76VgspCjDEUVRazsWQzXZM60iw6taGbKCIictA9//zzZGZmEhERQe/evfnhhx92WnfJkiWcc845ZGZmYlkWTz311D5vU0SkIRlj+ClnAY//9AIP//AMD815kvFL3iRPdw2KiOyWgsEicsg7I2swJzfvQ7GvhOX5K8n3FdK7WU/Objccy7IaunkiIiIH1dtvv82tt97K2LFjmTdvHt26dWPw4MFs2bKlzvplZWW0adOGhx9+mLS0tP2yTRGRhrQk7xf+u3QS64s3kuiNx+Pw8M367/nP0repDPoaunkiIoc0yxhjGroRh7uioiLi4+NZvnwrsbFxDd0ckSOSMYaNpZvZVpFPvCeOlrHNFQgWEREAiouLaN8+icLCQuLijvy+WO/evTn22GN57rnnAAiFQrRs2ZI//vGP3HXXXbtcNzMzkzFjxjBmzJj9ts1q6hOLyMHy0s8TmL9lIUcltg2XVQQqWVeygeu7jaZbcucGbJ2ISMOob59YI4NF5LBgWRbNY5rRNakTGXEtFAgWEZFGyefzMXfuXAYMGBAuczgcDBgwgNmzZx/UbVZWVlJUVFTjISJyoBljWFe8gXhvzQmmI1xegiakVBEiIruhYLCIiIiIyGFi69atBINBUlNr5sxPTU1l8+bNB3Wb48aNIz4+Pvxo2bLlXu1fRGRPWJZFclQSJb6SGuX+kB8LiPXENkzDREQOEwoGi4iIiIjIHrv77rspLCwMP9atW9fQTRKRRuKE9GPBgo2lmwmEApT5y1hZkE2ruBZ0bnpUQzdPROSQ5mroBoiIiIiISP0kJSXhdDrJycmpUZ6Tk7PTyeEO1Da9Xi9er3ev9ikisi96pXansKKImeu+ZlXhGtwOFx2bHMXIo/5AlDuqoZsnInJIO+xGBj///PNkZmYSERFB7969+eGHH3Zad8mSJZxzzjlkZmZiWRZPPfXUPm9TRET23lbHFua4v2GO+xu2OjRDvYjInvJ4PPTs2ZMZM2aEy0KhEDNmzKBPnz6HzDZFRA4kh+VgYGY/7jruFsb0vJY/9byeW465hoy4Fg3dNBGRQ95hNTL47bff5tZbb+Wll16id+/ePPXUUwwePJjly5eTkpJSq35ZWRlt2rRh5MiR/OlPf9ov2xQRkT1nMMzyfsrH3vfJd9iTeiSGmjCs4mz6+gZgoQkBRUTq69Zbb2XUqFH06tWL4447jqeeeorS0lJGjx4NwGWXXUbz5s0ZN24cYE8Qt3Tp0vDPGzZsYMGCBcTExNC2bdt6bVNE5FAU740j3hvX0M0QETmsWMYY09CNqK/evXtz7LHH8txzzwH2iIWWLVvyxz/+kbvuumuX62ZmZjJmzBjGjBmz37ZZraioiPj4eJYv30psrL6IRER2tNy1hOejn8BlXKSF0gHY7NhA0ApxU+mfaRfo2MAtFJHDWXFxEe3bJ1FYWEhcXOPoiz333HM89thjbN68me7du/PMM8/Qu3dvAPr160dmZiYTJkwAYPXq1bRu3brWNvr27csXX3xRr23Wh/rEIiIiIg2nvn3iw2ZksM/nY+7cudx9993hMofDwYABA5g9e/ZB3WZlZSWVlZXh50VFRXu1fxGRxmKhaz7lVintg53DZemhlvziWsxC13wFg0VE9tBNN93ETTfdVOey7QO8YA+KqM/4j11tU0RERESODIdNzuCtW7cSDAZJTU2tUZ6amsrmzZsP6jbHjRtHfHx8+NGyZcu92r+ISGNR7CjEWcffH524KLL0BzURERERERGRg+GwCQYfSu6++24KCwvDj3Xr1jV0k0REDmmtgm3w4yNAIFwWqPqXGap96/LhIGSC5FVsYWPZGrZV5hIyoYZukoiIiIiIiMguHTZpIpKSknA6neTk5NQoz8nJIS0t7aBu0+v14vV692qfIiKNUU/f8Xzv/prlrqUkhZIxGPIcW2kX6MAxvvrnozxUlAVKWFYwn/zKrRhCOCwnTb0pdEzogdcZ2dDNExEREWkQwVCQXwtWsbpoLQ7LyVGJWbSKbYFlabJgEZFDxWEzMtjj8dCzZ09mzJgRLguFQsyYMYM+ffocMtsUEZHaEkwiV5fdzODK4bhw4cbN4MrhXFV2E/EmoaGbt0eMMawoXEheRQ4x7jgSvclEuWLIKd/Ib0XLGrp5IiIiIg3CH/Lz9vL3eHb+K0xeMY23l7/H0/Ne5tM1s+qVt1xERA6Ow2ZkMMCtt97KqFGj6NWrF8cddxxPPfUUpaWljB49GoDLLruM5s2bM27cOMCeIG7p0qXhnzds2MCCBQuIiYmhbdu29dqmiIjsHymhNC4ov5yzyy8CwIOngVu0d0oCheRXbiXGHY/L4QbA7fAQ5Yoht2ITFYEyIlxRDdxKERERkYNrXs5CvtzwHamRycR74zDGkFu+lY+zP6dtQmvaJhyeqcFERI40h1Uw+Pzzzyc3N5d77rmHzZs30717dz755JPwBHBr167F4fh9sPPGjRvp0aNH+Pnjjz/O448/Tt++fcOzLO9umyIisn8drkHgaoGQn6AJ4nLU/Ap1OVyUB3z4jZ+IBmqbiIiISEP5OXcJDizivXEAWJZFSlQyS7ctZ/m2lQoGi4gcIg6rYDDATTfdxE033VTnsuoAb7XMzMx63Y6yq22KiIhsL8oVi9cZQXmwjGhXbLi8IlhGpCuKSKdGBYuIiEjjUxmsxGnVDjFYWPiCvgZokYiI1OWwyRksInIk8Yd8FPnyKQ+UNnRTZA95nRG0iG6DP1hZdQ7LKPRtI2RCtIzOCqeOkJ1T3kAREZEjT/sm7SgPluMPBcJlZf5ynJaTVvEtG7BlIiKyvcNuZLCIyOEsZEKsK/mN9WWrqAxW4LRcJEc0o21cJzxOJRfYW5XBckoDxbgsD7Hu+AM+Y3WrmHa4HR42lK2mMlhOnDuRFtGtSYs8vC90SvxFbCnfSEWwjChXDCmR6US5YvbLto0x5JSvZ2PZGsoCJcS642ke3Zqm3lTNMC4iInIEOC6tBwu2LGJ5/kpi3NEEQyEqghUcm9aDLk07NnTzRESkioLBIiIH0cay1fxatAhX1YRj/pCf9aWrCBg/XROPU1BsD4VMiDUlv7K+dBWVwXIclotEbxJHxXetkcJhf3NYDlpEtyY9qhVBE8BluQ/7c5dXkcPSgvmUB0uwcGAIsaFsNZ0Te5LgabrP219bspKVxUswGDwOL7kVm9jmy6VjfA/Sog7vILqIiIhAgjeeq7texuyNP7Jw61I8Tjc9UrpyfLOeeJy6c0pE5FChYLCIyEESMkHWl2bjdLiIcdsTa7gcbpyWk60VORT584n3NGngVh5eNpWt4beiJbidXuI8iQRCAXIrNhIMBeiRdCJOy3lA9x8KhtiWl0dpaQkVFeWUl5dTUVFOIBAgFAoSDAYJhUJ4vRHExMQSHR1NTEwsUVExxMTE4vEcGpPpBU2QlUVLqAyWk+hJxrIsjDEU+LayqngZPZqcuE/B7spgBevKfsNp/f67H+WKodC3jTWlK0mOTD/g50pEREQOvMSIeE5vM4DT2wxo6KaIiMhOKBgsInKQ+EI+KoPleBw100G4HR5KAkVUBMuJb6C2HY6MMWwoW4PDcoZHAXucTuKsJhT488iv3EpSROpebz8QCJCdvZKVK39h48YNbNmyiZyczeTm5pCTs4ktWzaTl5e7T/lvPR4P0dGxNGvWnBYtMmjZMrPq/1a0aGE/EhObHPBRx8X+AkoCRcS448L7siyLaFcchb58SgPF4SDu3igNFFERKCPOk1ijPMoVQ5m/mPJA6T5tX0REREREROpHwWARkYPE7fDgcUZQESzHu11+YL/x4bJcNcpk94ImQEWwHLfDW6Pc5XARMiF8oYr6bScYZO3abH75ZQkrVixl+fKlrFixlJUrl+Pz2TNfezwemiQnkZScQnpaC449tg8pKWmkpKSRnJxKTEwskZFRREREEBERicvlwuFw4nQ6cTgcVFZWUFpaQklJCaWlJZSWFoefFxcXsmnTBtavX8vXX89g3bo1lJeXhdsXFxdPly7d6dr1GI4+ugdHH30MrVu3xeE4EHPA1hV03vfJ3pyWC4flJGiCOLYbARw0ARyWE1cdM4+LiIiIiIjI/qerLxGRg8RpOUmPbMWKokWUBoqJcEYRCPkoDRSTEpFOvFspIvaE03IR5YqmwJdHJFHhcn/Ih9NyEuGMrHM9n8/HwoXzmD37K+bM+YYff/yO4uIiAOLjE2jfvjM9ex7PhRdeQcuslgRTKnHEgWU5AEOsK4HOiT1rjXLdX4wxbNuWx/r1a1i/fg2//baCRYvm89FH7/Lyy08CEBsbR9euPeja9Rj69DmZE0/sR3T03k/0FuOKJ9oVS0mgkHh3k3CaiNJAMU28yfs8iVysO4F4TxPyKrcQ72mC03ISCAUoDZTQIiqTCFfU7jciIiIiIiIi+0zBYBGRg6hlTBYBE2Bj2WqK/QW4LBfNIjNoF9/lsJ+A7GCzLIsWUa0p8uVT5Csg0hVFIBSgLFBCauT/s3feYXpUdfv/nGlPL9tbtqSHhISQhBo6CgYpggJKUbELWF57wV5/oq++1ld9UTpIFVEQJDSRTkhISO+7m+379Dbt/P6YzSZLFggp1Plc116Q2ZnznDOTzZ65z33ubzNJoxbwxN+nn36Mxx//N48//ghPP/0Y5XKJcDjCIYccycUXf56DDlrAjBmzaGhoGn0OrnRZMvQIqcoASaMGIRSkdEmZQ6zPreTg6iP3yzMTQlBTU0t1dQ3ttVOYHjuIkw44k9inYuh1CivXLGf58iU899wS7rzzFn7/+59jGAaHHnoUxx9/EieccDLTps18RX3TFI3J8ZmsSj9LqjKAqmg40iasRZkYm4Ei9s6FrAiFaYk5rEwtIWumAIkQgtpAA5NifnVxHx8fHx8fnzcfUoI/vffx8Xk9IuTehB36AJDNZkkkEqxZM0gs5mce+vj4vDwVp0TJLqApBhEt5gvB4+C4Njk7A3jO0vEKjEkp2VbcwtbCBkp2AVWo1AWbaNbbeezf/+auu/7Kv/71D7LZDPF4gkMPXcgRRxzD4YcfzYEHzkXXX7yyddZM8/Tgw4S0MLqyo9Cb6VSouCUOqT2WqL7/Up63Pt3JmsVrKecqCAFCEdRNreOgd80mEA2Mjn/TpvU88MC9PPDAPTz66IOUy2Wam1s5/viTOPnk0zjuuJPQtN1b+82aKfrK2yjZBSJajIZQyz7N8rVdi6FKP6ZbJqiGqTbqUBV/Xdpn78nlskyfXksmkyEe9+dirxX+nNjHx+etjpTw9NOCRx8V9PVBayssXOgye7YvDPv4+Ox/dndO7L+B+fj4+LwGBNQQgReJMfCBgXIPG7IrKdg5AGJ6gsmxmdS8oCCcEIKWSAeNoQkMpPt4+IHFXPvPq7n//nsolYpMnz6TD3/4Uk4++XRmzZqDqu4qKL8YEheJi3hBju72CAV3P66lFoYKrH1gPUionVSDEALbtOlb3c/WpzuZetyU0b5MmjSVSZOm8uEPX0KpVOKJJ/7NAw/cy+LFd3PddVdQV9fAWWedxznnXMABB8x+yc+NG1X7Lf4CQFN0GkIt+619Hx8fHx+ftyKudAH2eiePz95z//2CW28VSAnRKDz7rGD1apXzz3c59FDfh+fj4/P6wBeDfXx8fHxeV2TNFKvSz2K6FaJaApBkzTQr088yr2YhET02eq7rujz00L+45po/cv/9/8Q0TebMmcdnP/tVFi16F1OmTN/jfkS1OBEtRsHOEderxuToxvTkmH7sa4a3pChny9RMrB51jWuGRiAaoOf5XqYcO3lcN3koFOK4407iuONO4tvfvpwVK5Zy003XcPPN1/D73/+c2bMP5pxz3s+73nUuNTW1+63/Pj4+Pj4+PvufvJWls7CRoXIvilCoD02gNTLpVS1KLKVkS66LDelNAExOTqQ9NuEtuestl4PFiwWBALSMrH03NEg2bYJ77xXMnSsxjJduw8fHx+fVwBeDfXx8fHxeV/SVuinbRaoCdaMvEgmjmuHKAH2lbibpMxgc7OeGG67kuuuuYOvWTcycOZuvfe0HnHLKu5gwoX1Me6ZTZrDSh+l40QQ1wYYxsQ8vhqpoTIzNYFX6WdLmIJqiY7kWASXIxOj0cWMr9hWuK2GcdyhFEbiOC5Jxv78zQoiRInMH841v/JjFi+/m5puv4Tvf+SLf/e6XOO2093DppV9kxowD98sYfHx8fHx8fPYfRTvP8tST5Mw0AS2E7dpsyD1P1hxmTvVhaMqLR2HtK1zpctem+7hvy0PkrAIAMT3CCW1Hc+qkk95yTuVt22B4WNDePtYBXFcH/f2CgYEdIrGPj4/Pa4kvBvv4+Pj4vK4o2Fks1yJrpVCFSkiLogoVgeCJx//D5bd/n7vuuh1VVTn99LP5zW+uZt68Q8d1oGTNFCvTz5CzvOxhCSSNamYlF+yWs7ch1IKhBOgtdVKwc0S1OI3hVpJGzb4e9hiSLQmMkE4pXSZc5cWJuI5LKVtmykGTQIDlmqhC260XLcMwWLToDBYtOoOhoQFuvfV6/vjHX3HbbTdw0kmn8qlPfZn58w/br2Py8fHx8fHx2Xf0FjvJmmmqA7WIkblAyA0zVOlnsNxLY7h1v/dh5dBa/rlpMVEjyoRoMwBD5WHu2fwAkxIdHFg7Y7/34fWEYYCmgWnCzmUpLMv7c/DVM2z7+Pj4vCS+GOzj4+Pj87qhaOfpL20jZQ6gWwZCCDRhsP7fG7jljzfQtaGTyZOncdllP+I977mAqqrqF23LlS5rM8vJWRkSRg2KUHClQ9ocYkNuJbOrxheQX0hVoJaqwCuPVJBSUs6UcV1JOBlCKLu/XTLeGKPtkDY2PbqJcraMoilYZYtkS4LALIWnBx+m5BQIqCFawh00h9t3231TU1PHxz72GS666GJuv/0Gfv3ryznttKNZuPA4Lr30SxxzzIlvya2dPj4+Pj4+byTS5hC6oo8KweDtapJI8nb2VenD80OrqDgmE0M7FslrQzUMloZ5fmjVW04MbmuDSZMkK1cKJk+W6DpUKtDTI1i4UFL94tNWHx8fn1cVXwz28fF5yzKkDLBZ3YiGxhR7OhEZ3eWcjEizWltBSRRpcluYYs9AZf/FA7yVkVKyIbsS27UIaREs22T5g8/xjyv+Tt/mXuYeOY8fff9XzDl0Hp3F9ay1llKVqaMjNh1jnNiHnJUma6WI6olRoVQRKhEtxnBlgJJTIKzt+sz3Bdm+HOsfXM/QlhTSlSRbEkw5ZjLV7btXnE0IwbTjp5BsjtO7qh+zaFLdXoUyxWITK8GCgBqkYOW8fGWnwqT4K3vh0nWdc855P+95zwX885938Ktf/YT3ve8UDj74EL7znZ+xYMHhezJ0Hx8fHx8fn1cBXQ3gSmfMMSklINHE/o+IACjZ5XFjs1ShUrLLr0ofXk+oKpxzjstVVyls2gRSChRFMmuW5IwzXPy1dh8fn9cLvhjs4+PzlkMiuTfwd+4L3EVaGUYgaHRaeHf5PA6y5o+et1J7jhtCf6ZH3QZAgAAHm4dyXvFDhAm/Vt1/01J2igxX+onqSVb8awU3/eE6tm3cxozDDuC8r17A+074CFkrzf09f8V0TQSeaLoxu5Jjmt65i7DrSgdXuigveElRhIqUldHK2/t8HNkyy257jsy2DNHaKAjBwLoBCkMFFpw3j1i9F0+REg5L9DIbVJOoVDnIDjDTNhAjYcCKqtA4s5HGmY0A2K7NU4MPojgKMT0JQFANU7BzdBc30RJpJ6CGXnF/FUXhlFPOZNGid/Hww4v50Y8u4/TTj+Gccy7ka1/7AfX1jfvmxvj4+Pj4+PjsMxqCzfSXuinZBYKqNy/N2xkCSoiaYP2r0odJiQ4e6X4C07EwVE+ANh0LW9pMSnS8Kn3YTioFnZ2eIDtpEoRe+ZRonzBhAvzXf7msXCnIZj038AEHSAKB16Y/Pj4+PuPhi8E+Pj5vOZbpz/C34C0EZZCp9gE4OHSqm7kh9GcanWYa3CbyIseNoasYUPuZOuIGzoksjxkP0eJMYFHlXa/1MN7QWK5J2hzClQ4xPUlYi2K7Nk8+8Di3/O4vdG7YwryFh3Dptz7P5DlTKVtFXNdlxfBTONIhriURisB2LQYqfSwbeoIjGk4c8xlRPUFIi1C0c6PiqZSSgp0jrlcR1iL7ZWx9q/vJbMtQ3V6NonqO5EDUYHDjENuW9zD9xBgDwuaKcIYNqomBwAIeM0q8sxzhZDMyKgjvTNkpULKLhLSxCxEhNUzWTFGw83skBm9HCMGxx76No446nhtu+DM/+tE3uOuuv/L5z3+DD33oEnT9pV1GtmvRW+qkr9SNK11qAg00R9pGX1B9fHx8fHx89h11wWbao9PoKmwkZQ4C3iLxlPjM0XnP/mZe/Rye6VvKiqHVxHRvUT5r5ZldM4N59XNelT5ICYsXC+69VzA8DKoqaGmRnH22ywEHvPK2pATlZZK3LNdCQUFVxt8tGA7DggVy3O/5+Pj4vB7wxWAfH5+3HM/oj2MJk4nOZAAUFCY6U1itrWCFvpSGShOrtefpUbuYbE8fjYWIyTgxmeAJ4z+cVDnNj4vYQ4bKfazNLB/Js5MYShBl2OD3P/ol999/D7MOnc1Pvv1LZhw0E4C8lSGsRxgy+zHdCrERIRhAU3Q0odFT3ILruig7zd51xaAjOpU1medIVQbRFQPLraArATqiU3dxDO8rCkMFhBCjQjB4Qqse1Mn25gB4yCiyXjOZZhtoI8Jvn2JzX6DIQXaQJnfXX88FtciA1oelmETUGA1OEwYBbNdGFdo+2xKqqioXXPAR3vnOs7j88u/wve99heuv/xPf//4vOProE8a9xpUOq9LP0lvqRBEqilAYNvsZqvQyu/pQXxD28fHx8fHZxwghmBw7gMbQBDLmMIpQqDJqCWov/TvXi5Jgn9QHiBoRPnTg+fyn+0meHVgOwEl1x7Ow5VCixv5ZdH8hy5YJbr9dEAjA1KngOJKtW+HaaxU+9zmXmt2o+Vsswr//LXjiCUG57Dl5jz1W0tY29rxNma082PkIa1MbCWoBDmk4mONajySs+/McHx+fNxa+GOzj4/OWY1gZIijHOig9J6agIPIAVEQZCbsIvgEZoCxK2Ni+GLwHlJ0iqzNLKdlFEkYVVsXmpj9ey1//dAs1NbX87Lf/S+OhtdjSpmDlsFwTRSi0RqYwXOlnPI+FIhQcaePiojDWytEc7sBQg/QUt1K089QFG2kKt1MdqNtvYwzEAkhXIqUc86JlmzbhqhAOkuf0CtWuOioEA9S7KmtUk42quYsYvFFdx1Wx32NaBRLpGBXdJK4mOLAyF2k51IeaiemJ3eqfmxpGlooo1bWIlyhrXVVVzQ9/+D+cf/6HuOyy/+Lcc9/BRRddzGWX/YjQC/ZeDlX66St3E9UT6CP5za50SZmD9BQ7mRibvlt98/Hx8fHx8dl9hBBE9ThRPf6y52bNNN2FTQyb/eiKQWOoleZwB5qyd5JAIhDnlElv45RJb9urdvaUp54SWJZg4kRvlrg9JmLNGsHy5YLjjntph65tw/XXKzz2GESjoOvw4IMKq1dLPvlJl+YmL1ass9DNH5dfQ1+xn9pgDelKhlvX/52u/DY+dOB5e30ffXx8fF5N/H+xfHx83nJMtKewSluBRI5uxzcxURA0OE0AtDithGWYjEiRlF7pX4lkSBngUPMoDHYtWPZ6Zns8gisdIloM9TWasA6V+yjYeaqMWpb85yn+8OPfMLCtj3dccCof+uTHObz1RIbK/WwrbiZnZUga1TSF26gLNqMKFU2omG6FgOKJmNKVmK5JS7h93Em4EIK6YBN1waZXbYwN0+vZ8uRWUl1pEo1xEJDvz2OEDZpmefm7AsYVtr1libFOHRubW0PXs03tZGpiBkFLI1KEPFk2yNUsCC5kWmLOyzp83GwG886/Yi97BlmpoNTUoB//dvSjjnvJa2fNOojbblvMlVf+L9/73pd55JH7+c1vrubAA+eOnpOz0rjSHRWCwRPpNaEzVOnzxWAfHx8fH5/XkKyZZnnqCQp2joASouJUWG0uI2dlmJmct09cwq8Vg4MQDo+dVSkKCFxyWRfGid7ambVr4ZlnBK2tkuhI+YmGBsmKZQ43/raP+fVrUFSFp1qeoyfQy6z6GaP3q2AVWdq/nDWpDcyq8ec6Pj4+bxx8MdjHx+ctx+Hm0TxrPMlq7Xnq3UYcbAaVfg6wZzPbPhiAdmcSh5lH8UDgXjIyTVCGSClDVLu1HF85adxM19crOSvN+uzzpM0hpJSEtSgdsek0hia86n2xXItcOssfL/8d/7n3YWYfMpfLfvldqlqr2G60rgnWj1v4pCnURnO4g87CRkyrjIKK7VqEtAgzk/Ne5ZG8ONG6KAeeNos1960l05NFSkm0OsyUY6dQ3e4tLMy1gvw9kKcWFX3k71KP4pCQCpOdsXEPnepmtmgbaXU6UDQVs0GilMFxBJv1Tk63JxGW0V36sTPSdalcfxXWU4+j1Dei1MRwhwap3HgtQjfQD1/4ktcLIbjook+ycOGxXHLJBzjttKP5znd+xoUXftSLxEABdnVDu7ivWkVzHx8fHx8fn/HpLmyiYOeoMupGf0+bTpneUhfN4XaqArWvcQ/3nI4Oyfr1ysgcBCgXcLdtpnlwgMLjsDrUQPshbYSS49dV6O4WWBajQjCAU7Gxh4dZ1lth7kkOtnRYtnE5IiFwq11U3Zu0RvQwpmvRU+jzxWAfH583FL4Y7OPj85ZjgtvGhwuXcm/w72zQ1qJIhbdV3snJlVMJSy/fTCA4u3Qhze4EHtP/TV7JcmTlOI4xT2SK88aZ7JlOhZWpZ8hYaaJaHEUoFOwcq9PPYigG1YFXp9r0dpY+toTLPv9lXMfl8z/6Ksec4mXQpsxBGoItL3mtoigsbDiZlekldBbWYzoVasIdzEjOpT7U/Gp0fxdeKH5up35qHdVtVaS7M0gpSTTFMcI7XLPHmWE2qiZrNAsFcJBEpcLplSgNL4iIsLFxcFDlyHEBbghsISkqJdyc+2I241Hcjeuxn1+O2taBiMYAUCNRnE0bsB6+H+2QwxHqy8eeTJs2kzvv/Dff+c6X+MpXLuXxx//N5Zf/jqpAHboSoOjkCatRhBCYTgUp5Wv2bHx8fHx8fHy8ucqw2U9ACY2ZsxhqkIKdI29n3tBi8BFHSJYulaxdK2isLhLYuhQnnSZZGyQZlax7cAOpzjTz33vwmLnYdrzELInr7igcl+vPUcjatLfpROs8lbjaqmJrvpvCUIF4oxfL4UovQiKoBl6Nofr4+PjsM3wx2MfH5y3JJGcqHy98lrzIoaKOisA7Y2BwfOVkjquchETukkf7RmCw0kvWSpM0alCE1/+EUU2qMkBPsfNVE4MrlQo//OHX+eMff8nBRy7g/ZddRFNjCxW3RNEuEFLDtEQ6Rs/fHmthuRVCWmS0AJkiFKbEZzItMZugOr7DY38jpWSg3MO24mbyVpawHqU51E5DaMKYlywtoFE7afyqJVVS5eOlKp7VyjyvDWCLPHOtCEdau2YZtzht1DkN9KrdtDuTvD4g6VN6aLMmIvMOy8tP4kiHmmA9DcEJGC94KXGHBpGV8qgQvB2RSOIODkCpiB0O0VvqpK/UhSNtqgMNtITbCWljfzaCwSA/+tEvOfzwo/jCFz7BWWedyDXX3MHE6HQ259eQMgcAgSpUWsIdNLwGDnQfHx8fHx+fHeiKQcWtjDkmpfTqY4g3tiTQ0QEf+pDLPfco9C3tRS2lqZlaxfTpCuEwOFaIoc3D9K7qo21+6y7Xz5wpqa0VbNkCbW2eIDzQY+GgcsDEzI7z1OlsFFsYyAwSa4jhSpctuU4awnXMrJn2Ko7Yx8fHZ+95Y//L7+Pj47MXCAQx+fIFN8Q4Oa5vFMpOCQmjQvB2dMWgYGf3yWe40iVrpXBch6geJ6COLUq2desmPvGJ81m58jm+852fcsEHP0xncSP95W5c6dIQbKYtOpW4UQVAxSmxNruCoXIvtmtjqAGawu3EtDidhY3k7SwKCrXBBibFDthFrNzf9JS2sjq9DFc6GGqAVGWQVGUQ063QFp2y2+0ossxW7QbWGk+QF1nW6RFWWQdxTun9JGRy9LwwYRZVzuDG0JWs0VYSkRHyIk/CTTK/dx6rc896f0OFoL/cTX9gG7OrDh0rCMfj5GvDVMJlDDVAIg+KFMh8DqW+ATcQYE1mKd3FLahCRREqqcoQQ+VeZlcfSljbNYbijDPOYcqU6Zx//mm8613Hc/31f2de81GkzEFc6ZIwqkgatbv83fPx8fHx8Xkrs31Ruae4hYKdJ6YnaAq3UxOo3y/ZvUIIGkOtrM4sxXQqGGoAKSVZK0VYi+zXorqvFtOnw9SpLo9eNczwRpX6STvmHqquIoQg25sb99qaGjjnHMkttwjWrQMQlK0AM5s7mdVRGD1vJlPZVNpCnzLImtR6ENAQruecaadTHazazyP08fHx2bf4YrCPj4/Pm5jgiDDrSneMKGe5JhEt9mKX7TZZM83a7HNkzBSudAiqYVojk2iPTkUIwb33/p3PfOZDJBJV3HHHQxx00HwApiVmMyk2Y7To2PaXHyklqzPL6C12EdVjhLUoZafE2vQyXCkJ6WHCahRHOnQVNlNyisytPvJVq+DsuDZb8+sBSTKww/WbtzJ0FjbQGG7FUHZvq+A/g3dwf+CfNLhNNDhN5EWOxwIPIxB8uHjpmAWIw82jSbhJHtf/Q7/aQ4vTxqzsgQznewhrsVHh15E2Q+U+ekudo8K05ZqsqsvT97Z2rHwGJagQL6pMWTpEqFxGX3gMaSdFT6mTqBYfbcvVXFKVQbYVtzIlPnPcMcyadRB/+9tDnHfeOznjjOO49tq/MXv2wXtya318fHx8fN4SdBc3szbzHK500JUAfaUuBit9HJCYS2N4V+fqvqA53EHOStNb6qZgZ5FAWIswLT5ndPfVGx1FgUSdQWaTu8v3pCPRQy9ew2DePMnEiZLVqwWViiRsWww+2oWZNdCqQiChPFDmeLGQxnlNZKM5gmqAaVWTSQRe3liyM/l8jq6uLXR1bWXbti6y2QzlcolyuUSp5P3XcRxCoTDhcJhwODL6VV1dQ0NDM01NzTQ2tqDrfl0GHx+fPcMXg318fHzexNQGGonrSdLmIFEtgSIUinYeTTFoCrftVduWa7IqvYSMlSKmJ1CFRsnOsyG3kqAa4u/X/5VvfesLnHTSqfziF1eQSCTHXK8pu05gc1aaoXI/MT0xKkqGtShD5X7KbpGGcDOK8LJtDcUgVRlkuNJHfeil84ZfjpyVYaC0jbJTJqrHqA82E9R2fTkqOgUv1uIFbuSQFiVnpilYOYzAy4vBWZHhCeMRatw6ql0vpy8hk2BLVuhL6VY6meDueD4CwUx7DjPtOaPHNpZXMSi7xjiAVaGhKjqD5d5RMXhTbg09lS6ik2ahrFqDNTREKqawYVqQufPPQD/qOHKlDbjSHdOWIhQM1WC40geMLwYDtLVN5I47HuKCC07n3e9+G3/+860sXHjcy94DHx8fHx+ftxqWa7Ilvw4hBElje05vjIyZYnN+HXXBJtT9sMCtKRoHJOfRHG4nb2dRhUZ1oO5NIwRvp3F6Pd1LvVzfcHUYJOQH8xhRg/qpL+2Arqry8ocBpFvFem0Sm5/YwvDmYaSEYCLIjOOm0DZtwm45uB3HYc2a53n22ad49tmnWL78WTo7N5NOp0bPUVWVWCxOMBgiFAoTDAYJBkMoikqpVBz9KhYLFIsFbNsevVZRFFpa2mhr62DatJkceOBcDjzwIKZPn4Vh7JqN7OPj47Mzvhjs4+Pj8ybGUIPMTM5nfXYFGXMYF5ewFmVidPpe5wUPVfpH8oirRwXaoBahr9DN1775af55/T/4+Cc+yzcu+zGKMn5UgCsdhisDZK00qlCRSCzXJKYnxpznSAchBY50Rj9LVTSkdBis9BFUw0T1xB5FEvSXtrE6s5SyU0SgIJF0FzZzYPUCYnpyzLlehIKCIx12lrId10YR6m7n7uVElpIoUuOOfTGJyBj9opeckoFdjS0vQLxI4Tg5eh9Mt0JfqYuQGiYQiMKCw9CyGVSrRDEoqDQfS0jTRu/pCwviudLdrTHV1NRxyy3/4iMfOZfzzz+VP/3pFk444R1jztkeJ+JKh6iW2CXX2MfHx8fH581OwcpScgrEtLHznLAWoWjnKToFYkriRa7eOxShUBWoo+pNEAuxM+VcmfxAAc1QqZ5YzZRjp7Dpsc0MbRoGAcF4kGnHTSE5Yffvq1AEU46dTOPMBlKdaRRFoaq9ikj1S4vnmzdv4O6772Dx4rtZuvRpisUCiqIwY8Ys5syZz6mnnkVLSxsTJrTT0tJKY2Mz6m4U8AVvjpbNZujt7aanp5uurq1s3bqJLVs28vDD93Hllb9DSomu66Pi8OGHH81RRx1PS8v+cZz7+Pi8cfHFYB8fH583OXEjycE1C8nbWVzpENHi+yRWwXTKeMKjN4mtOGW2ZbZyxXd+z/IHn+OCL36QUy44hbJbJKzsmjlruxar00vpK3fhSAckCKFguhVMtzIme1jgZd7tLEzmrAzD5iBm1qK32Elcr2JKYhZJY/yibeNhuxYbsiuxXJMqow4hBFK6pMwhNuZWM6fqsDHiaEiNUBOoZ1txK7rQURUNRzrk7Qy1waZdROwXo0pWE3eTpJUUEWfHvUkrKaIyTq378kJ9lVGLphiU7CKhERez5Zo40qE22ETWTLEpv5qe0lZ0EcA2LE8wTyQxZIKCOYjtWgBUB+oIKAEKdo6IFkMIgelUcKWz2wXgIpEoV111Ox/72Hv52Mfex6233jcaC5Ixh1mXWU7GSiGlS1CL0BaZQmtk0n7JR/Tx8fHx8Xk9oggVBRUHB3WnV3FXOihCQRW7Jwz6gOu4bHx0M1uf3EopV0bVVKrbkhzwjgNonFFPqiuNEILq9irCVa/cAS2EIFYfI1b/0rFqa9eu5I47buaf/7yDVatWEAwGOfroE/nc5y5j3rxDmT37YCKRXefBe9KfRCJJIpFk+vRZu3y/UMizcuVyVqxYyooVS3nuuSXcdNPVSCmZNGkqRx99AkcddTxHHnkcVVXVe90fHx+fNza+GOzj4+PzFkAIsdtC5e4S0iIIoWC7NqpQ6ezfyK+++Au6VnfyiR9fyknvOJWUOcj67PPMrjp0F9Gvp9TJttJWYnoCXTGQUlKwchRklrQ5RFxPoisGZadEQA2iS4OinSesRSnaeXqKW1CFRpVRi6IopMxBVqaWMK9m4bgRD+ORMVMU7Cwxo2q0f0IohLUo6coQFac0pi0hBJPjM6m4ZdLmEFK6gLfVc1p89m4Lm2EZ4WjzBG4NXUe3upWEW0Ve5Egrw7y9ciq1Tj1pa4hUZRCJJKFXUxUYW4wtadTQFp3C1vw6SpU84L1kNoVaCatRnks9QcHKoaBQcgpYFU9krwk0UHaKBJTgaGG4qJ5gcnwWG7IrSZkDgEAVKs3hdppCu+8mMQyD3/72Ws455yQuvPAM7rzzYZomtLAqvYSclSGqJ1FHokrWZ1cQVEPUh5p3u30fHx8fH583MlE9QdKoZrDSN7qzypEOBTtHQ2gCIfXVLYr7Rqb7uW2sXbwWI2JQNSGJbTr0rRvANh0OvXABrQfv3mL2nuC6LosX380f//grHnnkfuLxBG972yl87nOXcfzxJxMOv/rPMRKJcsghR3DIIUeMHhseHuLRRx/kkUce4N//XsxVV/0eRVE48sjjOP3097Bo0RnU1Ly5nOI+Pj67hy8G+/j4+PjsEdVGHVVGLYOVPvJDeX788R9QSOe59Fef4bBDjkYVKhEtxnBlgJJTGBUet9NX6kIVGrri5ZoJIYjoMcpukbhRhe1aFO08ATXIrOQCALpLm8iYw+StDKrQaQm3E9A8B3HSqCFVGWCg0kOrNnk3RyGRwAslXDHyHTlODoOUkqReg+N6Lp6GUAvN4fZxM5BfihMrixAI/m0sJqUMEZYRTiu/h5NKp7Ext4qt+fVY0gS8LODmcAfTE7NHndhCCCbHDqA6UMtwZQApJQmjippgI6vSz1K081QH6jHUIIPlXhxpkzGHEUJBQdARnTbmmUyITCRpVDNcGcCVDjG9ahcBencIh8NcddXtnH76sZx//qn8/oZrySoZkkbNaFtRPUG6MkhPcYsvBvv4+Pj4vGVQhMLUxIFYKZOMOTxyVFBl1DIlPsvfLbObSCnpXNKFoipEa725jBFSqJpQRborzdCmYRpm7F0c2nhUKhWuv/5PXHHFr9i4cT0HH3wIv/3tNZxyypmvy5ze6uoaTj313Zx66rsB6OrayoMP3sudd97KV75yKV/96qdGheF3vOMMampqX6ZFHx+fNwu+GOzj4+Pjs0eoisbM5DyWdT3JNy79MuVCic///iscMHUW4ZECa4pQcWXFi4F4Ac7IlsidEUKgCJXmUDuN4VZMp4zlWtjSwlACNIZbKdo5VmeWUnHKu7h2hRCU7dKL9nmb0sXTxuP0Kduocxs4SMwnrEXJW1lienIkJkJStPPUBZt2KawyXOlnZXoJBSuPIhQkLqZbJq4nSQZe2QRaQ+OkyqkcVTmBjJIiJuNEZYxhs58t+XXoikFMSwJeBEd3YSNVRs2YSuNCCKoD9WPyn13pkKoMElTDniNcSyCCgkxlmIKTQ0EwLT6HCdFJu/QpqieI7gMHeU1NHddddyenn34sn/3Yx/jMrz+HEhj7rHXFoOgU9vqzfHx8fHx83kjE9CTzao9iqNxHxS0TVEPUBBpe8aLyWxnXdilnyhjhsQKsZqi4rqSSr+xRu5UKrFwp6O2FcBgOPFBSM5I+dt99d/Gtb32BLVs28s53nsUvfvEnFiw4fG+H8qoyYUIbF1zwES644CMMDQ1w99138Le/3cKXv3wJX/vap3nnO8/iAx/4OIceutBfmPDxeZPji8E+Pj4++4lyuUxn52a2bt1EX18v2WyabDZDsVigUilTqVRwHJtwOEI4HCEajRGJRIlEokSjMVpbO+jomExVVfXrdkLmViQ/+NQ3yQ8VuOwP36N5YgsRPT76/aKdJ6rFd3EFA9QEGkhXhnA1d0fBM6eMKlTiRhJVqGwtrGeg1IMtLRShUhWoY0biIGqDjWzNrx9T8ExKF4kkpIWRUpKz0pSdEkE1RExPskZ/nqvCv6dP6SUog1REmUeNhzjFOh1lQCVlDqAKDce1iehxJsZmvKCYmsP67ErKdpHqQN2ocJw2B9mQW8XBxsI9KmAXJkzY3SE6D1cGsKVFXKsaPRZQg5TsPIOV3jFi8PgINKFRcUvb/0hUjxNSI6TNQWZVLaA53P6K+/lKaW+fxBV/volzz1nEVT/+E1/44ddHsxCllF5kRbBxv/fDx8fHx8fn9YauGLvx+/y1R1oWcngIQiGU+P4pbLcnKJpCtC7K4MYhwjsVdbPKFoqqEEqGXnGb6TRceaXCqlXgON78r75ecvTRq7n++i+yePHdHHXUCVxxxU3MmHHgvhrKa0ZNTd2oMDw42M+tt17PNdf8gTPPPIHp02dy4YUf4z3vOZ/46+i5+/j47Dt8MdjHx8dnLymVSjz33DM8/fTjrF+/hi1bNrJlyyZ6e7uRckfMQDQaIxZLEIlECAZDBAIBhFAolYoUiwUKhfzo184kEkk6OiaPfE1ixkGzOHj+Alpq2lH3QSG4PaVSqfChD53NmjUrufnme6mZUs3azHOkK4PoSgDLraAqOu3RqeMWRGkJtzNU7iVVGcRQDVzp4kqXlnAHSaOW9dkVdBe3ENXiRJUEtrQYLPWwBsHE6HR6i11krGEiWszLG7azxLQkcb2KFamnGCj3YksLTehUBWv554S7GVIGOcA+EIFAItmgreXR5L/5qPspUqUByk6RqJagMTxhFwE7Z2XIWxkiemKnfGFBRIuTqQzTW+rElS6qUL2CbOorfxEBRlzUu4r/Qig47q4O6xeiCIXGcCvrssuxXHM0jzlvZ4jqCWoDDXvUr5fqb6oyQMUpEVBDJI1ahiv9dBU2UmzO8KGvfJzffusXHLRwLsctejsKCkUnj64EaA637dO++Pj4+Pj4+Ow9UkrsJx/DvP8e3P5+hGGgHbwA45TTX31RuAxKHtwoMFJbWAhB2/wJDG9Nke7OEK4KYZsOhaECDTPqqe6oeskmx+O++wTPPSeYNEkSDEocR7J48a+5+uov09zczP/9319YtOhdr1uDxt5QW1vPxz/+WT760U/zyCMPcPXVv+db3/o8P/zh1zn33A9w8cWfp6Xl9b944ePjs/v4YrCPj4/Pi2CVLAbWD1LOlQnGg9RNqUUP6vT2buPJJ//D008/ztNPP8aKFUuxbc/hO336TNrbJ3HYYQtpa5tER8ck2tom0tjYjKruXoVo13XJ5bJs3bqJzZs3jHxtZO3GlTz473vJ/I9XHbl96kSOOPwYjj3y7Rx++NHU1796Lkvbtrn44gt46qn/cN11f+egg+YjpcRQAvQUt1Cw81QH6mgOt1MTHF98DGkR5lQfxrbiVoYr/WiKRn2whcZwK7Zr0lvqJqSGCajBUVE9rEVJVQaZHDuAmVXz2JRbTd7KIoSgJtjIlNhMugqb6CltJarF0ZUElmuyyl7GKrmMSc50xIjQKhC0OK10a53kQjmmGbNfdLyOa5M2hyg5RXTFQN9pK6dEkrFSLB9+youqAEJalOmJOdQFm17xvU0a1WxFYLvW6JZRRzo40qY6uHtFPiZEJpKz0gyUe3CkM9KnCNMSszHU4Cvu04tRtPOsSj9LyhxEShchFAwlgOmUAQhqERa+82gef+ARrvp/f2LWwXNI1nuCfUd02ph4Cx8fHx8fH5/XB/azz1C+7s8gQampRZbLmP+6Gzc1TOhjlyJ2c067d52AwP0C4xGBkhO4MYl5lKRyggQNGg5oYLblsumxzRSGi6i6QvshbUw7fgqq9sr6Vy7DkiWC6mpJMAiVSoZ//vMiNmz4GxMnfpr//u/vc9hh+27+9HpFURSOOeZEjjnmRHp6urn22v/jyit/x7XX/pFzznk/l176Rdrbd40Z8/HxeePhi8E+Pj4+45Drz/HcX1eQ7koD0JvpYtXwUpb3PsPy55cA0NExmfnzD+Pccz/A/PmHMWPGgWja3v+zqigKiUSS2bMPZvbsgwHImimeHXoUyzXJbcuz8tkVLH/mWR544B5uuPpKhBAceuhCzjjjHE499Sxqa/evyPb1r3+Gf/3rH1xxxc0cccQxgOfSqA81v6KCYCEtwuT4AUzmgDHHy7KIIy0CSoi8lSVjDmO5XjE1RSgU7TyN4VZqAvUU7BwChYgWo+QUGChvI6xFR0VPQw0QVqJUnDKWYo4x3W4vECfGceJuJ2MOszqzjKyZImelyZrDVAcaqA54wuxQuQ/TrVCnNRFQg4Aka6VZk1lGTEuMyTXeHWoDjTSEWugtdaEKFYHAlhY1gQYagrtXGVtXDA6sOoRUZZCCnUVTNKoD9btkIO8NUkrWZVcwVO4jblSjKRqWY9FZWI8iVDqi00BAUA1xyTf/i8+dfQnX/fBqrrzmNsJ6dI8iNXx8fHx8fHz2L1JKrIcXg+2gTp4KgIjFEeEwzvPP4axfizb9gJdpZe8J/FMQ+ptAhsGNS5QshG4WYELlVC8mrOWgZhpnNlBMldACGqHEngm2tg2WBboOqdRa/vrXMykWezn99Nux7dNQFAnjFBV+tSgWobsbVBXa2mAfvG68LE1NLXzxi9/ik5/8HFdd9Xt+//tfcOONV/Lud5/HpZd+iSlTpu//Tvj4+Ow3fDHYx8fH5wVIV7Lq3jU8t3QJzw8v4clV/6ZrYDMBLcj8mUfy619fxdFHn0Bd3b7dbv9S9JW6qbhlqoxakhNraJ3YzklnnkLKHCBZamDTM5v4299u5hvf+C8uu+yzLFx4/KgwvK+zvm666WquueaP/Oxnf+Dtb3/nbl9nupVR9/DLbbELKiGCanhEzMwhpUQRCpZbwZEuXYWN1IdaUIRKTE+OXme5Jra0CYixome93UhtpY7uYCdJt3o0JqJb7WSiPZkJzvgZupZrsir9LDkrQ1xPYigBektdDJS3YbplAmoQy7WI60mC2vZYCEFcryJtDjJs9tOsdez2PYIdhfmqA3X0l7bh4lIXbKIp1IqhBna7HUUo1ATrqWH/LAwU7TypygARPY42EleiKAIQONL2IipUr7BLbXU9F33jI/zisz/lhmv/zEc/9Jn90icfHx8fHx+fvaRcxu3tQSSSYw6LSBRMEzk4APtZDBZZCDwicOPgjmx8c+Kg9HrHzWMkcqREhaqrxOp3rU3xSohEYPJkePDB1Tz99IkEg9Wcd97jWNZUHEfQ1ubu5Yj2DCnhiScEd90l6O8XI2Kw5KyzXKZOfXX6EI3GuOSSL3DRRRdz/fVX8Nvf/oxbbrmOCy/8GF/4wjepqXllBZR9fHxeH/hisI+Pj89OlMtlbrr2Gv7wm1+ysW8N0VCcQw44igtO/gQHNM5BmoIjjz6MaN3eTTpfKSWn4LlEdxJRvUgChWhNlHPPfT/nnvt+hoYGufvuv3LHHTfxxS9+gm9/+wucf/6H+chHPjVu1ldfHyxdKhgYgNpamDtX0vgSaROrVi3nK1/5FO997wd53/s+uFt9L9p5NufWMljpRUpJMlBDR3QaCaP6Ra9RFY3W8CS6Cpuo2CUQYLsWLi4BJci24lYmVgapCY4VOkNqhIAapOKU0JTYjvtnF5g3NI9nAs/wbOAJIkqMChUi5TAd/e0sN5+gIdRCU7gNZad84+FKPzkrQ8KoQhEqESVGqzKRgXIvEklzsJ0B0TPmGmD0OdmuvVv36IVois6EyCQmRF6/W/FsaeFIh6DYMZVQUNAUjYpTwsXLN3Zcm4FyLxPmt3Dcu0/gB9/7OrOOPpBDJx87KiK/URkup1kxuJKsmacmWMXs2plEjchr3S0fHx8fH589JxBAJJK4PT1QuyOeSlbKoCiIePwlLt43KIOgZAV2y1g3rlsFWrdAGfTE4X2FEDB16kp+8pO3o2l1HH30vxgaqkMIWLTIpeHV83+MYc0auPFGBceRtLZKHAc2bBBcc43CZz7jUlPz6vUlHA7zkY98igsv/BhXXvk7fv7zH3D77Tfwuc9dxgc/+EkMw3j1OuPj47PXvLHfwnx8fHz2EVu2bOTqq//AjTdeRSo1xKzWg/niud/nkFlHoaneP5VW2aJQLOBYL1/Ea18T0eL0yk6klKNCo5QSiUtE3yFM19TUjlYG7unp5sorf8fVV/+BK674NWeccQ6f/OTnmDlzDgDr1nlVk3t6BJomsW3Bww9LPvABl+nj7PzK53N87GPvY9KkKfzgB/+zW/02nQorUk+TqgwS1iIoikJfqZu8leGg6iOI6i8+k28ITSCqxSjZBRzXRlN0QiKAIlSyVpqe0tZdxGBDDdASnsj67PO4louhBMhZaVKVAarNao7rOp4NkQ2UjCIxK86EXCuNbgMpOcjQiPA7PXHQ6D22XHOkOF2OvJXFkQ6GEvCcr45Ff2UbZaeILW1Cqjc+8FzQilBfcnxvdMJalKAapuQUiCme+1wIBU0YVCjjSgfpSgbKPaTNIcJalA9/9hKevu8pfvPLn9HwgyYmx2e+xqPYc9anN3L1ypvYlu8djRmZlGzng7PeR1PkNXpr9PHx8fHx2UuEoqAvPIbK9Vfh9vciauqgXMbp3Iw2dTrqtPFdwTkrw7biZtLmEAElSENoAg2hCUikVzDYHEJBoSpQR3Wgbty4qIJVZGuui6BjMDvQgSgoyJ02RYkCyCDI2C6X7hUbNqzl858/iebmGi688B4GBuqoqoJDDpHMn//axUM88YSgUGDMvHzKFMmaNYLlywXHHbfnfXMc2LQJhoYEQ0OeCzkeh9mzJcnki18XCAT4+Mc/y3vecz4//el3+e53v8zVV/+eb37zJ7z97e98UxbY8/F5M+KLwT4+Pm9pVq58jp/+9Lvcc8+dxOMJz/F6zkX0LB6knK2MCsEAhaEi4eowkZpX3/nXEGqhp7SVtDlIRIshgaKdI6olqA+On9Hb1NTCV7/6fT71qS9zww1/5g9/+CW33no9J564iMsu+3/ceedMBgYEM2ZIFGUkA3ad4K9/Vfj8590xeWRSSr74xU/S19fD3Xc/RigUGvczX8h2IbAqUDPqng0oIYbNAXpLnUzRZ73otZqiE1BDKCjEjFoUoSCEwJUupiyTNgfHva49OhUFhW3FLZSdIgUrR0SL0RhqQ1EUOopT6ezfgMSlPTINoXuT1opTYltxK43hVpKGZ7UIqREKdo5KpYSqePm9GXMYW1ok9RoCagjTNSlZGfrKXSSM6tGIhOZQO0njzbt1TlcMWiOTWZddTsYc9grHuRVCWpiaYD2mW2HA7CFrpQlrURpCEwioQc7+8Pu4+pdX8Oz7H6dt/hR05Y3nJLFci1vX/Z2+Qj/Tq6aMRJjYrEtv5M4N9/DR2Rf6L0M+Pj4+Pm9Y9COPQabTWI88iLNhLUI30GbNJnDO+YjArpFVWTPF8tSTFKwchhogL7MMVvrImiks16S33IUrvaiFrYX1tEYmMSV+4KggLKXksZ6nuWvTvxgsDaMgODdxCieuPxxDCZJSs1hZi9qhBOJEA3f36unuFoVCng9+0ItVu+22e6mtrQNenVgIKSGVAkWBRMJzKO9Mf78gFBor+CqKd14mM36bxSI8/rhgyRKBbcOcOZIjjpBUVe3cLtxwg8JzzwnWroVKBZJJaGmRtLYKLrzw5WMoamrq+NGPfsUHPvBxvvWtL/LBD57FySefxo9//GsaGl55AWUfH59XF18M9vHxeUuyfv0afvrT7/K3v91MR8dkfvKT33Hmme8lHPayZkOlLp6/exXDW1IYYR2zaKIaKpMXTkILvPr/dEb1OLOS89mUW03GSiGA2mAjk2IHENJeWpyORmN89KOf5oMf/CR33nkzl1/+Xd72tnlMmvQpFi68DGXU1elNAjs7Bd3d0L5TjO611/4fd9xxE7///fVMnjxtt/tdtHOAHBOjIIRAFzqZyjBlu4gjHUJaeNyohaRRw7biFlwcFBQc6WA5FQJKyHNG7+SU3o4iFNpjU5kQmUhfqYsVqadJBGpGXzgEAhcXVzq4OKgjvwoNJUjBzpOzMqNisCp0pHc2ujBwpI2Lg0CgKRoBNUhADSKlgyJUFBR0NUxtoJGEUUPaHCJp1Lxpi6W1RiZhKAbdxc0U7TxJo4aWcAf1wRbydpae4lbWucupCzajKt7zPeW9Z3DHtbdyw2+vZdEfz35DisFbsp1szXbTGpsw+mx1RaMpXM+a1HqGysPUhl7FvZs+Pj4+Pm9qpJSkUsN0dW2ht3cb+XyWXC5HLpelVCpimhVM08Q0TQKBAOFwmFAoQji8/StMOByhpqaOtrYOamrqXnLRUmgagdPPQl94DG7vNkQojNI+EaGq456/tbCegp2jKrCj3ZJdYENuJQKFZKBm9Pd9xSnRWdhIdaCB2qC3k2Z1ah03rfkrEklHvBXHdblj3mKKqRKhFQF6i4NUpIkRNZhYnsjB+YMIRHe/jsJL3devfvVT9PR0889/Pr7fCzDvzMaNcPfdChs3CoSAGTMkp5zi0ryTx6OlRbJmjTIy3/WO2bbX7/EiIioVuPpqhaefhmDQE47XrlVYsULy8Y+7JBKeI/i66zwhOJfzrksmoVwGVRX09sKtt3rGEF1/+XHMmHEgN954F3fddTtf/eqnOf74uXz3uz/j3e8+318Y9/F5HeOLwT4+Pm8purq28LOffZ+bb76GhoZmLr/8d5xzzvvRXzDbmXBwC0bYoGtpN/mBPNUd1UyY20Ld1NfO6VkVqCVpLKTo5BEIQmrkFU2ydF3nrLPO45RTzuLyy/+HP/zhR3R1XcfRR/+QWbPejxDK6ERT7mRC6Ovr4Xvf+woXXPARTjvtPa+oz7oaAMkuoq3plEnJQZ4YeAAXh4gWpyM6jfrQWJdzc7iDLfn1ONKi7JZQhCCkRdCETsKofsnxq4qGoQZHspV3FYwd10K+oDK0AFThTbrLTpHhSj9hLUJEi1Gwcziug4JKUAvhSGd0XBE9joLCQdVHsDa7nP7yNnpLnShCpTpQzwHJuQTVsUXt3gwIIWgMt9IQmoCLi4Iy+kziRhJVqPSUtmLJCire+APBAGd+9Gyu+MH/smH1WuYcOP+1HMIeYbsOjnTQXrCAoSkajuVgu69+lIyPj4+Pzxufnp5uli17hs2bN9DZuYXOzs10dW2hs3MLhUJ+zLmqqhKLxQmFIgQCAQzDQNcNTLNCsVikWMxTLBaoVCq7fE44HKGtrYPW1g7a2ibS1tbB5MnTmDv3EKqrd6iMSk0tyssUCLNdm1RlkOAL5qVBNUxvqYugGhqz8BtQQxRGitBuF4Of7l1K0S4xvWoKALoCTdFGnrCX0ShqCSWC6CGdsm3y/OJVmLbJwo8dgaqPL07vLjfddA233HIdv/rVn5kyZZyMtP1ETw9ccYVCX5+goUEiJTz2mKCnR+FTn3JHYxoOP1yyZAls2ACNjZ6Qu22boKNDMmfOrhERK1YInn3WM3Rs38RnWV6sxFNPCd72NsnGjbB+vaCxUdLdLYhGvXOlhG3b4NBDJVu3CjZvZreL1AkheOc7z+KII47lG9/4Lz796Q/xt7/dwv/7f7+hqalln9wzHx+ffYsvBvv4+LwlKJfL/M///JDf/vZnxONJvvWtn3DhhR8jGAyOe74QgoYZ9TTMePUcAruDEIKItvtBaZ5g6aIKbXSCHgwG+epXv0y5fCH33vtV7r33Iyxf/n8sWnQNg4MTaWuTtOw0b/vud79MIBDka1/7wSvub22gka3aerJWipieQCDIWVlyVpagFiaohdAxyFopVqaXAAJtxEEa05PUBhtpjU6iv7QNXTHQFB3HtVEVjeZwB450KNkFNEUnqIYwnQqmWyGohtAUnbieJKR5UQ8xPTHaLwUVVWjIEdVbSknOShNUwxhKgOeGnyBtDlK0C+TtDI2hVqoCteStLAPlHhRUBDvcvpZrktBr2JhbxVCll7hehaboWK7JQHkbetZgVnL+m9YhIYRAZdcXsrAWpSHYQmdhA6500RWDilPmyFOP4p5r7+LnP/shf/7zra9Bj/eOCdFmakPV9Bb7aY15PyxSSvqK/UxKdFDnu4J9fHx8fF6GTCbNsmXPsHTpUzz77FMsW/YMvb3bAIhEorS2tjNhQjtHHHEs55zTTmtrO62tHTQ2NhOLJQgGg7s1r7Btm1KpSKGQZ3Cwn87OzWzZsomtWzfT2bmZhx++j87OzZTLZQAmTZrK/PmHMX/+4cybdygzZhyIpr24bKAIgSJUbNccc1wiEbDLgjwwcmSHmDlQGiKkjp2Tq12C4ICBbTiEGzyhWZcGheEi25b3MLhxiIbpez5P7+np5mtf+zRnv/tCzjj13D1uZ0946ilBT4/ggAN2OH7jccm6dV5h5+1ZwJMmwfvf73D33Qrd3QJVhXnzJGec4TJeHb8tW8C2x0ZL6DoEg5JVqzwxOJ8XVCoSwwDXZdT9axhgmgIpvUJ1lvXKx1VdXcNvfnM1p59+Nl/+8iUcf/xcfvzjX/Oud72699fHx+fl8cVgHx+fNz2PPvoQX/rSJ+ns3MIll3yRSy75ApFI9OUvfA0YUgZYq63CwqLDmUSr0zHuJPrlMJ0yW/Lr6S9340iXKqOGtugUEkY1AJoG553XjG1fw9q1H2P58g9x9dULOOKIP3DJJWeOTgwfffQhbr/9Rn7+8/8jmax6iU8cn6geZ0ZiLuuzz5MxU54TV0oMNUB9sAlVGYloUAP0Frt4cmAxQTUCSMJajEmxGcxKzieohhkq9+FKh5iRpC0yBdMp8/TAQxTtPN6rhgABUroYapCWcAdN4Tbqgy1sza0j5Q6iChVbWtQE6wkoQfJ2Bsf2cuGCaoj26FTWZZ4nZ2cIa1EiWoy0NUR3YTNtkSlE9TgZc5i8naVOb0IiKVp5JFAdqGVrYT0RLY6meDdQVwzCWozBci8lp0BYi1KyC+TtLJrQSBjVu8RjvJkQQjA1cSCqotFb6qRkFwmoATqq53LpJ7/I1772Gfr6et5w2XJRI8LJHSdw85o7WJNaT0QLk7PyVAWSLJp44mgkho+Pj4/PG4e8laW7uInhygC6YtAYaqUp3Ia6j35Pl8tlHn/8Ye67724eeuhfbNiwFoB4PMGcOfM5++wLmDt3AQcdtICmppZ9toCsaRqxWJxYLE5jYzMHHjh3l3OklGzZspElS57gmWee5JlnHuf222/Etm3C4QiHHHIkJ598Gu94x+k0No7dxaUIlYZQCxtzqwi43mL89kX2mJ4EJLZro43M+Uy3ghAKCWPHwmlrrJmVQ2vG7CRz8i6KpaDHjdFjQgg0Q/WKOg8V9uq+/PDbX0cTOic2nMm/f/cfGg9oYNKREwnE9j5+4uXYutUTbHd+xJoGiiLo7R3r+J0zB2bOdOnr886pr981Wxh25A/39UEgANXVXkG4fB66uz3379q1UFMjiUYVTFMSi3nXGAaUShCLSdJp75zW1j0f38knn8ahhy7ka1/7NBdffCGPPvoQ3/nOz3a75oiPj8/+R0gpX7vymG8SstksiUSCNWsGicXevJXjfXzeaKRSw3z/+1/lhhv+zCGHHMnll/+WadNmvtbdelGe0B/httANDCmDgCQiYxxTOZF3lc8d13X5YtiOxbLUEwxWeggqYRShUHKKhLUIc6oOJ24kR8/t6oKnnxZs2ZLl3ns/zrPP3sL73/8xvvWty9E0jbe/fQHxeJK//vUBFGXPc28t1yRtDiGlJGMNszm3hqrAjuofplOhs7ABgI7oNBCCgpVFESpza44goVdTdrx84bAWYbDcx4rU04AkqIUZLPWRtYYIazEaQ61UnBIZM+U5j5UAjmuhKjoxLUFVoJamcCthLcpwZZC8lUFVNGoC9QyWe1mdWUqVUYsYyYLNW1m2FbcQUiNE9BiOdHCljaF4DpagGqI1MpmEUc2SoUeIavFRkRvAdi2Kdp55NUczWOmlu7CJiltGFSoJvYrpyYNGXpb2D2WnRKoygCMdYnqCuF61z14wHddhQ2YzvYV+InqYA6qnEtbHj8OoOGUst0JgZLtoJpNm7txWvvjFb3PxxZ/fJ/15NZFS8vzQGp7sfYaB4hATYs0c3rSAycmO17prb0lyuSzTp9eSyWSIj2eX8nlV8OfEPm9U8laGZcNPULCyGGpw5He9w4RwBzOSB2O5JjkrgyIUEkbVbi/k9vZuY/Hif3Lfff/g3/++n2KxQEtLGyee+A4WLDicuXMPYeLEKRTdPBWnTFANEdFir4udRMVikeXLl/D004/z8MOLefTRB3Ech/nzD+Md7ziDRYvOYNIkL0fAdMo8n17CUKVvpFCcJKRGmBybSX+5m/5yj5exLz3HcHO4jQPic1AUDYTC1mwXv132Z1LlNI3hehzpkF+eo+2BZqqDSaJVnolDSkkxVcII6xz1iSNpPXjCHo3t6Yef5Iz3HcM5h13E6ce8F8dyKWVKNB7QwLxz5u51/MTLccMNgsWLFWbM2CHFSAmrVgnOPttl0aJXJtE4Dtx+u+Dvf1dYsQJUFaJRT9wdHBTk89DRAS0tcOSRLpUKLF6s4DiSnh5BoeBdM2ECNDXBe97jjrqT9wYpJddf/ye+8Y3/YtKkaVxxxU20t0/a63Z9fHxenN2dE/ti8D7An/j6+Lz+uPvuO/jyly+hUilz2WU/4vzzP7xXYub+plvp5OexH2BSYYLTjkAwrAyRUoa4qHAxh1kLX7aNkl3mP9ue5IHOB+nMb2FqsoN5jbNoiNR6hUfMAdqjU5meOGjc66WUXHfdFXzzm5+jo2Myxx//Dn7/+59zzz1PMGvW+Ne8FFJKBso99Je7qThlkkYNTeE2MuYwy1NPjSmslq4M0VvqJGHU0BTeYUUYrmzv85wx7T47/CjD5X6SgRos16S7sBkAV7o0hVuxXIue0haEFDSEWtFVnbJdpDHcyuyqQ8e8ZEkpyVopMmaKrvwGMlaKutBYp+pQuZ+QFh51ByeNGi9DWNpEtBiBkYiKJwcfwHFtInp89IWnYGfRlQAt4Ymsyy4noAYJqREcaZO10iT0aubXHjXqJt6X9Je2sTb7HCXbc89oQqcp0s60+IHjvsgW7bwnjgudpFE9RtTe5VyryHWrb2Vp/wpMx0QIQWusmffNeDdTkhN3q3+f/OQFrFq1nDvueYC8nUEVGlWBOgLq+PEtPj4vhi8Gvz7w58Q+b1RWpZeytbCeamNHAbSKU6bslGgJtzNY6aNkF1CEQkxPMC0+m2Rg/CzdgYE+brnlOv7617+wfPmzKIrCggVHcOKJi3j7209h+vRZo59hOmXWZJ5jsNKH7ZroikF9sIWpiQNfdwVWU6lh7rvvH9x99x08+OC/KJdLzJgxi3e/+3ze976LSCQTDFX6Kdg5NEWnJlBPWItiuSb9pW6GKv0IodDkSFoGlhMYWoZUNKyGI6hMeDsrC/38c9Nitua6UYTCVH0Stf9IklmTJhALoAU1zIKJYzk0z2nm2EuOIhjfs/nCexadzJoNz/ObL9yAoXtOYKtsk+vPMf+9B4+JnyiV4PnnBQMDnsB64IGSqle+WW4Ma9fCb3+rIqWkqckTgjs7vfY/8xl3TFzb7vDss4I//EFQVQXDw4J16wTDwzA87LmJEwmYMEFSXw+ViqShATZuFPT3e9cnEtDWJjn4YDj8cJfZs8d3H+8pK1c+x0c+ci6p1BC//vVVnHjion3XuI+Pzxh2d07sx0T4+Pi8qbAsix/84Gv84Q//w8knn8aPfvSrXbazvR5ZqT9HSgwxwz5wNBaixq0lpQyxxHjiZcVgx3W4cfVt/Gfbk0hsLNfi2f7VdOb6OGPqiTREajCUAKnK4Iu2IYTgggs+woIFh/PhD5/D7373M0455cw9EoIBNuXWsCm/Gncks3io0kdfqZsZibnE9SQZc4ionkQVCgU7ixAK8Rc4ZFWhUrbHbgN0cUedOwCWa+FKh4AaomwXKVg5hisDmE4ZhGCgso2QGiFuVDFY7iVrpUbjMlzpsj77PF2FTdjSpGDlKTp5dMUYLVC3vYJzY6iVjti00X5sb2M7hhpgQngS67IryBU7PSes9F7spsfn0l/uRhUqYc1zt2wvgpc1UwxXBnYpnre72K7NYKWXjDmMKjRqAnUkjVrKTpE1mWWYboWkUQMIKm6ZzvwGYlqclsgOwdaVLhtyq9hW2ETFraCgEDOSzEgctMs4t7N46795vOcZ2qItRI0IlmuzKbOZG9fczkdmv5eU2UfOShNSIzSGW6kNNO7idDr7nAu44PzTufnhK2mb2Y7AyxmelphDXfCNFR3h4+Pj4/PGRErJcKWfoBIa83sqoAZJVQZYl11BTE+QMKpwpUvGHGZl+lnm1R5FQAmStVLkKlme+vej/O2W21h8310oisLJJ5/Gxz/+GY477uTRomyOdBiq9FFyihgiQF+5i95iJxE9TlSLU3HLdBU3oiramIXw1wNVVdWcffaFnH32hRSLBR566F/ceeetXH75t/nZz77LmWe+j4su+uQuMRS6YtASmUhLZCJKqZ/Isp+hZjfgBmoRdong+r+gZtZzwJzPMu3gjzFYHkYTKtXBKlIT0jz2pycYWDdApWCiBzWaDmxi3jlz91gI7uzczKPLHuDDb/+vUSEYQA9qSMcdEz8xNARXXqmwZg24rgAkjY2CCy90mb4X9eamToWzz3a56y7Bhg0CIaC5WfKud8lXLAQDPPccOI4gmZRUVUlqaiSLFwukhFjMc/vm85DJCHI5WLkSams9EVhVYeJE+NSnXKZM2fMxvRQzZ87h7rsf41Of+iDvf/+7+Pa3L+ejH/30/vkwHx+f3cIXg318fF4XuNIlbQ5hOmUCapDETq7R3WXbti4+8YnzWbr0Kb773f/mwx++5HWxzW53KIvSSOrt2P4a0iAnsi97/fr0Jp7uW8aEaDNSWAyUIaCE2JrtYcXgKlA6yFkZ4nqSvlI39cHmF703M2YcyFlnncd///f3uO++f3DXXbdzyilnvqLxFOwcnYX1I5m5O7b2pcwBekpbmZmcx9rMcjJWCikdDDVIREpC6o6IASkljrSJ7lT4DUBBIaiGyVopIIIqVASCopWj6Hh5vA42AoWACBBQQiOxEV77ZadIAk/g7C9vY2thPUE1TExJEFHjdBY30F/ehqEGCKoh8lYWQwm+rFhbtPNEtTihkXxjiURXDHRhMFDqxsYmoI7NSlOFikRiurtW+t4dLNdkZXoJ/aVtXh4zkq15g47oNHTFoOQURyIvRooHqt696C11URtswnJNgmqI3lIXm3NrCKohqoxaXOmQNVOsSj/L/Nqjd3EnVRyTJ3qXkAzEiRoRAHRFoyPeztr0Ou7Z+g+aYtUYikHWTDFQ7mFaYjYTImO3Bk6ZP4VkXRWP/ONhLpn7X0i8jME1mWXEtARBbfzICR8fHx8fn32JJjRMymOOSSkpO0UMJTg6F1GEStKoIWUO0lvqZPW65dx+8038++8PkB5MM3H6ZL72je9z7ns+SFXV2MXUkl1gZXoJKXMQKV0vCsHKUBNsHN0RE1RDuNKlr9RFe3QqQfX1mbEaDkdYtOhdLFr0LoaGBrjuuiu46qo/cMMNf+bQQxdy0UWf5JRTzkTXx+56Mnr+g5rdiF01C0Z2KLmhOvShZeiDS6BxIQ3hHTFi1W1VnPLNk+lf20+uL0+kNkLtpBr00J7vprrtthsI6EFm1h+JZe0ooOa6LhLGtH333YLnnxdMniwJBLzCahs3ws03K3zhCy4vUof6ZRECFi6UzJkj2bQJFMUrFhfeg2mP43jxEmvXQmenIB6HqiqJ4wh0XRKNehnCgQBs3SoZGhJEIuA4EtPcfj089phgypQ92zQuJdi250J+sVevRCLJlVfexg9/+HW+9a0vsG1bF9/4xo9f1zs3fXzezPhisI+Pz2tO2S6yKrOU4Uo/rnRQhEpNoJ4ZyYN3exL84IP/4tJLP0AgEOS22+5nwYLD93Ov9y3NTisKKmVKBPHG7OKSF3mm2Qe87PVd+W1UHJOY4W3H0xUD0y3TFKvCEUWyZgoJONLl+dTTVOKzaIuOv/xvmibXX38F73nP+ZTLZT72sffx85//kbPPvnC3x5M1U1TcClXGji2UQggCSpju4mZ0xSAZqKU+1ExEi6ErBs+nnyFtDRPWop64a+eIaDEaQmPz4IQQtETayaSHKVg5gmoYKSDnZMeI6RIX27VRhCCghijZBXTFIKDsmLkPlLaBlKN/zwJakKZQGz3FLaQqA4S1GJGRQnbJnQqdgOfucaSNkIINuZX0lbopOQXSlUFCWpS6UDO68F4oUpVBLOlV2Q5rkdE2LNcccQtHxrQtpcRyveiFslNkoNSD6VaI6HHqg00ERl4We4pb6St1EderRmMminaeLYV1NIQmeFW8XzArFwh6S11ezIXrZR+XnAIqKqGRfqhCI2FUkTFTDFf6d3kGpmNScSoE1bFFVjRFJWdlKFgFqoxpo5+dtzJszq+lPtg86ugGGDB7OOLkhTxxz2PwdVCEQlyvImUOMlTpo0XbvbgJHx8fHx+fPUUIQWO4lTWZZaNzqO0F0ECMLmrvOF9hw4oN/OrPv+CZh58kHI1w7DtP4IR3nUTd5BrqQ827FN2VUrIht4rBSh8JvRpN0ShaOQbLveTMFFEtPvo701AMinYey628bsXgnampqePTn/4KF1/8Be69907+/Off8clPXkBLSxtf/vK3Oeus80YFPy29CqmFR4VgANQgSAc114nVuGv7QhE0zGigYUbDXvfVNCVXXXU9bU2L2LguxOatJVomB+loc8n3ZojWRqmb4s1dczl47jlBXZ0kMDLdUVVob/cKwG3cCDNnbs/6hccfF2zbJmhq8qIWZs58+aiFWMwrELc33H23YPVqr7+67hWQ6+xUqFS8/mo7KT7lssBxvAJx23ePVyowNCRYscJzPr8SXBeefFLwyCOCwUFobISjj5bMmyfHHbuiKFx22Y9oaprAN7/5OXp6uvmf//kTgcD+L9rn4+MzFl8M9vHxeU2RUrIuu4L+8jbiehJdMbxssfI2tKzBrOT8l3T3Sin59a9/wo9//E2OPfbt/OpXV1JTM36G2+uVvMgRkAHanA42autIyCSq1BhWBmlzJnKEeczLthFQA4DElS66YlAbaGSo0kfQcBECdCVA3KiiyqilYGfZWthAY6gVQ9118nXHHX+hp6ebSy75ApMnT+dLX4rxmc98mEqlwgUXfGS3xrTD4yxh+/9Jl1Sln7JbwnU9IdWWNtWBeibGpjMtMYeu/EbS5iASqArU0xSasEsVb0c6qEInqsUZrgxQtL2iKwoKmtCwpT36mQ42ZbuMrhrY0iKqxYjvFHtgueYu2bkRPUZVoI6GUAutkSnEjeQYZ6ztWmzNb6C31IktLcpOiYpdIhmoxVCCuEDZKZIxh0ajDsJ6hKItUVBIm0MYIkDRyVNxSjQEJ5DQdwjNWTPFlvw6UuYgJbvouZJUA00YSFw2qWEiWpySnWew0oeCQnIn0T2kRkiZg9iuhUAZfbEFz/UyVOkfyTxMEtTClOwCA+WeXcRu7754ovQLieoRWmMtPD+0mqpAcvRndKA0iKYotESbxvzchrUYGXOYnJWhZicxuOKUmTl/Nndf+3f6untpnLDjOu85+vj4+Pj47H9awh3kzDR95W4c6QAQUsNMiEwka6XZXmbn+WcHH7ICAAEAAElEQVSe4y9/uJZljz9LU0czn/j2pznxlJMJBL35lOlUGK4MkrPSxI0dgnDZKTJU7iOiRdFG8vgNNUhADVK0C5huZdQdXHFKI997fQrBGXOYvlI3RTtPRI/TGJpATE+gaRqnnHImp5xyJqtWLee///v7fPrTH+J///cXXHbZDzn22Lfj6nE01xrb4Mi9lfr+H+8f/rCE3t41TD/2vyHZgdPXyeZlKSqDMP2gKDMXHUAw5j0H0wTbFoTDYwVSTQPblliWJ54+9ZTg+usVCgWIRiVbtwqee07lfe9zOfzw/VueaWgIHnpIob1doigwMACGAcWipFSC6mrPsZtOe+7jQkGiqrBzjGgg4Ink6h7UzLv/fsGtt3oxF7EYrF4N69crlMsuCxe++Ng//OFLaGxs4tJLP8B5572TK664eZcFFJ/XN5ZrsSXbie06tMZaiLxIAWmf1y++GOzj4/OaUrTzDFX6iGrxUcHKixaIMVjupeQUdnFkbMe2bb7+9c9wzTV/5LOf/Rpf+MI331BbjVxcHgjcw+LA3QwrQwgpCMogqtQwMDixcgrHVd5Og/vy2akza6ZRH65lS7aT9ngrYS2KLS00vUhLuIXmcDuGEgABIS1KzkyTt7NUq3Vj2pFS8r//+3Pe9rZTmDZtJgA//en/EggE+cpXLqW2tp53vOP0l+1PVaCWkBYhZ2WIaUksaTJcGSBjpakyaqm4FbLmMK50yJjDZMwhGkITmJWcD0BvqZO+UhdrMstRhUptsIHJ8ZmoQmNlegkD5R5c6SCEQEFBSIhpcQw1SMHOYTomEhcXh5KTBxFGVwI0hTpG4hQ8EkYNnYVNlJwiilAJqWEMJYAiVJrCbdQE68eMS0rJ2sxyuoobMZQgIBgq9yGEQgLP6aONRD+kKoMIvEIztmMTVqNMjs1kbXY5vaVOHOnFRuTtDGsyy5iemEPZLbE89RQFO4uhhMhYw5hOmahMUB1uwHTLdBY2YigG9aEWLNfEdMpoZZ36YDOIHU5gXTGI6UlSlX4CWhhVqGStNK50qAs2jbqRdcMgVRkka6WoCTSM/gxtF8rH+/kTQnBC69FsyXayNr2B6kCSkl0mb+eZWTOJ6tDYYgWudFGEskv0S1WgjokHdgCwaunzNE7woisUFKKaX3zKx8fHx+fVQVN0ZlbNp6nSPlrQtDpQh+lWWDr4GA8/sJh/XPk3Vi9dSevUdj73ky9z0DEHEw3GxywYa4qOY+ewXiB4OtLGkQ6GCIw5N6YlGaz0UrByCCEwnQqWazIlOtWbt73O6C91syq9lIpbQhU6/eVueotbmVU1n+rAjjnTAQfM5o9//AtPP/043//+VznvvFM56qgT+PbF53GEUFFK/bjBOkCi5juRgSrs6v2bkZzNwj//+RCaFuHgg09EUTRoaSHVlWOLpnLW2Qlq6ndERFRVeUXVVq4UJBI7nK59fZ7I2toqqVTgnnsEliV3yhCWbNniHT/oIEloP2rc3d2QSsG0aVBbK+nshL4+gWF4xePa2iAe96IhCgVPsFUULz/YHFnrtyxQVcGsWS7gFczbsMHLIG5re/FiebmcJwaHQtA8kqRWVwdbtkjuu08wf758yRiNd77zLOrqGvngB8/kve9dxF/+8k8SieS+uzk++411qY3csu5OunLbcKRDbaiaRRPfxpFNh7xhIhp9fDHYx8fnNcaSJo50CImx/xxpQqciS9gvdA+MUCqV+OQnz2fx4rv52c/+wPve98FXobf7lqf0x7gleD1BgrTZHVREhS51C23ORD6V/woRIi/fyAjVwSrOnnYGN6/9G2tTG0BAWA8yO9lMR7x9jAPYi+JQdnHcAjz00L9YtWoF3/vez0ePCSH4/vd/zsBALxdffCG33PIv5s079CX7E1BDTInNYnVmGVsL66i4ZSpOGSklaWsQx/Xcu4pQcKQ94qTpp6u4ibhRxeb8WhCCkBbGkTbdxc2YboWEXkNfsYu44cUieNs4M7i4WK5NSNfQFJ2yU8LFc/Y40qVoF4loMbYW1pEyB5iamIWhBNmYXUXK7MeRDhoaqqITUIJMjs+gJrDrXsWclaa31IU+8jJnuyaq0JB4BWVqA42eW9gugYDhSj9ZK4UuDGZXH0JVoBYXZ7SgXViLYroVuoqbiOpxLNeiYGepMuooOQVAEtUSVNwyRTtH2Sl6zwSFgBIkadQwWO4lb2WJjzh9i1aevJWhM78eIRRcJBW7RESLURtoQEEQ0WJjxpUM1DBQ6iFlDRLRYrjSoewUaQi2jHEd78yBtTP4yOwLeKjzUbZkO6mP1HFG0yKqIhoDpW4CahBFqEjpkrPSJIyqMa5s8JxYw7V9NHU0s3zJUhacfAiWU6Ex3EpVoG7cz/Xx8fHx8dkfKEKhJlhPDTtEzeefWs4Pv/EdVjy3jMkHTuWLP/8aJ75tER2x6TyffpqyUxwjBpedIgE1uEv8U0iNENYiFO08urHj/IAapDpQR1ALUXHKGEqAjui0F43yAm+XT2dhA8NmP4YSpD0yhaiReNHz9xW2a7MxtwpbWlQZdaNFdjPmEBtzq0katbss+i5YcDi3334/9977d374w6/ztvM+wntPPoqfvhuqS/0gBG6ghvKUc3Di+zcaamgIenufpq7uYE8IBghGCbdG6e4WpHMONTt5ABQFTjrJpbtbYc0aQSzmuW0VBc44Q1JdDZs3e+JrY+NYF2x9PfT2Cnp6oKMDurqgWPSOV49fl3ePCIU8J3Cl4v3/tGkwbZpkYAAKBZgwAbq6BLW1MHkyxGKSJUu8Y4WCV2BOUSSTJ7u87W2S55+Hm25S2LJFkMtJSiWFxkbJ298uOfJISXv7js/u7YXhYU8w3pm6OujvFwwMQGvrS/f/0EOP5Kab7uGcc07mggtO44Yb7iIaHTtH3R7XMTTkOZrnzJH79B76vDKGyymuXvkX+ouDtMZa0IRKT7Gfm9b8lWQgwayavais6POq4ovBPj4+rylhNUpADVJ2ikSUHU7AslMkqIZGM0x3Jp/PcdFF7+aZZ57gyitv48QTF72aXd4nSCT/MR4EJC2ON1MyZIDJ9jQ2autZp69irrVg3Gu9bf29WG6FkBahNtiIoQQ4uH427fFWVg+vw3RMWqKNlOQgvcVOAmoQVdFwpeNt1Q/UE9OTu7R92203MG3aARxxxNhoCkVR+OUvr+Tcc9/BBz5wJnfe+TAdHZNfcoyN4VaGKwOkzUESWg0lu0DRymM6FUASUiMgBKbjUHaLxEUV/aVuzzGMS3IkOkHHQBM6g6U+MuYwumqM5uMKIYjpCYJqkJJTomR5Wy0VvIgDRuIqhIC4niSqx8lZaZYPP4npmvSVuwmpkVFBWkoHVdGoDTaNbuPcmayVJlUZwJUO7kjBNtMpoyk6plshb3u5xarQcHERUuC4Frqqk9CrWDL0H7YVt6IJjUq5REiLUhNoQFN0ekqdCAklu0jR3oztWliuOZqxW3ErlJwimtBAeKOL6UlKToGMmSJlDhJ0wmStYZCCkBZBVwJUnBKmW2ZCdDJVRjXPDP0HW1roYsfLqCpU6oJNRPW496KqGnREp9MRnfaShRwPqJ7GjKqpWK6NpqgoQqFo57HdCmlzaMSD7QnaU+Ozd1mAiBtJZlcdykHz57Ny6XMElRAd0WlMiEzcrQKSZbvCtkIvmlBpiTahKnuwx9HHx8fHx+cF9PX18P3vf5Vbb72eOXPmceONdzH/iMNQhUpADSGEoDUymTWZZWTNFIYaxHJNbNdkYmzGLvNXVdFoj05lVXop6coQhhrAdCsIYFZyAc2Rdi8qQgmOznHGo2wX+XffPxkob8OVnpNzVfpZDq45ksnxmfvzlpC30hTsPJGd8o2FEIS1GDkz7RXS1Xfd1SOE4OSTT+PEExdx441X8v3vf5UHn13HLy/7FCcceRh21QG4ofpdrtvXxGKQyTxDe/sZY44XChAMjo1O2M7MmfCJT7g8+qhg82ZBTQ0cdpjk4IO9GY6ue7m8lsUYB7Bte8dzOfjtbxXWrhVUKp5L9+ijJaecIsdk+e4pHR3Q0SFZu9YrcqfrnrN3YEBwwgkukyfD/fdDJgOzZ0sWLJA8/7w3V4rHvfmxpkE06mUGX3+9YN06QTYryGTEaHG5pUsFc+bAxRe7HHqoN/ZgcIcQvdP6xuifd7e43oEHzuX66//Buee+gwsvPIPrrruTcNj7+enuhj//WWHzZgBPvG5pkVx4ocvUqXt///YUkQbjUYG+VIAK1sES80iJHH8z65uK5YOr6Cn0Mb1qyuhcvS3WwprUep7qXeKLwW8gfDHYx8fnNcVQA0wIT2J97vnRybTplHFxmRiePsZtAZDJpDn//FNZt241N9zwDw477Ki9+nwbm7XaSrapXQRliAOs2dTI/Z857OAwoPYSk2NnngYBJC5pMTzudUPlflZlllC08yNHBEmjmllVC4hoMaqDSY5sPmT0/KLdgOlUSJmDMBKQkNCrmJaYvYvYZlkW//rXP7jooovH3eITCoX4859v5YwzjuH880/jjr89SO1OFgpXuliuiSY0FKHSX+pmXfZ5LNcioIYJa1Fv6yUatrRwpYvAi3kAgSVNDBmg6BTGFHkDbyulxMV0zXG3TcaMasKuTdocxHIrKEIlIEIEtQiutFGFRtktoQmdhFFNd3EzJauAruijL2xS86qGq0IlY6XGvf/D5X5KbpGgEiakGMiRTN2yU0JVNPJmBhAEtBAhNUJMj6MJnZJTZF32eQbK21BQCKph3JEq4mKkOE3BzJKzM2St1IhA7WC5JjmZRlcC3n1FwZImETWOKlSEENQFmnClS9KoIaRGsFyThF5FQAuN3LsYruXSU9xCS7iD6kAdA+VtRLU4mmJQdooU7QIRNUrZKYEQJPQaJkQmjpsp/UKEEBjqjhfXsBZlbs2RIzEvRQwlQG2wgaA6fpZY3KjipIWnc89tf2dmcAGJWPJlPxPg6b6l3L1pMb2FflShMDHRxrumvJOJibbdut7Hx8fHx+eFmKbJ//3fr/j5z39AIBDk8sv/l/e974PjxpC1hDtQhEJXYRMlu0BQDdEcnUFrdNK4bTeGWlGFRndxEwUrR5VRS3O4ncZQK0KIXea84/Hc8BP0lToJqlEM1cB1XQpOlmeHHqUhNGFcMXafMTo3fGEWrASxo1rEi6FpGhdc8BFOOOEdfP7zH+ecT1/GhRd+lG9+8/+9gr1we46iDFEobELXDyGdhkTCi47o7RUcd5xL3YtsSJo8GSZP9gwAL6SpCaZMkSxb5mULe3nC0N0tmDVLsnixwvLlgqYmSXOz507++98FsRgcf/z4mbpSwpo18PzznoA8cSIcdJAkPM40Stfhve91ufpqhU2bQEpPMK2pkTz+uOCGG7yIi5oauP9+hfvvlwghmTPHi7sQAiZMkNg2/Pa3gvXrFQxDks2C43hCsRCeKLxuneTGGwUzZ0qiUWhpgalTJUuXCiZN8orslcvQ0yNYuFBS+wpep+bOXcC1197J+953Ch/84FlcddVfCQZD/O1vCps2CaZOlWiaxHVh/XrBbbcpfO5zLvqLr5vsN0QWIlcoaCsEMiZBgrZWQVsjKXzEhddn1Pc+I1PJsn13586EtRD9xaHXplM+e4QvBvv4+LzmtEWnoCk63cXNVJwSET3OhPBEmsPtY84rlUp88INnsXHjOm6++V7mzJm3V59bFAWuC13BEuNJLLw4inq3gXNLH+Aga/5etf1yqKg0OS2s1JdT7+6IIyhTRkGl2t0xg5JItildbFLWs8FeQVJW0Wx4Lw6udEhXhtiUWzNusb3twtxwpY+yU8JQAtQEG8Z94XjssYfJZNIsWnTGLt8DLy83lAjxvd/9lI+fcyEXXHwqn/3p52kKtxPTEwxV+inaeTRFx1ACDFcGKNhZVKGRM1Oe8CsULNcGJJY0UdEwlABCqJhOhY7INPJ2lqyVJrTTq4Ht2iiKSpVRy+BIARYxMgmpuGUCSpCDag9ja34967LPE9MTxPQkaXOQklNEIJAjDhohPLeu3F7cTnpF6Uy34hWDc0r0FLaOCqfbcaVL3s4QUII40sZFQyAIqhFsaWEIz+WjCIW4XkVYi2GoBrowKDh5UuYgES2O6Zq40kVVNHQkBTtHwc7hujYVt4IrHSqUiIxk5uatDODlDSKEl+OrRkeev0vRyVMXbGJB7TFkzGGGzYFRN/F2AorXN0dazEgchIIYeT55r1CchLJbIqxFESj0FLdQcvIcVH3EaEGbV4KuGDSFd1+UnT59JlJKOjs3k0jMfdnz16Y2cP2qWzEdk6ZIA47rsHJ4LZmVOT5z8MepCu7/7bI+Pj4+Pm8uHnzwXr7xjc+xefMGPvCBT/CFL3zzJYtaCSFGxVzLNdEUfdwIrp3Prw81Ux9qHl0QfyX5mq7r0lXchCp0DNWbxymKQoQ4eTvDlvw6ZlW9/Py1ZBfoK3WTtTwTRn2waTT24aWI60lieoKsmSJh1IzGRBTsHDXBxhet8fFCmpsncP31f+eaa/7Id77zJR566D7+53+u2GuDx8vR1bUVgCOPnMzQkKC3FyIRWLhQcuaZe1boTVHgzDNdslmFDRs8MRYk7e2S6dNdfvc7lUoFtm0TVFXBpEleju4jjwiOOkruImZKCbffLrjrLmVUjH3gAcns2YKLLnKJjSQoZLOwZImgq8tzPJ91lkupJOjvl/znP4ING7xCbpomKZUEluUJtRs3egJzNOp9qarn+i2XGY2NUFVP/A2M+AEsyxONFQU2bvS+5szx/vye97gMDSk884xXbC8SkcybJ3nXu1xewV9tAA455AiuvfZvnHfeqXz60x/ke9+7gbVrVZqadrioFcXLat66VbB1qyfUv9oYzwi0lQJ7moTtz68s0Z8D/TmBddj+LRr4WlMTqkYI791s+y5KKSV5q0BrrPk17p3PK8EXg318fF5zFKEwIeKJv7a00IS+y2qjbdt88pPns2zZM9x00z17LQQDPGzcx+PGv2l1OojKGC4um9UN3BS8mg57MgmZ3OWajEhTEkWq3VoMXt7BIZFsVTexWd2IhsY0+wDq3AYEgqPNE1mrrWKLupE6t4EKFXrULg605jLDPhDwHMR3Bm/hocB9DMl+0uoQdVUNnJA+iemlAzxhUI8yVO6j4pYJjlN5WlM06kMtL9vXu+++gwkT2jnwwLljjtuuRVdhE93FzfSVuqiES7z7S2dz1df/xF23/o3DTj0cgSAZqCVuVFFxSmzNryeohomoMUxZxlCCVNwyhgiBUsZ2BapQUdGQSKS0qQnU0xabQsYcIpMepmBlCWoRHNcmb2eoNuqZmphN0c7TXdyCgoKiqBhKgPboFKoD9WiKTtocQlcDBNUQuh0gZ6WxXYe4kcCWNioKQigYagBH2uSszKhQ6uKOOHkLPDf8BDOT86gPeRMbVzo40qHaqCNvZ6i4FaSUKEIhpiU4qOYIfvOLn3H9764cc/8a2hr54c0/QREKhhJEIMhZaRShoCsGJbuANpJVHFCCODhUnBLSzqArBgE1RF2wicbwBCJqjLyVZdjsZ7gygADCWozpiYMIqEFvzELHcitjBGHTrWAoQc9hrGjMqT6cnJXBlib9xR62FtZRFdjxIhhUQ6TNQQbKPUyI7N8MP4C6Os9hPjjYv1vnP9mzhJyZZ0b1jj2CU/VJrE1tYPng8xwz4cj90k8fHx8fnzcfuVyWb3zjv7jppms47PCFXP7r33DQrAXjRpWNhyKUV7xw+lJRSFJKJC4CZYxA62J7i+MvuHa7I9d2zZf93LyVZUXqSbJW2ou0kg49hS1Mjs+iLTq+suZIh+GRBf+4XkXJLpAyBxAoSFyiWoLJsQNekbAthOD97/8YxxxzIp/5zIc566wT+dKXvs2nP/2VfVKAynVchBAIZUdb2WwagAsvTKJpDqmUVySupYVXLFzuzIQJ8JnPuKxYIUilJMkkTJ8uufxyha4uL0NXCE8QTqc9N20m4wmw28Xgchkeekhw5ZUKy5aBrguamyVtbRLLEtx2G3R3K1xwgUtDA/zpTwrr1m0vBieIx+GCC1wUBTZtEgjhtW0YXrbv+vWeUKsoUKkIVNX7Xk2NZHhYkM97wrOqen1xHM/hrGmeQL1djHUcr7BcZ6cklYLHHxc895yXHRwIQDjsiccbNngZyRMmvLJ7e/jhR/Ob31zNhz98Ns3N38N1v4v6gvUVVd3ejz1/ZnuDulYgjZ2EYIAggEDbJLEOe2369Woxp3YmHfE21qU30hhuQFNUeov9VAWTHNa0f81UPvsWXwz28fF53aAIZUyV5e1IKfnSly5m8eK7ufLK21iw4PC9/iwXlyeM/xCTcaLSW2ZXUGh3JrFeW81q7XkOsxaOnp8VGf4RvJ0l+pOYokyd28AJlXdwhHnMi26Lc3D4a/AvPBxYTEakyCppVDQWVo7j7NKFzLUWcF7pQ/wr8A/6lT4MDI40j+Vd5XNHhean9cf5Z/BvJN0qJpvT6SlvIR1Ks7jqHuqtBqrs6pHJuD3qfN2j++G63HvvnZx66rvHvnhIl9WZpWwrbsFyLUpOEdMuM+OoaRxx2kLu/NXf6JjbQXVzDY60CSjB0X7Y0iJp1GCaFUy3jILwKmmrASJajIAapOKUEEKlPTqVWcl5BLUwYTWK6Zh0FTeSM9MjebbNTE/MIW9lsV3Lc8+6ZRRXIRqM0RqZjBCCuF5FS6SDrYUNlOwCOStFaaToWt7KUXLWogrFy/tzBVknRcUt40hvRumFVqgE1TCWW2FF6imSBa/oW41RT0gNU3aKVAfqyZgpHOmgKxqG8Iq5WW6F+o4GLvrZRTsWNRRv66ciVHrLnaO5xLZrYtoVBAp1wSZs16Lo5AmpEXTF8KrzBpowZYXpidl0xLwMLiklGWuYvJVFG6l2vl34jelJaoIN9BY7CeOiC4OyU8JyLSZGZ4yu4AshiBtJADrzG1AVbcxzV4SCQIy6kvc3tbUNAAwM9O3W+T3FPiL62P2SqlBBCFKV7D7vn4+Pj4/PmwtXuuStLEueeYIv/9enGB4a5As/+DpzTz6YnBziyYEHaQq3MSk6g0Gzj97iVkp2kbhRRUu4nWRg30eKSSnpL3fTXdxMwcoT1iK0hDtoCE1ACIGmGCSNavrL2wi64VGh05JePFZ18OVzd7fm15Ox0lQbtaM7rPJWls35NdQHmwhqY3+3VpwyK9NLGKr0IaVXLSGgBGkOd6AJjbAWpS7YtNvC+Qvp6JjMbbct5uc//wH/7/99i1WrVvDf//1HwuNlIuwGhaECm5/cysDaAYQiaJzVSPshrQRjQXI5b04TjyeorvaEyn1FNAqHH77DEbp8OXR2erm8huF9hcOSwUHPtXvMMTtiH2wbrrtOcOONCj09YJqeQ3fLFsHmzRCJCFRV8uijglxORUpJd7cgmYRUCopF77zvfU9B12FoSGCaXiQFeC5fKb3zhPBiIhRFkMt5wu72jF/D8IRn2wbX9bJ/TdMTnF1XUih4kRX33+/FSaxYAT09nhvaMLzM5HLZE54ffVQweTLMmiU591yXxCvYsLVo0Rl89avf40c/+gaLFs2iUjmHaHSHqNzbC3V1cp8+v5eilCnT/Vw3gxuH0Q2VtlQjk+xm4AULOhLkTq+xruNiV2w0Q0PRXr4OxhuFmBHlolnv486N97BmeAOudJiSnMg7Ok5kUqL95Rvwed3gi8E+Pj6ve378429w441X8stf/mmfFYtzcCiLEoYcKz4rKEigIspjzr0ufAVP6Y9S5zZS5dbQp/RwXfgKNKlzqDW+C3GJ/iT/Cv6DsBsmq2YYVAYoiSKbwxtZo63kQ8VLONI8lnnmYQyovQRliFq3foy4/IzxOAC1bj2OansxD+VaesPb2Bhcz7zcIRTtPDUvkcm6O6xYsZSenm7e8Y7TxxxPm0P0lbqJagmyVhplJOpBAqd96nTWLVnLTT/4C5/45cVUnDK2tEYKqKme8KsY1AWbyJopyk4BVdGYVbWAhFE96oitCdQTN3ZswxRC0BGbRnO4jYKdQ1N0oloC062wIv00eTtDQq8mpEVQhUraHKarsIlpidkIIZgSP5C4XsXqzDIc6VITaPDiFOw8RTvnZfYqISxpYrkmqtBxpIuKQlALowudop1DFRp5O4PlmuiKwXCln4AaomyXGCj34EVMSFxcaoON9BY7yZjDaJpKbV09lltBIIgbNahoXhSFUyaghAirIRzFpmQXAIjp3vgKTg7btTEUg4pTxsUhpIaoDtSPuT9Jo4akUbPLcxRCMD1xEKrQGCz3UBlxZU+OHUDrizh+AmpoVAzfjpQSFzluPvP+IBgMEo8n6O/fPTG4OdLI2tQGpJSjIrbjOgigKuBHRPj4+Pj4vDhZM82a4WVc+bs/cMefbmXKrGn86Nc/I9boFTXWFYOKU2Jzfg3D5QEKThaQqMKLNBss9zKragG1wYZ92q+t+fWsyjyLIlSCaoiUOUjaHMJ0K7RFpwAwI3kw6YEhcnYKXQmM7lpqCrXREnrpnTy2azFU6SOkRkaFYPCKOQ9V+ugrd9MWmTJmcXhLfi39pW0kjCqvfoN0yZjD5K0MC2qPeclid7uLqqp84QvfZMaMWSMu4RO4+uq/Ul/f+PIX70QpXeLZm5eR6koTigeRUrL2/nWkO9PMO2cumcwOMXh/09srCAQETU1eEbRIhJE8YS+P95hj5Kjjdc0aePhhBdOERMITY6WEfN4TWjXNcxeHQrBpE2zYoBAOw9q1nku2pcVzI69b510Xj3vCrWVtL/K3o1+uK0b/XKl47lrD8MRsTfNyhFXVE4PBa89xPGE5EhGUSvD8856A7YnOngBuGJ4wPTgoaGiQo/14/HGBqip86EOvLDbi0ku/xJo1K/n73z/E8cdPZs2aBUQikmJREA7DokXuuBnK+5pSusSztyxjaPMwRkjHsV36h/rJ5NIcNDwTUa2ABGUQZBDsAyRSSrYt72Hr050UhooEogY1c5I0HdxALJgct0D1/kBKSWGwgOu4RGojqNq+K7LcHG3kY7Pfz1B5GNt1qAvV+EWc34D4YrCPj8/rmmuu+SO/+tVP+Na3fsJ73nPBPmtXR2eaNZNHAg+MxjYAZESKsAzT6uxY2dyormOFvpQ2Z+KoizjqxNioruPhwH0ssA4fKYI2lmf0J5BIUuowA0of1W4tNdQxqPTTp/bw19CNTLdnEpcJ/j975x1m11VY+98+9fYyfUYzmhn1LtmyLTfcCwaDjbGNbUzvCQklIQFCC4EEAgkJCQRICI9mwNh0F3A3LnJTr6M+vd25vZ223x9nZqSxuiyT8N79fZ8+2zP31Hvlu/c6a6/V4XYd8TwzIk1A+q5PVWgkjAZSlREs1yYrJ0hb4wTVEF2RBUdcVufisl3bzA5tK65wmecsZLl9xmERFxs3voCqqpx55sy1TUUnhzfp5lUc/xrFZEmICAje+ql38E/v+yKP3/k4r37ztQgEphpEV0yKTh6BIKxHCaohJqwxZgU7WZI484SWABpqYNrx6kmPTRPPMFTqRUXzhWRLJ2HWE1CDjFYGmBNdjKZoKEKhOdjOgcIuGoOtxPQEFafMYGk/hqLjeB4RI4EnXYpunogWp+z6xS+aouN4DlW3gpx01saNOjRFx5Mu49URbM/CnIzjUIRKSPHL3npym5ESxvvH+ezrPoNmaMxe2sGr3vNqtA6/fbzObKLqVnClM+l4bmHCGqPg5Eka9cT0JHk7S8W1Jt9zhTnRxUT1xHHv1xSmGmBpcjUlp4DtWQTV0GEZwofSFGxjqNRLwc4S1qJI/JxizQviZlvJceSG7dNNQ0PTCcdErGk9k3Wjm9iXO0BLqBlXuvQXhpgdbWNF48vbpl6jRo0aNf54sdwqD2/5Df/8sX9g79Y93PTu27jyzVeTdcfRRN103ENQC2O7Fr3F3TQGW4nqvoAopSRrpegt7KLebDqh8YyUklR1hPHKMLa0SBj1NAVmTR/Lkx778ztZl3oSy6tiqgE86ZE0Gyg7BfqKe2gNzUZXDGZH5iIE7MhsJFMdx1BNZkfmsTxx9hFL7o5yRtP/VnaKpKvj5J0s2zPryFRTzIktJqrHcTybkfIAQS00LfoKoRA1kuTtNBkrRUPg5ATbY3Htta+nq2sub37z9bzmNRfxgx/8ivnzF53w9gObh8j0Z6jvqkNR/XsRSoYY3zPOaM8Ytm2hKMpJ3KdTJxQCISRLloBh+PnE5bIvtK5aJWe4iPv6xKTw67twKxVfjJ2KQUinfYduZ6cfCSGE/3tF8bcZGBAoisSyfOfvxAQoiph09PqvORTX9YVgRQFVlTiOoFiUeJ7vEpZ+H+D0dqoqaWqC9naPvj7BpZdK1q/3c4injuO6TP+zXBa4rl8y19Ym2bJFMDQEbScRJyuE4Itf/CZ79uxm3bobeP/7XyCdbqCpyeOccySLF7/EN+gE6d8wQGr/xIzPVDVZZW+5n46xNppHfGOGjELlGg9nIQxsGGTLPVv99zME/cND7Nm3k+hQgLaLmuiOLKQl1PGynnduOMfOh3aR7ssgXY9IY4R5F82ledHxVw+cKEIIGoKHG1Nq/PFQE4Nr1Kjxv5ZNm9bxyU9+iLe85b285z0fPO37v9S6il36dnZoW4h7SaqiiiUqXFK9mi73oIsypY5RpTotBIMvsoZllBFliLIoEZaHl2YUlTyq1BhW9xGSYVT8J6YCQdKrY1QZZpe2g9XHCJea7yxit7YDDw8FhZiewFVcUiLFLK+Tzsh8WoOzp5f8H4qLy92BO3g08DssqoDgYfM+zrYu4PbSOwlwUCDctm0z8+YtxDRnOkFVoSEBKT1CWoScYmB7Fo5noysmncs6ueCGC3nk+w9x2XVXooY1XOk7W9GilN0yFa+CABrMZubFlx1z4mR7FqnKCCW3gK6YNJjNBLUwo+UBRsp9KCgEtRACgeVVSVfH/axbKZDMjMmwpT1d5FL1ykgkphpCUkZKD1MNogoN8IhoMSpuCVX45w8SRzrEjXo04U+AFKHieR62tOkOL0QKP89PEQqpyghZp0DXki4aPnojTZ3NFFIFHvjO7/j6+7/OX3//Y7Qnu4jqCRoCgUkx2P8KLrlFqm6FilsmYTSgCZ2Ck6Up2M6K5NlE9DhSSipuCSklATV0QpPPEy1ySRqNzI8vY19+JxkrhScFqcEIu59ayi/21BMMStaskVxzzZGbrE8XjY3NJywGz0vM4fbFN3HvvgcZLo6gCJXlDYu5ft41JGrO4Bo1atSocRTu/NV3+dRH/pJ4MsEXvvsvLFq5hJJTYKIwQsUrEefgSiU5WXZrKgfHS0IIglqEnJ2h4paOG48gpWRffif7CjvwpIsQCkOlXoaNfpYnzyaghdieWcfW9AsU7Dy6omGjkPVSSOmSNBopun7R7NSKoI7wXDrCc/38YE5c3NQUnYZA63Svg+1ZjFWGKLslDMUkpEYZqQxQdgusqr8AgZguvD0UBQVPysNWFZ0Oli1bxa9+9ThvetNrue66i7njjntYteqsE9o23ZtGM7Vp0Q5A1VUkkB3KkUgk8TyPfD5HPJ447ed+KEuWSFpaBIODsHixZP58GBvzc3RvucXP7p3CMHxXrqJAJsO02DuF5/ki8N69kmRSYpq+mKvrvou4XAZNOygSSymmBd0pUVdR/GNYFmia79z1oyB8AXjq+L7I7BcsT23reb5YPHXcfN7POwY/bgJ8pzD4x6tU/CxmVfUd0ePjvsv5RPE8eOopwe9/H2LOnLvYufMM7rvvT/npT+9AVV96nvTJMLprHCNkzPhMmVGTfHOewZUTxOJJEODMl7id4Dou+9buRwhBpC3McKkXr97CyOlUt3nklubY4W7AUM0Zq/5OJ9VClY0/30x2MEe0KYJQFXJDOTb9agtnhc4gOfvopZg1/v+iJgbXqFHjfyXZbIb3vOc2Fi9ezmc+86WX5Rhd7lzeW/wQjxsPsVPbRovXxhrrwsNygKNeDE1qVCijCpW96m4G1D6yIk2LN4tedR+LneWH7X++s5iN+jo8vIPlHtgIFKJenIKSw8E55jmeZ13EBv15dmpbafCasLHJaBOcb1/CjeG3YnL0Zfw7tC08GvgdSbeepKwDoCgKPGP8niXOcs63Lp5+7fbtm1m8+PBrqDebCGsRsnaaqJYgaTZhexa2tHGxqXhlrn37dbxw//M88N37afrLFhR8Z253dKGfMexVCaohGgIt6MrRS/fKTpGt6RdIW+NIX4ImpEVYHD+D0cogmjAxFBNH2uiKgaGYlJwCOTvNvOiSadEW/MlandFIf2kvITUyuT/ffSOEgqGYmIpfuFZxKyTNCBW3RMHKggK6MDCE4k+8Dhl3SiYLSYRAEdohx/NF4SXnLaXoFtCFjpirMGvRLL74hn9g88ObmP+GJRSd/PQSVCkleTtDU6CNhNlIqjJMYbI0blHiDP+aFJ28nWVffgfpqn9fEkYdXdGFh8VESCnZmtrBhtEt5Ow8c+NdnN1yBnWBYw/6hBC0h+fQEGglZ02wdq3C43c3EDJMGhslhQLce69Ctepx220vX0OyoijIF9tXjsEZTctZWr+IoeIwmqLTGm46ZiFPjRo1atT4/xcpJV/5yuf58pc/y9lXrOEvPvtxQhFfyNWEjiq06Y6BKWxpo6Ie1g3hSRdFKNMPnI9FwcnSW9yFrpiEJoVjb7KQrb+0n6geY2tmHZZXRVEUXDw8r4KhmBSdAkEtjCIUNHH4tP1Ulpt3RuZNunrHKdh5SnYec3KMFtIjBGSIjDXOeGWIWaFuYkaC8coIphKYfhBddouYSmDaLX26aW+fzS9+8Si33/5a3vjGa7n77gdZtGjZcbczwwauO9MYIKUEKdEDGomwPxbOZCZedjE4mYRbb/W4806Ffft8gTMaFVxzjceaNTPHOosWSWbN8iMaikVfUD00qkFR/J/Ztl9QFw5DPA6plMR1feF36vWOIyYdwwcFXdP092FZ/mtUVWCaclq4hYNiLjC9TyEOuo/LZdi2TaCqgkrFNwdUq5MxXe6Uy9jfv2FAV5ecvNcQjUL9SRhIH3pIcPfdfjxGPD6LFSu+ztq1t/C5z93Bpz/9xlN8R04NVVeQ3sz3S07eHDlLoXr+zN+VsxXKmTLBeJCSU5gs9w5BHMoDVcxCGDtWYKjU97KJwaM9Y2SHctR1JqdFbL0jQWr/BP2bBmticI1pamJwjRo1/tchpeTDH34X6XSKn/zkvsPcqqeT2W43t5ffeczXzHcWM89dyFZtI1klw6gyAkhcXFxs/jv0H7yn+AHmuQtnbHee9QrW688yYBwgr+SwPRtLWLS4bQggIevodo+c4zpFuzebd5f+nIeM++nRtxGSYV5RuYzLq9dMC8EeHju0LezSdiLxmOMsYImznF3aDqpUpoVggLCMoKKxSVs/LQZLKdm+ffOMPOZUeYJ0NUvSjLMwvpKd2U3k7AkqbgkhVMJqFF0xiOoJ5jQs4s/+tMBX/vnz/Nl7Psqc7vkkjHqUE5gkHcr+fA8pa5SEUY8q/IKMnD1BT24zmtAxVANVSZKxxqm4ZQQCx7OIKQlmR+Yd5pZtD3czYY2StsZgsryu4pZIGHUE1BDeZAyEUCFdHfcnd4pKg9lCd3QBvcU9vvAsDYTwnciq0Ahr0RluIE96k8s5G0lNTpg8XL9QL2LS3NFMdihPvdmCKjQy1vh0A7epBJkXX0ZLsJ2yU6TqVTCVwPS+K26JrennyNkZQloUAYxVhig6eVbVn49AMFYZ8l83vovHe5+n5Fjois4LIxt5bmQD71x2Oy3h4w84A2oQT87iuYcVgvrB5XyhkO8ief55hcsvd2k+DRGJrueyaXwbm8a3YTlV5ifnUqmW0fWjPyw4Eoaq0xl7eZfa/U/iSY8dE7vYmtpJ1bWYE+/kjKZlBLXg//Sp1ahRo8YfDeVymT//wNu45zc/49Y/eTPnvfE8Smoey/Jz9YNKCEM1p/sFpjKDFQT1gWYKTo64UociFFzPoeQWmR2ae8wIpimy1gSWVyVpHMxbUoQfHTVaHmCsPIjtWYS0CFW3guVVEAhs1wIkeTtLR3gOYe305DWFtAgr686jr7iHLRPPoasmcSM5vZrIf6gqKDoFhBB0RuaTt7OkrXFMJYAt/Rir7sjCE16BBFBxquzO7MP2bNojrTSGjl3AF48n+MEPfsXrX38Ft9zyKn7+84fp7p53zG2aFjUxsHmI0kSJYDIIEgpjBYywSeO8BqoZ356aTk/Q2TnH38hzEHYeqYVBPbkxyPFYuhT+6q88enr8Qrf2do+2tpkZvuCPt264wSObVRgY8EvcFAV0/aCr92DcgyAQkCxZ4kc1pNMS0xREIpJUSkw7fg91MkwJvlPirmn6zmDDOBhdAWKGIAz+8aaE5khEUCr5zuSxMchm/X3Z9sHr8TzQdUlnp7/voSH/da98pTctBruunznc0+MX282fD8uWSYzJW18owKOP+rnAU+PQCy+8kZGRN/Lf//0Bbr31QhYs+MOVlLUsbmZ8bwq7bKMHdT+HN1XECBs0dNcd9no9oKEaKnbVwdVtwF8R6lZdhC5QAwpSMSg6+ZftnMvZsp9jfIibWQiBEdQpjJyERbvG//PUxOAaNWr8r+Pb3/537rvvl3z72z9l9uxjl2GcChUqVESZqIxNRzccCwOD20vv5N/D/8hWfSO6NIjIKB1uF/OdRezStvO48RCxapxN+jpyIkuD18RKezXvLn6ABq+Rnwd+TFEUafHaiMgolrB4TeU1NHnHz1rrdOfw9vKfUClXUFHROeiAnYqCeCzwABXK/vlKk/OtiwnIIwtGQgo8cXBp38BAL/l8jiVLllN2Kvxqz/08P7yBolMipAU5q3kV13RfwnC5j935rcSNJHG9DolH0cmTtsZ45zvfz/e/95/81798nW9844fHvaYXY3lVxqpDBNXwtNNGCEFET5Cz0jQF2vycPb0BQzEoODm//E2PsSRxJnHj8AFZzEiyIrmG/uI+UpUREkYdjmejKQY5O03Vmyxo08I0BFoBP6ZCIgnrceZEF7OvsHNSTAZVqMwOz8VQTfqKe6lUy/7EUDokjXrmxBbz7NijpKtjGIqJRCKqKqnBCZINSZJmPV3R+YyWBynYOUw1QGOgdbo8L6iFCTJzueloeYiclSFpHmz9NpQAaWuMPdltjFWHyVopbM9hqDhCWzyMziw8T8X1XHakd/FI35Pcuuh1J/Q+ZDKQzQrq62c6HeJxf5lfKsVLFoM96fHz3ffycO/juNJFFSrPDK9jMDfMfOWlh8BJKTmQ72dXei9SesxJdDIn3vVH5xqWUvKbvb/jdwcepeJUUYXCY/1P8vzIEt629FaixolPwmvUqFHj/1dSqTHe9Jbr2LZtM+//4odYctFS0tVRsnYKU/FX6miKQVyvo85souBkKTslTDVAd2QRUSPBjswGMlZqUl4TNJjNdEcXHufIh3Kkpe0STzpYroWpmHh4BLUQ0vGwPdsv5JWCOqOBBfEVJxQPdaLk7DRDpV6/2NazyFgpKm6JxmDb5CorSWCyG6HObGJF3RoGivvJWhNEtTitwdk0B9tP+Hi70nv5Sc8v6M8P4kqXuBHj4vbzeVX3FccsnYrHE/z4x/fyutddxhvecA2/+MUjtLUd/bjNC5qY94o57Ft7gNS+CQAC8QALL5lHfFackuIL0OPjoyA9jMHHMPofRK2M4xlxrLaLqXZcBaehFG+KcBjOOOP4q54uvFAyZ47L5z6n8MgjgmhUUi7777ll+YJrIODvr1z2nbzt7ZJi0RdOy2VBOOw7i6cK6A7iu4Tha7jul8nnhxFiJdXqvyKEH1V3vI+Xn2EsWL7cL24bHFRobpZkMn5Osa77r1m6VNLd7Y8lw2HJpZdKrrzSPxnXhZ/+VPDYY2K63O7hhyVr1sAb3+jHUIyOQjotaGubec+uuOKr3HHH7/nQh97Bb37zwGn9+3As2lfNIt2bYWjrMJ7rK/JGxGTeRXOItkQPe70ZMWlZ3MLep/b5kRYKuJZLZcwiMidEoNkgY+eJaIdve7oQAUGmmCI3OEGxkidfzFAo5ymlSjTMaWToZ3sIh6NEozHi8QSdnXOIRF6+86nxv5eaGFyjRo3/VWzbtom/+7uP8u53f4BrrrnutO67TJkHA/eyVv89FaVMs9vKpdWrOcs+97AlgC+mxWvjUutq9mq76HC7iMgoQekHqCa8Op41nmKXvoNRZYipQX+3M4+3ld7HnxY/wuWVV/G4+SDjyhgtbhurnTWssM88qfM/NON3im3aJh41Z0ZB5EWWJ8xHuLh6Jbo0yIscUek7SipUsIXNUnvl9D56erYDsHDhUn6z97c8cOBRGoP1dETayFkFHux9DIClze0YiknSPOjm0BSDjDVBIZLjL//y0/zFX7yb97//Iyxbtuqkrk1KDynlYZOCqWK+OrOJilsia6cwFJOgFkH3LJoDs+gIH91dHTOSLDGSSCnxpMtoZZCxyhCOZyOEwkh5gKTZMC1ABwmTtSYYKvdyRt35JM0G0tUxPDxiepI6swmBIGE0MFYZxPIskkYDLaF2AmqI33/rCVrPbiLWFKOStvjFt+5CUQRXX3st9WYzmqJPN4KfCEUnjxDKjNZvIQSKVNmZ24TlVlGFSsWtgpBETB3XtbCsAKqiUh+oY/P4Nm70rkU/gclNLAbhsB8NET5Ely4UfIfw6SiS25vdz2P9T1EXSJIMJACwXZtHc1lyonTsjY+DlJJ79z3IAwcepWAXAQhqQS5qP48b5r36j6rpeF+ul4d6f09UD9Mdmw1A1a2ycXQLT9c9z1Wdl/zPnmCNGjVq/C9n377d3H77a0nnJvjrb3yCFStXMVzuJ6RFqDhlXOmgSQ3bq9IUbGNF3RqqbgXbs6ZLZQFWN7yCVHUEy6sSUsMkjSZ09cQEw4TRMB1rFdIiCOGvVKp6Fdoiixgp9xNQw5ScPK4iCChhoISUHm3BTtY0Xo52gsc6EWzPYnd2K1WvQlOwjZHywKQjushEZRRTDRLSIjROPiT3r6H+sGiqEyVbzfGD7T9luDhKV6wDTdEYL6e4Z98DNIbqObf12HnADQ1N/PjH93H99Zfyhje8kp///GEaGo682kkognkXz6V5cTOZ/gxCEdR11hGu88frLS1thMMRdu7cxqsWqIR6vodUdDwzgVJJEez5HsIuUpl38yld60ulrQ0++UmP8XHBtm0HHb2RCASDknnzoKFBsnGjwDDgnHOgpUUyPOznEU+5h6tV3+XreVMZwIJw+Cfk83+BYfwHUq7Bcf4Fy7qGYHAHrts0HUXx4rSuKc1VCIjHJTfeKNm2TWBZ3qQ5wC+ss21IpQQ33ih5zWskuZwfD3Fo18TWrfDYY4KGBt9BXK36AvHatYIlS+DccyXBoB9rUan44vcUnhdnzZr/4tFHr+LnP/8xN9xw68v5VkyjmRorrl9G6/IWckM5VE2lvruO+Kw4QgjK2QqpvSlc2yXWEiXRnmDexXOpFqoM7xzGLQjyokC0I0L9xRFyThpdMWgJzT7hc/Bcj+HtIwxtHcEqVUl2JGlfNYtAwmTPnp1s2bKBzZs3sGXLBrZt20Qmkz5sHwLhR+Y9AXzv8GM0N7cyZ8585syZT3f3PBYsWMw551xALFbr4fh/mZoYXKNGjZcViWRIGWBMHSHiRely5x7Vjet5Hn/91++nq2suH//450/7edwZ/D6Pmw8S9xKEvBD71T18L/RNRElwln3ucfcRlhFCMkKd1zDjGoqiQL96ADzJImeZH1+Awy5tO78I/IQmr4VnjacoKQVCXpj5ziIW2EuOK0CfCDu1rViiOiMKIirjDDFIiRLnWRfxpPkowwwgUHBwWGWv5mz7vOnXp1K+81WLmjy7dz0NgfrpdtiGYB0geWFkI8mQIPCiZfy+iOqXm9100+18+cuf5bvf/SZf+tJ/HHauFhYpZYygDJGQM/OqDCVA3EgyVhlCrejYEw5CF7hJv1W7MdhCQ6CZ/tJ+UpUhFKHSHGynLdR5WLHJkRBCoAqN1tBsWicHYL2FPYxXhg7L/NMVg5LjL6OKG3VHdB03BdtoCh5ei5wZy3Dfp35JOp0mmoyyaNUSvnrHf3H+3MunJ5Ung6kEkPhC+aEuiJJbmJxYhtGVADmrTMmuYpgaquoghIeUKhKJIsQJf9YiEVizRvKb3whUFRIJXwgeHBScf76faQe+6JqxxklVx5DSJWokaTBbTii/cG/mAGWnTNch8Q66quOUbLJK8aTuz4vpSe/h/v0PE9aCtEfaEEKQrmR4pPf3zE10cWbTipe0/z8kuzP7KNpFOiIHP2emahLRw6wf2VQTg2vUqFHjGOzatYMbb7ySaDTGZ7/zDzS1N1N1KzjSIaCGCKghyk6RhkALQgiqrq+8mWoA80XxD6YaoDU4m9HKAP2l/fRkNxPWo8wKddEYaDumS1FKiYrGQHmfXwKrhQiqIRoDrXRG5iHxKNkFIlqMjJ2i6lbxpIuuGkSNBJasonH6xOCsNUHRzRHTkyhCpSHQTNpKUXFK5OwMnWYDC+MrTyoC4lhsS+1ksDjM/Pic6QeyTaFG8laRtUMvHFcMBpg1q4M777yf173uMt785uv5+c8fOWqEnBCCWHOUWPPhTkdFUVi0aBnbtq7HPGMIqQZwo37kgGvWoZSGMQYfpdp+OTJwauL3qZDP+5EKpukLqu3tkr4+gW1P5fxKurpgzhzJ4KDgkkskf/ZnHvE4HDgAX/2qwsCAoFDwP4fhsL+dbR+McSgWv4JpvpNg8G2oqiSb/QaOcy+G8d/AR6lWDwrBh4rCU5nBliU5+2zJBRdIRkehp0chHvdfFI9POYf9bOBg0BdyBwZg1y5BLOZHR/T0CMplwYEDMDzsn9tUed7Gjb4Y3NLiF+6tXSswDD+buFSCoSHBZZddRjB4HZ///N/wyldeR+hFrcZS+vucKsM7Xai6SsuiZloWzVwaN7xtmO2/3UkxXUIAqqnRtryNpdcs4owbV5IZ6GJ8bJQRerEaizhqlbAWpTu6kDqz8YSOLaWk55Hd7HtyHxJJupzi+V89yebh59k7spNKxV8V2tk5h2XLVvLud3+AWbM60J0A2Z48oqARNiLUtzQy76I5tC5roVqtUioVyOdzTEykOHBgD3v27GLv3l1s2rSOX/ziJxSLBVRVZdWqs7jggku48MLLOOus8wgEjh+NU+OPh5oYXKNGjZeNChV+FryD54ynyYssAYIssBdzS/mtR4xHuOOO/+aFF9bys589hGGc3tyuPnU/64y1tLqzpoXIhFvHbnUndwV/QK+6H4lHtzuPZfYqDA4//mJ7OW3uLPZqu+hy5qKhkVZSZJQJTEw6nK5p0U1Do8Vt46HAfYS9CK3eLFqddvJKlvsDv0Iiubnyphn795CMKy6qFNRJ5YQEPEc4HGn5oYICQnJr6W0scpaxVd+Ai8siZxmr7TWE5EHbZyaTJhAI8kToUTbOe4KIGaJQ6MS0A4zE+8kZGdy0YInRQoc7043hSb/dwlQCaJrGG9/4dr7+9X/ik5/8wvTTZInkWf1JHjDvYUwdwcBkpbWaayuvn34vhBDMDs+n79khhtbtx8uDVCWBVp2V16wk0OIP+OZEF9FoNiORRPXEKQmsUwTUIGIy/+9QQdlyKzQGWk9pCdpURIYnXcpOCUUoBNTQKS9nawq20l/aS85OE9HjCKBg55HSQxGCklWlL99HwS5StEvkqgWaw3VACNu1mahkuHbOlSdVMvPKV/pujWef9UtPgkG44ALJjTd6kxMEvxl9f6EHR1r4fgNBU7CNJYkzj1kSCEzbTA4VuF3HxSpWCEaP3cp+PLZN7DxMaE4GEoyVx9k0tu1lFYNLToFUdQTHswlp0Ukn+EsfZh322RHw8tX41ahRo8YfP3v37uLmm6+mrq6eH915Dz3uxoPOODn5/1Xpl7/qio4EHM8+5j77i/vYlduMxENXTFKVUdLVcRbGVzIr3HXEbUpOgS3p5yi6eerNJspOCdurEjCCLEmciakG6YospOQU6S/uwZUuuqITUOPUm81krHG2Z9ZzRv35J93DcDQkfl7s1BgzoscJahHydhbPc1lVfx6h07iEvWCXQHLYypyQHmSicriD8Wh0d8/ju9/9OddddzGf//zH+exn/+mUzmflytU88tBvUG68EC84U5DzAvVouT2opSGcP4AYLCU8/LDgoYcEExO+m3dwEJqaJK99rSSX83OBx8ehUpGMjAjmzJHccotHctJT0d0Nn/60x/i4wpNP+p9t24Zq9WABnetaSPkCtv1RXNcXb5NJQSp1Ofn82hnCqaL45XeW5cdRCOH/qauDd73LIxaDs86SvPAC9PdDa6svAvf2QnOzZMUKSankx0GsX6+Qz/vjyEWLJKGQZGDAd/1GIr5gXK36Qvi2bf7xhYDXv96jXFbYts0vqzNNwapVkte9zuOKK77AJZes5Bvf+Gc+/OFPTJ/3tm3w2GMKBw74+z7/fMmFFx7MIj7dlDJltv12J1ahSn1nHUIRVAtV+p7vI94apfPs2SQ7EiQ7EsyT8yk6OTzpEdaiqIpGbjjHyM5RqvkqkcYILUuaCUQPF1rzI3mevP9xNuxfy7p9a9k31IOm6ixqXc6br30fV91yDUuXrjxiIaLneORG8kjXI9ocRTP9MWkoFCIUCtHQ0ER39zxWr14zYzspJfv37+HJJx/l979/mB/84Nt89atfJBAIcOGFl3HTTbdz1VWveVk7fWr8YaiJwTVq1HjZeCBwDw+b99PkttIm2ymJIhv0F5BI3l/8qxnu2vHxUT7/+Y9z881v5txzX3Haz2VEGaYoCrTLg6UDEklWybBRe4FBpR8dAw2Ns63zuL30rsNiGeIywW3lt/Pj4HfZp+3CxSMqY5xjXcBWfdN0pMEUtrAZVYY5wzl7WvwOeAFA8Jz5FJdXr6Fe+pELO1WL+8wCvaqNgmC+Y3BtNcws79hi51xnIQ+bv6UkitMCb4UKDg6LnKUYGKyxL2CNfcFR95HOTaAndH5Tfyd5OU5BCvpae7DUCrFyHbKiYHVUeDr8GAydi1rRCWlhPOmRt7PE9QT1Af9p+a23vo2vfOXz3H33Hbztbe8DYL3+HN8P/RcgSThJSpR40LiHjJLmvcUPoU1+FVV2O8hnTIJqFFo9FEeFQZWJB4vYs2xypNmd20LBySGl9J+uxxbRchK5dYdSZzaRMOpJVUYJ61FUoVJ2iqhCo+0oE7sTRREqYf2lT6YiepxF8VXsyW0jb6WR+OUvichcNk48z1BhmIJdJqQFUYWCxOFAdpiJ/AimZrCkfgGXdFx4UscMBOANb5BcdplLKuUv8zu08CRnpzlQ3IWmaMS0BOBPokfK/SSNRmZHjl2KOD8xh7AeZrwyQeOkA334QD9IyZrlvmNdTlpSjiWiO57NYOkAw+V+HM+mPtBEzsoeMRtYFRpVt3pS9+FkGC0PsjO7cbqJXkyWDi1NrD7MYXaizIl3EtL8yXJdwJ/1Wa5NwSqyquv4reo1atSo8f8j+/fv4aabriIajXHnnb+loaGJ8cwgg6UDBJQwihC4noMnXTRFw1AC5OwMbaHDS6kqbonR8hB5O01vcQ+a0KejskJahJydobewm+bgrCM+nB4q9ZGzM9SbTQghSJrgeg5ZO03GShHUwphqgOXJsycziZVJcdbvT3A8m3R1nHQ1RX3g+EWwJ0JUTxBQgxSdAhHdz35SUHC8KoYIsi29Hk3RaQq20RxsP2z11MnSHGpAVVTKToWg5n8fSinJVHMsbVh0UvtauXI1n/rUP/KJT3yQc8+9kFe96sT6EA5l9eo1/Pd/f42x0gU06CWkftABLZwSUg0gT8P47UR4/nnB3XcLTBM6OyW7d/sOWE0TgKS+Hq64QrJ1K7S0wCWX+CW+iYS//fAwDA/7xW65nEAIX8R1XV9odhwQQiLEOFK6eF4zUvqZvA0NkkikmUJhp3/tAkDieYJCwRdwGxr8AuFQyBeW77hDYdMmuOQSjxtv9Lj/fsHu3f5KsrY2yetf76vPX/ua4OmnFebMkSxa5OcYr1snSCSYjo8IHlJroml+0Vyh4Au5yaSfs9zbC6WSwDQl8+ZJolGor5/LO9/5Z3zta1/mllveSltbO5s3w3e+o1IoQDLpO5d/9CPB2BjcfLM8bh7yqTC+Z5zSRIn6Ll8IBj8vuJytMLhpiM6zD8ZA+B0oB+MWhrYOs/Xe7VRyZYSiID2PvvUDrLphBdEm//PoOA733fcL/v0rX2bzjnWEzDCrF57HDRe/kTPmn4ssglAV1pxzIap25L+jiqaQmHXyMQ9CCLq759HdPY/bb38nnuexfftmnnjiEX7zm7t5z3tuI5ms4/rrb+ENb3gLy5ev+oNlONc4vdTE4Bo1arwslCmz1nicuJekTvqCT1hG6HS76dG3s0/dzTzXL98YVYZ5/9+9BVd1ecPfvhkHZ1ogPF1EZAQNnSoVAvgjkAllnH3absJemCXOCjQ0iqLAM8YTLHSW8grrssP2s8hZxl8VPsMG7Xk2GevIijSG9B87D6h9dLl+O7FEMqD2YsoATV7rjH3EvTh92gHSSop6t4E+xea7wSwTikuLp+IBz+sVxhWH95eSxOXRB+Ir7DM52zqPZ40n0aSOQFAVVVbZZ3Gmfc4J3ZtBpw834NIt56IQpCe3m1Isha2XiWTqULMGSxMLECbsaz7A3L4F5CdFt3qzifmx5RiK/3S4tXUWV1/9Gr7//W/x1re+FwQ8bjyITZVkqY6sncKTHoam80zg91yqXsVS188v7ls34E9A2g9OeNyoS2YgS39PHwPNPVSc8kGHrJNnR2YDATV4Sll2mqKxJHEme3LbSFVHsWSVkBahMzKfBvP4xX5/KJqCbdSZjWStNCCJGUmKTp4Hex+l5FRoCMWQEkKGTtmusmdinLmxhbx6ztWsbFhKxDg1t21jo//nxaStcWyvSsQ4+EtN0dGEzmhl4LhicFesgys7L+a3+x9mvJxCEQoDO/3JyCvPuZLtmQ1MVEfRFZ2WYAdtoa7DHLae9NiR3cBgqRdN6KhC4UB+Fw55HM+m4lQJaP5n0nZtLM9iQfLY53WqWG6FXbnNk23xDQghcDyHsfIgvVqc+fFTE27nxru4pOMCHux9nLFyClWo2J7NsoZFnNd2/GW1NWrUqPH/G319+7nppqsJBkP89Ke/o7HRf1A9OzyfnJUhZ2VQhUbRyaMIlZieJO9kiOkJZoW6ZuwrZ2XYmnmevJ3B8RxSlRF0xUQgiBt1CCEIqWFKjh/bNFUGeygZaxxd0aeFEtuzyFkZJqwR1qeeouQUaA93owgVVagkzHpUoVFy8kj8VVeedLG8ymm7RwE1SGdkAbtzW0lXx1CFTtUrUXaKOKoLNni4jFWGyFYnWJhY+ZIKWBfVLWBJ/UI2jG6hMViHruiMlVMkzTivmLXm+Dt4EW972/tYu/ZxPvzhd7N06Uo6O+ec1PZThpP79hq8ZdEIUjWRegzhFFELB7Cbz8eNnHie68ngeb4Yahh+lu6TTwqkZDqCS9N8MTSbhbGxgyVqmgbbtglyORXbhlBIoqpgWYJ8XrJtm8L4uO/2tSxffPU8iaKA5x0U6RQFVNUXijMZMR0BYZr+zxVFUC5LpPRL6ZqaJK7rFwjH475Z4IUXBHv2qLzznS4f+5jkwAF/2+5u+P3vBd/6lsIzz/j7LZcFjiNpb/fL7vbtEwSDvht4YuLgPZk1C0IhQSbjX/+TTwp+9CNfZO7slBSL8LOfCYpFuOkmyQc+8DF+8pPv8dWvfoG///t/56GHFIpFyYIFB+/1xASsXatwwQUu7afmGTkmruWCYFoInkI1VKySddTtrJJFz8O7cCo29d31CCHwXI+JAxPs+f0eui6fzY9//H/47//+Ov39B1i94lzed8XHuPiCK2esms3n8himdsoirPQk2aEcruUQaYxgRo7u8lUUhaVLV7J06Ure854PsmvXdn7yk+9x110/5Dvf+TpLliznXe/6AK9//W1oWk1e/GOi9m7VqFHjZaGkFCiJElFv5tP1kAxToUxByYMLz+hP8h/r/oknfvoIy79xBt/r/BbbrM3cVno7QYJH2fvJM89ZxBxnPj3aNjqduQQIcEDdR4UyK93V0+JzWEbQpM4mfd0RxWAAD4/njKfYpK/HkAae5jGhjFMVVYbVAcqiRFEUaXSbaXdmU1ByBL2D15JTsoRlZDrn9zm9wpjisMg1Di7bcxV2qzYbtSoX2aEjngeAicmbSu9msbOczfp6PDyW2is5xz5/RhTEsRh3RhG6ICwjLEjMpaBnSam9YKmUzSKrkktYkJhD2SsyEUwx1jHBDmUrVaXMSvds2u15xLzE9P7e9KZ3c+utr+L559ey/OwzGFIHkBWPrJVCFwa6YqC4CoNOP2vzjxL2goTVGMWJAmboRZnEuj9qHZ0YplRfIGk0Tg98YnqCtDXOSHnglItNQlqEZcmzKbtFXOkSUsMnlEH8h0ZT9GlXUNW1sB1J1VbZlx6jM6FgKCqW6+K6KnG9iQXJeVzQdmIPA04a6bdSHx5fIKYdvcdCCMGru69kbqKL7akeqq7FEw9nGWptY1TtpVjMYSpBql6FHdmN5OwMSxJnzpiMZibf94gWw1D9AWxIi2KFbWbHW9ibO0BY8+M58naBpXULWR5dTN/6foqpEkZIp2lBE5GGlxZLAb44XnKK0+IA+A8aAmqI0coAc2KLT8lZJYTgurnXMDfRzebx7VTdKvMT3ZzZtPKUBf4aNWrU+H+V/v5ebrzxKnRd56c//R3NzQcfxMeMBKvqz2O41EfaSlGwshScHB4uQTVMV3ThjNU8fhzSdvJ2loReT6o6gisdbNdioFT2s4aDLXjSQxHKUccNhhrAlS7gr2YZLQ9QdkpI/FLb3fmtZK0JliXPJqiGGSn3U/UquJ6DFCCkwFBNTOX0jYcBOsJzCGkRRsv9FJ0CbtVByiIhNTy5jF2l6lYYLB+gOdR+3HzTilOit7iHieooAPWBFmaFOpFIRkr9rJk1n2RIpyfVS9musrR+EVd0XsS8xMkJueB/N375y9/k6qvX8N73vpFf/OLRk1qu3to6i9Wrz+UXzw9z6yWXo4+vRxT6QA1gN55FecGbeDmspFu2wIMPKvT1+U7XM86Q9Pf74ucU4bAvrDqOoFr1x1OWBdu3CyIRaGyUDA3BE0/40RGhEKiqnxVsWb5oq6pTxXFi+jKCwQZKJZVweARF8cVi25bAKIrSQjx+MF+4vh4cR9LQ4Lt3t27173lbmx+5sGCBpKdH8OijCu9+t8fy5f4xNm+GX/1KwXUlmqYAklQK1q8XRCK+q1cI6Oz0xe1KxR9ONjSAovjFcYmELxQ/8ohA12H2pCafSPjXtnatwsUXuzQ1xXjHO/6Ur371C7zvfZ+hv7+Z+vqZ489kEkZHJUNDfgbz6SbaHEXVfOHXmJy7SCmp5iu0Lj26oSQzkKUwXiTZnpgeMyqqghJR+I/v/xMP//VvsOwq11//Bt71rp8yt2MRT397LeXxMnqr/2DJsRwq+SqdZ89GUU/+QU1uJM+O3+5goi+D53gEYgG61sym+9yuw8TtIzF//mI+8Yl/4KMf/Tsee+wBfvCD/+JDH3on//qv/8AHPvCxmij8R0TtXapRo8ZLQk6mV7443zbmJah3GxhRh4i5iemfZ0SaqIzT4DYxpoxwd/CHrP/8czSuaOaKW15F0S3wtPE43e5cLqu+8iWfX0akOaDtpUSJdnc2G7V1rDUfJ+xFcIRDo9dMtzNvxjYKCo5jM1jaj+VWCWoR6s0mVEVjQOnjN8G7+L3xCMvtVYTxR3FNopm1xhOUlAJSSqJeDBUNV3HplwcAQcyLk1eyjCmjXFl9NfWeP7juV21CL8oI1ib/fVxxjnuNAQJcaF3Khdalp3SPhPQHbQCaotIRb2OfsR3bs2kLNLHE8R+1W9gcUPfyy/BPiXoxdHTuMX7GVmMj7y5+gBbPL7p6xSsuo6Ghid/97tesPnsNYSfMXrmTVmUWmvC/dspYeK5LKjfMTmcjqtAoRwWizyRcf1DociwHoQhERPrJtIcM0IUQaEKjPFn2dqoIIU5bUcrJ4noujnQxDnEOHeu1Tww+w2N9T5KuZhktjjFWKdESVLEVQVCNEzOijBf30hhseNnOOWbUTcYuVKYjEFzp4ngWDYETc1QLIVhct4DFdf5n656+/0PXgm4KTo66QwR/y60yUu6nLdQ5YzKat7O40p0Wgqf2GTGiXNSxEteJsn50E670uLrhUlYFl9Jz1y4metMgJyf6Tx9g2auX0LzopS299aSHRB72/0BFKP7vpAenuMxWVVRWNi5lZePSl3SONWrUqPH/MplMmltuuQYEfOP730NJ+MW2mtAZrwxTcHLT0QeNwVa2TDyPLnUUEaDsltiZ3QjI6YLZslskbaUIaxEKTo6cnUVVdKRrI/EoOFm8sktACdES7iCkHnkM0RxoY7Q8QMkpUnXKlJ0SqlDRhUHSbERTNFLVEVLVEerMRnbntiCBoBrCkx5VWUbxFCzv9MYcCSFoCDQT1iJsy6xjwhrF8RzGK8Pk1DSNgVZMNUjRyZOz0kcVg6WU9Bf3sH5iLXk7gypUQmqYjJViqHQAKSUVt4ymaLREorRGVjA7vJDZ0S6yVoqB4j7Ceoy4XndSDsdYLM43v/kjXvvaV/ClL32GT3ziH07q+l/96tfxxS9+itGvfZ9456tQKimkEceJLwDl1GMxHAd274ZsVlBXJ5kzxxdnd+6cijGQNDRILAvuu0/BsiAYlDRP9pI1Nfki5v79knLZF0B37RK4rh+bMDrqO4TTaXBdQT5/sOht6vh+NASTrmA/5kFRDAxjNbb9EJHI9agquK5EyoeQ8k+pVHy3cqHgn6+qQioF/f0Cz5M0NUkyGd8VvGqVpK5Osm+fL1RP6fDr1wsqFf+80mn/PDTNdyBv2ABLl/oC87x5Hi+8oNDRIadd0Ok0XH65/9/Dw36JXjI5U8Ctq4Ndu2B01L9Pb3rTu/nqV7/InXd+E9P8JJUXmedt27+OQODlaVmom52kdWkz/RsG0YM6qq5QzlaINkaYfebRrcjS83PLp4aMnufx+Mbf8b37vk6xUuAd73g/73nfB2Y8zFp4xQK2/3Yn43tTCCEQiqB5UROd55y8g92pOmz+1RbSvWliLTFUQ6U0UWLngz2YEZNZKw4vxz4amqZx+eXXcPnl17B160b++Z8/z4c+9E7++Z8+xwc/+HFuuvn2mij8v5zau1OjRo1TIi0meNx8kHX6M3hIzrDP5mLrimmBU0fnUutqfhD8NgfUvSS9eoqiQEZJc3H1SmZ5HfzeeJjdT+9g7PERXvPDGxGKICKjBGWQZ/UnX5IYLJH83niY+wK/YFDpZ7+2BxuLNm82ES+GisJSaxXrjWd52niMkAzT7LVR7zZAxaNloIEt1RcAX+iOBOLsbt3DC8Fn2aA/T0HksUSFZfYZ1Ml6FFQySppuZy7nTObzurhs0zZR77XgCZd+7QBhGeHK6qt5bfmm6XNt8DS2aNZh5+/BYREREomLi4o6LT4VKfJo4LeMKsPMdrs5wzpnOprjROhMzuHx1ENMiBS2sNiqbySv5KiqFQJegE1iHQutpfR5e6mIMolqgnrRSEgN0yra2alt5QnjEW6svBHwlxNdcsmVPPLI7/ibv/l7VpfWsM5cS07PEncSVESFXm0fjaUmOsrtxEJJEFBcOIbT75EZyBKqC+FaLoXxAo3zGmicmyRTHEJKDzHpEJVS4kib8CE5XH8slJ0yj/Y9xbMjz2O5Fp2xTi7ruPCYLpnH+p/izp5fYqomSTNOxamyN9fL9tR+zm09C4lkX7aX5nAjZza/fEVpSaOBWeEu+ot7KTsFv4RPutQZTbSFTm1p5fbtmzn3mvMJKIEZE0JDNSk4OQp2dsZkVBUaIGeU0IFf3Bczk5w16yKu7Lx4+uebfrmF1P4Jv+RDh4pZIjeaYeujW6nrTKIHT72IMKonMBSTslsipPkPMqSUlN0ibaHOyXOtUaNGjRovB67r8id/8ibGU6N87ntfZCTQx/BEL7qiAwLHs6ZL0w6oQTShUXTzM1Ya5awMe/PbqQ80T8deMblN3s6gCIWIFqNIHturIqWk4ORIhhqZH1t2VCGzMdBGV2QhfcU9ZOxxXBx0xaDObJqRJ5+zMxiKSUiL4EkPVzoIoVBn+FnD49UhWkLHXutueVUmqmM4nk1EixI36o8rsO7L72SkPDA9nsIDW1qIiqA16GcoHysiYqjcy6b0cxTsDAElCALKbglV6AyX+tAVg/bwnBn3ua/Uw2i1l7JTBCSq0GgOzmJhfOVJlQKvWHEGH/zgx/nKVz7PLbe8lXnzFp7wtq961fV89rN/zUMP/5brrrsZNz7/hLc9GuPj8IMfKOzcKbBtiWkqLF0que02j8ceU8jlYMGCg6bjaFSyfbugWhXs2ydpavLF1UgEzjzTdwF7HsybJzEMQSwGmzaJyUxgMekg9vfnujPPRUpfCPZLf6FalXjeh7CstwJn4brnIOW/AEU8722k00wKxKDrfhyE44jpOIuGBn9/w8N+9m9Hh6Sry38t+NEXfX2CQsEvuUskfEF86lr37xcEg34R8WteI0kmfUF4aMi/D9deK7n6al+0DYd9Abdc9rOFpygW/fMKT/pF6usbuPnmN/H97/8Hn/70X3LPPaFpB7Jtw759gq4uyfyX/tYeEUVTWHbtUmKtcQY3DWJXbDrP6aTr7A5irbGjbpeYFSeUDFIYKzDiDPDt3/wLPX1bWd19AX9y+1/yyve+6rC/t7NWtBFriTG2exynahNtitI4r2G6EO5QpJR4joeiKUf8+z++Z5xMf5ZkR9JffQlEm6Kk+zL0rx84KTH4UFrCHbzj/A+zJnY5v37uR/zFX76br/7rF/inr3yT88+/+Pg7qPE/Qm2GUqNGjZOmIPJ8O/zvbNM3EfeSCCn4dfBudmnbeW/xw8RlAoDzrIsAeMT4LSl1nJAM8drKjVxVeQ0CgSWq7PqHnTQsa2Leqw8O4nRpUBall3SOO7Wt3BX8IQoKKhoOLgYmeZFljX0hOSXD7wMPYUuLouIPSHvYQYPTyLX9ryFmxUga9b7Q5Tn83niE57RnmSsX0uS1IBWPrJJhk76O862LSSsTONjT1w6gotLoNWNg8IHcxykoORJe3XRpHPiioLP7BfZNPMse16G7aSGtHWeQCodp9lSWO/6kxMHhKeMxnjYeJ6NMMNvt5qLq5VREmb+PfIID2j484RHwTBY7y/lg4eMsd844oXu1tH4F9oTFiDvI1vBGbGySXh0Ovit5i7YBPaOSrMSZiI9jlyuMiH7CWpTGQCtxmWCbvgkmxWCASy+9mrvu+iHDw4NcWH8ZW0ovsDW+hUGzH1xJW2EW546eT0CEUBUVRahE54RwPYXgthDFVAlFV+k4YxYLLpuPEoNh6wBpa5ywFkMgKDp5gmrklAvkjkbJKTBU6iVrTWCoJk2BWTQGWk9bOYLrudyx4y52ZbeSDAaJKCpDlR5+sOMAb1z4RuYnDxeEy06Fx/qfJqAG6Ij6A7WoEUFKyd7sfvoLgwRUk3mJbl4z92paw82n5VyPhBCCBbHlJIx6xitDOJ5DndlIS7Ad4xTK0gYH+xkaGmD+4oXTy2mnmIqdeLGgWmc2ElBD5O0sUT2OEALLreBI57DPg1WyGNs9RrguhB0pM9E4hBUo43V4FNKjbO+NsXzBmaf8/oa1KO3hOewv7MSqVlAVDdutEtajzA7Pq5Vq1KhRo8bLyOc//zc8/viDfPTfP0ldewORyTHCUKmXvJ1hVribiB5DSknWmmC42kdzsH3G/5sjeoysNUHWmqAx0EpQDRMzkoyV/e84VaioioqhmBiKSZ3ZSMUt0R1ddMyVRUII5kQX0RycxcaJtUxUR2kMtE6LnlL6DzU1oeN4NkEtQsKox/FsFKGiKRo5K411nALUieoYOzIbKDo5JKAJjTqziQazBVfaRPQEdWbjjGuuumWGyr2U7DyOtPGkiyMny5W9NEE1gqGaJCcfxEopqXplVKGhKwae9Ogr7sXxbFSho6v+UnkFhaKTw/YsdMWYccygFuJAYRdRPU5joA1FKFTdCgOl/YS1GF3RBZwM733vh/nxj7/Lpz71F/zwh78+4e/b2bO7WbHiTH72sx9x3XU3n9Qxj4SU8NOfKmza5AuQoRAUCpLnnxcEg4L9+yGRmFlkFgr5wubq1X6cwsiIH41w8cWS66/3iEZ9cXbPHvja1xQyGT9awXF84da2D4q+R0II37XrOL671/PeAIxRqXwaGAZWAfcBzZNlc75dtVwWhMMSVfWvI5cTDA1JqlU/szeV8v+0tUlyOXjgAcFzz/lFcj09/jFnz5aYpp8BbFm+O3nePLj+eolpwq23Sq66yiWX8x2/8UM8HdEonHWW5L77FExTEotBueyLzWecIek8pOfxXe/6c77//f+kUPgx5533NjZsEAwM+LETHR2SW2/1CJxah+8JoZkac87vovu8TpCH5wcfCTNi0nF+O3/7qY/w0Obf0F7fxYev+TvOXnU+K1+74qif4WhTZLpc7khITzKwcZC+9f2UM2UijRFmr26neXHzzL/3BQuknBaCpzBCOqV0CenJE7qOQymmimz65RbK6RKL5y9j8YLPs237Zn709De58cYrueWWt/LJT36BZLLupPZb4+WnJgbXqFHjpFmnP8sObSvz7UXo+IO/Rq+ZHm0764xnuLR6NeAPCC+wLuFs63xySoaQDM/Isc09nSH18BiXf/+a6S8eD4+MMsGa6gUv+RzLosgCZwm7jR2EZJCojDOmjDKqDgGClBjjHPt8goQZVYYoiSLxcpzmcjN1WuO0A1UoCrtju/Acl4AI0ixaGVEGicgYeSXLuDJKQeRQUanzZi7P9/BQpEKDbKTZnbmE3nJtvr/tpzw7vI56Q2dQdXnWuxPFvIOOrnks8VZgczHQwa8Dd3Nf4BcY0iAkw6zXn2WTvo5+5QADai/N7iwMDPJKli3aRr4Z/gp/UvxL+tVeXFy63bnMdxajcvjyt6ZG/7xW95/H8PxBmt026r0GwjLCuDLKuDPM/PF5GGaAAWWQgBaadsUE7BC2ZhOQM/PsLrroCoQQPProA7zhDW/m1YUbmN83n3KwgnQ8yHjo6EQCcZTJJfSqomEs1Dj33DWU0mU0QyWYCE4PYpYmz2JvfhsZawKJpM5spDu6kOhpdAbn7SybJ56l4GTRhI4rXUbKA8yJLqY7euKuk2OxO7OP3mIPrdE4qtCQUlAfMMlZOR4ffOSIYnCmmiVTzVIXSMz4+ZxEJ450eMPC17EgOYfWcPNhZWsvB4pQaAm2nxYh/v77f4WmaVx12WsY8PZiuRUMNYCUkrydIaiGDluiGtIiLIgtpye3hbQ1DviCcXuom9bgTHey53pITyKDHpnmQSyzilENgAtFpUCf3E1LpZWm4Km5IaYm+xEtOp33mAh10RaaPaM9ukaNGjVqnF7uuusHfOMb/8yffewjLDh7IVHNfzgopcTFQSCouGUiesyPEtJijFYGqXplIhzu3ptacSWEoDuyiKKdJ2OlcDwH3dNRhEpDoAVT9VexRPToYfs4bJ9CENFjzI8tY3P6WWzPmn7AWXTyGKpJQ6CZslNEIJDSm45AktLD8WwSxtGjn2zPYmdmIyUnT9yoRxEK6eoYWzPPIyUoQvjnbbYwOzwPXTUwFJOwFiFvZ7G8KmE1RlkUcaSF9CS2Z1H1yixKrCSixRivDHOgsIuCnUURKk3BNlqDs6k4RQzVpOKWp1fqqEKjSsWPT3qRq7jsliady/Fpx7GpBrC8KkOlXmZH5h3mRM70ZxjYPER+tECkPkzb8lbqOv2yvkAgwGc+8yXe/vYbeeCBe7jqqmuP+35M8eY3v4ePfOS97N27izlzXpp9dGAAduzws2lDkzUfkQg0N0s2b1aIxyXZ7MxtfFevZPVqyapVfgREIOBn9h6qBy5aBIsXw+OPC8bHBbmcLxJ73syIiBejKAedw5rm5xQXi3+K47z/KFv4Tl4hwLYF5bK/f03zYxuqk88jptzG27fDl76kMDQkaGiQzJ8v2bNHMDEhCAQgEpGEQn7WcH29ZNEiyaHRzvX1/p8j8apXSfJ5jw0bFIaGfGF5xQrJLbd4KId8PObOXcCFF17GPff8iB/96C3s3QvDw4JwGBYtOvhevNwIIeAE9dNdu3bw3o+/kT17eviTW/+Kq858Lcn2OtqWtc6IyTtZ9j65j50P70JRBEbIYHxvioneNMssl/ZVs6ZfF4gHEIqCYzloxsH5QrVQpWl+40kLwQDD20cojBVomHNwNcKKVWfQEvs7dnkb+K+f/SsPPngvf/u3X+b6699QM0n8L6ImBteoUeOk2a/uRUGZFoLBj4XQMdij9nApV894vYFBg3d4Ludd/3QHzYtaMV9nsk/swcAgK9J0uF28onr5SzrHCSWFIQ9/HKwgqIoqOZFFQcXApN2dTbvrC0i97h5KFBnQ+hlVh3CEQ9iLMKgPUKXCU8ZjREWMhFdHVkmTE1n2aD00ek10u/OoiMp0fqiFRUad4MLypUcUYbeldrJudBOd0XZCeoi9DT1saO8l56ZRy0XWR/cxqD3La8s384T5MKY0EShURJlWt50N+vPs1nbS5nZMl+0lvDosxeJ5/Rn+PvoJTGkCggABzq9ews3lN6EzcxneVNu2MaLTOqedRc7BfNKIGyVZSBBwTBYpK9jkbmRMH6XRbkIVKmPeMI6QnG2fN2Of9fUNnHHG2TzyyP3ccstbWBBbjpJTGCsNUnKLZJkgYsSni9+klFTdMh3heWiGRqz58AlWzEiwsu48ym4RKaXvDHUy9BZ2I4RC0mggoh99adaJ0FvYTcHJzlg+WnIK9BZ20xycdVqyhXsL+zE1UNCR0j+GlAJTNUhVh7FdG12d+R5F9DABzaRkl4joBweLRbtESA8xP9FNR3QWf4zcf/8vueCCS1nYvByZ9Rgp91NwcggEATXEgvgKgtrhA+SWUAcxI0m6OoYrXaJ6fHoifChmxCTZkWT/+C5ss4JZDvt/P0sWASWEFlQZKvWeshgMk+J4qIOWUMcp76NGjRo1apwYrnRZv+5ZPvKR93HzzW/muttvZKC07xCRYSpGSMGV9vR2qtAwFIOKU0YafuyUlJKCnSWk+W7gKZJmA2fUX0BQC3OgsAtVaMSNelShkrcyNAdnET+J8trGQCtdkQX0FfdMP8Q0lQBzY0uI6UnCWpT6QDNjlUFMxX8QXnHKxI0Ercf4bklXxyg4OeJGctppm7EmsNyqH3OmJXCkTW9xN8OVPuqNJlRFR8N3HTuejSMddGEQVMPYwsLxHGaH52EoJtsy6+gr7kERKmEtgitd9ud3UbTzaEJHFRqqUKedwBIPKfHLgoWYjveSUlKa3CagzlTpVKHhSHsyY//gd/hozxibfrmZSq6KHtQZ3zPO0NYhll27dLqk6+qrX8PFF1/Jpz/9l1x00RUETtAKesMNt/KFL3yS//zPr/IP//BvJ/w+HolSyS8+C76o5y8YhHweli+X3H+/YGzMj1ywbT86YdYsyZIlfjFb+1Geres6vOIVHg8/7GcMSymQ8thCMByMjtA0pl2+ug4TExLPO7IYpyhMuoT9qIZUyj+e4/g/F8IXeWMx36k7MgLnnSepr/fdymedJXnqKT8iIhz2xXBVhcFBX1wulU5MoA2H4W1vk/T2uoyPC2Ixbzp/+cVce+0NfPzjf042m2L+/Hrmzz/8xpQmF5v+ocTho3Hnnd/jYx/7c2bN6uDee59kyZLTE+lWzVc58FwvRlAn0ujPU0J1ITIDWfY9vZ+WJS1ohn/z6rvrqe+uY2zXGOGGMKqhUZoooRoqHatPbQxbypRR1JmxFEIITNPg0vmv5k0feAef+tSH+dM/fTN3330HX/nKf07PPWv8z1ITg2vUqHHShGQID/ewn7s4hOXxXRIA+/fv4dFHfsc/ffVbtFZm8bzxFEVR5Hz7Yi6wLqHNe2mOw9luN+v0Z5FIWrxZ7NS2EZA2EknEizKiDaGjkfRmDuTLZoUxY5QB+nAUDwFs17eA5VGMFIgocUbEMEEZpNOZQ0od5+rKa3iFdSkb9Bf4TfBu9qm7ics4ARlkqb1y2in9Ynbn91KRFYJ6kKpWYV/LJoJSJZBrQbVMFgWX0aNt45eBn7BL3U5ZKVPFb0gw8RuqbWEfVlwlkaTVFIqjsMBZgopKVmR43HyAec5C1tgzXddTX8jqsIqBQUHkiUy+jxJJURTo9DpocBu5KHMZjyce4kBgP65nY2DySut1nFc9PA/qggsu4a67fgj42a9LkmdStBeQrlYZKu8jY/WSs9OoQpsuGFFQGCkPUG82HTE7bqrszZMeu3Nb6C/ux5F+3rKpBOiOLqIjPPeUnjo7ns1EdZSAGpq5rFENk7bGyVoTp0UMVgUoipgeWE9hOS5h08CVzmGCfdSIcE7LGdy77yF0xSBhxijYRfrzg5zdcsZpEYJdz2G0MshEdRSJxFQi7JkYZHNqOwKFVU3LOK/1LML6yY+m9+/3y0XSaZg1y18W2dAAExMpnn76cT7/+X9FVTSWJM6kLdRJ3s6iKSp1RhMB7ejHC2mR474nQgjmXNDN4BP7KRQsREnHcz2EqtDQVYdmCEruSyshrFGjRo0aLz+WV6WvsIedA1v4yNv/nK6Fc/nYZz+DrVr0lzw86aEIBSEUgmqIolPAEAfFwYpXIm7UY6gmE9Y4qlDxPBdTDTA3uuSQvGCfiB5jdf0raAvNprewh7JTRAqFjshcGs1WDhR6cDybsB6jMdCKrhgcDX8VyWKagrPIWhMIBEmzYfo7TBM6SxOr6S8mGSkP4EmXruh82kNzjvhAdApHOoBE4IuoJadA1a34rk5FRZ+MLgLwPBdV0QmqIT/iQbogBJ50qcgSqlQJqCEMLch4dZjRyiAZK0XVLRPV4kS1OKYWxFBMJqwxGgNtFEp5QlqEklOg5BZwPIegGmJObAkVp0DaGkegIPEIqCEUoeJIe9ok4ZfMlWgOzppeKQbgOi67HtuDVbKp7z5YMJfuT7P1mS1osyXRUJyAGuKzn/0yl1++mv/8z3/lz/7sr0/osxQIBHjrW9/Lv//7l/jIRz5DXd2JC/svpqnJj4FIpaDlkEWAqRTU1UmuuMIXKJ94QmHnTomm+VEKt9ziETmBYeUvfqGQSgkaGyGTgXJZUir590PXfZfwoU7hKVFXSl8U1jT/T309pNNT7veDr50Sjj2P6Sxiz/MdwuDvZ8qRW6kIKhVJoeD/+4YNAk3zy9+m8oXzeV8E7uvzhe9EAp55BqpVhbe/3ZuRBXw0hIDOTujsPLbq/cpXvpaPfvT9/Pa3v+bWW98643cDA36MxbZtvut52TLJlVfKGe/RH4JSqchHP/p+7rrrh9x885v5+7//V0KhU3cBv5j8WIFKrkJiVmLGz0N1IUrpEuVMiWiTf9M1Q2X5dcvoeXgXY7vHsYoWkYYQ3ed307TwyEWRxyOUDCEnV+FNOYullDiWQ7ghTHNzK9/85o+48cZ7+Mu/fC9XX72Gb33rx5x11rkv6bprvHRqYnCNGjVOmuXOKh6TDzKsDNLs+W2nY8oIJgFW2CeWU3vnnd8jGo1x3atuJmSFuMS68rSe4znW+TynP0mPto2YmySshulTDxCVUXIiS0RGUV0VhYMOhDFllKpSpb9ugO7xTjRbp6SVSHpxqqKJVHKCsigTlmFSyhgSyU3l27m19HZ+EvouzxtPo0gVR9gURYlLqldxS/mthOXMkd6ESPFA4Df8MnIfe9RhqlqWRCRG0cxTV2giQw5d0RAImt1WerTtDKr9xL0kDdJ3WBdFgTFlBFWqlEWJqIxNi8JZkcbBZVDtZ1wZpdlrpcudCwg26esOE4MbGpowTRNrt8WZ1jk8bTxORMYwpMGEkmKWPotmWqm4ZVYUV9FWncW+wB6y7gQrtXO40nwt2hG+ThYuXMrQ0ADZbIZ4PMGBHDxwIEZPGhANdMe7WFi/G03JIPDF2N7iLnqLu4kbSZYkzjzqMvvRyiC9xT0E1BBRxX9NySmwN7+duFFH3Dj5XCoxOYH0pPOi30jE5O9PB/MS83hi5FHGCgM0hxoI6gEqbhVPuLSEWjAmc/em8nKnJkCv6r6Col1i/ehmhksjBLUgZzav4OaF173kJVeudNmeXc9QqW/yvXDZmztAfy5FsSzwkGyf6GFHqod3LH8jQS143H1O8dxzgh//2M+703WJbQueekry9rd7PPPMPbiuy9VXv2b6WpNmA0nz6EtiT4VkR4KlFy9lw3AOkdExTINoQ4RgMkjGHqcx8AeeGdSoUaNGjZPCky7bM/731Lc+/+84tsP7vvCn9JQ2syi+ioRRT7o6TlAL+XELSMJaGEda5KwM3mQpW3d0EW3B2YxVhyjYWQw1SFOg9ajjBk+6mEqIruhCNKER0eOkKiNsyTyP5fkP6AWCOrOJpcmzCKhH/34UQhDV40eNtzJV3yk8J7oYiTxmedsUES2GJnQsr4KpBvGkiyddhJCoio6HxPEsFKHgSUneylBRiv7xhIEU/nl50sPzXN/dKyWWWyFpNpG3MwTUEBWvTKo6QkuwA03RkdIjbiQJqsGDYwfpENFjLIqvoiXYQdUrM1oeoujkMJUgjYEW+op76S/tw/IqfqSEWyagBpn9ogf5xfEiudEsgXpjesWdrVepnpkmpWawhlNEolFmhbqYO28Rt9/+Lr7xjX/hne/8c4Ivtugehbe85T38+7//I9/97jf40If+5oS2ORKJBLziFZJf/tIveItGfdHWdeHSS30n7Q03SC64wKW/XxAI+Bm6pnm8PcOGDfDIIwLP848TDksmJgB8QVhVj1wipygH84Q9z3coRyIHxV9N8//pugejH6ZiIlTVz+lVFImi+MfWdX+batWPYvA8/79tWzIy4r8+EvG3lRImJvw84O5u6Ojwyxg3bRKsXSu48ko5LV4fye17MjQ2NrNmzYXce+/PZ4jBqRR8+9sKBw74MRYAjzyi0Nsr+dM/9UgkXtpxT5SJiRRvfvN17NixlX/7t+/w+te/8fgbvQgpJeneDCM7RqgWLeKtMVqWtBCM+w+6NFND0VUcy8HQDj6QcqsOqq7OiIMACCWCrHzdcsrZCk7VIVwXOixD+GRoWdRE3/N9TPSmiTZFEEKQHckDArts0beun8Z5DVx55av57W+f4T3vuY0bbriMT3/6S7z97X9Si434H6QmBteoUeOkWeAs4TWV1/PbwK/ZqW0FICrjvKr8OpY4x1/y4roud975A6677mZCL9OanRavjXeU3s/9gV+xW9vJInsZq+UamrxWWrw2FtpLec54kheMtdg4/qRBhpnnLmJ/Yjc5tYQogVu1GQyNMBGfIKxEibtJckqGgAwxy+3gtvLbWWv+nqeMx+hwO5krFyCR9Kn72altoySKM8TgMmX+T+g/2GxsINYQwTQ19pT3EYqpONiUJ3PXWsK+6OsJD1vY6NLAETaudFFRMaQ/OG70mnBwGFYGMAmSExlKooQmfTHZEQ67tZ1MKOM0uS1URPmwe6XrOitXnsULz63lX0r/TYfbyTPGk5RFiQurl3KJeyVWqEhfYQ8Vp4RhacwtziGun8XyunOOKAQDLFiwGICenu20LzmP/9qsMFgUNAYlnhQ8N9RModrIxZ3PUaRA0qhHVTRc6ZKxJujJbuaM+guOOEgYKw+CZMakK6RFSFtjTFRHT0kMVoVKU6CNffmdBNQQqlAnc2uzBLUwyWMsB5VSIvEQHLm9d4qJSobf7nucLcO9pKopFLGbtnADc+vaaAwlWVq3gqpbob+0n7HyABJ/aWlH2HcGvWnxzVw++yLGyxPEjCidsfYTmiwej/HKEMOlPiJ6DF0xOJDrY6yYpjWSwA0EcV2NilNl0/g2No5t5dzWs05ovxO5Kv99/y5GjRTtSyM0eYvQvSg7dwruvVfh2Wd/yerVa2hubn3J13A8Zrd0k9KHmGgaI6xHUFDJOhMYSoC2UOfxd1CjRo0aNf7HmKiOM1YeYvPDm3j24bX81Zc/SfesBaStccYqQyxLns2B/C7Gq8NIoDMyn6ZAGzk7S8Yax1BMmoKzaAq0IYSgUz9+Tmy6Ok5PdhN5O4vEw1SDNAdmMVzuRyKnI6Vc6TBeGaGvsIf58WUv+VqFEAgEBTvHSHmAklsgpIZpDs467CF5VE/QEuygv7QXy6sipYfEA4QfN4H/wHfKQZyz0ri4CPx4hrAaQyJBeFSxUISKrhrEjTp/xZbQqLh5FBQKdo6qUUafzDQOKEG6IgtoD8+h5BTQFYOYnpweBwXUELMjc2ec7wJtBWE9ylCpF9uzaA620xGeM11UB+B4Dn2lPYxXh1GKYGASMxKU2zOUAlmUtE5ESQAue/M7MFSTd7/7z/nud7/Bz352B2984ztO6D7X1zdy223v4D/+45+5/fZ3vqSl6698pSQchieeEGSzgo4OyUUXSc491xci02nYvl2QSkE8LmhqkjSegBFz7VoFKf3IiXJZkM/7pWyKIgGJ44jDIiOmRGDD8MXfcFiQyUj27Dko/jqTvocpx6+UvuAbj4NpSlwXLEtQVyfJZgWu6wvBU5ERU9v29/uCbjAosG0IBn2XcKXiC+SK4ruDTRNCIcnTTwsKBVi3zj/vFSskl156Yvcil/PdvroOXV2+GA3wqlddz+c+9zHK5fL0g4Dnnxfs3y9YuFBOC87JpGTXLsH69YJLLz1OzsZpYHCwn9tuezXj42PcffeDrFy5+pT2079+gO2/24lVtFA0hf71A/RvGGDV61cSbYoQb41RNzvJaM8YifYEmqFiVxwKqSKdZ3UQTBz+cEQIQegIPz8VwvVhVly/nJ5HdpEdzOJYLpVMGUVT6H2ujwPP9hFpDLPs2qW0zGnjrrse4HOf+xif/OSHeOGFtXz5y984rU7pGidOTQyuUaPGSSMQXFl9NcvsVezWdgIw11lAm9d+WGTBkXjiiUcYHOzjDW94y8t6nl3uXN5T/CBZkUFFJSpn5skucZZztn0++9TdaGgsdJbwlP4YD4bv4UDdPqjzRb6iKBAiRL3XyDn2+dhY7NN2s9o+l7CM8Kz+FCEZmo5WEAja3U56tG1s17bQaB0cXG7RN7Bd38w8eyFGzECZH2DXrn7G+sfwEhWs0CiLA8toC7fg4DCiDNHsthCUQfIiT0aZAPxyvkavmYurV6Cg8ozxBFklTZ3XQFiEAUGQIJrUCckw48ooHh43V958xHt1xtlnc9fdP+A5/Sk63Tlclr8GgZgWer2YR1yvY7QyiO1ZJI1GWkMdx1y6OG/eIhRFoadnO/2J8xkoCBbVSaa6CZKmZPuERyRgs7wpijpZfKYKlYgeI2NNkLczMzL8pvCbtmeKoFOTD8eb6ey1PYtUdRTL9V0zR4ugAJgV7maodID+4l4ADMUgqieZF12KeRS3z3hlhIHiXnJ2BlMN0hbqpC3Uedj5uZ7LHdvvYv3oZubG59Fk19NfHGCwOEpbLElTaC7DpQMMFvfh4WKq/iRuX2EHWSvFiro1mGqQWZFWZkVOr3iaro4jkdNLXCcqGUBBEQpScXFdjYBmIhDszuw/ITE4VZ7gS0/+hGf1PYTaPMYViHltrLBvpqWli40b+3noofv41Ke+eFqv5Whois7S5Gr25neQqoxgYxPX6+iKLpgxCa1Ro0aNGv/7KDl50qkJ/usL/8EFV13EhVf58VSmEiBjjRPSzmRx8gwcz57+PvOkh4tHwc5S8crkrDQRLUb4BIrfqm6F7Zn1FJ08MT2BIlRKboFduS14eLQGZ0+PO1ShEVADjFUGmRtbcloe0k65j8tOEU3R8aTLYOkASxJnUh84OK4UQrAgvnxaYFVQSRgN5O0MlldBExqOdPxCYxQQoEgxKQh7eHgkzQYUBEUnT3d0IWOVIRShUnaKVN0SFbc8PbofrQwS0iLEjCR1Ad+4cCKRTVNoikZnZD6zw/Pw8FDF4Y7EPflt9Ot7MFo1qvsc7IDFqNYPpoU6bhCKhAhGAwjFF+IHivs5p/NSrrrqWv7zP7/Kbbe9/YTdhh/+8Ce4++4f8qUv/S3/+I9fP6FtjoSqwiWXSC68UFIu+5EJUyJkby985zsKvb1+XIHnwSOPSN78Zo+Fx+kmHhiAtjbYuVNMZgb74mq5LACJokhU1c/kPRQhfNHYMCAelxQKvqAbiUCh4IvCiuLnFVerfnyZqkKxCIWCwPMkpukLu1PO4krloBCsqmAYknJZTLuGXdePAovF4IUXYP16KJWmRGL/9f39vkibSEiEgPvvV9i9W/K+93kkDx/uA/4xH3lE8MADfkGdqkJXl+Smmzy6u2HNmguxLIvNm9dzzjnnA3DggF+ad6jzeCo/ua/v5N/fk2X37p3ceuurAfjFLx5h3rxTK6Gu5Cr0PLobJDTM8U0pnusxtmec53+0jkDUxHVcok1Ros0RckNZPE+iqArNC5uYf+lLK0ecopgqMrh5iNxwnmAiQMvilukiR4C6ziTnvOksCmMFdj26m4FNQ9R1JtEMDc/zyPRl2H7/Ds57xxp0U+dv//bLrF69hg9/+N1ce+2F/PCHv6G19Y+z++SPmZoYXKNGjVNCIGjz2mmzTj7b9yc/+S7z5i3kzDPPOW3n4+HRo21jh7YNF4c57nyW2isxMEjII48uDAxW2WexyvaFrRFliKfMx8iJLGEZIeElSStpskqagswzx/Ndv2llAk3qnGX5pWkVUUKXM3PipuInbGHN+PmIOohEUhJF9mg7KSzM09BuEhyrI0gbyViUaqhED9sA6Hbms8xZya8Dd7PIXkpGyWCJKgEZJKWMcb59MZdVX8m4MoqLywPmvTxm/o6iUmBA6UMTOgJBSRRpl7M5x5oZEQFwQN1L30UHSH1tnG+N/wvxziTL7TO4vfTOaQH9VAqyAoEAXV1z6OnZRnyRIKwfFIIBdBVc6ZGqmIdNBlSh4UkX97DIBp+E2cBYdWg6HxB8gVigEDMS068r2Fm2pteRs9OABAQJo54lyTMJa/5ksOwUGa0MkrXSpCojlJw8umLgeg6e9AhrUQ5kR7l37+NYns2i5HzOal5FxAgzWh5kW+YFbM/GVAMU7CzbM+spuyXmx5bOOOe92QNsn9hFZ6yDsB4iakTwhMNoMUW27DIr1EXaHmOiMkpbqHO6DC8oI6StFCPlwcMcNqeLF0+aVEX1nUIvwpMepnr0TMRDuWffA+wp7iBU7aY+YCJwSYt9bNV/wSL+hJ6efyMYDHPLLW89HZdwQoS0CEsTq6l6ZTw5lV94euI/atSoUaPGy4cqVL73xe+gCMF7P/7n0z93pE1EPfiwf+phr5SSPfntHCj0gPS/1yaqY4xVhlhed85RoxqmSFVHKDg5EocUk4a1KHkrTcU9fJUVk1ELHOG782QZrwzz9OhDkxENQcJajLiepODk2JPf7ou3h4ybVEVjdmQeHeG5SDzKTpEtmXX05ndR9cooKCioSDwUoeIhQLq+gUNKKk6JoBamIdDKnOgiCnaOopMnb2cQQhBWI5S9IlJC0ckT1MIsTpxxWMbyiWK5FXJ2BoFC3EhOv2eedBko7mdnZiOqolF/UZSJYonqgIXV5CCLFRJGlPru+ulsUl0xsbwqjmfzrnf9OTfeeCWPP/4QF198xQmdS11dPX/xF5/kM5/5CG95y3tYunTlKV3TFJrGjExcKeGXv1To6xMsWOCLk54Hu3cLfv5zhb/4C49czncUr1/vC52rV/uicizm5xHv3i0RwhdVDy1zA4GiHDxetXrQFTwl2lqWL4yqqmT5ct/Nu3+/X6rmun5khab5/0wm/Rzgctk/z1gMRkdB0ySu64vPQvjHjETAMMSkS9l33aqqYNEiiWVBtSrYs0fQ0OAL0rkcjI/7kQ3nnOO7ewEaGyU9PYLnn/fjI47E+vWCu+8WGIYvAjsO7Nol+O53FT78YY/Fi5cTDIZYt+6ZaTE4HpfYtsKhfx/9DGVBPH5qf0c91yO1f4JMXwahCOo6kyQ7ktOfxSk2bVrHbbddS0NDI3fccQ9tbafeg5Puy1DJlEkeIrwioJqrsm/vPhIdSQQwvG2UpvkNLLvWn3sEE0HqupKo2kvM4QAy/Rk2/mwTudECuqnh2C79GwZZ8spFtK86KOAqqkIwESQ7mCNcF5qOp1AUhXhbnOxgloneNE3zfQPGa197E4sWLeWNb3wtr3vdZfzkJ/fR2TnnJZ9vjROnJgbXqFHjD0qpVOL++3/Jhz70idOWEeTh8avAT3kwcC9lSoBAR+Ms63xuL72TAMdvFq5S5d/C/8h6/XkUoZBSxplQxvHwqIoKOgZbtQ2MK6PMdxfxmsqNnGn7YvYSZwW/NX9Ni9c2LQJPiHEsYeFgU6JECD8OIyTDpMUEvcY+KqKCJnWcmI0Vt7imfB3vKX2QTeV15ESWJq+FFfaZbNU3UhB57g/+ijq3kRa3jZJSZIGzmNXWuQgEjZ7vEonKKBLJMusMVNdkRB1EVVTatA4ur15DXCZmXLeNzY+D/wf7PL9cJPBEkOb2Vp7Tn6LBbOKmyu2n9J7YZZvxvSnaG7rYunET17zeo+qpvHhQpgiNuKFRdoroxkGRsewUCWohwlrsCHuH1lAHY5XB6aWfnvSw3CrNwVnUmy2T+5f05LaQtSdIGHUoQsWVLmlrjN25raxIrqHo5Nicfm7SQVMlU01NLiVtI6LHKTsl1o+tY/vYIGVboqDw/PAGNoxt4W1Lb6W3uBtHOjMybstOkcHifmaFOmc4ZTLVLJZnTRewld0CIEkGkpTsClXPbzyXSMpuiSj+e6UI36GbtVPAXApWkS2p7WSqWZJmgmUNi0+p1O1QkkYjfWIvllvBUAM0BRsYKAxguTbC8yd7qfIEhmqwpP747oZsNceW8e101jdSDpvk8xCPq8RpJyv62TW+jb17v8W73vUeIpGDsybbs3E8l4BqvmwZYkKIw5rMa9SoUaPG/26e+u0TvPDIs/zZFz5ENBlFepKcPeELtmYDWWtiRkRB0ckxUNyLqQSmVzFJKUlbY/QX9rA4eeYxj2d5VQQc9sAwoIYpuSXKbnH6O15Kj6pTZnZk3gyR9lTIVMfZMPE0OSuNqQaQSDLWOLZXpc5somBnKdi5I66acqVDX3EvQ6VeHGnTFZmPh2SsMohAJVUdQRUKCIEmdTxcXByKTp7GYCsL4yuI6HG6ovPZkFpL0c77OcTCJahGiOkJVKFiqgHyVo7hUh+KUKk3m6kzG4/7vS2lZLC0n32FnZSdIgJBWI8xP7aMpNHA9uwGDhR2kbMn0BWTUqhA9Po4if11FCs6+UaLZGOcYPjguL7qlonpvqB83nkXsXTpSv7rv/7thMVggLe85b1897vf5DOf+Qh33vnb0zr+GBmBPXsEra0HXaqK4ufo9vUJtm6Fe+9V2L5dkEz6btm77lLYtUvyrnd5nH++5LHHFFQVAgE5XermOEw7hQ/m9x4Ug6eOI+XB146O+r8PBv0/xaIkkYCGBg/H8eMbIhFJd7cvpu7Z4wuw2Sy4rm+oCAT8yIdSyXcKuy54np9fPH++JB6HPXt8odg0JZYlsCw/MzgQ8KMn/IgLH03zr2vvXl9sPhJr1wocx88fBl9InjtXsns3bN0qOO88jSVLlrNt26bpbVatkjz1lO+sbm3178PAgC9wr1hx8mKw53hs++0O+l7ox7X9gGYtoNG1ppOFl82fFoR37NjCG97wSubOXcD3vvfLl1RMeDRKE2UK4wWcqkNhND/5ZECy/9kSDXPrWfLKxcfcPjeco/eFflJ7U+ghnbZlrbSf0Y5mHP7/Ltdx2fbbHWSGcjTOa0CZzBXJDuXY/dgemhY0YoQOzt8828NzPVR9psyoqArSk7jWzHDrBQuW8POfP8wtt1zD9ddfyo9/fC8LF84009R4+aiJwTVq1PiD8uyzT1CpVLj66mtP2z53azt40LyPqBen05tDVmRIKWM8YP6Gec4CLrGuOu4+HjLvY63xBEL6wmpRFBhU+9HQaHJb6XC7iMgotqjy2tJNXGRfPr3tK6qXs03bzA5tC3GZZEQMc0DbQ1TG+FnwxzxlPM5rKjdyln0uC+zF5JQsGSVNm9uOgkKRAiVKZJQJGrwmrqoevDcPm/dzd+BHhGSYVredQdFPVslwXfVm3lp5z2Gu52X2Ku5Tf8Vj6cewMuAKQTE5gRL1GJHDPK+vZYV9Jgb+F/cebSf7tb3MTyzm+fnPMPB0H4vfsJx6r5F1xjO8qnr9YQV4xyM7mGXzr7aSHcohcwqDqX5CG3agNC9kuKjSFAJPQm8eYqbFigaFimeTqY5jqAFsz3dTzwkvwlAPuk6klIxVhhgq9VJyCphqgAazlYnqKAUnh64YVLwyw+U+2kKdFJ0c2WqKiBabnpypQiWsxUhXxyk6efbld5K3MySNBsarIxiKiRCK7x4qZehJ76FgZym7ZTQlQWe0Hcdz2Dy+nWeGn8M0ygTVmVEZATVE2hqnYOdmiMHJQBxTMSjYRSJ6GFf6I/aSXaEuECegGlRcFYGY4YiW0qPkFBktD/FA4V4e3P8MqbLvqHGkQ2OwgTctvonljUtmnIcnPdaPbmb96Gay1RzzEt2saV09nUd9KA2BFmYFuxgo76fo5DFUaI82sz89wmBuFAmEtCBXdl7M4rrjLzmzXAvbc4gZQRYskGzZIhgfB0XVyRou+viP8LwK73jH+wHIWXkeOvB7XhjdiOPZLEjO5bLZF9EVm3SiW2A8J9BfEIgSOEsk1nkSr5buUKNGjRr/z5PNZvjMJ/+Kq199LRdffQUZa4KCnaXqljHUIOPlIXLWBJ3h+XRHFyGEIGensTyLpHHwofLUw8BUdRRPuscUbkNqBAlkrQls15re1pMuzYF2bM8iXR2ffNBsEzOSdIRf+uqd/tJ+LLeKrhiTmb0qqlApu0Uqbumo20kp6clupr+0F0MxUYVO1k5PRjFoaIpOQAtgKAEUFCpuiaAaQhU6ET3G2fUXo6m+XbM12Ek2mqHo5FBQCGohokYCBZXx6hBD5T4GS70EtRBBNUxfcS+dkfnMjS4+TEidGruNlPuZqI6RtsYJaRESRr0vdFfHeWb0EaJGnInKGBE9NtnboCPxKKhZWpZFMWUMYVlYapmyo6Mp+vT9mBXumhbt3/WuP+ODH3wn+/btprt73gndc13X+cxnvsSb3nQdd931A2666U2n8tYdEV8sPZjNO8VUkduvf63wwAMCwxBMTPgxC52dki1b/MK1NWskF18s2bvXd9uapi/kqqpkcFBMi72e5/8RwhdtpxzIU2+HovjCdCjkbzMlFNu2nwmcSPjRFo2N0N3tZ+tmMnDFFZJKRbBxI/T1SaQ8WCg3lTsspaBalcTjvsu3UICODo9Fi2B83L/OurqDwnhfnx874XlQX++LyuGwx9EYG4Pwi1Lp/LxgQTbrC7uLFy9n3bpnp3+/YAHccIPHPfcIdu3yf9bQAK99rTctKp8MIztH6X2ul3B9GDPiz03K2TL7nt5PfXcdjXMb6O8/wG23XcusWbO54457iMWOvvrArthYRQszYqKZGtV8lYHNQ4ztHkfVBE0Lm2hb1kpydoJgIkhhtECsJTZ93HKugh7QCcQCKKqC67jkRwv0PLrnmGJwbjjH+js3kBstoKgKlVyFgQ2DjO0e46xbV89wOY/uGmPHAz3sfXIfiqYgXUldVxIjaBBtipDuS5MZyE47fQGMiEG8Lc5ozxiB2EFjRyldwoyaxFoPN/m0t8/m5z9/mFtvfTU33HAFd9zxm1POV65xctTE4Bo1avxBeeyxh2hpaWP+/GM/tTwZerTtlEWRWW4HG/UXGFIHsLEpiSJfi/wT87ILaXePXhBVocITxsM4VMmqaUqygCP8EY4mdRQhSMoki5xl9Gjb2K3vmCEGt3ntvKf4QZ40HuU540lS2igtXhsrrbPQ0BhQ+/hR6DvUFxomS9+aUVDIKBNIJAGCLHVWYgubIbWfDrcLgJzI8oB5DwYGHVYX+3O9lPOCifAoT1Wf5ZLytdQ3znRidDvzCG5vIpPcgt4gyIczVLQSatHgCfE4fdH9nFt9BW8svwMDg4qo4GBjEmDOK+ez7Y5NXPzFKwkEAhSVIhUqhDlxMdh1XLbet4PsYJZER4K6hnp2j24n3LOX1aEIPcFOejISKV10dZSljT1U5AiudJHSQ1ckSbORtlAnzYGZ2VEDpf30ZDfhSRdNGGSsFGW3iCIUwpo/eSg7RXZk1vu5xmbDdEae67kowi93U4SKJ12qbpm0NU5QjSAOcf4YikmqMs5wIUO2WkRV/DiLHantOJ7D/MQcgmqAbaldnNHWQdHJ4UqHgBqcdh8rKGjKzK/Y7lgni+sX8sLIRtrCzUgEqXIWz4OVzYtQFZWgFp4MsxB+KZ2UDJV7KTo5hBQ8sP9Z9meGmBOfTcn2GMwPs3l8O5vGtvGGhddzw/xXEzdjSCm5d9+D3LvvAT/aQTHZNrGTdaObedfy22mPts04N0UoLEyspCHYwkR1DJCsqDuXbGOFfbkDCARzE93MjXcd7pipgL5RoO0CFHAWQ93SJO2RVnZl9jGvNUI4BEPDMFIaozUQZdtPvsMNN9xKS0sbVdfie1t/wvqxzSTMOLrQeWrwOfZkD/C+FW+lPdxG8E6B+bhAqoAOWo+CvlFSfLeHd+p9LzVq1KhR44+Ar3/9y1QqZb7wua9R11jP7tw2due2kjQbiUyuIKq4JfYXekiY9dSZTX6h6xH25Ulv8vv52O7PmJ7E9ixGq2OTUVZ+wm5Uj7O64SI8XEbLg1helbhed9wehRNBSknWmiCkRXA8m4pbmowzUvGkn33cGpp9WIkcQN7OMFLuJ6zFMFXfORvUQpOCtTIZpSUo2jmklH4ngOZn+M6NLZkWgsEXzWeH5zJS7kcV/tik6lYYLvVRcLL+9qhU3QqGEiCoGPQWdtMQaCbxorLd3sJu9uS34UqXgpOjaOfwpEtIDePhUXDylJwCWTuFJz0kEk0YVL0yhhLAkxZ5O4smNBbGVoCAscoQFadEQAvREZ5La3D29PGuvfZGPvrR93P//b/ife/78Anf+8svv4Ybb3wjf/M3H+Tcc19BR0fXSb57R6a52XcB797tRytMDaEGB/3M3scf9/N+IxE/XmHHDj8POBCA/fsl554Lt97qsWGDwgsv+IKtrvsCq2H4RW+27WcR+/ERfoyDpvliraL4r6tWfVeuLyT7kRJS+qJsJuPHOITDkE77JXfj45BI+OKwEJJQyI+QGBjwryEahXLZzxxuavJF3VmzfBG4uVlyzz0q0aikpeWgC7dY9B3Kzz4LgYDvZu3p8d26Cxce3a3b0SHp71eQUk7fv6kSvYaGqdd0cs89P5/eRgi4+GLJ8uW+61gI302cSJza+zjaMwYwLQQDBONBiqkS43vGMRo0brvtWgzD5Ic//PVRhWDXdtm39gAD6/uplmzMsEHL0mYm9qUZ359CN3WkJxnZOcbE/gmWX7eM+ZfOY/tvdzK+dxxFU8kN5XxXfX0IRfXnL6qmohkq5UyJcrZCMH7kVbG9z/eRGykg8Uj3ZfEcD8/x2HLPdkJ1YZZe48/PJw6k2fSLzZQzZVT9oBvYrti0LZ/qLTm8KUgIwZzzu8gN5Ujtn8AMm9hVGyFh3sVzCdcdeWVeY2Mzd931AG9602u56aar+N73fsG5577iRN6aGi+Bmhhco0aNPwgZkeZp4zHufuqHNF3WzEOB+zjPuuikXadHwsVfcrJP202vuo+IFyNGHKHAmDLMHcHv8KHC36Bz5NKwkiiyW99JSSmhSR3J5LIqYVMULnVePa1uOyVRJCeyPKs/xbnaK1jgLJkuWGvz2rmpcjshwowr4yx2lk1/RXa5c9iqb+RzP/g42769iYG+XgSC2OIESz+6kiVXLsfFJa1MgBSUKaOi0q8eYEIZp9uZz7aJnezP9mKqBuFKjAG1l2/t/T+8l3dwZtOK6Ws5kOvH3W1yWeJ6+mb3sMvYQGu6E1H0z7PebGSt+TjLnTM4yz6XNredmEwwpozQeXMXL/zbWp75zeMor1NYYZ9J3EvMmC9JJANqLzu1bbi4dLtzmessnI7HyA5kyQ5kibXGUTWVoBmmbJWIJEOED+ziVZc0MeAY7C9sxdB20xyKoIlGbK9KwcnRFGhjcfKMw94j27M4UNiFEAoxPe67ge0ceSczeXqCkBYmZiQp2nn6i3tpMFtwPYe+yh4URUUTGlE9icTPAQ69aNIWVMPkRQbHsyk5FTRFI2IEqbplXFfFVA0O5PqZHZ2FxENTPfJ2lkx13HfcqEHiRh2OZxM36nA9l11Zv2gmbtTxf9l77zC7zvLq+/fsenqZ3otGvduyLdtyL7hgY0zvxQESCIQEAgHyJW8ISUjeJARCSCBv6KEZ02KDjU1xr7J6HY3K9D6nt92e7489OtJYM7IEMiXMui5d9uyz9z7PLuec+1l73WvVm028buXLCelBdk/up+yWSBhxepLN9CSaKDp5ik6B+kATmqKTtqYou2UKTo4aowHP05ku5mkON5KyZhjLpwCN+mAtBbvIAwMPYns2b1/3RkYL4/xs4FFiepT6kF8pe9LjQOoQPxt8hDetfvUp51gRCvWBZuoDJ8LpmkKwomYpw/lRnh7dzr1Hf0pdsJYLmzawNLEEShD6qoKx1b8GSDAflmiXa1x/7VWMFMY5mD5M3IiiNxVpF4LkHnhoYpR3vvNPANg3fZDdU/vpjnUS1PzitS5Yw/5UL4+PPM3rlJdiPqngNkrk8dralWgHBcajgvLLX/hE6EUsYhGLWMSvB+Pjo/y///dpfv/330tjo//75EmXgBqc4/sb1MKUKgVmypPUmA0kzToCaoicnSaqJxBC4HgOllemPbzkef3ipypjKEIladZTdotIKX2FrdARAurNub+X5wK++jhI2S2SNOuZLI9ScguAwPIsEmbtggF1BSfn+ycrc0koX83s0Bbu4Uh2H6POAK50UVEp2FlMM0BMP9VyIqRFaA110Z/vxfIq5KwMJTePgoqu6gT1EK50KThZonocR1qkK1NzyOCyW6K/0IciVGJGkrJbxFSCOJ5NqjKFJ1086aAL3VdpC4ntVTDVICE1StkrYnsVHM+iK7acZfG1aIpOt7sSx7MJqKFTHryHQiEuv/xa7r//nrMigwH+5m8+yRNPPMJ73/t7fPvb96Oqv7zfqqbBzTd7fOlLCgcOCEIhX2kbDvskq6r6JGwg4P/TdRgdFTQ0SIKzucWJBKxbBw895PvuCuGvFw6DrvuK3GQSDh3yLR0cR1SJYPDtI6SEUEhiGL6yNxTyrR5SKUEsJqlUBLYtURTf5iEahdbWE+RrXR2cf75kYsJfcDwkr7PTt2QYGhLcfrvH+vU+Ud3XJ9m5U9DZ6RPJ6TSk04J43B+X5/m1WzgMpimrgXjzYcsWXyl9+LBPrts2jI0JVq2SrF7tb5NM1pLJpPA8r2plAL4iuabml68TXced3z5EgFW2ePvbX8PU1AR33/1I9XtqPvQ9fIS+hw5jhHXMiImVr7DzO7vxPI/289pQdf+es4oWI3vGaF7TTNvGViJ1EcYPjFPJW9R0Jdn7w30gmSXIBY7lgBCYEROnYgMBPMfDKllopo5mqEgpmToyjed6ZMeyaKaOGfHfLzeW4+BPD9F+XiuxphhDO4Yp5yrULqnFsVxy4zkCUZNSpkRhuoBne0TqwiTaEqccY213Lee/5jwGtw2RHsqQaIvRsq6FlrUnzksxVWTq8DSu7RJtjFLTmSSRSPLNb97LW9/6ct74xtv4znd+yvr1p84JF3HusEgGL2IRi3jBkRUZPh/+N7ZNP83U7gm639vDN4Jf4oC2l98rvJsgwV9q/93OMhSpcVQ7hCJVXOFSkkU8PFY6azmiHaJPO8AqZ9282+vSIEMGFZV2t5MpZYK8kkMKQEoSXpJhdZAhtZ9JZZxGr4lPR/4vm6yLeV3xjqofMMCMmEZHrxLBEskx7TCH1IO4XQ4dH+uifnkjhjSwv2Lz6Kt/Ruej3cysm6LJaeO+wA/o0w6iS502txMPjylriv5sPwgPDxtHsQhrIRzb5cHBx9hYv7Y6MSjaJSquRbvbyojXR9AOo5VM8k4ey3OQFYE0JAe0PVxgX0yD18SWytV8W/0KxVU5AhsD7PjuNhKvSWDmAjzi/YSr1Rurx/JT815+FPg+GZEGICiDbLGu5pWlN6Ch4VgunuNWfadCgRDFSh7VUKkULBKKQ7y2REEexVBC6IpvV2GoAYLSZbIySre7koA6957I21lKToGoESdnp8laKYRQUIWKlFB2C0yVx2gOdRLUQmStNAP5PspeGVtaKJ6CJStVn73VifMJqGFqjHpGSv0E1AAhLUJEizNVHvMnmpqBrigMT00yXsgS1IJkrRzTpRkUxaMubBJQQiSMGopOgYKdo+gUaAt3E1DD7Eo9jSudKlndGGxldeJ83rL6NUyVpik4JWJGiOnKKBPlYTwp6QwvpT3SgxCCdGWakWI/qlCpCzQxUpiYVTSpFCslAqqOJsLYno2u6DSG6tkzfYD+3BCj+TGyVpaVyROWDopQqAvUsH+6l4prnXEQXG/qMF/c+w0mipOEtCC7JvfyzNg2Xr3ipVx++AKMZ8BtBxnyi22RBeNRwfnr12CuexOPjzzNsewgXfF2VgeX8UfvfS0ve9lrq55gY7PHdZwIBn9CHDOi9KWPouYElDlBBAOoIOMSfY8gf7tNb+owE8UpwnqY1bXLf2kP5UUsYhGLWMRvBv7lX/6WYDDIO9/5/uoyVzpVYkZKicSbVQILXPzOroAaYllsLb3Z3aSsSXwVm6A+0Ex7+PlDiibLoxiK4T/clU6VDE5Zk6StaWrMUy2XzgWaQ+2kKpNIJE3BNvJ2lryTJqYnuKju6jkZBSdDU3SEUPBwUU+a4rvSxlSCdEZ6mK6M+R1pahiERBM6eSfDsfxB1iYvnEN2CSHoia0mrEcYKQySqkwS0RK+z7D0RRiqULGkxPIsn5h6zphydpqKW6wSxKYSoEgeTRiU3AIS6at/RYWQFqbg5NAVHduzaAl1Ynn+A/F1yYtoD/dUxxdQg6AuPHd40Ytu5c/+7F1MT09RWzv/+ZoPsVicf/3XL/CKV1zP5z73Sd71rvc//0ZngDVr4N3v9nj6acHIiKChwWPlSsnXvqbS0eFbMhSLvv2DacLUlKShgSrR+cADggcfFIRCJ7yBj5/thgb4yEdcamvhG99Q+OEPBZOTJ2wjFMXfb7nsB7k1NkrKZZicFLP78W0ecjnQNIGug6L4IWuuK8hmJdPTvqrYdSWNjZK2Nklbmz/W40Fxtn2CfFZVX80MCr29voVENOoTw5GIoKtLMjPjr5tM+vYRe/YIrrhiftK2sxOuu87jsccEqZQ/xi1bJLfe6hGaLfcSiSSe55HNZkgk5g8O/2VQ213LyK5RHOvEHMcu2wgh+K97PslTTz3CN795Lz09yxfcRzlXZmjHEIGYSbjWF6SYYYOpI1NUivacz48RMvCcHJmRDI0rG0i2J0i2JwAoZUuM7R0nM5Kp+hcrqkIgalLbVUMgHmR45wjHnh6glCqih3TaNrbStbkTPaBTmC4AoAf87wkpJZqp4ZRtpg5PE2uKkR3JYIYNFEWhpiOJXbQpZ8tYJYuZ/jR1PbWsuHYZenB+oVWyLUFyHqIYYHTvGPt+fIBSqgQCVF2lZW0Ta25eTTgc4Ytf/A6vetUNvOENt/KDHzx4xnYvizh7LJLBi1jEIl5wbDWeYJ+2G/2n/g/G+VduRnVUdunPskvfxmZ7yy+1/9XOOjbY5/G4+SCWsKrkW51bT5PbzKg6TEEUFtzeUsrUyjryMostbOq9JoRUKKh5XFyOqIfo1fYD0OZ1cqF9KarUeMJ4mA63a47Hb5PXjI1dtScYVgfZq+2kIsqsvWkDbW4nvdo+CiJP/V83oX5eZfuzz7BmzXpS6hSj2iC1XgNFUeRJ4xHyIsegN0jOyZIwYniKS97M0DDRStyLMZofJ28XiBl+CFdTuJ64EWOm5PvEZSs5UrkylVml6/bJ3UR0fY7a97r8jQxUDnJ34/fR3qyT/0CO7sGllGqLfCH0GRqcFta46zmq9nFP4DuoUmOluwaBIC1meNC8nyXOMjbbW4jWRzBjAYqpIuHaMKFABMuukJ3KUdtRQzAWIO0WcD0HXZurCtcUg5JTwPYsDMUkbU1RdPJoioGu6CjCt3vI2RmEUNAVHf9AJKYSpOKW/SA6xUAIwUR5mKgWJ64nydkZbFlBSklADVFjNCCEoCu6nJyTIWVNVX354notw9kMUroElSjSDVF2Jpix03i4TJVTbGlbQzwQJWHWIKWvdrFci6KbI6YlmSqPYqomAdUvSG3PZrw0RI3ZQFu4m/pQHccdtmoCvtJHImdbUX2EtSgVr+RPYgVE9CC6ojGUH0dRXFRFBQkFu0hYD9EYbKAvc4R0OYOqqAjht7SqnNinKz101UA5acLnSTiagcGcwFBhRY2kNnD8NY8fHn2AmdIMq5LLqxOx/uwgPzz6ABftXUNIDSFP4l5lDMSwQDskWLFuKStqThRxH/rQu7Fti7/8y3+oLgtqfkCOJ705aqeKaxEzY8gS/pxHMrer14GKbvGfu7/Knsl92LPEe3u0ldetfAU9ia5TPuuLWMQiFrGI3x4cOXKIr33t83zkI387p+06adYxWhwgY6UoOFlsz0IVKgoqMe0EEdQUaicy203kSJuwFqPWbDxFTfp8UIU2t0tKvnAdKU3BDopOgeHCMfJuFkURtIS6WBFfvyARDH4QbFRLkLVmiBlJFFQsr4LlVeiMLPe7qewMMcMPWzuOEFHGSoN4eBTsLLpi0hzqoDnUgSpUWkJdtIS68KRD3sni4c16LntVIw5H+g+lE7PdUWOlISbLoxRnH5KH9TiGMIjoMfJOlrJb8usJKam4RYJamFqzAYkkb2eQQNZOoQqNzshy2sLdZxXq9qIXvZgPflDys5/de9b+v5dccgW///t/wj/8w19yySVXcN55F57V9guhs9P3Aj5O4lqWr9R1HFiyBI4d85W6rusT8ddc47J0qW/f8PDDvg9vfT1Eo5JCwV/uOP5/77pLYfly33ri2msljz3mW00cJ4RXrfKVtNmsJJeD6Wk/EE4Igar6PsChkE/itrRI+vsFR47A2Bg8/bQfIqdpPmlcU+OTwI4DR474xHO57Ctwv/UtBdP0WLbMJ6nf/W6Po0chnxfU1/tk+D33CCIR3zLjOCYmFj5vzzwjuO8+wdiYf/0bGyUveYnHhg0nLDfAVwYDpNMzLwgZ3Ly6kfH940z0TqKZGkiJY7scsffynXv+m3/8x//gkkuumHdb6UnSIxnG9o2RHc1S1zP3c6wHDUrpMk7ZRtUUiqkS5WyZ/GSe/FQB6ck5Xr7BWJA1t6xm3737sUu2r/oFAlGTni1LGN83zu579iKAQCyAlbfYf/9BKoUKLeubOfzokSrxLKWkkqugB3XMsFkll4M1YbITecC3xmhZ10x2PEd6KE3nRe2suWlV1cP4bFBMl9j/4wPYRZvarhqEIrAKFoPbhok1x+i+uItwOMJXvvIDXvrSq3jd627hnnseobZ2MSTkhcAiGbyIRSziBYGNTUZJEZBBDqr7MKTBwNZhkktrCDf6FYA3q5o9UzLYk66v/HhOQaih0ei1EJIhpJQEZRDN0/GExy5tG13eUpq8hVt2ol6cFc5qXFwKSp4xZZiUMoNAQUelolRwcQnJEEEZJOYlfK8mGeFp/TGur7y4qgQ+376IR42f06vto9FtoVfdT07kaPSa6HJ7iMgo6+zz6BO9xO9M4BUkb9jwe1hOhSeMR1h5kr1E0qthr7oT4YKVLJLVPFQUarINtAwsYaY0RWe0G3M2ZK3kFFAVyaamdfxs4DFSAyVyy/NoahATk/pgHUVRJJMr0qi0cZwjLNtFQlaYGqeWrhu7+fEHf4T1rQo9b1vKgHmMh/T7WeOu54C+h5zIstI9McaErGGKCXbqW9lsbyGYCNJ1YQe9D/Zhl2y8kl9ueIrDkku6UDSFkAhjqCZltzQnYK3iFv3QE1T2pJ5hsjyKK13/XKtRTCVAzk7jerbv/zurAvKrQYEEbK/iJ0sbSfJ2jogeQ1M0wrpPlrueP5kpeQVMAkT0OOfVXMp4eZicncFUAjQEW0gV7ufhoSfoiOosTy4lqAc5mDrM0ngXb1nzWhStMKs08ov2oBYmqIXxKi45J40jHWLqiWLUJ7NVJsujtIVPTa4QCNLWNGlrCs+btZXINVBXrueY0DiSOcrRzBBBQ2Egl8b2bDxPwVR9gnhpopuSWyKkh0gE4sSNGPXBWoZyo3REWxFCUHEtZippbm150SyRDpYL3+4VPDmqUHIAJPUhwcuXSS5olEyVZhjIDtEUapzzuWsJN3EsN8gxOUSSeVQQ83T77dz5LF/96v/jox/9JxoamqrL19SuoC5Yw7HsIB3RNlShMF2ewZMeFzZuxGmUyCSow+C2+vsWBRAleKxtG1vHd9AdbSekh7A9hyOZY3xrz/f4SOXdhHf7nw1ng8S6SCJ/OTvHRSxiEYtYxK8Q//f//hUNDc285S3vnLO8MdBGr7Kb0dIAfjWgUpK+bUDpOSFrET1GRD89aWF7FhOlETLWNKqiUWc2UWs2MlkexfGcKnlcccuoQiNhnLna9GyhCIWlsTU0hzrI25nZ96udQ+DOB03RWBFfz86ZJxkvDQP+Q+W20BLawz1k7Rnkcx66AtiuxUxlEtuzCWtRSm6JdHqKnJ1hZXxD9be/OdzJgfR2TDVIUAlRcgs40q6G8LWGu4hoCfaltzE26zXsSY+SW2C4cJSOcA+GGqA20MR4cRBdMWe3V6g3W9BVk3qzpfoQuznYQX2wmcZA62mD/uZDfX0j559/Effff88vFAb3Z3/2UZ555nHe+taX86MfPU5LS9tZ7+P5YBhw8cWS73xH0NgoaWrySdGJCcHatR5vfKNv0TA5CZmMqAa2KYpvz5BKCbJZ32t4cBB27vRD4xoaJIEA1NdLNM23lQiHoaNDsn+//3qx6PsKCyHJZv3rq2k+Gd3X53sYK4qvQPY8AEFbm6SrC/J5iMfh6FHB8LAgmZT09MDy5b5VxLe/rfCnf+phGD65vHQpHC8Ily71PYYzGX8f4NtN2DasW3fqA5beXvj61xUsy/cedl0YHBR8//sKXV3eHP/fQCA4u7+FQxZ/qesVMtjwsvWM7B5h4uAkQhVQ4/CB9/8jt9zyMl73ujvm3c4u2+y79wCj+8aoZCukhjKUMhVa1jYRiPnKCyNsIBSBY7lkD03OevM6eK7H4PYhQskgK65dPocQ7r64k9xYjiOPH6UwUyTWFGPZVctoWtPIk1942ld3t/gnORALoKZKDO8c5aI3bKJ5TRP9zwxSkEXfciSgk2xP4rou0QZ/Xta6oZnJQxPkJnJVFbP0JK3rW1hx7XIK00XK2QrJjgR6wP9uklKSnyxQSpcwQjrxlvicMQNMH5mmmCpVieDjx68HdUZ2jdJ9cRcAtbV1fP3r9/DiF1/GHXe8km996z4Cgfl9kBfxi2ORDF7EIhZxTiGRPK0/zs/M+5hQRzGkSZEiFVFh5uAUtavq56xtyOdvVU9VJhkqHCVtzWAoBs2hDlpDXaizxXlB5NlmPEVPeTnHlD4c4aC5ChW1wjH1MFdXbqTV7Vhw/wYGV1auY0QdwnBNppRxNKmjCZ24F0egMK1M0uA1URYlMkqahJfEkAZFpYhEVsnRWq+eO4rv4u7Ad+jV9pNTMjS6zWy0LyAio0zuneCb130Rp+wQDIf40ue/w7XdN/Ex9UPEZGKOFX+QELqncWH+QhL944y7EzTRSF2miUKpRNpKc3vjOlQhOJjZxVhxENuzqItoXNq6ju/0jWKGk4jOEkFdwVELPrE6WIdQAzBb22qKRk7P4gqXeDJJ643t9P7XAbre2EVEizEQOIYsSywsjrdZngxN6pTEieJryWXdBGIBhnYM4fb77ZoXvnITTat9AjCghmgJdXE0tx9XuhiKSd7OkHeyhNQID4//iJJbpCHQgqkG8KRHxpohoASIaAmydoqKU8ZQTGJ6AqEolJ0ijmdjexYNwVa6IsvZnXoGR9pVX2fw1SuqUNHFifsuoIXojJywUwC4fenNlJ0ye6cPMFzwVb63LnkRr1v5CpKBOEey+5ksj1a9uoBq8ImpBk+ZjPoQSHlqWrKUkr7cPgbzfb5yaSxE6AHwBgO0qfXYiY3cu+F+1B6XtXXd1IcSbB87xJHUGB2xGGtqVxHUAgzmR7i4aROd0TaEENy+9MXc2fsD9qd6EQgUobCubhVXd1xWfe8nRwUPDgkaQ5LOmK8SHsj5BHFnVKIIgUDBY+64feW7gGUKbAVKcNztRWRBahJn2YnC3nVdPvzh97Bq1dpTJvX1oTpes+Jl3HXobg5nfLVCVI9wQ+c1XNC4EU+B0m0ewe8raAdmN9IlhQssftj6c+r0GkKzthC6otEV7GB49xAD+46yzlvpL98j0PYKCnd4sICDRKaSZfvEbvpzQ0T1MOvqVrM0cXZqpEUsYhGLWMS5QW/vPv7nf77NP/3T5wgG51oCKELBUEzietJX6Qqf+FRQGCn10xbuxlDNBfY8F5ZbZk9qK1OVcT+8FclQ4Sht4SXUm01MVcY4/oRTCIW28JLTKnTPFcJalLAWPWW549nknSwKChE9XiV3bc9ioNCH5VUQKICHhkZTsB1N0ZBILK/CcLGfmJ4grEVRhcZMxZdl1plNVa/VsltirDhAS6iDuFEDQEuok7yVYbQ0iKH6HT0gaA620RVdTl2gmYnyCOOlYWJ6okpe60JntDzAWGmoGrDXFVnB0vgaSk6evuw+8k4G1dNwpUOt2ciaxPkkzV9OCXj99S/mX//1H3AcB007O8rDNE0+//k7ufnmLdxxxyv47nd/Rih07u2nrrnGt0t45hmFQkGiaYJLL5W8/vUe5uztG4n4Stxo1PcSjkQkuZyvFDYMXw+RzQrSaZ+ojcUEpukTu7GY/7oQkExKVq/2g97GxwWBgGTZMnjmGcnkpG8T4c2WeroOhnHCm9h1fT/j7m5/vLmcoLnZD4traGDW/xja2iQHDggeewy2bPHHdzJWr4bLL/d45BFf6SuERErYtAk2bTqVDH7ySf84V6w4sWzpUsmhQ7Br11xbiVwuC0A0On9w27mAGTbovriL7ou7cF2XV7/6RkKhEP/wD/++YK147Ml+Bp4dJNYYJdYcxXNdpo7OMLp3jI4L27FLvldH09omsiNZZgZSqIaKETJIticIJgIcfbKf2u5a6pf63ztSSg79vI+xfWOEkiGiDRGsos3QtiEidWGK6RKB5wTIBRMBpo/NUMlbXHzHZqQnSQ2mMSImZljHrtg0r2mibqn/uWtc0cCK61dw9PFjpAbTKKog3honUhNi27e2U8lVEIogUh9h1Y0rSbTGOfBAL6N7x7CKFpqpUbekljU3rSKYOPH97dquf08+hyRWNQW7bM+ZV7W3d/HFL36HV77yet7//nfwb//25cWa/BxjkQxexCIWcU6xTX+a/w79P1w86rx6yqLEkNpPXskx2TvB2tduRCKZUiYIyhCrF/DxPY6ZyiR7Us9QcUuYapCim+dgZicFO0ewNsYOYyvH1CMcVPcRzgXpFj1MB6YoqkVCXhjpeizPrEDo8/945ESWgshzobUFRzj8V+jTVKgghW/zUBRFJB4ODmmRJi4T2Fh4eKSUaa6u3FANTzuOTncJf1j4UyaUMf4j/AnGlTFqpN++VLOsllc++kaGCgO03NnKe9/7e3z3uz8hfl6SEWVozn48PBAKNV4Nrwtexk8HnmI0P8mInERTFDY0rOC6jis5mjtIf76XoBomZiSwvArN8ShLom20j7diGJKZ6BgAdblmMkctnKUuFbfEdGUCy7WIyTiOZ+PgsOZ967jvmns4/PU+6t7VSIPXjEDQ7naholKiSHCWUXNwKIoiK5zV1XErqkLbea20bmzhoNiBcp9C86qWOce2JLoCXeiMFPvJOz4RrAmdoBpmvDyEKx0y1gz1gWYUoRAzkuTsFGvi66gN1NOX3QsCYnoNnuegoJI06liZ2EiNWYdA8X12i8d88lcxsD2LvJOjJdgxR5E8H+JmjHesfxNHMwOkKmkSZpwl8c7qpKsx1MZoaZCUNTW7L0nRyRPTk7SHlsz65JWrid6u5+BKm7pA0ynvlbImGcz3YagmNcU6ln63i1B/gGx9Gi1ooB8y2XhkI9tfv5eZ5jS1RitXtrSCfJK4GaXiWgghuKL1El627MXVQunCpvNoiTSzd/oAJadMS7iRtXWr5njzPjMuMBRIYENFopgGHVE4mBLsmxFc0VrDsmQ3z47vJKpHUBU/gGIwN0JrtJmWNW3Yh0Dfhj9X9gSoksplkpNuCb72tc+zY8dWvve9n887KdvYsJYliU56U4dxPIf2aBst4RNqZOtyidPtou8XCBucNsnUkgL5Z4sElOcUvBM6MuNSbLVwj79VGfSdYOwUWJecOuGYKk3z+T1f41DqCJrQcKTDQ0NPcFvPjVzTsZhmvIhFLGIRv2p8+cv/SX19I694xetPea3o5LE9i/pA8xzF7PHun6KbP2MyeLjYz2RljIRR49tB4HdajRT72ZDcTGOojVRlCgWF2kAjdYEmHM9mojxCzs5gKAZ1gaYqaXq2sNwyU5VxbM8iqPp2Caqi4Xg2U5VxCnYWTdGpMRrIOWmO5Xopuf7D/bhRw7LYWmJGkoF8H2OlQaJ6gqRZV32Q3pvdRbu7hL7sPmzPpujk/eA4NUBIjeJIh7hRQ97JUHT8tvCAGsaVNnk7Uz0uVaisTGykJdxZVSwnzfpqnQOQsaYBiSJUHM9BQUFRNAKzGRFt4W6SRh11gSY/M8KsJ6onGC8NU3KLRLQoDcHW51Vynwk2bbqYYrHA0aN9LFu28qy3b2ho4otfvIuXvvRq/uRP3sZnP/u1c05EBQLw+tdLrrzSZWxMEA57LF06l0RtaIC1ayXj44JgUDI15RO/luWrbH1bCd97N58XHD7sq4ArFd+L13Ul+byvFn772yUXXCD57/+WPPaYQiYDhcIJEvjkw3NdfxzBoE8yj435y1TVVyirqh8qd9zuYWYG9u3z1cKf/7zK449LbrlFsmHDiZpLVeFVr5KsWgX79vkWGStWwIYNJ8LyTsboqB+4dzL8TD9BOj13eTabBphjJ/NC4rOf/ReeeOJh7rzzxyST83/2HctlZPcogWgAM+J/H9Uvq8fzJJnhLGP7x4nWR+i4oJ2V1y9n69e3Y5dtog0RgokggVgAIQTFmRJTR6aqZHBuLMfgtiGC8WCVaJWeZPrYNCO7R2f9fx3M8InvQLvsoKgKU0ensffa1C6pJdYUo5QpoZkazWub6NjUXvVDFkLQvbmTltVNZMdzqLpvX7H7f/ZghAxqOmvwXI/MaJa99+yjpruG/qcHiNRFiDZEsIs2o3v9ueemV59XJX9jTVE/RyZfqZ4T6UlK2TLda7pO+Yxt2rSZf/mX/+Kd73wDF120hTe/+ffP1SVcBItk8CIWsYhzCA+Ph4wHsIXNUsd/jBuQARpEE9PFCYrDBcqrShzQ9hCRMW4s38bykxmj50BKyWD+MBW3RMKoq/5AVNwSjyg/YU9oL3mtgIrKoHIMGfY4v3ghTYUWHOFQUAsUZR6Rd+E59lFFUeBe8wc8bTxGWZRIerVcVbmem8u3UwqUOKwfxBY2hjSReBREnowyQ8ANkBVZxrRRWt12rrSum3fsAkGj18yNlZfwpeDnGFIHqHMbKJslZlZMcZG9hfcs+SBD227lXz7/d6z4zCoG1KOURZll7koUqTCgHqXVa2eNvQEMl1eseBEj+UlyVh5TV9jccAm6qjNaGiSghqqKi4DqE7V14Qh9M5N0z6ykPdXjH7ddosgoEcNk69QjFJ0cALWilmS0hmOBwzSsaKTt9nYOfmo/rW/v5FLhe2CttTdynn0hW/UnCckQKipZJcMyeyUXWZedeg6EoFgqEAqFT/lxV4RKZ3QZbeFu9qafxZMeNWbDbECajiJVP6HajRPUwigoeLNPi1cmNlIXaGKgcJiclUZXTVZHzqMjsrQaSAewNLYGx7OYrkz4ydlCpSHQwrLY2nkL+uMegMdfU4SyoO9sWIuyNnkBx3IHSVt+CkZzsIPu6ApCWpS0Pc1w4SglJw9C4EmPukAzTcH2U/Y1U5nEkTYxNUlib4zwQJB8TwELm5yWZbh1gsjBKB07u5io972rVQXaIs1sbt7EVe1biOph6kOnKpVaI020Rk4loI+jMJWndtdRlGnf8kI216Gs6UaIGJVZ37pblryIydI0B2YOk88rFMsudUY9V3ffjBkxKLzFw9goUA8AmsRZBfZ6Wa0wDh7cy0c/+kFe97o72Lx5YUuYmBHlgsaNC77utUGl7UTxH5NRWqPNHJzpI2HGqtdtenqGuBujLdB8wqoigB9S0Qtccuq+fzbwCAdn+lieXIo+23Ewkh/j3mM/ZV3dqnnP7SIWsYhFLOKFQbFY4K67/pu3vvWdGM+VF+KHpSlCxZEOGifIYEc6KEJFE6e3VDgZE+URDMWoEsHg11Epa5K8k6UjshQjEmRcGaUky5QqBfaltpGypnwlsfQYLBxhWWwtreGuszrOdGWKfentFJzs7BJBrdnAkugqerO7mKlM+oIDAUiBI20CWoiIFsPDY6Yywb70s2youZix0hCmEqjWQccfpKcrU+SdLEIKEnotHh5FO0/JKRJSotQaDWSdFBlvBiF8F+CCk0MI5RRvZCF8Anoh4tuTkrydI29ncT0XW1qAxJV+6NxkaZS4XjOnVosZSWLGufd4XblyLQD79+/+hchggHXrzuNf//ULvP3tr6GnZzkf/OBfncMR+hAC2tuhvX1+H2oh4GUv86hUFJ59VjAyIslmfTVuLOYTyrGYT+jOzIDnSQYGfA/fUsnfRyYjaG6W6LpvMfG610nSaclddyk4jq/+FcIneR3H35dhgGFILEsgJbO2Er6qeMUKf6y9vb5iuVTybSomJyEel3R1SYaG4KtfVYjFXLpPckfTNDjvPMl55/l/WxZs3y7YtUtgWX5o3gUXSKJR37/40CFlVi3qr++6fr2efM4tk8tlEUIQiZyqpj/X2L17O//3//4f3vWu97Nly1ULrufaLnbZQTNP2Jxohk+8qprKsquW0n5+K9HGKEIIwjVBoo1Rajqec3ACPOdEh15mNItdsok2njhWoQiC8SDp4TRNqxo5/OhRNFPDCBs4FYf0YArXcjny6BEU1T+niqrQeVEHK69fgaLOFTYdhxk1qY/6pO2Rx7YhJVXbCFVRSbYlmOybIjuWI1wbIhg/YX0Ra4oyfWSa7Fi2almRbE/Ssq6FwWcHKWXKqLpKOVcm1hilY9P8diy33fYqnnzyUf7qr/6Uiy66lFWrTi8kW8SZY5EMXsQiFnHOUBYlxtQRkp6vgp1Qxtir7SSv5JjZOQ3AkmXLeUnpFax01tDpLjnFcuBkuNIha6cIqKE55J2nSh5LPoKKwSpnHQJBxS6x1XySA6F9rCmsp6QUyWoZ1qbXE7Pic9pOJJI7A1/lYfOn1Hn1JL1aUso03wp9mU3WJVSUMoY0ESjYwkJBmSWFJQYGfdoBojJO0qmZtU5YGBdZWyiKAj817mNEG8SUJhdbl/Oy8mvR0UkxzZDXT1kr4wmPvfoOjmi9dDlLaXc7eVXpjXRFutnv7CBrp0gEA9SFItQHmumKLqfsFnE86xSlq6GYLK/pJFf2OJDqo8ZM4HgOWSvH+Y3r8EQeyykRN2pRhELEi3PN1HXsaNrOTCRNx0d7GDl/mOCnw1z4B5cCYGLy6uKb6TC62aPvwMbimspNbLGurCqfn4t0eqYa6jAfFKGSs9PVa6yi+mnTTr7a0hgkTNktYSgmEc1Xi9QFfD8/26ugCq1qGXIyAmqQ9TUXk7FmZpXlgerxngzbsxguHGO8NIgz26LYFl7yvMqUuFHD+pqLqbglEAJTCVTvMT/opZ7p8hiudKkxG9AJ8dTYdiquRWukiWWJJX7rpjyRjGakDEAgVRCuf68mzDgz5jSJiRPX2PH8iVVPoosl8c7TjnMhlHNlWrbtZORoGlkTQghQDg1gTWYIXHgBrbNP7Nujrdyx7O188s69jA9OoVhxnMIafrijgehrPNavB+tiCRef+h6FQp63v/01dHZ289d//YlfaJwLQREK13dcyVBuhN70YZJmnIJdwlMcbsteS9NzW0w9Afqpky3bs9k1tY/aQE2VCAZoCjfQmzpMX/roIhm8iEUsYhG/Qnzve98in8/x+tf/3ryvB9UwdYFGhov9aEJDU3Qcz6ZgZ2kMtc1rr/CLwMbmrsDXeMJ8mJzIYMogNSRZ662h01iCIvxumbyT5UhuP7VmAwHtzOwEXM/hYGY3BSdbrU0cz2GsNEh//hDF2UDcoBYirtcwVRnDcsvUzCqHVSBu1JK2ppksj+FKB0XMrYUUFCzPAq9CWIsyURnBk57v54tL2p6mWeug6OQJaWE0xQAJ5dmgX1vaz3scUkpydpq0NcNw4Sh5J4OpmEig7BYBgakGqDMbkXj0ZfcSN2qet8aSUlJwsjjSIaxF5xDIZ4La2jqamlrYv383L3nJK89q25Px4he/jI985G/5u7/7c8LhCH/4h3/6C+/rF0UyCX/wB34YWzotGByE735XcPiwwHX9EDnP84lWRfGVvDlf68GaNZLmZpiagi9/2a81L71UcvHFkieekCiKr751Xap2EcftIWIxP2zOsiSRCBw8KKitldx8s4frwuioysGDkMtJRkchHveJ4kTC9wQ+cMAPgOvunp/odhz41rcEjz7qW1IoCmzbprBjh+Rtb/O4+GLJ9u1w9Cg0NflE8PCwoLNTsn793H1OTo6TSCSrVicvFKSU/Pmf/zE9Pcv5wAf+6rTrGkGdaGOE6aPTBOMnpM9WziKUDNJ+XuucELba7lqGd4/iWA6a4X+W7bKNEILkSQSxoin41nNy7vzY9TACBj2XLcEq2owfmCA3kUfVVYywQcktk2hLVPddzpYZ2DpE48pGaruev7OhlCmhB+Z+xwhF4HkeTsUhEZiryjaCBvmJPFbBmrP+6ptWEW+OMbJ7FKtk0bKuifZN7cQaF/7e/su//AeefvpR/uAPXs+99z75gti2/C5ikQxexCIWcc5gygBRL8akOk6IELv1HZREkZgXJ3sgA0BlZYmojNHl9jzv/o6rOypeac7yEXOItJ5inbMJofk/gku9FUwWx5kOTDKtTxJxY1yavpzlUyuoC88NvxpWB9huPEOL20pC+j9+ETfKgHqUIbWfuJdgWplClwYFUaBMiYAM4Xscm6yzN6JjcFDby3+FP83vF/54weNRULimciObrcv46N99kCuvuY51LecxmR/nU9//e/Y+tpP1H9pETMbpspZQpsQ+fQ8b7PN5e/GPiMoYGLCp7jJS1hS2VyGkRogbtQjhFwKGalLxynNaJStumaZwPe9YfxlPju5g//RBIkaYF3VdzaraLg7ldhI3klViVFcMOu0ldI720Njcgd1o86U3fJZ7P/V9cq/J4tTYPGD+iJ36ViSSlfZarrdups09PRGZTqdOm+orhEBXTCpueXaBT7KWnRJlr4jlVchaKVzp0hlZOmeCJ4TAUE8fJqAI5bTefp502Z/ezlhpEF0xUFAZyPcxU5lkQ83F1eC5041/vomfIlSagm00Bf2n3NsmdnHnwf9mqjSNQKCrOpsa1/O6lS8nbtSgCAXbs7CjNkiQroeHR0gLE43UID2bx4wdTJdSSDwmSlMsiXexsX7tacd3OoztGyeeTTHZUsu0oxDQwNUCqOMzrMmPsiLZVV1322MJZp65nM1dklCNP+k4ehTuvFOhu9sjOs9pklLywQ++i9HRYe6994kXpHBbW7eKt617Aw8PPUF/doiOWCuXLb2Q65+5CKGBnL31RAqkLrHXzj8h8eZJhvfDCc/5kBexiEUsYhHPg69+9T+59tqbaG/vmvd1IQRLo2uwPIuZysQswenbOCzU/bMQ6s1m+qzpagcR+CSmrhjsj+zhvsAPqPUa6HFXUBB5dhhPk21I0zG9pDqWiBYjZU2RtqZpOkMyOGPPkLPTxPQTtZgQgqJTIGunCKkRdEWn5BSwvApSSlw8ym6RiOITSMqsktfxbKJ6gqnyGAE1eKKTziuhKxqudMlYM3jS9S3BpOPnHEiL0dIAICi5RYRb9gUQqklYDZCzM6c9Biklh3P7GCwcoejkSZX9LiPHc6qq4OPjDM+SvylripnK5GnJ4IKT41BmNylrCk96BNQg7eEe2sM9Z3VtV61ax969u854/YXw7nd/gGKxwN/+7UcwTZO3ve09v/Q+zxaKAj09AJING+DZZwVPPSWw7ePWCb6St6kJQiH/HNm25NAhwdiYX7dZFnzhC74yV9N8FW+57L+WTEI0Kjl8WFAqSWxbUCwKDEPS0gI9PZKaGrjwQklnp68wfuc7XZ54QnDffQrJJJx/vqSx8XiXHQSDkrGxeRKFZ3HgADz+uEJTk6zWkZYl2bPHP7brrvP9k++91/cYVlXfMuP2271qAN1x7N69ndWr15/7E/8c/OAHd7J16xPceeeP5+1aOBlCEXRt7iQzkmW6f4ZgPIhTdrCKFp2bO4g2zS2em1Y3MnZgnImDE2iGhpTgOi4ta5poWN5QXa+2u4ZQTZDsaI54i98ZZ5cdKrkKXZs7McIGq29aSccF7VRyFYyQTu/P+wBRJYLBD5bLTxVIDabPiAxOtCc49lQ/kfpI9XPoWA6qoWKGDcq5Mkb4xDkpZ/2/QzVzvxM1Q6Xzog46L1o4z+e5CAaD/Md//Dc33ngJ/+f/vJ9//Mf/OONtF7EwFsngRSxiEecMKipb7Kv4hvZFDmkHyIsscZkko6QxRk3MRAAzHOAp5zG2WFc97/4UodAUaudQdjeWW8FQTTzpkbd9f9mQeuLHJaRG6LZ7iOTD3DRxC412E5qjEtUStIa65+x3SpkkL7K0ybk/QgnPH+uWypVYwsKUJhklTVZkqFBmWp0iSJCSUqTFaafOa+CAtodHjJ8jLcmQOoAhTVY6a4jLxJx9h2UEZ9Lho+/5MyYmRolG40TXRqn/UQOFa/Ns5xmCMsgyZxU9zjJSyjRBeeL4NEWnPtB8yjkyVJPmYAeHc759gKkEsLwKZbdIR2QZS+NLWZpYiuu5COGHiI0WBwA5GzBy0vUTKtKDZfZKNEXnQ+/9a37wzTv5u3/6c2o/Wc9BbS+1Xj0KKo+aP+OYdpg/LPwpDd7CNgRDQwM0Np467pPRHGpnf3q66rEbUIOE9Ai6pxNSwgT1CC2hDlpCnQtOAspuqeorHVDnMR5bADOVSSbKI0T1RFV1EtIipKxJhovHWB73W5FKs5MzgULCrMVQzsyLEGCmnOau3rvJW3lWJJeiCIW8VeCJka20R9u4tuNymoJtjBYHGFhmkaiPYB4x0Np1wkYMY1xFbWrn6S0HsKWNguDy1ku4oetq4uYv7quXHc8RNhU2NQuOZSWTRYFpKNQnVdaoabTZ28NxfGVHIiE5mc8NBmHrVsEnP6lw3XW+L9zJr3/1q/+P733vm/z7v3+VpUtX8EJhVc1yVtUsx/Vcf2LsCdxxgf6QgFF8QjcI5Ws8nDWnbq8rOhvqV/PjYz+nLliLpvizqrHiBHEjRk+i+9SNfk2ouGUqbpmAGjxjP8xFLGIRi/htwo4dW9m1axtf/vL3TrteQAuxseYSUpUpKl4RUwmSNOtmlaoZNKGd0lk2H1rDXaSsSaYrE7Pdar7nbWukm69HvkxUxqn3GsCFmBulyWphKDDAqDlMW2VuHSkXIL3mgytdJB6KONFC7nshV1BQffWvoqEKzSdqATkbVHscnnQBCGph4kYtWStF2prCVIM4no0rHdrDSxkrDvpksOfh4s5aYjgIoVJ086hoBLSgLzDQDJqCbeTtHFJ62J61oCp3sjxKf/4QhmISUiPk1QwKKilthqPRo2SMFDE3Tld+iU/Yz/6+Hh/3fHA8Z9aGY5KIFps9/gKHsnswFJOm0Kl2Wwth1aq1/OAH3z7tOp70SFWm/BpPCGoM38f4uffNBz7wf7CsCn/5l+/HMEze9KZ3nPE4zhXKZZASJiZ8f+CuLsnkJAQCgkzGJ3BzOWhokGSzAkXx11UU3zbCcWD3bsGPfiS48kpJY6Mf0DY5eUJZHAz6xGxDg6S1VbJmjSSTEQwNwciI4O67Yc8ewZvf7NHT45PEzc0eX/+6Qn39iXtTSiiVBM3NpwYnH8fRo741xMmCAl0Hx5E88IBg6VLJ+edL1q3zlceqCi0t/vE8Fzt2PMvtt7/6XJ7uU1Aqlfibv/kIN9xwK5dddvUZbdO4ooGNL1vPsaf7yY3nCMQD9FyxhM4LO065x4yQwcaXrWdk9ygTvZMIRdCwooHWdc1VL1+AQDTAqhetYO+9B5g+6lvVKaqgaU0TkfoIz35zO5mRDJqp0bq+hbqejqpnr5SS/FSBwlQB13F9crpw+i7X42jf2MrkwUlmjs0QqgnhOR7FdImG5fXULanl4E96yYxmCcQC2EWLcq5C9yWdVVuJXxbLl6/mYx/7BB/4wDu54oprufXWV5yT/f4uY5EMXsQiFnFOcXnlWtIixZ2hr5BXcuieTr3XSGGyQDaZISCDZJTUGe+vPbyEgpNlojQy66km6DKWskRZwYg2XLWa8PAoBy025S9hrbeRYXOI0bpxwoEcHjqNXhNxmaDOayDqxQgSoiDyROSJCiQvckS8CFdY13NE7yMnckwxiYqGFJKADBLxYhzU9hORMRrdZqJenLsDd/GM8RhF4Qd6NLhNvLr0ZtY75885lk984j+r/79P28VHo3/GUa2POtdvZ88rOQ7oe1jiLCfgBc54UtEV9Ym20dIARSePpuh0R1bSHT1BwB0vwAEiehxdMah4paq/sCMdcl6W1kBn1TevoaGJD3/4b/jLv3wfF96+hYuvvAx91puv1qvjgLaXp/XHuSp/LVk7jSpUkkb9HJLq0KED3Hrry087/pbgiYRq38NYEDeSLI+vp9ZsRCDmFEyWW8byKphqEIHgSO4AY6XB6oSlKdjOkujKOUrphZC3M3jSmzPREUJgKCYpaxIpJf35QwwU+ii7JQSCkBZhSXQlqlCxZy065rOfOI6DM4eYLE6xPNlTXSdihAnrYZ4e28b1HVeyKn4eNWY9k4ExJl6TZul93cTH4igzKm69RLk5yG2bb+Ea5yoEgqhx+gC8M4EZMfFcSVSXrK8T1ftt+qhLNHHiGjqOrybRZ0+nlHDggODoUchkfK+3gQHYsEHw1rd6hMOwa9c2/vIv38eb3/wHvPSlL2xxfhzVe1yF8iskzgYXrW9WubBU4iwDFugevLr9co5k+ulNH8ZQdBzpEFQDvKTnJhp+AywiHM/maO4go6UBHM9GV0xaQh10RVdUlWyLWMQiFvG/Ad/4xhdpbe3gmmtufN51FaFQG/AVc1JKRksDDOT7KDqFWaVwEz3RVdVMhflgqgHW1VzERGmEtDWNpujUmY2IgEZezRN1ImgpgZoH4WosEd3Y0Qo5stV9FJ08phI4qxC5qBavBiMf73qyXQsXl4AaxPUcpOK3gatCxZUOmtCx3DK2GsSTnm8xoddSG2jEUEzW1VzEcOEoGWuGiB6jJdRJS6gTUw0wWhqc7SLTcKWNItTZAF4xex6CKELBcstMlsewvAoeLlk7RUOwla7IMsyTHrZ70qM/30vOzvgqXylBQiqc5qcNPyEbyKB4Alfx6Ev0EckmWFLoQRUqUT2x4HlJWZNk7Bniek3VAiyixElb0wwXj9EYbDtjdfCqVWv593//Z7LZzLzBYq506U3vZKQ0gCtdQKIrJt2RFXRGls15HyEEf/7nf4dlWXzoQ+9GURTe8Ia3ndE4fllMTsIDD/i+ulL6xO3YGFx1leTAAd9HOJUSeJ5P5q5YIXnmGUE2K9A0ME0/bM40/W23bhXcdJPk9tsl3/62oFLx7SBcFzo64I47PK6/XmKa8LnPKfT3C3p6JIYhsW04fFjwne8ovPvdHkLAxo2SBx/0VcitrRJFgZERqkrihaAo/m1zHOWyTzQfOQIDA1AsqqxbJ3nNazw6T9OIODExxujoEBs2bDqHZ/1UfO5z/8Lk5Bh/8Rd/f1bbNSyvp35ZHU7ZQdXVWZuH+WGEDLo2d9K1+fSdl02rm4g2xZjqm8KuOETrI6DA7h/soZK3CMaDWHmLAw8cJD9VoHF5A5N9U0wcmiA7mgPp20rYFYeR3SN0be4glDx9V0OiLcGGl6/n6GNHSY9kUFSFJVu66dnSjRkx0UyN/mcGKGcr6EGd5Re0s2TLCUGFlJLMcIbUYBopoaYjQbw1flZq/9e97g4efvgn/Nmf/SGXXnoltbX1z7/RIhbEIhm8iEUs4pxCR+dl5ddS5zXw7+F/psVtpclr5SepHxFIBMgqac6vXHTG+9MUnTWJC2gNTVNwcn6isllPzK7nm/qXOKDtQcfAwqLd7eS18g6OtRzhoeCDZESKlDLNhDJOUIZZ5axhg72Jl5RfyQp7Ddv1p2l3OwnJCCllmoyS4drSzWxwNvGa0lv4bOhfmFGmSMgkcS+JIQxiMs6MmGZEGabRbWZI7WdSnWC5s5J22YWHR796hG+GvkRbrnNBL91d+jY0NOIyQVEUCMuIb7GhjDOgHuGqyvVV4vW5sLE5rB0kL/I0ek20uZ30xFbTHu6h4pUwlMCcdOfnIqLFaA51+nYITLMnvpMDof0IRXCpuJpat4U2z1e73HHHu/jaz/+LXe94lgueuBi9xh+TgkJQBtnqPEpkSsf2LJglSpfH11EfaKZYLDA01M+yZatOe41VRWNl4jyaw53k7SyqUKkx6+dMOuBkQmwQx7MxVROkIO9kCGoRIlqMilfmWP4gQFXV+3zvDRIpJRKJJ33FjCtddMVgqjLG4dw+NKGTNOqQSGYqEzw2fj9hLYqmaChCpT7QzKrEefOqZyzPxlcazS3+DEWnZJeQSFRFoyXURUuoC2qB86DQD8J1cTtAhnzbgphx7oIxGpfXM/DMANmxHNGGCAjITxYwgjqNKxur65kmLFsGTz4pqKuTzMzAsWN+C2BNjWDtWg/DgB074KmnBN3dh3jjG29j9er1/NVf/eM5G+9ZQQFnBTgrzuyBSkOojndueCvbxndxNDtAVA+zvn4NK5JLX+CBnhkO5/bTn+8loIYIaREqXpkjs90APbGFQzgXsYhFLOK3CVJKHnjgh9x226tQ1bN70DVZHuVAegfgK2Vd6TJcPEbFLbGx9tLTPjgzFJO2cDdt4RPEhY1NjVuHNZNHT/te/lIDzdFYkurGcy0yYgZXumhCoye6+pT8htMhoIVoD/dwJLefjDWDJnTKbgEFn8TOOxnKbhFFqFS8MkE1RHdkJZZXoejkUYSgMdBKT2xNtVupxqynxqzHk96cB+k90dUcy/UyWDiCLgw0Rfc9ebEwlAACBdezcZBYbpmikydmJAlrMSQex3K9FJ0c62su9v2GpcvBzG76833VzixFqEgheSbxJGkzTWepE0c62J5FNpznZ9Efk8i9iu7gstPad1XcMlJ6p2RBGIpJ0SkgkafNGzkZjY0tAExPT85LBo+XhhgqHiOsRTDUAFJKSm6Bo/mDJM26U8h9IQQf/eg/4XkeH/zguxgbG+H97/+LsyKzzhbZLHz+8woHD/o1mKL4YW2FAqxaBevWSbq7YedOOHIEEgmYnhakUv4De8PwFb/lMpTLvt9uoSDIZOCSSyQdHS779vmK4kQCNm2SVQuG0VHo7RW0tPhkMvjCgJYWSV+fYHgY2tqgthbe/GaPH/xA4dgxn7Du6JDccotLV9fCx7Z8uSQc9onomhrYv98XF2garFkDyaTkyScFgYDgTW9auJ576qlHATjvvAvPyTmfD2NjI3z60/+XO+54N0uWLDvr7YUQ6MEzD7YEkJ5k6vAUU0emcV2PmvYkDcvr0Uz/sxGuCRGetVqQUrL9zh1UchVqumqq96RRMBjbN07LmiYSrXEO/bwPoSmomoqqqyQ7khTTJQa2DrLy+rldfJWCRWoghfQkidY4wUSQ2q4aajqTWHkLRVPmHFPnhR20bmilkitjhIw5r0lPcujBPo4+1Y9d9L3I9aBO1+YOll+9rKpcPpPz+Hd/969cdtka/u7v/j/++Z8/d1bndBFzsUgGL2IRZ4Ljjy1fwB/7/224xLqCPfoOtupPAoJ8MYcdtan3GrnMOrPWmuMQQpA06+YUjxfal1Cfb2C7/gzjyigGBmvsDShS4e7gXQAkZS3HlCPo6BSVPDNimseNh8iINK8tvZWsSLNP341E0uS1cEP5Vq6r3IxAcLF1Of3KEUqiyFJnJYY0eNJ8lLSSQkFQEgVmlCkm1XEa3eaq97CKSpfbQ6+2n/367gXtMHIiS9SLEZYR+tQDzChTKFIlL3K0uO1cXXnRvNuNKEN8PfQFDmu92FiEZZSN1oVcZl2FoRo0u21oaBxRDzGjTBOXcZY4y1E5MQkSQrAstgZV1/hs+F84FDhIrawnqsV5WnucEXeYdxXeT6PXjKIovP1f/4gPX/Eefvq+H/HiL76sWmBk3DTxYhhV+HYGEj9E5GBmJxEtRl9fL8ApKc5TygSPGw+xV9tFgADnW5vZbF1GwqglYSwcNteX3ctAoY+AGiKohSg6ecZLw8T1GkKzqp+Q4k/ExkqDdESWPq9lRI3ZgKkEGS0O4ngWrnQRQsFQDVbGNzJeGkZKj/BxElZK38/YLRI3akiadVhehdHSIEEtwrLYqT4ErZFmAlqQTCVbtXWQUjJTSXFV+2XzK4oNcM++1jwrJNoSrLphJb0/7yM14Kv1A/Egy67soabzhM+zEHDNNR6HDyscOCDI5SSZDEQi0Nkpic06VZgmPProMB/60E3EYnG+8pXvY5onWRk4YGwV6M8KRNYnaq1LJN7pXUR+ZUiYca7puPzXPYxTUHIKjBUHCarhqrrtuOp9pDhAe3jJ83pnL2IRi1jEbwP27dvF2NjIGamCT4aUkuHiUTzpkTD9OkIHNKGTsqZIVSapCyxsaTUfdHS2FK7kyfxPyOpZDM3EFjZFvUBrsY1Gp4WaQANBNUR9oIWa54aWzgPfr9fGUAIoQqErspygGmK0NEDJKdIRXsqMNYknXerMJgpOlrydIaSGWZu8gGWxdXh4s2SwQkiNzCEijz/Yfm5dIYRgfc1mpivjVNwyruchpW+tpAmNsB4jadZRdkvMlCcQ0qYttARXOhTsIhWvxOHsfkwlwNL4WmbKEwwXjhDRY7jSIaCGcKRNRsszFZoiWoki8YN8w1qUmJtkMjBJpC7BCnXjgp1UzG4jZsP0tJMIYdurkDTrz5gIBojFEgDkctl5X58sjczJnxBCEFTDpKxJZiqT8yq9hRB87GOfoLGxiY9//C8YHR3m7//+39D1syP6zhTbtwsOHfKJ0+NvsXw5PPaYr55dudKvx1atkgwNCfr7YXTU9/oF31N4bEzQ0ODXbLW1kmDwhDVDayu0tp4gWsfG4P77BXv3+orhwUFY+5x4CsMA2/a7xo5j6VL44z/2GBnx1cctLSc6yhbC0qVw3XUeDzygMDDgE8+6LunqgrY239e4sVGyc6fC9LRL7QJThO9+9xusX38+bW2/WKDymeBTn/p7AoEgf/zHH37B3uNkSE9y8Ce9HH2yH9d2EQL6nxqgaVUDS6/sAQShZBA94J9kz/FID2cIxoNzvhPMsEluIk85X6F5bTNj+8fRgzqKqhCuCRFKhshP5Zk4OMGK65ZXtx3ZM0rvTw9RmCmClATiAZZctoSui3x7CzM6v1WZZqho89hCTB2e4vBjRzHDRjUorpQpc+TxYyQ7kjQsO3OFb21tPR/60Mf48Iffw+tedwebNm0+420XMReLZPAiFnEaKMVxjOGfok9uQ6oadsMlWK3XIM+hOu9/K3R03lR8B21mB0/rj+OWHGrNOt5WeM8ZhcedCbrcHqaUSXboWxlXR3nGeAIPjxkxxUX2Fp42HsPDo8arIyvSZJUMF1lb2Kvv5D/Fpzim9zEjpjEwqHXrub7yYkxO/Lh1ekuIyThhGcHAYJ29kT3aDkbVYYIiREEUaHZbaHbn+pcpKICkLEoshC63hyeMR1jmrCTmxRlVhymJEkE3yOuLv0ez13rKNg4O3wh+kQPaHjqdHgIEGFCO8MXwZ/if4J20uu3UeHWUKZNWUhRFnrySI+EluKnyUq6oXFfdryJUpuMpsuEcFzmXV4/bczwOaHt5yniUl5T99OXLG67hok9t4bE3PUj3TbtY9ep1jCujeLZDT3E5wdmwFIEgpidJWVNMVybo6/OViyeTwdPKJJ8LfZLDei9RL4aDw77Qbg6rvbyp9A60k36WJJKD2l72aDvJyjRlNctyYyUh4RO+ATWIAMpeCU+6Vd89UwmQd7JU3NLzksFhLUpICzNcPFadQElPzga62b7a5aTJSNktYcmKr6qZtVUwFJOAEmS8NER3ZHmVqPOkx0R5hII3zvnNneyfPkoml0FTdNLlDM2RRq5sveS043uh0baxlfqeOlKDKaSEZHuCQOxUYnHJEj/J+tFHBfffLwiHYcMGv1g/Dsua5M47byQYlHzzm/dSV3ci7AIJge8JAj8RoIA0QDukoO+UFN7u4bX9Ko72VJScMoO5YRQh6Ii2Y6gvzGTul0HZLWF7FlEjMWe5qQQoOnnKbnmRDF7EIhbxvwL3PfA/hMIhEitjDOT7aAi0zBvS+lxIJHk7d4qXuqZoSOmHrv0iWFtaz6h1mNHQGEVRAA9a7TY6nSUYisHy+Lo5wbYLwfFs+vN9jM1a/YS0KO3hJTQG22gKtc/xwU1VJunN7CHvZFCFRnOog87IclpDXb5lBCpR/YTKdUQZYpv+FEfsQ6hFSVe+mw5lCW3hbpLPIagDaoiyW0JKBxC4ngeaIGHWEtTCBNQQU5UxImqMkptnqjRG0c1XA+e2zzzOTGVy9jdHkDTqZpXERVRFwfFsBBA3ErSovj2FoZhYWLiapDHYhuacnn5ImvUkjFpGi36wry6M2TBXhdZQ91mpcGOzT6uz2fmD8FzpztbsJ3B8/6fzNRZC8J73/BnNza28733vYHx8lM997uuEw7+8hddzMTTke+WeTKy2tEjiccHhw36eg6LA1JSgo0MyNSUIBiWqComEJJ32t1m6FGpqJBMTgiuv9IjMDnVqyg+jGxujqjqenvb3Wyr5YcH9/YLubqiv98PkJiagrs4PqzsZqgrtZ27pjBBw662SFStcHnpIkMkoLFnik9PHGwNCIV8dnc8zLxk8MzPNz35231lbN5wNZmam+da3vsy73/0B4vHEC/Y+c96zf4ZjT/cTiAUIxv0ar5Qpsf+BXgaeHSIYDxCMB+nc3EHnBR0oqoIeMijOzP2u8xzPVyUHdKQnq+re4zYRCJ94FtoJ0VB2LMu+ew/gVByS7QmQMDOQYsddO/EcjyWXdJ2xkvc4Jg9P49ruHCuKUCJIKVVionfyrMhggDe84W18/etf4MMffg/33vvEWXeSLMLHIhm8iEUsAFGeIrT7U2jpXqSZBOmi9f43WqaXwtr3gLY4+X4+RGSUl5RfyU3ll3K42EvQCNLjLj9n+z+iHuIboS9SpkyH04VEss14mmFlgBXOGvIihyn9iYFAwcP1CVT1KHv0nZjSgNnlPwp8H1OavL/wF1XVwXr7fJY7q9mn7aLOa0BFpc5rpM3t5NbyK9jobOLnxv08ZD5Ai9dW3S4vcugYtLgLM1wXWJfwtP4Yvdp+ar06mt1WZpQpVjhbuMq6ft5tDmsHOaz30ukuIUiQoihwROujLMrkRRYk3B24i5ySJenWgpAoKAyoR+nXjrJD38qbi7/PSsd/xD+qDuHizSHAFRTCMkyfdrC6rN3t4k9v+j+MvnqYn77vXgobCnSu7uaSmctYUl7KyW4WQohqsvXBg/tobGye05r3mPEQh/Velturq8RvXuR42nyMi+xLWetsxJMeaWua+4P38FDoJ9iKjePaTNdNctg6wq3TtxPxIqhCQ1N0bK+C4zkY6vEU8HJVpQwQ05PP8Ry22Tt9gP7sIBKXspymMdQ+610mMdQAllthpHiMGrOB6co4Uvq+fZ508TwPIZQ5lhCqouF6jt8uit9+eVzJjIT2WBOGLpgu5kgVHC5o3MhlrZtpj55K+v+qYUZNmlY/v2Kqs9NXlVxwgeTTn1aJRmU1xCOXy/LII7cAM3zrWz+ntXXubEAdAPNRBbdWIo8LbTyJdkBgPiQovf7MQ3fOFZ4Z28EPj97PeGESIQStkWZuX3ozq2tfuLC7XwSmGkCfvc/Vk0IzLc9/KGH+GoPkHM9hupxCV3RqAolf2zh+V/GZz3yGf/zHf2RsbIwNGzbw6U9/mosuWtiG6dvf/jZ/8Rd/wbFjx1i2bBn/8A//wM0331x9/S1veQtf/vKX52xzww03cN99971gx7CIRRzHTGWCe+6/i5UXrmbM6mfUOsZQ4Shrkpue14vX9/MPk7am5yx3Z0lPQ/nFanZTCdAs22gqtjHhjOC6DrqnM+NNEND8kLbng5SS3sxuhopHMJUAmmKQtVPsTT9L3skSN2oIqiHCWmy2E66eC+qvIGvN4ElJVI/PGxj6T//013ziE38zZ1lgWYCrH72Oq8evZWZmknU1F1JjNmB7Fn3ZvYS1KBEtRt7JYrsWZVFEILDcCp70sLwyASWErhjMVCapeH49pQsDT7hITzJZHkMVKkEtjDL736JToOgUMKVBa6WDQl2RsBtFQUHiByw3ua10Os8vBnE8Gyk9Kl6JnJ1CSklIi7C25iIaAi1nfvE4oQzOZtPzvl5jNjBZGZsjKLA9C0WoxIzkvNucjFe84g3U1zfxtre9ipe//Do+//lvn1L//LKIRsF1xWwd6i8zDN/bt7bWVwtLCVdf7XHwoGBqShII+ERrJAJ9fYK+Pt9XOBwWXHedx403+jXXsWPwhS8oDA35ityBAcH0NFx+OTQ1wd69AiEglRLYtqS/XxAM+srkV73KmxMa/ItCUXx1c3Oz5MgRievCybxeKuWT2gupgv/nf76N53kvaD7FV77yOUDy5jf//gv2HpV8hcHtw0wcnADAtV0qBYtEawLwCduZ/hTlTBnNVKlbUkcpXWT/fQfRTZ3WDS20bWhh7737KWfLmFETz/XIDGeINkSo7a6hkrfQgzoje0axSw6e46IHdIQi6L60qzpXmjg0RTlbpra7BqfiMHFoklKqRDlb5pn/3kp6KM3am1cvqA6eD07FmfdBjlAEbsU56/Olqiof//inueWWy/iv//g3Xv/q3yNcGz5rkvp3HYtk8CIWsQCM0UfR0r04ydVwXBnolNAnt6FP78BuvPjXO8DfIujoRIIR8vn8Od3vNv1p0mKGlc7aKhG70llLv3mUo9ohQjLCjDJJUIYoixLtbhdZMoypo+hSo0E2YUgDF5cJZZT7gv/Dq0tvot3rAiAkw9xR+EN+Evgh2/Vn8PC40rqO6yo30ekuAeBK6zr267s5oO2l1qsjL3JklQyXV65hmbOwV25S1vC24nv4ufljdurPokiFGyu3cU3lBmLyVF8zgIIoYFEhIH2165gyQlbJUuvVUxR59hg7/BA7KcgpGVzhEvcSNHrNWKLCkDLADwLfZml+JRoaQRlCCnmK/5olLGJeYs57b7a3cOff/JhX7b2B/bft4i/v+Xu0IAx4fVWiFPwiXgiFsB7liSce4YIL5n5O9mu7iHixOQrgiIxiYzOoHqOr1M2BzE76OMB3I9/GLJq0iy7CWgzTMjgc6GVXZDuXZi/3iTAlSMHJYnkVVKFSdotMlkfRFZN96e1oQqPWbGBFYgMBNUTJKfPVfXeybWInjudSccuUvTyXt13IJS0bq8ehCpWyU6TGrGe6MkbKmiSoRnA8BxeboBJGRSdVzhDWQ5ScArVmQ9W3L2enGS4cnQ2BgXQ5Q0RNEE/EWN659oz8jH9TsWwZXHGFx89+JpiYEHhenqeeuo1KpY877/zJvF5qar9AFECe/HxEAa9Gou8VlFyJqIDxmEDfKhA22Gsl1uUS7wXIhuhLH+WbB79LxbFoj7biSY+h3DD/vf/bvPf836cx9JsTSBHSItQHWhkqHgZ8JXrFLVN2i3RFVpzirf2rwq7Jvfz42M8ZKfjEwJraFdyy5EXU/wYE7v0u4Fvf+hbve9/7+OxnP8vmzZv55Cc/yQ033MDBgwdpaGg4Zf3HH3+c1772tXz84x/nlltu4etf/zovfelL2bZtG2tP6gG+8cYb+eIXv1j9e47VyyIWcQbwpMtMZZK8nUVTNGrMhuf10vWky/bBJ+nddYB3/cUfkzTrkVKStqY4nNvPeTWXnlYRKoSgJdRJ2pomb2d9z2DPIe9kSRq11JinfibOBFE9QdKspzezCyldwmoET/GQeCDhWL6X9cnNpx1b3skwXh4mrEWr39eqUBkuHGWmPEHMqMFQTRoDrSyLr0VXDD+Q93lsJ1xcalbVct49F2HkNFShoGoaE+YEu+p2UT/cwGD+MEmjnoyVouDkiBtJFKESN2qQSMpugZydJaon8KRLc6gdTRjsSz9LySniSQ9fNOGhCg1TDSKlixQKZbdIquJfI03R0JUoFa/Mxpnz2BXZzYHgXgwMbCySspaXlF9JiOdnD4/mDjBTmaIl2IUQYHs2RSdPxpo+K79ggGj0uDJ4fpuIplA7k+VRUtYkmmIgpYcnXZqC7dSe4T1z5ZXX8b3v/Yy3vvUV3HDDZv7jP75Ga+vV7N0rKJV8peyGDfIXJk7Xr5c8+KBv19Da6pO8Y2MQj8M73uHR0+PbMpgm/MmfCHbuFFV/31DIt5cAuP56jxe/WM6Sy/42d9+tMDAgqK3163hF8fff2+sTsv39kEz6yxKJE97D69dLtmw5tw/x43G47DLJ3XcLHMcnwbNZKJXghhtkVcl8MhzH4fOf/zeuuebGud1oCyCXg8ceE2zb5vsab9zoH0cisfA25XKZL37xP3jlK9/4goWVWUWLnd/dxXjvJEbIv3gzAynskkVddy2qrlLKlCimShghHd3U0QMaelOM9FCaga2DtKxrpn1TO/mpAiN7RilMF0AIYk1R1ty8qurha4R1MtszSEBRBaV0iWAiSCB2opa0CpXqg4epw9MUJgtV4ldRFUZ3j6IHdNbftva5h7Igku0JBp4dxLVdVN1n+13bF9gkO+Y+eLGKFpN9PiEdiAaoX1ZXPS8no6t2GdddeCv/+M8fpXaijfaeDpZdtXSO1d0iTo9FMngRi1gAWmofUg+fIIIBtCBIFzXXv0gGnyWi0ThjYyPndJ+TyjimDM4pDJNeDQ1uEzPKNBEvSkEUyKlZEm4Sicd24xkcfO9iQ/o/LCoqNbKOcWWMA/pe2itd1f3VyjpeXXozLym9Cik8gjI05/063SW8o/Bevhv4Bj83f0xBKVDn1ZNWZjig7WGts3HB8Td6zbym9BZeVnodArFgYNxxNLhNRGSMtJghKWspzSo6yqKEg4MrXAxMHGxsYRP1YpRFCUv46dS1Xh2Daj/D6gCd7hLW2Buo8WoZVI/R6nagoDClTKBIhfPtU9VlLeE2vvPVn3DrrVfwnte9ha/e+QNCWoSUNUlADeNJF8sr0xBoQauYbN/+NH/7t5+asw9TBnGY+wTYt1uQKFJhf3o7KWuamZoUjubSWEqSltOoQiOuJpmxp9gX2M3FmS1U3BKGGiBp+sFueSdLwc4hhEKN2UBADWJ7FuPlYURGYV3yIp4afZZnxrbTEW0jpAepeGX2Te/hyZEddMfbaI74hd6xzBCHUv3sGBuhPdJAYzSOxMZQTZoC7Tw1upOD049juQ5B3WB9/QpesfQi9k4L+rOClGUjhYEuxjmaG6DslEFA1AhSdhyWxdaevtXRA/1ZgfG0QJkBp1tibZG43Qtv8quCosDLXy5ZsQKeeGKMz3/+NorFg3z1qz/kwgs3zL+RDuAnjZ88jxMOeBHAgtCXFfStAhmWoELgHgVtv6T4Bx7ewlbSvxC2je8kU8myMnkiLbwn3s3+VC87J/fyos6rzu0b/pJYFve9qCfLw2SdErpi0BFZxpLoyufZ8oVBb+owX973LYp2iYZQHbbr8OjwU0yXU/zhxt8juNg584LjE5/4BG9/+9t561vfCsBnP/tZfvjDH/KFL3yBD33oQ6es/6lPfYobb7yRD3zgAwB87GMf44EHHuDf/u3f+OxnP1tdzzRNmp7b+3saVCoVKpVK9e+FSJdF/G7A9iz2pbcxWR6dJRIhqIZYEV9PQ3DhTpicneHJRx9FepILLvP9H4UQhLUoGWuGopMnrJ/ejqEp2I7lVhgsHiZnpVGESl2gieWxdXO8Z88GQgjaw0s4nNuH9CSV2QfPMaOWiBYjVZmk6OZPaxVRdPLYnkVEmzXYl/jevV4FTejE9CQuDoOFI2iKfsYPizOKXye1JdtIB2YIznaO1Fp1jGtj5JQcbrGf5lDHbN160g+wYDZgTiWsRdlQe3HVVsuTHqnKBFkrhSNtQKArBiEtjEQi5Wz4rpSMlQdRUDEVExePmF5DnW0QH0ggmk2ygSy1Xh0b7QvoOIMCxnLLTJRHCGlhDNWv0XXFQFcMMtYMWTt12myJ50LXdYLB0ILK4IAaZF3NhYwWB5kqj6EIhcZgK03B9qpS+Eywdu1G7rvvSd71rjfymtfcxNq1f01Ly5+hqr5t3OrVgjvu8E5LOi6Eri54xSs87r5b0Nfnq4CTSXjxiz1WrjwRZ7Nvn+8N7Hk+CWyaPpm6datg3To/GO6BBwS7d/vrtLXBI4/4QXNHj/o7yWZB0/wwub17YXzc/7tSgcZGuPRSycSEv94LEaNz002+qvnRRwW5nKCmRnLFFf6/+fDNb36Jw4d7+fd//+rz7rtY9FXQO3cKIhFfZf2d7ygcOCB5xzu8qofyc/G9732Tyclx3v72P/plDu20GD8wwUTfFMn2JJrh33dCFQxuHSQ1mKJuSR1OxVfyCkUQrj3xZMGImBTTRRzLQQ/orL1lNR2b2shN5NECGrVdNVVf4fED40wdniaYCCIUgRExiNRFsIsWI7tGaNvQglAEkfoIUkIlV6GYKmKEDRRNwXU8gokgCDj86BGSnQla17agaAt7gB9H48oGGpbWMdE7iR4ycB2PcrZE47J6mlafCK3OTeTY9f09pIfS1W+seEuc9S9dS6wpVl0vP1Vg1/d3c/OKV/HYrp9x/+7vcZt4A6V0iQtev4lI3am+xYs4Fb91ZPBiS9wiflWQWgTx3PYvKUFK5K+xLfe3FdFobEHPrrNFqpyh4lZoMlp5xnhijlLAw6PJa+ZCawsqCtu9Z9ij7ySlpsioaYIyjIqKLSw4qbawsVFRCHnzP7oPEpyz/nHkRY7H9Ye5P3APU8oktV4tCbeGw9ohvhT+LH+Q/xNqvDq2GU8xqPYT9xJstC9gyUnpYAZ+wZsVGXIiS9KrnVc90eq1c6F1CT8zf0zJK+LhUhA5YjKBSQAkVESZsiihoCBQkEBB5Il7CUJEyJLGweWAtoesyHJp5UqeMh7lkHYAgKiMcXPldjbaF8x7Hhobm/na1/6H2267ivf+we/x2S99lTF7kLQ1ja4YtIeX0B7u4ec/uR/XdbniimvnbH++fRF79R1+gJ70Q+dGlWHiMklTsZlJe4i4nkQVKkL4QVnS88jbGRqDbYzLMdyKQ8aawVBMuqMr6ImuxvEscnaGPalnUBW96mNsqCZhYkxXJig4WbZN7MJUTUK6P+kxFJOmcCMHU30czQzSFK5j1+R+fnzsMVSpUxcssXfqAPXBWt6w6hWsqFnKt3vvZs/kEJoQhLUA2UqJnxzdxc7xSxA0IYRJyY1RtFdQE0yzNCmpDfpPqctugUOpIxyrHaQ73jHvOQYw7xME7hYICTII5jEFfbekeIeH8xvgYqCqEIkc5EtfuhXbLvGDH/yUdevOW3B9e7nEqxWog+B24Fd3JRBZgX29h35AoO8At0sij4sTGiVar0B/SlC5+dwqUCZK0wS1wBxCXgiBKlTS5fQ5fa9zAV0xWJ08j6KzjIpbJqAGq2Fyvw48NvI0WSvHisTS6jmMGmEOpQ6zd/oAFzRu/LWN7XcBlmXx7LPP8uEPnwiyURSF6667jieeeGLebZ544gne9773zVl2ww038P3vf3/OsgcffJCGhgaSySTXXHMNf/M3f0PtQv25wMc//nE++tGP/uIHs4j/VRjMH2G8NERUT6IrvmVS1k7Rm91N3KhZsJNBIjm6/zANLY3UNp7UXSCEX3fjPe97CyHojC6jOdROwcmhCo2onjgrj9n5xwZBNYypm2izxKSuGLjSwXYquN7pW5x1xUARCq500ISO5ZUpOQU0oaGrBqqiogsdT7qMl4boiiyf1xZiPhQO57hz9dcQBtRd0MCGj5xPsCFAU6aefCmNTZldM09TG2jEVAMUHF8FDL59RcnJ0xBoxVQCOJ7NTGUC27NoDnUwU5kibU3hSoegGkERCiW3gIJK3soQUIN4nocUEg+dhFFHxS2Rtqaxyxb1Tpy1kXUsja72yeMzgO9N7J5i66EJFU+6Z2TL8VxomobnLXz/BNQQ3dEVdEdPX1zlLdgxKRjIQViDtXWSJfETpGhtbR2f/OQ9vOlNH2P37r8gm32Im276MprWwJ49goceEtx22y9Wy1x6qWTVKklfn69o7ek51TZh+3Y/z2H1ahgYEBQK/jV2HN/i6957FXp7BfX1vh/vww/7IXHRqK8WltKv7TIZPxgukxE4jk+aahrMzAh27fJ9g3+RrLypKdixQ1As+mT2mjWSmue4v2gaXH+9T/7m87462DDAceDIEV/R3Nrqk93FYoF//uePcfvtrzlt/XkcO3cKdu/2z93xhhfbluzb53smX3XVqddGSsl//ucnuf76F7N06QtXfKcG0yiKqBLBANH6CKFkiOxYDkVVqBQs7LJDvDlGtOEEc20VLKKNUTTD/4xJV1LOVciN5wD/oU/90joyo1m2fmM76aEMgaiJ9CSe4xFriBKqCZObyGEVLcyISePKRmo6hxndN4ZVsjGEwCpaqLo6u56NW3HY9s0djG+YYN2tazAjp//OMkIGG162nv6tg/T+tJfsWA49qFOYKdL3yGGWXbEULaBx4IFeUoMpajpqUDQFz/GYGUhx4IFeLnz9pqoNxNi+MfKTeTpXdvHiS17OPY9/m5df80by43nG9o2x9Ipzk0/0vx2/VWTwYkvcIn6VsBsuxJh4ElGeRpr+r5VSGEKacZza394W718XotEY+Xzul9rHTDnNPUd+zO7J/dieTahGR9mk0xveT7PXgodkTB2m3e3iVeU3UOvV87Xg5ykoeZJeDTEZR0iFuwN3MaVMYEiToAxSERXySo52t4tl7sLWDs+Fh8c3gl/kR4HvkxYp6twGskqaZ40naXe7mGaSu827yKs5jqqHq+1yj5g/41WlN3KJdQUARYrcG/g+TxuPURJF4l6Cy61ruaZyIwLBqDKMFB7NbhsvL72emB1nu3wKVElTqIWACCHxmFDH0KSGKlQUqVASBVzh4krHH48ySb3byHcDX+eIfggbm4AMstRZyU3ll2Jg0OX00Oy1ztuG53gOI8V+Uolx/vRfPsJHf//P+fCfvpfPfubroPiha8eVFA8//FM6Orrp6pr7Y7zZuowj6iGeMh9liAFAEnaj3JC/jdpKHRNyAFXRaLHaCHpBsmqGsBfGkb7yWTE0XmTfxqa6ywiooWrrqaZoONJGAroyt0rVFZ2Sk8ea9RZWT0qxFgjqA00cU4coOyXGiiM8NrwNUwmyrnYdqlCRUnIofZj7Bx6iJljD1vEddEU6SQYSHEodIVWeYry4hAMpaItsZV1dBz3xBp4cL3M0s5rOWAFB2j9WPcix1BR7pvYvSAYrUxD4mYKMSNzjD8tbfGLUvF/BWe4xz+X5leKZZ57gLW95GXV19dx11/3Pm+Asa6H0MknwLoF2QPjjVyX2JknlSon5UwGuQAZPKsY1kGGJfuDck8Gt4WZ2TOyeY3HiShdXer/RNgchLfK87da/Cgxmh4jp0Tkki6EaeEimSzO/xpH9bmBqagrXdWlsbJyzvLGxkQMHDsy7zdjY2Lzrj42NVf++8cYbednLXkZ3dzeHDx/mIx/5CDfddBNPPLFwOMuHP/zhOSRzNpul/WwShBbxvwae9BgrDWIoZvV3WAhBTE/4HT+VSZpD8//uRbQ4o0dGaO1pq34vSykp2jniRi2hMwhpOw5DDVRDNX2riWnKThFTDRA3alHE86vYADzPY7BwmP7CIbKz3rURPUaN2YAQgqJTIKiFn/c7OWHUkjBqmalMEtMTOJ6D7dkoQiGixarj0RWDslPE9qwzIoOv2HgdQ58dwFnu4B6x2PvPu/jpbT/m9u+8jKgaxZQB6gJNhPQIk6URaswGck6GVGUSRWi40iasRemKLidrpziQ3kHWTgMSgYKm6L4y205RcLKzmRAKtrSJ6nHqzCZc6eBJiSdd8nYGR9ooQsNQfK/7/nxv9cH9mSCgBglpUXJ2es45KLlFTDV4Ql19FiiVigSDv5y57XQZvrBH4eCMf0+7En4+BLcv9bjypCDdI0c0Ojv/mhUrLuPee9/MV76ykWuv/QyJxO08+6zg1ltPZC2cLZJJuPDChWuhVEoQCEi6u/2AuUzGt31Ip8G2YWAAli07QeROTEClIrEsKBR8b2AhfOK1UIBQ6ER91NwsCQZhdNQno2+77fkfzhxHsQh33SX4xjcUpqZ8creuTrJmjeCNb/RYNc+0yzSpErZHjvgK3v5+3z6ivl5y442SJ574Z2ZmpvjgB//qjMZx5Ih/7UzzxDnUdf9fb+/8ZPCzzz7FwYP7+Ou//sQZH+8vAlX3w6tPhhCCaGOUms4kkbowdsUhVBOikqvgzHrsFlMlPMelY1MbQhF4jsfee/cztG0I1/Gv0dEn+2nd2Eo5XaSSr2BGDMyIiVAElVyF6WMzJNoSmBEDdZZQNsMGG25fRyAeYM89+3AqNtHGKOWs/96aoRGImMRbYozuHSNcE2LVDc/fqVbOlpk4OEl+skC4NkyiLYFbcTjy6DHcikvn5g5SAymiDdGq2ljRFKKNUVKDaXITuao6uDBVQNFVhBDccumruOfxb3PP43dy08pXUZgqnLNr878dv1Vk8G9KS9wifjdgN1xIueNmzOGfIoqjAEgzSWnJy3GjS37No/vtw9kqg11cHpI/5SfOj8mIDCvddWT22RyZGqQpWE9YDzE5NY37VJSmC0JkY1kEgo32hdxafjm1Xj0zYprtxjO0uu3UnmQ8usm+mK364xRFAVv4yccNbiOvLr6JBm/h74KCyPOE8TA79K3Y2FREiSeMR7Bx8IRHTsmQU7JYVDisHSQkI3wn9HWWOitY5axDxZ9MD6hHuTtwF2vsDURljO8Gv87Pzfup9eqo9epJKynuCv4348ook8o4x7QjSCRtTgeXTV9OMhNls7MZT7i8yLiR/Y37eTb2DJawiHpxlturGVWHGVL7EVKhyW3Fm7WJkEj263vodnoIEiInsuzUt9LsNfPq0psXPHZPehzI7GCk2I8mNJas7+H3P/Yu/v3D/8o73NfwH5/5Gpp5goR9+OGfcPnl15yyHxOTN5TexkX2pex2drJr4gDpgRIPFrfRFxmhIWoS1mI0Wc2cn72Ip2NPMKVMEFCCFPUyq511XOe+mKR5aphMQA3heJL+zCB1wQYSAX/yWJ61kwipEdbWruJAqg/bs6uT1aJdpjHYzNVt1/mexzLIqmQ36iyxLYSgKdxIf2aQvvRRclaexkQ3E8UpDmeOoisGhrKJigDIc2DmEHEzSlAtMeNFmCrXURf2CTLX1ciVXYrOwsnm6oBAZCTuyda7AtwGidYvEBmQiQU3f8Fx773f5w//8E1s3HghX/jCXSQSZ+bNZW+WuB0SfZ9AlMFtBXu1BAOkAbNuIXNtJGxYQKz/i8OFSybPY9uxbRztO0JTvBGn0WPYG6M92sKG+jP3QPtdRX2ojpHC+JxljueChKhx5qTNIn6z8JrXvKb6/+vWrWP9+vX09PTw4IMPcu211867jWmaiyKKRcxC4p4UxHUCs51b0l1wS03RGD48zCU3bfH9W4WO49kEtTBLoqvOmMA9GZZbZn9mB9PlcVzpoMz68K6Kb3zezopUZZJ96e0M5g8DEl0xsWSFjJWi4paI6Uk0RaczshRNOb1MUhEqq+LncSCzg7Q1jeVVEAiCapi4UYMnPd/yyy1hqkFM9cxsdm685iU06E18PfQFxteOsvySVTyz/ilG7hlmxS0riRnJqkewoQaoeGXWJS9iqjJG2SkS1RM0BdsIqEGenX6ErJ0iZtSgChXLq5C10jSG2qmTzUyX/SBdVVGpuCWag+0oikpIj5C10rg4VJwyYT2K49lE9QRRLUHOzjBYOEJbaAm6+vxyUkWodESWsj+1jXRlGkMNYHsWUrp0R1cS0M6uILBtG8dxCAR+OW/9BwcF+6cFyxISXfVJu+E83HNEsKZWUje7e1+ALOjsfBFvetN2fvKTd3H33a+ko+NVbNnyKaQ8x55XJ6G7W7Jtm4LnSZJJSCYltu0Tuz6n64fEHYcf43K84PKJX8fxi7BYTLJ6taRYlExP+zYRlgX5vKSrS3LppWf2gF5K+Na3BN/8pkIqJairk1XVcW+v5M47FT74QY/gApcnnYavfEVheFjQ3i7RNN8v+TOfeZyHH/44f/RHH6Sz88zm5KZ5/PrMhesKgsH5ye3/+Z9v09jYzKWXXnlG7/GLon5ZPQPPDlFMlwjG/c9/KVNG1VWWXtlDwzJ/DluYKXLggYNMH5mmmCoSiAXouWwFrRv8YMWJ3gkGtw0RrglVlbqVQoX+J48hJdR0JPFsl1KmTCAWQA/plNJlNDPPkkvXzlEmh2vDnP/KjYQSQQ49dBjP9bDyFVRDRboe8ZYkgWgA13IZ2zfO0it7qnYU8+HoU8d4+itbmTo87Yd1h3Qq+Qot65oRqmBs/zi1S2rxXO8U2wlVU/Bcr0pwAwQTQVzbRUpJLJzgxs2386Mnv8OWthtYmlxUBZ8pfmvI4N+klrhFf7TfESga5WWvx268GC3Th1Q0nOQqvPDCnmeLWBi1tXUUCnny+RyRyOnJAonk36x/4U7xNUpuGcVVeST3KEzFeVH0RdTqPhEYNSIcSrm0bF/Bq86/xVd5eo1VVWtByVGmRPw5zFmPs4yckqXHXkZe5Knx6rjSvpbLKqeSl8dRosQXQ//Bdv0ZDAwOq70MK4PYwiIm46SUGQQKUS+Kho4UHopUmFamWYZSJYIBWt0O+rQDHNEO0eg286zxFE1uM8nZIjHsRuhTD/Ll0OfocLtpdztRUBi1Bnky9yAr5VrqjSY86ZGrpLlw5CJudV7J06HH2a4/Q1qZYZm7ko32JpY6KzAwafCaqXcb+FroC3S4XQRnbSiiMkaD18iz+lPcVH7pguF1aWuK8dIQES1WVWtcf+OLrFpc8AABAABJREFUURSFz/5/n+ENb7iVz3/+28Ricfr6DnL4cC8f+tBfz7svDY2OYg9373yQsZk89cE6FKGwe+ogRhqUTmgON3FhajPxQpRjoWPUROo5z93M+fbmecdouTb3HfspP+5/mLHCCKZmsqKmm4ta1qIq0B1ZQUALcUnLheyZ3s/BVB9BNYgr/ULisraLOb/+fI5k+jFVE6Vs0dA/QnRsBsfQGGiNkKsLENXDBLUABbvIWHECV3ok9BBjRQVVSOJGjFQlzURxiqRZS186j+1oOI6O5ylUHA9XerREmhe816QuQRHgAifVQ8IGqUrQQMzgk6i/QpGo53n8+7//Mx//+P/HLbe8nE996gsEAmfnDes1Q6X51EmEs1oiY6CM+usgQKQBCfZ551AVLCFwt2DFj1v5A+O1fL/2AfpTQzApOG/TOm5Z8yJqAolz937/S7G5eRN7pg8wkh/zPYM9h4HcEK3RZtbU/gb4mPwvR11dHaqqMj4+l5AfHx9fUNzQ1NR0VusDLFmyhLq6Ovr6+hYkgxexiONQhEqNWc9w8ShBNVxVFVa8MrqiV+0J5kM6nWJibIzLNl5LZ2Q5RSdPRIvRFGo77Xanw+HsfsaLQ0T0OIZqYnsWU+VReoV62tC3rJViT2or4+VhJBJDNXE827d7UD36zEPEQ7VsDlxJk7aw3dPJCOtRzqvdQsaaxvYsxkpDDOQPM1w4hiNtPFxMJcD65ObnJZdPxvn2RdTka3lWf4qxxAiDXQMoAyrL9bUEtdCcUFxXuiSMGuoCczsEpsvjZK0UsVmLLvAttIJaiLJb4OL6a9EUHU96HMsdpC+3D0Xx10saDXjSY6YyiSNtXM8losVIGvVk7TRpawrLq7B16iG6ostpCrY/r21HY6AVtUZlqHCEvJ0lpidoCXXQHDp9B9J8ON6RGA7/YrZKIgVyHHb0C5IBiX7Sc47mMPSm4FBKUDfb1dTd7VsuTE1BfX0TL3nJd9i371v87Gd/xN13r+eqqz7Frbe+4hcay3yQ0g956+/3z2l9veTAAUFDg+/tPDkpWL5csnSp5OhRX9V7/PS7LoCktlYQCoFl+WRrOi2Jx30l8oYNcDQtOTItKDoQ8+Dql3qc6ekcHIStWxUsyyeng0EIBn2St1wWDA5K+vpg3QINt7t3C4aGfEXz8eaU+vop7rnndbS3X8L73vcXZ3yu1q71g/gmJ6FutgFsZgY0TbJ+/anru67L3Xffxa23vmLBzphzhfqeOpZc2sWxpwYozvhiET2os2RLF/U9J7rVwjUhzn/VRvKTeeySQ7gujBk+Eaw2dWQa6ck5lg1m2EQC5VyZeEuc+mX1TPROUslV8FwPz3FpXtNI18Xzf76WX72MQCzAgQcO4rkewXCIREusqtBVdRWn4uDa7oJkcGY0w8OffpTcZB5vltCt5CwmDk1ihk3ql9dRTJVQNIVQTZjCdJFE64m5XmG6QLgmRLT+xKSnaXUjg9uHSA2liTVEufnCV/CjJ+7ikSP3cfOqG8/yCvzu4reGDP5Naolb9Ef7HYIQuPGluPGlv+6R/NZj2TK/D6iv7yAbN87vR3sc+/9/9t46TK7zPP//vIeGaZlRqxUzWZZJZnbi2A5zG8bml7RpoEmTNk3b0Ld100ATO9DEjjFmRslixhUs8+7M7PAceH9/zGqltdiWQ92PL12+tJo558yZszPPud/nuW97N/fIuyCr0EAzAsFQbIwuOcRuuYNKjgppISNIX3KAYqv0uICQYqeUsFPEqDJMtX20YB9VRqi3Gvlc8qt4pBcDY5JYeyK2GRvZpm+k0WpmQO0jK7KUOZX0qB14pJcx4mREGq/wwnjAhoELt3QTVUZOsEWBRDKiDJEUCSrl5EWGnMgSU6KszF9EQBa+cMOxACniRN2jlFtVKEIhbBQXAtzSLt4u3s812TfRpbajo9NoteDiaEGwS9tGTmTxyMmdFR7pJaqMkBapk4rBCTOOLe1JY3tCCFasXkXp7RX822f/iZtvvoxf/er33HXXnYRCYS699JoTbgtg2/AuDsQO0xJpRh9/3yLuELtH9xFLW1T7FSzHYq61iGvtWyjP15zyBuKpzuf5/aHHCbtCtESmM5geYPPAbmzH4b2z30Z9oGViH389791s6N/KntF9uFU380tns6h8HqqiUhesoUGJUHPfE0zvySDGfcIDao7Gyy9h7oWzmFXUyvqBzSTySRShkLWyKLThUmchRKHjx3IsSr31eLUOhlN76NULqdyj2SitRdNYcIruU2taoWtW6wCriYIgnANlWGDOdvD+REHrFEhdYs6XZK+WyDc4OHd0dIRPfer9PP30o3zyw1/gSxd/He0hBekHc67EeZ1rZHYdZG5w8DykoO0FBEg35C5xMBefOzFY7QbXcwoyJGktaeFzNDHgDKPvVolUFJG94Jzt6i+ahaVzefO0a3mq8wUOxtvRFI3mcCNvabmekOvsx3inODsMw2Dx4sU8/fTT3HTTTUBhsebpp5/m4x//+Amfc9555/H000/z6U9/euJnTz75JOedd95J99Pd3c3IyAiVlSdfvJpiimOp9TURyw8TzQ1hqO5xGwGHOv+0U4q6+/btBmDRnOW0nmGA2qnI2mkGs714NP9E3aIrBj4tyOh4hoBfP3G905fpJGOn0NBAKTxPFRrtrsPsrNrBkD6IV/ezR93H8vxubsm8cyL74VQIIRjxjrJL38ZwYJC4MkgkFsLjeMe7qQWx/Ai2Y52xxy5Ag91Mg91MKpXkPzq/TdWVtSiKMlEzSSnJ2GnK3IUO37zM41G9FLlKUYRa8OlFHtfRrQoN27EKXsfoKEIhaERQhILp5NEVA03RKHVVYjkmOTuHR/WiKjpD2V4ydgpb2rgUFylrjO2j60gHkjQFZ56ynhNCUOqupMRVgYNTyMB4jb7P0Wih/i4qOkv7JwvcjwiMFwVOQqDWC4wiiZh1NNfgyBEd21NaXQ2rVzs8+qggGhVoGgjxNt7xjkvo6voYH/rQ27nvvt/w1a9+m/r6JkQaRBKcIHCWuauWBffcI3j5ZYV0GqDghVtTI8lmC6Lv6tUOV1xR6MZ95ZWCcFxTUwgCtm2JphWsJDyegg9vKlUIbWtuluRysGsMuisEmTLIpgTeoORZU6Fp1KH1+AG94xgeFiSTEk1TUNWjtZzbDdlsodvYNI+EGx5PPF4400ee6zg2jz32Phwny+rVv0TTzvz3pLUVrr7a4cknFfbtKwjpPh9cfrnDnDnH73/9+pcZGOjjhhtuOeN9vFaEIpi+uoWy1jKinTFAEqmLEK4OHXftCyEmeQYfy8mssVVDxRP2khxOUlxfRM2CatLRNPG+MUIVAZa+awm668RCrlAFxY3FtFwyjVwqhy/iwz8uykopSUfTFDeVoLm0idC3YHkA9ZiVk92P7SU5lERzaTiKjRw/3bZpM3hgCHfEg1AK3b7NqxrZ9fBuRtpHMbwG+UwezdBoOr8RzXX0/Q5WBJl7/Rz2PdNGYjCJisFli2/gqR2/51u+753JaZ+CPyMx+I3itYzETfmjTfF/naRIkBFpIk5xoVg+A1paCl5C+/btPq0YvCbzMkljjDrZPPEl6Ha50KROt+zClvZE90LSTNEUqpv4+7F4pY+L85fzO88v6VQPI5H0qt1kRYbLs9fhk/5JYumJkEgOaHu5z/0betVuQjLCgNqHikpIhhjCIC5iBJ0QGTVDSiRRpEKpLGeONZ82dS8JMYaNPSE496pdlDilNFktxJRRDOkiIeKEjlH0osooutQLwXXjCAuEUEiKo97LQghsHJJO4WdhGSFsnVgZLHcqCckwo8owpc7RhbIRZYgyp5yIc/xEhETSo3aywfsKnfIAM+w5lFplRwP7pM28JQu5775nePvbr+OGGy4knU5z001vPWXXaH9qECnlhBAMFPzzdD9ZE5aXrsZyTFyq57TjoWkzzZre9QSNABXegn98sauU0ewo8WwWlxKedH2EXSEur7+Iy+uPH/tyqQY3Dxcx1B6lvdSNrRtIJFUpgwU7RqG/l1tab0QiebLjOWK5OIpLYUZxjJSZZjhTQs6GpFmKmnXzpmmVFLkb2Du6B13RuKLhYi6ru4iAcYqWXg9kbnHw/EpB2w8gQJHYVRKtXUHkJHaZRJjgfkpBGZCkPupwmkv5NbN+/Ro++tF3kcmk+OX/e4Ab2q5Fu7NwXMIB+2lJ5m0Sc9HrEG0F5C+RWK022n6BsMBqkNhHxPBzhHpYIJIFsR1ARaVKKUcJAtshe4vDadaFpqDwuXNp3YUsKV9Ad7IPQ9FpCNUe59c9xRvHZz/7Wd7znvewZMkSli1bxve+9z1SqdSEldq73/1uqqur+ed//mcAPvWpT3HRRRfx7//+71x77bX85je/YePGjfzoRz8CIJlM8rWvfY2bb76ZiooKDh48yOc//3mmTZvGlVde+Ud7nVP8eRE0IswrWkFvup1obrgQ0uqtocJTd0pBb9++XWiaRnPzuZksMB0TW1oT3sFHKGQIpDCd/EmfG89H0RUXLtUibxbszXJajnVla0hrGcqzlVRQgy0cnnU9TqVTxerc6TvRnjee4n7Pb0iIMdJmkmT5GM2R6VzcvxolC3knx4HEbjyqjxnhBacVQL/2tS9wxRXXUlNTR39/H9/61y+DAkuvWM5Qphev5sej+cjaGVShEs9HGcr2U7AEUAi4wxglXtLeNOlkGo+dmQjfBchYKYpdZZPC3IpcpZS7q+nLdKKJQjBezskS0MI4MkrMHEExVfJOFhB4VC9uzUfSSpC2ksTNKEl7jOnBuae16hBCnLZR43SMjh4Rg8/OnsH1nMD9kMAJAfWSBQIeHYWyHQJ7qQQBgxkIuQTNoaMKnBBw7bWSujrYulWSSAhaWhyWLCmlqOi3/P73v+NrX/s8F100j49c8im+UPJ3BM0QTliSv7CQoXCmL3nDBsEzzwhKiiV1NQVRuqur4NH70Y86VFQUBN4j3Habw/33Cw4eZPycQEsL+P2SeBykFFRVSlpS8B43+GPwxD7BcwlBexFUFhWsI/rzgofbFVoiDsppNPpAQOLzKfh8csLTGAoexrYNxcWCurqT+w8XBrULIXiKYvP44x+gvf0JFi9+gNbWak4mIp8IIeCaayRz5tgcOCBwnEKYXGPj0W7pY3nwwbuprq5j8eLlZ7yP14MQgkhNmEhN+IT/7lgOyZEUQgj8Jb6JILVjKW6I0LWpCzNrTnTpmtmCHWLr6mn07x5gpH0URVVwbJtwVYg5180+qRBs5W32PbWfnu295FN5svEcY70JwrVhvGEP2UQWw+ciXBNi3R0bSQwUpuX9pQGmr542YW8x1DaMlKC7NSzAzFqFpg8pycQydG/qwl/q49DLh5l+yTQW3rKA7i3dJIZSFDcVxOvSaccv6JRNL6WooYixvsJ+Z7+rlfMvvI/f/PIOPvjhj7/u8ND/C/zZiMF/SiNxU/5oU/xfJSHGeMR9P5v1deRFjjK7kktzV7PUPO+EgWPH4vX6qK1tmOj+OBX2+NLmsR/i3pCOHhDk4g6pcAqv5mMoM4wjbVZWLTvpB/4luSvRpc4d3h+xV98JEoqcEnbqW/mZ73benfoQXk7sQSaRPOS+lydcv2e/tocBpY+oMYotbdy4UVAIOhEE4AgbVar4ZYAWcwZzrUUoCMZEHL8TYJ+2eyJALihDXJN9E9v0TbxkPEOnepgd+hZmmrNpsKcRV2JYmIRlEZo8+gXtGBKRk3gd38T7cVDZT1wf5cnQ44R9JVyevYZl5qoTFtAlThkrcxfzqPs+siKD3wkQVaLj5+mq44RxieQR1/085X6EKCNEfcOst15hVfJilidWYto5LGlR4a2ltrSJBx98nhtvvJjh4UGmTz91EF9A9xUsYo8J8ALI2TnC7tBEWveZMJZPksinJo33K0KhyF3ESOwQ0WyMav+Z+8JXtPUSrJmLr8hF1sri072U15ViHDyEvX8fRZdcxofmvYfzqpbws53/y2BmmCqfm6z1FJZdiktbwLJyP8sqJcsq/Li1t5C1coXgCvXMXpM1E5KfddB3CkRK4pSBtglc6wVWK4UiCnACEn0v6HsE5oJzG7Jmmibf/e43+MEP/oXFi1dw++2/YNozdWj7BNZ0CboECWo7uO8VWK0S+domMSdwqiBfdW5fxyRUAHm8N7ENjj75Z39qSCnZNbKPTYPbiGaj1AdqWVa5+Kyu7XNNyBWc6gT+I3HbbbcxNDTEV77yFfr7+1mwYAGPPfbYxERcZ2cnyjFJRStXruTXv/41X/rSl/jiF79IS0sL999//0SgsqqqbN++nTvuuINYLEZVVRVXXHEF//iP/zhV805xVgT0EK2h+Wf1nPb2Q9TU1GMYZ/YdeTo8qhe36iVrp9GVox3AExkCpwh986hexvKj+PUQKStJzs7Q5eskakSpylTj03z4tACqVEnIOGv1F7gkd+Up6+BBpZ+HPfeiSIWZ9lx6Mu1kZIiDrv24PDpL0ktQhIpp5dg/tgOfHqDOf+qpxL6+bj760XcRjY4QLiqied40/v5/vkokEiZpjpE0x1CFRqWnljEzSspKEhr3BO7QD/Pb0C9JutP4jAC4JfUjdayIr8Il3OTsDLpiUOtvnlSjKUJlZnghIaOI/kw3tmNR4allKNuLnyBezUc8Hx0Xgwt1fNpMggC36ibvmPSkDmM5JguKV56wkeNcMjhYyHwpLS0/zSOPIQ/GywLpgSN9E5cJSRuCvUnQeiDvE7hUuK7RoeJVdY+iwPz5kvnzYbJYKbjhhlu47LJr+PHHvsMPnvw3fq3fwVeW/SPvcd6L57cqOJC77MxqoG1rBcsH4bw+gWFCbykU1UnW9ArWrBFUVYGmwcyZktLSQvhca6vk4MGCEFpVJbnnHoXNmwu2EooiqesU3JyAmWOCTFiyvBNWdkjWNkr6l4+LpnlJZ1wwmOa41/5qmpqgtVUyPFzo7h0aKnQBp9OFDubLLpMTlg0nYu5cSWOjYP9+hwMHPsjBg79m8eJf0tJyNeedd3IP8pMhBNTXQ339qc+xZVk89NC93Hrru/5ggmIulWdg7wBj/QkMj07p9NKJzuChA8O0PX+AxEACIQThmjDTV7dMslIAKJ9RTtWcCnp39h+dDHAcKudU0ryqiZqFNfTv7ic5nMIT8lAxs2zC7uFEdGzo5PAr7QWLhjI/wYog/Xv6ycSzeCNeKmdXUtJUzIEXD5GNZwiUBwDBWP8YOx7cydJ3LCZYESwE1olCwJ3m1pGAlbUKKxgqhKrDhKuDdG/pIZfIseRti04o/p4IzVApqo8w2DZEzwv9zKldzM/++4csiqxi+uppJ+2inqLAn40YPDUSN8UUf1xsbH7l/Snr9JcpdcoIOCG61A5+4f0xWlpjkbnstNtobZ3F/v2nF4OXqcu5M+9nxBigJF/wAHYMG21mhpodrYxkY/TbQ0TcYW5ovprllYtPui0VlQa7Gb8MsCR3HtVOLSoqGZFmo76W6a6ZJ+3oOKDu4wnXQ3ilnzq7gR61i1GGySsmGipSgguDBfml5Mmxw9hKiV1CkSyhV+1C4nBh7lKuy9zMLmMbXWo7ISfCAnMJu/XtPOS+B10aTLNb2Sm2sc3YzIg9TL3dxA3ZWzmo7adN20uNXYeQgpHwIJXpMuy8yRr9eQaUXjRTJe4dY2NoEyk9ydOux7gxcwsfSH/8OK9kgOuzNxOSIV4yniWhjFFvN3JR/jKW51cd99hd2jYe9dyP1/Ex25lP0hnjoNzHs4En8SY91NuNVHsbqPIULDhqauqZN28hY2MxvvSlTzM8PMhnP/ulE1ruzCmZyVOdL9A+1kltoBpFKPSnBnGpLhaXnd1NZMgVIGD4GMsl8OtHK9OxfAKf7iVyth6wtoVb99IcOjrxIaXElkzMYAkhmF86h79b/mme63qJ7UO7kSLB6gaDmSUdlHnT1Hgbcak1gMCtnb2YIosgf+ExY3UPKjghOVmwdAOOQBk8twLq/v27+cxn/ort2zfzuc99hY9//PPoOQ19h8Apk3BkjUKAXQvaIdAOCMz5b6CQew6wWiVORKD2jncHCyAHIiYwL3HOaRfyueb57jXc2/YQWTuHW3WzY3gPGwe38f7Zb6c53PDHPrwp/gh8/OMfP2kN/Nxzzx33s1tuuYVbbjnxyKvH4+Hxxx8/l4c3xRRnzOjoMMXFZznKfwo0RafG18T+sR2M5aPjQWQ5LMeiKTADl3ryQLFyTw1D2T4c6VDsKieWHyZJHASE9CJK3ZWoQkUiSYokncYrfN/3T7Ras1lmnj8psPgIB7R9RMUI0+1ZQMGCQbEFRt6gI3CY82LnI6Qo+AVLyY7R9QxmeggYESo8NYSM42fyf/jDXwFgORYbh58jZSUJ6hGEEASNImK5YYJ6hGpfI4MjfQT0EKpQSStpnip+nGFthPJMBfWihag+wv6yNiqoZsbYTMrclVT7mo7zFz5ybuv80ybE6tHcIN2pQwSNMKrQ8Gp++tKdgCDnZAr+w6qv4LssdIJ6EdFxK5ES9xu7mLlv326KikrwGQFi3TEMn4E3cuoQOpEGZUzgBI7WMxUSPolk4yDsmgW+Gof5pZLZryEPzp/y8ZWif+B9b3o/X9n193zixQ/xo9Lb+WLLl7nm+RvInQ+c/PIsYMGcdYLablCDYKkwox3KhwUvpOCeqMKR+KOiIsmb3yxZsUISDMLCY3IY3vMeh8ZGwYYNAicDb/VImopAmwVmFvqloCEqWbFb8OhiSd4Nzrjv8Om6ggFUFd7+dgdVVdi0SdDTU+jyXbIE3vOewjGd8lz54Z3vzPGhD32Egwd/xbJlv2D58lu56iqHlpZTPvV1sXXrRkZGhrj22je/cTs5hnQsw9Z7tjPaMYoQAulI2td3MuPy6YSqQmy/fwe5ZA5/qR/pSPp39zOwd5DaRTUEK4OUt5biK/ahGSpzb5hD6bRShg4MAVA6rZSKWeVoLg3NpdG86swC92zLpntLD4bXwBMqXJCGV6dqTiXx/jHmXD+L8ullHHjhIKnRFCWNxRMCdKQ2zMihEfp2DxCsCNK4sp72dR2YWQtVlyiKGG8GgpLmYqrnVyIQuINuRtpHGT48QsWMM1/AiXbH2H7/DvLpPJcvuZ5/+92XWffCGjLxDEvfsXiSh/IUk/mzEYNhaiRuiin+mBzS9rND30K93YhfFlbZgnaIA9o+njOeZKG59LTdwdOnz+S++357XDfoq1ngXcA1gzfxoO93dHgOogoVy7aozTbwzQXfJCAD5OwcVf4Kwq4Te75BIVRsy+B2fhf7HTuUdmYVt2JXOQXvJOnFLT1s1tefVAxu0/eQEknCToQO9TCGdIGQWFjkydOnddFgNZNQ4mjS4EOpT9FkTWeftgsHyUx7Ngvzy3DjpipXM7HdETHMC8bTBJ0w5U5h4anarmWXtp0ip4i/SX6ZKqeGfdou7nffRZfajhSSEsrJllkcjh+CvCRNkoHwIIdKDlEqKiixyhhU+nnO9QQVTjVvz7zvuNeko3Np7mouzF1GVmTwSt9Jx/B26FvJkaXBKaSyBvQQs9UF7FS3ki+xWZRdScgonrBxGBkZ4tlnn+Bv//brZLNZ/u3fvs66dS/x/e//D9XVk610qvwVvHXGm7iv7WEOxdtxkBS5wlzTdBmziqYzkh1gONuP6eQpcpVR5qk6aaiKR/OwsmoZ9x14GDWtEnYFSeZTDGSGWFW9girfWXSEAOq8RdgP3oO0KhHjfmQyOorw+VAbJyfUlntLua31TSypmMHB5G5UoWEoBvH8KPH8KJZjUuM/s8LrdDhFoA9O9qfDBqR83UFynZ2wfbtgeDjJmjXf4LHHvk9dXSMPPvgCCxcuLTwoDUKKQojdsSgUml/OvknjD45TAtkbJZ57FbQ9FMRgVWIukOQu/tMVsqPZOI+1P4OmaLQGC79LUkr2xg7weMezfCT03qlxuCmmmOLPlmh0hEjkNShrp6DW14SmqHSlDpO10nhUH9XBBqq9jad8Xqm7kubALDpSbViOiU8L0qLMpsPoIagUFWpBYL+2h236JiJOEW3afrbrW9iov8JfpT9JhVM1aZvO+Df3kTrZrwVJmnEcaYMohLPZjgkUAvcS1hiqohPND9Of7qKkqJqd4e3s1Xbikz6W5FdyXv5CXLhIWnFSVhKfFpz4HhBC4DOCpKwxRrKDJM34uPWWm45AO0P6ANXZ2oJxqpSUORWk1CTR0jFWui9HEeoZf6dIWZgkO/La3KoXrxYgYcYK/seqF1vamE6eoB7GrXnI5JJk7fQZvpNnR97JMZodxJIW23Zuoq6ikTU/WUcumUNzaZTPKKP10umTgrcmvR4/WEUOUXOI3ppe8lqOcLqYqq5qrsl5uXCWXbCweo0oQyASUNVcy0/q7+RDCz/GP7z4Rd625i3M2jmHTzb/Lde+6+ZThpZp+2F+CnZoAl+wIKqN+SDQAXMycHhWwQLBcaC7u+At3NAgefVwdCBQsLW45hqJ0gHeb6l02zC0GYQKilvS7YOmGISGob8a+lKChWWS0tMJ1uOUlhZsKzo7IZmEsrLCnzOh0FTydnbvXsM3v/kzLr/8FsrKHPQ32JHqlVdexOfzM2/eojd2R+O0v9LByOERiuojqJqKlJLEYJK25w5Q0lRMJpaluKkIIQTZsSzpWIbEQJJMPIM37KFjg5+518+mpKkYzaVRs7CamoWvL8zDytmYGfO4UDjVUMGRhc5eIB3NoKqTPy+EEKiGNhGG13ReIx3rO2lf24GZt5BOod72FHmpmV898dmhGdqEdcTZ0LOtl+xYjuLGIpZVX0D4sSI29b1EXUkTg21D1C6sOf1G/o/yZyUGT43ETTHFH48hZZAcuQkh+AgRp4h+tYcMmZPaLRxh2bLzuf32f+fQoTaam6ef9HFCCD5f+gVmjMzmqdgTJJwxZqqzeUvwFloDZ7YUbDkWv9l3Ly/3rKdf7SWh5djZc5jB/hgLF03DMHQUVCzMk27DxkYg6FW7yYgMdXYDOZFlWBnCOy4mL8gvZr61lFnWXOaYC9DQOM88dQpVn9pNTInSbE0+B3V2A3ElOiHOtlqz+Zvkl+lWO3Bw6FTb+WXgJzS4m2hjD116BwN6P1JIAnaIgAyho6Ois0XfwLXZN52wOxgKorAuT11NpUUS9VVfE7pi4FMDeN1BInJy98sPf/hddN3g1lvfQ1FRMcuWnc8nPvE+LrtsMd/61n9w4423Tnr8orJ5TA83czDeji1tGoJ1RFwhDoztYm98KykrgSMdNEWn0lPLstJLcKlubMdmMDOMJlRKPIWV6MvqLsJyLNb0bqAn2Y9Hc7O69gKuqL+QztQB8nYWj+aj1F15yo4gAGPVRTh7d2G37QW3B2mZCEVFv/RKlMbj7wDydpa+bAcuxYVPL4xbefCRMGN0pg5S4a09q3Twk5FfIdH2CpQBcEoBE7QOgV0tMWefnZBpmrB2rWDdOsH+/dDfD+n0vRw+/Fny+WGWLfsKP/jBZ6ipOfpdKANgtkiM9QInIie6aJV+cCIFj99XI6VkeHiQQ4cO0NV1mL6+Hnp7e0gk4liWhWWZ4/+3cByHcDhCWVnF+J9ySksraGhooq6u8ZyJnflVErvORtslENlCh7A5V56+E+ePSGeim9HsKNPCR68/IQQVnlIOxdqJ58dOuTA2xRRTTPGnTCwWpaHh3CycDih9tGl7sbFp1lpY4rkQ2zHRFP24kDQp5YSQeayQWh9oodxbzVg+hiJUAkaIjLRYqz5PUIYwsdimb8InfSw1V1LslGBjs1fbxfOuJ7kt855J+2myWgjKMINKP+VOJT49gM8OkhRtNI42Ydl5VKEVciCkhUtxEdTD6IpBv9XDhswr7CndS1CEGWKAfd7ddKiHeVfmg+NCyglCuKQk62RoT+4jZSVIWQk0RSMrkmgRDenYGKp7oj7xSh8xdRShKKdt7jiWgBHCO+4LHNTDCCEocpWSshIoQiVnZzGQ+LQAEVcZtrQAgaG4iedH6ct0kcjH8Go+yj21FLvKXntgXG6IPbGtpKwxJLB962aaQzOxTJNAmR8zbdK5vhPHtJn/5nkn3o8G+y9r43DfbkzDQtEUegKd9DmdzKtahmg49b3O6ZABkB4QKZBBWFq5nIdvfZpXdr7Ev+z4Jh/+4jtp/unX+eQnv8Cb3vS2E4akqT2C0jAYFCwYPJ6C8BtPCRYYEjlejykK1NbC3r2CPXsEFRUnrhWFgKQJXQehJwkZV8FDOG9I3GEYdQT709AfhdqA5IYm54Q+uydDUaCh4ezO09q1L/Dxj78H0zS5++4nWL78+AnGN4p1615i6dKVZxVQ91qxTZuBvQN4Qm5UrfD5JIQgUOpnpGOU4YPDaO7CZ4OUkpH2UfIpE8On4wl5KG4sJtoVY9/T+4nULZvYxqkwsyb5VB6X3zUplO1YDI+Ov8TL0MERhADNraEZGvlUHs2t4Ssq/B54izw4tj2p0UtKiW3a+IoL05qaS+PiT13EgQUH6NrcjWM65FI5FE3F8B5dlLEtGwFn3cmb6E9geHWEEGiqxsULr+Kpjb/nuplvPWth+f8af1ZiMEyNxE0xxR8LvxNERSFHbpK3bEokKbMrThvEBnD++RdjGAbPPvvEKcVgKIR83Fz6Jm7mTa/pePeOtrG2byOVvnKK3AESxjCGqTE4OEpPzzA1jSUkxRjzzBtOuo0GqxldGgU7BjQUFAzpwiM9zLbmY2Ex11p0wg7ckzGo9POK8SIH1H20qwcIORGKcsWE8iES2hiG6sblHA3r0NBosAvdqM+5nkBFxS08CF0lpaXGizFBWqTxyyASiU/6yIssGZE+TgzO2mn6093E8yPoikGpp4oSV8UJC+JmazprjBcwMdHHfQFM8tjYNLyqLWJkZJif/ey/eP/7PzoR1LFy5UU8/fQmvvCFj/GRj7yThx66h69+9dvU1NRPPM9v+JhfOnvi7/H8KLtjm0lbCXTFQFFUTCdPZ/IgAT2MQRGPHn6KrkQvilBoiTRxQ/NVVPsruaH5Ki6qWclINkrQ8KMo9vi2khy5UQrqEWZHlhA4SYo4gFJcgvuvP461aT3Wvj0Inw9t7kK0BYtOeJ5SVpKslSFgTD7XbtVHxkqSsVMElPBxzztbzKWS7Ai4n1XQ2iSoAqtOkr3NQZ6FDug4kkd+3Mnup3uwFIO2Thcdg39HLvcEtbXXsXr1dxkYaOKlJx3eNQ+UwUKnjDlbkrvCQe1S0PYKpFcicgLphsy1Nlaul7FN3WzpOszG/XvYsGEN27dvJpEYm9h3OByhsrKaUCiCpulomoamaRiGgaIoDA72s2PHFoaGBhgbi088r6iohEWLlrFo0TIWLlzGwoVLCQZfu/hp14Fd96fbCfxqVKEghIIjnUkei7a0UYTyhvsuTjHFFFO8kWQyabze12c6L5E8ZzzBw577iInRQqq9DHJJ7iquy74Z5RgfIEfa9KQ76Et3kLNzhIwINb5GilxHWxbdqhe356jw9/b0+6mya1hnvMhh7SB+x89S83yKnYK9hYpKsVPCNn0Tb8m8c9LUVaVTzWXZa3jYcy/7lF3o0iDrzzAjMZsFicUUu8vRhUF/pgtHSny6D0NxgYBh1xB5K8PM9GxwF17DmIix3vUS55mrmCZbCeoh4vlRQkbxhGiUMGPk7AyG5qLUVUnUHAbpoGYFxbEI+UCeUqMKgUAiiSmjLE2eR2fyAKadx6f7KXFXFo7jFBiKi8bADPbHtxPNDaEKDUuaVHnr8GlBetPt6IqLkFGEI22SZpyIUYJEsm30FbJ2Bl3RieVHGMj00hqaS7Xv1N3bJ8JyTPbGt5O2EoSMYvLJPIP9/Sxvuhg7mEMzgmiGhlAVBvYPkehPEKw83i81bSU5ULcPVdMoORRBZAWO5jA4Y5D9DW20KmdnZfZq7JpCPWW8IrBrC5NdIgar1AtY8sVHWNu0nu9975/41Kc+wLe+9RXe/vb38453fICKiqPd5tIFHjcsWiDp6ILBQXC5wOOXDIcK9gxHOFK2ZrOnPq4XDwhsC5YoMBwCW5Xk01DUDuaFDnMXSi71wYIySdHJ86FfN4nEGN/5zjf58Y+/z/Llq/iP/7iDysrX1+WayMOGfsGOEYEuYG6pZGm5xH0CJcy2bdavf5mPfexzr2ufZ8MJJ2bH/+oKusnE40gpMTMm2bEsulfDTJkFkVgRBMr9jPUnGOtLEKkNn3Q/tmVzeG0HPVu6yY2LwTWLamhYXneciJxP57FyNsOHRhhsG8LlNfCEPaiGSrAyyP7nDiAdCFb48YQ8jHZECZT6QUByKIm3yEvl7KOt6LpLY+YVM5h5RSFQvndnH9vu20FiMIG3yItjOsT7xwhXhShtPjvLIF+xl9GO6MTfL118Hfe/+Gu2tL/CIv/Cs9rW/zX+7MTgKaaY4o9DqzWLJquFNm0vdXYjbulhRBkiKzKcb158Rom/Pp+fZctW8eyzj/PBD554UedccTDegWmbBAw/fsdHnd1Au36ItJ5j//BBMi3DzLDmcF7+QqAQxmZiEpaRiRuGmdZczstfwF3eO4krMUBiY1PqlFNp13BYPUCpc2azTmnS3O/5DY+6H2CftosxMYYiFWL2KB3aQSrsKiQOi0aXEMsPEQ5EjtuGKnXkeOdHhV1Ju3qAhBjDHp/PT4g4LlxoUqPULqfImfxlmraS7BhdT8wcRRMajnToy3TR4G+lKTDjuEJkkbmc9dYa9ug7CTsRQBIXMWZb84/ziP7xj78PwIc+9JlJPw+HI/zwh7/i6qtv5Gtf+zwXXjiPT37yC3z4w5/F7T6+muzPdJOyErgUN/p42Jqm6CTNOLui29jW289YPkmFtwxb2mwa2MZINsonFnxwIswq5ApiOxYbh18ga2WIGKWF4ALpEMsPc2hsD/OKlp+y80SJFGFcdhXGZadPCNcUHVVRsR1z0lifLU1UoaKJczTPpkDuGom53EbtEkjDwWqGM1iHmUCaJv0/uQv/L19ikT3C/fH9HBzbh0svp7LyPpqbb6C4WOJJSep+q+B+SaKKgpBuVwgy73RIfdTB2CBQDwvMkMlz1uM8ePt32LBnJ/tiozhIin0Blq68kI9//PNMmzadxsZp1NU14vX6ULvAWCPQDgicIOSXScwlxydop9Nphob6OXBgH5s3r2fz5vX8939/j3g8hqZpXHTR5dx0061ceeUN+P1/2QERTaF6KrxldCV6aAzWI4TAdCwGM8NcWL2SgPE6fUKmmGKKKf6IZLMZ3O7XN55xSG3jAc/dKFKh1S4sMg8rQzzmeoB6u5H5ZiFfQkrJgbFddCTbUIWGpugMZLqJ5oaZE1lC8Ql8cgG8eLkmdxNX5K7jReMZfun9yYQQfAQHG1Vqx3XWCgRX526k1q5nu76ZhBij2Z7ObHMefUY70fwwKWsMS5p4VF/hGMY3MSKG0KWBEMpE729Qhumlmy61g+nWLJqDs9kT20w0PzQh7qpCx1BcBIwwCgq6qjNmxnAsSWOyma3l20kpKUxpMawOUJYoo6K3lP3m9oKgDESMEmZHFk8E7uXtHP2ZLkZyA6hCo8RdQbmnhipvPR7Vy0Cmh0Q+hiktVKGgj3s3J8wYSTOOIlRK3JVMC8ymbWwHeTtLxCiZqMcSZpz25H5KPVWnFaFfzWhuiKQZJ2REUIRC5/5OHOlQXVVLQsYIEUFBw+U3SA4nyYxlTygGx/Ij5J0s4boSclUSJSOROui6hyH6aJFzjuswPysEZG5xELaCtlsgegrdwrmLHLJXSxZ6l3LHHfexa9c2fv7zH3L77f/O9773T1x55fW8+90fYtWqS7BmKTjFkkgMgrMlcg4oY9BnC57RCvEWR4alUynQdaitPfUC+Lbtgsw0Sf2QoDR6NJ5inwEslLx15hu7gO44Dnff/Uv+6Z/+nkRijC984Wt89KOfO6VdxpmQzMNPdirsHBa4VYkjYcuQYN+o4N2zHIxXbX737u0kEmMsX37qKc9zhaqrlLWUcviVDjxhD4paeONSIyncARdN5zWyK7aHeE8cw2vgWA5W1sIVcE103gpFFCzjnFO/RwdeOMSB5w5i+HTcATe5VI69T+zDsWxaLjoaWCmlZPdje4l2RSltLiY1miYTzzLWP4a/zE9qKEUuUQjGHmobwl/qI1wTIjmUAiSRuggtF08jUHby2rRydgW5ZI72VzqI946hqAqlTcXMvHIGuufs7psq51bSt2eAeN8Y/hIfZYFKmstmsLVnLWUtXzmrbf1fY0oMnmKKKc4IFy7ekf4gv/XcwSG9jRxZwrKIq7I3sSq3+oy3s3r1lXz7218lk8mgu3QEAlUpfBPnbZOhzAhu1aDIHXldY+GqONoBIhDMMucRcYrZZe+hVIZ4W+Y2luZXFoLxPD9lm74JS1jUWQ1ckbuOWdY8NDTelnkfYVnET7w/ICVStNjTKXcq6VLbqXSqWGAuPaPj+b3ndzzheoh+pRev4ycoQgzZA+TJgwZ9ag8Xxy5lxdgqOqw2Sj2V+LTJAtdsax5rXM8xJuKUOGXMtuaT0F8kqoySEAl0dErscvwywKX5qzGY7IfWnTpMzBwlYpRM+PymrRRdqQOUeaqO65YNyhAfSH2cF11Ps0XfAMAl5pVckLt0kl1INDrK//zP7bz3vR+muLjQ7dGhHqJP7cEtPbSas7npptu47LJr+O53v8l3vvtN7vjVnXzo//s33vWma/HpR9/nnJ3Blja6MvnYVaHRnRhiKDPM7KKZE9eGX/fTFjvItqFdXFhzNBx0zIyRtMbw66GJxypCwacFiOaHydppPNrr60I6gl8LUmSUMpDpISg0NEXDckxSVpJqbwNu9fWNFL4apxic4tdWlJtrX2TwsQf55WgXzyW3I6XkCmMmF0cu5t7IhYyOUgj36BbUj0L+PNB9EuxCSJz7boX4Z03WlK3hgbV38/BP72V4eJAqj5eVtY28a8lKFgdC1GWzuK6+Hvdb3jZp/+ph8P1EQekTyJBE7QZ9lyDbA9mb5KQAN6/XS319E/X1TVx66dWF1+44HDy4nxdffIYHHriLT3zifbjdbi677Bre/Oa3c8UV102yi/pLwat7eXPLdfx67z3sie4vjDQjaAk3cVXjmX/+TjHFFFP8KZLL5TCME3u4nim79O2MiTiz7LkTPyt1yohqw2zVNk6IwSkrQW+6A7fqw6MVvp89qo94foTO1AGKTmNToKExx5pPkVNCj9qFz/ERVUaxMBlT4rwt875JXchHEAjmWguZax3TqaZAeXEV0dwQGSvN4eQ+MlZqYhFZSokrbxDzxvEYRxfQHRwkTPgXF7lKWVh8PkPZvkJ9o/pAwt6xbQVhWoBfD+HXQqStJKWykrpcK1s8G8gqGWZm59LUX4/P9hF0RcYX0G1Gc0N0JNqYGVlI3s6yI7phQgiWSAYyPURzw8wMLyTiKsWtetkR3UDCjKEJnYQZAyGo8tRT4alBU3QCepiMnSJhxvBq/knn2qf5ieejJPJxit1naCw7TsF+QiLGz31H+yEAIosCpKqH6VMcAmPFGN0+dI+OJ3jy9taJCksH54guZY6LbucAGYHUhx3UThBxsItgbxL2PCEwTWhshLlz5/Ov//pffPnL3+Kee37NnXf+N29969U0Nk7jTW96K29ecQsL1s9G2w9QmNJyXecQ7RMc3isIhwthbakUrFghaW099TGpKoy44enlkuoh8GYg7YJno4Ibjs9EPGdIKXnxxWf4l3/5Clu2bODGG2/lS1/65+OyRl4rGwcEO4ehKSRxjQu/KRM2DMCicsGissn19Lp1L+FyuViwYMk52f+Z0LCigWh3nJH2UTSXhmPaqIbG9NXTqJpbiVAEB54/SGIoCQoIVVA6vRTDoyOlJDmYxFfiJ1DuP2kuTzaRpXtLN+7gURHZ8Bkkh5J0be6hbnEtLr8Lx3IYPjTCwN5C+JvL76K4QWKZNsMHh4n3jVHWUorLV/jssU2baFeUOdfNpqihCKTEX+qfELVPhhCCxhUNVM2tIjGQQBvvOD7d805ESVMxs6+aycGXDhHvG0MoghXzLuTel3+J4vnLuyc4l0yJwVNMMcUZU+PU8anU33FYPUBGZKh0qih1zi6c65JLruDrX/8C3/7dv2A2GYBgfslsIu4Q6/o3M5AeQlc05hTP4PrmKynxvLZAkZZwEx7NzWg2SpE7gopKJFtCg9nMu0O3sSq3nCxZbvf9Gzv1rZQ65XgdL7v17fRoXXw4+Rma7ekYGNyYvYVp1nR+776HbrWThBijxZrBTdlbJwLgTsWoGGGjsRafDOAIG78TwJAG/oSXtJomIMLk1Cwrxi6glHKizjDx/MhxYvB8czEX5FbzsvE8PaKQ1DzfXIxLelClQEWj3mnkwtzlLDaXT3qulJKhbB9uxT0hBAN4VC/R/DDx/OgJrROKZQk3ZW/jxmzB7/dEPnI//vH3sW2bD3/4M+TIcbfnF6w31pAWKRQUauw63pZ5L3WemTS85V+4tOmDbLrjU3z902/mZ3dcydf/4Z+5cukcRnNDjOSGydsZhu0cmqLjUt3oioEtLaLZFD598o2DpqgIBKPZ6KRjkjhIKSde+4QX4LhdhOTcdTgIIWgJzcWSFtH8MI50UIRCqbuSacFZfzLBXqOjI/zgX/+RO7esw3YUlnqWsyqwEiPvpii7j9r0Dg4VXYArD5U9AlEl0Y/o5SociBzmJ4/fzm9/+xsGRvuoqqrl5qtv5LKBIeZV16EWHf1ddQYHsDa8gnP51SihcOGHElxPKyj9AmumLLSdWKDvAv9/KBgvSMzFkvyFErv+1UdfQFEUWlpm0NIyg/e//6N0d3fy4IN388ADd/H+97+F6dNn8qlP/R033HDL6+4m+VNjfulsyr2lbB/eTTKfpMxbyvzS2VNdwVNMMcU554iX7rH1whuJz+cjnX59gWJZkUE5QY2iSYOEctSuKGnGyTt5IsbRrlAhBG7NS8KMYzp5DPXUXamlTjnXZd/M9/3/TKdxGFvYIKHYKSVNGgfnhILwiVCFSom7ME4dNMLsim4imh/iiL1VqauCvSVtlCoKfhlAIulS2yl2SphpzZnYjlfzU+8/mquRtpK4kvvI2KmJzl6JJOukqfTUsTp/I9eZbyEvcuQyabbn1xEwwscsoKt4NC/DuUKYb3+mm5HsACFX8YQ1UaFTuJNyTzUl7gp60h1Ec0NEXKUT107WztCf6abKV0/QKEy9KSgIlHFJ+yiOLFxzr+W68+tBNMUg52Rxqx42r19PVWM1eqkCWQXLn2c43IU64mN6xVwCFSeeKAobxbhVDykrgX88B8KRNlknQ6O39cy7gjOgbxdonSDdYM6SheC5I5eoALu+EMD38MOCxx5TyGQKtg6KIlm0SPDudzsEgyHe976P8N73fpj161/mV7/6KT/+8Q/4TuIbtE6bxU2Lb+HGJTfTvHwGehN8oF/y4ouCnTsFHg8sXSo5/3x52tC1BQske/YoJGxJ+7gjxfAwqCFoaTn3XcGZTIa77vpffvzjH3Do0G7mzl3Ivfc+zYoV57Yjd/eowKUyIQQD+HSwHcGhuGTRq9YcNmxYy4IFS/+g+VH+Eh9L3rqQ3l39xLpiuPwuymeUUdJcqKsrZ1dQOq2ExECCaHeMQ2vaSUfTmGkTK2eh6AqaS+XlH61FURUq51RQt6RuUkhiJpYln8oTKJ/cDe8OukkMJUnHMgwfGqF9XScj7SOMHh6lZFoJukdHURU0Q8XMmAX7iLyNa/z+QNVVVE1ltGOUhuUnKd5Pgctn4Gp6feGhQghqFlZTPqOMsf4xFE2lNl7Or674EWvXvsDFF1/+urb/l8yUGDzFFFOcFRoaLfaME/5bb7Kf3aP7yVpZKn3l1JdUc8i9n6QyRpldQas1m7L6asJVJTx4311c8qmCn/dv999PNBtjWriBKn8leTvPS73riOZifGzBB3GpZ98t0hJpYnXtBTzd+QL96UHiuQRJM0WtvwrTschYGXZ7trNX30Wz1TrheRyyIuzTdvGy8TzNmaO+xrOt+VRGq9mT2UoyFydCEW6vC9tjoSqn/iiNK1HSIkXIjqBIFWfc1kGTOh7LS1AJksOF2zlSeEiyIssubRs2DsVOCZ3aYRJijJnWHGaZ8+jUDgOC6dYMppuziJujDOX6MdAJ6cVI1/Erw4XRwRNzOrnyZGEinZ2H+eEPv8cHPvAxSkrKeMr1CM+5nqTCrqRW1mNhcVg7wK89P2PRoX/kuS4P1Q3TmfmNR9j/yoM899PP8b4bF3HB6su49F2rCbcEkICNhe1Y5J08mqLiVr1EjHIGxjon7d+RDhJJ0Jhc1GtCJ2dnGMkNYKgufGqQgB4mZSUpcpUWumbOIV7Nz4LilURzw+SdDG7VS/iYDuw/JkNDA/z0p//BT3/6n9iZHBd55zBLvxIPPlQH8o7AtCAby6CWStr3C4JeSXV94WpZ17uW/9z0PR48cB9hLczNV7+d6z5wC4sXL8c5sJ/Md/8FJTi5uBSBAM5gP3IsDkfE4CxobeCUyImsG32XQO0AkQZ1VKA8J9D3SlJ/7ZxUEJ7YRwoaR+r51LLP8fFb/oYNnev43vf+iY997N38+7//I5/+9N9x001v/YMEgPyhqPCVUeE7u26pY4lm4+wY3k08P0axu4i5JTOnxOQppphigryTozt5iP5MN460KXaVUeNvPqXP/rkgEAgxNhZ7XduotesRCPLkMMZrOguLrMjQYh2tWwtBcgIHZ5LFme3YaIp2xh7sEaeIgBNkupyFW3oIyCA6Oi+7n2WetZDZ1vyzCmIDCBlFLCo+n8HxDl+36sHvjpBSTTapr9BNJyApckp4S+adlJzCqsyr+an1NXM4uZdYbhhV0TCdPD4tQL2/BSEEXrx4pZdBmZ4I0jsWgTKxuD6SHUBV9Ennx1BdJK0x4vlRStwVDGf7cKmTmw7cqoe0lWAsHyVsFEQfl+qhyFVKX6YTQzFQhFrocDRjBI3IhGh8Nvi1EJXeOrqSB8jkUmxds4nl16/AG/ShjrqQIwLhVTHmSBpm1Jx0od6r+Wnwt3IwsZvR3ODEOSgySqj1nVnIoUiA92cK+g5ACnDA9TRkr3fIXTq5Ej90CJ54QsHnkxMha5kMbNgAra2CSy4pPF4IwfLlq1i+fBW5XI7nn3+S3//+d/znw9/hX377NaZPn8lFF13OqlWXcO21F3LrrYGzCnk77zzJ3r2SbdsABFKC2w1XXOHQePYWzifEcRy2bNnAI4/cx69+dSdjYyNUVl7PxRf/P1auvPC0VhavBV0B+wSblYB2gvNz8OA+li5dec6P43R4wh6azz/5idZcGpG6CJG6CCWNxfTu6CMxlER3awwdHGHk8CjuoBtpS/Y9uZ9Yd5yFb5k/ERBneHU0l4aZyaMZRy158hkT3aUR7Yiy/9m2QuPGeIDb0IFhbNMmUhtm6MAw0e4YVsake0sPLp+Bv9SPJ+zBdpwzCq57o9E9OsWNhc+YcE2Iysoann760Skx+BT85dwhTTHFFH9UNvRv4e79DzCSjSEQZIJxUsE+whEvuqKhSp051jzqu+ZRccF02u5ZR8jy4Q356RzrJpqL4VJd+HUf6D68mpf90YPsHW2bFDB2pihC4cZpVzM90szPd/2G/tQQQd2PQPDrPb9j98heapYEcXAmhd8JBEEnRLt2YNL2EmaMPaObSFkJ3IqbpBxjb2wLyXyMGeGFp+z+LHJK8Dl+TJGnxCmjW+1Ec3QwbJScYFQbZXq2lTKzgrSVoNvXw7MlLzCiD5IQCQaVfgIyRIlTgorGDHMO701/mIgsQkrJocSeQuCHzAMFS4UqbwP5YpsnPQ+TFGM05qcx2z2XRCKGR/OiisLH/xF/3ojr7GfApJR88YufoqiohE9/+os4OKw1XsAnfYRlEQA6Oo3WNA4pBxnsj+HXPYRdhTM9Y+WNNCy+mpcf/zX7n/gHXnzfU7QsauHyd11F7cKa8ddTuDkpdVUyp346XfFf0THWTaWvHFvadCd7qPCVMa901sRxZe0Me+NbsaSFlJKMmSJtJonmh6nwVFNkVLF9eDeaotEUqsejnZskjEJ3z9l1yr9RSClZs+Z57rzzRzz66P3ousFb3vIRGraWsyCxicGAl8EhyOfBZSew0SmbW8Ws86C1xaF+i83Tr9zP91/6Lhv61jEtMp3vLPl/vL3pXdhfciOPLOIXFYPfj4zFECVHryEZjyECIZRI0dGDUkHqIMaDfZUoqD3g+EAR4JSAXSnR9gqM5wSZ95z8pkDbDp7fKaj9gAMyrLDy0hUsvuMBtu3YxHe/+00++cn3c/vt/853v/sT5s9ffO5P8p8ZB2Pt/GLPXXQneifGhhuCdbx39lup9p9+wmGKKab4y8aWNrujmxnM9mAoLhSh0pU+RDQ/wryi5RNdkm8EoVB4Umjoa2G+uZhZ5jx26FsIyTBCKkSVEaZZrSzNHxV3wkYxAS3MWH6UkFGEIgpBtVknQ7NvFqZi0aN2YUgX5U7lSQXdnfo2DAzmmkdtH2xs1hrP86/+r1Nr1zPDms0F+dXU201YjknaSqIpOh7Vd1zdKJF0qx10Gu3oHoNWa9ZECPB70x9mZf5CutQOXNLNTGsOZU4Fp6Mx0IpfD9Cf6SHnZAjrxVR66457L4N6ZEK09Y3/m5SSjJ2kwlOLrhzxLHZOuB8xLv4qQjlu8qowpSUmvV4hBE2BmWTsFLH86MTPfVqAluCc1xSKKoSgJTgHn+bnyZcfJhVPsfiCpTRMa0S3XZhZEzTJqBikLbeDSLyEEnc54WM8i49Q62siqIcYyha6ooNGmDJ3FYZ6ZvWi8aJA3yqwmiW4CudD6QP3IwrmTBvnaBYcbW2CVArqA6DuEihJ0ALglZKtW4+Kwcficrm44orruOKK68hmszz33BM8/vjveeSR+/nxj3+AqqosXLiMVasu4bzzLmTOnAVEjq3HToDPBx/4gMP27YKDByWGAbNmSWbM4KxE5VeTzWZZv/5lHn30AR5//EH6+3sJh0spL38by5d/nObmZkwTtmwRxOOST3/awXcO+zXmlUg29gsSeQiM9xaNZMCjQWvRqzrTHYdDhw5w223vOXcH8AYQrAxO+F23PX+A7i09FDcUTVgsWDkPg/uHGGwbompOob7zFnkpn1FOx4ZOFFXB8BnkkjlSIykaltbRt7sfJIRrwkgpSY+miXXHiXbFSEfTZKIZVF3FsR1SIylSwymSw0l0t47uNSh+8+vr7j3XCCG47LKrefrpR/n61//9T2ZK80+NKTF4iimmeN2MZmPc0/YQWSvPzEgLtmrz4rSH6BYdLBheRGvRTDIizWZ9PXvTPTReOIe2e9ez6+n1LHnTJSTNFIZiEM3FqafgEeXWXNhSMpIZPc3eT0yGDBtca3iy5HE2t66jPjWNprEZxIkzZA/w6PDjLI3OAH8hFO7Y7pC0kqbZnGyu1Z08RMpKTISRAeTsLH2ZLip99RPdDiciJMOcl7+Ih933UmyXklQSDCp9mL48JaKUunQdi4eWMJofYmtkE49XPUZOy1FhVxJTYiSUOFI61Ni1aFJno7GWcqeSt2feRzQ/REeyDV0xCGjhieN6Ofs0j/AQh1yHcVk6uqpRG6rj+tRNWFmr4MkrJbrqoikwc2KE8Gx4+OF7eeaZx/if//kdPp+fHDlSIolbTg6B0dGxHEHelFS86lvH7TJouOhm3v2OIAdeeZHf//x+bv/M/6N+ZgOr334ZredPJ2IUEzKKmV00k7fOeBOPHHqKzkQXQijUB2t587TrJtmJDGS6ieZHqPTWkbezJK0xcnYWW9qMprM80vZLRnMxVKFS7a/gLdNvYGbRdP4SiEZHufvuX/CLX/yYgwf3M21aK1/5yr/wlre8k/b2In7TeYC5Sic1mT2ES0owU3m0dJRDkfP5xHdaKKuyeeaZx7j84b9lX8ceLiq6hN9dcD9Xha9BKArZqyTWMX7FSkkp+tIV5J98FGwbEQgg4zHkWBzjplsQxwa7GWAulrgfVHDCEjEGWKBIkL7xjmEbhAOuZxTM+TbWrMLzjkUZBO//Kog4WE2AAsqQxP17gVMO8xct5uc/v5ft2zfzuc99mGuvPZ+PfOSz/M3ffOWEoYX/F7Aci3vbHqI32c/0SDOqULEci7bYIR44+BgfmffeqUJ5iin+jzOSHWA4109Qj6AphZlyj+pjNDdEb7qD6aG5p9nCaycQCNLf3/u6tuGVPt6X/igvuJ5ik74OG5uV2Yu4MH8pRfJojaApOq3h+eyJbSGejwISRahUemrpjwzwK+/PGVIG0NFptWbzpsxbT2gJVrClmCxa7ta306m2U0GeEqeUZ12Ps0vbzg3Db0LGLLJ2GkWoFLvKmBacPZFdYGFxv/u3vOR6hqRIIoAyu4Jbsu9igbkEFZWZ1lxmWmf3HgghKPNUU+apPuXj3JqXBv90DoztIpobLnxHSBOfFqTBPx0hBKXuSoayfZhOfiLX4Yi/ccQoBOmVuavZH9+OpZoT11DaTmIoronHHMGnB1hQvJLhbD8ZK4VLdVPiqsA97uOcd3I40sGluM/4+0kVKrW+Zga3DOEPBVi4aGlhexqgS/oyXSTzY6hCI2aO0J06RGNgxkSn9LHnLewqIewqOfnOToYEY6NABuSkgF+nArS9oO0X5KuO1lFSghIHV5cCWUCTKAMC3R6vi05CNgsbNgi2bfNi2zdx3XU38rWvOYyMHOTFF5/hpZee4Y47fsj3vvdPANTU1DN79jzmzFnAnDkLaG2dRXV1Hfox/hEeDyxfLlm+/GR7PTWmadLefpCtWzewefMGtm7dwO7d2zFNk5qaeq6//i1cffWN7Nx5PuvW6bS2Hn19Xq/k4EF44IGCb/LYmKC5WbJsmaRk/G04EINtQ4JYDuoCsKhMUnya3MmFZZKV1fBKn6A7WZiQ9Gpwaa1D66sa0Pv6eshmMzQ1tZxwW3+KjHZEJ6wcjqC5NKQjSQwkYdxJRghB66Ut2KbNYNsQiaEkhkendlENdcvqWH/nBtwhz8RjS6eVIBTB0IFhzFQeb8SLJ+JhtDOKlbWQdiHITh3vPEa+sQGDr4VLL72aX/zixxw61EZz81/Gfd65ZkoMnmKKP1OklHQleojl4hS5I1T7K/9oN/Nt0YOMZEeZHm5GCEGvq4MevZP8MGx2tmNaFtPCjZS4yzjk7ibgr6J11QK2P7aGJTddjE/3MZgZmmQHYdomChB0ndjT69U4OMRFDJd0o6LyC++PWG+sYdAeJhYcJleRYG9iG3q3H2lDysjzQv8oVbU+Dmlt1FuNaOgMKv0IKVieX3V029KhJ9+JrTo44uhYoUt1k7ISJMz4KcVggGuyN6EgWGu8QI1VT7mooN5q5sLcpVSnq8hrWdYWvcSLxS8S12OUOuX0qj1E1REq7CqGlEHWGM8TkEEcJPd4fs2V2euJ5oawpElQK1Q0UkoOpzvYl2lD6w/hEhUUV7owKiUdvnZeiazhmpHrKXNXEtDDFLvLCemn7hY4EYnEGF/5yt9wxRXXcdVVNwBgYNBgN7NJX0eJUzbRTdOhHKZfO4xY/C26h5pZkDmfirHFCARjOTBUFb9bY/HqZbSsmsbhTe08fucj/OzLP8EX8rHiypXc9JbbWFyyihWVS5hXMouuRC+qolAXqMVQJxuhxfIjaEJDEQpuzTtxY3F47CDbBrdiSYWWcBO249CZ6OJXe37HZxZ9mGLP2Z+HPzRiFNRuwAVWI2CAZVmsXfsC99zzKx588G5s2+bqq2/i29++nRUrLpj4XAgEJMni6Wyq+AgtA08SirYhigLsqbqcvsbL6e7fySc+87e89NIznHfehTz69z9hRWwZ2mGBXQz5ZRJz0Qk6VG64GXQDa/1anOFBRCCIcemVGJddddxjc5dK1G6JvhOUQYFIgFMO1mwgD8Y2gdoD0gD/fymYrZB5j4NzzK+XvlOgDAqsGXLC38QpL1hQ6BvFxDHOm7eIhx9+mf/6r3/nO9/5Bo899iDf+c6PWbr0vOOO6y+drkQvHYluav1VEx1XmqJR6augLXqQwcww5d43MCFmiimm+JMnZY0hpTMh4kFBFDBUF7H88Bu672AwzL59u1/3dsIywg3ZW7g++5aC5/FJfHvDRjFLSi5kNDeI6Zh4NT+dvg5+67sTkJQ7laRI8Yjrfp42HmVF/kIWWktYlj9/IkB3mtXKy8ZzE7YUUWWUDvUwLulmmj2DSqeaCqeKrWIDD4i7uNK5Bq/mx5IWvZlOTGmyoGgFilBZb7zMk+6HKHJKqbbrcHDoUA/zW88d1Nh1p7SDOB05cgwpA7hxU+yUnrTTudbXjFfzM5jpIWtnCBpFVHpq8emF11vhqSGaHypYiDg2iIIlV72/ZaIGrvY1EMsPM5TtL3QIH2k68M/Apx3fWW4oLqq8kz2hslaaw8n9DGX7kNIhaISp97dQ5Drzc/Dcs0+yfNX55Mhh2DkM1UU0P0zCjBHUI5S5qxBCkLISdCT3U+KuOLed7zac8NITIF5VRjVWS7xDELMlwfGXmLclqS5YEgNM4FV+v/k8/OIXCuvXg6aBosD27Qrbtwv+6q+m8e53T+Pd7/7r8dDdfezYsZVdu7axc+c2/ud/bicaHQEKOQyVlTXU1NRRUVFFRUU14XAYr9eH1+vD4/Hi9fpwuz2YZp5sNkM2myGTKfx/ZGSI7u5Ours76enpor+/B8cpdI9Pm9bKwoVLue2297BkyQpmz54/UY8++aRCIDD5ROg69PcL7r4bSksFui7ZskVh40bJBz/o0I7g7v0KY3nQFcnLPYK1fZL3z3GoOUU/i6HCO2ZIFpVJDscFioCWsKQlUphKO5ZDh9oA/iTFYOlIRjtGGe2I4tgO4eowJdOKMTw6lmlPfqws/O5pxuTFKpffxYKb55HoT5BN5HCH3ATK/Nh5G92tY2VNCBRWMDRDo7ihCDNjIm1J5dwK4j1j6G4dX5GXXCKH40iq51ZhZvIMHxylZkHNH+x8nAmrVl2Cy+Xi2WcfnxKDT8KUGDzFFH+GxHNj/Hbf/ewa2UvazODTvcwvnc0t02/Eb5xbL9QzwXQspCxYLOTsPLvH9pG0kxi2H4mkM9FNPD9Ga2UjoQoP2iGdmotnsfvZjbRv2Yenzo02bltgOiZ526Qz0UNjqI5ZZ9CxuV3bwlOuh9mv78HCotQup1/todGehsi6iCUyKEh6QoepGptG6Ug1mkzgsVU0NAJOkA7tMDYWEaeEm7K3sshcBsCwMsgDxt0MujtRTIFhuGm2plPl1EwElJ3JOJsLFzdmb+WS3JXElSghJ0JQjnvweWHUN8K+wE9wKx78MohPFs7dqBymX+nFEQ6GNChySoiJKJ3qYZ5yPcICuYBjHX8PxA6zKb4VR7fwSA+ZEYueUYsK4cVT76UtvA8reg0uxUNzcNYJj/VM+Pa3/4F4PMY3vvG9iZ9JKVkydj77gnto0/dQ5JTSp3axW9tJyAlTG06xW13L89YmZnW9i6Ley0nkBSur3NQHyuhKHkBVNFqWtjBz2efoOHCIjY9uZOMTG3j6rif5zxnf5dZb382b3/w2WsumnfTYdGHgyONHGaPZKJZjU+UrzOepqkpTqIF90QPsHNnLRTV/eI+wM8YB1+MC1zMCJSrIKTme40nude7l8bW/Jxodpa6ukc985ku89a3vobT0eLuKxkaYPl2ydesMoi3TCbiyjMY12rsHyR36JD++4Rc0NbXw85/fy+WXX4sQggwSOPVqv3C7cb/pFpzLrkTdnMa9rwh1sxu7T2KukFgzmbhEZRBSf+2g7xGobRL3AwKRA2yBvk2gDAikITHnSeyqgvAr71dIv9+Z2IZIjW/vyGXvgDJS+KNvLojldnXh33Vd55Of/FuuvPJ6PvvZv+bNb17NN77xPd7zng+dk7flzwXLsXAc+zh/c01RsaWD5Vh/pCObYoop/lRQhV6IV31VGr0tLQzljQ1TCgaDxOOxc7Y9Mf7fqdAVg3LPUfHif113kBc5WqwZWFh06DsYVYbJiAwaOrv17ezQtvDB9CfwST+LzeVsMl9hh74Vv/QzoPQTF1GarelU2lUTx6HnNHo8XXh1PyoqGjqa0BjNDhLNjVDsLmODvgZFqhQ7hRZIFZUGu4l92m52adu5KH/ZxHHmyLFb306/0otX+phtzUORKtuMjQwo/UScIuaZi6hwqlhnvMSTrocYVAbR0Zltzuem7K0UO8cv/gkhKHFXUOQu46C2j8PyIHvSO7HSJhkjg9vjY2bJHOalljOWi6IoChGj5DibhWJ3BZZjknfyhI1iqnz1hPSiUzaspEnTrbUjHUksNkgsN4JH9aIqGsPZfhJmnPlFKwgZp1+0HxjoY/v2zXz3Az+hylPHULaPpBUnnh/Fo3opdR9tnvGqfqL5YWL5kXMnBgsw50vcDylQLifUFmW0MAVlNU6uqWZ44RI/PDUGfaOgCsjaMLcaLlQEah/YdZN3sXOnYONGqK0Fw8iSTg+QTqdYs8YiHDZZuNDGsixs28JxHJqbpzN37kJ8Pj/BYIhYLMrBg/vp6mqno+MQvb3d9Pf3sn37ZsbG4qTTKTKZkwc6KoqCx+MlFIpQU1NHbW095513ATU19dTXNzFv3iJCR/IiTkBFBXR1jYdHjBOLweAgzJhRqFUBbFuyb5/gkWcEBxsEjpTMHL8EbCnZHxU80S54/5xT16maAnNLYO4pOq2hIAZrmkZtbcMpH/eHRjqS/c+2cXhtB1bOAiSKqlI9v5KSaSX07x4gE8vgDhWm38YGEghFkBnLcnhtO+HaMOHqEEIU7FoKNhNHt6+5NKrmVbH/mf1oLg1XwIWZMRlsG8IdcGHlbaycjXQkQoDu1rHzNi6fi0Cpn1hPHPtVgvSfAl6vjzlzFrJly4Y/9qH8yTIlBk8xxZ8ZUkrubXuIV/o2UeuvotZfTTyf4KWeV3Brbt42481/8GOqD9YQMHyMZqMkrTTppInH9JP35gnbZZR6ihnKjNBmtnFR5BLmNJ7PQ+Ix/A1FPPXr+7jmH97L/Ok3MpwZpX2sC8uxkNJhOD3Cv2+6nSXlC7mgesUJhe7d2nZ+4vt/7Nd2kxYpsmRJGHF0aVBnN1LkDiMNh1F1GBTI+dKY/Ra27dBY2kCaIS7KXUazPR1TmNRY9RTLQjGeI8cd3v9mp7aNJn8jwWEfCSfJdn0zal7FnXfj1XwUnaHfroNDj9pJm7YXiaTJamGmNRcNjV61i6gyOt4FPDDuZexGQ5uwXvDJ8TRo4RCxi9ihb2aeaz6j6WGiIophu2kf60RVVVRDIWEl8YY1MgmLaHuOYLXAVh1UpTAe91p59tnH+elP/4OvfOVfqKkpVKg7h/fydOcLdCd6yep+tFqNRHWC7mQ3wYEQMzJz0JDMyns5YA6wL/8j5nZrrJ7WzA3TKlC1WWStNH3pTpL2GFmZpryxgnd+5r383Re/Ts+WAX539y/51re+zDe/+UXOP/8SLr30Ki6++AqmTWuddJNR6qmkP9NF2kpOBMWlrARZ20Tak7/2CiEngmQ+9ZrPx2tBZjKYa1/C2rQOmc2izp6HseoilLITew7rGwT2vWnujz/O/YP38vjhh0mYCaaFpvPOt36Qa256E/PmLTrlzZaiwNvf7iCEwr59Cl0ZD0ND/8327Z/H7/fwzW9+n3e84wOTRgbPBPUwGGsF+ith9AMRHD841RKtXcHYDum3OZjLjynAXWAukEgd5PMCbbNA3wciLXCCBfHYrgO0goewvkugDMORe1e7nIK3RB5QQdsp0LpBiYFIgv87KtnrHHKXHO0cbm2dzQMPPM/XvvZ5/u7vPsHhwwf48pe/har+8QMv/hDUBCop9ZbQnxqkPlgQP6SUE38v876GUdgpppjiL4pidxnupJeEGSOghwHIORmQcpJo+kZQV9dEX1836XQar9f7huwjKRL0ql0T9eGx9mASSa/aRcApCIKDSj+9ajcRpwhViVMki6myatmub2Gj/goX5S9DSIXrs2+hyZrOHn07ACk7yTxzMfq4v5EjHXJkCcjAJHFaU3QkDlm7ILiNKTHcTJ53V1AKGRziqCgXFzHu8P6QXfo2bOzCyLv0IZFkRBoFBRubZ12PszS/kudcTyCRlDrl5MnxsvEscRHj46n/D+PVHkwU6t7feO5gu7OBwIAX2zSxhY0n5cFOSV4qf5pLQ9dyTe6m48T2jJViZ3Qj0fwwR0Q+08lT5Co95fTcOv1lHnHfx4DaR97KoZUILo9fTbFZaJN1KW6i+WF6Ux1nJAbfddeduN0errz8ekKhMHFzlLSZYk98CwqFTvfJnHzRW4wVAm6dIo6zrDoV+Qsl2j6Jtl8gPRJhCqQqyV1+fDiuqsM76qFFSraMCbIWzCqRrAhCOCXoSkbZs24Xe/fuoq1tL4ODfezZM0h//yCm2U8+PzZpey+/fPrjMwyDSKSYkpIy6uoaqK9vZtmy8wkGm+jra6KvrwG3W2X+/DTz5ycRIoNhuHC7PbjdHnRdf13TqCtWOOzYodLRAeXlYFmwa5dA12H6MT1AqgrFxZJX2gSyFKYfY+ugCij1SPaMCpJ5if/ss8aP49ChNurrm/7kgodHO6McfqUDw2cQri40EuXTebq39hKpi9B4XgOdm7pIjaaRUpJP51EUQfsrHQDoHo36ZfW0rm5BvLodepymlQ1kE1n6d/UT7xsjMZBAOoXu4vRomrG+MYKVQaQtSccyCCEIVQdxbAfbtClp+tPyDD7CvHmLeP75J//Yh/Eny5/WlT7FFFOcloH0EDuG91Dlq5iwUAi7gliOyebB7VzZsJoid/gPekw1/iourD6PJzqepTPRg5mWaIcDWK0jKIZFyhkj447jz7q4lKvpb+1BNI1S+6VG9nxwE9FcG5+99K8JZiPsHN7LPW2/ZzQTRVVURjIxftf2ew7FO/jg3HdOspKQSF5wPc0hbT9pkcIvA5TIMnpEJ4PKAOuMlyhVy8l4Rog7w9iqxbCvDz0XoaaqkuqqUtoYQhUqs6x5x72uPfoO9mu7mWZNxxVwQ04QSYZJMkaXPMxsZT7Tg3Nxq6e/cbGxudf9vzznfoIshQQtAxfn5y7mtsx7cEsPBgYRp4iIU8SoMoxP+tGkga3YCClQURlRhnBLDy32DHrUbv6n5L8wpIo/4cPK2WQUm3I9wi720kEPOgaGRyGXtonnkiwQ8zEcHbd+GpOtk9Db283HP/5eLrnkSv76rz8FwJq2tXz/wR/Qt7+d/ECKxECUxGAUK5M/ei7Zcdy2uriPR4C/VVUqKqqprqmluKKEyvpKZiyaxbyFy6kK1RHSi5h1meDyy64hFovywAN38bvf/5p//Mbf8dWvfo6iilIuuvhyrr/izaxadQklvgoa/K10pg6M35QUbibCeiXbs5sp9VRMFLF520QR/EHFMGmaZH/9c8xXXkZ4vKBp2A/fj717B54PfQKltHADlEiMsXHjWtaufYEN973E5t6NmNJknmc+nw1/jhuL38RM/0ySlxe6UM6EkhL42Mcc1q3r4Bvf+DBbtjzNu971V/z93/8TweDZp8VruwuJ2cqwQGsHkRCILDhlYLVK1HZwPyYKx3eMXa8yDN5fKQgLspdJtE4wNgukC+yKo500uIA4iOzR55pzJOYMgb5TgCkpZD0K7AqJuURCCty/V7Cm2ZO6aTRN4x//8Ts0NDTz1a/+De3tB7n99l/g9f7hpyn+0Hg0D1c1rOY3++5nX/QAPs1L0koRNAJc3XgZunJ2CwBTTDHFXx4+LcD00DwOjO0klh9GUuierfO3vOFi8KxZc3Ech7a2Pec88FMied54iifdDzGiDKNJjSarhbdk30G1XYeFhYFBhV3NLn0rOFVElREkckIwdksPLlwY0mCXto2kSLDG9RwpkSTohLkgv5oPppbxXf836Fd7aLCbUVBIKylyao6F0cUTlhW2tEmaY2TsNFk7gyMdWqyZPOF6mLCIkCePR3qRwkFDpco+eu6fdj3KVn0jTVYLbjw4ODzhfogUSa7J3oQHLxLJQW0/v/T+hBKnlFarEMLsw4/H8rJP38UubRs+fMREjCKnmCa7BQWFtcYLvGg8zYyuaTh5mw7fYEGQllkaUo24ht08UfUQs6y5NNqTJ7Q6UwcYzQ8RMYpRhIqUkoQZ40BiFxFXKa4ThK/t13bzG+/PMDGptRqImsO0uXbzZMlj3Dr4DsJ2pGBVoriIm9HTvte2bfOLX/yEG2+8lXC4oByGjWLCRjEpa4xDib040kYZn+jL2GkMxUXoVWK1SID7YYG+WSByAqdUklstyZ8nOU3DOVAIxE192MHYIND2Cxyvg7VgvFZ71fPtahCNkvPbBNNbR3mp53le6FzDDzt3sjuzi97f9QCFGqaxcRoVFdUUFdXiOMuoqSnD56vA6y3DMPx0d2vMmaNx880CTdNQVa0w5ZVJk0qlSKUSxGJRotFRYrFRBgf76eg4zGOPPUBXVzu2XejuFELB759OMLiUefOW8qEPLWb+/Hm4XOdmQmDOHHjrWx0ef1zQ2ytQVWhqkng8YLxK1HWcQjPDyfpO8zaM5cGnv76gO4D+/l4qK0/tr/3HYLR9FCtrEq46WqcbXqPg6ds2zKLbFlA5p4JYT5yxgQQd6zrxhj14IoV7vWy80CEcqQ1T3npiuxXNpTH3+tnUL61l6z3bsXIWZdNL0d36hPCciWfQfQb5VB5/mR/bcoh2xShpLqakubjQOXwSsfmPxfz5i/jZz24nkRgjEHjjQlD/XJkSg6eY4s+MRD5JxspS5nl1EIOP/vQgyXzyrMXgDvUQ2/RNjIgRapxaFpnLTjhCdjKEENzQfBU1gSp+secuEvkE04ZXoAUsRsq7SRspwoMVXCavJzMtxa8yd5LqtilqrKdoeTcv/vMT3HFTM591vsxwZoRYLs7M4ukT9gsZK8v2oV3sGtnLorKjoq2NzUF1P8nxztkjwWVhp4hRZYQO9RAxZZRStQRsyRCD6CGB/wKTeXoTaT2JgYsGq/mEr2tEGZro0EUBs1RiByRpM09ejLDYvhCvdmZC0m5tO8+6HidiFxORhc6GLqWDX3v/h13adpaYKyixS+lRu5ljLqRdO0if2oMqFIrsYsrtSgzchGWEOruRuIjSp3ajoTGtuBXDpZEeTjHkDJPM28R0i5SaRtVyKDmNSm8Js4ZnMTczl7gTpcF/9t5JpmnykY+8E1XVWL58FZ/4xHvZvHkdHR2HAfBFApQ0VFE7sxn3Kj+5gI2xyqSqqYKIq3giXCAj0wyrQ7wz9gG0Hp2eni56ejonfMdeeuZZEj8Yw+VysWDBUlasWMV5513EypUXEQ5HaLh0Lg21KylNzyPVNkznlv089dxj3PebX6PrOosXr2D+/MW0zplF3Yx6auvqKfaUUe+Lsnuog7bYQUo9pVjSYigzzMyi6cwpmXHW5+O1Yu/bjbVpPWptPcJX6PZ2yivp2b6Zff/1XTZLm7VrX2Dnzq04jkNpaTkX+C/kbc1v50rrSpo8zUgXiBwo3eB6XmKuPLEYrHYVUq21/QIZhNxSh58f/h++9o3/j2AwzK9//TAXX3w5yMLNj9SZJNqeEgdcjykocbCrJFqHwK6UKFnQDoJTWeji1foFai/YTUefqu8oWEJYrRIUsKeDPSBRRkHtEziV4+nbQ+CUSuxjP468kH6vg/tJgefOws21Uy+xmiVOCAiOh7TsEdh1x5+XD3zgY9TXN/LhD7+DN7/5Uv73fx85bcr2XwIrKpcQcgVZ17+ZwdQQ1YEqVlQuYlq46fRPnmKKKf5PUOGpIWwUEc0N40ibgB4hoIfe8EyK1tZZCCHYvXvHOReDt+obudv7C3SpU2c1Yoo8O/Wt7NK3UWnXYAmTJquFOruevfpOutQObGws8owqIxQ5xZTYBQHFFjb7tN1sNtYTdEKEnAhjSoy7Pb/Axua2zHv4jffn7Nf2AIUshfMzFzNjbAZJxlAQDOUKgWmG4uJwYg9JM8Y8ZSH3eH7Nw/p9BSsJqeF3glyTu5EZViEBKkeOTcYrRJziiS7irMggkQggpkTxOF4Eghqrju2ezZTak6eN3HjIkObn3h9iKnny5HHjZrY5n7en389642WC2QCerIcudyeqUHFJNxklRdJIEcqE6LC62K/tmSQGW47FYKYXj+qdEFqFEPj1EPH8KLH8COUnCLHboK8lIRLMGBesvfiozFYz6BvggHc/SxLLx7dv4jFO38Tw7LOP093dwbvf/dfH/Vutr4lYfoRofmhcrHZQhEqjfwb+Y/2MHfD+WkFfVxCBnbBE6S8sYEvNwVx2ZovvMgK5KyS5K07++HQ6xfr1a1jT/wwvr32OrSObkUhq3XXMKZvHLVe9k+krZjNz5hyamqZjjCule/bAf/6nSiAgqQxA9WBhkb3bgpWrJLNnn32Y17332tx1Vw/FxQdIJA4xNLSd3t6NPPbYb3j0URNd15k1ax4LFizlwgtXs2rV6tcsrgkBK1dKFiyQ9PYWBGCPB/71XxV6eqCmpvCYfB6iUcHqFQ67fYKeVCE4DmA0A+v6BH4DvrtZZUaR5KoGh+qzz8OeIJEYe02NEW80ju1wolUIRRE4llPo0q0KEaoKseeJvSDAW3S0UckT9pCOpRlsGzqpGAyF31nNpZNP5SluKEJ3FxoFDK9BzfwqksNJ5t44BzNnMXJgBNu0CuHSY1le+dl6fMVe6pbUUTmn4px+Z0gpSUcz2HkLb5HvOC/kU3Hk+2THji2sXHnROTumvxSmxOAppvgzo8RTTMDwE83FKDsm8CeaixFyBSlyR07x7OPZqL/Cb7w/JypG0NCxMHnZeI73pz9Knd14xttRFZWlFQsp95by/S0/wrItqqOVtMRm05cewLQtrpx/NT8dup0Du4Ywsh5UNYfvulpGv7yRJ3/0ONd84E3sjx7Er/sm+fB6NDe2tOlJ9k0Sg4UUeKSHnMjik0dFWbd043P8xJUYLpklJZJ4NQ811GBrNqO+fnZb2/BILxfkVk90TbyaoBMGwMRERwcBjgcGAkPMNOfiTZ15R+EefSd5kZsQgvuUHvboOxhWBpHGBgbVPtzSS9AJMaT245VeGqxmrrCuJeCE2Gyso9gpxSt9RJVRRpUhPNJLk9WCqqjYQYnu99A5NMTYQCdX+a4m5I7QZu6ljgpm+OqoSZTjwsWYFmN9/kWydp5Z6vzT+upJKdm3bxef//xH2bjxFQD+7d++RuOM6Sy78ELqIgspaamisa550pf/3mgbRXN1hloOEbAC6BjY2AxpA8y1FnJp8hq0Gcd/Ddm2ze7d21m37iVeeeVFfvnLn/L973+LsrIKrrvpLQy3WngrQ8yumAkVsHTVKvbHDlKVK6ZuKMLaNc/zyCP389///T2gkFI+b94i5s5dRH1TFarbJJ6P4vZ7ubh2FVc3rMajnf4mI23BM53Qk1So9DlcVl9IJD4bpJT079jK7r5uDsRHaBseom1kkLaRIRK5LDz7MFVVtZx33gW8611/zYoVq2hqasH3XyreXwikH8aza5AakCoIryIO8lX1q9oOvh8rKP0CJyQZ6hzgQ3e8nyeHHuetb30v//AP/0owGELbDa6nFbROgWMUumtzl0vkaQpqZRS0LoFdXhB0EYADjh+UUYGIS/CAVDku/EQkxx8/HrAiDbAbC9tUe8GuLaRrS02Qu8w5TqCWRZC5TaIelGhdMGk9Z9xPWJzCBveyy67h/vuf5bbbruZd77qB3/zmUfz+Mwur/HNFCMGs4lZmFbf+sQ9liimm+BPGrXqp9Nad/oHnEK/XR2NjM3v2HD9F9HpZa7zAmIhjYNCn9+CVfmLKKO3qQWaKDLV2PeuNNYSdCKtyl7Bb286QNoAtHCJOmPnmEnR0xkScPHlyao4Ku5JSpyC0BuwgvUo3L7qe5ouJb/K5xFfZo+8gKzJU2TVMM2fQ62+nM3WAvnQhOC6sF1HsqQCgP9tNd7oPNaxRZdeQEkksYaEIQaVdg4HBQXU/zxtPsk5/Ga/0IWxBhV01IQQDOOKY3kkJ7rwLY0RBzwkcr8T2QU7J0qN2kVKSzDcX45U+kiLBBmMNPidAWqQwnILgKMeTzo5sX4pCFoOQAofJuQwSBwnH1ZMCMe5DfXyOA8CgOoBHHq2/vJoPl+LCdEzGlLGCCGQnEUJQcQbX5J13/og5cxawYMGS4/7NrXmZV7ScgUwPcXMETRiUuisodpVPql3VQ4UFa7tWTtRbdgDUQxLXC6IwgXTiXMIzIpvN8tRTD3PPPb/m2WcfJ5/PU1ZWwQXLL+F9lR/iourV1LTUYy6Qk8Jzj6W1FS67zGHz7xXmvwDVaVAVuLQYKp+DbKPkLG7hAGhr0ykra6CurmHSz3ftyrN48RZ8vo1s27aRF198ijvu+CGaprF06UouvvgKVq++klmz5p21AOj1wrRjGsxvvFFyzz0Ke/cWxGAhCsL2NZdKmrNw136FPaNgO5J90UIYXFNIoglY0yvoTSl8fL5D5EybGl5FIhGnoqLqtT35DcQddGPnbdKxDN5w4ffFNm2svEVpy+TmMCtnn/B9EIqClT19PoSdt7AtB7c+WXBVDRWEwFfkpai+iMZl9Wy9fzvta9pxBdx4Qm6iXXFiPTtwLIeahSfvsM7EMvRs72Xk8CiaW6diZhmVsypQtON/sVKjafY/08bwwWFsy8FX5KVpZSNV8yrP6HqbNm0GHo+Xbds2TYnBJ2BKDJ5iij8zIu4QK6uW8vChJ7Ecm4DhJ54fI22muazuorMKkEuLFA+67yJDhhnWHASFAm+ftptH3PfzodSnTysUvpq6YA23Tr+RBw4+RlvsEAiIuEJc03w5tYEqtm/fj2KrlJQUlKvwEj+xlV0c/PZeem/rxqf7yDvmpG1KKZGAa9znayQzyvPda9g6tJOusjiZWSYj3hEqlSpsbOJKlLATQUFhmtVKSEaIOEUEZJBetYs92i6azOlcnb+BRfnlk3zjoODxdjjeyWgyjeYLsqd8N82uZgzpojPTSX/vGOUjFvfpj7CwfC4NwdrTnhcbiyMltYXFfm0PpjAJyCARp5gZ1hzatL3U2HW8LfM+UiI5Md5nijxlTgUbjbWMKiNEnCJmmnPYYKxBO+ZjXFEErbNr2Gt30TnYgzEWZLmxipaSEur9tYy6h+kyOsioGbxpD3ut3SzwnceN2VtPmLjd0XGI3/72Dh566F4OHNgHQMusWTRevwB1WhDFUEH1oGc1hGuyf5jpWAgEl8or2Ww9z0Ft34QjW41dx1sy75h07Meiqipz5y5k7tyFfPCDn0BKyfbtm7n77l9y929/QSIep7ylljmXLmf2pcvQXQbl3jLG9DQ3XvpW3v++jxauk5FhduzYwo4dm9m2bRMPPXQP3d0dE/vxBwJsrqnnyepfUVvbQE1NHdXVtQSDIXy+AD6fD78/gM8XoM/08w8bvLSPKdiORFU07twt+cZKi6aAOZ6unCaVSjI0NMjgYB8DA/0MDfUzMNDP4GDhT19fN9HoKACGqtJcVEpLcSkXN01nmmUze+FSWr78zePOiTXTQaBBVhZ862wQqUInLopAGQT7VWKw8YxA6RdYMyQb+9fzzpduwbIt7ln1e1Z96krsIGj7wPdTFZEAu0QisgWLBXVAkvqgc8oqQWqAWhBdnSKwi0AdoNCdO54LovYIzNmyEOh2DHYEREYiYuMitgCrQaL0CZwIhU78mZC/wMFcePIOF2ueRDuggHXUWkKkQGoSq/bUnTFz5izgf//3YW655Qre976b+eUvf3/ORiCnmGKKKaY4O2bOnHtKMThnZxnJ9ZOzc3hUL8XucnTl9Eahu7VtdKiHkICGRkYcJq5E8To+Qk6YUqecEqeMfdpuEiLB3yW/QbfSyfOuJ9mqb6JbLdQNBi7mmgvYp+8m7EyeJonIIgaUfkaVYWrtBs7PX3z0HwXUB1pwaR4SZpyAHirkGRwJVtVgKNvDtNx0wkoRNjYCwYDSxxZjPQ1WE7/13cmoGEbHoFvtIKHEmSXm0WA1o0sDSxkj6BSKAEfa7HG2UZooozxWTt7J4E66yQdz7K7YhhSS1vxsvONNFH4ZoNyuZLuxiRn5uax1P0upWoQ/6yfhHcNERUgFT95D2pVBMVSaUy2TXr+uGBS5SulJt+NWvRP1YNpK4lY9J/X6rbZq2altIWtnsKWFKlQirhI0OnCNaXQk21AVlWpvI8FxH+uT0d3dwdNPP8q3v337SUUil+qmzt8MnHgiEEAZEZA9KgQfQYZBGSgsvqMUwuDOVEmx7cLE1733/i8PP3wvicQYCxYs4Ytf/CYXX3w5LS0zEY5A3ygwXhEoL4DSBfnzJXbL8dtTFLjheskVOwp1U7QVQiWSkgjohwTidwrJz5y6hns1Xi+Yk2+/kBIUxUVr6zIuv3zpxM87Ow/z7LNP8Oyzj/P97/8z//zPXyIcrmT+/Gu54Ya3c/PNKzGMs1fMzz9fUlNjs2uXIJWC6mqYP1/i88F5SMp9NtsGBS/3CoazsLRc4htvNgi7JPtjgq1DgktOU/+djHg89ifVGZxL5dn31H769wyQiqYY7YzijXgIVARwTJvSllIq51ROek6kJkznhi6svD3RQWubNrZpo7k09j21HytvE64JUd5ahuaafJH87De38y//+RUun3cjH7nt/5v4eTqaxhNy4y/1kxxOsf3+Hex/pg0pJe5ArrDv2jBjfWO0r2unck4Fqn58B29qNM2Wu7cR7Yqiu3Uc26ZvVx9djd3UL6sjXBPGMx6EZ+Vtdjy4k6EDwwRK/bh1ldRImp0P7UJzaZTPONrl7NgOji1RdWXy4s74PeW2bZte57vxl8mUGDzFFH+GXNN4OS7VxZre9cTzYwSNANc2Xs7Fteef1XYOqW30q300WtMmRF8FhQq7kjZtL1ExSpE8e0P45ZWLmVHUwoHYYRwpaQrVUewpYvfoPkioqMfUhKYrR/hTFSTfO8SdX/gRn/3CN9g2tIPRbJSIK4xE0pPsJ+IKMbu4lXhujJ/s/BX7ogeIGCHCPZX4qWCksZN82WE8omAXUeyUUO5UUeFUUXtMWkPEKWaRuYyPpD9DhXP86m/eNrl7/wOs69tExsqSFoLRgIU55yCqD/o2pfDFwgwS5yHncV7uXcfbZryZxeXzT3lOmq1WnnU9Xgi5ExmSyhgex0dSSVDilCEQVNrV9Kk9VNk1VDtHBWZd6tySfSdX5q4nJZKEnSKiygj79N2MKMOUjFt6SCSZ8BjXLb2Yi7qvYyyfQFHzJK1BpBfa9YPoGJTIMoQKalrnydKHabamM89aVNiGlKxf/zI/+tH3efzx3+P3B1iy5Dw6O9u54OLV1P/VCkYyozQEa1GFSiw3Rmeyh5RZ8B8tckcwHZP2sU5q/FWcHzmPVakV7NC2MqoMEZRh5pgLCMnwGV9PQgjmz1/M/PmLufUTH+TLP/sSw2sP88yP7uWVu55g+S2XU7RyEUlZw+Exg5CrkBxcXFzCxRdfXrBBGH9vNx7exL5Du0kPJXBiOXq7u+np6WTNmufo6uognT5FkJyioRhekA7Ssel1bFY7Fpyk48XlclFaWkF5eQVlZRUsXXoeFRVVNBeVUvfic9R6/Bi1dQghcMbiyP5e3KuvOOG27CZwiiRKt0CJgfRKrCaJXTMeTPLqLt486G0Cp1jyi10/4zNPfYwFoUX8Zsbd1AxVkX5Ukn6fg/G8ghgDa3rBx04CMijRtwu0NrBmnvx0yDDkZzm4XlIKwW+NEjEmUHsKx6eMgN0gyd7sTOqi0TcJ3E8J1D6Btq/g9WtNkyipgm1E6iMOdhWFCuU0a1H58yTadom+X+AEJFgF+4z8ilMf+xHmzVvEnXfez1vfejWf+9z/z955x9lV1vn//Zx269w7vffMpJFGEggloYXeQVQQUFREwIJl1VUX/a0NV9e22AFBadIEBAREOoSQkJDek5lM73Pn9nva8/vjTCaZNJKAq7s7b168IJN7zj3lzj3P83m+38/nk/zXf935d2+HnmCCCSaYYF+mT5/F7bffipRyn+/hEXOIDcMrSdgjo48FQb5RyFEF8wlqB25jsbEZUAZIiSS1TgMCgVAEg7KfjMhgSN/o3gRFbjE7tC1oUmeyM42m9BS2ahvZqm3CxaXRbibi5vNj7dv0Kd3kyShhmYeCQlIkCcgAYffAbfMKAk3RvZyJPU7PUkwc1/HEXIWxAoWIjDKg9PGU/1HiIsZUewblTiUrjWUMKP2s0VaSI0uhW0TEjdKhtuGXQWLuILZpcVbifMJqmFbfdmJuDF/Sx6RUM1qeQd5eSmdABkmIXpoyzSzTXmZ19G0aBhuIJiNktSwFTj4WJvFAkhPiJzHJnbLP87ku3ETcHGYo14+u6DjSQRUqjeFpBA5gqTYvdyx/5c+skG8QNaMgIOlLMj+2gPpMA6qqYqhegNza4WXMKDjmgPf77rtvIxQKc/HFHzzgPTgUZFh6g8js+KwDEQeRgfCPFZS0wCmWmCdLzOMOXCmcTqe47747+e1vf0ZHx07q6hq59trPcOmlVzBp0ni7Nv/TAv+TAilABsH3moKxFrInO2AIL3x3msT1CsrRhqBiSODOlRTn796PU+PZdqlt4+253ol58yRr1wpiMcjP94Tgzk7v/6dNGy+u1tY28JGPfJKPfOSTvPSSye23v0FLyzMsW/YwL798O9/+dj0f+tDlfPCDH6K5+fBs2OrqoK5u/2JuY9SrBO5JC9L2biEYvMpoVUi6kof1duN4r31lbRd2jEDcFBT5JfWRQ/c1llKy8ZmNdLzdSag4RPWcaobbhkn0JlFUhelnTaViRgW+0PgFsbKppZQ0F9O7uc/zFRaQTebQDJX2lR2jthLea0unlDL7kpkYQW8fq1a9xX33/46mhqlIYKhtCF/Ih5kyQQiaTp6Eoqms/fPbdK/vBiEIFgRwcg6DOwZRDZVANEBqOENmJEu4eN/f+/YV7Qy3D1NUX4iiKiQHUvRs6qF3Ux9d67uJVkRoPLGB+gV1DO4YZKh1iIKafDTDky3zq6IM7hyifWUHZVNLsXM2rcva6FrThZ2zya/Op35BLYV1u8WG6dNnsXTpK4d/A/8PMCEGTzDB/0AMVeechsWcUnMiCTNJxMjDrx1pRdv+HrjvXgyJ+iL7CKQqKiWynB65kyF1EKs4Saykl8yUNL5vhXn1S89z4jmnsLjhZF7rXEpfuh8JFPoLuKjpHKrCFbzY/hpbh7czOX8SuuJ9hZUOvZ/Xe18ldLRDsFKlzKnkROsUcjmL+/Tf0el0U0Ip/qBOXBlhce7s/QrBAEu73+LljiWUBUupi9TgSpfWRDvybZtQIIg6NMyMgqkoQkFKSWu8jT9vf5bpRZMPajUw25rHfPN4lhtvkCVDUiQxFYsqt5oK1yublLgoiP1W6YI3MYiMegH43SoW5k7lWf8TxMUIARlgRIlR4BZxlnM+U0o864uudCvrh/vpEt3Ywh6rZhES/MKPJUzW6m8zNT2DJ554iNtuu5U1a1bS1DSFW265ldmz53LFFedx9NHH8Ilvfpm7tz5Mc34jquJNVgr8USpD5SStFBk7y6Zhr4qjNlLNB6dcQkgPgoQF1uEtVByIyUVNHHPqIjrnT+KUzCUs/eNfeeG3f0J9cAll536B2wkxqVDh8ikulruTZd0r6Ur1ENICtCc6GczGsIWDVqYyeeokvjj9mxQHvAWPWG6Qjd2r6B7uIJvOEnDC5DkFbO+z+MXyNKqVwOekEYoKikrOVbHQuGaOweQSL2E5GAxRUlJKaWkF0Wj+AYVFq6GJ7OOP4G7e4LVW+vzoi05FO+b4fV+bgu1PKkQVqPBJQgboUiDiEqVXYJ60e3Iwhgo51eSrr3+R3277FR8v/QQ/zfsvfAkDEQf/c4CqeN6++eMDTWQIMEHpFTA6AVB3gL5GIOLg1IJ1tERGIXe+RO2Q+F4SKMMCbJA+L2Qle6HEmi2Re4yrtU0QvEeBnMQ81gt/U9tAyQgy50lyF+ybtH0w3CJIf9LFeF2grfMmS+Y8iblA7mNNcSAWLFjIT396BzfccBUNDU184Qv/dugHMMEEE0wwAQBJa4TudDtxaxi/GqAsUL1PC/7BOOaYE/jhD/+dtWtXMWvW0WM/d6XLlpG1JO04+UYRilBwpMNwboDt8Q3MKDjmgO/Ro3ZioBOWEWLKMCEZxsFGCokiFfxyt9qXFRmK3JKxriUFhSn2UeOsxFrUbQyKATYaawnKEIVuMRVOJTlhcmbu/DErsP0R1qP4FD9ZJz0mjjrSAQtkBEa0EQrYvX1cjKBLnX61j1KnAoGgQBYx3zyOnWoLO7StlDmVXJi9jHKnkjfUl2kV26lKlVMdr2a69Mbg1U4tCSVOxkpSPTyJh4L3M6gMjBsHDyoDFLhF6IOCU93F7CjYQW9xN+FkiIJcISErRJ6I0jDYTJEsZp1/OdOiczD2CIXL0/OZU3Q83el2Rswh/Kqf0kAVRb7xvsV74sRMjnOOY03RagaNQUBSP1RHfV8NhUYJAX9o7DMwbA7QkWphcnTmPvsZHOznd7/7JVdd9QlCoXdhGgvYk8FukmO5A9I/aou1QyB1r2LYDUvUTgjcLcABc9H4udTw8BB33vlL7rjjF8TjMS6++IP88pd3M2/egv1+VpV+8L3kLay7o8WObpHE94Ig720Ve9TywVcoyVwmsRZIMPFS1fZSc6TmWXEZrwvkJnAqwJouvUDeg3DMMZLWVliyRKGnxxODCwvhootcqvfIj8zloKPDEzV1HZ56KkBR0anMnn0qcAvbtr3OqlX3cdddv+aXv/w+s2fP433vu5IPfODq96zqttAvMR2FPeewUoIjBVHfkVUFg1cZbITyealD0J+CqB9mFUvKjyBnuD8N92xS2DosMB1JQFOYXSK5fIpL+J0bGkj0Jujb0k+4NA9/nnfzyqaUEiwIogc0qmZXjnn67oke0Jl96SzaV3bQu6kXKSXl08roXNuFEfQRGvUStk2b3k19dKzqpPGEBlKpJJ/+9If54Q9/xc9+dgslFcWUNBWT7E9RWF9I9dFVVM6ooH/bAMNtMaJV+WRiWZBghAyy8Szx7jiaoaLpKrpv/zJj35Z+/GEfiqpgpk36tvYjHS/ALhDx49qSzX/bSqgoRGYkg3TlmBC8C3/YR6IvgWu7rP/LBtpXdmIEDTRDpWtdN0Ntw8x9/+wxQbiqqoauro7DuX3/Z5gQgyeY4H8wAc1PQDtCYySgwWmizKmkU22jzmkcs4noVbuYbx1/0EHtkVAXqWF6cBpyyGWgYScD5R0otkZooJDGixpof2UTP/mX7/LYCy9xTPkNtI60oSkaUwomURL0PJF2jOxEV/QxIRjAUAyq0vUc1zqPC0NnEpQhWgbbuGfTQ9jREIOVHbT5OihOF3Nl4MNc4h64amB579sYik6+z1OwFKFQn1fD2sENxOIpJuXXoQhPrBVCUB2upDXRTstI20G9OH34uDp9HdPsmazS3yKtpHBxmWPNR5c6Li5dagczrbmUuRUH3M8uBIKLsh+gzK1gqfEqMTHMjNwcFpmn0eTsPo5Co5SAFsIyc6CNDkAdEC7YeaC4Kq/f8yI//t536O7u5OSTz+Dee5/g5JPPoLu7kwsvPJny8kruuutPvB1fD8Lzh8450JWEgawgbQUpDUS4ae5H6c/0EdB8NOU3HtIChYlJi7aVHCbVTi2Fsoi2eAdrBjaQtFJUhsqYUzqTiOFVsfg1H5c2nce9mx6hx+0j/IGrKZ77Zcznf03XvV/i1VfuZOCjd9CZCCLEAyTMGEE9yPqBTSTMBMeWz6Mpv4GMnWXdwEb+tPUpPjHzalJ2gvWxt0jpCUoqypC4ZJwU+b4wU7Mn4Lf9FAck/j2emlkb+jOC+SfbnHyYQev6wlNQJk3G2bwBLAulrgF1UjNCHd9SNZSF+/6msCUu0BfBGWtgVq9gclYS6BE4zS6ZS9191m8Ghvu47pUPsmL7Mm6d8Ss+mf2kJ/omwCmFzgJB8n6Iu5AfhDxdUrjr190GhESOZk8YrwkCj3gVxKgSHIG9RJK+1sUtBlnkTT7cqEQanpisxLxt5V4FFvoSgUiCPfoRNYslYiaoLV47pL2fdsh3wi2C7IUSLjzyCcBFF32Abds28+Mff4eFC0/j2GNPOOJ9TTDBBBP8X2PEHGLd8HJSdgJNMRjK9dOb6aQpchS14aZ33r47TnSkmKA/zL2/+B3VX/sWhXVeBkbcGiZuDZOnR8fGX6pQCep5DOX6xomr+yAFeTJKqVNGt9pJn9KNXwbGLBUEChJJXIyQFEnON993wAX5ITHIXcFfo6JQY9czpA7QrrXSr/TxvswVXJB930HPMaiFqQzV05rYTB899GrdZGUG3TCQEYVurQPXdQi7EWLKECNKjDOy57HctwRb2GO6V1QWMMUOECTEhzOfYJI5ha3xtZRniil0IySsGI50sIImumpg4KPILWFIQtTNZ5F5Gn/2P4QpcuS5UUaUYRwcFqSOJ5dLU69Nojk2DYkECZ2ZVlJ2grpQE34tiOnk6M10YCg+puUfvc85ToocQlsOjO1nMtOY2T+XYW0IBQWZtBmwehC+3fdBEQp+xU9/tpvmyIx9BNWf/ez7KIrCZz7z5UN674OiQ/oql8AfFbRtApED6fOqde0GGLWKxsn3FrN9LwrMYz2xdXh4iP/6r+9z99234bouV1zxUa6//nPU1NQf9C3VDi9nYU9LCLVjNF8hIHAaXKTPCwUOPCJwGj3R2KmQXgVwiLFxoL5ReOHCf1O8RXFVYh0lSF/j7jMmG3faOlx+ueSYYxza2gSaBlOnSsr20PJXrxb8+c+C7m6BEOA4ksFBOPbYXRWvCs3NizCMRZSX/4Q5c57kkUfu41vf+jI/+ME3+dCHPsbHP/6pd7we78TcUskb3dCegIqQ96vRkYACH8wuObKxoG3bZLMZ3hiMsHqDFz7hAi+2S66c6jKj+J32sBtXwgNbFNYOCOrzJAENEpZkSbcgzxB8cMo7H2M2kcPMWOSVjq/i94UMsoksZsrcrxi86zVNixppWuSVhre80YqzwiVYtbtoSTM09IBOz4ZeGk9o4Gtf+yyLF5/LSSct5mc/u4VQYZBjrpyP67gIRYz9zuWSOaSUhAqDBPIDJAdTBPL8qLpKNpkjNZBm0qIGfHn7nwMquorreuefGkxjZy38UT/ZkRxCUcgrDTPYOkj3uh5KJ5eAEDiWM85yIpc2KaovItY5Qvf6HvLKwvhC3vsFCgIMtg6xc3n7mBhcWVlNPD7ynld+/29gQgyeYIL/w4RkmAuyl/HH4F1s0tZhSB85kaPGqeOc7EWH7Rf8Tvg1H5c0nUtqY5p+pQV/LkgoVkChr4BZhUdx9M9mcOdxv+Ir/3IjT9/1xn69eEN6ENvd1wDfkS4RLY8iWUzGzvDQlj/TlxqkNjeFgp4KbJ+JlXbR6qMEphy4gjdlpsa8iXehCAWBwJEOcq/n964/HkrlS4AAi8zTWGSexum5c7g7eBvb1C1oqDg4VDt1XJi97IATkb3R0FhonspC89TREJF9j8GvBWmOzKQ70clAphc52rZn50k2v7WZl77xVxJr41xwwWV8/vNfY+pUL7V6aGiQK644F03TuPfeJ4lG86lwyjAUg8Fsis1DYfozXktW3IyRdRawpLuKD06uRN3r8KWUpKw0uqrjU3cvh+9Qt/JA4A+0aS1YWBS4RdT0NrHz7RgjuTiKUHCly+tdy/n4jCspHV0QmF40hc/P/SSr+zdyz8ZqAlOizFp4PkM7VvP8rz7NG99ZSMHppzH94ikcW9VM0k55EwnNT1uig5q8SgKan8pwBRuHttCb7idh95GyExQYJWP30qcGGM4NUBnsIc9Xx3AOKvZ4asZyEPFJph3hmolaUYlacfCgise3C1YPCZpNgb9Qsu5YWDoimZ+ED3W5OM1emNqedO5s5/JLzyExMsJz017kxMHjEY73SZUhaI3C29uhNg79+SB7oOs1habjXEryQWsVOFUSe7pEDIH/CQGu3G27YEu0LQLfcwLzBImxRCDSIBxv0kRSIgcFvmfx2if3GKtqnaN2Dnvg+e4JlKEjF3PfC2666au8/PLf+MxnruFvf3trYsA4wQQTTDBKQsR5zXiBFfqbuMJbyD4xdypFstjrkkpsIW0nxz1Dk1ac1uQWSgOVnjXCAejfPsCax9aSGc4ys2YeLy15jtP/eDFHnTedyhkVuNJBSndMCN6FgoKFi7uHVVNSJNiorWNQ6ScgA1TYNfQo3bSoWwnKECoaKhplbiUCGFB7GaCPgAxwUu50FuUWj+0rLVKs01YzpAyQLwsYFAN0qDuZZs9CQSFux8gKL5Ctxqkn9E6pq8CkvGm0+VrYmFsHDihBlc5oDyOhEUqdCkzFpFNrI+zmcV72Us7LXkJapHjF9zx5bgRjNIh3p9ZCnd1Ig91MW3Ib7antBLU8QloEBYWeTDu92U6qgvUIIcg6GVShUugr4ezsVMJuHq/6XmBEiVHl1HKSuZgpmWmskktQRgOcBYKcm8V0smhCG/u5ofoIyBD92W4anKn41XcO4N0fljSxpY1P9aOiUmx7lmdxZQiJ9KqmR7Fdi4yTwYfEle64kOn29lb+8Iff8LnPfY3CwvHWdkoPaNuEl0swaT9dVAfALYfUZ1zUnXjjmxEI3aXi7iUIuoWex7AccLj3xTu55ZabsS2LG877HNed+CmKakqw8uV+ezD3ROrSMwK2R3Mh8DIXUL1uK1RAgFMN2hbQNgnMRZLc2ZLg3QraFs/eQowI1BZPJLZneNuQBX2VwPe8IHvJwY9EUaC5GZqb933dzp1wzz0KyaSkstL7+7fegt5emD4d8vbQLDUNbNvPeeddynnnXUp3dyd33fUr7r77Nm6//VbOO+8Srrvuc8ybt+Adrsz+acqH9092eWqHYPuId5plQbioyaXmCLOAd83vBrOC2fmgKhIpYfsIPLpdobnAxbevBe5+aU/A5iFBTVgSHB0DRwwo8UtW9ArOaZBEDlId7NouuaSJa7ukY2lChbsXu3KpHL6QgRE6hPLivc5t77mqEOC6Lo899gBr177NX/7yxj7bKntN6vSgjnRdrIxFSVMx0pVkRrLkkjn8ER+Vs8qJVkZZ9+R6HNOhoLaA8ullY1YUFUeVs+HpjVhZG8d2kFJiZWzPYmI0HE/z6WTiWYonFZFfHWW4bZhIWQTVUEkPpRFCUDO3imR/Ett0xoTgXecYiAYYbh/GsR1UTaWqyguf7OpqZ8qU/YfG/19lQgyeYIL/4xxrnUBJspS39eUMKYNUOzXMs44bS0l+r5lZPJ2bjr6Ob/ta6dAVaosbKQ+VehXOxTDzV3NY8b43+fnPf7jfFf6ZxdN5vfNNetP9lAa8UVlvup+g5mdGsadWbYu10DKyk6HsMCO5BOD56YLg2daXuKDxLIL67omJK2HTEGwYFMRyzbTEX6IoUIpv9AGYstIEtSD1kVo6k91EfXljNhEdyS7KQ6WHFCK3J9PsmdyU/Cqr9LcYUgYodcuZY82naNT/93A5mHBfFqhisXEh3dxGq7KNxI4UK778Jj1/7aJyQTW3P/kgi+aeNvb6oaFBrrzyfIaGBnn88ZfGknWb8hs4unQmj29fTk8qSoHPIOcMURSMUlE4jb90JZhR6mNW4W71b+PQFp5ve5W2eDuaqlFdUcrshqmUaWXcG7yDDrWNOrsRA4MO2nlAu5vawhnMyx6HEALbtdk8vI2/7nyJq6ZdNrbf4kARC6sW8txOBQkYKpQ3z+fyH7zKq3/8Gisf/ilrt2yg6UtXo5T6caRDnh4maaWImwkK/QX4VR9D2WEydpYRawhdMcaHDggVicSvxTm/XnLfFkF7EkIapG3vc/O+eknpgee474rBDKwbUCjXIOiAi2cbp+fDCwVwYkpQGRjvV7xj+1Yuv/Ac1KzC8wtfoaFgEvJNIONNhJIVsH6FQNchHIJtDZJ0raB0vSS5TFA+WWLXSjIfdJFh0JcLlCHGV+xq4BZLL227zLOQwO/9DAAH1F6BvmE0zC1/96ZOGRjtYjR3fBQbwLOd+EeiaRo///ldnH76fP7t3z7Hz372u3/sAU0wwQQT/BOQJs3vgr9gtb6SPJmHQOGxwANs1NZxferz+B0/MXOQgBYa9wwNaXnEzAHiZgx/YP8PStd22fbydrKJHEWNhZyYOo0fP/BN+od62fqSj9LmEsJ6lIAWImUnydO9B4WUkrSTJKoXjlUFb1U3cXfwNlYabzKg9OPiokkNTWoUO2VklDSGVImLEWxsvpT4JhVuFTmRpcKposFpGluM71Y6uSv4K3ZoW0dHj5AhgyvcMU/fqCwgKiEj0nSrh9aC7AiHpUVv0K300+A0gYAKwuAquMLhhuQXUFAodIvJlwUMiyGanalsdtezXds8NtarcKp5f/ZKFFehO9OGofjHRNmIUUDWSROzBllvvs2INoIrHEryKzkqYKNJjVPMM1lonkZapAjKEBoappbDrwXJ2EnC+u4wOss1CWmRcWF9mqKTsdPYrgWHIQZn7TT9uR5yTgZd8aErOjkng6HsWYShoCkGlswhZYiYOciIOUjWyRDWo7w99DpTorPHPgv/+Z/fIhot4BOf+OzuXUjwPSfwP6sgRkYXw6MK2bNdcqfLfbqp9ou623NXbQXp9zyD99T8RRqWJ97kpo9+htXrVvCB867me6Xfp7K3DP7qJenadYLMR1yc2gO/ld0Edo1E2wl2I54HcQ6E6QUFy13DagVAICzvnKyjJamQg75EoHUIyJOIJFhz2X2Ofs8OTF8hyJ5/6BZae7NihWBwEKZN2+17O22aZxmxYwfMHnUGdBwYGRGceOLu8WlFRRVf/ep3uOmmr/Lgg3dz223/xQUXLGLevOP40pe+yUknLd7POx4YIeDESsnMYknriHeuk6KM8xA+FFwJ22PQmRRkLE8Wi2jOWFGLEFCTB+0JQesITDnE4o+UBTkHgnspbQENhnOClMUBxeDhjhgbn93MSNcIyf4kgy2DFNYXUViXTy6Rw0xbNJ7QcMCq4P1RUB1F92tkRjIEot7vq2M75FImoSk+vvGNL/LHP/4Fv//A3caO5dD65k7aV3YQ700wsGOIwvoCiicVMdIVx7GCTDt7CkjB2ifW49oOQii0r+qke0MPcy6dhS/so+boKobbhund1EdmOIOVtlB1laKGInwhY1QcNsmvjKD7dWZdNIONz25muG0Yx3YJ5AdoOrWJiqPK6V7f4+WuOO440drO2YQKgyiK97PKSq99s7NzQgzemwkxeIIJJqDBafIGpUeI7dpsje1gMDNMwG8wWNbNGv9bZMlwlD2bheap48TlynA5Z/rP5An/I9TZ1WODbxOT8nMqef8Xr+KWW/6N6upaLrr4A8StYWzXJqSFmV44mfMazuSvO19i0/A2QBL1RTi/4Uwao3VIKTEdi+5UH2krTXGgaLSaRNKbHmDHSCvD2ZExMVhK+PN2eHhkHf0FK8hUdRFPZhls38Dc4lKktEhZKRZUzOPUmkXcveEBlsdWkNBGyLpZSn2lvK/pnIP6BR+IMreCs3IXHPF13x85xyRhJgjpoXEWIoVqCR/MfJR/+Y/reeH2Z8iri3L9XV/gM2d+eZxHXW9vN1dccS59fb3cd/+TxPNy3L72HmK5ESZF6zm15kSW91QwkluBInIEI/XkigrYGdxBot/hJ7mNfEmbyXR7FluGt/O7tfcxYsbxB3U2sp5ndw6Tb/spOybEsNbPotxijNEyCD3lJ+vkMKtHENu9kaamaJQGilk3sIGMnRl3nQ0FKsOwfhBKRn+sajrTLv0ineUDpB56mns+/yNO/uQlBKb6SVsZFKGMVRcNZAYp9OdTFiwmmexlODcw7lpKKQGJrhh8Zq5LxC94crvCUA4qQ3B+o8s1R/39qlkzDpgO5BdL2OnZK8gQ+AWYFmR0ib2HLfeGDWu44gPnUWQX8vgFz1BRXYULWC4Yr4FICkYsSSYDdQEwdegugb5CiciH4KDgw1e7hOYDuy6z5MC24hKUYRAWOHsW46ggVYlIgNyrisI8zkVfq3ptjWUgbK890qmVWDP+sZXB4IWifPe7P+Wmmz7O4sXncOGF7/9HH9IEE0wwwT+UNcYK1umrmGQ34xtN1LKcCrZoG3jLeINFzmkIoeDu1TolcQGxT0XvnqSG0sR7E+QVhxFCcPTkBaiKysbBVRSEioj3xCmsK6QuPJktI2sYzg2gKwamm8On+KjPm4wiFDJkuD94F28byxgRMSJuFIGgRduGIX3MNGspcArpV3tQhEpcifFQ8G6OMxdxfvZS6vZI2pJIHgs8wDZtM032VHR0bGyWGC8RU4aZbc0bE4QlkpRIkhVZnvc9TcTN5yh7FkG5f9uKAaWPPrWbYrd0nCBZ4paxQ9uKKXJMt2fh4vKs7wme9z3NsDKEgiDsRjjank+jPZmZ1tEUyEJyMoPtWujKbmFIEQqF/lLalZ10RboQfhUrYLMmtIEWp5Ub0l+g2C1FQxvLoQBvvFUTbGR7YgOx3CC6YpB10qiKRkALjBP6PWuO4FjF94DSx4iIUeSWkC8Lxq7NnoUKMXOQDcMrSVojo2qi3KPrbhifGsByc9jSoi7URNbN0JXeScKKoQiViF5Aoa+UoVw/G2IrmVe0kK2bN/Hww/fyne/8dJxXsLYJ/E8oSJ9kl3Oa0iPxP6Hg1DpjVlWHilPrBe3qbwuceokMQHIwwb+99GXu7Lyd6dNn8dhjL3HqmwvRVwnsJgm6F2qrbRP4H1FI3eQeMGgOH5gnuagPKWjrAcOzYZBBL5x3F2LEE6Xtmt0/syd7xyZikuBdCmqPQCwHp1ziVOOJv7o3VsPhiMXg/n6B3y/HBaAVFUFxMfT2CnbskBgGxOOC+nrJwoX7jumCwRDXXHM9H/7wdTz33FPceut/cPnl57Bo0WK+/vXvMmvW3MM6pogBs46shoasDfdvFrzVo5BzIWdJEAqJ3Pju013OxPuPit4/ZUHv2IayULLHOthQDor8ksIDaK65RI61j68j0ZcgUh4hVBSke30PwzuHcG2HaEWE5lObqD/+MMI1gPyafGrn19D65k7SwxkUVcE2bYobi+innYGBPs46a3eVtuM4LF36Knfe+UtaW5OoqsrWl7ax/dUdGEGD0smlDGzrZ3DHIFbapKS5lIbj6wgVhVh+91v483y7RWfLoX9LP+1vd9K0qBEjaDDnfbPp39rP0M4hdixpxUybGAGdzEiG1FCaUHGYqtleIVJeaR7HXDmPeG8CO2eTVxoeqzIuaigiXBIi1hEjvzofoQhyyRxW1qJqdiVC8T6s5eWVKIoy4Ru8HybE4AkmmOBdEcuNcM+Gh9gwtIWcNGmfso6sNUyzv4k8NcwT2sOs01Zxffrz4wTh48xFrNTfZLO2gRK3FAeHAaWP6fYsbvziF5E7JZ/7/LWMBAaomVWDK218aoCqYD1n1J3MrJLpbIu1eKESuRHeHljLS52vU5dXQ3OB5wm7p/AnJWN/7k33U5Xn+fJuH4G7nafomfEQmpFFdXV8pUlim4P0xAqZES3ivPIzWFS1gIAWYPaCBtYOLsFJpokE/IiKYV7Je4rJqUaq3YMs+/+dsXNZXlj5KK8PvE3ckOSFizih8lhOq12EoeosX/4Gn//8x+nq6uDfvn4LH//4pzGM8cvSra3b+dCHzieXy/Looy+wVevgybV/xXEd/JqPjUNbWNm3hvLQp1HEqYTze1jpW4EQEHUiWDLAkNbHPcHb+Xzy67zS8QYxc4RJ+fUs9b2CqaQoyxaR6bZID5r0VnTTpbVTb08aOwbN1Mn4k+MmEUIIXOnuM9EUAk6ulmyPKWyPSUoCkHVgIF3G9FnHo88I0PXQap6/9UGmXrKAxPEGESOC6ZjsGGlFAhfWnE1QD1Lmr6I/00XKThBUw0gkCStGQA1R5C9DU+DjMyQfme6QMCHP8MKm/56UBKA0KOkD8pql5103AAMGFCmS4uMl1pxdrXpLufrqC6krbOCJxqcpqNrdy+hUSZwagdoBESloTHqtgBuaJX2FgIA+A2SZRAtB4E8CZVDgVEvsyRK3QKD0wFjWjANKvyB3qotTBW4YlBFwI3gTjgwIB5yS0dbGPbBnQuaDLr5nvSoWqYI1VZK97OBedv+dXHbZVTz33FPcfPMXWLz4nHcdRjPBBBNM8D+ZVnUHwJgQDKCj45N+tqgbWaycQ4mvjLbUDvyqH0WoSClJWCOEtDyixoHL6XZ5UcrR53vIH+aohqN5c+MrnFB3OmK0sqsqWI9P9dOdbiNtpyjxV1AZrKXA5ylBW7WN7FR3kCNHQAYJyzxc3NFMBod+tZdqq45WsR1HOBjSh1/6eVtfRp/Sw2dTXxnrzOpTetiibaDELadP7SYlUhjSoMFuYoWxjDX6SqbY01GkQou2nU61HVPk2KptRCCosxv5cOY6apz6fc7XLwMY+DBFjjQpBpS+0eM00NHxS09AWa6/wWP+BwjKEJPsyQyLQdq1NpaLN4g6BazWVzDDnk2hLCaohYlbnpi6ix7RxXBgmEBhBMPn3bd8p5jN2nreNF7nvOwlY69NWDHakzsYzPWhCIV8oxgpHXJulkJfHeWBavqy3SSsGLriI+dkAUl1sJGsmuFx/4OsNJaREWlCbpgStwyJZESJUW9P4iTzNCZb01maeom1obfRDR8lVin1mQaS2RECqicqZ500huKnPjyF6lAjCSvG8v6XcKVLvq+IoBZGFSq6ahA3hxnM9vHd736d2toGrrzy4+Ous75GIDKMC6R1KzyRWFsrsA/Bs3UcCmQud8FW0LcKVva/xTWrr6Lb7OI7//YTPnzdJ9Hb1THbLGNE4JRKnCpvDKa1CNROcPbTRCgGIfiQgrZRIFLeGoFd6wXFGa8J1FaBjHiBccKG3EkuzqS99hGH0B0K+gqBsARKj0TpFyiDYM2WKAOC3IkuHHnMDBUVkmXLvK7IXYKwlFBZCbNmedcznYaTT3JZnA/lTygoA+DUeqHCe6y3oCgKZ511AWeeeT7PPPM4t9xyM2effRwXXHAZX/nKv9PYeAQBEofJq52C1zoF1WFJngG2Cy9oPjqGTTK2V8UL0JWC0oCk7jDsJ4oCcEKly1MtgpwDYcOzlnMknFojD2g30betn0RvgoLagrFK17r5tfRt66e4vpB5V8zFFz78wHghBFNOn0xBbQF9W/qxshZF9YWUTysjHq/n4d8/T6w9RnIgjT/Px60PfZcp04/i05/+F1RVJT2cpmNVJ4FogOBoAF24KMTQziGMkI9jr56HP8/PjiUtWFmbaOXuRSZVV9GDBn2b+8Y8jFXdO7dEXxIjqGPnbJIDSYIFQSqml9N4YsM4r2ShCKIV+04SfCGDo86dzoa/bGC4fRikF0RXO7+Wmnm7f9k0TaOsrJLOzrbDvnb/25kQgyeYYIJ3xRPbn2Vl31oaIrUkiwbZWhnDHhLEk1mmVkynApfN+nreMF7hwuzuKrsKt4pr05/hOd9TbNE2oqJydu5CTs+eR4gw3//BL9jStp7v3HQzt/z+R9RPaiTjpNmR2IRPDVAdbqAyXM4jW5/g2dYX8ak+gpqfFX2rWT+4mYgRZjgXYyg3jCY0TMckqAXI90XY071reaqb9sonyCdAOOGNrqKKTfvc9fgHJvP1sqvHqiEGlD7eiLxIQ0E5FW4VAC4um9X1POd7io9mbhjbr4vLTnUHfUovYRmm2Z42VgH7XuPsbOHpP/2Ax6z1+G2IaiFGKkp5JNlDIpNgy6Nv8qtf/Zg5c+Zz112P0tS0b0nEqlVvcfXVFxGNRnn00RfwFYd5Yfn9hPUQZUFvguRKl83D2ygJvIXlLqadIWxhUeSWkM6E8OsO0/M1epVuVmlv0TKykwJfPoNaPzFlmAK3CNVQScVNgiNRtAqNHco26vDCC6O+CCLoovT5xoRgV7r0pQdYWLWA4H6qr2cVSz483eVvbYKelMBQJWc3wMzis3l4yzDqh1SsCGx65E1qhqaz4PpTkELSGK1jYdVxHFPuhZ+U+CtozJvGzuRWYuYAIAhqYZojMwhpuwckmgIF72IwfTj4VFhcK7lvk8KmIklBniQZE7gSLilzCQeBNwRvxF7n8s+dx4yZR3PPZx6j5P58bJfdQqwKTv1ohcgpkvVPC1ZkBL5JoAovGbq/X/CROknpH1TP2sEn0VcruMs9wdl4U0Hb5PnaiZzAaZDkFntir30UqDtByQApkAY4BZA7hd0VxrsQYC6UWHO9JG6pj06ODtGH7d3gutDZCaYJFRUQPIC9hxCCb3zjP1i48Ch+85uf8oUv/Nvf/+AmmGCCCf5J8Us/7n7q4mxhERztma/Lm0zSTjBs7uqwkQTUEM3RGePsBfYmVBSkoCaf3k196EEdRVE4efZZ3Pqn75JQh4hUeM9fIQQl/gpK/PsP2M2KLFmRxhIWYelto6AQlGHiIkZOZGlXW0mKBH4ZwC/8VDt1BGSQzdp63taXc3ruXAAsYZIUSbaqm0go8bExY0AGKXaLqXbq6FN7cHCIixghN8x0axY+/FhYbNe28KD/bm6If57+TDdD2T40RafEX0FpoIqZ5lz+FLiPpIhjChMXl6zIMMM6mspRtfAN42XACx9ebixhSBlkUOknS4Yd6lYq3WpK3DLOyl5AcaSY+OAwI+YQfjWI7VrERYx4JEmxsft6qaiEZJjN2nrOwxODk1acNUPLSNlx/GoQy7VJ2iOU+atYUHQaqqLhSpfOdAudqVZyTpY8PUp1qIHyQA1/8P+WV3zPU+ZWUOgUs1pfwfO+Z6hx6phsT2O5sYRN+jpmpmfzt5InSetpFKGiSIXGTDOL+8/EtSXT8o/GUH3owkAdDYiOGgUEtBB+LUhQ270o6/kFSx5/9EFeeOEZ7rjjoX2KG0QK0PYj+KrS+7sjwC2B5Kcd7v7Jb7j5yS8yfdJMnr19GY2TmsGCwEMK2laQAW9so/cJ1F4wp0uEDZj72ykE71fQV3pZDbLc67hS24HjIPUpF2OJQN8ocENgzZeYC+Q+FcbGWwJtg8Ca6YX/atsAB7QdIHICe7rEPO3ddV/NnStZ+qRkx/OCqrBXANAuobpactVVLiWjFbrGi4LgIwqYXmWztl1BXyVJf9TFnj5+n0IIzjnnYs4443wefPBufvSjb3HKKbO58spr+cpX/p38/IJ3dcwHQkp4s0cQ0r3ijrSdJGHF0HwBzNwIS7tNGqIGWVuQZ8B5je6Y9++h0J6AuAk5GzqSgnwDjiqWnF4rOa7iwPchl/Q+JHv79AYiflzbPSIheBeuI1FUhfyqKP6oHyEEa/68jr4t/QztHEZRBPnVUURSwY67aJYxliOTGkqTS5oU1OSP22e4JExmJIudtWEPsdxbMDiwF0vn6i7WPbkB6bj4In6CEsyMReXMSqafM/Wg29qmw8COAZJ9STSfRklTMcd9bAGDLUPYpk1eSZhoVXSffRQUFBKLxQ77uv1vZ0IMnmCCCY6YoWyMNf0bKA+WENQDdAaHQJEUKSUMZWPEcwmivgh5boT12houZHzLdZ3TyMfTnyYj0gipENhDOUoxwo0/uInvXfvvfOdT3+C7d/yI8uoKbNeiK91KVbCennQfr3cup8hfSHHAqzwpCRSzNbaDfF8UXdExFJ2sk6MqVI6hGhT4ozREd5cK9Pi3khMDaENNOIaFqusoUsOXLaE3fwWO+BDa6FflDnUbw8ogk/cYzSgoFLulbNLXkc6kCRIkLVLcH7iLVfpyMiKDJnUanSauSl9LpVv9nt4DmU4xeM9tvOpuIhTJo8KfB+kU4R3dbO9P842bbyDVO8JXvvItbrjhC2javl/7Tz/9GJ/+9DU0T5nBLb98jFBJMdtHVjKSG2FKwe7VeUUolASKMJ2lzC5ZxCMxg3SiHinz0DSTpqZNFOYP048gLkaI+CJ0JDpRcJFIVFRc1wUBxVoRMaeUQaWPXqWbgAzS5++hwWrA6C5j09BWfKpBxslSE6ni9LqT9zs4EALmlUlml0hiOfCr3go8VPPZudexpn89Q5POYvX0N7jjlh/Rob3Ff/3yLvLD+eP2J4SgPm8yZYEq4tYwAoV8X9FePnb//RxfIfGpLq92CnpSUFsiOTkhOe05gdYPq+OruXLpRTRUzGPK9U/yIyXISXVwRjv4awFlNPwkIchc5uKcK5k8R/Lm3Qpt2wAEiiKZN1lyej9eUNyutQLpBcUpA5C6wfEqbeICp87FnCeRRYAL5lwXf5fAdUHmgdRHxeKz3AP68sngXj7Ef2c6OuDhhxV27BDYNpSUSM46S3L88eNbHndRXV3HRz/6KX71qx/z4Q9fR3Fx6X/fwU4wwQQT/BNxlD2LF+Qz9CrdlI4mcA0pg6hSY7bttXUHtTBzio6nP9tN2k6iKwbF/vJxi6n7QwjB5NOayYxkGWwZQlEEU/LmEA0V8Ebn83xAu/yQjrHSqSbfLaRDacckh46ORGKgE5ABMiJNn9JDWqRR0ZhsTxsLfFPR6FG6xvZV6lSQEHG61A5qnXoUvPyATrUN6RbxxcTNpJQEg0o/9wRvJ+iGx6qmdXRqnDraZAuvDz+Hm3PQhI6ULr3ZTmqsIY5Rj+f+4O+IK3F80o+KRrFTii1sVhhLOcE8mQGlD+lK1igrSJEiSw4LE1dIEsoIQWcKy/QlLDFeZmbgaCrzqpjRPxMtraEpOnmRAvpLBilyy8dZNZjCJOzuvidd6Z0krTiFvt3Bf343wECul8FcH6WBShShUBOaRFWwHtu10RQdRSh0KR2sNt6iwqmiQBaSFili6hAhGcIUJgVuEaVuOeu1NdwfvotIKo/abAOqUL1K6uAmSiIlzB2cjxDKPiGDAoWwHmUw2ztODLZci5GBEX707e9xySWXc845F+3zeXAagNcFWHt45FqAPb5a+HDIZDL8679+ioceuoePfexTfOMb/zEmQusbBPoWcItBmF5WgnRA6Qd9I9hHSZz95AWrW0BfL3DqJLucRdxSbx/G64LcyZLs+yTZPYpY1A7QVwvPMkL3FuP9zymIDOAX2I0SmSdQu0DpBLtZkrzB5V1NPyRMWi34VEbwaAZah0C4gjm1krM/5VJS7OVH6OsEgYcEMiCxJ+/aVqJtBd8zCvbU/VtlaJrGhz70US655HLuvPOX/PSn3+Pppx/jO9/5Ceedd+khhXQfDq6EtOUVXSStOAPZbq9C3+8nwBBVkVZKAoUEtCKkhCVdgu4kLKiQVL5Ds1hHAn69RqEnJSgPSQr8kpGcoDQIx5ZLlIOcSiDqfY84loOqj1rRSImZMimbcuTj0NRgijV/Xs/wzmFc18WxHDJDaQKFQXKJHI5pIzWV9HCGypkVCAEjnSNkE1n8eX6MoIHmU7GyNr49guusrI3mU9ED3i9ZQW0BekAnE8sSLPDm9LbpYKZNyqeVjZ1b69LWsdcDkB8gNZiie0MPjSfUjwXJ7U0ulWPlA6vo3zYw1lUSyPcz/expVBx18HTIYDBEJnOEK0H/i5kQgyeYYIIjJmtnybkmecbogNpVQYCqKDjSwXItADJWlmxHjg3LNhEsCFA2pXTsi14g9uutZjo5AiE//++Xt/D1a/+Fr17zeb592w8oqiki52RxpENXsoe4GWfqHoKlEIKSQBFCCKryKhhID1KiFWO6Jn7Vx1n1p1Hozx89/hw9fctJ9fWxM5PBMHSiZWXkVVZiC0mxf/wTW0VFINg7F1giUVDGBtzP+P7M68ZLVDu11MoIObJs1jZwf+BObkp9dUxcfi+wN65nsGcHiclBSgl6xxAKs351Ky+9+jLh6iL+6767qW+ezKrBddRHaigOeAavlmXxve/9G7/5zU+Ye/KlTP34Xdy+PURgJxQFNByp4EgXbY/U5pxjoioOV0+zyWZaeT69hSpZQUlxP0VFfTjCRgIlsoySinruibWgpQVKRCXtZsjGLPLygpQVF5FwK0YTvQVJJUGzPZUzxHkYDXm8HVpLLDdCfV4tx5TPoSRYfIAr4KEpULzX2CHfF+Wk6hMAuLjpXE6etohrr/0gH//IZdx775MEAvsONgJaaCyM5p+BXWL33FKJ5YKvD/J+rCCysKl4Kxe8eA7lwWZ+UPo4K1qDrJsC904RtKnwqS0SDQEaWMdKzJO9z21zM3zhCy7r1wsSCUlJCRytSEK/Usd50yE8zzmtVZAphswH9zIQtiHwiMC3REEd8qpZpA65RZLUJ9/lxOM9JJmEu+5SaG0VVFVJdB36+uD++xXCYYdZs/a/3Wc/+xX++Mc7+fGPv8v3vvez9+ZgcmCsEGhrBMIB6yiJNV9yCCH0E0wwwQT/EJrsqZyXvZRn/H9mk7YeAYRkmDNz5zPTOnrsdbpiUBk8fKUtWhHh2Kvm0bupj3Qsgz/PxzV5N/DbO37KN4b/g4KCd05tqnJrOMk8nRZtG31KD1mZGQ2Q06lzG2mypxJThtjJDmbb8ykfVeYkEgebQnf3GCMrMviln5AMM6wMYUgfpjAJyjB5Mo8BtZfp9ix80o+L3Kfry5A+/AnPyqDGaEAZHUPlnAydqVaSBTnKnSpmWHPIigw+/BQ7pbSpLbylv8Gx2RPwJw1afFvwmz6KRRF9/l70oIGKRlpkeEtfOuZlbAubluB2ctUmn4zfRJmsoEvv4HXxGp1qO1Wj1cbDyiACwdHWsWPHGjMHMFTfOLFNUzzxOmXHgd0KpiJUDHX3eDCmDJESKcql1ykXV0bIkSXi5pNVMuREFkMa6GgMa8M02I3YroWqqhjSR8gJsy6wmhP1kwlp+z4EhRBUhxqI5QYYMYcIqCEcaZOyktzzH3/A7/Pz7W//ZL+fB3OuRH9Tom0WuIXSyziIefYQ1tzDr5BNJhNcc82lrFy5jFtvvZP3ve/KcX+vtgBSYE/3LCqUQcCQiLRAiUPuHHdcp5SIg+95ge9pgb4GRKPAmSRxR7vr3TxQEmK0U2v3dvoKQfCPCmIQlLhEWy/AFl53lSPRU6BUCexpEvNo0MIC80T5rsdjSif4/6YwrULSNBO6kqA4ktouQW6ngJ3ge1lB6QF9q8AtBFSJPUliWtArBc4S2DxFMHmhJHqAwOBAIMCNN36RSy65nK9//Sauu+4KzjrrAr73vf+ioqLq3Z3EHqgKTCmQvNwJljuIBPxqEDUQQTfjnFLbQX86zqb+Qhyp4lMl6wcV3uqTXDvDpeEggcevdQm6U4JpBbuLDVKWZHWfYMswTC868LalzSUU1BYw2DpEXkkYRVVIDaYwwj6q5hzZ+Usp2fTcFga3D5JfE0UzNHo395HoT6H6NaysjT8SQDNUz7N3IMV3rv8FwzuHGemM45/qJ1KWR1FDEd3re8ivjKL5NcyUSWY4TePChrGK5fyqKPXH1bHj9RbSsbT3vSKhbErp2PFnRrKkhzME9xJ8A/kBhttjJAdS+xWDkwMpltz+Bh1vd6IHdALRAPk1+VgZm41/3UxBTT7+yIFbN4PBEOn0hBi8NxNi8AQTTHDEFAUKKQkU0Z8eIKQHKU5U4jeDDBh9hJwoYSNMf7KXvv5emv86k5bNLUgJO5e3M/viGeRX5x9w3wEtiCJUwqVhbrnzx3zjuq/w1Wu+wOd/8iXmzzsBVaj4VB+aomG6Fj5192A855gU+gv45KyPsHZgAy0jbRT485lbOpNphZPHXvfn7c/Q1bGNsM8gE7Rw0iqpHW0ETZvI0XHO1C5Gs3d/TU6yJ1PiltGptlHj1CMQWFgMKH2cnjuPAAHSIsVyYwmFbtFYMIefAPVOI9v1LbRoW2m2p71n90Am4oQsgV/opLExbIVXn1/DmhXbaZ5ZRc2nzuJVaxVPr34NV0ryfVFOrzuJGUYzN95wNW+/vYwPfOaHJOd9DnRBhU+SsmDDYBNpq4T2RCf1kRoyTpbNQ9vYFttBdV4lD2x+gOObJzFYt4lB5TUMp5IYLj1qJw12M7OteQQrQ/SmB1jStQy1P0CvOkBBJI/6GUV0BFuIyHw+lr6RBrsJU+Q8GwlUKGTcfXqvOOWUM7n//r/wwQ+ezfXXf4g77nhov5XS/4wIAYYKxgaBMiToqunh0gfOJWQU8vnj/0J5KsKcVojNk6R8kqUhwfHzJTOlxKkcrfbdo8UtPx9OPHH3hEjbhFetsXcnsOv9XO6nkkNfKfA/IVC6AQlOMShJ8L2pYL0myVz+jw+EA1i/XtDaCk1Nkl23u7YWtmyBpUsVZs3afyxIQUEhn/70l/mP//gGn/jEZ2hoOPKQTcBrI71X4Fsqxq6p/rbAWi1IX+tygLyhCSaYYIJ/KALBmbnzOcqexVZtExJJo91MndM4rur03eCP+Kk7dnfuwsebb+TXt/2Ie++9g09/+kuHdIyXZq4g3yngntDttKkt+GWAGqeOhbnTOMqexU61haf9j5Egga22YZIjoYxQ4VQz25o3ti8Ly/PitYKMKDHiYoRit4QKp5q4EsPGC5gqdkspcyroVNsJO7urbfuVHsqTxeSJ6JgQDOBTA6TsBMPOIAoKZe54ywsDw7OnGFlHuSxHrVQZCgxRYBaimSqKLvDrQWxsUiLJJGcyg8oAQRmkwqlms7aejf51VOVqqXUauCRzOX8OPMRmbT3gCfinZ89l7h5isKH4SMiRccchpfS6ucTB++Lz3UJCMkxcjFAgC9GkhkCQFWl80o9PesJMlhwKCkVGKfHsMFknjSIUbMdCUQzq8prHXac9KfaVM61gLm3JbaTtBIpQ2fLiFpa9tITf/e5hCgv3r6zJKKSvdTFeEuirvM9o9gQX81R52PkEIyMxrrrqAjZv3sD99/+FBQtO3PdFGuCCUymRhkRtFygJkEJinjBq7bCLLATvUtBXCaQqkSqoLQIlBuZ8r0JYbQe3QI4L4RUp8D8uIC1xJoH+lOcPLHWJ9IGSFJ5IHAO1TyCDEqcC7GnvfiymbReIBDhTPHuxmtFrKDMS33Ojtm6FEtkAap9XNqBtFYwYkpWtArcPNFdw74OC/OWCj3zEpaHhwO9XUVHF7373MH/5y6N87Ws3cfLJs/ja177Lhz98HYry3gR2nFwjWTtosyXmJ99nMOxqqNFKjGQ7RX6NJ7cVU+gzmTQqMLpSsmVY8NxOhU/MdPfbVQawaUiQb4zvOgvpXphzR1IwvejA90MP6My6eCZbXtzKYMsQruMSrY7SeGIDRfXvvCi2P5L9SQZbBskrC6MZ3iDYztoYIYNsPOd9gytiLGzNzFhIV4IQCHU0u0URTD97Kq7jMtQ6hGM6aH6N6qOraDppt4G1EILJpzRRWFtA/7YBbNOmoCaf8qllY9XDuk9D0VVs0zuGXdimVw2t+fedl5lpk1WPrKFzTTeqoaEHdNKxjFdxPL2c9HCawdYhqmbtp/x+FL/fTzabPaJruCfp4TQdb3fSv30QzadSMb2cylmVaMZ/g9/d34H/GbPgCSaY4J8Sn2qwuPYk7tv0CNtiLeRnIuRvqyZeuxG93KHF2EqiI0nzmhkssBZiNPiQrmRo5xBbXtzGMVfOG3v47E2+UUyxr4zeTCf+ghD/fvstfPez3+CW67/Ff/7818w990Sa8huoyauiNd5GY7QeXdFIWWmGcsOc33AmdZFq6iL7Xw4fyAyyrGcl1f5KIuEgq8R6kuEEVsKG7FZOURdxrnvm+GOSBVyU+QAPBu9mo7YOFQUJTLanc2buPAAyIk1WZMe14YEXGmKSIy3S7/7C74FSUkqB8DM7q/Os7OT5RzbQ1zHEcYsmYZxaR8JNEXGyNEbrUYRCX3qA2/98G+t+8wJBf5D7Hniep90TUWwoHxWj/BooIkx74gJs9wFe6XyD9kQnOSdHeaiMmnA1y3vfpi3ZyTnHXMaKgtdp11oQKCwwF3Je9lKiMh8UuKz5Ak6oPJZtqe28HXiTzrJtOIZNpdPMablzmGXN9SaT/0264VFHH89X//NBvnXTJXz6C5/ilz/9NcrBerYOA5nNgutAIPiet7TtQmQh6SR5/+MXkrWzfOfk54n6i7BNCIx+tEI6WBrsnAyT6w7twtr13iRGawd7EqMJJqD2CMwFErnXGNS2IfGMQN0u0F3QKkAo4OSD2gb+PwuyZ0nk38fu7bAYHgYQaHv5B4bDku7ug2/7sY99it/85qfccccv+M539l+BdKjo6wW+ZQKnkt2VwDnQ14K+XGCe8s8hnk8wwQQT7I1AUO3UUX2kPfaHSXFxKZdccgV33vkrPvnJz6Hr72zYqaNzlnkBZ5rn06t0jwm3f/E9xp2hX+LgMKj006HuROLZfIVkmEZ7Mnl7KIQFspA6u5F1+tvMsY4ZE7y7lA6K3VJqHU/FMjA4M3c+9wRvZ4u2kYgbJaUkEVJQ7zahyfHHvCskr8KuQkcjLVJjnXEuLnExwrHmifRlu5iqHMWi2Km8GnmBrJ5FOlA7UouI6OzwbUGXPvqUHpJKgk3aeuJiBAuLXmX3Q22ReRpT7Ols1jbgYFPvTNpHwC8LVDOQ6yVjp/GrAcAL/vOrQYr9ZRyMCreKOeYxvOx7Dsd1CLohNKkzpAww11qAgUFGpLGESblTiTSgXFSTtOLk3Cy23+XU3GlU+r1FAFe6xMwBck4WvxogahShCIXyQDWl/gqyToaB3n4+ecs1XHrpFZx99oUHPT63GLKXSbKXjD5bj0CrGRzs5/LLz6Wrq52HHvors2fP2+/r7MmeiKsMeO/rFktEEtQuyJ0px1lm6RsE+jqwGyX4QEmB2iYRA6NVwnFQ4uDUep1g2XMl1gKJuh3UHrAbQO0ViJhABkEqwvMkFoCDZ4ibAyG9wDn5d8y5ENI7ZwICwiBViRvxjh8bNq8VxE3BNFWyrVFSMR22bRM8/LDCF77gor7DPTn33Es48cRT+e53v8bXvvZZnnrqT9x6612Ulx9Y9DtU6iNw7QyLezYP0JmIEvW7NNYWEGtZQ0/KT8ryMa1g97hMEVASkGyNQdLyvIb3R1iH/r2meu7obvyH8BkMF4c4+rLZZGIZHMslVBhEeReJ1SNdcWKdI6i6ii/PR15JGC2ggZQIvIW41FAadVTMVA2VeHeCcEl4nEdwsCDI/CvmEusYwUzlCOQHiFRExgJAk/1JhnYOI11JQU0+086ast+5kC/PR/m0MlreaEXz6xgBHdu0iXfHKW0u3m9QXP/WAWLtsTGLCs3QUHWVTCxDoi+Bqqm49v4LO3ahaRqZTOaIryN4QvDKB1cxPHosru0ysG2Q4fYYMy88ah+v5/8JTIjBE0wwwbvi+Ir5GKrOq51L6Un1MTd5HFekLyevQCfeGWfosTjN5lQMv9dCIhRBuDRMrMNrBckr3X9/tCIUpuYfjU8L0pfpxIgY/Ntt3+Gn3/wBN33y4zzz9Sf43I1f5fIpF3PfpkfYMeJVHeuqzjFlR3NG3SkHPe6hbIyklaI+UkuRU0i+EqVX6SEVSuEmBR8a+QhF4ZJ9tltgLaQiWcVafRVJkaDSqWaONX9sIhF1Cyh1yulQdxJ18ne/nzJAROZT5uw//ORIUSdPQ50+g4bXnmP1qhWkHYu5l06jrKYQf3kTgz5JY6QOIQRWzmTLQ0tZ/ugLTDr6KB77/V8x/SUklwkK/OOFqIgPRHI6ulJL0lpLbjSATyBwpM3UgmY2Dm2hvzXB54yvMqD0oaJS6BaPm2gIIagKl1MVLudkTiSdSZPNZojKfK8K+L+RzUPwx80KnXnncNRHf8ufb/8YsmgKP7/5C+jv4vntDg1i/u0Z7FUrwHVRJ0/FWHwWat1Byh4OBwlkAB2scpePrr6SrUObeeaDL5Fy6ohlwJ+CbVM8qwYn5G20n8X1A+OHzKUuwXu9RGvP001i10uy5433/h0agnvuUTj6ecGJfYKUAaqEsjLPgkEGQIwItFaBVfCPFzgLCkAIiWXBnnpCMimYPv3gxxcIBLjiimv4/e9/w9e+9h2CwSMv31W3A5ZAhvd4T58XuKdvmBCDJ5hgggn25BOf+Ax//ONdPPDA77nqqmsPeTuBoNytpMyt4Dehn7JBX0Oj3YyBj16jGxdJpVPDLHsuutTpUHfytP9xPpj58Nj2C81T2aluZ5O2nrDMIyPS6Oicl7mEoAzRq3QTlCHmW8fhT/l53fcS7WorNVY9J5gnU6IXsSW9Btu10BTvwZN2khiKn1ksoMXawTL9dUIyD13qDCmD1Dr1zE3Pp93dRlALsyB1AtJyiGnDBKwQlpXl1bxXUaRCUsRJijh5MoqFxRZtI45wODs7XiAtdcspNQ/spVkW8MTZznQLw6bXQh3UQjRHZozz6T3Qdb4s+yF0NFYay+jRumi2p5IUCSQum7X1qFLnRPNkCtwiXvO9wLBvCJ/hIyESTHNmcK7rBdll7BQbR1YxnOvHlQ6KUCnylTI1/2j8agBFqOjSx798/np8B7GH2C9HONQcHBzg0ksXMzw8xMMPP8e0aTMP+Fq7GXJnuPieU9D6AOEF7uaOl5jzxj/b1W7AFTA67raOkkhDoG3abWthzpE49Z74G/yjQqrQ8Tb2VjEQWe+/uwophIVXRRwBcuA0SKyp3oK+vkrg1HovVPrBeEOgbRLIAFjzJOYxclzn2H7Pr8mrqFb6wN21RmCBGBaIDKgdArlTgqog/RKpgzkkCTjQFJX0lcHayRJV9QLndu4UtLVx0OrgXUSj+fzgB7/kwgvfz2c/+1HOOOMYfvazOzjttLPfeeN3YEqBn8unZuhIbSSqF/JoY4Q/v9FHxklgKAaaMl5Jd6QnCqsHqfc4tlyyeVgQy0HU8Brs2uJQHJAHrQreEyEEwYIDpBwfBoMtg2x8bjOpwTRSSrRBlXh3nHBxGDNt4toSx3bIJnKkh9P4QgZmKkdeaYSpZ0xG94//YCiqQmHd+CoPKSUtb7Sy/bUWcskcSDCCOnXH1jL51Ob9Fn01nzSJbDxL/7YBEpaDUBWKGgqZfs60/Qqq6eE0KBAqCjHSOYIe0BFCoOoqqYEUxY1FRPYjIu+Jqqo4jnMEV3E37W93Mtweo6i+cOw4cymTrrVdVM6qoGTSwS0N/xmZEIMnmOB/AVJKOpJdtIy0oQiFyQWTKH0Hj9X3CiEE88vmMK90NjnHxFC9YAksGB6OsbRrGWqJts820pVI9+CreD7Vz9TobBrDU+imk7sjd1D2cBWTvpXmqW//ieWvLeHmX3yfL8y7kY1DW0hZaUqDxTTnN6IpB/96yzPCBLUASTNJgT+fQreYQreYvvQAKJIK/cCD51qnYawyZG80NE7Lnc3dwdvYrm6hwC0irSRJigRn5M6n3H33q9l7InSdt5qa+PiP/h91oTA3n34mgbpqio4/nfWlgid2PIsQgta3N/O3Xz1IcnCEGe9fxGmXX0RRUQlpC8KGJGF6q9m7SJgQN3swnU4aInU4rktxoJCEmWTL8HZKgkWEjTAt8Z0oKGOhMu9EkCBBue8AJ2NnaIt3ogiF2kj1ONuPvTEdi4SZIKSH8GuHFvAWN+G+TQo9aWiIQvMFH0YObOaJ336V6dOO4qYPnHVI+9kbmU6Rveu32OvXohSVgKpivfEq7s4W/NffhFqxn/vteP5ySlLgFEvcKg4YtKZtBN8LCupOT2S9pfd7/KX3SR6Z+2eOtubQbUJfC/T7oC0lUN8Q7MyTlFdJphcenrhoz4Dk51z01QJlBJxSsOZI5B7eaFLCY48JVq0SNNdK/D0CW4ORFPT3CypLPV9hGfQqRP4ZmDFD0tAg2L5dUFGx2zM4EIDjjz/4dxDAlVd+nJ///Ic8/vhDXHHFNUd+IAeYkArX81qeYIIJJphgN9OmzeSyy67k+9//Buef/z7y8/dtNZFIbGw0tH0sK7qVTjZp66lyavATYFAZIKEkKHUrsJQcfuknLPPIuVlW6m9yXvYSYmKYZ/1/ZpO+nrRIY0iDsAwzw5rDMdbxJEWCH+R9kwGlD5/0M888jnNzFzMzdTQSOXYMVtBkJDdIX7YbiQsSdNWgITyVAq2Yq9OfoN5oZJm+hJzIcVbueE7KnU6UKD1KG6abIyKizLHms1luIGunUFRJI81k7Axr9JXoGOTJCBoaFiomOZJK8rCusSIUmiJHUR6sJm7GUIVKga8Yn7r/AKe9CckwH8p8jLNyFxAXIxS6xaiorNfWkFISlDhlTLVnoKDQbE9jpfEmcRGj2Z7G8eZJVLhVSCnZEl/LQLabPL0AXdGxXJO+bBd63OCogvkAfOtbX2Hp0ld54IFnDslH+t1gWRbXXXc5Q0ODPProCzQ1TTn4BgKyF0jsaQ7aFgEO2PUSezr7qC3SK8D2/hWAD+wZEmXQE3Rz53oVwwBOPWhrwXhBkHm/xC3xqo1lwKv4FWkQtkSqYrSjyxuzWbNGO5CGJGLQ25fSB8HfKOgtAjcswQJ9rUBtgcwVcr/BbrtwKyF7lov/SQVtI14FgAMyIMfsxdxCEK5ExARuSBKrkLxqCmSzZLABzNFxjqaB43gdZofDwoWn8txzy/nc567lqqsu5Kab/pV/+Zdvor5TefFBEEIwKW8aOSfDsDlAoNhPfHiExoBNSzRMe0LQEPGs2iwHBjKCxbUuwYOM2Y6rkLQnYGm3Qk8KQFIahMsmy32yTf6euI7L1pe2YSZNyqeVMrhjCIkkHUsT64whXYljuWQTWS/vR1MREYWmk5qoX1BHuPjQih+G22JsfXk7qqaMWVlkYll2vN5KtCpK+dR9Owx8eT7mfmAOw+0xMsMZjLBBUX3hWGje3hghA6TnL5+JZcjEMqi6SjaexR8NUDO3mkj5O4WVKl6I+btgYNsAvpAxTrD2hQwSfQniXfEJMXiCCSY4dHa1i73bdnJXujyx/Vle6nidhJkCJIX+fM5vPItFVcf93drV90YIsY8wl1caJlwcItGfJL8quruVZCBJfnU+4eJDS00yVD+vB15hu76FKfZ0jrp5FjtP3MFT1z3Kl06+Af1WnQsXvf+wjrc8WMqskqN4tfMNQJBnhBnJxRnIDnFOw2JCviA71R04OFQ5tfg4NNER4FjrBNS0yku+v9KrdlPgFnGueQmn5M58x22zZOlXe/BJPyVu2Tv68T3//NNce+NVHHf8Sdz2k9sIqhqioBChaQz1rMROZnnyrj+w+aWV1Mxs4tJvXEdPMEZToSdmB3U4oULy6DaBIiDfBykLulMSwXoqQ5Ks4/fGrRLCRpjBzBCDmWGydoZCX/4hX5cDsbxnFY9vf5qWkVZcKWmM1nHF1Pcxo3jquNc5rsPrXW/ycscbDGdjhPUQJ1Qeyyl509HjKURBAcoBJggbBwVdKZgU9cLmAE6/5tv0tazm1v93LVctfpuiosN/iNtrV+Ns2ojaNAUxmiotCotwNm/AevN11IvHfy6Vfgjcr6BvEZCVyLCCebRL5v0S9tDIRQqMVyHwqAouuEWSF7c8xy0vf5MvL/o3Tr3yHNwVUJqStJUI/lwh2FkukQpUpOCq5YLyZoF19OEJsm7ZaEvjARgYgHXrFCoqJK1+6N8mqegHxSdwRsBFoORJ7Mmep93fG8eBbdugt1cQCsG0aZLgXmsNoRB85CMujz6qsG2bwLa9KuYzz3SZPv2d36O2toFTTjmTu+/+7bsSg+0pEp4DMcSY7YZIAq43EZxgggkm+L+G67gM7hhkqG0YgMLaAooai8Ym21/72nd5+unH+dGPvs23v/3jse0kkpX6Ml41nqdX7abELeXE3GkcYx2PMqpqZUUGU+RQZD49SheDSj85soRlHimRwBYWSPBLPwklQZfSyf3B39GmtlDqllMgi+hTujFcH5dmL2eruonbgv9FWkmR50bwiRzP+B8nrsT4WPpTKChYWGzU1tGptqH7dCpS9fgyBqqiUegrIaoXIoQgIIOclbuQM3MXAIyN9aQqKfVX0pHegZQuRUoxc8x5xNwhyiI1zI8v5NehH7NF24gpcnSp7SgoFDulTLNm0qW2H/Y9EEKQp+eTp+cf8X0scksoYnc33QJrX1/dBdaJ+/15yk4wlOsnpEXQR6uodcUgqIUZyPaSsVM89tBD3HHHz7nllls5/viTjvg4D5VvfOMLvPXWGzz44F/fWQjehfAqhO3mgz/PraMkvmKJ2upZQaCMWi24AlnGmBAsesB4W6B2gb5eQdshsaZLjLcVxDDIkEQkRt8YEBnALzCnu54Q7AKmwK30jsd4VaC1CKwpcmxxWoyA8YaCdayD3cz+kXgFCQaYJ7qIjCdaOzXg+5sYE7SVQS/sTioSbScUVAh6BLRsFjT4gRrPLqO3F4qLJVVHkIdWVFTC73//KL/85Y/4/vdvZuXK5fzyl3+gqGjfTs53wpXQloCUFaIqcAK14T4SkzLAr6h3q7h8isH9m2DjsPfbqQjJ1ELJWfUHv7+GCldMlRxf6dCWEPgUmFIoKfw72nXsj9RgipHuBHklYfSgjmpoxDpjpIczSNer8hWqgqoqOLaLL8/Aztn0b+1nxnmHMDgepX/7AFbaJNq4ew4VLAiQGcnQt7l/v2IweO9fVF8I9e/8HqXNJYRLQqSHMpQ2l5AcTBHvjuOP+pl98UymLJ78jnpHOp0iuPcE4TBRDRXX2StEXkqQEuXdtJj+A5kQgyeY4L+ZvvQAr3a+wZr+DeiqzrzS2SyqOo6wcWTtx6v71/PszheIGhGqCiqQSLpTvTy27S/URqqpj9S8x2dw6Gg+jeaTJ7H2yQ0Mtgyh+TTsnE0g6qf5pEmH7IFkYrJWX0mxW4I2+rVVd1ojVy/5BI9e/0duuPwqVl+/gi9+8eZDbuMWQvC+5vORSNb0r6c/M0DYCLG4dhFTmmr4Sfi77FRbkMKlwq7i/Nz7mGPNP7R9I5hvHcdc61jSIoVP+tHfoQdLIllqvMpffU/Sr/ShozPNmskl2Q9S4u7/QfqXvzzKDTdcxWmnnc2vf30fPt9uwdq2bTb+bTmvfPsBHNfm+OvPZ/Ipc+nNDlAeKuP4yt3nckadxHJhSZegMykIaHBytc36gVWAoDRYTEgLEMuNEPXl4UqXgcwQUX+EeWVzDumaHIjtsVZ+u/YPbBvYgZJQEDloMzrZ1tfCj8/4FlV5uytrX+5YwoNbHsenGuQbURLZER568RcMdqhc2JeHCIXRjj0e33kXI/zjR11pGyQCR0oGvW4j8v0KJ15/G099eQ5f/sqN3H7bA4e9eOL2dCGlOyYEAwhFQYTCuDu27/ViCPzRCw1x6rzqWRGT+F5RkBHX87WTYLwm8D0r8L0pEMNee19rqI1r3ryK06pO59+Kv0FmuiR7nkRfLljwOygslLRJ8DkwTYfiOFhLxovBYtjzpnMLYT/F2YdEJgOm6VkvpALwxCLJ2UsEZYMS0xaYIYmcIcleJo8oEE0MjSZxx8AtAGu2RObv/7WpFNx7r8KqVQLTBEWRVFcLrrzSZdJeQnRVFXzqUy7d3WBZUF4OvkNf3+Hqqz/Bxz52GWveepu5oaPB8iZCh+OJbE+B7GIX//MK9I7+cFcb6RGkm08wwQQT/E/GtV02PLuJ9hXtOJYLSFRdpXZ+DdPOnIqiKZSXV/K5z32N73//Zq688mNMnToDgCXGy9wfvBMbm6ibzzZ1C1uDm0lm4yzOnQNAmVuBK11e8v8VENhYjCjDZESaCreKkPQqyQaUfuqcRnZoW9iptjDFnj5mY5XvFrBZ28Ay/XUeCtzLBn0tIHFUlwAB6u1JrNZX0Kpup9yt5A/B37JKewtb2EhcigIlXJa5imOtE5BIVuhv8obxMr1KN9VuHSfmTmGGPWfsmgghaI7OQBEKvZlOknYcQ/ExM3wMDaEpdKg7ecN4BR2dWrsBU+TIigwSsIRF4Egf7v9AbNfEkTaaMn7QoAkdU+Z4c/nr/Ou/foqrr/4EH/nIJ//ux/P73/+G3//+N/zwh7/ef1jcu8Qtg8wHJYFHBNo2QILME5gnSrRtXlWx0ge+5xWUpFdvjinxvShQ2wXpDzsoMYHdJtC2epXDSsITa6UGar/A7ZBe91m1xBodX+jrBDIqx3UpySiIblB3ijERW+kD43WBtlEgfZ7IrHYLRA5A4hZC9hKJNVMS+IuCmy9xKyRqGyg9AmXIOye1WHJePty5RrDtdQhMFiTywO+Hc85x91m4P1QUReHTn/4Sc+bM54YbruL88xdx771P0Nh4IDV7X/rTnm3clmFB1oGooXJcZSWz6rxwxZ6ubk5qmk55SLJ2QJCyoDwIs0okoUPo5FIENEahMfoPHtuNRrIIIYiU5aGogvRQBqEI7KyFETRQNAXFcnBtiebT6FrXTTaRxZ93aOq1nbX3O3dSNAUrYx10WzNt0rW2m97N/QigdEoJVbMqx8LmdhHIDzDzwhls+utm4j1xVF2lanYljQsbqZ1XfUhzt3h8hNra+kM6pwNRcVQ5AzsGMdMmRtAY80r25fkpath/mOU/OxNi8AQT/DcymBnitrV3syPWSr4viiMdHt76BNtHWrh2xtWH3PK+J6v61o628HtfQgJBZaicjcNb2DC4+R8qBgOUTy/Hl+ena1036cEU4ZIw5TPLyK/MP6z9CARyj5QxiYQChVmPzKPyP6v43Q9+wWOPPcjNN9/CRRd94JAeDHlGmGumX053qpe4maDQX4ASdvlp6Hv0qT1UObUoUqFTbeee4O1EklEanUMfbCgohOXB21Z2sVpfwX2B3wFQ6paRI8dS41USyggfG/4ymvQR1hlLp/3Tn+7npps+xnnnXcqtt941Fq7iui5PPPEw//mf32L79i2ce8ElLLjmbLbbHTjS5tjyuZxedzJV4QpsbCws/Kqfi5vglBrJYAYiBpQEFf64uYm/tr5ISaCIGcXTWN2/nq2xFkzHRCJZWHoM0Wh4XGvk4fJ61zI29mwm0hUmkPMjhCDs2rQnOrn71Yf413NvAiBjZ3m5YwkB1U/1qEAc3LoDOgdYHvRzUlUjBSNZzGeeBEXFf8n4itzSgGQgDTtiAscVICRhHUJ6JZd8/tf88Vvv5+GH7+H977963HbZRJaRrjiKqlBQk4/m28vyJBgCKZFSjvvMyVwOsVdLq9oK2mZP3N0llMoCcHMSY5kgd6YXEBJ40DOCk4CMSqz+HB9ZczkhLcTtF96NtlNF7XZxmiRKEoQUNKqSxj2ssNw8ULoBOZpA/YTAWKlAFtyoxDxZkjtNHraXXkmJJzwPDEBNDfSUwv3nSILrocSG8z/oEoh4kxN9hYI9WWLNOzRhWN0Bwd8rqB2jlSZInJcE6Wtc9pdZ9NxzgqVLBbW1knDYE3lbWgR//KPCF7/ostd6AEJA5RG6tJx++rmUFpbz1M1/5KS6+WB7ydm5MyS508eHwxwQBbIX79FG6oLTCNb0d/brm2CCCSb430bf1n7a32onWBjEF/bGv9lEjrbl7RQ1Fo1VlF177We4//7f8Y1vfJEHHniGnMjxnO8pFKnQ5HhVmyWU0am28YLvGRaYCwnLPLZoG2jRttGv9BGSYfJlAXE5QlyJUeZUEBPDjKhDGNLP4tw5rNFXYmCMyzNQUTEwWKK/whp9BRmRZtcXfpoUcd2zPBhQ+tmibWC5voQ6p5GgDCGRtKkt/ClwH5Psyaw23uJh/324wiHs5rFSX8ZGbS1Xpq/lWOsETCfHiDUEQGPeVOrCzZhuDr8awKcGcHBYoS8FKShwi0iJJBEZHfMw7lW6mWcdS1Ik8MvAWPHEPztBLYxfDZC1M4T03WPmjJMmOZDi5us/xdy5Cw7PJ/gIWbZsCTff/Hk+9rFPceWVH/u7vY81V2JPkmgbBCIJ9lQXGYLwzxW0zQJ1B4ikROoCfHh2YllvHKnuUEjf6IKUhH6lQAacWolwQdsqUNu8HILsBZLs+S5uCZ4iaHpFAaJoj1C5UbsKOfpRUfog9BsFtUUg8yRKjydY23VgLfDGOmoHBB5RsGscnDJv3OoUegveokbie0HBLZTYTTCvAEKFkiVrBG0jMP0UyYITXY466t1fw4ULT+Xpp5fwoQ+dz4UXnszddz/O0Ucf847bOS7cu0lhzYCgJiwJ6jCUhWdaBIGGOgzDYMuWjZx00mIqQlAR+p+5WB8qCpFfEWGgZQgjUIBQBK4jsXMWuk/Dztqeag3eAFlKFMWzcTRT5iGLwflVnpecbTpooyF0ruNim/Y+/sJ7YmUsVj+6lt5Nfd7cSkr6tvYzsH2AOe+bvc98q6i+kOM+eiyxzhFc2yVSERkLlDsUEokRIpH8Q379/qiaVUmsI0bnmm5cJwFS4svzM/nUJiJlhzbf/2fjf8ZTYoIJ/pewrOdttsdamVLQhKZ4X5hZO8fa/g2sG9zI/COoskxaKQx1vJIghEBBIWO/u9TM94qCmnwKavLZqm7iVeMFtumbiLhRjjMXcYJ5CgYH/zI3MJhtzeNZ3xMUOcV0d/WxuX8rfaFedEVj6tyF3P/UX7ntRz/mxhuv5ve//w3f/vaPmTFjzjsemxCCynA5lXiet3/T/0KX2slU+6ixVsNGp5lN2nqWG2/QmDl0MfhweM14EVOYNNueNUKQECJewt9ay9jYnqLACTIpKjmz3mXpU3fypS9dz/vffzU/+tFvUFUVKSXPPfcUP/jB/2PDhjWceuqZfPcnP2XKUdMI6xEUNGzXIayHyIkcz/r+zFLjNVIiSa3TwKm5MzmK2eTvsR5xas1CtsVa2BzbBlKQtFIoKFQ3FqFMTfN86BHeFi9wie9SLrY+cER+yBsHNyOHpScEhz0RUENDNRU2rd6CeYq3+jqUjTGcG6HY7/XXy3QKp7uTQiNKS8BmQJUUGWUgJfabr+OedgZKNB/YHRy3NSbIOZBvQNQHPWlBSIPPXXgRzsar+PrXP8fxx59EdXUdUkp2Lmtjx+stZGJZUCCvJMy0M6dQ0ry7HU09ahZKUTHuzhaU6lpQFGR/H0LT0OeOryQXSS/0Y29hVIZAGRGeNcRSrzrabvba8pQUfCnxL6xOr+Jvi1+mSCsGRY5V9roFeHHOJuz5a6QkwBqtjg08oGC8LnBLJLLEC5kLPCy8gI/DDC0LBGDxYskDDwh27IBIBBIJcCJw6cUuoREw/iQQtgBVYiwVWCsF6Y+7HHRdxIHAowpql8CeLMlKaBsGfaOg8mEF5/PuOD+7bBaWLxcUFHhCMHjhcPX1kp07PeuIGTMO69QOih7XOLPobP62/VnsRT8EFZRe8D8qcIsYq7x5RxSwp4E97X/m5GKCCSaY4N0gkXQrnaSUBEPbEriuHBOCAfx5PlKDSQa2D46JwT6fj3//9x9x9dUX8eijf2T++49jQOnbJ6ugxCmjXdtJt9LJoNrPz0K3jAa9BUmLFJYwqbcnkXHTRGQ+IJluzeJk8wxmWXNpU1uwMPc5ZguTuBIjJZK4uERkFBBIXIaVITrUNvJkhGeMxwnLCMHRh7xAUOPUs0XbyNvGMl7wPYMPgyq7FoByt5IWdRt/9T1BxUgZbYltpO0kIAhoQRrzplIZrMfF5TXjRV4xnmeFsdSrKnZqGVQGGFC8irqcyFHoFvGmvoRnfU8SkVEWmqeyKLf4n14UNlQ/1cFGtsXXETdtDNVHzsmRTqb46Rd/gKKp3PQfX2RdfBlF/jLKAzX41Pe+5z6Xy/HFL17H7Nnz+eY3f/Ce73/8m4GxVGAsESgpgbNSkjtFkr7GJXCfgrFC8fIEFDB9klRW4KiQZ0m09d4ulF5P/HUaPJ9gCZjFEmUSKDFB9iIXpxZEDPx/EmjbvEpitR2cBs/OQu3xihLsRom6Ffx/FajbBPZ0r1hA7fGEY3UYnGFwi72uKG2jNz7LnSIJtiqoOyRukReOJ0Yk9lTGurqmFsG0YyTKsCBxkcshRowcEtXVdTz22Etcc82lXHbZGfz2t/ezePE5B91mxwhsGRbU58kx79+SAOQcWNbvY+q0Waxdu/K9O8j3CCklfVv66d3YSy6ZI78mn8qZlYQK919iragKzac2kxlZy0DLIKqmkkvl0AMGoZIQuZSJnbPRfBqu7aDqGrlkDn/Ez7qnNlIxvYzKmRXjvp/3R+mUEkqaS+jd3IcvZCAUQTaRo6i+kIoZ+7/Zwx0x1j6+jpY3WgkVhohWRQiXhHFMh97N/fRu6qNq9r5zSlVXx3yJD5d4PE4kcvCQuXdC82nMvGAGlTMrGemOez7JDYVEyt/dfv+R/HM/HSaY4H8ZW4a3E9KDY0IwgF/z4UiXjkTXEYnBTfmNvN23Dsd1UEf3m3NMhIDq8JGHlVlYrNZXsElbB8BkezpzrPnvKNweiC3aBm4L/pxhZYACt4hOtZ17g3fQrXZxeeYjIKE/M0jCTFISLCJijFeOFufOoUXdzhtDr9OW7MTMy+F3ghS/WcvyjtWkm3L88Be38eE3P8k3vvEFzj77OC6//BpuvPGLh9U6NKwMoaKMCcHgDegDMkCv0n1Y5+y4Dq3x9rFzqgyV77di2cWlS20n4u5O6rIsnQ2rFtIzEKTIl0UTkuW9gpeffZhXfnY9H/7wdXz3uz/DdV3+8pdH+dWvfsyKFW9y/PEncfdDjxFuDpCwY6weWopP8VEZrGdSZDoIeDBwNy/7niNP5uGXQdboK9mhbeXjqU9xlD177BjKgiXcOPujLO95myd3PEfICDG7aTItM1eRMZIUZYsYycR5NvIkQ8EBPpP6MpplsG5gI22JTvyqj2lFk2mI1B6wUjskg0hzNCxD7LoeEumDQI+P4c4YZc2lJM0k3ckeNg1uIeqLUmMHKDJzpPLDBIDQaFmDiERxe7qRsWGI5rNjBO5Yp7CqXxDWJWFdMJIDRwom5UuEgIQp+Pa3f8KSJa/wta/dxB/+8Bj92wbY9LctqJpCQV0+0pGMdMdZ++QGFnx4PqEib7KnVlbh+8BVmI8+iLtjq1chHM3HOP9i1Nlzx9/nUrw05iFw9+gmUobBLZG4UVB78YI9BLjV8MKy5/h13y/5WfGtzPcdAy0Cp0piTx1NoZ4usScJr+K4SoLhVXZIQ5A70UFt92wXnKrdQXBOENQ28L0iME+QHO6v9EknSfx+ePVVQV8f1NbCwoWSRUUS389VnEKJLBg9vjRsXyIYNAT550lqa3dXt+8ik4HMBght847zrT54dIugOwnChro+wXnzYcrJu7cxTcjlBH7/eFFV1z0f4UxGAO+d4KqvE5wdPpt7knfRnm2jJlKLWwnaVtCXiUMXgyeYYIIJ/o8yLIZ4JHAv6/TVZEWGcqOeKrWefKLjqnFB4Drjg34WLz6HCy98P//6r5/mvnlPoc8yyJEjyO7V1ZzIoksDkxyP+R8gK7JEZJRitxQHh0G1n2K3lDwZodqp5abkVzEwxjqbqpwa0iLNm/prNNqTyZcFdKsdRGQ+utRRUBCjvsAaOu7oM8bFpdgpJSuy6HulgXr7lvQoXQwpA9TZjeP+vtQtZ4eylWeyj5IvCygzKvETIGXH2TKylqAa5q3IMh4O3IsmNfLdQnaqLXSpHTTYzYRlGEtY7FC3oaDSqbaT7xbQr/Ryf+BOEiLOhdnDy9TYE1e6SCSqOPKQrkOhNtyEpuh0plvJORk0U+cXX/gZ7W1tfOVXX8cOm8TMQQZyPfRnu5lZcOx7Lgj//Oc/oK2thdtvf2Cs2+7vgoTAowLf3wQyD9ygRN0JoT8opK92SV/p4n9CoCTBcgTWsECNQToEIifYKSSlDgRMwGKfziI36hUEYI6+14MKvqUCu16CK7xx4QrP0sGZAubRLsF7FLQOgbYWUEAWeBYTmIDfex8xAhTjjdUVEDmw5knSrovvBYHaK5A+iVMLdsP4jimRBumTR2xRdjAKC4t44IFnuPHGq7jmmkv54Q9/zeWXf+SAr4+bAtOVBPZSwcI6JC3B9JnzWLnslff+QN8l21/dwbaXt+PYLqqu0rupj+71vRx92SzySvdfbVFYV8AxV82je0MvI10jpAZSuK5Lqi+FqivkUiZO1kGo4NoSiVfpGu+OM9Q6RN+WfuZcNvugFbi6X2f2JTNpW9FOz4ZepCupmVdD7fyacdXFUkri3XHaV3aw/fUW4t0jOLZDJp4hE89QlLEorCsEAYM7h/YrBr8b4vEYeXnRd37hO6BoCiVNxZQ0/c8Li9sfE2LwBBP8NxLSg1jueP8cOdpi7lMP3yIC4Njyo1nRt5pNw9so8EcxMyax7AhzymYyq+TQDeD3xMLivsDvWOJ7GTk63H3F9zwLzIVclb72sMLUwKsEed73DMPKIFPso8YG3sNikKXGq8xKzmPZpnWs7d9I1skSNfJYWHUcZ9WfiqZ4X1PFbikXvXIFI4851JgJfLoPJ18gQyrpdJrNHdt4u28Ni08+nb/9bQV33fUrbr31B9x//52cceZ5vO+aj3DsMSdQGig+qIVEoVuEO/rPLkFYIsmINJVO9SGf80BmkPs3/Ym1w9tIOBmiepgTSufw/skXENDGx8kqKJQ6FWzW11PmVgDQ21tJ/2Ax4aJtlLmVFDmQ2PQcz9z6EeaffgWf/ey/8tOffo97772D7u5OjjnmBO6//y8cv3ARKwZfZcDuJRaJYSom4WyYXDJHQAthR1zeMpZQ4VSRP2p4WuQWs1XbyIu+vzLdnjXO8qHQX8Ci+uN4VjyFqo2wpayDYW2IyOZKEkmXHCqaFWFH01beEK+xeW0nawc2IqXElS6RtjwubDybU2sX7vc6HVsyj5et1xnwDRERYSSQVFMEcn7qrVqEFHQmu7l308PEzQTD5ghpO0vMdJlCirjjcIyopmq0EkfGRxCh0JhFw2udgsGsIGqA7UK+z0v1HclBfQRiOYibEIlEufnmW7j++itZuvRVAt1RHNMhvzK66yZRUJ3PYMsgfVv7aSgKYbtehUGi+lgKb5xKVfdmhGuj1jeilJTuc65uOZjHuvieE5Dzkp6VYcCC3CmeIG5XSXxtCi6S4Yo4nxy6jlODp3GDegNuDKxZkswV7u7q4gCkP+ISeFRB2yIQFjglktyZLvZM0FcLREoi9/rouvmgDAuUxHhh+lAQAhYskBxzjCSX87x3FQX0pwVkQNZ7r+tIwF1rBDs6wOxRMDbBvHku73+/F/LmOPDii4JXXhEEOgRXbIa+CsEjMcg4UJ0H0oKWAfj9QwqfneJSXu4FGQ4MQC4n2bYNpk/3PIyFgKEhyMsTVFW9u8Tgfc45BacVn4EqVJ5rfYaPzboO8LyX1cH39K0mmGCCCf7X4eJyf/BO3tLfoNKpoUyWk2zM0L28E7/jo1n1uqKsrA1S7rf664c//BVnn30cX/vEZ7nkb5ezPLKEgB3AT4AcOdrVnTTaTSzxvcxWbSNlTgVDygAODrawUKRCu9pKjVNHo908NpaVSF42nuOJwCPkRJYutYNWbTvFbimzrXlcnP0gm9R1PCefJCjDJJUEGdIIBIb0UWc3IgRMt2bygu8ZSt3ysfFjTAwTlCHqnEaWytcwhYkudwsr27UttNstVFNGR6iNHWxjsj2dGlHHUK6f1tx2XvQ9S0AGqHJqsUZ9j3eqO2hRt3K0dQwJkUBHpdytpNFpAqDQKaJX6eZV40UW5k6jUB78QW9hoaCMifKWa9KRaqEn3U5aphkI9SGjKvPE8TQ7Uw+6ryNBEQrVoQYqg3XEUzE+esP72LFlG//y868yfcYsDMW7V450GMr10ZNppy783nXqbd26iVtv/Q9uvPGLTJnyHngYHASlB4w3Fa8IYPS2OAWgtkiMFwQsAHRwVYHpgmmA7kLpMMTyJX+aDMfsFJyyBbRWEGsE9iTPKkLmeUUFTrHErfAW/vV1ArtaIiNgFUqcetBawA1B6oMuwccVlAGwqyVKh0DtHfUXDnjHp/R71moooMRB6RAoPV5gnEiDdez/Z++94+O4znP/7zkzs30Xu4veAYJg76REUhKp3q1i2XKRi+Iiy7527Di/69zETpzEiWM7cUsc9xLLVbaKbVWrU51q7CRAgui97WKxfXdmzu+PAUFCLKJKfG9sPPrgI2J3+ix23vOc530ex5dYJpwOuMC/StxPCkdF3KCwAyAnBLnLbNR/k4DS6/Xy/e//is985hP85V/eRCaT5v3v/18nXLbUq/DpkmRBETpmODuVhxq/onHNen718++RTqfw+08v6Py/G6mJNN3bezG8BuEZMYpt20x2x+h5ro+VV538M+sv9VO/ppbxjnESw9MESgNIIVCjCs2lo7k0JJDPFqlYWE75wjKEFFhFi/HD4wzvG6Zp4wm82o6BO+Cm9dyFtJ678DjbPHD84Q8+coi+lwYYOzRGPplHaBIhwFvipZApMDWYIFgZBFuhnWam0OnCtm1SqSSh0Osng//YME8Gz2Mef0CsLl/BS6O7iOXiRNxhAIbTo5S4QywtXfSatlnqjXLTyvfwcMfjPLHrGfJTBVpzTSzqbKI/P8jCcxacdlDbEew1dvKs+3GqrBqCM0/utEjxnOspVhTXnDAN+FTIkqVbO0yZXT6HaAyrKCNiiJ8N/ZLRgTQ1/krKvFHiuSl+13k/bs3NRY1bUUpx8NEOXvzJTiITpXgCAQyXMxWerclh1VlYWZPhqVFoAMMwuOmmj/Oud3+QL3z/c9x+yy08+M57KFtUy8XvfDOfeuf/oSp04lC2NcUNPO56iA69jRqrHolkWBskapdxRnHzaZ2vrWx+3n4nD07uIh+pxDKiDOWSHB7ahu7yccPCNx23ztmF8ziktzGg9VFuVTKR1kmRplGEKLcqGevaxd1feCvhxjVMpfNs3NiKYbh4y1tu4L3v/dCsJcZIdoDDsp1n659l3DWKjY1HeWicbsY/HUKG3WREhvojjB2OcqXULqdP6yZLFh9Hp+4zZPix/9vsbHyKWCZO3p0mV8gz7SrgmSgjXyjSPx3DdPl5mmcYHE/RHGrEo7tRSjGcGeXe7odYUtpKtf/4a37eorN4yvMMu6f2kwynnZCDbJAlYy20ljdTUhPijv57GEwOs7X2LNriHYymR0mrDJ0linPHbK4RIfDnsacT2JMTuK64ZtYioishKHEpbCXoSYJpCzThEMPTBWcWvHLmdN/0prewatVX+PznP82n3/zl4/2BpQAhKGaKjGUcz7GOuKMw8Olh1lacwdsXKU4lKMleq1ABMJ4VyKTAqlQUzlOOQhconqVw7XX8c//+0N8QMyf51rpHKdZB5t2K4ioFL1NW2FWQ/rCNHAFRAKsSR8kB2CGF8kjHe+4Y4YBMgh1U2K8i5E1Mz4S7xRzlSXGVwvtyOzDlLGem4SdtgrYULPSBqxTGQ4onnpBEIjZXX6149FHBbbcJfD5w1SgmewTP7Yc+N2xqdshdmYSF1bA7Jdi9W1BRobj3XsHDD0tGRmBoCAYHBU1NispKh5y++GKb6urTPy+lYHgYJicd24sTqZftMgi7SthYvZmHumfIYAViWmCuf2OJ53nMYx7z+GNDj9bJAX0v9VbTbF0ZWepienmO4b3DRChHQ8MyLaqXV1G5+PgJ1WAwxHe+83OuunoLD3/mPkLfCLPT9QIhFUZXOhY2fXoPO+QL9GidJEUSzdbp0juwMCkKE1CE7QhrC2diYzOg9dKpHeJ2789xKzebC1vJiDTd2mHSIsVluWvYVNhCrWzgl94fE9MmiVgRisLExMSLjw3FjUTsUs7LX8Ih/QDt+j5CKkyBPKYockH+MjYXtvKC8SwHjN20mIsxMOhTXXRYB1gQX0BlqgrLD9OuBG36XoJ2EE1ojIghpmScarMWAAODNcUzCNhBDusHMYXJWYWtvOR67jjBQqldTrfewag2RNQ8MRnco3XymOtBDhkHcCsPZxbOZmv+Qrqn2hjO9jGhT7DPvRM7Z5Ox09xS+102cx6fTv3zG24/kbMyjCaG+PMPvp+2ffv48g++iXeha5YIBtCEhi4MJnOjJySDbWWTMZMowK8HkeKVxz9KKf76rz9GbW0DH//437yRp3RCaGMCkVRYLxM+2qVO+JvrOUdZmwaMQYE/y4xlhKJ7leJQK5z1U4m3X2EHQY8JXLsUVo+j5pU5AZYi+HmJ7VfIccfaQaScGlFFoOhzBAF6n0COCMzFDtlr1SvklIAMaIMCc4FCPwwiLZDjoO8SkFbYYXBtE8gxQfr9NqoUJyT5pxI5JRxriX6B1gtWLeSuVuSv/O/toNJ1nS9+8T/x+QL87d/+BcFg8LgcEIDGIKwuVzw9KCizFD4dYnmwFJxbr4gE16GUYt++XWzcOFfIUrQd67lYThB2KxZHwf3fK5oHYGpginwyT2nz0UkyKSW+sJfxw+Nz/HpPhOEDI0x2x4g2RtB0jZLqEOWt5SQGE6y4ajnx/in6Xuyn4lgbPENDM3QmuiZfkQw+FicSXA0fGKF7ey8un4FtK4QmKKQLWEULTdfwRX1kE1mmR5JIQ6N0QSm2ZSO1N4YUnpwcRylFNPo/M+TtvxPzZPA85vEHxLqKlfQ2nMuTg88ylhlHAWF3CVe3XEZj8PRVpy9HhaeMxQcWEOjwEKwI4I64ySZydDzWgeHWad7c9Kq2d1Dfj4U1W7AD+JUzO9qm733VZLCBgUd5SInknNdNTHLFAv2xIUpKSun0tpOSSYKeEK4pP08ObWdL3SYOPNbO9lueIxPPkpcFZEYiTYEZsvCMudHCElWiiPrmMlN7pg4wudzm0n99P9P7Rtjx22388h//k99+7b9481Vv583XvoNNm7agaUcfoKV2OX+W+Qi/9fyKHr0LhU2TuYArc9fRaM1t7zsZeqb7eTJxkESknIDLS1AJip4Sxq0Cvxp/jqsbzydgzGXhNhQ3kcpO87DnPgb0XvLuCkL2RpanF3DoqV/x8Lc+gm0WiHe9gFaY5rOf/Vfe9rb3HDfLmVQJtpU9SsI9TU2uBg2dtEyxv2Qv5WY556s3IZGYFDGO8QfIiiwldvg4G5Dn9ae5t3AX+QlFLqnIjQssQ0PUprHTPvxxPy7bxfDBaUIlA0SMutkgRCEE1b5K2uMddMQ7T0gGuzQXN13zXu77yUMMdA2idEXUjFATreKMS9Zj+A32T7YT9UTw6G7WlC1nOtRApphlzDPApe46ol0x7MQAwh/AdflVuK+4enb7ETcMpiCeh1QBpmwcMlhBRxwuanSKQnAKq8985l94+9svY9/mF6nOLJgzw22ZFkKAO+zjl+2SvROO55hXd4jlpwYlJS6b61pPUfB6IPcmRe5C5bTNhZjT4mcugcx7bJ77wTZ+cOA7fGXdf1BzWZPjs3YqglNwwvetJjCXKYwXZ6wifI4aWaSgcJmaJY1fCdoAeP9LovfO7AxFplzwxDLF83GBxwObPYqqblD7BVkl6EpAaxA8boVZDeEwZLOK554TnHOO4oknBH4/1FY5CpOeWpgaAXdSoGIKTQnwgL1IoU8rJiehvR0eeEDi9yvOPBOam6GtzSGFa2vhrW9VnH22Oo7MPRkyGbjjDsGOHZJkEnw+WLpU8Y532ESO+TopLleYiwWXHrqcf+v+AlbMxD1hYEePEvnzmMc85jGPEyMhp8iKzNxQXQ+o6/NMLRkltCuAXwQoX1hG9Yqq4yZjjyC2fpKV/7qWZz/xBK3nLUF/m4FLudlc2MIe1w7KrEpqrHqScpqkSJCVWVAOiWhhEbFK0TF4xr2N++RvOKS30at1MSqHWW2uo1sepk/rJidypElxh+fnbCycQ73dyM2Zv+DHvu8Sl5O4lYeI8tFstXJl7i1oaNTZDXw4/Zc843qcdmMfQTvEhuJmziycjY7O9bl38RORplvvIGflGLH6qcxUsmpyDbLoTBCH/WHGvGOMyGEqVDllWjVu5SErsniU01XmUR7qrEZKVIRPJT9LQIXo1A+RJoUqWmSsNAC2rjA016yH8cvRp3Xzff9/MCKHKbXLmJJx7vD+nP5iFwtyTQiXxvO+Z8iINEEjRFmmnNRUhruqf0291cD7sidWXr5aKKXoT3dyaGIfX/qLz3F4bwef+ebnaFzRyFh26LjlbWzkCWwrpvITdCbbmC7EAQgYJbSElhJ1Hz+xcCwef/whnn32CX7+87vxer2nXPYI5DAY7U6Xl1XneOSeLqti+xS4JSI71zZBZEB5HXsFFYCBMyHWCbVZsDSFNOHgemgahdr9YC0DFVBY9QqtzyFe5YRA+WcsGgpgdEu0HhBTAmkKMB0lvMgBHpDjEpEEsdDxJ1YBUC7QJkC04QggloIdtXE9K1C6wjxTYTbhZHy0CTwPC7JvV7ieFOidwhEtSJBxhZwQiALkz7NPK0j49UIIwWc/+0WSyQSf/ORNBAJBLr/82pctA+9YbBN0CV4cEcTzgjKP4rx6xeZqhV2xDI/Hw+7dL80hgydz8JMDkoMxMJVAE4qWEnjvMpuq/+ZzE1I4da1ijv2GshVS13ilOY/J7phD7upH/250l+50Y46n8ZZ4Tki8KmW/ISrdkf0jMxuE3FQW21JIXWAWFKmJFIVcESGc0O6SmhBtv2+n/aFDVC2poPHMRrwlr88SpqurA+BV2Ub+qWCeDJ7HPP6A0KTGW1rfxPrK1fRM96EJSWuk5YQk2avB1GCCic5JItVhXD6HzAuU+bFNi/6dAzRsqEczXv/U5ZHnz5gc4YC+l7zIUWc1sNhcfkqFgIHBGcWz+Y3nVkIqTEAFMTHp0TspSUYZiGcZKt2DLSxcyk1SS6D8Aitj0jPQy2P3P0EhXUAEBJZlk7Xz+AteZFaidMVUKkF1UxVrq1fO7tNWNk8ObHcCPEpq4exalp+9nv6OTnY8+gyPbnuAX/z8R1RUVHHZFVfTumk1VUsaqSqpZFnpYj5h/Q0dWjsvuJ6hW+vkt95f0V3s5Jz8+a/Ybhcrphi2M/iMCgLKeYhqCMK6n/HCNIfsJOuYWzkIBOcXLuWM4lnsm9jN84/v49Zb/4mf7LgXKzuNkDot57+fmrPfzV9ft4kVFSbdegc9dFFj1xK1yxiVw+zz72HCNU59rhF9hmUM2EGmzBiHSw7zweJi6q1GuvXDNJsL0TGYFgmSYprLClcddx/vTd3LCOOUiWqkV9KfHkRZgoJZxAjlqMrVYbuKiEkdmfWjnaQisdTJ1ZP1C+p410evp3tXL/GhKSKlYaqXVc3OgHt0N+miE4YohKDEHSLoCpAoTJO95k34PAtQU1OISAQZmdtaurFa8Wi/ZCglKPUopvKKrOmogzUJy0ttYjkIupxQ3S1bLuDsLRfxg7u+xaev/QaTPTECpX5sS5GOpSltipKrqaDjgKDxmPCJEjfkLMfX+dImhf+V7Oa8oE4y5kgsSfHRHTezae0W3vmDD5GutOcUf68KEjLvsPHqEuOAQIw7iuDcFYr8+adHYtoW8DOJfUhgLlegK3JpGHpEkH5a0LFCMTkFT+2XBFICwxFQM2zCoimwqwTWjG2D1wuZjOM1nEgIytxOyJyMw7ANCV0wklVkDfDVKscfOQxW3FEF79snyOWgqck5tooKqKhQtLUJzjhDcd6rDMT7/e8F27ZJqqoU1dWQTjvBdJomuekm+yip7IPMn9msS20k1ZGia/QwC1cuIX+JzWnOEc1jHvOYx58sonYpfuUnKRIz4W0OEv44ZWeWs2npxlfMpBiXo9zjuYPaDzTQ81QXnf/rEDXL64mtnKBf9tBiL5qtz1rMRezVdzEtE0TsKB7bR0iF2FDYTEok+aXvvyi3KmmyWkiJJCPaIDuNFxAIQiqMR3nIiDS7jJf4RuBLnFM4n+Xmaj6T/DwvuJ5hQo7RYC7gnOK5LDKPWrLV2HW8NfcuyB1//PVWE59Mf4Z92i5eSj3NULEPgcDnDqCKIAoKmQG31MkXMgT1MEtdq1hRWMNT7kdxWS78KkBGpBnSBhzFst2AQLC6sJ7fyl8SLATx2j4KIs+oPsKq6XWOYvgEpdmTrscYkUMsMVfMdu2lRYpO+yBlhIm54mREmpAqQQpJ0TCpSlexV+3lfs/v3jAyOJYfY9/IS/z7X32Fjj0dfPY/P0/jqgbGcyMgBBkzjU93auaCnUcpmwrv3NnvjJli/9RLZMwUfj2EEIJEYZIDUztYEz2LgHFyf4JvfeurrFq1jvPOu+S0jte1XeC9QyJiMy8YUFivyLzLPq5z60SwFkCxRWHsn/Hx9TgdVTImyF1lo7ygd0iCYUVbJWRcEMxCcApGKyHUJQjPELfg5FHYFU7Xmd4hyK8/Gt5mlyn0AwJ9EuwaBVnQR0BYAqtcIb0KOSRQu50wXOOggBwoTaF0sP2K1EdtZMaxjzAXMoc9sqMKY5cge61yrCXCTvCc857zvt4u0HsE1qmEEm8ghBB86UvfJJmc5iMfeTe33PJbzj33ojnL+A142yLFZU2KTBEinqMKX6nrLF++hpdeem52eaXgd4cFe8cFzSUKr67IW3AwLvj1IcnH1tjI11qnvwJMG0ZDZRwobUDEiiyL2kSliVW0yE7nWLhlwRyS90QwPPpxPuzOiYHu1ihtLqX72V4y8Sy+iDM4yafzoAQVJ+jSeLXIpwtohmRqaBppaChlobt0UAJpSJRlE22OEqwIko3n8JZ4UFh0PN5JfCDB+revmeU3jsXUwBQDu4dIDCXwhb3UrKyhYnH5cerkzs4OhBA0Nra87nP5Y8M8GTyPefyBIYSguaSB5pKGN2yb+WQOq2BieOcyUC6/m3yqQDFbfFVk8CJzGdvcD5ESyVkVR1qkUDNhGV8OfI5JOYEADFysL2ziXZkP4OXkM+oX5i9jRA6y0/UCA/QiEFRbdWxKX8AXtX9HFGzKjJn2FAVD+REm/MPsPXiAVDJFwOdHWhLdoyGzkoJWwMgaFLwFSktKec/Zb5tDquetAhPZ2HFBdPWtLaRLbW78h3fgGxX84vb/4o67biX94+8hNElJUzktK5fytsvewcCl3XQE2wmqIALBbz230q7v48PpT86kSZ8Yfl8U4fZBNgne8OzrVm4a6S/B8vhns61M06StbS8vvridl17azosvbqevrxuAptYVBEtrSY5bnPW3T9HQupKLGxTRqm6+5ruFbv0wBQoIBFIJ3HiYkOOMMkIwESJiRxFCYtoFvNKL7nGhFTVuyLyfn/t+RI/eiYWNV/nYmr+Qc/NzC+GiXaQn0YcW0QgYPjy6m0QhSU5lydkmhqZje0wCVohKK8rK4HL2Tx6kwlc+G5IYy03hN/w0l5y6xShQHmDlxSf2vNpQuZbbO+4mU8ziM7yOiiQ5RKk3wpLoQqQnAidp/dlQoQi6HAI4UXAKOk0oPBqMZQS3HNCoDcCiiOL6VpvdE4LIlf/C0399Jj+NdXJB5XoaMlN4DEHjhnpatiygUxnkTPC97Anq0yFREKSLvDIZfAp8+cv/xOjYML/45T1QJZFD4HpRoPUL7DJFYb1ixhbwpBCTM+v0CFRQUTjbJncFyDRYZfAK8xmzOHQInvqV5Oz7JEgIxqGxUjE1JZhICZYEFK0e6BkXhLKg61BeruhPCYaS0FECLW6cEBKX4+nb2KioqgKfV5HbJZBZgR1WoIMUimxGcBCbxibnfnXuFliW4rHHBAMDkEwqLAuOEfRjGIrcCQbfp0IyCc8/L4hG1awKOBiEujrF/v3Ovurrjy5vl0Hzx5fD9+DFC3ZT/c5FJxxgz2Me85jHPOaiwWpmVXEdT7sep9Iu4ld+YnKSvMiyNX/haYUTd+jtxOSkQ1B+p4TM+WlGrxii4ZFmxheP4sXLCtYgECw0F1OkwIQ2SkiFWWQupdaqx6f8xOUkE3Kc1YUNCAR5kcXCIiWmceGobm1skIoiRe7w/oI9xg5KVJiVxbXcmLl5Tufcq4FP+VmeXUU6PkmJEaLdsx9b2uCXSAkyD5qlE/VXssK7Ab8e5Nrc28nIFG3GPvLkceFiTXEDb8neMEvibpw6i5f0pxkKDDItk2hKY2F2MWtG1zIZHCUfLPKS8RzjcpRqu5b1hU0c0vdTYkfm2Lf5VYApMUFWZMiRRQmFPCJqsCV5I48bDzH5xpnlt/Xt4Z8/8llGeof52L99gvCyAOliEhtFlbeWrJkmlhsDIZBCUu1twKsFSBRiBPQQmtQZzQ6SNpNEXEeJoBJXKfHCOKPZwZOSwXv27OSppx7l29/+2SnzRI5AjoHnDgH5GTWwcDIFXM+B1SzIX3QahKcG2Xfa8AuJcVhAUaF8gvwWm/ylTmibsVdR3SZYXoBYCgwLhkug4UHB8jGoToM94dQlRyASAhQcOzyRUzgKfN05TjkBKMfGQRQEIg0qCHr7jEDB7dRidg0U1ir0UYHRLph1IHl5zaMBtvOjXID5sveV86NeR038WqBpGt/4xo95//vfyvvf/1Zuu+1B1q0787jlQi7n5+XYsuUCfvzjb2OaJrquM5mDfZOSav/R4Dm3BnUBxeEpwWAK6k+c4Xba6JqCRAFawxCYOaas6VjSvTTiZbqmlenRJC8kcmxKD9BanKJ8YRlNm5pecdsViysY3DNENpHFE3JUtplYBsOjU9ZSRrQhQvNZTfQ828NEPINCoRsadevqqFr2+gRrANGmKGMd4+SSOfxRH7npHIVMEdu00T0amttFxeJKpgamKG2OzqqUfWEvk92TjLaPUb9ubgf1RNcku+/cQzaRw+VzkRhMMHpwjMUXL6b5ZbYWXV2HqKtrxON5Y0Mn/xgwTwbPYx5/BPCEPGhunUKmgNt/1Fsrn8rji3iPI4lfCauK6zgrf+5MgNzMkxxYXlzNPmMXeZFjyUwQXEok2e56giZrARflrzjpNn3Kz/szH6Wj0MaIHMKrfCw1VzLmG8FTJUj3SbKBPIahk83m0YoufIsl3UM9SCkRYQmjoOkagUCAbDKLp+impnUhV9x0GSWRuU9ht+ai1Buhb3qAMu9RtWjWzKFLjYgnzNK1rTzJLrZudRNNehlp66V//2H2P/EC/+e32wBwlXvx1wcpbS2jYlk5j7c+REVdJVeE3ozfH8Dr9SHl3Oqo2VtJQ+0qDnc9g8jn0CyN1NgYmZEYfr2OW+/+B77d20tvbxc9PV1ksxkMw2DlyrVceulVbNiwifXrN/Hgg/fw6U9/nM985VdsumAFTSEbnzfL13zfp0s/TKPZTFEUecL9CEmRYE1xA+VWBYfcB+gt6cE37cdtufHoPtLeLK32Urx4abEW8b+Tn+WQcYCMSFNl1dBsLZwzIADIFLN4J8PopToFLYfL8hA0AhSFMxhoTi1mWXEV2SmTgCfANS2Xk7XyHJo6jFfzYionkOTCxq2vywZla91m+qYH2Dm+F9N2Ks2oJ8JbWq8i6nm5ae1caBIMCW5NUeZ1irdMEcayYCsIu6HMo9g1LuickmRMCDauo371RRx+/qeE/uZmTH+WD64C30zSRGUGQm6I5aD0mPmPWA4q/Yrwa8uCBKC3t4sf/eg/+cQn/obm5oVoHeD/L4kcFSiPQuQlru2O2re48cSDDjkM/h9ItO6ZdYoS17OQu8Y+4UBFTIHeLUCA2XLUW7i/H370I4m/R9CQUriLAt+gwLVPYEiFT1NoeUj0QSYFa12C/iJMZgSaV2HnYH8WjILAs1cw6VHIsOD8823CYdjcqLj/YYErqghrkMw7Nh5XNCp8uuDwBGQKMD0NPp8gk1Fks4KeHkdhvGaNYwdRKECxCAtfRpDHYvDSS4LubkEwqFi9WrF06VE/4FTKUSmXls69Jn4/jIw4ZPHLEY2WUlVVw4GBvVwlX3tC+zzmMY95/E9DliyH9XbyIk+d1UCVfeq0d8s2mcyPkbFSuKSbN9vvwOv3sdP1AqNimLAd5YrCtWwpXHji9bGYFgncyoMPHzY2BfJMyDFCgRKi95Sy/6I99F3RTXRbGbH6SSbVBN5xL5nRNPmJPMakgRyUDI70c2i0jcx4mvj0JPlCnjuLvyBfzGEVbeyihSooKCaJFcfCdhwAAQAASURBVCfAAOETCK9EeiVpXwpviZdnSh7ngeDdrPWfSXV1DbW19dTWNlBbW09p6fGKtBNBCIFAUmlXM2IPMSnH8Qk/wi+w9CJ6pY8L1JsIEgYgqkr5SPr/o1M/SFzECKsIC80ls2FvAMVsjssyV5LN5khoCfyWn7p8A0lzij3mSzwUeIBJMYFHeXhePM2zricAyIu5s6g2NllfDrfuJZArInWJqUy8lgdpS+KhKTIizdLiSt4IdHS08+c3fIBcIcfHv/lJalprUCjydpa8lcdyV7G27Gzi+XFsZSOQTOSG2Tn5NApFQA/SHFxK1kwjkXOuvxACTeikzRM8zGfw3e9+jfr6Jq688rrTOl69XSDjMx67M7tSfidM1nj+NMlgHFuv9Mdt9E4QSYFdYWM14GzTB+kP2bieF9TuFdg9glgf1HdDQApK/QojKeAJQf4cG7vCURYLy1HiYjOrzhV5wAa73Pm3SguH/dGYFaXYfpAFkCmB7VPYJWC2KlQZWEWFsVNQ2GyjwhI5prCrjpyEEy6XP9cGLxQ3KPQO4dhd+Jzty2FHpWwu+sPbablcLr73vVt5+9sv44MffBsPPPAc5eWnR2xedNEVfP3r/8JLLz3Hxo1nkzOhaEH4ZcSxW4OC5ZC2rxVdU/BvL0n2jUuKCqIexfWLbG5cqnhmSPDsENQHFC1hD7kyweFRLwfsEBe0pli6vPSEitmXo2JROc2bmuh9oY/0ZAZQuPxuWra2EG2MIIRg0fkLKW8pJdYbR9mKcF2Y0qYoQgqyU1mkoeH2v/K+ToS6NbUM7Rsm1hNHd2u4/G5Q4Aq4CDdEUEUbZdvoLn2OXYVmaI7KfyhBRWs5/bsGGT80DgISw9MUs0XKFpTO/t0nx5J0P9NDzbIq3MGjg7Guro55i4iTYJ4Mnsc8/ghQUlNCRWs5g3uGCJTa6B6D3HQWq2C+JosIA4N3Zt/HcnM1bfpeFLDEXE5KTLPX2Mkic9kscRhQQXzKz/PGM6ckgwGsjEWwI4qe8OIOuHG3enCH3bSsrGBC5kmMZMnlCrg9LppbKog0e/D2eUn6U/gtUFEQCZBZgStv4G/wcekHLzqOCAYnmfic2k38LPFrBlPDlHmj5Mw8g6lhVpQtZVFkAYOpYToTPdSHagiWBahqrmfNFefQOz3Ato5HyA/HcY0J8sNpBp7vp+eeDsx0kZ28wKf5xOy+XB430q1jeFz4XD50W5Is5Eilkwxk5hbammFg1TfS1NjCmWeew9vffiOrV29g1ap1eL1eLCyedz3Nv+//Ij//hx+w+UNbufTdy1hoOYXULmM/vXoXC8xWXLjo19qwhEWJHWFSTrDYXM4ScwX79F3EQnEWmK1MyRilVHB+9pLZ++bDx5rihlPeL7/hozW1jInhIVK1cWwRxw7mKGrTmHmbwdoOEtkxmvRlvKP0OloizXxk9ft4fngHHVOd+A0fq8tXsKZ8xWkNkE51HO9feQPtsQ4GU8O4NTfLShdT6St/xXWFcEhfhCDidqY2xrOgC0FxZpmACxoCikcHBAtCiho/rLn8Zu7+4vUEYrs5qK9h0LI4UkZU+mBTtc2DPYKc5aiA4zmnrj6/XnGKDIdXxBe+8HdEo2XcfPMnwQbPvRI5LjCXHBl4KLQe8NwrMFfO9Zs7AvcjAq17ZrCiOevIYfA8ICmusY4qSRS4nhJ47hOOWgSwKwTZqxXFjY637/i44EodAha4i87mcoBuCzwKbE0RGhTYGvQr6Jgh2d15gZWFoKmwJMT2CpqicPn5ihVLnM/ylRtA/RaeVTAaE/h0xVl18O56RTgv6PiQxZ3bBC++KFm6VCGlYwmRTgva2wUulyIYhFRKsGKFYv36o4ON0VH4/vcl3d3OcsWi5Jln4OqrbS6+2FkuHIZwWBGPOwTwEUxNOQrhsmMUN8di6dKVHDiw97Xd4HnMYx7z+B+IDr2NX3l+Qr/eg4VFSIXZkr+Aq3JvPaFNWM7KcCC+g8n82GxXWUAPcUXxGi73XEtaJim1y0/oZ6tQ7DRe4FH3/QxrQ7iVmzMKZ7GyuBY/jk1CuBDBnDKp+ngNfZ/pZmLVGLJG8tOu76GKc4mn6fA03kovvnI/rio3wdYQXpeJ5bZw6S48uhfbpRh3j6DcioiMEsyHSeTjaBkNK2dSlqiAaUgmEnQOHmJ0coiRoWFyuezsftxuN7W19dTUOORwQ0MTS5euZPnyVdTVNc7WQV7NT8RVxmhugNVyA936YQb1frwFN2Wuapboa9jDDnzKT5PVQqkqQ0ObY0fxcjj0sqAu38CxU+82FnuMnaRIsnTGDkKh6NDb8CgvBZGfJZhtbHq1LqKqjLWhsxmY6mQg00dKTGNJi75IP7sje/Dh5/rcu17Nx+eE2LbtIT784RsorSjj5v+4mWhVGR7dh1KKgm1jK5PR3CBL7bU0B5eQMVPsnHyajJkmYIQcQYqZpG1qJ6XuSixlz8l5UEphKXPWYuLlGB0d5q67buMf/uHL6PrpUSLiSOH4spJW6SBeZXcShpMTMcvKHru9EshfrHAFYfEeMMaEQzobyiGPw44fr/thgV3uWDLkLrKd0LYeJy8CDZSFQwb7QcsIlNcJkcME5QY7ADIHdqnCLofieuV4+x7h44QTXmeXQ+5iG++dEm0AR0EsBGajIn+hc/yFsxR6p8DYAZhHVcrZy2yE6aiq7fLjr91/J3w+H9///q1ceulGPvKRd3Prrfef1r1es2YDpaXlPPLIfWzceDZlXijzKiay0HCMvmoiCxGPovo1egZnTPjrpzQOxSHiVnils83v7Nbw6xadCYFPP6oU9gTdLAu4ORQXJKpDuHynR7JLTbL44kVULq0k3j+FkBBtjBKqCs7+vQghiDZGiTYeFVCNd07Q9XQP0yPTSE1QsaiChVsW4A2fvBN4amCKkfYx8sk8oaogVUsrCZT5OeOGdRTSBYb2DuP1GlQurSRUHSQ5miJUHyZYGSQ5kjpue8q2QQp23rGbicMTGD4XZr7IaNsYgYoAaiaQDsBf6ifeHycxMk1F8OgYsaurg7PPPv+0rtWfGubJ4HnM448AQgqWX7EU3aMz1j5GPp3HE/TQsmXBcW0VwGwhuEffSVJO02g1s66wkbA6qrJ04WJDcRMbiptmX3vU/XsA5Mv6hHRlkBVOW8nL1aVHkBpPsfs3e5kamJote0KVQZZfs4zFi5dRXL+TJYklqILA8En6/J0sLq7l7Pqz+MnKX+Hdm8eju1FBKBYK5OoKXPThC4g2OsecLKTYN9FGojBNxB1mUaSFMm+UTdVnsD92kIHUMC5pcGbVOt6y6E0Y0iBvFTBtE5d2dKZToRhMDWMFNaLrKqlxO8qXZDKDlILICsWynhVsGjmH4fgwt+z6JYOTg9gFEztvkUFjYdkC3ta0heGQi94SnXzARUl5NZsbl/Lu0kWE5cmV2vd47uCu/G088b5HCC+PEPpSmO8aX+cDmY+yxFzBtEhgY8+2VKZEEl1pGLjIiSwKxYriGqZFwrlPAlaZ6zk/fwnLzdUn3e+JoEud82u3MNg+RDo+hVmapruiDTceavJVlBvlFEJZjIYUJUUXmBD1hLms+QIu44JXta9XgiENVpYtY2XZyQdDJ0OlD/qTiomcQEORKTpBDD6d2XYvXULedFTEAAvOfBP+aDUdD3+f2nf8J/H8keQGB9e0KEIGPDMsSBcFdUHFeXWKTdWvXf3w0kvPcdddt/G1r/0An8/nJDf3CqwqNad4tmpB73HCQsylMy/aoO8D4wWB9zcSFVCOCmSGLLYrQT+k0DodqwkAvQO8v5BoEwpsgdJASyl8t0pSVRZ9fYIqTdEwJhgphQWDzvhAzHDMwobDUagvgjRhf0HhRlBlQD4PS4qQElDrgWVBiArBuj2g/1iQfZ/C1aC4cQVcbCrGvIqQCxpCoHeD1aCobIXBnzvK3SPie8OAzZsV27eD2w1VVbB2rc1ZZykCM/55mQzccotgxw7BypUOYQyK/n745S8ltm2zYoWipga2blXcdpugv98hh1Mphwy+5BKbipPYpC1Zspy7777jNd/necxjHvP4n4SkmOZn3h8yrA3SZLZgYDAhx/m953eU25WcUzh+kN2TPMREfoSQEUWXOraySRRiHErsYYOx9ZTZC3uMHdzi+y4F8kTtUibFBD/q+xYVj1Uy8fQ4gwf76T50GPLO8lqJhp2z0cY1LvzMZVhLbULlJWwu3Up9pJGflv2ADt2ZOK+yanhb5r0853qKbe4HqbSrMTDIiAxCQF4UqLJqWFVcxzPux5FKo9auZ0NhEwJBUiRIyASfSX6eMquCWGySwcF+Bgf7Zv7fz9BQHx0dbTz44D3E446dQklJmGXLVrFs2SqWL1/NgsUt+MoDHJRt7AnuYsw9gllqoXkMfqPfTkxMIoWk3mrimuzbeHPu7ScNggOIesoZzHRTsPK4NEcRV7DyZGWG0cAItVb9bH1+xKZtUk6wrrCRNmMfo2IYAVRYVbwt914WupfQUNZESbGU2zw/Y5d/BwnvFI3WAt6Vfj+X5K96jZ8mh6T94Q//k3/4h09x/vmX8rmv/BvPpx7DUpZzzFaaol1AopEuTvPi5OOsiW4mZ2WPs4IIGRHihXFsLPyGYx3hN0IIIG1O49F8VHprT3gcDz10LwDXXffO0z52q2HG4zdxjB2DCVqfMwHvflRQXKJ4BdH8CSFHnQl6Y69j41BcrzCeF4isY7NgB3BqQanQhgRKKITphLohFOiC7FsdwlbvwFnYBcWVTmgwlkJ5QCaFU836ZmwkTKeWFMyE2B3R15ggJwX5s22MHQI5BJgKGRdQFFgNitxFNvbMMFP5IP0+G2OjQOtXKMOxpnA/JdDukigDissUuTfbc+wtToqc42EsEo7i2VzMnMDl00VVVQ3f+c7PedvbLuULX/hb/u7vvviK60gpOf/8S3jkkfv59Kc/j0eHixoVv2yXHE4oSgxIFh0rs8uaFMHXJpjloR7oSkCNHzwz45GAC3qTit8clrSG1ey4ZPbYZsYDxZNHsZwQQggi9WEi9eHTWj7eP8XuO/eST+fxR33Ypk3Pc71kJtOsf+e62ZDPTDzD0N5hpgYTZKeyTA0mAEfV27/DZmDnIKvfsopQZZCtHz2H3XfuYbInhrIVicFpAuV+ll66GMu0Gd4/THoyjS/qDF5S42lcPhfKUkx0ThBpiKAZGmbBJN4/RTqWIT2ZJljhfGhtSyGknKMuLhaL9PR08p73fOjVXbA/EcyTwfOYxx8J3AE3q65eQWZrlmKmgDfsPWnryDbXg/zW+yvSIoWOwVM8ynbjKT6Y+RgVs/0/x6PebMKDl4SYomQmncDGZkrGODN/1kmJYKUUhx7tIN4/RbQhgtQltmUT749z6MEOrqt9B6lgkt6Sztmg1IXmYt6cfQdVZTVsPWczT4SfhUEbURAYERdbNm1mzWKnTa0/OcgtB35F73Q/KEgUpskUs5R7S/HqHkq9US5vuoj1lauo9lfOFpH+lBcjrnF4tJOGcB3+Uj8FigynRylzR5HuPCmRxK8C+PweRqcmCGsRrl73VlYX1/OVF7/NVEGxwLcWr+4BFBO5OHGrwPpzzueC+nNICZtJYRFUElOM86TrLrq1DkrsKOuKZ7DCXDN73UblMI+7HubAB/ZQnCjy9t/cSIkW5pA8wEPu+1hsLqfcrkDHICPS+JQfn/JhYZETGSqs6lmivtyu4Krc9VySexNevCe9N6+Es2rOwLRNHut/mi67HSpgTfEMVpQvnSXRD+ltPCUfY4W55jXt41gkCyleGNnJgclDGJrOqrJlrK9cg0t77YZjZ1QpBlMCr66IZSFrKQqWQJMwngFDCjy64wNmznC5mm6w4uL3s+N3/079m79IxDNXguvW4LJmxfkNiqwJAcMhlF8rlFL84z/+FcuWreKtb51R3IiZn5fzy/bM60f2p8B9v6PwFTknjE2MCLRhsEoBH1jl6rgUYteTAmOfsyNlgLRxwhwmwdgpKCtTxFMSw4SpIKS8YJhgFkELgGnBkILmghMyN2FBQwRyRacdMSpAaoL2ArwpAWV5YEAQOCxxPwGZG2zyZ9rUPiKoEaA0EF2ALshfYCMM8HicULdj4XJBVZXi2msVLS2KcBhKZgZl+/fDrbdKHn5YYFmQTkuamxUlJYrOTsHQEExNSRob4eyzba66yrGaeOIJQSIh8PsV11yjuOSSk5P6S5Ys59vf/iqZTBqf7w8Qjz2PecxjHv8XcUDfw5DWT4u5eFYFXG5XkBLTbHc9eRwZXLDzjOWG8Wh+dOksL4UkZIRJFhNMFWKUeipQKA7q+9mr7yIj0jRZLawtnsGjxgNMHByl8HiBp5/eRvzJGNaoCRrUr25i/fqN9LyvC7XMpmpJLRXlVVj7izx36dOM3jHCP/3ia3grvRTIc6v/FgwMzs6fT05kiMs43XoHmwtb2WfswhY2GTL4lI9FxWUc1tuJyQna9L1YWERVKSuKR+u0CTlOrVVP2I4ihKC0tIzS0jJWrVp73HVTSjEyMsT+/bs5cGAP+/fv5rHHHuBHP/omSik0XSO4MkRwS4jarfVMnD/OuHsUiUaNVYdSNv2yh9u8P8PG5j3ZD57w/tjYDAaH6LZ7Kaay+M0AUasUr/AR8VeS9ecpsV/+TFNoaFydu54r8tfSp/XgUm6WmMtn63uX5mGttom1bGIkO0Qqm6TJbjllYPQrYXo6wd/8zZ/zm9/cykc+8pd8+tOfx6RAabGClJkgXUxh2kW8mh+X5sayLSzbomN6P2FXKSCOs4LQhYGlLJaF13F4+gCpYmLGQqKEltAygkb4hMfy4IP3cMYZZxF5WfDwqWC2QH6TjftxgZoENND3C0QBlFvg/bnAE4HsdTaFs05fHCDHwf+9GXuvEgWTM5YUEwK7SsEoR+0fig65q8ICq1JR2OJM/rteBHOlIPUpG6NNIHIKq0ZhlYLnNwLfbRI5DkqAMJ1sCSywyiF3lY1A4L1bIg/OKJBzAjwK126B5xHQOwVoUFiusBpBHwTvfRKr1T5qHeGC4hpFcQ0YLwr8d0uUprAqFeSc8D2RlKQ/ZnOcTXjKUS2rEkdF7PupRO8ELIfwNpdA5j32aWdeHIvNm7fymc/8C5/73P9h/fqNXHHFm19xnQsvvJzbb/85AwN91NU1cFa1wqPZPDkoGElDcwlsrlasrVAoddSC7NWgLymx1VEi+AgCOgyn4fIFim39kiqlZkng6bwzBqkPnvjzZdnw0phgx5ggWYBFYcWmGkXlaYQbHov+nQPkpnOUNkdn/+ZcfjcT3THGD09QvbyK5FiSnbfvITGUQOqS8UPj2Laialkl0foItm0z2R2j86ku1ly3Cl/YyxnvWs9YxziZeBaX16C8tdwJjFOKhVtb6H62h8nuGEKAO+hm4XmtxHom0Qx9ttNZd+kEygNMHJ4gE8sSrAiibMX0yDShqhDhuvDseezdu5NCocCaNafuhv1TxTwZPI95/JHBF/bCKdo3JuQY93l+i6Y0llgrwASrT9Fb6OHByH282//+k67bYi1iY/4cnnA/QkxN4MLNlIhTbzWyJX9izzdwTOone2IEyvwIKUiOJkmOJcmnCyTHUtSuqeGT6z/NPn0XcRkjapexwlztqCAEXNNyOWsqVtI51Y0UkoXhZuoCNQghsJXN7R1305voZ2G4mayZo294gIlsDLfmYmGkmaHUKE8MPMOq8qWzD7SBnYO0PXSQ2kIVz/pfYmIsRiQYRlZLpBAsCDTjsQWH9UNMiHGn3QybzeYWVhbXYtom24dfwJDGDBEMICj1ROid7ufpgee4oP4cAkoSUJIBrZfv+75Bv96Lz/aRF3lecD/N1dnruST/JgD6tV72/nAnvb/p5k0/fQvhZkf1XG5V0at3MS0SROxSmswW2vV9VNhVBFSIoihiY1Ft1ZInx6DeT6ldzobCJnyvEG2sUKRFCqk0fPhQCmJ5p5go9YAmJefVn83G6vXcLe/k3pIpVqq5CuMSO0yf1nOcMlzN/PdyJfmxsJVNd6KPWG4KXWo82vckB2KH8GhubGWzY3Q3h+JdvGvpW2YHla8WW2oVByYVvdOChpAibQoOJxQuKRhMCfqSCpcm2FDppAoPppxzbzj3gzz36y9g7vkFC6448SDMrR1NIH49uO++3/Dii89y6633o80koykDlFS4nxVYNWBVK+wy0AfAqlGYM/kIchg8j0iUX2E1OwW8sVMgpxRiWqBCjo+bWQ/mgqNSAn23Ex5iNQByhnPOgTYGWieccYVi/7Mw0Q8hCdNecE9DtgQaIgqPAauqIJ2Ey1ttfveURPPAZBLCBTAKUNScQqMmD14LsgIitkDvUQS+Kdl3o830RkXNIUFNAexGKGy2KaxX9PU5xO/hww4jXjejPunuhqkpwT33OISvzwfr1tksX6746lclExMzFjYBZzB+4ABIKVDKWbahATwexYMPCioq4MILFWefrUgkHHsI3ysUzBUVTop5LDYxTwbPYx7z+KNHWjqtuy8nAb3KR0xOHvfst5XlpMW/rAtKCg2FjY2FQvGg+x7u8dxBVmQgJ/j1Qz8lfWeS3m3d5MdzCF3g3uCm7L3lhLaGSZ2dpM7XwLrCRkJ6mH6th5RIUmFWctOCT3Dz7Z/k/e++nhvedCVn/vYsMksz5ESW8/KXOMeuoNKuoVM/yEJrMYutZXhtH17lw8Bgn7GTSTlO2I7iV36EJSiKAruMFyixI2houJWHC/OXY5yGRFEIQXV1LdXVtVx00VEbtXQ6RXv7fr536N956YXniP16kv7/6AUd9DN05Pka2a0ZyjaWY/ktihTY6XqeS/JXUmlXH7ef+92/417Pb7B8RULpECKnKLXLeRNvZaFrKU/bT9Grd7HQXIxAUCDPLuNFNHS+5/93VhTXcE7hfKrtEytogVf0hz4dPPfcU/z5n7+PRCLON7/5E9785ncAoOGlxtdIb6qDgizg1twY0kXOyuLV/UTdFUwX44SMMKCOs4Iw7SJ+PUjUXcGGslKSRUeZGDBK0MSJC7RMJs1TTz3KX/3VP766k5CQfZtDhhovCow2wAWF9Qq7ElCgDYD3NxKzxXJeOwZiGlwvCbRDAuVVmKuguFLhelagdwuKs/ZejqpW73IUwXbQUfHaERApMRPa5lg6aMPOslqXwHuLwP6kRWHT3I6y7AcVxc0Wwb/XMDoVtgsQAjviWDe4dktSf2Fj1VoYu4VTOwZtXM9KQCGnBSIpQIJ7u8AcsVFBgdED7gcF2ffOJSaHU+C5XxDOgHsJjn2aF0yvwjgE+kEwZ2yntTbwf1/i2uVYS5gtCjsEMiEwFyjHkiILxh6B53eC7PtfWwfezTf/BTt2PMdf/MUHWbVqPXV1pw5yP++8S9A0jUceuZ8bb7wZIWB9pWJdhSKWgycHBb/vEdzdJVgYhgvqbVrCr+6Yyn3Od2fBYo7FXMaC+gBcUKfonFK0xQRht6JgQcGGrXU2C0+wL6Xgd52CB3tnyFsN2mKSneOKD620qQmc/rElBhO4A645ky+6S0MpRSaWAaD72V4SQwlKm6Nk4lmkrmEYkqn+KYJlAQyvgb/Uz2R3jEK6gDvgRnfr1Kw4/ntMCEHLlgVULnGC5BAQbYjgL/UzPTTt2EUcg2hjhMRQgkwiy0T3JChFoDzA0ksWoR9zMbdvfxKv18eqVetO/+T/hDBPBs9jHn9i6NQOEZcxFplLYUQif+dG69OoL7bSHxxm77L9LLtgyQl9hiWSt2dvpMFq5kXXs6RFis3mVs7Jn0+NfbwdxRHYlo1t2UhdEuuNE+uLAc4Xfy6ZY9/dB9hUeiYbG8854fpCCJpC9TSF6o97bzA1TPdUL3WBGnSpM5IeI2fmqfJVMF1IUbRNmkMNtMc72DG6h8ZQPZl4hoOPHkJZNudUb6SaSvaYBxiKj7I00sqiBS10xLtoLLRQblcxKcYZSUxQ767jA94PIZFYWNjKPm4q2KGtFJay5rz+kOs+BrRelhSXI5HYymZQ9vOg+x7WFc+kzK4gMTBF+2f2s/x9q1l0zdLZdYuiQJ4cP/Z9hx69kwJ5JJK4iOHHz+b8FvIiT0HmGZT91Jh1XJN7O3X2qQudPq2bB933cEhvQyBoTK0n13YVgyNVWAoagorLmmyWlYJX99BsNOPSDIpmcc5AKCWSLDKXzg4G8+R52v0Y242nSMlpFhWXcW7hIpqtuQlfifw0t7b/hn2TbeTMPLH8FJPZOFtrN1Pidlp+koUU20deZH3lKlaULeW1oMoPH15t8/SgM1MOsDCsMATkrBkBroB1FYoqP2wbEAynBe5wAwvXXUhi5x1o8sRk8GuBraA9BgcmBQUbmoI2X/jiZzn//EvZutWZVBEx8P9Qog0CeccCQm+T2BWK4kpF9s02zMxB6N0CMa2wFju/K2OmUJYCmQPLL1BuEJpCToE1054nU84yiGMKa5cTQiIQLFmiuOpdNh3fFqxpE6RNiCqoyitEAiYrFQKoeofi5ksU8S8pJiedQVrvc4JiDKYVXACEFExIKMdRAKdLoa9f8LNvS/a2gGHDFrfiJhu8d0m671D8Oi1oMyGbhWeeEUQiUFfnBLsppfD7HUVwMgl33SX5r/+CyUmH0M1mHbuHxkbI5ZzfKyocpXFpqcLrdYLptm8XbN2q8Hic904H0agjS4nFJqmra3yFpecxj3nM4382KqwqNPTZjiRw6pyEjLOpsPW4ziO39BI0wsTyY7ikZ5ZMyFgp3JqXgB5iSA5wn/gtE/eOM3r7MF33H6KQLBBcFaL6z2oJnxsheXaSqLcUDQ0LCyFhUk3woOcezs6fxxJzOUmRYEgboENrJ7UhyfptZ7Ln2h08e/6TVN9aS/HCIj1aJwtnHpBHaheXcs+KGzIqzYg2TKd+kEqrmk2Fcyhi8oT7YVIiSVIlGdT6qLbq+Ej6LzmzeDYFCuwzdtGht6MpySJzOcvNVXNC3U4Gvz/AuvVnUnp+OefdfDGVVjW7u15i11Mvkto2TfF7Bbr+pYNu92F8m/2Er45QelU5UyWx48jgYTnII577CaiA09nnBdtr067voyb/Isuza7k29zZ+6vs+bfo+JJJurYOMyMxev/s9v2W/sZsPpz/5hpC+L0exWOSrX/0nvvGNf2XDhs3cccdD1Nc3zVlmQWgJaTPJVHESy3amDNyal1J3JVJIFIqQKzqjLJ/Ar4cQQpAuJvHoPqq8zhhECo0S1ysrfZ944hFyuRwXX3zlqz8hFxS2OIrcwJckyq2Okr4CrDqH7NTbBIXKo/WV7IfA1yV6t8AKK4QucT2ryF8M+iGBHThKBIMTTGdHQKbBqlBgCuQYyJgCSyAsgexX6AekU8e5gA7wf0sje71N4Zyj+xYp0PoFwnSOz6oCu8LGjgA5x3pM7wVzBZgrnOBwz90CkQNhCbQ+gVAzquIkGPslVrVC5MB7p8RcalM8Q2HZcE+X4KkeyTsPOodlDgmWlyoqfDh1qyWQMWe0pHVD6G8lepeY9So2XhCIonCO/0gGmBesKoVrvyQ/aWG/BnWwEIKvfOV7nHfeaj796Y9zyy2/OWWeSUlJmHPOuYA77/wlN9548+zrpoJbD0peGhWEPY6Nw/ZhQVdC8uFVNo2hVz6WI8KbteWK6gAMJh1LO5fmhFErBVc029QF4cOrnPFLW0zgM2BDpWNJJ09w6P1JeGJAEnGr2YBrWyna44JtA4Iblpw+ke4Ne0mOzQ1ftG3byRvxuzDzJhOdE/giM0HqM5s2vAa56Ry56Ry6Rz8Sd3JaEEIQrAgQrJjLWlcscnKRctM5PCFHRWzmTMoXltO0sRHDa+Dyu6hYWHacn/H27U+yYcNmDOO1d5j+MWOeDJ7HPE4TSim6p/vonOoBoCXcRHOo4XUFY/1fRQHknW5Et4aqszA9efS4QffTPQRCfpo3N51wNRcuzi1cxLmFi057V76oj0B5gFh3jOmxJJpLx+U1yCXz+Ev9mHmT7u09RBrCr/p6FqwiprJmVaMZM4sUEk1q2FYe27YRQuDWXIxnHe+2WE+c7FRuNiW1lQW0uhaQNJO4h1wsumQxPz7wSw5PdSMQWMqmtljHObFNtN92kJKaEDUrq1lbsYqew30UrMKsZUIin8QlDTZWr589xgwZ2o19lNmVoKB7uo/+5CA5K0c+muS+1H282/9efvxX38YdcFP/L01YWGhoZMkwLAdRQrHH2EG1VUOQICPaEEEV4sbMzSwzV2Fi0q93gxI0WM24ZyuoE2NUDvMD3zcY1AYotyvJ2xY/t+5DRvrYHP8UfitIexyG05KPrrFpCkFDZgGy6OZh8RDluSqqPdWIoEN6n1U4D3DaFX/t/QmPux/Gq7y4lYcn3Y9y0NjPTemPs8BqRaHo1br4Qe9P2TN2kMWBVpqMBp4cfJZUMcXhqS7WVaxyigJXgP7UEF2J3tdMBoNTZF3XqqgNwEgGFoWdAilnOT7BsRy0xwXvWGKzuVoxnnWC4e6+9go+97m/IpVKEggcH1T4aqGUUyg/2CvJmg4XO/rSXXR1HuKLX/7h7HLupwR6m6C4SlFcptBGQBsBBGSvt2cVFcAcuwgEyLjArpwJM7Gd1jq7UiFHBHqXwFroVGVWPeiHFXJSoPyOjYSYSZMuLnTsE845R5FcrhD/Kql6HLwIhnPw0JTgcQvsECxNKq624KqrFLfdJonFII3iBWAdcK0lMBWUCPBqoFywJyEQeQXTgrEBqBiFB/POAOXdKxTFnYIrfBA9RzGxDHp7YXAQVq6Eri7weqF8JhsiEnGI3dFRQSAAZWWKUMhREA8MgJQOGSyEYNEihwgGZxsTEzAy4vgFnz4Z7LDpk5Pjr+kzMI95zGMe/5Ow2FzOyuJaXjK2E7XLcCs349ooJSrClhP4BQshaAy0kiwmiBcmcEs3piqiEDT5FvHM40/w/Xu/wTP3b8NMmJQuK2fDxzez6LplZJdkiIsYMTlOURbQbA0TkykZI2SXMCWn0NFmPYfDKoqy4Bn34ygUS2pWsPyBNdx74x30XtlFyVfD9H2ohybLsTewsVFA2I5yeeFaGqxmnnM9RYfeTnNxIavMdfhVgO2uJxEIgirE+uImolYp3XonPVonefL83PdDnnc9hYUNKB5RD3Bu4UI2FrbQqR/ExqbZXEiLteiE3VECQcSO0qN1IoSgqrWGyPJS5EckeZWjYncl5jaT8YfGGPqrAQb/sp+JM0d585Xv4Ior3kxtrSOO6NE7mRYJFltH8xQkkjK7ggPGHvLZPMvMVXw89dfsMl5gr76LCTnGhuJmymcYzCq7lnZ9H0+7tvGW3A1v6Genq6uDj33sRvbu3cmnPvX3fOxjfzXb/XQsPJqPtaVnY9pFRnODlBhRfEYATWikzSRu6aHcU0XYFaVz+gCJYgylIOSKsCC4hJArcoK9nxxPP72NxsYFtLQsel3nJ7Icz6bMDGNmw+ZwCM7A15y6TvlBpAVmi0KFwL1NYpUqKL5sOwrsUjAXz6iChROkbZc6JKpdMrN/nNwH8jhhwygnZLhuxt84C4GvSPQDAm3U8SMWeSj6cWb4XTj7flkAnkg7daQ2OkPUJnB8umdC6WR85nXNIY7N5YoXpwX39whKDIVWISgbhMMm7J0QnFWj8FrO8nbEqUPddwn0PoFdBWqmNhOGkx2hdYO5kKM1rhtIAlleM4LBEP/8z1/nAx+4nrvvvp2rr77+lMu/4x038pGPvJuOjnZaW5cA0BaDvRPQXKJmc0fKPA7h+tSgoDF0auZzIgt3dQr2T0pMGxaUKExbEM8JTBuCLsV1zYo/W+5sp9oPb13k3NeXw7ShZxpyJtQEoGdakCpC7TFcqhQgUdx+SDKQVNQHFRurFQtKjtvcHNSuqmG8Y4LUeAp/qR/LtJkeniZYEaB8YZmjohECZhS7nhIPhs8gl8pjFiwmuieZ6J6kmDNpPKMBw/faydjKxRU0ntlA/0v9pCZSgMDwGbSe28LCc1tOyh1YlsXzzz/NzTf/xWve9x875sngeczjNGArm7s6f89j/U+RLmZQQMDwc0H9OVzVcilSvA6z0D8wWqxFROwok70xKvtDqAYL22WTkWlawovwJN0M7Byk8YwG5OsxQQWyiRyj7aNMjyYxvAb5bJFsPIunxENmykRqTnKpy2sQ75+imC2e1Of4ZKgJVFLuLWUsM05DqA6/4cNWNqlCiqArgN/lRylFzspTE3BMrWzLJu/OEQ9OEMiFcFkOcSo0gWXaVHjL+OiaD7BrfB9jmXEyvVn0nQJvxsWEa5LhAyMM7R3m6ssuZdf4ProSPbMBKQLYUruJC+q3zB6jRDqkMhaH4p10THVhSANd00gXM/y+61Emdo/xyEP3848/+jL9gV46tHYAdDTK7ApicoJF5tJZ1UnQLGF3fgeP7X6E9HNFQlVBalfVzPFJOhWecz3NgNbPEtNRKvcnBWKiDMrbyaVfoGLyAoIuhyB9blgQcSf4xb47SeZ17IU6h4MdHM4fotlawAc8H+KM4mYAurXDPO96mmqrhpKZQMJKu5p2fR/bXA/RmF3A7zy/5kHrPnZOdiNKBDnfFBlzCW7NjUd6GM9OkiqmCbqOVDPqpK1+rxZHbPMEjsdvYOYjLmfqGaWc8IYjyb0XXXQ5f/d3n+TJJx/l8suved3770rAw32SgKFomlEPvPjAvxFZtIVc9SaOFHvGTsfeAQMwwGoGq0mhHxTI5FwjYbNVYUcE2pATLicKIBKOosOqAhV2Bg1yhkw+gsIW5YSMFEAmnU3aUSf4xFynnIJfQTgNgYKAdYoXlOLb+wX5oqJKQMoP256Q9A8qPv5xm499zGLXLkFPBfQ+Kqm2wRV3/IiFC7wGTPkgloKwDd1FiJqKBVLQ6xU8FoOGDpBuwSpbsXBQEI8oFixw7k8+D0oJgkHHoy2ZhHgc0mmB2z2THm459hL19c57UoLfD8uW2Xg8MDzs/N7eLjAM+OIXNUIhxdativPOU7xS0PQRMjgWm3jdn4d5zGMe8/h/HQYG78ncRJW7hpdc28nLPCuKa7gofwWLzBOHupZ6Klkd3chgppfpQhwta7D97mf5zM8+RX9/LzWt9TR9tIXN15xL+dKjaZ1DKkOtXc9ScwV3eH/BmBxGx0WpXUaT1cJT2mOUW3M7MgIqSEpMIxD4lB8RFrz5jnfy+7+7i/ZP7KPjxXZWfWEdgVCAPr2HGquOlcW1s+KGMwtnMynHYYb8jYsYUzKGV/lIymksLHz4qbHqaDf285j7AZ5zPUmt1YBfOXVKXMT4pffH/N5zF5rSAIFX+diav5Drcu8kIxzz+6AKzSqpNxe20uFrZ0KOUWqXU2aVMyHHnGCwVRJ9lUHNx+spmyin4bfNxO6c4POf/zR///f/m/XrN3LllddRe00DLOU4qw4LC0O5ZonoaruW6nwtAslB48AsEQxOjRpSYdqMvccRgq8Vpmlyyy3f5Qtf+FsqKqq4664nWLv2jFOuo0ud5ZH12HGLtJkkbSos20QTGguCS/HpzrVeW3o2aTOJwsavB5GvoT48dKiNpUtXvKZzOxbFFQrPfRKqjrF3mAbcYDU6dZocAN+vJHIQVGSGxE2B0S4orFdOaFsriMFjgukUaDPLZ95lo0JODSeHwf9dDc0FMg4y64T/Imd+dGeS3vWMIDSsoXxg7HWWtcMzAYIpZzn9INhlzj7tsMKqcbav9zrbtCMzZHNOYZeCNiWQGUcdDA5ZjAA7otBGBVob7OgRGGOCGgld1VAxAE3T0OsWxMcgOAXm0pkwOAXGfgHiKBEMjiJa6Qo5IRAFJ/QOQE6AXa6wy1/fPbv88mu44opr+bu/+0u2br2IcPjkEwmXXXYNkUiUW2/98Wzw3HBKULQFWVMxlBZIoNSrKHEpDk+dKOjjKHIm/Hi/5MCkoMqvCBgwNiNQ2VhlEvbCilJOS13cn4RfH5R0JZwuw7BbUe136uBjI0IGUk4n4pF/H4pLXhqF9y63WHWKIL+qpZUsurCVnmd7iPXGEJokVBVk6WVL8IScm1K5pIKup7vxlHjQDY1oY4S+F/sppAvYpoXUJJpLY3o4Qf+OQRo3HN/hezqQumTZpUuoWlrJVP8UQgoijRHCtSWnFJG1t+9jejrBpk1bTrrMnzrmyeB5zOM0sH+ynYd6H6fEFaQ+4HhqTebiPNi7jZZwMyvKlvxfPsLTR5ldwRW5a/l9+l6S9jRFTw4bi6hdRrO5EOnVKGQKmAUTl/4a41GB5FiSXXfsITE0jdQEtunMHBpeA8Nj4A64CFYG8UV9ZCYzaNrc9M8TwSpaZBM5DLeOO+gQuF7dy6VNF3Drwd9waKoTHQ1TmWQtk9ZIC5lihp7pPvJWgW0DT7Nzcg9WzTQdb9uL6S9QYkVY1bOB1YfPIJvIseCsJqQmCWoBttRuIjed45m7t2NhEWx2lKHKVkx2xwjtCvG1S/+J2zvuZuf4Xryam3PqNvPW1qtwH3PtPHhYU9zA3cadjKUSeDUPPsNLwjdJhVmFZzjAt770JS659CpuuuzjTEyP84LaTtEo0CQWsNfYwTb3Q3PaD5MjKTJTWTqmDrJ0dC2T3TGG94+y+toVeBa52WPsYFKOE7JLWFlcR6ma+8Tv0Q7jU77ZgULGBKlcgCLlGQScCV+PlmXn2ADJwjPsHtvHyuhq1vavZco3QdyMYyc0Fq5YiQw72xnU+smKLI1qwey+BIKoXUaH0cYO8zkect+LHvfiL4bwuzzkVZY9+g585SGmPWNk016mtQRBAkxkY/h1H4siLa/lY3gcmksUJS7JWPZomIKlYDInuLDenuPZBcwqRx555P43hAw+PCVIFx0vMIDBA08zenA7m//yN+waE1zUcAxbfSKoo4X4EdhlkLta4b1TYryIk/g8rRhzC5IJcD8NZY0CVeUQx0dQOFPhfkhgPO+oP5Qf7ErIXWZjvCTwfU8gLGd/chiKa+HuJwVD01ClCfJ5GGlXjAahrU0wOCi58krFlVcqPFeD8QNF32MClbQJdwncRYFyQV5XlGUE7UrQF4V6zTldrwums9AeEyz0QjYA5fGj56nrzmcyEFCMjsLYmGByUpDNHrWHKC+HWEzg8zmksGUJ1q2z8fng2Wcdj2EhIJVyCvaNGxWBgGMp8etfC2ybU4bHAXi9Xnw+P7HY5Ku48/OYxzzm8YdBLpljYOcgo+1jSE1SubSSujU1r3qy/ViEVAlvyd3AFbk3UxQFAip4yjwAgLC7jKnhBLf+8OfceuuPKRTyXH319Xz3u7+kbG0FXw39M7ayHP9ToEiRKS3OluyFXJa7hpRIcsDYTZVVS7lVyag2jFt5ZgPOjmBKxomoKAqYFglKVBipS674wrVYay06/+Igtz3+U9Z+90zWn30mb829i7CK0Kt1kRc5qqxaWs0lPOt6kjK7nCkRY1yOYWKihKJDb8ONm4AdZJoCD7vvp1vrZFyOEbFLqbcasIXNoNaHYRpsNM9BIIiLGPd67uSAsYe0SIGAxcVlXJa/mjqrkU2FLYzJER7w3M0O43kSYoqAHcSNGwsLj/LQZLVwofdyrnnz2/BfG2B6OsHDD9/LPffcyZe+9Fnyn8tTuaWayZvG2XjFOegunQJ54tokV2avO87b+EjX2MvJ4yKFWQuQ14tnnnmcv/3bv+DgwQO8+90f5LOf/RJ+/+mZlYZcEVZHNzOc6SNRjOHRvFR4ain3HLXHEEIQME6DLTsFDh9u5/rr3/26tgFQOEdhHHAm6lXAsdASRchdaGPOlK3GXoGIOSTvEQJVBZ0ANzkiwABzscKOgutZiRjGqfUikL3G5ogLn7ZP4L1DYnQxq+5VtvNvVa6g4KiDjV1OkLDZqpBTAjkkwA0qqLB8oA0I5BRggr5PoKKK7JsU7qcF7m0SMe34DdthsEMKvUdgGXCk/Uzg2H3hcgQEctDpRvP+VpLSBD43aAUYsmBXAyyKQ+UkSAXFdYrs9fas/YMqwbkgpjrKSkln2wiFHJgJlEvghAtfdHTdVwN1pLSe+cj/0z99jfPOW80///Pf8OUvf+ek67ndbq677gZuu+1n/PVf/xOGYeDWYCAJXVMSa2a7bk0QMBRb605dPx6ICQ7FoSWsZvNGgi44GIesJXlXs33K9Y8gY8JPDki6E4KGoLOt8ayjWLZsh/St99rYvSNk943RmjEpa45SXVuDCnjoTMD93ZJlUfuk4ddCClrObqZmRTXTw9NIQxKpj8zx423e3ERiKEGsN44AbNPG5TXwBD0EK/y4Am6C5QGyU1m6n+mmelnla34OCSkobYpS2nT6gY/btz+Fy+VizZpTT0T9KWOeDJ7HPE4D+ybaKdhFSr1Hv4DKvFEmspPsn2z7H0UGA5xXuISwr5QX9B1k02lCwRBN+RYnDCQRJ9oYwfC89nYOpRSHn+hiajBBaVMUqUmUUsT7p8gnc/jL/JQ2OhYNZt4kM5Vl4ZYFaC6HiJaaRHfrc7Y3uGuI7u09ZKeyaIZG1bJKFm5twR1ws7l6AyFXgO3DLzGcHuWaUB3TxRTpYoaxzDiJfBK35sK2bV4se4K+soNUadWUTZQR803wUOtdJEYSbK25iMYz5vrsTg0myE5liTQenTkWUuAv9THZG2OZXMIn13/4Fa/JxfkreTH/Im2Bh/HrPmJqFDIa4cONHPzZ8xSyeT75mb/jhZGdPNz3BGOZcVzSRbZS4l8WwnZbs8W7VbSI98YolhaocdcRqgrNXN8423c9y+41z9JjdHEkwq3aquXG7M20mkdtFkqsKLHkNPnxXqaYImX4yKg6PFi4LKfIHknvYdf47wkaozw7dAghoMxXRrW/kmi6kiiVtGUO0TXVw8JwMwBu5UEAJia5Yt7xdjV8FESeoB1it74TS1g0eKvp9k6Qyxax3SZj2gi+QAorp8iEx3mm4UEWdK6mtFjOxY3nzm7/9aLKDxc32NzdJTkYB5dUZE1BU4niggbFnOn0GVx00RX87ne/nhNa8lpxpEw8spkX7/wy0fqlVK+5kmPDvotrFZ7fSCio2cRlOQ4qxKzNw7EobFFYdRaBr0omxwV7BPjTCjXtqHInpgThcy18RwRVCty/F8iYQwCLwszAwlDoOwT6gMAOK8e/bZ+gOwZPW/D7doFuQ4nXSTo+OC0QFWAYjkr3d79zLBne/nZF8UabphaB8bhAy4MaVpCHaFyw3aW4xSWwowozBzETduWc8cCjRTiQV4QUGDNzGMUiZDKCNWts0mn4+tcluZwgGnWuhW1DKgXV1Y4n8PCw83tTk+J977O5805JNKooFgXFovOersPIiGBwEDTNsYl4+GHHGuOVQuRKSiLE47HX9iGYxzzmMY//JuRTeXbetpuJrkln0K0UE92TxHomWfOW1XNqq9cCL168yvuKy23f/iTf/e7XefDBe4hESrnppj/nve+9maoqx5NWKcXFuSu513Mn7fp+dHSKFFlSXMHW/EV48fKx9Ke40/tLDukHmNDHqLCqeHP2HbQb+xiSA5SoMEkxTVzGuDR3FSmR5Cn3Y+TtHD7lJyYnWfbOlXxo/Sf46Ue/xzOXb2PxB5chP63xH6VfolM/SFEUidqlLC2upNyuYI++gz6tmywZBIJyq5K8yLHH2EGVVY3LdtNu7CcuJ7FskzF9hGFtgLAdxcIipEpmSVYPHjr1QwxqfZxRPAuUY2cxqPXzsfSnKLXLObNwNk+5HiUgg9Ra9fiUj7RIs9RcybXZt1Omyik9RgoZCpVw3XU3cN11N5BKJXnwwbv51s++wnPvfYqdFS9Q/95GGj7QxPqaTZyfu/S4+7KkuIKop5R+rZd6qxGBYFpMURAFNhQ2Excx9hg7mJIxInYpq4rrCNklpM1pQMwocU/MHg0O9vO5z/0f7r77dtav38h99z3D6tXrT7jsqRAwQrSWvH7V7smQTE4zPDw42/b/emBXQfpmG89vBZ77JCLl1Gl6NxgvQfFMNZPPAHYNyL0CaTpBvyIHmq0w1zpqWesiKJ5pofUKlO5YPthOQyMiDe57nUlsq1EgRwRWpULrUggLbDcIG0RWoQ055KzdCPpTODWkrRDTTr1n1TnLCFNg1drk3qZQEnz/5XSkWYsBG7Q+R7FbaHLOR8xM6KNwOtaqHMJYG3aUxdoAtCyHw16osUEWoFNC2xmQKthcv0oRWOUch/sBgbHPIa+xFXJUYFc46mo5DrYfclfZ4BbISSgugcIWm+K60/e8BUgV4KlBwXMjgoks1AXh3DqbMypr+fSnP8/f/M2fc+ONN7Ny5dqTbuOd7/wzfvjD/+TWu+/lmjddi2VDsggCRbnXGTLEcjBYgLrgK1tEKARube5yIZeid9oJsB5OC7y6YmH45AHV7TFB7zS0lCiORPxU+R2S2JCgKxh5sh1fRy/eoiDqFpS1jSJiI9jnrqXK52UwJRjNzLWUOBG8JR68JSf2UfOFvWx45zpG28dIjiYp5kwQEK4NY3iP8ggiKkmOTpMaTxNtfO2Tkq8WDzxwN2eeeQ5e7ys/s/5UMU8Gz2Mep4GcmUM7QfEjhSRvFV5xfatoMd45QXLE8cuNtIQJV5bw8tCNPxQEgoaaRh68+n52aS8gPYK6WBNLn19DlV5H4xkNiBM5058mCqkCk92TBEr9s2pfIQThmhJy0zmklEz2xEA4xGrl4gqizVF23bHHaUURgsqllSzY3IQ37GV43wj77t0PCHxhL2bepOuZHvLJPGuvX4OQghVlS+d4ytrKZiwzwcO9j/No/5MsibSS92TI1k5RZlWQFSYlNWEqUhWMaEMMX9rLUmsRgejcp6I44on0sue7Qwxy2tep1C7nz5Ifoa93kjF9FDudwTUapH/vEAOP7qH5HWeQ8GS5/cBdWMqmzBslZ+Z5oGcbS1UzZWdU0qkfos5spJDJMxDoJ0QJi4aWzx6nr9TP7+p/x6Q9wjJz5aw/3mH9ILd7fs7/Tv09BoaTBHsQDg2MMl3sRAkFQlAo7cLrq8cTW8pkdoBnh2/HtLMsK62lc2qYkfQ4eyfa8OleStwOAT2z89nzXGIux5cN8VD2IYgZoARun46/3OAK680c0tswlIFh6DQ2VrK3rYvJ6TFsj0DPegjZQcobg6QaRqgJ+bkp/wFaIwveUG/uS5sUNUGLXWOC6YKgpcTm7Jyi8naB3iFQQShsVBTOcojYCy+8nO9+9+vs37+bFSvWvK59t8x4jMVzoCba6Xz+bi746PfJ2Bqry48qAvJbFFqHwjiAo5KwHZ+33BU21kkCv+1SwC34dQS25WFrGGpMyCl4PqFo2Cl4372g2aA8Cvm45FFT8UwREgVYUgoXdgtaOiB/nhPcoRTc3w93HxIMxWG04FjL2TrkbFA65HNgGIrqaocUfv55yfnnW1RUQGGzQt8nZm0uZFygEjahnKB8RDF8SLDLD3YOLFtRbwgCwGQRfjEBtXWKkT5Hybt4seLMMxXd3VBSonC7IJuAVNZRAqfTgmefVWzYoKiuFixZAh/4gEUyKZiYgC1bABTj445KOBZzvIhra6FQcCwlpqcFIyOwYMGJr/GxkPJ/jj3QPOYxjz8NDO8fYaJrkmhDZDYI2MybjB4cY/TgGLWr3viAsGPR1raXz33ur3n88YdYvHgZ//qv3+a669553GBcILgsfzWNVjN7jZ2kRZoF5kLWFTcScuSCVNrVfDj9SUbkEHmRo9KqQUfnYfd9POV+lEk5gV/5uTr3Vi7NXY0lTAIqyIuuZ4nLSUrtMs7LX8yWygt5568dQudfvvC3/GbbrSz9wQpWrl/r+B/LUZ5xPc5l+at5ydhOp36QBquZtExhYKArgwk5hi51Kqii3KpgQhtnUptAoZgizsSMzUT0mC6sYW2QjMhQbdXNErphO8pB/QAvGtu5NH8VT7kfZUrG2VTYMqu0TokkPXonljAptU7eEx8IBLnuuhu45rq388Pub/Czn/2Qnu910vmVQ8S2TqBdp9i09RzqAs1U+xqQQlJt13Jd9p3c6fkl7fp+BODGw5b8BVRZ1Xw98C8Man2z8+LNyQVsnNiEzDvHFnKFaQkuJeI+ely5XI7vfOer/Md/fIlgMMS///sPectb3vX/7DPy8OGDALS2vvYcimOh3KANC8fqQDo2Ee5tAuOAIPm3FlY1gMIqA8MUyJkgY0zQTCgaCqvCWddsndvBdQRaL2gTArNJoTwK16SYsX4AbcwJklNlytm2DuZyUAaOCtkHIunkNIgMyBkbMbtEUVytKC5XeH8uETbM+bgVwdgjHJuxLFDAscKwnfdkEigKRyFcrZAZ2CIUu2xBm4ByN1hZwXhasXItLFihEEnw/UBi7J/Jqig6thZaTCEGcRTRQchdaZP6pALPjJfyzLm8GuQtuOWA5NlhwVgapouCpwYV93VrvGmB4n1v/QDf+96/87WvfZ4f/ej2E26jYEGnezWlLRv42g9+zN6y68iZUOtXZExBPO8MD10aeAxwvcJHvsTlyHRMmzmK3GQBpguCL7+okSwqdCFpDCluWHLiQLrpAmRNQV8KckVHSOE3oGhBwKt4b9kEL8T6SVd5yZleQh4Q2IixSUTHANbSVjQB2hswtHL5XNSvc+Tr6ck0owfHsIrWHDLYLlpI3bGM+ENhfHyUZ57Zxpe+9M0/2D7/J2KeDJ7HPE4DLeFmnhl6YU5QWN4qYCuLBSVNp1y3kCmw9+79jLaNMhYZZv+ynYwkB6kKVnFR5FK25C/CxyvI0N5gZMjw48B3aNu0k8BggNxkngN1uxmvGOVj2f9N1cLKV97IKaCOMKcvf8gIh4QqnGlRsApERAmLm1sJhYPsun03qYk0vogPZdt0Pd1NcjTJuuvX0PdiH8qGSL0zQHD5XegenbGOceL9U0SPUe0egRSSKn8Fo5lxAkYATWpk3CkKRo5IvoxJpsgHCzQ1NBASASbkOPlkbrZd8QgiDWH8pT6mR5KU1DjpxbZlk57M0Hhmw6tqd1nqX0LD5GI6+8eo8Dbi1b3su+8hjOoAJWfXc3/PIxSVycISRwUbcgXx6G52dbWz2FPPREOcQ8F2NI8kmAmxZd9F1E0e9c5LeGIMVffTZDWha87Xu0RSbzbSr/fSo3XSai3h8FQ3+zs7cfkNRKSIgYadAzGWwyjWcWhKR8b3YKsEZ1YtpCYgyFk1xHIJ0sUMI+kxStwhJnIx/Iaf1vBR5kzlBOpAkExVARHJOqqTYhG9o4mG4CIoh53G84zlxtHLFJElBhOjJlrWIOjzs2zRAhoaK5jUK8BrUj9dc8JW1ENxeGFEMJYV1AYUG6vUaXlsgcNdryqDVWVOIIPWCf5bJHLcUcPKKfAddjx4s+9UrF+/CSklu3e/9LrIYKWgJQxb62we6xPsuv0/cZdUoa+5gRWlis01RwcAKgyZm22MHQKtG5RXYa5wwuBOWhDrEDMVO8YFkSCM+GAEwIKVaWh9QcK0wuMHEYef98E9PoGhgUeHB3tgbxI+KQW1unMsXVPwmwlwu2FFRhDWobsAkynIaYDXUQKXlzvBbVJCZ6cTzFZRAa4XBMZ+JygFDeQeSHVI1qQUn9LgQB5+kYMnUCxVgpYCuHQougS7dUUmAItrYOVKm82bFZEI7N8v2OQTrLLgd4MwZSuCYUGXVzGRhPFxwXXX2VxwgSIYhAceANsWaDNKDL/f8R4uFgVer8LvP3p/kkkYHBQsWHBqdYdlmeivZC48j3nMYx5/YEz2xNBd+iwRDKC7dZSCqYHEHDJ4UPaz23iJaTlFlVXD6uIGIur0W3CPxcjIEP/2b//Ir351C01NLfzwh7dx2WVXn3IiVyBYZq5imbnqlMtU23NnQC/PX8O5hYuYEnFCqoSAmgl3VfC23Hu4LH81aZEiYpfi4YjhKNx008epvLiGv/7zj7L9gidJvmeasz59LjXV9XRqh+jUDrGmeAZD2gCLzeWMymEGtD7SIkmVVUO1VYcQkBVZihQwRRGhJAWRJyPSVFu1hKyjqUxx4XSPHOvPq6HhVm4GtF4A2vX9hO3onDonoIIM0Mug1n/Ka+OcsuK3nl+xY+0LLFu7ikWfXcr+X++m+0edfOkT/0RpVRmXv+tK3vueD7OiwlHpbi5sZYHZSru+n6Io0GA10WQu5D/9/8qg1k/rTD6FXbDIT0xz0DzAWt1ps47nJzhg7WBt6dlIU+fWW3/MN7/5ZUZHh/jgBz/OJz/5aYLB12fh8N+NoaF+AOrqGl9hydODsUegt4OcdLqslA+UdALQfN+UTH/FxlwocD8tULbCqhCItEL5oLjSUQ4bbYLimlPUHY4NNdhgV0BxjULrAjnhdFjltyjMxY7PrmuXwCpTyGmHLJYTgO4QwVqPQBQVaAKrXuHeJpFpR60LoA0Kxz6izfEsFnmBNurY4yGcY7DLZzrWcgL8itz5CqtK4Xpe0KzgI0XFA5qgQwO3rbjardi6zMatgetFhyQ3W2cyMQC7RqG3O9fCqoXiGS+rdY8ZZlk2dCZgIClw67A0qoieJPx334Rg9zhkTchagnKvQhMwloXH+gQVPoOPf/yv+eQnP8i+fbtOWN//vkdwV5dg4YUf4rnv30xisI2D2jICLji7RjGRczrTQm7omxYcnhI81AshFywvVbP5I0ewvFTREBR0JgT1AUfVO55xFMNCQLlXUeOHgq3oSgh+1ib5y/X2bFDdEUxk4PAUWLYgbTqktUsDQ1O0lgjyZoJy3WRxUwn5YUe57PJIcLtR/WMMNSxibcVRu7w3Cr6oj7IFpQztGcbwGmiGhmVaTI9OU7GoglDl3CBupRSp8bRjQ6FLoo0R3IGT+4DkTCc0z2/M0SGdEPfccydSSi6//No34Mz+eDE/kpnHPE4D6ytX89LYbvaNtxFw+UFBykyzqmw56ypWnnLd/h0DDO8bIb8sw5NbHiQWHMMz6ad3sptflP0X/b5e/izzEfQ/4J/jXmMn7fo+FplLcdW4sStsTNPisL+NrvxB1uU2vK7tuwNuoo1RhvYO4wl6ZtWz3b29dJjdHMgdpqib+DQPmwsbOHvPGaTGU5Q2l84u6wl5mOyaZGj/MOnJDJ7Q3IeDy+ciOZokO5WFE5DBR+AzfBSVE9HrMj3olkFezwOgz4ROpEUKr/IeHVC8bD+LL1zE/vsOMNkdc0TCCqKNERac/eqsCzSpUR+sIegKkLPzjO/qI9Uxzso/v4hSX5TD8e45dghF2+TwVDcHY4fJ7MlScjiCHg2ypXYjDc81kusuoBoUQjoE9fRUEiOk43G75yiZHYWwhSVMANpjHUxY47j9Bi3WQkxMMCAnbELeTmqW/ZTqXXUMT+k0hZz7UR+oYTIbo3Oqh57pfmwUHt3NZU0X0BQ6GgiwZ3w/mX6by3NvI14yhqkVCGTCjA8m2F13gM3edUzHC+w2HkbLG+TLshQXxmgtLOMceSbumckWhZoN3ns5nhsW/PKgJFUAr67YNyF5aVRx4zKbZaWv6pYA4H5MIieEk8J8JBgjDq7tksJZFu5GL3VNS7jjyd0kVkhaw4oNlYrISQrQY1Gw4JkhwfYRQaoAiyKKc2oUTb4873j+Vs688mb+bLWLDRX2cUWj8jv2D5xm7oHyQXKRIrpNsthUBDIQd0MhDSvzgiENnohBBEfl8fi4oCoKkSqF8jpJxO0x+L2puGnEUWe0TTjtcA0hyOSh3oIKAXtt6LUgW3CI4kWLHFVwIgFeL4RmxoL6PoFyOwMHrQ+swxC3IOwTWEEIJ+CGFBy0BfsExINQHVWIIAx2C1Jt4PEpKiqcbUejUJdWXNUr2JNTJBUs8gg8KfBJwchCRbjMWW7bNsHevZLhYUcBbBiC1laHINY0p4DXdefvOZ+HQkEQDiuSyVe+1qZpnjARfR7zmMc8/m/CcOvY1gl8JxVzvB53GS/yC++PmJDjDvGHzZPWo7w//VFq7dMP+clk0nz721/lW9/6Ch6Pl8997qu85z03YRiv3WrsdOBT/pN63IZUyay6+Lj3FoXY/NhWst/Ksv1fn6T91/tY/9GNNP5lC+OhUdYU1+NMEyuq7BoiqpQ+rZv9+h40RsiJLDmRo95qmiWBC6qAjUWNVUeP3oVf+ZFI4nISv3KC545FQeQpsSNkyeJTPsbl2Jz37RlVglu9cpHRr/XwhPsRInYppXYZI7Kf1W9dw+iNYzQ81cjEd8f5xdd/yt0//h3/63/9f3zgxo/h8/mptKupLBz14e3TuunWO6mzGmbzKVwpHXchRNwbI1fMEVIlGC4Xw/F+vn7rv/Dr//oZExNjXH319Xzyk595Q2wXTgbTdupXXb7+sVKh4HR0ejynUcSdBuQUM2FnMx1aM3WkKoJxWKANQuYDNvphDX0SVEBhNYG5QKFKQG8TaF2K4pqT78NsArNOofeD2QJWlcIqBeMAFM5QpP/chiLoBxTGHonnHgG609UosoAusHWFTCnsEJiLFMUNjq2EsVugDOUEF88E04mk4zOM5dhPoM9YRFgzIXdhQCiKixXp99tofQLXcyCS0BqEhbYiMw6euKB4nY05M4TT22ZqwmO+HpQPlFdgLlLk3nLq8LVfHhS8OCLJW87faKVP8LZFijUVx683kIJUUZDIC0IuhTEz3+LRnCHSnnHJX11xA41f/xe+9rXP88Mf3jZn/emCU79H3LDw0nex/87P0XHXF6h9z0/omBLkLaiZ+QqK5aB7GlJFaItLwCF937vMng2LBiec+r3LbG5tl+yfcILfKv0Ooe0zjhLbbg2aQ451xKG4YHX50fNLF2HXuMCrw1hGYdkCv+5YRLikwFKKx4d1WpRCoFgShT3jgsksuHKKgkfSFFJc22K/IqH6aiGEYPGFreSSeeJ98Rl7Q4jUR1hyyWIQAssGTTph7h2Pd9L3Qh/5dAEB+Mv8LLlkMVVL5orSkgV4qFfw0qhzzZpK4KJ6m8WnmLu8667b2Lr1IqLR1zAw/BPCPBk8j3mcBvyGj/cvv4Fnh15k5/heBII15Ss4q/YMfMbJp9WUUgztG8HlM9i5eBex4DgN4y0IJchMZgmW+nmp5jnOLpzHUvPUpPIbiVFtCIXCNePCL3WJS5cECNGld7zu7QshaDmnmeRYkonuSXSXTjafZW+xjR2teymYJsqEOPDbw/fhGTCo99XOsVzQDA2lIJfI4w64SccyeEuOthmaeROpSVz+Uytz11WsZNf4XuK5BGHClE/Vcji8H5+nhFJvlLiIMSHHuTx3zXGhJEdQvbwKf6mPsUPj5FMFAhUBqpZUnHL28mTw6V4WRxZS4gpxz9e+T8Xiei666AoOxg8DkC5mKJvxph5MDdOXHMSju2kNN1PhLWckOcbeti42bDqTyenYrN0GQGNdI0uiy+jXegiYRxOrh7VByq1K6q0mAIp2EQuFjYVbefDOKFJMkUSzwF/ey7KKZQxMFbCVjRQSl+ZidflyClaRpaWLWF+ximVli1kcWThH+RPLTSEQ+K0g/thRcj0jLYbSI/z/7J13mGVVmfV/+4SbY+Wcuzonumm6QaDJIFGCgjoKDmIOM2ZndHSCzug45qxjQFAREAFJkkND59zVVV1VXTnHm8M5Z39/7OrqbjrCOPNNqMXD88Ctc/c9Z58b3r32etfa0LEV90gpC+tDTBUOk8vkGJy2yUkLV0hVhxYWo9oIF2Uvx8vRraWpPPzxoMB2VIED6nPWMzjNs5sTNJ8XwfC9Bm+oLBjt4BTKoxS3MgJiGLQ+wUMWOKXLad23i7Ix2DqssWVY8u6lDkUneSlHwj1tguf6BB5d7do/16exf0LSNPgwmcQU//j+tzLvFIETpw0JRcXwFhsYAp8ucAQkdEkyK+k2BOU5GO6CfWOCEQvOHAcsgQyBE5SU5AX7c8AL4A4rdYtIg5EFzQs9EtxeMNPQkJFMOJArgcpKSSymVLVr10oqZ4Rc0jWzkAC0AUHaFuQ0SDrQEYMpDWoFLAX6gZEMTI8LcoOQz0uyWdi8WbB7t2D7dsmHP+ywZEDQ75HcmRRIDbIGpGyI5CAUhr6c5M47NVwuqK+XzJun1MKbN8P4uKCwUGKaEq9X4HIJxscVoV1TI9F1RWafCrZtoetzJdQc5jCH/14oaS6mf+cAmVgGT8ijbKEm0xgeg6JGtShOkeL3nt+QEHEWWktUFgE2bcY+HvH8nttTHzqljZmUkgce+C1///efYnJynNtv/xAf+tCnCIcj/wVX+fpR4BRjGCaL3j+PxW9bxuZvbGDLdzay/eebOe8Tl7D45hVUuqs5YLRQ6lSw29hBr96FGzdhJ0qv2U1KJIhai5WIgAAT2jhldgWVdi0X5y5nj7GTHDnOtyvYZW6n2+ik0WoGYEBXqtQ2o4XNoY8zqo0wrA0QtIOUpEoQWcmEPkqZp4yF+VP75nbpnSRFgiq7BltaZO0MLs1DyAqRXJvgwzUf5y3veTt3//jnfPXLX+RH3/sW733vX3Hrre89KtTNwsIRNro8/Lsm8oAQSOEgcYhPx3jw1/fx8F1/IJNMc8MNb+XDH/o0DQ3z/rw36QikrATdiXbGMoMAFLnLqAk04TePFW+cLvJ5RQab5p/Hu9SJKE9fGeDozi1HID0SfUhgLZbk1qhaz2rgGNsDbQo8Dyp/XLtcBa05JUeM5YbM9Q6+X2kYLWJGpSvJN0vSNziYuwTuhwRmq8BoEYiMRPol0lB+wk6lCphzDEUEOyUctpAYBy0jcHwzxHFOnZjIKCswaQIWCKmOdzwzBG6BJH2jBBuMAyCmwHVAIMPgRCHsg8wFDkfElSj7kRO5K56ipHppQPBSv+oGDLpUjd0VU3V2TehYhbBLV0rivA2BI8hnR4LPVDYSOQw+/OFP87GP3cG+fbtYtOiwEn8yo8jkUp/EMNysueGTPPPjj7L0hs/RZzTTMiEo90scqYLhhIClReA3lQ1Ex7TgN/s1bmpWquhyvyJBQTWhypl7aDuCWA4KPfLY85eCZP7oxzumYCQFZ5RInu4Vs+N5dKV8Tufh91Yl9YWwYnqSM0MZziqXDE1aJJJ5KtaVce5Kh/AJlrDxRIKekUkMHLyGg+M4GIaJy+XCNF0Eg6GTdsb5C/2svHEZB57rID4cxxvxUntOAzutEBu2CKazUBuCpfFBpl/oxB1wU1hXgHQkscEYex9pIVgcwF+omPa8A3e2aGweFhS4JS4NdowIuqY13rPMoSly7DkMDPSxadNLfP3rPz7hec5BYW4lM4c5nCbC7hCX11/I5fUXvqbnOXkboWv0Fh0kkAkeVWB78z6mmaBf7/1PIYPTU2mGW0fIJrJ4gh5K5pfgDXuQaY1kJkvGzOFxHy6GsiJD1Pnz7KCFK8KsvuUMBvcNERuM02X3sjGzHccniboi6JpO1s4ykhplg9jCDdmSo56v/GglLr/yItrzx70kx5N4ZzyDY0MxChuKjmsRcSRWlS6nK9bDS/2bGEqN4EoUULKgmkC1wZC/j6AMsj57KVdm33TScUJlIYKlQZJWnIydJq9ncEnXa/ayXVA4j03D2xjZfJCprhHe8s8fVp7Ums55ZevYPLyd0dQYhd4CemJ9pPNpSsuipKom6RXTROPF9A6NMOQd4w3vWKMI6mSWWNkUPQvbsf0W49ooMXOaEqeUlEjik34uz16LX6rCvzZUTQA/Ezk3KTNJQAaxbQfLdtBKLSrsas4reQN7+ttom+qg3FeKBAZTwywtWsiHVt5Osa/ouNcXdoeQyFkSWSKR4+PEh9rx9WfY6t9DdUElxcMlyGEViNca3s3uylfYxmaCRhAbm3nWfC7JXnnM+Acm0xycnqQm6EbKIsxsipqXH+TMva/gpNOMvxAlctGFuM6/EHE6yk1DJTFr06963AZHs9mjTfN4t4/ihmUMb3+QuoCNI3RaJxXJe8NxvN0OoSsGGwc1Sn2SyEzRVeaT7J8UvPKbu1ixYvWfzbMOVEuff4NGyTLJ5g6BnZOETEHVGBgWlFVDYQj0aUFAQEaDpAF+C7QREBOCHBAoVcpcEtAUB5cDMQ+4yyXeSUE8CdN5WO8TSE2ytwS6uwVeL6xZI7nppsNqA2uZxLVNYE1CblIpifUsdKCIYG1mXjzAIXvBTEZ5+BqGIBiE0lJJLCbYsweeeEJj5QhULpbU7Yf2PkE2Cy6XatMbGIHdQ4JcTlBSotrIFi+WLF8uefJJwd69gmBQ4HZLKiokzc0qLM7tVp7BgYA6/lSwbXvOJmIOc5jDfzuUzC+hbm0t3Vt6SYwlAIHLZ9L0hgYKZlLYDxoHGNL7qbMaZ2tSHZ1Sp5xWcx9TYvKkdhFjYyN85jMf4o9//D1XXXU9n/vcP1NdXfcfOu8cOVrM3QxofXill0XWMkoOpWf9GbHAWsz8/GL2mNspj1Sy8gtrKHxvMS3/uIenP/Mo1353Pde9+y3U39rEzuJt9OndlNkVLLAWU+FUoaHxkvsZ+vVe/DKAaZssHl9MeayCgBNggVjA/NAi7g3cTbfRSVakGdD76Nd6qXCqcEsPNjadxgEKnWKCMkSPOEhrbjfB4dXoUqdIFlAj60n4pygKlJy0zjzUVagUeBpCCKR0sIWN1/YiEJRWl3H7597DZz72j9z1o5/xla/8Hd/73td473s/yq23vo9gMESFXU2ZXc6Q3k+93aTGNCU5mSG9P8Nv7vkVTz3wBFY+z5lXruGKt1/NooZlRCMnr8H/I8jaGfZMbmYyO47XUMKb3mQHsfwEywvX4dFfX497LpdDCPFn+w3PL5fIQtAGBY5LBe+SgJgAxyOZzoGfmXpoi1BhvYeCgccV6Wq+LBCOAFORtq6XJKnbHOwj8gusRZD4qCJ+xTQ4xeq1tUnw3alBUvnrSrdEepXa11qivIqNHpRHr6VC5GZhKyJaRiC93OJfHvl7fp26iyE5RAUVvFN7J5/1/S2aJdTYPokMqcDhfCM4BRL/DzT0AYFdIcGQaNMCp0iSunWGCNZAGwL3nwSuLWDsF4gpsBZLZHAmHNmnQvROBCnhlUGBz4DgobkTUBeCthHofEijtkUJGPKrJLlzJIsKJMU+GEhKknlFCGds9VyvLom6odALN974Nr7+9X/iRz/6Ft/4xk9mXzPsVt2HqgsRllzyLjb+7stsv/9fWPqXP+XsCoeumCDvQIlXhcf5Z0hnQ1Pk7KNdgpYJnbAb6kKSS2sdHujQODgtKPNJMjYkc5LhpMByoPiIt/RoCqaz8FI/bB7SKPNLmqOQcw77FEfc6nU0oY4dywjceYl0meRLCnl2wsvkcD9npQcIa4Lms0opmJ9nw9N/oKVlN4OD/YyODjM2NsLo6AgjoyNkM6mTvt81TaO4uJSysgpKS8spL6+ktLScyspqFi9eTlVxHXsfamGiawIJTPZM8eiwl7YqH8GQC58BW4YFA5uGmZ+Fhhp10UIXhCvDjB+cYLR9bJYMbp2AXWNQH5T4Zua3wCNpnRS80C9oihz7vnn44fswTZPLL7/2pNcyhzkyeA5z+E+FEILieUV0vHgQT9ZDzDcFgJWz0A0NV9AE5Gm1gr1WTPZOseuB3cRHEoAiV30lXgZXjPEKm+kRcXrcL7KwqpHGxkrGzRF0abA6v/bPdg7+Qj9N5zYCsHdfG7FtSarc5eiaIuncuhu37qE3PIgVs0mMJfEX+JBS7Q56I15K5hXhi/rIJLL0bu1jsncK3dQpaS5h0RULj/LFk1ISH4oz2T+NEFBYV4C/0M9NzdeyunQlXbEeDM1gXrABy0kznZii0Cmm3KmcXRC1Tx3k5YHNdMd6KfIWsqb8DFYWLyUvcxyY3s1oZpC8k8PQXBR7ymkOLcWlH7u9mnfydMd6ydp5qgLlhN2qT2hVyTK2De7kO3f9LaXL68lUCHoS/ZxVtopbFlxPsa+Ql/o30TbZwXRuGn1BmtSSBLvdB9Wc5X249UIsmVe7rWtqaDP2cb/vToa0QTzSgyEN0iKNjsE52QtYkz/7qM2GJUULWVt6JqMjo4y4+5kUcUROw1OiU1FZziXZKyn3lXLbkrfyx84nODjdA8CKosVc2XDpCYlggOXFi3mm50XapzqpDFTAwU76+1rw522axnLsLpkkWlCBXB5FGOpXvWlyEYnxLOtWLSUcClDt1LAyt4aIPLzIcKTD830vc3/7C7ROTtOfMCn3NnPDlklqd29kOlhMKlSEHhsn97u7ELqG6/yLTv0m1SG3RuK9T0OEpVJ22DAVG2PvJXt41u2ib6yeQE0Z+UyS6aEOopXNRN2SnaOC65vkCdus+uKCtAU1RwhYhABfboTWTY/yxS/866nP7zXAaFULhJJCOHNU0jUqmM7BlBBUuyDpk4yPClIxCGtQICW7PbC6FPRpSCUF41HJhcskThXkxwULxmFtH7w4LTEzoPklCQnLC+FNdbDAhN732Yy4IRyG6uqjfbxyqyTpLTByv4ZvGMIpSbslaANyOgSzkJXQqx+2brBtEEKiaQK/XxG1oZAihPfuhekGSeEI3LBW0rkBshaURyDVI9jTJzC9ctYLuLdXYNuQTILHIygokJxzjiSRgPZ2wfS0IJtV97CkBG64QVlSnAqWlZ9TBs9hDnP4bwdN11hwyXxKF5Qy1TcFQlBQG53NPABlQ+DMkIdHQkgNRzizNgXHw2OPPcjHP/5eAH74w7u5+uob/8PnHBcxfuH7IbvNbVgotqbIKebm9K2szJ/5Hx7/SLhw8c70HTwg72GvuZMYMerKGnj31z5M5NYCvvH1L/G9L/4rri+7ab5lEVUfqGFd03mzpOtiaxntRis2Fk25ZuoH6/AnvGT0DGV2OW3WbvbLFroC7VRb9TTSzIDWT7/ezdnZ88mSYav7FeZbi2brTlfWYL/Yg88Tos6qp9AqBlvSFW8l6i4i4jqxSGO+tYgCp5A+vZtqu46AEWI4P0BCi7MmvhYkxHOTBMwwixtW8JWvfI8Pf/jTfPe7/8rXvvYP/PCH3+Qzn/kHbr75Vq7IvIm7fD9lv7EXc8qk6/52hv59gOE9QwSjQdZdu47zb1pPUWkJpZ4qUlac1umdrC46D0P789uCjGQGmMqNE3UXoc2EeHt0H1O5MYbT/dQGXp8iOZfL4XK9djHHiSDDkLzNwf9dDTEtyOiwNwUyLxmxBA89Ilg05XDdxRJXA5g7NCVEcM+QwhqgK8Wu8gWWGG0Cz8MayQ86HPkxdUoge/ER5JcE81GBmFCKY70XpBdkUKKNC0QSDFs9btVJtDTIMWVnoU0ob2CRVj7A/9b6FX449QP+vfRnLLYXs216K3+ZexchK8yHtQ+rlDIp0MYkdhVkrpOYuwT64IzNmgZOFYhJiRY/IlBvCnw/1TDaFUnslIHRKdAH1TnLKGQvdbBOcTtTeUWAHgk9B8ZBgewATUplh/M7DaNNUnO7w03NDqMpjQNTMJkFrw4Rj/KcvaBa4jMATN785r/ghz/8Bl/60rfw+RQ5GXHDmjLJY10ampCE3B4WXfUxttz1aW68/bO8bWEdliN5skewaVCQsgTJPFQGJGkL9k9oJHKSiEsRzy0TgrZJjUR+RtU8LbCU7om0DcMpwYEpSZEHRtOwe1x9Q99/QCNtg9cQ1IckS4rBZzBLUmcsZT8xnhG4dPWYW4dlFQE624bYfHAjg2MbGR45wIHftBCLK/VLNFpIdXUtxcWlzJu3kPkrz2N/rhz8JRQVRHAwiOV1GiPwxto80s6Ry2WZnJxgaGiAoaEBhocH2bz5ZYaGBpiYGAPAbXqoiNTSXLeIxqoFyPJVPG0X4RpKURIxKfYq/+bpZIbhnEE9h0XyKrRddf8ewnBKYNkCn3n4fS8ERNySjikxG+g++xlxHH75yx9y8QVvJNaZYMqOEa4IEyoL/lnDyP+3YG4lM4c5/CejZnUN450TVG6pp+uCTsbyY/jSfsJVQYajAxQ7pSz6j6iCpQQnD9phN3XHdmh9qo3EWILCugKEJrAdm52799A7NkDgoiCrvKvZnd/J9rb9DBn9LJhXxzWZG1ieX/WaXt6xHSZ7p0hNpnD73RTUFRzlS3cIBZ4ouhDk7Nys55dEknfyFBZEqamvYXzzGBPdEyAE/kIfCy5uJlCs1KzzL5xH9RlVJMeSmB6DcEX4KFsJ6Ujanm2ne1MPuVQOJHhCbuatb6JmdTWNkToaI3WHT8g+cgolaTtD+1Qnv9x7D5PZaUKuAH2JQXaPt/CmxiupDAfpT3XhN4L4jRA5J8tAqgsNjUXRM4661oPTPfyu7Q90x/qwHIsCT4SLas7noppz8Zk+wi2S5PA0b/3ch6goqWVJ0UJWlSzHY7i5rumNvKHyLAaTw/wh/gC/L7kLQ0QJxhUBO+kaY7qhA5zczGXYPOy+nzFtZLbds95u4qDeTsSJcnP61sMBKjNw6y7esejNzIs28MeJR+jiIJ4ywZryM7jEumw2sKQhXMsHV9zOaHocARR5C0/5Q1rgifKOxW/mV/vupXNgH6K/g0oCXOafz1JPAS9oWxke7yPU14tepyQPY5kJqqjhbfatFKSPrzLZPLSde9r+gCFclHhLmMhkSfc+i7mnk3jhKkaMQsoD4C314/R0kX/+Gcx15yJcp24DzK2X6P1KwUofpLxJtl+4len5MdxOI4YwCFQrpUxfxyailc04KFXCyeDWAVQL2ZGJvR0v/AYBXHfdm095bq8JFuijIA5CJVBRIrEzsCspmDYkYgA8cXDloVyDOgF/AnbllRjFZcPaGrikTi1QbJ9EK4TbvIL5McnmSUHGD1c3S86pgpJRtaAoXQKlJ9jPki74hVswWAhnl4N/F3iTkqK8wLAgoMELEvqiEAQSau8K2xa4XFBQoIo/XQfLUvMpz5LIDkFzDm5bLvnNXsHOgzCcgumgpKhQ+QtrGgSDkt5eAUg8HkkopMYZHVX2EADnnAPLl0sWL5aEj28zeRRSqRSZTIZo9D9PETWHOcxhDq8XQlME8Ik6p+rsRkqcUgb0XmpslVPg4DCsD7Ayv4YCeSz5mE6n+Yd/+DQ///n3ueyyq/nqV79PUdFp7JydBp5xP8E2cxP1ViNefDg4dOud3Ov9FQ3WvBNaeL1eFDrFvCv1fsa1UTKoLq9Wcx+dy9t4108/wKf6vsi9v7yLn9z1beI/jTF0/gAr7lhN4xXN6IZOvd2IJnWiyQKMlE7Gk6NC1tLEQoaMAXLZNPOmmyGsauFKp4q8yDKsDxDXYkScgqNtOPISYQp8pp/qbJ1iRgyYyIwwkR09KRlc6BRzfeYW7vXexX5jD47ukMwlqEvUUzdey7Qzjt8IMT+8fJawraqq4ctf/hYf+tAn+ed//hyf+MT7uPPOH/MP//B1zpbn8Ytf/5BdD27DztqsXr+Wd/7b7ZScUYDQNfxGgKi7GLfhwdBN4vlJpnLjFHn+/CrueG4KgTZLBIMKh9aETiw/+brH1TQN27ZnSKTjF3KJ/DQj6UEydhq/GaDEU4HXOL5HNUDmWgmOg/tPGv2tkLMgVQwtKySWAU89oBF6FN7mARyJyKhg3dxaB+8DOnY1hxkxDexyiXFQoI2+Ssl7BIz94H5Ww/N7gTak/HhFFqX+NUFYYO4TSANEUmIgIKvyG7BBpFRnnHSB2SrYnHuZK+uv4fLglWhDUJ+r4zfOr9mc2gyuGT9kFOlrVUqlaN4pkF6JsJUqFw7brOm9ArtB4touMDoF1nwJBuSKJVqjxGgRWIsk6TfPKKBPUlMLAQsKJc/2qm67Q7ct2SfwxgQV5Q72obdJVmLuAfcuwfnrJI1hm3vaNDYOgpSCeVHJGxsk68oPk4s33vg2/u3f/pHHHvsD119/y+zjVzZILMdhy7BGdwxqL3gPLQ9+hYN//Bfs9d/nnjbBM70qxC2VhsmMYCipbCimskrpW+xTIc3zTMlLA4KprFq6B0wIG4oYzqXUY7VBReoOJiEys3xJW1DqV+PFcoK2CagJSSYyAkOD6Zwij20JAZkl3voi+YOPcffuRxnv3QdCMF7TyBnLlnPxFW9EL1/BaHg5GW8FpQHBuRUqB+Xf92rEBgXNkcPzm7aU93LJUodlxSe+PwCJRJztW7bwhx/dT9fwAXYf3MrjWx4AKRG+YtxNlzKy/BoWrbuc5ZV+XGUR0vsmSeclPlO9oJWzEEJgFvjZOarmaigJDnLWa/gQUhaU+Y8NknviiYfp7GznL9Z+iJ3371Iqaq9Jzapqmi+ah6YfvQn6fx1zZPAc5vCfjECRnzPesoLo7gjZ/hT7G3aTLJjCDmUolWXclP4LCp1TfMMeD46Fa+A5XAPPomUnsYO1ZKsuxipcQXwkwVT/NMHS0CxhmrLSjHkmicRCGDkX+Aspd1fQarfh7jD5cOFfU6OffrKuRLI7s4Mtf9xKujWLPxcgSgHFVcUsvWYxwZKj/byWFy+mMlDBSGqUlJUmY2dJ5VOAoNxfwsI3zMde1sT0wDSaJojWFuB+lR+wL+LFFzm+oedw2widLx7EHXARLFHtjYnRBK1PHSBUHiJaFTn2GqRk68hOXuh/hYHEMO1THWhonBVehW5pEICBzDCPdz/J+tqlhNxB3Lp6fbfuwZEOo5lBUlYCn6FI63guwZ0t99Ab76c2WI2pGYykxvh9+x+JesKcUbKMn/9ULaa+eP3nj3stRd5CiryFbCp9Dm/eTXbUQWpxbOngZHRCZQHi5jhkYUQbots4SLl9WN0sEFTY1fQbPfQaB5lnHWtF4DW8XFh9LhdWnzt7P4UjOCQIklKqEDehUXISJfCrEcvFeXlgC7FcHCeVwJt3WOarYrldiIHGelHLb/VJ2of3U1QaIZFPkbWzXNlwCQWe4y9cD6mCBYLaUAV+E3aOeQjGI9iJCboLHErdMD+qggpEJIozOYGMTSOKjv1sWY5qN9s4JIhloSkqOfcGh6Y3gD4g6A4NMFUcI+Ivoj6TJeiysajH8AYYHzpA1obpnODC6pOHLywokJT6Bd1x5Y+lCxWCcPDFu1h+9hspKjz9eT0d2MUSbQIcAw6t5TUvVIxKfiKhXYc1IYk/JtilwzZNUGJIzvbBAi9UF0maayX6ER87bRJcZZKzr5Nc+DAgIGPC9FboyAgOnCcp6oUmxZXT3w9TUxCNwhNPfJ/vfvffGBoaoqBgGQfXfJNYwVmsD0FDTDIQF+zwwZ8s0DRJwKfUu9ksxONK2ZvNClIpmJiATEYyNCT4xX7JdedIirZodOyH+ISgMycZk5KArgjlZFIpfktLle2ElAKXSxKJSLZtE6TTAk2TTE7C9u1QWAjr1p3ePI+NDQNQVHSCFdoc5jCHOfw3RlCGuDJzPfd472S/sQe39JARacrsSi7PXHOMX/DBg+3cfvub6ew8wJe//G3e8Y47/mwKKwuLzeYGIk4EL0qRp6FRY9fTbuyn1djHmvzZr2vsg3o7m82XGdL7KXUqWJM7e9YCQSAocko4oO/nR/5vMqD3zTwOTY0LuO1T7+OSj13J55/8GN3f7+Sht92LJ+ql8o3VFF1TwifXfpFsJsaINUCxVk5Qqu4vS8sjHYmZNsiHD5NNfhlgWpsi5ERmfYMPIS/zCARu59hdVSlPrNI+hLW5c6m26thn7iIj0pRblVTkK7GCOUzNRaG7BJd+7NgVFVV86UvforGxmR/+8Jtce+35gCKL3/P+j3DJtW+kpqIOvxFk4+gzaEInaIZn772GhpQSW1rHjP3ngKm5kEemIc/AkQ4u7fV3VBYWFmFZFrHY9HE9rkczg7RMbSdjpxBoSBwGkt0sjq4i5DrBJrABmRslvY029/2bjh6QpGohb0JRTlkhvJISXH6pQ7gJtDGJPgRaTJyUBD0RjH3g/6mOmAAxocaRWRUGrGUEohuQSn1MSOJEAVt1gTkmiIwKcsutVgSt2Cw4e+Bsfnzwx7SuaGWhM5/twZ28lHyJr5T9K1ZQItLKA9maJxEu8H9ToA0LFWLdodTCVuOh/A3JTCwNWi9gyMOskwZOGdhJiVMEduPpXfP5lZL9E8pqLeKW5GzITcFFKcm8yBEHugEEepeEdSrULZGHoBvyjiRpCUZTEimZnfu6ukbOPPNs7rvv7qPIYJ8BtyyQXFxjM5mFsNvNko9+gr//+09x8Q3v5aX0GZT5JCGXZNeoIGNLeuNKgCCEpCEk8cxctybApSk7h4ibox43NIGpSS6qkUQ9Dt/epmMj2TcuCLvVMUGXCo/zGJJUHpYWOYykBG6ZI7f7fnI77mWg/UmcbAJ/tIy6My5n6dWfQatZzwcW6qxeEubZERf3H1D+xkEBByagY1IjYzscnIaQ62iVrdcA2xGMpNU1nQyBQJDVK9eRWSEx3QaDwQJ+lwwTH9hBT/tLZFr/yPCuuxi528X+JRdQvfBiFpk1xLonoNCLYzmkpzOY8yv5daKMg0MC2xE4SEaSAkdK5kdVLuJ4RvlBryyRxHOKWD903t/7zteYV76IumgTobKQWrdMZ+h8uYtgWZDKZRWn94b7P4I5MngOc/gvgL/Qz5L1i1nEP3BQb6dP78GddjM/v/ikvmwng6fzfjwHf4/UXUgjgDm2DWNyP6nF78Gxm5HO0TveaTtDRqZBt+nSujB0kzKnnHqjnonMFGbGo0ytTgMSyRPuh3j22efw7gliVWax/BaFmWLsgzbaIxpr/mL1UbtvlYFybmq+ht+2PaC8cK0Mhmbg0V1MZKb43YEHeev86/EXlJ/klU+M4ZYRHNvBFz1suBQoDjB2cJzR9rHjksEbh7ZyV8u9WI6NW3czPjlBVWc5yViCiBZCBqCsuZh9ha0MpUYp9B6tznBpLhJWjJyTxYcig/eO76c31k9TpAFjxg6jIlBG+1QnrwxuIbF/lLa2Fr70pW+d8pqEW9Lsa8S0/YymxzE0nTJ/KengNKlcUh0zs2yTSCw0UsKLLm000uov8vSqzEOLv6yd48X+V3hlcCupfIqmaAPrq86hPlxz1PEpUmx0v8B2czN58izNr2Rt9lzua32Elwe2UOYrodEoYtwZ4gmzlwAm6+1K1tgl6BMVbPA6TEqHMn8Jb6hcw9kVa054blk7x1h6gpBLbTAUeWFtmSSfCiE8Gk2uSRaWF+GZEaTLRAItEEAcEY5yCFLC/e2CJ7vVjrrHgGd7NfaNS25f4tC4UJKMpZFx1a5U6M2yrmKUF/tL0EMlDA2P0zkNtUHJ4kJVTJ5oTRx2w1uaHX7TqnFgUs2xkxhh+uAW3vqhn57WfTkZRAL0LlR7YR3gAyekgkzEBOCoABhfOWwZghcS8KQXgm5JMC1YpkMmL3B3Sa6uVUEfZqfAsdRz9QH1/NybJdnLVdGee1Rw4CVBVw72lEr2HhDUfVXnknMcDiQFW1oVeTs+/ls2b/4En/jEd9m2bS09Pd/kT396I5WVLTxZXoJdAoOD4PNJnFFBKiHw+SQNDQ65nKC7G2IxmJiQWJa6F8EAjPbB97+jcV85LGiQTPsE8RKJY4ARV69dUKAC4qamBJOT6h6ZpqSxXJIcEWRigsJSycQkVFVBURFs2KBx9tk2VVUnm3GF0VGV/F50nI2GOcxhDnP4n4B1ufModkrYam5iXBul2q7jzNw6yp3Ko47bunUj73znm4iEwjz4z99jflEp9t5d6PMXIcz/uDWAg0Ne5DA4eiwNDQnkT5g2dXLsMrbxS/+PmBTj+KSfnWIbm10beHvqdlbkVwOQJ8+93l8xpPczz1qAjk6eHK3GXh523887nfdw+zUf5KmbHmVgVx9Dvx9g5MEhOu5q4+3eq1lz7jrmv2E+6y+sBMUF45cBNKmR1bJoHN5ZndQmWJFfzaL8Uu7y/ZRJMU5EFmBjM+GbIJyKUJM+LMjIOzk0TSNoRo65tiFtgI2uF+kw2gg7Ec7In8Xy/Coqs9WHDzoJV9rV1cGTTz7Cn/70CK+88jz5fJ558xawfPkZbN22kfHJUcbtYcbNAaYnRwmbUYKuCLHc0WrcjJ3C1NwEzNNoqXkdKPaW0586SDw/TcBQE5y04pjCpNjz+tYKwKyifXR0+Bgy2HLydMT2knOyRMwisk4Gy8kzkRuhM9bC8sJ1J94IETDug+1+qK+DQ5bE2qgglIUhrySGsulySmZquG7ltav3g32oX94BfUiQXyI5rlZIgvtpDREHGQRhgAwBOYHIg10o0UdQAXYRqRS9adAyYFWDPijAK8EE1ysCAoAFn9I+RSwfY8XGxejo2Nj87ZX/wOWfvhnrqxJzN+BGWUCkJdqoQJshlcmC0QoiK3CCKqDOqpC4XhIYbQJtQCAqJPKIdabIC2To1Jsdh1ATgvcsdXi+X7B/QlDohfNckotHBVrkVQc7SvU8lYW79muMpKAxpLx8x9KSx7oEpT5YV3GY4Lzpprfz6U9/kOHhQUpLj35/HVL4Atx22/u5++5/51/+/iM0fPR56kKq3jc0SW9M0JdQYxZ5lafx7CnNBNeFXJCylLevrin7C8+MfUXWVhYWQlP3z5GHuxAFag2jC+iNC7o6Wxh98ScMvPgrsvFxfHVriV76Kc4+93KqG5Yy0DFBxyRUDU8x1tHOc68EeL5qCd5oARUzy6NiL/TEJU/1KNJ5PH30NFqOsm0LnOCrXkronIb9E4KMDdV+D/6yEJMdY3T7qsATYkHDanJV60le+EUKtH6Gdj5Cav/DbP7dZ9giJW1LzuUN8y5lWeNqGs9v5InQPA7EdBrCErcuyVhKSJPICTqn5IxfsiDgUvfxsS6NxrDk0jqH2MEtbNn2Mu+96NOEyg9bI3kjXtKxDEN7h+bI4Fdhjgyewxz+C6Gh0Wg302g3/8fGSQ3j7n8ax12A41PqNMdXij7Vhrv7EYJLVxAo8pMYSxCpVLv4aT1BLpVhvGiE1qIWpC7plhEqkw0UmAWzRNvpYFDr5wnrEQp2V+KNesHrx3EcxrwjRKoK8PX5mO6fJlpzeAddCMENzVfTG+9nLD1OsbeIiDtMXbgar+Hh5YHNrCpZxqLC+a9rTvLpPLrxqtYPAXnHIp6IH9MOlrPzPNn9PCBoitSTyWdZ0N5EtDdE3J/AH/Vhxg3EJklkaQhPg4fp3LgiX4XAo/uR0sGlufEeEWIRyyVUMIV2tFWG3/Qzkhrjpz99iEWLlrJu3XmnvKZ6u5GXXM9QH61hXlRZKuTJ0y4mqZvZSi9xyqiz5vGCaxyLpSSFDw0HOMCqvH+2BfR04EiH37b+nhf6XsFneHEbbl7sf4Xdo3t55+KbWV6skq2zZLnT/yM2mRvwSi8aGvuNvbzkPM/4NFQHKwi5gjhRC1N2MpBz2GAMsc4uw5W3WTEuOHP9W+HsS3BprlkP6RPBrbso8EbpjfVR5C2YmU9I1/tJ1BeyanwMV7IE6Q8gJyeQsSmMiy5FeI9VkfclYEO/RpH3cPJw+Uyo29O9Gg1hZzaU5FAI3qqycUr9KVoKg3gyg7h0GE4KvrtDZ0Gh5NpGh9IT5JgsK4bqoEPLhCBjSfY+8xiPApdceMkxx2qD4NqgCmcZgPxqSe5MefQvtQVGB7heEphbBdqoQGTAKZRkL3Cw6kHEJUaHQEsqX96NE5BNQloIcnkoF7DardrO9qclWokKIhG2IH21xPcbDaNLIIUigF0bBU7EIXuF5Bf74KUuQdMCSTgL790HwR7Ib9Bp9krciyTdi+DOjV+nsvJ2AoFbCYcFXu/3EeIRpqZ+RjT6KbJZKCyUnHWWpK1NKRHOPlsFwbndDvfdJ+jsVH6/mqa2KpIxgSejpmH/BOzfL2hqluTzEAyB16eUySMjgqoqSTIpiUYll6x3cHYKWloFwzGhlBkJgbtYUl0tiUZheFjS36+edyoMDioFWXn5aTDHc5jDHObw3xACwTxr4XE7hw7hscce5P3v/wuWNC3g+yvXEnzqCTKA0A30Jcvw/MW70EKnRwQ6OCREHJd0s8/cxSuuFxjTRqi1Giiwi2g191LslM5uTE9o4wRkgBq77qTjSinJ2FncumvWTiBHjoc995EQcZqtRaREggKKGNYGedh9P4vyy3Dhostop9foptqqR0fVISYuypwK9rl2MZ2Z4urMjSzNr6S9uRU+BY0fa8beb/HYYw/y0B/v40ef/x4/+eIPqJvXwILli6hf0oBYJhk8qweP5sctPYzpI/iln/OyFzHfWsyQPsDLrhcYFoNKBW3VsXx6BVYmS0xMIXFwpE2Zt5pCz9EdKH16Nz/2fZtevRu/9JM1smw1N3Jl5vrjBiHH4zF27tzKjh1b2LFjMzt2bGVgoBeXy8XZZ6/n85//ChdffAW1tQ040uG5jkf492//gHu+dTcvPfg8H/z7jyHnO4TMKC7NzVRuDJfuwXLyONKhLjgfv3H664fXgrBZwLzQEjrj+5nKjSEBj+6lPrSIqOv1d1ZVVKjf7v7+Xpqajl5zxPJTJPJxvLqP4Uw/GTuJIx0kkk57P3XB+UTcJ7HtKFRBtFNTaqMZQORgKi8IhyVRD2DNBMfF1YZ+6l0S3281jP0gXRKRFdjlksxVR/sFH4JIK6sHp1ARstIUOGUSbUwg4uqzbVVKyEnya1QWhvsVgRNghlFUGk+RA21KYAUkMgz3Df2OX1t38/NFv6J56SL2tu3kk3/6aypS5dzedivSo56r94E2pWGHJU5QPVckgZyyrsidBZn1Et+vNIxOEAmhVNBPCnJnKaJYHwTph/zKU9dcR6ImBG8PyVmfWNMtcG8COQ7OjLZKG1FjW4uVsnYwAfOih+3ain0Qz8PLQ+IoMviqq27gs5/9MI8//hDveMcdJzwH0zT5yN98kw+881ImHr+TsfPeQWVAUhuC4lJJgReibkhbgoMxqAgoC4eBhKAmKFlUCM/2KnI4bwsKPJJSnyKGKwKSUh9UByR7J8Cta6Tyyts4ngcvGVqfvYfuZ39KsuNFvKEillz0DpZe+pckQwvojAlyftjenSA1ZlPrznNFIEFBJELvwSlE/z6KrjwLjth8K/LASFqwstihY0rQn1BzlXdgOiuoC0sWFR57n6SEp3oED3VqxPPqraULydLiRpqHE0yPpbACIXK5DNWazUhRGVnXPMyzP0LphR/hivJJfPvv5p67fshX7vsstbUNXPPmd9PVcBtVxUUzVntKtNMckcTzKqdFF/CnHuiNCYp9El1INg8LehMafT/7OpWl1ayoXXPUmt+RMGnpdHTmeXKzxryIZF2FpPw0RXD/mzFHBs9hDv8DoSX7EbkJ7Miiox53vCXoyX5ccpp55zey++F9jHdNYHh0+nK9yIjFYMMovkwIw6UzkhshbuX4q9oP4TdPP5W33WglmYpTmfVBUP1AaGh4HC8jvkFKc5Xks8e2jbl1F7pmsLJ4KVXBCgxhzH5Z9ycGOTjd87rJ4GhNhMF9Qzi2g6ZrTGQmOTDWSXIyxdaJ3eza28IV9RdT5leKgPHMBKPpcYo8qnrwTLsonShmIjiJ5bbJmTn0Qp3kQIqK/lJKPBGGs30IqR0VZLE0euasdQRAoSeKQJC1c7hn+u2llMRycUrSIZ588hG+9rUfnVaL5Rm5s9hovkSbsY+oMutiUhtnvrWYM/JrZud9Yf4GHnC3khQZPPRjoyFlE5aswTniBz+eU0EDUY/aHX81Oqe72Ty0g3J/KWF3iPHMJLFsnD1jLbRPdfOmpiu4ov5iBiJdbDM3UmvX45vZ5s+TZ4uxkVyBj6akIqpFYSF6dS2BvgNMa2PEhtqIpmyMRUtwrzsXzTi+5ceroQmNcyvXcuf0PQwkhij0FpCxMvQnBym45nIi+zVk636coUFEMIixcjVOMknmrp+jVddgrFg1u2DtiwsSeUnlEaJhIaDII2mfUrvyxZ5yQmaEqdw4fkMFDgTd45SX+RkYmUAApX7VovbKoGAqo/GhlQ7eE/yiRj1w9kzBef/mJ1i8eDklJUf762l94P+xht4rkEEJOTD3CPQeSL9Ztd1po+C9S8O1RWDsVwsCdIETkOj9AqNNx2pURDACshXwxEFBNg5/IWBMwAEJhRaMS5CVkhodrsyBsRvMXRq5AzYiBla1xK5RqhRtHDyPaiQqbHbvEUSKJaaAtbsElaMw7Ie2cWj2Cc7rgRdCWcbHt9HY+Gl+8hONTEbZNkh5EYnEy3R0QEGBYMkCSawXwgLe+i7JuRepOfrFLwSxmKCgAPx+QXwSMgnIAhMWBAywbBU017pf4PGC1wtVVZJwWBCPQyymbCAuvBD+ugbsPYLf1cKP29RnoNyGJg8UFELeUp7Ep2Evrd5DfT34fH4i/4kp6nOYwxzm8P8TP/vZ9/nc5/6Kyy+9iq/UNGEODaE1zUfoOjKdwtq2mVx5JZ7rT+59L5HsMLfwjPsxBvR+xsQIU9okRU4JISdEj7sLAx1Tumgx9hCSYbJkcITDJZkrqbKPb18mpWT76G6e69vAUGKEsDs022U0aPYxoPfhki5ecb1ATJtGIAg6QbJahn69h3q7iTx5LCzMV6mSDWmSEinyIoeQKofhkL0EAPNg3ryFfPCDn+SVtud45Mn7aN3ZwvaNW3jktw8C4C8MEF1TQGRVATV1dZxVeSmlFeUYRQZvSb+Ts3Pnqy5B6WG+tQjTZzBAD+PZYTQ0SryVlHmr0MXRm+VPuR6jT+9mgbUYbYYpHNGGeDTzAIGWAKnuFL29Xezdu5Pt2zfT0dGGlBK/P8Dy5au49tqbWL16HeeddxH+V3VPxfNTSJ/N+//mo1zz5hv49he+xmfe+Vdcd9tNXHf79SwuWsV4dpip3AR+V5Bybw3lvqO7xv6cEEJQ6a+n0FPKVFaZ/IddBSf17j0dVFRUo2kavb1dx/mrIhoncmOkrDgu3YMudHJ2lrSVoivRxgr3iT2liovhzDMd/vQntZkdDEIsJUnZcFOpJJgBc5dAmwIRUxvxrq2QfJetFLRjYFdI8mfI43oFiwlFIIskSANwochdP9huiTYG+eUSkReQAy0lsEOHiTwtDk5Y+f2KSeUnjAe0GHwq+0k+EfoUN+s3kyuSLPUtpa+rh288/xXeHbhNqW3DEhkCMSJVDRoQ5Bc5INT16EOC1JsdzDYVGGc1SzAkTonA3KKUyNY8JTTIL3cw9gjM7WDVQ36ZPKmi/ah5mFlC5ZdKspcq72RjdGYuQpC5UgXSJXsABLo4msz0GjCZOXrMSCTK0qUr2bTppZOSwX0J2Oa7kOIz38L4Hz/J+LJriOUiTOeUPVzGgguaJAUeycMHBUNJtcXVHJVc1+QggbG0xlhaEHI5SAlJC84tlzSElRL4jQ0Oo2mN0ZRkNAWjVpb8ln8n9uSXyE8PEF10EWd/7NcsPPsaDFP5cQRstYa5ocmivbUd00ozPywwhQRDI1wZxrU3hjU4gavh8Jsrayv7irPKJH0JuK9NYyqn5rjAA5fUSoLHUQbvn4Qf7dKYzEHIhDK/pMgLOxJF1J1/Biv7J2gdNTBCQZpK/SyKuhhISnrjcOM8yS0LQhjnvJf3vOs9bN78Mnfe+SN++M0vYDl/R/N5t3DOzX9LuEwJmty68kZujEgGEocIfjkbKFjgkWxp6eClx+7nEx/4AnrWwMpaGG4DCbSMSwaG88QWF5BLQOuExs5RybuXOVQd20T6fwpzZPAc5vA/EYYHNBfYWTiCUBN2RtlG6B7KFgVxB9z07x6kb6KHsZoBAgsMwnEPQ8Np0uksXrefyHyTFRULXvMpWGELWeQghjQIHJHwOaHjDrpng99eDVPTkYB5RPKwlDMhUdphhtLKWsp3zXt6bYgVSysY3DvMeNcEjtehZayNXDKPXm0gK+HFgY0MpUb4wIq/JOQK4jU8uHWTjJ0hgB8SECJAzBsnY2WZzsZJ5dOEg0HKZTFW2qbIX0baTmI7FhKJLnSir1IILCqcT3O0kb3jrZT5SnDpJsOpUQJmgO7H91BQUMSb3nTzKa/HsRz0SYO3uv6SjaUvsMPcggDWZd7EBblLCcnDapwevZh6y8TLEEmRwCd9FDrljGle9hk5mpIeHjko2D4iyDuCMr/k4hrJGSVHe0MNJAbJWBnCoWpiuTjbR3aTtlL4DB+JXILn+19mKDVK7dlhLKxZIhjAxCQgQgwUTJCYShJ0BRBCQ1+0hHQAQpMJQpFleOYvwzjzrNNWEx3C2vJVpPIpnul7ib7EAC7N5MzSldww7yr86wtx+nqQ8ThWRxv5p/8E2zaDbiBtC+OVDXje9R60omJMXe1eW/EEhiaVlYQQ5B3wuxRJbmgeFkdX0xHby1RuHCklYVcBRnA+ybbNNMycut8Ev6lI5H3jglWlJ1c4OI7Dc889yS233HrM39zPCfQugYyANi2QOkhT4npJI3eWjV0D3t9qmLuECj8UAmyJyIJwqTZDbVhg9KB8n13QOSI4kIC8gGUuuEiDVgv2SSjOw/n9cLsjaEQiDKUwdj+jgyGxKwRiRDASliRrIdQP236msWWH8hpbUQjmMAwGIW4ponYqCIUOlHSOI6VNb28p4+OCigqJ1wtDQ6Xk8634fCr8wtcBBZbg4hK46AXIFUB2uaSlRTAxAeGwCoGzc0pV4QDSUUXzrNWbBF2XxOOCjg4xa9thWVBaCrdc7RD6hQZlkttLwROS3N8qWBCEUFaQnpB0T0J5Ocyff3oKlf7+XqqqauZSiecwhzn8r4PjOHzpS3/D9773Ne644yN89k23kP3+N9HqGhC6WnkLrw+toAhr6ybkFVcftwvnEHaYW/i57/tkRZaAE6TDbCMjMgRliHJnEeUOdBitlDhlLLKW0m60EnIinJlfx5m5sxEIJJIWYzf7jN1kRJoGex75Xp179/4RW9qEXSGGkiPc1XIfU9kYZzQvJCWSdBmdODgEnAAOkmFtiAnGSYg4AFV2LYVOEcP6AJW2IjUlkhF9kCZrPkXOyQPyhBCsbT6fxfXLmciNqgeTgs69HWzbtolNWzew93s72DOxg0d4AACv10dNTR3V1erfUCjEBt+zeL1+fD4/Pp8Pn89PypenW3STy2XJZrPkclkmE5PcZ91FLpZjaGKAWO80sZ5pYj1TZKez3M+vAfB4PMyfv5hzzrmAD3zgE6xcuZrGxvno+sm7sGxpz3RE6dTPb+Qrv/wW9/77b/jtD+9ky/Mb+d537uSMJWedNHgta6cZz45gSwu/ESTiKjoqAO71wKP7KPOdvmDlVDBNk9raBlpb9x3zt5AZxdRdjGYH8eo+RcZLcKSNz/AznZsgY6XwGCc+n2uvlbhcsGmTYHRUECqVXLlOclVM4NqoSFNMkEUCa4GD+YrALhBkbjpJDSLB/SeB+0mBNgH6sEBMQH6hsl7QxtUxTgFggNQhc72N+0UNvV/VRvoAyAKBtUSi9cz8vw9ERimEU6QQAYGwBGJSYrYIjLSulNGhGTWzo1TH0hToMchXKmIXHYRXXZeIg/t5geOXzAjulbggAuZ+QeZKJXhwb9AUoSzArUnyKwWpdzhoIyp8TrrBmi85aX6kBpnrJPmVthJCaGDNk9iVatxSv0TXJGmLWcGGlErxuqzoWIuKNWvO4eGH7z/JC8JL/YL+pODa932Fn71/MbHHPk/oum9zYMaW7IJqWFuh1LyLCiUDSSXwrgwcDkC7Y6nDM30anVPKOuLKMsn5VXLWEmJxIXxwhcOGPovf3nMnu+77J9ITvay46Bbe84HP8nxmIS5NYhyxRE7mwWvCwrCNlh1D6BqmOIIj8LiYCkXonTaoSytFcMaGwaTgnAolOumNKyVwyCUwdSV6eXlQY1mxzdIjxPixHHx3h8aBKUUYT2ZhNCOo8EOhR7JbRvjYNSGm9mpsGoYRTdmiZGzBhdWS6+epsQeTEDAFa9aczZo1Z/OJv/lXPvhvv2TPQ1+n7fm7WXrpuznrzZ9hwiinyCsp9sL2EZAIXPoR3IOAjns/hb+ggne9//3sf7iN4ZZh3AE3SUdjuDuDXhikdEklBMGRkv0Tgmd7BW9f+NrU6f/bMEcGz2EO/wNhhedhhZowJvdhhZtBdyHyCbTMCNnaa5Azlg/RmijRmihuXeMPwWGqrBqWyUYakxmy2TzSnyflj6HFtdngsNNBkzWfkDfMxLlDFN1biezWcYI2TlZSkCqi5g3VJwx6W1GylD3j+0nlU/hm1Mhj6XH8pp/maBOpyRSdG7oYaR1BSkm0Ooq/yI+0HUyPSfG8ImUI/yp4wx5W3riMni29bNi4iYSZInpmFBaAx+sl7AQ5MNXJztG9nFu5log7zPLiJTzV8wJew4vf40UzBH58FIUKqAtV4zbc+OJeMu4EulenwFOEI20saaELg+ncBNP5KQqPSFH2Gh7eufgtPNT5BHvH9pOyUtSHa3hD4Zm88w9v5N3v/hAez8m3vkcOjNLxQifx4ThCEzTWLebiC64kVBKabWU8Ev26RZH0Ue4cbT8yQo5RYfNyi8a2YUGxVxJ0Sbpj8Mt9GqZmH5UO69bdIMB2bPriAyTzSYq9hUxn4/hNH82RJjqnuzBjZcjjcP0e00Wlp4KeeD8V/lL8po+JzBTpsJ+rVt9CtG79Sa/7ZNCExsW157O2fDVDqRG8hpcKf+nsgkSvrsUZG8X6xY9BCPSFytJCWnms1n3knn4Cz5vfRtN0J0Xt03QmHWpzI+iRMNmGhUzphayvdmYV00EzzPKCdaTsBFJKfEYAx/cKuemHjjovl66KkvHMqYuJPXt2MD4+yvr1lx79B1slMuujILo47POsq5RmvVuAkBitArtaYu4RiIRKowbQxkDkBNIFZMAugHiTZNsLguzMcFMO1OkQioBnUrLEEdwhoVkAIZUwrXq8AFsQQ7I9JpjoF+R6oDAp2dwtCJbD8LBg94Tk5wlY7hXoFjgO2LYk7QHvjNpiakrg8ahWSV2HZFL5ADdXw40CLi6ReMrAyQiSB8D4mcavGxz27BHE44JcTmIYEscR5I+YrrwSoSBQ6mDLUvMQj4PHA+GwRNfBcSTdewSL0wJnJsjn2malBtk2BH0xidUBpfPh5psdTnet2dbWQn1906kPnMMc5jCH/0GQUvK5z/01P//59/nCF77KHXd8hPzWTUjH4SjWAVQrRS6LzOdOSAY7ODzjepysyNJkzWdEG0JDo8guZkDvo9ZqICKjlNhlxLVpLspewc3pW48+JyR/9Pyex9x/ICPSCDSe4XEmXBlKjCaaffMAKPIWMJIa5YX+V1hbsQoCgikxTo3dMOM/LDFmlr2jmvJ9D8kwl2au4j7f3bQZLfiln7iIEZEFXJa55rj11qvRaRzgRd8zHDD2E5FRzsq9gfNKL+bCCy+fPSYej9Hb20V390F6e7vo6emip+cgL7/8HIlEglQqSSqVIJPJnOSVFAy/gRly4Qv7CFaHqVhTRfNNi0jVJ3hryV9yWflVFBYWv67NyqAZxmv4SFkJgmYYwzR4yx1vY8G6Bfzk777PDddcwt99/iu8853vOe7zxzPD7J/eScpSZLsmdEo8FSyMrMTQTk/Y8V+F1avXsnnzhmMeNzSTSn8dA6lusnYWXeZnAuvcRN3F2NIiL3N4OHHB4PXC9ddLLrlEEotBJAIBC/iFQPuNhvQKnAKJ1eDglIA2LHFtFWTfeLSn7pEwtws8DwikF6xm1b3lflFg7hfYRaCrKUcG1RjZSx2yFyj1rWurwNwuMPaiFLxxiTQETpFS+TohcLySKzNX8S/DX6baVcOCvsXsHtjBN3Jf51bvbTilaqddm1DqXwCpqw4yNSbonTP+rPfpGPtAujX0cUl+qUS6QXrBiYAMSDyP6MiAZNYFJg3mZggMaWiTApECUIri9E3y5HYSAuw6sOuOPWZBFJYUwvZRQYFbKUnH0oKoR3JO5bHHr1lzNj/84Tfo6+uhqur4qve2SUHYJQkFKjnnls/zws8/zcIL3kay5CzOrYLbljizFgcu/Wjf4EOYF4V5UYecrUQo2qs+ro7jsOmJu/nWv/0TXV0dXH31jXz84w8xb56y9Zne5/Bsn4apK9VuIj9D6lZKKiI6Q+UhhltH8UY8CCFocYI8k4kyVmGQ8EQ42Cso8io7iqW+NOfkJ9j0CkzEC1hYqfxA7Bl/4pYJeLJbUBGQFM4sX7cMCbpjAr8pCLvVPOYdpZqOZZVoY/uI4Pp5DvMLBLtGBQ6wtNBhVYnkpX7Bc/2C6azAa0hWlUiubpTUlBXx4Q98jN+e/z4OPvldWh7+Knue/BkNl36Iz3z0Y3iMKH5TCUKOzGzp2fUMA1se4MZP/4pgOMiy65bSUxlmcM8gI1MOkw21lKyshbBavGpCqYn3jQssRx63W/b/CubI4DnM4X8iNJP0/HfibfkJRqwDcEAzyZeuI1N/7TGHV9m1lNhlDOr91NmNBAJe/AEPHUYb9VYTZc5rM1Mvdyq5LHM1D6+8j5hrmtArBejDJuHCMOvWn828FSeOhl1TdgZtkx1sHd5J3rEAid/0c1ntBVToZWy7dzvjByfwRbzYlsOeh/fi2A6R6gi6oeN9xcPCyxYc1wDeX+hn4WULeLzwGWLTSaLhw+F8hjazCEiNzT52VcOlTGWn2TveSt7IUxIpoGg8yoLyZiKhMJmpDOlshvAqP5qhQkw0oeM6om3veEnPRd5Cblt8CxOZKfJOnkJPlPt+dzepVJK3v/3dJ53byb4pdj2wh1wqi78wgLQdBvYMkZrKcObbV6H7daazMbYM72D/RDse3UV+4Qri0ULKj/hKt5AIIDatsXdMUBeS+GZq8ZAL2qYkz/drLC1yZn9MFxTMo9RXQle8l8nsFC7NRdbOk3fyVAXLcekmUkqCkwUEyoOMaEOzHn/TYgqE5B2Rd9BdOcbusRYmslOEXEGubriM9TXnnPS6TxcBl58m1/F9kO2OAzgT4+jNhz0QhWEqBdPObdhvWI951w+4dsrFfbVX0R6ohXgaY38vq1frXFh9tO+dEOIoL7zCggi51PRRx+QdAEnYfepzf/bZJ/D7A6xe/aoWQ6EUF9oo2OWo1GVUG6A2qvzPnGKl3hAx0AZmkqMFzObT2Kr9zy4Cx4ZtPYLxNOgG5PPgtWAHoOmQd8FKCxotgTCADAgLpSYxgRjsHhGMuqDADaksOLagA0k4DNmsJNYuOJAFfw5qdUX2jowIqoISp3BGJsIQPp8iZkUWZH4YXSvDGYdgANIlgr3bBVNTkMsKYhnJgxGN+rMkg4OC6WmBlBJLHj/D2EEVgpmMIqNdLhUeV14O9fUSx4HndgsuDUuMSbCDKo35fWdI2jpgYFog3iFZcI66LlALGnOnQBtVwSz5pUe3akop2bNnB+961/tPfcPnMIc5zOF/EL7zna/ys599j3/5l+/yF3+hahW9shoRCiPHRxEzwVtSSpzREYxlKxDB4zAdM0iKBINGPwW2kpTp6GhoGJgkSZLU4kTsKHmRx8DElMd69XTrnfzJ/Uf8MjCblTBhTbIz+Dih6kIYP3xsobeA9qmDDCZGKLPL8MsgE9oYh4xSgzJE0Amx39ijNlilyUJrKe9OfpiN5ouM6sOstM5iXe5c5tmn7pg7YLTwY9+3mdDGCDtRJrVx2nz7GNIGuDHztln/42AwxKJFy1i0aNlJx7Ntm3Q6RTKV4GD2AGmZptyooshVjMvlxu8P8Hv/b/ij9/fMy8/HhRuJpEc/SFCGuDL+JiLy9dsXmZqL2kAzbdO7mMyOYWou8k6WpoXN3PPg4/zgq9/ks5/9MM8++wRf/dr3mS6ZIqZNU+gUUZaroHV6Jxk7ScRViBAaOSfLYLqHgBmmPvj6LOD+s7BmzTncd9/dJBJxAoGja78afyM9iXZS+QSapuPS3ATMEBk7jUf34dVPz6YiGFT/gqphsusl5naJXS+RXmbamxRJqsVVd9aJyGDXJoGwZ2pEAD9kL5KY2yF3HuRWSKQpES6BVSs59DZwKiFTKclcI9H3g++XmhorowLWJGCXq2yKb/Z/iy/kPs9Hej/ISNcIFVoFf1l0B39T/DlEDOwy1ZNlRyT4IN8g0WyB0aauQZoSLSewKyXWtEAflGi9Ave4CpkTcbBLJdqgQCTBPpJr9SpvYddzkDtHIqtRQXo94P2tEkI4r8Mm2qXDOxY5lHcLts10Ry4rllxS49AUOfb4NWvUWmXz5g0nJIMDLqVoBVh59Ydoe+l37Pnh2zjr77aypCg4SwSf7vm9GgcO7OfjH38vmzdv4PLLr+EnP/ntMd8d1zVJMrZk96hgICGxHIHbkIyn4YEOjcZlDbj7ppnoniQdDvOEHiHjwKISQWGTTmdMMpEWnJPupXzXATqm0wwloMzxMrW0idbiGuJ5QdaGRA66pjX6E7Cs2OHaRpWzEnVLUnlBxlK+vpajAuhGU8qq7qd7oCEsuG2xw/kzeRwTGXi8W/BEtyDkglKfJJGHJ7o10rbDrYskF9ZIgi4fL5V9kv7L7qDr8X9j+4Pf5CPP/YCPfexzXHXLByn2mhyMQXUQhGPz5I8/TrTpLG57i7ItcvtdzFvfRNN5jbzcD8+06BRHZz9ygOo4NI9DxP9fwxwZPIc5/A+FHaonsepvMCf2InIxHF8JVmQhaMd+rL14uTJzPb/2/Tv7jb34pI+kSFIgC7kic92sWuJ0IRBcmr2KGruO3c07SMxLUJduYBVnERaRkz7Xa3h456KbWV26gu5YL4ZmsKCgicZwPX3b+5nonqSgNopu6Ix2jOE4qh3NdJsU1EaJDcXZ+0gLyfEk0gFf1EtJczHuwGE2rtBXyN7JA0e9rpQSiSR4RFBe2B3ivctupX3qIGOZCdyNJvmNeaa7p5mcmsQdcFN3bg3ZNdMMJA4SkCF0oeYqZ2fRhUbYVcCJUOA5PBd/+MM9rF177mxwxYnQv3OATDxDYV2B8n31pDArHYaHeulrK6FwURE/2f0rWiYO4NHd2NJmKt9PauVFeEJVlDomeSHp0yzqbJPwlAdrJsH2SERd0BcHS4IpDs/HzfOv4zetD9A+eZCJ7BRRQlQHK6kOVs3aeTTazdRkSnjc8yD7jb0AePBwXvYSzuUCLlhsMJIeI55LUOwtJOw+8WLxz4qZ8+PVihghQEJ+x1bs/j6Wzl9EtXyZ/bKUlMegpHM7i5vOIuA6diPlSDTOEIZ9cSjxKSL4UEvVkuOEK7waW7a8wurV63C92pxWQ2U5OBxmPSVgoaoUCU6pCvtwbVKKCTkTAiKyMwSuVI9JH4xEJL4DgvIgjDsQsSDpwHNShZoIIdhXJxEJIKHC6uSM35s0YUKALw8NbjBtRQZvCkg6opCfUMTvuICQBrqEAiBtQWECsiZsj7jw+Vbhcj2N330dsl+gpxymp56mTP8AZVNQ6oPWNkgkDpO5m9OCVBa6umDePId9+zTSaYGFxKeLmVRjyDlqenQBbq/EHxCMj0s0TeB2q2vcv19QXi5xuyF+saTgAeW/7ETAlYBlFsy/ziH9xiO89IbA9+8q8ATUvLufkqTe6mAtVccMDPQxOTnO0qUrT3m/5zCHOczhtcLBoVM/QL/ei0d6mG8tPinBJ6UkMZokE8/gCXoIFPtflyr0d7+7ky9/+W/56Ec/O0sEA2hl5ZhvOJ/8Yw9jx2IInx9nahItGsV10WUnfS239OB1fAxrg0yKcXLk0KTOhDY2S/7myDKkDXJ+9uLjXme70UpCxKg6gjnyCz8ey8tgpJtl42tmH09ZaXJOmrbYDswJnUgkQpFWAoaGDx+FdjGvuF7gafdjbJMbAYjKQq5Lv4U7Uh95TfMlkTzlfoyJmRyHQ8TvuDbGBvezrMudR5Xz2vx0dV0nHUpxX+ndtBp7yYosESfK+dlLuDR7FTo6F2WvoEc/SIu5G+U+KimQRVyXvvk/RAQfQqWvDrfuYTDVQ8pKUOwpo9xXS4G7mC996Zucf/7F/NVf3855Vy5l9e/W4lnqxSt9LJ1cTpVVTqGrFDFjC+HS3Lg0N0OpXmoD8/7DdhGvFyIG5m7l0+tE1SbvmjXn4DgOW7du5PzzLz7qeLfupS4wn854C4ZmYGpu0lYSB4cqf8PrVjk7ZcqmQSQl8ghhsTYGdq3EOYl7mjbGUc8B1AZ+UGA1OOTPObJ4PBYiCd4HNVwvK3UvmlrLKbsxZa9g6kH+tfTrfI2vIxICaUoyV0iEBGcPaJMgsooITl/tkH6LRJue6U6zwPdLHcur1MZ2k0SLCfReiRgWShHsAm1C4L1boGUAUxHHcqaxQBtX5zX7NtbBrgWjTWDsE2TPlfQnYP+kqgdrQ5LmyGHrhRMh7IYbmyVXNkjyDgTNY5cJh1BYWExDQxPbt28+oaXfmaWSlnHBZAYibpPLP3YXv/roag786n0suuSXHEk5SilxLJVlI07BOuZyOb73vX/lG9/4ElVVtdx//1OsXXvucY8NueDdSxy647BhQPBMj8CyBT1xSeukRrG3kGsvX0W0pZNnh0xSbi9LSjXCFWE0HeZHoXtykqktrZRFHaK1UZJxQeeBJPlNreRWBkhEihhLQ95RgdumJnm2TwXauTSJ1xDUhCQHpyFhwURaBWVX+GFduVLbHpgSPNipcdM8hwc7BTuGNbaOqKXNsmLwGhKvocbeMaLRX2NTFYSzyiVryiSxXAjr/C9gfeb9fOubX+KLX/wkf/jDPdzxNz9iq3cpB6eh+9mfM9W9k7/98YuseJWzj9AE84uhsAt641ATVPc+bSmrkAtrnDky+P/3CcxhDnM4PrJkiWlTBJwQXk7gx2b4yJeceVrjrcmfTSQZYZO5gWF9iEq7mrNy5xwdivEaIBAstJay8BBL8hq+TF26ycqSpawsWXrU4/GRuOpUN3QcxyExmsDlNXEsh0w8ixACw6XTu62P6cFpvGEvSIhUhVn+pmUES1T7x6rS5Wwe3kFffIByfymOdOhJ9FPiLWJp0dHJ2bqmM7+giUOaBdkomR6MkU/n6Shr5aGyXzPMAEWDhRQni6iSNbgcpcao8NVS4D65pxzA+PgoL7zwFP/0T99Ur4GkXW+l3diPFJJ6q4lmaxE6OvGhOC6vidQkk8UDJENTOJpNLpSjxdlCokujZaKNeZGGWd/lqXSM1r0bcFZeSZ/XjyEFiy0X12eCjBkaAkneBvOIHehEXqXyGq+6b0uKFvKJYCWPHHyS3x/4I0FXgOZII46UdMS7KPEVsaxoMcXZQhZaS2gzWrCxabCbaLIWqLZKAaW+Ykp9xfxXQm9oQkSiyOFBRJlSjkvLwhkfxXXx5TA+hjBMhKYRJc06uwsA2x5GH+o75fhVAdCQ+F3QExcYmvIDu3Geg/801gZ79uzgppvefty/WTWgdyp17yHLFulVQSIyqNr4JKpoF5oAH5ABbBC2ChKRmsRqkDy7VKIPCRpsSVgX9Hrgfgk7cqA5kgUL4Jq/c7AfF8iHwfEDJuhxgZaUdPvgKRPWmZDOCJ4rlvzeFAiXxMrD2JhAM6DbkfRKOEcICoAhW/KiX3LGWxyur/oId9/9LgrFKobTZ2FY30CXST7v3MbqtKCyW/JUGLJZpegVAuwUZAW07xeEwwK/X2IYAseBiiLJEi/sHRB0J5VtstTA7VGewYe+gCIRRVbH47Bvn+CyyyRcJEl5lS+zNiGQPknmQknm4qMXTe5HxFGBJ0gwOsH7e414kwNe2L17OwBLlqw49Q2fwxzmMIfXgCxZfuP9BZtdG2YsEQQldhlvSb+TZdaxG1C5VI79T7QytH+EfDqP6TUpW1jKgkuacflOMxETeOaZx/nYx97DLbfcxic+8XfH/N191ZvQi0vJb9yAnJzAPPtcXOeuR29qPs5oh+HCRYFTxGOeB9GlUgTnRE5ZMTgFjGmjTGoTLLKWclX2+uOOIQ87xB8+H91FgSfKeDxOMp/Cb/rI2jn2T7QQCXpIRScI2xE8locYUzTl5xNwhdlhbiUhYiy1V1DuVCGR9Os93O/9NbVWw2sib9MiRad+gCKnZJYIBihwCmk1hunXe14zGZwnz53en7Db3EaFXU2ZDDCmjfKA97f4ZYDzchcRlQW8N/nX7Da3M6j34ZU+luRXUOlUn3BcKSX7Jw+wY2QvsVyM+lANq8tWUOA5ljwWQlDsKafYU36ckeDSy67iHS++h5/d8j2ev+gprvjJdUSujLJX34mmOxTxqnBcoWPJvOqi+/9ABuvd4PuFhj4TJAYSq1Yw/9b5FBQUsXHji8eQwQD1wfm4dBf9yS6ydoaAGaLSV0+F7/iBhqcDpwSy5zh4HhOItCJ3tSmQLkH2QuekrIxVJ3Ef1GDG2xWAHKCdnmLW3CZwPyYwBoWii3VmusEUISwNyF4jZxS6oPdLzJ0a+qDEWgi5NRJzn6pVk+9zyK8FNHB84JTPhNrlQM7oY5wiRXBrw2KW1HXcEmNQoI2oNaSxA/R+yJ2hbCS0tLK/OAqH3jJpeLZX8NBBjamsmgK3DmeVOdy8QJ6WGtdrcKLV9FGor59HV1fHCf9+VrkKWtswoDGcAjwNnH3H93n+m2/jqQcvms0FGe0Yo3tjDy3DNgO+CIGaQlYuibCyXDsmcHrbtk18/OPvob29lfe//2N89KN/c0pLQSFmhBWTyj+3LjSjfZGSA1OCl31RPnzjCvo6YN9BnejRETcEhkfIp7KEFqo/FPshHQiiDY9TNDpMt78ICXh1Qc5R3aXVumTnqODCGpU5U+mXRD2CjkkYS0GhV3BW+eFA7XKfIs5/lNFonRCEXBJNCBwJu0aVitiSYDtKYNMTg6qgCjx/vEuwdUSQtQUV/jJu+sg3edObbuZjH7uDD9+yhvd98NO87dr38b4/fI6rrnsr779yzVHXl8rD7jHBSBrqwrB3TG0kABhCckapZH3V/22/YJgjg+cwh/92sLF51v0EL7ieZlqbJOAEOTu3nouyV+Di9Iv746HZWkSzteh1PTc5niQ5nsL0mUQqwqfc4TwRxtMTvNi/kd1j+9A1g1WlyzmnYg1+04fpVTYEUs70hUtACBxbYrgVQTx2cBwrZxMuDREqD5G1soz3THDg2XZW3rQcIQQLC5q5cd7VPNb1FAemD6IhqAiUcf28KynznyIQRBNEKsO0GLv5rf/nZEhT4pSRLs2xP9VKOp7lDfkLKPdUU+qtOi2lw6EwgrWLz2ese5wXGv/E0/7HSIkUIPHg5Q3ZC7kp/Xb8hT7a+w+yP7gXwinMnJuoVYA+LXAqLF4c2IzX8BwVwBdxhwgNtHFhqJsl9W/AlIJqx0BDUFQgqQ0JOqYFtSFVMI2llSL47Ap53N1xpRB+E5WBMh7tepr2WBe60NQcNl1FiU9VnnV242zb5n8HaCWluC67ktyD92O17kUYytNQb5yH68JLyW/diLTyR4WfSCmR2Qyi6DBxLaXE6e9FTk4gwhG06lqEEGiaCk/7zJkOA0nV3lUdOLUqAWBsbIShoYETkoj5VRKjTXnbahkV/iHdqJa7Bok2DDIgyC8Bo0OipwROIUrdkQarQoIbMtdLrBjc54U35MApkLQaMKlDWQJWrpT88z87RKOQrJbogxrmDqEWFZZEalBYJWlJCbpsQWGFZKBektsnSE0q/18pIZcDKQXPumCPDR4HeqUgPwAbvqfT3HwzCxvH6Oz4AllniEaxgn9yPcrS4hLmmxKnH9bk4EnA0aACSUSHvhxYMyud8nKIxyWZjED3SaovkXh7JelNGnlHEcnFxSoszu9X3sKJBPh8h7yE1flqBuTOk+TWSrSYWry82u5PTIOrRVO+eIcqIwFWNRjdAqMTrMWK0C8oKKK8vPK1vDXnMIc5zOGUeMn9DC+4n6LcriQsI9jYdOkd/Nb7c2oSdccoP1ufOkDPll4CxQECRX5yyRw9m3vQdI0lVx1d68lUEmvvbuT0FCISxVi8FOH1sWvXNt797ptZv/5S/uVfvntcpa8wDMxzzsM857yThoe9GuPaKH16F8V2KRktTZ4cbulGl1EuzF7GObn1FDulLM4vP6HoodFqxi/9TGjjFM6wXnlyhMMBagcXMpwaJWfnkEi0qjSJpeM87G/nEI3sOA6d4gDFWhkCqLfmUe6oDi2BoNKuYb+xhxZzN1XZ0ydvDWnikq6ZOu4wrJlfsGF9iBfFM3iljwXWYvzHC1p4FZ51PcEfvPeQI0uncYBKu4YGu4mszPCi62nOya1HR8eHj7Py53CUmf5J8HTvC/yh/VHSVgaXbrJ5aDubhrfzl0veRrm/9NQDHIF+vYfBuj6uf+IWXnr3szx4y+8494sXUv++RgaNfmxhYeguipwSSuwyMnaacm81+nG6Fv/TYYPnfg29R2A1zWzyWmAcEHgf0Fm39lyeffYJPvnJLxzzVE1oVPsbqfTVYTl5DM31Z1E2Z65WFg7mSwItBvkFkDvfIb/i5KRU7myJuVPViXapREuC3i5wilV9qA0p5fGJ4HpZoA+rT4UMqDZ+kQUtBU5eqDYvN6opKgjWAtBGVUix0a7GsJpUjZk/49jxnWJwIhJtEpxDH2VbgBvsAom1QGJumcm2KAJpK1Jb6wOXpUKQ881SPUceJrxFEjCgq1DyYKeOQLIwqkjPeA5eHBA0RODc4/j/vl5UV9fx4obn2DAgyNlQGZQ0hg/bCZgavKVZsrbcpicmcOkw/9yb+IfhP/G3f/tRVq9eS5hCdty/i81OATtCdaRzGrTaPD+W5pzFAW5bIgm6wLIsvvrVL/Cd73yVZcvO4NFHX2bx4uWnfa6d0zCUFNQED6/nhIByv6Q7JhjNCKrCAl2DjKX8iYdSykbOPZ6jVNdwpLo2WyorNdwaiWyWrKX2DIRQz3l5EPwGHJiClgkNQ0C3rgLk3AYETFha5FB+hNWJocFUFqaygvqQxNTBP6XOZSwDo2ko8arXTuXh0S6NhYUOv23V2DgsKPJIfIYK6u5LaLxryVqeeGIz3/zml/jOt7/Mz3/6LfL5PF/42388al7G0vCzPRptU2qtIoGAKTmnwqHUBxUBJeZ5LZYe/1sxRwbP4X8NVKtcgsneKYQmKKiJ4i88PV+n/054yv0o93rvwid9hJ0IcRHnPu/dpEWa6zPHb1n5z4KVtRg/OE7Hhi6m+qZwLAfDZVBQF2XxFQtf8/xOZKb40e47OTDZSdgdxHEcfjv1AO1TB/nLJW+jtLmE7o09xAZjBEuDeCMexrsmcftdBIoCZGJZUhMpPCE3iVCKDfZWeuhDlkka+mupGK2grKQEIQTnVa1jRckSumO9aEKnIVyL1zh2l1VKSTKfUv6w5mGGaIPrORIizvxD5LkGWtDghegLFGTKKBuro3dTH/GRBO6Am9L5xYQrju3xklLy21/9ksU1Kznw0EHixk721e0i/MZCamsaAJgWkzznfoJ51gKStTaPdj9NhdeH3/aQymVx50eo81aD0JjMTpDM2pR6i2cD+A7ZN/ikRpN99IaB11B+Wb/dazPSP8mQ4cUXDXFVvcPZ5ScuntQcns3y4iX0xPvQhU79Cebwzw0nHsNubYFMGq2iSqWYa6dXhLsuugy9ogpr13ZkPIZe34ixag1atABjxSryLzyL09mOVlkNApz+PrRoAeYZSmEvE3Eyv7sba+d2ZDKJ8PswFi/D/ea3qb9LScAFza9xX2bPnh3AiRWluXUSY69SXkgTlebsSLLrHIz9AvNFgbkdcIFdDdIrEWmBdEk0N8gQ5FdIcsskq7+j8fygYDeSah+U5wR4JZVLJe/5gCKCkUpRnL5aYkdVe55TJsk1Q2RQMH8jPDstma6Q+EIqlC2VEpimg2mq4gogb8GIDjlLbTC4EExNSQIBSXXhB1mf/yA3CCiJg6tE+XtpeRieFGTiknIgbsIuTbBFSlJS4BISyxJMTyu/49pa9T5tOwCaBpopcQNnnilpaIAdOwTpNHi9yhYilxNEo5KiIigqOuI97uLEChoLsFUb41HQUUpt+/B9XLJkxetqw57DHOYwhxNBInnZfEHVfjICKI/dOruRA8Z+WszdrMudN3t8ajLFUMsw/kI/npD6XfaEPDi2ZKhliIY31M8G+doDfWR+/hOcrk7VYSI09IZGsm96M+96103Mn7+IH/zgLgzj1MvC1/Ld12a0MKVNcV7uIhIiRkJL4JIu0iKNjsZl2avR0IiLGINigohTcAwpXG83sT57KX9yP8KYMYKOjkWeFdYqbi17H2PuKcYyE4yavfym9EfYLklZrhwhBWOuEaQ0OHP8LC71X8eDwXsZ0gePvp6Zf3Iie9rXBUr1vCZ/Dg947iEio/ikHxubDqONSTHOI577sbDRZgjnt6bedVIf4g3m8/xj6DP06F24cKGhMaGNM66N0mwtYlKbJCsy+E5kKnsCjKbGeKzraUzdpDak1MO2Y9M61c5TPS/w9oU3vqbxEiJORqQp91Vx9Z03suEfn+WFzz/N/ta9LP7iIkQShNQY1HopsUpp1BYQNCNM5yYImpH/UqsIfQCMToFddcQmrwF2pcRoF1x33pt596dvob29laam43saa0LHpf8ZGSMTshdIsudJtQnv4bQ6K+0GSL3Lwf24htkiMNpVdxSWwHu/wP2SJPV2B2vx8Z+v93M4JHgG0q3EBIrxe9V5SFUvZa6Q2E2q88xuPLGnsfRD9nyJ9z6B6FHdbNq0RFgCuwZESuVeOIUgJtQ9scskRocAB1JvkTg1Et+d6vrsQonIg4gLcmdJ9kQFsQkVCHfoKyjoAjMN20bEn5UMJlxNT/8g/75HgBB4dDizzOHm+YfJQyFUMFxd6PDr/uM/fp0tW17mfe97O393yzcYyhm0lNXixaFS5LCyFpmJDFv6vTRHdc4MjfL+9/8FGzY8y0c/8Y989AN/hWmeHjU3kIBnegXP9Qk2DcHuUUGhV3UwlvgkfXGYzsG2YcFZZZIlRYKtw4KRlPLtzTlQ7w8xNeCwd9RhSYmGoYFbOJiajVEfxWNIUhYYQiAdaJ9SCl4Q6JoSbekCoh7JFfWSrcMCjcP3R0oYTkHQJUnlxKxdYVVAsn1UkLbArSmv41hOEHRJnu4VHJzWSFuChYWSyIwDZMQ9k3XTp7F0uZtPfvKLeL1+vvzlv0XTNB5//CFuvfW9s79PjxwU7JsQNEckpi5xJHTGVFfnzQscfHMM6CzmpmIO/ysgHUn78x10bewmm8ghBLiDHuatb6RmdfX/mIV7SiR5zv0nAk6AipmWr6AMY2Dwsus5zs9eTKF8HQ76rwOTPZPsfaSFgT2DxAZjmD4XBTVRvBEvI62jSFty5ttWob2GCM5Ng9ton+pkfrRxNtAtbWXYObqHPWMtrCpfzsLLF9D21AEmeyYBgcvvwvAY5FI50tNpHFuiVxo85nmaMTlJiCB5kWOjsR1vm5cPFrwbj6F+PUKuIEuLTqyE7osP8ET3M+yfaEcgWFQ4n8vqLqDMX0KP3kXoCAOvfr2H/cZehrVB/hC7l67f9VPRWUOZKEc6ku7NPSy6/Nhgu90v7mTn3i385SV/RbQywhC96N0mkXsLcG5PQ0gSllGGGWKHtoWBTBqz0kXUDCFyIPI6MSNBm6eDaDZAkd9Pb7yDTSMbWVSwmBJvCaPpcQKmn3mRhmOuUUpJ2d4NvOfpJ4j3j5A33QRWn0nBGdcgtOAxx78aYXeIpe7XpiaX+Tx2ZzsymUQrK0Mrrzztz6DVspfsb+/EGRgAJHg8GKvOwvOWtyNO0TIFaqFqLFqCsWiJOhfHwd63m9wjDyFTCfTmBdhdnTgDfSAlWnk5rquuR6+tByD7h/vJv/g8WmUVWlUNMh4jv3EDmK7D7WqvA7t37yAYDFFbe+w9AkXmpu5wMLcIjDaBdDlYC8G1RWA+pxQkIinQBiTagMApUYnM2iTYUchc7ZC9VGLuFSx8WfB2U/K7hGB/SiJ1iR4XlJTDgw9qPPknyZt1WNyuYbSANg0IgV3qMBaDXwVgWxmkNegcFpRrsG6dZNkyh1AIdu6U/PzngsEBAZby1pUzAW4ul1Ls2jYUlkue36dxqQ05DaaHVQF7hlfS5YF4Gu4XsMkHYwbEkwJNA48H/H6l8K2sVOFu6bTgjedIhlsENSa0TSgVxOgoJJOq+DzzTCgulgSnJLXjEOsTnDEpVHL2Kd7qsgCseom5S2BFDqtS9EFwilTiteM4bNu2abYFcA5zmMMc/lyQSJJaApc8Oo1Un2FvsmSOejybyGFlLGWXdQRcfpP4SJZcIosv4kU6Dtn7foN9sB29sRlhmsh8Dru9lc99+DZisSkeeOBpfL4/v3jCQoUEa2iEZZSwrZTNQ9oAeZEnIeI84X6Yza4NpEWaqFPA+bmLOT97yex1CwTXZt5MkzWfXeY2MiJNk7WAVfmzCIoQhQVFzAfuNdpIGUnqsg0YQrEOZbkKOt0HSJpJ5jkLWWKtoN1ow8aeHT8lkugYVNmvvf3/wuzl9Ou97DS3YpEHBBnSaEKj1ConKMOkRJIOo427fD/lk/Ev4nt1WwpKQf0z/3eZFlP4pA/3jIlqVqQZ1UbQDYP12UvwyNNpcj8andPdjMcnaAzWzaq6dU2nyFPA3rEWMlZ2tmY+HRTbZYRkhAltDI/mJfAPIUqWljNyxyAbOqdo+O58Sr3FWLbFsHuYcDaKHbPQhU7YVUBzeBlB8yTmuH9O5A/baB0FA7Dh0rVXEg5HuO++u/jUp/7+v+acDkHn9DwLjoC1EKxmh8DXNUgLrMVSjSPB6ADP7zUSTY5S+L4KTlARtiImVNaEWymDsQFD4gSkyqOYEbBro+q/82dI7OOXrccge5FEusD9gkCbElhVEpFWnsLoAixFCqNJnAqlZLY0pUjOrZfgguR7HFzPCMxWgROA3GUOufMl1rAqyo4kGq0ZMjJjvbZ5PBnGM9CWLSaXnKQxmMc0TWJZeKFfUBOEC15tY3EEfD4/P/jBXVx99Xn86y8+z/k3fJMkOjWojSbDZaCn07jyeR57eRd/8/WbiCVSrP/s4wwtuoAf75NcXufQcIqPx1gafrJHo31KMJSElAWxnCCeg5E05G1wJETd8Pt2wbZRwU1NDsm8RtukCm4r80NDWQmueJThjkmilpeoB4piKfpCUYaLy9FjoAuljtaFsnHI2oKwC2qDihiezEIyL3Bpkjc3S37XptS4PkOSyAkibnhDpc1jXdps0FxDGLrjMJFWnYFZS9lG5GxlR9EyoYLrhBCcWSbRZ+551K0yW3IOjA728N3vfpWrr76R0tJy/uZvPsLOnVv553/+DnnNw54xQYlPztojagJqA3AwJuicgiUnoVKkI8ln8uguHd343y8dniOD5/C/AqPtY3S80Inpc1FYXwASEmMJWp86QKg8RLQq8v/7FE8L49oo02KakiPj64GoU0i30cG4PkKhdfpksHQkuWQOzdQwPacfeJBL5dj90F5iQ3GsnIUn7EE3dKb6pnD5XYQrQkx0TzLZN6WCzk4DidEEL219hdRUirHhcQLFAfxFPryGB9ux6IsPsKp0OZXLKiiqL2SiZxIpJS6fi8meSSZ7p9BdBp6Ah03+7YzKCWqoQgDpdIZIKMKBdCd7x/ezqvTULTajqTF+sudX9Mb6KfEVk7MlD3VuYMfIAJ88812U+MsYNgfAgZg2xV5zF3nyBGSQBU8tx+gy6W5up9guotguJTYY48Az7RQ1FuH2H5YX/v6ee9A0nfPXXopmaEhTkq1NE+wII1p15JmqitGlzlB2iMFkhvnVTfgMG03Lkc/DdDaOz6MTdnvwmRWMp1L0xobZPLyF6kANYVeYy+svojZQxeDeIQZ2DZCeThOujFBujuH+468ACBUXQTqN88wjZFKTeG5//0kVtzKTwWrZixwbQQSC6EuWoZ0kORyUAin7619id7ZDLg/BIOaZa3Ff/xaE++gKVeZyoGmIGTWSE48pInh4GK1xHsIwcGLT5F98Fr2ySvn+vgZIKck9+hC5Rx9C5nIIw0Dms+j1TbhvuwMRjqBX186SzJOD44y/tIW0twxDRCixwRsKg21j7d6BrK97Ta9/JPbs2c7ixcvRTjbfAVUQ59arYtPYA+ZuDccvMbpUOyBu5TGnTUhkDrLnShLvd3Bm3Drc31LhIOfVwCIp2Z+Crkl4Ii7ZsBtinRpNKck5KUGoABoMlDWCLbEnBIkBaA5A/3mSggLo7xcUFEhuvdWheMZJY82ZUNYq+Kf7BHkH/MCYhJytiGApBS0t4DJh1JFkHEGFAwmgOwmmXxD3Q7gAki5J37RAZsHtBseRlJbCJZcoBbIQ0N4OCzXJjZsFxpQiTTaG4dkoDLrh4oslnZ0wNSWYPwXntQvcMfAHoXETENNIvcvhVV+rR0NA5nIHfUDDaBFIv0RkBNKjHpch2Lt7J2NjI5x33rH+gnOYwxzm8B+Bhsb8/CKedz9JqVM+60MbF9O4cVPhHB0+6414cPlNsvEMxhFdWtl4FrffNUsSOwN92B0H0CurEaaqA4Xp4k/ZDPfv2srX/u6rVFW9fh/Uk6HObiAgg0xoYxQ66gfEwWFCG+PC7BX83vMbnnM/SZFTTIFTyKQ2wT3eX6JJnfW5S2bHmRKTJLQ4VXYt1XYt9XYT2qt2Z7NGFq/mI+dkiesxEnoCx3HIOBlSvgxu28Pa3LnsMLew39hL1CnAEnmSIsmZ+XUszB+dYXE6CMgg705+mFZjL0P6AC7p5mHP/WRlBjceWozdDOh9ZEWWDr2NequJd6TvOMpjGKDF2MOwPkhABsmTJyFimNKNQCMlEsSEUle/+ppPhdhQjLan2hmbGsdlG3gDXgpqo/givpmYMfGaxTKFsoizs+u533s3vXoXWTLYb7Nwz3OTf1Oeu9/8C2556DaKIgXo4xoxI0Yd87ClxXh2hH1T21hV+IbXHcL2WmCXg10q0YfArjv8uDak8hjMWg9XX30j9957N5/4xBdOWp+dCiIJeq+y+LLrUKHA/wnQJkDvF9g18rDKV4BVBUa/wOgG6zhW3vnVSiyguSTaiEBLzFgyCAkekB6B0SaR3hkLtYCqfez6IwbJgOsVgWubgAxYSyS5c6SyLAPQZ2rYcyTaEHh/p2H0SPQ+DbISLSlwMjMEc6mEPGjjguwVDodcEO0aSL9TkrakEmDM3JLqoFJ4xnIwmVEKz3Qe0jY0RBzlLCEUCZq2lJ/wqbRKUkLrJGweEoylBdVBiSYg61IXlE9OYkZKCLkV6blxUJyUDAZYtGgZ3/7Gz3j3e26GZ76NduVXj3g9dZIDm+9m990fobB2Cav/+t7/x955h9lVnVf/t0+5vU/vM9KodwkJRAdTRDXY4IZxj+MSYycxcUnyJbHTHMc9ju3EjrtjwMZgMGAwvQoJ9Taa0fTebu/nnP39sUcjDSogbBIHaz3PPKA7p+xzzp173732eteisbERTUi2jQv6UxrXzXeoC0BD4PjXsHlE0JsSBE1JryVoCcJEXpIpQ7GgyNvmIJzbIPGb0DEl+dlzaYJdPZxf0KiYV4UWqwbdAxeuYmxLL6n8GFEvrD2vGadyHi9MuZEIfIaycCjbIFFZKYGZZ3XYIs8QsHdK8OaFDhGPzZYRjbE8nFHjcFadpDmoVMX7pgVNAYnHgJBLEnAJVlcpO59DCeV7PJ1Vx7Qc2DsFPkOwskrd81wZ6gKgSZtbbnk3gUCIz33u63gCUdxN6/jmP3yAB7fs4+pP3s6ku4U639xnpWlq/mA5yjv82PeDZPTAOIee66cwmcH0mjSsqqd1QzOG+7VLmb52r+w0/qAwemAM27KJxGZW3AUEq4NMHppionPi/wwZHHBCeKWXrMjOaQXLijQe6SPgnJyIOxoTnRN0P9tHeiyFbujULaul7Zy2OUTliTB5aIr0eIZQXZDUSArDZWC4DeyyzfRwHKNC/X8pW3pZY0mOpNjxs52ks2nyvgKpZJrMRIZYa4xoUwQQmPqRyskddFO3rBarZONYNhVtsdmCdWD7IHc//mu0jEZJlLCLFkIXRGIhRuwJhrOjrOOlyeCtYzsZSA+xKLaA4YzGwYQgU4pyKN7FRGI7l6y5GG3VLob0AVIiSYY0JiaxdBWh/TFERMPSy4wwTLVTS7AmSHwgTmIgQc3iI77E2/dupr1uMX6PWm6POhUIswtH2IiM+iYtUaQkSrQU2xlkDwKBgwvDkGh6Eb80CHlU8W4IF+tqllLtqyBZTLI8toir266hPdJG91M9HHy0CykdTI9JYqiP4b4OlhgmlctmwkWCIYTPh7V7J07PIfT5C457f5ypSQrf/zb2wf1I2wEBekMTnpvfiz7v+D7B6fg0//RHb+XBA7uZKpVYWl3LX647h+WPPIiIxnBvuhoAe3iI8qMPYe3djdA19DXrcV10CfahTpzh4VkiGEAEQyA0ir+8E21eO/opWEY4QwOUHv41IhBAr1JMoLTK2AcPkO7uo//CsxBpmK9Jpgpw5+YMK8YKJGIxrAlByA2rqyRhvx85PUVqepLgS5DhJ0JHxz7OOeeiU9pHH1VKCj0rwBbIiMQOgnTPeCFWKx+5w0QwZVRIikuAIakEzg7BkynoKEBeF0QaJOdo4M1I8qOCRA2EZjj6/BjkbcECTdJfhpEqCAQkBw8K9u4VXDhDUnv2CG4uwvONkscnBeUylIsgbZUPY9vQ3yMQBfBLeMaGRgsqS1AS0JOHNlMyHAQWwZJJSSYDpqmUvqGQZGxMKYPjcfDmBFdaoHkl1kJlSeHshsiowHWBZOE6ydVXSzY/LFj+Uw2PBayEunYJpvIHdD8gyL/z5EW8vQCyH3YwNwuMfoEdcyifITnsFPPYYw/h8/lZv/7sU3qOp3Eap3EaLwcXlC7hgLmHA8ZeYk4FRVEkKzKcU7yQdmuuxYAn6KFxVQOdTxxCOhJXwE0pU6SYKbLgwnbcwZkP9lJJ+fmYR2q/yWyGv37qN1xS38SNl1/zql1Po93C+cVLeMhzL3FtCpd0kxFpmu02lpSX80P/f1BnNxCdSZ0K2EH69R6ecP+GjaXzceNmq/kcP/P+iEltApB4pY+zSudzY/7tc3I06mQDHtNPwpUkb2fQLB0pJHH3NEPuQUYyQ9Q5Dbw/+1GedD3CHnM7LsfN+vLZnFu66BVnchgYLLNWscxaxZA2QLdxkCkxQVpLUxYlYk4lXuklpSW413MnC+zFnF26YM4xyqKEJnU0oVFt16BrOlktg40SC6wun8GG0jmnNK5CusDOX+zGHNUI14RIammcuKScL1O9tIqp8jSXtl6IWz/167668Ab2mNvp0jvwyyA4As96D7FHK9i/aQ93XP1D3vGd96DFQBg6mqWhCRdhV4xUKc50cZxq7/+A774XCpskvp9oGB2oRd6sQAaguEkpaG+44e386EffZvPmp9i48fyXPuaLIZUfr/t+gT4hQFMhaIUbHKzjl9e/HRx1zmPWBbSZ153j71baKDF3SPQugQg6mL1qTmU3Q/EsiTEMCCie7+DEwFo4owg+vFZQBt+PNVzPKlUxBphdGsYeSe6PnSOEMIAJru0Cc4+gtE4iVjtoU2B0SLSUIpKNg8ob2FoqKb7uOLXZixiqJTFYXwO/6ISxnPLAlVLgMSR7JwVPDSmS8tFBwUhG+ddurJNc2CRxnUDc+eyI4LYOjVwZPIZkz5RG0ZbkyuomCu3Iji4dshazpPPJcOU11/PBxz/Ov//k87RXLiZ65s0EnTL5ZI4Dz36Brue+w9LXvZumN3+NRdWeWcI3b0meHBLsn9ZpCkJzUHJ9u8OCF+U8diUEPkMSLwo0oewy3DoMpGcEGm7liRt2g2M5uEYm2Z4ss3ooTiBbwBgeQi5oorRuKROan46Fy6mqXcQ1S2yqIiZnOODsdHikX6M9KgmYsHNC0JuCdElgzoz3cLyPEOoeaxqsrISVlce+CW9e6nB7h1ImlxxlLTEvDDU+SXdSIJGM5QSOlNT7IVkUJIqwb0otBFgSirbKuvnGv3+ezZuf4mc/ewhfMMr39mo8WXEzFR9awdT338CP/2wD4Ztuo3XlhVzUdMRLeSyn1MXNoWPfb46Eh58cZ/fdeygWbMyQh1qzSOrBDvLJPMuvWvp/psv8VHGaDD6N1wRKuRLacaT8QoNy/nfYP/IqIypjrCufxYPuezGlSVhGyIgUw8YgZxcvoM55ecXTVM8UO36xm1KuhD/qwy7bdD1xiMxUlrU3rkYzNEq5EuMdE6TGUpgek6oFVUQaVG9KKVcCKTHdJobXoJQp4RgO0+UExekiXYd6CDlBmvVm6l6UHPxiSCnpeaaH9HiaVS3LGJJj2F4bI+8iPhgnE8gRdAVYEjuylF3Kleh9ro/hPSPYJZtwfYjWs1qpnFdB4+oGFibaSfZvhzjYtoMmBZO9U0z7p0lGE3Qv6CTixIhJVZ1kSlkGM8OYmklLqBFDM+hPDeLRPSSKGnunBFKCr1AimyvSnezh1wObWN5/EYNnPslApJ+iWaQyW4tvZw1jwwkczaFYKhCITCsFwEzmwWH/XgDbttnXv5OLFl81255XY9dR4zQQ16ZJVUxQ1LPkRY7l5dVsMq6kyzfOUGaE1lATpZKbvD3TGioBaeA4GoYmqA9UURuIsKqmnQXReeQSeXo292F6TQKVaiHBH3ExsafAQKiOiqMKGBEIIocGcCbGT0gGl351N9beXejzFyDcHqRlYXd3Urzjx3j/7FOzCqOj8ecfupkDXQf4/KbrqYlWcPf+Xbzrwbu4b9MbqHvuaVwXX4ZMJSl859+xe3vQKqqQJYfSr+7C6T2EsXINIGeJYGmVsfbvxenuQpbL8MV/Ql+yHM9bbkareGmFvN3dBekUYtFRNheGyZgZpfvBbfws8maVnO1VBVRGVLEuGqa+PE021MB0AfZPC9ZrcbRgkES5TPTFcbwvA1JKBgf7aW5uPck2qvXJpR3VBudVvxAFAdrM+0qgEqBj6kccHSBjgwyD9CjrB+mB6YRgzyQUHQj7wPSCKw6mKdDLEM9AwFFFXNkS6LpEk2DOHFfX1XgmJ4+cxtwi0C3BO8+WJJ+H5/qVWsB21I/QIZtU/+/xwQMucNtwdhkqTUjZsB34z0noHRfYtiKQhYBIRBKNgmGo1xYvhk05hzO6New6SBbgc88Jnh0CowihsuCZA7B2reD9ZzhEnpOUG6A4CMnNglEN/CGo2iEQb5S8VIaP3YjyFzyOauCxxx7knHMuxOV66cnz4fT2zSMvMJodpyFQx1l1Z7Ag+jL7LU/jNE7jDw6t9nzen/0oj7t+w0FzH2E7wpnlN3Ju8eLjqkLbL5iPMDSGdgyRT+Rx+V0sOrOFtrNbZ7fR6urRKqtxxkfRm5VVwKcf/CWalPz9pdeg19Yfc9zfFQ5bPDTZrbxgbiatJVlkLeXM0rmM6sOkRZoGOTe0LeLEiGtTpLUkKSR3eH9IVmRYaC1BIGYzFprtVs4rXTy73+rSeu723M5TkR3EypW4HJOMniEmqrCcMk+7HuOGwk3UOvXcWHg7NxRuOkah+1KQjpztvsolC0QawzStaSTaFCErMvy377tMaGOkRYqsUBK3lEhgSIOwjBKQAR5zPciG0jkYR03Bm6xWqp0aJTzQMtQ4dZSdMqPaIDFZxZvz7zzm+RfsPAU7h1vz4DWOtfgYOzBOcjhFc0sT54uzeNx5lonwFFbGYmJ4mrWLVvK6pvNO6foBSnaR6dIEtstiOatplq1M6ZO8YG6m0F4k8psYqdcl+fHN3+f1372eZbWrZ/fVhfI0KDmn5s/826C8QZIN2pjPCfRRgV3vUN4osWYsgtev30hbWzvf/e6/H5cMlratbM9SKbTKSrTm1jmkkLEfvLdpithsmQmo6xF4f6CR+ZiDPPWS8aRwqsBqlpgHBFZwrqWVXS2xj/5zyoP7OYG5VaANg7DA8UjMvEBGoLxSKoWxS4XDGR0Cu+FId9rRMPaDuUXVSIfrKNtSgXauZwWFq4/axwHzBYEMz6iOAduv9jW3S6w1yrfZblRZFy8jWxFDg6vbHB4bVAHCHkN54zYFJZM5+O8O5f1tS0nMo3xxb+8UTBfgzYuODcvOlOBXPYqEXDTT4CqlUucmc0ropBuumddVANraauclieDDuPXv/h8Dg/3cc9+nkGaU8ILLGXzkbxnfdQcX/NGXueyGD/H0iI4xU9vHC7BnSql6hYAqr6QrIfjePo2PrXGoOspdJuiWlGwV4HZ4yunS1T1xSXDrAq+hfpEeT1OIZzECQWLNEYbGS7hlEXFwkP2eGrr81RQd2DplkNprcPMSh0Ux2NQqZz2CvQbMC0sG0+oTU0rlwZsuqTA9QxOsr3E4Wa58jQ8+vNphMK2U29U+Ff73UJ/GVEE9L1tClVeR2F5DUrAEOVuyfRyWVAg2tToU9t3Lv/zL33LLLZ9g48bz2TEu+E2/IFOC+vbVNP3l83T+59tIfPtyet78PYIXv5VanyRvKRL7ynaH2HEcBx/rg8d/0487b6PVREnZkJBeFrryuHeP0HJGE6HaVyYG+n3HaTL4NF4TiDZGGN0zimM7aDN9C3ZZJf6E6/5v/fFeU3gjeXLscG1lRAzhk342lM7hhvzbX3bh2rd1gFK2SKzliKLW5XMx0TnBVN80gcoAO3+xi6nuKUB9sPdu7mPxpYtoWtuIv8KP0DXyqTyG2yA1miKdzFByLLSAwJV2Mdw0ym0Td1HRWEFD4MSEsFW0mOqN44v6WKhFGXHG2S33M+WJU86Vacw2cOPia2mdCbZwLIc99+xlaPcI3pAH3dQZ65ggMZRk7ZtWE2uJcf6Ss9mbOkAqkSbkC2D6TEblOHahxJ4XdrB53a/xVLg5o7CR6KEmHu97lqn8NIbQaQ0388YF1xB2hyg5JUayqr3dXy6QTeUQhk2N28OQXWRyW5FYD/jmt5KvNyEdY3BimmBNBRU9MRLuJFP5NJNVU7iSLnxR3xwV+t69u8jkUqycv454fwJ/pQ/HcmienEfV/CrG2j3YtsXK8lrOKp1HUA9xzfzL+O8Dd3Ig3olbd5Mt57DI4Dd8mKbG4covU85S5YsQdasl49RIikKqQEXLEdsOYZh4/TrJtEWxDJ4ZDksWCgjdQISO/7fhJOJYe3ai1dQh3J6ZYxnoza3Y/X3YPYcwFs5VKeXzee5/8hG+vu5sNrQtQAjBR8++iEcOdfDffV38aTCELOQpP/8Mdm8P+qKliJlQDlFRibVvD1plNZgunFQSLRRWSuGeQ0jLQmubj1ZVg7VtKwXA+8GPvrRCWBzbCjSVh74kWH6N9oj6bcc0HExonFUXYHTVhcx78g6E4yA8YZzpFHlvksh115F84iEikejxznRSxOPT5HJZGhqaZl8rFGD7dsG+fTCYESRjoDdCdRDOrZecWScpL5U4tQJtSirTXVsiSoCjWh9FibmFvxvKa0AfkmjTKjjFVQarDEUgUoCxPsELOViRUwRtTUGRrpoEU5eUUG2EyRmPXcdRP5GIupXaGOg9IF2StTVwQb1kS7+gwgTLgrwpkYagXAI0cASEvPC0Djuz4NYgG5I8P678x6RUYXGappTBkQgYhgqA+/CHHSIRCHxNA48kUYC/fVLwQI8qeo2yIDcFba2Sbdugwys4twC5xwTWuMDjgAdJQYOueoFvEiIvY9JxPGQyabZseYbPfOaLL2v750e38d8H7iRr5QkYPg4letk+voebl97ImupTb0c+jdM4jT8MtNnttOXbsfM2GtpJ6z7d1Fl4YTutG5opZkq4Ay5cvrmLVcLrw3XZJgq3/Qi7q4MHpyd4pLuDr599MXWvv/FlefH/NtDRWV/eyPryxjmv50QWr/SRFRkCMoiUDjk7y6g2hN/y4yqbbPNuZVKbYJG1dJYMjcgY03KaLa5n5pDBMVnBhtK57Da2q3Z1vUy1U8fC8mJyWpZ95i4o3HTkvpwiEQxw6KluOh/rQkowPQb9W1OMH5xg9RtW0rF4NweMPawtncnzrqdJaUl8jo+USCE1ybLyKurtJqb0SdIiNauGBphnL+D84iUkPHGG9QGG9H5KokzYCfOG/Fs5q3SEtLUdi+70AUby/ZSdEoYwqfE2MD+0FFM78uxz8TxCgKZrrGAJFSJGt9PLlJ2gzW7mLaveQMj10rkRhzGsDfI0j3CwtAev5WE6P8akGKHCjuHz+imLIpPaBPZ8C/fDHkoXFbnn3Xdx3ncu5rBmxHLKCKHh0Y/1Tf5tIaUkXU5Qcop4dT8+I6DmPwKspWAtPf4irxCCD33o49x66wc4cGAPixcvn/2dMzlB4Sffwz7YAaUi+HwYq9biedNNCL8qJsznBSLHLLmMCVa7xOgQmLvFcYnVl39RYO4QuDYLtHGlOC6fIyle5aBPaBgHhAoTLghkEApXOcjDt7YMvu9qeH4D2rRAJFUosRMCLHB8auF7NjR3ZnqhpY8/FGNAICyBDBx1PYbKrjD2C3gRGSzKyjJjDgRIv6C0waF03qnfl0RR2QZc2CRn1akAMQ88MaRsHlZUHnltugCbRzXOb7Spf1Ht15+GyZygNXxkHELA/LCkVyoyeLjgxiNhNCeQUrJvSvCV7YK11ZINtUfC5I4Ht9/N13/4Xaz3lXjoVx+hpm0NE4e28oG//T4ffcdbeHZE4AwfURoPZwUFC7y6IOxS9g7tEcmBacHOScElzUfGubYKto4qQlbTBDlL2Tj4TbAcSdEWVHkliSIMTFqMmUHatDJLfSVsP4zl3NiZNJnBOOaSahZGYXEMelJwR6fGrWc4tEfg+gUOv+oWHEyoXJB5EUmqCJMF5fnr1lUY3LkNcEGjIo93jAviRWUHsa5GziGxNQHNM9NOKWFVpSRRcLClYLogMISk2qvuhyWVJUTAJTmzFt67wmayZyev/9DbufTSq/j4x/8GgN6Ues66puYFBCpZcst97P/++0n+9GbK/iT11/wxFR7J+lrJyspj33e5MjzWVcadzuCPeMFQBHi6BAO2h1AxS3Yqd5oMPo3T+H1G/Yp6RvaOMdU7jTfkQUrVIlXVXkn1oqr/7eGdEvwywLvyH2Co1M+0mCIsIzTbbS+7cJWOJDmUxBP0zFm9Nr0mjuWQncwy1T3FROcksZYouqmrImosw8FHO6mYV0FFawxfxEv3s72ApFgu4eQkLsNEhgX6SqhaVs2BXCfPj7zA9QuuOu5YyoUyI3tGiA/EEUIQ06JcENzIIq2dYXuUXDbHZS0Xs6Z55ew+0/1xxjrGCdeHcXmV+tQT9jDdG6dvywCxlhhrq1ewurCcJ51nGQtMIgBHt6iJBYl0h4js8RA/f5w7EndQ7PLSzkJag02UHYuOeBc/3H87V7VdyrMjWzkYH0FQTT6To6CP49bDSKqI69NUePwEpv3U1dYyfGCMjmIvDc0VjJ8xipHVqJuqwbTc9E73s7B2Pu3nzzvSmgk8/fSjeDxeXv9Hb2Bg8xDp8TSartGyupmFF7Xjd/zKSPUonFGzmpgnyraxXUwVpmkI1KEL2BV/jkw5RdkBy7EIuf00BGqo8yqCUTc0hCZwbAd9prVJaBqiug7tUCdiOo6sroBCHqe/F33hEvQTpCdTKiEtC+F9UcFuupBWGcX0zYVtW9iOjcfrQ6aSiHBEPTvD5IXBPrRLrkb4A9iHuhA+/ywRLKXEGRnG7thPPj6NphvIgX5EUxNO50FksYgWjWK0zVO+xc0t2B37cPp60NuOb1dxGPr8hRCOIsdGEbV1AIwmSpiZBIkzL0GfWcauD8CuKZjKCwY2XIljmNTvegxPOkHCE6J01SZcmy5j4uc/oqLi1D9PhocH1Hnq1bPK5+EHP9DYuhWmi4K+lNquYRmkz5d0JwSZMlzaIsm91cH3Aw3XpETvB+kDuxpETlBeKSmvOqqwEVB8nYPRpeF6UuC4IByULE3D1oKgLwfFPGwPwjpDcr4EvSygD3QvuIJg5CXPuGHcBCcPg4NQVydZ0yDxf0vD6BAYnZCdhOmiJOKBahPKEgoOlE0xG+ohNeXvRkalHMcdsJGMJyFbEHg8ikDWdeUXbJpg24LqmEO+A8a3Q/RCsFslrh0aD/fAtjHwGoJaH5CDhAE9PYKGBskL04KNYwIxIsj4wHSDcATVScnUFGzZLnh96yubmD311KNYlsWFF176ktvmrQL39z6MLR0WR9uBmQ6JVB/39zzMsorFuPRjlfWncRqncRqHoXMSpuFFcPmOJYGPhnH2+XgDQQpPP85Xv/4vnDt/Idf8zT+jL39pSy2AaTHFU+5H2W1sR0dnbXkD55QuxP9yJH0nQIs9jyXWcl4wN1Nn1ZPNp5gU4yTNBGdNncPezAtM108ggCJF4poSL0SdGG7cpEXqmGM22E202u20ziReeaVPqYlRwWy/DXKJPL2b+3H5XPhnPJoDUjLdN033Mz30L+lBIKh3GlleXk1aSyGFVPZiTiVLrZWM6cMEndAcGzhQftFvyr+DFnseT7kepV/vpclu5rLi1awrnzWn/u/JdNCT6cCjewkYStTQn+1CIlkSWTO7nTvgRjpytiutXtRQr9cwmZqidXHLKRHBu4zt/MD7TbqsA0jTQQ8buB0PtmbTZ3WT0bIY0qTSqcaiTEvTfMx7NHZevY2/fPfH+cy3P0e0JkbezlDpqSPqfvl1lIPDbnM7LxjPk9GSzLcXcWbpHCqdI3ZseStLR3In08VJLFnGpbmp9TbSHlr2sryJb7zx7Xz1q//Ml770j3zrWz8BVPhw4bYfYe3aoQKGZ2rb8lOPIwJBPDe+DQB9QiBf5E06S6we+xY9JbgfFXjuFIgyOH5wD2i4dkPunY6ytNoi0AcETpVD6QyJfVSjn+sJ8P1UhcVpCYEUEukHLJBB0MZB7xXYbcq7VxqAkNjVxx+LNJjpUIQ509Gy6kKbc/lTYEclrm2ayrrQj7wuA6qmeyXwGqp7rjDX9YZUSYWPVXnnHjfqhvGcZDQnqA/M/Z0ujngMz7lOoNJVpA+oDZnEi4q4tKUgXZLEi7BnUtCVgJuXyJP6EhumwVe+/h3OOWcxQ52b+chHPsGn/ugtAKyslDzSL+lJQVNQEY8lG/wuqPOrQWlCjXM6P/e4K6skV7TBw30aXgPGsmrcNX5JjQuyZckL44JcSZC2w+iGRQNJLE1nTbVFfxr2jgiqfDCvVimsNaF8hgfSgu6ksuW4sFGyvEJyKKEe+PyIIqC3jAr604pwXxiFpRWSbWOC2w9qpMtgCsmzIxrPjkreu8yZJYBn77GEh/sFv+rRSJWUs4nfVOF9o1m1gOMzYEFUUigLKrwO9+8Y4PMfvI6m1gV8/es/QJ+ZQxqa8hc+WpUsdIPat34b3RPiuW9/mMvqM7zvA396wuc0kYe4Y9DkNaBcBtQb2mdCOmdTEhqG57VLmb52r+w0/qDgDXtYc8NK+rb0M94xgdAETesaad3QfNIC+fcVAkGj3UIjryDUQygvueTI3CrEsR0QAsNtMLpvDG9YqW5BrYwHqwNM900T74tTOb8C27LxxXxIyyFvFin4i/gMHywDuU6Vpr6Sl/704HGHUcwU2fmL3Ux0TlDOlclMZckl8lS2xahvrME/5sUdcLF0oSIkraLFdH+coR3DlLIlok1HCjghBJ6Qm8RgAulIdE1nQ341VYUI2VABW1gMuDvxCDdu3DgZQZVTw97+QabEBGcGNmBKE1M3aQ/PoyvRTdEq8sYFV/ONnQ8zkulGd3L4RIwmLmQYgUCnVrMxLIOojLDIWMQLiZ1gC/IVeXIXF6g7FMQecZj2J2m6tIbpqkEmJ/uIuaup9TXx9NOPsWHDOTQub6R+cT25RB7d0PCEPSf1HpoXbmFeeO6zD3v87Jh8HqHZ+Ewf1Z4qFkaWz/quRZujhGqCJIeTRJuiCE1glWwKnhiNZ8zHSG7F7jyAcLnRl63A/aa3I07Q7i4qKtHrG7APdSGDodmxyvFRtFgF2lEK18MIBIKsW3cW3xjops0wqa6u496RAbYPD9AcCGJe+DqEriNCIeRRZLLd2429bzeyUEAPBBDhGPR1I6emwLbQGpowFi9Fi8302vkDyMEBZOYE8oWjoNXV47r8Kkr33oV9YC9oOq5ph76WFYysOuLfG3KBV5dM5MGpMBhcfwUjqy5iZDRFpCLIpee6ETqMjY2wYsWak5zx+JiYGAOgulrJY7ZvF2zdCvWNMBKHcAi8NiQPQvtSkA3w6IDgrDpJcDmk/5+D6zmBuQW0MeVJd3gu6blXUJpJezY6VXFvtUqMbQLHL3nUhl4gk4dMSeLRBYmi5Nse6PPDtUKy2A2NrRIWqBCSA8MwPCQQQvn3zmuS7PuKhpYQVLVI7vfBMwlB7hGNXFAyWJCYtsBlgqVDLqeK2qgh8LogUZBIBC0mZD2CzKSygQgEIJ0+YhHhODA/BW/YLfBNQNt3dPx7JKVzHIoNki2/EQQQ5BwQeXUPglWSyQQkk+DNQrYIeQeilirydQeyPoHwSA49LZDXHdsu+HLw8MP309o6n9bWky9AAAxlRhjPTdLgr5t9TQhBja+a0ewYY7lxmoL/A36Jp3Eap3EaqM8fY9VaHu7tojM+xRd/cBfGitUva9+EiPOf/q/SYewlKMM42HQa+zlo7Od92Y/g4ZUpizU03pJ7F5pP4znnCeLmJGEZ5dzMRZybP5+MlaKYyBIPT9PjOYSF8i5ySy8e6eFtpXcfc8xGuwkbm33GbtqtRXjxkRYpiqLIGaWNx2x/KkiNpiikX9R9JQT+mJ/kcApPyoftVV6Z8+wFTJTHGNNHcbCpdepJaHHSIs3lpWtw4z7m+C5cnFe6eI7a+cUo2QVGcv14dC8+QxHxXs1ACBjPD9ESWDD7es2iKvo2+4kPxAnVqjouM5HB5XNRt/zk9m5HI0+eu7w/ZVJOUJerw6v7sHHo9/QRsiNkjDR9eg8BAkSdGIutZTTYzWTa01TcVcmT1z3G3/zRJ/nM9/6Z+XWLmRdaPGMX8dKQSO7z3MV97ruwRBmXdLHD3MoL5nO8L/sR6p1GpJR0JHcyXhgmYIQxtTBFJ09/tgtTczE/tPQlz+Nyubjllk/wF3/xITo69rJo0TKc/l7sjv0qZNinCi4RjkCxgPXC8zibrkYLhrAbJUaHNiPxnDmgpUY/x0f3FCFS4H5IKKVxq3rNkRLjELh/Lcj8mcS+/vhqZxzw3qkh0iDDAplRAXEiL6EETljVT+bzKA9hS7Gi5XUSa/nxiVpriUSGQBsBpw5EQZG7ogDlNTP75MH7C4Frq4Y2AfqwqlmthXL21hQuc7Abj3uKl0RjEBZGJdsnBPNCKogsV4apgqDGL4+5E0VbWRj4jGM9bFvD0BiUDKRhfnhGjeooFXCVkcXj8fCXZ0ru6Ya7DgkWRhTx60gYysCvezRaQzYXNp7YQ7hcLnPLLe8ikYizYcM5/Pu//ysLFy7mjW+8ifoA3LREcmenoCcpyJYBAUtjkkqV/YkjFVFa4Z17XE3A1W2StVU2dx3SeGpYefl6dVgUldhS8syIRsQjqdcs5MgUk5qXJ0QlV2qjxJwCAY+OOT9G0H/krh0mVmcaqwGo9ELli0j2q+bN/XeqBPd0K4uOxdHDY5d0xAUP9Gn80fK59hq9Kbi3R8MQanshIGTAQwOCeEni1gUFS/L8KHh0mIpPceALV+FIk2UfuYe9qQDrZxZgFkbV/RpIK0W1JtRzt6RG05u+yLomP5/5zCcAeO/7/5SDceVHHHFLFkbVNXsNMF0GhaZ6gnsPIt0u8LiwSjbu6SThVRFizafeFfp/BafJ4NN4zcBf4WfppiUsvnQRQgjEycxrXsMQQtCwup74QJxcPIc34sWxHBJDSYLVASrmVdD5WNex314z/3Qch+m+OIVUkea1qtCy4g5DyVG8JR8Mo4x9dEHeLhLzHP8Dsv+FAcY6xok2RQnVhRg7ME5qNMXI3lGK6SKBmiCLL12EO+BmqneafffvJz2WJhfPkxpNoZk6lW0Vs8+xXLAI1QZnxxmuCxE8FKRVtJDQ4wxqnbhLblUMxNQXv5MBXDYFkcc7owoxZlSz6XKGy1svpta3lH/d2s+h7hTVxSp0b5A4kwSZImz1oZk6hsekiio8jpdl2TU0aHWYXhOWw6GmXsJuD4OhTkRJoKExURxlJN3P5s1PccstnwRAM7RZL99TQbyQ5L6eh9g1sZeCXSTqCXJuw0I21p+J3zii7DDcBksuX8yee/cy1TsNQr0XqhdWsfi6i3GXr8AZG0F4fWit82aVuceD0HVcl11J/gffwT54AC0URmYzoAlcl9+IdgKrhK997bv82cfeywWP3IsuBEujlVy9eBl7MymMtesBMFevw9ryHM74KERjOL3dyGIREatAb5mHFo7geL045RKidR7kc0eIYEDGpxHBIKLyBPIFVE0+koWiLai+cBPeljasPbsgnyPpmsfDrvXMC/hnHfgkUOuXuHRBR0LgNyTZspdgzMtlix3cuio6xsZGuOSSK1/mkzuCw2Rw5cyYDxyYSbU1IVcW+E2J6QLDgsrnBUsWSXqEYHweBOuVkqJ4iaR4CeidEt93NfQhQWZUwk6N4FMgq6QKnCup4lxPC+51O3xyWFAoCQwLQPmReXXw1sHIfMltEfUn9Zm/dHAHoEWHj+ehtxceeEDQ0aHx3G8EW7ohGIFgWjCYgooaCE/AdFyQslWasAOUMur+uzSoNGBjveT5CcF0VqJ7BONlicejvImzWRU+5zhQzMBVOvxFCWIWDHoEac2hfL8gtVkjfp1DYZdOdEQyVRTkA+Cqliq52waZFqzeKynH4aBUgXWGDrkKScYLTgp0+7iP5yVRKBS4996f8853/vHL2t7UDHShY8u5J7QdG01oGNrp0us0TuM0/mdh2zZf+MJnufjiTaxbd+bL3m+L6xk6jH0ssJZgohbp8+TZab7ALnMbG8qvPFCzQlbyntQHqUpGKWgFaqgnaqn6QjcNhotDpGWKlEgQkmE0qRPXJvFKH43W3AXzneYL3OH5IWktyZDezyGjgwqnkla7nbOLF7DxRaFtpwrd0I/pvgKwLWVPt5QVPC4fYlgboNZpYIW1hpx4mil9goIoUBJFLitczUXFTa94DEWnSNkpzRK+h+HS3KSsPEU7P/s7f4Wf5dcuo+Ohg6TH0kgJ/piP9vPnU9EaO97hj4teo4thfYDafB1JpgCBjk5FuZKSVuTC6ddRMPK0GQtpsJvwoJgrAwO5XOPf7voBH77yHXz70//BHbc/iOsUAutGtCEecT9AQAaothWBbWPTYezlUfeD3JR/D6lynOniBAEjjEtXJLtH92E7NqP5AZoD7XPsM06EG2+8mS9/+Z/40pf+kW9+88fIXBZZLMCLOuSEz48Tn4ZsFoIhSmdKXNvA6AK7FhX6Oyqw5knKK1+5RYQ+qOwdrKNVtALsGtCHBdqkChHW+0Bk1P87KicZbRj0cYH0KPEANogsiJJA2CB9yh5Cs0DYgEvieIEcGHsE5TOPHbfdBIVrHDy/0HA/BFoCkAK7UaJNKnWx536B+2ENp0ZSXq7Gau4VaElB8XxJ+QxJee1RpPkpQhPwpkUORVujKwGWI3DpsL5WEnNLftOv4TclQVNlcPSmBAujkvmRY4/l1uEN7Q4/2q+xP35Yey9pC0sm0p20tMxDCJUbEjAVEZwrw+5JwVQB4kX4+g6dgbTDDQslvheVdVJKPvGJD/Pww/fzne/cwUUXXc5f/MWH+MhH3k06neZd7/oAq6okCyKS7qQimO/r0SjakC0rIng4K2gISFZXHc/eRBHX+6YE1V7Jspik7EBXUjCWFayrllT7wLFdjBV1xibT7NdMFiWzVGhlPMub2eWO4ZtSVx71SGwJMY+kKfjia1FjcukcN4yvN6nUtfPDc59VjU9ycFqQKikf4MM4EBekS8wSwRKYKAgMITB0SdGWFC2QCLLFSUa/fzUyO8VbP/cEqWAdv+hSquGIGxZE4MaFDt/apdGbUh7GtlSktlvX0F/39yzLwmc+8wmeGDIIXfAxbEeRwItikpuXOlR5YVml5JmmNtoLeTyDo9iJNEVbp7IxwlnXLZ0Vz70WcXpGchqvORz2DP5DRuOqBnLTeQa2DzLdO43QNcJ1IZZduQRvyEPVgip6nu3FF/XO3q/cdA5XwE20MUJiKIl0JEITaEKjLlJDX36QVD5FsBzAtmAkP4bX8LCu5tg2Qyklo3vHcPvdGC4d0KlbXkuwNki8L07d8lqWXrGEYHWQYrrInnv2kp3KEa4PE6wLkU/lGe+YwHQbhBvCFJIF7LJNw+qGWZVqw8p6RvaOMt0Xx6jU8ehe7AnQmx3kUkXCmGENOajhcR1ZVi3ZZWVZ4VHF8MJYmE+duYKfFBLsPJTDKUqqzEME7KeROYtAbRB3wIWv4CVoBpjKTdOox3DcgsligrydY2moDp8RwK0rhYwjbXbv2042m2H9+leuRik7ZX64/3Z2jO+h2ldJxBVmLDvJr7ofo87fxLKKuTYPlfMqOPOdG5jonKCUL+OP+ahqr8RwG4AXrerEBOqLYaxcg/ePPkz5mSdw+nrQWtswN56LseaME+7T2jqfO+96jFwuS2pinOpQmA9+4sO05nOzz01fsRrXla+n9MiDOHt24oyPIcJhjCXL0GasJUQsBj3dGOs2YD39OHZ/LyIag2wGJz6N6+LL0GrrjjuGsRzc2anRMa1C4Sq8kktblnDu9YsRAhbnoHqHRsc0LEwdwj89wjg+1i9dwmWL3OybEgxnBTU+h7Pq1MoxKE/k8fHRWauHU8HU1CTBYAjPi7wZjZkWMNuBmiK8bQhWTYG/X7BaQsuYhvY+B6dB+fTq/QLPrwSjfYJfGLB3poi7vBuuKArE62aUKA4UhkHr1KhFtfflTJAlKEjw63DBBRKfDzo7weeDXQcFbW2SykrweiGREOzfL6itlcQMgT4uOITkwW7BulpJXRTiWTCLIIWyiNA1kJYq7iqrwOVADMFFEXhWg8A8Sb0Nti3p6lJ2GVKCkNAI+BwwkbgzgrYCDO0V7PIJmobhlyM6e0JgV0JNm2RkHOykoFQAWZZc5IflAZC14B6CgzoEAMcjiBQlPRq0nv3KVMG/+c2vSCYT3HDD21/W9o2BelpDTRyId7Eg3Iau6ViOzVB2hNVVy6n1vfy/w9M4jdM4jd8F7r77Nrq6OvjqV797Svt1GPvwSO8sEQzgxYvEoV/v+a3IYABHOlQWqzA112wNBSpsrNfXQ6VdyXy5gEF9AAuLJdYKLCxG9CFmxMJMaRP81Ps9kiLBmaVzyYscPfohEto0FxQv4U35dxxjuyGR9OndjOhDeKSXReVl+DixlUS0KUKoJkhiOEm0KYKmaVgli1w8R9vGVha6l3Bd/s38ynsnB419ACy2l7Eiv4b15Y1UO7XUOMevW2bHJCW2tNCFcdzuMbfmwdTclJziHPuDol3E1Ezc+lwJYdX8SqJNUZLDqqYP14WUkOEU4ODgIHFrHjQ0bGmjCx0NFb5Vm61lcXE5JaM8SwQDjOrDVGXCrO4u8K333sLbvvAZPnnrB/jCl79z0s64o9FjdJESSRbZR9S9OjoVThV7zO2U82VKThFLWscQvqbmouQUKDull0UGu1wuPvaxT3PrrR/g3e/+IOvnLUQEw8j41BzxgTM9hVZRoWpSwJ4H2XfaeO7X0AcF6FBeL8lf4yBfvhPHMZAmyl7BAo5+ZGX1ukiB/w5l3SWKyvqhdKZD/jqVJyH9gBcoSkRpZrVeQ2U/ZIFpQWmdxFom1etu0LvB/YSgvH7mtaMhoHihRN/uoPdoWAskdrUidj2/EkjA9bzAiUmcmbUGpxZKXomWEhSucXDqOTERnAfXToF+CKRbeTxbizlmHDU+uGWNw8E4pEqCCo+kPaLUoHlbsmNcMJyV6ELZGrxtsTPHX/hoLK2Aj611eGZYsGVUMJlXx9l94ACt81UuiltXxKKUKuBtNAdhF5RtgdeUPD6oETQdrl8wl7D95je/xE9/+j2+8pXvzIpI/vVfv0kwGOLTn76FdDrFn/zJrfhMwfJKWF4J9QGHX3VrDGVUZ97SCsnr5zvHKIMtR1llPDWk1LhtM+8zD9DgVz7DOUtlppTRKDXWYvsKZLIO7nnzWLPYx8F0FX2dOnlLEby2FFR61L2NHjVV2T0Jj/RrDGYEbl3lmbyu+Vjy+zByZZguiBmVtvJ2fvGf+2Hl8eHXk0UYywrCbonlKIuIaAi6BgbJ/NflkJ1g3p88gLdmAWEduhJwMC7YUKtq+jctlMyP2NzXrbFrCobTgoAHzqyVBNwQvfGzjKZtHvvOn3N5RTPLzr2evAW7JgS/6NR473KH69sdsmWD/Z6VaA0tuDJZmqtMbjw/Qjj82iWC4TQZfBqn8ZqEZmgsvnQhjWsaSI+lMVw60eboDCkIrWe2EO+PM907jeE2sC0b3dRpP38+gaoAjiNx+V3kkwV8ES8B08/yikV0jnczUTnNdC5JhTfGFa0XsyS28LhjkI6DOOoLWDd0QjVByrkytUtqCVarb6+JQ5OkJzLEmqNouoYONKxsYGD7IBOHJrHKNi6fi7aNLTSuPpJ6HaoLseoNKzn0ZDfJ4SRVRi1dZ+xHv9ghEAiQ0pK4Wx3qRuoYjo9T7avEcixGcmMsirazvPIIkdoWhk9eGWLnU0mGdw6xL9XPM74R7NoKQm1hksUUI9kxLjUWsPbRThjdSdbvwruynYXnnkNDNDRnEqMJnbGeUQAWLVr2ip9jx3QX+6cOMi/cgtdQxw+5gxyMd/HU4HPHkMGgLFOazzh1wvJ4MBYtwVi05JT38/n8+FraSCTiPP7Eb/irv/qn2d8JTcN95bUYa87A3r2Dwk++j4jG0BuPJKHJXA7h8WCedyF6UzPlxx/BmZ5C+P24r30DrkuvPO5komDB9/dqHIgLGvwSl65Wq2/r0PCbDmurJTU+eG97loPf+Qnmzi24ilmCHo2akRYqGt7JmiXzOV7bXWfnAaSULFr00i2HL0axWMDjOVLNLVsGzz0HTlF5dfWnBBd0ShblYWoB9IcEDW5JdEBQvkN5rrk2a2hJcPbCcBFGvaBXqLq6MA1dJUFzVuKpADToqpJ4OgQbBTwnwC8hoCu1RDIlyMcl3d3Q2Smoq4D//EdB1AOXXOxwybskO3aoULdQCGQWhJD4NchbkpKt1LzpNOyVULZUoXz4B9T8JeOXvBCBaAAiNlz5RoedOwV79kC5LBBCeRm22LAKGAD+G8EHKsBOQXNRUIhISiWBnICeKYlhgCsO9TWSwijoBbjSB7cmwIiBrJa0JMDIQLYs8PdJEiHB8NkOl1z8ylQ6t9/+Q9au3UD7iTy2XwRd03nDgqv5/r6fcjBxSPk9CmgNN3Nd+/Hfu6dxGqdxGq8WHMfhi1/8By699CpWrz7xgu7x4JM+bGEBIIqgZ5W6MOQNYhi//TTSrXsJmmGmixO4NPfs52POzlA2y/hFkPnWIuZbi1CGQ4Iuo4OUlpg9xl5jF+Pa2GzQXEAGWWGtplvvZEQbPoYILlLkdu8P2OJ6hpzIoaHRYDfxttx7WGDPDcc9DMNtsHTTYnbfs4/p3rgiMjTVfTX/3HkIBBeWLmWptYJO4wASyTxrAXVOA2KGOM2JLC7pxnjR9FtKyWh+gMFsD3k7h1f3UudvweX3ogmNCqcKgcClu6nzNdOdVmSzW/NQcorkrCzNgfnHKIYBDJd+SkrgF6PFnkeVXcO0awp/KUjaSqIJnQnXGE3ZZuqcRq633spd3E6HsRe/DJIVGar3JXn9F3qxt29hidvNZxYs5ZN3/IhFbe388cc+/bLOffi5HX7uh2Fj45ZuBAKv7seluSk6BTxHkeFFJ49H9+PWXr6NyVvf+i5++tPvceutH+Shh7ZibjyH0gP3qu41fxCZjINVxrzoUoT7iNTRWg6ZJQ7aOGCiFuV/y695uxWsFonRJbDaZ3x3S0p1XDpb4rlPw9wlsJsl0gdaXNlKOCEoXiCxmqVS+vYq6aWQKH9fl/oRRQlhyVH8PTKivIQP23C9GNowmP0a5dUSGTnqF8OKRBYFgVMzt86SAdD6wftzgTaplMrl1ZLSGqmUzF6lWvb9l4a5C3XjJPCospQoXHusktilK/L06Frdp8G7lzn0NsF4TnXcLYxy0pA3UKFjnQnBSE4QMlVAWk/XfqIL3kuuDGurJXunBMMZmMwrIrjkqP1qvJK+FHxjl0ZHQnJWneSsWsnjD9/D3//9p/jIR/6CG2+8efZcQgj+5m/+hWAwxD/9019x8OA+Pve5f8fnU4tQKyphccxhNKuI0nr/XC9cy4EnhwRPDQmSRcGeSaj2y9kQOlAKXFNTY63wwI4JQbwIRcsLLtgSbcPvcuhI6qyrlqTKgkRBKWV1jTlE8N4p+O4enawlqfBIcmW4q0tjPCd59zJndmzzwlDtg23j6lgFW80FCo7gvAZJ4EVfEy0hMDR1PJ8JOUtQdOTsHCLghtJ4B+lvqi6KxR9/gnJ0EYmipNannrp9lPOHCv5TY3DHBXlb4DEk3WnBMlMSdAu8l/8jkXgfD3/5HVTUPErtgjOo86tnO55XiwwfWuXQlYDpQoiIO8iCKCdcSHgt4TQZfBq/N5BI4mIaHZ3wnG+Z03ilCFT6j2tNEKj0s+6taxnePUJiIIEr4KJ2cQ1VC1QMa7A6QPPaRrqf6aGYLmK4dfS0zur2FYQvieCpddMSaiLsPuIKL6Uk3h9nomsSu2Rj+lxM9yewyzZ2yUYzdTRDw+UziTRFZvcr55W042hFty/ipXZJDU7JZuX1KwjWBAlWB44hUSpaY8Sao+STec7Q1vJQ9b1scT/DkNaPXwa4IXADzYuW8Fjv04xkxzE0nY1167lm/uV4jblLrbqhsfbCFpZvqGNjfDEL4vPZHN/GSGEUt+FmQ6mCK57sJlSMka6uRGYyeHcUKNZZ7KvUZkM6DqO/q59YdQXR6CsvwifyU1iONUsEH0bYHaY/PYgjHXJlja1jgn3TAlNToQRrquVxW3leTUjH4ZHbfogzNcG8tgUMGhqf/fzf0d6+iDe/+Z3HbK/X1aPX1eNMT1F66H6cVBIRDEEuizM4gLnhLPSmFozmVswzz1GhdH7/rHfb8bB/WoU6tIdnrl9KluQHyO/fT2+nzcqLWtHbF1L7wm+IHnoEq70REWzGJcvYPYco/Pf38f3ZpxBe7zHHPnhwLwALF546OW7b9pxJ8+rVkrPOgs2bBXoZqiclTROC8WrQQpIqr2RJlcQOg/s5gTTBapU4VTBwUMNKwht1eMSQZFzQ4IFkDoYHBPNrJCIFobTAAgImSi6gQa0B+TQUStDbIRjJQaMfziqCnoOxouBXXRpNg5KiX/n6AthVEj0m8I0KdCTZkgpG6S/D2FEr/C6Xyl6wLJichOpqwWhC0j0s8HgkVVWSiy6CZ59V6Sp+v7J4aCmrcRo29NswYoFPQsABOSZwLNBrJREHampUd2brtOAMH6xbItkwLnB1CEgodUy9D9w+GE1LpoJg/ZHNFddJQq8gCHhycpxHH/01n/3sl05pv7ZwMx9b+8fsntxPspgi6omwonLJKYX2nMZpnMZp/C7w/PNP093dyRe+8B+nvO/K8jqedz1DLpMmNhkGG4oiT12qGremkYmkCJivPGVdExotgQVkyikSpUlcmgdLlpHAUm0VI+LXWFgYGAgENjYlSjTbbbPHyIvcLFk4pA8wpA1QFHlAzFqEHY3H3Q/xmPsh6ux6mmQrFhY9Rhc/8f0Xt2b+5piAt8OoaKvgrHevZ6JzklKuhD/mo3J+5azQAqDaqaW6dMSTVyLZab7Ao64HGdYHCMggZ5XO44LipbPewYPZbg6mdgPg0jwMWwNsTT/LsGeEbLTAfGshVxfeQIs9j7bAQiSSkVwfGSuFIUxagguYHzz12uTlICCDXF18A7d5f8BUYBrbskg400TKUS7PX8uq2JlEqKAu28Cz5pMM6QO074H1f3of4YEchCOQz3ONYdBZ18xnP/93LFi5losvfmm7jAXWYmJOBUN6P412CwJBiSIJfZpz8xehS52clcaSZcayg/iMACEzioONLW2a/PPQT8GWSdM0Pv/5f+eyyzbwb//2L/zZRz6BCASxnn0KJ5NGq6vDdf7rMDaee+zOuvLS/Z3BhPyNKkDY6ASVSCexFkvKyx38P9SxWxURDIqAFgVwPSMoXiQpXi7R4gI5InHCAqQi1K0GiawEYzeQVerR2esfVzWUSJ2ADI4r4la+yPNXBkGbEkivRIuD4wKpAxpoY6ANgmlpONUSfQLcD2tIn/IaLp0tkQaYOwRWmwTPTHjapNquvNzGfumoBrWPUMTkvPDLX/jfNiY4MC1YEFEq1kImQTE5Sj6yhO0TSn3alYAH+zTiBUHZVERwc1DSmRCM59Sz6YyroLWnX9jBd//8HVxxxev5xCc+c8z5hBD8+Z//NfPmLeDjH/8A+/bt4j//8zbmzVPpfwJF+lpSqZS9R7197+8R3NMt8BgQNCWWFOyfUl65LTMfwRKo8kpsR7B5VJAsqmMIQ1kqJIqCOw6q+fayisN7KHQmVDje2fWKmH1sUCNTZrZDEiBYkmwfF1zQCO0R9VrABetrJI8NaJRsiW9GyR5yQbwg2D2lyOaOaYHtQHMY1lTB1jF1L7NlSb4sqPJC2YFc31Z6v3EVmr8a77sewFfTQLKk7s1kHoKmoCV0hA2WEn7eqfH8qMCtQ9BUXsMDKXAJFR6o6xr1b/sv4v/5Ou7+h+t5278+iyfSSKIIebXWiaHB4tjce/KHgNNk8Gn8XuCQfpAHPL+k2+hEQ2NJaQVXFq+j1ql/6Z1P4xXBF/HSft684/5OCEH7BfPRXDoTB8exLYf6FXU0rWkkVHds0S+lpPvpHrqeOEQ5b4GAUq5EfCCBU7bRdA3pSAyPydIrFyvv3xn4K/xouka5YGHOpHVKKbFyZRrXNtKw8uTvAaEJfFEfPny8qXgzl5SuJKFNE3FixGQFVMO6ylVM5KZw6SYVnthJlXkun4sKX4zrG67i4uJ5TOQn8UkXoa9/E+m40NvbOOxg64yOoD+7A+/SC8hHsnh1P0IIyk6JgUP9LFh4rMIkV85RsEuEXcE5vnPHQ8AMIASUHQvzqII2WUwT80TYMd7NPd0hOuKSoKsCcLF1VHBOA7xt8clTbn+XkIUChdt+xORdt/HF3VsZLeSJeLxcedk1fOqfv4Zpnrgt0XXV65HZDNauHciRIXC5Mdasw3XDW3FGR3BGR8CxkbkclIpoNbXoi5YijnPMRFF5TLl0tbzc8MKDtD57tyKSkeR2ezHP3Ijd2YEIhfFEI4dHgd46H6evG/vgfoxVa4859oEDe2lsbCEYPPVJr21b6PqR5+d2w9vfLlm1Cjo7JYFpWPuYht0ErrAKQ9AFSEcV51YjsyqMhBsmgCWWpC4HnRpMZAXtFuS7BJ4BQAgapSSEZK0FhwzBITdggy4kG6Jw+fkOD+3SWBcHrQBOJdQIScc4bH1SsOJ1kl05QbksMU0or5IIB6IJyGbgBRt2SUiVFRGsacz6/4JSErvdiuz1+dQxnnlG433vc9iwQdLbK3C5FIEcLYPbgUQeKENxGkwHikBYQFdY0hEAUurend0k2fSMxtK1Dv4AmAdVayamUpRIj6TChkg75N/sULjxlRd2d911G5qmce21N57yvhF3mPMaznrF5z6N0ziN0/hd4O6776ChofkV2VatLp/BhblL6Y7vJUmCoq+EGzfzywvR8oLOyb3Ehhoopot4w16qF1bhDhwbkHYyVHpqWRnbwFCul1QpTtCIUO9txqX76La66TD2UT1jiDqhjdFmt7OudOSztd5uxJAGe4wdDBi9AOjSYFIbx5Amh7QOWnvd2AcPYJeLdC75JcFlPiJCLdabmLRZ7XQbnew39rCufGJPZU/QQ9Pal5+AtcPcyvd836Qo8kSdCqa0SW73/pApbZK35N+J7VgMZLvRhE7QDJMRaTpc+ymXikQSIQjobHNtZlwb5ZbsJ6igigWhZTT751Gw87g1Dx7jxPYWvwtsLJ1PpVPFVnMzU9oEDU4TZ9hn0RRond1mobWUhdZSpOOQ/Y9bKQ3kEI0taIaqc51Ego+GK+jW4IMfuIl77n2ShQtP3mlV4VTO22t/AAEAAElEQVRxXeHN/Mz7Y/Ybe9AQCATLS6u5qHg5PekOutJ7sKWNR/eRLifJWVkqvbW0h5fR4Gs96fGPh8WLl/PhD3+cr33tc1x77Q0suPwqXBdegsxlEcEQ4neghn+5sOdB5s8czD0CkZQ4lVBeJjEPCChK5It0C04AtKxA5FTNJm0HX15g7gO7GewmB6cacFTYsJZQfsNoYG4V6KNgN0DwX3VKGxzyr5ccnQ8po4okFimQR/nDijQ4EYldCb67BFhKEezElHAAA6wVEr0PjP0aIo8qZB2UtYZQ+x99LqcCjAkwugT2/FePnOtKCExNzipAx7q2AhBqWk5PEs6ph3cslcwP2/zbTh23Dm0hyWhW2SF4dAi6lAp5anyU7/zl9TS1LuKrX/0umnbiSdj117+FJUuW8773vZkrrtjIV77yX8w/61ru7NQYSAtsCbU+yZVtkvW1kukCPDEkCLsV2TmWE0TcMJkT7JhQXr+OhKGsInPbww7/vkvD1JVKuikIrSFJ0YbnR8VxbR40FAkNiojuTykv4aMRdCl/47GcoD1y1EKCUAR5wJQUbEHQJanzQ19K8N29GiVbULQVoatrktVVcONCye5JMUN6S9IlwdDWuzn0X+/AU7ecyvf8EsNfwVBG4DMl8aJShV/R6lB31GLFcFaR2PUB1bnYnTwSDDecZdYD2fB4ufbTd/Lft57NXZ+9jnP/6jFioQDVx+p//qBwmgw+jf91DGkD/Jf/64xpo9TYdTjC5in3o4zqQ/xJ9i8IHf2N8wcE27JJDqVwbIdQbRCX7+WHLvy2mOiapOuJQ6TG0spftzlyQiIYID2a5tBTPRgug3Cdel7De0exizbheuVRphkaju2Qj+exihamR5F5FW0xqtorGd0/hi/iRTN0cvEs7tCpFdyHEZMVxOy5Eb6mZlIfePnpyYcRdocIu0PYI8PkJifQKirn/F5UVuH0dzDq6ebp4C7M8WkqZCOtwbMY7R7h6iveMLttupTh172P8sL4Tkp2ifpALZc0X8CqqhPbSCytWEhzqIlDyV5ago24dRf7pg/SGe+m0hvlQw99kqm8TcSopjrQzoLK1xFyreOZYY211fZMG9Wrj9ITj1B+6jGuWrGWq8+5CGmVsHdsh6k4xp23Y1+yCb3x+NYVWjCE5z0fwOnrwZmaQguHEQ1NlO65k/LmZ3CGh3CGBkDT0Jqa0QJB9OUr8dz8XrTQ3M+GiBsEqhioGO+h7Zlf4Gg6g3VLqfYLNG+c8uMPg2Wj1TfMHYhpIm0bmc8fd5wdHftYvPilLT+GMrB5RKUDRz2SM2oklmUd007rcsG6dZJ164AiBKck2hjYR83p9AnlG3f0R6DZKLHHBZQEZlJiTAj6bXC5JeeVQMsBGrjCgnQMiMMFSck2D0xLqDPh1rMlvQvAtUV5x8kgiJn2QY8bpgW8OQ1b2iUHOwWBgMS2oVgp2bQatu0W7O1VZC3MtYcARQxHItDcDF6vJJkUTEzA3XcLhoY0vF4IhdQOjY1QToIzLBgqQYsLqtwSf1ZgOrBbwg80GExAsQiRiCRcBE8ZygHQx0FqEkICLQ3YalFAjwMhsDa+8kmElJKf/vT7XHLJlcRiFS+9w2mcxmmcxu8ZLMvi3nt/zo033nxScuJEMDC4KHUp7ryg6CmhWzoVTiUhGSFRiHOwdz/ep6fQyzpSqHDfVdetOGG9eCJE3VVE3VXHvP6+3J/wkPtX7DF3AHBB8TIuLV6pFvtnsNhazjx7AXd4f4RHevBJP3mRo9apxy1d7Hnyq1TfKZHJJA4Oq/x7qdhYzfDNzTgzql4TE4kkL3KnfI9OBAeHR9wPUBQF2i0lDqgApsUUz7ue4vzi6wiWghTs3KzFw7A+SEakqdSr0UuCaMlFUAvTYexlu7mFS4rKf9Ste4/xCH41scBawgLrpdXHcmoSe2gA3B7EUR1/IhhEGx/j88vWcVPPfj74wbdz333P4naffOHg7NIFNNrN7DV2khVZGp1mVpbXMp0ZZ/vU07OewF7DTzAQZcjpoyvcxWPVj7HeOofXFTfhl8faZ5wMH/3op7nnnp/x8Y9/kDvvfBjd7Z5jC/E/CRmC0tlz6xinSiL9GiI5165Bi4MdlXjuE5i7BaIscCokdhs4lYpMxgK9H+wWiRMAvVugD4GWFpTbHazlisB1PySQbihcd+TcdgOUV0pcTwscW+L4VZCcloZyM7gOCuyo8gjWEiASKtjObpVo0+B+UiBSM5YGDogegVWUCAnyxY6DhzU7r7JI02tIbKnNnqh7y68IVDbiq1+OZyY82tBgYz08P+rw+JBGd1Ipgsu26qBrCYGG5KlvvBdpW7zzM3eyLR5geAD8pvL+TRYFHXFVKy+IwsoqyeLFy7n//mf52Mfey3vecwMrX38rDdf8HS1RF5pQQdg/OaARcdvYElJFRbI+OyLIltUN0oQipTsTiiQOGBKfKSk6ygKx0uMQmxGYgMoq8RmgaZJMSal6QdnslR1YVqGu2dRUx97Ei6ZEZRs0caxncM5S2y+Kqdq5aKtz5i1JX1qwoUbOWlBky7BtHFZWwcfPcEgU4aFDJb7wT5+k/+F/w7viOmJv+QGhoB9NgMuWNIck66phQ51kbfXc/I9MCfI21JhKjVzlg9Gs8jgu2dCfhqaAIqHHtRouvPVu7vvr83j26zfzr1//+ayS+Q8Vp0wG33fffdx5553EYjHe8573sHjxEeVbPB7njW98I4888sjvdJCn8drG866nGdWGWWwtV21eEsJOlC6jgx3mVs4vve5/e4j/45jui3PgoQ6SIymkI/FFvcw/dx6Naxpedb/J5HCSXXfvppguEqhU/sGj+8fJJwqsv3kdrqCL/cZuOox9lEWZedYCwoMVlLIlKtqUysIq2xSSedwBF0LTqF+ueqfssk1iKEliIEHVAlX066bOymuXE6j0M7JvDLtsUb2wiraNbcRaonPGVkwXGe0YJzuZxRN0U72wikDVqRV5rwTC60W43MhCARE4omp2innuurSfqcFuLv5RBv9Ekbyrm3jbQUYHh1myeKW6H47FD/ffwQtjO6jwxPAZPrriPQylR3jP8ptYXnl8jzq/6ePtS27kto676EsNkCgkGcwMUeGJUkgWmM6lsKQgVR7CKUhSuTHObHVTdlbSnRQsr3z1W12kbWNtfhoRCKKFI0jLwnr6SezuLigUsPbvo/ST7+F534fwvOmm4x5DCIHeOg+9VSnViw/dT+k3DyCiMZxsBikEQkrIZhHti7C2baVUW4/njW+Zc5zFMUl7RNARF9Qf2oueSTJcvxwNQVNQovkjOBPjyGIKOT0JR5H7MqlsKA4H0znxaWQijojG0CJRDhzYy3XXvemk96InCd/eozGSFQQMSUdC44UxyE/bJ5+Eu6F4qcT7Ew2jU+IEQcuoNrvSORK9T6hoYU2Fpw31CeIJGK8Eawp2ByX9JlySA6eggiekX1J9maT4tKBqAC7TBaWI5C0rJQs/5pBPCvQ8lKcE7oQahuOCtCYxw3DPdkFmjaS52cEwBD4frF0r8Xph/whcNB+2bIFDh5S3sJRKtSuECobzeARLljh0dSkiWNPANGHfPsH0tNoumRSk05Jhv6DallQhuLpSYrtgQkiesuFvs4LClMCTVefu6xP4PJJLApKgBpRmVCV1krItmErClnFBDMhMSTzf1lizROK0qkkMpzB3fvrpx9i3b9ccz+v/aUgpsXduo7xtCzIeR2ubj3nW2ej1p75QdhqvDk7Xxafx+4ynn36MqamJl/z+OhkO+/A22DG0mTAI6UgSAwnKhTL1zRFM6cKxHab74nQ80skZb1tLSktSFAViTuUxPrkvF7VOPTfn/4hcXpG0xwt5MzE5s3Quj7sfApSnbJPdSpvdjr83TuieZ7C0M3EtXoYuQCtOU/tUL9aCIUYvaAEgJRJ4pJda+9Q7Eot2iZJdwm/6EAic4UGckWFSQYvhdX1U2HNJ7qiMMS5GGNGHiGrL0YSOJS0MTNIiiSENhCOQAqSm7r+OwYg2fMpj+x+HpqEFgzguN2SzSL+S8EkpoVgk1Dafr33801x11dl8+cv/cNxW+hej2W6bYwuSKE2xe3oLeTuHT/eDgClrnCltiownjafgZVqmuMtzG6PaEO/JffiU3n8ej4cvfOE/eOMbL+Ff/uVv+NSn/v7U78OrCLsRSqsd3E9qOMUjnsGUlSDAfETDjkrEhMTdPePlGwZtRCI9oE0LpfAtS0QBsKCw0UHOaDYOk8auzYLiJZJZLl1A/k0O0hS4dmgYCXCCksIVEmO3QGoS6xxg5rgiDcYhodTKAyAyyjNYutRYEaAnBNKUaNMCShJmyEkRB+kF62SqYKmIbW1UIH0SayFwipz9iirJ08OSyTzE3JJDz99L/ZqrZ4LdVKtbqgQ/2KfNkrndSUiXFNG7ogqagpLdD/wnvdt+zaqP3cvzuUae2atumCPhP3YrArVkC+IFVf+uq5F8Yr1DQzDEt799Ox//py/x02/8FYMv3EvoT75Bw9JzaQvDgbhS8p7XoLo9DytpK7wSgbJZmC4Iqr0ODoJMSbBvCkq2oC8FvUlBwASXAfV+iS1VsF5zSLJtTKDlFO9edmBVFZwx4/msa7CxXvLTA2rMYRcMZVWInluDbWOKEF4YVdfTGAApJGNZ6EsLEjNqkWQBot65XsR+UxHFuyeVPcfnf93FPf9yE5mhvbS/9as0XvwhKrxKXFO0odKrrhMBS2Jyjo8yQKVPkcCJgiKCV1VKvAZ0J9TcY34YNrU5lGx4algwZS7nqlt/xN2ffT29j3wD3v3BU3vTvMZwSt/MP/nJT3jHO97Bpk2b6Ojo4Gtf+xrf/va3uekmNcEvlUo8/vjjr8pAT+O1i369B5/0zwkHUF/aknFt9H9vYP9LyCcL7P7lHjKTWUK1ITRdkJnMsu+B/XhCHqraX1255/DuEfKJAhVtR+wUXD6T6b5pRg+Mse28Z3nYcx8FCghAc+u0tSxkkbnmyEEcqQILNDFHMih0gZQSx5775e4Oully+WLaL5iPU3ZwBVzHkN7p8Qw7f7GLxGBSkV2OpHdzH8uvXkb1wmPVJL9LaJEoxqq1lB55EOHzIXx+ZLFAj72Hcc805//MxDD95BurCRYtxp/sRkrJwrZ2ADoT3eyZ3E9LsAm/qSYyEXeIg/Eunu18lIWdk2STE3R48+Qba6mONLAoOh9DM2gNNfFn6z5AT7Kf+7ofwhjXCVshnktvxa1H0XQ3kik0W6eQyXFo/EmioRXHfFm+GLJYxD7Uiczn0eob0OteoSVLuYzMqsA3AHv/Huyug6Dr4PUhKiuRqST5b3wFY9lKjGUrTj4uy8J69imEP6A+EdIptFgFSIlMxCGXQ4tVYm3bgrzy9XP8fb0GvGOpw8+7NIrbyuRtDbchmBeW1M60FAnTRGtsQZaKWAcPoMUqkIU8MpPGdf7FiOpaCj/7b6znn1VBdn4/uZVrGBrqP6kyWEp4qE9jNCtYEj28ai0ZycL+hA0vYQlSOlPi+GzcT2noQ1BuUWoQu0bi/5ZKjC74JckeQUVa8riAH09DVUGysBKubpf4ugSOlGCAKAl0r6Rlk8TcKlhwpoNzlsRaIbGisHJIsqwMe/NQ7VHqhpGkJGvD0+OCcV0yOCDQdcEZZ8CHPmQzNCR4+GHB9LRg/nxJRQUMDCjFLqj/er1KcSCEsogYGQGfD7JZgT3jL1xRIdF1WLHC4cABQTgsmb9eUtELlVHo06FnpeQH+wRiSGJagnBYKZQHBsCpEwTPlrgGBRgSEMi8SiTuDoKrTtI8CrkUJH8pyO0UVFRJyisEuXc7vFyR0De+8UWWLl3JBRdc8vJ2eBVQeuh+Sr+8E1kuIzwerP17sLZtwfveD6C3vUwjvdN41XC6Lj6N33f88pd30NbWzooVa1564xMgZEbxGj6yVoqgGQGgmCmSt7ME7QoMqaRVmq4RrA4wMNnHTudZOsJ7KYsydXYDlxavYm15wysew/FI4DljlGEa7CbarAUz5Kn6zg3s78KTKKO118/WlY3epfSYI2jbdjJ5kY+CyJMWSc4pXsQ8e8HLHlPeyvNw/5M8P7KNvFWg0VPJlbsy1O7rQ2bSSI/A+EgX+cWtRH1H8iOKFDEw8UkfXt1PpaeGoVwfhjDwSj+2dBBlsP1SdQchsbHmqKF/XyFiFejLV2F1HcSeGIOBArYOSAe9ph7PDW9l+bIV/Omf/iVf+MJnufzya0851HAk248llSJY1wyEEErRbTlESzGkC+pkAyE7zA7XVjpL+1linbz2fDHOPPNcPvWpv+cf/uHTrFmzgU2brj2l/V9VCCi8WSLDDq7nBVpS4FSp0Dj304JyqwqeM/oUoYqj9sGjfH2dClQonRf0PonRKdCnBFbTkbnZYR9gkWJOzSSDkH+HpHiFjUjNeBWXwP2EhjOjKsWc6WrzgxwAkZFoY5oaw4wqGG1mbDZgCqwmB2O/6lYTRYHjlhSudU7sF5wH7+0C1wsaIidBF1itkL/JwW4+wT7HwfIKuLxF8siAxtbOvaTGeli99hqubHNYNKNHerhPsG1M0BaWLI4ptelzozCREwRNSXLkEI//163Mv/iPcC26gmRRKX9NTTKSEeycAMsR+F0qMM1y4KkhgSM1/u5shwqP4Nw3/TkDtZvo+sH7ue2TF7Jy0/s59x3/iE+PMJYTNAYlFV6lCq5V6x+UbOV32xyQ7J3SaAxKlsZU/Z0qKguGVAmCLoEuJN0JQX0A/uIMhzPrJGuqFCFrSVgaU52MR6tkz2+QjOfgmWGNZ0eU5YLlKGXxf+7ReGQA3rbY4co2lVXzQK/gwT71Ges1IG8JCrbEKKv9jrYu1ARYtuRz3/kJt3/pTwjG6njb55+mev4auhOwc0KwOCZZEJkRmVjwwpigLSS4om0uh1DhgY11Dvf1CsqOsrKIuiULonBlq+SiZklnXJHoV7U5NIcgcN4VRAc+yN9/9pOcf97FLzsg+rWIUyKDP//5z/PFL36RW265BYDbb7+d97znPRQKBd773ve+KgM8jdc+Yk4VBbFzzmsSiYMkKF95KMX/VYwfHCc9nqaitUKRqai2u6neaYZ3D7/qZHB6LI3pUcWVRBIvJBjPTZJP55kYmmCz6yFidgVtM0VpXuQ40LQb9zI/0YEo3rAX3a3jCrjIDeSIHZVinJ3M4Q17CNcf/7maHnOOZ9RhSCnpevwQicEE0eYopWyJUq5EYijJ3vv3EWs5Z06Ax6sB11Wvx0nEsfftgXIJNJ3hK6K0HtJxOybJZtXLb3sMhiJq0lIzo2SZyE1iOeVZIvgw2hLQ9LOfsd98hJ9VTzHkLoE/iKutnZWNa3n7khsIuYIYZYf5cXClswTNAImJJI6UBDWNaUXJI/UyphVhLD1MXcxmYfTEbLDd203xpz/C6utG2DYEQ5gbz8X9+huO68V7Urjd6G3zKL+wBVFRhX2o84hM1LLRvD5kJIrs7aH463tfkgymWEBmM7OEO1LOEKkS6TjIUhHhD6jtyqVjwt5q/fChlQ4juSZEp443msUVUPddWhYym8V1xTXozS2UHnsYZ6BPkf2brsY8/3VM/eznOA/fh7u6GrOuAZlKsvm2HwCwatWJJyyZsgpfqPbObV+q8YGl+0lnXqL1VIC1EqyVjupWO+oYufc7aA8LDt6hMRyHgaWS3nqo7gJvv8aNLQ5nVwMdoA8DusCJSKSmwj6cBpDnOuiTAuMegVMPnhx8sAbucyTbhgTlMrS5YF9CYOiQ9SqFcakEjz4q6erSWbgQkiPQPwwuU1AqKc7fNI/4Bds2BIMQCsGWLYLhYaUSDofVRYVngj2yWWhpgfp6SbkseNObHL73PZ17qyWBAMTjUHAgGhXU1EgiEchkoLJSWVDY73Io/0bD2KVulD0OEwL0Csn8BHgsmKqCASAp4ZwmFVDiekpQ3PTSivl9+3bx6KO/5t/+7XuvejfGieBMTlD+zQMIrw+9TSnWpZTYHfso/eYBPO/70P/a2E5D4XRdfBq/z3Ach/vvv4t3vOP9v9VnhVv3MC+4hIPJXUwXx9GETsHOo2dchBKVc8Qcjsvm4XW/IuWfpIEmfI6PXv0QP/D9B66si+XW6t/BlR2BhUVJFFlgLabWbqBf76HNVgvxBQoIO0GECkxxpLapdKrRWIzI5NiBTdiJcHnpai4sXo7Gy7PScKTDTzvu4qmhzURcITyGG+epJxh7qgvXvNVULlqKt1RizXP93B/eRaC1mpBeQYkivUYXC6zFtFuLVUZHaBllp8RUcZygFSBoBpj2TeGOeAnvncLq7mSj9LK6IYBcYCP0/+GE4FOAEAJz/VmU7roDu5SjLIrIkoXt1ujZ4OBaMcFGR/LhD9/K/fffzcc+9l4eeGAzHs9xiv8TIG0l8BkBLFmm5BRwaR4KegHdMhAWWFHVSeWXAcqUGNWHT5kMBvjQh/6cbds289GPvof77392NuTr9wHSB4U3SIqXSVXnRcD9lABHIMoSY1h5COMB4UhEHrR+MLJC2U7MlM5OJciAwBiQWIuYfV1LgBOWOJHjn9+pAg7rcNIg3eoc8qiMXJEHWQ3lOom3H1Xb2oChxo8OoghOQFK42sF3h4YYVh3CwiUwukHvldjHibdxPyJwPyGw66RSNJeUCtn7Y43MnzkvWyGsCbh2vmR1tc0Xn7gbt9fP3990HvMrVC1fsmHruLJ9Oxzm5tJhQw08PghdcYc9X3gvRrCa5W/7PBkBdX7lQVyyYSQHAkHOklR4lYIVwJaCA9OwdVRweavEdiAbXc6yW5+i9qlvsvf2v6Rr8y9Z+NYvc/YNb0ATcEmzw+ODOtmymnMYAhoCStE8MqFR6zsy/+hPq1DxSq+gISApz+R5BE2oD6jtKr2SC5okTYEZ5e2L4NLhrYskAoe9kxp+Q8xeW6KklLv3dmssq7RpDSnP4ArPke+CeWFFiL8wLuhLCebPeAwXbZjs38tdX/lT9m19hNZzb+LqP/k3XD715tE1dX1eQyCE2sc7E5r3/KhgU+vceRbA1fMkHgOeGRakSoKIR3LtfEmNV/LlbRqjM0F/poDV1ZK3L3H4q7/6J5544jfccsu7ufvux0+ab/NaximxJ52dnVxzzTWz/37Tm95EVVUV1157LeVymeuvv/53PsDTeO3jjPKZbHE9w6DeR53diINNv95LlVPNyvKxQU6vdZSyJUDMEsGHYXoMslO/Oy+zE8FX4We8axKA3mQ/HfFDlKwS3pybQ+UD9KT2c6ln0+ynh1f68Hv8JDZOUNhbIBfPo+kamqbhq/Bjl2ySIymskoVuaLRfsBBP6MRF31TvNCN7R8nHcwRrgtSvrMflNZnqncIb8TLZNUl6PINjS6SUpMfT9G3tZ/45xw/D+11Bi0Tx/vFHsLsOIqcmEaEQwWVjlD/7AsXD3+6HryFfUqueB8ZILEjiM3yAoOyUMTX1ZSNsmwWP7yA0neWuc2MMGx7ayjH08TgFV5ytxnaqvBVcl22g9Ku7cEZHqAxPka3PY2g1CAEeSngRpIAiQfJ6mbCMcWmzmE15fTFkPkfhx9/D7utFb20Dlxs5NUn5wfvRKqtwXXhqKkhnZAhZKmH3dGF3deBMTYHjIEollSLt8aAJga0JZCJx/GOkkthdneDYaC1taLX12J0H0Grrwe2BXFZJVw0TfH6ciTGMFasRJwhzEwLq1q2gsPsMyluew/b6QNcpxZNMNSxioukcGppiNH9wBaKYB9PFdNngVy9MU/fr57FEDbJUTVtJ0lztYfMLaWp8ftoqTqxA14UqKl8keseR4ApVkkpMvvyb+qIix26C7Wsl33xWUncmeH0QAtbVwqH7JAf2CS7sU6EeWCDyEiEF7qehUC0ZCUtc39KJ2mD6JVgCkZN4NXjbZZKLD0hGDgruG4UhCXkBBR/4ZkLhsllID8M7m8BbgG8XBRPboORXicOBAJRKcpY8LhYhmYRcDvJ5RTQftkzWdfW6y3XEViKXg4YGWL1a8txzSgHc3S0YGhKYpiKWW1pgxQpJJgOJhKAYgOwHHPQBNcEZ+JXG8GOCqAkTETXnmAqDKw+FAtgmGH6J+cLLI4O/+c0v0dDQzDXXnHpw3O8Kdl8PMhFHW3DEdkAIgVZVo9T32QwcZVtzGv/zOF0Xn8bvM3p7D5FIxNm48YLf+lj1vhZ8up/xwghFO4/XE6BvbITpyST5hgJBGcZ0TDpc+xhtG2S1vgavoxZig3aYg8Y+nnI/+jsjg4sUecz9IM+5niQrMjTYzawqr+MF13N0GPtUUBE65zSuo1qbQGbSiEAQiWTcGSKTHWB85XzWlc7k7NKFNDktp3T+3tQA28d20eivI+QOImyHNT058qZGt5aiUoBwu3nd5LlMb36YvVU9DMdG0dCYZy3grfl345rpiffoPlbFNpIoTVKw88Rc9Tzqv5/oHU/S/ugkgbyLOqeRkOunFM8bx33DW34vCOGsyDCo92FIkxZ73qwVg93bjV0T4+CiNDKfxXQHKYfciEySx3r+jej8Kpawgq985TtcfvmZfPGLn+XTn/6HE55HZtKqOyYSRQiBV/ORH+igeWCSVDlBuimK0aDjSAfbA9YM6WRjI1HzlFcCIQRf/vJ3uOKKjbzvfW/i3nufwufzv/SO/4OQgSPKXScACImWBEpAUJGt2uiMnZZP2TGYOwVoErtBIv1gNyglsT4qcWqU16/IQmmT5KSCfAnasPIYttolrmc1pFcqQrgAer+gvFySe7dEG3NwPScQ2SNdoyKnFMDF8yTmHg0tI5BR5V9MXuLaIpAejcwnHDj67V5SFhZO+Ei4Mm6w2iRGHxidYC1/6Xs3kVPK2HQZYqbFs7/6L6664lraK4/MUS1H+eSaL1ojcuvQGJS4N3+JROfTfPobj3DZ2T7+c5fEcmDvlGAkCxP5I5YJ+lG1vS4UqXowLoh44PFBjXgBhjMGnpUfZuGC1zNx50fZ8e9vofz0mdR+4q9Zs/FSzqpTat2IWxGkERfsmlQhcYc1UVLCeP4IwbsoKgm71esH4oJnhgW3HxQMpgUOKqjumvnKi/d4OJRU4pCIW+KaeQ5hF0wXFNndGRe0hiTjOaXmrferGvzwnKgnqYhjB7Ay0+z7+d/R/+g3aWpq4+JP3EXt6qtw+Y5aUJTHaGIApSwu2sf/nUuHK9skFzZJsiWlDrYl/MsWjck8LIioe54tw+ZRQX1AcM08H1/72ve49trz+epX/5k///O/Pun75bWKUyKDQ6EQY2NjtLUd8e256KKLuPfee7n66qsZHBz8nQ/wNF77WGwt54b8TdzvuYsuowOBoN5u5A35t1LnNLz0AV5j8Ia9CAGO7aDNhC9IKSnly6ccyPFKUL+8luHdI4z2jdFp9aBLQWW6AqrAtcQkWcrQVxpgUax9dh+BINoeZc0Nqxg/OEG5YBFtjmB6XSQG4mSmsnhDXupX1FG14IiyOVVKs2+qg0w5R5W3gvBAgIMPdlHKlTBcBmMd4wzvGWXxJQuQEjJjGVIjKVwBF4bLwLYc0mNpuh4/RPO6ptlQuleKkl0mW87hN3249GOPJQwDY/GR9OPFTPJkTQXW/glkjbI6yWTTTI6niRom3TsTlOJbqFlVRXOwkUOJXlpDzbh1F+XeHiKjceKLFzFopGlxApi6jgwG8UwlqGhtYlvXk5z/aBZfrohW38A6GWZLfBvDRg9ur5uEmUAgqCCIjxKWXeKmxmW8vp0T2kRYB/bjDPShz5uPMNVERFRWYWezlJ95EvP8ixEvETLjSIfx3CT2yCChH/4UOTiI1tSKM9gPtqWsIxoa0aPKasQp5EFo6AuObcMpb3mO4l0/Q06MI5FokSha+0IwTJyxUbSqKuU/XCqj1TfgjI+gRytwve7ykyqdhMuF5+b3oM9fiLVtC8PxEk/UXcbOeeeTHa7CPwFn1zu8cYEPCXx/n8bYoTjzSxmy1c2kbVXMmZrkufFRzqqsgWQCorHjns9nwspKycP9GlG3xNRV4dWfhuqKSvYVcuRyOXy+VzYpmZoSlB2B13ekWMtmoapS0twl0JIgQ0olgQtwJIVxuDcP/mmBacMLYfBXCxY2SNYPC6JFGJoHnxsS9Dsw4RIcyoPmQG0GWnTQinBIClYXYV6npFQBm6olDw7A8BREA5JUUSAsgdTA41EK4elpdf0ej6RUEkxOCnRdkcK6DosWSdxuGB6GhgZJdTVccYXD+LjGffcJEglwuSShkDrenj0AAsuStLSo7dHAblE/PYbDVzsEoaDk3DJcnlHvjUJBqYkNQ20v7Je+10NDA9x112389V//M6ZpUrRhx4RScQAsicGqKon7VZ6LC10HoakbcPTfpG2pf/8ekAF/6DhdF5/G7zP27lVdd8uXr/qdHC/iriTiVjVcn97NjoueJ/vLImJAYHpMajMNZNam8FV58Yq533VhJ0q/3oNEzlESvxJIJD/3/oRH3A8QkAG80sc+cxc9RhfX598yEwSXpcapZ0nzUlj/E8rPPolwuRl2jzCe7WJsiY8t59tMeO5lj7mD9+U+QsvxJIgnwFh2nLxVoDmomDjNtjHzRWyfj3QxjeVYmJqJXwa46VftjLZcyfT6RvxOkIXWEjwvaoPThEbMXQ1APS2se85H4sFeZOUiQo3NmJg4iQTlx3+DsXgJxqr/PbGMRPK06zHud9/NlD6Bjk6LNY835t/GfHsh9oF9TFfDYKugwplHceZ5R/eV8A7EeX7xsyyxVrB48XI+/vH/x+c+9zdcccV1rFmzfs55nKlJSg/ci7V7B9Ky0VvbMC/ZRO3ePXgevAc9VyCGRslr4D6nke1XRMg2lAkZUSws+oxD1Nn1LCmfuir4MILBEN/5zu1ceeU5fOQj7+Jb3/rvYwKBf19gLZXYDQJj/8zflwX6iFCWI81q8d4pSCiC0aHC3aQJTkxiLZJIL2gTAhmS5C+TFF934oVzkQLvHZoKqsuDdKtjaePK3gtDYC2W5N/iICNQeIsKljMOoLyBLYETkJTXSYqXOvi+ryPGQdjK/xcbRFrgfhTyb1akr8jO+BnboOUEjvdF43OpaxY5wUslz+2bgh/t1xjLKaJz+LlfMDjYy+e/ftuc7bwGtEfg+TFBheeIInWqAGZ6gAf+6//x/vd/lA9few4Ai2KSH+3XKNuKoA2akqm8wJJHSGGXLpHMeOdqkru71HgvboKDCZjMC+LuRlZ8+OdcVLqfX3z7s7ztbVezbt2ZXPqO/0c2djmZssCWktGcoD4gaQiqwLn2mcBpXahgtYagJDijV5JA0ZI8PCAUmR1QvsPDWfjxfhVUN29uLjcSyJXVnPLoO3p4julI9QPKF3gwo0QeAhUUN5YDU5MsjxQYe/q7PPbDvwW7xK2f+Hs++P4/4ec9Hh7q06j0ylkbiZJ9JOTuMBwJ00XBJc3OSW0QfQaz4XbbxhUhX+NThPVUQZH6Ll0FfW9qlaxZs55bbvkkX/7SP1JvtdIQaCNUG6RxTSOV837/bXl+FzilT7MNGzZw//33c9ZZZ815/YILLuCee+7h6quv/p0O7jT+MCAQnF96HavK6+jTu2dWzRe+pD/YaxVVC6sIN0SY7osTqPQjdI3sVAZP0E3Dqlfo63oKiLXEWH7VUh6/7ylkr0PQHURWglwLUVcVHumht9zHQuYjEBQpUhAFVtlrqV1aS+3SWia6Jul+pofUSApN16heVM388+bhixxp6e9KdPOj/T9jKD0CgG7pLHlmPm3uFqrb1GRDSsl0X5yB7UMEawMM7RzC8JgYLvXRVc6X8cV8lHJlpnqnqV1c84qu2XIsHht4hqeHN5MqpYm4w5zXcBbnNZyFrunkrTxlxyJoBmYJyKRIkNFS1G98E8WO/6A40E8qpuOMFzGGIeoJ4lm9kkx/ml237WVBbQuesMFQ/Tjpugzt5RL1nmqmw9XYpDAPtyZqOjhFXFIjOzZMKW0TaF+JEII6vLyd9dw//QKW6aG3WELogqjjJlIscU5kPe8+97yT+wVn00q5a85VMwufD5lOKSnoSVKTDyV6ubf7QXqSfdjdXTTKKa5Yuo52Isily7H278V65gnE9BQOM8ax2TR6+yJcV1wz51j2QB+F238MxSJa+0JkLou9bzfl7S9gLFuBcLsRLheGyw2miRatQF+8FNe5F6C3vzh6WEGWSlhbN1PetR2KRfQly0jd9CG+1VVBwYKmIAgkiSI80i9oDkLQJTkYh/m1EYTPj6+QQgtXES/Ansk8+yfHeMfZFyHCkZO+jza1SoYzkoMJcKRAApUeybrFFTwGTE9P4vOdgpnZUQgEACTlsvp35z7Bun2ClQlYaUlyXnAZEme1xG6A9B7BcC+UMxreMnzXgZ4syEmIjsBCL7wrA1+5R/BIFkIugV1WBVw9sCqriuBhG7wSzpKwIy3weqA1JrmsCIUJKIdguiR5YlrQYMJ0QLJ5WhXXHo9SAEejknhckMspq4fmZqX27ewEt1twySUOExPwve9p7NsnsCxBMKiKbinBspTiePt2OOMMwaZNDkfPxXbtgrvv1hhNwP4eQcqAZUWBtCWaR9DSIlWwSlJQvNh5yXv9rW99mUAgyNve9h6KtpowbB6Bw1qEp4ckZ9XB25ccUUm8GtDbF6LV1Cgrk9Z5yrqnVMKZnMB16ZUI7x/md+TvE07Xxafx+4y9e3dSV9dAxUm6Wl4J0iLF933fon99Dy2hBbi3+8hN5jhYu5v29e14Qm7KVhmTI4vqGZFmgbX4tyaCAYb0fp53PUWNXUt0xrLs/7N33nF2XfW1/+7Tbr9z7/TeNEXSqEuW5CrJvVdcAGOKqYEQQsgj4RF4QAgJCe8Rkjx6M2BsbGNcce/qvU/T9N7L7afs98cZjbos2eYFEi0+2J659557zj5nzvnttddvrRwnjxatke36Zj4Z/xyN+j7a1Ba6gh3UvW8pc2pqmN7xKk0cYHDBHFIXLiIv6ifHcmjS9vO85ynuTXzqjPcvoPtRFQXTMTFUA1vXiOVH8Te2komGUYX7cHCmp1AMHzXB81AzJ9oMZMgwroxiSA8jyhAxZZpcJ4/cAy2EzSBa6IhpqhKJYA32Yx3Y959KBu/TdvGg7z4UBOVWFZYwadEauc//A/4y9j8x/AHMoTjuDG9mPKUr91M8PobVI3kwn/jEX/H44w/zla/8Dx599KXZGlsmE6R+/kOs/XtR8gpcz/w9u7D27sJIpwnlFTFR7GA5JvrYNHVv9JNeWcZLtZ30i34UBEV2KXcl30846SWz+UWsnduR6RRawyL0Cy528yjOAPX1DXzve7/i3ntv53Of+zj/+3//4PShwP9JkCFI3O3gu19B7ReoXa6fr50jcTxuMLFd4ZKyyrBA6ZCzitv4xyXmYul6BGfP2DgchgXKyAzhGwUkeB8WGOsFdrFEFoIyCUpckFnuYC0DJ+Rg1TIbCJe5QOJk2xjPC4x9bmicuVySukoiLFAHZryCC47yLdYk6iD4fyBQLAWRBjsiyayRWEUSvQWs6JHdFBMgAwKn8PR1XsqCh1sUxlKualYg2fbct8hpuIxDvmWsOYr2FAIuLXdon1JoHBdkGZLxtEvY9j/wJXR/hDV3f3nWE7c24iqDHVxrBtNxiVnTgYEEjKdAV8WMpYIk6oGdQy6JqypwXqEkbkqGEy5ZfNcVV/LBG6/g1Vef51vf+hr/+BfXMW/RKpbf9j/IXXgtVSUaq4skMRN+vl9hY7/rEzycAFO6at7D8T09MZdUtRxBQ/YRYrsq7AbVbRt0M1aOhiJcgnv3sGAyLXB0d3spy91W1MOs/cPKQsm+UcFgwn1t74hgcHiIzObvc2Djd8lMDXLL7e/ny//za+TnFwJwebmkY0rSMiHQFIntCPwanF8kaRp3w/BChqvEnhORrCl9886+w0hbLiG9Y8i1jvCo7rlJ266f8WHF9y0XvJff/vIB/vWXX+erd/8703umGWoZYdGNDRTOLzzj7/tTxVmRwX/5l3/Jhg0bTvra2rVreeKJJ7jvvvvekR07h/9+yJIRFll/urYQY6kJpjPTZHujhIwzTCg6CTwBg8W3LKTl5RZGO8aQjiRaFmXOxdVESyPv3A4fBSklbZOdtE91IYDaimoK31XEG5u3EIiEEDmAKsidKqJgqIKRwm5a1INuAjIWi8ylrMy4q6LjXePs+M0upoem0b06iqbQtqGd+GicFe9ehubRSNsZftP0GP2xAWoi1WiKSro7zcTIJAOVQ+TjksFCCIK5ASb7pqi/oo6WF1tdGw1H4tgOqq6SXRnFSlrY6TOQ/J0Cz7S/xONtz+DX/ISNICPJUX7d9FvGUxOYjsnOob1Yjk1FVhlrKs7nUNE+NhmvMa1M4b8owMWZy6j9fR9m4yCZIUirOoGcAOMDSSZ6JzGTJp6YwTx/LWWHSsgrz6PhylICB/8vnsEJQpU6oyJNrvQi4zHICjMiksxJKoQN/zEK2FqiVAwXM1K7moGCZfS29mEpNvMW1TF/9Vy8gdP7ron8QjAMZCyGCB65Tp3xMbRFS1z27uhrA4mJO6kbTozy0/2/ZjAxRJE/H2ssRlOWzZinlY9n5lOAH21eA4yNIKVETk6A14O2cjW+j38aNffYCam1dzdyfAx1bgNyatIt1KcmkPE49sH9KDV1GJdfhefq68+I+JKWRfqBX2Cuf82tqlQNa+8uRl/bSXLZp6goi84WP1Gv2+K0dVCwJM/17xI5eQzNXUXZ1t+DBL8nzJ7W3UjgwiuvRzmFKvgwcnzwySUOe0YEA3HpJg3nSoYOuROOsbFRSkvfGhk8f76krEzQ1uaSqmWNggunJMO6YNojEBkITkqydgvUZhhrEphpSSQLfp+BDgdKcLn5gCNomZT83zh025ClCALAuAYeDUKWpA/BpA19DswDlgF9lqv4TacFIUOyygvpAfi6FzodiKUhJyVQHNCDrnBVSkFursTrlfT3w9q1krIyGB2FhQvh4osdFi2S3HefoL1dUFQEvb2S3FyYmnJJ4Lw8mJ52vYnf/W6bJUuOjEssBg89pDA5CevWSVpbBb09gt8PSG5LwpJiSXZSIloEZr0kfeHpC8nOzjbuu+/7fOYzXyAQCLJlQLC53/VlC+gzfsczbWYLc93C/Q8FEQzhufVOUg/8ArtxPwiBEAra/IUYV1z9B/veczhznKuLz+GPGfv27aah4Z1RBR+N/fpuutUOaqy5aHM0mGMTxKBFa6Mwk0eFVU2b1ky5XYUhPQwrgyAkF5hv364CoF/pIyamKZHHPk9z7Ty6tQ5+5v8uO40tmJiAwOP1cOkVV1N66RU8HjhInTV/1htYQSHPKaRZO0hCxAmcYcJoXbSG8lApbVNdVIXLMRSdxoYi6tsOUTPqQGACJ5XEmRhDP/9ilOMCPyWSjcZrPOF9mH3abvrVHgw8lFrlFDml3Cwslgj7hIm6UAR2TxepB36JjE2jVlWjLTvvTeuTtwrHchhsGmKoeQgrY5NTmc3m89aTDqaos+YBYEiDGqueZu0g+/RdrDpvFd7mV/BOmDhhB8URhLomSeX4aF+ocb59xJJD0zS+8IW/573vvYGXX36WSy91n23Wvj3YTQdRa+oQhitQEJEo5gvPgIDwwiUEpEXGTuMEoS+2h9C4yTWpm/BIL1kySr01H1/GIHX/jzE3rXe3o2mkmxux9+3G+9FPnTEhfMUV1/Gv//oTPvWp9xMKhfnqV7/1R+nZb9dA7PMOmQvA97CCsR0QbpeXXQZmA6htEmMnaC1uhkTqKofM+W748PFRPfp2ged5gTogQIPMQofMComxR3GJ4Ij7PicHsCRalyB5t3PCdhBgzQNrniRxvGo3w4yf8FH+ABKUBIgU6HsVrCUSOwLqGPgeEWRWg+wSaK0SOxuUpKtWTq91sMtOP0btU9AXcwlZRUDXnlcYOrSdy7/wNAfHFCbSNpGjNDE1EfjYIof1vYLdw4KxlCDdt5u+jb9k0fv/nV+3hxi0JHfUSVRFUBkCny6ZSAuGky5hajqSmCnw6RJHuuTwlRWSshC82MXs3EQAQR1SxoxdgnTnwmvXXsmaNYdJ4b/nl1++jby8Am699T0suuse5tU3sCRPcnAUFCGoDktG04KDY4LJtDusXhXyfC5xfPSlKwR4Vdfm4WSK6rWlkr0jks397riBxJKQ74drq5xZNfHyAtfC4qEWha179hF//duYu+5HURTmrns/0TWfZt0FteQfZUeR74c/W+ywc0jQMeX6AusqvNqtoCluvd0fg/Iw3FjtUHIW9EppSDKdURhJuuppgTueAwl3uwNxKNEzdG/p5e41n+DrD3+OHb0bWLv0asa7Jzi0voO8ujxU7b92F95ZkcFr1qxhzZpTP8jXrVvHunXr3vZOncM5/CkhYSZ4/NCzbB/aTdJKETKCXFi0kquq1s36w54tQvlBlt6xhOREEsdy8Ef9KNofZhXadmx+d+hpXu3ZSMJ0PYlDRpAVBUswijyMqKPkqS456ziS6MEyLtQvJjccxBRp5loLWGGuni2gm19upX//AMphcyQh8AQ9DDYOMdw6QlFDIYcm2uma7qUiXI6muDdZj2agaJKh+DAZx8SYGTvpSIQiyK3OofqiKrq396B5NXSfTqIgyQG1GVu3KfeXoyYcJjKuN2u2J49cbyGKOP1NfDI9xRt9m8kywhQG3Ba9LE+Y7qlefrL/fkJGEdneIkJ6hj3D+3kt61nU0gSllFFolRBTpnjsih6uWnEFS39dw8C2Psan/4OAN8Rk3ySqpuIJClRdJac8B61fR2tUyLmwHPvyq8l75AHO75G8kDfJdGYUv8dDrChIyAhyaU4ZSmIbUsojagnHQXUcysvrqLn0fKSUM8N8ZkWpOqcWtW6e2zKZk4uIZsPIMCIQwFh72ex2bGw2Ga+z3niFMWWEIrsUfTiLvng/86J1CASm6iUYt4lNDtMzvJ0cOxcRiUJuHv4PfBSttt4lsHLzTrp/Mh4DRUEIgdXWijPYj0RCMoEzPgrjY5jrX8O4eB14fTid7TijI4hQGLW6BnFcq57dfBBz83pEUTHKjJ+wNE2M3QeoDq7HKT9WpedR3ZCCiFeiCEnKgvaLbwMgv2kz/sEuWsa6Kc/Np/p9Hz6j8fXrsLro2GLKyj5MBg+7v0iBvl+gDrhpyuYCiZN/+u0GAnDPPQ4/+5nCs88q3BwHWxPoBRJTAaNPEE8KonHAC5oNCUWwKQ0705ALxHGF3+kMFEhBnwO3+kFokp05sD8KczoEq8YFG2wYA7JVyQUIQg6ETJjWXSI3GhZMxyUvmIIt9kwLl4RJAap0MxZB4vVCd7frHZxOw9iY5EMfcmhocMniw57BBw4I8vLkjJWEgmlKQiGXfC4vl7NjsGjRsePS3CwYGIA5c1wriEWLJHV1kv5eeC0Di1ZLrLTAqnfInHdk4nIq/MM/fJGcnDw++tHPAMxYQ4hZIhhctYYjBU3jkvP+wKIBbcly/IXFWPv3IOMxlIIitIWLEX9kvoX/XXGuLj6HP2bs37+bu+56/zu+3QkxjoRZj9jDCDhBxtVRPpD4OA/67qNDO4SJSdTJ5sbkHbOigbcDKSW+jI7mV8mQxnOU3UJSJJlmiq3GRkrsstkQ6nExxkveZ7gsdQ0CBQfnmKA4BxsFBUWe+aTfq3l4z7x3cf/BR+ia7sGWNoGSINXvfQ+Fh0zo60X4/HjWXIZx2ZUn2G9t0TfwPf+3OaQ1M66MkBFpJJDUE0Qy2axfMETR6zZlqRrETLiaTCWxB/rdnIWWRoRmYG16A3PLJnz3fhwl/611x50K0pEcfK6Rzi1dAAhFoX/fAGNt0wTfGz7GT1ZFRUEwpUygrbyOaPdN5G/9MZnedjzSRyLXx8Z3B9DzS1kVv+iY71m79kpWrryQf/zHL7F27ZUoioIzNOj6yhpHWDkhBGg6MjblfqfQEF7Jo+ftZEf2QTLFg2i+Vkrtct6T+BB+GcBq3IW1dTNqSfmsAEJaFlbzQcxNb+C59qYzHo9bbrmLWGyaz3/+k2RlRfjc5770Vof2nUUCjK0CfZ9bZ5sLJJkLJJkLHYL/pGDsFphzJTIbSIHWJpA62JUSVIGxQSC9kLpJoowAmhsQp+0H/y8UyEicPInIgOdVBa3JDa2Tx9U/MuTaTIipE0nl08KAzCKJ9zWBMuraTQjTJe5QwSkFZ2atwy4BtROUHkjc42C8KtD6BU5YkrlSkl4nTzSUPQ6W4/LOqnDnVBvv/1/kVS2mbMkVJC2wTyIsrgxDZdi1MhhOChqf+huixbWsu/FDTFmwvk9hZaFNUAe/IakIgS0lr/cq5PklGdsNbpuTJUnbYDqCRXmSbB9EvYKBOBQf9n6WMJQQXFTizFoewBFSeO3aK9m/fzcPPngfDz30C77//f/DgkUr8K14P0XLrqemwmXDLVuyYQCGE24AnqoLOqdddXJ52CWdwR3nlC0oDpxcUV0Rhk8vcXguKnijV5CwBDURyU1zJCsKDiuPJU1NB3js/idY//zjxDu3oWSVkH31l1l45UdYUhFlKOGqoG+Yc6zNWsQD68rcGjthwT9tVUjZkguK3DmCI11v5S0DCsvynRPC406F4gAzoX+SybSr3E5ZbohfxICumCCcmSY5mWTJwvNYfXAtv3r+B5zfsJZAtp/YSIzkeJJg3lsX+P0p4G2b3lx33XX86Ec/oqio6J3Yn3M4hz8pSCl5pOVJXunZQJ4vh0J/PpPpKR5r+z2KonBt1enDuMZS4/TFBvBqHirD5WjKkT9JIQT+6MnVkNKRJCaSKIrAm+U9KdGWtJI0j7eRttOUBIsoDhSe9H17Rg7wQtdrZHsilAddj+bh5Aib+7ezMHceu4b3MzY+ga5opOw09ZFaPhi+m7xk7gnbciyHjs2dOJaNLzuAoihIW5KaTGEmM8TH4oDrzWs7NvpRx0uu6wmljAmsMgtD0XFsh+mRGCULivBHfNRdWktiPEkqnmJPzkF2sY+EncKf72N/2wHqJktYUTgPIQQ9iXaKfOXMiyyZJYQtLEaUIQzpIXumtXA4OcJUeoqyUOnMOXUgkyFpqhyaWEGuby1DiSBBPU5Jzn46s1+maDqfgrB7z/M5PgSCLZHtNCw/n0RTjEQqTtSfi5W2MAIG5pSFb8Yiwx/xER9PkBhPEFp7GUokyrWbXiNn4gBbcy2mCrOZV7GYi0tXUzetk9zejnOoBaWoGBwHu68HtbQcbcmy2evkzeAMDyGnJiGchbVtM3ZXBzIRx+nrRfj9qIuW4b31DrSGI0zbc54necz3IKrUCMoQB/W9dOX1EiwsRsm4gVZqQTFzHn+VyGiMsG1gO0PIdBqlvBK1fh7KcUrg46EWFYOU2PEYdksTcnzcfVpLQCg4vd0wOYF1YB92axPW7p3IRBxheFBr6/G+5/2I3DysXTuwdmzB2rYFu7sDLf9IhSp0HU84SGn3bpqs62fTgB0Jkxm32KqPuuECB8YEpQE/iXXvY+v8q5FT4+z8P9dw5TU3IXy+kx7DmaCgoBjDMDh0qIV1S6/E/1MF/QBgu+fOkytJ3iUxl55eZVpZCXff7XCoVVDb5aYC61kw7oAvAcUDrgUGOgRUweua5EUpGJKwREDYtYoj3wIETAMTDixIC8rHwPFKJgOSVQkoDQkeyYCiC9QsyPRDcQoGUuBkIGJByhHs9UtCuAEcqOA1IJ6AtOk6Q1rWkf2PRl3B9r//i8IlF0ryKqGuTlJYeOx7cnIkAwOCcNj1uJuacovVyy6THH8azJkJw2H7XJGEYI8g2gWdCsSrJfrFzmyr4umwbdsmnnjiYb797R8d4+18srPy/1MPpBQWYRSeq7H+VHCuLj6HPwaMj48xONjPvHlv3S/1VMhx8lAQmGTQj7q5TitTNGQWUWFX89nYF2lXW0mLFCV2OVH59pSr0rIwN7yGteENyiZHuWHOBFuu2IloWIaOwbSYZFIZJ5wKEJvsxts6iSkESn4B0ZIyBnx9pEWKAruQbrWTSrvaXdQmw4gyxJXp6/Fxds/5ynAZf7Xiz2gZbyNpJSkM5FMadOsaGZtGeLyIk9huOTi86nmeXrULBxsVjbDjByGJizhtaguR5Ys4sKKdgk1NaIa7+CenJpGmiVpQjFpaNjsudtMBMi+/gPfO976tMT4eY51jdO/owZ/txxtyCWnbtAk0ZiG3K8iLj/g/uypsyHZyEYZB5M6PUrlqPpsHH6DL18vg/CDZ2fV8IHETlfaxKmkhBH/7t1/jllsu5emnH+X6629DBINI6SAd5xgiXXg8kNKQZgahG7xe38qGOS0UdOpkFzTgWCW0a63c7/8pn5v+EmpnB1jmMZ1wQtMQwRD2gf1wFmQwwPve9xGmpib5+te/gM/n55Of/NxbGdp3DkkI/ExB3wFy5k9R3ynQDwjiH3BI3usgfqKgdgkYkyg9AiUGmVVu3gJIxDj4HlMwdrihbihg1bqWESIB1owrmwScgETfA0rc9ea1y6VL1IoZm4awdK0kzhLpWyRa32G/X7D9oIxLHMv9jqPhhEGdEFg1DuZSiUjMHPsZ1HkA5SHI9UkGEjC24af0HljPu772PMNJhYYcSfQUDZZyxvog3fwcnTuf44a/fRhV04morjdu55RgdZGkMiw4NCHI9UlsByxc8nleRFIZdsnotklXHZzjhWsqJY+2KjSNg6FIkragLCy5okKekvhsaFjMV7/6Lb74xW/w4otP84P7fs6Wn30G+ZM/J1pSR9nCdZQtupR49jqSag7r8iDscS3yXuyC9X2CFfmSqQyMpKAkIE/b6VYehg8vlNy74Mg+xWLTbN60gxde+D3PPvs47e2taN4guYuuIbj2ryg/7xYsodNnQtGMd7DpzJDtp1h365iEgbigMnTkexQBBX5J64RgLO2O2ZlAETA3x91O2hakbfc4yoKS4aRrEaHqKoqm4FgO77vq4/zFv97NExt+w3VL70BRFRT9v7YqGN4BMvi1114jmUy+E/tyDudwIhwLbeIgSmIAqQWwshcijT+e9PSBxBA7h/ZS5M8n6o0A4NO8ODhs6NvC2tIL8OsnErq2Y/N0+wu83ruR8fQUhqJTlVXOnfU3UxY6fWjeSNsoh95oY7JvCkUR5FTlULNmDqH8I4VO01grDzQ9Sl9sAEc6BI0AFxSfx801156gVt4zcgDHscn2Hnl65/vzODjWTK4vh48vfj+7hw8Qy8SYE6liRcESot7jHOZnMDkwxYQYZ6Cmh1RBEsPykD9RSMTMIT2RRs4sOpaGioh4shhOjlLgdwlDqcHA3BEqD5QS74qTVBJICZHiLGrW1iCEIG9OLkvftZiXNr7Gjom9hNQAdfk1qPnQFmtl91ArddEaKrNKyNhp+pNd5HoLKfCVsEPfwnOeJ+hX+9DRacgs5ob0bQT0AB7NQ9yKow7FcToOYcUS7AtdT8powPCreNUUU5kQo/ESTM1HOmbBUaveWU6EndYOfmE9yFDFJCP2GH4zCytjYaVt/Nl+wgXudWtlLFRdRfPqCCHQly5HX7qcK4DLpcSRDuqMWpps8N5zL5knH8Pu60HGY0jLwhkbI/WzH6KvugBt1QUnKGQPQ8amSf/uYcxd21217eQkcmIctW4u2pLlmLt3Yvd24+zYijAMnEQCY82lTKqTvOJ5joATmg1xzHMKGFCH6S1uxepYhYZOgWVQNm7S44GQrUHK9R4WqoLs74VTkMFSSuzGA1hth5DT05hP/g6GBtxK0xKgagi/HzweN0Dk5edd39SSMpSyCkgmsfbtJvXgL1EqqjCfeRIcB2d0GKe/D3PrJvTl56GEI+750WzywjrPTglCuruyP54WlIUkFxS7K9Tvm+/wcItC85ggk4TsnDwqjQ4G+zu57rpbT3ocZwpd16mtnUdj4z48Lwj0PQKrWoLXbYlT213/NWuOfFM1RV4elJRKBqYFJeMQl+Ao0BKWRMYEQQ84NRL/kKBnAnqnIaPAJqBIQoECUQGNuASq1wNdAmoykgV98G8CNuqQH5EEpRvkILLgjX5BvQPlNphSErcFzzvQ7QiKs2Ew7qoMdClJaYK8YhiflsRiYBiCcBiWlktSLYKdA4LBXZLCAvDXwJU3OjQ0SF5+WSEalSxc6BZvXV1uO5rXK7joIofLLz+xUC0rk4RCCqOjkryA29IoRgTDKcmF2ZD7gCDTA8m75SmLT3Cvya9+9fM0NCzmttuOTKjnZcP6Pret7rCKIpZxSfe5f5iu3HP4E8e5uvgc3gqmMtMMJYbxaX6KAwVvuwV9amoCgGj0nQ/BCcgAadI843mCIqeEEruMpEgQlEEuyLhKeQ2NWnvuO/ad6ScedZ/1Hg+q38/yHQEizd28+LGdtC0P4ZM+Lhm/kImdL7E32IEcdWtaa7AfZ2QYcZ4PHYNbU+/h176f0ajtA1ybiHnWQq5IX3fW+zQlJtnl28ZwYJCQDFNmFiAc4XZEhU9eJwMkRJwutR1TZPBIL1NMoqAgpEBFZVqZJOWXbPxYORfV3oBnz4wyV1WRWzeilJTObktoGkpOHtaeHchb70Doby9I+WiM90xipe1ZIhhc8iTfm8/IgTwOrWumwC7CwmJA7aXGqmehudTdL0WhuPoSbq6+mFFlGAuLvFgB6ikexKtWXcTatVfyzW/+L66++ia0BYtRCopx2lpRyitB05BDA4jcXJTqOdgtzdiRIJvX7iUwlCbir0YtLEZFp8qaQ7t2iGb9APMNw7UtO6rDDnBXkr2nzsc4HT75yc+RSMT5+te/wMjIMH/3d9/4T/MQNnYL9J1gVcDsWkYS9J1gLBVkVktin3IwdgqUXvC8Bk7hYSLYhRCgtoCYBnOJG96mb3c9h835x9ZdWq9Aa3fnbFofqO0KdrnELpQocUheKZFvoXEps1KiDILnNYGYki4hnQvqCHCcC6AyDU6edH2NFThDZ5dZZHngqkrJr7YO8/JPPk/FJe8nXnYpeV7J1ZWnDyjTFcmuX3+B4rnnU7PaXUiQM//UFPBq8L55Dr9uUjg0Icg4EtOGhlyXhAYYSkC2V85aHqwrkxQGbHYOCcbTgqqww3mFkvwziIUwDINrrrmZqlU3881XJ5DtL9O/7yU6d7/Enme+D0KgFCzk0dJ5lFbWUlxeR1VWLZ16Pa+aWSRMgU+TZBmSjinhhsoddfxSSmKxacbGRmhvP8TevTvZt28X+/fvoq2tFYDc3HyuuuoGrvr4/6E9+zIqogbr+wQpCQENYqZrnefTBKuK5Kwo52SQh/9x3Dk4nCHyJrmAJ3xmdaGke8ol+b0zt57uGGR7Xb/oLD1MoDCLvkPjBEqKuWrVrfz21V+wNPcCGi5owJd1hszznzD+OOMwz+EcAJGZxn/wR+gjO1wpGmAHy0nMuxc78s4VmG8H46kJ4maCQv+xPd5ZRpjR1BgT6amTksEb+7fxZPtzZBlh6iLVpO00TWOt/OLAb/jL5Z/Ap5385jPZP8We3+0lOZkkmBvEsR169/QSH0tw3t3L8QQMpjMx7m98hMHEMFVZ5WhCYyw1wfOdr1Lgz+OS0guO2WbSSqKKE28FilBIOxkW5s5nYe78MxqPRmcfQ3l9jIdH0S0D05OhtXCCgngJxckyouURAHJ9Oawtu5An254lOZHE7/gY1ycJl4W4ZNWFRIfDpKfT+LP9FMzNP6YQza3OwUzbRDsjzM2pRQjBcKqfsCfIYHyCtoluKrNKMFQPcWuKsdQQo6FRfuH/ASlS5DuFmCLD654XGVNH+DP7czTkzGX9weeQrQP4LegN1jKh1ODPdBKYDKMWFOFRMwyls0glcyEcO/a4E010TfcTmhgjtyofaTgMeIeZyJ6mMJNHfm0uulfHTFnERuKULy89JkzvMIQQs6Ejh6EtWIxaPx9z/WukH7ofmU4hPAZ2Wyt280H0oUG8t9x+wraklKQeeRDztZdRCosgmo3T3IScnEAUFkNPN4yPoURzkGYGp7+XzEO/QvF66b84i3ExRtFoKZPJKVRNwR/1U6PVMBzcTKPZSI2oI9TZT8LvQcnLpUQpQTNCKHn52D1dWAf3oy1cctLrJPPCM2SeeBSZSiITcZiaPGyMBV4/aCrO+BjKTGCb09GGkl+Acnhy5fejllVg7dkJ+3ajRLJR8gsQefk4E+PIkSHs9jaUxcuQ8TjCTLP8imUkKx22DAoyNqwrc1hbKmfbsgr88GeLHHrjbhtRoR++9Y3fkJubzwUXvH2fw/nzF3Jw/150Q+BEJbOdrQLsctAOuf5t5vLTVzpZWbB6tWRDJ5SOC/KGYEwFfwJ8foksgMxqCByQ3LIftsch4UCfhAMKxH2QSEvSiuBCHyg26D4w/ZA/LkiqkscjUDYFo0kYTIMzJvDa8IIXFFvi2EAE2sfAn4ZAyi2AAwqs8glaCiTv+7LDE08Idu8WlJRICg1JZpdC06jEZ0COV7AwBQMdkmeeUnj33Tbd3ZKWFoGmSYJBWLhQcsklDldeKSk4RfdrURGsWePwzDOC6QOCYJ9gwicpyIPLl0kcDTybFcyVNjP2hifF008/yrZtG3nwwWdQ1SN/g4vz3LC4Tf0CR7rVqSLcRYTFeX84v+BzOIdz+O8B27F5vutVXu3ZwERqEo9qMDe7lnfV3UCu760TuYmE24nlf4ctZXboW/iV/8fYwsKLl0NaM91qB6syF/Le5IeZZ73zSmRnaBBz/WuIaDZKnltvh/MKaDgUoPjxMAN1t1JAKSUb+nhp91PsutVPxg7hsXSEZRIb70SZKGaOt45l5koK7WL26buIixiFTjGLzGVn7BUMLgn8gucpHvDd56qRnQghGeYlzzO8N3EvC62lp/28V/oIyRCWMAk4XjQ0LCxUVAQCBZUpMcEcXx1Zl9yA5xKXtDQ3voG5Y+uRemkW0lXPvsMetu7mTnzOhewsljjL8VqCXrUbDY2VmQu5MXX7CeMoEOS+mQ/WDP7mb77K1Vev5re/vZ877rgH73veT/qRX2N3tiEciYhG8bzr3WgrVmNtfI3pfRtJZekEcurR8hfBTDCyho7EISkSqPXzEOEs5GA/FBS5QazTU2BZ6EtXnPWYyGQCc8MbfNJRCF50GX//g28z2NXOt7/7SwzjDKWpbwMiBvpugdIPMgBaEy5xdvSUYua/1SZgNcgcSM8spqtDLsl7NNQegZIBs8i1egCwghKtVaD2gjPjwauMg9YkkIoks0KiJARqu0Q75H4ueZuc/Z6zhgqpmyWZ1TZqt0B6JFbVjOp550xQnR+UUddHOH2x2wX3VrGmRPKjr/wVhqZyzUf/ifoSh/OLJOWnEWQIAcGel5jo2MnFX3nOvZYk9B5FLgKUhuAvlzm0T8LOYcFLXcqM9QPELPd0XVMlyfIc2W5RAIZnAq2zPG5NfTaozoI5xdl0Bt/FRRfchn9I0N7dzdjBl9F73iAx2sL+5lfZPXUkvFH4oqi+CKo3zJAnwHpDpyqq4hEW4+OjjI2NMj4+ink4uRoIBkM0NCzm0kuv4dOfXsyCBUuor29AVVV+3Sjo7lYIGpKqLDcULp2GpCXpj8P5xZLLyl2bB8dy1WHHW2BWhaEgIOmNQUUILAk907BvVFAQcEPuVhcdGbtTQUrJUNMw2bv7WNqaptsTpr+slFQ0QrYHbq11yPXBjiGVVwrnY7buRd83SXn2TQie4sW2x7j5Czf9UfqCv9N422RwRUUF+ju4CnkO53AYns6n0Ac2YIerkHrQVQlPtuBr+jmx5V8C7a23bb9TyPJk4dd9TJsxIp4jKoDpTIyA7ifLc+JTRUrJ+r4tGIo+q4r1aT6qsyrpmOqicayFpfknL6Z79/SSGE+SU5U9e4PyBD2MdY0z1DRE2bJSDow20RcfoCaretaPN8cXZdqcZlP/di4uOf+Ym1tdZA7bBnZhOuasajhtp5FSUhl+Exf+o+DgsLHiZcyCDIXDJSQ8MZykQ8qTImHEiC7JIloWmX3/utwLsTeYtO3tIJ3KUBEpYeGFDSytXohSe/oV9pSdRtf0E27SCoKMbR71G/f1N4xXiIsYddYMqS1db7tm7QCNxj5unXMt8Zee4aCSpj/Hz7SIkiOy8MhBxpNjRFNZqF4falKidq/AWP0Gw8oQWU6EccZotprIG61gvqcBPOD3+fFG/IxdO8mynkXE++IkxpMIRVBQn0/dpXVnPK4AaJrrGWqZaHMbZo/bGRnGfOMV9NUXupYLR5+PgX6s3TtQiopRotnIVNL15w0GcdpawHEQkShC113VTCQKqRTm66+gLLuF2Eiczt5OjIQXIdzrTJsvqM+qodhTQu94P6WJcUp0HxXlK4gevSAiBFgWMh7D3LQea8dWpGWjLV6CWl1L5tmnEB4vakUV5tgooqAIOdjnflRT3alHIon0+lBKykBVT/RI9QeQ42Pua7Xu4pDIiqDW1GPv243d0uS2Ahoe9FUX4D3/fK71Sa6ucq0H1JNcYkJA6WHPLsfh8ccf4vrrb0M7hfL6TDCShNd7BN3+Rew5+CiPr3S41FY45mhUZlTRZ7bNa691vXVf/D3UtQkqU5BXLxGLwG52i2VzrmSuIliyERQVevIlO4fBsAQLpKBIwDIdulOQSkNmWjDmSBwvTGmCyahkaQ5sOQR7YhJbQlh106RTFhTp4HhgeoYMTjtgemGDR1JcLZk/XzI5CSMjgvp6iWe3YFsMHB1SJvRnJLs0Qc4YTHthdFTwyU867Nwp6OqCcFiyeLGkuvr0YyEE3HCDpKgIdn5TMBmUrKqAS8ollVkz09geUDsE1ryTT1ISiThf+9rfctll13DxxZce85qhwnvnShblQuOY+/m52bAoT6L/8YWJn8MfAc7VxedwNtjQv5XftT5NUA9QHiohZaXZOrCTlJ3mU0vuPcY+7EwgpeTgWDNPND4NwP6pZmoz8wm/A111KVI85X2EJAkWmysQCOIixiG1mTynkMXm8rf9HSeD09cDkxOIendFT6aSyGQSNRAmt8ehYngRSnYOydZXWNaUz8E+jT1lfRiWiiMkdjzJ6oMFLFiwBIBip5TidOlpvvHkkFIy1rKJl1v/D0/Xbqe/LokaijBiZMiyIkwqEzzqe4Ca6bmntZzQ0Lg8fR2b9DeYUicJOEFGlGFsTDQMNKlhSC9r01fh4QjrodbUoUSzcQb6UItnrM0sE2dsFOOaG07ZJfZWkV2Rje4zSIwnZm3rrLSFlbZYXLeE62LXMaQMoKOT6+QjR0Ywu7YhdM0NfjtN8K+U0rUvA0Q4CyEEixYt4/LLr+WnP/0ud9xxD9r8BahVX8RuawHTRCmvnA18U6+9CePaG5kT+Ba79W3kWUfGaVJM4JN+iuwSlPJKjOtuIvPUYziN+9362TDQLrwE7bzzz2o8pGmS+uVPMbdsRPj83F1aRc7iVfz1s08wetc1/OjnjxIKnY1Z7tlBGQX/jxW0ZgABDihj4FpdH1ffSAHaiTWPuUSiNSqQOiJKUAbc2s45eu1JAzsHRFqg9EucPFB6BWJcYheByIBIg10KTlySWQDpq8+OCJYSeuOuh23U4wb1OoXgHGVXkHiPg8+joB8QiFFwsiTJmyWZi079XWIctFZ3fOxqd9+PxzPPPMZLT/2ab3/7R9xxaTZnKjnd8/T/Ja+iAVm5jsYxgcTd95trHPKOutw1BWqjUBuVLMixeaNPoWvKtSk4v0iyvODI97VNws8PKPROw+H0vIoswQfnO5Se4W3bUOGOeodfHlDYMQQdU+DLKSN71T1E19xDxAMjSUG5MUlnRzM9nS2EEl3o5hR2appkMk7GNkl7TCqyFebNW0h2dg7Z2blEo9lkZ+dSWlpORUX1KVXwcyLwcrckabnWe0Fd0jktsGxYWyb5UINDtp3k4LOdDDYOAZBfn0fFygoC2e7g+XW4vlryQJPCgTGXCB5JuQrjgCZ5sFlh36jkIwsdwqdZe+nY0kXT803YpkOtRyN7aIzM9CB5Vy1i2aJsioPQPgm/OqiQ9GVRcs1KRO8wYxNp5lt/xssv/xtpkSLAO7uY+seIt/3U2Ldv3zuxH+dwDsfCSqEPbcLxZrtEMICiYYXnoE0fQptoxMo9/cr7/w8UBwpYlNfAG72bXDsGPchkepLx9AQ3zbmGwElUwaZjMZGaIKAde4MxVB0Hh+lM7ITPHMZU3zS671gSVJlhtRLjbltqwnL/fZgIPgyf5mMyPYXr5Hnk88sLFrN9cDcHx1sI6yEkDjEzwZK8BhbnNZzROCQnkgwk++ms7iD74gI8T3rQ0zq2bpM1KYjlTZF1fXA2kVM6ksZnmhGNsKiwAd2rkZpKMb1xiu7sXipWnJ6ErolUsbFvK6Ztoqs6PtXPaHoEW1oUB11S0pxRk4eMbBoTB9FTfqTvSJuYBy82DqPKMIvNWj7QE6ErmMW0x8uwKOJxPUhAFNGa6WYiOQEyRcYpYtnoHVxDJdvEBrq1Dpy0JLu7lGVDFx7T1mIoOs3eFnLv8lIwlkfpRAW1wbnkVuacXTKplcQ49Hs80w8iaiSmniFpV2PLECInF6f5IE5v94lk8NgoztAAwh9Ejo1AVgS8XmQmDek0QlEQuo7MZEDTEF4fGB7skSHkgx3M1RQOLe1Hz6lBs3Sm4iNM9nRy7dAi3rUnl/76xXjWLiN75Al05UjxK5MJV9lSXknyvh9j7diC8AVAUUg/0uT6wE1OoixY7L7fshAeA7KiyOlJZCTqJpxJifB60M6/CBJxnNER1OCRqkiOjUIwhJAOOI5LCguBOqfW/TkRx7j+ZtTqGtR5C2Z9+xTBGRm+bt26kf7+Hm6++c4zP1fHYSwFP9ir0Doh8BQtwErH+aG3gzZrDh+TEu9hP6wRkGGBXX7y8IbjYRguATq9VjI5AREfBCOQEsCTAs9rCtohSbEHVp8necKShHKheIdgug8WCsliBYZSgqgErwI5YcmrBiwPgF+VxPoE9jhcEpFscMARUGcI9LBkzwSMpSSaLlAl9GkwZUomBIQ08E7Dd7+rcM01DoWFrtq3fBCGLElXTLhhcylB2wQEhCBkSaamIBKBlSslliXYtk2wb59gwQLJxRdLco+zKE8kYN8+weioq5ZesEBy2YUS9RDYVUe98XC9fZpq5x//8UsMDfVz//1Pnny8VTclefk7m81zDv9Fca4uPoczhe3YvN6zCUPRKQq4NxhDNahSy2kea6Vloo152We+eCyl5JmOl3i6/Xl6Ot0W3mf6XmZg9wQfXnj3MXZgbwU9aif9ah/FVulsHRmQQartWvrVHgaUPkqd8rf1HSeF1we6hkwksHu6cLo7kZm065+bm4fMpOlS25nwHiKcGeP2DQtZUF7CweJ+FEdh7qYMK4suxVjw1pWbUkoyz/+eoWe+g0drxiqPM3e7QPdZdK4I0OE7xIrManrVbtq1FuZbi067vUvSl3OX2swv/D9kQOnDFBkkDgIFvwzwrtR7uTCz9pjPKHn56NfcSOaxh7Eb94OqIi0LrX4+xror3vKxnQqR0iyqVlfQtqGD+PgIQgiEEBTOL6BkUTEaGsVOKdJxyDz3FOZLzyEnxkFRUIpK8Nz+brT5J4pb7O5OMs88id3cCEKg1s/DuOo61NJy3v3uD3DvvXfQ2LiPuXMXIHy+Y/IsjoZAsC59JW1aCy3aQbKdXFIiybSY5pL05VTY1QghMC69Eq1uLnbTQfeaqahyFcPqWdTigH1wH9aOrahllYiAO4e7rrCYqMfDp3Zv5dZbL+O++35HUdHp7f7eKjzPC7SDAqtOgu7ajOm7QG0X2MPMkp5iEqQqsU4yhctcINGaJPpuAOHWSIpL/B4TsOuAjII5zw2V09oEyjhILwgH9IOutzCOSxg7h86OCI5l4DfNgj3DCnEL/BosypHcWeIQDDOr+pU5kPiIg9IPIg5OPshTO7BgbBR4HxNuEB4gIwqpKx3SVxwJlmts3MenP/1Brr32Zm6//X1nvM89PV28+PwTfPXvv8PSxdAbc/CoMD/niOXDyTA/B+bnnLy+txx4pEWhLyaojUpUIbEcaJkQPNqq8KklZx6YVhuBzy53+NE+hYwD9VGIZSTNM167tpR0ZsJYhSsIZp9HTfTIOZNA15RgQanD/1l7ZnOR47EoV7I0X7B90K2bBW443CUlkg8vdNDSabY/spvR9jF8YXcl4tAb7Yx3T7D8rqWzXcArCyV5Ppvftgh6phUW50JVliSgQ8aWHBgVbBsQXFp+8msuNZ2ifX07qq4SKYkAkFUoGescI+9QG0Wro4Bg+6BgIg1zoyCEAXUlFEgYyP8r7Je+w69+9SP+/M8//5bG4k8JZ0UGj4yMEI/Hqag4YjSzf/9+/uVf/oV4PM7NN9/Me97znnd8J8/hvx+Ek0Gx0zjqcXYJiu4qGq3Uf86OHQchBO+qvQFVKOwa2s94aoKgEeDaqsu5svLkCeK6olEcLGT/aBN5/iPLsAkzgS40cn2nNqL0Z/sZbR875nfScQsCI+A+OQsD+eiKTtxMzJLRUkrG0xOcX7QCRRy7opflCXPvwrvZ0LeF3cP7EChcW7CIC4pX4nsT9bWZMml+qZX+/f3E7Bgjd46RKEtSd4cfdbeBNqJiFqeZWDlIXlHOrPJxsm+SoZZhwoUhPAGXpNO9OpP9U3Rt7aJ0SfFpCdNl+YvYNrCL/WNNhPUgjnSYSCYoDuVSEAgxkR5BAomM5P79j7Ev3cukOsFwJEl9fTlZWQFMTAQQcrIQXh+KL0BkMEMgXUChkWRPzhiHKGRRMkUmUsO4Nwdbhrh3rs4l6blcYV7DmBjFSkh+2H4/tnZklV0KGBKDDHszDOXsI1Tpp9Au4ob07VyfOgv/WcfC3/gTjJ6XMY040gZd3Y8y2cnkUBW2qUEiDsep0KRtY23egN1+CCQI3QBdR4RCOMmk29qkCJzpaUglUYpLEZEodnMj9vgY2s4f8C6m6H15iv0rd7LpmhCqOcDiZ1Os+9UGnPGNFAYC6JesQ6mfh9Xc6CY+S4m0TPTlK0EI7F3bUSvnuEQzrqrC3LweHGfWNU7Jy3cnBH6/eywjw2BZIB2kZaMoCtoFF5N+9DfYHW2ISBQZiyHj0xiXXonddACnpwulvNIl+jMZsEz0q65DaDqZ3z0MjzyIumQZxiWXzraYStvGbmnCbj+EUBTU2nqUqjmziwW/+92DFBeXsWLF6jM/X8dhS7+gZUJQH5Gk5y3iVcBr7WB7zhz2tQtWCYlIAwJS1zg4Z5k3FVYhe0ggpsEpkFi1bhp05gIbtUcgPQ5Xl4Fng2DDq4JaASNhyQYD8lNQ7UDQkXRkBJPlsKwbrp0SeFNwyIJnYjAhIDApOM+U7I9BOiaIaGAKSJhQHoSpPElYQkMDFBW5xdmhQ4JXXlG45x6HZ59VaGkTDKcElpTowp2AZBxXUTw5Bnv2CG67TXLffQrbtgkCAYmiwBNPKBw8KPnYxxxyZm6Xg4Pws58ptLaClK6KorRU8BdzJVUHhJtyHQAkKINuqrU19+RF45YtG/jxj/+dL33pn6iurn2rp/oc/pviXF18Dm8HKTvNRHqSkHEsk+DTfJjSYjI9dVbb64sP8nznK/g0H4W6+6yryZtD41grr3Zv5Jbaa9/W/iooCASOcI4R0jkzJKbCm7dLSEcy2jHGaPsoAJHSCHlzck9oFz4aanUNamU1mTdexZkYR/EHXII4kcBJxNn63Jf59adsIit6uWRDB+bEKNXWMhZ3r2BD4V7eWNHNG0tfJS8wwjxzAbX2XMrsymOEEW8Gp7+XzLNPMewbZ6wmm0zURiQF4Z4EBe0+msss3vjSy/Q/3sOzw0+wsGEpX/va/2bJkhOtCBwc+tUeLktfTbfazgbjNYIyRJ6Tj08GiItpBpWBk46nsfYytLJyzL27IBZDqahEW7L8iI3WW4SUElIpMIxZklQIQc0l1WSrE0ztPIiNSmDlMvKX16J5jlAI1s5tZJ74LcIfRKmdC7aF09lB6v6f4//s386qecHtaEv9+HvYPV0oBUUgJebGN3B6e/B96rNcdtm1ZGfn8uCD9/HlL3/zTfd7vrWID8c/xcue5+hU24g6OVyTuZk16Stmz68QArWsArWs4k22dnrY3V1gWbNEMLg+zqvLq3mgdi4ffvFJrrpqFd/73q/eEXuxY5AEfZfAyT3KHkGAucBVDKudRwhQPJBe42AuOLHukQGI3+tg7HYtHqQB0ifxPquidoFdBNigdQvsHEnmQgfHByigdQoC/6EgplxS9jAZrPYKlGFX2OCcmC1+UjzaKnijT6E4ICkOQLxLsHmfIGtC5YO4yt/0xdJlqgQ4xW+6SdRO8D2kgOXWwwhQhiS+xxXsYhtrAYyNjfKBD9xGRUU13/nOT8/KBuAXv/gBgUCQO29/D4GAZOmZuZ+cFj0xN3iuNChRZ3ZFU9xAt0MTgoGEayFxpsjyuOKFA6OCAr+k0A8BXdIdE/TFoCYCtiM5MOYGac/6I0twpCT4NpxOvBq8v8FhXo5g97BrobEo12FloUvkdu4aZKxjnJyK7Nn7vT/bz1jnOAMHBqlcdeTvsyoLKrNcJfW87CPXsaGCR5UcGDs1GTzVP01yIkm0/MjipxCCQE6Ayb5JklMp/BEfw0mBVz3WI1kI0IK5zLnoPXzvR9/jkts/S0O+zuHHU9KCPSOC7mnwKO5CQHXWO+7Q8/8VZ0UG//mf/znFxcV861vfAmBoaIiLL76Y4uJi5syZwwc+8AFs2+Z97zvzVZZzOIeTQeohrFAF+sguLE/27F+ZkhpBGmHs4JnbF/yhETKC3DP/Tq6uHGEqM0W2N5vsmTC5k0EIwZrSC2id6KBtspNcXzZpK8NQcoTlBYuoi8455WeLFhTSv3+AqYEpgnmuZ/DUwBSB3AD5de5TqTZSzeK8Brb07yDizcKjGIykxoh4sri45OQtUdneCNdXX8n11Vee1bE3vdhCx6ZO/FE/ueE8ajvnscX7OoGsIKXvK8XEpE1rodqqYU6sfvZzyckUdtrC8B/71PEEPaSm05gJEzV8ajI4ZAS5d+F72dC7lZ3DexEIrqpYR11OORnp+uQlMjbPtT7FtBmnOlTNfnU3nYN9JJIpFq+uYijQS5ldRYO1iHRaso+FDHe1IgdB8agszNqFzC2mr6AOy1tLrk/h4hKHC4rdh0+Ok0cOeUhDMj+nno39W/FqHnyal7iIkTAzZOX7scMKEyJFQsR5TPyGBnMR1faZkU7a2D6MgQ3YWVXYeSpWcyPpVonZ2YmTGsGKqYhgGPvgfrQFi2cLeHv/Hqxtm1GLSnGGBxEBP9J2kEMDKKEQIq8AOTSInBhHKa9ArZ+L3deL3XEIvH4yngLwFVAxFqP48W4W7lQg6aN6u0owO4ooCyNHRzBfeRHtuhvRL7kUc9N6kBJ99YV4br2LzPO/d1OgvUcWFISuo0RzcAb7cQb7EB6fG/yWiMPY6JEDV1WU0gq05Sux9u9BVM/Be/eHyLz6Is7ICEpWFto112OsvRxrz07Sv/kV9sH9oAiX2K2fj9PdidnajMiKIhRB5unHsZub8H38zxHBEOmHf425/lVIpZBCIAIBjCuuxbj2RkzT5MknH+H229/3tkJBmicEAc0NldCihWSXzqWn9XnKb7md1jzJim5XiZFZId/UK/h4qO3g/4WC2oMr29XBXChJvM/ByXPDNcANV776YsnVGdCaBer5ktEgmBYUpyQH22HzAbhiGnRF0J0ALQMlwKUm/OsIZCwoENDqwEgCVAk5CrynUnLJLQ6/2K0QHIe8FpC9ArsMykolbW0Cw4BPfcrhiYxC648gbrmhEiru/y0FFAU2bxa89ppg506orJT4Zi4by5I0Nwu2bBFcc41r8fH44wpNTYLaWomuSyzLJZ9/7oG/WQneHbhsNS4RnLrWwT5JR3AymeSzn/0Iy5at4sMf/vNjXpNSuosSmvbfwjPsHN4aztXF5/B24FU95Piy6Z7qOUa1GzcTGIpB1BM5q+0dmmhjKhNjbrSG8Uy3+x1eH1Eni53De7m55pq3dT8rsyspsypp11qZY9WhoODg0Kf2sMBcQuGbMDbSkTQ+30THli6sjOV2EWkKpUtKaLh2HuopktuFYWDc9C4yLz8PmQxSUUBRUSuqGKn2Mb1vPYVdq9EbVjN5aYjoKwfpHnmV31yn0lyfRA/n0laykwn1FUIyzAJzMRdlLuW25HuOsWE4HezWZpiaIF7pJ5hIUDDio6Nkmsiwir8vxtDfT+Dsd1jzwyv4bPiLPPPw49x559W88sruY5SiXWo7j3jvp01rZVqZpM05QG1XLjX9WSghL0pRIaPBELuNbVyXugVbWHSobejo1Fj1BAmh1tSh1pyl3dhpYO3dReaVF3F6uxGhEPr5l6Bf5JKZmYfux7N5PXmpFFJKlOGd4H83LDliCWJu3QSOdPMpABQDpboGu/kg1r7dGJccsV8yt2/B7u5CnTvf9TkGRCSK03wQa+c2jMuu4rbb3sMjj9zPF77w9TOy3JlnLWSutYAUKQyMU4bUvV0Ij+eUYXT1tfU8++xmPvGJu7nzzqv5m7/5Gp/4xGffsWA5IQEHTlgfUMEukaSulOABBFg1Emsupw7N9UFmtYTDWgcJMtvB87RA63IVv1KRKNPg/5kKGliVkuR1DtIAYYEyCWRAJAUyy0320loFmdw3r2VHkrBrWKHAJ4l4XBuvvP0gdNjmh+t7oPDXAhGD1A1nXhvrewViEvfYZ06PUwhKE+g7Bcn6DB/72LuJx2M8/PBzZ+Wnnkql+NWvfsIdd9xDIHCWiXWngeW4pKwqwJ45VFW4Vna2BPstiHQX5kgKA5K2STe0rjAAGce1NrtpjmQ4CW2TgtEUBHR3qGKmS+auKDi7ucjx0DIZqvoGCLWNoqiCfC0fX1EBoDLZP4WiimMW/hRVQdEUxrsnjiGDAbRTPKpsCZ7T/IkrmuIGmTvymL8Bx5IIVUGd6aguCki2DSozf8/ue/rjgsZxCK7+c8Zf+il/96PHuP7GO7h7noMt4af7FfaOgMTtcnyhC26odri84k83Q+SsyOBNmzbxs5/9bPbn++67j+zsbHbt2oWmafzLv/wL//Ef/3Gu6D2Htw8hSJdfgzbVjjZxAMeTg7DiCDtNquJ6nMAfpgXn7SDfn0u+/8yWRBfmzud982/nhc5XGUwMYyg6V1Ss4dqqy0/rD5dbncP8q+fS+nob490TCEUQLghTf0XdrN+OpmjcPe92igIFbBnYScbOsCR/AZeWXUxt9E0MOE8DK2MzeHCQoZZhpCMJ5QXp29OHP9s/G4Z2cd/lTIbG6SxpZYpxNE2lzK7gruQHMDhC/HpDHlRDxUyaxxDCmXgab5YXzfvmxV/Ek8W11ZdzbfXlJ339N02/Yzw9ydxorbs6rMGh7Bb6xwbxDUtWepZyR/J9BJwgu1/Yy4iZRaiyEGWkHzNhMT3l43IRJ+e9KzFzJUUBm5yTCKWFENxScy0xM07TWAtpmSHlzSBMm8qGPAyp4kiHMWWENrWFZu3gGZPBaqwTHBOph1Cra8gcGiDV2IriddBDKexQHUp5BebrL6PNa0BbvMw9V/v3uq2DK1ZiNzfi9PW6Hr6ORF+xisD/+DvsjjbMl5/H7u5CDg8hVA0RiaLVN6B3uh7HWiQbXyrNvA1N2FIBYaCEwyiaiszNxR4exnr5RZziEhRNQwqBtWWjS6Jl5yLkiQ9HYRgo8xditzbh9PYg02mYnp6JM1bAH4CMiTM0iLV9C8Lnw3rpebzf/A7aeauRsWmEzz9r+6CvWIVSXIp9YC8yEUcpKkHGY6Tu/7nrWWe47xN5BdgtjVjbNiMiUczXXkLkFaBkRdzifnSYzLNPotbU8eT+XYyODnPnnW/vWebTwDyqkKtcfhXNbzxMyV0SZZEkVnX2xUMmA7ExKPmVgtotsKrddkERB32bIGUIdi4AVYXaWkluP/geVFDbQO8EOSTw1oJdJNF6BfMHITshMGzJSKFkakJgARMSaoFltuAFG7arUJIDuipxkoJERpJeKilZKFF+I/CbIPwSkZjxtZsjMDVIp12y16mSeAuEuw+W+zsUV9Tu8buCpM2bBbYt8PmOjIumgd8vaWx0yeDRUTh4UFBUJGcF8ZoGZWWSlh7BgVtt6la7Chapu4pgu4KTWoP88z9/hd7eLn7600dmQ+OkZWFufB1r/es4UxMopeUYay49ZZvq0XBGR7AbDyAtE7WsEqWq+hyR/F8c5+ric3g7UBWVS0pW84up39A93UeuL0rSStGfGGJZ3kJqIlVvvpFj4HZKABg+t1Upk0whPe/MRFVH55bUndzn/wFN2n4UVBxsSu0Kbkrd/qbK4JG2UTq2dOELe/HOtAmn4xm6d/SQU5VNyaJTk8lKJIoypw5VOmQMwd65E2xcNUmH7yAVO+IEpiRmqUr7XQsYW1LI/thrdNY4LBJr2V7YjKMkKbXLmFQmSIg4L3l+T4FTyGXpa87s4KUEBEV2CQeV/Sw9mMNYVprO8jgkksQenWbFb87nQys+yYLMEhZ8bgnPP/8U9933fT7/+a8CMC2m+Lnv+3Rq7ZTYZWjD4yR8g7SqI4Tj+eR2GSjdXXiXz2M0kuIp76PsNrYxIcYQKBTZxbwreTeLrGVnts9nAGv3DpI//xEkEohoNs7wMKkH7sMZGULkF5B59UWUwmK3VrJtnK4OUr+5H/9R/r1ybNRVah8FoSju1Rg71vrO6elCeDyzRDC46lp0HafXXcC48857+OEPv8OLL/6eq6++8YyOQyBO69P8TkCdO98lrvt63I46IXAmJ8C20ZedR25uPr/+9dN885tf5utf/wIbN77Gt7/9I3Jz376EVPrcLAjvGwpOjpwlhZVBkNmCzGXOKVW5Io6r5o3ASYdIuORwZoHEsxH0LQJ9p4KTK7EqJcJ0xQT+mIJVLVF9Aq3DJWulB0i7hK7aeRTBfBpMZ1yFZTQAWKB1ACr4QzCmwEQZ5PeD8bogfZFEnqG7jYjjjstxZZc0JM64zV/91UfZvPkNHnroOUpLz04l/vTTv2VsbIT3v/9jZ/W5N0NJ0A2N2zwgmMlUI88HqiKpi0oK3oJlbY4P3jvX4eEWhY4pVxE8lRFkGZJn2gVRn2ROBPrjkLZdMlpTYGWRZF2ZZDQF+0cEjWOuz7BPh/KQxKu6c5qiACzMlfhnavDpDKzvFezoMlHX7yFnZJj8sIomHPr3DzC6tJQF183H8Ok49onPIsd2MAInSpLnZkue74LhpDsmAFNp99+LThPgHCnJIlQQYrJvkmiZKwiyTZv4WJyKFWV4Qu68cEWhZPOAG3RXFJCkbNjQ547FRcsWMbVwDd0vfofNK++kLOReVLuGBdVhiXfGj7s/Dk93KMzLsU9rFfLHjLMigwcGBqisrJz9+aWXXuLWW2+dDde58cYb+cY3vvGO7uA5/PeFlbOY+MJPY/S+iDbZgh0sJ1O8hkzxmv80Pb6UksRYgkzSxB/x4QmemaLgeAghWFGwhCV5CxhLTeDVPGcU7iGEoGxZKfn1+UzNrLBllUTQjGOXyAK6nxvnXM3VlZdhOiZ+zfe2SAnbtNn/1H56dvUiZkxXO7Z0khhLUD7j7ysdidMtWb39MooileReG6FuQT1LvStOSBeOlEbIrc5h4MAgwfwgulcnNZXCTFnUri094XjeCvpi/QQzQUQC8MMcq44SUc4eax9rR8/nI5EPoqERH4sz3DJMIC+EZ84SiFejxeMoGYh5gizKDRN8E44/x5fNJ5d8iObxNnaa23i97tfYh0yMgHscCgoBGWRUGWFanLzt0z7UgrnxDeyebpT8AvRV52OEDNwle4nweHD8BYjwKHqOjaNH0asuQni8WM0HyWxa74Zw5OQiM4c9gQ20hkXIObXIVAqnrwd93gKUaDZKNBttyXKc/j5IJrBHR0j9+LuIcJhIqU46liY5kcTjaHjSaYRQUbMjqIfPjVAQjo0zPIhSVo46bwEAMpHA3LoJfd0VEAi4vsV5BW7RPDEOQqAvW0FmoA+lugZneAhp6AiPFzk86Pr94kDGwpmaRCQSyNRu7J4utJo6N+juOKjFJajFRxaIUg/+0t1F48jfp9A0d6xam10iWYKSFXFfEwKRm489MoTZeIAf/ejfuPjiy6ivPzPP7FNhSZ5k+6BgPOX6ZlUuu5odj/0rcmgfc8+ff1bbsm14/XVXPRtsF9zeDN55UKHOdOr5oTsO3fcp/KJaktGhIiz47BQEVbDmgTBBPSTQDkrUQwJhgs8S5PklZloQ6XedTgwBGd39XL0Or9hgS4EvIHEzLyUTPbC1Bz77e4WSSUm3LQinXIWy9EpGmwQ55zkUz8zts3PBVyIRIwLdgoAfHBUSCbegysqamWtzYlC6ZbkuIuCS4bbtEsBHQ9fd32ckWIvAWnR68mPHji384Aff5vOf/yq1MwGEAOknHsV85knweBB+P9aenditzfjuuRftKCXU8TC3bCT92wdxRkcAgfD70M+/BM9tdyLOhYn9l8W5uvgc3i5WFS0nZaV5pWe9Kw5QDS4uWc3Nc65FVc6uFpoTqSJshBlKjuALuTfN2OQkE1mpE8KD3yrqrQb+Iva37NK3MaaMkufks8RcQc7JUpqOw2j7KHbGniWCATwBg9gIDLYM0rO8nR3GZqbFFHXWPM7PXELBjH+SCIdRs7PJJKf5zW19bKnuwGPpTKXjbLwkiVF5kEUij4AaZKIhn/0eD4b0MG1rTCmTRJ2cGSMLhYzIEHay2KC/yrr0VWdkb6FW10AwRNkgjBYXMjw6yEWv+Bk1Uuxe6Qd7ktvTd7Fyo590/6MInx+vorJly4bZbezTd9GptVNj1aOZDhwcIlrrYTLLob8S8sjBGRxkuG87dqSO140XyZIR6uz5ODh0qe3c7/8JhbFi8p3Csz95x0E6DpkXn4NkArXW7d6TtoV9qIX0b3+DCGeBqh2plVQVpaIKu/kg9sF9KBe66mG1ag5OeytSls5eYzKddru8Co7dTxHNATNz7H5IiTRNyHLru3lVNSyorOH+f/gil+zdi7pwMfr5F83ux38W1NJyPDfdRvrx3+I0HUACwutDv2Qd2opVAGiaxhe+8HXOP/8SPv3pD3H55Sv49rd/xNq1Z9d5eQIEZK6QaJ0SvVHg+CUiI8CA5I2nIIJT4H1WYGwSiITACUsyl0jS6+SJDFAGfL8TeDYpqPtd5a8zLcALdqnEmiPRWsGqcr9famDPcbcjpkCJgb5DkLpBIk+dGwi4ZGXYkEykodieURcH3NyJLAdyHXCy3UV9dQisMySD7RLcAtLkiJVGGqxhkw+13s2TB37Hv//7z1m16qIz2+BReOqpR1m+fBU1NfVv/uazgOm4xHhvTCCQGCoMxCHbK/hgg/OWg4rn58BfZzk80iJ44pCgOiIpDULGhs5pQcQjqYtAT0wgBDTkSG6Y47C+V/D7DsGuIUHMFGR5JCFdMphQCBpQEXKJ6rlRwQcXOHhV+Mk+hd0jgvyOPkJdw7RFokzrKssKJKQy9O7upXB+Afl1eXRu7WZ6OEYw12W546NxNI9GQf2JCyZ1Ubiq0uH5ToWDMw6ZHhUuLnFYln/qGl/zaMy7sp59T+xntMP9oFAEOVXZzLn4iCiuNAgfmO/wVLtC55RgJOVu//xi1y5j2Q1/weP/cCtWz3Y2h5ahCgjpEu9RfzuFfmgadz2eS4J/murgsyKDw+EwExMTs95oW7Zs4d577519XQhBOp1+Z/fwHP5bw8puwMpuAMcGofynmrKkY2maXmhmsGkIK23jCRqULitlzkVVZxcIdhQ0RTtjNTHAqDLMhBgnEswmr+bNP2eoOob69omI4dYRenf3EcoPzSp5k5NJJronmOiZIK82j5H2USZ6JrDSFvkDxUTjEdjmgdsVOM4GWSiChusaUHSVkUOjJMaTeIIGtWvnUH7e27cAmeyfIrDeT9YhP4oBMh/kQomRYxCSYer0erSZ2994zwQjbaM4loOiKQTzgkRK8jEUQXowhpk0z+g7dUWnIaceR0sQqgwwvDGOjT3brpYhjYpKlX2iDYi1ZyfJ+36CnJoglpvDQb9FfNvvKV26kPN9+XhjXdjBcqRlIzwqSsCDndvgEqiZNE53F5m+Puy9uxGhMCInB2mZyHTaVWB4fTPKW/WY1kIhxCyJKoIhRDCEHB8jkJNL4bxCJvsmsXtHccI5eJw4mnFkwV3GpsCxEYp6pDUQEH4/wuPFGR7CuOZGMs8+hdN4AMRM0bzucmQyhfD70evmkdm6EWlmwPAgLRvSky7TZ9swPYXU3OvXajyAdqZtkT4/wjmxt0paFkowhDM95Upnj38dhZ0tB9i1axv33fe7Y16zbZvBwX4SiTjJZIJMJoOu6+i6js/np6Sk/IR2xqX5ksvK4bUehcEEWMVrUD1+ol1PUxV+czI4lYKNGwVbtwoaG2FgQFBSIjnfI5EZwb5mSCOYO1cyOCg41ANBBeZXQ8wDWTtgpEPgvUIS1sGaP6PwaATSrgebUyjxCfD0SrKnBV6gUHFD6qK2ROqu+kPRYXqmwHcc0DSBxwQ2w3Ve+GEGDiKJpAXTCYFjSG6aJwnPZAsuXiyprxe0tUlGRgQyDbY9o7QqkuTnS5YulYyNCfr7ocQPWq9gYhAUC5avcT0q8/JcX+LOTigUUDDuXpO7U5CTKyk9g4D48fExPvGJu1m0aBmf+MRnZ3/vDA1irn8VEc2e9ZZW8gqwW5vJvPgc6sIlJw2ccQb6ST/8a2QqjVo3D4RATk5gvvw8alk5+oWXvPlOncOfJM7VxefwdqEIhXXlF7G6eDnDiVH8uo9cX86bf/AkKA4UcFXlWp5qe55h6RqItvYd4qLqdawpu+Ad2+d8p5Ar09ef9eccW560jBcCdis72Bx4ERAY0qBR288ufRsfjf8FxU4pwudHu+AS9u/8PtsLWykZycE/DdMJD+3Vgt6sGD1WF/XWfJIkUdHx4sMkM5OR5bIqEokmNXzST1yJYWEd07l2Kiil5RiXXQXPPMmyvRHGDYWEOUG6fgG35f05n1vyP3jk89/m/LmLyNF1nurtYvue7VQWH3koTYgxcByUwWHMzg70rkHKIllMRMboC4xQMp1kMhJD9kOWbRCvK6RAc+srBYUqu4ZGbR/79F1cmr76rMf/eMjpKey+XsSMclWmkli7dmCPDMHUJAiByIqg5OSiRN1C/rCiV6aOZLfo51/kLpw2N6LkF4Bp4owMoS1YfEKAnL5kGeb6V7G7OlBmxsbp7UZEouiLlyLTaVI//xHXeP18p3k/yc529KYD2Af24v3wn52RN7Ld1YG5aT12+yGU7By0FavRlix7RxZDjIvXodbUYzcdAMtCqahCnVN7jNIZYN26q3jhhW185jMf5j3vuZ7bbnsP/+t//Qs5OcfN3RxQhmb+87AP76mOqwzif+ZgbBaoHQIny8FcJrEWnPz9vscEnucEThScqESZBN9DbmZD+spjiStjs8DzqsAulCgBgaO6dhDaQVdRLIO4WSCVEl4DYUqUKXf/0SGzWKKMg9YiMBefnhQLG3BhseTxNoHjQJ4BEw5MCrjVkkQAkXRVx/IslLHmEolVJ9AaBU62RBkRmI1J7hq5nRdTL/KLWx7i0hXXc7bOC8lkkldeeZ7PfOYLZ/nJk8ORsHtYsG1QsHMIdgwKVCGZzgimMpJsDwR1N1fj7cCrQn9MkOODbA8MJlzrhYqQpGsaLq+QlIUcNMVV3m4dFDzeJhhLChwpKA5IJjPQMSUIGWDZkOOTRD2wb1TwYpegJAj7RmFOlsQ3Ngw+DU9QZTjpqmYrwwbTg9NM9ExQu7aGuktrOPR6G6PtYwjh2kPWrashp+rEvCQh4Poqybxsm5YJ91qpypLUR91G0tMhtzqHle8/j+GWYTLxDP5sP/m1eei+Y+dp9dlQE3EYTMwc/yGF7BkdUfV51+IL59K39WGK65ahK0d5LB8FyYyO6U8UZ0UGr169mu985zv88Ic/5Le//S3T09NceukRH6Dm5mbKyv54vFzP4b8QzlId8U5DSsn+3x+kd3cfwdwgvoiP1FSKlpdb0QyV6gvOtpXv7JAgweO+h9hmbCQuYgRkkPMyF3Bj8vY/eFsUwFjnOEp8kqBvGtsKYIdy8GX58EX9TPVPo3kNxnvGQQpUXSW7IpvsiiijHWN0b+9m7hUnrqT6srwsfddiYsNxzKR7oz6cJPp2kJhIsuuRPYT6AmiGxhTTBDsD2OMOHef1Upifz+I8V/EZH0vQ8korqakUiqaiK4KxznFSUynCBSG8YQ/+7DdZ4j4OlfYcSnPLGRzoZ0yMoAkNG4e4mGZF5nyWmiuPeb+0LDLPPAnxGJ0XruQ3q+bQF/UjEwkUVeONunv4s50PkDu+H29wkpSVxvRUYofKkUjMPTtxBvrRFi1xLRImxrEP7EcE/NhtLYgZTyyZjKMtWoa25OTthUpBIfp5q8m88AykE/h8Op5wEmlkoV14A9Yrz2Pt2Y2dSrp/jwoIjxcR0FCix01cFQVhmRhXXYc2rwG7pckNjauuQamuIfWT7yNUFWd6Cjk8hDM06Eo7zcxMv5I2S14jcMNIWpvP+BzoDQtdC4y+XpQiV5oqR4YRuoa2eCnOyAjWjq1IM+OG6wEymUCoCj/fuoHKyjlkZ+fyq1/9mH37drFv3y4OHNhLMpk45XcahkF5+VwqKhayePECVqxYyLx5C3lXbRHLC2zaJwWq0ElesJbOHb9HiM+d9hgsC+6/X7Bhg0DXobVVkEiAogjG5ktkBPLT0N0NFRXQ3w/hlCBRKUl53UKpLBsyTTA8KghHJNIHmZUSMe6mQmdWSZwcUMbAGBeUZSSHdIFUoWAaxlWY0iSOIojrbpicSEI8LvB4JFVhSXRAsDoHDCF5eRK6MzAvI1kTgvkXydmiOxKBD33IQdMUHnsMpqcFfj8UFTlkZwuWLYPLLpP4/Q5P36/QsgFEGnwGXJMNa15XMAsdWCe5+irJrn9SWLpDEJWuZ3CdR6Df4RB8k4mD4zh8+tMfZHp6kocffm5WwQng9PXA5CSift4xnxG5+dj9vcipSUT0xILVOrAPOT6GUj9/drIpIlHssVHM7VvOkcH/hXGuLj6Hdwo+zUd5+AxWs04DIQRXVqyjIlzG9rxdvMovWeadx8cWvf+EkDqASTFBRqSJOjmzC+R/SETLIrQrAjNpzk7KrYxFzI7RNHcv2U4u2TMSRweH7rEd7N3xb+SMXejWKBdewkDpBiy9hXBvgrxOi9p4hMh0EkebZP/CHbR7DpEQcSrsSjzSR0a4C/FpkXIX6KVKvlPImDLKMnMlOmcmmBBCYFx7I2plFdbuXXgTMdQ5dWjLz0PJivAvN7+bv/7Xb3DJ84+hCoWG/EKuK6ti/+S4G3oXiRJNh3GGOkntbUGNpZHT01Tu8DDkExhxG9NJUTbg5/zXA2y8epy0yEBtw2wrjJj5X0LE35HzIQwPwuuFVBKIuqG6g30QcDsVlWgUp7MDa98e9AsuRqiqa9VleGaJXAC1shrvhz5O5rmnsTvaQFHRzluNsfZy8Bzp0JKJOM7YKGp1DdaeXdgtja7aOL8Q48ZbUSurMbdvwdq1ndUNS/nnxj0cNHSWlpZhHdiHtW0LxqVXIGPTrvdwSxPC8KAuWIy2aAlC07Bbm0n+5HvIoUEIhXHa27B278S46TY8V5yhJcibQC0qRi1680SzgoIi7r//SX7zm1/wla/8NS+99Cxf+co/c9tt73XFGK3gfVJB63DrBqtSkrrewa459TadAkjd6Hr0ng7KMBhbFJw8OasatoOg9h6xX+Co6Y2xzbXWklnu/5Vp3PpwRKCOgGVIEBK7HMx6iToBpAU4YBdInDLQWoAzXPu8ukqiq669wEiOJNIL15iSq3QQCTeUzlwhXbXvcZjKjDOQ7CFmTuLXghT6Sol4cpFBSNzr4HlB4HlJkGqOc+vYzWxOb+Q3V/6OKxNXYf1MEv+Mc1Yk8/r1L5NMJrj66hvO/EOnwbMdgifaBJYj2DfqkqZCCDyqRFUEY2lJ2oFdQ4LLTxGSdiZwJIymYCghaBl3LSEU4YbM+XVIWpLiox4Lm/tnrkPphrQZqhvYlrQg1weWI5hMQ4Ff4tMkj7QIPIobcpfjlfhUBaREEe73jKUEleEjHttCCKpWV5Jfm8d41wQSSbQsOqsSPhmEcEPvaiKnHofpDBwYE6QsKA64FhiKAH/ER8V55W86TqoCxUFYZEte7IKJNES9oKgac1beSNumR3n3J/+eqEfweJtCgV/OBsqNzfguV2X9aaqC4SzJ4K997Wtcdtll/PKXv8SyLL7whS8QjR7R7j/wwAOsWfMOp2eewzn8EWCqf4rhlmHCBaFZa4hgbhDHnqZ7Rw/ly8uOSdZ9p/G47yGe9zxJnlNAmV3BlDLJc54nEVJwR+rMvQillDg9XTh9vQifD7V2LsJ3ejJZplJ4Nz5F+bbX8XscHN1LuqSO6cWXEcpzCdfkdIpM3LXOCBeEya6IoqgK3pCHoebhk5LB4D78QvnvrMnOwIFBpgemqKgtQ0/otEy0MeGZwjNsUDVeyk1rr5tV3fTt7SM2HKegvoDR9lEc20H3akwNTKMoCkvW1OA5iY/R6RCSYS6JXs62zCbC41mYURMVh3nW+fx17MsnBFs4Q4PYfb1YJSX8bnkl/RE/cwanUSyLVGKYXZWVPHjBZ/hgVxOyYhqR2kymuQfsHmQyjnOoFaW4FLV+nlvYFxYhbRvhMTCuvsENVhOgLV6GvvrCWXL4ZPBcfx3G1G6sja8hk0lEVhbapdcjbn0f9trLSN33Y9cT2HFQiopR6+ZhtzQip6fcdkJwVb6JOOqCxW6xW1GFWnHsYolaU4u54TXsndvcVkKfz+3/B5f8tWzQdUQ4ApEocrAfc9d24l/5AsLrQ1uxyp2YnOLaVaprMG64lczvH8duOoBAQigL46rrURcsRolNo+3ahnVgH8IfBMcmHo/xW2yeeP1FhBBcf/1FKIpCTU09CxYs4brrbqW2di7BYAiv14dhGFiWRSaTobMzxiOPNNPWtpft2/fy6qu/w7LcyVpDw2Juv/1ubr75TvLzC+m77ib++q8/Tk9P52k9y5qbYetWhZISNzRN0wSFhZLJSTgwImgph4YWUMcEVo9bSA8bkoPVEjlTpEwFwVFAHj1vFIDhtuA5ee7PTi6YCySeLYJFuqQ9LdidK9mYA+0KlOc6jMUE8bjAMCThsCQalVzeAEYfqKOCFR5YEZBYORJ9QOD4JGPHdQ2XlcEXvuBw5ZXw2msKAwOuAnnZMofLLnND4y5dK1m6QdIyLMgUS+ZEYU4UtD5QnxOYyyTLg5KlPujPk/QJCASg3i/JaRPEG11LjFPhO9/5R1566Rl+8YvHKCurPPZFrw/0GaNj71ELU+kUwjBmfaqPh0y7CqkTVEeGgYxNn3pnzuFPHufq4nP4Y4MQgrnZtczNruWbeZ8lEDdOIILHxRhPeX/Lbn07ljApsku5In0dS83z/qD7ll+XR/HCIvp29yFUBSEEtmkh59uMLhmgzjly887dPcySX/YRGmgnZfWiKCrqvAZCH12C17eHRY/Eyd0/AZpKWZPBnFfSvHp9gt3v0imWpegY2MLGlg6KVOhVevBLH2V2FRPKOFkywtr0FYiTGcqfAkJRXLXrgsXATE3d34fZ3UVJcyP3X3MbqdxcYuk0+cEQn378Qco8XuyWJpTzVlO/1aIsk+ZQjUnxZDaKmmQwK0bZQZu778ujMlOBp2ccvaiUQUfhkL0Le3gQdSaALk0agUKB/eZE5MngTIxj7d6B09vj1ncLl6CdtwrziUexpcRqPIBMxGBkxFUEF5dBIonT04V9qBnh8yNj0+jnX4RaN/eYbWu19ag1dVjbNpN58VnsxgMkmw6iVtfgufFWUFVS9/0Ep6MNiQTbRonmYNxwC/ry81xhxGsvkX76cez+PuYvOw+/brC1t5NlJeWuzVfTAbQVK0n95PtuuLCuu/XupjfQ116OcdtdpJ97GmdkGHVuw+wz2Rnow3zhGfTlK2d9jv9/QQjBnXfew2WXXc2XvvRXfPrTH+KRR37NN//635n/yBzUIYFd5BJJ+n6BMqwQ/7SDU/D2vlcZATEN9nFRMU4ElFFXEOAcRQaLOBwWyNtlEnXIfQ8WiAnQEgJrniSzXKLvFmjPuFZjIu3WoU472OVgl52ZRFJX4OpKySWlklgNFDyqEN4nECkJhsBcJEne7pzg/zuaGmT/xHZSdhJNaIykBxlI9jA3soRCXylODiTvlLQ2N3PPy7fTk+nmt7c9xYWll2CZEq0JPE8IrGUSq/LIMZ8Ozz77BFVVNdTUzH3zN78JhhPwYpeCX5NEvZKdQwI5c5AKwlUE24KpDDSOyRNs084Gh0Po2idd5W+O1/15NHmYxDyWwBxJulknmnCJZHefXMz+LNzPHhgVJC1BtlfSE4exLoXFvgLqzUHUtIlER1MkyYkkulcnu+JIXRTICRDIeQtmyCfBwTH4daNCfxwcR5BxoDDgeh8vzHWtMc50/CrDsLrI4ZVuwUQaPBp4Ft5C4oWfUJnez+I5DTSOS5rHBT5NYjquovvyCofK8DtyOP8pOCv2atGiRRw8eJD169dTWFjIqlWrjnn9rrvuYv78s/NBPFv8x3/8B//8z//MwMAAixcv5t/+7d9YuXLlKd//0EMP8Xd/93d0dHRQW1vLP/3TP3HttdfOvi6l5Mtf/jI//OEPmZiY4MILL+S73/0utbVnFvB0Dv91kY5nSIwl0H06yakUZtIiXHjsU8MTMEjHMmQSmT8YGTwqRthmbCTPKSDXcVu5cp18JJKtxgYuT19Ltnzz4kZmMqQfeQBz8wZkPOauxpeW4333PahzTn29p595klDzJlKaRiIQwSCD79AOrIyFU3AR2XXZjLSNons0IqURIqVZs+m5ji2PSQ09Zn+kJGWnMBTjrHzxRpVhetQuPNJDtVV3Qnvf9NA0iqYiFEFJsIh8fx5TmWnixKkOVVIXPWLTMNE9ge7VySoOoxoqk32TWGkLzVApmJdP1flnHjAgpXQVE5rGRdnr+N/8PVd13oju1SlzKjkvc/6s993RELoOqkpHdoCe7CDlozFUKZHSwWNL8i2FPVk+RsovIyxVjE9cgbJpPdaObdjDg4jCQrSVFx7jj6tkZeFMjKOvWEXi+ovZaWyhX+kjIl9gsbmMUvskxyUl/u5H8VR2YlUsxLY8aFocVd1Dov9lMuVXEfzi15DJJM7oMCIQRISzSD/wC8zXX8YeHABNQ6aTaPMXoK++8JRjpS1fifL0Y1i7dyAiUVdtMjGO1HUwLZcInlOL8HqR4+OQSuL09+Fk58LoKFZrE3Z7K973f+SIH6uUIG1QNHeF/dIr0OcvwG5tQh5WJZeUuavT4Sy8936CzMY32PLsEzzStJ/ftx4glkyiqipf/OI3WLnyQurrG/D7T68MTybhuecUfL4rufZaiccDw8MO/f3tLFq0nZaWh/iHf/iffO1rf8OaNVdw/fW3EQiE+NWvfjwbKnMydHcLTBNCIdcuQtddtbC7fdhzsWTQgeou0AyIL5A8MiDw5B0p3jrDEM2Cq6ZB6QcMV+VhV0qwBGoXR1QXEszzwH+LjWZDb48gNCW4sVKycKFk61bB+vWCdFqQnS2prpZEh2F4UlCYBjUG2ALVAVSJDEHoWwrptZL0FUcSfYWA886DFSsckklX/H00xyrGoXJMUL7QVT8fhl0IWqtA7XTbEL0KBNZKjhbQiEaBvsedsJwMr7zyPP/8z1/hL//yf3LppSe22KrVNajlVVhtrajVNe5EMx7HGR3GuOaGUy6kqCWl7rWfiB9R4jsOcmoK9aK1pzzH5/Cnj//suvhcTXwOp0NZWQXd3R3H/C5Dhp/7v88efTv5TiE+x0+72sp9/u9jxA0arMV/sP1RdZWF1zeQW53DULMbRpxXk0vX0lakTyItiUCgxU1qHjiAHE8xPi8XzZwP6TTW3l3MfcrLwjIINQ4wXVOM7dWZVMaZGocbns5l4dzLmarLwcGhUdvHEnM5tyTvYpe+jUGlDx0PFXYVl6avYp618M13+hRwpiZJP/IA1t7dyNg0dtNBRG4+vrw8/MEQk6kkr3cd4nN1C9yHN+Dd1cJ7u+p56uYYhwpGMKt9FDZL1v0qztw9Fkp0HBGOoNbUs7ob9kQbaSo+SIFiYGExog6zMLOEBeaSs9/fgX6SP/kedvshhKIibYvMKy/iueFW1BWrSD90P3JkCFQNfH5QFJy2FpTaeqR0QAiU3Fz0625Cv2jNyS2TerpI//ZBt1OmsBjpOFh7duGMjiA8Huz2FtQ59e6zNZPGPtSC3XQApXoO6Z/9ELuzHbu/D9nfh2JZLMktYFtvFx8DsG2XEN68AXvfbteaYabudSbGMV9/BXVOrWsNkV94zOKsyC/EaWnC7uz4/04GH0Zubj7/9//+gttuew+f//ynWHPLYj5Z+mk+fdVfE/W7RJkVlGiNAn2HIH3N21MaOiE3dE7EXaXvYYgYSL9box0Nc57E+5QCjqskzixyvYGVOEgDMhdKUjc64GfWgkE4AickEWnQxsEplqcMsTsB0lUph8cglAX2Rxxi7aCMCWTEwZoDx2lncKRD23QjGTtN1MidPcdTmXHapxrJ9RSgKTpPPvkIf/Wjj1LkK+Gld2+gPsddZNK6BXqjS4Y7r4JV7hLOp1ViOw7PP/8Ut9zy7rdtMyIl7B8VjKQk87NdRatEoAgQEkzpkq4Sl3SNm4Jp07XVOBopCzb1C7YMCJKW6/d7UYmkMHDi90kJhiJI2dIlhx33O3UhsaSgN+aSm7YEvy7pmVYo8MNAAjJpGz2dQbd1EqZK2CPI8brvn0y7at3pDJiWYNyCDd5iRHSU0qE+vEgCaUk6rFK5upLsihO76t4uYhl4oFFhKOESuc3j0DEF+0YEB0YFdVG4tsrh8vKT2xMdDyHg9jqXQN4yIJjOwLuuXMfBH4TY/drvuHR5Ax9f5LB1UNA8LvDrDkvyYHHumW3/jxVnzV7l5uZy0003nfS166677m3v0Onw4IMP8tnPfpbvfe97rFq1im9/+9tcddVVNDU1kZ9/ovH0hg0bePe73803vvENrr/+eu6//35uvvlmduzYwYIFrsHON7/5Tb7zne/w85//nKqqKv7u7/6Oq666igMHDuD1vv2W9XP404NjO7Rv7KRrWxepqTSqoRLMC6KoCpl45pjQuHQ8gydozPro/iEwoYyREHFK7GNbHUJOFv1aD5PKONn2mxc35uuvkHnpOZSCIpTScjBN7I5DpO7/Of6/+tuTEh3O9BTW5g14S4sIRDQmeyfJOBoaAdRD+1G9cxlodFs/HMth4OAgZsokvzYPO2OTSWSOMWsHd7K5Z+QAr3Svpy/WT8gIcn7xeVxccv5p/Y1tbJ7xPM4r3ucYF6No6FRYVdyRvIc59hEvWV/Yi2Pbs20puqKR7YkghUMwcuwxekJebNNGCEFWUZhwQQjbtBnvHqdwfiHKm5kSHd63Qy1kXngG+1ALqBr5FZUAzD3YwJVlp/fVE7l5aHVzyYx1YgnQbAcpHeTkJEp2NkYwTBw543oHSiiMccU1GFdcg7l7B4l/+ipOeysUlyKyIu65mJpCCYXoi07y0+BP6VAPoaJhYfGK8RzvSX7oBAWQkujDGNyE7S8Gbw4qbkEipzvw9L7oBjdqXldRXnrkWvTc8V7U2nqsPTuRqRTavAVo5606ra+bEgqjr70c8+BBFE0FBMxfgJyYxD64D5CIdBoyaUglER4v2rLzUHNdqamMx7B2bMVefSHavHl4el9C738NxZzGyqolU3oFVnQeSmHRMX7Gh2GaJr/87a/58Y//jba2VkpLK7j7/R/nvvu+zz33fJyPfewzb3bKZ9HUJGhvh+pqiTFzGygoUEgm5+A4c/j+99/F5OQYTz75CA899Es++9mPoOs6P/zhv/GRj/wF2aeYmHg8Rwo5rxcKCyVtbW5SfDgMk3HYJwSJOyWrP+jgGwXruwodTYLcXIltw9iYIHylRK2SsFdABsylkvTlDmJc4H1coB2CeAo2JeFgpUNWTHDppZL3BY5qQ7RhTlBy9SrJ7i548UWFrZsUtu2H3LjgCq/kphwwhgUiATIoyJzngOkGksgwZC44dmIjxJFQuGOgASoI+7gmSBuX5dZmFCzqiRMlqUvEtICTtE/29HTxyU/ew5o1V/CXf/k/TzrmwjDw3HU38lc/w2k/5A6+rqOtWIVx1anrG3XeArQlyzG3bnK9t3XdbYUtq0C/4OJTfu4c/mvgP6suPlcTn8OboaFhMZs2vX7M7xq1/TTqe6myamdtxsJ2Fs3aAd4wXn5bZLAzOODWQUKg1tTNeq8fDc2jUba0lLKlR2wGgoqPbCeHHrWLMruCrOZRPP0TdNSozJXlrnrX40XJySO0o4N1nfkM+boY9I8DkBJJzJwA2b0BJprHmKpzw+IiTjYDSj9/lv4c16ZvJkMGU2Twy8BZKYKPh5SS9MO/xlz/GkpxKUpBEa8dasJp3Mscw6AnK8w/vfYcVYaPW3IKETM+sdKyKBoL8dFXljAYnsbUbPK6LGj6PfhU1IZFqEXFCH+A4mGL9z1Rx/qKStpyM6ioXJ26kStS170le7j0s09hH2pBrZuH0DRX1dzVgfn802jX3Ij6+itIR0I6iVJQBIqCHB3B6elGX7Ic319/ESUn97SEmLllE87w0KwqV+DmUVi7tiOTSdSCApz+XlcEEAqjFJVgNh5ATk5id7aj1s1DySvATKVwxsdYqqjc19OJOT6GIkBbtATz1RfBHzhWABGJYvX34nR3uiT1DPk+C9vtjxf6H94K5c3w/9g77zA5qjPr/26Fzj0555E00kijnIUEEkKARDYZk8Fg8OKw9jrgdVh7vY7rbNbZJtgm5yhARKGIchppcs6hp3N3Vd3vjxqNNGiUMF7wtzrPM48eVXfl7ur3nnvec846axVvvLGd393+Y/7n7Z/xxz//ns/P/xKfnHkXbt0NDona8ffvxyqE5DSJc53AwCZ/xaBNtsbOs5DvUTImFkv0XRJtn8BKlwjDtn+ILZdErreQw51kmKDW25PuwsBWGKdDMguEFGg1YBxn/lOEwPWIwLFDsclpLxhVkshVFuaE99RvBmh7QasTBPUw4fEBPOm+UZ9Dr+YnaAToHurgZ9/9Pn/5yx+4eMpl/K70j7gz7a4ItUOg7cG2tSiXmDmgNQg8DyiEPm+NIswPx86dW+nu7uTcc0/eI/1wNA3B6kaFde2Cff02oVuaIm0l7jBJawyHyUlsdW5nBB7Yo3B6kcW0LLtuNi34W7VgXYfApdoK6+cbFPb0ST453RpFCFsSdBUmZ9phfcGEQBGSijSJJW1C+dEDCoG4fWuT1kGCWpIRChDoiRATKqXhIFmxISIzK+mNumkMQobLPp5Q0raNGEwIYobG5vHT6M7J42J/P3NKBFnlmWSWZQyHz3+w2D8g6AjDuFT7WjUNgV+3Q+AAFCTP1yuMTzMZd3yrccC+nmcU2Yp1Gw7WnnU2Tz33CNd98pNke7I4p1RyTuk/ry3Ee3HST0XLsrj33nt54oknaGxstP0/ysu5/PLLuf766z8Qc/aj4Sc/+Qm33XYbN998MwC/+c1veP755/nTn/7EV77ylSPe//Of/5yVK1fyxS9+EbDb+V555RV+9atf8Zvf/AYpJT/72c/42te+NlLI33///eTm5vLUU09x9dVX/8PO5RQ+umjZ1sb+V/ejexz4c30YMYPe+l6kKRnqDOLLttDdOrFAjGQ0wYQzxv1DLSLSrUy80seQMkj2YX1DQ8ogHstHmnX82TZpWSQ3vI3weEdCIHA4UMdNwKqvxdi3F33Oke2BciiAjIRQsnLIyvXgzfAQGYyBkYJsrscpYyiZXkK9YXSPTnQwRvf+bsyEidPnJH9KLiVzRnvgbevZxf17HiZuxkl3ptEb7eeh/U/SF+3niokXH/UZstmxjmfdj+G3UphoTiFJggatlgc8v+cLoa/jH65scifn0rylhUBbAH+ePfUd7Arh9DvJmzy67yq3Mof2Xe2E+8J4MjxIJKG+MKquoqiCoa4g/hzfMZ9rZnMj0T/+Gqu3ByUrBwyDzE3rSXO52b39Xc455zhksBA4LrqMwkfuI7Wjm25FkNs3ZBfKk6fSo1lMNByky9FT5Ml33iT29OPInh7Mvh5EzQGU8nEo2bnIYAD17HN5wfUU9UObqRgsQvdlQFoqDVo9T7keYpIxBc9hpllKtBuRGEJ6Rxt0Wc4MlFgfSmIASxtb2azPX4Q+f9Exz/O9UEvK0IqKUIpKbc867MkH2daMFAKlbJztFdxQh8jLRzksdEN4ffZkRnMTfnULzpbVSM2D1Nw4Ot9BH9hHeOpddvjke/D666v5j//4IrW1+7loxfl87+7/4rSVF/Htb38ZVdW4664vntR5DA2BlLZ9wuHwemFw0B6TpKdncP31t3H99bfR2FjHL37xAx566F4WLpzEv//7f3HttbeO8q4FqKyUZGRIWlpse4VJkyTBoO0RbJqSvj7B3LmSyy63EDrk5sFtt1msWSPYt0/gdMJ551ksXy6xMqBmkaSlXmC6YWI2pE+UGFWSLY/Dj/6sUhcQJOsk2pvw8MPw/e+bFBWBtgdcqxXUFoFlwpvtkg6voCxL4nUotOdKft8LNX3gjkKnJoknobJNsKxCUh6z/ekSi+QR7X5iyFb7YtrFuZVlK1gSUyycbytYKdJOhLZAawazUGKMGw5bsRidGG2AiAvM8iNbFEOhILfccjler49f/eo+1DEUTQehlo3D869fxti3FxkOomTnolZMQmhHf8YLXcd1/S0oZeMwNq+HeALHipXoS5efkK/gKfxz48Oqi0/VxKdwPCxYsJgHHvg9fX09ZGbak6m9ahcm5hGEYqqVTpPWgESeNFEqpSSxZjWJ1c8jB22CVknPQF91EY5lZx33O5BpZXNp7Boec/+Vam03JVaAchkmV1RQenifu6aBkaQ0UUJWMk624cLCJCIiNKg1gBg1F2iIJG7LPXI+Dhw45N8v3rA62jB27UApKEZJSwMgkl/Aj2v30vnmC6SqGmc7PXwmNQN1aJDYI3/BfeNtaJOrMLZuhniCvCG7ZpWEMMZXgKrZtZDuQEbCmC1NjC+eylT3lwgHoUvpIKgM0a/0kmqmHWE5dizIUBBz7y5bMXvQf1gIlMJirPoarN07wOvFMW8ByR3bkAMDCJfTtvHq60E/866RyfjjXRfh9oxW5Q53fpltLci+HlDshFq1tBylsARCIYy6Ayj5hTaRm5mFWjER80A1c4IB7knEOdBYx7TzL7EnXd9+3Z6oPfz8TMMmfJ0utGmzSLy22hZHOBxIy8JqbkTJLzxmF+T/Jnw+P1+9+lt8SvkXvtv3Hb79ztf5zbZf8dVF3+QG5UasrBMTohwTAqKXW2Ap6HsEotMOgYsvt4iNoTq28iB8m4X7cYG+WYAmSCyyiFwt4TDiWMRACYCVbyuED4e2H5Sh90zIS9tLWNtr20oYpXaIsXOtglkokUU2OezYKJCaQuSWw6whEuB+UOBYLxBJQdKn4kgoqDnAREba4CSSXet28PWf3k1XRwc/+tGvuWHaLfj+oEGTxMwFtW7YNqPY7jTDAcZ4iVYD+m5BYvHY5N67727A6XQyZ87C93sn6I7AH3crtIUE2W5Jsy6oGRCEk1DolfREBFJAqsMmb/tjoAibKN7ZJ9g3oHLpBIvlJZIDg/Bul0KhV+IffpTleyXVA4K1bYLLJx46D1WBUr9t/7AwTxKzJJqwSd/aQcGObkGmW1KZbhO7AzGoD4AeCqMFQmQ4BXkMMcXowhEcJNmaZKB0Ln0xlVy3pG5I4NXBo9mWE70xQUWagsjNZdacHCpz/7GEacwES9pWDZ1h+0Pj0sBK2nYYWW6oDUB1v2Dc+/D0TVpJXmp4jdB4Qc0z+/jaC99myZQlXDLhvDE9+P9ZcVIMlpSSiy66iBdeeIEZM2Ywbdo0pJTs27ePm266iSeeeIKnnnrqH3KgiUSCLVu2cPfdd48sUxSFFStWsH79+jHXWb9+PZ///OdHLTv33HNHjrGhoYHOzk5WrFgx8npqaioLFixg/fr1Ry184/H4qHTooaGh93tap/ARg2VYtLzbgqqr+LPtL7rm0FB1lUDXEFmlGQR7w8SCcTsBc3kFZfNP3Erg/SBDZjIvfhqrXc8CtiJ4SBmkT+llVexi0uUJtF4kk8hQCOEeLcUTmm4XVdGxwyiU4dl7GQigeLx4Muw/q6+XrjYnCd3LwN4uzISBUBV0p0Y8ksCb6WH6xdPInpA1iig3LZPXmt8mYSaYkGYX+JlAX3SADR1bWFK4kAJf3pjHsk5/CyHFiNWCExfjjInUaQfYq+1kQXIJAKn5KVSdN4UDr9Uw2BYACb4sLxXLJpBWlDZqmzkTs6lYNoGGdY30N/ZjJE1iQzEcisnuP7yMFg6QkyWYfNEMPMuWjukZmlz/DrK7a5Q/mUhLo/LdtezauPb49wY7jKLw1rs4u7uap7MFzZMdeP3pDLl0UqXCuQ2DmLs2YcTjqEXF4E8h9uSj9qArNw+iEayhAHLnNsTseTgvvpyhYi979j9E5lAchvpIajpKYTHFVZU0OBuoV2uYaswcOQbpSEFqHkQyhHQcqvxEMoTUvUjdP8aRv3+oEyejTpqMsWsnSl4+wulC9vehTpqMWlllK4I9XszsbGQkOmpwIaXto6WaAzg612J68pEu+3tguXPRBvfibFmNkT5lxCzqwIG9fOtbX+b111ezoHIqT158DZMVDV5fQ2NtDffd+xs++7m7j6rUPRqysyWaBpHIaKXr4CBMnWrbOxyOsrLx/OQnv2Pv3l10drbx1a9+hj/96R6+/vXvc9ZZq0bOMy8PLr1U8sQTgupqQTRqWytffLHtuZuVZVFWZo+rDqIoBz5+lWTHbklPj8Dvt7/eL7wgWLNGIRCwi6GsLMEll0imTpX8x+MqrUNQUCjRdfs8du+Gn/5U4WefsvDeqyKGJGaOZGcrtNUKJpWCyIaYBQ1RQX0C9iQlqgkpQlAlobFJsisg+Gy5pGQASACHfX30dwXuJwVKD2CBTFOInW0RP0cSP0+idkm0Wjg4uLdyJdHLLHBhp2dvPJQYjbDb/owKSWL26IIvmUxy++3X0NRUzxMPr8bfUEf87ddBd6BNrkIpG3cEUSE83jEnx44F4fHiPPd8HOecB1IekSx+Cv9/4sOqi0/VxKdwIpg/366LNm16h1WrLgHAZ9m/70mSo8LTwiLEBKPyqERwX7SfoUSITHc6KY7R9YBZvZfEM08gnC6USVNASqzODhJPP4ZWXII6YeKY2zwcCxOnU2yUsVffiZnfSZnvFdLbstBz7R8OKSVWb7fdJTS+AtfDNZSFixFOJxERZnCwgSFPhMGJdst9SASJiDDzk0e3rHq/kIEAMhpByTs02XfBrAWsKigl+earoOmoxaW2NVV2DlbdAeJPPYrrptvRdm7D2LXDVrZaFlgmjnPORxSVYLy5BrOxHqFpaBMn47z8amJ+lcddD7DNsZmwCOLCzcTkFD4evXnEOu64x3uwzei9pLwYJs8ddiuSyMpBnzMPs7kJOTiA8PrQZs2xf9dOAEp2LuaOrSOdeQBWIIDV2w26jnQ67a6xaBSrrhYZDqOWlSNDIdszCpukVidMQqSmM3Pfbuhrp2buPOZeeS1CVdFmzMHYswsZiwJg1tdiNtRBMomxa7sdXNzRhnmgGikthJSI3Dycl199xBjow0RyliTn7Vx+7v8l/zL1s/znxm9w1yu38zP/j7htxme4LHL9cW3KjgeZBpHbLNQ2EAE7EM7K5YiJefvN4Ngq0GqUER9dxxYBqiByrV17gW09YWWD2ggcNvy0PYcFVoY1apvOFwWuFxVExN6v07D9iI0pckSNK/1g5Ev03QKl0yaaARxvCtxPC6wUsLIlriwvafFMugY78PfqkKPQ2drBb374C7a+sZnTTlvK/fc+RUVFJaaE6DUWztUCrd32PzZzIDlDHvIJVgEEInh0onDXrq1MnjwN/b3F/Elgc6egNSSoTLfD1WblSHb1QksIinyQ5ZH0RwUmkkhCoCiSCakwN9cmfNtC8HKTYHaupHlIkDAZIYJhOCxNlbzZKpiYLilPPfT60mKLAwMqdUOQ5YIhE/rignSXJJgQ5HkOPRbSXeCPQlprN2dHG8jI8+MRJqSqJNNTCPX0cUVeH0+nZPNmi8C0bMJZCDucLs8rmZplWzWY7/tqnTgKfRKPDoGErao+KD6OmIJCrx1QCJB4nwfzWvPbPFu/muwpdjdsoKaLN1LeIWklubnq4/9QAez/Jk6KDL733nt56623WLNmDWeeeeao11577TUuueQS7r//fm644YYP9CABent7MU2T3Nz3KPtyc6murh5znc7OzjHf39nZOfL6wWVHe89Y+N73vse3vvWtkz6HU/joIxk3iAVjo6wgAHS3jpBQMq+E1MJUkpEE7jT3Ee/7R+HC2OUIBJsc6+jQWvFYPlbFLuaC2GUntgGHA6W0HGPbu4is7ENFWnDIbr3LO4p6zeNFX7yUxJOPYGGTnDIYxOrrIVo0md6AiqJYuNPctj2Bz8LoMDCTFrmTco7wCx5KhOgId5HpHk1gZ7jS2D9QS2eke0wyWCLpV3rwytEzcRqareZVRoc05VflkTUu0yaDgbTC1JHk6sMhhGD8knHkTc5loGWQurfrGYhG8fU0oIUHSOheWlpU+NNLVHU247ru5iN80sz6GvCnjFZCOF1M8afySs3YzyYYHtjU1WAcqAbLRC0t55zJM8l2mWx0ROlVTKYmNM54cxvZjz1BrL/fbrNRVXB7MJsbkeEQxGIIt339ZSSCSM9AP+NM4r/5OvLCGGpKBkL3IONxzKYGSPUix0s7wOPw8/CXY2RORe/cgOkvwcKDEu9HSfYTG3c5Un9/s6AymcRqbkQmk6hFxQifPYgUDgeu628l/vRjGHt3Yw0NovhScF50KfpZ545cz+Q7bxK7/49YgUGU1DT7urW1INLTcRb5ED1hpO+wCRkhsFzZqEN1YMYYDMX40Y++xf33/5aiolJ++2/fYOmBGoTLi5KZiYxE+Olf/oBP17nt1rtO+vwqKqCqSrJ1qyA72/YM7u0Fp1NwxhnWUX2kbr/9M9x11438/Od/5JFHHuCGGy5hyZLl/PCH91BWZvtaz54tGRiQPPaYQiQicLuhv1+hpcVi5sxDRLBaC843FOQ+qKsX7LQk72YIEqpEUQThMBQUSCZNsseebW3w6KMKNTUm7e12MN1BiwuvF1JSYMsWwcBLgpQ2sFLtMBLLAHzgGIRkXLAvDp1DduBEX1IwToWwCZ3Aohyo7ofXGwTXrZCjQjqUNnA/LBBRbF84AUq3xP2MgplvYkyH8F0W+i6B0m23NyanSTvwDpAp0PPxIXY/sIWmrXU0Bxto1JvoGuym77UegsEguq6j6zrd3Z0MDg4wpXIq93z5XxhvmIz3+BjvTaE4Nw/3xZfjXH72Sd/3o0EI8f7TPk7hnw4fVl18qiY+hRNBUVEJhYUlbNx4iAyuMqZTbJZTrx2gxCjHgZMepQspLE5LnnHENkKJME/Xvci27l1EjRgpDh+nFcxnZflydMWuq4yd25GxyKGwWCFQCwoxq/eQ3LX9hMhggEKrmMJ4MaRAfGk2ieeewgwesMM9gwGUnFycK1YicvMw9+22SVVNw2FZTNLyePO8JJsmd4DSiY6DhYkzWBwf/b20gkPI4JAtdjhGoO6xoGRmIXx+ZGBwxAICQA4NIpwu9DOWH+rCAygsxqyrRQaHcN9yB8l3N2Ls3Q2qij5tJtqc+QinE2vRYjvczeFAKSlDaBovOR/kLecaCsxCimQJERFmu76ZODGujd5KjpWHdpzhvPD5USdNJrl+LSI9Y2Sy0upoR2RmoS87C6vuAFZjPUppGdrMLGRfL3KwH9elV428X5omVlPDCBF+eMcWgD53PsnN6+3tFBSCJTF3bQNNQ5syHauhDjnQj3A4sYYCCJcL94VfwFj/tk3e+vy2vYQQEI3gnzWP7MZq2o3kSO2tLzwNo3qPbRVWV4MMDILbg1JcgnFgH4RCuG6+HaunG6u7yya0p04f07Lkw4RZDJHrLdxPK0xqr+AvVQ+yZea/8cPOH3D3f3+W7//+m1x//W3cdNOd5B1tjHYiEGAWAUXHfptaC85XFKRHcjBWRITAsQGMSYLEkkOJYvGlEk+jnTthZdpqYbVLkJgt7Zru4DabwPXye7bZBfpuEAUC8g6NQ6QPRB8oQ6C22jZmrueHrcfSQTaCmSOo1KqI5Ubp7O/g+Uef47l7n8KfmsKPfvk/fPzSWw+NxQQkFkkSM23bDefTAsceZbRXcgJQju1zvHPnVhYsWHICF/roaA4KXKocISvzveB3SLZ1w6R0uGyixDAl6zsErzYp5HolE9Ls2hogzws1A4LmoG2BIBk9t9MWEmzvBVXAL7cr5HjgwnGShfmSyRlw01STNc0KrUGBU5VcUG6RMOHVZnFEuapJi0hc4hcG4fouegYiIwI5I2ESD8Y5b4JFR0ihNSTojYBXE/gckskZkoEYpDoEZSknFiR4LCQtWN8u2Nxl+/dOTJecXigpHr6HpX6Yn2fxRqtASkkoKUhaAo8mKU+VRA3QhKTkfYS7RY0Ya9s24tO9FKbnk16QTbChl4lnzGJnz17aQh0U+U/ue/n3hAH+I3FSZPCDDz7IV7/61SMKXoDly5fzla98hb/+9a//EDL4o4S77757lLpiaGiI4uLiD/GITuGDgu7UcKW6CPeEcaUc8sdLRJMouoorxYk3wwMZ/7uzy27cXBG7jhXx8xhU+km3MkmT6cdfcRhCCBxLz8KsPWCnGmdmIeMxZGAQ/bTTUcaNds8362tJvP0GVu0B8PlQJlYi+/qwujsRbg+OlReAqCDx0G68mXZLmJQSI2rg9Dmxkiah3hApeaOfwC7NgUt1EjPio1SCcTOBpmi4tbE9CQWCMnM8Gx1rybXyR5QrUaKoqGSbR8bu6m6d7AknlmTgzfRiJpJEG1rwNOxFHeiAjCxcHh3pcdGb0Ahv2Iy+cDHqxEqsuhqSO7dDKIjV34cMjlZCScuiMiWNPzUcIBAYJDU1bfTrUpJ44RkSL79gE7pCIHQdbf5pzLnmBuYa9r21uruIPPGUTaROtpXHMhohuf5tzI5OlLRUxEEPt1Qw21ox91djbN+Cv36QomAB75Y2UzyYiktx4NZU2sN7yTGXUP7exAShEJl0I85AmMQrbxCv7UYqDtRpc2DK3FENiaZl0hRsJWbEyPfmke4a24zJrK8l/tiDNnFtmChZWTjOOQ/99DPtFsXMLFw3f5Lk2jdIvPoSMjBI4uUXsDracZx3EUpmFtq8ReiNDSQ3vIPR0WZ70KVn4Lz4cpRCN/QKkAYIO2HN6upA9tUiVS9be5/kzu9/k4GBfu6++zvccuMdmL/4EZbDaYd+AbXhIE+1N/HV6XNxd3fBMbyOx4KmwfXXW2RmCrZtEwwNCUpKJGedZTF9+tHVBhdffCX33PMjHnzwXh577BXWrHmRb3zjC6xcuZCf/vQPTJhwMQ8/rPD664L+fju0LS/PViG/+KLA54Nzz5WodeD9vYLSK6gPQaRFcJ4TJvtg7TR49XUIhQRVVXbAgapCSQ5U74IaBJZ1iAg+CF2HWEwg3xJozSB1+/s224BlUWjxCIyQpMMBPg26o6Aj8SgCXcIAEAhBlgG7gxA/Y7RFhL5LoPYJkpMPLbfyQDkA+laBMV0ifXYhfxC9vd1seuEdNm1ax8aNa9m9ezumaaIoCvn5RRQVlZCTk8v4yZPwe/wkjCTvrHudgYF+FixYgjeisae+jlcSPYSTMQAcikrla89x9hXXsfKyjzNlyvT/b2b6T+F/B6fq4lM18UcdCxcuYeNhXUpe6eOGyG084n6ARq2OJAnSrEwujF7B/MRoFa2UkkdrnmFt2wZy3dmke9MYjAd4tn41mqKxqvws+33BIYQ2hv2CqkIo9L6O27HyApTcPJKbNyAH+lEXnIZ+2hLUkjIA3LfeibHtXYyaAwiHg6KqaayckUtpbC9xEafILGGSUTVClMpYjPhLz2FsXIeMhBD+VPQlS3Gcde6hINoThJKTiz57HonXXwbTQPj8WIODEAwisnIQ7kMWHNKykIFBrJYmkq+/ir5kKfrSs3AsW3Hkdv0pKJWHDFdDIsgmxztkWlmkDXcBCgRBMcSTnoeo1vcw0ZjMOfELjsiBOBxRJULrqgl42rbj278b1eVFJuIIjxfHeReilY2Da24g/shfbVK2vw+ZTNq1v6ohDQOru4v4I3/BrK1BJhMoqWnoS5bhOO+iEesJdXwFrmtuIPH8U1itLbZPb3omqtOFNnkKVlo6VmuT3amYnY0+bQb6wsUoaenEeroxq/cgPD5kLILw+XGcdyHFW96mtbV55FyEx4v75k8Sc3uw2lpQSmai5hcisrJBSsz9e0nu2IrrkitO6p5+GDCmQ3CShTZ8euNLZvJb54M0Nzfwxz/ewx//eA+//p8fc9mKhXzyqouYsuhCTP+4fwijpNcIRAgOj6eRPpAa6NsPI4OB5HxJNA7O1wRKrwAnxJZZxC46FBYMoB0QtjXDYe5zMh2kyyaKjcpDy5UBsPwSbafAuVqg7xOIGAhDQBQsr03qajtUNndu5n9afsFAZICrb7qRL37+m2Snjt1VihvMcZC4QKK3glZnK4RFEpQugVElSVaNXatHImFqaqr5xCc+834u6QgyXJKEqXC4fYZXgyy34IwiiwXDpPjULElXRKKKQ0QwMKLA1QRUZkgyXNA6rCoOJGBbtyBmSOblSkpToD0ED+9XyHKZKAoE4oLT8iWFFRYZbtvWYUuX4PUWmzA9uC/DgoilUGpEqN/ZhQA8bhUlkiTUG8bpc+L0O8nywmdmW0xMFzxea9tdFHglgbhAUyQXj7fIOoqtuZEwGWgeIBlL4sv2HdWKUUp47IDg9RaBrtgk+Jpm2xv5tmkWZSmHAt8KvPB6iyCUBMOSFPrsYLuuCMzNtQP2ThbBRIhgIkSa0x4T5k0speNAEykOPx3hLgbigydMBndH4O02wc4ega7C3BzJ6UUS7/sXm3+gOCkyeOfOnfzwhz886uurVq3iF7/4xd99UGMhKysLVVXp6uoatbyrq4u8vLEfAHl5ecd8/8F/u7q6yM/PH/WemTNnHvVYnE4nzjHaxU/hnx+KplAyu5jdz+0h2B3EneomGTMI9YbIn5J7hM3A/zbSZQbp5tFtISzTItAWIBk38GX78KQdehprk6tw3/JJEq+9gtXShOLzo515No6zzhnV0mzU7Cf2p9/YHrhpGbYVQSKBtvgM9GVnoaSkoqSlk1ffi+elOhKhBMlI0k6GdaikFqSgex1jtkm7NTdzc2fxXMNqvLoHv8NHwkzQMNTMeH8ZnmYXO9/eDQKyxmWSOykHdbjP4/TEcvbpu6nV9pNt5pIUCXrULmYk5lBpTP27rquUkvBzzxOvqcM3ZD8z5EAfVjiEllNETHGRiCaxWpuxOtuJP/UYhIKgali93XZSstuLUlIKlonV3MSU8gmwYxN79+5k0aLRahurrobEy88j3N6RgY0MBTHWr8WYWDkSOmUe2GcHUU2cfMiCwu0Bjw8iIWRuLsrwcmlJ0FRQFMzWVgKxABmvJRHnxNmV04IjoeBIE+SGUzh7dSrqrnuJ5xeizV2AWmBXambSQf8GgVmXgpKeB7qX2L4w6r1/xn3nZ1Eys2gLdfDI/qepCzSSNJOkOVNZWnwa55aeCZ2dGFs2YXW0gceLsXWz3QZYXAKajtXVQfyxB1HS0tGmz7L3WbOf+NOPQyiEyMmFZJLEG69i9fbgvvOz4HSizp6L2duD1dyAkleAc9VFaFOnYyTDmL5itKFaDHcZyV07Ed1NaM4wv93g4mubb2FiUSmPvvouJSXlWEMBIv19iMMI35+ufY18fypX5pdgDfSfhAvfIaSmwjXXSJaeI2noh8x0GJ9x7HpdVVX+/d+/y3XXXcSaNS9y9tnns2DBEj7/+du49dYrmDbtX0lP/x6G4aCgQJJIwIEDglmzJH4/vPOOYOlSSfqbAqVPEK+Q1L4lSKbb5G5pB+zOAU0TxGLQ1wf5ubZHr9oMeg8UBxR8ScFAryTzMNFMICCYnC0pHhBIKbAybdI2JQLLg3D/oKQ9DBEFLJ/EqQl0A4x0EAkwg2BGBXFdkjpVYsx5T3hcBKTCEa2K0ilRBw75zfX0dPHkkw/x2GN/Zffu7QAUFZWyYMFirr32VubNW0R5eQWOYTZbrbEV0lod/HDvd9m9ezvf+OLPmNx2F64Xe9GkoM9vUZO9nazxT9MXaWTL/j389r7f8OPf/JTi4jLOOecCVq26mEWLzvi7iWErMIjV3Aiqilo+YRRBcAr/f+DDqotP1cSncKJYsGAJTz75EMHgEH6/PUFfZo7nX0P/TqNWS4wYhWYJGfJIi6T2cBc7e/ZQ4M07NCjWcrCkxbr2TSwrPg235kYtH0dyw9tIwxghBqWRRJomynCg7slCKAr6nPnoc+aP/brXh75kGfqSZSPLcoHcxNgSyPgzj5N4+QWUjCyUzByswCCJJx4BKXGuuvCkj8/5sSvB5cbYtA6rtxvhT0G/6jqMrZuxOjtQy8YhTRNjz07M6r0I3UFizWqS727EsepCHGevOu5vTFiEiIooaZYtEDAw2KFvoUvpwMLCLd00qnXc7/kdrrCLyca0I7axUV/Lc64n6JnSif/LCWauU1lck0Ne6hS0mXNQJ9qMnDZlGuKOzxL96Q8wBgdQsrORoRDxP/8W88BerNZWzLoDqMVl4HIh+3pIvPA0IjUVx9KzRvanz5mPNmUaZmszQlEwe7qJ//l3EIuj5heg5hcgLQtz/170xUsRQtjjkzs/S3LzBqyWZpTsXLR5C9AmVlJQUERHR9uocxJuN0pmJkphCdrkw7IhhEB4fVj1tSd9Pz80OMF4j5VxSUk53/7at/j6uRn87fEnuOflLTz00lqqir/LFRdfwoU3f5P8/MKxt/d+YTC2fYQK4j15fAhInCFJzJcovSA9MKZjoXXo/YBd2plgZUmUfoHaaneeiaCtCI4vlTg2CXt/mm1poXQDCegZ7OYXiZ/z29d+TURGuOrs67njG19g3LgT84E2KiB8k4nrZQW1VYAG8aUW8fMlR8tj3Lt3F5ZlMW3arBPax9EwK0eyvh1aglDgtS9DSxDSnTAj+1B9nOqEKZmSt1oVUp0SXbFD4JqDtiXCuFTbE/fSCRZP1AqqBwTtITv0bXIGlKbYYXQlKbC7D+7ZoWBYgqgBQkjyvIKrJ1lUZdrE88wcwZYugVeTKAr0xwQxA6J9YUKhJIbLRTCpkeGwUEwDyzBH7mWKA66cJFlcKNnQIagPCNKdkrm5ctQ5HY5Ae4A9z+9lsH0IaVroHgeF0/OZtGISmmP06KspCOs7FLLdkvRhnVi+tL2R32gR3DRM4DtVWF4iObNY0hO1Q/F29doE8pxcyaJ8ORIodzLwO3z4HT6GEkH8Dh95E0s4sHYbA+EBvLpn5PfweOiLwu92KdQPCtKcElPCozUKdQHJJ6ZZ7+vYPmicFBnc399/RPvY4cjNzWVgYODvPqix4HA4mDNnDmvWrOGSSy4B7NCONWvWcNddY7f2Llq0iDVr1vC5z31uZNkrr7zCokV20FF5eTl5eXmsWbNmpNAdGhpi48aN3Hnnnf+Q8ziF/0VIiRLpQIkPYLmysDxH/+wejqJZhRgJg+bNLQR7QmgOleLZRVSumIiifnR9IIPdQfa+WM1Ay4Ad4OZ3Ujy7iIqlE0bsGrQp01AnT4VIGHQH4j2SQCklyddexurrRZ005ZCdRH8fxrZ3cZy5AiXNLkozSjIonVdCV3UXDq8DRVNxp7gID0RIL0rDlzV2+905Zcvoi/Wzo2c3beEOVKEwzl/CrKYqdu/bgyUkihS0bm2lcEYhUy+cgqqpTDKquDF8B6+6nqNVbUGTGiti57MqdjEOxlCknASstha0netxuAtJxNNwJYcQLhcyEiY2EMKR7cCpJrFiUYy3Xgeh2L62gCgbh1z7BmZLI1ZnOxhJlLwCJt90OymvPc/69W8dQQYb+/fZJGlJ+cgy4fODqpDcuQ1t0RKMbVuIPf4w5p5dWIFB1JIyOxBDSpSMTEyn0056joRB05DJJMKfipKTQzQzheZ4D7l1Ope/WEVtxQBdmSHSagaYUu9izmAdhqsduXkDyY3v4LrxNrSJlRjbt2DU7EedMntEKaPmmpj795F8dyPWWWfxwN5HqB1spMRfiFN10hvt45m6l8huG2DyCxts9bjThdXeitnRjr74jBGfNrWoBONANcmN60bI4OS6t5BDgdGEt9+PuX8vxr49yNAQ8ccfRkbCCIcTc/8+4qEwIiUFtaSM6KSbcFf/CaVhA/pgI1Gvn395GR7cVcfVE6fy1akzSU/alazweBFp6VjdXZCewZsNNbxcu4//Xn4+zuHX3g+ktGd9VzcJeiM22VqSIrm8wmJC2tHXW3j6ucxduIzv/Ne/s3z5SlJSUvn97x/m7rt/yQMPfIXMzE34fA/idBbidEJ/P7S1CSoqJKGQIBKEnFo7+dmSdn6KqkJYBXqgdb1ClwGDg5LmZiiKCLT9ENds37d5BZKuQXi6F2IhgcuAcAycTrix2ELrV8AAJQSWFxQHTFDhojT4fZ7E0Q+WBlMXWhw4IBhISFQPOFPBrJQEInDepUcGx1n5gJCjA+AsEGFBqCTCs88+y6OP/oXXX1+Nqqqcffb53HHH51iw4HQKC8dWHKp14P2DgtIn+FHH9/n2tm/w9Qnf4prXPk1fC1hSYjkj5Eof+W0L2e3I4voVL3KdNwVx2TVscTlYvfpZXnjhKf74x18xZco0PvWpL3DhhVectFeclJLk26+TeOk5rN4eUFTUwkKcl16FVjX9pLZ1Ch9tfFh18ama+BROFMuWnYOUkueff5KrrrphuBNLRVd0KozJx1x3IDZIOBmhwDt6giHF4ac/PkggHsStuW0f300bMA/sQ8nIAiRWfx/aJJtw/N+G2d6GWbsfLAt13ARwe0hu2oCSnYsyHISmer1YHbYVleOMM+1g2pOAcLtxXXol1opzR2wn0B0QCRN/5gms7i6kqmId2Af+FLQ581HyCpDdXSReeIbOSW42VLbQpjaTbeYxL7mQSUbVKM/mNCuDDCuTPqUHv5lCr9pNr9KDR3qRUpJt5eKVPvZre1nreOMIMviAtpeHPPdiYFBijMPKNHnrY03ssUJ8LrSCHGv0fTV378Dq60Gfu8D2NAaswUESLz2PkBK1avpIdobIycOMRkmuext9ybJRFmrC7UarmASAUliMMXUaxvatiNR0hKpi9feilpShLzhtZB21pGxEHHE4MjOzaWioO/L6e7wg5Sh/YgAZj73vWu4fCZEMofdsRYl0IHUfyayZWN6jE7qO9jdIDW3nX65Ywe1XXsArW2p5+NVNfP+3D/Cte+5j8eIzueyyj3P++R/D5/v7Mz2MMonUJSLIISuFJIioIFl1lJZ/F1hjzb1IUJtBbQe1D2QNWOmgNQiUPlAGBUaFhdTswDnLJ4mebds1ON9ixEvY8Fts6H+Hvw4+wF8G/4IudG73fZJP3PwZ0j6fz8kO/YxpEJpiofSD1G1P5cPRHrLDxhIWFPsl23dsxeFwUFl5ZBj1yaAiDS6faPFCg6AmYOfe5XjgkgnWiOXBQZxfLumOSGoH7XBqie3Fe3mFhWuYtVtUIBmXJtnXL3ihQdAalEzPPuSZCzAUl7QGBYvyJSV+O1CtMQgP7Vf44lyLFAfcOMViQppNCCctSHdKagYg04yh5megRhMkYwYDhqCsOB1dhVB3iKzyQxOHhT64rEICYxPAB2HEDXY/t5fB1gCphamoukJsKE7DhibcaR7GnVY26v0tQUEkCcU++9hbg4KOMATi8FJSYWWZSd5hNIMQ9jW9aLzkovF/f3CdW3OxpHABj9c8R2e4m6zxBZiGSfW+3Zx/+oUU+U5MFXyQKJ+ULjlIIUUNyY4e2N0rmPMPDtk7EZwUGWya5hGJ54dDVVUM473TRx8cPv/5z3PjjTcyd+5c5s+fz89+9jPC4fBIkvINN9xAYWEh3/ve9wD47Gc/y9KlS/nxj3/M+eefz0MPPcS7777L7373O8Bunf/c5z7Hd77zHSoqKigvL+frX/86BQUFI8X1KfxzQiSCuGofxNG9GWGEkbqfRO4iohOuBO3YFg+KqjDutHKKZhYS6Y+gu3U8GZ6PdPuwkTDZ9exe+hv7SM1PRXNqRAaj1L5Zh6qplMwrxuGxfzWFEDBG0dutdLJJvsH+KY+SWuZiRn86U9rzEAhEegZWdydWWytqsW38pGgKk8+dRDKWJNQTRpoW4f4I/lw/E5dX2P62Y8Cre7hl6sepDzTRHenFp3vwtXp5teZ1GvKa6db6SBF+JibGYe2wyJ2UTd4Uu2CdbsyiKjSdQTGAEyc+OXYBZOzfR/KdN7Ea6hFZ2egLF6PNW3jUUCerqRFnbJCCglIaYqmYiQQOQ5DQUjFiBqWyDWd2JkLTbfVoxaHeJkXXUUrLMffvRaamQzyOVV+L9YsfsWRCJWtefo7Pf/5ro3doGmPLRlUNEgmMdW8Re+gvyMFBUBSs5ibMmv128evxQjyOSM+EgV6scAgUBZGTh5qegVYxieopBRwoTWV6/RDGgRiFDX6cg9ATT7K9yodVORldqEgpMWuqSbzwNOqEiVhtrSCUUS2TQlURLhdmQx0H+ktpCDQzPrUMh2q/J8+bQ2wgzMBTD2IFfSNBepZhQGszZu2B0QnWXh9WzyF1mtXYgJKSeoTnsjQtrKZ6khvXgyURKanIYBAcToz6GhIvv4Dr1jsxMqoIzf0PzM1fpqEFPvnOfur6B/nhyo9xadVMjOo9mDX7UcdXIDQNfclS4g/eT6iliW+88iynFZRwvtNtB9e9xy7lRLGrDx45oKAJSVmqxLCgflBw/16Ff51tjcxqj9x+y25peqtNIfX8H/Du1xfw5V/cx39++hbcmmDp0s9SU7OAnTuvYWBgDqr6F7KzV4wEvA0OQm6uxJ9qk7Rql23tkJEBtbWCUADKYtCelFgeUFXBgf3gb4Y8DXotmJYDsypgbp4k/zXBS0OSIVMwPlVyTR5c0akgHWBMlqj1oAwIUEBJlcxeIfnm5y22bBE88YTtZ5yXJ6muE4R84B4Hm9ME84sksxccWegkp0uMiQJt37DqWIXBtgF+0fdTfvvV/yEQHGT27Pl85zs/46KLriA9/ejdEAfheFMgeuHfe77CT7f9iK8s/Dr/WvhVki8J+rIkoRSB7IsQ8UBmxE9eexENTSrlbg+eiZNYNm4Cy5adzXe/+3PWrXuTX//6J9x1101873vf4JOf/Bwf//jNeE7QX9Lcu4v4Yw+BqqFOmASmidXSROyv9+H53JdQck5sYvIUPvr4MOviUzXxhws1UIvetREl2o3lKyKRuwjLdxxjzg8B3mIf805fxJ/++j8MTRHUBxpxqk4W5M1hWfFpePSj18TprlQ8mpuhRIg05yHbr6FkEL/DS8pworqSnoH71jtIvP4qxs6tCEWx1a9nnm0Hhn0AOJizYNYesPc5vsL+XT+srpNSknj9FRIvPAOBQSR2zaFOmowMBVHKx4/apkhNx+rpsuu6EySDrYF+jF3bkYMDKBmZqNNmohYWY/V0E3vgVxgH9rFn/ADby9sZcAUp6UxhoTmDQmWY+MvJJbB/HS83/oQNM1x4pY992i62ONZzVfRGFiUOiQecOFkWP4eH3PfSpNYzpAwREREkFuONiSM5GvmdTmKtmzHkftTy8SP11rv6BoIiSKVxiMyaYExin7abnfpWVsRHB8MZO7fbk+aOQ0p/JS0NMxzCMk2093QACK/P9uxNJOyE2zEgXC5cN95Gsuw1jC0bkckkjrPOtTsNc4/S2n8Y0tMzGBjoP2K5WjUdJSsLq6kBpagEVBXZ3YVQNbRZJxcAeyIwMdmqb2KLvpGAMkCFUcmixBnkW8dX6CrRbjy7/wdtcB9Im1h1NmUTrbyZZM7Yx6p3bURqXqTmQQNWzZ/IqnkVhNu282jbeB5+Yyef//xt3H33p1my5EyWLTuH5cvPHcmcOFkYU2xrLuc7ArpAqiBidtBbYo5Eqwa10VbUGpXStn44yrDY8YbA/YyCCABRcGwSIyHC0iGwsiRoAitFEr3OxMwH3KDtARSFPYndPDrwNx5ufJDmZDMlailfcdzNzSm3os1wE7tePWkieAQqI/kTh2Ntm+CpWoWB4SxUhwLdW+soKRk30n32fiEELCmUTM+WNAQEQkjGpzJiE9AVgc7wQa9buGumxa5eQU9U4tagMl1S9J7hbq4Hcj2SpGkTvIfnwBgWdEUE+V5bbQy2xUS5H2oDsK9PsCDftik4p1RyTqktKPnPDQpZLmln7Pi9UJSDFjcYMBRkoYra039EFtCJoq+xn8H2AGlFqSMdv+5UF8logrbtbZTNLxm1bYcCYI+n9vQLWoI22R0zIJSEP+xSuGPG0e0oTurYotAbhRQnowL1lpecTsJM8k77JuLZAkVTye73csXEi0+YE9o/YCuvD9cSujUwpaA1JJnzERgOnBQZLKXkpptuOmo72OFpwv8IXHXVVfT09PCNb3yDzs5OZs6cyUsvvTSiymhubkY5rCg47bTT+Nvf/sbXvvY1vvrVr1JRUcFTTz3F1KmHWsq/9KUvEQ6Huf322xkcHGTJkiW89NJLuFxje5eewj8HXLUP4Wx9BdNTgPTkocQHcTY/j1Q0YhOvO6FtODyOEQL1o47+xn4GWwZJK0pDc9hfa0UVBDqG2HjfJho3N5NbmcP4xeV40o8s/FvVJn7v+SUtSgOO4kHiIs67ej8XbJ/K8n2TbE80RbEThw9DRmkG86+fS9f+bmKBGO40N7mVubhTj/39UYTChLRyJqTZytgnXn+GV31vY2oWPrx0yC5a9XaqnBMpaywbIYMBVFQy5dG9gI1d24ne9wfkUAAlNQ1r/z7M/Xtx9PcdvRVQ10EIyvPiqEKjrcZDIhRDTwYpFR0U55ThvPTjtgRUHk5uSUiGkU0HkOEQSmExDA4gjSRm9T4WY/K1jma662vIOayVSS0tR2ia7Zvms4t5mUwgo2HUCRNJvPISKArqjFlIaWFs3wKxKDIeR1gmMhoDRUXJyEKYJtIwYKAfmZGB42NXktQDbDqjAm+JJG9fE1o8QfuEfLahECzMRA5zE0IIlLwCzKZGu4j2eMEyj1RaGAkUfypDiSEk1ggRfBC5YXC2d0DF6SPrKV4vpsuDHAqMhKxIKZGhAOrM2SPriqxsW6l7+FU1DBAgh4awOtuwgkFkX++h66+oJNe9jfPKa22S2OFnXTN84rnNpHl9PHbNbUzOGf7MDCtHRm71kmXIaJQf/Pjb9IZD3HvGuTjmL8J52dVHhAOeKDZ2KCQtKEsb3ocC49PsGfbdfYLTC0cToq+3CB6tEbg1GD9lDvVnXM8jv/oSZbNWcNeyErKyICNjEVdfvZknnriBurrzSCT+gqZdidMJhgFLl0p0ByQXSbS/CcQguJwQDkBBBDo12CkFgYAkPV3id0N7r6CoQHJRKZxdPuxX5YEvA3fNgqHJFn6H3WbGJlA7BLHpErMARECCBKVXkFgs8XrhjDMkmZkma9cq1DVDaDoMeiBVAa8TujPgwTqFm6usUf5n0guRWyzcfxVEXg3wy8af8avBX5BUktxw0yf5+LW3UHHYhMtxYYFaI/lM7V38sea3fG/Zj/mX2Z8lWQ+qKVFV7GdBJIKMRAhLi/Swn2SXhX7FUpSycSObEkKwePEyFi9ext69O/n1r3/Ct771RX72s+/yjW98nyuuuP64RWDy3U3IeBxt4rDyX1VRxk3ArN6DsWsHjrPOOfFzO4WPND7MuvhUTfzhQe/aiKf6T4j4AFJ1IbrW4eh4m0jVHRjpU46/gf8FhEWIZ12Ps0XfQPTyJLs/sw12P8rMorkEEyEer32O9nAnN1ddg6qM/dtX4M1jRnYVb7dvxJImXt3LYDzAUDzIWSVnjCKSlZxcXFddi7z0Srtd/xiTJCcLaVnEn3mc5GuvIKMRW4TmcqFVTUMdNwGhqqjjK5CmSeKZJxCajphk3wfZ14uxeQOYJjIUHFGNSikxmxuw+vuIP/042tTpaLPnofhHZ11IKbE6O8BIIiNR4g/eh9nWgs2ESdSSUpw3fILk6ucx9uzkrQsEzy0IkhBOHE2DVM8cZJdzHzetTaWsNxMpJJ1qB/Gkj0pj7ogSuEmt5wXnU8xIzsEjD008np5YjoLCm45XGFD60aTKRGMKE4xJCMOi7Mn9eNfuIrffQ1T5Ieq48Tivug61qIQepQu3HM2WCAQKgoAYPPJCq6qdMvteOF2QiCPDoVEKahkYRJ04CY7zfFBSUnFe+DEc518MlnVSnw2/P5VgMHDkoeYX4LzyOjvguq4GKS1EajqOCy5Bm/H3tfW/FxLJs67HWO16BonEIV3s1/ayXd/MbZHPUGyWHXN9Z+NzaP27MdIqQXWAlKhDdbhq/oaRVol0HClsEVYC3vu9FIJUj861F5zJ5f/y37S1tfDMM4/y+uur+da3vsjXvvY5yssncOaZ57J8+bksXHj6CU9io0H0aolZAfoOm7w1qiwSMyWuFwTO9QrEAWmH+MYusIgvP7LzS2kD93MKUkjMybYvsGOtQK8GMx+MSRKzQCJV0GpBbRNECmNsWbeBtW++zmvrX2Bn73bStXQu917B5dqVLIjOJ+E26csLsCNtOzytMuvyGfhz3l+w9XvRHoKnahUMS1KZbpOBwQRsau8iIy3/+Bs4QaQ4RttCJC14pk7wTrvCUELiUBTGp0quqrSozJC0NQs2dAhebRJMypCsKJFHKIln50rWtUsODAhy3Pa2uyL2+CLfO3rsoSq22jhqHinSMKVNtDo0gSzPR2zaC4YL4XFiRSTR7gAZ6S4Mb5y77rqR119fTTQaoaxsPD/96R+YMePYHSDJaBJpyREi+CA0p04imsA0zFFk8KQMSa5HsKcfWocEfod9zKZlB8nVBwTr2sXfpQKOGfBMvWBTh0IoaZO007IsLp8oSXGAruhcOP5czihaRF9sgL3jXiEjbNtHnCg8un2fD4e0h1M4PgIWEXCSZPANN9xw3EHQPzok46677jpqC9wbb7xxxLIrrriCK644uom8EIJvf/vbfPvb3/6gDvEUPmQokQ4c3ZttIthltzJY7myQFo7OdcRLz0c6P3otRH8PEuEE0rJGiOB4KE7nvi7iobi9zJQ0bmgi1B1i7jWz7Vm/w/CK4wVa1SYqjWlIhxOzei9dpZI1VdVMb8wnfVcnSn4B2qQjCRpPuofyhWXv+9gtabExsY2YiDOOUnzhKLmdAeLJITq1TQzETzv+RoYhTdMmUkOhUbYDVmcHyddfQZ+/aFT6sZSSiBFFnTAeJTsX2dZEeWk5xXk+Yq1htMYGXKefgfum21FycrF6ulHSM5BdHajpDtSB/YhABzLQiiM3jXhLDbhSUdLTkf4UFocCyA7Jq3/+DR//zx+P7FedMg1t3kKMjetsNbCqQjRi23iUlJJ44RmU/AI7VTk1DeH1ID1eiEVRisvscI/QEEppOUpqGjIaBSMJbjfC66PU78ftS2PHDBet8ycjLEky0E/djldZYqTgONwZ92C8qRBo02bYfr1tLSgFtsJJdnciHC60WbPJdDtQhUrUiOLWDg0wAskQbs2DclhVqOTkoqSnYbW3Yw0NIRwOrK5OREoa2qJDybz6wsWY+3bbqdbDnsFmcyNqYTHKhAqsR/6KFRi0W0+Hp1at7i7Mxno7fC8llc2b13PLk3+l0pfCb6+4gVR/KoIEeqQGV3YfWmYYy4iA5kGoKvsLCrh3306+fPtnKbzpc7yr5NPXL0gJwfRsedIzzd1RcGvvKbqEPegaio9eHjXgrTa7UCsaridW3fFT7tv7Jr//9s18bPbLVFWpTJwo2Ls3h5Urn2PNmltoabmOrCyLuXOv4mMfkyxebG83vlgyuBMiLygk6mFKws7Ue84B/RYoimBoyL50Pgf82zhJwaTDjrMThCXQi20Fs5TQMgQH3DDegPytAneRRKgghoZVInMPnVNVFVRVWaxvF9y7EeY1CrwBAVISbVfY2S/ZnilYVDT6OgRrBvn5c7/knuqfk5RJbi+9g89N+iIpZ2QTrzjB4i4KIgoJV5Jb13+Cx2se5p5zfs/1U21lpO4GQxUkEhJ0HaWgEDkUgB7AJ8i/8WycK6cftWNgypTp/PKX9/LlL3+L733v63zuc5/gscf+xg9+8CvKy4+uIpd9PYj3DI7tZ5FAhobGXukU/inxYdfFp2riDwFGFFf9Y2BEMdKr7N9PKdEG9+FseNImfMSHaykmkTzsup+1ztdtK4HJ+QiPStuWPUycUkG5MYHURArbundxeuFCJmWM/TwTQnD5xIvQFI3tPbsZiAfw6z4uGHcOK0qWjr3OSdrqnAjMvbtIvvoSIiUNtbTctuLZsonYfX9ALS5FpKUhvF5ISUMGh1CnHLJLEFnZmP29KE4XVvuw96zPh7ljq91xlZ2LuXsnxvYtaFs24brlDpS0dKRpklz7JvHHHrQtwNz25Da6A33eQoSqIk0Ts6aa+F/vxezpJlCRxWuzNuM0NEoDmVghMBu6qTujj1enVHPrW6cRifYQdsQQJaMtIfLNIpq1eprVhlE5GAoKpyeWsyCxhE6lnT94f0mH2kpSJCh6s4Oc53YSzFLxV8xBiWRhVO+Fv9yL+3NfoshVyg59KxI5si8TEwlkjiGP1KbPwti9AxmLIlx2IWT19SKys9Fy8jAb61GycxEuF1ZfL2gq+unLx3wGGpZBfaCJoUSQLHcmpf4iZCg4YqlxorYcsVgUl2vsokyfMw+1YiJmzQEwDdSycf+Qzpsmq4HVg88geyXOHhfR3gjx3hhv9L/C7q6dlIXGoSjKyJ8QAiGG/4+Jq2utHcblHyArxUN2mpdsv5NcZT/ezA34KpajvkeMkMycibv+MUxPASjDnXXxfqTmwUi11b+FhcXceefnufPOzxMKBVm79nXeeONlXn75Of70p3tQVZVJk6qYOXPuyN+kSVVHt75yQuI0SeK0QzWYY53A+ZbAzJPIFGxRQCe4nlMwJpiYpaM3oR+wxQlmJTZRLOztWulg+cAol8SNOLt7d7K27g3WfGMNG5reIRaLkp6eydKZy7nb+AbnB1bi6NIxh5LEPUna5/XTNWUALd1BX0M/ze82U3XeBzPxtn9AMBBnhAgG8DsgGejEzP2AvZkPw9utgpcaBZkuyaQ0iJm29cO9exScKuztE2S4JJqAt1sVGgKSO2dY5B/G72e64NapFi81KuwfEEhp20hUJST7+pRhcY/93mDC9tgt9B5ZY+sKVKRL1rUrZI8vQu0NIJo6SSSDeC3wlzgpWJjH1Tes4rTTlvKXvzxLZmYW9fW1R4SkH0QgDjt7BX1RcCW8mKpGPBTH6Ts0eR4dipI9IQvNOZqSTHPa4XA/fFcQSNq6ZyFsW4rxqdAZkezp+/vI4NWNgtWNgiy3HbwXSsDadvua3TLVGrluqc4UUp0plBSV0dHRelL7mJUj2d4tGIjZ5wTQHoZUB1RlfPgWEXCSZPC99977DzqMUziFDw5KfNC2hnDnjFpuOfyokQ6U+CDm/2dksCfDjaKrJCIJHB4HQ11BktEkqkPDne7Gm+3Fne6mv2mA7poeCqcf8rqJEKFa302WlYOCAuXjkMEhstvaqS3qpj6+jXnZ03Feed1Ri7ckSXboW9ir7cLAYLJZxczEPNy4h9WgQYSmjXjHHo5APMiQN0hKl5/85g5m7dyPJxzFkpIQMVLVJ5Bnz7Y9dY8DOTiA2daCyMkdbTuQk4tVU43V1oLIyMRqrKe5YQcbgvvZ6Y+iOFwsm1/InLUR9P17UBB4NA115Xm4brh1RCmiZOegr7yA5OP3I+t2YJLAHIqiOgycrh7Mjj6SRqFtuqpqZKsOpqWm89qbr/Lxw45T6Dquj9+EMWkyye1b7YC+aTPQ5i1ERiKg67YK2OWGaATh9qH4fchYHLWkFKO/D1xuiEZRp820z90wMBtqkYFBCvOnsKRwAa80vclgYgin4iAgA+Q7M1nSYCGLbOWvtCyszna0aTMR2TkoioLrsquIP/skZk21fc6paTguuhSlbBzj6/azeMDL+lAtKZmFOFUHvdE+lPRUMitnYzW2I3wpCGW4AHG6UAoKkKqCNTiAOm4CjnPPG/GSA9Bmz8Mx0E9yzWrM2v0ITUcrH4fj4ssxO9qxOjuQsShmNAqWaV8/AJc9sNvS0sS1117A1Gkz+d3iFThr9iM89aRm7MfhDiEKc1AHn8TYtpfIlDtIOHP4t3+7g8rKqVz86e9wzwEnDQF7qxJY0yK5frJF5fGdCUZQ7JM0BEYXXUkLEEcSy4G4neyb7jxUBLh8aaz6/L089u9n8T//89/811e+zM03Wzz/vGD3bo2lS//Mrl0q+/dfz6JFCZYtu3Zk3dYeuL9ZcNog7DUFpgJdBriiUOGCrhSIxexCql/AtmYoiWCnfCMhaHsOS49NBL/SAE/XCAJBcBsww4IzAzB5EljnWMRPGx4QvAe1naA12USwlSZBAVdEonVA/fZDZLCUkicff4hvfvkLROJhbp35ST4374vkeHNR20C+Aok5csQzbkxEwfWKQN8oiAYj3LHl47zatJr7Z/2Ni4sut8X7JmhBSOZJXGHo7gPNraFbmTgdEF0pKbhg9jF2cghFRaXcc8/9XH75tXzl3z7NWctm8+VlX+fOq/4VFmhY78ldUkrLMfbuHqWwl0YSBChZOWPs4RT+WXGqLv6/By3YgBpux/CXHWIOhMD0FqIF6lEinVjeE/MT/EehVW1ih2MLhWYRKVYau0KtpC8uYOiVbhqvr6eYMnwOL63hdjrCXUclg8EO0rl+ypWsjCxnKBEi051+wuE5JwMrMEhywzuYu3eAqqHNnI2+4DSE22M/T+MJ1OHJfNndBd2dtkWW14s6aYqtAN72rk0KvweK7kAZX4GSloaxawdWYz1mcxNK6TjbH1fXkckExp5dJNe9heOc84n99T5i9/7WtkFwuYe7lYJ2sNpAP9LhwOpsxxrsx1rfjkhJo3lOBgPeCBO6bKJVpKajBAJkNyepS2kk0J2JPhCl9YwsuqalcvhPqYmJgooqxx6aO3BQYpVxS+RTPOJ+gGaljinv7Ea6dUoyZpFvFCPcArV8AmZTPeaBfcyfdRqbHeuo0faRZxZiYdKhtlFmjmdmcu4R+9AXLLKttbZvRUoLhus4x9kr0ZetIPnisxjb3rUn6AsKcCw/F232kdvpifTy1+rHqRmoJ2ElcAsHVV0Wl+yJ4wnH7LC9xUtxrFh53MmDSCR8THWrkpKKMueDsYXo6+th944dVK/dyZ79O9jTuovO3nYGBwdGdZiBbZXnyHTSl9VDzBlFSNu3/eCflBIpLSzTgHAnliUJJ5roCYRJGodLBV9FURTS0zPJysomKyuHzMxsygvzmOIwqEx5h4riXNJcArCIF52LmTrxiGP3+fysXHkRK1dehJSS2tpqNm58h+3b32X79s089NC9WJaFy+WiqmomFRWVlJWNo6SknLKy8ZSUlJOennEEsa9vE0iVQ3WfACsPtGrQ9gnM0vcQWgeD6AQkzATNQ03sD+5h/+Aedod2s6t2N7UDBzCliVf1smjK6XzpS//BkiVnMmXKdLReBe8vFPRdglh2nKAahDSB4TEJp8dQFAV3qovuAz1MWSmPakl4MkgM56K9d04jHujCWXFideLJwrDgnXZbGHJwnODWoDxVsr3Hrs9nZB8KP8t0S6r7BRvaBR97j2CiyG8TwkMJ+/8pDqgPQHfEDlzLdEl6ItAaFuR5YEu3wKHaBOhBJKNJpvZ10rZ9gG5TRSvIIZ6TD4Ew0/IUzl6ayc9/+18UFBTxs5/9YWS9ksOybw5HaxD+vFehMWALY6TMoNybx4TWVjIzk2gOjWggiuZQ8WZ42PnULizDIrM8k/yqPHS3zswcyWUVcP9eQb5XkuqATLdtF5G0GPFQfj8IJWBDpyDdCdnD1z/dZQft7eoVtIUPCXYOIj+/kF27tp3UfubkSBqL4Z12ha6oPc5Kd8KF44/0i/6wcFKX8ZZbbjnue4QQ/PGPf3zfB3QKp/D3wnJlIXU/SmIQ6zBCWIkPIh0pWK4jE5P/2ZFWlEbOxGzad3XgSfcQ7g1jJEwcbp3UAtuPVdVVsCThvvCodRUUBAITm2hDd6DNmovs60JVq3Gd9zHcuVeOBMe9FwYGD7rvZZ3zdaxhz6L1vMFsfQHX71qB8tJrWI31oKpoM+fiOGcVymEeoE5VJyUjBaVjiKo1+1DjcdpT/VgKSE1nenMDiTfW4LzgkuNfCIcDNJUuM4gldHKkGx3F9jPTNKRpEvvzbwlsXktnVzU5GCwqzmfbmVU86E7QsGo818opaPEkakEh6uSpI2EZI7s482w8xi7k2noSnQZ6ioXuskj0xFCUJEqoDTOSgNQ0FK+PpQUl3FdXTTweH2klllJidnYgIxH0WXNQyieg5tuDR5mSijppMua7G5EuN8LlRpoGDAVR8vMRGVkIpxMZGkJ4bHI9JiwiwX40nxdPZiZCCC4Zfx5FvgLe7dpOIBFkceEC5uf4yOh8FrN6j21TYRiohcU4L7hkRB2pL16KOnmq7cs3HL5idbQR/dF/Yba3scqMM1mLsn56nJrKPAp8+awoPYPScSnE7/s9RvUeCASw2luRQqCWj0fLyMRx7vlocxcc0R4oFAXn2avQ5y7AamsB3YGSV0D80b+S3LgOmUxAKAjBoG3noetIVYFEnE1bN3HTD7/B9OmzeeCBp3FLSfLdDfj3/wqH4sTMmYeSV4QhDbSBfbhrH+KXa0327NnBc8+9w0utTuoDgoo0iabYnll1AXi8RuHf5p54yutpBZKdPfa6uR67yOuICCamS6ZmjS7a/A7w6JJQ0lbqHkRaxRlUnvdl7r/nW1w7/myqVs7mxhslg4MmhgGpqb/jy19W+MIXbkFRTK680lYbvvOOoHCvoMIBj6ZKQpZAC4MehQlJGDQgbAoURTK7TJLWJVAHQTFtD2CjQJKcDGqLoC5T8uQBgSqhShNY4yW15ZL1PXDL2ZIFY/j/HoSzQyCjYKXbRDDYdhBWBJz1doXd2trEV77yaV577SUuK76C75/+E3LzDrXgmbmg1QvUFjCOxjVIcD8qcL6pUKvUcs26y2kcqueRBU9y1qyVKN0C0QkIiZkP1pUmaU8r6DsFyQGwXCDOlkz9ylECUQ7flZTItk60PRaO3jwubFrJuZN38p/1/8F/vvJ1ntv0FH+78DHSPpOPeZhVqD5/EcaWTZgHqm1PRNPE6upAq6hEnT7zuPs9hX8enKqL/y9CjKiBR+GwLpsPG/1KH2ERpEiWAOBw6qSckU//Ky30bmsjMT2OMIX9PNVOzAIk25NFtufoFl1/D6yhALE//hpj1w5kMomMhEm8uQZ93iL0M5Zh7NmFjEZGFK5WTxfStOygX9O0lZhZ2QiH07amisdBU7E6O7C6OpE93ahV03B+/CYcfT0kXn6RxKur0WbOPhRcqzsQKakY27ag5BWSeOEpSCRQysoRioo1OGB3ZYVDGPt2QzyGjERskjASQQwNIVqiKJYyHIQMJOIoBYUwMRU1MIheMp6UVacRWr6FNvcWPEY6GhoWFq1aE2XGOErNcchEwq6HhEApKsHSVGJC4pKCMnM8nw99jcbEHpy9P8bjSMFtHFIvCqfTtsQIBik2Z3FT5A5ecD5Fs9aAgsrc5CIujF1GmrRremugHzkwgEhNRcnMwn3zJzF2bcesqwVdR5tchVo5BaEoqFdfj3XeRRCNIjIyxyRyLWnx8P6n2NW7lzJ/CW7NxcCezazrrMHjKuRS90RkIEDiyUdASpznXXTMz0YkEsHjOXbWy/tBb28369a9yZ49O9izZyd79+6ks7MdAK/qZap/GouzFpO/pJi+ZYNsLHqbcRkT8WX4cGd5cKa56FDb8Eov3wz+CO1olIqUeHb9AkfnOxjpU5AIhiJx+jrq6QkmaMn9GL1DcXp7u+nr66W3t5ueni42b14/SoWYm+5nwrjxjJu8kQkVQ4wfP5EJEyZRWFhyhKpYCEFFxWQqKiZz3XWfGL6OYXbt2s6OHe+yffu77Nu3i9Wrnxnlx5ySkkpRUSmZmdmkp2eQnp5J5r4UUsJ+3AEfTs2JYRmYlonsNIk9lyReY5BMJhkcHKCnp4uepi7667rpWtPFQPzQtjNEBlW+aSwdt5w7Z3ya6eY0ZhXNJf4F1Q4VHobjbYHaLkicJgn1xOjaN0Cqw092fSr9pUOEsmJYpsThUY/qWXyyKEmROFRbOesfrstNCdHBDsqLPjibiMORMO39uTEJdkUw4naWjCfdTSTpwqEKnOqh3xhFgM8h2deR5ExPGG+GZ1SXrxCH/IEBxqfBbdMs3mhReKdd0BAEnwYpuuSVJoWtXZKbqiymZEIikmD7EzvpPtBDpaXQE5EE21tRJpUw96JKFhcKHCq8/PJzLFt2DrfffjXr179NXl4BN910B9dee+uoc5MSnq5XaHzv+GpKFY42DzmRNsyEQUZJOkbSpGlT87DyV9C2q4Ou6m5mXDoNh8fB/DzJO8MNHVnuQxYeScsmWt8vhhIQTtpE+eHwO6A7AkNxYAwy+OWXnzup/WgKXDlRMi/PpDEgUBWYlC5Hhd992DhpZXBpaSmzZs06YobsFE7howLLnU08fzGuxmdBmkg9BZEYRIkPEht/BdIxhqztnxyKqjD1gipcKS4693YCoOoqOZNy8GXZTxwpbXXge32QXbiYkZzLK87nyLCy0NGRiqA9L0qunMZk51Uo741cPQx79B2sc7xBrlmAf3jqOEqELcnXmbB2Awu2O1Gyc8FIknj5eayONtx3fGakjVoGIbcri2TzOryREH2ZmWgenagzRpY/H58owHh3A45VFx7X07VDRHis0uRA2y6k5idX+Dk7ns/U5gG08nEYB/ZjrHubdk+cplwPuWoKha39uN9pQLtkEVuj7cyffi4zc6YedR9CCDTRR8JQSLT0gGVhukw0NYlQLYRlIPt7wDBQJlZy3oCbX+3bwYsvPsUll1yF1dtD9Pf3kFizGjk0hNA0lLJynB+7Cucll9uq4Y9dSSw4hFlXg4xEEKYJmj4cliJslQogdQcN8QCdkQH03l7qzjmTcJmTy2MGeWgsyJ/DgvzRPk5m3kSMHVuR/f0oeXloM+eMss4AUDIyUebbCfNmexuxB+9HBoOo5eNRFYWJHW2U7YuTXLiSjBmLRjyElbu+QOzpx0g8/TiioAht0mQUlxuztZnEi8+iTpqMOEoYmJKeMTJJkFz7BslN6xFFJYjqfcjgEGj6sLIyG+lwsKWvhzu/+1VmzlnA/fc/NaIccc+egMtKx3JOQBn5rjswvUVs2/wmP/jBK9xxx79SMnkuf9koyPNIDtpUKcJOrm0NCpqGYOIJNhCMS4WlRSbPNijs7bdnmhfmSS4ab40ERByEV4cFeZJn6wW6AmkuCCego0Hw8eJv8qTvFW79ytWsWb+W1GtzSZtz8LdW4b//+zeoqsq//uttmKbFNdfcROsewVU9kJcUnG3Co0mJqguCUUg3wAxKXCkwtUpySbcg04Lw2RJnv0QkQQy7Fpj5kuptMNQHU/0gcyTGJEmaB3p7YetWcUwyuEpK3kYwqEDa8LJBbAeUKf0Gf/jDPXz/+18nNTWNe3/5OFesvwSpS0ZRsgagMhIM0t0NXV3g90NJCSgKqK3g2KKw2nyJm1+7jgx3Jq9du46pQ1WYGoQ+Y6J2CqTLDjmRPvCfbpFWDfF+gVooERUcdxBhtjYTf+JxxCs1JLstknoxqZGLSdVm8P3KH3HJwiu47rkrWProQv7mfYLKb8wZ2aZaXIrr5k+SeOk5zKYGhKKgL1mG87yLjvCjPIV/bpyqi//vwUgZh+ErQgs1YaRMHCaGLdRwK8nsOVju44di/aORYqXilh5CIoifFIqKsunvH8I53kffX5tgiqAh2EyeN4fKjIpjbktalt3d5XAeYX/zQcF4dxPGjm1YwSHkQD9IiRUOYe7bQ3LtG3aIbWsLANqMWXanhZRgSZSMQwIPkZEBRgpmzX6snk6s3l5IJhA+P8bmDcRdblzX3mRbS4x1LsOEvrlnp22/peuIYd9WkZIKHe3IaBSrpRmRmgaZ2YjBfkRRMRIoWd9H9gqDlqxOSpudCKFgVVbQNz7GWfHLyR5nE3PnMZEuY4Aardruog8kmNiaxkUsRfTtIfL8M1jtrZhC8O6Zc9m4ailD2elkWiqnJzwsSLoYr08nmjsNs3ovHKZxkaEQOJyIbFsMU2lMpcKYTK/SbWduWNm2Ui8eJ/7CMxgb1trruD3os+fivPgy9HkL0ectHPNeKSmpcIxQwOZgGwcG6ij2FeLR3chIGG9HL5m6n+2+JOfEdfyeAiwg+c5bOM4485idf8dTBp8MGhvreOmlZ3jppWfYvHkdUkry84uoqprOVZfdwOyGmcwKzaRkyjgUt4LSB0oftBf38Z3zQgTFEGlmBgJBjBiDSj9LYyvQDqq5x6orhCBeej5qsAFtYA9S85JpxsjMdlC86HqqSs8/6vGGwyHq6mqora2mru4AtbX72bJ1M48+9ldisRgALpdr2ApiHjNnzmXWrLmMHz/pCILY4/GyYMFiFixYPGp5IDBIU1MDTU11NDU10NraxMBAH/39vdTW7ifcHSQyFCJYHSRuxtEUDU3RUKWK1qChvqChqipp6RlkFeeQXZrLdP90CnryyHHlUugrokqbRo4/B+EWiLB9Tcx8SexSiZU/+ndT3yWQqXawsDvNje7SCBlhshPpePvcDPpDJEJxyheVfmDB7hPTYH6exTvtAkfUtkwYCEYxokPMHf+P6eZyaZCajLD1QISU3p4RT13D4yaZl487x4OU6sjcohEz6GoKoQ90s/7NetypLkrnl1A2v/So6ugJaVDgtWgIKHg1KBv+2kopqQ3ACw0KRTLI7qd3U/9OPf5cP9n5Xor9LmKhONH+FqYms3Go9hixubmB++//Lbff/lk+/ekvs2PHFr7+9X9F1/URgQpATxRqB2w17+Hjq6IMjSbnRC6ZXkap22CwNcDWR7bhzfLi9NpMtpEw6NrfTceeTkrnlVDkgwvHWTxbr1A9YG/LocCifIsF+e+/5kpzQYpDEogzapx28P8ZY/w85OcX0dPTRSKROKlQQSHsceK41I9mjXhSZPCdd97Jgw8+SENDAzfffDPXXXcdGRkn0Ut7Cqfwv4TYuCtA6Dg616JEu5AOP7HxVxIrO0qA2P8HcHodVK2azIQzxtOxu5N9q/ehKLbywzRNhjqG8GR4yJ5wpLLj7Ph5NKsN1Gi2NYCFRbrM4OLoVaQegwgGqFX3kxTJESIYwI0H0dPPvowEp0382CHlRWqaHaK0Zxf6nHnEg3G2P76TwuZclGQuSbWRQRFENTRy0rKYmjUZbSiOjCfANG1m6Shob+ngR2/8kvpQPxlmAb6eIM3uDh6UnXyiZDpVF1xC7G/3ITKy6E7W45ROLIeDUF4GqW095PSGaXRbdEW6j3m+Vn8fQy/swTrQDoZhB5u0JTBSdNz5SRL7NfvJbySRvb1MXLKUhdEgDzzwey4+/1Kif/4tieeftmdBc3KR8ThmcxPxhx9Ayc7GsWwFSm4e7k//G8a+PcjBAaz+XtvHrasTq68PbcoUxOnL6Ohrp7OvDel2033+StovXkmjFiPusrgrkoFzjMpULShELTjSA0uaJnIogHC5Rtl5mLt3YPX2oFZWjdxHtagEff9evPsacMw+lHytZOcgVBWloBBt4uRD+xxfgVlTjbF7B47Tzzzm9QVI7txuX+v9+5ADw8FxyQQgkOEQ25IO7uhtY+bEKaOIYPuA4wgriVRH/1D3hy1u+MVbzKiayle+8p8MJMGStrfvqOuj2MuN4wtHAft9jx4QrG1TSJp2kaIAhT5J7lHELOeWSaIGbO5SqBsETxBOr4WPh3Uuu+xRznp4MVe+dAkvyjUo+W6s4Y5jRVH4wQ/uQdM0vvCF23EpLlYcuJaiAGganKtD1BKsNaBdBaHCpAIoWCIp9kD2bgEZEvcBgdoDmIAEtQOCXzYJpilYr4MxEcxsOVIh6DpEo8e+DlXFkhXrYY0H2obXc0qY1dTE3Zs+zuaWDdx44x189avfwe9PIRGwcL6tYPmlTf6aoDULjHJJOB+efkSwYYNCMGi7rlRWSq6+2iKvE3688/t8c//XOLt8JX9Y9QBprjQsDJRmA9NrYC55z4V3gjkDNE6sELMCg8T+/Dvk9kac/UXIdJXEUB2D0d8jCj6Hs2UC83Pm88a1G/j445dy3h/O5KcTf8/F11yF1dmBsWcnMhJGm78I56VXInx+lKN4qp3CPzdO1cX/B6E6iY2/Gs++P6D370YqGlgGpr+U2PjLPxLK4FJzHFOM6WzW11FoFpNXnE57yMvAVQX0ffcA7769jpmnLeCqSZeQ6jz6BJWxZyeJV1djtjYjHE60ufNxnr3qhGy7TgZG9R6svl5kcAiRngnSgqEAGEmswQH0lRciDRNz3247HwHs3ISycpSDXVXxOAiB6+M3YuzdjfnYgyhp6ShFJailZWBJjE3rMKbPRJ1QgfD7kb3diOzckfVlKIh27vl2hoHTicQmw4Wi2H/+FGRgEJlMIlJTob8X4XShVVSC14O/toaL96byyIJt1FSZKJkZKOlJKo0qzo0fGn8UWSV8JvwVdqibkWteJ+/VA2T2xXEk7iXc0YaSk486bgKvTSviuSl5ONrqSPNMocXn5i+uIRLCYmnCi2PpcqINtZj1tYisbIhFsXp70OctRB1/WGgxKrnWaIVjYvXzJF542p78LyxGhoIkX3sZTBPndTcfl2iTpmmHwb1HHRxORoiZcTz6sGghGoVEArfHR0AYRISBXzoQaWlY3Z22hdhxyGC3+/2RwVJKdu3azksvPc3q1c+wb99uXC4XZ5yxgh//+HecddZKsofvv7YLfL9SMKYBwwpLKwtEBHJfz+RjS6/hEe8DVGu7h0P4VGb1zGXVEyvx71KQHkgulMQXS3t9I4Leux012oXlSCVS+Qn0/t1oQ7WYrkySOQswso4ddOf1+pg+fRbTp49+n2VZtLU1U1d3gJqaanbv3s66dW9w//2/RUqJz+dn+vTZzJmzgHnzTmPOnIWkH0WIkZqaNuY+DkL0gfe3CnqtsOs1E9ReICywssFKk4goCAmxVZLYxRKSoG8VOLYKRASSkyXBBRI0idoEKGCOAzlGjSx1GGlUdelklmfSW9tLIpxgsCtAwDlE7uRcSuaWHPPanQxUBa6plJSnwpYuQdSA6Y5eXgHK8v4x3cRWwiD/wAGMYCadige/M4ah6QTQKWlpxCSPJl82xakgLEn1/gFiQwaVagh/to/oYJTql/ejO3WKZh3d17glCH0xQYn/UP0bMaAnImiuH8R8YgcZTU0oSQPLCBDuCZM9MRt/to9Qd4jBlkGyx9u8gWVZTJ8+h7vv/g4A06bNorp6Dw888PtRZLBhHWV8JWzFNZqK06cy2DaImbRGiGAAzaGhOlR6anoonVeCELCsWFKRbrKvX2BYtpJ7Ujocljd30vBosKRQ8kSNoD0MaQ4IJe1rtbzYGnP8VjCcpdPV1U5xcdn73/lHDCdFBt9zzz385Cc/4YknnuBPf/oTd999N+effz633nor55xzzgc2Q3MKp/B3Q3MRq7iaeOkqlPggljNjzLTW/x/h9DoonV+MZZo0rGukv6kfhMCf7aXynEq8mUcWVVlWDv8S/je26+/Sobbhs3xMNWYekZArpWRoKMDAQB8DA/2EQkGqtd306t00JOtASrtzUko62zpwN3pY07h/RDHl1h14+3rI2LmV7NJSBg4EGGgeoLi0kLLAJbj3d5NiQCThZlxOKWnOVMy+PehLliGOMQsX7A7yzFMvUGc2UKgWoKRCIhjB75IE8yNsmzSXabn5yJjtVeaSToJJ2y7DcOqoCQM1GkO65ahgtLGQ3LyBZJ+FqyQT2dOFGTRRUgUyLsHng4Jc6I8jfH60WXNx3/Ypbiwr5c47r2Pf4w+S/+KzWP194PEgIg47AM6ysAYHSK5/G/2M5fagw+VCn3VI1Ssv+BhmY71t3VBahvR4eNZqpj/QS64vg0RaKm6g3LSo1ZIc0BJMM8ZOuH8vjB1bSax5GbO9FeF0os2ej/Pc8xA+P9ZQwPblO+z5LqVERsLEX3wGs6EWtbQcffEZtqVEVxfiPSqOg4puGTgyEXpMWBZWR7tNTru9yOHUbkJhWpMJPt3fQVVaBn/60rePUIxY3iJMVzZqpBPTXzZyvJ/6xTMEYwaP/fZv6LpOpgrFftuDy+845PXbGYYsj6TkBB8XW7sFb7baAQTlTpu37onCS40KE9NNKsZQF7s1u/BcXmLSG4WcZwUT2hTMSZBGCY9+7BlWPXwm1665gvvPegIKDn32FUXhu9/9BeFwiC98+XaemFhJi3cORXGJdAou0mHxEOx0g+WSrC2TNDXBrjZBNAo3xkENgZUG6EAS1DbQXlVQl0sSOyCYahdKAIYB4bBg8uTjsOOT4QK/ZPxmQXUOOHwQP/Ay39p4Hb50P08++fooRUr8AonaK9FqAcu+v2aBJHa5xZq3BatXC7KzJRUVEA7byuRIZIjaLbfz3P4n+OKcr/LV07+JKhTMhjqUPQOYMkj4Fy+gLV6E4+xVR1i8nCiMndswmxtxuitRdQ3LC4r0YfbtJRJbh1OrQOmV5Obl8eLK1/iXtbdz5xeuZ++mNdylu5B9fbYns1BQJ1fhuum293Ucp/DRx6m6+P8mjKyZhGZ9Bb3nXZRYH5Y3n2T2PDus+CMABYWrIzehujX26jvocXYxbnoO5+at4OEX/0TfCzV84a77SHEd/YfOqN5L9M+/g1AQkZUD8TiJ555Cdnfh+sSnjtupdTIQQiD7++zAXE3FCgQhmbS7oOJxRCiIvmgxxhYHGAbanHko2TlYwSBWh93aTziENm0mjsVnIDvaUadMRa2oHF27mBbm/n1os+ehL1lG/KEHSG54BxCQkoLjtDPQF5+BuXMbibf9iGgE2d8HKalIywLLQiktg0Qc4U9BpGfYYbcZmXYwWlY288/+GmW5JrvcuwgrIXIj+cxIzsErR/ccp8o0Fq31EHu4E9x5KCXZGHt2YnV0gNNFNMXLO9PK8CqC3PpmVHcGmRUTaVWSvO6IMD/hxjVjNq7rbiH52suYnR0IhxPHygtwrLzgmPdHhoIkN6xFSctAybGV7MLpxBKC5PYt9u9n3tjt8TIUJPHGGox3NyATSbTKKehnrkAttpPEcj1ZpDj8DPQHyG5PR230IdsmEY/1kZFrkCaH7dKCQYTbgzhOt8zg4AApKSfXURONRnnwwT/zu9/9nObmBlJT0zj77PP5whe+zrJl54ypNFZCwiYh31M2SC+IIMwLLqJIlrBH30FURChqK2bBL2bhaXVjpUmUAGh/EygtEL+sE8++36AN7gMkSInpKyI6+TZiFVef1LmMBUVRQqugsQABAABJREFUKC4uo7i4jGXLzhlZHgwOsXPnVrZt28z27Zt5+OH7+eUvfwhARUUl8+adxrx5p3H66ctHiK3jQWZC5JMWjncE2i4BTpAOUAbBmCxH1NBKDzjfVEgsMLHyILlAkhyjm8yYfuz9JedKtEcUiEpwQ0quH2/YTcwfI/2CTPIqi8YMHPt7ICLg3yZYeQBWOiVGFdSnJvkaoGkffCAmQH/TADmd7SyKDrDLmUPE50e3TGYMtjF1sIWgHqShwEetdBMLJYkHEyx2DVHlTKAIDX+un8G2AM1bWyicUXBUdXDYgAFbRE6O27aneLVFYSAqmbWvjo7+EAHppUCLkpLmJB6M09fQhyfdzvw5fHIzJyefiYcJfcD+XL3wwpOjluV4oMgnqQuATz+0iY4w5HgkRcM/O0IRyLFEGpYcFewshO2LXOQ/MUHHiWJFic1ZrG0XdEcFHk2ysszi/HFyzDnd7OFui+7urv+7ZDCA0+nkmmuu4ZprrqGpqYl7772XT33qUxiGwZ49e/D5Tiwd9BRO4X8D0pGK6fjggy4+CCStJJs6t/Fu53aCiRCTMiawuGA+Bb6/v8VQCMG408rJn5JHoD2AoqmkF6eN8hc6HLFYjNbGJgJ1A/Q0dVHdt5tXBl4YIX0P/js42I9hGGNuYzPrjli2BXiMA0e++Z1X4d8/DYCm6vjcfjwuL37TJCUZxyNVUpt3U5zioSAvnxK3k6L9eygoKMY/RuHYuq2NrqFe9GwHbsXu7dDdDqKhOLrbS7sxgEhLR8nOxmpvpzArn55IH+FkhIyISczrpFofItdTwNSsymNeW6upAenPxswuQImsQ421gWIRj+oEGzxYUkGbXAW6jjp5CsLjZdWqS8jMyOS+e37ElwaCNmNoGFg93Yhg0FaomCbWwIDNvo1BfAunE23SoR/hOBbdKW5ESgkJeajod6FgAkPCPOZ5HISxZyfR+/5gB9Vl5UAsRuKFZ5C93bg+8Snb89SykIYx4vdr1NVg7t+HkleA1d+P2dSAsWs7rptuRy0uwayvGSM8SxxhR3E0qOXjkb3d4E+1E8DjMaQQRD0ePhfsx6eq3LPiYvyzjwwNkbqXeNmFePbfhzZYjaX7ueeZLTy/tY2//PQ7FJXYQTmqAqvKLboiCtUDAq8uiRp2mMOqMmuUn++xsLPHDp47mBIrhF0I7eu3k4Ar0o9evOR67D/voIDDfKtm5Mzibxc/zhVPXMTt91zP/6z8K9phXstCCH74w19Tv3E/t9Veyp/mbMJfnYNvwMTCoMISVGbGeWOFYPc2H319kJoCDW5JV7PAkyUpHn4UKBEIuqF+k2Bbj6C7D1paYNw4iccDLS0CXYft2wVCwPz5kvcKH6WEN9cJXu+Ayl6YeEDydORH/Kn/31k6dwW/vPc+MjJGqyusHAjdZaHvFqi9Estvq0diFqxfK0hNhazhj4vfD0K8yG9+80kUZYi/XPQolxqXYsQlZnstckcjIplFbEobMhkh/vRjyFgU1+XXnNhNfA9kfx9IEOIwTzq3QOhejFCr7YMxHFLn6XPzm5vvozwwju//5DuEJkzh7guvQFEUZCKOsXsHiTWr3/exnMJHH6fq4v+bsHxFxH0nRqh8GEiXGdwW+TTtSishZYgsM5dMVxYzvzGdj31sOW++spoLL7z8qOsn33oNGRwaRagKvx9j53bMmv1olVM+sGNVK6fYpICRtKd+TRNMA3QHOB22AtXhRCktR0lJwXv3t+zAufVrMXZsRSBQZ85GP+30wyajxZiTMdI0MTatJ7H6eazeLlA1cLtRVG3E81mbPQ992xaSG95GxuJY3V229VdJKe47Pkty83pkWyvKuAk2kS0lZnsb2uQqlNx88lWV/ETpMc9ZSknynbdAiEPdWqYFKSkwOEB/aIBBxaKorQcZjyGD9mR6pqXSpZr0KibFlo4+dwHazDnIwQG7s+sEVNtWYBAZCqFkj26BFympWH29WIMDKGOQwTKRIHrfH+ygvpRUhKaTeOs1zLoDuD75GdSCQrLcmSxIncX2l3cR642iuFUSyTRS6jxM7JNo5RIr2IvV14Nj1YXH7Zqprt59hCfp0TA0FOC++37L73//C/r7e7n44iv50Y9+zcKFp6MfJ6jOzJLgUhBBiTzsEioDkJwEuCHfKiQ/bt8r93MCZ6uCUXmIEBVD4NisoOS8jqbswkirBNUB0kQbPIDrwF8Izf2mvewfAL8/hcWLl7F48TLA/ow1NzewefM6Nm9ez+bN63jwwT8jpWTSpCksW3YuZ555DvPnL8Z1DAsYKxNiF0m4SCKCkPJtFSvv0HmDraLWdktczwqsHFtwkJwukSeZ155YItHqJPoOYNjXXE3RMa9WKVs6dljZ3wMRAv0+g2jTEKpUSQmk4XhboM+wa3JF+Tvkp8eAkTCQpkXJQCdFoptk2ItDmiiRGNKyyIz1cVZWL9GyIjoODDK0o5oJpaMnMZxeB9GBKEbcGHN8/26X4NEDtvK1ZlCQ7oJADPpjkGZEKIgMoKV5CYc1BnsjpCckDq+DWDBOoGMI3aWRUZI2sr158xZRVzd6TF9fX0Nh4WiVtjY8vrpv72Hjq6TAq8N55daI2CSjNAPNoREbiuFKsT9/yWgSy5T4crzUv9NAMmbgy/aSMzEb3fXBEvP2cUrOKJIMxGy/4NRjaEicTvsYE4n4B3ocHzb+rmkVZVgtJqXENE+MeDiFUzgF+wf68ZrneL35bTRFw6E6eKlhDbt693L7tBso8n8wSdTuNDfuNPfIPtvbW6mrOzDqr76+hpaWxhH1rs/nJysrZyQ8oLi4lOnTZ4/8PyMjk/T0TNLTM2xiVoXnXE+yybkWQ5goCsj+AarWxrjoVyGcuh+9ogKRV0i4qZGQx03i4ssJCdi/oZrGHY3glUTiISLRILH+dob6uuiKhdgQG6Snbh9y7ZqRc/L5/JSUlFFSUk5pqZ2EG94Xw1CjWJkWprBQhYIy3O8/FAmR58lG6DqOZWcT++u95HSHqVSy6OlpwYhEqJ5Ziju/hCsmXkSG69iVi/CnIJNJTH8FZmUO5tY3MWprsCIg0p1okycgcvIgHBwhbx0OB1cuOJ2/vPwcnykcjyscsjeWTCDDIWRgABxO2xf3OAXrQTgR5FgatVqCLPMQGRzFQgcyrOOrdqSUJN58zW5przhEgguf3w4QqT2ANmO2rfit2YfIyUcmEpjbtyC8frQ581H8KUhZiFmzn8QrL+I47yKS27Zg1dWg5OUjDQPZ2YZaXoE2bcYJnZs2uQpS0iAUtFOZkwYyHuWbZpwmI85DS84j78ZPoOTkjrl+ouBMpJ6Co+NNtmzbwr8/sptP3Xgty6/84qj3Tc6AT82w2NAhaA0KsjwW83Ilk0+iyztmHtkKBXaNnDzBSWyzTKLvHPanGK47l+WfxV9mPcQ126/gppsu5Te/+Su+wwZ5LpeLB259jOXfW8h/NFzOo7N+gGjz44778BoqyoyNtHid5Oacx+mnq+g6+NvB+bygvk+Q4ZD4EURN2GGA05Tkp0lmFMHu3YKeHoHHI0kmJW431NdDTY3Ctm2S22+3yDyM262vh6eeUhCKpG7JEL9dfSsHWp+gctJX+fJ3vk5GxpGfRdEHzg0CbY9AOkHxguMdQaJVkNgFqSVAjiRuDvHWW19i164/kJm5gv/+79+yYloxyQcl2gETsSuGiY/olHaiU1pRtALQNZIb19mWK1knr9QTqWmAxMqwoEW1vYw1ML0R3Ik8lKDEioJ2QGCWSOKrJHd2rUJf/QL/uWcrKRvf4tOLltnkRUYWxrYtyAsvfd9K5VP458GpuvgUPkoQCAqt4v/H3lnGyXGdWf9/C5qne5h5NNKImcGyZdkyycyx45hjx3HiMMfZ7AY32TCtE0PiGGJmOwZJFqPFo9EwMzV3Vd33Q41gRJZsObDvnJ/mg3qqq25V91Q999zznMPh5uyzZy/gzDPP5Yc//DbnnXfJsIXGA5CGgVlXi5KcMoxQFV4fMhHHam+F00gGa/PPQB0/CXPbFqx43A79ldjet0kBRCBg16j9vagzZgGgBJJxLrsQ57ILj9qfOnY8Ys1KZCiI8NqLMTI4CAKsxnrCLz6DWbnnYNeRkpWDMmmqbWe1ZSOOM5bg/sTtaOMnkXhvC3KgH618DI5zz0dJy0DNzSX6yB/tMF6HAxmPo2Tn4LjwkpNXTBsGVlcnJCURFiEMkcDpcyOkRBoGju3voZ85hvBgkKSePky9HrW4jEhaAKcUuOVhn4um2TYRJwklkIzw+ezcisNUsnKgH+H1Dgt5Phzm7h2YO7ahFpch3PbcQmRkYu7dRWLtKtTLbdXrxP5xDEQG6MzpJk4cd0oqOW0G7rY4XfFm0tMVHEvOxXn+xSccZ3t7K52d7UyYcOLasbu7iwce+AV//OOviUYjXH31x/nkJ++juLjspK+JWQbxSRLHOoGVJpEu2y9Y6oL4GdZwP2AL9EphB+Ye9rr0g9IYx7G/C3NKziHSV6gYSSVog3Vo/VUYqeNPelxHIixCxInjlwEUTkxUCiEoKiqlqKiUK674GAC9vT2sWvUmb7/9Os8++xi/+91Pcbs9zJ+/mDPOWMqZZ55DScmo43a1SA2kLiE+/HWlB7Rq2xZCBuyATbNAEP64hXkKHK70QugWC32XQG2SSAcY4yRmwcnv46SPJSUdWxtoTK4iODqIIhRSQmmM2z0BbZ29zUfV3ePPSsLpcxLsDCJjBklmDCklkWgCd8CF5lDJTHOQmS3pCgtWWXEq2xz048CpSnK8oIfi+LOSEKrAMi173juE9jA8USkIGzA3R7KjS9AZEbRHJLoicanYIdkCRLKPgXCYvv4gSZokHkkQD8apOHs0qUWH7gW3334vy5cv4uc//z4XXXQFW7du5M9//l9+9KNfH3V+E9Lt+dWGNnt+leGxmJUtqTjs1pJWnErxrELq1jcQ7ArZa3GKwJvuoX5DI/Fg/GBea3ppGpMvm3SQND6d8Oocle9yLBwIgY/H4++z5b8XTpkMjsViB9vh3n33XS688EJ++ctfsmzZso9s9WQEI/i/hrqBRta0bCTDnU6KKxkAy2uxt3c/7zSt4WNjj6/WOFkEg4Ns2rSW9evfZd26d9mxYyvhsG2NoGkaRUWllJWN5oILLqWsbAxlZeWUlY0mLS3jlB9+d3Av8/Uz2KvtJLZrG8XPVzG+fjR6ST9mbTVs2YyS10HatJk4r7gGbbzdJzRr3EI2PLgW2dON12kgXC7CagCpaky/ZhrppWnE43E6Olppbm6itbWJ5uZGmprqqa+v5Y03XqKxsY5EwvaQE6rCzgw/qVlZJGdmYAY0clKLmJ0zwz7vOfNxqSqJFW9R0tFGZlkunZPKmT1/HqMzK/Dq759YrE2eRmLtu7anXFY2YvrZiJBAdHSg5BfavsahIPqZS1ErDhV8V+WV8HvL5ElFcgPYfni2IR2YFsLhwGprwaquQh01+n3HIRAsjHuo0eLUKwnSLZWYkLQpBpMNJ+XmSSgPEgmsxgaU5CMKf4cDq7WF6ON/Rps8Df3MczCysrEq92D1dCE8XrTZ8w6GYQkhUDKzMRvq7DTqG2+2w7NamkFV0WbMwXHRZSftM6jkF6LNnI3x7kro7wWHg4dknFcGovwkp4ipX/uuTRgf9+IIEpkzqDeyue7nv2DylFl86Tu/P6aXY5EfivwSTtJP9kiMTZVs7VBIWBJ96BEYMUARkmI/ICXqYB3qYC1Scdhp0q7h1zs+W6JvlmiVAivT9mRTugTnLriIR+5+jtvvvZZLLlnMQw89S15eAbEYBIOQPD2Xx2b/jXPfPYv7Hd/mF7O/jBgMIwa9hOZ1seONUjL97ei6vbgUzIZomcRZK+hygSdHsrcHIp3gSoVoBqR7Yd48yc6dEAwKLAs6OmxVcEqKZPt2werVguXLD12vHTsEg4OQm1vHY49dRDDYxPLlT2Gal7Bzp8WkScOvregG7+8VtCqB9EmUDoFWDWYqJE2RpKuCtj3Q1/MWL++/lWi0m3nzfk1u7u2MG2dh5ULoXguxroP4b5/HytEwsxKH1DnJqVi1+7E6Oz4QGaxNmoqa9wbx5kpEejFau4IRa0ERPpxFc4gVglFqYRZCbJFEpgMtBtcXjyKUnMxPVr+Fz+HkE9Pn2mbOpmH/HNl/OoL/Exipi0fw74Yvfel+li2bw+OPP8z119989AaqiuIPYLY0DXtZGgYCcZBgPV1QHA48n/sqkf/5IbK5EdxuLIcDKxS0ieDBQaz6WpTsXPQFZxxzH1JKrJYmrMYGUBTUKTMwt22ySWRp14dKYTFWQz3WwIDtMTxEhFt11RjxGEphMWblHjhjCcKXhOOspTjOWnrUsbQJk3Hf8zmMrZuxujpRsrLRp808ppr2uNA0YjkBmqr+To3TxMQkuUhjTH0cb3M/yZEwo+ra2TqhmGIELikZqNpNnzaaC97dhWdzFWFFsWu02XOPsuc6EYQvCX3uQmLPPwVCIJKTkYODyM529KHcih7RTaW+ixgxCswiSsxRdk1nmgeJYMC2NfMHMPftPfhaX20f+Wk5jMkpx7ASOBQHolDQtbuZ2JT5eJeOPe5i/uHYtes9ACZMmHLM30ciEX760+/ywAO/AuDGG2/n9tvvJSfn+B6qx4UCkessrDSBY5NACQrMIknsLIvE1CPqQwGWx7bcGgYTQIIWRSpHsEuKDtIE64MRSf2ij1ddz7NV30BCJCgwilgau4DxxsmJLA4gJSWV5cuvZPnyK5FSsmfPDt555w3eeed1vvOdL/KNb3yWoqJSFi8+hzPPPIf58xfjPfzv3Q3xaRLXywpWwLZywAB9vQALEpOxO91M0KoErmcUQvdadjjwycIJiWmSxLRTOrVTRlesjcr4eyheQVIsgCUsOv3tbJsUo2BvMcDB+eXphjfdS+G0fAbbBgn39WMZpq0HURWEqpBWnEpasT1XiKSlUOlMJ1bbgQz4MDSN9uYIuUqcpEyLd39jd+Zmj82iaFYhroCbnV2C7qigIsW2PJibK6nth+5GgUORpKe6iaWl4G9tI5GWSldGFiVuD46+XpKykpj98Rlkj80eZj8xZcoMHnjgSb73va/z05/+JwUFxXznO//NZZddd8xzLAlASeD48yuhCMacPZr00jS6aruRpsSb7mX/imqkaZFWmooQAjNh0lHVRe3aOsaee+Lu3dOJiAFNg4CAwqRDliGJxP/HZPBdd93FY489RkFBATfffDN//etfSU8/ubbfEYxgBIfQONhEJBGh0HeoYFGEQqozmT3dlZiWiaqcmh9bd3cn69e/y/r1q1m//l127tyGZVmkpWUwZ84CPve5bzBq1BjKykZTUFD8vi1TpwINjSmJGUzuqyD0x53ISDFqfj7kg1ZegVlTBU4Xrjs/jZpl22CYloFh1pGhbKKtOU53yB6Py++i/OqFpJXYD0GHw0F+fhH5+Yda7qSUWE0NWM1NmJrOrvYEK55fS3V4H+/1bqKztZmWXfuxDJM9f4PtP3uZceMmM3bsRMaNm0D5svMYKM5iT5qTqKZQYTgoTjjxngQfqI4dj+Piy0m89hJmlV38qhOm4Jo4GZL8oCho5WNQx4wd5nlUkJvHFUVl/L61gUtKx5JUF7LJK1Mg0jPQFi6G/j4SG9edFBkMMN1wEo8EeNMRoksxcQwRxMtjPrRjxhof+cFpdltga/OhaxuNkNi8HquhHqlqyIY6SPLjWH4Zriuuxdizi+iDf0BJOoLYNeK2jYSu22TauIlYHe0IXUekn3iBwWxpxti2CdnVhcjMQp82E33cRBJvvAo+P+sw+Wl1A7cmZ7AsOx+row1ORAZjL4bceOMlqKrGAw88ceLvu7TQ+ipRgw1I1UkidSLSZUtfzcZ6EpvWY7W1omRmo8+YhVp0SOYwI0uypUOyu0eQpEssCVEDpmfBhJQErqrHcTa/hUgMghBY7kwi5deTyDqU1G3lQPhWC+cbCloloEBsiUXsXMkZ6Ut57rl3uPHGS7ngggV8+tPP0Ng4g74+QZJXcl3FHH7W8XU+ufebTBbzuL3oMowZ+7BGdaK8WQKxwUOnqcCuCsmETvD4wcySmNWCFAn7CiXBobmkwwFdXYK2NkhJAZ/P9tVqaxOoqmTDhuFkcCgEkcgeHn/8XFTVxXXXrSM1dQw1NZLBwSG/58PgXCfQqgTGaDsxWm3CVgfHwCFhUmkjT6/8BvUtj5CbuZizznqTWKyUadMs8g90ZWtgTnQSy28DCYo4NLGUwRC6lYsWTIUEtjfyKUBJTcN1wy3Enn6CeF0dhmKhyWzc45ZjTq5ANEi0BgWtEdQOSfRCC6uoBBFI4Y7kVILxOP/5zqt4dZ3LdBf6vIXgfv+FphH8+2GkLh7BvyMmTZrGFVdcz3e/+2XOOuvco8gzIQTavAWYf3kIq6sTkZYOiThmXQ1qfsGJF2M/IPSKcShf+RaJte/agWhJfhAKVncnxGLoC8/EsXjJQW/awyENg9hzT5FYvcIOoVMURGY2+tnLIGary9Qx4zAqdxOvrkL29QLyIIEqAdnbi6Vq4DykPJOxGMauHViNdaA70CrGo5SUIoRAzS9EzT/5ECtpGFgdbQhFRWRlYwqTV5Z2kVnfQXqNh2imj7gZpDU1SFZQkISTi55bQcLroWZ8OZkNzRS+8gaX/fD3FIQMjOw8hKJg7NqOuXcXrk/cgThBq/+RcJxzvm2Zse5drJYmcHvQz16Gc/llbNE38KT7ETqUdiwsvNLL3PgZXOy1ye7DbcAA29f5MAs3VVexTIkmVLQDmRFSIpwu9MLCkyKCwSaDk5L8x/Tn3LBhDffddxtNTfXceednue22e0k7SSuy40H6IHq5JLZMIqJgBTg2UyJsT1ztMYEYxLaVsECtBzNHxxoLWqQVwxE4KEJQwi1YrvSDWRanggQJHvb8nq36BtKsDFyWm936Dpq0Bm4PfZrRxgdT6QshGDduEuPGTeKuuz5HKBRk9ep3ePvt13nnndd56KHfous6Z5yxlIsvvpJzzrmIpCQ/saUStUWi78Kew4QlIgGJ6RIOfAVVMPMlWq1AbeEjUfZ+WLSGGzAUk4yBdDtHA0gJptHr6yY507Zl+aiIPyEEo88qx5PqYfMLu2mq6iEqVYwkH8nFuUw+azSqbv/tvNWsUjNuAqNde1FbuyASIajqdEQ1kmt6SU51I4Gtqxp4vl5jYHQpjWGFziiU+MGl2Tkg41Il73UKBuKgKoKu8lE4giH09h5SVYVUl0FaWRoTLhhH9rhjW1YuXXoBS5decNqug6IqZJRnkFFuCzdadrQSHYiSUnSoK0XVVdwBF217Ohh9VvnB6/JRYkuH4IVqQVtIgLBDwRf4PlrrkH8WTokM/u1vf0thYSGlpaWsWLGCFStWHHO7p59++rQMbgQj+L8KbWjFWCIRh5F2CcvA5/CiiPe/0Zimydq1K3nxxadYs2YF+/dXAlBQUMzs2fO54YbbmD17AWVlo/9hITZWRzuyrw/lsImF8HpRR1dgNTfBkD1Cb6yTyr7t9NdvxJwYxDElifQ6jdRGi6SOvSTVmGDNslW2R0AmEsSefoLEuneRwUGEojI6Jw/XvLNpqh/H2bvG0FXahjrWID/RwUCklarkZKpUwSuvPMvvfvdTe1yahn/cGNLnzsQ/fxbT58znM0mjSZUnfsgIIXAuORd98jTM6ipbgTJq9HHb6g5AnzaTT61fzfNNdfwpFuHT2bkITUUqGvqCM1CTUzATCay21pO+3gLBvISbaQkn3YqJSyqkvc/4h71fUdDnLSD26KEJn1FViVVfh8jNR58+ExQVs3I30Qd+C4aBPm0GakEhVn2d7ZenqsiY7annOHPpIbWwph3ywTsBjN07iT7yR6xOmziW8QTG6hUohcWoeQU0xMJ8bvsGFgRSue/Mc1GkxNixDccZS7B6e7DaWu22xvzCg+R7IpHgzjuvp7Gxjueee4fMzBP4cBtR3JUP4WxbDVYUJFiebCKjbyDS6ST68ANY3V0Ilxtj8waM9atxfewTaEPJywEn3DrBtprY3iVQBUzLlMzJkfi71uJqeMmeAPgKAQt1sA535cOYSUVYnkMqIrMIwrdYiJBN2nIYd1hRMYEXX1zFZZddxre+dRZTpvyUKVNuJhgS/LgfvnbhaD7pnMNndvwXYy7rZ/bCALoimVFSydO7x5OcsN1HpIR1FnRPlkwYA/RANAfeNCXRw9YfEgmb4DVNSE09JKh2OCRNTdDba/9faQX9PYG2fjtr3l2GLymTK658FZ8vB8OAWExQWnp08Jy201YEo4GI2cEt0i8JDgzyg/U/4Be1/4ND9XFm2q/JGHcbroBg5kyL884bHuqgBJLRZswm/uoLoOuI5BS0Jh3/1hm4PJNRH87DfBOiF1h2QvgpQB01Gvd9X7YXRUzDnvT3uXH+UqC0C8xsW22mbxQoHQrBT2XjOGsp8Zee477sAgbLKvjaGy+Qce4lLDtrJEjs/ypG6uIR/Lvi/vv/m1Wr3uKLX7yLhx9+9qh7lD53IVZnB4k1q7D27UEoCmphMa5rbvhQymBpGMhIGOFyI45YpFWLSoYttgIHg9vEMewsDsBYv4bEGy8jUtNRcvPBNLHqazHf24L7vq8crM/Mqkr74YYA3YFMxG17LiGQqoIIBVGL7ePLSJjoI3/E2LIRaZiAJO5LwnHBxTjOXnZK93Rjxzbir7yA2dwEqopaVk7DZeNZPaeHGbFZlL3agr8jhOXwsPsCB++mGNz6xkyyA8ncXhWkfcXTqPurcLe04IqbKMmpSIcLdfI0MA2MbVswdmxDnznn/QczBOF04rrkCqzFS5C9PYhAMkpqGl1KB4+7H6JerSEmYoRFCEWqtLibyJlxO7NeTsNqqLM74RQF2dsD0kKfMfvgvrPHZtGxr5N4KI7D60BKSbgnjMPrOCj0OBns3LmN8eMnD7vW8Xic733vG/z+9//DtGmz+NOf/kZ5+dgT7OXUIb32z4kQWyBRGsG5WYFmAImVCZGrQeYvxburGq13F9LhRxhhpKITLb0M6TyBFZ0RRYn3Yzn8cFiY9V5tJ7v0bZQYo3APFYfJRgr7tN2scrz1gcngI+H1+jjnnAs55xzbeqW2dj9///vLvPDCU9xzzydwOp2ceea5LF9+JUtvuIBAow+1TSCC4HxVYB3RiCVVEBa21da/IELGIFrAAbUCDLsmVaSCjEusZLt2jcWiH9nxFU0hMLGAPVYR1e0JsowQODT2az7amuFT6XZ+ye5uQUqaG/KnYA6EIJ7A3dJNeEMlMjsNX0AhJFXWJuewP+QgvzOK9HppDcGGNsGcHIlDtRe98nwSEYTaQXDpAerGzyCro40FnkGmTnLiKMmi3h2gvhXKkiXpJ85UP+2Qlh0pd+T9VagK0rKQ1ukNkTsW6gbg0T0KYUOSn2SLYZqC8NRQVun/12TwjTfeODKhGcEITgMqUkeR5k6lYbCZoqR8hBCEE2EGE0HOLT7zuH9nlmWxadM6nnvuCV588Sk6O9uH0mSX8pnPfJXZsxeQl/fhll+t/j5kdxfCl3TSq/cHILw+cDqHCv3D1BWRCDidCI+XqBlmd99WwsEuPK29CN1F1K3QV2GR4wSfJwezrgarqeGoSQFAYs1KEm++hsjIRMkrANPAqq2hyHqdzOIJ9OytQispJDUgcDoysQYKOLe7C/en7kOrGMeWcBc/rltHYvte+ja/R/Pr71D9uwfZCjxfWsqZMxcye/YCZs+eT3Fx2XE/CyU945Ra0LVpM8lbtpyP11fz4L6dXJueR1Zmjq2GCSTbrYzhEErOqftFu1DIsz7Yw0mftwirq4vEmpVYe3fbXr+p6WhTpoNlYWzbgtneCv39RH/7c4zJ09DnLyIRi2JW7bWXMhQVbfxEHOddNGzfVmcHZkM9wqGjjhqNOEIdKeNxYs89hezrRa0Yb3ttWhZmVSVWZydGIJn7tu4mNSnAT6+9DYfHg1mzH2maxJ5/isS7K7AG+hFOF2rFOFxXXodIS+cLX/gkK1f+nYcffo6KigknPH9nyzs4m9/C9OUjHQGQFupADa7dD9K/2o8cGDg0NimxaqqIvfgsasV45OAAxr5KQkGTQGoZZ+TlMSYVUoa++nr7WiQKluuAWkXFTCpF692F3r2dmOeIllJhK1OOBZ8vm4UL30LKe9m8+Q46O59g6dLfoevFvNg9ivs/Wci2n9Zy218eZu2s20iSvSyd1sEe1cGearuwMk0IBGDSxy1YIBkwIbJdsOePCmqzJDPTtmpsahL4/TYZHAqB12sTwqZp/2Rng7YDPH9R2LRvPd/edAGpahkTfS8Taksj6IPubsGoUZLp04cXbmHDzgRJitv2yFKDhJbgT+0P8J2ObzMgB/jUjM9wzZQvsjMUYPdsSVa5ZEaJHWh31Od3/nIIh0ls24xoChGovRJdLUZOzsdyg1oHkT8rhG6xCJQf0ynkuBCahlp6yHPQ8YZAaRXDErQNv0TbJ3BsE1jLLkTJzsXYsoH7R5XT/sYLfH7t20xUBCevHzu9ONhF0dqCcLnsMCj3P7i6/z+Mkbp4BP+uSElJ5Yc//DUf//ilPP74w1xzzceH/V7oOs7LrrZJ4dZmhNOJOmrMKalPD4e0LIy17xJf+TaypwsRSEZfcAb6gsUnJHqFosD7TL4T69eAph8KqdU0lOJSzKq9mLt3osxfBIBaPgYUAaqCSElB9vdDOAgJA+F0InLzUMfZK4eJNe/anVqFJQiPByklsr2N+CsvoFWMO6ZC+Vgwa6qJPvwAMjhoh+4aCRLvrkCvepvULxr0zp/N1hkluDrCmG6NpoxBEru2EnstiCepEK27i8z3doGUWKgoKQFEcgpWazNWRiZqUQlSWpjV+49JBstIGGPHe7Yq2eNDmzBpWG2vJKdA8iGCcre2g73aLvqVPgQCp3QRVAZpVOr4ccHPufdTVzH2kUqS91cipASvD/3sZWgzZtvH2r2T9O5ecjNMWjsHsdoBJA6Pg7JFZQTyTi7U27IsNm5cy8UXX3XwtYaGWj75yY+xc+c2vv7173H77feinqxH8+mGGyIflyTmm6jNAumCRIVEpgJMJjjpszhaVqAN1mK6M0lkzyeRMePY+7IMnI2v42h+CyXeh6X7iectJlZ4Hig6HWobJuZBIhhsMUjASqFW3X+UsOh0oaRkFLfd9mluu+3TNDU18OKLT/HCC3/jrrtuwOVyc/bZ57F8+ZWcteQ8Mnf7bAVwCQfrI7UNzGyJeXpicE47fHqAtvRGrGyJ0m6/ZioWSqogeUoGuq7T1dX5kY5ha6egug/Ks3Q0JRmAZAmVvYKNbYIlRRKFIT5dCAgMTRL21iNV9aBPcJVMolF4yTF7CMSgtNBLKAE1/eDTBflJku6oYEqG5O4pFu80KlT1CjJ8CouyNGan+qi0vLxdm0SvaX+AKU7B+cWSxQXypGpn04KYBS7Vvs1+EATyAjh9TsI9YbxpQ50bliTcG6ZoRgGa80PFnZ0UNrcLemNQkXJozlDmh81t9gKBcoqd2//qOKUr+uCDD35EwxjBCP7/QqorhcvLL+Jv+55jb28VALqiMytrKovy5w7bVkrJjh1befbZJ3j++SdpaWkkOzuXSy+9hosvvoopU2Z84Mlof0s/rbvbiQ5E8aU4yWjbirp9PXJgANxu9ElTcVxyBYr/UPEmwyGs1hbQ9WFqTLCDJLQJk0i8uwKhO+yAilAQq7XZTnnOyqYrXEfIGCBZ+jASEqEreKPQ74OOFEht1+wninH0UrKUEmPtanC5UFKHEqw0HaWkzE4z7ushN0OgZhy6Hoo/gNHShFVXAxXj6Ex2450zg7Ez58FQQHGwrZ331q0mtHoTO97dxBNPPIyUkqysHGbNmsfs2QuZPXs+5S4Pcu8uZCiEmleANmXaSStkhKbhvPJa7hk7jscuX8JvXTr3Z2UjUtMgHMZqa4HkFPRZc99/Zx8AVncXxntbsXq7UVLS0KZMQ0lNQ+g6rsuvxjF/EUZjA7EHfwdON0pyCsbeXVgtTQh/sq3MycnDbG6Edatx3fVZrP2VyGDQ/tzHTTxIMknLIv7qi8TffgP6+0BRUHLzcV553bD2UqupEau5ESUv/1BauaKg5uZh1NfyQE0lezvbeOr62wl4PMhoBBmPIYD4S88hklNQS0bZE5DNG4jGYvx0sJcnnniYX/7yQRYvPtrr70jobauRmtsmgu0BYPpLkfs3I+sUlIIJh8YmBEpuPlZLM/EXnyG2fi1NDT10RyDk8lM5cSnPL7yYy8cIpmdJRHwA1OE+sQkpiCWgLxjGI0+enOzthVDIyznn/IHOzit44407efjhycyb9z36ku6gLflSfnt7nNlffp7/fOBpfnDTAnyTLuSOs9LY9p6ksVHi9cLEiZKSA2ssGkyZKrnqKos33hA0NQl0HSZPlkycKHnrLYVwGLq6DoasEwjA7MkS9zMKq/a/zRWbL2FS1hR+u/R5Nm5OZkMrxMdJli2zWLxYkjokAjIteKtRsLJJUOASnNcGhhZh48Bf+E3VT6kM7eX6pI/xtWXfIZxdyB8SguYiUAOwo11hSz9cX2ExJfMI/2GPF+cNN6OffS7OVxM4YzmYUz2gQL2A58cKKgcFxmpBcURyXrFF6cnNRY+C0iyQruGBMaiAKlHahpT202agT7MnfL+87W7OO28ut956Nc8/v/KEad0fBWQ8Tuypx0hsWGt3UWgaSn4hrmtvRC0d9Q8dy/9VjNTFI/h3xtKlF3DVVTfyrW99joULzzpKTCCEQM3NO6kun/dDfMVbxJ98FFQVEUjG6uwg9tgjyFAI5wV2kNhB4rKzA5GUhDZh8qFabwhHWRQAsr/vKJJaqKqt+I2E7W3CIazebtB02yZCCPAmIUwDkZOMkpyKvnTZwUV+Y+smhNuDGFqFFEJAVjbW3l2Y+/aeNBmc2LAG2deLMmYchENYe/dgdnXi29fJhb2C5uU6jVdPJlRod1UNqq3kZ07C4VQx9+1B9vfb5+f3o6RlQDxmd1GpKlZ7my2akNL2dzoCVk830Qd/j7l3DxILYUnimVm4rrnhYHfTkRgQ/XQobXjx4reSCYsQQWWAqAhTrVXy6KR3KPpWHjduWU5RfwZKYTFKQRFWcyPRh/+IVV+LlJJSRSE9axyRWWejpiSTVpxKIC9w0vOVDRtW09bWwnnnXQLAihV/5447riUQSOG551YwZcpxiNUPiiho+0BEBFa2xCyE9+VXVTDGgDHmaLWimTyaSPLJWb45G17GXfUoUvNiOZNR4gO4q/6CMKJER12Fz7Jt2QwMtMOom7AIkWvlfSRE8JHIzy/kzjs/y513fpaGhlqef/5vvPDC37j99mvxeLycO+NCrrau4pzQMpxuJyIhkH6Inmf9y8Yl5LqL6I620Tmhm6ScJGS/SdA9QMCXRmB0NtnZebS2Nr3/jj4EWoN2CLV22HqXIuz73Ov1gq6ofatqHhCkuuxsEimhVzrwY5E6dNtrkS40JKolUXUVTYFpWZKYCXHLPsaiPIuzCiUFSbCs2KK7roedL+xisDPIpgRUDah4s9PJOGMiuJ20heDZaoW8JJPRJxC0mxasbhG82yLoi0KmBxbmS2ZlnRyJHA/HifRFcHgc+NK9lMwuomplNbH6HlRdJRFJ4M9OonjOyd1zPyy6IgKnOnzsQoAV6gIg+cicnX9zfPT0+ghGMIJjYmb2FIr8+ezuriRqRsnz5VCRWo4+ZCHR09PNQw/9lr/97S/U1u4nLS2DCy+8jIsvvopZs+Z/6DaF1l1t7Hp5N5GBKKqmEqhej9K0jsDYQlx5BchQkPiKt5CJOK6b7wTAWL2C+Buv2AnImoZaWo7z8qsP+qYJIdDPOheztgZjfyU4HChuD9rU6TgvvQohBHErBgiUJD8iKQnZ349ISUUzIeoEq70VJTMLJTef5mAbG1q30BRsJtWVwrSMieQP9oPLRSwUw4wZqE4Nh9dh978IwBrelm6Hh8iDlhND7mXDVtJ92VnkXX4RxRdfxn3hVPr7+w6G761fv5rvfOeLxONx/LqDBelZLM7KZWFGNhmTpuG6+Y73tYg4AKEopEyayme/+G3uv/+LXL7wbKb09SItE6WgEOf5yz8SksasriL68P9iNTchFQVhWSRWvInr47cdVD4q2Tk4snOw6qqJv/oiVnoaVkuzreY14kinEzU9A5xOrPo6ZHcn+oLFxzyesXkD8RefRfiSEOUVtnq7vo7Yow+iDLVsSimR8sBndUS1IATV0uRXNXu5ZdRYKvr6MQcGwJKok6fZnsFuD8qQ/YNI8kNhCT974Ul+tec9vv3tH3Hp8qswdu+01TC+JLSx448m7qVEJILIIwhbhGIHzkjT/v4cDksiI2Fir79Ml+VmV3IF7gxBSrCDOTuf582sQh5nOgVJElfyWLTevXZIoFCo6xc09oXxRzVeqitCiytcXm6RdRJ2sj4fuFyScBiKi8/lxhvf4913v8qKFZ8mJeVh3jjzq1x/wQ/4WkMa3/z5H1n2if9iWvFyfEIwe7ZE1wU7dwpee01QUSGZMUPi89kFzqJF9v9bWsDphLw8qKyEmhoYHJQoCiQSgkhEUlYG8zIl63es4dL1FzE3fwGPLn8Kr+6ldIbkypBg4F4L9QhnjneaBH+rEng0qC9u47/iv2PF87+h3+jiwozl/KngYaamTccMSn4QFbRlwOixEgL233DtADxfI6hIlbiOqFxswiIfPUkgvAookm4Bv9MFdSrkKhIisLVD0BZUuHuKRfbJZ+0c+rqkSkRcYZgHsgQMgZV89GQwOTmFP/zhMS66aCH/9V9f4zvf+e9TP+iHQGLVO8TffgMlM9vuokgkMOuqif7lQTyf+8opBQ6NYAQj+L+J++//MatWvcnnP38njz764kmTdVZ/H+aObVg93Qh/AG3S1KOI2wOQkTDGO2+A03nIZzclFautlcS776DPXwSGYROX+yoBiZQSNTsH5/WfQKsYh1G5h8SqtzHralBS0tDnzEebMx+hqqijRhNf+RYiO/fg+GU4bC+AZecgIxEiD/0vxpaNiCQ/SlEJVn0txCIoJWUoySmoJaU4L7j40PsTiaMUyeLAqugxxArHvU7NTeD1gZQHFbpKcgq6ZeKRcdLe3E0oRaV5+Ti6lA5MYbAocA3eW7NIvPsO8bf/jvD50CZPA0XB2LoJGT3Uum51dyGcLrSKo60C4q+9hLHjPfscvV7bBqtmP7GnH7e7tY7xDHDjxhAG/tYEGXXNxIJduAMWjWM0rICXHDOPFm8Hr8yp4VOhKxAIu1vrb3/FrN2PWjbaJqtjUQLV75Fe5Md14R2nLFp57rknyM0tYMaMOaxe/Q6f+MRlzJmziN/85s8EAsmntK/3g1oL7kcVtAbABOlRiE+3iFx9mAfuRwAZjyPb69H2voTl9mN57VAE0xFACbfiaHmHWP7ZjBOTyDULqNGqKDZK0XEMfVdM5sQXfXQDPA4KC0v41Ke+wKc+9QVqaqp44YW/8cLzf+OaPZfjV/1c6b+aT5Tcwrgl00gca80hCmoHSAdYWbw/6f4RIc2VSUXyVOoH9xHMGkTJVkh35DPKPx5dd5CTk0dLy0dLBic5wJRiaJHLfq0nCnt6BEkOO8QsZAi6Y7C1A/wOgQTS87LI72nE7Ash0zw4pEk8ZqLqCt5Ue1LhVCHbKzi70OKyctsq4gCMmMHuV/YQ6g6TWphCR59CxDJIb2vH2u1FzhhLrg/29sB7nYLRKce3Z3itXvBctcChgE+31cg1/YLmQfA5wLCgIEkyNnU46W0ZFjVramnc0kQsGENzamSPzaZ8cRm+TB9tu9uIBuOkFiSTOykXX/o/pmbN8UrWtynDPhNLQqTPlo9nnmLX9L86RsjgEYzgRLAMtL49KJEuLGcyRsq4o5R+HwaZnnQyPcMDD5qbG/n973/Gn//8v0gpufjiq/iv//o58+cvRjtBK92pIBFJsO+tKoyoQXpJGooRJ217NVFLwwo7yHE6UJxpoKoYO7ZhNdYje3uIPvEXUNSDxIKx8z1kKIjn3i+Ax0ti1dvEX30Jq7MDpERxuWx/tXPOt1UagFv12LlpikQdNRpj+zbM7k7iupe02n5QVRznXUR1rI0/7for7aEO3JqbqBFlQ9tWLixwMvbFKnq1MJZpoWgqPq8gxavhnD6HxKq3kOHQoWCQjnZEkh91tJ1AOtpwkCQV2hWTLEtFIAhjEUUy1bA/20AgmbPOXMacKYuQtwORbjZ+8/Osa6pnRVc7X9y2HkUIJm98lyX7dnHeZ75ERcWEky52P/GJu3j++b/xxVVv8NK3f4SroQGR5Le99GIxhPP0fcekaRJ79m+YbW2oY8YhFOWgFUPsub/h/vTnD342AI4zlmBU7sXYthmzsx00nc6iPPafOY94YSE5/WHKG+tw9vfZPsP+AOIIRUpivZ1sqwyFBaI4UEpHYe7bg7FtM0h7GznQh9XTjRwcQJ045aAVQ7ylka/t2ExhUSmf//Fv0PZXgWGglo9BKR1F+EffHRZWIqXkx5vX8Ps97/HFG27n1mtuIvL7X2Lu3D6Ufg5KQSGu6z9xkPyWsRjGzu0MbI/j6N+LMt6Dlp2MEAIR70fNSEYpzSRe32S3xh64bs2NoGnIeJz6lAqIClw6RFKySA72MrF5E88WzmB3tyA7dxF652a03l20WRm09yZIt/po8c+h1zeJ+nZBKKHw6SkW79f5FAjA9OmS115T0HVJIOBn9uxf4nReTWvr1/nc5y7jT3+awqc//SWmTt3BvV//Bq+/vgxdd/PYY4J33x0KMVFg0ybB1q2CW2+1OJAD6PHAqMPWISoq4JprLF55RdDRIXA47Ncuv9yio2YPV264hJnZs3n84mdxaUOzJQU0AboKhy/JRA1Y0Qjhug3Urvpf9rzzFxRVY+rsT3BW+T3cPLuUwGTJgGLS0STY1yHISD80CRMC8nzQNChoHITy4ygUrBTAAqUL3gsJGlJhfEigAkaOxOOQVLYKtqiC82dJ26fiFBCfLnGsGwqJyQUkqI1gpUsSk49dKE+YMIWvfe17fPOb93HGGWezZMl5p3bQDwhpWSTWrUJ4vIcWqxwO1JJRWLXVGHv3HFQwfxToifbSEmzDqTopDhQcXOQcwQhG8K+FQCCZH//4t1x//UX84Q8/5/bb733f95hNDUQf+gNmXS22v5GFmvcGrhtuOWYArtXVidXbg5IxfJVQpKVh1ddhdbRjrFuNsWeX/bx1OJCWhVVdReypx5DLLiL614eQAwMoySmYtdWYVXtxdHfiXH45+oIzMHbvwKzcYyt7E3Gs3h60GbPt4LgtGzG3bUYtLkW43Ghl5Zhd7Zg7t6NPnoa+8Ey0qdOHLexrEyYR219p1xBDtbfV34dwulBKyjhZKJlZGPt2I/t6sHq6EMkpdngvgtzkScQ8DWSsrmPjeQ6SnBmcG7mIufFFKGMUtDFj0ebMJ/rrnyGS/HYtW1SCUVsDA/1IXxIyNIi+dBnqEWSw2dJM7Jkn7Gvf1oJISUMtHYVSVIxVW425f98x1cFjExMZs8/DYLCZAUMQ88SIq1C+WcXtc+Kc5CLHzKdaq6JdaSXbysVqrMes3o9aUHTQB1o4XSjZORi7dyJ7uu0QwpOEYRi8+OLTXHnlDWzcuJYbb7yEWbMW8Mc//u30d9hEh4jgOoFRIkEH0S9xrhRYWRBbdvo9SqWUGOtWE3/rdWRLNUb/DvSKYrwL0lF89vlZzjS0wVqUaAdJrgo+Fr7V9nLWajAxCcgUzo9ewuz4gtM+vlNBaWk59977Fb5Y/lXqH9nLEx2P8Uj9Qzyw5Q9M2j+Fj22/meVfvRa/P2DnLGwQuF4TKB0CdFtVHbnEwjpBtMdHiWx3PhmuHMLGIIpQ8ai+g3O5nJw8Wg8L2P4oMCld8nYjNAzadS7ApnaBYcGUDEmmx/6+7O21yd35uRbJTpiQloyRP4qa1bV01/WQphloqT7Iy8AVcCMldETs8LjJmcOJYICehl4G2wcJ5AZQVIWYBcKhI31uRH07ctIocOhoimTwBBl6fTFY0SRI0jkoskhzw4ZWwc+3Cor9oCqgq5KZWXaH3wFBR92Geirf3IfT68SX7iMeTlC7rg4jbjD50olkj/3nkK4zsiTrWiVVfYJs71CAdljgjrYihCAt7eQtIv8dMEIGj2AEx4GI9uDZ87/oPdvBSoBQMAJjCI+7Dcv74VvmjkRV1V5+/ev/5umnH8Xr9XHHHZ/hllvu/khuOn0t/YS6QwRy7ZYtJRZGiYdRAgGi/RGMqIHu0hH+AFZrC7Kvl/jadyGeQC0vtXfidKGOGo1VvR9j1w6Ez0fsqcdA0WxfNtPEaqwnsept9GkzEUPEYLorm2RHGr2xLtzpAcT0KYS6m3EORsjLLMC9dCHqmLG8+t4f6Qh3UpFSfvDB3DjYzNNKFzfHBWnhZmQgFSsSJdHeR/eMuZRcdiVEw7ZyImEAEuFLwnH+cpSCIpRwO8VGkPNFMi95DPaq9hNOQzAz4WJO3LY56GvqY987++lr6gcpyYrXMykumHXOcu5VFDpDg7xTU8Vbu9/j1688w49feIK8vELOPvt8zj77PObNW4z7BL6cmqbxs/95gHOWTOM/vvMV7p8wFVAwVryJNm0mrhtuOW2+nlZzI00te3mvVNDmrCRDuphiZlCQl297M7c2D0vEtro6EaZhs3mGQXtqEs9dfx77Zk5CQSLCEcbqYa578Sl80QRKWjqOxUvQ5i48aBkie3vgiPELxe5tir/2kq0kcnls0juRwGxtRibiKClpyFiUhzta2N7WzLPPvoNv5lyYecg6QxoGSlq6rbZJTsGSFt99+1Ue3rqer06eySfv+Czx11/G2LzBnvh5vEgjgVmzn9gTf8b92S+DkSDy8AOY722B6ABafw/Ktlfxzi/HOz4NYYSJ5S1Gv/JMzEf+hLl3N0LT7GPn5KLk5GFs20TMEqiHEYqm5sAZ7kcAERMsXwHhSZ/G0fA6LZU7CQkvzZnnUek/F6/qYHa3JPNtgfirgi8L4rMl8bkSju72BODCCyXhsMW2bQptbRKXS3DOOQu5+uq3qKxcyU9/+p/cfvu1lJSMoqGhlu9+98tce+3PWLNGITtbHiR+43HYuVOwYYNgyZJjT3SEgHnzJJMnSxoaQNMgPx/efruNz312OSlKHt9LfZrIoAtXCjYx2iZITJBYQ+IwKSU7d27jsaee5IlnniLUWYsvLY+5132LSefehtObwvZeQc00k0lD88REmkRu5CiliDyg/B96PWxAbT+YEor94HdAYorE9azE+Y6gdYxAC4BuZ1ai1oO7VcEHdOxU8G6QRK62bFXKScIcBeFrLFwvCrRaAYrthxe9VGKd4JFwyy138847r3PffbezcuWO065qOiYSCdvCxXXE36Gu20r1SOgjOaxpmbxa9xYrmtbQG+tHVzSK/QVcPeYSivz/gnHiIxjBCDjzzHO58877+M53vsTo0WNZvPic424rpST2/NOYtTW2B7mm2Qul+yuJPf047s9++ahguL4kD2tmjaUxJ5WA4mBiUy9lHQMH8ySQFsbO91Aysw8uLgtFQSkqxqivRT79GASDqKMrDtaDVmc78ZXvoM9ZgFpShvuWTxJ/63XMmv0IjwfHwjNxnLUUoeuY+/fZvWCH3Q/V9CzIK0QpLMZx1tGWUvr8RRi7tmNs22yTwaoGLieORWehjhptB9uZ5lHneiS0mXNIbNmIWV+HjMftbpKeboTfjyezhLHxXML97Yzu/CSZgbH45XAfI61iPPqCM4i/+TpW73vIaNT2s586HceFl6JPnIxSVj5MjCCjUaJ//iNmfZ1tcyEUrJYmZH8v6rSZYFlDNfLRyA6lcPvvs1ld2MPuGYKQ0yC/zcn0tyUBRaWl3EOfO4REYg0t+8pYDGkkwDFcyCAcTmRwEHmKIVyrV79Dd3cnFcU53HD9+UwfU8Sf//MedCWOPM1SXW0faA3YRPBQ7SWTQQbBsVYQW2ITxKcTxpaNRB99CIRASc2EoE50Uw1WxMK/fAZCVeyuNc2D1G3hQ7lZweeD36JaqyQmYuSbhWT+sxjUIyD6wblSMDp3LF+dfj9fsr7JG3Wv8vDGB/jKI5/lm098ieXLr+TGObewePV8hAQrU0LcJodFv0LwHmtYcPI/EqpQSdKTj3o9Jyef7du3fKTHLvTDVWMsnt8vqO63lcDBBBQkQWdY0BOFDLek2A9tIcHc3ANWZwLml5A1OoPexj4mAVlqgFW9bvYOhTz7HTAvx6K6Dza2CbI9UJ5i78uMm0hLogxJdZMd0CjBUhRU0wTTwrAgYQmK/cdfEGkPQX9MUHTYNgNx6IwIYqakIEkScNrntLYVypIFi/MlRsygcXMTDrcDX4bNgmtODUUTdFR2MNg+iD/bf7zDfqTI9cFN4y1erFVo6BcgYGK6pNvRyp7UdPT3uef/u2GEDB7BCI4DV81TODo2YPjLkLoXzCha7y7clQ8Tmvolu5X8NGDr1o388pc/5NVXnycrK4evfvU/uf76W/D5kk7L/k8GltOL5fKiDg5ieA4Vy3KgH+H1IlJSkW0tiCPGJDQdkMi+Xsy9u5HRKNrooVRfVbXVoHt3Yex8D8cQGawrDsanzKBmcC/d0Xak30t62kyKfaNJc9nMTE+0l9r+erLcmcMK3Aw1jQ2JeirnLWRGQxt6TwsiKYnBkmn0Zk4hO6bguel2jJlzsRpqQXfYQR85Kbj3/A5H11aEEeUSVxpjy85ne+EcEkJQYGqMN5w4EIR7w7z3zA4GO4MkDT2gBnb00d/SjzImgdPrJMObxJUTp3F5Zi5xIdh+zrm8+c4bvPHGSzz00G9xudwsXHgWS5dewLJly0lPzzzqmhcmEnx5/FS+uXUdS6bM5KyyMchwmMSm9WjjJhzXguFUUdlfwx/TmuhyKzhVJzFM1qsdXOXIY8LQZOYAzJYmoo/8Ednbgz5pKkZBPj1t1Ux+8U28UiHk9xIODbC9PI+iiGTZvk6s9naif3kIF6DPP8P+6EtHYbzyPFZwEBGLIXw+REoaRCOYjQ2oZeUoQ4SYyMrG2LoRJTsHrWI89ULwP//5JW699R5mzjzaP1loGo5FZxH98x+J19fy7Z1beHLvdu4fP42PXXotIjMTY/MG21vP4cAa6EPoDtSiEsz6OszqKszGepssLilDuNzI6Cismm2ENraglZUgJ1xFLPdMVM2F+57PYby3BaurEyUtHW3yNFuV9N4WMrQo+2Iu26HEMtFiITqyx6ApkjyvXRiZScUMjL2dP3abGFIh3Wsvz6e3wKIXBHSAkSdQGySeKoHaDJFr5THb5rxeuOkmSXOzSXe3wO+3KCoCRRHMm3cG8+adwfr1q/nVr35EfX0Nf/rTb3j77U0kJd1BXt4lkEhG7RK4Y+COSnZuPz4ZfPgxxw79ST/+eJBvf/tiYgmDS6a/QKg+hbY3JM4KiV8XWFmS7jMH2bx2I6tWvcmLLz5FTc1+UlLSyJt+GXmzr2TyjEUoql16DMTBo0PgMPI70wMlfsmOboHPIYe806A5ZKcgFybB9i54ukqhNSiwgJKE5GIkkzPASrZbD1OlxBBg+mwzGLVVYOZJoqmQFgJ9mwBDIfRp61AlZIHaAiSGlL/HEOgnZkuMCRK1DlDAKAbeZ91GCMGPfvQbFi6cwE9+8l3uv//HJ37D6YDDgVJYbBMZ6RmHWp8HB8DpQsnKeZ8dfDBsaNvCizWv4dN9jE4uJWbGqeqr4ZE9T3LftDvx6P+k2d4IRjCCE+JrX/tP9u/fy513Xs8LL6yivLzimNvJjnbM/ftQcvMPKmaFoqDmF2E21GM11g+zu2pVDP6YJ6i+aB5aSyuGP4l1ZZlcuL6Kua/uRJ85G5GeiTRNhHP4zVSqKrKvD6O9ze40MhKgD5HFaRnIqr2YzU0omVmoo0bjKiuHaARUbXjH0oFFsAP7jUWR4bDtJ3ycib0MDmL1dGH1diMjEVtYMHch+tnLiL/xCsaGNchIBHXUaByLzz6uxZdWMQ7XNTcQffJRzOp90NeDkp6JOna8bcPV3oYvr5RMzwyEPHpaLlQVfdFZJNatRjbYSmyRlAR+P0p+ASgqib+/ClLatVXpKIxdOzCrq1ALi+xOPbcb4fYguzowd+9EKytHLT46oBlAtrYyviETqUBJTxedqoWJiVcNUNDiobdtkD2j2qgwxpFl2c8RJScXJSUNq7MdNTf/4L6sznaU3DyUY9TAJ8Jzzz1BblYm3/rOV5mQm8TTd47CV/0gRs9awuPvwvKcPhJUCQswOYrwlW4QYRBxkKeR+5GWRWLFW2CaqGXl9otGKbq6l/j+ZhJNJThzHGihJqJ5Z2EdFjTswsV4Y/LpG8xpgtIBSr/AKLDrSVVRWVZ6ActyLqCjrpUHCv/EX175E0888TBjfeO4ccYtXJP1MdKS0zA8Eq0a9D2CxPQT16P/aOTm5tPa2nRMn/LTidnZkrGpkuo+QcMg/HyroD0MXVEBEmr7BYV+iVsbEkYcBl+G7yCZWiBhVtBkf78gYcK+Hnhyn0JjUAASnw5jUuDCMovFWUk4fE7CvRG8qR5yfNA0KAk2RRD56QRNB929grJkybSs438ubg0cqt0BOHRrpisiiBg2GX1AkezTwanAlnabDI4FY8RCcZy+4YW20+ck2BUiOhDD/09c6xidAp9JtmgP2x7OGW74+pPtZGX9ayzAnE6MkMEjGMExIKI9ODo3Y3pybCIYQHVh+orR+ipRB2sx/SffJnYsVFfv45vf/Bxvv/0apaXl/OhHv+Xyy6/DeRotAo6H5NwA3nSv3SKSFwDdQbh4Co41L2JpCap7g7jjFukDBr6FS1DyC1HyCkhs3oDCoVhYmYiDEIjUNKxdO45WoQ35q8nBwWGvezQfE1JmEDUjWNLEpXpQDiPXFaGgoCCHNZtDIpGwi7bsAvoKFyISMaSqYVqCeNsA8VAcb5oXfep0mDrdfpNl4t3+E/SODZieXKQzDTXayfidD1JiasTzzhx2jNbd7Qy0D5JekoYYikNVx1UQ3beS5rr9tExKJSpMMg0no3ra8S05n8VLzmPxkvO4//4fs2/VW7zx6gu8tXU9X/nKPXzlK/ewcN5iLpmzgHPnLCJp3ESUQDLm/kquysnn7f7RfPX153nxxk+S7vUhNJ3E9m2nhQw2LZOXQzvp9emU91koKUmApFYZ5GWjkorceSiHFe7Glk12MV8xHiEEPX4Hzfk+yja8x0B1PbXTxuMaDJHUP8jmTDdLXqvGUVaO1d1F/J030WbORTgcdutnU6NNPLncYBoIVUMdNwFhWgeJ4APfETWvEHQNx02385WrziU7O48vfen+456XNmc+IhLmy/d/kRf37+UH887iisuvw3HhJVi9PVg9PchQELlnh608UjWUzCyEqtn2EJs32H7GB76vrlSssWdiVO5mwHsxjsKzDh5LycjEcfayYccXSUmo4yZQsHUbQSuVYJ9KariTlsxSNubPZXIGVBxmI60JSPPpVPbAgUbJMVsEnh7Ymw9ZORLTA6IXHOsU4vNNzOPkJAhhK3Tz849dnM2ePZ/Zs+fT0dHGBRfMp6VlF/H4bezedRfZ7unk6rPIdc0mYc3CZRUjekGeIBjiAJqaEvzgB1cTDldzzTUrUDIK2DzKJLaxFaN1Hd2Z77Juwxp2PLIV0zRJSUnl3HOX893v/oz58xfzWqODZ/YLuuOQ4oRQAppDgllZNsGrtINaJ0CD5dkWXRGFyl6BpkgMS5DullxcZtEbhUf3KPTHoDhJMn4LlK0WeIMCmSRxNNmtnhOKIcUv2JchKa0VKEC9E/zADF1iloBeJdD2g1EBagO4nlbQagTCHFL8LpMkZh59naUXjPFHvXxC5OTkce+9X+GHP/wW1133CcaMOcUdnCKEEDjOWIJZXYW1v9ImTqJRZH8v+ryFKB+BN7mUkjUtG1CESrbXnvy7NRdlgWJq+uvZ3bOPGVlTTvtxRzCCEXx4qKrKr371MBddtJCbbrqMF198l5Qj8hCklDQMNtPm6CfF4SD/sOwFVBUsE3nYAjPAm44QtWqCitQCZEsv1v5mWlM8vFaezvj+WeReeh0iJRW1sBhj9w5EwO5Yk5aFsX6tnREQj2G1tWI1N6GNn4SSkWl3PygqwnmI9BVCgPvoBSdt7HgSK97C7O5C9nbb1hSDAwjDQCkfjdXXi5J86EEYX7uK8Pe/g9XUYFvtBJLtjqDa/YS/fz9ycMCuIRxOEmvfxayqxH3bpw7aUB0JffY81ImTif7m5yQ2r0PJzgdVxayrASlxnLHkILF+LCTefgMZHMSxZJmtxJYSs76WyP/8EJGcbLf7AMLjQV+0BJxOhGWhjBlnk95dXeBw2Ardnm70O+5BOcy2QcbjWK3NoKpIVcOhe5kWmkaPFqVZbaBercUVixPSY1R69pNqFXF+9FLUoRQOxR9AX3IO8acfx6yuQviSkAN2vofj7GVHWYmdCK2tzTz99KOowmJcnp8nv/MJXD43hpVA69mNs+4FIuNuO+n9vR/MbIn0KIh+iUw+9LrSA4nxIE/3+mUsamdZHPZ9M1LGo1sG9OxF2dOFvn8BpnUhMj4bJVNgnRqX/g+H9IJ02uS5POyjViKQk5zLvXd/mbu/8UU23/o2D+5+gG+u/DLfXvVVlpdfxk0Tb2GxtRil5583/uOhtHQU0WiUhoZaiopKP/T+pITtXYLN7dAXE5QGJLNzJDlemzidminpCNtEriIgzWl71gbjsLtbsCjfIv8EOjEhID8J8pMkL9UI3moStEdsZbEq7GM2DkpeqVUom+KlcEYB1SuriYfi6C6N0kiMrnQXTWNL0FTB2UUWSwolycegJfqa+mjc2kxvSz8eVyn7vWmMzXfi0mx1c9SUlHttsvgANAWiQ48Hh9eBw6MTD8Vxeg99aeyx6LiS/vmpg4qAnMNsijs6WsnIGCGDRzCC/y8gjDBYcaRj+F1Xam6EGbN//wERDof4+c+/z29+8xNycvL53e8e5fzzL0VV1fd/82mC7tYZfWY5u17eTXdtN4qm0qzkYZWU4Y1vx1HbQMKhsnV0CZOWzGCCEOjzFtpqg7oalMwsZCKBbGlCLR9DPG8UYed2lK7NOPMLD4bbSSMBgJJxbKsLl3psSV3A4WdsWjmrWzbid/jRFBUpJa2JDtKVVFIHA5AukA67XSzaF8LhdeBOPnp/Wv8+9O4dmEklSN1ePTV9hagD1Tia/k48ZyEoh26Foe4QqqYgFNu/to8BLL9K94QSNiZtYjBWhxAKimkxYVweN82fiws7ICX2xKPkbd3Ix0MhbioZS/+8Jbwe6uPZ11/iM6vexK2qLCks47Krb2R+fiEIwX+ds5yLH/kddzz3V/5y5U3oQoBl0d86wEDbAJpDI7U4ddjD8mTRFu6kMdJGftF4RKgS2dUJuk62EaUtSdCxYA4ph6lirI42hMN5qA0TwOlEZmXi86faKuJEAg2BoesYTfWIwQG0inFY3V128e/1kVi3GuH12S2R4RDS5QaPxw4MHBy0FUCHfd9lPIYS8PPIXx5g/fp3efLJ1/GcINyqu6eLW3/xfbY1VPOr7/+Ci5ZfifD6iL/2EvHVKzF2bEV2dkJyMmp2LpgmZlUlSlo6SnaOPXFSVUCiiV4cagdIg7i3F4z3b2cUbg+um25DKf47Y9ZtornfpLLiPJqnnc254zI4o8Aa5s8lBCzIlewfWvXP1iClDprdgjSPJH3oayuTQbSD2igwi05OIRGP22Fvvb2C5GTJmDF2121mZjYPPfQM55wzmzGjv0JqKJv+/rXsizzPxr6fAbCyN5NXLhxN1oQsMjNzyMzMJjMzG9wqu7v20tjfhI5GgTePtx9/h7a2dYwadTFr136H/v5a+vr2k0jYPgx5fcXMmTOPaz/+CWbOnMvo0eNQ+xQc2wTK3+BiP6gByZumQm2/7WM2J0dyZZmF6xWB6017MoaAyZmC+y6SbBgtaQ9DmksyJVOS54PX6wUdEcHYFEluHUx/V2BoUJsjwAMzGkDfLygskHw8Jnm6R1Cj2JOVrE64eFBSngzSBSQkYkAgBiSePylolba62UgBtQ08jyqEkkyMYwvkThm3334vjz/+IN/4xn08/virH1pp0tvYR9vuNiL9EZIyk8iZkDMsYEMbNwH3J24n/tYbWI31KD4v2pln263THzKA9FiQSLqjfXiPUP8e8AsejAdP+zFHMIIRnD4kJfl56KFnOP/8+dxxx7X85S8vHmyJHYwHeaLyWbZ37mIgvxv3YBMTtGIuN0pJwoHV1oKSmY2af8gOJoLFLi1GhlTRXE7k9FnI3h7yIhH2p7homXYe+ZZdlznOvQCrpQmrcjf4/FiNdcjGetsLV9Ox9u/F7OlCrl+NOn4iMhJBKxuNWna0RzGA2VCHuXunbauQlY2+4AyiTz+OVVttm+cLgXS6SaxeRUQouL/8LRRVxazZT/RPv7froZw820dzYACrsQ4lPZPExnU4Fp11MCxPZGRiVu4mvvIt3MchgwEUjxf3XZ9B/furdmZCKIiSm4dj8dloc+Yf930yFLQtNDKyDimxhUA4XRhVe9GmTLfzIITA6ukm8dZraNNnARJ8STaJvX8f9PeCUNGmz8Kx9JB3vbF9K7GXnsNqaQZFQSkqQaSkYTU1kO4aTYaeRVG0gIGGrTTPTGdRypXMDM4n3yocNk7HmUtR/AESa1bZooLJU3EsOAN1wqkpWX/xix8iLQu3S/Dkt64n4BsqkBQdy5ON3rWVaHzwqPnZB4VZBPHpFs6VAhmyawOlB6RHEFtsnv5wM4cTEUjGam2BA4S86iTun4juGIVj3yeR5Np1ZpVAXy8J32Rhnv7129MGKwcS4ySOdQJTl0gviKBtHRY9w8JKBwWFxdOXslRfSuv5Hfx19yM8uOMBntz7V8YnTeCOUZ/hokVXn5QgSvSCiGJbkp361OikMXmynauwZcuG00IGv1oneLFWkDAFTlWys1thc7vktokWhX6bLH6rQeBSoTcGjUHwamBI2xKtPFniPAmqIGLAulb7OLpyiJBNcUlCCUFPVLK7W3DZGWV4Uzw0b28h0h9h1Mx0zpxWgDfftuADjnm8rppu3nt6O5H+CE6vk0nxPfR5i9hr5OBO8xI3Id1td/MdgGHBYFywpNDer+7SyZ+aT+UblYS6BS6/i0QkwWBnkPzJeSRl/+O6o08W1dX7mDXr+Pfqf1eMkMEjGMExYLkzsNwZKOE2TP3QDUmJdGC5UjG9+Sd49/Hx5puv8OUv30NXVzv33PMl7r77Cyf0lv0okTM+G0+Km7a9HUT7IqyPtrJ5tpcxKZfiC0aJu5zsFt3sa32bsvwJuMZNxHXdx4m//rKt1NB0xLTZ1GfOpvnP7yFbneR1gffNtfgnlKM7FKz2VtRRY1CPEVJxIgghWFa8hNZQB1V91QgEFpIMbxrzR81CrrEYtII4fQ5ioTixYIzyRWW4/MO9xKSUyM56zIEglt87rKazHMmosR6EEUY6DvkSuQMuLFPSaXWzRm6kSbYQk3GaRreS5cpglpqGiMeIBvzs8JusiFdxCeXE33iFxKq3CaUW0SZH0d8v8ayv5rzQHq6YfzatgQAvVu7i+Z1buPn7XyfFl8SyjByWqyq/vfgarnviQb748lP8qHw8bZFkah7cQCKcQAjwZvgYv6yCjPJT84+2LVYFIiMLfUaK7RkXCqJ4nGgpTrQxwwNHlMxsZDx2sCUqYCk4JJixOCFdIKNRLLebvpQA8zftRU9ORfZ0YzY1oBaVIrxejJoqjHVrkJGwrbx1uRCGgdB1rGAQNZCMVV9rTzhUFTk4gAwOEj7zbH701Xu46qobmT9/8XHPacu2rdx08xVE4wlu/MGbpC2YQ9QnUd94gfjzT4E/YCuDFAUG+jGFguL3275+ug6hIOqkqZgvPYs7L4TPuRuFqL24kWdiKWuJGItBO7EcRElOwXXJlWRddBlZlsVkVUcRoIhjk7gzsiQRA/7eIGgNCUIq5OmS3HR7xR6wVe9CIk9yQby7Gx5+WKGyEkxToCiSUaPgxhstsrJg3LhJ3HDD7Tzx2K8Ym1pFasbd6Dp0Wh1o3g04nRvo6a8l3trGvn176Ohoo7f3+PIMTbjp79iLOzmX7OyZjB17LVBOVtZ0vv/9nGE20WoTeB5QUBsEqBKXIbg6Q7LwCpOmiXbCcJ4XHNsErhcF0isxx2BbNTRC0VOCtPssrOLhYwjGh77XAgr2CfQ4dGeDGpMEBRhjwLHRTgefGxZM7RFUWxIpoFQHnyKIT7OvsXQLrHQL5+sC51sCFIHaJtC8EqMcxCDo6wRGxelpXXQ6ndx//39zww0X8+KLT3HRRVd84H217Ghl18u7iQXjqLpK8/ZWWra3MOmySaTkJx/cThs/CXXcRAgF7UnoKSi0ThWKUChMymNrx3ayPIfuVREjiiIUMtxpH9mxRzCCEZweFBWV8r//+zhXX72Mb3zjs3zve79ACMGz+19mTetG8rw55JXMpHfXRtZH96NF+rimxY/w+nAsu9C2PjgCB+6gYqiTDCSKmkAJawfTRrWKcbjv/DSJdavtPIOONpTyMWiTptq1R30NdDZjxWPI7i6U3Dy0K649ZuBuYvVKos88gezrBYTtwTp6rE2oGgZWXy8yGrUJ4YE+Yi89i1Jahvvaj5PYugk5FBQnnC47TT6QjOzqwuztgfjwkF8hBEpKGmbV3qMWuo+EcLtxXnQpjrPPta0nkvzv6zmMlPbPEQt4VncXmBbCn3xwYVFJTcPo7LCFGGmZGCvfQoaCCFUDp8vulIrFkF2dNoldW21bg4WCKDl5YFmYe3ejJNkkslWzH4nELxRSSs9m9AW3osZyjzVKhKKgz5yDPnPOB26rb2pq4M9//gOGYfDj62aQnuw78iAIywBpHnsHHwQCIldLrExwrBOIMCTGQexME2PS6TvMwcOpKvr8RcQefQirvQ2RngGRCFZDI/7oXYjkUhLlil3oWBKtSuB6QSF0r3XKobf/MAiIXGEhYgr6XgExiXQJ4jMl0UsO1U/xeRJ9j0J2dyafnvg5Pj36PlZvX8HPO/6HT//8Vv7z8W9w8813ccMNt5N8mHL64GF6wfWCwLFDgTiYmZLY2ZLErGNbq31YpKWlU1RUyrZtm7j00ms+1L46wvBmg4JblZQM+etaUlLZK/h7g+CmcZI3GgQb2gWDcdtSIWhAdxTcKlhIdvUI9vdJRiWf+FhhA4IJgSqGXxZdAcOSmBbETFBUhfypeeRPPRR6ISVsaBesahJ0RiDVBQvyJHNzhmzbLEnN6lqig1HSStIQQuAxTC5sq6a9qZOy6dPITNVZ3ypY2yoYiNuK4GDCtpyYn3vo+1AypwgzZtC0rdkWPzk1CqcXUHH26I/UluODIBwOUVW1l1tvveefPZTTjhEyeAT/NugMd7GudTP7eqvxObxMy5zEtMxJqMpHoKhVncQKL8C99wG0/n1YjmREfBAhE0TKrkI6T6Kv+jD09fXy7W9/nieeeIQzzljKk0++RnHxh7OZOB0I5AYI5Abojw3w5/VPkyZSkW4fg0l2AVZouqgdaKCmv4HxaWPQZ81FmzzNVkzoDurrYtS8sheX34Vn/GgGApcQ3bwCc18L6aMz0RecgfO85ShJp24Cn+vL5lNTbuG9zl10hDvxO5KYmDGOTGc6td56mrY2ERmI4vQ4KJldRMm84mHvN5saiL/yAtaOtUS6qtCKw3jmj0fLssM5lHg/pjcXeQTplz02i8pN+3i2/1W6vb2kkcJgNMSAYxBXkpNgQSmprmR0ICXUxqb29zg/ZyGJDesYdGexuyOdUFTBoUn0SJzBzhCJQYWCknQ+OfcM7pyziN0b1/CipvDilvX89fnHyPN4WZSRzSv79xDQvSzJTMGd5cCflYS0JH0t/ex6eQ9zPpF0FOF9ImR7MykKFLC7u5LRyWVoKalIKWnvr6XQn09B0vDUK23aDBJrVmIN+QEmYVHRVk9tTgabJpcT0QSDCuTUNjJ/9XZ7IhWPI7u60K/6GMLtwaqtweruQmRmoQyxg9KysNpaUbxJOK7/BPE3Xsas2gsIhNOJPv8MfrZlHdFohC9/+TvHPZ+/Pf0Y9913O968Ccz58lO0p+bx4C54ryXOVWvW4PYHEGnpKLqOzMu3ieZIGCMnh33nLmbd+AL0lAjTzprN1H1r8IRfRsY1YjEXoKEW5eNMVGK1rCBWeN5xx3E4hKqCqr7vw1QIWJQvmZEt6QhD1qAg50UF84AfnWmHnJlZnBT5KCU884zCjh2CkhKJ2y2JxWDPHsGTTyrcfbeFEPD5z3+dp5/8Cw7xHUrH/owWFWalZFKWdSGqeSF1UuCZbPG5RRZpLnh0x5O8tOc1xqSVo2ka2gqDX/7uO5yfehFf9T9INOalsxQ2TpIMJqC2VrBggYXbfdiYJbhesIlgY7QE1R6wWgM5ryj4vmhxIANG3ywQhsA84Emm2kodbY9A3ymI5Qy/Fhke+wCGBZ4QGEMXPm4JAk6JWSoxawRqk0AZBI9XMlEABrYnYMjet5VuT0ysZHtyIQbAypNIBZQg6DsERr6F2mp7rZ0uLFlyHmeffT733/8lliw5H4/n1HtQ4+E4Ve/sx0xYpJfaBKu0JN31PVSvqGb6ddOGFdJCCPgHedAvyJtDZe9+qvtqyfCkEzPjdIS7mJwxntEp/8LSphGMYAQHMXfuIr73vV/yhS/cicfj45Of/zzbOneS7ckk4PRDlp9UxwJk0z52hfoZKJ1F9qwlaBXDF5jdKExIOFmp9pIq3WhDXQItikGqVCgzhxOhakkZSmYWVn8/kV/8NyQSBxWvSImSlY0VDKKUlqGmpJJ49x30WXOHWR5YXZ3Enn/K9mQdsrySsSjGts22n7DXBz3dKNk59oK0lMjmJuKvv4rzzHOQPd32NrqO7OpECmwi1jIhHLRD0XR9GMkiY1GU5OyjCFuwRQlIiVAUzPpaEtu2IPt6UHPz0abNRBw29mPC60MdXUFi7buI1LRDQb093QinEyVl+HxEqCpC03HMXUB4zUqkZdnPAI8XtWI8MhQkvmYVrosvJ7FxHbKvF2VIWQyglo/BrNyN8+zzUNLTkX29iNQ0tHETjkn0HwvvR+QExSBdSgdJ0k+adWjh8Cc/+S6mabFw/kKuPHM0ItSC5RtSmksLJdxGImsu0hE4zp4/IFwQO88mFkV8yBriI+Si9HmLkP39JFa9jVm9D+Fw4spbhNOahlmgHDq2AmauRKsTKG1gHcHDK6FmHC2r0PqrsJwpJLJmk8iYftoybU4FMhVCd1moNaD0CawUC7OUYQR2YookfJWF8w2B1iSQumDeWYuZetkiKiN7+f3vf8ZPfvJdfvaz73PNNTdx2233HFLkJsDziIK+TWBl2pYeagt4/qwQdlokpnw0fsNTp85ky5YNH3o/tQOCvphk9GF/roqAdLdNCNcMSF6tU8h0g2EJkh2SSEgQMsCpSjwatIcEf9qlcNdkizzf8Y/ld4BPt1XAA3FQhMDvkMQtUIXAoR0IoTsaq5oFj1WKg/tpGIBH+gWhOJxTLIkOxuhvHcCb6kWakp7GHgbag1iGiRZrJ7Xcy9jl4ylLhlEpsKVDEDVgXJrFvBxJ2uGiEV1lzNmjKZpVSLg3gtPnwJPq+ZcjggF2796BZVlMnDjtnz2U044RMngE/xZoDbXzhx2PUNffSJLuJW7F2daxg4aiJi4bdeFHcuOI5yxEqk4czX9HDbVg+ouI555JPGfRKe3n7bdf4777bicSCfOTn/yBq6++8V/uRmdKC0taKEcQ64pQ7MTgw1bhhdOJWlCEZVg0PbMWzanhTR0qEAtHEc4soquhhaSrp+Of8uHaagJOP4vyjw4RG7WwlKKZBcSCtteQ7h4+obC6u4j+8beYDfX2xCKWTnxnNUbXAIErZqM7wwgrRjzvrGEWEQBJmUmIBRo9W/vICKWiShW310XA68cQJk3BFlJdyYDd/pywEiQiQaxIhIZgNuGoIDXJJuL8egyEoL89hD8Sx+F2IISgIj2LCVOm8c0/Psmav/6Jp595jFd2bEUCf92ziX2xn/PFj/+HraJRBcl5AXrqe+mq7h62gvt+UITC8tJl9Eb62NtbhSY0DGmQ6cng4rLzcKhHTMRy83HdcAvxF57BbKoHBAXFFUQvu4BCo4e+tkYWhCTTXtpARk0j0jDANNCmzjjkq5tI2OrDWBTpctuqGqGAYSAAfeFitElTDgYOKnn5tDgcPLD4Pj71qS+SnX206sQwDH7wg2/yq1/9mPz5H2Pp3b8lxTdkEWLAllaLcjOL+X4VNH3Ip9hEKSjC7OvhpRsuYuW8CTgGgqhpbnb6nZiXTeKCdZtIDHhQUp0o2Tko2bnIYB16+7qTJoNPFR4Niv0glkniXRLHVgEJAImVBZGrLeRJ8HadnbBrlyAnRx5U5DqdtpdwVZWgudn2Fk5Ly+DTd3yJH/zPt/jEWXdTXjia0qGaWRmEYr9kpwV7ugUL8iR7B2vIzszF60tCbxE8/NCPGOUt55I5N5DwdBHd5SV7DziDgj05kilTJOecM7wIV3pAqxKY2UNEMIAAswC0BoFWx0HrBaUfpOuIIt6WtCOO4cYzOUNSlizY1ycoS5Pk7hJ0hwQ+pyTPJ5GAWSwxs0DfJTBLJGY6qJ0CrRaUiEAEJbHrLaIXSPStwh6DD6QGKHYIndINarMgdoZ19CA+JL797R+xaNFEnnjiYW666c5Tfn9/6wDB7hDJeYeqeaEIfGleepv6iPRH8RzDMucfgQnpFdw47mper3+HtlA7uqKzpHAh55csPep+M4IRjOBfF9dffzORSJhvfvM++mP9RM5MIvUwIYSSkorfP5WBYCvxGcvRAsON7qNYNMVayemuYrzspl8V9Poy6U0uxIOTC6I+UuThdlFxu8Nq7So7vK2pESsWRc/IxGppAkva4ZeGgVpUQjwrQHjfVhr3/o28Bdfil/b90Ny3F6unG3X02IO1tnC6EKnpWHt3IaPtdsfSAQVvPA4+H8SimPsrUfILEYZhe6x3tA9ZSgAJA5FfiDa6HNnYgCwqsT12B/qRkTD6nAXDansZi5FYvZLEuneRoRA4nVjNTWCaCF0nkYijrlmF6+Y7UAuOExLAkP/7OedjNTZgVu62Q2/jMYTXg1Cy7ZXmw66hNBKoo0ZDJIJaVm4rTy07q0F4fZhNDZi7tsPFl2O1tYB7OPkiFAWEggwH0WdeeOpfnBPAwODvzpdZ6fw7/UofLulmcnwGl0Svor+hlyeeeBhVVfnxTx4gJitx7/8rWu8upOpGGCFMbx7R4ouGnfNphX56w+KOB6FpOC+6FH3BGVitzQi3Bz1RgvgfHY7TXXbkmrQ6UItnxy9Qg41I3YdmRtE71hMtvZxYySUf+TkcEyqY5WAebwFdQHyRJDFDorQATjDzbMuHCavG8pvA7/iPy/+D33b9mv996jc8+OBvuOCCS7njjs8yyzkLfbdd08mh8sZMAm2/xLFKkJj80aiDp02bxSuvPEs8HsfxITqrNDtCxw6AO2yclrR/V9NnE7cT0mzSdn+fYCBmf+y9Mcj3wdR0yb5+wYZWwaXlxye/6wegNQRtYRiICzojElUIPDpkeySzs2By5tHvjxjwZoNAU6BwaB6S7oaWILzdJJibK3FoCooqMA2Tzuou+lv60ZwaQlUw4yY1q2tJLUohf0oeC/MkC/Pen6R3+V0nJXSSUhLsDJKIJPCmenH+A32Ft2/fgsPhoKLio837+GdghAwewb8FVjStoX6gkYrUUajCLuC6I72salrHzKypFPo/mG3DCSGEvcqaOQvMGKiOU1ptlVLyhz/8nPvv/yKLF5/Dj370G3JzP4JxniRMyyRhGThVx1FkdIozQGmgiG2dO0l2+g/+vjXUTro7lSJ/wVH7M+IGsWAch2d45aS5dOK6j7hy8grWDwLdpaO7jl21GVs3YTbU25MBVcUKLEDzv0e4soHmVzvpTirHCiwgw1NKXmoch2f4A95Mt0gtTqVIL0BKyHQm6G7vJWLEGIzbYXhSSroi3czJmYE7kM5gWhb91QbuwKE6NaG68CoWYUsQG4zhcDuQlmWToPmFqE4nC2+6k4U33cn3YzHeeOMlvv7Fz7K5ZjU3/Md5XDD3Cs6dfQmZKTn2XCSWOOXrVJZczD1Tb2Vr5046wl2kuVKYkjmBHG8WMhzCbKgHIVCLShAuF9rY8ajlY+zJF6Dk5jNV05i4ZxeRZ5+02/QKRyP96VgN9Shpabju/gxiiJEUPh9KTi7mwAB0dyJV1SaCnU47QE5RECmpKHMXHBzj9++6gZSUNO6663NHjX///ko+85lb7Datu39EZMZnSTlsRdylgcOhsydpFPN6alC8XtTCIowd7yF7e+lO8bG9NIvCqkbcaelo7lRiJlSl6AzkZuEfd4SfnaIhzNgpX+djQe3fj96xESXahekrIJE152AKtvRA+GaLeBWoLQLphsRYeVJhbgDRKCQS4Driz8zlsoniSOTQa7fccw8PPfQ73lzzNWb5n2TAFKhxicMhEKUSFEHUtAs2l+qk3+pHSsmbv/ob/fFevnvuT3BoOskBhTGLJeGdgssDkvl3W1RUwFH1sRz6OfJ2eUBkexi/apRKtF2KXREf2D4OKBLzsJwGS0LdAPRGBWcWWGS6BXujCqV7YHKXJKVAEugDpVNgjLGD39RO7H1oYBZIzFzQ9kgSk+22UAClD6yAnSau9ICVZI9bRCVokJhz+tUmpaXlXHjh5fz2tz/lYx+7Fe0EwUHHghBDkU3HGpoQH2g+ZMQMpJTHvaeeCqZmTmRi+lh6o304NSf+0+TtOIIRjOAfi1tv/RRCCL7xjc8yrnMOnuvOJMeXdfD3vdF+/I4k0lyHHlwJJO84Qqw3mnAO7sBhmaQJHxkJk1BXI+6IxZzkWZRawyfy8VdesO0a/MkoqenISBR2bCWx6m0Y6Mfq6rRzC9weevevp8ptoWoDbDMfozNpD8sjVzE7MR8s037UWBZWawtWVztYEqGokJ4BdTUQjiDdHkjEkcEgSm4+wuOx74EzZhN99EEIBiElFSJhiMXA6UTNzsF1850kXnsJq3qfzey4PTgWnYU+/5BQRFoWsScfJb7yLVtNq6gY7/wdFAVt0VmoqWlIy8Lct5v4Ky/gvPUurL27MTZvwOruQikoQps9F8XrAyFQ8gtx3/UZEpvWYdbWoKSkoo6dQHzV25jbtyA8SSBsf2FtwiT0aTNJrF9rh+dm5Qyv+Q0D4bQLByUrF3PHe8NsHaRlIaSFknL6bX1WOv/O0+6/4rOSyDbyCCsh3nG+ToQwb3z2RSzL4stf/g8KCoqJyyIsbzZ6+wa7hvKXEs+ej+X7582jTgusBI62teidmxBmlETqBOI5C7FQMHIkajO2onaoXlJbbasq6/DcKilxNLyCGmrESB1/cG6qhFtxNbxCIms2lifnQw9VHaxH79yMiHZjeXNJZM7Ccp+aXd2xID0c8kCOg+chBX2TbReWqWXybeV+Pnf5F3kw52F+99f/4cILFzBnzHw+47uPc0svPBhcCGAFQG3mUPfXacaUKTOJxWLs2bODyZOnH3ujBLai3M1xrTzKUySZbkFjEIqS7Hli3ITuqODcIgt96JRcGqS7YJ+0t9GEbbPQN+Qh7NEk9YPH71hLWPDkPoWoIfA7IG5KEpYgYYFpSaZlSj4+zsJzWNl5oL7e1mELLUYFhu873Q0Ng4K2EJSnOMgcnUnViv2EusM4PA5Uh0q0P4o3w4fT56BuQwM5E7JRtdPXuR3ui7D3tb101XRjxE1cSU4KZxRQOr8ERf3olfDbt2+homLCh1oQ+FfFCBk8gn95WNJiZ9deUpzJB4lggFRXMh29XdQNNHw0ZPABCAHaqRGbiUSCr3/9MzzyyB+4++7P85WvfPdgqNo/GlEjxsqmNaxr3UTYiFDkL2BxwXzGph4K3RBCcG7xWbQE29jTuw+v5iFqxnBpLpYVLznmZF5zanjTPPQ19+MOHFKhxUNxNId2XGWalJL+lgG6a7oxDRN/jp+MsnRU/fQ9NMymBoTuOKT80DyE0+bwnlZBX3s2rtSxENHoeGUv3fV9TLlsEprz0O0w2elHKOD0O211tHRR6M9nW8cOwoaH5mArA/FBMj0ZnFW4EEXXcSxegtj8OmZfP9KvIxNxwpofb0Y+nlAbYsCHpcawOtpQCwrRZ8weNman08mFF15GoTman//2v3j5vb/x/OrHeWbVX5hUPpcJY5awPX8Uae4+ZiZcTDGcnAztY/V0k9rXx5KkcVhNe0lsWIvsfZ2gpiF7upGxqO15l5uP85Ir0CZMRmgaamHxsP2oFeNwXn4N8VdeQNZU2ZOa8jE4LrkSLe/QYoE6ugKlsBgRCtm+dZEI6DpCShxnn3vU+Hbteo9nn32cH/7w18NC4yzL4oEHfsn3vvd1cnMLeO65FdT75/BS7dFFkKVpOIqLsGp6QFUR2bkoPT2Y+/dRN7WcAb+XLHey3eaoKLiA9kAR/ZqTQKwX6UxBmFHUwTq0np0k0qfhaF1FPHO2vQj0AaC3rcVT+SAi1oNUnDhaV+JoWUl4wl2YgaEqWANjLBhj7fOREhoHoDUs8Gh20JnrOE/pjAxIS5N0dkLhYTkunZ2QmirJOWwe4HK5uPcr3+ALX7gNZ98WJjmnE3YLurJspbJPgVyvPYaZ2VN5vPIZtr6xmo073uX7o36Cz2Xfe9Pdqbh08ORK0pIg8zh+elYqGCUSfbvA8B9Sa6gttsebWXxo2/gcib5F2uFt6dK2y+gRxCdIEhPtMfXH4K97FXZ226S1S1WoSJXccIZJ6njIWilsnzoBsaUWsaUS6QLHaoFWLTALJFIHtR3wCuKLD7HRVgqgSxITbDWz2gtYtko4utg6beFxh0NKySfvuo/zls3lpZee5uKLrzql9wdy/fgyfAy0D5KcF7DbqC2LYFeInPHZuAIn/7wK94apWVNHR2UHUkJ6aRolc4vwZ5+6tc/h0BSNDM/7tD+PYAQj+JfHLbfcjRCCr3/9M0QSEWbdeC4Bp5+B+CCDiSDLS5fZ1hFDeM0Z5AVHkIKeRnTTIOROpk9IKgwX4/AQCfaQ6hw8SEgCWH29JNauQklORcmyWS9tdAUIsGqrMQf6wEhAVjbxFBehwUayNgriOakkZY6mWvTzhOdhsoM55BeXIr0+zA1rkD09gLT/DfSjzpiFesYSjLffQPb3g8OBUlSMkpZhL4qXlduL1cmpmL4kRHDAJkqzctCmTINIGKGquD//VczKPRCLouQWoBSXDAvltOpqSGxYi5KdixJIxupoB92BtEw7MyElFaEoKJk5GPsqUV59kfhrL9k+wi43ifWriT70B7tjKZCMOnY8jmUX4Tz/YgBkIgHxGGppGcaGtbYFhiVRxlSg+JKIr3gLjATC48FqbUbJybPtMoJBZDSCNn0mAPrMOSQ2rsOq2Y+SmwemhdnSiJqXjzb51LI+3g9x4qxyvIVbusm17Dmb23KjS52Xn3qGjWvWkJWVwyc/eZ/9BiEw0iZjpJ1aAN2/NCwTd+WDOJveAkVBCg2tcxPOxjcIj7mR2IVTcD/qRNsrkE6JiNmWCNELLDh8imRG0Ht3YbmzhomULHc2Wu9u1IGaE5PBUqKEGhFGGMuTOyw35QD0zs249zyAEu0EoYNM4Gh5m/D4uzD9Hz5M7eBx9gj0bWAWSdueA7CyJEmVXm6beQfXrbiF119/kd//5H+4ZvPllO0v577ZX+SasR9DV3VE0LYW+6gYrQkTpuByuXn33bePJoOj4HxH4Fhre02beRA7yzqm17TfAZeWSx6vFOzptWdvipCMT5WcXSQJxiFJh7YQNAUhyQGWBQhJussmhGsHBFkeSHUdv2Otrt8mbq0hMrkixVZqx0yIm4L+GLRH4ECZGDXg8UrBpnaFvhjU9EFnSDAj+1C4ddS0g+QONOGWLSylfW87PXW9WIaJCAscHgcZZWkomkqkN0w8lMAdGBLv1fXQtLWJvuZ+PClu8iblkTM+G6GcnHTBMix2vrCLjsoOkrKS8Do1In0RKt+sQnfrFM0sfP+dfEjs2L6FadNnv/+G/4YYIYNH8C8PgUBXNEJHBAbIIfmZqvxrfY37+/u4447rWLPmHf77v3/Ptdfe9E8bi5SSJ/Y9y8qmtSTpPpyak20dO6juq+OWidcPI4RHJZdw15Rb2NC6mYbBJlJdKUzPmjxsm8OhqAqFMwrob+mnv8UmhBMxg3BvmLxJuSQfFmJ0+Hjq1tdT9U418VAMECiqIHtcNhOXjz8tqjQAkZyCTMSHqR3ae3R6Yz5SSpNx5tmKByNm0L63g/bKDvImHbInmJA+jlxfNvv7asj35aEpKh7VzejUckoDhThVJzOzpzE/d+bBhQjX3LnkLu2l+u19OBODaE4XsnQUjYkZZHZux+kJg5FAn7sAxznno2RkHnPshdPzuW7p7RSklfHAip+QmZJPU1cT26u+i2/XIxTdegObPnYVV3jzWRrz2gEphoFISx82GZGRMLHnn8bYtN5WAHd2QCiEUlAE0sJ4bwtoOvrcBSgpqVhNjUT/8hDuT38eNedoqwYhBI6FZ6JNmopVVwOqilo6CnEYgQugFBbjuOBi4i89BwP9CK8XNB1t1lz0mYcsP+Im7O40+coXvkBWajbz24LEXn4effY8mkKDfPazt7F27UpuvfUevvzl/8DjduNqC/GG4qEvppA8JCoKJWwSdfK8ChyOCzHWr0E2NaBkZaMvWkzXhfNRipLR9MAwdUxD2iia8+dTUvsuhJpR+/ejRLtsYjgxiGfnr1Hz9xIZczMcsE+REpEYQCr6CQPmRCKEq+ZJMKMYKROGesMstN7duOqeJTTpc0e1OUYNeLJKsKlNIRyTFLUJpvbBwlxJ6kSJMYZhigO3G84+W/LYY4LqavD7YXDQLh4vuEDiO8JPrGTRx/Bk/5jGrd8gcdtLuHUIGRDphctGHfIxW5A3i637tvCLP/yBMdOmMsuxgGgkRnF+EV7dA5btBxebbRE1YCBuh8EdrjJAgej5Fmq7grZHIN32pEYmQfRC62DBD3YKdfg2C8ffBXqlAAdEzrOILZEwtN2z1XaoRoFPkuSAYEKypUPgUhVumWARKZNEwkOk82FrUOEbLdxPKmh1Agyw0iSRpcO95RITJWahsP2Nx0nMCCgdIAOC6JUSbTvoOwUiCkYZJKZL5Am82k6EiBFlVfM6NrRtIZKIMGraeH76i++xfPmVp2QdpLt0Ri8pZ+eLu+mu6UbRFEzDIpDrZ9QZZSe9r1gozrant9Nd24Mn2Y0QgobNjfS39DP92mmH7H9GMIIR/H+Nm2++CwuLb379Pryah3HXLcDvSGJZ8VmcWXioy6dfmKzWoyRbkBILgerCLxUGsWhQDQpMN5YcIGwESXEeUhjKnh7k4CBK3vAuNLWwGLO1BbWoBBmPQyRCxBok7pCktFu0jnEyMCGbQlNlr7aT9/QtFOZdjlY6itialUinE+F0QyKOyMmBSBTHgjNQXE7MPbvtsFlNg0QCx/nLUXLykLEYZnOTbR+RnIKiqlixGGZ1le3Pa5oo/gDKzDnHvFYyHCL25quYNVUohSVDogRpz1mkhblnF1ZPF0pSwK5VHU4Sb75mL7CPGYsMDmJW77MtHACRkYmxZhVWawuuW+/C2LYZY+27yEgYJTcfx+IluD//NazWZqIP/oF4XY0tKpVDz7mYgrl3t11yDNV8+hz7M1NLy3B/7CZiLz+P1dJ8cAzOi68Y5sN8OjAoBuhTevFbycN/0Qhb79uAUBRuvfUe1BME8P27Q+vbjbNlpZ1X4gighNtQB6rRO7ei9ewknj2P2MevRFRNRekUWDkW8enyKK9ghGr/mPHhr0tryGbr+NdQCbfjrvoLWs8uhBnDcqUSyz+HWNH5h4hlI4Jr/2OIxOARNewuXLXPEJp032mz6lCbAFMgPYeJPFSQPom2V6BerHLeeRdz3uKL2f3lDfx85Y+5+/Xb+PH67/PVcd/gmvRric8/zGf5NMPlcrF48VJeffU57r7784d+IcH9hMC5QiD9tipY3wtanUro5mOHD87IkuR6JTu7BeEEZHthUrrEo0OqE84ssHhqv6AjbIfLhU2bAxmIH6h9IcsLE9MlLUHw6hA4wikhboFp2dYSLtV2uVGw1b+WhIgJjYOC0Sn29V7ZJFjZLMj1Sgp8tl9xZa+tEl6UL7EkNAUFM7IkeUPTPU+ym0mXTGSgfRDNqeP06HjSvOhOjcGOIA63jj6kZOmo6mT7MzuIBWM4fU46u8N0VXcT6Y9QtmD4okIwDpW9grgJ+UmSfLsxgt7GXrprewjkBQ7yBL4MH6YxQOPmJgqm5qNoH43gbqBtgO1vb2dv5W7OKD+fmjW1FEzLP218xb8C/rVYtBGM4BgQQjAjawpPV71I1BXDpTmRUtIcbCPNlcKYlH9+ENsB1NfXcOONl9DZ2c5f//oy8+cvPvg7iWSPFmejFqVDNSgwdWYnXJSYH13LQd1AI5vatpHrzT6o3Eh3pVLVV83bjaupSPl/7J13mF1Vvf4/a7fTy/TeZ9IT0kMSeu8dFAsqInZRsV0botdyxXvtXq+iqCgiXUCK1ABJCCmkl0kyvfeZ088u6/fHHiYZ0inqvb+8zzM85MzsvdfZZ5+9v+td7/d9GyYRB2XBYi5vuPCo9186uwRpS1rWtJAcTqEaKrXLalxC4iArfrHeGI3P7iE1msKx3VALI2DQuamT3Kocqpcc2jftWKDPXYD50gqctpaJycVgexzFCKCX7quqNI/mKpU7RyeRwbneKNfNuIYHdv+N1rEOHGlT4MvnndOvYGnJwoOSLUIIGt59JklvAQO7e5EoSEUQKAhQdMM5hEq9LpkYCh+WrImWR5l39VxyKnMoKCzkBw/dgqewkPO//RNannueHd/4D3Z+6zb2XHAO3sIG5o0lwLFRK6swzr0IbbrrZ5R5+AGyTz2OUlAE0VycrZsR2SwUl7oFo8eLUBTMjjZidTV4wkH8GzdibdpwUDL4NSiRKMoJ+wz07a5OzLWrXcuI3Hy0BYvQTz8brX4K9q4dSNNErap2g1zG2+HHsvD7bYLnH36MTRufZ9a53+Tn5nwu+dvjbPn9L/nhulXk5hVw771/Z/ny09CGtuBpfILFo82clz6ZpxKn0OMpBUVDFZIlJbCgXMdT9U6c08922yzDEURhEdP1DCt9I4w5DpFxf8IRYaOhQsP7SPpn49v1O4RjYhYuwQ7XIvUAIjuKp2sFZuESrLw5aMPb8bT+DW10L1LRMAsXk666COnNPeAcqWN7URPdWJH6fQWzULADpWjDjSjpgQNa7Z5vFzzfLij1Si7dLJiyAbIJSGkC33NgneaQulJOemqfdJLE64UXXxT09UFtrfva4sUHto9tG9GZfuU3WP/zawn3rcQuX07UA1ED6qOS1+oor+pl4++eISeay03f/Sr+p8PM3VyBZ9CDjIEyLLDKJc/UwJOvKIxmBQHdTQheVCTZOSwYSrsF6rwbHIq2uGFuTp47qZloDdwPdiWkrh8ndFVAdYPljHWCgRhsKxYUl7rFMEBQd5XM2wYFPUkoCTBBHE/abzXEP+OgtrktfHYZB/gxywgk3+/gfVBBa3LtIqwGSeY8B22nwPN3gTAFKBJjJZjrBckPOsijyK9xpEPTaCtj2RhRI8xz7S+xunsdAT2AoejknFHL2h88wv1P/oWrzju2pOriaUX4oz56d/aRHksTzPNT4HSjPvYnkqOjKHUNGEuWoRQfWh30mrIjryp3opD2RX0MNg/SvbWb+lP+dZ6tx3Ecx/HPxQ3XfwJN0fjylz9FtV7GN77/C6LB6KS/GVBsxhSbCktHqgYimwDAJwUxxSGBBQh0ZXLdK8Jh8PmR8RjCs4/dkPG463dbXIJSWIzdvAdnZCdSU0mUeRmcW4zt1RCAisaYGHFtdEpKUapqXSP9TAaRl4daVoHT1YnT0YbvU5/HWv8K9t7diEAI7YS5qDNmI4TAbmmCTBo0BREMIUdHIBHH6evFKSlFFBdzKDhDg6R/9yuyL63Abm3B7mjH8npRp85EWjb09YDHg3AkTm83zs5tKEUlONJBnTYdaWaxOzvc81BcisxkUHx+aJiGvXsnqV/8CKerAxEMI/x+7J3bSbU04b3ug5irX8Ju2oPaMBWh6Ujbxt7TiFJSir7sFLAs1Moq1KkzEPo+IkObuwB1xmycznZQVZSyin1ddW8hgjJE2IkwqgwTsaPu5+tIHvvYQxPWR1dd9W6XxE4lXUsy38E7DP+3Qh3dC3baJYKzo64Fg5PBMcIgLbRYC0r218TP/iJO8DBqR9VDtnAxvuaHcLx5oHrckN54C46vGCtn+sG3c0x8O36NPrgRO1CJ1HwoqX58e/6MNIJkS08DQBvbi5roxArVTK5h/WVow7tQ0v04voMLWo4V0mCftdj+UyMT5P5aEx/M+vxi7qy6j+0bNvHtbbfyodXv5z+Kv8tnzvkKl9hXv20LCRdccBmf+tT1dHd3UlLi5raobWCsU7BL3DA7cLvitD0Sz7MK1iznoJYRpUEoDR5YowsBF9dJAobkG6tURjIuQWw6kLYF8aQkrEF9RPLgHoWRjMCrShYUSS6qlQTGv9LlQcjzSfaOCuR+h0mYkOOReFSJMX6aHAkv9wiC+5HKM/IkGVvQOuaGv+X7JDPzJFc1OJP4//zaPMrnlNLb2I8/x49mqKRGU2TiaaqXTEHzaDi2Q9PKZrKpLLnVuRNz33h/nJY1rZTOLsU3LlHeOgD3NCr0JNxxBXWFpaUOVzS4oXW2ZR9AwBp+g3Qsg5mx8GhvPZcy1hvj1Xs38eTKv4KUTCs4ge2P72S0c4wTLp/9thHQ/2gcJ4OP438FTilfSvNoK1sGdmBJG6Qk6o1waf0F5Pveel+rN4ING17huusuIxKJ8sgjL1JXN1lRu1pPc493jJSQ+KWgUTV5VU9zXSrCTOvtMUHvTvSSstNUGvtsNIQQ5HpzaB1tJ21n8B2jBcb+EEJQPq+MklnFpEbT6F4NT/DQ76Vv9wD9u/uxLQdFFW7rnpRoXp3Ozd1UL6kiMZhguG0EBORU5hyTOs0Z6Mdu3gtC4LngEsznnsZu2g2A7q9Hya9CyXkdeSdBNQ4sIOqjtXxmwUdoG+vEljblwRL8+sHHIh3HLbrbW5lTrjBYUU7/iIJmaFQuqiBylC3XMp1GplNES8PMvXIOFZdOZ+On5/LidR/mqc9+gZO/8RVO/fYtbP/tH9jy81/xztgjLC2p4GPzlrBwexynuwvfR29ChMKY69agFBSh5Bdgd3W4D+FIjlvwB4IITWcgP0J3UPDwqQ1Ij4dZhRoXJsc42hLP3rub1B3/g9PTjfD5sTJpzLWr8V59LfryU1Grag663TOtgg0tSVr/9k3y8qs5ee5yXtmzmQ89/xcSw52886Qz+MZv7yEYDJHp28HuDY+RzmYoCxRxbeB55sQ3skU/k2Tp2TTkKszO31fcKLl5KLn77gmzLQ8nZ/28ZCTpwkICPgSnZfxMlwGypadgdD3vKnb2a32TRgTibbB9FdnW53C2PIQMORiz6tFzBd6Wh1ESnSRO+KxbiL8eE0kR+3/AHFS9YDpuQRYyYEY3TF8PiTDESqAtA1G/pPA5gTWVSapWIWDRIsnChRLTBF0/tFgja0P54itpe2wuA4/fyhXffBKBuwq/v93Www/fy/PP/Z077rifc2dfDFPAWS2wX3ED3TInOvy92k0X9usQMSSxLPxph+DPOwWG6ioZJPBsUPLeUxwaokfpu+t3z5H3PoH3aQEKpAJg2oK8QRDzJHL8q+TXYDDlKiUOC23cf+8wsCsh8UkHpRuE5foMq53g/4OKjEjsvPHxZ12VsLFKkDn/8O9pKD3MXTvvZ+fQHjJWBtMx6Un0saDoBHLH/TULTzqdXfeu5mc/v+2YyWCAcHF4ws4h87e/kn3sr1i2Ax4P1vYt2BvW4v3Qx1DLDz6pjPfFEYJJBa1QBJpHY6Rj9JjHcxzHcRz/exATDtu1DAnhUOhoTLUM9CPI697//o8QiUT57Gc/xHvbO/jtb++loGCfh3BAKnilIKlIfKEytIEdCDNFVveg2w7ZzDD5ei65nslVhpJfgD53PtnnnnatnkJh5NgoTl8P2vRZyKEBRE4uWs5iUjLALm0L9bs0YjVRwA0ms7Ept917nVBVRDSKdkDYjwRFQYnmYJx5Lpx5oHWV09GGyMlFOI7rC5zOuC03jgNdnWTu+gPqF7+OMAykZWHv3oXT240IBLG2b8Xc9Kq74O44ridxOo217mXQVNe72OODbMYlmVMpVwEsBE4qheztdeuGcWstV+UpEJqGzKSxNm1Amz0XJTreypObh71nF9m/PeQugpeWIzR94hyo5RU4A31oU6ahVh/6QSgMA7Xm7V388+DhpOzp3Ou7kx6li1wnj7W/Xk3X8+3kNhQwb9FC8rNZ0r/7lWvBIQTanHkYZ5+Pkv/mfWr/JfBaJ6uUKIlO16bBV4iSGUKqXqzIFLThrRh9a0kfjgwGMpXno8Za0Ie2jNeaEsdbQKrhWrd+PQi04e1oIzuxwg2guUS7EyxHjO7F6HzODUgXCvus2F53PxDjNm2vr23fBMwZEm+OqxC2y9xDihEQjtuJtT+cckjc5FDbOpvfpu9j/eB6fvCrb/LxT17HT372PW6++WtccMHlb7kt49lnX4SmaTzxxMN84AMfBUDpEYikRL4uUsfJdetHkeSYu8gUAScWQ64XMragJCBRhCRuwkBSEPJI9o643Xh5XknShCdbFBKmwwdmSoRwSd2zKiWNw9A8KsYVwQKPCgHDId8H03Pd82o5kDRdBfFr8GmwpFiiqYLTyyUnlzlMzQVdgdRommwyO0HgRiui9O7qo3tbD5pXI1gQpGRmiSvy2tyFN+wl1hvDnxOYJILy5/kZah0m1hvDF/EynIY/71IYSkNtBFQBwxnJM22C0iDMjPjQDJVscnLGTyaeIVQUnFAhv9Vo39BBrDfOjoFNNFTMoLKuimwyS8+OHsrnl1FQ93/DCu04GXwc/ysQMoLcMPu9bBvcSUe8G5/qYUbeVMpDh1Yw/iOxZ88u3vOei6mvn8bvfvcAubmTCeqEcHjcE0cAU8eVwBLJHsXkSSPONMtAfYM9LsPpUboTPfg0H5WhMlRl313dp3lRULCkhS72railrQwRbxj9IBYbCTPJ6u51bOrbikQyp2AmS0sWEjIO/VRTdZVgfuCQv58Ya+sw6Zh7837NWN62bBIDCWK9YzStaqZpZTPpMTfAyxv2UndKLdWLKw+rppVSkn3uKcwnHsUZGQZAyc3HOOtcPOP+c2WZMP1/2016LI037EVKSXI4hebVyK89+IKCrujURasP+55kNkvmnj9hrlmFTKcYTAdoMstIF9SiFBQw3D7ClNPrKZ5edOh9pFJkn3kSa81KnFQatbgY/fSz8SxYQO7MqVz04uPs+Mb3ee5LX2PvY09y7Qeu4yNnnE6TqfCntS/z3sfuY35pBR8preLMl1dhLFoCyQSifN/ECCFA15HJBIrXS2/YS0ueH8swCJg2Cd3h+QX1xH1FfBSJdpjrUUqJ3dlO6mc/dJUoM2ej5Oa5qpr2VjKPPYw6Zx5K6EASPGvD+j6Bs/sFujo2sfTUD/HQnz5Nd8cW8mtP4pZTLufSYg+mHuKP2wVPbgmSSF1JyKNQkE2wLNjKtbkvMS99J/GiMjc84zDQEFydDnGC6WGvZiKAOktnim2gODbayA6UeNt4yFvlpGI93TzK2MbH3BZWq580QdSW3YTOn4tRNgV9cDP64GbMwkWTjmlF6rEDpajxNuxw3USLnZrsxCxaiuOdXECYtrtq71OhpFmgWpAMuSJZB0gFBWIUtK0Sc+6+7fr6YMMGQXc35OXBvHmSigOzHgF3tX9Dn8rcyz7HUz98Dy/+diuWnI0TBe1SoAFGRob5+tdv5oILLuPccy92N/RA9jRJ9jS3eEyY8MQriqvODbpThpGMYMcgxEyYkQdTcqDAJ9kzInhgt8LNCxyOdgFdbQPPSwpOnsTJhVxcwnloTFDaAuYcdxyu+lhS+FYJhxRwyvb9U2sSiATY+59Pw21d1F89PBkspeSeXX/l1d4tVIUr8Gs+tg3upC85QFe8Z4IMVhSFGecv4eVf/o2OjjbKD0HaHgl2dxfZ555CBMMohe59RjoO9q7tZJ/5O7733XDQ7XS/juPISXY6ALbp/ENTmo/jOI7jH4s9apY/ecfoUk1AoAIzLA/vTYUnOmgOhcsvfydVVbVcf/1VXHDBcn73u/uZOdP1dS1yVGaZHlYaKdRwCVEzgRPvwk4nKJA6BUYBUyMnoCkHtth6LruKZCZOcsPLqJ1NBCIF6Cefjr78ZNK//RX27l2oJaWUiDwSfYL2iiy7lhgklD76lR5qrSnMNd1nsdYwFfPpJ3FGR1AiUQBkIgGOgzZj9uFPjq6j+LzInFzYu9vlw15LalVVsk88irZkGcayk0n/6XdYm191PXxtG6etBQIh5GA/orgURoaR2TQkk67crbQMpagEzCxOf5+7gpvJuL3cw8NYlolaUARZE2maKJVV4HXrVZlMuuF44+/nNYi8AlfxbJqIZAq7tRmh625XWDDoktEb12O3NqMUFo8rh49t+i8zGZeUfpPhSadlziEj0rxkPMemDRtY97XVzLlqPpvv28A1N15K+je/xO5odcfuOGSffgKnvQ3fx25CBP/JQaRWEmFnXKL1GMLEJ+0iZ7qrCk71ILKxfVYPjo0dKHVrRcVASfYecV/Sm0fihM+iD7yKmuhEagHM/Lk4gbJDbqNkRhC2OUEET+zLCKOk+sFOg+bHDtVi+0tQE+3Yodr9atguzPwFb5kqGNy6K3WFg/dBgdY4LhLyQ+Z0h+zCg9RZ6r4F/hNYwJ0n/5X169dw2223cuON1zJjxmxuvvnrnHfeJcdkv3U4RCJRTjrpDB577MEJMlj6pctamsB+XwuRcoOI5Rv8qgykocAPCMlYBuR4Okx1WNKfdm8jFeNfhYAOhirZ1K/QHrOpHJ9ynVEhCerwqy2C3cPg0SS5XklZEK5q2OcFrCsu+fpylySq2OgeDYRb5xf64Jwqh7ooZJNZtj67h54dPVgZC9XQyCazSMtBqAqaV0VoCkZAZ6htiO5tPSBA1VRGOkcxfDr+PD+BvAC6VyPenyDeH2fvyiayiSzNkSJ6EwYNOe4pBZcQH83Amm7B8nkR8uvy6d7WQzA/gObRSI2kkbZD5cKKty1AbrBpEOGDTXte4arTrgNcNfJYT4xYb+w4GXwcx/GPhlfzsKDoBBYU/WuFCQwO9vPe915KQUERd975VyKvK9QA2hWTAcWm2t5XAAsExVKjXbXoV2yKnWP7OtqOzZOtz7GifSUjmTEMVacuWsM1Uy6lNOgSoNNy6ykPldA82kpNpApNaIxmYww7ac4sOQPtdWRwykpxx9a72Ni/FZ/mQwA7BhvZPriLD81+r+sZehiYjsmuoT10xXvwai5hv79y28pYCMVtDXsNiqLgWA6WadP43B40XSWvJhckxAcTND67m0hxiNyqA9vxJ87Fzu1k/3ofQjdQG6aBlDhdHWQefwT/Jz6LWlNHseVQPZChbV0H8YEEEonHb1B/Ui251Qfu2zZt+nf3M9I1hqop5FbnkluVc0BxYb2yCvPF5xDFpaS0KLt2G2TTKQL9TejVBSQGEmx9dBu+iJdI6YGr9VJK0vf8CfOl51AiOQifH7tpL3Z7G14Es5dP5bmgzUnf+yZ1553D4x+7iR997FO8r2EKHz/jEt4xbS7PN+/mFy+/wI3rXmJG2x4+9aVvcZrPjxwbReQXun7CoTDOQD8iFEaprKJ7WOAbHKJ30Xx8qQze/gH8BfnsnFnMbjPLdPvgZNBrxHvmgXuw1r4Mqop8JYZaUY06YxZKSZnrd9fShDJ77gHbW9IlhLc98Z9omofVK35NXkEtl7zjNjL1F1DS8Ve6vSq/X6fwTJsgm4ziVzKkLZuQmubvYw2U6GOcbzejJLvgCGQwgIpguu2Z9J5EZhj/9l+jD25GSXSgxloQmWHMoqVumMZoN/H1QzjU4CnRUexcFD2M2TFIclUj+jUngnRQkt0HHlDzk667Bv/OO9CGt4Kig2Nih6pI11x2gHzXp7mtXduHQLVBjtc1WROCpiDEeLvZfvZwTU1wxx0KnZ0Cw5CYpuDFFyXvfrdk7twDC+iFRZJX+yTPOlehGl9kx6ofkzvtdkJD8Py9CrUBmz/+8SukUkm+9a0fHvJcDqVhNCMoGvd32zsi2DYACUugCOhLSuKmYE4+lAcl7TFBW8wtOI8Gaus4CTvezBAEznIkd3uhbQR8KYhZkLLgkjp5gF/aW4o3OI/oiHexY6iR8lDpxD0z7AlhqAZd8R7qc2rxjqvJC+fXoBoaDz98Lx/72M1v6HhOazNydARl6ox9Q1cUlLwC7J3bkOk0wntgB0jhlEJa17Qx1hMjVBhECEFiMIHqUQ+7eHUcx3Ec/3uRxuEeb4xu1aLONtAQpHB4VUtTZKhcnTlyJ9P8+Yt57LFVvP/9V3Dppafx05/+jvPPvxSB4PJMiKyA7VqGzsJawpFipqaynGNGKdPzD5rz4UiHpwfW8VzDMFYwRDjlp6CkkouWnk95uAzv+24g+7eHsNta8QDVUy6k5WoPscJeBA6nZs7m7MR5BDbuIb1rJwQDqItPxH5lNVa3q7wVikBbvBRt/qID39B+0KbNJJubj7P1OVAURDQfx7YZTKcYikbpHxlk5Dc/Z+CBu+jb/Copr5cMkMpmSHV2kLVMbEAqCo6i4AC6ZRGQkkBXC8GhXvyOTSCTwa/rBBSNoD9AbmyM0oEUJVKiSRChEGpNPZgWTnur6+Gbzbg/+4XvyVQSAgFk4y63CywQAiR2azMYXoRtkXn0QVBUhK6jzZmH993vPypy1e7qwHz2KaxtW1yl8dz5GGecM6n7CikRWbeTxCVKD/3g1NG5KH0l5durufaK85k9Zx6Lc5fRntPCqf4Qdkcb6tSZE/kXIicPa88urE2voi8/5YjjfSshkXQrnTSzEb1nNTPbuilN6FihajKV52Plzz3mfdqhWtLVl+Bt/itKdgSRGUKRDravAMdf7CpunSy2/yiFTpofs3g5R2qQeg2ONxcUHWEmkPo+AY/IjmCH60F1ryupB0jXXoV/1+/QhrciVS+KlcYKlh20hn2zyC6TWPVukLCwwCpyLdHUjvFa8Aj2rAsWLOHuux9jzZqXuO22W/ngB69m9ux5fP7zt3Dmmee/JaTwBRdcxpe+9AkGBwfIy8vHagCrCrRmgVUjQQcxCmIMsufISQTxsSCoQ9QD+T6JlG54m0+TmDb09yjkeCfX92EDuhOSwbSgMuz+TghYUiJZWGSzZwTa464yeHruPiIYIBvPUNHayYq2AKulRq4XPIVhLK+HU8ocaiLunG/7Eztp39BBIDdAIM9L765eRtpHKJlVTF61ey/o2zNA8+pWSmYUkVeTS3osTcernSSGkghV4OmP4wkaGAGDkY4RNENjsGmIweYhYoUFKFVzUcXkk+bVYDTrZhTNumgGht+gb1cfmbirTq5cXE/5CYde/Hg9EglIpyEahaNxFDH8Ots2vko6m2LRdNdn/TX+QjX+71Co/3feyXEcxz8B6XSa97//ShKJOPfc89JBiWBw1YkqbqLn/m14FhIVgfYGOm5Wd6/j4b1PENKD1EaqyNhZtg7sIGNn+fT8D+NRDXyaj2unXcGfdz5A82grY6EIo1V1eItOZVWkBsuOcWY2QGicfdrYt5VNA9uoCVfh1VyyIju+3/W9mzilfOkhx5Mwk9y5/R429W/DciwkkgJ/PtdMuZR5ha4Sw5/nxxfxYyZNzJSJUASO6aD7dRRVwcpYRF8jTAWECoIMNA3S19h/WDLY2rgemUqhVu6zJVDKK7F3bsPasgm1pg5FU5h+zjSKpxcx3D6CUAS5lTlEyiIHErwZiy0Pb6VrWw/SdtuvdK9O7fKaAwKazPVrQdNRwhH6ulTSWYWcfC8M9qOODhGtrmWgeZDu7b0HJYOdthasV9eilJRPKFiI5mDv3Y254mnOmz+XXsWiUcvinLmE5WueoP99n+LXzzzPYy1t3LTsdC6ZPofTahpYuXoFv+rr4iOf/RB1hSV8qKSci2cvwIjkuMEog/2gqjiD/WiKoGfWdFrnTHUVKCVlhKdMpccwGLRtsA8YqjveliayjzyIUBRENAehqa6XWGsTIifHVSYqykGftI7j8OIzj7Hittvo2rkajyfAKad8iOmLLmNYDaNbSUpG2/j7go+zdVCgCSjxjKDYaYZkLh3ZKFXGMCtjlZznB6kdQY1uZ932Oy2ANCZPfLxN92P0rcUK12KFa9AVD9roLkTH37EjU8j0QyaTi5gyHUY2IKwYQgi0vBBW3yj2UAxdlUj94Ip5s3AxcV8het86RGYAJ1COWbgYx1foqnx6e5DpFEpRMcLn5/RKh5YxlVcjkhpL4G+FsgFBvgO5rQI0cK6R4+cR/vY3he5uwbRpEkVxC7bmZnj4Yfe113N/IQPeUeXwartGfuXH6Wu6lRMqv0NtfgGtzfCb36zi3ntv5zvf+cmEJxpAxoYdg4LeJPh1KA9IfJrbtiYEtIyBqoAmAAF5XkhakqZRWFDkBlnYhw4+PhA6vJb+/tqt8mwLvMOSZ3MFvbYg1ys5uUxySvlb16r4elj1EhkAZQCc1zpUMyDiAnP+4d9Q3EyQtjL4A/uq7gJfHiEjyGh2jLSVwat6GM2MYXskC09ezgMP/PkNk8G8pvx3nMnfO8dG6N5DVr2RkjDTz5tG4zO7GW4bBgSesIepZzSQX/evYb90HMdxHG8tmjSTdtWk2tYnOoB8KORLlQ16mgszQfwHM7t8HUpLy3nooee46abrueGGa/jSl77JJz7xBaKofCgVoU21GBE2OU4elYqG8ByakFnb8yoP7X0Mn+ajsGYGGTvLulgnQzvu4TMLPoJv6nTU+ik4vd2AIFhcwjsUwYWxGAoKvv4Uye9/i/jLK10FrRAopWV4LrsKEQiCbaNOmYY264RJnsT7I5lMsHv3Tnbu3MaOsSF29XXQl04xEBtgSDpuSTTc4/7xi08R9XjJ83gJ+gN4NB0P4BEQciSKpqKqOop0UByHrFCIKzAoHdrMDIl0ioTjkMimSSIhNjQxDjU5Qkk0hzIzQdmTD1HuD1JZWk71ORdS3dxMsLnJtXQwDOTYqLsQWF4Jmg6RKNgW6B6c0VGIdaDWNaA2THetJhJxzLUvI4qK8V529WE/X6e/j/Tt/43T1ozIK0BaFtnHH8Fpacb30U8hAkHUWAuelofRh7YDYObOIFN9KXbo0Pkfvb3dfPjaaynMK+ZPdzzKddddxumnn4vW0Ybt808KQha6DhLs7s4j8YFvKSSSv3se4UnPwyRi61AiQ0RnhLm4q57zW0bRYi0k5tyElTPjyDvbH0KQqboYKzoNo+t5/Nt/hZIeQEWitD8Nmg8zfy5m0eK35X1Z0emYebPQ+9ZhB8pg3DMYIFN+5iTFs1m8lLivAL3vFZRUP06wgmzRiTiBt6cj1ymEbKFE3yDw36+gdrtWYVa5JH2544YoHwFLlpzEffc9xcqVz/P973+D6667jPnzF/OVr3yHpUvf3GLCuedezJe//CkefPDP3HDDJ8ELqXc5+P6koLUCNsSCgtTJDvqp8g3n2eX7YE6Bw4oOhYqgpMDn1ts9CVeE4byu7E1Y4NUEIePAulRVYGouTM09sFa2LZvND2/F2tHLubn5bDUKaI8ZKMkUFyyJctHUIIqAsZ4Yfbv6CBUG8YbcLgUrY6F53bC4nIocFFVB2g52xpr4/g61DuM4bpeZqimAIN4Xx8xYhItClJ1Qii/iw8pajDX1ERYdJPJrJ7yPpXS7DucUuO/LG/Iy59JZJEdSmCkTf47vqEPcYjF4/HHBhg2CbFZQUiI56yzJvHmHn0OUzC5lw50vUxQtpbygGulIRrvH8EV9h+wo/t+I42TwcRzHG4TjONx00/Vs27aR++9/hoqK6kP+bZWtU27rtKomdbaOgsBC0qNYLDZ95B2hLe+AY0uHlV2voCsaxYHxdh1NJzc0k62JbrYN7WZ+gauYrI/WcvOCj/FcYi/35QgUr59KNYgp4FEtTpdqcUMyioFg72grAjFBBAMYqoGmaOwZaTosGbyifRVrezdSHSrHr/txpEPLWDv3NT5CTaSSqCdCYUMBHSUdICATz+JYDrpXQ9FUgnlB4gPxA/arqIJs8vDr3nJsFKG/LpBECISqIeOxfa8pgtyq3MMSywCdm7vo3NJNpDiM7nMfNsmhJE2rWsivyyenIrrv2MkEjLfOpbMCVbgcVjZjEWsZxMoEcUybxGDioMdy+nqRiQRKWRlKohMlO4IUGjJo4HR3kRNP8VERZZuWpV+1CHjCzPzxnbR/7WZ+vPZ5Pv/Eg/zqlRV8ctpczi6r5Ixv3samdIqf/Og7fOmZx/nZnp3c0DCDK2fMwf+Rm1ArqnCsLDsKYUttCRUpCz03z/UZFqBiEpSHnghaO7Yhx8ZQps1AGRrEaW9D5OVBMoHd0wXpNGpJ2ST/OXex5E5+85uf0tS0h7yCElTdx7IvryVneJDmJEhMzhxbT2D2LPbkNBAxYTAlEJ4IIpkkQowRO0zWEcRTGczCKqzc8XZPx0Qb3o6SHsLxRLGiMzD6VuNpexIl3Y9UvWSLlpKpuRSpBxGZYfT+9dj+ogl1hFl0Io6/CC3WSrbsNJJFU3BW/wVFgh2sQE32IMw4SM1NVY43Y5dWYuYdulPBDlVjh6onf979fWQevAdr53bIZlHyCtDPPJvZJ5/OB2bavOBRGF4LCzaAoYEnIFFGwPFBbKtgVQOMDMGWXW5B89q8SQgoK4P2dkFrK0x9XeEsTZP4pg5KRzTqT3s/v2n5d4aa/pupRV+noMDi3ns/xuzZS7juuhsnthnNwO+3K2wbBFu6HshFAUGJX7J1UCGelaRMgU9zk9J1IdAV1883YQraxiRlQUnZYQRISjcYKwXaLoEMgjVF4uSA2u56+SJATcFZ3YIlSx0Gl0r8GhzE5vsthV0FmTMdvE8qKIOAIsEBc7Yku+zwBWShL5+wEWI4PUKB320j82k+SgJFSCSd8S56Ej14NR+nlC8j+s6T+PCHrmXXrm1MnXpkpfvroTZMRSkoxOloQ6msRgiBzGRwhgYxLrxsUljQ61E2p5T8mjyG2oaRjiRaEcUf/b8V2nMcx3Ec+5BB4nDgBNAjBSkhyQiJ/2ht3v0B/ud//swPfvBNvvvdr/Hqq2v5wQ/+h9zcvPFOuCNP1qWUrOpei0BQGnC72jRFoz5STdNYK9sHG1lQdIKrTC0tn7RtSIZdy69f/4DsimcQ/gBKRSXStHA628k8eC/Bn/8GrWIfOWnbNo2N29mxYyuNjdvZuXMbu3Ztp62t2Q0vAyora6gtKmZmdxcFvgD5Xi8FikaeZaGF/Wz+3EeZsWYjXs2gKFpEkaNi79yOuXUzDPS7C3OBgLsSmohBJIqSm4vw+ZDpDE53pxuSFslBVFWTUlX6UwnaO9vpmbeAvmnTaWvazd69u1nR28Hgri3w3OPu2CI5zN4YYlY4h9lFpcw+9Sx87W2oVTVo3mnYHe3I2BiKz4dMJ1EqKidsIUQgiJKTi7VuDfK8iw/aMfIazLUvY7e1oE6ZMREoJ/LysBp3YG16Fc+8afi3/hw11oLjd4NKje4XUeMdJOZ+/oCQXIBYbIz3vOcSLMvkT396mmg0h8bG7ZxzzkUo0RysbOaAawPpoPyDLSK2a5t5xHs//lSKWX0SqVfR5Utyf2ULleZyZnUMYHQ8c+xkMIAQ2NEpOEPbcHwFSBQUJ4OwU+CYWKGqt41wRdFITv8QXj2MMbgRsiM43nwyFedhFi8/4M/tSD125CCpv28T1Cbw/0mBlMQqlQjHtezy/0Eh/mln38L8EbB8+Wk89NBzvPDCM3zve1/jyivP4sILL+erX/0uVVVHCJE4BAoKirjggsu5447/5vrrP46iKG5I8c0Ow9vgyV6FjUisgELdbjlhsfBGcHm9JGNJtgwIOuMSryZYUiIp8ksebxb0JyHHC0kLOuKCeQXyqLvvXsNQ8xD9u/uJlIYp8EGD7MeUMNo2SHl7Kd4l7twmNZommzIJFY1/B8fD/lRdw87aWFkbw6dgZ63x4HiJmTJJj6XxBD1kEyaR0jCBXD+DLUOMdo5SNKMIX8StMzVDIzdsUD3ay4ZYHVFDoqswmBYU+CSnlE1+GPmjPjiGGtWy4I9/VFi7VpCXJwkEJHv3QmengqbZzD6Ma1De1Bw2tr/MsilnMtgyBBL8uX6mnzP1mPKM/tVxnAw+juN4g/je977Go4/ez69/fTfz5h2+7UxHcGU6xJ2+UXap2XEHIKi1DS7KBCf+fbQwHYvh9AiBcSKrNxymsaSIuNfLWLaM3/psfGpmoi3er/sZKa5CM5LMsnWEdH2Zoihs1TLs1LLMsTwYio4jD1xddKSDcbCQrHHYjs0rPeuJGKGJkDVFKFSFy9k90kTj8F4WF8+ncGoBFQsr6Xi1Y8IEXtUVKuaX48/1s/3xndiWTSaWJT4Yx0pZZFNZvOHD94Ir1bXIV17GyWSQXR04nR042TTCcdDfQBpx744+NF1F9+k4tkNyKEk6liHeH6dtXTvR8n1qYnX6TOw9jUjbxu/RsB1JvGcEJ54lHhBkuscwE1lCxWFsy57wSn4NIhBAqAK982U0s9cNHgEYdnDKZiE8XnwoLLS8YLm/0u0dVJ2vcPK0KOua4Vsrx/jUqmcpLyji6opyrrnmvfz+zr+yfftmfvKf3+LWJx7mvztbuGnWbN61ZBk89jBVrc2s8WbpTmbIa2vBnj2HjvwwdbZOnaWzRcswqNhEHIVploHvNaWQmQVFoCgKon4KZjyGHBp0SfEuiaisxnP51aSk5OmH7+Vvf3uAp59+nEwmzYUXXs73vvdzbrzxnVzzno+w4KQGGtuKCI90s1DtYXHNQmJ1c5AbdYJINEWQ1qL4vFlEehTsDKNZWJabIl1xFZk165Dtu/GOrsaTP4Ye1UCoOJofJTuK1AI43nyElcDb8hBKdoTkzI8hrBTCzuJ4cva7iFTsUA3CyZItXoaezWJ4B7E2bME0gmSHLITdjXAsfDPyobSO1PTrkN6jXx2W2SzpO3+LtX0LSkk5Is/jksP33IXwB5izcAmzch28zyj4Y6Co7up4pkjSgmBwpcKzOZJmL+wdEGRUmB058qzd2raZ7CMPEdrZzjm7FcyxKjbUXcymTb9k0aIvsGPHHxkb287Xv75mUvDGU62Cjf2C2rDEq7mKhKZRAMGJJQ5rehQSbjg8tRFIWZLBtBtKETddpzO/5vCjDQK/JqgMSRYWuURE2oKyMSi9Q0FtF8iwhE7QtwmsaokyLNB2jq+saBJzgcQ5TRL9R1nZCkhfJLHqbPRtAtKuT505XyKPUAPm+XJZWrqQx5ufIeuYBFQ/3b29pEZSnJ09mXJRRk59lClV9dRGqjBrTSKRKA8+eDdf+tK3jnmoSjQHz2VXk7n3Luyd29yWaKGgzT4B4/Szjri9J+ShZGbxMR/3OI7jOP73odzWCEuFAcWmcNyeTCLpFzbTbYPIYRaDDwZFUfjCF77BnDnzufnmGzn77IX85Cd3sHz5aUe1vUQykBycqGdfg67qSCnZMdhIwkwS0P1Mz204IMjX6e7CXPcyaBpiPLtAqCpOUQlOTzepZ59i7wlzWb36BVavfoFXXlnJ2Jhra1BSUsbUqTM577xLmDp1JtOmzaShYRqBQBC7q5PYpz+MvXM7xJIgHbrLCnnkvRfTOquaks4e8rbu5NX8MLNtL0Ujwy7pWloGtg2jI0hNde0ZVBW1bgqipg6ncTtOPAaZtJsYNTqCX1GodByqS8rxf+QzGK+zREgk4rS3t7B9+2ZeXf8Km15ZyTO7d5LesQnlhSepi+QwO5rHCbUNnFRdT1U0F6uzHaujDdTXTfUND2QyyGzmoGSwMzyE092JtW4NeL0TRDDgii6kax+hl46gxlqwcmZOKEodTw7a0Hb0vlfIVF04ab/ZbJYbbngH7e0tPPjgs5SXV9Ld3UksNsaUKdPRyioxX1mN092FKHItE5yONpScXNRZc47qWnqrsFl/lYxIU5/yImwTYQ9SGYuzNS/FVrGSmUxHG93jFmhvwIJAZMfwdD2LHazEKVyCMF3RijCTaGNNiPQA8nXZEm8J7AwoGqkZHyaTHkBYSWx/EWj/GsSWsVYgRsCaBrjTVNc+YqdA3yjInH303WBCCE499SxOPvkMHnzwz3znO1/l1FPncMMNn+Smm/6N0EEyTURi/LiHOB033PBJLrnkFJ555nHOPtu9vpMq3O4o7FBd8tJQJOv7BO0xhY/NdSg/ihA5x3bIxDKohorhNwgbcMNsh9YxGM4Ioh6H6rBbWwtgVZdC06hro7CoSHLNFAflGC7DwZYhNj+8ld5dfcT7ExhBD/6oF0/Igy/oYaRjZCJLwhv2ons1soksnqAHoQgCeQH6dvfjj7rBbuB+VrbljIfMmW5QvK4CEm/YS7AgSDqeId4XR9NfNw8WMCvPoareYW2PIGPD4mLJGRWuVcWbwZ49sGWLoKpKEhh/xEQi0NgoWbFCYdYs55Bf4b899gCx5Cif+vLnydHzUcdtLH3/xwQTx8ng4ziON4CHH76Xn/3sNr7+9f/gggsuP6ptGmyDTydy2aJnGBMOeY7KbMszYdFwLDAUneJAIbuG92DklrOlspyspuKNx/FbKYZzvfxRH+MTyRxKxov9vWqWiFQmEc8+FCygV7EA1+N3RccqBlPD5Plcomw4PYKmqMzKm3bI8Tg4ZGzzgEA6BcUls2zX7FTVVGZdMJ2iqQUMtQwhFEFOZQ4FdflkU1m6t/bQsbGT1EgKx5I4toPu1enc3E3JzGJChQdXB+gLl2CuWY313N/dIlvVwDTBY2C9shp7ybIDFCWHg23bCEVgWzZ9u/qJ98eRUpKJZ9n9/B5CRUFqlroKPGPZKdhbNmLv2k6+P5+WeIixmESPlEBuASJt4ov6SA0n6d89cIAXp1o/FSNP4GzZjV1RCl4vTjyFkxogWDyCNPtxjH1t+yI9gG/X7xE+ib3wMuY1jPLQacOsa+3kN9uD/PrXP+GHP/w2J554Mtdccx3fue2XfP7L3+YnP/keX/nKTdz+0+9zc3kNp884gbHyUlbVF9IkTYyuZqb6ZnMuQX7rH2WXlsXGLTyqbZ33psKUOTpKeRVCVZGpFCIURl+0FLurA3vvblJLlvJcTQ2P//R7PPPME6TTKWbPnsenP/1vXH75Oykvr+J3v/slY2OjfOajH6O8XML0EBACpgBgSKiJSDb3uy1RnXGFjFpESs/DUS2K8nwsqgkTu/PX2M170cb2kkwOkC7IJ3jufDyVQbytj4LuJ13lhqBJI4xUfej961BjTdiBCmx/MWq8A9vYVxAqqV4cIwe9bx1G9wsoFXEGVnVhj2UQhhepeEH3oxUtI7bw6wjPsalV7F3bsXfvRK2pn5iEqRVVriXIS8+jLViMmhL4RgX2NIk13trVl4StPYKarGSOlASrYKAEtjVBcS4UBNw5SWcnlJRIqvbr0LTbW0n/4TfIsTGCNWUoA+DpauTasIfPJvvYvPkPrF//bWbPfgfLls2b2C5lwYY+Qa5H8lpQryKgKgzNo3BNg+S8apufb1JoGxPMzpMkLNg9LNg25BaphgIvdyvYQEkAQrrg9q1uG5xPg4I2yfkxwdnT9lM4j4DarZB8j40SF4i0mzBtzji8B5sjIZYFjwpvWbCwAtZMsGYeux3FRbXnEtQDvNT1Ch3NndAjWWrNZybTMLtNvG0G+VfnoEQVPB4PF154BQ8++Be++MVvviF/O33RiShl5djbtrghkSVlaLNPQPj+NSZ5x3Ecx/GvgQKpcUrWx+OeBHE1i18qjAqHsFQ4MxtAeYMNzueddwknnLCAT33qA1xzzbl87GM387nP3YLnELYMr0ERCmWhEjb3b6PIv0/6F8vEaRlrJ9nyDH7NhxCCilAZ755+FbWR/R5y6RQykUToBkIITMdhWyLG2uFB1g70sOGWz5DIZvB6fSxcuJQPf/jTLFlyEjNmzCEazTnIiMbHVVKKdvLp2Fs2gWWRCvq558NXMzKljsqmTganNVDS1kPhjj305OSQl04hUkm0Exag1DUge7qxB/uwt21BKSxE1E/B3rgOp7vLZU4Mj0saOzYivwBFCLQTT0I/iKdxIBCkYdp08mYUcMaV5xOVOViWRWPjdjZuXM/6h/7Cps0beLh1D5bjUJOTxyl5RZwkHU505MRkX0qJHOxHnTkb8ToyTNo22ScexXzhWZyRYZyOdmQqiZJfiBIKI6WDMzSIHB50X48PgWJMDlMTKqgGaqx10r5jsTFuvPFa1qx5kbvu+hvTp7tyvMbGHQBMmTIdtboO49IrMf/+GPbunQhAFBThuewq1DcYrno4SMb9VQ9yvSdEDE3qSKGgZIYBCYqB6ghS9iD64CYylee/Ye9cJT2Ikh3D8peAUFyvZUCqPrRYM2qqH+stJIOFmcDT9jh6z0uu92+ogkzF+VgF89+yY7wVUHoF0icn5zUogArK8Bvcp6Jw5ZXv5vzzL+MXv/hPfvGL/+See+7ki1+8lXe+8/2oqorSCZ6/C/QdrgDBnCHJnCNxSibva8GCJcybt4jbb//ZBBm8eUDQOAINUVfRCpDjkewYFrzcJbhqyuHrx96dfTSvbibWF0fRVIpnFFF/Ui2ekIeaCNSwb3tDhSsaJKeU2/QlXfu38uCxXYYDTYNsvH8Twx2j2KbDUMewa9fo1fFFvegenYqFZRN1aLg4RLQsQsuaNnSfjjfkRdEVNI+KUAWjXWOY6SzxgQRISd+uPlSP5lpBjghyq3II5geQUuKYNnrAwLH3vSfbtMkms9SdXEN9reT8aonpgFd9a6yp+/oEpskEEfwacnKgo8OlCg6Wiyml5Le//TlnnHEec5f/a31P3mocJ4OP4/97SCS71Cwb9QyjwqbK1lloesmXB/96xGJj3HLL5zj//Ev58Ic/fUzHypUqp2bf/ORcCMEp5UvZO9LCZo9FXFUIjgwSN5OUBAqZq+bQqFhs0tKUZN1lyYhU6BPWpP044w8Zn3TvuDPypnB21ak82/Yifan+ccsIL2dUnMLs/OmHHI+u6EzNrefFzpcp8OVPPERGMqMEFD+lehFSSuysS7IWTS2kaOrkNFpvyMu0c6bSva0HoSj4cwwC+QEiJWHGesZoXtXCnMsO3s+h5OVjnH4W1pqXEIEQwjBQiopRqmpx2lswX1yB+o53H/acOkhGhYOBoLChgMG9g4x2jhLri+EJeVzTeCHwRrzseaGJvJo8IiVhlMIifB/6BNmVK1C3bqZ8OMkuo5x0tAQsh0BugNyqHBJDSYZahg4gg4VhEFkaITGSS2YgSdwcJWZAalYppfUKRUPbJ6UD64ObUZI9bnuaUFDyC4AClgQUFi5r4Nb//AtPPPEQf/nLndx8841IKZk6dQYnnngyX/rSt3jqD7/i42tfZFFXG18yM3y2qZbukAdjzx6mXBDiL6f42aJlqbU1vChkkTSpJvd5Y3wimYM2aw7a3AWk1q5mr2mxcXiAV3s62JyM07T5ZQDmzl3IzTd/jYsuumJSS5bjONx++0+48MLLKS8/0FPOGR7C3rmdi4ezpBPVbA/Wk+eF3iQoisbJFSofmKFT9dCfsZr2oNVU4OlpwhElWH1pEit2oL9zASgaWGlEdnRfkW1EEIlOlFQ/driOTNUF+LffjjayC8eTg8jGEFYMKzoNT/uTOP5SsqaJmpeLWqYhkqNQUINTPB2zL47W1IY2/dha+p2RYbCdCSI4jkGTko9ZEKCif5Aq00R6DJygRBkGe9zNpDcpULKgG5Dxu4rh+adJnh0UbNouqMmVZLOC3Fy45BJnkl+wuf4VnMEB1GkzEUIwZS5s2jiVkq6t1EYWsnr1N8lm+/n2t2+ZVHhZDpjOgXYMmnCJ16zjhlV8cq7DnTsUmkfd1OOMLcn3CZaVSDYNCEIe8CgQz0LWgv40aApMi0rGRgR/KYWgCsvHPaplBEQPiJQgc+bRkbCb+gXPtAm64gKPJllSLDm1XKIK1+f4qBUTGRAZkEE4CsvMw8JQdc6pPp152ixefGEVIW+QUK67eCALJIPNQ7SsaWVueRSASy65mrvu+i3btm1i1qy5b+iYamn5MS16HcdxHMf/n7ggEyTf0XhZTzGs2EwzvZxk+pliv8HUo3GUlJRx992P84tf/Cc/+MGtPPPM4/z4x79l9ux5h93u5LIT2TW0l6bRVgp9+WTsLBv7tpB1stRFq4kYYUzHpGm0lbt3PsDNCz+OoehsGdjOqqGVKOUprPUdvDzUycvDw8RtG5+iMM/j4yPnX8Yp13+ME05YgHGwWf8hYG/fgr36RUQ4AlXV9NSU0jWlhorufkQwzGDAw7aPfID8F1bi3buXzKxZBAJBCIUQmoYoLQXLRFl4IjKTxvzbX5H9Pe4D3ON11a5C4HS0o4TCGOddhHHuhYiDdLNtsdfwvPUgLdE+hGEwM3sCF2WuYMaMOcyYMYd3nnsx6d/8N6Pbt/Ly6BArujt4srOF36eS+B++i2VFpZxaWcPJwQglhSVoc+a7/r99PSgFRWjzFuI07Sb7yAOIcNRVMXu8mKtfxFr9IuqS5dg7tuJ0tIPjYL7wHJmsH09pavJAxwPQHM8+K7aeni7e+95LaWtr5o9/fIRly06d+F1j43a8Xi+VlTUIIfCcdR76vIXYzXtBUVHrG1DCb1Ia+Dq0qy2sMJ5mp7YNvwywxFzO8szpeNlXONXY9aw2XsCSEo90QChkNQVLVamJB1GsBFJ94+pAx4i4HWxmHGc/Va4wY0jNj+OJvpm3OBnSwbfrdxhdz+N4cnAUL0bvGvT+9SRmfQKz7Iy37lhvEnaZRN+kjCuux1903B/nTXLjfn+Az33u67zrXdfz3e9+lc9//qPccccvuPWzP+C8dWeitgqcfLfm9KxQUNsliY87yP3Wi4QQfPCDn+ATn3jfhK1XbxIcKdBVud/fQVCXNI+5tgmHQv/eATb/dQtm2sKf48c2bZpWNpMcSjL/HXMP6CR9Dfk+JgXBvQbHdhhuG2a0ewxFVciryZ0korJMi433b6Z7Ww+qoWKmsji2RPdqOI6DlbFJj2Uw/PsW8Ibbhon3JzDTlhu+7ki8IQ/TzplKfk0eo91jdG3pxs7aeIIezIyFY7pdsEJxbSBifXGsjIU36mPKlEKG20dIj6UQ4wHyhfX5VMx1a1dNcX/eKoRCIITENGF/x7REAsrLQTsEE7phwyts2fIqf/zjw2/dYP5FcZwMPo7/7/GCkeJBT4yUcMPd1upp1uhpPpiKUO4c6Hf2n//5LcbGRrn11v98SxJK3yjmFcwmOz3Lf2kdWGYaiUN1pIIpOXWoQkXDZlDZZ/mw2PSxU8syIGzypKvWa1VMCh2VmZZ741eEwqV15zMrfzp7R5qRSOoiNdRFq1HE4e/Op1csZ89IEzuGG4kaETLpLN7dGrNGGti1qpFtme0omkogx0fx9CJqllbjDU9uUcsm3ITQ0lklCEWM+w+BP+qnf+8gZto8tGF8NoNSWo5aPwUQEx5pMpqLvWPLYce+Q83wlCdBu2qhAjNPilDcUUD3Y41YmXEfJAmR0jC5VTkMtQwz2DxEpMRVVijFJXivfCdc+U6c+zfj39xJ0fgDWPfpCCFIDCYPusyZGk3T0ZVDd+RMOhikw9tFV02C4XyFcGKIs7s3clb5WRPXmrCS7oav+zyk6kVkRxkK6pS+43K+dM0VRDoH2LjyBV5++SVWrnyePXt2ARAxPGzp7eLKu35NXW4+F06bzbSsSUthOU8r3RgIeqWKEALHskgODvD4QC9tbaOMtrbR1tJE095Gkpk0qhBML6/m5HMv5jPLT2XJkpMOSvQCPP30YzQ17eHHP77jgN+ZG9aRuf/POP195Et4v+5jz5STWb/8XUQDGnMKJLPzgLFhkju3I4pKEMJBSAuh+dCKPFg9I1i9CRzNj5oeQDjWRBkm7BSoOlJ3PxezaBlJxcDoeAY13oaw4gjHwtP9EkqiAys8htmURs0NohWEEVkDqTtkC4uxdm7D6WiDYySDlUgUFAWZTrPVX8ND2hz6lCC2P0E0qHFOp8F51ZBdKvHdI1AGwckFmYSyXkl/PfSNc305ZdBwsaR0BBoUSV6eZP58SeXrBDSytwfh8SKE23ple2HaAsCrca1nMd9+bh2nn34BCxdO9oUL6lATgVf7BDkeOXHpDqQg4hFUhBz2jsDeUcHsfMnMPAjpksdaBAnTtYJImO62inC3i0ko8kPKBEtCGbDXgRWGYJk9Hrghx/9zlGkxmwfgjm0KKUuS75UksvA/mxR+vx1qwpLSEJxeLplbIA+tMkiB9xmBvkagpAR2qSRzmsSc9zqFyhtAuj+DkTQIFu7rFRRC4Iv6GGodxsraaIbK4sXL8Xg8rFq14g2TwcdxHMdxHEcDFcFS08dS04dEHtGqzEaSReJBHFE5rKoqn/zkFzjzzPP41Keu58ILl/PpT3+Zj3/884dUCc/Km857pl/NU63P05vsR0Hg1b3U5bhEMLiig5pwJa2xDnYPN9Ez0sPP7/kJ7a/spO/VZizTptLQuCovl3P0ENMl+OYuIPit/xpfND82WBvWITMZhM+HCATxaQa6opFRQE+nET4/o1PraZ1ZT9qxWJDIx/fCC2Sf/BvOnl1IoaCWlqGdcTbZe/6EnU4CAnx+tz7NZtEWL8WJ5qDPXYD32usOGIM0TVpf/D0da37FlLEEU6NRmk8v5qUzn2ZA7ePj8c/jx4+Sl4/3w59EW7+W85r2cH4ggFLXwPad23j24Xt5budWblnzAo6ULKifxuWtuznPHybkDyDNLOZLz4Npge4KKQCUymrUZBJ72ybk008g02lEXj7azNmIUITEhh34tSSqpwk76BYearwN6cnBLFwIwK5d23jPey7FcRweeui5CUXwa2hs3EFd3VTU/awolLx8lLxDM39SSnYN72F97yaG0iNUhctZWDSX0uCRrY7a1RZ+6f8RPWonUSeXUWWYP2u/o01p4brUjai441iQPZG1+ip2eZ+mMMcL0mJIH2N2n8HiXj9WqMQVHRwrrCRarAUpVMyCRXjaH0cKFemJIrKjqIlOMuVngZ3Fu+celGQ3jr8Ys+jEA0L5lEQXRtcLaCM7cTxRzMIlmIWL3cWG/aCO7kHve8X9jISCNrAJNT2AkhlGXfMVErM/RbruKlD+kRF9B4e5SGKskWh7wC4BHFC7BXa5JHvCWxMYXFpazk9/+juuv/7jfP3rN3P1DedyUeElfPO8/6A+vwEAJ0ei7RYYrwoyZ0w+7kUXXcm3vvUlbr/9Z9x2239PCjzbv8bM2II87+HDhtvXd5BNmuRV71s8Mfw6/XsGGGoeoqDh6O9btmmz7fGddG7qxM666gpPyMOUMxqoXOBOHLb9bTtt69tRVAXbdHAsx/WwtmykLREBD+GSEOnRFNKRSEey8+lG0rEMtcuqsTIWZsYi0RdHCEHtshpifTG2P75j4niekJdMIoPu0TACBiUzi1B1FX9egNKZxUTKIvTvGaBvdz92xiKnKpeSGUUT1pFvNaZOlVRWCpqaXKsIjwcGBiCbhaVL93Ulvh6/+91/U11dx2mnnfO2jOtfCcfJ4ON42+Eg2aO66cUagimWMWFd8M/GsLB53BNHRTDVdu/oDpKdapanjAQfSEcn/f2OHVv4zW9+xhe+cCvlb0Pb0rFACMGJJQu5Vq/nAX2EqbaOX3OXCh0kFlDg7LvLLTK99CgWLxlJdik2AihxNK5Oh8jZL8BOCEF9tIb6aM0xjacqXMGH57yPlV2v0Diwh9DmALltEfKCuQy1DZEeTaP7DajOYc+LTYz1xFjwznlonoNcCwoTRDBA0kqRstI0jbZQZ9SgAdrQFrThnYDEik4j+1p/jqJOSiPGzCL8hzZt2qtmucM/yphwKHRUTCFZEc4y/T1V1O0ZItk6gi/qI5AXIFgQmPBUlc7BH/IF9fl0bu4EITDGw+cy8Qyq5q7S7o/0WJpX793IyPZyEpkmdlk5eNJFFA/EKC7cy4A0eXSohcrhvUzNdck6O1AOio4w40h9/H1JicgOs7r8BH4TGCIuJApQUuvlmtIrueKKdwEwMNDHS9+7ha2vrKZL19nc00nL8CA/WfWcu591Lx3yPKnBAEOFxdSW1zB3/mIuu+Ja5s9fzJw5C/D7j07tfvvtP2XBgiUsWLBk0uvOQD+Ze/6ETMRRG6aBEPhHR5i94ykWLaxAX3Tavr91HNfLSlGQehBH8yOsBFINu+ptKUAPQ2YIYaVBOggriRprxsw7ASvi2lEgBGbhIsyChXha/opv95+xA8VIPYxI9aMme9BsC8t6bVVdAsI9NvLgfUVHgDp1BmrDFLr2dPKXhouISw9VI80otslw3Yk82qRQEpDMO81VBos1Cq90SZ4Nwq6FoM6QFAjwAVkbjAhcuExyctmhi2RRVIxMp2kekTSNKaQsUJDUSItOoxchBIpy4PZCwNmVDq1jCjuHBRFDkrLcs3BetcPL3YJn2xUS49mOfg3OqHAo9ru+wgL3RzIuFhrfqcAlWIUAuxRCuwUDhsQGNAlqBzh5YE07cuEvJTzf7hLBDVH33+0xwXAGnLRrM9I4BK2jCu+b6TC/8CD7lOC7R+B5QcGJSByfRGsEtVUhKRyXED5KyGQCa8NarL27EYYHbeZsVKXADXRzJELdd09zLBvDb6CM3+e8Xi+LFi3jpZee48YbbzrqYx7HcRzH/5+QUtI4vJd1vRsZTA1RESpjUfE8ykPHFj51OCLYQrJKT7HKSDGi2BTZGqdk/cy3PEckkGfMmMNjj63iRz/6Nj/60Xe4//67uPXWH3DWWRccOAYhWFQ8l7mFMxlIDZG1TX726u141MnPWZlxaF25na/+9hOsX7UKO2tRWFfOie88jxMiXmq2bae8L8lMkYtn3mK8H/7kGyKCwe3kUXLysGMxSCYp7RmitrWHbTVFVHX0EygsIqMKuhWT5ZaPfKEhTj0Tbe4CnNZmUFXU2nqyLz6PTKZQZs/DadqNkl8I0kEODiB7uhGaDv7AQceQfeJRYo/9DhGJ4QsWYvRnWfCHNgKZBlZcvINt+iYWmW6wsxKJYpxxNpxxtuuVfMevqG3aTW1OATeceBqxSIRVpWXcd/cdfG3ls3xb0zl/6kyunDmXBQP9OG1tqHP3KbiFEOjTZ8LIEDIRRzthPkp5BcLrzjPsTA0jLb3kVAXQRhvd1/zFpOuuxo7Us2rVCq6//irKyyv5wx/+SulBulZaWvZSW9twTJ/LS11ruK/xEZJmEq/mYWP/Fl7p2cD1s9492T7kIHjBeJYetZNp1qyJ6zcmRlnnWc1y81SmWG4gXFTmcEPyk7w0lmGL8yCOUcyl/RHO6i7BU1CEk2g/aEDe4aD3rMLb9ABqshuEgu0vwcyZhZboQKR6kXqQTNkZWHlzCG68DSXV64o87DRG1wqSM27Eyp+LkuxB716Bb+8DYMaRvgK00SxG31rSVReTrn/HJFZSTXYj7DRSD2L0rERJ9uIYUaRiIMw43ub7kZ6oa3vxT4ZdCcn3OXgfVVA7xy0bZkrSlzrIo4/nOCrMm7eIhx9ewZOfvp9bHv0SS/44hw/P+zhfWPJVot4oaBKl/cDtDMPggx/8BLfd9g0+9rGbmV1Uz9M+SdMoVIZBFW43oyZgQdGB278G6UhGu0bxhiYvkOleHWk7JIdTh9jy4Oja0k37+naC+QE8QQ9SSuL9cRqf3U1OWQTHduje2jNRdzqOgzo+/9Y8GqqqUDS9EN1nkE2aOLZDrDfGWPcY4aIQiqpg+A0Mv4Gmq4y0jxAfSNC9rYdsIos37EUz3P2puo/kcAqhKVQurKR09mS/jYN1B79d8Pvhve91uPtuhZYWgWVJwmHBeec5LF9+8Pq+v7+XRx65j3/7t3+flKPyfxX/GozccfyfRRbJvd4YLxspMriJ8zlS5dJ0kOXmP9/LsFk1GRYODfa+FVEFQaGjsUvPksg4BMY9faWUfPnLN1FdXXfM9hBvJxbbAdZ6TDpVmxLpGj90KRaljso8c5/yVkNwaSbALMtDt2ISkipTbGPi/b0VqAiV8c6plzMcGGHN82vxVXmJ9cWwTYdwaZj0WIZsIkvBlEIGmgbpa+yneEYRYz0x7KxFIM+PL8dPrDdOuDjkWngM7KGnuZfhmlGe3/oydZEK3qfZ1A6uRYx7EUvVQAstIJuXi9PeilJR5bafxMaQ6RTaohMPOeZVeooR4TDV1t3iUEIIhT1+i4ZL6gjd00heVS7KeN9KJp5B86hEyw7eulY0vYiyOaV0bukGx3UlU1SFyoUVFNRNVjt0bu5isGWI/Cm1dHXsJZsaISR9iHYvSp5B/pQGuoXG9qFdE2SwlTsTs3ARevdKpCeCVAyUdD8dwQL+UrsIv1Qoc1zld4ticrdvjM8mcolIlfz8Qi7+yGc4S9FxRkdQlpxCMpXgt+tf5tete9C8Xqbc+H7y3n05laofpEvKjxVG8fkDfCmRS1gevG3p9VDi7Ri9L6PEWmnKqWRDNo+XXnqOH/70QFWwtX0LzmA/6pTpE0S+iOZgDw9hvrIa/aTTJv5W5OSiVtdibX4VEZmOHWlAG9yE09OJFgzg8Q9he3Ix8+agmGNowztAuu7HaqyN4LpbMEtOIlN6Omg+kBZGzyqXWPYXgWqAHkBK8JdnSG9M4SSSaEoWM1KC09GGyMlDm3ZsqmAA4fHgfe8HaXzgFQbjHqaMNKP4fSjVUympLmT3CKzrFcwrlCSukfyl2mFFu4ItJHttwVBakNsOU3IkWVswK18enODcD/qCxfQ8u5L+bbsR+eXkq5LAQAc7tQh/ev4uFi05hZdeepaRkRHiapQ9IwIJ1EUk9VH4yByHlV2CvSOC6ggsKZZ4NMmvNqtEDEnFuF/ZYAqeahMsKJJkhgQFKoQMyVjWtYnwqGA77r8rQ64fmF0lGR6FOV3g6RQICU6uJHWFxDmKwj9tQ0cMcsZr6VgWuhIQ8UDCkuiKoCIk2TMiebZNcEK+RH3d7U5tA2O9gl0ikVH3NSsHtD0S4zmBeYI8KssIJzZG+o7/wdq6GaGoSMfBfOl5wsvPIJBbzmjXGNGyCEIRbtJyLEP1kqqJ+wrASSedzs9+dhuWZaEdqnftOI7jOI4DeLl7HX/Z9RAJK4VP9bB5YDuv9L7KB2Zey5ScurfkGE944jzqSeCRgqBU2KNladJMsqkwS80jt8kbhsEXvnArl1/+Tr761c9y3XWXcdZZF/CNb9x2UBJQV3RKAkU40qEsVMLOod1EPRGG2nvZ+PhKtj27BjOVpWHWTKZdcSLzTllGUfk+8nvPqfPZ1N1C9fwbyK2Z+6a699TqWsyN61GqapBNu2EgxUX3PEXykmW0L5yDmFaNrlrMsjxckglNkItKJIoyZx+p6nS0ITwGWjiC2dYM6RTC50OqKk5fL0peHtpBAtKckWHMlSsYzjNJFUfRpI9sxEegI0bVs53op+cxoPYdsJ2Uksxjf8XZ24jaMBWh6UjHIbhxPWdv3sQZiofeqqk8nE3zUOteHti2kapIlMs8AS5vDlNeuE9hK5MJ8HgQmo5SWz9JaCH8flIjOagNn0H3uAFodrgeRw/y57vu4Mtf/iSLF5/E7bf/hXA4gsjG0AY3oWRHcXyFmLmzkVIe07NuNDPG481PoyCYltsw8X53juzhiZZn+eic9x/yM5dIdmhbiDq5kxYyQjJCFx10qG0TZDBA8UAf79njQWv1IpwxZKQcO1qGyAyA0MkWLT3qcWvDO/DvvAOcLFaoGqSDFm9BeAtJzPo4CMW1cPAVEdzw7yiZIaycWW5xJSXaaCPevfeRTXbha3kErX+9m3HhLcA2IljRqSjpATydT2MWL52kInZFIwpKogclNeAGJys6wk4iPTlILYzR9byrSP4XUAdbMyE+1UHpARRwinnTtl2HghCCy066mkvHLuFH8f/iv175D/68/Y9899T/5N28G3lgxhwA11//ce6447/57ne/yq9+dTfvmuZw/25lPGAZoh64tM5hTv5h6nMB3rCX0c7RSS87lis00v3H9ln07OhFURU8Qc/EewsWBBlsGmKgeRDV0LCyNtHyCIMtQ6i6Mt71KrFSJqHKHIKFQUY7xyieXoSiKePq4MniLHD/7f7OIRPP4Al6sDLWRCesEALHdhDjHbX/bFRVwWc/67B3L6TTgtJSh8LDcNE/+tF38Xp9vOMd7/vHDfKfiOOzjeN4W7FWT/GikaTIUYlIFYmkU7F4yBunxjYo/ScrhF+7vb3+di2F27Is9vvFAw/cxZo1L3H33Y8fk+/Y240yR+e6VJhHPXE6VAsBTLcMLs4EKdjP97hVMXnaSNCoZ1EkzLa8VNgagbfhKZvoT2ClLYwSg9RYGlVXURQF3aORjmdRVQXpOPTv7ad9QwfD7SM4lo034iNUGGS4fYTB5iEGMoN0jHYiChUKFhYS8mfY2b2WP6ba+XzJHAyfq7QV2VG8Y+sInX8mscc3Y4+HUgiPF335qejLTj7kWJtVk/B+wXrCjBFI9SMNFaNKIVIVZrB1CM2r49gOSKhcWE5uVe5B96cZKrMvmUXRtCIGW4dQhCCvJo+C+vxJxA+4Rv66V0f4wqQDFQgHQCITfkwxGyc3DzHSRHac9AZA0UlOvwFPsAq9ZyXCTpMpPZ17py5mJFpBte2StRpQ4+jsVk12aFlOHJ+4qfVT8F7/EbLPPInT2kwgGOLTn/0K18+dz3/98of88Uc/x3ffg4x89+tMu/h84sJBSsEFGd9RE8Ha8A782/6bVGaA3zecyPpcwfpf3oka8NFyxSn0KBbF+3/3MxlXNfr6FViPBxmPYyPZqGXYqKeJC4e6d1/M7PgwOTu34fj9mKP5aLpKcHEpsmgaqbLTyZachJLqQxvYiHfvPSjZMRACNdmNtvN3qGPNJGd8GGGlULJjOOMqa8eTgxWZgjayE39Fmsyoh2RLL2kljJ3OIPKjeC6/ZqKV8lihFBZhnnUJ6vYMhq8SEQy6ZsC4hOlw2v27PSOwMqFQUCKJeKDSkjSPCnYMQdKEq6Y4nFIu0SVs3gydnQKvF2bMkBTtp0ZQK6pYddoNGH9/kIpYOyCIVUzhgf5WpKJx7Wd+zNp3zuP7f3iQ1JwbGBu/1EI6nFHpcFGtpCYi2f8u+ZddAtOBvP24gDwfDKQFipDML4KNfW5IXH8SxqSgyCeJj9tDFPjcsLmBFIg6WDrbIT0KeMGcLt3i/yhgKG6AxsC4gCJuQtZxVcoK+/yO87zQkxDETZconvR59AhEUiIrJr9u54LWLRAJkEeRE2itWYW1+VXU2imI8VZoZ2gQ+fIKpl98Pdu3CoZah9xjqgqls0uoWjy5u2T58tP53ve+zubNG5g/f/HRnYTjOI7j+P8O8WyCvzU/hZSSaTnuQrGUksaRvTze/Az10ZojWnsdCcPC5kUjRUQqFI0/r/NslVbV5FkjwQLTi3GUPjoNDdO5++7HePzxh/jGN77AGWfM48Ybb+Kmm/7N9dl9HRShcGrJibz092dY+ff7GdzZiSfsp+qs2bzrXddzwbzz+cH6X+D3TCYaLGyy+TnoRaVv2sZNX3Qi1ro1WJ1tKLUNOP29FHf189EXd9Nx1rXEKCI3qTLVMg57HkROLmSziPwC1Jo67OYmnNgYJOLIYAh92SlocxccsJ0c6EeOjaLU5WOK3olHcDrPi787RmDAJFR4INEiR4axdmxzrbS0cUIplcQZ6EcO9oPXR0luLh/OZLgxt4BNFZXc19zIr3bv4Kd9nZzTvIuPLz2NKf4gTm836uwTkH19yOEhxH72Dc7QEEphIRRUYo3PicbGRvnCp97Dww/fy7vf/UG+/e0fYxgG6the/Nt/hRprwTFtsm1xbKsQMTKIfQyewK1j7QykhqiP7OtcFEJQ7CugaaSFkcwYOd6D708gCMogo69LInPG01M8cj/xzPAOAlt/jsgMQ2QWysgOlIH1aLFmsoWLSdW/Ayv/8D7Y+0PvfRlhjmHl7BMRWJGpaMPbUJNdZCpdtbw61owaa3M7AF+7foXADpSjDW9DG23EMcKAguMtAiHQRnbgeKI4viJ3f2NNk8hgM2cGVrgao+8VcCyXCLZS4JjYoSocI4SSGUVYKaTxzyeDAdDA+QfFH1hzJYGVPr4U+TLvfd8H+LcXPseNT7yPu4vu5Lsf+BkV1B6wjd/v50tfupWbbvoga9euZtGipdRHHfaMgCUFVWFJnvfAY+0PIQTl88oYbhsmMZhwPYMtm9GuUcIlYfJrj00KbaatA+aYQrjqatt08ARUkNKdcwpIDaVwTAfbtNE9GqGiEKNdYxh+ncpFFTiWgy/HRyA/QLw/TqQs4na5SUl8IEG0PEIwP4gv6sMf9ZGKpUmNpFB1FduysbM2JTOLCeQdvOvhNTiWw0DTIIPNg0gJudU5FNTnH9Iv+Y1C12HaNDichzNAU9Nu7rzzV3zxi988bMDo/yUcJ4OP423FBj2DBkTGSSSBoMzR2Klm2aFlKM3+cy/BOlsn31HpVCwqHA2BwELSL2xOy/rxjxOl6XSab33r37j44qs45ZQz/6ljPhim2R4akgY9ioVAUOyok3zdehSL3/pH6FRsCh0VR0ieNRJ0qiYfTeYQfAvVwQCaV0Mo7sqgZmikHZfhcmzHtYUQ7v+3b+gCKQmXhFF1heRQkoG9g9SdVIPtOGzb1kiiPk3B9HzwCfz4qFcV9pgZtkvB3PHjSSMCiU68hQ72576CvXMbMpNFKa9ArZ+CUA/9UMmTKr24wXpKvB19cDOOlUKNFFLauo4F8/JpnHoO/a1JVEOjeHohxTOKDlgp3R+qrlIys5iSmYdntTRDcwlmIC9QRFdqCNOTgxpXkAFI21kEkurwZNJI6kHStZeTrr4EpI1UdZqCA3jl5IecOt6onxKTLS20aTNQp06HRBx0w1WsAt/73s/44Ac/zs3f/ByvvOtDdJ9/Nuf/x3c4v2QKi8wjVDYTg3PwND+Ekurjkenn81xpJeXJOL0PPMOUMxewNyS42xnjk8mc8fGBUlqG0DRkIo4YnxhKx0GODKMsOpGHPXGeNhJIQBeCLVVBXv3iB3nf318lf9delLyT0OYvJFtfi6l5JvyUHX8xaqoXxRzDyp018brIxjB6V5MtORkrZya2vwh1rBnb6xZfVs40pGqgGs1433kOMl1JJhFC84XQps08OiL4tc/iIBPS4iCoPi9m2DtBWEoJ8aygNuJ+Vi1jgrQFkfG5nk+DGXmSsOEGR1xSJ0kk4Dd/UNi4ESzLPU5uruTqqyWLF++7FhpL5hC/ejoDqQ4QguFAlA0fqqPy9BuJVsxk3pLT+dtf/8y5cz/ItPHaZyANT7Qo1ERsZr/Ovi9t49o9vA7K+GvXz3TYXCRoHpUkTLf0CupumFt7DJpGFIbSUOyXnFXlMKtYknkD83ZVgaWlkrt3CobGg+mQMJQW5PkkeV53PCkb/JrEc5DbgAxIt58vC+xHFCspcMISeXCLywNgbdyA8AcniGAAJTcPq6+HHHuQEz9wJv17B7AyFsGCIHk1uZOK3YydZcrMmQSDIVaufO44GXwcx3Ech0RbrIP+5CA1+7XFCyEo9hfSOtbOYGqIAv+bS1zqUSxGhUOtPZkgynNUBhSbQcU+Jss3IQQXXHA5p59+Hr/4xQ/4+c9v4777/sRXv/pdLrvsHRPtuN3dnfzxj7dz112/pbe3m8pZ9cz81OXMOGkhp1QtY3nZYlShUhepZsvAduqjteiKhmmbdCV6mF90AsX+N9+CrBSX4L3+w2SffgK7cSdKXj7anLlEzj6fwoJC95lxFNBPWID50gqctjaU+qmI3FzsPbuhsBjfhz6KceZ5ExkXkxAIgtdHeSyfHm8/MTFKUIYQiRRD/gQ5/kpmmXMP3M62wXFgv7rX6e3BiY0hDA+KbiDjcZS8AhjsZ14qxYLyWr629DQe01V++ZffcfF9v+f0cA4fqaxnfm0dIi8fq3mv6xscDCJHhsG2ME4/CzFOBK9Zs5JPfeoDjI4O88tf/olLLrl6/OAWvl13osZayChVxJ7bgtnSh8jsRekeJpNIYW3fgjbj4AHRkz4ToSIQODgT/r4AjnRcy6sjLAAsNpdzl3YHY2KUsIzg4NCqNlPgFDLNnDXxd0bHM4jMEFZ0BgiBFalDTXSgpHpJNbwLs/SUI4510riTPQcGzgkFEIjMyH6vHVKihJIZQep+nJyZMLwDbBuphxCpfpREF46vEBAHehlrPlLTb0BkRvCOtaAkukAPYIfrscK1bu0brkFqryPtHAt1bA/CSuH4S93OubcJIjuG3r8ONd6JY4Sw8udih6rftuPtD6seUlc6eB8VlHeXcGfDn3hXxbv59OZPcur75nHzzV/jxhtvQtcn3wevvPLd/PrXP+Wb3/wCDz/8An5dMKcAjkQ27o+yOaWkhlO0rW93xUOqQk55lBnnTz9mD9382jwGmwdxbAdlvAXOTJkomkKkJEywIEgmkaW3sR/VUNF8unu5qIK82jwMv06oKES0PMqupxsZahvB8Ov4oz5sy2awZQjdo2GmLbwRL/Wn1KFoCsXTi2hf34HSp+I4DplYBoDCKVFOuPzw32nHctj+5E7a17djmw4IaFnTSvkJpcy8cAaq/tYSwkeD//iPr1NQUMz113/8H37sfxaOk8HH8bYiKRx0+br2gnENZlYc/Q3z7UJYqlySCXKfN8ZONYuKwAbqbYOzs/sejE8++TB9fT18/vO3/PMGewSoCMrGA++klDSNtdIR68KreWguLadTsZlmGy5JLCEHld1qls1ahmVH0e53LMirySVcHGKkc5RAjp9YX5zUaBrHcYiWRxjriSGEwM5a5NfmTdzwQ4UhhtuHGe4YZc67ZjJkjKIKFXz7riGPULClJGab2IAFGDDeTuWg5Beg7GcrcCQsNr1sV7P0OmOUD24mg6Q5r47SbJoqS2Wb0s7Y7D6KTltO5fiCwVuFommF9O7sJRPPUBospjvex/DACB7VYDg4xshYjHmFs5hTcAhLAkXlNcq3ztJZaaQp2i8MJoGDjqDYPvBWL4SA4IGSx4aG6fz1D4/y+OMP8dWvfZY7l5xKwWe/yvwbb0LRj6waUFJ9aGN7GQtV8nJuAXnZDKlNuxhq6eXcL1xNXirO3oBGs2pSP55crk6dgTZ3AeYrqxGhMELTcYYHUUvLGThlOS8YSaJSJW98UclGsisCay4+nctrpuP0dCETcchYoE++lrWhrThGzqTAPWmEIN6KGm/HyptDpvxs/Dt+hTrWhOPNR1hJhJUgXXslyVmfAKFwtGWZOtaE0fEM2vA2pB4at6Q4DdR9JOHsfMm0HMG2IUGeV6IJ6E8JSoKSE0vd+6Im9vnt7j+/seU+xesLLwjWrhVUV0v8fomU0NYGDzwgqKuT5I0LCypDktVxg3ixq6bZ+ujPyabi1Jz1MfJ9MP3Ud7Hh+x/Cl2hH+F2JbIEPhtKwZcANiNsfdRF4sQMyNhMEa9Z2x1YfBa8Gi4sl03JhY5+gK+GSwXMKJBfXwmDaJm25xzDeZK13SplkIAlrehRGs25InEAyLUeiChjNwGhGcHqFg/cgFY/VAFYVaHsFVrUED4gREGOQPUdy1B/8oW4L4x+gL+qjckHFAb8eSg/zdOsKNvZvdVujZ9byzIon+OQnv3hUh5VSMtI+wlDrMFJKImUR8qpzJyYDx3Ecx/F/D4pQUITAkZMXel8jxVTlzU+ivSgYCDJCou1Xx6eRGAh88o3VQj6fj5tv/hrXXPNebr31C3ziE+/jZz/7Pu94x/vZuPEVHn30AbxeH1dd9W6uu+5Gpk2bhelYaIoKo6M4W7bgqCpXVJxJ3EzQNNoyse+6aA2X11/wloU7q1U1eK//iLtwriiIQ3j7HnYftXV4r3k3mUceRDbvAUCfORvj4ivQFx/aakApLkGbOZvc1S8yU6tjd6SdRKyHvN40qbNmcLXvk0TtA5VrIjcPtbIaa9sWRDjitoE374X+PqSuIyMCkU5jp9MQG4PeHpzcPPx+P+++8DLeccrZ3PWTW/h1407esXUty/s6+GjddBbNnoewbWQygVJSinHKGWhLTyKVSvH979/Cr371YxYuXMp99/2diorqfe9/bA/a2F7sUA2pFU1km3rRS3NRlCB6+wh2JkXm3j+jfr72iOe3NlJJcaCQ9lgXNeFKhBBYjk1vsp+TypYQ8Ry+JX1Z5jTalVZe8aykC7dTqsAp5OrUe8mT44sn0nEVuJ7cfcWXamCHaxF2CsU+Ni9XADtYid6/FjXejrCSSNWL43U9h/f3HrYD5dihGrSRnVjRaRM2EWq8HccTAcVwlcLBCrShrUjHD0JB2BnUWCuOLx8zZ8aBxw/XEVv2X8i1t2D0r8MOlOP4ilBjrQBkys+eFDynxNvx7fod+kgj2BmkJ4dM6Wmk665CZMdQ422gerAiDW/aWkJJ9uLf/EP0/g2ARGo+pL+U5NTrMIuXval9HxUEZE+WmLMkWpOrpF1Wex7P6q/ygx98k+9+96s8+ODd/OAHv2Tu3IX7xq0o3HLL97n66nN45JH79i1+HAMUVWHKGQ2Uzysj1htHNVRyKqJviAQtn1tKX2Mfgy1DeIIeHMvByliUzSklrzaX0c4x1/fXp7v5KppADXpQVIXp502jbnkNLS+3sv6uDSSGk6iagmqoeIIecsqj5NfnYWdsgkVBSmeVTlglhgpDzL54Jrue3U2sN4436CFYEKTh9Hqi5VGyySxmysQT8qK9ruDv3zNA+7p2/Ln+CXuLbCJLx8ZO8uvyD/AafruxYcMrPPLI/fzwh7fj8721vMi/Mo6TwcfxtmK6ZbDbk8VGTqgAEzhoUlBu/2u0oywxfRTbGlv0NGNCUuZozDU9E2pmgL/85Q8sXLiU+vqp/8SRHh2ytsk9jX/lle71pGxXkdurnkpUm4Gi71OK6OMRTz2KiRtJ9dZB9+rMumgmWx/dxlhPDMOvTwTIOY7EHzQonFJA99buAx56ht8gOZTAIzwUBwtpHN5Lnm+fJcOYHsIQCmld5QVDYApByM5Sq3uJRKcf81gXmF4GhM0LVje7AiGEHqIylWDRyAA/n7KILk1i6zbewBCLTR9XpkNH3Rp5JJTMKma4fYSOjZ3Y/TbVdgXRQIj+umEKKws4t/hMlpUswqcdWZV7iulnp56lUc2S52hkhWRE2CwyfTTYx7bC/JqC5+STz+S2r32W737nq9x7x3/z3c/fwolXvuvgKpaJjRVAIamqpFSNsJVlzcMvEsiPUrN4GqYUxJGs1lPsVrPkSZVZePC95wMoldVYa19GptMYp52FfuoZNFUXEBdjlO8XhqgiiJoOr+7ewDk/vQslkwEESlk5nne9D61h3/dU6kGU5Ot89cYn0HKcoDWLl5OSFp62J1FSfUjVQ6bqYtI1l08ikY8EdXQPgS0/dgM6PLkomRG0Hb9FibeRmvbBiX35NXj/LIenWwWv9gks6ZKnZ1U5lI93zE7Jde0hupNQ4nfnBEkTUhYsKHKJ31deEYTDktfy+4SAigpobITGRsHSpS6Ju6xUsmUA9o5Agddh3cM/pWjhFcypr2RGnsPU5ZejaJ9g5wt3s+jKz+97P0KSNF+LgduH+YWSdb2CLQMCvybRTbCSglnl+zyM+5Lwm60Ke0ZcH3YbeK4d3jHVYVHxW7cQaKjwjqmSk8ttuhOCnoTD35oVtg+5lhXFAcFJZQ5nVh7imB5IvcfBd5eC1izAksiAIHO6Q+b0ox+ndsJ8rB3bkJk0wuN+X52hQYQ/gFp38ICcpJnkjq1/ZuvgTvyql7SdJVUsaHlmAz2JPooDh1e3SSnZs2IvTataMJNZ1wZFVymfV8aM86e95W12x3Ecx/GvgepwJWXBEtrjndSFq93FdcemO9nHkuL55Hiib/oYlbZGvaWzRctQY+t4UUgIhz7F4oxsgOhR2kYdChUV1dx++z389a/38I1vfJ5bb/08mqZxxRXv4tZbfzCpRVdXNMwVz5B98jHkYD9SCHJLy/nEJReza/apjKRHyfFGmZk3Fb/uPhCztsnL3etY37uRuJlkak4dy8tOpCx4bBZPh1o4PxboS5ahzpiF07zXvU/X1CGOsE8hBJ4r3oHMZinfsZWitnISARtn8XQWn3sTHvvgbeRCUTDOuwinpwtn5zacRALZ4ZJ+orgUJRzGGeiHsVGwLERFJfripQjLJvnofbywoIfGWxPMza+h7O8xdv+yn/esfpaFG9fwyZPP5pQrr0U/6zwUn48VK57mq1/9NB0drXzta9/jQx/6FOrrOvGEnQHHwjYFmcZu1EgAYWhI20JVwDI8OF0d2Hsa0fbzWj4Y/Lqfy+sv5M8772fHUCOKUJBIaqPVnFdz5K5NL17em/oQS82T6VDb8Egv08xZ+4hgd8Q4nhzUWMvkjR23g9B5vYL2KGBFp6AmOlH6X0Vq3olF4mzJyZj5+1mEKBrp+qvxb/8ftOGtIHSQJo6/BDN/Hp72J8DOYofrUDLDKIkuRHYMYYRA85JqeBfSewh7Ac1HYsFXsZsfRO9dg5IZwvEXkak4D7N4+b6/szP4d9yONrwD25uPovnBjONtehBtZBdKqg8lMwiKjhWuJzX1vdjhN+5R7t/2S7zND7pENwKpeZFmAt/uP2PlznQ7P/8BkDlgLthX8wUIcsst3+fyy9/J5z73ES68cDnXX/9xvvjFWwmOf3eXLz+Ns8++kH//9y9zxhnnTbx+rPDn+PHnvLkcJX+On3lXz6V9QwcDe/pRdY2SWcWUzy1jIKvy4FabNUVzCNZqVMX6mZIaIBBwlb6jnWPEeuPseHIXmUSGcLEbGGemTMy0SXwwSdXiSqadfXAOpKChgJyqXGI9Y0iJm/kjJTuf2kXX1h6stIkv6qNqcSUV88onOmsHmwexbWeCCAYwAgayHwb2DvxDyWApJf/+7//G9OmzuOqqd//DjvuvgONk8HG8rTjR9LFZy7BLyxJxVCwkKSFZbHqZZv3r+O5WOTpVmYOT052d7axY8RS33fbLf/Co3hhWdq1hRcdKiv1FVBrlONKhMzHM3ngXU0PBCWJRInGQBN5kQX8o5FREOfEDixlqGcbKWAjVtY1QNZXcyhxGOkfp2d6DlbUmEkgBMvEshVMLUFSFU8uX0TzSSvNoK3m+XNJWhn7Lpj6/gZTPwGuOoUuHYUVhfcE0poTLOFYnVwXB+dkgJ3X10df9AkqwmsJMih/WzaLP46VhpBNFidMXUXjeSFLiaJyWPfChLaVk72gze0dakEjqIjXURasP69unaiozL5hOycxiRjtHEaogrzqXcEn4mJUttbbBB5NRnjWSNGtZ/FJwajbIGdkA2hsgr6WUGC88x+elygXLzuAb2zZw1Wdu4L33/JGv/PrPhHIPXnA63gKsnKnk9a2lMLOEds3D9r+tZPYFi8AXpckXok01ecpI4Bu3Yak1dN6vRCg67yKMcy8Ex5mw9tBk8qCjN7u78HZ2oOYXogRDSMvCbt5D5u47UT/3ZYRvfEJYvAz/8HZEZgTpiYJ0XN86bz5W7ngLkxBkS08jW7QMJTOI1AJI49hDD4yOp1CSva4v3PjnJzLDGN0ryZacih2dMvG3eV6XxLykTmI7ENAnK4DLAnBRjcMjTQo7hl3hqa5IFhW71giO41otv56Xd/chyGb3FbXTcuF9MxyebBWse+FxYj17eOeX7uD9Mxx8GkwrCVM072K2P3/XBBlsOu5PffRAQtSvwwdnObzcIjCfENRtFZQKyK0CHIG5WPJkq6BxWDA1R6IpLnndGoOH9gqm5UpCb+HtXwgoCwJInmxRyNiurYbpCLyqZHmJxH+YaseugPhnHLTdIJICp9jBLufQat+DQD9xOdaObVhbNiKEgpQOwjDQzzwX5RBk8Kb+bWwb2oVlW+yONZGxs1h5Cpl4invW3c+nTv3oYY851DrM3pXNGD6dSIl7vWbiGdrXt5NblUPZnNLDbn8cx3Ec/zvh1TxcVn8hf9p5HzuGd6MgkEhqIpVcWHv2W6KMVRFcnQ6T9Y7SpJlY44rgBaaXCzPHToi9Hu3tLfzwh9/h3nvvJD+/kI985DM0Nm7n3nvvZP36l7nppi9x+eXXomka9vYtZB64BzQdpX4qOA5OWwvqPfey8KbPo1TPn7RvRzrc1/gwK9peIpx2UA0vT461sXVgJzfOuY7y0L57o5QSOTjg5hbk5r1lquLXQwmFJwXLHdU2Obn4PvxJnJZmnNERIjk5KFU1RxyjNnU6vo/eRPblVWTv/gOipBzh2MhMBiwLvF7o6UYUFWMsPxVl3Ld3Tc0uHlq8lWCwlJJYhMgCm8BvdIJ35bD37hTv+/tDnLdlPe/etpHfbtnAc889yeLFy/ntb++loeHgYgw7WIXjzUOJdSMdiRhfpBRmAlXVyQrXy1Sa1qTtRjNjbOjbTMtoG0EjyOz86UzNqWdu4SwK/flsGdjOWCZOUSCfEwpmHVEVPHFOUZhizZgUFjcJQpAtOQX/yC6UVD+ONx8cEy3WhB0ox8o7sp3FJEiJ3rcWRw8jg14UKw4SdwamGKijexBDGYR0kKqB9ESIz/wYRvdKhJnACf0/9s47So6rzv6fV7FzT85ZOWfLcs4BB5wItgGTcw4/ll2WJe0uLCwZNgEmGAwGG5yjnIMkK2dpomZGk2d6ZjpXer8/ajTSWJItyTKGXd1zfI7PqPvVq+rqru+7737vrcUuOw2pR1FT3ejDW3DNIpxwLZrn4BXMJNd0A1bl2XiRlzfalUaM7KxbyDVe448dKJ7StQagJXaije5GOFnMgRfBzfnWE04WLbEVq/JcX7XsWmijuwjt/B9Sy744EVR3fFCS+zA7/gQIfy4oCCeFyA2jju1GG92DXfb6WmYtXLiUBx54np/+9Id861tf4YEH/sy//usPuOSSKwH4yle+zUUXLefLX/4c3/7268sThItCzL5oJlx0cK0xkIH/2qqwYzyCo9hk1QD7C6OMFxVzqdKH0zuGEDDQPEg+nUfV1UkhgREyyCQyuHmHROfoyx5bM1QK6/wNPCkl2+7eTueGboLxAGY0QCaRZcd9OxFCULvUv089KY/4WyYEeO5ftnv87rvvYM2aZ7jttnsO29D6345TZPApvKYo8zTely3gOT3Ldi1PAMFyO8AqKzihTP3rxx//eBuBQJCrr77h9Z7KK0JKydreDQTVAAUThZEqVOblFZ6yMuxxEizUKvCAbtWhWKoscI7RFPMEoAd0ymcfWd1W3FRMcUMRgy1DhEvCqLpKJpFFNdTJB8WS0gW4c10e63yagcwghmpwXt2ZlIQg4KUJZ4cB0IIVjBhhurJdlIVfnoA9GgqDtdSODuHm4LmKGfQEQsxKjqI7GZz4LAqlyrj0WKNnOdcKTrGL8KTH3S0P8UTXM2Qcv4UsqAU4t+ZMrp3+hpdt2VRUhZKm4uMOCzgSZrgG07M6uQkl/rEqmD3HY6B5kMGWITzbpaihiBJjHOeR+xHBMAtPP5s7VpzBr9Y+zXfXPcNj5y3i3773Uy644LLDBxOCXON1hDL9XNr2PP/cY5IZGafu2gtpL1vANsMmJBUWuCYqAhtJs2pxr5niPVk/oOCA111aeBhSoAPdik2NpyMQZF2Hscw4Z+8bQZnYiReahtrQhNfRhrt3N9oif3FoVZ6DmuzA6H0WkekGKfCCJWRnvO1wDzTVwAud4E6056AndvkF7SHFjTQKEOn9qKnOKWTwAQSP8hQWAs6vlUwrcNk1IrBcqIv6vsEHOq3mzpU8/rhCWZnkQPbe6CiEQlD3EiXs4jLJ/BLJ9d/4HgsXn8aX37RycppLyiSnX3oTf/ratWzfsZWC+oWMW4I5hZKl5UcuyKI6XL0ezC0gYxIvBEo3iF8rjDge2z1BWVByIM9CCKiJ+OrkjjWClbsESgKcaRJ7lfTJ11cBKeHeVoW2McHMAp+A9iS0jArualX4TIGH/nI/C4afYn08fm+HQkSiBN/7IZxNG3Bbm8Ew0ebNR5274KgL9770AIPpIcbtFGEtSESPEKhT6AHuXvNnPnDWezDVo7PmQ23DODmHgqqDyhkzYpIeTjOwd/AUGXwKp/C/GPNLZvPJpR9gy+AOxvPjlIVKWFg6jwLz2JR0KeGxUcvRolkEpMICx2SeY0zJm6j0ND6eKWKvZpEUHsWeyjRXn+z0OxHs39/FD37wDW6//VYKCor40pe+ydve9r7JttytWzfyne98nU984j1897v/woc+9GnekLPQ8zm0+ongMFVFaZqOu2sHzvYtGBVTn9vtY520r3mIK7f1UjKcwdNUBqdX89gsi6e7X+CmOdcD4La1kH/gHtwDit3pMzGveCNqbT1/LRCKgto0jeOlJ9S6BgJVNbibN6C6DtJxcDZvxO3p9j2FJ873ABHsKC4vnJZAz0sqMyYioqJ3ZdA0k6G3SP51sIkXR8r4txef4aEf/CuFBYX853/+lquuuv6wZ9yQMkCb2oKKynQxC6Pucszm2zELbHIdCTTdBNVEC8TJjmcgXoBad/CaD2WH+dn239CcaENTNBzP5ZnuF7h62mVcVH8uVZEKqo5T4X08sKrOQcn0YvY8iZbYCULBjdSSnf3O41aqivwIxvAW3MI5eGYRwk751g65Uczep9FGm1FyQyhOCs+II7WQr6SO1CCDpVihMt+yQguQnv9hzK5H0AfXgZRkZ72TfO3FeKFjvBbSQ9gppGIiw3Gf0B3ahJIbRpqF2EXzUawkarrHF1DoEaRRhvBs1PR+f4hACQgVtKAftjy2F2146wlZOujDW1Ds9IQlh3+HSz2K4g6i5IbBcwFI2xm2DO6gL91PWA8xv2QO1ZG/nGpU0zQ++MFP8YY3XMPf/d1Heec7r+PKK6/ja1/7Lg0N0/jKV77N5z73IS655MpJkvivBc/tF3SMCeaWKfT151EsG9sMsMcJ09ifRW0fRHqS9HB6cl0rDyFphRA4loMZO3auYLwvyf6tPQQLgoSLfGGOGTYY6xlj37pOqhZWomoqxfVFdK7rxM7a6EFfkGfnHKQnT8qa+FjR39/LP/zDJ7jqqus5//xL/2LH/WvBKTL4FF5zlHsa1+WjXJd/dW1WrweklPz+97/iqquuP+H2j780UnYG8yU7veXJNBUdCeSCGvaqNgIo81SuzUWpOo4AkJMJzVBZcM0C9j7ezGDLEFbGIlISpvGMBspmluIhadZshupnML+6kbJMltkiQt4dZ/PIGnRjOnbsoOIu4ObIOEksL0/gkKAGT3oMZ0dQFZVCs+CoxIxTMIt85VmY+1eTz8URTh16th/PLMCN+QuQAIKU8JOHDx1l5/BeHut8irgRoz7m+4ImcqM83vU0MwqbWHQ0z9/XAAJB8DgWap7rsevRPXSu68Tz/AKga/N+ir0hZo5nMISC/cKzyGyGtxcWccHK8/invi7e9rarue66G/nKV/6d4uKSKWO6sUZSSz7P0oEXET/4JsUzGjDOuw7HCFMsLRbZgcnFpI6g0tPYrVmMCI9iqSKRPGlkeNzIkFBcRoXLmOIxJDyCCIR0WdDSw5kt/RA6RA2i6UjXReZyB/+mmmRnvwer8my05D6kYuAUzZ/i03Y0SCR58hgYKLzCBoNQkFoQJZt6ySAeIJDqMQbwHTqkgPoY1MeOTFCed55k717Jrl2CeFySz/vZMRde6NHQcPjr9+7eyosvPMGPf3IbmwYFWwch4whmFko+/eaLeeQHxQys/S2NMxdyQa3HWdV+YN3kfNKg7sMPgDTBeFHBLZfICRcXtxDUdkngSQFngHjJT4siQOkTGFvASIEMgLZXwdgkSb/Xwz08tPmYMZSFvQlBZfggAa0IqI1KOscF+8Z9P+PXEiIYQj/jbPQzzj6m1xuqwUguQUgPEtT836xwWRyhK4x2DdKcaGN+yeyjvl+63hHVy0JVcG33hM7hFE7hFP52UB4q5ZL68477faPC5dbgGDs1Cx1wgef1LBdbIa7OR6ZsdhsI5p8E0UAmk+H73/8X/uu/vkc4HOXzn/8q73rXhwi9xCd24cKl/OIXd7Ft2ya+//1/5Qtf+Bhf1XTeWNvIzYVFzCr1N3GFEKAIZCp12LEGtr7A0kc2UeqZZAuiKLZL3fo9XDBQwKaiHXizr0X29ZH9+X/hDfShlFcBEmfDWuRAH8GPfgblKN1Ph8IbH0NmsygFhVPCQ/+S8MZGsZ9ajZdKoS9cgjZ/4eS/CU1DqavHfvoJ5GgC6dgosTheeuKaWfnJ12YNm9Fgmng+hsxm/bYj2ybsGnTlx/nV4BgPbm4moGosCMfYMDbKd77zNUpKSjnjjHMBv2Z61LyfRwP3kxAjCKDUK+ea6W/mzMBHMbN3Yd/1FPkhgSxtoF7dy9qx/RjnX4xSelA48kTXc+wdaWVm4TS0iUC03nQ/D3U8zoLSuZSHXrl+e1VQdHIzbsaqOnfCHzeAXTgHtONv5ReeBZ7jK2eFgjRiCM9GH92JsJJgliC8PJ42YWkmpF88KRqOUUCg7U6Emyc76x3IQDG5GTeSm/bmiXke+xaBNrQZs/MhtNQ+PNXEKZyLmupGG2/xSVdFxYnPxCpZ6gfeKbpPTANSaJPWFsIaQwbLJq8TgGKNH/d1ARCujWfEUZw0rh5msqDxbFBMnIIZDGdHuHXH7ewZaQEBrufx2L6nefOsa1hRsfiEjnsiGLWGGYsM8sF//wiLH1nCL7/9U849dyFf+cq/c+ON7+Lhh+/ls5/9IE88sYni4tf4/jwO7BwRxAxJOBqgsKaAka5RyIxTuH+Q4b5eKsICz5MMdyTIjGYQiiAzksGMBvA8FzvnECwMUjn35TccpJSM9YzTu6OP9hfa6dvZjxkLECkOoQcN7KyNYzvkMxb5ZJ5QYYiymaVULqikZ2vvhIueQLqSinkVVMx97QILXzrvz372g2iazr/8yw9fs86Qv2acIoNP4RReBmvXPktHRyvf+c5/v95TOSYIIZhZ2MTT3S9QHiqd/FFLWSlqx4Z5R20ITS1AwQ/Ji8rXN2QoVBBk0bULyI7lcC2HUGEIVVdxkdxlJnnazJLDAxOCYYXz83D2uI4iFFzpoImD1h6OZ6MpOtohDNSekRYe6HiMzvFuFKEwq3A6VzZdcmRFgVDIzroFN9ZEeXonutAYLZlLMFSHNGJIJAnhscoOTFHOAOwc3k3etSgOHvS4KwwU0J8dZPvQrr8oGXy8GNmXoHN9F8HCIIGoT1g6lsPAi92UdY1SvK8ZKQRC1/G6u6iw8vz8/Mt48OP/jy9/+XM8++wTfP/7P+fccy+aMq4MFDNSdCZrntzIJz7197zTqqfTsflJeBTzJcSqjiCF54dKSnhRz/FHM4UJVLs6JUKlWbWJSIULrBD1rs70XSNo3T3IGdHJ+1yOJhCRKEpV9dSTFApuwWzcgqMTa1PmjmSrvpEnjUfpUbuIewWcnVnFuT0xjGQPUg/iFC2c3CQ4cAyr4iyCe3+NsMZ9mwnpoibbcMOVOEXzj37AE0RNDXzwgx7PPy/Ys0cQjcLy5ZLTTpMcqZ759a9/SkVFFfas6/ifbQLXE2iKZH2fwqyiAFde9SaeXv07/vFHX59Mdj8Afa0gcJ9AHfADNqQqUQfAfUkHn1cEgSHBIl2yOiMoCkgm7MHoG4bSPkGTJnEO7ONIibZbYD6mkHnfkcnNY4EjIe9BKuMH1ukKlAUlQQ08CY73ymO8FK4HSdsPyTuagvvVYEZhE6qiknPyRHU/+CnjZjEqIrj9WdJ2+mXfH6/ylfR2zkYP+L+Fru3i5J2/qKriFE7hFP628IKeZYeWZ7prTHYQDQuXJ4wMC50AjROZHo7nsDfRSk+6n4BqMrd4JkWBw0PLXgmrVz/I3//9JxgY6OUjH/kcH/rQp19RYLFgwRJ++tM76O7u5Fdf+gy/f+Jhfvurn7Csqo4bFy3nsqaZaDCFRDyA2MZt5DMWo9NqEBOdYnY4QHFnDzX7xxEI7A3r8Pp6UGfPO6iCi8bw9u7C2bQB48JLjjo3Z+d2sj/9Mc6WzYBEbWjCfPPNGBddNmlx9VJIy8LZugl3zy4A1Flz0BYuQRgn7plkrX6EzHe/gezvQ3oe2VAI/dwLCH/x6ygBv57TzzqP/J/+gBwZQpRVIDwXRfqdM+6+DpS6BpRYHLN/iHhCZXhBA0XdBl5/L6mBDJvX5dj1XB6ZSvK24greHy4g7jq0rDqPr3a1csMNF3PddTfyj//4DQaq+7gn+AdML8BMdw4SSbe6jztCt1FV9QVqrvo31IXteM8/g9vewiwlyOCebaROO50DVLrjOWwZ3E5hoGCSCAaoCJWxJ9FCy2j7SSWDLSxyIktYRlAP1V8LgRepxYscHvx6PPACpbiRGrSxFhw9BkL4vrvZPjyzGMUe963JtCAi0wNCwYnWoljjSC2Ap5Rj9D1Hvu4yvEkS9vh04trINsI7/gNhj+MGyhB2htCO/wAgX3Mp6CFwshjdj2F0PoiwxlE8G2Fn8EK+MhgtgPRc//8PwMmCUHFf2mV3jHDDlXjhKsgMomYHkYoOno1w8+Rr34AMlPDYnj+xa3gvMwqa0FUdKSX7kt38ueV+ZhY2HbM9yKvBuJVge+JFMk6KgBrmtEtOZ/qK6fzpB3fyqU+9l4cfvoe///uvc/31F/O5z32In/3sD381pKJvmeb/f1F9EYF4kPyebsT4CPHGEpqmR1BUBUVXSHQmcG0X6UnG+5IgfKuI8llllM86eoaFnbXZ+dAuOjd0Mdjid6x5rouqq/TtHAABgXgAN++g6irta/Yx97LZqLrKgivnUTqthMGWIZCSkmklVMwtRzP/MhTlb397K6tXP8gvf/mnw4RN/1dwigw+hZOOhHDJCUmxp560oK3XC4899gCVldWsXHnW6z2VY8Y5NWewa6SZ3YlmigOF5F2bpJ3kjKoVLI82ojqvvReOlJLUYAo76xAuCmFGj66YEEIQKpgaYLdNy/OEkaXEUyiQ/qJkRLisNjJMC0UpGC9mxBokpheiChXLy5N3s9SE56JN7FR3jndz647bGcklqAiV4UmPdX0bGcwO87El7yVmHGEhoppYNRdRzYXMD46xRssSkwoBXIYUhyKpco59uDIg79moR7CmUIWK5eQmd9T/GpHoGsW1nEkiGEAzNMK6hxweQharKAUFAMhAELo7ITXOm254G+eeezEf//i7ufHGN/ChD32az3/+qxiHLGweeeQ+crks11/7VuJSpdETFHsq/YpDjed/ThJJv+JQ7+qUer4q+Dk9C0JSMxF8Z0qV+Y5Cp2ozyzVZ6Jg451xEtqUFt3k3SkERMpdFZjMYF1yCUlP3qq7JBn0tvwr9N5bIUeAVMWa3k9t+P8m+MNWO33ovzUKy02/Eqj5/8n35motQU50Y/Wsg1QkIvFAF2Znv8P2KXwNUVWGVAqIAAQAASURBVMENN0gO2BuI3BDmvifQBzcghYJddhpW9QVYIsg99/yBy697J8/2mRSakqKA/x7bk+weEcxdcT29t/0n27dvYeEh3oZqC4R+p4AjcRp8wl7bCWo30CSRpUwqyUQGCEnOnemxp0Vh94ggqEksVxBKwhv7PQoPXTcI8Mqk79ebBnkE27m0DZsHBV1JP3xvXrGkKT71K6Ur0JeGjjEI6f5s2lRBcVAyo0BSdxyNHVL6x1vdKehNCwKa5LRyycX1ktBJzD2dXtDIioolrOlZz0h+FCklAc2kqLaCfF+SsldY8JbOKKVyXsVkEKdQBFbWpnR6CZXzXrsW2lM4hVP428ZmPU9UKhgTXsM5JKaEfsWlTbVodHWyTpbbdv2RTQPbsFwLIQRlwRLePOuaY97g7u3dzz/902e47767OPvsC7n99vtpajqyh/rRUFNTx+e/9h0+9KPv8Nj2Tfy+t4vPPngXX9MNbpi/lLeFg7w01qhiMEN/LMqYlSRuxnx7KQ0Cjs1iyhBC4O3vQpiBKaSNUFVQNby+nqPOx23ZS+qfPo/X1oKIRJFC4Gzbgrd/P6gq5kWH22dJyyL3m1tx1j6PlBPP6mefRFt5JoGbbjkhQtjt3U/mW1/HGx5EVFYjNB0Sw9gP30++to7gBz6OtCzkYD9C1yAcReayvu/ytBlolVW4G9cjEyN46TRavIBzom/ijprt7OnP035Hjpb7RlFMwaKzSvjnjjLqtADSthEVVcwOhvjNinN44C238M8/+AaPP76YK394Pdb1eRq8g4FidW4ju7TtbNM2U+PWo9Y3ok7YfSxq3gV33saevbs4o+TlA1PBrxdPFnLkeMJ8iBeMZ8goacrdSs7PX8oye+UUZfyrhqKRb7gadduP0IfW45nFqKlukB5utAEt1Y6n6AjP8TvJhOp79EoH4Vp4gRK08RaU7MBBMvh4ICVm12MIawynYI5fONkphJTgWShOGk8PoWb7UPIjCOnhRmpQMgMIJ42a6cWJNuEEy9GTHSjZfjwtjGKn0EZ34alBAu33YGf6sCrPPi7vYLtkMXbpCvSBtXhege+n7Oaxi+eTmf8Rck6ezQM7KA4Woat+8SWEoDZSRctYGy2j7SwrX3T81+Q4sT/dTsZJUWgcFFmZJQFu+cd3c+Vl1/O1L/49b37zZdx887v50Y++xe23/4KbbnrXaz6vY8GycsnOYUHSgqgBwYIgOSGIBFQaGmMoKuTTeRIdCbSAhlAEju0gXIEe1CluKiI1mGL/1p5JC8eXou35dtrX7iM9lMFzXALxAJlEmvRIZqKDw3+dZmpEy6N0b+ymal4FhXWFaKZGzeJqahZXH3Hs1xKdne18+cuf5a1vfScXX3zFX/z4fy04RQafwklDQrjcZ6bYpuexkJR6KhdaYVbagZP7YP0LYseOrSxcuPSvZofvWNAQq+V9C97OU13PsTfRRlEwzOWNF3BOzaqX9a49WciOZtn1yB6GWodwLJdA1KRueS1NZzSiaMemRN5KlkwqixiVpAM6oYIgRYrKgOKyW3d4Q8Fido9uYtQaQeKhCZ3qcAP1kYOLjHV9mxjMDjGncObk5xc1ojSPtrJlcAdnV59+1OOPd4+xYFMXI2aGlulB8uVhlhYUcKEXZZp7eNE+Ld7A093Pk3PyBDQTkNjJLkjsYpHTRyTX7AeUVZ5z3Dv6xw3P8YvJ47pnD3+toVhohoK0LbzECAgFbAulohLpuLhdnZTV1HL77ffzn//5Xb7xjX/kueee5Cc/+fXkYm/16gdZsGAJNTW+F1xEKlySD/PHYJJm1SIkFZLCIy4VLs37QXc2kkHFIfYS1bqJwMNvbwXQ5swj+N4PYz+1GrejDaW8An3V2ehnnvOqvq8ODqvNB3Gwme74SuJFbUPM7Uuzo0gl6jYR8kKoqU4CrX/AKZp3sEDXgmTmfhCr6jzUVBdSC2IXLUAGik54PscDkR8lvO2H6CM78CbC8rTm36CN7uaR/pkkEsPMO+8mnrOh9pB6XVcgbkjSFWcSDkd44omHppDB+kaBSIJziLB6aGWCnoZ2BhoHUIt1qsfqqG9tRBvVyV3tUV0GH4l4vNgnaB8XFJoeKyKwYlzglsIUcbgDaHCkLMtEDn6+Q2HXsE84u8DjXXDNdI/zag4uDJ/d7z9lCgNguRJdgTELMg68Y+7xkbhbhwS/2KGQdyXFAUnOgbtbFYZyknfN8yaVzq8WilC4ec4NZOwsifwoMT2KoepYNQO0b9xHffTljZQ1Q2XB1fMobiyif9cArutSNrOMmoVVU9KZT+EUTuFvH3nXwvEcQlrwVdekB96dFB57VYthxUUisYD9iq/8e6rrBdb0bqAuWk1ED+NJj47xLv6w924aYrUvq8hzHIdf/OI/+OY3/4lQKMyPf/wrrrnmLSc8b7W6lth7P8wVD9/PZW2tdKTHuWNkgDu3buBnbziTGTNmc+WV13Plldcxe/Z8gmXVTOvtZIuSYSjr2xWoHlQFCpnWsNy/BsUlU2wSYCJMznUQhUdXP+fv+zNeVyeirAIl7FtcyFwOb2QY674/Y5x5LiI4VeBgr1+D9dD9iEAAEQojSspAVXDWPoezYBH6suMPybIeeQBvcABRW4dyIEm2uAQ3k8F6+AHMt7yd3K9/hrNxPe7gANi2v9MZi0NiBGkGUBqbCLzrA6hl5XjxArLPrWb3hXfQsnY3oYYwi762hEtmVnLNDwaIpUYgZqCWlKIuWIyIRPF27+C6iy7j0qe38bnPfZDb3v5TGp+cTuM/z0CfeOgKBAqCtDjczqOhYTqGYbB7945JqwlN0VhUOp+H2ldTEixGn1AH92UGKDDjTC9oPGyc44VEclfwt6w2HyIm44S8MG1qM52hdmRGssJe9aqPMQnXQh1vB89CZIfQ0r2+H3DBLNxwNUquH2GnkXrUD5FTNISTQ6oBpB5B2ONILYRnFJzgyTqo420TvryH2DAIBaREyfSgZIfQRrYiPBupBnGjTf5cXN/iwguUgKKQbbwOVB01uQ9tdA9IBy9cjZpsQ0tsRxvZTnr+R0ELvvycDkALkZ7/IQIdNeiD6/E8F6dgBrn6K3DjTUgni4d3WA6MEAIpfRvAvwQS1hCmMvV3V1cMPDxWXXQWT5y5ic9+9oP86EffoqlpBl/4wkeZNm0mK1ee+ReZX87J0JvtIpEfRFN0ygJVlAWrUYTC6ZWS9jFY16ew388upFJTqApLIrpvepgaTPsdZqaGdD2KKgtRdZXcWI5A1ERRlSlev4fCztm0PddOsi9JejiD9CSu7YfF27k8qqqAJ3AtdzIfZ2TfCCOdicnAudcDlmXxsY+9i8LCYr7ylW+/bvP4a8ApMvgUTgpsJLcFx9ii5Sn3NCIo9KsutwfGMaVgiXP8fpmvN6SUbN++mXe84/2v91SOG03xepri9diejSrUEwpUOxF4rse2+3bQv3uAaFmUcLFGdizL3seb0QM69ae9smIzOZCkZf8+EiUQ6PXVKKGiEOWzSlFCYAMRPcaSkrMYzQ9je3lCWoSoPtUPuCu5n4gWnvI3TVERCIYmgueOhNHuUTb9YQuZkQzL4gHmb0ySsxyaFlQz/Y3zOZJ17JKy+Wzsn8emwe2EtCBKbohcYjdLDYPlgTDqeAehsf9ByQ2Tm/bqgwhFbgg11QWqiROfAYqOltiF0f0o2lgznh7FrjyHfPUF8DIBVABFdQVopkp2LEcwPmETkXdw0AnUVqGVRf2kbc9DFBXjDQ/j7t5J9nvfQK2pRz//Yj70oU9z5pnn8eEPv51LLjmNf/7n73P99Tfx5JOPcMstH5hyvLPtIHGpsE7PMaA4LHRNzrCDzJgg2TX84MlmzeJQXWQODwUoPIQx1GbNQZs1B2nboGknZdNmVBmhT+2hxPOPLqSkvrcbx4yT0vIkvXFCIowbqUUb3YWW2IV1qFpDUXGK5uEUHVk55fb24LbsAc9DbZyGUlt/cjabPJfQ7p9htt/te9IhcKP1yFAF+uAm/vz7J5gzZz4NMxfy7K4jvB3QDYOzz76Axx9/mE984gsHT2lYII2DxOtoKMGGprVkapMEc0HyMs/Ows0ka8aY27SE/MX++ZSF4IqmQ1TLKfAqBWonuI34jEQe1GFB7lIPjrB+eLJLsH1IMKNAYqj+OD0puL9NoTHmEjMgosOGAUFdVDKrELpTgnELKsJgS0n8OERXUsITXYK8K6d4DId1yaYBwXk1MK3gaO8+fiwpXcB7FtzME53P0pcZIKgFOGf+2ey54wXGxkYpLHz5jQQ9oFO/oo76Fa9ODX8Kp3AKf50Yt5Ks3vc06/u3YHs2TfF6Lqo/h+kFJ26yvtg2+V0gT4tqk1RcIp5CTkBWeKzT85ybz7O2bwMxPUJE9wlPRSjUR2toHmtjT6KF0yqWHnHsrVs38rnPfWiifv4Af/d3XyUeLzjhuR6AOm0GwQ99AjmaYJ6q8rVYnC/m8zz11KPcf/9d/OxnP+K73/1npk2byeVLTuOinMvp8TqG4wquladgYIz4nJmEFiwDQF+6AvuFZ3H3taNU1YD08Lo6UYpLJgNoXwppWTi7d4KiTCV8zQAoCl5/L15iBDV4UOUmM2lyt/43blszIhzxHzJmAHXmbPA83D27TogMlqOjAAeJ4AmIQABvbIzcg/fibN6IMn0Gyv5uvM52QCABT9WQ+7tgwWLWpZPc9x+3cf/9f2J4eJBVq87hUz//e+ZdtoiYEqfMLiPT9HlctX3SgkuOJsDKIwGZTlJYWMT//M/v+eKP38Gvv3UHd/7ph3zgXcvQ3jibvkWFgKDSO1z5p+s606fPYs+eHVP+fn7tmbSNdUwJkAvpQa5uuuxwiwjXQkvsQM30IrUIdvHCV+zE2q90sc54nnK3kmKnkNJ1PZS/MEJupJeepn/DWv5NjKbDA3+PGRMkqzbWit7zNPrQBrxgOXblOQhrDCU/gmcWomZ78Iw4Wm4YxRrF00IgXRQrgV04HzwbNd1NvuoCvPAJKieFhmfGfTXygenpUTwtgJbtR0/sQuJbVwjp4hkxv3Y0YqjjrX5wnIBc0w3kGq4CxcBs/zPB/BhO0Vw4kFHjZNEH12MMrseqnJqbIHIj6EMbUXJDeGYRdulSP4gOP5AuO/tdvg+yZ/sBfRM1cVALMrtwOs/uX0txoHByLTuQHKJoNM6ce+oJCQVnnsRaKuE1ohoMNUDWzUz5m5QSgb95UVpazi9+cRe/+90v+dKXPo2iKLzjHW/k4YfX0tAw7ciDniRknTTbEusYtYbRhI4nPQay+6m1R5gZW4ipCt4+R7Ky0qVzXGCoUN1QQMeITmYkQ6gohJ2zkZ7EczwUTUEz/PWUUBRy43mMCGRahhhoHqRidvmUNUs+mWe4YwTX9jDCOnbWt4KwczZI0IM6qqFSPrOU4sYD9mXiRLOaTwqklHz+8x9hy5b1/OEPjxKNvvZWI3/NOEUGn8JJQbNmsUvzW8uCE2xZxFVoUSyeMTIsdsy/OXXwwEAfw8ODzJ//2regvFbQlZPY03wMSHSNMtw+QkFVfDIZNFISYcwep3NDFzVLqw/bVTwUnuux86HdxNQ02mWl6MUqSt4jNZTCC6nIWQU0TXjZqUIlEiilRbPYj6TSc6n01Mn7rCRYxK6RvVPHlx4SSfRIFhET2PdiF5mRDMWNRQghCOO30PTt6KV2aRVWY5yk8NAlaPi2ByEtyDvn3cjsvg1s6d+KnmpheaSQ0ysWEVJ1XPCTifevxqo695jCywCGhMM6I0ezahH1FJZYOitbHiLQ/RhKbgRUHSfWhFV+BsGOuxH5Eb8FLd2LtvtWlHQP2dnvelmVcFF9EQ2n1dO+dh+Z4bSfuiUlFSsXE27vRvZ0oS09DVQVZ/06vLZmlIYmiEQZadvN0P69bAq+DXPlCv7zsWf42Rc+z6c+9V7uuut2EokRzj9/asukQLDICbDoKBtEAsFZVohW1aZLsSnxVPK49HljzEvlmZPOQ7huyjkJ/eTd5wEZxJAGOZEjJgEpUV0PR3ioaOgcOJZ/nZDHFtQlpcR64jGsB+6BsYT/x0gU4/yLMK68FqG8ig0bKQm0/ZFA8+0IOwmKija6GyXTg112GsmczUNPPsenPvNPzCjwQ+EGslA+4XiScyCZF5xX4lF9zmV88YsfZXQ0QUGBv2vvVUvEi8qE3QnsK2kjbSYp7SnDnQdeXKJaFvvmdFFUW0th4Mj3t4xA9gaP0O0K2m7fexghseZLchcfXhl6EjYOCgpNiXHIz4bjwdPd8EKvSnkQFpVKBjM+KVwYgMIJ+wspYU9C4B1H1Zl1fLK58CXC2qgOPSnJQEYwreDkVbFCCJaXL2ZJ6QLGrRQBzWSrtoH/4d8YGup/RTL4FE7hFP73wnJtfrXz92zs30ZRoABD0dnQv5mO8S4+sPAWGuMntgl0hh3kETPNdtUiIgVpRaICixyDnPBYp2fIu3kMdeqz9QAZk3Pyh43peR7/8R/f4Zvf/BIzZ87l3nufYenS4yc5Xw5CCMQhv4mmaXLJJVdyySVXYlkWzz77OPfeeye3PXQPPxpLUB+NcWFpJavKKilbuIzQ296FEosDoDZNJ/CWt2Hd/2fcjlbfy7W8Ev30M3D37Mbduwd1+gyUmrqD5IemocSiE632lk8Cg9/e7zqIYBgRmdombz//DN6+DggGUUpKfZuIVBKveQ+i/MTtfNSmaaAqeJkMSsh/mHueRKaSqAuX4G3ZhFJSilA1QIJhgufhpdNskB4P5zM8+vh9DD34R2pq6nnzm9/Otde+lfnzFx88iAeooC1djtfZgdvaghwdAc/zsyQUBSYCAN1d2/lkl8mSsxv55sYu/uV7z/L+F1qRf9/AzHPOY5G97IjnMWvWPHbvnkoGlwSL+eDCd7JpYBsd452E9TALS+Yys3AqsSbyo4R2/Tf60GY/BE2AG64hO+c9OIVzj3rtBtQ+UmKcallL3X3NNNy9FykgE1LRntxDcsN3iL/7E2iz5hzvxwLSI9D+J8zOhxG5QfTEDjw1iAyU4OlhpB72VbmejVV/FdrQJlB0FDuFp0cQbs4vfhQNxU5iVZ1PbubNJ241JwRW5TmEdv8MJTuEFyieqFsVcC1wLaRRBKqBdHKAQE134xTOxTOL8AKlpJf8P+yyFZNDqtl+0IMHiWCYUAML1NE9cAgZrI61Edr4c9RsG2gOAG5XDZm5H8QtOEi4S31qkOQBXFR/Lu3jnexONBPSQuStPKEOgze1XE5NqsS/tdcJtB2CzDv8jJmThYEMvNgn2DK0kLTTzrzSDNPjDgjJuJ0gqEUoMn0xiBCCG298J2eeeS4f/ODb2Lz5Ra644iyefnrba+pF253pIJEfptAsmfx9zrtZ9qc7KA/WUGAUoyowpwjmFB2ojeOoZzfR+kwbw23D5MZz2DmHWEWUfDI/SQpbGQs7b4HnBxNv/sMW6lbUMefiWZOdvtnRLJ4jUTSBHjSxMzZW2sKxHaQryU2ExUXK/N/F7FgOLaC9pqpgT3p0J3vIOFnKQ2UUBuJT/v0nP/l3fv/7X/LDH97KihUnsQvgbxSnyOBTOCkYFi4uTBLBBxCXCn2Kgw2ceETC64MdO7YAMG/e3y4Z/JdGPpnHtd1JIvgAzLBBPpXHyTmokaOTweO94yQ6R1lYHmZgwKW1QsO0FZx4kD7F4sKUymLPf9K3qza/C4zTpdo4SCJS4XQ7yDW5KAaC5RWLWd+/hc7kfirD5XjSpSu5n7JQKQtLjlwkSk8ysm+EYHyqj5wZNkkMJLkr0c+2+TZtqk1S8SjyVOY6JudbIc4nxEV153BJySyi6c14egx5yELKC5ajJXahpLsnyeBMIsNQ2zCu5RItj1JUX4ii+t+hXsXhp8FR9qk2YRQsKcn1PcnMfXdQqRbhFM4BN4+W2IPR+zyeGccpWjhZMIrcCEbfs1jV5+HGjq4gEopg5oUzKGosYqhlCNfxKKovpHxWGaKriNxvfoHbuheZTuO2t6DU1qMvX0l3SGVHuUHB3jbiTzzBwyvmsKlM4z0/+jHnnXcxn/zke1EUBXkCbVzLHJNsLspqM02/N05wtIVzBpt5S+t6iqWKVXEG2Rk3Ty1ETxIiMspSeyUPm/cQlhHCSoSO8hKa2rdQ5DRR4PkLUSU3iDRiuLFj2/X32lux7r0LoaqIWf79J4eHsB6+H7WuAW3xkRdKxwIl3YXZ/RhZtYCMEkBXY0T1HEpuEG28lXs29JPNW1xzzVuojsBlDR73tyvsGvHfP9YqEa3wlKugisvxPI/Vqx/j+uvfBIC1QmKslWh7BE6Fx/CMQYKDIYiDWyWRQdAwcPNjpNxxCjn6ZoezEFIVHvoOgUiDWw72fHlEVbDfBjh1DdScELzQI0jakkIJ+11BbwbiBkQMKA1KJr5CDGV9j7TG+LGTt4YKYR1GcnBoBJvlgapA2Hht5Ayqok4WrKUTgUgDA/3MmHECi9FTOIVT+F+B3SN72T60m6Z4PUHNJx6LAoXsTjTzbM/aEyaDY1LlNCvAvolgVl0KSj2VEqnSqTj0aJIZhdN4bv9aSoMlk/XQmDVOUAtSG52qUuzv7+XjH383zzyzmg9/+DP8v//3lSn5AX8JGIbBBRdcxgUXXIb9bz/hueee5N4//ob7n3qEn7ftQVv/LIs3Ps+ZZ57LmWeez7JlpxM8bRXa/IW4He1IKXF3bcd66H5kOg1IRDSGcckbMC69YlIpp114GfaGF/FGhlGKSpBC4HV1Qj6Hl01jPfEYxlnnokwQQPb6tYiyMsT+bqRlIQwDGYki+/sQ6RTqiRCOgH7hZah/+C3u9q140RjoOoyNQjiCedW12M8/iwgGkekMI1ae9dEw60ZHWJ1JMJAaoiIc4aqaRq793D+y7KrrX7ZDSVuxitwv/wevZz+itMwngcfGQFXxOvchXRfrofsJjDmctfRqChfv5gcPv8gPn+/hqs+rfOWPNxKJH1mEMXv2PFavftBXWh4yh7gZ47zaM4Gjt9qb++5HH3gRJzYNtBB4Ltp4M8E9vyK5/J+OalcQlmF0DET/CNWr27FiJtnyEJoYoLBGUNi1Hbn6v5HTvoHQjq/O1AfXY7b/CanHkZF65Hg7INGGt+AZMaQRxzML0VL7sMpPJzv9rQjPQiomwkkhtTBKfhglO4hnFuKFa1515ohVfT5qpg+j92m0RD8oCm60zncMV4MIJ40brkKxxgAFdbwdN1iGmunBLluBXbJkynhSNf0NkJdCSlAOXi9tjyTy633obefjRq7Gm9uFs3QPWnorwZbbSS394ita59VGq/nI4vewrncjbWP7KO6Jc+auhcwvmYszsZciMmC8CPZigb3i5NRonePws+0KXUlBQCtiJK/SnEhyenU780v7CGtRZsUXElCn5sjU1TVyzz1P8eUvf5af//wnnHHGHB544HmmTTs+r/RjxXCuD1M1p3QAm2qQtJNi3EpQYBweJiyEoOnMRooaihhuHyGbyNC5sRs5kbScHskgpcTJ2eghA81QKW4qJhgPsm9tJ/HK2KTHr5QQKgySHcviTiTVOZaDlBJVV9BDBm7eoXd7H9HyCIqq0nh6PYW1Ba/J9RjMDHHH3rvZk2jFci3iRpSzq1dxWeMFaIrGgw/ezb/8yz/wiU/8Hddff/NrMoe/NZwig0/hpCAmFRQgj8Q8RAGcUiT1rsZfVp96crBjxxai0Ri1tQ2v91T+ZhCMB9AMFStjYYQOLgRyyTyRsshk4v3RYOcdXNslJhQu35hjV41Oa4WGa3vUNKd5yzkNBIsVsnjcHhhnn2rR4BoYQEJ4rDbSlHoq51thZhfO4M2z3siD7Y/RPrYPRShUR6u4bvoVlIWOsksrQA8apIfTU/7sui7dqsOesEuv6jCueGgShhWXZtViJOCiITjPCiG1IFIxEG5+qh7RzfmWDaq/oOvd0cfOh3eTTWRBgKqpVM4rZ/6V89BMjSeNDPtUm1mugTqhQl3WuYY+1SMULicqFdCCOLEmAkOb8MyDRLCa7kFJdaGmOgnuvpXM3PfhRQ5fNDpd+5AD/aiNTZTNKKVsxktIvOkzCX7673B3bsfe+CI4DuqyFdiaSquaQQBGUQmV3YPMG7PYXajwkJniU9e8me9//18ZHOzn+usv4jOf+Uc+9rHPox6Ssp3oTNC3q59cMk+sIkrlvApChaGJj0Fwth1ieV4lveuXREZ2UayXIUINePkRzM6H8Mwi8g1vRE3tQ0nvR2ohPxxDe/V9Ym/IXcOIMsQ2fRM2FsPTFN4+XMOKgSC61u77qgmFfP1VuEe4rkeCs2MbMjmOOmf+5N9ESSlOYhhn6+ZXRQbL0XZ6RsbZZi3Hzo2iWVBhZFig5XBHe/nRs2lmL1zJWLCOCgmX1Eua4i47hwW7tsCWDQpRTRIphkymlkhkPr/4xUNcd92bEAK8Ski/2yPwgILaJjAzBuOV49i1PhEMIKWvv1XFK5cVXhnky165YFcVWFgieahDoTQosTzYNSLIepKILqiKSHRVMpQRZBxJVIc9o4LQRGCdqkgub/CoPrLghJQFO0f8YI3SoGR2kU8Gr6qU3NEsGMn5CuG8Cx3jviJ4ZoHvY5y2oSjASQ2UO4DSUj9hb3Cw/+QPfgqncAp/M+jLDOJ67iQRDP5CPm7EaB1tf1Vjl0iNQk9jjqtP6dzLCkmJp3Fm7Vk0J9rYldhLoVlA3s2Tc/OcX3MWDbHaydc/9dRjfPSjt6CqKr/6zT1o06P8cOtPybt55hTN4szq045ec70GyDpZ+rNDzF6xiHPPvQiA1ta9PP/8Uzz33BP8+tc/5fvf/wamabJs2emccca5LF68nFmKRvSJR1FiBZMhtHKwH+uBe1AbmtBm+5u45jkX4LU2k7/7Try+XmRyzLcFqK1HKS3HuudOvN07CLz/oyiFRX7WQkkZ4IfWSQAJMpdFnTkbbcHiEzpPJRQi8q/fJfPD7+C8+ALSslCnzyJw0zsYX3kGTz/6AGuef5x1YwmaE0MA1BomlxSVctXZF7KoqBSRShJafvorWlUJ6fnnoOuQzvjnO30mSmEx7u4duG0tuN2diPIKYjLOKrGKZZct5bel6/nGU4+Se/dH+c/b7iEYPJycXbp0JePjY2zZsoHFi5cf+wVwshgDa30/W22CkFNUnGgTWrIVbXQ3zktIzANocmYyzZlJev+zaKMZxmeVUK7sp0btp9CLECn3kNkH8TaUkV32yWP3wAX0/rV+CFuoHOFkJ6zaBMLJoGT6cY04ipVEamHfzkI1kBN2blKd6MYKVeKFKo/9WrwUB4jaAwShopOd+Xby1eejprqRWgAl3Udoz604hfPwe/YF6ngb2vAWhJ1EyQ1jla8iO+udfqDdIXCKF2P0PInIjUxmYii5IVAN7OIFAKitEP7PHHpbAbIwj8iZaE8vQAzGcS4fRx1vR03te1mxygGUh0q5atqlAAS3CMyswDnkI5EhQAq0vRJ7xZHHOB5ICY/sU+hOCWYXSRQBTcTpGA/SNlLAlQ2jNESLMY4iSNE0ja9//Xs0Nc3ki1/8JBdcsJh///f/5oYbTj75qAj1sO63yZDKl/leCyEorCmgsKYAgKqFVTQ/0czwvgRO3iE3nkMIgRkxiJVHKawtQFEVsqNZenf0TZLBsYoohXUF6IM62bEsmREwwgaKohAuDVO7qJpE9yj5VJ7qBVVUzq+kdEYJ4mSFbxwCx3O4bdcf2T68i9pINQEtwEguwb1tDxM1IxQlgnz0o7dwxRXX8rnPffmkH/9vFafI4FM4KZjlGDS6Os2qTa2nEZCCIcXFRXKGFfqbs4gAPzxu3rxFf1Phca83CmoKKJ1eSs/2XiIlYTRTIzuWw3M96pfXvmKAXLQ0QiBmkklkiKgRlrbZLG2zGd0/RqQkTEnMZ3V2axadqk2Ta6BP3FtFUiUlPZ7Xs5xrhVCE4Myq01hUMo/OZDeqUGmI12G+jIeuEILqRZXseGAX+VQeM2LiuR59PaOMlBkYMwsYFx5xTyGAQhKPjOJR7qg8o2c4wwpiGDHsshUE9t2P1CN+65NroY234RTNxYlPnwzZszM2xQ1FCEVgZSy6N/cQq4xTf0Y9O7Q8RVL1iWB8p7eS7AhJPUxCcYm6E9dSNUFREY5PYGuje9ASO8FzEG4Wvf8FIvY46fkfxY37O9PeQD/pf/s6zvo1yHweJRpFu/RKQh/7DMpL1DxKNIay8gxEJIK7dRPCcUjqgrSQxKWCls1gxWN4gQAVnkaP4rBzsJs9e3byve/9lI6ONr71ra+wadOL/PjHvyISidK9eT87H9qNlbZQNYX9W3ro3tzDkusXEqs86N0UTeyhsncTTqRushiXgWKkk8bY/wRKdhCjfw3CToGi4EQbyM5+9+R5nhCcDDGp8n75CVq1PQwo/USVGNPnlkHvepzRXUg9hl1+Gnbp8iMrNtw8ijWGp0cPzjubgSNYQQhNx0sfHqwickPoI9vBtXCjdbjxmQcL+5dg3YBOUcb3CowHDZx8kn3ZMJ4spC9nsGX3g8y75TP8aJPK8grJzbM9ZhRCU0yy5y6FQkPSNFGPFxZCQ8Ol7NhxG11dHnV1/jHdaZD+qIcyDEV2Df3eFlQth0EAKT3G7VFCWpigUsYLPYLuJAR1mFcsaYiduLDl/FpJy6hkb0KQcWAo5/u0FQcPWkdEDEnKgqqo5NJ6SfOoIGJ4LC2FhaXyiMduH4Nf71LoSoJEoArJnCK4Za7HebWSkRys7VMYyEg0RTCjUHJ1k8efWgQbBxSyDhSYknNrJBfUHlQjnwxEIlECgeApMvgUTuH/OEJaEITf9nqo8ivr5KiNvbr09YWOyRNehlbFptbTUYEexSEsBctsk4ZYLR9Y+A6e2b+WvYkWykOlrKxcyumVKxBC4Hke3//+v/Ltb3+Vc865iO/94Kc80P8Ez+9+GFMx0RWNlrGH2TGymw8seAelrzEhLKXk6e4XWN35NCO5BIZqMKd4JtdOfwPTp89i2rSZ3LR0JfnTz2dvy27WpMZYO9DH//zPDxgbGwWgyDCZU1nDnNJy5pRVMqe0grp0Cn3HtkkyWASDBD/4cYyLLyd31+9xnlyNOm8+SkW1H2zlOLjNu3E2rMO46DLU+Ytw7/sz6twFKOUVyKFBZDYL1dUEbnkfYqLmkq6Ls2UTztaNyGwWbeYctOWnobzEb9kbGcZe8xzuzu1I02Bk1Rk0r1xJ895d7O3bz6Z//wp79/rBAA3ROMsLinhvYQnL0lkqojG0OfNQK2tw29vQTjsdpfKV7yOZzYJpop12JsJ1QQiEaSJTKbzEENJ2fIWn40y+x8DklnnLqMvZfHzjC7ztbVdx6613EotNbdleufIsCguLeOihuw+SwdJD738Bo+85lOwQTsEMrKrzptR2wrMQroX3UkJO0cFzEe7hViYHoKNzY+ZdPKT0klE78WQ3tWoCwytAd+uw8lmE4hAcWY/b9yxWzcWveI0mD58fRU6oY6UWxA3Xoo7tnZhvHpEbRsn2kq+97GD48MuNlx3E6HkSfXAjUtGxy1diVZ13RFsFJdOP0f0o+uB6EAp26Wnkay9GBopBCLxILV7E38hRzX1II4aSG5ychxttQEnuQ2i+rEsb3YvWfgd24w1wiP2XXbKEfO2lBLofg8x+QCC1ELm6K3CKFwJgPC1QRgSyugtphJGKjQznUFuqcburEKX7/cDr44UCvsfYEQQFJymfO23DnoQvEjiUs6yLGjSPGqSs8GSGxcvh3e/+MLqu8fnPf5SPf/xdrFnzDF//+vcIBE6euXF5sJrRsSEcz0ZTdKSUZJwkpmJSaBybJSFAcUMRhe9YQWowhfSg7YUO2p5to3RGKXpQZyQH/WOQTauM9NlMy/mCiEAsQOOqBvasbsZzPIwJj2A9qFM+uxwjbFBUX0h6JEPTWY3EKl47f97m0TaaE600xg520pSHSsk7ee5edzcPf+XXFNWWsfT9l7B+YDOLSxccZoX0fxGnyOBTOCkIoPD2bJw7AuO0ajY2kgJP5ep8hJX23154HEBfXw91dQ2v9zT+piAUwfwr5qIHdQb2DJBPWQRjJjPOmza5i/hyCMQC1K+oY+8TLYzuH8MI6eSSeRRNoXFVA6ruP+kzwsNDThLBBxCSCinhYSEJTPxbxAgzt3jWMZ9D7dJakv0perb3khxMkReS7nKdDdeV48Ql48KjeiLEzESQxSOAwqjikhQexVIl13gtSnYQfXiLX+wIgROfRnbmLaDoDLX3k0lkKK4vmtwdNUIGelCnZ1sPdavqUJnqdSqFwki8nqK+F1ECh6hR3RxesBw8ByXThzbWghQqQni4oUrs0pVo480EOu4jvfCTeFKS+tLncda9gIjFEQVFeMkx+h69m/bZVbjX3kCFpzLHMaeo/NXps1Abp+G07EFpbEDRJMZIAmMsSdflF+MZBg4uKoK1jz+KEIILL7yc4uJSli1byYc//Hauvvoc/vvHv6PryT6kJylp8tuXpCcZ7him9dl2Ft+wcHIDRthpP/VYnfob4mkRtLFmtHQXTrgWGakDz0YbbyG062ckl3/poFLkGKFkev2Ww6FNICV20XxmN1zJzMiEpUgQck21wLVHHkB64LmY+x/D2P84Sj6Bp0exqs4jX3c5am29X77m8whzYqHgOMhcFm361KASvX8twebbUDIDvjWxGsCqPIvszHccZo2RseGR1DyuNiqokR0MB5vQzQiRTJJ01uSubj8Y54xz34AISV7ogaa44Pxayfg49PcLioqmFrWzZ1/G9u3/zgsvbKGu7hBljYDeEGzva6Q3P46udhE3kwQ0CGkRqoILuXVHmF0jIKUfVLO6E66d7nFOzfG17rke7B2F1lHB9LikOiJpTgi6kgLbgwLzgJbFt5JwJVRH4PJGyeVHWCTkXX/MoAaOhDv2+u1/0+ISTZFkHdg6JHigXXDzHMlbZknOrnHpSwtCukdTDG7brfBcj6AsKCkLQiIPf2wW6AqcV3tyfYRLS8tPkcGncAr/xzG3eBbloVLaxvdRH61FEypDuRE8XFYeJcDtWFHmadyci/EnM0mXaiOBEk/lDfkI01ydNtUiX1jG1fFricmpLEsiMcLHPvZOnnjiYT796S/yyU/+PbtHW1jXt4macBURwyeqKj2X3aMtPN/zIm+cfvmrmu8rYX3/Zu7Yeze6olERLifv5lnTs56UleYji9+DfPJxrD//EWnlmW4GmJbJ8Y4Z8zH/5Qf0qAobv/dNdm56kd2OzYN7d/LT9c8DYCgK0zavoe6+O6iurqO6unbivzpKSkqIV9eiVtZMzkNoGgSCOHt2YVx0GcZZ5+Hu2oHb2owIhxGhMCIYRD/vIj9EDp/Izv/5D9irH0G6LkLTcDasQ9u4DuUd72XEdRgc7Ke/tZm9v/8Ve9tbaMmkaRkfJeXYAASDIWbMmM2KFWfw8Y//HatWnUNpKoX97JO4u3fidu3zH4Keh9e7H23BIszr3/qKghfpeXhjo3gjI3jDw2gNTYgJKyNveBCltAy1aRranLnYzz+DiMYQuo70PNyuDs5fvorfffbvueU9b+KGGy7mt7+9j5KSgySopmlcfPEVPPTQPfzd330NALP9boJtf/Q3QtQQZtej6IObyMz/yGQ4r9RjOLEm9KGNOGbR5I6zkh1AmoW40fqXPa9qr5a3V3+TvvLPEe/bTHmtRHo14Hh4mTTKtJlII4jRv+64yGCnYCbayFa/JhSKH7Lm5dFG96DYY0ingHzNJeSmv+UVxxK5YULbvo+e2I1nxEG6aKO70Ub3kJ7/kSn1oMiNENr+Q7TELl8tLSXBtj+ijTWTXvhJ5EuyUtxIHfmaSzD33YuWG0YqBkq2F9UexQ428XyN5KmSbQxoT1CW/zOnhf+BZe7ZvrhLUcnNuBmndBnq6F5/jVMwCzc+y/8cJOjNArckiPDiKPmET0gHLXAU1B4bp6HqmDvrplzfeWA+LxFJkBOnJMZBqhJn9nEPd0Qowq8ZR3OQsv16M274tSYcMUf8qHj729+Pphl85jPv53e/+yXbt2/mpz/9PTU1L39/HiuqQvWMWsMMZHoo2BujYkMZsb56grUxwufFsRf5WR/HAkVVJsnaqnkV9G7rRQjoTMKuYYHlSIKjNq2VJbRvUXjPfI/KMDSuaiBUEKRjbSfpkTRmxKRkwlYC/O5gM2KiRlSGssMEtSBh/fjWaMeC8XwSWzpTOmkArKE0D3z5FxgBk9M+ehGbR3eyKbGD06uW87Y5N/zF85X+2nCKDD6Fk4ZKT+OjmUI6VYccHpWeRoE8vm06F8mY8DARhOVJlFqdAFzXRVX/b35FJBKBII9kj2YxLlyKpMoMxziMgAU/OG7/1h6SA0nCxWGqF1Yy/dxp2FmbUEHwMA/hl0PTmY2YUZOujd3kxnKUNBVTt7yW8tkHi8dyT8NEISk83y5hAiOKy8KXkJjHC81QWXDVPGqXVNM2OMqDsSx7ZgboK1RwpUta8dCACk+Qm7BFsZAUSoWw9I8rzQLSiz6FltiJkvW9Ze2i+ZMEpTfhq/TSNhlVV3xfZSlYbpvcbaYoRsX0nb14rv4MrhjeReVICyJQgXDzqNl+8jUXIvU4ZtfDiGwfGDE8PY5TvBg0Ey9Y4SsT7CTulp042zYjiktQ4r5CY+eSmdx57kKGqgIY6ghqQGeeY/D2bJz4xHdYmCbmTbfAb39JpKONeplhOBZk78Vn03zRWRh49CoOy+0g61Y/wpIlKygu9nelL7zwcu6992ne+c7rufq6c3nfWZ9l5cqzJs9bKIJwUZjhjmGslIUZ9QtcL1SO1CMIaxRpHgwbULID/rkEy/wCE0A1cGLT0cZb0Ud2TAm7mISbR830IbUgXqD0oL+yNUZo+4/RErt9Yl0omD1PoCXbSS/+3MuqN0RuBLPrEfTBdahjbSjZQdxYA16oAmGNE2z+LcLJIhddhTp3gX/tYwWgKMjREdTps9CWr5xybsG9v0JYKd8XWiiI/Chm92O40YbDFiVJGwbcIjYU3cx5Y7+kMrcdhCCJylPq6axv205J/QKixf5mTECD9f0+GRwIQCAgyWSgoODgmEVFZ6KqEbZte4i3vOUgGdycgF/sVOhNq+jKMlSlkYrwKBfXKywpKePh9hDbh33y1lAlUkJPGu5tE8wpkpQeY+3nePCHvYJn9ivkXZ/0DWmwotxjIAPr+gXNo6AJgan6fr6VYcnZ1YcTsqN5WN0p2Njvk8gzC6GpwKNjXFAXlRxoVghqvgpk86Dgqml+yF51BKoj/pid47BlUFAdlsQn1l8h3S+Sn+oWrKqSmCdJlQK+b/Dg4MDJG/AUTuEU/uZQFCjgxtnX84c9f6Z9rAMPSVSPcGn9hZz2KslggLmOyTTHoEO18ZDUeTpDwuUHoQQdmo0DFHkqF1khzpvo8uvu7uTmm69kaGiA3/zmXs477xIAupLd2J49SQSD74Ue06PsGNnDG3ntyGApJc/sX4OUctLPOKgFMFWTvYlW9uzbQOMjDyBME7XBb4ORnoe7dxf2Yw9R+94PU3H1DVyQTKJOm4XQdcZyWXb1drFzz046Kqvos22ee+4Jurs7SR/SzaMLQcVzj1ISihALBIgaJpFcjvhQL9F8mkAgiBkNY5SX4PX1YisKXlUNXmIQ+8ffxnFsrMEBMi88g6VqJDyXwUyKwVSSwUfvZuy7X51yrgFFZVpJKTPKK7mwcQZNts1026Xp7e8heN2bJwPyDkCb4YshpOvitjYjR4YRBYWo02f6xPURrqXX2oyzeyfSsXHb2nBb9kAmjdfXg93ZgaiuQSktR+g6xoWXogQCmFdeixwa9I8hJSBRK6oxb3grp82czZ13PsZNN13JNdecx+23PzDFgu/SS6/mjjt+TWvrXmZUxgl0PYxnxCatEjxZgza6C7PzAdxQFWqyHYRCvvo8tGQ7WmIHnlmIcDII6ZJruOaYbBaMYIyqGz6BdtcX0ZIDWNlhUPwgQXXaDLB6QNqvfAMeAqvybPSBdf6cAqV+WJsaIDvtBnJN1+OFqvy5HUO7lNH7LNroHuzCuQetGpwM+sCL6EObscsP1o7GwBq00T2+7cPEa71AKfrAOoJ7bsUuX4VTOPegolgIctNuwI01og2uR7HGUMd00CI8PDvKb2u2AhC1YuwWzezU/42ManOOdeHE+xWcwrlHDuoT4MVB7RK4ZbNRBjegZAdAmAg7AAGPXOO1J2TrZi+S5M8C4zmB6PaDiKUusc6Rfv7EScCGAUHnuKBj3A8mDmhQF4WgJikLcdwhwjfe+E4APvOZ99PSsodLLlnJT37yq8nfzuNFPpVnqHUYK2sRKQ4zp34J03ZMJ/ZHEzWroBWYGLsNRIsg82YP65zjvy4l04opn1PGvi19tGUMVKFQauWQFTGM+VXsGRM81il4+xzf67tibgUVcyuIVUVpe7YD1/awsjb5ZI58xiK5KMMPdv6UwcwQpmayvHwRlzVceFJJ4eJgIaZikLLTRCbu85H9Azz45V+CoXDd1z9IWZlvNJ2y0qzr3cCS0gUsLpv/csP+r8f/TabrFF4zqAga3ePfYZFINmt5VptpehUHQwqW2QEutSJTyL6/JDzPneJx+noih8cLRo4NepYcknmOyZl2kDLv5H2FbSRr9CxrjCxjwqPMVelTHBKqdyBQmNmOwduycYoOIfn7dw+w7d7t5JJ59IDOSPsIfTv7mX/FXKoWHL/nlaIq1C6poWZx9WSi6UuVC02uzmLL5AUjS6FUCUjBsHAJS8G5J8GWRCiCwrpCds1UeCEwzqjiMqa4HGhoGlBcBP79Xupq5ITHGfkwgUP2i+089A9V4tplxCtjhA9RqkbLo2iGNmlFAb46Njeeo/60eoQiOLtrnGz7eva7WYYaqhmY0Ui8bB7OnA+itj7m++QqOrn6K8g1XoPUY3jhakI7foQbacANVx1UDUgHhIpUNNx97ZDLQrn/2aSDJndfupKxiMm0Xe0EZyzBKg6yWctTbWS4Nn9QTaDW1BH85OfRW/cyag/x64YgexvKUHAIywwr7QCXp0y+89RjfOADn5xyTWfOnMt99z3Lu95+Pd958Eu8X/s0F5929eS/TzhcTdnBdqONWGWnYe5fjedkkFrI9yUTKl6w/LA2OSEdlFQXoR0/wemZh122AqviDBAaRu/TmPseQMn2g2piFS8iN/3NyEAJsu8F3MRuOKTg9gIlaIkd6H3Pk2+85sj3iZUkvP3H6MNbcPUIarINxU6DauBF6/12vEwfRu9T5GsvJvCu92M/8yTOhnXguqhnnoNxzvm+r+AEtJHtKNlBv8CeaAuWZgEyP4zR+9xhZHDMgJgh2WKfjlVZR1V2C4aX4cV0DQ94i+jbNp155x/0KdMVyE3cyMEgrFwpuftuhVBIEo9DPg/d3SaNjRexZctDwBf8z8KDe1oVBjOCOYV+8SdlMS1jxTzVCUtLPdYPCAqMg/YNQkBl2Ff4No8KSkPHVpBuGRQ81S0oDR4kXpsT8LMdCrYLybzAQU582ySaEJSWSjwJWccndsE/z1/sUNgyKCgO+MTvml7Bi/0KKQsaX9KtZiqQtAWWe/icRnKCtC2pnRoUT4EBo3lBygLz2G0FXxGxWAHj46Mnb8BTOIVT+JvEvOJZNKz4GHsSbXRqDkasjPpAMa6jnJSuaBPBLNe3KkgJj18Hx+hUHepcDR3BgOJyl5ki7qmEtrdw881XoWk699zzNNOmHexq0RUdCYcFgdme7dtdvIawPJuBzBAxc6r6MagFcKTL8L49NCRGUGYclA4KRUEpLcdt3oNMJdGWrkBbvw5n13ZErIAIkuVZi9OvuJ7g+z6MCPs//lJKxsZG2b+/k64Na+n4/W305LIMC0jl8wyOJWjLZkhbWXJ7tpPLZclmM1iWBYCiKBiGgabp6LqOrhtojoOWy6EHghSHwpSGI8wqKafIdigtLaf23R+gtLSc8B2/pWhsDKO+EXdfO86uHWBZyHQS+87fIfp7MW9+J97AADKdRCktR50xyw+uVVW0mS8vnZRSYj14L9bD9yPTKWRyHK+9DVFdi3bWOcjeHpyWZmRfL6K+icDNt6AtO80/r/IKgh/9NM62LXhDg4hoFG3hksn6Zu7chdx995O89a1v4Jprzue3v72PWbN8le+5515MIBDkoYfuYfabzkHkE7iFhwTrCYEbKMPoeRp1rHky6MyN1JCrvWSiK24vbrQOu+IsrIqzXnpqR4U2ey7KTZ9BWfNVNBmGaDlKaRme8NjvSDritWQGn6TQKKEqVE9YP3IQ3gF44SoyCz6O0fUw+vBWpGqQbbqOfN3lU0QNxzS3xE6kFprq2auFQLqoyQ6fDHayaKN7MLpXIyUHXys9tLG9aKO7ULL9GH3P44ZryM56x6SVA0LxA+LKVoCUxJ77JOMRySNluzE8hbpsAQCVOYvWiM1jwQdYbq0ixCsTeNYqSahV4OUqscpWoY50o+7RAYm37xr0e2fhnefhTj+uSwI6ZG+U2Isk2l5/zeDMkDhzOSmsVuc4/KlZoSIskdLPjkhbgs2DkgUlgnfO84ieQDbmjTe+k0AgwMc//i7C4TA333wVn/3sl/jEJ76AcgQLuaNhuGOE7fftIDXgb0gpqkJpUwmrWhcSEiau/5XCBdQuifmIwF4mkUfJzjgaVN0XRg2F46Sf6aNQc/FmNCBn1qMWRCjNSLYP+XkdoUOu+8zzZqDqGvu39JAdzWJGDDKL8zyuPQNJKA4UkXVzPND2GIncGO+efxNuzmW4YwQn7xAtjxKriJ6QRWdjrJ55JbN5sX8z5cFS0l0j3PvVnyNCCos/fekkEQx+17CddmlOtJ0ig1/vCZzCKQBs0yx+FRzDEpISTyUnJA+ZaYYVj/dm45O+qX9J+Mrg158MdpDcHhjnBSNHEIEq4T4zxQ4tz/uzBSeFEJZI/mwmWW1mMBAEJNwdyJIRHmdZISqlRhaPLVqeqJnknbk4AoHruLQ804p1wPt24sd7dP8YLc+0UjazFM08sfkJISZtIV4KBcGNuRilUuVFPUdOSGa5BudbYeY5x5f8ezQ4SO43U7RpNiDRpcAVEgf/IZtFUutpVHk6Z1hBzrcOPmkHW4fY9dBukgMppCcxoyZ1y2uZce40FFWhsLaAqoVVdK3vIjuWQ9VVcskc0bII9ctrsdc8h/anO7h4ZIhx4ZEJGmRWrqT0Te+grugsUgWrUPLDuKkc9o69eJvuRcRieHPnoJcs9XfglYlqxbVQsgPk668ELYRSXgGGCZk0RCK0NlQwUBKjflcbqqYhQj6pXSRVNuo53pCPTFFaC8OgecEM7g+WEhGSZTZkFEkKD0MqtK5/kWRynAsuuOywa1pUVMxvf3s/H7zx7fzH3f9G12A7t1z2EYRQSA+nqV1SjRHSUUd3o4/sBOlgFy9EGgXoA2tQnDRurIl8zSUY/c+jD26cVO0KJ43e8xRqth8vWI42tBl9cD3qWDNO0XyCu38OCP/1bh5z/xOMpPu4N76IHS1/QiQHmO+FuSReRZURBKEg1QDqeNtR7xF98EW0ke3YBbN9RYrQcEOVKLkh1FQ3Tnw6XqAYbbwVNduPLJyLeeU1GFe80Z/zEYod4ebwSfGpxaFUTYSdPOz1QQ3Orpb8sVmwmyr6w1WkLGjLC/TEFrKjfTQs9dUHroSxvOCs6oMp0BddJBkZ8di4UaGvT6JpgpkzJfPmncf3vvc5crkcgUCA3gzsSwoKTN9P19R8xUR1GLpTgq5xv4XupackAKTAnQi08CQMZUEVvt/YgdfbHnSM+6rgDf3gSUHc9N8zbvkBbuM5UBVBXUwynoesK9EVAElnUvDz7bCoVPCueR4RA3YMC3YOw7S4JDDxM1QckGwdEmQdGMhAefjgPAey/msLj/ATEjclQU0hZcspC4GkDRFdEj7JXWaapuG6R2ClT+EUTuH/HowAm+pq2GLkySHRSNDo6Nyci1N1SA3Yrdhs0nOMCI9yT2WpEziuGnGHlqdLdZju6mgTz/1qT6NFsfj9use5+6ZbqKmp5ze/uZfy8qmb/bOLZlBgxuhJ91EVrkAIQdJKYXkWS8sWnpzrcBQYik5psIjWsU604lqGoxE8IQgkxxBjGnEj4j9sPG+qd7/ngqL4ZGkoTOA9H8R+9imczetBUdAuvgz9zHMniWDwn9sFBYUUFBQyd+5CrKo6rAfvxRsaQA4O+AHCMxZgXnUtxoWXolZWAUz+nh9pXWGtfoTc7b9CnT13Sl3gdnagVFYRnqinMqsfxU0kkJk07t7dfndZSSkgUaprsTdvxN6+FSUU9m0KdMO3g7j5nSjRV/bq9NpbsR55ABEMotY14OzZiTQMSAwje3pQ6xtRauvxmvegzZmLfkhXE4AIR9BPP/Oo49fXN3H33U9y001X8Ja3XM599z1DTU09oVCI8867mIceuodPvOVC/zPynInwNR9qth91vBUvUIITn+0ToqkOzO7VpJb9A9lXEbjm1Z2OnX0T5v7HgTG8VJq9psnekum4gQiKM86oNcRgrpcFRSuI6gUvO54brSc79/1k3bxfyx1PG7o8WEhJLYTw7MP/HQ9UA214C8G9v0FNdaGOt6LkR7HUAG58OmpqH9rYXiQKbqQBJz4DbbyN4O5bSS3/0uHEtBC4oXL67A6GjAw12Yn7RXogodQtZ786yIDaS4M77WVO3kLN9OIsMsj3VmG8oCD6i1E6SyAN9ky/a8xYI9CaFdLv93i54Y4IDZyF4Cw8efZcB7BjCEayLnMKBbVR6EsLkrZkOCuZXwwrKk78mNde+1ai0Rjve99bqKio5tvf/iqbNr3ID35wKwUFr7xR4OQddj64i/RQhsK6QhRVwc7Z9G3up6W/g/kzp1oiumWgdQuUHnBPIErFCBkUr5hGvz6NaAGo6itzMZqpMeuCGTSeXk8+ZWFEdH6086d4o5JpBQ0AxIgS0oJsGdzBpp3bSD+ZnFwv6yGd6oVVzLlk1lE5gKNBVVRumn09USPKo08/wJPf+gPxymIu+rubyBhH8BCX8riI+P+tOEUGn8LrDonkaSNDDsmMCXVCXEJYKGzTc7RYoUnVwl8SruuiKK8/GbxHs3jRyFHjaUQmVNKVSPaoFs/rWa7JH32XOo8kM2GloL0Mob5fcXjByFLiqRRJlTHhoiDREPSoDhWOShCFSk9jp24xnHcpkRrpoTSpgRSR0siUAjZSGibZnyLZn6Sw7vh2wo8VIRSuzke5JB8mLyRRqaAc56aB53j49leHPwwsJK2q3yZZPKGEDktI4ZIXcG0uwpvycYryILqSJOwhYhX+Z7Hj/p1kEhkKauJ++moiS+vTrUSKw1QvqkIogrmXzSZeGaVnay9W1qJqQQW1S2uIkCZz1++RloUxax6lQuCNjyGfWoNZMxvOuQAUFXvUIXfrz3DbWxCKhnQd7CeLUK9aQdRYi5bYjk8qCpziReQargRAW3U26oyZuNu34rml5BWBZ9uouTzKnHmIYBAXiSslOeFbt7zUcGqNliUzQcAD4EocO0mbluPB9U8SDkdYsODIKc7heJhvfONH/PCb9dz25H/Q3tnKe8/7NFUN1TSd2Uiw48+Y++71Q+EAFJ18xVkkl38ZIT2kEQdFRRoRtNFmtNFdeIFStJGdaOke7KL5vupBCN9eoedptJEdCOngxCcKJT1CQhj8977n2BrcR6lQ0aXHI2N9tOZTfKJ8NiW6ifAspFl0xPMAUMfb/aJdNZDSRSo6wvNV2CI3DPHpCDuJ1EJ4+sGF2MvteLuRWlANhDWONA4W40o+Qb581RHfc0GdxAOe2S/ozwiCmuS66R4PrnmIViOEVX0W3UkYtwQNcclZh9gphEJwyy2S8893GRgQhMMe06fD7t2n861v2WzbtokVK1aRdfzQtZStIKWvMK4ISxpifmGvKLCwRPJQh0JZ8GCg2lAWogY0xiV7E3B/u0LnuEAIqA5Lzq72COoH/+5K6E+DKiSywFeM7xoWDGZ9Qtl1fbI2pEHrmCCkQ5EpcaSgMuLbPLzQK7i4XtKX9knlgHbwfIXwCWEpYdeI4IVeMBQIG1AfhYvrvSOGwdXHYG6xZH2/bxUR0iEx4Sd3WcNBsvlkQVXVU2TwKZzCKQDwiJHmBSNLracTlQo5PJo1m9sD43wiU4iGYKuW5zfBMYaFh4lf/73gZbklG6fpGGvoceEnFby0Xhy552HuffdHOX35Kn72sz8cFgIGUB2p5I3TLuee1ofYnWhGINBVnbOqTmdl5bKTcBWODiEEZ1afznNhyZa6CpRAGM/zyBeFmF1SRpO5CqV8LV7XPpSGJr+zxbbxBgcwLrwUEfJ3BZXCIsyrrsW86ijZAEc4rnHhpah1DWR/8G3cRAJtzjyIxrGfeBSvo43ghz6BUlzysuISdcZMRCyGHOhHlPvqNZnLIdMp9KUHLa+0Jctwdm7D29+NzGWhqASRToIZQCkpwWltwUsnUa+4BiUQQKZT2C+uQZSWEbj+ra94Pu7e3chUEqVmwsvV83wrCVXF6+tFrW9ECAVUDXmCz6eysgp+97sHufLKs3nb267m7rufIh4v4PLL38gnPvEe9ucLmB6pQ0u24cRngFDByfl5GEYMN9Z44KrhxmagJbajD24kX3/FCc0HAEUlO+sWnILZ6IMbSHpZWsJxtFAVkQnSVEpJwhqkK9XG3MJjtGh5abjdy710dA/G/sd9f2CzAKvyHOySxRgDaxH5Eb8WlRIl04PU4zihSkK7foaSH8aJTcMz4pg9T2AMrievh1CTneDmfNu4cAUoOk58OlpiN/rwVqyqcw+bg1V1LoH29Rh2jrwIEvQkIjOKVAvIajF0mcOUR7d20PvXYnbcg5reD4qGM2su6cU3oW+rJHCXwFkhkRMltVcq0fYIjKcE2WlHJ1hFbghj4EWUbD+eWYxdtvyYLEBOBP17BtjxfIqRdAFdSppoWZTamgIUTWHfOGgngRK46KI3cOedq3nve99MPF7AmjXPcNllp/M///O7o66bDmBkX4Jkf5J4VXxy3aoHdILxIN37epmVnYZ2iExX2CA14FVQKE1xSVFIoTcDNRN0g+vBYE5wfq03RRV8KIyQgREySFlp+jODFAamPjOiRoSusf1seGojNYNVk+vlfDLPvrX7iJSEaVh5/L7KMSOKs3aIJ/75dyxatpyf//xOWnP7+Pn235K2M5O2FGP5cVQRosiczbjld1n+X8UpMvgUXndYwH7FpvAl/sIRqdANDCnO60IG/7XYRHQpDg5ykggG354gJhV2aRbXHGGzK4/kyYnFQ2pCbX2uFeJ0O3BEC4Vu1SElJNWefwwH8IQgIiExYY+gAwEpSAqPnJAgfRJVKALpelPGk65EKAJxJFblJCOAQuA4N2qTA0k61nYy2DKInXOIlUWpWlRF5dxyjJB/r/WrDhJQ8Ym2A2eiIFDww+p2JEZ5rqsXOZSlriPLnBGIF0dIDqQobiya3HEMFYXIpy32b+2hepGvEtEMlfoVddSvmBqgYD2xDpkYQZl1UCGixOK4w0M469dinHOB/7qH78dra0adMQehab7P2752Uo9uh49/CiPbjLBTuOEq7JIlk37FiqYR/so3yXztH3B276J641aiFy5hdPECypecTodq06FYDCguNZ7Os0aa863IFK/oXtWZtG8RdhJteAdmbgA1WszOJ//EsnkzUV9mt7V6YRV/929fZuGdS/jy9z/Fdx//R37+szspDPRg7roXqYZwo42T45u9T+MWzsGqPn9yDKd4Een5H8LsehgtuQ+cJE5sBnmnEm/LRj9NurgUNTiOlk/gBGvI7+3F6hoGKVlbpbKbDLOLilCDJRj5AUpch535FGtSA1yt+6oMu2z54SfgOT5ZLaWvAk7uQ5px3HA12lizH3qnGAhrDC3VTb7qXLzwsSW/OwVzscpPx+h5CqmH/VCP/AhuuBqr+oIjvkdTfELy7GpftRvRoTAAf9z9CItOO5+ZpSZZB86p8TizWlL+kg4/IaC+HurrD36R5sxZQCAQZMOGNaxYsYoNfYLRPDieH55me9A+JhjMSs6qktREIFwr2Tsq2TMqCKoSWwo0Ibm80UN6cOsOheGsry5uGRU8u1/hTy0CXYWYDsvKJbrqq5dbRgVVYclI3vcHTtsSV4IiBMM5iRD+XHKOIKlAWJeENQhpkg39Phkc0ploW56qWB63IeP4x43ovrVExoaYKZlZdOQfE0XAjbM8NEVh55BgMAtRQ/KGRsn5JzE8bvIz1TTy+aOnoZ/CKZzC/w1k8Fiv5yiS6uRzN4BCvavRrlm0qTZ1rsY9ZpKU8Jjj6oiJANpm1eI+M8VHM4XHtFle7Kko+JvhxsTrN//slzz92S8w/+qruO27t2GaRye4zqlZxfSCRnaPNON4DrWxamYWTEOdEFZIJEPCxRFQ6qkvK1I4VrhIulSHTO0stBoDc7wHhnrQhEplpAS1oZHNdpAzrnkz2d/9iqHNz5HIj2FLBzF9BhWrllDzyoc5KoQQyMF+pJVHP+NshO7XkLK0DGfXdrK3/hfC85CZNOrs+ehnn4taXTv5fm94CGk76Gedi/3U47i7tiMV3x5NW7IM/YyzJ1+rrzwTt7UZ68H7kMlxX+kcCKLOnIV0XLz0OCIaR+i+ElWEIyjFpTgbXkReftUk6X00SNdFCHGw9iwswhUKuC4yk8HZvQNvbBTSafQLT8zvFKCkpIzbbruHq68+h/e+98385jf3ceGFb0BVVR589GHee/XbCO76Gdro7ol3KLjBEqRZ/NKLD4gjdk0dfnKe/59yFOpD0bErz8KuPIu+dAe50Q0UGgWHHEoQUEMM5wfwpIsiTt76UEvsJLTtRyi5ITyzCDXZQWh0D/nay8jXXIzR8yQi3eOfhlFArul61PwoSqbX9wgWCl64Crt0GfrAevSB9Qg7ifAc3Ggp8gApPTFn4aSPOA+7fBXFuXcyZ+zfeLGgi0h/HHO8lGx+DgNOP6flZ9BgP4PipXCjDdhlK3yBBr7FWWjXT/1Q61AleDb6wAuIghHsoi8iQ+FJItifBHiFEr1ZkHUlR/K8UcdbCW3/D9TUPn/u0sXsfpTM3PfhFC04adcfYKB5kC1/2obhRVAKCsg5YLWPYGdtSmeWk3YEMwu8Vx7oGLB06Wk89NAa3v/+G9m4cS2u6/DGN57Lv/zLD3nrW2856vtc28XzJIo2dX2lxBTsmIvsdSCq+eSvDWq3wJ4ncWuPPN6xoCgAVzZJ7mxW2DUCuiKxPUFjXHJR3SvXvgHNJKKHGcmNURQ4KA7LOXncjIs3LCeJYPCD5PNpi+7NPdSfVndcdhHZbJYvfOFj3HHHr3jPez7Kl770TXRdJ+bGOb1yD+v6NuJ6Hp4UjObnYWhv5q6W6TzY4a8/rmw6+V1+fws4RQb/jUHit6lr8Kp9Uf9aoANxqdKrOJQcQghbE56Qr1eQ3F+LTYQByIkAsUM/c1tASB75HrjHTPKomSE6EWrWozrcFhjHQ3KmfbjXkyl9gtPFv7ciUkwSv3FPmSRCBxWXYk+lxJtQyhaHKawroH/3IHpQR1EVpCcZ7x+nqL6IWPnLe2uN947TtXk/ic4EwViAyvmVVM6rOCxY7WQiPZxm4x1bGOsZIzeeIz2SpjPn0vJsK7WLq5l/1XxKmooRCEqkSl565IVk8pEnISBhm8jxtJVAhDzUuRG65kUZ2Zpi7p+7UIWYJIKTAcHOWo09S4oxVYWskeE0K0DwaHm0tuVrcV/6ANQNZCYDgJccx921HVFaMRkAIoRAqanDa2vG7k0iFx49rEVrnEbkp7/F3bSe0EA/58+fxWMNMdYIlx41jwfEpEKhp3BnIE1ewFWHKNArXY1ONTdR7G1AyQ1iGXGkYtK6vY0PnT8NbWgTTukh6gk3jz68DSWzH1CosNO8/8wuLmh4O2/+l7t5000Xcde/f46VSmqSCAaQehQpNPTBDVPIYACnZAlO8WKEnSK08RuIHc/gdG9i4oIg93ehlQuonU1q9XYyLXl/seNaNFc5uI0aWo2OZxZiFy9CH9lBKDtCe6INKmaRbXyTX2hPTkZi9D6D0f0o2uhu1MQe1Nwg6lg70izADVfjmYWomT6Em0bJjWBVnEl2+lsQdsr3OBav8HumqGRmvxs32oDe9xzCyZAvuxSr5qLD07Glh8gnQNGQRpywzmQhk8mkWf/ic/zTP32Ldy87/gJW13UWL17Ohg1rGM3DxgE/BK4zKRjJgzZBxqYthbOqHQwVykPw4YUeL/YJmkcFUcNjcSksLJX8uUUwkBHMKpBsGPDJ1CJT0p8FxxJIKRnK+SEdi0ol/RnBun6B6x181pWYkPUkvWlQhcDyfGVu2hFUh/35CAEHznZ+saQkKGkf98dVha9UHsn6yuRVFb6CWUo/hK5jHLYNClZWHrnALQzA++Z79KQhbUNJ0C+UXwuoqobjOK/8wlM4hVP4X42ckOSEN0UQABBAYAEZ4dGlOvSqDjUTRDD4G9eVnkaHajOguFQcg13EHMdghqOzS7Oo8DR2fv8/WPOPX2fmh97Nd7/0XUz5ykrHqkgFVZGKw/7eozjca6bYq/k1RrWrcZkVYf6rsPbqUmz+GEjSrtl0KTYDSowFwUJKYnk0RSeqh+lQHTbJHOcsWcazuV00P9eLkdPJFxewtzpEUc99vLe4kIbYiTMmbncXICaJYMAPie3vw+rchzZvAegG7qMP4O7aRuD9H0WJF5C/+y7sTeshl4FIFG3uAkRpGbgOan0j2oLFiOBBv2URCBB4+3tQp80g+5PvgaL4nsDxAtz93ZDLocycizh07WKayGwGmc+/IhmsNjaBbuAlx1GiMURZOaKyCm/7FrzRBF7ffj/UNhLBfuFZtDnz0eaemN/m9OmzuPXWO3nLWy7js5/9AN///s85//xLue22n3LLLR/AXfYl9OEtCDuJF6pETewksO++qbu7no1v/1V+cGDPRskOIFUTGShB2CmMrscw+p5FeHnsogVYNRcfojCe+Lgyvej961Cy/ZhGGKEDxtTOOE96GIo25W+vGlJidj6EkhueIHYnvr+5IYy+Z0kt/QesilVoY60gVJzCObiROgLtd/JSSzGnYLav5raTKFmByPajpLsx8kO4sem44VpQNNxQ1VHm4uIWL+SN7ldJdf6BFqUbpyRIea6Lm7aPcnlLK6HQatyyUqQewul9hszc94NQMLoeRtjjU2pmR4/4Xs52M4hFE0a2k1cThV1Q2IrZBU7RXL/2P/DZSo9A6x9Q012ThDdSoo3tIdDye1LLZk2xEXl1H4Fk37p92DmbxXUKXV6WZj1GULMYG3Ho67WZVakftTY8EZSVVXDHHQ/z5S9/jl/84j+YNm0mn/70+9iwYQ1f+9p3CQQOLy6j5VHMiEl2NEuo0F/LW65Ne3cnyZoU4WGTGVsaKAsWE9KDOI2S3Js8jrbcPFacVSWpCLtsHRSM5aE+JllSJo+p/tUUjTOqTuO32++kfcQi6kZwTY8hY4QavYoKu/Sw7lzN1LAyeTzXQz1GOXZ39z7e854309y8ix/+8Fauv/5gVoqpGrxtzptZXLaAlkQbzaPlbB9aQGkgRlEQsjY83OHbwL17nncs2Y7/q3CKDP4bgYtkrZ7jOSNDQrhUehpnWyEWOebfPCmsIFhlB7k9MM6g8AnhPJIO1aHR1ZnlvD7afV03yOWyr8uxD8Vsx6RAptmvOFR7GgKfpLWQLLUP/yXuVxzWGjlKPHWSXC9wVfYpNk8YGVbYwUnFxwHMdA0qXY0OxabR0zFRqHBVBnSXEiAhPN86QsBF1iFBaQqELpvGmOmSbB0lmvZASqJlUWZfPOuw3ctDkegaZdMft5AeTmNGTMb7kgw0D5IeTjPjvMMTBTwk7apNv+ISkoJZjnF0QvVl0L2lh2TfOJqpkRvPEYwFEYWCfDLPcEeCHQ/sZNW7V1IT0llkm4wYLpr02yYlkiQ+UZrO5CnpyBCJBhAJSSoAzQuiVG4KEtk+ipN3yMV07l8eoKNUJZe3UYuC/GcwwV4lxC25gsM+BwClphahG8hUEhHxCVjpecjxMdSzzpm4GP515qXqWyF8KaT3yuSfoigoy05DB96IpCSb5TvhEcJSoc7TKXd97+4BxeFuM8UKK0CF9JnG050g2/Q8ne4QtfYY+XAFXaEYhTv3MjaWZeU0v13tABks8qOEdv4X+vAWcPNo423gZHDiM5kVKeKpzy/k6u9v5Y0f+yp3f2oFE3kkh0xWRXjWkU9ECKQRJe/UEhjej4iUgRHF93lO4CTGSabjODs3oxWEUPUsipUkLsBJhpCtGxFz43iROvLBMjKDWwmWziW56D3IwFQVitHzJMHdP0d4Dup4G4qbRaoBf13gpNBGtuIUzCW16LM4pUvx9DDaWCuRzd9GsZO4oUrytZdgl618+SRpLUi+/grydW8A5BEJZG1kO2bHfWjJNqTQsEuWkG9846SH8rZtm7Btm9NOO+OwQJ9jxdKlp/GnP/2O8byvpK2NSirDkp4UpByBqYLnSaoPCVUrDsJljZLLmFo0d6d8+4pRCwazEDfAUEFkBarwlbf7xgU1Ed8HeF6RZMewQBEQNiQjWV8VHFWgdQzynn9OmvDbu2zPt30wNLisxL//S0Nw02zJH/YKWsf8r0zM9BXHwKQdhBBgqv6mW2dSsvJlOhCFYMr5vlbQNA3PO2UTcQqn8H8dMalQ7ml0qQ5x9+DCeER4xKRChaeREC5jwiOvWgQQlHgqMXn8YoYgCu/IxrnPTHHv7b9izT9+neWf+Thf/n9fY5574iFw48LlF8Ex2lWbSk9FRdCm2vwqOMaHMgU0nkD3X1p4/Do4TodqU+tp5PDoVRxadI+oiFI0QX6rEmwh6U3385i1F+P0hZQG/Wd7k5TsTjTz6L4nWVW5nPbxTlShMrOwiWnxxmN+bopwBDx3yrNWjgzjDQ2gNk5DrfeJR1lRibdnJ9ZTj4NtYz/9BEp5BaKoCDk6ir1hLea1b8a8/KqjH8xxQAiUiiqcHVvx0inUskpkLosoLkEpiJPHZbeSoEfJYFh9zK2eT9MRrD1eCnXmHPTTz8R+/mncgX7fT9kwfIJaCNSSMpSyckRFFbKzA+u+P6POnD0pSjherFx5Ft/73s/48IffTm1tA+9+90e46aYrWLfuOVauPGuKAMANlGD0r0FLbMcNVSOki5LtxSmYjV26DKREH1iL2XEvaqYXFB2raAFqPoE2shVpxJGKjtn9KFpiJ+lFn8aL+J15WmIXoZ3/iZLuAUWnRnq0lkwjJR0i4QaEEDiejeXlqA1PQ3mlTf3jgHAyqGMtfu12yP3mmcVomT7UdDdW5dm4BVPD/9xghf961zpIikrpq37dvH9uQkHYWYRroQ1tRGQHsOrfgFM0j5dCH9yA2f4n1FQ30azCP2ybxuayy7Hi/cxN3Elpvh3F0SBpwv9n763D7Lruc//PWpsOzhlmlDRisC3LJENMMcSOYzvgMFNDDbVJ09v01zS5hds2KaRNgw3HASd27MQQM8u2bFlMw0xnZg5tWuv3xx6NNJZkS44cuFfv8+iRNHM277P3d73r/b5vwserWYc9eD/W+NOoWHX0t5WC9CI4EBg555WsW3oI69dhdkHQDhg+VvAdzIp7UE0l4rsjC7hS29W47a+M7u/CMOb0XsJE08EaWAiCVCvmbA/GbDdh+dLDjuPFIPRCZoZniZfFsYXmCjlMo8qw0y4jXww4NznLtWvKqT7BOZi2bfOFL3yJdevW86lPfYCGhiZuvPE77Nmzk2984ydUVi4cg6Sqk7Sc1sy+B/bj5l200uwf6GZCZsmdVuQXNVNk9iRp8xq5ZN0FVJ1Re9zBcUeCENBZDp3lL44MX+kupX1fM1uDnXTLPixM2hItXHfKlYzao7h5Dyc511GhNaWZIg2rGo6ZCL7nntv54AffRiqV5he/uO+Idhu2YXFa7VrWVK/l7zdJquPQOufEl7LANjTPjAl6ZyNbuP+XcJIM/iPBHXaeW2I5DAQpJdhheOyL+7yhVMYZ/kub0vu7wEYvzpQIecguskv6WEBnYPG6UtmLIvyOBh/NNtNln+EhECwNbVYE9hED6jo6FtPVtfeEbfvFolGZnOvG+V5imqdNFxtBrTK42E0c8dqPyZAZEbJULSyuK7XBhAzJyvCwQJGklry2lOaH8Rn2Gj4aTQzJy90ESSUZlyFxLVjhO9QpiUIzJRQ/js2ys92j8JZmzMkaVveHvDxr09RZRzxz9ClDrTVdj3RTmCxEKty5Aig/WaD78V4a1zSQrDr4Biui+GFshs22SwmFRNAaWry+lD7uQURvzzhDKUGf6SKXJqh1JdUzUUCCk7LJjeUZ3z9B4+oG3lnMMCVDtpseRRG9BFtDi+bQxCvmkSEEc6rqVEkzkYaZzgyV+wtM9WfZcXYlu2oEeRni1tg4GYucEXBjPMeK0OHcI6i0jaUrME8/A/+RBxHxBFgWOjuF0dqOvTHy+BJlGYwlS/EffwRRXoGYI4XV0ACiuhrZcXxpDAaC9tBaQAI/Y7rMSoVCUxSaf09k+VihkkptsCKwuaFUxl1uLwOJMgwrwanTk1h33Q/A6cvbkbm+eQWH0/srrNFNBJlOjNJYFAwyZ3/gVa0lnWzm1o8aXPvlHVz1jw/z40/XcO5pc0nSoYcICvhV6573GAqT5ajpShKNJQRRC5wiRm52MYXxHEa8BdvqR5YmwXBYLVL8Rhn0zozTMf40fsN5jJRmsBP1rF78ysOIYEIXp+/XIAy0aSOUT5hsRJYmEaFLkFmKCFyCimUUV74bhCC+69s4Pb9EW2mUlcLI7iYxs5+CVvj157zwhZlrgTzsek3vJbH1y1GIYKIBoQKcvtsxCoPkTvkkmAk2P3A3Mcum5QffpWDfiLnhTKzzLjymEJkDWL/+LL785X+iNNlH2m5j2oX6JCyrBNCMFSFU4pjUAdVxzZZxiRVoQhURwRCRwILIt7cYRKSuLaEUCprSkfKgKRkRwHuykPUEQkNtQlMZ0+T8yCYjULA3C1cv0pxziCfyuhrNooxmT1bgK2hLK27rFjw6KOEQwlrr6M/R/M9+1zAM46Qy+CRO4iQwEVzoJfhubIZ9hkeFMigIRU4oLnWTZJTk57FZ+qRPUSriWhDXBksDC1fAqsCmVh07MVyjTRp/cT+Pf+jPuP4t7+SfPvYP2OFvV4dvNT26DZ/OQ4LpUtpip+HzmFV6UWTwdtOl1/Dnw+6qlEEKiYemz/BpUCYBmpxQrPEdeme7mPVyLKs4KDYQQlDhlHPb/rvYNLyZUCsEkDDjXNJ2AVcvuux5CWHt++jZGYwlnYhMOWqgD9nYHIVx9XaDBqN90cHtSYkoryB48nF0GCIbm5BzwVGirh5UiP/Q/dgXXHREFa/2PErf/xb+Yw+DZWM0taBGhtGFArEb3oSanib78F38MPcIW9MlwpKHrjWobpvkNaObOavhCLZXh0CYJs4Nb8ZYuhz/mafAdbGqqvHuuxujtX2BSlk3NBEO9KKGBzEOeAy/CLzqVa+jr6+H//2//5IvfvHrLF68lG984z8488xz5zaksIcexB74DdLNIksTCL9AmGrGqzsbt/1VaLsMc/xpEtu/CspDxesRyiXW/XNkaRyv6VK0HYkrVLwOc2obzsC9FJe9BVRIbN+PkYURgorVIASm1qye7WWL4TBlJiJSFUFNrJGW5KLnOZrjh5bWXEDwc6wbdDAfZHwkBNWnEpSvwJzcikrUo4WJURyaz7Hwq9YhU82Y03sQpUmE8ghTLRRWvPuwQDtzbDOJLV8E5aNSrch8SJxnOSM3iRyfxjCGwRLglyECA1kYwx57EkIXiiNznsUpzNlu9HgMv+7siMBVUQ2jqtIUb1DEfygx94FMPo7ReCdhZR3eknKQGlkcIdb9C4KK5YTly5DjCqNbY04ZaClQjRA0HxBHzIXonSBIU2LHbYrTRSBOQoScbUyywR9janaaM1pOO8xe7UTida97C6tXr+OjH303w8ODbNnyFFdddS7f+94tdHQsFEctfdkSYmmHZ256lrG+cfJ+gURFnHRvGt0AegPcPvUQbj28Ofmal26njxGBG7Dvrv2cPrWO05vWMSWnsQITu9dCVkvqVtTRv3mAWNrBsEyK00WclEPbGS/8TJmcnOCv//oT/OQn3+NlL3s5//7v/3MYgf5czHowWYrs6g5FmQ1Dec1ESdBWduKt3/6Q8Qcy7DmJ58OUCLnPKZCaUwAA1GjoMnzutguc6scW+Hn+McJE8Co3zTl+nEEZENeSRaF1Qo/LQ/PD2AyP2kWCOdOFO3SeRaHFGX6Mam2yLLDnt7l48VJuvPE7J2z7Lxb7jShAztaSGgQuGlNDR2jjHOH8JLUghqQgNKlDbCQKQhHXksRRbDdWhA4fy1eyw/QoCkV9aNIZ2mwzXX4Um2V8TnG8xXJZ78eYlCFbTZdmZdEoTKaqLZ6uDVlSSrPEe352aLtX4LHBEYq1krTl0aRMKpVBoiLORPck04MzWFUJsjIkoSX32nketos0KYsybeGj2W/4/DA2y0fzFQeVyi+AQRnwREWIO+hSjEnCMpNZU1JwAmomwbBMwiAkKEUFTGfo8PnZGp6wSnQZHlXa5CI3wdfj02wWJmFR4/k+wjJJxqLwK1UKWLSxg2RVgjvrCmRTBjpmUGvZxAwLX2n65tolj0QGC9Mk9vq3YrQvJtj0KLpUxDj7XKyNFyDrI8miEAL7slegBvoJd21HxOJoL2oBdK58JTJTDkTPjnEZzj07jOftIkhriTNnDbLb9JiVikoVDawsoekyPG51cry5lEEgONOPc/p4yPi+uxgua8APC3x52y46WqpIO27kkStEZA8x+igqVgVmPCJjhUA5VUh3IvJISzZRlozz0795O2/83He47vM38cOPjHLpumZEUCCoWotXf/SEagAtY0yPLsGvrsKUWbQ28FQd7uw4CA8Vr0bbOZSZQFtpWg2Ha0bHuL3cYdfsEIH5DGWpZq5edBmrqpYdtn7pTiKL46hYNUZxFBG4GF4vIighlIs5Y6Ji1Qg3i/CmEUEBe+hBVLwOFa8BIIxVYUzvxen9FX7N6ZEH2osIqbSHHkCWxudbCjWg7HLMqR1Y40/jysU8dfNPWZFMY3guqlDA/dmNhF37iL/zTxBHaD87Ek4//SwAdm55lI2rWvnFfkEpBDcUTBYj4vaaJYqqY5iPPL1Os2kYJkuAgFIY+fRWxjSmhJGCoMrRlALoK0K5A8srNfcNSAZzkb2D0qC0RkrB2hpNSxqG8lEwnhtGJPINyxXlz+k6TttwWu3B4u7UGtg0HJHZ1XOnYjAfFYMrq/4wikAhJOoYFP4ncRIn8X8/TvdjGFpwn11g2AioUAZX+inO9xI8ZBd5ynZZFzrswacoFDNC8bhV4hw/wSvc1HGF6z766AO8//1v4oorruGLn/93jBOggpyQIcyFEh+AQJDSgn7jxU16zQiF4mDYXZU2aA5Ndhs+gyKgx/DJo1gW2pzpx9knzagk0QrzEL/XselhxmeHWVW5jIp4OQDjxUnu7LmP5ZWddFS0stfcSU7kqFV1tIYdoDTBIw/g3fsb1OQEMplCNjWjhgYJd++YX7dsaITqmgX7rT0fbAuKBUTTQmsKkc6gpiZQ09MYRyCDw+3P4m16lPGKOGOyCGUO1a2rqR7MIhIJYle+kk0VWZ7p+jWtxTiJTBOytZ2BeMBNe26js3wRVfGjh+ICCNvGOmsj1llRzRV278d/9KFotnThkUQ13gnoqf7ABz7B3r07+cxnPsJ73/tRvvSlLzA42E9jYzN2/2+I7/42IAjjtWjDQeb6EW4WK7sHc8u/4NduQOQHonqxYsXc3qWRTiVyel/UmTVHBiMk2s5gTm0DwMj3Y8x2RRYKB45FCOqtasomu+mpvxAvXkPKTFPl1GEczXP4xcKw8es3Etv3I7RfjrYilbk5s48w1UJQsTLaJW8We/gBrNEnQQj8mvUUl74Fe/A3WGObkaqIX3M6WlrYww9HdXasGi9WjVA+MteHSrfPBxMbsz3Y/Xfi9NyGObUd4edQyRZCYRImlqDDFRjBI0g5hg4rEPYsaAdtatAesjCIstLoWD3aThNUrEKWsnP73Yp2KjByPZG3cPUp6EbI/ZnC3CGIDT6BQhM0lc/pHQQqUY85+SzW5Fa0v4zYt5uQXgeUbUeU0shJgZgCtaifMNlImGo/YZdAGpKmUxrZ/uudFKdLxMocQl+RHchS0ZihquP5vzMnAqtWrePWWx/iv/7rX/inf/obBgZ6ueKKs/nOd25mw4aD4dHSlLh5D8OU2Mttpkt5aoxK6J/7wEVQ4WTYNbn3RXcF/jbQWpMPZpj1pzGEQdAvmB2NQu8Mw6CSCrCgUFlkfP8EZ71tA6nqJINbhgi8gLrltbSf2UZV+9HPudaan//8R/zVX32cIPD553/+Kq973VuO6ViTFqQszawHmUPGCoUAHCOyt/t/DSfJ4D8CDBkBWaFYFC6cyatRBiMyYOIYvcB+X9BoemVAt+ljaOgMbeqOsr+1yjxMtXqi8LRV4mG7SKMySWnJrFA8bhZ5yirxlFWiUhksC23eXMxQqQ0WL17K0NAA+XyOZPJ30Bd8BGg0v3byjMqAUw+xBOmXPrc7eVYGDvtMjx2GiwaWhw7r/MjzbYvp0hZaxBHMCMWkCLnCSx3mO3coMtrgrEPUxlMi5MexWWZkSGdoI4kK8NudPL5QrAli857ONdrE0/CwXeQCL3lEohpgk1XkB/Y0KVth+4oJw2fYCFjtO9T5UWjGtpjPpuQE4zKyaBgwQmqUQdnctiwEHaFFj+Gz1/SP2XPuXrvA8Npy2rZNYk2HFKVGJS2ynkd5mYXpGAgpSNUcvN7VOvK1OxQVgwZ9pRJVyseZzKMNk5GGJNawoDWr6Xh5O1XtldTERlHxPPXaxJ4jrE3AQdBn+EyLkMwRWjlFPI590aXYF1161GMx2jqIf+Cj+E88Stjbg6yoxDxlfaToQHObk+MuO8+gEWBqwVrf4b3Fcqr1kb9fFdrgVD/Gzc4s4zKkUkkCYFZoWpRJszLZarlMuwf32a1czeRAkvTIE2TLmtm5Y4DlnWn2WiHVjediA0IFiNA/2C4m5JwgM7K0EHquFV6HxNMVfP1HD/L+d1zNa774MN/8s9dwxdVvw6vfiHbKn/faGp3LMFIGamYGL12HryvRhRLa97HOPIfg8QdQpQCRTIMRQ5V8zpqQrK9YykDZBKXa5SxxYjRknyAMJ/Dqz0ElD/qqaTOJNmOIoIAyYwhvOvq54cy36pkTW5ClCdKP/QVhshHhThBWrl2wn9ouwx55lPRDf4qQJn7VKbgtl6JSx+5XaMzsiwr6QwsfwwatMQrD+NtGeHawjwuWrkTWR8egSzWEzz5DsO1ZrPUbjrLmhaipqaO1tYOnnnqcv3jFaxgrwvd2SrKuwJSacgd2Twl2T2mWVixcVmvom4XuGYElYWml5pWLFf+zLQqiG8pBzBLUxzUCSJqa6gTMeoLFGc1lHSFNKXhwIFL8ZuzI1iFhgGtonhoV9MyAmx1g8pefYnb7r8Ev8O6OxXzxi19j3bqjp9evq9Fc3gb39kt2TQFoKmKCqxYp2v9A2sNKpSLx+EsoRTmJkziJPxoIBKcFMU4JHIpC42gxT4I+aRWJI2gLLSqUwbAMKAjNtAg5z4ux+DhUt9u2PcNb33otp59+Nv/+798+YbkZZVrOafn0AmK6IDS14YvbRuVc2J2LxkEgEawKHPJoklqwKLBYHtic4cep0AZLKxZTm6ild7af9rIWLC+k9rFn6Rl9ipaMTWpyC2H7ImRjE9XxSsaK4zw89Sg3t3yPveYuAnwSOsmp3hlcf3cH4ns3gmkiyitQ+Tx6aADrrI2Yq9dFvq2Zcopf/0/Urh2weCnSttGzM+hiHvu8K/EffgA9M40oP/jy1LPTiFQamTmypUOpay+9k13sMKLzKYBeYKlvsXjbM1gXX8bmqoDy5OmUJerBiBStzVqxO7uPnVN72Rh/rg/X80M2tWA0txB27UMsWoIIArRpogb6MZcfrDHCvh6CzU+ipiaQDU1Yp21APocIPxTa9/EfeYDg0YfRszN8ZuWpPPLA3TzwwG9IJJJ89av/xmc/89c4/beDtAjT7UCkB7WKT2Lme3EbL4y8ZbtvRhRGCMpXLNyGGQdUFPh7KEIXdUg43PzJPBRCkA4D2pwGwvTh1nUnEm7r5Ri5PqzxJyH0I2V5soHisregrSTCz5PY9h9YY0+i58KgzYkt+DWnk1/zQUqLXwcqQDsVWCOPYo88Eql254LjtDAQQY5gzlbBmN5D8tl/wxp5dE5pPQs6RJbGEGEBKiGsXYY1psHU6LAc4Y8hyKMdFxHmQYcIwyGMVWFO70WWJtB2CpGfwZzpIkx6hE4FWlqknvwCQVk7fuMF+GesxN5cRE6aR2h8ExD62I8IrC4Db8W1WLERRGIrBHHMqSL+bAWlNdeDeWIDG1pPb6UwVWRwyyD5yTxSSiqay1n1ipWYzu+GY7Esiw996M+44opr+MhH3sHmzZu49tqL+Id/+A/e8IZ3AOCXfAa3DBIrixGPxREuaEcjKgViBPQEeI5HdbzymIngwA1wcy520saKvfgENaUV+2a2M1DowlNRB7ZQBm4mRMjyBZ+VhiAMNIZp0HnBEhZt7EAFCtMxn3e/+/t7+Yu/+DB33XUbV199PZ/73L9QW3u4R/3R4BiwsTGyjxsrRLkfhSCysltXo1n0BzIG+F3iD5dBPIl5xLTAOqAIPeTJWUJjEQV9/aEiRHOLk+M+u0BORDFo5drgqlKK8/z479TveLvhIjSkdGRzsM10yUtNHElKS5qVyRbT5RYnx1tLGRYtil6a+/btYe3aw/1nfhfICkWX4VM35xV8APXKZI/h8ZX4FH1mgJgrZB6jxOlmjFeVUqgY7DE9PKKguY1enMvdw5UGbs4lcANiZTEMa2FRvtP0GJUhnaE1b6WR0QYSnxERcuZz7r2UFuSEpiAUzhFIzhKKX9t5AsOgYmUt3NNHMh1jKgb7pIs96JGrc3houYEwQiqVwbQI2W94FIVJyyHnwSLKIiiIY5vFC9FsN12c1TWI8z2cB/rRw3n0aJHZuhizy1K4Yx4tpzVR0VJ+1PUopZn5Xg/1HUWmVpQxG4bg+1g5l8ZHNYtOXTE/o9lJDCUKhHOCCo2mIKKBS0JLSkKT+S2EiLK2DufKaw77+d12jm/Hp5mQIRpBiGa/6bHf9Pm3mVoSR4rtBa52U+w3PLpMn6zUWGgaQpMVgY0rNDkU/iGt9ZvjkltPfT3Xbf0F6YGd9HRP8NpXrOSmNdezqm4tL/NBmwmCiuWRSjZWg0rUwcw+pDuJNhyUXY5wJ6NgjOp1xJIZ/vu7v+GDH3wrb/37H/Olxiu47rqjXw8AQo8UT2KeMgIjXWhX4xYzzEwtxjr9ApzXvBGkIPj1HsT4MNpOo7XGWd5IbUucdl+isk8htEIbMazRx7GGH6Kw5oOEZZHlhrbL8OrOItZ1c5RGLQxQfuTPJgzk3DFoI4p8tIceRHozhJnO+eIdFWCNPYkoTSCTjWgjjtN3G2Z2R+RflziyWa32PHQ+h0gkEY6DitVEvssLPqQAjbLLmHz2fnoKOdY2HMxIF7EYOgxRg31wjGQwRL7BTz75KKaMLBpq43BarSLjgCVhb1Zw017Jx9crDliEhwp+vk9wf78k7wNoyhyBQcRfNyY1+2cEOQ/6wkiR25qGt69UrKnRVMcj6wc3hKq4Zmm5INARGVyfgG0T0DUDsjTF+BfPw1z0MlJvu5XLV1ZzdfluMpnnv1+kgFcu1qyvC9k/HfkSL61Q1D4P9zpehH3T0dNncbmm6iUKjjuAaBLyBJi9ncRJnMT/NZAIks+pu1w0xtxrOaONeV/hnYZH8jh8g3t69vOGN1xFR8cSvvGNn+A4Lz7Y7blY4zs02iZ7DZ+W0MRAMCwDElqw4QjZF8eCA2F3O+fC7mwEozKkWZm8q1jOqueIBMrsNK/uvJof7voZuyb3cNZ9O2neOkR8TZKEk4wC0rY8hQkYjc1oFA+a94A5Q1uwmBgxZkSWB6y7SExprrRqMFrbo5VnylGOQ7BjG84rXoVIp3Fv/QV6apJgzy7Ezu3I6lpEUxPWOedjX3UtOp/Hf+BeCENEKoXOZgmnszgbzkJNTyNt5zAv3t78IFOlLGm7DXtugt0LPSZzAyT8aZag8UIPU5oLQuQEAq0hVMevwhaWhXXlNfj/+Leom38aveBtG2PpCuyrr0UYBv7mJ3F/8D+oyYko88L3CB5+gNjb3r3AJuMAtNa4P/0R/t13oG0b3BL2M0/x+cYO3rrpIc4990K++92v8tF3vYlMcWK+uwrAnO0GrdEyBtJCxWtRdgZnZj9GYQCVPjixrpwqtDJgZB+qkEBW1SB0DqEC/PpIbRkmmwjTrZjTuwkyy+eyN3QUXJZqJ0y9eAuMY4W2UuTXfAhjcjuF7Day7gjjyXosqaj3slSPbcYae4og0wkHbCOCEtb4k9ijm/AaL5hfl199Cn7VWuyxzYSxapAmsjhKmGzBazh/LrDuVxgz3QitUPFahOEgi2OIoBDVqUMPEFaOooslxEQT0ipBvh7KtyOcGbSOlP4iKGHkB2C2J6qL5+4vv2otQdUanIG7wZtBmSmcwW7s8c0UVr6XoHIV1timKABw7j4WQQGEQVjWgbNDoFIaJdbgeX+GYTyCEH3IyXo8eSZ+TRuyMIK2kpGS+gTAtA1WXbmC1vXN5MbyWDGTirZKTPvETIgdD5YsWcYttzzA17727/zt336aT3zifdx00w/56ld/hK0d/FKAk7SpjlWRtBJk3RkydhkyEORm8hQripzZcHRBxAGoQNGzqZfeJ/twcx523KL51CY6zm4/jA84FgwXe+nO7SZmJKiwy9BosrEJvJXTzGxLUF4RjY211uQnC9QurSZWFt3Phmk8r0fw5OQE//mf/8Q3vvFlMplyvvGNn3D55a887n0EuLBFk/fh4UHJvmmImbC+TvPapQrjt2+E+aPDSTL4jwBtocWiuXTfRaGFjaCIYlQGXOglKX8RIRG/Kzxjutxp5ynXBs0q+oYNypBfxGZpVRbt4YufgTpehId0M80IxZQMyWgZtZoJiBHZcGy1XCbdkMWLIzJ4//7dx00GZ0XkM5sTihoVeaweq5XBoTjgGPpculPPHcNWy2Vl4JCcW3cBxRNWiTWBwwcLFew3fGaFoloZC4hUAK/gsff+fQxtGyb0QhIVcdrPbKfplMb5WbmSiAim53oqp3SkNpgWIeWHKE2zQlGnjKOqjweNgBEjoDE00ec1IYbzsCdLmdK4QhGUZei7uhmVNOeV8CktqFMmgzIkKxQVc/d7ViiSWtAYHttjTAK2FhQM0Je1IdfVYO6YYCZboGtRjMV7QlacVkXbhlaEPPokxeTADP7uMdYNxZiZCpmulJiuSfWOHGrKoXRR9fxnL3IT3BibYUSG5NFoDQ5QqSSLQ4vK4/DxO1aUUNzl5BmTIXEtSWuBQJATiq2my82xPDeUjjz1mdaSPymUM2QEFNE0KZPyuWvZJwM6A2v+/AN0GwGT8SYeOOPDjN/6M7T+OVNv+EvG21cQ831e5gNC4LZegTm9B3NqK9rORP65hSF0rAZjthvMRBSsVh0FzlmWxZe//B0+/vH38qEPvY1SqcTrX/82dHYKDAP5nCAUp+8OYr23oDqWEtR0oscHSAQjWBsqKVzyNkQ8ReyN74AmB3Hf/4DysDqaiLXFMMIxUB6YNQTptmiFWmFObSfWdTP5tX86/+BwO65FulliPbeCNNCmAyqMlrdSaKciUh3H69DCxB55FGNyK0HlWjAczJl9yMIQftUp8ySzStRjTm3FHnyA0pLXLjguHYb499+D/8Dd0eAwXYZ57gWYq87AGn8Sme+PCGQVYM52ESYb8atPYev0LwBYW990cF1zprjCOb6B9/r1Z3HbbTfxq70ev9wXJ1CCiRI0JKGzQtOc0vTNCvpmoWPusjwxKrizR1DhaJpT0fPqiWHonhVc1KLJuoKGBFhSkw/g9FpNoOGRYcF5zXoBqWwIWFSu54M7pt1oYFtuC0q3/wNkWki+5psYAh53NR3lHZze+MITREJAcxqa088/G6M13NMn+FW3YKoU/awyJrh6sebcRv2SpQ7n8zmamo5dLX4SJ3ES/29iVeDwSydHeEitNiMUDtH44ViQz+d485uvIZ1O893v3kxqLrz2RKFSG7y5mOGnsVn6DZ+QqLvxcjfF8hfhFwxRzf6WYoabnRw7LI9ZFA2hycvdJCuPEjx9Su1qmlL17H72PqpGu4gtPZ3zauL8xNpPYKUxp6ZR3fspVpdTMkqomklWhkuJE72AMrqCQmmSJ5qf5uLaRRw6fygqKlF7dhKODhPc9gv8hx9A1tZh19YT9nSB52KdcTayuZXSV/4NXSoi6xvQuVn0dBYsE5lK4z/2MP4jD2LUN2Jd9gqsUw+SOturBZmYRdl0iVK5CUKQdBWhUuxrTrFUSFZXr+Cu3vuoS9TMB51NuVmSVoLWsoXvFB2GUUjcC7zIwt07o2NsbJ6zaw0jQnZsFN3cgnfzT9H5PMbyVQgh0EoR7t6Be9vNxN//kcPWr3q78R99EKqq0AP9qIE+dBCwfnaWt9Q28cNHH0QL+OYPb+SvTncQQXGe9BPuZEQ8SuNgcJoZR8eqEH4uUs8acRQG9O+lMG6jvClM8y6IxRBty3BXXotXP+dJLE1Ki19NYttXohrViCHCEipWQ2nxqw9u46WGNBkJJgkG7ySV6yclJFOpBvY0XUB6doyEMA4SwTCnjJUY2d1wCBmMGaew8v2Evb/CHn0cdIDXeAFu6xWoVDPCz2NO7URZSQzlo60ysIHSFMKfBmEhpAmFnVBlE6QlQrtI1QXCAwzQgjDViiyNIXP9kZWFVkgvi4rXIPND2HOCjwNkutJNmNO7cbpvJr/6g1ijmzAnt0ZdbjpEBEX8urPxq0/BTgB+dDhatxEEbaDBHAMh7iO16RsYxVG0GZ/zjb4Gbf32k+dCCMrqyyirf3Hy0DAIyfZlCX1FujZFvPxwD7ViABNFiJs8r8WalJL3vOfDXH/9G3j726/joYfuZd26Ft5yzftYUliH8CSV7ZUsyyxhV24v01PTSCHIOgXOb9nIOY0v3AHQ9WgPu+7ahRmzcFIOXt5j5127CdyQ5ZcefzjfUKEPKSTxOQGMQFCRqKbQWKDUk2eiG0zHxC/4JCrjLNq46HnH2wDT01m+8pUv8rWv/RtKKd71rg/ygQ98krJjCMQ8GmwDruvUnN8cMlqIAuRa0ifE8eaPEifJ4D8CGAheW0rz3fgMXYY/55EFpwQOVx5B6fmHhKfNEorIy+sAGpXBDsNnh+H+TsngFYHD41aREooATUhEkAigao6UcxDkUZSEpjFTTnV1Lfv27T6u7ew2PL4fn2FQBhwgUpcHNm8pZhYQaceCjJas8G0esotkQomJiGw3DH9eIZI8hGROIBE6UoSc6cfpPEqRrZVm26076H9mgERFglg6Rn6iwNZbtyEMQdPaqO2rIYzUFjmh5glejSZAszKwGZIhvoaElkyJkEBozvMSR/V6NnTUyhcKoMxGv2kF7JnCHSuQSwgWtbbxUJNHhZojo1FkpaJaSQalzy7TY0lgUxSKvNBc6CVoOkZbEUGkQPlpbJai0MQbklgNCfLSZ1EouG5JJTXihe9Hf7aEHfoURRmN3QGN3dHPVcEm57rEAw+ICrZabfKOQoYfxGcpCU1cCwwN5VpyiZc8oZ7YuwyPR60i+wyPTWaJEpoaHR13iMabaxv9ejxLSgs2eokj3o+V2uSaUopfOHkmRUhJaKaFIqMll3qpBRMDmdIsq4ceZs30GDfefw/xTBllK9YwTOT7fQBhppP8uo9jD96LObWLoG0pYao5sl1AEGSWRj5vh3gTGobBP//zfxOPJ/jEJ95L4e7beV15DUIKjJVrsC97BUZDIygfe+h+tJlCJeqQCaC2HhUUcXK9+MUugvgahOPAy9+Ouf5UnL47MXK9KKcCL9mIPXAPYfKgihYhCRMNmNldCG963qJCWykKqz9IUL6U1JYvEcbrUE4FzuC9KDOB9GdQTmXkSRerQiXrUYkmjFwPQgXI4ijKqSSoXL1gW9pKY2YP+gwegHfXr/FuuhFicURZBjWdxbvx+3D1tRhrX4fTcytmdmekpki1Ulz6RnSsmm1oUqZFq9LzJLAa6EOUV2KsPLjtaRcCDRVOpJY9EtavPxPP8/jWb7Yyk95AzNAUfNg1JSgEsLpKozTz6neAJ4Yj/+yK2JylYHSgBEowXIg8flOWxjbmvIMDQXuZZjAv6M/Bkuh0EzdhUSYil6tiEfFaCiHnR6F1+7ffQnLpy1E3vobp3fcznWnipovfR8M73slrlp4Y799dU/DzfRJTHLTCGMzDT/dIGpMhi8tPyGYOw+zszO/NnugkTuIk/niw0Yuz3fTYZXiktcQXmgA414uz5Bhr7M985iMMDvbz618/SnV17Uuyn0tCm4/lK+k1fAKgJTRJ/JYB0TXa5B2lDBNuiCegWhnYL1BX1SSqKTdbKIoyjPolbAgDthlT7JBTxNIa5Y2isr0sbe+gp3aKmF7I2CTMMrJxKIQzJDgkrKiQjzpwZqYJtmxGNjbP5zfImlqC/XvxfvlzRFkmCgeWEgp5ZMdi7MuvwvvVLYRd+5ANTQjLIuztJvzO1xHxOObyyDc231LP4KltnLl9kvLeYUCgTINnVjaSXBlNMF/QfA67pvayc3IPKTuFpzy0VlzcegFt6Wa01oTbtuA9eB+qrwdZWY119rmYZ56zQE18AGp0JCKnm5qRh9wbYU8X3j13og0Df8vmyOd+YhxZVx8de30j4b496IlxxHPsItRAPzqfBxWi+noiawzHQSWSfHh2hodKeaak5Kvf/DofvugvqRi7G23EohA4YSL8acLMsoNWDzqMLLtUiJHdiQhd8Hy8Sc108UKCZAe2GEEP9hNma7AueAXyEO/foHINuVP/HHvkscgLN16HX3fmvDXFi4WHx25zOzk5S3VYy6KwE3mUez6c7SG29d9wCmMEiToAmqa7SZSyTKVbKddHmuRWYBz8jgtvFlkYACNOqfMNEZmtQzDi80yXliZICyGM6G/los0E2owj/BmQEi0MwnQHQaodM9+LVgYyG0YTAVqg7UxE8np5ZJhDlibAsFCJOvzKtZhT2xAlf2HWhxCEiQaMXD9C+eTXfBh78P7IGkNa+LVn4DaeD4aDf5rGelYgpkFniIIYB0E0Poglv4bOE2V0BAViXTch3CzFVe8/jM0TWRAFUFVESpyXEFP9WXb8eifTg9OEgSJWFqP9jFYWnxsRnlrD/f2Cu/sEEyWBY8CaasU1izUVz6PRqKqq5uab7+fL//FPfP4Lf8HXf/JvpJw05zVdxrqZc6ipq2F103KycprkhhRrL1jN4vL2+Ymgo8ErePQ92YcVt0nXRrWmk7SRk5L+ZwZoO6P1eYPgj7jO0MUUC8fkQggSFQnqL2hDb7cpTBXJNJXRtLaJTMPRSfdcbpavfe3f+MpXvojrlnjrW9/PBz7w8RP6fqqOMy80eTHQaPaaO9lubsUTLq1BB2v90+YnD/9YcJIM/iNBk7L4SL6CnabHjFBUa4Olgb3ANuIPETmpsZ77cI4aliiJ321Qz6m+wzNmjM1WCU0UKDcuNYtDaz5peVyG1IcG1XP/X7x4KXv27DzmbZRQ/CQ2y4gI5q0VXDTPmi6/dvK8/iiKzKNBILjcSzFshOwxfCSR3UG1NmgPLPaa/mHLaMELltfZgWlGdo9SVl+Gk4wIYztpM9WfpXdTHw2r6pGGpDO0We/FeNgukdICG8GkUNQqkzcWy9hmujxpl5gQIeXa4GVunI3+0R+CzcqkPbDYZbosCW0MxyBcXUWPUcaawKazUEZKTTIlA2aFYo/hUxAKhUYgqAolWmiqlMFVfpxzvQQCQZEio8YQjo5Rp6JW+4LQWHP7fADn+wm6DZ8tlsucUy0VWnK9nz4mIhggVuZQkzHJuj4FwyZuRQrGwqxLoizGysaF67nCS1GhDR6yi2RFSIMyOc9LsO4YfY6PBU+ZJb4Xn2Z2jnDOyShApkJLUlozLENmZIgGSkLzcyfHDsPj3cXyI3YWXOIlqVQmj9lFxmXIKt/mHD/B0kMmF2RhmEu3/wfDM9ui2ehNT7CmLUHF8OPYzetZ85zjC9PtFJe97biOS0rJ5z705+hNj/LZ235G5UWv4OWtiwgevBc12E/8gx/DiAmkN4N6bquYEQMVILzZgz8TIkpgrjplrj3NxJx4BnvwPiL9/SFtlVqhhVhAUB9Yh9d0CaXpPdiD981Nz2iM4gjaqSAsi1oihTeDitWQX/tRhCoh/Bzm5DacvjsOS3IWoYuyF85y69ws/v33QDKN0Tin8M2Uo4aH8B++n+L5n8WrOxtztgstLYLM0nkPtd0zWVa0LUIUC6hd29GArKzCuebVGM2tjBbgV12CrROCUEdE7GVtimVHyGvo7Iw8+Mb6thOu2MBIEUwhMATsmYq8g5dVRApggD1ZeGBA0D8bhbs1JzVtmYOkcKiivw88/QUgRUQoSxEpgQ851VzcpuiekeycEmRszUQpKsCSFnjj+/HG/4uGSz5K+cWfJujfxN4f/Ck3pmxe8edvInEC5hqfGYtI7+WHeCI3p2DHJGwZEywuf2neY9nsFBUVL31oyUmcxEn8caNGm7ynkOERu8gO0yOuJaf5Dhv8+GFdXUfCjTd+mxtv/A7/9m/fZMmSw8NTTyQsxHF5GB8LBCLKQjiOR7FIJBGWBW6JdCzO27xlPGGMsbXQTbG8ho4119PZ0sqNcj9TeoJKdbDja8KepjGxlFT/LKpuCpEph0KBsK8H85T1EEtAoYBoblu40cAn7O/FvuRyZPlcu7TnEu7fS/DUJlRfL8aSpQg7qp2MVJpg9w78h++fJ4NXVC3l6+vb0UuX0Tg0g1Ca4eoE2ypD3lW3HIDGVD3vX/d2Hh7YxO7sXtJWivV161hftw4hBP5TT1D6ztfRxQIyU07YtY9w7y7s7BTOlYe3XavRYfRMFtm5fMHPZWUV4fAQ+sffR/V0IdJRjkFpoAdvcD92++KjBlbjOAghCAcHQBrRZD0gwpBERSV/V1fHDY/dC0LwxbuG+MuXn4M18Qzke8GwUXY5YaIhssdSAebMPoQ3jXJqKNVuQAYF/GeexNCjJCqmmfYTuCxCV7cR7tmJ3Lkdec55C48z1ULpOLIbXgiDsp/vJr7GfnM3ASExHWOtv543FN9OSh+uvA8H78HJD1Mo74yIWsC3UpRP72c4XksoBKI0iY5F944oTaCljV+1DrTC6bsdp+/XyOIE2rAJKlZQ7HzjguwLAAwHr/YM4l0/I4xVYuaHUJZGBPkoYNnOEJYvw6/dAAhUWCBMNWLMdqMRaKccbSYw8n1I7aHsCrz6s9FGHNBIdwpZGgdhI/xc1DE3f5KjulsbNjpWibvoVbiLXnXYufDO0Bjd4DwiYQiE1YOoewI6/wd0niBxOpgxtJ1BG3HssU14s5fNd9yJGYjdIrCfluCCqtK4F2u8jfoIPsW/PbyCx9abtzEzMkOmIYNhGeQnC+y5Zy/xTJymdY08Niz40e4oQ6M2rimFcH+/JOdr3r/2he0Jrjnv9eQv8/jPu/8OpOb2rp9xV/fPWV19Opeefg1XvvFqFp3T8YL+xoEbMLp7jJGdI4ztG6eiuRw9N8aGaIw7PThDYapw3GRwuV1JX2E/CZ2e7wbwlY+Uksa2ZqqXvbC375YtT/Hd736Nm276Ib7v8aY3vZsPfejPqKs7so3e7wsazR3OLdwau4m8yCEQSEey1j+NtxXef8Tv+B8qTpLBf0SIITkleImNCk8woiCz0oL2tRIKiaD5dxx6F0fytmKGtYHDdtMjoyR9RkBKSyZlyDQKSwsu8pLzBOL69Wfy4x9/F6UUUr6wgqHL8OkzfNoO8dh1ENRog2esEle7Rw5wm+iaoG/zANODMyQrEzSta6R+ZR1CCBqUyZ8UytliuozIgDJtsCZwGJYBXWaWWaFIz60zJxSCSAX9fChMFQjcYJ4IPoBYOkZuMs9TQY5dycgmoi2wuNo12GK6FIVmox/jAj9JR2ixMnR4uZekIDTlShJ/ARraQHCtm+LbMiJ6D6Q2tIQm15TSxJCc7cf4RnyaQSPA0VHa9LRUlGlJAsnb8xkWKxsDgULxsH0/dzi/ZNwYxdIWFeEybH01U7ISZ86P7mI3SQJJUkveWSxnh+/Or39F4NBwHPdieWOGFaurKT02xChpJg0Lu1SkTPtsvHAR6cRCctVAsNFPcI4fxyfyOj6RXtneXMigK/R8u2U2cHjYKjIgA2qVwfRckndcS5YFFktDm92mx5NWiYu9w7sLJILTgxinP8/zxun5JWUTu8lWL2efDTtGHmbjKVWs3/VzGsvXsUqfmAFf+MRjfKptCeOBz8fuu51vv/otrO9cTrh3F8EzTyE3nk+YqMeY2UcYO6jSEf4M2owv8Jo7+Esx3/oXZjoJk40Ysz1RISkEqABZHMZtumg+eRmYJ5a1laK47O2oRAPW8EOoeB1Ca/yyJWjDwRx9AiPXTZBZhvSyDNedxoNOiS2tjSTqUmyY6OHcvKIsCKKiGYFfu7ClS02Mo6ezURr5obteWYnq60WPj6EXd+LHDicMe/u66Tz9LBIf/RRh936EYWIsW4GsriHvwze3SXZOCuoSGsfQbB0XDOQkf7JO0fac+So7niRZ3cZE706SqyBmCEKtCbUg52kmS4JXLFLEzCjo7atbJDMueEpQDDTbJwWzPmScaKSetqEUaEaL0XpMCeVOFN6wuFzT8pz6qbMc3rdO8eCAYG9W0JKG5pTm2XGB1opYy+kkLvs8AGvXnEI6u42dd/437idODBk84wmsI0xcGkIze/hc3AlDNjtJeflJMvgkTuIkXhjV2uRqN83V7vEtt2fPDj796Q/zute9leuvf+NLs3N/gDCWLMVY1EmwaztG+yJSTozT8hm6Vl/GwIXnMNjcyNNaEuhXMyC/Q1EUSKgUU3ICKSSXNryPxLnduJs30Ss9ZsrTVL1sI52XXQ/T02A7UCjAIb7vanQEYZgRaToHYTsIyybYuR20mieCD0Cmy1B9vfP/X1ezmjMb1/PY0FNsL4sxl8LLWQ1nsKZ65fznanyLV8pORMupiLr6eWJGBwH+Xb8C18U8hNxVw0OU7ruLfSsa6Jd5LGmyrGIJjan6uayCGBQLcEi3iprOovp6UFojKqoIQo9Ry8UNC8S6JylO9eNuPJtTM+nDdHLG0uXI+gaC3TvRVlR/a89DuyWMRYtZG4a849Kr+Nbdv+Lf/vNfueGNW2luuzIKKbNSmNndOP2/wZzaDtKIJtODImH5ErBSKCuFKllolcE2RjGCHKFOR8pnIdDFwrHdKCrEyPWC8glTLWAem+IvIOCH8W+xy9xOR7CEGDFmxQyP2vdTqap4denw75qdH6AooxHCPIQklBIlTYotLyc1eB+iMBidLzOB23oFQeVarJFHiO35HhhxgnQbIixhjT6OCPLkTv10FCSnFcb0HsyZLrSVwC9fhpndhTYmkMURQIGVIqhaE3WvCRmR7VKizdScKttCxasBgZY2sjgK0kTZ5VhT2zFmu5HFEYQK0IaDUxrBr95AWLkSlI9RGMBreBnaqTrs+BfACCm+xsPfEMPe9ThO/lvo2CBmYTs6ZyCUh1+zIcr/sDOIfD+yMBrV8CEkviexNglUrUZXgZyA+PcF2gL/zN9yAl+DuQ3shyXGAKh6GKmbYGZ4horWCuQcq5uqTjLV59P/9AANaxu5vz+6rgdq3IQFjqHZPiHYm+WIYoxDMT04zYrmU/jf7/0vPv/tT2JbDueuuZiHnrmHf7jtU9za8wPe8pb3cO21NxzV5sfNe2y56VlG94wRuD6zI7MUJgvULa+lvKkcAL/gYzomduL4x3BNyQ7G3VGy3jgxI4HSISVVpD7WTKVz9DDJmZlpbrrph3zve19n69anaWho4t3v/jBvetO7aGxsPupyv0/0Gz38OnYzjo7RGnYAUKLIZmsTS+2VvNy96ve8h8eOk2TwSbykOMOP85RVYpfpUaEMQjQzQrEucFjtv8Q9G0dAHMlGP8FGP4FC85hV4hGrwLhUrFQO53rxBYrNCy+8jC9/+Z/Yvn0Lq1ef8oLr90RkP/HcL5alBQWhF4RvHcDIrlG2/GIrXs7DTtnkJ/KM7Rtn+cxSOs5uB6JQkPP8helG1crgHC/Ow3aRASK7CwPB2V6ctXPHEKLZYXrsMzwAFoc2KwIbJ+UgDUngBgtmEb2iS3+Vyc8zeQI7Cnx6zCqxIrD5QKGctDYOU6NntHFcIWgdoc2H8xU8a7pkpaJSSdYEDpk5heoFXpI77QJdho8UoATUK4tVgU2fDNlteRAIHrQKPG4PsNfcS7VKsCJsZFQW+GXsAeJ6hLPcj1AQDr9wcozIkLcXMxhESuF1QYx1x5ijUZgq4OY9Epk4TtpBSMGaq1Zg2ZLenePk8kVitTYrzlzCsvPbjroegeClcB4bkQHDRkDdId7JqwKbYRnQbQSMyBAfTUpLmkKTVhV1FMSQ7DI8Lub4rWaEn8ca34yK17FYJ0nnPEaHpllz7QY2zGRRIwMEtU0vvKJjQNjbjZFI8o9XXMc7fvYd3vuLH/CjG95JuxCo4WGQBm7zJSS3bMOcfJYw2YwIS8jiOF7j+fNKgaNBWylKS24gvvObmFNbo2A4rQgynbjtkUpG5vpxum7CHnkchMCv3UCp4xpKi66n1P5KhDuD03sbsf47cHp/FfnNxasRKNxdX+Vb5W9lR9kSMjpJtm4NPyirZNfofj6480FiMk6p7Sr82jMXnuNUGmIxdKGAiB0cgOhCIfK9ex4LgZ6eLi655EqM1vaDATdzeHY8KjyXVmisubmbjK3ZOSV4dEjQVrbwy2wKSDetYGpoO4uT4IWaWQ9KoUYDjSnN+tpomfv6JZMlwYZ6zZOjMO0KTKHZNw1NKcGq6mh5yxD4SpMrCGoSmvGioCGluXbJwRC6Q9FeBu1lmgPSr03D8Oy4RKQa8KtWMFYUNKY0DUnYX7Mc97GfkTnG10uoYNqLEoaTRyCP28s0jwxKQqXnlRuBimwxWl+iiX/P88jncyeVwSdxEifxkqFYLPLe976B5uZWPv/5L/6+d+d3Bl0sEvb3Ym48D61CVHcXOvC55ZUbefL8U2mpbSITWswIRZ9YTXPwHkxxM3mZoz1czMvcl7NBns3UW0J+cMNF7JAlXMckEU+zNnB4TVUH5rIV0WR1cysikURPTqA9D1FZiTAWjhC0CpGpNGp2Bh0EC0LjdC6HXNw5/3/HsHnjitewpnolOyf3gBAsr+hkXc0qbMNC+z7er3+J//D96JlpcGKYa9bhXPtaZHkFemqScHgIUbOw1dqvqeLG7CM8/dRX8dNJNJpyJ8OrllzBuW0bMJYsJXz2aWT7YojFCPfuJtzyFNoPImP9eJx8fhIhQ1KWQ6ykKFZZ/GKJZqrvAV6x6NIF25PpMpwb3kSwbw/hjm0ozwPDRDa1IKtr0ZPjfPAd7+H7D95NGIb83d9/li996RvzHX1B1Tq8hvMxcj1oaaOFIL35H9HyYJUtKqpQ/ZMYSYmYW1Ln8wjTOmyS/UgwZvYT2/N9zOl9CB0QJupx218ZeQ0fwVxUFsewhh/CzO5kZ3mJ/Ss20SZXEZuzjUvrMqpUDU/Yj3C5+8rDlIN2shUNzCqXmBHVfFordOhiJ1tQLVfiSgdztocw2YBfdxZB+TJAREFtiIiwBrThEGSWYmZ3Y01uxa9aS3z3d6NutiAiwrWdibrcEvWIII+c6cYafYywfFnUvab1nJ1aFUiDMLM08ggujgFRCI+K1xKULcEZfhCZ70cEpSjnJlaD8GYwimMYA3cQTm0jKO8kqFoXWVcczZxVBdgDd2MP3ot0s6hYDUZsNypuEabXYvT1I9ws5mwXAF7jhRAWQdoRWQ0Y+8HaKghbNXquTA6T0c+d+wX+Bv3C7bPPA+txQeL7ElEEldZYT4MoBcikQHYsXLEVtyhmixR9zVgRMs8ZBCYt8FUkqnih1gbDjrof2uoX8/n3/Cd//bWPcN/mO3nfOZ/GaZM82ncPn/70h/irv/oY5557EZde+gouueTKBWRq35N9jOwcobylHNOOPMcn9k8wtmc88jfWMDuWo+XUJlI1SbL9WQa3DpEbz5OqTtK4uoHy5vKj7mOZXcGayg305vaS9SYxpc3i5CJak4uRYqFQamJijHvvvYPf/ObX3H77LXiey8UXX8EnP/lZLrzwMkzzD5ui3G3uYEbMsDxcNf+zGHGSOsWT1mMnyeCTOIkDqNIG7yqW84BVYIvlYmrJJUGSc734C6pIX2pIBGf7cc7yY4REDeLPVWxu2HAOiUSSe+6545jI4JbQokJLRmVI/ZzaVKMZEyErQpvMc1TBKlDsf7ALv+hT2V4xP3s/O5qj65FuGlc34KSPzGpYCG4olbE6cNhtemhgaWCzOnCwEARofhyb5Vd2nhEjoCgUCSV5mZfgT1vLqGyrYHzfOGUNGSzHpJgtki157DmzhkrTpGIukdpDs910edwqcZl3YjwsK7TB+c8htw/ARrA4tJgKHCq1ga0FGS2RCExCdhoe99lFZoSi3xhiRlSgZDtp3UdO7iamFyHoQoldNKrTKBOSZ6wS+7yFNgcvBDfvses3uxnZOUpQCnBSNs2nNrH4vEXE0jHWXbeWJRMF/IJHoiJx1Ov0UsMk8iEOhJ6vJUwkZ/lxMtoDrRmQAatChxZlkZy7BwMdWUq8KGgVDQDmXu4j/VMopdnY0kBCzZLXxz/zHnghY3vGmOiZREpJVUclNUuqkRVVBG4JxzT5z1fewA0/+gbv+Ol3+MGG82grK8OY6cIeeQQRljDy/Riz3QSZTkodr8LtuOZwm4cjwK89A2WliHXdjCyNEpZ1UFz0GnSiHlGaJPX4X2CPbpozuBGYE09jTm4ld8bfouI16HgVpWVvRnpZRGGEoGLlfCvfo5kke8UsywoFDKcCnCY8meGpjiYeTazldLOZMNV2WHEsq6qx1p2Gd+9vwLIicjifQw32Y208H1F35HarXG6WiYkxWls7jvj7sSJoLbDkwWskBKQsTffM4QWpENC2aDlj9/+SYhCpGUwJWVeQcTRVMVBzPO2+LFTENGkb1tdqumdgtACugjPrNe9creieEezPaophtNyBEI111ZqauUdCoCKv3rGiIG3Biko9r/LNefDL/ZLGNPQuO4fpsd3ETc1gDm7eJxCDe2hqbj2qB/KheGZM8JteQX8uOh+n1Wqu6NCUHfKYWF+neXRYszMrqIlFBPh4UbC0QnNq7UtjETE6OgxATU3dS7L+kziJkziJz37243R37+e22x4ikfjDzh95IehiAV0qRX68R/C9PQB/06N4t/4CNToCUiAbm7BfeR3jzfXsWldJUyxF+dzQOKMNAmXjieV8MLeBJB5JncLAQKP5WTzH5rIYrSpNUgtmUTxiF0kged3r30LJNAl3bkcNDSLLynCuuJpgx1bU+BhyzkNXTWcRQmBfchn+vXcR7tuD0dIGloUaHQbTwjpz44JjcAybDfWnsqH+8GBr79678H55E6KsHNncis7n8R+6HzyP2Ls/ENkzWBZ4CyXkm/Qgj5UXSNd1kFu6mslUkt7CFCPjT9OaaaPldW+iFIaEe3ejZqYJu/dRzKQo2hJrbAJlBrgixK+qwLdtwniJ4bPXErS08NjQk1zcej4xc2GNbK5cQ/Jv/p7iP3+BsK8PUV2DiCdQo8NYZ22kesM5vO99H+VLX/rf/PjH3+Ud7/gA69YdDNNTiTrUnLeu8POE8RqMwtC8z6/R1o6R3Y0/4eIVi2gvBzNZzDPOxli64nnvJ+FmSWz/CsZMF2GqDS1NjMIg8Z3fQtkZgqq1Cz4vC0Mknv0S5vQ+tJHAVyOEs7tI6STqkIyIuE4wK6cpieJhZLCqWkUSk2ByJ9OxMpAxkkEBHauhKtZA+sm/QboTkX9urgcVqyYoXw46QBZGoyC4Q2E4UTCbO4U9/AhO3x2EiTp02aJ5oteaeJrZ9s+gUq0IP0di+39HimJvFhEWUU4VxZWvx8j3YU7twmu4AFkcRoQe2kwhSmOUOq4h1n0LKB9ZGkc5FcjSJAjQZhwtTURQQMWqya/+APpI3XpziO3/KbGumyJ/aCuNNbYJM7sLr+F8zOxOhJtFulNowJzcHk0EOGn8qlOicwEYEwLcg0TwAehykKMgiqBf7OPOhdgdAgJNMJevpoDkrjjGqCDIB5jJubG/1ri5EpWrG4iZggonsk47NDSuGIAhBBnnhUOPaxZX0/1YDzPDM/jjineu+SRfffIf+Pf7/oaPveJzfOHj/8HnPx9w660/4447fslnPvMRPvWpD7J69SlceukruPjiK5jeUsBO2hERDFR3VKFDzUT3JCO7Rsk0lNG0tpHlL1/G6K4xnr1lK6UZFytmMrprjMFnh1hz9Wrqlh/dt7fcriJTUUmoA4SQGHPjRKUUW7Y8xd13/5q77/41mzdvQmvNmjWn8uEP/zmve91bqa9/4Uma3weUVuT9AjHTwZqz+VNE1+y5vJFAEnKMarM/EJwkg0/iJUedMnm1W8b1bjRwPpEt8icCAnHUL4Jt25xy7gX84N5fkvvUO6hVJmf4cVYH9hGPo1IbXOgmuMXJs096xJHMCEW5llzqJpHPWaY4XWR2LEeyMrEgbTdZlWCqL8vMyCw1z0MymghOCWJHtA/Zarr80skxJAO0iELeCkJxUyxHlZa8/qpVbP/VDiZ7pgi9ACftIF/WwsRZ1SzXkqwIyUqF1GBoeMZyTxgZ/EJoDS02WSVqlDF/zqLQP82AEVBC0xma9JujVOoQoeP0Gg2E9JAgpCgURTkFClJa0k+koD1WMjjUivse2sXgviGaYg7pTAx31mXPPXuRpsGS8xYhhCBVnYTjUNbmJwtMdk+iAkVZYxnlTZkXTHF+IdQrgyWhzdOmS3IuZDBEMyRDzvbiXOkm+c9EFgdBYo78zYoQIWDti7Sd0VaKoGIF9tD9qFgVO3vHAFhR6aNiVQSZJce1vsANePaWbQxtHSLKO9P0PN5D6+ktLD3lNMSmhwn7e0k3NPG1q2/gtT/8Gu/Z/Ag3/fknqdr2ZYzp/WjDQVkZpDuJ9GfxajfMp0+/EIyZLuK7v405E6kNjPwgsjRJYeX7cLp/gTP0ENpKE1ppQCG9aeyhB7B7byOo3RCFbSRbI6uKso55IhhgR2Urcb+A7WYJ57zTbCtFaNjsq2/jVPfoPuL2NdejiwWCbVvQA/0Qj2FuOAvnutcd8b7RStGzZxcAbW2LjrjOlDUXBq4XhsYVwyikLVCRb++hqz9n3Qoe+/m/4rouxSCGIaEyFvn2dpaDIaO5gaQFI/lombQNa6o1QQh7p+GCFk1VHKrimvXPw3FOu/CdHZKt41EInUDTWiZ484rIwmJ3VjCYh8UZiL3mI9z45+eRvet/I1a9lmDwMYr3f5WrPvJfFHye1yZi2wR8a5ukEGhq4hovhNu7JWNFzfvWHlQoV8bgXasVv+kVbB2PnvqXtikuadXHrD4+XvT2Rvfh0Qj9kziJk/i/F3mh2GK6TMqQ9Fzn1PGGD78Q7rjjl3z3u1/j//yfr7B8+eoXXuAPFDqfw739NoInH0O7LkZ9A9aFL8c87fTD3pHBnl2Ufvgd8DxkcysohertxS8UKXzy45SSUBnAWGkCL/RImHESTpoZU+FKk4bwIIszJEO2mS5Nypy3fivTBrUKNlslLq+povx9H0b19UTq3to6RGUV3i9vwvvN7QQ7RxFaQyyOdf5FWGefhw4V4Tf/G+++uxCOg7GoE/u612Keup5jgXZdgofuh0QKWR/5awrbAdMk2P4sqrcbo30R5trT8O6+HZFMI+JxtO+zubAfd1UnQ+vPomTbOL6PLqthZzLJN1U/n60/nfiHPk64dzfePXcyUZzgmQaT2NgUbUGevJSYBGjLpNBQjTNbYHRFOwkrTj4oUAxKh5HBAObiTpJ//fd4D91HuO1ZRCyOuX4D1lnnIkyT97znI3zjG19Ga8Vf/dXH+fnP7zly7WMlcduvJr7r25jZnSgrhdQzqM6VzI60w74AmU5gXnQp9oWXLFBfHwnW+GaMme6IYJwLmgvLFmNObccafugwMtjpuwMzu5egYhVIgzq7jkwwQDbYRrnXMp8JMSHHaA7byKiKBcsLP4fT+yscpbDCgMzMEApFmGhAtV9HauhBQBOUrwAEsjBEbP9PCNOtBJVrCZMNWFPbURxSXAVFQKIBZ+A3aCTamatNhSBMtWJObcWaeBY31Yq2UuSXv5NUaRx75DFQAegAe+h+vIbz0FYSWRwlTLYCCmO2B5WoI6haix56gFC3IJQXLac8tJVCBAW0XYmy00h/BjO7C/8oZLAsjuIM3INyqg6S/MqH7E7MiS2R/7C0UU45ws8jVIA5vQe39XKKy985f51UmY4UC0XNof4kIgdhLejfom4zRkGOCFTdQiFAdWsV9dlqeveOkGhNzHsG2wmb1vUtGBLObdJ8b6dgKA/VMSiGkT3a6ipNZ8VRNngIKlrL6bxgMZt//DSTPVkS8RTvO+sv+MbT/8Q//+p/YaVM3vAXb+Y97/kI73nPR8hmp7jnntu5885b+frX/51/+ZfPYxoWzVXtLG1byeKmZSxuWk7T4ja0ho6z2+g4u52y+jQqUOy5bx9+waeqoxIhBFprsn1Z9ty/j+rFVRjWkd9JKlQM7xriqYefYG/3Tvqz3XQN7mHHzmfJZqcoK8twwQWX8OY3v4cLL3w5tbUv7CP8+4LWmqdGt3Bv34MMF8ZIWgnOadjABS0b6QiWkNAJpsQEFTqyPfHxmRXTXBJc+Xve8+PDSTL4JH5n+EMjgY8FT5slwmsuovuDn6RvbJie+mq2mC6vKaU59yjK1pd7Saq0weNWFKy2LnQ4x48fMTTDsAykIVDBwlnB0FdIQ2JYL149vc10GZQBQkCVkggEaST9wucOu8Bra8o4/Q2nMT00Q1D0SVYneahOo6xptuHSbwT4c5csQOP4v7vrd7of4zG7xC7Dp1YZaGBUBjSFJlmpqNEGEklKpRkzRqjQJSYoxyKNRxahJXFdMb/vEo4eZPEcdBk+P/bHeWJpiFjVQF0RztrtsWjERIWKgc39tG1owYodnyFp/9MD7PrNborZEgBW3KT51GZWXLoMeaTe+DlMiJCnrRIjMqRCRb7hh3ocCwSvKqWYjiv2Gh4H/MZaQpNr3TRCa2qU5AmrxG5DkNSCQEB7YDEpA8ZFEIWvHA+EwG2/GmO2B2tyK7v27KOhPEZFXFLquAYdewE/sOdgePsIg88OUVaXxopH57U069L7RD81S06h/Pob8H71S9TeXdQKwTeufDU33H4T7/3Mh7jlPc1YuoAsDM+pgAXWxBbST32B6Qu+Eqkjng8qIL77u5jT+6OWO2lB6GJOPEts/0+whh8FrVCHHJOK2ZjZ3SQ3/wM6XgvSxK9YifSmDwuHSwQ+gYwfpvzVaJwXeCbKsgyxd38A1dOFmppCZsqR7R2I5/iXa60JNj2K/8C97H7yEQAaR0fQSh322TXVmvqkZv90ZHNgyIjAzbma7PZdfPe2LVSqPO2r2ll10WkYqRTnnbKcf9GKluFdNNatoVQFBQPKbMH5zXOz4wLObtB8f5cg60btcKGG7lloSEXq3mPBbV2CJ0cElY5GITAFdE0LfrhL8rH1CneuK9UQIJvOoOGdP2Xq1s9Quvtz2FUdbHz7PxOsfgNPjWrObTryNrXWPNBvkPdh6SFFeMrSbBsX7J6ClYfcwg1JeNMKTSnQCBFZSryU6OnpQghB83MDiE7iJE7i/2oMy4BvxafpMrworAlNozJ5UzFD5wkKXiuVSnz2s5/gggsu5fWvf9sJWeeJQv/sII8NP0n/7BA1iSpOrzuFpRVHtnrSSlH6wbfxH30IWVGFiCcJ9+8j7P86cUNGYW6HIHjiMfTMNObyg229cnEn4a7tlD/zLEbdUh7MduFOj6C0wpQm8YomVpS1U6UWPvRzQuEKTUItfL8mtWBEKnJCUSGMw2ya7Kuvw1y1lnDPLrRSGIuWYHQuI9j8BN4tP0NbJkZrO7qQB0NiVFcfs2BA52ZRszOIsoUTzCKVRvf3obNT0T5ceTVqcoxw+zZ0GCKEoLQ8zfgZ5zLjmKRzM1FeB4KCKrC9sp5+EdBiWZgrVjH57OMMqVkQlViNzXglk/TIGLrko4ZHCcpS9K9fymR7PZOzfbSWNZO2jy6akDW1xF71GnjVaw77XTpdxgc/+Em+8IXPsGnTw9xyy0945SsP/xxElgHazmAP3o8sDOJVrsFrPB9RuZak64JpviAJPL9P7tTcP55j6WGlMPIDCz+sFeb45qhGlNF9UuemOG9qGbdWbWJWbScuO5kSkxiYXORejsXCOtEafRxrfAte/dmAwHSnQHnI0iTBTBfCyxJUrJ6vI1WyMSKmR5+ILDOaLsKa2okx24WK10FQwszuBASJPd/HmngGLYyoky12kBAGgQgPqsRjA7/BnN6DX7UWbZeDcjGzkcCguPh1xHp/iTkd/T9MNlJacgNB5WrCeB1WfiharZ8HYURErjQBFY0JtMYoDHK0qAWZH0C4U4QVB1XbKlaJssowZ/eD1mhpzQUQa4J0GyregF+1dkFIXtAJfqfG2iYIWzQ6HnkGixJ45+rfivnSNlHwiwccQgGYocHp1WsxTt3N8Pgobt6jsrWcjrM7qF4UFZMbGzV5H+7rF/TMChwDTq/TvKZTcSxDfSEE7We10fVIN0JKEhVxYmUxPn/2v/O5b32CL/zoz2g8pYGXv/pKQj8kU1bOtdfewLXX3oDv+2zZ8hR3/Og2nnjsMXb2PstdT9wSPecMi8pUDW072mm6q4X6+iYy8Qqmt+aoqa8j3hdHuRrtRerecCKAez2UEzA+Psr4+ChjYyOMjUX/7tq9j97BLkIVqWNryxroXLSSd73jQ2w872WcdtqZWNYJCPT4HeCJkWf4zvYfEaiAilg50+4MP959M5OlLK9Z9krOdy/h7tivGGMUU5uURInlwWo2ui/7fe/6ceEkGXwSJ3EUBGhud/JUX/1y5Ec+xezPb+fUd7+dPulzh5Pn1CA233J/KCSCDX6cDf4LBw3EymLULKmh78k+rISNaRuoUDEzPE1FSwWZpsyL3v8ZoSgKRa02FhDxMS3JSUW/EbBKO1Qc4v+zJPQoCegxfMqVpExHdhNDRsi4DNkvPabnCt0aZdAZ2seUVn0s0GiyQqGBam3wjkKGO5w8u00PAZzrxbnAS/DfiSzunCVCa9jOuDHKlJhFk6Jc59ln7KUuXEZVuBwfzbOmi9DwqFVkTAas92PUHIX8nBQh/xOfpkt7xGcCEimD0XKDO06J8arHilTmHUqzJXYX8zxVLug2fKqVwRl+nFMDBzk3eNtv+Ow2PQKgLbRoHnTZeecuQl9R1V4JAtxZl57HeilvzNC07sitMT3S51uJafqljzFn/fGAKvKGOXuQA2hSFh8qVLDVdJmSIRklWR047DQ9fhKbZUqEpLTBuAiZlCE1ymRChtwYm+V+u8ibi5njstAACNPt5E/5BPbww2wf/xuWL+4gv+6jBJVrjms9AGN7xxBCzBPBALG0Q24sx2RPltpLL8RceyqqpwukZN2iJXzrjW/l9a+7jPd+dYj/eX0lKlZ5SOGuGXIHuNvbykDlImqUyXo/Rps6vAAxZrswZ/ZGrYUHiFzDIUw2YU1sQXjTh1lNiKCI8LIYYRElBGhFLNdD6FShY9WE8dq5Fj3N+pGdPJY5g/FEFeVzFgwjMiSlJStfIOgRogJQtnWwpyxk8+gWpp99gLayFjbUn0J1fG42+oF7cG/8Hmjoy80SN0ySN/8Mz7JxLrl8wfqq4/DG5Yqf7JH0zApCBaHWdG65g3Wbf0rSn8XHYOpRxfYnVrHqo+9hZXXUfnfFXds5O72WyTIYPE+z6MqQ5YfY2m5s0gzm4fFhyXAehIh8fG9YphZYLxwNOQ8eHxaMFaFnRhJojSEkZbZGa0HXNDSnNSlLMuVqJkuQXHU1raddxWQJGlNwWq1m7zTsnBSHkcHbJ3bx0ODj7M/28vBQAq076JupoixWRnOqmapYDb6C0aJg5RH822K/o4qpt3c/DQ3NOM7vx3rmJE7iJH730GhutXPsMzyWhDYWAoVmn+Hz09gsH8tXzocb/zb4ylf+hYGBXr797Z//1p1JJxK7JvfyrW0/YKw4QcKMs3ViJ5uGN/O6Za/irIbTD/u82r+X4JnNGM2tB0PZyssj9ep9d2OsO23B8amxEUQ8waxQjMuAECiXkowQJEfH8fflGa2MU1FWQ1Ip8hKGgmlO6dtJunphjVajDNJz4dN1h0zOT0pFuZKHkccHIITAWNyJcYgPsC6V8G67GVwXa0Wk0tZao/btwbvtFowVqyNrhxeASKWRZRlUdgrKDo4d9OwMJJOIOQ96WVFJ/H0fIdy9AzU2ikhniKe66a6UGH6BvAwwEaT9EMfzkU6CgTCgZa5+6ksE+MonI2JoaTCxqIFYRQp79x6ebUkweuVqCm0tTM72IYALWzZiyhd+eXpodpseM0JRoSSdYZRv8ba3vZ+vfvVfEULwt3/7F1x66VXE40cYXwmBX7Mev2bhJIAAiB1fF1w0+a8jlesh+y78HGHyCIFWwkDohaKe6wZXUDU1xm9WV5AloDNcxvnupaz3zzxscSO7K7JdmxMvHFDGSj+HMdsd/U6IyNu3OIpQYVSHulkA/NozKSwv4vTeiiwMgQoRykPbZSg7Q5hoxJzcgjX6OH7DuZFq18+DNAnTc5POoYs19CDKLkfPdbFhxAgySzBn9lFa/GpmN3wOc2YfWkjCTOd8953bekUUHpcfQIYFROiipYU2E2AmCVNtyNIo2jp60II2E2A4iKCItqLJA23EQFrR+rRG6CDKTBQSQh9tpxAqXLgiC4pvUnCjxNwtEF6kFi5drSMy+LeAqoVgmcZ6XBAkNNiAD0a/wFrlsOrNq1hSWELoh8TKYkhDMlWa5pmxrYwWxih3Mrxj9SqgloQJTamj2ycfCQKBNCRl9WlS1Qc6H2N89u3/wl//90f54F+8iU8+9Xk661ZRVp+m46w2ajprsCyL9evPZHnHGjb/+BmyfVOERkjv6H56J/YRpF28WJHh4UG2bHmSocFBiqXnCVn8ycF/xuMJamrqqK6uoSxRTlvZEl624nI6O1bSVr8YU1nMjMyy9uLVtJz6hxkGdyQEKuDuvgcItWJxedSlV0kFk6UpHh9+ivObz+Y68XoWhZ08Yz5FSRZYGqxgg3cOGV3++93548RJMvgkTuIoGJMhQzKgpbya1gvOY/cvbuHUd7+dOmXSa/j0y4BlJ0Cp0fmyxRSni0x0TUaSNyBdl2bF5csxzBcvQesMbSQCF80BSsFHo9BklIF1BL/Y9tCiUkn2GZCXmgKRR2Z7aBEAf5eaRBK1mVvAGj/GG0plpI9RdXs0DMmA2+wcO63I+7gjtLjcTfLOYoZZoRAIRmXAk1aJGRHSawSYGupVIyv8U3jaHMNkkGrVS7m3AalfyX7DZEqU5ov1HZbLZqvEo3aJtxcyRyQGn7FKDEifpaHFYABGIaAhgN4ag51NJut3u+TSBt+uKTBlS8q0pEf6PGAXOc+L8dpSGY9bJW538uSFQs+FxnWoAksKLvUtlfODk1hZjMJ0kaFtw0ckgzWaX8Zy8/fZPNEsfX7hzLIksOaug4gKdy05e24Coohiq+nyP7FpJLBi7j591CoyKhXtSrAidOYHmjfNDTSt4xxoqkQ9pUXXsX34f3HRRZcf1jp38GA0Mj+Ake9HG7Go9c6MgdYIdwrt5UEcXcUJRKrYtQc98s466zz+67N/yjv+1/9hRbXmU9fUAhq0ZmtVK/+99iq6kgHKLqK05hG7wJuKGdY9xxpDhC4oH/1cBbG0wZ8lqFiGld0RhVnYGRBgzPaADlGxmkiFMXccRq6PUBg4Q/ejrCTaTHFarIqLw2p+Y8UYEZHaK6PgqoLJotDkWE75/QOP8NM9v6ToF7ENm00jm3l8+CneteZNNBoZ/N/cCaaF0dJGf9cumssrEYkk/j13YZ15DjK9UCm0sgr+LKPYPx15lt3x2Ajrt/6CZMKkUBUNRvMFD3vLs8zccQ9N/a+hzq5nTG9n5QqNGAVrk6CwVuBXH7xujgFvXK7Z2BgykBPETFhWEXkIPxczHmwdF8x4kRXD6mqNG8K+rGC8KKiOaWwjCteYKEafK/qRLcXZjZFtw3gJSj5M6MgSoiMTKXe1Fphy4cDs6dGtfHv7j8j5efJegeFckZDtVDtrGStdwLbxEmV2EdtIMDA7Z4n9e+JJurv309Z20iLiJE7i/yVMCcVOy6NemfPvYomgJTTpM3x6DP+3VgcPDPTxr//697zrXR+ms3P5idjtEwKlFbd138VkaYrlFZ3zdVLvTD+37r+TNdUrSVoLu/HU6Ai4pYNE8BxERSVqaABKRYgfXEY2NjPy7JNssQqU5uoNM1Qskz7xhELsfox17asYq23ANW2SgU/D4AClkSFmzziDtH3QdqpCG2z04tzq5PGlT1pLpufUwle4aRLHkYmiBvpQI8PIQ4KehBDIhkbCoX7UyBBGc+thy4Vd+/C3PA2zM8jmFsxTT8c872V4N34PNTSAqKhC53OokSGss89FHqJSFpaFuSqq1wakTw9pzNw+Qq8ISpFzHGYMg45kPalSiNm7D13ZhkilKSxbzHBTBUsHxiiVpdBSEs8VGV7expMXd1De3kwpKNKcbuTClnPZUHe4t/FzMSIDvhubYZ/pEaIxEawIbN5YzFARj/O+932Uv/3bTwPw1a9+iQ9/+FPHfH5fDPzqUwkyizGzOyPyV1rIfD9a2vj1Zy/8sJD4dWcR2/sjOEQIEMsPcVlhJWfkP0ORcuI6gTzafWHEgOeQmlojiuMIKZGFIWRhDOFNRYpbrRFhgSDZAqELhoPXdCFe3VkYhWHswftwem+NbCuEJKhYgXSzUeDe+DOoVCPCz+PXn4M/J+AQYQkZFFDmc1Tc0gEVIPwC2ik/jGwH8Os3UhAGsa6bsPvvwsz1ghEjTLcRlC1B+DOoeC1+9SlHPedh2WL88k6siWcJMp0RMexNI0tjKCMeHbdhoUUUpGaUhlHFKoLKlYetS9VB/gMKow9EAcI60Ccij1dA8VUKMS0x9879AE3QoSm9RoEEJ3VwLNE/O8g3t32fnpl+JJKQkNr4g7xxxatZWvH8vtVH3LwU1CyppuvRHpKVScSc15twJe849WP8z+Yv8fc3fpqPXff/sTi/kumhGU65fi01i6sBSFYmWP+6Uxh4doipnkna1rZSt/x11C2vRRoH780wCLn3a/ex/aHtFHJ5zJhESYWb85BC0rq+hbNffzb1DQ0kDwmz3vWb3ey5dy/Vi6oP2+/R3WN/VGTwjJdjtDBGVWyhh0eFU85oYS8jhTEaU/Ws98884gTPHxNOksEncRJHgakjT94AzbJXXc0dH/kE0z29WG3NGEQTgicCiYoEp7/+NMb2jlPMFrGTNjVLqhe8UF4MzvXirPJtnrJdPKWRCEKhsbVgdeDQHh5OhgoEzcoiFygMBKGAciWpUQa3O3mKSrPRj2MhyAnFJqtIjTK4zj36bO8LYVqEfDM+zX4jGgRJYItZYkgG/EmhgkZl8ohV5MexGXIi8g0elyG3O3maA4tq3cCFXiMXurMsDS+kUTXTF3hs3TvEA/3DdEoRteksq0CZgl1GpOx+VzFzmHXJhAiRCGJJm2R1kumhGWwFtisYMkLcvEfPhY3MxCXLQosBGTBkRGrb7rjPE5ZLQSpaQ5PWMLp+eaHYKl2oM6kN1AKfJcM08AreEc/LmAzZb3g0HuKbLBA0KZPtpsvfJycpSkVSSc70Y1zgJXEQbDZL3Ozk2GG67DN86rRBLIiI6xkRqVbGZUgwV3A3hSb9hk+/4dPxIgaaruvS3b2PZcsOL8gAUD6xvT/EGbwf4U+DMAnSbbgtl2NNbMGa3Eabl2B0oo0wsxgjEwUTuHkPaUoqW49upnXF6z7Ipx/5EX9zWx8XLNrLeS0hIYob176fp2oW4zkxXCPA0DBOyE+dWZYHzgJ7hjDViopVYxSG55OYAWRxmDDdRmnRq6P2u5l9yGIJUIgwD4aDShz0uhLKQ3oz6NI4KtmI9At45SsprvoTrkouZnUhoJs89tgTrOp9kJaZEVSqFbflMoKa0456jJOlKW7bfyemMFheGamJlFbsnNrDHT338pb02ajJcVRNPeMF6JvJUx5PQVUNur8XPTIMc4NlMQtGZEdLfFFECvfnwOnZRaaUZbbu4DWMx21mYhW4dz6OKa9nec0KdhV2YqSAFIg9GvsBgX/K4YFzHZmImD0auqYjX+DeWUALpNAsLhe8cpGiFEY+wfbc18SSYEtNKRQHAqy5rjNSHN+yH7KlSO28olJTEYtIZik0qw+pRUMVcmfPvRSDEh1lrTww+BhV8RSTpTQTpQFMWYHWNpMlj+pEnAcHJMsqFafX/XYqkheL3t4uli49yvfpJE7iJP6vhD9X3xjPIYxMBIqoW+23xec+9ynS6TI++tG/+K3XdSIxXpykd6afukTtAjVvQ6qO7pk+emf7WVG5dMEyIpUCw0B7buSNOwedzyFrasFeWENPnnUG+56+h8rd+9D19QilcQaH2NvRSGFVM/7uJ1kxnmXZVA7XNHGCgGJphqxfohAUF5DBAFe4KeJa8JBdIicU1crgPD/Bud4LdwUugGGAlBAuJAN1GEZdSfJwUYj/8AOUfvpD9HQWYZroMMB45CFib3knvOo1FB+4i+zI08wmPEqXLqHm6nNoO8rk5rOmhxvLsMHq5EmRp0gI0sDQgjFPUzbQT+2XfkwhUY599bW0r1jGbZevJ9gyQEf3FEJpBla1c19njJdteBVXLXo5xaBEmZ06JkWwQvOT2Cw7TZeO0CKGpIDiadMl7eR4aynD9de/gb/9209z5pnn8qUv/R1XX/1qOjqOL5vieKDtMoor30ds748wszswZvsQQR6VqCe290d4pUm8xgvmu8bc5ksxpvdgTWwBFGiFtisoLno1MtFM8jlfXVkYwR5+GCO7C+WUo+0ytLCRxfFIlaw11uijmLkugrJFCK0xx59AS5sw2YQgEiMYhX7soQfwmi+JVmzGCcs6EL23oqUzv3/aSuHVn4U1rGDOi9ivPwev8UIw7LnPpAlTTZhTOwkOyb0Q7hTaTKGSDUc/YULg15+NX3cmwp3F6fkFTv9diKCIDPKEySZKnW9AJZ5nHdKkuOxtiB1fxZzeA0qBMCKFsVOBkAbCm+EAASuCAtpK49cehYiTELZqZL4fGbqEQROYx/ndPAJUPeQ+pLC2C+SkRmUgWK0PC6XTWnPr/jvpmelnafliDGmgtWb/TA837b2VJeWLjuij/UJo3dDKRNck410TOCmH0AtxB0sksgk+u+Qf+Gf1N/zTT/+KT9zwNyzSK+nd1Ef1oqr552q8PM6S8xbBeUfOFYFobLru0rUMbxolaacxLQvQOI02VR1VeEWfjKxcQAQfxOEPGiEEWv1+6ukXi7jpEDMcCkGRjHNwwrEUuliGReIE3Et/KDhJBp/ESRwF1dpgWWDzuFVk8fXX4PzV53jiP/+b5n/8XywNbFqOQKY+H/qkz2NWkR4joEpJTg/irJoLojMdk4ZVJ9ZEPYbkr3JV/J/UJNvNiHDMKJOVgc3rS2VHbTdcEdjsMTyWHWIB0Sc8imhODa15xUpKS6q0wZNWiSvcJPHjUEIcimdMl27DZ+lcSxhAJpTsMHwet4pc7CW51cmhiMLStpgBjhYUhGbUCFniW7y7UEmrioqMwA2YvGUX09sGSEiXlJaIh8fgzDrkVYuoEwa7TY9poSh/TihLpTZQaLSA6sXVCCmYHc+TVyGVsyENFy3invPLqNUG00Kx3fRQaOqVZEqGjBgBYzKkPZh7tOZ8Unf20PbMMG5/nu78BBUtFfPWHH7Bo6rj8OnqSRHyuFVkUAZUK4OkFoxJxbAMGBchQ4aPS6T+npIhP4nlGJeKc70YP4jPUBSaOmUyZISURKQSXhXYaMBAoGF+WGnMDzSB0MUevA975JFINVC1Fq/pQlSyaX7fhJvFGnsSWRpD2+XsmoyjlKKz88iz3PbAPcR6fomK1UYKC+VhTu3AGbgXlagnTLXQ1KYZ7uulf08RVbEsUgEIaD61meolR/cfVslGPv6BD/Pg9k/xpu8M8+SH0uQbW3i0YQVZO0bScChXEg9NVioetwoMuH0sJj3fBqftMtzWK4nt/T5mdlcUPOJNo60UbttVBNWnkDvtU8T33YiR3RsV10ojwuJ8+6AIcpHHnJCoZBNe4wWI0mSUwDy5BRUrpy2nab/pb2DrI4h0Cq+zkVjDFsyZfeRX/clRCeH90z1MlrILfBOlkNTEq9nft5W8VUYpO8PuXJzBRJLB2QIpJ8mzAy7LTJvEXDul/aggdotAjgIiCtIoXaOJr9ZYMqq7D0WgwJICy9VgC5ZWr+CB/nsPnvsMGENEYpbjaGAIFPxkj6R3VrAkozGlxg9h15Tg1i5JdQy8ECZKEDPADcFXUOHoea9eS8L5zZqNjZqf7BHc3y8ZLgiGC2AbkVfwupqDhedkaYrB/DC18Wpm/TxFv0hN3AJdwUC+gBBjWMYiJFnW15r4KsndvYJTazTGb9f08KLQ09PFpZe+4ne/4ZM4iZP4vaFaGTSHJnsMn7S25ierh2VAlTJoPkI30/HgkUfu5+abf8yXvvR10s9R0/6+YQiJFBKlFxIGSkedYQcS6Rcss3QFRtsigv27MdoWg+OgJ8ahWMA653yEsXCZnYvque89b+BlN91Jqm8QLSVTp63jyesvI1ZRTqY7zURpipp4FaYX1cwTpSnqkzVUOOWHbd9CcKmX4nwvSV4oUlq+KBsP2dyK0dZOsGc3xpKlCCnRYYga7Mdce8p8GNz8OclO4f7yJghDjOWrIqIlCAh378C/9y7cN72K77x8E/1ZRSntMFszREb/B9cWb+B87+LDtp+fE0G0WBVsdSzi0zOccdcjrL5/E1YQMrZyKbtOXclZj2+n9KPv0vAnH+XMlZdwR/IeHjmtDhuLGcNnUXkHFzSfQ9yMETeP3ZZhQAbsMTyaVUQEAySQ1CuTrZbLpBtSVVXDRRddzuBgP7W19XzkI+/kppvuxjBefPfkCyFMt5E/5ZPEt/0XseIYYaoZ5VRg5AdJ7PgaXlCk0HQ+toxhOuXk1/4p1thTGLkeMBP4VWsJ04cTbjLXT/LZf8WY7UKbicjOQZioeC3Sm8ScGkYEBczZboKyTvz6s9BTUXcaoYsM8viVqwnLliCLo9jDDx0kg+egnOoozO2QFqeI0G2ktPi1lBYfwXdZSNyWyzFmujCzuwidKsxcN3K2Bx2vIbb3RrymiyJl8NHapoRExzKUlr0Fr/UKjJn9IE2C8mXHFOisUi3kTvsM5tR2pDeDitWQfvjjmNO7CeMNSDOF8Gch9EBKvKaL5i0lDjvP+QHie76PObUToXzCeC1u+1V4DRf89m1fCfBPf35yc7I0xe7sPuoTtRhzEzpCCJpTjfTN9tM90zsv8DgelNWlOe11p9K/uZ+J7kni0zaxGZN+d4hMeRl/ueQL/P3Oz/KPP/hLPvTK/0U8czbqOUKkY0GqNkVleyV+wUcYAssxSVYnMR0Tt3sSv3i4+3N5cznSlLh5DycZTTIEXkDoh9QsqT7s83/IiJtxNtSfyi37bidhximz07ihR/dMH8srl7C4vP33vYsnDCfJ4JM4iaNAILjKTTEhQ7rKBC3vfjNb/uNrrPvzj3FdvGqeuDwW7DE8vhmfZkwGJLVkt6l5yna5rpTmZd6Rg+hOBFq0zd/N1rLN9BiXIWVasjKwDyNBD8VZfpxnTJddhkeFNgjQ9BshaS1pfI4PmqMFRaEpCU38RU76jcoAAQvOp0CQRNBnBHQbPmMypCM0ecJymZaKKmWQUhIXzagMuc8u8OZS5JE2vCMKI4s1JPHKYmgtEbM+PD4CyyrRKzNEsQmHY63vcK9tstfwuWf9Ocz29s//7gngK0D7u9/CWf/0efJSURKaaiXn+DBJRkmGRciIoajxNeLW/bBpBKPGRjckcDdnGd4+TGmmhBW3yDRmaD6lacE+PGOWuDE2y6gMGJEhe02PHUpygKsbkSGBAFdoYgiqlUVWhGyyirgoJoViRWhRRJPQEdFbEoppoUhrQa8MWTHnRwgwZATUhgZNPsR3fgtn8J7IMkHaxLp+jjXxNPk1f4pKNSNzvSS2fRlzpmvO0kTT/XQegKVLDyeDXa1QY48QM+KoAwnChoM2E8jCIEHlSlSsGjMGp12sady2jz4ayNYtompJFcuW1b+gVYpI1vKtt3dyxt/v5m0/9fnHj1cykqrGVgFJFVl1JFVA5VQ34ybM7LmX9OQIfs3plBZdj0+K0dJpSNuiWj9J2hjGK1+GildjZndiZvfgV61mZsMXMIqDIEycrp+T2PZlZHEMbZch3alIpWDGCNMdoHzMXEQEG/lBSlt+ztQ9o4RdXZBIoAdzFPftI3F2J2Ur8jh9txNUn3rEAlUKCWLOLuOQXzft7GfZIzsJRBdT+/upzu3AW7Sekl+kOVWO19fHrvUbOLuxGWMvxH8kIdQEc5yy0Q+JH0pqPhRSvWIR2fvLSEyN4lfWEWrIFQM63UlSG6/5/9l77yhLr/LK+3fOm26+dSvHrqrurs5ZLbVyQFkgQBIgookmmeScx/Z4PDNO4zS2sQ3GGBDGIEAICSSBJJRT51ydK+d08xvO+f54b1d3dW6B8MBXey3WQtX3zek5++xnb8Q+QVdqCV+c+lzo2yYEIgv+EjjXHFB/Dl4YFByaElQ5cEmDJmVrjk4LFiQ0x3MTLQOaYprerKA1qbEMKPqCviyhUliEFhEHpgRdVScIWkPCW5doVtcGHJwSKA2LqjTLq+HkTEbHdLCkhatcTGliCInSAVFrGtuwaIyPV8iIGWoidbgKhgqC6YqFxU8TMzPTTEyMseCU4KF5zGMeP98wENzqJhiKTrPf8EhoQV5oIlpwazn+Y9lxKaX4/d//ZS65ZBP33POun+Be/2RQHcmwpGoRLw9vJWUnZlV0vdkBWpJNtKfaTltGRCI473ov/MeXCI4cBs+DVArr5tuxrrn+tN+7wPCqpexYspLo0AjaMCg21JE3fGLK5MrmS/ne0R9S8kskrDiT5WmEgBvarsE2zk7EOwicc9TU54MwTZy73ob+988T7N8zWwcYbe04b7zntBDY4NBB9NgocvHSWbWfME1kbT3+zu08KUy2Vu9mUWoNdThoXzNg9PJg5Bss91dRpxrmrK+x4nk8LgNqs0Xu+vuv0fn8Vjw0EWmw5GA/xZZmZGNH6NO8bTNvvPuttKfa2Da6i6yboyOzmCsb11IbOV3ccD4UhQ7rWTW3/oloQVYoCkJRrQ3e9rb38KEP3cvf/u0X+PSnP8A//uP/4ROf+PWL3t7FQJTHsSd2hB1klU6wkpPhsDdG3+QLFMwyEStJS6yTtvhCvKar8bj6nOt0er+PMXMYv3pl6AUMyMIQ0stSWPZBpDuFOb4DtKqoXgVIM1QQmxHAwK9ZGwoT3KnQ+/cUeA2X4Qw8iTFzaLbjzcj1oCO1ePWXnnXfvLqNFFZ+DLvvUeyh5zGyR1GROvzkQqzxbZiTeygu+wBu87XnPXcqWnei7r8YGE5YD1fgNl8XduaVx0MPYTMORgRlxc5uTecXiO35J8zJvQTxNrThYBSGiO77N7SZwKs/3YP8Jw2lFVrrsIY/CVIIlNaoUzymLwbJ+gTLb10GAST+SjJkDdGXGsaPBJgxm99a88f86e7/xt89+D/4vff8JTcY1130NuyYTVVzmqn+aTJtVbN/L06XsGJh9+ypqFtUS+u6Fnq39JEbC8lvFSgaltX/xAVvPw3c0n49k6Upto7uYjA/jClNlmQW8o5l91xQ18PPCn5+jmQe83gN0KTM2WCuI+//OL/x15+l+p+/QecnLtyvSleC6MZlwLLAnlV79EufR+086z2H9I9RSJ4PUSQb/QtnNOqVyYeKVTxtF9hllrF1aEPwvFViSmhOsghlTAZ0+TaWhsftPFusEi6a1b7DlW6Mmgs4rpQ2CNBo9BzbhiKaGmXMErdTQjFVCUc77p9rIqgPDHZaZabKAVXaYPTgGEJAbSRCXBdCa4SkBcMF9OEphlfFucKNkjrDwKpWm/xCMc23nSz6R99FKZ+6wOTacozN3Tv5+3vuwXzLrTznFEkoGabbAtNSkVaSJmWyx3TZZ5QpD8zQtW8MpymKmzJZGk9QuyLC+JFxitNFuq5bRMflHcRrTnxQp0XANyJZpqViaWBTr0yeswocM3ySWpLUEimgSUlmpKJHeqwIHNJaMiQDeg0fm5BMjyFYFFjsM1yyaPoMj7QyqNIapTXHpMekCMhJhYvJ31qHuVaOcnWqnZhR2ad4C+bELpz+JygueTfRQ9/AnD4cev5KE7TiaN8DVCejVCVPHEdeKH5g53nFLKCXrGVBvp2bp2ZYnZ0CwgA2EHBS8IPlCMYureUHXbX0NDViAEv8HHeW4iQGi0wPzCANSXVHNdH0ifvZnDlEQ+c6/uWT7dz1vx9h3Y8KOG+2yRsSApcVA1u5av9DpGf6cA2LRSUBRhVO7/cZOzrNliOXkh3Jo5XGSW6k89JGVsd/QOTY9wjb/TR236O4rTdTarsNoT1KC+/GmtiBNfIysjSO8LJoaeBnVhIkWrHGd2JMd4fpzXYVxa0DmKO7iS4wcCOtaG0RTOYpbj5MdMFyTKcH4efPqJxYlO6gLlpLf26QtmQLQgiiAyN0PP4KjXYtQ23Leb7YyaqDT9F48BUKuWkSsVomOlaxc/07WekKGrYJxAz4J/H1QTuY+8DeIbjlphae2HMb9pMPYExOIAyThZRpWrsE8203UPgiND3bihu4jM+M0ZALC3z3Kn3mWRXg6Az8y07JUF4QtzSHpmHrqGBdHQSa09KTj//35Y2ax3oFU6UwNM6WIAWkI/Ddw4KIATe3n3gJSRHaXayoOftsVMpOsqZuJY/3Ps3CVDuZSIah/DCuKhEx1pKw0uS8EZoSzcSsGNN5iJqa6H9BhbRr1zYAVqw4yyBnHvOYx88tVvoOHytU8bxVotfwqA8MLq10kf04+OEPv8eePTv59refRMr/gnaH80AIwRsW3cJocYzuqUMIBApFQ6yOuxa/Hsc48/Ebbe1EP/ObBIcOoItFjKbm05S0x9EehF7MWVOgW8LfBGhmhOJ63+b2RbeSclI81/8iWS9PR6qN69quvCDP2x8XxqIuop/+dfztW0Prh+pqzNXrkJkzkauVb92pk8dCoIRis/MyaZ3BqaSFCARNQSvd5h66zb3UuXPJ4LV+hK6gyFN2gfYtu2nfvp/xxhoihRJGJE7WlDQd6kWVbLAd9OQ4UkjW1a9CNHfxlF3gecNnl9Jc6RW42o1elFimQRlklMGYDGg6KYxvTAbUKIPaigjlxhvvIJOpZu/enXz847/Kn//5H3LDDbfQvGoVB0yXgDBvpFGd/8Mt3CyoMtrOnNGG4ziMwhCiPEWQDhWcGthrCo461cTcAhENpaBA98wOpBC0xs/eeg9A4GKNbQ9J0pPU7iragDm5G6EDyu1vQFtJrPHtlS2KMK9CGojAQ1kOoUJAIdxJ/KbTidkgtZDC8g8QOfR1zOxR0BrlVFFecDtBvOW03584MQKv/lL89BKMXB8qWh+GKwMKMGYO4xx7CLdh02zY3WuN0uK3Yk7sDK0jtEaZMTAs3IbLz0rqWuM7MKe68dNLZ20wglQn5tQ+7MEn5y6nvDB0T9phBshPKCyiJlpNR2oBu8b3krKTsxM3g7lh6mO1Z5zguliIaTAGBDXNNVQNJ5nIT5GJV2HEDT7Z/pv8kfgt/vw/f4cr33k5q1atu6h1S0PScXk7O769k8meSSLpKH7Jo5x3ad+0gGT96eMVaUpW3rGcms5qRg+MoQJF7cIamlY2zgkI/3HhFlxG9o+Sn8hjRS3qu+pI1J1feX6xiFkx3rfyHVw/08tocYy4FaerauE5Jwd/FjFPBs9jHudBXEs2eVE2pRfyylvezVc+/4988hd/+YKT3qeE4qjh0aCMOWRnozI4aHgcM3zW+K8dGfxq0KRM3lZK8dZK0SkQOFqGwWhaEdOCKamIasE1bpSvRrO8bJWIIjA0PODk2G26fLhQRfV5COHVvsOTyuSw4bEgCPWqg9InrgUbPIc2ZdGgDI4YXsVPL/QYywtFa2CRQDJNGNwR+h+ExZOtBUt9h91WmVEREBWKaeHTHljc5sZP8ws+jsWBzWcK1fRHUyihaQ4sHq7J8dA/PkRyYTvXbrqOrapMr/TxhUYrSGuDFb7DiPQoowmEZmqmSLZUZqDFpkNJFgibyIIIseoobt5j0TULiWViaDS7TZeXzCLbrBLdpsc6z0EiqNEGiwObcRlgacFC38IxxKyH8LAMWB5oyloRBB7VZUVPTBLqYUPfa4mmLBRFLbjKt7m5nKDX8Og2XcbMAFsLbA27zDIvrbiSJ7MFfrd7O3EV+tUpJ4M5sQNZHKnMsjefKGKFpC9r0lrtYGQPE1QtI0BzX2SGF60iGSWJm3F2peBIuomPHd3Hitw0xwtcbZ74eO9NpPlC8xJyiWpqK4r0V2SBmacPs/KZCVTeAxH6XS27aQkta8LQPWVXYSqPG6+8lF++J8tffPUFbrz7FXqv3Uj19BFu2/Y1bDfLRDRNtVumceogplPHdGQdOx6fIGcPkm5vQxqSwmSBmRceIlj8LEHL0tn2M5kfILb7H3COPoi2kwSxJoodd1FuuA577EVkaRxZGMatvQQRlDDyvWjDmfXJiur9yBUlDOnhiwMU8s2UdRqvZwy/fxSjYcnpAXYVpJ0Ub1p0G//Z/QD7Jg9gCIMVOw5S75rUrdrIC0MWM5bF7jW30XXkRQrTx9i7+EZKN/0amaRDKQiQkwJtn0KWKhBFjbEPam8U3PmR13N43QLc7duIlbI0re7CX3cZX5uo5ugKqN8fBj907+6nblkd7hv0WdvktIYf9IRE8NKMppJxwUgBdo9JoqZmuACtyRO/Hy5Ae0rz+kWahA1//orENARVtqYlEXoQDxXgR32Cq5o1sYusxe7ovInx4gR7J7qJmDZCCCzpkrAXM1SI05qwWVq1mJkyTJYEb1yk/kvI4G3bXiEWi5/VdmUe85jHzzc6A/tV+fefC//8z3/Dhg2XcdllV17UcgrNTrPMDrNMUSi6ApsNXuQ1ETC0JJr4xPoPsX10N2PFcVJ2ijV1K2iInVtdKCwLc9n5Pda7AptL3QjP2UUcJJaGGaHoDCyucKNY0uSmBddyXesVlPwyMTM6297904CsrsG+4abz/s7oXFQJyRvAaGlFockpDzk+ROzqG1DmHuQpYofj9W5wakgZoeXb+wpppBbkDvbgGxIjniSSKyGAYsQm7ivU+BgiGkO2hCTWFrPMl6LTuGiqtMGI9PmPyAxTIuDNF5EjktYG17lRHojkOGp4JJRkWgZILXidG5u1jnAchze96V6++c2v8uyze3niiUf44Kffx7U/+g5TURMQpLXkdeXYWWt8UZ4kcuQBrNGXEYFHkGil1P6Gs9p0aSuBNqPhZL2dJidg0BQkPJcoAteMY5kRst4UvfnDNEUXYJxLMSgEWhoI/9SskNDOQVdqaz+zHOXUhKHEiQWoSB1BtBFzcg/YVchCP7I8QZBop9xywxk35dVfhle9GnvwaZzeR5ClcSKH78cafoFy513nVMcahQGkO4NfIYKPQ0UbkMVhjMLgLEl8QdAaY+Yg5uRehA7wUwvxM6vOScQfR5BaRH79bxA58kAY3iwM/MxyiovegrbTZ1xGliuB7KdMIikrhcz1z/63NfwiztHvYOQHQJp41SspLXobKl4J9Q5crLGtmBO7AfCrV+LVbQB5/gJUCsntnTcyVBhm3+QBomaEkl8mYcd5/cKbTwvEfFWIgLY1jm+xbsFKtvXuYaowjfI1EWHzFx/9LL/97U/wrnfdyQMPPElHx6Lzr/MkNK4IJ46OvXSM3FgeO2Gz4LI2rJjNK1/ZglKa+iW1tKxpxo6F59qwDFrWNM+O0X7SyE8U2P6tHUwem0TrsGvySOYoK+9YQePyhvOv4CIhhKAzvYDO9Okhnj8vmCeD5zGPi8BHPvIZ7rvvX/n2t7/Gvff+wgUtIwktNf1T/h5U/u1CH8IAzRHDY1ooqrWkPbBmScHXCicXU28oJ6jWBs9bRWZEwBrP4Vovhodmi1ViQWDOJij7aLoNlxetIre7556ta1Im7yil+JaT5ajhoYA6ZXB7Oc7SipL6jaUkX4xOc8BwGZQ+NoIqZbA4sBiVAe2BRU1FPVC7uJaBnUN4JY/miEXcFQyUipSExfqWJq4qZMicZyBjImhXFspXTCmPZ+1pBv/jW1z6Sx+lWphc6xrsM8rsM11SSrIosMgLxU4zDHxb4jsUEj4Rx4JcQDwqcXR4Lr2ChxO3Zz+cz1lF/jOSpSw0OaEYkwFbrRJr/QgNviQ6NUEspqkqlFk6pYm21bEnppGArSX9+TG2BmMYU6NE9+0g39LJriWXY0YS7DdcXKFpUSZdvk2PEXDY9HhHKckD5DhseNRogz2mSx4bRZTvNNbgC8HvHdhOyvcRykWZMYQ3g5E9iunOgDBQsQb81EL6x3O0ZKIIFd7hBw2P7VaJBcoioSUy1kbt8IvsT8ITVSlWDe+BwEUlWhHuFDhVICTPxU2mnQSLZTW6MpBJbJ+g+FQv0/E4jS3VWB7kB2fY+8h+Uo1JkvVJvIbLsEZeRBaG+b13Xsuzu46x5WN/zPrH/pFbxrdT7RbojdchgVrl45hVyOIo01MlZmYS1CzXqIqXVrwmTs3YIYrTLlZHRemsA8yZQ8j8AMquInAaEN3PY734Paan1uB3vQ77qstJjn8ba/QVhPIQ5QmQDipSjVEcIjADvFwcbeSQ0TzxVD++a+ErDxEUcBuvPmdxuanpEhrj9ewY3UPWzbFqf5SWmgRZlaAUQNQU+BhkE3WUA498QxeHCg631GmqIxC0aeznQ69jJMgxMPcIjD6BKGpkVmPdIVh+1Vq4em34DCv47A7JlhFBXY1m/J5m+AH8w7J+jA+vYWXH2Z+fYgAHJqE2eoIIBqiLwv5JzcZGza4xyYEpTdyErCdIWHBrh8IxQuK3Mw2GUIwUBEdmYLAgaIxp3EAwVoQFZzhdvoLuSRjKCyJmqBauqnDs1ZEqPrb2/eyb6Ga0OI4tLTQwlDd4YbCBrFdLb14SNQRXtyhuaT+70vi1xPbtm1m9ev1r6oU4j3nM4/8/2LVrG88++ySf/exXLmo5jeZBJ8ejdgFfaEzgZavEK2aJDxWrzltHvRpUOWmua704wvpCYSJ4RynNwsBmi1WiJDTX+zZXuFHq9IlK3JIWph12ee0w82RFwMLA5hIv8poc87mQE1metZ9gi/UyCsVa/xKuqrme+B1vpPyt/2SieycDEYFbLjG1aAFHblmDnhrmWPIZqqnFkeEHcEyOktQpFgZnDl2r0yafKWR4VGaIBJpyKk2hXKaMJp0tUDM+jXI87MuvwrrkMnw0P3Ty+EKz+PjEhTYYFT7P2kWu9KLUX4BC9zhucuMktOQZu8iUDFjkW1zjxdjoze1qfMtb3sW//ds/sn37K/z2//0X3nvrNbzyP/+SO/7w9xHAiAx42MnToizW+HMn2APlMnTsq2QLh0glqllcyGNP7SOe6yW/5lP41atP268g0Y5fvQpr+DmC5EIKdpyy8ki5WVS6q2LbAI6MUg6KlFWJmDzHmEdaePWXET38TVSkLiQrtcbI9aAitfiZcBJYRespLXor0YNfxZzcFXoFu1NoOwV+HiPfT7ntNkqdb0YlWs+6OaFc7IHHQ1I53grSxMgeI7b3X8jZCYKqZWdcThsRtLQRQXmuUCEog7RDu4YLhdY4R79D5Oh3EN4MAH/+0GG+vX2a7r4xoo7NxvVr+Z0/+GsWd6084yr8mrXkMquQxeHQMiNSd04Fr3Kqw38P3DmEsPRm8KrCZ8Cc2Els7+cgKIXBdsrFHnoWWZ4gv+430dIiuu/zOANPcVyN7/T/gHLTNRSXffA0ovlM6Mos5JfWfYiXBrfQlxugNlrDxoa1dGXOoyC/QOgYuJdoIt+T1CYyXLdkE6NTE+jegPjyBPIDMe5724O86U3X8/a338EDDzxJQ8M5QvxOgRCCppWNNC5vwK14BO/+7m4Gdh/CsAyEEIx0jzB6YIx196yZHde+ljj87BHGj0xQ3Z7BMEM7oen+afb9oJvq9sxPZR9+3jBPBs9jHheBrq5l3Hzz6/nsZ/8Pb3vbe+akHp8NaW2w3LN5xi5SFchKMrSmV3o0Byad/rlnGFWg6J/Jc391kUOxUAEb1ZJVvsO9pSSpn1JxaiG4zo1xjRvFByxCsvhBJ4cPs0QwhEV3XEv2mS63nzoBfgas9h0W+xZHDY9AQHtgzfHHu8SPUJc3+GJ0ihcqCuSWwGRIBsS04OZyfLYtrWlFI6PdowzuGUaIcHK4QUDr+g5WdrRiXMD58koex17uYWDHIP1xzXZnC+70DKvede/suVgaOEghWO05DEufERmQ1pLLvWiommmoQXTV4W4dJtcoKZkWarqMm3dZePVCTMekgOJRJ48ElgQ2UyJgWPoUhOag4ZLZeZDUYB/68mWYuRxqx0FaB+uZuWwV3TGJ9Es8X+7Dnplgbf8QkQDGul/CzU3Re+Xr0cJgYWDTFVhUaYMJEbDZKnKDG2O/6RLTgr2mS1FoaoSD6ZYZEgabq2r4QW0z9/TsQvglvPrLiBz5DrIwBEERbWcwpg8giyMMjIxx6dK20CsXGJY+HqHaBMKi1q+/lHSxj6MxH9+Mopa8myDeTOTYg5gzhwBNz5KVxGML0M6JkAFr5zh9LQ7HNtWgkjYRT7O8x6btiSFGD46HZHDdRsqdb8bpeYRodh9f+vAqNv23Jxn/lX9k6S8txHMypIKA1uwYS8t+xd5CQ3kGRD1YcxNhDUvgeye8vGRxJCxAhREmSg9048+UMKRJxDjI5A89gn174IMfIFqzBmvwaWRhEBWtR2NglqfQyTqYGEbHE/jKwpIzWMUj+PEa9CV34Lbddt57sj3VNttW5g6lKW0/gu/5CCyqHM1kQeGXCigd4JlxHC24ulVhSfAu0djPa8z9ApXSWNsFcgpUg8ZbrZFToaewygT4lbFB9yTsHhN0JEMVrnIaENLgCP08MSVZodVZa3FDgCnC8LeTEVSyTDbWw+WNiheGBCN5WFWnuKpJs7TSERu3IOdBT1ZgijBILuvCSEHQmoD4GWq9ggdf3ivZOirwFKA1jXHBO5YpVlYyCG3DYk3d6YONNy8Ojzfvaepiis7UT6xT8KKxfftmbr/9Tf81G5/HPObxc4fPfe7vaGlZwB133HVRyx0zfJ60C1RpOTvZ7qPZZ7o8Yxe48yLUn/+vwEFwrRfjWu/cqrxH7TwPRfKU0NiEJPjLVokPFtMXRXL+OChS5Auxf2Sr9TIJnUAg+aZ5H3vNnXz4uk+Tba7nib3PoCen0B0dvNKoePnYt9FHhzCWTDGQ+S4L4wuJOREMbXJb+U6qSrW8PL6VGTdLbbSGZdVdsxYcBoLrllzGwOPP4IxMMl1VQ1vfEK17DxFxfazrriLy/g8j6xuYrNSqNafkiNRog27pMSj9izpPBoKrvBhXeFHKaBzEGcUua9deQjKZ4qWXnmXJr3+SFb/6cXb/6d9yz3iWhcuXU3/JWp67bDnbzNIcMnhKBHxNdLO/pZGS04WtBSuyU7y35yANo9uw+x4/IxmMEBSXvAeUhzWxm1hJYVe1Ukq2ITMnunc85WJJG0uen4gKQ9oOY03sBHRoN+dUU1p0LzpyIjDZbbmBINmBNfw8kcPfRBtRvMxKkAZGrg9zcg+i7dZzbssa24o5feSEtRsQpLswp/ZgDz5DsWoZaI05vg1rZDPSm8ZPd+E2XI6fWYo1uvmE1UJQwsj34TZfG9opXCCM6W4iRx9AGxGCxCoQgqe7t/DxSzUb3rkaT8b5g/v38q63XM+TP3ieaN1i0Dq0XzOcE3YU0jih2D0DZK4Pe/AZjOwRlJUOuwan98/xDNbSxm26HgB74CmEl8XPnOgq8M0E5lQ35vh2EBJn8GmCeCvaDt91ws3iDD6NX7ser+HyCzr+lkQjd3XdcdrfVaCYHpjBL3nEa+PEMq9OKVy+TWOMaqydAsO3aBP1+Mug+G5F4ECNU8d//MfDvOlN1/Oud72B++//Iel01UVtQ0iBE7cZ2DXI4J5h0k0prEjIXfhuwEhlzN2+8ce3vjgXvKLHSPcosUxsNk9GCEGqKcVk7xSTPVM0LKt/Tffh5xHzZPA85nGR+NjHfoW7776Rxx//PjfeePsFLXO7m2DYCDhohMyoRlCvDO4uJ4meI4Fp9OAYB58+xP1LBN2LIyzwTDpaq/ASBi9aRWJa8q7STzcVWiI4Xu5MiIDDhsuo9KlXkmp9wgrDrwSfXCiiSJYHZ7feWKAsfjdfy8tWieetImMyYKVvcbUXZcVJRZ/pmKx582rql9YzfmQcISW1i2poWFZ/3jAyAK00ux/aQ9+2fuy4Q0KaDHzzG1RvugIjeqIlaUYoqpXBu4sp4lqy3SzxhdgM8eMkthDoNy6kZGmcfVPk8jPE4g5d1y+mY1PYbtJn+IzKgAVB+FFNa0mbsjhouAwGRY64M3htjSwby2MGmgMrFkE+S3RymjcYbWS7n6dnaBfrqcLSBsRqSdgJDg8fpXGon9b6pdSfdE0yWjIiA0akT0xLxqUiLzQZJRHSIYjWY6gsVcUJXo5K7iqN4jdfRxCpITq2Ga/u0tDXzC+gpY3M9zEwkaeh67JZS4WYPqEOP07Qq1gj2UQ1TZ4iv+n1CDMkYL36jRUfMEWmvonDEWgMTtwzg3HNrg11xJIWjXlNLiJ4arXDomKaFeVKkq2QlDrvxm24HHP6INXS5P/85dv5wEffz7FLGnjzaoGQNsnJgVDtYDigfSJiEuwErlE9+yHUWjNZqGVR+hWsoWfBSoDyEaVJRFBEBwJVKGBGBUKWiTpTTC+9jWDfHtyXNiPf8g7KC27D715G5Oh3kIVhAOwaCz1h4JdslE4itQ/peow3/BrulW+/aObRXLcB47mnSHbvJUMTUgpaJoc4UB16wVlOhGtaFde1hGoGVQeFDyqc70ucJwUyB/5S8Ls0OgVBCsz9YL0s8JeFy4wWBb5m1o5BGgbxTCPM9NOXBV+DdZbddgxYV695+IgkE9E4Rsi/92ahPgZLqzVJO/zNmVATAVdB0RM0xTW2AUYAOU/gaX3GfognegUvDAraU5q4FRLPR2bga/slv7FRkai8tJSGQ1NwaDp8KhZXaRamYVUtzHox/hdhfHyMnp4jrFlz5pbVecxjHj9/8NBss0rsMsq4QrM0cLjEi/xYgXHHMTw8yLe+9R/89m//D0zz4oZ8RwyXnFC0nkTqmZVW/G1m+WeSDL4QDEqfHzgFolrQoSqER6Xb7Um7wNt+SjX3DmszO6zNLPS7iBAqMT089pm72Oy8wPSqq3jqklqWBTbDQ/vZs+XrpEyLUmIpi/o7mBjbzlRNkU1NV3I111M1Xs9f7/0nemb60FpjSMnSTBfvXXkv1ZEMANGlK2m79S7qH/seA7rA4IJWji5YwLK2ZcTueSey0rESqVixzdqzVVCukOfRV3nvSgRRBFopvG2b8Te/iJ6eRi5ajHX51RhNzVxyyeW8/PLzdI6+jV8sB3y2KsPn7v8Wf3tjlrpXtlI6dg3Tb3nbnPU+4OTYHAQsHp8h5gvyhsGWqhoiKuCXZo6FogStz1iLqWgd+bW/hjFzAKs0RUqNMahKJAFLa0pBkbIq0hpfcUFksI5UU1jzy1hjW5C5HrQZx69ZS5DqPO23QaoTY+YgCInbdDVCB5jjOzAKQ5iTezByPZQWvpXi4nvBjJ62vCyNAXqWCAbCsYmZwMj1VFS7DxA9/E1QLlpaWMMvYA2/QGnhPQgvjzl9kNBXLAyuKy2696JqVmtyD8LLzrGV+M5n1mONvkKQNHGbr+Af25aw8P2fZf/3/pyNd3wEp/dRzOwxlOHgNV5FecHtZ8zUOA5jqpv47r9H5gbQVgwRlAGNcqqR7jRCeQTResodb8CruyRcJnsMZZ3yLFeU2rI0hiyOgFazRDAQ/v+8wpzYdV4yWGtN1ptiyp0AoMquJmlVIYQgO5Jjz/f3MtkzSeAGOEmHtvWtdF2/GGle3LOjk5D/sMI8AHJEoBPgLddwErfc2trOffd9l7vueh3ve9/d3HffQ0Sjp98v58NkzxRa61kiGMC0DaQpGT8y/pqTwUpptNIYp5wjIQRaa7R69aF8/3/GPBk8j59JaDQuoTr1tbZKOBWbNl3NJZds4k//9A+4/vpbLqidt0GZ/FIhw06zzKgMSOhQUXpya9qpmOqbYvu3dzJkBQx0NFA3E+BNZBnJ+jSvaaLBMdlulbi9HD+vL++MCNhtukyJgBptsNJ3TpCW51lui1XmsOES15LVvsNyP7Ru2GKWuD8SWjsMSZ9R22dRYLHKj5AXigBYdxHBdRcCiQj9m73oaYFzJ8N0TFrXtdC67hxBCWfBZO8kQ3uHSTakcOI2I5NDTG15mYV/9qccmcyyqCpCVihGpc+NbpzayjVcFURoUnl6DI/OwEIgKCctet7eyc39kqsmHWLVUSLJE+fEIlRE+GjCsypY7ttIDT2FLLFcgXVDBa44OAIC9jVVUZgap3XbEJfcs5o/ObKDduVjxU5U41EzAoHCys+gBYiTCPmCCBUXSS3Z6EV4yirgVvyFNZoZJ4GtIlR7NoWaeqbWr8dKr8Dp+S64OYSlUVYCISRIkzwxxnKD1C850da5PLBpCUIP6I7AwgImhKIk4HJVNUsEhxcqFqYiA5tUmZ16il7p0aBMXBS7NqSg4LJwTIXEclEzbWsOL46S46QkWyFQ8RbcSjDGbW+8ilu/9U1+/wtPcuefXEM8HsOvWooxfRCjOIy208RXbCSj1zJ8ZIZYdQxpSPR0H0urDxC1S5hT3YAOwyXcGZRTSxCk0H4ZZccwmMIQBaRUqFQVwf694b74JbRdBSrAyPcj3Cw63ox5ySZ0OY3IZpFiCLn8AxiXvuOi708Amakm8oGPIr//EI0v7KI/B8PLrubY2uvhR39LR0Lx7uUK46RHPFgAhV9U6LjAeUTir5lLfOqoxhgN7wSApA1eoDk4Ca4SRExNrLqZmbEBMpFQ+Xsu3LRA05vV7JsApQUaqIlo7ukKieBzYaIMtRHwkpqsK5hxNZYUdKZClfJQHqpPerX4Cl4aEqSdkAiGUJ3ckYRD07BvUrCxQeMF8I0Dgmf6JaWKajluwY1tijsXzbW0CBTsHoc9EwI3gK6qkLx+LX2Ed+7cAsC6da990vU85jGP/3oEaL4eyfK0XQg5GwGvWGW2mSU+UEz/2J1fX/ziZ7Fth3e+8wOvYmlxxhpLwzkkDD8dFFFst8ocNbxQ5enbs7ZiPy6O27EtDU4iPBBUa4MdZpm7T5rofi1xxDwEMEsEA1hY2Nqh29iL0puIIhEIBgZ2oZRHJtmKKwII0lw9cyP7jh1k1fIrWN2ynr/u/ieOzfSyKN2JJU1KfpldY3v5zqFHeN/KtwMhqSJuez3fu3YlzzNDzhQY8QStTpp7SwFLgoqllpZs8CI84uSJCUlCS1w0xwyfpb7FwuDcHY9aa4Kd2/BefA41OoLR1o51xdUYi5cA4H7vQdyHHgCtwHbw9+0m2LaZyAc/xqWXXsE///Pf8BuPP8vUoaO8/567+P0v/jv/cOwwH990Oe1PPIex7ipoCVuNxoTPTqtMoytI+C7KVMQDaC0W2JXMMGQKGuxz2w4gjVlLhSV+AT29nfHyKHl/Bks6LEh00Z5YcsHXVltx3KZrLui3xvQhECZIC3NkK0b2aOiVq1IIr4DT8xDaiFDqevtpyyo7U9lgMCewTvg5glgzMt9P5NjDKCuOilUsRFSAObkba2IXuQ2/gzWxE1meREVq8apXXXxwnPLm/KcIXIx8H9qIhiS1EEyXwzdKffkAia1/Ftb0kTqEXyRy6D+R+QEKqz4xx2NYuNPI4gjaTBA5dD/m5H6CZAfKyaDNaEh2S4vc6k+B4YTBeSeNP4J4M3a+jzn0YcXuTttVUBFznBnnFg5orTmS3U9P/gCuCkVglrRZEF9Ee2QJu767m/EjE6SbUmGX6FSRg08fIpKK0H7Zq/CmNSsB0cvPvl9Ll67k3//9Ae699zY++tF38vnPf/2iJwilKc546GciaF8L2DGL6vYMAzsHiaQiiErRnhvPE01FSDef2UN6HufGPBk8j5857DLL/Mgu0Cc90trgci/KVW4U66dECgsh+KM/+kve8Iar+dKX/oX3ve+jF7RcQkuu8C58Jq5/xwCl6RLOxjqCiEG8CEZVlOJ0ifx4nlhTgmGpyAt1TjL4qOHxpcg0vYY/e4Y6A4v3FtNzkntPxYQI+Hx0im7TwyL0PH7GLnJHKc6lXoSvR2bIS8063yajJXvMMntMl6zQtCqTa93T/b5+XBRQFIQirY3X7HpnR/P4boBT6UV/fMtDpOMZbqzdyKF8mT7hEReSm9w4byydmKlOaMndpRT/EZlhn+EiEHhoWgOLjVVpqlOnM2BtgUWHb9JtuiwObAxEJVoN3nQky7v/4UGs5atO/H4iT3D0MMbCxcSESXWkisPTPXPW6SkfUwiWyyr2iABHCKq0pICmR3qs9h0WBCZNgckNboyvRKcrPsySiBYsVTFy8SgrvSgmaUCEKoTpfSDtsCjVPtpK0pcPp56bmk54lsUravWvRbIcq4T+JbTkpnKMq9yz3/+rfJu3lpI86oSEugJ0a4KmvTOUJ4uoiBUWHJM+emkKL31udc5//+9/yXXXreF/PKn5X290QBgE6S68SA3FxW/Da7qW1Stc4s8eZmjfCIHns7p9P63pAl7TG1DFIWRxFJkfQHqhEhqlAIUQZTQmGgdD5PDdMiKRABUQ3fevOAM/QtuhhYU1uhkRFCBWhZlKIBM5gtRV+KvfeM79Px+Mpmai7/9Flt01w+QAbJlJk8+XALiuxWN17RkWEhC0AZYOH+hZSTSIvCBoOVEWhyFvkqF8SJgKIShEW/DG+7myWZ9XGJKJwMfWKnaNCQbymqwLbQlN+wWIyaImJG1N2gFTaEqBIGoqTAljRXEaIesrKAVgn1KLGpW6tejBU32Cbx0QvDAkqInAsmpNQwwmSvDoMcniTDBrJ6E03H9Q8ESvwFMCiebpflg7IvjASnXR4XUXim3bXqGqKnPRIR/zmMc8fjbRbbg8ZxeoV8ZsKJuLZrdZ5iWrxE1u/DxrODuCIOBLX/ocb3/7+0ilLn6Q3OVbJLVgRAY0VGrFMmG2wS3eq9+vHxc5ofi36DQ7zTKCMH/jSafAreU4d5TPHg58LhT9IoemjhHogHxtA0SZrcWO46dFghdQ7DVdDss6ZkQDivAbdByB8InqGHFlsA2FRlMoTWGaETQaDUS1QIpQKpPz8hyd6eXIdA/tyVasiko0Yjo0xOrYPbaPydI0mUh4jzzvlHg8HaNOJWnTEh84Kj2+GpnhVwrVs4r129wEE1KxyyrRh8ZAsCiwuLeUOi9Z7j39BOWvfxXtuYhYnODoIbztW4m+94OI2nrcJx5DJFPI+tCOQCtFsH8P7uOPcemlV/Lnf/5HlJ98CjtVjVmf4W233MSXHvoe9c0NvMuK0dndDy2h7UNeaMpCk3HqUE4VsjSOcjJEfcGYAXnDxG2+9oKvT8SMsab6crLeFK5yiZox4uZrp5LXVgK0j3BnQqsDO402Iggxg4pUoyK12EPPUG6/Ay2tWes1P7UIr24DQXIB5tQ+gkQ7WpgYhQG0mcBrugpz5iDCnSLInGSfJQ1UpBZrdDPFrnfh1V/2Y+1/kFwI0kJ4ufBYlIsIXASaINqAUprf+tyjXL68lTVVUwRlC6/hyllyXlsp7NHNuFP78KtXgvJxjj6I0//DUPXrzmCObUMbNrIwgDaiBOku/NRCzOn9iKAcLncKvKZrsMa2YWSPEsSaEMrDyB0jSHXg1a5DSxOn//ET+w0ILweIMPzuHJgoj3A0tx9T2mTscKxSCgoczXWjhiSTvVNUtaYx7fBZjFfH8Ms+vVv6aLukFWm8Nm+ajRsv53Of+xrvfe9d/NqvfYS/+qvPXZDd5XHULqzl6Is9FKeLRNPheK6cKyME1C957e0ZhBAsvLKDmcEZxo6MY0ctfDfAsAwW39hFtOri1c7zmCeD5/Ezhm1miX+PTlNEU6Ulg5X02gkRcPdPsWVtw4bLeOc7P8Cf/ul/484776Gm5txpx68GM8NZrKhFJK+JlTTZqCCTD//NL/lkZUBGydM8u06Gj+abTpY+w2dxYGFWCMoDhst3nBwfLqbPWjg/ZRfYZ7osCexZ4nVE+vzAyVNGMy4VSyohdp2BRa0y2WuWySiDT+arWfwTDLgronjEyfOyVaIoFLXK4Ho3zhVe5IIL/wKK/aZLSWiaA5MFyjzjspYTlrEqUCDg8S0Pc8P627jilSKXD0qWLKqmShtnJODX+A6NhQxbzRJP20UGpM+o4fNP8SnWexHeVE7MUWSbCO4uJ/l3McNew2VGKHyhWRBY3GzUIuMJ1Ngosja8v3SxiC4VMdeFbU5XNF/GgakjjBRGqY3W4CmPozO9tCWbeXdyDY+6ms1WkWEZ4AArfZvFvs1fxyaZkIpmJbmpHGenVcZCUBsY5IWmTpm8zo2F50f5GFP7AQHCQkUyoYKpOMhQ/ygATU1zFdiLAptfzmc4aHqUhKIlsGhSxjmvlUBwtRdjvR+hX4az81+KTjPdZRG18hQmCkhLEmtNIGsDqsZ2YVBNkF4M4vTCqa2tg09+8jf5q7/6E97yvidY3hhFGw5BatFsy1w0HWHlHStYckMXqlykbue3QLegzChBspMg2YksTyLL42grgiEVIhKgyuCbGRQJgukC+D7Wxk2YU3twhp8nSLSFQR+AijVhDf4II3uMIN2F23Qt5Y43oiNnYmtPQOYHwjbC0hRBrB6vbiM6Un3a75yqFNdXwbVaUyhL7gMao6fGVZ6At1bjdwjMA4KgUYMBagD6I/BkHahDgpW1mseOSTIRsA2YcQV+ACRb8I79iEsbLsxOIWpCc0LzdL/k0JTgRwqqHLimRXFHp+ZsQoIqB9bVaX7YI2lPaTIRTTmAw9OCVbWaBae86h0DFlWF6uDa6AmieqoMcVNwYFLzwpCgPyfQCHKeZuuoYG2tpjkBo0XYMy5YWRMeV/ck/KhXUu2E2wYo+bB1BF6sEdzQ9trYSbzwwjNs2LCJibJg15gg50JtDFbX6NeMgJ7HPObxX4cjpheGdJ1UT9gIYkh2muUfiwx+5ZUXGB8f5a67TlcMXghalMkt5TgPO3n2GuXZem6173DVRQgbftJ4wSqy3SzRGVhEKvTsqPD5gZ1npe/QcR5V6qnYNbaX+w98l4HcEApFNN1EYdNtDMYbaNHhujw0EyLg9W7iVamCfTT90ieo1J+Rs9DKRwyPr0Sm6TN8smIxx+SNFKxxNnlDOHhMigmkNlnjb6BKRXnRLnHE8IinmhgZO4JHQEJLGpWJF3hIIamP1uIGLp7yTrMxsA2bgl+cVS9qNC9YRSIVJTSEHWydyuKQ4bHPLHNp5donteRDxTSH3BhjMiCuBUt9+6zHdhwqO4P76MNgWpgdYZCW1hp1qBv3kYcwr7wWPT2FXHrCy1VIiaytJ9i3h3VveDOGYbBjdJg3NzRjBTbJNavpGRjkW4/8kNdffh3pk8K96pRBRkkmTEGsdgPW+HZkeZKJSJyM61PVeEMY4nsREEKQOq66fY3h165D9/0wVLoGLtpMILxsWI/HmtBmDFkaxRp8GmfgRxj5fkATxJopLXoLhRUfIXrgPzBmDiG0TxBrpNTxZvzMKuzBpyqk66lTHwot5Cl/OweUhzm5N1QQO5kwCK8SiuzVrMFtuAJ76Bl0JdCQoBjW2Yk2fvWz32NvzyiP/cEd4G4hiDXNUWmH1gw9yMIgVK/E6X2U6KGvoew0QaQOe3wH0p1CORkCpxXh5TDHt1WmReQZxwcAXu0Gikveg3Psu5jZo2jATy6muPwDaDuFV7cRt+lq7MFnOFkO6zZdM2s1cTaMlYfxtU/KPHGPRM04xXKBsfIQOlCYtomrymS9acpBkcDQeNNl/LJ/IgRNBZgTO7DGd4Ly8auW4NVtBPPVC61uuOFW/vqvP88nPvFeOjsX8+lP/zZaa/pyAwzlR4lbURZXLcQ2Tn+P1i6sofPydo6+2EN+vACAYRks2NhGw9KfjldvVWsVl7xjA/07BpjunyaajtC4opG6rnOPqeZxdsyTwfP4mUGA5nE7T1loumbTa2FMBDxnF7nKi86qF34a+O3f/h88/PC3+JM/+V3+z//555/4+uPVcSaOTJIua1Yf83h+mY1naGRZ4qclttC8vhybE9x2KnoNnyOmR0tgMC0UEzLsjY5qwf6KZcWZQh40mq1WmcwpCtw6ZbDPcDlkhj7BYzLAR1OrDdoDiwWBRUxLlgTn9826UGg0/xnJ8oxdIKMNUtpg2Ai4LzKDgAtSW3cbLv8RmWHA8FFAXAsu9aLcU0rinFLs1CysIVGfYLJviv3+Acamhrl66c14JY9Vy5rpUOc+tnplUhCaYRnQqExSWpIVisftPArNu0tzFTqdgc29pSR/F5tkXGosLSgIxf1dGd7+xjdQ++3v4I+NAAIhJdYll2FtugKATY0bGC9O8FTf83RPHcIUJp3pdu5d+mbq7CTvLGlucGOMSJ+kluw1ynw3ksNAENeC3YaHDbyxlGRGBkwJxeLA5io3SnvFK8/I9SJLE6Ff8MxBZPG4B5lFX2Vy4lQyGMBSihWeiSEurs01ftL9c7kX5dsxn8yyamqDagJ3hl73GEsHj7F+x2PYwsaru4Tisvef0U/sYx/7Vb7+9S/zu3/0B3zta987fQZcK8yp/dj5vrCFTpXntNIBKDuNdqrRdhVeZinKHsc/fBizNEExm8LPlrGuvxHzsisxBh6BoDxLBAOoSDV+3Ua0FSO78Y/C9r5zzMRPTcHWx49w6PldxBljfccRNi78Lk7mhxRWfpwg2X7G5aSAmF0J+fGDM/4GQKeh8H5F9EGJeUBQcuGFFHx3paY3EASHBA8dgRk39NNNWDBV1ngK9rY3s3NrH4N56LwAoVk5CEPduicFC5KaiAnjRfjuYUHagetaz06qvmGhZrKs2TwsGC2EHsLtSc2NbQpPQU8WLAOa4+Gxv65NcXhKsm9SkHE0pQDcAC5rVOyZkFQ5mpwXhsRVR2CyFJLLjfHQHsI96ZQdmhKUglDdfBwRMySdd4y9NmRwoVDgxRef5kOf+Z/81WbJYD6cOhFoFmdCRXLdq8sXmcd9u981AAEAAElEQVQ85vH/KMRZXiUK+HGjgR999EHq6hpete2MDzQqk1W+w6D0qVYGG3yHdV7knHXna42tVomYlnNIx1ptsE+6HDTciyKDRwpj3LfvfqbKM3SmFiClZDg/irf7R0ysu5WZSBIDgQKW+zbXuxf/Ej5suHzLyXLM9FFoGgKT28sJNp5io+ai+c/IDL2Gx6LAxqAGRzew27J4xh6mRe0mpuPcVL6dNd4GDAzeVUzxkJMj374KRvaixnroiDRRDEocLoxSV7sQ1bgQx4fqSIbR4hhN8RPhXyOFMRakWqipeAYrwjyMU/M+jhPg+VNuWAPBksBmydlLjtOg+npRY6MYnYsIhOBobYKZqE06Kmg71IOZy4ZWZErByTZ8gY9wIjipFMtXrGGrW+at2SzLcg0sicS47No38IG+QX59y4s8XJXheFUaQ3KdGwtt7eJJUs5V5IJpyijeVE5hpy7eTu6nCT+zktLCe4ge+hoiKGIUB1F2Ci+zHBWpQ+ZDXXb0yAPgF/CTHYDAyPUS2/cFcut+ndyG38HIHQXlEyQWzNol+FXLUE41Rr6PIN4W1qaBi1GaoLjwhjm2DGeDLI4S3fsvWBO7Q5sFaeJVr6S4/EOoaD0YNoXlH8LPLMcaeRmhyvhVyzAntvPr//Advr91gEd/72ba4iX86KLwTtMa4c0AoI0IIMIaPyhjDzyONuOoeAtG9ghCq/A8uJOo0jjSLyDKUzjFMbya1Sj7LMWqELitN+JXLSHa/eXQg7k0SuTw/ZTbX49fvZLC8g/h1a7HnNgZnq/q1SERbJx9HDiQG+K5/lcYLAzQmSrQkW7GqhCrEoERExi2QS47w4QcxVNlpDAoTrp4LSX6vMMsZBloReTgfTi9jyKUCwicvkfx6i8jv/KjYJ7jXRSAnARtgz5DE+Xdd7+Dw4cP8Gd/9od0LVvBxAKPV4a2kvcKmNKgM93OO5fdQ2tybmCfkIKlNy6hrquOiWOToDVVbVXUdFS/ZmrmMyFZn2DZTRduyzKPc2OeDJ7HzwxmhGLACM6QXivZLz0GpE+DMimicIUmqeWPrUwtothllRmUAVEtWOk7NFfI05qaWn7rt/6Y3/qtT/Cud32QSy7Z9GNt61Q0rWpk18AYWxoDcnGoGfeY0j5m2qYtHuN1xRRXnocI9dAEaI4aHr2mj3fS7GaVMiidw/dIajhz/pvmiOHRb/jEtcTRgsPSY1T4pJTkDd5PVqHdJ322WSWalElVRamQCiRHDY8f2QUu9SLnVGrkhOKrkRmGpE9nYGEC00LxpJ2nURm87hTljRO3Sdy9lIeG++iJ13Pdu59n56DHbX6S1vWtZ97ISZgWAS/aRWq0pLayvzXaQCjYZpW5xT2RsqzRTImAh50croArvdDuxENz0PR46PVX8pGWRQT7doPrYnQuwly9DhEJBxGGNLhz0a1c0byR/twQjmGzMN0xO6MrEDQrk2ZlMiECno4VSWo5O2lSTzhQGTZ8fjVffc7zqOLNuIlWZHEUdIA2bHqKW0mnUsTjJ4jYGXeKnvxBJsujoSol0sKCxGIc4+Jnsl9XjjEqfLZaZQbNACd3jMXjR3j30AhGehnKncYefAoVqaXUdbr/biQS4Y//+P/wnve8ie9+937uvPMtJ/7RLxHd/wWc4RfAL4XC5/IUQge4kbpZ9bCR78erWUsQa8TI9WImJcbKhZTdS3GTtxDrXIPsWIiQMgyng9ODSAIXlWhFO1XnPN6JCfjcPysOvlgiJurwjC6ePXYFN83s5xfWf4XI4fvJr/nls5LJUkqklChVGZmVwOwGmRME9ZpgISBBtUL+owo5Dt/cK3gwJ1lcA0tkqNDZPwk9M4L2VEi0HvfnHaptwStMkS8UIH3+QfH+STg8DQvTYYgchAFyRR+eHRBc0zLXp/dkpB24oVWxe0xiSkHS1vha8DdbJQkLfCUwJCxKa+7uUiyugg+vUTzdLzgwKaiPwaYmTdLWvDAISzLhdvuyYbhczIKcB1Ol8DlceNJ44WxvxdfSiOiFF56iXC4z0XoruSIszYTnxgtg/4TgoSOC9638rw24m8c85vGTRVdgE9OSMRHM1gtFFGU0a3/MzIVHHnmQm29+PVJe/CC9hOK+yAyvWCW8450WMmBZYBP9Ked0nIqA0K7BRTMuAwLCel/DXP/PC8DOsT2MFMZYlumanSxuTjQy03+QrshOli6/hbzQLAhM1voXH+o3KQL+PTrDoPRpVSaGhkEj4KvRadIFeULYAhwyXLoNl6SWTMqAjDJYGXQR1VNkZT13lday3F/MwqBrtstqnR9hue/Qb6U4tjzGjkNP0TPTy7iE8qJ1jC29gn+Ll0lrSfXqWzi2+Rscmj5K3Iwx42WJmVFu6bgB83i9g2Chb/GiXaSeE91cOaGwgMbgx52iAGFZCNNk0jb4+rVL6W5M4xoSp1Bk2aEG3rN0GVZ9Par3GLK9MwyHKpfxJ8Y58Kbb+H4mS3nDMl7YspPJG2+hevtOdBDgaM3fXnUj9zz1CB/93U/xjW/8AMcJa7Ib3BgRLXjaLjJhBNSJGq7yYlzNf43CXaOZkGOUKVOnGgjTNc4CISrBZ+uJ7/g7rJEXCBILULFGZL4P6WXxkwsxZw6E9gWV+zhILURO7qZnZjvjNW3UVbXTeLxDzy9gFAZDr+HOu4ke+hrm5C7CJ0vj1ayl3HbLOQ7gRC0SOfhVrLEt+KnFIUHpF7DGtqAPfJXC6k+F+2NGcVtvwm29KVxcKX7/V+7le1sG+f7v3caCBW0Um65FC0F8999jTB9A+qXQM1oHeHWX4FevQrozyPL0LMErgnLoLxxtRHjTGLnecCKB43WcJrb/38iv+43ZkOs58EtEu7+EPbqFIFofBuiNbcHIHiW/9pdDa7nGK/Earzx92TPgxcHNfOPAgwzkB8h6U2wd7qYr085tndfgmBYKTWN9K0bXDPs278FPlIhEY/gzAY7lULU2Sm/hEI3xVlIzR4j0Pkbg1JzoCvQLWMMvYtesmT2Xp8LcAZFHJUa/QJsab62mdIemmCrzyvA2dozuxtc+a99yLTfs2MwnP/FeLv39N7N44VJaE82UA5fuycN8Zd/9/PKGj56mEBZSUNNRTU3H6Z2K8/jZxDwZPI+fGTha4JwlvdYkVA5/08my2SrhCk1bYHKjG2elf5Fm9xVMiYB/j06z23QrrlxQrQ3eUkpyWYWEfde7PshXvvJ5fud3PsXDDz93QWFyF4rRJQmeb2mnL5tHFHyKEYFKxGhPxKmybQxPnFc90qJMpIZ9tku1MmbJ1GHpMy0ChqXPAnV6ESIQrPcdHnBy1GOcZBMRoBEUhKbdtxgzAhSaiIYBwyeuba45yRdWEypkRyqheR3nsI4YkT67TZeCUDQGJit8myiSMRmQF5pWNbcIr1Lhv2WFInMOz+R9hsuAEYbbHSc7q7TBjFa8YJW43o3N2acB6fOtJYKJrgaaJn0CpehfY/CCGWdDUZA4Dx8zIxQFoWk4pWhOasmE9JgSapaEfdQusNMqsdMs0xyYeJhYCCwELcrkkOUzumIJzctWnHljFdRGa6iN1pzzNwOGX1H+zr3edcpkWPpMnEUlHiTaCJLtmNPd+OlloTJVa8zpffTlTJqaTqTH5rwZdk6+RN6fIWLE8JXPkew+sv40azKbZgccF4ookveW0lzn+YzlD1G75/ss8S2cSquZttOhX9rwc6H1whmKvRtvvJ1bb72TP/zDX+d1r7ttlrh2+n+I0/8EKtqATrShhYExvhuz0Ic5sQOM2Kw3cnHJu3GbrsWc3BO2o0Vq8KuWh8ejdViQl8bQVgoVqcHIHSVIdIAQYchFeRw3ejky14eKt5yVzH3uOcH+XUVW1R/AiCVBeExmozy5YzGXLVzP8sg+ZGkMFT27LY1hGPi+j9ED0S9JzB4gAB2ReOs0xXcqdAwQkK+CFxDUxDVW5fESIrRc6J6EQ1NQ3RD+TWsoxZoAsPKDwPl9bbOuIFACx5j70MQtmC6HyuGzBbL5Ch48LJlyoS4akutaa7aPCJIVVbHSsHNMMONJPrNesTAdEs9an7CKODAVWl2UA2iKawbzguGCCFOJBfTlBZc2aNbWn9jHxVWaiBHaTFRVPh8lP/QlXlN7SvCehv489GUFloQlmfMH5J0JTzzxKA3N7eRSy1iQZJYktwxoiGl2j0umysHs/sxjHvP42ceiwOJGN8YPnAKjBMhKMO5GL8Jl7qsngw8e3M/hwwf4gz/4s1e1/It2iRfsEm3KJK7kbC33sJNjiW+fsW78aWGt5/CFaIluw6Uowve4RlOlDNqDi6sxcl4h/E6f8k2OmVHkxBBv+jHt57ZbZfqlx9LAnq0zOwPJPsPlZas0SwYHaB6z8+wxy0S0RAhIK8lK36FGp4iqJFe5689Y6zoIFgY2C1NLuX7dEo65k3w2MYMRidCuLMwARmVAb1s7V8r3MHHwBUaL42zIdHJ1y+WsrFkKgDFzCHvwGW7VkxxcvIGDiQaqjQyuDEntTV50Dnn9aiHbO5Ft7XyrK8P21gwLJgrESmWyhSxbNy6jurGae+96G+X//EoohBACpGTv+mX8xx2XYciAWEMDvRNP8o+fupcPvnQ5C3qGwHZYsHwln/vQx7nnnhv5ld/7JL/2l/+Xem0S15KrvBibvCgFoYhp+VMJATwTxuQI33G+wW57Ox4ejUETt5TvZKN3+TmXU/EWspf+AZEjD2APPYMsjaLsKooddyLzA5jZo3NqyzEnwpdX3ciehlqysQmSWnKJG+HtR7ZQdfQhjOII2rDxMyspLPsARn4gDJZLdoS2ZGfothPuDE7fD7GGnwflESQ7sUdeJIi3nlCqmjGCeFsYPFcYCGveU/A7v/tpvvX9J/nC578J7c0cMqMgLdJGnrjWYdCzMEOLByHDIOfSOEGsseL7PEHgVM3uoyYAI4o2QFlJRFDASy3Fr9+IObUPc3wbXuNVp+2HNbETa3wnXmox0s8jgzJBvA0jdwx74EcU010XfF3HixN88+BDuL7LmppVjJT6mXan2DXWTZUTY11jF9VOPY3xVmru8OjR3eQPWAR5hVNjkdmQIrksypQ3TjY3RfVYNwTFufZwZgxt2FijW85IBpv7IP5vBiKvCeo1wgXncUkw6vL5G77BC6ObMYWBFAY7Rvew/IPXsnnPS2z7u0dY85drEEIQMR06Uws4Ot3DganDs++Hefz8Yp4MnsfPDGJILvEiPOzkiQtFQku8SnptZ2DyglVim1WiWhtEtGCv4dIb9flgIc2y4OJH0E/aBXaYZRYFNg4CjaZH+jzg5OjybTLawDAM/uf//FvuvPMavvzlz/He937kJ3KsPpqHInn8WJQrUnEmC2V2JAPGouBrMAOXvabLUcPj7aXkWQnWuJY0ByYvAwWh8VGUhSaqJVVKss90udQ/88z4NW6MA4bLftPDIFRjRCueYAdMl41+hF7t0S/9WZ/blsCktTJIKKH4diTHy1aRrFA4SJb4YbhEvTLJCkUJRZU22GOW+Voky6j0Z/OrV/g2v1BMk9ASGyiiiVWOU6MZkQFloXjaKtAV2CypBLCdiqJQaDit8IsgyAk1qzI5jhetIkPSZ3kQQWTCZTw0Bw2XnWb5vLYUaS1JKMm0VEQrBHYZzYD0MbUgmfV5ZWcvj/T0kbME8eUZxCVx+m2NJzSXelFsRJjxJcPgi58EIlpgEappoggYLUD3FJQ9Yo1RrOYMZxQnSJPSorcQ2/PPoXJA2qBcVKyRAXeKupNsogYLPeT9GTJ23ezgKmJEGS8NM14eoiF6fmX1qRAVT+ol2Tzx8aP4VSspCc2g9JmSAUnHoLFUQPh5ONPMPyfC5P7mb/4Xv/M7fwJa4xx7MFT6Zo+FiqJYI35qMYEOcJuuRVsxtJUMAzjSS0EI/Np1c/fNyxM58BXskZcQbhZtxSuFqIs5uRuUi5kfQEkLp/+H2KNbcOs3UVzyzjO2eO3YIUgmAiwZoCr3ayZZZGA8xcHBBpY3HApToc8BwzDxSx7R+yTmUYHfqcEGkdXYzwtUraD05uOaidOd4iAMXmuMa2KmYN8kxExN0RfEE1UAlAsz57lqIWojGtuAvBcSwMcx5cLSDETOMZPVm4XnBgRT5RN/m3ElgYak1vgqVA8vrtIcnApJ4Subw+M6eVzfmQoJ4n0TgoVpzfp6zaGpkCRelIZ3r1Bc0aSJnVQNLcnAta2KJ/pC4lhUJgPX18NljScF+Sh44JDg6X5J1tMIoCEmeNsSxZqLtJF/4olHuOSqW1AIzFOeeVNCyQu3N495zOPnBwLBG8oJlvg2+00XD01nYLPKd06zsLoYPProd4lEolx99euAsAYxOL0OOhu2mSUcxGzGgUDQULEJ22+6LHD/68jgFb5NUYQ1YKJyPEElE3VY+iy9iHq/PlqDgIqfbnhMWmvyXp72VNsZl9Foug2PvWaZstB0BBZrPIfoGawzpkX4vT61Ro9pwZA84e3/klVim1XG1oIIEFWSKanYYZZpUAYrfIfUBaiShRD0JSJMRN3ZTA+ABmWSlS7ZhjY+nVpTmTA9sU/m+A6iuz9Lr+HTV7OAdf2vcCxZz2TtaqKxZq51E7zOjZ+xxr5YCMti4p3voNs9QNORPqL5MlpAKlONrmlhu+1yx8ZLSDe3EuzZiS7kGWmu5/5NbaQjMaqUwWh9AztHxph0DB67aiUfW3/VrIq5s7Od1//Fn/HNT36ao5d2cckH3sd1bowb3BgmgtQ5xCNnwrD0edEqcsD0SCnJBi/COt95VeeiRIkvxv6J3eZ2GoMWkqToM3r4cuxfiOQjrPLXnXsFZpRS19spt9+BcKdDCzMrjnPsIVBeqKQVEgV8uaWTLTGHVm3TEthMCcVT9NOQf4l7yhP48RZEUMIafg7hZcmt/00wzvHs+CViuz+LNfIy2k6FAWu938PI9RO03DDnp9qIIkpjCL9wxlV98Yv/BMA9b52rPv673/skH1wUp9R+J9LLoaWFcmowp/dhDT1P0PV23OYbiHb/OzLfT2BVYZoJjHwfGoG2kmEAnlVPULUE4ecRXh4jP4h3hv2QhUGEn8MefQlZngitSUwHZaUwp/ad+1qcgv2Th5goTbKkalHFq7uFqBGn5PkcnhrgnsVvpjXRgS0dZFxSe32KqssCnCCKmTSRpsAesljyxCJae2qIFtcjawbxrytCqnjSlo57PJ9A3iuwc2wP009PkBEJVnctp46acGyT1Ozt2cfLB7fS2tJM3ArHHm7gsm/yIMs+fh1b/uQhHviLf2Xxh68m6+eImVECFHk3f1HnYB4/m5gng+fxM4Vb3DijMqik14YkXntgcakb4ZvRLB2BNetlVqUl3YbH03aRZcWLI4N9NFusEhltzBbkAkGrMjlguBww3Vl18CWXbOId73g///t//z533PFm6uoazrXqC8Kg9OmTPs3axLQko9UBeVPTrAR5qalRBhJ4wS5ymRdh8Tlm69uVxULfwqiQn/XKoDWwmJIB7jmIxow2+HAxwzYzDKiIacEq32FE+hwwXQxgYWDTWVGa9hg+DfrEK+Vxu8DjdoE6ZdBc8dHdYZYpRqZpUybbrTIumowy6DM8TATLAhuBoEz42x/aed5cTtLl2+wyyywILKLADsOl23SpVwbfjeSwEFzuRrm3lMI+pUBrVCYOkBVqtsVPo5kUARv96BxPZICjhkdCyzlhZxYCTVgUng8pbXCFF+FBJ4+SHhMi4Kjhk5OKzmnNw9/sxu2exI9p6gOB3jnFyiNVdL9lARO2Ylj6tCmLERlQp8yfSFseQEdg0elb7Dc9Fm+ZxnroCP5MibHWCGosyr+NurxuzUJW2/HTBi5+9Wpy634Te+Ql8sUBxuK1+A2bmP6/HyedPtFfP+WOY0l7ziDDkCYaTd7PnnG/RHkyVLs61ehIqG4elD7bzFLF09pgnR+hOdaEtqoolUd4OZViSgQYgCoPc6SqDTfpcN1ZLk9bWwe/+Iuf4vOf/3s+/vFfo9rIYQ2/hPCylSA3HYZruDNoJ4Nfswq39ebznlPnyDdx+h4jiLWg420IbwYzewy3dj1e3aVEjtxP4BfxMyvQdhpRnsTpewRtxSh1vfO09ZkmBEYCbSaQ7gzKyaArjRCWN4afWhh6sJ0D6XQVM0cmMacFfkdIBAPoJKgqjf2yoHSrhijETFhRrXmqX1Id1RiVyzaUh/YkfLAmYOyAYNAXsFRRWx/nUSCfP/O1PBWLq2BVreaVYUFdJPQMHiuCKQTXtapzWSdzaAqGCoK0fUJpO+OCG2gKJ/HhpgSEYKp85neZKeHtSxVf2is5Mg1Kh/YS17Zqbm7XrKjWc4hqCFW5b+nSLM3A7vHQL3lJBtbVzyWNXx4WPHIs9ChujoeERE8Wvrpf0pJQ1FxgB2pPzxEOHz7AZ37jT3glohkuQHNFlKM1DBcEK2r0HA/jecxjHj8fkAiWBc6rEi2cDY888iDXXXcTQwmDJ6wpDpguDpKNnsMNbnxOkO2Z4ArNKQ0dszWRfw57sZ8GBoyAOm3Q7ptMCoVJmNVQFIqnrALJStbFIt86I0F7MtbUraSraiH7Jw5SH6vFEAbDhVHqYrVc3nR6SJRG8307z/edPAWhToTqWQ7vK6Z53aVL6es7dtpyfR96Hzf/5f+eXUdBaJpO6sR6wSoSQ7I0sDloevhCYeuwFqrSkte5sQsmH3MinOY9jYBGzuaGzFFCqwDn6AN8r66RBxduYsYMP7ix8jRX9LzEGxruJWL9ZO3fSh3t+JZDcmAMo1RGxOLI+nrilsmwCMgLRXVTM0ZT6FnaZxWZiU7TFITXM15fh/I84uNZempNShWRQ4DmvugMpfe/mRU7t7H91/6AmuXLGbtqI1Etz2utdyr6pcfnYtP0ynBcUJaabWaJW9w4bywnzhmKfCbstXbSbe5hkb8Uh/B5TwRJus29PGs/eX4yuAJtp+ZkU3j1l2EPPIE5uYcg3syxeBX7HMkC18VJNaMRZJQkmOnjubo2bsoFJAIfbUbxzSjm1D6siV3nDEazxrdhjW3FTy+e9RzWkWrMmcNYE7txm6+d/a1RGkFFqlGVbrJTMTDgnvHv0X1fgN5+tJMhcE6Er2kjilHoR3g5tOGgIrUYMwcxpEOQXoyfXowxfQCjNIYf70RFarEmdyHLkwh3GufYg/iZZfjVq+aeR+lgzBwGLVGRapAmws9jzBzBTy08/4U4Cb7ywkmWyj1hCpOMU0s5prANi47kEoyKB7MpLeqjzRwLDhCz40ghsMcsWr/YQLo/hdMSRVONsflq7KlR3LdshqgLfgkRlPBq189ud6Qwxhd238fBqSOYOdDNmka7nvd5b2WVWgoROGj3EJSCWSIYwvDIuBmjXJ9kyQevYvffP07+m4rmO1YyXpxAaUVPtp/LmjZc1HmYx88e5sngefxMIVFJrz3oxhiVoWftMt/mRbuED3NCLQSCKh16ywboi5rFVYQF76k03HGNxKnavN/5nf/BY489xKc+9QG+8pUHX5VH26nbOXnub0QGRLSoqGZDBVpaGwzi02N45ySDFwc2KR22zx0P3CijGSNgiX/ulq+kllzjxbjmpCnVGmVQow16pU+bMpEIskJRRM+mDJdQvGgXSWlJTWUWPq4FbYHJD508GWXQqSzS2mCf6XLQdLnKjc5+RB0ENdpgm1XmDeUEby+l+FpkhgOmx7jw6TF8WpXFpZ6DRRjQ9qxdZHFgn6bcXRRYbHAjPGcXSWkDRwvGZUBaG1x7hjCQahXu08nQlSuRuEC/uFvKCTTwtcgMBw2PKIJlnk37K6P0Hx6juDiBYxqgDUTeo2XzJCPrqji6IkW/9CkKTUQLbi7HzzuYuVCYCN5WSvHl8ij5Rw6jfZ+9b2pmsjVCXEt6ij7bgiHe6DRwVzlx2kCimGjhazU38aJdYkYEOEgOFSbZ1HpC7esYEabdiTnLaR0OTExxCuPml4gc+Rb24NNIL4u2ErgNV7B16Zv4UsxlWPrYCFw0z6gi7xG1rG2+honeb2HNTNBmpUgUJ1DS5Ecdr+NQtMyqXDB7v52KD33ok/zTP/01X/rSv/Drd3SG8lEjgjaioQWBEUUWhlCGTZA4syLoZIjyFPbQ86hIPdgJRFBC2yn8RBvmzGHclhsRysPPrJz1CtaRGpTysIeepdz++jBM7iSsX6/Zv98iW7eSVG4LsjjC0EwdGXOIpYsKlDvfdtZk5OOoq2tgbGwkTCY65fHWEZAlgSiDrjwmN7Vrjs5o9k8KIobGCwRx4BOHNev3G4isBgP8/YKjN4UDwulslu7J0AaiLqppS57Z+cKQ8K5lCqEFLw1LtIaFVZpb2xXr685NKIyXBI4MidjjiJohIQzMEsS+ArQmcw4epTUJn9mg2D8h2DGmeXlIMJyHL+8VpG3J69oUt3fO9S82ZEj+rjsH9/7SkEACtZVzaQroSEH3pGDXuDhnQN7JePLJxzBNk1uuv4HqKc39ByTdFUV2zhNkIppbO9RZ/ZXnMY95zOM4SqUSmze/wK/+r7/ic9FpRmRArZZkUXzbydEvAz5QTJ82EX4yVvoO+x0XHz2rJs4KhY24qIC21wJFoTB1aI1wHBrNFtNjn+PSYwZIoDkwuaecPKdVXNyK8f6V7+B7R3/IrrF9eMpnbf0qbuu4gbbk6e3tPdLnMSdPFEF7hbwvodhulviOI/mzR58gEmjaAxMDwSv7d/Khe19P9O7bmREKQ8OQ4ZPWkk1eZHbfJ2VATAs6VOgh3Wt4lISeJYIvxj+6QRnIiqjiuJhFo5kWivXB6euRpREOkeOBzmtxtGZFbgoBTJgmz1TX0uX1stZaecHbvxDUKYO0FWG6vXVO8PeE9MkoeVo2TFSH31qfsIEt3hB+mKdHh1lQUzN7jx6qKLbbA4ul//O/M7N7L8+95yPc8PRDPNVoscmLXNRY8Em7SK/0WHaSzceYCPiRXWCjF6HlIu1SxuUoCjVLBB9HSqXpNY6iOUEmXgxUtI7Cyo8TOXw/5tQBciQoxOqwrWa0fZzI1yRL00zZcfKGSSKoqCeMCOgAURo/5zaMXE/YmWaeZAVoRAkS7YjSKMb0wVD04E6DVpQX3nNGq4lzHkekNtzGybkbWiOCIspMEtvx11jjO0DISu1uUmq7jdKSd2IPPEVs9z+ABnPmUNgtqDUqUovwcsT2/BO59b8117ZCWqEaWFZG3VqH/5NGGNqmggsK0QNYkGojbsWYLE9RfTyQUSsG80Msrurkh71P05poYklmEaY0aY93kfWmmSyPotEseLmV9EASc2UUHImvMwinD/NANfY2F7VsX+ifXL9xjofxQ0ceo3vyEF1VC4nZNowpDtQc5T+s7/B75U8R8R1MbaDN0+tRjWZRVQcjXWOkbulg4vsHSbRV46yoJmUn2Tayk9s7b5xDIv8so0iRHdZmDpsHsLTNSn8NS/2VyP/CQNT/FzBPBs/jZw4GgqWBzdKTCsG4PqFYOLkNrig0bRUV7cXARrDcd/iRXaAWY04RkNSnF8M1NXX83d/9G+985+v5+7//Cz75yd94dQdXQaMyWRBY7DdduiqhZ0U0M0JTpSQpLWcJSvPMKW+zWOM5rDMdtlplnEqZUUSx2nfY8CoCSuq0yT3FFPdHZ+g2KgmnwFVuhCsqHndFockLPXtdjqMoNJNCsUTb1FUKwEZl0K2hT3q0KPPErKoW+EITiLDF7ZcKGY4ZHo/aBZ61C6zzndnfJrVkhLC18VQyWCJ4eylFozJ50S5REor1XoTr3eice+g4LvEjbLZLlUBCAw30GB612mCV76C1ZmYoS3Yoi2Eb1HRWY8fmrsdBcKMb5xkrJMRbg1ChIrpncG2DcSskSdMAcYvYgGblEZep5eG13eBFuMKLsuo8ZP3Fok1Z3LtH8fyI4tjV9bitMRbNQNzVlPMBRTfP4/V5lgV2OIBSftiKZsZ4MhbwmFOgVknSSPplwHhuht0Zi0kRkNEG1U49vfnDTJRHSVkZpJBkvWkiRoyayFzFfOTIt4kc+RYqUoufaEO6Mxg9D/O9Ba1MJFew3LMQeydh9xjjxTLfXVBF7eI38FjaYGnfcyTLBUarF9PdfgMzTRuYFB67zBIWkkHpEdcGq317tlivq2vgLW95N//6r3/PL2/6RYJEO0ZhEFkaRZvR8FiDAkG8lSB9jpRa5WNO7Q9VCfn+0MtsbDNCByi7Cj/ZiQiKyOIgwi9SjDZT8F1ShokpJNpKIEujoa3EKWRw6+oe7IM7+MHACEa0gaqJxbQKmztvH6Pm5g/gJxec9xrX1TUwUhxGNwjkpEadZDdmjIO/RM9JF25JwC+tU7wyJDgyI0g7itcdhGVbBKpGEzQDHphHBXXfDff3vu15siWDog9pG65rU7xvhSZ2yrioHMDDRwTdUxJVUUyYEpoT+pyqYAgnwpoTMO3CWFFgiNAj2BACW2rKQUgE9+cE7WnNqtpzE69RE1qTmv/slvg69EU2BIwWNQ8eETTEYWPDxSnepssQOaWKOk7YFs/fRDCLxx57iI0bryCVSnNDUlMTVbw0JBgrwmWp0Mai8yyB2POYxzzmcTIOHtyHUoriuqWMyIBlgTVbK2WQbLdKdHvRc5KkV7hRdpplug2XuJZhLQZc5UZZ8hPwjf1x0KhMTCCPIl6p7oekzwHDo1mZLAksAqBHetwXmeEz+Qx1+uzD3bpYLb+w4l6ybg5fBVQ5qdM8hI/joOmSFYplJ50DA8Gg9PnX6DQLF9jYwBLf5h3FFC/83+/T0rGQ66+4liPCRwloCyxuL8dnyWyBoMO3eNEu0YDBAmXRpkwKKAaNgEsuUs263LdZ7tvsMMvUaQNLC0ZlQI2WXOmdoeYXJruqGsgZJu2FE2391W6JsYjN9ojJ2ovag/OjShtc48Z40MnhSo+klkwLhSc0ry8n5wh7AJb6NnXK4EWrSEQLVFP4QZwYHuatCzfMTmxMygAPwvvCktz5xX/hy9ffxnP3vJfE975B0cyQOM+Y6Tg8NHvMMjXamCOOCEPLfY4a3jnJ4HER8JJVpNsMn6F1XoS4qkIg8PDmhMblZJZ2r/MEERyUMaf2IfwSQaL1jL67pyJILSS/9teQpVFiMiCalkxoSfVsWSMYj2VomOmnyj9J8BKUAYl2KsWiCrBGX8YaeRnpTuNXLcNtvBrhziBLY8j8QNihVskAUZEagswytFONLI4QpDpxW27Ebbr6Qk7zHHj1G3H6HsOc3kcQX4AWEiPXi3ZqENrFGt+Gn14GRvjsyHw/zuCPcFuuw226Bvwi8Z1/g1EcQtlVqEgGv2ZNaDUxuQt7+CVKC+86sUEpCZLtCL+AdCdBq1Atne5Cm/HQeuMCyeD2ZCtXt2zihz1PMVmexpYWPdk+pstZNNCfH8ISJhsb1/GOZfcQNWOsrb6csdIQBT/HguFG0ukqcCrvKiHwGlchRqbxg+vwm2rwq1fi1l82azM3XZ5hz/h+GqJ1WNIiaNVYY5KObCtHkn0c8o6x9vBSlnYsJpKJMl6coCYaXuesm0PpgK6qRbw8tI3UjR14/Xn6vrSFa//gXpYvWMVAfpD+3CBLMufPCHlNEIB5AOSQQMfBX67RFze/MIu8yPGF2D+w3doMCDSKJ3mUW0tv5A2lu1/VJMzPC+bJ4Hn8XGC579ASmBw2PNoDCxsYFwoXzeUnKU4vBje4MQ6ZHvsMl4SWuJUZ29vcGI3q9I/DddfdxKc+9Zv82Z/9AZs2Xc1ll11Y+uiZYCB4YznBF+U0+4zwoz0hFbUqVEILoF/6VGnjjITmyYgieW8xzbKgxHazhAbW+A6XedHztgqeDRv9CB05k31mGOARoFHA03aRrsCmOTCoUUaogDjJ5mBUBkjBnFn/tDaIa8GoERD44UtJoxmVPpu8KLFK4WYQKkFalRsGbJxyTU0tKJ3F9iKK5HY3wU1uHB9NBHHWe2Klb/PmUoJHnTwHDQ9BOPi4q5SgzhXseWwf/dv6cQseQkKiNsGK25dTt7h2znqmRejP3BKc1KpoSiwVTjZoATkUcR1aUJQMuMyL8ulCZo5S4icNKwiDSUp1DhEtibuVtkEpiOdCH+f9Rpl1fZtxeh/ByA9SchK8dOlbSNjtjEnNYcMjEJogm2UwE+HPY+O8b8JlNHsANygz7Y4zXhombqXIOLUkq5Yz4jg0B6FSRZSnsAefQkVqUbFGAFQ0wrF4mn5ytJTLiCcHEU/2QaCpsgSlXZM805pjy0evZ0fndbR4Aa4VQ4sw/biM5tuRHPnKPaDQPK4N3lZMsbEy6fGRj3ya++77V77+7GHev8rCrd+EmT2MLI6irSjKSlBquw1zYifW0HMYhSGCZAdu09UE6S5kvp/ovn/FmtoPXh5r5GXQAUFiAdqwkMUR7MIQfvUK8okOHim5PDP1Cjlp0WBFuDHVyNW6gLIzYUvaSdgxupsvdX+dsRabUvxSpssZRoWgsamOhVcvR19gl2ZdXT2HDh3AfZPCeUwgcqBjICdBRwXlGxSnzo7VRuG2zoqDsIbEtyXaBnU8k9AGf6EmeSCOEILNvVnSrQJDwHRZ85W9ElsqPrR67vP3o17BD3sF9VHNqhooB5rDU4Iv75X8yiUK5xw19qI0pB3NglQY5Jb3wFMn1v9sf0jgbqjX3N2lLii0bddY6AG8NHNCBdwQg+wUvDwkzkgGjxdh57ig4EF9DFbW6NnQu8VVmh/0SJpPCqwr+mBITdOZ7atPX//4KE8++Sh//Md/BYRimLV1mrXnUU7PYx7zmMeZsG/fLgDyKzupOqVWiiHxCe0HVnJ2MjijDT5cqOJFq8ge08XRgvV+hI0Xqax8LbDEt1nnRXjJKpKudHttM8vYCFb7DpJQRdqpLPYbLrsslxvc89dUSfv8LEPYqDK3fjxouIwaPjXKZGlgUUSz0yyjjXHuv/8+PvKRT/OJYjXDMsBH06jM01TZV3sx9louB6VHnTZwhWZUBKz3Iyy9SFFABMkvFNM86uTZZpYoCc0q3+GmcozOM4wXVKSWQnIBhl+c9ZxFa6Q7hRFvJx+tgdKFb1+h8Qgbk8419rq1HCemBc9YRWakokGFBPHJVg6lUomBgV52Dx5lz+g+9vcfpdw7gN87AMCuj/8Gf2z9Ib9bLFAsFsgXC/hBwPcjDqbjYEajCCGY3Lufby6/jL51l9HY0ERdXSP19Q00NbXQ3NxGS0sbDQ1Nc0LAJWFHXfkUW5RKMsE5n4Nh6fO56BRHDI94Zfy4xSpxtdtFm9/JIXM/bUEHtnYYlUNILbncDW0WjOkDRPf9G2b2CCgfbVdRbrme0qK3hkrWc0EIVLSeejQbvSyP2wXKWpPQkkkZ4MeaeF33M8SmewmiDYighJHvxa9eiVe9CrQmcujrOMe+G5puSAdrbCvR/f+KxsDI9mDkegliTfg16yrblJQWvwOvYVNILBvOeTvYzgYVa6Kw4sNEDv0nZvYYaEWQaKXUeQ+RQ19D25lZIjj8fTPm5G7MyX248RbcBbdiTe4EIQlSC1FOBkTlmkobWRyau71oAzpaj+/UIFQZdIC20xj5PoLkgnN7KJ926gV3LX49bckWNg9vZyQ/hiEsFqY7WJpZhBCCnJvnuYGXaU+1ceOCa7GkTVMsFHlEawVmtzzFhkeirGrKS6+nvOq607YZ6IBAKSJWhZhvBr+oMY8Y6JxCjQb4CzVtb+vgJn0tP+x5ipHiGACWYdGZ7mDLyHamylOhdcU7VzL0t5vZ8nffZ9Gfd2FIc9ZL/acNkYfoVyT2NgEuIDRBi6DwLkVw4bl+s3jefoqt1st0+ouJEL5jRuUIP3AeYqW3hoWvZqU/J5gng+fxc4GElryrlOJrkSy9hocPpLTk1nL8vIFfZ0OrsvhooYqXrCIHDI+0PhEccLYC51d/9b/x/PNP87GPvZvHHnuZ6uqaM/7uQrA4sPlkIcNWs8SADNhllhiXAWMyYJSAKm3w5lLygojDGJIbKgEKZ4NGMyRDr646ZZA+T8hCrTbZ5Bl8PZLlebtAqfIBS1bO+7VulPsiWY5Jj2ptkBeKGanIqLB4P46EltRok0kCDkuXGJIZoWhUJje6sdPO9YIgnE/PCzVLZvuESuQV5ymaLULCeJcReh53nsFTTiB4nRtnvRfhaMXLeJEfelEf29bL0ReOEcvESDYk0Uoz1T/N7of3cPn7LyOSPKG6SGlJXEtmpCJWCZLTy6vxd42RzAe0OjEGDJ/pySJGTBLvyPDmUuo1JYIB0k0p7JhF2Q1mfUi0Bq/kUdVShSsEYvogsT3/DDoIBwrap1Q4hiTgaF07DoKYMjgwncNJJtljFHgg6GGDKtIcW4CnPabLE4w7DjtalzASjaCZoCkweX05wYaZCaSfx4+FfnDCyyP8IjKWBuUjB6YQzw2GXgDVETQaTwWU90/RuC/Lng0pqkRktgtgUAbkpULrsL019HjW9Bg+345kWZK3SGmDrq7l3HTTHfzdt57jvWs2If186L2lFEb2KNpOgNDEd/wNwi+gzRjm5N7QK3nZB3F6v4c1vgM/tQhZnkJLE+kVEeXJkNSWNrI0QiCj/Of4YZ70NDVujrgV5ZhX4gu5EYxkmg1r7p4TIOcGHg8efpSxgknefxNWJEZHMsdUaYbtuSyf31XLZzZwQR60tbX1vPjisxTv0qgasJ8XiCx4K6F8fYC/+jwrUCCnQwJ5DkzIeRLTTCDdLI3xCumuYSAv+M5hyb1LApKV2tlX8PygIG5CdeWxiJjQmdYcnYH9k7CmlrNiTZ1mY4PgpWGwZbitnhlB1ITl1ZoZN1QZb2gIbSoAsm5IHKcdSJ3hVVDwK8Hkp7y+IyZMlE///a4xuG+fDEPkBEg0y6oF71upqI7A1S2anWOhxUZNNLS0mCwLLqnXLK++MDL3gQe+jhCCO+98ywX9fh7zmMc8zoV9+3bT2tpObSLNYTE3NklVPvrRC1BHZrTBbW6C285s73lGaDS90uew6SE1dAX2HG/cnwRMBO8qpWhWBq9YZcqENWtMSzL6ZDIvJG3zQqG1RqORr5KkOo6FgUVMi9luKBdNn+EhtKA9CDvbYgjalMWT3/s2MzNTvO1tv4BEnPM8LAls3ldM80M7T6/hY2q4zY1zixs/LQfjQpDRBveWUtxJAldo0mcQUMxCCJozl6FFN+VcD9EgvGfKdop8qoMl6sLGUAGa560iz9pFpkRAkzK5xo2ddcxkIrjBjXOVG6MgFP7UDPt2vshnt29mx47N7NixlWPHDs9ZJtbYQKy1GaepgX4g0bmAa1duojoSJxaL40SjPBMp0esXiRfKiJJLrlQg0dPL0IOPcKB7L265zJYtLzEyMkQ+n5tddyQSYdGipSxbtpKuJcuwm5KMNvgcaI5SquliQcNyDNOiT/rUKHlOkv5pq8AR02OpfyLYekIEvGwp3lr6RZ7XX+GIdRAPj2pVw+tLd7PRuxzh5Ynt/RxG9hh+alGlphwjcvQ7qGg9butNF3QtBIK7SgkSWvCiVWJaBNQqg+voYFPqKtTUw5j5XrS08Roup9j1TjAjGNmjOP0/CD17o2EKrpjqxh59GS+9DLfpGsyJnRi5HozSGF7dRkrtr8ervywkgM1XN94+GX71KnJVSzGOk8HJdpA2kUNfrZgkngknBfsmO9HmiyinZo7VBMpFRed2KPrpJXh1G7CGnkVFatFmDJkfQBuRMDfkfC1sp8CUJpc3beTypo083f8C/flBlmQWznYaJOw4MTPGS0NbuXHBtXOW9TZo7Jc1chhUfXhIRh/oDPgrz1xPVjlpOlJt7B7fS9pOIoQgWAz9mWGqShnq72wi16XAlrxR38bymiV0Tx4i0IoFyWYePvID3MBlUbqTgfwQmaoaxAfW0/MXz/KDz3+Dt3zmQyw4g13OTwPODwT2C4JgQUUN7IN5WBD7qiT76wou8lbbar1MTMdniWCAWlXHPnMXB83982TwPObx84BFgc2v5DMcMj3KaJqVSeOPWYQ2KpM3li88OME0Tf7hH77EzTdfymc+80G++MVvnbXd7EJQr0xucuO8aJUYkz4FqYlqwVrP4WY3QfNPqMieEAH3R7LsqaQjVymDq9wot7rxc6ZPb7FKPG0XqK+Qx5ow4fn7Tp6PF6p4ZynJk3aBcRkQ1ZK3FZPsM10OG37Fw1gwJsMi5Q43TiDCNq8rfYvLvSitZ2jBWu7bbHQjvGCHadeGhpxQdAX2bKjfmaDRPGcVedjJMyZDT7nGwOTN5cQcP7a8ULxoFdluhuzQGt+hU1igoX97P4ZlEE2HvxeGoKo1zcSxScYOjdO67sRHM6Ell3sRvuvkMaRPWhnk1mZwj9TQsXmKejdLFVCKSuqvXMwVdR3E/Z9MWNy5kGxMsuCSNuoPD7O3xSBX8pHFACfuYLclkRqW9z4XKl7T4ccxLgS1geRlw8XTHikcdBCgcnnsZIKYV6Y7anJ9vgohBAYmpWQbP8wYBKbH0iC0auk3fL4SnaEqlma1GUeWxpGlcYx8LyJwWaQVd6khvme/h2jeg8YYoMkKRUYaVKWi2D8apby6hgOWi4nAJ3wmEkrSqE8obgSClsDkkOFxyPBYXzm3H//4r3L33Tfy/X1ruLNpP+b4dlSkBr9qKaXONxM58m0A/MyK8IRpjTm9n9j+f0OWpwhSi8CMIfP9aDuNMiIIbwYj34+WBkGknn7g5aGt1NWuo748gpE7RiZwOejbPBztYGnLTXM+vv25QQZzQ2iuIeclqI2MIQRURw3y7jEOTrewddThpgXnJxjr6hoYHR0GG8o3aco3aCgTFk4X8ioywO8A+xWBqtOzy4g8zGgQTgr7pDBAKSBlacaLgheHwFUCT0F9NPT3jZ3yirJlGLRW8E5PRD4ZjgHvWaFYVi3YNir4UZ+gJaG5pP54F52mJwuPHhWsrNE80y94bkCQ8wQJS7OpSXN7h55j41AfCwndcsCsKlnr0Pv4iiY1Z/sFj/+PvfcOk+Mqs/8/91bo6jTdk6NmRjnLkmVLso1zzgmcwIABgxcbWNKy7P7YJS/LfsGYJSxLMhiwARsDxgTnbDnIVs5xNDlPT6eK9/dHjUYaaRRtE5Y5PDyP1VN1K3R39XvPPe853LtFMmAzqiS2fVjbJ3h4l+DamSEJ/Z75AY+1SLYMhtd6en3AmY0K8wi/yvfffzdnnHEe5eWHYMYnMIEJTOAIsWnTembNmssS12KT7tAvfEqVxAd2aS5VgcbsQ1hEHCsCFL+LZHnczI906ChKlMaFdpyzxlnYfy2IK8kldpIL7AQuigciWR6O5MZ4rjooUAEdnRv48tbnGbKHmZJq4rSGk4657Xmyb3CaE+MxM0eP9HFR9IuA6kBn0j72cTEl2P7jezjh7HOpqak77Lgd0qNDejT6BvO8CMc5Ecpfhyl6DEnsCNYl5xoNzDUNVsWqSTh5hDQZjJYykyQn2EdmJ/cnM8cDI4HOiUCySXPYGnN5a6GEpePU5vl8jqeeepRHHvk9zz//FDt2bAUgHk8wf/4iLrjgMmbNmkfNpEbum57ArK+mzghbbnzH4fbKRmrf9hYuv/LdLNtn/KuEz69G5jPOyHzmNCeK/NMzvOsdV3HGGedy//2PIYQgkxmivX037e2t7NixlU2b1rNx41oe/OP9FHMFAKRlsLmxjJLFc2m6/FqmnnwKV0QnjVl42BcKxRrDoTTQxqiHS5WkW/o4ooZ/zP0Lu7TtOMKmzp9EiQptL/T+tSERnJo+qgIOopUIN4PZ/iRO/dlHTFBaSC61k5xjx8mJgBKlYSLwJp1LtuZkZD4kPYN4w+iYWmY7wsngl+7JzVDIXAtKjyPdYdxkE0G0EpnvRMvupjDlSuzmK46aND0spIGfmjbmJbfyBKwdvyIIakYtKmShO8zqSO+1dnOqlmC2PY4+tCkkf90ssthDEJ8UWiyMOY5Gfta7iUSrMLueR7o5/NQ07MYLcSteW3Ca67uAOGABytB0Cl4hDJvb575586F4icJ6RKJvDGvvoByKVwT4B3GIk0JyfvOZtOc62TiwhYQRp+AXMQ2DS2efT7xxryecEIIZpVNHn33bh3bRke2iIVlHbTygGNgMFAYR5RFKLp7CrvvXUn99ajT07s8KG4yXBEHpPrYQOnjNCn1naB3hLTi6IQOCA36Dwn8LAoLxd/o7wQQZPIH/U7CQh/RB+3Ogrq6BO+74Pm9/+xV897tf573v/dBrGu/3kSy/j+TQEZQoyaDwWWs4LPb814UM3pO+u1K3qQ80okGYOPyAlR1VFB8Mq3UboRhVEQsE1YHOBs1mi+5ykZ3gRDfKkPCJK0kMyU7X5VuxAV4yCviCUbXoJXbikMTzHhgIri+WMM03ecUo4grFPDfCMjd60OIMYLPmcq8VklhTfQMF7NZc7o5mqMrp1AY6BQJ+GB1ilV4MPX6B9brNet3mXbkUTtZB36+3XUoJCjz7QJPQ8+0ELvCiWWC35hKVkgWXzeXUqTZOawapScqayyhrKn1NiwaHQ2GoyFD7EFKTlE5KM+Os6egbE/TmBtjcYBKPGASlUWxLsrSgWNSxPlxVH4GuFGf19/NiqpQ8PgkU9oiiojyZxgwChBqbUL0lajJo6sywHeLRsBia4hts1BxejkeZWXsq8TV3IIv9BJFUaLNgD3FG21ryqSd5XszDw8cXgpgSTPdMhCpQmlf8Qz7NyohDy4jX3DTP4CexDHI/tdOeEmzfwMdTppeyeGol3/jVM1z4z2cg7R6QJnbDuSirAq3QjZcMU4T3FGt+rBZ9aEv4mjaSojzSPqaEQAt8fMMEzUCze+ke2kU2Fqe+fA5erAIvNQ3h25R4Lh2+Q8bLU6bvfU5JIRBCMuwk0YW3j5hBIaRCF9CZO7L3urKymnw+Ry6XJRpLsG0Ytg6GA05LKaamD1TG7g/n9ABjs4a+BfxKEEWQA4LsXIVancQpDI/Z3g7A8RU/36SNllSaUORdQdRQo4rmATtU9w470FtQOD6HJE2jOpzWoJiWVmwb1EhH9tqpQbhW0DIs+PlGwYpuQToC1TFFxoHfbRf4Ct48fe9MeF65Yna5YE1PqOTVBXQXBFFdoRQ80iKYklJMLoGtg9CehcmpvfcrokGFpXi1W3DpVEVMhykpmDwvoOiDHtoUHjF27NjKihUv8K1v3XXkO01gAhMAQgLtZaNAp/SoCHSOdy2ajjLQ6f8iNm5cxxVXXMMSN0q79HjOLNAtfQRhuNhbiiWUHabz61iwRrd5yMyHOQkj3VBd0ud3kSxNvnHIoONjhY5AR3CSG2WlEXocVwU63ogwge5dLH/118QQRPUoL3SuYFP/Vm6adz1zymce9fEkgivsBFN8g9W6TVYEaIBUYAuFN2L91bK7hd7Hn+FffnD3Ycd8wsjxi+gwORFgqtB+YI1u845CivQb8D6NhyiSd7jVPGeU8Gq8iIfiDC/CKU7ssF2CAAPC58lInqSSoyKcCqWxU3N51MyxyLUwEbS27uLhh3/PI4/8nueeewLbtpk6dQZnnHEeH/7wv3DccYuZMmXGGLsGF8VDiV5cofb4dOC7oXp52JT8wsrwiJljjhfhFDdKdaDz7kKKTulTEAGVgU5SSTjzAr72te9z223voKyskn/7ty+RSqVJpdLMnr23ZWpVzzq+vfKHxAsmdkeG7pZOdmzZTvcfl/PK3Y/xCrBm1twRO8BTOOmk0w4g/A0F/ri2dQpNgYY2rhJRuMOAOsAOQulxpD0IyoP9w5gPgyiS6H6WgMqIj4o9xp6ANkLsKsJAtQDhOyihoYQGCJSRwB+paZWZfn2JYN9By3egtEhI5O4zttNwLvrgRvT+DSgtglAeSIPi5CsJ4ntDn4N4PXb9mcRXfx2z49lwWKsszAKRB86ZlZmkOP0G7ObLwS+EVhSvAwE6KVmPpUfI2MOUREJRWaACBu0hltQsOnDOJ8C+UOEu8tF3CJQEb7pClY0z+D6YWTaNfzjunbzQsYKdmVYqoqWcWLOIeeWzD3uOeyxPEmacpdXH013oZaA4SOHCOvJdlXz5U//KBadcRDpdekz34FghXBA2HOBkZAABiOKhxSTj4Tj3eDbp63FwMEfStQdEP1EVY6p3iJyYvwNMkMETmMAbgHPOuYhbbvkIX/jCv3DiiSezaNGJxzROj/B4yiyQUpKqkQKrCo2t0h0pfvYm3B4rXtWLPGXm0JWgWypqAoOaQMeVLs8aed7kRA+aOl0UCv0ghYA78qA2EaPhHTaKx80cQzIkh/2RZ7l3iOaf8RBFcpob4zT3yBNOXzGK5IQaDf7wUNT5Ops0myeMPJfbCVYZNmv0Is2+TlYohmRY6L9oFDnetChrKqVlRSvx8vjoD7lTcNEMSaLyQL+5CIKr7SRnOjH6pU+JClOSs7MCrJnVo0nPbxSUUux8YRfbn91BYaiIEIJEZZxZ58xk2rx6/j9Ry8tGkXWajY5gXiHC8UWJrltIZxifvVXIyT0t3BQM8eV0Mxk9wMyEZHBjrJS8bjArn0MpAzGyCp6RAqECLLlXVSIQRJF0SQ+77gxiG3+AcnOIICzq/LL5RGNVnFfcTn9yBj2dDomqOPWBSdRWDAzbNJ3YSFronLGPB6BCMcs1ec4sUubL0e9El/RJK8nkPYodFWDt+g3/eP5kbvzWi6wcLGX+5FlowzuxWh8iP/3tKKHRletgZ66PrJsjYcSYYiWo0eMIIRH2AMoqI4hW85+PDvGbV7vY2ANRM8+yyTG+eEkZJfUelj1AwSsSM6IgTZQ0yTtdxAyL6L6tdC40b6/jslfO5EFlMFwRRZmDIBXDbpYKqxxDWpRFjqzwaWhoAmDHzu1sNo/j8d2S/Mg6RVSHMxoCrpym0A5BWnqzIHeTT+RRib5boCxF8dKA5OmKyG+TFHLDZJxQ5Vv0Q3uGmBF6/FbHwvo978LqXoUbCLYOKvKeYOugIO8qyi347TZBW1Zw45zgAPXw/jBkSMjmvdB+IqKFx/MC8ANY3SsotUL/3z3XKYEXOgRnTVJjbCreOSfgoV2CV7tDBXNCVwy7godaBDk3HG9BpWJBxZ7AurHnoktwfIG3j5BACEZ9hI8G999/D/F4gvPOu/Tod57ABP6OsVlz+FF0iG7pEUFiU+R5s8ANhRIWHkMw7v8VDA9naGtrYdasuRgI3mwnWeZGadU8DBWGLyePMSvicFit27hCUbFPJkTNiDhgvW6/IWTwHkwKDN5VSPGwmWO77qIBx2c81r36GGkjORqaVBWtYPPgNh5reZrZZTOOaRFeI/RPXjTyOfuBGuTH0QyrpU1ECWKBZPCnPyNZWcHVZx382e6guD+S4X9jQ+RlQEkgqQ0MJnk6q3Wbx808Vx5FZ+KRwg98ht0sUT1KZB//1biSnOvEOdc5QsP7fdApPYZEwJSRWitHQKvm0iY9NgUO9h/+yLof/pjnnnkCXddZtuxUPvnJz3POORcxZcqh27MNBMd5ER6K5KgItLAjzAm9SzIRjawIEFLweyvLOt3mvYU01YE+ri3HVVddz8BAP5/61IcpL6/g1ls/dsA2Wwa24eFTV9cAdTB58RyWchab+7cxjXqahyp54YVneeaZx/nRj74DwPz5izjvvEs477xLmDdvIYtdi/usYYoEWISB3+0ytPc7VADjHrsx4WZRxsicQimk3Y9btRTEG0vbeKWzCSLlaNkW/EQTCC0MPc51EJTOHSVnw2BpCz9e+/ocWCmMruexdj6AzHeANHDL5lGcdg1BLDxGEK0kt+DDmF3L0QY3oYwEbuVivPLj9pLGgU90y0+JbvkZWnYXClB6FBWtQstsI7rhu+QWfmKM7zAqQLjDKBkB67V3aBW8Imt617NzqIWobrFlcDslZpKibzNQHGRSsp5ltQfnBIIacGqOjuhsKplEU8mkw2+4DxoSddQlamjNtjO1pBlDM6iL15Bz88yrmMW1X/v/OPfcE/nXf/0Q3/zmjw86jlIjlPLruCig4uA3grE6VAfvmSrLflBJgV939Erek5zTWa+vYZ2xGlOZ+MJDUzpn2ucz1Z8ggycwgQm8AfjkJz/Hiy8+w/vedwMPPvgMlZXVh99pP7RqYYE1Y5/2M4GgUmm0jfztUGrYg2FYBGzRHHZrLr+ODLNNc0kFGh347NI85nuRMN1XBuRFcFBlwCzPZLUeqgj2qHrzBOgIGv0DV69fMYq8YBZpCIxRv99B4fNEJM8cP8KcN1DV3S98rBFbgR3YdGby9MYE/ZagXfNZZ9h4KDwUG3WXds0jQKEIvev/aOb4yAmT6N3eR9/OfmKpKL7nUxi2iZxcz5rpEdByTPbNUe+4PShTGqW+ZIVu82NriC7NJ64Ey5woZzgxrP0TvV4n9GzpZdMjW9AMjfKmMlSgGOrIsPbB9cQr4iQq4pzuxDidfUh1DZza04lu+Rmi2BcmDPsFjMx2rtTnUixU8rtokYHBsIVuMG2xzE+ysCgZcHqJ6QlAIO0iMllDTO4lyRWKAqEftPQLBPE63NK5ocOvHkXpMfAdUs4OzjyhipXPBjhbsthC4AhB1YxKmk48sF9KIDjbibNrJPAxhsQmDKu7vJigXGlhsdm5nEjro1wxz6I8YXLvEyuZP/l8/Hg9+tBWELATg4GO5fQZpZhahP5CL4mhzQxNOo/GigX4O/7EsCiAEefJbXn+YanJ4smluMLi337fz0X/28ULXzqRWV6RV4d301wyCUuzKBR7qRnayHlVc0h1v4BbsRBFiujdAvN5gwtzp5Lwu/ne1Cid5VVkqjcTN0uIGrOpjMHMMsWKLkFvIfTEnVuuxg1NmzkztLd49OV1bGpYSDqiaBrpFBsowqO7BTNKFQsqD/3Z8eaBNycIA+hMwII40FSRoNPPkPcgN0KW1icUcV2MEsEQkrVVUYiboVXDYy0CUyrmVCumpsAN4KVOmJ4WnHUY+4ukCY4Pz7ZLoroiooXEryGhOq4YtAUpc+wYqQjszgr6i3s9iwFKLbh2puKyqYp1fXDnWo0KS9FbFPTkBHkPdmTCsDkFdBdU6FZCaCnRnRccX61IvkYBolKKX/3qZ1x00RXEYke+qDWBCfy9w0fxYCRLn/SZ5ZuIEY/47ZrL7yJZZnuRN3yh9VjRKzzW6w5FEVqZzfTMgy62Hwva2loAaGwMu1vEiH/tpD+DYjovAsY7ikRQOEi47+uJab7J1ILBkAhr0A2dq1g53ENZ6V6yUQhBZbScXcOtZN3cEYXGHQobNJs1hk1NoJFTgpxQDOLR+ZN7ueHNN6LrB59mP2rmuNcapiAUVb6GJ2Cn5hKgqAo0XjGKXGInXrfPh1KKl7pe5fHdz9KT7yWmR1lWewJnNZ6KpR++9u6SHr3SJxlIJgVja1wLgYnAFmGY7wq9SE/rbgZ/8HMyd/6SNZ09NC9dwte+/gMuOO9SSkpSR3XuZ9oxtmsOq3SbjPBoK4YBcjKVxBGKeCCo8Uw26Q7PGgWuOgSJ/u5330pfXw9f+MK/UFZWwfXXv3O/+zT+fkIKyqqreMt5b+Mtb7kRCANgn3zyER5++EH+93/v4Ctf+Ry1tQ2cef7FlFx6JjvOWAwREwWklcalxcQhc0G89GzcikUYXc8TRMpRmoVW7EYZJdgNR24RcawIolUUp11LdMvP0AfCIEolDYJ4DeAjC73gF5DOIE7dGaHa9nWA3reK2IbvQ+ARxKohcDA7n0baA2QXfWLUi1hFSrEbL4TGC8cdx+hbibn7T+BkUCNBzcIrIAtd+PF6jIEN6APr8CoWhcftXUmk5Q/ow7tQmolTfRJ20yUo89gWYbJOjjvX382anvUEKLzAp+gX6Rjowg1cSswkWTfL/Vsf5MbZbxldpPpLwNQMLp96AXet/wUbB7ZgShM7sKmOVXLJlPNpKGviC1+4g9tuewfnn38pl132ljH7Z5xB2nLb6XN6MIRBbayRulgT+usROCfAPjtA36mhbw4JYVEIuxTtcwKCY7AxTqk0N+c/yArjBTbr64moKAu8RSxwj0e+QXPwvxVMkMETmMDrCIVil+axQbexI4pbv/dD/uWic7nxxsu5775HiMePrvC0lEADXMZ2SzhCoSuOKVhirW7zSytDh/TYrrkMiAABJBCYSjAgAjZqDpN8nfp9SNvxcKJrsdIosklzSChJIKCI4gTXGteuY61uI2HMmGkVktBbNOcNJYMbAp1XKbJtWzfiyVa0KhNvXpKooZGrTdBuGnRJjxwBQoSWHKYKJ5mt0mOdYVNoLGXRm49jxwu7GNg9iBEzabuyiVULE+T0LIowtfd0J8ZldmKManuFbnNXdAhfKMoCjWGh+JWVpV8GXF9Mvq5+envQsa4D3/VJ14eFt5Chx3Hf9j66N/eQqBhfAWI3XoC0+zE7n0XkO0Az8crmUZj1Li5100wPHB7s93kWeLNZx6XFSvzUCewY3siAHSbVLgkq6Jdl7DSgLgiQQIfwKFcaJ7gWgVVOYCQRgT268g8gnUGUkaR68VyWTInQs7UXz/YoqU5SNbMSwxq/0GgMDP4hX8qLRoGdmkc6kCzyLOp8jRbpUr/7SVIb70TLtiCFweXzovzmmTV85q0ng2aAkOSV4B5RylI9yWxVRHlFhIDtZjlP+DGShatJ+OXMyj+NpbJ84eYLmGG0YVTMQqD45lyDKTd9hzW7BrhxwQzs6HS2Du4kbg9yZW4Tc6TPpEyAtm49fqIRp/gerKea8WvBmhbnZLea4lCWP/VXsL2yhEQizuS0yel1Ab/aKtkyAAqBUorGEsHb5wQ0l4y9D6lUmtraBl5esx6rTlBm7Z3ZlFqhLcKutlZOLKwG38FPNuGVzRs/pVqC2m/8SRVJErlhrlwQ0JkTTClRFH14vFUcME/RNaiIwqIqxa6hMFTNGFlXMrRQqbui+/Bk8EO7BIO2ImVCwYOMJ+jOK6aXwq3TA+7ZpDHsMsYfeHjErzh9kEdKVIedQ4KiD1FDsCMDlq5IW9BbDFXHgQqtIrJOOPawI6iMKs5tCl7znOzll5ezfftWPv/5O17bQBOYwN8ZuqVPi+ZSuw8htccjvl3z2K25b6gK9VixUi/yc2uYnZqDLRQRJTnZsXhXMX3IOutoMDQ0BEA6nX5dxjsaTPNNXjbGigNswuC28cQBbwQEYtRaIaKZSKHhBR6GZlDwirQOt7N9aBcR3eSlzlc5pX7pGHXs0eJ5o0BeKJZ4UXwUDoqWR59gx+42Ft94w0H3yxPwnFkghiRCgDFSf0sCuqRHOtAwUPio140MfrHzFe5a/wsUUGalybl5frX1dww6Q1w/86qDKvuKBPzGyvKSUWRYBESVYLYX4ZpiclSM0ugbTPUM1uoOw7t2seHz/4/hnz+AjEepv/4qlt70Tvz506jPpwl882g7vKlUOgtdi1W6TbcMyGQyALjpBNs0lx4RcLwXIRVorNftQ5LBAB//+L/T39/Lxz9+C+l0KRdeePno32aUTeXR3U8x7GRHFwvybgFf+czez1qkvLySq666nquuuh7HcXjhhWd46KHf8dBDv2P3nd8hnkpz/JVXcNp1N3DxcSfTrA7zWZMa+dk3E4nVhh62fgG3bC5240V45UdpknqMcOpOx082ofetRngF/EQjSggi7U+OBC0nKTZeQLHxwjA07nDwihj9q5HFXgIzjVd+HMoYO/cw259AeHm80r3WBp6eQB/ciNG3Grd66RGdu963GhHYCBXstXLTowgvG4Y+awbSHgi37V9LfN23EW4G36pC+DbW9vvQ8p3k5n/wmKwinm1/gVe71jAl1YylR1AoHmt5mpyb46zG06iMlo8qh+/f+nvePe+t43/vfBujfy2y0E1gpvDKF+xVih8KKgDfAS1yRAsH8ypm84FFN/NK92p68n1Uxys5vmoBdYkaAK688jr+9Kff8slPfoClS99EdXU4V8s4g6wZeIGcN0xEi+IGNhuHVpFxB5mTPv41B3UCeHMg914f80mJvgP8OnBOUjin7FUKHy2SqoQznHM5wzn3NZ/f/yX8zZDB/f39fOADH+CBBx5ASsnVV1/NHXfcQSJx8C9HsVjkox/9KPfccw+2bXP++efzrW99i+rqvQrN8b6Ed999N9ddd90bch0T+L+Np8wCv40MMyRC8kvMSHLpvT/hnguv4L3vvZ477/wVhnHkRfEU36TRN9ipuUz1DXQEBQJ6hc/5TvyoJxADwuceK8Og8KnzdXZpLqVK0it8ukaC3GJK0CM9kkpyoW2yWg8tBKZ7BtH9Vs/SSuM9+TTLjQKrDRtDhe1zyxxrXKLaH88OwvXRBgu4mskbuTi3xI3yVGcX9i+3oAWK7jeVoed9yrsLqF6X7HEG1ZrBS0aRlBKYI0VuEUVcSXQl2KjbnNVYSnpSGt/x2Rbx+HVyCBPBTD/cvl8EPGzmafaN0XZVH8VjkRy+UEwZmaSmVfh+vGwUON2JUv8GKHcKQ0X0yNjHvBACBLgF9yB7AVqEwsx3Yjecg5bvRBlxvBG/LQHM9iN09YfeA6dEyvE6smR781RHJjN10lw0UxKRUSY5Lr8RWXZpoWlIc2BwsZ2gMTBQZgqn9jSs7fcRKJ+s1PHtXiL2EE7TpYhoJemYGCWyjwTVgc6lI5OAPAEPWFl+HC2iFft5R8vPqDM0JqVnYQzv4soT6vjB8g2sXf0yC6fV4iWbaDdSrFM6xfqLmeb0EPULZPUE28wqnu0ZJJofZk7VVfRrFyG9PP0DvdyU/w9moyPjlQwN9QNQZrlU1Z3GB5svZdvADio2/YA6lcSoWIjSzHBy2vU8kY2fhYEPosX68N0aEqVzuLSiigs2CTbOC3CXKCYlAn62SbKhTzAtrTA1hRfAtiHBLzdLPnx8gD7yvdnjcTxr1lzad6xj6jgzroWFR1my826i3f2AQGkmbtVS8rPfc0RJ0KYZwcgO8/Y5YUAQwIZ+eK4jDIwrGZnruAFkHcH88gClQm9gY7/aWhOh4vdQGHbg+Q5BbTxUQ3fnBXkv9BuOG9CQhBOqAx7aJZEoSiLhPl0FwdmTAioOckl9hdCHOVDQlYdAhd7BED6GLB2iGswqUyRMQb8NS2sCTq7bq7R+Lfje977OlCnTOO20s1/7YBOYwN8hDsYn/TVqgoeEz91WhlWGjSvCKBsfn19EXZJI3llIvy7HyWQGASgpeX3GOxosdi1eHhEHlCqNAEVGBMz3Ihzn/vmzPKaXTqUhWceOTAt18WpW9qylK9+D63vUJaq5Z9P9tAy38bbZb0Yfx0v0SNCmeaO2GxqCKIKZZ58F2V1EiyVhW9k4yMiArAi7pLqkT14o4koQUYJhGRLCF9uJ161rzAs8Htv9DABTUqGVVDqSwipGeKnzVU5vOIX6EQJofzwcyfGomacy0KgJDHJC8aJRQAHvK6QQhD7H57Y5PPTNz7Hye3ciytJUf/VTTLv+GhZaaQak4kWtwNcT/TT5BvPdCJfYiSPubOwb6SBMKY208vH7s+SBIJ1EoMjLgB0j85noEYwphOALX7iDgYF+3v/+t3Hnnfdz+unnADC3fCan1C/l2bbldOQ6QYQL8CdWL+L4qoMTsqZpcuqpZ3HqqWfx2c9+hY0b1/Lb3/6SX/ziJzx9553cM3UG11xzI1df/Vbq6hrw8HjVeIk1xisURZFZ3lxOcE6ixExRnH49xclXIPwiykwdGen6OsJPNuMnm8e85lUtDe0rtMhYm4VDQOY7ia3/DvrgRggCEOCVTKUw5334yabR7bRsC4G5X2GlmYBCFnuP/MQDB4IAJQTSyexDOgvwCigjRhApC+0kNv8EbXBTGAptJAiscpRZgtH7CvrghlAkcZRY0bWahBkfVdoPO1ls38HQDLJOlspoOVHdojZWzfq+zfQW+qiMjbWmEMU+Yuv/B6N/7eg985OTyc+5OTzXca/bx2x/ArP9CaTdjx+vx2k4B7fyxMOSwg3JOhqS44dcCiH4j//4BmedtYiPfvR93HXXbxBC0JbbQc4bptSs3GuZ6Nt0FVqpizVRFjlM6+ERwpsJ3swAPCC0rJ7AG4C/GTL4rW99Kx0dHTz88MO4rstNN93Ee9/7Xn72s58ddJ8Pf/jDPPjgg/zyl78klUpx2223cdVVV/Hss8+O2e6HP/whF1xwwei//xIr6hP420en9PhdJItAMHukdbFIwPaFU7nlRz/mjmvfwsc/fgu33/69I/bWiSC4tpjkJ1aGbVpI3mlKsNCzOO8YfL3W6zbd0meGb1AYCQiIKihVGj4KHyjKAA1Bra/xRCTPkFBIoM7XebOdPEC9W6o0LnQSXOgcftVyjhfhFcMOfbSUgBXduM+3UzdQQOlRNs6dxNQ3TQZLZ4vu0Cd8kkoy0zMPIKKPFrWBzsIXhlg76NKxpAw7rpMe9DDTFgw65PsKzCy3WG2Efse9MmSpdGCyZyBQ7KFPhRDoEZ2NkQI5EdDo770n5UqjV/qs1u1RMjgjAnqkT1kwtkhNK0mXDIn4N4IMTten6N3WOya11vd8hBDEyw7Tmi4EQWISQWJ8HypnxLNt+1O7KO7aglNwkVKSqith7sVzsOpjTPFNPpgvpVN6BITvwb4hgcXJl+MGDvld9+Pb/fiaxfaK2bSnqmjMbqY5cWyefgrFfdYwT5p5qgKd+T3biRf7ebV8GoVoBbPdYc5q7qcsJnng2bUsmDOb4rTriZgJ0nhU5XejaQY9kUo6o7X0F3MM2gZ1CZO4AR4WmBZmeSlPuRfSmPstqWIXn/z2ck6aXsaM404lX38GutSZHUuTdPsJUlNRIwW0LPagFbqRwiZI9qCEEaY4ewWcmpOJKp05Agql0FeE9X2CmrgaDVvTJUxKKHYOCXYOKZp2voL73FMEHW3I2nqml5ax/oUXmAIUvb2KWavYxpuyPyeVDvBK54EQCDeL2fE0fmo6duMF49/QfSClHPUI24MZaTipLuDpVkF3Pjy/vCuYWaZYVqfoyYc+v0N2aN8AofJ22BWcXXFo768hJySVK6Ph9Tck1ej+OzKCwSJcOlXhq4AVXZJdmVARfGZDwBXTDqSLBm34zTbB6h5J6zDszkJcAzFCLflBmFFTZilsX1CbgLfNfn2Thltbd/Hgg/fzuc/dHgZQTmACEzhiVAcak32DdbpNwg9zE/Z08EzyDSb9mVSoR4NNusM63aYgfNJKx1QCH0W79PhlZJhrCyWvucYBGB4OlcHJ5OuwYnWU2CMOeMrMh+HCwNlejDc5MWJ/gTbcqG5x3cwr+emGX/Jy1ypacx2kzCRNJZNYUDEH23d4sWMFJ9YsYu4xhMlBuADdrhXHvOaN/JakDiHWKAkkCSUpiIBmX2er5mILH18oCkIxyTcOGd58tMg4w/Tk+yizwiCoQAUM2cMEQcBAcYjufM+4ZHCegBeMImklQ7stIKkEDYHOCqPACjfC3Jzge/97B9/4xn+hlGL+Jz5EyQdvYlokTZmS9Eif1XqRglQkXYmhBE+aeQakzz/kS49I+bxVd+gXATElcAQY21pASmRDDTmhqApCG4uIb7DYO/yiNoCmaXz96z/k5puv5Z3vvJL//d97OPfci9GlznUzr2BO2Qw29G/B9R2ml07lhOqFR2SnAeEcYfbs+cyePZ+PfezfefbZJ/jlL+/i9tu/yJe+9G+ceupZNN80lf6r+0AHXemsMJbzqvES787dRlqVgh5FHcEC/Z8NQhydfYJSWNt+gd6/Bi81M1SrBi764CasLT8ht/CfR9W3QbwOo+cVxlRagQsKgsgRhpcFHrLQi9G/DgIP4Q0j3GwYROfbCL+Il56Nn2wmtvbrWDt/gwp8pN2PMkpwKxYSRKvAd0Lf4mMggwPlj7Ec8AKPIAhf27catfQIQ06Gglc8YAxr+68wel4NBTi6Fd6zoc1EN91FdvG/jtvFZ+34NdaOe1HSRBlJjIF16IObyc9+D27tm476OvZFWVk5X/nKd7jxxsv5yU++x9ve9h76nR4iMjpmfmZqEbJehqybed3I4FH8zbCVf5v4m7i9GzZs4I9//CMvvfQSJ5xwAgD//d//zUUXXcT/+3//j7q6A1c0hoaG+P73v8/PfvYzzjrrLCAkfWfPns3y5ctZtmzZ6LbpdJqamvFXRMeDbdvYtj367z3tKhP4+8YWzWFI+MwcIYIBLCQlSpI/ewm33/49brvtHdTW1vOJT3z2iMed4pt8JF/Get0mN5KMe6w+c3t82ySCGJAMBF2aj6UgicaJToRdmodUMCwUKSQzfB0faJEuP7MyfDhXNloUHi1OcC1WGzardZuSFT2U/Ho7ShNUJWOksoKtT25jIFfg5esmscFw8AkXApt9gxsLJa+ZME22FYjFIjQVNDodMAwNzQ99go2Cjy1hqmfgEyYhSwSlgYZJ2Jra6I99ZBaFGjfAzwAKYm9ZE1UCS4W+eel9KgIbhQHE1NG9l1kR0C49IiNFuXaQz0L9cXV0rO+ib2c/8bIYylfkBvKUTy6jauZr+7H2/ZAsb3u1g9qGWpLVSXw3YLB1iHUPrmfpO05Ej4TndtD3TYuwpWYxO02fSgwCM4VrJhFell3ZLVRYNSSNo/OWA+iUPiuNIrWBTqnSiCCxVOhp1xLRaao9hUiug/PmDfDHrUU+tOgTBMkmGnpXcnN2Ld7wThJGHCV0dkbruVOrIWkupTQy9nfC0ARPRd/MkuZp/PirH2Ndp82D37uD/PyL9xbNgRMWtcbeIlof3gEqQJX4oEAFJaiIiSx0I/t6QavGGxEAOH5IfCb2u4WGBp6CwooVFH/zHfA8RCKJv2YVk9tb6GrbzsxEjg2ZxKjidfbwOur1PlLle4NAlJFA6TGMruePmAwOgrHkqCbh2hmKaSlY2RNaL8wpC1hSoyi1QquGk+sCnmoV9BRGyGIv9C4+pf7Q/aIpExKmIjMSUrcHGQfiOqStkPy9YZbinEafwRHCuXqcebTjwTdXSlb3CBqTihmlMFAUtOVAEwIhQtV1ZTRUBffkQ1/m1mF4uUvQmhVUWIrF1aFFxbHihz/8NslkCddcc+OxDzKBCfydQiK4xE7QJ302aS464QJuVaBzuZ04JvusNxpZAgZkQHLEfgpCJWlqJJthi+awwH/twXeOEy5Zm+afX4kL4WL4lXaSK+xQHPBG2F8dDaaXTuGjJ9zKp579EjEjyuRUE+VWKVJITM2kNdvOrszuYyaDl7lR1uk2LdKlOtBxRbgo0eQbzDuE7VkMyclOlF9bw6SUZJ4XYZfmMoDPKY7FR/Ol1I5TNxUI6B0JYC47ijo8qlvEDIu8VyBQig39mxi0M7iBS6AUmwe2sbBy3gGL71kZkNsvm6RHemzWHFo1jy9ufpqtN3+Uwa3becfb38c//uMnWV0b425rmGgQvvu7pMOwUJQHGpMCg6SSxH3BRs1hk+4c8j7twZ4qQQOEAnvdFsxpzUgrQhHFgAgwEZzshJ2JR4pIJML3vvcL/uEf3sp73nMN3/72T7jooisxpMHUdDMtmVZW9G9h88B2dgzt4qzGU6lPHF1omqZpnHba2Zx22tl88Ytf53e/+xV3/uLbPHXToyT+PcniW5Yx7+0LkSWCdfpqlptPcYF9+eEH/iuHLHRj9K3FjzWERDCEQdHJJvTBLWjZnaNKV6f2dIze1WjDu/BjNQjfQcvuwktNO2J7DLPtMYzelfhWOdIeBOUj3CF0L4dXMpVi08UUZ76dSNujmO3PhiSzFyqvpd2P0bcKp+YUEKD0Y/MSX1A5l19v/QPVgYchdeJGHCklvudSGtk7l+kp9FEeLTtQFWwPYPa+Elrn6dboPfOSk9Ez29CHto6x0gAQxV7MtkcIzPQ+YXtVaEPbiLT8IbTYOEYfX6UUeS/LglMW8uZrb+Dzn/8kF154OYY0sf3CAdsKQD8Ge40J/GXxNyFJef7550mn06NEMMA555yDlJIXXnhh3H1WrFiB67qcc845o6/NmjWLxsZGnn/++THb3nrrrVRUVLBkyRJ+8IMfHKB62h//8R//QSqVGv3/pElHl+A4gf+b8AXjmSAgEbgCrrzqOj71qS9xxx1fGk2gPVIklGSJG+VMJ848L3LMHmK1gY6uBBl8NmkOAzJgUATs0nz6RRgel1baCBEaqh4kAgPB5MCgW3qs0+3DHudgiCF5Vz7F24YTTH66lzKhM7emnAVWkpKKBImKOCs3tbO+p596X2emb9LsG2zTXH5hDeMfrdHYfmhIJUnkA/y8R02XQzapkTXDDhQrajAgfM63EyzyrPA1JcjIkHg90bGYvp8P4R4fPHuf8/JQFFFjtrWQLHEtBoXPoPBRKIoE7NA8pjmSWT1bwkKmZwXss1Ic9PbgPPEI9oO/wV3xIkEhzxNmji/F+/havJ+vJPr5RmyAVjm+5UOyKsnCqxZQO6cG3w2P27SkieOuXIAZG3stSincgovvje3bV4HCc/wDnot7trNiEawSK1RLmxrphhRD7UP07ewf3dZFsUGzedbIs1a3cUbul1KK7mIbxWgFz9VM5d6aGv6YjtGaSFMMigw6fYd6Ow+KAemTFYqSEWVOT+kU8lYp1cNdOEJR1Ez8eCPnHFfPq7sydBUthDNMbPNdzLCS5JPTWKsnWRsrocRrZVlFjHnTLqLPFmPCRfqLkIhofOt7P+SPL2/nl79ZTsXi68aoJ4JoNX68AZnvHEkmUQh7CFRAUCrw60B2lCM7qhHtdei7wTkR3HnhgSqiUBtXdOfHXmN3Hsp0l8pnHgRNQ5s2A1lTizZ9JtNTYSjFSf4Krp8V+go3l8BZ9Q7NqZDEHgOpI/wDFQrjYTwyGEIbiJPqFP9wXMCHjw84vzkkggGkgGtmKm6ap1hYBVPTcM30gPfODyjfb96Wc2FdH6zvg7wXhsedVKsYtMNrtn3oLUBXXrCoKhhD+lbFYEbp+ETwjiH4zHLJb7ZJ2nOwYUAw7MJpDYopKYWhhdYT5Zai6MGTrYLuvODezZL3P6px3xbJhj54tEXyrVUayzvGfwa7AWwfgq2DoSp7f+RyWX760+9zww3vOmoP+QlMYA/6+/t561vfSklJCel0mne/+91ks9lD7lMsFrn11lspLy8nkUhw9dVX09XVNWYbIcQB/7/nnnveyEs5JkzxTW7LlfKWYoKTnShXF5N8IF/6huYOvBZUBmFNte+vq0Jhi9CGyn+dOFPPG+kg0/6yE3Ix8r/XEwUCHjdzfDnex+fivdwXGaZLjvOQ3Q9JM8Hk1CQqouVURstH/SyVCgOCj9UiAmC+Z3JNMUlKSVo1lz7hM9czeXshReIwNm5nO3EusRPoSuIJxTTf5OZCKZ/NVR1ABAcoHjdzfCnRx38l+vmPRB93WUMMicP4LI0gqkc5sXoR3fleXux6hd7iABHNRClFVI/wTNty1vVtPGC/kkBSqjQGR44zJHxW6TZdgUPhP77NC6dfjmPoXPHkH/jw5/+L8vJKljlRljkWHdJjo2bTonkYCkoDyUbN5iW9QJf0ccTeDrzDYYpvkFKSgDBzJLN+E4k5MygJJI2eQVWgc0OhhHcW00dtrWGaJv/zPz/joouu5H3vu4H777+Hglfgh+vu5oEdD1HwCgTK54nW5/jumrvoyvcc1fj7Ipks4brr38Ftv/8XTn7uDCad3MzT//Yo3519B8/9y5OIXbDKeOWIxnIDly0D21nft4mMM3zM5/SGIbBDIcR+lhJKRhCBi/D3ziXdyhPIz3w7gVmCPrwTWezBrTiOwuybR3xwDzPvDFwibY+hzCRO/Vm4VSfil87AK5uHm57F8JLPkj/uowSRUozOZwmsUrzUDITyEF6BwCxF2P3ova/gJ5rwyucf0yW/qX4pM8umsWVwG9uHdrErs5tUJEVZNE1/cZCeQh/bBnfgBT5nN55GVB9bAAs/vGdqfxsOGaqqx7sPWq4NaQ8QWFVjb0m0Ei3fiSwc2+fVDzw2ZVbzUu+TvNr3HGfdfDYBAV/8z/+P2tgkfOVhj8wblArIuANE9ThlZtVhRp7AXxv+JpTBnZ2dVFWN/XDpuk5ZWRmdnZ0H3cc0zQMsH6qrq8fs89nPfpazzjqLWCzGQw89xPvf/36y2Swf/OAHD3o+n/zkJ/nIRz4y+u9MJjNBCE+AZt8gMRLAtmfF3kcxIHxOdqIIBLfc8mE6Otr413/9EFVVNWNCC/4cmOGZHOdFuD+SYVCGQRDxkSA6qaDW17mpmOLnVgZ7f74IAQhy4rW1TMeQnDioY/cqjFgSa5+WTlViMLTbprLXI1odFnQmgiZfZ7vusktzRz13jwWT5tfSvKkT2gv4miLn+wyW6PhNCSan4ixz4lxZTJAVAc+bBdbrDpYSHO9aLHWjYywOABa6EeYaJmt0m4SSSARDwmeGb3K8O/ZH/hw7zqAIWDU4wFBHFmloHFet8Q/r7yfduyb8oUfiR6txq5dg7xqm+KeX8QfyoYpTaqw571TuveF8TBF6STtCsU53yEczfChXOm4rZumkNMdfuxAn6yA0cQAJDNC7vY+dy3cy1JFBN3Xqj6tj0uIGujf3sPuVVgpDRRIVcRoXT6JmTjVCCLwRBZIRHTue1CUqUKOexP3C5yfRITbqodJbA6Z5Jm8rllDpawxLyR/KyuiIxTBUOFleGzOZLUuZc5iFuX1h5xxaX22la1MPvSUS/5IyBtMJynWDXKyCNdMvZtam+2ju2UhCJdB9hzNOXgLffZWnnnqE606djpZrwyidw2yhGJJDDMqAYraH2WKY5xa6FFcX2NweozSiyHtge4rMrz/AluW/4d57H6axcfKBJyZ17ObLiK3/X/TB9QRGCvwCwrdxK6fgX7yWYOMQcnsl0ummeFaK/NmVMHJbDQnnNyt+vF6yaQBKTEXWDdW1Fye6ife2IGsbxhxy1rSZmI8/yLpnH+G9b3oT54wEtOkDzehDFr49iIqkw42Vj7AHcevPOqL7fDAy+HAwJCyrVSyrPfh7+mKn4IFtgq58SCPUJBSXT1Wc36zwFTzfLmjLCmI6nNMYcPnUI/t8DNrwo/WSjf0CXSpKI5B1YW2vYHG1YkEFTEnBGQ0BD+4QbBqA6aWKmaXwUpegdTi02mhOhXYSuzLw4HbB/ApFfJ85++YBuH+rpCUjCICamOLiKYoTqvee5y9+cRe5XJabbnr/Ud/DCUxgDyas08JQqfOOwJ7qrwFzfJOZXpjBoAAdQVEodCWY7BvUBa/PdMzzPAzDOCZ7pb9muCjutjIsN4tElUBH8Acry3rd5n2FNFWHuX+Lquaztm8jWTcXdv0oRVe+hxIzwczSg/hwHgEEglPcGItciw7Nwxjp1hqvY2x/mAgutZOc5sTolz7JQFKhxr+O540C91rDRJSg2tfIC8WTZp68UNxcSB3R8c5rPoNXulexsX8zlm5R9G2q45XMq5hNR66LFzpWMK9irOrQQnKaE+UX1jCt0qVH+vT2dtP3jo9SePx5ln7kAyz954+yLQqrikXOdOJEkby9mGKJF6VFOuiRHJt1m07ND7NUgPaRe2UdYSlRFeicZ8f4tZXFR1Fcvxn13utJK0Gt0lnqRHlrseSALjlVLBL09yKiMWRp2UHHNwyDb3zjR0QiEW677R3c1vOv7Jg8yLTU5NGAwYpoORsHtvBCxwoum3r4LqrxsEGzedIs8KxZR9fx13Dc9wJO/vQW1nz3JVZ9/xWcb7/I9su3cNmtb2b27IMTklsHd3DflgfYlWnFD3zKo6Wc23gGZ0w65fX97iuFzLUinSECq5IgVn34fQBhDyLcHEEkjcx34qemjf5NK3QSRCvw4/vwJkKEHrfVy9Cyu1FaBFHsw9r6M/RsK4EWxa19E8XGC0A/cLVfuDmkPUhglIBm4qem4qfC77XevxZllITzKN9FeEXQIvjxOoSbQx/eibD7EG4OFa0mP/vde8PalEIf3IDR9SKy2IufbMapOZkgPr7HbplVyvsWvJ2XOleyeWAbMSPK/PLZ2IHD8+0v0VvoZ3rZVN5Ut4wTqo87YP/AqsCP16MNbcM39yqJZaGTIFKGn2g88C3SoqBFEH4BJff+Hgq/iNIiqHHu15GgJbeNluwWYnqCuJ4kWeFy+c1Xcs/Xfsw733EL9fWT6SzsJueFCxExPc6MkgVYx3i8Cfzl8Bclg//5n/+Z//zP/zzkNhs2bHhDz+FTn/rU6H8vWrSIXC7Hf/3Xfx2SDI5EIkQif53qgwn85dDk65zqxHg0kqNP+RhCkCdgmm/yJjf0fRJC8OlP/xddXe3ceuuN3HPPH1my5OQ/2zkaCM634zwWyeEphYmk3jeY7BsUhUKJUA3c7Jts13LUoY2qO2zUqFr4NZ+HpWNGDZy8g5XcS5raRQ/fkJjxsQRjBIGLoigOJH8Uijbp0aZ5WEow4xD+wlUzKjnhvNmkntlOz4Yc8zbbyOlpJp/ZyBS3jJqRa4sqyWV2ksv2W4R18g7tazro3tyDEIKqmZW8bUEVLyRNXjGK+MCClhj+o3FuX6cRicCSJYqTT1ZYEVjy+y6SK1rIZm10ASdXPUuyZi1+7SyUkUAfWI/V8iBGyyNsWF1FW6KcxvJyKoxGlG3zfNTB62pnSv10AEwlmOob7NBc1usOJ3gHb48TmkDbP8GLkAheee8q7JxDNBXFyTtseGgTO5bvxMm7aLqGGTPo29nPQMsAvjOXhkX1IasLOMM27FMfOjkHPaITr4ijUPwmMswq3WaKHwYQFgnYoNvcFxnmlkKaltI6dkVyTC66GCNFbL/w2VCSZshJH/R69n9fVv1qNV2buzGjJmYXlDznsvEEh5nVpaQ0g2cnv4mVqQresnsTQd6hkJpCvGopc+cu55lnnuD6kxsBBUJjp2HjaBaNSiNp2OD5pAwX47gMM4VJR79GQwLW33Ubqx67hx/+8D4SiSTd3eFCYzKZIhrd6/XmVp1ATo9itD+JPrwTp/Y09KGtKBmBSI5g3ipE007s1HTyi6864Jd5UZXC0n2ea5O0ZGFKOlTLLnA9CrqBcmyEtfe9N32fRRXVPPvqi7x3n3G81EzsulOJtD6KKvaANFCFQdzUNJz6M4/oXgshCYIjU/McDbYPwT2bJI6vmJxSoEI/37s3Sj64yOfKaYozJykGilAS4QBF8aGwpleweximpBX9tgQU6UgYIteWFZRGFNVxxTlNipe7BSURRV08VCkXvVCd3VcQDNnhfnUJ2Dkk2JWBOeXhMXoLIeHckxc0JBRSQEcOfrZBko74TEtDEAR8//v/zUUXXUlDw4FF/QQmcCT4a7NOm8DhoSG5NV/KV+N9tEsfH0UqEKQCjfPt+OtSVwFYVhTXdXEcB90Mu6o6pIeFYLYXGQ07+1vDJt3hZcNmUqCPBifXBBqbNIfnjAJX2El8FC6KyDiq5CU1i9k2uJMXu17F9V0UihIzycWTz6Ux2TDeIY8KMSRT9xMquCheNoqs1IsUhWKWZ7LUjR5g75BSGin/4EpuD8XTZgFdCRpGFMNRBZFAsM6w2eG4Bxy7QEBGBpQEcrQejurR0ftQHa/ClCZlVhopJBl9+KCK11OdGAp40iywctc2ui55OzJb4Mr772HqmaejUAhsevZRaesI5nkR5hGhS/NZMRLMXKIkPtAv/YPW9AfDYtfiSTPP5t5egu4+4nNmUB1oXFGMc6GdHNMxqZTCe+4pnEf/RNDXizAj6AsWYV56JTI9vseTpml89avfJRKx+O9Pf565bz+NmW/eS2JKIYnrcbYO7gjfl8Aj42SJ6dEj8hJeq9vcGR0iKwJKglJ8vZxXdItZk6Kc8u9JFnxsMc/f8yTdX+vinHNO4Morr+NjH/s3mpvHLlYM2kPctf4XdOS6aEw2YEidrnwP9239HWXRUo6rnHvE9/RQEPYg0c13YfSuRHh5lFmCU3MKhanX7LUw2B++jbXj15jtTyLdLLhZpN2P8AsEkXKkm0FJneKkt4zrP6yMBF7pbPTeV4mv/w7Cy+NblQg3i7X1bkShi8Ksd6P3r8XofRVZ6MZPNuHUno5vlaHl2vGtvaS/cIZRepRgz2taFD81DaP7BQKrEq9sHn6yCZndjfAdho//V/z0XssYs+NJopvuQrjDKM3C6H4Bo/NZ8vPej5+aPu4tSEdSnNt0Ouc2nT7m9ZNrT8QdsY84KGEvdeymS0LxyMB6AjONcMOOn+K0a1Dj+Cf7JVNxUzMw+lbhpaaHxLCbQ8t3Umy6aK/w4yjgBx4d+RZMaWFpIblrCJMrrr+Gx+59hE9/5mPc+/NHqIs1Muxl0IRGWaRydNsJ/G3hL0oGf/SjH+Wd73znIbeZMmUKNTU1dHd3j3nd8zz6+/sPWrDW1NTgOA6Dg4NjVA1dXV2HLHKXLl3K5z73OWzbniB8J3BUEAgutxM0+QYrjSI5oZjhGSzZr/iTUnLHHT/krW+9hLe97VLuuuu3LF16yp/tPG2hKAs0FvoRJAJ95NzzKgw5GxxRMq/RbTZrDpWBjicUPcJngRdhjnfsytw90COh+nTjI5vRBwtYKQu36OF0DBOZm2JXc4x9NZY90ietJLUjnr1qxGbABX5tDfO8USA3EnTX4OtcWyw5wNIBQjK+6cRGamZXk+kcRuqSdH0qJEkPo1BwCy4rf7Wa7k3d6KaOAro2d1O7vY9zr5jPhZEEnZ3wP/8jaWkRpNOKgQG45x7Bjh2C8xa3s+3pHSQTEaqbkkgvQyq3hu5Og4pqC7PYiz60he5kDf92/LU8fdU87EiEeLHAqZu6ef+Tu+ipryba0YWqakYY4aTAGIm+GjpIu133lh52Lt9FpmsYY+S+Ny9tQo/oKKVoeakFO+dQ1lQ6WqBIQ7J7RRuV08pJ14er07HSGEPtQ+xYvpOauTWj6lDDMulv6SeaiuLZHsVhm8bFk8LwOuGzznCoC/TRCYmFpD4w2Kw7tEmP9mialOfg+Rl8EYaTRYWgGK+gU0SYP74Dxhh0beyme0sPpQ2l6CMpaxfsDnhYDdB/msVgBSSVZG7yeJqnnkZ+n0nDnNnz2LppdRi8oMXxnF56rRhxJRFKkcj3srNuCZUywdaYx2kLbOY7FlJA/RWh3cvVV58z5nxuv/17XHvt28e85pXNxSsbKdJVQKTlD0R2/xE9sw0lDdzy4yjOeBvoMWShB5lvR+lx/JIpICSzy2B22dgPqVL1aNNn4q98GWVFEaaJchxUeysnz1nA91Y8j+d56PrIT73UKMx4B37JNLxNj5PZ1UHH4Hx6WUhpPsPkk8qIJA79uxcEAWIkXftIFChKgROAKQ8davxqt2DIhlmle7ebXAIb+sO/TUqGRGx65PQCBe3ZcOy6+N6QvPEwZIfPuMqootxSdOUFJaZCk9CVCz2H31Qf2kPk3fBvENpbQKjCzrmhRcWeaxJi798BVvUIOrKCWWVq9PXmEtg0AC91CqalFQ8++Cu2b9/KHXf88LD3bQITOBgOZ5125ZVXHrDP4azT9iWDb731Vt7znvcwZcoUbrnlFm666aZDftcncjSODIs8i0/mKnjKKNCiuaSU5CQnysnu6xcSVVUVrsy29nby5JQSXjFt7BEDs5pA57pCktn+397cZrd08URAfB/7BI2QXFyr28SU4AWjSF4GNHoGZzixMddp6RFunHMNJ9YsYlemFV1qzCqbTmOy4Q1RUQcofmkN85SZRwN0JVin26wybN6WL6EESVLJI7LSyIuAPukfEEiXUJJWPPqlz9SR3yYXxWNmjufMAhkRkFChN/HZThwTQXm0lKgRpTJajjHiI6qUYtjJsuAgJKKG4EwnTumqbdz91msxEjFueORBUk2TaJet7NJ20KpZRGhnsj+LE9xlY65LV4K6QMMRMCCDUFTi68SUYKfmwhHUeAAPR/Ls1jzkuq3h9c+bSZvmc5c1zBInSo3a+9nwXlpO8e4fg6Yjy6tQxQLOE48QZIaI3vJBxEFsVKSUfOlL36DT7uHhH/+aSqOMxZefMfp3J7BJGAmea3+JJ3Y/S1+xn7ge46S6Ezmr8dRRFfH+CEbelxwBM3wTRSm+V8U6bTfrdANHtBBLZbnqXTdww9Xv4rd3/5Lbb/8Cp502nxtueBcf+tAnqa2tB2Bt70bash3MKJ2KJsLrqE/UsnlgG8s7Xn59yGCliG7+MZH2p/ASk1DxeqQ9QGTX71CaRXHaNeH9KvRgdD6LPriRwEwhi/0YfSsJrAq8eD3SHgqJVGGi9ChuaipO7Wm4VUsOeexIy58QXh4vPSt8CVB2ArPjOWSuk0jXM2jDLYjAQQkdLzUDu+4MdH8nWrYF36pAenm0XDtOzcl7iVshsBsvQBvaij6wjsAqR3gFhAB78uVjiGDhZLB23A8ovD1hcioIRTs7HyC34MOHLmr3gxACUzu8d69bdSI5LYLZ9jh6ZjteohE/Xos+uIVI6z/ix+tw6s4I7+FI12hh5tsRG76LPrAJ4Q6BMHFqTqI4+aojPr994SkXN3BGnxF7EDEtrv/Ht3H7R/6LRx75PeeddwnpSMVBRpnA3wr+omRwZWUllZWHDzE66aSTGBwcZMWKFSxevBiAxx57jCAIWLp06bj7LF68GMMwePTRR7n66qsB2LRpEy0tLZx00kkHPdbKlSspLS2dIIIncEzQECz2LBYfQqEJYFkWP/7xr3nHO67khhsu5oc/vI/TTjv7z3KOJUoSHQ0z21tcZmVAFEFSSUqUxk2FFI+YOXboLpqC890459rxo/bjOhialzVjZx3a17TTt7Mf3dSomVFF+UWTaZUeW3FIKUlWKnwUlxQTRJTgT2aWl40itlBoSrBNc2gMDBoCiQfsHAm6+2i+7KCebZFEhMppR/cd71jfSffmHtINaXQzfHS6RY/O9Z3UzK6mfkEdzz4raGkRzJqlkCOHzuVgxQpBycAgJQLiZeHKqal7ROOCoYwg35/HinQR+A7/dOo/8kTNDOL9OcqGcwxHTR44YTpSj1PXO8DqpsrQQ2sEeyZ6ZcGBxW3Plh5W/Wo1TsEllg5Vvxsf3kSuL8+Cy+cR+AGDbUNERzx/90Ag8GwPqY+9f9HSGPmBPIXB/Oj28y+bR9eqHoa7hjFjJs1Lm2he1oQQYRusIxTpYOw4lhJ0yzA125AGpZEKSolj+wWkkET1OI5hoh2hkeJAywBSilEiGMLP+RnPDBL3k9RfVE9FoI0JQQHQBzYwK9rOI1vWEV33HYSXxSh2Ue3HcbUoZfYQw7FKNk4+K/TPJCxKtZHLaW93juj8DoCQ2E0X49ScgpZrRWkWfnIyEGBt/TmRtscRziBKs/BKZ1GY+Y7RYIgxwwhB5KprsbPD+Nu3oVSAkBJtxixOP24htz/xJ9auXcnChXtJIzSTbrWIV1YKCoMFoqUxAten98ltDHdnOf6aheMqyJVSeC+/gLNuNfT1UPjGVzBOOQN90eJxJ9NKwas9gqdaBZ05KLXglDrFSbVq9P7ti76CIKKFAW5deci6AkMqvCD0C96DYQde7ITHd0sGiuFxq2KhlcTSGjVubR56FysCBQsqFev7oCcfKn2bSuDyqYpT6xSegnRE0VcMvYotDcqjsHMI4gZE9fC6dmehLqFoLtl7jAE7JIf3JYiFgKiu6M4LPM/ly1/+NGeeeT6LF49ft0xgAkeCvzbrtP/4j//gM5/5zLFf0N8R5ngRZnsmHowuxL+eqKwMyeCHBnaxwpxOQ6CTVBIPxU7p8vPoMB/LGYf1s309oVCs1G2eNwrs0FyUUFQEGnO9CItca1TteihEECjEiAp17z1zhGK75tKieSSUwFKSVYbNdt3lXfnUGEJYlzrzKmYfYIXwRuBFu5MHc5solRaN6WZ03WAYnyeMHKtKikwKDKZ4Jmc7UaYfhpyPKkkqkPRJn/Q+NUyeABNI7VP7PWTm+K2VJalCr9+MCLjfGsYRisvtJPMr5jAl1cSWwR3UxavRpU5XrpukmeTkuhMPeg4vv7ycG2+8jJpJjcz/zY8YqK6gh12s19dREEkSKkNWvsCdscfJF3Kc7uxddDIRVAY6ESXYobs4KHTAERw0/Hh/5AlYqRcZFgGda9ciIia1zZOxlWSH5vLDaIZ/zpchEKggwH3qMVCgNTYDIOJxhGXhb1iHv20L+oxZBz2WEIIvfO5rtBW7eOL7v8Z1HJa++Vx6C31IITE0nZ9s+CWakJRG0uTcPPdteYCMM8y1M68Yd8xhEbBb86gYef8EguneLEqDCtboGY5zLc5wqpnvLsLSLW688Wbe/Oa3ceed3+Yb3/gyv/jFj3nnO9/Pbbd9nGEnixCMEsF7EDdi9OSPLWdjf2jZFozeVXiJxlFlaRCthMDF7Hwau/FChJcltuYO9MGtKD2O8HLoA+vw45MIykKLi0CPgtQQgUvu+E8SRPfzkw1c9IH1SHuIIFqBl5qJ8Ito2RaCSPmYTZWZQut9FT2zdUSpnMY3kwg3iza8E7P7RZxJ56INbkLPd4Sk9aTzKE69BsTe551XOofcgg8SaX0UfXATfrISp/ZUnNrTx5C7WmYbMt+Jl9onXFJI/Fgd+uAm5PAutGIvKB+/ZEp4f14neOULwuA8pTC6niO24fsIv0hglGD0r0YfWEfBHsRpPD+8jYlJ2LVnoA1tRwYeStOR9gDa8A68yKKjPr4hI8T0BBl3gIi2d7HSCWwWn7qEk089nc9+9p8444zzMM0jF4gppdiZ2c2Wwe2gFFPSTUxJNY/6uE/gL4O/Cc/g2bNnc8EFF3DzzTfzP//zP7iuy2233cZ111032g7X1tbG2WefzY9//GOWLFlCKpXi3e9+Nx/5yEcoKyujpKSED3zgA5x00kmjCogHHniArq4uli1bhmVZPPzww3zxi1/kYx/72F/ycifwd4J4PMFdd/2Wm2++lne84wq++92fc845F73hx60JNBa4EZ4xCxCE6oIhEdAnfC6yE5SMFCvTfZNpBYNhEWAocVDrhWOFbmrMvWg2TUsayfXlMGMmqboShCZIFGyeNgu0S5dGX+cUJ8bxboQfRzO8ZBRIKIkGvGgW0ZRgWmAiEBjA5CBsjdyg25z4Oipu+nf2I6UcJYIhtLtAwcDuQeoX1LFxo6CkZC8RDBCPg+tCa5tkQd3eSY9DiqKsIKa34HsBwnBZVTmFl6omU25niQ/nQQmidoEuzeDJWbV8/JsvsmVmEzstQVUQ4AhFh/SY7ZnM3k+xrZRi54stOAWX8qa9bVNmzKRjfSeNJ04iXZfCiJm0+TYtc0xaKjSijmLqahddGxuWBuDbHpqhoUcM5MhFVk4rp2l+I07BRTe1MURiRaBRFmj0SZ/YPoRwr/QpDTRqAp1FboRtlkONmSJF2ALVLT2SSjHtCFXomqmhgnHaDQNFpa+NqxKXuTZi677NrFSegZxDj2NRLbNoegwRrWVQ2HRXH8e2ptPpS0+mQ7qUBxpT/MNPXI8UKpLG26eFK9LyMNaO+wki5fjpmQgvH4YKBi65Rf88biKwVltH9IMfx1u/FjU4gCgtQ589l0W6Tjye4OmnHxtLBgMtr7SSH8hTPrl8lMiNJC16tvbSu72P6pkHBkA4jz+Cc989+JkhNMBfvw5/8yaswo0Yp5x2wPYvdAp+skHiKUXKhJYM7BgSZBy4aPKB71VDQvFUm+TFLugrAgoCBI4vOKnORyl4uk3w222C5Z2CghsqfWeVKXoLoZ1E0vSZW37A0MyvUDSlBFsGBfVxxewyhaUpmlOCW4/zOX7E5kQDTp+kuHtjaCtRakGppWgZFkih6MgJWhVURRVXTAvGqJErrHChwFewJ59PKch7grpEwC9+cRfbtm3mW9+668ATnMAE+Nu1TpvI0Tg67KlV3ghUVYWdjy/1t5JSM0ZtIXQEzSO10Ubt0JZSrzeeMvPcG8kyJHzaNI8hGWAoeNmwecYscEOhhOMOcz6zvAhplaNNetQHOgJBRvgMigAfxQzfJDVSu5b7ki2ayxNmnlkF83Un3A8FP/B5cMfD3N3xNJvdfkqEQWuqlllzL2R7ZSUDWoAKFCkludfKcK+V4TQnyoVOgsWuNa73rzHiS/xzK0On9CgPNPIioE16HOdFmDpSkwyLgGfNAiklRy3PEkqiSXjeLHC6EyNlRDlr/pvp2f57NvfvIOoqZpTUc2Hz2cw4iHfyypUvc+21F3DccYu5885fsTUZ4df+AE9EunBFCQ2+YqbfTrmqY7e2i0civ+dE9yRiKg7ALM/kF5EM3ZpPRAgMJWjVvHBBRB14vX3CZ7VRpFf6lI3MV8yRvJIBEVD801OkTl2K1A0spbCEYLPu0CP90Du6WCDo6Uak0mPGFfEEymlB9fUe9n1sSNbxhX//Kp82P8Gzd/2erqFuFl97Fuc2nsGa3g3oQqepJLQXSUVKiBRMXux8hTMmnUJ17EBSMKIEESWwRWiBBeFzoCSoYJKf4iJ7AXP3C7+MRqP8wz98hLe97T185ztf4zvf+Ro///md3HDbLTAFXN/F0MaquxdWzTvstR0JhDOI8HKoxFgLFWUmkcU+hJsh0voI+uBWvNK5IeFrD2AMrEcrdOM7QwQjnreBmUIf3oEs9o0hg2W+g+iG76EPbkIELsgIbsVx5Ge8A2UkkMX9iO3ACc8LEQYwm+FqvNITCK+ALHQTGAkKSz6PLHShjMSB5PMI/PQs8ulZI8IacRCFrwBCW7Exr6oAWeghsfI/kfZAeC5WOXbTJdiNFx2VWviwCBysnb8D5Y+qpANCst7a/XvcmpNRZhJtcCOxrXeD1HGqlgEKPbuL2Ibvkz3+kwTx+qM6rBSShvhkNgwOknFCQthTLrZXoD4+mc99+nbOPfcE7rzz27z3vR86sktRAb/f8QgP73qSnJsDQuua0xtO5sppF6HJv2zo6d8z/ibIYICf/vSn3HbbbZx99tlIKbn66qv5+te/Pvp313XZtGkT+fzeyPXbb799dFvbtjn//PP51re+Nfp3wzD45je/yYc//GGUUkybNo2vfvWr3HzzzX/Wa5vA3y+i0Sjf//4vef/738a73vVmvvWtu7jkkqvf0GMKBFfb4Y/oasOmR/oklORsJ84FdvyAbUvU6/eA7pAe2zUHgWCyr6Mh8KoiVFbExvh9HedZLPAiuIAxch5rdJuVRpEm3yCGJECFgX1SsVu6owqQPSFv+aPwIjsSSF1DjRNophTIEaljLAaOc+DfAUprYtjZIonKeNhmL3TavKVMopUEu7AdyZr+OsqfGqZUd8g2WDiBA55HfLjIUEIjGY3zNq2ORwOdLuljIFjmRrnMThyg2HaLLl2buikMFCgOFogkLJLVCcy4yXBPllxvjtKGNMbSWv6g9TFUq5PwBT1J2LwsxknbSqkcdvEcD93UcYsu2b4cTSc2Ek3tVRIHQYCQgkj8QMI1iuRsO8bPrWG2aQ6pQGNYBgQozrcTJJXkZDfGFt1hjR7euACIKcEFdpxJR+ijWDmtkpYVraHSNR1FKUVxqIg0NCqnj79ab3YtR+Y7mDJ9AfACW7pdymfPQR9YR0XVGfx4zqm0aB4RwMEhqQQX2/HRCef+0IZ3hm1dA+tDb7Xqk3HqThuXwB0XgYfZ/jhKj42GdCgjiVcyDWNgI/rgRryy8QNFRDSKsXisqscAli07lWeffYIPfOCfxvxtoGWQSHKsGtywdJQfkOvLHTC+ymVxH3sIzAgqnkAr5NGmzcDftRPn0T+iL14yxrPY8eGRllBHPW0kA6MyCm1ZeHiXYEm1omI/a7ETahQ/Xg87hqA6FhKqw47A0hRbBgUvdCh+uVnSP0IU18bDIL3NA4JltYrWLCxvl8wtP9DvpcSEm+YE3L9Vsm1Q4CuYUaq4YHLA8fvNFU6tUwQBPNkq6C+GlhG3LAioioVBdOkIHFepqB37qGRBpWJSq2LLgKAuHlpFdOahzFIcV1rg+q98jssuewvz5x+9SmMCfx/4W7VOm8jR+OtBeXklQgiGuruo2o9Y1AhVuvZrDAE+GmRFwMNmHh2FLRSBgGZfZ1AGeARkRMADkSwzPfOQXWd1gc4VxQS/sbJs1BxAYBGGIrdoLiX7KJ0FgrJAo0V3KaKI/hnJ4Bc6X+H3Ox5Bi0aJlzdS4imGBtt5seV5gsoLiCuJQrBdc8MgNKF4ySjSJcN7cbYTH3fcU50oeUKyd5fmElGCE90oby4mR9W1fdJnWPqjdmp7UBpo7NBcXtILtOoeq5I62apLiOQH0XyfyUY9891wIb5AwHbdxUXR5Bs4nd28611vZs6c+fz0p78jGo1yvAcxlWOn8RBVfhWVykMbIcwq/Sra9VY6ZTtT/LA1v2Gk1g9EGKrtCYWJIK0EO3QH11aj9f8OzeFH0SHapIckDGJ9wsxzY6GEqkBnONND9qkXmPzlcNEqJxQxFdrdZUVAFUDEQqRLCTo6oHxvC3t2uI+il6VTDdDkOwe1dNiDpbWL+f5/3ctnGz/Lb7/6DWaZUzjxs+/nqbbnKbPSY7Yts0rZNLCV7nzPuGSwheQE1+LBSJaEkCSUxEWxU3Np9o1DCh+SyRI+9rF/453vvIXPf/6TfPNzX6RmZhODNyxhxuw5GNKgM99NKlJySHX30SCwKlFGCTLfjXQzyFz7iCWBiZeaSmCUoPe+SmCVwwiJp7QogZFE2P2IYj+MkMHSyYSBbL6DtfmnGP1rUHoMmd2NtPtC5a0eRbhZzM7nCYwUTu2pRDffhSj2hz65gYOe2YoyU+DbCLWPLd6eOlZIpDOIMuL4xpQju9BDKFL91HT8eB3a8E78kmnhcQIfObwd4WQQRgIvNSM8br6D6LZf4Mfr8CqOrMbTMtswOp9Hy7Xhx+twq08aE7QHoOU7kfkOgujY4D4/WoM+vAMt14pnzsbsfAHhDO21swCKicnkO59n+St3sLNiKXPKZzK/YvYRk6410XBBtyW3jYKXQ5c6U0pm05yYgV5qcOONN/PVr36eq69+K7FUgs5cN6ZmUhevHrdbcGP/Fv648zESRpxJiTqEEAwUB3l091NMS09+3RYyJnD0+Jshg8vKyg6Zktzc3HwAUWNZFt/85jf55je/Oe4+F1xwwZjE5AlM4C+BSCTCd75zNx/60Lu45Za3cvvted7ylhvf0GMmleQdxRSdjs+Q8CkPNCoPkmL8ekCheMTM8VAkz5AIKIqAAeGTUIKqQKc2MDjfjrNoH3WIQLBvedQqXTwUsZEJg0RQFuj0C4de6cNIbZAVAQZQ47++11M5rYLWlW0Uh22sZDjxLQwV0QyNiqmhHPGEExQbNgiGhiCVCongnbshVhaw9Pw0XY/H6N/ZT6wshu/6tHVU0R69hsTQIP1tNoPAlM196ArKK/N0nhcjVxklX5Ikqpk0X3otdVWTWZRT9EgPUwkqfDD7VqH3rkT4Nl7pLNyqJbSv6qN/5wCe7WLGI+T68wz3ZqmaVoEQAsMKScptJ5ZSyLjUbMqEBqwC3EqLtvfNYsFve8jsyowQ3oKaWdXMODMsVvYog/d4Bx8Mb3KjWAieMQp0S49GX+dNToylbvheJ5Xk3fk0qwx7dJIz24swwzeOWNFTOa2CySc1s+vFXeT6wwVBI6oz+aRmKqeO72cls6E9w5S6MoSALW19LJszCaRBZa6HW/OlvGwU2K15pAPJQs8aVeDsDy2znfiaO5C5dgKzFJnvIta/Di3bQmHmO49IKSC8AtLJEBj7hWroUVAewh46onuxL0455Qy+/OVPk8/nicX2sq9WSYT8yH3yFRTR0AMPpRj9XOyLoKMd1d+HbGjEVwFyj7d0ZRVBdydBd+doOyaEyt7uvKDCCn+TvQC2DwlahmGgKPivFXDNDMXxVXttHUpMKI8q6lyBG4Sq9PqkYkqJoqcIf2oRFP3QvkFKgakpSmV4rK48xA1F+4E89igaknDbwoDOfHg+1TEwx6mJNQlnNSpOqgvtKRJGeMzDocyCm+YG/GabZPtgOImdnFJcPDng8fv+h+7uDv7pnz59+IEm8HeLCeu0CbxW6LpOeXklVmc/fcKnYp8Q4IwIiCpJ3esUVnckaJehp215oDEoHZJB6JMbV5K8CCgNJB2ax27NG7eDZ1+c4saY6pts1B08FI2+QUb4fC82hAdj1NZFEZAKtDEigz8Hnu94CU1oTLWq6BFFigakSxvYHdhIJ0ssWoIrwsl3RaCTIcBAEkXwmJlniRsdN+RPR3Cxk+AUN0qP9IkrSW2gjamRSgKJpSQ5oRj56UWh2KiHtdV3Yz67dZcyX2OxFyFt1TAofJ6WDjOUTRTBvZFh2jWPAEgVHJa/5zoQgu997xejobhDwqdd0xBiEB3Q2BtqZQsbU5lovkFLphVN6gym0pQqSbMTZUgGKBRlSiOiBD3Sp0/61AQ6AYrfRrK0S4+ZvjlCBiu2ai6/iWS5wI7z6z8+jfI8jIvPol/4CKAy0KgMdMpH7DKEpmGcchr2z35E0NlBUF7K9s6N5HZuZnd9iqcyjzNpxXaum3klk1MHD3J9WS/yu4SH+Ow/cfzsqfz+/f/E7q5O5rx7GXlZIGkmRrfNeXksPULcGJ/MBzjXidMjfVYZRVoJNaeTfJ3rikkiR/A5raio4mtf+z7XXfdOPv6J9/PcZ+6l97zFzH3zKUytbOaCyWcxLX2EJOhhEMRqcSsWElv/P+AVQIshAhv8In60OjxboSH2saxDt/ATkzAKnQhnEHwb6WSQhU6cqmVEt/wULbMdFUkjiwPofSvxSqaCFv62KCOBH6vF7HmZzAn/jix0Y3Y+hyh0gNDwSqbh159NdMtPEI4XqnqFBN8GIVFaBL9kfHX7sUAZcYrTriO68YfoA2tBaKAClB4Nu8BKpo7W9kG8HjmwHqPrhSMig/WeV4hv+C6i2IvSYxg9LxPpeJb8nJtxKxfvPQfNAmmC74x5wAnfRkkDNXLvZLEbtY+dgxu4rOxZizncxm7f4Imix9Ptyzmz4RTePOOyI7JlEEJQG2ukOlqP7RfRpYEh9z6jP/axf+P+++/hn77wQRrevJDeQh+G1JleOoWrpl9CbXwsgb2hbzNFz2Zyyd7vXKmVprvQy5re9RNk8F8QfzNk8AQm8H8Zuq7z9a//kGg0xoc+9G6KxSI33vjGKtQFgtpAp/bP8BjYqDn8LpIjMqIIftEo0CN9hhFUBwa7NZefRjPE85IZB5kQWON4xjX5Bjs0l8yIzYUtFEPCZ6kbZdrr2M4PUD2zisYTJrF7RSvZnixKKQxLZ/KyJiqmhGTw0qWKXbtg+XLJ7g5Fl/ApVLpMujbHAycHLKuaQfXTHQy2ZhjOFBkQARlRBW0JvN488bII8bTJ7nKTyu1Zyp6H1lunko8YXFKMU1cbEgUmgvrAABVgbb2byO4/hG1WaJjtT5LZ8QLbl59AvCJGrjeHbmrIuEFhoED7uk6alzRSPrkMhWKT5dJkpSi1Ejg5F6kLrLTF1kiAdVMt8zYXsbM2VsoiWZmga3MPA7sGaF3fDkDW9nlkM6zslegCTqoLOGcSo1YZglDBcoJrYRMqQvZvhYwiWeZGWXaMth5CCqaeOhnf9Wl9pRXfC6iZXUPTiZMQcvwiO4hWI3ybqKkzqTLFlra+kL0PXJRVSbnSON9JjLvv/jBbH0Hm2vFK540Wh6LYj9nxFE7tqQes9o8Hpcfwo9Vome341l6vA+FkUJp1TH5kF1xwGZ/97Cd46KEHuOKKa0dfr19QR8/2Pl7ORNgYq2ZI6WjZPAuqEpw+ZRzy3LLANFCOjR8EaCNvrnJsME2ENfZ9s7QwMM72IQGs6xPsyoRWC4ZUtGfhR+slQgQcXxXOWgMgYcL8ckViJMAtqodBcd2FkESO6gopBGLE/3fPW+v4AieA+RV7Jya9BRgohkreyhEeXAgOUPTuj2EHVnQLtg4ILF2xoEIxr2KsF/DB0FQSEs5d+ZBkr4lBIZfh2q//J9dffxNTpoyfPj2BCRwNJqzTJnAoVFVVU9LWS4nS2KS5lClJEUVeKE5zojS/zrXRoWAg0BF4+7Va+ygkAm3EJuBI+7hqAp0aZ2/NWiCgwdfZoblM9g10QtI7KxTnu9Zop9ifA0op+gsDxPSQ0J3uhUG5/Zqg6NsI5TIpkAyJvaIGF4WlBJWBxk7NHSVCx0O79HjZKNAhPSoCneM9i8n7vJdlSuN41+JRM48WhLkJWzWXdVoY4ptQ4f3Oy4A1usMyVyOtNHrwed7I06n5DAifyb6BVIrffOij7Fy7hq/+9o9UV9eiUDxnFPh9JEePtGgTzWyJrOAEZxaNQQKbIm3abmr7m7jrpfvoyvUghUZp3Wy8hadQIpNjRBp9Iuxui4x8Bjqkxw7NpT4wkAh85VP0bCoDyW7Do8LWqfztUwwsmIPWWEuJ0kgoQVQJTnGjY7q2jFNOR2WzuE89Ru+GFfQUOshOq6fv3FNoSFtsH9rJTzfey0eOv4WYsV+bErBdc7gnmsERimbfoPGa64jX1/HCDe+l4zM7mHPr6USaIqTMJAWvQMtwKwsr59FccnB7nLiSvKuQYqsTo1t6xJVklmeOfhaOFMuWncpjj7zCd797B1/5yufpe2U3n//sV5l/4pyjGueQEAIv2YTSYyBNhAoIzHK8eH1oX9a7Ard6GdbWn0O0KiR0lQq72VLTUWYJ+vAulBHHbrwQpcDsej5UrgqJ0iy0wQ1ohS78Ym9o5+DbgAK/gFAehVnvwmk4G5lrR+kxvNLZCC+PntlOZOcDaMM7UZoZHtdM4lYej1P1+uYxuFUn4sdqMLpXIN1B/Fgd2nALkdaHDhB5KM1Cs/sPP2jgYu34FcLJ7J0zKIU2tIXI9vtwy+bDiGo9iFbhls/D7HgKz4iH99l30LK7cCsW4iebAfATjRjdL46mG7dlO+jKdjBbt4inpjEzNY1Be4gnW59nQeVcZpUdeS0qhUZUP7BwLi+v5NIbruOeH3yfi0+roqGmEdd3ebV7DVk3xwcX3UxU3zs3sH1nVEiyLzShUfTsA16fwJ8PE2TwBCbwVwJN0/jyl7+FZUX5xCdupVDIH7EXz1871uo2BRG2nbVKl0GpqAt0BqRPVgTM9k02aS7LjcJByeBZXoQylWO39GgIwhJfIySEJwU63ki72OlOgrOc+BEHUxwppC6Zc8EsqmdVMbh7EICyplLKmspGCUfThBtuUCxZ6vGDzgKDsSLTZgeUVynaZMCv5mi8vWkOk3odHvjpcvIFE7csQrIng1VtYeYVM1p0/Mo4fY0GZpeN1e5wQl2KD+dKDzgnfXATkdaHCSLlqD0Eom+T3bAFu7OG6lmz6NvRx3B3Fj9n49keOD6xdJRM5zDpxhSmEgxLsJIWVjJU6wYoICBiaFTNCEnI4nCRlfetpndbH1KT9O0Ii54vfncLLzRV4iuBAh7cqfHHGQ4fWayoRRt9HwQC6w2amPmez7rfradtVQfSkGi6pGXFbnL9eY6/5rjR69oXbvVSIh1Pog1tYVpdKVtau9EzmwmsSnyrNGyBizcclIQVTgajZwXa8E6snb9FmekxxaGKlCLy7WjDu46IDEZq2A3nENvwv2iZ7QRWRagWLnbj1LxpbxryUaC5eSqLFy/l/vvvGUMG182v5and8OhOE5X1SQQFirEYK2vqeLjH4Kr02Km5rGtAmzYTf9UKcnaRdDSOsosEHW0YS09GVI71Wii1YEFlwBO7JW6gaM+FRG7Bg5o4zK+A7UOKJ3YLFlaGlgoxHWaWKp5pk5RH1Sj52p2HdEQwsyxgeYdkSokiFQktHOKGwleQcRS18dAuIu/Bb7cKXu6SZN0w+O34qoArpylih+FABm34/hrJhn7QJfhK8ly74txGxRXTxg+n2x9yP8L529/+KoVCjg9/+F8Pv/MEJnCEmLBOm8DBMHPmXHatXs3X8mmeNvJs1V2qlMYy1+IUJ/Zn9dBt9HUmewbrdZuSQNKrBWiBICsC6n2dfs2nxtdpPMZOriiS64ol3GNl2KG5BCiiSvImJ8ppzoEk3x4oFB3SJycCqgLtoPZPRwMhBJNTTbzQuYLqWCXNvkGp0mjzcsS8IVJ+lFigM6y5eAQ4QiCB+kDHFqFVwsEUoptG7BO6pU8UgU2R5WaBK4oJBGIkZ0Gy1LWwCVhl2HRJn12aS0UQLrZv0xwMBKWBZFAE9EifukBHA1o0lwEZjCpyN/7qN2z/2S85/ru3M7xkLhRgs+ZyrzWMIFS0alzFKiPLC+Y2+n2NuNKpGq6n/0UfmeumJlaFpzxaWl6hv6oUNWkBM1VoCVckoFN6nOHERoN9A8JFAaEUbdlOtg/tIufmULqJla6j34YdDz/KBe+7hUo3xrDwSSqNUxyLM/az1xCaRuSiy9CXvYmHHv0v2oMmUpNng5REgKmpZnYM7WLjwFaOr1oAgINilxZ2IK4wimSEz0x/r+f0klNOQzxyH+uuvIkVX3wA/SOXYTYkiWgR5lfM4dqZV45RXA4JnwEZkArk6DVqCGb65kEJ/yOFYRi8//0f4/LLr+VTn/owt9zyVv70pwf4whfuIJ0+cK5wLNCHd+GXTMFLNodKVC0Kmok+sAFtcAvFqW9BG9qC0bdqxA8vQJml5BZ9ErfiOGSxDxUpJYhWkVz+idDuYeT+KCOJ2mMpUehGz3egZduQziCBVRHWwPEG/GTzKOEJoLQI2UWfwC2bi7XjN8hCF4FVhd14AXbzJSirbPyLeQ0IEpOwE3tJfrP9SWh9GAJ3rw2cChBeLlQ6HwZathUt24ofb9jH4kIQJBrQs61o2Za9cwYhKE69BmkPoA9sHFFDC7zUdAoz3jZ6P52akzE7n8XuX8fzRHmkdzOGm6EvVkV7LPR9TkdSdOS62Dq446jI4EOh+szp8APofnIr0986g6huYekW2wd3sq5vEydULxzddkq6iSdan6Xo2Vh6qGh2fBc3cJlR9vopuidw9Jgggycwgb8iSCn53Oe+SiwW49Of/jh9fT184hOfHW3J/1tFVgajD5vCiJevhkAqgStCpW9SSdo096Bj1AY6VxWT3G8Ns3lku6gSXGonuLqYwBVgKIH5Bk50pCapnFpxUOsBCBWxxiyPwvHDLFRa6GWnoMTX2KY5PB7JQW6QwmCB2KQStGKACiBrghHXKMnYnDtUSZ8ZYcBWvH2gnONLxg9B0IY2h6m6Jfu0hmkRMCykM4AQUDWjkpKaEno29zBcDAv5ttUd9GzvY/KyJk44v5b7YlnyBMSQKBSt0qNMSWbu42O2+5VWerb2UtZYimZoVHnhOeXXbqGhZi6yMk0hbdM2aZjfJT3aZMAyy+ASO3HY9k+Z78LoWo6W200QKQ9X44+EQB1B77Y+2td2hp7IsfBYvufTv6OPtlXtTH3Tga1zfslk8rPejbX9Xqpiy2nrzxGYpaAC4hu+D76DipRiN5xNcfKVIPf+XMpCN7G13xwpzhTa0LbQB1q38BN7WqDCoo3D+NLtC7fmFArKI9LyR2ShG6VZ2I2XUJxy5bjeZkrByh7Bcx2CzhzUx+Hk+oD55XtrzCuuuI7PfObj9Pf3UVYWLhi4SDZXNFCr+VSqAkKTxNJR+hzJc+2C0xt8yvcR+wopiVx9HXY+x/Czj1IvJMHuXehz5hG54i3j+oNdOkUxaCueaRMMFNWIDQTMLQ+J3nIL2rOCnLvXhuHcJsXOjGLjgCCuK+xAYEjFpZMDZpcrtg4qdg4LpqYUW4I99hAwoxQumRowuwx+uUXwcIukMqpoSkLGgcd2hyrkG2YdWn/2bJtgbb9gRkoR5iAq+grweKtkYZXP5NQRv5UAbNu2mW9/+yu8970forb26AI8JjCBQ2HCOm0CB8MJJ5zE7353H/VZn5uiaTwUGvxZSeA90BBcXUxSiCrW6TYOPru1gFQg0EbsIi4dJ/NgfwyLgG7pER3HHmGab/KRXBmbdIeCUNQGGk2+MW4YG0C/8LnPGma9bmMLRTrQOMWJcr4Tf81K4lPrl7GhfzNbh3ZQGS3H8R0o9HFx1QJO9Rv5tZ+nXXp0SZ8yFXbClQeSbZrHPM9k0jikuI/i95Es/dJn9oh9lkKxQXP4UryPqkAbudZQYXx9Mcm5Tpx+6fPDaBj4GkGQGrGf2PNUsIXCRWGjmKQMBnGRCJxsjif+v88w7ZILmXHdm+kLQg+2V4wiOREQV5LlZpG8SKGpm8mzGVflOcGuY2hrO72Z55ld1jR6/jP1GKvXv0A03cSOlEGAQkOwwItwsb23+6om0KkLdFa5vXT3rEMpRcyIMhCNUejZxk/+8EsymSHed/ZVzMyWMyzCvJNDfXb8VJJdNTGUirJvsrMhDQIg64TeUps1h19Zw+zWXAKgc8SGbV8IBOlZM7jt4T/x2+vfwTNfuJePf+kLnHPORUxJNY16sdoo/hDJstwskBuxZjnBsbjUTrzuQdz19ZP4/vd/yf33382//MuHWL78aW6//XucdtrZr3lspVmgfNCs8L9H/xCAFkFF0uQW/CNGzyto2V2gx3DLF+Anp4AQ+FbFPmNFIdgbqKL0KH5yMka+E6NvNUIFKCFRQiPQo8Q2/Yi8kRhjmTC6b6SU4owbKc64cURNzKjVxJ8DbsXxeKWz0fvXhopmoSELnfjxBpzaN42/k1JoQ5vRBzeGPsxOJvRbHrONjxIytKPYB0GshuzCT2D0r0EWewkipXhl81H7WJIEiUn0zLyJH77wFVZldlBwbQaI8acgRs1gC/MrZh+RNcTRYlDmmH72IlY++AwnXnU2ZjSCqRkESjFoZ8Zse1zlXBZUzOHVnrXE9ShCCLJOlnkVs0cXZCbwl8EEGTyBCfyVQQjBJz/5eUpLy/n85z/Jli0b+e//vpN4/Mha1v8a0eybPGcU8FCjLWEeAb6A9MiKeU4EzAkOE+jgRpnqGWzSHVwRqk6aR4p+8yA8jwoCgp3b8VtbEBELbdYc5H4pw683eqRHQSgag7HFZFmgsUV3iRgezUhkAMR0ZNzEzNhkI5BEIoQk3pOntCTJvMpDrXSPP3GpqChQkg4Y7Bgm3ZDCK3oUh22ELilvLKVqRiX5wQI7nt/J3OZSWuZFWWkU8UZMOMqU5Mpikop9fKS7NnYTiZtoIUuGFRlR3dgZKoYH6GxK0D17EKI+ql9naFCyscGhXw5xa750NN16f2iZ7cTWfgstuwslI4jAJtL+OPmZ78StOfmI7nemI0PgBaNEMICma7hpl0f83/NkTGCpGPPchczzjkPuadGsOgG3bB7F9HqU10dgJNEz2/BLpqA0C1noxtr+K4JIGU7D3uI60vJ79P51eKWzQ2WA1DG6l6P3riSIVqGkiZbZQRCtxi2de0TXAIAQOHVn4FSfFKoqjHgYmHEQPN0m+MXmUH2bMOCVbsH6fo23zgpYWht+IS677M18+tMf43e/u4+3v/29APQXYaAoqSoVJPbxKC6TsG0oDD4r38+xQ6utI/rBj5O/90ek5y8kessHyU6ppSXoINu7kZiWpCbWQFkkVFKXWvD+4wImp+DOdZL6ONQlQsUtQN6DpKmI7FP3TkqG+zzfLljRLYgbAWdOgqU1oSr3prkBf9gpackIZpbBmY2K0xpC8tvSQ1uIlzpDr+KKkfMPr0OxoktyXpM/+npfAdb0CYZHQuEWVSlW9ghKjD1E8Mg9saB7ALYNCSanjjyUUinFJz5xK7W1DfzjP06ogicwgQn8eXDiiSfhui6rV69g6dI3HTXB6aLYrrkURUCdr7/mPInGwOBD+VLW6TYt0qVLehgIagKdRa7FpODgLRs+ikfNHE+aBQalj6kEs7wIVxcTY+qTGHJM5sShxvtZdIiVukN9oBENJP3S5wHr/2fvvcPruAr0/8+ZdvvVVe+yJMuW3O04iZPY6b2HQAoJhNBrCHUpS9sFdn/AFnZZYFl6TSAhQBJI7704dlxlW7Zsy+pdt0475/fHKLIVl8hJKLtffZ4nPPj63pkzM9fSmXfe874ZYmicdgQ38UxoLWnh7YvezH17H2FfugdTMziv8QzObTyDIhljXjbC82aeO0JZRjQfH+jUPZp9c1oZ3IEMaD57dZdqaUyJ4AJBTkj6dI9FXogKZaBQdGouvwtl+GS2hFpp0uoVWG8WAKiUBlW+R7fuUUDSLVwGDY+Vbphj3TC7Ii4Oiqf/5ZsURkY57Z/+gV4hOWEyimJU+LjAFsNGAcVSMKRZZMUCtugCwwozUONR7CxApb2pIzE0nUg2zRl7emlsaWFCk8SkoM2zSHJAtAOCs7Mh7snuYSgeoUiE8a0Icddj3nCGJ++6l4rqapYsWY5AEJrB99LUDOqTtbw4sJny6H5xMuvmsDSTimgZI8LnF5FxBjWfet/AQDAkPDoNl0ZpUDn5/fRR+CjmlVVzyy33ccMNb+OrH/0E1o3DzH3XDVAczNXvDWX4UyhLsdKo9g0yQnFfKIsv4M2F5FF+o14ZIQSXX34Nq1adzEc/+m7e/OYLeP/7P8bf/d0/YFmv3oHsli3H6nkoECDDZaAUWmEI9BBu6aR4Z0Rxq9fgchgR9KVtVZ2IMbo1iD2zkkG0gxHBL2pGy/UjjSgqVIyfbMaP12OMtWPtuw+37Jgjd278BUXgl1BWgtziDxDafSfm0FpQPk71ydhzLkTGJh/6+zbCy6PMBAhBeOcthPbejfCygTCc2Y2W68WuOyco4FMSPdOFV7zgAEPJARhh3IojlwM+VcjxvFXG3IY2+nNDdI7uJGpE6Up3Ux2rwNQswnqIllTT63YuauKV1J+zmO33rmXjvU+x8tLTcHwXTQhSoenf9YgR4fpFb6a193nWDWxAKsXyxsWcWHPstPztWf7yzIrBs8zyN4gQgve//2O0tLTygQ+8lUsvPZWf/OQ26urmvPKH/wY5xg3xrGmxTXcmc8tgr+5R6xuU+DpdWlActmoGmbFlyqDMndmPLmXb2Lf8CvfZpyCfRwnQK6oIXXkNxtKZNb6+GhJSwyBwCBwYjZAVEksJ7Po4ojIKvVloSKAaE4gtDmIwD2UJxvvGMSyD5jVN0wTOl+OlWoNW3pcmawBeAUtkaDtzLi88b5F9cTOZzl60vEZJaYTissDRG01FyI3myO4a5e1z59HuRtinuZiuonHQo1pTqGK13/UpJleCTRINBU+lPS+PjiBTkceJeURGLAqOQHdhvm+yVXdYbxQ471D5u0oR7vw9emYPXvGiwP2qFHp6F+Gdt+KVLkWZcURhBGN0C8K38ZONU86DlxC6BiiU2j/eTHiCe067jcHWXsqtcnw8Hrce4lz7Ii4pXLHfWWSE8fUYhjaCkd6NVzRvaoIpo1UIL4vV8zBO7enB+LwC5sDaID5icomYl2xG2KMYY+2YA88iQyXIaCX5+W9FhUvICMk6o8BezSWqBEv8oIzusE4tPYSM1Rz2ugPkXLhvj0AXisZU8Fp1TLF7Au7dI1heEQit5eWVnHzymfzudzdPicExE0K6Iu8GBWlT2/QgpAd/fyhEOEzGdSlasJix1ho2j67FlgVMzWTEHmSgsI+2ouVURYMldYYG586BLcOwY1TgSYUugjiGCQfOmaMOKnFLO9A+GsRADOUFd+wKoiBOqla0lcD8YslwPih6Kw5Nv08YdyDnCqpj00XbhAVd6WC/ZZFgPL/YqrF1RDCUB8eHyhiUhBXJwx37Ea/Gwfz61z/jyScf4eab75oq3plllllm+XOzYMESYrE4zz77JKtWHVmkeTl7NJdfRybYo7u4QNFk5MKFdvw1uWZjSuN4N8LxHN3PwqfMPL8PZ4gpjXrfJC8kz5l5CkLywVzxUY9pp+6yzXCY4xvEJh8KV0kDR3N53Myx2om85tK5xWULWFjaypg9QUi3iB2QSRtF4xQ3xio3SrthMzIZI7DAsw7rGhWT/6kDkpVtgpiHsNKmzoFAUC9NdusuuwyXRV6I1W6EdsOmU3cplzoNvkG/5iOFYEQPiv1yQlIqdeZ7Js91buX5//pvln/yRvqbqyiXOic4wTWrlwZ3ah4FoaiQBmkhGRcKiyCuI6YgroXYU1vLvK4hSrKB69aXQdFbpZmkRhpsMbJssQJH52IvzFlOlEppMJAb4v7NN5EZeBZV3Ug6WU7K9VniRUn2j7HvqXaues87D7kS6XAIITil9iQ6RjvZMbaL8kgpBc9m2B5lVeUxzEs185hp03tAaR3AYj9Mr55lvW5zgtJQQJ/mU+ebLHfDRGSO/zz1PL66axef//evsvOuO/j7D34S/+JLeCpeIKU0KicNEGEFQgbO6jOdKBV/pgLH2tp6br75T3z3u//G1772BZ544mG+971f0dDw6sQ/r3QZdsMFhLruxcj1AUEmcGHORXilR+fktKtPRRvfSaj/ScjsBUCFSyk0XorV9yR+qhWlGbw005KhEvRMV+Am/isIvq+EjFSQX/AO8t7VCOmjzHgwGfUKhLruxup9FM3N4ker8ZLNhPfdh7SK8ZPBtZBGDGvwOaz+J5ChUkDix+rIt1wViMOvgk1D7YT0EFa4hCorQZWToS83QMbJsmm4nTnJOk6vX8P84tcvkuGk6uPZOryDulWtvHDno8w9ewW9hX7mFTezqLT1oPfHrRhnzzmVs+ec+rqNYZbXzqwYPMssr4Ih4fGcWWCX7pJQgSNhsWcdXtx5lZx99oXcccejXH/9Gzn//JP44Q9v4fjjZ+aW/FuiSOm8M1fEI1aeF80Ci7wQWeGjodGve5RLnXPsGIu815aj9XLcJx/DefRBtKoatDlNKN9H7umk8JtfEq2fg1Z8eNdtHslGM5hEm0qwwLOmTRaPRItv0eyZbDNcGn2DMIIRIckIyZl2lGdDBcbPbyB1Wyd0jIMmsIstIqkw1dVllNQXU7u0hvJ5h4+jAPCL5lOoOxd/63249hjxuIdp+rjlx1Ky6CyO814gu/5R2u0ESi8llR+DjVnk8pVTx+67QYnHYtei+MUhOp/aTcdIjk5Do2xuGa1nziNaHKVqQSVb79mG7/ropk5k0hmcw6WQKsaNeGi+wPXFZG5qEP8RQtCv+QeNvYCkUw5hinEaUnMpeWkJkxD48QaM8Q708Q6EdIls/zlafgBQKCOGU3MK+XnXTomxpY0lmFGL3EiOaEkUIQTrKp+lq7aTpdoKSr1gOdaQNsiDobtZ5q6k0d8/IfJ9H11IQB408VRmAs0eCfLB9BBM5ilz4I2bbk0+tVfYdWfhla3AK12KjJQzKnx+bHSz3e/DyI0ghc4TZooLxRxOV4eO/3hFpEvPBAwVwtTFpwufFRHozwn6s9Aw+WD+8suv5sMffgddXbupr28kacExFYp792qEDEXChLwHXRnBinJFQ+IQ+5wknZ4gHk/Qmd6GKx2KrbKpm7MJZ5TO9DbKwlUYk9fG0uHaNsmv2jV2jQs8pYgbgtPrJafXTx/7cAF+tkWjPyeoTygMAb05+PU2jVTIZ1FpkMlbfhjzVnEIYqZi3Amcwi8x7gTldMXhQPC+ZbtG+0jgJDY1iOjQnYaBnKA0DJWx/Y7loTwkTGhJzdwVPDw8yJe//Cne9KZrX5flmrPMMsssM0XXdY45ZhXPP//UUX0uj+SXkQl268G8xUIwrPncFcpSInVOdl+ba/Zo8VE8buUxJ8uOASylY/mC7YbDDt1hgX90QtG45mPDlBD8EkmlMa5JckK+pvzgceGzazJqoFlLEDvMtkIIls3AyQxQIXWafJONhkPc14JytclCwKTSpuIfIJiVSOClGdciz+KaQpL7rBwDmkdGSCJKMN8L0yRNfBRdusetkTRvzRXxwFf+i0h1JfM+8l5avBBn2zEaJp2xx7kRfhKeYAifPJLRydLmpNKJKoEtoC1URo/KscWwWekV8KXPvmwP9Ykaaivm863oCJ26S7VvEBKCh6wse3WX92WLuHXH7bSP7KDSUeR2biZmRhmzx+lN1LHj/j0IIXjDVddMOzdZIXneLNCu2xgIFnkhjnHD0+LiFpe1cf2iq7l/76N0Z3oJ6SEubDqLc+acjq7ppIVEvKzYODEZk5YWPmkh0RCscINYi2KpUfjtr/EffYjPnngacyqr+fJj97HvK5/m76I6matOptKfft2LlEan5jImJK9y1ndEFIoh4ZM1Fdd/6GOsXn0a73//Wzj//JP4/vdv5qSTXoX4pukUWq7GLV+JMbYtyKpNteInW47s1j0URpj8wvfiVq9BT+9B6SG8ksXo2R6s/mdQSnLgI3fhZvDjdfszef9WMaL7H9EoRaTjV4T23o2yipBGDH1iJ1b3A6CHAqPJJLKoGc8dx4/PwStfiYyU4ZavDGInXiWaEJPnMYhBWVGxlL5sP1uGt7G4bAHXtL2RxaVtr2tcxLLyRVzd9gbcizLc/tnvse25Fzn1zLO5fN5F08rjZvnbZlYMnmWWo6RX8/hBZIw9uksUDRvFc1aeCwvxQ7sfXyNtbYv54x+f4N3vvoorrjibr3/9u1x11XWv+37+3JQpgzfaCS6bzAhTQJfu4aKo842jbtR9JZRSeM88iQhHpmIhhK6jzWnC79iGv3Uz2kknH/KzWSH5aWScDZPL4RTwUCjHOXaUi+z4K4r+JoJrC0XcHJ5gp+HgENxsnG3HuMSOoyG4b6EkW9ZKYvMY+YyNKA1zZnMjq6IzDybNjxdYv2EBQ5t1VHaUaEzSuKqemsWngyfQH7ubeMqkpqaeiT0WKpZAjQ7h79iGt+RYhBAU1wfnpr99gE1/3IIAYsVRfNdn3/pu7IzNcdeupH5FHcOdIwx2DCGEIF/IA2BXxOnWknhjafIVCs1T1MQUldE0Y7aiEAlRoqZf2226w63hCfaRR289npTnctbwCBcM7Jv2LRCFESIvte6mWgENYY8S2ns3frwBp/YMAFJ1RbSsaWbn47sY7hwGBFuP2UBptIzisv2Cf6ksY5vRz05j+0FisGaGg9ZkNxM85Z9Es0eCJXHa5IMKI4JbspRQ9/2BO3hyYqXl+5HJJgqt102b0D0s+tle2EHb0E5MLRCTe0JD3BcaZaF5IpXaEZTXlyEKw4T23o05+ByldorwxNtwzUpC0f3jdWQgcIYO+O1+/vmXUVT0MX74w2/zpS99A4CLmhVpV7FhUNCTUVi6YEmZ4upWOVXe9nJ83yeXy2JFLTLuOFEzPs2lEzMSpL1x0u44xaH9DzLqEvCRYyQ7xyHrCqqiktr4wfcTG4cEPVlBa/H+ArmGBGwbhWf7BItKjyzIFoXgxBrFnbuCDyetwGk8XIBzGyWlYdg4BHvTkPcEhha8B4KxZFzwFOwYDURsqQRhA85rlEcUyF/Ol770SUDwxS9+feYfmmWWWWZ5nVhx3An87Mf/jackxgxv/tsNh726y1zfnHLHlkuDrO7ypJVnjRt5VYaHXs1jk2EHQuuoR92uHFE0SuYUH7Lc9SUKQjGq+STk9PFH0PCAcU3uVz1fxi7dYb1hM6b51PkGK90IpUqnROqECArs4gfMS8aFpEoaxNSrn4c+Y+a5PZRhSPNRQFIFUQiNvklKGiz0rFc1z9UQXGQnGNbG2aY76Ag8FKVSJ6bEtC32aYHb96VCPoHgODfCMjfMkObz+9AEL5owfzKSzUQw1zfZpjtsGe1l8+138PHPfoW3+nUYecgJhYPCmoz1uNSO8dPIBJ5Q+JOlzZUyEFSTSqM8UkqD4WGFB+nN7kQXGgtL5nPO/Av5frHHg1Zw7cc0n8qCR4Ot2BlT3F/Yw7aRndQnaombcTYObcbxXWJGlH1j3XT+8XEWnnkcxzbuz5DNCsmPImNsNBxMgpVrz5kFtusOby4kpzm8l5YvYnHZAsbtNGHDmiZUlUkdUHioKZe1QqEhuMxOcJYTQ1NQqoKsar+3G+/FF9BqatFSxby1tIzakjI+cudv+MAnP8ySZbcy0dpC5IDv7cRk1nLRa/h+HY5R4fOHUJpNpoMtJCmpc+rxC7jjT0/wgfdey9VXn8+Xv/zvvO1t7z36jQuBn5qPn5o/888UQO8FTPBr2O+b0HSGyxqYqEhSJItJqqIg/7ZoLsbYDrxkE+hhtPwgwi/g1Jx2yK6Mv1W07L7A5Rytnir09sMl6GPb0DP7MLUQyggjo9XIcDkYUfxUC/nWt74u+19SvpB1gxvJuXmiZgRTM4ibMeYXz+X6RVezoOQoruEMEUKwuuZ4Vly7hO2/ehLvmSE+8LF3HJV7f5a/PrNi8CyzHCUPWjn26C6tvjWV79WveTwQyrHcCx82G/W1UFpaxs0338VnP/thPvrRd9HevpnPfe6f0PXX3oD8l+bATLQm/8/71FdlM4jQ9JsNoetBXlWhcNjPPWvmWWfYNPnGVDnFkPB5wMqxyAvRPIMm4GppcEOumE7dJSdkkNc2+d241E5QonSeLM2TOSVCyZjPqmGNJe7ht+vjs9XYyE5jOwLB3MJ8Mnc4DGwbJFFehV5RR3Ysx4bnJNSMU2mN4ff3ojc0Uan5DI75DI3rGHoxqi8DyUFqjplDxfxylFJ0vdCF4/lE5qRACcIREyNsMLJ7lKFdw1S2VrDiTcvobx9gdN8YmqHBz+G8VSHshYrHMhF6ZI5I/TixXAdPDqbJR2MUpy1SXgMkAjVtVPj8KjLOgPCZ48eIOIIBmeen9XPZGUuwMD3Got4N1Maq0bwcWn4Ar3jh/hbicAnKHsHqe3JKDBZC0LymidKmEoZ3j6DbQ6xtzDIR7SY8mMOP1eLH60EIDiUn+r6PFi7CLVuO2f8UMlyJMsJTJW5O7VnTlEt7zvkYEzsxRjej9AhC2ig9TGHuldOEYB/FRm8fpdkBjFBpUA4BVPke7TLH7sxWKpPHv+J3CQKnRGzTf2EOb8QPl1KvO7S5z7CuZxmNDU0YVhTHh56s4PhKRcUBD+VjsTjXX/8+vv/9b/GRj3yWVKqYuAXvXCzZMwFDeUHSksxN7c/0PRSZTBqAeCIZFOa9rKBKohBoh3QeWDosKAEOeQUCJuzAG/JyMTpqKAZy4oiffYkLmhQa8FSvoD8niBqKC5sUFzQFn/WkoOCB7QliBwSNawIiBlRF4PwmhSshrCsWlSnaimduhHn44fv47W9/xb//+w8oLS2f2YdmmWWWWV4H8kgeCGXZcdoSxv5tmM/seISrF57IMV7oFYXctJAoOCgmIaYE40LiAUc7Y1trFPhNZIJh4ZMbypEbz1KVzXP6w2OUW2Faz5xH7dJDRyKFlaBU6uzTPUoOcFrmkJhAsTz0/PcpM8+t4QkmhMRC8KQJT5sF3pEvosk3WeyGeM7MUykNoipwP7tCcbITedVRGHs0l1vDaVwULb5JVkieNPM8buVp8iyKlMYc3+S6fJLaI2QkH44m3+SGbDHrzUJQPicDYfHOUJZ23SGmNPJCEVaCi534VBfHS1gIaqRBToPoywRJbfKbce/Nv0TTNN54xVt4yMrxvFkgPxkhcZoT5SQ3wgV2nF26S6fuUeobdBou48KnRBpU+QYT+BBJsnjeGVTWrKLNNjg+XM3PoxmeMycwEUQKBQbTveyRLu1dO4jbNuuL23B8h7Aepj4Ro+AV2JPeR8bNMfrsXtyMzRdu/CphY78T/DmzwAbDodk3CU1et4yQPG3mWe6FWepNd41rQqM4fLDZYsnknH677lAtDXQE/SIoUT7RPTjWQY2PobJZtKr939sz5rZyy+Vv5YN/+i13n34xc777FZJrlqLbeSKxUvRkKWe7RVPREUfLqPDZbjj4wBw/KNoTkw7xmyITvGAUqJYGJdJgWJPcFk4Trkjyy1/ewT/8w9/xmc/cwNatG/nyl/8d0/zz3XeZzwrCdwn0AYHSwWtSFN4oyTYU+FP4dzxtPUZOZImrBCc5p3Ju4RL0Be8msu0nGOMd4NuoUAmFpjcEYvD/IvRcb5CJHKufek24GTQ3h2aPoE90ggZieCN+qASEjmy44HXb/7GVy2kf3s7z/S+ikEgFlm5yRv3JtBbPvJD7QHzPx07bmGETM3L4703UjPD+d3+EG298J7t27WDu3NdfeJ7lz8esGDzLLEeBQ9CIXKr0aaJmhdTZprt06u6fRQwGsCyLb3zju7S1LeZLX/oEHR3tfPvbPyOZPMqK+/9HEEKgz2vFeewhRGXV1JNKlUkjQmG02rrDfna9YRNBTGspLlUag5rPDt09SAx2cJBIwuwXnpVSyLxLk65hhKa/30JwphPjmH0+G+7fRm7XKFlX8lQiRMOx9cw9uRlN379vD49fR37K46GHcAiy1pJ7S2jbcwwLahdhhSddHlVJRrvG6FrbRcWJickpvsIyYHGTQ9+IwUCfg/Bc6s6eS92aNoyQQc732DA8ykBKYls5wkpQ75s0WSZKSQrjgXBuRS3qj6mj/pg6tPwA8WiY2M6b+Myi7bj1q7hPX8hXsjsZDJlEjRJKc3kSHU9zt/0U85a/i6pYBVsMm17NZ55voguBW9zKgN3JtliUbquWlphFquwMzhcNnDnYM3kxp9+8KD2EcKY31QohSNWlKCmxiW78Aaf1DPPLljHcgkso14tmj9BTUU1CJWjxpmdZSSkxzRC5Be8iHCrFHHgWzRnHTzRiN1xwUKOxjDeQXfZxzL6nMCZ2IEPFuOXH4pUuO/jLZI8C+vRj0AwQAq3Qj0oo1EA/yvfQKqoQxqF/fpiDz2OMbMZNtYEeXO8ra3vIdEXYPlCMjMUQKFqLFZe1yIPEy3e844P893//Oz/72ff48Ic/HQxDQFMRMy5GS6fHASgrKidlxRko9GJqFprQUEqRccdIWWUkzNSMtvdyyiKAUHhyvyitVOAmrk9IHB8G8hDSgvceSqAN6XBpi+L0BsVYIXALFx1wT9iQUJREBHvSEJp0USsFWS8okkuGYHWNou4onMAvMTIyzMc//h7WrDmDK698fdwes8wyyywzQaG4NZzmUStH6uQTiFSU8/Rvfo3ztYWQL2LlK8QSlEsdg0BsPdDBOiYkS73QUd8wTgif34XT5IWivt+lf/MIybDOQGuCnVik7h9i6z3tJCriJKsOLtbSEZzsRPlFeIJ9mkup1CkIRa/msdwL03IIM0FaSP4YyuDDVISEj2Kb7nKPleUdhSKuLiSIKsEG0yatSYqlxkV2IHa+WjaZBUaFpM03GBM+60ybCSFBQU74VEmdHbrDbeE0H8wVzyhu7OWUKp0zndi01+p9k2fNAnt0lxKpc6wXZuER4tbqfINtujNZFfxSmbNCScVTP/0pF170Ru6rMXnCylCidFJKZ0jzuSmcRkdwohvhXfkU91hZ1pkFBlSQaFw5eXxbDQcDiFqw0wqzWWkM2nk2mDZlUqdb5Rno3UqhMIGRLKNQWY+99l62jwxSrZsMFYapiVXRWtJCfaKW7aO7GHryBU4742zMiig/2XwzoGgrmceGhjpCk1FkLxFXGraQPJbfQ39PL2EjxMKS+ZRGDh8JF1cab8sX8cdQhm2Gg49knm9xnhM7pGFFpIoRsRhqYhxxQNTcPMPit5e+mbfvfoENb7mRovdeTvLj10F6L/P6d3Fy6hSOMjIbCMwpvwulGdYkoIgrjVOdYJVip+6y1bCZI80pV3ut1NijuTxm5TnBjfCVr/w7CxYs5rOf/TAdHdv43vduorT0yBF0rwajHaI3aeAovCqF8MDcLNDSGr/99O/5Y+p3lMoyKv1qxrUxbg/fikJxKVeSOebvMcZ3gJdHxmqR0crXZUxatgdzcC2aPYofrcItPxYVPlIp96tEKYQ9Hoi+Y1uRsVqUmQyi7vw80ioGJJo9gXBzaPl+/Fgtoe4H8BNzcKuPLtv9UESMMNctupoVlUvZOdaJIQxaS+axoGTeUUdDKKXoXt/D7mf2kBvLY4YMapZW07y6CTN8aFH44ouv4LOfvZE77/wtN974mdd8PLP85ZgVg2eZ5SgQgA54L3tdTf7vn3tBixCCd73rQ7S0tPK+913DxRefzPe+9yva2hb/mff8t4tCscF8gSetR+jRuqiWdax2TmOpewzmKafjtW/B37YZrbgM5dioTBrzpJPRWw7/5NIXCvEyfewlYfXAl0fFCA+E7mKd9SweHm3uYs5yziexJ0XnU3sY2TuCEILKBZU0n9hIJLV/JujZHltv30J6zwhF1UXolk5+LM+OhzsIJUI0rNz/dHm9+TyPhR6gwq8iqVIAZIbzDKh+qqJVVMrqqfeG4hbZkTxUL0SrrkF27UVrbsEyBfXlDjWj7RgnHEvkzMUgoFtz+WV0nN1tYWrX5YkqjYJQbDUcpOtTgiCUmO6wEIURohv/k4QFmUwGzRkn3PFrmnbFmaviFNetRAhBMpdHU0m2ZrezfmAT5zWdQUYEwRsvPUzpicTZFW8m4uWJawVaRQn7ymr4vRmn2TNYKEwy3X2MD0lc2yUcD1GZHEAuWH3Ia2d1P4gxvoM15slsyr3AxtJ+dN9BqhcxXMW56joa/OmFGqOjw8yb14ayisi3XU9Hy8WsF6N0xJKUEOJY16HNn54JLqOV2M2XTUrzh0ZHsDyb50+JCOW2jzHppO23IhQ5YzR2Fcjf8k38nTtA+ug19VgXXIyx+GBRWU93AWJKCAaoD2X4VOmdrNf7GSg7i1Q8zIL6aiLmwTea5eWVXHXV2/jBD/6Ld7/7xldVapZOBwJ8IlHE3GQrBT/PmDPES+2CMTPJvOSiGU88lZRQyEMojNB1lpQpmpKCHWOCqqgi78GeNMQMQMHXn9fozwpMDdpKFZfNlVQcJsYyae2PgDiQ0ghc1KToGFX054L3BA4KCOuwoFRRe4S0H08GsRMhPRCPp45FKT7+8fdQKOT5j//44ewyuVlmmeUvSpfm8YIZOARTms7CKy9ny823svirf88jVpblXmiaieHlzPMtFrsh1poFypROSAmGNJ+IEpzsRo86IqJDdxnUfFp8k8HBEaRSRC2ToqxkZ63F6rokEzuGGewYOqQYDLDKDWMLyUNWjj7dJ6QEq50Il9mJQx7LLt1hSPNpPkDE0yfFym2mQ9oOMoHfWihi2PbJCkmZ1F9zTFlaKASKFw2HLt2lX/PxUPgEcQYTmiIiBesN6NY86l+FO/hQ1EmTOnvm21rlRnjBLNChuVQpA59AXA89spa+3Z2c8x//w0OmTY00prKTE0pjt+7yiJXjuMk8XpPA1NDiWegoBjSf3bpHTiiKlWBEl7R5Bj7wx3AGV0Cjb7Inu4+MPUEimkIiyIcilFfMRd/bQXlyDj2ZPjrH95Kw4kw4E+S3DTGxb4i5N67ghxt/CSKYiz/Z8xzueCuhY84Hsf/4XemyJ7uPoc4XWLe7HYCySClXtV7Gioolhz0v1dLggqECxWPbGXdztJjF1JcvAuvgTGqtqhpnyUIKD92Lnq8iXlIF4+PIiTG8c05j/vlxrMYi1n7/d8Se28oZn3wrw4Uu1jaHmDPvohlfK4AezePWcBobxXw/8OwPC8m9VpY630QKFWRgv8ztnVQao5pPTkiSSufaa99JS0sr73rXVVx66an85jf3UFNzeEPMq8F8WiAy8JLXQgFei0J2OIxs7qOsrIJyGayci8jg58lT1qOcbp9LUivCK17wuo7HGFpPtP2HaLk+gqwKhd/9ALlF78dPNL5+O1KScOfvCe25Ey0/gD62HRUuxStqRZ/YBWi4FSvQc73gjCNDJYDCLVsJShLZ+Ru8ksWoUOo1DyWkWxxbuZxjK5e/pu10v9jDxjs3I4QgUhTGLbhsf6gDO+Ow5JJFh5zfhsNhzjjjPO666w+zYvD/MmbF4FlmOQpMBMvdMHeFMpSiYyJQKLo1j1KlM+91LkA7HKeddjZ33vk47373VVxwwUl88Yvf4Lrr3vP/pADxlPUoN0V+jCMc4jLBi+ZathobuTp/PasbTyPy7g/gPPIg/o52tEQC44JLsE4+PYiLOAxL3BDtYWcqKw1gTPiE0aZuMnLk+HH0O2y01lPql6Gj83joQba5W1h17+mYnRGixVGUlOx6opN0f5qVV6+Yeqo63DnC2L4xUnXFGFYwllhpDLfgsW9dN/Ur6hCT6+U3Gy8iUYEQPCEQHTqJzhS5QoEBp49KY78Y7GQdimqKMBJxxKVvpPCrn+K3b0YYJspz0esaCF14KbZQ3BbO8LiVY51hY55byVCpweIX0iRMg4zr0dszQUN9JWXNpdPOj9X/FMbYNuKxKGk/HEQwRCqI7X2AxZEm8rn8/jcLgSlMBvNDQOCi1xDYKEIIejUPJXSUlSChF+MbYapQtAuHTWWNpNKNGF2PoakYId3CHBmhe7wS5i4ndYhrZw6vR1lFFMkYH9y1irWpbnbGRohN9NNadDZzy98w7abW9322bt3EJZdcAUC7bvOzuGRQixFTii0izwtmgSsKyVflHDpF1LJrYhPtqRpCGLiaIOJkuGTPTqK/78XrG0WrrkPoOn7nTgo/+yGR99+I3jS98VeZMUAGNtbJf+fCdygaX88pejvSux9lxnAzy8m3XocKFR80lve+9yP84hc/4NZbf8Fb3/ruyRNgYw5vQE/vRukWXskS/ETTIW236XQQE5FIJEiYKVaUrmaw0EvBzxHSI5SHqggbgTorFbw4KFg3CBkbWlJwfLWiLDKZ5/3Cc7iPPIjs70UkizBXn0J8zWm8fZHB7Ts17t0j6M0KTE2RtxTf3aBRG4cFJQrHh6d7BeO2xg3L5bSyuJlw9hxF2JD8cKPGnjRETKiMwPIKxRXzDnZVQ3DaXxgQ3LdH0JcVmDosKZNc1KwoCcNPf/o97rnnDn78499SXV17dAOaZZZZZnmNDGiBuFk3uTpt8ZuvYu1/fY/sPY/Se+HZZCeFocNhILi2kKRM6rxgFsgKRbNncoYTY7F3dEVtEDxUDxyo4Ds+mjaZr69ATuYBCSFw8+5ht6EhOM2JscqJMKD5RJWgbDK79VAIDh0FFfzddEqVTulrKIs7kBrfoE/zGNd8IkoHfPzgGSk+CgfJkC4Z0D06ded1E4OPlibf5G35Iu6xMnTpHjqCY90IPQ88T3FxKXUnHE9WjFP3spzmlNQY0nz6NY9fRSbYpjtUSIMYsNVw6J18aFCiNGKTZXxbDIfjnTCDehDsbADlBYcxoCDAMwxMz2OxrYEeo8gq4oyFp/B49zOMFEZpjNex9nd30Lp0Cf3lWWrj1cTNwBmdc/Os37cFUdNMbc2KKTF0c66H8dwQixxBQ8l8pJLsmejilu2305hsOGRMBMD6gU3c1H4bQ4XhQKRE8GSqkbcvvobK6P64J096/Knzfp6p7WHOHElj5wZK+7bTULOA5EVvYOfiYvIdGznlygtpapvPnf/yM/5ww9c58cOX8GJiM29oufCo7tM2Gzajwp9mRihTOqPC5wWzwGlOFAvIIqeVIqaFpELq0yJBVq1awx13PMqVV57HG95wBr/5zd3MmdM847G8EnqvQMVe9q/PAEc4GGM6oWyU3X195HIFwhGLosoI6aIRRrVhkv7rvMLVtwnvvBmtMIJXvDiYzyofY3Qr4V23kV360aMvwTsMxvAGwp2/R5oJ7NqzMYfWoWW7MQeeQWkGfrwWr6gFPbMPP96AMmNo+SEwQvjRWozxdozx7bgVQWScKAyjZ7pQRhg/2YJwM5iDz6Fn9qHMJG75CvzkEa6bV8Ac2YiW70dZRbily1DWoR+2HQrpSXY/uzdYbVkbXJdQPIRhGfRt6aNxVcNhH96dd94lfPCD19Hd3UVtbf0h3zPL3x6zYvAssxwlZzhR9ugO7YYDCCSQUhqXFIIc2L8ULS2t/OlPT/GP//gpPvOZG3j44Xv5l3/53p9l+c9fmkHhsdYssFd3KZY6y70w8w6R05snzz2hOwCY57UFL0rYq3dyb+hOjnFWEWmaS6RpLsrzQNMQ2is7QE50I2zWCmzwshhSoYVNTD1YmvXS0sSN5gtsNTcyz23FIrhRKpXlPJ97GqsszvlcNjXpCyfDDO8aZmD74FQ+np21UYopIfglrKhJfqKA78mpv/OEG0wEt+lot4dgUENIMPuh0OeRX5zHjFrkx3Ioqag/JhCSjWXHECkrx1u3FjU6glZdg7HiWLTSMu6xMjxk5QgpCAOJZISBY0rYFNZZdXs/EV2QXpii9vQFGKHpvyr0sW0oPUw8EiKTdyZfDBEyQpTaw3QptT+WQylc5VIWCQTlBZ5Fq2ex2bAplzoZIclpimKp0TB5boNJryCT83hu52qqzQi18W3oymGU1WzrayOy3uO4RWpKMH8JpYVABt79uG9x6nATpw41YoxuJrdgHs7Lbgl37dpBoZBn0aJlSBT3hLKMaPsn38LN0u32cX+mnxN2bSdcegxO5UnTHLpHIlV8DO/bvZv1w2vpiMZIuDYr0mnqdibJ9I2hz1sw9WBCmxtHbtuC++xTB4nBbtkyQnvvQs/sDcR3BGb/k2iFIZyq1fhF8xDuBKHex1GaSX7R+w+a7DY1tXDhhW/gu9/9N6666m2EhEt0y/cwB58HGdyQKytFvvlNOA3nHnQsmcx+ZzBASA9TF2s66H1KwZ274J72EVRukJCfYZOW4uliuGJ5gpaOrXi/+jn4PiJVjBzox77pZ8ixMWouexPLyxXP9EF9XFETD0TYwRyMFgK3SXEYYqZixyhsGREcUzGzmIuX0AScWqdYU+OzaxyGC4KikGJe6vCZyZuG4edbNBypKI8obB8e3qcxUlCs4Tm+9KVP8Pa3f4Bzz734qMYyyyyzzPJ6EFMCY/JBaxhB+eKFVCxZzLabbqH5gnMIzaC8Kql0rrCTXODEsVEUKe2IbuIjMcc3KVI6g5pPOBkmM5xFKhiLaSze46LnfYSAePkrFy9H0JgjX3n8Tb5JhdTp1jwaDshV7dc8VjsREn+GAi+AZs/AFgpbQEQFZWRKBSsJCxqMKYlSMCEUvwhPUC+tP3tnxuFY5IVo8ywGNR9jshjtqmef5rjjTiSBjgXkUUQPuO5ZoUgoQYfu0qE7zPOtqWxpQylsETQGRJWGhpgSj4d0nzCCWs+kW3dJWHFSwsIw4ri6ycK+HppcnXZpUx4tZXXN8ZxYfSy27/Crn/2Qrp27uPG7X6VD9EwJwRBklJZgoPV20VO3BH/yfO91x6nr66W+EAjQmtBoSNaxY3QX20Z3cEL1sQedj5yb43cdfyTjZllQPD94QCE9dozt5J7dD3Ldwqum3vtY99PcueteUuEiRi4+m5GRUUYG9zBnTivvXXMJWs+zSAVD+WEman2WffYCtv3Po9z/j79kzTsvRp2gjkoMLghFMCOe/pkQgglN0uybtHkh1hkFaqVBRGmMaD62UKxxogdlYDc2zuW22x7g6qsDQfjXv76LefNeH0euX60wdgUO3Ck8MJXJhJnjyWc34oyrqTWW+m5Fy7JqkuHU67L/lxBulvCu3xLqeQw/Wo3mjAZuXKHjR2swRtvRCoPT+j1eC+bQOpA2Mhrk8jo1p6AVhjDG2vEjVQg/P3lKJq+lk0YZEWS4dP8cXUmQPqE9dxDquhfNHgHNxI9WgZdHzw8EUXPKI7TvXnKt1+FWHbw6UssPEtny35gjWwjMI+An5pBb+G78onkzOh4765AfyxNOTo8VCiVCZIaz5EZyhxWDzzzzfEzT5O67b+ed7/zgDM/gLH9tZsXgWWY5SoqVznvzxWwwbLo1lygai9wQc/4KT/ojkQj//M//yamnnsXHP/4ezj77WL71rZ+wevVpf/GxvF7s1Vx+FB1nn+YSRuAAT1l5rigkOfFlzsw+vZshfYAqb7oTr9yvos/opk/vpskPfkEfLov1ULid4yx9pAMZcempNIlZBqsrKzizqQJt8pd3r96NRE0JwQAaGvqEyVBdP2J4/yRMN/Ugl3QoO/VatDiKpgeumAOD+Qtpm7LmUnRz/01Lq7eIZ5wnkXfoaKMaNPq4hku+NEvJC2UM7xkhVhIlnAwz9+RmapftL7bQa+vRX/aEtoDkGStPkdJIKA1TOSgNUsUxhk8IMVxXFhSEVcWoyCcO6u1SZgIhXZLREBO5/UEJxaEkloyyc7yTmlgVCujO9FIVrZhaohcmyGe7O5Rlg1kgpjQySrHEtSiefJiSQ2IAxX022bRgZM7ZjIqzEEiUMFDJAuO9E9gZ+6AJi1t1AkZ7O8LNBm5apdCz+1Ch4qCI7mVs3vwiAAsXLmVI89mju1RKfVIIzmD2P0O9PcrOVDXddjdLNj+LPt5BvvXtoM3g4Y+mE256E6vTuzllogNCGl7VAvJ7ngRenOZQF0JANIbs7jpoM35yLvn51xLu+A3G2FbwbfRcP17xomCSJwTKKsKL1WINrcPO9SBjBztUP/KRz3LOOcfzwx/+Fx+5oBWz/2n8ZDPKiIJSaNluIp234ZUsRManf2+GhgYASKWOnLnWnYVH2vsonWinwhgjp/tszb/AvXsn2DCiOLanj1MjBisrJ+MwSsuQA/14TzyCXHMqawcqCRlQPznfzLhQHIK8F4jC8SKFpQfu4+1bR1AP7sbJO5TOKaF2WQ2x0tjhB3cAugbzimHeDIrpHu/WyHkwf9JwnSAQpDfsHeEnX7maRYuW8YUvfG1G+51llllmeb1p8S2aPZPthkujbxBC0HTNm3jui//E4v4CodjMRaiY0pjZT9HDUyENzrSj/DGUpdAQJu1F6ZY+FYM+bc9PMLonQ/m8MipbXx9RBoJIg4vsOLeE07TrDiYCB8Uc3+QcJ3bUURevhIfiYSvHA1aWtJBIpUgLOXVzHaS8AkpgAXEFo3qwzP89+aIZj8dDsd6w2WAUyAtFq29xrBvGFoonzDxbDZuI0ljphTnBiUzL0T0UOmKq38R1XdaufYaPf/zzNPsm8z2LjYZNvTSJKsGIkKSF5Bw7zrh2cMmgLYKyPw1BTiiiSk2W0gmGhU+NNHlrPsmzVoEnIsVoiVJkIc2i/jzNAwPszfQRM6JT8wFNaOTG0/zLN/6Ba655B/Xzm+no6j7oGDTgeC/CMbkUu3QHTSnua99EdHQIEas44H3BXNrxnUOei53ju+nN9tNc1Dgl1JqaQUWkjE1D7aSdDAkrjitdHtj7KEP5EXqz/SilqIpVUNE0l812N49N7GCkooZ9G9Js7N5GRDPRQzqp9y7C+8N2Hv3e7/nY6Lv5p3/6FtHoYfKtXka1bwQPFJBTHSYSRUZIWjwTHcG1+SThsGCLYTOs+aSkzmV2nNWHWclWV9fAbbc9yNVXn8/ll5/FTTf9kcWLl89oPEfCPVFhvQj6LpDVgAd6t0A0WGwzexmeGKOqtJSQCFMgz+DoKPYmk/iKBK9XxqKwR4lu+jZW3xNo2W6EPQbp3bgliwM3rdAIRFI5840qiZbrBUBGqw/qMBFeFsQB95dCQ0YqkIVh3PIVaM44xmh7ML/O96LCZbglC1FGDC3ThQyV4hW1YPU9TmTnb5BmEi/VivBtrO4HEW6GQuOlYESC+5l0J5GOXwfREtZ0R3Vo122YQy/ipeaDHgbpYYxvJ7L952RWfh60V9YpzLCBFTZw8i7hAyICvYKHbupYscMbYZLJIlavPp177pkVg/83MSsGzzLLqyCmtElh8tUXTryenHfeJSxbtpIPf/jtXHnluXzoQ5/kE5/44p+1NfbPgZp0ZnZrHm2+NVWy0aW53BnKsMizpi1ztFQIQ5m4wiai9l8LVzgYysBSR7+0MT+WZ+Ptm3BGcpxQlUSMKTJDY8AYY1dFKZ8bOK8jKoripWWQB8QOhDzC6enfCzWZFXug6FvSUEzZ3DL6tvYTK4liWAa5sTyaodFwbP0098AxzirWdaxlZCiN1zgOusJHUlNSR938OizDZMUVy4iXJwgd4Rf11DEKNTlpF8SVoFIadOkucQS+qTFcH0ICF9iRQy4rdctXYvU+Rnlcp28sE0xycj2EI+Usa7yCbb3bGd4+jDlgMi/SyJqVJ1Ju7Y+aKFE61xSSXGLH6Rcev45MsEt38ZSLPzm+49wwbY7Lek0gfRkI6pMzRt/10Q0N3Tx4bHbNaehjO7AGnpl0CCuUVUx+7hXI+MEZaVu2bKC6uo6SklJGlI8+6fYH0Cd2B85bM8qC3g3Udj+FkR9Hn+hERiqwGy95xXMNgBD4ySb85H4XrUimgpAZKae51VU+h1ZRdcjNODWn4ZYsxhhtR8/sg87f4iXnTnMAKzOOKAyiOROHFIMXLFjC2972Pv7t377CW5veRq0VC4TgyXHKWC3G6GaM0a04LxODe3u7KSkpe8W84T3DBbITQzQaGVwrxbrsffSqPUREklzWZLc7TF9NmIgaZaEM1FVRVo6/ox3Z38uEU0nogPl2zISByZXE3uTFcSWk+zP0d+0g6Y6imzpDO4fpbx9gxZXLiZe9ViljP1JBVxqKQtNF45DwWPvf15HPpPne7+8nFDr6nzezzDLLLK8HJoI3F5LcFJ5gl+Hioai66lL4/FcYuun38K4b/uJjOseJUSUN1hkF+qt0YptGqX52mNKMouqUZhpXzTliS/2rYZUboUIavGgUGJ+MzVjhho9q5d7L53WH414ry+3hDBElKJcGQ5qHqQSVvkZGwJgmMRTECOZahoJaX2en4TAh5FQu75GQKH4XSvNQKBesJhOCdWaBx808LpJ+XZJUAk/4tBs2ezSXawvJGTu6N29+kXw+x6pVa9ARvDmfRItMsN1wsVEklcbZdpSVboQ7wmkmhMJFYk7Ox0qkTpfmMdfXsQUMaxJNKTIoBAZn2FHmSou5BYtzRYznCjke3XEPw+Pd7FaSimgZ5zedxbzU/qXvX/vaF1FK8elP/yN7/F4e2ffklCgLkHGzoGBpaRsrvDArJssR00Y5j9gdVEXLp+bQ406aiBGmNl7DofBlINlrL3PsakLDlR6+8tk20sE9ux/kjp334EmP4nAxUSPM9tGdDOVHsKMxfqT1ICI19BmSvK7w8YkrMEyT6isWc+6a87n9P37Opk0v8oMf/JrGxrmHHM+BLHJNFroWG0yblNLRgWHhUydNVk2KvcVK5535IvonM4LLpfGKDviKiipuvfV+rrnmQq644hx++ss/UN/aTMKME7de3dzJa4XctZLwXQK9X6B0cBcrus4bgr4QLbEWxvUhxhnFxKIt1kp0rIi9Y53MCxchrQQYUZRS9OcG8ZWkMlqGoc1cqgrtvQdzeD1u8WKEM4Hw8ggE5uhWZLgcPdeDW7p0xq5gfayd8K7fYozvCo6xqJlC85vwU/tLqL2i+Vg9jwSr614SW30HlMQrW4FbdgzmwLMYg2sJ9T2JkC4ohTGyGWVEKTS/ERUqxep5BCUMZKwGlI+W60fLDyL8PMbIJryy5YFbOD4HY3wbxti2qWgJCIRwa3h9IFjrkyYZzcBLNGFMdGKMd8wol9kIGdQuq6X9/u0YIZ1wMoxX8BjvnaBiXhmputQRP3/++Zfy2c9+mJGRYUpKSo/43ln+NpgVg2eZ5f8I1dW13HzzXXznO//KN77xJR5//CG+/e2fTU04PBRrzQLrjAI5oWjzLVY5kdctM+31YEJIdhgOlZO5si9RLQ126i6dussyb/94a2Qd87w21pnPEvGimFi4OPToXSxzj6VGHn1BwsD2QTKDWUobS6YiCIqqkwzvHqF3Y++UGLzYXc794T+xV++kzp+Dhsag1k8sGWfunjYyQ1liJcHEZqJ3gkgqTPm8/dljmqGx5JJFhIvC9LcPUEgXiFfEaDqhkcq26ROVKFHOS1/G494TTDCCkIJKv4oavx7HchECUrWpQ4qjhyKhNMqlTpfukfJ1FngWCtinuRSEmszpi3KefehJoVe2HLvpUmqKN7BuZx/G6GZUqJjC3CuYU3U257xQxc7tu0EpIukIw/cMsbl/C4suWohu7B9jXGnElcW7cynuDGWCCa/UuMSNsjKv0xsfwEl5DO8bpqyhDE3X8GyP3Fg+aLU91E2kESW36P241WuCHFzNwitdgh+fc/B7lWTzhmdZ3NYKSlGMRqtn8YyZJ+FrWLlelJulcridM3c9TsXInuCGoTBAdNN38OP1eGUrZnTODxrm0uW4D9fhd2xHr2sAXUf29SBicYzjTjjs51S4DLd6DZ6bwRp4Cq0wgozvd5lo9gjSKsKPHL6N+ROf+AK///2v+eJPH+QH7z1u+l++lEcsXfSx9iC7TI/glSymt7d7Rnm4hj2IkA6+GWfI62HA66ZYLycv4lh+jmbXYqdh84TRNyUGk88jQmFEJMp8S7FjTEMqhSagLqHozwkKviJsBE7hzgGP+PAw86MOxUXBNqSUDHeOsPf5vSw87/UrIxFAWQQ6xva/ppTige99hKFN9/HFb91OXd0hvl+zzDLLLH9BaqXJjbkSduoOOaGoiJahXfwmvv/df+P6t7ybcDj8yht5HdEQLPfCLPfCoKVgaTVeq4fQxIznK6+GJt886hgGH8WzZoFnzDwjmk+jZ7LajdJ6iIgyCHJZH7VyOCJwaroofBRSKAy0qcfKJVIjqTQ8oaiTFhGlIQXT5rhHYpfu8lgoT5nUSU3O1z0UD5u5IPLIiU4Jv2khedbKc7wbps2f2cPJ9vbNACxevJwCkn26R6tnUe+bVEuDOb7BNt3lG7Fhtpg2uzWXrYbNYs9ioRfCAqIIFIJmz6BH9xjQfJZ4Jh/MlbDigLzpEqVzbmohp6xopnN8D1JJ5iTrp0RegA0bXuCXv/wh//AP/0ppaTlJmWJ17Sqe7H6G7kwvQgSu4xOqV7KsfHp59ml1q9k+upOtoztIWUls38GWNqfXraGpqAEIrnNGSMJKI4RgTrKeknAJfdkBauNB/8ZLguTKymV0ju/l51tuYff4Hgp+sBJuwkkT0i1KwyV0yTSuEaU5nMIa6iNlRCiZcyzZ/BhVrkatGTjArWqL393+EB9471s4++zj+Kd/+g/e9Ka3HDI2Yjg/wiP7nmT94CZc06K65Tic6mYMw+I0N8rpTnTK2Q1BkMSBf54JJSWl/OrmP3LJm07n6msv4PQvXUNFbTUnVK3k3MYzCBv7r5tUkh2ju+gY6wSgJdXEvOLmg8qC3WMV7hKF3gvKAFkDhZyL1muwyFuCZkNB5ImoKLqn0ZNdT+TFb5IQDtKMsyO1lFtth50T+5BKUhev4YLms1lU2sqhECMgbJBlgCExB59FWsVgJfCLF2AOrQe/gHCzmIPP45Uuxm667CB376HQsj3ENn8XLdePP2msMIc3ouUHya74VCC6Am7lKry+pzBHNiInS+CEPYZbtgy3ZBnm4PNY/U8hnAns2jNRZhTNTSOtFG7FcXglSwL3cWEQZcZBScyh9egTuwLXsXQwRzcjpItbcRwIPchiU9PNCcIrIAojQcieX0BGKlB6CDQrEKr9I9VdT6fxhDnYWZuejb0M7x7BsHQq5pez6IIFaPqRz90551zEpz/9IR544E9cccVbZ7zPWf56zIrBs8zyfwhd17nhhr9jzZrT+cAH3so55xzPP//zf/KGN17Db8NpHrZyaCooCtli2Kw3bN6dK6Jc/W38KBCT/x2+AEQc9OfL828mLSboNHZMuTnmevN5Y/6ao1oSmBGSJ8wc671hNMOm3HJo8M2pAgYzbJAdzk29v1bWc0Xurfw+cjMdRjsKKFIp3mi+mflNS9nbv5fhPSMIIYiVxmg7e/5BbsVwIsySixYx79S52BmHSFEYK3roG4/y8jJqrBqahpumohGUUkyMTVCzrOaobqyMSbH355FxOnWXEqlRJYPbiaV2iLcWklQdKfZEaBSaLqd06UZ67v9Xsgveg1+yABmrpX9jL70b+qmsKZ86Fifn0LW+m/J55VQvmu56nRA+t4cybDJtckIhNMkLhR4eW3cXA2Nd6JU6NQMV1GyboDRcjGZoVLVVMnfNwVm1U+gWbvlK3PKVhz8Ho1uxdt3Glg3Pcv1p84i9+C8Umi/nQm0OA5pPh+5ixGIUeWHO2/oidfkswkqBdBFuGs0eIbznTjIlSw8fF+E7GOPbEF4BP143NXkE0ErLCL3lHTi/+w3+3j0gJVpFJdb5F2O0vrKQqcw4du2ZRHbchEjvQYZSCGcCzZ2g0HQ5Knz4KIdUqpjPfOYrfPKT7+Nda0o47rgamHRfCHsEpYcwBp4nsvM34OUBgR+rpm/3lhmJwfOKBaV6lm47gSvSSOUjCFOQOs3WGHpVNcmeXeyLj+Aj0QoOsms3+qKlaHOaODGvWD+g2DYqKAkrpISkCUkL8h74eUGjyNA0tpNUw36XsqZpRJJhBrYPsuDcttetUFMIOKlGsWNU0JeF8gg8fcvX2Xj3f3PaB77HWy46+3XZzyyzzDLLa8VETBMCP/GJL3DKKUv42c++x3vec+NfcWQBL+8g+FvhrlCGP4WyaAjCCu4LZXkklOPKfJLznNhBTttBzaPdsBkTCp1gpXuQUQygWOiF6NI9HCGJKUG9b1HjB8aGU5zojPOLO3WXPIpKJRgVPmEliKBhiyB/9cBxJZRGN9ClezMWg0dGhkgmi0iHdX4WGWOH4eATzMUbfINVToTfh9N06i5ZIUkojREhec602ad5rHIjvD+XIo9kh+kyz7e4uhDmdCdKkdLZoTusMwsMC596aXCsG6HKCLPwEAKfUorPfe6jzJ+/gOuvfx8ApmZydetlLC5to31kBwDzi+eypGwhlj59ntqQrOO9S9/G493P0D6yg2qrkuOrVnBC9XEIIVhrFHjIytKvB2WEq5wIZ5DgvMYzuG3HnbSPdhDRQ2TdHNXxSs5sOJXbdtxBwctTFE5SZCUp+Dau7zGUH0HXdHKaJFxSQ3NqDt25TQgEJeEiVDRJ3Dep9UL0ZQcQIliZdc89z/C5z32EG298Jw88cDdf+9q3KSpKTR3DuD3BDzb9gm2jOym2ilBOlv7nbmVBSSvvWnodxdYrZ2zPlBfGN9Hy/jUMfPW3PPn12zjrS2/hjtw95L0CV7e9AQBf+tzWcSeP7HuKglcAIGyEObXuRC5vuQj95fPfEPiN+/9YGS2nLl7NzvHdtJhNxEQcpRRdg09Tl99HY2wOMlLKaGGEn278KbusMiorj0MTGjvHd/PTzTfzweXvYE5y/0o1bRhCtwusTRp44Fcq7LMkSDkl9PrxBpQRRUt3oac7carXkFv4bmS8YUbnxux/Fi3bs7+ADvDMBMboZsz+ZwJRGVBWEdklHyLU/SBm/zMgNNz687BrTyfc+XtC++5D6SGUHsLqfwIZqyG75CP7Vwp6OULdD6GPbkPPduNHa9DzvUgrhfBttMIwvlWMnu1BZrpAM5HhIFpiCukS6robfXwHmj2KMuMoK4FXugylfGS4DD8xc8OCETJYeP4C5hzXQHY4hxU1KaotekUhGKCysppjjlnFPffcMSsG/y/hb/M38iyzzPKaWLHiOO6991k++9kPc8MNb+d3999J9N8/R1VZxZSzwEfRrjs8buV5g534K484IKE02lyLJ6w8KT8oLlEo9ukelVKn+RBOjxpZx42Zz7DF3MCoNkxKlrDIXUaUmWVyAeSR/CQyznqjQHmJQULAdlVgyPRZ6YYJS4GTc0lWTz9Px7snMd9bwA6jHV94NHpzqZI1cDLULqphvGccTdcomVN8WJE3M5ih8+k9DHYMIoSgsq2CxhMaiab2i1xKKrIjOdy8R9+WfsLJMInKONJTRIsjNK46elficW4YBTxs5RjUfCJK48pClLPsGJGZBHgJQeWcReRtm8HESlKxwJ051DkMMO14ragFCoY7h6leVMWo8OnRPcIKHjHzPGXlqZUGtVKj38/xe7+HVEmEk7wa9KRGX2qQsf4JLqg5mwWN8yltKnlNriIts4/o5u+ydlM7/WN5Tlg0B3PgObR8P7XLP8WHVAkbDJtcdwfNu/7EioEOQiI0+aQimGwqM4me3hs4cSPlB+1Dn9hJpP0nGBO7QHkoqwi75nQKc6+YEl6Nea3oH/00smsvyvfQ6+oRkZl/b+2GC0AYWN0PojljSDNJfs5F2A3nv+Jn3/zm6/nlz7/LR3+1gyfmlqJZ8WD5GhI/Wo05shk/UR9kkkkfPb2Tvr3tLF994Stuu6Sshsuqn+DWfZXsVtWkZQSpDKrFII1Jhda4lJw3xpz+ArJrKxgG+vwFhK68FqHr1MThPUslD3YJ2kcEUQPevVRyXJUi7YClg9o+zubncqAi02riX4oUmfqzgt0TMJgTxEzFvGIIvYqvzvFVitECPNQleORPv2T9L/+eVVd/jv/vw28n+r8rjWeWWWb5f4imphauuupt/Od/fo1rrnkH8fjfxnzvb4kh4fGolSepNOJSY6NpM6h5pFF8MzbCLsPhLfmiqV4DgAySYU0SUlA8aahIKo1ezaNC6vx/6XL6dI9fRdKMCh8b6NQ9WnyLc52ZL8X3UOzTXfbqDr4AUwnqfGOacSKLZETzUSjy+NMyfV+JkZEhSkvLuTOUYYthM9e3CCHwUHToLtsi4+Q1RV5TpKSOiaASSY/mE0JwphPlXCeOQlEoKHQE1uT+nzLz3BKeICMUIQRrKfCsWeDt+SKaDuG4/t73vsnzzz/FLbfci3FAz4epmayoWDLVPXEk6hO1vLnt8oNeX2sU+FlkHE8oSqROVih+F84wrPlcW3ci5dEyXuh/kZHCKA2JelZVH4MQgt7sABXRcrozvYT1EEkrwUhhlJyXx/FsorVzKV1wKkJolJQ0EI4UkU4PIorKcFG40mOkMMo5jadh6SZW3OSb3/whp59+Lp/61Ac566xj+da3fswJJ5wMwPP969k+uovW1NypiIRyv4ztIzvYPLiZNbWrDjq2CSfNluFtpJ0s5ZFSFpTOJ/QKJcd5L88j+56kpLSUq/7xg9z0yW/yxL/8jpV/dxF37LqHknAxJ9Ucx87x3TzU9TgloRKKk4GQOloY56GuJ2gtbmFp+aIj7kfXdC5sPpufbfkNW0e3EzWi5N0sZfkBLk1WYBU1o4C1hTy7pWChzOHrOsqIETdjtI/u4Nm+F/aLwTZEfqphbhLIKoVKgL4Por808S45BtO7HRmpAk1HhstA+qhIGbmF7zmoB+OI487tCwqpDzQVCA00C30yQxhAy/Wjj+/Aj9Vir/g0arIYTh/bjtUblNip0OTqtWgtxugmrH33kV/4HpAu0S3fx+p7AqVHEEpiDr8IKNCDfctQEULaCC+PMbIBr3QZheYrg/1MYvU+TqjrHrxUK8Z4B8IvoOUHMbsfwitdQn7+26bGMFOEEMTL44cs+PR7e/C3bQHPQ2toRG+ZPy3u7oQT1vCHP9xyVPub5a/HrBg8yyz/R0kkknzrWz/h9NPP5dOf/wj2sQ9y9v/3ZYqufCNCCHQEKaWzybD/ZsRggeBcJ0637rFddzAQuECx0rikkCB+GCdFlCjHuodfWv9KbDJtNhk2zb5JuLUCUTdIeNcEY5UhupSkbMAhXBSmdunBmWMpVcxx7okHvR4riRIrObKwlxvLs+7WFxnrHieaiiCVYufjnYx1j7Py6mOm8n87Ht1Jx6M7EZogWZlgoj+Nk3NoOaWZtnPaKH6FDKdDIRCsciOsdMOkhZxymxwNVVWBS7Svr5tUanKicYQuLgn8ycrwSCjHmJD4SPbpPgs9ayo7r5AZRmZH8KtbIN2B6brUl9fQbnSwu3Yfp84/uEH3aLH6n0bL9fKzZyeoKU1w2rFL8DSCJ/6Dz5GsP59TRwaI7d2K1b8L3c2jNDeYCAoDaSZRmo7SzGAZ1svxckS2/gBjYhdeYi7oIbTCIOHdf0BGynDq9jtJJ2Se7dEJHOnS4MWpU5GZO1o1A3vOBdi1Z6A540grGZRMzOSjmsZX//k7XHzxyfzzU1E+fUkLyojilB9HaO+dKCu2v5xC0/ETc+kZ/j0XFs1ASRUax69czRz9Fl4YdPlNOk9GPcfiZAJRupJ+dwLRPJdTVqwm6lcgkkn0eW0Ia//NS30C3rZQ4cogKfolQ0L15D10tqmEnUUR0gNpEpUJhBB4toedc2he04QQgpwLN23TWNcvSLsKSxe0pODaBZK6ozTXaALOb1Kw6wFu/tG7uOANb+O73/g8f8aVzrPMMsssrwsf/ejfc+utv+AHP/gWH/nIZ//aw/mbY5/uMSYk83yD9YZDv+aRkjoxFHmheM4oEA9pXF/YX/o2rkmiSqMgfGwklhK4QiEJVl9VKINqz6Qqa7LRsEkLn0ppsMwLz9gVDLBTdxkRgZM1JXVsodhmBGVoxVJnq26zT/coCEmeQHQd0NwZ5x6PjAyTLClhs+lQJY2p8jkDQYNv8LCVxUBMK44z0IiiiCiNbt0Dgjll5ID9pYXkT6EMEmibFH4liu26w91WlvflzWnje+GFZ/mnf/p73ve+j73uJdg+ioesLJ5QzJ0cS7GCqPB5wSxwmhNlUWnrQXEEw/kRDE3Hkx4V0TI6J/aiC42aeCXjdpqW1FzGi5sQyWpcFNFIitb5p7Np671MDO4iq8J0+DC/ZC5n1J88bduXXnolK1eewA03XM+b3nQ2H/rQJ/nYxz7PrrHdhDRzWlaupZtoQmP3RNeUGOyg2GLYPFfo4cnuJ7F7O4hmxzGERmvJPN628GpKwqnDnpPRwjjjTpqycAmxeJSLP/9Obvn7b/PwN28l8Zb5/GjzL3iq9zlSoSSu9CgO7y8rKw4X0Z8fYPPwtlcUgwEWlrbygWVv57n+dfRk+qgQgtMYpCVZP3XLMOzaoIUwpI10MygjhhCCqBGlJ9M3tS2zXWBuB69ZwWTqjR8HYzuIHefhrWjHGNsSiKvSQWkWdtNlyNjRxQbKSCVC2kEcw0tzcqVAOshwOShFaM8fCe+5E2GPggAZLqMw90qcmlPRM3sRXg6VOGAVoxDIcBnmyCby0scY3og18Ax+ogllRJGxSqzex9Gz+xD2GF7xQvxkE1phCH1sO17RPLKLP4RXOj2ezup7HKVZ+EUtCCUxhjeAm0VDIkOl2HVnHdWxHwn3iUewf38ramwk+AkTjmCesJrQFddMzeHb2hbx7W//C+n0BIlE8nXb9yx/HmbF4Flm+T/O5Ze/Gf3Mk/jy5z/On97zIbbechtn/dvXKGqox0cdlYPgL0GNNPhgrpj1pk235lKkNJa5YRqOFFvwGtmruUhU0NYb11BXzkfctwdz9xhjXoH5c0qZu6b5FYPzj5aejb2Md49T2lSCNvlUNZIKM7J7lP72fhpW1pMdzrLnub1YUYtYaaCEVS6sZGT3CLplEIpbeLb3qpdeGohpbpejobo6EMf7+npoawuy28qaS9m3bh9O3sWazPR1cg5CQO+8GHeEMySVRotv0ic8XjQdduoOldLARGD7Dobr4sUMbEMn7AbNYVEjzFBu+FWN8+VomS5yvsmtj23m3Rcei/6S0igMtEwXke0/w+p5FH1iJ0K6SM0EzUCFSlB6KCilUBK3fCXKOniiY45swpjoxCuaP1UoISMVCDeN1fMITu1ZIATrBjZy6/Y7GMgPopQibsZYXbuKN7RccFSlGRhhpHH0WZArVhzHhz/8Kb72ra9z4iUPs3LZKpA+kV23oPTp25twNIbSDltkC994XqOtRHFStaL0MNqzjNdRfsL7uGhkI8tG6/hl32Z2uTZudoBUqIhLWs7npMYzDsqceznmYf46Vhqj9cx5tN+3neFdIyBA6ILqhVU0rAzcH3fvFvypU1DwwPE1NKHozoCvND55rDxo20rBcCEoqSuP7BegX2Lz5he58f1XsGbN6Xz3m9/B1P+2fnbOMssssxyK2tp6rrvuvXz3u//G2972PoqLDx8jdCDjwucpM8+Lpo0GLHfDnOBGjkrMfDkKxYiQFISk7ADx8a9JSAl0YEwohjSPhNIwEThCYSGokQabTYdBx6diMpvVmHzdQWNQ80lrClNBudSZ71lTR1UtDaqdVzc/G9A8dhsO832Lfs1jVAtyiD2gVGosd0PcG86iFIQQpJRGpW/wuFVgiRtmwQyiIkZGhkgUl+ChSLzMEGAhCCuNnAjKkl9CTv5/C4GhDn399uguA5pPs2/Qrbl06x55FJaC9WaB0YKcKvUbGxvlfe+7lqVLj+Ezn/nyqzlVhyQnsqw1n2GDuZnHLYcqfwmS5WiT8keR0ujTgozj+kPcY5SEi2krmcfTvc/TUtREa3EL20c76Mn2EzfjxKwIZ8fa6NJKadcdkkpDm7OEiqIUc3s7WZWFefE6llcsIWkdbLqpq2vg1lvv47/+6+v8679+mYceupdzP3INBc2hK93DUGEYDUF5pAxXusQmH/bnkfwiMsFaI88Wu4dsdQUlNXXM7xugurebjUNbuavzfq5d8KbDnpuEFSdqRMi4WWJmlGy5T+XbltL7g3WE7o4x94Nn0pPpZevw9kOWymloONKd8bVoSNbRkAwEWeGMk5h4AdxMkJMLpAwLqVx8YUwzWeS8PBXRA/pWhgFfQHi680QWKbS9VaTf9jGs/icxxncgzQRu+bFB+dpRxoa5FcdhdT8UuH4nHcVGtgsZLsetOA5jZAPhybmyX7wAUOiZLiLbf4kfrwucvQgCG8z+eyzh24FxQ2gYmT0gvSDOojCM8PLIUAlarhfhZoIcYT2EjFai6yGE8oi2/wivaD52w3lTRXbCmUDpYfTsPoyJDtBDyMQctPwgxuhmIh2/Jt963VGfg5fjd3dR+N2t4PtorYsQQiDHx3AffQi9sQlz9akAtLYGDwi2b9/KypUHO9ln+dtiVgyeZZb/Bzi+uJbTf/wd9l15OWs/8hl+csKprPrCp0m+7y0c675++VOvF8VK53Rn5svlX4kh4bHDCATfRt+kRhrTHAmhSdfDFJVR1LVt9E+kqclbHB+tmlFW0tEy3j2GHjKmhGAA3dARmmCiPw3ARF8aO21TMmf/zZsQAiUVOx7sYGTPKOF4iNplNTSd2PgXy+PzXR/LCSamndt3ccrJEk3XqGitoGZJNd0bXlpGpRBCULOshvuWRDFRVE7eTJUonRKpMaRJhjWfKmkQNSK4oTAJxyHiBBNNpRRZN0d94tBt0EeLjFRwx7OdjGdtrj1j2eQwFUJ66Nk+jLGt+JFKZNUa0Az00fbAIeCMowmBH63Erj6FwmRm2MsRThpQ+5uFXzoTRhzNHgPpMmhP8OttvyPjZJlXFBRxjNpj3L/3EWrj1ZxUc9wht304vOAeEeMov6Yf/ejneOyxB/ngB6/jnnueoagohVu8gFD3Q8FSOyHwleB724MlaUOxhZgTsHVEY8Og4r1LJWWHMyMbEdyK46mqOJ4b53nsnthLwXOoiVcd0rHiS9g5Dj1ZQUSHBaWK5BFWOtavqKOoOsnQrmF8xydREaespRzD0sl5gRjclxWYmiJqKDwFYwXBI13whrmw4ICi430ZuHOnxvZRgVTQkFSc3yRZMPnP7sUX13LNNRfS3DyP//mfmzDN2WyIWWaZ5X8PN9zwd/zqVz/i29/+Bp/73D+/4vszQvKjyDibDXtqVda28ATths27cqmjXkkEMCJ8/hBKs8V0cFCUSZ2znBgnuOGj6nh4vWn2LRp8k82GjSMUUanhosgJRbNvTsU/5MT+meI8z6RS6thozPVNCgI0BYOaz4lu5BUL4hQKG8WY5pMXgYj88tVvY0KSFYpFnsUcYTKoeUggrARSQK1vMMczKVVB6XKZ1EkojXbd4UXTnpEYLITAVFAlDbp0l/gB8Q0Dmk+Tb5EVks2GzZjwCSlBRlNEFWhK4aJ4wSjQ5llED/hOBBnKgbN5px7M5UwEQ5pkAJ8O3eF4L4JSio9//D2k0+P89rf3vW6/WzMizY+i32aDuQ6BzqCep09/Cts7g8XulQg0bBQGguhhBG0hBJfMPY/RwhgdY50opaiKVdKYbOCsOadyUs1x1MVrGMtLHrdyvGgEJV1nxdpY3XDMVCzfkdB1nRtv/Aynn34uN9xwPf/x/r+n9ML5WCeWE7bCoGDH6C5KIyW0lcwD4Gkrz3NmgVg2izHUTb2VoBCx6KiqpCydocItY8PgZi6Ze960cr4DSVhxjq9awR8770cIjX3pHhILK3Eva2Potq0MnbiXplULea5/PRNOmoJnT5XKFTwbiWR+qvnVXBqUVYRTdRLhQZdZzgABAABJREFU3X9AaRYqVMwxpsEDuGwTcao1C8236c32k7TiHFu5fPKDCkL70JId6EpDagtRKijcFhnwWkFFyrAbL2HmdWmHxk80klvwLsK7foOR2QeAF6+jMPcq/MQcIlvvR/gF/OTc4P7BzaCEiTGyidj6f8WtOB5lRNAnOvGTzSA0hJtGuBmcpstAiED0Vj7G0DqMzN6gj6QwAr6LEjpaYQQt2wMIZLgEaSQAgdX/BMb4DrJLP4Jf1IKXWkBo711ohSGQPjJcCr6DMuPIWD1W3+PYdWci40dfqj7tnLRvQY2NoLctmlrBqBWl8IeHcF94fkoMbmlpQ9M02ts3z4rB/wuYFYNnmeX/AaqlwUV2nNvPP5PQmvto/8LXePzTX6T6pt/y7n/+Diw7OuHpfxNPmDnuCGcYERJQJJTGaU6UC+341GR9oRfiAStHnxZkEwOMahJVHGZFOIXmvXYh2Pd80n1plIJERRwjZGDFQ0hXTnufUgol5VTmrmZowdIiKaeKGib60gx3joAQWFET1/bY9sB2nJzDwvMXvG7FWYdjsGOIzXdtpW9LHzEzycM/f4i6sXksuWQxVW2VLL54MRXzK6byg8uaSyluLec31ggxuX9sETQafJN1pk2v8IgLDZksxlKj6NvXkc1mKQidvvwAZZESVlUfvhDuaHArj+enT/SwprWcuZUx8AsYmb340QqEMxaUPUzmcbmVJ+LHajCGNuAVt+GWH4tbtRq3/JjJJ/8HI6OVoFnB5NDcPxHX7BHcipWgmWwZ2c5QfoTW4pYpd2xJuJjRwjjP962fsRg8XICH9grWDwYi5rJyxWn1isoZPksxTZNvf/tnnHvuKj784bfz4x//FqfubMzRLRijm5HhUrblSnm8K7hu8+a2UJIETyq2jwqe6BZc2nKEbJBJDM2g5Qg3DjkPftUeRDrYEgSKqpjgmrb9guyhSFYlSVYd7M7Ou7BnQoBSlBxgcjaEYiAPHWOCBaXBuMdt+PFmjd3jgqqoQtegfRT6cxofXCbp3fo4b33rpbS2LuIXv7h9NnNzlllm+V9HeXklH/jAx/nmN/+JSy65gqVLjzni+9eaBbYaDi2+NZUBa6PYZDisN21OdGcWSfQSDoqfR8bZaDhUSZ2k0hjSfG6KTBBWghXe0a9ueb0IIbiykODHEUmn7tKnBbEMZb5GVAo2GDZJpRE5QDQsUwaXFhLcFk7To/mB+CkEy70Qpx7GzFBA0qv77NEc1poFnrDyTAhJsdJo8SxOc6KcdUBZXUppxJVGWihKlU6JH8wB+zUPXe6Pe5v7svxdkyCmYSYUFRWzc+d2zrFj/DwyzjbdoUhpZJEIBG+w49T4Bt+MjbLJsMmIyXgMFJqmAmHSKtDgm1xTSNI42e3R5JmkpMbjVp64EiSUjkRREGApwdNWjmO9MD/58Xe5664/8MMf/ob6+saju3BH4GnrMV4019LszSNEGA2HDfoAHcYjVPnLScj57NY95nsmLYfIL36Jymg5N6x4F5uGtjFSGCFhJVhc1jbN6VusdC62E1z8GiL3li49hrvvfoYb/v5d/OnmW4huLKHizUswSyKEjTCmbmL7QTzIWrNABIHl+0gl0YVGzLYZjscZTsQpGTfJuFmcyfcfjvObziLvFXimdy0jhTEs3WTReavo6tG591s385bmT1BkJSiPldI5sYfw5Iqxgl9gecVillcsftXHaze9AeFmsAafR+R6qDSiXN94Gr9ScfZmB1FKUhYtZW5RI/fvfZTbO/7IOe4Ax2Z2o81x0LOgwqW43hWo3jMBcE48eC6qlKIvN8CYPU5xKEVltHzG90he+TFkShahpzsB8BNNU/N+zRkLMoW9HObwRvTsPrT8AHh5NHsYLd+PUAql6RijW4IN6hZO9Zqp2Aa3ZDFR6WKMbMWPVKC7aZTQQQ+jzCReci7Cy6BPdOKWLkcmGwGQ4XKM0c1Y3Q+RL2rBqT0dc+AZzOF1KDOBcMYRvo0fr8MrasEYa0fP9bxmMVjZNkKIg8+faaKy2ak/hsNhmppa2LZt82va3yx/GWbF4Flm+T+EQjGg+UigQurTWoZPcSI0eSZbrTjO1/6DzOVv4wef+BiXX7CG669/P5/61D+QTBYdfuOTTAifF0ybXbpDVGks8UIs8KxXdEHMFEfa5LwMhjCJGYnXJGzu0Vx+F84gUcz3g0CMIeFzt5Wj3jdZPnnz0eSbXGTHuSuUpV130YfzpHakOTFnUldiIOdYr8kZPLx7hG33b2eibwKlIF4WY96pc6lqraB7fQ+ZoUwQAaEgPZAmFA9R0VIGQElDMYmKOOM9ExTXpVBKMdo1iuf6VM4vJ5oKbjoMS6dnYy9zjms4ZOD/60VmMMOGP2ykd1M/ru1SFClmPD/CvnXd2BmHVdcdR3lLGTVLqqlZUj31OYWiRpps1m3KD5iv1fkGg5pHUmmMCp9iQnxAa2LM380OfxipJG3FLZzXdOa0NmEpoVCAUAj0o0y52D1h8sjmPr7zgTMx0rsBgZeoo9B8FdFtP0HpB5T3aSZ+sgXh2RRar8duOO8Vt++lFuCWrcDsfxIZLkfpIfT8IMqMYU9GROS9PMBBMQlhI8SEk57RcaQd+NFGjS0jgpJw0Cx+926NjjHF+5fJaSLokWhoaOJb3/oJ1113Gd/61te48cbPkF1yI9a++zBHt7JLa2XUzgEQL61DKsi6gWC7bnBmYvAr8UiX4KkeQV1ckbACp/PuCbi5PYh0iB+5C+UgTG0yvvplPz7U5EsCGCnApiHBs32CdQOCY8oV4clZUcKEraOCn9x+Hz/5wps49tgT+PGPf0ss9re3kmKWWWaZZSZ86EN/xz333MENN1zP3Xc/QyRyeEG3Q3cwAIvpq6gAdunuUYvB2w2HbbpDs28EkVxATGp0aA6PWTmWe6G/qjt4rm/xqWwp1b7OA6EchhKMaz5degEJ1EmDH0XGuS6fpHYyUuBEN0Kdb7DFcCgISa1vssizDumafs7Mc7eVZbth06l7ZJEYQBSNfuHjGw6jmk+IwLAAUCENVrhhHrSyKBmULI9rPqNCcoETo1TqPEMB94DINw+Fgzpk4fKhSKWKGR8fZYUXJpQXPGnm2au7NMgQJzgRVk5el29PVPC8UWCv7vGiWaBDd1nkWcTRcZBs1m2+ExnlxlwxtdIkgsYxbphHrRx5wBE+UkBK6jT5Jl26z9Mb1/KP//h3vOMdH+T88y97Ha7ifjaa64ioKKHJYNkmzyRPOVuNPl4QG6n0K2n1olycM/CUjXmEqK2IEeG4quVTf7Z9h33pHizdojxSOnWf4kmPZ/vW8UzfWkYLY7Skmlhds4q5qcYZjTkSiXD89ecy1ujx4g8eZN83nmLptacy7w1n0y0neC7fxTIW4QK6gmQoTsQIk3VzU1EOEhjIDTG/ZC6p0MH3dL706ZzYw5g9QWm4hDe3Xc4Z9SfznRd/xO7xLpaWLWTxh+fzi4/+K3/4+o847QtX867Fb6Ur3c2GoUDUXFq2kFXVK4nMsKPiUCgzRn7he7GzF6Dlh1ChIuoTTXxM+eyd6MaXPs/1r+ex7qcQCNqcYcyRp9kYKqa1dTXF22Po2X1Y8iacaCOF85txj5s+F804WW7dcQcvDm4i5+aJmVFWVCzhjfMuImrO0DGhh/BTbQe97CVbMPuewhxci57rRfg2SB80AyE9lBFDCS3ILG68GISGH6/HK144VSIt4w340WqMse1o7hjCGUMIDT9cAZoWOHxFWeAanlZkJ5ChYoyxrQD4ySayS27AGN+Olh9GWTH8onl4ybkgXdAslPHaV9vq9Q1gBMKviAXfN+X7qPQE+ulnT3tvW9si2ttnxeD/DcyKwbPM8n+EvZrLHeEMO3UHBdT7Bufb8allYgJBgzRpcCYniMtP4Yq7n+ZHP/o2X//6l/jjH2/ji1/8OpdddtVhBdhR4fOjyBjthotJkFv2hJXjPDvOBXbsNU3klVLszXTQlduF7efRhU5JqIJ5ycVEjJk3Lx/IFtNmTEgW+PuLKsqVwajmsN4oTInBAsEZTpQFXogXNncxcncHoVGHGBprzS5qFlez6IIFryqCITucZeMfNpEbzU0VXaUHM2y6cwsr37yC+We0sOuJToZ3jyCAcFGY+afPm8onNiMmC85tY9OdmxnePYzn+uRGciTKYxTX72+HDSfDDHcOs299N/mxPNnhLMmqJLXLaihtKj304F4Ffe39jO0bR0qfaHGU4lgpGTmBbuqkBzJ0re2ifFLIPhCB4GQnQkfEoVN3KZM6NooBzeMsJ851uSQ5TVEkNSKahlr8VkYKo/hKUhounnJFKwXPPit45BHB4CAUF8Pq1Yo1a9SMReHvf/9bRKNRzn3LZ8kZLjJSgVfUAnoId+DpICIhWj2VryXcDOgmfmyGMRWaTm7BuwhFq7D6n0LzcnjFbdgNFwTZZUBNrApDM8h7+akJtVKKcWeCVdVHdmy9xPpBwbZRmF+spvJvyyKKHWOCtf2Cs+fMXKQ966wL+NjHPsfXvvZFqqpqueqq68gXtZCXPu4+ndwf/5VQLEVOi7O1TzBWgLQL/Xl4pk+wqurVC8KeDLaRsAIhGIK4i8Yk7BqH5/ozpEKdGJrO3KLGGU3ioybMLQrE6pECRAxwJRR8KIuCVIp/W6vRl4XujKAvByjB8nJF1Awu/di63/E/334zZ5x2Nv/zPzcTDv/1nGuzzDLLLK8Vy7L4z//8EeeddwJf+Prnuf7LXyUhNeoPiM7q1zx26i7dWiBYHohCkRWSXs1lh+7Q7JvTTAdHYkT4QdTny4TSIoLc1h7NQ0dQ/jIjw1+ShNJ4X76YJV6I/4yOMiEk1VKnybeokjo7dZfbwmk+mCueMkDUS5N658jCa7tuc1N4Alcosih8IK9JYkqjxNdxJ8+rj87jZo7VTmRK3L2sEEcncIN2ay5xpXGBHeN8O44jFE97ebYZDsUqOGvDms88z+IYd2a/r5LJIsbHx4BgldxC79Arngw0TvCiLPYl66wCzdIkjk4eyRbDoU9z2WYGgvaZToxLC3EW+SHmexYmAl9AXGlU+ToTmiS7t5sPXX8Fra2L+Pzn/78ZjVUpRW+2n7xXoDJafsgs25cQL4t/MxA05z12+YOofc+R3t3N0/172GyGaUjUckzFUs5pPP2QAuqB+3+27wX+sPNuto10UPAKzEnWcUHz2SipuH/vo+wc76QsUkpZpIRH9z3N5qF23rnkLcwvnjujYxQIyhbW8/ZvfZr7f/QHXvjhfbRv7Cf21ffhN1QQCk8wzzPZGXKoMsI0JhvYPtrBgJ/DdzRG+7ZRY0U5e86pU/PmlxjOj/Cr9t+ybaQDWzpE9DBLyhdydesbOKvhVP5z3fe5o/MeklaCue88kXVf+xNDd7TTcmYT84qbOaPh5MOMOjg3nvIwhDFzE48QyHgDMt4w9ZIpNOamGtk90cWzfWspDRdTEi7muMF9pKwom6UkxD5iJ7Shjdahpzdht67DXtx00OZ/3/EnHut+iupoFdXRSiacNA/vewJTM7m67Q0zG+NhcKtOIrTnDkKDa1FWEcIdBhTKKkJaCfT0bpzqkzEmOpDRatyKYw+5HRmtwi1bjtJCmEMvILwcCNDsUfRsN36kHKUESky//xRefprT1y9ZTG7h+wh3/AY/0YCyUiBtjImdeCUL8VKtvFb0tkUYK4/DfeZJRCQGhoEaH0Vvmot14ppp721tXcTPf/7917zPWf78zIrBs8zyf4BR4fPTyDhduke1DLLDdugug5EJ3p9LHbZ8zTAM3vOeG7nwwsv5whc+xgc/eB0/+tG3+fznv8bxx5900Psfs3JsNRzm+dbURHVQ83jAyrLMDVH3GkreevN72JHehCFM4kYST3n05bvwpMvy0hPRxNGXnOWRaIdoVLYQpLXp4pVAUDTkEPnTbioLkJxThtAEdtama90+Sit8mloKgMBLzUeFipkJ/dsGyQxlKW0sQWiTS/9qixjuHKF3cx8Lz1tAZWsFY/vGQQRO4Ehq+tP28pYyTrj+eAY6hsiP59n5yE7MqDVNnHYLHoUJm813bcUIGUSKwoz3TNC/bSCIb1hQedTn71DkxwtIzwcFuqGRipSwe6QDTdcQAsZ7J1BSTR3rgSz1QlxbSPKAlaNf87AQnOHEuMCOkUQnecB9pxCC0sjB+QCPPSa4+ebJ85iC3l745S8FmQxceOErC5JbNz3Pj3/0bb541QqqdnwHZcRwylfix+tRegin9kzM4Y0YY1uCxmDfRnPGcKtWB0/0Z4iykhTmXUOh6TKElw8mZgdMzNtK5rO0fBFr+9ZTFEpiaiZDhRGqY5WsrplZxtbeCdDE9JI1Q4OQrtg1LoCjE2g//vHPMzQ0wMc//h5isRgXXfRG0HTml4A/0km4dA7rBwRpJxBXTQ3COtzUrlFk+bTNrJPoIDwJeQ9CLzNT6QL6ssP8ZNOdWPoLCCGojlXyxnkXvWKDtaHBZXMVg3nIuQJPBeON6HBspWLdoMZgHuYXg6Ur0o5gICfYPgbLyxUb7v4+z/z3h1h6yhv54Q9/PJsRPMsss/yfoKF1AWf//Wf55Re/xK7L1lC/5kQWeCGuKMRZZ9rcHcoyLiTjwmef7oEIfnc7KJ41C/RrHh6wx3CZ51lck09Spl75djKpNDSCuIgD3cZ9wqOgKf41NoxC0OCbnG/HaJtB3u2fAx3BXN+iVpq0+iGKlTYl/NZKg126S6/mYSBYbxTo13zKpc5yL0y1PPR5eMYskJnMH+6wXMJKYCFwBWSFJKk0skKhAeOaJCvkVN5sFI2rCknOtmOMaz4pqU+V/oaU4B35FA9bOV40AwfzuXaM05zojIuBU6kSxsdHkVJO6684HPak8ziuNBSKLYZDt+6SUAJfCnQleMDKEVGCc+wYLb5Ft+Yxf/LBQQFJz/AgGy57CyHD5Kc//R2h0Ctf66H8ML/dfiftozuwfYfiUBGn16/hjIaTD1lEu8xdySZjPQXyhIngK59nxp4jryZo7Tbp6t7KuD3OiGZgaSZ3736I3uwA7136tqls3JezaXgrP9j4c7aP7sKTHkIIHu95hkf2PUVdooYJewKFwpMeFdEy2opb2D7Wwf17H2VeqnlGIumisjae619HOm6if/8rJDbsIHfjV8ld+GGKP/IeHv7URzhZpGjxLbbrDtHSWspjUYYKoxTt3c5yLcWaeatYXLpgaptZN8dQbpjbOu5k03A7cxL1U+VxT/U8j+M79OcG0TWNuBljtDCGUWFw0nUX8PCP7+TBy+8l1VaF4zvUJWqoi9dMHYtUkmd61/JEz7MM50epiVdycu2JLCtfdNQrOyecNM/1raN9pIOu9D660j2sqgrMEWHfRgqDiB6hPzfEwlKFLBFoCISVO2hbg7kh1g1upCpaQXE4EPiLwyk85bN24EXOaTz9kN0Vh0LYo1i9j2EOb0DpFm75sThVq7HnXIw5vGHyp4NAhVLISCU4E+iZfZiDzyOkC37+sNv2SpZgjmzGS85Hz+zGGOkJ7lmVQp/oCDpLUFj9T+H6y/ETzWj2KEK6OFXTBVh7zkVohSHMgecQuR4QOl5xG/nWtx/UYfJqEKZJ+Nrr0Zta8J5/BmUX0NecirX6FLSK6feYjY3NDA72k8/nj7gCZZa/PrNi8Cyz/B/gRdNmr+4y37emHBVxZdKuOzxnFmiwj/xLoLa2nh/+8BYef/wh/vEfP8Vll53G+edfymc/+1Xmzp0PBI6Q9YZNkdKnhGCAMqmzTXfpMFzqXsEdcTiUUuzL7kagETeD/E8dA13ojNiDjNrDlIYrjnq7tTJwBNuoqeWNPoockhbv4LEO7RomP1agtKlkahITiloUxzZSuvmH/z975x1mV1Wv/8/a7fQzZ3qfzGQy6T0hoUPoJXQEBCmiIooNy9Wr1+712iteERtwUUARFOm9k5AE0utMkum9nTlt1/X740wmGdIJKPo7Hx+fJ8ycs/fa+5T5rnd91/sSSmYjMbxgCZmJl2JVnHTQMWSG0wjBOHFUCIHm00j2ZwuYUGEoaxNxAAKxABMWZm0SBLD12UYyAR1fxIedtulY18lIdxzF0PCPehEX1hWQ7E/R9NJ2ihuKULXDF9TfTKgghFAUpJR4jkeeVkDfSBep4TRhLYQ/6tunEJwdt+AoO8Bc28+A4uKXgrxDnLBA1hbimWcEmgY1o40EBQXQ1ZUViY87ThKLjX9OtpNJYshsSMp/ffY66kuCfGzpPJxQEcKK42t/GoD09A/jxiaTmvVxfC2PocYbkUaETM1ZmNVnAgI13oRwLdxwDVI/hI51LbjP7VmGqnPNtMuoiVSyousNLNfi+MrFnFJ9PFWHGJQX0sGVe4u+tieIGIfmF7gnQgi+/e2fMTIS56abriEUCrNkyZlUhUHr24CvbBrtSQhrgowrmRCFWUVZ4Xl5l2BqwVvrDvapUJ8Hr3ULigJyLPB4+/AA3almyoL9NOTX40qP1pE27t58P6WhEkr3SJjeFyfXSAZMWN4pGDIlhiqoj0kWlXncu0WlJiJRBJQGIWrAoCnpGDTp+OPH2fz0b6k/7SP84Ic/QteP/HOTI0eOHO8GHvMl8T55NWWPP8qGD3+GKa88xYqYR5/i0KO4GAimuDoeGp6QbFMtTCRx4dGvukxxdGY7BmlgrWaiBUa4MRU7qFXYVMegztXZptpUexo+KWhTbLarDsWeSmQ0BG2LZtGruHw0FTuiBoN9kcZjq2aREpIyT2WCq+9z3JYAT0hCnhj3e02Co0gaVYunfCk6Rxe1LSQvemmuTkf3GdrWpTqEpUABNClA8VAQOEgcAZ4EIbNCa56nEJJ7i5sFo57BGTzWaBmGhUehVJnsGFxiRrjAzFoYaYfZVV1XV49lWbS27mTChIMHgsWkQrmrsUOzwWPM5ssBAmTtNOKKx3I9w6lWiPdkovwhMMw21QYkMpFiwyXX4wwMcf/fnqOs7OD1ju3Z/N+mP7O+dxOV4XKKAz7604P8ZdtDBPQAx1Us2us5i63j2aitY7W+AoFgyBymV/Qwc2A+do+C6ZhUhioYsoYZsRLMK5nNxv4tbBzYwvyS2Xsdb9myF/n8dz7F9s1bcOMWeVdPJX9uJapQSbopBjKD+BSDkmARg2acLQONFFYUUOQvYudw8zgrhwMxv2Q2K4cbubfMT3csgnrsLMLP347zs3vY9qPb6PrrE8iffZevzT2TTZrFJs1iqlKE1b6DzrZWmq0RupLdbOrfygX1Z/N6zxpeaF9GZ6KLLYNNVIbL0ZRs927ECFMaLOLJ5ucJG2GOKp2HIhQ86TGQGSJVmKJh5Q4+/rnrOfbrlyJUhZAe5JiKhVzcsBRd0Xmi+Tn+1vgoAoFPNVjXu5Ftg9u5atqlh5XzMWQO89t1f2DjwFb8qo+uZA/N8VYieoiZRdPo8ZVQk2xG4qCqRlYwdc1syPEencW7GLZGSDsZivzjuxQiepjOZBdxa+SQxGBhDhJa9zO0/vVIPQjSQ+9ahm/7/Ug9AoqOHalD8RWhJZoRmX6UTC8oGtpwEwgVo+MF7OIFsI+5gFVxIlrfSvTe11BTXUgtiOKkkYqGYqcBDztcg2oO4Wt5FOkrwC6aS6b2fKzy8Z3aUg+RmvER1OozUFNdeHo428Syn3yTt4IIBDFOPQPj1DMO+Dhdz27zc13nbTt3jneGnBicI8e/Af3CQUGM21onEASlQod66F/Exx+/hMceW8YDD9zNd77zVZYsmcvVV9/Apz/9JQoKs1v/5T66DXf5cL5VXOmQcVMYyvg/WJqiI/Ewvf2vqh6I2baP6brBOs0kJlUUslsVa12do/axhc61XBCMW82OsJPa4LPg6jj58wGJmmglsPUu3FAlbt6kA44hWBDEk3Jct6yUEsd0iByCt69DduIxongUjU5eJh5Xh5m06NrYTaIviZWyMBMmQlMIFQTHvIdd26V4UhGJ3gTJ/hTR0iMPviqbVkJBbT4jvQkGWgeJOAWYboaB4V4URUEzDjx5k55ksKmP3q29OBmbWFWM8hll+MKH0BnSB/39gqKi8e/BwkJobhZ0dzNODF6vmTxjJGlXHQKeQPnVL3l17VYe+fR8DF3HUzSkvwAXMHpXYaY68UIVOPnTcGJTwc1kvb0UHTW+Hf+2P6INNyI8BzdQjFl7HlbFkjE7icMlbIRYOvEMzq49FVd6GOrhTXxnFkmea4X2BJSHsp/BnnRWXJ1d9NaEWUVR+MlPfksymeADH7iMP/7xIRYvPp7u5o0svPBMBsNQHpLk+STFgWxnclCTdCUPvxN5F0LAkhqPxiGFLYOCfL8k40DjUIp83zYm5xsIkfVXrotOYPPgVtb1baK05sBisE+FK6ZITqxy6UoKgrpHfR5sGQRXgjb6soV0mF0seW1rG9tvew9O51pO+Ohv+dT11zD9Ld7HHDly5Hi3MSI8VugZCoXB0v/9KXccdwqvfe6/OPbWH7NKzxCSyliQm4rgaDvA65pJjavRqjpMtHXqRm23QmQtEraoFq2Kw4SDCLd+FK5O53GfP06jamMrWcuEPClY5PjQRu0jwu7uRoaqgzQyHA47VJu7/XFaVRsXiQPUuBrnm2FmOv5x3crlrkqxq9GtukxwdwuzPapLkavxmp6hR3GZ6hokRdbuaoNq8qvgEN8aKSb8JiuMMlejSbXREFR6KpsVF92DtAIOHgMK6BJ0BMfbwXENF3vSqTjc5R9mu2bjkfWNneIaXJ3Oo0CqDAqXFXqaJjVrJzHH8TPzIJke06dnhc+NG9cekhisIjjNCnGnmhV4E0LiSolE0ODqBFHwZHbH4ojwmOIafDpZwEbNYsjO8Iv3XsfIlkbuu+8pJk5sOOj5ALYNbmfrQBN1eRMIjHr7VoTL2DHcwkvtyzmmfOFe3cFBGeL65E2sMVbSqG5hx0ALbCxillzMi+YyfKqBEAKfYpBy0hiqjic9elK9+xxDIpEgXSjRl1bi/nEHCBgy42ScDJrQsFwLRWS7paNGmBE7QdyKY3t2dudXeoC+zAClweKxa9gXfs3HpFnnENU7SQuHEArhvBi+b/wnPe+9mOEbv8TjZ11C8Orr+fYXv8NZeQU82/oS92x5mogRpjpcQcrJ8GrnCtb3bSLlpInoYWK+PKSUdCQ60RWNeSWzRgVcH/3pASrCZWP3UBEKhf58OhJdlFw6nW1f28jw8y0svuQ0hsxhnm55kfJQKbOLZ/Bsy0uknQxxa4SEnUQXGrqi8+iOp5hXMvuQ69plHavY2L+FhthEdFWnNFjMYGaQLYNNlIfK2BmeSNVIIxWpFgoCU1ASbSjWAHbh3KzQ+iYK/PmE9SBDZpyS4G7ruiFzmIgRPuSuYKPzRbT+9Tj5U0HREXYSo+tF9L43skKrk8bX9TJOdCISgZrqQCoqUgsifXnYeVPQ+9didC/DqjwFPDcbVK0FQAviBUpIzfoUwbU/QUs040bqsQPFqCPNKHYcqWio5kBWSJYOwh7JNqRE6sa8h8chFNy8Bty8Q/tsvVNoWnZsjpMTg9/t5MTgHDn+DYhJFRfwkOOKvrSQFHuH19WmKAqXXHIV55xzMb/97S38/Off5b777uJjH/scM256P4/EPEr22ObXq7hEpdhnp+2hogqNgBoibg8SYPfKqe3ZKELFr761LSYBFK5N5/GCkWKVbuIiOd0OcZIV3Oe2xmhZBFVXMZMWvlB2VTPmbkJxk1A8F0YLJTc8AW1wPXrv6wcVg0unlNC8ooX+5gEixWGEIkj0JgjE/JTvEbC2L7oVh7v9cbZpFjYQkILZto/LRZRZ582gdvEEUv1JNj6+BVVXGOlOIIRANVQUVSE9nMEa6KMo1EMgEYHCOXAEgQ8Awfwg894zl5GeBO1rOij0ZTu2k/5hGqqnMNg2xEhPgkjJ3kK3lJLGF5pofHE7nu0iVIW2N9rpWN/FvEvnEMg7sMddIJANjMtkILRHg0UmA4YBwT0W3TdoJr8PDJFCUugJhttW87fv/JSzF8Y4tXIY0fkSTl49TsEMpBFBZHpQrCG8Xb7AQozdK2HFCW78FWp8B26kFqnoqKlOAlvuxDNiOMWH5vG7P1RFReXwu0/r8+DCBo+Ht2e9gwWQ5xOcN9FjxhHYROu6zq23/pFrrrmQq646j//+758wNDTI3OnT2BiUTMzL2jBA1sM5aQvKQ4ffibwnk/LgvIkeD+9QaE8IKkKSKflvIFiLELs/J0IIBIKklTzA0XYjBFSGoTK8W9StjkChX9KdgorRt2ly6zNs/96VGP4QP7nrec44Zj5BPScE58iR49+HhPBIjXaUhmoncPqPvssjN3yM/IZJpP/zgwTkPuyzpEKpq5EWkgKpjrPdCkpBpyJJiUP7/i/3NG5K5dOmOGSEZJme4mUjMyYE7zpnAIUO5e0TETJ43OOP06xaVLo6WzSTdtVhrW6yWjc5wQpyZSY6ZvPgR+FMK8Q9/jhbVIuIVBgRHkEpWGj7edKXpMLT6FAcNmkWaSFxkXSpDj8JDfDJVAGRPbp7j7YDrNEz7FBtylyNfsUjodr4JZhAQEqmOj7ONcMcb+27RvOQ3OePs1WzmOga+EYtF9ZpFn/zjXCOFeJ3gTjbVQsfCjaSl4w00xydRXZgzPf4zbZpJSVlFBeXsmbNqkMOcZvr+NHTgkd8CTpUG79UaHANqkbv35BwyZMKMS97D2JSZXHG4KabPsiGV1/mD394iNmz5x3y6zdkxnGkMyaiOkjaVYcdQZ2tTicT1X5OIp8822N550pWda8h7ZhMK5jMcZWLODp4AuuTm+gY/B1m2MavBRixEwCYrknBqCWZRBLcT9DW9KPnkT9cz/BwKxl2oAkVXfWTctLY0iGm+giNio+7OoAzjklXsoeYP8pP3/gVtmtTFCzk9Aknc2z5Ufu0UZBSskXJUKaGQZcoBgRH30u+GZOZ8syfUW+7lye/+h1efvwRvvaN77O2uBW/5qc8lN2qb6jZBYDn2l5mUqyOinAZaSdD1AhjuTbdqV7i5gh5viiD5hABPYD2Jis+D48RO4EoNZi39ASW3/sk009aSH5xPsNWnGWdqygOFrEj3kJ/uh8pIaD5cTyHQXOYFd1v0JPqPeSdbuv7NxHSQ+ij4nFIDzKreAbLOlewrn8TfaFSmgP1nBeewHSfD6n7yFSegll9xj536RX4YxxdvpBHdzyNJz0iRpi4NcKwGef8+rOIGofQHOOk0Ltfywq3ozYLanw7wopnxV4jglU0B73rFdRUJ54awDMieIFyvHA1bqgSL1CMNrQFrW8NUgtmdx0mO5CaH6v0GMza87K+wRUno8W34+RPR9gJtJGdeL4C1EQbOIlsr4V0Ea6JOrSZ4ObfEi+ag9TfnaHGqpr9Lsh1Br/7yYnBOXL8GzDb9vGskaJJsanyNBQEnapDZLRwfSsEAgE+9rHP8d73vp+f/OTbfP/7X6fojl8x78v/QdNV56MpCi7gR3BOJkTFfrzSDgUhBJWhWuJDA4zYwwTUII50SDojFPvLiRl7B5IdKjGpcr4Z4VwzjGTvLXSpwRTpoTRGyEd+TYyKmeW0vtFG2tBQdQVLdqHmBQiXR/ccMAgNYQ0f9PyBWIA5F85i67ONDLUNIaUkVh1jwkn1DFQFGJY2lZ62V2CKh+RP/jgbNJNaV8ePICEkr+gpRHeKs9sUwsVhCusLkZ4kXBTBStmkh9IE8gIoqkB3+6jIrGJGdSex7c/h9lSRbriS3uLZNKt21hvPyXZyHA75VTEKqvNRNUGdUYO+VccpTlMxq5z+HQMMNA/sUwwe6Rphx7JmfEGDYEG24HYdl/4d/bSuamXyKQdeyS4shNmzPZ57TsHvl4RCkE5Da6tg/nxJZWX2cRLJc0aKpJBMdg2UZCfrvvUD3LTFVdcfT1zEiboe2vA2PH8RCBWpR7L/3gd632rU+M5st/DoSrwbqUUb3ITR+cIRi8FvFSHg5CrJjAJJ45DAA+rzPMreWt7iOPx+P7fffj833/wBPv3pGwA4Zf4UknFoHMqKtZoCXamsxcIx5W9dOJUSHt0peKJZYcQCQ4G0I4gaku7UCFKWjU2abM8GIcZ1ehwuMR+cPkFyf6NgS5/Fzke+y4a/fJOymUv4xS/+j2P2EYCYI0eOHP/qxDyFPKkwJLLhZdMvv5TBpu28/I3/YXptGerlF+Agx+okB4mNZLprEFc9BoVLeA+Rc0C4RKRyWE0HymiYMUCzauORRr4p2yGDpOgwLKQORqNm06La1LkG21SLdtUhz1PxC4klPDZqJn/yx7k6HUVDEJEKi2w/YamwTE/TqThMdw0WO34GhUuratMhbbpVF11m7dIcYFhx2aiZvGikOMfcXQNNcQ2uTOfxmC9Bt+pS6apMtw3mOD7ypEqFq1LlGeME5DfTpjg0qjbVnj5me+ZHocxT2aBng6ObNIsGRyclJG2jIX9rtAyrNJMSqbLECnKOGR7XNCKEYOHCo1m1avlh3dMZjo9ptk5gqJ9X1ARauJSUojIkHFJCcq4ZJDBaW3qex3/918089NBfuO22uznuuJMP61z5/jx0kQ3c9Wl+NmgmLapD2o4TjVXwWMhis9NPbP1TrG1fiU8x0BWdxuEdbBjYzA2zrqE6UkVID/BSx7LR8F4Ty+1BV3QqQmXsiLdQ6M8noPnZGW+lOlwxLoCtPdFJxIgQ1AOMkA2hFQASPFyivgizi6azeWAbnYluApqfYTOOLW0SdpIJ/gIMv0Fvqo97Nj+ATwswo2QGfsTYe78l3sbjzc/yfIFKT1EZYX+URF4BrtAxpCCFR1TV+Oi1H2XGyVfy5S/fzEdvvJqK+ZM44YPnwR76ZvYaM2ijImZA81MTqWLLUBMJM0FPqo8hcxjbs1lSdRyNQ9tJOxkCmj9r3TfSQVgPIYRgwVXnsPXl1Tz3mwc4/z+vx6/6GTZH0IXOQGYQ23MoDuyum1zpMpiJ05vuP2QxWBMqnnTH/aw6XEFvXh2zS2Ywo3AKleFyZhROJS2yln0oB/6OOHfiGWiKxrLOlXSnetEUlVNqTuCMCUsO+DxhxfE1P4TR/Sp6z0pw00g9hBcoQ0l1INUAws2AUJCqH6tiCXrf6zjhKtRkB07R3gsdarINfeOvwHPw/EUIJ41/x/0omT5SM2/CiU1BOGl8LY8CHkq6C0/xIZw42TU6iVT9ICXCc9C7l6P1rcUu3zvf592AOpro7bruQR6Z459NTgzOkePfgGKpcXU6jwf8I7Sr2e1jJZ7GOaPhDUdCYWER3/zmj7j++o/yP//zXzz0kU9Q+4tfc+rXvszsk5cw0/Uz1TX26jY4XMoDNTieTWtyO0kngSpUKoITaIjM2Gc4xOHyZrHVMR22PruNjnWdWEkLza9TMrmYhpPryauI0rG+EzttEyyfQ4HRDcE9ig7PAeniRiYc0rljVTGOumoBib4k0vPYWqbx+1CKbrUfRQpqXZ0LzQh17u7u6mbVYZuWLfx3FdR63MRrH+CplEXkr73kqRqVc8oJ5PkY7hyhuL6I7i09pIfT+Bgi6HUQizkY9fNx/CpKYievDDzNA1X59BvZTO8yT+PCTJi5zqEvGmh9q9F6VhBOJIkWalQWlNE52Jr9pchaQeyLwdYhrKRFpG63h5eqqfjDPro2ddOwZNJBAyfOP18Sj0s2bhTYtkRVBTNnSi67zGNX9kkGSZtqUzA6Qd356EOsvPd5Tv70lbTNrMNe93fwACeDNrQFz1+IOWEpXmDfvtTCGsr+401bsjw9gprsOPDN+gdQHITi4NvfxRoMBvnlL/9AIpHg2Wcf59affZPPfPM3PNYWonFI4EqoCEvOqfNoOLQ8xX2yZRAe2aEQ1CRTR48zkIGO5EL86lo2DzVSGijC9Tx60n1MLZh00AC5g3FKtaR323K+840b6W3ZxKlXfYGvfOHLNBTk/IFz5Mjx70kAhROsIPf5R2hTbGJSZeKXbqaltZktN36OGWXVbD1xwVj42IBwmewaLLYDpOjm/4IraFEzFHsV+ORU0kLnbDN8SAFy+2K24+M5TxvXyNCxRyPDsHBZr5kMKB4xT2Gm4zvkYLQ9SQtvbPdcp+oQlAoGAheJiyDqCR43kmzSLKJSYYptcJYVYobjY4aTtbCSSJ4wkjziS9IvXAZVD1NI8j2FiJSMCEnMUynxNFZoGc4yQ+NE14WOn1mOj07VQZfZLumD+SzvSUZIbCHxeeOf45OCuHBZr5v4PcFqzaJLtelRXBQJPgT5o4F1jxopKl19zApkbGwLj+EHP/gGpmkeUpgbZIXLBxofYdvwTlLCZF1eAaUNxzOxbDrnmkFOtrIL/rZt85nP3MB99/2B73//l5xzzkWHfM27aIhNZEpBA2t7N+CLFNGiAIlhQhJmVS+i3POzYmgLme5VzA+VEzGyQny557J5qJGnm1+gPzNAb3oA27XpS/VjehZ+1U9RoJCMk8GTHnGZ4Hfr/4Cm6EzMq+HSyedTHcl2GfhUg0J/PpHiEI/zMmknjeukMFQdRQhCWjDrURsspCRYzPGVRxPzRXh857NMzJuAoWbnYpV51Sz3W3wn1MukSB8VrsaJVpCyeJzfrL+LjkQXxWIKfUWlxAdb8dkpRGENQ6ogJBUuy4RZYgVRy0P85jd/4uFH7udTn7+B+26+heOvOod5556AqmtYrk1A8+HuIbBOyq/D8Rya4jvxkJSGSjix6hhmF03nrk1/Zl3fRhzPAyQF/nzOqzyGZ9tewtE9Tr7+Qh7+wZ1s2biN1gqbosr5PFESxCmdgNW6EVe6qELF8RxM1yKkBzFd85Bf47kls9jQv4WUnSaoZ7vj+9IDFAeLuGzyBUzMO7T51p74VIPzJp5JUA/w+I5nSdhJVnStJmmnuHDS2RQF9rGFznMIbvoNevcreP5i3FAVeu9KjK5XsEqPA0A4KdD82ZDpUaTqwymcg2LFEeYQ0hfLPtZOgmejWMMIz8LJm5J9vC8fqYfQe1ehDjeijrQg7CSKOQAo4Nmo5lD24EoAqeoIJ4PUg7jBMtRkG+rwtnetGJyzifjXIScG58jxb8IU1+AzyQJaRsXgKlcbExHfDurqJnHbbfewYsWrfOMbn+e3l17GvHlH8YlPfIGpZyw9MtNgst0JNeFJlAdrSDtJNEUnoIYOO432UGl6aTvbX95JMD9ArDKGlbJofb0NpGTOxbOpXZwtPERmGmL1TtSBDdmUWEDJdOPkTcYuOerQr08RRErCbFMt7g4OYomsj5wrYJNmElc8PpHMH5vopISHiSQoRxN7HY+erX1Iy0IU+wk2FKC3p9j5agulU4tBSqy0Tem0EkZ6ErgdPVSUZag7rh7Dnz3mmqpF/LmiHGH206DWIoEW1eGeQJzSpLbfJOw90XtWEtx4K1V5AdZ3VRDJDDMhEqStYyOZ4Qx6QCdWFdvPTdj3jw9HxozF4CMf8WhqgoEBQTTq0dAA+h4uJQaCoKcwqLj0rtvCXz/xbeqPm8G865ayKhLh3O5WCrs3IDwLiSRTdzGZugv2e07PX5gdu2ePS+RV7DhW0ZzDGP07Q+sIvNwhaBoSRH2wqFSysFSivoWPv+s5eHhoQkcIgaIoVFdPoLK8micfe4j2N07j1zf8EXHMBMxKKAuDcYT66fo+QcaB2j2a7wsD0JepZErBVfi1Z9g+1IwiFJbUHM9ZE5YQ0ve9lfNQSCYTfPe7X+W3v72FWbPmcddjrzJz5twju4gcOXLk+BdgiRVEAC8aKQaFS1AqfPGHt3BXSz+PX/kBPv7oI3TPqEEC51hhTrICNGorecV/B67SRZ/i0Y5KmTuL96du4GzzrW9TLvU03peO8ld/YqyRoXS0kcGQgp8HB2nZI/ei0tW4JpM3buH8UChzNUJS0C9cXCS+0Q7cFJKYVGjSbHpUlxpXR5eCZUaGDtXhplQ+BaM1WZvi8IQviR/BsXaAl4w03cJhUPGwhaTY05jsGkjAFvtK18gKs7WHOfaxa/BU8j2VXsUdtxOvT3Ep8jTSwmOjbpEREg9QRju8E0LSp3gUuBo7NZNvh/o52vaz0AlwjBXAQHDqqWfxzW9+geeee4IzzzzvoGMZMoe5feM9tMTbqApXUCoUWgd6UFc9yaVzqpgZy4pkyWSCD33oCl5++Vn+93//jwsvvPwtXbumaFw9/T08sC3IE0MbSCRSlPnzqa1dRGVl1vtWDHUxIDNjQjBkLbjy9AhPtTwPwORYPbMKp5Gwk3QmuhECLm5YypA5zDOtL+HXfJQEirE9m439W7lj473cPP9GQnqQhvx6qiLltCc6AZha0ICRX4DpmZxWfRIziqbSleqmKFDIvJLZVIbLeKblRRBiTAgG2FpeRmdhHrrnMVnCZtVie8CmvmMz7SOdTC1ogIyE/jg7CgsYdNLEUknq/YWca4Y5ywqNa74595yLUSdG+Oa3/5MXfv8gqx99mYVXn4Y2Ncb8kjkk7CTdqV7yfTGSdgqE4MyaJUwtbEARCmE9hF/z88FZV7OxfwsdiS78mo8ZhVMpChQwaA6xvHMV0bllRKqKePqeByn56nuonDCHNX4X+6hzkD4/Qzs3ABIhFIoCheT78wgfSsjyKIvL57NlsJHXu9fiSRcJBLUAZ9YuoS66d0DcofJ6z1r+2vgoKgoVoTJM1+TVzhWMWAlumvuBvTyNtaHN6H1v4EYmIvUwXqAE4ZloA+vRe14BJYBw09gFM7JzA8h2C/vyMavPBsXAaH8KkWofPaLAKjkKbWgb0hgvPksjD5Fow+h8GX/TvQhzADdcjVSMbFbJyA7UeDNCmmArSC2A5y9FcZJILXBoIdb/JHI2Ef865MTgHDn+jdAR1B9hJ/DBOOqoY3jwwed57rkn+OlPv8P7338J06bN5OMf/zznnXfp2NaQt4quGOjGO3sNZtKifW0ngTw/wfysqOSP+kFAz7Y+Er0JIiXZ/VbSX0Ry5sfxtTyC3r8m+/zqszAnnIv0HX475Gt6hoSQTB4NYUFCg2uwVbVYq5ucNNpNUeqqRKVCv3ApkCrNqSQ7o5J0QYiyYY9wRuKLBbDSNqmhNFNOm0zLylbSQxl8IR+VMxzmNIxg+HeP8bVYMRlVoyGdxAlmi8k6V2OTarFeMym3Rv942y79OwbIjGQIRP0U1BWgaip4Lr6WRxBOmpp5E+ga0ejpLqLQX87rba9hDg5Re8JU8iqie184kF+Tjy9skBpIESoMjZ3LTJjULp5wyMK/qsLkybA/GTkbfOPn/wYbefWyq4jVVXPudz/AzmiUCek0xYFazNI89PgWUtM/gjnhnAOezymci5PXgDa0GTdUBYqOkupE6mGsihP386RM1sdhjxRfJdmBGt8OioYTmzrWOXAk7BiGX69T6EoJ8gxJWwI29Ak6EnDhJHnI2XaWm6E50Uh3ph1PuuQZBUwINxAzCtm6YROLw8fw6brPcMXrl3L81+bwlVnf5LrPfxTvpCPvpM24oIi9X0tVSCJGHR+YeR1xawRVqAdM45YSLC9rM7Gv65ZS8vTTj/LFL36Svr4evvKV7/KBD3xsrIMhR44cOf7dURGcaoU41goQVzzCUkFIiNx+B/+x9ExuveIKbnvoGY4qrERDYVAM8JfAH0iJJMdZc5AI4iJFq7oeIV7B4NIjGs8010d90qBFtXHJhrr5EPxiVAie5Opoo12821SLB3wjfDKVv9duL8jaWqiw1061Kk/jKDvAM0YSFxhSXDQp0MlmMWxXXYo8hSpPI4hCnivYrNqs1NOcYWXFxUbNIi68sZ1wi2w/LxlpMkhCXtZWIiIVNqsWp9qhfY7vSIhKlZOtAH/1Jdiu2kQ8QVx4qEJwuhXiDS3NU0aKck9lRJGj5gMCTUr6FJcBJUWvkg3Aa9YctmrDtCo2V2aiTJ48nalTZ/Dgg38+JDF4be9GWuJtTI7Vj1kpTI5OYMtQI2s632BmrJ6+vh6uvvoCmpq2ctddf+eEE0454DEzjsm6vk3sHG7BUHWmFjQwOb9+rC4s8Odz/cyrKHBauU/tY5a/BGOPRWGp6qgy+3d+z1rS9mz6Mv3URWvxa9l6LGKECeeH2DzYiF/z0zO0HQFUhbOWBoaqUx+ro2loBxv7tzCneCbN8VbmFs9iZDSvQBUqFeFSZhVP533T3jNOhN5FzJ+HgsBybQxVJ+kzaC0sQKSHKVOy2SVFErarFi8FHAr1wNhOyCkdnZQNDbHeG2ZescIHy6fs147v9MmnoH3zhzxw9v28/LuHefp79zBp/gy+861b6IuM8HL7clpG2vBrPirCpXSlemiKNyNGr2NOyUyunX4580pmMa9k1rhjXzXtUirCZbzW9TqFFy9i588eYa4xj4ZYHXgQ9xezY/IialOSiCtRFY0Re4SGWD0N+fUHfM33JKAFuG7Ge1lYOped8VZ0oTG1sIGG2MS33BQkpeTF9mW40qUuLysoB/UAAS3AlsFGtg42MrNo2rjnKKku8KzdXrxCxS6an7WGsEdI11+G3vcGaqoTbXAjSA+ph8lMvBQvUk168vtwCmehDawDz8PJn4ZdOJvIqm+gpHuBPWzI3AzCGsbf9Ce04W1IPYLI9IFiYBfNxS6cjb/pzwgrjlA0EArCHs6KwqFqvMioSO5k0HtXog1tBkXHLpiFUzjnoDYa7yTpdAoAXX9n5/M5jpzcDChHjhyHjRCCJUvOZMmSM1m27EV++tPv8NGPXs0PfvB1brrpP7jkkisx3mFB90iwkhZ22iIYG99daAQNkv0pzIRFZA/HAC9cRXr6DWTsJFKIbKrrW6RLcQhKMW6ysivSY3iPEJYiqXGsFeARX4L1iklvxCHl86GoCroHr0wzOHGDhe7XsdMONQuqqZpbSWoghebTKBzsIrDzNRwpx5SxAV3H77rIwO6iVSBQECRGz53sT7L2wQ0MNA+QlC4ZTRCty+f482ZT7EujJtpwAyWEfJKjT87QukNju1XG/RtTTDxaMumUhv0WbpGSMHXH1tH4QhN9O/oRQiClpGRSETULq97yPd0XCwYlX7zkA3hSMuu+22kNjVDXv51rdm4gkuhHOCmsshMwK08+6LGkHiI14yMEGv+ULf48Bzdcg1l7Hk7B+MJZSbbja3kUvW81ILBLFmBWnYnR/Sq+1scRo9u+vGAZ6YYrsUsXv+VrlBKeblHoSQmm5e8WfvvS8EK7wuJydywg7UC40mXD0Ov0ZNrxK0EUodCTbiduDTGnYDHbNm/i9PLTmL1gAcvnr+UbL32Z/1z9We696Y9899b/Zebxhx4Esy8m5sFzrWC64ButXW0XbA8mxbITuzzfvhcYADwJyzsFL7UL+jJQGoTjKyVHlWbviZSSF154mh/+8JusXPkqJ554Gn/+8+OHlJyeI0eOHP+OBFAIeArDwuX3wWE2RmHG/b/jpSUXcP3l5/CNex/gPYWT2KptokfppsGZOuZuGpNBUl4+K/RXODdz0VsKQN0TAzHO0qxTcdiu2lR42ph/sYqgytNpVm3aFIcJ3u6Ovi2qxYtGih2qTUQqHG0HOM4KoI8+VyC4JBOhyFN4wJdgvW6iAXWuTpvi4CKpcQ2CKHQrDjtHz/H7wDA6guOtILs22++q3co9jZm2jw2aiQDiwmO7aiMlvK5laA7ZLLD9HGcFDjuXYX+caoWISJVX9BR9issU1+B4K8g8x8fIqH/ziOKRFhJrNFA6JlXaFQd3dME1pEgUF0qlymtGhmPsAPWuwQUXXMYtt3yfVCpFMDi+xt2kmizX03SqLuWuSsruAyHGeeqKUauEjmQXzc3bufLKpSQSI/zlL08xa9aBa4SUneKOjfeyumcdHhIpJU+1vMBZtUs4p+70sZpSCMHRvgpeCfnpQVLpZUXvYc/CKKymysinPdlJZagcIQQjVgLTtSj0F6Lu025O4kqX7lQvoTd1WeqKBgI2DzbydMsLNMfbcKRDaLT+P75yMUvnXcKU/Ho0Zd9yyrSCyUyM1bJtsInKcDlDoWKGsAmbGapLdmdkFHoq7aEISXUPL2cgL5miYHAHC4IzxoRgB48/+OM84ct29k90dd6TiXDahJM4puIoes/5DKteeoUf/883uWzpqVx11Qf40MdvhrBC2jG5c8O9KEJlWn62Vk87GVZ1rWZidAJn1e0t2If0IEsnnsHi2hMZPrGTvr+uY8vv/kLD8acDMCdQzpBwoKCZTNdONOkyJX8SV069BJ96ePNAn2qwoHQOC0oPb8edlJK1fRt5peM1OpLdVIRKObZiEdMKGuhO9pKnjw+L82s+POkykBnc+1h6BBDjdwIKBan5cWKTyUy5FrP+UvSeFajxHUg9iFM4J5spAqBo2MULsIsXjDuuVXESgc13oKR7s/kkbgZteBvCSeEGivF8BaBoSC2IYg6iDW3GU4PZ7mHPATcFjodA4hkx7PITsAtmg5MitOFW9J7Xdl0BRttTmNVnkGm4aiz4/B/Ntm2bCYcjlJYeOCg9xz+fnBicI0eOI+Loo0/g6KNPYPXqlfzsZ9/hM5+5gR/96Ft89KOf4YorriMQ2Hcy8j8Tf8SHL+QjM2KiB3ZPKMyEiRHUCeTt2z/37diSU+5pbNGs0YCFYUzXRFf9yFCQfG/8H+2lZpgOxebegEVAUQgO2JTFIewK1tTp1Pa4RHemKWkoQjVUNKFhBLPFlx04AV/PcrTBjbjBcoR0mdS3ibX1x+KEysakaGe0u7bEU5FSsumJLfQ09dFZG6AzKHEzDr7GTja/ILn0nCnMUnVML40iYgSCMHmGzfkBP//9MAxrwygH8CaQnmTCohpilVF6G/uwMw6xyjzKppWOjfvtwHVdPvWRqxlq2sFtDz5JpHA6QSvFjEyaiNKGG41hlyzEKjvhkIV9L1RJcvanUNJdCNfCDZbDmwpdkekjuP7naMONeP4SQOLb+RB650so9gierxA3fzpIDzWxk8CWO3DDVXihyrd0nWkXtg1BYWB8B3ChH7YMSlrikirRAQi8YNl+i8IBs4d+s4s8vWAsbMSvBhm0+li/4w36k31MKZ8GOkSI8P1TfsJ7plzBJx++kbOvOJYPfujj3HTTZykq2rfn8sGYWyyZWSRY2ysIG9lQlhFLMK1QsqD04CYiz7QI/tKYnaJHDdg2mA25S9sSsSMrAq9Y8Qrz5h3FH/7wd04++Yx3zH4mR44cOf6VeNFIsV4zmeQaGJUTqXnofu698DK+dN5ZTLj7YZwGG5AobxI0NXQcYePiHlAMzuBlO5A95ZBFUQeJK0Dzxv9ck+AqckzYhKxQ+bvgMHHhki9VOoXD3f44PYrDezKRMfHWh+AMK+u3+pqWYZmRpkd1SXsSHUGDa9ClOKzWMthkbR4sIbnHP0KfcJnv+AhKhcHR8wgEk1ydQcWl0tWIednfuQKkgC7h8Cd/nB2qzXXpPIy3oVNYQXC0HWCx7cchO4nfdX0TXIPJroGQkrQiaVFsMkIyKFzSQqIBYamgIlinZ5jk+OgXLi/paapdnfPPfw/f/e5XeeaZR1m69JKxc76mp7nbHyclJGGpsFO1GYmqmMIZ84mVSOLmCO2JTowej/M/cBLhcIQHH3z+kBZdl3WuYlX3GmqjNQS0bP3dm+rniebnmVY4ZZxfbLmncUEmzN/8CVYO76Bv5wpS/a1UKiEWBirpGGln8+BWQMFQNI6tOAq/6ueplucpCRajjQrYA5khQnqIurwJVA028Xr3GspDpWPnsVwLT3os71jJiJ1EmJL+jm4cmd323tbWwiuvv8SK0GvMrJ/FzMJpY53Huwhofq6Zfhn3b3uIrYPbGUi4+KhjcvFUykO766WMkJT58hFCpz3RSVmoJBviluigwJfPnD0yEr4d6udhX7Y72YdgmZJhvW7y+YTkbMKE8mqoPbeG806/mN/eeSs/+uG3uO+v93Dhpz/LsWcezaA5xNT83U0bAc1PxAizovsNzqxdst/aKKAaBAIhZnzyRl773Jc57kufJ7++DqGoTAhXctnUS4mV9RPUAtTHatGVt2aH8lZ4tXMl92y+H8uziOgR1vRuYFP/Vi6bciFFgQK2D++keI+OXNO1UIRCni9vr2M5BTOzAdHDW3Gi9aD4UDK9CNfEqjgJhMjuCqxcApUHDqIDUIcb0XteQ0l24AZLUdP9KOluUA3ccDUg8KJ1eJ6DGm9CCi2bR5LuyTaQuBm8UDV4FsLNIFwTVIPMxItB82O0PYXevRwnWjc2nxGZAfytT+IUzd2rYeUfxbZtm5g8eVqu1v4XICcG58iR421h7tyF/O5397F583p+/vPv8eUv38xPfvJtPvzhT3HNNTcQDkcOfpB/EHpAp3p+FZuf2spIbwJ/xIeVtEgNpak7unbMwuCdYJHt51VlmCfiTVhDXbhCYoXyqEpo1KhzwbdbnNQRBKXCNNtggqfTP5gh0RVH92mYAR+bRIbj/ToTjtrbYsEL15Cc+VH8O/+ONrwNhMpCUcGLwXo2G4JSz8UDehSHBtdgtuNnpCdB/84Bhir8tAYlESnw+Xx4JZJ40wDfUke4aMIM5jU+Sj8aBVoeky3BlMggqiLY3JliXy7KdtqmeUUL7Ws7cC2X/Jp8ahdPwF8dZa1u8rpi4pc2MxyDau/ICkjP8/jSlz7Js88+zp13/pUl0xaADYgA1JxDoubAlhAHRAi84P5XuY2uV9GGmnDyZyDcFIo1ghcoQe9ehvTl4+SPFvRCxY1MRBtcj96/BvNAYrCUCHMA4Tl4geJxgq4mQFcg/SZLLleC4iSINv6ZiPsCAAljEr2hMyB/InmV0aztxyhJZwQp5ZgQnL1UgaH42LB1NQBTY+O30i0qP5pXjl/B94p+yI/u/DZ33HErl19+HTfe+KnD7rgN6nD9TI+X2wWregQSOH2Cx3EVkshB1ggSFjzbJvCrUDnaBR1TMyx//u986r9voXvzy8ydu5C77nqQJUvOzBWmOXLkyDGKRPK6ZhIdDVQDKJzcwFWPP8gfL7qCa88/lV/c82vCR0cZUPoo9LJesB4e/UovJ5qnYbDvL2kXyfNGiheMNMPCJSQVjrb9nGaG8B9EFC71NCpcjTbVpm6XpRbQpbqUuNnf7Rr/M74UI8LLCqGjjxsQLsv0NMdaAareVFPoKBznBDnGCZAWkg7F5mfBQVboGdqVrOduSKpEpGCG48MFlhlpjrMDnGgFedyXZJNiYmVdvphr+fh0qoCXjBStqsMMxxiziEjh8YaeYbHtZ85hhPQeDEHW4mIXEkmtqzHL8fGGlqHS1alyNFbqGQY0j4CEIArlno4mYadqs1xPoyF4mAS9isNV9bXMmjWP+++/e0wMzuDxuJHEFdlckl1sLWugOW8FW4a2UxEsYetgE83xNhKru1j2520U15Tz27vuZ0L1odUCq3vWEdD8Y0IwQFGggE2DW2kc3D4mBg9mhulO9VCt+blIZvjlhgfxpwap8xcQsFNstXuZUTiFmUXTcKVLdaSKKfn1DGaG2B5vZutQI0EtgO3ZCBROm3AiEyJVHFexmE39W9kx3ExJsBjLtelMdRHVI3Snehi2EnRvbKbjFyvGxveXW37HX/gdlcdNYf4NpzOjaBrXzbiCqDF+rlMeKuWjc66nI9lFwslwf16AHUZ295MBJIRHv/A4SymleOJZPLLjKRqHtgOCkmARF9SfQ1Uka1+xWTF52pciKMVYaKOHR4viclcgzplWcGzRJu1TcT7xPk649nQ2/8+Puedb3+Cvvy2i5qI5TDl1fFizqqhYrn3A1ygsFWbaPrqvuRT///yI1b+7nZP/+2u0KjblnsYxRgmBkmwtO2zGGTK7yPNFie1DcH07STsZnmh+FoGgIZa1pSijhJZ4G0+1PM8p1SfQNLyD9kQnxYFCTNeiLdHB5Pz6rD/zm5B6iNS0DxLYcjtafAd4NtIXI1N7AVbFyYc1Nr17OcHNv0OYA0jVnxV2jTys6otw8qfh6SEiK78JnoOdPx1cEzXdjbDiKOYAUtGRqg810QzSQRp5uIESUAyUZDsUzEDvfR2pGuMaW6S/AFLtaENb/mli8JYtG5k+ffY/5dw5Do+cGJwjR463lalTZ/KLX9zJZz/7FX7xix/w3e9+hVtu+R5XXPF+rr32hnfN1uzaYyYggdbX20gOpND9Gg0n1lN/4js7vnpHp6BpBZmgjZZXgk+oFMWH8G1dwTN5XVw3/YpxRZorQBUCv1Aoayhi0K+R6EkgPY9gcYjZF06iZHLxPs/lxqaSnDMFYQ6AUMn3xXi/Y/GEkqJJs1CAk6wgZ1ohIlJhIOPg2i5dgWzytG+0qFR8Gj0TfGwKuoQbTqUs0UdZ7zocr5VeqVNR0MDE2jq2bNu61xg8x2P9QxtoX9uJL2Sg6Crtazpo7R1m44cmsSOqZLcFAk9LlYszEY6xD9xN7iDJCElAinG+fKlUko9//Doee+xBvve9X7JkyZmH/wIdAWp8O1LV0QfWoyZawDVBUVFSXbhv3ka4a9ujndzv8ZRkB/7t96P3rUGY/Xj+Asza8zErT0NNthIAFhRN4u87DWI+SUDLWia0DKapSr3OTPU53HABTVv9NK4bIWk9iVc4jWh1KdPOnEp+dQwAXehI9vbac6RNy7Zm/JqfBrMBXNjVAKZ0gygw+Pjn/oP3fuED3HHHrfz2t7/g//7vNpYuvYQbbvgUevVCXulUaR3JWjccUyFZULJvH+OoAWfXSc6uO5w4QehKwUBaUBn26Nj0Khue+T+2vvQnzOQwBQ3H8oNb/8p7zzs7JwLnyJEjxz7wRLbjdE+i1VUc88R9bL/oej5y4dVcf89N7FiyjSFlEJ/0ERfDVLrVnGLt/2/sc0aK+/wjBKQgX6qMCI8HfUlSSC4z92/7A1nbiDPNEH8IxNmi2oSkICUkISk429otJqeEpEW1KfRU9rTeypcKPYpLh+rsJQbvQkEQkoKozLoMtyg2A0p2Z4qFR6VrEJPZrtctikuX4nKc5eclI0WzkHhIglIhrng0ahabNYvIaOftLoIouECb6jDnHchRyuDxkpFmuZ4mJSRVjsoMx0eH6mAJyQRPJ+iolHoKmzUbAQwoLhkhAUmFazDVMdii2dzjj/O+az7EF/7jJhobtzBp0hQ6VZcdXhx7oJv29DCa7seIFqOF8jEWnEPx6mWsbX2NtsE2rCc6GXhuO5OOm03d1Yt4su9FJldNHvPA3R9SSgbNYeJmgr70AKpQCWh+/JoPgcDDw/VcHtv5DC+0vcKwNYKh6MTtBNJ1Oal4Rvbvux9GrASNQzs5d+IZTN7Dr7Y4WMRH5lzH8s7X2TrYRFgPMrdkFnOLZyKEYEbhFK6a9h6eaH6G7mQvmqJzdPlCCv35/HLN7ZiuSfXMSUy4dTK2Z7N1sBGB4MqplxLUA2Qck9W963mu9WXOrz9rr2sUQlAZzjYSXG3Z/EGNs1O1s2GGCBbbfs6ywoSrjmV20Qx2xJtRUKiP1Y7zIl6tmySRTJC7F/MVFGJS0qbatCsO1V5WtH/WSLFGM5kUK2bmd79D3/Xv56HPf4EtP3+Cwae3cMaH3kNpfdXYLsXFtQsOWiedZYboDDhsuXgpGx54kNJvf4FSaXCJGSGAgulaPLL9SZZ1riJpJwnqQRaVzePciWeME/rfTrqSPfSm+sd1dUP2Ne9MdlMZLueShvN4pvUlWkfa0VWducUzuXTy+fu1sXDzJpFY8BW0oc0IN4MbqsILVRzewJw0/u33gZPGyZ/JqGcZ2tBG1OFt2c5eBE5sCnrvSpzYVOzSo3GTXfjan8ANlOL5ClGTLVnLCjxwMyh2HJHuRBvahFV9BtklqXdXfet5Htu2bX7LgZE5/rHkxOAcOXK8I9TVTeIHP7iVm2/+Er/5zS3cc8/v+dWvfsySJWdy3XU3smTJmUccNnckqJrKpBMmUrOgikw8G7rmi/gO/sQjpDPZTXzn65xgRNDyUggpiWQyDHp+1vdtoi/dT3Fw93amaY7Ba3qaDB5+XaVoYiG+CXlkhMOZlfmUuQex4RAC6d+dYFvnGtyQ1rPBIwjCcnehHi4OYUT9eAMJtLLdq8xWwqRlRhlSF2hqjFcX3EjJwDZIdRE3glwcPI7ps1pZs2bVXqcfaBmka1M30bLImBVEMD/Ao0UWm8wRFrhFGAgkkjbF4UHfCFMcYyzBe09cJC/raV4y0gwrLgWeyolWkMW2n56uTq699iKamrby+9//hTPOWHrIr8nbhTTyUBOtKOYwUg8hjSjCsxFeG2qqGzwHdonCrpntEN5PV7CwRghu+F/0vtUomb5sgISTwtf2DG6oHC9cDYrO+YF6OkI3sjZRgetlu2rL3U7eZzxEsKCWjlaNdWsDqKpHUbSHTHCIwVaDdQ+uZ/E1R+GL+Cj0lRLUwsTtQSJ6HgKFjJsNf9i5fgdzZx+FUq+hbhFIv0TYAumHzAUeXjEUUMjNN3+JD3/4Zv70pzu59dYfs3TpcQSLJlAy7zwmHrWUnoaT2DSgM9wAp9YcnuC7P1KpJOtWvMbWv77Is6/eQ7yrkUhxDXPP/SiVx74PvWQqpy1yDzlEL0eOHDn+f0IgmGP7eMiXoAR1zJ+3X3gUFRXxxfse5SvXvZdbL/kxn/ntlxFLBcNiiOOdUzjGOpFKr3qfx03j8YKRIiDFmBgblgq6cFlhZFhiBSmWB56CznP8RFIKy/UMHapNmauxyA6M61BN4NGhOHQpDlGpUO5pVLvZ8ymATx78y/8ZI0VaeJxlhnjBSOGR3ZU1pHgk8VDJTpYDUvCML82I8DjVCqKN1i0tisPffAkKPAXrTUGocvR/hzKOw8VDco8/zitGhpDMLuC/blgUeypXpiOUeBpdistvgkOUuyoJkRUMe4WHQ9byYbpjEEPFcAXbNYszL7+c0h9+i1/84gf8+Me/pm2ohUZtO15qGNWT9Nt9OOkOgqX16MWl1C86l2DTRjK/a2J4Zy+n3HAJc889nqSTYuvgdnbGW8dZPLyZESvBfdv+zqb+rWzo34IiBD7VR1GggMJAATEjysS8Wl7uWM6DTY8RNSLURmvIOGlWdL9B1IjiSQ9VZOvFiBGmPdlJR6JrnBgM2QC6s+tO5ey6U/cahxCCo8rmMqd4Br3pPgxFpyhQyIqu1STsJBE9NCZqp500npRoQmVl92pURSXPiKCrOiu7V3Nu3enjvJTfTJWn88lkPls1m6TwKPJU6l19bEEm359Hvn/fHZV+BArgwLh+fAeJKiEwajVnI3ldz5Av1bGFk6IpU3jfA3/hwd9/j87v38Fdn/4BNcdOY8LSucyYMosTq47Z75h3USQ1bkrlU7j0Cv7rtjtY+MImLl54MiWjfsaPbH+SR3Y8RYE/RnmojBFrhEd3Po0rPS6fcuE+j+m2NmO//CLuts2IvBj6wsVoi45BHGKwr6Ea6IqG7drjBGfbtdEVDb/m59SaE1lctoCuVDd+1U9FuOygixSoBk7hW+hslRI13oSv7Sn03lU4sWm7E42FwA1WosWbUNLdeMFyMpMuR1gDaENbsg9xEkgthFV2LOpwE4qdxNOjCCEQno2n+hF2HDXRDoBTNBe9d1U2tHr0+oU5BKofJ2/vzud/BG1tzaTTKaZMmf5POX+OwyMnBufIkeMdpbKymq9+9bt87nNf5W9/+xO33/5LrrnmQmpq6rj66g9xxRXXUVhYdPADvUMYQeNt9as9GCknjelaFKs+fOn02M8DWoBhM07KSY97/Hzbz1rN5A09k518SJAqHGMFmem+tZV2gSBvH2KrETSYuLiGTc9sYLB1BEIG3ohNX4FK54QAGV3S6Fro+HCLpuIylR2qzUlJHwsWHM2jj/4V0zTx+XaL6oneBK7tjbvHniJomRLG12eiFwMiO6ZKT2OrarNNs1i8j+7gJ40kf/MnMKQgIhXaVYe7/HG2rFvNz6+6EoC//vVZZs6c+5buy5FiF84mZA0hpIOnBrIJb04KN1iKcE2MnuU40UkgXdRMH1bRPOyifY9V73sdbXAzwkkinCSevxghndGfJbB8BTgFMylMNvJp9dusmPRF2mU5AR2O6nmaioE2XDGZlu06ngsFRRIlo2CIJPnV+Qy0DNLT2Ev1vCr8WpCpeXPZOryWYWsQicRQfNSEGli/ei2XXnIliRs9jJUCdadARjzsuRJnvHMEwWCQ6667kcve+0E+ffvzrHv57/S98Td2PnkLvlAeJbPOpLV+HslTpjJn+jSqq2sZNBWSNhQFIXiAisRxHLq62tmwYS3Llr3Ea6+9xLp1b+A4Dv5wPgVzzmPJR37JxDknkfEUdsQFxxZKit99luU5cuTI8a7hRCvIVs1iq2rhQ+AAuhScaQWZEgxz551/42Mfu4bvXvMVvvrV7/GfH/jmQTsIhxSPYeFR+KY6I18qNCk2fYpLsXvwKegk1xgXLLcng6PBdwPCJS48bCT9mkefcIlKlRpXY7Jz4NrOQrJeMymUKlFUJrkGWzWLgBSMCEnvaBdtvatT4WrcGRimSO4WzQWCKk+jUbWZ4hhsxWZIuGMdxe2KQ0yqTDvION4KTarNKsOkwtOIji7qlyDZrNqs0UyuywQo8jQmOTqbNYuJrk7MFbziyxDwFI5yAlSMCvUBBCYSx29www2f5Nvf/hKf+cx/8UbvM2ilQbzSWuLpIVzhQ7XSZMwRJmSKWP/gQ2z64l34A34u//bHqZhaC0BQC5BxTVJ2ar/jl1LyQOPDvNS+DCEUVEXBcm1s18b2HPozg5xUfSx10Rru2/p3fKpB2ajPrmaEKfQX0JvuZyAzONZA4XrZmL83e/ceKoaqj3XwDmaGiRghYkaUAXMIRahoikrcHMGTHpa06E71EDbC9KcHAIkilNF9bgfGj8Js5/DHeJIZ4NcBlU7FpdIDDWXMk3uJFaRoVNZxkdhItDcNRRWCqR/4JNecuJTXfv97Xrj7IVpf3UL+RS7mZy+G0fvreA5bB5tGg/WCTCuYDMDyrtdZ17sRgaSguIgd9/2Vkvmn4XouG/q38OjOpwnpQUqC2d2Kfs0HQrCyazWn1ZxIYaBg3HjcndtJ/+Z/kV2dEI1BVyfuxnXoHW34LrnikHZ0VYRKacifyBujViO6qmO7Nu3JTuaVzKJitGM4bISYZLzNOz+lREl3I5w0brAMVB/+pvvwtT6GkuxAjW9HyQzg5E/FyZ9GtoN3Vydv9trcyASS8/4Tve8NlEwfOBn8zQ8hfflIIw+paAjPzFrEeTaKNYwbKAcnDVJilR2H1r8GvWcVCBXwQKiYVadlO5L/CWzcuA6AhoZpB3lkjncDOTE4R44c/xCCwSDvfe91XHHFtbzxxgruuONWfvCDr/ODH3yd8867lOuu+wjz5h31b7+duyRYRJ4vykBmcNy2poHMIPn+GMWBwnGPD6BwXTqP2Y6PTZqFKmG662Ou7R9Lyn47qVtcy4Kw4Ok12xkayjA8McDm4wpIFRmEPIW0kKzRMgjHj18KAjKbVr1w4dFYlsWGDWuYP3/R2PE0Q0OI7LYhRclOWDyR7WTQVGXc673rX2/KjAFgWLi8YKSJSIWy0S6EAldl+WOP8JXrP8bU+qncefv9lJUd5lauA6Ak29H712TTfkNV2IVzx1be94UbnYgbqkFNtqGYfQBINYBdfBRKuge7YDqKkwLFR7ruIsyas/cbYKcObkAdbkTN9CLVEELNIJx0ttNb8aGme3CEghNtIDCwnkXuS2TqLwXAnwkj+kyQksSIguEbnRFIF7TgWMiflbDGzlfkLyXPOIlBsw9PuoT1PIa6B+nu6mDhwmOQhWCemd1aejCGbA1j8hmcPvt0QtpP6d2+msblD7LzjSdY85dHufEPIwDovgDh8qkY0RJ8hkFZno/KPB2f4UNRFAYH++nr66Wnp4v29hYcJ7vPtry8isWLj+Oyy65h8eLjiFVN5w+bNbYNwdZhgapIZhVJLprk5bqCc+TIkeMAFEqVD6divK5naFQtglJhtuNnppP14PX7/fzqV3fzrW99ka985TOsWPEqP/zhrw6YAxGSggCCpPAI7bH7KCE8AqOLuUfKa3qaRtXmGDvAJs2iW3WwkDSpNottlcszUQKHGFi366/aRNcgJSSdqs2IkHQpDvMcP1eko2hCIAUob+ry3fVfkx0DDcGrRpqu0WjefKlyQSZM5RFmIeyLjtHrje5xLwWCAqnQqO22ILgqk8d9/hEaVQtLQLGnEZLZ8Ltd9AuPPKlS6qlcffWH+NnPvsNP//d7eGcWMksWsDxWwGB4NEsjFEW076DlP/6XznseInLsVI657hQqymrHjjeQGSJqhMftcnszvel+VvdsIN8Xo32ki/JQKZ70GDLjCATTChsQCLpTvQybw4T13XYJAkFtXg2dyS76MoMUBQpxpUfzSCulwZIx8fKtMGzGeWj7E6zuXY/pWgT0AJo9gis9PNejwJ/PgDmIhkZZqARVaEjp0TzSRtrJjLMrebvJR+NjqXx+HBqgVXVBuihk33sfS+aPPc6PwmTH4Dm3Bz1jEwrko+v+7OssNM6rPJYbvngimU9nuOuu33DLLd/jhL/N5PLLr+WDH/kEz428xpre9dijYXnF/kJURaUj2U1Q8+NJSd78Su7/2z188LOf4eGdT7C2dwPr+jYR8+Vhuhb1sdps44kRpXWkjUFzaC8x2Hr2KbzuTtSpM8bmAt5AP/bLL6AvPha1ev9d5bsQQnDRpHMZsRJsH25GIlEQTIrVcdGkc9+xOaWS7sHfeC96/1rwLLxACU7eZHydL+AZMezioxBOCiXdiza4Gc9XgBcoQU22YxcvxAvsnv9JI2+3H7Fnow1tRotvR/ry8XwFgEQxB5FaELtwDhLwQuVjYXbJmR/D6HkNbWAjUtFxiuZgF82HA3Sov5P8/e/3UV8/mcrKfe8cyfHuIicG58iR4x+KEIL58xcxf/4ivvKV73HPPbdz5523cd99f2DWrHlcd91HuOCCywgG9y2Svd0k+5P0NvZhZxzCRSGKJxWh+d65r8aoEeGkqmP5a+PDNMdtokaYYWsEx7M5d+LpBPW9rzuAwnF2kOPsd/6eCEVw7KxaCuaUc78yzKpIirBUmerCoOLhk4I0HptVk2JP4wQrQJmnUjh9Nn6/n1Wrlo0Tg4vqCwkVhhhqHyZWkYdQBO6QSVWzRstpMVzkmMder+ISlQq17viJU+tIO3/rXs6y9DZK/YWI8pkUFU5k5c9+yYvf+DaV553Fz398B2WB2Nt2H/SuVwlsvTO7Uu85KNYwni8fs/ps7NKjUcwB9K6XUNK9uHmTsCqX4ORNwS5ZhDcQBF8eSIkbKEa4FghIz7gRN1iRXb3fj1cZgDq0FV/rk6iZ7uxzPQ/VTY6u+kvQ/AjpZgtNsw0tvo3AltuRioZVcRJ2ySJ8Hc+hDm8jFpvKzj4fef5+pBbEDVXi2lnrhED++LZZXTEoCewW059a+SgACxYsPqx751OzwXa2C0IXlNTPo6R+HnPe81V6UnBVRQt/eXUzb2zYjNe7CSfRT9o02dE9xMiASVCxcF2X/PxCamsnsnDh0dTU1FFTU0tDw1QqK2vGCvyUAxkHPjLHo2kYhkxBgV8yOT87hhw5cuTIcWBiUuUUK8Qp7Ds8V1VVvvrV77JgwSI+/ekbOPvsY7jttruZNm3f4URRqXKUFeBRXwJdCGJSYURkO2UX2wEqvSOvsTZrFkEEQRTmOT4GXJ2k8GhXbE6wAjTsp6N4TwwEcxwfT/iSFHkqBoJ5jo+gFFhC8v5UHovsAAGyHZ9TbIOXjQwFrjK2rb9LcYlJhQbX4CjHzyLbT7Nqo0nBZNcYW7x+u9llPbFnDQVgIsmT6pgMXuZpfDQVo01xSAtJu2rzV3+C7apNvqeSFB4p4XG6GabU0yAU5vrrb+J///eHnLPogyQ1gWjeiBGNYmg+7BffYOR/7qJrMMMxP/suxilTEaufZme8hZgvRtJOMmIlOKP2ZEqD+86zAIhbcTJOmqgRxfYswnoYTdEI62ESdpLKUDlxa4SUk6YkWMT24WYKA7sFz0J/PqWhEhzPZstQY/ZaQ6VcPvlC8nwH9qTeH67n8ofNf2Fl92pKA0WE/fmk7TQDmUGCmp/CQD4jZgK/6iegBxg2R1CFiuVZFAUK8KkGfZmBA173wdjVlduX7idshJlW0EBA212rnW2FmeYYPO5LMiw8al2ds8ww0T0WPhJWkuSWJ2npX8VGN0PIH6WgdgFldQs504pS5mVFQr/fzwc/+DGuvPJ6/u//buOWW77PPffeQeUJUzjxvUspLSvH8Vxe61pFe6KLKfn1dJsj+DU/dcfMYMfja/j6fV/HXxujNFhCm6+TjJth88A2fKpBdaSSEWuEoB7cK0hOmibu1s0oBcXjm0LyC/B6uvFamg9JDAaoCJfxyfk3sKF/C4OZYfL9ecwonDLuvr2tuBaBjbeh963GDVWBVoiS7ibY/SpSDYzZMzijAW9qqgu9fzVusBI3MoFM/SXst1NB0cnUv4fgxl+jJNtA0RDWCG6oGrtkESBR7BGs8hN3P0cLYlWcfNgBd+8Ew8NDPProX/n0p7/8b9/c9e9CTgzOkeNfhD7hsEbP0KLYRGTWK7XkIJ5r73YKC4u46abPcuONN/Pss49zxx2/4rOf/TBf//rnOOeci7j44vdyzDEnvmPewl2butnwyCbSQ+msVYEQFE8qYs6Fs95R/+DTa04iqPl5vvkV+kcGKQ0XcXLd8RxbcdQ7ds7DZarn42Q1zHbXZapr4AFbVYt21cEWMCw8Lsz4ucSMIhAYhsGsWfNZuXIZH/rQJ8aO44/6mXHONDY8tpnB1qFsgEJA5yyjiBdCEbaoFiEUTCSaFJxlBccKVYDGoe38dv0faUn1kAmqtPa10rbyZQbvWMnAuq3M/szHmPnlz1GUihxK0+ohITIDBBr/iHDSOJGJGL0rUVLdqPEm1GQnsvFukB5uqBKphzA6X0DvX0tyxkcwq04l0v0KyvBWpFDRND+eFiJTdxFuZOL+C8BdSIm/+SGE9LLeYiPNSCU71RP2CCCQQsXTgmiDm1GTbQg7ifClCWz7A3r/GpKzPklq2gfxN/2Z+vJmeprL6R0uIFBdj5M2SPQNUlhXSPGkA9uzrFq1nLq6SRQVlRzW/Svww/RCycvtgqAu8alge9A6IphWKCkorSIzYQInTjuD8B66/844VIbhcws9tIMIuSkbnmgWvNYlyLiC0qDklGrJ8RX7DqjLkSNHjhxHxtKllzBt2ixuuOEKzjnnWL785e/y/vd/ZJ+T/rPNEEnh8YaeoVtxCEqFo+wAl2Yib0v3ZEAqpGXWM9hBEpEK1Z5KSnhEOfSa8RQzyA7VZptqo8GYn+770hFO3GMBXiA4zQrRrDlsUa1sYBYSvxScbYaQQI/iMsHVqT8EIfpImeoYFHsq2xWbOk9DQyEuXFJCcrTtH3ePFQQ1o93Jk12dqKfygpGkW81mLyy1w5xg7b7WD33oE9x552003vsamQ/ORDcNgk0DDP3qMcxn12HMrKb073cips1mnu3jmEwhL7W9Ql96gLAR4owJS1hSc/wBx1/gLyBshDBdE13RMV0LTdHIuCaGauBIl6AepMAf48SqY9keb6El3kZhoICMk6Er1cs5dadz5oQldKV68Ks+phRMImrsv2P9YGwfbmZD32YKfTFM18KTHpNidQDoqs5RZfMxnTR+zU+eL0p/ZgDLtSjw5xPRwwhFoIlDf++1KjavGmkaVYs8qTIt6bJ+7V/Z3LcF23NQhGBCtJr3TXsPNdGqsefVegYfTu/7PSal5J4tD/B65wpmBQsZ8EfpMgcZWf80Z7t5LC07aa/PXzAY5MMf/hSXXXkt137jKtb+7SXufumHzD7rWBZdciq259KfGWDzoEdYD+NIFy0qEIbKptVruXTONRiqQVWkgqahHViexY7hFgKan55UH6dOOJGiN+16RFURhoGXGBn/c290R5dxeJ+hgBZgYencw3rOW0Ub3IA+uDEr+o4Kzm6kDm1wI4q1OxTaC5ZjlR+P3v0abrSOzKTLsUuOwgscuKZ2CueQmPf5bLdv/3q0wY0gHRRzAM9fSLr+MuyyY9/Ra3yr/P3v92FZFpdeetU/eyg5DpF/bSUpR47/T9imWtwaHGSNZpIQHp6A37vDfDSVz7lW+OAHeJejqiqnnXYOp512Ds3N27n33jt54IF7uOee2ykrq+CCCy7joovey6xZc9+2lUYzYbL5iS3YKYvCugKEEDiWQ/eWHnYs28nU06e8LefZF0IKqlrKOW7VAoYTCcJGkPx4FCff+Yf6Fx+MiFTRRz0EDQTTXR91rs521abUU7k2ExvXkbJgwdE8+OCf9zpOcUMxx1Tk0b+jH9d2iZZGiJZHmW66vOalaVJtop7CXMfPHMc3VqhKKXls57P0pfqZmz8ZR0mz8fHnGbjzaYziKOf+/Y/Ik49llhXYy5vwSNCGNqGke3BiU9EGs//2/MUIJwjSQU11IYXALl6ANKJ4wQq04c34Wh7GDdVm7RiEyHb1OilEKJANojiE966w42hDm3EDpbjBMoSTRkl3jf3e0/wIz0ZqAdREc/ZnoTLs4vlI1Y/Wvx6j+xXMmnOw82cQmLKD6ZNG2LbaZqQ3jSJdqudV0nDyJHT/gbeurlz56mF3BUP2Ms+f6DGYUdg6CFIKEJLaPMllkz06kgLTleOEYIA8A/rTgrQDkQN8DKSEe7YIXmpXyPdLQpqkOQ53blRQhMeC0rdpVSBHjhw5coyjvn4yDz30Mt/61n/yX//1KZ5//kl+9KPbKCwc3w0ZQOF9mSinWSH6FJeop1DjaW/bNvqYp7BFt5CAhkCT2ZC6aldj6kE8el0kGzSLzZqJAyyy/MwXProUl7DM+rlOdPf++1jt6Xw0FWOlnmGnYpM36k28VsvwJ39W1Kp3Dc4yw2/JE3ZfDAqX1/Q0WzWboBTMsf1McXSW6WmGhcc2zaIJiyJPpUhqHG8FONbaf0ekQLDQ8TPf8ZES2XC7N1uO5eXF+OpXv8vHP/5+Gk6dQv8zTzJ070pQBMFPnkn5zZ8mXVaH4QrOtiLMqljEMWXzGbGSBPUAvgPsfNpFgT/G0eULeWTHU4T0EF3JblJOComkOlzJYGZoTEQs9BdguhbPtLxAX2YAn2KwpPo4zqs/k5gvj4b8t8cLtivZQ9PwTlzPwXJtFKGQ54tQFakkqAW4bPL56IrOQGaIlpE25pfMRhEKjuewbWg7i8rmU+DPP/iJgB2qzW8DQ3QrTjYDA4dHRDvSn+HoUBkhPYDt2jQO7+RPW//Gp+Z/GE05uGzTMtLG2r6NVIcqiPoiTADwx2ix2ujeuQq3+DhUdd+1n+4zmHreIiaftpDmp9ay8q/Psu7xVwkfW4mzOEA0GiFqZLuuR6wEWlWI5PY+nECYnnCIUN5CKlo02vu20Zfux3QtTptwEhfUn7XXuYSmoS1cjPm3+5D5BYhAEOl5eC07UUrLUKccud+ssEbQ+1cjzAGkrwC7cC7yCBYLdqFk+kZt18Z/ztxAOfrQpnFhblL144UqSE+9DqvylEM+hxeuJhOuhomXZK0jhreBa+KGa8aFgr/buPfeOzj55DPeVsu+HO8sOTE4R453OQ6SP/vjrNIyIKB4tGuyW3W5JTRAnasz3X3nulj/0UyYMJH/+I+v8bnPfZXXX3+N+++/m/vu+wO/+tVPmDRpChdf/F4uuugKJkw4suJvoHmQZH+S/Jr8MYFZMzQCUT+dG7ppOHkSqv7OdCS3rGxl8xNb0P06RbEC7IzNjld24JgOsy+c+a7ZWtPg6NS6Ok2qTa2r4UOQEhINOM0KjROCARYsWMStt/6Izs52yssrx/3OFzKomFk+7mclnsZSc/+F2ZAZZ8dwM6XBYjo27GDDr/7MQGs3+Rcdi2/pJNJTizjW8XOB+fYuiAjPyXYZS4GSbEeq/qz3llAQTgo5Gv6gZPpxjWg2IThQij64CW1oK05sKp6/COGkkYqOFm/C1/EsdukxB/bwkhKt+zW0vtUIO4X0RXFiUxHRiSjJ9uzWsJJFSCOK1rYTZSCAl5mFl1+CCIeR+SC1AFr/esyac0Dz4+RPo2ghFMz1SA2lUTUFf57/oO+xVCrFhg1ruOCsy9mxbCdCCApqC4iUhA/p/VkchI/N9dg0IOjPSKIGzCiUhHRI2RJDVUjZkuAec5IRC0pCksBoZZJxoGkYTFdQGZaUjjYu7YzDGz0KVWFJdPSrL88HTUOS51oF80okyrvjI5QjR44c/3YEAgH++79/wkknncbNN3+IE06YyX/+57e48srrx+3kEgjKPY3yt9kqYUR4rNdNIp5CWvHwkKSEZER4HG8F9rKa2hMXyf2+EZ4z0tjCG7N7mGf7uSZ9cJ/hEk/jnNGaI4HLf0R6WaObKIAmYbtq06zafCpZcEhWFQeiTzj8JjhMo2oTRGBLyUo9g+5BSsn6MVe5Gm2qTUpIzk2FON0OjV3TgVAQhOX+H3fxxVfy61//nI3/fS+OaVJ9wclM/PT7YdocUuEotid5XzrCrFHRW1M08v15+z3evjh34hnois6L7a/iSJthc4SYL4+yUAlHlc0bExGFEBxfuZiFpXPpzwwS0PwU+GMHPb7jOQxkhtAV/ZDG1ji0nd5UH4X+fAoDBXjSY9AcYtgc4eTq4/CrPlRF5ZKG8/i/TX9iy2BjduFfQl3eBJZOPOOQ6iOJ5Bk9SbeS3XknEJiuycaRHrzqyXhOK2RMdFWnNlrFjuEWdgy3HJLovXWwiZ3DLSQDhYStEKXBEnyqMZpVMkzCTlKg7vvehfQgE6LVrDU3sPiy06k/Yy6vPvAkjY++jnxZ0nlsBuP02fiLwqiKQKsKk9oY54VJdTi+rPhp1NQQ3rKChQmPj8+9/oC+0caS03DbWnDXvgGOm/X7LS7Bd8kVKNHDey+9GXWkmcDGW9Hi20d/InGik0jNuBEvXHNEx5a+gqxtm2uCunv+LVUfTrgGbXgrUguimAMo6T6c2KSDdgMfEEXHyZ9+RGP+R7Bt2yZWrVrOr371x3/2UHIcBjkxOEeOdzmtqsN6zcIDijx1TIAr8wQ9wuEJI8n09L+PGLwLIQQLFixmwYLFfP3rP+DFF5/m/vvv5pZbvs/3vvc1Fiw4mosuuoILLnjPXh0ph4LneNlM1zepRoqq4Lke0ntnugsdy6VlVSuaTyNSmhVC9YCOoil0b+5mpHsCqq6SGkzjCxlEyiIHLS67FIeVeppWxaFQqsyz/Uxy9SPuwPGjcGU6yj3+ODs1GxuISMFpZojjrb39ixcsOBqA119fzrnnXnxE5wZQFQVzKMXyPz7M9hfXUT5lAlf+8GaYWExzvJUrzQLOTMX2EqX3RNhJlEwvnh5G+scXpdoGMF5WUFvAKwXraIm9QOJEJyKNKEqmd49HS4STwgsUo6RHuwL28KUQrgWuifDcrB2EZ6KONKMk27LevqlO1NoLcQv37bEIoHe/QmjbXdn0YGmDa6INb8WN1OLkT8PzF5FY9N8onUGiL9yDVP4Cdj1qXKB2gT1DIsJOVrx+E4qmEC7atx/kvlj9xgocx0FtDrChbxMAvrBB/QkTqTum9pAmPH4N5pXs/Tmqj8GUfMmaPkHlqPjbn4GMC8dXSjQFGofgni0KrSPgepDnUzih0uO8iZKetCDtSGretI6Q74eulCBlQ/jd02CfI0eOHP+WnHHGUp5/fg3f+tYX+fznb+Luu3/P//zPz5kzZ8E7et7Nmkm34nKiHSAuJENKNkjLRJIRMlvb7ee5W1WL530pCj2V2Kh1QhqPVXqG6Y7BCYeRzXC/f4SVeoY8qRCWCq6AYeGwUbV4UU8esRj80qiFwGQ3G04HsEOxWOHLMM/y0a66DAgXCbQrDncG48we8R+x+N7f38fPfvYd1q17AyklDde8l8n/+12KPAVbwJDnMt/xc9oR7kz0qQbn1Z/JKTUnMGzGcaWL6VrEfNG9bQUAv+ajMlx2SMde3bOeJ5qfZWe8hf70EIaqM7toOidUHc2MwmmoQhDQAmO1jOu5bB1sQhEKbYkODMUgYoTRFZ24NUJ1pBJ1dDF/SsEkbp5/I2v7NhK3EhQFCphdNJ2Isft+jFgJWkba0YRKbV7NuG7pDJJG3co290iwpY3l2miZJOn8YoYDQSIZc/Qe+bBci4ybOeg1r+ndwP3bHqYj0UV/emBUoI8xr2QWI3aCqBEltI9ckl0IITim4ihebn+NP239G0knhVwExpTJeMsHGXytlYEXdhCeU0bhKfXk11XQ8UIHg0O9lBgBVEVlUJO4k2Yx06k6oBAMIMIRAh/4CNaWDbR0N9Mc0hiaOZ3qgnLm2B7+QwyA3Avp4W+8G224CSc2FRRtNJhtC4Ft95Cc82nU+PZsFoi/CDdSe0g793Zh58/AyZuMNrgh26mr+lHT3aAaJOd+FiXdT2DTb1AzPXhGDGEnCK39KemG9x5Wd/C/Grff/itisXzOOOO8f/ZQchwGOTE4R453OR4SU2Q9lNQ9VvEF2Q9wp+r808b2j0LTNJYsOZMlS84klUry+ON/54EH7uFrX/ssX/3qZzjhhFM544ylnHba2VRVHVrgQLQ8ii9kkB5MEyzIFkdSSlJDaarmVrxjIXJW0iQTN/GFxwv4vrCPeM8IGx7ZRKIviZW00HwaBRPzKVtSRjQW2Wcoxg7V5vbAEO2KM+Zht0xP855MlGPsIw9PqPZ0PjESY31XHynTpqooj7roeBVuuDNO18Yu0kNpSovKWfbSS/sVg5P9SYY74iiaQsGE/P3aYrS07OCXv/wxD979W4ShcurH3sOc044BIdgeb2ZmsJIlkYb9C8HSw9fyGEbbkyjmAKgBrOKFZCa9B2nkoa8SBO9SEEnwohJ9A2ibBJlhME+twqw6Hd/OBxEIFLMf6aSQvhhOwSz0rpezRaQRy57LSaOme7CK5qIPbgI7id6/GjXViVR9CM9CSXUQ2vw7EvO+gBcs3Xu8nouv9Qmk9LDLTkDvXTEaXmejDW7AKj+B9JRrkXoY3+MKdC5ETnsKSRdSlqIMC7TGIdwZErvkyCfiLzz+ND49wMSqBoKx0Nhrt+35JvKrYuTXHNpWyH2hKfC+aR7+bQqb+gW9aYj5JBdOkpxQIRmx4K5NCh0JQW1UoivQl5Y8ulNQHMw+VlMEppsVnHeRsiHml7yD+Y85cuTIkWMPiopK+MlPfsOVV17PF7/4cc4551iuvvpDfP7z3yA/v+AdOWearOCro1AoodDNinQ9ikNKSDzYr4zUqNmYZEPzdhFAQQfWa+Yhi8ESyTI9K9DtOpYmoUBqdCkOr+smCx2TIeESkypTHAPfYS7Qr9VM8qQ6JgQDIMBCskPLBsLlewoaAh2XnarNn/1xPpbKP6Tu4DczODjAbbf9hN/85haklHzhC99kcHCA3/3+F5x9w40Mz64nJAUnWWFOtkIYb5PlR0gPHlCkPBRcz6U33Y8iFPrTA9y58V6GzGE6kt0MZYZwpEtLvJWnW18kooeoi9bQUFDPaTUnUZdXw0BmkNd71iGlxKf4MD2T7lQvIT1IWbBkzDt4F4WBApZUZ32RHc+haWgncWuEAl+M1kQHT7Y8T18qO56qSAWXNCxlakE2WExFoElBpznE9oEWhs04mqJiew6mbaJKD1e6WK7FwGggWmW4fK9r3pOUneKBxocRQH2slq5kDyE9SF+6nzd61lEcLOSc2tPwqQY9qT5W965nIDNIUaCQucUzKAoUknFMlnWuIuOapJ00juegKxoFhUVo55TineGirU3S92wTzT96mXBdtpat2DGENdnAdiyKRBC9sIa0Wg4H16+Rhs5DCyfxrFGBJeTo53aYuZqfaw+hU39fqIlWtKGtuOGarBAMoOi44Wq0/jWEV3wNLdkKThqph7GLF4zV14eE5ic1/QYC2/6ANrgJ4Vp4gULMiZdgVZ2Of8cDyEAhmbKjQfWDlKjJVvxNf8YNT0BJ9yCcJF6wDCc27cA7Bv9FWLv2de6441a+8IVv4vP9+zWo/TuTmzLlyPEup9LVKHE1WlUHFw91NNU4gYeOQt0BtsP9OxIMhrjooiu46KIr6O/v46GH/sLDD9/PV77yab74xU8wdeqMUf/hc1mwYPF+w+fCxSEmHFVD00vbySQyaIaGmbKIFIWpPbr2HRu/ETTwBXWslDVOELZSNpnhDF0bu8mrzCO/JsbWdBN/aXmYzMMmpfUlzCqexrl1p1MYyE6wJJLHjSQdisNU1xgr/lsVm4d9CWY6PiLyLa6sjzLSk2Djo5sYbB3EtVyaIj6s+VU0nDQJRVPo2tjF+oc3kolnUDWV6kg9Lz71LIM3DZFfFRs7jvQkjS80sfO1FqyECUIQLgox9YwplE7ZvX1q48a1/OIXP+DBB/9MXl4+H77pZpyFYdqcbjYPNSKlpDRUzCUNSwloe3fAjt3n9mcJbLsLqQXxguUIO4mv7XGEkyQ19eP4ntDAlDiTs4/3AKUDfE8LrKMEmYmX4IarMdqfxtf2FMI1cYNlKKkuvHAVLtWoqQ5IdYKiYpUsJDXl/YTW34LR/SpKqhvPV4DAA9fEzp+FmmjFt/Mh3OgEhJvBDddkt34pGsKOo6a68HyFSD2EVXosaqoTYSdQUp2k69+DUzwfMQT6VoGbNwXpXICmPYhQ1kOeQEn4yGinjyYOHxnLlr3MxJLJhPJ3F8fhojB9Tf30NvUdkRgMUBiAG2Z5dCSzIm5JMGv1ALBxQNCegPo8ORYkVxyEERte7RB8Yp5kYh5sHRRMiEr8KgyakLDh7LqseJwjR44cOf5xLFp0LI89tpzbb7+V73//azz00P186Uvf5vLLr0FR3t4v5QpPw48gLjyiozWORDIgPI62/ePF0zexvz1fgmwdcKi4QGbUNstDjtVfGgITSZNq88vg4JgwPckxuDoTpeQwunY1BN6bRqzJ7PmGhUuht1solkC+p7BdtWlXHKq9Q58bxOPD/PrXP+O2236K4zi8//0f4SMf+QyFhUWkUimee+5xHn3/h3ng0ZeJBsP7vb8SyU7VoU2xMRBMcYxxovuhIqWkJ9XHiD1CZbjigLUewJaBRh7e8SQt8TaEEAxkhnA9h4AWIGWnKA+V4XguO0da0O0kcXWEokARyztX0TS0kwvrz6Z1pB3TMQnqQYpGPYodz2bIihPxRah4U0eyJz22Dzezvm8Tz7W+QtwaQVUUMo5JX6qfiXm1TMybgCtdWkbauWvTn7l5/o0UBgowEJQODvKEbMOXGSAsdEzXZjAQQMR7aNz2ImuSfSSsFC4eMwon82zLy9TmVTOzaNo+PZm3DzfTmeymLlpDRbgMVaj0pvvwpGQwM8RlUy7g5Jrj2DLQyJ2b/kRXshtNaLjS5YW2V7huxnvpS/ezpmcdpcEi+tMDFPrz8aRHb2YAQzVIiATqoiKOP20h6Y39bHlsHQm62fyJ/2HBZz/GpPdcQDSST6vukbAPbXflNtXmOV+KfE8hf9QGMYPH63qGaa7BSfvYhXhQPBvhOUjlTZ8BoaENb0MxB3AKZiK1MMIaxmh/DqmFSU+99tBPEaogOeczqIlWcNN4wcqsH7GU6F2vIo28rBAMWSu5UBV6z3Iir30JkNkPrGpgFS8kPe0Dhy5EvwtxHIfPfvZGpk6dyYc//Kl/9nByHCY5MThHjnc5u7bqb9MsWhSXiPTwhMRDUOfqHHsYW9r+3SgsLOLaaz/Mtdd+mHh8mBdeeIqnnnqEu+++nVtu+T75+YWccsqZnHbaOZx88hnk5cXGniuEYNJJ9YRLwnSs2E5qSxMlyQ4qdI/g5jQydhwi9Pb/cdZ8GpXzqtj85BaUgRSBqB8rbTPcOQwehEtCBGMBWmUHz/heIqGmiMRDeGmX51tfoTfVz01zP4Bf8zEkPJo0izJPG9cFUu5pNKk2O1V7zM/treBYLuv+voGBHf3k+/PQ0Uj2p2l6pgl/xE/lnAq2PtuIk3EorCtECMHsaQu487FfsP7J9Rx/3XHZUUmHzg29NL7QhBHyUVBbgPQkwx1xNjyyiUhJmHVb3+CWW77H008/SmVlDV//+g+54orrCAaDJO0Ua3s30JPuJ6wHmVU0nZIDbT/zbIz2p5CKke0MAKQWRKo+9L430JubULum4hWPL1a9EtB2CNQOcKap2GXHYJcdQ2rmxzC6l6EOb8t2ERTNw43Uog+sQ9gjuIEynIKZID2sipPQe5Yj7DiKECDUrHicPxWtbzXBzb/Ojkl6oOhY5ceRmnwNwk7gKRqKk8RyCogP+xGijmg4gaHoeJHa7CDF6P+lwHEuxHVnoaibEK6L6JyEWTLliLsMTNNk7daVLJ1/+djP/LKPkGxH8SeQ6QNv/TtUhIDKfXzEkhYgBZoy/vUJajCQAV2BK6d53LNZoWlYYLuSiCE4rcbj5OpceFyOHDly/DPQNI0PfvBjnHfeJXzrW//JZz5zA7/97c/5xCe+wLnnXrzfxfnDZaKrM9/y8YqRIS4FPgT9wqVAqpx4kJq43tUxgLhwicrd4pMFzDyM/A0VmOroNKkWA4pHnqegAkPCJaNIhAfVroZ/dMfWZs3kfl+CD6fzDtnCa4Ht489+iwzZLfOSbNdzWCpkRtMLJBJTSBwhqXcNbCGxxKH9HUwkRvjd737Brbf+mEwmzTXXfJibbvosxcW7dy8Fg0F+9as/ctZZx/CNL97MT37ym30ey0byF/8Iy/Q0KZEdW7GnckkmwjznwGLunvSkevnZG79heecq0k6amC+P8+vP5voZ70VT95Yt2hOd3L7xHgbSA5SFSpFSsrxzFT7Vh0/z4VMNFKFgeilMx6QgFMORLrZnEdUjvNj+Khv6N+NKF9uz0RWNQXOIgBZAArZnk++LURutRkqJEALbs7lv6995pf01NgxsYcgcpsAXY2bRNIYyw3SneykNlaApGhoa9Xm1bB5sZF3fJk6uPg4pJeaWVwjna8jSWlIiW9gVp0bwtr7IQLwDKSWaomE5KVb3rKcz0c2EaDWzi6dz7fQrCBvjbb9c6eHJ3csZswqnkfFM+lIDKIrg7NrTAPhr0yP0pfqZmt+AIhQ86bF1qIm/NT1KebAEDzBGu2mFEKhCw6cYFPoLqAyX4Xoul0+7iFknzmDLx1XeV1dJsLyUFz73ZV779g+Z86H3E7rhSuqj4zup90eTln1/58vdwq0fBR+CtZr5lsRgN1SJGyxDTXXiRnf7LKvxJoRr4uRNRurZHY7SF8NzTfSe5WTqzkf6DqPRQSi4kTfvRpUgHaR403ed56CONOMFy7DKjh9tAhnB6HwJL1hGZtLl/Kty220/ZePGtTz88Mvo+v9fDWr/DuTE4Bw5/gU4wQnylUQRdwSGaVVtfJ7CJNfgkkyEWQdJTf7/hWg0j6VLL2Hp0ktwXZfVq1fy9NOP8OSTj/CXv/wRVVVZtOi40a7hs5k0aSqKqlA2IUTsiRdwetcjwmHok2Tu2YCzeSOB629EBI7cauHN1B09ASfj0La6naH2ITSfRumUEgZbBjGC2cnIem8TSVJUqxVknAx5IkphrJCtg01sHNjC/JLZu3XBNx1/l1/ekfbiDDQPMLRjiOK+fHx9GrhgiBCe7tD+bBuBggCJviR55dEx37V5kxfzu0d+yrJlz3Pc0cNERpahmEMMNhnkq5OgIGthIFRBoNjHcy89wfce+gJrN61iypTp/Pznv+f88y8bV1CE9CDHVBx1yOMWdgI1M4Dni42/L0YUkWxB0A8aCHv8vRMWoIF800dK+mKYNWcB4xORrfITxv6tdy/Hv/0v2W5e10IKDTcyETdcNRYmp8Ub8fQwwjVR0t3g2QT712K0PY30F6IkO+hslqzrqGA4GULgUhh2mHbsNHyxKdmxRMGeIjFeFXgxkOokXHsSajO4MXDrD6e3ad8sX/4SGSvN9PL/x955x9lV1vn/fertZe703mfSeyEJoQRCC1WqAqIuKrLWddXVdfW3dtddddW1o6I0C6AC0jshQBJC6kySSTLJ9F5uP+35/XHDhEB6ggQ879drXjNz55znPOeZM3Of8znf5/OZjWNZVEjPUCxeRHHiVAQtAuZmlD4Hs3jhcR/rQBT5BYqcqxh+NWBOCBg1JE4pEUgSVAThE3Mcdo5CypIo9juUBo7K9s3FxcXF5U2guLiUH/7wN1x//Yf43ve+zk03XUtDQzMf//jnuPTSa1DV47v9lJG4OhOmzFF5UcuQlhwWmj5ON/00HMand5Kls8Tw85yeohcbiVyo3CzLy3zjyEVLCYmzjSAtmkmnbDIm21jkfIvzHIXZlmfC89SDRJmjslXN0ivbR+zpu8T0s0M12aBmcfYKwWEhc0Mqwt2+BH2KjV9IaEKixtbxComwkCk5TPuDg/3cccev+PnPf0AiMc61197Ixz72WUpKyg64fWPjZL75zR/wyU/eyJIlp3Pllde/YZsXtTRP6SmKHIVKR8YB9igWf/TGqUpq5B9BhXDWNvjqC9/l+e6X8KlefKqPwfQQv9z4OxzH4pzaZbSP7UGRFZry6ikNFPNSzzoGUoNMymtEkiQc4eDXAuwe70CTFVQ5l5+RsbPYwmEkM4rhmLzSvxlwkCUZISCshehN9uPVooS1ECkrhemYFPkj5AUkfrf951QFqpgcm8aukX6e6liJT/UiI1MaKMGws7QMb0OVNQKqn95kH43ROryqB1nKlWuMG3EAMnaW7qFdVPWMk9m9HcsfJD9QRFEizRNdLVQEy2iONfBCz1q8qgdVUrCFQ8ybx8v9G6iL1HB+7Vn7jV1VqByE4Ik9zwISiqxQGijGxuGUwjlEPWHaxzvoiHdTESxFlnLXpizJlAdKaR/rIKKHEMIh4omgKRoZK4tX9eIg0BUNXdY4t+4cztt77KiwkVWVgusuZ+Gvf8S2H/+S1T/4MXzvR+RdeR1V7/sIU6bMOOTv/E15fK/6yNZchK/116gjW3C0ELIZB+Hg+IpyVbuv7YMWQE4PIJmJoxODD4QkYxXMwdP+VxxfyURxhjrWhuSYE6sBc8cN4XhjaH2ryNRcAoepgD8ZaW/fwX//939y440fe9O94l3eHFwx2MXlbcJppp8lpo8OxcJEUG6r+I9b7ntnoijKRPjcZz/7n3R1dfDEEw/x6KMP8J3v/Cdf/eq/UVBQxMKFS5gbK2Tmrp1MnTUfZW8arshksDesw9r4CtqCRSe+f5rCpOVNVM+vJDWSQg/oeCNeVv3yRTLjGTwBnV4GCODHNh0UTUH1angUHYFgMDUEQETINJs6q/Q0EVtGQUIg6FQsShyVWiunpMX74wzuGMK2HMIlIfJrYyjq4SfmRsJA6nHwDKk4UUADHPAO6lgbDMT5bxQeywoqKYyU0N7xV8I7X0Hx+RFqkJCxgWmhVnaaOk+3jfHcxsdZ3ZITHac3z+E3v7mHs8++4IQsJxVqAEePIGeGsD37PAslMwGyF7swijHNwfOsjBMQ4AFMUDokrCaBfWS20xMoo9vwt94CVgYrUImsBvEku1HG27AijYCEMrYd7AyoAZTxHQg1gGSbKMku5Mwgmdp3MWA0snoTGGaScCyNQKNvrIzxlsnMX2Thi2ogQeY8B6VLRt0qgSbAkhBRyFzsIA5SzJ4ZzzDQNkgmnkE4gmBhkEhpmED+G0PlnnjiIUpLypk+axZK9wsUxh4n6/hJGCUE8/0E/WnYeit2sBIncOCbx+OhOQbTCgRr+yQKfAJdhoEMRHRYWr7vmtPk3LZHcjuRtaF1WKIvBUENpuaLCVsKFxcXF5cTz4IFi7nzzgdYt2413//+N/j4xz/A//zP1/joRz/DlVdej64fezGDD5lzjCBnGwEsOGL/WgWJqzIhJlk6m9UsNoIm28Ns03PU/qSzLQ83piI86kmyW7GQhaDW0titWgReZ9HlERKmLMhIR/7ANihkPpCKsEk16FRMPEhMsnRqbI06R+fXvjEyiFwQGTnrirOMwBuODTnrhdWrV3HrrT/l/vvvRlEUrrzyej72sc9RUVF12L5cddV7ef75p/m3f/sYs2bNo7Fx8n4/f0nLoCFN2EIoQLWtsk0x2XKEXszrBzayrn8DET1MnjeCYZvoXo3+9CC/bfkD6we3YAkLISDqCXNR/bl0JXrwKt6JgoS20Z0kjDhCOIBKxsrQmejJif6OhWEbaIqOI2wSZhKv4iGg+aiOVNKXGiBppJia10zKytCf6aSpoJSwx0tXvIfhzDDj1gjd42MokoJX9WIJi5AcxKvoDKSHCesatpNre+tIGz7VS0gPgWAiFG8sO862kTb6U4N4hz2YjsWI5kOK1GLaJvnePEazY2TsLPmePCQJBtPD2MImrIVY07eO82qW7Rfk25noJmGmGMuOo8g5I4/eZD9V4XLOqjxtQijPVTfvf31IkoQQDvWRGtb1b8RyTCqDZewa38OYEcd0TNJWhua8ek6rWDyxX0BSiEbyqB9MU1JbS8F3vs51n/13en95O3+85af86bZbmD17PtdeeyOXXHIlgQOstqyzNDweiTHJJrL32skiyCCYdhzFTkbJqTh6GL37WeRkJz0l87GjzVRsvQM5M7RfdoecGcLxxnC8J2bVW7ZyOcroFtTRLQjVj2QbSOYojrcQx7t/4LlQvMh2FskxELy9xGDLsvjsZ2+moKCYz3zmy291d1yOEVcMdnF5G6EgUfMP5hF8Iigvr+T66z/I9dd/kHQ6zYsvPsuLLz7HCy88x7ceug/Dtgi89CxzyiqZX17FvIpqploW2q4db4oY/Cq+qA9fdF/lceW8SloebGG8N04wGmDAHsSb9hIti6AHchNMgZhILJaQON8I0qNYbFMMFCRsBDGhcEkmiB+Zjpc72frYNjLxDCChqDIlU0uYftHUw4bk+Xw+tDGVjG6ga3snZTKkvBmKMjGKrXyCBQHG++KES0IYCQMhBDPKJrNm94tIoXnY/mIs2+HJnm7ue341j2x/mvGMQVVxHZcueQ/TC+dz3gcvoHTqoROihRB0JrrpTfbjU3005tUd0DcNyPlwlZ+Jb+utyKkeHE8+kpVESXRiFs3HDjeSvUigDAvUbYAtgSSwqgTpq5yjfmfU+1YhZUex8qaCJOGopRglS9B7n0PvfxHbX4Jkp3P+ZI6ZmwxKMrIxipBVQCCb4+zunU5SUigu7MWONeEESglrMQZ3j9C3tZ+ahTmV2qmAxEcd9HUScieIPIE5U2DXHLh/gzuH2HT/FkY7R4n3xTFSBppfo6ixkKr5VTQva0TR9j0cePzxB1l21nnMvnIW4okHUPsk0nIRBZU5AdlRZdSRTWhDr5B9E8RgTYb3TnEo9ku83C+RsSWmxARnV9l7xd+jYyQDt26RaR0GS0ggBGVBiesnOzQeZxGIi4uLi8uhmT17Prfeei+bNr3CD3/4bT772Zv57ne/zkc+8ikuv/za4wqak5E4WslIRWKO5WXOUdgXHAgJiSWmnzmmlwHZxotEwJH4r+AwA7JNpbNPdBuUcx6/xfbRTTC8yMyzvMx7XV/PNPzkOwrPa2m69hYALDZ8zH6dNVgymeDee+/iN7/5KVu2bKC2toEvfOHrXHXVe4963L/xjR+wbt1q3v/+y/nLX54hP3+feBaXHDxif0H+VfuyzBHaVuwa6yBrG4T1ED3JflJmCgeHrGWQdQya8hqYmt+MhERPso+/tD1IQ7SWrJPBcRwGMkNsHGwBwKf6CGh+0laapJnCEQ6SJCNJMgXeGHEziSorZB0Dn+qj0JdPc6yBdX0b2Da6g7HsGHPL6ijw5RFQc+HNQ5lhehMD2Jj4NI2g5sereEhbGQKaHwmIeiJ0xLtImilGM2NYwgYEk/ObmZKXC6l4qP2JnCgLJMwkhm0ymB6mM95N3t6qXFs4uSVRgGGbqLKKrug4wsGwzf3GzbIt7tn+AI5wmF86h7HMOBk7g0fxoinq3rhFqAiVURospifRS224eq8ILOhJ9lEXqWZR2XziZpJHdz+FLMnEvFESRorSYA2X1J3HqRWnvMGizaN7qE/BpxMFCED1SfCxL/DZmz7Do4/ez+23/4rPfOYmvvzlT3PZZddw3XU3MmPGnIn9m2ydUw0/z+op+rCRAQvBTMvDfMOL6RhISKiv9/89HJKElT+THYVT+JsnwQ7VRCC41JjPsq0P43eyOFoYOTuC5GQxKq4E9cSsBHX8paRmfBqtZyXqaAtCC2GHqvDu+ktOePbtHUMhUDL9mPkzJ2wr3i7Yts2nPnUjq1Y9w+23339Aod/l7YErBru4uPxD4fP5OOOMczjjjHMAGL3lJ6x78D5elgVruvbw89Ur+e7KJ9BkmRnbNrBoRwsLF57KrFnzyM8vPEzrx0f1vEoQgj1rOqhLVNPu78Apd8iriZK1DHbHOygPljK1YNLEPmWOyj+n8nhFy9IrW0SEzEzTQ7mjkRhIsPXxbTiOmPD0NdImXeu7yauMToiLByNWGKXUU0RHuht/1o8mKySNNLIiU69VoQmNpmWNrL5tLdufbMO2HIQjaAo28vjIk/zyqQ427V7LX55vZXAsRXVegPfMKqeh+eMUxaaQHktT3FRIYcOhn8ZnbYM/bfsrq3vXkTSTKLJKTbiSd096F9XhygPvU7EcyUyidz+JGt+FULwYpaeSbroWZAWnABL/7KC1SsiDAicE1jSBeGOh7GGRU70I1befR4EdrsXMjmDHppItXwYShF78D6TsCEgyCAcpO47kmAhZR050EB+bjuaRkRQFoQVwvIU5uw9JIj22fyyzyIfs2Ye/wbKyFi0Pt5IcTGCkc2K9Pz9ANpElMZRk18p2vCEPdYtz/m7t7TvYsWMbX/jC1/FHfQSrNJS8CvJCrx9nCclMHv1gHSFhHa5sEqyoExh27nv5GC0gHt4tsWFAoi4i8KoC24EdYxK/3ybzr3MdvO5MyMXFxeVNZ9q0WfzsZ3eyfXsLP/jBt/nKVz7H1772ec477xKuueZ9LF267IT5Cv898SFT9Rrh96xsgD964+yUDULIjEsOkpBYZvhP2Io+CYmZlpeZlheBeIMPcWvrJm677Rb++MffkUwmWL58BV/84jc57bSzjnkFlt8f4NZb7+Wii07jhhsu5Q9/eAS/P1fx22TrPKEkKUWZ6EsSBxUoP4xtxXBmhNbhNnqT/TjCoSfZh+lY6LKGEIKUlUbgsGWolbSVpjnWQGmgmJaR7fhUHyEtxIu9a+lN9TOQHtorHqqUBouoDVUzlBlhw8BmCvwFgMCwDUxhYAsHTVYZzY7Rm+ynMlhO3EhQG6li28h26vMqUaV9jxoCaoDRbIKiQBTD6UdXdCpD5Wwf3UnWzmI5FuPGOJawUWQl91mSkSWZjvFO/rLrIS6pP4/NQ6005TUwnBkhYSZRJAVVVrCFja7odCS6KA+UockaY5hkJItyX4yQHqAn0cuCmjMnqoIHUoPc1vInHtn9FLawGc6MUhooZm7xTDRZo3Wkjf70IM004FF0Lqw9h9ta/kjryHa8ipeMnaHAl8+KunPQFZ0VtcuZkt9M28hOHBzqIjU0RGsnbCVez8jIELFYAcrrrj9N07jggsu44ILL6OzczZ13/oa77voNt932S6ZNm8W11/4Tl112DeFwhCsyIZotnU1qFgtBk63TmMrQnljHcHYg5z/tLaU62IhPPfJJ+pBk8xv/GJ2yRamTuy7vmrycPt3L1TvXEjQSOP5ishVnY5QvO+J2jwTHV0i27lKyXJp7QQgkYwzv7vuRzHGE6kfODiP0CNmq809KjzMhBDvG2tk52g5AfbSWukg1Qgg++9mbuffeu/jxj3/HaaeddeiGXE5q3FsgFxeXf2gCs+Yxb+1LzI8V8JGFp2HZNq1trazuaGddJMzvf/9bfvSj7wA5H7zJk6czefI0pk6dweTJ02lomHTCDPNlRab2lBoqZ1cwe2wmtSM1PNO/ip3JPWiSQl2kmiuaLias7/8EOU8onHmAkIWhXcNkxrPk18YmJo66T0PzqnRv6jmsGCzlycycORX/yx46RC8ZK0vYF6LJU02Zp4REuYNP+FB1BdOboX18OzuHt7K9ZwMAn/rpQwTzfNTOK+E9ixp4b3mY/BFYNViHUGUaltZRu7jmsBXKT3U8x1MdKyn2F1IRLMNwDHaMtXN7y938y9yP4FX3r4SxHZvto7sY9FaSV3cdkzx+NG8MJ1Cx/4TLC+as43csswMVaANrclUcr7YvbFA0jLLTMCrOAuFg5k/H0/EQcmYIyRhDsuK5CSISamIP9dGX6eucj/CDUHLnJByBIwS+yLFVMA3vGSHeF0cP6AzvGcEb8iKrMsJ2MJImwfwAna90U72gCkVVeOKJh9E0jaVLcxNjK9KIOrwxF3j36s2Anc0FZwQqjnvsDodfzX0cK0kT1vXn7CZeFX0VGWrCgt1xiR1jMDX/xPTVxcXFxeXwNDZO5oc//A1f+tK3ufvuO7jrrt/wnvesoKyskquuuo6rr76B6uq6wzd0krLU9OEXMiv1FH2yTePe6se5xxHoeygkchWeLS0buf/+e3jggXvYvr2VgoIiPvCBm7n22huPyAriSKipqee22/7K5ZefzUc+ci233PJHVFXlVMPHFjVLq2ISEzImgrjksMD00XSI5f6re9dx9/b7GEqPYDk2pm0wZsSJeMI4QjBuJBAIFEnBdCx6kn2krQwLS+ciATFvlGWVS/mftT8mY2WRkfFrPmLePEYyo1QGKygLltCXGiDmjeaqXc0Uo9kxtg63kTIzdCS6GUgPockaS8oXcGHtOdyaGUaRFCRJIPZWPDvCQUamwJtPiT9D6/B2Ip4wRf5CuhI95HmjBLQAftVHUAsQ0oN7hWmFPfEu/rbzUc6sWIzlWKTMNJqsURepxnIsQCZrZygOFBLSg2Q9XsanL2Y0mo+m6nizNi/v2cHUSOWEVYMjHH6/9c9sGmohqofJWBk0WWP3eAceRacuUoOMRFDbJ6DOKppGxBNmbd96+lIDlAWKmVcyi8pQee5akiTqItXURQ7vl5ZKJclk0sRih55EVVRU85nPfJlPferfefLJh7n99lv44hc/yVe+8jkuuugKrrvuRubOXTgRNJiyErwyvJa4NYZfCSAQ7E62ETfHmBk7BV05sr+jV7QMnbJJs61PVKmHCfJQ01kEys/lgqSE0MJwsFWGJxJJItNwDY6/BL37aWRjDKN4EUbFWbkA6pMM27H5y44HeapjJSkrDYBf83NmxRJevOVB7rrrN/zgB7/i4ouvfIt76nK8uGKwi4vLO5Ysgi1qli7FQhcSUyydCmd/4VadNQftzOWYzz6J1dcDQjA5GGLmRz+DfsHFQK5actOmV2hp2ciWLRu57767+clPvgvknn43Nk5mypTpTJ48nSlTZjBlyvT90piPFtWjEswPcm7emSyqnk9Xohtd0akJV6ErRy4821bOm0563RNnWZGxsvbhG5CB81Rm9U5lymgD2bCJ3/Qisg6bprbw/FMv8PC9D/DyKy8wEO8FoDS/gkn100nsSkDQ4ZJPL0RTNNrNJL9MdvK+xndRN30aul+noL4AVT90FZDpmKzqWUNQD5DnjQKgKzq14Sp2xzvYPrqD6QVTJrYfN+Lc1vInNg22YDomMjK1kSqum3wlFUf45F0y4qjDG5HMBE6gFCs6eSLw4YB9LFmM3rsSdWwrtr8chI2S6sIOVWMU7g1UkGSS0z6OEm9HGd+FZKdw9DxkK4WQVWw9jzL/VsoCYXrjzXhLY4iUQbw/TrgoRFFz0RH1/bXIqV6CPY/Q6H2ZcauSfieGpHj3dicnCKseFSNpYBv2XjH4IRYuXEowmHvgYJSehta/GnVkM463CISFkhnCKJyNWTDrqPv098awwXIkvMr+or8qg+MIDPtAEYwuLi4uLm82hYXF3HTTp/jwhz/JunWr+f3vb+WWW/6P73//myxefDqXX34tZ511HkVFh7aROtmQkJhneZlrebDJ+ee+vnL3RCCEYOPGddx//9088MC97NrVRjgc4dxzL+Lf//0bnH76cjyeEy9Az5w5l5///E5uuOEyPv/5j/Ff//VjKh2NG1NRntZTbFUNAkLmHDPAaYYf9SDn3pvs54/b/krGytKUV4+ERMpM8lLfOtJmBkvYuUA4xbfX81YQ9UQYy47TEe9CkRTKQ6UMZ0apDldS4i9k8/BWRrPjBLUAcSPB7ngHMW+UAm8ebWO72DEGuqwxZowjSRDxhAhrAUDCQVAdqmBm0VRKdhfTnxylJBQGIWELQcpKURUuoTxQybypy1jZ9SKbhlqZWTiV9025hrnFM7iz9R52jLYT1AJor7E28Klexow4KStDRbCU57peQiDwq7lCjriRIKD5KfUX4/XnoZ96FVVKmrp0goyRYixPQSmdwiVWJSV6bj7YPt7B1pE2qkIV+FU/Gwe3IHDwqh72xDuxhE1jtJZJscb9xr02UkVt5PgfDgwPDwIQix2Z166qqixfvoLly1fQ29vN739/K3fc8Wv+8Iff0tw8hSuuuI6LLrocK5ohbo4R8xRO3L94FR8jxiADmR7KAzVHdLwe2UJFmhCCIfd36EWmQ1cR9t/ZJ0zWMCqWY5SfnSsYOcR9xVvN5qFWHtvzDHmeCNXhSoQQDKWH+e43vsKOh9fx3//9My6//Nq3upsuJ4CT9yp0cXFxOQ6SksNvvWNs0LLYgIMgTyhcmgmy5DVBFpKi4Ln8GrTZ87B3toEkoTQ0IdfUTUxCamsbqK1t4KKLrpjYb2xslNbWzWzZsoGWlo20tGzkb3/7M6lUbul8Xl6M6uo6KitrqK6upbq6jqqqWiorqyktrTjoBN1IGexevYeejT1Yhk2sOo/qhdXkVUSPegwiZWFUj0I2kcUTzB3PsmzG++KUl4ZJDCYJFhx4yZUQgsHBfnamt7OrbBu7dm9jx6ZtbEtsoz2+E+tvFpIkUVvWyIzq+cyevoDJ1TPIC+XT5rTT9mQXXU9vpiSbRPfIFEsyazIefvXiKPNHNyEpMtGyMFNXTCFSFjlgHyDnlZYy0/gUH2OSzR7FYlC2UAWIuMHI66wK/rbzMdb0vUJNqJKA5sewDdpG2/n91j/ziTkfQj3M5EsZbcXfcgtKYg8gQNIxC+eQmvxBhH5gTy87XEtq6ofx7rwXJbEbkDELZpOpvxKxN5BCMhNowxsQWgDJSiHZJsITxFF9ICtIwsYrx5k+c5T40FTGRwwkySS/Np/ms5rwRw/iZeaYyNlRHC0A6r7rWhltxb/5pwTGd6MExxlPtNErzyFrVGHrMcy0QaAggJE2iVVG0bwaqVSK559/is997iv7mg9WkJr+cfQ9D6KNbEYoXtJ178otaztB/mpvJhEPVIYEW4Yh6tlXuD2QhohHojJ05GE+Li4uLi4nHkmSmDNnAXPmLODLX/4ODz54L3fe+Rv+9V8/nMshmDGHs846n7POOp9Zs+adkKDZvwcS0gm/0U6lkqxZ8wJPPfUoDzxwDx0d7eTl5XPeeRfzta99jyVLzjyuYL4jZdmy8/jv//4Zn/rUjRQXl/Kv//olqh2N92YiWAhk2E+EOxBbhrYynBlhUl7jxHy7PlrLjrHdBPUAtrBwHEHMF6V9rAPLsUhbGVJWmj3xTlbULmda/mSe6HgWVVYo9BcwT/OxcXALQ+mcBYNA0BitYyA9RFO0gf70AMOZUZJmmrAeZFnlUvyaDxmZhJlkT7wTRVK5vPEi7tp6N32JMfx6Ts4uDESpjzTQHJ1BSItyVfOlXCnEfgUXTXkNIOUKGfS9FaeWY2ELh6gexqN4OLdmGRsHW9kT70STx3IVx5JEY7iRjJ3BX9ZAn19nvh1EC+WEX4GgVTHYnvUyM5s7VtJMkrGyBFQ//pCPtJVmT7yLhJHEETanlM7j2slX4FOPbWVZ0kyxZWgbSTNJob+Apry6/QTuoaGjE4NfS0lJGZ/4xOf52Mc+xzPPPM4dd/6a//mfr/L1r3+BpumTmXfWfM664DwKS4qwHYuENc6oMciW0bUIBCW+isP6CMeEgoXYz0ZFIMjikH8Y65I3FUkC6eSW4DYNtmI6JjFvTjC3TYt1v3mcHQ+v44pP3Mh73vP+t7iHLieKk/tKdHFxcTlGntNSrNUy1NoaPmQEgi7Z4q/eBI22TtFrJgKSLKM0NKE0NB1x+5FIlIULl7Bw4ZKJ1xzHYffunbS0bGT79lb27Glnz55drFu3mu7uDhxnn/BUUFBEaWk5ZWUVlJZWUFJSSjgUZbhllEyfQSQSJRgI09XZQXdbD4uvO4VoefSoxiCvMkrZjHI61naQGkljmxa9bf2krAQ9yQ5eWv0snlINb7nO2PgIIyPDDAz0sWtXGzt2bGN8fAwAWZaprKyhrqGRM+rPpb6hifr6JqZPn03/ukFaH99GwV5PYoAhMUyothTniY3sGCyhYWYlY0MOqbFhuj3jxGpi2KbDSMcYm+7fwsIb5h/UKsKneikLlvDyyFaGIjoJycGHTDwbJ+1VWZ3nYwkCBYm4kWBd/0aKvPkEtJwwqis61aEKdo7tpn18Dw3RQyw9tTL4tt6KkujEijSDrOZE3L5VePxlZBqvAUAyk6ijreCY2KEaHH8JVv5MEnlTkVPdICk4/tJ9tgrCwdf6a/Tup3F8xdjheqTxHbkqgeKFOP5SJGMcJd6Or/ZcFjaeS7w/gSxLBIuCyMoBbnyFQO95Fr3jYZR0P0L1YZQsIVNzEcgefG1/REn1YhXPRMqOYbUPURjqoWNQIy5UNK+OqinIskzV/CokWWLVqqfJZDIsW3befoeyw7Wkp91M2kqDpPx9ltSdIGQJllc7dMYVto1A2CNIWYCAC+scCk5+PdvFxcXlHwa/38/ll1/L5Zdfy+BgP0899QiPP/4Qv/rV//G9732d/PxCzjzzHJYtO48zzjiHaPSdnQKaSMRZvfp5Vq16hlWrnmX9+jVYlkVhYTHnn38JK1a8i0WLTkNV//639Fdf/V76+3v55je/iG1bfPaz/4kkSQetBH49WTsXDvZaMTXPG6XAFyNhpSjxFdGXGiBrGUQ9EQq8MUxh4lF0zqpcyg1TrsGreqgKVaDJGnEjQUgPsqBkDgOpQdrGdnFBzXIGM0NkbYOacCUZO0Pb6C5ahrajKxqOcIh6cgUJWcfAtC1sYTGtYBKfDNzExoEtjJpDhHQ/TXkNlPkr9xMhX7/y7ozKJTRvbWTzYAthTxZZkrFsC4+iM6toGlWhchRZ4dPzbubbL/0vu8b3UOjLpyJYhhDgVX1UlExhENBeV9EaFDK7VQv2isEFvnxCepCR7Cgxbx6TY01UhsppG9lFgT/GZ+d/bGIufLTsGtvDbS1/pCPeCYAiKUwtmMT1k68i4skF6m3ZshFJkqipOXZLF1OWMM5ZRPmK2VyW+jrJB55g9+/v4o8/vpM7vv9bJs2awswzZzL59Cn4Yz7SVpKW0ZcZzvYzNToX5RAFHjNND8/oKXYqJhV2bjS7FYuIUJjzJli2CEdgJA0UXTms/d3JTtbOoki5lZsj3QPc/1+3MtTRy4z3L2P+RWe8tZ1zOaG8va9UFxcXlwMgEKzRMoSFjG9vWIeERLmjslUx2KYY+4nBJwpZlieqiF+PYRh0de2ho2M33d2d9PR07v3cxQsvPENfXw/j42PY9oHtG+QfKkQiEUKhCOFwBL8/gBBir8AsEEJMfP/q169+b6SyjI6MMJ4Yw7CzB+x3JJJHLJZPLFZAY+MkzjvvYurrc6JvdXU9Xu+BKwuUqSqd67sZ3j0yUWWcjWcJlEYIF8XYsXmAmiWnMbStA0t1iHhCuZsFXSFaEWG0e4yh9mGKD2KDIEsyZ1aeyhOJHfQOt1Psi2FbBmTi1FTNYVdhPjvHRpnS34oW30NFajejwf0npl7Vg2EbpK3MAY/xKupoC2q8HStcP7F8S2hBHE8MvW8VmdpLUUdb8W27DSXZBdgIPY9M5Xlkay8BWcUJvnHpnTLWhjawFjtUjdAj2HYW2RgBR6Aku7DDdSAEQg9jxaaiaArhkhCjnaP0tfbji3iJlEf2u+nQe5/D1/pLQMLx5CNZKbw7/4RkjJGtPBclvhM7UAmSTKwqD82n4QsO4tu1m7ZkDVpe3kSIYOnU3BLcxx77G5WVNTQ0NB9kgN6eyunUfPjwTJvnuiR2jUlUheCUUsH8EtcewsXFxeVkpaCgiCuuuI4rrrgOy7JYu/ZFnnjiQR5//CH+9KfbkSSJhoZmZs2ax8yZ85g5cy5Tp8486HzlZEcIQU9PF5s3r+eFF57lhReeZcOGl7Ftm4KCIhYtOo3LL38PixYtpalpyhuEyLeCj33ssyiKwte+9nlSqRT/7/9954j7VR4sQZEVUmYav7ZvflHoL6DZE2E4O0bGziIQTM5roiJURkeii3kls/nAtOsI6rk556RYAwtKZvN8z2r0tI4qK8TNJAtK5nJZ4wV8b+1P8e71mfUqXqrDlXTFexk34piOBexdDZce4pTSeXj32mkV+PI5s2rpUY2HX/Px/xb9K99Z839sHWlDkKtsnlYwhaubL0ORcwLb9ILJ/M8ZX+UvbQ/SOrwdy7EoChRwTs2ZJAIVvML4G4IBU5IgZu8rDijxFzG/ZA6P73majJ0lqAYYyY4R9AR4V+OFxywEG7bJ77fey554J/WRWjRZJWWmebl/I4W+fK5uvgyAl15ayeTJ0wmHD76671AIBHd74zylpwgKCV8gSOrqFdRecTYXbn2ZkYef5/lHnuVPP/wDzvcd6mc2cMb5Z7Po7KX000Wxr4JiX/lB2y93NN6TjvAXT5wOxUQAxY7KhZkgtfaJLWroa+1n1wvtJAYSqLpK6fRS6hbVoPlOTKbM35v6aC3Pdb3I+kef55lf/gV/NMQV37qZRIFNXbTmre6eywnEFYNdXFzekRgSb0i3BRBIWG/B/FnX9YMKxa+y8/ldvHzfOnzFHlLZBMl0glQmwfDAEBknQ8ncQuLxMcbHx0ilkkiSjCRJB/3ILaXMfZYNhfiOFEUlRYRDeYT8YUL+CGJcorC8mKUfWowkH/3ABAuDzLx0Gtuf3sFYV66SeFbFdHqLBhmauZ0dL27k9PdfzIg1hiRLTJL2eZcpmoJwBGbaPOQxphdMpnzORRjtL+GMD6DpPmpqFlBXu4jx8U7yX/4DgZEO/MLhivGdbItvp7ViBQktZ+swmB4m6olQFji076BkZcCx4XVLz4TiRbazKMku/K2/Qs4OY0UaQFKQ0314d92N4y/BLFl0wHaVdB+SnUbouQmzHShHTvagxnciJzpRR7YAYBQvxiycS2o0zab7NzO0axjbtNE8KkXNRUy9YDK6XwfHQu94JOdjF6nP9dETRahe9L4XsfKm5GxwX70hkyBUFCQck6lp6GbK1PlkPVV4Qh4UNXdjYpom9913N1df/d6T4gbzRNMYhcaowPUHdnFxcXn7oarqxGqsz3/+a3R3d/Lss0/wyitrWL9+DX/5yx8wDANVVZk0adqEQDxlynSqqmqIxQpOqve2wcF+Wls3s3XrFrZu3Uxr62a2bdsysSKruLiUU05ZyjXXvI9TTllKQ0PzSdX/13LzzZ/G7w/whS98nFQqybe+9SMU5dB5EACTYk3MKpzG6t51hPUQqqwynBmhLFBMaaAEa3QnFaFSUmaacTPBYGaIybEmLmtcQZ53nwCpyirvmXwF9dFa1vVvIGsbTMlvZnHZfGLePOqjNTzb+QL5vhiarBLSgpQEihjJjjKSHUWLq4ybcUoCxZxVddpxj3N9tJb/Of0rbBpsYSgzQsQTZnrB5DeEPxf7C/ng9OsZSA+RtbMU+QvxKDr9tkWRo7JTNqlyNGSgT7bRkZhn7RPNJUnisoYLCOkBXuhZQ9xMUBwo4LSKxSwpW3DYfiaMJC/2rmXDwBYkYEbhVBaWzqUj3sXu8U5qQlVoewsj/JqPQm+Mdf0bubDuXAKan9WrV3LaacuPeZw6ZYvVWpoSRyEqctdLvlDYrsBgzTROv9DDpLOaGBkdZtvK7Wx6aiO//s7P+dW3f0bznMmcfe75XH3++6mtbTjo72yqoVKQVOjTNbyql5q9q0VPJP3bBlj/541YGQt/ng8ra7Htie2kRlLMumzGMd1bvdWUWjFaf/QUu9a2UH3qFGa99yzGNZOZBdOYXXjyBd65HDuuGOzi4vKOQ0JimqnzsCdJEcqEKDwqOQSERLV9cj6plWQJxVIJKmFiRQUTFQGjgVGiFRFOef/CY2674+VO1v95IwW1+6f+ppQUZsrEtpzDhrkdjFh1jAXX5ZEcSoIkEYj5KRosIt49yM5HX+HFtSsJBPKZNtjEJN8+MTybyKJ6VAL5B/YtfhVZkqkpnoJW0kBpxkRRdFRVRzgWyzbfTWR0J1ZkCsgaISVMbe/zjHc/ysrC00hZabK2wQW1Z5Pvix3yOHawEqFHkDODOL7C3ItCoKT7MAtmoYzvRE715sTWvRYQjr8UdXQcve/5g4rBjhbK2SvYGVC8IGuYhXNBAsk2MAvnYxbOxSg+BSHrbHlwHf2t/UTKIqjeXMBb5ytd6H6NqRdMyVlKpPtwvPufj9DzkJLdIMnYoSrUsW1YkUk5UVgIlGQnVrAKpbAO/+usHp566hGGhwe5/PL3HHKM3gkkDGgZlkiaUOQXNOXlAuVcXFxcXN4elJVVcPXV7+Xqq98LQDabpbV1E6+8soZXXlnDmjWruOOOX03Yc/n9ASorq6msrJnIb6iqqqGysobCwmJCoTA+n/+4hUAhBOPjY/T399DX10t/f+6jr6+XgYFeenq62LathaGhASBXKNDQ0Exz81SWL7+ApqYpTJo0laqq2pNW/D0Q73vfTfj9fv7lXz5EOp3i+9+/5bDWFbqicf2Uq6gOV/JS78sYtsHpFUvYNb6bjYNbKAkUUeQvoDfRT8bJcmHdOZxddfpEde1r8Sg6p1Us4rSK/edhO0bb6UsOsHNsN1tH2qgMlVPoL0CTVc6rOYuSQCFjRpxFkXksLltARajshIxHQPOzsHTuYbeTJIki//6eu0WOyrszYe7xxNmlmDhAgaNwQTbANGv/uZtX9XBh3TmcVbWUlJkmpIeOKGg6Zab41eY7WD+wGb/qRQjYNNRKy/A25pfMwXRMTH+Agb1hwrFkEj3rIWmmyNpZUqNxdu5s41//9ctHPiivo1exSEqCCmf/CVi+UEj6I0wtXMr2wRfwqn5mXDWfq69RGR8dY9Vjz/HUQ4/yk//6Pj/8+n9TXV3HGWecwxlnLGfJkjMmApA3DGzmsT3P0JXoQZNV5hTNoKxmGb69NhcnAiEE7S/txsyY5Ffvm5Nrfo2+ln5G54+SV/X2sbIZGhrkllt+xC9/+UMCwSCf/O+vQGPuHm1W0XQWlc7Df4wV5y4nJ64Y7OLi8o5kqelnq2qwVTEIChkDARKcmfVTY59c//qEEHSs7WTnyl2M94wz0jFKtCxCfn0+julgWw5l049vguqP+VE0BSNl5KpL95Iez1DUWIiiHZ8aJskSwcLgxPeziqbx39d+mzU/eZBgq8Onb/pn+v7Wx9iecXxRH5ZhkY1nqZhVRrT88EvMFphe7vSaZL1+wkLGQWAkdlI3vAtvsG6imrckUoXmZJg93sEGO0kkWMqSsgUsPoIqCSdQTrb8TLztf0UyxxGqHzkzBJKClTcFKTsKEvu8gPciVF9uuwM2mjOodRQv+uDLmLGZCMWbs5mQPSSnfZRs9YqJzeMdvYxsaSHmGSeQccjKVXiCRQRMP71b+qhfWofX58dRg0hmHOHZN8mUrBQoHoQnSqbucvxbfo46sgmheJHsLI63gEz9lQf0/L377juYPHkaU6bMOOw4vZ3ZMQq3tch0JnKrBDQJphcKrp/sEHr7WCG7uLi4uLwGj8fDzJlzmTlzLjfc8GEgF7bW1raNzs72vRkO7XR0tPPcc0+wZ087mUx6vzZkWSYQCBIMhgkGgwSDoYmvdd2D4zg4jo1t5z4MwyCTSZPNZshk0iQSCQYH+8hk9rekCgSCFBeXUFhYQnFxKYsXn86kSVNpappCbW3DW+L3+2Zw1VXvxev18dGP3sDo6Ag//vHvDmshEND8nF97FufVLMMRDluGt/J8z0vURWrwqjlrh4a8WraP7qRtdBfn1izbb39HOHTGu0laKYr9hROBVwB7xju5ZdNt9KcGmZI/iY54J+3jHaTNDMuqTsWn+rCEzfzi2cwvmTXhHXwyMM3yUG9p7FRNLARVtkaeOHjBhk/14TsKK6+X+zeycXAL9ZFqPHstNDJWlvUDm6mL1JCqmsxTFVVIvhACCa9p4tuZZkkqTEQPc/eTtwOwcOGpx3yOPiGjAAbwWgffjCQIComIEqQuNInUSBxLWCiohKMRTrv0TBZdvJgm7wxa1m7hyScf4amnHuHWW3+KpmksWLCEKQtms6doFL0sSJG/EMPO8tDuJxlID/GhGe/dLwjveLANm3hfHF94/7H3BDwk+hMkh1JvCzG4p6eLn/3s+/zud78A4L3v/RCf+tS/H7MFiMvbh3fGu4+Li4vL6yhxVD6civKCnmabahJwJGZbXuaa3v08uE4Gerf0seWhFiRZpmRyCQM7BhnaPUxiKEFRUxE1C6son3V8YnBeZZTi5iK6NnTji/hQdYXUaBrNq1E9v2q/ChQhBLZho2jKcS1vinjCvPvKG/jVr37MpO82UqoX0/7ibuL9cTSvSvX8KmoXVR/RMRYZPjpki9V6mu6cBwIzTYsaU8LzOn/AgmApRYrG5+Z8GDlcgywdodAtSWTqr8LxFaP3PIM6vhPJjOcC2Xb8MXfU7Oi+Cl8AIZCMMaySJW9oTk4P4Gv5JdrIZiRjDDnVhyd+X+5QQmD7S1GHt2Dmz8QJViBlR1Be+SWl2W3Uhbajk8ZMBugzT6fDex6p0Sxm2sQbCmGULsXXdidkfBOewUp8J2b+LKxIE8gqyVmfQet7ATnVg+MrwSxeiB2ufUM/x8fHeOSR+46rwuPtQMaCu7bKdCUk6iICTRakTFjdJ1Hsl7i80bWQcHFxcXmn4PcHmDFjNjNmzH7Dz4QQDA72s2dPO0NDAyQScZLJBPH4+H5fJ5NxEokE4+NjyHIudFVRcp+DwSAFBYV4vT48Hi+BQIDCwhKKioopLi6lqKiEoqISAoHgAXr3zuTii68kHI5y003vYcWKJfzmN/dQX3/4cGZJklAkhaH0CI5wJoTgV4noIboSvTjCmZjTDaaH+MO2v9I6vJ2slSXsCbGkbAEr6pajyRoru1+iLzXI5LxGJEmiPlJNwkjyUt9aXuh9mchea4qX+9fzcv96Pjj9+v3E5BNFwkiyYXAL/akBAlqA6QWTKQkcOCejX+5lk/YKSSlBkV3KdGsWfnHo1XPHwvaRHSgoE0Iw5KqMJSRa5BTGpAVkxzvxD3ahKRpjmkK8polGqxJFVrjttltYuvQsSksP7tl7OBotnSpbo10xqbU1NGBcchiTbM40QuhIFPnKKM/W0p3eTdIaBwGqrFMVbKA8VEPF8lqWL88VVOza1cZTTz3Kk08+zG9+/H+YWYNALEzN7EnUzJlE8aQKNg22sm1kJ1PzD5KNcZTIqozu10mPpoF9grBt2iBLJ71ncHv7Dn784//hD3/4LT6fnw9/+JP80z99lPz8gsPv7PKOwBWDXVxc3rEUCpWLsqGJ5N2TESEEe9Z24DiCWHlu6VKgwE9iIEFiIEnTGQ3ULq457uWCsiIz7cIp+KM+ejb3YmYtYlVRahbVUNRUONGXvtZ+9qzpID6QwBv0UDGngspZ5cjHuI7+ssvezfe//02eeOJBLrjgMoqaCjFSBoquHpUthReZ6zJhFpk+OhULj5CYokwl4imEVC9OsHLfuab6sIMVqMGKN1TxHhZZxag4Cys2ncDLX0dxLOxgFUJSUOK7kbOjaAMv50RVWUNO9eL4ijHKTt+/HSHwbr8DbXAtdqgeEWnC9hTi6XocoYfJli5BqF60gZeQzDGSsz6Hp+NR/MlH0YrjOKhYchhdxKl27iedlukPn4svkptsZqvORzbG0HtXoqY2g+LFLJhNetL7J8Lv7FA1dqj6sKf8t7/9mWw2y6WXXn10Y/U2Y8cY7IlLVIcFrxbC+zXI9wjW9kmcXyvwu7MiFxcXl3c8kiRRWFhMYWHxW92Vk4KB1CDDmVHCnhAl/qLjmnOeccZyHnhgJe9737u48MJT+clPbueMMw7uLZsyU+wYa8dycgHKEmDaJtpr7A7iRpLGWN2EEGw7Nre33M2Ggc1UhsrxBXwMZ0Z4YNejBLUAZ1efzo7RdiJ6aOJcJEnCEhYD6WHyvTGa8nKZC6ZjsXV4Byu7XuKi+nOP+bwPRH9qkF9vvoO2kZ2AhIPgsT1P855JlzOzcOp+225QX+ZO/6/pl/tyi9CQaLam8v7UR8h3Ck9ovzRFx8F5w+u2cOgNBYmGi6mwVTqcLjJ2llo5hBOrIEsxLes2smbNKn7xi7uOqw8eJN6dCXOnd5zdiokN+ITEEsPHsmzOikCWFJqjMynylTNmDiEhEdULiOr5b7hGX81lueF9H+bzT32V7i27GNvSS/vLLWx+/CUA/EVhhue1cNEZlzJ//iIaGyfvzVc5NmRFpmJWOVsebCE9lsYb9mKbNmNdY0QrouTXHtqe7q0gmUzw1FOP8Ne//okHHriH/PxCPvOZ/8d73/shQqETZ6Hh8vbAve1xcXFxeQtxbIfkUBJPYN/TeVVXiZZHc9W5HvWE+cbpfp1J5zRTf1odtmGjB3RkRcZ0TLoSvfS19jH46CCSJeENeUgMJNh8/2ay4xmaljUe/gAHoLFxEjNmzOHuu+/gggsuQ5IlPEHP4Xc8ADISjbZO46spwJqPbNUFeLffgTq2DUcLIWdHEaqPbPWFB7RDOFK0wZdRUj1Y0Um5FyQFO9qEZKVwfAUgKUiOiVl8CpnqC7DDdfv3NdmFNrwRO1CJ0HJVHXJ2CKF6c1YOegShBbC0EOrYVrTBl9F6nkGXRvCFfHT1xVBVUBQ/mj1IqfoSwbnXoHr2vm2rXtLNN5CtODvnH6wFscMNcAAvvcNxzz13sGTJGZSVVRzzeL0dyFgStiPQXzfv9yiQsSUMG1cMdnFxcXF5UxEIOmWLXsXCJ2QaLR3PW7RiLW1l+HPbg6zte4WEmcSv+phROJXLGy8kpB97RXN9fRMPPLCSm2++nuuuu4gvf/m/uPHGj71hPrtxcAt3b7+f7kQvQgjCehCBRNvYTipDFXgUDwOpQZAEi0vnT+y3Y6ydbSM7qAlXTniYFvkLyNoGK7tf4rSKxUS9YbqTvfsdbzgzguXY+1lCaLJKRA+xfmDTAcVgwzZ5ZWAjW4a2AYLJsSZmFU3HcwRzzIfaH2frcBtNefVosoYQgl3ju7ln+/00Rmsn+p6UEvzJdzuj0giTrKlISBgYbFE38LDnft6Tfv8Rj/2RMDW/mZVdLzKSGZsI4xvOjOBRdGLhMuLIVIXLqQyVIRDIkswOxSBhOvzud7+kqKiEc8656Lj7UWNrfCqZxzbVJCk5lDgqNba63wpOWZLJ9xaR7z1wNfXrkZAoCOaTnJRi1sIFnP7+i0kMjbF783Za12+ge9cePv/wx7Btm2AgzIyps5m38BRmz1nArFnzKC4uPapzqJpXSWokTdeGbpLDw8iKTF5llKkrpuybs7/FDA0N8PDD9/PQQ3/h2WcfJ5vNMmXKdL761e9xzTXvw+c7cosRl3cWJ8cV6uLi4vIPiqzIBGIBhnePEIjtM+W3DAtJlvAeo3B6KDSvhubNVVxsGdrKfTsfpmOsi4GdQ4R9Qc6KLiUm5UEeJIeS7FnbScWcCvzRY5ssXH75e/j617/AyMgweXkn9il5tvJcHD2K3v00SqoHs2g+2fIzsfJnHVe7UqoXOdmNnupBckwcTz5WpAHhieCEa0nM+CSSYyG0UC6g7fX7W0kkK4Pw7as6ko0xhOJDEg6SYyAIIDkmkhFHGd2GZCaQnSzhwiiWYjM2LGNZEpqsU1xqEpuk7V/HIUk4wQqc4KFFXMmIo4y3ARJ2pHFCnAbo7u5k5cqn+O53f3Fc4/V2oDwoCHtkBjOCwr2XshAwmIHJMUHY9Qx2cXFxcXkTySK42xtntZYmKQkUoMrWeHcmTM1bEG784K7HeHzP0xT5CqgNVxE3EjzbtQqAG6ZcfVzFCOFwhFtvvZdvfOPf+fKX/5VNm17hG9/4wYRtRn9qkDta72EsM0ZdpBpFUuhN9WPZJoX+AgbTw5iOSZ43yllVS6mP1mDYJrqiMW7EMWzjDT65IT1A3EiQttLML57N5sFWBlJDFPhi2MKmLzWIV/VQ+LrQNgcHWXrjw3TDNrm95Y+80LM2F/qLxPPdq5lfMpv3Trl6QhDePrKT57pfZNfYbmLePBaWzGFyrInNg60U+QsnPGolSaIyVM6u8T3sGGtnesEUANrUrfQq3dRaDRNCqI5OgVPEem0Nl2auOqF2ETMKpnBm5RKe6VxFb6oPgcCv+llWdSoxbzk7SWAhUCUJCQkLgYGgoH+cb999Ox/4wM1o2om5Xr3IzLBO3L2OJEksKptP29gu+lODFPhiqBEv6vQo5y26hndZF7DzyZ1sa99MW38Lu/va+N2tv+QHP/w2AKWl5cycOZempsk0NDTT0DCJ+vqmg1bNKprClPMnUTWvgsRAEtWjEqvOQ9GOLZT7RJDNZtm+vYWVK5/ioYf+yurVzwMwf/5iPv/5r3HuuRdRXV13mFZc/hFwxWAXFxeXtxBJkqicU8HwnhHG++L483zYhs14X5yCunzy6/LftGN3J3r5XcsfGcmMUqjkk06nGPaN8LDzFJfLKyiQYvjz/AzvHiYxkDhmMfiSS67iP//zszzwwD1cd92NJ/YkJBmzZBFmyaKcsnewGxfHBMcGxXPwbSa2tdEG16IkO3C8BSCpKIkO5MwgjjeGUb4MVD+Hcph1/KU43jzkzABOoAJsAxDIxgiOvwyheFGHNqAkdiOnB/GZvwMri5QeQhES+flFxPI1nGwKRSRx8qsx9dBRD4/W8xzenX9CSfUCEnagjEz9VZjFCwG499678Hg8XHDBpUfd9tuNkgCcWubwULtEwshZRIxmJUI6nFVlcxz22C4uLi4uLoflKT3JU3qKEkehwpExgHbF5E7vOJ9K5uHl+MJ8j4ax7Dgv9rxMvjdGvi/3oD7PGwVgw8Bm+lIDB/W2PVIUReE//uNbTJkync997qOsXfsiP/7xbcyYMZuNg1sYSA0yaa+nL0BZoISEmaQxr5azqk7PWUiMtrO67xUe2/MsEU+IU8sXUh2qwKd6SZjJ/SqYR7PjlAaKCWh+5hXPojfZz9Odq9g60oYkSVSHKwh7gsSNBIG9VbkZK0vCSHJB7RsDdDcMbuaF3rWUB0sntk+ZKdb0rmNGwRQWls5ly9BWfr3pTkayY0Q9YYbSw2wdbuP0isXYwsH7urAyWZIRwsF29j3et7AQOCjsLyCqqJiSiYV1XL+H16PIClc0XczMwmnsGGsHoCFaS0O0loQtWG1n2aoY5O8NrRuWbRosnce/+hUkSeKf/umjJ7Q/J5pFpfMYzozwTOcqto3sQJVV6iLVnB9ZRve9Pfh9fpaceganSmeSTWSJDyQoPbWAXrODV15Zw4YN6/jjH2+np6dzos3i4lIaGpqpr2/aKxLnvi4vr0KWZUJFIUJFRz9PP16Gh4fYvHk9W7ZsmPi8bVsLlmXh8XhYuvQsvvOdn7J8+QUUFBzf37PLOw9XDHZxcXF5iymdVoKRMnLhan0JFE2mdGoJk89pPipf3aNlXf8G+lMDTM5rwjZtvLKXEstLt9rHVmcHBUoMK2uh6AracSx1KioqYenSs7jnnjtPvBj8Wg5UoWuM4+l4GK33eSTHwIpOIlt1Hnbk4LYX6mgrSqofx1eCZKUQuhehh5GTXQjFi1H6xrC41yP0MNmK5fja7kLpewE51Yec6Uc2xkHWUPtXo6S6wbEQigc53Y9sJgCBkuzMicbefBRJRnjCGCVLcPxHt3RNGd2Gf+ut4Ji5UDkESnw3/tZfk/AXY4dquOeeOzjnnIuO2ifMsRzGe8cRQKgodETXqe3A+kGJjYM5y4bmPMHcYkHo71iRe3G9oMAHq3okxrIwv1hwWoXDpJPP1s3FxcXF5S1EIBiXcs6qUSEfd/iwheAFLUNQSET3imweoNbW2K2YbFPNE1oheTjiRoKUlab4dVWyQT3IYGaYsez4cYvBr3L55dcya9Z8PvKR67joolP5t3/7KiVn5UK8Xl997FW8jGbHqY1U8bddj/HArkfwqX7CepCRzBh/2PpnLq4/n+mFU1jVs4YiXwF+1cdQZgRbWJxeuRh1b3bCJQ3ns6B0Dh3xLjRZoyFSyws9a7h/16O0DG9D2lv5OrtoOkvKFr6h39uGd+AIMSEEAzlrB0miZXg780tm8+jupxk34kzKa5g4l/7UIKt711EaKKJ1dAdRT2TiZ33JAWLeGNXhfVkXNXYdURGjX+6lxMkFRgsE/XIv88xFhMSJ93KVJZnmWAPNsYb9Xg8L+KdUlCf1JOu1XOjKuZkAhS+1cO3tv+IrX/numyoqDkgWL+hpWlUDvyMzx/Iyz/SiHcXfnyIrXFx/HotK59GV6MWj6tRHaul4voM9iSwFdfs8hz1BD8nhFAwprHj3u1ix4l0T7SQScXbu3E5b21ba2rayY8dW1qxZxR/+8FsymQwAXq+X4uIyCgoKicUKKCgooqCgkPz8wr2v5T4XFBQRjcYmAihlWT5o5b0QgmQywdDQAENDg2/4PDw8QH9/H1u3bpkQrL1eH5MnT2POnIVcd90HmTp1JlOnzsDvP/EBhC7vHFwx2MXFxeUtRpIkahZWUza9lORQCtWjECwMHnJ5noHgRS3Ny1qGtCSYZOksNn0UOUf+b30gPYQu60iShKqrBAuCjHSOomgKo+oYlmEz1jtOQV0+kfLI4Rs8BJdf/m4+/vEPsGfPLqqqao+rrSPGMfG3/AKtdxWOJwayht7zLOrYNpIzPvUGn99XkZNdIIFZshh1eCNKshuEjdBDOIEy7NCR9T9btQI5PUhg0w/BymCHarAUL0piD9rQKwg9D8dfhGTEEaoPyxNDNuMIJNRUN5KdxcqbjFF2OunG9xy+ovl1aP2rkYwxrNi0idfscD3qyCa0/jW8tK2PlpZNfOELXz+qdofah9n6+DbGe8YRAoKFQRrPqKdk0sGDeBwB97RJPLFHwhYSiiRY3Suxtk/ixukOkb/T/a8qw2kVgqXlAkeA8vcrwnJxcXFxeZvQI1s8pCdo0QwEUGdpnGcEqLWP/emlgSAtO/jE/m88GmADKemNgV5vJlFPhLAeZDQ7vp/dwlh2nKAWIN+Xd0KPV1/fxH33Pcu3vvUlvva1zzP5gZkUv2c6ZmRfWJwQgoSZoCZcxbgR59muFwjpIUoDuflFxBOmJ9nHyu4X+djsDxLUgqzpXUdbopeA5ufsqtM5pWTufsctDRRP7A9wdvXp1EaraBnaTsbKUh2pYHrBFHyq9w19zonF+68DEwiynhSjvgH6zD464l0U+vYPNCvwxdg2uoMz8pcwYozTMrKNgBogY2fwKB5W1C2f8OoFyHcKOTtzAX/1/pFtagte4SMhxSlxSlmeWXHcDyKOlnyhcEU2zKXZ3LnLjuCiL/wLkydP44YbPvymHbdftviFb5RdiklQyJiKYKOWpV0xuToTQj7KcSj0F+xnCeLYTu53+rr5tKLKmNk3Vl8HgyFmzJjDjBlz9nvdtm26uvawY8c2duzYRl9fD4ODAwwNDdDauonBwQEGB/tJp1OH7eOrovA+gVjGcWwMw3jDtpFIlFisYK/oXMjll7+bqVNnMmXKDOrqGlGUt86awuXtiSsGu7i4uJwk6H4d3X/4Gw0bwe+946zU02hIaALaPAab1CwfTEcpPkJBuMhXiGEbCCGQJIlYdR5mxqR7vBdlRGY8O0asKo8p509GPk7V7PzzLyUS+Rd+85uf8aUvfeu42jpS1OFNaANrsSMNCDVX1eF4C9EGXiSw/rsYpUuxw7WY+TNz9hF7EaofBDlrCTt3IyghI1kZJDORs3w4wE3DG5AVQGBFGrEjTQhZA0nCTg/gbf8zVqQBx1+MNrAWoQVBOGAlMUtOxcoOA4LE3C9h500C6ejHX84MIpTXqaySBJKKlB3h5z+/m7q6Bs4888jTs5NDSTb+ZROpkRShohCSLBHvT7Dpvs14gx6iFdED7rdjDJ7ulIl5BXne3M2FacPmIYnnuyXOrz2U6caJR5JAcW0hXFxcXFxex5hk8xvfGDsUg2JHRQbWaRm2qwbTTA9Dik3YUZhreplteVCOUKDyIVFmq7S8Zvk9wLjk4BPSEc/dThRBPcDisgX8ecffAIjoYeJmguHMCGdXnU6B78TblHk8Hr785W+zfPkFfPwT76ftP/5A35W7mXPeUnRVoy89QLG/iFNK5zKYzlUnVwT3XxWV54nSnewlY2VZUDKbluFWBjKQtbKs7H4RW1hc1rhiwqf39UiSREO0jpAeYm3vetb3b6Ij3sWcopnURqr227Y51sDTnc8TNxKE9CBpLcn6klXsCewgm99Jv7qdvtIxSnv3LxKwHAsZmbpIDXOKZvJSz1p2je8h5s1jXvFMpuZPekO/zsleSLFTymptFSPyELVWA4vMpVTY1cc56seOuvfa/sUtP2TdutXce+8TqOqbd50+p6XZqZpMsvSJv6tRyWaVnma+6aXhOB7GAIRLwrng7LSJ5stdH47tYKRNCuqP/HpXFIWqqlqqqmoPOYdOpZIMDQ1MCMWjo8PYtoMQAsdxEGLf17nvc1/LskxeXoz8/ELy8wvIz89VHZ8on2YXl1dxxWAXFxeXtxltislLepoSRyW8t8LERtCqGKzU0rwre2SeVbOLp/Nc9wtsH91JWbAESZJIlqdpLK7noti5VOSVk18TOyFpuIFAkPe85wPcfvstfPrTX5wIEHkzURKd4NgTQjBCoIxvRxlvR4m3I6f7QVIwixeSnPIh2LudFZuG7S/C0/UUkBOQcUzk7DCSEcfT9TjZ6hVH1Ac53YdQ/YjXpE47egRH9YNjgKSAJCHZWaT0ALKVRB3ZjNBCWJE67GjzMQnBAHawGr33uZzI/GobwgZhsTuu88AD9/LVr34PWT7y9vu2DpAYTJJfE0Paa7IbrYgwtGuYns29BxWDd45KpC2oeXWlY8ZA7x6gaNhg45iXs0tjE6GGLi4uLi4ubxUb1Cy7FJMmW58QwyRH8ISeZqti0GTr7JJNNmgZurIBLs4Gj6hyU0LiDCNAu8+kTTaICYWMJBiTbJYYPmrsv/9t+Tk1ZwDwfPdLDKQH8Wt+VtQu57zas97U4y5efDoPP7qamz/3Pp797cO0P72R5uuWsOyU5VxSfx4VoTL6U4N4FS8pM43nNQ+2U1YKr+pBkSRua/kTvckBJuc1oskaw5lRHt3zNEX+Qs6oPLil186x3fx60x10J3vxKh6ytsHz3au5pvldzC+ZNbHd9ILJLC6bz6ru1XSmutg55RVGY73USPU0a5MZVYYYnrSbeCpFOBPGp3qxHZv28Q4qw+XUR2rwqh4uazz8nFFCYpY5j1nmvOMa2xOFEIKOeBePPPk3/vMrn+PGD32MhQtPfVOPuUnNEnWU/R6wRIVCLxa7FfO4xeCChnxKp5bQtbEbVVeRZAkjZZBfE6N8Rvnxdv8N+P0B/P4AlZU1J7xtF5cTgSsGu7i4uLzN2KOYZGBCCAZQyHnQbVGzRywGlwaKuWHKNfx1x0N0xLsAaMir5aL6c5kUO7in7rHygQ/8Mz//+f/y+9//lg984OYT3v7rEVoAEBNiqGSMoo1uRUJgByqxYtOQzCRa7/N49noJAwhPFLN4EZ7Ox0FSkLPDIOtY0UkITwS95xmyleftrfw9NHawGm1w3f7hdpKC4y1EEg44FghQRrcjCQNHjyBnhpHiu7AD5bl97Cx6/0uog+tBOFj50zGKTwH10IF+Zskp6D3PoI5swfaXAQIl1Y0dquWnD6whFApz1VXXH9WYpkfTSLI0IQRDrspG9agkhw6+HO7VUxcCpJFx5Oc3wtAYQRNUDVaPR5lx6XSCBa63mYuLi4vLW0efbCGxryoSYLdiYSAIColKJ/fgclCyeUZPMd/0UXaEVb0zLA/vS0d5Sk/SqVgEhcSZRohlWf/f3QoAQJM1VtQt54zKJYxkRgl7QoSPIaz2aLEciwe6HyN6RROnzgyz/jdPsO4bD+CcP8IV38gJp4W+fKYXTubZzhdQZZWgFiBhJulN9bO0fBED6WE6493URWrQ9noE5/vySJhJnu9ezekViw9otyaE4G87H6U31c+kvMa9gW6C3fEO7t/5MFPzm3K+wHvH57KGC7Eci4dSDzEa7afKqmdmdAYB/ATtIMlAipGGJB0vdOE4Dki5ILyrmy7Fq/79PKBPJGkrze+3/oVnNz7DY//xOwomlxM8v5ruRC9lwZLD7p8wkmwdaSNrG5QHS6gKVRzS+u5VdCTsA9hyCPb/ezxWFFVh+sVTiVXn0bOlF8dyKGwsoGJmOb7IEaz4c3F5h+GKwS4uLi5vM14NURCI/W4eLEngPcrJUnOsgU9Fb6In2QfkBOJXgzdONOXllaxY8S5uueVHvO99Nx1VReqxYObPwPGXoIy1YYfrkNMDSOY4QvZiB3NLAYUWQKg+tP6XJsRgACdQhhWdhOMtQBImjhZBePKQMwM5qwjHPCIx2Cxdgt63CnW0JSfuOjZKqhuzZBFmbDr6wOpcZbBj4OhBhBoECexABZIxijK8GU/PM+g9z+6t7pXQe1eiDa4jOfUjhxSEHX8Jqen/jHfXX1BHtwISRsmpDBWeze2/P53rr//gUVdo+/N8CEcgHDEhCAshMLPmIYXcxqggqMFgyqF4TQvS0Bhmfh4pS6YmajHUPsy2J7Yz+8qZR3TD4OLi4uLi8mYQEgrOXglKQsJCMCjbqEj42fe+ny9ktso2uxXziMVgyAnC0yydpCTwCAn9LRCBX09A8+8XkvZms3VkBy90r6E0UEzTgnrmzz2FNfc9xfN3PMS5y+bzja98j8suezfvaliBYRlsHmqlM9GNX/WxoGQOlzVcwPqBzThCTAjBr+JXvcSNOJaw0KQ3rjgaygyzc2w3Jf5i5L2rpiRJojxQSnu8g93xTibHmgAwHZM/bfsLq3rWIJU4KKrM8Mgor5ibmFs0E6/qoUAqJFwa4epZ72YsmSSo+ZmS30zEc/Shb0KIif6cCCzHwhECXTm6lVePtD/Fk21Ps/p79xGIhLnws+9j29hO7tp6Dx+f/aFD3idsGdrK77f+mZ5kH45wCGgBTimdx+WNF6IrGo5w6En2YToWpYFiPK9ZOTfP9PJ7b5wUDn5kBIJu2SZPKDRZJyZtWPWoVC+oonpB1eE3dnF5h+OKwS4uLi5vM5otnTwh0yVblDsqEhIJySGNYK556GrRA6HKKpWhE7886kB88IMf56KLlvLoow9w7rkXHX+Djok6vAkl1Yejh7BiMxB7q1qEt4DUpPfj2/Zb1LGtyMlucBysWDN2sBKEQM4MoiQ6kYw4Wv/qvf7BOra/DKGHc6Fx+t6QDyGQM0OYRXP38xg+FHaohtTUm/Ds+jNqvB2QMYtPIVN3OXaommztpYRW/gtSqA5J8YBjIDxRbH8Z6tg2PF2Po/e9iB2syvkKA1hptP4X0QvnYJSdcejjh+tJzvgUUnYYkBCePG6/5f9IpZK8//1HX51dPLmYPWs6GN4zTLAwhCRBYjCBP+qjdHrpQferCcPyaofH1iUY7x7DDoRwbJmSAFTlKThakMGdQ6SGUwTy3epgFxcXF5e3hhmWhyeFwi7FpNLWAEEKgYKgxN4nBjuAhDimm2kZiZB460Xgt4r2sT0YjklIz81rZEVhwaVnEZpZRtsfXuSjH30fv/71T/n857/KBxddz+7xTkazY+R5o1SFypElmZJAEbqikzCSBPXcvEEIwUh2lNlFM1ClA/9mckUU0oTw+ioHSi5oHd7OS33rqAiWkdYD9Ktt+P0BBlPDdCa6aYjWEpfGKHeqmFswG6Xg2AK8OuJdPNO5ipbh7fhVHwtL57CkbOExVxaPG3Ge3PMca/rXY9kWk/MbWVa5lIpQGRaCDWqWjWoWQxI02TpzTS/BvasN01aaZ9tW8vJ3HyQ1NM57vvNJ8qIxvJaPttF2do7tpimv/oDHHcuOc2frPQymh6mP1KBICqPZMZ7oeIayYDF1kWrubfsbO0Z3YTs2xYEizqtZxvyS2QAsMXzsVEzWaRlycW6CiFC4KBOk9O/sqe3i8o+A+1fl4uLi8jZACIFjO8iKTKmjckkmyJ+9CVoVc+/NiMQiw8spxsm9zGnu3IXMnbuQX/ziB8ctBkvZEfxbfo42tD7nhUvOliE15YPYkZzNhVUwm0S4HnWkBXV0K972v2AHKwAJdbQVdbQFKTOMFKwisOF7GMWnkJr8QexIA0bxQjxdTyL0MYTiQ84MIPQw2Yrl+3wPXsU2UMe2g2Nih2oQnujEj6zYNKzoZNSh9WhD65GsNOrAOoSs4QTKcPzFKMLGCr8mgGTvTYqc7AacfUIw7K0GltB6VmLGZiC8scMMlITw5oIxbNvmllt+xEUXXUF5eeVRj7k/6mPGZdPZ9sR2RrvGwBFEK6I0nlZPpPTgVTCSBCtqBSUJg5dfcJDyZPICgmI/aDKYmoKTyGKb9lH3ycXFxcXF5URR6qi8Ox3mXm+CXYqJAEochYwkExT7VmZ1KhYFjkrjCapY/EdClRUETAQYv4on5ueyz9/I7EQ93/jGv3PFFcs5/fTl/Nu/fYXZM+fu10Z9pIaZhVN5sXcteZ4oHkVnKDNCUA9yeuWBLSIAYt48GvJqeblvPRFPaMImojPRTWmgmJrwvorR3eOdGLZJUAvgS3jJj5fQF+kEW6U33Ys/pmFKJkuNZSgcmxDcGe/mZxt+S0+yjzxPhHg2zl2t97JnvJP3Trka5QhWob2WjJXl1s13sW5gE3l6BFVWeLrzedpGd/Hhme/juXw/T+spBCADq7UMr6gZ3p+OEBYKu7vaeeDLvyYzmuTKr95MfmXOFsKreDFsg6R5cEuwluFt9Kb6aYzUTfQ7zxtlzIjzdMfzPNO5it3jnVQES1Flld5kP3e03k1QDzA51oQPmfelIyw0vXQqFrqQmGzpVDhupoSLy5uBKwa7uLi4nOQMtA2yZ00HYz1jeIJeKmaXs2hWObW2TquaxQAqHZVmSz8hnlpvNh/84Me56aZr2bTpFaZNm3XM7Xjb/4revxoz0pALf3Ms1LFt+LbeSmLul2Dv0jOhhzGLF2IWzQfHwtP1KNLYDtTRVgRgR+oxi05BMkbw7vgTcrqfbNX5pOuvwvGVovc+i2SlMQvmkK06Fyt/5n79UEZb8W27DXW8HYSF4y0gW70i5yu892ZEHdmMv/VXyOk+kHVwDDzdT5CaehNmyWLUrb9BHt+JkhlEMpOAgxOowAlWQqJ938GEQE52oY5sRkl0oCY6MApmkam/AuEtOOyYPfLI/ezevZOf/OS2Yx73vIooC66bR2IwgXAEwYIgsnp4yw9JghkNQRKVXoxkknBwn3icHEoSLAy6VcEuLi4uLm85My0vjQmdXaqJDcQciXu8CbaoBja5h5YxIfOuTIioODYR8B+ZprwGQlqAgfQgRf5CAJJmCsMxmF00jdPmLGbS3Bn86o8/4w8/+xXnn7+Ic867kC/829doapoCgCIrXDv5Cor9hazuW0fWNpgSa2ZZ1dIJm4cDIUkSK2rPpj81QOvwdjRZwxQW+d4ol9Sfj0/dV1Sh7hU0hRAoKMzcvZjNFavZ5dlO3D+KhwbOTV/EIuO0Yx6LZ7teoCfZx+S8xgkBO24kWNO3nsVlC2iONRxVe5uHWtk01Ep9uGaisrjAl0/r8Hb+PLqR1tLpFDjKxHVrINikZnlJy1C/tYcbrrkYO2Ww9ItXUtK4TxgfyY4S1kMU7/19HYi0lQF4g4DtVTy0j3cgSxKN0fqJca2NVNE60sYLPWsnfmc6EjMtLzOtozptFxeXY8AVg11cXFxOYvq29rP+3o2YaRNv2Eu8L86m+zaTHkkxaXkzZcbb79/4BRdcRnl5Fb/85Q/5/vdvOaY2crYOL2H7inJCMICsYoXrUMd3oY5txYpNf91OMunm67GiTfi2/gYl0Y4Vm44drEJO96MNb0RKD+Bpvw91rA0zfwapqTeTrVmBZBsI1f+GiuBcdfIvUJLdWKFakDXkVDfetrtwvAV7BWgT744/IGeHsfKm5doQAnVkM97tvycx61/xtN+Hp+uxXKCcpIAk4XjycfwlCFlHyo4iPFHkdB96/4tIZhIrbzpC1vF0Po6cGSI5618Pa1/x85//L/PnL2bWrONLq5ZkiVDR0YfM6H6dusW1bHmoheE9I+g+jWzSQPOq1J1ai6K5N9UuLi4uLm89fmSmWvveUz+UirJFNehRLHxCYrLlcZeuHyM14UrOrVnGQ+1P0DK8DYFAkzVOKZ3LgpK5bBnaym0tf6K/bJA5X76QrlXbeeEvKznzzNm8613v5qabPsXUqTMJaH4uaTifc2uWYTgGQS0w4QN8KKrDlXxs1o283L+RnmQfMW+UWYXTqAiV7bddc14jIS1Af3qQIl8BfiNEc+t8NJHHhZOWc4F8HiFx9N7AryKEoGV4G3meyH6VzCE9SGeih85E91GLwT3JPmzH3s9iQpZkQnqQ9c4IArHfAwwdCT8y9z3/OE9+4GYikSjf++mtPDjyDDvHdpPniZKyUsSNBMuqTqM0UHzQY5cGitFkjYSZJKjts+4YzY4R80YZy45PCMET56oFJnJLXFxc/r6472AuLi4uJynCEexa1Y6Vsciv2WcFkBpJ0bGui4rZFYcM7TpZUVWVD3zgZr71rf/gc5/7CqWlx+BX7GSRbBNHe935yzo4JpKVPfB+soZZeipKuh/ZGMOKNiNZKdSRzSBsHE8ewhPDCtejDbyMp+MRMg1XIeQDL1HTBtehJDuxopNyIi7gBCtRR1vReldiFs1Hie9GTXRgBypzYXHGOMpYG2qiA3V4M0LRwc5ixmaA4kXICo63CCXVhTK6DaNsGXr3E0jJTpTxNiRzHCs2HSvaCFLOb1cb2YQ6vBmrcM5Bh2zVqmd48cXnuOWWPxz9eJ9AKudWoAd1utZ1kRhKkl8bo2J2BYUNh69sdnFxcXFxeSvwIjPH8oJbsXjcSJLEeTXLaMqrZ+tIG5ZjUR2uZEp+E7bjcE/bA4xkRmjOa0CWZBpXNFCysB7tlRSr7nmau+++g1NPXcaHP/wJzjzzXLyqBy9H56+b74uxvPr0Q25TE67k7KrTuKftAfbEO/GrPjyKh2Wly7g0eikecXwWIZIk4Vd9jGbG9nvdEQ4g8BxhPsVr8apeQLzBgiNrZwkoPhKv296xLDZ9479p+a8fcOqSM/jJT24jFiugqK+UpzpW0pcaIKQHWV59Bsuqlh4y3K4xWsfsoum80LOWiB5CV3SGMsPEvHnML5nNw7ufwHLs/QThhJlkemDKUZ+ni4vL8eOKwS4uLi4nKdlElnh/An/e/qFwvqiPofZhEv3xt6UYDHDddTfyox/9Fz/4wbf45jd/eNT7Cz0PO1SJOtyCpUcnKnbldD/CE8MOHTol2IrUg+JBMsaQjTEkM4XjyctV7/pLQPHgeGJo/S+QqbscDuLZJhnje7/Y/+dCDaCk3ljpIJlJtP6XkLPDIOtIwsHT8RBydpRM9Yp9Vc6AjY0a38345BsxC2ejDm3Cv/12zFAdVmwavFr9onpBOCjp/oPeowoh+Na3vsT06bM577xLDjk2bzaSJFEyqZiSSQevLnFxcXFxcXF55yJJEvXRGuqjNfu93ja6ja54N5WhiokqX1VWKQuXkl1i8ODHX+L5xx/nZz/7PtdffwkNDc186EOf4PLLr8XnO/oQ5UOxpm89a/rWYwkbw85VHq+oO4fzas5ElU+MjLKwdC53tN7NuBEnrIdwhMPu8U4K/QVMjjUedXtT85uJeWPsiXdSESpDRmYoMwzA6b4qnhQyQ5JNvlAYbN3K3276OP3rN3LFv32B7938RRQlN5+dXzKbOUUzSJhJvKoXj3J44VuRFa6ddDllgRJe6n2ZrJ1lfvFsllWdSrG/iNbhbbSN7qT8NZ7BAc3PKaVzD9u2i4vLiccVg11cXFxOUhRdQdGUN4Rq2aaNrMgonrfvv/BQKMzNN/8r3/72l7jppk9RXV13dA3ICpnqCwnEO1BHW3A8eUhmAknYZGrfheMrOuTuVt5UsmWn4+l8AjkziGTGkRE4viLsYNXEMSTH4sAZ0zkcXzEggW1MeBQjBJIxhlW0AAA7VI0VrEQd35Xz/M2O4HgLkbPD2IEyrHA9ns5HUBKd2NF9PneSY+dEaMWDVTAbq2A2SrIDdXjLPiEYwDEBCUePHLSfTzzxEKtXP8/tt993yKoOFxcXFxeXdxoCQbti0S2beIRMs60TEoe3E3D5+2M7DrZwUF73kF2RFBzhIClwySVXcfHFV7J69Sp+9rPv8bnP/TPf+taXuOaaG7jqqusnfIWPh7bRndzZejeGbTIprwFHOHTEu3mhZzULS2ZT6D8xq5kWly1gz3gXq/vW0Z3sBSDmizJ38iTawi0knFKq7FqkvZkgQgjsls1YmzdAOo1SV486ex5SIBc0XBoo5sqmS7i37X62j+wECYJagHOql3FhZCqSkebBTA/Pfu8n7PzhL/FXVfCBh+/nC1OXobD/34QiK0Q8R2eD4df8rKhbzrk1Z2I5Nh5FZzQ7zqqeNUiSjGEbtI3tIqKFKAkWc17NskN6PLu4uLx5vH2VBBcXF5d3OJpXo2xqCduf2YEWUDELMqQ846RH0uQ1FxCtPHafspOB97//Zn7xix/w3e9+jf/9318d9f5WwWySMz6O3vUE6tgO7GgTRulpGCWnHn5nWSXddANWdDKersfwmOM4vmLM2DRQvCBs5MwA2coL4BDVH2bBTKzoZNThjTj+UoSsoaR7EZ4YRtneQBFZI1t5Ptq6b6ENvgx2FslM4PgKsfIm5ywohEAbWIPQgjj+YhAOcqqHbMXZCE904nhG2emoI1uQk504vhIkO4sS34UVqcfKn3HAPjqOw7e//WUWLjyV009fzsieEUY6RwGJWFWUSHnEFYhdXFxcXN6RGAj+5I3zkpYmJTlISJQ6KldlQkyxjn4ZvsubS3W4gkJfPr3JvgkPXyEEfal+puRPIubNA3KVxQsWLGbBgsW0t+/gllv+jzvv/DU//vH/MGvWPK688nouvfRq8vJihzrcQVnd+wrjRmK/6tyGaC2tw9vZMLiFs6qOPTTutXgUneunXMnisvl0JroZ00Z4pfw5Hgvdg4WNX/iZa5zCVen34hVejAfvw3jwPkQ2jSQrGM8+ibr6Rbwf+DByJArA/JJZNERr2T66A9OxqAlXUhYowTAMen95C0/+7zfJZDKc9dF/5sZPfJb5ahQvJ/bhiCqrqLLKUHqYn2/8HW0jO/GqXryqh6xtML1wCjdMuQaf5j18Y8eBEILtozvZOtKG7djURqqZkt+EdhD7NxeXfyRcMdjFxcXlJKZ2SS3x0QS7rBYy4XGQQc1TMUvH2J7cxCRtJrJ08oRuZREMyhY+IRM7TMK23+/nE5/4PF/84ie5+eZP09w89aiPZ8Wm5SwThHhDuNthUXTM0lMxS5Zg5U3F0/4X1PFdCMWDbCaxwnVkK885dBuqn9TUm/Dsugd9cD2SncHKm0ym6kKE6kcZ3YbQI+i9zwIOjhZGdgaREAjVj2TG0Ye3INkGkpPF034fjieCHW3Gyp9BtvbS/Q5nFi8ikxnB0/EQ6th2kDWs2DTSTdcjXu+fvJe//vWPbNr0Cnf/6TG2Pb6d3S/twcxYCEQu0G1RDQ2n1SPJriDs4uLi4vLOYqWe5ik9SYmjUumo2MBu2eQu7zifTsaIHGau4vL3JaQHObdmGX/afh+tI234VS8JM0mhr4DzapYdMCCupqaer371u3zxi9/kscce4A9/+B1f+tK/8J//+RmWL1/BVVe9l9NPX46uH7nHb39qEL+6v1ApSzKyJDOaHT/u83x9u415dVTnVfC94NfpVNupterR8TAmjfKM53HyRSHnb5uD8eiDSIEASk1uRZ0wDKxN6zFXPo3ngn02YHneCAtKcjkSpmlyzz138F//9Z90de3hmmvex6c//R/HltlxlDzT+QLbR3bSnFc/Ya0xlB6hZXgbval+aiOHtnU7HoQQ/HXHQzy25xnSVhoBaLLKwpI5vGfyFUdkfeHi8k7GFYNdXFxcTmI8AZ3yCwrp795OJFOKR/Pgj/qxZJPu9G4KvMUU+d78ydzhEAhe0DI8ricZkG10JKaaOpdkQ+Qd6EZLCJTEbm44fwE//2klX//6v/Pb3/752DtwPJWtkkSm7nLsUDVa30vI5jjZaDNGyam5Kt2DYaVgZAutQ9t52dJJhucwOVLFnFgj+bv/gjq8GckxwEohZwYxShZjB2vQ+1YhADnVl/MVFg5C9SJUH5KVRMkM41gZ0o3X4vhLXtdXmWzNhRilS1ASHQjFix2uO2j1ciaT4Rvf+CLnnnsRdbFJvPzYK3jDHiJlEYQQpEfT7HhuJ9HKKIX1boCbi4uLi8s7B4HgRS2NT8hE985FVKDG0WhTTFpVg4XmifWZdTl+lpafQr4vxpq+VxhMDVEVrmBhyRyqwhWH3M/j8bBixbtYseJdDAz0ce+9d/GHP/yWG264jGAwxJlnnss556xg2bLzD1sxXBYsYePglv2C2GxhIxDk761OPtG0qa3sUtuoserxkBOioyKPlJPkRe05Tt/pRcTjyOWVE/tIuo4UjmCtW7OfGAzQ1dXB7bffwp13/pq+vh7OO+9ibrvtLzQ2Tn5T+n8gNgxuJuoJ7+exHPNG6R8ZYNfY7jdVDN4+upPH9zxDUAtQE86NWcJMsqpnDU2xBpaULXjTju3i8nbAFYNdXFxcTnJGzQH0gE5eLH/iNR0PSWuc4ezASSEGr1Oz3OkbRxJQIBSyCJ7V04zLDh9J5aGxT6yVMsP4tv0ObWg9kpXia++q4/r//RvPPvUQS8847605AUnGLFqAWXRkE0N1cB2ebbfT3fMCamaIZtnHS4EGft1fwmZrkJuVDHKoBqF60bufRkn1oKQHsAOVmHlTUMe2IRsj4Ng4nhiSJCP0CI6vBDkziJLcg6/tLhJzPg8HWMomPHlYnsPfjPzqV/9Hb28Xd9xxP/0tAzi2jS+Su/GVJAl/np/UaJrBHYOuGOzi4uLi8o5CAAnJwcv+D4yVvd+npYNnAri8dUiSxNT8ZqbmNx9zG4WFxXzoQ5/gQx/6BC0tG3n44ft45JH7+djH3o+iKCxYsIRzzrmQ5ctXUFf3xqC2BSWzWd27jraxXZT4i3CEQ3eyl6pQOTMLpx3P6R2UlJTCwsTD/vYlPuEnLafIKFkOamyy91K2LItnn32cW2/9GY899jd8Pj9XXHEt11//QaZMObCd2JuJIue8nt+IdMAq7xPJtpEdJK0U1eF94nlQC6BICuv7N7lisMs/PK5zvouLi8tJjgAOVvd6MtzGCATP6SlsIah2NAJ7LSLqbI1W1WC7arxmY4F/663ovc/h6FGsSDOXLmrilIYYX/3SRzEyBsI5Gc7q4MipHvytvyIxuo2NtsOIr4ICzc9F2Q6WijjrRnazUsnPef0qXhxvIQDK+C6QpJy3cdkZ2L4SHF8RjjeK48lDqH6QJITiQagh1NFtqKOtx9zPoaFBfvCDb3H99R+ioaEZK2shyW9825ckCcuwD9CCi4uLi4vL2xcZiQZLY0RyEK+ZMSUkBx0otd26qH8EJk+ezic/+QX+9rfnWbt2F9/4xg/w+wN861v/wamnTmXevHpuvvl6br31Z7S2bsJxHKrDldww9RrqIzWMZEeJmwlmFU7jfVPfTZ734IG9x0OJU0pQhBiVRvZ7fVgepNQuJ1o1AykUQgwNTPxMmAajQwP8zUjzz//8XmbMKOfaay+iq6uDb33rR6xbt5tvfvOHb4kQDDC3aCZxM0HGyk681pvqJ+IJ0ZhX/6Ye2xbORPDea1EkGUtYb+qxXVzeDrjvgC4uLi4nOTFPId2p3ZiOgSbn/K1Mx0BCIuZ566s5TaBHtgi/Lpnbh4wNDEn7hEYl3o46vAE7WIXQc5Np4S/i0ytO58r/vZevffgLnLP4cipmlVE5twJFPfm8/LTBdcipXrqUMIYYJKJ6GcVLcaaXpnQ3L0uwIZtk2d7tHX8JYmwrUnYIhA2SAnYWO1SdC4BL9eaEYAAEkp3GijSDYyAZiWPu59e//gWEEPzLv/w7ALHqPLrWd2ObNoqWG1fLsEAI8iqixz4gLi4uLi4uJymnm362qSatijGxcikuOSwyfDTYbojUPxqlpeVcf/0Huf76D5JMJnjuuSd54YXneOml57j//ruxLItoNI958xaxcOGpzJ06neVVp1JcXEpJoOhNDdwtt6uYZyziSc8jpEQCnxNgWBlEFx7OzJ6LWlWHvuwchu+/l01bt7BhfIRnert4eWQIWzhMmzaL97//Zs499yKmT599UoQDn1q+kLbRnWwcbEEgcIRDSAtyYd25lAdLDt/AcVAbrkSTNRJGkqCey9UwbJO0nWVq/qQ39dguLm8HXDHYxcXF5SSnyFtGia+C3nQnEvuqgUv9VRR4S9/KrgGgAfmOwm7VovA1BaZZBDLsJxJLxiiSlUIEqyde6+lUMPtns6iijTueuYWpFQsY6x4jNZpi8jmTTorJ7GuRjHju8+uWt5mShl+YOEjIzr6KA8dfguPJR7ZSqCNbABBqgEz9VUhmgsCm/0PO9CP0KNgZhCcPx5eP5Fg4/qJj6uNTTz3KXXf9hu9856fk5+cqk0umlNC7uZf+tkF0f+6hgpEyKJ5URPGkYzuOi4uLi4vLyUytrfNP6QhPaSl2qAZRoXCuGeA0wz9hF+Hyj0kgEOTccy/i3HMvAiCVSvLyyy/x4ovP8eKLK/nud79GOp0CIBrNo7l5Ks3NUyY+V1fXUVxciqqeGElFQuKK9HUUOIWs0p8lYccp3lVK7Y4GVq9/nlvW/4j169eyc+d2APy6zqJps/j6J/+N5Sve9XcJhDtaQnqQG6dfz8bBLewZ78SjeJic30R9pOZNP/bk/CYWlM7hhe41yKlc+F/GzjItf9JEuN4JwbFyH4rn+DJMXFz+zkhCiJN7Pe7bgPHxcSKRCFu3DhIKhd/q7ri4uLwDsR2L/kw3w9kBJCDmKaLQV4YivTWVs47tMNY1hpmxCBQE2FAs8TvfGAEhU+AoZCVBh2zSZOl8PBXDs/eGS052EVr9/3BUP8Kbj3Bg5eNeBjqyaNE0H/njLTRXTePjK/4Dy7BZ9IGFhIqCf9dzk4w4kjGC0PMQeugNP9e7n8K/6cf0egp5qX8jPtWLV9YpyfSwNtTMXxIjfFiHpYXNCMWHku5HMhOYeZOQJAXbV4RZehpW3iRwLPxbfo5v66+RHAs7UIHjzUey0mQrlpGectNRTywTiThnnjmb2toGfv/7B/cT07PxLB2vdNLX2o8kSZRMLqZ8VjmegJuo7PL2Jh4fp7m5gLGxMcJhdy72VuHOiV1OZrIIVHBFYJcjwrZt9uzZRWvrZrZt20Jr62a2bt3Mjh3bME0TAFmWKS4uo6ysnIKCIoqKSigoKCIYDOH1+vB4vHi9uQ+PJ/dhmgbpdIpUKkk6nSadTpFOpxgbG6O7u4Ourg66uvfQ19vDq1KN1+tj2rRZzJgxh5kz5zBz5lzq65tRlJNvBd3JRNY2WNu3no2DWzBskyn5TSwomUNIPwH3FlYKT8cj6L0rkawUVqSJbNW52FG36tjlreVI58RuZbCLi8s7CpHNYq56Fmv1i4hUEmXSVLSlZ6CUnXxPy48GRVYp9VdR6n/zUnePlMRAgs0PtjCyZwTLsPEGPZTNLufi80p5xpelXTHRhcQMy8MVmfCEEAzg+Mswihfi6XgYxzFI22HG+wUBn4FWNJUPXfxpvnPnF1k7fSWTQ3NIDCSOWwyWjDEkI47jzQf1EKnhtoG3/T70nqeQjARCD2KUnkam5uLc0/69mAVzsfImUTK8mcneMHuSffitOB2Kn+cIMK9+KXPUNPJIC1JmOOdTKEy0kRYAFC0AsooVaQBFJzXtnzELZuHpeHivZYSPbNUFZGsuOqYKg69//QuMjAxx992PvqGq2hPy0LC0noalb65Pm4uLi4uLy8mGxxWBXY4CRVGorW2gtraB88+/ZOJ10zRpb2+jo2M33d2ddHd30N3dxeBgP6+8sobBwX6SyQSZTBrDMA5xhL1hvv4APp+fUChMWVkF9fWNLF26jPLyKioqqigvr6Smpv6EVSAfL2kcdqomJoJqWyNPnLyCtEfRWVw2n8Vl809sw46Nv+UW9J5nEHoEIXvQ+55HHd1KcsYnsKPHHn7o4vL34uT4j+Li4uJyAhC2Teau32GufBrJ6wNNw37kAewtG/F++KMoZRVvdRdPalJmiu2juzBsg4pQGaWB4jdsY1s2G+/bzNCuYSKlYVSPSnosza5ndzIl5GHRokp6ZRuvkKh0VOTX33hJEunG9yDUAHrvSnRrEMVTT8ZTgxyuZ9G0RhZNO4Nf3v99vvL/27vz+Kire//jr+931kz2PWEJJGwBRAUpAasVBDVorW3RqkVb1KJWvbZVb39qW6UrWL2119aWait6b7Vcty5a14IURQSkoBTCKrskSPZ1tu/5/YEEY1hCSBiSeT99zMPkO+d75vOdMyGf+eTMOdMexu09jgQz0oR/619a/2Lv+DII9Z1MsP8FYLfv17/tr/i3PIvjS8NJyMEK1eLf8gw4EVqGXNnazniTaRr5TXxb/0rRvtVkeANssxPZmzmGS/qczanZI3BsD/XN5diN5SSs/z0uDJHkIrBcWKEafDtfx0nqvz8WyyKcfxbh3PHYwWocVwINtVC/sR63t4n0ARkdfh7efvufPPHE7/jJT35JQUFh5587EREREWnH4/EwZMhwhgwZfsR2wWiIeWueZPnOlbgcF04oQlOwiRE5xVw7+ioykzPx+Xwn3XJoR1LmCvKcv57drghRIMPYTA4GmBJKPORmbb2Vu2Y93r3LiSYNwHj3z7x0EnJwV6/Fu2sBzSoGSw+gYrCI9BrRzRuJrHgHu08/7I8/nmry+hBdv5bwm//Edfn0GEd48iqr2sjTG/7Khw3lOCZKsjeZs/tO4OJB5+O2D/6qqNpWTc3OGtL6puL27T8eSA8Qbomwa9VuBowtINV9lCUH3AFahlxJcMBFWKE6skwNGxfvwN0cwRvwcu3Ub/Gdh77G0//6A9MGTOvcBRlDwsYn8e16DcefjePPxg5WkbDpj2DZBAtK2zS3gjV4P/wnjj8DJ7B/HWbjSQTLxrvnTYL9L8D4M1rbO4F8mkfeiBWqxe1EGOTLYNCnknknkI+7djOuln1EUotbC9DGl44JVuPZ8ybBfucfnP1ruwl7Mti4YBO7Vu8m2BjCdtmk5qcwYupw0vunHfGSm5oauf32Gxg37rPMmHFj5543ERERETlu73+0jlUfraEou5CEjz+ZFoyG2Fq7nS3B7fTN7lmfWqy0ojyVUEelFWVA1IMbqLCj/M3XSJbjZnTEH+sQTxi7cTc4odZCMACWhePPxFOznmbjwKf2FhE52egVKiK9hrNrByYUbC0EA1i2jZWWQbRsTQwji41wc5hdq3az9qV1bFi4iart1RxqmfjqllqeLHuO8sYKClMLGJY+BJ/Ly6vbF7C8fFWbtqGmENGI01oIPsAb8BBsDBEJRego403BSepH4VlD6XtqHxqrmti3tRJqXFwz9VaWrn2D1/7xt7bnGNjVACsrLNZXQdg5dN924y68e5cRDfTFCeRhPElEkwowrgS8uxdApKVt+2AldqgOx5ve5rjjS8cO12G37DvMNaRi/JmHX84h0vzxA7Sd2WtcfuxQHQe3A9xv16rdfPD2NlweN5kDM0jJS6Fmdy3/fnEt4ebwoR/jYz//+SwqKj7kv/7rd9i2fr2LiIiIxMoHtVtxoLUQDPuXLXDbbjZUbY5dYJ30b0+QcjtCkePBi4WNRb7jJoLhXU/L0TvoRYw7sD+Fd6JtjluRZhxvCsTRLGnpuTQzWER6D68XjME4DtYni2HhEFYgMXZxxUBLfQvv/XkN+zZXAgZjDNuXbWfIpMEUjh/Ypu26qg1UNO5lSHpR64Z0WQmZ1IXqWVa+ss06W4H0AG6fm2BjqM2mYy11QdL7p+Lxe445Vm/Ay+nTTqV6Zw2N+xrxJHg4r/BcNjS9x1133cr48Z8jIyOTYBSe22SxvNymIWRw2zZFaYavFjv0+9SywnbLPqxQPSaxf5vjji8dO1iNHarBcecdPO5NxbgD2OE6HPfBmQ12qB7jTsT40o75ugCcxL5g+7BCdQdnDxiDHawi2HdSm1kDxhh2rd6N2+sikL7/jYPb6yK9XxrVu6rZ90El+SPzDvUwvPXWGzz66EN8//uzGTRoaKdiFREREZGu4bbch5yE4RgHt+vY8+VYa7QcrI+LwJ+UgEWlHT3MWb1TJOMUokl9cNdtJpIyaP8ycMFqrGgz4fyzO7Xnh8iJpqlDItJruItHYmdm4ezcgXH2Txk19XWY5ibcY8fHOLoTa8e7u9i78SPS+qWSWZhJVlEWtstmy+IPaPiooU3bpnAzGHDts6HcQMvHOxe7/dS01LZpm9Y3ldxhOdSV19FY2UiwMUjN7losGwo+U4Bldy75sV02mQMz6HdGH1KGJuL4IsyZ82sikTA33XQ1kUiEN3ZYLNxhkeg2DEuHfkmGjVUWT5bZhD6Vg5qPi7tWuK7NcStcj/EkYjzJbdv7swjljsdursBqqQQngtVSid20h1DOOJyEnE5dVyStmFBuCa6GHbjqt2M3leOuXouTkEOo35Q2bZ2oQ7AhiPtTBXXbbWMMhA4zM3jnzm3ccMNX+exnJ3H99d/qVJwiIiIi0nWKM4fic3mpaqluPVYXrMeyLE7JPPJ6wyejbMeFDYQ+8ak2g6HBchgY7XnF7eNhfGk0D7uGaCAPd+1G3NVrscN1BPtdQLDPpFiHJ9IhmhksIr2GnZ2Db9oVBJ+bT3TjOsDC8vnwnPk5PGeeHevwThhjDOXryvEn+XB5Di5PkJiVSOXWKqq2V5OUfXAqbWpDErlvZWLqo7iMC5MIZrihNqeO07JHtunbsi1GXjQcX4qXPWsrCDaESMpJpLBk4GFnrXY05r0tu9nWsImmSD02LrL8ufz3w49xzdXT+NGP78a64AGSvZD+8cRdvxsGphi21sKmGhiZebC/aPJAIpmj8FQsJZrYH+NJxg5WYQerael/+f71gD+lZdBl4ETw7l2O1bwX40ki2P88WgZf3unrwnbRVHwN0cR+eMvfwoo0E+pzDsF+5xFNHfKppjZpfVLZs66CQHpC64YioeYwLo+LpMxAu+6bmpq45ppLSU5OYe7cJ3G5Tt4dnUVERETixfCMIUwuOJs3dr5FRdNHGAw+l4/P9Z3A6Tkjj97BSeaUsI8hHg9lnhDZURduLPbaUTIdN+PDCUfvoJeJZJ5Kw9hZuKvXYUWbiSb1J5pcpFnB0mOoGCwivYrnM+NxDSwiUvZvCIWw+xXgGjIMK86KZMYxh05GLNp8ZC3UFKLpn03k1GVS4duHz+fD0+Am9E6QvM9mc9b49jOqvQEvIy4YTtHZRQRbIiSl+HG5j+/5rQxWsK5mFY6JkuBOxDFRdjVtI+uUXH7wgznMmvWfTPCcTvHEq9qc53NBxNSjH+cAACc8SURBVIHGsEWb9Xctm6ZhM0iwXHgq38Nq2oPxphAc8HlaCi46ZAzGk0jziG8QHHgRdkslji8dJ9Dn+JM6d4Bg4SUEB14MTgRch95gz7Is+o/tT+W2Kqp3VBNIDxAJRWiubaHvqHzSC9quZ2yM4fbbr2fr1s288MJiMjIyD9mviIiIiJxYtmXzxcEXckrWcDZVf4CDw6DUgQxLH4zL7nnvSxKwmdGcxitOI2s8LbRgGBXxcX4wMe5mBh9gvMmEc0tiHYZIp6gYLCK9jp2dgzf73FiHETOWZZFbnMOmRVtIzAxgu/avCNRc04w34CW9X1pr248276OxvJFRI0aQ3LCTPQ0VRFIjpNekM7RlEEWpA9r132g5/NPbxIrEFkKWoTgS4ZxQgAKn84ng7sZtRJww6b6s1mNuy0t1aB9fvPoy1q1bw3OP3oAnp5ixY8a2tqkLQcBjkRtov5Oc8WfQNOpWXA07sEK1OAm5OIHco8biBPJxAvmdvpbDsuzDFoIPyB6UxWlfPpWtS7dTX1GHy+NiyMTBFJ05sHUcD/jtb3/BX//6NL/73VOMGHFq18crIiIiIp1mWzZD0wcxNH1QrEPpEpnGxfSWFBqCSYQxpBkbS5ulifRIKgaLiPRCBR/PMK3cVoXb68aJRrHdLgadVURy3sH1coMNQcCQ4E1geMZQhqQV4RiHYCCIt8nXrt8whj/661jhaSbVuHADi73NbHKHuKEpjb6dKAgbY6iP1OJz+dscd9tujHFoiTYxZ86vWb2ujBUPXUrCrHfom59HUwRqgxZn93UoSD5M55ZFNLl9QftkljMkm+xBWYQaQ7i8Lty+9r+qFy16jZ/97Hvceuv/4+KLL41BlCIiIiJysok4EbbV7aAlEqJvUj7p/tQuf4wko62nRHo6FYNFRHqhQHqAMy4fzYf/Lqd6RzWegIfcYTnkDMluXYsWICE1YX/BNBzF5XHhtvf/WqhvrCd7UFa7fte7Q7znaWFg1EPg4z1IszGsd4V429PMZcFjLwZblkXAnUh1cF+b446JAhZelx+/38+fnniG8y6YwJrffoXUuxeQ6PcwsZ/DlAHm5FqeyzjYzXsBcBJyO7TMhBVugGgLxpsOtgvLtvAlty/GA2zevIFvfvMqJk26gP/8z1ldGbmIiIiI9FDb63by9Ma/sq12J2EnTLovjUkFZ3H+gInYlgq4InKQisEiIr2UP8VP0ZkD4cyBh22TPTiL9IJ0KrdWkpSVhMtj01jZhCfBS7/R/dq1321HiGBaC8EANhapxsVGdwiCnYu1T8IAqoP7aAjXEXAnEjVR6sO1pHrSyfTlAJCf34cn5j3NtGmTqXnyq/z44T+SnHByrVHmqtmA/4M/467bDFhEUofQUvRloqmDD9neCtXj2/Y3vHvfwYq0EE3sS7BgKuGccYcsIm/ZspHLLjufnJw8fv3rJ7RhnIiIiIjQGG7if8ueYXvdTgYk98Nre9nbvI+/bX6ZdF8qJflnxDrEozIfTzBZ7QlSa0UZGPUwNuwny6hsJdLV9OchEZE45va5Oe2Lp9Dv9L6tm5Wl5Ccz6uKRZBW135DMj4UBnE9u1gYELYfk4/jIWG5CPwYnj8Rtu6kNVdMcaSTDl82I9DF47IPr7J5xRgmPPDKfNxa8yLdv+SqhUKjTj9nV7MYPSVz7GzyVq3A8KTieJDz7VhJY+5vWmcJtOFES1j+Gf9tfwIngeJJw1W4msO53ePb9q13zzZs3cOml55GSksqzz75Oampat1+TiIiIiJz81lVuZEfdTgalFpLgTsBlu8hPzAUs3t6zItbhdcg/vU3MDdTwhreJtZ4Qz/rr+U2ght12ONahifQ6PaYYXFVVxfTp00lJSSEtLY3rrruOhoaGI57zyCOPMHHiRFJSUrAsi5qami7pV0SkN0nMTOT0aafy2ZnjOfMb4xl/zTjyR+Ydsu2IiJcs42KHK0IUg8FQZUWJAGPDCZ2OwbIsBiQP4TNZEzkj6yzOyDqbMZlnkexJa9f2/PM/zx/+8AwLFrzMzJmXEwx2cjpyF/NWLMPV8CGRtBEYXxrGl04kbTiuhp14Kpa1a++u3Yj3o5VEkwbiBPIxvnSiaUOxoi14d74O5mDBfdOm9Vx66Xmkpqbx7LOvk5199I3wRERERCQ+1IfqMYDHbjuLNsmbyL6mSowxhz7xJFFlRXnF14gHi+Kol8Koh+Kolx2uMK97G2Mdnkiv02OKwdOnT2ft2rW8/vrrvPjiiyxevJjrr7/+iOc0NTVRWlrK3Xff3aX9ioj0NpZlEUgPkJyThMt9+KUHchw3lzWnkOzYbHKF2eAK0Wg5TA4GGBf2H/a8jvK5/GT4ckj1ZhxxbbMpUy5k3rznWLz4H8yceTktLS3H/djHy27cieP2t13ewbLB9mA3fdi+fXMFRIMYb9vd7xxfJq7GnRDdf02bNq3nssvOJz09Q4VgEREREWknKyEDl+WiJdJ2kkRtsJaC5H5t9gw5GW11ham2HPKcg+9DbCxyHDfrPSGacDremXGwG3bgqtsK0ZPnU4QiJ5MesfhKWVkZr7zyCitWrGDs2LEA/OpXv+LCCy/kgQceoE+fPoc879vf/jYAixYt6tJ+RUTi2ZiIn8JGDxvdIcIY+kc9FDhuLE5skjlp0gXMm/c81147jeuuu4w//OEZ/P7jL0h3luPPxoq27J/ReyDhNgacMMbffjM+x5MCtguiQXAd3CzOCjcQTcwH28umTWVceun5ZGZm8fTTr5KVlXOiLkdEREREeojijKEUZwxhzUfryAlk43N52du0j4AnwFn9xsc6vKM68C7i0/OXHctgGTr8LsNVuxn/lv/DXbsFTJRoYj+CRV8mnH3yr5ksciL1iJnBS5cuJS0trbVgCzBlyhRs22bZsvYfve3ufoPBIHV1dW1uIiLxJN24KAkncFY4wADHc8ILwQdMnHgeTzzxF5YuXcxVV11MZeVHMYkDIJw7DuPLxFW3ZX+BN9qCq3YTjj+bUM5n2rWPpI8gklKEu3YzRJrBGOzmvVjRJsJ9zmHhote55JKJZGZm8cwzr6kQLCIiIiKH5HV5+NqIy5nY/7NETISaYC0DUvpx9YivMDJzWKzDO6pBUQ9ZjovddgTzcUk4gmGfFeWUiJ+EDpSurJZKAuvm4ql8H8eXgZOQh6t+O4F1j+Kq3dTdlyDSo/SIYnB5eTk5OW3fBLvdbjIyMigvLz/h/c6ePZvU1NTWW//+/Tsdg4iIHJ+zzz6Xp556kQ0b1nH++eNYvvztmMQRTRlE0/BrcQK5uOu34a7fjpPUl6bh38BJKmh/gttP8/BvEMkYjrtxJ+7qtVhOmMb+F/OTP73N1VdfwtixE3juuQVkZmaf+AsSERERkR4jw5/G1SO+wvdKvsNdJd/m9rE3MSbn1FiH1SGpxsXFwSQ8WKx3hdjgCrHJFWZw1Mt5oUCH+vB8tBJX/Q4iacMx3hSMJ5Fo6hCsYBXe8ti8PxA5WcV0mYg777yT++6774htysrKTlA0HXfXXXdx2223tX5fV1engrCISAyNH382r722nBtvnM6ll07he9/7Gddf/60Tvj5aOGcc4YxRuOs+ACCSMgjch1+6Ipo8kIYx38Nduwkr3MTeUAI3/ed3Wbz4H3z3u7P4j//4f9h2j/i7rYiIiIicBNJ8qbEOoVPGhxPIj7pZ42mh3jL0jbo5PeIjxRx+P5NPslsq93/xyX1HLAvjDuBq3N0NEYv0XDEtBt9+++3MmDHjiG2KiorIy8tj7969bY5HIhGqqqrIyzv0jvcd0dl+fT4fPp/vsPeLiEj3MsbQUtuC5bLwJfmwLIv8/L48++zrzJ79A374w++yfPkSHnzw96SkHH9CbAVr9s/cjQaJJg8gmlzUdqO4T3InEMkY2fHObQ+R9BGsXv0uM2deQnNzE0899XfOOWfKccctIiIiItJTDHA8DAh6OnWu8Wd8/IVzsCBsDFakiWhA+0GJfFJMpxtlZ2dTXFx8xJvX62XChAnU1NSwcuXK1nMXLlyI4ziUlJR0+vG7q18REek+1TuqWfnUv3jrkaW8Nfdt3nv+fer3NgDg8Xi45545/OEPz7BkySJKS8ezYsXS43o8z0crSXr3XhLXPERg7VySVv6UhA2Pd9nuxC0tLTz00By++MWJ5Obm8eqry1QIFpHDqqqqYvr06aSkpJCWlsZ1111HQ0PDEc955JFHmDhxIikpKViWRU1NTZf0KyIicrIIZZ9BNKkf7pr1WOF6rEgTrrrNGF86obwzYx2eyEmlR3z2dPjw4ZSWljJz5kyWL1/OkiVLuOWWW7jiiivo02f/X3h2795NcXExy5cvbz2vvLyc1atXs3nzZgDWrFnD6tWrqaqq6nC/IiJy8qjfW8/q59+nYuNePD43Lo+LXat3896f3ydYH2xtN3XqJbzyyjukpqZxySXncMMNX2XHjq3H/HhWyz4SNjyB3VJFJLWYSPpIHE8Kvp2v4t3z5nFdizGGF154lnPOOZUHHvgR1157C88/v5C+fbXskIgc3vTp01m7di2vv/46L774IosXL+b6668/4jlNTU2UlpZy9913d2m/IiIiXckYg7O3guiHuzDh8LGd68+iacSNRNJHYjd/hKvxQ6JJ/Wga/g2iaUO7KWKRnskyxphYB9ERVVVV3HLLLbzwwgvYts20adN46KGHSEpKAmDbtm0UFhbyxhtvMHHiRABmzZrFD3/4w3Z9zZs3r3V5iqP12xF1dXWkpqayYcM+kpNTjvtaRUTk0Nb/YyObF20msyizdT1gJ+pQtb2aUy85hYKxbQup0WiUZ599kvvuu4eqqn1ce+0tfOtbd5Kamtahx/PufoPA2t8SSR/RZv0xV+0WoqlFNIy9t1PX8f77/+Kee25n+fIlnHfeRdxzz30MGqQkVaSz6uvrGDYsi9raWlJSem8uVlZWxogRI1ixYgVjx44F4JVXXuHCCy9k165dR53MsGjRIiZNmkR1dTVpaWld1u8ByolFRKSzoh/uJvS354hsXA/RKHZ+H3yln8d9+hnH1pETxdWwA0yUaFJ/cGmJT4kfHc2JY7pm8LHIyMjgqaeeOuz9AwcO5NN17VmzZjFr1qzj6ldERE4e9eV1uP2eNhvD2a79RdrGysZ27V0uF5df/jUuvngac+c+yMMPP8D//d8T3Hbb95k+/Tr8/sNv7gZgRZsBq+1GFIBx+7BCtccc/6ZN63n44Qd45pn/ZdiwEcyf/zKf+9zkY+5HROLT0qVLSUtLay3YAkyZMgXbtlm2bBlf+tKXTmi/wWCQYPDgpzLq6uo69fgiIhLfTEM9LU/8HueDjVh5fbHcHpwd22n+4zwSEpNwDxnW8c5sF9GUwu4LVqQX6BHLRIiI9ERhDP92B3nV28g/vU3stSOxDqnH86clEA21fR6NMRhj8CUd/q/+gUAit932fd5+u4zS0i9wzz23cdpp/bjjjht55503cRznkOdFE/uC7cEKf2LdTGOwg9VE0ju2SVxtbQ3/+7+PcvHFZ3POOaeyYMHLzJnza157bYUKwSJyTMrLy8nJyWlzzO12k5GRQXl5+Qnvd/bs2aSmprbe+vfXMjciInLsIv9+n+jWzdiDh2GnpWMlJWEXDcbU1RJZ+laswxPpdVQMFhHpBk04PJZQy28SqnnOX8cf/bU8mFjFv9wtsQ6tR8sfkYc7wUNdeR1O1CEajlKzq5bEjAA5Q7OPen5ubj7/9V+/4803/811193Cm28u5Mtfnsz48cO477572Lx5Q5v2kfSRhHI+g6t+K66GHdhN5bir1+IE8gn1PfewjxMMBlm06HVuuulqRo8u4K67/oP09EwefXQ+7777AVdfPRO3e/+Hc4xjqNpezQdvb2PrO9uo21PX7pMuItK73XnnnViWdcTb+vXrYx1mO3fddRe1tbWtt507d8Y6JBGRHmm3HeYNbyOvexvZ6ArhEF+5oFO5Dwuw3J7WY5ZlYSUlE92t3y0iXa3HLBMhItKTLPY28a6nmQFRDwFsHAw7XBGe89dT2Ogh3bhiHWKPlFmYwYgLitn8zy1U76zGsiySspMoPm8YSdkdX+u9qGgI3/3uLO644x5WrHibZ575I/Pm/Zb//u855ObmM3z4KIqLT2HEiFMYPriEkQP6kFy1HCvSQrDvJEL9z2/9+FldXS1r177Hv/+9uvX/GzeWEYlEGDKkmDvuuJdp075Kbm5+uziciMP6f2xgx8pdRIIR+HiG86CziyicMLDNchgi0nvdfvvtrftZHE5RURF5eXns3bu3zfFIJEJVVRV5eXmdfvzO9uvz+fD5tBajiEhnGQyLvE383ddIreVgAT4sJoQSuKwlGQ/xkQvaaWn7P+0XjWK5Dr5PMo2N2Kd0/vebiByaisEiIl3MwbDc00KysQl8/AEMG4v+UTebXGE2uEOMDyfEOMqeybIs+o/pR87QbGr31GHZFun90nD7OvfrzLZtSkrOoqTkLH7yk1/yxhuv8v77K1m3bg1///vzzJ37C2D/x6VTU9Pxejx4vK/hOL+gpaWZ5uYmGhv3LyHh8/kYPnwUY8aUcPXV1zN69DhGjTr9iAXd8vUVbFu2g0BaAv4+qRhjaKxsYvM/t5DeP530/mmdui4R6Vmys7PJzj76pxsmTJhATU0NK1eu5Iwz9m+os3DhQhzHoaSkpNOP3139iojIke2wI7zoa8DGojjqwcKi1oqy2NtEYdTDhDh5z+A65TRc/QqIbt6Iq38BuN04FeVYfj+ecRNiHZ5Ir6NisIhIFzNA0DLt/pJ/YF2eUJx97Ks7+JJ85Aw5euHkWPj9fqZOvYSpUy9pPVZfX8f69WspK1tDTU0VoVCIUCiIbdv4/QkkJATIzMzilFNOZ/Dg4talHzqqYv1ejDH4U/ZvZGdZFklZiezbUsm+DypVDBaRNoYPH05paSkzZ85k7ty5hMNhbrnlFq644gr69OkDwO7du5k8eTL/8z//w7hx44D9awKXl5ezefNmANasWUNycjIFBQVkZGR0qF8REel6690hai2H4VEv1sfvHVKNi30mymp3S9wUg+3UNPxXX0fw+f8jun0rRCPYWTl4L7gI18hTYx2eSK+jYrCISBdzYTE84mWRt4lsXNgfJ3Y1lkPAWBQ4nqP0ICeL5OQUPvOZCXzmM90zIyESjOByHWL5fpt2G+WJiAA8+eST3HLLLUyePBnbtpk2bRoPPfRQ6/3hcJgNGzbQ1NTUemzu3Ln88Ic/bP3+c5/7HADz5s1rXZ7iaP2KiEjXC1sG6+P/PsmNRbMVXxNIXEWDSPj2d3F2bseEw7j69ccKJMY6LJFeScVgEZFucE4owCZ3iPWuEKnGRdAyRDBMCgUYENU/vbJfZmEGFWUVOBEH272/KBwJRrAsi9Q+qTGOTkRORhkZGTz11FOHvX/gwIHtNqGcNWsWs2bNOq5+RUSk6/WLuvGwf/PpA8vLRTA0Wg7FUW9sg4sBy+3GVTgo1mGI9HqqSIiIdIP+jofrm9J429PMBneIZGMzNuxnXDih3V/+JX71OSWf8nUVVG6rwpfoxTiGcEuYvBF5ZA/OinV4IiIiItKNRkR8jA77WeFpIcFYuIA6y2FQ1Mv4UHwsESEiJ56KwSIi3aSv4+GyoAeCsY5ETlb+FD+jLz2Nnat2s3fDXmy3Td6IPPqd3qfTm+KJiIiISM/gxeKq5hQGRT2s9LQQwjAxEuDMUAIZxhXr8ESkl9I7TRERkRhKSEtg6KTBDJ00ONahiIiIiMgJloDNuaFEzg1pfVwROTEOsWuNiIiIiIiIiIiIiPQ2KgaLiIiIiIiIiIiIxAEVg0VERERERERERETigIrBIiIiIiIiIiIiInFAxWARERERERERERGROKBisIiIiIiIiIiIiEgcUDFYREREREREREREJA6oGCwiIiIiIiIiIiISB1QMFhEREREREREREYkDKgaLiIiIiIiIiIiIxAEVg0VERERERERERETigIrBIiIiIiIiIiIiInFAxWARERERERERERGROKBisIiIiIiIiIiIiEgcUDFYREREREREREREJA6oGCwiIiIiIiIiIiISB1QMFhEREREREREREYkDKgaLiIiIiIiIiIiIxAF3rAPoDYwxADQ01Mc4EhEREZH4cyAHO5CTSWwoJxYRERGJnY7mxCoGd4H6+v1P9hlnFMY4EhEREZH4VV9fT2pqaqzDiFvKiUVERERi72g5sWU0heK4OY7Dhx9+SHJyMpZldXn/dXV19O/fn507d5KSktLl/UvX0nj1LBqvnkNj1bNovHqWnj5exhjq6+vp06cPtq1V0GKlu3PinqKn/zxJ19FrQUCvAzlIrwU5oLteCx3NiTUzuAvYtk2/fv26/XFSUlL0D0YPovHqWTRePYfGqmfRePUsPXm8NCM49k5UTtxT9OSfJ+laei0I6HUgB+m1IAd0x2uhIzmxpk6IiIiIiIiIiIiIxAEVg0VERERERERERETigIrBPYDP5+Pee+/F5/PFOhTpAI1Xz6Lx6jk0Vj2Lxqtn0XiJdB39PMkBei0I6HUgB+m1IAfE+rWgDeRERERERERERERE4oBmBouIiIiIiIiIiIjEARWDRUREREREREREROKAisEiIiIiIiIiIiIicUDFYBEREREREREREZE4oGLwSaCqqorp06eTkpJCWloa1113HQ0NDUc855FHHmHixImkpKRgWRY1NTXt2gwcOBDLstrc5syZ001XET+6a7w6068cXWee15aWFm6++WYyMzNJSkpi2rRpVFRUtGnz6Z8ty7KYP39+d15Kr/Twww8zcOBA/H4/JSUlLF++/Ijtn3nmGYqLi/H7/YwaNYqXXnqpzf3GGO655x7y8/NJSEhgypQpbNq0qTsvIa509XjNmDGj3c9RaWlpd15C3DiWsVq7di3Tpk1rzRt++ctfHnefIr2ZckE5QHlm/FIOK6DcWA7qcbm3kZgrLS01p512mnnnnXfMm2++aQYPHmyuvPLKI57z4IMPmtmzZ5vZs2cbwFRXV7drM2DAAPOjH/3I7Nmzp/XW0NDQTVcRP7prvDrTrxxdZ57XG2+80fTv398sWLDAvPvuu2b8+PHmzDPPbNMGMPPmzWvz89Xc3Nydl9LrzJ8/33i9XvPYY4+ZtWvXmpkzZ5q0tDRTUVFxyPZLliwxLpfL/PznPzfr1q0z3//+943H4zFr1qxpbTNnzhyTmppq/vKXv5j33nvPfOELXzCFhYUamy7QHeP19a9/3ZSWlrb5OaqqqjpRl9RrHetYLV++3Nxxxx3mT3/6k8nLyzMPPvjgcfcp0pspF5QDlGfGJ+WwYoxyYzmoJ+beKgbH2Lp16wxgVqxY0Xrs5ZdfNpZlmd27dx/1/DfeeOOIxeBDvaik87prvI63Xzm0zjyvNTU1xuPxmGeeeab1WFlZmQHM0qVLW48B5s9//nO3xR4Pxo0bZ26++ebW76PRqOnTp4+ZPXv2Idt/5StfMRdddFGbYyUlJeaGG24wxhjjOI7Jy8sz999/f+v9NTU1xufzmT/96U/dcAXxpavHy5j9Ce8ll1zSLfHGs2Mdq086XO5wPH2K9CbKBeUA5ZnxSzmsGKPcWA7qibm3lomIsaVLl5KWlsbYsWNbj02ZMgXbtlm2bNlx9z9nzhwyMzMZPXo0999/P5FI5Lj7jGfdNV7d/TqIV515XleuXEk4HGbKlCmtx4qLiykoKGDp0qVt2t58881kZWUxbtw4HnvsMYwx3XMhvVAoFGLlypVtnmfbtpkyZUq75/mApUuXtmkPcMEFF7S237p1K+Xl5W3apKamUlJSctg+pWO6Y7wOWLRoETk5OQwbNoxvfvObVFZWdv0FxJHOjFUs+hTpqZQLygHKM+OTclgB5cZyUE/Nvd1d0ot0Wnl5OTk5OW2Oud1uMjIyKC8vP66+b731VsaMGUNGRgZvv/02d911F3v27OEXv/jFcfUbz7prvLrzdRDPOvO8lpeX4/V6SUtLa3M8Nze3zTk/+tGPOPfccwkEArz22mvcdNNNNDQ0cOutt3b5dfRG+/btIxqNkpub2+Z4bm4u69evP+Q55eXlh2x/YFwO/P9IbaRzumO8AEpLS/nyl79MYWEhW7Zs4e6772bq1KksXboUl8vV9RcSBzozVrHoU6SnUi4oByjPjE/KYQWUG8tBPTX3VjG4m9x5553cd999R2xTVlbWrTHcdtttrV+feuqpeL1ebrjhBmbPno3P5+vWx+5pTobxko47GcbrBz/4QevXo0ePprGxkfvvv19JusgxuOKKK1q/HjVqFKeeeiqDBg1i0aJFTJ48OYaRiUi8ORlyCzk5nAyvBeWZIvFJubGcKCoGd5Pbb7+dGTNmHLFNUVEReXl57N27t83xSCRCVVUVeXl5XRpTSUkJkUiEbdu2MWzYsC7tu6eL9XidyNdBb9Cd45WXl0coFKKmpqbNrI2KioojjkVJSQk//vGPCQaD+mNLB2RlZeFyudrtnn2k5zkvL++I7Q/8v6Kigvz8/DZtTj/99C6MPv50x3gdSlFREVlZWWzevFkJbyd1Zqxi0afIyUa5oBygPFOORDmsgHJjOain5t5aM7ibZGdnU1xcfMSb1+tlwoQJ1NTUsHLlytZzFy5ciOM4lJSUdGlMq1evxrbtdh9nktiP14l8HfQG3TleZ5xxBh6PhwULFrQe27BhAzt27GDChAmHjWn16tWkp6crQe8gr9fLGWec0eZ5dhyHBQsWHPZ5njBhQpv2AK+//npr+8LCQvLy8tq0qaurY9myZUccOzm67hivQ9m1axeVlZVt3gjJsenMWMWiT5GTjXJBOUB5phyJclgB5cZyUI/NvbtkGzo5LqWlpWb06NFm2bJl5q233jJDhgwxV155Zev9u3btMsOGDTPLli1rPbZnzx6zatUq8+ijjxrALF682KxatcpUVlYaY4x5++23zYMPPmhWr15ttmzZYv74xz+a7Oxs87Wvfe2EX19v0x3j1ZF+pXM6M1433nijKSgoMAsXLjTvvvuumTBhgpkwYULr/X/729/Mo48+atasWWM2bdpkfvOb35hAIGDuueeeE3ptPd38+fONz+czjz/+uFm3bp25/vrrTVpamikvLzfGGHP11VebO++8s7X9kiVLjNvtNg888IApKysz9957r/F4PGbNmjWtbebMmWPS0tLMX//6V/P++++bSy65xBQWFprm5uYTfn29TVePV319vbnjjjvM0qVLzdatW80//vEPM2bMGDNkyBDT0tISk2vsLY51rILBoFm1apVZtWqVyc/PN3fccYdZtWqV2bRpU4f7FIknygXlAOWZ8Uk5rBij3FgO6om5t4rBJ4HKykpz5ZVXmqSkJJOSkmKuueYaU19f33r/1q1bDWDeeOON1mP33nuvAdrd5s2bZ4wxZuXKlaakpMSkpqYav99vhg8fbn72s5/pH5Eu0B3j1ZF+pXM6M17Nzc3mpptuMunp6SYQCJgvfelLZs+ePa33v/zyy+b00083SUlJJjEx0Zx22mlm7ty5JhqNnshL6xV+9atfmYKCAuP1es24cePMO++803rfOeecY77+9a+3af/000+boUOHGq/Xa0aOHGn+/ve/t7nfcRzzgx/8wOTm5hqfz2cmT55sNmzYcCIuJS505Xg1NTWZ888/32RnZxuPx2MGDBhgZs6cqeJiFzmWsTrw7+Cnb+ecc06H+xSJJ8oF5QDlmfFLOawYo9xYDuppubdljDFdM8dYRERERERERERERE5WWjNYREREREREREREJA6oGCwiIiIiIiIiIiISB1QMFhEREREREREREYkDKgaLiIiIiIiIiIiIxAEVg0VERERERERERETigIrBIiIiIiIiIiIiInFAxWARERERERERERGROKBisIiIiIiIiIiIiEgcUDFYREREREREREREJA6oGCwi0sPMmDEDy7K48cYb29138803Y1kWM2bMaNPWsiw8Hg+5ubmcd955PPbYYziO0+bc9957jy984Qvk5OTg9/sZOHAgl19+OXv37j1sLC0tLcyYMYNRo0bhdrv54he/2JWXKiIiIiJySMqJRUQ6R8VgEZEeqH///syfP5/m5ubWYy0tLTz11FMUFBS0aVtaWsqePXvYtm0bL7/8MpMmTeJb3/oWn//854lEIgB89NFHTJ48mYyMDF599VXKysqYN28effr0obGx8bBxRKNREhISuPXWW5kyZUr3XKyIiIiIyCEoJxYROXbuWAcgIiLHbsyYMWzZsoXnn3+e6dOnA/D8889TUFBAYWFhm7Y+n4+8vDwA+vbty5gxYxg/fjyTJ0/m8ccf5xvf+AZLliyhtraW3//+97jd+381FBYWMmnSpCPGkZiYyG9/+1sAlixZQk1NTRdfqYiIiIjIoSknFhE5dpoZLCLSQ1177bXMmzev9fvHHnuMa665pkPnnnvuuZx22mk8//zzAOTl5RGJRPjzn/+MMaZb4hURERER6WrKiUVEjo2KwSIiPdRVV13FW2+9xfbt29m+fTtLlizhqquu6vD5xcXFbNu2DYDx48dz991389WvfpWsrCymTp3K/fffT0VFRTdFLyIiIiJy/JQTi4gcGxWDRUR6qOzsbC666CIef/xx5s2bx0UXXURWVlaHzzfGYFlW6/c//elPKS8vZ+7cuYwcOZK5c+dSXFzMmjVrABg5ciRJSUkkJSUxderULr8eEREREZFjpZxYROTYaM1gEZEe7Nprr+WWW24B4OGHHz6mc8vKytqtpZaZmclll13GZZddxs9+9jNGjx7NAw88wBNPPMFLL71EOBwGICEhoWsuQERERETkOCknFhHpOBWDRUR6sNLSUkKhEJZlccEFF3T4vIULF7JmzRq+853vHLaN1+tl0KBBrTsnDxgw4LjjFRERERHpasqJRUQ6TsVgEZEezOVyUVZW1vr1oQSDQcrLy4lGo1RUVPDKK68we/ZsPv/5z/O1r30NgBdffJH58+dzxRVXMHToUIwxvPDCC7z00kttNuQ4lHXr1hEKhaiqqqK+vp7Vq1cDcPrpp3fZdYqIiIiIHI5yYhGRjlMxWESkh0tJSTni/a+88gr5+fm43W7S09M57bTTeOihh/j617+Obe9fOn7EiBEEAgFuv/12du7cic/nY8iQIfz+97/n6quvPmL/F154Idu3b2/9fvTo0QDagVlEREREThjlxCIiHWMZ/cskIiIiIiIiIiIi0uvZsQ5ARERERERERERERLqfisEiIiIiIiIiIiIicUDFYBEREREREREREZE4oGKwiIiIiIiIiIiISBxQMVhEREREREREREQkDqgYLCIiIiIiIiIiIhIHVAwWERERERERERERiQMqBouIiIiIiIiIiIjEARWDRUREREREREREROKAisEiIiIiIiIiIiIicUDFYBEREREREREREZE48P8BzSMqyZIa740AAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(10)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(10):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + "\n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(int(cell_label), xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " inter_distance = distance_matrix(xy_mean, xy_mean)\n", + " mean_inter_distance = np.mean(inter_distance[inter_distance>0])\n", + " norm_distance = np.max(inter_distance) - inter_distance\n", + " # print(p1)\n", + " # print(p2)\n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", + " \n", + "fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=10\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (10-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (10-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (10-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(Xs_pca_reduced_mds[:, 0], Xs_pca_reduced_mds[:, 1], title=\"PCA Reduced Data (10-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_mofa_mds[:, 0], w4_mofa_mds[:, 1], title=\"MOFA+ Reduced Data (10-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"GFA Reduced Data (10-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples.bck/uci_digits_biomed.ipynb b/examples.bck/uci_digits_biomed.ipynb new file mode 100644 index 0000000..ff3eb26 --- /dev/null +++ b/examples.bck/uci_digits_biomed.ipynb @@ -0,0 +1,542 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: mvlearn==0.5.0 in c:\\users\\paul_\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (0.5.0)\n", + "Requirement already satisfied: wordcloud==1.9.3 in c:\\users\\paul_\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (1.9.3)\n", + "Requirement already satisfied: matplotlib==3.3.4 in 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uninstall: scipy\n", + " Found existing installation: scipy 1.9.1\n", + " Uninstalling scipy-1.9.1:\n", + " Successfully uninstalled scipy-1.9.1\n", + " Attempting uninstall: adilsm\n", + " Found existing installation: adilsm 0.0.8\n", + " Uninstalling adilsm-0.0.8:\n", + " Successfully uninstalled adilsm-0.0.8\n", + "Successfully installed adilsm-0.0.7 scipy-1.12.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: Failed to remove contents in a temporary directory 'C:\\Users\\paul_\\AppData\\Local\\Programs\\Python\\Python39\\Lib\\site-packages\\~.ipy'.\n", + " You can safely remove it manually.\n" + ] + } + ], + "source": [ + "# !pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "!pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.12.0\n", + "\n", + "# scipy==1.12.0 not used (due to changes in SVDS) to reproduce presented results in ref paper" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Obtaining file:///C:/Users/paul_/OneDrive/Pro/George/adilsm/adilsm\n", + " Installing build dependencies: started\n", + " Installing build dependencies: finished with status 'done'\n", + " Checking if build backend supports build_editable: started\n", + " Checking if build backend supports build_editable: finished with status 'done'\n", + " Getting requirements to build editable: started\n", + " Getting requirements to build editable: finished with status 'done'\n", + " Installing backend dependencies: started\n", + " Installing backend dependencies: finished with status 'done'\n", + " Preparing editable metadata (pyproject.toml): started\n", + " Preparing editable metadata (pyproject.toml): finished with status 'done'\n", + 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" Successfully uninstalled adilsm-0.0.8\n", + "Successfully installed adilsm-0.0.8\n" + ] + } + ], + "source": [ + "!pip install -e .." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coucou\n" + ] + } + ], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "from sklearn.metrics.cluster import rand_score\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "X_car_p = Xs[2].copy()\n", + "X_car_p[X_car_p<0] = 0\n", + "X_car_n = -Xs[2].copy()\n", + "X_car_n[X_car_n<0] = 0\n", + "\n", + "Xs_concat = Xs[0]\n", + "Xs_concat = np.hstack((Xs_concat, Xs[1], X_car_p, X_car_n))\n", + "\n", + "# Xs_concat = np.hstack((Xs[0], Xs[1]))\n", + "\n", + "\n", + "for X in Xs[3:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "\n", + "m0 = Xs_concat\n", + "\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "\n", + "# max_values = np.max(m0_nan_0, axis=0)\n", + "# # Replace maximum values equal to 0 with 1\n", + "# m0 = np.divide(m0, np.where(max_values == 0, 1, max_values))\n", + "\n", + "# df_m0 = pd.DataFrame(m0)\n", + "# df_m0.to_csv(RESULTS_PATH + r'\\m0.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "\n", + "list_columns = [str(i) for i in range(m0.shape[1])]\n", + "score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-kar-p', 'mfeat-kar-n', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", + "n_items = [Xs[i].shape[1] for i in range(2)] + [X_car_p.shape[1], X_car_n.shape[1]] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", + "# score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", + "# n_items = [Xs[i].shape[1] for i in range(2)] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", + "n_scores = len(n_items)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error ism before straightening: 0.4\n", + "error ism after straightening: 0.7\n", + "condition number(9, 8) = 3.09\n", + "error: 0.7\n" + ] + } + ], + "source": [ + "n_embedding, n_themes = [9,8]\n", + "\n", + "h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0=True, update_h4_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated_sparse), 2))\n", + "error = np.linalg.norm(m0_norm - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0_norm)\n", + "print('error: ',round(error, 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[302.1345837237795]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1)\n", + "\n", + "n_marker_genes = list_cell_codes.shape[0]\n", + "\n", + "stress = []\n", + "\n", + "w4_ism_mds = mds.fit_transform(normalize(w4_ism[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 3.16\n", + "0.8744\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(10)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(10):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + "\n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(int(cell_label), xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " inter_distance = distance_matrix(xy_mean, xy_mean)\n", + " mean_inter_distance = np.mean(inter_distance[inter_distance>0])\n", + " norm_distance = np.max(inter_distance) - inter_distance\n", + " # print(p1)\n", + " # print(p2)\n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=10\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (10-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/uci_digits_gfa.ipynb b/examples.bck/uci_digits_gfa.ipynb new file mode 100644 index 0000000..5044b00 --- /dev/null +++ b/examples.bck/uci_digits_gfa.ipynb @@ -0,0 +1,1151 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/100: 132 iterations with final cost -1369484.296532\n", + "Run 2/100: 133 iterations with final cost -1369466.989440\n", + "Run 3/100: 133 iterations with final cost -1369407.701255\n", + "Run 4/100: 133 iterations with final cost -1369500.475922\n", + "Run 5/100: 134 iterations with final cost -1369414.021918\n", + "Run 6/100: 133 iterations with final cost -1369473.535264\n", + "Run 7/100: 133 iterations with final cost -1369485.893255\n", + "Run 8/100: 133 iterations with final cost -1369459.946172\n", + "Run 9/100: 134 iterations with final cost -1369416.110993\n", + "Run 10/100: 133 iterations with final cost -1369456.055691\n", + "Run 11/100: 133 iterations with final cost -1369467.927528\n", + "Run 12/100: 132 iterations with final cost 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"cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1640.2007565428682]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1)\n", + "\n", + "n_marker_genes = list_cell_codes.shape[0]\n", + "\n", + "stress = []\n", + "\n", + "w4_ism = model['Z']\n", + "\n", + "w4_ism_mds = mds.fit_transform(normalize(w4_ism[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9 4.45\n", + "0.9001\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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OOOEE7r///jrByvr165tU6WlFUQ49tQxMURRlN6ZNm1bv9ur8ij0tUbrtttuYMGEC9957b4ONZ9iwYQwaNIinnnqKUChERkYGw4YN4/nnn683cMrPz6/579NPP52ff/6Z2bNn1/r6f//73zrHZWdn8/3339fa9sILL9SZWTnvvPNYuHAhH374YZ1zVC9tGjNmDFu3bq23iWYwGKSyshJwe8oA/POf/6y1z1NPPVXnuP21ceNGLrvsMjweD7fffnvNdl3X6yzBeu+992ryfKrFxcUB1AngqvuX7HwOKWWtsrx70r9/fzweD3Pnzt3ne9nV2WefjWma/Otf/6o1hueee46srKyaqmXdu3fn5JNPrvno378/4M7YaZrGxIkT68wE7Wl5Wn33PmvWrDqlkqvzcqppmkbv3r0Bapav7bpPfHw8HTt2rLO8rbS0lLVr19bck6Iovw1qZkVRFGU3zj77bNq3b8+oUaPIzs6msrKSr7/+mv/9738MHDiQUaNG7fbYPn36HJJ3gKsb/b322mtcc801PPvssxx33HH06tWLK6+8kg4dOpCXl8fMmTPZsmVLTc+QO+64gzfeeIPTTjuNm266qaZ0cdu2bevkB4wfP55rrrmG8847j1NOOYWFCxfy5Zdf1pkhuv3225k8eTIXXHABV1xxBf3796eoqIiPP/6Y5557jj59+vD73/+ed999l2uuuYZvvvmGY489Ftu2WbFiBe+++y5ffvklAwYMoG/fvlx00UX861//orS0lCFDhjBt2jTWrFmzX6/PvHnzePPNN3Ech5KSEubMmcP777+PEII33nij5kEZ4Mwzz2TixIlcfvnlDBkyhMWLF/Pf//63TsPB7OxskpOTee6550hISCAuLo7BgwfTtWtXsrOzue2229i6dSuJiYm8//77FBcX79NYfT4fp556Kl9//TUTJ06s9bVFixbV9MpZs2YNpaWl/PWvfwXcn63qn71WrVpx88038/e//51oNMrAgQP56KOPmDFjBv/973/32hCyY8eO3HPPPTXFI84991y8Xi9z5syhZcuWPPTQQ/Ued+aZZ/LBBx9wzjnncMYZZ7B+/Xqee+45unfvXpMbBO7PUlFRESeeeCKtWrVi48aNPP300/Tt27em3HL37t0ZNmwY/fv3JzU1lblz5zJ58mT++Mc/1rrm119/jZSyVsNVRVF+AxqlBpmiKEoTU1/p4kmTJskLL7xQZmdnS7/fL30+n+zevbu85557ZFlZWa3jiZUu3pP9LV2881iq2bYts7OzZXZ2dk052LVr18pLLrlEZmZmStM0ZVZWljzzzDPl5MmTax27aNEiOXToUOnz+WRWVpZ88MEHa8oz71y62LZteeedd8q0tDQZCATkiBEj5Jo1a+qULpZSysLCQvnHP/5RZmVlSY/HI1u1aiUvvfRSWVBQULNPJBKRjzzyiOzRo4f0er0yJSVF9u/fXz7wwAM1ZYSllDIYDMobb7xRNmvWTMbFxclRo0bJzZs371fp4uoPwzBkamqqHDx4sPzzn/9cbxnhUCgk//SnP8kWLVpIv98vjz32WDlz5sw6ZXqllHLKlCmye/fu0jCMWmWMly1bJk8++WQZHx8v09LS5JVXXikXLly421LHu/rggw+kEEJu2rSp1vbqn4H6Pnb9Hti2Lf/2t7/Jtm3bSo/HI3v06CHffPPNvV57Z6+88ors169fzfdn6NChcurUqTVf3/U1cRyn5pper1f269dPfvLJJ/LSSy+tVWZ58uTJ8tRTT5UZGRnS4/HINm3ayKuvvlrm5OTU7PPXv/5VDho0SCYnJ0u/3y+7du0q/+///k9GIpFaYxw7dqw87rjj9uu+FEU58gkpD6IMiaIoiqIoB8y2bbp3786YMWN48MEHG3s4TVZubi7t27fn7bffVjMrivIbo4IVRVEURWlE77zzDtdeey2bNm2q6QWj1HbXXXcxffr0WjlXiqL8NqhgRVEURVEURVGUJklVA1MURVEURVEUpUlSwYqiKIqiKIqiKE2SClYURVEURVEURWmSVLCiKIqiKIqiKEqTpJpCHgaO47Bt2zYSEhIQQjT2cBRFURRFURTlkJBSUl5eTsuWLdG0g58XUcHKYbBt2zZat27d2MNQFEVRFEVRlMNi8+bNtGrV6qDPo4KVwyAhIQGAX35ZT3x8QiOPRlEURVEURVEOjYqKcvr3b1/z/HuwVLByGFQv/YqPTyAhIbGRR6MoiqIoiqIoh1ZDpT6oBHtFURRFURRFUZokFawoiqIoiqIoitIkqWBFURRFURRFUZQmSQUriqIoiqIoiqI0SSpYURRFURRFURSlSVLBiqIoiqIoiqIoTZIKVhRFURRFURRFaZJUsKIoiqIoiqIoSpOkghVFURRFURRFUZokFawoiqIoiqIoitIkqWBFURRFURRFUZQmSQUriqIoiqIoiqI0SSpYURRFURRFURSlSVLBiqIoiqIoiqIoTZIKVhRFURRFURRFaZJUsKIoiqIoiqIoSpOkghVFURRFURRFUZokFawoiqIoiqIoitIkqWBFURRFURRFUZQmSQUriqIoiqIoiqI0SSpYURRFURRFURSlSTIaewCKoii/JlVVsGSJIC8P4uKgZ09JRkZjj0pRFEVRjkwqWFEURWkghYXw2msaK1aAlAIpISNDctFFDr17N/boFEVRFOXIo4IVRVGUBjJ1qmDJEkGnThKPRyIlrFsHkydrZGc7xMU19ggbn5QQLYDIdnDCYCSCNxP0+MYemaIoitIUqZwVRVGUBlBVBQsWCNLTJR6Pu00IaNMGcnNhzRrRuANsIkKboHIJRHLBKofQBihfDFZZY49MURRFaYpUsKIoitIAbNv90PXa23UdHAei0cYZV1NiB91gBQOMFDASQE8Bp9LdLmVjj1BRFEVpalSwoiiK0gDi4yE7G/LzRa2H7u3bITlZ0K6dehK3y0BGQAvs2CYECD9ES0BajTY0RVEUpYlSOSuKoigNQAg45RSHTZs0li8XJCRIQiF3ZmXUKIe0tMYeYRNQ/faYBHZeFSdjQYtaKacoiqLsQgUriqIoDSQ7G667zmHmTMG6dYKUFBg4UNKnj5pVATCSQA+AXQ56ohucSBtkCDxtQKi/SIqiKMou1J8GRVGUBtSqFVxwgcSdPlB2pnnA3xGqVoJVHJtcEWCkgq9NY49OURRFaYpUsKIoiqIcNp50d3YlWujmqGgBMJuBZu7+GClBxgoUCFMtF1MURfktUcGKoiiKcljpce7HvrArILgJokWABDMFfG3dSmKKoijKr5+qBqYoiqI0SU4IKpZCeBsgQGhuf5bKpW4ZZEVRFOXXTwUriqIoSpMUyXeT8Y1k0P2g+d2+LFYFRLY39ugURVGUw0EtA1MURVGaJLsC0NwZlWpCgNDdIObXSIgSDGMZQkSxrA44Titq13lWFEX5bVHBiqIoitIkCQ9Ix02w3zmpXtqgeRtvXIeKac7C7/8vmpYHSKRMIhw+mVDofEBv7OEpiqI0CrUMTFEURWmSPOluuWO7PBa0OGCVu9vM9MYeXcPStG34/a8jRCmW1RXL6oGUXny+jzHNWY09PEVRlEajghVFURSlSTISIdDZnUWxS90PzYRAJzCTG3t0Dcs0F6Jp+dh2B9xZFIHjZADg8fzcqGNTFEVpTGoZmKIoyhFOytjsQ9TtW6L7G3tEDcebCWYqWKXu50aSO7PyayNEEDc3pXZ+ipQ+hChulDEpiqI0BSpYURRFOYLZQaha5XaEl7ab5+FtAf72biL6r4HmcZeE/ZrZdhbujEoQqI42HYQow7JOOqxjEaIQw1iPlDqW1QUIHNbrK4qi7EwFK4qiKEco6UDVSogUgB7vLpFyQhDa4HZ697dt7BEq+yoa7UM02gfTnIvjpAIGur4dy2pNJHL8YRqFxOv9DK/3UzStENCw7dYEg7/DsvocpjEoiqLUpnJWFEVRjlBWGUSLQU9wZx+EcJeACQ9EctyZFuVI4aOq6lpCoQuQMg7QCIVOoqrqRhyn9WEZgWn+gs/3LmBjWd2wrGx0fSuBwMtommpsoyhK41AzK4qiKEcoGQFsELv8JhcmOBGQ1q9nKdhvgZRJhEJjCYXOBRzg8NZnNs2ZCBHBtrNjWzxYVicMYxmGsYBI5NTDOh5FURRQMyuKoihHLM3nBiYyUnu7E47NsJiNMy7lYJkc7kAFQNPykXLX/BQt9rVfaRdORVGaPBWsKIqiHKH0BDDTwKkEuwqcqNuHBAe8WbU7vyvK3th2NkKUAXKnrRFAYNuZjTQqRVF+69QyMEVRlCOUELE+JB6I5IEMge4DbyvwtGjs0SlHmkjkBExzDoaxDNtugRBRNC0Py+qFZfVt7OEpivIbpYIVRVGUI1h1k0Rf21ifFW/dHBZF2Re23Z6qquvxej/BMNYgpU44fBrh8FmxpH9FUZTDT/1JUxRF+RXQPMCvsFmicni5VcC6IkQJYCBlQmMPSVGU3zgVrCiKoiiKshOBlCmNPQhFURRAJdgriqIoiqIoitJEqWBFURRFURRFUZQmSQUriqIoiqIoiqI0SSpYURRFURRFURSlSVLBiqIoiqIoiqIoTZKqBqYoiqI0uihRdHQ09R6acgTTS9dg5v2MXpWDHZdFtPkx2IntD+qcThSiBeBUgTDBbAa6anuj/IaoYEVRFOU3LhRyPxISQNcP77XX6qv41juVNcYKfNLP0ZHjOD58MgECh3cginKQzO1zCSx/CREuQhp+zO2z8eT8QFX3q7DS+h7QOe0gVC4DqwSQIAHdB4Eu4ElvwMErShOmghVFUZQjlAxLiABxIDSx38dXVcHXXwtmzRKEQoIWLSTDh0uOOkoi9v90+229voYX4/5JgbadVCeNIq2Qd/1vskXfzGVV16BzmCMnRTlQdhjv+skQrcBK6QFCgJQYpSvxrf+QitSeoO3/I1doE1jFoCeB0EFKsMsguAaMpFgzWEX5lVPBiqIoyhFGVknkNAs5y4awhFYa2kkGoue+P9xLCe+8I/jhB43kZInPJ1m7FjZt0tA0h3795CG8A9d3nq/J17bT1eqBwI2OykUZ8zyzOC4yjC5Wj0M+BkVpCHrFJoyKbVjxraiJ9IXAjstCL9+EXrkFO6Hdfp3TsdzlX8LnBiqxU6IngF0KVqmaXVF+G9TiYEVRlCOIdCTO21HkR1FkUCI9ApY7OK9EkEvsfT7Pxo0wb55Gy5aSFi0gJQWysyESkXzzjcBxDuFNAA4Oq8zlpDipNYEKQIJMJEyIbfqWQzsARWlIQkMKgZC7BPlSghBIcQCzhNI9nF1nOas/P8T/RhWlqVDBiqIoypFknYOcZyNba4iWGiJVQCcNKiXOdxZy14el3cjLEwSDksTE2ttTUyEnx10idigJBPFOAmERqrXdxgYEfqlyVpQjhx3fFjuhHXrFJpCxKEI66JWbsZKyceKy9vucwgAzGWQwFrTEOFUgPKAn7vZQRflVOeKClWeffZZ27drh8/kYPHgws2fP3uP+7733Hl27dsXn89GrVy8+++yzWl8XQtT78fe//71mn3bt2tX5+sMPP3xI7k9RFGVPZJ5EhCUiofbbrTJFwGYHwvt2nrg4iWFAJFJ7e1UVBALg9TbQgHdDIDgmejxBEaREFCORWFhsMNaSabega7TnoR3AQZJSUlRUyLJli5gzZyazZ//ErFk/MmvWD8yc+T2zZv3AihVL2L49l2g02tjDVQ41zSDUcSxOIAOjeClG8TKM4qXYcVmEsseA2P/HLSHA1wa0OLCLwSp381ew3e26v+FvQ1GaoiMqZ+Wdd97h1ltv5bnnnmPw4ME89dRTjBgxgpUrV5KRkVFn/59++omLLrqIhx56iDPPPJO33nqL0aNHM2/ePHr2dP8Q5uTk1Drm888/5w9/+APnnXdere0TJ07kyiuvrPk8ISHhENyhoijKnomAQGoCGZUIc6eAJQikCzD37TydOkH79rB6taBdO4nXCyUlUF4OI0dKzH08z8EYEh7GVm0zszw/kiu2IYDmdksuDF5Kskw59APYB8FgkMWL57NgwRwWLZrH1q2byc3dRl7eNkKh0N5PgPumWHp6c1q0yKJt2w706NGHXr360qNHH9LTmx/iO1AOFyulOxX9/oyZ/wtaqADHn0E0fQCO/8ATS4xESOgN4RywykDzgicDzLQGHLiiNHFC7uuagSZg8ODBDBw4kGeeeQYAx3Fo3bo1N9xwA3fddVed/ceOHUtlZSWffPJJzbajjz6avn378txzz9V7jdGjR1NeXs60adNqtrVr146bb76Zm2+++YDGXVZWRlJSEitXFpCQoOZtFUU5cDIocR4Lw2YH2V5zg5NiENsl4kIT7aR9fw9qyxZ46y2NdesEliVJSBAMHOhw3nnykM+sVJNI1utr2Kpvxit9dLV6kCiTDs/F6xEOh5kxYxrTpn3OvHmzWb58MZZl4fP56NGjD23atKdFiywyM7No0aIlmZlZJCQkIIQABJqmIYTAtm1KS0soKiogP387ublbycnZytq1q1i6dCEVFeUANG/egh49+nD00cdz0kmn0bVrz9i5FEVRjkzl5WV06ZJGaWkpibuuNT4AR0ywEolECAQCTJ48mdGjR9dsv/TSSykpKWHKlCl1jmnTpg233nprrSBjwoQJfPTRRyxcuLDO/nl5ebRq1YrXX3+dcePG1Wxv164doVCIaDRKmzZtGDduHLfccguGUf9DQTgcJhzesRajrKyM1q1bq2BFUZQGIdc5OG9HYKN0k2zjgUE62nkmwrN/D7qRCKxdC1VVgubNJVlZHJayxU1JKBTiu++m8umnH/DVV59QVlZKu3bZDBo0hH79BtGv3yC6deuJ2UDTTY7jsGnTepYuXciSJQtYtGg+P/88g2CwihYtWjF8+KmcdNJpHHfciepvhqIoR5yGDlaOmGVgBQUF2LZN8+a1p8ybN2/OihUr6j0mNze33v1zc3Pr3f/1118nISGBc889t9b2G2+8kaOOOorU1FR++ukn/vznP5OTk8MTTzxR73keeughHnjggX29NUVRlP0iOmhot3phjQNBCS00yBIH9I68xwPduoHbbu63ZcuWTbz00tNMmvQq5eVldO7cjfHjb+DMM8+lS5ceh2yGQ9M02rXLpl27bM44w/17EwqFmDXrB7755kumTfuct956Ba/Xy8iRo7nwwss47rjhaNoRl2aqKIpy0I6YYOVweOWVV/jd736Hz+ertf3WW2+t+e/evXvj8Xi4+uqreeihh/DWs1biz3/+c61jqmdWFEVRGorwCdiPvirKDkuXLuTf/36CKVPeJT4+gcsuu4bzzhtH587dG21MPp+PoUNPZujQk7n//r+zadN6Pv30QyZNepULLxxJVlYbxo69hLFjL6F163aNNk5FUZTD7Yh5myYtLQ1d18nLy6u1PS8vj8zMzHqPyczM3Of9Z8yYwcqVKxk/fvxexzJ48GAsy2LDhg31ft3r9ZKYmFjrQ1EURWlca9as5Pe/P5tTThnIrFk/MmHCo8ydu44///mvjRqo1KdNm/Zce+2tfPfdIj7++HuGDj2Z559/isGDO3PZZeeyYMHcxh6ioijKYXHEBCsej4f+/fvXSnx3HIdp06ZxzDHH1HvMMcccU2t/gKlTp9a7/8svv0z//v3p06fPXseyYMECNE2rtwKZoiiK0rRUVVXx8MP3ctJJR7F69QqeeeY1fvppOePH30BcXHxjD2+PhBAMGHA0jz32HAsXbuaxx55nzZqVnH76EMaNO5NZs35s7CE2ebq+Cr//ZeLj7yUQeBbDmM/hWPYYicD8+YKPPhJ89plg7dra/VIURdk3R9QysFtvvZVLL72UAQMGMGjQIJ566ikqKyu5/PLLAbjkkkvIysrioYceAuCmm25i6NChPP7445xxxhm8/fbbzJ07lxdeeKHWecvKynjvvfd4/PHH61xz5syZzJo1i+HDh5OQkMDMmTO55ZZbuPjii0lJaRqlNRVFUZT6ffHFx9x335/Iz8/lj3+8gz/+8Q78/iOzQUUgEMe4cZczduwlfPLJ+/zjHw9xzjnDGTKkF3fccQWDB/8OKZMbe5hNimHMJy7ueYQoQsoEDGM1pjmXYPD3RCInNvj1pA3RAqjcDjN/hrnLYXOZwHYECQlw+ukOp54qf3NFLBTlYBxRwcrYsWPJz8/nvvvuIzc3l759+/LFF1/UJNFv2rSpVgLikCFDeOutt/jLX/7C3XffTadOnfjoo49qeqxUe/vtt5FSctFFF9W5ptfr5e233+b+++8nHA7Tvn17brnlllo5KYqiKErTEgqFuOeem5g06VWGDx/BO+98Tvv2HRt7WA1C13XOPvscxoyp4OuvJ/HIIwsZPfoWRo9+hvvue5zMzNMbe4hNhI3P9zFClGFZPQA3QtD1Dfh8U4hGByJlw/VMkzZUrYRwLmzfDmYxDG8vKZSStRFBbp7g8881una1adu2wS6rKL96R0zp4iOZ6rOiKMqRzolAZDvY5SBMtymdkdQ0yxxv2rSeK6+8kNWrl/O3vz3N2LGX/Op6l3g80wgEXsS2M3GcZN57bwl/+ctXlJZa3Hjjn7j66nvqFIv5rdG0bSQk3IvjJCNr9e6JYhhrqai4G8vqudvj91c4FyqXghYPs+dCUTGkJ0NAgxVhQYElWLFCMHasO7uiKL9WDV26+IjJWVEURdlfUkJpKVRVNfZIjmx2ECoWQdUKt5N2cANULITQlsYeWV3Tp3/BaacdTWlpCR9//D0XXnhpEwpULAxjEV7vJ3g8U9G0+svo7wuP5wek9CJlKkJojBnTm19+uZ5rrmnH448/xvDhffn6688acOxHIhPQAXuX7VFAR8qGXVxiFbv/r5lgO6AJsHAD+iR9x9Ive9fhKIqyR0fUMjBFUX578vJgyxaBxyPp1An29c3iVavgq680NmwQ6LqkXz/JiBHuu5lFRZCUBGlph3DgvyLhLe6DmJ4CIvYWl10JoQ3gaQZ6oFGHV+O1157jnntu4sQTT+Ppp18jObkp5RUGCQRexuP5GfdhWeI4aQSDFxONDtnvswlRhpS1S+cnJPj5v//ryZgxN3HnnVO45JLRXHTR5Uyc+HiTLyRwKDhOGtFoDzyeH7CsBNzgxcYwNmJZnbDt7Ia94E6TJRnpUFggsB3pxktAWRl4vdC+vZpVUZT9oYIVRVGaJMuCTz4RfP+9RlmZRNcFrVvD2LEOnTrt+dj16+HllzWKigQZGZJoFL74QmPaNElCAoRCAr8fjjrKYfRoSVzc4bmnI5F0IJoPwrcjUAHQAmCXgFXa2MGKRNc38u9/P8j997/BVVeN4b77XkLTmtYSKK/3Gzye77HttrE8CYmur8fv/y+23RHH2b/qkpbVHa/3Cxwni+pcDCEqAA/Z2UczadJVTJr0Gvfeews//zyDZ555nX79Bjb4fTVtglDofDQtH8NYFdsmse1WBIPjcIOXhmOkujOPThRat4bt+ZLyIvDEwdIcQW45nHDC3n9/KYpSm1oGpihKkzRrluDzzwWmKenSBdq2hU2bBG++qVFevudjf/pJUFAg6NxZkpICGRnuUoxffhGUlEBWlsTjkUybpjF5slDlRPdity9PE3jdPJ5pvPDCRdx//xvceWdnnnzSJiHhOYSobOyh1WKaPyJl3E4J3QLbbhd7kF663+eLRIbjOC0wjCVoWi66vgldX08kMhDL6oEQgnHjLmfq1DkkJSVz1lkn8NRTf8P+ja1BcpwsKivvpKrqekKhMVRVXU1FxV+w7a4Nfi1POniag1MBRgiO6gQ9O0HIDxmd4IorJGPHSnTVy1VR9ouaWVEUpUmaOVOg65Ce7n7u8UCHDpK1a2HZMsHgwbt/Ul63TpCYuGONeCQCublgmu55vF73Q9MkCxZonHqqTYsWh+GmjkBCAzMdwhtA+nfMrjhVILxukn1j0bStvPbaRO699xfuuOM47rlnGI5TiWn+hMfTiXD4jMYbXC0SIYJI6dllu/tiChHe7zPadgcqK2/E4/kK01yO46QQiZxJJHIKO/9p79ChE1OmfMcTTzzIY49N5LvvvubFF98mLe230ydMygQikRMO+XWEDnHd3OITVhF4BST3g25pIPQmENkryhFKzawoitLkSOnmlQR2WV5kGCCloHIvb5qnpkIwuOPzcBjCYTf4MXda+ZGQ4Cbfl5Q02NB/lXytwEiOLfsqcR/EpA2+No27BOzbb//D7bf/zA03DOaee4YCxGYv4jDNnxtvYHUILKsXmlYIODu2ihKk9GNZ7fZ8tCjE4/kKn+9tPJ6vEKIQANvuSDB4HWVlT1Be/jDh8GikrLum0TRN7rxzIu+//zVr167ijDOOY8WKJQ15g0qM0MGbCXHd3cDF29zdpijKgVPBiqIoTY4QkJ0NJSW1l2hVVbkBS0bGnt+lHDTInVXJywPHAV2HUEji8bhLwqqVlbkBkervume6H+J7Q6CLu8zF2wYServBSmNZv34NV1/9KKed1pwHHzx5l4pfBkKEGm1s9QmHT8a222AYS9D1zej6GnR9G5HIcdh2590ep+uriY9/iEDgRXy+DwgEXiI+/iF0fc1Oe1VXvdqzwYOP47PPfiQ+Pp6zzhrK9OlfHPyNKfskQpQI0cYehqIckVSwoihKkxONQs+eDvHxsHq1oLjYDTw2bBD07OnmsOxJ//6SUaMkUgpWrYItW6BjR0lGhqSsDEIhKCiA3FxBv34Osb6yyh5oXvC1hvgeENcZzNSD77Giafl4PF/j9X6Maf4CRPbpuKqqSsaPH0OzZmm89NLx6HrJTl+1EaKYaLTfQYwsgq6vwDCWAcG97r0vHKc1lZW3Eg6fjeOkYNudqKoaTzB4Kbv/U2zj872Drq9BSk8sAHMwjOX4fO+w8yzNvmrVqi1TpnzH0UcfzyWXjOall55GtVs7dMpFOQvMRXzrm8G3vhksMBdRLir26ViJpFSUUaAVEmxiwbeiHE4qZ0VRlCZDSjexfvp0QX6+IBh0H6IqKwWBgOT0091mauZeivhoGpx+umTQIJtNm9wk/TZt4NtvBTNnCnJy3Gpgp5zicNZZskk2Nvy1M4z5BAKv7tRrRCca7UdV1dW7NPCrTUrJn/50NRs3rufTT2cQCMxB178CtiOlB00rJxrtSCRy4gGOazE+3zsYxkbAwbZbEAqdRzR6zAGdb2eO04pg8Pf7vL+ub8Y0F6PrubGCARpugKLj8fxIKLQZ297/Vujx8Qm8+ur7/PWvd3PffX9iw4Z1TJz4OJqm3r9sSEER5BfPQkr0EgKOu15yg7mJUq2MQZEB+OXuK9ZViiqWmssp0AuxsPBJL22s1nS2OqKp95mV3xgVrCiK0mTMni144w0NkDRrJjEMKCwUdOrkcPnlsk4Oy96kpUFa2o53jc8+WzJsmKSoCBIToVmzhh2/sm+EKMPv/w9CFGFZ3XEfwqswzdl4vW0IhS7c7bGffvoBU6a8y7///SZduvQkGOyCbWdjmnMQooJwuAeRyAn7XQoYQNNyCQReRNMKsKy2gIaubyEQeJWKilRsuz2GsRRNK8Nx0rCsruzL8qvd0fUNGMYCNK0C225FNNp/p2phADa6vgkhynCc5lTPwGhaAbq+ESGKgf0PVtxr60yY8Ajt22fz5z/fQCgU5JFHnkVXpaoazDY9lxK9hGZ2ak2A4bd9FOpF5Oi5dNhNrpKDwyLPEvK07SQ6CRjEExRBVplr8OKlvXVg33NFOVKpYEVRlCbBtt2ZDyklHTq42xITweeTrFypsX27Tbt2B3+dpCT347fOKoNIPsgo6PFu2VXNu/fjGoJhLEfXc7CszuxYAhXAcVIxzZ8Jhc4Fdq2cBeXlZdx3358YMWIUZ589JrbVJBI5oUGqPZnmXDQtB8vqSXXvEtvugGEsxev9DE0rjfXrsAEP0WhvqqquRMrkA7jWDwQCbyBEUc21LKsHVVXX4ThuCTzHScJdGiepu1RMIMTBL1G75JKr8Pv93HLLlUQiYZ588iUVsDSQUlGKLvVaMyEaGprUKBWluz2uUCumUCsk2UnCjPWCiZdx2MJmo76ZtlZrNbui/KaoYEVRlCahshLy8wUpKbXXzycmQk6OJD9f0K7dr39tfXX+gDiEa9PCOVC1GpxILO9EQiQH4nocnupeQkSoXs5UmwchooBFfcHKY49NpLS0hAcffPKQjEvTSnCDgp1fe4GUfrzer5AyHsvqBPgQogKPZxaOk0IwOD52/DY8nh8xjNXYdirR6CAsq99O5wtimkvQtA34/e8hpXenwCiCYSzG6/2MYPB3uK+NNzabsxJNy8dNpI8A3thMS8P8Cb/ggt/j8Xi5/vpL0DSNJ554US0JawBevDiibl8bB4mX3b8zEBZhbJyaQKWaR5qEtTBRons8XlF+bVSwoihKk+DzQSAgyc2lpuljs2Zu/olpChIS9j+Z+EjilBQT/f477PnzAdD79cM8/gS0lNSGvU4Yguvc/CAjxQ1WpAPRYghtgriG75VXh2W1Q8okNK2gZhYBJJq2nUjkOMBf55jFi+fz8svPcM89f6NVq0NThswdi8SdOakOpCSalo8QVbHAws0zkDIe287E4/mFUOg8NK2EQOBpdH0zUsZhGCG83p8IBscQDp+Jrq8iLu4xDGMDUoYxjE3YdlscpzlSJuL+OY4QCPwL05yNbWcTDp9EOHxirDO9GyBJmYiUOo6TGQucGsbZZ4/Btm1uvPFyDMPk73//9yENmH8LMu3mbDI2UybKiJfxAJSLCrx4yLR3X9XDL30Y6ESI4NkpaA+JMElOYp0gRlF+7VSwoihKk2AYbqPG+fPdfigej7stPh5OOEGSnd3YIzx0ZHk54Zdfwl6+DJGUBEIQ/d8UnNWr8F11LSIhYe8n2UdWCThB0FN2VPMSGmh+iBaCtEAc4r8MjtOKcPhEfL7/xfIu/AhRguNkEg6PpPbMhuuBB+6gc+dujB9/wyEbVyQyAI9nGoaxHNvOws1Z2YbjJKFpBlLWDqKk9CNEIUJU4vV+iq5vxrJ6sCO3ZCs+36dI6SE+/m/o+gakjIu97k6se/0SotFjMIxlGMZK3CDJwjRnYxjLCQbPwbJ6o+vrcZxMhIggZSKh0LkHtPxsT8499yIsK8rNN4+nZctW3HrrX/breE3LwTQXIEQFtt0Cy+pXb9+XI1GFqGSjsYntWj46Oll2S9pYrfYYOKQ6KXSPdGOVuZoirRiBwC/9dIl0JNXZfb30VCeFDDuDrfo24mQAQ5oEtSAgaWe1UUvAlN8cFawoitIkLFnilhhu0wZKSwXhsNtXRdclxx3n7LUC2JHMWjAfe+UKtE6dEbEblekZ2CtXYs3/BfOEYQ17wfreMI8tB5O7+XJDDyAUGoPjtMI0f0LTirCsIUQiw7Dt9nX2/uWXWfz003e8/PK7mIfwB0HKZlRVXYvPNzkWOEgsqxvh8In4/W+hafk4Toua/d3PmyNlAMNYVisJHsBxWmCa84iLeyoW9LRASj+aVoymBZHSh67nY9s56Pp63OpjXXGcLBwnC8NYgWnOo7LyT5jmbHR9A46TSjTaH9veS/3uAzRmzCVs27aFRx+9n3btsjn33Iv26TjT/AW//xU0bTvuT5DAsrrXysE5UlWKSuZ65lGileKTXhzhsFhbRrFWTL9IH/TdFFkQCNrarclw0inWigFItVPwsfsqYNXH9Yr0wGOa5BrbCYoqfNJPe6streysBr8/RWnqVLCiKEqTsGCBwLIExxwjqaiQVFa6JYdzcwXbt8eepH+lnA3rwDBqAhXA/W/DwFm3DhowWNGTQHjBqXQT68FdEuYEwZcF2mH7q6ATiRxPJHL8Xvd89tm/k53dmREjzjrko7Lt9lRW3hYrqezgOJmAjq7n4fNNRoggUiYgRDFC2Nh2Szyer2OBS/IuZ3MQohSwkNKPlBIhgjhOIkKUommVSBlF11eiadtxnNZYVsedxpKBYWxGSpNweNQhv/dqN930ZzZsWMutt15JVlYbBg8+do/7C1GBz/dfhCirJwfnE4LByw/LuA+VzcZWSrRSmjmxql4SokTJ0fNopRWSWU/lORubSlGFjk5A+mlpt6jnzHWFCbPF2EaunodA0Cbamgy7GYkyCVM9sim/UeonX1GUJqGiwg1OwF36FR/v/nd+vjvDcriVlBSzatVyVq1axrZtmykuLqKoqJDi4iIqKsoIhYIEg0FCoSCappGQkBj7SNrlvxNo3botXbr0pFOnrvj9dfMx8MeBbdXZLGyL/a7XvBe6D/xt3bwVqwjQQdpgJLhNH5ua1auX88UXH/P44y8cxqRvUWsGBSAUGo3jJOPxfIOmFeE4abGmlj8Cbk8Uw1hOOByPlCmARNc3ImUcUjroeh6mmYOURuzDg5RxOE5zLKsjmuYQifSr1WNGiDBSeqBWMnUIXc9BSl8skGr4eTAhBI8++m+2bNnEFVeczyefzKB9+4673d8wVqLr22I5NNXj8eA4GZjmXEKhMUf0crB8rRAPnlrLr0xMHGFTppXVClYkkm16DmuM9VRqFWhopNlpdIt2Jm4vr0GECPO8C8nTt2NK942LfD2fMiuToyJ99nvc0WiUvLxtbNu2lZycLeTkbCU3dxuhUBDbtrFtG8dx8Hg8eDxePB4vfr+fli1b07p1W9q0aUfLlq3xeOoWu1CUw0kFK4qiHDaOA6Wlbi7KrmkY2dmSuXM1LMvtrwIQibjVsVofwofoqqpKFi9ewOrVy1m5chkrVy5j9erl5OXlAKBpGs2btyQlJZXU1GakpKTSpk07/P4APp8Pn8+P4ziUl5fFPkopKSlm8+YNlJeXUVZWSn5+HuA+BKand6RDhx7079+Nnj170L17L9r36IE14zuc7XmIdPfBR+bng8+H0bNXg9+ztxXocRApABkBPQE8GaDXE0c1tn/96wkyM1vu83KkQ0cnEjmJSGQ4UEF8/N8RIhfL6gbo2HYrvN4v8Xh+wLbbI4RESg3bTsbn+wZ3dkXizrZE0LRCpAxg260wjEKklOj6WiwrAbDRtAI0LYdQ6MLYg77E45mB1/tJbNZHJxrtQyg0tk5g1RA8Hg8vvvgOo0a5ne6/+OJn4uLid7O3hTvzuetyKAOI4hYsOHKZmNi73EOZVk6RKGGpZzkVopI2divSnGZs1/JZ6FmCg0Oc45Yb3qJvJSRCDA4P3OPsSI6ex3YtnxQ7BSP2WlpY5Op55Op5tLZb1Xuc4zisWrWMOXNmMmfOT6xZs5Jt27aQn59XU10QIC4unszMlvj9AXRdR9d1hNCwrCjhcIhIJExVVRV5eTm1qhJmZmbVBC8dO3Zh8ODj6Nt3IF6vqkimHB4qWFEU5bBYuRK++kpjwwaBrkt695aMHClrGjMOHCiZO1eyapVbvlhKKCkR9Ogh6du34ZaASSlZunQRb731Nd999xWbNv2IbUfQdZ127bLp3LkbF110GZ06daNLl+506NAZn2/Pa8z3ZsaMCl59dTlbtiylomIpa9YsZfHi16isdAOizMwshnfuwQnoHL09jzjTRMQnYJ52Blq37g1x27UIAWaq+9GUVVZWMGXKu9x885+b0IORm3RvGCsAHcNYEZshaUEkcgK6vo1w+GQ0rRTTnIeuFwF2rCSzgZRarD+KQEofmlaGEFsQogJd1zGMNQhhAWHAixBvoOubcZwWeDxT0fWcWHWwMKY5H8NYRVnZE7DbPAiJGzCY7O8sTEpKKq+//iEjRgzmnntu5qmnXqp3P9tuj5SpaFoOjlOdUyHRtFwikSG7NLo88rS0Mtnu2U5IhPBJH4VaEbl6HiDQbYPNxha2G/n0Dfdmi7EFC4tmTuwflzTxSJMirZgCvYAWduZur1OoFSEQNYEKgIGBQFCkFdcEK1VVlcyfP4e5c2cyZ85MfvnlZ0pLS9B1nR49+tCjRx9OOmkkLVq0omXLLFq0yKJFi1YkJu5bg6lIJMK2bZvZvHlj7GM9mzdvZOPG9Xz55f94+OH78Pl8HHXUYI4++niOOeYEjjpqcP2zxorSAFSwoijKIbdhA7zyikZxsSA9XWJZMH26Rk6O5PrrHQIBSE2FK690+PZbwcKFAk2D4493GDZMEneQK0gKCwv4/vuv+e67qXz33dfk5eWg6wGaNRtKr16PEAgMpWPHLlx1lVnTkLKhlJXBF18k4vcP5pRTBiMEhMOwdq1g6NB82rb9hW+//Ypp0z5n0tpVeAyTwd16cdLJp3NS545k/4bLx3799WeEQkFGjx7bYOfUtHxMcw6athUpU4hGB2Lb9XcE1/WNmOY8hCiOdZgfiJQpGMZyDGMJbjK925fFcZKx7XY4ThLh8KnExf0bKT04Tlt0fVPs2oUAOE4KUgo0rRIhQjhOS4QIoWlb0LRiLKsVmhZCiAiGsbomP0aIMsBECBsII0QQn+9jotH+BIN/2GX0EtOcjcczLZbcn04kMiyWI7Tvy+myszvzf//3D265ZTzDhp1S7/fCcTIIhU7H53sPw1iOlG6ZZcdpSTh8BoejZMOhlGW3oMQqYbOxlVKtjAKtEB2dFnYmSTIRKSVFWjFrzXWERAivrL1sSkdHIqnaSxPP3SXqSyRWMMrHn73HBx9M4ptvviQajZKYmET//kdz9dU3M3DgEPr1G0ggcPDL7TweD+3aZdOuXd0SjLZts3TpQn7+eQYzZ37PK688yxNP/BWPx0PfvgM5/fTRnHPOhaSn7740s6LsLyF3niNUDomysjKSkpJYubKAhITExh6Oohx2kyYJvv5ao2tXWVMuNxJxg5jx4yWDBtX+NWTb7rv/+5KiICVUVLj77hzURCIRvvzyY95661W+//5rpJR0796LQYNOYdOm08jIOJaWLb0151ixQjB4sOTKKx0aMj6YO1fw/POCjh3d+9q+XVBV5TbBzMyUPPSQQ/WS8PXr1zB9+pdMm/Y5M2d+RzgcpmfPvlx22bWMHj2WQAPnrzR148ePYdu2LXz22U8Ncj5dX08g8C90fQPuTEMUKdOoqrqCaHRgrX1NcxaBwGsIUYC7vMnBsjpQVXUVgcC/8Pk+Qcr4WL+YYjRtCyCw7bYEg2PweOZhWW0QIoTX+z2O437vNK0Yt/t8FUJUYlndcYMHG8NYGstR8SOlH8dJRwg3aBGiBE0rxnGSamZm3POUYtvpVFVdiW33JxI5GimT8Hi+w+9/BSEsHCcFIcoQwiYYvJBw+Aw0bSsgYzMhe+5YL6Xkuut+z/TpXzB16hzatKlbsc0Njn7BNH9G0wqxrE5EIsfhOIemJ87hJnEDko36JlZ4VtPcSq9V1StMmLAWId6Jo0QrrVWa2MamRCulf6QvWXbL3V4jR8/jF888fNKPX/qwLYtfZvzMD5OnsvB/s6mqrKRfv4GMHj2W444bTpcuPRq9eafjOKxYsYSZM2cwY8Y0vvnmSxzHYfjwEVxwwcWccsqZBz0zrRx5ysvL6NIljdLSUhITD/65VwUrh4EKVpTfur/9TWP7dmi1y5LrFSsEQ4c6xMW5Mw2pqdC/v6RPH7lPAcOmTe7SspUr3eCmZ09Jp07L+Oqr13jvvTcoKipg4MAhjBlzCSefPJLmzVswZ44bPHTuXDsYKiwEy4IJExzid7c0/wDMmiV48UVBy5awcKGguBhAEA67M0bPPGPTvZ6VXlVVlXz//TTeeusVpk37nKSkZMaOvYxLL72q3nc8f20qKsrp3TuL22+/n2uvvbUBziiJi3sS0/x5pwBBoutrcJxMyssfANyAQogK4uPvRdOKsO0OuIGBjWEsIxrNxuNZBETR9VwghKaVxpZu6YTDxyCEjaYVE4kcg5RxmOYPGMY2HCcOISKAFasS5qsp1axpebGGkQZg4zjJSNkSxwmgae5SMrdPix8p4wEdISpisy06kUh/pMzAsroRCo0gLu4lIIhtd6t5BTRtK0JUYtvt0XU3WLHtdoRC58WqeO1eWVkpp5wykIyMTD78cDqG8dtcmFGgFfKzdzYJTgLGTotTqkQVDpIu0U4s9SxHk4I4GYeNTZleToqdzNHhgbWaPO7KwWG5uZKZa39k+qufMu/DHynbXkKr7LZceM6ljB49lg4dGq4R6KFQVFTIxx+/x+TJbzJv3mySkpI566wLOP/8ixkw4GjVaPQ3oqGDld/mbxtFUQ6rtDTYuLH2NsdxZxemTRNomiA+XrJ6tWDePDjrLBgxYs/vo2zYAC+8oJGXJ0hJqWD9+sl88skrlJT8SHJyM8aO/T0XXXQZnTvXjgRM0w1SHKd2sGJZoOvuR0Nq21aSmKgxbx6UlQlSUyVCSAoK3Gv9738anTs77PrsFwjEcdppZ3HaaWexceM6Xn/9Bd5++1VeeOEpTjzxNC6//FqGDTu10d9ZPVSmTv2UUCjEqFHnNcj5hCjEMFZg2y3ZsQzKnQkxjHUYxhosqzcAur4aXc/BsjqzYwmTDpj4/R8jRGUsYABNqwQ0bLsZ4I31Pwnj9U7F5/topxFE0fWiWOf5bHR9C0JUAhGECKLruUipIaUGGAgRRogcIB3wYNvJsV4sUdwZlVAsd8VAygBCGFhWJj7fB3i9U9G0XKT0YhibsO0sHCcDKQN4PDNiMx89AIFhrCAQeI6Kijv2OAuSmJjEs8/+h3POGc6zz/4ft976u1iuzqGpSNZUpTjJJDlJFGslpDjJaGhYWFSKKtpZbWhrt0ZGJeuM9ZRqZWhoZNhpdI9022OgArB86WKe+sdDfPbph6SkN2PEOWdz3rnjOK7nUDRxZPw7T01txmWXXcNll13DmjUrmTz5TSZPfos33niRbt16csst93D66ef8an9vKYeG+mlRFOWQGzjQrfCVk+MGCZEIrFsHwSDYtqBbN0mbNtC5s8Tvh6lTNfLz6z/Xtm3w+uuCu+7SmTq1kpUr/8bkyW356ac/kJDgp2/fSTz99EYmTHi0TqAC0LGjpHlzd1amel45EoHCQkG/fu71G1JmJvTv71BQAJYlKS+HggJITIQ+fSSbNok6gdyu2rbtwH33Pczcuet57LHnycvL4eKLz+Lkk/vz7bdTG3bATcSPP35Lt249adWq/nyS/SXE7oLf6h4+cqd9HXZtjylEFbq+AojGmhx6AT9gxT73xxpE+jGM9QhRjKYVxqp6FcZyUxKJRntRVvYElZW34DipGMZmNG07UhqxPi1GrPqXBlShaQXYdiZSpuA4zRDC7dUiRIjqJH23PLKGaS5GiCogAjho2vZYY8m5eDw/4vH8iBBBbLszUiYiZQKW1RVNy8Pj+Xmvr+GAAYO47rqz+Mc/Hqaw8A4SEu4jEHgmtlTut0FHp0e0GwlOvJs0rxVSqpWR4aTT2eqIQNDeasvxoSEMCQ/m2NDRDA4PJEnu/t3l+fPncOml53DKKQNZvGg+jz76L+bN3sA/JrzICb2G73eg4uBQIkop0oqJEj3YW94n1cvklpsrWWQuYaO+mTYd23PXXQ8ye/Zq3nnnC9LTM7nqqos4+eT+fPLJ+ziOc1jGphz5VLCiKMoh17ev5OyzJboOq1fDxo2C9HRIS5NkZdVe8pWRASUlkg0b6r5bW1gIL7+sMW1akDVr/k5OTgfWrXuQ5OSLufji1VxwwRdkZY0hP3/3laPi4+H88yUJCe4ytBUrBBs2uEvITjnl0KyKHTpUkp0NzZu7QUqnTjBggCQ11c1jsfexsmsgEOCiiy7jyy9n8dFH35KUlMK4cWfwu9+NYuXKpYdk7I1l3rxZ9O9/dIOdz3HSsKxO6Po2oPohSaLrm7DtFtj2jj4ilpWN42Sg65trtmlaDppWiG1nY1m9cAOZMO6MyUY0LR8hijDNmRjGUjQtiuPEYdttsO3WseVbyXg8c0lM/DNC2JSVPUll5dXYdhukTMKyehCNHhULWjSEsJDS/XcgpY/KyvHYdmscx4eUAdxeJslIGUBKP5pWFKu85eCWC9ZjOTACKc3YMjIfTnWlKmBHVbJte30NPZ7vufdeaNbMy223Lcdx4vB4vicQeAm3fPFvQ6qTwpDwYI6K9KVntBsDI0cxKNyfgNyRU+bBQzMnlWSZVKs/y85mzfqBiy46gzPOOJZ161bzz3++wg8/LOV3v/vDAfc2KRGlzPLO4Sffz/zkncUM30w261uQh7ip7gZjIz9757DSXMMGYxMLPIuY7Z1HlXD7UB1//Im8/fZnfPTRt2RktFBBi7Jf1DIwRVEOOSHg1FMlAwZINm8WGIZDy5bwyCNanQd1x3H3r29J/M8/h/nmmxfZuPFhQqEiEhKuoH37u6msbE1lpVvu2Lbr9nDZVZ8+khYtJEuWCCor3SCiZ0/Z0P0Xa2RmQu/ekvXrITubmuBs0yZIT5d1cnn2RgjBoEFD+OCDaXz++Uf89a93c9JJ/fnd7/7Abbfdd8RX4ikvL2PlymVcddXNDXhWQSh0Dpq2LZbI7kOIEFKmEAqdX6tpoZTJhELn4fe/EdvXi66vi/VF6Rar5hVA11fHHvKjsdySUgxjNe6Du4EQIERJbEYkgq5vRcoEhAji9X6Grq+jsvI2LKsjfv9/kTIplvyvYduZQCS2rMxBiEpMcw3RaA90vQgpK2LX1rDtdrFmkhbue5Ce2OdhhChF08pisy+BWJCzMxmb9dnbz4wdqywmeeSR/lx88Qw+/3w9I0d2wTSXYhgrY0vLfhu8eGltZ+19x3ps2bKRu+++ia+//oxu3Xry3HP/5YwzzkU/yDWoYcIs8CyiVCsj0UlAQ6NCq2SxZxnesJcMJ/2gzr87laKSVeZaNClId9xa9DY2BVoBG4yNdI92rdl30KAhvP32Z8yZM5MnnvgrV111EV279uDOOycyYsSoQzI+5cinZlYURTlsUlPdQKFHD0hJcWdcCgoEkYj7dSndB/jmzaFTpx3vBEYiEV577TluvbULK1feTocOpzN69HKysp7DslojhNuTZePGHdfYm4wMOPFEyahRbjWyQ1loyzBg5EiHuDjBypWCLVtg5Uq3mtNppx34tYUQnH76OXz77ULuu+8RPv74PY49tjv//OfDhEKhBr2Hw2nBgrlIKenff1CDnte2O1JZeQfB4O+IRgcQCp1DVdUVSJmCpuXV2jcSGUpl5Z8Ih08nGu1LOHwWtt0mFuysAGRsFiMO226JrlfEZleiuEvIDNyKYxJN2xYLjOKQ0hvLW+mOYazCNOcQjZ4AaHg838eS6XU0rRxNK0fXt8RmhbrjOJkIIYlGs6msvJFgcCyW1RkpvWjadoSownHSapaTuTM6qVhWOyKRY7GsHjhOGoaxMrZcLIiur0bKNKLRwXt87YQIYhhLMM2VXHBBHqecEsedd35NJLICt8hAUYN+r36NLMvi+eefYujQPixdupDnn3+LqVPnctZZFxx0oAKQp+dTqpWR6qTgwYOBQbKThIXFZmNrA9xB/Qq1YkKEiJc7KpPo6Pikjxw9F4e6MycDBx7DpEmf8vHH35OR0YLLLz+P8ePHkJu79xk+5bdHBSuKojSaU06R9OzpzjgsX+4uyYqPh/POkzUVuebNm81ppw3mnntuonPnYZxwwlJGjHiJ9u3b062bRNOgvByKiyEpCcaOdfZ7puJw6N0brrnGZuhQh+bN4ZhjHK66ymbIkINfnuHxeLjqqpv48cflXHjhZTz22ETOOGPIEbs0bP782SQmJpGd3aXBz+04LQiHRxMMjkeIEIHAq8TH/5WEhPtipX4rgSCalotltSMYvJSqqluJRnuhaSV4PDMxjDl4PN9gmrOQ0gf4se0WSJmI4yQCJlI6NYGLm1/i5sG4/VgycYMZHV3fiOMkIWUqjpOG2whSYtttY0FUCVKmxs4Zj2V1QddLsO32hEKXYVn9asoTu31gknCrmoXQtEKkDGBZ/ZEyASmTCAYvxbI6xBpbbsG2W1NVNT5W9Wz3hChC1zcjRBApM3jiia5s3Wrxz3/ORtNKcXYq1avUtWjRPM4441gmTryTiy66nG+/XcioUec3aKJ5WIRBUGfZmSlNKkRFg12nrgP/HTZgwNFMmvQpL7wwiTlzZjJsWB/efPMltTRMqUUtA1MUpdE0awbXXuuweLEgN9ct5duzpyQz0+1e/sgjE3j55Wfo1asfX3zxM15vP559VmfrVkmLFtC6tcS2oXlzwdixDiecIPe6BKwxdepUPWN0aNaPp6Y2Y+LExxk79hKuv/4SRo48hgkT/s4ll1x1RJUMXblyGd269WrQBzk3CInGHuYFPt9kvN7PcZwW2HYGmlaK1/sZhrEIEGhaGY6TSCQyjGi0N37/R1hWF2w7C13fhhBhoAIp3dkTx2kWm13w4uaLOEjp4FbvsnArhbUhGu2D221eAhZSJqJpBYBNJDIMiMQ+d9C0jbHE/jVYVpfYce71THMOprkYTduCEFUYxgYgjmi0B0I4aFpCrExxC4QoR9eLiUSOJxQ6HxCx5WbhWFUxG03bhuPsvgeIaS6NBTxRNK2cTp0CXHVVOv/4Rz7XXitxnK67Pfa3rLKygkcfvZ+XX36Grl178sknP9Cv38C9H7gPIkTYrhcQFiF80ochDZACGwd9p4AlIiI0tzMa5Jr1SXFS8OKlUlQRH1tO6eAQEiFaWR12m7NTTQjBmWeex3HHnciDD97FHXdcxwcfTOLRR/9Fu47ZbNNz2a7n4+CQ4aTT0srca2U15ddF9Vk5DFSfFUXZP9Onf8Gdd/6RwsJ87rzzAf7whz9iGAZSwvffCz79tLpfibvs66yzJMceq36V7ayqqooHH7yT119/nhEjRvHYY8/TrFlaYw9rn5x77km0bNmKZ555/aDPJUQRPt//MM05CGFhWdlEIkMIBN5ASjNWycvlJsevIRw+Gimb1+R7WFYnDGNVTblfNwCxMc35GMYq3OpbnljjxRBSxmPbyQgBtp2FEBVImYhtt4qVB5ax5H09VjI4nYSEuxGiEl3fEKsgVhLrxeLgOC2x7Uwsqx+Ok4BhrEPKBHR9E5pWGLu+gRClOE4iZWV/x3HaoOtrYkGGgWX1IxI5GmKNDHV9NX7/axjGetygKZlweBih0Bjqex/T6/0Iv/+/gA9dX4sQQbZtc+jSZQV33DGIa6+dcdDfq1+b2bN/4rrrfk9RUQG33XYfV155I6ZpNsi5y0Q5CzyLKNFKsbEJiTAmBgKBRJLipKBLN2dFR2dAuB/pzqH59y+RrDHWscpcjYWNhoYtbFLtFPpH+hK3Uz7Yvvjhh2+4447ryMnZwtjbrmDwTSehm+69OThk2s3pF+mDh4Z5LZWGp/qsKIpyxCgvh2gUkpP3rRt9YWE+9933Jz788G1OOOFkJk/+irZtdyxPEcKtrNWrl2TdOnemIDtbkpLiJtavWAHr1gmEcEsU79r4cVehECxZIti0CTwe6NpV1kqAP5IFAgEeeuhphg49hT/96SpOOWUA//znqxx33PDGHtpe5eRs5aijGiJfJUQg8G88nvnYdgaO48U052IYC9G0CqLRHaWthQjHZjR0pGwWK+2bCGzDNBfEZlCqfzBMwMRxmuM4WzGMItwcFh132VcQTdNxHLfKl213QtdzMM15wHJsuzmOk0UodAG27Tb5i0YHEgg8GWsaSWzpmLbT+YowzZ+x7VZYVsfY7E4REMTteK/jOFno+mb8/imUlT2FbXcmEjm9zqsiRCWBwMvo+gYsKxvwoGn5+Hwf4zgZRCIn1znGttsCJrbdEttuixAVNGumc8UVIZ5+egkXX1ym3oyLkVLy6qv/5v77b6N//6P54IOvadOmfcOdH8kycwVFegkJTjzb9e1UiSARIhjSxIcXoZXik14SnAQ6WR3qDVQkkjJRjiUsEpz4A56tEAg6Wh1IchLJ1fOIiAgpTgot7Rb45f53rz/uuOF8/fUvPPjUXbzx8PPM/Oo7bn/tr6RkpmFhkavnkaPn0NbefV8g5ddFBSuKojS4wkL4/HPBokUCyxK0aSMZMcKhW7fdH/PZZx9y++3XAZJ//ONlzj//4t0uXUpNhdTUHTMplgXvviuYMUOrSdb3+2H4cIfRo2W9jR6rquC11zQWLADHETiOW9b4jDMcTj1VNqmApaoK5s51k/N1XdKzJ/TrJ9mXN2lPO+0s+vTpz003XcHYsadxw5V/5uYrJ2DECfSkpheYSSnJydlCixYHn3hkmoswzSVEo52pnlGwrFQMYz5ClMVyLdztQlQiRDmOE4+UO5rtOE4qur411t+kNLaMDMCO9UdJIxrNQNc34S79krE8Ex/h8FB0PQdN245tZ+I4Sej6JhwnjYqKO2s1YYxGB+CWKy5H04oRQsbKDntjyfDJaFoJ0WgPpGyOYcxA1zfi/hkvoro8MfjQtFw0La/eZV1ClODzvYdpziIa7Qa4pY0dJwMhyvF4viMSOYldGz1aVk8ikcF4PD8iZTxSGuh6ETfdNJyXX36T1177NzfccOdBf8+OVFGiFOpFVFZV8Njt9zNl8jtceeWN/OUvDzXYbEq1MlFOkV5Mop1AsV5MlQgSkH68eLCwiXMCBGSA/uF+pMpkdOr+AiwX5Sw1V1CkF2NjE5B+OljtaGe1RRxAk0+BIMNJb7CKY4FAgIsnXEvGWe146ZLHuPPEK/nTaxPpMqgXGhrb9QIVrPyGqAR7RVEaVDDoBgHTp2sIAYGAZNkywSuvaKxdW3d/y7KYOPEuxo8fy5AhJ/Ddd4u44ILf71eOxcKFgu+/F6SnS7p1cz+SkiTTpmksW1b/MT/9JPjlF0Hr1u6MSvfuEq9X8sUXGps3139MY6isdHvL/Oc/gl9+gVmzBC+/LJg0SWDtY2uLFi2yeOO5z7lp3AP884W/ceutV1I4N0rVcnCaWHuMoqICIpEILVrsPn9iX2laDm7+yM7v7gqkTEfKBDQtP7ZPFZq2FU0rQdOK8Hh+RtfXAFas7G+AaLQTur4h1t1+A6a5FMdJRcoEIpHjiUSORkovQrhljN3cFTcIcqtwtcS2s4lEjkPTyjCMNTVfF2I7Xu8HaFoZmhbEnaUxcZxEpIzHLU+chpQ6hrEa05yJpm1FiDIgEuvh4kXTyoHgTk0lazOM+cTHP4Df/xa6vh6v9+fYbI/7QyBlHJpWQv09U0yCwfEEg7/HtpsjZRzh8CiSk//CRRddwXPPPUVl5aFM4m668rVCfvD+zGc5n3HRWafz+acfcfdzDzPhgUcbPFABcIQTq7AlqRRVeKSJiP1PCkm8TCAiIjjCrjdQiWKxwLOYPCMPn+Mj0UkkQpSl5gpy9NwGH++BEmh0GNSFR6a/SPP2WUw46yamvjYFqFtEQPl1UzMriqI0qGXLBCtXusuzvLHejElJkpUrBTNnCrKzd8yI5Ofnce21FzNr1g/cf//fufLKGw8oEXzJEnd2JDFxx7lTUiAvz2382KtX3XyWefMEgUDtjvUZGe5SslWr3NmgpmDOHMHChdCuHfhiz9zl5W6w1a+fpFevvZ/DsSC0WmP8aXfRsnkb7v7HeIqrCnjsmrfQ/AH8DbdC5aDl57slhNPTMw/6XLKmlKrDzg/vQlQRiRyDbbfH45mBYaxB19fG9ndiQUwBur4WTSuNLQerbs7oiVUUG4njJBAIvAaEYjMrWmy5VBCI4vV+FcuJMXa6toMQZXi9X6LrqzHNRej6RnR9S2yMAogDQrGiAB6kJNad3o7NhHlwnJTYDExFbMmYjpRucr/juJXF3D4sJo6ThhCl+P2vo+sFsa71xbHZkfU4ThK23RFNKyIaHcTuHg2qA5Rw+MzquwHg+uv/xH/+8zxTprzHuHGXH9T37EgTIsRizxJmT/+Bl8c/QXxyAvd+9STpPVuyJbKNNnbDlyaMd+IJyACVWgUICdL92Y6KKB7pwStNQqIKR9T/O6xAL6BYKyHFTqkJZhJlAkWimE3GFlraLRp8zAcizU5lnaHjy/Rx34dP8vpfnuGFPz3OcYsW8fCDT6sn2N8QFZoqitKgtm93AwfvTk3khYCEBMn69TsCkblzf2bEiMGsXr2C9977iquuuumAK1ZFowJNq/uHWQg3Z6b+Y+rms7hN/Pa9o/zhsGSJwOPZEaiA2/QyGhWsXbvn10vaYJVCeDNY5aAnwlknjuO5CVOYs/Q7xj86grw1hcgmVCXUik0XNcQ70tFoH2y7ZaxRYwQ3EMlBSkEkMpRQaCzl5f9HNHo0jtOSaHQQUqbGSg4HMc2VsfMMwrI6xkoJW4TDowmFLiAaPQ7bboVpzkfTCnGcZkjp5r/YdrdYM8kdfSN0fT0ez3QMYxk+3zvExT0VC4y2x2ZJqquIud9XIYJAFUJEESISK3ucgaZtR9fzY8vVBFK6PYrcxH+J42QSH/83EhLuJSHhHgKBf+LxTEfXc7GsbBwnE8dpEVteFsUwVmIYy5EygXC47hKwukStfVq1asvw4SOYNOmVevfWtO0YxrLYLFbTeBOgoWzXC5jy/CSevmAinQd05+FpL9K1e080NLYcot4mJgYdo+3R0HGkJCiCVBEEBKl2MlUiiF8GSLGT6j0+KEJIqDPr4pEeKkTFIe92v6/SnGa0t9oSEiFK/GWMfuwSLnnmBma9/R03jP49OTmHrneM0rSoYEVRlAbl9keRdR74g0G3VLGUklde+RfnnXcSrVq15csvZ3H00ccf1DW7dJFYFoTDta8nhKRjx/qP6d1bUl4uagUzJSVuUNChQ9P4Yw2g69UPonXtqXhAtBDK50HZPKhcDlYx2LE+kccddQqv/20qm3LXcvHdw9iyaVPDD/wAVReobIiyxVKmEQxegW1nYRhrMYzlCCEJhc6raYIoZRKGsQJd34phLAbCuEntDlIaRKO9cZxUwINttwGiseaNOQhRRDD4O6SMi+XAFKFp5bEk+GwcpzVCWGjaNjQtF9NcGFtm5Y/lo5ixGRkZ67FixmZHHEDE8mRs3JySZjX9VtzZmhBCRGKFADKQsjWOk44QJqb5A4axAsdJxnECeDw/4PNNjiXv64AgGu2PZXUDTIQIEo12p7LyWiyrzwG91uPGXc4vv8xixYolO20N4vf/h4SE+4iP/z8SEibg97+IEOUHdI2m6OV/PM17f36FUX+8kLsmPUJ8sls73ZAGIXHoGrO2tlvRP9yPDlY7vNKDhiDBiSciokjh0MnqgI/6k9v90o8GWNT+JR0RERKchAPKWTkUNDS6R7syODyALtGOdLKyufmCO/jgw2nk5uRw+ulDWL16eWMPUzkM1CSaoigNyu2TIli3Dtq2dbu35+W5MxZ9+1Zx443X8P77b/GHP/yRe+99GI/nwCrQVOdrGAb07y+ZP99dLlU9oxOJwIABblACbiBj227ivRBw7LGSZcvc5Wl+vxvsCOEm5WdnN8Qr0TB69ZLMmyeoqqKm031xsRtUuT1b6rIroHIFOGHQ40GY7sxKdDtoLUHzQs9OA3jzvu+4+vEzOfvcoUya9AlduvQ4jHdWvx3V9BvmgcmyelNefj+GsRJNKwUqEIJYgrnb80TX1yNEObbdGk0rQIhgrGywha7nxCp26bHxCbzeTzCM5YCFbbckHB6BppVh26lImRYLGtzGkradgmGsQ9Py0LSCWF6LHZspSUSIChwnAU2rQEo/QpTEkusDuEGLW5bYttvHyhTbgI4Q1ZXCLBwnAbdCmUBKG00rIRLpX/MaWlYCpjm3VpEAKT1YVk/AIRIZQlXVLRzM+5cnn3wGaWkZvPXWq0yc+DgAPt9HeL2f4DjNse0OseVvUwEIBq+kob7HjUFKyWOPTeTFJ5/kjLvHcsGfLq/Jo5BIwlqY5tGG721iY1OklRARYQIywPHhIfSL9GaLsY1SrRS/46eFnbnHMsXpdjNSnVTytQISZDy61KnUqtDQaGO3brCxFmnFbNa3UqaVEefE0cpuSbqTtl/BkECQ7qTVup/Mvs35/POfuPDCkZx77sm8++4XdOu2D+thlSOWClYURWlQKSnw+987vPeexsaNAtt2SwuPGFHG44+fw/z5s3nmmdc599yLDuj8+fkwfbpbaQzcqljDh0v+8AeH2bPd7bruPuQPHCgJh+HTTwXz5rmzKB07wkknuQHJ1Vc7zJkjWLFC4PdDnz6Sfv3kPpVZPlwGDJAsXy6YO9ddXielW2b5xBMduuymwXs4H5wg6CluAKbrYCSDVQiRQjBTQIagXZtOfPj2d1x63ZmMGXMaU6Z8S7t2jRupVQcrDVulLIDjNMPnm1yT2A4Cy+pMKHQGUhpI6UHXc2LLsQykNNG0KJq2GcNYEeuxEsE0FyKlF8eJx50Z2YBhrMay2sSqgIFhLI4FBxEsqyuO40HXy5AyGgtCDDStAk3bFpthSUfKcCzHROA4qbHKZIlI6YuNyYPjNEfT8mKvE7HE+tTYbIwH2+6EYSyK5d7s/AKasd4vKej6ZqQsjiXkF8Tyb87iYBdaeDweLrjgYt5++zXuvvv/8PtDeDwzcJy0ml42UjbDtsHjmUM4fOYem1A2ZVJKHnjoTl545inOm3AZA24dylqxgWQnkWQnmaAI4nd8tG3AB3+AClHBQs9SirQiHBxMDDLt5vSMdKdbtP5fBhKJg4OGVhMkGBj0jfRiubmSfL0QR4QJOAGyrfZkNlDzyDxtOwu8iwmJEB7HpNgoIdfIo2ekW4MERBkZmbz33lQuvHAk559/Ku+88zk9e/Y9+IErTZJqCnkYqKaQym9RKATr1rm5FfHxBdxwwyjWrl3Ff/4zhcGDjz2gcxYXw7//rbFmjSA1VSIlFBcLunaVXHutU6d7fTgMzz+vMX++oFkziWFAQYFbNezaax3aHCGVL8NhN3dlzRo38OjaVdKtG/WWZAaoWAaRXDdAqeFAOBcQYCaDngC+VmCmuv1tzj57GJZlMWXKtzRv3ngJtgsWzOX004fw1VezG/DhwyEu7lFMc16sE7wJRDCMlbGGjxsRIgePZ0lsyZ2O+/AeRkoPUiYSDg/HMNZhGOsIh0/CcVqiaXkYxnJ0fSOOk4ZtZ6Dr29D1zbGqYClImYLjxGMYCxEijJQpsRmUEO5SrjCO05x//aucJ54oJTdX0rt3PI89djT9+h2Fpm3H55sKWESjvREiFAsy/ICfaLQvEIeUWqyh5BakTMKy+tW6f8NYSlXVpQghMM0ZaFol0Wg3IpFTanq9HKzVq1cwdGhvXnrpHUaNOor4+Ak4TmYsQKsWxTDWUlHxFyyr+27P1VRJKZlw/2289OLTnP+3yzn7mosQUpBn5FMpKkmxk2ltt6KD1Y40p1mDXdfBYZZ3Lnl6Psl2IgYGESKU6eV0jnake7Rrrf1tbDYZm9msbyUsIiQ5ibS32taaoZCxamIWFvEyDqOB3r92cPjJO4sirZhUJ6UmSCrVyvBKD8eHhjRYB/ri4iLGjTuDjRvXMWnSZ/Tp079BzqscnIZuCtmE3j9UFOXXxOeD7t0hM3Mb48efxKZNG3jvvakHHKiAW8Fr7VpB586SzExo0cJdCrVyJcyfX/et+OXLBUuXupXJMjMhLc3Nb9m+XfDjj0fOEhSv113qNnas5Pzz3T4ruwtUAPQASKd2rosUoHshrhskHQ3xvdxABaBZs3TefvszotEI48adQUlJ8aG9oT3wx8qzBYPBBjunrm/GMFbFck6qE/c92HZrdD0Hx0kG4rHtVBynBbadGetZYuJ2m8/D6/0GNxerHY7TEiFKMM25aFohUiYAAk0rQ4gSHCcFy+qMlElo2iY8nrmxpH2BG6CUIaWNEGHA5t13K7j99lLuuacVs2dn0rNnBmefPYOCgkoMYwNShpHSwTBWoGk5hMPDKC5+n2Dw3Fgp4xl4PD8gpZ9gcDxSJqPrG3AT7oMYxkpsO4to9GjC4TOoqHiIsrInCQava7BABaBTp6507NiFr7/+vKassxAltfZxZ58ScBrwQf5wcRyHu+++kZdefJpxj13DuVdfjE/68OKljdWK5nYGzZxU+kf6NmigAlCilVKoFZFkJ2Lilir24sXv+Nmq5xAhUrOvRLLcXMkicykVmpswn6vn8Yt3AXna9pr9BIJ4GUeyTGqwQAWgUlRRppUT78TXWvIV78RRJYKUavuWs2RhkadtZ5O+mXytIFauubaUlFTeeecLsrM7M2bMCH75ZVaD3YfSdKhgRVGUAyalO3vyv/8J3n3XLU1cVbXj65s2rWf06OGUlZXy4YfT6N273+5Ptg/Wrxd4PO4MSTXTdB/cN2you39ODti2qFVJSwhITJR7raR1JPNkgB4Hdgk4EffDLgEtDrzNQeh1l1m1atWWSZM+JTd3G1deOZZIJFLfqQ+51FT3nd+iooIGPGsYIaKxJVc78yCEQzh8ElImxpLZbTStHCGqYiWAW2Pb7bDtTkgZh+N40fW1eL3T0fXNuAGIXVNKWNcL0bT8WE+WLbGkdhnLU3EDFHd2pBwpDRwnlSeflFx2WW9+97sh9OhRxQsvbCcQsHnrrcno+gqkbE44fBah0LlEIoPRtGCsJ0xVTeUwACl1LKsPweAlOE5irKjAFiwrm2DwDxjGKgKBp4mLexSv9wuEaPig9KSTTmf69C9wnHgikWFoWjGatg0hqmqaVUYiQ3Cc5g1+7UMpFJL88Y+38Z//vMAN9z/C8X8YgUntinV+6SMsItg0fDnBKFFsYWPuElSY0sASFlGxozdOuahgs7GVgAyQ5CQRJwOkOilEibLO3FBT7StChBChBq/+pcX+54jawYWDg0Cg78OjZ4Wo4GfvXGZ7f2G+ZxE/e+cw2zOXoKj9JkZQBMlPLeK2yX+jTfcOXHjRSGbN+qFB70dpfCpnRVGUA/b994KPPtIoLXUrUwkh6dZNcMUVDvn5y7nwwtPxen189NE3tG7d7qCv5/dLios11q93A5a0NDdh3rYF8fF1/+BWJ6Q7Tu3KWaEQJNVf1fNXQQ+4MyjB9WCXuduMFPC3d4OY3encuTsvv/weY8eexp13Xs8TT7xwwOWkD1RysjvdU1jYcMGKbbeKLdHKwbZ3NJXRtBxsuznh8OnYdjfi4h7GNH+J5bCkxGYGKrGsblhWD0xzHpqWg6YtiQUhEXR9C1L6cJykmnyS6k707v+7wcyOkr1R3BkbN3iJRDzMn1/CHXc0jwUQIYQQnHSSYNasAiCFSKQvjuMuzZMyHsNYit//BoaxBtvugWUFABvDWIXf/x/Ky+8nGh2Irq9HShPbboff/w5e75excxiY5lxMczaVlTchZcPNApx88kief/5JFi9eQO/eowCJx/NdrGR0HKHQ2YRC5zbY9Q6HTZvgttue5ocfnqFnz3+RX3QeG9fPIq5VFL++I2AJizCJTmKDzlJUi5NxeKWXMq2MsIhQKdyEeE1qNLcz8MkdteIrtAoiREio6TPkzqL4pY8yrYxSUcpGYwt5+nakcEi2k8m22jfYbFBA+kmzU9libMNje9DRcHAo08pJcZJJcva8LEgiWWquoEAvJMV2Z32iRMnVt+OVq+kX7Q1ApajkF88CivRiDFNn/OQ7+PdFDzHu4jP56MNv6aVyWH41VLCiKMo+y8uDtWsFQriNHj/5xK1I1K2b+059JOLmVrz55gKee+50MjKaM2nSZw2SAxEKuQ8N69YJ1q93l0YFAtC8uSQ5mXobP/bsKcnIkKxf71Ym03UoKHBnhAYNOvB3E7dvd5tfhkLQqpWkSxd3hudQW7kSfvxRsGWLoHlzOPpoSe/est5kdDMZjD7gxGa6tACIfZhLP/ro43n88ee58cYr6NChIzfccGeD3sPemKZJWloGeXnb9r7zPgsQDp+J3/96rKRvQqwzvY9weBTgw7K6UVb2bKz/yb9jy5csLKsTltUVt5RwFW7jx9bo+hY0LYzjxMWKGOQhZTJCbMdx4tF1tzHkrn1F3O+VuyRMSo38/Hhsu4SWLdegaSI2U2KSkeGwYkUE8CLErmv+bExzObbdYqd8EB3Lyo7NpqzAsvphWW6FJMNYjsczHdvOiJU/dsdgGMvwer8lFDqvwV7pQYOOJT4+gWnTPqN3736EQucTDp+KphXhOMlImdxg1zocIhF44IGP+OGH2znqqDsYOvQqyioctsxNw+PPpWPzeExpUCWCOEjaWq0PSXf1eBlHmpXGAu9CLGxMaWILCykkaU6zWsutDGmgoWHjYOzUS8XCRpcaiz3LKNSKCMgAujTI1fMo08oYGO5Psjz4d3EEgi7RzlSJIMWaO3sncRtPdo92rdPfZVdlopxCvYhEO6Em8DMxiZdx5BnbqbKqCMgA642NFGnFNLNT0dBICaTwp0kT+duo2/n9JWfx2Sc/0bJlwzflVA4/FawoirJXUsK0aYIvvtAoKakuBezOqBx//I4lRR4PCLGcxx8fSdeu7XnrrU9ISUndw5n33Y8/uvkq3btLtm0TBIOS/HwIBgW33urU20+lWTO48EKHyZM11q1zZ1gSEwUjRzoMGHBgwcrs2YLJkwWFhe5NmyYcdZTk4oudmpmcvZGWhb1wAfaSRchwGL1zF4z+AxG7VgjYyfz5gjfe0Cgvdxtsbt0KixdrXHCBw9Ch9d+L0NzSxfvr/PMvZt261Tz88H0cddRgjj122P6f5CC0bNmKbdu2NOg5I5FhSJkYe5d/K5FINyKRoVhW35p9pIwnGPwDur4Jn+8thCjGNEvRtOJYcnsVUiYSiRyPpm3E4/kldmR5TVliKUVsVsWJzarU/d5U5xJJGYfb7R6EsJHS7YHizrpEY6WGq2LVwIjtV4Tbp8WBOknK1TM2tft76PpahAgiZXatfaVMxDDmAQ0XrJimydChJzN9+hfccss9sftMxLaPzOIy//vfHL788hI6dRrD0KF/dd+oSdDInd2LfJ9J69O3E9JD+KWPztGOtD4EHeureTHxOT4QbgJ9wPER58RRJaoo0ArJiFVdS3VSSHISKdFKSXaS0NHdJV9aiHQ7jUKtiFRnR/d6n/RSoBex2dhCcrRhppwTZDxHhweSp2+nSgTx4iXFTqZAL2SFuQqJpLmdQRurFV68tY6Niig2dq1AC9wgLCwiRLFwcMjTt+OTvlrBYUoglWvf/jNPnXwvl1xyNh9++I0qbPQroIIVRVH2auVKmDJFw+NxZxGkhAUL3KChsNBdjgVQXr6F7747Hb+/OW+80XCBipQwa5YgEIA2bSTt2kmKiwWOAyUlkvT03Ze67d0bOnRwWL1aEIlAmzYOLQ5woqewEN5/XxAMuhW5wC2l/O23gtatBSNH7j0Ako5D5IPJWN9+Q1WVJBzVMb+dQ0L/efjGX4lWz/q0aBS+/NIN0HYuV7xli+SrrwT9+0vi493XacsW2LxZYJruGPcQ/+zRn/50H7Nn/8SNN17O11//Uud7KaWbJ5SfL0hIcJtvNtTsUlZWazZv3tgwJ6vhNkKMRvdcLUjXN2Cas9G0wpoAwjTzMIwVRCKDcFM9NRynPZGIJ1ameBMQQUpfrMt9AaAjpdtc0q0utnMeg9sBXkqdZs2S0PVC8vKAmgRiQV6epHlzCUTRtG3oui+WpK8RCp2GrudgmvNiieruD7+m5SNlMrbddpe7cmdA3Y+d/6FE0bQCPJ4Z2HYmtp3NjlTWKjyenzDNhYAkGu1LJDIE2HtEfuyxw7nvvlsJh8N4vd697t9U5eXlMGHCBSQm9uG0016O9bZxeW0/pV/0o//R5XgTo8Q5gQarcLU7RVoxzZxU4mUcDg46OgJBviikTCurCVYMDHpFe7DQXEKJVopEoqPTympJwAlQoBfWmt0QCLyOSbFW0qDj9eCpCd6iWMzzLCDHyMWUJgIo0AvJ1wsYEO5X67VLcOLxSR9VIlhrKVuVVkXACRAXm03cXb+WpOapPPHfl7lm1IVcffU43nhjCvqeKpIoTZ4KVhRF2auFC90H9LaxZyAh3H4lq1cL1q6FtDRJMFjI+++fjuPAzTd/SlpawwQq1aqq3JkbgPh4anJUgkFBOLznICE+3u3HcrBWrHCDs86doazM/by4GCoq4F//0mjTxqbHXvoqOmtWE/puBmsK0tlckkIkAh4tQodPl5HV9ieanT+yzjH5+bBtm4g9vO7QvDls3CjYsgWys+GDDwQzZ2pUVLhLw5o3F4wZ49C79/7fq67r/OMfr3DKKQO4445reeGFt2vyVyor4e23NRYscAMow9Do1Enyu985ZGbu/7V21bVrT/7znxdiD/uHN2fG6/1fLBckCyEisSR2J9bEMQ0hbDQtH8dJx3Fa4jibcRw/UmbizlbEAZVIaeEGHxJ3lsU9v5R6LICRse72PTnqqI1Mm2YzerQbUNi2zfTpDtdfL5AyQDh8Cu5sSlws4BoYK5m8HsNYiuOkxBpZRgmFzq7Tv8SyuiNlMpqWW5P7IkR1sJNBIPAMUvqJRAYRCl2KlDpxcc9hmrNiVdEEpjkH01xIZeV1gH+Pr2GvXv2wLIuVK5fSu/dRDfjdOXxCoRB/+MMF6LpgyJD3qajwkZKy4+vVvwcyAnFodYtUHRImJraoQJNarQaUCFlnaVWKk8yQ8GAK9AKiRImTcaQ6KWwyNsfCVlnrYT8qLALOnr+vByNXzyNXzyPFTq5Z2mVjk6/ls83IpZ21o468Fy/trbYsN1dSLErwSg8hEUYgyLba1RyfaTdnlbGagPTX3H+FqMCHj4Gdj+aFF95m3Lgz+L//u4f77nv4kN2bcuipamCKouxVRQUYRu0H5YQEyMx0qKiA5cvDvPPOOZSXb+eccz5n9OisBr2+EG7J4eJiUascb1mZm7vSqtXhaRcVjQIIwmFYsECQl+cGUH6/+/Dy+usamzbt+Rz2+nXkbQyxZnsKpgeapYEv3kNeZTzL3l2MU8+Dj8cDhlF9/drj0XV3RmPWLMG0aYJAwJ19yc52x/T22xqFhQd2v1lZrXnkkWf59NMPeeed/9Rs/+ILt/RzSop7rawsybJlgkmTNOwGKITUq1c/Cgvzyc1tyLyVfWOaswGJlOk4ThaW1QHL6oTjJKPreYTDpyJEOab5M17vFDye2QgBjtMSKeMRogIhrNhSLE+sY/2Ob2p1oOLyIEQ5t9zi4aWX4PXXBcuXC667zqayEi67zM1Dse3uVFbeQVXV9USjR+Pmp/SksvImwuHhSBnAtjtRVXU1odD5de7JttsSCp2FEGEMYzGGsQiv90ek9BCNDsSyuuM4zfB6v8Hr/QyPZw6mOQvLao9td8a2O2FZHTDN2Xg8c/b6Gnbr1gtN01i8eEEDfEcax91338jSpQt57bX3GDq0Obm57qxlURGsWQNer+DEEw99A9kIEdYbG/jZO4dyUU6ZKKcqVhFLIinTyvE7ftLtuh3rPZi0tFvQ1m5DmtMMDY0MO514J45irQQbG4mkXFSgoZFlH7omncVaMQJRq/iAjo6GTqFWVGf/bKs9fSK9SHbcmeZmTir9In1qLbNz+8akU6yVUKAVkq8VIIWkc7QjcTKOE044iQkTHuW5557g/ff/e8juTTn01MyKoih71b49/PSTwLJ2lA22LEhPhyFDLCZPvoKysnncddfXjB3biWaHoIXC0KGSFSskK1YIkpMlkYg723L88W43+sOhdWuJ3w9r10JpKaSmuoFUebmgY0dJYaFg9mxBmza7D55CYY3iYvAnSPw+951Njwd8fofcYp1Nm6Bdu9rHNGvmLun6+We36plpuq//5s1uz5m2bWHKFIFhuGMCN7hp3x5WrXL7zRx33IEFdKNGnc/06V/yl7/czKBBQ2jevBNz57pNNqt7ffn90LatZM0at/hBfflD+6O6GeSSJQto0aJhA1+3NHEO7lKuTHZ9z87t/l4dcQncP5MyFiQbhELn4jiJxMc/GKv+5QBhDGN5rJdJf4SYgaaFYt3nLWrTkFIgZRxSenGcRM4/vz35+WuYMCFCbq5N376Czz7zkJ6eimWZSLmnd7w1wEDKQGy/+pa7CMLhM7GszpjmEnR9DZpWQSTSB4iL3XcSjpOKaf6IZXWO3fvOS778gIauLwNO2OMrHAgE6NixM0uXTkfXj41VYDu0S6Qa0pQp7/L226/x1FMv0a/fALp1k6Slub8Dq6qga1cYNsyhT59D+yZJhCjzPAvJ1bdjoCORWMIiR88hwUlAExp+x0e3aFfi5b4lpwVkgN6RHiwzV9QsEfNLH12jncm0D105aR0dKerJ30JiyLo/swJBG7sVre0sbGx0dMJE2GBsokSU4sVDczuDAeF+5OnbKdHK8GCSYaeT4iTXnGf8+BtYunQRt912DZ06dTtiZ/p+61SwoijKXh11lGTWLMmqVbU7x3fuLNm6dQKLFr3Diy++zRlnDDpkY2jTBq6+2uH77wUrVgiaNYPBgyXHHrvv727m5blLt2zbzX3p0IH9eme0fXs4+miH//5Xo7LSndUJhQSJidV5NO5yrfqSqquFWnSiiniS7AJs3DXmuhXER5D1cUfRuaLuMULA2Wc7FBdrrF0LjuNeo3VryfnnOxiGGzzt3E8Gqu9NUFV1cA9Vf/3rk8ye/SN//OOlvPTSd4RCPhISap/T74dwmFp9dg5UVlZrUlKasXjxfE455YyDP2GMYSzG5/sw1jBRw7I6EQpdgG13qNknHD4Nr3darEt8OuCgaUUIIQiHh2EYy4mLewbTXE11EFOd22IYG5EyHik1HCcNd/mXW0HM/ZnQqc55cY/RcJxkLKs911zj5/rr19bs5wZN0dhSsa14vR8DWiwfRkNKA6/321jCfRiPZzo+31uEw2cSiZxQU2bZttsRiQzDsnpj212w7S6Y5ixM8xd2zT+R0h/r4VKMrm+NFRSIx7ZbImVibGx7f2wwzTkcdRQsXfo1CQkCy2pNKDQWy+pzcN/AwyA3dxt3330jo0adzwUX/B5w/12NGCE58URJOOxWITzUMyoAOXoOefp2UpwdTRsTnHgK9EJa2Vlk2GmkOWk1ORz7KsNJJyWcQrFWjC1skpwkAnsMiA9ehp3OemMjVaIKv/QjEARFCIEgw8nY7XHVszGVoop5ngUUxWZopHDYYGyiW7Qz7a12tNnNjK4QgocffoaVK5dy3XW/56uv5hDY10ooSpOhghVFUfYqORnGj3eYPl2wcKFbunjkyP9n77zj7KrK9f9da5dTpvdJZjLpvYeQhC419K6AKIJcbIAgKteCYsfrtaJeUQRFEQUVRYoUQ4fQS3rvyfR+Zuacs/de6/fH2jPJJDOTmTTgx/ny4ZPkzNntzJk5693v+zyPIpH4I1/96ve56aZbOOOMg5+dUFUFH/mIRoezYEORMzz/vOCBB0xXA8zi+qijFBde2DtkciCkhA9+UNPQoPn7303Y5IgRpmjIyYHqakFJycAD7LnTR7Nj/MlUrHmMAq8eLQQIyea8udRVHUFJSd/blZXBtdcqli0TNDWZrsa0aTu7G+PHa556SjJs2E4r485OM743fPj+FStZWdn84hd3cc45x/GHP3yX4uJvUV1Nz7HBjJzl5prz3F+EEEyfPpulS9/c/52FWNYm4vHbkLKJIKgAFI7zOlI2kEh8qSdrJJU6k66uF4jFHsCy1mEKDId0+khSqYVkZf0Ey1ofdkfyw7GuFkyHJY1tr0brOJ43D8taFS74XYRI0DtrReL7o5CyC8uqR6k8YAy2vR4TYtmJKYZyiUQex7I2IWWCIBiBUoXY9hq0tgEbKXdgChxBPP5bIpF/EgQTUaogFMkvobPzKjxvPkBP8SFEU6+MFXMehTjO20i5DSEaAQvL2hh2W6y9FhyWtZ54/A5mzozywANteF4FjrOZePx2EokvodS710pWa80XvvApHMflllt+vodeynEGbyKh0dTLBqqtWpIiSYHKoyIYTpYeIOhoNxqsRiSy1+iUg4OjHaI6wsigaoCtB8bB7hHkHwqKVRHjvDFscDbRKRohLELG+KMoC/Z+HhvtTTTKJopUYY9ep120s8ZeT2lQOmDBFo1G+fnPf88pp8zjO9/5Mt/73s8O1GVlOERkipUMGTIMipISuOgizfnnmwXXxo0rOO20a/jQhy7j05++4ZCey1A119u2wT/+IQkCo7EQApqb4amnBKNGmbySweK68OEPK+rqJA0NUFlpiphNm4yQf2+WyNGooPLyM3nqt+Op6FhJTjRNtTOGVXIGxx8Z2UNEvyvxeP/5MMcco1m+XLN6tRnRSqehtVVw+OG9HcT2ldmzD+eaa27k//7vh/zkJx9jx45xbNigKSgwgvtEAk45RR2QYgVgzpzDueuu3xAEwQFx8nGcF5GyDt+fRrcjlu/nYNsrcN3XSKUWhs+0SCS+Tzp9EpHIk0AKzzsC3x9FVtYviESeDHUpKSAIBejZ4d9dlCokCEYSBCOw7bVoHQ2PF0OIAEgDdvi8sQihwu5MG1LWhkWLRoguTHEjEQKESIf6lw6CYAq2vRzL2ooZWYuwM4CyKyzIoihVgVIVWNZaIpEH8bw5gINSlaTTRxOJ/ButE2idhZSNaO0CSUDiebOx7Y0Yx7BqHKeTjo5r8bzZe3mdX0aIJqqqxtLV9RYNDZqSEjN+5jivkUq9e4uVP/3pDp588lH++McHKCzcv1nWjfYmVjprCAiQSKqtGrbb1cxJzSJPD85KV2qr33T5g5HlcjARCCb64ylTpaFGRVOgCihUBf26enUTEFBDPUXVZeQ2FCK0IF3UiS6FRqeRJtlEVjBwt2TcuIncdNMt3HTT9Zxyypl84AMnH8Cry3CweW+92zNkyPCO4zjgeZ186lOXMmLESL73vZ8dcsemobJihaClBSoqdhY6BQWmyHj99aGfe0kJXHaZYtQoqKkxIY1FRaaIGT9+79sfcyyc8IlJ1Mw5n5fLL6FhxDzOujDCeeftewdk5Ei46irFEUeYzkpODpx3nuKyy9QBsxS+5povUlhYzCOPfLnn+lMpQV6e6TgZN6sDwwknnEZzcyNvvrl3QfdgsKytYYDirt9vM5YlZd1uz5ak06fQ3v592tt/Qjr9AWKxv2Dbq9DaRqlcTJ5Jc2gnLACF1tmkUgsJgnFY1naUioa6lSSgUSoHrSXGNSwb130Dx3kFy6oOHcZyUCofpbJDPUsRoLCsjRiNTRFCdCBlS+j+lQw1MRbm3qOPEGkgipQ1PVejVDmWtSPU2AAIurouoavro+ExfDxvKsnkuQiRJgiq8P0ZpNNH4vtT8f2Z+P4o0unj2Ns9Tinr0DpCRYURRm/f3oaxabbDEbZ3J5s2recb3/gil156JSeeuKcj31DoFJ2sszdgaUmRKqRA5VOsimiVbWywNwGm85IihcfumqadlKkSBJAiBZj8kWbZgha6T0H9ux2BoEDlM84fwzh/LEWqcK+FCoBWULCinKK3RhDbmkd0ey65S4aRu7wMEQx+GXv55Z/imGNO5IYbrqKlpXl/LiXDISbTWcmQIcOQufnmz7Np0wbuvvtFXnopm44OKC83Y0mxQY4+p9OwebPJ66isZNCBivtCOm3+3L2mchzTFdgXJk6EL3xBsWWLCZusrGTQ1y4lHHusZsECTXu7ufbBbjsQY8aYTJlk0gjsBzveNlji8Sy+/OVvc911V/Jf//Ucn//80XR0mJl+9wDrp2fPPpyCgiIWLXqEuXMX7ONeNFJuw7Jq2Kkf2TVrxNgKKzWwzbYpKLbieTOIRFrRWqN1XhjW2G72pOMEwUh8fxJCJHGcRsBGCD/srmikbEaIZKgNqQ5F9t3feA/Lag11MjZCdPRoRowdsgzPu3sEMhUWKhLofhN3jyAG9BbapzGBkbtmnkRIpc4klToVIVJoHQ+1PP8Ivy5RqgylyhAiERZ0e+9wKVWBEC8wfLhpsVVXtzNrVhngh4YG7z6CIOC6666kuLiUm2/+wX7vr0W20iWSFO3yvhII4ipOvdVAdVDLVnsbzVYLlpYMC8oZ640huls4YllQyki/ik32FrbJHXSFRW+ezmWHVUO+yjvo2S7vBlSDReG2cppyGog4LgKB8CR2dYyC0hIKCwv2vhNASslPfnI7J544h6985bP83//98SCfeYYDRaZYyZAhw5D4xz/+wp/+dAfXXfdrHnhgOg0NIhxT0UyeLLj8ctXjSNUfK1aYsazt202wY1mZ5swzNYcffnDcdSorwXE0XV07i4IggERCMGnSvockOA775UTmupCXB6+8InjtNUF7O0yYoDnySE3FEEywtDYC++ZmE9Jo25Cbazos5eWQNfgx+b1ywQWXcued/8fNN3+BRx55kdzcg9OgtyyL448/hUWLHuW///tb+7CHFLHYPbjuCwjRjhDJUNuhQ/2FwrY3o9TwvQZFmi6FjdZGDG/ba1AqHym9UCgfDQX1SSKRhzDOWQrfn0g6fRK2/QaOs7Sni2ECJ5NhV6Y9TK7PDospNyyePKSsRcpWPG8kUjaExVEk1Kqkw8wWMEVKd3fHQYgugh4dQArL2kE6/YHwHHfHDvcHQTCCIBiJba/B9yfRXRxZ1jZ8fxxBUMmewZK9SacX4LrPUla2FYDGxgZs2yMIRuB5B8+AY3+47baf8Npri7n//kVkZ+9jiuouiPC/3bNMQJMWad52l5ASabJ0nICAtfZ6EqKDuenZvfJSLCymeVPoEJ20yTYKVT45KgcLySZnMwLBTG/afp/vux2vGbJUFh1OK12yC6EFOqKJd+Qwom4UWQU7f8E1yxaqrRo6RRe5KpvhwbBeTmnDh1fy3e/+jGuu+RgLF57FOed86J24pAxDJFOsZMiQYdBs2LCWG2/8DGeddTFtbR+nvd1Y6gphnKCWLTNZHx/8YP9FR20t/PGPRuheWWm23bED/vxnSUFBsN+2t30xZYpm5kxTEGRlGUF9S4tg9GjNEUccmoyWvtAa/v53wZNPCqQ07mLr10uWLNF84hOKESP2vo81a+CxxyTPPSeorhY4jsYPJ0sqKmDsWM2JJ2qOO+7AZEJIKfnGN37Ieecdz9///qcex6SDwQknnMr99/+ZmpodlJcPLQMiEnmUSORRgqAcrUcgRBeO04GUtdi2C9j4/liSyYv3esffLPI9hGhG6yyCYASmm5HCaECKkbIJy6onCNJ43gKUysW2VxMEE/H98UCEIBiPbb+F6z4LWAgREAS5aJ2HZdWitYuULWhth1oYidHCWOHXGlGqItSSgFIFQAQpW9lZRFh43mSE6MK2l2OKsylh/sreRm5skskLicd/jW0vCzUyqbADJMnN/WIYHnkkqdRJ9JVmr9QIOjs/RTR6P/n5D9PU1ILnHUcyeSFqANend4odO7bxox99m6uu+izz5x99QPZZEBQQ1zHaZDt5KheBIEDRKbpwtE1KpikKdo5ARXSEOqueetlI+W6vUUqkaZfte4rIlaDarmGcP2bIbmDvRjpEB9utahIiQYwYw4Iy8nexILaxGR4MJ6ETJEliYREPcskJdnaWdljVLHGXkySJjcU2K2CrvZ3Z6ZkUqp3dl/POu5jHHnuQL3/5WubNO+og2KNnONBkNCsZMmQYFKlUik996lJKS8u57LL/o65OUFW1c7QqEoHCQs2bbwqSyf73Y8IUBWPHmvGhSMTkirS17Zt+ZDC4LnzsY4qLLjIC8Jwc42b2iU+oft23DgVbtsCLL0qKikyHprLSFH87dgiefnrvr8WmTXDnnZJnnhFs3w6gqa01zly+b/5sbIT77hO8+uqBe23nzz+Ks866gFtu+Rqdnfs4RzcIPvCBU5BS8vjjDw1xyxSu+wxa54ZuVyYJPp0+HKUq6Oq6mPb2r5JI3BwK7gfG82YgZQvR6MM4zqtY1kYsqxbIJghGAVEsqwkIsKzNOM7zgEUQVGLbS7DtGoKgIlz8t4eal0goaHfCjooRtiuVE1ole3jeLHx/LFpn43mzSCbPp6vrHHx/Mr4/EaVKw7EyEY6mOXjeHFpa/koqtTAcP1NAAsd5C+NCNjC+P51E4r9JJi/G8xaQSp2I1jaOsxxIIWUdsdjdxOO/hX70Fr4/lUTiK2RlFdHYeDQdHf9NEAxCzPUO8IMf3Ew8nsUNN9x0wPYZJcIkbyI2Fg2ykQbZSLNspkgVYuMQUZFeHRcHB4WiQ+7pW54iRVp4uLr3uFdEu3h4JMUAv2zfIzTLFl6OvMYKZzXb7WpW2+t4OfIaO6xqAJwCQID0bPJVPuWqnKJUCa52oNBDo/HwWOWsxcenWBVRoAooVkUkRAdr7HV00dWj/em2M3Ycl+9858vv4JVnGCyZzkqGDBkAc5c/nTajTX3dgf/Od77MmjUrePDB5+jqykFrk56+K7ZtFskDpZi3tJjtdtWPCAHRqKa+fuCMkv0hHodTTtGcckp3NsZBOcyQ2LxZ0NFhipRupDRF38qVvUM4++Lf/xYsXSpoaxMkk5q8PDALc01WlsmAsSwIAs0LLwjmzdMH7Lq/+tXvceyx0/nlL3/IF79484HZ6W4UFhZx/PELuffeu7jssk8MejshupCyE6V2n38z2hGlSgiCwVuk2fZ6tLYJggKkTGM0IQmk9FEqhmW1hAVDBCHSWNZWLGsNShlrWa3jSNmG1hLL2oaUyVB07yOlh9YRII0QHkIkwkwTH8uqCTUtNlK2Y/QxklTqDCxrC1I2hwVJBOMgliYIxmLbq8PxNx+lhmFZTdj2nQjRRDJ5yV6vV6kRJJOmrReL3YWUrfj+VLrvbwqRwHVfJp0+bgArYwulLCCHd+t90aVL3+Svf72b7373Z+Tm5h3QfVcGw8lOZlFn1ZEWHjkqm/KgjDcib9MgG3v9mlOh1sjRezphRHWUiHZJiRSO3vnLIClSuLjEdHSPbd5LaDRr7HW0iwTFoeBeo2mRrax21lISFOMUO0TKIVVtSm9FQEJ20FRZS0PVVnLJoUDl0yE6ejpZEGa0aJu1znoarEZcbUIjx/tjKSgo5MYbv8kXv/gprrzyGubMeXeOKGYwvDt/g2TIkOGQobXpaPz0p5Kbb5b87/9KXnhB9Co4XnvtJe644xd89avfY/r02YwapcnLE9TV9d5PQ4Ng3LiBxfIlJWbxHKR6b9vVJaio2LdCpb3djENt3mzE7nvj3VCoQLcoXe9xzp5nvjbQ2NaqVUb3U1cnaG42+puaGvN9E8IUnp5nxu6Ugpoa8+8DRVXVaK644jPccccv6OjoI8nyAPHhD1/Bm2++yooVSwa9jdY5BEE5Ujb2elyIFrTOQqmh+Su77vMoVUY6fRqeNwulsoEoQrTgOGvCka1Yj35FCI1lbUXKapQaTjJ5OkK04jhPA35Y2Ah2hkVqIIrWMYTooFsoL2V9OHrm0J1Sb1mbcJxn0doOBfi5aF2I6fJUImUr0ehfAD/svpQQBKNRqoRI5OleLmGDwbaXo1Q+uy4XugMrLWvLXrbW71qnQK013/rWfzN27AQuvfTKg3KMfJ3HBH8807wpjAyqiBCh0jfjjB2iE40mIKBFtpKtsyntI28kSoQRfiVJkSQhOvDw6BAddIouKvxhxA/wCFiaNLWyjhpZ19OJOJh0ii6arWaydVavIiNX5dAuErTIVoSE+CTIngZuhaK2ahvr5iylftoWpBTUWfWscleTEr3PNyXS1Fn1dIouLG0hEGy0t/CG+zZp0lx88ceYMmU63/jGF3uyuzK8O8kUKxkyvM956SXB734nWbPGFA1btsAf/iB4/PHuLAqfL33pambOPIyPf/xqAEpL4YQTFIkErF0LW7fCypWCsjLNySerfosBndbMbPOo3OGz+l8BLc8EtG/QrF0LJSVDF9grBf/5j+B735P8+McWP/yhxc9/LtmxY79ekgNOe7sR0T/9tGD16p2dpwkTNCUl5vXr/qzs6oL2dtMF6a9Y8X148EFJEEA8brJOLMu8HqmU+bOlBZqaTAH3xhuCDRtETyDmgeK//utaOjoS3HvvHw7sjnfhpJPOoKSkjD//+fdD2MoilToFkFjWOoRoRsrtWNY20mlzBzUa/Sux2G1EIg8jZf0A+9II0Rp2OIyNsJQJlCoMiwgfKZvCDkcQFhc+tr0BIRIkk2eRSp2B581Cyna0zsF0QkRY9DiYLkQ+vj8JrbNRqgjfn4BS2UjZhuMswbaXhiGWNTjOMow25TCUKkepPDxvOun0B0KtyubQVWwnSpUghEmmHwpa54SZMr32Fn5tYAs7rd+9xcp//vMIL7zwNDfddAvOgfL2HgQVwXDG+WNQQtEom0yhorKZkZ5KlL67JOP8MUz0xiORdEgzdjnBH8tEb0Kfz9dommULNVYtbaK936yW3am2ang++hIvR17jlchrPBd9kS3W1kFvv+/0/R7ZdVROSHDLIDmlmY0zVmKVBmTLLGI6RqEqQGvw8WmTbT3n2ypbSYok+SqPHJ1NXMcpUgU0yWZqrDosy+LrX/8Br722mAcf/NtBvsYM+0NmDCxDhvcxqZRZ7IPucbUqKYHqanj6acERR2juvfcXrFq1nEceebFXON9pp2nKy01XprkZxo5VLFige4007YrWGvVPj/wnAj5WKXjYcdhQJ1ENiolHCc64nH637Y9XXxX8/e+CWAxGjtSkUrBkiaCrS3LddeqA2AHvL6tXwz33mAJKa4HrwuzZmksvNa5pF1ygue8+wapVxlXNsjRz5xpBfH9UV8OWLYKJE824mBAa1zVaIaU0nZ0m/yQ31ziB+b4pYO69V3LNNWrIQvu6OqM1qq83749ZszSlpVBRMYIzzjifO+74BZdf/inkgVDw74bjOHzoQx/lT3+6g69+9XtEo4Mbe/G8I+js1EQij/WMUyWTHyIISsnO/kGYzu4AHq77NJ2dV4f6k90R+P4UIpEngAApW8K8kwCl8pCyLRzpAiPCD3o6J0J02yU7+P5heN5rKDUC6CISeR6tA4TQoRtYd/ckF9+fjtZZuO7icJSrCPNxHYThjW0EwQhkuHDVOo7WeWG3piMsWHx8f3I4UgbQhdHJDO1OfDp9JPH4srAIy8e4g21EqRJ8f/qA22qtD8p7Yn/xPI9vf/tLHHXUBzj55DMO6bElksneREb4FbTKdixtUaQKcOi/YLKxmeRPYLQ/ki6RJKqje9gcd9MpOlnmrKDeasQXPq52Ge6XM8WbNOAx2kQ7S5zleMIjX+UjgHaRYJm7kngqTrHav5DM/ojrGIVBPtVWLREd6RkDa5PtZKss8lXv8byE7CAg6GXZLBDEdQyBwNEODbIRiaRZthDRbs94GdDjttYpOyGAY489kZNOOp3vfvernHLKWYP+/ZLh0JIpVjJkeB/T2GisbouLey+Mi4th0ybB229v5X//95tcfvmnmTFjTq/nSAmHHaY57LBB3nWr1vBSgC4RjCoSfGaST32nINioKC0TOONd9u5WtBOt4YUXzPOHh0ZRxkpYs2EDrFolmD37nW3td3Yal7OaGiOgt21NImG6WcOHC844wxQmVVWaFSsEXV1QUaGZNGlwuSWlpaY42bhREItpgsB0bSxLEIkYW+R43OSvFBVp1qwRbNpk/j1Y1qyBu+6SVFcLbFvj+4JnntFcfrkJwPzEJ67jzDOP5oknHmbhwrP2+bUaiEsuuYJf/vKHPPLIPzj//L1rLgwCzzsKz5sfdkbiCBGQm3s9Um5FqSqUKkVrB9teQTT6Dzo6rqev92A6fQKOsxTbXoJJiHeAFEEwCaVaQ3cuH60jBMEwhJB43hTAIRr9B74/K3QUi4Xi+mJ8fzSWtQ2tRZhl0o6UO8LU+YKewEmjZ+n+qLbQ2kHKVrRuDoMu8zAdpM24bgdaR9E6B8taimXtwPPmEATl2PYGfH9a6Ew2eNLpY8J9P4sQ2zEdoVK6ui7d6zid7/vvymLlnnvuZP36Nfzyl384ZJ2fbh1Gu0jgYFMcFFERZO99w12IECGi+y5Suo+xzFnBDquGPJWLg0NSpNjobMbFZbLXv06r2qqhTbYT1RFaZSsxHSVP59IgG6m2ag5asSIQTPDHkZCdNFiNWNoiQBEjyiR/wh4FlqMdBAKFQu4yHOQJnwKVz/T0FFY7a6mXjbjKRQuNvYsWSId9l131QV/72vc54YTZ3HHHL7j66i8clOvMsH9kipUMGd7HdIf5pVKQvcvnZiplFv6//vXnyc7O4cYbv7H/B6vRRh8cFhZCQGmWRleCqFHQBgwi26s7UyQIoL7eOHvtiuuaDkZr6zs/g7x6tWDHDhg9emdAY3a2yUB5+WXBKadompuN5XMiAWVlMG5c34WKbtewSqHTmmHDJJUVgvUbBOPHw/DhmtZW2L7ddKc6O814WDRqOiuOs3NEbCghmL4PDzwgqa8XTJpkxtKU0qxbJ3jgAcnnPqeYM2cehx22gNtvv/WgFStjxoznmGNO5Fe/+jHnnXfxEBeYdugI5hGP/wjXfRqtI1hWNVrn4HkzCYJh2PYqhGgKn7srGgjw/UlhwOQWlLIJgnH4/jiE6MSy6pCyDiEU0AxEcJxl4WiYwHFeJZU6Ed+fgm2/TRBUEARVWNY2hOgIU+1jCBFD6yi2vQYhWsJRsyhS1od6Fg/jGibQOgffn4Fl7QhzZOoQwiMIqlCqJLRO3oLrPofnTcX3p9PZeTlD/9h36eq6nHT6aCxrM+DieVPReuAEdd/3aW5upKjoHbTb64POzg5++MNvceGFlzJ9+uxDckwP3xQRdjW+8BFakKtymeFN7WWpu7+0ylYarEZyVW5P5yGmowTKZ5u1g7He6D5DJDWarfY2mqwmHG2jEVhI8lQutrbDMMqDR77KZ35qLju6rYt1jPKgjHy9p+lBSVBEjsqhWbaQr/KQSLpEEh+fEUElO+waGqwmNJooERpkIxvtzYwMRmBpSatsI0vHKQt2WkSPHz+Jyy77JLfe+n0uuugyiovffRbb73cyxUqGDO9jCgth+nTFM89IYjFNPG4WtFu3CrKyHuLpp//Jr35194FxyomBtjWkBL1Gs5Maors91g/btsG//y1ZvVqgtRGPC2FGknp2lwQpNYWF73yx0q0f2dXRq6XFuIC1tMCXviRpbDRWmsYhTTNhguCKKxTFu6wF9bIAda9nCj40xAUfGW3xfwURVq6URCKaVEowapTmkksU//qXpK7OBEK2t5v/k0nIzTWPDZYdO8y5VlTs1M9IaYqjzZtNITZiBHziE5/lk5/8MEuXvnnQFoCf+9xXOP/8E3nssQc59dSzh7y96z6H6z6P1nao5xBI2YzjvInndTta7f6e0UQi/yIafQAh2jHakghS7kCILixrI0K0AJ2hfiNAyiazpc4OxfY+kchDpFIL6ez8BNHofTjOUsAjmTwZpUaidRFKFaBUEZa1FSFaCYJSotHHkXI7QqSRsgml8oB8pGwhCMaiVClKrcF1X8eMkUnAwbI2EwTlpFInYturSaVOp6vrMuhndGjvSIJgAkHQt0aiL+rqalBKvesyLP7617tpbm7k85//2iE75kZ7E5vtreSqbFxcFIoW2coSZzlHpeYPOJ41FFKk8YRP7h7dCJekTPZpgQzQKJtolM0ILYjqGBKBh0+zbCGuY+R5uXtsc6DJ0nHG+3tP2HVxmelNY6mznBbZikbj4jLWH02OymZlZBVRHSGmY+Zr2qXGqmW73EGuziFHZTPFm0S27u0U+PnPf43777+HH//4u3zvez87WJeZYR/JFCsZMrzPOftsTWtrt1WuWeiPGdPBP/5xHcceexJnn/3BA3OgsRIxSsJahR4rEa5At2tEk4YzLURs4LvlTU0mU2TzZiPkB+MgVl8viEaNViadhupqwbRpZpRqIJJJM+LU1SUoL9e9MmN2p6sLXntNsHy5KZKmTjXjWwO5noFZ1GdlSVpajAi+qcmI3evqzAjXK6+Y0a/p02HiRBPmuHKl4NFHBR/5iLlG3aRRf/agWaPHCbAEtED5yoDrT/N42Xaoq4PiYjOSV1EB7e2aO+80JgnJpCCVMt/XhQs1RUOY5uh2Kdv9dRHCdLi6v37aaedSWTmS22+/lVtv/d3gDzAEFiw4hiOPPI6f/OS7LFx41pDHd1z3hXC8qh0h2tA6H6UKkbI+XNCftUdXxbLWEY3+C62jBMEohGjEtteGehcbIdrCrkccrW2kbAlfGyvUq3SFdsXrcZzn8byj6Oy8JhT0p8Ixqt6LR89bsMu/8ojFfg8k8bzhoUA/EibcmxBIY1/sAvHwePGwc1RPEIxDqWKCYCT7XqjsGzU1xuXi3VSsKKW4/fafcfrp51JVNfqQHDMgYLu9g4h2iYTfAwuLfJVHi2yl0WqiPBiaO11/ZOksIjpCl0gS38X8ICmTxFSMaD8jZPVWAw4W2SqLpOgKiydBijRxHaciGNbndmk8aq1aWmQrNjalQQmFqqCXMP5gUKgKODK1gCbZhC98clQOOTqbDfYm0vjkhR0ZgaBIFSK0wMLisPQsCvvRBxUWFvHJT17Prbd+nxtu+Gqmu/IuI1OsZMjwPqegAD7zGcWaNdDYKMjL0zzyyP/Q2FjNffc9PORFYToNb79tXK+EgEmTYMYMjeMK5CUu6k9pxGYNgTIdlSNs5Cl7v7P45puCzZsFEybonnyXefM0ixebIquhQeA4cNRRmnPPVQNqPt58E/70J0l9vRmRisclhx2m+NCH9B6i/GQS/vAHyauvmueCMRVYvlxw+eUDi/hHjID58xVPPSVobTU6IFNYQFWVZvVqQVGRcQMbMcJ0PkpKNEuWCBIJTXY26JUB1Cn0BImQ4feiAHSLpnB9wJmf3/PX+OGHa/72N0Fnp9HJFBZCTo6mutoUSAsWDK7rVFFhCq4tW4zmprtIqa6GUaN0j1bItm0+/vHPcMstN3HzzT84aKM/n/vcV/ngB0/hP/95ZMjCaGNbnEMQTMG23woLBgsh2lBqPMnkOeyuV7HtFQjRThBUhf/eAKgeXYlxCmvGaEniCNEUPqbQOtmTl2Lby8nO/l887zHS6VNJp4/d5VidSNkWdk16v5nS6WNRKhfXfR7L2k46PYt0+lgc5zUikYdRKg/LqkaILoTowoQ0mqBJ0EhZi9b5BMGhWZjvSnX1NgCGDRuia8YBojGlqEtqsmxBZVwgheA//3mEDRvW8dOf3nnIzkOh8PCwd1tuSWRPmOGBIltnUeEPZ4O9wYjQtUNSpggIGB1U7XEO3QQEWNqmXBXQZDXRGdoqx7VJks/We2prkqR4M/I2daGTnhaw0d7MRG8cY/0hiOJ2o0skaZWtSC0pUPn9dp0cbMrUzoIi6ASrIYvyztFEIw7psg68AjO+ZglJjsru9fy+uOyyT/Lzn/+A3//+Nr7wha/v8zVkOPBkipUMGTJg2zBlCoCmubmJO++8lSuuuJoxY4YqxoW77xa8/LJAKbMYe/ZZzZFHwoc/rHGqJPJzEVij0AmNLJUwRuxchA/A9u1m4b1rEKVlwbBhmunT4eyzA6JRKCoauEPyt78Jfv97SUsL5OUJhg/XFBVpnn1WUFpqCqCmJlPEFRebwuv112HkSHoKk64ueOMNmDXLOKb1hxBw4YVmUf/ss4Jly4ymZNo0TTptTjIWMx2X1lZTrNi2cfLyu8PBuwD6eI2iQD+6nDVrTJF1yilmfMtxzGu1YQM895xg/vzBhUM6Dpx5puKPf5SsXGlE/MmkoLBQc8YZil0dXy+88FK+850v8+9/P8BHPvJfe9/5PnDkkccxf/7R/PjH3+Gkk04fUiHt+5OJRB7D96ehVBaWtR0hWhAij46OqwmCPUdQhOgOGxKAQsqGsCjxANkzGmYKFK/HyhiMw5fRm7RgROmFWNYOYrE7AEk6PZ9I5BFc92mkbEepfNLpE0ilFrLzo1ng+7Px/d6jdUEwDMvahuv+Bym3IkQaU6QYq2ZjU5xAygRdXecSBOOG8CofGKqrtxONxsjPP3CajMHgKc0jOzwW1/u0+ZqIFEzIlVw4wuU3v/kZc+bMY+7cBXvf0QHCxiZP5VFr1RHT0Z6uQ0qkcHDI6aMQ2B8mexNwtcM2ezspmSau4owKqhjpj+h3G+P+JZBIyoMygvC/VqudEUHfxeZmewu1Vh0FQQF26LCVEAnWOhsoDUrI0Tl9btcfGs1GezPr7Q10ii4Eomdka29Fht8GHcsh1lFAjhsgA0lsex6JSfUkKppJC49hwd7nXwsLi7j44sv5/e9v4zOf+QLxvbXOMxwyMsVKhgwZevHrX/+EIAj4zGduGPK2b70leOklQUUFZGWZhXR7O7z4omDaNOMeJqICZlhDHhTIzwffF2F2w87H02lBaakalO3xww8bYXhXl3EQCwKjvRAC8vM1d90lePxxSTJpXLTmzlV0hcVCLLazMIjFjHZj9Wo44oiBj+m68IEPaI48UpNKmWMXFkIiobFt03ExlsWEOhzjYpYXyoTEMIGyNC3bNClLkJ1tXlvRrGGm1ecxW1oEQSCIx3sXM7m50NBgisrIIKeCZsyAa69VvPaaoLZWUFamQgez3s8rLi7lyCOP46GH7j9oxYoQghtuuImLLjqVf/3rr5xzzocGva1x9Ho7DDksCV20jDWv5x3X5za+PxZww7GxnHDUqyEsTEZjChMnLF5EWNxotFaYAqYd04kpIQjGABaWtQ7XfRwptxCN/gul8lCqAClbiMX+CPikUucMeC1aF9PZ+WEc53V8fwyW1RSGQ7qhaL+VIBhLIvEF0ukTGYrL3oFi5cpljBkz/pDnrDxX5/HoDo8CVzA6S5IM4M2mgG2rX+XFF5/httv+dEjPRyAY7Y+k2WqhSTYT01E8fDyRZpQ/krzdrHn3Fxubif54xvijSAuPqI702PX2R3lQSnlQSrVdgx26baVJUxIUU+HvOQKm0VRbNUSU21OogBlDa5BNNFpN5PhDK1bqZD0rndVYWprclNC+eIm7nCNSWXtoTHrORUPXZgg6IFpgk2VFaJLNWO0uckMW7WXbGabLqfL7/4DQaJIihaUtrrrqWu666zb+9re7ueyyTwzpGjIcPDLFSoYM73O0NjkabW0gZQN33PFLrrji0/s0s7tyJYDoKVTAuHXt2CFYtUpz2GH7fp6zZmmefdaEHFZUmAX+jh1mAT5r1t7HmlpbzQhUdrbGcQS2bToHyaSxDdba6FciEcWECZBIwH/+I8nJ0fQVbqyUsfIFUwA88IAZUysshNNOU0zczSXUdWH+fM0//iHp7NRUVwsSCUFTkxk9a283RUZZmeaUU3YGa1bnSFa1OuS96pOwQESgKkszfIbEOabvX+GFhRrL0nR10WtMrbW1f7exgaiqMmNrewrQe3PmmRfw1a9eR2NjA0VFOx0CEgnzPsvO7r/rNViOOeYETj31bL75zRs58cTTyM4e3KIoCMbQ0fFZIpHHse2VYZDiGaRSJ9PfR6HvTyWVOpZI5EmE6AyDJRvQOoplbUCpGGYELAj1ImECt7DCojqB1oV43nykbETKZqAD216BlNtRqgilysPzy0XKbUQii0inTwjDI/vHtregtYvnnY5lrcW214eJ9lkoVURr62/ekY5KN2+88TLz5h11SI/pKc3ihoC4LSiJGkeIbAmjsiT/uvUXlA0fwemnn3dIzwmgVJVwWGoWG+3NtMhW4jpGpT+OUX7VQdN3ODi97HkHwsZmVnoGRaqIaquGLpEkS8fJVlnUWfWUBWW4u41j6VAztQd6b78l+maHVU1A0MsBLF/l0WA1UmvVke33Pcqo0+C3gIybmxmFqoCYjtEZ64RWm5L62ZQVFuD08zNeK+vY4GwMM28kw8cPY+HpZ/Ob3/yUSy+9sle2WIZ3jkyxkiHD+5j2dvj73wVvv23u+K9b91M8T3PllZ/fj70eHBeuqiq4+GLFP/8p2LjRdFhKS+GssxTjBrEma2uDzk5BSYlm69adTl2NjUbboZTAdTVbtpiOxNixJtSuttaELra20tPtaGsz4Y1TpsCqVfClL1ls2bIzhf6BByw+97mAs0PTqnTaFArz52tefFGzaJHoOV5hoRn96ugQnHuu4ogjNCPCiQ3Pg3vus1jvWBw+RlBZHUBS8xQ2YyZbHDeq7wyLCRNg4kRYulQwbJixMG5oMIXCMccMbgRsXzjttHP4ylc+y6OP/otLL/0427fDY48JVq40B5w0CRYuHFwXbCC+9a0fceyxM/jxj7/L17/+/UFvFwTj6ewcD6QwH397W4jYdHVdThBUkZ39A7TOxvOKkDKNEB1YVmMo2k9ifLkjgIdSsbDL0YTnTUKIJhxnE5BCylZMt6WAdPr4Xkczo2LVSNlAEOytCAvdDRAEwUSCoAopWxGiAaWGhZ2cd4a2tlbWrFnJpz51DUI0hl2sg7/c6Aqg3dNk7XYov7marc/+nSs+9x1s+51Z9pSoYorTRfgEWMheGSEHgybZzFZrexiuGKciGE6JKu63OOp21HK1ywp3Jc2igxbZyma2UKyKmJOeSSwU7QsEZaqUNfY6lM7quZYu0UUEh4Igf8jn2yW79tDUCARCCzwxFF2PIKZjRFWMQEG2ol+vtXrZyJuRJXh4xHSMgIC19nqOv/YM/r3wnzz++EOcdtrAXc4Mh4ZMsZIhw/sUreGvfxU8/7xk2DBNdnY9jzzyS6qqrub110s488yhFx2TJsELL5gsj6ywa59IGCeqvblzDYa5czWTJ5vQR61NfsnuOSv9kZ9vRObptBkB27TJFB1dXeYOoW2bx6WEDRuMliU3F5qbYcoUzapVJoUewHU1xxyjmTFDc+21kk2bjEC+O89k2zb4v/+zOProgBUrBE8/LWhoEEQiOgyI3FmklJTosJgRzJu3s1Dp7DRGAKtWCVIafl8boaNDE/gClYIJD2sWfFj1Oc7lOHDZZYp//EOyfLkZNSss1Jx4oubwww+epXNJSRkLFhzDww/fz6mnfpw77jDubSUl5pgvvijYvl1y9dVqSK5ku1NZOZLrr/8yP/zht7jooo8yceLUIe5hKM5YLlrnhIntR2NE+U1I2YqUtQRBGa77cpix4qJ1HkFQie9PxHWfR8p6hKhB6ziW1YAQnSgVw7J24Lr/wfOO6RHwS5kA4mFw5MD4/gS0zkPK2rA7E0MpB9uuw/PmwUFeDA/EW2+9gNaao49+ltzcVwiCMlKpU/G8IzmYI2lZNhRFBNu6NAW7dA9feeAOLDfGRZdcftCOPRgEot87/AeSGlnH25ElJEUKRzk02c1U27VMS0+hqh8NChhx+ypnNQGKYlWEQBAQUC8b2GhvZoq385f4KL/KWB5bTdjaQqGRSMZ5Y8jTQ7c6zlN51MkGNLqnoAowmrG46l87IlxwCiFVbf4upPlsCBJgxcEeYMpus72FFGmKdnEwi2gXa57F7PnzuO22n2SKlXcJ775o2b3wy1/+klGjRhGNRpk/fz6vvPLKgM//61//yqRJk4hGo0yfPp1HHnmk19cvv/xyhBC9/j/11FN7PaepqYlLL72U3Nxc8vPzufLKK0kkEgf82jJkOJRs3w5LlkiGDze2um+//SOEEMya9XkWLxZDCg/sZtYszbx5Zt9r1ghWrxZUV8OCBZrp0w/MIjkry1j9zpgx+EIFzHOPPFLT1mbS3EeMMCJ3rQWlpZqyMo1tm1GlZNIUMt2i90su0Vx9dcBZZynOPFPxmc8oLrpIU18Py5cL8vN3OoVJabJM6usFd94puOceQW0t5OVpkklYsUKSTgvGjDHJ9bGYOUZnp6amRvDWW/Cd70iuvlpy660Wb74JS5eaoimZFvja2B0vWSL5+9/7X/gVFcGVVyq+/OWAL34x4EtfUhx33MHrqnRz5pkX8PzzT/Lcc8097m3FxcasYMIEzaZNgjfe2P+T+OQnP0dV1Wi+8pXr0H3N6R1ApGwP/2ZjAhmLCIIxBMFwHGcZShWGxcwEgqAkLFAa8f2xaJ2HEMke164gGE4QTETrbKRsxbbfQIhkWADVkk7P7yOYck+UqiSZPB0pE9j2cixrDba9Et+fRDp98kF8NTSWtQnXfQbHeQkh2vb4+tKlvyUvz2bixEhoZrCZrKzf4DgvHsTzAksIji51UBq2dig6fE1dV8CqRfcx88TzmVp+aMX+7wQKxVpnPWk8ioJC8nQuxaHV9brw8f5olE10ii5yVU7P4t3CIqqj7LBqeooHgLiOc3hqDtPTUykLyhjpj2BuejYjgkq2WtvZbG2hRbSG42J7p9KvIEtn0Sib6BJJOkUnjbKZQlVAeVCKj9/r+N0IAdGRYOdA0GJGwoJmEBbExoDsp62i0bTIFqI60qvb5OCgUFz0mct59dUXeeONgdeYGQ4N76nOyr333ssNN9zAbbfdxvz58/npT3/KwoULWb16NaWle87Xv/jii1xyySXccsstnHnmmdxzzz2ce+65vPHGG0ybNq3neaeeeiq/+93ObIDIbrcqL730Uqqrq3niiSfwPI8rrriCT3ziE9xzzz0H72IzZDjItLaaDsjw4dDZWcdbb/0fc+Z8luLiIpqbBW1tO7sjgyUSgY9+VDFjhmDlSrMwnjwZZs7UQ9ZJHAwWLjTn9MILgtxcc8e/uNhY/a5bJ1i7FoLAdEdaWsyfp56qKC01uSjTpvX+4O3qMuNjjtP7ccsySe9vvy3IyzNOYk1N0Nws8DzN2rWCkSNNJgqY4kgIweOPw+LFkpoaU/xEo5BICJJJTSQCubkiLDYE6bQxDDjvvD3tlrsRAvr41XhQOf30c/nqV6/jiScewHE+vod7m+uaMbz9JRKJ8N3v/oxLLjmdu+/+LR/96FX7v9N+CIJytHZCTUj3XV6NbW9GiBRKVQLNWNa2MAPFx3GWhkGMnUQiT2FZ21EqN8xWEShlBPFStuI4rxEEI0ilTiCZvHCQZyVIpc4lCEbhOG+EFssTSKePQOuDtSj3iMX+jOs+E5oHCIJgOF1dl/U4llnWZhYtepkjj6wERpi73EEBlrWOSOTxMEfm4OkA5hdZBMrl6TqPppSmbd0bdNZu5toPX4I8xGL/d4IO0UG7bCdLZfVahGepLFpkKy2yhVLVt7W4xhhD7I5EoMP/diVKlLH+aMZi9CRbre28EHmJLtEJQuBoh5H+CCZ7E/c69panc5mTnsl6ewPNVgtCC0b7VZQHZaxwVlFvNSIQDA/KGe2PIqZ3pgjb2ZAzE9L1pqMiIuCWmAKmP0Q4LtYsW3pdsgrHK48/5VSGDx/BX//6R+bMmTfguWc4+LynipUf//jHXHXVVVxxxRUA3HbbbTz88MPceeedfOlLX9rj+T/72c849dRT+eIXvwjAt7/9bZ544gl+8YtfcNttt/U8LxKJUN5PrPPKlSt59NFHefXVV5k7dy4AP//5zzn99NP54Q9/yPDuoIEMGd5jFBSYLkJrKyxb9lOktDjssBtobjbjUnkDtM8HIhIx9r/z3oW/310XzjxT84EPaFpa4N//FixeLNEaxo/XKCVYuZKwMIAjjtCccUb/dwZHjYIRIzSrV5vnd6+FGhrMaxuLCfLzNTt2GP1IKmWKkNZWzYsvCo47zmSpbN5sxPorVwqSSdOZiUZNwQQQBALPM4WU75vjmBE1wZYt7CHmfycpLS1n/vyjWbPmn1RVXcnuix/PM1k+B4LjjjuJyy77BDff/HnmzTtyH8bBBofvT8X3Z+I4L4dOYhEsqyYMmGzFstaHIZCp0N44hyAYRip1AtnZ/4tlbUGIToToQAiPICgBHDxvLpZVQ1fXxWGmygiGNibVt7XxwaEL132GSOQRgqAMrauAAMvaQDz+e9rbq9C6iOrqJbz0Uj2//nVvmzyjx6kJndUOXodDCsHRpTZziyya05pb//kAJSVlnHhM345v/78hkQgEWqieH7022R52LLp4PfIm471xjPZH7uESVqDyiRClQ3T2uG9pNJ2yi9HeyH5zWgDaRDsr3FUEBBQp0xlMiiQb7E3kqTwqg72vlYpUIYXpApIihdQCX/i8EnmdVtlGXMUI0Kyx19EiWzk8NadXBouMQrR/d+Y+GeFX0Og20SE6iesYAYpW2UquyqFMl3LuuR/iz3/+Pd/61o9xnMGZFWQ4OLxnxsDS6TSvv/46J510Us9jUkpOOukkFi9e3Oc2ixcv7vV8gIULF+7x/KeffprS0lImTpzIpz/9aRobG3vtIz8/v6dQATjppJOQUvLyyy/3edxUKkVbW1uv/zNkeLcxbBjMmqXYvj3NkiV3MmnSFbS0FNLeDkcfvfd09vcy2dkm7+T88zVTp2o2bYJ16wSWpTn2WM03vhHwta8pLryw/64FmJGvj388IDcXNm40YYmbNpkF+ZlnmtGytjYzEhcEZhSqtBTy803x8dJLkpoawdSpmpwciERMwRSNmoIkLw9iMd1ja+x5ppAqKYFoVB8Qd62DwTHHnMDmzS+Sk6PYssUUWUEAW7YM3r1tsHz96z+gqmo0n/nMR0kmkwdsv71x6Oz8BMnkuYCNlF34fmUY+mg0JkFQQRBUoXUMpQpIpU4iGv0XoEKxexTwEaIRy9pGEFQghE8QjCaVOgOlqngnLIb3hpTbicVuJzf3i+TkfAMpt2IyXQRgEwRjkbIax1kKwL/+tRjXlZx+eu+VoxAdKJWF1gP8QB1AopagLAKPPfw3zjrrwveNq1NcxykKCmmXCRSKNtlGtVVDh+ggqqNoDcudlax0Vu+xbbbOZow3Ck94NMomWmQrDbKRvCCX0f6oAY9bbzWQZOcIWXfnAozT12Ax20WJEGGbtYNW0UZxUESWziJHZ1OoCqiXDdRadUN6XfqiMqhggjcOLRSNsok22Ua+ymOmNx0Xl/PPv4Tm5kaeeeaJ/T5Whv3jPdNZaWhoIAgCysrKej1eVlbGqlWr+tympqamz+fX1NT0/PvUU0/l/PPPZ/To0axfv56vfOUrnHbaaSxevBjLsqipqdljxMy2bQoLC3vtZ1duueUWvvnNb+7LZWbIcMjoDixcvvzvpFINlJV9EteFc881Quz3A0VF8JnPKJYvF9TXm8X/tGlGwzNYTj4ZXDfgN7+x2LDB7POUUwI++1nj+nXXXYKmJlOoeB60tQnGjtWMGqWpq4OPfCRg1iz41rck8bgJvuzOQZESsrIE6bTRuxQUmM6M5wkiEZg0ac+8k3cDhx9+JP/7v9/kiCOW8fbb01m3zjxeUmJCJsfumb+4z8TjcX71q7s5/fQj+fa3v8R3v/vTA7fzXdA6j2TyI6RS5wEpXPc5bHtTaGO8Ca2TmMyVDoTIJQjG47r34vuTAI1S+TjOWwiRCDswdQih6Ow8e7dOgw51ICK0L37nChghGonHf45tr0Op0vC824FXSKePBIx1s8mY6QTggQee46STxlFUtJ0giKB1rMeQoKvrdEzRdmh45ZUXqKnZMaQ8nvc6AsEkbwJJkTQCeNmEh0e2zqY8KDW2vnSyzd7BKH/kHvkl4/2x5OocqmUNSZmiKCigIqggSw9898rHtHx3dxuzsEiL9D5dS6PVjIPTa5/d3aB2maAPCcuQkEgm+RMYEVTQJtuxtEWhKujpIE2ePJ2JE6fwj3/8hZNOOn3/DpZhv3jPFCsHi4svvrjn79OnT2fGjBmMHTuWp59+mhNPPHGf9vnlL3+ZG27YGajX1tbGiBFD7E9myHAIyMqC9etvZ+7cY/nWt8ZTWKiGJFr//4FYzLiM7SudnbB8uSQe18yYAVoL1q8X/PWvcN55mvXr4e67jQ4lEoGyMmM24PumsBk71gRQjhsHixcLioqgpsYUk0FgCpbRo02h09FhxsDy8jTjx2suvFARPXRrv0EzZ848LMsinX6RL31pKhs2mMXGmDHGYe1AM3nydL72tf/hppuu59hjT2ThwrMO/EFCtM4CshDCBySeNwulcsNRLw+lSvG8w1CqAvDoTpb3/TkoVY7rPhmm2ptumeM8g+9PJAgqQoewRTjOCrQG359GMnl2OB7WjYdtr0SI9jBschx7DklopKwJuxnl6H1MSXfdl7Ht9fj+VMAiCEZiWWuRshHL2kEQjEWIBKbDUsHWrZt4/fVX+OUvf4LnNeE4a4AUWueSSi0klTptn85jX3nggfuoqKjisMPmH9LjvtPk6hzmp+ay2d7KG+7bRFWEfJXf40QW0zEaZRMJ0bFHsSIQlAdllAdlfe16wGMKLfDwe46j0aRJU6QK9+k6oipCIHtXJN3aGUcPfvmq0dRYdWEBlqRA5VPpV5AT/lxk6Syygj0FmkIIzjvvYn72s+/T0ZEgK2vffo4y7D/vmWKluLgYy7Kora3t9XhtbW2/epPy8vIhPR9gzJgxFBcXs27dOk488UTKy8upq+vdbvR9n6ampn73E4lE9hDpZ8jwbmTt2lUsXvwsP/zhHxg2bOhhgRng1VcFb7xhRPRmZMyMfr3wgmDmTM2CBfDgg5raWnAcgVJGmN/QIJg1S/cI4I8/XrF+vXEKy86G+nrTQRkxQvPBDxonrxUrjLNYYaEZpdrfvJKDRTyexdSpM3n11Rf56EevOqBjX/1xxRWf5tln/8N1132cf/3rGSZMmHJQj+f7I4F0mIkygSAYD6Sx7VWk07PQ2kXrwtBaeBhGkL8eIdL4/nTS6WMAcJzXyMn5HEqV4TjLAUk6PQuI4bpPY1lbSCRuROsipNxOPH4Htr0a8NE6hufNoavr8nC8ykaIJmKxP+M4b4fi/wJSqVPCQqH/yW8hGnDdl7GsTShVEOppNmCKLXM3OwhGI2UtUlZjWVsAFyFa8Lwj8f2p3HnnV8jNzePkky+joyOGba9BiARBUL4Pepz9w/d9Hnrofj70oY8i5Xtj4j1Nms32VmqsGhSa8qCMqqCyZ5xqKESIMMqvYrO9FYXqZZns42Npa4+gx/2hJCimPChlh11DREWQCLpkkrwglxEDpMcPxDBVxg6q6RAdxHUcjaZdthMjSknQt0lAX6yzN7DaWYtCYWFRJ+uptmqYk5rVK4SyL8499yK+//2v89hjD3L++Zfs03Vk2H/eM8WK67ocdthhLFq0iHPPPRcApRSLFi3immuu6XObI444gkWLFnH99df3PPbEE09wxBFH9Pl8gG3bttHY2MiwYcN69tHS0sLrr7/OYWH89pNPPolSivnz3193azL8/4VS8IMf/JZotJhXX72QtWslRx2lOe44zXtVS1hfDxs3CqQ0gvmhmgRobQIcXXfwWpDlywWO0zspPjcXtm8XvPiiYM0aSWmpyXJJJIx1b3U1HHec5uyzdybVjx0Ln/yk4rnnjOWz1jBhguKEE0xRIoQR8x8MTKaLYN060/2ZMkUzdSrsz5j/4YcfyX/+88jen3iAEEJw662/45xzjuOjHz2Xhx9+nuLig2OFZlmriEQewbLqcJylKJVHEIxGiDaESBONPonWiwEPy2oJuw8Cy9qI1nn4/gRAIkQLUtYgZRNBoOgOlrTtDaTTR6JUIba9Atd9hVTqFGKxu7Dtpfj+OCCGEM1Eo3/DdZ9CqcowGHJ7qImpROsSLKueWOwetI7vEULZjZRbycr6xS7FiUck8hRBUAa7WN2aImYervscWjsolYvnnUoqtZDm5jbuvvu3XHnl1WRnm/as7x/cgnEgXnjhaRob6znnnIvesXMYCh4+b7pLqLZrcLWL0LDSWU291cDc1Gyi+zA+5+JSEQxjjbMOJ7BxcfHxabVaKQ1KyVdDd1FJkqRTduFqhyy903HMxmZWegb5Ko/tVjWBUIzxRvU5ajZYyoMyJnjj2OBsolE0AYK4jjHZmzDoLJcO0cEGZyOOdnqZBjRYTWywNzHHmzng9lVVo5k79wj++c97M8XKO8h7plgBuOGGG/jYxz7G3LlzmTdvHj/96U/p6OjocQe77LLLqKio4JZbbgHguuuu47jjjuNHP/oRZ5xxBn/5y1947bXX+M1vfgNAIpHgm9/8JhdccAHl5eWsX7+eG2+8kXHjxrFw4UIAJk+ezKmnnspVV13Fbbfdhud5XHPNNVx88cUZJ7AM72kWLUry+ON/ZNSojxOPuzQ1wb33CtrbzfjSewmt4fHHBU88IWlpMfbEJSWCc8/VzJu392vRGl55RfDcc4L6eqOtOPpos+3ebsp22xyvX2/0JCUlEI9ooj5sWGPc1iZNgvJy40AWBNDYaDJfdtebjBoFo0Zp+rIPPRgoBW+/DbffbsIbc3I02dmCZ581uqXzztv79ffH3LkLuOOOX1BXV0Npaf/d7ANJbm4ef/zjA5xxxtFcccUF3Hff48QGckjYB6TcQVbWr8JMlHlY1nZsew2WtQGtC1CqmCAYBigsaytaZxEEI7CsGpQqxvcnh50WY/Nr7JBzAB/IQql8pGzAsmrCsEgHKbdgWatw3cVo7YQWySVY1raw2GnA80pw3aew7XWhfbEZvTHHXo/rLiKdPpa+bIMjkQexrA1hcWGF57YJy9oEuFjWVoLA+GwLkcDzZpNIXE8QTKZ7GXHXXd/D9z2uvLLvm4eHmkWL/k1l5UimT5/1Tp/KoKi16qi16igI8ns0E4FWNFiNbLerGeuP3qf9jvPGkBRJaqw62kkgkRQHJUxPT9mrnfCudKe7b7G3kRIpHGzK/FIme5OIhiGrLi4T/PGM98ehw5DI/UEgmOCPY1hQTotsQSIpVIW9bIv3RrNsJUmKPJ1Hh+hEIojqKHEVo8FqxPO8Xq5ifXHeeRdz882fp7GxgaKi4j2+nsaj3qonKZJEdYzSoHiv+8wwNN5TxcpFF11EfX09X//616mpqWHWrFk8+uijPSL6LVu29Gr3Hnnkkdxzzz3cdNNNfOUrX2H8+PH885//7MlYsSyLJUuWcNddd9HS0sLw4cM55ZRT+Pa3v91rjOtPf/oT11xzDSeeeCJSSi644AJuvfXWQ3vxGTIcQJJJ+N3v7sfzmjn66KsoKDDi7bo6kzJ+7LF6vxLGDxXNzfTkozz5pCQ/XzNhgik+tm2D++6TDB8e7HVc6plnBPfdZ+4Q5ubChg2wfr0JxhzIbKC9HTZvNm5fsZjRl5Rpxekyzdx0gL9D8LTv8OJWm6RnOjCVlUar4vWfzXZIUAoeeEBw992SDRsgHhd0dAiqqkz2zFNPSWbMCBg/ft/2f/jhRwLw6qsvcsYZ5x/AMx+YysqR3HXXPzj//BO5/vor+dWv7h5wDMgI4pvQOi909xoYx3kFKXfg+9Mw1sGF+P40IpFHgDS+P4PucSffz8G2V5JOH086/QGys7+Dba/FFKMCKZsRwkepIpQqRMpGTLGgwhwTDXhonU0sdje2vaxHf6K1hZRd4b8lSuUDCliJZW0nCEbTPfZlCqDGXQqjXa+/PQy2LGfXQiYIRoQjbcdg2yuw7RXhvkpIJi8gCKb1XGdXVxd33PFLLr74ckpKhqZ1OFi8+OIzHHXUBxDvRru8PmiRJkBxV3tgC4mtbRplU0+WyVBxcZmdnkmrbKVDdOJql0JVsIdt8d7YaG9mtbOWqI6Sp3JJC4/N9lYCEXBYenYvEXy3G9iBIkdnkxP0rRfRaDw8rPC/3RFAh+ik1W7DFz4CQVRFyVJxYsQGdZ5nn30hN910PU888TAXX/yxXl9rE+285S7p+f4JBAUqj1npGeTo95kA9CDynipWAK655pp+x76efvrpPR774Ac/yAc/+ME+nx+LxXjsscf2eszCwsJMAGSG/69oaoKlS2+nouIECgrG9TxeWGjGqOrqeNcXKy++KHjgAUFjo8kpaWuDww4TSGmKi6oqWLnS5JtUVvZfcHR2wqJFppDo9sEoLobt2+HJJwULFuh+wzGfekpQVycYPdqMoBW3K0SL5gXLYub0AL9Wc2R1GlGmeavEJZWCNWuMoP6dfn1XrYL//EfS2WkK1fx840K2ZQsUFQm6ukwhOH78vnV5hg+vpKKiildfXXxIixWAWbPm8stf3sV//ddFjBgxkq9+9Xt9LFp9IpFHiEQWhdkfWaTTR5NMnoNxueoby9rOTuvebgSQRgi52+Nm8SRlPeCQTJ5HPP6bsOjIQohWQOP7k0PNyQYsayNCJLBthZQ1BMFIIAhF9/loHUHrOFLWhkWWHWa/xNG6K/xaI5DEuHQlkbKeIBi9S6Dl4PG8+XR1XYxtr8EUZxPQuveb9/e//xXNzY188pPXD3n/B4Pm5iZWrlzKJz953Tt9KoPGxkL3sW5WqCGJyftCIMhX+eSTv0/b+/hssbfiardnlCqmLaQW1FkNtIq2vWo/DgZ1sp4N9ibaZBs2DpX+cEb7o3rpcwICOmUnGkVcZwGaTtFJwkowNz1nwOyYboqKSpg2bRYvvPBUr2JFo1nprKbJaqEwyMfCIiCgyWpmpbOaw9OHHdCi7f3Me65YyZAhw/7T2rqVxsYXOOqoP/R6vKvLaBaGmlx/qNm0Cf72N4nva8aPNx2WRAKWLRM0NmpSKRFa/Jrux0DU1UFTk6C8vPei3BQsgpoa+rTa9TwzOlZUpJk8Geq2g/NKgC6AakfSOdyiqVmjHc3MVp9VBQ7KEaTTJgByMPqTtjZzTa2tpriZNu3A5d+sWmUCKGMxSKXMY90GC3V1HBCXsenTZ7F69Yr939E+cNpp5/LNb/6Qr3/988RicT7/+a/1+nok8gix2J9QKg+lShCinWj0foRI0dX1sX72CkqVAmm6uyMGDTjsKWA3ieBa5wPg+7Po6PgCjvNiOFo1Ctt+C60FSsXRWmBZzWgtEKILy6rB98di22tQKh+tXWx7NUJ4GJvkFEJ09TiCKVWE1nkIUYfjvI6UTUiZQGtBEEzAeL32vvusdQ6eN51I5EmUKmTnGNhWlCrD98ejdT6et+f4C8D27Vv50Y++zeWXf5pRow6gJ/V+8NJLz6G15ogjjn2nT2XQlATFbLA3kRAdPTbBSZFEIilTB0d7NVhSIk1KpImo3g4sLi7tIkFSJiE4tMVKnaznjchbpPGI6RgpUqxwV9EhOpnlTe8pEmqtOnJVDkmRIimSPY9bWEPS0hx99PHcf/+f0Vr33PhoFwkarSZyg+yero6FRbbKodFqIiESme7KASJTrGTI8D7kpZceQkqbaPQM2ttNSGJXF2zdKpgzR/NOOG13dsK6dcYBq6pKUzKA2cvSpaJHCyIEFBZqqqsFDQ3Q2iooLjYWv21tRgAfBLpfsXg0ahbpqVRvkXwyaR7vT/LgeeZ/xwHbhuF5GpUFOhsaW6Azac4tMhzcek2kTdMYkQwfbvJc9F5qlQ0b4A9/kGzbBmZkSDN2rODyyxVlB2DSJgjM+ZWXaxoaBL5vrkMIU+AVFMC4cfunnRk5ciyPPfav/T/ZfeS//utaurq6uOWWm7Asi+uv/wpgRr8ikUVhoWJmBM04lY3rvkgqtTAci9oTIzB/Cttei++bHxTb3orvj0UIsKz1BEElQgRY1haUGo7nHdazvSkoChBiC1rH8bz5WNYmXPclpGzG9yeE4ZLFKJWDZW1CiI1ANLQQjoaPJdG6O5ByWHhdnShVjBAS296A1m54jcXY9gqi0QdJJi/Y45pSqTOx7c3hqJcLpNE6n2Tywp5Cqz++/vUbyMnJ5cYbvzGE78zB5cUXn6GqajSVlSPf6VMZNIWqgAneONY660MxucbBYbQ/csg2wvtCt71vjazFEx5FqoDhwTBiOkZEu0R0hJRIEtE7R+RTIo2jHWLq0AR97nquG+3NpPF62SInlUu1Xc2ooIoClQ+YEbBclUMJxXSIDgKhcLVLktSQjnnMMSfwq1/9mHXrVjF+/GQAAhH0OIztiqUlgVAEIjhU8sP/78kUKxkyvA957LEHmT//WI45Jo+lSwXbt2tcVzBjhubii3c6VB0qVqww+pIdO4yWIj9f8oEPKE4/vW+Bd0eH0Yd0n2d5Obz5JnR26jAdXtPcbL745JMwfrzgoov6djkrK4PJkzUvvSSIRExifTJpuirz5mlCY8A9iMVMx+XllwXFxRrhgLCgMSHIjsDoIs36rRD1IK8cps/QjM5WpNOmIMvP7//18H3TOdq+3YxhWZYZ0Vq9WvDgg5Irr9z/79GYMWBZmsJC8xrU1ZnXPpEw44DHH7//4Y0jR45m27bN+L6Pbb8zHzfXXnsjQeDzgx98g3Q6zRe/eDNSNiFEW9gl2YlSBdj2WqRs6LdYCYKRdHZeRTR6L7a9DQDfrySZvAghuohG78e2N2OyVcaQTF6CUmaxKUQr8fjPcZy3w5GsACF8PG9OGBoZxfPmQi9xroPWMSyrhSCowPfH4/tjkbIZy9ocFiLdOhgHz5uDZW3C9ycCOWidjdYuUm7HdZ8hlTplD92KUiNIJL4YWhdvRql8PG9uaMfcP48//hD//vcD3Hbbn8jNPTh31jt9zdr2gLSCirhkWFTsVYeyePEz+9xVafU0q9sCugLNsKhkbI7ECo+ntabF0wggz9n7eQwFgWCsP5qSoJhGqwmNpkDlU6DyD/ookUaz2l7LOmcDCoVEssOuZntQzWHp2WTpOCP9ESx3VtJOgqiO4gmPDtFBVVBJ7iHuHvj4tMq2PYT2Ee2SEAkSIkFBOPKWp3JptdvIVtkUhOGrAQGe9IgPYSxy3ryjcByH5557qqdYyVZZxHWcDtlB3i7Oah2ykyyVRbbK5LIcKDLFSoYM7zPa2lpZvPgZvv71H3DFFYpNm6CxUZCXZxan+2NXuy80NcGf/iRpbDThh5YFDQ2ahx8WlJTA/Pl73pqqqAClNJ5nnLXWrjXicKVMIZNMGsF7UZFx4LrvPoHrwgc/aPZVW2u2y801gvfzz1ckEpI1ayAIBJalmTpVc8EF/RcFQsCJJyo2bpSsXi3IzYUuSxI0ak6a4jOqWNMa1yTXaJaX2XTlCrwU1NcLTjxRUdz3VA1gRPubN5sOU/f3w3Vh2DDNypWCpqb917xMm6aZO1fw8stGryKEGYcbPx4+9amAww5jn53Auhk1agy+77Njx1aqqvZNIHwguP76r+A4Lt/97lfo6uri5pu/HGpG2sOQR0O3dkWFd2X7w/dnk0hMCd2yIAhGQeiI5Hmzw2R7KxS57yw8XPdFHOftsJBww2MmcJy3Qt1KDuzhIuTj+zNRqhrHWYFSuYCHEB7J5Gkkk5fQ7j7PamcxS2U9naKZI61tzEvPx2XnXXCt80OdS/MexYr5ehGp1OBTultbW7jpps/xgQ+cwllnXTjo7YbC6raAv25Os71LoYEcW3BEsc3ZlQ627PsHs6mpkRUrlvKpT31uyMdb0Rpw3+Y0NUkFQETC7AKbi0a61KUUj+7w2ZAwIYXjcyxOHe5QGT9wGS4CQZ7OJc8/CMmpA9Am2tnobCai3Z4FvELRKJvYZG9mqjeZ0f5IFIrN9lYSMoGDzWh/FJO88UMqplKkqLPqSYoUMR2jNCgZct6LhYWjbZIi2etxhUIgejlxjQgqqbXraZYtZKkslAhIiA6KVRGlwQC/hHcjHs9izpz5PP/8k3z8458BwMFhnDeape4KGmUTrnZJizQ2NuP80YPSw2QYHJlXMkOG9xlPPvkonuexcOGZSGnusI8Z8871qpcvN0GHEybsXByXlpoRrldeEX0WK7NmaSZMELz+usks8X3zeFYWCKHR2hQhtm1yU/LzYfFiydy5AYsXC157TZJIGKH71KmaD31Ice21itWroblZkJ+vmThxz5BMrY1o//XXBc3NglGjTEGzerVg7VrBiOMEhzf5zGn12PoEeM2CVRGL+9pcOp6QTJwIJ52kOPfcPa8pCIOaLcuMl3WPZe2KbZvH0+mBX9NEwrx2y5YJbBtmzNDMnat76VAiEfjIRxQTJwqWLBEEAUyfrpg3z4ypDURjo9n/+vWC3FzN7NmaadP2zKYZOXIMAJs2bXhHixWAq6/+AtFojK997XNUV2/jV786hYKChwEbpfIRoh3L2kY6fXyYPr83IgTBxD4ejyJEgkjkeaSswfdH43nH4fuTse0loZB+5xvLjJ95KBULxfHVPV0dKWvROko6fSxKDcN1n+3Zh+fNJ50+ilrZxG/cZ9lorSNH5+LJepZbm1nnCC7zjkaGC0lTiGXvdaxrMCiluPbay2lvb+X73//5QXHcavU0f9mcpi6pGJ0tsQU0pTX/qfUoi0mOKul7+fLyy88BsGDB0Dor7Z7mb1vSNKQU48JuSsLTLG70iduwsk1R3aUojZprfbXRpzap+PT4CIWR90boZH80W82kRYoctfMOiEQS1RFqrTqmeJOQSMb7Yxnpj6BTdOHiDKkzAdAiWnnLXUKrbAPYxTlrZk+a/O5oND4BFrLHClkiqQwqWOGsJIkZTVMoWmQreSqXomDndRSrImamp7PO3kC7bEciGRFUMskbP2R74aOPPp7f/vbnBEGAFd5FGhFU4qZdtlrbaJcdFAWFVPmVlKrBh1Zm2DuZYiVDhvcZjz32IFOnznzXzHN3dEC3JmNX4nEjnO+LnBy48kpFfb1kyxZBXh4UFGiamiCREPi+pr3d6EmiUZNfsmOH5v77JStXCsrKzHhXIgEvvSQAyVVXKYyref+F2zPPCP7+d0kyCZGI5q23JBUVmiuvVFx6qdlOBxYv/lHybANkTRB4VZIFCcH69Wbk6qyzeovk6+qMq9iSJSbMcvZszeGHa4qKTOp9t+2y1ua5o0YNrOdJJOC3v5UsXWrG2rQ2gY+rVws++lHVqwCLxeDYYzXHHjv4YrW62ux/40ZBLKZJpyUvvwxnn61YuLD3fioqqpBSsmXLxkHv/2By5ZVXU14+jGuvvYIzz9zAPfdcRFXVCmx7begGdjxdXR9hf5LWI5HHiEb/jBAeSmUTiWzCdd+ko+OTaO0iRNDndkqNIpmcSDT6ILa9HACt80gmz++xQ04mLwR6dzGec59ko7WOif7Untn5TtnGC/Y6jlTlTAhGImUzUjaSTF4wKHvmvfGTn3yXRYv+zR//+MBBKUI3dwT8cUOaJ2s9SiOCiBRUxAVFEUm7F/Byg99vsbJ48XOhXqWqz6/3x5r2gOouxZjsnWNf2Y4gxxM8Vu0hgUl5FjL8Wq6jWduueLsl4Piy93axIrRE9/Ge14DYzaLMxcXV7h7P3RsKxXJ3Fa2yjUJVgEQSENBoNbPKWcPc3eyPwYjoN9qbaZVtONphRFDBKL8KG5vR/kg6RSfb7WoSogOJIE/lMsOb1ssNLEmKHJXNgtRckiKFhSSmB2dZvDtHH30CP/rRt1m27C1mzjQ6NIGgPCg7JLqi9zOZYiVDhvcR6XSaJ598lKuu+uw7fSo9lJUZ7UQyudOBSmsjlJ8zR/W7XXExjBxpdCrd4YpvvSVYsgTSaUFLi1nUT5xoOgpKwerVRoxfGGoyc3OhokKzfLlg+3YGzGNpboZ//9s4eU0Mb6grpVm9WvD446bYEQI6k4KHVlu0Dzfn53mmKJg+3QRHrlwpeoIqm5pMIOO6dYLCQlNYPPSQZONGU0A89JBk7VpjndzeLsjJgYUL1R4dl1157TXB0qUwdqzuKUw6OuDVV2HOHMHs2fvXRXvyScHGjYKJE7tH1ExR9fjjklmzgl7if9d1qaioYtOm9ft1zAPJGWecz4gRo7j88vM54YRb+eMfb2fGjFKUyg/F9vteqAjRQiRiujVax5CyGZBIuYNo9F+k08fiui+HXQ5TNEhZh9ZZ+P5UfH9SOEa2GmNpPHGv57TceZs83Ts3I8ubz3bRyibZwBSh0DqXZPLs0JZ5/3jiiYf50Y++zY03foMTTjh1v/e3OxsSAXeuT7Oy1cdX0OZplrT4tPsWU3IlUUvQ6ulerky7smLFkp6F5FBIhVpoa7dduhKa05rymOgpVAAsIbAF7Ojs/3fUe4UiVUBMR0iIjp4Oh09ASqYYkx51QDQzbbKNFtlCrsrp6ZBYWOSobBqsRjpFJ1m7jGTWyjrejCwhTZqYjtIluljurKRDdDDDm4aNzQxvGiP9KhIyga0dilRhT6GSIsVqZx01Vg2+CMhSWYzxR1EZDN/n65k9+3Di8Syee+7JfXqPZdh3MsVKhgzvIxYvfpb29jZOPfXs/d7XqlWmK1FdLRg+XLNgwc5F/FCYMkUzebLg7beNUN1xjK6juFhz1FEDL6yHDTPdDcsymos5czS+D8uWQWWlZs4cUyhs3Gi6KW1tguzs3vvMzjYalra2gc9z0yajFRm3M5YGKaG0VLN2rXEiW7xYsHix4PnnBUIYdzOlwrDIMvP3rq6dx3/tNTNKNXGixrbN15XSLFpkxrJOPFFRU2P2PW2a4uijTejlQKxYIYhEeo+wZWWB7ws2bNDMnj3w9gORThsr5aKi3u5qpaWwerVmwwbzOu/KyJGj3zWdlW5mzJjDww+/wBVXXMDZZ1/Iz3/+O0477dz93q9lbQ51IW1YVjXGutggRIrOzitIpY7HdZ9DiM2AGQNLpc7C9ycBAqUqSaf3kmK6CxEdxad3wqjSDn4wCj95Ie3eDJQqRuvBz+f3x8qVS7n22stZuPA0PvvZ/97v/e2O1pqna30aUooJuZI2LyDLFvgKtnUqKmKmUJmaZ/U7erZ27SoWLDhmyMceHhPELUGrB/nuzvNpSmuq4pKu3RpiWmt8DXnuez9HI1tnM94bx2pnDfWioWcxXxaUMNIfWoeqP4xv1p7OWVJLtFAEu/ys7HT7Svdy++oiyXa7mpF+Ffk6z+TH6Dzyd7NNVijedpexw6omruNEVZSEbGeJuwwrLRke9OOashdc12X27MN5++3X9mn7DPtOpljJkOF9xH/+8wgVFVVMnTpzv/bzyiuCe+6RdHRAVpZmwwbJW2/BpZcq5s4d2p37SAQ+9jHF448L3nzT5JDMnKk55RTF6H4mTLrvqh5+uObllzXr18OwYWaxn5MDkyebYMMdO0BKTWWl0Zb85S+SlpbeOTItLaZgGUjwDjvdx3a3HFYKQPPQQ4KnnzZC/ngcNmwQCGG6O9nZRusiJWzbZkaphg2D9esF0agpVILAFALbtplzWrRIsHmz4IQTFJ/8ZN+uaH3hON3ntPtrtv/mCUKYa9h9/92vSV/nWFU1mmXL3tq/Ax8Ehg2r4P77n+T66z/OlVd+iI997JN87Wv/Q3y/gmzccORqB1oXonW3iL4By9qGlPV0dV2J583DttcBFp43JdS+7LnoFaIV130axzGLI887nHT6OPQuAXyHe0ewzl5Fh0iQpbPRaLZbWyjQhUxIH02ghu/H9exk3bo3uPjiUxg1yuV3vysmK+tHpFKnhiNqB4aUgvXtAUURQaErKItpdnRpolKT8DUrWwVjcvrXqzQ1NVJfX8vIsZOpSypyHUF091ZJP4zMkswtsni2zqfNE0QsaElriiKSk8ttHt7usaVDMTxm9retS1PgCGYWHJpllNKat5sDXmsKaEorRmVJjih2qMo6MCNoo/2R5Ks86mQ9vvDJU3mUBaVDFr/3R47KIUvHScgO8tTOUcQOaayFs3bRv3h4tMk2Yrq3JXJUR+gQHSRkYo8CZVeaZDN1Vj15Khc31IdFlEuDbGSFs4qIjlCg8ns6PIMlIGDCpMk889R/hrRdhv0nU6xkyPA+4rXXFnPEEcfslyA2mTTjUL6/aydFs2EDPPqoYPp0TSQy0B72pKAALrpIc9ZZpjOSk7OnWBtAr1eo53xYryBfMGK+xUc/bPPIY5Jt24zmY/p0zVlnKbSGmhpBVpaxJo7HjT7jr381BUF+vtF3tLQY0XvpXnLXxo7VlJYKtm41GhghzIhXfb1gxgzFQw8ZRzMhoKXFdEYsS9DergkCM9YmhDn+669LTjpJEY9rPE8Cmpoa2Lp1ZxekqkqTn69ZtEgyaVLA1KmDey2nT9e8+qqgvd28jmDGzWIxMxK3PziOMTd45BHZ0wXTGrZvN3bHfeWy5OXl096+l7bVO0Q8HufXv/4zd9/9W26++Qu88MLT/OIXf2DGjH1rP/n+OIwdcRdKdX+8BggRoFQMy6rG9+fi+7Px/YGPIUSCePwXOM5boQgfbHsVtr2czs7P9riYHZE6jg3WWl53XsYTnrG81YWc23UR5QeoUFmzZhkXX3wypaXwr3+dTG5uFlK+gW1voKPj2jD/Zf+xBDhS0BFopBBMz7fItgO2dwIaJuZKPjI6wticvqvuFatXAvCMGM1LK5LkOYKjSiyOK+3fPawbIQQXjHAZHpO80ujT7mmOLLY5utRmTLZF3BY8vN1jQ8KMe5ZFJWdVOIw4gG5gA7GoxuPB7T6B1sQswYZ2nyUtistHu4zPtVAoUiKFrZ1emo3BIhAUqgIKVcFBOHtwcRjrjWFZj3OWQ0p4uDiM88f06rhYWNg4pHbLQjG2ygJbD1xAdYouAhH0FCoALbKFJtlMrainU3RRqAqZ5k0ify/Of2CKp432JrbZ1TDdZdPv1rM1tY0RkcF3QDPsH5liJUOG9wldXV0sX/42F13Ufzr3YKiuhrq6PRPfy8tNcVBTY7Qa+8JAN7X1mgB1RxoaNTpfQLOGNYqpJysm3eBQU2OKlWHDdnYQJkzofY7HH2/+/dxzxs0rHjcF0u7C8G7SaTPu1tRk9CKnnab55z8FK1eKsIugGT/eWChv3Gi6M9GoKYCk1Ni2IJUyhUtenhnNKioyupeHHxYcf7zRldTVQW2tQGtzzEjEjJcVFEBNjclXmTp1cIXGnDma1avNSNq2bWaBFouZkbJ9GdPbnRNO0GzaZI4B5pxzc+Gcc1SfdsrxeBadnR37f+CDhBCCj370KhYsOIarr76Ms846mhtv/Caf/vQNyCF7Nzt43lSkbEDKxp5HlSpFqSyE8PvYRiNlHcYRrIxu62LHeQXHWYLvT4AeC+IUjvMmjvMK6fTxAMSJc3nnp5lvH8M2exOujjDJm0alOjDjO0uXvsmHP7yQYcMkDzzwIQoLy8JxxSJsewWuu+iAFSuOFMwtsvjXNo98RxO3BeNzLKQImJFv8cXJUXLc/r8n97+6HKQkMmwMFqZLs6otoNPXnFW59zsoEUvwgTKH40ptNPTSqMwrspmSa7GpwxQro7MkcfvAjYD5SiNF72N205hSPFnrE7OgPGZ+uWltBP5P1HpE82vY5GymQ3biaJtKv4Ix/uh9KloOJlVBJZFUhG32dhKig9KglBFBJSWq9y8OC4tKfzgr3VWkAhcXt5fbV/Euo2F9EdEuUkt8fGxsEjJBndWAj0+WipGjsmmUjbzlLOWI1Dwi9P/e0GiWusvZYm8joiJUTh6FUop/b3mY8ydeSLHaTw/5DIPi3fVOzpAhw0Fj6dI38X2fOXPm79d+bNuM+/i7rbtMJ2FPu90DgdYatciHZo2eJHs6Q7pRI14McI6wqara+8LBtuHkkzVHH23yV3Jy6Nemt7HRJMivXm06HVJqRo2Ciy5S1NcLEglToE2YoPnudyWRiMB1zUiXCZY0upXcXBO4WVqqaWgA19WUlsKaNabrcvrpikWLJDU1ptNTXGyCIAvCG5xC7PlaD4TrwqWXGjvhjRtNUTVhgmb8+P3PTQGT7/LpTyvefluwbZsR/0+bZl6bvsjKyn5XFyvdjB8/iYceep4f/OBmvve9r/L44w/xxe/fTNW0URQHZcQZ3HhYOn0ilrUFrfMRwkeprDDUsSnsvOxEyu3EYveFyfEBQVBBKnUOnjcXy1oDWNBrIRUBrPBrx/c86uAww5/NjL10a4bKAw/cx+c//0kmTqzgwQfHk5tbhkKzTbSSIqBC5ZJtrwWC8Fz3nw+UOuzoNKL6QBvRe2nU4kNV7oCFSmNK8fbKFeQOG0MrLttaA3wFyUDzq7VpqrKsQY9sCdG3BDvbEUzLP7BBVOvaA56tM/ktWbZgQbHNkcU2kV3G17Z2KlrSmnE55vrTSrO901gpb+hM0VywjcnDEuSJCB4eq9zVdIkuZnrT+xWTa63xNDiCg2I93RcCQbkqpTy9lzY2MNofRYfoZIddTTsJBILc0O1rb/klRaqQApVPo9VIbpBLi2jDw8PRDvk6HxeXQlXQMy42Iui/Q9Ikm9lh1ZAb5BLBZfxEEwi5ccU6Nk3dTHE6U6wcCjLFSoYM7xPeeONlotEYkydP26/9VFSYXJbly7vT1c1ievt2wYwZ/Se+7xedwEaFLpa9P1gLgdXAdgUVOxcyTU2wdKkZhSouNqNRu+pUYjHz/0D861+CpUsFY8ZoolHTPVm/XvDss5LrrlM93ZvaWgBTjDQ1CfLyNNnZmuZmQTIJJSVG49HSYjpH3eNmrqtpbxdcfrli9uyAf/5T8sQTgsmTVY9bWWencUobapK8yVYx+SoHg+xs9mp+0E08nkVHR6Jf96Z3E67rctNNt3D4iUdy41c+w0Unn8roq8Zx1Fc/wGnxczg2fdJe59zT6WNxnDex7RVhbkoH0EYqdSK+P6XneWbM61fY9mqCoAJwsKyNxOO/oaMjjilM+rI5DoBoH48fODzP4zvf+TK3334r55zzIX7608spLLydzUETf7FXssZqwCOgGJ+TvZksOIAZ69mO4IqxLmvabWq6FDFLMDlPUjBAoQLQmNI0bllDVuUENiU0WTbk2pBWUJfS3L0pzehsi1zn3fMeXNsWcOeGFM1pTaErqE9q7t2cprpLcclIt+fnxRYCIQSBBl9r3moOqEtqfKXoEj4vbSyhM5HPyeNaiFoRHGWzw65htD+KvN2sqgOtebkh4MUGj5a0ZnhMcnSJzfT8/k0LDjUKRbNsJkdnM8qvwlEuOWRTHBQOKhvFxmZmehrL3BU0yWbaZTsWFoWqoEcv0/1znBIDh1YlRIKAgEg4UhbLiVNSVU7diu00y1YCgj1MAzIceDLFSoYM7xPeeONlZs48DMfZP8GklCbxvb1dsnYtGGGwZuRIzXnnqT7v3uu0hrcVapXxB5WTJMy0EJFBfjjagCsgqeklRPYBGX4tZO1a+OMfJTt2mHMTQjN2rOCKK1QvW92BaGyEZcsk5eU7gxQdB0aMMI5XW7bQI/7PyTHdk8pKM77V0CDwfVOY2Lb5s7bWdFNmTFIURiDpQ2enZOxYM1IyfLgxGfB9yZIl0NBgwim7ukzXorJycIWB78OWLWaUbNgwM3r2TpOVlYVSilQqRXSXVEqtob7enGtp6Z4BnO8UPj6rT1rO7JMOp/mXTbx1y2vc+/c/sOQbb+J80OWo4AMDbq91IR0d1+O6z+E4y9A6iufNJZ1ewK7dB9t+C9teG7qAmZ/JIMjGtlfiOM/iefNx3aeQsh6ljPuDlA1ABM/bP4OMgaitreaTn/wwb7zxMt/61o+58sqrkTJBIniY30UWsZKAEaqAqEhTJxP8SdbhOK8w11twwM7BkYKpeRZT8wa/CMx2BImtq8g98oPYgh5hva8h39E0pzWr2wIOL3p3LHu6nc+a05oJOTtvwrSkNa81BhxVohiZZa5/bLZkeFSwpUNhCahLanIdTUsayvI6GZ7js64xzviiJOOLu4iENsQdsoO8oHex8tgOj4d3eNhSkGXB8taAde2KS0e7HFb4zr82aTyWuMuotmpQoUNYXMeZ5k0eUohjjs5hfupwWmUbS5xlNMrmXiNbAQECQVwPfNfKxkEgQr2M+XCrmjyGrSs24Gp3yCL9DPvGO//OzJAhwyHh9ddf4bzzLjog+xo5Eq67TrF0qckzyc83d/Fz+8ib055G/dmDxQEoDQLUCwLmKuRlzqAKFhERMFciHvTReRoRF+hAIzZrGC5gQjgekYa//U1SUyOYMEEjpemIrFkjeOghycc/rnoJ933fZK9s3WrsfidNMp2hVMqI57sF6t24rnk8mdz5WDxuugz33y8YMcIUba2tJpxywQLF7NnwyN+hbKXPzLd9IoFmu5DkzXCYN2/nyWRlmaDLRx81TmvNzabYqK2FX/5Sctllqpdtcq/XWBsdzm9/K9mwQaC1pqJCc8EFmnPPHbrhwYEkHjctrc7Ojp5ipbYWHnjABHT6PpSVGd3Q3Lm6T2OFQ8laeyUrnWWMCcYTvyaL2RcezvM3P8nb17zGZ37zUb533a2ctvCcAfUsWheQSp1NKtW/RbgpPIBeCzCB1jnY9ha6uj5BKnUGkcij2HZtuN9skskzD6gDVzdKKf7859/zve99lUgkwt/+9h/mzTsyPG4Or3sLWBt5nIlIXNkGuAz3ZrEal+fdpw5osbIv5AYddDZsJ7t8AkqbzKKkgq5AMybbwhLQGRycTuO+kFKwqcM4n+3a0chzoDap2d6pGRl2g2O24Pwql79sTvNyvUeHrxEISqKSwpw0lu2jNFS3O4wv7sLHx0LuEd7YlFI8V++T4wjKoub9WxKFjYmARTUeM/ItnL0YERxsNttb2GZvJy8wTl4aTYtsZbmzkoIgn+gQuooSSYHKZ4o3mdcjb9Akm8lSWSgRkBAdFKsiSoKBbSBLgiJyVDbNsoV8lYdEMnxKFc/f+wQjgooD2FPMMBCZYiVDhvcBO3Zso7p6G4cdtn96lV3Jy4Ojjx7Eh/9SBYsD9DCByDYfkLpTI14NYIaE+YP7NSRPclA7NCwNwA8/IsoF8kMOIm4+MDZvhi1bjJNW91rScaC8XLNihclJ6RaBd3XBPfdIXntNkA4nAQoKTNdo7lxNaammurq3pqW+HgoKNMN3M1k66STjYvbCC6InvPHYYxXnnqvJimtmve3RvDqgWgk8C+ZEAkq9gOxaF0p23j2Ox6GxUZCfD3PnGretIDDjZ3//u+SGGxR9NcZeeUXwgx9IqqtNl0cI2LRJcvvtGtdVnHfeO7dIi8fNC9jZmaCwsIiuLrjrLsmqVYJhw4zBQHU13H23JBYLmLZ/U4r7TbNswsMjHrptZZfncOqvz2HklWN441uvcNWVFzFx4hQ++9kvcfbZH8Tqxw+6WTSREkmKVEmfd4S1zg//1lvvIUSCIJgBSJLJD+F5c8KQSPD9iQTBOPYnuLIvli59k6985bO8/vrLXHjhpXz96/9DcXFvXUGTziftTwFVjCd8lMpD61yyZTV1VjXIbVgiIAiGwwGyux0KDQ11AIytrKTah0ArXEtSFZcMjwma05ph0XfPXXBHmu5Pa1rRIhSNKU2gIWymEN3tbTUlz+K6iRF+qjXLWxXjciQlEUGrzKaBBnwCc3MGj1arleKgZA9nr+qkpiWtGZvT+3Uojkhqk6b7VBo9sO+tLtFFs2wBoEDl72FHvCvGdnsHrnJ7nLxEmEzfJJuptxoZEVQM+RxKVBEz09NZZ2+gXSaQSEYElUzyxu+1W+PiMsObxhJnOS2yFY2mfHIlzTsayW/MgXdB9/r9QKZYyZDhfcCbb74CwOzZ8w75sdUa01HpLlQAU1xIjVqhsAZZP4lsgbzKhdUKXasRcWCyhcjf+eHqeTuF/rti20b/EewiATABjiY8MjvbdCe2bYN//EMyZkzAKado7r7bJMjn5UF7u9nu1FP1HuNVjgNnnaU57jhNY6Nxx+ouivR6TWx1QGyeYFiWOVdpCVgToJ7xkVN3joA0NsKqVcZprbsosSwzfrZli2DzZvborgSBsZJubDT6mG5HtVjM6GaeeEJwwgl7nvNg6Ow0hYTrGq3Svgj0d+2sgAmtXLfO2Bx3j36NHg1r1sCLL0qmTXtnE8HzVD42Nl10EWPnwip2RJyLHrqM459cyK0/+x+uvvoyfvSjb3H11Tdy7rkXEQtFUI2igYei97PUfQMPj2HBcE5OnclhXu83uufNIgiqsO1VBMEotHawrB1oHSGdPjp8liAIxhME4w/KtdbWVnPrrf/DXXfdxoQJk7n//kX9BioWqCIkDl2qgMgud7fbZTWzhE9ezjcwJgHDSaXOxfMOPyjn3B9NTcZ97RMzy3kIi8aUpixmUuZrujTzi+09FunvJJYQzCuS/HqdT5unUdqMhiWVYETcFFm7UxSRnFPp0pROkWULbCkoUPk0pRS2lSKnoI6ETFEclDAjPXWPEaWoBFdCKoD4Lqu/lNK4kl6i/v1Fo9libWO1s5Yu0QVATMeY6I2nKqjssyOh0fjC30MDIhAgIBB9abgGx7CgnNKghE7RhYUkpmOD7ooUqUKOSi2g0WrCxyO/Isbt/JDqbdsozMsI7A8FmWIlQ4b3AUuWvMGwYRWUlx+Y3AWd0LBDGa3ICIHY24dcXzf29+Fmv3AETLMQ/dx9HzECios1tbVQGRq8aG3GjsaP172sdV97TRCL7eycCGG2Wb3aFAzHHaeJRgOee06yfTvU1X2TxYu/zQMP7NzH2LETeO65ZT3/zs1lj1E4XasQSQ25stdHoy4UiC2K9npYt02gFMTjJpNld0e17tBI3zcF2cqVxiY6FjOFzLZtpjvU0gKtraZgyc4GIYzov3ukbLBobYq5xx4T1NcLbNuYKlxwgWLEiMHvB+gZ/UqGs3NNTaC1cU7blZwczdatQ9v3wWCCP4UJ3hSWOW9RGYwgqmM0yHp84XFM+kTmzzuaP/3paN5++3V+9rNb+PznP8G3vnUj5557MRdc8mGeXfAflrlvUaaGkaWz2WJt4o/x24l0RJjmz+o5jtb5dHZ+gljsHmx7A+ATBKVhmv2s/k7vgLB27Up+9aufcP/99xCJRPna177Pxz9+9YB6tsnedMb7k1hhL2F4UIlLhAZrGzF7IycGpSiVC9hY1mbi8dvp6MjG9ycf1OvYlaYmM1Y3q6qMMbEoT9f6bOwIiFqCE8osjitzsN7pGcPdGJdjo3SatDKdFikkRY75+9LWgOP76ATNLLA4qsRmcYPPDlMDELUKuKDc55hYFpGUQ6Eq6FNLMTJLMjrbYlVrwOgciSsFnb6mPqk5vswm7wCaDzTJZla4K0ELilQhGk1CJFjhriI7ldUrmb4biaQ4KGKTs5WsIN5TTKRECkfbvcIkB4tGkyKFEpqYjpKj+7F/3AsuDsOCUPRYbOwZ6+vr9mlfGYZOpljJkOF9wObNGxk9uh/BwxDQWqOf8dFPBOgmBbZAjJHICx3EiL7vWsoJFurpAN2uETmh5XCHRgByyoG905mTAwsXav76V8maNWbxn0gICgrg1FODXh2Xri5TBChlOio7dhj3rlTK/FuIbkctRRDAT36iaWqawr33PtqzD8va+69QERdoKdCeNsVWzwlATVryfz+W1DWavJL8fEEqZQIiTbFhnlpTA0VFRht0++1GhO/7xtigsNB0XFpbTYfHsgRtbSYEU0pBTo4acldl2TL4y19MWOXw4Ub3s2yZIJGQXH+96tfuuS88zwPoWQibYs7sc9e1cSIBY8YM7Tz3lc3WBl5wn2G9vYp8Vci89FHM9Y7AwsLB4aNd/8Xf+BOr7OXUizryVQELu87iiPSxPfuYOfMw7rzzb2zYsJZ7772L++77I3fddRu50/OYfukcRn1wLPGSLHKCXNbYK3k+8lSvYgUgCMaTSHwVy9qIEB5BUNUTAHmgCYKAF198ht/+9uc88cTDlJcP58Ybv8lHPvJf5Obu/Q0SIcJlnZ/gn9F7Wekso4VmytCco4qY5S1A95gEjMe2V+A4zx+yYkVrzZa6egDy8wspi1iMy5GkzK+ovQZCvlNs6VSURowdcocPtoQiV1CT1LzZFHB82Z7FoyMFF410mVVgsz4RUNOlsQTk4pBszWZ4tuzX1cuWggurXO7ZlGJTh0KF1sWzCixOH27anIHWbEooEr6mOGJG6PbFJazGqiUtvB5Ru0CQq3NpkI3UWnV9FisAo/yRNFpNNFiNRFUEXwQEBIz2R5GvhvaLrF0kWOuso95qNGGpQT7j/DH9HnuwFBeboqWhoXa/9pNh8GSKlQwZ3gds2bKRSZMOgBjgTYX+m492gCoJHrAiQN2lkZ+LILL6+FCbLuFIC/FiYLoxhL7+8y2YdeAtH489VlNQEPDqq5K6Ojj8cMWCBXva/06dqnnoIUlLiwl0FMJ0FNrbjVh9/nyTTQI7x8osbVO8thghgQkSUTSIYmuihBECNih0gYA2DQlNql3waNSmzRKMG2eOb7JWBK5rOjzZ2ZrOTiP+P/VUxUsvCV57TTB6tCYW0ygFb7xBmN9iujO2bToXTU1GX3PiiTszW7rRGrZuhZUrBZ5nOlKTJ+8cy1q8WJJMwoQJ5t/RqBnbWrfOFC0LFgy+LZZOmxRq1zUq/2nTNCNHCtavF1RW7gzFtG3BkUce/BGw9dYafpv1c+pkDbk6nx3WdpY7b1Od3M65SWNAUaLK+FTH59ght9EpOihXw8nRfd/VHTNmPF/+8nf44he/wc9euIW7/3IHi296hhe+/BRls4Yx6uSx5J9awKbDNqDRfYye2AdtzMv3fRYvfpaHHvo7//73AzQ01DFx4hR+8pPfct55F+MO0YKtVJVzVednqZM1pESSKucl8qP/xN/DJCAby9pyYC+mH7Z2KB7Z4fHgyhqceC53bNGcNixgdLa1h+7j3YavzA2FooikaBcTDEto0rr/nzFLCMbnSN5q9lna4pMKp6OybJ8Tym3OGO70W2BUxiWfnRhldVtAuwdFEbMvWwrqk4r7NqdZ0x6QUpBtC2YXWJw/wiUWBmAqrXmjKeDlRp/6pKYqS3JUic3E3N4vdoo0Uu/5+1FqSXK3ZPpdydd5zE3NYbO9lUarkRzlUukPp3KIYvYUKd5036bJMqJ6gaDGqqVdJpiXOoxcnbP3nfRDLBYjOzsn01k5hGSKlQwZ3gds2bKJU045c7/3oxb7EIAYHX4IuaDHSsQGDcsDmLfnrxThCOQlDky1UKsDUCAnWjBDDt66eAjs2hEZiKOP1rz8subppyXRqA4X+TB5sulaPPWUZNw44x6mlUavVWxcv445HxlDVEaYUzaPL9/4HUacO3rg84kJxCUO+rspeDqAtAYJnRGBzFKMnK0hHKMbPtwUS5MnK4qLYds2QVmZ5vDDTfr8t78tyc/XPRkxUkI0FMSOG6fZvt2EVYLRmUydqjnnnD0XPc88I3jgAUlrq3m9bFtz2GGCj3xEEY8TdnZ6b2dG04z72+6kUmZMLRZjDzevVMosTCKhJVlWlrFp/utfjXOZ75uu0cknK2bNOrhGABrNosi//x977x1nx1Xf/b/PmZnbt3dtVV31Lstyw8bGDTdcMKZDKIYEAnkg7UlISPKk50nyJIEfEAgYNwwGG1NcMDa2JVdZklVXfbu0vd46c76/P84WrZrVbBOyH7/0knX3zsyZuXdnzud8v5/Phy59mPn+4onJT7c+zDPhJ1mbvYgqYwW8CkW1OfWeN9d1ueSKK9h14zaqDtXQ+thBDj6xj81ff4XM36eJFkcZvLiflSvPY/Hi5SxatOyUKhqng2w2y86dW3n11ZfYtOklfvnLx+jr66G2toFbb30/119/C8uXrz6rPA2FosLYMKWQcwDbz3m0ScDomND+jUVfxvDt/Rnak4ZguI9IQTGv9QccTgmfnhumIvrro1E5HurjmrCGoZxM5L/4RhjO2bask2Fzf8D6bp/yiJ7Ytidj+MWhHPPyHObln5ipRRx1TECmEeE/Nx7kh//2p/Rs+gVBJkXejFm0/va/k/e287ihxhLbXx7K8XC7DyLEXMXLvT67hgLe3xBi6RH7LJB8WlTrFMtfgyFQAYVy8u99geSzNLfILoadIQ45XfTrfkqC4onjRyRMj+6j3e0gP9d45jsHysoqpsnKm4hpsjKNafyGY2RkmL6+HurqTj6pPiUcFiQ+1YtIebYkIYMn9ihSnqJzhsPL7S4HDypKdgsrE8L8+cdObt8sVFbCZZcJ27ZZohIKWbJQUyP098PevbZVLBYDthpW9Kzkny78CrPr59E1coh/eeZvufkLV/DLea+St/DkD18VAGGBKmAAEOgeUcwf9Nnf6dBZM3krDoVs4v0HP2g4UtiTTFpr5qMXw8erPrNmwcqVhp4ehTFCMgnr1h0bftneDo88olFKWLDAXv9kEl56CWbPVlx+uXU7a2tTU46fy1kNzJFVmoEB+MUvFJs22QrNvHlw+eVmIoMGIJNJj53X5NJxba21vm5ttedUXc2U0M43CimVZK/bRJmpmLJKW2rKaXJ30OIcnCArZ4KF/hLq/VkcrNzHzPfPofF9izhsOml/pZXKn85g39NNPProwxMErr5+FosWLaWxcREzZtQyY0Y1ZWUVFBeXUlxcSigUsq2XR/zJ5bIcOtRBZ2c7hw6109nZQUdHK9u2bWbbts1kMhk8z2PhwqW85z0f4vrrb2Xp0pVvSOBfLreCIKgfMwmoR8QdMwmIkctdeM6PdzQ29we0JQ1z8zUHR/pIFJQwN0/TNGzY2OdzbfWJK0ciwu5hw6t9Pr0ZoTauWVPiMuMcERwR667lC0QiKQacfkAoNIXkSQKFYk6eZl2ZyzNdPt1p26aV9G1S/brSk0/Ptg3YcsqRIZelYU1POqBpKDgpWTkSWSPsHAxY39zD1z/9DmqWXcytf/sgscJS+tv2kSss5MlDPhFHoRAe7fSJOVAZtfuviAj7Rwy/OOSzsMCZaLmbEVTSFnTQ6/QRM9b1I6mTFAVFVAWVp3s5TxujehRQU7Q7CoWHy4AePOv9l5aWT7eBvYmYJivTmMZvOFpaDgCcG7JSq1Gv+HbSPQbJCkqDKj7xZGj/fvjmNzWdnYpYTMhkNC++CLfearjkkjfHVnfc7au/HwoL7YS5qsoK1GfNmipqz2YnQx0BzKaAy0qvgHn2Ab2wfAnLq1ZzwdcX8+Pvfp/3/c3HTnpssyuAdmAY2yQeUURyiqF+KHktN0FWjIFUSjFz5mRVaGjIit03bVI0NyuSSTj//MnsFNcVwmENWC1JIiGMjtpzXbLk2Gu7e7diYIApRDEWs6Rm40ZLVtatM2zd6nDgAFRUWKLS0aGYNUtYvNjuM5WC//ovzdatiuJiS/ief16xb5/mxhsNVVWWhIyO2lJPPG61GFmyDOg+4ipBQ8Mbo884ERxx8fBIk57y+nhA3Lhd6pkiLgk+mPoE34vexUF3HzmyFJtSfmvFb/OOhe9Ef1Hj+z579+5i27bNbN/+Gtu2beaee75Jd/dh5CStPydCIpFHeXklCxYs5oYbbmPlyrUsWrRsSgDnGwWRIpLJTxKN3ofj7EMpnyCoJJO5Ed9f8oYfvzNtcLRti0oO9hLJL0YrRURDR+rk1/L5noAHW7MkfSHsKLYOBmzsDfjgrBBz8s6uf6wrbfhpe46dQz5DJHES3Syo6aK2ZJSwhJiVa2CuPwetFLfUhpgZd9jSH5AKhPn5mvNKXYpCJydNGTNRkJ0CpSB3il+jpC/cczDLlv6Al7/1T0hhNYmP/D9y+S6FcU1eZT2b+gJa+32GcoaMsW1355UeWUWzeS8dKUPfEdbHUYmyMruM/e4BuhyrJ5qZq2e2P5OovPHfzbDYjJajWy8DAmLm5EGQp4KysnJ6errPej/TODVMk5VpTOM3HM3NlqzU1589WdEXOJjtAbLfQIWCLKhOY3UZJxDLi8Cjj9qgxgULxkP/hLY2a7m7bNnr2+oODNgJc3HxsbbEp4LhYXjgAc1rr9k2qXjcaieuv95QVaU4eNDa5zqOnYQPDCje9jYzWcUYFsSb2jFdGC1iZnw2Bzv2TZ5rv8Be2+pGg0ZVjF2TYaBHIA7k271Ulgsj/XCgE7paBB1TdHXZYMk1a+xsY3TUkrytWxWJhBCLCQcPKh5/3F4337cE55JLDMPDil277OEcR1i7FlautPsZHIS9e62Iv7/fTmiOXmh3HNvOBbBwIbz3vYbHH1ccPmzdwFasEN71LjNRAdm+XbFzJ8yePUmcfF949lnFtm0O9fVQXy/AEI7jEI3FeC70FE+FH6NX9xCVKGuyF3B15oaJTJM3GmHCrMyu5aeRH1JoiokSxWBodvZTFVQz7xwIwuuCmXx+5H9z0NlHRqWpDuoolMlylOM45OUVUFExg9HRUYIgIByOsHdvE62tB09KWFzXxff9Ka+NjAwzMjLM/v17eO65p5g1a+7En5kz5zBr1jxmzZpDInHmPfonQxDMYWTkj3Gcg4A1CYDYG3Kso1HkKQJjqxipoV4KKuoQETIGikMnXjwZzgmPdtgeo/EKxHil5fHOHLNPIlJ/PSR94Tv7M+wdMRRGcmS9PgaHw4zuaaBc9xItGGC3t498U0ClKcfTirWlLmtfp5JyNBrzNZv6bWUkNFbNSPpWbD8z/vrVoSxZfjUwwqtDippohJFNj5K/8FL2/8tH2d60gYKyKmZd8zG6V7+PIg8WFmgGskLzqLBjMKAopImP6VhyRnAVhI8yMsiTBMtySyZNNt7E/J2KoJz9bjMDepA8k8BXPkmVwsVhRlD1+jt4HZSVVfDKKy+cg5FO41QwTVamMY3fcLS0HCAajR0T8nZGWKjR7wthHs/ZrBMXON9F3+iiosd/uA8O2lDDysqp6eRVVbBnDxw8aCfex0NPDzzyiGL7dk0QQHW1cNVVhiWnuWj78MOK9esV1dVCTY0lLy+8oPA8xW23Ge67T7NnD4DCdW2S+tvfbsckAgMVGnnJUOALeuwBPTIyTHPyALfW2gefvOhjHvItKQEoAPUOF3WliypWiM+UrLwQwuwioQuNSoOJwIUXCldeaSgf+6hefVWxfftUQlBWJmzerEilLKlYu1ZYtUrYvRuamhRBYLNYliyx4vUXX1Q8/LCie2wRUClL/gYGbIUJrN5kaMgStPH3rF0rLF9unck8z35eR35+hw6BMYpweJIQbd1qLZSNsePcs0fR2TlEPJ7PC+FnuTf6LRwcik0JSZXkJ5EfMqgG+HDqzjctCfqKzLW0O61s97YQEABCuank1tT7iR/Hictg2OvuYu9YMOMcv5E5/vzjWsOOw8VlTmB74kWEPXt3sX7906xf/xQbNjxDf7/NBHFdl/r6WcyaNZdrr30Xs2fPpbJyBolEHvF4gng8Mfb/eUSjUZRSdjKeyZBOpxgZGWJgoJ/BwQH6+no4eHA/+/fvYf/+PTz99BP09k6u/M6aNZd16y5h3bqLOf/8S5gxo+YcXlWHIJj9+m87x1ha5PJst8+BUUMuk8aJxGhJCgWeYkXxiVc1WpKGnoxh5pHZT8qmuh8ctRWCkjPU020fDNg/Ypid0Ay5STydodZxaB9y2Nkd54r8HGnVR6dziEpz/Hty1gi7hwwDWaEgpJiXp4/JQFld7PJaf8COoYCoozAiZI19fWHBic89IGCfe4AWt41t+Ukq57lEklUMdR5EOr9N0VV3knfd5yg5tIVXvvYHlKQdzr/x/bhaUxwWqiKK1iQcThlmtjjPKAABAABJREFU5TlkAqErLVxS7lJwAoL4ZpKUcSQkwdLsIl4Jv8o+7wA5lcMTjzq/5pxUdmwb2LRm5c3CNFmZxjR+w9HScpC6uoZz0rOulII1DnqZhm6BEFB6cmtLrUFr61x1JMyYE47WxxfCj7cZ7dxpReaxmNWRdHY63HlnMOHU9Xro7YVNmzQVFZMVnPx8qKoSXntNc+21AV/4gmHHDkUyKVRWWi2N51nHrJ//XNP0WphXN/1vztt9DTfMn4Ejnfzzpr/GcR1u+uR7kFaD+X4OyQFzFWigS5Af+zhVGlY7UAF0A3Gx4p4shIsVl9YZLvi9AKnSxGJTCcHevbbiET7CKai8HBoahAsvFD70oUmSV19vz3X/frtdNGorSA88oMnlZMJxrLMTOjrse4qK7HmOjNgWr4sumkoaw2G73+MhFrNETsTut6NDkUwqolEhL88ee84cYceOIUKhfJ4OPY5GUx9Yf+I8KSAsETaHXqY1ezV1wTloUzwFFEghnxz9HDu9rXTqdmISZ7G/jBJTdsx7AwJ+FLmfp8OPk1JJAKIS49LMlbwr/Z5jwuvG0dV1iF/84uesX/8U69c/TVfXIVzXZcWK8/jQhz7JihVrmD17HrW1DSfNNrEQHKcFpZJjovUCIpEIkUiEwsIiampO8AEBAwP9HDiwl717d/Hqqy/x/PPPcM893wSgoWE2559/MevWXcwll1xBRcXZrza/2aiJad5TH+KRthzZwDASKIpCinfO8JiZOPGEXWO/s0ffeYxYS/WzyUbszQiCtRgeby/UCqJeQPeIN3Z8TVZlSQdCW9KOojZmCUnraMB9zVmax6yFtVLMztO8vyFE+RG5K3me4iOzw7zc67N1IMDTsLTQYU2Je9Jwx33uAXZ6TYQlTJCJolQWKTmASEDFvJWs/difs20woGzhcsr27yDzzLepueMDdtxKsajQpSfj054y+AJawaICh2tnvPmEBKxpBnDcxQ4HB4Uea/uK44qmXw2wKfQa52fWnBWJKigoYnBw4Iy3n8bpYZqsTGMav+Ho6GilurrunO5ThRRUn9oTPT/f2uI+95ymoGDSdau1FSorj7UUHse2bYrdu6cmneflWUvfDRsUc+eeWmP28DCk0xxj3xuP24DC4WFbibjwwqn76+21LVitrZYsZeMdPND5Ye5p66U0Usrahet45K+epbS2HPPzHPQDC44gbhUKmgLMqwHOh0OY93jwPeumRggoAIxCLXeJzlbHNRoIh2314ugETZHJigZY0fzf/Z1m1y5re5xIwLPPQnm5HKNPqa62Opj6ettWl0rB/PmGCy6YGpr5eli8WCgtFQ4cgLo6m5Pi+0IkAjNm2LFpDb7fixfJp8fppvCofIMCKeSQaqdHd7+hZEUQfHxcXBSKMGGW51aznNUn3W67u4Unw49SbEpoEPtF7Vd9PBl+lHn+Apb6Kyfem06neeyxH/P979/N008/DsDSpSu59db3cdFFl7FmzQUTup1ThdaHiEa/i+vuRKkMxhSTyVxJJvNOOEllZxyFhUWsWLGGFSvWcNttdsLZ29vNCy88y/PPP8vzz/+K++//Nkop1q27hJtuup1rr30XxcUn/iJo3Y3r7sC2fM0kCGZxYmuNNx7Liqxt7uMhWFTk8cUFEWLuycczM6GpimrakoaZcdvy5RvhcFo4v/TswhHzxrYNRAiLXWUwCBnfoaAgi8HgK5/unjL+qSXNobRBAcUhTYErPNcTcCgtVEcV8/IdEq5i12DAD1tzfHJOaMrCUL6nuLzS4/LKU5t0Z8nS4rYRljB5kqAsFLBn2CXmaOLlRZTOnEN5RLNMwYdmhbhr6UIeefknCJOfsKcV8wscLqtwKQ1rSsOK+fnOSQnSG4F+PcBBp4Vep4+weNQE1dT5tVMWEFqcVgbUIDmVI1DWkECJolm3MtNvoOYsHOs8z8P3z8KubBqnhWmyMo1p/IZjaGjwnCXXnymuuUbo7BT27LHVGWNs0vzNN9uKyfHQ03Ns0rlSkJ8vtLQcO4E/EUpK7Db9/baVaRz9/fb10tLjb7dxo6KlRTF/vqA1vOu6ewhahV1NitvW5LjmfcpmyACSFEQJSk2dQEpYoYbtOJ13hTCBgk0BMiIQVaiFY4GaJ6hMLV4sPPec0Ntrz0PEEizPs7bEYInHl7/s8PLLkEjYYMtUyjp3tbYq8vKO1aeEw/a8P/3pM881KS+HO+4QfvADxb59dhy+r5gzx0xcZ2MgmTxIbX0DcZNgRA9TGEyyxpRKEiJM3llkHpwMPj7rQ0/zfOhXDOoB6oKZXJK5nEX+slPafpu3hZzKUiSTJKtIijmsOtnmbWFJbgUvv/w83//+d3nkkR8wNDTIqlXn8zd/829cd90tFBWdTfhclljsG7jua2NhkVG07iIavR+RPLLZS89oryUlZbzznTfzznfeDEBfXy+PP/4THnroe/zhH/4Of/zHn+XSS6/kppvezVVX3TCFYHneeqLRe9HatpeJJMhmLyWVei9v5XQi4ihCylAScV+XqIy//6aaEPc3Z9k1ZBiXWsxKaK45SUbJqWBxgcOMqGb/iGFGLEaYGB1pH3SW+vIeenUfucEynttfRMY3VEc1vhGe7fYZygmuEhKuoi8rbO4PWFnsUB3T7B4K6EgJ1bHJvJOejCACZRGFPoUxp1WGjMoQH3PnqotpejNCbzJExapGOg/spjtjuLjcZWWxy/09+ymsqKVpyFAUUvgCIzlhTYnLzbUhvLcobLNP97MxtJmkThIxYYZ0htf0dobUMEtzk5bkXU4PSZ3EE2+i9cvHZ0SN0ul0nhVZcV0P3/cRkTfEaW8aUzFNVqYxjd9wDA0NMm/em5MkfSJUVcFnPmPYvFlx+LBtE1q61FrkngjjSedBMFVUPzo6GVZ4KsjLg4svFh56yOo5CgrsxHpkBK6/XiZ0G0ejvd3aCGsNjAqyMUAPCKGUQ/sWwSRzqKtcuMFFVWlQU1PqxQhqVCYyaVRMoT/gweUu9IoV2tcp1FEP/EzGVpXa2qwb2fLlwtatNuASbEXoyisNixbZfz/zjGLrVls5KiycFOZ3d1v9DTAlLd4YSKcVDQ1nH8C4fLkwe7bVprS3C489ZnNTkklLrDo6FJnMAZYsup4Lsuv4fvheejujDO0vJalGkJkHuCCyglneaXygp4EfR77Po5GHCUmYuCTY5L3MbncnH07eybLcqtfdPkf2+NqUHDx/96/4yj//I/v376G6uo6PfORT3Hrr+5k9ex5KDaDUECJh5AzNA1x3B667E9+fC9iJljE1KLWPUOgpstlLOJXqyuuhuLiE97znQ7znPR+iu/swjzzyIA899D1+53c+TCQS5Z3vfBef+MTvsmxZOdHo3SiVxvcXAgql+giHf04QzCSbvfisx3I2MEf3mb4OFhc6fCYc5rWBgOGcoSyiWVp0dlUVgIKQ4n0NIX7YmqU1achKGRWREebWH2JmUYZKfxY7umoYzjo05tmqzkBWCIwVx/sCcVfhKttS1jpqaMx3yImQDuzv84GRgJ+15zgwahBsXsu1M7zXdTELSwhPPLIqiyceMddqe1rSGXK/9S7uv+UP6Hzo/9J85c387VOb+NH93+SP/uo/SFS67Bw0hB24qsrlojLvLSMqAAfdZpIqSUlQbImJQFqlaXc7qAtqKTKFABgCcvjEiU0QGBcXQRhSI2c1Bs+z02ff90+hlXMaZ4tpsjKNafyGY3h4iLy846dvv5nIz+e0bIoXLRKqq23SeV2d4Hlw+LCdwK9de3oWr1deabd/9lnF0JB11rr66iNE9IHAIBBlwiiguBiyWSto5qCBfsGUKHJ9UFSvkXzgaR+1ykEtc5BGH7VDkBIb+qi6Beo06rypNp9UKzhBlMfoKHznO5rNmyEIrHtXQQGsW2coKbGWyr5v28NeekmxaJGwcaNt/TqS0MXj0N1t31ddLezerSgrswYHPT32eo47jp0t8vKs69jKlTBzpvCTn2ja2mxb25w5Po8+2kxjYwOXpa/hsReH2OCsxxTswAsihDesYXDTh2m53ZmSzXIucFh38lz4KYpNKaVjQuYyU8EedxdPhn/GktyKk4rkIcv8oJb1BKRJEyFCkA3Ycu9GXvyn50g1J7nmmhv5u7/7D9atuwStNUqNEg5/l1DoeZRKYkwR2ewVZDJXwwn0LSeC1v3YnsGpYmCRfLTuATLAqVuwCkKb08wBZx8uLvP8BRPXRetOPO9F6us7+fSny/j4x++iuRkeeugBvvvdb/Dgg/dyySVL+b3fi3HZZW+fqCCKlAA9eN7zbzlZcRyXIPBf/41HoCKqeccbEBw5O8/hc/MjNI8acgZqYnnEvUrIWL3KS6kMIe1PrMiP+oIoiDiQDhTJAAo8RdiBgaytoBR5moqIpjttuOtAls6UoWps7DuHAnrSwqfnhak8yfmECVMTzGC3txdlFBGJoNwsJXkpViy+kOY//i6vfecveOnuvydWUc9Fn/obbr75DorDekLP81ZXEQICenU/UYlM0amEJcyIHmVIDVNEIQAFUoCinSw5PDwEIaMyhPCISPgERzg1uK4lKLlcbpqsvAmYJivTmMZvOCxZObdJ2acL6TLIqwHSIVAMeoWLqh+b8KTEOmjFQJVMPmgLC+EDH7BJ5y0tk0nn114rp5107rrwjncIF18sDA9bTcd4WKJsDjBP+tBhIKxglUZf5bFsGTzzDDQfhOpOQcKKjmFNQQSWVwZQqGCXQfYb9CUu+qMhzJM+arOBQOBSF325iyrXJJPQ3GyPV1/PCVvfnntOsXGjoqFBiEZti0dnJ2zcqPngBwN+/nPNvn22PQ6EujrF0BCUlkJXl93vOGnJZCxBvPNOw9atiq1bbfvdRRcZ3vEOoexYPflZY/FiaGw0dHba1jORVv7lX3LU18+k82CY4Jsf4aK6q3BqOgll8ijomU3THoennzbMnHlu83banVaG1ACNwaIpr5cF5bQ7bQyqgSntXZNIEQ4/Rij0DJepYWJqhIfNS/zyO0ma/nEHqbYk829czD9962usWLDmiO2ESORuwuFfYEw5xlSgdR/R6N2AJpO55rTGb0wp4KFUEpHJL4xSAwTBZLXlVBAQ8HDkAZ4J/4IRNQwoik0JN6Vv5+KgkljsqzhOO1ZMlSUUeoqGho/xO7/zRe688/P87Gc/4mtf+1Nuuuk1Fi7cx2c+cz633rqIcNhFJIJSw6d1bm8EPM+bsMj9dYCn1VGVDjuxTvnCQNbQNGTozxjKIw5aQWAEH0VpWJEKoC8rZH2D8TQZA++c4ZDwFBt6crQnDY35eqL1K8+1QZivniQIsytt2DoQ0O/XoUqypAq6SDkDeHgUJGvYsKeBOevmc8Fl7wSsHfHeYcOz3T431oQmjiUitCaF9pQhrGFenh3X2UAQRtUovvKJm/hJhe8KhSsOaTX1sxYEBNwjFgXq/Bqa3VZy5EirNApL0DxxKT+OocaZ4K0mb/9TME1WpjGN33CkUkmi0Tcn9+B4kAMG819ZaDdISKFyYNYHqPd4MCjIUwEyIKgwsNRB3+ihCuwDYM4c+F//y3DwoK0q1NbyupksJ0MkYv9MjG1rgPlOFkkDpQoygvqZj+kR6j8W4j3vMfz4x7B/s0ZyUFEmXDfPZ2aRIONdJ2PPRlWscW4LITcKGFBj4WgbNyp+/GPFoUO22lBZKdx4o7BixbGT81deUcTjMkGklLItdLt2wd13a3p7rbGA59mMlb17FVrbcyoutjocra2hgNZwzTXW5nnpUiGVsuTnRETpXMHzrOAeYMMGm/FTW9tAW5u1W66jCtU+KR4qKbGVH9+XKcGcZ4uIRHDxyJIhfMTEPqMyhCRE+Lj2pUI0ehfh8JOIFICJcOjBQX70h69xqD1D482L+dD9n+SWWe8lcZTORus2QqGXCYJqZIwEBUEcx2kmFHqSTOYyTodg+P4CcrlFeN5GgqAaiIxpRRSZzBWcjqj9Ve8lnoj8hEJTTHVQhyC0Oy08GL2HxqCI2U4nvr8I21YmOM4eIpEHGBlZhOuGueGG27j55gY2b/4j/vmf2/jUpx7hy19+is9/fh133hkmCK445bG8UbAagl8fsnI8pHzhOweybB0MyBph7wi0jOYIaejJWCG+hxB3FTkBUYolhQ631YVYPWbF3J0WPM0UjYpSipCGzvTxCf/OwYB7D2bpylgxv3TMpq6gmptmGurCUTZ1hxjK5JiXN7lPTysKQorN/QE3VFtdRtYIP2rN8mJPQDIQNFAZ1dxWF2LBSeyST4ZRlWSnt4tup9cGNkqMWX4D9X7tcR2+NJqaoJrtoZ1kgixhQhgMg3qQPElQGkyaQ1QGFcz06+l0DqGMTbM3GIpNMdWvo1cRhBE1wqAewsGhJCieEhorYw8Arc99ZW4ax2KarExjGr/BEBGy2Qzh8NmVvM/m+OanOegwyHyN0mNtVQcE+VrWvimuoEIhSVBP+5hRQX8yNKHlCIVOT6NyWmP7lQ9JUPPGHzgKiQu8ZmCfYc0axcKFsK/CR572aVgMibyxc+gUKFZHbDu2hyOyBg4ehPvu06RSQkODJQttbXDvvZqSkmBiUj+OXO7Y0EulLFFraRknKvZ114XaWqGtTVFfb/92XWF4WBGPw7XXBrz//ZPZNtGzD20+bbS0HAQsWenrA7AW1keeYyZjida5fubP8edT789iv7ubWf48PDxG1Qg9upvr0jcTO05woePsJxR6gSCoprVV83u/9yiPPrqH666bwZd+eAs1Nf8IYz3yR0PrHpQaRqR2yuvGFKH1AFoPYEzlaZyBSzL5CSKR7xEKbQYGMaaUTOYacrl1p3Mp2OS9hEEm7JkVipqgnq3eBtZ7G0kH5TjubiqCGeRJPkFQh+u24DgHCIL5APj+MtauvYYHH3yBXbsW88//vJM/+qMn+MY3CvnSl27k8svfWqHxr1tl5XjY1B+wpd+nMd+hNqbZM2xoSwa0p6AopMj3hHSg6M5AUQg+Oy/ETbUh3CN+OQpDitxYEKY6otqRDaD0ONkwmUB4uC1Lf9ZMaGQCEfYMRHiu2eUTc0IoxtrnRBjI2bazQGzeS/yIX9YXenyePuxTFlHUxDSBQPOo4XvNWT6/IHLaep+AgM2hrXTrbvIkgSNRkjrJNm8HnnhUnyC8scGvY0gP0+kcYhhb1UtIgsXZhYSZfNZ5eKzILKPYLaLD6cQooTIop8GvIyYnviEaDE3eHg66LWTJolAkJMGi7Hwqxlong8C6i02TlTcH02RlGtP4DYbv+xhj3jKyQo/AfoNU6QnyoZRCZgg8bqBeoxrHbvZRkLCG7Qb2C8x5YyY+YgRG7QOeNoMU2TXqwLd2xamUoqBbSLQJ4XlW/7HkAw7GD2CHATEoo2zo43UequzED6tnn1V0dMCSJbbiMDwMmYwVxGezmg9+0FZYxp93S5cKjzxiM2HGqwz9/UxoUo5ujfY8+/qttxr6+xV79yqiUcOyZTYU8q1+jra07KeycgaRSIT584WKCkVzMzQ0WHIyMmIzXq65xpzzsYYI8Z7Uh7gn+k0OuHswCCFCnJe7gCsz1x13G8fpwJhRvvrVbv7iL54mLy/MPffcyo03lgIphodHkeMERwKIFCASGxPWT5b/7L/jGHP6ujGRElKpT5HJdI/lrFRwOjqVcYzoAWaTZL6zlxwuHUEFh3SGZvcALWqYCpKMqDYOOvtZmltJ+ZhAWakjWVmIZPIThEKzmTt3A1/9ajWf+MTt/PEf/5IPfvDjXHTRffz5n/89CxcuPe3xnQvE4wlGRt76drSTYfdwgFbWjSziKM4L2WpFxhgW5CvmF3gcSgntyYCurLBzSGjoNywvUrhj98/lRS7ru30OjBiqY/aXpiNl3bpWFB07pWtNGtpThpqYniA3jlJURjX7RgJ6s8LsPIc8FzYPGLozQiYQBCHpK/Jd25YWdYQXe3zCjqIoZI/rKmhIaPYOG3YNBqwtPb0pZY/upU/3UWQKccemo/Egnw7Tx8agmSK/gph77I3Bw2NFdin1upZhPYwrHmVBCZHjVC4jhGn05zLPnwMcP4/laLQ7Hexx9xGVCHlSjCAM6iG2hraTl8kjJlGMGbdnnyYrbwamyco0pvEbAEmJ1eLGp/bQZrMZAEKht4isnAg5ICuQN/XBoRIKaTVIcwCVCpU4t4RFto7pU9oNEgK6BCKQjMGWLYq+PlC+UJ6Cpp9qrloEFRWgChT6kyF4LcC0GVRUoRY6qIbjP6j6++HHP1Z8//vWxWtkRFFeLnR2qjHCAjt3wn/+p+Laa60rGVjXsp07bVtULGZbvQAuv9xw4ICivR1mzZo8zuHD1gK6sRHiceHyy8+t7uNs0dx8gLo6q5wvKIDbbzfcf79m924AhefB+efLaRkvnA5mBnP4vdE/Yae7jRE1TIWpZK6/4IRhjtu2HeKLX3yGl1/u47d+axV//ueXUVAQQakWjClETiLKDYKZ+P5SPG8DQVCDSAKt+9B6kFTqGjhOJefUoDAnSDo/NSS5SbeQDW2myOShgGE3j28SMEyWIr+GBp3hcFBKv+5jp7eVCr+GIKjH92dN2ZNIHpnMTWQyNwKG2bMd7r//T/nFL37Gl7/8+1x55XncccdH+P3f/3PKyirOYsynj5KSMvbs2fmmHvN04SjrknckfAPhsbYuX6AlGdCbEVIBvDbgczgtXFLucGud1Y1UxzTvbQjz4/Ys7SkDYlux3lntURc/9n4UCBg51r5cK/u6EaiKKFYWu3x1T4ZAIOqAoKmOQl9O+NqeDBkj/OqwT14IikNqIk/GGdtxKjj93+G0SmMwE0SlKy3sHg4YVS7IEFtaklw7I8Kq4mOnqhpNqSmh1JxaONSpkJRxtLsdKBSxMa2YQlFoCujVfXTrHuqDWnK5LFrrabLyJmGarExjGv+NId0G87gPWwMbxzzPQV/poursDfQtL1WXKpilUZsDJH+sDcwYaBuz7lVTH3DSYuCAwdyXQz3uw3IHfbWHKjp70iLbAqudSYKUKEiPCfvTcKDV0D2gKc2H0hFDb6VmfY/LyIPwqU8ZlBpzCVvr4qw9ar9js49xkpjLwXe/q9m8WVFQAH19iuFhm3nieTBjBvT2KmbPhrw84amnNKtXB1RVQVmZFcS/+KKiqUkRi1l74FWrhC1brG6lqcluNzJiXcCuvNIQPzN33Dcce/c2sWDBkol/L10KNTWGnTsV6bQwY4Ywdy7nVKtyNGISZ1Vu7UnfIyLcffd/8id/8jlmz87nyScvYc2atUAIpfpQaohs9no4aeK1Ipn8ENGog+dtQanDiOSTTt9AJnPtuTyl00I4/AsWOd28HFSwTY0Skyil+jAXqX4O+I2kcisZDm2iSneTT460c4gmEfzM9czAOcEkQTEu1lJK8Y53vJNLL72S73zna/zf//uXPPzwA/zlX/4z7373B9601rCysnI2bPjVlNdyRtg3Yhj1hbKwpjam3tJWtYUFDi/0+AznhDzPjsVVEIiiNKJpHTX0ZoTCEOicYlbCIeIoNvT4rCx2JwT7iwsd5uZFaE0aRKA2romcIJSxNqYpj2g6U0J9fLJt7HDKMD/foTRsx1ERUdRENXHX2icXhjTlEdjQHfBIe5ZlRS4RFw6MCNnAZ1WxS8JTjPpCSENVVHMoZXih12fPUEDC06wqdlhZ5ExUhY5GWCJoND4+Kd9h24BPxkB+Xg5SBbRnFN9rzlIcUsxMnJkm5kyQIoMnU7/5luwocmPC/v7+XgoLi6cF9m8SpsnKNKbx3xQyLJhvZaHJIOUKHFAv+JgWg/7tEKpCTwjrU6nUWzJGpRT6nR6mS1C7jK0AdVgBOqUKDhrEBSq01YC8EEBiTMMSgHrCxxwW9KdCqOP0Y58qRATztA+jwDw9tsamkDgMvGwYPqyY7RqcJPQVa3YsClOuoKnJtnFVH8dqWDoN5hnftq2FQK1xUBe77D6g2LFDMXOm1WcMDMDgoCKZtO1cfX1QUCBUVVkhfVOT0NysqKoSZEAo7jBc0wDXvF1PiPQBVq0SYrGADRs0bW3Q2Cicd56QSAhPPqlwXfta5enIIt5AJJNJdu7cyvve91tTXi8uhgsv/PWpAGUyGf73//5d7r33W3z0o7/Nl7/8CQoK7sZ19wMGIzFGMpeSy1z5umuzIsUkk59B6za0HsSYCsw5ch06MxhCofVoU8Ziv5GEu4/DupNeU8xMJ8UVwQz6pYJfZC8g7L2IcZppwdCPJhv5KevUMHekPjJFWHwieJ7Hxz72O9xyy3v58pe/yOc//zEee+zH/P3ff4XS0rOpDJ0aysoq6O4+PKHl6EwZ7m/Osm84wBeIOYrlRVasHj2F4MizQSYQRnwrlD+SRCwtdLig1OX5noCOlCUaMU9RCaQDoT0VoBQMZqEkbJ3BXK04lIYDI2aKu1jYOdpt7PiIuYprZng80Jxl91BA2FGkfKE0orlqhjch1Dco8kOKxvzJfXanDX1ZQ0lIUxfX5HmQDXw6UkJ0yKcm6jCUE9aWusQc+PreDG1JQ56nyI76vNaf46UCl/NLHSoiDjVHkcVSU0yxKaJb99CXiZESRXEiYxeHhmuoj1mXs019wZtKVopNEQe9gySCxERFJkcOQ0CKNId1Fz39PRQXn1pVZxpnj2myMo1p/DeFbAlgryDz9GQQYbGgdgnyUoC6XuN5HqFQiGRy9C0bp5qp0Z8JYR7OwcM52/o1U4Mr0ITVp6QDaBXIA96mUWPtDJJnSQ67DCw7i4dVGmg1SPGYh9JYrooSGChweCQUZk6t4CQU/UUaoxThdiHXLKSeCpDLFKp6sjolhw3mG1lLtooUDAEP5JADhr55YYJgUtC+YoWwa5fNN8lmoaJCaGwUYjEIAtue4WjB/DKHPB4gvcYSz2qNvsVDLZw87wULYMEC60KTy8GDDyqee84hnbbtJUVFcOONhosvfuvJwNatm/B9n5Urz3urh3JCdHa28/GP38727Vv4m3/5N2a/fx4/dDaSn1vBktwq2tydvOAcoN3bxmz1Dd6euYo5QSNgRbhp0oQJH9VWpjCmFmNqj3/QNxVmzPo4TELyWJxbziKWIRhaIg+RcvqQQGghx4s6SUriFJlaLsq9jTQp1oefYm4wnwuybzvlIxYVFfMv//JNrrrqer74xU/z9rev5B/+4atcddX1b+B52jawdDrF6OgIkViC7zVn2TUU0BDXhDUM5WB9t09hSHFDzeuTrzNBIMIzh3Os7wkYzNrqybpSh0srbIiipxW314dYWmTYOxyggdl5moGc8PRhn+2DhpwR6uOaxnxbkRCxlrwnKJycEpYWOnSlXV7ttZWLC0tdzi/zqI1N3tPGr9NgVigYMwnpzxpyxraejfhC1tgMGU3AQBYW5CveXuHytgqPh9qytB1hqTycE17u9dk6kOWlXlvBWVls29nGCZyLy/LsErZ7u2hX3YSiPiqIQt8sGKlAKUVYQ0/2zb2f1fu1dDnd9Oo+YhLFJ6DH6QVgn7efAxykaWAneSVvbSTA/yRMk5VpTOO/KeSQQRCUN/nAUVohsbEQwzHEYnFGR88urXfimCmBYas1GQ9PPO77RgQyAgUK5SpbRcmBmqGRuZNCTykQaDfoWzzkUd+ezxF91yqiIBCkS06j4/g4CGFdx/oEeg2y08CgQCBUZBVebZitjkttKZADs8WnazcUBUL5vWmCXynUhzyci2wbkHneh2axDmdjD15JCmqzobzEoLUmk7GhjIWFVpcxOmqrLMuWCZGIJRetrbb1qzEXYH7oQxiYq63+qEUw9+bQn1dT8mfGsXGj4qmnbNhjYeF4Yjz86EeahoaA2rd4rvzqqy8SjcaYP3/xWzuQE+DFF5/jE5+4A9f1+K+HH+SFC57jWfeXgOAT8P+cw4QlTH0wi5BoXgo9x353N58Y/V36dR+/Cv2CQ047+aaQi7KXclH27RO990cjS5bt3hZanAOEJMR8fzENwezT6qM/M7jkcgsJh5+yVR5gkDRhNUy5P5tAEuxyt3FId9ClD1FqypjnL8QTDw+Pwzhs8TaeFlkZxzXX3MTq1ev4whfu5CMfuYX3vOfDfPnL//iGBdSOa2S6uw8TlMbYPxJQH5tsjyoIQSpQvNIb8I5KeUOqK788lONHbTmijiLfUwzlhB+25sgYuG4s/8TV1o54SeHUxZfVxS5zElke7cwxN08TGxtfV0bI9xRzT6GKcjx0pQ3fPZBh/4jBiNXGtKWE8FG3lFkJzQVlLs90+fRk7DgPp4WYI4z6hue7DRkjjF+2C8oc/nhRBFcrfCPsHAwoDiv0mNvYtoGApC+4WigOQZ6neLbbpyikeOcRWTBxibMmu5K+/gF2dqZIhBI4Yn8uImQCmHGSZ80bgUIpYFV2OfvdA/TpAbIqi0ZRHBSTJwkMhv6+PsKlkYnA2Gm8sZgmK9OYxn9TqIQCo6ZYWAKojNjAwjHEYomzrqxIVpAnfWRDACNjZOViB3WZa8nI+PuGBPNoDjYGVkRfqdCXe8gSBS22CjFlrIkxG9iwgnptKyhHHjcYS00+vgHTKUM5CnWBi9yVRbYa25Sdp2BIEY3A28nyg74I+7KKxKBh+DVQAbw9L0dsVGCbQf7FYGZrdJVjW+/ymCAqACqmwDc0eAGzZrns2aOoqRHCYejpgaoqYf58aGmBZFLR02OrI0uWCB2PBMxKC87MsQmJAzILVJNBthrUpceSlVdftRkrhYVjx1dWD7NrF+zcqaitfWurKxs3vsDy5atx30hByhni/vu/w+///qdYvXodX//6fTxW+whN7g7m+Qvw8OjSh9jubaHQFFFmyglJmFJTxi53O3dFv0GP04WvchSZYrqcTu6NfYt+3c+70rcfc6ykGuWu2NfZ5L2MTw4B8qWA69O38PbM1W84Yclm34Hn7WCXt4GfqkEOOgN4GJZnLuFto5+kPf5fbPdeI0eOAEOf7qHcVOKJhyMuWTJnfOyysgq+/e0fct993+bP/ux/sX7903zta/eyfPnqc3iG48eyrWY9Pd2EChvIGpsIfyRirmIkJ6SCc09WRn3h2a6AhKsmkuXzPUVX2rC+2+fiMm+iYnE8RBxb8RnKweYBn0Cs3jDuKK6Z4VITO/3xiliy1DRkmJXQhB1FJhB2DQU83JrjY3NCE/djrRQ314aYmXDY0h+QDoQLShx+2Jpl77BQEoaSkNWodGdgKDt5HK0gpBWpnL3n9GeFgZyhwIMhX+FoRWFIkQoUL/UGXF4pRBxLcjpSghFheTyfl4mwd9BQGbGW64dShoqIPq7A/nSRI0dSpQjhET2JbfE4ik0RRdlCcvi8En4VjSZ/LFvJwSHdmyR/URFdTg91Qc1Zj28aJ8e0jcE0pvHfFGqJA8XYFX5fECNIh0HCCr1i8ikdi8XOnqz83Ed+6CNJQQoUMiLI933kcX/yPb5g7s7Coz4CSEIhB4Xgu1nYaSw5SB6135yAVqi4Qp/voMS2WIkRJCOofQZmKFh09v3K6mIHGjQMCaBgVEGxggtdLs4P+ND8HI2NoNuEuSbgQxUZLqn1bVWoSMEBkJ/bCQRxBdmp+x9v1/ASig98wLB4sdDTozh40Arrb7tN+Mu/NNx6q+A4VlQ+e7Z1/Nq9AfZ1akxwxHi1sj1rw8cnHaOjx1oZj/PAzJnPL88JRISNG1/8tWwB+9a3vsLv/d7Huf32D/G97z1KrCzOa96rlJuKieTsETVCRCJkVYY+bds/FIp8U8AL4WcQDHP8RkpMGXXBTIpMCevDT9Gju4453obQr3jZe57qoJYF/hIW+ItxxOGnkR/RrlvfsPMUhGE1xGhQw5b0jfy7yrBFDxA2RWRzjfzE6ePf8v6BLn2IJbkVlJtKQhLigLOXXe42cuRIqSQL/bOzIlZK8d73foQnn9xIaWkZN9/8dh5++IFzdJaTGK+s9PQcpiKiyXMV/Ue1D/VlDGURW/U41+jLCIM5Q+FRhKQwZFuiujPmBFtOIuYqPjQrxG/NDnNNlceNNSF+e16YKyo9ssZaFPedwn7G0ZkW9gwHVMcsUQGrdamKaJqGAw4fFSLpacV5JS4fnxPmM40RbqwNURzWhB1IBtCbFXyBxjxF2gj7R8aCEZVidbHVr6QCIWdsp+2oD1HHam98I6R8oSNl6MkIe4cD/l9Thv+7K8U/70rz7f1Z1pY4LCtyGPWF4ZywsMDhg7NCE+TvTCAI+90DPBvZwHOR53kmsoEt3lYyp0DCFYoQHhmVwZOpN9vhvkESxXnkVPYEW0/jXOLXb8lrGtOYxilBVWv07SHMQznYZyfKFIG6yYVFkzf3ePzsKisyIMgG3+aRVIztN08hnQaeC5CLXVSegr0G2WagQaPGXGcoULDHIM8GqHUO3OcjvWJJVg7UAQO1GhZoiIDqEXjKh92CuAJ1Gn27h8o/+8mFCivUCo3s11CurbFTsW1Tk2HFqmjA6s8GZF9I4qQEVWbdXwCIKsAg+8bc1VY5mG0G6RcoxF779rGQyAWaGTPgs581tLRAKmVT6McrIOm0zUa5+GKZCEcM+hSjTUJPN5RXjrWV5QSFQpUf/9wbG4WdOzVBMLmfVApcV97yFrCOjjYOH+5k5cqTu3C92fjKV/6Jv/qrP+KTn/wcX/rS36GUIkMag0zRnXi4KBQy9t84htQgvvIpPUo0X2LK2OPupFO3U3qUzfCr3ovEJEZMrGWbQlFpZrDL3c5ubwc1maOSQc8BmtztPBH+Gc3ufjzxSJJkwBSxKHuZDbgDXNXHLyI/Z3FuOXP8RpJqlDanGR+f3e5OAgKW5VZxXvbCczKmurqZPPjgk3zhC5/kU596P01NO/jCF750zpwKi4pKcByH7u4uKiKKNSUOvzzskwoMMRcGsrZKe2mFd0J3qrNB3LXVkaQvRI+ouCZ9+3riFCs5YUexqthlVbH9t4jwYm/ALw/n6BlLr19U4HBdtUfxEb1cgpBUdjUoJjHSPgxkrQYmfNT5hh1L3A6OBrQmDVFHMTdvktCMI21s+9aaEpecASO2Ja3Qgz0jwrA/+btxcbnHwaRh24BhJGdI+faazM+3JGbXYMChtCHmKP5lZ4oh3+a0zIhpNNCeMgxkhTvnhojXagQbcqnP0m2rxWllu7cTVzziJkZO+Rxwm8mpHKuyK06pslkYFNLitpKQ+MT7h3oHySspIC6/plaMv2GYJivTmMZ/Y6jVDrpRw15jHbZmKlTx1If/WWtWegQZBmqPuqkXKaRTrA4kT1ldSU7gKK9/KQTVZuAjHnQL6oXACus1UK/R7wnZFipAXeshax1rbRzC2h6fhQvY0VAlGiIKqVSTWhMR1KhAjUJrhVutYLcBURNchTRW9D5OwlY70GxgQ4A6PEYUixXqJm9CiK+1DT88Gjt2KBIJmZLi3j3LI3+/obLJIBHAF7vf+RoW2zf29cELLyi2b1dEIjBrllBXJzQ1KQoLbYVmdFSxerWwcOG5bQFLJmHDBsUrryh837auXXSRUHICM5xXX30R4NeqsvLVr/5f/uqv/ojPfe6P+OIX/3yi/SUuCRr9BTwfepZiU4JGUxZUoFxNVmVImDwEoV/1YVRAVVBNWqXJOyL4Ma1ShAgTlWOzVLJkJ4iQwdDptHNYd9LptLHRe5HV2XXkj+0rqUYZVkMUmKLT6oPv1d284r1Ah9NGhjSbvVcIVECZKSdLlhdDz1EoRSz0ZXJypsBXOSvexmFJbgXFpoR23Uq/08c70u/kxvQtFOKDGjlhGObpIBKJ8G//9m0aGxfyN3/zp+zZs5N//ddvEYud/YRPa01FxQza2ppRSnFjTYjCkOKFnoBRX6iPO7yt3GVV8ZlXaYdyMiFAz/cUOSP0Zy0ZKA5rlhc5PHXYx9NCngsjPnSmDOtKXSoiZ3Yf2zIQcH9zBrAVikwgPNftM5gT7pwbxtOKfj3AbncPe9NJNrcVsruzlCAdpyzkkvRtpWPeES5fh9KGnizcdzBLxtislLqY4o6G8JSslrgDZWFFa9LQcIQb11BOiDlQdgRZSniKj84Ks2sooD1peLHX5+CoIRUITf0Bw77Vu5RHFLuGDT1pw9srXeJjJG5m3Dp/vdpnuLnuzKemgtCr++h0DpNUSTqcTpwjWrhse6PmsNPNgB6kaCwA9WSoD6zgvk/3E5MomWyG1PAoVUUzKAtKz3is0zh1TJOVaUzjvzlUnoIVJ34Ax2Lxs2sDywMVARmFKQ6mI4KK2J+Pj0O0shWBI9ssRoFqhYop1G0eXOha++IoMGeqPS+MEYpTdISUvrFqzqigyjUsOnZ/MNZu1mSQwTEStMMgtdoSkEO2IqLPs7dDfbmHedHAYQMxZcXuvkCRRp0/li3hKfYs9dja4pLZLeSXKGZerVi87vUnJNEoE2GP4xgs0DxVF6amMosaDeyd+RIXfa2Liit6e+Eb39Ds3m2Jju/Da68pli4VFiwQduxQhMOwerXhoouE0Dk0O8pk4Dvf0bzyCsRitir08MOa7duFO+80FBcfu82rr75ETU09FRVV524gZ4Fvfesr/OVf/iG/+7t/yO///pen/EyheEfmnTQ7B9jlbicmMTIqTW3QgIdLp9OGQYhLgqszN+AT8GT4Z0QlRkLyyJCm2TnAotwyZgZzjjn2Un8lP/K+R0WQY5e3jWZnP1my+MrnFe95dFzzwdFP8GJ4Pc+HfsWoGiHfFHJJ9nIuzVx5QtH+OFqcA3wz9h+0Oc14hNjrNDGkB7gwcxnFxk6kyk0l7U4rPbqLcmO9rT0JW02Ksu0wLi71wSxiEscEhncHsymL/weO046IRy63lnT6RuQIknYmUErxmc/8AXPnLuB3fudD3HTTZXz72z9kxoyz7/tvbFxIU9MOwFYorqwKcWmFkA7sKr9zhqv06UB4rCPHS30BIzlD3FWUhaE1KewZNmQCK1C/tc5jbYnD9kFDZ0qIOlY4f3Nt6IzyOESE57p8coFQHFYMZIWwA3VxRdOwYftgQFaneS44QH/OsO9QLft64mQC8HSGgazCFejOCIHYSsVQzmasiFIUhjQFHmQNHBw13Hcww+/Oj0y6dWnFxeUe9x7M0jxqKAopUoHQnxEuKHOpjR1dsVEsK3JZVgRvr/R4+nCOB1pyDOesBkVQdKVtRWbEh+ZRQ1lkMq8n4ii6TqPN7XhoddrYHtpJdoyIdzndhCVMXBJExkJdQ4QYZoSUSlFE4evus9gUsTK7jP3uQQacAYa7BgFYXrD8hAGz0zi3mCYr05jGbzBGRyEWK6ajY9+Z76RcwRIH9ayPaA35WNvfLoG3u5OVnPka6hVqr0EatHW26hVUFtQF7uTDulrBcXJLTheyPcDcm7OkQoFoBfM1+kMhVPERov/BMS3NNgM+SApICnQaqz2ZrdDXeBNp9OoyF/VagPwqsI5mLpCvURe6qDX2lrlpk+KuuxxGRiAvXxgdAO9BxW3a8La3nbyqsXKlsG2bYmgI8vMnXbx0rUP8dz10wgVHoQomz+GFFxS7dyvmzRNEoLtbkU7D888r/uAPAm6/3R4zk4G9eyGdVlRWCjNmHJtcfcx17DHWerlk6jHHsX27YvNmWyUat2P2fWH3bsWLLyquuebY833mmV+wdu25aR86Wzz44D38yZ98jk9+8vPHEJVx1Aez+J3RL/JiaD0Hnb0UmhJW5tZQH8xit7tzjLzUUxPUM6KGGdaDbPU20U4LGod5/gJuT33wuBOXC7OXst3dwquhF2l2DhASjxAh5ubm0+gvZLv7Gv+W+Hs6nDYKTCEFpohBPcAD0e9iMFyZue6E5yYIP4s8RJvTQqO/CI2m2TmAIy673Z2UZSsIEWJmMIdWp5k2p4VSU46PT4fTymx/HgCHdSf5UsCQGqRP9/KB3HIqo/ejVIogKEepDOHwj9G6m9HR3+VcTB2uvvoGHn74aT784Vu49toLuPfen7Bw4dnpYxobF/HTn/5wymshrQidZafZox05Hu3MURRSVEQ1zaOGxzp8DFAQ0jgImwYCmpOGP1oY5sqqEH1ZQ4GnqIvrM25l8gVaRgM6UsLeEZsX4yihJKRxlXDPgSxdMko/eQwMJxjKuLhKKI/lCHQOnXNwghBhrYg41v54Tp69GBHNhL4m7EBDQtMyKuwdNiw+wqnsvBIHQ4hfHspyYMSQDKy18rw8TU7gRJ4BEUdx9YwQA5mAllGFAYpDCkcpVCZgKActo4ZVxdYgRkRIB0JF5Mw/rDQZmry9toUsKMFgGNYjjKhR+nQfMwK7eJIjh4NDeIy8nArKTCml2RIyZNmw/SkAFsz+9XQ6/E3ENFmZxjR+AzEyAo8+qti4UbF//2wOHPgFr7yiWLVKXnfyejSUUuhbPCv+3hrY8Ma4gotc9I2TokMVVej3hzD3Z+GgoHJAAahrXdQF53b1SVKC+UEO+gRp1NayOSOorQHmiRzO7ZOlBfO4dSeTWdraLfsC+wIoU+iPh1A1eoqjmYoq9GfCyOoA2RzYlqzlDmqd1ebkcvDYYzaBvbFxckxtbcLjj9trnDhJx8zatcLBg/DCC5qODktWCgttPkpN3RE6mSOwbZutqIyOwpYtisFBEFGMjAhf/7rm//wfw+Ag3HuvprlZ4ftCXp7m/PMN73rX8SstMiw2+2ZTgKRA5SvUBQ7qGndKZay1FXxfEY1OkhLXhVhM2LXrWLLS0nKAnTu38bnP/fGJL8KbhC1bNvKFL9zJu9/9Ab70pb896ep2hanihvStx7y+KjdVd5Mn+Xx89LPsd3fTrbvIM/nM8xcS5vgTn5hJ8MHRTxKogMPqEDNMDZVmBlVBNQ4Orji8EHqWFbk1lJgyBnQfYQkT4PNM6Ekuyl42oXc5GgOqnz3uLiqDKvSYX05UogxoOOx0sM3bREMwh4qgiqpgBg4Ou90dOLhUB3W8K/Ue9ru7eT70DIf1IRKS4Lr0zVzl7EepUXx/AWC/oyIJHO9VDoR/RBNhIhJlkb+UMlNxSp/F8bBo0TJ+/vMNvO9913PbbVdy330/Y+nSlSfdRsQ6SA3lhJKwovyIye2CBYv4//6//8vIyDCJRN4Zj+tIDGRtS1NxSFE2dqyhrLHtUxpKQuBqTaERDqWEH7b5/O1yj9r4qU2vRIS2lNA8YtAK5ubpieO4CnoyQmdaqIoqQlqRM9CZNmQCIWNgRmmGsJNmaCRBEEAObbVuKFzHJ+uHKfYUi/IdPjY3TMoX/s/2NEdLaDxlc2JG/am/z0op1pY4tIw6NI/6xBxrXHD3wSy7hgx3NISmBF8ejairSfpQGp6sbOW5GlcZhn04lBLyQ9b5qzikWH0Wzl8DzgAplaTIFAGg0RSYfFJOiiE9TElQiijDsBqmKqg8pRawKdcCRYQw+/fsJRwOU15XZe3233AL8mlMk5VpTOPXCCKQzVqXpzPVnRpjJ60vvKAoKRFKSmaxffthvvWtJK4bY/ny09czqHyF/i0P2lwYEOuOVa2OmfypOo3+fBgOGKvzqFKosjfAdHCvQVoNzFLWNQsroJcyhdoUINcLKqaQpFiiUjqZC6NchTQ40GJQo0whKhPnEVeoK1y44thbZHc3dHQoKirGrqMP5KCiDJpbFW1tMH/+iYceCsF73yucd15Ac7N1CmtstFWQk22Ty1nSMjioKCoSlBKCAJqbFQ8/rGhrs85jDQ2WnPT3C088oSgthcsvn/qZi4yRvWd9pMq23cmAwCM2NO7VshCtrRCPQ3//+DZTqzS+P1lpORKPPfYIoVCIyy676sQn9Cagu/swH/3obSxcuJS//dv/OKM2nKPRqdvZ7+5Bo5njNzJ3bDJ/3OPrwzwR/hmveRsnhPo1po4VuTVTJjcplSKnsgjWOWxQDyAYPPEokhJ6dTex4Phk5Ujx//i/bc9+NwEBe50mOp0OYibGytx5fDj5KQb1ACEJMcefT4QIS/zlXJa5ikHdT4EpIl9ihPM/jzFT+/uyhOl0dvJo7DCviP1ZqSnj3akPHkPoTgelpeU88MBjvO9913HbbVdyzz0/YfXq84/73sGc8MOWLNsGA1KBkHAVK4ocbqqxqfSNjXalu6lpB6tWnRtzh/6sncDPGHOkEhH6MsK4WZ8/Vnx1tSLkGA6l7J+GU0hcD0T4aXuOZ7p8RsZIQlFIcX11iAvKXAbGrIDDGjKBraoIEBghGdi2roTjMqoErYSQaxjJuqQDhasFjYMCjEBB2FY14i7MiGqahgKKj+DXQzmIOeq42pqmIWu/XBXVE9WYUV94qdenMd/hgrITTyXn5LvE3AyDOQHsYtmIL1RGFUbs9c2KrdZcMyNEbfwsnhdy7GJPkSkkrdIM6WGG9CAhQtQEM1iYWzBB8E8HWXK8tO95yudU81ziefJMgpn+TKqC0yPtWbL0On34BOSbPPIlb5r0nATTZGUa0/g1wfbt8PTTmpYWSCTggguEiy8+ff3Bvn129b2uzq7w+/5MAAYG9vP000tYtuz0qytgV9ioVfA6TlPKUzDPPqhFBOkz1ua3VB1DDETEalocThoyOWWbFoP5UQ62G+hUSLVYB7KwskuRWbEEAiCDzXs5et8hUIHiTCIkQiFbWcilQToMtBnIQTYMTsLBdceskU8Cx4HGRktSTgUrVwovvaTo6RknKrbFLxqFujrhmWc0IsKc2YaaAUNlp08kLewLNK897nLppUwR9NMhsCVAqjVqPJOnQpFJGrZ9w3B3OfiuJggsaU4m4dAhqKy0hGVgwJKXFSuOHf9jjz3ChRdeds5Wts8E2WyWj3/8PQSBz3/+5wNEIpGJifyZTFAE4fHwIzwW+QlDyvarF5lirkvfwiXZy4+ZZAyrIb4Z+w+a3B2UmXIUimbnAIedDkqDcupMA2AF9UYZik0Zm7wXyaosBaYIjaZHdzGqW2hxDlIbNBx3XEVSzFx/Pi97Gyjwi+jRXfTpXqISI5CAMBHSpPC1z5rshcwO5lkN1lHIlwLyg3EtimBMEUlvGwecQ6RJUSBF5NQIGd1H3F/MgmAOBkOLc4AfRO9mpj+HYjlFodlxUFBQyP33/5wPfOBG7rjjWu6772fHEBYR4UctWZ7v8ZkR1VRFFYNZm/wecRTvqg0xd+58tNbs2rX9nJGVAk8RcxQjvky4ZUVdG3zoqslk+UAEIzbbJeTYtqbRsftQ4gRWyVv6A5445FPgWQIhQGdKeKgtS21Mo5XNamnM03SmrbjfUVAWUQQpwdWKhIkz4A6SF0synMlDAaM5KHAc0hn78CgNK5YX2emeUopLKzyaRw37hm2QY8qHIV+4qMyl/jhkYe9wQCZgii1z3FU4CrYPBiclK7MTmjUlLlsHAgKx943KiCbmQHVM8/6ZYcJaURlVeGfp0lZsColLnCE9RIEpmHD08/BYnFnI7GAmIQmRJ4kzIgaCsDW0nV17tlM1rwZHXHp1P4OhYXRmGRVHOQGeCN26h23eDob1CIIQwqPGr2FhrnFaA3MCTJOVaUzj1wBbt8J//ZfVQBQVCd3dcN99iq4ueM97To9cdHcrMhkmWpEKC2eP/WQ/HR1LSKePvyJ+riGHDeYnPuwI7ASpWqGv8lBLx4jMQYN5woc9gXUGW+agr3SPm9Y+sc92g/l6Fmk24InNTBlWMCjISo3qFljuTIj+KcBaI283SEhQIWX7HXrE/qzm9B9YJSUwf77h+Qcgng5wYwrfhZZDinn5PvU9AnPO7a117VrhmWcUu3dbcqCUJU1FRbay0tUF8bjiklyWle05tAHfVawcCRgYDMju8YjOP+K69gsyKlY/dARa+hXpLmHBCiFbaPUxbW2WrKTTil277PuiUXjb2wwrVx6VY9HXy4svPsdf//X/e91z2rkTnntO09wMpaWwbp2wZo2ccUXxSPzZn/0vNm16iR/84Bfkzyjg56GHeDn0PFkyLPaXc0nmCmaYUxd0b3e38Ej0QaImRmOwEEHodNr5UfR+aoI6SwKOwCbvZfa4uyZCJgGKTSm/0k/Q5jYzaoYBhYPDxdm3s0/v4YnIT5kx1hqWVmk0mjxTwHZ3CxdmLz3uuBSKa9I30qnbaXK3c8jpYEgPUB5UMsufS1ziOLj06i5G9PApnq3iVSkk5G6lTxQjxOniAIW6h0FTTn9QD9gWm9qggT3uLpq87azLXnLK1/N4SCTyuOeeR3jf+67nfe+7ju9979Ep4ZGH0sK2wYCqqJ4IVywOK3wxvNzrc3mlR340SkPDbJqatp/VWI5EcVizstjhyUM+YMj3FKVh2DtsDRjBum31Zw0KxcoiF2OE7+zP0TRsmeHCAocrKr1j8kK29AcYEUrCYyJzYEYUdg0ZdgwFvK3cpTiscbQwK88SJlcpUoEhEMOoL4QkREVQjkn00Z/Mkg7AGM3gaJyoUiwoUNxaF6Ixb/LYSwodPjgzxK+6fNqThoSnuLzS5dIK77gVSHOC9RelbJ7KyTBOJDNBhuGcNTrICXgarqwKMSfv3E3OQ4RYkJvH1tAOenQvGo0oQ1FQyEK/kTw5swWUgIA+3U+v7qPVaeNwUzsrLzmfqESISoQ+3c8Bt5nybNnrkqA0GbaGtjOqkhSaQjSKtEpzwD1IniRo8M+9lflvAqbJyjSm8RbDGPjlLzWjo8K8I+Y8fX3w4ouaiy8OqDkNo5xEQnBdIZOBcBhisQpcN0Zf3z4WLuSUKjUSiLUPzgrMOCI35RQhScF8J2sduKq0vdPsF8xdWfQnQ5BQmG9modMg5RoCQT0RYNoM+tPhEx7PrPetMH6ZhjgM7hR2JTWZXYqqYWH2Yo17uQMHBNMWgAcSHtOobAGJA4UKyhTqnZ51EDtNKAU3rA7o/R7sMw5krHNxbZVwS3EO/SzIGmdKuv3ZIhKBj3/ccPCgZmQEiothYEDR0WGJRDQKzqCh6LDPcJkiGGu9680J88UQetZHGo+YiBTZIE4ZgnEzHDEw2AYmrjBj118pqK62GqirrzbE47b9a+ZMYfbsY1sV77//ZwRBwObN19PdrTnvPOH884WjQ+y3bFF8+9v2XAoKhKYmaGpS9PfD1Vefne3yPfd8i+9852v8wz98lWVrVvGfsX/jFe8F8iQfV1weC/+EXe52PjX6e1SYU3Mr2+JtJEOaBmOJv0JRHdSyw93KNm/zBFkRhL1OEz+OfJ8W5wARIlQF1UQkioNDgz+HSlPJ8tx55MjSEMxmcW45j0S+z8bQi2R0hhFGCBGiPphFoSmiyzl00rHVB7P4zOjvs9F7gR9GvgcolmdXUySTbVxDepA06VM61wHVz3ec/SwJ6linRqhRaXxCrEd40sSZw6ROzVaphOzRCalniFgszne/+zB33PFO7rjjWh544DGWLFlhz2EscPDoNqWEq+jJ2BDBfE+xYMFidu3adk7GAzaPZEmhw6gPu4YCWkYNxSHNlVWKHYNWP6MQ4q7m/DKHy6tcvnMgS0vSUBZWCPBsl09r0vDpuWEKj1D7D/tyTDVBKYVS1oEs4iguLXf5fmuOzrQQc4TWUcNoIKwockgGwu4hQ0Eoig6qmBvNMa9SmBWNMphRNCQ0K4vcKXks41ha5LK40CHpQ8ixRgQnwqw8B++Qz0hOJqpEmbHwx0UFr38PXVPsEHMivNDj05EyVEY055c6LCk891WEGUEV8XScQ85hMipDnuRR5VeclhX4kRhUQ2wNbadfDzCqRunoa2e4d5DqxklSEZEwQ3oYnwDvdabV3U4Pw2qE4rEKKlidWUZlaHPap8nKCTBNVqYxjbcYo6PQ1mb1JUeiqAi6uoSODkVNzalP4ObNg/p62LdPUV8vRCKKRGIWg4MHuPDCqfkex4O0GswPcsgB656lShTqHS7qYufU+/63B7BXkLnWHpgAmAXsFsyGwDqKdQgyX49pThRSJKjdAq8FsO74t6bsdsNAWjG0V9GW9viFOHSlFaQhPKhYVQK3r88S2ZxDJQXpFltFqVRQhP3/PoHzHdR1Z377qzDCZ+qybCty6Etp8sPConJDXhI4LDCCrdycBY7WL1VWwrveJfzkJ7Zy1tpqhfQFBbB0qRBvMkR2wN4hRWXCBkR6IUXRTGz1atSDceH/DAXLxhzeAg0JkF4hnIUd9S7+EW0rStk/JSW2NfFE2LsX7rrrJxQUnAfMYO9e2LVL0dkJt946WR0MAnj8cUUqNdWgoLMTfvlLxdq1QlHRmV2zlpYDfOlLv8d73/tR3ve+32Kz9wpbvFeYGcyeyECpMFXscrexIfQr3pV+zyntd0gN4smxLN/BYVRN2oI/Efop34r/B3ucJoadIfp1L5W6mtW580mITbue7TceI+SvDWYyK5hNaVCOUYaoRIlLHjvc1yimlJ9EHiTPFLA0t3IKCRlHqSnnqswNJCSf/4p9ldgReS9ZMgQEzA0aj9nueDjg7qVL99CcvZgBlaJIDZHF48fuNpqdNmpUiojY0uyA6icmcepO0KZ2KjAiDGTtpD3PUxMVlttvv5oPfvAmfvrT9cyYUUNxyAYrDo4J68cxOEZSisaqLY2Ni7jrrq+f8XjGkfSFR9qzbOqz+pi4q1hc6LCq2KEsbEXwncmA9T0B6UCYl6dZUujybHeOlqRhbp6eEJQXhYQ9Q4ZN/QEL8m32StRVzIwptg0IgQiOUoz6Qn/GkPKFyjFSdlG5S8hR/LQ9y8u9hoyx59+WNJSENEsLFT1ZqI65nFcSYU2Jc8oWzVopEkcEs/tG2DIQsHXAtn3Ny9esKnZZkG/JxYYeH5We1OosLXRYdQqCeKXstVv8BpCT46FA8inw8896PwEBW0Pb6HX6KQwK8LTH1qZXAchrnPw9zCmfmInhnEKLqa9yAMe0o7riklaZacH+CTBNVqYxjbcYoRCEw0IyOfX1XM7qDI50YToVRCLwgQ8Y7rtPc+CAIpsV4vFZxGL7uOiik+9LRscqIgfHkuVDIIcFeSCHk69si9UpQHoECQy0aqvpyAjkKySmoCVAxTUSZ0IcLyLQK8jBgOAu0AOCOs+Z0hLW0gJNr2kSrQEHI4pHel3SjmJ5nSE6IgzPcVi/XlEliitXKKQKaA+sZmVEwcX2fNglsMHH/CuoFS5qtWOzak4HCYhEYXWxQR3x+UiPQEJxhot4EziRfumaa4TiYvj+9y1Rqa+H+nqhvBxKjJBohY6ctTAuKbEVkGKNnV04465OoLXCv9Fj2yFNy6uCm4P5MwyDF2s2dIWYE0xqXLq6oKBAMWvWifMPROCxx7K0tz/O+ef/IVVjBYveXli/XrNu3WR1sK/PGhSUl0/9LpaXw9691qDgTMiKiPAHf/A7FBWV8Od//g8AtDstBARTwho1mjzJZ6v7KovcpUQlRnVQd1Ity+xgHi+HnsfHn8g9yZFFEGrH2qLadAv/nPhrepzDRCTKqAwzpIbIOBkKpJCKoIqIRFiRW3PM/pf4y5nrL6DJ3UFlYJ0WtrmbaXdbSas07U4rglAd1PLB5CeYcwLisSK3ho25F9jibZxwEEuqURbnVrAyNzWgs0/1ss3bzKgapsxUsii3jCjRsYmS7fUfkQQjY2GQFUEdPaqf3e5OSkwpGTL4KsfbM1fTEMw+eiinhB2DAb/ozNGWMjjKtiddXeVRnF/Ad77zI6699kI+/OGbeeihpyiLxVldYtuxfDETxGU0J1xR6RIb08MtWbKCnp4uWlsPUlvbcEbjEhF+1JblmS6fsrCiKqQZzAnru60r2MIC+x2oijncWjf1ntiWFELadraOjOWLxB3QSvhpe5afd9isk/GAxOKwYtdgwFBO6EoLyUAoDime6/apjztURjVrShw2dCtqYprqKLQkhfakYcegoSer+cOFEebmn910zojwo9Ysv+oKrLZLwat9sLk/4KOzwtxW55FwFc/3+OQMnF/scEO1N3HdfxPRq/vo14MUBgW4uMRNjMGdvShHE54bJyAgo7LkyFEX1JySHi5hEjhosmQJjQWXCUJaZajzy6eJygkwTVamMY23GOEwnHee8PDDmrw8IS/PEpUDB2xlZO7c099nTQ18/vOG/fshlVLcc89Mnn32ETzvdTbcHkCLIHP0pH1trYI9BvO8j3OqZCWBFXEnbSsWHtCN/XedZ9uQdtnJqojAHmP/DAjkG+QHOWRjgP5YCFVphd4/+IEmqzyuiwf4okhqRZERku0QqVLk1SvyDhpeChzekW9QLQId2MbyIQNaoEBZ6+VRQTYFNhxys4P+aOi4GSMnxGyNmqVg11imTAToBzUktgoVPvMHzuvply64QIjHhZERzaxZTHymw2UOfoliSdYw72JFLKGs7fIeGLnIYf1TmpdfttbL8+YJXV2avc0O2QigrcznglVCbZOtiESjQi6nCIXguusMlZUnHnM6DevXP0EQjDJ79g0TrxcXQ1OT0No6WR20BgVC9qjOofEqUvjUow+m4MEH7+FXv3qCu+56aELcH5YIPgGDuh9XPOKSwGBodg6wz9lNh9tOSELM9Rfw7tQHTtgWtjq7jpe8DTS5OygxpTbR3ullQW7JBPn4WeQhDjnt1AT1eHhEidKlDzGqR9jmbiZHljl+I/vcPeSbQmrMZLtHTOJ8OHknP4n8kB3ea4ySI6syFAXFLMutwsMjIGCfu5sHo/fweyN/OqGFORIxiXNH8qPUhOtocnfioFmeW8O67CXEj0ig3+lu5Z7oNznkdAAKjWJ+bjEfSt7JbH8e5aaCdqeFumAmCoWPT1KNckPqNmZILbvd7SRMAWty61ibveiMJlh7hwO+sz/DiC+UhTU5sWL57rTwyblhyssrueuuH3HDDW/js5/9CF//+v1cX21tcl/sCejL2IrKOyoc5iQcnujMEQgULbwArTXPPfc0d9zx4dMeF9gQxc19ARURRdFY21a5ozBieKEn4G3lckLRfIGn6MsIXWlLQAAKPRjywVPC4kKHeXmarBkLRQwrqiKK3UOGmAvz8x0qoprdw4b7mrP8zrww7UlDS9JQHVPsGDT0ZmwwZZ4n7BoyfG1vhltq7Zp82IHGPGdifOPWyANZodBT1MSOdXEE2DdseK7bpzSsKRg755wRdg4GvNCTJRDFM12+tTZW8EKPdQ94d33opC1kvw4wGAICXNzT+q7mVA6DmRC9azQDW7spm1OJHw7oCfqIEmGWP5N6/3WcZ8ZQYoqpCCpodzuImDAODkmVIioR6oNT28f/REyTlWlM49cAV1whdHUJmzdbHYJSQm2t8N73GiJnuErvuoxpYIS9e5dz993/Sm9vDyUlpSfcRgYAYWoCPZZ8qEOnXuFR5RpJY5WUhWPLjL7YfacFtUJjNiqrP4kB+8aqL+UKtcRBYqB2GcxTPs4dIdraYP9+RdlizYF2j2CHbVkqcYTRQBGfpYnGFCEDKRTBgODuCqwLmYetLHQItAqUYcnSLAeJAC/7mDZjxxgDtdpFrXNQpSdeJVOeQr83hLk/B3uNzZRJYEMyLzvz26rIpH5p7txJq+Cj9Uvz5kFtLezfbyf/hw4pUinFXj/EJ0syxFvHzgdFZrbiu91hXn1Wk59v2wC//31NXx9cfLFQOscet6sLnn9e8+EPBxw6pNi/X5FIGJYvF5YsOfm4XRf27ftPiopWUFKycOL1cTexI3VSBQWwZInw9NOaREKIRKwOpqVFMWeOMHPm6V+3np4u/uzPvsBNN93OFVdca68lQoYMre4Bdqqt5EkeJaYMx7i0OgdZ6C+l3p9JWqXZ5L1MRqX57MgfTqx2HoliKeHjyc/wdPhxtngb0aK5IHUzl2auJDEm2j3g7EWjJyov+aaAmIlzwN1DoALrHOT08CPnfp4JPckHkh9jqT+ZKVJhqvho8tP0qV4OOx18I/7/iJnEBClxcKgN6mlxD9Ls7D+mutKjurk/+l+8GNqAi0ttUM9l2Su5JHPFFIehFCm+H72bbqeLef5CNJoMGbZ6m3g08mPel/ooN6TezQOxu9jlbsPBJcBnlj+X96Q/TJU5B4muwPM9PoM52z5lJ8+KPFdoGg7YORiwothl4cKlfOUr3+UjH7mFv/u7L/FHf/RXXF8d4rJym4Je4MILvT5f2ZNheOz+4ukIVfOW88yzvzxjstKftRWO8qMCCvM9q48ZzJ2YrFRGFJ0p265VGrJhiAdGhLSB5UUOJWP6kfEgxgMjAWGtWFPiUB3TE0Qi4gj7hgP2DtuqU87AQFboz9o0e0cpXAMihi0DAQdG0pRF7Np+ZVTz7voQ1VHN91uybBsISAZCzLHtWLfWhcg/avz7Rw3pgAnzAgBPK+Ku4pmugGQAMRdq4/a7NJwTNvT4zMlzOL/013MqmcPnoNtMm9NOTvnkmQQFJp8IESISpjQoJXQc0j+OuInj4ZFRWSJjAZJN67fSuG4xDX49C3ON5Jm803IX02iWZReTb/JodzvwCZgRVDHLb6DYnGH/6/8A/Hp+w6Yxjf9hiMXgox817NsHhw8rYjGYP1+IxV5/21PBmjUXALBx4wtceeWxidjSa5CtBtkRWKvhEVCJyQe1GhZoPPV+YzUCVGpIGevYZYCYgkUaCUDVa/SNLvKEj2w10A9UKNRCB/LsbV9KFGpbgPhCNqsIAnDDiqb5HtmEkHxRs99RjHqaolIhAvR5mnWSw+0IbM5LKdCLnbhHx/5/GGjUSB6wP4CtBnnFWB1NBGSzQb3moD8ZOjlhmaHRnw3BfmPtl8sVzDj+quWp4kj90pG7GdcvtbfbCkU0CrfdZvjSlxz27bM94Y4jJGMuj8xUVF7gUxQWVIVip3HZepdi5kyZcIHbv9+6e/X2QmmpPVZ5OezaBV1dakzkfurktKurlba2n7Ngwb+TStnvrzHQ3Gy1NkdbNL/znUJvr7BrFxijAEvO3/1u8/rVv+Pgz/7sCwD8xV/808Rr290tPB75CaVBBTiKUUbpcboJXJ/aYCZLcivQaDwJMcufy153F03udpb4K457jApTxe2pD3Fz6r02cA+XYTXEK94LZFWGCFE8CTGih8gzVrCk0aRVmiJTzAp/jXUnQtjv7OGh6PeYN7xwivBXoSiRUrImg8FMEJ9xOGKJQ6D8idcE4Zfhx/jbxJ+y390DQFjC7A12st/djY/PFZlr2epuZpP3ErvdHWwJbaTen0W3c5iEySMuCcpNJa95G7kp/W7W5i6kcqSKLd5GhtQg1aaWFdnzKJRzN5k6OGKdtY78fQk7NnujJzP5fbnyyuv4kz/5G/7yL/+QOXMaue22D5DwFAlP0TJq+FmHj6OgcYz0jOSE6MKL+dUz9yMiZ/T7WDhmVzzsy4QWBqzIP+4qCk5AVAC6M4aikCJrFKOB/S0qCUNXWo6Zzoa0wjdWrF4b11OvhfUdYcQX5uc7lIQ0u4dti5aj7H1p1BcMitGcENOKeXmaQGzF5oHmLNVRzYu9PtUxTY2rGPZtRcTT8P6G0JTjjfuBHH3NBOjPCb6BuvjkMyDPU3SlYetA8GtJVgRhu7eDZreVkIQQhJ1eE77yKTSFRCRMoSlgeXYp+SdwCSuQfGb4lTR7LeRMltGuEdp3N3PtF9/N4twCqoKTlJtPghAhGv25zPFnTfyeT7d/nRy/ft+waUzjfyi0hrlzYe7cs3NDOh5qaxuoqKji5Zc3HENWZFeAuTsHh4x1AesR+EWAnIdtm+oSiCqcdachjoxiXaYWOqg0VmCfr5A+QUWU/fMOD1npII/4mJ/6sFzBkSuZBsaT16qr7aT68GGoq1N4NYrCPsWOHYo8z7pVdXQoSucKbyOAHWL9NkPKWhw72CpLAOSDWqCtRua1sfDKfKBOw4jAiEFeA9kQoG44eQ+yctVEpsy5gG2DOjX9kutaErNqFXiekEjIWNuVw5NK8e6b7Hs7HlEEwdQEehG7TW/vEedyhAD+dHHvvd8iFotx3XV3sGuXwvcBhIoKePe7zYSN9jhKSuDTnzbs2qXo7ratj4sWyTHvOxU8/fTj/OhH9/Ov//pNSksncw42hJ4hpZKsya0j6Y/So7tJk+TV0EtTEt8BIkQICBjUA697vPFKx1Z3Ew9E7+KQ04EAKTWKi0eGLBndhcZhVA2jcVicXT5xPIWiJqij1W2m2d1Ho7/omGOUmQpmBLXsd/eQ8CfD4g45HZQGFdSM6WTA2iR/JfZPHHT2ERKbLJ9RaXqdHpRyeCL0M1IqxWORH9sgOtVNk7ODfc4eiqSYuCSo8xsoD6rI6gxZssSxLmP1wazT/0BOESVhxeH0VB1UIPY7Gj9KB3HnnZ9nz55dfPGLn6KhYQ5r1qwDoGkoYNiXCaICNtOkatnb2PvDf2X37h00Nh57fV8P5RHF8iKHX3X5mDF9zFDOVlSuq3ZPWFUBa7FcE3eojCgGspNtYE91+fRmp97bR3JCzIH8kKY/KxQdUdQb9m1mS2nYGg9cVuHQNBww6kNYGbJiXditAYaiMGyJn6ugPq7ZORhwcDSgOqonqij5HgRRxdaBgO6MUH6Es9qchCYxVjkqG3s9FVgHtoa45uDosZo1R0HWnPvn1bnAgB6kw+0kIQnCEqLTOYwoGasyCoWmgH5ngO2hnZyfWXNcsqBQLMotJCpR2twOtq+34vqbz7uNytMMgTwenLH/pvH6mCYr05jG/wAopVizZh0vv/z8lNclI5gHc9AjSKN15pJqAxsCaDIwU6GqNPpKFxZNTu4kJbAtsOSjQMFiB5U44mY/R6MatE2an6VRIYUMC6pf4HpnMk2+RMN1LmpHgPSAVNtVPcmOvfciawEci8FVVwnf+55m925rz+x5ltyVldlWoiVLDJdcIsxSHsE/BLA5sGL3eQpKNHQbeNnADA1RhYxXRBTWzlgBeQp6gCzItgBuOPEyv6QEdhpkRFAlChr1MaGXp4sT6ZcOHrQhn0daW7e0KIyxFbgjUVQkbN9uqxXj+xwX1o8TkvJyoaVFTXGGGxqy75058/QmH77vc999/8XNN9/Bb/92nKamgM5O+5ktXHhiZ69wGJYtO7uJjojw13/9J6xdexG33vr+KT9rd5rJM9YRyDpWxRGEJtlBv+6b8t7kGNEoMSdukTwSfaqX+2Pfpk/1Mtu3QW49uotufRhXPIwy5FSOyqCaimAGM8zUXvTXW0V1cbkmfRN3xb9Gk7udhOQxzCgqiHD16HXETHwi9+L50DMccjpQShE3th3FkTgpNcqIGmGvu4shZ4CEyaPWNNARakXjEIwlpzrisMfdRY/u5orMNee0enIyrC112Tlk097LI4qcgZakYUZUs+go1yilFH/7t//Ovn27+e3f/iC/+MUr5OcX2IwP4ZjqScXC83G8EM899/QZkRWlFDfVhtDKCsw7UlYjck2VxzsqT176Kw0rMoGMBR1Opt4XhzQK2DccUBLWZIzQlxHOK/FYUqi592CWgyMBxWFNyjccHBUKQ4ofNGdpSGjWlrh8fE6If2/KMJiDqijURDUb+wI8ZQMbD6UMnoaikCIn4AfHEr+Yo+jPGkZ84cgIw/q45opKl8c7c+waDFDKrhWtKHJZUaS560COUV8m9pczQjqABfm/npPtYTVCDp98CZHDJ6mShMcqLGmVQaHID/Lo0/0MqiEK5fj2jR4ujf5cZvr1/PxX9zNr1lyWlS1/c09mGtNkZRrT+J+CNWsu4K//+n+TyWQIj6uYDxqkXaBOTThzqSKNnA8MCvqTYdQ8SzbGIYeMdQzbZ3vEBaDeR38whKobWz0OKfQdIczdWVSzQGAgomCdi77SPuxlSKxmJargnS7qYR92CqJtuBpLHPSlkxODCy8U8vMDXnhBc/iQsHS+Ye2FwvyFiiAQnA6BYYFShfO7YYKvjMXTz9C2StOrYb6yVZ8dAbTY5HkiTIZIgm0Zy2DHewJIi8F8N4scNJAWq4tZ4eB8MHx6Qv3j4Hj6pZqaY/VLnmcJiDFT805yuamhnwsXCoWFNtyxutoSlkQC8vKsVuTgQfu3MXDJJea0DR2eeOKnHDrUwfvf/zE8DxYvhsWL35zV1scf/wnbtm3mBz944pgJa5WpYZP38nhyHwA+PqWmHE88WpwDlJpy0irFId3B8twa5voLTum4O7zXOKw7JzQfYKshs/xG8iWPddm3odDM9efzePgRdnivUeAXTiRqtzmtVAYzqPNPXLlY6q/gzpHP84L3HC/nDjDaV0a0ay1PjSyhKz/LdTUepWFNlz40ZvbmEqgAV2xLiRXGZ0nrFCmTZLaZNyb4H6XcVNCju+lTPWgchtUASikuy1x9UiKVIUOLux+Doc6fRZQzT5ddUeTQW+3y1GGfPcMGV0FdTHNrXei4bVahUIh///dvc/nlq/jTP/08//qv36I2rvE0U/I/fCOk3ShzlpzHc8/9kt/6rd8+o/HFXcUdDWHeUWUYylmHriMzUk6ElcUuL/UGNCeFqoi9P7YnDXMSmrdXemwZCOhMGcJacW21yxUVHnHXEqSnDufoSQs9GSEbGIxoDmWEvSM+WwYMH5oZ4v8sj/HDliydacOQbzUmvRnD7kGfrGgcZbUpRSGIeoodgwFxVxFzFeURxUBOyPf0hHZmHEoprq7ymJ1waBoKyBqhIeGwqMBBK9gxaHip18fV4ChF0hcWFDisLjl2GikiDPtj6z8nqUK9kfDwUCgMBsEgSlCipgjtHRwrvlfBCbteR9UoB9xmDjtdPP3846y86HzSpM84t2UaZ4ZpsjKNafwPwerV68hkMmzduonVq8+3L/rYxuijAwxdhQqBqlBTiYoI5uEc7DbIXOsYJr6g9hjMgzn0Z0MTYYiqTqN/L2zfOyLocg2z7M+CR7PwdIAM2FYtNUej7vBsy1lS0DXa5oDEJo+tlBVmLyGHecaH/QbpVpgDDqrFEDQZSAkqTyGrNPpdHvJkYPNiNKgajfqMh3IUZmcAWwy87Ns2sCSQEBBlgzALFGrV8VcMJSeY72WRzWO2yKNjmpyDPkFI4X7yDO2sxnCq+qX584XiYqGtzYrtlbLtcMkkrFkz+eStqYFbbjE89JBi924ARTwOH/mIobwcdu9WhMOwYoWwatXr5/Acjbvv/k+WL189Ed73ZkFE+Kd/+kvWrbuECy542zE/X5e9hG3uZlqcg1QEleRUjjanmeXZ1azLXcL60NN068OECHNJ5gpuSN92jEbkREipJNZFa+qELyYxohKbkt8SljA9Thc7vW1ETISMSlMkJVyfvvV1J/tzgkbSvXN4dV+GChHKIpqsY4XNAznhU3PD1AUNRCSKJx4Z0igVQYnGJ8BBMSe3gJQewWDIqgxCQKkpJ0eOPt1Dv9OLQpHWaV7xnmeO33jcidhr7iYejnyPDrcNQagMZnB9+lZW5dae0jU7GloprqwKsabEpS0peBpmxjXhk4Sp1tY28Fd/9c987nMf4x3veCdXX3sz55W4PN/j42XAVTblfU6epuFtb+eub/4rvu/jHp1KehooDWtKT+NXelbC4fb6ED9rz9GWNGgFNTHNjTUhFhQ4vK3CZThnKy/RI6oe55W4rChyaB0N+OqeDGURdyLxXkTYM2J44lCOT80NM3thhH0jhsAIz3fn+O7BHCOBkO8KWQMdaZvbEneFpiHBUTYxPuooqqLqhIRQKcW8fId5x6mW3NEQYl6+w5Z+n5zA4jGicrRQv3XU8PihHHuGbHVmQb7DlVXeRJXpzUJpUEy+yWdAD5Jv8vCMR1qnUUBxUIxCMaqSRCVKnjl+/2lSpXgltJl+3U/mUJKOPa1c9fu38mp4C6szK08qzp/GucU0WZnGNP6HYPHi5UQiUV555flJslKnUaUaOSRQM2l1qQ4LzNdQctQDrUtsKv2MSWtj5SqkTluheZtA/REEI6JgqTOxVisjgvl6BvmRb62Ea7UNbHwtQNKC/t0w+mS2v5sMwXeybD6k2TDqcWhAcfX3sywsMBS+zYFqhQyA+mUAN2j0H4ZtBcUBGvSEpbAz30EuFcxXQF7wYQAYwjqSFYyFYK49waz9oLFal74xkhLH/t0HPJjD3OShK87uwXwq+qXycrjlFuHBBxW7dtnzCoVg3To5JrzxgguEuXOFpiabz9LQYDNalIJrrz3zKkhr60Gefvpx/vEfv3bG+zhTHFlVOR6W5lZye+pDPB7+CW1uC654LMot57b0+6gJ6rko83Z6nMNEJUaJKTutY1cF1bjikVSjE3kmgjCo+1mZmZpnMido5DMjf8AroRfo0K2UmnJW5s475WyS9d0+GSPMybPfxziKmCPsGTY0DQVcGL6U9aGnGdQDBGqUJKPkdA5HHJb7q/ncyB/xjcS/0u60EJMEDi5pUgzrIWLEqfHrGdZDJCTBs+FfUiJlXJ++ZcoY2nUrd8e+wbAeotqvQ6Fod1q5N/YtikdKmBnMOa3rdySKQnqKVuP1cNttH+Dxx3/CH/zBb7N69Tpur69iVsJhy4BPOoCF+Zq1pS77Lr2cf//nv+C1115l5crzXn/H5xCril0WFTi0jhqUgrq4nrD3dZSiMHT8e5ynFSO+1avMyTtyoUZRHra6kYGcUBTSLCpwyBnhkfYcCws0qcC6hSVcaIjD3hEh6mhWFyvaU1Z7Mu5w9o7K05/6RRzFhWUuF5adeNuutOFb+zN0JG1rnwGe67ap9Z+aG5niNPZGw8NjaW4Rr3nbGdJDOGgMhrCEccWlT/ejUMzPzT2uAyBAu9NBv9NPSVDM8+u3AHDeBRfSrXs47BymNqg54fEFYUSNYjAkJD6tTTlLTJOVaUzjfwg8z2PFijW89NJ67rzz8wCoPIW6ykV+kIOmAIkp1IhAiUJf7U60hk0gB+LDMQtKHtaaOHviia9khODbGXjIt+/NV9AiMGCQZRq1zxIhlk69qY+3KHmOYJ7M8VyHw/f7QuQCqPcMelTYnNLMOQy1+UCJQrIKXghQl7vWYew4UEUK/bEQwUwFzwc246VWo69xUevcE+tP0sBhY6tSJUwKQYrFuo1tDuCqN2cV8fzzLfHYtUuRTkNtrU2FP95CclmZ1fecS9x993+SSORx4423ndP9vh5er6oCVhdycfbtrMqtpV23EiZMTVA/UQ2JEJkQqg+qAV4MPcd2bwuueCzLreK87IUnbPVo9BexzF/Jy94GCk0RHiF6dDflppKLspdNeW+KFACXZa6cknVyzDkhDKoBAApkrGVMhPZkcEwrTXgs86M3K1zmL+azo3/IXdGvsSX0/7P33nF2VfX6/3utvfdp03vNJJPeOykktNBLIAhIBwGlKIJ67eUqehW9ioqNIogoVTpSpIaSQkjvyUwyydRM73Pa3nut3x/7ZJJJZlIgcH9+nScvXiHn7H7O2Xt91vN5nmc1XaKTZJXCKdGzuDn8FXJUHosil/KP0N9pkHtAC2qMKhQumW4BPbIbE5NxziQvR8R6n9Oi5/RhfdZbq2mWTYxzJva2iZW6I9hmbmKN9eHHKlaOFkIIfvGLP3HqqdP52tdu5NFH/8n8XJP5uX2/9KnTjiMrK4eXXnr2Uy9WwBvcj+qHodjV7bK0yWFnlyLNJ5iVZTIry8DcW8xIgSE8f5D9SSZHewyJuV+7Y8z13MLyg5IMS+BqT2fSEtNs7LDJ9AtKkw2GJXuMS4+jcTX0uODr57bY42gqE0L6YUnyqAMfV7c41IYVY1IlMnGc6ZZmR7diXbvDSbmfLhORqTKYF5tNs9GKTZy4iNMq2+mSXWS66Qxxiw/p6NUq2zC1iUSyeck6CkeWkJ2fSzMtdMguhgxgRtIhOtlmldFqtKLQpKhkRjkjPrJ72CAGi5VBDOI/CvPmncy99/6GaDRKICGAECcYGJkCtdJFNHn6FTnH9ATyByJXIPIEuk73tnQB0KA9m9+iQwzStyiv9coPJAnPyjgJz32swQsa0+377D27u+HttwWrVgnicZhc7LJgm+atdhNDwLAsTU6PItMHFRHYuVSTX6Uw8wVkCwhrz90raeAHrsiTmFf40Rd5+xdHMvNXKDwmRWkQ+51vFO/c4gOs9wkhPx/y8z99R56OjnYefvg+Lr/8OkKhpE9134djVfZHSCcxyh074PudooMHkn7PZnMDIR1CodhgrabM3Mq14Zv6DV+0sLg6/AWK/CWstJYRFzHmxOf3SXN3cVnsf433fG/RIdsJ6RBz4idwZvT8g4qgKmMXr/pfoNzaBsBoezznxC6g2B1KTsCzrN0ftvLspve24Ey1ZzLRnkq9rKNbdJGr8snUWYBXBOWrQk6Nns0eWcsp+gzW+D5kme9dpJCkqFSGuSNIUkm0ilZiso2w6CGo9xUrrbL5IHtVgcCvAzTLxsN+BscaWVnZ/PrX93PVVefz17/ey3XX3XLQMqZpsnDhxbzwwpN8//s/Q8pPtw2pP+zocnmoIk5zVJHuE7R0K8q6XBqjFhcUWwghGJEsKQxKqnoUpcneoN9Wmqao5sRcs0/hGjIhLyDZ2e2S4ZO9xU23o5DA3lB7UwpM6c0Rddka+2BjL1a1OLxcZ9MQVQi87Z5XZDE988iHiVVhTcCgt1DZu29DQH2kn51+CrCwKDjAucuLzzz8vd6nfSgUSilWv7aMWeeegE78sXT/1yVGjHW+DbQbHaS4yQgknbKT9b5N+GI+slTmMTmv/zQMFiuDGMR/EM477zP86lc/5r333uy1MBbCc/MyJh6ephY+gTzTQj0a95iYlAQT40u0ToUGfgDoOuUN8NOFx0CE8BSYfjzb5CESmWg7i8fh4Yclq1cL0tM1pglLVhr4dxrUxyUlBd7gPGxAPKbJcqA5LumOQPpOBbuAWYZnvXwEOJrEeZElYaIBSxJsjIUnyDc05Bre+f0H4P777yYej/PFL/7Xp77vhx66hxkz5gzIqhwNPvQtZYu5gVHOGKxEO0iP6GalbxnH2XOZYs/od71kncL50Ys5J7oIBwc//j4DoFf8z/NU6G+kqDRyVB7doosXA08TFmEuj3yud7lm2ciDoT9SY1SS5xaC0Kzwvc8eo4Zbu7/J3Ox0yroUdREv7TyuoCYxkB2/38y9iUmxKulzjD2imycDf2OdbxVh0Y2fAKPtcdwQ/hKOsPHrACk6lW3mJjZaTXSIdtJVBst873JObFFv60qeKsDFQaF6mSnPVSlKgRq4FeaTxIIFZ3HNNTfy059+l3POWUReXsFBy1x44WX89a/3sGLFEubOPfETPZ6o6020DKS50VrzVr1DQ8SlNFkSMARSSFpiiqVNDrOzTQqCgoAhuHCIjyd2xynr8goHAYxJlZxZ0LdwlkJwQq5JZY/Lrm7PZSziarpsTVHIIOx65O/e/TdEFWNSDLIPuN/t7nb5R1WcuPJ0Q3tNAf5RGSfHLxmSdGSFXrqPgwohrTWugpSP6ZR4LHGkmSYFKo9a6lj34Spa9zRx/IWn0CW68eMnd4DW0QajkXbZQaab0ftbsVQ6LbKVGqN2sFj5iPi/n2oYxCAG8alh9OjxjB49jpdeeuYjb0PMNJBf8MFsE5EqPBesz/sQJxym2EnkADBEgNDQkWgbC2voATHWgNHeLWnrVsGGDZ6NbmGhp88onQg7LQPVrYlHvWKlOwp1riRJQrqpsQKAD+gCnZrQzHwCkNdaMEJ4owitIRPIMhATJGLS//u9yW1trfz5z7/j2mtvIjf3021tqKur4f333+Lyy687JtvbYm7wBOr79a0n6WQcbHYZOw67volJgEDvAChOnGcCj/Pb5J9Rbm6jwiyn1qgiW+WSq/JY6VvmtWMlsMpaTrVRyRhnApk6i0yVzWhnPFXGLtb4VjA90+DCYpOAFFT2KJpjinHpBleW+vsItPvDy/7nWOJfjE/78Gk/HaKdt/z/4lX/ixwXP55W2cIH1vtUGruIESWkQ+SrAl4KPs0S3+Le7Uyzj6PYHUqZuYUO0UanaKfM3Eq+KmBmfM7RXvJjhm9/+yf4/QF+/vP/7vf9mTPnMGTIMJ599vFP7Biqw4qHK2L8ZGOE/9kU4emqGG3xg1mExqhmcUOcyh7FkkaHFc0O9RFFps8Ln6wJ71tnfJrBbWP9XD7Ux8IiH9eN8HPLqAA5AUnE0Wxoc1iZaLmali65cpifkpCk29ZYQnD+EB83jfKjEZR1utSEFds7FWmW4IwCqw/zAbCu3aXD1pSEBKYUWFIwNEnSGtesb98vgFRrqnoUmxKOZlr3ZXSnZZgEDUFtWOFqjaM0VWEvWHNSxkefG+8QnZSbO9libaPaqCH+KdHXeW4uo+wRfPDsYjKKssiaU4gUknH2GNJV/1bHEREF6GO+IRD4tEWn7PpUjvv/RQwyK4MYxH8YzjvvIv7859/1tTA+BLSjPTet/fQrYryBMYAWZCCI8RKdITwR+wQJuzW0aXAF4kQDebWvV7RfV8dBQYZCQPN4k6SIorZZEgy7mGHBhoBJpaWZa7kkOdqzHB7BJzoVI6YayC/50a856CYFVsL97CIf4j+AWbn//t/iOE4vq1JeDitXCvbsERQWambN0ow4Mv34UeOZZx7F7w+wcOFFh1/4CODTfs+6tB8czh2sQ7QTEWEyVXavSPdfgRd4Lvg4ERkmVWXg4rLd3IJCMdoZz05zO82ykTzlMQE1RjV+7e8zuDEwMLGoldVIIViQ72NmlqY+ovAbnruUcZhk9i7RySrfckxtssXaQLfwBkoODi8Hn+F7HT+l2BjKxuAaQjqJdJVJqTuCYncoVcYu3ve9zbz4yZiYZKtcrgvfwj8Dz1BhlqPRTLAnc0r8DHabO1kpl5GiU5lkTyNH9R+Wp9HsMnZQZez2ErztCWTpI8u1GQjp6Rl84xs/5Hvfu53rrruFyZOn93lfCMGFF17K3/52P//zP7895P2uMaoo61TYWjMkJBmeLA8a1B+IhojiLzs9QXmW38s2eX2PQ3VYc9NIf6/mw9WaJytjlHcpHAWmhKaYZ1E8M8szIDnQFTnbLzn5AKOOsk6PAamLKJSGZFMwJ9vkgmKLqRkGnbYmYHg2xVprCoKSVS0OzTFNcZa37NCkg+/brTGNT/bNqxFCYEloi3n34Pa44h+VNls7XSKuJtkUTMsw+MwQX2/RPCpFclGJj1frbHZ2ecVXbkByfrHFkNBHuyHXGLVs9m0lQjQxISDIVplMi08hpD+6dfaRQCAYHh3G+udXcNZnFzHDmUqWyiRJD9z2GtABQPdhIQHiwibZ/Qipt4MABouVQQziPw7nnfcZfv3r/+G9997k9NPPHXA5Xa1Qix3Y5nptXscZiJPMvuGPh4GOaNim0B1ecKJYZME/bS89PgvIl3C8gbzK10fQHgz2nyHSqQTF50ryOzS7Kkxvu66ktFSTO16DKeiMg6zRBIPyE7vBCSEQJ5roaQbUeMUKQ/c5pP2/jNbWFh544A987nO3kJOTx5o1gkcekXR2Qiik2bpVsmoVXH21YurUY6ul0Vrz5JN/45xzFpGSknpMtjnFmcEa3wq6RCcp2ttmk2wkpJMZ44zvd51O0cFLgWdYa60iLqLkqDxOjZ3NOHsSy3zvkq1yaVaNaDTJ2kugrzGqyHJzCOoQqfsF0GWoTOKi70yxRuPgkKH3tYykWoJU68gnCHpENxERptasokf0kKVyEHg5EzuNMv439Q5SdRoOLj7tp9gtYYg7zNuXSqPDaCMiwr3XZJg7glt7vkGTbEAnBmMPh+5lh1mGxktnzPMXcGX4BiY4U/oci43NU8FHWO57t9f2OUtlc3HkKo6z5x7xOfWHq676PA8/fC///d//xXPPvX1Q3s6FF17G7373C95553XOPHNhv9tY0ezwfE2c1pinBfJLz0r4kqG+Xhev/rCy9WBBeYZPU9bpsrHdZXa2dwcq63R5o95jKFztDbxc7RUsK5tdTskzGZVy6M+209Y8URmnIaoYliQxBbTFNW/X2+QFBCfkWmT6+xYbE9K8nJTDoSgo+KAZlNa956G0p20pDEm01jxdZbOq1aE4JBlienkt7zU6hEyvbW3vPuflmExMM9jd4yLwrJyTP+J9MSKibLXKcHHJVlm9399G2cwuczcT7CPLRfo4WLb0XVpbmrnm/M9T4g457PJ5bg6pKpVW2UaqSkEi6ZY9+LAodgs/8eP9fxWDxcogBvEfhjFjJjBy5BhefvnZAYsVXatQf45DrUJnCS/48FkbXamQn/cdkRBd13rBiexKhEdKvJT3z/sQTRpt44n4h4iDBhgTJmiyszWVlVBS4hUsra1e8bLgNJgyBbZsgbYKSH/VZlySS1QJlm8Q2M2a5Khgc8hi/ArB7Nn7Bsy6Q6M3ul4LWoZATDIQKUdRfLnay42p9kYcYoxEjDv8YEApBcsUVCrIE3CigdxvKtV1ob0dAgFI+nS16keN++77DVprvvSl/yIWg5dfFkSjnguZEF5BUVHhvT5+vMZ3FLa0h8OaNR9SUVHOz372u2O2zRnxOZQZW1nuf48aKgGvDezM2EJGOgcL85tFEw8k/Z5t5ibyVCEZKot6WcejoQc5M7KQLtlJgVNMkSih3NyKROLXflplC5XmLk6NndXrQgZei9VS/ztUGhUUuZ7mpMaoxIefqIjwuv8lhrhDGe2MPyr70wyVhaktmmUjuW5Bb5tat+zCETYdso0x8fG0yRYcYbPd2kKKTiVH5dEh2ylyS3ptmfdCIMhVXtvfQ8F7KDO3McoZi4WFQlFhlvNM8DFGdPXNalnhW8I7/tfIdfMp0aVoNNXGbp4K/p2hbmnvNj8KTNPkRz/6FZdffg4vvfQMCxde3Of9MWMmMH78ZJ599vF+i5WGiOL5mjgxF0Ynio5OW7OkyWFoknGQy9j+2NnlJdvvz8D4pEBpqI/ua+va2O7SHNMUBiXtcU2P492THOW5eS0a4utlYXoSLWHGAZbH2zq91qvhybLXOSzTL+hyFCtaHObnmAfdRw8HrTUb2l02d7jURhTVYZfRKSZpPmiIeAzT1AyDmohmS4dLUUj2CvwzfAJbKVa1upyWr/sI/1Mtz5K6OaaojypKzcMzgf2hVbYSFmEyVUbv99fAIKQD7DEaGGuP/sQtgV944R8MGzbiINZuIAQIMNWezBZrG+2yA40mSYcYZY8gW2UdfgOD6BeDxcogBvEfBiEE5513EQ899Cfi8Ti+fkaTapnjFSpjZW/7l87UiA0KtiqYcvADQmsNmxVqrYtuSSzXA0yQCJ9ARzVik0KnuYjrDu6b3h85OXDZZZqnnxbs2AFaC5KT4fTTFccd5wnuZ87UMBN0iSTyhEvduxp/FJJSoWq0wVrDYMMTkvR0lzFjQO9WqL/FoXrvIEKghzrIz/kQxYdvUdAxjXrCRq9wEXbifA2Qs0zEySaUioOtngHVqFDfi8ImF2ICTA2lEu4IIMcarF0rePNNQV2dwOfTTJ+uOftsTepHIA50j/Yyaxo9FzQxSSI+ZubL/mhpaeYvf/kT1113C1lZOezaBfX1goIC3evgLAQUFMCePYI9e2Do0ENv82jwj3/8jYKCYubNO/mYbdOHjysjNzDDns0ucwcGBqPt8YxwR/cR4raJVv4ZeIYl/rdYb60hy80mQ2eRo3JJdlPYaZSx3lpDQAfplp2McsbiCJs6o4Z20YoQktn2PC6JXN1nu8PdUVwavoYXA09TYZahAaVdwqKH5wJPEtABQiRxXOx4Lo9cd8TJ2X78zLKP533/23TKDpJ0MraI0S5asfCRqtPJVNnkuPnsMWqIEWW3sYuYiOLicmL81AEHgh2inS2+DeSp/F63NImkxCml0qygwixjvDO5d/mV1nJMbZGeYIoEghK3lO3mZraYG8iNfzzd00knncbpp5/LT37yHU4//bxep8O9uPDCy7jrrh/T3d1FcnJKn/e2drq0xjRjUiUC6IxrokrjaFjVah+yWEnzCWJdfdlDrT1nwVBCaO9ZFdt02Rqf1KT7JGk+QVxpOuJeZsqYVI+9WN7s8toem+aYV6wUhySLir0wxrDj8VfmAfeYgAEdce+9oy0H3mt0eLY6jqNhaEiwoxvWtzuMTJbMzrI4q9Aiyy9p7HCJuprCAzRSSaagNabpdvYVKz2O5umqOOvbXMKuxi9hVIoXlJkTOLp7kUrEyh8oiBcIL5V+oNj5Y4R4PM6rrz7P5z5381EVghkqnbmxWXSKLpRQpKgUrMHh9sfC4NUbxCD+AzFlykV0dPyML3zhLaZMOZc5czSzZ++XXl6u0Kl9B98iKMBR6DqF6K9YWeygnncQUe1lkGxwIVN47InPE7vrAhCbXWgxPXvhQ2DaNM3w4V6QoW1rSko0xcX7Yk16j2uOydoGg2WVmpJCTWeGpCNNUiIE27fDihWC0SMV6jkbqhV6lEQYAu1oRLlCPW8jv+Q77MNIr3BhqetZF/uAjR5TotYpWOogZhjIK3yIjL7bUf8bhVUu5AgoACICyhTqx1E2fzPEww8bxGIekxSLwWuvSVpaNDfeqPrNSxnw+JoV6i9xKNdeD50G/ZZEXm4hJh+b2cc//elXANx889dwXaishKYmT19UUrKPRXFdjw0zjuGkp+M4vPjiU1xzzY0Yx3LDeNqUic5UJjpT+33fxubvoT+zzrcSpTWGNonKKBvkGgwM8t1C0nUGnbKdKfEZvOt/A6kNxtmTyHCzqDEqOSl+Grf2fLNfJ6LZ9nwmOFOoMHZQZVRwT9KvaTNa8Wk/QR0kzy1giX8xpe4ITo6fccTndX7kEt72v0a5sRVbxPHrAPm6kD2yllSVRopOZbI9DR8+dppltMhGRjtjOT9+BnPjA7tnOTi4uPh1Xw2IgYHCxaGvBqhHduGn77LegBOiCUHyx8V///cvOOWUqTzwwO+59dZv9Hlv0aJLufPO7/PMM49x7bU39Xkvrrx7SlzB5g6XpqjG1hpHQcT1WJCkAYwMpmUYrGl1aYgqcv1eCGJNWJNhCSakG2xoc3h0d5zaiDeobopBt63ICwj8CTvh0mSJJQXbOhVPV3ntgEOTJK6Gyh7Fo7vj3DbGT35QYkn6HI/WXsEzO8s8rL7mQHTZnjuZJQXDEnqSkcmKbV2aISHB54ZbWIb3erZfkGwJ2uOarP1azdrjmjSfIMMnUHiTQP+qc1ja5FAUkhSbgojrMUtSxLl5lP+ojjNDpRPAT48Ik7xf+GpYRhhmlxxWU/Zx8e67b9DR0c6iRZce9boSSbpO4xOup/5jMFisDGIQ/2HYtg3eeGMyKSmT2LLlzyQlncv27YLGRli0KHFnTRZeGv1+0DqRgdKPw5Zu1ejXnIR2w4AW7SXaRzR6p0LMkN6IwAe6E8/q98BtVCvUageaQOQLxAyDtELJrFmHv9s3I9mWKRGlfZcNBjUNDd656F0Kir1CBUCYAl0kETsU1GsoOEyxssZFW16Qpl7vQq2GTAGdeFOaqxXKbyNvsHoLH1WrYKWCVGBvu1kIyAVdoVj9KITDmtGj9+0nKUmzcaOgvBzGHUVLtvqX4+mDRkuE5c3UUqFRz9jIkfKQttJHgvLybYmB4DcJBrN56CHPWrq+HnbsgKoqweTJmowMqKvzWsAKj2GL9vr1q+noaOeMM/rXHYCXcbDGt4LN5gZcHCY4U5gen02I0Mfad5m5hS3WBkqdkXSJTnazk2SVQqdsp8rYRb5bSLfoIt8t5MLoZQgka30f0iI87cvC6CVcHL2iT6HSLBvZZexEIhnljCVVpzHBmcxfQ/dQZ1STr4qwtI+ICLPb3EmOm8dK3/KjKlaSSOYLPbdxj/UAlXYbXfEkor5mtD+LUjEKiSRAkCn2DLSGk+Onc2X4hsNerwydyTBnOButdaQ5+1p06o1aslUuQ93SPsuPsSfwauB5CijuFR33iG4s7aPoCHQA/SHqarZ1unTbkOUXjCodxeWXX8ef//w7Pv/5L/dhV4qKhnDuuRfy5z/fzVVXfb5PsVsU8vQf61odGuOaVFOQIqA+Cu0xxcu1cT47tH9h/qR0g3MLTd5ucHpthnP8kguGWGT7BX+rsAm7mpmZBmEb9kQVUQW1UU2OHzJ9cOEQj2Ve3eoQcTWjE3bUFjA8WVLWpdjY7jI/x2RymsnKVod0n8AnoSWuSbNEH/anrNNlRYuXGp8fkByXZTIuVR40GVMbUbTEPf3LXggpKQ5p2m1NSxzyE/r1nIDkuCyDt+odbKVITmhWoq7m9CExygO7aDAaiNoGb7aPJN2f3KuvCpkeQ1Te5VIdVn0E/pU9Lh82e4YEOQHBjMy+x5qskxhul7LdKqdFtGJgYGOT5qYy3On7HTsW0Gg6ZAc9IoxP+3jyHw8zduwExoyZcMz3NYijw2CxMohB/AdBa3jzTUlnp2DOnNt4440bOeOMchxnFO+9J5kzxyU/H+RxBmqLQrdqyMALQaz2ku0ZJdArXdQWF5Sn20ACbcDIxAMxGS/0sQdoVRCXXp5Kk0bkij6sio5o9PM26uk4RIAs6RUFy1zk5yzEmMPPomdmAujeGf2WFmhqEtTWQn6+Ih4Gw+HgO54J2sVjggZAayuEw5DdqTEtPKvlBuUVdH48G2afQGcLjzWqN/cVPs3Ks2dOPaBQCIJugvZqTcYBbVJJSWDb3vGPG3dk03K6W8NGF50rekX+Qgh0CVChYaeCj2GprLXmu9+9jaKiEm699Zu8/bZg6VJBUZHmhBNg7VqvaGlvF4wa5VlOX3ih4ljm8C1dupjk5BSmTOk/9yROnEdCD7DC9z4CiUCwyvcBG6w1XNfzxT6J7EeLFtmMi0NQh/BpPxkqkxbZhKV9tIs29shaYiLGvPgpJOlkLo5cyWmxs2mXraSrTPJVYe+AXqN50/8Krwdeok20IBDkqDwujFxOuspgt7mToE4iqL2CIUWn0iHaaJZNvY5ee7fTKOtxcMhTBQPOMoeaZuNrDOJP/QBCDYTCJ9DdmUV5wRZ8RY1IbdBg7GGYKuXc6IVHVNhJJGfFzqfOqGGruYlknUxURPDrAGdGzydNpwNeeOXWDpeuntOIB+KszNxISWoUx4jRLbqYHZ/PWGfiUX8e1WHFY7tiVIY9VyxLwLg0gytuuJ1HHnmAZ555lCuvvKHPOjfd9FXOO28+r7/+EmeffUHv62NSJCNTDNa2ufilx7B0up4b17Bkwdo2lzMKFOkH2nXh5ZycWehjWqbJ7h6vdWtkikGaJajq8fQa+QGJ3xBMywKrDZrjiqgLhUHBGQU+TkykujfF9EFW1HtZiC5HY0rB5cN8FIQEK1tc4kozJd3klDyTkQlx/ppWh8d2x+hxvBat3d0O69tcLh3q6xX774UlwBReLoq536k5WmMIsA443YVFPkKGYEWzS7utSbcEc4e4mMWb2SnbCeoA3Y6kWXejfVFyyettIwyaEI1A93732W2dLg9XxGiNaZIswY4uzdpWl4tKfMzL2XesI53hpKgU9hj1xGSUDDeDYrfwkI5cHwVxbDZZW6g3G7Cxaa1s4tVXX+SHP/vlMd3PID4a/u2KlT/+8Y/88pe/pL6+nilTpvD73/+eWbNmDbj8U089xQ9+8AN2797NqFGj+MUvfsE555wDgG3bfP/73+eVV16hoqKCtLQ0TjvtNH7+859TuN+U4LBhw6isrOyz3TvvvJNvf/vbn8xJDmIQnxC6u6GyUpCdrUlNvZylS7/HmjW/Z8GC37F9u6a6Wnhp6MclXK6WuIgG7bEiOQJxgYl+20Uvc0F7OcAsc6FAoJUGnRgo+wW6VMA6DV0a3awRYQ0+kKebvSGMulujHoyjn7a9QiUJiCkYKdAtLuol4bECAwSt7cXkyZqhQwVlZZ7Yu7pa0N0NpgmbNgn+lmJwdYbAbNAwdF8LBfUaUSg80fsB6OyEF18UrFsnicVgXqvJyV022cFEm5tPQ6uGLqBJQVCgo0DPfgVGqYQ0CR2J4mYv2jUySSCGCHp6PI3OXtg2SKlJPhqXSxevoDzQ+GBvfXKIYuxI8Oyzj7F06Ts89thLdHcHeeQRSW2tp0vJyYFJkzQlJR67csIJmosuUmRkfLx9HoglS95hzpwTMAfojdtgreFD31KK3BKStHfxIkRYa33IFN8M5sVP/sj7TtVpCCRx4vjwMdmezmZzA1XmLgI6iERybvQz+LSfXyf/D42yniyVw/z4KYxxJvRhVDaZ63gh+A/8KsBodzwaTY1RyaOhB5kSn4GjXXz4iRHrbZ3yaT8tsomRzhgAamQVLwT/wQ5zGy4uRW4JZ0cvYLLTVwTsas3re2zM7tGc7o5FtHvHURHtYU/lMFpzHqQnWEmeKuC06DnkqyOnwkY74/liz3+xwlpKpVmB0AJb2Lzpf4VVvuVMj86nqnwOq5rB0SkY4nyi1bOoKVrJuNIyznEWcUL81F7Ny5HCUZ4mYlciGNMnBWFHs67NJTN/KGeddT733vsbLr/8uj6p9dOnz2L27Pnce+9v+hQrphSclmeytMnG1aC0oCgkKAkZGAIaY4ouxws8HAi5AUnuAXoMKUAi2KuQy/ZLjs8VVIcV7THNzaMCzM02elmEdAtWRxTpJp7+Qwgc5bHZWYlCKdkSnFfk4/R8T1MTMrxJCaUUT1XF+WN5nLa4l2syOkUyPs2gKqx5bY/N5HSjTzE0NEkyJCTZ0OGS4xMETUGSCfURzZxsk8wD7iUBQ3BukY9T8jyNSqolqPHvYpPRTpab6RlJWIIsn6Ql5pAf7CEt4STXFveWz03c912tea3OpsNOaIUS16AmrHh9j82U9H0OYgJBvsolX+UedN211uzoUmzvcnGUd07j04wBgzkPhZ1WBVVWNSkqhVSdwov3PkIoLcSoKycfZEM8iE8f/1ZX/8knn+RrX/saP/zhD1mzZg1TpkzhzDPPpLGxsd/lly1bxuWXX84NN9zA2rVrWbRoEYsWLWLTpk0AhMNh1qxZww9+8APWrFnDs88+y/bt2zn//PMP2taPf/xj9uzZ0/vfl7/85U/0XAcxiCOBbtXo7S669uCArv5gmmBZingUTDPA5Mk3sXnzX+nubkNK8PsT27BBnGUiv+pDXOtD3mAhv+H3QhaXu+h8AaMNxGiJHirQNYlHcpXedxzFAnIFjDIQAWCixLjeB7P3zfDr5S76AxekhqIE42LhsQEpEl2ZaNE6DNLS4LrrFEOGaCoqJEoJSkrgpJO8VqoV6wVbii3vjrfdRdcoxHaF8IM8yzrI3UwpePxxydtvSyxLk5Oj2ZRisq5d0rIJiAM7NdTiFQmNCj5wPUvmzH3bkqkSsdAEV3jFX4eGPQq6BMyTTFsoicc93YdSEI1CRYV37GPHHkWzcyowXCIaDvge1CeYsaEf/Vbf3t7GHXd8i4ULL+a4487gwQclFRXee1p7upUtWwT5+VBYqJkwQQ9YqLgulJXBe+8JVq70CsojQTQaZdWqZcyff8qAy+wwt6FwewsVgCBBJAbbzE1Herr9Yow9gVJnJBVmGT2im6AOUeIOY7Qzjpt6bufbXT8hXaXzSOjPVBjlSAyqjd38PfRn3vC/3Gdba6wPiROjQBUh9/vzobWUJ0MPU2tV0iKb2G3uoNLYRYtsolE2kOlmc0rsTDpFBw+F/sQa60OSVQqZKptd5g7+lnQ/O40yABSKD61l/DLwC/5Z+k0aRt9Pc/LW3mMY6g8hoznEe7IxtEmHaOOZ4GM8FnzoqAL3StxSLolexYWRy2g2Gik3txERYXYZO7gn/BbPtO0iLwBjUg2mpiQzwxhBbtVnuaj2J5wZO/8gt7Ejwe4exe5ul5LQPqeskCnI9gvWt7lc84WvsXNnGa+//tJB695881dZuXIZq1Z90Of1wpDHroxNNTgpz2R8mulpNGxNmiUPGrgfCQqCXrBiXVihEr/JqKOpCysCJkSV124VV5rnq+OsanGp7FG8Xu/wQbNDQ0Sxo8tr05qY3pcV9RuCJHOfi+LDu2x+uy1GY9QrblpjmhUtLqtaXfIDguaYoi7SN6gy6npyirqwYkmTy5v1Dm/XuxSFBOcVWQNq+EKmIDcgCRiCXfEuttdms6Iqnc0NIWwlmFYQxtWCXT0OHXFNbVjREtPMyjJ7BfYtMU11WJEX6NuetvdYq8Oq333vD601r9bZ/Kk8yj9rbF7dY/PAzhh/3xUn6h6dUCSOTa1RR1AFCGg/4c4eFv/9FU6/7gJ6kiO0yfaj2t4gjj3+rZiVX//613zhC1/guuuuA+Dee+/l5Zdf5i9/+Uu/LMfdd9/NWWedxTe+4YntfvKTn/DGG2/whz/8gXvvvZe0tDTeeOONPuv84Q9/YNasWVRVVVFSUtL7ekpKCvn5n25S8yAGMRC0rdGvOuglDrrTC0IU4yTyEguR2f/AVCtNYL3LtAbBK5sMUvM1U4puYqX+BUuWPMBJJ32DkZkK93EH1nstXowyPCZkmLdN920XbI1I2a/POSjQfuG1d0U1bFVokQgYm2MgPmchsg9mR3STQr1pe21U+z+LQ0ALHmtxYPvUITBkCMyapSkv14wY4TETeydWfT54L2oy7UZQK1zEHgVTDeRcAzH64PaoXbtg40bBkCGalIR5UGik5F3HT9hxOFcqjAYN6UAW4AiPvbBBtCQS7fden5ssr93sJccLwUyWcIFEfsnP8QFNayu8/76krExjWYKRIzWXXaYIHYXMQgiBPN1C1WjEVoVOFp7RgSUQZ5uIzKMfbO3Fz3/+A6LRCHfc8SvWr/fc2UpKoKFBkJSkCYW8trvycsjOFhQV9T/QCIfhsccka9cKYjEQQlNUJLjiCtVHs9MfVq/+gGg0eshiRWL06w6k0Uj98R51QYJcHf4C/wj+nQqrjDhx0lUGV4Sv58zYQuLEedv/GhYWQ9xhaDR+4WenUcbDwfvwaR+TnGlkq1w6ZBu+/YTpTbKBTdZ64iKGT/mxsekSnVjaRAlFWPSQ7xZwU89XKVYlLPEtptLcxShnbG/r1whnNNvNzXzge58RkdG84X+Z5wNPYitwDT916UvpTN3EtLobyO+aTpvuos7azSjpMMmZikDQIdp4z/8Gw9zhzI8PfJ37u76L/a/RJBsYux+LVN88ghbZhOvLgkRbWJpPUB+F8i7FpPT9tqE1EdcLRjzQ7epARF2vVct/wM82YHiWw+Omzua4447nnnt+zVln9Z14PP30cxk+fBT33fcbZs58svf1TL9kTpbJq3tsHO1pMjptTdjVnJ5vDiiwPxQMITi/2KJjl6asU9HlaKp7FEJ4YY5P7o6zvEkyNEmwpMkl0yc4PsdgW6eiKqyJKpdzCy3OK/bRGFW8UOOwq9tLvJ+VbTIj08AQgo644tnqOAhINsEQ3jXssr28l9IkmWjr6nsO/9pjU96lmJ1tEnE03Y7HgKQlCr/DYUuHy7NVWdTEYvjxIYD8lDinjWxj7ohOmupKCUc8RuWMApOT8/YxaKbwmCdX9/Uwc738YY7kcu/qUbxZ7xA0BCUJ3U3Y0axqdRidKnvb644EjrCxhYNfefTZ239/CTtuc/YNF+HiYgv7iLc1iE8G/zbFSjweZ/Xq1XznO9/pfU1KyWmnncby5cv7XWf58uV87Wtf6/PamWeeyfPPPz/gfjo6OhBCkJ6e3uf1n//85/zkJz+hpKSEK664gq9+9asDtiPEYjFisX0K4s7OzsOc3SAGcXTQ7zrolxxvDDBMQhj4UKFsG3mLr9+2Kf22g37W4VQD9uT42FwrcWpyKci8nKqqP/KZc27D/4QLWzQ6BzAFYoWDqlTIL/oQhfLQ3phFEnmeid6koEsh8iRiysE5JjqiUc/G0W+7nmOYxmNTHAU5cm9YB7Rqz8Uq/8gHClp7WSWpqZDcpchqcREausMS15WI8QbG+MNrN1pbvXayvYXKXsg8ybKYn5PGOKT58IwCol6xyGgBnQr1moNs0TBEQqFAmhJu8qOutaAeyPQYF/AezIsWaebNc6mrEwQCiuHDwTq6zhgAxEiJ/KIPtdxBVHo5MsZMAyZ9dFZlzZoP+fvf/8wdd9xFfn4hS5Z4NtIjR2o6O6GlRRAIeC5mdXWCc89VDBlAL/3224Jly/YVgI7jFYWPPy75xjcOXZwtXbqYzMxsxo4dWN8w1p7AYt9rdIg20rRH7XQJ7947YT8b3Y+KYlXC7T3fptKoICoiFLjFvaGNzUYDLbKJPFVAjBhbrA3sNLfTJttwsKkzqplmz+aC6CUMdUewzlrd21pSbVRiEyeoQ3TJDtJVBkERokO0karTMbTBKGcc58QWAdAqm9HoPhoVgSBJJ1NrVLPKWs4fk35Fh2wnW+WSFiimo3U0sfQKdmS9SlbnZLZHujAyahmTFEp4ckGazqBFN7Pa+qBPsdIlOtlibiQsu8l18xnjTOiz7xgxdpjbyVI5uIk2UFNAUCXjCIdO2UGam77fsYKrvH1qrdnUoXinwaYu4hUJs7IMTsy1BmzlKQwK0n2C5pgmdz+zj+aYYkjIY0FuueVrXH/9xaxa9QEzZ87pXUZKyc03f5VvfetL7N69k2HDRvS+d3aRhd+A5c0unbY3yD6n0DyqQe+BKE02uHW0n1WtDk/sjlMQkkxJk4Qsias12zoUa1q97JS9rENuQNIY1XTZmrMKLDrimocrYgmWR9AUU2zrVDRHTc4p8rGtU9EWV2T5oN2GTht82kukjziwvVNxQq5J0X4GG122Zk2rQ7ZfkO1P6AnxnMZ2hxVVPYphyQPfJ6Ou5vmaOHYsRFFaO6aII12L2i4/S2p9nDK6jZmpIXzxAEFDHPRZZvgEY1INVjQ7pJieHkdpj20ZEpIMTR5gwi3xRyAo71L0uJri0L5lg0ldpKU0slq6DDOyyHdz8XH4oKeADpCskuiQnZhxg5fve5r5F51GoCAJF5ck5TGAXaKLOqOeLtlFSIcocPPJUOmH3f4gPj7+bYqV5uZmXNclLy+vz+t5eXls27at33Xq6+v7Xb6+vr7f5aPRKN/61re4/PLLSd0v5OC2225j+vTpZGZmsmzZMr7zne+wZ88efv3rX/e7nTvvvJM77rjjaE5vEIM4Yui4Ri910Unsy9BIA20C25QXwjhy38NBd2r0Mgf1oNfekTrW4MaRDtubJfW74VTji3z93YfZsfhpxpUtQo/eJ9LWGRI2KNz7YogkAa0a3a6hRSGypBeSWJsQnKcYkCuQZxz6tqKejqMftD3huYvnpmXi6VW08hidGDDZQC40+y+8bA2rXNRq19OIjJXIuSbDh0v8Pk3OpjhT6hz8UY3SkBcRpA0z0K7pMSC7FdjAEIlIO3j7aWkan08SDus+g+jubsjK8gSh5HuBlsTxrIxbNKwF3e6g1rueRmW+gbzAQpgCGZAwrP9rkpMDOTkf3+NSFEmMi49NCqPjOHzrW19i4sSpfO5zNwP7AiszM2HGDC+0s7XVY66OP15z8cX6IGtp8HQ4H34oSE/fVwCaJgwb5hUsZWXikGn3K1cuZ86c+X00CAdigjOFE+On8Z7vLfaIWm8fmMyLncxUe+ZHugb7Y6+2ZJe5A40moIOku54TVkAH8eEnQphKaxc7zG10iy4MLVFC0ma0soX1OMLmyvD1DHGHss3aRI6bT4PcQ0SEyVa5hEWYdJ2BqSxMaTLcHUWJW0qnaGeTuY5kknFw6BKdNMg9pKsM/InclbDowcXlT0l3UWNWkulm0yqbsdMbMfUYnO58qs1qDLsCldxO5si3MWVJn3O08NEjenr/XW5s49HQg9QaVQAYmEy2p3N1+Au9yfYmJrbtY0esA7vLm4HO8UuCKQ3QNBLXMXsnOXoc7/sxIiEI39Du8rddcWKup7Noi2ueqbZpiWsuLenfTjzTLzkhx+TlOpuwq0gyoMPW+KVgQb6FKQVnnHEeI0aM5oEHft+nWAG46KIr+cUvfsj99/+On/3s7t7XfVJwVqGPk3I1XQlNRuAjaB/6O97SJIOQKRidKnsH7oYQZPgF5V2a0fsxyFII8gLeOe1Nie+0NaNT9rVMNUYV7zY6HJdlkmKBKQRRV+Mqj3WKuPscc3MCgkVDrD6hjBFXE1OQfkCyfNCAmAs9fZ2nD8LuHkVdWDFUOrTU9dBiN2LHIsS6HTZtchi9TePE3sKx44RCyaSmppGamkZ6egbZ2XkEAgHOLbRojip2dntMrAbyA5ILh/h62/sijkYBQUPTYDZRZVTRKbtJ0kG6AwVAVu810am1qKxykojhGoK1vhqyVRbT41MI6kOba0gkpc5Q1vs28tZLr9BS28gpXzyXsAgz3CklRSfTIltZ69tAt+jGxMTBodqsYXJ8IgXuYNfNJ41/m2Llk4Zt23z2s59Fa80999zT57392ZnJkyfj8/m46aabuPPOO/H7D7Y0/M53vtNnnc7OToYMNN04iEEcLXqATt1XsA0QAhHD00UkoBuVJ2Bfr2CXJwKnw8EcbzBxCExI0dA4luePO4U/PnUX505eiNx/Wj8O1CmoAj3VAEdDN7DKRee4UJM4nnSBfsdBNWvkVQdnjeyF6lDoZxyIaG+wD1CrPCexHiAHEBKO81gCkd9Pnovy7Hh5x0VLwAKx3UWtU6Sc62NahmLoSocOAzqCEteFkjzN6BoH/aJGb1GexsYFkSURZxiIk/umP48Y4WlG1q0TFBdrgkFvUB6JwPz5Gp8y0FtdtCO8NriIhtXKK4TGCXSuhFYQb7jofImY9+93q73nnl+zZcsGXn55aS+LPGGC5s03oboaiopg6lRNQ4N3XS69VA2YVm/bnh7nwPdNc59W51DYsWM7V1xx/SGXMTD4bORqJtpTKDO3olGMdMcywZ5y1CLuA6HRvOJ/njcCL/eyNUk6iZNjZ3BB9LNkq1wmxqfypv8V9hg1CDyxeZw4Ekm36GanUUabaGWmOZvPh2/lDd8rbLM2kaxT8Cs/4+3JrPWtRAMuLiYm2SoHhWKnWcZ9yb+lR3RRa9TQJlsoN7eRrbIZ7ozGxAAEDcYeIiJCqkonpEOY2qJHdKMydpPrt6jWjbhjn0BnbKE2tJkVTinTndlY2sLFpUt0cLJzOhpNubGNu5N/RrNsZpI9jQABwqKHldZy8vwFXBS9AoCuuKS9cRZVOU+SJdOw3CR29TiQtJS8jEx6GmdQIV1UIv7nuCyTiekGSmveaXCIubq3eAFoiytWtbjMz9EUD2C3fWahRbpPsqzZpj2umZBmcEKuxcS0BGMpJZdd9rl+gyCDwSDXX/9Ffve7n/PFL36N4uK+dnxBUxzkyvVx4eINxg/scAtIz3Wry9bk7pdj2WF7bl6W9AT5OQdoO7L9gh1ditqIYnKaZHiSZEWriym99Hhbec5bQQNmZ8k+dsEA6T5Bpl/QFNW9QnbwtCT7C+HB6xSpqqqgomIHFRXlVFSUs7GsjLId5UTb+p/4XX+Y65GWlk5ubj75hSVkDZ9E9siJjBk3hdNHjiE3ZNAUVbzVYLOxzUUBuWldZJVsJSM5gl/7aJVtRHNaSWsfRmd3KSnBKCpzJxpNpCuDYWkGGQqaZBO7zErG22MPc0RQ5BZCDH7xx28x7sQpjJo4jmK7iFJnKBrNdqucsAiTrbISGUGadtnBNrOcbDd7MPTxE8a/zdXNzs7GMAwaGhr6vN7Q0DCgliQ/P/+Ilt9bqFRWVvL222/3YVX6w+zZs3Ech927dzNmzJiD3vf7/f0WMYMYxDFBMl6+R4OG9P2efl2gg4n3ElBvOlCuYISAZgEBIAqUKXSOgBgIC77+pR+w6HMLeDrjcS4Ze/W+B2OthnYNoyWixBsI6FwFW5RXyAQ0TJKIIRIdB9YqVKqNce0Ao9ZK5YnR08S+u0+xhICCVmCmifEZH0w5hAPYLu25kWWL3qIoHtbUvaNZvkYRdwVBW1OfJMnNFeTlaQoLBWa5Rv3N9tioEgkG6AaNfsbByJKwX3CiYcCVVyosS7Jtm5dBk5qqWbhQc+KJGhEx0JsNxEYXbQnvnNo1TBCIva1sWaA7NHzowqdQrGzdCsuXC/bs8RLl587VR5XTAp5Y/oMPBEuXLueZZ37IJZd8kylT9rESJSVw8cWK55/3tCtaQ1qaYOFCxaRJA283GPSS7NevF2Rl7WNf2tshKUlQWDiwoLarq5OGhj2MGDHqsMdvYDDJmcYkZ9qRnvIRYbu5mVcDL5CkkyhyvcyFNtnCG/6XGe6OYqo9k0XRS9llllNmbSEqIkSJYmIikbi4KOHSYOzhz0l/YII9lc9FbiYajbDF2Mjfku4nKqKEVBItshlTGGSoTLLcXN7xv05cxEhyU6iydhMTEfzaj0/76ZJdrPAvJdPNJEvlsNlaT4abSUAH6ZDtZKgsQjqJRllPZ9KHpOg0ploKS09mhdNJubmduIgzwhlFs2zy2BKt+XXST1nnW8lmcwPJOpmYiDDRnka6ziBLZbPa9wHnRC8kSJC1rS665hTGhKppSF2DI2xA43TlsTBtGBOSktjc4SKBCekG0zMMfNLThNRFFJkH6CPSLUFjVNEQVX1afPp8zkJwfI7J3GwDR3ttZweyMBdc8Fl++tPv8q9/vcDFF1/V570bb7ydhx++jzvv/AF//OPfjtn3ZCCUhDy3sLqIZmhSggnQmua4ZlKaQVR51yLdEvQ4muaY5oRck5KQSNgL99V2ONorfCwpkFJy9Qgf6zsihB2vmxYgyw+T0022d2k6Ei1ke+GTglNyLZ6ojLO72yXNJ+hxPM3H6bmSuu1reeK9t3j//bdYuXJZb1t7UlIyw4ePoqR0FMao4wkUjGBIQT6mL4jh81Pn+BidlcQXxqYSCoYwTYtwuJvOzg66ujppa2ulsXEPDQ31NDbWU1lZwYev/YO6h+4CIBgMMWbsJFTxBFTRREZOnUdaySjeb4mRGs3j4jE9JAUdknQShtnFsOI6tmwsoDPQQipRol3pZPslxSHPNDmgg+wx6hlrjz6sm5dAsPOdrexYs5V7/v4oJ0SP72137BbddMgOklVSryZLIEhRKXTKTjpkB9kq61h8VQYxAP5tihWfz8eMGTN46623WLRoEQBKKd566y1uvfXWfteZO3cub731Fl/5yld6X3vjjTeYO3du77/3Firl5eUsXryYrKzDf+HWrVuHlJLc3IOt9AYxiE8awhKIE0x43PYYgkwBYY1o1DDHhGGJh2FEwwYXneMN6nWW8Nyh0vCYmT0KERFwvMFxp8/j/DMu4Rfv/4RzSheRVJriCSp2uWCCGLKfoD5ZomWiWJlr9mpShAk6H28A36p6hf7a1tCswS88a10p6BNwbQBJ3mvyfAsx/dCaEl2lEBGNLkk8fDVsLRN0dMEw6dJSJAm0eBakSUmaocMSi3V7BYU+XnjPfeW1TVHmoj50MQ5Iec/KgptvVtTWQk8P5Oayz+UqRSA/70OvdqFcoXcAUiEmGn2nTwP0Ybo+KXz4oeCxxyQ9PZCcrNm9W7JhA1x+uWL27CPbf3k5PPigpLa2jeXLryQ9fTbR6B28/rrgzDP3bWPuXM3YsZrycoFSUFqqOKDb9iAIAaeeqti1y6CsTJCRoYlGPdH9ggUD61wAKirKARgx4uCJoU8Lm80NRESYYe7w3tcyVBaNZgMbzLVMtWeSqbO4pee/qDNq2GFsJ2yGe9tFJBKNxsWh1qjku2lf5jPRK7gkfDUznNk4EZvX/P+kU7bTQRtCS1J1OlusDcRFnMnx6TgyTlh2k+8W0SO6kUhG2mP4wLcEEAx3RlFjVlFl7iakkkjRKbTJFlxcukUXyTqFmfG5vfkts+x5BAnSLJuIixgKTZfo5C9Jf6LOqCVDZRLUQdJVJq2ylY3WWubGT8Sv/cRkjLiIEdRBKsOKkE5mQu0tNLdtpStQi+WG6K4fTzA1h3nDzT65GXvhl54oPnKAc1NceW1NwSNowRJCYA2wWHFxCbNnz+e55548qFhJSkrmm9+8g69//SZuuOFWpk8fOP7gWCBkCs4ptPhHZZztnS5+w2vbyvVLrhhmUZNw5GqKKYKG4LR8k3OLfCSZgqkZBm/UO6RYmoAhaIsr1re5WELwYbPtCdsDkumZBq7S9LieBfHIZAOF5wzW4/QtVgDmZhsYwsf7TTa7qippW7+Y8OZ3+P7Kd2lvbyUUSuL440/iu9/9KRMnTmX48FHk5ub3FoVLmxyeqYoTcTXCEHS5mqF+yaWlPvJT991LQ6EQ2dmHHiu1tDSzZcsGNm9ezzur17F2/TK6X36YTcolrXAYWfPm0THpDLakjmPecM+HPUmHKErtYPxIm21hg4gFI9INCoMG/sTjSiBQCZ3L4dChOvjOT25j1JwJ+M5NZ5VayyhnBFkqE++BITxn/j6b0olHiSJKFB++QYvjTwj/NsUKeO1Y1157LTNnzmTWrFn89re/paenp9cd7JprrqGoqIg777wTgNtvv52TTjqJu+66i3PPPZcnnniCVatWcf/99wNeoXLxxRezZs0aXnrpJVzX7dWzZGZm4vP5WL58OStWrOCUU04hJSWF5cuX89WvfpWrrrqKjGMdJDCIQRwhxPxES9Y7LrpJI/zAqSZy4QGWk3sn5IRAjDfQtgstKpENAsyRyEVeC9T3/+dnnDBvIvdW3c3X1fdAgzaAQulZEO+Pvbkegb4vEwDdDUQSOSarFOoNG92gERYwRnqWxrs0GNpbP47XBjZUIMYdQXChmTivxLn19HjOVNkBjRuE7gyJ5YdUS1NXJxgxQuO3tNdmFtBQoaBGg6PRWRKCIJr6n9kXAoqLB/gMkgTiRBNOBL3RRf0pjo6BSLRHa60RHRqmHab4StiaDmQVejjEYvDaawLb1uwjejW7dnmvT5miCRz4OR10DPDaa5LmZti163qU6uHiix+lu9vkzTdh5kzN/vM4GRme89rRYOxYuPFGl3fekezaBXl5XuEzf37/Ope92LlzOwDDhx+eWTkWsLHZY9QgtUGh8hLXYyKGTMyoxoiy26xgj6yhTbZiaYszYueSrwopVMWcFjuXhtAeDG0QlVEMLXFwUSgsfIR0ElER5T3fWzjYfLHn68yJn8DU+HHUG7V0iHb2GDW0yBaiIsJ7vjcZooax0/RsiQUCP37CIky7bAMgRIhd1g6iRIiIMGGjBxSMcsbSJToZrkYhEGTqfR+ihcUoZyxZMpcUnUpMxBnmDGe5/z1SdDJh0Y0jXOIiRrrKoEO20SKb6JBtTLSn9mpWUk3hzfRrk9yeSeT2eBTbtphLyiEsf/2G4LgsgxdrbJJMTYoJtobKHk9wPmIAkfWRoCWm2NDuMvzki3nyV/9FRV0Dwwv7VtSXXnoNf/nLH/nRj77BCy+885F/f922Zk2bw/ZOhU/CxHSDyenGQe5bx2WZZPgEa1sdmuOaoqBkZpZJYVAyLg2Oz9G0xTUppiBtv+t2RoGPhqhma6ei03bZ3aMQwKgUycoWlw3titPyTbJ8EinoFeoD1IaV1/LVz+cQj8fZs+R5PnjkQZYtewcpJdOmHcfnPnczJ510GtOmzcI3UF8ncHy2QYbPz5pWh+aYZkhIMivbZMgAbNihkJWVzQknLOCEExaQWxUjvd5huC/GzjXvUbb8FSoXv0L0qUfZ8YtkVpx2HDPOPJ6Jp03HzPExNd3HjLQQSwMhpI7iT9hhKxQRGWG4XdobTjkQYsT47bP/y+7NO/jv1+/Gh0WD0UiX7GZWbAapOoVMN516oxG/9vW2gXXITjSajdYWHGkTVEGGOSUMcYv7ZCsN4uPj36pYufTSS2lqauK///u/qa+vZ+rUqfzrX//qFdFXVVX1EWAef/zxPPbYY3z/+9/nu9/9LqNGjeL5559n4kTPUaa2tpYXX3wRgKlTp/bZ1+LFizn55JPx+/088cQT/OhHPyIWi1FaWspXv/rVg1zGBjGITxNCCsQCCz3X9ITdSeIgnYgIChhnIN530JkakSwQsw30NgEOyJt9iFlGr5i+uHgoN958O/fd/zuu/MoNFGWWoFsU+jkH3QUiNTGwbtKQJbwbdrPel9YO0KIRWcJLut+scB+Ne0L2XIGOg/hAQTaee1krXhq8FpAL4jrrIOewfs99jPSyTGo1uhhiMYGIaJK0ZluhSWOeSUOeS36NS4crcCo1ARf0MAFbBGxSXpFkAFWe0F5/jHR3AMZKrx1utYtOF56OpllDjkTO7/82q9s1+n0HvSahhp0ikSeZA1pPD4Q9e6C+PhHmuR/y873X9+yB0tJDb6OjA3bvFjQ3301FxT+54ILnSEkZQlKSx7hUVXntWx8Xo0fD6NEKx/Fa7Y5kfLhjRxl5eQWkpBy6PfdYYL21mpf9z1Fn1CREtyNYFL2UUncEi4EeuthkrafeqMOnfdgiTo1ZxZ9Dv+OW8NfIVrksilxKh2hjT1ItLboZgUSgCeoAJiYpOhUTk3xVyDZrMxVGOSPdMQQIMMz13KmmODMAaJB72Gytp0O2EdJeC4qLS5Sox3CIKBpFnDgdsp0CtxgfjbSJVjpFOzuNMk6Jn8H5kUv4R+jvtMoWstW+BNJW2YLUkiZZT6kzEoTAxiaok7Dw000XYREmJmJERJidZhml7khOjZ3dO4M8IU3y2h7Y2u4yLNlzftoT0aRYgvyMZl4OrKBR1pOtcpken0WR2kejnZpn0RLTrGtz2BPxGJVhSZJLh/o/UrAfwI4ul7/vilMfVcQnnIfm63z3r0/yy9tvZUjSvt+WYRj88If/y6WXnsU///k0559/yVHvq8vW/LUixuYOF0t61rsftjjMzzH57FBfH1E7eOn2I1P6v9ckmaJfi+Q0n+ALI/1s7XT5W0WMmCuYlmEQSMTO14YVHzS7TEo3eL/JwU5YL3fYmpirmZdt0BLzNDGWFOzYsZ1HH/0L//jH32hra2HOnBP4/e8f4rTTziUtLf2Iz10Iwfg0g/FpH/PeeQCSLImrNLviPuqGn4o1bAElV/yE7qr15Fa8QtPqxfzhiz9FmgazzzmBtKu/w7zjT2a4PYxyayctohWJxMEh3U2n1Bl62H3uiu7iiZ89wPGfWcCkGdNAg1/7aZYt1Ji1TLDHMdoZRY+M0Gx4vxmFwha2Z2YpYvi1jy7ZzXrfJlzbpdQZdkyvy386/q2KFYBbb711wLavd95556DXLrnkEi65pP+b0LBhww4bpDd9+nQ++OCDQy4ziEF8VOiY9tqEkgViADHpoSCCwmMqBoA83URVeQGIOiAQce21MC0ykf3oKL785W/xxBMPc+cLP/R6uW2JbtKw3IVa5c0WJYM43/IsJF9w0buVp6PpAASIU01EQOC+7yAiGvbLMdEhEFUScZmBqlSeuD5PIE+1EHOO7KEnciVikQXP2rBVkWxDriPZkW1SXWLhmoINU/wkYTO8zcWXC8z0XL/05pg3orCk1+YmFCiBOHwG2aGPyRLIq33oYgdWup7T2WwTecq+jJr9oXs06qE4bEwUNwLEPx1UmULe7O/XoWwgmKY38HcOSKnfWxAciQ2yZUFb20pWrfoW06ffzogRCwEvxFEIMM1j28o2gOt7v9i5s4wRIw4TxHIMUGGU8/fQn+mhmwJVhIvLFmsD7bKNG7u/wiR7Gov9r1NjVJKsU4iLOMXuUKbax7HL3MGH1jLOiS0iSJCbwl9BaoMHk35PTMSJiQgaCOgQlrZI0snkuHnsNLfTKTv6tkXuhzxVwIz4bN72/4tUlU6yTmGPUePZGbvjaBFNGBg4OAR0kAAB8t0itIR8VYiLTZEzFInBlNgM3g+8TVRESFbJtMlWr0Z2ZrLc9x6dsoMW2UxMRImKKOkqgySdTIbKZLdRgS1iFDslXB3+AhOcKQDURRSv73Hodjzb2a1dkO0TjE2VTBm6hxfyf0eDuccr7Iiz1LeYa8I3Mj5hJR00BVeX+jgx16QhqkkyBaNS5Ed24bKV5rnqOE0xxegUiUzNpWzmAja9+RT/vPhGbhnl78OgnHDCAk4//Vx++tPvccYZCwkcjoI8AKtaHTa1u5Qm73P56rQ1y5sdpmaYjNtvIP9xGFS/ISgJSUwpGJdmEtivqMkPCnZ2KcanWWT7BR+0uHTb2hPvC8HSJof3Gmy6Vr1I9b8eZOPK98nIyOKSS67iyitvYNSow4vPP01MTDP4u6vZ2aFJ90GyAW12AJU/jfzZRZz/izPoamxj63Nref8v/+Kzl5zJyJFjuPqaL3DypWfTkxkhJuJkqHSK3UJC+vCBVQ/fey89bd1c+YObel8TCHxYdEjPUCNDpTM7NpN6o4Fu0Y2BwW6jCguL5ASbE9ABOkQnu8wqip3iQdH9McTglRzEIP4PoJVGL3HR7ySCAoMe6yHOML0C5BhBFHmuWnqVC7s9vYox1fCYgH6QnJzCt7714z693PJyC2Ya6F0KDIEYLb2WLQU6RcISB92sESUSeaoJM6X3YK7R6LS+ZLgICFCeE5hxmeVZFPs9puhoII830UMleqvCH9O0lRu8vMEguUWQkgKdnYKuVB/5V2uC53mDBPWmgx4iPRajKeHclSM9sX/rMbANThFeEXd2wh45MPDARK9zYYtCj5KIRIuGztGIMoVe7SIWHPmtubAQRo7UbNjg5Z+Ypleo1NYKJk7UFBYefhuu28GKFVeQnDyF2bO9NlqlPMF9YaHnjvZ/herq3Ywbdwj1/jHCSt8y2kRrn2DDkc5YysytlFtbuC78RVplM02ygRSVRoEqYog7lIAOEtJJlJtbIZGFIhBcF7kFhObJ4N+oM6qxsAgRwo+fkc4YIqKHkE4iS2Uf8rgujFyOT/tZ6VtGvlOMz/BhaT8ZKpOhajjVqpIycwtJOgkHm07ZSYbOJKSS2Glt583Ay6zzfUi2m8vM+Gz2GHV0yy6GuEM5OX4G+W4RLweeZbu5GYkkKqJ0ynZaRBMWFrvNClwckpSX4/Ja4J8UhYeQYufy6K4YO7sV49MMxqZIdvcoYgpOyjfYWPIoLUYT4+yJvW0zO8ztvBh4ilHd43od2qQQlCYblCZ//M+wJqyoCSuKgxKZ+O2NW3Axr9x5I5t219A4ZDh5B9xff/CDn7NgwTQeeOD33HrrN45qf5vaXQJm3xyRVEtQF/ZCC8elGQk9is22DkXI9NrB5mabR12QGUIgAdVfkKLw9D9nFvo4KU/TFFU8sjvG7m6Nu/VdVv7lhzTtWE/G+Hmc+M0HGDbvPPLTk4j1oyX6v0aWX5Dmk4RMFzthxZxkSoYnB/CHC8jrSGV2hsVl116Kec0v+eCD9/nrX+/lJz/+Nnf+7AdceOFlXHvtTYyePPKI9ldfX8eTf3iIU246j5ySA8yXcAipfbbHSTrECMejqZtlCxXmboKqry1ySAfpkWEiIoKlDwjqGsRHxv//vqmDGMR/APQSF/24jfYBGcLLCnnRRndr5BXWIWfftEpkmzhAgfAKgENAZEvEWUfeWnTppdfw0EN/4vvf/wovvvieZ1s71kCMPYD5MIDRElUhoEWhW13UJoEsFJ5wPVtAuYb9WsW1rRESRKrwCpRD298f+ryKpLcfYH4Mwq/D8uVeoGNKiub00zWnnbZfERIAEQA9WiJiiaIlCLpSwzF8pgjLawM7FHSlQguN8O1nXGAJtF/ATgULjnx/UsKFFyo6OiSbN0N7uyASgaIizZw5ikNEkwCeUcnXvvYF4vFmLr30X1RX+1BKoDXk5WkuvvjQgY2fNLq6OklNTT+m29RoamU1lWYFBgaj7HHUGbWEdKhPr/neXvdW2UKKTmV2fD5VRiXjnIl9lrOFTfIBAxMLiy+Eb2Nu/EQeCT7AUv+7pKpURjij8eGnxqhmXvxkSlxv8BMhwnrfKrYbWzAwGO9MZrI9nRAhLolexRmx8xKhkZnEieMImyyVwwZrDT9L/h47zXLStEOaSiNNZVBubiWoQ0yyp5Gkk6k0KqgUu7it+9uEdBJJOhkDgyXWYrpEl3cOKoWQTsImTotsxtEOAR2gQBUikXTKDtZaK3nF/wJTW65nV4+iNFkmcjEEk3ySXd0uKzrbaZK7Kdyvd18gKHKHUGNWUWtU9ba8HUu42psL2X/uo/S40wCo37gE94ThB60zcuQYrr32Zn73u59zySVXkZdXcMT7k4L+uzMSHh61YcWDO2PUhhUZPkGHDU9WxqnqUVxVenCb2KGQasG4NIOlTQ6plhekqLWmpkdRGJSUJjQ+AUNQF9Fs2Lie3Y/eQeXqtykcP4t5P32Z6sLZhAMCaRlsaHfY0eVyVamPqRn//xkKdtmaoCGYm21iK+82nW4JAgaUdUlSImkU+BPPIgFz557I3Lkn0thYz+OPP8Tf//4Ajz/+EPPnn8J3vvM/TJt23CH397//+yOC/iAXfvUq2mQ7aSoVgaBbdGNheZbG/cDSVsJAw8Zgn/urLWxMzI9tlz6Ivvj/zzd0EIP4D4GOafQ7DtpHrx0wKQLt04jVLiww++pA9l+3UqGetT2WwwWRJ5Bnm4jjjs1PWXdrZB3cefvvuPCWBfzmNz/lG9/4Yf/LdmjUg3FEecIGWQBLEon3t/qQ8wxUuULXuJ4Yv1F7DMZoAz3i2MoP/X5YuFCzYIGXrJ6WxkEDbDFeevknuzV6qPDawDq8bBox+1O+FQbot/VM2Bo+QmEwZAhccIHid7/zcmXS072k+WeekQSDmilTBmaOfvaz7/Hqqy/w4INPceKJw9i0SdPYqElOhokTNdmHnvjvF64LLS1eG1pm5pFpUwZCT083ycnHYNo9AYXin4Gnecf/Rm9mSpbKJkWlERHh3oTsvcsCvUn1453JpOtXqDWqewfiLbIZqSVT7YMHRQLBJGcad3b9nnfir/OO7w3aZCtKa06PncvC6EUIBGHCPJx0L2usFUgkCs0S/2JOiC3g0si1mJik6fQ+SfBo7/gK3WK+0v0dHgk9SItsIk8VsMlcjy1sJthTSNVpAAxzR1BmbqHc3MaJ8VNpkHt4Nvg4b/hfplU2kaSTcYWDpS3SVSZREcXAYLgzEhOv7bNFNgGaTdY68p0e0GZvgN9eJJuCtphE64Gr5CNxZ/ooKE5YBNdH91kEB9OySC0ZS8/WpeT6r+13va997fu8+OJTfOMbt/Dww88dcavW5HSDje0uEUf3ZrO0xhRBwxPAL22yqQ0rxqTuY3q6bc3qVoc52SZjUo9c7yGE4KwCi4b9gxS1l3i/aIivl6mpqtrFL+74bz549Ukyh4zigjsepWjOuSxvdgm6GksKcgKSnABUdLu8Ve8wKd04qsJpL8IiTKtsBzQZKoOkI2i5OhxSLEGKJei2NYX7ifU7bU3I6Gs+sD9yc/O5/fbvcOut3+S1117kl7+8g3PPncfZZ1/At751B6NHj++zvBDtlJU9wJNP/pVf/vIETk5vYb3Op0N2ooQiSYUYFR8xoCVxqk4h282izthDmpJYmMSJ0yPClDrDCOqjaykcxKExWKwMYhCfNjq01/p1YHBiBujGhM1vP5N7ukOjHo57ovBiCaaXE6Ies5Ep4mDm4wihtfYskN910etcdI9mqjmVr8z5Nr+5+05OOul0Zs06/uD11ruwU6HHSETiQa2zNGKbQq90EWea6AYFf7Y9bYoEkoQnLP9T3BPJ12kv9X6uCTPkR3bk2YukpH0p6wdCZErkpRbqH7bH+KDRQQEnG0esl4GEy9fHPE450cB9x4V6T7MDeJ+7TxxkoXwkUAoWL5aA4OSTNVJ6A5mKCnjhBcGYMf07gj3yyAP86U93cccdv+Kss84Hjt7l60Bs3QqvviqpqhIYBowZo1m4UFFw5BPWfdDd3UVS0rErVtZaK/mX/0VSdTpj3PG9yfRtZismFruMHRS4RbjCpdaopsgtZnLcE7wPdYdzYeQyXgw8RZm5FdAk6xTOiJ3HVHvmgPuUSBbEzmJu7CRaZTNJOpl0vc9NcrXvA9ZYH1LilBJMVKudooMlvsVMtqf3mxuz29jJPwNPs9MsR6PJdfMZ60ygW3RTY1RR7JQwxpnQu3xURGiWTayylpPvFvJc8Am2mZvRqMQ8sA+NZpwzkQZZT6vRjIuLkRgmCAQSgxhxlHBJ87lIYRF1dZ+Wpk5bMyY1mYgqYYe1jVHO2N42sDqjmmHOCIrdw4uePwoChuDsQosnK+OUdbr4pBd0mD1xPi2bFmMO0G6akZHJr351L9deeyGPPfYXrrzyhiPa33FZJts6FWvbnN7Qy4ABC/IthidLHtvtuXHJ/e4XyZagNkKiiDn4t94eV5R1KRwFQ5MkhUHRe7/JC0puGRVgY7tnc5xiCiamG+QGJK7rcs89v+ZXv7qDpLRMJt/yGxZceA2WadEYVUSVRgpI3m/Ul+2X1EcU7XFNyPBa15T29psykCc0XrFZZVSz3SonIrxE1wABRtnDKXWGfSwnrIAhOD7b4Nlqm/qIx0j1OJqGqGZOtknxYdqkDcPgnHMu5Mwzz+e55x7nl7/8MQsWTOfii6/kv/7rBwwZMgwIYxi/59Zb72bcuHRuuKEUv/8l8pzR1ESuQ+k00lUafgbOyxMIJtrjiIkY1WYtYRFGIilw8hhufzLf7/9kDBYrgxjEp41k4SXJ93hi9150471+YDJ9AnqjC9XKa2PaOzgYJmC7i1rhYnyEYkW3KtRTNnqJC9tc8AOjJJQIbg1/lfdy3+LLt17LG2+uIjU1re+6tQptiN5CBcfLetGNGv1SHNms4QPHG5CnCBgmYZyAKg0vOughAkolYotGbY0j2k3EaR+dOtcRDdsVulsjcgWMODhYUkw2kEMlbHfRMZDFEoaJIyo+9A6Fet+BHQrSQMwxEXP3uakdFUZJ5EIT/aoD25THSiULxNkmTDh668+mJti1ywuD3Nv2tdd2uaZGUFXlOXHtj3feeZ3vfOfLfO5zt/D5z3/56M+hH+zeDQ89JGlvh/x8jevCihWC5mbJl7+sSDnKdjutdYJZOXZ9emusD1FCkeN62Q8CwRB3GFutTcyMzaFFNlFv1iG1ZKwzgUWRS8nS++ilE+ILGOOMp8zciovLcHckxe7QIxqgBQn2ccPai83megyM3kIFIFWnUSuqWOJ/mx3mdsKJnJep9kyiRHkodA+1RhUFbjESwR6zhpiKcmvPNyh1R/KW/5XebdUYVWy01tIuWlnue4+1vg+plTUk6WSajUY6RBs2cfwEqDWqSVWpaCBFpdItukjRqb05MbaIUeqMZHpKOitT42xsd8kLeLqN5pjGknBCro9A7CIeNu5jq7kp4VwWI1flsTB68SfaIjMzyyTdJ/hnTZxlzS6O0qSOn0fFKw/w5rZKThvb/0Dy9NPP5YorrueHP/w68+adzLBhh29TCxiCa0p9TM80qOhysaRgTKrRy6SETGiN911HaY9XOpCRAljV4vBCjU1zTKHxWKoTc03OLbJ6mY8UywvE3B81NZXcdtv1rFixhFtu+Ro3fPl7PFhlUBFWFIc0WnvOYEFDUBjcd3+Jud7ntbNL8foemz1Rr1jJ8UvOKrSYm230e29sk+1s8W0HTSKHBLpFD9usMlJUKjkfMyDx5DwLW8GyZpe6iCJgeNfhgmLfEU8UGYbBxRdfxfnnf5ZHHnmAu+++k+eee4Krr76Rb35zAQ888CTbtnWzePH1GEY+jlNI0NxKsbmVWOz8I9qH1N4z11QGGaQjMYjKGJv8W5kRm4ZvsBXsmGGwWBnEID5liJAnpudFG+3TkAF0g6jWMN0Tr/eL9oSbzAEDcJ0kEHuO3s5KK4+VYa2CLs/+mABQoSGoMUb5uDt2P2d+MJ/vfvc2/vCHh/ueR7IAN8E0OAmmZY/2AiebQK2Mg197dxkTL73eL6ABr7E8IBH5ieDIchd9fxz1juNpbGYanuHAAIVANBqlpaWJcLgb13Vxah2cf0ZRe1wMLTH9PqyxPnwXhghkBsnKysFK2GKJNAGzzKOa+9NlLuqBOLRqdIaAamCnjW5QyIsPrTHqD0IIOM30sm92eo3ZYriXQfNxWJsjXXXt2pV8/vOXcsopZ/LjH9/1sZmivVixQtDSIhg7dl92SkqKZscO2LBBMG/e0bE2kUgYrfUxZVY6ZQc+3Tc/QiAQGorUEG4I30qdUY2BQZFb0m9GQ67KJzeef9DrxxqNsoHX/C+RpXJ6gyXH25MZ5Y6hxqhkjDOh10I42Ullm7mJldZyToifwmZrHdvMTQR0iHW+VUSJMMIdwwx7NmusD6kyd5GpsslVuSjp0iZa6aYbbShG6rGUOEMBTbvsoCehaTG1yQh3NGdGF+KTkqtKfbxUa7Op3aXH0eQFBKfm+5iSbiCciXy5+1us8X1Ig6wjR+UzIz6732LtWCPFFLTGITcgKAoZRGedwDrg/lffYWTxVQxL7n9i50c/+iVLlizm9ttv4Nln38IwDj8B5DcEMzJNZmQePJw6LstkZ1ecTluTagmU1lSHteeYltZ3UmJPRPFMdZyICyNSvE+1Ja55Y49DUUj2u32tNc8++xjf/e7tpKam88wzbzJnzgkAXFXq8lyNTWWPJ1TP9EkCBqQnxs9hR9Mc08zKMni+Jk6Xoxma5KUJ1Uc1z1THyfL7+2V/GoxGYiJGlptNS9hEaUFmUNDua6HeaPjYxYolBecU+Zifq2mJeVbM++fHHA18Ph/XX/9FLrvsWh544Pf86U938dRTf6G7O8IPfnAykyfv/R2baB3ENLcdsljRaHpEGIWi0WiixWgl383HSPwOHVwaZRMNRgND3AFCugZx1BgsVgYxiP8DiDNMjwFY7XqtX0EB0yXyMmtgZ6x0T8SqHb2PzQBE9+GDB/tFhUZvU15x1AD4SCTJa6jSiKEwJDiUn171W267/3oWLDiTz3zmin37nWx4bmbVGu1or9XLEpAGkHD96sFLrU8TXrG1XnkOYAqPWdLaC6isUlCviQZjVOzeQfkb26kv2kPrkFZaWpppbW2mpaWJlhbv756e7sOf31vAH/f9MyMjk+zsXHJy8shJzyPbzCEnLZecEXnkjSxk1KixFBcPPWjgrrVGveVAu0aP3deqpls0YpkLx5tQdOSD/XAYNm4UtLYK0tNh4jR51IzDgcjJgWHDNJs3C5KT97WB1dZCQYGmpGTfsuXlW7nqqvMZP34y9977mGegcIxQVSVISuob8uhtXtDcfPTtZd3dXQDHtFgZ5Yxhs7keheod6MeIIjEocIvx46fUPTInoWOFic5UVvs+ICLCvQnzdbKGJqOe8fYUxjleNlicGJusdTQYezAw+qRlCwQBHaKaSsrv3Mq/nnmRpqZGzAIT/7UB5n3jZIarUb3Fl4uLRuPTAQrcYpJFKtXGbkIqmRvCXyLbzWWx/zXW+D6kWTYSdJM4JXYG58cuZog7DIAMn+TqUj9tcUXU9Zyc9mcMClUxhdFPf8C2rs2hMeppRYQQBLNzyRo6lqp1S1jTevmAxUpycgp33/0XPvOZBfzpT3fx5S9/82Mdx5wsk+oexcoWL0cGIMsnuGCIxe5uxdNVNh1xLwRzb9r83mMGyPYLOuIua1rdg4qV9vY2vv3tW3nxxae46KIr+OlP7+7Dfg9LNrhtjKS6R+Fo77b+bHWcyrBCaRdLwLRMgxy/5MMWl1EpgqgLhvS0P9s6HF6pjVPWaWAnWsMmphn4DUGcOC2dIZZWZrGny4fWgsyQzdiSOMVpsY91zfZHqiVItY5NjksolMRtt32bRYsu49RTJ6M1fPBBNbW1nRQVeRlOQtgoNfDNuEt0s80qo9loQaPoEj0IRG+hAmAmfl+Nspl8N29QaH+MMFisDGIQ/wcQQYG8wvLE9M1ezgpDxSEtfMVkA13ieIL2Yum5cdVrRIpAzv4IN/ROjYh52SekAnWJ1/0C4hrd4zl3XbTwct5pfoPvfOc2Zs6cS0mJ514khkrkJRbqRQe93IWI8MThBYbHoqQLiOLFUvfgFStdgAZb2eyq3knZknK2N2+hrH4rZc42dr9Zgau90ImUjalk5WWTlZ9LVlY2Y8dOJCsrm6ysnN6/k5JSkDUg/qEQeQaG38DVLq5yiLfEcYVD7GKHlq5mmprqaWpqpGl7PU1r6tnSvZ6mWCPtdlvvJUlOTmHs2ImMHz+JMaUTGJ82gTEFE0guD0H2AZqaTGC7hhoFRUc261dXBw8/LKmo8ATwQmhKSgTXXqv6FBRHCylh4UJFU5Nk2zaB36+JxwWZmZrzz9+nV6mtrebyy88jNzePhx9+jtAxtvnKydGUlUnYT0CtlFc4pX6ETMdwuAfwBhrHCrPj81ljfcg2cxNZKgcXl3bZymR7BpPsg7UhnwZmxOew2VrPasvL9NJAq2wmTaUzxtknDPbhJ1Wn0Sj3QEIHsrf9TKOJiggbf72WZQ+/y913P8iYMeO5f8vv+euX/0RDUh2xW2O42HTJDixt0SO6iRHt1aWk6wzm2ieyIHYW4BVRTZEGAHJUXp/iaH9k+D7arHd/CBNmo7WWeqOWkE5ioj2VAlV0VNtoszWG7GsdPmTKPHaufo+m2KGL5tmz5/HFL/4Xv/rVHSxYcCYTJkz5SOcBHutyxTAfc7JNqsMKvxSMSZUsb3Z4rc5G4+l9KnocIk7/ejifIei0+x7z+vWruf76SwiHu7nnnke44ILP9rt/Q4g+hdlXxgTY1unS7Wiy/ZJRKZIXqmN02JoPWjQ9jsYAsv3QYUNVj8O2LoUADAFTM0yuGuZDqDTe2yHp6faTHXKQQtPYY1K5pZDOjBDlRoySkMHkdIPkj9Im+wninnvuQinB3XfP5s47NzB79n385CcLuP76YrQ2cJz+tWdx4qzzrafFaCNFJSO1pMVsIyIiZKi03jyXHhGm1WglKqO0Gq0UuPmMtIcTYFBw/3EwWKwMYhD/RxBCeK5fRyg8FqkC+Tkf6jnbs7d1QRQK5JkWYsxHKFayBDqIV0CUSGhyPeG/0l7BUqO9PJbxkp/97HesXLmcG274LM8/v7h3plvMMRGjJfqbymsBGyO9wqcWL7neryEk6Knv4sOOD1iq3mOZWsJ2dwt2xIY6yDFyGWWO5cTMBXx+wq2MzRnPyMwxpFemIq72IU889G1KWy4qOY4e0reFSoc0IqyRcwOIzMSArlah7o6hc4EhnoNZfE+MpvZ6dszbydbIZrZu3cjKd5fx2N//gqO9pMUiq5ixaROYM34+JwxdwLiciQhHeN6l/iN7GGsNL7wg2bHDy0OxLI3jwI4dgqefltx+u+IIuk4GxIgRcNttijVrBHV1kJ2tmTZtH6tSVbWLSy89GyEEjz32MhkZmR99ZwPguOM0a9ZAdTXk53uuYN7/ayZPPnpmxTS9WUnnwMTLj4E8VcAXem5jsf81NlvrCegAJ0VP4+TYGQQ/jpf2EUChKDO3sM3cgi3ilDojmWRPI0iQa3tuZqp1HGXmFgxMIoRZ4l98UIEgtSBL52BissPcTrEzFImgzqghXWew+4OdnHnmQk477RwALhh+CU+/8Ajb12yhwChCIukQ7TjCJc8tICqjKBRBFSLZTWFGfPa+fSHJUx/RGeEjoFW08NfQPWy1NqLQaDRZ/hwui1zLDHv24TeQQK5f4ipPH7JX3J4zYhLrXnqIHNOGQwinAb7+9R+yePFr3Hbbdbz00lKCwSAtMcWqFpvVrYoOW5HjlxyXZTIt0yT1EANyKUSf5Pr6iOK9RocUC3IC3mtaa1a0uHTaiqijetPpldZ025qRufu+A2+//S9uvPFyxo6dwP33P0Fh4ZEzV0FTMO0AhiasBNU9LkmmINkSuBp2diva4zA5XTI20QYWcTRrWh3GpEpssunpFmSktWEK7zcajyhqWtJ5v8tHdZKD0g5jUg2uG+4j/RgWsx8H77zzOg8/fB933vk7rrxyOBdf/Djf/e4Sbr/9VZ55Jo/f/OYbFBTM6HfdRqOZVtlOppvRy07mqhx2GZU0yxaGuEEiIkK1WYODS45KQqHZYVbQLXo4Lj6935bSQRwZBouVQQzi3whiiETe6vMKAwfIF4gjHCwfhBKBmGzAcsezHh4lYbuCCFAkEHNN5PleSGVqMI2HHnqaRYtO4ZZbruKhh57BMAx0m0Y/ZnuWxHuU19qVI4in2qytWMmS2Lss7XifdU2rcHDINws4vuBELjWuZLQ7ltHxMWQZ2V6b2HyzN7ldKw0oxJEw6IWJ1rMW7WW77EWzhpEy0ZbmQW9W3nLj9rEk/qIAxd1DKJalnHrrOeidCvXHGLEZcSqCO9jatIltGzexqXU9dy39GT997wdkBbOZn3kyJ4xcwInJZ1DM4WmRhgYoKxMUFureVHnThOJiza5dgpoaGPoxTWTy8uDssw8uCsrLt3LZZefg9wd49tk3KSg4upnqI8X48fDZzypefVWwe7dASigp0Vx0kSLrI7SxB4Ne8RCNRo7pcRapIVwV+Tx2xEYgMD+FR6FG81LgGV73v0REhBFIpF8yw57N1T03EiLEbHses+15AFQbu9noW0uzbCJHeWYADg7tRhsLIxczxhnPc4EnqDWr0Wjy3ULOjX6GZTPf4ZFHHmTnzjJGjBjNno21dC/rIv2XGVjah0Tix4+lTUIqiUnOVFwUXbKDTJXNLPtg579PC2/5X2WTbx0j7TH48KPR7DYqeDbwGKOcsb02zIfD5AyD95sk5V2KgqBX7kWyR4JS5HVXA+MOub7f7+f3v/8r5547n29+8xa+/YsH+WtFnFWtDk1Rha0FJi7Lm13m5jhcN9xPlv/IBuTVYUWnrRmdsl8LnxCUJgm2dAjKuxTZAY/JaIlpikOSOVne9/PJJ//G179+EwsWnMU99zx6TJjR1pjH7rgaXOXp3uLKaxvL3k8n4jeg29E8tjtGtl9ixFLJ0tAjeojako7OdJKEjxTTYFSKga00Wztc3m10uKDYd4gj+HTQ0tLM1752IyeffAbXXHMT8bjAMCZy113bOP/81XzlK/cwb94P+PGPQ1x55Q0HMVwR4d2D9i84klUS6TKNsAjTIltpkx04uBS4eWSodAD82k+T0UyTbCE/8TsexNFjsFgZxCD+zSCkOCqNxIDbEQJ5mYVKArHWRZsC5hqIyQbiJAOR27ftafz4ydx332Ncc80ifvSjb/DjH9+FeiqOft2BuKZFNfPP2ud4a/frfGh/QESHSTPSOT77BH6UfSfzck5k+CljkQHhWRrvVLAHGCk8DUtiAK+1p5khR8J+jJF2E25flQoMgRgrYYhA5Ek43kC85qJ7FIRAtHsBkHKB2deQIKzhgPYQAB0QiI5E0v1aBzo1/nEBxjGRcfkT0aMVvOUSC8VZY67kvebFvN+xmBffewZ9ombEiNGceOJpnHTSaZx00un4/QfP3Nq2lyx/oETEsrzX4/GDVjkm2LBhLVdccS65uXk8/vgrRxV6d7QQAubP10ydqqmq8s512DDwfcSxit/vtU4c62JlL/b2k7u47DS3U21U4tN+xjkTyT7GA4udRhlv+l8hWaf0BiJGRJiV1nLG+MZzcvyMPssXu0M5NXY2//K/QKvZjKlN4iLGaHs8J8ZPJUvlMLJ7LFXGLrRQFDvDCBJk+q2z6Orq5MQTJ2EYBq7rMvxHoxh3ySTqdA2ucBntjCcmokRFlLiIo9CMdMZyXvQzvXqUjwqNptrYTaWxCwuL0fZ4MvXhK9UYMdb6PiTTzcaXYD48p7ah7DS3U25uO2J2JdsvuSYh/t/do9AaJo0ezRKgq7YcJh26WAEYN24Sd911H1/60jVECyYQP/kWYq4mzZIkW4IuWxNxFZvaBe81Olw45Mi+5KZH6OJq7//3QiMYn24wK9NgQ7vC0ZqTck1OyrPIDQjuvvtOfvGLH3LllTdw552/PyZaM609AfukdIO2uKYlptHaa+tzteo1IXG1ZlO7y84uRdCEppimNixIkumMT8+iNqzAcfEJ0WuNbEkvE2Vdq8vCIt3HvvnThm3b3HzzFcTjce66677e+79ShcTjhcydu4C3376Fn/zk23zzm19kzZoP+dnPfkdgP793v/a+k/tr3QBCKkiRKqDILWSDtRGDdDLVPtbawkSh6JE9cPQ+OINIYLBYGcQg/oMhkgXG5T702dpjRTIF4hA+9qeccib/8z+/5Tvf+TL5yQVc/c61vF77Ms9Hn+bd7rcRCOb65vOV3G8y/wunMvGUaZgRA/cDG1YpLxumXHn2v93aG926eInPlV72iUBDlkB+xoIA6FUuuk2hVruwWyEcQINOFYhzTTjVRJ5vobMkLHU8Z7OJEnmSiZjUl3YXecLbg617nca00ogeoDRx3m2g/X2NaIVfokdo/CNDzJt5KvNDp8I4gza3laVLF/P++2/z1luv8tBDfyIjI5Pzz7+Eiy66khkzZu/LSMjzEuHr6mD4fkHaDQ1ey1bRJ0B2rFixlGuuuYCRI8fwyCP//ERav/pDcrLHsnxcBIPezHE4HP74GxsAMWI8EXyYD/1LiOFlRmSpHC6OXMVx9txjtp9ycxs9ors3sR4gqEP4tJ911uqDihWB4NzohQx1hrPBWk2P6GakO4YZ8Tm9GS0+fIx0x/RZ78UXn+LZZ5/gj3/8GynjUrmv/G6WffsdVKFiyhUzKXZL8OGj0qig0B3C1ZHPo1AUuMUfWwzs4PB84Ene979Nj+gCBDkql89ErjjstXRxcXB69TN7YWCgE9s+GpQmG3xptKQxqlFArr+EvyenUFFRfsTbuPDCy9i4eSP3/ekHzMgYTXz0AjITNUlywpbYEJp1bS7nF+sjClYcmWKQF5BUhRXDkjyL45iraYtrzi60uHCIj/OLvWP2SYHrunz3u7fz8MP38fWv/zdf/er3jpl7nxCey1ab7TItwyCa0Je1xxXvN3nCfICGiKaqx7NmHp9mUBCQhB2HrZ2aVEvjaEHYhUwffayRBfur1/7v8OMff4sVK5bw5JP/GpBVTkpK5uc//wPTp8/i29++la1bN/LnPz9JcbHHmue5OaSpVFplG6kqFQNJt+zBwGC0PZI8lUuD0UizbOmz3b3BspYeFNp/HAwWK4MYxCAQ6cJjLrYqtAWMlAMWLVdeeQMrVrzPT+/+Hr/kDuLEmZEyix+X/oJzMxeR2ZYBGQLjjBBipPfgMkoEqiuOXqegwvWKlBThaVyKJJS7cJxEDJMQEIhxEiKgfh2DKo1uVlCtoVCgZxley3mDhpcdxEgDMUwiTjbRJxqeVsZ3MHsCwGQDxjiILRqdnWBZmjQUSWQixV4UCfhAe0VMwvBAuxrhgphuIhfsu21mksXChRezcOHFAGzbtolnnnmMZ599gocfvo/S0pFcdNEVXHTRFQwdOpwzz9Q8+qhk+3bP0re7W+D3w5lnKo6x1p133nmd66+/hOnTZ/HXvz57xFklMWJ0yx5MbZKqUz5WwNvHhWEYpKSk0tHRdviFD0AkArt2eazV0KGQNkAH0XLfu7zvf4tCt5hUnYZCUW3s5qng3xnmDidH5X3Ms/CghNvv6xKBK/oOxLeaG1nme5cqYxc5Kp+58ROZbs867GcRJcoP/+frXH3bF5h40TT+kvQH5BxB6p50an5ZiXmtSZfoYLQznrDoYYY9+5gGNK7yLeeNwMtkqWyK3ZJeluUfwb8zxB1KvioccN0gQcY4E1jme5csld07e90o60nT6ZS6h889ORBSCPL3u48NHz7qqIoVgP/65h28tGoD6+66gZIfvArDxwIffRCeYgk+M8TiH1U2ZV3eQNYQMCndYEFegunTEHa9XJvbb72aV199gV/+8l6uvPL6j7jXgTEn26SsS7EnqsnxC2IaOmzN6BQDV8OOLpfKHkWPKyhNkhQFJX5DcFyWwbImh6qwIssvSbUkxSFBZqIdzlGa9rhmToH5f8qqPP74X3nwwT9w552/Z+7cE/u8pxOf4v6/q89+9hrGjp3E5z//Wc46aw733vso8+efgh8/U+1JbLa20S47UCiSdIiR9nByVQ4AxU4Rzb4WekQPIR1CoWiXHaToFHLdnE/vpP8fxGCxMohB/IdDK41+w0G/6UA7XtJ8oUBeZCEm7mMmKisr+Mtf/sTzzz9JU1MDqcE0uiKd/KzgLq4adv3+G4SY2JfMDohsibzZj/pLHN2koERAnoQsTxSvUyW0aORN3sNa2xr11xhUKvQICR1AUEMbUK4Qkwx0HrBVo7e7XpFDokXuENpZkSSQ1/lQbzqIda5Hy88zkaeZvZkvYqaB/sBFbFfofOlloNQrGCoR0w/dlz527ES+972f8e1v/4Rly97lmWceTaRK/5hZs+Zx8cVXcuWVn2XDhnRqamDsWM2cOYpJk4744zoivPzys3zxi1dz0kmnc999j/dqPw4FjabC3M0us5KIiGBgkOlmMMEeR4o+dtbBh0KD3MNa68NencY0exZpaRlHXaxs3gzPPCOprRW4rudSduaZmpNO6murDPChbxkBHejVQ0gkQ9xhbDe3sMXcyEnxY1OsDHdG4SdAh2gnTacDnsNQWIR7Xcg0mmcDj/O30P10iy5yVB7Vuoot5kbaoi2cFjtnwO2vsj7g5cCztEfbeDv4L9amrsTFZoY9l0bq2am6iRNjq7UJR9jMjZ/E8fGTjsm57cVqawUSSabyQjQFghK3lG3mJrZYG8iPDVysCASnxc5mt7GDbeZmknUyURHF1CbnRS8iV338XJvhw0exc2fZUa2T5DO55ad/4c7Pn0H1ry4l9JPXSc/Ko8sBvwSlBZPTjSNiVfZicoZJQVCypdMl4kBeQDAuzUAKeK0uzgctLt1xlzV/+DJlb73I/X9+grPPuuBoT/eIMCPToCNu8k6jy+4ehSlgXJrJBcUWLTHNpg6XuOsQNBRTMzzrYvBaxUYkS6ZlGJxRYLG5w+XNeoeyThdTerqXkSmSE/M+nWFmHBuFix9/b/GxdOk7fPvbX+Kqqz7Ptdfe1LtsRESoNKrZY9QDgkI3n6HOkF7Hrv+PvfOOs6I6//Bzzsztd3vvy9I7IkiVIoodFY29R41GE42mmMQ0TWIS02NiEqMmdsWOvRcUBel9Yenbe7t15pzfH7MsLCywS0n8Jfvw4SPee2fmzL1z5573vO/7/Y4adQyvvbaQr371Mi688FTuuONuvvKVW0hWyUyKHkeLbMFGkaASuhg/5tk5tMfb2ebaTr1oQCBIVImMiA/FexBRhz4OTF+w0kcf/+ssV+gXLXQAGOSUZYltGvVEHPkNwaqKFfzlL7/m5ZefJTk5lfPOu4S5cy9iSM0wbrr5cn5UeTuJIpE5mec6HipxAQMFIqEjK1HXoRTmA4ZJxBoDPWQv88O96wW2KPRW7QQILoG2cPxaTKBKoQdJR1hAKOhlr4dIkxgXuNFnaSdY8XXNwohMibzajXotjtjU4S4/wUSeZiJSe9ZEaxgGxx9/AscffwI///kfef31l3j22ce5/fabCAa/yxVXfIWvfvVrZGQcmYnwLpRS/OEPd/PrX9/JWWedzx/+8GCnGebBKDcqWOdaj6lNkuxEaDNpFC2s8K1iojX+qDehbzDX8C//36iSlZiYWFh86HkHb7KXpqaeByu1tfDYY5KGBigq0hgGVFXBM89I0tLsfQLDkGjfxyRS4pjjxcSR84wYZA1jSnQmH3repoZKDG0SFVFGxEczMeYY+b3ueYl7g/fQIppI0ElUGDtIUWnk2vm843mN42JTum0y32is5zH/A8SIUnLKQDb+aj3+fgEShibgWxxg2x+3MOjyoeRbRVQYO5geO4nL27+CnyObzmsRzbh110mZ6PgTFgcv5SuyS7ih/TY+cy+gzCwlWaVybHwCo+PdKzT1lv79B7FgwXu93u70/mls/sU8Hr5xFqW/uoj077yEzxcg2ycZliSZnmUStTUNMY3PoEfqVxleyfS9jA6f3R7jrao4QQNWPfRD1r3xGONu/TsJY0/v9ZgB4kpTHdGYwgmIuss2SyE4McfN+HTNumaL5hgUBAR5fkm/oGBcmsmgBINHtkS73KLbLY0pJcOSTLa2a6rDmsIOU8lEl6AkaDA21TjqSmBhEWGTWUaVUY0SimQ7mf5WPxrWV/PlL3+JiROn8bOf/aHz9VGiLHGvoE7WdfahrHNtoM5oYHz0GNw494K0tHQef/xlfvGLH3Dnnd9h2bLF/P73D+Dz+UjuaJ7fG4lkiDWIAjuPZtmCoQ3SVOq/RcDjv52+d7CPPv7HUYudDMOuzAISVD/Ngo/f4a8X/4mPVr9LUVEJP/vZHzj//Ms7V+l1peIPJ9/Pd977OjdVXEsttXw58wZIAvElF9rS6Fcs9AIL3aTBLRApjjcEDQI6em51TCNaNGL2HrejKI4/yy6lswwBtdoxrowDFp3mmKLo0H4MhfcAnjbFEnm928nkCCB5P2VlPcDvDzB37kXMnXsRlZXlPPDAvTz00H38/e9/4IILruD6679BcXF/tHZKl1wu6GF80YWmpka+/vWreOed17jtth9wyy3fQ8qevTcazXZzBwApTekENqbhavahlaY9oZWawmZyD0XOq4c4vQ5PUydrGWqNQCBQKDaZ61H5ip3l23u8r1WrBNXVgsGDHXNMgLw8KC2FxYsFI0d2LeAZao3gDc98slVeZ+lRq2jBjYd8++Aqb+2ijRpZhU/7yVI5nau6MWJsMNdQZ9QQVIkMs0ZyfvgyBllDWeVaSkREGGqNYFxsEgk6kUpZzku+ecRElByVh1t7UCjqZS1JKhklbHYa2xhmjaJB1LPC/Tm1soZklcJmYyOtopkh1gjy7ing45++z5qbVlBbW0N1ThWjrjqGibdPA8vpATkmNv6IByoAg6yhbDTXdWlCDhPCwCSvB+8lOEaS50QuPOJjA8jNLaCurgbbtnvkTr+LTK/km5P7M/Avz/GDq05C3H8Vp9/1OBNyEzgm2WB9q+Kd6jj1EY1bOiVdZ+S5SXL3/J5RG1EsqrdI8wg2v3Avq567l1k3/YqMmefzQU2c8Wm7sxo9YUWjxRuVcSrDCikE/YOSOflu8v373hOU1nxSG+eDaoumuBPcFAYk5xW6O4OOlU0myxstTOGsK2lgeKLBgjqLbe0KtwRLgVvC6XkuTsg++j0aFhbL3SuokrV4tZsYMbaY29hUu5HfXXoHeXkF3H//k10WbCrNKupkPalqtwSxrf3UyToqjWqK7ILO1xqGwfe//3PGjBnH1752FZddNqdHJbUBHSBgHzlvqD76gpU++uijQaF9zpzcUhYvb3ievy7+PWtqV1KSdQzf/vbjXHHFOaSk7NWsniPxfinAb437yFiYyU82f4/aQC23X3gncrKJ/sRGv2qhk4FBEqKgtzr+MDQoqAVtCISlYYSBmLzH7SivI7Cp1ZAlEPkSXa0dp/sUAVUKERcw0YChEtt23NptG3JzoRsxri7ouHaa/ENAtoDcfVcdhRCO8eMRJCcnjzvuuJuvfe07PPzw37j//j/x6KP/YNq08ygp+Tax2Bh8Ppg0STNjxm4zx4OxevVyrr32QpqbG3nkkRc54YRTejUujaZdhPGGAySuzsJs9RAPxkBo3K1e7HVurBFgJvf+nHtCubGdHcY28uyCzsm+RJJrF7Bh0DpKX1rb4321tjotUXvHaV6vpr5+35bfqdETWGOuYL25mmSVSlzECIswk2LTGGTtXyVAoXjX8zrved6gUTbg0V6Gx0dxTvgiJJKH/X9jnWsVNk6vSpFVwmXhaxkfn9Rts/lmcyPttOHXgc5tJBKf9lNplDPAHoxbe9hubOFB/1/YYWxz+l1QVBnlZNiOepk7wcPMX55Mwa+L+cy9gGHxURxjjcfGZpuxmUzbyeY95fsX7aKdEmsAY+MTeiwLfCAmxaaxwvV5p+GmJeI0iybGxicwNN67WkelNTHllFodqYby5ORkAJqbm0hN7V3wneQSXD7tWPo//ByXX342S+++mK//8znWtQue2BpFCEGaGxpimmd3xFjWaHH9QC/9g7JH46+JalriGr38ZT74+w+YcNGtjJ17PW2Wk7FpimmyDiB+siebWm0e2xojbGuyvBJbw4pGm6Z4lJsGeffxhfm8webVcouACwYmSOIKtrYrHt8S4+YhXhJcgiv6uRmVbLCu2UYKGJZksKXNZkWTzcAEidnR31cdUbxdZTEq2STbd2SyKhpNk2ymUTYhgDQ7jQQd7JQEdmsX9UYDMeK01TXz4Hm/IWqF+ftjT1OWto2oiJKqUsizc2kQjRjILhLEBgYCQZNs6hKs7OL00+eSnp7F5ZefxQUXnPJvFSvpw6EvWOmjj8NEaw3lGiIasmRn+dP/G0okbIrz4vrn+NWCn7C9eStD005kRvpr+PqdyJo1gt/8Bs491zEZ3BMxycToL/n++rvJeimPHz/+bWqX1HHPBfdhfKzQHqesCgAfTv/JVoU8yUTHQIRBDJCIsQYiuEcpVqpETDdhvuXIEQdxvFj6ScgXiCLD2eY4g7JtghdekGzbJlDKMSA844x9x7oLvVOhHo+htyjH2DEIHGcg57oO3bOmlyQlJfO1r32Ha675Or///cM8+OBvef/9ceTmzmbIkDsoL59MdTVcfnnXHgutNXqVDQtsJ8N0jMEzO5/g9jtuZMCAITz11GsUFvbb73H3h0CQoINEajVmi4dYStjJsKGIJkUx6tKIVh+9YEWjQThu7PE4hEKO7LEICoIDEyjbvoFYLIa7BxrIGRnOHuPx3RkqraGtTVBcvKd2aAwhouSpPK5rv4UFnndZY64koAKMj09mcnTGAU3cFro/5FnfY3i1n1yrgIgI8Yn7Q9pFGykqnRXuJfSLD8CHnzhxNpulPOV7mG+0fb9b1S2BwI2bDDuLHcZW3NqDgeHInoo2iqwSiuwS/hb4PTuNbQyyhnYoZWkq5U7KzI0MtUZ1lpwU2EVsVOuJygjrzdXsUuYqskt4xP8PIh19SR+63+ZD+11ubLuNNN2zJuCwpVnZZLO93cZrOApRJUFJrsrnutDNvOd+k3WuVfhVgBPipzIzOhtPD2v2ba35rM7mk7o4DVFNpldyfKbJ2BTjsIOWpCRHRa2pqaHXwcoupkyZwWOPzefSS+dw2WVzmHTHk9jaR0lAsKVNUdamCNmailqbpliYM/LcnJnvOmhPS9CEtk1L+ODu6xg8/RyO//IPAQhZGo8Ev9nzc19cb9Ea1wxM2B0o+RMkZW2K1U02kzO6Tv0+q7NAaLI6TCo9BvQLSDa3Kda12ByXZuIzBZMzzM5tba15tSJOilt0BioAmR5Baatic5s6IsGKQrHOtYFt5nbiWGjA6/IwMF6CjSJOnCajGVvYtO6I8sDZvyHc3MalL9/MppLteLUHiaTKqKbCqCSgA50KXXuihT5gudaECVOYN+8tLrroNM4770SeeOJVMjMPv4+qj57RF6z00cdhoGsU6pk4eoOCmEakSMRMAzHL7FSS+qKzJmUVP1hyK4trFnJSwWncPOxxPthyLImZgozxAlvA9u3w5JOCvDxN5l72EyJTIjIl1027hfTJWXzjG9dQX1fLfdkP4vN3TYULd4enSqbEOP7Atx9xiulkUT62oFHDBAN5vNml6b++Hh5+WFJZ6YzNMKCyEh59VJKYaNN/LwEhHdWox2KwUUE/ifYAjSDetdFpAjH73ysv6fX6CAZvYPr0a4FnWLTol7z77jRKSi5CiF8yfXo2xcUdY1ca9ecY+uk4tGuiOsadf/4+j7Q9yAVzr+Dn9/yxR4303SEQFFkFbI5UExdxlLRRQhMjRkD78Zpu7PYjdtr7kGsXkBMvYEnDVsIrBhGNSKShEUPL6Zc/gGVqEdu2lTFw4MH9MUaN0gwYICgtFWRlOddEdbUgM1MzcaIGQni9r+NyLUDKMJZVQHH0FPLDV6LRPVI/s7H5yP0OhjbJ61iJ9WovbtvDKtcyBJIMOxtfR6mVCxeFVj+2mmVsNTcx0Op6HnHiFFklpOg03NpDVESok7UobNpkK8Pjo7ggfDkNso4ycwM5dl5nICUQjLBGs8DzPqtcyxhgDcbGpsooZ2psJmdE5tIqW/BqH8l2Kv8I/gmf9pGtcthkbqBC7mSNayUVcge3tH+XIdaIA557S1zzly31fN7SihI2HuUnuyaRM3PdnJDtJt8u4rLwtVhhC9nxpze8WxXnxZ0WhnQm8GVtNlvabKJFnn0m2b0lKSkZcDIrh8PEicfzxBOvcsklZ1D2zXOYfdc8GmOJbGxVGNKZsNdHFSB4p9qiOCgZk7L/sbfENR9vqeLDn12CkT+c7Gv+SKvlBPH1Uc3JOS4SXD3/PakIa/xm12zxroCiIbbvRL0+qvHvVWK26/Vt8QPrnh1taeIqo5rN5lZ82kuiTnTGJNrZ4NpEYTyfqIgRI872tTZPXfBTrHCU6Q/9DCs3QEvcIk86vlIKRb3RgGmZGBi0ixB+7dwvQyKEqc2DKnaNGnUMzz//DhdeeBpz557Ak0++3ilt3MfR5eh2PvXRx38xOqZRj8RhiQ3JAgolOqrRz1noT7uXKf0iUVdXwze/eT2nXjWZZn8Tj138Ig9Oe4KG6Bh0liBzikC4nBXufv2grk6wevWBfzDnzr2Ihx9+kc8WLeC8d09ja3kZ0JERaNHonQoNiIyD//AKQyAnm8hvepB3eTFu8HQJVABWrBCUlwsGDtQEg+DzOWNtboZFi7o5xkYFWzS6RCI6Gk5FqkAHccrWDvLDfKQJh2HHDsjIMBky5EIuu2wJs2ffT1XVO7z++lDuvfeXRCKO94deYKOfjIPWrM5YxTltp/BU+6P8IuV33DP2T4ccqOwi286i0MzF0CYxHUehSNSJZFpZEJcYR77FoRM3booXn0flhkTqsldjlZTS1m8VLVtTCay8HICysp5JzgaDcNVVirFjFTt2CDZsEKSkaC66SFFQoPD5/oXX+wRCtKKUG5drNYHAfZjmsh7LNEdFhHpZv0/plF8HiBElJNrx7NW078FLnBiRDi8XgDpZw9O+R/hxwjf5c/AeAiqILWwSVBKZKptkncK06Cx+1vxH+tkDUCgUep8AIFmlkW8XEifGe543+MDzFjaK0yJnMz4+mROipzA5Np06o4YW0USaymC563PKzFIEAr/2s9K1hH/4/8xm48Dv88MNa5nfVkptyqc0pi2kNuN9NrvW8UplhMrw7omwidmjQKUuqni5PMY9a8P8el2YJ7fF8Rqa4oAk3SMpCRpIIXi3Ok7EPrzv5y7fnnD48H17xo+fxBNPvkbz9vW8+r1zqKhvJK41QdNxg5dCkuEV2Fqzqmn/vwcRW/NwWYRf/+BrCCvG4Fv/yZa4h3erLWoiiikZJqfk9m4RJcsrCFld3ytbO0K9ya59P5PCgKQ5rp0qgT3GJTt8WLrDEIIxKQZNMU1c7d6uKqJJdgkGJByZ6WWVrEGj8Wlfp1hDgg4SI4YWGg9utpTW88R5P0TF4pz2+F2kDsgiFPGwo9mH3XFJSiQ+5SMkwwyI98MWNnWygXrZgC0UA+P9SVcHz7YNGjSM559/F8uyOfvsmb1Wl+vj0OjLrPTRx6FS6pgb7pr4Ao4PyBYFH1noicYXMrsSi8V46KG/8Nvf/hQpJXfe+Vsuv/w6xxG5BVr+IPHWC0e9qwMhnL/hHhiJT59+Is899w7XX3UJp7x/PHfV3MN53gsRDdrpEcmVqC02cqDs6i6/H4QQ7K8ap6lp3/4EIcDn01RVdWNJ1q4dE4O9G1/9wnG3jwIu0BENaxW6SoFfIIZLRMaRW9vR1Y5CmtsFAbegsU10jF0yYsRVFBXN5Z137uKpp37Cxx8/xI9+9CtmfXwS0XCI34l7+PvmexnoH8xzI95gVOMo+EChL3cEBw4VgSAvNYNmv01yfSJGUOASJna7QLjBfRQrHpSCnfPHMjh8O8bUTwklVhJoySVl/URaVxfh8wV75Y/R2AiVlQIpnQC2sRFef12Sn7+ZkpLPsO0CdId8sGWlYZrr8HjexLLGQA8CFo/2kqbS2GlsJ5X0zsdDoh0fAZLtZGplDYl2cudzNbKKZJXW2WjeIpr5h/9eSs21nVK/FcZOMlRWp/pVod2PY+LjSehYUc5U2RR2yCoHrcROIYLV5jK2y61IBAk6mSw7BwG86n2BIrtkH3+TWqOaOllDUCXQLJpokg24tYdScw3vu9+iJDyw2/Ouo475LVsQLhcZIgWhBDFiNAfXs7k+lU2tPnJ6UfpTG1E8UBZlc7siaAoao4p1LYpBCZIc327X83SPoC6qqItq8v2Hfo17vc5NLRqNHOSVPePYscdx5wOvcse1Z/DhD84m91vPEHOl0hLTpHkEaW5Bu6UJHcDPck2zzavP/Yuqz17mrJ88xoAhBTTENBtbFOPSDC7v5+5x+dvOkGJDi01MOdLBW9pscn0SS0N5SJHvk4xI3vfzmZphsr7FpqxNkeGRxJSmLqoZmWwwOHH/n+f0TJMt7YrSFqePxVYQMAVz8l1k7ifIORgKRatoQyAI6gBxEes26BUIDAwC633MO/dXIE1OfeJOEvOyMJAoT4jaqmwqpIeCJEfVT6MxMRhoDSDHzqHBcFQGU+2UXnlKFRWV8Pzz73LhhadyzjknMG/eGwwePPyQzrePntEXrPTRxyGimzRYGrH3TTkBaOiY+B7eYvcR5/333+KOO25h69YyLrvsWr75zR+RlrZ7skUSDBgKG18TaL27XyISASk12T2csI4ceQxvvP8ZP7j+Fm5796t84Hubn+X/mqRhjmEkL9roBImYaqI1lJVBWZlACOjfX1NSwj5+GN2RkuL0Iyi1O2DRGkIhQW7uvuUOZEq0T0ArkLjH4w3aEQHwg27WqH/GYK0CpUGDzpDIC12I0T1XEOoObWnU/LjTc9ICmJqLbIsHWj00BSVJSRCPQ0VFMqec8mvOPvtqfv7zb3L11ecxMmUM9c211Os6biv4Ll/J/Tou6YJmBVHtCBcc5h1deiBhqEFos4Hd7MR1hh98xeBKPrx9H4hIxCnpyzGKSVte3OW5JhtycnrujxGJODLFdXUwYoSjCBaPw/r1gtde09x0Uxi9V0ZEqXQMYxsQhh6oZBkYHB+bxaP++yk3dpBmZxARISqMckbHxzIlNoPH/A9Qaq4jSSXTLtpQwubM8JdI1c7q7VLXIjaa6xloDe3sYUlXmWw015Gj8pgVPbXb454WOZtafzXrzTX4tI8dxjZ2mFuJEyfdziQiQlQbFYyKj2WHsY2F7g871bUGWINJ1MmUyx1EiFJlVBKW7SitcYsoZWIjr3if4/Lwdd3W729wrSGkIwRJRuDUBbpx49JuGmUdNr0riVlYZ7G5TTEoUWIIQZJLsL1dsTOkKAhI0jt6yCK2xi0FvsP7+uHxOIoVkUgPVl16yMXHjyP8t1f5+fVnsOXOU1HfeJj8fkMYnCgpDyk2tSqSXDZLGixGJhu491rAWrFhI6vuv52Rp13OgKlnIIF0jyTsh8ZYz8UFPqqJM788TnNMg4CwrWm3NLbWuKRgUKLB2fmubqWEByUaXN7Pw9tVcSrCjtfKCdkmp+S49hnvnqR6JNf297CyyWZbu43fFIxIMugfdI5hY9Mkm7GxSVSJB/UaqZX1bHBtpEW2AJCsEvEpH5awuijMWVgIBHUbqvj2eV/G8Hk4+5lvkZSagaFdaFeY1riH+oYUQonxzrFERIRCawAGBkk6kSQrcb9jORg5OXk899w7XHDBKVx88Rm8+OL75OcfOYPVPrrSF6z00cchIlIFygTCuqvbewtO03oPlZz+HYTDYX760+/y0EN/YfLk6fz9708wbNiobl87aZJm+XLN+vWC9HSNZUFjo2DUKM3w4T0vwwgGE/jtd//OtOaZfHflNzi1agZ/PPYfjM+b5KiCLbCwJxq8+JLkvfckoY7KjEAATjhBMWeO3kfRaW9Gj9a8/76mtHR3z0pFhRPEjB/fzViLBOIYAxZY6BQBPgH1jiSynOH0Gdlvx2CFje4vEV6JVhq2atQzcWTJ4Qko6E9seM1GpwKDBUQFxWU2FxPl4QYfVVVgGI4/yMUXK0pKhvDnPz/M9ddfwkcfvQPARcnXcHX2zbikCZaGmEAMkUdMHMBMgoTROD0qCowgiMOcJB4MjwdSU2HnTthTITkcBtMUDBgwgpUrl/ZoX1u2wM6dgsLC3dePy+UIL6xalUJTU5BgMA7sLtMSIoRSaV0eOxiTYtMIixDved6gwtyBR3uZFDueueGLSdPp+Nr9fOR5l+3GFkrsgUyJzeC42JTO7bcZmzExuzTbO//vZqtRtt/jDrVGcmP7N1nsWkiZUUq1rCTbyqNNtpCiU9Fa0ygb2GxuJM8uoNRc17ltjsrj5MiZPBj4MzVGJTERw9QmXu3Gq32ERTvbzC1sMNcw3Bq9z7HjIkpi2lZCW/sR8IWQ0vmO2dFEhCtMv0DvrsG1zTY+A6rCmrDtyN+meSVb2xR1EU26x2kwr45opmaYpHkOL7u5S67Ysg6Q6ujtPoXguhnHMuq597j66i+x9cezyfrG31g24hQqI5oEF1RFFA+WRZmYbnJhkRtXRwAQj8d58IdX407JJu3Cu/ig2sIlId8viSn2Ue3aH+UhxfzyOFrD4ESnqb7N0uxoV0zLMJmYYZLvlwds8h+RbDA0SdIcc4KbnvbIJLgEUzJMpuzVT9Qom1jjWtfp9u7TPkqsYkqs4m4zGC2ileXulYRFmAQVRAN1sgGP8JBgB6mXDXi0By00MRFjy8vruPeGn5OSl8a5j92BkePGNNogbCJb8onVp4ANtq+KWhlGIMhQ6fSzint0Xj0hLS2Dxx9/hbPOmsGFF57Giy++T1pan1P90aAvWOmjj0NlkEQMMWClQufgOKfXaQQgjz985Zojxdq1K7nxxsvZurWMu+76HVdf/dUDji0/H669VvHOO5LSUvB64YwzFCec0HMp3V3oZs2c7LmMHTuBr7/yZc576hRunvgdvjrgVryNblYvFbz9tiA5WVNU5GRFGhrgrbckAwbYjDhwry+pqXDllYoXXpBs2eKogRUWak4/XVFSsu/rhRTIC13oNAGLbEdpLEUic4XTT6M1erHlzFkbNdoLJAOFAsq00/My9tBm7lpp9MeWo5C2q6TMC3KQZPgWxW2nx9meZuL3awYOBI9H8fzzT/PjH3+LSCTCOaf+geYPK3iq6R6WL13FXcl/Z5yRj8yXiIu7n2S3tzs9MaYJRUW98G8RUNkEGzYILAuKi50x9cKaolcYBkydqnn0UUFFhROwRCJQXu74omRmHs+3v/0oTU2NJCenHHBf8bgjYW3u9etmGBCNphCJ9CM5eRWWNQBwI0QjQrQSi51Nb34SJZITo6cxKTaNalmJXwe6+KwMt0Yz3BrdZUV4T4I6AZt9J80WFkF9YB+HAruYAruYteZKNrnWk6rTWCtXA055TEAFaZHNJKqkLn01AsHs6Blorfk85VNMbRDUCXi0B1vYuDCxsSg11ncbrOTbReTmvERlYyUNzTmYhoXSgnbZxPScKIX+3l0gMeWUQcHugk2fBK8BjTHtuKELGJ1scGb+4YtfhEJONigQCB72vvZm4rAhvPPax1x701V88vNLSDvrm4y79HYGJrnwmYK2uKNyNjLZ7my2/93vfsrW9cspuOM1tth+Ug1NNA5LGixS3JIv9+9ZcFbaatMc1wzeQ/0raAp8pqAyoikKdP+57GhXLGm0qI1ocnyCsakmuUdAwStKlBWuVTTLVpJUQkdDezvrXBvwaS+5ds4+21QaVbSLdtJVWud3yK1SqJP19LdKEG3ZbNJVRGOaZX+Yz+t//hujTz+Or9/7A1rNBFY1xFCBZsxwIm3bhtIYhWkZFtM8GsuKk6CCpNmpNMtmtphbUShSVDJZduZhmTZmZmbzxBNOwHLJJWfyzDNvHdSHpY/e0xes9NHHISJMgbzMjXoxjlhto5udbIuYZcL4o7wU3QOUUjz44J/52c++R79+A3jttYUMGXKQ2X8HxcVw9dWKSMSZ5PVAMbZbRKpAuQR5ZgFPX/Aa9372a36/8Bc8u/IJvjfjTlpLLyQeF6SmOlMVIZyJal0drFkjGDHi4Jmcfv3g5psVFRVOOVhOzoEn5cInEHNcqBMNeNVCf2ihlwlYaqMVsM52emRM4dwhMwSMkM5syup5ZmkfLKBFw15eYcIlQCmyEzS5E50m19dff4nf/OZO1q5dxWmnnc2oUX9g+fJ8Ck62uWbVyTxbdhWX1h/P14b9lpvvuQQ5qOv1pjUsWCB4/XVBfb3Tu1FYqDnvPMWAAQceptbwzjuCV16RtDjVGHg8MGmS4oIL9CFfCwdj8mRNKATvvy/YuVPg8cBxxzljbm+fhtaaRYs+ZvbsMw64n8JCSEvTVFc7nju7zqm6WjB8uIHPdxGWFcY0twA2WgeJRk8mGp19SOMO6CAldvc9HsB+m8xHWcfwoX6bCrmDbJWHQFAlK/BpP6PiY3t0bBcuDEwS7CT8pp9m2Uhih4FkjCgCydjYhC7bCATFqoRMO5tm2QRAREQwMUlVmURFGEvGuj1eiT2Q6e4xvD3yObw1Q4g05hJzNzI2JcJNvvMRqucLNFprWi1NaxyyfeA1JLZSVEY0SS7BVwa68RmSFLfo4uNxOLS3twIQCBydyWROSiIvPTKPq+/8OW/efxebqlcz+Hv3QzCJoEtQHlZsbHGCleXLP+ePf/wlM798B7Hh42i3FGHbOUevAV4JyT0wlbS1Znu7oiGqqXVr0txgdLxXpmC/ogQrGy2e2BajIarxGPB5PXxaZ3NZPzeDEw/v96vGqKNZtpCqUjqv/6BKoE40sl2WdxustMk2zA6/k10IBBLJ2tYon64dTHNTkGW/v47qpe8w5VuXcdk3LybRDJAIKNxsiyQQdbcgPW3MTk3hlBw/QTuJBt1ImbmFhZ7FtIo2PLgJKD8mJjl2NqNjI3F3IyfeU4qL+/P44y8zd+4svvKVi/nXv553ekD7OGL0vZt99HEYiFSBvNIF9abTPJ4hupaE/Yeoqanilluu4f333+Saa77G9773M7y9TIs4jepdH9NRDWUKYjh+J+kHWYUbIBFDJSxTGDkGN4/+DqclnsXPl/2A61+5nMJVf6Ww8NfAcXsdWxON9nysUjoZod4gKkF9ZINPoAsFQkj4LO641geAYhwvk0oNERtdIqHgMFYdXUC+RKxUsEelgG7X4BKIDHjnnde4556fsHLlUqZMmcHzz79Hfv4U7rnHICdHk5IioXgaF4aWMv/Vm7hn7TVs/NO7/PKXfyQhYXf99erV8PTTEik1RUVOKd/mzYKHH5Z84xuKlAMkJ7Ztg1decbYdMsS5Dlpa4KOPBP37O0HF0UBKmD1bM2mSpqbGufZycnYFsP3IzS1g4cIPDxqsJCc7+3nuOScz6PdrWlsFaWmaU05RaN2ftrYfYpprO8q/8rDtInrSWH8kGWAN4ezwBbzqe6GzVCtZpzAnct5B5YN3UWwNoNDqx2azlCHxEWww11Ara2iXreTYeZweOYdx8Yn7bJesUim2+7OZjQQIYOLCoz00yHoCOsiY2PhujyeRnB++nAJ3MYtzPqYtdwVDrRFMiZ5FvnL6VRSKiAjj0d4D+tRURzSW0hQHBY0xaLOcHrOAKUh2CQYmmBQGjqxgaXu7k1kJBo98ZmUXUkpmXv4tWrOGs/S31/HIV2dy9p2Pk148pPM1Wmt++MPbGDR4OAVn3QxSEnQZNEQ1CkgyoSKsqInAgAPEVa1xzZNbY3xSF2dHyKY2osjwCkYmm/hN5/kZmftO86K25uWKOG2W7iwbU8rxiHm1Is6AhAOXjB2MmHBu3rsCle1NHlZVB9jZlkqiG6xki4npRpcA1K/9WKgu8uEaTVgpVte5adu6ioU/v4xoezNzf/0IqXMCVIUM0jpuewV+SZbPQz1RjvOaFEpnVaVJNLPEvZxG2USrbEVqSUzEcAs3yXYSFUYF6WYa/azD6zcZPnw099//FJdeeiZ33HELd9/9py9MdcV/A33BSh99HCZCCEj/4tyU3n77VW655RoMw+Cxx+Yzc+bJR2S/epNCPR1Db1cIW0AyiOkm4lRzv6pewhTIS9yoYEf2qQUGFQzhX1c8x0d8wHe+8x0WLJhMTc1FTJv2UxITi4hGnQzJ3h4pRxpdaiPagULnB1WHNDQBSTheMHU4pX2WhiqBmGsicg598iSEQE43UZti6E3KuWaiGmoVCzUPJZoAAQAASURBVDI+4jff/jlLlnzK+PGTmTfvTaZMmQHAqlVOOdeuLIE7qgmoBM448WGWrjqFt16/iaXvfcIfZ93PsTMmIaeaLF5sEI3CoEHONi6XI1xQWgqrVgmmTdt/wLF+vaClhc5ABSAxEWprYdkycdSClV0kJDh/90QIwaRJx/PJJx/0aB8zZ2rS020++8xptJ8wQTFpku70rAEPlnXMkRx2rxEIZsZOZoQ1hk3mBgD6W4PIVD2XXXPh4tzwxTwQuJcKuZNMOwu/DJATH8s1oZsYEx/fbX9AlsphduR0HvM/SEi0I4jSJBxlpLMjFzDQHrLPNo2igYgIk6YymB47kemxE7s8r9Escn3Ch563qZXVJKsUpsZOYEqse3PNXSrhw5MMwrbjNSIFJJqCFsuRw43ajvnk2mYbDQxONDgmxcDbAxXB7mhrczIrR7tMZ0iiwWfHzua8e9/jjZ9cwqNfncmEq39MxolXMyjR4NVXn+fzzxfy+BOvssBt0hjTZBiCTC9YCgzhKKF5uon1tNZURjTtcc0ndXEWN9gUdzS0b21X7Agp2iyLPJ9gQILBxPR9p3nlYUVVWJHrkzTGNNvbbRpjGiE0zXHNznYXRcFDz674tR+BwMJmR0OANzelELEEpjtES3uQx5uj1EddnFWwO02ba+Www9hJo2wiQTnBZKtsIxbysnzeB6z403dJLRjI+b9+iaTcfNqsRdQZIZT2sivmich2UvCRLXd/vtvNHYRkO17toRWBHx+2VoREiLhIwsCkyqg+7GAFYNq0Wfzyl3/httuuo7CwH1/96m2Hvc8+HPqClT76+C9Ba8299/6Ku+/+ASeeeBq//e3fSU/PPPiG+9ufraEdRyggimOmWKGgSKJdQJ2Gly1EmoBJ+7+VdMk+hYF0J/s0rWUWbzz5Kbf85DHef/+HPPjgMPr3/yp5eV9jwoTC/TrQHzEsnPqgXcRwFLX8QIJ0yr8atBO8GAIx8fBL+8QIA3mFG/WORXxHlLdrXueBHffx2RsfM2bMOB5//GWmTz+py4pcSgr4/RBu1IytiZNbYeOKa+otmC7PJvu40Xxj1bWc+/wp3L7mR3xl1c1UW378/q7vn2E4k/6WlgO/r/F4x1j3mg+aptNH8p9i0qTpPP/8kzQ3N3Wa++0PIWDUKBg1qhtFuC8YGSqLjFgWCsVS1yKe9z5Js2yivzWISbFp5Kr9pwzrZA0L3R8RIoQlLJJVCmeEzuWM6Ln4dWC/2wkE50cux42XNz3z2WZuQWrJ0Pgojo/OwsbuDDCaRCMve59lhXsJMaJk2FmcGDuNCbGpXQKhBe73eML3T0CTpFOoNMp5zP8P2kQrp0XP3mcMWV5BlldSEVb0CxpkdiR+t7c78rnpHsGT22J8Vm91HuWzOovVKSaXlbgPKWDZFawcjZ6VPRmTYrA2zWSx6MeEu99kzb9+xIK/fIuiD+ehf30vP/vZ95k161RmTD+RWGWMp7bHqW2waIppYkpjacGgRMmgvbxKmmKKZ3fEWdts0xbXbGq1yfIJAqbJsCSDZLdkR7vTvzIx3cXZBW7SuxElEB1/66OKdc2KqNJ4DEE4DnURm7er4lzdXx5yZiDDTidNpVIt6lhUmUrI0mQktSKR5NoBQhHBx3UWkzLMTonjJJ3I6NjIDjWwVgQQ3xbm8Zv/wIoP3mX47Is56Ru/w+XxgYZYQyGunFIaZANePMREDIGgJF6Mew+hjAbZiEd5iIl452MGEo3TqC87/n2kuOiiK9m+fTM//el3GTx4GLNm7avq10fv6QtW+ujjv4BoNMq3v30D8+Y9yje+8X1uu+0HSCkdk68K7azkaxD9pVO+dYAfIa01LLZRH1hQrSEoIA30duWICuzy8sgU6DaF+tTGOECwAl2zT7pBYz8Xg5U2wTj8duBFfDD2XP75zm9ZvvxeNm36A1KexujRNzBt2izkwSTBDhFRLNFugW5ViBjQqJ2AJQwMFYgBzmRNVymEKRBZR2YcmxPKeCL0EE+//Qh1DTWMHXsc//znc5x00undfi55eTByhEI+HaeoLU4sKKjXgmCzzWBL4S8q4tkr3+TXH9/Fzxb9kI1tGxg18T7KwoEu8tNOEKK7qG11R2EhuFya9nZHmQ3AsqC9XTB06H9u8j958jSUUixa9DEnnXT6f2wcRwON5mXvs7zmeRElFB7tZb25muWuz7kmdBPpKhOXdneZhIUI8ZD/PtaZq8hU2ZRYA6g2qvjI/R45Kp+x8eMI6P1PygM6yCmROWw01xIVERJ0EgjNvwL3sTOyjXMjF2Nj86j/fpa6F5Fp55Cig1QZFTzqewC39jA27pRvRojwrud1TAwK7X4ApJFOlazgQ8/bTI5NJ1l3rT10S8HsHBdPbI1R2mITMB1PEp/hPF7WplhUb5HrkwQ67jlhS7O00WJkY/cZg13URBTb2hVSwMAEo1NVKxRqc879KPWs7MJjCC4udjMqxWBDi8msH/0B6+KL+dudN3LGaRPRWvP3vz8JOB4nz++Is6bVdsqitCZma9Y1a367PsIZeW7GpBg0xzR3rgqzvMkmwRRkeMDSmvIQpLoV/RMMCgOCfL9gY6tiYrqr20AFIM8vyfVJ3q2xsGzVqbIWtR01trXNNpURTe4hljSbmIyJjWKJ2kx9yEXQF8GrvaSqFPzah8+tKW1VlIdUFz+WLJVJejSNBtXIo/+4n3t/+UsSklKYfsejFE46A1fHOC2lqanJZkbQS3ZiFa2ylQyVTqGVT47dNTPpxUOraMOnfUgkFjYGsrPqMy7iZNmHvqjXHd/61o9Zs2YlX//61bz11mJyc3tZo9zHPvQFK3308f+choZ6rr76PFas+Jx77/0nc+deDHS4xr9uod+ynMZuATooECeacKq5f8PKz2zsR+NOQ3mKgGYNyxTENGLYXtkFP1CvOybFB/9h01GNejjmKKhlAkFBwnbFmQGTs35xB+HCb/Lcc0/wz3/ex8UXn05JyUCuvPJ6zj//chITkw66/14xRMIxEuZZ6BbllH61dzzXqtHNGlo0IgRijoFIOvRSv3A4zKuvPsfjjz/EwoUfkpycwrnnXsJFF125XwnpXQgBF0y3KX8xTlVU0GJJ3G7oVyDwVWqoExjK4Pbjf8LAtCF8+42b2PDBVgZMf55Nm1LJznYECzZscJrWP//cydSMGqX3yZ4ADBumOfZYwWefOY31pgmtrYKBAzUTJhy5FcimJvj8c0FpqcDn04we7UhR709xrKiohPz8It5557X/umClSlbwvuctEnUSGXYW4PR+LHZ/wo8SvkWGysKjPRwXn8KJ0VPx6wBrXCvYaK5jgDUYNx4iRAiLECs8n7PZVcqY+DimR09iVvTU/faOfOx5j+3mVkbHx3VKKDeIej7wvM3Y+HFERYQ1rpUUW/07MzVBO4FN5gY+dL/NMR1lZnWyhjpZS7rqKtuapjLYZpZRbVSQbO3bKDU21SRoCj6rtygPKUYkGxyXZjI0UTJvewyl6QxUAHym48W0vsXuNlhRWvNmZZz3qi2a444yY4ZXcla+i2NTTRobG3G5XL3u3zsUPIbg2FSTY1M7xlk0jRNHvMGkSUOIxaLceONl3HPPX8kYNgG31ExKdxGxNWVtClM6GZaPamxqIlEmpzumjZ/V2wRMp4Rua0gTVyDQ7AwpigKOCEF91BEoyPLu/37lloITs128VWURU07PkEKTYApGJhvURjXlIXVYymAB7We8PZzXaCMeU+RKd2cmLqbAEHSbHVuzcgXf+tYNrF69nKuu+irf+c5PWNzu5eVyiw0tNoZwKnMHJhjMTswlPZbfpc9lb/KsXGrcdWg0SSqRJtlMDEeyO06MHDuHfCu3220PFSklv//9A5x00nhuuOFSnn327b6G+8Ok793ro4//x+zcuY2LLjqdxsYGnn76TcaPn7T7yQ0K9arlGFMO6fjRqdPo1yyMIgkj9p3A6LhGvevIqYqSjm1ShNPPsUKj61RnU73WGtGkYWIvZJrXK1in0P0FouPHVCcCpQr1sY1/SIBLL72GSy75MosWfcxDD93HnXd+h1/84oecddb5zJ59BscffwJ+//5LXHqKcAnIEk4zvV86DfApGqqF06+Sph11tzMMxIze3ypbW1v4+OP3effd15k//xmam5uYMmUGf/7zw5x66tm9mjAFo5oBOZrccYJoTOP3g6cGqAfCHQakJpw77CLyI4Vct/ASahdOZvbsV9iyZQBlZQKvV5Ofr1m1SrB+veD00xUTJ2pSUrqWfLlccOmligEDBEuXCqJRmDVLMWXKwbMyPaWhAe6/X1JaKvB4NJYlWbRIc+KJMHdu90GUEII5c87jiSf+yV13/Q5Xj3WYv/hsMzfTIpoZbA/rfKxR1lMlK4kb28iMZhES7bzgfYpqWcHVoRupNapQaNx40GhWu5ZRbuzEq72gNWFCPOt7HLf2MCN2UrfHXeFaQqJK6uL1kqJTqRaVbDY34tU+LOL7lJQlK6fMK0oUL178OoAHDxER6ZLNiYgwbu3Bp/dvtDko0WBQN+pTAvZbnLO/u83KJptXKywCJgxKkChgZ0jzzPYYuT7Jli2bKCoq+Y81Pv/1r7/DNE0effQl7r77Ds4+ewYnnXsVnHIro0sK2dCisLQmwyOIKoHSkGAKXqmIE7I0XkOT6nbKsywFVbYToNVFNdURha0FEVtzSo6LjIM4yI9INhiTYlAZsvEYAr8pyfZJ3BIaYprDtLQBnOByfLKH1yvjJJs4gZbSbGtXFAck/fcoc2tvb+NXv/oxDzxwL4OGDuNvrz1NybGDqddNTErIIN/vYVWTTbulKQpKxqSYJHVkzA7kPJ9n59JqtbHN3IHUkoAK4MdPlp1BP7uIXCunS8bySJGamsZf//oYc+eewK9+9SO+972fHfFj/C/RF6z00cf/I7TeJfErWLt2JZdcciYej5eXXvqAkpKu8qlqlY0IayjeYyKQIaDeRq2yMboJVmjWUKshba+bf4mAUqBUOfK+boGo1ZAokFN370dHtFNGlUi3Tfe6VoOiM1DZdS46SSC2qi6PTZgwlQkTplJVVcFjjz3As88+zhNPPITH42HSpOmccMIpzJp1CsX5/cHsudNz51gsDUsVlEhE3u4fTd3fUTyTc12IiUaPzRaVUqxevZz333+T999/i88/X4hlWfTrN4DLLruWiy66in79DqIbvD8SHANLPxDYFTCkC7SBU7rm0oBAt2sm+CYy/6cfctlfzmL+/OOZOvVlBg0ay9ChjuJWa6vm888Fv/qVZNQoGDzYUcnaU9DA53Oa1GfOPDp9QwsWOAHToEG6Q2Za09AAH3wgOeYYu1uPHICzz76Av/zlN3z44duHXAuu0WwyNrDatZx20UaRXcIx8fEH9TY5mhjaROBkU3ZlQbYaZcREhFSVTprKwMAgQSey3P05ZbENBFQCoFEomkUTNUY1SSqJNtlKgk4mVxWwXWzhI887TInN6BKQ7D6ugaK70j6NRJKokjAwiBLFs4f7eJtoJd8u6pzkpeo0RsbG8oH3TbzaS0AHiRBmh7GNsfHx5Nu9b14enGjwQY1NS1x3lnG1d0iHD0vqPlO0rMHG0ppMr/O8ART6YX2LYk2zTVlZKf37D+r1WHrL9nbF0r38S2RzFQ899BduvfUOpkyZwYsvfsBDD93HPb/5Ke0vPkbVqZdhnHwz3pQ84gpqI87nUhmGipCTLfFIxyzSY4ApBT7DacRPMCWGgHSPYFKGi8kHKJHbhccQTMs0eblcUxhwSu0spdna7mRUBiYcGfn9k3Jc1EQ1a5ot4gokjnLX+UVu3FLQ2trCP//5V/7+9z/Q3t7Gzd//PqNumkTYE6FUbwIEyUYSxySNYlBi7x3nJZKh8cHkW3m0yBYMDNLs1B4HKApFuwghEZ3CAT1l/PhJfPe7P+WnP/0uEycezwknnNLr8ffh0Bes9NHH/wN0jUJ9aMFKBS74PHERV/zlXAoKi3nssflkZnajIBQBbex7a9WmQIT2cyCfMykm7AQincQEDJQwUjrmiHUKCiTiXBMx2ECHNfodC/2p7WybLZAzXDCua5OmSHBWS7Wld/e+gKPKldf9j0B2di633fYDbrvtB5SVlfLuu6/z9tuvcted3+GHP7yVksQBzOw/m7HTJzDk9JH0HzgId0/MQOJARLPHHMzB7GhAdXPAQKWhoZ7S0rVs2LCWRYs+5sMP36G+vpZAIMiUKTO4887fMmPGSRQXHwFZs0LhSEB/rtD5ONmgsIZ0JyPUsl6zsF6yolUisiVjEwfy2GMfcM01Z/HKKycwe/YzSHki0SgsXy5obgbbFliWYtkyQUWF5KtfVeTlHf5Qe8KKFYKkJN3FDyclBaqrHYnlkpLug6Thw0czcOAQXnjhqUMOVt7zvMGL3nm0CWfiotB8an3E1e03kqbTD2mfh8sgaygZKosdxlaK7BI0mnpZhwZy7fzOACZBJ1LODiplBSPjx5Bt51JmbsCjvMSJERUGEtkpI5yokmmSjbSJVlJ06j7HPTY+gafMR4gQxoujU14tK0nUyc6Y7Gz6WQMoNddRaPfDo73UyRoiIkKySuGf/vswtMlwazSnR86mVTaz1rWSODEM7WJ4fBRfCl+2X6+ZAzEsyWBKhsHHtTaVYWfibgjBhDST0SndT6Sb4hrPXuWtQjilY2FbU1ZWypw55/V6LL1hZaPF41tjNMa6+pfE5/8Nt9vD1Vff6JyLYXDNNTcx9/zLuf7Xf2LRU38i/urDJE2/FNepNxNPyiPVDTtDioaYE6BkeyU7QoqABpeEsA0ZHvjaYA8T0kzcBr2SHD4hy0V1WLO8KU5jDGK2JssnOSPXMbI8EiS4BNf0d7OpzaQmogiYgqGJBrG2Jn7zm3t54IF7CYXaufDCK7nhplvZ1r+SJtlMuu2YQzZbNhtUPeta11HcMoaxKS6yelmeJhAk6gQS7QRs7B5fjzWyllJXGS2yBYkg1U5lSHwQib1Y2Lj++m/w6acf8bWvXdXXv3IY9AUrffTxBUfVKZp+HSO2XhMPwE61nCsXzmVYzkj+9fgLJGZ038shiiV8YKFjGtFhLqbjGhHXTqN9d9sEhBNgvGKhvRoSgQiIbQqGGlAg0Jst0BrdoGCBjcoW6Nds+NBCJwnwC9iqsR+JYQg3jNtjYjHMgHwLUabQRdJxiq9xasvlpIOv5PXvP4j+/QdxzZgbaX6ono/LP+Ddpjd5ff18Hlj2F/g9mKZJSclABg8ezpAhzt+SkoEkJaWQkJCI3x9wAigvUCwRy2x02h49N02g/QKdI2hva6WxsZ7y8h1s2LCW0tJ1nQFKXV0N4Bxv6NCRXHzxVcyYMZtjj53Ys2CpFwgpkBe4UTIGaxVUakSCgAtctI80eOg+gzX1gmCRgER46jnBmM1Z3Hvvm1x88UW8/vqZuN3PYhin0djoeJGEQs5/U1I069YJFi0SnHPOUVZg68AwHHnq7jiQnoIQgnPOuZB7772HUCiE37//8qLuqJFVvOp9AUMbDLVHAhAnxjrXKt73vMm5kYt7tb8jRZJO5tzwJTzl+xfrzdWAICxCJKpE+lm7s3EWjjJWQAdJ1WlcGr6GZ7yPsd61mrAI4dJuhlgjyLadGvxW2UyqSt9vo/2U2Ew2mutZ6VqKQqHQBHUCZ0TmkmcXIhBcFrqOp3wPU2ZuICZiBHUCXu1lsXshJiYazULPBxwfncW17V9nq7mJBllPokpikDWs24xOTzCl4EuFboYn2WxocaSLByYYDE82cO+n364kKFnXbKO0k3EAiCmNBJKJUl6+nf79Bx/SeHrCLv+Sdnu3f4nWmg0NYd55/H7OP/+yLn5IAKmJifzhu9/l4bOv4/nH/sqOl/6M/uBRkqZfQvI5N6NSCwkYinYL0j0SQ2iqwpqWjsDsnAI3x2eah+SLEnQJzit0URlR1EYsDCmIKZhfESfBLSg5DPniPTGlYEiiwZBEg/r6Wn7/q9/zz3/+FcuyuOyya7n++m+Qk5NHvWygRZaSqBIRCGqjmlVNmgh+TLOBVTWtLKwNcHmJmwG9zPw0yEa2mtuol424tEm+nUeRVYhrP9PgRtnEMvdKYiJGQAXQQlFhVhEWYSZGx3fJNB6Ivv6VI0PfO9ZHH19gbBsW/cnG8y5s90kqa0r5Xflc8oMDeGj4UyRsDXYxGNwTcYyBXmw4/iZJAgROj8lQiRi7/xu9PMWFatKw3EZUCkemeJgB+QJetSFZwCDplHstUUSr4oS2amJeiS8oHI+MZIEuU6j34shjZGdJmEgQyEvdqHkx2K4hrhEpAnGmCeN79uOj4xr1VpygSOCUaXM4hTkANG6rZ2PrekqP28iGHWvZsGEN//jHuzQ2NnQ9PylJSEgkISGJBHcCwdYEXAtNIiJCJB4hYoVpk600vtBAPL5b7tI0Tfr1G8DgwcO4/PLrGDRoKIMHD6Nfv4FHPDjpDpEqkNe5YYeGVu0YkGZKlr4vWBsSDJhMp7t8NKpZtQqOPTaBa655gb/97QLmzz+fY499Da2n0doqSE3VJCc7/Sp+v2bHjgN1CTjoBo1eYqO3KUgEOdpwFOJ6OVEaO1Yzb54kEtHsat2pqXH8XAYOPPAYzj77An71qx/z1lsvc9ZZ5/fquJvMDTSJBgbbwzsfc+Em2U5lmWsxZ0cuOKCR4dHk2PgE8uwCVruWExYhKo1yPnV9REiE8GgvFnE2m5sIqkS2GWVsMTeRbxfy1fZvUmHs4Cnfw2w1NpFqpxEjSoOsJyxCHB87Yb8lL0GdwJfbv8ZK11K2m1vwaC9D4yMYYA9mp7GNZa7F1It6hljDmR6dhRcfm41NPO97kn5W/85sTItoYoHnPUbHj2WENeaIvSemFIxKMRmV0rOpyoQ0k+WNNqUtinSvwNbQENUMSzIINpShtT6qZWB7+pfs+k4IIWj/7AVCTXWcftEN3W6X45PcNiadKXm384MZV1P79kM0v3YvS997mOCI6Qw58ULsUafSEAviMSTZfidQOTnH5Kx892H14LxTbbEjpBidYuLvKAXb0q6Yty3GLUO8eA7R02ZPtNYsXryQ5557nKeffgTDMLjyyuu57rqbycjI2v26jj8S57Pb2GITtTUpPgkmJAahrFExvzzO1wf33LiyUTaxxL2ckAjh015CMswauY4W0cox8VHdlnbtNCqIyAhpdqrzvAa37aZBNlJl1FBkF/T4/FNT07jvvkc599xZ/OlPv+Qb3/h+j7ftw6EvWOmjjy8wq1YJqj/S5HvBTtjOn9eeSaKZwVeynkeEguhyhdjPJF8EBfJqN/ojC5bazmMzTMRUE5HY/U1ehzX6YxtdoUAIdIlATjFhnET/Oob2s1vC1wVbQ4KPXpcMj1iUJwncmwX5+ZqhQ53JNdUa2nC8SnaNq79E3uqBLcppDM8XiNRepPXrNbrGmazvSXJ+KuM3TWTitOmI4R2yw1pTW1vNli1ltLW10NLSTFtbK62tLbS2NtPa2kpLeROxiii+iBev14e3IEDC4ERS0tNISUklJSWN7OycIx6UNDY6Zo+pqY46V08QQkBh1/PetEngcu0OVMBR8QLBli2auXMN6usf56mn5vD553NISnqb4uJxDBumO7MY0aggNfXAssS6UqEeiMFWhXYLRExjf2wjzzYRM3u3ej51qmbjRs3KlaC1QGtHJvn00xX5B6mSKC7uzzHHjOf555/krLPOd1TvqirRkQgyKxtxgDez+/4Mp0zkSHotHCrZKpfsqJMViREjwZfEZ+6PqBFVGEgEghbZxKveFxAdf4bGR3F16Kvc2nYHz/oeY41rJfWijiSVzFnhC5gW3W3eqFAsd33OctdimmUzA6zBTIxNZUJ8CsfFJ7Pd2MJacyXzvc+wwr0EoQVefMSIUWAXcVXoBraZm/FoT2egApCokylnJ5vM0iMarPSWbJ/k6hIP71bH2dBi4xbOhP6EbJMFb5QCHNVgZZd/yZ72TVprVr34V3LGnkjJAY5tSsHIFJNxecm0XnwL8XOuoerj56l4/0k+/91XMH1BJs+ey7gzLmHI2MkMTHRR4N9Xhr4qrPik1mJDq42/Q5HsuPTus1EhS7Os0SLdI/B3lH2ZUlDol2wPKTa3KYbupz+oJ2zYsIbnnnuCF154mh07tpKbW8BXv3obV199I6mp+6p1JKpEAjpAq2yDSCItcU2CCbhDiHAywvKT4xXsaFdUhTV5/p4FK1uN7bTL9s7SMjREiVJpVlJkF5Cm9i2RbJUtuLSrSyCzq3wsLMOOJ1cvOO64ydxww6388Y+/YM6cL/1beqf+m+gLVvro4wvMihXgNgRhqvjZ+jm4hIcfjpiP3ZRKS4si4SCZaJEsEGe60Gc4X/UD+qtYGvVoHD6zICDABWKTQkctRLbLkT9O2L19xIInSt1EQzZjfZCZDO2W03Pg92uKPR0eLd2IXgm3gMGH+CPoFQi3I4O853iIAm4BezXvZ2Zmd9/T0835Y/S+Ub+3tLbC/PmCZcskkQgkJ2tmzHCa2Q/FUsbr1di2ZO+siNZO0JKVBbfe6mbq1Gf57ndPoaLiNILB9wgGh2HbUFnpNNQfe+yBJ+vqHQu2KPTg3ZkyKhT6NQs9ykCk9XzwwSBcc41i5UrB1q1OdmXYMM2AAfuaUXbHuedewo9+dBs7Viwj49NPsUvXQyyOSE3FPGEWrmkzuv0c+1uDSNTJ1MgqslQO4JRWNcp6Tome9R/LquxNnDgtsok5kfM4PjaTcrmTCCFe8D0NQpNrFyAQxIiy2rWMDzxvcVbkfL4cuolqWUmbaCWgg7j17hvEnl4utrDxaA+rXEtZ6vqMa9u/ziZzA8/7nqRB1LHRXEdMxCi2+zMwPhSXdlFqruUV7/Od+9oXZ0X8P01BQHJ5PzcRG6SgMzOwbt1qUlPTSUs7en1J+X5Jnl+yrV3RP+hkV3au+Yzajcv50i+eOaCcMECiC0Ykm3xcazEkM4kR516FnnslqzaW0bDgabZ8+CQfvvgwBQXFnHvuxcyYMZsxY8ZhulxsalUsabB4u8oibGtyvIK4dmSet7WbXFTs7iyN20VMOfLHvr2yJy4JSkNU9T6A37lzGy++OI/nn3+StWtXkpycwhlnnMvcuRdx3HFTDuib5cbFoHh/VrnX0mTWY/gEuCyE5Uc09EOw733uYCgU9UY9PuXrEnh48NBKG62ytdtgJaAC1MmuWXmNRguNRx/aotUtt3yf+fOf5fbbb+Lpp9/4j6nS/X+kL1jpo48vMKGQYFtiC/duPIeYDnHn8DdJdWVjRzWRNPb1PdkPPboprlfopRYUSqd3BdBZGrFOoZZZTqakUjtlYEBpvWRbnaBfmqDFJ0lrVohkScyE2o2a4gKNOMnssZpWTxHJAkYZiHctdEAjfMLpy9mmnHK14kM7njhCDaUHQmt48knJwoWCzExNRoYj4ztvnpMdmTat95ODkSPh448dNa3UVOcY9fXg9cLw4c7+/H6YMSPAa6+9xOmnn8iSJSdjmh/i9/cjPR1OPVUxZMgBxh3RsNpGp4uuKm/ZAko1bFbQi2AFnABpwgTNhAm9PmUuuOBy7vnVj/nbHbdye3Y/ZG4eeDzoujriz8xD+gOY44/bZ7sclcfJkTOY732WDeZaTG0SE1EGWEOYGZ3d+4EcYTSaz9wLeNf9OrVGDV7tZXxsMqdE57DMtZhW2cwga1jnpMuNh2SVwueuTzkzch4SSVAn8LH7fT53LyQqomTZOcyKnkq2nct7nje7eLnYts0Gcw3PeR9nk2sD4GR3trGZVDudalnFNmMzg6yhHc38pYxrPp3qUBtWPJdUX4zU5AZajTo8eBlgHb1+kN4ghMC31+xm4cIPmTBhylE9rksKzsxz89jWGBtaFYaARU/fR1Jef7529qn7BAt7I4TglBwX1RFFWZuTBdRAXlF/bpv2I4b/8icsWvQx8+Y9yoMP/pnf//7neL0+Ckcch2vQZGIlE2kvOJa0gBePlHgk7AhrHtocJao05xZ6OuV+ARJdgnyfZEOrTZJrd+lafdRRYDuYz4pt26xbt4rFixeyePEnLF68kPLy7Xi9Xk466Qy++c0fMnPmyXg8PevvAMi38/BGvWyXlWyPN9NQHyAznoOIJaG1pjKiGJhgkN1Dw0qBwK3dtMm2LnHOriyrqbufBufauVSYTrN/ggqiULTIVhJUwiGbSPr9fu6++09cfPHpzJv3COeff/kh7ed/kb5gpY8+vsAUFrbxp7VnE9dV/LbwNQa0FaJaFLVCED/VvU9J0OGgyxUiLpysSgdCdsgKb9SIGSY8HkfvUJAqaKvV6JDGNUSyeqCHEatjpDTa+MIQNQXWVBPPzEO/xeh2DVENSWIfGWR5ugvVoGGdDZYzTkok8nxXt5LJR5rGRti50ym96tevawnWgdi2DVaudErldqlw+v3O4x9+KJg0qatCVk8YMUJz0knw3nuS6mrnsWAQZs/eNwBJTk7hhRde5ayzZrJ27Un84hfv0a9fHllZPchoHLyl5d9GIBDk0lPP5p/PPMpNX55KUodhqMjPxy7bRPyTBRjjxncbpM+Onkm+XcQK11LaRAsl9kCOjU3sVi3rYNg2lJbC9u0CtxuGDNHk5Bz6eS1yfcIj/vtBC9JUGiER4hXvczSJRgbYTiCwd329QHa0xjsyxo/7HuJT90ekqXQCKsA2YzMP+//O2PhxtIrmLv06trBpFS08ErgfhabY6k9KxyqzgYkXDxXGDgZZQwFBfVTx2boRtIeK2SEbENLCl15K+qDFzFBTGGgNPfSTP4qEQu0sXfoZP/7xr4/6sYYmGdw0yMOKRoutVdXMXzif277/SwYk9uyLneWT3DDQy6omm9qoIsEUjEg2Op3ed0m6/+IX97JmzQqeffd9XvngQxpeuo9o290I001z/7GUFR6DL7OQxJxioin5vGAX0WYl8+X+nk6jTSkEJ2S72BlWLGu0idia5rjGJeHMPDcZHQtN8Xic6upKKivLqazcSWnpOhYvXsjSpZ/R3t6Gy+Vi5MhjOOOMuYwfP4njj5+1j5BAb0hXaaSrNNza5vH6KKVRjduwiSnI8khOz3X1uF9FIMi381gl1xARUbza48h9y2aCOki63X2mLV2lMiI2jI2uzTTLFgSCFJXM0Phg/AfwDToYM2acxDnnXMhPfvIdZs067ahm+v6b6AtW+ujjC4rWmhdeuJ720GrGHfsOy1NHs6NV0R4RJI+XHHdZz0pmeozbmY3u40YfA4ICMdlwrIPfs9F1miy3xpMnaesnEImwaIKHlEZF1WZN1ijJ+Cu0E0T0Et2qUa/HYYntHDtHIGe54Jg9mlZTBPIGt+P7Ut+hjDVEInq42naoKAVvvy14+21BY6PANKGwUPOlLykG9MBCpb5eEA5rCgu7Pp6cDA0NgrY2R8K3NxgGnHOOZvRom7IyR6Z1wABNcXH310dGRhZPP/06p546k1tvPY3p0z8mGExk7FjN6adrRyBhL4S3I5v1loVO3UN2ukJDKtD/0MqndLWChg6Z7Nx96+8PxBUzTuHv8x7miXXLuX7C9N1jTUxE19SAZdFd5CcQDLdGM9wafUhj3kUkAk88IVi8WBKLORmtlBQ45xzHQLO32Ni873kTrTUltnMxJepkfNrPcvdihoVGkqCTqJM1ZKiszm0aZD2nRM/EwGCjsZ6V7iUU2EUkaGeymGSnsMncwCpzGTaKOllDnDhe7aXMVcpWowwAqQ1qjEoaZT2GNmkVzRjaRKGwsdkhymmsGUNSexYnBDIpNww+FUupqM2mNjmFpsKnqJaVfKP9e6R1ONlrNC2iGYnsHM9/gkWLPiYejzN16ox/y/HcEhJcku2fvIJGc8mXLurV9gkuweSM3dMzW2vWNttsbVcIYGCCpCRoMHr0saxOHEHt1OsZGBC8tWQFtas/pm39J7SteINww07qrRgAFcDKpHSeyS9iaEkxCQlJuN0ePB4PdVGTtQ0xQjEbFwqXjvNgSzVPtFbSUltOTU1Vp8cXQEpKKsceO5Gvf/12jjtuMqNGHYvP5+NIMyLZ4MZBXpY1WtRGNVkex7Mmu5fSxYVWPi2ihXKzsjPDkqCDjIgNw7sfVS+BoMDOJ8vO6pAuliSpxCNSKvrjH9/DtGkjueuu2/n97/9x2Pv7X6AvWOmjjy8ojzxyP6+88iS/+tUjJCYey/LlggZpcOyxmilTdI+bsnuKGCbRqQJ2aHR+R1alWSPiII41EFIgZrjQE0xo0PT3Co55XrBwoSA5qvF6BaVNBt5cwelzbOShBCq2Rj0Wg0VOyRFBAVs06uEY0uWGkbt/KIRLwHDj31olv3Sp4IUXBD6fExDE47Bli+CRRyS33qpI6l5FupOkJI3XKwmFNIE9TMFbW53nDvUzFQL694f+/Xs2SS4vL2LMmNf58MPJLF/+ZaZOfZo33pC0tGiuuUZ12zsjZ5morQqxUTn+PbYTJIozXE6JYA9QDfXYq1ai6pphdSJG7XCIJyD8TjAkz3XtV/xhb7L79efs/P78c8nHXHXsFDwdcqC6pQU5aBAcZXnQTz8VfPKJIDfXCfC0drJtzz8vKSmxe51haRdt1BrVpKiujceJOokKsYNScx0WMZa4P8WrfWSrXEztosQawIyOErZao5ookX0CgxSVSr2so0JuZ61rBR7txcamVTbj0wH6W4OoMaowtElEhAiqBGIiSq1RTZ5dyAZzLZ72PJJ2nEFhwMAWMda71tBiVOCKF+KpPJ5I4WJe976EFpq7Wn7LDmMrb3jms9G1HrRgWHwkp0TnkKP+TWY+e7BgwXtkZmYzYMABah2PECsaLZ7ZHqc2qlj42oukDZvMy00JXJiku3iXVIQVi+sttrcrUt3OJHxI4r7KenGlmbc9xsI6i1iHPoTPcDxSzshzEdOO0aKQkoFDRxHLHY5n1rUYMUh3KczWGuz6HRRGyinfuZWElh00Nu5k587tRKNRorEotW0RbCRuQ6IMg7jpQiVlEcwbwvmzTqYwv4Dc3DxycvLIycknMTHp39ZvkeeX5PkPT9jExGR0fCSFdgGtohUTk3Q7rUfyw25cpKt9xQAOh4yMLO644xd861vX86UvXcqUKTOO6P7/G+kLVvro4wvIypVL+eEPb+XKK2/g0ksvADRz5hzdGhyRJZHnuVDPxmGDcsp+vAKmG4gJewQJPgF5AgO4+GJFerpg8WJBJOI4ks+cqRg58hAHsUmhVynH/2RXOVqSgI0K9YGFHNF7mdwjyWefCZSicyJqGE6AsHEjrFkjmDz5wJ9Rv35OqdDSpYK8PCc4aWhwVMFOP13Ti9LuQ8a24f33BQkJgzn11Ad56aVzKSr6PYMGfYOVKwVbttDFzX4XIlMiv+pBL7Nhu4IEkKNMKOnZ52Fv2kj00YdRFeXocg1VGplRhGvcZUiRj1hgoQB5patHn7ExeAjXnHg68x78PS8uXciXRo1H19UipMQ15fijfp0sWeKUfu3KRAkB+fmwfj2sXy/Iyend99WrfQRUkBbZRIq9uyQtQoR6Wccb3vkEdZB+1gAqjB3UyRrOCV/I3PDFnWaWwY6V330d59uoldV48RPQFnFitIs2WkULSXYqw+Ij8Ws/ZWYpESJY0iLfLiLbzmNs/DiGWMOxq8fxfCjAmuQVbDfL2GqWYWgDjwDDDpBp51IjKvjU9RFLXJ8x3zuPcmMHmSoHjeYdz2t84v6A0dY4suxsxljHMsAa0itH8EPl44/fZ8qU7kUXjiTNMc2z2+M0xxWFopWXV33I5Ot+xqf1Fnl+wewcZ9Jd1mrzr80xqiIKvylYa2uWNNicU+Di+Myu2cAVjTYf11pkeiWJHf0mDVHFO1VxBiUaDAwafFZnEbU1BX5JU8zJwlga4kKSkJ7LkJIC8vwSf4vNyTkuzinYPfkvDyl+vS5Cmkd0loeB4xlTHlZ8abC3154mByIkwlQaVbSJNrzaS7adRdK/IesmEKSqFFLpZer6KHHRRVcyb94jfOc7X+Xdd5f/W+Tv/z/TF6z00ccXjKamRq699kKGDRvFD2/+JXq5DQbQXzor0EcBHdGOlHBAIL7a4eURB1kgoUTsU85lWU6fRTwOs2ZpTj5ZE406EzfjMH7XdI1GxDQEui7t62QQO5WjAFav0aUKbBDFTq9KT8rNtK1hqUItsaAFGCCQE01Ebs9LCmpr95UZds5X0NqqqauDDRsEtg39+mny87uWYhkGXHSRwjQl69YJamshIUFzxhmOIti/g1AI6uoEKSmalJSzGDfuNj788Hays8cTiUylrk7sN0MjkgRiRu9/NnQ8Tuy5Z9FVVciiwbBToTIsVHQzVtkruI/9ClpLxEobqkzIOfjnKTwehn/9m8xa8Db/WP4p56TnYKZn4DrhRIxjx/V6jAciHneCk2XLBOGwE3A2NOxbZSaE8zcW6/0x3LiZHJvGPN9j1MtaUlU6YRFio7GesAhRYg0kVzm6ziOsMWw019Mu2rr02gy2hlFk9afM3ECR1R8vXuplHfWyFlObDLNGoYWiRlZTI6vYzEY8eBAIBlvDSdIprDFXEtABLg9dx8T41M6SrlJPmHL3ClpVOUJEUWhA0R71kJ67DiEViTqRelnHAvd77DR3MCQ+HImkTbTSLBtZbayg3qglVaWxQL/HOeELmRE76VA/lh7R1NTIqlXLuPzyrxzV44CjvFUTVQxIkGx4902UFWf4tDNoMQWL621Oyna+V29WxamJqi6ZlPKQ4o1Ki1EpZpcm+NXNjkZu4h6PpXoktVGb9c02J+W4WN5osKrJxmcKkt2CXL+kMarpFxQMDBoEXIKaiMJnwLC9pIhNAYYAe6+vvKWdx4+k7kizaGGpewXNRjNSO71WW13bGRUbTo59cMXG/yaklNx99x858cRxPPHEQ1xxxdG/Pv8/0xes9NHHFwitNbfc8mVaW5t5+puv4fodqIaOmU+OQM51IUYf/iqX1s6kHwW6SqFftBxvFQUiQyJPd/xYumPLFpg3T7Jtm8CyIC1NM3u2Zvp0fdg9NCIo0LJD3cu9x85CoLOBjyz0a7Yjo4xA+UBM7SgfOsCvqtYa/bKFft1y/t8DYgOo5Qp5rRtR0LOApahI8/HHkpyc3ecajYKUmoqKXb0sAIJgEKZPV8yZo7sEcGlp8JWvKMrLnYxKRoaj4vXvwut1AqT6eqfHYurUn1FZ+Rnz51/E8ccvITHxyDd8qh3bUTu3IwoKEHGJiitEwIVwZaMaytChWkQwA12nnc+2hyVUMjOTm3/9F+bMmcb8If25+Ms3InxHtj5SKUet7YMPnH4glwtWr5bYtsaynAzKrs+3tdV5vrDw0ALPGdGTaZJNfOZewAZzDR685Nj5aKHJVrmdrxMIMu1stptbaRQNnZkVDx4uC1/D476H2GJuwiJOgk7iuNhkVruW49cBDG2QqJIpFMW0iTYaZT3toh239uDWbgrtIi4NXcu02KwuY2tNWoXMXYR75wSsSBrSrSCaBIFKdMF7ALTThk/7CIl2vMrX6Uux2dhIk2wiQSeQqJMYYo2g3NjBq97nGRk/pnP8uwiJdlaZy6gxqkhQiYy0jukMmnrLW2+9jFKK448/4ZC27w2xDqlfCWxa8ArZg48hMTOfcEQRsTUKaIlrtrQpMr1de7SyfYKyVsX2dsXIZGOPfToSzHsjcErEAqbgihIPi+stVjbZSAFn57tojGk+qbPZGdEQ0SSajtLYoISu97pMr6B/gmRFo43fkJhSYGtNeUgxMNEg338IeurdoNGUujbRYrSQZqcikWg0TbKZ9a5S0u00XBxchMDCokW2AhyxHpK9919hVFJt1KLRZKoMcq0c3D0Y2y7aRYhqo4aYiOFTPrLtzG5LzoYOHck551zIH/5wN+eff/lR6fv5b6EvWOmjjy8Q9933W95882X+edez5H2UizY1DBCggB0a9XgMmelB5Bz6D4jeoVAvx2GjQoe04ySfJmCYBAF6k8L+ZRQGxRH9DeR4A8Y42YuWFvjXvyTl5YKCAke5qrraCV6SkxVjxhxmdmCIhCKBKFPofhI8QJ1GRIASA16x0abzOiEENDoN/5RIGH+A21mFRn9goZOcYEwAWmnEeoV6x8K4smcp+KlTNatXQ1mZE2TE41BT46h7rVgh0FozaBAI4QQDb74pKCzc18NkV8nQfwKXyzmPJ54Q1NRAWprJzJlP8PTT41i79mL69XuVI/HToLWGtjYnnWTbYCuQBvhAeISTzfMYoG1QFjRrRFA412IvGDduouNqf/8fOevK6wke9si7UlYGn3wiycjQJCc7j1mWZuVKR2BhwwZBYqLTvxSLweTJikGH6PfmwcMF4cuZHj2RaqMSvwoQFVHuDf6SOPEuE564iGNqcx93+ny7iG+0fZ/NZilhESbbzsXE5BfmD2mQdZ3N+W7tIdfOw0BSbVRSQxUJOpHZkTOZFJu2z9hqjSqy+39Kgi+BiposmrVNe/bbGLmfEgk20KDTaBXNzI7OocQeyBZzk/NeYVFjVOHXfsIihEc7xks5dh6l5jrKzFLS4ruDlVpZzYP+v7DJXI/GmeRmqxwuDV3DUKv39aXz5j3K5MnTyc8vPPiLD5M8v8RnQEN7hM2L3mLixbeitaYxqjk+08QQAimcYGZvCxOlnfvC3oHJkETJknonENpl7BiynMWSko7yrASXo+p1QraLmNKYwglmjkvTlLXZTk9b0CDXC2VtinXNNg0xTVFAMiXd4Mw8N42xaFe5ZJ/k7HwX5iH0HnZHWESoNxoIqEBnECsQJKoEmmQzTbKZDHXghZIqWcMGV6ljGgkEVZA0lUJIhImICKkqlUIr/5DLyiwslrtXUW5UdliwQqVRRY2sYUxsdI8CljpZzwr3KlplG0I7712ySuaY2CgS9b7qJbfd9gNefPFpHn74b3zlK7cc0rj/F+gLVvro4wvCkiWfcffdd3Djjd9klutkx3Vxl3GiAbofsM7p6TjUYEU3aNRDMdiu0DnSUWKqUoCEKIg4Tj9BpYJ6hf7Mxn4axGkm8kYPq1dLdu50mst39S/n58PGjZpPPxWHHawIn0Be6kY9GYOtTnM/icApBsIl0G3AENFFFYw6jVquMMYfYMfbFbRqGLz7fRNSOL4hG2x0VPfID2bgQLjiCpu33pLs3CkwTc306Qq/H15/XTBkyO6yr/R0R+J46VJxUMPFfzfHH69pbnYm4WVl4PHkcu65j/HUU7P5/e9/wu2333VY+7e3biH+1pvYmzYipEQOGQoJCeiqSkRRMRQI51puqUKk94PWdESzhtkmIr331/b3vvczpk0byR//+Au+972fHdbY92bLFqf0q7h492OmCZmZGp8Pxo/XrFkj8PudoHTiRH1YpZDQ4WTfkUmJEiXfKmK7uYUSa2BnT0qtrOKE6Cndqmy5cDHYGt7lscnR6bzmfYGoiBBQCTTJRnw6wG2tPyRTZRMVEfLsAnJUXrd9JEGdgJQ2udlbKcjewWDZwALXZ+w0dhDFwNIWM6Inc3vLndSYlXzq/oidxjYy7Gw0mlbRQpAEstTucp/uvnGveJ5ng7mGgdYQXLhRKDabpTzjfYxvtf0Yb3cus/th585tLFjw3r9Ncak4IBmfZjLvldeJh9tIGXc6G1oUWV7JtI5elCSXYFiSwYJaiySXxpTOIseOkCLHKynZqwR2bIrJikab1U02XtN5bUzBuFSTEXuUdG1qtfmwxmJzm43PEExMN5mSYVIQcI6rtea1ihhPbouzPaSI2k6Z19AkgztGeLlpkJfVTTYNMUWSSzAy2SDZfWSyKg77uweKjmcPfI9sFi2sdK8iKmIkqgQUmnKjgo2uTaTbqXi1j0bXZqqNGo6NjSFFJfdoVDY2IRHCwKBBNlFhVJKkEjqzPBYWlUY12UYVhXbBQfe11rWedhEi3U5DIDpMKRvY4NrIuNgx+3y3+vUbwIUXXsmf/vQrLrnkywSD3cgx9tEXrPTRxxcBy7L49rdvYOTIY/jOd+6Ee220t+ttTQiBFkDH6tehoFfYsG23C7kuBxKAdg07bScYaHX6QWgCsoCQRj8dx27WNGd6IGLuI7QUCNDp8XG4iEKJ/IbHMRoM40jaZkrs52OARoi9+llcIEIHCQZMgI4M1Z4TSQvwdyx19pBRo2D4cEVjo5OlSEqCF17oCJ72mn253U5p0H8SrSBeB/F659+uFHBlOHLH06bZVFU5n19h4TSKi+/k7rvv4PjjZx2yQo2qrCD64D/QVVWQmYm2bayPPkQkBEFrVOl68PjQSW3QmoyZfDLS7YIzJPKUXprMdJCfX8gNN9zKn/98D5deeg2Fhf0OaT/dseta17rr52vbjuT03LmauXOPXjDqwcP5kct41PcPSs11CARxYri0i9Wu5fzU+B7j4hOYGjuBYDcrt7s4I3Jup2Fkm2wl3y5keuxEJsaO71zpPhDD46PJtnPZbJZSZJWQqJI5Jj6eLJXN1NhMToicwlB7JBJJspXCeeFLeNX7PFs7MiwKm6HxkSR2TCIrjZ2kqDT6W7vTUM2iiTWuFWSqHFwdGSOJpNDqx3ZzC5vNUoZZo3r83j3zzGP4/QFOP31uj7c5HKQQnFfo5s1N75Oc24/s4sEMSTaYku6iYI8g5OQcF1Udxo8a59rK8ErOLnB3UQwDCLoEV5Z4+LzBYnWTjSFgZLLB+DQTT4en1KZWmwfKojTGNKluQUPMURCrCCsu6XCuL2tTzNseZ3u7wmMIUt0QsTUrG21+uy7C3WP8XeSSjzQ+7SPVTqbSqMarnT4pjaZVthLUAZLVgaUUK8xKQiJMunKCgCgxLBFHoTEwSdQJaDtIvWygzNzCsbExBxVvqDAq2WRupk22I5FYWMSJdylHMzERCGqN+oMGK02ymRbZQpJK7Dy2RBJUQeqNBkIiTKAbj5Zbbvke8+Y9wj/+8SduueV7BzzG/yp9wUoffXwBePDBP7Nhw1peffUTTNPELlKI1XTxPNFx598i8zBKwKoV2mC3cWJAAMKZwDfhZB9sIApk4yhxeYAtGl60SMmRiAZBVIFnlAGm80Pb2ioYMeLQg6i9ES6xO6vUgSwwUIaNDutOPxVtaURIIwYf5D0ZaECm5cgyF3UEfhGNaNQw1cna9AbDcDInu8jLc/pWIhGnJwScyWx7u2DQoCP3vvQWrSC0EaLlOAubAmJV4KqBwHCnfyZtD1XOG2/8Jm+//Sq3334Tb7+9pFfO07uIL/oMVVmJHDJ097WbnILeXIY560SIxVA1NcjJeRgjj8NI7geJwikBOwxuvPGbPPHEQ9x11+3cf/9Th7WvPRk0SJOUBJWVjgqcEI5IQSi0b3nf0WKQNYxb237AatdyKuROPvK8S4toIkqUkFHF0+ajbDJLuab9a/vNPLhxMzt6BjOiswmLEAEdxOzFFCBFp3JZ+Bqe8j3CNnMzCkWyTuWK0PWcEp3TJeARCI6PncCo+Fi2mJtoFA184HmTHcZ2NprrsbBI0AmcHpnbpV/FwsIWNh7V9bozMLGwsbB7PF6lFY8//SDj50xmdfIKBlvDSNZHXwnKLQU1G5Zw0pQp/Hh09/1Tu4wfVzbZ1EYUQZdgZJJB1n78QxJcgplZLmZmdR/Mf1hj0RjTDErY3bDfEtcsabCYkmFSEjTY2KqoDGtMCckd/YAuKYgqxfoWm7XNNuPSjt6UUCAYaA2gTbZTJ+sxMbBQePEwKD5wn3LGvQmJMAZGZxAQFVFsoXBhEhfxzmP4tY8G2YiFdcAemBpZywr3amxsAsqPLWxqjFrixMlUGfsE8FIf/P6k0Cj0PkGS1AIt1H6zR3l5BVx22bXc99ffcubV55OVlE1AB/4tSnn/X+gLVvro4z9MZWU599zzE6644npGjRoLgDzORC1RiPUKnSVBaUSNhoHysBrsRbIAe3cQJLIFertwyqRSgbh2ghY3kOQclxrdkYGA4XkWJcJF6TpBjlC4BkhqahwVsEMxwusVIyWMloglNjrYkQ1pVui0ZpS9GbXEizFkKGJPA5M9zlue40I9HYP1TvAgDAGjDeQJh7aavycjRmhGjnTUopKTnTKg+npBv36aCRP+cyVgViNEK0D6QHbM/7TtZFpi1eDdq29GSskvfvEnZs8+jr/+9bfcfPN3e31MtWULIhDo0jwsXC7nmvN4cJ9/4eGc0n7x+wN873s/5etfv5qFCz9k0qR9+y4Ohbw8OOMMxfz5kvXrncdMUzN+PAeVqj6SJOsUpsZm8prnBdpFG0OtkZ3BRoQwK11LWO1azrj4xAPux40btz40mdTB1nC+3fpjyswNxESMQrv4gI3vSTqZMXFHme2Y+HiWuj9jm9xKok5kVHwsA+zBXV6folMptPqxxrWcJCulc7JWLStIVxkU2sU9GqeNzW9W3MXOrdvJ+2sh/wj8kQyVxXnhSxkbP+6Qzr2nRCIR1qxZwZe+dNkBX5fgEkw5ApmMmNJsbrNJdXdt2E90CSrDUBnWlATB0pqwrbvIEwOYQqDQNMZ6dy2vb7Z4cluMlU02XkMwLcPFJcUuEg5QOpaikjkuOo4Ko5IW2YpPe8m1c3pUspWgAtiGje4IBgQCNCihulzPNqozG9IdGo2FzTZzB3HipKnUXU+QYaex3dxJo2zqfDxKFIBMlXnQMSapBALaT5tsJ0kldh6vTbaTrlLx6+4b6GPEmHnrHB554h/87O8/5PwfXE2OlcXQ+JBeNfb/N9MXrPTxP4uuUqiFFmxyHLTlWAPGyt1Zh16g4jhKWi4QvUx8/PjH38LvD/Dtb/+48zGRJ5FXu1FvxBFlypmYTzeRJ5uOW/shIkYb6A9sRJlGF+BkVNIFWBJShKNXWacgiBOwtAOtgB/wCXweyZWTLOYvkqwvN2hLcWr5Tz5ZMXjw/o97JBAegbzCjR5gwxILwnEs9TFW8zvwUiMIEHn5eC66BGPAwH23H2cg8z3oNQrC2un7GS4dd/bDxOeDK69UFBcLliwRxOPOezJ9uibj0ESMjghWixOcyD0WqoXU6FiY6KomXOE2ZL8SxB51fUOHjuS6627mD3+4m7POOp/i4m5MVw6ATElGrY90eUxrDVoh/PsGkkeSuXMv5qGH7uN73/s6r732KV5vz/sb9ocQMHOmpqTEZt0657MtKIDhwzX/CWuEUnMdfu3vkhXx4iNGnDc983nP80ZnMHN89IROueMjhRcvw63Rvd4uWadwQvSUA75GIjk1OodKYyfrzdUEdSIREcKFm9PDc3ucGVns/oRnH3+MYFECkyZOQ1ua7cZWnvL9i3y7kEx19GRy165dSTweZ+zYoxsU7cIU4DOc0i9wvmu2BqU1EvB2rG0V+SUBUxCyFAku50Fba+JKk+IWJLl7fh9c32zxneVhKsIanwGWVpS1KlY3W/z2WB/u7lxlOwjqAIOsAb0+zxw7h+1mOQ2ykaAKYnRIHwMEtHNfsbCIiAiDrAH7ZA01miqjmq3mdlpFG1VGNX7t7Qx+ABJ0An7tIyRCKMORdzC0QZFVQLZ98GDFjZuB8QGsdq+lXjZgYhITcfzKy8D4gP2WW65zbaAlr53Z153NW397kTk3XsCWtG1IDEbGh/X6vfpvpC9Y6eN/Er1Toe6PwU6NDoKIglphQ7mBPKtnpnQAKgaRbRCrcSaFRhC8BeDu4QT1gw/eZv78Z/jTnx4iKSm5y3OiRCKvd0MzYHBYQUrnPnMk8hIX6oU47FCgncBIXGciBkp0vUb9KQofK6fx3sLpf0zAUXFKF2QGNFeNsWhstLC+5iGzvzjaZuG7xx8QiNkmzDaJzX+D+EsvIgsKEcEstBVHb9lC7Kkn8N72LYR39yqWbtfOuWZLRPaRbBrdTWIizJnjeKZofXh+MwdCaw1lGr25I0PUv8MLpyfXrGVhr1+HXReD5i1EXn0X2X8AngsvQmbv1gu+7bYf8NJLz/D979/Co4++1CszPWPsOKwlS1CVlYisLFAKtWM7Ij0D45DdQnuGlJJf//qvnHrqJH75yx/yox/96ojsVwgnKC8u/s8LJfh1sLPsZRc2NjuMrdQZ1RRb/XFrN294XmKNuYKvtN9Cnjpwrf0XicHWcG5ov5VP3R+x1dhMusrkuNhkRlrH9Hgfb+58hZ1PbmfanSciOybOxXYJ6801Tk9M9OgFK0uXLsLj8TB06NG91nchhWBCmsEzO+JsabOpj2qaYoqI7aiF7WrYH5pkMDXD5OXyODtDjudKXIEpYVSywfCknt+wntwWoyKsKfSD2fH+tsYVSxss3qmyODX3yEfxCTrIMbFRbHBtpEk2A1BkFxAVMVplK63aKQPLtrMoie/bs1ZhVLHCvQobG6/2AJo62YiJiwzbKUVUaBJVIiVWP8yO5sZUO4UMld6jvi6AAjsPX9RLhVFJu2wnUSWSb+eSvJ/sUbtop9KsJqgCnH3DJbz+l+f49LH3OOGmOVSYlQyw+uHbT0bmf4m+YKWP/0nUuxbsVOghuw0Fda1CfGg7Erh5PTAZVNC+3glUpNfJqlhN0N7mZFdcaQfePhqN8v3vf51Jk6Yxd+7F3b5GCAHJvTy5gyBGGMgBErY5vioU7TabFKnAnV70H2LopTZEtZNtkcBA6ShzAaJRk1YokANAHKVJ+YHQ0SjW4kWI5GRE0BGrFaYLiopR27dhb9yIOXIUulKh3rRgbUet+xADOdtE5B2dgAXgAIuKh41WGvVCHP2+jQjhlEEEQMzsCLL3khk1k0CYoCLONWpv3YK1bQckFmKmaoQ7G3vNaqJPPIb3a7d0Zlj8/gA//envuOqqc3n55Wc588zzejxGY8RIXHPOJv72m+iNpSAFMjML9znndgmIjhZDh47k9tvv4s47v8OsWacyderMo37Mfydj4uNY4v6URlFPcochZKmxjnbZxojYmM6sQbaVx3pzDQs873JB+Ir/5JB7TbHdn+Jw7zJ6e/LBb9/Ck+ph1NVjOx/btXoeFqHDHt+BWL58MSNGjPm3OpJPzXTxeYPNqxVxYgpcwpE5Dluat6vinFfoxiUFtw7xkOGVvFERo9XSZPgkx6YaXFjs2ac87EAsb7TxGbsDFYCgKaiNaFY09j5Y0R1/DhYQpKlUJkbH0y7anWPqICERptaoJY5FUAfIsNP3yao4inJbUNikKic7l2lnstMsp07Wk6ASMLSkWbaQoIIEVAAtFAEVIF2l9ThQAec6y1DpB5Vh3kVUxIgTx699+NJ9TDprJm88+AIn3zCXdjNElBg++oKVvmClj/85dEzDOtuRrd1zcpcuYL1Cb1U9msxaTY7CkpEAsqOsVHqcPoFIOZip+6pD7cnf/vY7tm/fyoMPPtOrlesjgfDu28C+C5ks0d/3wAaF2majP7JhmwYv6GagQYEAeYJ5SCVz4GQ69GIbvdoGCWKkgRhndDbOH5R4HB2Nwt4N4C4X2rYhEkE3atQDMdii0BmO8YBYYKF2KOSNbkTawT9jrbRz7k3aKZMr6mEG42ixRsHbdudYAKjX6LdtR0RgxF7u1CngzYPoToi32ajqKDqYi8uoxRUrR/j8yOJ+qLIyVFkZxh61fLMnTWf2xGn88LtfZ9qY8SQVFPVoiEII3CeehDl2LGrrVpAGUg9Al7mxV8cQ/STiGKMzQD4aXHfdzbzzzmvcfPOXeffdpftkLf8/c0x8PLMip/Kh5x2qRSUaaIwpiGWzsC1Mm7kIHawg2WuTo/JZaS7jAr64wYqFRYtoxqt9+Dl8Q89t2zaz4fG1DPrZEAz/7u9DmDAmJrkHUXQ6XJYtW8wJJxy43O1I45ZOuVdJ0CDVLTAlpLkFbZbms3qbKZmaXJ/Aa0quHeDh0mI3lRGFSwryfALZy3ua1xAovbvsrN1yrsFWCxbVK96ujDEty9XpC7M/IkTZam6jwqx0DBjtTPpZRQT1/stFJZKEPVTvAtpPwDrwvSkiIrTJdvxq9/UV1AGy7EyqjGrqZQMB7SOgA8REjFXu1cAu89V0RsdH4dOHX1LaHT7txa3dRESUgPZzypfP4aN5b7L43Y855qSJR+24/9/oC1b6+N9D4hT67i13qwAhED3sZ7PDzjZyr9cLD9itdKovdUdbWyt//evvuOKK6xk06ItXkypMAcMNjOEGeqZGv2OhP7Wd96xQIGeaMO7QUio6pFEPxmCFjfY4TZIsU+j1Cnm5q0d+JwQCyMIi1KqV6JTU3apTjQ2IYBCZm4teZjuByuDdfUg6RSM2KPQSGzH7wMGKbtKoJ2PotQoRAu13gip5gQuR+O8JWLSlYZlCrbIhrNGVGkLaKf3qQKQJqFGoNTbGXsGKEODr78gVR3dGiC1agylaMH1tiF3KSl6vE/y17dZYtj77lNj8F/l+MJ1Tmpv4zdUX8oP/Y++846yo7vf/Pmdmbt3eG8sWlt5BULD33o0lsUcTE9PLN71+801iuia/mMSoMXaNsUVNjA0VUBHpnQWW3WV7v3Vmzvn9McvCwi7swqKY8PjyBdwyc2buvTPnOZ/P8zzf/jHmcScMmazJrGxEZhb6eQf1vIOIOWhDwGsu+h0Xeb0PkX5ozqOUkl/96i5OOWUm3/jGZ/nd7+47JPv5IGBicmn8o8yy57LV3ExLd4jHWrbTXPRnnISNiOcgIlk0Za2nNe09TGWiUMNaHX4/oNEssRbzsv8FGo0dBHWQo5LzOD1xLqF9TFb3h9tv/ykZmZnMuW4e683VZKtcHGw6ZDsz7TlMsA9de1Z7extbtmxixox9hT6NPLodT0hfGhZk7iZwz5TQ3K3YEVOYAlZ3ukQdyA8IJqUbe9kkDxXH5ZncV50k4iiUFjTEvLYzS0K6BY9vt+mwNZeWDu4maGPznn85jbIJf6+V8WazmjbZxlHJmYQGsPjdHxwcmo1WIiKCT1vkubkECGBqE1MbOMLBr70xCQSpKgWFYpI9gXSVxhprHVEZJUtlIpHYODQYTYT1ZibZEwAG/B1pNJ2iCyUUqSplny5keyKog4xyi9lobkajGD27itHTx/Dvu57m/JMu6RcE+9+MI2TlCP7rIEwBsyTiaQedpREBL2iLWg25wluhHgJ2khSt+ovqteNpV/blOnj//XcRifTwyU9+4cAP5H2CCArEuRb6VNPLPUntPYcHCL3EhRUKXSH7iImOasQSF2YYQyJBQgh8J51MfNtW9Mb16IwsiMcgFsM89TREUTHqJRttiX7VH2EIjyDV7Ft7oLVGPWHD2y6USnQpntHAIgcVBONjB9bioZqasBcvRK1fhwinYM6ahTHrqH4C974xqN4xvOKCAm0C611PRzRGwm5VKG0IRHyvTXjHLMHKATMzgPHiFtyaGkR5xa4XdHVCKITI9QSk7rZtJB5/FGyb4ikzuK6rhb8sXcjNDz9IYX4BxrjxQz/gWo1+0fEMGsoMBHgBnCtd9JsO4uyBb+qbNsF77wna2wWjRmlmzx6+UUFeSQFf+/H/8s1bP8tJp53JpRcO3Gr5YYRAUOFWUe6M4Z7tSboiEl3sINJq8CfyUckwqrsUN6WODqONzcYGqtxhfG7vA5Zab3Nf6A+4uGSrHKIiytOBx2kTrVwfuwUXl3pjOxpNkTtqSBPAmpotPPbYX/nWt37MmZzPy4l/ss5aRVincmL8DE5KnH5IJ39r164EYMqU6YdsHwPBL71qR9ztf11LKs/ta3vE5W81SVoTGtFrpDguzeCaCl8/crM/aDQ2NleUSVZ3GLzX5tDpeIL+oAET0w2mZpq0JhRvtbgcm6soGMSOudFookk2k6ky+/QhYR2iVbZRa9QPW4QfE3GW+VbQIltQvRbBqTqVqclJ5KhsCt0CNlnVWK6FDx8OLp1GFzluNhVOGa2yjS7ZTbpK6yMkFiY+7WONtZ4dRiMSQb6bR7lT1lf96RCdrPWto112oNCEdIgqu4ISd+Bw1YEw1h6D1JLtZh1xI85pN17Anz/7S6xNAsqGdRr+Y3GErBzBfyXkSRZqm0asUeAqhAayBfIi00tFHwLMTK8FzO0EIw2QnjYAF3yFg7eAJRIJ/vjH33DxxVdRXPzhEb6KgGAY4dGDQq9XaJN+FRQREqAVarOLMcSKjTFxEoEbb8Je8Bpq6xZEYSHm0cdgzjvWW/1PB+HsTUpEUsO+88egUcMqF10kdxkbpIEukIhlLvosNaQ2st2hGnYQ/9MfUTVbESmpYNu4K1dgbtuG77LL965YVCuvBS9bIDK9255Gw0IXXe0iJnmXb53QCAVizL7HIwwD64STUPf/BXfLFmRWFjoWRbe3Yx13PHKU9110V65Ad3T05aRcP/tY7l26iL+sWcqXl703LLKiNykvu2f8bpUgv0CnCnjPhQHIyqJFgkcflXR3g8+nWbxYsnix5sYbFaWlQ9tvnVHPemsjWVeNYtZL8/na129l1Mxy5pYeM+SxfxgQcWBjt4vhs/G3TieZswwnuAOC4CbSCPWUYYQN6oztB0RWhqolGC4Uilf8/8TBodL1QiHTdSYhHWaZ7x1edst51/cWNcZWQFPilnJe/NL9upD98pc/IjMzm4997CZCbojrYp8kGUsikcPKlDlQ1NXVAFBSUnbI9tEQUyxuddjUpUizBDOzDGZkGczONni2ziZsalItga002yKKoqDk3TaXHkdTEhJ0O+AqzYoOh5caxD6rH7tjZ9hiu+zACEhuPjqflVtKuH8zBKRgXJpBYcj7nmT5BBu6FQ3xwclKl/QqueZuKb0SSTwRYGFHjLakQ1lYkh8YWuvtJrOaRtlEpsrAxKsmtssOVllrOTZxNFV2JTERp9Fookt0I7Ugy81kcnIiEokrXBQKY7fxuLi0GW1ERIywDmNpg83mFtpkO0clZ2FoyXLfSjpkJ2kqFYmkR0ZY6VuDP+Enbx+23rvDxGS8M5YKp4y4SHDs2XP523fu4f6/3MV3v/vTIW3jPx1HyMoR/FdCpAvkJ3ywSqHrFQQFYpJEFA39piwtCI+H6AbPIhbtiewDo8G/Dx3x448/QGPjDj71qS8d/IF8GGGCGCgnUTHsK5IxbjzGuPFoxwHD6HdTk1NN1GsuukbtMkzYoREpAjl9P4SoR6PjwJ73mhDoLqAH2I+Bwp6wF7yGqtmKHDse0WsVptrbcRa+iTlnLkZZfwcbXa0RMaBstxt1iYQ8BRs0OqXXDSyiYaqBmNH/mFRnZx/xEBkZGJOnYBw1B7/W2K++gmpuQgSDWOddgHXaabta6bq7+p3L7FAKV0ybw19XvMPNjQ17nZJ9YrAClhj4ue5ueOYZgetqxvfOrZXSrF8veOEFyU03qX3qwADaZQcrfWtwcclUmdzy8//hG6d/kk9d8zH+/vQrlKYNkfF8CCA9KRY+FcLqKcOK5yFCrWgUbss4QpUv4Qu1DNtNKEqU1/0v8bb1JjEZY6I9hROTp1HiDk23tD9ERA+Nxg4yVf8fUZpOZ4vYxIOhezAwKHZHIRBsNTdzX+iPfCby1UHHsGjRAh599D5+8pPfEgrtaiHaX9jgSKKubjvZ2bkEg4dGEF0bVfx5c4K6qBckuTWiWdHhUhczOTXf7BO418e870ZpWDI90+Dp2iSuhkUtioQnOcQQ8Gqjw7nFPgL70R62yw6W+pYRFTFCOoSLy1Z/NaXlUWZ3TMBRoh8pSSivJSw0wHYTJNhi1rDB3Exbr73vzon+uuYgL9dk4MbTWKwSpFmCE/NMziyy9qmrSWLTYDQQ0sE+UiqRZKh0OmQnbbKdPJXLrOR02mUHERHFpy0yVHrf9yNNpRIgQERE+6om3bKHHhElTaeSqbwVrpAO0Wq0UWfUE9B+OmRnX9sYQIZKp1W2sd2sJS85vHJwXwZSEK644joeeugevvKV7/b7Pv+34ghZOYL/WoiAgNkGggPTXgCYaZA6A5zOXuviMBj7uK64rsvvf/9zzjrrAqqqDq+2jPcLcrJEveWiu3Vf1UK3a/AJjEFE//vDQG1UolwiP2Khnu7N0gGvSnGO6bVR7Qs5EpEm0G0aCna7SbZrT2eRPbw2OK017upViIysPqICIDIy0I0NqG1b9yIr3tdS9wV4AgifQFdIL9OnpPex6RbimP6CdbemhsR996BqahACtAZZMgr/Nddhzj0aY9ZsdFcnIhhEBPt/YWVxMSgX7Th95/WGmfO4f+kiHty0ms8N47hFhUSFBbRqz8AC0LZGdGrEyXt/Zlu2QHMzVO5mBCUl5OdrNmwQdHZCRsa+97nDaCBOnByVjUCQnpHBNx68jW+c8Uk+/cmr+ft9L2G+X17bB4BO0cES3yI2GxsI61Sm2jOY5EwbsLoRMj3twabmYoKhdLpxCXaMwe0uQQdaMNK3MsaZzXhn8pD37+DwQOguFvteJ6xT8Wkfr/j/yXpzNZ+IfIESdfBkL6CDhFSYTtlBlruLsCRI0CU78ONnTvLYvjaaSmcc68xVLLEWD0hW4vE4X/nKJ5kzZz4f+9jHD3p8B4q6uu2HtFr+aqNNfVQxLk32Td5bE4rXmxxmZZlcV+GjusekKa4Jm16r16oOl9YEtCU1AQOyfd66UHNcs6Fb0Zb0xPf7Qo2xnYiMkeNm9X0mfh2g09/E2NzRvLk9RMjUpFmChKvZ2qMYl25QkdL/O9spung58BrNhteqlRRJ4mI7uSIbs6uYV7aGSSrN5BQfqVrSktC8sMOhKCSZnjn4b1bhooTC0P1fI5FoNK5w+/6drbJQUrHF3MZKuRpLW4xySxjtjKLcLmW9tYk2kcSnLVplG0IIcpysfts0tUm77OgLs9zzt+nTFj29jmUHimuv/QR33vkrnn76Ma644vA1yHi/MOwr9nPPPccTTzxBVlYWN9xwA+PH75pwtbe3c8kll/Dyyy+P6CCP4AgOZwgDrKz9vw7guef+TnX1Jn772w+X4FfbGto0BMTBi6KnGzBfIRa5XtYLeJWtkw2YMLLtJuJoEznBgGovU4YKicjY//hFhkDMN+AZB+0oSBXQ5VU6xOkGImX450BYFtrt7v+g1l6Bwdy7HUpUSa9dqklDfu9EP6YRNojLfcjTBr58a6VIPvUEuqYGWTUWYRho10Vv2kjyqScI3Po5hGkisgYuDRnTZmAsWoi7cT0iJw+EIL+liYvHT+XPLz/HzbHY0FeORwvESQa86EKLizaF14Y3USKOHfrtR2uvrXJfVZWeHti4UbBamzAmRG7urhcXjynl5r98lTsu+T7f+c6X+L//+82Q9/1+olU286fQHWw01+LTfhxhs9D/KmfHLuLsxIUD9sCfVmhRF8si0XYccXMT3SQg0EJaxQvMEBO4PnIL6TpjyGNYb65mqe9tRrllpPS6LuWpAtaZq3jD/zKXxD7KcutdVpvLcYXDOGcSM5JzhuXkZWExL3k8jwUfoE22kKmyiRNjm1lNmsogSKjfsQoEfh2gSTYMuL1f//pH1NbWcM89T/TlqnwQOJRkJak067pcsvz93bt2tlzVRBQlIZMxqQZjdpllURqWRF1NUmmy/d65kVpjCI0Ugq09LkWDtGrtRJvsIKB8/T4TCxONZmZRNzqWxvIOhx0xTyNTlSq5vNSHuZsbmIvLQv9iGoxGgr0VkCiCuEjQJFvoiKfQncxlUqqPNB0GIcgNCDq6XZa1u/skK378pLvpNBnNBHrF+gBRESWAn7TeNHmAZtnCu/5lJEkS0AGiMsZKuZo22cYEezxBHaLWrCMqYuS62XTrnr3E/i4Kv/b1OXW5KIzdCEtC2OQNIURyXygtLeeYY47nmWceP0JWGCZZefDBB7nmmms488wzWb9+PXfccQd33XUXH/3oRwFIJpO89tprh2SgR3AEH3Zorbnjjts49tiTmT59dv/nGhR6hUJ3K0SuREw33jfHqX1Baw1LXNS/HU/H4QOmGchzrCFN+geCsATySgtmGOjNvTbIVRLGyb1yQvY5tgaFXrnbOZs28DkT6cIT7g93nGeaEARedz3dRaZAXGAijj+AbQmBcdQc3L89ho5FEcEQWmt0XR0iKwtj7Li93zRKIM400c85sNb1rJeldyxi3uBj0DvqUdXViMKivqmFMAwoLkZt2YKqr8cYNfiESqan47/uRuwX/4W7eiVaKax58/nMDTfw2KWn8uCDd3PjjZ8e8nHL8y2oMFArXY/sVUrErIE/q4oKyM+H2loYPdojJ64LTU2CefM0aWkD7ARYvlzw+OOCxkboohgnM8jRJ0eYe24bUnrai7EnTOaLP/kOP/vyd6mqGs/1198ypGN4P/Gq70U2mGuocib0CcqbZAP/DjzHNHvWgFWNvIDklqog8zsq2JDIpDawmtSsLmYGPsG0rtnDIioAdcZ2bJJ9RAV2ttRkssZciRP8C2/4X0b1Jn8v9C3gPesdboh+alguXicmzqBdtvOW7w2azNVY+BjvTKbALeZl/wv9ksU1moSIk6/27q9dvXo5/+///YIvfOGbH3i1uq6uhhNOOO2QbNszsRQkVP/+yZ0dteYgfCMvIMnzCzptTVtCYUqv+pFqSdItQdTd/76DBOgRPf1aN3uXWUgzLK6p8LEtYtKc0IRNj6zsaVvcKttoNJrx4SPQa3KQplMxMLyWTZ1KjsoiV/vY3Z3GJwVd9r4NUQSCSqecTtlFq9GGX3lEXwPj7DGEe8mGRlNtbiVJkiyViUAQIUKb0cMyYyUNRhPFThFTk5MJ6xDtsoPF/nfoFj2kai/PKyqiGEgK3HwyVQaZKoM2o60vq6VHRvBhMcot3v+J3Q/OOedivve9L7O1aytmpoVP+8hWWf10Nf8tGBZZ+dnPfsYvf/lLPvvZzwLw6KOPcsMNNxCPx7nxxhsPyQCP4Aj+U7B06dusWrWMBx/8R7/H9SoX9YANTQotAQ36delZuw5DQ3NIsFzh/tUGF88pLa4RLzmoDo38hO+AXcGEKWCygZh8YBddvdLFuasWtW0dWjvI0CjklDEYNwRGLKFeWAJxqoU+zoSIhhSB8B04gbSOPR61pRp3xQqUY3umDpmZ+C64CDmA1ZUQAk43MSoleq1CJzWy1IAp0mthHASqvQN321Z0PIaQBjInB1lWDpYPlAuus9+xyvx8/B+7Gh2NgtaIcJgK4MILL+f//b9fcPXVNw059E5IAVMNjKn7/6xTUuCCCzQPPyxZtw5MU2PbgvJyzRlnDKxXaW6Ghx/2WsQqKkCZPla3mix4Jo1wYYSqOR1EZJR0lcYnPvo5Ota38p3vfJHy8jGceOKhmVgeCDSaZdYSMlRWP+erXJXPenM1m80NlCQHbsFKtQTzc03mkwucCEDE7uEdayGrrGVIDCbb0zkqOW+/AXN+HUDDXnbHCZHAJMabvlfJdwtJ1V4Pf5wYy6wlvGMt4oTkqUM+Xj9+Lo9dw/GJU2g0dhDSISqdcbTIJtZaK9horqO4NxNlh1FLjspjVvLofttwXZcvf/mTVFaO5dOf/sqQ930ooLU+pJUVUwpmZxs8XWuT5dMEDM/FsjaqyfVLxqbufd1riivWdLrkBQSNcUG6pXG0YFRIkuMXdNqQH9j/9bLYKaLZ10JERAnpIApFp+wiRYfJUzlIIShPMShPGXwbEemFcUrdf3+WtlBCMcpKZSUmSeXlxgAorYk6eq92soGQq3I4KjmTGmM77bKDDJ1OiVNEsVvU9xobh07ZSVAHEQhiIkaD2YiDiwAcXGrM7URllLmJ2WSodCYmx7He2kirbAUEfvyMtceQp3IRCKYnp7LOWk+r0d5nXTzGqewLhVQ2OG3en0bQM+YRQ7xFnXrWOXzzm5/jT6/8njlXHI9EkqnSmZqcQtpuiwn/DRgWWdm4cSPnnXde378/8pGPkJuby/nnn49t21x00UUjPsAjOIL/FDz11KPk5xdy3HEn9z2mY732tJ0aPUEihEA7GrFBoZ5zkDdaH1gIodYatcBB2OzSeKQKdFh74YQbFUx4f1Z4dEx7+4trdJbAvuNV7NXPoH2dnlY74sd8bS7kfQTz4wee0TAQhF/AULJf9redcBj/DTfhrl2Dqq1F+P0YEyciiwZfgRNCwBgDMWZo51nHYyRffAFaWsBV6LRU3G1bUe3tyLx85OiyYaXIiz2Enbfc8kWeeOIhXnvtRU477Zwhb2c4OOooTV6ey/Llgo4OKC7WzJihyRqk1XLVKkFTk2D8eN1LZiwm5GSyvCPGhndSKZ/bSpFTSJVdSUiH+M53fkp19UZuvvkKHn30n3tVOT9ISARKKFplM12yE0MbZPc6Cg3VBhUgKiLcE/p/vGctwa/9aDRLrbdZa63k2sgnCezD1m+SM5UclUuNsYVStxyJpEO0kxQJStzRtJrNfUQFIEAQS1ustpYPi6x025qWhCJsFjHdX9x3nStQRVwTvZmnA49TY2xBA2XOGM6LX7JXZemOO37KihVLefrpBe9rYvxA6OhoJxqNHFLNyvF5FtsiitWdLkp7hY4sn+DCURYZe1gQL293eHRbkpaEJupquhxNUsPMTM9dqyWhmZ5pMC5tCGTFLSRiR9hq1dAq2voySibZE/Zr3rCp22VJm0OjCXKUD18ghiXcPhewhEhiYjLdn0d1msHqTpdMn0AKaE1oRoUkc7KHNlXNUpl9CfUDwUBiYREnAUCn7MLBxa/9JEgQ0gGCOkibbKfJaKbYLWK0W0qOyqFNtqHRZKpMUnS47/eYplM5KjmLHhHBxSVFh/tE/k4XRNb15q4BCLCyPWMeOQQTtp5RMSqPHs/ypxZz1kcuxMWl1WhnhW8VxyTm7FVh2Zn34giHVJXyH5XRMiyykpaWRmNjI+Xlu4SgJ510Es8++yznnnsutbW1Iz7AIziC/wS4rsszzzzOeeddirGbwJotvW5ko+UuEbUpPIvcdS50WDDItVfXKvRSF92sEQUCMdNAFI5gJcYG6hU6vX9kjAgJsBW6RQ9j+nTg0JsU6qGkl4OjQHdHcFdvR+eDzBjrEbx4J077G8gFozEuO2lIuhodjaIadiD8fkRR8ftCCoXPhzltOkybfki2765ejd6wHjljFmr9WnQiAZYfXVeH8vkIfOIWRODA/acnTZrG+PGTeOKJhw4ZWQGvBWz06F2tH9q20cpADKBHiEb31rMEdIAiX4DM5lROiBcT0IG+yYVhGNx55wNceeU5XHnl2Tz66D+ZMmXGITuWoUIgmGzP5M7wL3GF3dve452DMncMY5wBWgUHwVLrbZZZSyh3xvQRk5iIssRazAxrDnPseYO+N0NlcUnso/w9+BAbzLWAJqhDnJg4g4AOsMlct9d7tND9yJRCodEDtqu4WvNSg83rTS6dtsIvBZMzDC4o2TXhHudM4gs94/aZs/Lvfz/Hz372fT7/+W8wa9bcIZ+bQ4WGhjoACgoOvv1nMKRZghsr/azpdGmIawLSyzbZ0x6429Y8sd2m29GMS/PuLYUBl6XtLtt6FFVpJmcUmpxauHfKfMTRrO50aY5rUi2YlG6Q7ZeMd8ZS4hbRKbsxkGS5Wfj2k33zbpvDw1uTdDuakJVBIJhFas4OlD+BT4IjXEAzxh5Licjh2grNK40Oy9pdXK05Ic/kxHyLvCFUf4YCA4Nip4i1vnUkXB8JkUBqSYIEfu0nqEMYvaL8qIj1vS+sQ4TdwTVZAtHXJga9LXKuILoB3C4wMrxqinYg2QwyCOGx+x6rjU2dUc9R5x7H3374F+LdMYKpIdLddNplB+2yg5zd3PS6RDerfWtpk+0oFEEdpMIZTblTNqyFjsMVwyIrc+bM4fnnn+foo/uXYk844QSeeeYZzj333BEd3BEcwX8K3nrrDRobd3DBBR/p/4Tq/X/Pa7Hc+ZxmoHRJvdJF3Z+EVtA+IKHRC13ktRZi7AhVOywgU/SGZe6276RGSLErf+QQQkc06oGkR5rKJFig32xDRipAWSC2AyAC6SBbcVuWgXsi+0rk1FrjvL4A+6UX0a2t4PNhjKnCd/GlyIKCQ35MhxKqvh5chTF6NCIlBVVXCz3d6GAAo2os5uyDT9a+6KIr+dWvfkRPTzcpKYe2FcHdvAn7tVdRmzchgkGMuUdjHXc8IrBrNTc/H6TUJBLg711I1Bq6uwXz5xsDrvyGwyncf//TXHHFWVxxxVk89ti/mDhx6iE9lqHAjw9b2DjYWFgoICkSONjDWiXdYK7BwOxXQQnqEKDZZK4fkKy0yCZe9v+T5ea7gKbKmcBx7imEdJhR7mjK3TGsNVfyEs/TIdrJ0N4qSlREcHGYYs+gQ7Tzmv9F3rUW4wiXafZMTkyc3k9r8kazw5O1NqmmoCgoibmwsNkh5sLNY3x94nETk1K3fK9xAmzatJ5Pf/oaTj/9XL70pW8P+bwcSsTjXirrobaZDRiCmVn7nrpt7HZpjivKU3YtghWGDGYJr1r7lQl+0gYIg2yKK+7bkmBzt0L3Vm4KgpIry3yMTzNI0SmkuPvo9doNcVfzXJ1NUnuuZGCgOibTpk2szFZSUm1SlUWlXc50eyoCQYZPcNEoH+cWe4qYPYnUSKDcKSMiotSbO0hiExdxUlUq+SoXA4lCARq/Hl6lTqGoNeq8YEeRILe5gLzucoJpvr62L2GCDIDdBKoM5D524QgHR7jMPf8EHv7Wn1j64iLmX3wKJgYKhS3svtfaOCz3raDV6CDd9XRAERFljbUev/b3a4X7sGJYdPULX/gCgUFW5U488USeeeYZrrnmmhEZ2BEcwX8SnnrqUUpKRjNz5pz+T4yWiFwJO3ZbRdYa0aigXELWAEQlqVFP29ANerxAVEr0BAktCvWsg3b3LUYcKoQQiHkmIgm6UaFdjY4oWOaiHY3a6KJXeH8/ZFiroE6jyyXC1xsOlpUA00a2FvUJPrUGkTTRKTGPYO0D7tJ3ST72CLq726uopKXjvLeUxP1/QccHiYH/kEAEg57DmNbI7GzMqdMw5x2LUVyCUVGx/w0MARdddDnxeIx//vPpEdneYHA3byLx5z/hLl6IdhWqtY3kY4+QfORhtNoV1DNpkmbCBNi0yRPYt7TAunWCggLNvHmDfzfT0tJ58MF/UFxcymWXncGKFe8d0uMZCtaYK5hoT2G2fTSjnQrGOuM5MXEaQR1kjblyyNsxtNkngN4dGj1gOGKX6OSu0G95wf8UcREjIRK84XuZ5dYSptuzqXTHIpGMdyZzQuI0WmUL683VrDdXU2ts46jkPCYmp3JP6P/xVOBRekQ3CeK84H+GP4Z+Q6tsBsBWmjeaHQLSy+XwG94EdVRYsrbTYUvPQAFMe4y1q5Prr7+EgoIibr/9ng/U/Wt3JJNea5FlfbDtaACuV4Rmz5gTUwokYA1CAl6ot9nQ5ZGccekGY9MkLQnFE9uTxId5X6mNKpoSisLd9HUymU5gx2ya1h/NjI4TOS92FrPsGXtV4CwpDglRAc/FbLo9hWMSc5hmTyFbZZGh0vFrPw4O7bKDVJ1GnttfR9gtulllreUV/wJe8r/KAv8bLPa9zSprDa2yjbXWepb5VtIuO7CxaaSZdtqJmtF+2xEGaOVFHewLfu0nRYUJj0qlcsZ4Fj39KgBxEceHjxS1q925xWihXXaS5Wbgw4eB0adpqTH/MzqehlVZOeGEEzjhhBMGff6kk07ipJNOOuhBHcER/CfBtm2effYJrrrq+r1ajUSqQJxloh+zYZ2LDgpEBMiXyLPMgVuTtmt0vYaiXcm+Qgh0oYRtChr0rhDEg4SYZ0CnhgUOeqPrOYJFAFPAPx3Uyy4cYyCvsA5KfD4YdFR7Am9rtwT07HQI10MiCC0CbSlIumBEME+buN92LnvhG6AUsqS3tzwQQAYqcVdswnl8Jea0mVC1bxH7XuNsUeglLnqbhgy80Mmx8n3XGxmTJkFWFnp7DRSXgJTo1haQAnPGrIPevtaa4p4Sjqo4hr/d/iAXZVzutR/uhyAeCOzXXkW3tiDGTdgVWNnVhbN0Ceb8YzHGVAEQDMJ11yn+/W/Be+8JHAfmztWceqqipGTf+8jIyOTRR//JVVedw0c+cjoPPviPvRcU3idoNN2im4jooUPY2MImR+WSolNppZmkSAx5W5OcaSz0v0aX6CCt1w2sXbTh036q7PGsMpexzazG0CbjnUlsMTax0VzXz4UsR+Wx0VzLUt9bnJw4E/DaaC6NfZQJ9hTWWatwcKhyxjPVnsl7vrdZY62g0hnXVwXKUwWsN9fwtvUmZyUuJOpAV9JLWN8dYVNQq6BjP65PSiluvfVampsb+cc/3iQ1dRB7uA8AiYT3+fj9H7xOoDQkSbM8XUqGz8tUSbiK5gScXGASGKD43mnrPjH+TjIjhSfGr40qtkYU49OGXrU3RG+DwB4fqdISFcsg1wkQtD4YoikQZKlMMpMZpKs0NptbaJcdSASZKp2J9oR+Vclu0c0S/3t0iC4E0GK0YWOTosOk6hSqza3YwiZNpRHqreSKkEEykKA7IQj7g+ys9qsYmBn716xIJBVOGct8K5h2wVye/enDNEebECkGFU45Kbu1ncVFHAZou7S0RURE+jnrfVhx0MlY55xzDnfddReFhUMXbR7BEfw34Y03Xqa9vXXvFrCdmCeRykIvcRCOgCqBnGMiikfmQt7TA9XV3t/LyyF1iJ07uskjPmK8hDl+xGIX9TcbJglEjjc23a0Rb7owTsLcQxC0lyvQCaDaReRLCAvIzkZml+DG30Wl1oBjgK8TOaUS68Jj931MSqEbG/ufBEejlxrotTbummZEZgwmSuTn/cjy/d+cdZ1C3ZWEGoUOeDki7kIXeYmFOP79DR+UhUX4L/0Iyaf+jtq00SN6qamYp52JMf3gdRn6ZQf1lMOFwUv5zpqv0nxPHTlv5yNv9CHyRm7ioR0HtXkTZGb1I3wiLc2zZq6v7yMrAJmZcNllmvPO07guhEL7zmTZHRkZmTzyyAt87GPnc/nlZ3L33Y/3M8F4v+D1yUdYbS0nTacjtUGT2UCNsYVidxSjhpEeP82exfGJU3jD/wp1eK2SAYIcGz+ZpdbbvONfiI0NaFJ1On4VwMTspwvx/u1ji7Gp37YNDKY6M5jq9P8+1coaNLpfu5qBQVCH2GRugASETEj3CVrimozdFjd6HC+wMHM/Cx4//el3eOml5/nrX5+isnI/Tf/vM2w7CYDP98GTlfyg5Pg8kydqbBa1uCSVwlaCoCFoiGk67f7nH8BRGoVnj7w7TOlVapz9F736oSQkKQlJtkQUlSleiKWrNTtiiqkZBnnDWAw6VBAIKpwyipxCumRXr9tWxl6T/hqzlk7RRY7Kos6oR2pBCmEc4RByQ3TITjpFF7k6p+89OuQSL+nC2uwnGVcYloFOgLAgMGpojmCFbgEiKeg5vZ0nvvcXat7azNnHX0CZU9qPfARVCBA4OP0qpwmRIN/N+9ATFRgBsrJgwQJisdj+X3gER/BfiqeeepTKyrFMmjRtr+d0m0I/7uVo6ASIdBB+2T81fU+MEogiT0uiK3urKloj6nvduXZ779tvC55+WtDU5LVK5eXB+edr5s4deAVTd2n0Ugf1vA1btXeFyJCISgkZ3oWWnN2qHKkCGjRqlcIYYY2r3q5QTyehUcEq7+JPiYRUgZxcipjeAY116EQCY9JxWMceP6AF8O4QUiLyC9Dr1kJevreflS56ZRyhJCIjExLAQoWKxRG3h/ZbYVH/cjyiMn63nJg6hf6HjZ5yaKoO+4J51BxkVRVqwwa042CUlXmZK/uYvet4HLTaK82+32uaFfoFByw49/iL+e7a/+EZ9STXb7wJ9bKDccWBtb+o1hacxYtw16yBYBBz+gyM2UchQmF0S3P/MTie7fJgJgEH6h2QmprGgw8+y8c/fjlXXXUO//u/v+baaz9xYBs7QGw2NniJ7iqbhEhi4UOj2GHUM96eTOUwBPYWFpfHrmWaPYvN5gYEgjHOOJplE/eH7qLILSFVp6HRNModrLdW7xV8B+Bgk6KHVr0IEuwT1u8+ObJFkrDyFgcsKTg21+SRbUl2xBSZPkHM1TTENDOzTMrCg8/gfvvbn3HHHbfx7W//hJNPPnPI5+L9ws42sMOBrACcVmDycoONL6bJ8kmy/YIcv2R7VPF8vc2VZf3HmenzqihLWh1STEAIMiwv1yXbLxkVGt5ihCUFF5b4uH9rkg1dnu24xqv6nFfs+8BcLneHRtMjeoiIGH58ZKj0ASf2TbKlr00sLhL4sDAxsbFJiiQpOkybbCMmYv0qHu2VDaigS9G2XEiAmQeBIs8RbCgQCArdAi4d/RF+kPUFYm90MGbe3u28OSqLbJVNs2wmRYcxtElURjExGO0eOne69xPv77LfERzBfyFef/0Vzj//sr0uztrVXr7KMoUuEpAHulXDUw6EBeKEgX+ewieQF1io+23EWo32aS8VvFAizzMRvY3KW7fCI49IEgnNTrlCXR08/LAkN9dlTwmDblSou5PoRS7UKvAJSAeyNKxxPS2M3PtSriXspvUbEei4Rt2fRFRr9NEG7FCwGY9AzZMYN4YQk+ZjMX/wbWgNGxRqiQstGkq9ipV17HEkNm5Aba+BzBz06gja2YHMGofMGQdSeK1vKzV6ibvPpHUd0V77Xp7oH2hZKGCDhmoFs97/AC+ZkYmcs3/2qFpasF96EXfFcq81buxYzKnTkcXFiLz8/u5bmxV0aBgryZI5nFB2Cs9s+BvXnXwzYoWLvlgPuxVQtbSQ+NOduNWbESmpaNtGrVqBuaUaY85c3MceQXd2QloauC5621ZEQQHG+AnDPSX7RTicwl//+hTf+95X+PrXP8P69Wv4wQ9+gWm+P7fJbWY1AsG85InUmFtolo2kqFTyVSFpOm3YQXAmJpOd6Ux2pvc9dkf4NixtkdpLQASCfFVIjd5CUiSpl7UUqCIEggZZT1CHmWbP7Hu/RlMva2mTLaTrDEa5ntOQg4OpTTplB4t9bzDGGUu2yqVVNmNqk5nOLmOH+bkmCVfzepNLQ0zhNzwCc0GJr18y++646647+L//+yZf+MI3ueWWLw7rPLxf2NkG9kFbKO9ETURjazgx3yKwm3hFCMWKDpdzbE1abztei2yl1qgjlpOkriWPnq4gpgClBTmWj8+MtUg/gDbfqjSDz4zzs7LDpSOpyfYLpmYYe9ksHwpoNE2yme1mHd2ymxQVZpRb0ldpsHFYY62l3mwgKZKY2iTXzWayPamvlWsnLEyiwgXt/WZcFI5IkBRJbGGT7qZhGRbdIkJIh5BIbGziZpyywiDp2dJ77wEethSSOXPms+SdRQM+b2IyPTmF9dYGmowWj0CpMBVOOflu3oHt9DDDQV+FR48ejWXt277uCI7gvxX19bXs2FHLnDnH7P3kZoVep6BMeHbAAIUCbSt4w0HPNwYNXRSTDeRnBPo9byIu8nuti/N3XQ2XLxe0t8OECbtaYkaPhrVr4b33BBUVeyQhv+CgNyivBJMlIA3oALZo9GwJa3vtFyMaEe7VECQ8oiQmjvDNZ72CrRpd4QnrSZPoMRq2KMgCJux/f3qRi3rURkQ0OihgWQL7ubXooxqQY6pQ22vQNQ0QExipszDzL0DsbCROBdo1bFWwr86ynR/PnoWqnSZuH/zi4aDQ3d0k7vkz7vp1iJwcdHs79l/uISkN5PgJGBMm4jv/QozRu7cf7Tqgk8rP4HuvfJWYHSVkHpgDkrPoTdzqzciqcYheUqC7u3DeWoz/pk9gHXc8zpJ30A07PHvigkL8l1+JGCzK/iBhmib/+7+/Yty4iXzzm59j8+YN/OEPD5KRMXh2w0hhJxlJ1WlMtqf3PV5jbCEwQNVjf4gSpc7chtQGpW45FhYJEd+L9Hi2q2mMscfRarT02hVDhs7k/PiljHMmedsTER4LPMAy39t0i25COsxkexoXxq7g+cCTLPItwMFhh1HHNmMzeaqAsc5EzkxcwNTdCI8hBKcV+jgmx8tZCZmCXL8YdKX9T3+6ne9+98t86lNf4stf/s6wz8P7hcOtspJQYO8WsLgTPinotjXJXqfJWqOelb7VtDsJ4kHB7Gk7aOpIoasrA7+hSA04yGA5cGCT3hy/5KT8Q09O9kSdUc8K32ocHHzaR7fRQ5PRwpTkRErdUWyyNrPF2kaKCpOqUkhiU280AIKjkjP7VViK3SJaZRuqt2rYITvRKASSNtlODxGKnEIsTNpke++lX1LkFFJhl3n334O8F8yZM5/bbvseyWRyQEIc1iFmJKcRFTFcXEI6OKCZxocVB30kq1atGolxDBm/+93v+NnPfkZDQwPTpk3jjjvuYM6cwQWRjz32GN/+9rfZunUrVVVV/PSnP+Xss8/ue15rzXe/+13+9Kc/0dHRwfz58/n9739PVdWufui2tjY+85nP8MwzzyCl5JJLLuE3v/kNKSlDs/A7gv9evPvuYgBmzTp67yc7NSIJhPa4iqUKbwU7ikcYBoEolvvUtbS3g2XpvXr3fT5Ne3v/x3S3htUu5AjYgVdVEUC69ibtUSBFQ5FE1Gq0oUGCSGiYaiBGuHqguzUojdjtTisMgc4QiG4gCfvItUP3aPRzjkcaxhuQ6CbZeB9qyyp0tUZUCERaGtYppyK2TUP05CKs3Y4hBhhA3r7vMCIkYLKBeNVBZ2ovI0drqNPeuaw8PJyKBoKzfBnuxg3IqrEQieBurwHDRCeTXgbNyhUkOjoIfOazyIxMLxg0E8/AoUgwp/gYHOWwfMu7HHPlif2qKrqnB3fjBkgmkaWliILCASej7upViNS0PqICIFLTUHV16MYGfB+9GnP+saj6ekQggDFuPGKooquDwNVX30RFRRU33XQ55547n3vv/Ttjxgy9DetAMNaeSHoggx2yjkJV3JuwHSUiIsy2B7h+7ANvWwv5R+AJmowGJJJRzmguil/BJHsqa8yV/Xrbe0Q3Pu3n3Pgl5KtCNpnrAah0xvazHH7W/wQL/C9S4BZTpEfRLbpY7H+dGmMLTUYDhW4JY9xxREWEzcYGumQn50Uv4WT7zAFba1IsQYq17+vG7bf/hJ/85Dt86lNf4pvf/L/DonVoMCSTnmblcFm8zfV7607L2l1y/V5ifdAUNMcVFSkGmT6vurDR3IzCxY5kkbR7yMluIie7CZIh6CmgR/awWvZwtDiG8AGQ5g8CDg6bzC1oFNmqN01WeyGQm6wtZLtZ1Br1BFWgz9rcj480lUaL0UKn7CRDZfRtb5RTTIfoZKu1jaiI9rY6Svzah0IRl3GK7ULG2lU0G83YwiFFhclR2cjhme4OirlzjyUej7Fy5XuD5goJxIfmMxouhnUWW1pa2LZtW7/HVq9ezfXXX89HPvIRHnzwwREd3J545JFH+OIXv8h3v/tdli5dyrRp0zjjjDNoamoa8PULFy7kyiuv5MYbb+S9997jwgsv5MILL+xHsG677TZuv/127rzzTt566y3C4TBnnHFGn2c6wEc/+lFWr17Niy++yLPPPsuCBQu4+eabD+mxHsF/BpYsWcyoUWXk5Q2Q35Ep0H5vYt0Pndqz3z3Ia05xMTiOwN3NIlEpSCYFo/ZsY/Ws5b1slRQB8d3GpIGIhpBEXOFDXGshpkrEBIn4mM8TV4dHdhIh8gTaJ/Y6N6IdKJbsN3Jim0I3K68dC3C2vYbbuByySzHMscjS8aAU7orFiHlBhONZPxMDuhQ0a6gw4Nj9kzB5ugmVErFRoTcoxDrt+emfbw4pnPKDgtpRD0IgLAtVX4uOxRBZWZ4eJJlAVI1Fbd/mtYgBIkcizrbABda5VLWPI81M4225GHnyLrLhrFxB7Gc/IfGH3xO/+0/EfvEzkk8/hXYH8Or0B6BXh7ITWmuE1mBaCCkxKiqxjj0Oc/ZR7wtR2Yn580/kH/94EykNzjvvOJ577u+ANxF6x1rE3cHfc2foV7zq+xc9ons/W9s/itUozo1fjBaKteYq1porqTW2Mds+mvnJE4e8nY3mWh4K3UOrbKbEGU2BU8QWczP3hf5IpTOesc4ENphr2WZUU21spM7YzlHJeUxwppCnCpiXPIF5yRP6EZVO0cE7voVkqCwydCYCQZpOp8gZxbu+t0iQIFWnoVA0ygZaZQu1Rg33pvyBl/0v4ODsY8R7w3VdfvjDr/GTn3yHL3/5O4c9UQEwDO83oNQwlehDhKs19p7WWoOgx9b8vdahJaHY2K1Y2GLzcqPNklaHoCE4pcDEEIJu2U1ERkhRKbhaYwYiIFyww2A4IDRuLB3HjNBoDDzP2gmFolW2UWPU0iibBvzMFYou0U2X6O7NMzk06BERIjJCeDd7X4CwChEVUdqNTmxhY+2RpeLDwhYOCZL9HjcxmWZPpsweTapKpcwppdQpIc/NpcDNJ8fNJipjBPAzyi2hwikjT+WOGFEBmDx5OsFgiLfeemPEtvlhwrAqK5/5zGcoKiriF7/4BQBNTU0cd9xxFBUVUVlZyXXXXYfrulx99dWHZLC//OUvuemmm7j++usBuPPOO/nHP/7B3Xffzde+9rW9Xv+b3/yGM888k6985SsA/PCHP+TFF1/kt7/9LXfeeSdaa37961/zrW99iwsuuACA++67j/z8fJ588kmuuOIK1q5dywsvvMA777zD7NmzAbjjjjs4++yz+fnPf05R0Yc/bOcIDh3efXfx4OnKFdJrn3rXRRdICAJtGuGAOM4ctAVsqJg5U7NwoWb9ei9zAqCxUVBSopk1a4+bXhrehHuJix7VW9lp7zXrF0C39sY0oVdEPn/vS4dW2mtt26ERQWC8ceDBkZUSMVXCWy46R0BAeLoTPxgnDGLpvDsMvKUYDVor3B1LEYF0hBHwHLKkgOIS1Mb1cPEWRHQm+l0X2jxdDhMk8os+5BBEpSJfIj/tRy/ztD4iTSCmGojRh29VBUCEU0AptNZe5oxlIRAo10X4/AjDAASqtXXXe04wMIoFaqWL2Q2zao5mifmWlxWEJ5ZPPvIQqqMDWVGJMAx0awvOv17AKCjAnNu/QmDOnEli3Rp0pAcRTvHGsmMHpKdjjN2/21MyCV1dEA57FsYjjfLyMTz77Bt84Qsf5+Mfv5xLLr2K6T+fwzsZb6DQGNrgHd8iltrv8PHoraTp9IPa30mJM6hwqlhrriQpkoxyy5hkTxtWIOQ71mK6RRfje9u3wKuSrDdXs93cws2Rz/GW73VWWcvw6QAz7TnMSc4ftGVEoXjV9y+WWIuRQpKq0hjtllPijiZFp5IgiSs8IlptbGSdtQpDm5japFt08kjwLyRIcHbiwiGNv6OjnU9/+hpee+1Fvve9n3HzzZ8b8rF/kAiHvYlxJNJDWtrBfQ92R7etea3JZmmbi60049IMTsi39il4f7XJZmmbw8xMg/IU2B51aYx5rV+Xj/YxLdP7rCXC+82jyfBJ6pSNUhLZ28eqNThKkGoJEvuwzk6QYIVvNY1GE65wEVqQqTKYlpzSl/fRLFtYb22iS3YBkKHSGW+PJUvt3WJ5xx238eMff4uPf/wz/OAH3nxzW8RlWbtLe1JTFPSCMXP8A58DEwOJxBUKa7dbnYtCIr2KigoSkzH8ahdhiYk4fu0fsDohkYR1iBQd7pccD9ApukjuQXBGGpZlMWvWXN5++00+9akvHdJ9HY4YFllZvHgx9957b9+/77vvPrKysli2bBmmafLzn/+c3/3ud4eErCSTSd59912+/vWv9z0mpeTUU09l0aKBRUeLFi3ii1/sL8Y744wzePLJJwHYsmULDQ0NnHrqqX3Pp6enM3fuXBYtWsQVV1zBokWLyMjI6CMqAKeeeipSSt566y0uuuiivfabSCT6xHYAXV1dB3TMR/DhRjweZ+XK97j44qsGfF5IgbzKhwrZiJUuuhNEpkCcbSDmH3xbVXY23Hij4vnnJevXe5P7uXM1Z56pyOl1WNRaw3sKtbBXr9KiPZ1KrvBamRwNVQbiPBN5ptVfRL4bdEyjHrbR77qIhEYLAYUO8koLMX74xyKM3nOTZSPedaFHQ5lAnmLB1CGQgDKJKJJQo1HlLlrZaAxkj/YcxYKA7j2WsEJ+P4Be6UKthnQQ040+e+b9QbW1ojasR0sHeexoRGnpYb8KDGBMnoJ45SUvkyUYQjU1Inu6EYaJLCj0ghe1hkSS5DNPobu6kCUlGNNmYFycAcDczmP57W9/huu6GIaBu3o1qqkJOXZcnzhf5OSiurqwl7yzN1mZewxu9WbcJUtQtu0RyfR0rHPPRxQPHpLiuvD664IFCwTt7YJwWHPMMZpTT9WMdMxFWlo6d931KI8/fj/f+NZneW7xk5x45+lMOH4yAEkSrLKW8ZbvDU5LnHNQ+xIIytxKytzKA95Gk9zR19qyE7J3WtopOsjS2ZyVuJCzhkgeXvH/k38En8AWSQSSTtnBCrkUG5tUnU6BKkQg6KGLbWY1lvYRIICLwxh3HForHg/ez1pzFUkRZ4wznnnJEyhWe7sUrVu3ihtuuIyOjjbuv/8ZTjzxtAHH9EHkRri4bDTX0iB3ENYpTHSmEN7N+Skc9v4+kmQl4Wru35rgvTaXdEtgSni9yWFzj+KmMX6Kgntfo2ylWdLqkuEThCxJyILikMR2FVsjmt0v4WkqjXSVTptsI8ufQWo8QIQYUiZwE2EiMZNsvybTJwjZg68GbLQ2U2fWk+6m4cOHg0ub0c5K32qOScyhR0RY5ltJTMRIVSloPFH/Mt8K5iaO6kcOli1bwv3338XEiVP6Hnu3zeHRbUk6bI3PcFia6ODtpMtF+RlMDGTu9V0I6zDZbhb15g4s18TAwEXRbXST7+SSpTMpc0tZJdfQKbsIKD9JYZMQCSqcsn6OXrsjTaX1ied32nxrNEmRJMctHcpHelA46qh53Hvv71FKHTZhqO8XhkVWGhoaKCsr6/v3yy+/zMUXX9znlnL++efz4x//eEQHuBMtLS24rkt+fn6/x/Pz81m3bt2g4x3o9Q0NDX3P73xsX6/Jy+svLDNNk6ysrL7X7Ikf//jHfP/73x/ikR3BfypWrFiKbdvMnj14v7nIEBjX+dAtygtbzN1NbD8CKCmBj39c0dnp/Ts9vX/+hH7dRT1iIxSQAqQL6NKICglnScQYA6ZKZMq+L4z6NQfedHqthSXa0YgtHoGRX5EH1CYmUgXGZT702dprS0sXQ642iYBAXmShHrCR6yXSnYDb8gq6JBdZJQGBbmtFhFOQo8sQIYGYa8Iw7Zedd94m+fe/oVpbvP2Gwpjzj8V34cX9dBiHI4zSUi+T5ZmnEM3NiHgC7bjICRMhHEZv3oQQEvvN1yGRAMMA18FY+Cb+6z+OLChgzpz59PR8h7VrVzJ58nR0LAqC/i5i4HkKd3bsNQYRCOD/2LW4R81F1WxDWD7k+AnIkpJ9Er7XXhM8+qjA54OMDE1PD/z974Jo1MtbGWkIIbjssquJnxjjp5/5Hs+f+yQNt9Rz7PdOwhf0E9IhVlhLD5qsjASK3VJWWu/1m9C7uGggS+Xs+817ICJ6eNn/AmGdwhRnBmvMFRgYONisNldQ5lZyVuwCWowm3vK9QatsJqCDJElQ7JaS6+azxlzBamu5N6FTuWw017HcWsJN0c8y2t1lSfjMM4/zhS/cRFlZBQ899A9Gj97bonW7sZUFvpdZZ64krFOZa89nXuLEYVWeDgQ9opv7Q3exwlpKkiQCKHFH89HojYxxPS1TSopXQYhEekZsv+u6XFZ1KMpSJMFeR68cv2Z9l2Jxs8PFpV5VINnbHuaTAld7/94zb9GUXgUluVv3lUQy0R7HMt9KOo12CkMuDcLFdg0sO5WiDIe0lCi5OoN8t/88aSeSJNlhNCC1pN3o8Kx9tUVIBWmX7bTLDpqMZiIiSo7K6vtO+lQmLbKNHUYDY5yKvnN3663X8LOf/Z7f/MabS0YczbN1NgkFY3O6IX892tdFt6N40fCRtIqYbE/C2m06KxBMsMeREAnaZUev/4km081ggj3eWxRwShHAFqOGuIxjaYsyu7RvLAMhV2VT4OZTZ9bj0z6klsRFnHSVRqmznwTaEcCcOfP51a9+RHX1xkOuoTvcMKy7aVpaGh0dHYzudYd5++23ufHGG/ueF0L0qyj8t+LrX/96v4pOV1cXo/YSCRzBfzqWLn2LQCDIhAlT9vtakSNhePOIIUMIyMjY+3Ed0ahnbehQ6EzpXQ0me9UIUgXych/Cv39yoF2NfstFp4q+ti9hCnQ5iM3aCxJ0NLSBKBKI2UZf29CQxh8WXhjkMCEmG8jPC/QKhbXtJHinGpXYiOpIhcYECDBPOwO5v5jzQaAaGkj87TGIxZBV40AI1I4dJO7/K86iNzEqqzCmz/C0FiNgZ6q7u3E3bQTXRY4u22+mzFBgzj0aOW48avMm3DWrcdauha5OaG5CFBejGxoQhoEYN97L83Ec3I3rsV/8J/6rr2XatNlYlsXbby9k8uTpyIJChGGi4zFEwFuJ1VqjOzuRRw1shCIsC3PyFJi8/98JQCwGr74qCAToS6hPSwPLgsWLJSee6DICp2ZA5JTkMee5+cRuj/LG919m20ubOf1356LnaQx9eJDTOfY83vEtZJO5ngK3CIVih1FLqVvGNHvWsLbVJBtol20UOaPw4UMgqDG2EBEutkxwfOwUPhK/hqRIUOZW8Ifwb7DwUe6MocgtISJ62GJuIqhDjHHGkaJTUSjWm6t52fdPro/dgm3b3Hbbd/nd737OBRd8hF/84g+EQuG9xlJjbOEP4V/TIOvJUFl0yHYeNDdQI7dxdezjI6oP2BMv+v/B29ZCRrvlhHUKDg7V5gYeDt7Ll3u+S4BAX2Wlu3vkOil2xDSu1gSNXccmhSDNEmzscWmKK15ttFnZ4bXhTckwODHPZEyqwdutDtk+3Uf625KaFFNQskf7WJbK5JjEHBqNJuIizniSdMhOopkRBC5ZbiHj7bEEBiGEtnDolhE6e0mBoQ3iMk6X7MbUJs2yhU7RhYnRrwIiEEgEERHpe+wb3/gsp5xyNscff0ofWdkaUTTGFeUpyiMq/i6Ip+FzBF1Okk2p2wnrMGOdMf3GlapTODpxFE1GC3ERw68D5Lk5+PD17b/MGU2JU+zlGmkLH/s2RzAwmJacQqbKoM6oxxEuxU4RZc6oQasxI4lx4yYCsHnzhiNkZV84+uijuf322/nTn/7EE088QXd3NyefvCvpd8OGDYdsUp6Tk4NhGDQ2NvZ7vLGxkYKCAcTLQEFBwT5fv/PPxsZGCgsL+71m+vTpfa/ZU8DvOA5tbW2D7tfv9+Mf6V6EI/jQYdu2aioqqg4bd5g9oV9zYKHraVJqXU/nUeQ5WOkmDU0aRg2BJNhATO8tejdAtyr0I0lEwBPMs1CjF7nIG3yIskNfxhb5EnGaRFKGcd6tOG8vxt2wwUt1nzkLY8bMA27ZcteuRre2IsdP8CbysRh6SzW6rhY3EUPHE7grl6M2b8J31cd6NSAHBue9pST//jd0U6MnQM/IxDrlVKzTzjjoljOZkYGcNRtz1mx88TiqrhYMA9XaSvJPdyJGl/XtQ5gmIicPZ81qfJEIwXCYqVNn8u67i7nhhk9hjJ+AnDgJtew9dFYWmBa0NCHz8zGPGTwTZzhob4eODkF2dv8KSlYWbN4Mzc0cMrJS5UwgHEgh/7MFlJ1awQufeJqHT/sLhZcWc+I3T4eBF5/fV4x2K7g2+gmeCzxJrbENiWS6fRTnxy8lU2cNa1tBHcKvA8RFlIAOUOFUUeqU0ywbSYoE58Qvwo8fv/ZzUfxKOkUnC/wvkamyMDDYYdTRLbqY6Ezta5mSSHJUHuut1Sx+43W+8ZXPsnHjOr797Z/wyU9+YdDv8wLfSzTIesY7k/smvV2ig3d8bzIveTxV7viDO3GDIE6cJb5FZKqsvmMwMSlzxrDN3MxGcy1TnBlkZXmrTe3trfva3LDgN7xOTK11v/OScDU+Ibm3OsGmbkVW76LSvxsctkYUZxZaVPco1ncp0n2CuKtxNZySb1Ec3Pv8BnWAMmdXG5NCERVRBJKQDvad79aEoj3p5bPkBbzrt1/7iBMniU2aTkMACZ2kU3ahhWa15VlhO8Ihg12hixqNQvWFkT755COsXPkezz03cFs/oXbwd0M8DaENbwuOH59S1Jn1VDrlfbbcNjbNRgsJkSCoQox2SwfVZJl4+qqhwofFGKeCSqcc4H1tR8zPLyQcTmHz5g3v2z4PFwyLrPzwhz/klFNO4f7778dxHL7xjW+QmblLHPXwww9zwgknjPggwQtamjVrFi+99BIXXngh4LluvPTSS9x6660DvueYY47hpZde4vOf/3zfYy+++CLHHONlXpSXl1NQUMBLL73UR066urp46623uOWWW/q20dHRwbvvvsusWd6q1Msvv4xSirlzRziy+wgOS2gFbhdoF4wUkEPkofX1tRQVHfrS8IFAd2r0vxywtadP8QtIatiuQSqv0jPUYoAfqDAQ7zjonN1uqi3ac9WqlF66uxBopRHrFep5G/nJ9zfFWObn4zvvgiG9VmsNaxVqhQs9IMolYpaByNjNnjce99y0eo9B1W5HtbVCRgYiGMKoHNOXGWIeNeeAgwxVww4SjzyEjkSRFWMQUqKbm7CfeRqZX4A5bfoBbXcgiEAAo9JbodTt7Xvnx4BXqtupZwEqK8f23TyF34//6muxS0pw312CdhyMOUdjnXwKRunI9HSnpEAgoIlEPGH9TkQiXrfZoTQMq3THcmriLF4M/IPY5BgzF8yh/oHtbP7uRr5y9C1sunkdn/nM//S1BH1QmORMY3zPZBrlDgwM8lTBAU2q8lUhE+wpLPYtwO8GCOoQtkjSLls5Onl8P8cwgIviV5CQcVaZy6gTNUREhEyVxVh7Qr/9R7ujrPneCv76xz8xZcoMnntuIROnTKXW2IZCUeSO6tMEgDd5XmOtJFNl99tOms6gXtRSZ9QcMrKSFAkSIoFf9fdKt7BwcYkLzzk0M9MTXbe1tYzYvielG2T5BTVRTUnI8wxpT2oUEDRgXbuiKlVi9gpRsn2aTT2Kdltz8xg/C1tsNnYpSkKS2Vkmc7KNIV1zO5NQGw3ik1AW9ojFM3VJlrS6RBxNyBRMyzC4oMSH9sV7E90t4njnokf2oNH4sAjrMBERpUdGaKKFLDcDgC7ZTViHKXTzqavbzne+8yUefvg5AoH+57ksLMkPSJpthzwUaE/+3+NAYVASkhY2Ni4uBgZdoptlvhV0yE7otRjOVllMT07pI0YjgfdbMwVe91JFRRXV1Rvf931/0BgWWZk6dSpr167lzTffpKCgYK/J+hVXXMHEiRNHdIC744tf/CLXXnsts2fPZs6cOfz6178mEon0uYNdc801FBcX9+lmPve5z3HCCSfwi1/8gnPOOYeHH36YJUuW8Mc//hHwPvjPf/7z/O///i9VVVWUl5fz7W9/m6Kioj5CNGHCBM4880xuuukm7rzzTmzb5tZbb+WKK6444gT2XwCnC6IbvT9RIAPgL4FAKXvll+yJHTvqmD599r5f9EFhnevZApcIaMGzLPYJMDRs1uj5Er0zGDGVfWpFhBDIkwzUJhexXqGzBMTx8jhCwLhdE3ohBbpAIjYpb9tZh6cQXb/ooJ5xEHGNtgQsctBvS+THfX0tbLKo2HO7ikURwRC6ucnrRbJtRLa3yrozM8TduuWAyYq7ahXsVsEBEPkFqI0bcJe+O6JkZXeIsjK0aaLeXYLIzkJmZkFaOrq5CWv+fERvzlRR0SgWLHip730yPR3/hRejzzrHU8IHgyNKStPS4KijNC+8IPH5NOnpHlGprRXMmaM5wK6+IUEgOC9+KeOcib2C8SQVl1RRecpY7vrd7dx55694+OG/8NWvfp8rrrgW4yCqaQcLA4MidXAnQyC4JH4lEdHDBmsNDjYmFlPsmVwSv3KvCVu6zuCmyGfZZlTTLtswtMkjoXtpMhopc8MIBGufW8krX/onql3z3e/exg03fJotgY38KvAjthnVaDQlbinnxi9hsjO9bxxhFabH6N9ipXrtCn2HULOSolMpdcpYZS0n09mluWiTLaTqdIpdr5skGAwSCoVpazvwyorWmlWdikXNDg1xRUFAMjPTZEWHw+ZuhQbCpuCUfIsOW2FJ+ogKeH83BdT0KE7IsygND++8KK35d4PNK40O7UmNKWBUSJLhEyxtd8nxC0rDkh5H81qTZ018YYUkrMNYro+YiHluX1qQQgiBwMQkX+ViCxupoUd6bV+ZKoPx9lhSdAqvr3iJlpYmzjhj15zSdV0WL36de+75f/xtaQd/bwnRlTBBJbBtP2mWYEyqJCZi5Lm5WFgoFKt9a2g3Osh0M3r1VQ5Nspn15kZm2NMO+LM5XFBRUXWksjIU5OTk9Nn87olzzjm04sLLL7+c5uZmvvOd79DQ0MD06dN54YUX+gTyNTU1/RwS5s2bx4MPPsi3vvUtvvGNb1BVVcWTTz7J5MmT+17z1a9+lUgkws0330xHRwfHHnssL7zwQj92/8ADD3Drrbdyyimn9IVC3n777Yf0WI/gg4eyIbrOIypGKmCAikFss1dd8Q/cBdiHHTvqOOusoa3kv9/QnmQDPcmAFb3EQQMJDWkC/Br9f0l0TEOe8HI0Zg++KieqDOTNPtSrLqLa9cIUpxvohQ4fVIx7T083t932PZ5//ilaW5uYNGk6P/zhL/dLIPUOhf6n44VOlhneeXI0Yr1GvepgXOaVnIwJEzGmTcN5911EWprXBtbRgSwsRPa2w2qtAY0wDlzPoKORfhWcPvj9qN50Tx2JeHqWZBI5qhQ5SIvqkPepFM6CBej2Ntyt1YhNG3F9FiIjC/Poo7FOO6PvtSUlo2ls3NEvWVlHo6jWVkRKGBka+ZCyc87RRCKKZcskDQ2aQEAwe7bmssvUfhcRDhYSyXhnMuOdXfcRwvDVr36fq666kR//+Ft85Suf5O67f8unP/0Vzjvv0sO2FXQoyFa5fCbyVTaYa+iQ7WSoTMY6Ewdtq5FIyt0xlPfG6rgxh4eD9/LO+kVs/OFaGp/eQcVpVfzxRw8zsWQKTbKBe4N/oNlooMgdhUSy1azmvtAf+Uzkq4xyyxAI5trH8pB5L92iqy/TZZtRTZ7KZ7w9ecCxjAQkklMSZ7HN3MIGcy2ZKouoiJAUCU6Pn0ehKt51rrJzaW5u3MfW9o3FrS6PbkuQVJBiCpZ3OPik4Owii3SfwFaawhSXkgA8vV3gDFD5dLQXuHkgeK/d5Zk6h5ABVakSW8GmHkVNRDE5Q/ZZBWf6BKBY3uFyasxPrpVNrVlPoVuAACIyAgiCOkhA+xEIQjrIKKeY0W6pl9OjUvvato477mRefnlpv7F84Qs3MWbMOD796S8zPtdPXiiH1yikM207IRzyfT4cM46FRbkzui9Zvk12kObu2raJSYoO02Q2E3VihPQh8Dh/H1FRUcXCha990MN43zHsO6hSinvvvZcnnniCrVu3IoSgvLycSy+9lKuvvvqQt3Xceuutg7Z9vfrqq3s9dtlll3HZZZcNuj0hBD/4wQ/4wQ9+MOhrsrKyDnng5REcfnDawOkGIwNELwc2wuB0QnLHvslKIpGgtbWZwsLDsw1MlEhUSIAWiKMNaNYeMWnyqh3idYXOFBASUKNx77MxEHDU4CvFYoyBMcZAO9rTv8RAbVXoWo0uY1cbWIPytpM5vGuFdl3c1atw160F18WoHIMxdZoXYjgAvvSlT7B+/WruuOMe8vML+dvfHuTyy8/k1VeXU1hYPOB7AJLVcRJdUbonRDGFQYpOIWD60Vkgliv0RV5SvfD58H/sWuTocpx33kbm54NSGBMmIFJSPWF5UyMiNQ059sDFkDK/AKRAJ5N9Qn2tFDoSQVZW4qxaSfJvj6EbdqCVQqSlYx1/AtbZ5x6wTkatW4vz738hyiqwyitx6+vQtbWoznZ0Vxfu+vVe8nxaGiUlpWit2bGjltKS0divvIzz+mvojnYIBDGnTcd33gUjGugYCsG112pOPdWltVWQlqYYPRoOxs1TKw3VGl2nwAIx3kAMs/JXUlLK7353HzfeeCu33fY9br31Wv7v/77FTTd9hquuuoHU1LQDH+AHCBOTic7UA3pvYHmAhl/X88azL5NVksNX/vA9bjn3iwSE97tdZi1hh1HHeGdSn0i+0hnLOnM1S6zFjHLLAJifOIkauZUl/kXUUQNArsrnstjVZOnsAfc9UpjkTOOmyGd4zfdvtpqbKXFLOTp5PPOTJ/arLo0eXc62bdUHtI+4q3lxhw14FYOdqIkoFrc43Dw5wXb/FjbKNjYh8OcU4WspYkdMkB/wxtAQ14QNwZSMff/uEyRoMzrQKDJVRp/V9VstDlprCoLe+/0GFAYFqzo0XUnNNq1IKk3IEGT6oC2h6UrCOHssMRGjTXZgC5sESVJ1CrluDgLh6VOEIk2nDZirkpKSyvjx/QlnKBQmMzO77/GysEkJU9hiplFr1GELmzQ3l3JnNPnKc2x1cPvawXaHoQ2Swh52OOlwERMxWmU7Gk2WyiCs9zaKOFhUVo6lubmRrq7OEc3zOdwxLLKiteb888/nueeeY9q0aUyZMgWtNWvXruW6667jiSee6MswOYIj+LBD9WY8iT0mQMICN+a17A/GzRsb6wH2OSn+QFEmEHMMeM1FBwE/iCRQKNFRjS6Su/QZGQJdrVCv2siZEmHsewLX1zIWAnm+hXooCes02tQIx8s5kWdZw1rY0K5L8onHcV57FWwbhMBZ8CrGjFn4r7m2z3lqJ2KxGM8993fuuedvHH30cQB8+cvf4cUX/8F99/2B//mfgRcnekSETdZGsmUaPTKKFtBBF3luDqn0ur3sNmyRkoLvrLOxzjgTHYuSfORh3CXv4La1ec+npmKddQ7yIPQaxpSpyHHjcVevRuTkgDTQzU3IoiKMqrEkH7wf3dGBKK/wnLhamrFfeN7Ts8w5MF2ds2Y1JBMYOTkeAaqrBeWC4+CuX0eiqxNn+Xv4r/84xcVeFam2toairduwn3gcQiFEXj46GsV55WV0PI7/+htHdDFLCM8NrKTk4K2KdUKjHrXRb7uIeK/Raa5EXmohZg6f8M2cOYeHH36O1auX88c//oYf/egb/PKX/8vHPvZxbrzx1sNWyzaSWLnyPX796//j+eeforS0nJ///A9ceulH+6pvO9EsmzAx+7l5CQR+HaBB1vc9FiTItbFPMN8+gVqjBr8OMMGeQrY+RDaKe2BnNc3BwdjD2WonKiqqWLJkEIH4ftAQU7QmFAV7ZKfkBQTNqptF5mowvVR2jcbJ2kTV6CTbayrY1C3QeBWPc0dZVO7Dar7O2ME6az0REUWjCeogVU4lZU4prQlPj7I7woZHNlZ1KkJmb6UYQcCAyhRJpl+QqlOYmziKRqOZdtnOZnML4H2OcRGnR0RIV2kUDmJ/PFSYmFQ5lVQ4ZbgoLMz+GiaVQlAHiYooqXrX4khERklVqQOGPY4Uaoxa1lkbiIkYAAH8VNqeEH8k9S2VlV5YbnX1xsO3zfwQYFhk5d5772XBggW89NJLnHTSSf2ee/nll7nwwgu57777uOaaa0Z0kEdwBB8EZAAQoB0Qu/1SdALMvH1rVnbsqAOgqOjwJCtCCuRHLHSxhLcc6AGO7xWQP+OwVxB3poBG7b0uvbe9abtGb1ZerkaVhKK9W5XELAOZ25vu3q496+KZBiJ7eEvgasN6nAWvIbKyEb0+zDoWxXl3CcaEiVjHHd/v9a7r4Loufn//qksgEOTttxcOup/NZjX145rIzcgkvT6VZJFDQiRoVW2E24IY5/oGJGtCSnRnJyIrC5Gbi3BdZNVYrBNO9HJcDmKSLkIh/Ndej/3vF3GXL0O7LuYx87BOOQ21beveIYy5eaiuLpwl72AcNQe1bSu6sQECQYxx4/YidgMikehj6bqtFVVX6yXeAzIzE1lZhbtmDc7biyk+/kQAtm+tZkb1dvD7kb3fexEMoS0f7orlqJoajF7b+8MNerELC1woFJAuvSrLNo16zEaOFsP+vu7EpEnT+M1v7uZ//ucH3H337/jrX//En/50O+ecczEXX3wlJ5xw2l6T95FGs2xkse8N1pmrSNGpzEzOYZZ99KBtXAcD27Z59dV/cf/9d/Hii/+grKySX/3qLi6++MpBW+FyVA4ODqo3XRw8QXdCxPcS8BsYjHMmMc6ZNOJjHyr2dd4qK8fy6KN/PaDQPp8hMKUgqSCwGz9OKghmNBE3IpS4uwwGAgSQJds5Li2LRGcBQkBFitwr1b0hplje7tBua0KBOImCDYREgkyV4YV5ih7WWutIUWFGh1NZ2OJQENhllJJ0FY729CyZEgKG5y7WFIcxKfTtz4ePUW4xo9xiStxiNpibaDc6EEChW8BYe8ywBO5/+9u/B33O6P1vT/jxU+GMZq21njbRjk/7SIgEJmY/t7CRRofsYI1vHQpFtvIc9yIiwnprA2kqlTw1cvaEFRVVAFRXbzhCVgbDQw89xDe+8Y29iArAySefzNe+9jUeeOCBI2TlCP4jYGWBlQl2K8ggfZoVYUJgP94K9fW1ABQUHJ5kBUD4BOIkE04y+6wx9VoXZWpICE+zsRNRDSneY1pp9LMO+mUHIp7WRacJxBkmnG72m5jreAxyNPL8g1vRcjdtQifiyN0CY0QwBJaFu2rlXmQlJSWVWbOO5te//j+qqsaTm5vPk08+zLvvLqasbOB0cBuHJqMZM9+g/fQ2sp/OILg2QFD6Saok0YkJ0k4IoVe5qEUu7FBQJJHHGLjWZpL33YtqagB/EBJxdDyOOWkyoqz8oI4dQGZl4//IFejzLwBXeZULIXDXrRk0hFG1NJP8619wlr7rhTVqiSwqxXfNVZgV+05INyorvVaueBzd0QGOA6YJSnmE0bIQ4TDuqpUETz+T3Nx86rZuRndGEXu2JqSmQn0dur0NDley8raDDoBI380IYjSwXqHXKMRxB2ezXVRUwre+9WM+97mv89BD9/Dgg3dz7bWPkpmZxdlnX8SFF17O0UcfN+KC/Ea5gz+GfsMWazMpKoWkSLLMWsLWRDUfiV097BVfG5t3fAt513qLmIgy3pnMvOTxNK7cwWOP3c8TTzxES0sTEydO4fbb7+bCC6/oC40eDDPsOSzwv8Qmcz3F7igEgnqjliyVw2x78EDdwxEVFVXE4zHq62spKRleNbUwIKhMlSxvdwkaEkt6GpX6mGJiWRephoVQuzmqOYJmrUgkO5juK6QyVWLssSiyttPlga1JmuIKU0KHjmO25XB+ZZyMVBuAVJ1Ki2ij3mhgXm4mqztdNvcocv2ShphiVadDwoU0E7odsJXGbwjGpAgsKeiyPSvj3ZGlMpmbnE1URKFXr/J+uWdVOOX4dYAacztREaXAzafUGUX+CBKGPdEom4kTJ2c3t7oUnUKLaGWH0TCiZCU1NY3MzCzq6raP2DY/DBgWWVmxYgW33XbboM+fddZZR4TnR/AfA2FAaALEt0CyBXDATPWcwKz9tEhHo1GAD9zCdKjoIxhjJKJSwjqFLpMeYWkH0aURp5kIv0CvdNH/dNBpeIn1AA0a/XTvKvR4E9XSgv3vf+GuXIFWCnPceKxTT0OWHGAOk95Hq49WAz58xx338MUv3szMmWUYhsGUKTO48MLLWbFi6YCv146Du6MetX07zXYXkblFpLRUYqYU0jIxSvnUIOnLFe5DNiKh0SkCUevgrLRx/M+iu5qR43ozV7RGb9tG8h/PYEya5BGrEYCORnGWvIPassXTgAQCICQ6Hu/T7mit0V1dEAxiv7EAmVkEncVQn8BdtYXE2vuR3/8ycvzg/dTGtOkYU6bhLH8P3d4B0ShKa4z8fGRvJpV2lZejgjcZ39HShMgoRHd1IdJ3IyyRHs8RLP0w7q/uYS+rbiGFVz4dwZzj1NQ0br75c9x002dZu3YlTz31KE8++SgPPPBn8vMLOe+8SznnnIuZMeOoEam4LPC9xBZzE+PsSX2ryu2ijTd8r3BU8hgq3Kohb0uheCT4Fxb4/42hTezaJM///Sm++cDnaF3ZTHZ2LhdffCWXXfYxJk+ePuTt5qkCro1+gqcCj1FjbEGjKXXKOS9+CaVuf6Kv0bSJVhxhk6Py9rlSHidOs9GAXwfIVfnvy2R516r3xmGTFSEEF5X46LETbO3xnL8EXqvV9NQwHXT0vXZbRLGx20EHFd1Nkjdb48zJNrlstA9frztYUmmerk3SkVSMT/Os4xtEknU9Ad7a7uP8Ca3sNBIz8JLYp6UaXF3u56UGL2hyc4+L34A0IN0UxFyv1WxGpklcQczVOGrnSPc4HsQh0WzsDwJBiVtEiVuE7m1ZO9SwhY3o/W93GBgkRHLE95eZmUVbb6vxfwuGRVba2tr6nLcGQn5+Pu297jRHcAT/CTACEJ4AwYSXsyIDe2tYBoLrOkgp99n+02sUNaTtvV8QlkBe6fN0JtUaYQMpwEmmV4UBL3skqRE5BrgaXaNhu4JGjfpFEq6NEF90F2r9+l6NhcRe+Abutq0EbrkVmZc37HHJykqE5fMmwmmeSFnH45BMYgySfF5WVskTT7xENBqhu7uL/PxCPvGJqxg9ugLdrNCrFEQ0Il+iS7tx7/8Tmb6V1MzJwdfRQ8TXTXRMDT0VufjGTyI9no56oVegOdbouy3pdQ24q7YgjynYZS0sBBQVoWq2ee1P48aju7txVq5AtzQjUlIxJk/pOxc6HsNduRK1ox4RCCAnTALXRW1Yh7YdZGkpIjOT5F/uxd26BQIBRDKJtizw+1DVmxHZ2X0hjCInB+JxRDANqlMQzRod8iHSytDVW3F+swbrW7MQxQN/+UQwhP+6GzAWLyL56ivozg6M/AKMiZPA8qGjUUjGMXvzqcLhFGLJBOb8Y7EfexTV1IjIzIJoBFVfhzlrNnJ02bA/9/cN4yXi3w66cFf7i+7RYIEsHvnJjhCCiROnMnHiVL72tR+ydOnbPPnkIzz99GPcddcdBIMhZs06mnnzjueYY45n+vSjhh00rNGstJaS0RvQuBMZOpMGUc9Wc/OgZCVOnHd8C1lpvYeNzRRnOjTA4/++n85XOml8rZ6O6nYMn0HOOXl84uuf5xvzf3TArmdjnYl8oeeb1BvbUSiK3dJ+OSvgVYmeCTzOWmsVLi4lbilnxs/vszfe/bgX+Rbwov8fNMsmfPiYaE/hwvjl5KjhX3uGg1GjyrAsi82bN3D88acM+/2FQcmtYwOs7XLpSGoyfIKJaQbdsoh3aKBbdKOSYTb22MhgD0ECpOk8uv2SN5sdylMM5uV61+ntEUVdTFEU2nUfCuIjI9jFju4wHTGTrJCDRuPikKm8xYTJGQYT0gS/WpdACk15isHCZgdXQ7oPOm1N1FG02jApzSDDd3jaz8P7l4WSprxFyd3F/QqFg0OGGvlFmszMnBENH/0wYFhkxXXdfZZ0DcPAcQ6t28IRHMEHgaEGQe6E67qDtnRoBYl6z1FM2WCmgb/Yazk7HCCKJfJzfqhWEAHyRX89ShS05d0G9AYFm5R3JZFAnSL5uyUoZwNy9lhE7+RFZ2Wj1q/FeectfOecN+wxGeMnYB57HM7rCzzXKyk9R7CqseieCImHH0Rm52BMm76LAEQ0bFEEVYBgWYiOjnZee+1Fvnndj1C/TEKzV5HRUpB0n8JufI1RgShdVbl0TMxDRuKocABfLELFezFC6QHclgQUCY8odXaCYUCWhjVAVMHuAeE7U6eFQDU0kPjL3bjVmxG9idQyPx//VR9DFJeQuO8e1JrV4LpeYnU0CobsrZYIhGWihYBoDDlhYp/Ll2ps8NrN5s1HVW/2QhhnHYU5bz6J+++DDgktGp0lEAagLVRCoxujqLcdjIsGX70X4TDWKadinnwK9vPPYf/rBW/8QoBhYM6e0yfg9/n8JJMJrBNOglgM+803UDVbEYEA5tHz8F1y6d6taocR5LEmarVCrO3NCUqCiGg4xoSxh3bcQghmzZrLrFlz+d73fsbKle+xaNECFi1awJ13/orbbvsegUCAmTPnMmfOPMaMGU9l5VjKy8fs1w3I0j4cufc9WQCmHphYJEnyh9ivWLDuJWLrY3Qt7+B3C35Gz5puALLG5TD65AqO/X4ZpceX0ZrTjFQCq/vg7JlNzL0qKTsRET3cE/o9G8w1FLjFmJhsNNfREK7nlp4vUumO7XvtMmsJDwbvRiDIU/kkSLDQ9xqdooPPRP4H35DTbg/gGEyT0aMrDiq0L2QKZmX1n2cFVA4T7LFsMqvZTivKrwmrMLK1CpFMIc2ClgQsa3f6yErvWhi7f3tTdJggPbSIJFERIyBsIjJGmkqj2N3V22xrQVtSkxeQBAxBeYpkfZeix9FEHc26bs3YNIPTCi3k+xjse7iiwM0nW2XTIlsI6gACQVTGyHDT+53XkUJWVvaIho9+GDBsN7Drrrtu0BWeRGIE6+VHcAQfYgxGVrSG6GZI1ACGp39JNoDdDimTPJ3M4QBhCRg3MNkSYyQsdtCdyquo9LqJ4QBVBnpLA7qrf7aIkBIRDKG2bj2w8RgGvssuxxg3HnfNak9DkZqG+967JJ/8G0gD4bqIBa96dsKRMbz82+fRbZrKlDFsNbfwo/XfprJ8HJdFrkR3ahgnEVKgujpx/7kM7TdIiblMf3Q7jRPT6cw18bc2kdfmJ7esCM4EDI3evB3VsNEjFFKCLxvpL0a1bEAWpnlBkVqja7d7wZGlo7EffgB300Zk1ViEaXnWw1uqST7xOHLiJNTy5YjKMQi/H9XRgfvKS2CamKechgyFvDG++C+MyjH97IhFXj56wzrMiRMxrvqYF8LYm2siS0bhrlmB0BkeUQFIdCGsECIzH6qH5qIlhMA662yMqirc9evRdhKjrBxj4iRE773AsiySySTCsvCddwHmccejm5sRKSmIgsJDbml/sBCjpJcT9JqDWKcgHcS5FuJYY7/udwNCa8z2NZityxFOHDd9DHbuLLS177YYwzCYPn0206fP5pZbvojruqxZs6KPvDz44D00NTX0vT43N5/y8jFUVFRRXj6GnJw8MjIyycjIJDU1jZK0UtZmriRsphBQARLdCeqiNeiIpqlpB0/1PEpPTw+trU1s3ryB6uqNbKheS3f7rvDF9PJMSo8vJ/Y/UVKPT+eU7DP7rVg7OPj0wPbhI4WV1ntsNtdT5Uzoq7ikOmmsM1ezyLeAyphHVjSa130v4QiHMY5nFx4iTNAJscFawzpzFVOdmYd0rJWVY0c8YVwgqHDKKXQL6G5tZWWTQ7rMQqhdxMsSguaE5pnaJNU9ipABUkBdVFGe4lVXpDZQkVzGZLSTHrDRaEY5xVTa5f1atkyhsRXURBVNcUWaKZiaIamPKRrjcEyOwaWlfkrDI0/kXVwajSaapTcZz1HZ5Lt5h8QQwsGh2WihS3RjYnquj3r4rds+fMxMTqXa3EaD0YhGU26PpsIpOyQOZJmZWVRXbxrx7R7OGNanf8011+z3pnNEXH8ERwBSyt5AwP5wI5Cs9wT7svf+roPgdkB8O5iZ+3YZOxwgZhnodyS87Tl8kSZ2VWAKBKIzBdGo0DGNCO92MMnEQekWhGlizpyFOXMWWinid/wa3dKMHDvec+NSCr15E8m/vogVL6GrrZOfrvs+DZF60q1Mzi45n/+59vtYr5jocuFpEgCMBPhsRCKEFq34u2xGv9WK1grd1obMz0dOz4IiAaEm1KIWdIZGZGV7CWz1FuTmIfKbcZe+i7BMzxErNw/rggshHsdZuxaRX4Do1XgIKWFUKc62LcimJsjI6Jv409aKNnoDONvaPDF9OAVME9XS3L9Lf+d3TIi98masE0/CfXMzqn4ThLPAiaOT3ZilxyGNUcPKuRFCYFSNxagaO+DzlmX16bQAZEYmZBwmpcIhQpRKjKt9nhOY4MAJltb4tz5NYMvfEU7U6/Os/Sd29nSikz+F9g39N7BTazVlygxuvvlzAHR3d7Fly6Y+clFdvZG1a1fy3HN/p6urc5AtvbjXI//mub6/Z2ZmUVFRRUVFFYVnlVA3oYbJFTPIqMjECnrf2VXmcpqMHdQ7tRS5Jd7qsYgQE9FDLoRvkU1o6NcaJhCk6lS2G1v7HnNx2WHUkbZH602AAApFmzz0q9Hjx0/igQfu7jMtGUkEdZCxspBXuhNEg4Jw7wzO1ZrmhKI96ZGTsClIKE2XrVEKklrhlxB3IS9gcXV+CeXJIkBjYfUjn1p76fR1McWWHoXfAL+ELJ8g3RKcVmDyqbEBAgdC4vcDF5eV1mpqzNq+x7axnRK3iKnJyQMSliRJ6swdNMtmJAb5bi5FbuF+nb8SJFjuW0mD0YT2yt0ErCCTkuMpcYdvjBPSISbbE5hgj0WjDwm52olwOJVIpPuQbf9wxLCti4/gCI5g//D5fCSTyb1uWG6PZ4UsU3a9VgiPvDjdvTbJh3nYtUgTyI/7UBk2+n4bfAIqBZRKhE8gUyZB8BV0Qw2UjQIh0I2N4PdjzDj4VU1Vu53E889j/+NZyMjAaG5C5Od7BKC4BJZK8Mc479hLOP+4S3e9cZ0LWySo3tDKnccTyESEchHJDs/Jq7MTnZaGjvSA4yBycjBnzEBIgZP9OoTSEHYZok2jAVXUjuP8C9Eg0XYSHY0gR43CuvQjmJOnoNpawXURe9goYxjguGjsfgJ8rbz2NKF1n3mAMAxEdja6tRVt2wjL8qo3O+oRmVnIMXtrD8zJUwjcciPJ2/6N6qhFZKZjlJ+GGToekZDIWSPnPHUoJmYfFPpI7AHC6NlGYNszaDOEm1bhPegmsJqX4qt7hUT5hQe1/dTUNKZOncnUqXv/lmzbpqurg46Odrq7u0gk4nTFutjorKPdaiEllMqE4BQqg2MJh1NISUklFAr3s9l9MvAIzwQeJ9fpr+/w42dqciZJmWSduQqBwMRkTnI+xyROOKhj2h/SVAag+9kbA0RFlDy1K53XwCDXLWCjuZY8dj2eJIFAkqEPfel6zpz5/OY3P2HLlk19gvuRxPg0ycwsg7dbHQKGwBTQZWuvfVTAuDTZ15rVHFd0JDVHZRvYWlAQ8FrMikMSBpnM18c0/9zhUBwShAxJXUwTcxXbojAjU3BFme+QEBWARqOJ7WYtqSqlr10viU2tUU+ekUfJHi1VSZK8619Go2zGRKLQ1Bs7aHHamLqbqcRA2GrWUG82kOFmYGGi0XTJLtZa68lSWXsl3ds4XvvkfqbNh8oeeXeEw2Eikcgh38/hhGGRlRtuuGG/rxFC8Oc///mAB3QER/CfAJ/Ph9Yax3H6iU772nEU/e4V2gVpHl5i+31BZEnkDT7cbg1rXBgtPaewHo0RLcI66zLc7mfRmzd6jdMZGVjnXegJtA8C7uZNJO75M+7WrehIBBIJ3PZ29LjxGBWVXt+D7Yfg3pNn7e/VbaQDzdqrBAFIEzN8IrZshPQEuqUFXVfrpdGPG0fgo1djVI7xtuFsQ03rwjB70HEfBBIotRy9aCMirQzzqLmQTKJra3BeeA5z3HhERiZydBlq9SpIT98l4G7Y4blrVVbiLHwTnZuLkBKZkYFwXbTPh0jP6N2vg0hJReblo6o3eV8U5SJS0/Cdez4ye+BgPDm1FN9nr4B/W9AeQLim1354gQlTR+7Lti+N1n8bzPZ1iGQXbuZu33XDj/alYjW9fdBkZV+wLIvs7Fyys/tbpZ7G2UPexjhnIv8mRKtsIVt536tu0QVoLopfQbEaxRpzJbZIMsopY4IzZS8x/Ehjsj2dIreETeZ6Sp0yDEwajXp82s9Ryfl9rxMI5idPZKO5jjqjhlw3n4RIUGtsY6wzgfH25H3sZWQwa9bRCCF46603DwlZsaTgqjIf5SmSpW0uSaU5JscTwfsN0U9DkuP3tCcT003m5gxturex26XT1oxLlRQFoDQMPY6mJa4oDRmMDg/9d67RREQEWziEVWi/eqEW2YZC93udr/e71Sxb9iIrdWY9TUYTGW5G7968/2rNOgrdAgoGMVTQvaTGr/xYmDg4RGQUB5du2UOTbKLM9WzWu0U3m80tNBstgCDfzaPSKT+kAZP7Q0pKKpFIzwe2/w8Cw66sjB49mhkzZgzY4nIER3AEHiyrd1UomehHVsxMMFLA7QbDrxFtGtcGZYJ/sjiw/vgPCMIUGFf4UPcnYYv28j/8AuYa+K6ah1aTUZs3gVLIsnJkzsElTWutsV/8p5fePm06xGPo9na0YXhuWAUF6IYGzKJSsINoRyN605i10ogYiIkGTAaedaBLedka3Rpj3BzkMWGcLa+jtm8HrTGmTsN3/oX9sl1k6WjcLa+gxtX3kQ530WZAI0tKPUMBy4LKMajqzbhrVmMeNQffGWeSaNiBWrcWkZICsSgEglinn4lRVYWqrUOtX4sIp6ATcc+W2LFxN2/09h+LY06ahO/Kj6JqalC12xHhsOcoNkCOi+7pIfmPZ3Hee9dzBQvnYc49HXPaTES5gcgZWVaslNpvnsZ/Dwa7NwrEIDbbhxPGOZM4JX4WL/lfoNHcgQB82s+8xEnMsOfgw0eJ+/7m5WTpbK6O3cTjgQeoMbfi4pKjcjkjfr7nVLYbjrKPoSfexcv+F6gxt+LTPqbZs7ks9jECHFptDUBaWjoTJ07lrbfe4Morrzsk+wgYgpPyLU7K9+4tSaVZ3qFIuP2/ezu/bcO5rfR2QXoQglQLUi2B0oN/swdCVMRYa62nyWjGxSWoA5Q7oyl3ykbMpatJtqC0ptloJtqbHO/XfiSCdqO9H1nR2mvDdrtBS40zykUKQVTEaDSaSIokGo2NzVrfBvLj+WgU7/qW0WF0ElIhQFFtbqFTdjInMQs/w3TeGSGEQuEjZGVfuOWWW3jooYfYsmUL119/PR/72MfIyjpMFMFHcASHETIzvd9Fe3sr4fCuni9pQmgcRF5XOKsUxAEFpq2wYqBHWZ64/UMCUSyRn/fDRgXdGnIFVHjCdUE6cuasEduX7urC3bIFcvORhgFVY3FXLIdIFB2LolauQI4uw7xgBuJVEzYodL709AeNXoCjmG1AnkDkS9y3YtAcRc9NwTjOjyiZjalneentPt+A7lXmvPk4K5ajN22EvHy0Y6Pr6xBZ2f0smYVpoXrHDGCMHYf/k5/GeWsxqmYbMicHY9ZsT6QuBIFP3ILz9mIv/LK9Da01qqUV3dCA7uzCOvMs/FdehczMwijd90RRa03isYdxFi5E5OQisnPQ7a3Y792NGJfAyjl+n+8/ENi2PWxr3f9UOOlVaCsFGW9BBXsrHMpGJDuxRw+9wvFBQSK5IP4RJjlT2WCuRaEod8YwwZlySPvw94cqZwJf6vkO28zN2NiMcstI1Wl7vU4iOSVxFnOTx7JD1hEgQLFb2q997FBjzpz5vPrqv963/fmkYEamwfP1Nll+jU96WU91UU2uX1KV6lVDkkrjKggYg2uyylIkQUPQYWsye22JnV79y4n5Q/v8FYoVlqcHSdUpmDpIVMRYba3D1BalbsmA78tRWWxjG0mS/drAAHLV3otdLg5tsh0Q+HsrMFERxRUu8d0CkrSCWLXnwqmTgJCkJnKpK99CPBAlKZIEdQAHBykk3fSwydxMQAfokJ1ku1l935+ADtIm29lhNlLmDC9LZ6SglPqvq2QP68rzu9/9jl/+8pc88cQT3H333Xz961/nnHPO4cYbb+T000//j+lZPoIjOFgUF3vhh7W1NZSU9J9cmkoRXpzEtgW6UGBojdGtkS9rdLFEHPvhWqEWfgGTD/2FU5im577lugDI3DzE7Dm422vQ22sw583Hd8FFGGXl6AqFesFBrHG95cCjDOTpFqJAoh0Hu+NVnNY30ZEu5LYs9KjjMYvmewQlMPjqq1FeQeC6G0j+6wWvAmMYGJMng/3/2Tvv8DqqM/9/zpmZW9V7syz3XnCl92YwNYTQISR0CCGbX3rfTdnsbnazSdh0AoQSQu8dDBhsY9yL5CJZ3er9tpk55/fHyLJluUi2bEjQJw/PE1/NPXNmbjvved/3+7X7DBIBdDyOEBKxx2aOMWoUxqh9m2LKnBx8iy/ErSgndu+vkf4AxvwFgNejoyp3QE8PpB98c0hVVeGuW+cpkfUKGohwGFVVifPuEsxjj+uTlB4uWlqaKC4uGdYxPxaUi9W8GrNlDcKN4aRNxs5ZiPYNXiHITRlHvOhs/FUvYMaa0MJEqARO5gziBacdwckPHxLJRGcqE52pH/dU+uHDxwRnyqCOTdLJTHAnH+EZ7ZuFC0/gvvvupbFxJzk5eQd/wjBwWq5JVY+itNPty6ikW4KLiiyEgGdqEqxqdbGVZnyywWm5JmOSBn5vjwlLTso2eavBoSXuYgqIujAhWXLcIEvJWmQrTUYLaSq1r0QwRSfTJtqpNKsZ5RbuM7uS6+Ywyinq12APUOgWkOcOLOkyMUmIhBcQ9S5nFZoEiX5mjO1NEdprbGKBCCJFk+wmk19XTHNSI02FTfgNX19wk+6mEdQB6o0GUpQ37p6BroFEIOgUnXxcxOPxYTGN/UdiyKsiv9/PlVdeyZVXXkllZSV/+ctfuP3223Ech40bN5KUlHTwQUYY4Z+cwkJvx6W2tmrA3/RGhWxQ+CZJxK5flQyBbtfwoQv/YMHK0UKEw5jTZ2C/9SY6Lc1bcKekIEIhjBNPInDTrYhe2V6RLzE+70N3aq8WInX3TqL98ovYzz/nqWwlJaPq60k88le0beM77fR9nlt3deGsWom7bRsiEMA69XREQQHC5/ca/v/0B9yKcmRODjqRQO/ciTF5CsaUoS32nFUfodvbkZOn9M1XloxBlW7GWbsGX9G+g51+c21tgUgP7HWsSE1Dtbaiu7s8w8ZhpL6+lvPOu3hYxxwsMWK0yRaSdPI+d9oHjVYEtj2Kv/olhHJAGPjq3sFuXE5k+p2DV/ESgtj4y3HTJmA2rUI4Edz0ySRyjx2SEtinnYjoYZO5nh7RTa7KY7wz+WPN7AyFBQu8Pprly9/jggsuO8jRw0OaT3LzeD8bOlzqo4qgIZiWZpDlF/xxW5w1bS7pPoEpYUWLQ0W34ubxfkbtJT8shOCiURYlSZJ17S4Rx/NUmZdhkOYbXHYqJmKoXqWxPfFrP1ERIU6CLtlFl+zG1J5kcIAABgYz7GnkqOxBSRcHdICgDmILhwSeFLOBJEWnoHt/XDtEJ5XNzUjpwwnFUSi6ZQ8ZMp3Jm2cSC0SxsyIYGCSrZJJ1EjERxxEOFj52h34eulfowac/vmDBthP4fJ+uTPZhffJ3OXRrrXF7dztHGGEEr6Y0PT2TmpqBwQpRPKnZvVWHAkDHP0YvmGpoQNXWeApf48YPkM09UljnLELtrEdt3eJJFeverMSll/UFKnsiUvrfY9Xehv3eu5CahszN9Y5JS0PV1OAseQvr2GP7KXPtek78T3/ELdsMlg/hOjgfvI951tn4LvREA/xXXYP9+quonTvBsjCPOx7fBRcN+b7o1lbw+/tlqYUQYFnotrZBjSFSUsAfgEgEwru9E3R3FyIlBRE6sNfHAefnOOimRjBMRHY2Qgji8TgtLU3k5++7tONIoVC843udt/2v0SZb8OsA8xPHc378EkJ66NdodGzBX/Mayp+JDmR6D7pxrOY1+OqWEC+5cPCDCYmdPRc7e/jKID9NlBtb+WvoT9QYO/oarmfYx3BN5IuHF5ACbaKVVb7lVBk7SFYpzHLmMN6ZPKxu53l5BYwePZbly5cOIVhJIEQXWifDIRpXBk3B/Mz+y7q1bQ4bOzyvlV0qXpk+TVmn4v1mm8+FBy56DSGYk2EyJ+PQlogBHUAisLH7BSxxESdJhdng28hOoxEXb92YpMPMSHhBioFBgZtPgZt/0POEdYhUlUKQIDERQyAIqRAR2UOS8r4DKswdOIkgydLC6P15tYVNu9FBejyLpM4UGrI7SFVBAtq7F57KXDajnEIajAa6RBdJOgmNpkt2EyBA7n6a948GiUR8JFg5GPF4vK8M7L333mPx4sX8+te/5txzz+0nfzjCCJ92ioqKqa2tHvC4KBBoE3REI0K9DeBaIzo0DKOc7JFAOw6JF57Dee9d6PQc3OWoYs+wcR/yucONzMoicNuduOvXoRobEeEwcvJkVHk50V/+At3VjTF5CtYJJyDzBzoH68ZGdGcncnRJv8dFRga6qRHd0oIo6h+sOEuXojZv8gwde8unVEsLzttvYc6ajVEyBnPefIyZs9DNTeDzIzIzD1gWq10XtaUMVV0FpoUxaRKysAhZNApWLEMr1dczo10XHHuf17PPezRmLMakyThrViMLiyAUQre1Qnc35rmLdvu5DBFnw3rsl1/yglTDwBg/Ad8FF9LgejXleXnD79R8IJb63uZvofuxtJ8MlUlE9PBS4Bm6RRc3RG8d8uLTbN+CcCLoXXLD4Kl4mUlYTR8NLVgZ4ZCJE+fR4F+oNnYw1pmAhUWP6GaF9T45/jw+E7vqkMfeKev4Y+jXVJhbsfDhYPOufpNLo1dySuLMYbwKrxTs/fffHsSRLn7/6/h8byBlO0qlkUicRjx+Foe5nwzAzpjG1ZqAsXt9JoQgxRJs7z4ygg+ZKoNsN3OPnhWTiIii0fi0RY1ZR6qbgg8fCkWH7GCDbxMnxI4dUtN6rptDkllJTMRId9MQCLpkNz7tp8DNx8WlyWghJS0H2WaBtkGApU06VBcdRid2OEZcxNlpNNAm20nWXnZlnD2GLJXJZHsiW81yWmQrACEdZJI9kXSVdkTu3WCIxxP9hHs+DQzpk3D77bfz6KOPMmrUKG688UYeeeQRsg5T4WeEEf5ZKSoq3ndmZZKEWRLxoYtOFeAD0awhRyI/4SVgzvJlOK+8BGnpiImTwbZRlTuIP/wQwa981VO6OsKIUAhzoWdCp5Ui8dijOEveQpsWwufDfuUl3A3rCNx0i7dY3/O54bCX7YhGPdWuXnQ04vWqhAfuyLtrV3vlZntKUPcGN2r7doxeNS7h8yEKDm4mpuNxEn97BGf5CnQigUBBWhq+Cy7EnDMXZ9n76K1lkJvveak07kQWjcI45pjB3R8p8V15FVgW7uZN6IadiJQUzEXnYZ1yaD0TbkU58Qfvh64uRF4euC7OqpXo1hbqjvV6awoGce3DhY3NEt9rvc26JYDXo+DTftb4PqQmsYhRvY8Pnt7gRuu9nFn1P46m+D8B28xSqswKSpxxfbvyYZ1EpsriI98yFsUuJsShyca+7n+RcnMLE52pfWVFtUYVLwSeZLo9m0w9fOuZs846n8cee5CKim2MGTN+v8f5/S8QDD6C1kGUSkXKFoLB+4E48fjFhz2PkOG9pZXW/WSNY0qTakma44ry3qBlbJIky3/473WJZKY9HZMymoxmoiJGUAeYYI+jyqjBr/xIIWkTbUREFIGkR0RpMpqHZMiYpJOYlZjOZquMTtmFRpOkw0y0x5OpMlAoDCTdha2kNGZgtQZRQQdXu/jjIeK53WSkp2C5RXSITrpkF0HXz/zEHDKUZ2o71hlDnptLm2wHBBkqnaA+OpUE+8O2E586QZMhrYx++9vfUlxczNixY1myZAlLlizZ53FPPvnksExuhBH+kSksLGbJkoHO0cISyGt96CLH61GJazjeRJ5mIoo/uYsirbW3kDYsjKxelSOfD8aMRW/f5sn0Llh4VOekdlTgfPA+Iju3T2JY5ytU6Wbsd5bgv/LqfseLgkKMyVNwPlyBNEYjwmF0Vxc0NmCeejpyX70cUu52it+bQxAVcV7/CPW3KIb9Oc/cMa8RN7qSxHPPEhw7Dv8NN2K/9CJu+XYQAnPOPKxF5yMzMgd9DpmRif+mW9C1NeieHkR29pCeP2DOK5aj29qQkybv7qUJJ+Fu20qN4S10jmYZWKfooNVoIU31f71SdRo7RS3NsmnIwYqTNmmgipcbQzgR7Ox5+32e7tHoZS56nSfmIGYZiIUGImlEcOZQiIkYDs4ATw6/DhCTURIiTugQPC5ixNhgrSFL9e9/yHeL2GJuYru5hUx7+IKV0047h0AgyAsvPMWdd/6/fR4jRCd+/+solYxS3ufHddORsha//w0SidPQ+vD6nKamGuQEJDt6FMUhiSGgJeE521sC/mtzjLaE9/2W7hOcW2BxcrZ52IJJIR1ibmJ2P58ViaTCrEQLRZ2sJyKiSCQKhY3NRqsUA4OwCpOskwaVHc1R2WTE02mXHWg0qSq1z5tFIilw8ylN20LbrFqSKjOx2oJEzAgNo2tIKfJhGNLrVSGZbtWNgTkgaxLSIUJuyPNj6VUbC+vQsJYODoWRMrCDcN11140ofo0wwiDZlVnZl7u3CAvEYgt9rglur6LWJx2t0W1tA3pDhGmiAR05+o66qrrKy5LsIecrpKfC5W7e1K+cCrzyB99nLkMnEqiyUlQigQgEMBYci++Ci/Z5DmP2MbhPPO4pfPXuZummJkhO2qdz/IHQnRp1n4NsmA2ZBjggt46FzDTc1L/jlpVhnXEm8rY7vEZ5IRDpGYf0vSuEQOyjIV+7GioUtGlIFTBOHtTfR9XUeFmpPXtpTBMhoL66kqSkZJKSBq+YdbiEdRJhlUSP6CJVp/U9HhE9BAiSrIbe1+CmTiA+6lwClc8jow292RSNnT2P9rR51GzdTFXVDqqrK6mu3kFtbTVd7R10l3cR6ewmqqI42sFxHRzTIZgeJCUtjdRU77+UFO+/1NRU8vOLGD9+EhMmTCYlZaTpfk8K3VGk6DRaZBNZe/QFNBuNTLKnkXKYi/ejRSgU5owzzuWFF57cb7AiZSNStuM4/QN9pbIwzSqkbMB1D+96M/2Sy0f7eKIqQUW3QgHJpmBmusHmThdTCiYke9+RO2OaZ6ptCoOS8cmHX5IsECTpJNBeY3qX6EajqJM7cYVLik5GIIiTICpjVJg76BRdJBGmwMljqj0FaxDLVBOTLLXvzZgx9mjaRQeN6Y00ZTSAI1DCJW4kSNvLZNJrnB943RpNrVHHNrOCHtmDRJLlZjLFnkTSIfTHHS6ef9uIGth++ctf/nKEpjHCCP98TJw4hWg0QkXFtv06GQtTDEdZ8lFBSIkcOw53xTJ0Ts5uJ/ZIBEwTmZM7qHF0LIa7bi3u9m1gWZ5q1tRp+/Q1OSiWDy1A7F26YzuI1MA+Mx8yM4vAbXegyrejOzsRGRnI0SX7Pb91/Im4W7egNmxACeE5xwdDWOcsQu5Hinh/6NUuNKagU6sRyV7JnA5FkK3pKF2Ctj25TSEEYj+u9IeDbteohxLozQoRB+0DMVkir7YQGfu//zInG7W1rP9YSqG1pr6n66j3qwQIcELiFB4PPoyPRtJVJhHRTY1RxSx7HmPc/Zfd7BchiI79DFs6g6x+/yU+3FDG2vImKne+R3Pzd/oOM02ToqLRFBaOIsVJJVNnEh6dRMAfxJQWpjYw2g1iUxN0pXTS0dFOZ2cH9fW1dHS009HRTktLU5+xcm5uPhMmTGbcOC94mT17HjNnzvnUmmzmqQJOiJ/KK4Fn6RZdhHSYdtlKWCdzZvy8Q/ZLCRBguj2bN/0vk6Gy+rIr9UYNGSqLcc7E4bwMAM477xLuuOM6amoqB0jYA2idjNYBhOhB71FatOvfXrP94TMjzaAkHGBrl4utoDgseb/JJuLC5D3UwAqCgrJOl3Vt7rAEK7vQaCrMSrZY2+iki4iIonrVunz4iBBBAAYGAfxY2qTCqsSv/Uw+zNfFj595iWNoMprpEt2YmCSrMKvFejplJykqBdErBhCXCcYlxg7ImDTKJtb5NqJRhFUYV7hUmdW0y3bmxeeQplOPapalo6Od5OTDE5r4R+PT+W04wghHgblzj0VKyYoV7+83WPmko10Xd/Mm3K1bAY3IyoakZPS2reisbEgkoKUZY85c5MRJBx8vGiH+wP24q1d5DyjlNaqffga+iy9FSInq6MDduAHd2YHMzEJOmeo1vzc2IlJSkOMn9PWPGJMmITOz0NVVUDzaUyfs6UH3dGEsWrTfjIQwDIwJg/sRFCkpBL5wM87aNaiKcggEMKdNR06cNOSMh65QyNQk3M4oqDBIAYZG6wQiloMcdeRMxrTWqKds+MiF0RKSBPRoWKNQAQf5RWu/12POX4izejWqqhKRlw+ui66pRhYUsmHFm0yZMuOIzXt/nBY/ly7RxTL/u2wzywjoIHPtY/lc9DoMBrfQisfjfPjh+3z00TI++mg5H320nLa2FgDGj5/E7NnzOO3ccYwaNZri4jEUFY0mL6+gz5DNfTCBfs9FTOy/gNZbFGKhgXHDvnc/I5EI5eVb2Lq1lK1bS9m2rZTly9/lkUf+TCKRIDk5hWOPPYkTTzyNE088jUmTpn2qBGwuil1OlsrhA98SOmQ7c+yFnBw/g6nOzMMa98z4eewwytlqbu5rsE/SyZwfu3RY+1X6znfmefj9fl544SluueXLA/6uVA6JxBwCgddxHAutUxCiE9OsIRY7HaWGz6Ml2RL91L16HK8MbG9MAV3O8KpSdsgOyqytCC0o0HnERJQuo4eEsDGViSUsgiqALRwkkqAO4iiXGrOOsc6YvrKuQ8XEJN/NY099san2JDb6SmmWrQihkVpS4OTv0+ixyqzGwenrg+kQnXSLHprMZjplFyXOaKbak49aL0tdXQ2zZn26lAZHgpURRjhCJCenMGXKDJYvf48rrrj+457OkNGuS+KJv+O8swSdiHv7RqblKWkV+VF1tQifD+O88/GdebZn2ngQnBXLcVatRBaXIIJBAFRrK86StzCnzwDTJP7gA6i6GkB4i+JotLdxX4NpYowdj/+qq5H5BcjMLHyXXkb88cdQW0o9A0jLxJy/EOvEgU7tWmvUljKczZvR8RgiGEQkJyOTUzAmTUYk73snU4RCWMcdD8cdf6i308OXQGOg7QSqfDsiKckrLeuxkLMLMSYdmomd7uxENTV615NfsO+go1nDBhedL/v6KURYoAvwzDN3mpC/72BFTpqM//LPkXj5JVR1JUIayDFjYfEFrL//f7jw0isOad6HilLQVOdnvnMNxxeeRXtwJ0k6mVFuyUF33ru7u3jzzZd58cWnefPNl+nu7iIlJZVjjlnADTfcyrx5xzJ79nzSB+NFY4FQAxd2QukDqs+GQiGmT5/N9Omz+z1u2zbr1q3ivffeZOnSt/nJT75NPB4nMzObE044lcWLL+Wssxb/0zfXmpickjiTkxNn4OJiYAzLznWeKuCOnq+y2reCSqPCky625zLePfhGy77QaGqMStpFG+kqk0I1qt88k5NTOPnkM/cbrIAgFrsCKaOY5lqEqELrEInE8cRiV8IR3K0fFZYsbfLc6U2526l+V+ZlOGmSLcSJk6UzEQjSdBqOdlBo/PhJkEChMbRBUHkLfp+2iMsEtrDx6f0HKy4usteocSgUuYWkxlJoMlpwcEjWyeS4Wfvc6OiU3fh7fVXaZQctshUTAwsLoQXVRg0KxbzEMUclw1JbW/2x+Vp9XIwEKyOMcARZsOAElix59eOexiHhbtqI887biMwsZGoa4Hl1qB0V+G64EWPyFITfhwgE9/l8rTW6vs5r8M7JRaam4qxf50n7Bnc/R2Zk4DbuxC0rxd28GV1fhxw/EWEYOBvWo0o3I8aOwzruOHQsjltWSvxvjxK4626EYWDOm48sHo27eSPE44jCIoyJkwa4tGutsV98HvvVl9HdPbjl5eiWZvD5kKOKMaZNI3D1tUM2chwsOhrB3vY0oqEAQRYYddDeAcHRGOMKMb6QN6iAr9+Yrov9xmteQNnW5vneTJ6C79LLkHsrNUZAx4H0vQYJgW4BDtByJITAPPZ4jJmzUdXVYBrI4tGsXr+aeDzO3LnHDmneh0NlJTz1lKS8XOC6kJeXz6JFeUybt//d4JaWZl599Xleeulp3n33DeLxONOnz+b22/+Fs89ezOTJ0wedudBVCr3JhbhXZagk0KYR6b1lkW3aE9GYNvQyGsuymDt3IXPnLuTuu79JNBpl5coPWLr0Ld5661VuvvlKUlPTuPDCz3LZZdcwb96xw9ZHqtGUmhtYbi2l3qil0C3mWPuEj93FvtrYQam5AVvYFLtjmGrPHNB8PxTSdQanx8897Hl1ig4eCz7Aems1PaKnV5lqHp+NXUPSHuVbixdfyt13f4H6+lry8wcqXWmdRk/P3RhGOVK2olQ6rjsODrHcbbDMSTf5sMVha5cis7dnsiWuGZ8smZM+vEtDV7iI3v8BpKpUoiJGu2wnRgwXhRaaHDe7T7o4KmOEVbjP+2RvWmUbFWYlrbINS5sUuYWUOMVDMg5N1skkOwcvtUtSSTQajSit6JCdGEhMLBxcQoTwKR+NRhPtsuOISxpHIhFaW5spLBxaCfI/OiPByggjHEEWLDie++67l6amBrKzB9fT8UnBLSsF20b0BioAIikZTT2qdBPWwv0vUFVrC4knHsfdvMkLIFJTMU88yesl2dfiSoNqbERVVyFGFSMMw3OCb2yA1DTo7vIyLMEQsng0avs23IpyTzbYMJA5OcicA5t0qR0V2K+/BuEwqq4O3VDvNVH3dKNqq6Grk5jjEPrWdz1jxWHGWbkSp+YtjNmnYlROQsfzIWCDbkec7SJmDr2EwPngfeynn4JwGDGqGKJRnA9XoGNRArff1T/4yRaIdIFu1VC4x2vQohGpAnIOvugVoRDGpN270CtXfkAgEGDatFlDnvuh0NEB998vqaoSFBZqTBN27oSHHpIkJ7vsMTW01rz//hL+8pf/4+WXn0UpxYIFJ/DNb/4bixZdxKhRJUM+v3rXQT9lQwcgNFribX43K9gpAA1BAacZMP3wF5vBYJCTTjqdk046nW9841/ZurWUJ554iMcff5gHH/wDJSXjuOyyq7n88mv32RMxFFZYS3kkdB89ooewDlNubmGN70OuiXyROfaCAcfb2LTIJgI6SJreOwI+fDSa1/0v8kLgKbpEBwAWFnMSC7kmctMhyxcP19yeCDzM+74lFLrFFOnRdIh23vW/gR8/V0Vv7Dv2rLMWY5omL7zwFF/84p37GVHiuuM5mt7aqT7BDWP9vNlgs6HdO/HpeSan51qk+oY3O5C6R1+IhYWJQZ6bg0KR7WahhKJddGBqkzhxojKGQjHGGb3PTEerbOMj32oiIkpQB4jIKBvlJrpEN7PtGcOe3Sh2C2kymmgT7dg4SAQxESOgA4RUCImgiy5iIjas590XdXWed1th4ZErGf4kMhKsjDDCEWT+fK9s6MMP3+e88y75mGfj4cbAaQXtgJEEZtp+bCRcl32VIWgh0O7+zcS06xJ/+CHcXaaEuSF0Swv2c88gJ09Bx6L9lbU6OhCBADI/H2flhwir92vJcbz//D6v7qf3nNrvx62vJf773yIsCzlqFOaJJ3tlZAdAbd2K7ulGpKSgtm0Fw/CCr1gMhKTBZ7KzrhL97N/JPmYexaPHEh5GhSt34wZP6nlsA25RF6I1zbvM1hXo9BMwIvnoYHDQQgPadXGWvguWtdsw0udD+ixUWRlq21aMyVP6jhchgTjFgCccdKWCFAFdGhEDcbGBSBn6D/xHHy1j5sy5+HxHR5lm/XpBVRVMmKDpbRthzBgoK4NlywSTJmm6u7t44omHue++e9myZTMTJkzmhz/8Ty5Y/Bmycw69B0A3KvRztqdiPUUghISYhnKFOM5AFHqvmxwnYbxEyOEvB5kwYTLf+Ma/8rWv/ZAPPniHv//9r/zf//2C//7vH3PppVdy111fZ/z4oZc0RYnyYuBpHBwm9WZSNJoKYxsv+Z9huj27L5uh0ay0lvGa/3kajJ348DEzMYfFsc+QrgdROjdIaoxKXgw8jaENJrvTEQgiooflvqWMdycNS3bkUGmUO1nnW0W+W9SnRpem03GUwyrfCs6JXdjXA5OWls5ZZ53PQw/9kS984Y5PlKJqTkByxWg/8SIvK+k/iCrgoZLtZpHv5lFr1mFpC6klcRGn0M1nXmIOlrYoNyuoNmuJywRJKkyJM5pid99y6DvMSiIiSqbK8AIT7ZmJ1pn1FLtFZKrhex8C5Lm5TLenss3cThPN2EKRqpPJdrMxkMSJY2ERVPuuMhhOdhlNf9oyK5+ejr0RRvgYKCgoYtSoElaseP/jngqqvY3o22voemI9PWsiRLZA11ro2QzKGXi8MX4CSInu2V0fpGNRcN1+i+AB59m+DVVWiiwZg0hN9QKKvDwIBFEtrRjTpuNuKcVZu8ZrpG9uxDzxJMzjT/Sa5RsavIH8fgiGPLnk5JQ+yWR39Sp0/U50ezvaVbhr1xK/7484u5r294sGhFcuZdte4NBLm89kZVEeO/wm9S1NrFm1jPfeeY3OjvZB3t1BIMVuv5akCLq4DlVYjerYiv3sM0R+9H1iv/xvnFUf9SlFHZBYDNXWBnv12YhgCO3Y6H3MXZxuIq6yEHkCEdeIHIG42kKcfWj7VitXLmfu3IN76+hWjXrRxv2POO7/xlHvOOj40Jt429oARF+gsotQSLN581a+8517mDOnhG9/+27Gj5/E4/f/leW/+wp3T97OmIpf4t/xLMI+NIltvUVBb1Zq14JTBIQX9NVqxLkmcpGFmGgckUBlT6SUnHDCqfzP//yRtWur+f73f867777JKafM5NZbr2bTpnVDGq/OqKbJaCBvDylXgSBX5VNv1NJg1PU9vt5czYOhP1BjVJOm0jG0yVv+V3kw9Hts7GG7xjJzE52inTxV0LdTHtJhgjrIKmv5sJ3nUOgUHcRElLD2FP1sbBpkPd100S5a6Zad/Y6/4YbbKCvbxAcfvPNxTLcPhaJNttMiW7HZ/aXvN8QRC1TA60GalZjBjMQ0UlQyQR1ksjORuYljCOkgFiYTnfHMi89hbnwWx8UXMNodtc8MiULRItsI6kC/v/vx4+DQJbsPa64KRaNsYrNVxmarjAbZiEZT4hRzcuxE5iXmkOmmk+qmILUgIqJ0yi5y3RxS9ZFX6KqtrUYIcVR9rT4JjGRWRhjhCLNgwfEsW/bux3Z+rTX2229hv/QydnkrSktkXj7ilEvRo2eRqPcyLMG9qkiMGTMx5i/AXbEcJaVXoO84mMccg3kAN3Xd0YFOJJB7+LHoeBxcF9WwE1lYCEqhm5shFMKYczrWhRcjg0HMs87CfuJxL/MRCiFMwytlCvhR7e3o1hb0jgrkqFG7A6acHNzt27DfeB1jxsz99n3IkjGIYADd3e0FKraDNk0crajMysR0XdISNlZuPmRk0NhQz5ayDcxbcOJhvwYAxrQZuB99hI5GEMHewGvNavTOek+m2LJwt21FVe7ArxTmvPkHHjAQQGZmej0kexg+6kgE4fMj0gbuLgopECeZ6OMMiAChXvnsQ6C2tpr6+hrmzTtwv4pu1ag/xGGLQicJcID1Cr1dIa+1hnT+9HQAjevSF7B0dlbywQc/orb2QTIyMrnxxtu55pqbKE43Ca/7b4yqCpQ/Axlvx9zyV8zOcnqm3wFyCApDWkMivu+/GXjX5MXCR51QKMwXv3gX1157M4899gC//vV/cOaZ8zj77MXcc8+3B6UaZGIitYEjHO86enFxMDEwtPeZ0mje8b9BXMSY4HhiEMkawirMJnMDZeZGpjuzh+W6HJx+fQ67MDBIDGNQdChkqRySVQrtshWBYKO5jm7ZSYQIQYJ8ZC3vJ/Zw4omnMX78JP7yl//j+ONP+Vjm3CJb2WyV0S47AE241+m90D06suM+LMY5YxjnjBnwt27RQ6m1hWajBYUiWSUx3hlLvjswEyoQ+LRFj4z0e696DjJg9r5XY8SxRYKgDg66j0Wh2GiVUmlW4gqFRmOaBqOcIqbbU/FhMcOeSliHqDSr6JE9mJiMcUqYbE84Ss31VeTlFWBZh6eQ9o/GSLAywghHmNNOO4cnnniYqqoKiosHflEfadyNG7CffhLlWOjcCUifgoYq9OuPIC7PAyuXxE4IFPe3JRE+H77PXI1rTcJZvxHhUxinTMc8cV7fYntfiIwMhN/vBQWhEKp8O6qyEtXSDI6N3r4NOXM2xqTJ6PZ21Pp1OO+9g++sc7BOPhWZlu45pjc1IqdOhY4O3NLNnnpWMIjIycGY099RXGbnoOrr0O3tiL0by3cdM34C5oknY7/4vNcTE42BnSCSlkp9RgqpXd2I0aORGRkgJeGkJOrranAcG9M8/B8Gc+5c3E0bcVetRGuv4V7tKEeWjMGcNs2rxcvIxK0ox37rDYzZxxyw4V4YBuaJJ5N46EHPtDEz0yux21mPMXsOcvxArxEd1dCgIQDkisMqSVm+/D2AgzbX6+WOF6hMlH2Bie7WiA9dmG/A9ME3os+YoSkuFmzdKsjIaGTdup+yYcNvsaw07rrrv7jnni/2KWX5tj6C0bkDJ2N6X52jsHuwGldgtpyMkz1nUOc0W9fjr34FWjuIRM5GV2bhFheDNNGuRrRqOPfIZ1MOht/v59prb+KKK27gqace4X//999ZtOg4rrrqRr71rR+TkbFv0zyAInc0Y91xbDY3MN6ZjIGBg0OtUcNsex75ymsMd3GpNapI3auJOEgIV9g0y6Zhu54x7jj8BOgQ7X2lVg4OnbKTM+LnDdt5DoV0ncFxiVN4MvAIlUY5Srj48RMgQJ5bwGv+FxntjmWu7WUdhRDccMNtfP/7/7LfRvvB4mpNtw1+AwKDzIZERIS1vvV0ix5SlGfE2C27WefbiD/u36+h4tHAxmaNbx3NsoUkHUZqH+2yg7W+9Zhxi2yViYODQmFhERERTCxaZRsJEmSodASCdtlBsk4i1U1mvbWJeqMeR7gEdYASZzQlTvFBg4lGo4kdZiUhHSKgvO+ROHGqzGqyVRYFbj4SyXhnLMVOERERxYePkD7y5V+7qKjYdtj9af+IjJSBjTDCEeaccy4gGAzx9NN/+1jO765aCfEY5BSCMMC0oGAstDVC+XqEAdql3y4VgG5R6D9L5NJ5+Nqux2r4PHLZAmg4cCO4HDMWY9p0VHWltzjftBHd1QmWiTAtVCyKrq1G+PzIvHxISsJ5710v4yAE5qzZBG66hcDdX0EkEqgtW7yeFb/fm6LPh+7uwi3fjqooR0cj6HgMfL6+Pph9IaTEd8ln8N95N+ZpZyDy8xEZmXSnJuNIiSgsxDr+ROjtGdFa9y7mh2cRKgJB/Ndej//GmzBPPgVjxkxkUTHGvAX9moZkRiaqoQHd2XHQMc2Fx2JddjkiJRndsBNiMawTT8J3wdXQJjy3+t5rUUts1E/juP8ZR/17HPUHG920/96jg/H8809wzDHzyTlIH4jeqNBh0S+DIpIEJDR6x9DOn5oKn/1sBx0dP+TRRyewadNfmDfv2zzwQBnf+MYd/SR9zbaNaF9Kv3urrTAoB6O7alDnM1vXE17/a6zGlcicNnxT1iNrajFWVaB3uIgyBWMl8uRPzr6fZVlcfvl1LFmyjp/+9Fc8//wTnHzyDB577IH9lhcaGFwavYpCt5gt5mZKzY1sM8soccZySexzfYs8A4MMlUW36F9qkyCBQPYFFcPBeGcyx8VPpsGoZ5tZRqVRTpm5iYn2ZE6Inzps5zlUzo9dwkz7GBIi7oUpOsg0ZxZz7WNRwuVDX//S389+9hoCgSAPPviHQzqf1po1bQ7/WxbnJxuj/HRjjOdrE0QH4YnSYDTSJbvIUOm9De4maSqNhEhQa9Qf0nyGi0ajmVbZRoZKJ6iD+PGRrtKIk6DCqKTU3MKSwFLeCrzLm/4lvBFYQoPRgIPDTqORrdZ2moxmwjrE9MRUtvi2s90qBwRBFSAqYmywNlFt1ABe8FFpVLHWWs8mq5QW2Yru/fFrks0oVD8FMj/e706TbO43bx8+0nTqUQ1UANasWfmp81iBkczKCCMcccLhJM4990KefPIR7rrr60e9wVK1tEIgiPThbU+4gCFACHSkGx0DX8HAJnv1nAMbFHqcQPgF2tGIbQr1WAL5Ff9+y3eElPiuuAqCQRJ/fQDshCd/nJbmZQBSUtAtLei2VkRmFiIlFd3ehu7s6pexsZe+i7PqI2TJGGQg6C24a2tw169DbVjvleZI6V1bdg6+62/Yr09K39xME2vuPMxj5niB1Ib15DU1ktdQg+nz4evsJB4O42pNpLubKdNmDauTuAgEMOcvwJy/AHdLGWrHDs9Ycw8pZx2LIgKBfvLO+x1PSnynnoa1cCGqsQkSAfQHafBrhXLiUCSRZ5sQ1ejHHLQF5Al0HMRyB9WtkXf6EENU/+ns7ODNN1/mW9/68cEP9uO95/ZAa43QgqF4vWmtefzxv/LDH36dnp4urrjiDq6++v8xeXIm+4pRtZUM7o69BwE0GIPwKdEaf/UriEQHTtoUEALz7BZk0TrczTXo9Az0jGzEsQYi65O372cYBtdffwuLFl3Ej370db785S/y6KP387Of/YqJEwfKEZe44/hy97dYb62mQ3q+ITPsY0jRqdjYxESUkA5zQuJUtofK2CnryFa5xEWMKqOCsc4EJtvTh2/+GHwuej3j3Umstj4kKiJMsaezMHESGfrjywTswoePae5MNjrrvCyQDvSVGwV1iBbRf3GbnJzCZZddw0MP/Ykvf/lbQxalWNfu8kB5goTSZPolMVfzXI1NS1xz3RjfAX9XYiIOemBJnaVNeuSh9XANF1ERQaP7VL80uq/Uq9wqJ0CAgAogEVSYlWihGe0Uk6Oz6RERmmULGW46x8UXEJFRGowGUtzkPglkn/LRLtvZYVaRoTJY41tPi2xGINFCs8OsYrI9kbFOSd+598WuMrMjgbLBaQcUGMlg7KdwobW1hR07tjN79rx9H/BPzEiwMsIIR4FLLrmCp556lE2b1h01mdddyNGjcTdtRPo0RpLA7cLTgG1rRdU1YUxowF/UX1ZZt2nY6KJzBah27K3LcJs2gGtitM3CV3oSYnrS/s+Zmor/0su8rA4CWVCAjkS95nlXoZVCx2IIQHd1IsLhAYGGu+ojRDipz8dFCOEpd7W3A5q+FWp3F0orZNrg5VOFlJjTZyDT0gg+9FdmVtXS0dqMvWIlXYUFVM+fQ27xGCZOmjboMYeKHDsOOXYsblkpsmSsVzrX2YlubcVcfMEBS+0GXE8whMwtRv1fArFBoXOAgEBsVbi1cURAoDWIUb2L6iDogERsUbBFDakUC+Dll5/Btm0uuOCyg89ttuH1qHRpRLLwdvcbNKSAmDS489bUVPH1r9/BW2+9wqWXXsm3vvVjCvLyMFs3YNQvBcOPnTEDFd5df2/nLsRqXo2It6L9GaAVRncV2p+JnXFg5TgA3BhGZwUqkNVXHykMjTG9C3/hcpg+hkTB0an3PxxycvL49a/v53Ofu55vfvNOzjxzHnfe+TW+8pXvDAjE03Q6JyVO7/u3g8Ob/pdZ6nuLDtlBtpvLCYlTuSB6GUv8r7PdLMPCxxR7Bp+LXUeQ4d1ltrA4NnESxyZOGtZxh4scNx8fFqa2+gIVjaZLdLLQHdjrdsMNt3D//b/lxRef4uKLPzfo8yitWdLokFCaccm7PjOCkOllW07OMRmTtP/PUlAH0XgL7l19NBqNLRyS1f43eFriio0dLt0OZPsF01MNgofY47Y//NqPRtMqW+mSPbi4BLQfGxtbOOQ6Ofjw0SO8zLvQgg7ZSdgNeWVjSpAQCQCiIoqNQ8pePjwBHSAqo2w3y2mSzWSq9L7gqEt0s9XaTq6bTYbKYAdVfRLLADYOGk3mESqVs5shsg3cHkCD8IO/CIIl/cuyAdas+RCAOXMGSon/szMSrIwwwlHglFPOIj09kyeffOSoByvWwmNxV61CbylFZuWgm2rQW0vB9GNWLsV4vRTlPxd9xpm7d+cSGmzQZgeJ1X9CtWxB+JLQSqHaS+GpHfgnfgFxoN3BYBCZX4iqrfFKz5JNRE4uqrLCS+MYBqrRK3eyTjujT+1rFzph00/2SSnU1i2AhsIipD/gOdwLge5sx17yNta5iwadudKOQ/yxR3HLt5E8dQaWY9PT2EBqXS2FMUXOKWfh93slb1op0BqxtwzVYSBME//nriT+8EOoinKU6yACQczjj8d35tlDH3CzgtLdmTAAneI9rls0TO6/+y8CwgscW/WQC92eeebvLFx44qBq78UCA71NwQoHavAyKikgzjNh9IHPrJTigQd+z49//C1SUtJ48MFnOOOMReBECG28F1/Dck+DW2sCgUyiE64ike8tbBN5J2B0VpAoX8bSTT5WVYxDGQuYfmIJ8yhi/6F2L9KHNsPIWEv/x7UDQqLNj8/n41A46aTTef31Vfz61z/nl7/8KR9++D733vvgAf2fXgw8xfOBJwjoEMkqhSpjBzuCf+bK6A18s+vfqDOqCeggJe64ffph/LMzzZ7FZHs6G6w1ZKkcLG3RaOwkW+VwfOLkAcdPmjSNE044lT/96TdcdNHlg/6uirpQH1Wk75UBTTahPgqNMc2YA7yh85wc0owUWo02klQSUgu6ZQ8BHaBoPw32pZ0uj+xIsDOqEMIrhp2QYnDdGB9Z/uHLJGa7WTjCYadswYcPQxu0yjZcFMk6abdktvCyHgYGMRHry4IIJK5wUELj076+fitrj7RtggR+HaDRaCKoA/3eq0k6TItopUW2UuDmU+DmUWvsRPZ+KyoU+W7ePpv9Dxc3Cj1bQMXASAUEqCjEKrzsin+vj+bq1R+Snp7J6NFjh30un3Q+ebnrEUb4J8SyLC688DKefvoxlDpy6eR9IQuL8N/4BYxj5kJLE9RvxyjOxTrvDKwF0xCWwH7uGVRZ6e4nZQrIFzhly1GtW5CZE5Cpo5BmMTKjGLdyNe6mjQc8rzAMzJNP8YKMmhqIRRH5BRAMev4m3V7du3XeYqwzzxrwfGPmTHRHO9rpldh0bHRXF0gDmZSESE5GpKUhU1PBMNEtTWjHGZzsL3gBQkUFsrgE4fMRDIXJKhlL+vhJZNbW4bMdVGsL8aefJPrD7xH94feIP/53VNPwNRHLwiICd9+D/467CNx4E4EvfwX/9TciwuEhj6WbdO/O3B69IUJAqvDKsLr2Oj6mwRB9zuuDpaWlmXfeeZ2LLrp8UMcLv0Bea2Hc4UdcYiE+ZyHv9iPONA+4WNu+fQuf+cyZfOtbX+LSS6/k7bfXeIEK4K97G1/9e7jhQpz0aTjp08CNEdj6CDKy0xtAWrQUX8//bvwJf1r1RUqjp1KWOJMHX5nBn/8siUQOMnFpkMg/EWF3IeJtXgmZsjE7tuEmF+OkHwFndyeCiDV7dSFHgEAgwFe/+j0ee+wVtmzZzNlnL+gTStibVtHCu743SVMZFLslpOsMxrjjsDB5y/8KQR1imjOLce7ET2WgAhAkyPWRWzkrvhiBIC7jzLbn84XIHYx2972gvOWWL/PRR8t45503Bn0en4QkUxDdq5wyocAQgvBBsh0BAsxOzCTPySUhEkRljDSVxuz4jH06rsdczRNVCVriiokpkkkpBiVJktIOl1fqhve9GRcJTG0R1mEEAle4hHSIkA72NdYD+LUPqT0zRkPvzg71iB4y3DQC2k+GSifLzaBddhAngULRIyIkhM0op7Avq7Q/PInlmRyTmEGem0Oum8PsxAyOSczEOgJ7+3YrqIgXqIhewc1dJWCJnQOPX736Q445Zv4nyqvnaDGSWRlhhKPEJZdcyf33/47ly9/juOMG7rodSYyx45C33AYP/xUSCYzpM/v+JvLyUGWbcdatxZg8Bd2k0GsUaNANpRAPQMCARO8O/JQwOuGgqiph9v4ljMFrANfxOM7bb6IbGxA+P/6rrsU8/gQvpZ+Vtd/yLevEk1GbN6G2bkGHQuhE3FPx8vnQhtGXDdB2AhIJdCJB7Cf/ijAMjDnzsE48yTOAbGrCLd0M8TiyqAg5YWKvGljUG3OvhgcRCKC7OlHNzcTv/zPOhytAKYTPj1u6GXf7VgK33I5MSzvk10M1NeGsWYVuaEBkZGLOno2cdnj1/iLZW09rVyP2UAkSUYGeKhBdoGsUZAmIg6hVMNWASUPbs3rxxacAOP/8wZucClPAdAMxiHIzrTUPPfQnvve9r5CXV8jjj782QO7V2vk+2gyjrd7tZCFwk0Zjtm3CbN1IIuTtgq5ZY7BmSzajp0KgVxciHtesWwerVglOPPHAgW286GxkZCe+hg8QkTpA4iYXE5n8ea9ZfwjEiLHG9yGbTM8DZaozk9mJ+QQIgBMhsON5fA1LEXYEN5hDovhcEnknDqwFGQaOO+5kXn11Bbfddg2XXXYW3/72T7jlli/3WwQ1GHV0ynbGOBP6PTdTZdMim2iVzRSoT6bXQ0T0sNUsJUGC0e4YctTw74rvIlNncWX0Bi6KfRYbmxSdekDVqTPOWMTcuQv593//HieffMagFp6WFCzINHii2qYtoUizBAkFO3oU45IkE5IP/hlO06ksSMylR/Sg0F4J1X4W7+Xdirqoojgskb3z80lBdkCwocOly9YkW8PzvuyS3QigxCkmLuJoNH7tJyqi1BsNtMhWwipMq2wjJuJeVkVCA40Y2iCog4xyvPehRDLDno5kE81GC914Km0TnfGMdUqIiwTbzO2Edajv2ntEBD9eoANgYVLsjqLYPfKmi9ruVTzf61YKE9ReSulaa1av/pDPf/62Iz6vTyIjwcoIIxwl5s07llGjSnjkkfuOerAC9Nb7avAPVPPSpoXu6kJvU6j7E1Cn0KYAEUDYLthAlvB6HvIFqhTYR+O57uzE3bwJHY0g8/KR4yfsbgBvakaEQsj9SAvvjczKwn/LbTgrluOWlSHCIYy580k89gjsrEelpAEa3drirdIjEe8alMJ98nHc7Vsx58zDfv5ZVEsLCBCWD2PuXPxXXo3ML0CkpqFbWhDZ2X3nVS3NGMXF2Ms/wH79VbQ0kH4/OhZDS4H+4H3c+QuRp59xSK+DW1FO/P77ULW1YFlgJzzp5quuwZw+iF6K/THNgEIHsV2hi6XXvN6oEQLkpRZENLzpoms1wgfMN5CXWkNurn/mmcc48cTTycrKOfS57odYLMa3v303jzxyH9dddzPf+97PCYUGllsJJ4Le2ydllzyxSvQ9tG2btxAI7PGW9/u96sItW+DEg1nomAGiU24iUXg6Rk8t2gxhZ0yDIZaAxYnzYOj3fOh7H9G7SFrme5cF5glc23MTaVv+ir/mNZQ/A2UlY3TXENr0BzQCO394fH72Jjc3n8cee4Wf/vQ7/OhHX+fDD9/nf//3PsJhLwAM6TA+HSAqIiTvYXYXERH8OkBIDz37dzTYYK7h78EHqTdqUShSdTqnxc/hvNjFB91ZPxwGez+EEHztaz/kc587l1dffZ5zzrlgv8cqranqUXQ7MCnZ4NQcxcpWRWNMYQoYlyS5osQ/aENHgSBJH7QAEkdpXC/x2g9TCKJK4+jhMxWytIlEoNEE91DW0kC2ysKnfFSZ1SSEjR+LZDcZVzh0iW7SdToODuv8G6l2axjrjCFLZTI/MZcO0YktbMI6REh7n9cxzmhaZSstRiuGNlBoTAwm2ONwUTTJZkI6RFgfnRJPI+R9P2nHC1CgN4GbAN9eJWCVleW0tbV8KvtVYCRYGWGEo4aUki9+8U5+9KOv89Wvfu9j8VyRxaPhnSVox0b0eodoxwE7gSwuQT1rQ4NCT5YIKTBCs1FL10KgCzE/HS1ANzYikpIGuNi7ZWXEH30IXdfrdm1ZGDNn4b/6WkQ4jFFcPPT5ZmTiO/c8ONfzVdBKIfMLSDz0gFdaBsicXPD7MeYv2H1N8TjuqlW4a9YgkpKQEyd5RpT1ddivvorML8R37iLME07EeelFVKQHwknQ3obw+TBPPZ34n/8AsThydMnu3c/uLnRzM87G9ViHEKxopUg8/xy6vg45aTJCSrTW6PLt2M88jTFh4gHllw+ESBHIa3yovyegWoOtERkCcbGJmO/5gOgFJuzUEATyh+6zsnXrZt5/fwm//OWf9n+QE0W48QGywQejpqaKm276HKWlG/jv//4jn/vcdfs/ReYs/Due8xrqdwUp8Xa0GcRNLuk7zu8HrQV763IrJfD7B1cuiBC4qeNxUwf61gyWNdZKPvS9T5E7um9RGxE9rPC9zzEdBZzesBw3XOgJAQCuPw2jYxv+mtewc48FeWR+qk3T5Lvf/Rnz5h3H3XffyOWXn8MDDzxDZmYWo9wSJjlTWWWtYIw7jqAO0SU6aZI7OTN+Pml68IIWR4sW2cTDoT/TJlsZ40zAxKRJNvCC/0ly3DwW2Md/3FMEvP6hE044lf/4jx9w1lnnI+XAz0lLXPFYVYKyTpeYC8mmYE6Gwe0T/LTZmpAhGJcs8R0Bf5/isCTDJ2iMafKDvf1vWtMUV0xLMUgdpqwKQIbKIEWl0C47SFNpGEjiJIjJGFMSk/BpH21GG2EV9qSNtQ8Xl+1mBe2yjTw3D0NL6o0G2mUH8xNzSVdppOnUAXL8YR1iQXwudeZO2mQbPu0j1U2lwWxkm1WOg4MfH7lOLnluDhYWaSr1iJU5WhlgZUKiGWTA+ypTETCC4M/vf+z777+DlJLZsw9iFvxPykiwMsIIR5Grr/4Cv/zlz7j33v/iZz/79VE/vzn7GJzly3DLShEZGZ58cUsLxrhxGIVz0M8pKJB9BndG/hzc8eW4FR+g1+xEWzbCdZGzZnuZBsdBmCY6EiH+t0fQO3cixo33HuvpwflwBbKgAN/iC4dl/kJK/Ocvxnf66biVlSAFzuuv4Wze3BeoAJ6yVlcXKtqDddqZ6KYmVFkpqrsLenqI/+Z/kbm5+M6/AJmejvP+UnRHB2LKFM+YcsxYLxvj20sSNJwEzc3ogzY87Bvd1ITaUY7IK0D0LlCEEFBYhKqrRVVXYYyfcJBRDnB/xkvkv/ihQkECKJL9elJEWMC4Q19o/OY3/0V+fiEXXTRQyUgkOvBXvoCvYRnCTeAklxAfvQgn8+CCEu+99xa33no1oVCYZ555h5kzD1xeGC86E7N1A2brBrQ/DdwEQsWJF52Fkzap77gpUzRLlmja2iC9d23d1gamqZk+fCq7B6XM3AT03333/r+mVKzkDLsLndQ/mFeBDIzIToTdhfYf2cBg0aKLKCx8nauvXswll5zGI4+8SGHhKC6PXkuCBFutzdjYBHSAhfaJLI5dekTnc6hsMNfSIHcyyZnal0XJUXl0mh2s9H3wiQlWAL7+9R9x4YUn8+yzfx+gDKa05rGqBKtbXUaFJWED2m3N240OyZZgceHQZI+HSppPckaeybO1Dlu7XEKGoMvWZAUkZ+VbfaVhw4GFyQx7GmutDbTL9j4Z4yKnkDFOCRXmDvzaj1/76RE9dItulNAkRIIkFSap9zMV0AFajFaqjOp99uHsIkCAsU4JUEKPiLDSt4pm2Uq6SsUiiSajhQ/9HxFSQVJ1Cikqhan25CNinClMCE8BowoSTaAVWLkQHAVmSv9jX375GebPP/6A5q7/zIwEKyOMcBQJhcLcfPOX+MUv/o177vk2ubn5B3/SMCJSUvB//gvYb7+Fu24NKIV59jlYp56OiKfjEu8nuyEMC2vyZzHMY9AzPsLZ/LYngVlWRry8HGPWbPxXX4u7bSu6vhZRMrbPdV2Ew4jUNJwPV2CdfS64Dih9SM3j0Jst2VKGbmtDpKVhTJqM8Ptxly1D2Pto+nQdhDSguxt3/VpIJBDJKWgpUV2dxB95iEB6BtbJp2KeeLLnd+L3exLJkQhGVjZOw06vqT/JK53Q3d1gSIxJkw/pGrymEvZRpCx6/zbI3f4DIHwCBikJPBRqaqp48smH+c53fjrQI0LZhDb9HqthOSqQhTL8WC1rMLt20DPzS/ttRtda87vf/Q//9m/f5IQTTuPeex8kM/PgZYIqXEjPzHvw1b2N2boebSVj5x7b2+Ox+w08bRqccYbmrbck9fXebQ4G4bTTFNOnH/69HixyPyUqZ6QlAAEAAElEQVQzGhAyCIbPkwbao7xM2D0oK/moqY7NnHkMTz/9FldeeT6XXHI6jz/+KsXFY/hSz9fZZpbSITrIUtmMcccf0XKqwyEivP6HvecX1EHaZMu+n/QxMW/esZx55nn853/+iMWLP9NPRrqqR1HW6QUqSb3N8+k+QdxVrGh2OT1XExpmCeG9OT3XIssv+bDFpTWhmJ9pcGyWyejw8H+3ZKh0TogvpNlowcYmrMNkqHQkEr/20SW7aRVt6F77RkfYxEmQwe4gXiDwKx+tsv2g50uQoNTaSqVZSb3RgE/7MDEI6ACdshM0uEIRcsO0yw7W+jZwXHzBETGAlH4ITYDgGC9YEdbAn4fu7i7eeef1wfla/ZMyEqyMMMJR5oYbbuM3v/lPfvvb/+H73//3o35+mZWF/7LPoi+6GABh7SoH04hCATs0elyvr4nWiHqJOXYs8e6nEYEgcsxYr0F9j8yJzM5BK9UXqPTh96Pa24k//CBqyxa01piTJmOdeRayaPANjKqpifhf7/eki5XyFMHGjcd/zXUYM2fhrFiGamnxskWAbtiJyMhEKIVbUY6ORhG7FsF2AjlmHLq9DWfNKoySEi/LsUdjgwiFME44Ebe2xvODaW3xVMZsGzl5CtYZA9XLBoPIyUGOHo3avBmSk/vusaqr9Xp8Rg29VO5o8fvf/5Lk5BSuvvoLA/5mtm7Eal6DmzKur/Hc8Wdgtm/CV/PmPoMVpRTf/e5XuO++e7njjq/yjW/8K8YQpKFVuIDYhKsOeIxhwCWXaGbOdNm2zfOaGT9eM3685yd6tJjizOBd/5t0ic6+/o8u0YlEMsk6EztNYLWsIxoYS31TmERnC6myg/jE80kbjIHlnjgxZKID5UsBc2iLq3HjJvL002/x2c+ezaWXnsnjj79KSck4JjtHMQ21B20Jxbp2l/aEJs0nmJlmkO7b/wuXqwqQGMSIecIFeIpRnaKDBc6R6f05HP7f//s+55yzkMcff4grrri+7/EuB2Iu7B0XhE1Bp63pcY58sCKEYFa6yaz0o7NM9OGjwB24eWdpiwRxHBRhHUQg6MTGFW6fKtguHOGSpnd/j2s0TbKZarOGTtFNsk6iyCmg3mig0qpCaIFEIhC0yFZMDBSKIEFcHASCDJVOs9FKo9FIiTP6iF2/MPffBfTmmy+TSCRYtOiiI3b+TzojwcoIIxxlUlJS+fznb+OPf/w1d931tSGndbXrosq3o1tbPene3rKrobIrSOn7tymQiy3UgzaiVKMDGhHTkC7Q06rRb9YgegMV6J858d9wIyIpyXOl770erTV6Zz26uwvnvW5Edg4Iib30PdyqSgK33Ynco7F9v9erNYmnn8TdtMkLlAIBL8tSupnEU4/j++ItmGedg7PkbXRZo7dbnZqKdeVV6Lo64o88BNGo128SjyOSUzBKSlBNTejm5v2e13fOInRdHc6G9dDdjdAaUVSE//NfQCbv30jtgPdcSnznLSbe1Iwq3eSJHSTiiPQMfBdciAgMFD84EG5lJc6y91E7KhCZmZjzFmDMmj3s0pYtLc089NCfuO22r/Q1YO+JEdkJyumvkCUEypeO2bHVyxjtMadEIsGXv/wFnnnmMX7+83u55povDut890RKmDABJkzYdybFdb3pHcJHaNDMtOdwfPxUPvAvoY5qAAxMjo+fykx3PtHJJURX3UfN2jKiXXGiKpW3O89n1drzucARnHTSILJAysFf/Qq+2je9YMVKJlFwKvHiRV7mZpAUFBTx+OOv8dnPns1nPnMWzzzzNkVFRz+Iruh2ebAiQW1E9SUe3wlJrh3jo2Q/BohT7ZlMtWeyzvcRmW42JhZNsoEclbdP35OPmxkzjmHx4kv5z//8ERdeeBmhkPf5yfILkk1Bu637eau0JzSZfkHKMPaMfNLpMrwgw9WKqIwBENZBQNMlu8lSWUgkERFBoylydvvG1Bp1rPNtxMHBpy26ZBe1Rh22sElTaUgk7XT0UwYL6ACOcLC0iYnhebloiJPY1/SOCi+//CzTps1i1KiSj20OHzcjwcoII3wM3HTTl/jDH37FH//4K772tR8M+nmqvZ3EIw/hbtqITiQQpomcNAn/VdcOWmXrQIhpBvIOgVrpIOo15ArkPBN3Z4eXOTH2zpz4vAAgJxfz2ONx3ngd1dnl1dq0t6Jtz9hRTpzcF1DpjAx02Wacjz70mucPgm5owC3djCwo6FvMC78fWViEu2ULNDbgu/BizNlzUBXbQUqM8ROQBYXoeBzV0ozzzNNonw+jsAhZXAxJyVBTjczffxmezMoicNsdOOvXoXbWI5OSMaZPR+Yfnmu5MWEigTvuxFn1EaquDpGdjTl7DkZJyZDGcbduIX7fn1BNTYjkFKisxF27Fuuiiw/NVPIA/PnPvwHgxhvv2OfflZXkbQsqp18zuHAiuOGCfoFKLBbj5puvYMmS1/jd7x5m8eLPDOtcB0tHB7z9tmDlSoHrwrRpmlNP1RQe3OdyyFhYXBX9PDOdY9hibgZgojOF6fZsLCxUuIDH67/Buk3bmVTSRcSXT7uvkESt4LnnYOpUTebeexpaYbaXYrRvAUD21OKvewftS0b5UhGJToLbHka4cWLjB+eJs4v8/EKeeOJ1LrzwFK6//mKeeWYJSUmHFqAfCq7WPFtjUx/1fD6kELhas71L8WytzZ0T5T77JgIEuCFyK6+5L7DKtxwHm7n2Qs6Mnbdf35OPm29/+yecdtps/vM//5Xvfe9nAOQFvGb6txsd4q4ibAraExpbwck55qDVv/4ZUCh82k+myiCmdkkb+2gymkmQoF12oNEE8DPRHt+XnXFw2GqW97rPexl3NOzsbcTPdrMwMUlSXqmX1BItPIlxv/aTqlKRSNxen5fgESgBGwyJRII33niJm2+++2M5/yeFkWBlhBE+BjIzs7nmmi9y3333cvPNd5O2H6+RvbGfewZn1UpkcQkyHEZHI7jr15N4/DH8t9w2YEdda43u6ACtEGnpg9pxF6Mkxqj+O7HSKNpn5oTmZoy58xChEL6LL0Xm5uEs+wDd2YE89jhkVzfupo39Mj9CSlQgiNpRMahr1rEY2Anw7VUO4/d7/iqxGFIIjNGjMUb3T9MLv5/AtTcQ6+hA19ch8gpAafTWMmRePubcAyuriKQkrOOGvylX5hfgO//Qgx6tNYlXX0G3tCAnT+l7XdXOeuzXX8OcMxc5TI2YPT3d3Hefl/3YXxbQyZyJGx6F2bEVJ2UsSB8y1oRQdp+jPEA0GuXGGy9j+fJ3uf/+pzn11EMrpztcolG47z7JunWCtDSNYcAbb0i2btXcfrsi53BVmZWN2boBI1LfK3c8EyuQwTH2fI6xB77nbBtWr7WIhyZTL/HMPQUUFHgSy9u2CTIz98iuKJfAtkfx17yKcCII5WC2b8EJ5eKkTwEh0b5UiOzEV/c28aIz0YGMIV1Cbm4+DzzwFBdeeAq33XYN9933RL++iiNJXVSzo0dRENodlBhCUBCU7OhW1EU1RaF9f5el6XQ+G7uGxbHP4AqHsE46oO/Jx83o0WO5557v8POff59LL72C6dO9zOglo3wkWYIVzS6dtpdROTnH5ITsT9eyLU15vjUODsHeEi8XFwODeYljSFEpuMIlVaUQ3kPAolt20yN7SFL9eyST3WRaZCs9oodUnUq2m42BQYtoxdImhjYIaD9BFSAqonTLHjLcdPLc4ZdqHwxLl75FV1fnp7oEDEaClRFG+Ni4/fZ/4ZFH7uNnP/vuoJTBVEszzvp1iNy8viZ1EQx5GYayUnRtDWKPPhBVX0fipRdxy0pBKYwxY7HOXYQxdtyQ5yoKi/bInHRCIAgdbZCegXXqad5i2bKwTj4F86STQWuElCReeB53/Tqv92WPQEkk4ohBBmgyNweRmYVubkLs0dOhm5sQmZnI3AMbvsmsLPw33EjihedR5dsBjZw2HfPU07Dfexd3sxdMyanTsBYeh8w7cgZyw4Xu6EBV7oCc3P73NScXvXULqrp62IKVP/3p13R3dx1wZ0/7UohO+QKBLQ9gdpWDctH+NGIlF/UFK5FIhBtuuISPPlrOgw8+ywknnDos8zsU1q8XbNwI48ZpdilFZ2drSksFy5YJLrzw0JvvRaKT0OY/YDatQigHADecT3Ty5/erjKa114q1P90Fpfo/brWswV/9MsqfiU4Zh4y3Y7RvQUabkNEGVMjbXVb+DMyuCmSsEXeIwQrApEnT+N3vHubaay/ihz/8Gv/6r78Y8hiHgqs0SmuMvYIMKUChUYN4eYIEB0jXflK59dZ7eOqpR/ja127nuefexTAMAobggkIfp+d6PSqplvhUZVR2keNmU+DmUWvUY/SWZdnCJtvNptgZhZ9993RJbSCRKPp/eAwkIR0kIqP4lA+f9hFSQbRIZ6xTQrabxQ6rkg7ZhUBQ5BQyyZ6AjyOrwLY/XnzxaUaPHsuUKYfhwfVPwEiwMsIIHxO5ufl87Ws/5Pvf/xcuv/y6g5o96UgU4nFESmr/PwSD6KaGfnK6qqOD+F/+jFtejsjNRUiJu3Y1qr6OwO13DrmUSQjhZU7y8nGWL0N3tGMcfyLmiSdhjBs/4Nhdqy5j+gzE22+iq6ugsMiTSt5ZD8EQ5uwDy9PqSAQcB5KTsU4/k8Tf/+YFG8nJ6K4uBGBecNGg1MWMkjEEbr8TVVGOU1qKW76dyLe+AQ0N3goonIRcvRp3zRoCN92MLBjeWiCte80rEwlEZhZibzWtISJM0+sed5z+f3BdtJTD1oBRU1PJ//zPT/nCF+6isPDAgghO+hS6534fs30zwk3gJo3yfFAAx3G49darWLVqBQ899BzHHnvSAcc60tTXg+sK2tqgvd17u2ZmakIhzdatA31ZhoK/6iWsnR/sFhvQLmbnNoJlD9I9fxzaGtjz4/PB9Oma11+XZGV5mR7w3p5paYKxY/svuMyWdQiV6MuWaMMHVhiZ6EBGdgcrwu5GmyG0tZcO6hA49dSz+bd/+x+++c27GDt2wlFx0M4PSvKCkvqooiQs+4QodsYUhUFJXvCfa9FuWRY///n/ceGFJ3P//b/jxhtv7/tb2BSEj3Az/ScZE5NZiRlkmZnUGw1oFLluDoVOIUL5WNPhUNHtmWROSDGYmOxl45J1EpluBvVmAxmuhdFb0tVt9FBijyZJh9lpNhITnfi0j8nORCba4zEwyIvnEhFRJJKgDnxsmbnOzg6eeupRbr757mHvQ/xHYyRYGWGEj5EbbriVv//9Qb7+9Tt46aUPDlhmIbOzEBkZ6NYWxB6u3rqlBZmahtgjw+CuXYNbUYGcMHF3CVZqGmr9WuIP/xVz/kLkqFGe4eEgZZGEZWGddDLWSScPyJTsD2P0aPyXXU7iuWfQ23sbrdMzsBZfiNyP/K9qa8V+9RXctWvQrosxZizmGWfiv/7z2EvfRTc0IMePxzrhJIx5gzfIUpWVxB/+K6qsFKeszFulJiUhCwrQsRiqqxOxtQz7nSX4r+ivMqUTCdzNm7xshmliTJzkCRvscQ90JIK7fi1uXR0yHMaYOg1ZNArV3Iz9/DM4GzeC4yBzc7HOPNsrnzvEHyCRlIQ5fQb2W2+iU1MRPh9aKXRVJbKgAGPc0LNn++K73/0K6ekZ/Mu/fGdwTzADOFn9g1CtNd/97ld4661XeOCBpz/2QAW8OK+yErZv32UaCaYpCIUUM2fu+zlaa6jQ6CoFFohJEpG112fHTWA1fIAKZOwWGxAGTvJYzI6tmG2bsXP2/Z494wxNebmmrEwQCGhsW+D3wwUXKHL3crPGjbOnxrg2Q7jhQsxoI8Lu8jKbdidmTw2xwtP7gpdD5frrb2H79i1897v3MHbsBE455czDGu9g+A3BufkWj1Qm2NKlCBqCqOtlFxYVWEfECPHjZt68Y7nuupv52c++y6JFF5GffwSap/5BsbAocUb3U+OKu5qHdyRY2eriaE/SOLDT5rRciwuLPC+YKfYk4iJOm2zre166m8ZMezopOpkep4e4SBDUwb4SM/Ckr5P0oUnsH4wO0UmT0YwjHFJUMjluNuZ+luKPPfYAiUSc6667+YjM5R+JkWBlhBE+RkzT5N///Tecf/4J3Hffvdx005f2e6wIBL3Spb//DbeiApGaCt1dEI9jXngxMnV3xkU1NoAQ/XpFdMNO3Ipy3Joa1JYytM+PUVKCecppGOPGIdMHXyYylEW2ufBY5OQpXlZEKeTYsfs9l45Fid9/H+7GDYjMbLAs3NWrUNVV+G+5ncBdX/ayCaY5pDloxyHx5N/R1VVov9/bSg8EwLHR7e2IvHx0SzPadXE3bkC7bp/qmY5FiT/0V9yPVnrHa08JzTpnEda5ixBCoNpaif/lz6jSUtAarZWn8HXhxTgfLsfdtMkr30vx4dbWoR56EH8ggDn90FP71rmLUI0NqLIylFagNTInF/9nPosIHH4z6KuvPs8rrzzH73//yGE1V//2t//N/ff/lv/4j//jtNPOOex5DQeOA93d3ltgVy9IWxs0Ngqys9WA47WtUU/a6KUuIup55ehMgbzUQizcox9L2Qg34Zkn7IkwvUDdje93Tnl5cPvtipUrBeXlgpQUxaxZmmnTBh7rpk+G2rfAjYHhLbKclHHI7hqQPsy2jWgrTDz/JE/eeRh2Zb///Z+zZctmvvzlL/Dmm6tJH8L3xaFwTIZJiiX4sNWhIarJCwrmZ5qM3Y8S2D8D3/zmv/Hyy8/y3e/ewx//+NjHPZ1PNB+1uixvcSgMScKmF7i3JRRvNdhMSTWYlGKQopM5Nj6fRqOZmIgS0AFy3Oy+kq6wDvfrcznSVBk1bPaVEiXmqYwhyHVzmJ2YMaCcTSnFn/98L4sXf4a8vMMTdflnYCRYGWGEj5nZs+dx/fW38vOf/4Dzz7+UgoKi/R5rnXQK0u/HfvcdVHMzMjcX84STME/o7yEgU1K8voHeDIiORnA3bYRYHDlmLDo9E7VmNe7yD3DeX4qcMBHrzLOxzjjzsNPN2nW9zM76tehoFGPiZIy58zCPmXPQ57obNqBKS5FjxyN6mwl0egaqdDPOsvfxF18Be0kuDwZVU43asQNRVAxbSr2tdSHA8qEjEYRjexq30QhYvn4mHM6KFTgrliOLRvWVnKnGRuxXX0aOGwfRKPG//w133RqMqdMR2TkIQFdXEf/rA+DYyHET+q7HGJOE2roFZ+l7hxWsyMwsArfe7t2zpkZEUhLGtOnIQZgqHoxIJMJ3v/sVTjnlLM4//9Ddyp977nH+9V+/wV13fY2rr/4COhJBd3cjUlP77sfHQX29oLhY09UlaG314gifDzIyoLbWUwlLToYpUzShEOgPXXjThWyBLhZelViNRj1uI4slIt97v2gzhJs6AatxGSqQ1RckyFgz2peCm3xgn4bMTDjnHM3BytDs7PnY2cuxmj5Cm15vhnCjxMZdRqzkIoQTQfvTcJPHoAHd2AjK9d6bQ/Cy2RPDMPjv//4Dp59+DN/+9t3ce++DhzTOUBiXbDAu+Z83ONmb1NQ0fvSj/+LWW6/mpZee+dQ3VR+IDR0uhqBfiVy6T9IQddna5TIpxXvf+PBR5B65xb7u0ei1LrpOIUICMc1AjB5YrdAjIpRaZbgoslRmn2hAvbmTdJXKRGdCv+PffPNlduzYzq9+9ZcjNvd/JEaClRFG+ATw9a//kBdeeJIf/OCr/P73j+73OCEl5rHHY8xf6EkaBQL79FgxZsxEvPUmqqIcOaoYtXMnqrnJWyRmZKI2rAfHhuQUtBDgKuxnnkJmZ2PMnOX5jyTi3uJmCP0VWikSTz+J8+YboBTaNHFWfYSxaiX+L9yETM9Ax2K4pZvR7W2ItHSMyVP6JIlVY4MXYJkmqroa1VDvGWEIiVu6eeg3dheJBNp1EJaFSEoG0/D6OmzbW1Aqhba9rIk5f0G/gM1Zsxp8vn69MSI7G3fTBuJ/+B10deGsXwdK4a5ehRw/ATl2HKKwCPXBUi8LtPfCPCUVVVM96HK6fve4qwvV1oZITUWmpmLOP3Cv06Hw61//Ow0NdTzyyAuHHLyuWPE+X/rS57nkkiv42t3fJPHsMzgrlqGjEUR6pifGcOJJgy5DHE5cF7KzYeZMTUuL18AeCMCyZYJXXpGsXQtCaEaPFlx3naJgpYs2QKT33gsBehRQqtCblBesaI3sqcVJKcFsXoXZugEVyEA4UYSyiY1ejArvfyNiKGgrTGTa7fh2vofV9BEgsLPnkMg/qV9PjKqpJvH8s7hbvRJMWTQK36LzMKYMNOkcDPn5hfz4x//DnXfewKJFF3HBBZcNy/UMBhsbWyQI6tAnWt3rcLnggst48slH+OpXb2XWrLkH3Lz6pNAjemg0mrBxSNZJfbLARxJXafZXDTgYAYbhQLdq1H0J73sADUqg33AQn7GQx/e//mbZQrfoIbM3UAGvH8ev/NQa9Uxwxvd7X//pT79h9ux5B+1l/bQwEqyMMMIngNTUNH7wg//gjjuu49VXn+fssxcf8HhhGJA0sFF3FzK/AP+VV5N4+knUjgr0znqEaWJOm462HejphswsRHe358qen4/augX7rTdxViz3FMQcB5mXh3XWOZhz5w04h2puRnd1IjIy+0rQVEU5zjtLICOjr9RL2zaqtBTngw8w58z1nOi3b+91opfIcePwX3sDMjcXEQ57mZn169C1NYAAKdEdbWBIVHs7Mi1tyPdXFhYhs7K9fpf8AmRNNSphewGfFOi6OmQohHWi15PTD8cesBsthEC3tqKamzCOmYeorkIrDQLUtm2e501Kal+ju7bt/iacPd2IoklDK2VLJLBfecmThu7u8kQKFizEd975w1L2tYtt28q4997/4o47/h9jx044+BP2QU1NFZ///KXMmbOQ//qv3+M89QT2W28g0jIQKanolmYSf3sEAOvkU4Zt7oNl6lTN6tUSw9AU9a4FV6wQdHfD7Nma4mIvjt2+XfDYY5K7Y5q9xYB2vXaiJ4psrydQ/SpWy1pEohO0ixYGWhiotAkk8k8ikXfisJRj7UL7kokXL/JMH/eBam/3Siord3iCGlKitm4h1thA4LY7B8h8D5ZLLrmSF198mm984y6OPfYksrP3bqgZXqJEedv/Cst87xEREYrd0ZwaP5sZzoEFOobCTlnHGutDWmUrOSqXY+z5ZKqDG9YeCYQQ/OIXf+Css+Zz223X8MQTrx81yehDod7YyXprExEZ6TVQFOS42cxOzCSwH6Wu4WBKqsGadpe4q/tU0rptjSUFY45SqaB6y4aNLnqCRPik19dWo+EZGz1ZIjK8jZhG2cQm32aajRa6ZTfJOpkMN6PPdFKJ/qWnW7duZsmS1/jVr+771DfW7+KT+wkYYYRPGRdf/DmeeupR7rnni7zyyorDdo02Z87CmDABVVGBu20rieef8xr06+rQvYJdOhbFKOhNkfssnHfe9uSA8wvA8uHW1qIefhARCvXtxurubhLPPIWzdg1Eo4jkZMzjT8A6ZxGqogIdjWCMLvGOtW10Rzsajb1qJWpHBW5Zmdec7vMMJd2yMhJPP4n/5lsxps1A+CzcDeshOxvhD0CkxzNxTCRwVizDd/a5B712HYvhbtqIqq9HBIMYU6dinXUOib//DV1f5zXVRyNoKZCZWciSMfgWne+Vwe2VBTGmTcfdtKlfwKG7Oj1X+8JCREoKMjsbt3IHOiMLWltQLS2IeBxRWIQMh1Hl25BFxeDzoZsaAbCOHZp/i/3KS95rmJaGyMlDd3Vhv/wiOA7+y68Y0lj7w3Ec/t//u438/CLuvPNrhzSG67rceef1BINh/vjHx/C1txH9aCUiJw+Z4QWwIjkFVV2F/c4SzIXHHvWSsAULNGvXajZsEPj9mljMa7gvLqYveLEsKC7WlJcLmqZJcspdtNKI3u1c3W1jxuoItT9BYMkrGJF6nNSJ2FmzEU4Eo7sSN20SkRlfGtYgZbC469biVlYiJ0zaHWwnJaPKNuN8uPyQgxUhBP/+77/h1FNn8//+323cd98TR2xBpdE8FnyQd/2vk6xTCOgg6601lJvb+ELPHUx3Zh/2OTab63kg9Hsa5E5MDFxc3vG9wecjtzHGHX/wAY4AGRmZ/Pa3D3Hppafz859/n29968cfyzwORpw4G61SEiJBlru7tGmn0UCFtYMp9qQjdu55GSYb2l02dLhYslfiGzg202RyypHP1mpbw2qFzhAIX+/GhRDoQmCLhq0KFkqaZQur/WvpIYqBgYNLq2zDxibPzSUmYhQ6Y/plVf7853vJzs5l8eKjl7n8pDMSrIwwwicEIQS//OWfOeechdxyy5U8+eSb+A9zESeCIU+VauIkVFMTzvIPPJPFWBzV2IhMSUEWF6O1RtXVoeMJjHET+kq/jDFjUVtKsd9fijFlqmdG+PjfsJcu9SSR0zPQ7W3Yzz0LpoXw+RB4MqO6rhZ36xZ0Tw9EIojaWnR+AbJ4dN/4wu9HFhTibilFN+z0jBpnzsJdvx7R0wM9PRAIeKVipoW7cSMcJFjRnZ3E7r8PtWkjWrkIpSEzE99ll+P/4s04y5ehGhowjzve66cZPwEyMxGO4zUu7IW58DjcDRtwSzcjgkGvhkgpZH4BojfLI8eMRbW1oVubIRJF11YjiorxnbcYY/oMEk88hqooR9s2MiMTc9H5GPvIVh3ompxlHyDS0vp8ZUQwiJISZ+WHWGecOSy9Kj//+fdZufIDHn/8NYLBQ8vW/PKXP2Xlyg944onXSU/PwFm/Dt3VNUAuW6Slo9ta0B0diMN2YRwaSUlw841R1q3oYF1ZCp3RELEYjB2r92xXwu/3Miwd001yGhWiVKHTBThg7KzDl/8uRl45orMLLS2Mrkq0FcZJn4obLsZqWYvRXXXQXpUjgWppRgjRLysohECEwqjamsMaOzMzm5///F6+8IXP8sILT7J48WcOd7r7ZIexnY98H5DvFpKqPV+mTJXNFnMzb/lfZZoz67BKwhIkeCrwN1plC1Oc6d4uN4ot5iaeCzzOnT1fQ3L0yxQB5s8/jm9841/58Y+/xbHHnsTppx98k+Zo02x45ooZKr1faVNAB6gz6vukgI8ESZbghrF+VrU5bO7wpIunpxkck2FgHQ21OA24mgFvD9H7x95kSaVZTYw4uSobE4M22Y7WmnbZgUaT4+b0UzmrqqrgkUfu4557vn3Yv///TIwEKyOM8AkiPT2D3//+ES6++FR+9KOv8+Mf/8+wjCtME/9VVyNHjcJ5/z10ayvCtpElY9Cugi1l3kI8EBzYo5KciqrxFje6rhZn3TpkQQEiNc0bOzcP5bo4HyzFd/V1kJyM2rYFVVHhlXqFk7yshJSobVsR+QUI20Z3dniN7L0rQh2LeZmYWBxMA22ayNRUT0UrGEQ3NoB58J3OxFtvotauQYwdhwwEvMCpqhL76ScJfPXrBGbs1qbViQTO0ndxPngf3d2NLBqFecqpGFOn9e0Wy7Q0/F+8GffDFTibNyL8AcxZs3AbGnGefRrt2IjkFMx5C3C3bUHV12Me52WajBkzEVISuOvLqOoqiMe9ICdlaL4Xqr0d3d2FyOlvWClSU9FVlejWVjjMYOW1117g17/+D7797Z+wcOGJB3/CPlix4n1+8Yt/4+67v9k3hkhO9nqSerq9DFkvuqcHEQwhDlDOeERQLr66N0mufo0Cq5VzjgkTyT6ZnzoXUFEdYM8qw6YmSEvT5M6QyHE+1DsOYqNCGDECo17FN2MbJKUiOkH50hEqjtG1AzdlLNpKRkTqEImO4Z2/1pit67AaVyIT7Tgp47DzjkcF+wd8MjUNrRVaqf59QbEoMvvwg8NFiy7itNPO4Wc/+y7nnHMh1iEIXxyMBqOeiIgwSpf0ezxTZVFt7CAqIoQOQ82p2thBjVlJkVPct9iWSArcIsrNrTTIevLVxychfNttX2HZsnf50pdu5NVXV3zi+lcULsCAgHGXGaPuFYpwcWmRrcREjIAOkKkyhiWISbIEJ+dYnDyMex0aTatso112IJFkuZkk64HfUcInYJqBeMtBZ2nELsPOJg0pAsZINJp22U5A+72xVCZ+7adbdtNFN/luHnMTs/spkv34x98mIyPrgMqgn0ZGgpURRviEMXv2PH74w//im9+8i/nzj+Piiz83qOdprdH1dejO3j6SvXarRTCE7+xzsc46B9XYiPP6qzgb1kM8hpw1CyMnD/u1V9CO079pv6fbU72id9Hc04PYyzRRpKR6DfMpKVhnnUPst79BtzQjklMQ3V3IwkLk1Gk4r7yMu2olrmWhIz0I4S2ijOnTEbm5JJ58HPejD9HxBMJ1cZuaYEsZhMMIVyGysr2M0H524rXj4K5aCenpfU37QggoGoXethW1pQy5YGHf/Uo88xTO66+hfT5PDOD110g8/STGxEmYJ56EdeJJyKJRyNRU5JlnYZ15Vt+5ZFsrqqwUt6wUEQqD6yBDYXzXfx7fFVeh29pw167xfFnGj8coGTOo13HA9WzaiLN+HaqhAdGrrrZr+193dUIo1Bc4HirV1Tu4++4bOeus87nttq8c0hidnR3ceef1zJmzkHvu+Xbf47J4NMakyTirVyFHjfKC17Y2dFsr1gUX9fMMOhr4a14juOUBtBFA+dMRdjdJO/7Gtcd089OdN7J1qyY11ZM2VgouvFCTng6kS4wrfWilMToqCa5ahpM0GoREGUGEG0WbYaTd2dtU34X2pQ4IIg57/lUvEdj+GMKNoaUPa+cH+Ha+T8+Mu1BJu407jRkzkW+9gS7f7hmyGga6vg7CYcy5g/cnOhDf/Oa/cvbZC3j00b9w7bU3DcuYexLUIQwkNnY/B/GoiJKm0vHpw9t53rWgFnttjwu8haY+DHPQw6Wyx+W9RoeiW37D8ttP5JqbruHFp14j4Bv+oPBQSVWp+PEREVHC2vscazRRGWWUXYSJSY+IsNa3nhbZikIhkWSqDGYlZvQ955OCi8sGaxM1Zi0OLlpAUPuZnJjEaHegKa483USVK0SZQgcFIqERlkCcbyLyvPdUUAdole2eYh+CFJ1MkhvGJ/2McUr6BSorVy7juece5xe/+AOh0NGTVP5HYCRYGWGETyDXXXczH374Pl/96q1MnTqDiRMPrN6jOjpIPPF33A3r0dGoZxo4bz6+iy4e0HwthMDIzcW4+lp83d1eZiA1DXp6cMtKUdu3QlExWJYneWqamMceB3hZBhEOQ1cn9C2QNWpnPSQSuBs3YMyahTn7GBzDQKanI1LTkLm5XplYcgqqrtZboKakQjzmZVR6elBlZTgfvI8oGYOZnILz0UpPSlhrhOVDjB+Pamsj/tijBG6/s2+3WDU3427d4pVmFRaibRuMvb7aZO/io6uL+JL3cSpbcaMu6t1XkfEOdE8nuqUZ4ranFFa+3et72byJwE23IIv28UOVnoH/pps9QYLNmxGBAObs2cjZc7BffQX77Tc940kpEfkFWKec4il57dyJyM7BnDMXY9T+XeG1bZN47FGc95eCnUB3d6HKy9Ft7Rhz5qJ7utENDVinnrbf4G0wxONxbrnlKpKTU/nlL/+MPAR1Lq01X//6HXR0tPHEE6/1awgWUuL7nNdT45aVomtrEEnJWKedjnX22Yc870PCieGrfR1tBPtKs7QvFaSPie5Sbrv2bN5YXkhNDYwdCyecoFm4sP+CVUgB/rTewKQLFcjCTR6D1boOEW8GYSHirQgnSnz0YlQob8A0tNJQrqCldxd2gkQMwqVcRhsJVD6HNvy4KWN7B3Mx2zbhr3qJ6NTd5nEyOxv/NdeTePoJVHU1Witkdg6+RedjTBqeXoLp02dzySVX8Itf/Buf+czVhIY58JxoT2WUU0KFuY0xzngsLDpEO12ik3MTFxy24tQot4R8t5A6o5oxrqfGpNHUGzVMdKaSqw5upqm1ZmdME3M1OQE5LG7z27tc7itP0BxXpIQzmPfVP7LkWxdw83e+w/3//rNPTNN1sk5itFPMNrOcuIhjaoO4SJCskhjrjkaj2WyV0iibSFNpWJjYODTKJjZbpcxNHPOJUnarNeqoNKsJ6zAB7Uej6RLdbPaVkR5LI0X395sSBRJ5uw+9woXtCpIFco4B03d/hxY5RTT7vHK5kA6hULTLDlJUEjnu7my41pof/vBrTJ06k89+9pqjds3/KIwEKyOM8AlECMHPf34vGzeu5aabruDFF98nHN6dirZtm86ONoSUpKamYz/xd5wPliILChH5BeiOduw3XgO/H//F+/fJEElJu38qkpLwXXs9iSf/7vVXOA4iLR3rpHOQvaVToqAQc+ZM7PfeQ7guIhzGXb8OtaMCkZ1D4m8Pe4FPaioyIxM5eUrfD6t2XdAKmZfn9bp0dyHS8xEFhajOduz33vEa9kcVI0JhxJZST01LawgEMebMQ8RiqK1bUFWVyNElOEvfI/H8s+i2Vm/nKiXF20FubUFnZfUFNLqlBaRB/KXXcCpbUA7Q2QS1WyCcAskBiETxBtHoeBwxZgyqogL7vXcHONrvQqalew3/e/TROB+txH7hWQgnIyZMBNdFbVhP9J23kUVFu8UCPliK/5rrMKfPQHdp6NKQLhBB736569fhLH0PkZOLSElBjJ+Au2YNqnIHWCaysAjr1NPwHeD1HQw/+tHX2bRpHc88s4S0tPRDGuOFF57kmWce4ze/eYBRo0oG/F1mZuG/9XZUVaXXv5Kd7ZX3HYlFl7Lx7Xwfq2EZwu7CSZ9KouAUVLgQGW9FxlpR/sz+TwlkYLaXMX1MI5Pn5eM4u6149nmKUC6J7Pn4a15BI3GTSxB2F2Z7KSqYhfalEh+9mNjoCwY8V3dq1MMJ9AaFiGu0JRATJPJqC5Fz4EDR6NiGiLXipk/Z/aAwUIEcrJa1RJ0omLs3J4xJkwjc81VUVSW4Clk8ChEc3oDia1/7ASefPIM//elX3HXX14d17CBBroreyMPBP1NpluPiEtJhTo6fyanxwzcYDRBgcewzPBT6E6XmRgI6QExEyVa5LI5detBSpaaY4ulqm7Iul4TSZPgkp+aanJxjIg/xva215u1Gh+a4YmKyRAhB/oITiXz+B7z+p+/yuxlTuPXaGw5p7OFGIJhkTyBFJVNr1BMTcYrddEY5RaToZLpEN01GC8k6Cat3uWlhkqyTaDI8Kd99lVh9XNSZDQgEgd6MnUCQrJNoFq00Gc2kOAPNcUWWRJy3/89tkVtAxI5QaVXRIloRCFJVCtPtqf2MIJ9//gk++mgZf/vbyxiH6IX0z8xIsDLCCJ9QQqEwv//9o5x33vHceef1/OEPf8M0TaqrKti0YTWdnR0IIUj3+Zm4cR2ZBYW7+0gyMlGOg7tiOfqMsxDJA79kdSKBu34d7pYyAIyJkzBmzCRw15dxt2/Hef891PZt2G+9gbtpI9Ypp2LMmYvvM5eDaeGsXYNbWYmuqUGMHoNxzByEYaAbdqKrqjy1r61bIC8flIvaWY8IBBGFhRjjvCyJqtqB3lIKHR24pg8cB6F1r1ywCVlZEIt5vQ2GAaEQujaG7u5GVVaSePpJzz9iwiQQAt3chG5sQASCqLJSz1MlHvP+JiVuVSM6axIyyYTyBFTG0E4AYQe90qpg0MsaxaJIy4dKz/A8YYbgh+J8uAKU9rJJeH2YKtLj9eikTMMoGeOV7JVvJ/H0s7BxDGK1iY5qRJpAnGwgTjVxN28Cpfr6W4RpYc6bj+vzYcw+Bv9V1xxWRgXgmWce47777uUnP/lfZs2ae0hjRCIRfvCDr3HWWedzySX7VyUTUh5SKdyQ0Jrg1ofxV72MFgYYfsz2UqymVfTMvBvtS0ObIYTTjfbt/kwIuxttBtGWd68HoxQbG/850A6+po8Q0QZ0MJueUWeTGHUOKpSHtvZdxqGes2GFC6MlJEmIatjgov4O8jYfQvTOx/CBsXeZk9jdwNt/VMCAfexSC5/PE5E4QowePZZrrrmJ3/zmP7nmmpuG3dl+rDuBf+n+HlvNzURkD3luAaPdscO2Iz/HXkB6dwYfWctpNHaS7xYyP3E8RerAaowJpXl4R4JNHS75QUnAEDTHNU9U24RMwYLMQ1texZWXWcn0i37fOSddeRcNVdv48bdvZ1pJMSeddPohjT/cSCSFbgGF+zBedISDi4up+2f3TW0SFTEc4RzM//SoYmMPCFAFAiG8ErFDQSKZ7ExklFtIp+zC0AYZKr1fVjAej/OTn3yHM8887xPzun7SGAlWRhjhE8yECZP5v//7KzfeeBlf+cpNfPtbP2bliqU4jk1KahpKaRqrd9CtEpwQDLLn8kiEk9AtvV4oewUrOpEg8chDOMuXeYX5gNMrI2tdeTXu6lU4Hyz1FvuhEG75dlTlDvxKYc5fgP+a67DOWUT80YdxLQtjDyd2kZeP29aGnD4dEglUVRUIgTl3Plor3FWrUB3tuGtWobu7wR/wSrdqa8CQiKpKRFERBEPozg6E1uhx44lohWxpxZ+aiszJxflwBbqjo1/2RmTnoNraPPWwUaNQW7cgUlKRJSXEn38RnZqDCPZ+7ZkWWD5ErBvtCyE04Pb+eFq+XtPIBCKYNTQ/lNZWL+jZRXc3dHWh/QFwHG+eQkBBAeqDavTGKigeA5kC3abh74635lTuPrf2RSCAzMo67EDlgw/e4Z57vsjFF3+O66+/5ZDH+c1v/oPm5gZ+8IP/OKz5DAdG53Z8dW/jBnPRgd5Fs1aYbRvw17xOdPLnSeSdQKDiKbT0o/3pCLsLo6sKO+843OSSQZ9L+5KJTr2FeE8tMtGG8meiQvkHlCnWbRrWuOhcgUjqfc8GBXqUhDKFsaGMoHoOo7Mcbfixc48jXnxeX2DlpE1CBXMwuqtxk0b3GprayGgT8ZLFYAYO+d4dDl/+8jd59NG/8Oc//4Z/+ZfvDvv4QYLMdOYM+7i7GOOOH7JM8dYuxdYuRUmSF6gAFIYEFd0uS5sc5mcYh5Q5NAT4pCDi9l/FKwSzbvkPMiO1fPGLl/Pss0uYNGnaoMZ0cKgya6g16nGwyVZZjHZGkawHbmINJ2EVJqgDRES0XwlVREQJ6gBh9cnqy8h2M2mRLX29NQAJbKSWpKqhiaLsTViHCbv7vt4//OF/qamp5P77nzysc/wzMxKsjDDCENhZX0v59jI62ltJSk6hZMwEikaVDEs5i1KKeDyGZfn61fyfeeZ5/OpXf+H2268lGunhtFPOIDevsO+c2XmF1FdUUN/Zzvis3QZtuqPdc6zfR2mPu34dzvJliLz8PjUm3d2F/cZruE1NuGtXI7JzkIW96jPpGbgV5dhvv+llUEzTK+UJBvsvzHdhGMiUVHxXXo1ubvb6NjIzUZU7iFVU4H60Et3RAWlp0N2NkZ+PMX0G7rat6HgcvW2rl6WJRKgvzKc8NUhXYzXStimaNJWZSWGMaASEGHDvteUDKfuVv7nV1YhnX+jfy2IFvRKwSDe4theoRGzw+5F5+eiuTnQ0grlgaA7CcuxY3IpydMHubMwuRSYR3uPHqktDG+ixErmr/Ccs0NUK3nUxzpiMs/Q9z/G9t3RHRyLe7Z14eD0Ha9d+xPXXX8K8ecfzi1/84ZDfv1VVFdx7739yyy1fZsyYj8eTYk+MrgqEE0Enlex+UEiUPwureQ1RrYiNuQhhd+NrXI6I1KHNEHbesUQmXbfvQENrT9FLK7Q/vf8xQqCSilAMUqUpotFxIHmv8wRBdncSXP8oVk4pKpCDcKIEtj+O0VNHz4y7QJroQAaxsZ8luPWvmG0bQBigFU7GVOKj9m0OeTTIzs7l0kuv5OGH/8zdd3/zE21iOFy0JzSu1gSM/iVAyZagOaZIKPAfQjWPJQXzMg2erbFJtTQhU+BqTVWPoiDZzw9+9wjXXH46V111Ac888/ZB/bgUinX/n73zjrOiOvv490y7Zdvd3gsLS+9NBBUEbNjFbtQYo0leS2KJSUxizPummWqKJiZGY8FeY29YEVFB+i5LW5btvdw+5bx/zLKwssDS1Jj98lHYuXdmzi07c37neZ7fo69ju1aDhoYqFTZqW2hWW5gam3RYBYuBTqlVwjq9gnbRgUcaxEQcgCFmGQZfHLMAgEK7gEa1iValDY/04AgHC4t8O48MJ33fBzgA1q1bxW9/+1O+/vVr91mb+t/Ml/+KMsggh4jt1Vv5aNm7xONxvF4f3V3baayvJTppOmUDXOHqDykl26o2sbGynFCwG6/XS0lpGcPKRvfe9E8//VyCwW6++91vYZpxLtnFeUdJTERJTSVSU+8uxicmIjs7IBhEO+6Eft2W7IpyN8Voh22sY+Nsq8LeuBG7utrtb9LSAraNUuyKMSUtHaex0Y12pLkXbrV0KNaHH/RxEJOm6damFJe4E/RdIgBqyRA8l3yVyA9uQgLCNBG5uSjZ2djbt+M0NKDm5qFPmYJIz6ChdjurqzZixmMk6AaysIgtPoPYsneZkZfrNuGKx906leYmnLY2aGyAyZP6pG4p2dkoedmI9fVIvRShAqlZoBk4/lSUEZNRRRtO1WaIm+A4yJYW9JlHoR05a78+T23GTOzVq5CVG5CZWch4zO3NommIjMzez9zZXoPQixD5n7JGDQjokCjFE1AnT8Ve/jFOTw6zcGzUKdNQx0/YrzHtSmXlei688BRGjBjNvfc+gdd74Kvx//u/3yM1NZ1rr/3+AR/jkKLsmPxIdk2JEo6J40lxt2l+IqO+TqzoRJRIM9JIdovVxe5550pwO96tz6K3rQUkVmAk0SGnYScPPbDxpQtEukC2SkjYRbC0SlRlO6q/GiswqlcQSSMFvWU5Wkc5VpobvYznHYOdWIDe/AnC7MJOLMLMmoY0Dm7l92C5+OIrWLTon7z++ouceOJpn+tYPgsChkAVgqgteyMrAF2mpDRRxTiI9ixzsnRqw5I1HRa2dL/N2V6FhUUGOSk+HnzwOU4/fQ4XXngyTz/9Jul7sS1vVdqo0+pJcpLw9DiqJcpEWpRWqrUaxpij9rjvDiSSsIgA4Je+AafgRUQUEwtNqnQp3cSFSZadTolV3G/a2MEikXQqnbQq7YAk1Ukl1QkMeLwJ0s/U+CS2adtpVJrRpEq+nUehlX9Y+sVEIhGuuuoShg0byfe//7+H/PhfJgbFyiCDDADbtilfvwrbtsnK3ukQ09HexoaKtRSVDMXjObBJ39YtlSz/6H0QAp/PTzgcZuXyZcSiUSZM2rmqf9FFl1O+fjX33PtXUlPTOfUUN3LgOA4iN5eknELExs2uZXBKCtrxJ6DPnd//64mCFQSaQDFAdNbhVFWBrqNkZbmTftt2rX5TUyEl4LqMeb1uNKUHdfIU1I8/wt64AZGSCkhkRzvqmLFoEyf1e25tzFi0Y+ZgbyhHKSpBdnVgr1njRlQiYWT1NixFwXPBRWxPMLA9guxAuiuGNA2fGaexvpaWI44mbdQorFWrkK2tPW5eUfAnYH24DCU7F/2kBW4jPMPAOGkBdsMD2PUVSD0RGQ9BRinC60U14iiGDzFlGuqQUtRx41ALi1CGDuvTVG8gqCUleC67HPO1V7G3VaFoGupZZ+Nsr0ZuqsTRDTBNlLRMVO0URESHXRc3g+5EVqR78FxyKdbYcdjr1oIAdfRYtMmTe22Z95fq6q2cf/4CcnJyeeCBf/cxbdhf3nnnDV588Rn+8pf7Duo4hxIrdQyON8PtdZI0xI28mSGE2UW85LSdUREhcBIL+1j9fhoRbSNh7R2oXZtxfLlIIdDr3kRvWEKs5FSstLGYaeNA3b2R6B6P6RWIYzV4xERudVxh2i0REYkxYjmke/pEbqSeCI6JGqrrFSsAdnLpTjewLwjjx09m4sSpPPDAP/4rxMrwJIWyZIV1HTZ5PgWPCq0xVyTPytQOKtqeqAu+NtRgQ5dGY9TBpwpGpSik9iignJw8Hn74Bc4441guueR0HnvslT3+DnYpXdjYvUIF3DoMj/TQpLSwr2W2dqWDSm0jbWoHAGl2KsOtYaQ6gb3uFxERPjY+oVVtQ5c6XunFQZLoJJJn5x5yFzCJZKO2iU36Vkzc6I2GTolVxChzxICbeybIBEabIxnNyEM6vv742c9+QHX1Vl56aelgA8h9MChWBhlkAAS7u+ju6iQxqe/qZVJyMm2tLXR2tJGVvf8rRZZlsnHDOoSikJbmro75/QkEu7vYumUjQ4eN7HPO73znZurra3j0sQcwdIPZs+fR2dFOakYWxceeiM92XJetlMAe+1fEGiAmRyIj7yLaQthGAsrWBojbKIaBUuTmwttNTRCL4zQ3oyCQ7W1op5zWx01ICQTwXP51zHfednuKANoxs9GPmb3XZn/aETNwNm1ERiPYmzYhLRM0DSUjE2XESKz167B/8iOSsjPILynBmpyB7Inc6LqB4ziELZPcr34NeddfMZ99FnxelOEjUIeUIiMRzNdeQR05ErXUXQXXJk/Bn5BI7O2lWJtrkcnjUafMwBhfjNKyCSwLpbAIJTNzvz/HT6OWDUcZVub2vNF1hN+P09iItXIFsrkJkZGJOn4iPJsOHzvIAiABaJeIDok4Q0P4BeBDnzkLfeb+RXf6o6GhjvPOOwmfz8/DD7+4d+cvK+JGGnYr8HZxHIef/vS7TJs2c69F9Z81ji+TSNmF+Cp70qQQoGiY2UcSL5i3X8cymj9C7dqCFRgNiooSqkONNKGE6lHCDThJRZiZUwiPurJPsf6+EEerCAOcty1okZApELM0NF89oifNb+cLcmucHP2LIQb3xcUXX8mNN36D6uqtFBUdSF8hMFvBDoPQQE8H9QvUikNKWLcOVq0SdHcr5Bd6ETkxqi2b9rgk1VA4JVtjWtrBr8LrimBsQGXsHlb0S0vLePDB5zj77PlcfPHp3Hff0yQl7R5dU1Fdh0NkH4HgCBtD7j0NKyhCfGKsolsESZSJgKRBbSSkhDkiNnWvfVK2qzW0Km2k2WmoPUIhRowarY58O49M5+Ca2H6aFqWNjfoWdKmTLJMQCCIiwhatijQnlVx7dwvxz5M33niJe++9k5/97PYB1x79NzMoVgYZZAComoaiqNh2X0cQ27JRVRX10309BkgkHCYUCpLg7zsZ8Sck0tLcQHd3X4GUkZnNT3/6OyzL4sGH7sFxbE4++UzGT5ja20SqP+evHTgmRLeCLJmAMnEGrPsAgcRpqUexYoiSMkR2DmpyMjhrcKqrcGq2IwwDfdYsjON274uhpGfgOXMh8vQz+60h6Q9txpE4tTWYr7+GU1cHXg9KcgpK6VCc8vXQ2oq0bfy6iqeuHrOri+Z585Ca5n4GAjxeH0iJU7kBFOFGQLo63TS13FycigrsjZW9YgVcK1d/fz0m8g48rWoHMhLGXrXSTaPz+dBGjXYjM7ukohkn9K0rkOdJHCUO6x2ol4gkAcdpiPn7/32SoRCyuxsRSNmtt05bWysXXLCAeDzOs8++SVZW/zdutXsbRvWLPWlPCmbWNKLFC5DevhOLN954ifLytTz99OIvTM+HHZg5s9y+J62rwY5iJxZhpY3drwgIgBKsdWtCFBXMKFrzarBiSCMVqSfieDPQa9/Ck1BIdNi5Az6uEAJmaCjTVIgAXhCawKw5Cr28AiXSguNNB8dE696CnVDQJ6oyIBwTrW0dargBx0jCShu/X4LqQDn99HP46U+/y6JF/+QHP/jZfu3rxCC0Hsw2eh2iFC/4R4Bx8OsHh4RXXxU895xCLAa6Lol+qFJa6uWrF9skprt9VvyHoM/KQBk/fhKLFj3PxRefxrnnnsCiRc+Tlta3tiLTzsDn+OhSukh2khEIYsSwsMm3995Hpk6tp0t0k+Gk9wodj/TQorRSp9ZTZu05HbJJbcHA6BUqAB48dBGkQ+k85GKlWW3GFBYBJ6V3m0/6CIsIjUrTgMSKiUW92kCL0gpAppNBrp190P18Pk1LSxPXXXcFc+eeyGWXfeuQHvvLyqBYGWSQAZCQkEhObj5bt1RiGB60nklze3sr2Tl5pKYd2IVXN9xi+rgZx7NLao9pxtE0HcPYfWW7oLCEf9z9ODd991ssevhfFBYPZfaxJ+72vP6wu9xVSzVgwPFfgdKxyG0VKL4AsmYjYsRoN90qKRl1/ASE14N+0slos45GKS7e68RU7KGZoHQc1yJ59UpkKIQ6bDjq1KkY512AMqQU+ac/uI5dhUU426vdFLSUFEQshn/IUBqb6kjYUIlnWBndRQW0VW8jJRgi5eGHCVVuwKmoAF0Hnw+nqwu5ZjWqprnpNPaB2U3uL05nJ7H778VZu9a1SXZsrMVvoJ9yGsa8/lPxAESaQLnSgJqePisZYp+9Nj6NjEaIv/IK9ocfIMNhRCCAdtQx6HOORagqDQ11fOUrp9LS0sxTT71BQUFxv8dRwvX41/4ZtXsbjjcLsPBUPYvavZXQhBvclKQe7rjjt0yZMoPp0w8+4nM4cBLyiCUcXE689ARAWjhhiWxqRnYHsdR0NKcOxalHibYirCD+tX/BTBuLndZPcaxjuTbEesIu9TQuQhWwyxpFPG82SrgBT91baO2NIBTspCIiIy7dr3oUEe/Ev/7vaK2rELYJAuzEIsKjvo4dODTNIPeE35/AwoUX8eij9/O97/3vfjUYjVZDvBW0lB7fAOleryKb3G3K/mnNQ05DA7z6qoLXKykpcbfZtqSiQmHN+3DBBZ+PB++0aUfy+OOvcsEFJ7Nw4TweeeQlsndJVd6R1rRer6BFbUNIN9pSbBVSYOXv5cgQFEFU1D4RGYFARSUognvdV0XBwemzTSIBiXIYGkFa2CiyH/dEhGuRvM/9LVYZq6lV6127YgQ11NJs5zEhPu6Q1aw4jsP111+J4zj8/vd//8It9nxRGRQrgwwyAIQQjB0/mXAoSHNzIzuW/tLSM5g0+YgD6voN4PX6KCwqpWL9KnRdx+PxYpomHe2t5OUX71EEGYbBH26/m/yCIn71qx/T3NzIT3/62/0ah9ANGDUdMWo6zuQW+PffYMtmnNQUsG1kOIR29GyMc85DGAc2U5BSEn/u31ivvQKWhdR1rOXLUZd/hOfyK9GmTXejLGvXgK67zmG6jgiFEJmZBPIKsAyd2Jo1xDZuoL25npQtWxnb1IrQDJy6WqRQEI4DqoYSSEW2tWJXbkDNy0cd8tnk9VtL3sNevRqldCiiJ/fYaajHfPlFtNGjUXL3PHEWQkDhgd+w4s8+g/nGa4jUNLfZZkcH5hOPAVBdXMiFF56Cbds8ds8TlOpenI52lH5SwIz6Jajd27BSx/QWmzuedLT2dejNK4jnHQPARx8t5cMPl3DPPU98djdaK4zRvLzH0teLlT4eKzByrzbBB4uZOQV9y2tQtxlpA8JGtRpRnS4cSY8A8aCGG0hYdyfByT/ASeiZ/Dk2Rt2bGLVvoEbbcTypxArmEs+b60Zq+kPRiZZdRDxvNmqwGlQvZuoo0PYvD8pT9Rx64zLs5KHuGB0LrbMS34Z/EZx66x5T+w4Vp566kHvvvZM1az4ZcO8eaUO8GRSPK1TA/WjVJLA7weoA4+Ccug+aLVsEnZ2SXQOzqgrp6ZI1awRnny3RPydzq3HjJvH004s5//yTOPPMuTz66Et9mrMW2PmkOgGa1VZsbFKcZNKc1H3WcfjwYWP3SSGTSGwcfLIfF8hdyLFzaFJaiGNioCORhEQIDx7S7YN31jIxaVJbCIkQBgZ+x4dEYmH1RkJsbGwc0p199/6pVxuoUxtIcZLRe1zK4pjUqHXkqNnk7SMKNVB++csf8cYbL3HffU/vMcI9yO4MipVBBhkgSUkpHHPsCTTU1xIKBfF6feTmFRxwYf0ORo+dSDQaoa62ms6OdlRVJSe3gMlTZ+xVfAgh+O53f0J2di4333wtTU313H77Pfj6sxLuQU0BNQHsblCT3QmBlOBoGWjnfwO97T3stasRuoE2ZSrazFkHLFQAnOpqrLffdDva9zjWSMvC3rgB87138JxxFsZpZxLr6MTZVIlsb+tJ48pHHTEKoShkZOYQT6vD2xmieN16Ah3dKD4fMtyGtGwIJEGw2y2wNwxkLAbNTahnLkQ5SIvfgSClxFrxMSIpuVeoAIjsHJzKCuyNG/coVqTjIOvrkKaJkpOzW/rWvnAaG7E+/giRlY3Sk/4hEhNxamv4+MlHuOLt58nMyOLei68i68mniIZCkJCANu0IjJNP6VOor3ZuRGr+vq5YPalTSqi2d9Odd/6WsrKRHH/8Kfs11gNFxLvwr70DvXWlu0E6yO2vEB1yFrHikw+bYLGTiulI+yq+lkfxyY/QrUa3qAKBdFKRto6CjZUyHCXSiNGwlOjQswHwVL+Eb9NDSMWDY6SgRBrwV9yDsCLESvZSfD6Awv+9YoUxmpbh+DJ3NqVUNKzkoWjdW9E6KrDSDz7lcW9MmTKDpKRk3njjpYGLFYnb1/LTl7uej1Y6fO7suBRHiNKqNmIJk0QnCUdmuSYen/MCeVnZSJ555k3OPfdEzjjjWB555CXKynYWiSfIBBKs/etrkmvlUK3V0KF0kuwkIYFupQuf9O4zrarAyqdVaaNebcARDlKCB50ycxgpcu+Rwjhx2pUOJJDqpPTp9A4QFmE+MVa76VrCXTpMcHwkOYm0qx3oUkcgiIs4mXYGeda+hUaL0opA9AoVoFdktSith0SsLFr0T+6447f85Ce/Zv78BQd9vP8mBsXKIIPsB7puUHgAhaN7w+PxMmPmHNpam3tEkJf0jGzUAbpQXXLJlWRmZnPVVZdw9tnzuffeJ/e4YqNo4CuF0Aaw23EnB467gukfnYmWdCacceYhe21O1RYIBhH5O/tQCE1DpASwV61Cnn4manEx3mu+jb12DdaHy7CWLkEZWoYIBNxO7/X1qJZFSls70nQQWdkIw8BRWqGuE7QMZEIianExRGPIjg60MWMwzj1/v528DhjbAaWfFARJ70xLSgmxmGthrGk4dbXEnn4KZ9NGsCxERgb6cSegHTlznxELKSWytob4O2/jbKtCGTe+z+Mvt9bz3TeeY9y4ifz9/CtIeO9dSM9A5OYhu7swX3kRAM/Cs3v3cTypCDv26ROBlEjdXd3fuLGcV155jt///h8HHE3cX4zaxejNK7ACZaC64koJ1eGtehYzYzxO4t77TOwXjoXR8D5641JEvJOW0EJM/wkYoU3E9SI0qwHFCaM5TchQJ06gCDtQhoi2ogSrARDxbjy1r+FoCb2iw/amoYRq8NS8RjxvzmGzGRZ2DGHHcdRPiV7FcFPSPv35HgZ0XWf27Pm8+eYrXH/9jwa0j1BBC0C8AaR3p/50IiB00A5/uc0+GTZMItLbeb15IxRtdzeaOkrXWL5+ZB6a9vm7ORUWlvRGWM46ax4PPfQ848b178o4EAIyhfHxMVRolXQqXQAkOomMtIbvU3DoaEyKjydPzent3J7hpBNwUvbqBFan1lOuVxISIQD80s8IcxiF9s57yEZtM81KC2lOKioqEkm70oFf+hkdH0Gj2gxIsu1SiqyC3cRO/4ieNLXdtx8K57K33nqN73//ai699BtceeW3D/p4/20MipVBBvkCIIQgPSOL9IwDy3U46aTTeeaZN/nqV89iwYJZ3HffU4wZ0/8KqpEFis9Nu5AxN9KiZ/XOAw8tQsEBhEgAxYswO6GlFqe+DsU0kfE4wuNBCQRQjjoa7YgZxPPysN5fgr2h3J3sp6QgMrMhEna7wfdM/pWUgOtY1tKCCAQQufkQCaMGAnjOPQ/lM7KCFEKgTpiA+cJzyKzsnf1m2lohMRFlyFDszZsw31yMs2UzeDyoEyZhr12NU70NJa8ADB3Z1ET80YcRiYloe+mjIm2b+L+fwXrvHZzGJuzqapyODrTxExC5edy57C1+/95rnDJkBL//032If9wF6Zm9LmfC68UBrI+Woc+b15sSZmZNx9PwPkqoDsefA1KiBrchPelY6RMB+Otf/0Bubj5nnXXBYX1Pd0VvWuYWh+/yBXX8uWjt69DaK4gfjFjZsWQvFJAS76ZH8VY/j0QFzYu34x3U7q1Ir4dQ6mn4ut/DiK4Hxy28twIjcIwAWqgWx+e+v0qkESXajpXQtx7A8WagBbejhBuwD5NYkUYKVlIxetsarF0aWCqRJqQnFftAIzb7ybHHnsiNN36D1taWvfYA2YEQ4C0Cqxusdre8R/aUm3mHuNeozxtfZhDn7MeIPT4aY/VYFFViOQ5y3AqUk9YAXwy75pycPJ588g0uuugUzjprHrfffjcnn3zWvnfcA7l2Dhl2Bh1KJwJIcVLQBzB1bFYaedt4nVX6xyioTDGPYHbsuL1O/DtEJ2uMdVhYvdbI3UqQtUY5CbEE0pxU4sRpVJvxS39vHYlAkOIk06F0EjADDLfK9vt1ZjkZbKeGOHGMHpvnGDEUBBkHaQawfPkyLr/8HObMOZ7/+78/DNapHACDYmWQQb4kjB8/mRdeWMJXv3oWp58+h1/84o+cc87F/V4YtaTPZrVSlAzHLD4ZmwJQfcj2WpSGNrRNm5GhELG/3oHn4kt6U8SErmOcez7alKk427aBpqIOH0HsqSewKioQaWk4DfXQ06WepGSEZYIZx1621J1gFxZh19ahBoN7tU8+lOhHH4NTWYm9sRK8XoTpWjFrx52AtEzif7sTp7ERkZ0DsRjxRfcjuzpR58zrTR0TRcU4mzZiLX1/r2LFXv4x1uuvQSAVZeIkN5Vs80aCKz7mp3aQZzau45rh47nu2z9AV1WioVCfyBaASE5B1tUi29uhR6xYGZOIDD0b77YX0TrKQYLjzyYy7DzspGLC4RDPPvsY1177PYyDSA3cX4R0+m3Y2PvYgRwz1oGn5nX0xg9AWlgZk7ACI/HULsbxZuL0uJ95VAu1cxt2LBm8ClHfBBSzG6HGUD1u81O1ewvSCGBmHQG4vVGk6kVYYaS2M8IhzDBS9fYxKjjkCIVY0QK07m1oHetxPGlunxlpEi05A8d/aPLu98Wxxx6PlJK3335twMJWS4bEcRCvB6sThAc8We5CyheBDdp6xPzFzCmI0rq6ADPoIaW4HTntQ8oTVczuk/qkEH2epKdn8OSTr3P99VdwxRXnc9VVN/L97//fgKP1n0ZHI3M/Ori3iVb+4f8zm7QKAk4aUjg8432UzWol3wh/B7/sX302qk1EiPZxH0txkmlRXfexNCcVWzhI4aDKvtNXBcWtpxEHdk3IsbMpsgrYrtXg9ERYFBSKrAKy7QO3o6uoWMvFF5/GuHGTuOuuh3sbPQ+yfwy+a4MM8iUiNzefp59ezM03f5vvfOfrLF78CrfddgcpKYHPfCxSQqQzF2fIUYjt63FqNgA+7OK5KEl+9PQw9rq1xJ99Bs9ll/eKKqEoqGXDUcuG9x5LmzAJe80aRGERRCPItlZkPIawbdTRY5FmHCHp6QYvMF98HtnSjOfyKz6TVDAlPQPPld/A/vhj7E2ViIQE1PETUcqGE7nlh1hL34eEBER7G0phESQl41RvQ+3uhl0jQImJrhjbC9byj3vO2VOjMnYsa4Id3LDyfWptkz/MPJ4zFl6AcdICZDgMCQnI7i6EZ+cNV3Z3gd+PCAR2HlgIYiWnYWZNR+3aDELFCox0XbGAV155jkgk/Jn3VTEzpuDb/Bi2P7fXUUuJtiL1RDc1bH+xwvjX/RW9ZblrQywUPFXP4VFfBTOIvUukRveGUX3d2NEYZnwYiBSiSZPxOasQ8XqUSDNW2iiipWdjB9zvq+PLJp45CW/NG1iqgdSTEGYQNVxDPHfOYRcMVsZEQuOuxahdjNa5CTsll3ju0cRzjzms592VnJw8xoyZwOLFL+9XFE5LBO0APtLPgriIgZBklLWTWdbRu71FcYhhYWIeErESi8Vobm6kra0Fx3En3qKPJbxA01RycvJJTU3b4yp9QkIif/vbQ0yY8Ht+8Ysfsnr1J9x55wMDinQdLB8bS9mkbWC4NRoNA4RGNg51WhNLjHeZHzuh3whLVERRUHZ7TJUKUeGmMHqlh4CdQqPajFd6ep8bFCF80kuKc2BRSxWVceYYsp0sWpU2QJDhpJFlZw64oeSnKS9fw4UXnkJeXiH33fc0/j30Phtk3wyKlUEG+ZLh9ydw++13c+yxx/O9713F/PlT+ctf/sURRxz1mY7D7garFbQhmeAvxWxvQGggk304mfMQ5hJwHOz165AtLYi9NGPUpk3H3liJ/fGHbi2LUFAUgTZjJug6Tvl6RNnw3hu3TErCXr0KZ9NG1BGHvxMxgBJIRZl/HPr843q3RZ94zBUqPY0hicZwKsoRWVkIRyI72iFjl8lDdzfK0GF7PId0HOzaGmQsioxGkB4P965dzm8+eYehvkT+fcl1jPn6/6DkuDVLwutDmzIV87VXcRCI5GRkVyeypRn9+BNRUnd3yXH8OW4a2Kd4+ulHmDJlxgE1+9tvrChGwxL05uWIeAeOqqO1rkEaSQhpIYVKrOhk7KQ9u73JShtnmQ31EnIFygwVUaaiN69Ab12JlTK8N7XM8eVg1L/jNiLCAXZaUilJXnS5DVKH4HizUU0LpTWGNAJYgZFEi0/FzJ6x88RCEB16HooVRm9egRKuQ8S7cXxZOL5MhNl92GpWdmCljcFKG+Omue0hKrU3pJRQLZEVNsRBFCkwSkEYA09fmTVrDq+++vx+n/uLSoFdRKJMol1pJa0nLcgtvm5mqjkDHwM3x4hEIqxevYI1a1ZQVbWF6uqtbN9eRUNDHZ2dHQM+jt+fQH5+IQUFxeTnF5KfX8iwYSOYPn0WmZnZCCH4n/+5gXHjJvHNb17ESScdyd13P8b48ftfx+KYEG8Cqw1QQM9w+9/09/XapG3AK31oeEAYgEBD4iOJjfpWRli1FNkFu+2XJJNwhIOD0ysQXIcvm2THTQUQCIZapXQp3bSqrRiOB1OYKCiMNIfj34dL2d5QUcm1cw5JA8n333+byy5bSFHREB566PnPZcHwy8SgWBlkkC8pp59+LlOmHME111zGwoXzufba73HddT9C/4z8NZ2Ya56kJAlISEBJTITkFISqIRUP0jIQHo87eY5G93os4fXi+col2FOm4myrAk1DHT4CUVxC9NYfQyDQZ4VRJCQgTROnseEzEyufxmltwfpwGSI5GSzbtYrWDWQQnI4O8PmQjY3IrGzXtrm5CTwetCNn9n+85mbijz/qirvt1dRXbeHm5m0saa7n8okzuK6gjORTzuwVKjswTnVz6a3lHyNrtkNCAvr84zFOOXXAr6WtrZW33nqVW2/97QG/HwPGjuOvuBuj7l2kaoBQUKwwjicZM2Uk0peBlTEZM2PyHp3A5Mc2zkNx6JbIBIHYKHE+sVEuMlCTt7thv12LtBQVx5+DEtyO2l3liiAhwAojVR+x4pNQzSBGx1LU7m1IxcBMG4sabcRfcQ9RM0SsZKc7mvSmERr9LRJX/Awj0oiTkI9jpODd8gRq12bCY6/5TJo0HqhQkW9ayOct6MTtfq4JmKyiXKwjfAMTLCNHjuUf//gT4XCot2HtfzL5dhEzY7N53fsCnaIDr/TRobST4WQyL3rSHmsxpJTU1Gzj448/YMWKD1m+/APWrVuFaZp4vV4KC0soLi5l5sw55OUVkJmZRWZmNunpmaiq6grHXY4FEI/Hqa+vobZ2OzU11dTWVrNy5ce88MLTtLe7DQ2HDh3OEUccxYwZRzFjxtG88soHfP3r53HGGbO57bY7OOeciwf82p2427Az3tJjLe1AvBGsPPAP//TXzCaJKI7oAKHhWrq5EaIYYVRUNulbyLWzd4tE5drZVNs1tKptJDgJCCCohEh2ksm3dzoqZjjpTItPplqtpUPpwOf4KLDzyLGzB/yaDifPPvsY3/721zjiiKO5++5HSUo6vIsT/w38x4iVtrY2rrnmGp577jkURWHhwoX88Y9/JHEvOenRaJQbbriBRx55hFgsxgknnMCdd95Jdrb7hV61ahW/+tWveO+992hpaaGkpIRvfvObfPvbO50a3nrrLY499tjdjl1fX09OzqBH9iBfbAoKinniidf4859v43e/+z/efXcxf/nLfRQXH/7+I4rXdfKRJq5Q8fogHEImpyJkBCHjyJZmREZGb/H33hCGgTZhIkyY2LtNStlbf7Er0nKb4YnPcZIkW1shFEQpHYpTvh4ZjYDHizQMaGpCmzgJpWw4Ts12sExERhb6ccejfsrZC1y759iiB7DXrUEdNoxXm2r5YcVH6EJwz8SjOCq/BHXCRNRRuzcmFD4/nnPPR59/HLKjAxEI9NocD5QXXngKKSWnnrrwgN+PgaK3rsZoeB87qbi3vsO242idG7AzJxEr2rvlp4xJnJdMZBzESBVBzyRvq8R52USe4wMcV7B8SuzEc2aimEG09nXuBkXDTinF9uciuraAFcb2ZRPPnQ2qe/tUQrV4ql8knjMT6d0ZqdLb1qB1b8XMmLLTRtiOobesRG9cSrzw+EPyfh1y6iTyeQspgFECIRRkWCI+tJDDFMTcgU0bRo0ai5SSDRvWM2nStP0aghKs7u374/gyMbOP6On/8/kVJgsEZ0bPJ9fJZ5nxLh2ig/GxyRwdn0up3Td3zbIslix5i+eff5LXX3+RxkY3tXPIkGFMnjydc8+9hClTjmDUqHEHUcNwRL9b6+trWbbsPZYse4sPP1jCQw/dA7j3ggULziAvr4Bvf/ty3nvvTW699bek9hNd/TTxRjBbdjbsBFfAxOrc6IreczlR1Y34fI8wU/+IlWoNcVmHQTLSSSQouvHgIcfOISzCdCtB0py+/Z580sfk+AQ26ptpUVsBSYGVxzBzKAmybwpVwAkQ6CnC/yIQI0ZYibDorrv5xU9uZuHCC/nd7/7+mdb3fZn5jxErF110EfX19bz22muYpslll13GlVdeyUMPPbTHfa677jpeeOEFHn/8cVJSUrj66qs566yzWLJkCQDLly8nKyuLBx98kMLCQt5//32uvPJKVFXl6quv7nOsDRs2kJy8Ux1nZX1Bqv4GGWQfqKrKd75zM0cfPY+rrrqE446bxve+979ceuk3Dkmxn5SSUChIW1sLbW2ttLe30tbWQmtrK40bWmhtbKM72o4d6sYJhd3+xXYHItruCor8AvjmandF91OriKqqkpISICkpheTklN5/p6SkkJwcICUlgG9IMd4N60lqbsTIyALTRFZtReQX9Dt5/6wQySlu3xSvF1EyBFlbA6EQxGKIxESMr14GHi/Wm4uRba2ow4ejFhX1m4PubNqIs7GScF4+t334Fg9vWcO8zDz+LymLdKGjn3Iqxtz5ffq8fBolLR32U6Ts4OmnH+Hoo+eRmXn4Vy7Vrs3gmH0L0VUDqfrQWlfvU6xQJ5GNEnJ2ibQJgcwBWS8x4xPxGi+ghrZjJxQAAiXajFRUYkPPwU4sRGtdjbDCKLEOjPp3UIPbkYqBGm5Aqh7UcA12Ugngps1p7RWowW1Yu4gVrWMj2LZbr9K1GeGYON40JAKtbe0XVqzICgc6pZv2taOOzC+QPgErbBigWCkrG4UQgvLytfslVrT2cvzr7kQJ1yO1BETLJxgN7xMZcTHxvDkH8pL2mx2XoU//KuroHB2fy9HxuX0aJQKYpsmSJW/y3HNP8vLL/6a9vZXi4lLOPPMCZsw4iilTjiA9/cALtQdKWm4GQ88Zi+/8DI4VZ2K1xOh4r4nyt1by+OMP0N7eRlFRCc8990RPTeNfWLBg73b1Ziug7hQq0OOGHXTNEPR0EKIFv/9vqGoN463hzHN8LBdxOqjBUhLxEqDMGk2yEyCiRFBk/1G/ZJnE5PgEYsQBiQfPHqNWXwQcHDZpW9iqbOPhW//Oq395mnOuuZRffe9PGMqgUDlU/EeIlfLycl5++WU++ugjpk6dCsCf//xnFixYwG9/+1vy8nZvuNbZ2ck///lPHnroIebOnQvAvffey6hRo/jggw+YMWMGX/va1/rsU1paytKlS3nqqad2EytZWVkEdi1GHWSQARIKdrOtajNNTfXoukFBYQkFhSUH7MxyoEyZcgSvvfYRP/vZD7jllut58MF/8L//+3uOPnruXveLx+Ns3bqRyspyNm6sYNOmDb0FoDvESTwe320/n89Pamo6AX86SV63W7LQQYm2o8Q7wedFTUtDJCWhfKqQdMckybIsqqq20N3dSWdnJ11dHYRCwT2O1adqJOsGyf4EsocMpeDHdb053QUFReTnF5KXV/iZrHaJ7Gy08ROw3nsHJb8AmZOLU7MdEezGOOtsZHc35qIHwTTB68V843XstWvxfO1y1NKhfY5lt7fz5NYN/O7Vx+mORfnZcWdw/vhpOK2tYMYx5h132JzPmpsb+eCDd7n99rsPy/F3Q+n/tiSwkQPx19Zw+wfZn9puAyrYgUIigfPxbnkCrb0ckEgjmVjxaZiZU0AoxP05YMdJ/Pgn4FhYqaMRdgynY4MrPjoqsf15oOiIaAtKvAOtczNWYBRo7hiloqKEatG6NoJjgwC1eysIBTNzKlhh9Lb1CCuI7c/BTtktn+bzwQb6FHT3oAKx/vpQ9I/f72fIkKFUVKwd+Lmlg6fqWZRIE1bquF61oHZX4d36tBulOozpc47pRgvijYANWhp480Ht51drxwR61arl3H//33n55Wdpb2+jpGQoF130NU455WzGjZvY532UEmTcLSVSvIc+UCSRrDHWUaPW4Zd+fI6XSAaknZHHtxacwK23/pY33niRRx+9n9ra7bS1tfL1r5/HrFlzuPPOB/a8GLGHccpdHtP1FahqNZY1BoHC2fZo/EJSpUTwyiRS40fglwm0Ke1kORl77dOy472t0xpoU9rQpU62nU22k/mFEy5btSo+6vyIRdf+lVUvL+Mrv/ofZn5jPuvsCqbEJ37hxvufyn+EWFm6dCmBQKBXqADMnz8fRVFYtmwZZ565+6rA8uXLMU2T+fPn924bOXIkRUVFLF26lBkzZuy2D7giJy1t97DoxIkTicVijB07lltvvZVZs2btcbyxWIxYbGfzra6urgG9zkG+fAS7u3h/yWJampswDAPHtqnZXsXwEWOYOPmIz9xvPSkpmdtuu4OLLvo6P/7xdZx33oksWHAGt9xyGxkZ2WzevIGNG8t7hUllZTlVVZuxbXfml56eSVnZSHJy8hg+fDRpaemkpWWQmppGWloGaWnppKa6/+3qfGJH3Zu0mxomwbZBVQ/o9VuWRXd3V4+A6aCryxUxndur6ajZTnc8Spei0NjSyIYN63jjjZdobm7s3V8IQXZ2bk9BalGPiHH/3vHv5OSUg36vhRAYZy4E6RB/602c6mqwLJSsbGQ4TPyF5xFeL0qJW7AupURWVmC+/hrKFaW9783y5cv48Y+uYWX5Gk4ZNprvzT2FvOSAe5LubtSiQvAdeFHpvli69B0AZs8+bh/PPDRYqaORWiJKuBHH706eRLwLHBsrY/K+D5AvEEMUWG8jyxSEKpC2RNQ4ME6FPIW4Mg8rbaxr0ezY2CnDXBewXb6PargONVTfazYgVQ+OPxe1cwNKvAsl1o4SrEFvX4fUPK74afmEyMjLsFOG4XgCqNFmpObv7cEizDBKpA4l0kTi8v9D66pyZ66al3jWNCIjLtuZMnaQtLW5gbz0dNgfEyJRLHC8QKdEpPQYVlgS0SUHnAK2gxEjxuyXWFGirWidW1wh2PNZtOkRQhlJ5DTXoXZtxsqYuF9jGCjSgVAFmI2A7urG6Ha370viuL69Xmzb5tVXn+Pvf/8Ty5a9R2FhCRdffAWnnLKQMWMm9Htds0MQqQKzDZCudby3GPR9Z2ENmHalg0a1mWQnGU9PrxDDMWhXOtimVVPgyWPBgjNZsOBMmpsbeeqph7nrrttZsuQtpk4t5YYbfsw113xvt/EbGW4amDTd1F4AJ+qWpGgB92dFacVVLq7gVhAcLRV8qLRIH1ERJyripDjJjIqP3OskPiwirDA+oUVtQ5UqDpLtWi3DzFJGWGUDEgA2Ns1qC52iCxWFDNsVSIdSPFhYPPvmk/z9mt8gbclND/6CqSfOIubEaFKb6RRdBOTB30sG+Q8RKw0NDbulXWmaRlpaGg0NDXvcxzCM3aIh2dnZe9zn/fff59FHH+WFF17o3Zabm8vf/vY3pk6dSiwW4+6772bOnDksW7aMyZP7v3H+8pe/5Kc//el+vMJBvqxs3lRBS3MjWdm5KIobSQmHQ2zZvIGi4tIDbgJ5sIwfP4k//vGf/PnPv+bppx/mxRefhV269+bk5FNWOorZs47niitGMWLEKIYNG3nAtpeqF+hdFBdwEOlnmqaRmpo2oFzrHUSjUerq3GLUHQWptbXuv1etWk5d3XZM0+x9fmpqGmVloxgxYjRlZaMYPtz9d1ZWjptSFIthV5QjO9oRgVTUESMR3t1X/UVyMuqUaYhlH6Dm5yNyckFVsV57Bdnejnr8iTufKwQyMwt78ybo7qY+1M0vf/kjnnhiEWPHTuSRS69hcksbwrSQoRCyrRUhHfRjZh9We+YPPniP0tJhZGcfRstdx0RrXYPWY5lsZk5Gb16B1rYGEKAYxPLnEs86ArVzE1pnJQBWynDs5KF9RIZQBMqZOk6XRFT2ROwAihSUM3SE4j7X8WcT9+85rU0qhhvlcXZ+L6zUkYhoK2q4Dq35I9RII44nEzPnSBwjGa1rE76KfxKccgtCShxfBsKMoESaegan4iQUYDQuxUnMx0ouA9VAxLswat/G8WUTK1qA0fAeWvMKBBIzYxLxnKMGHFHo7obnnhOsWKEQjUIgIJk9WzJ3rmRAX5NhCmKGCu/ayGbpTtxDEsoUxKz9+56NGDGGRYv+OeDnS6G4n6V06NJiPJW3juWBOmIiTkZBnKP8yziK8QdsJbs3zDYwm0FJ6nXIRvGD3QaxevAPg2Cwm0ceuY9//vMvbNu2henTZ/HPfz7G8cefutdI+Y4CdbMTVD8g3PPZYVcIafuov7ZDbnG7EwfND3qmm4b1aSIigoWFBwNTxGlSGomJKLo0UBSBidnbzT0zM5tvfOM7XHHFtSxa9E/+93+/x69+dQv33vtX/vzneznqqJ0RdyPbHW/cbQzv/qeCt3CnWHF6+7D0hDCBFBzmsIVqu5QOcyRe6SHLdqMj29TtdCgd6Ohk2ZmkO2m9QmKbVk2L0kaanYba81mHRJit+jZy7Zy9RmUATExWGWuoUxuQwk0v9ugeRpplDLFK9v5mD5BwOMxPfnYji/51NxPmT+OqP91Marb7HhgYdBMkqkTBHhQrh4LPVax8//vf57bbbtvrc8rLyz+Tsaxdu5bTTz+dn/zkJxx//M5c4hEjRjBixIjen2fOnMnmzZv5wx/+wAMPPNDvsX7wgx9w/fXX9/7c1dVFYeFn0zl4kC8WdXXb8fn8vUIF3PSo7q4OWluaPlOx0t7expIlb/LOO2/w7ruL2bZtC6qqMn78FADWrFlBIJDGDd/8X06dcCnEVYQKeir4hoF6+BbvDylSSuLxGKqqomnurMPr9VJaWsaQkjK3TuZTi2u2bdPU1NDjrrONqqrNVFaWs3z5Mh577P7eSGmyP4GyolLKdA8jhMbIxBRGBNJJGTkaz8WXomRn7zYW8923EQiUSVN2bg+Hcaqrka0tkL2LUYdlE5eSu//+R/78t9/j8/n5zW/+xvnnX4oIBjGf/zfW6lXI1hZEWjr6sXNRp/VfaHuo+OCDd5gx4zD26LCi+Cv+idGwpFcYSC2ReNY0HH8eAhsreShW6mi8W5/Bs/1lhBkEJFJPIlZ0EtHShX1SqESJgvJtD3K1DR0SUgVivNobKRgIjj8XK3UUeuMHWHoiKDpSMXD82Vipo1CiLUhvJlbGhN70NCu5DK1rM3r7ehBgJ+Tj+HJQoi2AxPGmowRr0bo2EU+eDao745RGMo43Db3ubdSuzegtnyAVDyDQ69/Du+EBrKypWCllmNkzeiM1n0ZKePRRhSVLBNnZkuxsN8LyxBMCXYc5c/adxiVUgXKeDkMVnE9siIAY5QoYkbZ/IiEnJ4/W1mYcx0FR9r2v9KRhpo1Bq3+H+0cGWZJRS040gUDYoiXR4OGMt1CiwzkqvrvpzcFiB3vSs3YxqBLCbU7ZvK2VBx75LQ8+eDehUJBTTz2bv/71QSZOnLrnA+6C2erWdmiBnV9TYbhRm1jD3sVKvBnCG9zotOsWAXodJIzuG+0B8EgPKgqtSivrtZV0Kh1IJDpu/5GpygSynL7GQIqicPHFV3D++V/lJz+5kfvvv4tzzz2RsrKR3HXXw4wcOQahueczWlzBJRT3vqCl7byWmuYUbPtVNK0C2y4EVBSlFpUUcmLHkGm7qa0REXWjJkorAoEUDlVaNSPMMoZaQ5BIGpTG3teyA7/00SLaaFPbSbH2Lla2a7XUqvWkOMno6EgkQRFkg76JdDudZHlwqYSrV6/gqqsuoaa2mkt/cw1Hf+0EUtg5pjhxdHS8zgDSVgcZEJ+rWLnhhhv46le/utfnlJaWkpOTQ1NTU5/tlmXR1ta2R0eunJwc4vE4HR0dfaIrjY2Nu+2zfv165s2bx5VXXsmPfvSjfY57+vTpvPfee3t83OPx4NlLoesg/z2oiorj9DNBkPQRMIeDWCzGRx+9zzvvvM677y5m9eoVSCkZOnQ4c+eeyNFHz2XmzNm9KU/btm3hJzffxA9+9k3+lvNbLl94I6cdfRFOgwfHhKQJfQssv4i0NDeyoWINLc1NqKpKYdEQho8ci276idWA2e6mLui+NrTmlci2ZpSUAOr4CeTm5pObm8/UqTtTRKWURF58ga3PPs6m+joqg51saqzjk0iIJ+MRzJ5am4I3Exn9wqOMWXAaY8aMZ+zYiRQWlrjF/rW1vV3id6Dm52N7DJyqrShZbj+EUCjIYx+/yz31VTR1d3L55ddw3XU370xJS0nBc9HF6AtOhnAYkZ7RbzTnUNLW1kpFxTr+539uOGznMBqWYNS/g5240/1LCTdiNH9McPIPsFNcpyWt5RO8257DMQLYicXu86LNeKuew0op2y09SAQE4piDuMUJQWToeYhYe28kBwl2ylDCwy8lYe1fQKh962gUHXAQZhArZThSdydFVuoo93E7jmaW4xgpuy2NS9WLFtyGDNe7PWA0H2r3NtRwA1r7OtRoM7pnKUb9u4THXoWdVLzbkKurYfVqQWGhZIcfTH6+u/3ddwUzZ0oGUq4ldAFHaqhHHtwUIRBIxXEcgsHugaVWCkF0yFnUiXJW+1cwpM0g2Qwh9URyvZPZIkK8bbzOjPjRaId4+iLUHi2wi0lc3IzxwHN/5a5nf4GDzSWXXMlll/0P+fn9Lz7GiLHMeI9P9A+JiihjrYnMiB2NP+JGpHctSRLCTamyu/c8JseEiOs3gZbaG3TC7IDoNldA7Eqak0qqk8pS4z26lC4CThoqGjYO25SNPO95issi3+o3FUrXdX7xiz9y00238u1vf43XX3+RuXMncfLJZ/HrX99JamoaRrYbZekPKdMJh7+J1/somrYZsLHtYmKxM7HtnQ1+q7RtNCktpDupqD0RmG4RZJO+mSw7k0SZwB6LZMQeH+lDnVqPhtZrjSwQJMpEWkQbrWorydaBiZWurk7uvPO33Hnn7xg1ahyvvrIMMcrDeqWCoAzilT5MEScoQhTYefuMAA0ycD5XsZKZmUnmACxLjzzySDo6Oli+fDlTprirk4sXL8ZxHI44ov9VxSlTpqDrOm+88QYLF7p2mxs2bKC6upojjzyy93nr1q1j7ty5XHrppfz85z8f0LhXrlxJbu7h7UI8yJeDwuIhrFy+DNOMo+sGUkq6Ojvw+f1kHYa0Gikly5cv48EH/8Fzzz1JJBImPT2To4+ey6WXfpOjj567xxttUVEpf77xCVbM+Zh7Xvk1P7nzW/z54Z9y6anXcvasK/AWJWMc/ubHB0xbazNLl7xJKNhNQmISlm2xbu0ntDe2MzZhLkpMR3hBVlcTffVe1K5tqAEV4ViIt9/Ec9HFqKPH9DmmU1mJfO0VStKyGDJiLMeFgphvvoFwJPaUaVR5VMqbGiivqaKiqZ4H7ruLlrYWwE2zmDp1BhO6I0xUdcbGo3hsB5GQAEmJ+Ecl4M+uob3lHv62KsQ961vpNm1OW3AG133vpwwbNqK/l+k2cvxUCpzs7sbeVgVCoA4pdRtQHgI+/NB1Tpwx4+hDcrz+0JuXIxWjj/uX48tCa1+H1r6+V6zoravAifeJKji+LJRIM3rrqsNSy+AkFhCa9H20lpUosVakkYqZMRGpJ2EnFaO3ruwzHhHvRKo+bH8udvJQYoUn4t32HEq0yZ0NOzbxzCmo4UZErA3p3enOpkabe9KgNNB8YMfQ2tcjEWAEkKoXK3U0ets6PFX/Jjz26t1ChG1tgnBY8ulAfkoKtLcLurvdGpbPipQUV6R3drYPuA7MSSygZuyZBD0VFHVnYKkebH8uUk8i4LTRprYQEkFSZACJJEIEFbU3velA0dNAeN0Ii5IgefX9p/jdvT+krnkbF5z1dW665cdk7CUSbmGxyPdPlnreRpcGGhobtPWs1pbzdeO7SBKIEETHg3dHA0kLlL1ErO1ON1VMTd75UQvF3SfeBr54X82roJBqJ9KqNJEiXaECElU6JEkf64xVtEVbSZd7vpAHAqncd9/TbNq0gUsuOZ0XXniKV199nmuu+S7/8z837rVnjm0PIxT6Aaq6HVesFAA7ByiR1KsN+KSnV6gAJMoEWpU22pR2kuxE8uwcyo0N2La/93khEcIrPaTZ+07/tbFRpOjzsyMcQOL09HzZH2KxGA888Hf+8IdfEImEufrqm/jOd27GMAxsy8bGplrbTrfSjYZGsVXEKHPEYHH9IeQ/omZl1KhRnHjiiVxxxRX87W9/wzRNrr76as4///xeJ7Da2lrmzZvH/fffz/Tp00lJSeHyyy/n+uuvJy0tjeTkZK655hqOPPLI3uL6tWvXMnfuXE444QSuv/763loWVVV7RdTtt9/OkCFDGDNmDNFolLvvvpvFixfz6quvfj5vxiD/UZQOHUFLcxN1tdtwbDfZ1+vzMXbcVJIPYUfbzs4OnnzyIRYtupvy8rUUFQ3h2mu/x/z5Cxg1atyAUjCQ7o1x/Kip/HnKY2yp2cA9T/2ePy66hbse/xUXn/tNvnHd1QdlXyulpLOznXgsRmJS8iFtFrdl8waCwS6ysvN6C0R9Pj+1VdvJyqojv7gYkMhVzyHaq3FyRqDlqgivRG7ZTPyZp/AOHdbH/teqWI8MhVCL3FVsaVkITUeacbTWZkZOmMTIzFzOGDoKWV+H9/obaU3ws2rVcpYv/4Dly5fxx4+WEonH0BGM9iYw0ZfIjHyNsgkaj250eGBJDYqArx5ZwNe//VNyjryo//cuGMSpqwXDQCks6q1TsZYtJf78czjNzQAoOTkYp52BNnlKv8fZHz744N0eJ7XdV/EPFcKO7R6y22GZ61g7t1lRoJ/QnlB6cmQOD1JPxMw9arftscLj0To3oXVUcNtL23lu2SYq6zrwenxMmdHND3/4C4YNPRsrUIbeshJhRbACwzEzp+Pd+jSe6heRZjdS87tCSEvEDoxEa3ML0pVoK8IK4XjSEdFWQAGhYvvz0NvXI8wupNFXAKSkSLxehVBIsqs5XHe3+9hhMozbIzvqytrb291I4wBJ0QrRfAV0GXn45c5rRLfSTZqTjk/62aZu4XXPS1Rq69HQmBSfzrzYSaTKA6tYVxPcupQPXvqQX//rJj7Z+D6zJy3grtueYsL80fs0aqvQ1vKhZwn5PZ3uwRUwG7R1PFB0J6MbZqGFPAST28mWuQzrGo2q6nj2cjmVvf/ry44IS3+PKUAb20l2fIDOjiITQ3oIiSBxEet3v08zbNgIliwp5847f8dtt93C73//c+65506+9a0buOCCr+5FuCnY9r6uF3uaxLsDK7aKaFXbaFZaep4u0aXBcHMYSbLvl9iyYN06QXm569sybBgEpmXRnlCJKU061E66RRBTmAgEMdPczXZ6TziOwzPPPMqvf30rNTXbOP/8r3LDDT8mNze/9zkqKiOsMoqtIsJKGI808Ev/oFA5xPxHiBWARYsWcfXVVzNv3rzeppB/+tOfeh83TZMNGzYQDod7t/3hD3/ofe6uTSF38MQTT9Dc3MyDDz7Igw8+2Lu9uLiYqqoqwLVtveGGG6itrcXv9zN+/Hhef/31fhtFDvLlJx6PEY/H8fn8A7Ie9ni8HDlrDvV1NXR0tKGpGtm5+aSmHvzyppSSTz75iAce+AfPPvsYphnn+ONP4cc//hXHHDN/YAJlV4RbUGp1uD+WFozgZ9fexdXn38K9T/yJ+x67g3se+SPnnnsJF174NcaNm7Rfbl6hYDcrP1lGY0Mdlmni9fkoHTqSUWMmHBIb59aWJrxef58x6ZqBE5eEnQ6EKEZ2tEDtJsjMBVQ3vcInIL8Ap64Wp3obatnOlAXiccQu76NITHStlZpDYO6cSMuWZkR6Okp2Dll+P8cddzLHHXcyMh6n+ze3sW7xK3zS0conwXZe7GrhvnYL1oIiBCOLMjhn9ljmD9MpsT8hap8N6k7BJKXEfPstrDdew2lrRWgaypBSjLPOATNO7LFHwbZRSocCEmd7DbFHH0ZkZqEeZK3cypUfM2XK4a2JMTMmoLWudnNdegoGRLwLFB0reaeFs51SBrWLwY731npgx0A6vdGXgWBH3FoB6biWtFrKgVnIWhmTCI35Fp7tL7Nk/VKuOHEi42ecQDjrSH71m19wwQUn8/bbq/BnTMLKmNRn30jZBTj+LPT6t1FiXZjpk4gVum5rCZ0bXXHSMyNVQvUo8Q53RwGOJxWp9b8cX1ICo0dLPv5YkJ8v8ft3uoItWCD5rLOTAz3pjx0dbfu1X6k1nJHmGFbqH1NgF+OTftqUFiIixKz4QlqUJv7h/xP1ah0ZThYxYrzoe5pqtYpvha7Hx/4X2HV0tPOTX97A448/yMiycdz/l5eYM29en6jG3timbcHE7BUqABoaQaWbZ9MeJjI+xuT1c0jpyqBLhtjkqWBSwVi0tD0fXEtyHRSd8E77ZCndn/Ust+7l0+TZhaTJDJqVOvKcnb//TUoDBXYxGc7A6ySFEFx11Y2cdNLpfOMbF7J+/Wp+/euf8Nvf/pRTTlnIJZd8g+nTZw74PiAQ5NjZVGqbSJD+XqOEbtGNhUVEROgQnaTIZKbGJtOgNtChdKKjk2ln9CnCB1ecPP644O23BZYlEALeeksyflUpI7/eytakzcRErCc6I/BJL1v1raTKFHLtPTf1dhyHt99+jV/+8sesXbuSE044lQceeIayslF73MeLB68zmP5/uPiPEStpaWl7bQBZUlLSp6EcuEW1d9xxB3fccUe/+9x6663ceuutez3vTTfdxE033bTf4x3ky0U8HqeifDXVVZswTZOExCTKho+mZEjZPi/UmqZTWDSEwqIhh2QswWA3TzyxiAcf/Afr16+hoKCYb3/7+5x//lcPyrFJCPDkgd3RkwrhA2lDupbPD751G9/91fe4/8G/8a9//Y377/87ZWUjOfvsr3DWWRfsMbVsB47j8PFH71NbU0UgkI6ebBAOBVm7ZjmGx8PwEWP2uv9A8PoS6Ozs6Hte6YCQaMKzYyDuLFUofQvtFQUpHffxXVCLirEEyGjUrQ/RdJTCIuz6BojHcZqaINgNQqDPnb9b+pWzZTNKXQ0Z048kUrGKyop2mm2LgiSNY4sDJBQVsWJLE7986B1+atn4jDeYNPEDphwxh6OOmsu0aTPRytdjPvUE6DpKUYl73vL1xB+8D6V4CLK7G3XkzpuoUlKCU1GOvXrVQYuVqqrNHHXU4V2Yiecejd7yCVrbWqTmd6Mp0iaWdyxW2tje55lZ0zEbP3CLzw03F1zEuzEzJ2JmTR/YuRohvAmciLuGKzTwZIN/+IHVY1mZk7EyJvLAs1cjVU+vyLz99rsZNy6f1atX9J9CpxqYqWPQmlagRDYhzCCON4NYwTxiRSfh2f4KItaOiDajmFEcXwZS86J2bkaVFpHhFyP13fPhFQXOP99B0xTWrxc0N0NSkuSUUyTHHjvwHimHil3TwPYHFZWLIpejY1ChraVR1JPsBDglupDZseP4t/dx6tQaRlpjeye8qU465foa1uifMN2cudfjNyr1NKh1+KSPUms47775Bjfc8A3C4VCvocX+LqBo0o1i7Lpqb2PTqNThlX48aQrlM5aQ1JFGUAapS91GofldCvYShVA84Ctxv7NWO25g0QIlAXzF/YuoZJnCvNhJPOV9mE3aBhKdJLqUTvzSz/GxU3rrOPaH0tIyXnppKb///c+4/fZfMGHCFFas+JCnn36EkSPHcMkl32DhwgtJStp3jUaxVUSr0kZrjy3xjm72XsdLhbGRLXIbRVYBo82RFNmFFNl7voaVl8M77yhkZkpSUtzvdzgMqz8yKBw7As/8GnSpY6CT6CSSJBPpUDrZqm0jx87eLfrR0tLEo4/ez0MP3cPWrZuYOvVInnnmLaZP3/v3aZDDz3+MWBlkkM8LKSUrV3zA5o0V+BIS8Hh8dHV2sGzpOzTU1+DzJeDxeMnOySUpORVN0w5L/xTTNFm06J/87nf/R0dHG/PnL+Dmm3/O7NnHHbIGk0aW2w8lWuMKFqG42/zDQPWn8Z3v3MzVV9/Eu+++wRNPLOIPf/g5v/rVj5k5czYLF17EySef2e8Nq7WliabGOtLSMjF6lneTklOwbIutmzcwdNjIg34NxSWlNNRtJ9jRTkJyCo6UtLe1kJyWQqa/ACcGpGRCZhHOlgqUwiQUX08fibo6lKxslKKiPsdUJ0xAGTcBe9UnCH9CT/OFKNoxxyCys6GjAzF8OPqso1F3Sbuyq7bS8s5bvPDcEzy98kOWhzrxahrHDRvND+bN5owhFWAZqLPmIrxeYqbFqvJKPiyvZklLOo88ch9//vOv8Xp9HJFfzCxfMsdMPZJhmobQdRg6DGfLZmQ4wqeXy4VwraFlx/5NED9NOByiqamBIUOG7vvJB4H0pBIa922MhiXorauQqgczcyrx7CP7NIiURhLhsVdh1L2F3vQRAGbJacTzj+3b7X4P2BEIbwTHArWnWNmJQ6wW1CTwFvQ3OInevBy9cSlKtBk7eRjxvGN6u9cDIJRe8bSDrq5OYGdk4dPoDUtJ+uB7qN3b3NeoaGht69Da1hCacANWxkQ8VS+gdlcjjTgoak+6HIDA0ZP2uNyflgZXXOFQV+dGVDIzIbX/YRx2dlwLDqTXWLqTyTdD11GrVhMSQTLtHNKkG5HerFWSKJP7WBh78CBxaFTrwOz/mCYm//Y+zhLPW3SJDkS3wvYfVbH6nhXMnn0cv/vdXeTl9fdF2DcjrTEkyxQalDpynDwEglalhaiIUma5lfCOZtOZ0YxE0q410Wq37FWsABh5PTUqTW5vEzUJPDm7O4HtyrzYSaQ4qSw13qFJaaAsPpJZ8TmMtSYe0GsD1zL+pptupbi4lBtv/AZz557Irbf+hkcfvY8f/eg7/PznN3PaaedwwgmnctRRc/v02dqVBOlnWmwydVoDDUojQS1EwEkhy85EQSEqomzVtpHsJFNk7/2z2LRJEI+7NVk78PtBVaFircaoeX4ynL5ZDF7pJSiCWFjo6DiOw7vvLmbRort5+eV/o6oqp556Nr///T/2K2o0yOFlUKwMMsg+6GhvZXt1FcmBVHw+9wKsqAobN6ynsaGWjIwsuoPdWGactPRMikuGUjZ8zCEroJdS8vLLz/Lzn/+QrVs3cc45X+HGG285LHUEQrje+UaOm2ogVHcVb9frtaZpHHvsCRx77AkEg9288MLTPPnkIm644UpuvvlaTjzxNE4++UxmzTq2d7IWiYSxLatXqOzA4/ESiUSIx2O97+2BIMNhcsorGVpRyVYrRndCAkpmJoH8QiZMPIKUYCLx9XXIio+hM4QS7kCp/RjpZOJEI4jkZIwFpyA+NQbh9eG99DKspSOwPlkOjoN63PFoM2ciUgJuJEZRem9o4XCI1+/5K08+cDdv1VRhS4cjNS+/8mcwP6+Y5IIhiJQirMZyfNndOHYc8OJRbGYW2Eydeh5fHXctEigvX8Pbb7/O4nv+xm+3buQXaz8kJzGZo0rKOLqkjCNMh4wEP7KmGhmPI3psnqRtg22h9NTzHShVVZsBKC4+vGIFQHoCxIpPJlZ88p6f40hkMJlo4HSiJafv9yTCauuZ7KXu/D4rBjiaG3HZIVbUri3oDUtRg9tRwk2owS2g+ZGqD629HL35I0Jjr8IOjOz3PI7j8JOf3Mi0aTMZOXLsbo+LaBsJa/6EGqrFTiwE1YOwY4hYB0bzx5j17xItuwCtYwN26nBsXx5quA5hRXA8AZAOarRp9xPveg7huoAdUqTs8fYd+KLCjmyHA12IEIh+J/MBJ5XNWmXfc/X88ck9X0fe9bzBy95nSXcySXgniZe/9W/CzUGO/cMJ/PPcx/GKA3fXK7KHcHL0LF7wPk2F5tYdqWgUWiUkOn3FdFB045MJpDiBfR5XCLf4f3+aRwoE08wjmWYeOeD6jIFy3nmXkJmZxZVXXkBrazP33fc0P/vZ7Sxa9E+eeeZRHn74XrxeL0cdNZfjjjuZefNO2k0AevFSapWgaIJmmkm303vH6JM+IiJKnVa3T7Gyq3Nbn9cvQHFUBAILG22XWjdTmHhiBsuXfcA7b7/BU089THX1VkaMGM0tt/yahQsv3K8eXoN8NgyKlUEG2QfBYDdmPEZglwtYS3MTphXH0A3ipkksGsG2bVpbmpBS0tLcxMyj5pKZtee82IFQXr6GH/7wO3zwwbvMmXM8d931EGPGTDjYl7RPFB2UAZj3JCYmcd55l3DeeZdQW7udp556mKeeeohnnnkURVGYMGEKxxwzjwnjJ4MQxKJRPLvY7UYjYZJTAhjGThHjdHZCJIxITetT7L4npG0Te3gR1rKljEpKpsAw6KxvQg3FyZ9zIr6CQqzKSuQH/8KpbwCPF5GqIa04IikR7aij0KZMRR06rN/ji8RE9OOORz/u+N0ei9s2n3y8lHffXcySJW+xYsUyTNNkbGoGN46dxoKISXrcgs4O6OjAWrUSPllBZ5aBeoSCseJlRF4uSloGVuoYosPOBeHetkePHs/o0eO5TE8k9OFSPvbovFe1iXe3beSJtcsRwJhlGcxSPcxc8wmTR4zFW1yEbG1FKSpGnThpt/HuDzvEyuGOrAwEucHGedmCbY5bQTxaRVmgIXIGXpclbffvT09uhOp25gbQWlbiX/93lGgzUqgYzctBMYjnHoWdkA9SorWvx7vtBUIpI/qdKd38vW+wYf1Knn3ksX5nU3rbWpTgdrfupCdtTKoehOYBs7u3wF6qPpAS6UnF8u689midlbtFcg4nwgxh1LyO0fg+wopipo8jXnBcv9bJn2ZHo1VdP7RTjSnmDD7RP6JJaSDTycbGZrtaRbqTyVhzYr/7WFgsMd7CiHpZd8sqlt+xjPwZhZz+zDl0Du9gY7CccdaB/84IBPNiJzHcGkWFtg5LmBRZQ6hSt/CM7xGaaSTgpBEWQWrV7UwzZ1JkH5rU4H2N61Azd+6JPPnk61x88emcfvpsFi16nu9+9yfceOMtbN5cyeuvv8hrr73AzTdfi23bjB07kfnzFzB9+kzGjp3YW5xvYoEUu41Rkxox4vscx9ChEk2TdHdDUk+pUCQCpgnTx/jQnACtajvJVhKNlbUsf3spq9/6mE3vlRMJhQgEUjn++FP485//xdSpMwajKF9gBsXKIIPsA4/Hi6qqmKbpWhXaNt3dHaiKW7QX7O7E6/OhqhrhUBC/P4FwOMSmjeUHLFZisRi//e1P+dvf/kBJyVAeeuh55szZfbL8RSI/v5BrrrmJa665iZqabbzzzmLeeed17r//H7S3t+LxeBhSMpRxYycybvwkAskBHMdhWNkoVFVFdncTf/F5rE9WQCyGSM9AP3Yu2sxZe72JOJs2Yn+yAqWgCJGYSAqQnJGFrKxA+eADZNkIzJeeh9Y6lLxMt4N5SoHb/8TjwTjtdIR3YAW5juOwbt0q3nvvTd57702WLXuPcDhESkqAmTNnc8tVNzFjXQVDykZiffQhiBhkJSK9PmRrs2tdo2k4I46kI7UAo2k1SqeNdvaF2CPnu5a1n0I7cibe9euYZZocPf0Yvj/xSOpXrWBJfTVLsHispZ67YrX4GzZzREo6s2fNYe7cYxm6hxSkgVJVtYXExCTS0j5fv2pZ7eDca0K7g8xSwAGxxMJpdFCu9iCSer4bdhxhx9yUsH6+L2oioLqpXzvsXqUEJ9bTO8Kx8G59GiXegZU6FiVc5/ZQESpaezm2LxsUDdufg9ax0bUp9gR2nsCx+PG1Z/DG20t47ebZFG+/CzO8lMjwi5DeXd5DJ9bTcEP0ETNSqAgz3NubxcyYgMebidq9pSftTEGJtoJ0MLN32u8fVhwTf/k/0BuWIPVkpKLj2f4KWtt6QhOuw0nce02UbbsmFKp6aKcak8xpnBw7izc9r7BBW48AsuxcFkYvJMfpP6IYFzGamhp476LFtK1o4ZifzWfy/0xHURXaaSWo7KXhyQARCIrsIX1EyHBrNHERZ6nnbbZpm/FKHzPix3BO5Ct90tj+05gwYQr//vfbXHTRqZxzzgk8++xb5ObmM2zYCIYNG8E3v3kdHR3tvPnmK7z++ov8619/5fbbfwFAdnYuY8ZMoGRcGdpEHyPGjKFgSDGKoiCRxESMQnvfocHRo+GooyTvvSeor3c7ZsbjHRQUbKS+ZgObf76OlVuXs37ZKjrqWtEMjbHTJ3Httd9jzjHHMXbsxEOWQj3I4WVQrAwyyD5Iz8giIyuH+roa0tIzUBQFyzSxbZvExGRCoW63U3pv/arAn5BAS3Mjtm3v98Vww4Z1XHXVpWzcWM53v/sTvvnN6zEG0sntc0DathsJ+JTzWEFBMRdeeBkXXngZjuOwdu0qFi9+iVde/jdPP/sYjz/5EIbhYWhpGcs+XsaokWMZuqWKYRsqSUEB24KqrThbNyM0De2IGX2O7zQ0YFesR8ZiOPX1yFgMZRdfViEEMpCKvWkjdmMD1scfIZuakFu3gBAoiYmIocOQjY0426pRR+ze06Sjo50NG9ZTWbmeiop1VFauZ/361bS3t7m1JEccxXXX/ZCjj57LmB5HM3vzJqLVv0PGYhCLgeFBCAWSk5GhYE+RvuaOVfURy5iOs6Eco1bDGNu/YFJHj8Fz4VcwX38Vp6EBNJXslABnZWZz7pixONJhXWMd761byXvbt/DzV57h1hceZ8iQYZx00uksWHAmEydO3W93uKqqzZSUDP3cVxudZRa0OMiRO9PtZJKC2CyRq22U6TE8219Gr38PYUexk4qJFZ64W98VLeDWX8XqwdFdveDEQEtwjSWUcB1qsNqNoAjRY8Kg4OiJKGY3SrwTx5uOcCykovatp5GSW75zDi8ufoeXbjmZgiFDcawwRs+YQhNu7E2fshOLcPw5KPEuV/D02A+LWCfSG8DMcYWI40kjWrQAb/XzaO0VCCuM1LxEi05y63k+A7S2tWhNH2EnlSJ1t0jC8eegta/FqHub6PCv7HX/SMR15zyYFM/+UFA4JXoW0+JHUqVuQUOjzBpJstxzOLhyVTmvXv48cSvOOS9eTN50N8WoW3ThwUumvWcP4TBhVhofUaG6NsljrPFMMKcMqDGlgcFZ0fOZHZtPi9pIopNMnlPwpbC2LSkZymOPvcLpp8/hwgtP5qmnFvdJoQoEUjnzzPM588zzcRyH6uqtrF27knXrVrFu3WpeePQJGv9YD4Dh85CSnUpyToC0rEzK0spIT8ogOTlAcnIKqqoRi0WJRiPEYtGef0cJh6N0d3ezZctWWlo2Ego1954/OzuXIaXDOO20c5g+ZxbTps2CRLCERZKThHC+GJ+BRNKudNCudKCgkGGnkbSLq9wgg2JlkEH2iaqqTJ02i+Ufv09zUwOWaeLxeFBVjZRAKqFQECkl8VgMXdfx+f2Egt0kJSXv1wRRSsk//3kHP//5DygpGcoLLyxh7NiJh++FHQROfR3m229hr10Duo42ZSr6MXMQyf05FCmMHz+J8eMn8Z3v3ExbazPLPlzC5s0b2bixnNWrV/DkEw8S70kZydYMhnsTKNO95Kk6WeEuCq+9jszsXDIzs/GuW0f8maeQbW0gBLKzA9nejlJaivDsknMejSLS0nG2bsWpqgJNQ6Slu6k1XV0E16yiMz2d0Ia1dNRsoaGhno0by9mwYT0bNqyjoaEOcD//oUOHM2LEaL7+9Ws48shjmDRpOp5+UtSUwiKU/HzsbduQHg8iGkUaBjLY7aa0qSpC1xE+V5gIIRBCQYbCux1rB0IItKnTUMdPQLY0I+MmkTv/jOhJnVOEwricAsZm53Nl5Qas887no1iIVxe/xKOP3s+dd/6O3NyCHuFyBkcccdSABHRDQ12ffgKfG9USmSD6iCah9zSaaHHwbfgXRt1bOEYqaF701lVoXVsIjb0aK338zn0USBjh2sHGG0FaYGSCJx+0RCCoAEpPAwtwvBk4RjJKrN1tNS4E2HGUSCOxogV9ivpv/sHVPPPi6zx6/TEkpBfS2BUHNFI8hSS2rUPrrOztYm+nlBErPA4l2ooSrkMJdYETR+qJREvPJp49A6PmdTzbX3UjKXYMEe8ACULR0VvX4tS/SzxvzoF5Lu8HanA7Qlq9QmXHGymNAHrbOvbV3aa72y2sH4hL1IGQ7eSS7ey7NvDppx/hhhuupHDUEMoeG0k0L0Kn00FURGhVmpkRP4ZSu3/767AIca//r6zUP0KgAJL3PW9xdGwe50cu7dPccG+kywzSrS9wV90DJD+/kIcffoEzzjiWSy89k8ceewWvd/faH0VRKCkZSknJUE45ZWHv9rqWGt6qeIOVG1fQ1thCtCFEpCHEyk0f0dXVRXd3J93dXTiOg2EYeDxevF4fHo8Xj8eD1+vD5/MzZUoJpaXzKS0to7R0OKWlw0hM3Dnhb1SaWGOsJyRCgOs4l2fnMC4+Fv1znArb2KzXK6jWarCwQEi80ssI0+3d8mUQtYeCQbEyyCADICk5hWPmnEBbazOxWBTHdliz+mNaW1uQ0qGjrRWP10t2dh62ZWHG4wOyNd5BNBrl+uuv4JlnHuXrX7+GH/zgZ/h8+98rYH+wbZua7VXU1mwjHo+RlZ1LcckwEhL27qzkNDURvfsfONVViNR0iEaJP/M09tateK+4cp8pVWnpmZx00hl9toVfeZnyH91EpSqodEwqQ128HO6gORbFfLsa3n6h97keVSXT6yc9OUCmP5EEBEpjA3pjFXpOLpqq4sRiOOEQYtgwrGWLCbdU027FaavB/duMEd1hU/zOc4ArCkpKhjJ8+CjOO+8SRowYw4gRoyktHd6vMOkPYRgYZ5xF9I4/QVsrTmurazmclQ3Z2chtVTBiJKLHvkbG3bxsNW/fEy5hGIi8fKRpoiQkIrs6gZ2rmDIUwmlqRH/maWYlJnL0qKn8/Irr+Li+mhcevIeXnnyIe+65g/SUVE446XROPnUhs2Ydu8eoXSgU/GzFimOjta1Bay8HJHZgBGb6BEgXiA19bXelIxGAotRiNH2InVjcW8fheNLROsrx1LyGlTauz4ReaOAtAk9P9tKuv55OQh5WShl6ywpsv4mwY9gJBSiRJlBACda6UdX08cRKTu0znvvu/wcAJ/7sNeC13u1/v2wsl05SMGrewE4scif9QiEy/BLsxGI8219FDW7DTiggVnwq8fw5GLVv4q+4F6l6cPRkjLbVKOFGrMBIzJThqNFGfBv+5TarzJp2yN7+/pCqt6ew/lO1N3YMx9j3ym8w6KZW7Tpp/CyxbZvbbruFv/zlN5x99kXc9us7WZu0ksX2yzSodXill5OjZ3FC7NQ9pmR9pC/lE/0jSqzS3s7zXaKD9zyLmWBOPiiHrS8LZWUjefDBf7Nw4Tyuv/4K7rjj/gHf+/IyCrjwqEs5/6iLkch+xZ+UEsdxDjhlK0qMtUY5EREhzUlFQSFGnGq1lmQtmWFW6QEd91BQrzawVdtGgvTjlSlIJEERpEKvJGAHCOwlWvjfxKBYGWSQAaIoChm7dG8PpKZRVbWJ6qrNNDXWIxSB7ThEoxGGDR9F6bDdU4v6o7m5ka997WzWrVvN3//+cJ9Vp8OF4zisXvkRlRvWuZM+VaW+tpqa7VXMPGreXicX1ocf4FRXoQwf2dtFXaal46xfi712LdrU/Z9AabEYwxSNYTm5nKLvFAZ2cyOdQhC57de0mnEa3nubhtdeoTXRT2s4RHMoSL0Zw9IUzGAHVlUQW0oURUVJTEBrrIPWFnQFUnWD0RLSvImkqRppEjLnzCPvim+Snp5JWlrGgEXJ3pDBoDu3S01z04W6u5HhMEpePqJsOEI33G7zjoNsbUUdPQZ13MBNE4Suo82YQfypJ3Da2xCBVGQkgvXeO4h4HEqGICXYH7yPXbGeiYFUJigJfH/2aaxpa+bVxu28+vpLPPTIv0hOTmH+/AWcfPKZzJ59fB+70e7uLoYP33MTtEOKY+Pb+CCemtfcRo8IUDRieXMIT7kUe4WAGgeygTBQIyFHoBfXQGO0b8G5EDiedNSurW6Birr7Z9rvPEooxIoXYNS+jrd5eU9FvoLtzyI67DycxEJsfy5mxiTQ+qY11dWESfzoFtRQHXZSCVrLKrTureBEEbE4nuoXEVaYyLDz0ToqUKKtSG8awcnfR3rSdw7IjuOpeRWpGNhJJaihWoQdx/HnosbasKSJnViM1r4eo2bxYRcrVvp4HG9GT93MEEC4/V8cc0B1M21trQCkpAQO6zj7o7u7i6uuuoTFi1/mlltu4xvf+A5CuC5Zk8xpdCrteKWPBLn3xZm1+koMafQKFYBkGaCWGjZplf+VYsUxwWx1+xUJHfR0mDRpGrf/8R6++Y0LKR1axo033LJfx9xb/Y4Q4qBqS1rUFoIi2CtUADwYGOjUqLUMtYZ8bhGMerUBgcAr3WiUQJAoE2kRrbSoLQSsQbECg2JlkEEOmKTkFMaNn8K48VOIxaI0NdZjmiaBQCqpaRkDWlnauLGCiy46lXg8xlNPvcHEiVM/g5FDW2szmzdVkJCYhN/fk4vu2DQ11rNlUwXjJ+55EmRv2oRISOwVKgDC60XaNk5dXe82KSX26lVYHy1DtrSgFBahzZyFOmT3VSyluBgSk5DtHchAKugahCMI0yJt6FASR45FJCdjmg7xzdUoI/tOop36etBVt6t7PI6SX4BSVIQQgvhzzxL/97OI4mJobEQGu5GGBxEJ41l4PvohTLWTkTDxF59HqBr6nLnutlgMu3w96vAReC6+FOvtN7HL14NhoJ24AH3uXETCXpom9IM+Zy6ytRVruVuL44RD4Nio049AyXGjNDIjA2vJOxCOoB0zG83rYxIjmNjZyY0tzVSddCIvV6zmpZee4amnHsbn8zN37gmcdNIZHHfcyYTDwd7vxuFGa12FUfMatjcT6XGNAUS8C0/dm5hjxiLPno58wkS+bkNQQoJAFqk48UTctu6ma2HXg7DD2P7sPtsGgt68ArQEt95FghQCYUcRCKJDFu457UrRiBfMx1/+T7TW1Whdm5CKjhAqdmA4ZvqEnf1heo8hMba/SnjMN7FT3BQkJd6BEm3F6XH/ElYEkEgtARFtRuuoRJhdKNE21O5qzOwjiOcd01Owf+hx/DlEhl+Mb+Oi3oiX1BOIFZ1IPPeofe6/bdsWNE0jP79on889lHR0tHPRRaewadMGHnjgWY499oQ+j2topDuZAzqWQCCR/Twi/ysTdOwIhNaD1UFvnaaSAKFxTWQtLOas6kv4/c9/RvbUQi6YffGA6noON5ZwrQA/LUhUqWIK65DYO0sbzDawuwEF9FRQk/edqWkKczehJhAgdo57kEGxMsgghwSPx7vfHeo3b67knHOOJy0tnQceWLzPLvCHkrbWZsy42xdmB4qi4vX5qaut3qtYEYkJbvH4Lkjp3s6Fd+cqtvnmYsxnngTLBp8Pe8tmrNWr8H71a6ijRvfZXx0+HH3aEVgrPkaGQ2DbYBiIQCr63Hm9tTBKYRH4fMjODrfPCSAdB9ne5toL9xPV0aZNx/pwGXJ7NSI7x+0g1tiIOmIU6viJ+/nO7R3znXewepzJHMdBURSEx4M6dCiytQVhGHguvhQZjbr9WfaQgiUtMDsA23WxUvx9b3rC48G44CL02XNcs4F1azHffadXqEBPc0jTQkbCbmH/ju0pKVBfy3CpMPbGW7jxxlvYsmUjL774DC+++DRXX30phmEghMLWrZuIRCKHPSVRb1uHcOK9QgVwoyWhGvS21ZhjZ+C8AqQKGK5CMogaifnMMMxZY9A712ElDwNFR8TaEGYIM3f2fk3itdbV+DY9Co6JnZiPk5CPVL2IWAda6xqU0HacxD1PuuN5cxDxIAmrb0fEO8GXjZ1chJU2GhQPargBiUOs5PQev2QHraMc76ZHCU36ASgqjp6I1BMQZhBppLj2xgiwowgrjNq5yY3qSAukja/ibnDixAsPn1OgmXMkVqAMvW0dOHHspCHYyUMHVC+zdetmCgtL0LT9n2rUK7V8aCxhm7qFgJPOFHM6o63x+5xUtrY2c/75C6irq+Hxx19j/PiDs/Aea05khf4hERHu7eHSIdrx4GWYNbDo+aEiQgRH2PhlwucWCYhWgdkOWkrP11hCo2iinFUIxeK06y+kculafnHd9yl5exhHJR31udddJDtJaGjERByv7LELRxJVohSZhQftyiYtCFW4jTtxXA2n6OAtcdNO9/arkmGn06S04OD0jsPERJUKKc5nZ1H+RWdQrAzypaajo42tmytpbW3G5/NTVFxKfkHxfjsjHWq2bdvCueeeQCCQymOPvdLrO/9ZIRSlxzlV9okAScdB2Ue4XZs0BfuTT3BaW3YWrNfWIgKpqKPHAOB0dGC98RoYHpQSt+5BSonctJH4qy/jHTGyj4OY8PkxvnIxqAp2VRU4NsLrRR01BmPBzvoAZUgp2owjsd5+C6etDWEYyK4ulMIi9KOPwensxNlejdA0lJIhCK8XJScXz2WXY778EnaPG5h25EyME09CCQQOyfvpBINEfvkzrDcXI5ubcSorEWlpaEfPRsnMdJtHCgE9r1n0U4C6A7MDwpVgB3Fteg3w5IJvaN+5txACkV+Akl8AZhxzybtI2+4T8cKyEJoO+5gslJaWcfXV3+Xqq79LTU01L7/8b/7v/77Hyy//m4kTCzn11LM555yvMH363m2kDxhpQX8TBqEgpI38xEE0SOQ0FaH2OIKlS6iAUPASEov+hta9BRwbqScSKzqJWP68AZ9eb1xGwtq/oHZuQKpelEgjTncVZtZ0VzxEGlDi3Th7OYbWshK9aWnPa3F7p9iJBYhYO3p7BUr3NhxvOkqkCcefC0LBTihC69yMGtru2hNrfuK5x+Dd9CgoHmxvJqqegBasRgJSdycvQvVhZk5GOCae7a8Szz26X9vrQ4X0ZhDPm73f+1VVbaK4eP/rAbaqm7jb/xfq1Vr80k9MRPnIeJ+FkQuZEz9uj/s1NtZz3nkn0t7expNPvtZvU879ZVp8Juv11azQl7mfARJd6syJHcdI6+CPPxDaRCuveV/gE/1DbBzKrBHMjy3YoynA4cKJQ7wVFJ8rVAAQktrCKixpkRVORfHBNX/6EdcfdSm/+f6tjLvjKQJ8vqlMqU6AAiuPKq2aqIiiSZWIEiPB8VNiH3zUL9bgmnYoiTuDuXbYFXZ6Kmh70RwFdj71ahOtShse6UEKB1OY5Fl5ZNpfPkOGA2VQrAzypaW1pYmlS96kq6sTj9dLa0sTtTXbGDN2EmPGHdxq28HQ0FDHOeecgM/n49FHX/7MhQpAVlYuPp+frq4OkpMDCCEwzTjxeIyior1PLtSJk9COPwHrnbdwKisAEGnpGKedgVLgRoecmu04LS0ow3beTIUQkJWNU12NbGtDZPS9EGtjx6F8+3rstWuQwSBKVibq2PGIXS2JFQVj4TkoRcVYH38EoSDKzFloM2dhVVRgvf4qTksLqCpqfgHGwrNRR45CLR2K8q2rkO1tbkQjJXBIJ93Rv92B9eorkJQIgQBEwsiWZqy330Q99XRoqEebMAmRuffUEycO4QqwQ24KAYrbdT1aDarfda7qD2XkaJTsHLeAv6gYVBXZ0YHw+8HvQ0YjvcYHsrMD4fGiDuvbBNMxwWyG1GARFx57NX+8/ZcsPPtCEhISeeKJRTz00D0UF5dy9tkXcfbZFx3QJHRPWKmj8Gx/FWGGdjpPWRGQNlbaGOQnDlITvUIFQCgC6RXY7bkEz7wFrcO193US8rETiwfslCXi3Xg3PYJ0HGxfLsKJIfUklGiz20U+oQDHSHHTyvaA2rUFf/k/EPFurJThKLF2RLQVo+4tQOlJJXMQThyj8UPMjIluU0VFRUh7Z8dKIFp0MiLejdHwPlpnI05CIZbiQQ1uQ+DgaAlYKWU4/jyEFUaJNKJGmgbUpPGzZtu2LcyYccx+7SORvOJ9jga1jpHWmN7V5lq1mpe9zzLRnEpA7t5DqLZ2O+eddwKRSIQnn3ydYQOsGdwXPnx8NfQtJunT2aiVo6Ex2hzPGGvCgJ3ADoYIEf7l/ytrjZWk25loaCwzlrBN28K3QtdTYH92n7t0cBdQdsmuNFWToK8bT9RLT+CJtNwMrvjt9dz+9Z/y5HEPcfkZ3/rMxtgfAsEYcxTJMokatZaYMCkxsyixiwg4gYM+frwJ14Rjl/dF8YHd7qbL7U2s+KWfqfFJbNOqaVSa0aRKnp1LoVXwhUih+6Iw+E4M8qVESkllxVq6uzvJzsnrnZh2d3eysXIdRcWlJCUfntUeKSWWZaGq6m4RnFgsxhVXnIdtWzz22JtkZ+/bBepwkBJIZfTYSaxdvYLGhjoEAkUVFBQOYcjQ4XvdV6gqxmlnoE+Z6kZBVBV1+HCU9J3iQ+g6aKrbBHGXlX7Z0xSRPaSFKFlZKHP3viIuDAN91lHos3bmzFurV7kpZ7rhCiTbxqneRmzRA3i/cz1KultDJNLSB/Du7B9OMIj1xhvg9aKkZyJ9fpzGRohEkC3NOB+8jzZ1Ovopp+1TIJntPUIlZWcURfWBZbr9QYy8vnNwGQ5jb94Eto1+0gLMl19CbtnkRsz8Ceinnwnd3dhrViFFjy2vqqHNnoMyfOdkzolBcD1YbT1p6BI0DDx2It/97k+44YYf88G7b/L4PXdw1x2/4Xe/+z+mj5/MORdfwamnnk3yQf4umRmTiOfMxGh4D4TmZpBLEzNrOvGs6ZAisDwCqYAKKI4bKxIx6RqiaT6sjANbgFA7N6KG67FShoNQ0NtWQ7wLqRionZuRika09Ny+jR2lRDZI5HoHIhKPsRTFbsHKcFfarXgnamclWnAbUvXjeFJxPAGkluimf3VWYvvzUEK1WIkF2Am7pIBqXiIjv0qs8HiUSKObDicFSct+gKMn4iTk9c6KhBUB1Yujfza1RfuD4zhUVW3hggsu26/9ukQnm7QNZNu5fdJzcux8NmkVVGmbmWj2re2rrt7KOee4dSlPPfXGIRXSAF68HGHO4ghz1iE97kBYq6+kXF/LUHMEHtwUpjQng3JtLUv1dznnMxQrisedeJutbsRXCFAdFTWmYRpxlF28LI44cw7TXlzMr39wCydNP5W8vILPbJz9oaExxCqhxComSowWtZV2pQMTi3Qn7eBSwRx2CwwL4V5L5d7CsT0kSD+jzZGMZuSBj+FLzqBYGeRLiWnGaW5uIDExuc8EMTExmebGetraWg6LWGlqrGdj5TpaW5oxDIPikmEMGz4KXXdrE2699UbWrPmEp55a/LlfvMuGjyYjI4uGhlpsyyKQmk5uXoHb4HIfCCEQBYW9kZRPo5QMQS0oxNleDaVDEYqCjMeRTY3oRx9zyNKvdmB9/BHETZTinrohVYXSoTiVFdhr16DMPvaQnm9XZHs7MhKCHb1T/AkoBQU43d3Q2Ig2YSLea65FGUBHeem2mtmt1EJobtRlV6w1q4k/9QSyvt5NHE9NRZt1NEpuLsTjiLwClORCMKM4U1dhb9wImoY6ajTqmLF90sWitWC2uI0Td6R3pCSm0lbbgR0CRYkyuaKSCf4MfnjsWbxav42n66u46ab/4cc/vo4TTzydc875CsccM//AXHtUD+FRV2Clj0drWQk4WOkTiGfPwI76CSY6WEME0gbhE+img2+rhZIsUCYd3G1M9BZPC+yUoaCoqF1b3P4qik609FyipWchzBBG3ZvoDe9jr88gsnQ+TjwfNB9Kbg0yz4BkAYZ0e6qoBmqoHqn2zOoUw3UBU3SE2Y3esgInsYDYkLNA7Vu7pHZtxqhfghqqwfbnYGbNwMyejt6wFOnL7D2GGq4jWnhCXyH1BaGxsZ5oNLLfwkFBQUgFR9jsWtfu4LiLKrLv96u6eitnnjkPr9fLY4+98pnW/n0WNKp1SJxeoQJupCBJJrFFq/xMxyIEeIvdBRWrHRQDpKWS7cujauQGYpqBRxo4OHQoHXzj1zfyw6O+yfXXX8nDD7/wuTeYBWhRWlljrKNbBAFQUcixsxkfH4vBgTVf1tPdhSbp7Lx2O3H3Wrq3qMogA2dQrAzypUQIBUVRsO2+yxpOT+3Awdgg7onmpgaWLnmTSDiEPyGRcDjMyk+W0dXVzrQjjuGJJx7kvvvu4je/+SuTJ08/5OffX4QQpKVn9imyP2TH9ngwFp5DbNEDOJUb3G2KQB0xAv2kkw/5+WRLS69Y6B2D4nY8l93BQ36+PufJzEQJpOE0NUBP0b/QDYRuQEoK+vEnDkiogJvq9WlzKylBxkDP3hlVcZqaiD/yEE5HB8qQUjftq7ER67WX8Vx+JWpgMs7zFnJrDBSBGD0F46QjEDm7rx5KCWaTu2oqdvm1SElOo6OrDasDlI0fYC3/GKW4mASfnzPHjOX0lmYaWpt5MT+LJ954kWeeeZTs7FzOOusCzj33YkaMGLN/b6TmJZ43u09thHQgtAFsR0EbBnKjg9PhEDcESrZKwjwFMfTg6s+s5KE4vkxXGCQVYyeXYicUorWtIp57DNFh54G08Jf/wxUqsUzibx2NEqxDLajGzD0SS8vH6FiKsq4KPXMDSAdHTwEh3ToazY+jeBCKgRppAsfC9mUQK1rgWiHv+ja0rMS//i6UaAtSS3Cd0hqWEh1yBtgx9Lb1rqpVPMRzjiQ69OyDev2Hi3XrVgHst/11kkxmrDmBtz2vEXDS0NCQSLarVeTYeQzdpU6jpaWJCy44Ga/Xy1NPvfG5RaoPJ36ZgOz5s2uhelRECThpe9nz8KCnQuI4N9Jrd4PihZEZJQg9QoOoJyiCKAhSnQDjE8fwu9/exVe+chovv/zsbv21PmvixFmrryckwr02xnHi1Gh1JDlJjLAOrAbIk+dGm6wO3Fm1A8iehrOBQzf+/2YGxcogX0p0XSe/oITy9avw+f1omo6Uko72VpKSksnMyjnk59xYuZ5IOERmdm7vClI0GmF7dRVJyan8+MfXc+65F3PRRZcf8nN/EVGHj8D7neux165FBrtRMjJRx45F+Pz73nk/UYqLsTdt7GMYIE0TECiZh3fVWfF6MU47jeg/7sKpq4WUZIjEoKsLdcoU1JkDTx3RAqBn9HRY1y1E7Xqcqs0IQ0WfXYZ0RiAUBXvtGpymJpSRo3pfr8jNxdm4AeuN9YiWkdDsILMVN8f8PQunwUG52oNI2sPq5qc2JycG6Aq2IwFr1UqE19vnsxPpGeTUbeHaggy+94eLWF7nsOidTTzyyH389a+/Z9y4SVx44ddYuPDCA24KaHWA1dljAZqmILIESrvEjoOVosKUg1+plZ4A0SFn4qtchNa+Dql4UKwIVvIwYqVnuYYMLWvQmz7EThqC2VCGHcpCKWxFibWjdlVhpk1Fxp9Fbd4EKWEQEi20HWHHkVoCUkvoMQtwEHYUR/OixLrwbnkcJdJIZMRX3eiKY+Hd+jRKvBMrdWyvOlU7N2PU/T975x1mV1Wv/8/a7fTpvaT3HkioAUIvSlUEQRQV7AW714Ze+/XqVbEjNorSREFBEKWHkgAJ6W2Smcn0PnPqbuv3xzrTkslkJplQ/M37PPNAzpy999r77Dl7vev7ft/3MfqOvREjXovIdCODhUq6ph3ZwotEskffRb1ei4XFXGchBfLI5ZLPPvskZWUVhyXJOi9zEY16PbuM7dkRSgr9Yi5Nv30gFyUe7+Oaay4mkYhz//1PvCZERfqqygCgR46Og/QiZxnFgVL26LuY4k1HQ6NNa0GTOiucQ2fdHArSBbstO9HWwCxQlYLRzsXI2b9iYLLcWUy3N4W4iGNKi6Is0TzjjPM49dSz+Pa3v8zZZ7/5sJzhJgodeid9Wpw8P29A9mVhEfQDNBhNzHJnHFYfkhbIErhmZV8sDLCKwCo5aq7i/99hkqxM4j8Wc+cvpqe7k+bmRqRUPi6RSIxly48nEDi4G9PhwPM8OtpbCUWiw0rdwWCI3u4uvvWtLxEIBLnxxu9N6HFf79DyC9BOORXZ24u3dQvu88+hlZahzZ6DmMCHlnH8iXgb1iN37lBJ8a6HbG1GnzMPfdGSI9q3392Fv2sX+D7a9BnK3Ws/mNe+F2k72H+7H9ndrTJUzjqL0Cc/PS7nOaFBZB5oloNz15+Qm55BE7bqW6kLQv3pWJdchozHlWOa58JQ2V4wBJtDICRynjZI3HI0xG6J3OAhVg2/7kIogpTeAzI0+HDNDRWwq32L0qhnMsMkOYByqOrdhdHWidldxYlmkuMvKudrn36ER16s4a67/sAXv/hxvvnNL3D55ddw7bXvZ/bs8a2ySxe1Stk/fzAFlAhEZsC9FzEBt5FdcTp+qBSzdS1apgMvNh277ETl2gUY8VrwXaQZQboaKrQS0ANo6Ta8eA7phtMIlL+EJTYghMQLlSI8F2lE0TIdCDeNlulAmlFkoFBJzoBAw2O4+QtwylehJZvQ43V44cphzUletBKjrw492YBbMHEOVDY294Ru51nrSVIiAQhK/FLemnoHxzhHVv19/vmnOeGEUw5L+lPql/ORxGdZb66jRWsiJnNY7CynwlfSWcdxuP76K9m9ewd//vO/mDJO2/iJgNMNqRpVXZASjCiEpquJ/kSixC/j7al3c0/wNmqMHfj45MkCLky/heXOkYWC+q7KTHHaBl/LNECgCsKzxjfRFtlqSj55B/zuC1/4Jueddzx33XUrV101vh6mg8FDmVKMh1y4eEgk2v55K+i4uPj4h22aoAUgNFX9TGLiMUlWJvEfi3A4wsmnnkVT4z76ersxrQDlFVXEYhPfqyKEwAoEiPf1DXvd8zy2btvMvx97mJ/85Pfk57/6ZfvXGt6O7WT+eDuyPzDSMNCXLCVw9TXDnL72h7Rt/JrdKv29vBxRVn7QiY8+bRqBd70b+5F/4NfWgq5jrjoV8/w3jTtwcSjc59ZgP3A/sr1dyTDy8jHPPhfzzLOGjUXTNIIf+BDWVe/Ar92LVlCIVnkQ665DQLMg0PcysvEpxLwytDy1hOl3deE+9m+0wkLcl1/C27YNr7ERvbxCScHCYeWiJqYgw1n3NSlJpZIk4n0Y3Qb+Vsg7vgTTHN6XFKjMVjG6UcTAg5xwAX2pDty/3YG7cSN+zS60vm70WXPQAgZawwZ8Q6DNWoSXoyysja4t5DQ8yPnnfYbzz7+YhoZ6brvtZm6//Tf89rc/45QVS3jvWy7g7AvfgciffUjHLi2knIdkBsSQ9QU/rVZ2xaHbq8YGIXALFuIWjCxdk3oAlRIp0ct7ECEb2RuCQIf6wNK9uM2LkFPK0PPiaPE6hJNESBekj1NyAkb3DiUPC+SBZqh96gFINmK2r8cpX6V0eEIf5g4GIHxPzRzF4U2k9O7tWA2PYfTsxA8WY5evwik9keeCT/F44GFKvDKmyGn4+NTpe7kz9HumeNMo8g/PqTCRiLNhw4u87W3vPKztQcnBTrHPOOB1KSX/9V8fZc2ax7n99r+xaAJDXccKLwXJrcqeVouo29jtVVkb0SVgHF4h8aBY6hzLTHcOO41tuLhM9aZT4h+5OsBuUURFjw2Sfj+jCItVOHHEa8mS5Vx00eV8//v/zaWXXnlEuU1dopPHA4/wkvk8PpIl7nJOz5w7puuR48ewMEmLNCGZdUdEkhQpKrzySfet1zEmP5lJ/EfDNC2mTLAzzEjQNI1p02fz8ro1pFJJgsEQvu/T3tbM3x78CyefvJpLL73yqI/j9QaZSmLf+UdkSwti5iyEYSATCdy1LyDKyglcfMmI23l1ddh/ugOvdg/CdSEawzj+BKxLLjtokKI+dx7B2XOUPbFhouUeGSn1amvJ3HuPyiqZNVv1v7S24DzwF7TycoyFB65wazk5aIuPrJID4G3ZpHqy8wa1Flp+Pl59Henf3AK6jsjNQ3Z14nZ3ozU3oZWXo5VXoBVXITarMkhnRzutLU24jkNuTx61e/egrTE57oRTh1UX9ZCSMditirAIE4qrwnT9qxH3348iiooRHe34mzYh6/dhVBXix7uxjp2LUZXtxxECL1KB0bMLLd2OHyqhsrKaz332a3zu4kX8495f8MuHt3Dtf32Hyu/9mHdddgFXfOT7FJccXL6jR8EqhfQ+VUkRhppMISBY9epJLJyCxQQDhejxWmThVIxltbjPVuN5JbjFM6DPwyjYS2DW0xg9O5BoSv4ldLR0K1qiGS9YhEg2I7wMbs4MRVQAiaa6cX0XLdUG0sdsfwmn6FhkIBekjx6vw82brbJYxgmjcyORTT9DpDvxA7kYyRbMzo2kU62sXbIZU1rkSbWIoqMz1ZvODmMLW4yNnGqPPatmKNatew7P8zjhhEOn3I8XP/vZ97njjt/wox/dwqpVE2OcIX3VMO7bWcervNHvLacN3AQY+YN8W+Qqq1q7ZeLJCkBUxo64krI/nA5AG16d1ALZBvqeia0Sfe5zX+O005bwm9/8lA9/+NOHtY+EiPObyE/ZbG4gzytAIPhH4AF26dv5YOJTh5Qv5sgYVW4Ve4y9ZEQGQxqkRYaQDDLDnfaah1dO4uCYJCuTmMQEYeasufT2dFFXW0NfTzcI2LV7J03Njfzq5juP2Anl1dBHTzS8nTvxGxsR06YPyL5EJILIL8B7cS3yvPNxNQ0pJVaWhMh0GvuOW/H31Ch3r0AA2dWJ869HIVKEcdpZKtF9xAxBDVE4MT0q3qaNyO7u4X0hZeX4O7bhvvQiWnEJWOaYm+fHA+m4yBHkY7K7C5mxMc44E2bOwtu1E7+lGdndhVi0mMC734uWLsLblcHenaIt1YTwBUWJErwSD38RNNbtpaiolPkLlw7btxaAYDWQNVOaarp0p1P0VFVREMtBKynB31ePt30bemkBOSdWoy+ZhdCHjDNrhyOHfDhG1yYijX/nbafM563nnMaGmmZ+ff/T/N8f7uN7v/sLb77wrVx77QdZseKEA/5GhFByFC0IdpOSrRi5EKwE81WMJ/Kj1aRmv53grjsxuzdjLNqCE1lAuuVMZKAErbqLYOBpgt6/kRjIQD7CSeCHSpGagdFXo2RhSNxINV64HL23BnwbLdODmzub8JZfYjU/i7C70VItBGvvx4tW4wcL8SKVpGe9fXiQw1ggfQK1f0dkunDzFwzMrLVkk8q1mW0QiGRdpnwXhIYmlMwtLVKHfb2ee+5JCgqKxi37OxQefPA+vvnNL3DDDf/F5ZdfMyH79FIq28jpIhs9rprII/PUfTfiNkl1KYferkIAOvjJCRnWqOgvvB1moe3A/fmqWtm/ILC/5DObc4owVE/GQHN9SAXWmsWHjjWaPn0WV199HT/5yf9w9dXvJe8wvjfXm+vYamxiljNvwLmr2C9lh7GVddaznJN586jbCwQLnLnEZJR9egNpkWGKW8JUr5oCf+K/xycxcZgkK5OYxATBMExWHLeKGTPn0tPTjWEY3PK7X7Fq1ekce+zxR7RvpwNSe8CNqz5oPQdCM8DMm5ChHz1kMkjfO7A/JWCRyKTZunYNje0tSCkpLatg7rxFROv34dfVIabPHKiiyEghbn0S54E1ED4dPVcnNE01g44HXn097nNr8PfuQRQUYqxYib5k6YhEUiYTKkByv9/5iST+Q3/HW/8yGAbGgoWYF7x5xF6Ww4U+dx7u2ueRmQwikF2BTyaRqZSqcug6hMMYS5Yi7fl4O3egLz8Gu6KCndtfJlnVS+7zuRjdBuFoFKfYofXEVmQxWJ1B6uv2HEBW9secrJ3ujq52TojlIAIB9JmzQErEwmMw59agJerxzKycy/fQk43YZauQgcEVTqN9A8JN4eWoHo2lM8u56ROX863LX+a3G0P8+qG13Hffn1i4cCnXXvtBLr30SsLhIY38BoSmKSIlXVX1eS2Iul1xGm7eXIzOzQjfwT1+On7ubHShAWHcXSfAuocRrovwW0EP4hYswM2Zhdm5keTsqzC6dxLY9wjBuocUOZAefrgUq+kpjL49eNFpyLw5uPmLMDrWI6RPevpl2FVn4ofGz86E3YPRuwcvVDZsNumHSjG6t7K8cx6bgy8wpa0BPa06g3tyijDzA1R6h28BvGbNk5xwwqoJtaqtqdnJxz/+Xi688K185jNfnZB9SgnJnSqV3chR95p0lI13sgYi80eehGuhrEufHPy9lIpEaGPwD/FS4KeUgrBfRjYWeClI16vxIVWvWbA66yJ4iPP0egcJmZGjqkJmYZZ4NIG01fv6CZsIqOdNpi67HUqK6fWiTDssRdrcTgjOhNAYguA/8YkvcOedv+PWW3/FRz/6ubGd9BDU67UIGGYxbGAQlEF2GdsPSVZAVQ+nuVOY5k45wGFtEq9fTJKVSUxiAiGEoLCohMKiEp544lE2b97AnXf+44j26fZBYmtWotAf7t2lXostVSvidqt6gEmpJvBW6fgXYY8GtMpKRDSmEusL1QRWSkmmvZ2XppTTsWcHkUgMIQQ1u7bT2dHGCZF8TM9F6ycqrpJdSMIIOw5aBrcrTCIF0aWqsXUs8HbvIvPbW/BbWhDRGNTU4K1/GfOii7HOPvfAsZdXqPA/x1Ehl4Df1oa/a4fqn4nlIF0H5+mn8NvbCX7owxPmdGYcuwL3lQ14G9ZDIAC+j3BdtFmzILNf4IppIjQNNxxm7ZrHaWqsJzQzQk10J/bONNG8GDnLCjGi6ute0wSe7x140P0wbdoMTE1je3szJ0wZIqV0XWROEalZxxHeegtG1yZUt7nEy5lJesalw2Zfwk2NuAScHw3zkUtP5Nov3sbjjz/C7373Cz772Q/yjW98nive+naue/NxzM6TSCuCU7gUL2fGhK0kHxbcNEbPTvSenaoCYeXixdyBjJRM9TkE6h5C2L3IQB5+IB9p5SLsHvxgAW7p8Xi5cwi0PIMfLEaaEfxwGVIPEqx/GDc2DWkp/ZA0wzilJ2B0bcEPlRxIVNwkZscraOkO/GAhTuESMEa49zQLqRkI3x6+WO67IHSW9U3lyZb72Wp1UBjIxdFc+rw6TqhbwdycuQcE3Y0Fzc2NrFv3LN/73i/Gv/FBkE6nef/7r6K0tJzvf/+XE0aC+vNC9OigFEqYinA47araoI/QXmEVq74Or2eQbHhxVYmxRuGU0lOLTpkmRYqErghDeDbDAhVHgp+BxGY1XpEdU7pekYfokoNvLyWka9WPdBQXEZqqiASmZ/tt4oCRDTIUoGmQaYR0nbpGWjBrcV6DkmBOHfyT9uKQqYdA6aHPoaSkjIsuupzbbruFD3/4M+MyHwFl4+xxYMqiIxyi/vgDTSaJyhsHk2RlEpM4Srj33tuZNWvuEeuq7ZbsQ3OoPjpPPbQyzWqFzm5GlVyEysxw2iGy8LUnLKK8AuOkk3EffQS/r1dloXR305wXoyM/j+KS8oHMm3AkSmtLE/sCYWYEg8i+XkQsBy+RJWp2J0xfgIiGVA94lyJpYyErUkqcfz6CbBtu9+s3N+E8+k+MY1eg7Zdubyxdhjt7Dt72bUpapgncl14E00I/ZoWSswEyGsPfuQNv82aMFROjKRfhMMFr34P74lq8TRtBN9AXLUbEYmR+82slrSsrU2SqsQGRm0tzcREte7ZRVFyGYRhYlsUeZweddgeVSUlxtAzf90ilksyYdeik5NCyY5mZk8/22hrkshPUdWxuQkSjGIsW4xbNIX7Mf2G2vYSW6cqGF65EBobLKbzcWbDvUfAyA30aeDb4Dm7eXDRN44wzzuOMM86jtraGW3/7I/74x9/xq1t+znlLS/n0BbM5ccksUrPfgV1x6vgupO9gdG5CTzQiTUV69h/fmOCmlUyrZY2a6UlJoPExMuWrSc1/D2gmMpCPXbGaQN3fFFExcxBOHD1eh1N6Al50KsHmNfhmDLd0uOWs2fwUmt07/JjZ8pHYLw1USzQQ3vJL1bCvpp64ubNJLng/fnR40Kw0IzglxxHc+wC+lQtGCHwPo68GNzaFip40H68r55FZU9ic00rMM3lzywLO2msgF23DLVo27kt1//13Y5omb3rTpePe9mD42tc+w65d2/jb354+bBvskdDvKLe/+3N/CGu//Gl/6GFVdUntzk70pepTCU4fPQQwXadIgwiq6rh0lLxRSoguYtQKS7+9sJ4/WFnUguo1u1WZZEgH0EEbMrNze9Qx0bOkTKhzyzSq7TVLybjwUCZ3IXX+dmuWTGUlXr4DaOp6+UnVlA+K2Hk9WVJzCLICcM017+Ouu27liSf+yemnH7hINBoWOUv5d+AfNOj1lHuVCARtWgsWFsucFePa1386ZKsPfUCRQOS+8UnZJFmZxCSOAlKpFA899Fc++MFPHvEqoJ8E9AP10UJT8jCvD7TokBBBD+x2MFuVtv+1hBAC6+JL0UpKcZ97Ftnbg3bCCaTLihEdrcPCOTVNw7IsOnXB7KXLcZ9dg8jPx08FEc0dEAkilp02eD118BNjG4fs7cXbUwPFpcM+D1FSity5A7+29gCyImIxAu9+L86//on3ygaQEq2wEJGXjzbEYUxYFr6U+B3th3+hRoCIRDBPXY156urB85AS8+JLcR5+SNk0A6KoCOvCi+i1dGWhmpXchUJhCgqKaWysp621CV3XcewMxcVlzJw595DH1+bMYf7CpWzfvgV/53ZAIPLzsS54E9osFZ7mRyrJREa/yZySlTjNazA71iNNNZMTdg9u4VKckuHyyKlTZ/CNq1by9RV13PmKz4//upazvv00J87ZzicvaeTk9y+A0Nh6koTdS3jrzRhtLyF8Nev0IuWk5r0Ht3B8JghW6wtYzc/gB4tUT44RVoSl6Qnc4mNwShRJVUn3vZhtLyMS9aAFcYqOITXnGhAC4SZH1LD5VgHC7hmMwJYSLbEPIX28yBACIiWhnXdgdG3FzZ2rqjqejdG9ndDOO0gs+8wBM9701AvREo2YnRvVl4NUVsjpOe8ktPUWpjjVvKeuEg8fDYFAYNibSCUaDk1WfAejZyd4GbxoNTJYxH33/Ykzzzyf3GxA6pHi/vvv5ve//yXf/e5PWXgI6eJ4oYfVBNtPqz7Afngp0INq4n4wmPlgHKMqC5DtIxyl8ue7anFJWIPVGhEAhJJSeX2jEx2vDyXPGnL7CE1VQjKNisx4cUU0rBJV/dDMrHGAA+YQjqdZ6rlityu62y+B64f0sg580SELZNlnjnQV2Rk4VX/4uKSn1AD4itDsv2B2zDHHsWDBEm699eZxk5Xp3iwuSV3B30N/ZoexFZDkyDwuSF3KYnfZuPb1nwrZK/Hvc+AVD5kCkSMQJ+mI8w2E+cYlLZNkZRKTOAr4178eJJGIc8klVxzxvrQw0DqCPtrPNlr6wx8I/e6nbifwGpMVAGEYmKecinnKqQOhjcHN65HtzcNCHAFc1yUUjhC48ipEYRHeuhcQfWlk+WzESWfATDVZGdCHj9EBUxgGQtOQ3n5LpZ6nLupBMl+0oiICV7wdecllIH0yv/+dkmYNgfQ8QCJi45ch7A+/u1u5j+Xnq76U/c9DCKzTz8BYsgS/pgY0DW3mTLS8fKyNL6lKS/81FYLSsgoymRTBUITi4lJKSiuYMm0mkcihy1FCCBasPotHXn4e69r3omka2sxZaEXjMzCQZpTkog8TaHgMo+15hARn6gVkKk5HWvtdM8/Gal0LsVLecU4FV511DP9Yu5Pv3/MMl//PI8z/88l8+IYbueiitx0yXC5Q+yBmy7N4sZlIMwLSw+jZRWjHrcRXfFW9BmoWiRg1XNFofQG9dy9Gbw34DlK38CLVgIbRtXWArEgrh+Sij6L37kZLdyCtnGHBjapv55+qspSVj+E7+MEC0IMYnZtA6Og9u9DsbvxwGcHdd5GWLm7RcrREA0bXNnXs/u11Cy9ajdG9HS2xDz86vNdEBgtILP0kZsdGtFQz0ozhFC5BZmVqWkqFbOj9mi/pgxCD1+cg0Ht3E9r+e3VNPHUOm/1FbNjw4mG7Pe2PPXt28elPf4CLLrqcd7zjugnZ51BolsoUSe1W1sOaqSbiAvW6dogZktBGJxhDIW31I/arPghTkYz9CmgHHstUH80B+02DnVRkSQup78X0XlVxjywCJCOLnbJO2HpQkbOhDmZ+SpG4YQtkhtq/n2Jgh9JXMjQjX1WKnM7h1SY9BMFpYJUNJT2Ca665ni996QYaG/dRUTG8GjjqNUCw2j6bhe4Sdhrb8PGZ4c6m3K+clHShFrP8uxxY4yLLNSgE2S3hARcsEOe9DrThh4lJsjKJSRwF/POff2fBgiXMmDH7iPdllaoVOa9HPZAkqqKgh9UDxo6PsFG2SfL1hn5iUlFRzY5tm+nq6iAvrwAhBH29PRi6TvWU6YhwmMDFlyDPPRevM0N8dwzf0dHVwvCA5GA0ffiw40Yi6EuW4vzrUWRuHsKykL6PrK9DVFSoxvHRtg8EkK6LiETw9tTg7alBVE9BKyuDtla0ikr0EayMh8JvbMDdsAHZ2YFWWoa+bPnAxN9va8N58G+4WzaD66JVVWOdex76gpFzP7TCIrT9XM/KyqvYsX0zPT1d5OYqqVMiEScay+WkVWdQWTX+tLKFC5eSSCbYW5jHnDkLxr19P2QgT/WyzDiUNCjLQrPLtJomuOD4OZy/chbPP/8U//Nkho985Fq++92v8oEPfIIrrnjXQDO+F4d0oyLpmpYh3PksfqBwcNItdNycGRg9OzG6tuJFKgjUP4LZ/hJS03GKjydTfQ4yuJ9rg5SYbevQ0q144QowYwg3hdG7E2mOIEkSAi93lpK/ZaH37cVqfAK9YyMi04vV8gxebCYIgZbpwik+htTMK7AanyC843cI6eAUHYMXm4LRW0N4y69U1QSJ8J1s7suQIWoWwncQXnrky6oHcEoOlMnY5asId21RVtOBQpAeel8NfqgMp2DxQT8l4cQJb7kZvW8PbkzZMGupFu6/+2aikTBnnnnBQbcdK/r7VIqLS/ne934+oc36QxGcokhKpnEwvydQoSbY/ZbGbl+WmOQPSqnGC83KVnFsGNIfjrTV8Q/mPNYPsyjbJxMf7F0ckMgGld1yP3xDVU0CPVm5lqaqKwPVdx9wVcq6MCCxPWtZbmV7aVAkw27Nftdm26GEmV0g8tR1AbX/8OxsT832IRIxoRrwkzvU+Mwh6svLLns7X//65/njH3/Lpz715XFfy2K/lGK7dNzb/cdjn0Ru9KBKG5R+lQqk58MzHvI0AxF6Y5K6SbIyiUkcBaxb9xxnnHHehOzLiEF0XtYNLCt7MnIgNBPw1QPFTw8+7Hwb5RQzwUnKE4m8/EKWH3s8Gzeso721GYBQOMyipcdSVj640iaCIYyKEJHgkBU7FFELzRj7qiaAec65+I2N+Du3I6VEStBKSrAufQtiiPvUSJC+j/3ne3CeeRoiEWhrw3/5RfxYDPOU0wi8452j5rq4mzZi33EbflsrmBY4DtozTxF417vRSkvJ/OG3eNu2IUpKEcEQ/o7tpJubCL7/g+gzZo7p/AoKi1mybCWbXnmJtpYmAALBIPMXLKW84vCcnY477mQCgQCPPfbIEZGVMUMP4BQtJVD3MH6oeEBXo6eaOXnJLG5779fYuKeFn/70f/nylz/BD37wDa677iO84/IPYNTl4/Zlm4EdB6/LQUYs9CiDS8vCUG5mqRbCe+7B6N6FHyxCuD6hmnvQe3eRXPKJYVUFLbEPzUkg9SBC05FCQ5oRhJdBpDvxoqOTQL1nJ5GNN6Elm5BWLtKKIpIJtHQbbu5sMpVnkqk+Cxkswo/X4UWn4OYvHCBsrpmD0bUJs/kZ0jPeihcuQU824eUMmh7oyWa8UAneISR5+8MuPwU9vg+r8QmMZDP9WTmpOe9EBg/+BWJ0bFREJXfOwAzYC5Vx17P1XHT8rCMK/evHt7/9JXbs2MIDDzxFbAKqlgeDEFlyUp7lyVnJrfQguV0tFPVXNDRL9aUEq8ZPWIQBVgWkdmblWoFsw3taHV8fvZCFkau+85M14LRkq+qm2o++H2fWLPDjijhYZWrBy24GX7lSg6uIl1WqCIrQFVnzkqDnqvGYJYqYpetUfyBCVWEi87IVqLQiL2ZhNhe1PruINqS30ogpUmM3DycrsVgOl132du6447fccMMXhsmBJ3EE6JaQlFC9380ZE9AjoU/CJFmZxCQmAdDe3sqePbtYseKECdunWQRGwRB9dHRA2k6wOusKk8zOybJOL9bEOelOGFzXoaW5kVQySTgSYfUZ59PZ2YGUksLCYqIHmZSYBeph7fXLC2Kj68NHglZYRPBDH8bbuBG/tRURCaMvWjImy2F/Tw3uM0+jFRejz56DTKfxu7uQdXVoi5eMWpmRmQzOX/+C7O1Fm7dAhUv6PnLnDuy//w1j5XH4O3eizZw1GHiZk6PyXJ5dM2ayIoRg5qx5lJZW0NrajJQ+hUUl5ObmH/aqdDgc5qSTVvPvfz/M+99/w8jnJ9Xqq9enPhMjT01eDheZ6vMwundidG1B6mGEnwHNIjXzrfjhMhYuLONnP7uVz372q/zylz/khz/8Fj/58fe4fPX7ePcVH6M0UgEyghOfRbDvefy8IrRwNl8k3Y60ctCTzRg9uxUpyEq0/FAJZudGzLYXhzXya3Y30gjj5cxATzQg+n21fB8ZLMKNjUJWpCRQ9xBashk3f7CDWguWIDybxJIbhjXFa6l2DmxMEEgjjJ5oACNEZuqFhLb9FqN7G76Zg+b0IjWLzLQ3j+wINho0k9Sca7ArTkXvq0XqAdz8BQfK8/bfzOnNVm8HZSXPbqlnZ3Mf37+uerD35jDx8str+fWvb+LLX/4OixcvP+z9jAdCDO/bsJuVa5cWUfezlGryn96jLOMPJ/gxmP2o7QYlvdIMsKYqa+5D/YkKkXUhawY/W+mRqMqMnxxuXyyzzfL9Nt+RuWrMdpv6nVkEgbLBhnirNJtbJLPbZccSmqqq125PVi2bd/Am+n4Z2wHnYcBIBb+3vOVqbrvt12zY8CLHHHPc6Cc/ibEhX0BEQC8wdO2sVyrCEntjEhWYJCuTmMSEY9265wBYseLEQ7xzfBhJHy2EWm0zC4Z46Oepf49nriDl4P6OFvp6e3jh+adob2tB+j5CExQXl7HyhFOIxQ5elfA8j+4uRWhy8/IxzcOfCYtQGOO48Wfe+LW1yFQSbeo0tZ9gEL2sHN/z8LdsGX3bulr8pkZEReVguKSmQVk5fs1u/MICpJQDVs2giIeIxvBra8c91mgs56Ck73Bw+unn8o1vfJ5EIn5Ar4v0IVWTldDY6v7Rw5Lckg2E0s+qFf/YdOzyVcNkUaPBj1aTWPZpzKY1GD078AP5OCUrcAuXDXvftGkz+fa3b+ITn/giP/3vm/jjQ7/k9n/+hEvOvIb3XvZJ5hSej5nYhdW5GSHzEW4S4bukp12EHq9D6qHhfSq6mpHq8brh4wkWIwO5+KEi/GhVtsdDfY5etAoZGoXseimMrm3KenhYzkkJRtcW9HjtMLLihwqyzWhDJvtSIrwUfrgCALv8VKQZxWp8Ai1Rj5M3G7tiNU7RMWO6vgdACLzYNLzYtDFv4oVKkJoBbnKAIP3fvWtYUJnDqpNOIX0ERMVxHD7zmQ+waNEyrrvuo2qI6U7Mzo0IN4kXqVQBl4dqKDlCZNoAbZB4C6HkUG6X+jkcsiI0lUcSKFeyKc0cm4NWP9J16thm8UCRELtJkQktPCjR8nrV4k5/NUMYyi0sUKm2cTshtVc1yxu5ipBoFiM2t+ihke2b+9G/P6cVnB7Vb29Gs5IyqapHIzk2Hnvs8eTl5vOvRx+aJCsThUqBWKLD0y7S1SACdElEHMR5+htWAgaTZGUSk5hw7Nixhby8fCorDz9UbTwQQpGT8QYkglrdy+zLhoyJwZCx8TxAxwIpJetffoGW5kaKikswDBPXdWhubuCV9Ws5adWZI67+t7U288r6tXR1dSB9n1hOLgsWLWfK1BkjHOUoQtfUCvd+hgD4PsI6RNPi/ulx/RACkBCJqsZ4zxveVJ9KISYwaPJwceaZ5/GVr3ySp59+jHPPvXDY7+wWNYHSAkpWgoRg878J1d2GlZcCK4LRtRWz9XmSCz84ZhcuP1RCZsYlZMbw3pKSMj717m/yngs+yz1P/4rf/+XH3PPIbzj3pMv40NlXc+LURoJsw49WY5evwi5bRWjrrw+wBO5PxJP7VSf8cBl26QkE6v6BHyzCzZ2DyHShOT1kqs4akIwJJ4HeswMhfdzYdDSnL6vbFNkm/qHH8tTEcD+rJKd4BVb9Ixjd25W7ltDRE/uQVj52abZSKwRO8bE4xceO6VoeDbj5C3ELF2O1rsMLlbK5Ic7D63bxqw+swq0+84j2/ctf/pBt2zbz4IPPYhgGRvvLhLb/Dj3RPHDN7OKVpOZfd0gTgCOCywF9fwMGJyM0uo8Hmjl+W3npqspIv7Vw/3jMUrAbVeO7TKsxG3kQnju8UgTqFk/tVQ34eOq9dpOSEkcXHt73fnqfkujKrPzYbQW/F4wiwFHSMats+DZuH2TqDU5adDaP3P8QH7r0RhVueYi+nUmMDiEE2uUmvgVivYfsybqBXaQjTn9jT/ff2KOfxCReh2hs3Edl5RjifF9j+BlIbMqW+AOAzPbF9CrP/yOR8uyP3p5u2lqbyMsrwDDUU9owTHLzCmhpbqSvt4ec/axOE4k4a59/it7ebvLyChGaRm9vNy+ufYZQKExxSdkIRxqEFwenG2WhmaNWEA+3cqTPmYvIzUM2NUJ5hZJypVLIeBzjgtFTk7UpUxBlZfiNDWjTpqttpUQ2N6EtWIBx8iq8F9cha3ZD9RQwTWRLMxjGYVWBDjh+shm9t0Y1mOfPQ1oHr2KNhOnTZzF9+iwee+zhA8hKJpvv0+/Kpvm95Dj3Iz0N21qgtPRSYvRsI7j3r8SHyK4mElYpxLpyee9Fn+GdF32U+x69lV/f830ueuZeLr7wCj7zua8MM7twS1ZiNa9BSzbjh0oBmSUFeThFB9rjpma9HamZBBoeQ3N68YOFpKa+icyU8wEw214kuPMOJRNz4ohMJ9LMQYaKEXYPwunDDuSrnBPpo/ftwYtU4ObNH3YcP1RCasH7Ce66E71vT9a6uJL0jMvw8uZM+HU7bOgWyfnvww+VYrau5Yf3PU9VUQ5vfu+3cAsP3154797d/OAHX+f66z/GkiXLEXYPoR1/QEt34ObPA6EjnDhW89N40alkZlwycee0H4wC9f0xtMjl29kK98RFvYwZ0ld9Il46GwyZzU3RwqpyEZqppGDCyH7XjfBn5mUT6YfaJ0tPWeCnGyE8fXxj8tKQqc3KP4sGHcG8XjXGYBWEpg+/Xl4yG24Zh1OPOZ/P3/RuGtY3U9ZbpsIt37iGVa8LiJhAf4eFPC+bs1IoEDlv3IpKPybJyiQmMQr8TFZbnE01Hstkd7x2jK8V7BZFVPS8wYexFlIPGaddNVlOFFzXwfM89P3sZg3DIOl5uK5zwDaN+2rp6emiuKR8IOm4sLCYluZG6mp3H5SsSKmqRam9KitAHUhJL8KzD09Kr5VXYL7pzTgP3I+/fauyAzUMjGNXYJx08qjbimAI680Xkfnj7fjbt6lE+nQKrbQc6/w3oRcUErj6Gux778avr0d6HlphIcYFb0ZfcgS5EtInsPcBgnUPITKdIAR+qIzU7KtwSo9H+j7YGbACSpY2Cs444zweeuiv+L4/LHVaZoav3pqZvRhOK2ljFmb/6rMQeKFy9L5atHQbfnh0knk4CJSrCVKmBXQvyFtPuJ5LV13L3zb/np/85pucdtoSrrzyWm644QtUVlbjFC0nM/0SAnX/wOjaDAj8YCHpmW/N2gsPgZRYbeswuraB7yKFgZ2/BLvqTNBMtEQDoW2/Qdi9uNEpWM1r0JOtSDOOHalEMyJoyWbMjg1IKwoS/HApqdnXDCTWD4WbP5/4sV9Cj9eC7+HFpg6Gab6GkG0+8gUPucuHHIG2PJfkkmtpDBzP3c/9ji9/6VtQefh9elJKPv/5j1JUVMqnP/0VAIzOLeiJRpUpk519SzOKtHKxWp4hM+3Co0J+Qd1TbociLMJE6Zt89boxzlxR31FuW/0LJ6PJqoZCStULJm3wXNUb1m8igZ19NoUUWbFKhvetjAS3R41lqCxL6Kqi4rQC4yUrveoZqeepf2uWWjjwI2rskYUHVksyzdlMmQI49YRzED8RPLPrYS4ueJd67pSPbwyTGBmiSIPxucy/rjFJViYxiREgfVVlyDRlvfENJbMKzz50qbyxsZ6VK0efwL4e4PagwiaH9vLqqsfS7Z1YshLLySUSiZKI95KXP+gyFO/rIxqNER2hZyWVSinVx34Tacuy6O3tAUAmk3ibNuJ3daLl5qIvXIwnY6T2oNxrss40fkYRGCNn9Ieh7OtDJuKIvDxEcPiMwjx1Nfq0GXjbtkDGRquuRl+wEBE49ETSWH4MIr8A96V1yI4OtIoKjGNXoJWpweiz5xD85Gfw9+xBOjZa9ZQBdzGvZjfuhvXQ04NWVY1+zDEHBFiOBLPtJYI19yLNCF7+guyKfi2hbb/D3t5E+vnt+J0daAWFmKecgn7cCQclLRdddDm33PITnnzyX6xeffbgeeUpGUg/kZfCAHSEdNEMgdGzV4Ub2n1IKweR7oCjQFaEDuF5ytFpoNG/wOTdq6/jyvdczR/+8Et+/OPvcs89t/Gud32Aj370sxROvxS75DgVapitOvmhA72wzZZnCW/9tWqwDhUj3CTB+n+geUmSiz6M2f4yWqoVN38RerwWze7Bi1ajZTrQ0x04xceA7+LmzlIk0YzgFi4d8VgD0IwDSdNrCNno4//ahlofGRYIW+Kv9RBvMvjl87cQjca46uojy0H585/v4MknH+XWW/860Bsl/Ey26Xs4IZFaAOGmlDaKo0NW9JDKKck0K9KCAYHibGbIOBY87HZI7VJEA1RVI1itQhtHW/zyUsr21+kCXNUeJG01LukBuuoH8fvAnHZootKPCV1j79+ZHPx/IdTYNA5S3elBuZgJKMgtZsmc43jypYe4eOW7Bq7RJCaxPybJyiQmMQLS9UrX228LKV2l7ZU+RBeP/pDp6+sjdxQb29cLhInSLe8Pmf3dBMKyAsyeu5D1Lz1Pe1sLgWCITDqFEII58xZhWQdqzsKRiMqUGbKaL6XEtm1yc/Pxm5vI/P63+DU1A89KbcpU/HM/gLSLBy00pUQLCPyU0nyPRFZkOoX9j4fw1r6ATCYRubkYp5yGufr0gT4SIQT61KnoU8efVwKgT5uGPm3aQX8vLAt97vBkeefZNdj33IXs7QXLgjVPoz23huB7rkOrGN2m1mx5DuE7eOHsCQsNL2cG2o5/w6ZavPh0RE4OXl0d/m23YiZTWGeM3G+wYsUJzJ+/iNtuu3kYWQlUKAmJ1wUiBClvFmk5laC+G6uvFz2xD4SGcPvwpUt4++9JLP3kxFRXpETv24vRuQnhO7g50xEFizDzTYTdi3D68N1CQqEQ73//DVx11Xu4+eYf84tf/B+3334L11//MT7wgU+QU3n6KMfwCdT/Eyl9vFwlI5NWLtIIY7a9iN67G2H30m+hJNyUuhGFBsJAZL3GZSAXNJ3M9EuO/LxfA/j/dhVRmashdPXlJ1t92v7eyB0P/4b3vf9jYwoaPRh6erq58cbPcPHFb+PMM88feN2LTlE20ZmuwfwbKdHSbdgVpx31ipMeykqjxllx6IeXUvbHvq0sgRGqtyS1R5GLg+VESZklKm2gRdWk3+lWJEWPAlq2P8UCIYdnrIwGI1eRJT81SG6kpxZzAofRYmnkqv148axDoxiUqwWrRpZ0iQDDnjunrTiP39z3fziOQ8ic1IBNYmS8DmPjJjGJ1xbSVQ2LwsxqgLNlci2W1eP2jb79/lKZ1yusIkBXGuL+HnAvoR4wRyOjZdbs+Rx/4mkUl5ShCUFJaTnHn3gaM2bOHfH9FZVTyM8vpK21iXQqRSaToa21Ec91se0M2/94K/HduxAzZqLPnYeYOQu/di/uc88hpQ+dHXjrX8J5/N+4zz6DbG9B2geyMykl9p/vxXnwb0jXQxQUIvv6cO65C+fxxyb+QowRfk8Pzt8fAN9Hn78AfeYstNlz8Wv34vzzkUNur2W6kPtrMFwX2daGFhDo06dDQQHd1ZU0Bk06Hn8UPz5SwijZ1On38fDDD9Dc3DjwupGjGnOtUjVp0kyL9LyrMHIMjO6t2ZVvHy9SjV1xOnpfDVbD40dwVbKQkkD9w0Rf+hahHX8guOtPRDZ8n/DGmwhu+y2x5z5P7IUvE1v7Zaz6R0D6xGI5fPKTX+K553bwzne+j1/84geccMIcfvrT/yWZTI583ulOzM4NaMkWjPb1aIkGkB7SzEG4KbRUK34/GfRdpBEC2T9jc/ADSi8knD68yOtfGjoSpCNhi4csFANEBYBiwXfWfRVLD3D99R8/omP8/OffJ5lMcOON/zPsdeUkdyp6qgm9dzdaogGjazN+uJRM9TlHdMxXA05HlhjkZPmryJIEqWS4B4PXqyoqWkxJq4SmvpeFpoiBzKpmhQ4YB1fC1el7+GPod3wr+iV+Efk/1uc/h1XtI13l4OV2qwq7WQTBw6ika5bqlRE6eJ3Z52OPskkOHqRt0yrJvj/73Fm5+DTiyV5qO7e/rrPBJvHaYrKyMolJ7AffUT9iv8V+YQJxtQo16va+h3aUdNQTCaNQ+fun67OhX6hVr+B0tWI20RBCMGXqDKqnTB8gdKPlf4TDEY478TQ2bniRjvYWbDtDX28fpmlQs2UjXlMducV5LBeSIlQPCdXVsG8rsug4nPotiFQfBIP43b34qUZMbzfy2BOHHVe2tOC+9CKipHRAXiUiEfx9+3CffhLz5JMPkIQdbchMBueRf+Bt2oioqlLStEgUoeuI4lLcrVuw4nFE9OCr2W7ubIyOV4Z1CMu+TnBcvHAFCd9jQ6qXNs/BDRlYyR6mPPEIx5z9JizrwBXrt7zlKr7xjf/ij3/8LZ/4xBcHXjfyIJKbNbgSIPSFZLafg57ahx8uQxoRvHAZ6AGk3YPRtemIr48eryNY82ekEHjZ/BJh9xLa+UekEcYtWIgfKERLdxDa/gfQDOzKMwAoKCjky1/+Dtdf/zF+9KNv893vfoWbb/4xN9zwBa666j2DVT7PJrjrj+hd2xFeBmmGobcGNzYVN28uaAbSjOEWTMPNnY3Z+jwgwEuj9+zAD5XhBYvRe3YhzRhO+aojPu+DQdg9WC3PoXdtRRoh3KJjlJXxRHwPCUAT4MlhL69vWsdd+27n2+//Efn5h2FFmEVrazM333wT1133EcrK9psxC0FqztV40SqspqcRdg+Z4hXYlaeP2Qr7tYR0YGhuST+EoaoPB4NvK54/0A8mVN6L0wd4WXNBHWQiaxE8wtfoTn0bt0R+QpvWQkzmUitq2GC8yIVz38rZuZdgd6DCIXMVWTlcQxWrRMlAnfZsP0xk9P2ZheoZk64Drxtm5S8GoCazgeOiiw5vEJP4j8ckWZnEJPaDZqlKip8ts/dD2oqwaIewV/Q87w1RWRECgtNU0JjbA4hsWf8ouoGq44oxJxYXFBRxymln09vbzcvr1uB5HiWlFWjxOLYv6RSwKR3nlEg+uhBgmGjxWrTG9bgUIYqLEVIiRVD1Tqz/F3L1NET54KTI72hHxuMD/SMD48zLQ3Z3Ibt7EGWvHlmRfX1kbv09zlNP4jfsQ3Z3I3bvxpg1G61yZOmXlBJ/+zbcl15E9nSjTZ0OC+ZjRqsxurao/gjpoaXqSTpFpDN5bDD6aHJt8nQD006TMTxqGvYS2ryBpcsPzD2IxXK49NIrue22W/joRz+HMcQsYf9APRkqxg+VKLerYVbP9gHWwIcDo2szwu4aICrqoJ7qF9GtAZmZZ8XQe2uw9v0Tu/yUYbqUsrIKvv3tm/jABz7B97//db74xY/z85//gE9/+stcdtlVBNvWEmheg5s/H6NnB9III4WO0bsb4SZwyk/FzZ8PQseLVBJofAyRakP4TrYca6A7fXiRctLTLsEtODoTMZHpIrLxxxidm5GahZAeVuNTZKacT3r22w/PUWLo/g0Bx2qIB1xkkURYAs/3+MrDn2FB/mKu/tCR9ar86EffwbIsPvTBT6GlWkF6+MGSQaKlmdhVZ2FXnTWyBfjrGEOlVv39G1IqMmKN0junh7JBlBkQwSH7Gnr6vmqu1ywlUbaKh/wpIHk0+CDtWhvz3EWIbENJi9bEv0P/4LiikygqGKVnapwwoiNnqYwEIbJhk8VZ50mRy9QpM9he/wpw9YSNaRL/WZgkK5OYxH4QOlgVkNqhSu5aMFttSal+B/0QX8rhcIREYmQ5zesNQqjzOdQ5vZbQNA3LtOjp6SY3N18RnWgUPRIhNx6nWzfo8hyKDAvZ2oKWG8VqfBQtfyG+PxeJjuHVYmi10N2Ev28f2hCyInJyEaEQxOOQMximKONxRDiMiI3Pp1RKqUIeD5OwOk88hvvyS2iz5yD7epG9vcjmZpzGBkRpGULXMS++ZFhVxX3qCew/3wupFNIKwEsv4q6thssvI5y7AaNzMwiD9Ly3EW9pp+PFzbQX55JvekRpQDM6iUbCGI5BwytJ5s2ZQyCSd8DYrrnmem6//RYeffRBzjvvooOfQ8FigoF89HgdXnRKtvLRg/BtnNKxOUZJmc1u0EbQvvsHLlkLN5mdFQ5/rPmBAvRUB8LuQQYPtMeZOnUGP/7xb/nwhz/N//zPV/n4x9/LT37yPW68ehWXTpd4BQsBMOJ1CCeBcJJIK4/k/PeCZmK0v4zV8ixO8QqkGVOVLCeBnthHcubl2NXnHNXeCqvhCYzOTcoxS1erKyLdQWDfP3FKVuDljSyzHA+01Sb+XonYqgIr7627g/WdL3Lvf/8To/jw+wzq6vZw220385mPfpzKut9jdG0FfLzYVNLTL8EtWDx8g1eZqPTnqRwu3zMLlTGL3Z7NR9GyafMhlSB/MGiRbFp9Q7YwaqiJvWYo0xDdQv1dZEMg3V5VxelXA/SJXmqMnZT4pQNEBaDYL2WHsYVafQ9F/sSRlcOBHh4kcwsXLWHz5g2v6Xgm8frGJFmZxCRGQLASpC/p3Z0i02SD5hOqMAnNjo4qXQIoKiqho6NtXMfr6+uho70NIQTFxaWEj6BZ9T8Rnu8p6Vh/Rcb30aqq0XZsw4/34iZs/EQSLAvz7PNw//VPzJ6NaKH2gX1Ix0FqurIO7n8tJRFuBXrVAtxtz6PJKohGkd3dyO5OzDdfjIiMrdTk+z61e3ezp2YHyUSc3LwCZs2eR1l51SHvmYHxeB7ui+sQubloeXn4pWVQWwuuo2bvHR3ISBjZ0YG0bYRl4Xd14jz0IBgG2tx5aj++j79jG+nntiGv/SCal0IKHYwgRlE7ft9PoGMneTSjGzZYJjKcQ0B3SXXtQey6D5a+e9jYhN3DisIeTlw6l//7zmc597STEaGRReZ+tIrUrCsJ7bozaw0M6EEylWdilx1aDuV0Q7pWTcKEphK7Q1MGq5pezgzQA4qAZHNjpDAQ0h/oE+mH5vThmzHkIZZ+585dyC233M369ev4zne+wju++mtOnV/Mtz9QyOLpS/ByZiCcOHrfXjJTzsePqCqX2bEB4dt4Q4mQEYJ0C3q6Y2SiIiV6z3bM9o0IL4WbM0O5hh1G1clsX6dIkj5YBpbBQkSyCaNn54SQFZEv0N5vwSsePbu7+c7Xv8ol57yNE6877Yj2+7//+9/k5ebx0ZU2ZssavHAlaDpG1xbCyWYSSz+lPusJhvQZUZ7Vj2FhuShZU6Bq7JbD/RAGROaDVq+sgaWvQhxDU0bPahECQrMV+bCbVYVFC6omdrNAkZaBsTrZfpghhMqQJrrUcRkeRurhoaFhytdXI/uCBUu45ZafHhi6O4lJZDFJViYxiREg8dnd/TK72rYrj3vpoKcFc6xFLFi4bNRV8+LiEtraWsd2HCnZvm0j27dsJJVSadfhSJTFS49l+ozXUQjca4xIJEZefiFtjfswunuQzU3gusR1nXAgQK4ZRZ83H+P4E9EXLUb2dOP87QFkTi4iHEa6LnLvHiXtam/DeepJROd0eLEIOsHwL0Y6Hn7LNmRLMyIWwzzzHMxzzh3zGLdu2cDmjS8hhIYVCNDYUEtbaxMrjlvFlKljnHD5Ptg29EusbBtyc9W/e3vQZs9Bmz0b2bAPb9tWjCVL8ffuRXZ2ImYP3i9C0xDFpXg7tkMyiQwa6Il6VaLIr6by5ByiT0HCi5BjhZCGhfCSJN0okXCI/M4XSKYvHKhE6H17CW/+OXrvXr7x5hLO/OZTPHzTu3nT+39w0N4Bu/J03Lw5GJ2blStZbBpu3rxD9lG4vSo0zk+rlWPpq+A5P5F14jPAzVtApmwVgcZ/I1OtoBkIuxcnZwYCibB7kWYELd2BsHuxp74ZjLHFYy9btoI//elBnrznf7nx299m1Q03866zl/Pld6ymJBJBWrnDJV1ehpG9ajSVmrc/pCRQ+3eCe/6McPpAaAQAp2g5yYUfQlo5B24zKjRFZPc7BnDEErChEGEBJxh8/+/fIu7E+dK3vnNE+9u2bRP33nsH3/3Mh8jN1CnJYHYW7po5GF2bMJufGZGsSJkNHlQO5hh5g25Uo8GLK6ttpzNLgkvVwtTQ/go/kw0t7M46V6GcId0eVGjhOHs7tCBEZoOcnq2SZF0YM42qAR+hKjBWsbq3PTy2G5vZG9yNWKAxa8Y8pqfmIHRBfL06B5ENuJWuqviHpg0vKIYJs8xZwSOBv5Hr5xMggI9PnbGHcq+KWd6RE9iJxMKFS+nq6qCpqeENkVE2iVcfk2RlEpMYAS3NDWzb+grBYIhwgVqRTcT72LZ5A0VFpZSVH9w2tqiohJdfXjum4zQ37WPTKy9hGCbFpRVIKenp6WL9Sy+Qm5tPQWHxhJzPGx2apjFv3mK6XniOlvZWAlYAx9DRbZvZtk/OOy/FWLZ84P3WGWchm5rwNr6iiIqUSM9FdHSQ+dMfoVtCYxS9/GyMJWchnHzMhmuRRU2It6TRKkvQSsYuk0gm4uzeuRUrECQnJw+AWCyX9vYWtm/bRGXV1DH16QjTRJ83H+fJJ/ALi5Dd3WixHNA1CATQ581D5OTitbYg29r6L45qgPZ99f/98JWGxWx7nlDzw+jJZqSm4wVLMOJ7mVmew+Z9Hq4UBCSkHBNfJplbmovptaGl21W1QPoEd92F3rsHN28eK49fyPkrm/j67U9x0Sm34R3/5YMSED9SiR0Z3WJ5f2Sa1Mq2kT84+ZQBNcF0OrN2r5pOat61eHlzMFtfQLgp3MIlOPkLCNb9HaNjEyKxD2nlkp52IZkp541rDACnXvIRnp6p8/s7buebf9nEvU9u5PMXz+O911yLXzR4r3l5c2HfvxUx6Xdf89Ig/RGrGlqinuDevyI1C69f5uSlMVtfxMr797jtjZ3S45TzmpseIGR6316E7+DrweER7EeIxx9/hFtu+Qlf+9r/HvGk8rvfvZHqqqm86/Q50Fw/vFwgBNKIYvTVHrCdlJDanZVIOSriQzMhMCU7aT9YtSQB8U0qOV0LqIl+erciPdFFgz0ldpsiKkPDcmVIvWa3Kkvew4EwlFeBdCG+NRvCmB2r3QJOKVjzHe6O/oFnAo+TIQNIIsEYZ4fexJvTlxGeLUhuzybYC7W9VTyy89Y56Qtp1BrYZm7Ex0cCpX4Zl6feQViOrzHRSylSJEwlGZ7o4sfChSr8dvPmDZNkZRIjYpKsTGISI6CxoR7Pc4lEB2v10VgOLYk4TY31o5KVqqqp/OUvd47JwrhhXy2u6wyQEiEEeXkFtDY30thQP0lWhqA0Y3Ncayd1sVx6TYOIplOVG6Skrh7nicfQlywdCDUUsRiB696Hv2M7fkszfkcH7mP/hkgUUVqKfMFFyla87gfRE9VoxfORIR1tbyWatBAl43NR6unpIplMUFQ8XIgejebQ19tNMhEnljM2izVj9Rl4u3fh79gOjoPs7kJEImgzZiBycpCui0AM9KzoM2YiikuQDftgylSEEIqgtbcSWDmHyN47QLq4kUqE72B1voIWr2N51emEU3XsisdIewFyAi4LIm1MjwaRZgRpKTmVlmjE6N6urHezE8ob33kGJ37sV/zh/ke4euE7J1Sq4/Zk7VqHTIiEnl1NH+owrAewK0/H3i8nJZE3R03WnT78UBl+uPTwBmIEcRZ/kHd9ciWXXvY83/nDQ3zlnqe5+bmf8eWvTOW88y5CCIFTvBKn+DnMtheRhpoECjeBU3wsTsmBRgVG1zaE3YOXv3DIuQSRVgyz9flxkxW7/DSMzi2Y7S+q4M94PVqmGy9YRHjb73BbXyA199ojzrZpb2/l4x9/L6effi7vfe9HjmhfO7e9wsMPP8DPP3QmufvuR+urRRphdR9lGYLwUnihAz87pwMy9arqoWWVfX5ayQbNXCWTGgmZJlWVGEaCXSX1cjrVpB+y1Zr9ZFVCU68dyrZ+LLDbFFHRY4PVEOkowrJ+2gs8WfgopV45OTIPgDatlYcDDzDXnc+cggXEjlHXQLqq8mgWMGL4YoEs5IOJT7LZ3ECr3kTYj7IosZwCrwgZGBvhkN4o4chjK1SOCZWV1UQiUXbt2s7ZZ79p4nY8if8YTJKVSUxiBDiOjRhhNVIIges6o267ePFy+vp6qa2tYfr00e010+kUuj78z1AIgdAEtn0Ij+T/zyDbWslPZSiaMjyhzc/Px29qgnQawoOaf2EY6AsWoi9YSObeu5GOjV5WBkhICMgpwU/vwGvdhF48H2EJ8CWyW4475dk0LXTdwHWdYba/ruui6wbGOMLO9Opqgu//IM6aZ3CfeAxv2za0qkq02XOR6QyyrhZRWYW+QE12RSyGddElZO6+E3/7VjWzkhJ91mwi83VEOoGbv0BdQ0I4+QsJ9uwmkGpg6ZQoizp3ktFihEQaXfh4Tg+ZyjcNTPKFdNWsRTNASoTTy+KCFFetms537tvIJR/pJThe5dIo0IJqUjkUUqpF6DGFlWbDL4durMdr0fv2IjULN38BMpA3tsEYQZzyVYTKV/G1Ez/F1Z/Ywle/+lne+97LOfXUs/j613/A7NnzSC78MFbTk5ht6wBwildgl5+CNA9cwRb9XdsjjPugvxsF0oqRWPxRzLYXCe6+Bz2+D7v4GLyc2Qgvidn2IvgeieWfG169GM8xpOSGG65DSskPf/jrI3Y7/P2PPktJToArji/HDQSw4vVYzU9hexm83NnoyQakEcEpO/GAbZ12VSwaGiGkh8BNqUn8wciK281Acno/hAHI7P2WJSvCBEb6GOQB3g2HBaffJn5oISl7X7/Cy4AYICoAxX4J7UYLW43NzHEXoAVUGOtYECLECucEvB4lZbM7oAeVjxScqvJQRkO6XpFAEVShltJVpErKQ4cjjwdCCEpLy8Ysn3414eLSqDfTorfgCZ9ir4gqt4IARzeQdBLDMUlWJjGJEVBUXMqemh24rjtg0eq6LkhJYdHo8qDFi5cB8MorLx2SrBQUFlNXu2dYFcbzlJF+bl7+qNv+f4dIFKFryEwGMaRJnkQCrahIJbwfDMkEGP0zXQFREG0gNAMZb8LZ8Te8tp1o8RyM5hUI79iB5PqxIL+giILCYtpamygsKkXXdRzHJt7Xw5y5CwmFxtc4rZVXEHjL5ViXvgXnHw/hPPEYcvcu1UQ/fTrW5VcMcykzVqxEKyvDfeUVZLwPrbwCY+kyrG3/g/SGN5ZLKwc/VISWbsPNm4MeqyAnXgtuCi9nDpmpF5CedeXA+71wBV6kEr13D8K3Mfr2gpfha6ekuftZm5/86qd8+ms3j+v8RkOgVE06vWTW7chXK9pa+DDCSn2X4K47CTT8G+EqBuSHykjNuQanZMW4xzZnzgJuv/0B/vnPv3PjjZ/mzDOP4frrP8YnbvgCOYWL8SKV+MFC/EjVQWdybu4spBlFS3fgh7JN+b6DlukmU3kmRvvL6KlWfCsXt2DxiITnABghnJKVBPf8BbdgoXJgA6QWw8uZidm9DaNnxwBpHS9uueUn/Pvf/+C22+6nuPgwK1VZ9LTs4k//eJIbLlyCkT8NH7ArVmM2r8Hs3IRA4kUqyUy/BHdo9SkL6cGIqwki+7uDQFhA7377kkpGNrQyYRWpfhIvoe45UGTGzygZmNOl7sNAxfgb7mH0CX5GS6PLA793BAIHe/wHQ9kkJzYr0wo9DGiK8Hlx1YNzsEZ/6SpLZGEOnqewgOhgOLIxgYsURUWltLePkpT5GsDHZ6O5hXpjn6pmI2jWW2jWW1mRWTZJWF5FTJKVSUxiBFRPmU5dbQ3NTfsIBEMqcTiToryimsqqqaNuW1hYTEVFNRs3vszFF79t1PdOmTqTutoaWlsaiURzkNInmYhTUlpxyOO8npFOp8hk0oRCkcGQvSOEPmcu2rQZeLt3ok2dBoEgsrMDkkmME09WoZAHgTZ1Ojz1FNJxEKaJqNbwWjP4vb1IZzNe4yaEE8GL2PhPvYKMNmFeeNGYnWl0XeeYY09g7QtP09HeCijr4sqqqSxYtPyQ2x8MQtOwLngTxvHH4zc0IAIBtOkzECNcU62qGquqethrXqQKo2fn8Df6Ll6kAqdgGXqmDR9BJmcabsES0tPejB8dvg90i8z0S4i++N8YHZuyk2fBlNJCPnVugO/95lbOu+hqFh27+rDPcyjMEgilhoSVair7Jzxr+Gr6WGA1ryFY9zf8QLGawEsfvW8Poe2/w4tWH5ZETEiX8045jtMeeYaf3/JLfvzj73Lfnb/mW1cu4YrjisGKYRevID3nHUjzQAcyL2cmmaqzCdT9HSPditRMhJfGyZmO0bWFQO3fVDULgZszndT868cksxNuMut8th851UPgZxB270G23A/9TmWdW8B32NDk841v/BfXX/8xzjhj/L0/++NPt/0Kx/V495tOGnjND5VgV56B0VtDcsF1OKUnjXjtQGVBZZr2yy7xADn65NkqyQYXpgcb571s/8pQEmzkQ6g/tDBbBfGSitR4NmgepGpUAnx4gbIidjoBXzX69zfKHwxmgRq/bw826/s2IGCBtohN4nlsbKxsyFdSJNDQme4NLnx5CXCVNwNG3uhN/067Mq0w8gelbcJS52a3HJysHGk48nhRUlJKa+vri6y0ae3sMxqI+lEC2c/Dw6NNb2Of0chMd/oh9jCJicIkWZnEJEZAIBDkxJNPp2bXdhr2qSbPqgVLmD5zDoHAoWdMixcvY+PG9Yd8XzQa48STT2fHtk00NzWA0Jk3fwlz5y0i+Cqnpk8EHMdmy6b11NXuxrZtQqEwM2bNZfachWMOgjwYRCBA4Kqrydz5J/w9NeA4EIthnHMuxqpTRt3WWH4M7gvP4W3fhsjNByEhtwtSGiRtRP5CtGodMV/HT7fjPPEYxrHHIirH3uyZX1DE6jMuoKW5gXQ6RTQao6S04ojPG0ArLEIrPDAj5FBwyldhta1D79mFHy4H6aLH63FzZ5Fc8jGkZqDZPfhWnrLbzaK1pZHavbvp7ekmJzefadNnMSNnJlq6A6kHkWYML1rNp96dz983/oKP3/A+Hnx0I4EhFS+/pwd/7x4g21czxrwaIVSjtFWa7RHon5AdxtPKbF6DRB+sYAgdL2cGRtcWjM5XsMNnj31nUmI1PYVV/zB6qpWoEeLzbz6Zqxd9iht/fAvv+dnT/Prxar7/7hNZmvkX6Bapee8Z8QTTM9+GlzsLo+1FNDeBmz8fvWcXgcYncXJnKQtj38Ho2UFoxx+IH/OlQ14AacbwQiXofXV4gUEtlLB7kEZEBYOO4RwDe/9KcO9fEU6cjrjN9V9/ijlVRXzhs18a+7U6CDzP4zd/upu3njidsqgiAAPjdFN44TKc4pUHJSqgSIfdpqpvA7JAR9kLm6O0+Fkl6n7KNKpmcSkV+Q3NGh6EOxBaWKT6V5we1SOj5w7m/WhhsDvBW69shftPJNMIdjFEFhz84zKLVVUm0wh+v9xRV6+dEDiRTc5aNpuvEJERPHxskeE4+yQWOcuUucBeNR7pDI4lPGuw52Z/eCk1vmE9OAIwFeE5GPrDkb30cDI01nDk8aK4uIyamp2HfuOriC69G1/4A0QFQEfHkAatetskWXkVMUlWJjGJgyAUCrNw8XIWLh7/yvjixcu55ZafjMk3Pjc3n5XHn4LjOAgBhjH2/obXG15Zv5Yd2zcTDkeJRGKkUgk2vPQCAPPmLzni/WtV1QQ/dgP+nhpkKoVWWoZWdujGYRGLEXjPdThPPoH38ksA6FefiPv0GoRtoJVaEAMQiEgR/vZteLW1aOMgKwCWZVE9ZfwPMOn7+A0N4Dpo5RWI4MTMBNyCRSTnX0dwz1/Qkk0gDJziY0jPunLAItffL9ujrraGdS88g22nMa0Ara3N7Nu1DWknmJY7E1E+a+CetoBfv+9YTv7qk3z/+//NF77wTaSUuM+uwXnwb8qxTAhEcTHWhRdjHHf8mMeuhw5PZjMUmt19oGWx0AChQiTHAav5aULbfg0I/EAhwk0S3HEbc+0ebv3sJfx7Ww+f+dXDnPy5u/ngeYu48fJnENMuUkYFQgyfLWo6TslKnJKVakiZbmJ7H8ALlw1mrWgmXmw6Rs9u9N5deHnzDnGyBnbV2YS23ozeW4MfLEK4CbR0O5mK0/FiI9yXvqvKE9nPU+/dRXDv/Ug9TCJQzeXfvY3ulMfjn1pIrGstdmQc5G4/SBf+fucD1Dc08O7rP4boqIUiHWlGEXY3WqqVzNQ3I/fLyTngNC2ILsha/7ajrH+L1GT/gPDQIRCaIib7k+CD3WN6RP34GbXt0H33f5R2c/a4gcFztFtV9eRgrmFCUxkqWki9FwHBCrDKQIhcrkt+lOesp9hovowhTZY5KzjOPpkAAexWSO9R1Q49D9Vv0wfJ7dnxjqA21UzFpaTcT4Lmjl6pFDoEKiGZDUcWgaz7WjorgZvgKLDi4pLXXWVFkxpyf1twQCJHlOtN4uhhkqxMYhJHAUuXHktXVyc7d25lzpyx6cTNcTRhvx7R19tDXe0eorFcItlQSysQoLu7k5pd25kxc96ESMKEaaLPGX9OgFZQSOCSy5AXXaJekBJ/4yvIRBJiQ57i2ae60Ccuo2I0eLW12H+5F3/PHvA8REkp1nnnY6w80EnqcOCUnoBTtBw90YDUTBVmeBArW8dx2LLpZTzfo6S0AlwXt3EbHU37WNcXJ6djK4H5KSKr56JFAgg3yaIpBXzuw+/jWzd9n3POuZDleYXY994FPohZs0FKZGMDmbvvRJSUok+bNiHnNRa4+fMJ1D4AsnpgpibcJGg6XqT6wA2kr0gdqEpU/3XyXaz6R9TkMHememsgT1kOd2/D9Zdw+rIZrPnR+/j5Ay/wzTue4O9rd/PDj+ucNSeI1Eyc0hPJVJ89EGI5FMJLK6tha/hsU+oB8G2EmxrT+drlpwA+gbqH0VKtSCNEevqlZKZdNGymqndvI1D/T4yeHfhmDKf8FDKVZ2B0bUU4cZzwFD7w/b+wfnczf/vGO5hensZtXYdddXhkRXqQ2Aa/vuUnLJ9zEtXT/ou+3t8Qbt6EGalFWlEylaeTnnHZmPanBZRUKzSGdQEpFYkQelY2lTO+XosB++L9JvsyK4PSLEUY3Dig2g1J16lKhzZCS4P0FeHINKoqBUDKVuOzSiFX5nFu5kLOzVx4wLZ2tv98gJQI0HOUpMvpGJmsmEWg14PXoxzIEEq6hp61AR8FgQo1Xrsha11sgDV1dIvow0VxcRkdHW3D+kRfaxT6BViYJEWSsFQX18ZGIinzxm5tP4kjx+vjjpjEJI4CfN/HdV1M03zVU3FPPvl0YrEc/vrXu/nMZ258VY/9WiGR6COTSR9g0RsKhUkm4qRTyQnrXzkSiCFORvqxK3Dv/yuysBARCKg8loZ9aIWFaIdBiA4GP6105sLIWpb2z4F7esjc+jtkfZ2SnBkGsrmFzJ9uR+TkoM89xGr6WKEHxtT30NvTRV9fLzm5eQB4Nbvx99QQDQXpyy+mz2lBvLQNYfcRO6sKPdOJU3oS7/vER3jo6Re54Yb38MAH/wuzL44+b/7gjqun4G/bivvK+leVrNgVqzHbX8Lo2owfLBpoZHdKjx8e7AgYXVsJ1NyL0auka27uTNIz3oqXNwdh96qm9+Bwqyk/pKp6WqoZP1yCZep8/LITuWhRiI/94jEu+dLveOfq2Xz76hXkx/+E3ruLxOKPD5PcAfjBImVi0LMbbwiZ0ZIt+IHCgYb5Q0II7IrV2KUnoWU6kUYEaQ2X3xldWwlvvAkt044fKEJPNGFs/x1aogEZKAQkN/7hMe57Zgu3ff6tHDevCnprEP7oLoijwW6Dzes2sW7bk/zf525HFBXSlfMp+rp3E53ag15RiBebNqEzYClV5SW9L9ssb4BVrioeI1n9HgxGgapk+EkluRIi268hFRlxOrN2x0JN7P2kIi9eGgIl2epMVjplFYGbUD0vwgItT+3P64PETlWt0EfxU/AzwH5jH2rDPBL0METmQjKbKQNKwhWaoc5tNAgNQlOypCWdlX8dpZ7y4uISpJR0drZTUnJkNtsThQI/nxnOdHabe0ho7QDoUqfKq6DCK3+NR/f/FybJyiT+4+B5HjW7trGnZgfpdIqcnDxmzp5PVfW0V420BINBLrjgUu677098+tNfedXJ0muBYDCEZVnZxvrBJb5MJo1lBQhMkLRpImGedjp+7V78zZuRvqdWT/PzsS6+FC1/+JPcdR321uyicftmtKYmCkvLqVx1GjmjZOFIH1J7Jal6H+kINEPDzFU5BXoUvE0b8evr0GbOHjAIENOm4e/Yhvv8cxNHVsYITdPQNA3f98G28fftUyQuGEZID6asQBibcHbsRUxtQasoBT9DsG8nP/zhLZxzznF87vc/439nLx02pxJCgGlCb8+rej5ebCqJxR/DqnsYs2sL0oqRrj6XTPW5oA8SZy3RQHjLL9CSLXjZEEuzYwNaqo3E8s/hB/LxjQjC6UMO7QcRqgIj3DhaNoRSZHqYG2rhHx+fx6+2FvPl3z3GI6808YPrz+QSsR6zff2BlryaQWbqmwhv+RVG11b8QB7CiSOkT2rm5cjgKDZoUmYbC4xBrZJujZyrIiVW/cNomQ7cvIWD1aZMJ1bzM6RmXs4vH6vnJC5YqQABAABJREFUR/e9xHeuO4cLT5wHvqOqLYVLD+9DQIUY/v3ZP5EbK+DMEy5Wp2xp2OZsEkBkAl2l+uG0Q2JL1nU7qAhDcqeqEITnjp0X6VFVwUntzYYxguoxqQS7PZt0b6GqbtkCmMhmsiQ6UHKzLP9M1WSdxbJ9IFoyK0WLqX07naOTFSNXuXoNrfL0u6ONVFXph1kEsTxVXZFSNdWPh3RoBjDBsq/90f/MyGTSR/dA44BAMNedTbFfRLvWgRSSPC+PYr8QfX/WOImjikmyMon/OGx65UW2bXkFwzSxrACtrU10dLTh+z5Tp8181cZx2WVv5847f8/69etYvnzlq3bc1wq5eQWUl1exZ88uQJkUpJIJUskECxcfMyZjglcbWm4uwevfj7dpE35jIyIYRF+48IBeFd/3efGFZ+h9+CHKtm0nkEji+j77HvgrVR/8KDnHjizZatvWRfdGm5Tdi6vZ5OQUkJcpRnoGseUgu7sBDnQyC0fxmxqPxikjuyR0SsgFUTRcDpabV0BBYTEtTQ0UhqPg2PiBIL2+S5lhEQnlIqJRZKPAsWZDbhlm52aMeD1zF3+Em276LddddwVT0hk+OX3GQBVLeh64LtoY06mllHieh67rR0z0vZyZpBZ9iJSXUUvqI3Q+Wy3PoSUacfMXDcwCXTOG0bUZs/FJZLAQ4fRitq/Hy3Tj5s1H+Bn0vhrSlWfhFiwg0PyUsiO2clV/iNB593kzOWfFHD7x84e46rt/5bKVFXzri2eQM1J+SMlxJDSLQMO/0fv24uXOwa44Dbvs5IOem9G1lUDdP9B7dyH1IE7ZyWSqzz243bGXwejZiR8sHjZbl1Y+ItHIg0+/xKdufZkPnzOLj52ai+zZjfCSuIWLsStOHeeVH4SUkn88dzfnnHQplnn0q6tSKjc56Sky0A+RVg5YgcqDu2DtDyEgWA1mfjYjRSpyoedA3zrlCkZ/tSVLjISlCInMbq8F1a2X2acIjQgCRtZlzAGzjIF0+9EQKFcEyesCkbX19jNgFR7a1lszQBuv9fdrgJF6RCYaCZGkV+tFkxoFfj4mB5dgCwSFfgGF/iHKUJM4qpgkK5P4j0JfXw97anYQCkeIxtRyXSQao6O9lR3bN1NVPW1C3JnGgpNOOo2SkjLuu+9P/7FkxXVd9tXvobGhHt/3yM8vonrKdNpam4j39hAIBpk7bzHzFxzeqqxMp/D37QNdR6uqRhyFvh4RDGGsGP3zaW1ponPNU8zctAXNsnCmTEG4LnLfPrp//Sui02Yc4NbV0dFO/foWRMbED9hI36etvYFMTpJyfTpOl4bIV83E0nWHE5ZkHK1i2RGdVyaTpmFfHX293ViBAOVF1cSejsJzHjIOIgwco6NdbCIiauKqaRpLlq7ghXSK9vZWPNMAO01eNMbCYBQ904lo24uM5SLLpuOHovjBIozurQTq/8kF53+CL9zwRb71w28y9V9/57JjTgIksrUVfdo09OWjm1VIKWncV8euXdvo7ekkFI4yY+Ycpk2fPeYgQi1eh9X8LFqiAT9UilN6PF7uLNAPvpSsJRqQ2n6x3kIDN0Nk448QvovMSrfMjvWKTOTNxSlartLhIxXYU85H2D34gTzCW2/BalkDQGVRDnd+6W3c++RmPvurBznhms/ztW+EeOtb33EAEXOLluEWLVMlOcSoy/9693Yl50p34IeK0exegrv+iJbYR3Lhh0Eb4XtOM5BGGC3TNfx16fLXF5t478//xgXnX8xXbvwQdud6hJfGLViMXXby2IM0R8DW5hfZ17aH8066fOC1frveg4U4HgmknZV+7e+tEFAyLT9J1lBj7NCjBzaVB6pUrwi68irQLNU4L+1swnwY1cfiKlIhUVJQ4WeloMGsRLRPfdSjVVX6xxBdqIiY26WOG5qmyNREBFb+p0Mi2WXUUGPuJU0agSDmR1nozKfEH8VKbhKvOSZv70n8R6Gvt4dUKkVJ6XA9aSQSI97XQyqVJBod51PqMKHrOhdf/Db+8pc7ufHG/3nVSNKrBc/zeGndGvbs3qEmXZqgvm4v5RVVrDr1bHzfJxKNEosd2FA8FrgvrsN+8O/IlmbQBFrVFKxLLj2s5vojRVdXOzk1ezGkJFOYXZ60LDLlZQQb9uFt2oh22unDttm7aycyk0sgZOFnv2kN0yQe7yFtpYjaEcxFi3GnTFPZMRWVYBj4zc2ISBTjuANX38eKeLyP59Y8TltrMyCREnq3dbJg72JCVRGoEoqw/MvF90F/x+Bqd2FRCavPOJ/Ghjr6jCDms89S5qcJaSFk6z68ziSB4xai5wfQEo0IP4MUGnrPTvAyfPgzX2H37h186cE/U11awcqyKsyTV2Gec+4B0rr9oZzInsZzXYKhMF1dHax9/mlSyeSYXPmMzs1ZOVczUg8hvDSBpqdIzn8PTsnBDQv8YAnCt4fpa7RkC2bHywgvgx8qQbhJpBnDKToW4fSSmnkZmakXD5ACaUYGKhpOyUrMlufRUm3KlQvJFcdEOfN/38In/5bg4x9/L3/5y11897s/papqhH6UgxggDIW1719o6XYVnNgv57LzsFrXYlduwy04MFARzcAuO5nQzjsQdq9yhPNdHnj0Cd7187VccP5F3PTT2/FMk2TV6kOOYaz4+xN3UZRfyorpp6lJNoBQlQJz/I7ch0R/Ac3fv1LhAdoQy+MxwulUFRkvqQiFVabS360iVbnxbUUkZAbw1XGEka2WoD5OPwWIbGFPy+a9GKr64/ZBeMbYQk+NHGWNLL2sydx/yGOln7h7aakMEY7C7LRJb2a7uQNTWhTKAnx8erReNlqbOTFzPGH5xosL+P8Fr47dzSQm8SrBNC0Mw8BxhjeDOq6NrhuvuuPWZZe9nba2Fp5++rFX9bivBlqaG9i7Zyc5efkUlZRRVFRKYVExTY376Onpoqy88rCJird7F5k/3o7f2oqoqESUluHvqSFz6+/x29om+EwODV03sOJx/P0MAnxAaDqyL37ANu3tTXhmBl0O3nO6riN8HcezlTQkJwfrndeiL1qC7O7Gb25GFhWhv+0K9LmHT8q2b91Ia0sjRcWllJZVUpFbTf6OfNqdFvwCDxEQiEKBLNPgJQ/Z4g/bPhQKM3PWPJZecQ2zL7yMUCiEbGtFCp3w8iIiJxQSaH4Kq+VZzLYXsVrXYnTvQLhJhBB896bfsvL4U/jwC4/R8o53ELj2PYqM7Q/fwWxdS2jbbwls/hWdG+5GeBmKSsqIxnIoLCwmEAyya+dWkokDr/HwfXkEa/6MlmrHzV+ElztLTeTdBMGae8E9uBbeKT0eGSxUhMtNg5PAan4a4dn4wWL8UCl+qFj1pmTakUYIo2sHwb33EdjzF7XdEPmKU7ySzNQ3I7wURtdmjO6tSD1I5Nj3cNMv7+EPf/gL27Zt5vTTl/Hb3/5c9QiNB9LH7N6GHyzcT84VA99GSzQcdNNM9TnYFaehp1oxujbzp78/wjt/9jwXnXcuP/nZHRP+Hen7Pg/87W4uvPgt5C7RCVRDoBqii7O9I0dhFiIMJa2SmWxFQ6rJvdebdQMbx9dSugH6XlHuXW5cSbniG5VpgBZQGSeaoSZT0gaZAi2iyIufyjbSh7IEyQVM5fplxAa5caAcIvPHTjxElvSM5f1SqrH2vARdz0DvBiVnexXUVmOGlNkKFdC3AXrXqh4h6U3scRr0RiQQlREEAh2dfD+PuBanVX/1nyuTGDsmKyuT+I9CQWExhUUltDQ3UFhUgmGYZDJpEvE+5i9Y+qr3TSxZcgwzZszivvv+xGmnnfWqHvtoo72tBc/zhl1TwzAxDIOmxn3MHqNl80hw161F9vaizZ03KJWZOUvln7yyAe3MV/dalpZV0FlWir9pq3IOEwLXdcGxCUciaMUHLg8HwxE6uvaRQzGmE8bTbfAFQWlCjjOgpderqwl++KM0r3+RPdu30KZJ9OY6qtcbzJu/eNz3rG3bNDbUEonmDFTzzKRJWEboMbpIJhLEsm5fxEB2SOiWMEKYuzAMrHPPw1x1CrKrExHUCe/8PwL1j4Dv4QcLEdIBz1GN9rUPkJr7LizL4uab/8SFF57CVde9jXvueYSqqqnDd+67hLb/nkDDYyBdHNdldmcT+aEF7JVvxs8uf0ejObS3tdDb2004cvAuXy3ZhN63RzXI998zQuBFqjESdejxvQfNKvFypqs8mt13YyTqEI4iRl5sGsLrj+oWSDMHLd2JlmxBS7bit6smdmnmkJ5+MZmpF2ZnkjrpWVfglJ2I3rsbhI6bvwA/pKQmZ511AY8/vp5vfvMLfPGLH+evf72L//3fXzBrliKoWqoNs20dWqoDP1SIU7xiYNv+sfhWLnq8fr9rqmZ30hil29oIk1z4QezKM/jFr27iKzffx1VXvoPvfu/mo1L9Xbt2DU1NDVx8yduwSg5tl3sweHHItGZduYJqP6NZEAerFXGws9sgVBUkPHdsk3w3DulalWHie6qSYoSAqCI96b2qEmKVqB4Wp0Ml03s92YxIqd4rTDV26TJQ1dEC6kcY6r+xxaOn0B8upITEdkhuUZUcUNchvReiSyE8beKPeTiwm5S0DQBD4juQ3KUqVpE5E3ecpEhhyOHTXoEABA6H73Y3iaOPycrKJP6joOs6x6w4keKSMro622ltaSTe18vUaTNZsGjZqz4eIQSXXHIlDz54H6nU2LIS3ijQtJEDs3zpox9hRonf2ooIhRBC4NtKhuG0a3hJDbe5+4j2fTjIzc2n9E0X48SiiJoa0m2tyK5OShIpIouXoi86MPBy6rQZxEUrTcY2HD2FcA3sdIZkuI2i5eFhK8otLU08vWMju9NxpGnhei6bN77EuheeHvequ5Q+Ukq0ISvubsTFC3qYaXP4Z9aH6lfJH72JXUQiqmeoqIJM5RnZngqJlulCeBm8/Lm4+YswW19A2MofNT+/gD/96UEA3vKWs6mv3ztsn2bHBgIN/8YLl+HmL8TLX0BCL6Qks5Wi9I7BsTsOhq4fOiw1G/aoxjbsiqjXGf0cneJj6VvxVfqO/RKJhR/ELViEmzsT8MHLBmIIgch0odk9eLkz8QMFCDeN3r2NyMabMNpeHDIegRebil15BnbFafuRDYjFcvjOd37Cvfc+SltbC2efvYKbb/4xWtc2Ii99m9C23xCofYDQtt8Qefnb6N07hu3bLj9F5bOkO9Ss1HcwenfiRapwCxePeq6eL/naz27lKz/6NR/5yGf43vdvOWoy1b/85S4qKqpZseKEw96H06VW3NM1qkqQ2qv+bY+yGK4ZEJ4PsWMgshBiSyC2fGyN9V4KEpsVWfEdRW7c7mzOia96UdyE6osBFSwZrILclZB3CuQsh5yVkH+WkmxZhaq3JOcYJR3zstsaOaqicqhelcOF2wmpneoc9Bjouarq46ezYY+JMe6nD5I1EN+srr03gY8y6Sl76f5FKc3K9geFVejmWMc4FuT7+dhaBsngd6CLh5CCiByF4E/iNcdkZWUS/xHwfZ+e7k6klOTk5rP6jPNpbWkik0kTjeZQWFQy5gbdicall17JD37wDe6//26uuOKdr8kYjgaKS8qxTItkIj6w4p3JZJC+T3nFGHMhDgKtogJv4wbchMTtEErDjIdM+GR6CjE6xqbvnkhMP+1MuiMx4vffR7Sxke5ImLppU9i3YBGlzQ1UVU8bliMzZepMent72L1zG23pPZgiQiQ/zJKVxxLNH6wQZDJpnnjsIRrqa7ECAeKJOHl5BeTmFdLYUE97W7MKaBwjLCtAcUk5e/fsJByJIoTAC3k0T22k4uVKQvEIMighLhGtEs4wECVj/9vww2W4eXOVha/0kFYOfiAfYfeg2T0IN6V6IYCqqqnce++jXH75OVx22VnceedDzJgxGwCjc0t2e6XJMQyTUG4pTlsrseQuWkMLcV2Xrq52ysqrKBjFInpwXLMx21/CtXIUeZESI16LG5s+coL7/jCCeHnz8GLTsRofR+/ZixepRk/sA9tHOH0I38GNTkM4CcyO9YocCR0Rryey/nv0rfoxfniEMtVBcOKJp/Looy/yne98mRtv/DTPPDCLX71rDgVTlmXPwcfo3kZw950kF34EaYZBD2BXrEZPNGI1PYVINmerSFWk5r1rxODJfnR3d/GRj7yLxx57mBtv/B/e//4bxjzW8UJKyUMP/YVLL73ysL9/pQ+pPWqCreerwpWUqrqRqlEN+gerlAgx/hBIgEyzmqCLMMjurBxJAxkHN6qS34UYWcLWXzXph7FfQTE0TVVtBNnMpYOMXXqqT8buALxsFad0fBUYu0tJ0TSTgaVpoauKjh9XJPBQRMluU4GeMqP2IX1VCYksHP91HQm+na36WMMXvkRA9Qh5qYkjc1O8Slr0Fjq0TiIyjI9PUqQo8Ysp9iYb7F/PmCQrk3jDo621mVc2rKOrsx2kJCcvn0WLj6Gi8sgmzBOFmTPncM45b+amm77LW9969X9Mo31xSRmz5y5kx/bN9PWpVDRd05g6bTZTpo5hYjgKjBXH4b7wAu7mXRAtR7N8aG9AVFbjVy0ltQeM/KOjdx8NeSuOI7p0OS89+S/27NuDNExERyu1bc007Kvl+BNPGyAs/c5a06bNoqurHV03KC4pO0DWteHlF2jYV4dhmgQCQRzXoa21Sa00Sklvb8+4yIoQgnnzF9PV2U5LcyOWZeG6LtZ8k8qKqZh1BrJRKjewsw20i8bXo+BHKpHBIqTQ8cODVsRaqhUvZ7rqoxiCyspq7r33Ua688nwuvng1d9zxNxYvXg7IA4TzJaXl9CVqaE4naW1uRGiC4uIylh97wqEnu0IjPeOtaNleDNW97OKHy0jPumJYrsohoQfIzHgr4a2/RkobL1KJlu7AC1cgA/mqitG3B6kFB5rqNc/GSNRjNfyL9Oyrxn4sIBQK8bWv/S+nr1zExz79UY77ciO//EQuZy6fCUJDagGCe+7H7N6OFyxSFsVTzic1912q/yRejzSCuPkLkOZ+Ujk3jdm5CS3dxit7O7j2s/9Nb18vt9/+AKtXnzOucY4XO3dupbW1+YiO0x+yqEWGqfvQI9kKRd9we+KJgNebDXjsBvwsH9WUHMztAqJK/qUdxmK8MJSkbDRIX8nPMk0MFAWddvUTWTgOwtJfZNy/qCjhwJr4fm+RqjIT36AqM0ahqlZJqayTU3sgumTseTUHg8j23sT7VEU2GlYMqL/JXpvAFqo8P4/l9lJqjD106T1oUjDTnc4sdwbm5HT4dY3JT2cSb2jE+3p54bkn6evrJTcvHyEE3V2drH3+KU457ZxDrsa+Wrjhhi9wwQUn8cAD93DJJVe81sOZEGiaxuKlKygrr6S1pQnP9ykqKqGsvApj/9yQcUKfOhXjLdeSufXvaN37wBUwawli1SVohQW48ayM4tUxdhuG1tYm9rY2EissJhhU7jGOY9NQv5f6ympmzhreFxGN5ZDJpEkm43R3dVBUXDZAWPt6e2jYV0soFMLzfDRdJ5D9XVdnOzm5+YfVZ5VfUMSq086mds9uOjtaCYUjVE+ZTll5FfQAXdmclYLxsz0/VEKm8nSCe+5DuAmkEYHOeqQLqbzFSLQD5kbl5ZXcd99jXHPNRbzlLWdxyy13c8bc+QTqH1ayMT0A0sOUPkVFxWTKL8UILSQYCFFSVjHmpm8vdxaJ5f+F2bYWLdGMHyzEKVmJHxk72euHU3ws8UC+cvVKNuGHK3DKTsRof5nIKz8AL40MqgYM4WVAM/HCFZht60jPevthzeLOPPUUXvjW+Vz/65e59MY/8vFLT+TGt84n2LkBYffhCBPhpgjuvgst2URy4Yfxcqbj5Yy8OKAlWwhv+SVG11bufLaWD/3mZWZXFnLXPfdSPf/g+S0ThaeffhzTNFm58qTD38mhLuNRyNsVpqo8SB+0qOp98V1Agp8AUahsiwcUhhMMp0NVd7To4GRdeup1u0X144wFZp6q8nhp0E0GVZKeyok5WGXEd7NkaZ+S4AojmwdTpPqFtAi4Pdlq1xEYaHlZ22Y9At1dyiYuFslHOup1q0hVnyYSRX4hhXYBGWw0NKxRMlYm8frBJFmZxBsaDQ119PZ0U1JWMaB5LSwqobWlkbramqNOVjKZNFJKAoHgqOF1y5atYPXqc/jRj77NRRddftQlaV2d7TQ21JFKpcjNy6eqetqwVPmJgqZplJZVUlo2gtPTEUKftxAumQvpFkRAh7wShKYhnSNfzTsSKGMBd4CogHKh03SdpsZ9w8hKOp1i3QvP0NxYj+u6aLpOcUkZK49bRTSWQzKZwLZtCotKaWlqwLFttS9NJ97XS2XlFErLRp5oy0wGd+0LuBtehkwGfcFCjBNORMtT2S05OXksXnrsgRvmAXlHdgHTM96CHyzErPkHbF2H3WKT7CrBfvIx9LWtWFdejZaXN2ybgoJC7rrrYa6//kre/vYL+PznvsqnVy0jUnMvmt2pHJuMMJkp5xKd/yaixuGZYfihYjJTLjii8+uHlzMDL2fG8P2bMYI1f0bvrUUTJmrG6uPFpiv52xF4rnqRaooqZ/GXG0x+9PgsvnbbYzz90iZuvSLMjGkz8GNTQOhIM4bVug67ajtu/gLwbIyeHQg3jRetVjI0KQnuvgvZuoFP/aWRn//tRa5cvZCfvH06ZuoJEvLEo16afOaZxzjmmOMJhw//u0cLq0m106lIxIAMLK4WK/bPPpkImIVqwi60bDUhAqSy5MUAbEhsUUQgUKncvMZ7KZ977il+9rPvs3Hjy7S0NPHLH9zN6gUXIzPgZZSzmJY3+H6hA7oiLGMmK4UQnALJ3Wry3++lLCwIzTg4EUjXK6mXCKj3ClPJtZwOsMpH3mY8kBLSddngzqxbW3dvJ9FQDqLXwNNVn094zlFyi0MQ5OC5S5N4/WGSrEziDY1EvA+hacOIghACw7To6+05aseNx/vYtuUVGhvqkNKnqLiUefOXUFh0cKubG274Apdcspp//OOvXHDBpUdtbHW1Nby07lmSyUS2Cd5nz+4dnHDSanL6XaDeANCjYOQauKISPXdwkuLHlQTsaDWlHgn2J1GbXnmRutrd5BcUEQgEsW2b5sZ9vPzSc5x8ylmEwmEsy8IwTAqLS+nu6iCR6MN1XSLhCCuOPwXLOvChKl0X+4+34z67BmmaCMPA27oVb+NGAte/b4CwjAcqRLKWjvZWDMOkonIKJaXlI5NwzSBTeSZ9/9yD+2Irono6ojqGiMdxX1yHCIWw3vnuA7aNRKLceutf+d73vsq3vv0V1h83m1uuKCQ/UqUqMgK0dCd6oh4vd/a4z+HVgAwWEF/+eXJSrWiZTmSgAC9SgRcqwejdhTP1zYfPpnWLzIy3EN5yM59cbbN6xplc++PHWPnDOD9471yurFRVK2nlQLweLdGErlmEtv8eo7cGfBcZyCNTeSZ22Sq2v/wkH7z5OV7Z28H33ncu73vTCjQnjta9QwVb7kfEJhK+7/Pss0/ynvd8+Ij2I4SaWPtpJcHKtvGgh9TrRyNnxCwCMxfc3kEXLS0AmOD3KiJjhFU/RXK7kiyFpo66ywFIX/WRxDsTLFiwhCuvvJbrrnsb6TpwShTXdbsUYdFzh/e/IIFxnK/QlUW0ka/IgZdSBC80TWXFjHSbSh+c5qzlclhJ4nxHERffzjbXZ8AsPjB0c6xwWpVZAgboeYAPPakOcqMFhOdlv/tzXn2Z7yRev5gkK5N4QyMSjSF95X7UPzGSUuLaNrGcw8v4OBQymTTPP/sELc0NRKM5aLpB3d49dHV2sOq0s8nLGzn47rjjTmLVqjP4n//5Kueee9FR6V1Jp1Ns3LAO13UozVabfN+jtaWZbVtf4bgTTp3wYx4tCA1CMyGxVWmnhZ6dpESyk5QjfJBJKUmlkmiaNqxKcigUl5Sh6wapVHKgWmXbGXzfp7xicMkzmUzQsK+WWCx3QMplWRa5+QW0tjTR091JXn4hVdXT2LVjC7FYLjmxXHr7uslkMiw/9ngqKkdeQvW2bcVd+wKisgotqpaWpevg7diO+/xzWOeeP65rkUoleW7N47Q0NYAQSOmze9c25i9cyoKFy0YkLLK1FXfzFkRZNSKilmhFNIooK8fduBGzrQ1RciB513Wdz3/+6xw/I48Pf+ErnFjbyq2fv5ylM8tUQ3zXZqzGJ0i9TskKgFewgOSSGwjW3IOwexBuHKMvgVN0LJmqM49o307xscSX5WI1r2FxUQPPfFXyqT9u4/0/f5JHt3Twfx+8gJygNpAIGN76a/S+PbixmaBbaKk2tF338oPf3Mv/3foAM8vzePg772LlXFX9lHoA4TsI7+C5MxOBzZs30N3dxapVpx/6zYeAkQvRZYM2xP3WxUdrwULTITQLUruylQUdMCFTr/5tFqrXtKCSo2YaVHXlUL0kTlfWTasXjo2cx/HnnYeV9WIQ5pDeGw28fapHxapQH7XvAFJJo8YDYShyEpo2tvdLLxs4qavvWCNfjcNPAy543WoMoWmHz8kzzWrhyej//HToTLVQmFOK0A7d0zOJ//8wSVYm8YZGZdVUdu/cSltr80DPSm9PF+FIlCnTZh6VYzY11tPW2jQwaQUIhyO0Njeyt2YXy445eFL2F77wDS644CTuvvs2rrzyXRM+to72VuJ9vRQWlwxaQWo60VgOzU37yGTSr3rWzJHAzIfYsuwkJa3C1axiZWt5JGhva2HrllfoaG9BCI2y8koWLFw2JoJbWlbJrNnz2bVzK7093UqyLgTVU6ZTPWWwd8CxbVUhiQwnQqZp0ed2Y9s2QgiWLFuJEIJ99XuxbZtIJMbCRctHTWz3a2uRjj1AVACEYSLCYbwtW2CcZKVm13aaGuspKi4b6Dfq6+thx7ZNVFZOIS//QOs1mUxAOg2Fw2dPIhRWAZeJOPFeCyEE0WjOAYTnvJUzWfPfq7nq5xs567O/5XvvO5d3nbMcaeWoKsFQuCnMjg3oySZ8M4ZTtAwZPArR5+NApvpc3NyZmO2vINwUXs5U7OJj1ZL7EcLLnUUqdxa4KaJeht+8ZS/nzKjkI3dvY9WWWn73gRUcu3w5Eonetxc3d85Ac8PaBo8P/9+/2dHUx6cuXcnn3jQVo3BQpqmnmvFDRXiRMWqJRoDIdGM1PYnZ/hKg4ZSsxC4/dcBsAOCZZx4nGAyxfPnBvw/HAz009urFRCBYraRmTofq1/CTanJuFg2v5mgBRVj81OhkxUuohRc/GxoJat9Ou/p/MaR1Qg+pnpL+42saoClCdLg5NWOFMLK5Me0ggooQCgOcbiVNC81Sn8ORfAf76QPVkm1dzZTklytSNolJ7IdJsjKJNzSi0RjHnXDqgBuYlJL8/CIWLl5OQcHRmcz09fUipRwgKqCkZ1YgQGfn6Cm4y5at4MIL38L3vvc1Lr74bYRCR9CdOAKklFkP+eETQwH4Uo6Yi/J6hx4e+6rgWNDT3cVzax4n3tdLNCcX6fvs3rWd3p5uTll9ziGrLJqmseyY4ykrr6S5qQHf9ykuKaOyasqwLJBwJEokEiWRiGMFBrUcyUScYDA8QIwCgSArjlvFvAVLSaeShCNRwuFDLBkbetZMSw4jAdLzwBqH61UW++r3EAyGhxkjRKM5tDY30tbaPCJZ0YqKEXl5yM5ORPmgkF12dtKeE6Nm2yt0xXsRQlBUXMqixceQP+RvUpoxphVFeOQ77+TztzzKx376IH9+egs/umou05YsHHifSHcQ3vxzzM5NKGsm8CPlJOe9F7fwwHybI4JnY3RtRk8245sx3MIlAzbMB0AIvNzZR0+uJn0l7+rchB8s5spjOjihspBr/tjDmd98gs9+dAk3XJEGJGgmyYzDt+54gp/89XmWTC3gqW9fxqwzP0Bg2+8QXVvxA3loTi8SncyMt6q0+8OAsHsJb/oJZvuGLDmRGJ2bMLq2kFj4Ycj2Gj3zzOOsXHkSgcAbqzfAt7PExFTOW263aqr3PZW7sr871UAV4hB92v2Blv32y6C2sZuz+xkaDyTAiGbzW7KmlkZu1qb5KEujhFBEze1VVRQRUDI3zYTg9Gyo5hH2DBo5qhol5eC+WjsaWTHn1CNq2J/Efy4mycok3vAoKi5l9Rnn09PThZSS3Nz8o2oPbFmBESeKruMcepIJfO5z/81ppy3ht7/9GR/60KcmdGyFRSVEIjF6e7rJy1dyNN/3icd7mTZ99oRWVTzPQwjxmuTXeClw2pQPvxYYnyRk795d9Pb2DMjkAIKhMB1tzTQ21DFj5txD7kPTNCoqp4xqj22aJrPnLuSltWvoaG8lFA6TTqWxM2kWLll+wL0SjcaIRsc2gdTnzEXEYsi2VshW0WS8D+E6GMsOXpE5GA5KYUeZlIhYDGPVqTh/vQ+/3kHEcpB9vfRm0qyfOotUZzs5uXlIKdlXv5d4vI/TVp87kMnjFC3Di1QSTdXww/efyQXHzeLTv3iQlV+s42PvqeBDc9MEg0GCe+/H7FiPmzs36xrmY/TsJLTjNvpWfi0bK37kEJkuwlt+hdmxIRusAV50Csn51+HlHfqemGjoPbuwWp7Di07BLViMcHqpLOnhn1Nq+drjku/8+CaeenYNv7+6lIeee55v3/0c7T1JbrzmdD5xWg6yfCWJslNImDECDY+h99Xi5C/GrlyNU3L8YY/LbHkOs+MV3Ly5g3bQbgqzdS1m6Ys4ZScjpWTt2jV84AOfmKCrMXb0r8cMnVAPZJa0qYm3UQCBsuFOVtJXDd+ZfeBnFAExC1QlwSrKNvUnIbMXZFYiJQzAVc5g2iFuQz8B6PuNyx7siXFa1fjMPECoMYSmQ/joCARGhHTVd6sWUGGWmQZlnyzTSvLmZxR5M8ffEjcMgXKw2xUZ0sLq2re0N1JyWhnmyCrqSfx/jkmyMon/CGiaRv4Iq79HAxUV1WyPxujoaCU/vwghBPG+XjRdZ8rUQzeszpgxm3e84zpuuum7vO1t11A0SlP+eBEKhVmwaBkbXn6BluYGDMPEcWzy8wuZN3/xqI5lY0V7Wws7d2yhva0F07KYOm0ms2bPxzTHv6J/OHB7Vbq0l0AFnfmQaYTIXCXROBS6OtuxLGvYtdB1HQkTbsowY+ZcNE1n986ttDQ3kkj0EQyG2Fe3B0M3mTNv0ZhteYdCmzYd87zzcf7xEP72rYBAmCbGiSdjrFg59h15GYSXobKymo2vvITn5Q4Q/USij0AgSFFxKa7r0NbajG3b5OTmkZdXgBAC8+xzEKEgzlNPIXt70IqKaJ75/9g77/DIzvr6f26bXqRR71pt717vetfdxmBsY0xvCS2A6SUBE0IJJJSQEEgIJNQATggtlADmZ3BijAHj7vWut2ubehmV6fXW3x/v1Uiz0u5qm0ui8zx67B3d8t47VzPveb/fc043eaNIU2Nz5R57vT4m42MMDw+warWomji+Ooprb8V35Duo2T6et9zi6s++jL/7TY4v3P4dfnL3fXz6E3/HLZ7HsH2NgqgASDJmeBlq9hhq6hBm/enJmWHA449L7N4tUS7DmjUO27c7zDUs8/XfgWfiUYzoCtHKZZuo6cP4e79NbtvHZs9/JnBslNwQWCWRT3NiBsopoBRGRcBmWLhLOJ4olieKrPr4+Esddrz4z7n1ra9h9fsex7RsXnpZDx/9o8tYVaPjqAHy7deBJGHWbxH3aO4y9szwXFctMwk4QtCtRk+9cq4mD+DInurcGtUPOKjpoxjNVzA6Okwmk2bDhovO6HadC+wSlEbEIgaSEID72kT1onBYfEbgtlMZCTCnILhhlrDoY1A8JrZXwmLirscF0QltEseUZLfyUp5z6bXg6zqBhDiiiuJYojIsqWJ/KyOE85JLqMysaDEDcWxzWmyj1oh21xNdv+yy60jmO/V7ZCTF9ZhZcX5Ps/vZaM9ex1w4jqjwzIjwJVmMwZHcqlFEVFaMKfH5G1rPOZEKtUYcozQgxpjITpDOJ1i7Y9W5mOkt4X8xlh6LJSzhDBGORNl6yeU8sesREtMTOLaDPxBk0+ZtVQLrU+G22z7KL37xEz784ffw9a//4LyOr2f5asLhCMNDAxQKeWKxejq6lhEOn7vhwNRknAfv/w25nEitL+Rz7H78YTLpJJfsuPqCV1kcR6RWW/kT0qzT4nW19uTuQI7jMBEfI5mYYnJyHFlWCEfE5NxxHBwHvGcgtF8MJElimZvYPj09SbQ2RjAYRtd19ux+lGIxz9ZLrjhjEimIwg0oq1ZjH+7FMUzk7m6U1WuQFpNxYxbwDd6FNvYHZLPAJm8b05FmxibHkRUF27JRVZU16zZhOw733vNLEtNT2LaN1+uls6uHiy7egapqaNc8C/XyK3HyeaRgkNwD96KNj1ZdkyzLSLJcCX6rDCO2nty2v0ZNHwbLwAp38/7rY7zwjQf54AffzR+99mW8aHs3H3zFdtatap7dUVbBsZFs87SXalnwwx9K/P73kggTVGD3bpndux3e/Gab2lqQjBzaxCNY/sZZzYmsYkaWo2b7UFO9Z9xyJudH8B/+LmrqEJKtY/kaKHfehN5+/aL6aBw14D7gZlWPkWQWuedwnr/52V+RyaQJhcIY5SLPv6SblfUaZriHcufzMOs2Vx9wAaJSGhQTRlt3i2gqeFshsOLk7UaO4kNyK08nHtDMeygOwN6H9wOwZs36+dtdANg65PaLSfqMbqR4TGSBeFvdzJLg7O8cW2xbHoNAj/h3eQSQZyu0MwJzIymOA0I7pzUI3YlVdM9dFvsqq8QttvJQOCoqEI4tyIIahXJcVGbsFKKUOVMBct9atR60ZrCzYszB1bO6DqsIpX43zd4Wjl6+roUJgz4ptDGOLswArLy4fiUoxuOUhRbFUycIkVoryFuh1zUbCwiSVR4VhEtrnNWnOH5RDSkNuZ+157D2pcXEMewy7HvoAABrNz45z8sSnnlYIitLWMJZoK29i/qGJqanJrAsi1hdA8Hg4ldN6+sb+Zu/+Sfe/vbX8Itf/JhbbnnZeRubJEk0NrWeUer5YnHs6EFyuSyNTbMtVKVSkcGBPnqWr6Ghsfk0Rzg32EUxcZiXZh0CMy9W6RZyknEchwP7dnPwwBNks2kKuRx9+cPU1tbR0tZJNpMiFAqf1H3rnMZsC2ctWZZnra0DQTSPh8GB46xYte6kDnKngiRJKN3LULoXDgQ8KVwthHfkN9ieWmzVTzR3kBuCAxxsfQFxPYzH46W1rYO6ukZ+d+9dTE9PEKtrqLigHTl8gFAowpp1YgIvaRqSW6YIhSKYRn/1KR0H27YXbpNUvJixjVUvrVy5lh//+G5+8uPv8tlPvY9L3/9Dnr9jFX/+yqvYsqIFuTAuLIPD3ae93MOH4f77ZZqaHCKu/MQwHA4elHjwQYnnPc8RFSbbwD5RHC9rYFtIlr6YOzsLs1gJY7RCnTiKF6UQx3/kOzieCEbTpac/RO16rFAnavooZnQFli3zPw8/wZfveITfHZxi69Yd/PjHd7Nly3Y+8IG384Yvfo+d+rv4iw/9BcoiqpxWWkyAUWYnnnZZtEGpUfA2nWRc9Vvwjt2H5No24zhIhUmMnJ+UtYFyAvY9uJ+AL0S9evI2yfMJfUJUh9SaWZIl+10XQQCrWvwuyWIib04DPW77U3m+QF7SAFvcF7vktm2ZgnQ4tvi3Y0LmcUEofJ1QOgZGWnwmyTKYOSgNC7Igh8BJCVJQyOcYShxFcjtzRyb6OZrYTdCKsUztxDbAiAtyURp1hfkBMSYjIT7vwhvFezUDxxbk07HmLObo4vx6QlSRnJK4Bn1MtHN5O8SxHXvWjUxSRfXGTIhrlXCrObJ7X92KkHSOxXRJAsUHh4/tx+Px0H2BTHGW8MzHEllZwhLOEl6v75SahdPhBS94Ob/4xY/58If/lMsvv4a6Cxxgea6wbZvJiTiBQKhq1dzn85NOJUmlEhecrFTEFSeu6ElzfrcAkslpeg/txePx0tW1nHA4ykR8TJBN06S9axmbL7qESKTmvA+5VCqSy6bnTdT9/gC5TJpsJn1WZOVsoWSO4Yk/jBXswPHWAGD66vCnDrFRPUDPtr+oMMHRkUESiSlidY0V8X0gEEQvl+nvO8LK1evn6cM6OpfR33eUxPQkkWgtjuOQTk4TDkdobV/834skSbzs5a/hpdds5I6v3sbn/utRrnnfN7lxTYAP3lDPxdf9MbZ2eo1Pf79o/ZohKgCaBuGww969gqw43lrMcBdaYj+mZ3bJWC5O4HhrscJn9neuJfaipo64Ohsxo7NCXciTvUh7fku+61K06HxnqblwtCCFtW+k+MiX+M737uRff3OcgakCF6/u4lvf+GduuOkllb/DL37xdtav38ynPvUhDh45xpe+9G0ip3G2M5LCDnfuLZS9YjXdmDw5WTEatlLueC7ekd9AfhRwMMth0vLzMWrXo3rg2OR+VrSvo3RMQqsRE9ILCTMNyNXVoJn7ahXcQsaJXXBzMkskVUzkzVx1dohtiOPKPkFW7LKrZ1FdDQri95igjwrC5BhCE1MhTV53H80tkgVAUeFg307e+tXrK+f6zLf+HIAXXvlaPrPmm2QfFyTCLrntXBGXjCluxSQlKkMzZMU2BFEpDYvxSW6VyMy51+4IoiL5QHWJqYO7PScYBDju9eqi9cvyCt6u1c0GY57PfJve3v2sWLGmyuBjCUuYi6UnYwlLeIogSRJ/+7f/zLXXbuYjH/kzvvrV7z7VQzolZhzPTtR12LaNBE+KZkUOiC9tc4E0ayUoHHQWwuTEOOVyiWhNDCSJWF0DkUgN8fgooVCEZ19/Cx7XRcuyLEqlIh6P57xck6aJ4+iGjp9ZwmKaBrKiLBj6eCGhFMZntRBzYHvrULKDSGahYkFbLov8mBMJiaZp6HoZyzLn/a6+oYmLt13GgX27SCWmQJKoqY2x6SzJoNS4mZe/6aO8dtMn+a8Hevm7e9Jc+4VBrrrrX3n1i4Z5zpv+iUDoJI5dQLkM4+MwMSFmqs3N0NXlYFmCtIiTyJQ7n4eaHURNHcD21CKZeSTHptTzEmz/menK5HISsKt0HUYSyETAGaUs2ZRlGU8jBNfMt3EF2LPncb71rS9zx89/iOPYvPi51/C1193KpiteNK//RpIk3va297JmzXre/vbX8LznXc7tt/+ElSvXnHSMjnUS/wS54i9wkotTKa56LUbjJSjpI+BIJIfXUrJWoHrEEY8M7Gdtz0Viop0A5fwXeasgqQg9xolwhPOUY7n5LG5nnW0A1qwNsCSL1ivzkPgskX2CWNh50QalRsSxHMN17XIzT1Dcib7rBmZlmUeaZnJLZlrJJAlsH1yy7hp2fkZH8otjzNXbzZAeyeOeS3GryhlRDZEkcT4zK7a3DaHj0+OunsYV7itFcR1IYtySXE0yJMR9c2T3PC6sgiA5M9ciaeIc5QnRJeldeQqSbYprdWxRXVqMu9ehQ/uftJbBJTwz8YzJB00kErz61a8mEolQU1PDm970JnK53Cn3KZVKvPOd76Suro5QKMRLX/pS4vF41TaSJM37+cEPqjUEv/3tb7n44ovxer2sWLGCf/u3fzvfl7eE/6NoaGjik5/8PHfc8SN++cufUiwWyGUz2PZC37xPLSRJonvZSgxdp1gsuK09FonpCcKRKE3NF3hGgviS9i8TrQiW20tuJsVkxb9s4UkfULEImlsRUjVN2AuHQng8HhzHoe/4Ee65+xf8z69+xv/86mfs2/M4hnGGLUAnQNM0urpXUCoUKBbyOI6DYegkpiepr2+kvuEkS9gXCLYaFKl3dvV1CZISwpkjJI9EomiaRrlUHSBYKOSI1tSdlMx1dS/n2c99Adc++yae9ezncd31z6e5pf2sx6xkjiAHannRy2/lga/cxnc/9DJsxcvbPvMdNm3u4M/+7E384Q/3zvu7yeeFsD4el0gkoFiEQ4ckHn5YIpeDLVtmy3Fm/RbyG9+D3nwVjurDrF1DYd1bKXW/qPo+lVN4hu/Bd/QHeAfvQi5OzBuv7a1DJPsJJfbMRFMhjRXpQI3JyEHXocrd3XEcDh3ax+c//zdcf/0l3Hjjpdx//295320f5bGd/fzj1+9k05UvPqVQ4Nprn8svf/kAiqLw/Odfya9//cuTbquGEZPYObIfxwasRbg9STJm7TrK3S+k1P0CDHUlkiLGZVkWx4YOsrJr/ewxLzA8dYDiVlGcWecuVBGq6FsGOOIzw0gIEuJpFo5glWO0COcvWXOPY4K3zSWTruB8Jm19RuiO5VYk3EoGinht5podxyUwLvmTPG77mNtSJofEuBxTtHvJHkGOzLQgIsakO/F3z2fnqVSQHXO2YqXHhbuWUjvHIEETehXHAgz3s3Fmxjfz2MuA5JIx2W3vmiFWhnhd9rsmAG5lRg7OF/7PwEhC9nHI7obcE5B5TIRgnsox33Ecenv3s3r1EllZwsnxjKmsvPrVr2ZsbIy7774bwzB4wxvewFve8ha+973vnXSf9773vdx555386Ec/IhqN8q53vYuXvOQl3H///VXb3X777dx4442Vf9fMsYjp6+vj5ptv5m1vexvf/e53ueeee7j11ltpaWnhhhtuOO/Xeb5hmkZFVxGtiZ2RrmIJTw5e/OJX8dOf/oDbbnsrf/mhT+L3B6ipjbFm7cZzajO7EOhZvop0KsFA/zGy6RRIEInUcPG2y/D7A5RKRZKJKWRZIVbXcFZOV6eDVgPhzUKwaufdoMiZ1c+ToK6+EY/HSyGfq1jnWpZJuVRkzRqhl+jvO8pjj/wBJEm0Oull9u55jFKpsKAI3jRNJuKjpFIJNFWjuaX9pKGSq9duoFDIMzzURzaTRlYUGpta2XrJFQvabGcyKYYH+0mnkwSDIdo7l51zblCpVGQiPoZZ1miVO2lKH8WKrgDZg1ROIhlZ9GUvEj0eLmpj9XR0dnP82GFKZT+qqlLM59E8HlauWntKYwCPx3N+dFOOyPGwvaIqJktwy2VruOWyNYwcfoRvH2nkP+9+kB/+8D9obe3gpS/9I2666UVs3LiFXbtURkZg82a70g7mOA4jI7B2LezYUT2LMmPrMWPrF3TOAlCyA/gPfAU1PRNa6eAZuovi2jeL/VwYsQ2YtWtQE/uwgm3YeR9aeRy8HorhawExKTYdm0f+8Ci/O/wzfvWrn9PXd5RQKMyzn30Tt932Ua6//uYzs2F3HFbUyfzPlz/COz7+T7z+9S/mg3/x17zr3R+c915p9UIsbkwgZgKuvkGrpZKqvhhIktC8lIZF5WI43kdZL7GidT2S4pKiCwwpIBy89DiQFK1Xsk8sYGgx96fGrVhYYtsTM0skCfydwlbXLgoSMtd1S5KE6D1XcoXyZdeu2HUklLwgW6LNykq5+hRDVGokr+sJ4UZgOaaoTMg+UDQhrve1CaJR6HWrQH7AA5It9EV2SZxrxmlMkgXhArfSrIhzqLVuVcglRDiuBiXgalOsOefXxHX42gHLdQPLzV6bp1lsN6NpsQuiAnVi1gyIfQqHhHZHccmPXRTGJ7L/5G2Fo6PDZLOZpcrKEk4JyXkGpMQdPHiQdevW8eijj7Jt2zYA7rrrLp73vOcxPDxMa+v8L8R0Ok1DQwPf+973eNnLhHj50KFDrF27lgcffJBLLxUCR0mS+OlPf8qLXvSiBc/9F3/xF9x5553s27ev8tqrXvUqUqkUd91116LGn8lkiEaj9PZOEQ6fYkZ1njE5Mc7uxx8mmRAuPv5AkJWr1rFm3aanJBtjCQvDMHTu+NkP+MAH38PGDZt5y63vJptN4/V6ueKq51x4HcgZwrZtkokpUqkEqqrR1NyK1+vj+LFeDu5/glwuiyxJRGtjbL5oO80tbac/6AWG4zg8setRDvfuw7ZtZFnGMk0am1u5/Mrr8Hi83HP3L8ikU8TmaIcKhRx6Wee662+u6EqEjW+cfXt3kpiaxHaXUQOBEBddvIOuk4hEHcchlZwmm83g9Xqpb2hecCI6NRnn4Qd/RzqdQlVVLMsk4A+ydfsVtHd0n9X1j48N8/hjD5LJpMABj1RmldrPpbWjqJKFo4bQmy+nuOo18yx6y+UyD/7hN/Qe2ku5VCIYCrPpom1sveSKJ+dzxHEIPfpRlNwwVmSONbhjoyYPUNjwDsot17Bz58P8+Mff4ec//yHpdIpIJEp7+zVI0rO4+OLrkOV1TE9L2LZENutw3XUOb33rGXz9OQ7BPf+IFn8Ys2ZNxZFMTfViRpeT2/ZXVbM4uTCO/8j3UZMHsNI65VIjmfqb2JusY/ehh9nd+zAPPH4PE8lR6uoauOGGW7jpphdy5ZXXnV2QouPgHboL3/H/QtJT2DZ86ueH+bufH+QFt7yEf/z8twgEqg0EbEMIrfVJhMtUPfhaqnUbi4GZg/xeIfr+/d47eednX8w9X+ijc1MbgVXn5hp1OhhJyB9yrcxNcU1yAEIbhdvV+Ty3Y4pzlYaF2xdQSXyXFFCDEFgpLJTNlCA1VlaQP8mtdNhlt6hpg3+5ICnanHGmHxLVCNk/Wym2dWFpLAfAU+tO/jtEhcOxIPuYSLpXa9wWNtut5qVEZUmNCscyIyGqNCiiZXYmoyq4ziUlhlgAKg6Ili8tNjsuxxJjCK6vrkjNoDwKuQPzXcLMlNC6hDfP3wfg17/+Ja973Yt46KFeOjvP0CxkCU9bZLMZVq+uJ51OE4mc+7z3GVFZefDBB6mpqakQFYDnPOc5yLLMww8/zItf/OJ5++zcuRPDMHjOc55TeW3NmjV0dnZWkRWAd77zndx666309PTwtre9jTe84Q2VVagHH3yw6hgAN9xwA3/2Z3920vGWy2XK5Vkj9kwmc9JtLxRKpSKPPfIH0ukUsbp6ZFkhl8uwf+/jBEPhk06onu4wTYPE9CS2bVNTW3fatPFnAsZGhzF0nde+5k18/V//mcsvv4atF29ncmKMvuOHn3ZkZcbVqm5OPsz42Ai7H38YSZKoq2/EcWxSiWkee+Q+rr3ueYSeRJK+ECRJYtNF22hoaGJ0dBBd12lsbKajqwefz08+nyOfy+GfJ4IPkstkyGUz1NTEGBrs48C+XQwN9pNKTRON1tLa1onPHyCZmGLP7keob2hasIIpSRK1sfqqFPcTYds2+/Y+TjabqYRWOo7D9PQE+/bspKm57YyrVcVigZ2PPkA+n6W+oQlJkikVC+xLawRi17C6vQ472IoV6lpwZjc81MfU5Dg1tTE8Xh+GYdB3/DChUIS1608yAzmfkCSM5itRD92OpKdxPFFwbJTMcexAM0ZM5Adt23Yp27Zdyic/+Xl27XqU+++/l5/+9F6OH/8L9u/XCQSa6Oi4loaGTRSLaykWV3LksMXI8ADlUpH6hmZ6lvUQi4Zc2+BqIiYX46jJQ1jBttnqkyRjhrtQswMomeNV4ZG6p56B2hfRO1DHIw8+wiOPPs7evm9QLOVRZIXV3Zt47vaX8fwXv5Arbr78nINsldwAvuP/hSPJWLUbQJL4yOuWsak9zJu/9Ute+MJruP32H9Pe3jV7TZpwsPKdYwFXDYkskvIoTD44iiIrdF3ajL/twhIVx4LiUbHiPzNJdmwxQdbjgqycT0gqBNeKCX5xUBAEHNHepYVFG5lWA2qdICZmWmS8SF7R4jVDAs2saOEKb5qv/XAQWo9KZQQq2SqBHmFZrIYF0TAzopphTIv/NwvivdDqZ0lUoMfNgmkVZMVIuy1ormDe0zg7BlkDuQb8sqiwzA1ttIui6nayLCvHcPU7J7zfkirugx4X90mNVv9pPfTQfTQ1tdBxlgsxS/i/gWcEWRkfH6exsVrgqKoqsViM8fHxk+7j8XiqWroAmpqaqvb5xCc+wXXXXUcgEOB//ud/eMc73kEul+M973lP5ThNTU3zjpHJZCgWi/j98yfLf/u3f8vHP/7xs7nU84ax0WHSqST1jc2V1c9IpIapyTj9fUefkWRlIj7K7l2PkEomcGybYCjMmnWb6OhcxkR8jHK5RDgcpWHONT8TkM9ncRyHq658Fo8++iC3/9tXWb1qLV6fn8T05FM9vEVhcOAYhqHPaftRqGtoYmJ8lNGRQVat2fCUjg8EyWrr6KKto2ve7zweDx6PB0PX8ftnV58NQ0dRVRFqODHOY4/cj2HomKaB1+OjWCwwMjxA17IV1NTWMTkxzkR8rJKtcqbI57LCRStSU1kwkSSJmpoYqWSCZGKKxqaWqn0MQ2dkeICpyQkURaG5pY2m5rbK30B8fIRsJl31WeAPBCkWCxybdli29bKTtnMZhsGR3v1IskzDHHKaTic5euQgy5avelIWDMqt1yJnB/DGH4D8EDgSdqCJ4qrX4fiqw2A1TWP79svZvv1ybrzxI3zhC2Xy+fvJZH7D8PDvOX78VxhGhp074R/+QaGhvpHm5hbqwz4agnBxq0JraxuBnqsJdmwjGq0hFIqgFBOU0gUMVaVEinxJJ1fUyeXzTI71c+ShL9E/WWB4eIChoQFGR4ewLCFWaGho4qJVO3j75o+wafkO1vdsxe8JoNWLVW35PDgrqckDSHqqQlQAHC3ECy9dTvfqLbzys//NLbdczfe+9/9Yu3bjaY525lDcqkLaN0pDYxOBjvNoF3USmFlR1VHCc9q1Zlywkq7I/DSPp2OLybSVc6sjtafeR1LEBN/T6IrJc+LcSnh2Ei5JgjQoQUEQ9DjghkPaZUTqfdvCInWtRlRjHNvVu7gCeEUTFRhP/ey4C4cFSVHrAVVUPoykOIcaFZWXGRKnBMXPyfQmc6FGBCkrDYrjI4sKkK+7qku0CnKA2Ra3mYqQKdzEUCC3V/xXq4HAmtl7fP/9v+Xyy685L4HFS/jfi6eUrHzwgx/kM5/5zCm3OXjw4AUdw0c/+tHK/2/ZsoV8Ps9nP/vZClk5G3zoQx/ife97X+XfmUyGjo7zn99wKpTLJZCYN2kXffvZJ3Us5wP5fI5HH/4DuVyWmto6ZFkmm0nz8IO/44ldj2AaBo7joCgKLW2dXLLjSrzeC+yXeZ7g9fpdVyuHN77h7Xzww3/KV772T7z+NW+i4UkWX+u6jqGX8fkDZ7TSm8tl54mtJUkCyX0Wn0Lkc1mGhwfIZTMEAkHaOrrmuVJpmofO7uXse2Inqqbh9wcwDJ3k9BQtre3U1Tfy+GMPUCoVaWhsJjE9iaKpFR1MNpOmNlaPhNDCnBsknFP5MM9BuVzikYd+z8jwABIStmNz9MhBVq/ZwIZNW5FlGUMXy7Mnfhaomka5VDzl8fO5DLlcdl5lLBgMk0pMkc2kT0pWHMchmZhifHwEyzSJ1sRoae04Ox2T6qO47s0YrVej5AZxFB9GbMM8onIi1qyBF7zAx913X4/Hcz319RAKOWzcsJ+J+LeZmp5kemqSsYGD7OqbIFvU+b4x8/6dWVhrfX0/nZ3LaG/vYsuWS+jsXEZHRxfLl68S1QxHQp90V+NtV0fRsHD//1nBNgBp/tK2rLChM8r/+3/38cd//Hxe8pJn8+1v/5xLLrnsPJ24GhMT4zSdQKgvGGxEKeJkVuanEfc7ltCIlONURPCyT5CuU+l2HFsYI+hxQYjUKHhnhOpzhyFBcJUgo/qUqwXxgm+50H4sBG+rG9SZF9uiIJy6VNGy52udrVaYGaEPkVRR8VCCrjheEu1a59IGNxPa6JQR9s2nMUac0QbpU24Lm+LeH12YFyghhMXzFHBYVOIymRR79+7ita99y9kNcgn/Z/CUkpXbbruNP/mTPznlNj09PTQ3NzMxUe24YpomiUSC5uaFW2Sam5vRdZ1UKlVVXYnH4yfdB2DHjh188pOfpFwu4/V6aW5unucgFo/HiUQiC1ZVALxe79n1HJ9HhEJhJEnCMIzK5MBxHIrFwjm58jxVGB0ZJJNJ0dDYMlspikYZHjyOqnkqeQ/lcpnBgeOEwxE2b9n+FI96cWhpbScarWVqMk5trJ53vP29fO4fPsUv7/oFn/70F5+UMRiGTu/BfQz0H0XXywSDIVasWk/3shWnrVLpuo7X46WQz1NTW1dZIbMsCwkIhp4Ehe1JMD01wSMP/Z5UMoEky9i2xdEjB7lkx1XztDRr1m6kVCwyNHicbCaNqqo0t7ZXtBmZTBqPx4ssy4TCEaanJyq2w6ZhUCwK4fm5COGDoTB19Y2MjQzi8/krbWCpZIJotGZeC9lA/zGGh/qJ1TVW/s7z+RyHe/fT0tpBQ2MzkWit+7dRqhB4x3EoFgosX7H6lCuaquZBUVRMw6iyWDZNA0VVUU9BPI4ePsC+vY9TLBZFqJws09reyfZLr17UQkK5XGIiPoZlmdTU1hGN1mLWrsWsXXvafWcPUuSGi9NsXB7m6GgIx4EVKxxMXeGRh3rYseMKlHISz9h9OIqXjOnBtCyuW+GlNHGAKa2beNMLyWTTwrq7MExw5H/w2FlCwTAh1SQYihC+5M2oPdefeiySEBmfTGh8rrAiPaB4Z1vlAGwTWc9Q7nweDQ1N/OQnv+YNb3gpr3zljfzrv/6AZz/7pvM+jnh87KwWWRzTrQqUxMRYjZ2eyClhsUJvFWaF/I4jJvqqq+1YCHYJrJIgjuWx2XR7xxFi9twBCPtBO0n3arHfDdREEIXysDhWcJ1olXJsUdmxSuIa/KuEI5ljCDJ0qom/EhLjnhm7JIEcE69beTBSorrimAgypsxupwQESXBcJ69zLVZIEpXQytNuq7hVwn7hYmaXxf5awxyTBU28Z2ZSVI0eeug+bNvmyiuvPbeBLuF/PZ5SstLQ0EBDw+mbSi+77DJSqRQ7d+5k69atAPzmN7/Btm127Nix4D5bt25F0zTuueceXvrSlwLQ29vL4OAgl1128hWl3bt3U1tbWyEbl112Gb/8ZbX94913333KYzwd0NzSRlNTK6OjQ6KFQVXJZdP4/X6Wr1h9+gM8zSBW56WqiXMhn8cwTXyBYKUK4PV6CQSDDA0eZ+36iyrZGU9n+P0BLtlxFbsef4hkYpqWpmZe8qJX8JOf/ic7H3+UjgssOhTi80c4cvgAfn8Qr9dPJpNm5yN/AIQD2EKwbZtjRw5y5PAB0aKUnCKbTdPW0Y0iy2QyKeobmp4yRzOh/9hFKpWkoUmQXMdxmJ6Ms+eJR6lvaKoKIdM0D9u2X8HK1evIZTN4vT43tV08W5FIlPj4CI7jEKtrEDqXbBbTNCgU8kiyxMpV60+pSTkdZFlmw8Yt5HMZJsZHkFUV27QIBENs3LxtXlViZHjAzXGZfT0YDJHLpJmajNPQ2ExDYzPtncvo7zuC1yscvfK5HKFwiOUrqnM4bNumkM8hyTKBQJBgMERLazvHj/Wiam6rnGGQSiZo7+jGMk0O9+5HAhoam4nWxJAkiXQ6yf59u0CSKtobw9AZHuynvqGJtetOrXUZHxtm186HSKdTOI6D1+ujZ/kqNm7etqiKn2OaGL+9F/MPv8fJZKgLBGjacSna9Tcg+XwMDqiiZcVxkIws2AaOJ4ql2/g8KvXRAJqvm2WORfayyyq5MwBK5mY8Y/ej5Aax/M0YzZdVOYE9VTBr1lFuuQrvyG9wCnGQNSQzi1G7Br31GkA8w9/97v/j7W9/NW94w0v5p3/6Bi95yR+f13GkUkl6zrAN0ipC/qCYxM5AjYh2oVO5icmaaE0qHHb3VcVEXfGBv3ue7EhoXPqgPC50G2ZKTLKVCOBqXcyssOjNPCwqLL7O6uNYBSFWlzyCHIBLclJQGhIko9A7q2cBcQ3+1eIc+jiiHap2dv8quHkoar3rdSHNnt/Oz+pYlJDQwtjF6uPYRVdo/xR89cle4Zhm9wgykt0znzBKKtiuI9n99/+Wjo7uJWH9Ek6LZ4RmZe3atdx44428+c1v5qtf/SqGYfCud72LV73qVRUnsJGREZ797Gfz7W9/m+3btxONRnnTm97E+973PmKxGJFIhHe/+91cdtllFXH9L37xC+LxOJdeeik+n4+7776bT3/607z//e+vnPttb3sb//Iv/8IHPvAB3vjGN/Kb3/yGH/7wh9x5551Pyb1YLFRVY/ulV3Ng/25GRwbRy2Uam1pZs3bj006wvRgEg2G3xcaqTFYsy8SyrHnJ4KqqYRq6247z1JCVmSqW47qwna46Ud/QxLOefTOJ6Uksy+QFL/5jHEnmfe97MytWrGLDhosu2FgzmRSDA32EI1ECASEM9/mFXubo4f10dS9fcILY33eEXY8/jKpqxOobUFWV8fERxkYGaWpuY1nPKtZv2HLBNQ0zq5gzvdVajZh85HIZpqfiRKO1lfsvuS5l6VSSVHJ6XsbJjD5koUT5ru4VDA32Mz09QSRSQ2NTC+NjI/jw0bVsOT3LV9PZtfyce6/r6hu5+tobGBrqI5NOEwgG6ejopqZ2gZanU5g5zrSSybLM1kuuIFpTy0D/MUxDp7tnBStXrasySZicGOfA/t0kpieRJImGxhbWb9zCps3bKJdLxMdHsC0bSZZpam7B6/Xyu3vvQnfNRHx+P2vXbWb12o1MxscpFgo0ukQFBBn0eH0MDfRVkZVyucTY6DCFQg6/P0C0JlYxBKirb0SWZQr5HL0H9xKJ1tCz/PSLLca992D8108gEECqqcXJ5dB/cQdOuYz3Za+gsamFcDhKMjFFvU+ki+qGRUGHNc0eFFlCsorY3tqq3BkAK7KcYuRJ0PxZZeRyUmTfaIuwnJcVSt0vQtYzaPGHwC6jtzyL4opX4PhnFwV9Ph//+q//yZ//+dt517v+hEQiwa23vuu8DTuZnKa2duFFxIXgOMLe1ki44mtlViRfPAqhzfNJx1x4msUkWY+7trlhUb1ayMq82O86bfncFf602MdMgCMJzYekIkiPCcVjgAT+OTI3KysIg1Iz+5okif1KQ8IJzM67ZKQGsEU1RH/IJRAzmSs+CKxYwFlLcQX2CWHJPANbR7RjBebk4cREZWhGJ+Lo4v75Ok59zy40ZA2kqFv1KlVXyOySIHqyH+6//14uv/yap26gS3jG4BlBVgC++93v8q53vYtnP/vZyLLMS1/6Ur74xdkWGcMw6O3tpVAoVF77/Oc/X9m2XC5zww038OUvf7nye03T+NKXvsR73/teHMdhxYoV/OM//iNvfvObK9ssW7aMO++8k/e+97184QtfoL29nW984xvPiIyVQDDEtu1XUioVsSwLvz/wjBKez0VrWwd1DY1MTowTiUSRZYV8PofX68VzQktJPpelsanlKdOsZNIp9u/bRXx8FMexidU1sHbd5nnC6BOhqmrVNp/97Fc5cuQQb3zjy/nVrx6gru70VcizQS6boVwuEq2pToILBELk8zlKxcK8Vi7LEu1UsixTUysm9vWNzYQiUTLpJJdsv4K2ju4LLpp0LLGqWh6nkshd0oRjjl3jVHINqiFaq87EtX0mxb1n+SqGh/rJZTNIksTatRvZdNElVXbH5wOhcOS01QeA1rZOxsdGME0DVRUzgkIhj6Zp1NfPEjGPx8O69RexZu0mbNuqbDuDVCrBQw/8lnw+SzhSg+M4DPYfI5dNc/WzbuTKq69ncmK8QigM3eChB3+LPxCskKhcNsP+fbuoq2/EduwFW1BkWarS9KTTSR558PdMT88kI4Iky5RKBdrauyufV8FQmFKpyEDf0dOSFadQwPzDfRAMIre2IplF8NdiqSrmIw+jXXsdvvp6Lrp4B48/9iDj2RRqKYxqpemur2FNowepnEDSM+jdLzi5ohjAsdHiD+OJP4BUTmJFV6G3XoMVnm/isGg4Np7hX+Md/jVyaRpHC6A3X0Wp+xZQT0785cI4gX3/gpo6LGaHjok2vQsztg697VlV26qqyj/+49eJxer42MfeRyIxyZ//+V+fl7/XVCpJzQmfJQtcomgTckmBMT3bwgRioi2HRVtYaVBMbGcE6ydCkmb1EqeCrYuqhuybrUTIIfG6kaWSCi96FmerLeXRE7JFZtZt5ny2OLoQktvm7GtGUlQQtDpxPcbUrI2w4whBf+GoIFVzKyOSJMT3Zlr8yD7x2WYXBbFxbBG6aGXFcWTNvW+Saz3denLHricTkiLyW/KuCYDsFVUvxxCfz6nCJAcP7uMd77jtqR7qEp4BeMaQlVgsdsoAyO7u7nmTD5/Px5e+9CW+9KUvLbjPjTfeWBUGeTJce+217Nq168wG/DTC/wZ7X6/Xx47LrmH/3l1ihdfWaWvvoqNjGWNjQySmJ4XYuZDH6/Wyes2Gp4SYlUpFHn7wd0xOjhOO1CDLMmOjQ2TSKa685npqF1odPwn8fj/f/OaPuOmmy3jb217N97535wUJWfR6faiqhqHreOZorXS9LFqMPN55++h6mUI+j89f3cfg8/nJpJLYjvOkuLvoE2IyMbfn3C4KgWowGqU2VsfkxBher6+i/8ikk0Qi0YUrFQsgk0mxa+dDTE6MY5omXq+Pto5uVq9ZT01N3VO6ANC1bAVjY8OMDPdjGCaOY+PzBVizduO8qhGIKsvc8W7fvpLh4YF52z3n2Tfyute+mcmJMUaGBlixam2VxueRh34vHPnmWDSHI1Hi46OMjQ7R2taJ5vFSLOYr1TrbtigWC/S4rWeO47Bn92NMTcUrmTO2bdF37DDFYpH2jhNCDDUPxWKB08FJJnDSaZSggzZ2H3I5Lex8fY3oaR/O9BTU19PW3kVNTYyxsWHsTA+NU/fQIQ2i5GwcLUi543rK7c895bl8fT/Fe/ynwhJB8aGmetEmHyO/8d1Y0bNzhPOM3EOg999wFB+2N4Zk5PAd/xGSkaW45o0VIYJUmkJL7EMyS1jhTrSxB1CThzBr11UIlpIdwHf8xxh1m+aZEUiSxEc/+nfU1TXwqU99iOnpKT796S+es4VyNpsmHF44HNVxRAhlaUi0U0mKmKw7pmhpqtq2LEiMXXYn5B7wti/c3rUYOLprrzvn61ANC9tjKysct2QAR1RdZJ8Yl1NyRebuR69a4+pHMqC4afFGGqyy0Nk4RQShcQmJGhLVG5hDxiT3GElRUTqxHUyrd924hoRORVLcgMt6yB8Qn3FyUPAiOy9IVmiTIHOVXBTHNQEYd93BIoJ0nSo893zD0wrIom3OKrq5Ll2i8vPfP78HgMsvv/bJG9ASnrF4xpCVJSwhEqnh0suvpVjIY9kWwWAYx3HoO36YvuNHKBULtHd0sWLl2qfMRGB0ZJDpqTgNjS2VL32fz8/E+CgDfUfPiKwAtLd38q//+gNe8Yob+MQn/oJPfvIfz/uYY3UNNDa1MDo8SG2sHs3joVjMUyzk2bB564K6H4/Hi9/vJ5fPVbXh6XoZRVXnEeRkYoq+40eYmorj9wfo7FpOR+eyc57o61MI1zt3iDMiUyMBVlroPx5+MEt8fBRN0zBNA78/wPqNFy+K+FmWxWOP3E98bISaWB2a5iGfzzLQd4T6+kZisQtT7VosfD4/Xd3LGRsdJp9LIkkSAX+QSLRmUWTxV796AMuyeOiB3zI1GSeXz/N3f//XbN8+E/gokc2m5+2n6zqSMv+9kxDmJ3X1jfQsX82R3v3kc1kURUXXy9Q3NLHcrYxks2kmJ8YqBgAAsqxQG6sn03+UbCZNJFoDzLRV5mlrP73+SQpHkOUy6vAe5LCw78WxkeNHUJ0okn/2eQ6GwqxYuRZYC+bVFFMHkYw8drANK9x9SoWynB/FO/TfQusScImh46Am9+Md/BWFDSvOXOFslfEO3Y2j+MT5AccTwVG8eOIPUe68ETvYhjbxCP7D/4FcnKgsr8v5UczoyqpKkBXqQE0dQE31YjRfvuAp3/GO24jF6nn/+99KMjnNP//zv5+VQYxTcGCfhWM5SAMOTs5BClVfvzElQhVnckMcC0qufgQJZJfjOIaYaIMgAJLqLkL0i7/vhUIJTwfJKwiPo1MhHrJHkA8QhERSROVjxgrZNsS5pTkfgbIqtCz5XkE2HFzi4gY26qZLsHziOmxd/MwkxlfGI4lrnmkLqxqrJKyRtfrqfQt9glwpc4IXJU20wRpTghjNoNgPpT53GxVKGdcEYL1olX0yIEngbRHOas7MvXQJ2y9+8WMuumgbLU+D0OAlPP2xRFaW8IyCJEkETgjcW7FyLctXrKkkkz+Vfu25bFaEes1ZnZQkCY/XSzI5fVbHvPTSq/jEJ/6RD3/4Paxfv5lXver152m0ArIsc/E2YRgxER8TGSJeHytXr2PNSfIYFEVh2fLVPP7YA2QzaQLBEIauk0pN09beXbWqPzUZ58H77xWidb+fVHKasdFhspk0GzZdfE5jd0zc5dBqSAA2NDa1ctW1z2Vw4DiZdIpQKExH57IqrcapMDkxxtTEOLH6WbetcDiKXi6zf89OQqEI9Q2N82ybnyykU0n27H4MVVVZtWYDkgTpVIondj1COBytaiss5HMUiwUCwVAlS2amtbCtrZN8Lst99/+OxsZm1q5ZX2mV859QPQNobGxmaOB4lYbMNEUqXKyu3g3hFO1xI8MD6HqZxqYWurpXVKoxlmWJ9roTQkbC4Qg+X4BkcgoHB0VRKeSyhELhRelVpEgEX5eMfiyH421G8vhwSjpWTsPfY6J4pzDpmb+j6sOs37K4Gw8o2T4kPY1Vs27OySVsfyNq8hCSWagS5i8GcjmJXJrG9lb3NDneGFJhHDk/ipLpI7j3i2Bb6P6NWEUZKZ8gmH0USQpB6ISFGuf0n4evetXrqamp5e1vfzWve92L+OY3f0joDFz8nFEb+991OO6IXJAHLOx/KiP/iQepXfyBOo5YYXesWYIA7mQ74WaWpAQxsNJicqs1zU7wlQCYutCmnA1ZkTWhbykdB8td4LDybk5JZPbfM8GNVlFUVbzL5ovVtRhEtohFEVsXJMp2r0WNCjcsqwhYsxUkZKpmXLbhvqaJljHZI/Qoc7++JFmYBVT2yQPKCdu4AnxrTtHRKgiHsrkmAPKMCcCgqw16Er8mJbm6cpbJpPnNb+7iQx/61JM3iCU8o7FEVpbwvwKSJJ1z+8L5gNfnA8d1GZrzbWAY+oKp5ovF61//Vvbt280HP/hOVq1ay8UXn19b5nA4ylXXPJfE9CTlUolQOHLa1fnlK9agl8scP3aIxPQkqqrR2bWcLRfvqFRMHMeh99A+crlsldg6m81w9MgBurqXE44s3DKyGGgxMKfFSm3FMcedBCjuXOtkgvnFoFQqYjt2VRUmk0kxER9HLxcplkrU1sbYsGkr7U9BAvPI8AD5fK7iuAVQG6sjPj7C0GAfjU0t6LrO/r2PMzhwDF3X8Xi8dC9bwboNWyrX1dG5jGNHD/GH+3/LTTfegmWZpJLThMMRWts7KZdLjAwPkkxMoWkasboG6hubmZwYw+cL4OCgl4q0tnfR2ib0Goqi0NW9/KQBtOFwlEgkSjqdos47Sx5zuSydXcsIRWoYHxlCliXaOrpZs3bjoklmaLOfQqKV8oCONZJE8qp417cT2uKgF+OcSRKOpGeQrBK2t646vVF2BQ6ORSUFD4RwQfHinErrchI4WhhHCyIZORzPbL+OZGQBB9/R7+GZ3oeSPkxZXo3hpLC1GlBiGHYj6vRxjNqLkf1upSo/gu2rW1RL2o03voDvfe9OXv/6F/Pyl93Af3ztl9Q21pw+VNFxsH9mwDEbZ4WMJEs49UC/jf0TA/ndHiRZEhP33Px2L1kTt8/XLm6dlRWTdsURk+q5qIQqniVmhPL6mKhEWG4LlWOCZcxqQyiK172d1eL62Wt2qy4aaCFRXSkdFa8pAaBBVDEchNDe0yRIlpmYfVQcU+xfOAq4AnlPPfhXuC2tM2GVM8YhtW7woiXOP7fdC7vadetkJgCyX5Azx6iuFj3Z+O//vgNd17nllpc9dYNYwjMKS2RlCUs4j2ht6+DwoQjTk3FqYvXIskQmnRKhg11n7yAkSRJ/8zdfoLf3ALfe+gruuOP3tC+iJeZMIMvygjqHk0FRFDZsupieFavJZTN4vF6i0doqgqPrZaYm45XcnxmEQmEm42MkElPnRFa8zWLSYSTdSYD7xe1pFsLWc0UwGEZR1EpGSbFYYGxkiGIpTzRSS6yunkw6xc5H7ycYDJ2TbfHp4DgO+XwO0zQIhSKoqkqpVFywmqiqGoVCHoB9e3bSe2gvwVCYSLSWUqnIgf27QZLYfNElgLAdzuTyFIsFNqzbSCoxTbSmlk0XXYKmeXjgD79hfGxE6H5sG4/Xx7KeVSiyzNBQH4qssnzlGi66eMei7cJVVWXNus089sj9TMRH8Xp9FWcxWVKYjI9SLBYolQQpDAaD+P2BeQGVC6KmhchVcfTL27EyJeSgB6UugJLurSIBp4JUTuLr+xnaxKNItoEVbKfcdTNG4zYAzJq1WMEWlGwfVmS5YMtmCaU0RXHZi13f2TODowXRm6/Ed/zHoHgqmhUl24dkFFCzw1jeGJJaQ9noRLYyyB4b21OPGVyGlNeRx/ajxHxIto6jhSguezm2f3HtijsuvprvfPbXvO79N/L617+Ir3/kTmq7g/h7WDBtHYBxBw7bOK0ykuY+h4qE0y7DMRtGHOiQQBGTcPOEHFLHEseeSWd3HPE3nduDaJGamdw7bsDgueTUSKBFBSExk0I3ojWIt86xRaVEqxOaCtk/m7LuOLPp8I4hQiSNadfZShXhi2pMVIYsN4hSqwP/ctEGNeMWVugV55BkoTlxSq4+JiDIRWlUfIQFVgkntPKYuAcOrt6jSZA9K+2GLCIIoOwH79y3eKbafILByMyizlPpFAbw85//iB07rqS19eTt2o7jujy6XahqRGTnPNVjX8JTgyWysoQlnEeEw1Eu2XElT+x6hGRiCsexCYUirF2/mabmk0QWLxJer5dvfOM/eeELr+WVr7yRn/70NzQ+DWyoA4HgPPvoGciygqIomCekutu2DZKEqsx+BM3YlZoZ8W+15vTtCrIXQhvEl7oxPTvp8Tadny+1uvpGWlo7GBw4TigcJp1Kks9n8Xp9lfavWF0DE+OjDA31XzCykstm2LtnJ+NjI9i2RTgcYfXajYTCERzHrrRAgiA1hmFQUxsjl8uKsYciBIIhSsUCstuWONB3hFWr1+P3B5AkiXt+cxfXXvtcXvCiP0KSJGJ1DWiaxp4nHmNsdIj6huZKLk1yeoqHH7yXUDiK1+vDcRxGRwapqY2xZu2mRbdidnb14PV66Tt+lHQ6QWNTK9lMikRiCseBQiGHaVoUCuM88Id7GR8f5fIrrzttpUxvvgp1eg+aL4MSawZbR80cwQq1Y9Sd3mUN2yBw4Ot4Jh7F8jdha2HU9GGUA0PklXdj1m3G8YQprnwtgd7bUZMHxH6Sgt6wlXLX8xZ1/Quh1H0LkpnDM/4gauEgjurHivSgZPsxIyuQjRw2g2BbSIqJVE5he2qRnRKZ2KvR1RWEa48hhWowGrYKwf0i4DhQOALLg1v4+kf/H2/8+A2853Mv45//9Keg+Ags0DkHgFuRmNGBSDPx8RpgAm4uiCQJwbXZ64qtfYhqS1b8nc/oKCruXnWgT4rtJNnNE/GLyf9c2OU5E9qom/q+0PXZrnPgqEtWMoAXpJSoWkiy22qWARRBTqy8OGZpGMqDrpYmBXZObKMExXHLw6A1i88i22370mKz7mXGtGgVkxQhcp/rTOZpAGbCFyVB1Mo+0TJXZRySF/v4usEYBzPvXnMY/D2z5AVcE4BgtQmAYwpy5e+qLgQ+2ZienuL3v/81n/jEyfWXji3srEvDbqsvs/cusHKJsPxfxBJZWcISzjOaW9qpq28iMT2BbdvUxurPmyNbU1ML//mfv+LFL76OV73qJn78418Ti52HEsIpkEol6D9+hMT0JP5AkM6uHlrbOhc1IdU0jfaObg7s343fH0TTNBzHIZmYIhKJUt8olkkdW0yUyqOzFsSoYoXT33N6wuLvFj/nG7Iss237Ffj9AYaH+snncng0D61tXUSjwp5VkiQUVSWfy572eI7jUC6XkCRpUdbapmkwNNjPww/8lnQmSX19E4FgmHQmzWOPPMDF2y4lVtfA5MQYoXAUSZLIZtJEozV0dS+nWMij62UCwSCD/cfI53M4jiA2Xp+ffF5YEQ8PD3DffffwjW/8sMr1y3EcRob68fsDVQGahmmQzWaoq2+qVOOymTSHDuyhpaV90U5rAE3NbTQ1i3Om00nu+Z9fEAgEGR0ZQlFU/IEghqFjmRbTk3EOH9rP9kuvOuUxjaYdlEqTeAfvQk0dBEnFjPRQWvUaHG/NacekJvajTu/BiK4AVTT9m94a1OQBvMO/wXQJj9lwMblgG+r0E8hmHivQKsiQeg626aqf4uo3UO64Abk4geOJIBcmCO75PCg+bMWL7V+GVhxzd5BQSjlMTye5yAvRlW6UDSeftJ8MVs61EA7CRRsu4asf+xlv/qvn84GvvprPv/8H+Dq0hRPlmySkRgkn7sCyOX+oEw5SgwSts695W8WEuTwm9BPIYmIdWFU9gZYU4YalBAVhcWyhX/F3zLpZOY5o5yr2ua1biAqDf5k4z4kwJl3nwIBov7Jc1y4zLXQhcmBWLJ/dhSBashiXVRJjkX3gTCA+oKTZ1irbtSbGmf855Dgie8Ux52h1JHGNti7uRyWtXhNkpxwX555rHCIHRaVBAsJbxfsFbjjkCVUvWZtvAiBJonLlcwvydlncWzMlrlGrd22WL7CW5Ve/+hm2bfP857/kpNuYCSgPiWdYjsyOtzzittUtrht0Cf+LsERWlrCECwBN0yoTsPONrq4el7A8m9e85hb+8z/vIryY1pizwPTUBA/efy+ZTBqv18vExDgjQ/1s2LyVNWs3LeoYq9duJJVKuJbTDuAQDke46OIdlQm7MeWuJPpnJ1l2SYhBtdrTZyhcSPh8frZecjnrNlzEgX27OXRwj8iWcb/VHcfBMs15OTUnIpGY4tCBPUxOjCFJEs0t7axZt4lIpGbB7cvlEo889HuOHjnI1GQcTfUQN0dpam6lziUoI8ODXHrZtRzYv5uJ8VEcHNo7ulm3fjORSA05OYuqagwN9GPoZXx+v8goKmQpJKZ54L578Hg83Pmrn1NbG+O666qt3Ct5NCfMYDKZFLKsVLm5hcIRJsZHmZwYPyOyMheGrmOaJpIkiFowKIRHsqxgOgZen5/4+DCGYZzazU2SKXe/AL35cpTsICgaZnTVoluzlGIcyTErRGUGtrcWJdtXJZKyA03ogYUtjqVyCiU3ALIHM7qChWf7C+0oYQfbsINtYBZRp/cildMoyQNY0ZXYTasp6CE8uX0oWo5i9AYK4edQKnbjbTg7PYKjiwm17K7Qb994Nf/84R/yjk+9lA//85v40kW349Hm94JJXgn5BhXrewb0WoAEEzbEQHquiuSffXYk2Q1CbHX1IqpbPV1gpVz2igm3f5mr0VCrH0MzLRY4kIQ7FoiKR+GoIBYn6l10N1Fe9gibYkl19R6OIC6S353YW4I0yGFE4vyY+CzSagW5cNzWNMnVrSgeV/dSFlUZ74ltaraoHs3V6sy4e1lFUa1hpt1Mpyrrpeo+z2hU3La5E6/vRGh1EL5YaPpsQ9wTLSb2tUqQ3y/IDwpgiev0don7fSEJy89//kOuvPI66k+hP9Pd92GuuYDsFUTOmF4iK/8XsURWlrCEZyBWrlzL979/Jy9/+XN5/etfzHe+8wsCgfmuTecCx3E4dHAP2UymSsCdSafoPbiPjo5l88IiF4LfH+CKq57D+Ngw2Wwaj+alpbW9at+ZVcm5q8Gyb/bL6akkKzPw+wOsWrOe+PgIk/Exwq6tbjadIlpTS0fnspPum82keej+e0mnk4TCURzH4eiRg6RTSa669rkLVt6OHT3E8GA/Pn8Ar9dHMCiCESfiowSDIbw+P+lUgkhUWHqXSkUc28YfCFbeq1AoTE1NLceOHiQQCCPJcqVKYdsWfccP09bRzW/u/R8uvmgbvQf3snHz1soYZFmmta2TA/t2EwpFKiYWhl5GVZV5znwgWvxONJhYLMKRKIFAkJTrnOfgICFhGDqa5kFRVSTp9I5/tm0zNRlncnIcHKhviNEgaQsZxy28vycKuN61cwiGZORcsfpprs1x8A7dhXfgTuTSFMiislNc+RqsmlWnH4DjIBlZ5MI4/iPfRU31IutJ1HQvdvoIRv0WvKFmUs6rKfk346gNUBJ6DH/32U02Zd/spFtyJ4lXb7uRv3/Xt3n/F1/NRz4R4u8/+6WF7/0lCkpQwn7QwvM/HkotBsqbPbBh4TuuBOZni5wMkrrw3TYmxdsz97NBCQtNiD65wGTeZvZAimiPMpOCIFhFcBKCvCjBOVoV18FrptVMDoMsgz1zrJloN7cKs6AZgVshMfNUSImkINLpC4IAyaZLhErg7RCksTDmhilqriWx6raqBd1WNTer5VTvteIDZYE1s/KI0N+oNS5RdDNhisfFffNcoGL94GAfDzzwOz73ua+desO579VcSGKdYAn/97BEVpawhGcoNm7cwn/8xx286lU38eY3v5JvfevHZ5WPcDKUyyUhjo9EqsXx4QhTE+MkElOLIisgKk2nmsw7J/tywl39fJogHI6y47Jr2Ld3F4npCRwHWto6Wb/hopMG4QEM9B8jlUpWkT6/P8DU5DgjwwMsd4MSZ+A4DkMDx/H4fMju5Nx2bHw+P/l8lnw+h2WZ1NbWVR1vIaLQ3rmMw4f2YeNQKhVRZAVN07AsE10v8+gjD5BMJti+/XKOHjlI17LlVdWeFavWMTk5ztTEOIqqYtsWHo8PRVXR1NmJfDI5TTabZu+enfQdP0xn13KWr1yzqHa3GXi9PlasWsvjjz2IbdvkcxkUWcUBonUxyqUiPT2rqlrSToRt2+zZ/RhHjxzA0IVgQtU0lq9Yw+Yt2xflGmjGNmJGulHThzHDPaB4kYtxJMdCb73mtGxAm3wU39Hv48heUdGxDdTUYQIH/5Xc1r/E8Zz8WZFzg/j6f4Ga2Ic2vResInrTlZjRVajJg6iJPWjJ/djtbQTXRZCkehxDVCW1+vk2u4uFHBD6ifKwm4OiiQn0c7e+lL/7cJ4PfOpWQuEwH/vY380jLJIkwXoFZb1C/e2NJFZMI20U93kmrd0xBUE50/a0k8HWF67ISLL43YlQa4UGQp9wHcUkd7XebY/ytIjWoxlG6xhiocTKilV+YxJUE6QQkHJbq7ziWE5ZHH8h8b8kiWOfqNWRcN8vv7AallVR2VCiourhmOK4Nm6rWlCQiMJx0fYmyaIlzr/8zIIeZwwMZI9rLGCJtiszL86XfQz8K88+fPNUuP32rxCN1vDCF77ilNupNe5zaM5xT7MQUqhTF7CX8L8US2RlCUt4msOyLBLTk5imQTRaW7Wafckll3H77T/hda97Ie961+v4yle+e8qJ3JlAuEzJOHb1UpbjOEiytGCgoxB360iSvKjQxRmotVAen21xAFdYKT25icuLQV19I1df+1ySySkkSSIajZ023DKZmELzeKomeYqiIEkS6VRywX0sy0KWZUKhMH5/gEIhj9frx3Fscpk0gVCYZT1ilT6dSnLk8AHGx4ZRVZWOzh5WrFqL1+sjGq0lVt8ohPSyhCTJHD18gFKphKqqdHZ08cH3/yWS45BITJFKJqrISigU5sqrr2dw4DhTk3G8Xi+xWAPHj/cyOTFecUtLTE/i8XpRVJVCocATux4hkZji0suvPaNnctXqDXg8XnY99iDDwwNYlkUwHMaxbZpb2lm1ZsMp94+Pj3Ckdx/+QJCYmyNTLBY4euQgDY3NpyTNM3C0IMW1b8bf+++omeNgGdi+GKWel6E3X3na/T1j9yHZJlZkhXhBVjGjq1FTh9CmdgvCA2CbqOnDSEYO29+Eo/gJ7v0XlGwfjhZBKqfAMdCmd6E3X45ZtxEr2I5SjFNc/Trs6AoWQwXl4iTa+AOomaPYWgSjcRtm3UVVpEuShG2upArNhFUUE1rfCnj1s16H4c/zkY/8KaFQiNtu++hJz9XQ0MTkZBwQk/DCEaGLcCxBDrxtQjdxrhNhNSxal+baljs2YIvfnQgl6FZRXHH8jHOgVg+RS1xy5orYbZ+YwFsFt6XOEJubScDvtpKZiIqKI5zAIltP3uVX0eqMilazGSviwCq3glx2yYMG2d3imN4OMVZ7plVMEk5qkjQr7DemBTELX7Q4EmiXwdLnVCecWUKGayHtIMIkJWVh2+azRT6f4/vfv53XvObW03YBeOpAbwRjgtl2OMvV1Ty1ObxLeIqwRFaWsISnMZKJKR7f+RCJ6UksyyQQCLJ85VrWrttcmSBfffWz+drXvs+tt76C2257C5///DfOORkeREp9a1snh3v34XMF1o7jkExOEQ5H59kcJ6Yn6T20z9VkyLR3dLN6zYYFW4XmnatBfDEZ04hPJUdMbjyNYtXz6YRUcpreQ/uIj48iSRJt7V2sWrPhlCF6gWAQyzSqXnMcB9t2FmwBkySJltYODh54gnA4SmtbF+Pjw2QzKUzTIhiOsOmiS+joXEYmk+KBP9xDMpkgEAyi6zpP7H6ERGKSy654FvUNTTS3tDM81Ec4HMWRbPL5HDgO0WgtXq8XXdcpFHKUy0Usa36ktt8fYPWaDayeQxRa2joYGuxjcmKc+Pgoul6mq3tF5dnTdZ2R4QEm4qO0ti3eZluWZXqWr6Z72UqmJuNMTIxhGgbRmhitbR2nrdSMj41gWlbVc+f3B8hm0oyPDS+KrABYkeXktn4UNX0ErDJWqAPHt7iHUS7EsdUTnntZEcJqXVhXyfkR/Ie+hZbqBUvH0cLYih+lOIoZ24hsZEHx4Kg1yOUkSn4UK7oCRwtASZ61STrdWPKjBPZ9ETV9DEfxI9k63rH7KPa8lPKyF1Zv6wqzfZ1ue5JvdgL+hje8nVwuy9/+7V8SCoV561v/bMHzNTQ0Mjk5gWOLtHojIdqVZDeFvnhcHNN7jpI+T6MQ6pupWYG6XRSLGwtpGmacxeQwODOCfA8gCVG9XCfGZCTBmnZJjQRY4InNZpRgQ2CbkDPZxVlHr1ORhYpWp+UErY4yZxwIcjSTMyN7QHZb3GYcxxwZ1EZBWCRAqhFGBcbUqe+nbYhEez0OmEKz4uiA7LqXecV14Y7L0UEfBV/b+XMO+9GPvkM+n+NP/uTtp91WUoXBgh6bbRHW6kXlarGyryX878ISWVnCEp6m0HWdRx++j+npKWpj9aiqSi6XYf/exwkEQizrmQ16u+GGW/jiF2/nXe96PYFAiE9/+gtnpRk4EWvXbSKdTjI5MYZjCw1BKBRm85btVZPGdDrpCvFTBEMRbNvm4IEnSCanufLq60+bvSF7ILherGrqU4AkJgCepqfWZvNEZLNpHrz/XlLJBMFQGNu2OXRwD8nEFFdec/1JJ9LtHd309x0llRJVC8dxSKUSBINBWts6Ftxnxcq1TMRHmYiP4vH6CAZCaKqHzu4etl96dSVZvv/4EZLJaRqbWitEwTBCjI4MMT42QntHN5fsuBK/38/o8CD5Qg5VUdE0DVmSSKWS6OUSliVKWfv27KSxsfm0LX5+f4BVq9ezavV67r7r5wBVJNnj8WDbNqlU4ozIygxkWaaxqYXGppbTbzwHlmUt2KUlSRKmeSZxkICsLdr6dy7MyDK8o7/FdtpmqxeW6D2y/Y1gm/gP3Y42vQcrslyQiHISz9jvcbQwSAq2FsbRQkhGDiQZWU9hAUphHMvfgB08eUbFXHiH/hs1fRSzZn0l1FIujOEb/CVG4yXYwfnWWbJ34cn3u9/9AXK5DB//+AcIhcK8+tVvmrdNfX0TfX1HRUZGSpCHmb/hGb1FeVS0Rp1LdUX2Cavg0qC7yIGYXPs6Fx67mRD7KCEgWv26mROCdK0WQushfxiMlJjEq6FZYiEHBNHxtbg6kjOEEpy1M14QM8L6E3UZ9hxh/5xnuxKEe4qgTMeBouu0KPnFj+yAkXd1P2WQbCrObJJLBmzdFeaf4vPXsUW1aaYKp8YEKT0Rtm3zzW/+Czfd9MJF54PJqvt+XhifmiU8w/A0mgYsYQlLmIv4+AiJxDT1DU2VPvtIpIbpqQn6jh+me9mKKkLy4he/ikIhz5//+dsJhUJ8+MN/c86EJRgKc9U11zM6MiTE8R4vra0d84Ic+/uOkk4naWpum9VQBIJMxEcZGx2kq3vFac8le8REw3fm89onDQN9x0gmEzQ2tVQm5oFgSLikDQ/Ss3xh8XRjUyubt2zn4P7dTE2MI0kSoXCEjZu3ndQ5KxyJcsVVz6G/7ygTE2N4vV7aO7pp71hWpbuYmBjH6/VXEQVN82DbFulUgvaObvz+AJfsuIrChhzT05M8cN89ZNw8E72so6oKmseLx+NhenqSPbsf5dIrnrWo50fXdUrlEpl0En8gUCFsjuOA46Cqi1sKzaRTDA/1k81mCEcitHd0n9Qp7VSob2gSehVXlA/CWcyxrTMmPmcLo/UaPFO7UdO9WIEWJMtALoxi1m3EqNuMmj6MluoVRMV1HHN8MWxvDUp+VES5yypmzSq0yceR9TSWWURNHQJZo9x1M462iNmybaBNPY7ta6gQFQDb34yaOoCaPoK+AFk5FT74wU+Sz+f5wAfeQVNTC895TnWmTGNjExMTcaEbceYvNsgeV+dhnXsrmBIUK/D2TNFSEtUOq1TtJAWueUCh+rUZPdxc618tBuHNQr8xI2KvbF9yqzPn4Ex9Ksg+QZjK46L9TJJdzU92VkxflV7vkhrZK153ylQE/VbRbSMrQ3nCzWxxSZwcFe1ejg4o4t6oEddkQXKrap5TVzHsMuQPisqZI2KzUCMQWDO/De93v7ubY8cOn15Yv4QlnARLZGUJS3iaolQqguPMEwR7vF4K+dyCjkuvfvWbyOdz/PVf/zmmaS0ohj1TeDxeupedmmxMT8bxen1V51JVFccWTlj/WzA9PYnH46kiBjPvTzqdxDQNJEme955JksSKlWtpbeskMT2JJEnUNzSdtqUpFI6wYdPFp9zG6/VhWfNbzHBA1aorWoFgCJ8/QHNrB6VyCY/Hi9frQ1VVTNMiEo1SX9/I+PgIuWxmHik9EfHxUXbtfJDJ+CjT05NkMikaGpupb2gmnU4SCIaqcltOhOM4TE9NcOTwAY707seybfyBALZlc/xoL9svvYrGpjObTLe1d9LW3s3wUJ8gSpKEaei0tnUtugXsXGHWriO/7q34+n6OmjqAZFvoDRdTXHMrqH5RLbHKOCfYR1mhTtTCOGrmOGZkOVagFSk0iaL6sEIdWLF16C3XYDRsW/RYHElBck5UnDuuQvzM2YIkSXziE//AyMgg73zn67jzzvtZsWJ15fcNDU0i1FOzQFZONFTDLldXW84HJFW0SZVHBFGRFNE+6u+ZnaB7m0UVwC7PWjtbWZcgnOA2qPjB2y6CHK2s2N4uAzZ42hauHpyX65CEdbBddIMuXTKlBMHf5o4nM6tZsXPCAQ0Vck+AORP1JM1qamxDkBbPCTxd8Yu2Mk+TaLWS3JY3qyw0Mp7ThEcW+2Zd1yTVFeqnRfBmeEv1o/WNb/wLGzduYfv2y8/XrVrC/zEskZUlLOFpilAojKzIVSvEAKVigda2rpPqUt7ylj9FlhU+9rH3kUxO8bnPfe28ie5PBp8/gDk5XvWa44YYaJ7z51B2rshm0oyNDWMYOpFIDc0t7WdkBBAIBDAX0J6US0X6jx9hePA4qqrR2bW8InCv3j9IIHCqPpAzR0fnMkZHBsnncwQCQRzHIZ0S+pWFiIIsy2y6aBvJxCST8TE0TcO2bQLBIE3NbaiqSrFYwDAWsFSag2KxwGOP/IFcLkN7xzI8Xh9Tk3ERnpnP09jYzOYt209aHbFtm317H+fwwX2MjAygl8v4AwE8Hi9Nza1MT8XZu+dxrr2uqYr8JRJTDA/2kctliURq6OzqIRKdPYemedhx2dU0t7QxOjKI4zi0tnbQtWwFnifxWbRDHTiqiCV3cFAzx/H2/Rel1a/DCjTjeCJI5SSOb3amLEjNNhxPBDXdK44T7qJw0fvRW04dhLkgZA2j4RL8fT8V1RVFxKEruSFsXx1mzZrTH2Ohw8oyX/zi7Tz/+Vfyhje8lDvvvJ+IS2wbGppE+58xQTDWgj4BBBAWwCXR6eSd0x13PqCPCSG/pLiTcEsEMdoGhDa6jlzNYjKvjwmdhuOIbQMrFrZR9i8TFQd9TFQa1IAgMCdO+kFoTcojoh1NUgQB8LSeHalRghC6SBAIuySIkqfObWHzCZJg5t1KRkycq3BEVFXkgGhpM6dEu5evDSTXdtmYBHmOBsVxqyfBVVDyivbbGaG/b7kI5D0Z7LLYXg7MHk9SQImINj8zA1qNeP3IkUPce+9/84UvfPO8tCYv4f8mlsjKEpbwNEVjUytNzW2MDA8QCkcrmhVN87Bi5aknGbfe+i5isRh/9me3kkhM89Wvfu+857DMRWdXDyPDA2QzaULhCI5jk0xMEwyFaGldXG/9hcbwUD+PP/Yg+bxYfpQlmaaWNnZcdk1F/3E6tHd0M9B3lEw6RTgi8lImxkfdkESZmto6SqUie3Y/Sio1zaWXP2tRVrnngo7OZaSS0xw72ks+m8EBgsEQmy66hGh0YZ/P2to6nvWcmykU8pRKRaLRWkLhKJqmCT1OMEToNEGjY6PDZDJpGhqbRR5Lawe1tXXEx0epjdXx7OfeQvAU5goT8VF6D+4FSUJRVKI1QWzHIpGYJBAM4g8EmZqMk8mkqHVb5UaGBnjskT+QL+RRNRXTMOnvO8KOy66hobG5cmyPx8vKVetYuerM9SbnBY6D//C30aZ2YoV7cNQgkp7GO3IvjidMaeVr0Jsuwzv039hmAUcLIpemkSSJ/Lq3YMY2CGE/EmbNylNaHZ8O5c4bUTNHURP7mQkHcTxRSstfju0/e2ulcDjCt771Y26++Qre9a7X82//9l/IssyyZcsBON53mEu3tiB7XB1aUZADX8fCFr8ng+MIXYk+KSbYSlTsP9Pm5dhCj4HsVhlwdReKaFGy0rN5IoFVboUlKybXau38drEZSDL4O8WE3zHFMU8sRDmWOFa+V5zHcURVojwO3sn5FYbFQtaEGP9EaPVuHsvgrHWzPuYaC8zk1Cbd6zddA4CgIBFWSpAItcZtwzPA0ymIWnAt+FzXMdl3evtrx0LYL5/I/RVcr+XZl/7hHz5JS0s7L3jBqe2Kl7CEU2GJrCxhCU9TKIrCJTuuIhgMMTI8SKlYoLa2njVrN9LcMksAhKuU7VoNi5Ur27a5aPNW/vQ9f8EX//mz3Hzz5Xz5S99mzdqNF2R1q7Wtk/UbtnC4dz+T8TFAaC42XXTJWekOzjdKpSJP7HqEcrlEY5PIOjEMg9GRQQ737mfzRZcs6jjNLe1s2LyV3oN7xXVKUCoVCAZDtHV0z8k8KTMyPMjkxFjVe3UhoCgKm7dsp7N7OcnElBCmN7acViAfjday7ZIreGLXI9i2je7qTnAcuns2MTTYx/jYMAAtrR10dC6rqvAZelnogWcqfJJUsQv2eDynJCogyI5lmrOtZhKoikY+n6P/+BFUVWTBHNi3i62XXIGiqOzdu5OyUa7k1TiOw+TEGPv2Ps41z7rxvLjgnQ8o2X7UxD6sUFdFW+J4a7CtEp7xByl3v4DiylfjeGvRxn6PbOaxwl2UO27AaL4CJAmjcXHP5Ong+OrIb3of2tROlOwAjhrCqN+MFT29jux0WL58FV/5ynd4zWtewOc+93E+8IGP09OzCp/Px/79T3D55dcQXAv+kpDhKL4zb/8qDYpqAiZiMjwBRlwYcihBQSRmBN5zIWuiTcoqzU50JEm0LZ0u/X0uJEX8OLYgPzPVByMLxjjo08LyGJWKMN6xoXhMiPADq0919GoNyulQ7BetYMjiPhrTwghgbjikM3OfLJdkuTky5ZKovlhpUa3xdVVXTxQ/ldDK00H2uWYJ2er7bhcFgZnR+ezZs4s77vgRn/vc185rBtgS/u9hiawsYQlPYwQCQbZtv5L1GwuYhkEgGKpaqY+Pj3D0yEES01P4fH66e1bSs3w1vQf3snfPTro6uviz9/wF//Llf+B1r3sRX/nKd9h2yfnvG5ZlmVVr1tPU0ko2k0FRFBoamxe05X0qMBEfI5tNU9/QXCEUmqYRCIQYGjjO+g1bFtUqJ0kSa9Zuor1jGdNTE8iyzK6dD2Lb1fohj9eLZZkM9B8TRLNUIBZroKOr55QWx2cLSZKIxeqJxc7M53nFqnVoHi/Hj/WSz2WJ1TXQ3b2c0dFhRob7kV1R9tBAH2OjQ2y/9JpK21w4EkWWq9sUHcehUMhR39CDruundIETrl0SPp8fn89PsZhHUVRKhQKq6kECPF4f/X1HxVhXriOTTlFTG6vca0mSiERrSSamyGUzVe1gi4VUmkJLHADHxIr0YIW6zrlHSTKySGYJ5wTCVqmg6FmcUJRSz0sodT4PySzgeCIXTAzheMLorddekGM/61k38KEPfYpPf/ojrF+/mZtvfglr1mxg//4nKtvIvtm4jDOBlRdkRVJFdQBc0pAUAY/B1eJ3skeQkrnCd8dAiM1PMUd2LFfH4gZWKuGF33qrIGyYzRQi7T0viIBa65IlHVE5CrljcIMwC33g654/hplgxvKoOJYSFJUUreHkj57l5rRInjltawFRLbGy4NS7lsZeIOuSoDk3XYkIW2o1LGyYlXP4aJZkYYSSPyjuieRx77cj2udm3ofPfOZjLF++ile84rVnf7IlLIElsrKEJTwj4PcH5q16jY0O8fCDv6NUKuIPhEhnUux89H4S05PEx0fx+nxEIjXE6hr42F9+mr/9zF9x662v5Cf/dQ/LT+JadTYol0v0HtrH0MBxTNMkVtfA6jXrnzZEBcC2LcrlMhNuHojm8RKtqUWWZTf1/USv0FMjFApXSMeRwweYnpo44Xw2uVyWQwf24PWKtPfB/mMM9B/lsiuuI1rz1MUwO45DYnqSZGIaWVFobGqhq3s5lmWhKIpLsPqpjdVXSIiu6wwN9tHe0V1xdmtsaqW5tZ3hoX5BomWFiYkxSsViJeyys3s5a9ZurKrIzKC+oZGjRw5gWsKla2S4n2QigWVZeL0yPn+AtvZOVM3D6MgQ9fVN7vhPvCD3v2fBLzxj9+E7+gPkoghzcDwRym3PobT85VXuWWcKO9CM44kil6awA7M9T3JpGttXhz2T1+LYqJkjqIn9SJaOGV2JUb8F1AtkN3WB8M53vp99+3bzp3/6Jnp6VrJ+/WZ27Xr0nI9rZtzWr5rZ1yRZTLaNKXBWiKqHtw0KvW4SvE8QCDsn2qZOVkUxs2IfM0PFtczTKCb0kuq2n6WFk1a5T1RUtDq3opFyz2G47WGOEKs7OuB1vQtUMYE3U/Pb3vRx0TqGLSb6xrSo2gRWndyq186JysjcewGiLc6YdIX3EUFkzJRLVBRxT5yiqK742qudz84FWgOEZCiNum5lERF+OXOtDz74e+6997/5+te/f8E1k0v434+lJ2gJS3gGwrZtDvfup1wq0dDYUllpzudzHDt6CNuyaZojru7o6OIjH/okn/uHT/HiF1/HD37wS9at23TO47Asi0cf/gNDA8fwB0TVZ2S4n8T0JJdfeV2VjuCphGmapJIJLMvA4/Fi2zbpVAKfz8/a9RctOJleLDo6lzHQd5RsNo3fHyQSrSGdSlIqFqipiVFXLxLqbNsWOo1De9l+6dXn69LOCJZlsWf3oxw/1oteLoMkEQwG2XTRJXQvE7k9kxNjSEhV98Tj8SAhMREfo7NLaBJUVWX7pVcTidQyPNRHKpWgXCoSidYSidZi6Dr79uykVCywbfuV89oPW9u6aG3rYmS4H03zEI7Wkk6lCASCtLZ1Uhurw+sS3nQygaIqRGtqSaemqatvqrSBZdJJmlvbCYVOrbE5EXJuCP/h74JdxqxdC0jIpSl8A7/ACndhNF921vfZ9jdSbr0aX9/PkewythZGLieRbINy+/WCjBgFgns+j3fk12BbON5aHNWP0bidwrq34mjn14jhQkKSJP7xH7/OC15wDW9848t57WvfzA9/+O3TVtfOGg6iVOM+Ut5WoeHQx8SkXVJAmyEeC5R0HMslKikx2cclGuURQXb8y2ZF+1bRrahIwLSri3FcC+bCrE4GRNXHsVxLYcVNuj9hHcQxRbUIhH4EgICY8JcGBWFa0DJYda/ZzVuZgewR7mrIQpeCJHQ5kjanDaxTpNGfL6IC4rhavfhxbHHeSiua4/DpT/8lmzZdzM03v+T8nXQJ/2exRFaWsIRnIHS9TCoxTTAUrpoEBgJBktOTOIhsCUVRMA2DYiGPpqr82Z/+Bd/9/r/zkpc8m3//95+xY8cV5zSOyYlxRkcGqY014HF7kgPBEBPxMY4dPfS0ICuWZdHfdxS/30+pJCa4sqyQz+eQgOUrTtNUfgqUSyUO7N3FRHyMcrkEiPayhsYWQuEItXPasmRZJhSOMD42gq6Xn1RnqhkMDhzncO8+gqFIJd8llZzmiV2PUBurJxoV1SaHE8sXYFom42Mj/PevfoptWbS0drBi1Vouung7a9dt4jf3/BKP10e9S878/gCqpjE02MfKVevm5cl4PMK1q7+vlZHhAWzbRpEVFEWmvmH2uTFNA0mWCARCbNq8jUcfvo/4+CiKomBbFtGaWjZu2nrGehVt+gmk8jRm7YbKLMv2NyCXp9EmHj4nsgJQ6nkZjhbCM/o7ZCOLFWxBb78evfVapFKC0OOfxNd3B46kgOLFsXXMmjVo8YfwxNZT7rjhnM7/ZCMQCHL77T/mxhsv4xe/+DGGYXD48AE2bLjorI+p1ogWKjs/q4NwbJF34u2eJSKSDIEeUZWwCmKirgRP3lJlpkRFRZmTIi95RSVEHxfVgWKf4ERKWLR0SV7XvjiPsAYGsN20eZdsOI4gPZLmvu5zicQcWEXxc2Iblhxwc1HyINcscC8i4sdMiSqGpIjx2iVh0ezrcm2WZbeapFTnrpwrrLz4kRTXsGAO8TmREN59953s3PkQ3/venUsOYEs4L1giK0tYwjMQiqKiahqGUW2ja1kWHq+XcChCMjGFqqokpqcolYoYepm6+ia++IVv8tGP3cYf/dFNfO1r3+f6628+63FkMils95wzkCQJfyDA1GR8wSyYJxvZbFqEI3Yuo1wqiTwUwyBaU4MsK+ckyn74wd9y5PABAoEg4UhUBCQWCxQKuSqiMgPHmVkMrr4ntm2TcHNKNM1DY1PLaTNYzgZDg31Iklxln1xTW8fE+CjxsRGi0Vqamts4frSXUqlYaeUr5HKkktOUS0VqauuRZImDB55gcmKcK656NpIkUSoW5onq/f4AuUyabDZTISuO4zA+NsxA/3HyOfH6RVu2E6tr4EjvfnY+9gC5XIZgMIxpGiSmp2hoaKKxqQVV1bjmWTcxPNRPoZAjFI7Q0bHstM5lC0Eyi1QtB7twZC+yfh6ygRQP5e4XUO64AcnIuZoUsWTuHfwlnvgjOIoPOyCImVxOoKaPYIW60CYeE/vpWbSJh1ETB3BUH2b9RRj1W8+pRe1CoqOjm6997Xu86lU3AbB//55zIiuKX1Q5CsdEmxSS+MtRY+BdwFpX9p5aozID22S2OjMHkip+Z6QECZADol3MLgO6K9rXxTmsnBuiqIJaNxuwKIdAdgXw3nZxjKpzyOLHsas/BSohmSd5a2fczAqH3NY1xP3wNIC/W4xJ8c2GSNpFQZoq1ZuzhGND8bjQyzi6uGdqWIxloRY7y7L4zGc+xuWXX8M11zzn3E6+hCW4WCIrS1jCBUImk2JkaIBcLkMoFKGto+u8OWNpmkZHVw/79uzE6/O5wYAWielJYrF6tm6/kvt/dzdHjhwEHDweL5H6JgLBEEcO7ePLX/p3PvThP+WNb3wZf//3X+GP/uhPzmocMy0eM25kMzAMg3A48pQTFRDkaWYc0Zrail5E18tkM+mzHqNlWRw6tA9ZUQiExCTd6/PhDwTIpJIYuk46laCmtg5JkrAsi3wuw6rV66taYwxD5/HHHmRosA/TMECSqKmpZeslV5z3ylS5XEI5oX9cksSE3XDzY1rbOulZsZrjxw6TTiXEfqUSsqzQ2t5VqQiFw1Em4qMMDBxj5ar1eL1eynq5ygbaMHQURakiXseP9bJr50OYponm8TAxMc7wUD+XXn4NPSvWUCjk6Tt+mMn4GIqrqdm67TIR8Ej1e3gusEIdYnnYLM1qRBwbycxi1qw9y4OWUXKDgIQV7hKzW8WLo8yZQZslPBOPYGthFD1Tedn21CCXp5GMDDgWkp4msO9f0KaeEMdxLLyjv6PccQPFVa85O0/cJwFXXvksPvrRv+PjH/8Ad9zxI175yted0/E8raKqok+5ovawaD06l2qB4uaDOHq1/a5dEqJ5Sdzuil0ysqhiWAagiqqN7BF6Ezst3p7AGnFcKyd+52kUrWgnfrzIAXEOPS7GICniXFZO6EqUar5fBTUs7JCNpCBNin/WlhmEhqZwRGhYZgwG1BphRKCcZVehPgalAZB8oNQCtiBL+UMQvnh+y9p//Me/cvDgPn7xi/ueFp//S/jfgSWysoQlXABMxEd55KH7yGRSKIqKbVkcO3qI7ZdeTWPTAgb6Z4HVazaQzaQZHRkknUyAJFbJL952OXV1DTS3djA9PUkkWoPm8RAIhJAkifj4CFOTE3z96z/gwx9+D7fd9hYOHtzLxz72mTMWQja3tBOJ1jA9GSdW34gsyxQKeSzTqGggnmqEw1FidQ2Mjw7jafSKNifHIZ1KUlfXMK89aTEoFPL0Hz9MKjmNLElYplkhATP3sL6hCcuymBgfRXLP2dDQzOq1G6uOdfTwQY4f6yUajeHz+13SOcHOR+/nuuuff17bxRqbWpicGK8il7quI8sSNVERTqgoClu2XkZbexeTEyLoM5mcZmR4oGossizj8XiZjI+xbv1FdHYvZ+/ux9BUDX8giGHoJKenaG5tr+h2SqUiB/fvRpYVGptExofjOExNjHNg/xNc86wWNm/ZTs+K1WTSospUV984L6vGNE1s20bTtLOeEBm1G7CCLXgmHsL2NWJ7osjlaazIMvTWMw9gVKd24Tv2I9ScsHs2w12UVrwCM1b9fkuOBbaJ7Y2hFOOzZEmSwbaQzAJGw8V4xu5Dm9qNGV0FLtmRSgm8I/dgNG7DrH2KMmQWgTe/+T18/etf4Pe//zW5XPacHPDOxm74dFBCotWrNCwm9dJMYKUiROhqFGzbbckKuRWLstsG5YAaBP8ml+iYrpXvSZzEFrqewHJBgszU7OtajauxOc0xJFVUUxZCaQD0UZGtIocAS1SkCochtPnM+a3jQHkMUOa0rSmifc5Ki/ybueYBY2MjfPrTH+E1r7mVrVt3nNnJlrCEU2CJrCxhCecZlmWx54md5PNZmprb5uRBjLN3z06uve6m8xIU6PX6uOyKZzE1OU4mk64kf8+sYut6aZ5uAkCWFUqlIqqq8pnPfIk1azbwV391G4cO7eOrX/0esdjiJ+8+n59t26/g8cceJDE9gWPbeLx+Vq/ZeEqyYpom42PDJJPTKLJYPa+rb7wgK3GyLLNx01YK+RyT8TFBHGyLcKSGjZu3nfF7kUxO8/ADvyWRmMKxHfKlArbtEI6IUMVSuYQky6xcvY6mplZGhgcpl0vU1MRo6+iqqjxYlkV//1G8Xj8+v5gNKIpCrK6R6akJJuJjtHd0n7d7saxnJaMjg0zExwgEgsIlrVSio6unypBBURRaWjtoaRW9Ngf272Z4aEAk2+s6qqriDwTdtkPxvK1es5FSscDgQB+5bAZFVWlubWfrJZdX7nEqOU0+lyPmkhcQlZ1QJEoyMU0hL1q7wuEo4fD82WmpVORw736GBo5jWRYNDU2sXL2e+oYzSBkE5MI4/oPfRMmPIlll1OR+bH89xZ6XU+6+BTtwZgsKSqaPwIF/RdbTmKEOJMdBzRwjcODr5LZ8EDs4e28dNYBZuwbP6H2YoW7UbJ+IHbfKSI6J3ngJestVBPd8AUcNVogKINLuC6MoqcNPa7IiyzIf+tCneM973sD73/82vvrV7z7VQ6qCJInketknNCq2IaodvnZRtZEkQVDsGd2HyNIUGhoNPG0LhzYuFkoAwpvdCklJtHCpsXNzrrYNKA+DVRa6HWRBypSgaGuzMmfREuaIFrgTs3HmVnLm4qMffS+BQJCPfOTTZ3cRS1jCSbBEVpawhPOMTDpJKjlNJFqdBxGtEXkQmXRyQT3D2UCWZRqbWmlsap33u5raOgYH+qp0I7Zt4Th2JdlckiTe+MZ3sGbNet785lfxvOddzu23/5i1J6z+nwqNTa1cd/0tTE6MYZoGNTV1RKI1JyUeuq7z6MP3MTzUj21ZOAjitW7DZtas3XRBCEtdfSPXPOtGRoYHyOWyBIIh2to7F5wQnwqO47Bvz+Mkk9PinksS/cePUCwVKJdLSJKEbdtEolG8Xi+1sXpidSdPCbdtC0Mvo2rVvRSyLIPjYBj6WV3vyaAqKnX1DUxNjDM+Nky0ppb1m7awes3GU1bV6uoayOezTMSFsF2SZTyah3A4Slt7FyBaE7decgUrV60nl83g8Xqpc6ttc69LVJmEXZJpGpRLJdGepqjz9EOFQp7xsWH0chl/IEh/32FGhgfxB4IoikJ/3zGmpia44urnLD5jxrHxH7odbXo3VmQ5Zs1aJD2FkhsUwY2hBcQQp4EWfxi5NIVZux4kSRhcRFejJvehTTxKedkcP1pJotz5PNTMMeTcMFaoA7kYB1mj1HUz+c23CX3LzAx5/gU8KS1gSuoQnvGHkAvjWKEOjObLsSLLFr3/C17wcv78z9/GHXf8iNe//q1cdtlT44B3Mkiq0Hr4Oly9iFZd1VAjs61UWKLlS/YLPcj5iMM5VYXkbGCm3VY52+0aNMGecgMjXS3OmQ9SiPmNCXD8s/fHdu+LMkePc9ddd/DLX/6Mr371u0TPIu9oCUs4FZbIyhKWcJ5RiX04Yc4tuXLKhaYfFwJdXcsZ7D/GRHyUUDiK4zjkshnqGxpp6+iq2vbyy6/hrrse5A1veCm33HI1X/zit3je81686HN5PJ7KpPV0GOg7wuDAcWpjdZW2omwmzcH9T9DU3HbaSadhGExPTWCZJjW1sdMmtc8gGAqzas2GRW17MuTzOaYmx4lEapBlmaamVizTYnDgGHq5hMfjEpRYPY8/9hCa5qWj8+QTvFKphKZ5mJqMEwwEkdzJeqlURPN4KqTyfKBYLPDA/fcyER8lEArjCwRdXU2qEv54Mhw7cohiPo+h65iyhKqolEslojUxWts6K9tJkoTt2ExNT5DNpAlHonR2LquQ81hdAzW1MRLTk8iyQmJ6El0vo+tlGptaKZVLWLZF37HDHD922HX9kgkEQuiGTjaTZlnPKgJB0YAfDIaZiI9y/Ggvse2LIytK5hhq6hBWeBmOKmZbjrfWTZd/gHLXLTge95kyC2iTj6PkBkDxY9Rtwoosn/fHLRfHhS5l7uuSBJKGXJycNwYruoLc5tvwjN6Hmu7F9sYwmi4TqfUuETEat6Em9oJZBNXvnmcSRwth1py9g91ioI0/QODQ7Uh6GkcNoE3txBN/gMK6t2LWLc7y3OPxcN11N3Hfffdw221v5de/fqzK2OHpgpmE+hPhaRSBkXLInfw7wqpY9ogqzFMB2xROaJI230hAn0TwWEUQKwk39DIDWuzsQiAlSTismUnR9iX7xTHtkrg/M5WabDbDRz7ypzznOc/jlltedo5XuYQlzMcSWVnCEs4zotFaojUxktOT1DVU50HE6hrO6wT0VAhHolx6xbM4uP8JpibHAYnlK1azdv3mqlakGXR0dHPHHb/nve+9lVtvfSXvfe9HuO22j56TW9ZCGB4eQNW0Kv1DKBxhYnyUifjYKcnK5MQ4u3Y+SDKZwLFt/IEAK1atY+26zed9nAvBsW23UiXOJUkSNbUxpqcm8Hq9tLV3Ud/QhKKoTE3GOXrkIG3tXfPGZpoGe3Y/xvGjh8hmM6RTCdLpFK1tHTiAXi6xYuXa81aBAyrEtaGxpdKWZRg6o8MDjI0O0dnVs+B+h3v389ij92NaJh6vF9M0UDUP7c1tSBKkU4mKJmVsdIhHH76PfD6HqmoMDR6n//gRtl96FS2tHaiqxuaLtnPvPXcy2H8MSZZRVZVwOIqExAN/uAdZlklMT5FMTFEul/F4PHh9fjyah2IxTzqdrJAVSZLw+QLu8704SEZOpMur1X8DjhpE1lNIZh7HE0Yqpwjs/zLa9BOAA46Dd/BXFFe8Er191uXIcaBoryeX9mOWu/H4EvgDQyhKARyjKhRyLuxQJ6VVrz7pOPWWq1AT+9AmH5s9keKj1PV8rOj5C3WdB7OAr++/wNYxYxsq51bTh/D1/Yxc7fpFu5E9+9k3cdddP8cwdP7u7z7GJz7xDxdu3OcZ3ma3WhEHy81KkT3CnUw9hQj+QsCxhb5GHxHCekkRWhFf9yyRstKiCmLlXP2N6uafWLPtYGcDLQbBdVAaci2cZbci1Tlb4PvMZ/6KVCrJpz/9hSVR/RIuCJbIyhKWcJ6hKAobNm6p5EGomoppmIRCYTZsuvi86FUWi1isnsuvvK7SonQ6O9xAIMhXv/o91q//DJ/5zF9x4MAe/vmf/+2cBLInwrIs5BO+0Krb1Bwm4mOMjgxiGDqxWAPtnd04jsOjD99HNpumNtaAoijkshn2791FKBSuJKtfSARDYWKxesbHR/H6fEiSRLlUpFjIIcsK01OiohCra8Dn95PNpDBNs8r9y3Ec7r/vHp54/GEcwOPxEgyFKRQKTE7G6erqYcOGLSxfuea8fvHH46Nomqfq+dM0D7Zjk0hMLUhWCoU8e594DMuyCIXCqJomdDr5bMVZbCZfxrIs9u15nFKxSGNTa4WkT0/G2bd3F41NrSiKQlNzK3X1jeTzOXw+P4qiUlMbQ1FUjh09iKZq1NU3kkxMUVNTi2VbFVc1VVHJpJM0NlUTrjPRWdmBFhxvjUiT98/24Yi0+RZsrzAa8A7fjTb5uKhiKF5wHJT8MP7jP8GMbRAp9Q4U+6E8eTmKUY9cLlDKtlLyNlLv+0+scBtG43awdJT8CI6sYQdbF9XG5Wgh8hvehTa5EzV9BEf2YNZtFATiAk4I1Ww/Sn4MM9w9+6IkYQXaULIDyIUx7FD7oo513XU34DgON9/8Er75zX/h5ptfzI4dV4pfWmU8Y7/HE38IychhxDahtz1L3J+nASQVgmtd0jKTX1J7arcuEMTBTItUesdy3csazq11rDwCxaOue5hPtKeVBsR/A2vdIp5bUfE0iGqKY7ivBYQW51zgqQfNtWd2ZFDmdK3u2vUot9/+Zf7qr/6e9kVW15ewhDPFEllZwhIuAFpaO7j62hsYHOwjl00TCot2mLNxnjpXiNXnxfcASJLEe97zQdau3cg73/k6brnlKm6//Sd0dy8/L+NpaWlnIj6KZVmVCWepWETVNOrqGuk9tJf9e3eh62VkWeHY0V4G+o/S2t5JJp2kvrGlUqkIR6KUJ4sM9B97UsiKLMus27CFbDZDfHwUj6YxOjqIaRoEQ340jxfDMBgbHSIcjtLa1jFPCzI4cIwndj2CaVkEgiFs26JQKBAKhQiFoxfEshhcYmJZ83/hCC3LQpiajKOXSwSDog1L1TQkWcLj8ZJMTtPa1lFJjk+nk6TTScKRKJl0imwmhWVZeH1epqcmSKeTxGL1OI5DPpdzq40pbNsmk0lRV99IqVDEVyue1ZkKlqrKlEslFFnB4/FSLBaExbPjMBEfJZVK4PX5OXrkIJ1dPad1T7MDzZRbrsLXfweSVcTWQsilJGBT7rwBFBE7rsUfxvHWzgrcJQkr2I6aPICaPIgeaMbKQXkI8AeR29tQEweQyn2Ui22kg8/Ds34NSnaAwN4vouRHQFIxa1ZTWvFKYW18Oqh+jJYrMVquPP22ZwCpNI02tRvZyGD5mzHqN4PiRy5OQDmJSD084VlxLDErPoOMl+bmVrZsuYR0OsXWrZfyvve9hbvvfoyAz4v/0L/hHf0NjuwDRcPX/1O06d3kN/1ZlSHBhcZMLomZFJUIJSyqCTN5KFqd+FnssUpDUOoTug5JgjJi/+C6s7NbdiyRcYLiEiVHHNuyoDgg7JE99eBpBqMXJD94Wlw75LwgFosd/6muS48LZzC7KAiYtxXK3izvfvfr2bTpYt74xnee20mWsIRTYImsLGEJFwg1tXVPCTk5X7j++pu5884/8IY3vJTnPe9yvvKV756XkK9lPSsZGxtmMj6K5vEKkb3jsHzlGrxeH4cO7EFRVZpi9di2TTqVoPfQPoaH+rEWmGxrHi/5XPacxzWDcrlEJp1CVVWiNbF5LVyNTS1cde1zGew/Rnx8lERy2g1LsEX1yucjl82STiXYcfm1Vfs7jsPhQ/swTZNgMIyqqYCGoqgUiwUURWVyYty1vFZobGo9b33+be1dDA0cp1jI4w8EKxomWZGRZImJ+Ng8m+AZwhCra2R8bIhioYCmaeh6Gcu06OxcRjgiTApm1von4+OkM0lXBy6TTk2jqB6KhTy4ZCWXTZOYniIciaC6xxsZGsCyLRRZwecLoKridc2tSqmaRigcEZbIiSkSySnKpSLhcJRSqcCjD/+BsdEhdlx2bVUlayGUlr8CxxPBM/p7kS4f7hTp8i1zLIsdE2fBCojj9tcIhyVHF/kTttSI3lqPrGewiyo53xZi9iECB78BVgk70AqOiTb5GJKeIL/lQzie8+jHu0ioiX34D34DJT9aqdBY/hYcb1S85thIxTiakRYBlJIMjoWSH8FovATb3wy2hZI5imQWRDUqcHJy/cd//CY+8IG385//eReve90L+exn/5pPvvv1eMfvxwq2z94Dpx01sQ/PyL0Uul+DkRBOXLJPhC6eD1H7iaiQi363aoC4XE+TyCY50QXrdLDz4ljIgvDAbF6LMgKBU/gTWEVREZEk13XMfYRtXThyyV5xLGNKuH05NmBCdrdwFvO2CNJVjgM5sa/iE3bIZ6NXmYvSIBSPIUI5NdAnQJ92+PC338HERJz/+I87ztj2fglLOBMsPV1LWMISToqVK9dy550P8I53vJZXv/r5fPSjf8db3vKn59SeFAiGuPzK6xjoP8rE+CiqptHW3kVHZw/9fUcoFgs0NrVi2xajw0Ok0wl0vUwhn8PBQVHVig7EcRzKxSJtc0Tep0M6lSQeH8W2bWKxOuobmivHOnr4AL2H9lEo5FAUlfqGJi7asmNeCKHP52dZzypidQ1MT03Q1t7F+NgIxUIO27ZRFAV/IEBLS3X/hWEY5PN5fD4fpqm7ZEVks+TzObK5NE/sfgQQhgyhcJjNW3ZURPrFYoGpyTiO41Bf30gguPjm+bb2LlasWsfxY71kMikcx6FULCIrMk/sehRVVamN1dPe1kWxVCCfz2HbFrqhIykyLa2dJBKTlMsl9HKZmpoY2VyOgweeoKtrOZFoLT6vj4G+owRCYTwej6iiWCa2bTE5GaetvYvpqQlsx8Hn84t8Go+Cqqhk82n8gQCKquDgEKtrYCI+RqFQQFFkisU8tbF6Lt52GeNjw+x9YicdnT2EQmEkScIwDEaGBhjp6GdZzypM0ySZmMK27SozB6A6Xd4s4Gjh6tmwJGPWX4y3/w4xOXerCXJpEscTwYq6VbwT/wwkGdtbI1pwZPCM/QFJz2DG1lc2MWuCqKletKld6K3XLvr9Oy8wS/iOfBelGBfWx5KMVJrGO/QrHE8UvfkKABRZQcqPigmCEgAcrHA3pZ6XIedH8Pf+O1qqF6wyjreGcuu1lJa/bH5CIPCiF72CT3ziA9x//7184AMf55Of/CCvuLSdy6RSNVmTZGxvLYwNkkuJ1qsZqFHRknW2uouTwcq55EIShFOSRNVCHxeEwXeGBR4jNUteZyApgngYE8ACZMVxBBkoDc7aJMt+kcXiaRK3VNLcUMq8+JE9glg5Yo2EwhGIbBXBlJ5mV1uiCDvkcyUqdllYIkvanPsfgB/89Fvccdd/8uUvf4dlyy58VXsJ/7exRFaWsIQlnBLRaA3f/vbP+Nu//Us+/vEP8Mgj9/O5z32N2trYWR8zEAiydt1m1q7bPO93M65pmXSatNviM2NrKyoAo3g0D5FoDblsBr8/wLKe0wuOHcfh6JGD7N/7OMVCASRQVY1lPSu56OJLGR0ZZPeuR1wNRT2WaTI6Moiul7nmWTfh8XgolYoc2Leb4aE+TNNEURTK5RJhoKt7OcViAduy0I0yPq8ff6BaxC3ySQIEAiGy2TSlYhFN82AYOuVSEUUOEg5HK5WPZHKaXTsfora2jqmpOPv27CSXy4IDgWCQdesXr21RFIWLLhbEZ3pqgnh8lMGB44QjUUKhCKahc+Twfg7sfRyPx0ehkMNxbBRFBQnCkRoikRqmJuN4vF78gQDT0xPEx4YYHuzjsiuuo7G5jSNHDmDoZWG77Dh4fX4i4RriY8OwZTu5XBZN89DRuYypKdFmJkkytbF6ojW1tLZ2MDIygG3bBENB9LJBbSzG8hVrWNazCk3zsH/fbpDA7/NXrl3TNCRZJj4+SiAQ5Indj5JKJnAch3A4wroNW+g+cVJ1Yrr8HJQ7rkdJHURNHcBRAki2jiOrlLtuwQoJcqxGRTCgXZidyDmWmOB520CZHsLRTiCUsrifcmn6tO/Z+YaaPoyaHcAM91R0M0pxEgkHxwHUAI7iRW+8VOh16rdg1qzC9jVgNGzD0UJ4H/4S+XiUkvx6FM0gwD4CfT8XpKXzpnnnDAZDvPSlr+b73/83HnroMD/4wb/xia98h1+9o1vM1Oc+u5ZJurgD0xTBg25RByMFheMQOs9yHdNNgldrZ48ra2DLonpwpmQFTuL26LpQn3i5ILQtpT5Em1et2M7KQf6weKaUkKiaFI6KSh7C1RzHEL9Xa8HOiHvkbRIVHe3sP5rnYUawr8zhlb19e/jb/3gvr3zOW7jlua84fydbwhJOgiWysoQl/C+FZVnkchlURSUQDJ1TNURRFP7yL/+WrVsv5f3vfyvPec42/uVf/u28ZyfUNzTh9fnJZTPkchkAJFnCNHQamlqIxRo4fqyXYjGPzx+gqaWNNWs3LkrjkUpOs2/PTkCisVkIwIvFAseOHqKuvpHBgeM4jk2NS8JUVVRWpqcmiY+P0Nbexc5H72ew/xjBUAS/P0gulyGfz2GMDtPW1kkgEKRYyGMWDLrWbJxnaCDLMst6VpGYnkRVVPKFHOVyEcPQCfiD1NU3omqeyraxWD0T8VGOHD5Af99RbNuivkFcazaT4ondjxCORGlqXrwouVQqkkxOc/TIQWzbrlSWSqUipqGjGwaWbQtLaElCL5dEBcdx8PsDBAJBojW1lRZH27aZiI9y9MhBamtj1Nc34fH5MHQdj8dLOFJDIZ9FdnUxfr9f2BEHQyyLRDH0MrIsk8tmCEdq2H7ZNUxNjpNMTqMqKk3NbURraimXSzz0wG8Z7D9OOpWkWMyjl8s0t7QRcR32HDeb5tGH/0A+n6Wmth5JkshkUjz+2IMEAkEamxaX5mcHWshvvg3P2P2oqV4sLUQusplybCMBxPxTCYKvS0w2jYR4UXJEsKCvHaxSG2r68AkHtoTuwHseZ5SLhW2K2f+cKpJkZHAkFUlyZnUqkozjCWMFWiitnHUscwYOkhpajy61IasGuqFSktowPT5Co79Dr78YNXMUybYwoysqYvn/z959x8lR148ff03ZXm6v97tceu8dCC1IE6mKCgKKKAqIX1Cwd6VYULErqCBdBfSHoPQSAimQ3vv1fru3fWfm8/tjLptc6t3lklzg83w88oDs7szOzm7u5r2fd/nEJz7NX/7yW1588T985Svf51OfuowXFpZw5oSdmP5qUBR7BSrlJ6mOQ/Xv6UGgaPZ5Njrteglt/0aGA9aTzbd/ANQzW6W/9GBPsJPcs6IhrJ7gtezAgVa6pacQP9hzg2LXzRiddiCj+e1ZMFYaoqv2vEWar6cWRcWOkAZwvH2i9TyHZf83lojyxbs+zrDS0dx+9U8O2PZZkgabDFYk6T2odtd2NqxfTSTciapqlJSWM3Hy9H4PQdzXuedeyJQpM7jppmu47LKzuPnmr3LLLd8YtHzlYDDE2PGTWLv6XbojYVLpJEIIvP4AuXkFOJxOQrn5DB8xhkmTp+P2ePvcsri5qcHuVNUTqAB4PF5i0W527dxOdySMy9U7Z0LTdECQSMRpb2uhsb6WUG4BLrcdhLjcboxMhlQqSTQawewycLndjBo9nnHjDzyPYsTIscRjMbZv24Sqa/Y3/0F75aGttZlIuAuP10dBYTE+nx8FezUpmYj3OvacUB7NTQ3U1e7oU7AihGDdmhWsX7sC0zTpjoQxTZNaaysVVTX2ig0Kpmmgqkq2ViSdSuFyudE1nUAgSCzWTU5oz4W2qtpzUBrqdzH/lDPwB3JQFIXCnqAqk0mTTqWorBwGQEFhCQUFxTQ3NRDKy0fVNCLhMJlMiikjxuBwOCgtq6S0bM9wxrbWZl558T/s2LEFVdWy5yCVTNDYUIfb7cESAlVRUFWVSCRM8V7nKi+vgOamBmp3betzsAIg3AWkai6kNdLFujUraN64A0tsJ7+gkHHjp1BYVIK70r7QzLTbg/iy3Z8ckCk9CWfLUrTIVkxvGYploMV2YQaqyRRMO/CTGkn0znWomW4sTxFGzuh+FbUfihkYhuXKR403ZYvYheZBNeIY3nKEvvsKW4CVRrj3tM4WApJ1TgwzB2egLXvhbWT8RJPT8bUvJbj0WyipLrs+35lDsuo8UsM+xLhxk5g5cx4PPPBHHn30P8ycOY9v/HMrp00sQ+9ca+9IcxIvPB8zUoy27z9p1b7o37fm/0jpQTsYstJ7akSEZady6X3PLM3SAuCqgNQuMJJkgx5Hrr3SdiBWCjsg2Iui2MHw7gnxiga+0Xawlqq3V2BUZ0/aWs/cFW3wGjb2ogftP0YnqDmC7/7mRprbGnj8u28RKHPvN+9Fko4GGaxI0ntMU2M9S99+A8PMEAjkYJoG27dtIh6PseC0D+BwDKAlzV7Kyip4/PH/8qtf3c1PfvI93njjZX79679mL0aPhKIojB03mby8Ala+u8T+tj6/iPz8AnTdYbfLVVXKyisPWa9hmiY7d2yldudWkqkkRUWldlqSwn4rTJqmYRhpckJ5NNTtzBaMgz0PRVEUfF4/sVg3hmlkA5Xd/MEcPIaPufNPxbIs/P4ggWDOQVey7HSs2YwYOYZIuAshLNauXYFhGFimhaqqdEe6SCUTlJZVomoqum6nOO27T123C/P7IhLpYvOmtbjcHvz+AMlkgkiki0QyTkd7S0/duJ3Eou39zTt7SjNMyzrgvi1hH3dOTh7jJ05l7ep3aGqsty+6FJWKqmFU14y0h3maBpOmzkRZ9Q7bttj1M4qikJubTzwRs+e46HtqH1KpJMuWvEFTUwNOp9vuTJZO0Z1OYZgGqe4wtbu2kxPKY/jIMWiajqIo+50rh8NBd3ekT+dqb8lkgrfffJXW1mYCwRx0RaGhfhfhrk5OOfUsQrn5OELgCO2/rZE7kfi4T+He/jR6rA6h6GTyJpMceTla93acG19Fi9Vh+ipIl52KcObi2XAfemSrvQKjucgUTic+9to9gyoHykwjHH5S1efj3voYetcGhO61i+QdfoTDA2YGEGjRXQh3AZmimdnNRQbSyXwc+nZUy8qmz2l6FCPuwLB0NG8aI3ccoKAmmnBv/yeWv5JM4Qyuuuo6vvCFT7Flywa+/vUfcfHFp/Nwyxe4bF4NipnCDA4j4xuH9o6GGbeDvt12p9kN5qoK2HUpruI9Xa5Q7UDFEbJTr/pLUcAzvCd4bbPPmR6ya0+stN3mWhj2aomjwF7g0nPAaO+dIiZM7BWWfWp0PDV2cb0ZAzI9j7PAVXX4lsoDpah2kX5sAzz29P386+WHufvzDzBq4mjch2gYIEmDSQYrkvQes23rxp6J4Ht+2zqdblpbmmhqrD/kRPW+0jSNm2/+KieddDo33HAVZ501i7vu+jUXXnjk+cuKolBcUs7pCz+Izx+kbtd2Otvbdt/JsJqRvaam70sIwaoVS9m0YQ1Kz4X+7g5jpmGQTqVwuuwLLcsySaWSFBeXk5uXT0tzAx3trfgDQQzDIBLuoqSkjKKSMtrbmlFVjUwmg8Ox52I6nUri9wd6DVvsi0Awh0Awh21bN9LR3kpldQ31tTvt6fUOJ7FoN431u5g4ZSZFRSW0tDT2avdsWRZGJk1eft8GR3a0t5JMJu3PhaKQl19ALBYllUrR1dlJKDeXdDqFx+sDIRBCYPUEJy63B8s0qagcRiwaIRLuJJiTi6IoGEaGZCLOqNHjUVWV0WMmkJ9fSHNzA0YmQ25eAW6Pl8WLXqajvRXTNPH5/DhdLnRdp7ikDH8gB8s0Wbf6HYQQTJk6K3vczU097YldLtJpO+B0ulx4fH40VUVVNcorqpk2Yy5FxWVs37YJsI9996qbEIJMOk0o1P/Uq4b6XbS3NVNYVJI9926Pl+amBnZs38rUQ3T8My2FpbUns3r1TLRoHVXDNCZMqKQitgjPxj+jGEksZxBn9G0cre+CqqFkohg5o0BzoaS7cTQtwuUuPOQAyUNREy24dj1rD5cUkCmYSnLER9HCm1ATLaSL52E5c3C2vIXevQ1QMH1lJEdcjrnXrBVFBeHOwXQVo6fXg+4DVUdJJ1GsEOguTH9x9orb8paid61Hb1lKpnAGF1zwYe666zvcffd3+OMfH+Oss87n7l/+gvMvXZ09ryr2sMHYZjC6egrL03ZXLk/1gSfNHwlFBe8YO6BIt/asjOXZgYp26JFUB9+nYs86ce4Z4UOqwa45EendD+ppZzzODpYyzWB22m2HESAS9nE49vmnrQftup1Uo73SoTjt7Z0lR3X0DnoQVsZe4gcP3MSVl17PR67/KI7cwX8/JOlgZLAiSe8x4a6O/eaq2GlaglgsOqjPNXPmXJ5/fim3334Dn/vclbz66gv84Af3DEq7XYfDwey5CyivqKKpsQFVtYOY8opqNE0jkYhTu2s7He2tuFxuyiuqKCwqpbOznW1bN+ILBPB67a8bRY6gubEe3eGgs7MNh8OJqmokk3EKCoupGT4Kr8/PjFnz2bh+Nd3dETRNp6ZmJJOmzMThcPSkLxXR3NxAKLcAh8NBPBbFyGSoGTFmwMM+oz3f9nu9frv4vb2VWLQbh9NJXkERs+acjGVZ7NyxldaWRnuuiWJvl5dXQFX1nvk3lmXR1dmOaZoEc0K9amYURbXrfIVAURT8gRzKK6poqNuFYdqtlKtrRpJMxOnsbKezvQ1N07IrGZVVwxkxciy6rrN65TJamhqyK1Vl5VWMHDWu53kUCgqLKSi0J7enUklefelZ2tvbyM3NR9M1uro62LJ5PYVFJZTs1TFNUVV2bNvMqNHjs5+hdDoFwh7ImWhtQgh7GKeu6QghCOXmMWnKzGzaWHlFNVs3r6e1pZFgMISiqnSHu/AHAlQPYFZQtLsbAb3eX3vAqovOzraDbicEPP20wvPPK1iWB6dzFK+uUahemeH/Zr1GpVf0rEKA5StHb3sXPbyZZOW52dkuwhnAchfgbHmL1LAP2WlaitbnK1MlHca75lfoHeuw3AWgKLh2PYsRGk1syq0I955AK1V9HnpkGwBGcATovX+GKDo4ChWSsbF2O+H4LjBSpKhGDyRwuruxlN61Y0J1oqa6AHC5XHz5y9/ii1/8NO+8s4Rbb/0m55wzl3/96wkuvvij2W2cZfZFeKrJbgWs5drBw5HOCjnoOdLsehLXUZpFaSYgsQ0QezqOZdsZB8E7HHwT7RbKRk/dk7ParlM5QHM19EDvVadjYfPm9Xzmc5dz8sln8KN7fobsUiwda/IjJ0nvMf5AkO5Iba/bds8n6c9wyL4KBnP4zW8e5LTTPsDXv34zS5Ys4je/+RuTJx8kJ78fHA4Hw2pGMaxmVK/bo90RFr/5Mm2tzaiahmWabNu6kUmTZ6DpOul0qteMG0VR7IJxYMy4SdTX7cA0DEaMHIPL7WHliqUkkwkKCouZNXcBqqKg6Tp+fzCbTqTrOjPnnMw7yxbT1tqMYWTwuL2MmzCFESPHDvg1ulzu7EqG2+OlvKIaYVm0tDQyrGZUNuCYNfcUlr31OrW1OwCoqBrGjFkn4e95XR0dbax8d4ndGtgy8fkCjB0/meEjxqAoCkVFJXh9/uw0eEVR8Pr85OTmMWr0eCZPnYWm6dTV7mDXjq20tDRhGhmCoVyG1YxkxMixOBwORowcS15eAU2N9dnzXFZeddDZJk2NdXR0tFFQWJy94Pd6/RiGQTKZ6PVYj8dLV2c78Vg0G6wEgzlouo7b6cQZ8RCPRnE4ncTjMVxOF6VlVZSW2QGPZVm43R7mzDuVNavfobWlCSEERcWljJ84ldy8vq1C9Xp/3G67+1JPkJdKJujq6qCtpRmjp2NcaVnlfmlntbXw2msqoZAgv+ejaJqCjWtSvCxKueK8WK/HW84QihFHtdLsnWwnNA9a9w58K3+KmmrHchWQLj+VdMkpB65lET1TA1UHjpYl6J0b7KCo58rXchehd63D2fwWqerz92ynezHyJh7yXHiqwIw5SHVMIuUbC8JE9Tnw5K9ErUthmWl7qCaAMFGMOGZoTHb7Sy+9gt/97h5+9KOv88QT/+OMM87hl7+8kwsv/Eh2FexAKxP9ZaXtFCzV1f9ZKYPN6LLrUrTQnhhT0exjy7SAqLGDD//4PYMkj/cx762trYVPfOIiysoq+P3vH5bzVKTjQn7qJOk9ZljNKJoa6+nq6sjWrHR2tJOXl5+9qBtsiqJw+eVXMXPmXD7/+U9wwQUn8/Wv/4hPf/qmPhfA98emjWtpbWnqlXoVDneyfu1KRo+dgIKSvbjczTRNvF4vo8dMYOy4SQghWLPqHVavWIplWegOB02NddTX7mDeSafv14zAsiwMw2DMuEmMGjUeVVMJBEPZYGGgysqr2LRxLe1tLeTm5aMoKpFwF06ni6pqO2XPMDJsXL+Kjs42nC43YNHV2cGuHVsJhfJIp1Msfes1OjraCeXmo2ka0e4w7y5/C7fbQ3lFNV6fn8lTZ7LinSW9VkXKy6uYNGVmNpCtGT6KmuF2cCiEXcOy93lUFIW8/ELy8g9+NZlOp6mv20FrSxMtzU0k4vFenwNd03HoDruF9N7bpZI4HM5edUEFhSWUlVexc8cWQrl5RLsjdIe7cDqdTJk2m1lzTyESCbNl0zpamhtxOBxUDRvBrDkLyKRTWMKuIxro57CsrJJNgSDtbS243R4a6ncRjUbslbl4jEWvv8ikKTMYO653Q4UdOxSiUSjfq7Ba0yA/D1bsHMHHMutR91690FygulDSYdhrxUPvWo8abwbNgeXKR4tsxRveiJLsJDX84j3bCwtn0yKc9S+jJlowfaUo6Z6BG3t/Ra9qCNWFFt7S73OhuiEwyW4mYMYdKA4HjjxQHWMwYpNwtK/EdOeDotnHEBxOumT+Xq9f46tf/QFXX30xr7zyP26++atceOGpPPfc05x33sWHeOberM4OjKVLsLZtRfEH0CZPQZs4CWGpJHfY09aFYQcEzjK7M9sB53seC4ID9zNWerqRCbJFYQdaSTmeotFurrjiApLJBH//+/8IBIKH30iSjgIZrEjSe0xF5TCmTJvNpg1r6GhvRVNVikvKmDp9zn6tdAfbiBGj+de/XuPOO7/Fd77zZV599QV+/vM/UdiTEjQYTNOkoX4XXp+/V2pOMBiipbkBRdHwBwJ0draTl2e3rk2nU6SSCcaOm5S9aI2Eu9iyeR0uj8dOrcIOSFqbG9m8aR2z5uyZZt4dCfPO8rdoa22yV1Q8XoaPHEtR8ZHnjgSCOcyYNZ9VK5bS0d6KEAKfL8C4CVMoLrGvdGt37WD7ts3khPKy72E8HmPzpnWUlJbb7Yg72nutXoRy82ltaWLH9s2UV1QDdiAbCuXR2FhHJp0hJxSitOzgqyIDaXedTqd4e/Gr1NftBBTi8Rgd7S3ouk5ZRTWKYncac7ncGGaGeDyG2+0hlUzQHelixKjxvQJFVVWZOfskgsEQu3ZuxeP2MGLkWEaPmUB5RTWdHW28+fqLdHeH8Xr9pNIpVix/m67ODmbPXTCg9DzRJWCzCSb4hwWYOfskVr67hK1bNhKPdRMIhigoLCY3r4DuSJiN69dQWVmTXb0DelJlxH6zNQzVh+YNosd2YbnG9PS6zaCmOkgXTkMxE2iR7QiHHzXVhhZvwvSWYOTuGSypxhpw1T1PumxBNpXLVftf3JsfAhSEI4jesR49WotQVehJN9tNERmEa2CdARXdLhjvdb7wEZv4eVy7nsPRugTFMklVnU2q8hwsb+8HL1x4HrNnn8QPf/h1/ve/JZx88un8/Od3cO65F/Xp82a1tJC67w+Y27aB242SSWO8/Rb6OeeRqTmfdIOC4gbFba9oJLbY599dedhdDxph2as7imZ36VKc+7QzFvbf3dXHMYg6jHQ6zbXXfoQdO7byz3++SEXPzxBJOh5ksCJJ7zGKojB6zASqqocT7upE1/Xst+3Hgsvl4tvfvosFC87g5puv5YwzpvPDH/6cCy647IhmvfSNgsvtZsq0Oby7/C07eMFuZ1tZPZwRo/aka3V0tJJMJnoFHKqq4vX7aWqsxzAMdF3HNE2WLnmDpsZ6cnPz0XtqVdauege325Ot1RgIw8jQ0txILBZl3PgpqLqOptoDEj2ePa2PGhtqURW1V7Dp9frojnTZqwlOp909aJ/32Oly0R0J97otlJvfK0VusO3csZW62h3k5RfhcDgwTZNMOkVzUwNutwd/MIdod4SCwiJCuXl0d0eIdkdwOp3UjBjD5Kkz99uny+Vm0pQZjJswGcMwcLnc2c/S1i0b6O4OU1S8p1VxKpWkbtd2ho8YnQ34+kosNbCeNKC15yvxoELxGSWcdPJCOjs7yC8sIi+vINuxzB8I0tbSREdHW69gZfRoQV6eQn09VFTYF8zJJITDCgvPHYaVN7rXDBYjNJr42E/hCG/G0fAqaqoLI1CDYiTsIY57sbzF6F0b0aK1GO58lHQ3rtrnQPNg+nuuyr3FKGYCPbwFLbIdM2BfbKrxRoTmJVM4o1/n5bDnzZ1PcvQVJEd+xL5aP8iwTUVR+PrXf8SFF57KP/7xEJ/97Bf5xCcuZM2aFUyadPjU0dTLi0m16qjjzkBTwqgigdXSQvr1ZWS001GDPtTd/0wcYHTbBe6u0qOfXiWEndqVrLW7dtl1PnZwl2oAI4Xdccywi9YHMnTyWDBNk5tv/hRLlrzBww8/w4QJ+w/vlaRjSQYrkvQe5XZ7cJcMfo1KX51++tm8+OJyvv71m7n++it48slHueOOeynpxwDDA9E0u/vT+nUr8fkC2Qv0SKQLr9dLYWEx/kCQ3Nx8mhrryRhpcnJyKS4p73Uxr6rqAdPF7JQwPXtba0sjba3N5BcUZbuABYI5ZIwM27ZuZPiIMQNKMYp2R1jy9uu0tTRhCcsuTC8oZtbcU3oFKgBCWAcpqlawLCvbSMA0jZ7ZMLZUMklJPy/Wj1RjfS267sieK03TqB42kq1b1tMdjaBqGjmhXMaOm0xlVQ3dkTDxeBSPx0cwJ3TIgFbXHb3aGgO0NDfi8fh6bedyuekyOwh3dfYrWBH1FtbjGUQKGKWAqthByzMGrkInPp8f0XMc2W16Pj/7fgYKCuCiiwT/+IfK+vX226dpgqlTBaecnUfM81X09pWoqQ4sVx5G/hSEw0cqOJxU+UKw0ihGksCSr6NY6V6ZRIqRBNWB0N0oqS4czW+hRevIhHrXTpnBkSiZKIow0LvW252m3HkkR3wYI/fQ9SkD1odcplmz5nHhhR/h29/+Ev/73xLy8wt58snHDhmsCAGJHSaxxjJEVQ2Ky40iEjgzG9ELBVZdHCucQCvs3dxDddmrGFYatKN8xZNps1v8CstOmRMmpHaCowR843vaGaft+hVXyZ6Vln1ZmZ65PWl7P468Y1fHkslkuOmma3jmmX/y+98/POiDfyVpIGSwIknSUVNYWMwf/vAo//nPk3ztazdz2mlT+OY37+LjH//kEa2yjBo9nra2Ftpam1BVDcuycDqdTJgyE39PXrU/EGTkIXKsC4tK8fn9dHV1kNtTcG4YGRLxGCMm7enulUgksEyzV7tisC+Ik4k4hpHB6ezfZDQhBCtXLKWpsY6CwmJ03ZFdZVn57hJOXnBWr/NTXFLOrp3be7VNTqWSaJpKYWEJhcX2oMWWliZyckKomk60O4zT6aJm+Oh+HRtAZ0cbW7dspLWlEbfbQ9WwEQyrGTXg1Tmny0VuXgGjx05k1Kjx+APB7L6COSGCOaFDbh/u6mTHji10drTh8/mprBqeHfrocruJx/cpVu9puaw7+lcEINZa0CFg3F4zbYoU6DLRVuuUT65m44bVeL1etJ5uZF2d7QSCOdnuZ3ubP19QVWWydq1CMmnXr0ycKHC77dSpzF71HL2oGqgehO4hUzgd967nEA4/QveCmUbr3oGROxZH6yqcTb9Ci9Wjd26AdASjaBZid99dM4XlryY6+WbUVCcKAiNn9H6pWcfDD3/4C848czq33XYDF1xwGU8//Rhf//oPD/oZMzoguUMFBdREI4ojgFCCpJ3jUMwuFKMdRbUQGTvtajeRsVsgK0e5HsQemtkzjT6053ZLt4MUTyW4Jhx08yyjG+Lr7f/upofsNseDPWdmX6lUis997gpefPFZ/vCHRzj33IuO7hNKUh/JYEWSpKPuvPMuZv780/jud2/jy1++nqeeepQf//i3DBtAK1mwA5GTFyykrnYHHe1tuFwuysqrKCwqOfzGPbxeH5OnzmLFO2/bBefYrXPLyqsYNWbPVYXP50fTdVKpFC7XnqAkkYhRkF+03zf9fdHdHaaluYGcUF52e113kJObR0tzE+FwZ6+ZIFXVw2mo30V93S77Yk4ILGFRM3wUJWUVaJrG7HkL7JbCzY1YViK7elFS2ntlwTAy1NXuoKmxHiEExSXlVFYNyw4LbW9rYfGil+jujuD2eIlGu2lubiDc1cm0GXMPG2SWlldSX7+zV2CViMdwOJxUVg4jJ5Tbr3PV1trMW2++QnckjMPppKmxjl07tzF1+hxGjBxLdfVIWpubSCTiuN0ehBB0drQRCAT6nQJGQhxwcKhwKyhhwZhxk+jq6qC1pdFuPiDA5/czZersg9aDVVRARcWBKqz7JllzCWqyHb1jNYqVARSMnJGYgWG4d/wTyxkiExqHkmxH79oIKGRKTwYjgRavI116KmbueMyjnoLZP3l5+fz0p7/nyis/xPXX30JjYz1vv/0G8+efesDHp1sBU0Ev9GNuqgevF0WLYCoFGN1OHF4TR7kHIwr47eDEStp1K57hR794XRh2q2V1n4+B6gQraqeF6YcpExKWXWNjdIOW0zPbxoRMJyS22i2Oj9bbmEgkuO66y1m06GXuv//vnHnmuUfniSRpAGSwIkknCMPIUF+3i9aWJlRVpaS0nJLSiqPSbetoCIVyueeeP3LRRZdz222f54wzpnP77d/l05++aUDf2Hs8XkaNHn9Ex1Q9bCShUD6NjXUYmTTBnFxKyyqyF+4ABYXFlJRWULtrG4FADrrDSTzWjYLCiFFjB3T+DcPANAw0b+8fwXaNjIGRyfS63el0MXf+aezauY2mxjpUVaO0rJLKqmF7ViiCIeaffCbRaATTNPH7g/u1GTUMg6VvL2LXji09HYgUdu7YSmPDLmbPXYDD4WTzpnV0d0d61YDE41F2bN9MzfBRh23/Wz1sBE2N9dTX7cim2amaxshR4ygs6t9YcCEE69etJNodoahkz/F0dXawbs0KyiuqGTZ8FF1d7ezYvoVIuBMFBX8gyLTpc/H5+jfWWylVEKqCSAkUl/1cwhIoUWCEgt8f4OQFC2lsqKU7Yq9clZZXEgyG+vU8/SHcecSm3ILesRY12Ypw5pAJjiTwzo8QDj+Wz06rzBTPQRGmPdhRc2I5c8jkTyM58qNHd2LgETjjjHO46qrP8Ne//o7S0gqefPLRgwYrIg1ooA4bhujqxGqzZ9wItwOhOHGdfz7a9IA9ULLDDhwUpz1k0nPwGbKDRtHsAMlM9g5YhAmofQuWzChkwqD69xTeK5o9yT7T2btIfzDF4zGuueYSli9/mwceeJpTTjlj8J9Eko6AInb3ppSOmkgkQk5ODhs3tsnWf9KAZDJplrz1OrW7ttszObBrAUaNHs+UabNPmIBlt1gsyl13fZv77vsVU6fO5Kc//T1jxx6lHPpBkEjEWbv6HerrdmIYBn5/gFFjJlAzfPSA0tkymTQv/PdfxOOxXhf/XZ32pPaFZ3/oqHRu27VzG4sXvUQwJy+7SpTJpOnsaGPOvNOorKrh2f/3dywhev2sEkLQ2tzI7HmnMnzE4dPKMpk0dbU7aW1pQtM0SkrLKS2r7PfnNB6L8r/nnkJ3OHsNGrUsi7bWZhac9gHKyquwLIuO9la6ujrQdZ3ikvL96n76QiQE1u/SsNpE5Cmgg9IqoEJF/ZwTpWRo/DtTE60E3v4aljOIcO71db2ZwdHyNqmai0mXzLXnpgy1frj7iMWinHXWLJLJBPF4jJUr63qtYO6W2AnxzXZKlGKksZqbscIRLDUX3zhwTy9FURSEsC/6RRpUz9FPndpbshbim+xOZKobMMGM2MccmHb4ie+ZTuh+t6eD2F6PtTL2VPvATND7F38fVnd3hCuv/BDr16/mwQefZs6ckwf3CaT3pe7uCGPGFBAOhwkGj/y6V66sSNIJYNfObdTu3EYoryDbZjYRj7Fl83pKyyr3S/UZ6nw+P9/73k/50Ic+zK23foazz57DTTfdzhe+8JWDttE9njweLzNnn8z4idPIpNP4/P4BpX/t5nA4GTNuEu8sfZO2liZcHg+pZLKnk9v0o9Ziuq21GSFEr4tBh8OJoqg0N9VTPWwEuq6T2GdYoxDigN3GDsbhcPaa1zJQimrXjgjL6nW7EBaKQjb4UVWVgsLiA9aN9Ov5PArqNU6slzMo71hgCFigo56hD5lABcByBrFcIdRkB+ZewYpixLD85aSqzsYMDj/EHo6v3alNVhx0h59f/PTPXPzh07Asi5df/i/nnPOh/bZxFtvzU4xOUD1OyKsELzjzwDVpr4GLyrGf8L6bq8xe/Ug1gtkJaKDngnf04QMVsFdQVA9YCdD2CkqsuH3fYK+qNDbW88lPXsrOndt49NFnmT599uA+gSQNkqHz01eSpINqbKhF1bReF/Ierw/DyNDa0ngcj+zIzJw5l//9byk33PBlfvnLOzn77Nm8886S/R5nWRYN9btYtuQN3nzjRTZtXEsiET/AHo8ur9dHTij3iAKV3WqGj2bO/NMoLi1HVVSKikuZM+9URh5hatthHWAtXVgWmUwGyzKpqh5BKpEgnU4B9rnv6GglEMih+Ag7ufWXx+OluKSc7m47tQ3oqUlpJycnl7z8okF/TiVXQbvEifotF+p33GhXO1Eqh9ivSs1Fuux0FCOGGqsHI4GabEOL1ZLJm4KS6sK97e+4tj+FFt5sV38PEVYKomsguspehYitg9HM5au33gHAL3955wG309zgn2DPS1EU++LfPQx8E+y6kKFA0cA7CoIzwD8FAlPtFZW+Bk+q0359wgSjC8yYHZwpKniq+xbw9NXy5W9z7rnzaG1t4Ykn/icDFWlIkysrknQCEHtNOd739qOVyZlIxHvmX7gO21L2SLhcLm677Tt88IOXcMstn+GCC07huuu+wG23fQev14cQgrVr3mXDulWYhoGqaezcsZVdO7cx76TT+12XMFCZTIaO9lYsyyQ3ryA78X2gFEWhsqqGyqoaLMtuXXy059AUl5SxZfM6kokEbo9djN7S3EBLUwMZI0O0O0xF5TAqqobRWF+LaZmAQiAQYNqMuUf8mgdi/MSpdEfCtLU22TcIu8HC5KmzBmUVLh6P0VC/i2Qygdfrp6y8Erfbg+IcmnUeu6UqPwBWBlf9i+ixeoTuIVV2OhgJfKt+hmKmAQvhCJIcdiGp6g/aV73HuX4luQvSLXaxuaLbReVmGK6Y90Wemfp3VqxYyksvPccZZ5yz37aaD3xje+pAlKE7UFHz2X8GwlVut1tONfSssITsFRvHII5GeuyxB7j99s8zZcpM/vSnxwZ1aK8kHQ2yZuUYkDUr0pHasnk9S99+g7z8wmyHpWQyQaw7wsmnnkVp2eCNZzZNkw3rVrFt6wYSiTi67qC4pIyp0+b0Gnp3NBiGwR//+Et+/OPvkJ9fxHe/+2PmzjmZV156Ft3hyE6aN02T1pZGJk6eweQp+w8RHGzNTfWseHcJ4c4OLGFlJ8yPGDn2GAy6HDymafLu8rfYtnUjRiZDd3eY7kiYQDCHsopqMpk06WSSseMnU1xSRjjchcPhoKS04pgFhQeSTCaor9tJtDuCy+2h7BBF7UIIzHVrMVeuQESjaDXD0WbORM3N2++xba3NLHnrNcLhTgAUFPLyC5k9b0GvbmxDhZLswNGxGsVMYvoqMEJj7dWVZCvCEUDvWIN33R8wfZUIZwCEQO9chxbdhREahRkcQbrsVNIlp9jtkY8xYUB4Sc/MyL1qSYQJZjfUulZy7odn4fX6+Ne/XmX8+MnH/BiPtr2n2x/rciLDMPj+97/CH//4S6644lp++MNfDMm0W+nEJ2tWJOl9qKp6OI0NtdTX7kRRVbvDkqIwfOSYXhPYB8O2rRtZs2o5Lo+HUG4BmUyanTu2Yhgmp5x61lEt5td1nc997hbOOedDfOtbt/LpT1/OzJlzOfO0hUycND37OE3TcHu8NNTtZNLkGUc1YIhGu1n69hvEY1FCefmoqkokHGblu0vw+fyDGigebZqmMXHSdOLxKFs2rScc7sTpcFFYVILX60NR/HR3R9i5Yyujx0zo92sTQhCPx1Cw0xQP9b5s2byeDetWEovFKCwqYcqUmeQf5Btet9vDiJFjD3jf7uft6mwnHO5ELF9G6LU3cKZTCN2BsXQJ2rIluD51HWrxnv2bpsnKd5cQiYQpLCpFVVVM06StpYnVK5dz8oKFQyoQ1Vvfwbvxr6jxJkCA5iZVPJfE2E9iBu0W4I6WZQjVYQcq2NPq9chWlGQbqisXBHi7NqIm2kiOuOyYvwZh2YHJfulMKiBgdM1E3G4PwWAOH//4B3n66Veorh66tTf9lW61V5bMmH0OHEV2etexSGPr7Ozgc5+7kkWLXuaHP/wF11xz/ZD6fEvSochgRZJOAE6niznzTqOuYgctzQ09rWsrKCuvGvCgvgMxTZNtWzagO5zZb651XUfTNFqaG2hrbaaouH/tZweipmYkDz74NM8//wxf/epN/PhnP+Kccz7ExRd+BI+nJxVJcExSWhrqd9EdCfdqnRvKzaOluZFdO7efUMGKPYxyCfV1O/H5A0QiYRRFsWuiVI1gTgiv10dHeyvRaARvP1ZTOjraWLfmXdpamwEoLCph/ISpB2x1/NabL/PWm6+STqdQVZWdO7awacMazv/QR6isqunXazIMg5XvLmHnji2kwmGszZsIenSmVJRTqDsRhoG5aSOZV1/G9ZGPZrfr6myns6ONUG5eNgDXNI1gKJe21iai3RECwcMMxjhGlFQnnk1/RUl1YOSOBUVDSUdwNbyCFaiyU7wAxUztiQSEhR7eDJaBcAQQzhzMnJGo8SZcdS+QLluA5Rn8mp9Dvg4H6EF7SKLi2vPP10ra9zlDGhMnTqWwsJgNG9Zw+eXn8sgjz1BTM/KoHZNl2IMjVefg1oTsK9Nm1+cI0y6iFyYkd9ipXv6JRzelbdmyt7jppmuIRMI89thzB20PLUlD1RDN+JQkaV9Op5PhI0Yzd/5pzJ57CpVVNYMaqIDdcjaRSOBy9+5G5XS6MA3jmBe1n3XW+TzzzBucd+6HeP75//Dl22/gzTdfI5PJkEzGqawcdtS/HUylknCAehKHw0ksGjmqzz3YOjvaqKvdQTAnl1BuHi6XE6fLhWVZtLe3IIQgnU7hcDhw9qMjWbQ7wluLXqZ213Z0hxPd4WTXzm289eYrRKPdvR7b1dXBsiWLsCyL/IIicvMKyM0rIBLuYtFrL/T7NW3ftonNm9bhdLkoUDVCqTTdbicrk92kLAtF11Hy8jFXrUTsNb/GsiwsYYGA7nAXXZ3tJOIxFEWx79unA9nxosbq8Wz8K462lQhXKHtFLZxBhMOPo+mNbAF9Jn8ySiYKVgbFSKCmIwjVCaqG5bKHcVqeIpR0B2q07pi/FkXpKZB3gdnVU0AeBpGyazU0L0yaNI3Nm9fz2GPP4nA4+NCHTuXdd5cO+rEIA+LbILIEIkshsgyS9UenF4EQPfs27DbGqst+rVoQMu12Mf3RkEql+NGPvs5FF51GQUEhzz77pgxUpBOSDFYkScpyOJx4fT6S+wQlqVQSXdePS91CUVEpt/zfN/jGV79HVWU1v/ndPXz/B18lnc4w/BCpQX3Rl4tSvz+AAtluVGCvUKRTycMOSBxqurvDZDJp3G4PDoeTYDCXdCqFoqikkkni8Rjhrg6KS8rJyen7pPnaXdsJd3VQWFSK1+vD6/VRWFRKZ2cHdbu293rs9i2bSCTivVYtVFXF4/XS0tyYrR/pCyEEO7ZtRtd1vF4/FhBT7dubMikajOTuB4Lau7h8d1e3zZvWsmvXNupqd7Bt6yZ2bt+C3x/Ef7zrC4XAWfc8gWXfx739KbTu7Tib3kRvX2XnUwFCc6Fm4tm/p8sWYORNRO9cjxqrg0w3SqYL01+VXUVRUp0omSRKJnZcXpYjDwITwVVq12zoIfCNA0/PgtqkSdPYunUTubn5PP30qwwbNoLLLjuLF198dtCOQQiIb4Xktp60NKc9zDG+0S5s789+0q3QvQrCi+3/plsPEPBY9uwXZZ/xMarDvs9KMOjWrFnBuefO5fe//zm33/49nnzyZar6uWopSUOFTAOTJClL65k0vmzJIro6O/D6/GQyabojXVRVDycvv/CYH5OiKIwZN8kufJ5zMkuWLubPf/kdX//Wraxet5qvfOV7/e5mk0wm2LxpHXW7tmOaJqVllYwcPe6AF+hl5VXkFxbR2tJEIJiDqqp0R7rw+QNUH8X0lMFmmiZqz9wSwzBwOBwUFZdimgbt7S0oikoiHqOyqoZp0+f0a8Wqq6sDTXf0qmdSVRVN1wiHu/q8n/5+qS2EIJVK4nA47SL8WIS40wGpBClNY0Wimzyh4O/qQJ//QRR9z688RVExDYN0KmWvBuk66XSKdDqJz+cf9FXL/tK6t+HZ8jgCyORNtldJBOjhzQhXHqavHDXZRqp8YbZYXrhyiU2+GWfj6zjaVqJYBmqiGSM0BoSJ3vYujs51CN2Ld9MDpMObSI78KMIxwNZVA6SH7D8HMmnSVLsD4NpVzJ49n8cee47Pf/5KrrnmEu6++7d87GPXHPHzW3F7Zovq3TNtXnWC0Q2pOnCV9C0lLN0EsY2A1TO9vg0yHeAbbXfwylLt1RQj2nt70fP9hzKIhfaGYfCrX93Nz372A0aPHs+zzy5+TzYqkN5fZLAiSVIvw2pGYWQybNm8nmh3GF3XGTlqPJOmzDiqxfWHoigKRcWlFBWXMmXabD75qRt48ME/8pOffJd///vvfOELX+G6675wwMnX+8pkMry9+DXq63bi8XhQVJWNG9bQ2tLISQsWEgj0rlNwudzMmXcqa1evoLmpHkNYlJRWMG78FPJOgJUVw7Dfyx3bNpNMJolGu4nFYpSVV+FwOLIBaM2IMYwbP5m8/MJ+v89enx/TMBBCZIMcIQSWYeLx9h4hPmzEKDxveIlGIgRDIcBe4UrE41RVD9/v/B+KqqrkFxSxfdtmUh0JEpk0ntw8zI52yGRIpDOs6Qozf8w4HKee1mvb1pZGTMti+MixRLsjpFJJckK5aJpGJNKFYRjo+vH7Fam3r0ZJdWHmTQTA8Feid29HycTQOtehpDuxvKWkKxf22k64ckkN+xCpYR9CSXXiXfdHHO0r0SJbUeONdqDjLUNvexdn42s4mhYRm/ltzED18XiZ+xk9ejyaprFx41pmz56P1+vlT396nG9844vceutnqKvbyS23fOOIgkkz0TPhfp8YTXXZ9TNWqne3sgMRhl0sD70DL6Pbvt1RBGrPx0dR7JUkYwOYcbtmJTvdPgj6IDWeW79+Nbfe+llWrXqHm266jf/7v2/Ibl/Se4IMViRJ6kVVVUaPnciw4aOIRbtxOl1HvWVxf+m6zic/+TkuuuhyfvazH3DXXd/ioYfu45vfvINzz73okKsCjQ21NDbUkl9QlG0D7fcHaWluYMf2LUyaPGO/bYLBEHPnn0oiEceyLLxe33EL3PpDCMHKd5eyeeManC43DocTl8tNuLODpsZaXC43LpebCZOmM2XarAEPu6ysHMb2rRvp7Ggjp6flb1dXBx6vj4rKYb0em5ubz/SZ81ny1qu0t7WgqhqWZRII5jB/wZn9Pq8jR42jbtcOGut34XJ7MNw6mdxcgijk60663G5SF38Ub17vQRXpdBohBMGcEDmhPStqsViUTCaDaR7fYMUuliebumbkT0E4QzjaV4ACqYqzSFcsPOSkeuHKJTbl/3A0LsK/8qf2akwmihbdBZoLoTpxNb6GeOdOYjO+huU//s0iHA4H+fmFtPY0agD73/sdd9xLWVkFd931bRYteoVf/vL+Aac1qU5Atwvrlb2u5UXGnv2y90qHEbVXYayeIMNZZAcYZtwOevYNajSPXYtjxUDdK+52ltqpZqkGu14HxQ5yvKP3BDUD1dLSxN13f4dHH/0LNTUj+de/XpNDHqX3FBmsSJJ0QE6nC2fe4Vcqjqfc3Dy+//2fcdVV1/Gd79zGpz99OfPnn8q3vnUXkydPP+A24XAnwrKygQrYAZrL5aa1pemgz6UoCl7vsU2X6Ytod4Ta2u1Ewl14vX4qqoaRm2tfmIfDnezcsQV/ICfb2cvnD+DQHXh9fqZMm21frOfkHlGjgrz8QqbPnM+aVcvpaGsBBQKBHCZOnnHA1af5J59BYWEx69etIhaLZlsXFw6g01xhUQlTp8+mtbUJISw0XSevchj5BYVYlkUq3IV1gBW3nJwQTqeLRCLe632NR7spKavE6Ty+n30zUG1fORsJ0D2g6piBYShmnMSIj5MacXHfdqQ6sPwVCFculupAD29FuHIRqgMsAzUdRo9ux9nwKsnRVx7dF9VHBQWFtLa29LpNURS+8IWvMGfOyXzhC5/izDNn8N3v/pSPfeyafn92tQA4ciHTCgR6hlOmegr9a/bMP8l0QGx9T02JZqdtpRrtwZSa144jhWlvv5uwejp77bPwo6jgHWGnh5lRexs958i6gMXjcX7/+3v49a9/gsvl4jvf+QlXXfUZuZoivefIYEWSpBPeqFHjeOihf/PSS8/x3e/exjnnzOX88y/mttu+w6hR43o91qE7ENArZQnsdKmBTmgXQtDZ0UZrazMKUFBYTG5ewVHvVNbe1sLbi18l3NWBquuYhsm2rRuYOetkyiuriYS7SKeS2dWO3XyBIEYmQ2FRCR7PYfJd+qiqejglpeV0tLcCdgBzqAv+UWMmMGrMhEF57mHDR1MzYjTdkTD5+YUoPaszkXAbfn+AYE5ov21CuflUDxvO5k3rSPfUvcRjUZwuF6PHjB/U986yLNpam4nFunG7PRQWlRx2FSuTP5VM4XQcLUvs1sOKhprqxAiNJlN2cv+e352P5fCjde8ELDtQwV69EZoLy12Ao2MtyYG+wEFWUFBMe3vLAe+bM+dkXnxxOd/+9pf40pc+y3//+y9+8pPf9atuTVHsFY04kOm0U7pUJ7gq7LknYAcdie12WpiW2xOYCDt1K7EdAtNBz4V0S89qjGoiTBUzquAsPPgEe81j/zkSlmXxj388xJ13fou2thY+9akbufnmrxAK9b0phiSdSGSwIknSe8YZZ5zDggUL+cc/HuanP/0+p58+jcsuu4Jbb/0mlT3pSCVlFXjXe+nq7CDUM9U8HouiKOp+KUt9YVkWa1e/y6aNa0mn7cs9l8vN6DETGT9x6lFLFxNCsHbNCsLhLopKylEUBSEE7e0trF61jKKSUpxOJ4qq9aQ07bk4zmTSOB3OAad9HYzT6aKktGJQ99kXDoeDseMm886yN2lra8bl8pBKJdB1B2PHT8bh2PNNcyIRJ5VM4PH6mDp9Dn5/kB07tpBOJSktr2TU6PGDOjsnmUywbMkbNDbUYRoGiqpSUFDEzDkn43K5aWqsJ51KEgjmUFhUuif1THcTH389zpzROFoWo5gZkuWnky4/A8tTiBpvxtG6DDXZiuUuJFM4E8t74At24colXXoK3s619kqNlUExUyhmHCNnDKBgOQch1dNIoKY6s22VByo3N69XGti+/P4AP/3p7/nABz7Il750PaefPo2vfOV7XH751b1WTA9F84B/sh18WBn776p3T8M4M26vgKj+Pbcpih2E7E7z8o4APbIR964XcGU2Yql+UnkLoOZ0lH1bfw0CwzB49tmnuPfeu1mzZgUXXHApX/vaD99TgzMl6UBksCJJ0nuKrutcfvlVXHTR5Tz00H384hd38OSTj3LFFZ/m5pu/QnFxKVOmzWH1yqW0NDcCApfLzZhxkwYUrDQ31bNh/Spcbk82+InFulm/biUFhUVH7eI9Fu2mva2ZYE4ouwqgKAqhUD5dHW10tLdRUFiSzf/PLyhC0zRSySTJeJyRU8b1+cLuRDCsZiQul4vt2zYTCXdSWFRMzfDR2cAjnU6xZtU7NGxch3vnTnyWoHDsBEae+0FGjZmAZZnoumPQV8PWrn6XXTu3kZtXiMvlwjAytLQ08sZrL6AoCpEuu1WzqqqUlFUwa84p2dUu4QyQqrmQ1LAP2TvrOTa9cz2edb9Hi9XbbauEibPuBeITPosZ6t3OW0lHcLQsQUm1YwRG4Io1ocUbsVy5GDljMT3FaKk2MsXz+vyalEwMR9MiHK3LUYRJJm8yipXC0bQYNR1GOHykS08hWX0B6H2f17NbXwP8s8++gBkz5vCd73yZ22+/gd/85qd86Uvf4qKLLu/TPhTFTsU62H3AIVvUOTMbyDV/Cc42TE8+Ko34zT+Tqq8jEbh20IbWRiJhHn74fu6//zfU1e1k/vxTeeqpV5g9e/6g7F+ShjoZrEiS9J7kcrn41Kc+z0c/ejX33fdrfvObn/Doo3/h2mtv4POf/xJnnnUBLS2NWJZFXn4hoVDegC5UG+prMQyD/L2aEPj9QeLRKI0NdYMSrMRjUcLhTnTd7t7V105Iuq4zY9Z8li1ZRGdHG2ZPrU7NiNGMGTvpiI9rKFEUhdKySkrVCsgAxQqKa09nshXvLKFh8euMfHcVnnAY0zTJvLmYpuXLKLv1KzgK+t/ZLZGI09hQSyIew+P1UVZe1SuVMJGIU1+3A78/mO1Up+sOcnJy2bp5PcFQHuUVVaiqRiaTpq52B4FADtNmzN33xe35f8vAvfVxtHgTRu4Eu+hBWOhdG/BseZzo9K9nWxkryTZ8q3+F3rkuG9QYwWEoloHlCIKioJoxUlXnkC49RGqZEHb3sObFqPEmtMh21HQXlisPFAXnrudQMlGMgqlY7kKUTDfurU+AmSQ5+hP9Pq+722z3RUFBEb/61V/5/Odv5e67v8ONN17Nr3/9Y2677bt84AMfHHDwqXrtQvpMh11wn00Di4IeAM0vcK37H2q6DaNkgr2yCZipDpxNi0iXnYaZc2StzXfu3MZ99/2aRx75M+l0iosuupzrrnuCiROnHtF+JelEI4MVSZLe07xeHzfddBtXXfUZfve7n/HHP97LAw/8gc997hY+/emb8B9hp7NMJn3ACytFVchk0ke0b8uyWL9uJVs2rSeRiKFpul3MPmMuodx88guKaKjbhdvtyaaBhbs6COSEyMu3L75z8wo47czzaG1pJJVKEQzmkBPKI9zVgWka5ITycPVjWv1QJRosrKcysMmyi56LFNSzdZilEQl3Ub9jCyPWbcDXHSVZVgaaRirajXP1apL/egrvpz7dr+fr6GhjyVuv0dnRvvsIyMsrYPbcBdlhoUYmg2EYeDy9CxjS6TTJVIryYA5qT2DhcDjx+QLU1W5nwqRpvep9lGQ7jvaV9nR6IdDCWzH9VXuqsxUV01eJFtmOFqvFDAwDwFX7PI6ONWRyx2WrxtVYPYqRJFlzIcIZxAwMwwyOOOQqgGvXs3i2PAoig5KOoHesxXIXYgZHYDkC6J0bUTP2EBHhDCCcAVB1XI2LSFeenR1I2VdCWNnz0lfjx0/mL3/5J8uXv82dd36TT37yUqZNm8VVV32WD37wkn4PtFUU8Ay3a1aMzmxMiOYBzwhQSKN3bcJyFfY6d8KZixJrQIvWDihYicWivPDCf3jqqcd4/vlnyMkJ8elP38g113yO4gE0oJCk94Kh33tTkiRpEOTkhLj99u+xePEGLr/8an7+8x8xb95Y/vjHX5JMDry0uKCwGMsyMQwje5thGFg9E+9XrVjKqhVLaWyoxbKsfu17544trF31DgJBQWEJgWCIluZGli55g0wmw4SJ0wjmhGhuaqCttZmW5gacDicTJ03vVafhcDgoK6+iZvgoFFXltVf+y8sv/odXXnyWF/77L7ZsXo/Yb+z2iUPEBdZf0/COiQgqUKQgmgXmwxlYZxGPR9EbGvF3dpEqLICelSnN7SHh95FevRKrJx2rLyzLYtW7S+jsaKewqITikjIKi0ro7Ghn1cpl2ffZ4/Xh9weIxbp7bR+PR1FVFe8+QYym65imidnz2QHQO1bjX/59vGt/h2fTQ3jX/wm9cy2Yqd4HpaiAlZ1mj7BwtC7BdOXtaW8FWN4yFDNh17FULLQvqA8RqCjJNlw7/x+W7sYIjUOoLoTuRU112jUz6TCKlUZobtRE657nceWhZCKoiQMXyh9KOp0+ZNtoNVqLa/tTeDb+FWft/1BSe967GTPm8Pjj/+XRR5/F5wtwyy3XMWVKJV/84rW8+ear/fo3qOeAf+qeIY/ekRCYCo48QNERugfF3OdnhzBBURB636voo9FunnrqMT796Y8waVIZn/vclTQ21nPHHfeydOk2br/9ezJQkd7X5MqKJEnvK4WFxXzvez/lM5+5mXvu+SHf+97t/O53P+eWW77B5Zdf1e/ZGpVVNdTu2k5jQy0utwcEJJNxALZt3ZgNAnRNZ/jIsUydPqdPaVxCCLZv24yiqgSDIQCcTicFhUW0tbawfdsmqqqHc8ppH6B213bCXZ34fH4qqmoOOqwylUqy9K3X6OhoJzc3H1XTiHZHWLH8LTxuL+WVQ2Mw4L6EEHRHwkSjEZxO1/6DK9eYsE0gRqoojp4L7xoFNppYi03cl3pxqgpWJoPY69wbpoHT6UQTAlJ9XwWLhDtpb28lFMrLHoeqagRDubS1thCJdBEK5aHrOqPHTmTZkkW0tTbj8XhJpZJYlkVuXj7pdApnT3qYEIJYd4Ti0vI9K11GHM/GB9ESLRi54+2AJB3DHdmKo3UZ6YoPZPOTtHgdRmA45t6zUgT2rJYDn9U+vVY9sh011Y4RsrvqaYkWtEQLQlHRu8JgJMFK21Xqe62GKJluhObBcvZ9yOduTU0NB5267mhZhmfD/ajJ1p7UNguj4SXiE27E8tspl4qisGDBmSxYcCZ1dTv5+98f4vHHH+Txxx+ksnIYl112BWeccQ6TJk07bJtfzQNa1QHuUDXSJSfj2fw3lHQE4QyCZaBHtmL6KuwUvYMQQrBz5zaWLn2TZ599mlde+R/JZJKpU2fypS99m/PPv1gWzUvSXmSwIknS+1JFRRU//env+dznbuEnP/keX/7y9fz2tz/hllu+wYc+9JE+By0ul5u5809j+9ZN1NXuAAWKikqoq9tBMJiL22N/w5pMJNi6ZQPFJWV9KuQXQthDOfeZERKLRmlpbmTxopfZsG4V5RVVjJ84rU8zYBobaunsaKegsDgbMIVy82htaWL79k1DMljJZDKsWrGEXTu3kUwmcOgOCovLmDFzHv5AEADRaV94ZwOVHiKgoDRYhEJ55IybSOyNN3G1t2MWFmJk0pjpNLmmhV5RhZKfv99zH4xpmliWhar1Tk5QVQ1hmb2+va8eNhJV1di6ZQPdkS6COSGmTptDLB5lw7pVpFJJHE4niXgcj8fL2HGTsgGQ3rURLVaLERixJ+XL6cPIm4DeuRG9YyVCD6CYcSx3Acnhl+xZRVFUMoUzcW//J5aneE8aWKIJobmxHAF7dUY7dNcqoeqAPVBEizehJNsRimqvLKgaijBQ0lHAROheEBZKsgM9uoNU+RlYvv7XbDU01LFw4Xn73a5kYri3PIKS6cbInWgHapaB3rUe946niE+4Yb9VooqKar74xa9x881fZcmSN3n88Qf44x9/yT33/BC328PMmXOZM+dk5s49hWnTZuP19r2Vd6ryLLRYHc7mtyFaCwqYvjISY662U+F6xOMxVqxYxvLlb7F8+du8884S2trsFadp02bx5S9/hw9+8JJsx0JJknqTwYokSe9rI0eO4Xe/e4gbb/xyT4HuNdx993e5/vr/4/LLr8bjOXw6h8fjZfzEqYzvKXxdtuQNFEXJBioAbo+HSKSL5qaGPgUrqqoSys2noX4XgYD97XR3d4TaXdvJZNL4fH4UVWHTxrXEYjFOXrDwsCs2yUQCYL/HOV0uursjhz2m42Hj+tVs2riOQCBIMCeXdDpNfd0OAE459SxUVUXJsYubRUb0CliUqIAxGoqiMGXBmWzetBleeB515w6cHg8FmoNgeSWOsz6A0o8VtWBOLoFAkEjPXJfduiNdBIM5BIN7VhMURaGqejgVlcMwDANd11FVFcuyCAZz2LFtM/F4jOKaUYwYOYaivdJ9FDMNlrnfiHPTWwaKg1T56ajpMKavjEzx3P2m2acrP4Ae3ojeucEOVjJx1EQzwpWDb82vsTzFpKrPJV166kFTwcyc0Zi+cruoPtVlD5l0FaIlmrLBCQiM0FiE5sZV/yJKqgvhDKJ21+JsfI106YI+d8ZKpVK0tjZTXr7/coYW3owWa8DYO3VN1bE8pTja16CkuxCuA88aURSFOXNOYs6ck7jzzl+xZs0K3nrrdd5663X+9Kd7+elPv4/D4WDUqLGUl1dRUVFNeXklFRVVlJdXUV5e2Wsm0e7C/e7Kj6MGZtLVuJGWcILGNj8N616jtvYBdu3azs6d29i8eQOmaeL3B5g6dRZXXnktM2bMZdq02eTl9T1IlqT3KxmsSJIkARMnTuWBB55i9ep3+c1vfso3vvFFfvazH3DddTfx0Y9+EpfLidvt6dNkc8uyUA6Qf6MAlmXuv8FBjBg5htaWRtramvH7gzQ31pFMxCkoLCYvrwBFVXG53DQ31dHS3HDY+SBenz00YvdF826pZJKSkvI+H9exkk6n2bljCx6P1z527C5vuXmFtDQ30NHeSkFhMUzUoNpA2WIhKlRwAC0CXAraPDsw83i8TLzhi0SmTMd4azHO7m5cI0agzzsZbcyYfh2Xw+Fg3ISpLF+6iOamBlwuN+l0EpfLzbgJUw84v0ZV1V4pR6qqUjN8NDXDR+83oHQ3MzAM4cpDTbRgeUvsG4VASzSTKZxBYtynDxgEKMk2nK3LUVKdZPKnYeRNRo3X42x6Exx+zOAohO5GTTTjXX8/QnOTKZ67334AhMNHYtQVeDfcj9a+EsWII3QvRmgUprccNCdKqpPE6KtREw2osQas/Aost90S2bP+T/Z7WXZqn87tzp1bAaisrO4JhJR9+gj33Lb3MSoqCmJPvc5hOBwOpk2bxbRps/jc527Bsiw2blzL4sWvs2nTOurra3nzzVeoq9tFLBbt0z735nQ6qaiopqqqhtmzT+JTn7qBGTPmMHr0+D538pMkaQ8ZrEiSJO1l0qRp/Pa3f+O2277Db3/7M37yk+9xzz0/5OSTTuPsD5zP1KmzGTNu0iHTxIqKS9m+dROZTDpb6J5OpxEICov6XihbWlbJrDmnsGH9aiLhTpLJJLn5BZRVVGWntDscTizTIhrtPszeoKS0goLCYlqbGwnkhNB6alacThfDR4zu83EdK5l0inQ6hcvVe3XL6XRiGBlSPY0RFL+CerUT658Z2GqBAUqhgvIBHSbuSdVSVZXQgtNgwWlHfGzVw0bgdnvYuWML4XAXOTnVDKsZSVFxWb/3dbD2upa3mFTlB3Bv+wdqJoyleVHTYSxPIamq8w4YqGhdG/Gu+z1atC57mxmoJlV+Os7mpWQKp9srIoDp8KN1bcJZ/yKZojkHXf0wCqcT9ZXi1X24ml7HyBmD6bMDFcw0engzQnfh6NyAmTsWy23XTJnuPLTIVpy1/yVdMr9Xof/BLFv2NqqqMtvxLsFFT2I5g6RLT7FbAQdHYHmK0WJ12Y5nCAst0UCmaC7ClXfY/R+IqqqMGzeJceN6t/MWQhCJhKmr20lDQx3pdCpbgyaE6NWUIjc3n8LCIgoLS8jNzTtqw2Al6f1IBiuSJEkHUFMzkqs/cR0ja4azaPHrvP7GK7zy6gvMnDGHz1z3Bc457+KDblteUU155TDqardnW7BapklldQ3lFX2vC1EUhcqqGsrKq4jFunlr0SuEw529On2ZpgkKfWo/7HQ6mTN3AatXLae5qYGUZZITymXc+CkUD5GVlUQiTldnO6qqEcrNw+fzE4mEe6XUJRJxXC43vsCeugClUkW9yQn1AtJAmYLiOXzqkTAFLDexlpkQETBKRZ2no5Qd/mKzuKSM4pL+Byf9kay5CNNbjLNpEWqinVTxnIPP8LAMPFsetVOldhfkCxO9awPubU+ClckGKrsJVwgt1mDXrxxigKPlLSU+6SYUK40Wq0NNd4EwUZOtGHlTsDxFKJkw5j41Kpa7AC3Riprq7FML43cWP8f4ihCFXYuxXHlo0V14N9yHFqsnMeYaksMvwbPxQfSONQjNg2LGMX3lJIddMGhDGHdTFIWcnBA5OSEmTJgyqPuWJKnvTphgpaOjg5tuuol///vfqKrKpZdeyi9+8Qv8/oP3Tk8mk9x66608+uijpFIpzj77bH7zm99QXFwMwF/+8hc++clPHnDb5uZmioqKeOWVVzj99NP3u7+xsZGSkpLBeXGSJA05yWSCbVs3UlRUytVXfYbLLr2CF196jmef+xfXXvdRzjvvIr7wha8wadK0/bZ1OJzMmbeAsvJKGhtqAXuVpLKqZkBT4zVNIxgMMWrMBJa+9RqRSBd+fxDTNOhobyM/v4CS0r4FG4FgDvNOOp1YtNvOow8Eh0RqihCCTRvWsGnjGmIxu7Vvbm4BBYUldHV10tHRhtfrI51OkYjHGDlqHDk5vesTFFWBSqXXPuHgKxdCCKynM/C8nZonXKBsNrFWWqjXOVEqh8C344pKpuQkMiUnHfahWnQXWmT7PjNYNExfBXrETq/CTIK2JyhR0t2YwWH2KslhWP4K4pNuxLXzP+id60HRSFVfQLL6fNRkO6huFCOGcOz5vaxkogjdg9AP3wACIVi2ZBELRub0DLy03zcl2YGz8XXSZaeSLj0Vy1OEo3kJarIVM1BDumQelm9oBNuSJA2+EyZYueKKK2hsbOT5558nk8nwyU9+ks985jM8/PDDB93m//7v/3jmmWd44oknyMnJ4cYbb+SSSy5h0aJFAFx++eWcc845vba55pprSCaTFBX1/gZo48aNBIPB7N/3vV+SpPeWeDxGMpkgmBMCwOfz8aELLmXhmefw/PP/4c233uDss+dw2mkf4MYbv8y8eQt6XRQ7HE6GjxjD8BH9q4c4lOphI4jHo2zZtJ621iY0TaeouJRpM+b2qZZmN0VRsp20hoq62h2sWrkM3eGgoLAEyzLp6GglnUkzacoManduIxqN4nQ6mDhpBmPHTzp4EJJMYCxahLFsCSIeRxs3HscpC1DL9+lMVS/gdRMRAqVARQGEJVA2WFgvG2hXHf4CfkgRJoowEco+waeiYTkCWO589PBmTH81QvegJlpQRIZ00Vz0jtUgBGbOyF7Bxr7M4AjiE2+0gxJFg555IqYzl0zuOBxt72AGhiN0L0qqEzXVQbLmMpRMFDXRhOUpPuj+d2xdz6baFr5+6Zm9By26clESjfagxeBwjNzx9sqRJEnvCydEsLJ+/Xqee+45li5dysyZMwG49957Oe+88/jJT35CWdn+y/DhcJj77ruPhx9+mDPOOAOAP//5z4wbN4633nqLuXPn4vF4enX6aW1t5aWXXuK+++7bb39FRUWEQqGj8wIlSRpyXC43ToeTdCrVK+0KBKedupDvfO+nvPLK8/z61z/mssvOYvr02dxww5c5++wLjlq+uqqqTJg4jZqaUYTDnei6g7z8wiGxMnKkdu7YimWZ5OTYXwSpqkp+QTGtLY24XG7OOOsCkskETqfzkIGZMAxSjzyEsXgxitcLDieZF1/A3LAe93Wf7RWwiJ0WdAso3/N+KaqCKFBQ1puItEBxDm5q0dFk+ioxfWVosfo9U+mFQIvVYwSHEx/3aTxbHkHv3IhipbBceaQLpuOqex4t3ggITG8JyREfPvRKjqLsH3CoGomxn4QNAkfnBjCTCEeAdMlJKIkmAku/iWIksdy5pCo+QKrq3D2rPz2e/n9P4nM7OG9Kce99CxNQENrhUx0lSXrvGQJr3Ie3ePFiQqFQNlABWLhwIaqq8vbbbx9wm+XLl5PJZFi4cGH2trFjx1JVVcXixYsPuM0DDzyA1+vlsssu2+++qVOnUlpayllnnZVdmTmYVCpFJBLp9UeSpBPL7gGL0e4w8XgMy7JIJOJEwl2UV1QRCuVx8cUf5fnnl/G3v/0Lp9PFtdd+mFNOmcgf/vALuvoxEb2/vD4/pWWVFBaVvCcCFYBYNILT2fti1A76FFLJJLqu4/cHDruCZG3ZjLl8OWpFJWpVNWppKeqYsVj19WQWvdHrsYpOz6yOfXZiAA4FTrRTq7tJ1lwCmhu9cy1aZDt651qEI0Cy5mKsQDWxqbcRnflNotO/Tnz8Z3BEtqIm2zByRmLkjEZNdeHd8Fe0rk2HfCotvAX3lsfwrPsDrl3PoiTbsbwlxKbeTveMrxGb+mWi07+OYhm4G1/H0jwYvnKUTBzP5odwNryy3z6fevoJzj1lNj4lhpLpaRhhmT2DFsuP22qK1dqKsXIF5ob1iHTfh4dKkjQ4ToiVlaampv3SrnRdJy8vj6ampoNu43Q691sNKS4uPug29913Hx//+Md7rbaUlpbyu9/9jpkzZ5JKpfjTn/7Eaaedxttvv8306dMPuJ877riD7373u/14hZIkDUUTJ0/HNA3qancQ7Q7jdLqoGT6GyVNnZVOQFEXhjDPO4YwzzmH58re5//5f88Mffo077/wWl1zyMa655nom9sxfkQ4uN6+Qjo4NBEUoe24Nw0BRFHz+wGG23sOqq0Nk0qh71TMqqoqSk4O5YX3vB4/WoNCAWoGott9LkRQoXQLlNB1FO3FWVXbLFM8h6srB2fQmWqwR01dBunQ+Zs4o+wGKmp3H4t76d5Rk254Bi4AZHI7esRpHy1LM0P4d4pR0BO/qX+HZ+rg9jNKVj+nOx5n3CrGJN2D5qzBDYzEBrWsDjvZVGMEahMN+D01/JVpkG866F+35Kz0zZFatepeNG9fxtdseJl3agLNlKVi77G28+w9aPBaEaZJ55v+ReeM1CIcRuoZWVY3zIx9Fq5ET5iXpWDmuwcpXvvIV7rrrrkM+Zv369Ye8f7AsXryY9evX8+CDD/a6fcyYMYzZqwf//Pnz2bp1K/fcc89+j93tq1/9Krfcckv275FIhMrKQ88/kCRp6HG53Myacwpjxk4iHo/i8XjJCeUdtFZixow5zJgxh+9858c89NB9PPjgH3n44fuZOXMe11xzPR/84KW9Zm1IewwfMZrG+l20tjQRCOZgmSbd3WGKS8opLevHFHSXC3vkhpVt7wxAOoPi7V3kreQqqJc4sB5PwwZ7eUXRFJimoZx2QnyXd0BmaCyJ0NjDPk5JdYLi2K+LltDcqMm2/TcwknhX/QLv5r8BYDlDKJluVEWDjnW4tz9NfOKN2f2pyQ6wUtlAZTfLGUJLttt1L057gOavfnUX1dXDOf2si4irCuny9WixeoTuJZM3CeEKDeBMHBnj7bfIPPsMhEIoo0ajZDJY27eRfuhB3P/3JRRfH5oGSJJ0xI7rT+Nbb72Va6655pCPGT58OCUlJbS0tPS63TAMOjo6DtqRq6SkhHQ6TVdXV6/Vlebm5gNu86c//YmpU6cyY8aMwx737NmzeeONNw56v8vlwuXqe7GrJElDl6Io5IRyyQkdeDL2gRQWFvPFL36NG2+8jf/979/85S+/48Ybr+a7372NK674FFdeeR1l/bkAfx8wTROX20NjUz3t7S0EgyFGj5nAhEnT96kZOjRt7DjU/HxE7S6otOfRiEgEUkn0WbP2e7wyU0OtcCHWWpAUKKUqTFBRXCfeqkp/Wf5yEBm7JmR3Ub6wUNLdYGZwtC7HyBmFcNrNGBztK3E2L0Iout2GWFERuh812YJwBHF0rEVJh7OBhXDlguqyO4LtVeOipsOYvpJsh7DNm9fzzDNPctddv87OLzLyJmLkTTx2J2MfQgiMxW8idAdaYU9mh8sFw0dgbduKuW4t+qzZx+34JOn95LgGK4WFhRQWFh72cfPmzaOrq4vly5dng4mXXnoJy7KYM2fOAbeZMWMGDoeDF198kUsvvRSwO3rt2rWLefPm9XpsNBrl8ccf54477ujTca9YsYLS0r4PdpMk6f1J13XOO+9izjvvYjZtWsdf/vJ7/vjHe7n33rs5++wLuOaaz3HSSacddKXm/aKxoZa3F79KMpGgpKScRDyGQOD1+vH3IwUMQC0sxHnph0n/8+9YmzeiCMDtRj/5FPS58w+4jVKiopScECWcgypdNBtn/SvoneswvWUgwNGxCiUdxtX0Os62ZT0F9x8hUzIfLboLRVig7lXMoygI1YmS7gSqsKfM24yc0WTyJuJoWYLpr0DoPtRkG4qZJF1+RjYF7N5776akpIwPf/gTx/YEHIoQiM4OFF/vuTSK7kAIEAOYbC9J0sCcEOvc48aN45xzzuG6667jd7/7HZlMhhtvvJGPfvSj2U5g9fX1nHnmmTzwwAPMnj2bnJwcrr32Wm655Rby8vIIBoPcdNNNzJs3j7lz5/ba/2OPPYZhGFx55ZX7PffPf/5zampqmDBhAslkkj/96U+89NJL/O9//zsmr12SpPeG0aPH86Mf/YKvfe0H/P3vD/GXv/yWj3zkbEaNGsvVV1/Phz98JYEh1k74WLAsiw3rV5NKJSksLkVRFII5IaLdEbZuWc+w4SMJBHL6tU995izUmhrMDRsgnbKL7UeM7J0WJiHcBcQnfh739qfQO9ejpLpQzARmzgiM0HhA2EMZN/6VqLckOy9F6D7UTATLGQJAESaYSTK54xA9twGgasTHXYtHd+NoX4WSbMNy5ZIY+VHSZXaXzmXL3uLvf3+IH/3ol0MqI0FRVdTqYZjLlyEKi7JfKIh4HKFrKIVyfIEkHSsnRLAC8NBDD3HjjTdy5plnZodC/vKXv8zen8lk2LhxI/F4PHvbPffck33s3kMh93XfffdxySWXHLA1cTqd5tZbb6W+vh6v18vkyZN54YUXDjgoUpIk6XD8/v3tcO0AAC/kSURBVADXXHM9V1/9WRYvfo0///k3fPvbt3LHHd/gssuu5KqrrmPcuEnH+zCPmWQyQbirA58/2GuFyecP0NrcSLirs9/BCoCaX4B60smDeaiAnR4UCXeRSiXw+QL9Kv4fiszAMGKTbkZJdeDe8iiuhld7pV+ZgRr0zjU4Wt8hXTwby1MMVgYt1oQab7Znu5gJMsXzSR1girxw5xOfeBNqvAElE8XylmTrVDKZDLfd9jmmTJnBJz5x3TF93X3hOGUB5sYNWFu3oBYUItIpRFsb+vQZaKMHb36SJEmHpojdI36loyYSiZCTk8PGjW3vy29OJUk6tIaGOv72tz/x0EP30drazMSJU/nIRz7BRRddTkHBe+8bXCEE6XQKVdUQwuK///knlhC9ghLDyNDZ0c6pp59DSenQmE6eSMRZ8c7bNDbUkslkcLvcVNeMYuLkaei643gf3hHzrvo5jtblezqH9dA715OqOJPEuE/jbHgN95ZH0bp3oKQ6UYB08TxiU76E5d9/5tmh/OpXP+bOO7/Js88uZtKkaX3f0EyhxZsQmrundqafaZRCoEW2oke2IhQdI3cclu/Ax26sXkXmxeex6utRHA60adNxfOAc1Jz+B9CS9H7R3R1hzJgCwuFwr4HqAyWDlWNABiuSJPVFOp3mpZee44knHuSFF/6DEILTTz+bj3zkEyxceP6QSpMZqLbWZjZuWE1bawuaplFeUU06nWLrlg3k5RfhdDoxTZP2thby8ws5feF5QyIQEELw5hsvsXPHFnJCeTidLhLxGPFYN5OmzGJCfy62hyjX9qfwbH4II3fCnoGNloHetYHEuGtJVZ4NgJpoRe9cB5aBGRyOGRjW74Dh3XeXcvHFp/PJT97At7996K6gWULgaFqEe+f/Q403geokkz+J5IiPYHmLD789gGXaK0j1L6Jk7LoT4cojMeIy0uVnHvB1CMtChLtQnC7ZAUyS+kAGKycgGaxIktRfHR3tPP304/z973/j3XeXEgrlcuGFH+HDH/4E06bNOiGL8js72njjtReIRrvx+wOYlkk8FqWouBRNc9Da0ohlmQCEQnnMnH0yBYV9vAg9yjo62nj5hWfweH243XtmcYXDnTgdTs4656ITvi21Gm/Gt/LHaN07sTwlgECNN2GERhGbepvd3WsQtLW1cPbZcykrK+fvf3+hz0G43voOvjX3grDsdDQzjRarw8ifQnTql0E//IR7R9NifGt+hekuQLjyANBidQBEp381O4NGkqSBG+xg5YSpWZEkSXo/ycvL55Of/Byf/OTn2Lx5PU888Tf+8Y+H+etff8+IEaP58Iev5NJLr6C8/MSZ4bRt6yai3RGKSsqywZbH46O9rYW5809n7LiJdHdHcLlclJRW4HId/uLzWEkm4mTSaUK5+b1ud7ncJBNxUqnE0AhWjDh6eAsICzM4ol+DFC1vMfGJN+La8W975QSFVMWZpKovGLRAxTAMrr/+CjKZNH/4w6P9Wi10NbyMYqYwds+QcYDh8KJ3rsPRsYZM0czD7sPRsgQQCPee99H0VaB3rEHvWC2DFUkagmSwIknS+4oQgrbWZhoaasmkU+TmFVBROWxIXRjva9SocXztaz/k9tu/x6JFr/DEEw/yi1/cyV13fZuTTjqND3/4Ss4772J8Pv/hd3Yctbc14/J4eq0KORwOLEsQi3VTPWwqQ7UpvNfnx+lykUjE8e41XDKZSODxeHG7vYfY+thwtC6360midYDA9JaQqrmEdNmCPu/DDAwjPvFGlEwEUPcLdrTuHXa7464NWK5cMiXzSRfP793O+GD7Nk2++MVrWbJkEY8//l9K+1OLJARa9y4s5z61IpobsA48xPIAVCOGUPdJK1QUUEAxkn0/HkmSjhkZrEiS9L6yeeNaVq96h3Q6haIoCGGxY/sW5s0/De8Qv9jXNI0FC85kwYIzueOObp555kmeeOJBbr75Wr761S9w/vkXc9llVzJv3oLscL2hxOP10dXV0es2OxNZ4HQcu3ocIQQ0C4gKKFBRQodPqcvJyaWichhbNq/HMk07cInHyKRTTJg0DYfj+NbVqNFaPOvvQ8l0YwRHgKKiRWvxbHoA01uE2YeJ9lmKku3YtTctvAXf6l+iRWsRqo5uZnC0rUCN1pEc+dFD1q1YlsWXvnQ9Tz31GL/97d+YO/eU/r1ARcH0luLoXIO1d0hrpgEFq48T7jOh8eht74Jl7gmwjAQoOmagun/HdIy0tcGaNQqJBJSUwPjxgvdA+Zok9dnQ+20mSZJ0lHRHwqxbuxJN0ygusbv/mKZBc1M9mzevZ8rU/SecD1V+f4DLL7+Kyy+/irq6nfz97w/xxBN/44kn/kZeXgFnn30B559/MSeddPqQKcyvqh5BY0Md3d0R/P4AlmXR1WHX8pWWVRyTYxBhgfXPDKw2EUlQAqCcpKOcq6M4Dn6xrSgKU6fPweFwsmvnNqLdETweL2PHT2HkqHHH5NgPxdGyHDXZipE7MRs0mMEa9I41OJvfJtGfYOVAhMC181n09pUoRgrFTNoT7DU37h1Pky49CctfdcBNLcvia1/7Ao8//gD33vtnLrjgsgEdQrrsVPTOdWjRXZieEhQzhRbdiREaTSZ/cp/2kSk9CUfr2+ida7FceSjCRMlEyBTNIpM/dUDHdTStWgWPPKLS0qKgKKBpggkTFK6+2kI2JJPeL2SwIknS+0ZraxOJRIyi4j1tSjVNx+P1UV+7g0mTZ6CegIMDKyqq+eIXv8bNN3+VlSuX88wz/+Q//3mKRx75M4FAkIULz+P88y/mtNM+0CuF6VirrKqhOxJmy+Z1tDY3gqKQkxNi6vQ5x2ReiRAC69E0LDERpSoUgugU8G8D3KB84NCrI06ni2kz5jJ2/GRSqSRerw+ns/+BoJLqQotss1cLckYjHEf+nqjpTlC0/eecaO4+p0gdkpnE2fgaaqIFdB+WK2Rf6Cc70dtXo3duIH2AYCUej/GFL3ySZ599mp/85PdccsnHB3wImeK5JNNhXLXPondvR6gOMgVTSYy+EvS+peFZnkLik27GWfcijrblCNVJetgFdiewPhToH0vRKDzxhEpXF4wZI1BVSCZhxQqFigqFSy6R/ZGk9wcZrEiS9L5xsOaHCspB7zsWTBMsC440k0hRFKZOncnUqTP52td+yMaNa3nmmSd59tmnePLJR3G7PZxxxtmcd95FLFx4PsHgsf1qVlVVJk6eTvWwEXR0tKFrOgVFxceuXminQKy1oFJFCfRc1BcrCMOCRSZigY7iPnxKmMfjxeMZQI2KEDjrX8S942n7oh8F019BcuTHyBTO6P/+9mL5ykBYvdObhGVPpPcPQnqTqqMmmlGEidmTIiYUDeHOR4vuROveud8mTU0NXHPNJWzZspH77/87Z599wZEdg6KQqjqHdMlJaLE6hObqaZvcvy8YLG8JydFXkBz18ex+h6JNmxSammDECNj9HYrbDbm5guXLFc4/X6aDSe8PMliRJOl9o6CwGLfbS7Q7QqDnQt00TeLxKOMnTD3mqyrRKLz8ssKyZQrpNIwdKzjjDEHlIDT4UhSFsWMnMnbsRG699Zts27aZZ599mmeffYobb7wGh8PBKaecybnnXsQ551xAfn7hkT9pHwWCOdnzf0yFBUocxL4LAAEFuntqWPoQrAyU3rEaz+aHQNEwcsYCFlr3Djzr78P0lmD5Bj78Ml04C2f9S+hd6zC9ZYCClmjE8pWRLpl35Aev6AiHH+IWWBlQHSAEajpsD2d05/V6+NKli7n++o8DCk899TITJ049+L6FQO9cZ6eYmSnM4AjSRTMPuloinAEM5yCk3g3RIGW3TAaEAG2f3gUOh32faR6f45KkY+3Ey3eQJEkaoGAwxJixE8mkUzQ3NdDW2kxbSxOFRSXHvO4gnYYHHlB58kmVSAQMA157TeUPf1Cprx/85xs+fBQ33PAl/t//e4OlS7fyrW/dTSIR5/bbP8+UKZVcdtlZ3Hffr2loqBv8Jx8qchWED4jsc3tEQFCxg5ajyNG8BMVIYPqr7NUP1YEZHImabMXR+s4R7Vu484hNvIF06SmoZhLVjJMpnElswg1HFARlKQrpklOwHEHUdBg10YKabEVoTszAsGy9SiaT4a67vsXFF59OWVkl//nPosMGKq4dT+Nb8WPc25/CVftfvGt/jW/1L1HS+75R7y9VVYJgUKFtryw+IaC1VWHkSPB4Dr6tJL2XyJUVSZLeNxRFYdyEKeTm5dNQX0sqlaCgoJjK6uEDS+s5AuvXK6xcCTU1InvRUVgo2LBB4c03FT784aOXllZeXsm1197AtdfeQFtbC//977/5z3+e4nvfu41vfvP/mDRpGqeeupBTTz2LmTPnDZkC/SNWqaBM0mCxgTBU8AKd9mqLcr6G4jq6wYp9cb9PypuiAEpPq+AjY/mriE+8CSXVAQiEK39QVw9S1eeghzegJtoRugsEYKUximaRyZvEli0buemma1izZgVf+tK3uPHG2w7blU7r3oF75/9D6N49M07MJI7W5TjrXyZVc+GgHf+JpqQEFiyweO45hUjETgELhxVKSgQLF1pDfWFIkgaNDFYkSXpfURSF0rJKSsuO7zDFhgYwDAWPZ09QoqoQDAo2bVKwrwSPvoKCIq644lquuOJawuEuXnzxP7z00n959NG/8qtf/Riv18f8+aeyYMFCTj11ISNHjuk1J+VEoigK6uUOLDcoK0xEBJQcBeUsDeW0o//r0AzU4Gh7x/56fPc5tAxAwfIO0oQZRek18HAwGflTiY//LK6d/0aLNYHmIJM3ic7yC7j353fz61//mPLyKv7979eZOvXwAxoB9K4NKOkIZu6EPTdqboTDj6Nlyfs6WFEUuOACQWkpLFmiEA7DnDkWJ500OKmiknSikMGKJEnScbB7NWXv61aAVAoCR78x1gHl5IS45JKPc8klH8eyLNatW8Wrr77Aq68+zw9+8BW+9a005eVVPasuCzn55DPIzc07/I6HECWgoF3pRJxnQTeQr6D4j03wlS49GWfLYvSutZieUhRhoSYaMUJjj7jA/phQFDIl88kUzkRNNGEpTp783yvc+ekP0NRUz/XX/x833/zVfnacO1hQrgLWIBz0iU3TYM4cwZw5svOX9P4lgxVJkqTjYNw4QX6+YOdOqKy0V1U6O+2uYLNmHf8LE1VVmThxKhMnTuWGG75EPB7jrbde59VXX+CVV/7Hww/fn+0+duqpZ3HqqWcxffrs4z4csa+UPBUGEGft7ho3kNUly19BbMINuHf+G71rE0JRSZWfSWrYhQccwtjHA0LvXIOjdQWYcczgSDJFs/ebPD+oNCevr9zG97//FVateoezz76Ahx/+f4wYMbrfuzJyRiEcPtRUO5a7wL7RyqCkwxjV5w/ygUuSdCJSxPHs1/k+EYlEyMnJYeNGe/iZJEkSwPLlCv/4h0JrKwih4PPBySdbXHyxYAgOoO+lvr6W1157kVdffZ7XX3+Rzs4O/P4AJ510GgsWLGTWrHmMHTvxsDULJwrDMNixbTM7dmwhlUxQWFTCiJFjyS8o6v/OhEBJdYKqDTxI6dmPa8e/cG//J4qRsOesCBMjbxKxiTcg3IO76mWaJi+//F/uv/83vPLK/5g+fTbf/OadzJlz8sB3KgTuLY/i2vUMimUgFB3FSmPkTSA28aZBfw2SJB193d0RxowpIBwOEwwe+XWvDFaOARmsSJJ0MJ2d9jyFTMbu/lNZOeQ7qu7HNE1Wr36XV199nldffYFlyxZjGAY+n59p02Yxc+ZcZs6cx/TpcwiFco/34fabEIJ3li1m86Z16LqO7nCQiMfw+4PMO+l0CgqLj8txad078S//AUJ1YHlL7BvNNHp4A8kRl5Mc8eFBeZ7GxnoeeeTPPPzwn2loqGXixKncdNNtfPCDlw5O/ZJl4mh7F73tXRQjhpk7jnTx3CML5CRJOm5ksHICksGKJEnvJ4lEglWrlrNs2VssW7aYZcveor29FYBRo8Yyc+a8nj9zGDFizDGfb9NfHe2tvPzif3C5Pdl6DCEELc0NDKsZxbyTTj8ux+Wsex7v2j+Sck8AFFQnKA7QoruwXLl0z717wJGvaZq8+urzPPjgH3nhhf/gdLq4+OKPcuWVn2bKlBknbJMFSZKOvsEOVt4b6/OSJEnSkOHxeJgz5+RsepAQgp07t2UDl2XL3uKxx/6KZVmEQrnMmDGXGTPmMHPmPKZNm4XP5z/Or6C3rq4OMuk0odw9XbYURcHnC9DW2oxhGMcl3c0MCzKdkO6J9RQdtCBoR3Aozc2NPProX3joofupq9vJ+PGT+cEPfs4ll3xMftkmSdJxIYMVSZIk6ahSFIVhw0YwbNgILrvsSsD+5u3dd5eyfLkdvPzud/cQiXwHVVUZN24SkyZNY/z4yYwfP4nx4ycf1/QxXXeAAkJYKMqeceKGYeDxeo/LypCZhGjrGBwiiENrxnSWIDJgdGQQ7i4yE87u06pKJpPhnXeW8PrrL/Laay/y7rtLcDicXHjhR/jEJ65j2rRZchVFkqTjSgYrkiRJ0jEXCARZsOBMFiw4EwDLstiyZQNLl77F8uVvsXbtSp588hFSqRQAZWWVPYGLHbyMHTuBmppRvbuPCYEaq0WP7EBoTozc8Qjnka8GFBWXEgyG6GhvIy+/EFVVSaWSpFIJxo6fdFyClUw7pDNVxArPJ9D1FFpirV1gnzFI+iaRKT/jgNsJIdi8eT2vvfYSr732AosXv0YsFiUUyuWkk07jzjt/xQc/eCk5OaFj+4IkSZIOQtasHAOyZkWSJKn/DMNg27ZNrF27inXrVrFu3WrWr19NU1MDALquM3z4KEaPHseokWOZmNPFJG8towtUnLqK6S0jMfoTGIXTj+g4hBDs2rmVVSuWEYtGAAVN06ioHMaM2SfjdDoH4dX2T2IHxLeAI1fgSqzCFVuBasVIMopUaA6BecHssdfX7+Ltt9/gtdde5PXXX6KpqQGn08msWfNZsOBMTjnlTCZNmoamaYd+UkmSpD6QBfYnIBmsSJIkDZ729jY2bVrHpk3re/6sY/OGlbS0dwKgqQrl+QGq8lxUFIUomXQu5TXjKC+vpLy8ivLyKrxeb5+eq7mpgY0b1tDZ0YoQ4PMHKC2roLCwhMKikkOuqoiMgBUW1hoTMqCMUVFmaii+I0+rSrdAdLVdo6JodkF8XfMONq1fz87YenZ1bWDTpvVs3ryBWCwKwPjxk1mwYCELFpzB7NknH/4cCIHesQpH6zso6TBmzii7S5c7/9DbSZL0viaDlROQDFYkSZKOLt+7dxHesYx1kRw21LayqznMrtYu6hub2BlWaWrrwLL2TETPyyugoqKK8vJKKiqqe/1/UVEJwWCIWDTCm2+8RCIRx+cPYJoG8ViUyqoa5p98ZnYlIpPJ0NHegmVZ5OYV4HZ7EKbAeiQDb5ggQGigpAVM0lA/5UQJ9C9gEUIQjXbT0dFGZ2cH9bW1rHljPZs2r2db43p2NG4ilU4C4PcFGD1mHKNGjWPUqLGMHj2OqVNnUtDPmTCuHf/Gve3vKGaqZ/5JCiNnFPFJN2F5S/u1L0mS3j9kNzBJkiRJ2oea6iA/N8T8ykrmT6jK3q53rCUx+gqi5efS1FRPXd0u6up2Ul9f2/P/u3jppeeor99FMpnstU9d13G53Pj9AbxeHz6vD5fbjUPT+PczT1JWVgkIOtvbEJaFy+3C6/FRNWwkZZkKrH9lECHAawcbFhaJl6OEo90oI1WCwRzcHi+GkaGrq4OOjnY6O9vp6GjPBiUdHW3Z2zOZTK/jC4XyGFk1nsmjZnPxaVczesw4xs8dS+WY8iMuildjDbh3/j+E5sEMjrBvtAz0znW4av9HYszVR7R/SZKkvpLBiiRJknTCM0LjcO16BkRFtguWEm9Ci9Xi3vlvtMh2vCXzqJx7MigL9tteCEF7eyv19bW0tDTR3R3mzTdeJhqNYlom8XiMeDxOPB6jNdxJfWMDiUSccLhzvyDisJYc+Gav10deXgG5uXnk5RVQXFzK2LETycvL73V7Xl4+xcWl5OcXoigKwgBh2TNWBqtxlx7ejJLqxMydsOdGVcdyF+BoXU5i1MdBdRx8B5IkSYNEBiuSJEnSCS9dfhqOtnfQO9dieYpQkm042t4BzQtmBmfL2zhal5Ecfimpmov2215RFAoKinqlSuUGc2hrayF/r9ssy6KtpZE580/HyKRZtnQRodwCkskE8XgMIQQd7W2UJyuYvHIaykgNUOgy21meXAxxlWBOEHWaTjTWjWkYzDvpdEaNGo/b7R7Qa1d0GPzmwkrPH0HvvQuEonI0nlGSJOlAZLAiSZIknfDMwDBik76Aa9ez6J3rUePNCFcB6bLTELodBKjxRty7niNTPA/LW3zYfVbXjKKlpYloNILPF8CyTDraWgnm5FJSWs7WzesBBZfLhcvlyrb79Xg85JghahpGgVBQ8hRWJbsIaEGKrRIYo6EUqhQWFtPS1EAmlRpwoHK0GKExWO48tFg9pr/SvtHKoKbaSZZeDKq8fJAk6diQP20kSZKk9wQzZyTxiTeiJFsJLPkmpurMBioAlqcYvWsdWvf2vgUrw0YQ7Q6zdctGWpsbUBSVnFAu02bMw+Px4vMHUbA7ce0uthdCkE4myRtbiOLSEC+Y0GyR9iRQHQqUqShle1YlVE0jlUoe5AiOH8tbTHL4JXg2P4resdpuOSYsjNyJpKrOPt6HJ0nS+4gMViRJkqT3DkVBOAJ2PYUwet8nLEC186b6QNM0Jk+dxbCaUXR1daDrOgWFJdm5KmXllRQUFtPS0kggGEJVVbojXfj8AYaNGIU6wwFjNKwNJrkdRWxLbMUaLtAcdrBiWSamYZCXXziYZ2DQpMsXYvqrcLStQMnEMAPVZIpmDcqgTUmSpL6SwYokSZL03qJ7yBTNwrXz31iuPNBcIARa93ZMXzlG7th+7S6YEyJ4gInuLpebOfNOZe2ad2lurMcQFiWlFYwbP4W8vAL7QeM1tPEaVcmR7HptBy1NDXh9flAgHotSUFhMZVXNILzoo0BRMENjMENjjveRSJL0PiaDFUmSJOk9J1n9QdRoLY6ONT0rKgLTXcymyqvZ3uXGoRqMDmjkOI+sUDwQzGHu/NNIJOKYponX6zvgoEi328O8+aexaeNaGup3IYRg7LjJjBozHq/Xd0THIEmS9F4mh0IeA3IopCRJ0nFgxHG0rUCLN5DR/PwjM4PXIz5ihv1rr9ClcnGlg+l5x/Z7O9M0AbJ1LpIkSe8lciikJEmSJPWF7iVTMp8MsKTd4PltKfKcUO5RsYC6uODvu9JUeFWK3PuvhhwtMkiRJEnqu2P301mSJEmSjpNl7QYqkOdSURQFTVGo8iq0pwTrwubxPjxJkiTpIGSwIkmSJL3ndRsCl9a7PkXpGfeelLGKJEnSkCWDFUmSJOk9b6RfI5IRWHuVaSZMgaYqlHrkNHZJkqShStasSJIkSe958wp1VneZbOq2yHMqGBaEM4JpeRpjg7KGRJIkaaiSwYokSZL0nlfmUbl2hItXWjJsjJh4HAqnFuucWuTYLz1MkiRJGjpksCJJkiS9L1T6VD5R4yJtCTQFNEUGKZIkSUOdDFYkSZKk9xWnKoMUSZKkE4UssJckSZIkSZIkaUiSwYokSZIkSZIkSUOSDFYkSZIkSZIkSRqSZLAiSZIkSZIkSdKQJIMVSZIkSZIkSZKGJBmsSJIkSZIkSZI0JMlgRZIkSZIkSZKkIUkGK5IkSZIkSZIkDUkyWJEkSZIkSZIkaUiSwYokSZIkSZIkSUOSDFYkSZIkSZIkSRqSZLAiSZIkSZIkSdKQJIMVSZIkSZIkSZKGJBmsSJIkSZIkSZI0JMlgRZIkSZIkSZKkIUkGK5IkSZIkSZIkDUkyWJEkSZIkSZIkaUiSwYokSZIkSZIkSUOSDFYkSZIkSZIkSRqSZLAiSZIkSZIkSdKQJIMVSZIkSZIkSZKGJBmsSJIkSZIkSZI0JOnH+wDeD4QQAESj3cf5SCRJkiRJkiTp6Nl9vbv7+vdIyWDlGOjutt+0GTNqjvORSJIkSZIkSdLR193dTU5OzhHvRxGDFfZIB2VZFg0NDQQCARRFOd6Hc9xFIhEqKyupra0lGAwe78M5YcjzNjDyvA2MPG8DI8/bwMjzNjDyvA2MPG8D09fzJoSgu7ubsrIyVPXIK07kysoxoKoqFRUVx/swhpxgMCh/SAyAPG8DI8/bwMjzNjDyvA2MPG8DI8/bwMjzNjB9OW+DsaKymyywlyRJkiRJkiRpSJLBiiRJkiRJkiRJQ5IMVqRjzuVy8e1vfxuXy3W8D+WEIs/bwMjzNjDyvA2MPG8DI8/bwMjzNjDyvA3M8TpvssBekiRJkiRJkqQhSa6sSJIkSZIkSZI0JMlgRZIkSZIkSZKkIUkGK5IkSZIkSZIkDUkyWJEkSZIkSZIkaUiSwYp0RDo6OrjiiisIBoOEQiGuvfZaotHoIbf5wx/+wGmnnUYwGERRFLq6uga031WrVnHKKafgdruprKzk7rvvHsyXdtQN5Nwlk0luuOEG8vPz8fv9XHrppTQ3N2fv/8tf/oKiKAf809LSAsArr7xywPubmpqO6usdLEfjvAEHPCePPvpor8e88sorTJ8+HZfLxciRI/nLX/4y2C/vqDka523lypV87GMfo7KyEo/Hw7hx4/jFL37Rax8n2uft17/+NcOGDcPtdjNnzhyWLFlyyMc/8cQTjB07FrfbzaRJk/jPf/7T634hBN/61rcoLS3F4/GwcOFCNm/e3OsxA3lvhprBPG+ZTIbbb7+dSZMm4fP5KCsr46qrrqKhoaHXPoYNG7bf5+rOO+88Kq/vaBnsz9s111yz3zk555xzej1Gft72P28H+7354x//OPuY99vnbe3atVx66aXZ1/3zn/98QPvsy+/fwxKSdATOOeccMWXKFPHWW2+J119/XYwcOVJ87GMfO+Q299xzj7jjjjvEHXfcIQDR2dnZ7/2Gw2FRXFwsrrjiCrFmzRrxyCOPCI/HI37/+98P9ks8agZy7q6//npRWVkpXnzxRbFs2TIxd+5cMX/+/Oz98XhcNDY29vpz9tlni1NPPTX7mJdfflkAYuPGjb0eZ5rm0Xqpg+ponDchhADEn//8517nJJFIZO/ftm2b8Hq94pZbbhHr1q0T9957r9A0TTz33HNH5XUOtqNx3u677z7xhS98Qbzyyiti69at4sEHHxQej0fce++92cecSJ+3Rx99VDidTnH//feLtWvXiuuuu06EQiHR3Nx8wMcvWrRIaJom7r77brFu3TrxjW98QzgcDrF69ersY+68806Rk5MjnnrqKbFy5UrxoQ99SNTU1PT6bA3kvRlKBvu8dXV1iYULF4rHHntMbNiwQSxevFjMnj1bzJgxo9d+qqurxfe+971en6toNHrUX+9gORqft6uvvlqcc845vc5JR0dHr/3Iz9v+523f35v333+/UBRFbN26NfuY99vnbcmSJeJLX/qSeOSRR0RJSYm45557BrTPvvz+PRwZrEgDtm7dOgGIpUuXZm979tlnhaIoor6+/rDb776I2TdY6ct+f/Ob34jc3FyRSqWyj7n99tvFmDFjjvBVHRsDOXddXV3C4XCIJ554Invb+vXrBSAWL158wG1aWlqEw+EQDzzwQPa2g533E8HRPG+AePLJJw/63LfddpuYMGFCr9suv/xycfbZZw/w1Rw7x+rzJoQQn//858Xpp5+e/fuJ9HmbPXu2uOGGG7J/N01TlJWViTvuuOOAj//IRz4izj///F63zZkzR3z2s58VQghhWZYoKSkRP/7xj7P3d3V1CZfLJR555BEhxJH/HB0KBvu8HciSJUsEIHbu3Jm9rbq6+oAXUCeKo3Herr76anHhhRce9Dnl5812uM/bhRdeKM4444xet73fPm97O9hrP9w+B/p7ZF8yDUwasMWLFxMKhZg5c2b2toULF6KqKm+//fZR3e/ixYtZsGABTqcz+5izzz6bjRs30tnZOeDnPlYGcu6WL19OJpNh4cKF2dvGjh1LVVUVixcvPuA2DzzwAF6vl8suu2y/+6ZOnUppaSlnnXUWixYtOsJXdGwc7fN2ww03UFBQwOzZs7n//vv/f3v3HxN1/ccB/HmAd3ASAvLjiIKAIRYpP2ogrS0VMkjmr80SBEErM3O4UktdxtYPfwwWW82gSYDLNWyNssIfzaErfsSmA4NxOmLAbOOwcBAZcIKv7x/ubpx3nIR3+OHb87Ex796f9+d9n/eL1/14ge83kHF/hqqhocFiDOB2zk0UeyWZrnwDgIGBAfj6+lq1Kz3fjEYjLl68aDFfFxcXpKSkTDjfu+VEZ2cnDAaDRZ85c+YgMTHR3MdZr6PTxRlxs2VgYAAqlQre3t4W7QcPHsTcuXMRFxeHgoICjI6OTn0y08iZcTt//jwCAgIQFRWF1157DX19fRZjMN/s51tvby+qq6vx0ksvWR37L+WbI8ac6vvIndymdIVEAAwGAwICAiza3Nzc4Ovre0//H30y4xoMBoSFhVn0CQwMNB/z8fGZ8uNPh6nEzmAwQK1WW71ZBwYGTnjO559/jszMTHh4eJjbgoKCUFJSgieffBIjIyMoLS3F4sWL0djYiPj4+HubmJM5M27vvfceli5dCq1Wix9//BFbt27F33//jby8PPM4phwbP8Zff/2FoaEhixgrzXTlW319PY4fP47q6mpz20zJtz///BNjY2M2v8eXL1+2ec5EOTH+dcrUZq+PM15Hp4sz4nan4eFhvP3228jIyICXl5e5PS8vD/Hx8fD19UV9fT327NmDnp4efPTRR/c4K+dzVtxSU1OxZs0ahIWFoaOjA3v37kVaWhoaGhrg6urKfBvXf6L5Hj16FA888ADWrFlj0f5fyzdHjDmV9xFbWKyQld27d+PQoUN2++j1+mm6mplFSbFraGiAXq/HF198YdEeFRWFqKgo8/2nnnoKHR0dKCoqsuo7XZQQt3379plvx8XF4caNGygoKDAXK0qkhLiZtLa2YuXKlcjPz8eyZcvM7UrMN5o5bt68iRdeeAEiguLiYotjb775pvn2woULoVar8eqrr+LAgQPQaDTTfamKsG7dOvPtBQsWYOHChYiIiMD58+eRnJx8H69s5igrK8P69evh7u5u0c58u39YrJCVHTt2IDc3126f8PBw6HQ68w5TJqOjo7h+/Tp0Ot2UH38y4+p0OqvdJEz37+Wx75UzY6fT6WA0GtHf32/xU4re3l6b55SWliI2NhZPPPHEXa87ISEBtbW1d+3nLEqKm0liYiLef/99jIyMQKPRTJhzXl5e9+23KkqJW1tbG5KTk7F582a88847d73u+51vtvj5+cHV1dXm99hejOz1N/3b29uLoKAgiz6xsbHmPs54HZ0uzoibialQ6e7uRk1NjcVvVWxJTEzE6Ogourq6LApkJXJm3MYLDw+Hn58ffvvtNyQnJzPf7tL/559/xpUrV3D8+PG7Xsv/e745Ysypvv9amfTqFqI7mBbqXbhwwdx25swZhy2wtzeuaYG90Wg099mzZ8+MW2D/b2JnWqj29ddfm9suX75sc6Ha4OCgeHp6WuzKZE9KSoqsXr16CjOZXs6O23gffPCB+Pj4mO+/9dZb8vjjj1v0ycjImFEL7J0Rt9bWVgkICJBdu3ZN+nqUmm8JCQmybds28/2xsTEJDg62u3A3PT3doi0pKclqgX1hYaH5+MDAgM0F9lN9HVUCR8dNRMRoNMqqVaskOjparl27NqnrOHbsmLi4uFjtfqVUzojbna5evSoqlUpOnDghIsw3k4nilpOTY7Xr3ET+3/NtPHsL7O2NOdX33zuxWKF7kpqaKnFxcdLY2Ci1tbUSGRlpsQXi77//LlFRUdLY2Ghu6+npkaamJjly5IgAkJ9++kmampqkr69v0uP29/dLYGCgZGdnS2trq1RWVopWq51xWxf/29ht2bJFQkJCpKamRi5cuCBJSUmSlJRkNXZpaam4u7vb3IGpqKhIvv32W2lvb5eWlhbZvn27uLi4yNmzZ50yT0dzRty+++47OXLkiLS0tEh7e7t8+umnotVq5d133zX3MW1dvGvXLtHr9XL48OEZt3Wxo+PW0tIi/v7+kpWVZbGd5/gPlzMp3yorK0Wj0UhFRYW0tbXJ5s2bxdvbWwwGg4iIZGdny+7du8396+rqxM3NTQoLC0Wv10t+fr7NrYu9vb3lxIkT8uuvv8rKlSttbl1s73ujdI6Om9FolBUrVshDDz0kzc3NFrll2gGyvr5eioqKpLm5WTo6OuTYsWPi7+8vGzZsmP4ATJGj4zY4OCg7d+6UhoYG6ezslLNnz0p8fLxERkbK8PCweRzmm/XzVOT2DxK0Wq0UFxdbPeZ/Md9GRkakqalJmpqaJCgoSHbu3ClNTU3S3t4+6TFFJv+5xR4WK3RP+vr6JCMjQzw9PcXLy0s2btwog4OD5uOdnZ0CQM6dO2duy8/PFwBWX+Xl5ZMeV0Tk0qVL8vTTT4tGo5Hg4GA5ePCgs6frUFOJ3dDQkGzdulV8fHxEq9XK6tWrpaenx2rspKQkyczMtPm4hw4dkoiICHF3dxdfX19ZvHix1NTUOHx+zuKMuJ06dUpiY2PF09NTZs+eLTExMVJSUmL1t0DOnTsnsbGxolarJTw83CJnlc4ZcZvouRwaGmruM9Py7ZNPPpGQkBBRq9WSkJAgv/zyi/nYM888Izk5ORb9v/rqK5k3b56o1WqJjo6W6upqi+O3bt2Sffv2SWBgoGg0GklOTpYrV65Y9JnM653SOTJuply09WXKz4sXL0piYqLMmTNH3N3d5dFHH5X9+/dbfCifCRwZt3/++UeWLVsm/v7+MmvWLAkNDZVXXnnF4oOjCPPN1vNUROSzzz4TDw8P6e/vtzr2X8y3iZ6H4/9u293GFJn85xZ7VCLj9uYkIiIiIiJSCP6dFSIiIiIiUiQWK0REREREpEgsVoiIiIiISJFYrBARERERkSKxWCEiIiIiIkVisUJERERERIrEYoWIiIiIiBSJxQoRERERESkSixUiIiIiIlIkFitERKQIubm5UKlU2LJli9Wx119/HSqVCrm5uRZ9VSoVZs2ahcDAQDz77LMoKyvDrVu3LM69dOkSVqxYgYCAALi7u+ORRx7Biy++iGvXrk14LcPDw8jNzcWCBQvg5uaGVatWOXKqREQ0SSxWiIhIMR5++GFUVlZiaGjI3DY8PIwvv/wSISEhFn1TU1PR09ODrq4unDp1CkuWLMH27duRnp6O0dFRAMAff/yB5ORk+Pr64syZM9Dr9SgvL8eDDz6IGzduTHgdY2Nj8PDwQF5eHlJSUpwzWSIiuiu3+30BREREJvHx8ejo6EBVVRXWr18PAKiqqkJISAjCwsIs+mo0Guh0OgBAcHAw4uPjsWjRIiQnJ6OiogIvv/wy6urqMDAwgNLSUri53X7LCwsLw5IlS+xex+zZs1FcXAwAqKurQ39/v4NnSkREk8HfrBARkaJs2rQJ5eXl5vtlZWXYuHHjpM5dunQpYmJiUFVVBQDQ6XQYHR3FN998AxFxyvUSEZHzsFghIiJFycrKQm1tLbq7u9Hd3Y26ujpkZWVN+vz58+ejq6sLALBo0SLs3bsXmZmZ8PPzQ1paGgoKCtDb2+ukqyciIkdisUJERIri7++P5cuXo6KiAuXl5Vi+fDn8/Pwmfb6IQKVSme9/+OGHMBgMKCkpQXR0NEpKSjB//ny0tLQAAKKjo+Hp6QlPT0+kpaU5fD5ERDR1XLNCRESKs2nTJmzbtg0AcPjw4X91rl6vt1rfMnfuXKxduxZr167F/v37ERcXh8LCQhw9ehQnT57EzZs3AQAeHh6OmQARETkEixUiIlKc1NRUGI1GqFQqPPfcc5M+r6amBi0tLXjjjTcm7KNWqxEREWHeDSw0NPSer5eIiJyDxQoRESmOq6sr9Hq9+bYtIyMjMBgMGBsbQ29vL06fPo0DBw4gPT0dGzZsAAD88MMPqKysxLp16zBv3jyICL7//nucPHnSYhG/LW1tbTAajbh+/ToGBwfR3NwMAIiNjXXYPImIyD4WK0REpEheXl52j58+fRpBQUFwc3ODj48PYmJi8PHHHyMnJwcuLreXZD722GPQarXYsWMHrl69Co1Gg8jISJSWliI7O9vu+M8//zy6u7vN9+Pi4gCAu4oREU0jlfBVl4iIiIiIFIi7gRERERERkSKxWCEiIiIiIkVisUJERERERIrEYoWIiIiIiBSJxQoRERERESkSixUiIiIiIlIkFitERERERKRILFaIiIiIiEiRWKwQEREREZEisVghIiIiIiJFYrFCRERERESK9D8rnFykPcluWwAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(10)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(10):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + "\n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(int(cell_label), xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " inter_distance = distance_matrix(xy_mean, xy_mean)\n", + " mean_inter_distance = np.mean(inter_distance[inter_distance>0])\n", + " norm_distance = np.max(inter_distance) - inter_distance\n", + " # print(p1)\n", + " # print(p2)\n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=10\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (10-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/uci_digits_gfa_screeplot.ipynb b/examples.bck/uci_digits_gfa_screeplot.ipynb new file mode 100644 index 0000000..5c7bc33 --- /dev/null +++ b/examples.bck/uci_digits_gfa_screeplot.ipynb @@ -0,0 +1,1062 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/10: 51 iterations with final cost -1667706.262056\n", + "Run 2/10: 51 iterations with final cost -1667717.480977\n", + "Run 3/10: 51 iterations with final cost -1667738.247669\n", + "Run 4/10: 51 iterations with final cost -1667766.018791\n", + "Run 5/10: 51 iterations with final cost -1667761.424712\n", + "Run 6/10: 51 iterations with final cost -1667746.082755\n", + "Run 7/10: 51 iterations with final cost -1667772.658538\n", + "Run 8/10: 50 iterations with final cost -1667713.089674\n", + "Run 9/10: 49 iterations with final cost -1667711.213274\n", + "Run 10/10: 51 iterations with final cost -1667729.890125\n", + "Run 1/10: 84 iterations with final cost -1593176.097569\n", + "Run 2/10: 84 iterations with final cost -1593186.563296\n", + "Run 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iterations with final cost -1203364.204412\n", + "Run 8/10: 184 iterations with final cost -1203307.579001\n", + "Run 9/10: 179 iterations with final cost -1203445.088287\n", + "Run 10/10: 184 iterations with final cost -1203276.771754\n", + "Run 1/10: 191 iterations with final cost -1185487.070112\n", + "Run 2/10: 187 iterations with final cost -1185768.193736\n", + "Run 3/10: 192 iterations with final cost -1185422.264815\n", + "Run 4/10: 192 iterations with final cost -1185573.091691\n", + "Run 5/10: 195 iterations with final cost -1185509.193873\n", + "Run 6/10: 190 iterations with final cost -1185623.100158\n", + "Run 7/10: 189 iterations with final cost -1185588.334943\n", + "Run 8/10: 188 iterations with final cost -1185640.824238\n", + "Run 9/10: 188 iterations with final cost -1185595.136359\n", + "Run 10/10: 192 iterations with final cost -1185440.233444\n", + "2 0.029687702574105454\n", + "3 0.03393038485414468\n", + "4 0.04555350844521267\n", + "5 0.05664384961059846\n", + "6 0.06879185561736835\n", + "7 0.08266301622764172\n", + "8 0.09814874862641586\n", + "9 0.11200661509068298\n", + "10 0.1292666861504051\n", + "11 0.14910991700527848\n", + "12 0.16992858676032446\n", + "13 0.1904716017887359\n", + "14 0.21206792427828847\n", + "15 0.23364475961582268\n", + "16 0.25460850083774605\n" + ] + } + ], + "source": [ + "gfa_cov = np.zeros(17)\n", + "for k in range(2,17):\n", + " model = gfa_experiments(Xs_norm, K=k, Nrep=10, rotate=False, verbose=1)\n", + " gfa_cov[k] = np.trace(model['covZ'])\n", + "\n", + "for k in range(2,17):\n", + " print(k, gfa_cov[k])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca = PCA(n_components=16)\n", + "\n", + "# Concatenate views then PCA for comparison\n", + "Xs_concat = Xs_norm[0]\n", + "for X in Xs_norm[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "Xs_pca_reduced = pca.fit_transform(Xs_concat)\n", + "\n", + "\n", + "# Plot the scree plot\n", + "plt.plot (np.arange (1, pca.n_components_ + 1), pca.explained_variance_ratio_, 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(2,17), gfa_cov[2:17], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/uci_digits_mofa.ipynb b/examples.bck/uci_digits_mofa.ipynb new file mode 100644 index 0000000..7b999f1 --- /dev/null +++ b/examples.bck/uci_digits_mofa.ipynb @@ -0,0 +1,845 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "from mofapy2.run.entry_point import entry_point" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])\n", + "\n", + "data_mat = [[None for g in range(1)] for m in range(6)]\n", + "\n", + "for m in range(6):\n", + " data_mat[m][0] = Xs_norm[m]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "Successfully loaded view='view4' group='group0' with N=2000 samples and D=47 features...\n", + "Successfully loaded view='view5' group='group0' with N=2000 samples and D=6 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "- View 4 (view4): gaussian\n", + "- View 5 (view5): gaussian\n", + "\n", + "\n", + "\n", + "Warning: some view(s) have less than 15 features, MOFA won't be able to learn meaningful factors for these view(s)...\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -9709296.32 \n", + "\n", + "Iteration 1: time=0.37, ELBO=-1404936.80, deltaELBO=8304359.518 (85.52998325%), Factors=9\n", + "Iteration 2: time=0.35, ELBO=-1304107.77, deltaELBO=100829.032 (1.03847930%), Factors=9\n", + "Iteration 3: time=0.33, ELBO=-1292170.50, deltaELBO=11937.273 (0.12294684%), Factors=9\n", + "Iteration 4: time=0.35, ELBO=-1285195.38, deltaELBO=6975.120 (0.07183961%), Factors=9\n", + "Iteration 5: time=0.33, ELBO=-1279820.72, deltaELBO=5374.658 (0.05535579%), Factors=9\n", + "Iteration 6: time=0.33, ELBO=-1274512.59, deltaELBO=5308.129 (0.05467058%), Factors=9\n", + "Iteration 7: time=0.33, ELBO=-1268085.65, deltaELBO=6426.940 (0.06619368%), Factors=9\n", + "Iteration 8: time=0.33, ELBO=-1260996.51, deltaELBO=7089.137 (0.07301392%), Factors=9\n", + "Iteration 9: time=0.33, ELBO=-1255267.26, deltaELBO=5729.254 (0.05900792%), Factors=9\n", + "Iteration 10: time=0.33, ELBO=-1251797.77, deltaELBO=3469.488 (0.03573367%), Factors=9\n", + "Iteration 11: time=0.35, ELBO=-1249936.84, deltaELBO=1860.927 (0.01916645%), Factors=9\n", + "Iteration 12: time=0.39, ELBO=-1248892.38, deltaELBO=1044.460 (0.01075732%), Factors=9\n", + "Iteration 13: time=0.82, ELBO=-1248210.98, deltaELBO=681.405 (0.00701807%), Factors=9\n", + "Iteration 14: time=0.43, ELBO=-1247693.58, deltaELBO=517.396 (0.00532887%), Factors=9\n", + "Iteration 15: time=0.43, ELBO=-1247260.66, deltaELBO=432.928 (0.00445890%), Factors=9\n", + "Iteration 16: time=0.42, ELBO=-1246878.89, deltaELBO=381.764 (0.00393194%), Factors=9\n", + "Iteration 17: time=0.37, ELBO=-1246532.61, deltaELBO=346.282 (0.00356650%), Factors=9\n", + "Iteration 18: time=0.37, ELBO=-1246213.39, deltaELBO=319.223 (0.00328781%), Factors=9\n", + "Iteration 19: time=0.44, ELBO=-1245916.12, deltaELBO=297.270 (0.00306170%), Factors=9\n", + "Iteration 20: time=0.45, ELBO=-1245637.38, deltaELBO=278.732 (0.00287078%), Factors=9\n", + "Iteration 21: time=0.45, ELBO=-1245374.72, deltaELBO=262.661 (0.00270525%), Factors=9\n", + "Iteration 22: time=0.47, ELBO=-1245126.25, deltaELBO=248.476 (0.00255915%), Factors=9\n", + "Iteration 23: time=0.63, ELBO=-1244890.45, deltaELBO=235.793 (0.00242853%), Factors=9\n", + "Iteration 24: time=0.48, ELBO=-1244666.11, deltaELBO=224.346 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"Iteration 369: time=1.01, ELBO=-1234875.48, deltaELBO=5.059 (0.00005210%), Factors=9\n", + "Iteration 370: time=0.92, ELBO=-1234870.44, deltaELBO=5.038 (0.00005189%), Factors=9\n", + "Iteration 371: time=0.89, ELBO=-1234865.43, deltaELBO=5.018 (0.00005168%), Factors=9\n", + "Iteration 372: time=1.36, ELBO=-1234860.43, deltaELBO=4.997 (0.00005147%), Factors=9\n", + "Iteration 373: time=1.64, ELBO=-1234855.45, deltaELBO=4.977 (0.00005126%), Factors=9\n", + "Iteration 374: time=1.16, ELBO=-1234850.50, deltaELBO=4.957 (0.00005105%), Factors=9\n", + "Iteration 375: time=1.11, ELBO=-1234845.56, deltaELBO=4.937 (0.00005085%), Factors=9\n", + "Iteration 376: time=1.17, ELBO=-1234840.64, deltaELBO=4.917 (0.00005064%), Factors=9\n", + "Iteration 377: time=1.31, ELBO=-1234835.74, deltaELBO=4.897 (0.00005044%), Factors=9\n", + "Iteration 378: time=1.12, ELBO=-1234830.87, deltaELBO=4.877 (0.00005023%), Factors=9\n", + "Iteration 379: time=1.01, ELBO=-1234826.01, deltaELBO=4.858 (0.00005003%), Factors=9\n", + "Iteration 380: time=0.96, ELBO=-1234821.17, deltaELBO=4.838 (0.00004983%), Factors=9\n", + "Iteration 381: time=1.36, ELBO=-1234816.35, deltaELBO=4.819 (0.00004963%), Factors=9\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "ent = entry_point()\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(6)])\n", + "ent.set_model_options(\n", + " factors = 10, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + ")\n", + "ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + ")\n", + "ent.build()\n", + "ent.run()\n", + "factors = ent.model.nodes[\"Z\"].getExpectation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1863.659405336067]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1)\n", + "\n", + "n_marker_genes = list_cell_codes.shape[0]\n", + "\n", + "stress = []\n", + "\n", + "w4_gfa = factors\n", + "\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 2.91\n", + "0.867\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(10)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(10):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + "\n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(int(cell_label), xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " inter_distance = distance_matrix(xy_mean, xy_mean)\n", + " mean_inter_distance = np.mean(inter_distance[inter_distance>0])\n", + " norm_distance = np.max(inter_distance) - inter_distance\n", + " # print(p1)\n", + " # print(p2)\n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=10\n", + "\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"ISM Reduced Data (10-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/uci_digits_mofa_screeplot.ipynb b/examples.bck/uci_digits_mofa_screeplot.ipynb new file mode 100644 index 0000000..a199860 --- /dev/null +++ b/examples.bck/uci_digits_mofa_screeplot.ipynb @@ -0,0 +1,2222 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from mofapy2.run.entry_point import entry_point" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -3500167.92 \n", + "\n", + "Iteration 1: time=0.07, ELBO=-1640429.31, deltaELBO=1859738.611 (53.13283966%), Factors=1\n", + "Iteration 2: time=0.13, ELBO=-1638157.42, deltaELBO=2271.898 (0.06490825%), Factors=1\n", + "Iteration 3: time=0.13, ELBO=-1637485.81, deltaELBO=671.608 (0.01918788%), Factors=1\n", + "Iteration 4: time=0.15, ELBO=-1636892.57, deltaELBO=593.235 (0.01694876%), Factors=1\n", + "Iteration 5: time=0.15, ELBO=-1636321.26, deltaELBO=571.317 (0.01632255%), Factors=1\n", + "Iteration 6: time=0.15, ELBO=-1635738.53, deltaELBO=582.725 (0.01664848%), Factors=1\n", + "Iteration 7: time=0.18, ELBO=-1635117.89, deltaELBO=620.642 (0.01773179%), Factors=1\n", + "Iteration 8: time=0.17, ELBO=-1634438.67, deltaELBO=679.214 (0.01940519%), Factors=1\n", + "Iteration 9: time=0.15, ELBO=-1633690.47, deltaELBO=748.204 (0.02137623%), Factors=1\n", + "Iteration 10: time=0.15, ELBO=-1632881.16, deltaELBO=809.313 (0.02312211%), Factors=1\n", + "Iteration 11: time=0.16, ELBO=-1632043.82, deltaELBO=837.338 (0.02392281%), Factors=1\n", + "Iteration 12: time=0.15, ELBO=-1631233.51, deltaELBO=810.310 (0.02315059%), Factors=1\n", + "Iteration 13: time=0.13, ELBO=-1630505.42, deltaELBO=728.089 (0.02080155%), Factors=1\n", + "Iteration 14: time=0.17, ELBO=-1629876.76, deltaELBO=628.657 (0.01796077%), Factors=1\n", + "Iteration 15: time=0.19, ELBO=-1629287.82, deltaELBO=588.943 (0.01682614%), Factors=1\n", + "Iteration 16: time=0.14, ELBO=-1628565.54, deltaELBO=722.285 (0.02063572%), Factors=1\n", + "Iteration 17: time=0.16, ELBO=-1627355.84, deltaELBO=1209.693 (0.03456100%), Factors=1\n", + "Iteration 18: time=0.16, ELBO=-1624994.29, deltaELBO=2361.551 (0.06746964%), Factors=1\n", + "Iteration 19: time=0.16, ELBO=-1620473.47, deltaELBO=4520.820 (0.12916010%), Factors=1\n", + "Iteration 20: time=0.15, ELBO=-1613215.26, deltaELBO=7258.212 (0.20736754%), Factors=1\n", + "Iteration 21: time=0.12, ELBO=-1604951.73, deltaELBO=8263.526 (0.23608941%), Factors=1\n", + "Iteration 22: time=0.13, ELBO=-1598764.34, deltaELBO=6187.397 (0.17677430%), Factors=1\n", + "Iteration 23: time=0.19, ELBO=-1595359.92, deltaELBO=3404.414 (0.09726431%), Factors=1\n", + "Iteration 24: time=0.17, ELBO=-1593649.24, deltaELBO=1710.681 (0.04887424%), Factors=1\n", + "Iteration 25: time=0.16, ELBO=-1592742.77, deltaELBO=906.468 (0.02589783%), Factors=1\n", + "Iteration 26: time=0.16, ELBO=-1592225.05, deltaELBO=517.719 (0.01479127%), Factors=1\n", + "Iteration 27: time=0.14, ELBO=-1591912.29, deltaELBO=312.759 (0.00893556%), Factors=1\n", + "Iteration 28: time=0.15, ELBO=-1591715.55, deltaELBO=196.743 (0.00562094%), Factors=1\n", + "Iteration 29: time=0.16, ELBO=-1591587.47, deltaELBO=128.083 (0.00365935%), Factors=1\n", + "Iteration 30: time=0.15, ELBO=-1591501.17, deltaELBO=86.303 (0.00246567%), Factors=1\n", + "Iteration 31: time=0.13, ELBO=-1591440.80, deltaELBO=60.363 (0.00172458%), Factors=1\n", + "Iteration 32: time=0.16, ELBO=-1591396.83, deltaELBO=43.978 (0.00125644%), Factors=1\n", + "Iteration 33: time=0.16, ELBO=-1591363.38, deltaELBO=33.445 (0.00095553%), Factors=1\n", + "Iteration 34: time=0.09, ELBO=-1591336.84, deltaELBO=26.543 (0.00075833%), Factors=1\n", + "Iteration 35: time=0.08, ELBO=-1591314.92, deltaELBO=21.915 (0.00062613%), Factors=1\n", + "Iteration 36: time=0.08, ELBO=-1591296.19, deltaELBO=18.728 (0.00053507%), Factors=1\n", + "Iteration 37: time=0.08, ELBO=-1591279.73, deltaELBO=16.463 (0.00047035%), Factors=1\n", + "Iteration 38: time=0.08, ELBO=-1591264.94, deltaELBO=14.795 (0.00042269%), Factors=1\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -4186825.51 \n", + "\n", + "Iteration 1: time=0.15, ELBO=-1586060.00, deltaELBO=2600765.506 (62.11783850%), Factors=2\n", + "Iteration 2: time=0.12, ELBO=-1578871.40, deltaELBO=7188.596 (0.17169561%), Factors=2\n", + "Iteration 3: time=0.13, ELBO=-1576311.75, deltaELBO=2559.658 (0.06113601%), Factors=2\n", + "Iteration 4: time=0.13, ELBO=-1572730.91, deltaELBO=3580.841 (0.08552639%), Factors=2\n", + "Iteration 5: time=0.13, ELBO=-1565479.56, deltaELBO=7251.349 (0.17319443%), Factors=2\n", + "Iteration 6: time=0.12, ELBO=-1551338.48, deltaELBO=14141.072 (0.33775165%), Factors=2\n", + "Iteration 7: time=0.12, ELBO=-1535958.97, deltaELBO=15379.513 (0.36733112%), Factors=2\n", + "Iteration 8: time=0.12, ELBO=-1528282.26, deltaELBO=7676.715 (0.18335408%), Factors=2\n", + "Iteration 9: time=0.13, ELBO=-1525060.86, deltaELBO=3221.400 (0.07694134%), Factors=2\n", + "Iteration 10: time=0.12, ELBO=-1522582.22, deltaELBO=2478.638 (0.05920090%), Factors=2\n", + "Iteration 11: time=0.11, ELBO=-1520218.78, deltaELBO=2363.434 (0.05644930%), Factors=2\n", + "Iteration 12: time=0.13, ELBO=-1517942.04, deltaELBO=2276.745 (0.05437879%), Factors=2\n", + "Iteration 13: time=0.12, ELBO=-1515771.89, deltaELBO=2170.147 (0.05183276%), Factors=2\n", + "Iteration 14: time=0.12, ELBO=-1513729.99, deltaELBO=2041.900 (0.04876966%), Factors=2\n", + "Iteration 15: time=0.12, ELBO=-1511854.75, deltaELBO=1875.239 (0.04478905%), Factors=2\n", + "Iteration 16: time=0.12, ELBO=-1510199.46, deltaELBO=1655.291 (0.03953570%), Factors=2\n", + "Iteration 17: time=0.12, ELBO=-1508809.00, deltaELBO=1390.461 (0.03321039%), Factors=2\n", + "Iteration 18: time=0.12, ELBO=-1507697.05, deltaELBO=1111.951 (0.02655832%), Factors=2\n", + "Iteration 19: time=0.12, ELBO=-1506841.63, deltaELBO=855.420 (0.02043124%), Factors=2\n", + "Iteration 20: time=0.11, ELBO=-1506198.26, deltaELBO=643.367 (0.01536646%), Factors=2\n", + "Iteration 21: time=0.13, ELBO=-1505717.74, deltaELBO=480.520 (0.01147694%), Factors=2\n", + "Iteration 22: time=0.12, ELBO=-1505357.67, deltaELBO=360.074 (0.00860016%), Factors=2\n", + "Iteration 23: time=0.12, ELBO=-1505085.77, deltaELBO=271.895 (0.00649406%), Factors=2\n", + "Iteration 24: time=0.11, ELBO=-1504878.60, deltaELBO=207.170 (0.00494814%), Factors=2\n", + "Iteration 25: time=0.12, ELBO=-1504719.13, deltaELBO=159.477 (0.00380901%), Factors=2\n", + "Iteration 26: time=0.13, ELBO=-1504594.82, deltaELBO=124.308 (0.00296903%), Factors=2\n", + "Iteration 27: time=0.12, ELBO=-1504496.40, deltaELBO=98.419 (0.00235069%), Factors=2\n", + "Iteration 28: time=0.13, ELBO=-1504417.00, deltaELBO=79.399 (0.00189641%), Factors=2\n", + "Iteration 29: time=0.12, ELBO=-1504351.57, deltaELBO=65.433 (0.00156283%), Factors=2\n", + "Iteration 30: time=0.12, ELBO=-1504296.41, deltaELBO=55.159 (0.00131745%), Factors=2\n", + "Iteration 31: time=0.12, ELBO=-1504248.84, deltaELBO=47.569 (0.00113615%), Factors=2\n", + "Iteration 32: time=0.12, ELBO=-1504206.92, deltaELBO=41.921 (0.00100125%), Factors=2\n", + "Iteration 33: time=0.12, ELBO=-1504169.24, deltaELBO=37.677 (0.00089988%), Factors=2\n", + "Iteration 34: time=0.11, ELBO=-1504134.79, deltaELBO=34.448 (0.00082277%), Factors=2\n", + "Iteration 35: time=0.12, ELBO=-1504102.84, deltaELBO=31.954 (0.00076321%), Factors=2\n", + "Iteration 36: time=0.11, ELBO=-1504072.84, deltaELBO=29.995 (0.00071642%), Factors=2\n", + "Iteration 37: time=0.12, ELBO=-1504044.42, deltaELBO=28.426 (0.00067894%), Factors=2\n", + "Iteration 38: time=0.12, ELBO=-1504017.28, deltaELBO=27.143 (0.00064830%), Factors=2\n", + "Iteration 39: time=0.13, ELBO=-1503991.20, deltaELBO=26.071 (0.00062270%), Factors=2\n", + "Iteration 40: time=0.14, ELBO=-1503966.05, deltaELBO=25.157 (0.00060086%), Factors=2\n", + "Iteration 41: time=0.20, ELBO=-1503941.69, deltaELBO=24.361 (0.00058184%), Factors=2\n", + "Iteration 42: time=0.16, ELBO=-1503918.03, deltaELBO=23.654 (0.00056497%), Factors=2\n", + "Iteration 43: time=0.14, ELBO=-1503895.01, deltaELBO=23.017 (0.00054975%), Factors=2\n", + "Iteration 44: time=0.13, ELBO=-1503872.58, deltaELBO=22.434 (0.00053582%), Factors=2\n", + "Iteration 45: time=0.12, ELBO=-1503850.69, deltaELBO=21.893 (0.00052290%), Factors=2\n", + "Iteration 46: time=0.18, ELBO=-1503829.30, deltaELBO=21.386 (0.00051080%), Factors=2\n", + "Iteration 47: time=0.12, ELBO=-1503808.39, deltaELBO=20.908 (0.00049938%), Factors=2\n", + "Iteration 48: time=0.11, ELBO=-1503787.94, deltaELBO=20.453 (0.00048852%), Factors=2\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -4868144.53 \n", + "\n", + "Iteration 1: time=0.17, ELBO=-1494687.66, deltaELBO=3373456.868 (69.29656357%), Factors=3\n", + "Iteration 2: time=0.14, ELBO=-1479768.09, deltaELBO=14919.570 (0.30647344%), Factors=3\n", + "Iteration 3: time=0.16, ELBO=-1477188.52, deltaELBO=2579.566 (0.05298869%), Factors=3\n", + "Iteration 4: time=0.15, ELBO=-1474637.62, deltaELBO=2550.904 (0.05239992%), Factors=3\n", + "Iteration 5: time=0.15, ELBO=-1470398.11, deltaELBO=4239.511 (0.08708679%), Factors=3\n", + "Iteration 6: time=0.14, ELBO=-1462624.22, deltaELBO=7773.894 (0.15968905%), Factors=3\n", + "Iteration 7: time=0.14, ELBO=-1452784.13, deltaELBO=9840.086 (0.20213217%), Factors=3\n", + "Iteration 8: time=0.19, ELBO=-1445839.86, deltaELBO=6944.271 (0.14264719%), Factors=3\n", + "Iteration 9: time=0.16, ELBO=-1442297.62, deltaELBO=3542.240 (0.07276366%), Factors=3\n", + "Iteration 10: time=0.13, ELBO=-1440323.50, deltaELBO=1974.115 (0.04055169%), Factors=3\n", + "Iteration 11: time=0.15, ELBO=-1438917.04, deltaELBO=1406.461 (0.02889110%), Factors=3\n", + "Iteration 12: time=0.13, ELBO=-1437665.42, deltaELBO=1251.624 (0.02571049%), Factors=3\n", + "Iteration 13: time=0.14, ELBO=-1436369.41, deltaELBO=1296.004 (0.02662214%), Factors=3\n", + "Iteration 14: time=0.13, ELBO=-1434909.93, deltaELBO=1459.482 (0.02998024%), Factors=3\n", + "Iteration 15: time=0.15, ELBO=-1433214.89, deltaELBO=1695.042 (0.03481906%), Factors=3\n", + "Iteration 16: time=0.12, ELBO=-1431276.94, deltaELBO=1937.949 (0.03980877%), Factors=3\n", + "Iteration 17: time=0.13, ELBO=-1429181.01, deltaELBO=2095.933 (0.04305405%), Factors=3\n", + "Iteration 18: time=0.15, ELBO=-1427086.16, deltaELBO=2094.854 (0.04303187%), Factors=3\n", + "Iteration 19: time=0.14, ELBO=-1425150.37, deltaELBO=1935.788 (0.03976438%), Factors=3\n", + "Iteration 20: time=0.13, ELBO=-1423474.66, deltaELBO=1675.707 (0.03442188%), Factors=3\n", + "Iteration 21: time=0.13, ELBO=-1422099.89, deltaELBO=1374.773 (0.02824018%), Factors=3\n", + "Iteration 22: time=0.15, ELBO=-1421017.48, deltaELBO=1082.408 (0.02223451%), Factors=3\n", + "Iteration 23: time=0.14, ELBO=-1420184.80, deltaELBO=832.676 (0.01710458%), Factors=3\n", + "Iteration 24: time=0.13, ELBO=-1419546.09, deltaELBO=638.712 (0.01312024%), Factors=3\n", + "Iteration 25: time=0.14, ELBO=-1419049.00, deltaELBO=497.092 (0.01021112%), Factors=3\n", + "Iteration 26: time=0.13, ELBO=-1418652.04, deltaELBO=396.962 (0.00815429%), Factors=3\n", + "Iteration 27: time=0.13, ELBO=-1418325.20, deltaELBO=326.834 (0.00671373%), Factors=3\n", + "Iteration 28: time=0.14, ELBO=-1418047.69, deltaELBO=277.518 (0.00570070%), Factors=3\n", + "Iteration 29: time=0.12, ELBO=-1417805.13, deltaELBO=242.560 (0.00498259%), Factors=3\n", + "Iteration 30: time=0.13, ELBO=-1417587.43, deltaELBO=217.694 (0.00447180%), Factors=3\n", + "Iteration 31: time=0.15, ELBO=-1417387.26, deltaELBO=200.174 (0.00411192%), Factors=3\n", + "Iteration 32: time=0.12, ELBO=-1417199.00, deltaELBO=188.260 (0.00386717%), Factors=3\n", + "Iteration 33: time=0.13, ELBO=-1417018.11, deltaELBO=180.886 (0.00371570%), Factors=3\n", + "Iteration 34: time=0.14, ELBO=-1416840.64, deltaELBO=177.471 (0.00364555%), Factors=3\n", + "Iteration 35: time=0.12, ELBO=-1416662.83, deltaELBO=177.813 (0.00365257%), Factors=3\n", + "Iteration 36: time=0.13, ELBO=-1416480.79, deltaELBO=182.037 (0.00373935%), Factors=3\n", + "Iteration 37: time=0.14, ELBO=-1416290.23, deltaELBO=190.558 (0.00391440%), Factors=3\n", + "Iteration 38: time=0.12, ELBO=-1416086.24, deltaELBO=203.993 (0.00419036%), Factors=3\n", + "Iteration 39: time=0.13, ELBO=-1415863.36, deltaELBO=222.884 (0.00457841%), Factors=3\n", + "Iteration 40: time=0.14, ELBO=-1415616.38, deltaELBO=246.979 (0.00507337%), Factors=3\n", + "Iteration 41: time=0.14, ELBO=-1415342.71, deltaELBO=273.667 (0.00562159%), Factors=3\n", + "Iteration 42: time=0.13, ELBO=-1415047.14, deltaELBO=295.576 (0.00607164%), Factors=3\n", + "Iteration 43: time=0.14, ELBO=-1414747.56, deltaELBO=299.576 (0.00615381%), Factors=3\n", + "Iteration 44: time=0.13, ELBO=-1414474.35, deltaELBO=273.205 (0.00561210%), Factors=3\n", + "Iteration 45: time=0.13, ELBO=-1414254.77, deltaELBO=219.584 (0.00451064%), Factors=3\n", + "Iteration 46: time=0.14, ELBO=-1414094.27, deltaELBO=160.499 (0.00329692%), Factors=3\n", + "Iteration 47: time=0.13, ELBO=-1413979.61, deltaELBO=114.661 (0.00235533%), Factors=3\n", + "Iteration 48: time=0.12, ELBO=-1413895.46, deltaELBO=84.148 (0.00172854%), Factors=3\n", + "Iteration 49: time=0.15, ELBO=-1413831.61, deltaELBO=63.851 (0.00131160%), Factors=3\n", + "Iteration 50: time=0.13, ELBO=-1413781.72, deltaELBO=49.890 (0.00102483%), Factors=3\n", + "Iteration 51: time=0.13, ELBO=-1413741.62, deltaELBO=40.106 (0.00082384%), Factors=3\n", + "Iteration 52: time=0.15, ELBO=-1413708.51, deltaELBO=33.101 (0.00067995%), Factors=3\n", + "Iteration 53: time=0.12, ELBO=-1413680.60, deltaELBO=27.919 (0.00057350%), Factors=3\n", + "Iteration 54: time=0.13, ELBO=-1413656.65, deltaELBO=23.944 (0.00049185%), Factors=3\n", + "Iteration 55: time=0.14, ELBO=-1413635.85, deltaELBO=20.800 (0.00042727%), Factors=3\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -5549941.88 \n", + "\n", + "Iteration 1: time=0.48, ELBO=-1446396.32, deltaELBO=4103545.557 (73.93853206%), Factors=4\n", + "Iteration 2: time=0.28, ELBO=-1423932.62, deltaELBO=22463.707 (0.40475572%), Factors=4\n", + "Iteration 3: time=0.17, ELBO=-1419380.32, deltaELBO=4552.292 (0.08202414%), Factors=4\n", + "Iteration 4: time=0.17, ELBO=-1413166.32, deltaELBO=6214.005 (0.11196522%), Factors=4\n", + "Iteration 5: time=0.25, ELBO=-1400843.50, deltaELBO=12322.820 (0.22203512%), Factors=4\n", + "Iteration 6: time=0.22, ELBO=-1382058.12, deltaELBO=18785.381 (0.33847889%), Factors=4\n", + "Iteration 7: time=0.16, ELBO=-1368209.03, deltaELBO=13849.087 (0.24953571%), Factors=4\n", + "Iteration 8: time=0.15, ELBO=-1362557.56, deltaELBO=5651.469 (0.10182934%), Factors=4\n", + "Iteration 9: time=0.17, ELBO=-1359826.36, deltaELBO=2731.203 (0.04921138%), Factors=4\n", + "Iteration 10: time=0.20, ELBO=-1357713.61, deltaELBO=2112.746 (0.03806789%), Factors=4\n", + "Iteration 11: time=0.33, ELBO=-1355783.65, deltaELBO=1929.964 (0.03477448%), Factors=4\n", + "Iteration 12: time=0.22, ELBO=-1354058.62, deltaELBO=1725.030 (0.03108194%), Factors=4\n", + "Iteration 13: time=0.16, ELBO=-1352681.55, deltaELBO=1377.066 (0.02481227%), Factors=4\n", + "Iteration 14: time=0.17, ELBO=-1351709.03, deltaELBO=972.528 (0.01752321%), Factors=4\n", + "Iteration 15: time=0.18, ELBO=-1351038.15, deltaELBO=670.872 (0.01208792%), Factors=4\n", + "Iteration 16: time=0.19, ELBO=-1350525.54, deltaELBO=512.613 (0.00923637%), Factors=4\n", + "Iteration 17: time=0.16, ELBO=-1350076.65, deltaELBO=448.891 (0.00808822%), Factors=4\n", + "Iteration 18: time=0.17, ELBO=-1349640.33, deltaELBO=436.323 (0.00786176%), Factors=4\n", + "Iteration 19: time=0.17, ELBO=-1349184.84, deltaELBO=455.489 (0.00820710%), Factors=4\n", + "Iteration 20: time=0.18, ELBO=-1348687.22, deltaELBO=497.619 (0.00896621%), Factors=4\n", + "Iteration 21: time=0.17, ELBO=-1348137.58, deltaELBO=549.634 (0.00990342%), Factors=4\n", + "Iteration 22: time=0.17, ELBO=-1347557.99, deltaELBO=579.591 (0.01044320%), Factors=4\n", + "Iteration 23: time=0.19, ELBO=-1347018.78, deltaELBO=539.216 (0.00971570%), Factors=4\n", + "Iteration 24: time=0.17, ELBO=-1346602.36, deltaELBO=416.421 (0.00750315%), Factors=4\n", + "Iteration 25: time=0.20, ELBO=-1346327.14, deltaELBO=275.219 (0.00495896%), Factors=4\n", + "Iteration 26: time=0.24, ELBO=-1346150.23, deltaELBO=176.904 (0.00318749%), Factors=4\n", + "Iteration 27: time=0.21, ELBO=-1346027.61, deltaELBO=122.626 (0.00220950%), Factors=4\n", + "Iteration 28: time=0.21, ELBO=-1345934.66, deltaELBO=92.950 (0.00167480%), Factors=4\n", + "Iteration 29: time=0.36, ELBO=-1345859.02, deltaELBO=75.631 (0.00136274%), Factors=4\n", + "Iteration 30: time=0.25, ELBO=-1345794.00, deltaELBO=65.030 (0.00117172%), Factors=4\n", + "Iteration 31: time=0.26, ELBO=-1345735.67, deltaELBO=58.322 (0.00105086%), Factors=4\n", + "Iteration 32: time=0.26, ELBO=-1345681.75, deltaELBO=53.923 (0.00097160%), Factors=4\n", + "Iteration 33: time=0.82, ELBO=-1345630.84, deltaELBO=50.909 (0.00091730%), Factors=4\n", + "Iteration 34: time=0.30, ELBO=-1345582.10, deltaELBO=48.737 (0.00087816%), Factors=4\n", + "Iteration 35: time=0.30, ELBO=-1345535.02, deltaELBO=47.086 (0.00084841%), Factors=4\n", + "Iteration 36: time=0.27, ELBO=-1345489.25, deltaELBO=45.765 (0.00082461%), Factors=4\n", + "Iteration 37: time=0.30, ELBO=-1345444.59, deltaELBO=44.658 (0.00080466%), Factors=4\n", + "Iteration 38: time=0.78, ELBO=-1345400.90, deltaELBO=43.694 (0.00078729%), Factors=4\n", + "Iteration 39: time=0.24, ELBO=-1345358.07, deltaELBO=42.830 (0.00077172%), Factors=4\n", + "Iteration 40: time=0.22, ELBO=-1345316.03, deltaELBO=42.037 (0.00075743%), Factors=4\n", + "Iteration 41: time=0.22, ELBO=-1345274.74, deltaELBO=41.296 (0.00074409%), Factors=4\n", + "Iteration 42: time=0.23, ELBO=-1345234.14, deltaELBO=40.596 (0.00073148%), Factors=4\n", + "Iteration 43: time=0.23, ELBO=-1345194.21, deltaELBO=39.929 (0.00071944%), Factors=4\n", + "Iteration 44: time=0.28, ELBO=-1345154.92, deltaELBO=39.287 (0.00070788%), Factors=4\n", + "Iteration 45: time=0.19, ELBO=-1345116.26, deltaELBO=38.667 (0.00069671%), Factors=4\n", + "Iteration 46: time=0.23, ELBO=-1345078.19, deltaELBO=38.066 (0.00068587%), Factors=4\n", + "Iteration 47: time=0.32, ELBO=-1345040.71, deltaELBO=37.481 (0.00067534%), Factors=4\n", + "Iteration 48: time=0.27, ELBO=-1345003.80, deltaELBO=36.911 (0.00066507%), Factors=4\n", + "Iteration 49: time=0.19, ELBO=-1344967.44, deltaELBO=36.355 (0.00065505%), Factors=4\n", + "Iteration 50: time=0.19, ELBO=-1344931.63, deltaELBO=35.811 (0.00064525%), Factors=4\n", + "Iteration 51: time=0.21, ELBO=-1344896.35, deltaELBO=35.279 (0.00063566%), Factors=4\n", + "Iteration 52: time=0.21, ELBO=-1344861.60, deltaELBO=34.758 (0.00062627%), Factors=4\n", + "Iteration 53: time=0.21, ELBO=-1344827.35, deltaELBO=34.247 (0.00061707%), Factors=4\n", + "Iteration 54: time=0.25, ELBO=-1344793.60, deltaELBO=33.746 (0.00060804%), Factors=4\n", + "Iteration 55: time=0.30, ELBO=-1344760.35, deltaELBO=33.255 (0.00059919%), Factors=4\n", + "Iteration 56: time=0.25, ELBO=-1344727.58, deltaELBO=32.773 (0.00059051%), Factors=4\n", + "Iteration 57: time=0.32, ELBO=-1344695.28, deltaELBO=32.300 (0.00058199%), Factors=4\n", + "Iteration 58: time=0.23, ELBO=-1344663.44, deltaELBO=31.836 (0.00057362%), Factors=4\n", + "Iteration 59: time=0.20, ELBO=-1344632.06, deltaELBO=31.380 (0.00056541%), Factors=4\n", + "Iteration 60: time=0.21, ELBO=-1344601.13, deltaELBO=30.932 (0.00055734%), Factors=4\n", + "Iteration 61: time=0.24, ELBO=-1344570.64, deltaELBO=30.493 (0.00054942%), Factors=4\n", + "Iteration 62: time=0.24, ELBO=-1344540.58, deltaELBO=30.061 (0.00054164%), Factors=4\n", + "Iteration 63: time=0.24, ELBO=-1344510.94, deltaELBO=29.636 (0.00053399%), Factors=4\n", + "Iteration 64: time=0.27, ELBO=-1344481.72, deltaELBO=29.219 (0.00052648%), Factors=4\n", + "Iteration 65: time=0.28, ELBO=-1344452.91, deltaELBO=28.810 (0.00051910%), Factors=4\n", + "Iteration 66: time=0.30, ELBO=-1344424.50, deltaELBO=28.407 (0.00051185%), Factors=4\n", + "Iteration 67: time=0.28, ELBO=-1344396.49, deltaELBO=28.012 (0.00050472%), Factors=4\n", + "Iteration 68: time=0.19, ELBO=-1344368.87, deltaELBO=27.623 (0.00049771%), Factors=4\n", + "Iteration 69: time=0.19, ELBO=-1344341.63, deltaELBO=27.241 (0.00049083%), Factors=4\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -6213151.75 \n", + "\n", + "Iteration 1: time=0.24, ELBO=-1388191.99, deltaELBO=4824959.762 (77.65720128%), Factors=5\n", + "Iteration 2: time=0.22, ELBO=-1350821.48, deltaELBO=37370.512 (0.60147431%), Factors=5\n", + "Iteration 3: time=0.23, ELBO=-1342787.54, deltaELBO=8033.941 (0.12930540%), Factors=5\n", + "Iteration 4: time=0.22, ELBO=-1336563.50, deltaELBO=6224.039 (0.10017523%), Factors=5\n", + "Iteration 5: time=0.40, ELBO=-1331356.61, deltaELBO=5206.894 (0.08380439%), Factors=5\n", + "Iteration 6: time=0.33, ELBO=-1326957.24, deltaELBO=4399.366 (0.07080731%), Factors=5\n", + "Iteration 7: time=0.24, ELBO=-1323511.59, deltaELBO=3445.654 (0.05545743%), Factors=5\n", + "Iteration 8: time=0.27, ELBO=-1320584.62, deltaELBO=2926.966 (0.04710919%), Factors=5\n", + "Iteration 9: time=0.28, ELBO=-1317658.59, deltaELBO=2926.027 (0.04709409%), Factors=5\n", + "Iteration 10: time=0.44, ELBO=-1314494.38, deltaELBO=3164.212 (0.05092765%), Factors=5\n", + "Iteration 11: time=0.89, ELBO=-1311039.41, deltaELBO=3454.971 (0.05560739%), Factors=5\n", + "Iteration 12: time=0.31, ELBO=-1307536.62, deltaELBO=3502.792 (0.05637706%), Factors=5\n", + "Iteration 13: time=0.28, ELBO=-1304566.06, deltaELBO=2970.560 (0.04781084%), Factors=5\n", + "Iteration 14: time=0.29, ELBO=-1302537.05, deltaELBO=2029.007 (0.03265665%), Factors=5\n", + "Iteration 15: time=0.35, ELBO=-1301264.63, deltaELBO=1272.415 (0.02047938%), Factors=5\n", + "Iteration 16: time=0.41, ELBO=-1300438.82, deltaELBO=825.810 (0.01329133%), Factors=5\n", + "Iteration 17: time=0.38, ELBO=-1299874.13, deltaELBO=564.695 (0.00908871%), Factors=5\n", + "Iteration 18: time=0.40, ELBO=-1299466.62, deltaELBO=407.506 (0.00655876%), Factors=5\n", + "Iteration 19: time=0.30, ELBO=-1299155.55, deltaELBO=311.069 (0.00500662%), Factors=5\n", + "Iteration 20: time=0.36, ELBO=-1298904.97, deltaELBO=250.583 (0.00403311%), Factors=5\n", + "Iteration 21: time=0.27, ELBO=-1298693.45, deltaELBO=211.520 (0.00340439%), Factors=5\n", + "Iteration 22: time=0.28, ELBO=-1298508.03, deltaELBO=185.420 (0.00298432%), Factors=5\n", + "Iteration 23: time=0.39, ELBO=-1298340.67, deltaELBO=167.362 (0.00269368%), Factors=5\n", + "Iteration 24: time=0.33, ELBO=-1298186.22, deltaELBO=154.449 (0.00248583%), Factors=5\n", + "Iteration 25: time=0.40, ELBO=-1298041.28, deltaELBO=144.936 (0.00233273%), Factors=5\n", + "Iteration 26: time=0.35, ELBO=-1297903.54, deltaELBO=137.746 (0.00221700%), Factors=5\n", + "Iteration 27: time=0.29, ELBO=-1297771.35, deltaELBO=132.191 (0.00212761%), Factors=5\n", + "Iteration 28: 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Factors=5\n", + "Iteration 39: time=0.29, ELBO=-1296359.60, deltaELBO=114.407 (0.00184138%), Factors=5\n", + "Iteration 40: time=0.29, ELBO=-1296244.33, deltaELBO=115.268 (0.00185522%), Factors=5\n", + "Iteration 41: time=0.27, ELBO=-1296127.82, deltaELBO=116.504 (0.00187511%), Factors=5\n", + "Iteration 42: time=0.33, ELBO=-1296009.68, deltaELBO=118.149 (0.00190159%), Factors=5\n", + "Iteration 43: time=0.30, ELBO=-1295889.43, deltaELBO=120.245 (0.00193534%), Factors=5\n", + "Iteration 44: time=0.34, ELBO=-1295766.59, deltaELBO=122.843 (0.00197714%), Factors=5\n", + "Iteration 45: time=0.37, ELBO=-1295640.59, deltaELBO=125.999 (0.00202794%), Factors=5\n", + "Iteration 46: time=0.21, ELBO=-1295510.81, deltaELBO=129.783 (0.00208884%), Factors=5\n", + "Iteration 47: time=0.24, ELBO=-1295376.53, deltaELBO=134.274 (0.00216113%), Factors=5\n", + "Iteration 48: time=0.28, ELBO=-1295236.96, deltaELBO=139.567 (0.00224631%), Factors=5\n", + "Iteration 49: time=0.29, ELBO=-1295091.20, deltaELBO=145.766 (0.00234609%), Factors=5\n", + "Iteration 50: time=0.28, ELBO=-1294938.20, deltaELBO=152.996 (0.00246246%), Factors=5\n", + "Iteration 51: time=0.24, ELBO=-1294776.81, deltaELBO=161.396 (0.00259766%), Factors=5\n", + "Iteration 52: time=0.30, ELBO=-1294605.68, deltaELBO=171.124 (0.00275422%), Factors=5\n", + "Iteration 53: time=0.26, ELBO=-1294423.33, deltaELBO=182.354 (0.00293497%), Factors=5\n", + "Iteration 54: time=0.33, ELBO=-1294228.05, deltaELBO=195.276 (0.00314295%), Factors=5\n", + "Iteration 55: time=0.25, ELBO=-1294017.96, deltaELBO=210.092 (0.00338140%), Factors=5\n", + "Iteration 56: time=0.33, ELBO=-1293790.96, deltaELBO=227.003 (0.00365359%), Factors=5\n", + "Iteration 57: time=0.25, ELBO=-1293544.76, deltaELBO=246.202 (0.00396259%), Factors=5\n", + "Iteration 58: time=0.27, ELBO=-1293276.91, deltaELBO=267.844 (0.00431093%), Factors=5\n", + "Iteration 59: time=0.28, ELBO=-1292984.89, deltaELBO=292.022 (0.00470006%), Factors=5\n", + "Iteration 60: time=0.22, ELBO=-1292666.18, deltaELBO=318.714 (0.00512967%), Factors=5\n", + "Iteration 61: time=0.25, ELBO=-1292318.44, deltaELBO=347.731 (0.00559669%), Factors=5\n", + "Iteration 62: time=0.24, ELBO=-1291939.80, deltaELBO=378.644 (0.00609424%), Factors=5\n", + "Iteration 63: time=0.30, ELBO=-1291529.09, deltaELBO=410.709 (0.00661031%), Factors=5\n", + "Iteration 64: time=0.33, ELBO=-1291086.30, deltaELBO=442.793 (0.00712671%), Factors=5\n", + "Iteration 65: time=0.28, ELBO=-1290612.96, deltaELBO=473.341 (0.00761836%), Factors=5\n", + "Iteration 66: time=0.29, ELBO=-1290112.57, deltaELBO=500.384 (0.00805363%), Factors=5\n", + "Iteration 67: time=0.28, ELBO=-1289590.92, deltaELBO=521.655 (0.00839598%), Factors=5\n", + "Iteration 68: time=0.35, ELBO=-1289056.12, deltaELBO=534.802 (0.00860757%), Factors=5\n", + "Iteration 69: time=0.33, ELBO=-1288518.39, deltaELBO=537.722 (0.00865458%), Factors=5\n", + "Iteration 70: time=0.41, ELBO=-1287989.43, deltaELBO=528.965 (0.00851363%), Factors=5\n", + "Iteration 71: time=0.24, ELBO=-1287481.32, deltaELBO=508.110 (0.00817797%), Factors=5\n", + "Iteration 72: time=0.36, ELBO=-1287005.31, deltaELBO=476.011 (0.00766135%), Factors=5\n", + "Iteration 73: time=0.36, ELBO=-1286570.52, deltaELBO=434.784 (0.00699780%), Factors=5\n", + "Iteration 74: time=0.22, ELBO=-1286183.04, deltaELBO=387.483 (0.00623650%), Factors=5\n", + "Iteration 75: time=0.35, ELBO=-1285845.48, deltaELBO=337.565 (0.00543306%), Factors=5\n", + "Iteration 76: time=0.41, ELBO=-1285557.20, deltaELBO=288.279 (0.00463982%), Factors=5\n", + "Iteration 77: time=0.27, ELBO=-1285314.99, deltaELBO=242.208 (0.00389830%), Factors=5\n", + "Iteration 78: time=0.36, ELBO=-1285113.95, deltaELBO=201.039 (0.00323570%), Factors=5\n", + "Iteration 79: time=0.30, ELBO=-1284948.37, deltaELBO=165.586 (0.00266509%), Factors=5\n", + "Iteration 80: time=0.30, ELBO=-1284812.41, deltaELBO=135.955 (0.00218817%), Factors=5\n", + "Iteration 81: time=0.33, ELBO=-1284700.64, deltaELBO=111.775 (0.00179900%), Factors=5\n", + "Iteration 82: time=0.43, ELBO=-1284608.23, deltaELBO=92.411 (0.00148735%), Factors=5\n", + "Iteration 83: time=0.52, ELBO=-1284531.10, deltaELBO=77.125 (0.00124132%), Factors=5\n", + "Iteration 84: time=0.29, ELBO=-1284465.91, deltaELBO=65.185 (0.00104914%), Factors=5\n", + "Iteration 85: time=0.43, ELBO=-1284409.99, deltaELBO=55.924 (0.00090009%), Factors=5\n", + "Iteration 86: time=0.33, ELBO=-1284361.22, deltaELBO=48.771 (0.00078497%), Factors=5\n", + "Iteration 87: time=0.25, ELBO=-1284317.96, deltaELBO=43.255 (0.00069618%), Factors=5\n", + "Iteration 88: time=0.24, ELBO=-1284278.97, deltaELBO=38.995 (0.00062762%), Factors=5\n", + "Iteration 89: time=0.32, ELBO=-1284243.27, deltaELBO=35.694 (0.00057450%), Factors=5\n", + "Iteration 90: time=0.24, ELBO=-1284210.15, deltaELBO=33.121 (0.00053309%), Factors=5\n", + "Iteration 91: time=0.23, ELBO=-1284179.05, deltaELBO=31.099 (0.00050054%), Factors=5\n", + "Iteration 92: time=0.33, ELBO=-1284149.56, deltaELBO=29.492 (0.00047468%), Factors=5\n", + "Iteration 93: time=0.27, ELBO=-1284121.36, deltaELBO=28.200 (0.00045387%), Factors=5\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -6859324.64 \n", + "\n", + "Iteration 1: time=0.35, ELBO=-1325704.79, deltaELBO=5533619.850 (80.67295457%), Factors=6\n", + "Iteration 2: time=0.42, ELBO=-1267546.18, deltaELBO=58158.607 (0.84787658%), Factors=6\n", + "Iteration 3: time=0.34, ELBO=-1258463.81, deltaELBO=9082.376 (0.13240918%), Factors=6\n", + "Iteration 4: time=0.24, ELBO=-1253346.83, deltaELBO=5116.974 (0.07459880%), Factors=6\n", + "Iteration 5: time=0.24, ELBO=-1249654.55, deltaELBO=3692.279 (0.05382860%), Factors=6\n", + "Iteration 6: time=0.27, ELBO=-1246521.99, deltaELBO=3132.563 (0.04566868%), Factors=6\n", + "Iteration 7: time=0.23, ELBO=-1243804.37, deltaELBO=2717.620 (0.03961935%), Factors=6\n", + "Iteration 8: time=0.30, ELBO=-1241871.78, deltaELBO=1932.587 (0.02817460%), Factors=6\n", + "Iteration 9: time=0.34, ELBO=-1240672.94, deltaELBO=1198.845 (0.01747759%), Factors=6\n", + "Iteration 10: time=0.35, ELBO=-1239841.24, deltaELBO=831.698 (0.01212508%), Factors=6\n", + "Iteration 11: time=0.21, ELBO=-1239198.51, deltaELBO=642.734 (0.00937023%), Factors=6\n", + "Iteration 12: time=0.22, ELBO=-1238673.58, deltaELBO=524.931 (0.00765281%), Factors=6\n", + "Iteration 13: time=0.24, ELBO=-1238229.40, deltaELBO=444.176 (0.00647551%), Factors=6\n", + "Iteration 14: time=0.22, ELBO=-1237844.26, deltaELBO=385.143 (0.00561489%), Factors=6\n", + "Iteration 15: time=0.21, ELBO=-1237504.36, deltaELBO=339.892 (0.00495518%), Factors=6\n", + "Iteration 16: time=0.28, ELBO=-1237200.26, deltaELBO=304.107 (0.00443348%), Factors=6\n", + "Iteration 17: time=0.20, ELBO=-1236925.03, deltaELBO=275.223 (0.00401240%), Factors=6\n", + "Iteration 18: time=0.21, ELBO=-1236673.48, deltaELBO=251.556 (0.00366735%), Factors=6\n", + "Iteration 19: time=0.20, ELBO=-1236441.57, deltaELBO=231.911 (0.00338096%), Factors=6\n", + "Iteration 20: time=0.22, ELBO=-1236226.16, deltaELBO=215.411 (0.00314042%), Factors=6\n", + "Iteration 21: time=0.22, ELBO=-1236024.76, deltaELBO=201.397 (0.00293610%), Factors=6\n", + "Iteration 22: time=0.21, ELBO=-1235835.39, deltaELBO=189.365 (0.00276069%), Factors=6\n", + "Iteration 23: time=0.23, ELBO=-1235656.47, deltaELBO=178.927 (0.00260853%), Factors=6\n", + "Iteration 24: time=0.27, ELBO=-1235486.68, deltaELBO=169.784 (0.00247522%), Factors=6\n", + "Iteration 25: time=0.23, ELBO=-1235324.99, deltaELBO=161.696 (0.00235732%), Factors=6\n", + "Iteration 26: time=0.22, ELBO=-1235170.51, deltaELBO=154.477 (0.00225208%), Factors=6\n", + "Iteration 27: time=0.20, ELBO=-1235022.53, deltaELBO=147.978 (0.00215732%), Factors=6\n", + "Iteration 28: time=0.21, ELBO=-1234880.45, deltaELBO=142.078 (0.00207131%), Factors=6\n", + "Iteration 29: time=0.19, ELBO=-1234743.77, deltaELBO=136.682 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"Iteration 62: time=0.52, ELBO=-1231906.56, deltaELBO=56.425 (0.00082261%), Factors=6\n", + "Iteration 63: time=0.55, ELBO=-1231851.32, deltaELBO=55.245 (0.00080540%), Factors=6\n", + "Iteration 64: time=0.41, ELBO=-1231797.22, deltaELBO=54.101 (0.00078872%), Factors=6\n", + "Iteration 65: time=0.34, ELBO=-1231744.23, deltaELBO=52.992 (0.00077255%), Factors=6\n", + "Iteration 66: time=0.46, ELBO=-1231692.31, deltaELBO=51.916 (0.00075687%), Factors=6\n", + "Iteration 67: time=0.52, ELBO=-1231641.44, deltaELBO=50.873 (0.00074166%), Factors=6\n", + "Iteration 68: time=0.68, ELBO=-1231591.58, deltaELBO=49.860 (0.00072690%), Factors=6\n", + "Iteration 69: time=0.66, ELBO=-1231542.70, deltaELBO=48.878 (0.00071257%), Factors=6\n", + "Iteration 70: time=0.65, ELBO=-1231494.77, deltaELBO=47.924 (0.00069866%), Factors=6\n", + "Iteration 71: time=0.56, ELBO=-1231447.78, deltaELBO=46.997 (0.00068515%), Factors=6\n", + "Iteration 72: time=0.50, ELBO=-1231401.68, deltaELBO=46.097 (0.00067203%), Factors=6\n", + "Iteration 73: time=0.48, ELBO=-1231356.46, deltaELBO=45.222 (0.00065928%), Factors=6\n", + "Iteration 74: time=0.54, ELBO=-1231312.09, deltaELBO=44.372 (0.00064689%), Factors=6\n", + "Iteration 75: time=0.49, ELBO=-1231268.54, deltaELBO=43.546 (0.00063484%), Factors=6\n", + "Iteration 76: time=0.52, ELBO=-1231225.80, deltaELBO=42.742 (0.00062312%), Factors=6\n", + "Iteration 77: time=0.57, ELBO=-1231183.84, deltaELBO=41.960 (0.00061173%), Factors=6\n", + "Iteration 78: time=0.51, ELBO=-1231142.64, deltaELBO=41.200 (0.00060064%), Factors=6\n", + "Iteration 79: time=0.52, ELBO=-1231102.18, deltaELBO=40.460 (0.00058985%), Factors=6\n", + "Iteration 80: time=0.51, ELBO=-1231062.44, deltaELBO=39.740 (0.00057935%), Factors=6\n", + "Iteration 81: time=0.54, ELBO=-1231023.40, deltaELBO=39.038 (0.00056913%), Factors=6\n", + "Iteration 82: time=0.54, ELBO=-1230985.05, deltaELBO=38.356 (0.00055918%), Factors=6\n", + "Iteration 83: time=0.54, ELBO=-1230947.36, deltaELBO=37.691 (0.00054948%), Factors=6\n", + "Iteration 84: time=0.50, ELBO=-1230910.31, deltaELBO=37.043 (0.00054004%), Factors=6\n", + "Iteration 85: time=0.50, ELBO=-1230873.90, deltaELBO=36.412 (0.00053084%), Factors=6\n", + "Iteration 86: time=0.54, ELBO=-1230838.10, deltaELBO=35.797 (0.00052187%), Factors=6\n", + "Iteration 87: time=0.55, ELBO=-1230802.91, deltaELBO=35.198 (0.00051314%), Factors=6\n", + "Iteration 88: time=0.56, ELBO=-1230768.29, deltaELBO=34.613 (0.00050462%), Factors=6\n", + "Iteration 89: time=0.43, ELBO=-1230734.25, deltaELBO=34.044 (0.00049631%), Factors=6\n", + "Iteration 90: time=0.31, ELBO=-1230700.76, deltaELBO=33.488 (0.00048821%), Factors=6\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -7533633.70 \n", + "\n", + "Iteration 1: time=0.35, ELBO=-1462136.02, deltaELBO=6071497.678 (80.59188862%), Factors=7\n", + "Iteration 2: time=0.34, ELBO=-1413126.58, deltaELBO=49009.434 (0.65054177%), Factors=7\n", + "Iteration 3: time=0.33, ELBO=-1399190.24, deltaELBO=13936.340 (0.18498829%), Factors=7\n", + "Iteration 4: time=0.33, ELBO=-1331202.69, deltaELBO=67987.557 (0.90245371%), Factors=7\n", + "Iteration 5: time=0.37, ELBO=-1229392.66, deltaELBO=101810.028 (1.35140667%), Factors=7\n", + "Iteration 6: time=0.34, ELBO=-1209296.96, deltaELBO=20095.702 (0.26674646%), Factors=7\n", + "Iteration 7: time=0.35, ELBO=-1200636.41, deltaELBO=8660.545 (0.11495841%), Factors=7\n", + "Iteration 8: time=0.39, ELBO=-1195935.07, deltaELBO=4701.342 (0.06240471%), Factors=7\n", + "Iteration 9: time=0.43, ELBO=-1193570.43, deltaELBO=2364.643 (0.03138781%), Factors=7\n", + "Iteration 10: time=0.46, ELBO=-1192288.75, deltaELBO=1281.677 (0.01701273%), Factors=7\n", + "Iteration 11: time=0.38, ELBO=-1191476.75, deltaELBO=812.004 (0.01077838%), Factors=7\n", + "Iteration 12: time=0.34, ELBO=-1190887.60, deltaELBO=589.149 (0.00782025%), Factors=7\n", + "Iteration 13: time=0.36, 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"Iteration 24: time=0.33, ELBO=-1187680.29, deltaELBO=175.965 (0.00233572%), Factors=7\n", + "Iteration 25: time=0.33, ELBO=-1187511.56, deltaELBO=168.726 (0.00223964%), Factors=7\n", + "Iteration 26: time=0.50, ELBO=-1187349.45, deltaELBO=162.110 (0.00215181%), Factors=7\n", + "Iteration 27: time=0.60, ELBO=-1187193.44, deltaELBO=156.005 (0.00207078%), Factors=7\n", + "Iteration 28: time=0.35, ELBO=-1187043.11, deltaELBO=150.331 (0.00199546%), Factors=7\n", + "Iteration 29: time=0.37, ELBO=-1186898.09, deltaELBO=145.026 (0.00192504%), Factors=7\n", + "Iteration 30: time=0.36, ELBO=-1186758.05, deltaELBO=140.042 (0.00185889%), Factors=7\n", + "Iteration 31: time=0.32, ELBO=-1186622.70, deltaELBO=135.344 (0.00179652%), Factors=7\n", + "Iteration 32: time=0.32, ELBO=-1186491.80, deltaELBO=130.900 (0.00173754%), Factors=7\n", + "Iteration 33: time=0.32, ELBO=-1186365.11, deltaELBO=126.687 (0.00168162%), Factors=7\n", + "Iteration 34: time=0.31, ELBO=-1186242.43, deltaELBO=122.685 (0.00162850%), Factors=7\n", + "Iteration 35: time=0.32, ELBO=-1186123.55, deltaELBO=118.877 (0.00157795%), Factors=7\n", + "Iteration 36: time=0.32, ELBO=-1186008.31, deltaELBO=115.247 (0.00152977%), Factors=7\n", + "Iteration 37: time=0.32, ELBO=-1185896.52, deltaELBO=111.783 (0.00148379%), Factors=7\n", + "Iteration 38: time=0.34, ELBO=-1185788.05, deltaELBO=108.474 (0.00143986%), Factors=7\n", + "Iteration 39: time=0.30, ELBO=-1185682.74, deltaELBO=105.309 (0.00139786%), Factors=7\n", + "Iteration 40: time=0.32, ELBO=-1185580.46, deltaELBO=102.280 (0.00135765%), Factors=7\n", + "Iteration 41: time=0.34, ELBO=-1185481.08, deltaELBO=99.378 (0.00131913%), Factors=7\n", + "Iteration 42: time=0.69, ELBO=-1185384.48, deltaELBO=96.597 (0.00128221%), Factors=7\n", + "Iteration 43: time=0.64, ELBO=-1185290.56, deltaELBO=93.928 (0.00124678%), Factors=7\n", + "Iteration 44: time=0.57, ELBO=-1185199.19, deltaELBO=91.366 (0.00121278%), Factors=7\n", + "Iteration 45: time=0.75, ELBO=-1185110.28, deltaELBO=88.906 (0.00118012%), Factors=7\n", + "Iteration 46: time=0.68, ELBO=-1185023.74, deltaELBO=86.541 (0.00114873%), Factors=7\n", + "Iteration 47: time=0.52, ELBO=-1184939.48, deltaELBO=84.267 (0.00111854%), Factors=7\n", + "Iteration 48: time=0.66, ELBO=-1184857.40, deltaELBO=82.080 (0.00108951%), Factors=7\n", + "Iteration 49: time=0.74, ELBO=-1184777.42, deltaELBO=79.974 (0.00106156%), Factors=7\n", + "Iteration 50: time=0.67, ELBO=-1184699.48, deltaELBO=77.947 (0.00103465%), Factors=7\n", + "Iteration 51: time=0.62, ELBO=-1184623.48, deltaELBO=75.993 (0.00100872%), Factors=7\n", + "Iteration 52: time=0.66, ELBO=-1184549.37, deltaELBO=74.111 (0.00098374%), Factors=7\n", + "Iteration 53: time=0.37, ELBO=-1184477.08, deltaELBO=72.296 (0.00095965%), Factors=7\n", + "Iteration 54: time=0.35, ELBO=-1184406.53, deltaELBO=70.546 (0.00093641%), Factors=7\n", + "Iteration 55: time=0.31, ELBO=-1184337.67, deltaELBO=68.857 (0.00091399%), Factors=7\n", + "Iteration 56: time=0.31, 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time=0.26, ELBO=-1183624.11, deltaELBO=52.515 (0.00069707%), Factors=7\n", + "Iteration 68: time=0.25, ELBO=-1183572.69, deltaELBO=51.421 (0.00068256%), Factors=7\n", + "Iteration 69: time=0.23, ELBO=-1183522.33, deltaELBO=50.362 (0.00066849%), Factors=7\n", + "Iteration 70: time=0.22, ELBO=-1183473.00, deltaELBO=49.334 (0.00065485%), Factors=7\n", + "Iteration 71: time=0.22, ELBO=-1183424.66, deltaELBO=48.338 (0.00064163%), Factors=7\n", + "Iteration 72: time=0.24, ELBO=-1183377.29, deltaELBO=47.372 (0.00062881%), Factors=7\n", + "Iteration 73: time=0.23, ELBO=-1183330.85, deltaELBO=46.435 (0.00061637%), Factors=7\n", + "Iteration 74: time=0.22, ELBO=-1183285.32, deltaELBO=45.526 (0.00060430%), Factors=7\n", + "Iteration 75: time=0.25, ELBO=-1183240.68, deltaELBO=44.643 (0.00059259%), Factors=7\n", + "Iteration 76: time=0.23, ELBO=-1183196.89, deltaELBO=43.787 (0.00058122%), Factors=7\n", + "Iteration 77: time=0.23, ELBO=-1183153.94, deltaELBO=42.955 (0.00057018%), Factors=7\n", + "Iteration 78: time=0.25, ELBO=-1183111.79, deltaELBO=42.147 (0.00055946%), Factors=7\n", + "Iteration 79: time=0.23, ELBO=-1183070.43, deltaELBO=41.363 (0.00054904%), Factors=7\n", + "Iteration 80: time=0.23, ELBO=-1183029.83, deltaELBO=40.600 (0.00053892%), Factors=7\n", + "Iteration 81: time=0.25, ELBO=-1182989.97, deltaELBO=39.859 (0.00052908%), Factors=7\n", + "Iteration 82: time=0.22, ELBO=-1182950.83, deltaELBO=39.139 (0.00051953%), Factors=7\n", + "Iteration 83: time=0.24, ELBO=-1182912.39, deltaELBO=38.439 (0.00051023%), Factors=7\n", + "Iteration 84: time=0.22, ELBO=-1182874.63, deltaELBO=37.758 (0.00050119%), Factors=7\n", + "Iteration 85: time=0.21, ELBO=-1182837.54, deltaELBO=37.096 (0.00049240%), Factors=7\n", + "Iteration 86: time=0.23, ELBO=-1182801.09, deltaELBO=36.452 (0.00048385%), Factors=7\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -8206618.17 \n", + "\n", + "Iteration 1: time=0.28, ELBO=-1451650.54, deltaELBO=6754967.628 (82.31122114%), Factors=8\n", + "Iteration 2: time=0.27, ELBO=-1393570.54, deltaELBO=58079.999 (0.70772148%), Factors=8\n", + "Iteration 3: time=0.26, ELBO=-1378547.54, deltaELBO=15022.995 (0.18305951%), Factors=8\n", + "Iteration 4: time=0.27, ELBO=-1305496.70, deltaELBO=73050.840 (0.89014547%), Factors=8\n", + "Iteration 5: time=0.26, ELBO=-1198832.18, deltaELBO=106664.521 (1.29973783%), Factors=8\n", + "Iteration 6: time=0.25, ELBO=-1178674.59, deltaELBO=20157.596 (0.24562610%), Factors=8\n", + "Iteration 7: time=0.55, ELBO=-1170835.16, deltaELBO=7839.431 (0.09552572%), Factors=8\n", + "Iteration 8: time=0.26, ELBO=-1166321.79, deltaELBO=4513.367 (0.05499668%), Factors=8\n", + "Iteration 9: time=0.27, ELBO=-1163580.89, deltaELBO=2740.902 (0.03339868%), Factors=8\n", + "Iteration 10: time=0.26, ELBO=-1161732.63, deltaELBO=1848.262 (0.02252161%), Factors=8\n", + "Iteration 11: time=0.25, ELBO=-1160338.27, deltaELBO=1394.352 (0.01699058%), Factors=8\n", + "Iteration 12: time=0.26, ELBO=-1159215.57, deltaELBO=1122.701 (0.01368044%), Factors=8\n", + "Iteration 13: time=0.26, ELBO=-1158292.37, deltaELBO=923.201 (0.01124947%), Factors=8\n", + "Iteration 14: time=0.27, ELBO=-1157532.70, deltaELBO=759.667 (0.00925676%), Factors=8\n", + "Iteration 15: time=0.25, ELBO=-1156909.63, deltaELBO=623.075 (0.00759235%), Factors=8\n", + "Iteration 16: time=0.27, ELBO=-1156398.03, deltaELBO=511.594 (0.00623392%), Factors=8\n", + "Iteration 17: time=0.25, ELBO=-1155974.44, deltaELBO=423.589 (0.00516156%), Factors=8\n", + "Iteration 18: time=0.48, ELBO=-1155618.33, deltaELBO=356.115 (0.00433936%), Factors=8\n", + "Iteration 19: time=0.30, ELBO=-1155312.96, deltaELBO=305.374 (0.00372107%), Factors=8\n", + "Iteration 20: time=0.29, ELBO=-1155045.43, deltaELBO=267.522 (0.00325983%), Factors=8\n", + "Iteration 21: time=0.28, ELBO=-1154806.23, deltaELBO=239.204 (0.00291476%), Factors=8\n", + "Iteration 22: time=0.27, ELBO=-1154588.48, deltaELBO=217.754 (0.00265339%), Factors=8\n", + "Iteration 23: time=0.26, ELBO=-1154387.30, deltaELBO=201.180 (0.00245143%), Factors=8\n", + "Iteration 24: time=0.29, ELBO=-1154199.25, deltaELBO=188.051 (0.00229145%), Factors=8\n", + "Iteration 25: time=0.28, ELBO=-1154021.89, deltaELBO=177.361 (0.00216120%), Factors=8\n", + "Iteration 26: time=0.26, ELBO=-1153853.47, deltaELBO=168.416 (0.00205220%), Factors=8\n", + "Iteration 27: time=0.26, ELBO=-1153692.73, deltaELBO=160.735 (0.00195860%), Factors=8\n", + "Iteration 28: time=0.28, ELBO=-1153538.75, deltaELBO=153.989 (0.00187640%), Factors=8\n", + "Iteration 29: time=0.28, ELBO=-1153390.80, deltaELBO=147.950 (0.00180282%), Factors=8\n", + "Iteration 30: time=0.29, ELBO=-1153248.33, deltaELBO=142.461 (0.00173593%), Factors=8\n", + "Iteration 31: time=0.32, ELBO=-1153110.92, deltaELBO=137.411 (0.00167439%), Factors=8\n", + "Iteration 32: time=0.33, ELBO=-1152978.20, deltaELBO=132.721 (0.00161724%), Factors=8\n", + "Iteration 33: time=0.49, ELBO=-1152849.87, deltaELBO=128.333 (0.00156377%), Factors=8\n", + "Iteration 34: time=0.54, ELBO=-1152725.66, deltaELBO=124.206 (0.00151348%), Factors=8\n", + "Iteration 35: time=0.52, ELBO=-1152605.36, deltaELBO=120.307 (0.00146598%), Factors=8\n", + "Iteration 36: time=0.52, ELBO=-1152488.74, deltaELBO=116.612 (0.00142095%), Factors=8\n", + "Iteration 37: time=0.47, ELBO=-1152375.64, deltaELBO=113.102 (0.00137818%), Factors=8\n", + "Iteration 38: time=0.46, ELBO=-1152265.88, deltaELBO=109.759 (0.00133745%), Factors=8\n", + "Iteration 39: time=0.44, ELBO=-1152159.31, deltaELBO=106.572 (0.00129861%), Factors=8\n", + "Iteration 40: time=0.45, ELBO=-1152055.78, deltaELBO=103.528 (0.00126152%), Factors=8\n", + "Iteration 41: time=0.45, ELBO=-1151955.17, deltaELBO=100.618 (0.00122606%), Factors=8\n", + "Iteration 42: time=0.46, ELBO=-1151857.33, deltaELBO=97.832 (0.00119212%), Factors=8\n", + "Iteration 43: time=0.53, ELBO=-1151762.17, deltaELBO=95.164 (0.00115960%), Factors=8\n", + "Iteration 44: time=0.87, ELBO=-1151669.56, deltaELBO=92.606 (0.00112844%), Factors=8\n", + "Iteration 45: time=0.36, ELBO=-1151579.41, deltaELBO=90.153 (0.00109853%), Factors=8\n", + "Iteration 46: time=0.30, ELBO=-1151491.61, deltaELBO=87.797 (0.00106983%), Factors=8\n", + "Iteration 47: time=0.50, ELBO=-1151406.08, deltaELBO=85.533 (0.00104225%), Factors=8\n", + "Iteration 48: time=0.38, ELBO=-1151322.72, deltaELBO=83.358 (0.00101574%), Factors=8\n", + "Iteration 49: time=0.47, ELBO=-1151241.46, deltaELBO=81.266 (0.00099025%), Factors=8\n", + "Iteration 50: time=0.61, ELBO=-1151162.20, deltaELBO=79.253 (0.00096572%), Factors=8\n", + "Iteration 51: time=0.68, ELBO=-1151084.89, deltaELBO=77.315 (0.00094210%), Factors=8\n", + "Iteration 52: time=0.68, ELBO=-1151009.44, deltaELBO=75.448 (0.00091935%), Factors=8\n", + "Iteration 53: time=0.61, ELBO=-1150935.79, deltaELBO=73.649 (0.00089743%), Factors=8\n", + "Iteration 54: time=0.73, ELBO=-1150863.88, deltaELBO=71.914 (0.00087629%), Factors=8\n", + "Iteration 55: time=0.57, ELBO=-1150793.64, deltaELBO=70.241 (0.00085591%), Factors=8\n", + "Iteration 56: time=0.43, ELBO=-1150725.01, deltaELBO=68.627 (0.00083624%), Factors=8\n", + "Iteration 57: time=0.28, ELBO=-1150657.94, deltaELBO=67.069 (0.00081725%), Factors=8\n", + "Iteration 58: time=0.28, ELBO=-1150592.38, deltaELBO=65.564 (0.00079891%), Factors=8\n", + "Iteration 59: time=0.26, ELBO=-1150528.27, deltaELBO=64.110 (0.00078120%), Factors=8\n", + "Iteration 60: time=0.27, ELBO=-1150465.56, deltaELBO=62.705 (0.00076407%), Factors=8\n", + "Iteration 61: time=0.26, ELBO=-1150404.22, deltaELBO=61.346 (0.00074752%), Factors=8\n", + "Iteration 62: time=0.27, ELBO=-1150344.19, deltaELBO=60.032 (0.00073151%), Factors=8\n", + "Iteration 63: time=0.27, ELBO=-1150285.42, deltaELBO=58.761 (0.00071602%), Factors=8\n", + "Iteration 64: time=0.26, ELBO=-1150227.89, deltaELBO=57.531 (0.00070103%), Factors=8\n", + "Iteration 65: time=0.25, ELBO=-1150171.55, deltaELBO=56.340 (0.00068652%), Factors=8\n", + "Iteration 66: time=0.26, ELBO=-1150116.37, deltaELBO=55.186 (0.00067246%), Factors=8\n", + "Iteration 67: time=0.25, ELBO=-1150062.30, deltaELBO=54.069 (0.00065885%), Factors=8\n", + "Iteration 68: time=0.40, ELBO=-1150009.31, deltaELBO=52.986 (0.00064565%), Factors=8\n", + "Iteration 69: time=0.25, ELBO=-1149957.38, deltaELBO=51.937 (0.00063286%), Factors=8\n", + "Iteration 70: time=0.31, ELBO=-1149906.46, deltaELBO=50.919 (0.00062046%), Factors=8\n", + "Iteration 71: time=0.28, ELBO=-1149856.52, deltaELBO=49.932 (0.00060843%), Factors=8\n", + "Iteration 72: time=0.35, ELBO=-1149807.55, deltaELBO=48.974 (0.00059676%), Factors=8\n", + "Iteration 73: time=0.44, ELBO=-1149759.51, deltaELBO=48.045 (0.00058544%), Factors=8\n", + "Iteration 74: time=0.44, ELBO=-1149712.36, deltaELBO=47.143 (0.00057445%), Factors=8\n", + "Iteration 75: time=0.52, ELBO=-1149666.10, deltaELBO=46.267 (0.00056377%), Factors=8\n", + "Iteration 76: time=0.54, ELBO=-1149620.68, deltaELBO=45.416 (0.00055341%), Factors=8\n", + "Iteration 77: time=0.37, ELBO=-1149576.09, deltaELBO=44.589 (0.00054334%), Factors=8\n", + "Iteration 78: time=0.29, ELBO=-1149532.31, deltaELBO=43.786 (0.00053355%), Factors=8\n", + "Iteration 79: time=0.31, ELBO=-1149489.30, deltaELBO=43.006 (0.00052404%), Factors=8\n", + "Iteration 80: time=0.28, ELBO=-1149447.05, deltaELBO=42.247 (0.00051479%), Factors=8\n", + "Iteration 81: time=0.26, ELBO=-1149405.54, deltaELBO=41.509 (0.00050580%), Factors=8\n", + "Iteration 82: time=0.27, ELBO=-1149364.75, deltaELBO=40.792 (0.00049706%), Factors=8\n", + "Iteration 83: time=0.35, ELBO=-1149324.66, deltaELBO=40.093 (0.00048855%), Factors=8\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -8875938.10 \n", + "\n", + "Iteration 1: time=0.35, ELBO=-1445930.59, deltaELBO=7430007.516 (83.70954631%), Factors=9\n", + "Iteration 2: time=0.38, ELBO=-1380536.37, deltaELBO=65394.211 (0.73675831%), Factors=9\n", + "Iteration 3: time=0.62, ELBO=-1359583.75, deltaELBO=20952.627 (0.23606098%), Factors=9\n", + "Iteration 4: time=0.60, ELBO=-1216072.23, deltaELBO=143511.515 (1.61686025%), Factors=9\n", + "Iteration 5: time=0.28, ELBO=-1156133.94, deltaELBO=59938.294 (0.67528967%), Factors=9\n", + "Iteration 6: time=0.30, ELBO=-1145420.38, deltaELBO=10713.555 (0.12070335%), Factors=9\n", + "Iteration 7: time=0.63, ELBO=-1139743.65, deltaELBO=5676.737 (0.06395647%), Factors=9\n", + "Iteration 8: time=0.34, ELBO=-1136450.15, deltaELBO=3293.499 (0.03710593%), Factors=9\n", + "Iteration 9: time=0.32, ELBO=-1134320.07, deltaELBO=2130.078 (0.02399835%), Factors=9\n", + "Iteration 10: time=0.32, ELBO=-1132792.21, deltaELBO=1527.860 (0.01721351%), Factors=9\n", + "Iteration 11: time=0.33, ELBO=-1131606.22, deltaELBO=1185.991 (0.01336186%), Factors=9\n", + "Iteration 12: time=0.33, ELBO=-1130637.36, deltaELBO=968.859 (0.01091557%), Factors=9\n", + "Iteration 13: time=0.32, ELBO=-1129821.03, deltaELBO=816.335 (0.00919716%), Factors=9\n", + "Iteration 14: time=0.32, ELBO=-1129120.14, deltaELBO=700.883 (0.00789644%), Factors=9\n", + "Iteration 15: time=0.30, ELBO=-1128511.05, deltaELBO=609.095 (0.00686232%), Factors=9\n", + "Iteration 16: time=0.45, ELBO=-1127977.05, deltaELBO=533.998 (0.00601624%), Factors=9\n", + "Iteration 17: time=0.32, ELBO=-1127505.40, deltaELBO=471.653 (0.00531384%), Factors=9\n", + "Iteration 18: time=0.66, ELBO=-1127085.83, deltaELBO=419.569 (0.00472704%), Factors=9\n", + "Iteration 19: time=0.68, ELBO=-1126709.87, deltaELBO=375.955 (0.00423567%), Factors=9\n", + "Iteration 20: time=0.49, ELBO=-1126370.49, deltaELBO=339.386 (0.00382367%), Factors=9\n", + "Iteration 21: time=0.41, ELBO=-1126061.82, deltaELBO=308.666 (0.00347756%), Factors=9\n", + "Iteration 22: time=0.49, ELBO=-1125779.05, deltaELBO=282.772 (0.00318583%), Factors=9\n", + "Iteration 23: time=0.46, ELBO=-1125518.21, deltaELBO=260.841 (0.00293874%), Factors=9\n", + "Iteration 24: time=0.63, ELBO=-1125276.06, deltaELBO=242.145 (0.00272811%), Factors=9\n", + "Iteration 25: time=0.58, ELBO=-1125049.97, deltaELBO=226.088 (0.00254720%), Factors=9\n", + "Iteration 26: time=0.56, ELBO=-1124837.79, deltaELBO=212.181 (0.00239052%), Factors=9\n", + "Iteration 27: time=0.44, ELBO=-1124637.76, deltaELBO=200.031 (0.00225363%), Factors=9\n", + "Iteration 28: time=0.59, ELBO=-1124448.44, deltaELBO=189.322 (0.00213298%), Factors=9\n", + "Iteration 29: time=0.70, ELBO=-1124268.64, deltaELBO=179.801 (0.00202571%), Factors=9\n", + "Iteration 30: time=0.84, ELBO=-1124097.37, deltaELBO=171.265 (0.00192955%), Factors=9\n", + "Iteration 31: time=0.63, ELBO=-1123933.82, deltaELBO=163.555 (0.00184267%), Factors=9\n", + "Iteration 32: time=0.72, ELBO=-1123777.28, deltaELBO=156.539 (0.00176363%), Factors=9\n", + "Iteration 33: time=0.65, ELBO=-1123627.17, deltaELBO=150.113 (0.00169124%), Factors=9\n", + "Iteration 34: 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Factors=9\n", + "Iteration 45: time=0.38, ELBO=-1122192.43, deltaELBO=99.294 (0.00111869%), Factors=9\n", + "Iteration 46: time=0.40, ELBO=-1122096.05, deltaELBO=96.384 (0.00108590%), Factors=9\n", + "Iteration 47: time=0.40, ELBO=-1122002.44, deltaELBO=93.605 (0.00105459%), Factors=9\n", + "Iteration 48: time=0.38, ELBO=-1121911.50, deltaELBO=90.949 (0.00102467%), Factors=9\n", + "Iteration 49: time=0.38, ELBO=-1121823.09, deltaELBO=88.409 (0.00099605%), Factors=9\n", + "Iteration 50: time=0.40, ELBO=-1121737.11, deltaELBO=85.976 (0.00096864%), Factors=9\n", + "Iteration 51: time=0.39, ELBO=-1121653.47, deltaELBO=83.644 (0.00094237%), Factors=9\n", + "Iteration 52: time=0.41, ELBO=-1121572.06, deltaELBO=81.408 (0.00091717%), Factors=9\n", + "Iteration 53: time=0.41, ELBO=-1121492.80, deltaELBO=79.261 (0.00089299%), Factors=9\n", + "Iteration 54: time=0.59, ELBO=-1121415.60, deltaELBO=77.200 (0.00086976%), Factors=9\n", + "Iteration 55: time=0.43, ELBO=-1121340.38, deltaELBO=75.218 (0.00084744%), Factors=9\n", + "Iteration 56: time=0.44, ELBO=-1121267.07, deltaELBO=73.313 (0.00082597%), Factors=9\n", + "Iteration 57: time=0.38, ELBO=-1121195.59, deltaELBO=71.479 (0.00080532%), Factors=9\n", + "Iteration 58: time=0.44, ELBO=-1121125.87, deltaELBO=69.714 (0.00078543%), Factors=9\n", + "Iteration 59: time=0.39, ELBO=-1121057.86, deltaELBO=68.014 (0.00076628%), Factors=9\n", + "Iteration 60: time=0.61, ELBO=-1120991.48, deltaELBO=66.376 (0.00074782%), Factors=9\n", + "Iteration 61: time=0.46, ELBO=-1120926.69, deltaELBO=64.796 (0.00073002%), Factors=9\n", + "Iteration 62: time=0.46, ELBO=-1120863.42, deltaELBO=63.272 (0.00071285%), Factors=9\n", + "Iteration 63: time=0.62, ELBO=-1120801.61, deltaELBO=61.802 (0.00069628%), Factors=9\n", + "Iteration 64: time=0.67, ELBO=-1120741.23, deltaELBO=60.382 (0.00068029%), Factors=9\n", + "Iteration 65: time=0.62, ELBO=-1120682.22, deltaELBO=59.010 (0.00066484%), Factors=9\n", + "Iteration 66: time=0.53, ELBO=-1120624.54, deltaELBO=57.685 (0.00064991%), Factors=9\n", + "Iteration 67: time=0.62, ELBO=-1120568.13, deltaELBO=56.405 (0.00063548%), Factors=9\n", + "Iteration 68: time=0.62, ELBO=-1120512.97, deltaELBO=55.166 (0.00062152%), Factors=9\n", + "Iteration 69: time=0.61, ELBO=-1120459.00, deltaELBO=53.968 (0.00060803%), Factors=9\n", + "Iteration 70: time=0.66, ELBO=-1120406.19, deltaELBO=52.809 (0.00059496%), Factors=9\n", + "Iteration 71: time=0.67, ELBO=-1120354.50, deltaELBO=51.686 (0.00058232%), Factors=9\n", + "Iteration 72: time=0.72, ELBO=-1120303.90, deltaELBO=50.600 (0.00057008%), Factors=9\n", + "Iteration 73: time=0.80, ELBO=-1120254.36, deltaELBO=49.547 (0.00055822%), Factors=9\n", + "Iteration 74: time=0.50, ELBO=-1120205.83, deltaELBO=48.527 (0.00054672%), Factors=9\n", + "Iteration 75: time=0.44, ELBO=-1120158.29, deltaELBO=47.538 (0.00053559%), Factors=9\n", + "Iteration 76: time=0.42, ELBO=-1120111.71, deltaELBO=46.580 (0.00052479%), Factors=9\n", + "Iteration 77: time=0.41, ELBO=-1120066.06, deltaELBO=45.650 (0.00051431%), Factors=9\n", + "Iteration 78: time=0.40, ELBO=-1120021.31, deltaELBO=44.749 (0.00050416%), Factors=9\n", + "Iteration 79: time=0.40, ELBO=-1119977.44, deltaELBO=43.874 (0.00049430%), Factors=9\n", + "Iteration 80: time=0.42, ELBO=-1119934.41, deltaELBO=43.024 (0.00048473%), Factors=9\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -9537298.64 \n", + "\n", + "Iteration 1: time=0.75, ELBO=-1282147.16, deltaELBO=8255151.483 (86.55649562%), Factors=10\n", + "Iteration 2: time=0.72, ELBO=-1176812.19, deltaELBO=105334.967 (1.10445285%), Factors=10\n", + "Iteration 3: time=0.70, ELBO=-1162675.93, deltaELBO=14136.266 (0.14822085%), Factors=10\n", + "Iteration 4: time=0.78, ELBO=-1147749.13, deltaELBO=14926.801 (0.15650974%), Factors=10\n", + "Iteration 5: time=0.70, ELBO=-1131438.32, deltaELBO=16310.804 (0.17102121%), Factors=10\n", + "Iteration 6: time=0.66, ELBO=-1120597.36, deltaELBO=10840.965 (0.11366914%), Factors=10\n", + "Iteration 7: time=0.70, ELBO=-1114652.08, deltaELBO=5945.283 (0.06233718%), Factors=10\n", + "Iteration 8: time=0.67, ELBO=-1110790.54, deltaELBO=3861.540 (0.04048882%), Factors=10\n", + "Iteration 9: time=0.48, ELBO=-1107986.88, deltaELBO=2803.661 (0.02939681%), Factors=10\n", + "Iteration 10: time=0.43, ELBO=-1105870.62, deltaELBO=2116.257 (0.02218927%), Factors=10\n", + "Iteration 11: time=0.67, ELBO=-1104133.53, deltaELBO=1737.084 (0.01821359%), Factors=10\n", + "Iteration 12: time=0.53, ELBO=-1102637.25, deltaELBO=1496.287 (0.01568879%), Factors=10\n", + "Iteration 13: time=0.44, ELBO=-1101345.48, deltaELBO=1291.770 (0.01354440%), Factors=10\n", + "Iteration 14: time=0.44, ELBO=-1100247.70, deltaELBO=1097.775 (0.01151033%), Factors=10\n", + "Iteration 15: time=0.41, ELBO=-1099333.42, deltaELBO=914.284 (0.00958641%), Factors=10\n", + "Iteration 16: time=0.35, ELBO=-1098584.69, deltaELBO=748.723 (0.00785048%), Factors=10\n", + "Iteration 17: time=0.32, ELBO=-1097976.68, deltaELBO=608.013 (0.00637510%), Factors=10\n", + "Iteration 18: time=0.31, ELBO=-1097481.80, deltaELBO=494.882 (0.00518891%), Factors=10\n", + "Iteration 19: time=0.33, ELBO=-1097074.02, deltaELBO=407.776 (0.00427559%), Factors=10\n", + "Iteration 20: time=0.33, ELBO=-1096731.42, deltaELBO=342.603 (0.00359224%), Factors=10\n", + "Iteration 21: time=0.32, ELBO=-1096436.88, deltaELBO=294.545 (0.00308835%), Factors=10\n", + "Iteration 22: time=0.32, ELBO=-1096177.69, deltaELBO=259.183 (0.00271757%), Factors=10\n", + "Iteration 23: time=0.39, ELBO=-1095944.76, deltaELBO=232.935 (0.00244236%), Factors=10\n", + "Iteration 24: time=0.32, ELBO=-1095731.66, deltaELBO=213.103 (0.00223442%), Factors=10\n", + "Iteration 25: time=0.40, ELBO=-1095533.91, deltaELBO=197.745 (0.00207338%), Factors=10\n", + "Iteration 26: time=0.63, ELBO=-1095348.41, deltaELBO=185.500 (0.00194499%), Factors=10\n", + "Iteration 27: time=0.49, ELBO=-1095172.98, deltaELBO=175.435 (0.00183946%), Factors=10\n", + "Iteration 28: time=0.47, ELBO=-1095006.06, deltaELBO=166.918 (0.00175016%), Factors=10\n", + "Iteration 29: time=0.64, ELBO=-1094846.54, deltaELBO=159.522 (0.00167262%), Factors=10\n", + "Iteration 30: time=0.69, ELBO=-1094693.57, deltaELBO=152.961 (0.00160382%), Factors=10\n", + "Iteration 31: time=0.80, ELBO=-1094546.53, deltaELBO=147.040 (0.00154174%), Factors=10\n", + "Iteration 32: time=0.54, ELBO=-1094404.91, deltaELBO=141.627 (0.00148498%), Factors=10\n", + "Iteration 33: time=0.55, ELBO=-1094268.28, deltaELBO=136.629 (0.00143258%), Factors=10\n", + "Iteration 34: time=0.40, ELBO=-1094136.30, deltaELBO=131.981 (0.00138384%), Factors=10\n", + "Iteration 35: time=0.34, ELBO=-1094008.66, deltaELBO=127.633 (0.00133825%), Factors=10\n", + "Iteration 36: time=0.39, ELBO=-1093885.11, deltaELBO=123.549 (0.00129543%), Factors=10\n", + "Iteration 37: time=0.33, ELBO=-1093765.42, deltaELBO=119.700 (0.00125507%), Factors=10\n", + "Iteration 38: time=0.33, ELBO=-1093649.35, deltaELBO=116.062 (0.00121693%), Factors=10\n", + "Iteration 39: time=0.34, ELBO=-1093536.74, deltaELBO=112.618 (0.00118081%), Factors=10\n", + "Iteration 40: time=0.31, ELBO=-1093427.39, deltaELBO=109.349 (0.00114654%), Factors=10\n", + "Iteration 41: time=0.40, ELBO=-1093321.14, deltaELBO=106.242 (0.00111397%), Factors=10\n", + "Iteration 42: time=0.36, ELBO=-1093217.86, deltaELBO=103.286 (0.00108297%), Factors=10\n", + "Iteration 43: time=0.35, ELBO=-1093117.39, deltaELBO=100.468 (0.00105342%), Factors=10\n", + "Iteration 44: time=0.32, ELBO=-1093019.61, deltaELBO=97.779 (0.00102523%), Factors=10\n", + "Iteration 45: time=0.51, ELBO=-1092924.40, deltaELBO=95.211 (0.00099830%), Factors=10\n", + "Iteration 46: time=0.33, ELBO=-1092831.65, deltaELBO=92.755 (0.00097255%), Factors=10\n", + "Iteration 47: time=0.31, ELBO=-1092741.24, deltaELBO=90.405 (0.00094791%), Factors=10\n", + "Iteration 48: time=0.30, ELBO=-1092653.09, deltaELBO=88.153 (0.00092430%), Factors=10\n", + "Iteration 49: time=0.33, ELBO=-1092567.09, deltaELBO=85.994 (0.00090166%), Factors=10\n", + "Iteration 50: time=0.46, ELBO=-1092483.17, deltaELBO=83.923 (0.00087994%), Factors=10\n", + "Iteration 51: time=0.32, ELBO=-1092401.24, deltaELBO=81.933 (0.00085908%), Factors=10\n", + "Iteration 52: time=0.32, ELBO=-1092321.22, deltaELBO=80.022 (0.00083904%), Factors=10\n", + "Iteration 53: time=0.32, ELBO=-1092243.03, deltaELBO=78.183 (0.00081977%), Factors=10\n", + "Iteration 54: time=0.32, ELBO=-1092166.62, deltaELBO=76.414 (0.00080122%), Factors=10\n", + "Iteration 55: time=0.32, ELBO=-1092091.91, deltaELBO=74.711 (0.00078336%), Factors=10\n", + "Iteration 56: time=0.34, ELBO=-1092018.84, deltaELBO=73.070 (0.00076615%), Factors=10\n", + "Iteration 57: time=0.32, ELBO=-1091947.35, deltaELBO=71.487 (0.00074956%), Factors=10\n", + "Iteration 58: time=0.32, ELBO=-1091877.39, deltaELBO=69.961 (0.00073355%), Factors=10\n", + "Iteration 59: time=0.32, ELBO=-1091808.90, deltaELBO=68.488 (0.00071811%), Factors=10\n", + "Iteration 60: time=0.32, ELBO=-1091741.84, deltaELBO=67.065 (0.00070319%), Factors=10\n", + "Iteration 61: time=0.32, ELBO=-1091676.14, deltaELBO=65.691 (0.00068878%), Factors=10\n", + "Iteration 62: time=0.32, ELBO=-1091611.78, deltaELBO=64.362 (0.00067485%), Factors=10\n", + "Iteration 63: time=0.32, ELBO=-1091548.70, deltaELBO=63.078 (0.00066138%), Factors=10\n", + "Iteration 64: time=0.32, ELBO=-1091486.87, deltaELBO=61.834 (0.00064834%), Factors=10\n", + "Iteration 65: time=0.33, ELBO=-1091426.24, deltaELBO=60.631 (0.00063573%), Factors=10\n", + "Iteration 66: time=0.40, ELBO=-1091366.77, deltaELBO=59.466 (0.00062351%), Factors=10\n", + "Iteration 67: time=0.44, ELBO=-1091308.44, deltaELBO=58.337 (0.00061167%), Factors=10\n", + "Iteration 68: time=0.32, ELBO=-1091251.19, deltaELBO=57.243 (0.00060020%), Factors=10\n", + "Iteration 69: time=0.37, ELBO=-1091195.01, deltaELBO=56.183 (0.00058908%), Factors=10\n", + "Iteration 70: time=0.36, ELBO=-1091139.86, deltaELBO=55.154 (0.00057830%), Factors=10\n", + "Iteration 71: time=0.54, ELBO=-1091085.70, deltaELBO=54.156 (0.00056783%), Factors=10\n", + "Iteration 72: time=0.57, ELBO=-1091032.51, deltaELBO=53.187 (0.00055767%), Factors=10\n", + "Iteration 73: time=0.71, ELBO=-1090980.27, deltaELBO=52.246 (0.00054781%), Factors=10\n", + "Iteration 74: time=0.52, ELBO=-1090928.93, deltaELBO=51.332 (0.00053823%), Factors=10\n", + "Iteration 75: time=0.53, ELBO=-1090878.49, deltaELBO=50.445 (0.00052892%), Factors=10\n", + "Iteration 76: time=0.56, ELBO=-1090828.91, deltaELBO=49.582 (0.00051987%), Factors=10\n", + "Iteration 77: time=0.53, ELBO=-1090780.17, deltaELBO=48.743 (0.00051108%), Factors=10\n", + "Iteration 78: time=0.59, ELBO=-1090732.24, deltaELBO=47.927 (0.00050253%), Factors=10\n", + "Iteration 79: time=0.58, ELBO=-1090685.10, deltaELBO=47.134 (0.00049421%), Factors=10\n", + "Iteration 80: time=0.51, ELBO=-1090638.74, deltaELBO=46.362 (0.00048611%), Factors=10\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -10192024.40 \n", + "\n", + "Iteration 1: time=0.44, ELBO=-1275674.10, deltaELBO=8916350.294 (87.48360429%), Factors=11\n", + "Iteration 2: time=0.36, ELBO=-1158772.43, deltaELBO=116901.676 (1.14699172%), Factors=11\n", + "Iteration 3: time=0.55, ELBO=-1141754.49, deltaELBO=17017.939 (0.16697310%), Factors=11\n", + "Iteration 4: time=0.61, ELBO=-1121944.39, deltaELBO=19810.099 (0.19436864%), Factors=11\n", + "Iteration 5: time=0.74, ELBO=-1103706.66, deltaELBO=18237.731 (0.17894121%), Factors=11\n", + "Iteration 6: time=0.41, ELBO=-1094358.19, deltaELBO=9348.468 (0.09172337%), Factors=11\n", + "Iteration 7: time=0.38, ELBO=-1089581.13, deltaELBO=4777.058 (0.04687055%), Factors=11\n", + "Iteration 8: time=0.34, ELBO=-1086483.91, deltaELBO=3097.220 (0.03038867%), Factors=11\n", + "Iteration 9: time=0.39, ELBO=-1084264.09, deltaELBO=2219.820 (0.02177997%), Factors=11\n", + "Iteration 10: time=0.36, ELBO=-1082585.89, deltaELBO=1678.202 (0.01646584%), Factors=11\n", + "Iteration 11: time=0.37, ELBO=-1081224.16, deltaELBO=1361.732 (0.01336076%), Factors=11\n", + "Iteration 12: time=0.34, ELBO=-1080077.07, deltaELBO=1147.090 (0.01125478%), Factors=11\n", + "Iteration 13: time=0.36, ELBO=-1079100.39, deltaELBO=976.675 (0.00958274%), Factors=11\n", + "Iteration 14: time=0.37, ELBO=-1078264.39, deltaELBO=836.007 (0.00820256%), Factors=11\n", + "Iteration 15: time=0.57, ELBO=-1077542.10, deltaELBO=722.283 (0.00708675%), Factors=11\n", + "Iteration 16: time=0.33, ELBO=-1076908.19, deltaELBO=633.915 (0.00621972%), Factors=11\n", + "Iteration 17: time=0.35, ELBO=-1076340.12, deltaELBO=568.072 (0.00557369%), Factors=11\n", + "Iteration 18: time=0.35, ELBO=-1075819.35, deltaELBO=520.766 (0.00510955%), Factors=11\n", + "Iteration 19: time=0.38, ELBO=-1075331.71, deltaELBO=487.644 (0.00478456%), Factors=11\n", + "Iteration 20: time=0.39, ELBO=-1074867.04, deltaELBO=464.664 (0.00455910%), Factors=11\n", + "Iteration 21: time=0.39, ELBO=-1074418.59, deltaELBO=448.450 (0.00440001%), Factors=11\n", + "Iteration 22: time=0.36, ELBO=-1073982.23, deltaELBO=436.362 (0.00428141%), Factors=11\n", + "Iteration 23: time=0.37, ELBO=-1073555.80, deltaELBO=426.426 (0.00418392%), Factors=11\n", + "Iteration 24: time=0.38, ELBO=-1073138.61, deltaELBO=417.194 (0.00409334%), Factors=11\n", + "Iteration 25: time=0.44, ELBO=-1072731.01, deltaELBO=407.598 (0.00399919%), Factors=11\n", + "Iteration 26: time=0.43, ELBO=-1072334.21, deltaELBO=396.806 (0.00389330%), Factors=11\n", + "Iteration 27: time=0.40, ELBO=-1071950.10, deltaELBO=384.109 (0.00376872%), Factors=11\n", + "Iteration 28: time=0.36, ELBO=-1071581.20, deltaELBO=368.897 (0.00361947%), Factors=11\n", + "Iteration 29: time=0.36, ELBO=-1071230.45, deltaELBO=350.754 (0.00344146%), Factors=11\n", + "Iteration 30: time=0.39, ELBO=-1070900.79, deltaELBO=329.656 (0.00323445%), Factors=11\n", + "Iteration 31: time=0.42, ELBO=-1070594.64, deltaELBO=306.152 (0.00300384%), Factors=11\n", + "Iteration 32: time=0.42, ELBO=-1070313.27, deltaELBO=281.363 (0.00276062%), Factors=11\n", + "Iteration 33: time=0.40, ELBO=-1070056.57, deltaELBO=256.708 (0.00251872%), Factors=11\n", + "Iteration 34: time=0.42, ELBO=-1069823.07, deltaELBO=233.494 (0.00229094%), Factors=11\n", + "Iteration 35: time=0.35, ELBO=-1069610.48, deltaELBO=212.589 (0.00208584%), Factors=11\n", + "Iteration 36: time=0.49, ELBO=-1069416.14, deltaELBO=194.342 (0.00190680%), Factors=11\n", + "Iteration 37: time=0.35, ELBO=-1069237.45, deltaELBO=178.692 (0.00175326%), Factors=11\n", + "Iteration 38: time=0.37, ELBO=-1069072.09, deltaELBO=165.358 (0.00162243%), Factors=11\n", + "Iteration 39: time=0.56, ELBO=-1068918.10, deltaELBO=153.988 (0.00151087%), Factors=11\n", + "Iteration 40: time=0.40, ELBO=-1068773.86, deltaELBO=144.248 (0.00141531%), Factors=11\n", + "Iteration 41: time=0.39, ELBO=-1068638.00, deltaELBO=135.854 (0.00133294%), Factors=11\n", + "Iteration 42: time=0.36, ELBO=-1068509.43, deltaELBO=128.573 (0.00126151%), Factors=11\n", + "Iteration 43: time=0.37, ELBO=-1068387.21, deltaELBO=122.219 (0.00119916%), Factors=11\n", + "Iteration 44: time=0.49, ELBO=-1068270.57, deltaELBO=116.640 (0.00114442%), Factors=11\n", + "Iteration 45: time=0.48, ELBO=-1068158.86, deltaELBO=111.711 (0.00109607%), Factors=11\n", + "Iteration 46: time=0.40, ELBO=-1068051.53, deltaELBO=107.331 (0.00105309%), Factors=11\n", + "Iteration 47: time=0.62, ELBO=-1067948.12, deltaELBO=103.412 (0.00101463%), Factors=11\n", + "Iteration 48: time=0.74, ELBO=-1067848.23, deltaELBO=99.882 (0.00098000%), Factors=11\n", + "Iteration 49: time=0.54, ELBO=-1067751.55, deltaELBO=96.683 (0.00094861%), Factors=11\n", + "Iteration 50: time=0.60, ELBO=-1067657.79, deltaELBO=93.763 (0.00091997%), Factors=11\n", + "Iteration 51: time=0.72, ELBO=-1067566.71, deltaELBO=91.082 (0.00089366%), Factors=11\n", + "Iteration 52: time=0.58, ELBO=-1067478.10, deltaELBO=88.605 (0.00086936%), Factors=11\n", + "Iteration 53: time=0.61, ELBO=-1067391.80, deltaELBO=86.304 (0.00084678%), Factors=11\n", + "Iteration 54: time=0.63, ELBO=-1067307.64, deltaELBO=84.154 (0.00082569%), Factors=11\n", + "Iteration 55: time=0.72, ELBO=-1067225.51, deltaELBO=82.137 (0.00080590%), Factors=11\n", + "Iteration 56: time=0.70, ELBO=-1067145.27, deltaELBO=80.236 (0.00078724%), Factors=11\n", + "Iteration 57: time=0.89, ELBO=-1067066.83, deltaELBO=78.437 (0.00076959%), Factors=11\n", + "Iteration 58: time=0.53, ELBO=-1066990.10, deltaELBO=76.728 (0.00075282%), Factors=11\n", + "Iteration 59: time=0.54, ELBO=-1066915.00, deltaELBO=75.100 (0.00073685%), Factors=11\n", + "Iteration 60: time=0.51, ELBO=-1066841.46, deltaELBO=73.544 (0.00072158%), Factors=11\n", + "Iteration 61: time=0.83, ELBO=-1066769.41, deltaELBO=72.053 (0.00070695%), Factors=11\n", + "Iteration 62: time=0.56, ELBO=-1066698.79, deltaELBO=70.620 (0.00069290%), Factors=11\n", + "Iteration 63: time=0.56, ELBO=-1066629.55, deltaELBO=69.240 (0.00067936%), Factors=11\n", + "Iteration 64: time=0.51, ELBO=-1066561.64, deltaELBO=67.908 (0.00066629%), Factors=11\n", + "Iteration 65: time=0.54, ELBO=-1066495.02, deltaELBO=66.620 (0.00065365%), Factors=11\n", + "Iteration 66: time=0.48, ELBO=-1066429.65, deltaELBO=65.372 (0.00064140%), Factors=11\n", + "Iteration 67: time=0.52, ELBO=-1066365.49, deltaELBO=64.160 (0.00062951%), Factors=11\n", + "Iteration 68: time=0.53, ELBO=-1066302.50, deltaELBO=62.982 (0.00061795%), Factors=11\n", + "Iteration 69: time=0.47, ELBO=-1066240.67, deltaELBO=61.834 (0.00060669%), Factors=11\n", + "Iteration 70: time=0.48, ELBO=-1066179.96, deltaELBO=60.714 (0.00059570%), Factors=11\n", + "Iteration 71: time=0.47, ELBO=-1066120.34, deltaELBO=59.621 (0.00058497%), Factors=11\n", + "Iteration 72: time=0.47, ELBO=-1066061.78, deltaELBO=58.552 (0.00057448%), Factors=11\n", + "Iteration 73: time=0.48, ELBO=-1066004.28, deltaELBO=57.505 (0.00056422%), Factors=11\n", + "Iteration 74: time=0.47, ELBO=-1065947.80, deltaELBO=56.480 (0.00055416%), Factors=11\n", + "Iteration 75: time=0.47, ELBO=-1065892.33, deltaELBO=55.475 (0.00054429%), Factors=11\n", + "Iteration 76: time=0.47, ELBO=-1065837.84, deltaELBO=54.488 (0.00053462%), Factors=11\n", + "Iteration 77: time=0.47, ELBO=-1065784.32, deltaELBO=53.521 (0.00052512%), Factors=11\n", + "Iteration 78: time=0.47, ELBO=-1065731.75, deltaELBO=52.570 (0.00051580%), Factors=11\n", + "Iteration 79: time=0.46, ELBO=-1065680.11, deltaELBO=51.637 (0.00050665%), Factors=11\n", + "Iteration 80: time=0.47, ELBO=-1065629.39, deltaELBO=50.721 (0.00049766%), Factors=11\n", + "Iteration 81: time=0.47, ELBO=-1065579.57, deltaELBO=49.822 (0.00048883%), Factors=11\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -10847689.95 \n", + "\n", + "Iteration 1: time=0.56, ELBO=-1348351.06, deltaELBO=9499338.894 (87.57015488%), Factors=12\n", + "Iteration 2: time=0.65, ELBO=-1246775.46, deltaELBO=101575.603 (0.93638004%), Factors=12\n", + "Iteration 3: time=0.63, ELBO=-1214550.06, deltaELBO=32225.401 (0.29707155%), Factors=12\n", + "Iteration 4: time=0.57, ELBO=-1114159.63, deltaELBO=100390.428 (0.92545444%), Factors=12\n", + "Iteration 5: time=0.55, ELBO=-1085485.04, deltaELBO=28674.585 (0.26433817%), Factors=12\n", + "Iteration 6: time=0.63, ELBO=-1075312.51, deltaELBO=10172.533 (0.09377603%), Factors=12\n", + "Iteration 7: time=0.55, ELBO=-1069134.19, deltaELBO=6178.322 (0.05695518%), Factors=12\n", + "Iteration 8: time=0.56, ELBO=-1064835.88, deltaELBO=4298.310 (0.03962420%), Factors=12\n", + "Iteration 9: time=0.58, ELBO=-1061688.40, deltaELBO=3147.484 (0.02901524%), Factors=12\n", + "Iteration 10: time=0.51, ELBO=-1059488.50, deltaELBO=2199.894 (0.02027984%), Factors=12\n", + "Iteration 11: time=0.50, ELBO=-1058001.44, deltaELBO=1487.065 (0.01370859%), Factors=12\n", + "Iteration 12: time=0.51, ELBO=-1056933.55, deltaELBO=1067.888 (0.00984438%), Factors=12\n", + "Iteration 13: time=0.50, ELBO=-1056075.12, deltaELBO=858.428 (0.00791346%), Factors=12\n", + "Iteration 14: time=0.50, ELBO=-1055317.91, deltaELBO=757.211 (0.00698039%), Factors=12\n", + "Iteration 15: time=0.50, ELBO=-1054608.66, deltaELBO=709.252 (0.00653828%), Factors=12\n", + "Iteration 16: time=0.50, ELBO=-1053919.14, deltaELBO=689.518 (0.00635636%), Factors=12\n", + "Iteration 17: time=0.54, ELBO=-1053233.70, deltaELBO=685.439 (0.00631875%), Factors=12\n", + "Iteration 18: time=0.57, ELBO=-1052544.35, deltaELBO=689.355 (0.00635485%), Factors=12\n", + "Iteration 19: time=0.53, ELBO=-1051848.91, deltaELBO=695.441 (0.00641096%), Factors=12\n", + "Iteration 20: time=0.53, ELBO=-1051150.50, deltaELBO=698.408 (0.00643831%), Factors=12\n", + "Iteration 21: time=0.56, ELBO=-1050457.33, deltaELBO=693.163 (0.00638996%), Factors=12\n", + "Iteration 22: time=0.53, ELBO=-1049782.08, deltaELBO=675.250 (0.00622483%), Factors=12\n", + "Iteration 23: time=0.58, ELBO=-1049140.13, deltaELBO=641.952 (0.00591787%), Factors=12\n", + "Iteration 24: time=0.57, ELBO=-1048546.58, deltaELBO=593.549 (0.00547167%), Factors=12\n", + "Iteration 25: time=0.56, ELBO=-1048012.81, deltaELBO=533.771 (0.00492059%), Factors=12\n", + "Iteration 26: time=0.51, ELBO=-1047544.05, deltaELBO=468.758 (0.00432127%), Factors=12\n", + "Iteration 27: time=0.48, ELBO=-1047139.07, deltaELBO=404.986 (0.00373339%), Factors=12\n", + "Iteration 28: time=0.45, ELBO=-1046791.67, deltaELBO=347.392 (0.00320245%), Factors=12\n", + "Iteration 29: time=0.54, ELBO=-1046493.08, deltaELBO=298.599 (0.00275265%), Factors=12\n", + "Iteration 30: time=0.41, ELBO=-1046233.92, deltaELBO=259.154 (0.00238903%), Factors=12\n", + "Iteration 31: time=0.42, ELBO=-1046005.68, deltaELBO=228.244 (0.00210408%), Factors=12\n", + "Iteration 32: time=0.39, ELBO=-1045801.26, deltaELBO=204.420 (0.00188446%), Factors=12\n", + "Iteration 33: time=0.48, ELBO=-1045615.13, deltaELBO=186.124 (0.00171579%), Factors=12\n", + "Iteration 34: time=0.39, ELBO=-1045443.17, deltaELBO=171.965 (0.00158527%), Factors=12\n", + "Iteration 35: time=0.46, ELBO=-1045282.34, deltaELBO=160.829 (0.00148261%), Factors=12\n", + "Iteration 36: time=0.53, ELBO=-1045130.46, deltaELBO=151.876 (0.00140008%), Factors=12\n", + "Iteration 37: time=0.60, ELBO=-1044985.96, deltaELBO=144.500 (0.00133208%), Factors=12\n", + "Iteration 38: time=0.63, ELBO=-1044847.69, deltaELBO=138.272 (0.00127467%), Factors=12\n", + "Iteration 39: time=0.52, ELBO=-1044714.80, deltaELBO=132.889 (0.00122504%), Factors=12\n", + "Iteration 40: time=0.41, ELBO=-1044586.66, deltaELBO=128.140 (0.00118127%), Factors=12\n", + "Iteration 41: time=0.37, ELBO=-1044462.79, deltaELBO=123.874 (0.00114194%), Factors=12\n", + "Iteration 42: time=0.36, ELBO=-1044342.81, deltaELBO=119.982 (0.00110606%), Factors=12\n", + "Iteration 43: time=0.36, ELBO=-1044226.42, deltaELBO=116.384 (0.00107289%), Factors=12\n", + "Iteration 44: time=0.58, ELBO=-1044113.40, deltaELBO=113.018 (0.00104187%), Factors=12\n", + "Iteration 45: time=0.46, ELBO=-1044003.56, deltaELBO=109.840 (0.00101257%), Factors=12\n", + "Iteration 46: time=0.36, ELBO=-1043896.75, deltaELBO=106.814 (0.00098467%), Factors=12\n", + "Iteration 47: time=0.36, ELBO=-1043792.84, deltaELBO=103.913 (0.00095793%), Factors=12\n", + "Iteration 48: time=0.39, ELBO=-1043691.72, deltaELBO=101.116 (0.00093215%), Factors=12\n", + "Iteration 49: time=0.39, ELBO=-1043593.31, deltaELBO=98.409 (0.00090719%), Factors=12\n", + "Iteration 50: time=0.35, ELBO=-1043497.53, deltaELBO=95.780 (0.00088296%), Factors=12\n", + "Iteration 51: time=0.57, ELBO=-1043404.31, deltaELBO=93.223 (0.00085938%), Factors=12\n", + "Iteration 52: time=0.38, ELBO=-1043313.57, deltaELBO=90.733 (0.00083643%), Factors=12\n", + "Iteration 53: time=0.39, ELBO=-1043225.27, deltaELBO=88.309 (0.00081408%), Factors=12\n", + "Iteration 54: time=0.36, ELBO=-1043139.32, deltaELBO=85.949 (0.00079233%), Factors=12\n", + "Iteration 55: time=0.38, ELBO=-1043055.66, deltaELBO=83.655 (0.00077118%), Factors=12\n", + "Iteration 56: time=0.37, ELBO=-1042974.23, deltaELBO=81.428 (0.00075065%), Factors=12\n", + "Iteration 57: time=0.36, ELBO=-1042894.96, deltaELBO=79.270 (0.00073076%), Factors=12\n", + "Iteration 58: time=0.37, ELBO=-1042817.78, deltaELBO=77.183 (0.00071151%), Factors=12\n", + "Iteration 59: time=0.39, ELBO=-1042742.61, deltaELBO=75.167 (0.00069293%), Factors=12\n", + "Iteration 60: time=0.35, ELBO=-1042669.39, deltaELBO=73.223 (0.00067501%), Factors=12\n", + "Iteration 61: time=0.36, ELBO=-1042598.04, deltaELBO=71.351 (0.00065776%), Factors=12\n", + "Iteration 62: time=0.38, ELBO=-1042528.49, deltaELBO=69.552 (0.00064116%), Factors=12\n", + "Iteration 63: time=0.37, ELBO=-1042460.66, deltaELBO=67.822 (0.00062522%), Factors=12\n", + "Iteration 64: time=0.39, ELBO=-1042394.50, deltaELBO=66.162 (0.00060992%), Factors=12\n", + "Iteration 65: time=0.36, ELBO=-1042329.93, deltaELBO=64.569 (0.00059523%), Factors=12\n", + "Iteration 66: time=0.67, ELBO=-1042266.89, deltaELBO=63.040 (0.00058114%), Factors=12\n", + "Iteration 67: time=0.63, ELBO=-1042205.32, deltaELBO=61.573 (0.00056762%), Factors=12\n", + "Iteration 68: time=0.63, ELBO=-1042145.15, deltaELBO=60.166 (0.00055464%), Factors=12\n", + "Iteration 69: time=0.67, ELBO=-1042086.34, deltaELBO=58.814 (0.00054218%), Factors=12\n", + "Iteration 70: time=0.60, ELBO=-1042028.82, deltaELBO=57.515 (0.00053021%), Factors=12\n", + "Iteration 71: time=0.65, ELBO=-1041972.56, deltaELBO=56.268 (0.00051871%), Factors=12\n", + "Iteration 72: time=0.60, ELBO=-1041917.49, deltaELBO=55.068 (0.00050764%), Factors=12\n", + "Iteration 73: time=0.62, ELBO=-1041863.58, deltaELBO=53.913 (0.00049700%), Factors=12\n", + "Iteration 74: time=0.67, ELBO=-1041810.77, deltaELBO=52.801 (0.00048675%), Factors=12\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -11497424.02 \n", + "\n", + "Iteration 1: time=0.63, ELBO=-1245003.47, deltaELBO=10252420.551 (89.17145729%), Factors=13\n", + "Iteration 2: time=0.47, ELBO=-1094697.17, deltaELBO=150306.297 (1.30730411%), Factors=13\n", + "Iteration 3: time=0.43, ELBO=-1078353.30, deltaELBO=16343.869 (0.14215244%), Factors=13\n", + "Iteration 4: time=0.41, ELBO=-1069597.79, deltaELBO=8755.516 (0.07615198%), Factors=13\n", + "Iteration 5: time=0.45, ELBO=-1062300.17, deltaELBO=7297.614 (0.06347173%), Factors=13\n", + "Iteration 6: time=0.40, ELBO=-1055840.81, deltaELBO=6459.368 (0.05618100%), Factors=13\n", + "Iteration 7: time=0.43, ELBO=-1050180.84, deltaELBO=5659.966 (0.04922813%), Factors=13\n", + "Iteration 8: time=0.44, ELBO=-1045201.52, deltaELBO=4979.319 (0.04330813%), Factors=13\n", + "Iteration 9: time=0.42, ELBO=-1040859.66, deltaELBO=4341.863 (0.03776379%), Factors=13\n", + "Iteration 10: time=0.39, ELBO=-1037204.84, deltaELBO=3654.820 (0.03178816%), Factors=13\n", + "Iteration 11: time=0.49, ELBO=-1034308.92, deltaELBO=2895.920 (0.02518755%), Factors=13\n", + "Iteration 12: time=0.50, ELBO=-1032171.56, deltaELBO=2137.358 (0.01858989%), Factors=13\n", + "Iteration 13: time=0.52, ELBO=-1030670.10, deltaELBO=1501.465 (0.01305914%), Factors=13\n", + "Iteration 14: time=0.58, ELBO=-1029617.68, deltaELBO=1052.415 (0.00915349%), Factors=13\n", + "Iteration 15: time=0.46, ELBO=-1028848.31, deltaELBO=769.375 (0.00669172%), Factors=13\n", + "Iteration 16: time=0.43, ELBO=-1028249.05, deltaELBO=599.256 (0.00521209%), Factors=13\n", + "Iteration 17: time=0.50, ELBO=-1027752.30, deltaELBO=496.755 (0.00432058%), Factors=13\n", + "Iteration 18: time=0.47, ELBO=-1027319.35, deltaELBO=432.945 (0.00376558%), Factors=13\n", + "Iteration 19: time=0.45, ELBO=-1026928.00, deltaELBO=391.349 (0.00340380%), Factors=13\n", + "Iteration 20: time=0.42, ELBO=-1026565.04, deltaELBO=362.958 (0.00315686%), Factors=13\n", + "Iteration 21: time=0.50, ELBO=-1026222.17, deltaELBO=342.875 (0.00298219%), Factors=13\n", + "Iteration 22: time=0.49, ELBO=-1025893.76, deltaELBO=328.408 (0.00285637%), Factors=13\n", + "Iteration 23: time=0.44, ELBO=-1025575.72, deltaELBO=318.040 (0.00276619%), Factors=13\n", + "Iteration 24: time=0.42, ELBO=-1025264.85, deltaELBO=310.874 (0.00270386%), Factors=13\n", + "Iteration 25: time=0.46, ELBO=-1024958.52, deltaELBO=306.328 (0.00266432%), Factors=13\n", + "Iteration 26: time=0.43, ELBO=-1024654.56, deltaELBO=303.960 (0.00264372%), Factors=13\n", + "Iteration 27: time=0.39, ELBO=-1024351.20, deltaELBO=303.357 (0.00263848%), Factors=13\n", + "Iteration 28: time=0.46, ELBO=-1024047.14, deltaELBO=304.060 (0.00264459%), Factors=13\n", + "Iteration 29: time=0.43, ELBO=-1023741.62, deltaELBO=305.521 (0.00265730%), Factors=13\n", + "Iteration 30: time=0.38, ELBO=-1023434.52, deltaELBO=307.098 (0.00267101%), Factors=13\n", + "Iteration 31: time=0.39, ELBO=-1023126.45, deltaELBO=308.075 (0.00267951%), Factors=13\n", + "Iteration 32: time=0.38, ELBO=-1022818.71, deltaELBO=307.739 (0.00267659%), Factors=13\n", + "Iteration 33: time=0.38, ELBO=-1022513.23, deltaELBO=305.477 (0.00265691%), Factors=13\n", + "Iteration 34: time=0.49, ELBO=-1022212.35, deltaELBO=300.882 (0.00261695%), Factors=13\n", + "Iteration 35: time=0.41, ELBO=-1021918.52, deltaELBO=293.829 (0.00255561%), Factors=13\n", + "Iteration 36: time=0.39, ELBO=-1021634.04, deltaELBO=284.477 (0.00247427%), Factors=13\n", + "Iteration 37: time=0.40, ELBO=-1021360.84, deltaELBO=273.202 (0.00237621%), Factors=13\n", + "Iteration 38: time=0.43, ELBO=-1021100.36, deltaELBO=260.477 (0.00226552%), Factors=13\n", + "Iteration 39: time=0.38, ELBO=-1020853.63, deltaELBO=246.737 (0.00214602%), Factors=13\n", + "Iteration 40: time=0.46, ELBO=-1020621.33, deltaELBO=232.297 (0.00202043%), Factors=13\n", + "Iteration 41: time=0.39, ELBO=-1020403.99, deltaELBO=217.344 (0.00189037%), Factors=13\n", + "Iteration 42: time=0.46, ELBO=-1020201.98, deltaELBO=202.003 (0.00175694%), Factors=13\n", + "Iteration 43: time=0.47, ELBO=-1020015.53, deltaELBO=186.456 (0.00162172%), Factors=13\n", + "Iteration 44: time=0.59, ELBO=-1019844.51, deltaELBO=171.018 (0.00148745%), Factors=13\n", + "Iteration 45: time=0.58, ELBO=-1019688.36, deltaELBO=156.147 (0.00135810%), Factors=13\n", + "Iteration 46: time=0.55, ELBO=-1019546.01, deltaELBO=142.348 (0.00123808%), Factors=13\n", + "Iteration 47: time=0.39, ELBO=-1019415.98, deltaELBO=130.038 (0.00113102%), Factors=13\n", + "Iteration 48: time=0.41, ELBO=-1019296.53, deltaELBO=119.449 (0.00103892%), Factors=13\n", + "Iteration 49: time=0.46, ELBO=-1019185.93, deltaELBO=110.601 (0.00096196%), Factors=13\n", + "Iteration 50: time=0.49, ELBO=-1019082.58, deltaELBO=103.347 (0.00089887%), Factors=13\n", + "Iteration 51: time=0.49, ELBO=-1018985.13, deltaELBO=97.448 (0.00084756%), Factors=13\n", + "Iteration 52: time=0.44, ELBO=-1018892.49, deltaELBO=92.642 (0.00080576%), Factors=13\n", + "Iteration 53: time=0.46, ELBO=-1018803.80, deltaELBO=88.685 (0.00077135%), Factors=13\n", + "Iteration 54: time=0.45, ELBO=-1018718.43, deltaELBO=85.374 (0.00074255%), Factors=13\n", + "Iteration 55: time=0.45, ELBO=-1018635.88, deltaELBO=82.546 (0.00071796%), Factors=13\n", + "Iteration 56: time=0.45, ELBO=-1018555.80, deltaELBO=80.083 (0.00069653%), Factors=13\n", + "Iteration 57: time=0.45, ELBO=-1018477.91, deltaELBO=77.895 (0.00067750%), Factors=13\n", + "Iteration 58: time=0.46, ELBO=-1018401.99, deltaELBO=75.919 (0.00066031%), Factors=13\n", + "Iteration 59: time=0.44, ELBO=-1018327.88, deltaELBO=74.107 (0.00064455%), Factors=13\n", + "Iteration 60: time=0.43, ELBO=-1018255.45, deltaELBO=72.428 (0.00062995%), Factors=13\n", + "Iteration 61: time=0.43, ELBO=-1018184.60, deltaELBO=70.857 (0.00061628%), Factors=13\n", + "Iteration 62: time=0.42, ELBO=-1018115.22, deltaELBO=69.376 (0.00060340%), Factors=13\n", + "Iteration 63: time=0.38, ELBO=-1018047.25, deltaELBO=67.973 (0.00059120%), Factors=13\n", + "Iteration 64: time=0.38, ELBO=-1017980.61, deltaELBO=66.637 (0.00057959%), Factors=13\n", + "Iteration 65: time=0.39, ELBO=-1017915.25, deltaELBO=65.362 (0.00056849%), Factors=13\n", + "Iteration 66: time=0.39, ELBO=-1017851.11, deltaELBO=64.140 (0.00055786%), Factors=13\n", + "Iteration 67: time=0.43, ELBO=-1017788.14, deltaELBO=62.967 (0.00054766%), Factors=13\n", + "Iteration 68: time=0.44, ELBO=-1017726.30, deltaELBO=61.839 (0.00053785%), Factors=13\n", + "Iteration 69: time=0.48, ELBO=-1017665.55, deltaELBO=60.752 (0.00052840%), Factors=13\n", + "Iteration 70: time=0.41, ELBO=-1017605.85, deltaELBO=59.704 (0.00051928%), Factors=13\n", + "Iteration 71: time=0.41, ELBO=-1017547.15, deltaELBO=58.692 (0.00051048%), Factors=13\n", + "Iteration 72: time=0.61, ELBO=-1017489.44, deltaELBO=57.713 (0.00050196%), Factors=13\n", + "Iteration 73: time=0.68, ELBO=-1017432.68, deltaELBO=56.766 (0.00049373%), Factors=13\n", + "Iteration 74: time=0.53, ELBO=-1017376.83, deltaELBO=55.848 (0.00048575%), Factors=13\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -12133355.00 \n", + "\n", + "Iteration 1: time=0.44, ELBO=-1262182.20, deltaELBO=10871172.804 (89.59741806%), Factors=14\n", + "Iteration 2: time=0.44, ELBO=-1109537.71, deltaELBO=152644.487 (1.25805672%), Factors=14\n", + "Iteration 3: time=0.42, ELBO=-1083550.97, deltaELBO=25986.742 (0.21417606%), Factors=14\n", + "Iteration 4: time=0.48, ELBO=-1050628.55, deltaELBO=32922.420 (0.27133815%), Factors=14\n", + "Iteration 5: time=0.43, ELBO=-1030062.82, deltaELBO=20565.726 (0.16949744%), Factors=14\n", + "Iteration 6: time=0.42, ELBO=-1022132.84, deltaELBO=7929.976 (0.06535683%), Factors=14\n", + "Iteration 7: time=0.46, ELBO=-1017885.52, deltaELBO=4247.327 (0.03500538%), Factors=14\n", + "Iteration 8: time=0.47, ELBO=-1015245.19, deltaELBO=2640.322 (0.02176086%), Factors=14\n", + "Iteration 9: time=0.42, ELBO=-1013399.01, deltaELBO=1846.183 (0.01521576%), Factors=14\n", + "Iteration 10: time=0.45, ELBO=-1011953.67, deltaELBO=1445.343 (0.01191215%), Factors=14\n", + "Iteration 11: time=0.45, ELBO=-1010738.57, deltaELBO=1215.103 (0.01001456%), Factors=14\n", + "Iteration 12: time=0.46, ELBO=-1009679.23, deltaELBO=1059.335 (0.00873077%), Factors=14\n", + "Iteration 13: time=0.45, ELBO=-1008740.85, deltaELBO=938.379 (0.00773388%), Factors=14\n", + "Iteration 14: time=0.44, ELBO=-1007904.15, deltaELBO=836.700 (0.00689587%), Factors=14\n", + "Iteration 15: time=0.47, ELBO=-1007155.34, deltaELBO=748.815 (0.00617154%), Factors=14\n", + "Iteration 16: time=0.54, ELBO=-1006482.35, deltaELBO=672.983 (0.00554655%), Factors=14\n", + "Iteration 17: time=0.42, ELBO=-1005873.94, deltaELBO=608.416 (0.00501441%), Factors=14\n", + "Iteration 18: time=0.61, ELBO=-1005319.74, deltaELBO=554.195 (0.00456753%), Factors=14\n", + "Iteration 19: time=0.50, ELBO=-1004810.77, deltaELBO=508.977 (0.00419486%), Factors=14\n", + "Iteration 20: time=0.46, ELBO=-1004339.73, deltaELBO=471.038 (0.00388218%), Factors=14\n", + "Iteration 21: time=0.88, ELBO=-1003901.33, deltaELBO=438.403 (0.00361320%), Factors=14\n", + "Iteration 22: time=0.63, ELBO=-1003492.31, deltaELBO=409.013 (0.00337098%), Factors=14\n", + "Iteration 23: time=0.49, ELBO=-1003111.34, deltaELBO=380.976 (0.00313990%), Factors=14\n", + "Iteration 24: time=0.67, ELBO=-1002758.45, deltaELBO=352.888 (0.00290841%), Factors=14\n", + "Iteration 25: time=0.42, ELBO=-1002434.28, deltaELBO=324.169 (0.00267171%), Factors=14\n", + "Iteration 26: time=0.58, ELBO=-1002139.08, deltaELBO=295.203 (0.00243298%), Factors=14\n", + "Iteration 27: time=0.47, ELBO=-1001871.96, deltaELBO=267.118 (0.00220152%), Factors=14\n", + "Iteration 28: time=0.45, ELBO=-1001630.71, deltaELBO=241.249 (0.00198831%), Factors=14\n", + "Iteration 29: time=0.41, ELBO=-1001412.13, deltaELBO=218.577 (0.00180145%), Factors=14\n", + "Iteration 30: time=0.46, ELBO=-1001212.67, deltaELBO=199.466 (0.00164395%), Factors=14\n", + "Iteration 31: time=0.42, ELBO=-1001028.93, deltaELBO=183.734 (0.00151429%), Factors=14\n", + "Iteration 32: time=0.49, ELBO=-1000858.05, deltaELBO=170.886 (0.00140840%), Factors=14\n", + "Iteration 33: time=0.55, ELBO=-1000697.70, deltaELBO=160.348 (0.00132155%), Factors=14\n", + "Iteration 34: time=0.61, ELBO=-1000546.10, deltaELBO=151.594 (0.00124940%), Factors=14\n", + "Iteration 35: time=0.63, ELBO=-1000401.91, deltaELBO=144.196 (0.00118843%), Factors=14\n", + "Iteration 36: time=0.51, ELBO=-1000264.08, deltaELBO=137.829 (0.00113595%), Factors=14\n", + "Iteration 37: time=0.63, ELBO=-1000131.83, deltaELBO=132.252 (0.00108999%), Factors=14\n", + "Iteration 38: time=0.53, ELBO=-1000004.54, deltaELBO=127.290 (0.00104909%), Factors=14\n", + "Iteration 39: time=0.47, ELBO=-999881.72, deltaELBO=122.815 (0.00101221%), Factors=14\n", + "Iteration 40: time=0.43, ELBO=-999762.99, deltaELBO=118.732 (0.00097856%), Factors=14\n", + "Iteration 41: time=0.45, ELBO=-999648.02, deltaELBO=114.973 (0.00094758%), Factors=14\n", + "Iteration 42: time=0.57, ELBO=-999536.53, deltaELBO=111.486 (0.00091884%), Factors=14\n", + "Iteration 43: time=0.64, ELBO=-999428.30, deltaELBO=108.231 (0.00089201%), Factors=14\n", + "Iteration 44: time=0.66, ELBO=-999323.12, deltaELBO=105.178 (0.00086685%), Factors=14\n", + "Iteration 45: time=0.78, ELBO=-999220.82, deltaELBO=102.303 (0.00084315%), Factors=14\n", + "Iteration 46: time=0.95, ELBO=-999121.23, deltaELBO=99.586 (0.00082076%), Factors=14\n", + "Iteration 47: time=0.85, ELBO=-999024.22, deltaELBO=97.011 (0.00079954%), Factors=14\n", + "Iteration 48: time=0.77, ELBO=-998929.66, deltaELBO=94.567 (0.00077939%), Factors=14\n", + "Iteration 49: time=0.89, ELBO=-998837.42, deltaELBO=92.240 (0.00076022%), Factors=14\n", + "Iteration 50: time=0.90, ELBO=-998747.39, deltaELBO=90.023 (0.00074194%), Factors=14\n", + "Iteration 51: time=0.99, ELBO=-998659.49, deltaELBO=87.906 (0.00072450%), Factors=14\n", + "Iteration 52: time=0.69, ELBO=-998573.61, deltaELBO=85.882 (0.00070782%), Factors=14\n", + "Iteration 53: time=0.71, ELBO=-998489.66, deltaELBO=83.946 (0.00069186%), Factors=14\n", + "Iteration 54: time=0.57, ELBO=-998407.57, deltaELBO=82.090 (0.00067657%), Factors=14\n", + "Iteration 55: time=0.57, ELBO=-998327.26, deltaELBO=80.311 (0.00066190%), Factors=14\n", + "Iteration 56: time=0.59, ELBO=-998248.66, deltaELBO=78.603 (0.00064782%), Factors=14\n", + "Iteration 57: time=0.59, ELBO=-998171.69, deltaELBO=76.962 (0.00063430%), Factors=14\n", + "Iteration 58: time=0.58, ELBO=-998096.31, deltaELBO=75.384 (0.00062129%), Factors=14\n", + "Iteration 59: time=0.65, ELBO=-998022.45, deltaELBO=73.865 (0.00060878%), Factors=14\n", + "Iteration 60: time=0.86, ELBO=-997950.04, deltaELBO=72.403 (0.00059672%), Factors=14\n", + "Iteration 61: time=0.72, ELBO=-997879.05, deltaELBO=70.994 (0.00058511%), Factors=14\n", + "Iteration 62: time=0.65, ELBO=-997809.41, deltaELBO=69.635 (0.00057391%), Factors=14\n", + "Iteration 63: time=0.67, ELBO=-997741.09, deltaELBO=68.323 (0.00056310%), Factors=14\n", + "Iteration 64: time=0.63, ELBO=-997674.03, deltaELBO=67.057 (0.00055267%), Factors=14\n", + "Iteration 65: time=0.60, ELBO=-997608.20, deltaELBO=65.834 (0.00054259%), Factors=14\n", + "Iteration 66: time=0.60, ELBO=-997543.55, deltaELBO=64.652 (0.00053284%), Factors=14\n", + "Iteration 67: time=0.59, ELBO=-997480.04, deltaELBO=63.508 (0.00052342%), Factors=14\n", + "Iteration 68: time=0.64, ELBO=-997417.64, deltaELBO=62.402 (0.00051430%), Factors=14\n", + "Iteration 69: time=0.62, ELBO=-997356.31, deltaELBO=61.331 (0.00050547%), Factors=14\n", + "Iteration 70: time=0.61, ELBO=-997296.01, deltaELBO=60.293 (0.00049692%), Factors=14\n", + "Iteration 71: time=0.66, ELBO=-997236.73, deltaELBO=59.287 (0.00048863%), Factors=14\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -12772449.96 \n", + "\n", + "Iteration 1: time=0.63, ELBO=-1339249.43, deltaELBO=11433200.531 (89.51454548%), Factors=15\n", + "Iteration 2: time=0.62, ELBO=-1210283.89, deltaELBO=128965.539 (1.00971653%), Factors=15\n", + "Iteration 3: time=0.60, ELBO=-1153269.11, deltaELBO=57014.785 (0.44638879%), Factors=15\n", + "Iteration 4: time=0.62, ELBO=-1042558.73, deltaELBO=110710.374 (0.86679043%), Factors=15\n", + "Iteration 5: time=0.63, ELBO=-1023541.38, deltaELBO=19017.358 (0.14889358%), Factors=15\n", + "Iteration 6: time=0.65, ELBO=-1014396.72, deltaELBO=9144.653 (0.07159670%), Factors=15\n", + "Iteration 7: time=0.70, ELBO=-1008412.02, deltaELBO=5984.705 (0.04685636%), Factors=15\n", + "Iteration 8: time=0.64, ELBO=-1004161.65, deltaELBO=4250.363 (0.03327758%), Factors=15\n", + "Iteration 9: time=0.60, ELBO=-1001172.78, deltaELBO=2988.878 (0.02340098%), Factors=15\n", + "Iteration 10: time=0.66, ELBO=-999098.53, deltaELBO=2074.243 (0.01623998%), Factors=15\n", + "Iteration 11: time=0.67, ELBO=-997609.75, deltaELBO=1488.781 (0.01165619%), Factors=15\n", + "Iteration 12: time=0.63, ELBO=-996455.75, deltaELBO=1154.007 (0.00903512%), Factors=15\n", + "Iteration 13: time=0.67, ELBO=-995489.07, deltaELBO=966.672 (0.00756841%), Factors=15\n", + "Iteration 14: time=0.60, ELBO=-994633.20, deltaELBO=855.879 (0.00670097%), Factors=15\n", + "Iteration 15: time=0.62, ELBO=-993848.67, deltaELBO=784.525 (0.00614232%), Factors=15\n", + "Iteration 16: time=0.62, ELBO=-993114.09, deltaELBO=734.578 (0.00575127%), Factors=15\n", + "Iteration 17: time=0.60, ELBO=-992416.89, deltaELBO=697.201 (0.00545863%), Factors=15\n", + "Iteration 18: time=0.61, ELBO=-991748.91, deltaELBO=667.978 (0.00522983%), Factors=15\n", + "Iteration 19: time=0.66, ELBO=-991104.23, deltaELBO=644.687 (0.00504748%), Factors=15\n", + "Iteration 20: time=0.67, ELBO=-990478.03, deltaELBO=626.195 (0.00490270%), Factors=15\n", + "Iteration 21: time=0.57, ELBO=-989866.15, deltaELBO=611.877 (0.00479060%), Factors=15\n", + "Iteration 22: time=0.46, ELBO=-989264.83, deltaELBO=601.324 (0.00470797%), Factors=15\n", + "Iteration 23: time=0.48, ELBO=-988670.66, deltaELBO=594.171 (0.00465197%), Factors=15\n", + "Iteration 24: time=0.55, ELBO=-988080.67, deltaELBO=589.992 (0.00461925%), Factors=15\n", + "Iteration 25: time=0.87, ELBO=-987492.47, deltaELBO=588.202 (0.00460524%), Factors=15\n", + "Iteration 26: time=0.48, ELBO=-986904.50, deltaELBO=587.965 (0.00460339%), Factors=15\n", + "Iteration 27: time=0.45, ELBO=-986316.39, deltaELBO=588.109 (0.00460451%), Factors=15\n", + "Iteration 28: time=0.63, ELBO=-985729.32, deltaELBO=587.077 (0.00459643%), Factors=15\n", + "Iteration 29: time=0.50, ELBO=-985146.35, deltaELBO=582.969 (0.00456427%), Factors=15\n", + "Iteration 30: time=0.47, ELBO=-984572.62, deltaELBO=573.730 (0.00449193%), Factors=15\n", + "Iteration 31: time=0.45, ELBO=-984015.11, deltaELBO=557.503 (0.00436489%), Factors=15\n", + "Iteration 32: time=0.45, ELBO=-983482.00, deltaELBO=533.116 (0.00417395%), Factors=15\n", + "Iteration 33: time=0.45, ELBO=-982981.48, deltaELBO=500.523 (0.00391877%), Factors=15\n", + "Iteration 34: time=0.45, ELBO=-982520.47, deltaELBO=461.003 (0.00360936%), Factors=15\n", + "Iteration 35: time=0.52, ELBO=-982103.51, deltaELBO=416.963 (0.00326455%), Factors=15\n", + "Iteration 36: time=0.49, ELBO=-981732.13, deltaELBO=371.377 (0.00290764%), Factors=15\n", + "Iteration 37: time=0.47, ELBO=-981405.02, deltaELBO=327.110 (0.00256106%), Factors=15\n", + "Iteration 38: time=0.45, ELBO=-981118.64, deltaELBO=286.383 (0.00224219%), Factors=15\n", + "Iteration 39: time=0.45, ELBO=-980868.11, deltaELBO=250.527 (0.00196146%), Factors=15\n", + "Iteration 40: time=0.44, ELBO=-980648.08, deltaELBO=220.030 (0.00172269%), Factors=15\n", + "Iteration 41: time=0.45, ELBO=-980453.34, deltaELBO=194.741 (0.00152470%), Factors=15\n", + "Iteration 42: time=0.46, ELBO=-980279.22, deltaELBO=174.122 (0.00136326%), Factors=15\n", + "Iteration 43: time=0.45, ELBO=-980121.76, deltaELBO=157.460 (0.00123281%), Factors=15\n", + "Iteration 44: time=0.45, ELBO=-979977.74, deltaELBO=144.020 (0.00112758%), Factors=15\n", + "Iteration 45: time=0.47, ELBO=-979844.61, deltaELBO=133.132 (0.00104233%), Factors=15\n", + "Iteration 46: time=0.47, ELBO=-979720.38, deltaELBO=124.227 (0.00097261%), Factors=15\n", + "Iteration 47: time=0.45, ELBO=-979603.53, deltaELBO=116.847 (0.00091483%), Factors=15\n", + "Iteration 48: time=0.46, ELBO=-979492.90, deltaELBO=110.634 (0.00086619%), Factors=15\n", + "Iteration 49: time=0.45, ELBO=-979387.58, deltaELBO=105.318 (0.00082457%), Factors=15\n", + "Iteration 50: time=0.55, ELBO=-979286.89, deltaELBO=100.694 (0.00078837%), Factors=15\n", + "Iteration 51: time=0.47, ELBO=-979190.28, deltaELBO=96.612 (0.00075641%), Factors=15\n", + "Iteration 52: time=0.57, ELBO=-979097.32, deltaELBO=92.959 (0.00072781%), Factors=15\n", + "Iteration 53: time=0.55, ELBO=-979007.67, deltaELBO=89.652 (0.00070191%), Factors=15\n", + "Iteration 54: time=0.46, ELBO=-978921.04, deltaELBO=86.629 (0.00067825%), Factors=15\n", + "Iteration 55: time=0.45, ELBO=-978837.20, deltaELBO=83.842 (0.00065643%), Factors=15\n", + "Iteration 56: time=0.54, ELBO=-978755.94, deltaELBO=81.257 (0.00063619%), Factors=15\n", + "Iteration 57: time=0.86, ELBO=-978677.09, deltaELBO=78.845 (0.00061730%), Factors=15\n", + "Iteration 58: time=0.45, ELBO=-978600.51, deltaELBO=76.583 (0.00059960%), Factors=15\n", + "Iteration 59: time=0.43, ELBO=-978526.06, deltaELBO=74.455 (0.00058294%), Factors=15\n", + "Iteration 60: time=0.50, ELBO=-978453.61, deltaELBO=72.447 (0.00056721%), Factors=15\n", + "Iteration 61: time=0.48, ELBO=-978383.06, deltaELBO=70.545 (0.00055232%), Factors=15\n", + "Iteration 62: time=0.46, ELBO=-978314.32, deltaELBO=68.740 (0.00053819%), Factors=15\n", + "Iteration 63: time=0.46, ELBO=-978247.30, deltaELBO=67.025 (0.00052476%), Factors=15\n", + "Iteration 64: time=0.48, ELBO=-978181.91, deltaELBO=65.391 (0.00051197%), Factors=15\n", + "Iteration 65: time=0.49, ELBO=-978118.08, deltaELBO=63.832 (0.00049976%), Factors=15\n", + "Iteration 66: time=0.48, ELBO=-978055.73, deltaELBO=62.343 (0.00048810%), Factors=15\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "total_variance = np.zeros(17)\n", + "for k in range(2,17):\n", + " data_mat = [[None for g in range(1)] for m in range(4)]\n", + "\n", + " for m in range(4):\n", + " data_mat[m][0] = Xs_norm[m]\n", + "\n", + " ent = entry_point()\n", + " ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(4)])\n", + " ent.set_model_options(\n", + " factors = k, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + " )\n", + " ent.set_train_options(\n", + " convergence_mode = \"fast\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + " )\n", + " ent.build()\n", + " ent.run()\n", + "\n", + " total_variance[k] = np.sum(ent.model.calculate_variance_explained())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(2,17), total_variance[2:17], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples.bck/uci_digits_mvmds_screeplot.ipynb b/examples.bck/uci_digits_mvmds_screeplot.ipynb new file mode 100644 index 0000000..f655ccd --- /dev/null +++ b/examples.bck/uci_digits_mvmds_screeplot.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "mvmds = MVMDS(n_components=16)\n", + "Xs_mvmds_reduced = mvmds.fit_transform(Xs)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5044781.8612027 7944222.32409934 12568748.05447858 13366678.6347922\n", + " 14098489.67788737 14605600.54982345 14799708.65795875 14892973.51596771\n", + " 14954847.57334482 14987699.70407477 15019119.01472625 15044878.81515035\n", + " 15057565.31688737 15067358.69493423 15079793.61560008 15090013.68605973]\n" + ] + } + ], + "source": [ + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "\n", + "p = Xs_concat.shape[1]\n", + "variance_explained = np.zeros(16)\n", + "\n", + "for k in range(16):\n", + " variance = 0\n", + " for i in range(p):\n", + " variance += np.var(np.dot(Xs_concat[:,i], Xs_mvmds_reduced[:,k])*Xs_mvmds_reduced[:,k])\n", + "\n", + " if k==0:\n", + " variance_explained[k] = variance\n", + " else: \n", + " variance_explained[k] = variance_explained[k-1]+variance\n", + "\n", + "print(variance_explained)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(5,17), variance_explained[4:], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/abis_all.ipynb b/examples/abis_all.ipynb new file mode 100644 index 0000000..7f1a293 --- /dev/null +++ b/examples/abis_all.ipynb @@ -0,0 +1,1617 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "# from umap import umap_\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import tensorly as tl\n", + "from tensorly.decomposition import non_negative_parafac_hals\n", + "\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "from mofapy2.run.entry_point import entry_point\n", + "\n", + "RESULTS_PATH = r'C:\\Users\\paul_\\OneDrive\\Pro\\George\\Wise\\analysis\\results\\abis'\n", + "\n", + "list_solutions = None\n", + "predefined_solution = ''\n", + "\n", + "# ISM algorithmic options\n", + "embed = True\n", + "max_iter_integrate = 20\n", + "update_h4_ism = True\n", + "\n", + "# Grid search limits\n", + "min_embedding = 10\n", + "max_embedding = 25\n", + "min_themes = 16\n", + "max_themes = 16\n", + "\n", + "# list_solutions contains one ore more solutions selected because of their low condition numbers\n", + "list_solutions = [[16,16]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'\\abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'\\cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "\n", + "m0_nan_0 = m0.copy()\n", + "\n", + "# create m0_weight with ones and zeros if not_missing/missing value\n", + "m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])\n", + "\n", + "data_mat = [[None for g in range(1)] for m in range(4)]\n", + "\n", + "for m in range(4):\n", + " data_mat[m][0] = Xs_norm[m]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -554078.45 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80263.41, deltaELBO=473815.045 (85.51407166%), Factors=12\n", + "Iteration 2: time=0.02, ELBO=-72448.31, deltaELBO=7815.094 (1.41046700%), Factors=12\n", + "Iteration 3: time=0.02, ELBO=-67384.30, deltaELBO=5064.013 (0.91395231%), Factors=12\n", + "Iteration 4: time=0.05, ELBO=-63970.45, deltaELBO=3413.847 (0.61613056%), Factors=12\n", + "Iteration 5: time=0.02, ELBO=-62161.20, deltaELBO=1809.254 (0.32653396%), Factors=12\n", + "Iteration 6: time=0.02, ELBO=-61281.89, deltaELBO=879.315 (0.15869856%), Factors=12\n", + "Iteration 7: time=0.02, ELBO=-60660.21, deltaELBO=621.672 (0.11219925%), Factors=12\n", + "Iteration 8: time=0.02, ELBO=-60037.75, deltaELBO=622.469 (0.11234304%), Factors=12\n", + "Iteration 9: time=0.01, ELBO=-59598.16, deltaELBO=439.582 (0.07933568%), Factors=12\n", + "Iteration 10: time=0.03, ELBO=-59364.67, deltaELBO=233.496 (0.04214138%), Factors=12\n", + "Iteration 11: time=0.02, ELBO=-59204.15, deltaELBO=160.515 (0.02896971%), Factors=12\n", + "Iteration 12: time=0.02, ELBO=-59080.27, deltaELBO=123.882 (0.02235817%), Factors=12\n", + "Iteration 13: time=0.02, ELBO=-58999.55, deltaELBO=80.719 (0.01456811%), Factors=12\n", + "Iteration 14: time=0.03, ELBO=-58949.40, deltaELBO=50.155 (0.00905203%), Factors=12\n", + "Iteration 15: time=0.02, ELBO=-58913.41, deltaELBO=35.983 (0.00649417%), Factors=12\n", + "Iteration 16: time=0.02, ELBO=-58883.82, deltaELBO=29.589 (0.00534023%), Factors=12\n", + "Iteration 17: time=0.03, ELBO=-58857.76, deltaELBO=26.060 (0.00470338%), Factors=12\n", + "Iteration 18: time=0.02, ELBO=-58833.88, deltaELBO=23.887 (0.00431114%), Factors=12\n", + "Iteration 19: time=0.02, ELBO=-58811.25, deltaELBO=22.625 (0.00408333%), Factors=12\n", + "Iteration 20: time=0.02, ELBO=-58789.14, deltaELBO=22.108 (0.00399001%), Factors=12\n", + "Iteration 21: time=0.02, ELBO=-58766.88, deltaELBO=22.266 (0.00401863%), Factors=12\n", + "Iteration 22: time=0.02, ELBO=-58743.80, deltaELBO=23.080 (0.00416554%), Factors=12\n", + "Iteration 23: time=0.02, ELBO=-58719.23, deltaELBO=24.565 (0.00443343%), Factors=12\n", + "Iteration 24: time=0.02, ELBO=-58692.46, deltaELBO=26.771 (0.00483167%), Factors=12\n", + "Iteration 25: time=0.02, ELBO=-58662.65, deltaELBO=29.812 (0.00538040%), Factors=12\n", + "Iteration 26: time=0.02, ELBO=-58628.74, deltaELBO=33.911 (0.00612020%), Factors=12\n", + "Iteration 27: time=0.02, ELBO=-58589.24, deltaELBO=39.500 (0.00712890%), Factors=12\n", + "Iteration 28: time=0.02, ELBO=-58541.91, deltaELBO=47.333 (0.00854274%), Factors=12\n", + "Iteration 29: time=0.03, ELBO=-58483.39, deltaELBO=58.519 (0.01056151%), Factors=12\n", + "Iteration 30: time=0.02, ELBO=-58409.34, deltaELBO=74.048 (0.01336420%), Factors=12\n", + "Iteration 31: time=0.02, ELBO=-58316.53, deltaELBO=92.808 (0.01674999%), Factors=12\n", + "Iteration 32: time=0.02, ELBO=-58209.32, deltaELBO=107.211 (0.01934936%), Factors=12\n", + "Iteration 33: time=0.02, ELBO=-58107.17, deltaELBO=102.146 (0.01843532%), Factors=12\n", + "Iteration 34: time=0.03, ELBO=-58034.88, deltaELBO=72.291 (0.01304710%), Factors=12\n", + "Iteration 35: time=0.02, ELBO=-57997.19, deltaELBO=37.690 (0.00680233%), Factors=12\n", + "Iteration 36: time=0.02, ELBO=-57980.63, deltaELBO=16.558 (0.00298839%), Factors=12\n", + "Iteration 37: time=0.02, ELBO=-57972.92, deltaELBO=7.719 (0.00139310%), Factors=12\n", + "Iteration 38: time=0.03, ELBO=-57968.42, deltaELBO=4.497 (0.00081157%), Factors=12\n", + "Iteration 39: time=0.02, ELBO=-57965.26, deltaELBO=3.162 (0.00057070%), Factors=12\n", + "Iteration 40: time=0.02, ELBO=-57962.82, deltaELBO=2.440 (0.00044033%), Factors=12\n", + "Iteration 41: time=0.03, ELBO=-57960.85, deltaELBO=1.962 (0.00035412%), Factors=12\n", + "Iteration 42: time=0.02, ELBO=-57959.24, deltaELBO=1.615 (0.00029146%), Factors=12\n", + "Iteration 43: time=0.02, ELBO=-57957.89, deltaELBO=1.352 (0.00024403%), Factors=12\n", + "Iteration 44: time=0.02, ELBO=-57956.74, deltaELBO=1.149 (0.00020738%), Factors=12\n", + "Iteration 45: time=0.03, ELBO=-57955.75, deltaELBO=0.990 (0.00017864%), Factors=12\n", + "Iteration 46: time=0.02, ELBO=-57954.89, deltaELBO=0.863 (0.00015584%), Factors=12\n", + "Iteration 47: time=0.01, ELBO=-57954.12, deltaELBO=0.762 (0.00013755%), Factors=12\n", + "Iteration 48: time=0.02, ELBO=-57953.44, deltaELBO=0.680 (0.00012273%), Factors=12\n", + "Iteration 49: time=0.02, ELBO=-57952.83, deltaELBO=0.613 (0.00011059%), Factors=12\n", + "Iteration 50: time=0.02, ELBO=-57952.27, deltaELBO=0.557 (0.00010056%), Factors=12\n", + "Iteration 51: time=0.03, ELBO=-57951.76, deltaELBO=0.511 (0.00009218%), Factors=12\n", + "Iteration 52: time=0.02, ELBO=-57951.29, deltaELBO=0.472 (0.00008513%), Factors=12\n", + "Iteration 53: time=0.02, ELBO=-57950.85, deltaELBO=0.439 (0.00007915%), Factors=12\n", + "Iteration 54: time=0.02, ELBO=-57950.44, deltaELBO=0.410 (0.00007402%), Factors=12\n", + "Iteration 55: time=0.03, ELBO=-57950.06, deltaELBO=0.386 (0.00006960%), Factors=12\n", + "Iteration 56: time=0.02, ELBO=-57949.69, deltaELBO=0.364 (0.00006576%), Factors=12\n", + "Iteration 57: time=0.01, ELBO=-57949.35, deltaELBO=0.346 (0.00006241%), Factors=12\n", + "Iteration 58: time=0.05, ELBO=-57949.02, deltaELBO=0.329 (0.00005946%), Factors=12\n", + "Iteration 59: time=0.02, ELBO=-57948.70, deltaELBO=0.315 (0.00005685%), Factors=12\n", + "Iteration 60: time=0.03, ELBO=-57948.40, deltaELBO=0.302 (0.00005453%), Factors=12\n", + "Iteration 61: time=0.02, ELBO=-57948.11, deltaELBO=0.291 (0.00005245%), Factors=12\n", + "Iteration 62: time=0.02, ELBO=-57947.83, deltaELBO=0.280 (0.00005059%), Factors=12\n", + "Iteration 63: time=0.02, ELBO=-57947.56, deltaELBO=0.271 (0.00004891%), Factors=12\n", + "Iteration 64: time=0.03, ELBO=-57947.30, deltaELBO=0.263 (0.00004739%), Factors=12\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "ent = entry_point()\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(4)])\n", + "ent.set_model_options(\n", + " factors = 13, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + ")\n", + "ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + ")\n", + "ent.build()\n", + "ent.run()\n", + "factors_mofa = ent.model.nodes[\"Z\"].getExpectation()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/10: 49 iterations with final cost -62237.691583\n", + "Run 2/10: 74 iterations with final cost -62236.513594\n", + "Run 3/10: 48 iterations with final cost -62235.318998\n", + "Run 4/10: 51 iterations with final cost -62240.281221\n", + "Run 5/10: 49 iterations with final cost -62233.905370\n", + "Run 6/10: 48 iterations with final cost -62236.270922\n", + "Run 7/10: 52 iterations with final cost -62234.902403\n", + "Run 8/10: 59 iterations with final cost -62238.596242\n", + "Run 9/10: 48 iterations with final cost -62238.554169\n", + "Run 10/10: 64 iterations with final cost -62237.835234\n" + ] + } + ], + "source": [ + "model_gfa = gfa_experiments(Xs_norm, K=12, Nrep=10, rotate=False, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def format_loadings_merged(h4, list_solutions, list_columns):\n", + " # Format loadings\n", + " df_h4 = pd.DataFrame(data=h4)\n", + " list_themes = []\n", + " for i_solution in range(0, len(list_solutions)):\n", + " list_themes = list_themes + ['theme_' + str(i) + '_' + str(list_solutions[i_solution][0]) + '_' + str(list_solutions[i_solution][1]) for i in range(1, list_solutions[i_solution][1] + 1)]\n", + " \n", + " df_h4.columns = list_themes\n", + " df_h4.insert(loc=0, column='label', value=(list_columns))\n", + "\n", + " # Add description index\n", + " df_h4['description'] = df_h4['label']\n", + " \n", + " return df_h4\n", + "\n", + "def format_loadings(h4, list_columns):\n", + " # Format loadings\n", + " df_h4 = pd.DataFrame(data=h4)\n", + " n_comp = len(df_h4.columns)\n", + " df_h4.columns = ['theme_' + str(i) for i in range(1, n_comp + 1)]\n", + " df_h4.insert(loc=0, column='label', value=(list_columns))\n", + "\n", + " # Add description index\n", + " df_h4['description'] = df_h4['label']\n", + " \n", + " return df_h4\n", + "\n", + "def generate_h4_sparse(h4, q4_ism, n_items, n_comp, n_scores):\n", + " # Calculate hhii of each h column and generate sparse loadings\n", + " hhii = np.zeros(n_comp, dtype=int)\n", + " h_threshold = np.zeros(n_comp)\n", + "\n", + " if q4_ism is not None:\n", + " i1 = 0\n", + " for i_score in range(0,n_scores):\n", + " i2 = i1+n_items[i_score]\n", + " h4[i1:i2,:] *= q4_ism[i_score]\n", + " i1 = i2\n", + "\n", + " for i in range(0,n_comp):\n", + " # calculate inverse hhi\n", + " if np.max(h4[:,i]) > 0:\n", + " hhii[i] = int(round(np.sum(h4[:, i])**2 / np.sum(h4[:, i]**2)))\n", + " # hhii[i] = np.count_nonzero(h4[:, i])\n", + " \n", + " # sort the dataframe by score in descending order\n", + " h_threshold[i] = np.sort(h4[:, i], axis=0)[::-1][hhii[i]-1] * .8\n", + "\n", + " h4_sparse = np.where(h4 < h_threshold[None,:], 0, h4)\n", + " \n", + " return h4_sparse, hhii\n", + "\n", + "def integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes):\n", + " EPSILON = np.finfo(np.float32).eps\n", + "\n", + " # Generate w for each score, based on sparse loadings and create tensor_score\n", + "\n", + " # Extract score-related items\n", + " i1 = 0\n", + " for i_score in range(n_scores):\n", + " i2 = i1+n_items[i_score]\n", + " w4_score = w4_ism.copy()\n", + " h4_score = h4_sparse[i1:i2, :].copy()\n", + " m0_score = m0_nan_0[:, i1:i2]\n", + " m0_weight_score = m0_weight[:, i1:i2]\n", + " i1=i2\n", + " # # Normalize w4_score by max column and update h4_score\n", + " # max_values = np.max(w4_score, axis=0)\n", + " # # Replace maximum values equal to 0 with 1\n", + " # w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " # h4_score = np.multiply(h4_score, max_values)\n", + " # h4_score0 = h4_score.copy()\n", + "\n", + " # Apply multiplicative updates to preserve h sparsity \n", + " for _ in range(0, 200):\n", + " # Weighted multiplicative rules\n", + " m0_score_est = w4_score @ h4_score.T\n", + " h4_score *= ((w4_score.T @ m0_score) / (w4_score.T @ (m0_score_est*m0_weight_score) + EPSILON)).T\n", + " w4_score *= (m0_score @ h4_score / ((m0_weight_score*m0_score_est) @ h4_score + EPSILON))\n", + " # if i % 10 == 0:\n", + " # # Normalize w4_score by max column and update h4_score\n", + " # max_values = np.max(w4_score, axis=0)\n", + " # # Replace maximum values equal to 0 with 1\n", + " # w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " # h4_score = np.multiply(h4_score, max_values)\n", + " # if np.linalg.norm(h4_score-h4_score0)/max(np.linalg.norm(h4_score0),EPSILON) < 1.e-10:\n", + " # print(i)\n", + " # break\n", + " # else:\n", + " # h4_score0 = h4_score.copy()\n", + "\n", + " # Normalize w4_score by max column and update h4_score\n", + " max_values = np.max(w4_score, axis=0)\n", + " # Replace maximum values equal to 0 with 1\n", + " w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " h4_score = np.multiply(h4_score, max_values)\n", + "\n", + " # Generate embedding tensor and initialize h4_updated\n", + " if i_score == 0:\n", + " tensor_score = w4_score\n", + " h4_updated = h4_score\n", + " else:\n", + " tensor_score = np.hstack((tensor_score, w4_score))\n", + " h4_updated = np.vstack((h4_updated, h4_score))\n", + "\n", + " # Apply NTF with prescribed number of themes and update themes\n", + " my_ntfmodel = NTF(n_components=n_themes, leverage=None, init_type=2, max_iter=200, tol=1e-6, verbose=-1, random_state=0)\n", + "\n", + " if q4_ism is None:\n", + " estimator_ = my_ntfmodel.fit_transform(tensor_score, n_blocks=n_scores)\n", + " # hals_decomposition = non_negative_parafac_hals(tensor_score.reshape((tensor_score.shape[0], int(tensor_score.shape[1]/n_scores), n_scores)), rank=n_themes, init='svd')\n", + " else:\n", + " estimator_ = my_ntfmodel.fit_transform(tensor_score, w=w4_ism, h=h4_ism, q=q4_ism, update_h=update_h4_ism, n_blocks=n_scores)\n", + " # hals_decomposition = non_negative_parafac_hals(tensor_score.reshape((tensor_score.shape[0], int(tensor_score.shape[1]/n_scores), n_scores)), rank=n_themes, init='svd', fixed_modes=[1])\n", + "\n", + " w4_ism = estimator_.w\n", + " h4_ism = estimator_.h\n", + " q4_ism = estimator_.q\n", + " # w4_ism = hals_decomposition[1][0]\n", + " # h4_ism = hals_decomposition[1][1]\n", + " # q4_ism = hals_decomposition[1][2]\n", + "\n", + " # Update loadings based on h4_updated (initialized by multiplicative updates)\n", + " h4_updated = h4_updated @ h4_ism\n", + " h4_updated_sparse, hhii_updated = generate_h4_sparse(h4_updated, q4_ism, n_items, n_themes, n_scores)\n", + "\n", + " return h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "condition number(16, 16) = 3.43\n", + "condition number (primary NMF): 1.3\n" + ] + } + ], + "source": [ + "if predefined_solution != '':\n", + " max_iter_integrate = 0\n", + " # Read pre-defined themes\n", + " df_h4_updated = pd.read_csv(DATA_PATH + predefined_solution)\n", + " h4_updated = df_h4_updated.values.astype(np.float_)\n", + " h4_updated_sparse = h4_updated.copy()\n", + " list_solutions = [[h4_updated.shape[1],h4_updated.shape[1]]]\n", + "\n", + "if list_solutions is not None:\n", + " perform_grid_search = False\n", + "else:\n", + " perform_grid_search = True\n", + "\n", + "if perform_grid_search:\n", + " # Perform grid search first to select solutions with low condition numbers\n", + " cond = np.ones((max_embedding+1, max_themes+1))*999\n", + " list_solutions = []\n", + " for n_embedding in range(min_embedding, max_embedding+1):\n", + " for n_themes in range(min_themes, max_themes+1):\n", + " list_solutions += [[n_embedding, n_themes]]\n", + "else:\n", + " h4_updated_merged = None\n", + "\n", + "for n_embedding, n_themes in list_solutions:\n", + " if predefined_solution == '':\n", + " # Initial Embedding\n", + " my_nmfmodel = NMF(n_components=n_embedding, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=0)\n", + " estimator_ = my_nmfmodel.fit_transform(m0.copy())\n", + " \n", + " w4 = estimator_.w\n", + " h4 = estimator_.h\n", + "\n", + " # my_nmfmodel = NMF(n_components=n_embedding, init='nndsvd', solver='cd', beta_loss='frobenius', max_iter=1000, tol=1.e-6, random_state=0)\n", + " # w4 = my_nmfmodel.fit_transform(m0)\n", + " # h4 = my_nmfmodel.components_.T \n", + "\n", + " # hals_decomposition = non_negative_parafac_hals(m0.reshape((m0.shape[0], m0.shape[1], 1)), rank=n_embedding, init='svd', n_iter_max=200)\n", + " # w4 = hals_decomposition[1][0]\n", + " # h4 = hals_decomposition[1][1]\n", + " \n", + " h4_sparse, hhii = generate_h4_sparse(h4, None, n_items, n_embedding, n_scores)\n", + "\n", + " my_ntfmodel = NTF(n_components=n_themes, leverage=None, init_type=2, max_iter=200, tol=1e-6, verbose=-1, random_state=0)\n", + " estimator_ = my_ntfmodel.fit_transform(m0.copy(), n_blocks=n_scores)\n", + " w4_ntf = estimator_.w\n", + " h4_ntf = estimator_.h\n", + " \n", + "\n", + " # hals_decomposition = non_negative_parafac_hals(m0.reshape((m0.shape[0], int(m0.shape[1]/n_scores), n_scores)), rank=n_themes, init='svd')\n", + " # w4_ntf = hals_decomposition[1][0]\n", + " # h4_ntf = hals_decomposition[1][1]\n", + "\n", + " if embed:\n", + " # Embed using scores w4 found in preliminary NMF and initialize themes through NTF \n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4, None, None, n_scores, n_items, n_themes)\n", + "\n", + " else:\n", + " h4_updated = h4\n", + " h4_updated_sparse = h4_sparse\n", + " hhii_updated = hhii\n", + " w4_ism = w4\n", + " h4_ism = np.identity(n_themes)\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + "\n", + " else: \n", + " w4_ism = np.ones((m0.shape[0], n_themes))\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + " w4 = w4_ism\n", + " h4 = h4_updated.copy()\n", + " h4_sparse = h4\n", + " n_themes = list_solutions[0][1]\n", + " h4_updated_merged = None\n", + " if embed:\n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes)\n", + " else:\n", + " h4_updated = h4\n", + " h4_updated_sparse = h4_sparse\n", + " hhii_updated = hhii\n", + " w4_ism = w4\n", + " h4_ism = np.identity(n_themes)\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + "\n", + " if embed:\n", + " # Iterate embedding with themes subtensor until sparsity becomes stable \n", + " flag = 0\n", + " for iter_integrate in range(0, max_iter_integrate):\n", + " # print(iter_integrate, hhii_updated)\n", + " # indices = np.nonzero(q4_ism[:, 0])[0]\n", + " # non_zero_elements = q4_ism[indices, 0]\n", + " # print(iter_integrate, np.column_stack((indices, non_zero_elements))) \n", + " hhii_updated_0 = hhii_updated.copy()\n", + "\n", + " if iter_integrate == 0: \n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, np.identity(n_themes), q4_ism, n_scores, n_items, n_themes)\n", + " else:\n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes)\n", + " \n", + " if (hhii_updated == hhii_updated_0).all():\n", + " flag+=1\n", + " else:\n", + " flag=0\n", + " \n", + " if flag==3:\n", + " break\n", + " \n", + " if perform_grid_search:\n", + " cond[n_embedding, n_themes] = np.linalg.cond(h4_updated)\n", + " # cond[n_embedding, n_themes] = np.linalg.cond(normalize(h4_updated, axis=0, norm='l2'))\n", + " elif len(list_solutions) > 1:\n", + " # Construct merged solutions\n", + " if h4_updated_merged is None:\n", + " h4_updated_merged = h4_updated\n", + " else:\n", + " h4_updated_merged = np.hstack((h4_updated_merged, h4_updated))\n", + " \n", + " print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated), 2)) \n", + "\n", + "if perform_grid_search:\n", + " row, col = np.unravel_index(np.argmin(cond), cond.shape)\n", + " print('minimum condition number achieved for '+ str(row) + ' embeddings and ' + str(col) + ' themes')\n", + "\n", + "if len(list_solutions) == 1:\n", + " # print the condition number achieved by NMF alone\n", + " print('condition number (primary NMF): ', np.round(np.linalg.cond(h4_sparse),2))\n", + " # print(h4_ism)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[760.5816119426518, 787.6095590397154, 1008.5321028509333, 704.4503744654254, 66697.78954415832, 1123.1274864519983]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "n_comp_pca_mvmds = 10\n", + "\n", + "\n", + "# MVMDS reduction\n", + "mvmds = MVMDS(n_components=n_comp_pca_mvmds)\n", + "Xs_mvmds_reduced = mvmds.fit_transform(Xs)\n", + "\n", + "# PCA reduction concatenated views \n", + "pca = PCA(n_components=n_comp_pca_mvmds)\n", + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "Xs_pca_reduced = pca.fit_transform(Xs_concat)\n", + "\n", + "# NMF reduction concatenated views \n", + "\n", + "my_nmfmodel = NMF(n_components=n_themes, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=0)\n", + "estimator_ = my_nmfmodel.fit_transform(m0.copy())\n", + "\n", + "w4_nmf = estimator_.w\n", + "h4_nmf = estimator_.h\n", + "\n", + "# my_nmfmodel = NMF(n_components=n_themes, init='nndsvd', solver='cd', beta_loss='frobenius', max_iter=1000, tol=1.e-6, random_state=0)\n", + "# w4_nmf = my_nmfmodel.fit_transform(m0)\n", + "# h4_nmf = my_nmfmodel.components_.T \n", + "\n", + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1, spread=1, n_neighbors=15, init='pca')\n", + "# mds = umap_.UMAP(n_components=2, init='random', random_state=0)\n", + "\n", + "n_marker_genes = 915\n", + "\n", + "stress = []\n", + "\n", + "w4_gfa = model_gfa['Z']\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "w4_mofa = factors_mofa\n", + "w4_mofa_mds = mds.fit_transform(normalize(w4_mofa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "w4_ism_mds = mds.fit_transform(w4_ism[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "Xs_mvmds_reduced_mds = mds.fit_transform(Xs_mvmds_reduced[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "w4_nmf_mds = mds.fit_transform(w4_nmf[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "w4_ntf_mds = mds.fit_transform(w4_ntf[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "# Xs_pca_reduced_mds = mds.fit_transform(Xs_pca_reduced[:n_marker_genes,:])\n", + "# stress.append(mds.stress_)\n", + "\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "w4_gfa = model_gfa['Z']\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14 11.46\n", + "0.9958\n", + "12 11.19\n", + "0.9927\n", + "13 8.78\n", + "0.9878\n", + "11 8.39\n", + "0.9875\n", + "13 12.13\n", + "0.986\n", + "14 12.83\n", + "0.9967\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(16)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(16):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + " \n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(cell_label, xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', n_std=2, **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " \n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " if l==14 and n_std==.8:\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=n_std, edgecolor='black')\n", + " else:\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4)) # Output: 1.0\n", + "\n", + "fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=16\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes)\n", + "plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (16-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (16-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_ntf_mds[:, 0], w4_ntf_mds[:, 1], title=\"NTF Reduced Data (16-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_mofa_mds[:, 0], w4_mofa_mds[:, 1], title=\"MOFA+ Reduced Data (16-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[], n_std=.8)\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"GFA Reduced Data (16-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/abis_biomed.ipynb b/examples/abis_biomed.ipynb index f828990..4497dc4 100755 --- a/examples/abis_biomed.ipynb +++ b/examples/abis_biomed.ipynb @@ -2,53 +2,56 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "!pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "# !pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", "# scipy==1.12.0 not used (due to changes in SVDS) to reproduce presented results in ref paper" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "from adnmtf import NMF, NTF\n", - "# from sklearn.decomposition import NMF\n", + "# !pip install -e .." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coucou\n" + ] + } + ], + "source": [ "import pandas as pd\n", "import numpy as np\n", - "from IPython.display import clear_output\n", - "import time\n", - "from wordcloud import WordCloud, ImageColorGenerator\n", + "\n", "import matplotlib.pyplot as plt\n", - "import matplotlib.image as image\n", "from matplotlib.colors import ListedColormap\n", "import matplotlib.patches as mpatches\n", "import distinctipy\n", "from matplotlib.patches import Ellipse\n", "import matplotlib.transforms as transforms\n", "\n", - "import sys\n", - "import networkx as nx\n", "from sklearn.preprocessing import normalize\n", - "from sklearn import metrics\n", - "\n", "from mvlearn.datasets import load_UCImultifeature\n", - "from mvlearn.embed import MVMDS\n", - "import seaborn as sns\n", - "from sklearn.decomposition import PCA\n", + "\n", "from sklearn.manifold import MDS\n", "from sklearn.cluster import KMeans\n", - "import umap\n", "from scipy.spatial import distance_matrix\n", - "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", "\n", "import adilsm.adilsm as ilsm\n", - "import os\n", - "\n", "\n", "RESULTS_PATH = './'" ] @@ -62,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -88,12 +91,6 @@ "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", "\n", "m0 = df_norm.values.astype(np.float_)\n", - "# m0_nan_0 = m0.copy()\n", - "\n", - "# # create m0_weight with ones and zeros if not_missing/missing value\n", - "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", - "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", - "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", "\n", "list_columns = df.columns[1:].to_list()\n", "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", @@ -118,26 +115,38 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "condition number(16, 16) = 3.43\n", - "error: 0.35\n" + "error ism before straightening: 0.33\n", + "error ism after straightening: 0.35\n", + "condition number(16, 16) = 3.43\n" ] } ], "source": [ "n_embedding, n_themes = [16,16]\n", "\n", - "h4_updated, h4_updated_sparse, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0 = False, update_h4_ism=True,\n", - " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", - "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated_sparse), 2))\n", - "error = np.linalg.norm(m0_norm - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0_norm)\n", - "print('error: ',round(error, 2))" + "ilsm_result = ilsm.ism(Xs, n_embedding, n_themes, norm_columns=False, update_h4_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "hv = ilsm_result['HV']\n", + "hv_sparse = ilsm_result['HV_SPARSE']\n", + "hhii_updated = ilsm_result['HHII']\n", + "w4_ism = ilsm_result['W']\n", + "h4_ism = ilsm_result['H']\n", + "q4_ism = ilsm_result['Q']\n", + "Xs_emb = ilsm_result['EMBEDDING']\n", + "Xs_norm = ilsm_result['NORMED_VIEWS']\n", + "\n", + "h4_updated_sparse = hv[0].copy()\n", + "for h in hv_sparse[1:]:\n", + " h4_updated_sparse = np.vstack((h4_updated_sparse, h))\n", + "\n", + "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated_sparse), 2))\n" ] }, { @@ -149,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -179,14 +188,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "14 11.46\n" + "14 11.46\n", + "0.9958\n" ] }, { @@ -195,13 +205,13 @@ "" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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" ] @@ -333,6 +343,7 @@ " xy_mean[l,1] = 0\n", " \n", " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4)) \n", "\n", "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", @@ -340,13 +351,6 @@ "face_color = 'lavender'\n", "k=16\n", "\n", - "# plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes)\n", - "# plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (16-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", - "# plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (16-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[1])\n", - "# plot_scatter(w4_ntf_mds[:, 0], w4_ntf_mds[:, 1], title=\"NTF Reduced Data (16-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[4])\n", - "# plot_scatter(Xs_pca_reduced_mds[:, 0], Xs_pca_reduced_mds[:, 1], title=\"PCA Reduced Data (16-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", - "# plot_scatter(m0_mds[:, 0], m0_mds[:, 1], title=\"Original Data (16-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[10])\n", - "\n", "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes)\n", "plt.show\n", "\n", @@ -357,13 +361,6 @@ "ax2.set_facecolor(face_color)\n", "plt.show" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/abis_gfa screeplot.ipynb b/examples/abis_gfa screeplot.ipynb new file mode 100644 index 0000000..b2e02c1 --- /dev/null +++ b/examples/abis_gfa screeplot.ipynb @@ -0,0 +1,1081 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/10: 25 iterations with 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cost -60021.103695\n", + "Run 6/10: 54 iterations with final cost -60019.508566\n", + "Run 7/10: 53 iterations with final cost -60018.533511\n", + "Run 8/10: 53 iterations with final cost -60021.146553\n", + "Run 9/10: 53 iterations with final cost -60023.868182\n", + "Run 10/10: 53 iterations with final cost -60021.737245\n", + "Run 1/10: 60 iterations with final cost -58052.156293\n", + "Run 2/10: 59 iterations with final cost -58050.492267\n", + "Run 3/10: 59 iterations with final cost -58056.787989\n", + "Run 4/10: 59 iterations with final cost -58049.311828\n", + "Run 5/10: 60 iterations with final cost -58052.339000\n", + "Run 6/10: 59 iterations with final cost -58053.213309\n", + "Run 7/10: 60 iterations with final cost -58053.059667\n", + "Run 8/10: 60 iterations with final cost -58053.841687\n", + "Run 9/10: 59 iterations with final cost -58051.578004\n", + "Run 10/10: 59 iterations with final cost -58048.055497\n", + "Run 1/10: 67 iterations with final cost -56187.170937\n", + "Run 2/10: 66 iterations with final cost -56192.270394\n", + "Run 3/10: 66 iterations with final cost -56185.656986\n", + "Run 4/10: 67 iterations with final cost -56187.350352\n", + "Run 5/10: 67 iterations with final cost -56181.629959\n", + "Run 6/10: 67 iterations with final cost -56184.546052\n", + "Run 7/10: 67 iterations with final cost -56184.650092\n", + "Run 8/10: 67 iterations with final cost -56183.219397\n", + "Run 9/10: 67 iterations with final cost -56185.738590\n", + "Run 10/10: 67 iterations with final cost -56186.226664\n", + "Run 1/10: 62 iterations with final cost -55886.583504\n", + "Run 2/10: 67 iterations with final cost -55867.680831\n", + "Run 3/10: 69 iterations with final cost -55855.370746\n", + "Run 4/10: 71 iterations with final cost -55845.366242\n", + "Run 5/10: 70 iterations with final cost -55849.413502\n", + "Run 6/10: 70 iterations with final cost -55845.566195\n", + "Run 7/10: 69 iterations with final cost -55852.152346\n", + "Run 8/10: 70 iterations with final cost -55851.374305\n", + "Run 9/10: 70 iterations with final cost -55849.519401\n", + "Run 10/10: 70 iterations with final cost -55852.645281\n", + "2 0.22694591404422354\n", + "3 0.3447063278586807\n", + "4 0.43491089381450376\n", + "5 0.5234815263248173\n", + "6 0.6080527439185863\n", + "7 0.6774380319837394\n", + "8 0.7241360875226066\n", + "9 0.7707051314744143\n", + "10 0.832421378820104\n", + "11 0.856068009678558\n", + "12 0.8696563070710126\n", + "13 0.8502564109925649\n", + "14 0.8439898525994537\n", + "15 0.8348636396739824\n", + "16 1.0102458494221684\n" + ] + } + ], + "source": [ + "gfa_cov = np.zeros(17)\n", + "for k in range(2,17):\n", + " model = gfa_experiments(Xs_norm, K=k, Nrep=10, rotate=False, verbose=1)\n", + " gfa_cov[k] = np.trace(model['covZ'])\n", + "\n", + "for k in range(2,17):\n", + " print(k, gfa_cov[k])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca = PCA(n_components=16)\n", + "\n", + "# Concatenate views then PCA for comparison\n", + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "Xs_pca_reduced = pca.fit_transform(Xs_concat)\n", + "\n", + "\n", + "# Plot the scree plot\n", + "plt.plot (np.arange (1, pca.n_components_ + 1), pca.explained_variance_ratio_, 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(2,17), gfa_cov[2:17], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/abis_gfa.ipynb b/examples/abis_gfa.ipynb new file mode 100644 index 0000000..5c46f8e --- /dev/null +++ b/examples/abis_gfa.ipynb @@ -0,0 +1,1156 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/100: 52 iterations with final cost -62234.366388\n", + "Run 2/100: 48 iterations with final cost -62238.057050\n", + "Run 3/100: 85 iterations with final cost -62236.052869\n", + "Run 4/100: 48 iterations with final cost -62236.163082\n", + "Run 5/100: 48 iterations with final cost -62237.015366\n", + "Run 6/100: 84 iterations with final cost -62238.479998\n", + "Run 7/100: 52 iterations with final cost -62238.542473\n", + "Run 8/100: 63 iterations with final cost -62241.274993\n", + "Run 9/100: 70 iterations with final cost -62239.231260\n", + "Run 10/100: 47 iterations with final cost -62239.043010\n", + "Run 11/100: 50 iterations with final cost -62234.862528\n", + "Run 12/100: 49 iterations with final cost -62237.187186\n", + "Run 13/100: 50 iterations with final cost -62237.780570\n", + "Run 14/100: 54 iterations with final cost -62237.436951\n", + "Run 15/100: 48 iterations with final cost -62237.784498\n", + "Run 16/100: 52 iterations with final cost -62234.562732\n", + "Run 17/100: 48 iterations with 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final cost -62340.284289\n", + "Run 97/100: 81 iterations with final cost -62239.102161\n", + "Run 98/100: 47 iterations with final cost -62238.662903\n", + "Run 99/100: 48 iterations with final cost -62233.406714\n", + "Run 100/100: 89 iterations with final cost -62238.114494\n" + ] + } + ], + "source": [ + "model = gfa_experiments(Xs_norm, K=12, Nrep=100, rotate=False, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[764.641798261622]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "mds = MDS(n_components=2, random_state=0)\n", + "n_marker_genes = 915\n", + "\n", + "stress = []\n", + "w4_gfa = model['Z']\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "\n", + "stress.append(mds.stress_)\n", + "\n", + "# m0_mds = mds.fit_transform(normalize(m0[:n_marker_genes,:]))\n", + "# stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14 12.31\n", + "0.9952\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(16)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(16):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + " \n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(cell_label, xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " \n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4)) \n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=16\n", + "\n", + "# plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes)\n", + "# plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (16-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (16-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[1])\n", + "# plot_scatter(w4_ntf_mds[:, 0], w4_ntf_mds[:, 1], title=\"NTF Reduced Data (16-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[4])\n", + "# plot_scatter(Xs_pca_reduced_mds[:, 0], Xs_pca_reduced_mds[:, 1], title=\"PCA Reduced Data (16-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(m0_mds[:, 0], m0_mds[:, 1], title=\"Original Data (16-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[10])\n", + "\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes)\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/abis_mofa.ipynb b/examples/abis_mofa.ipynb new file mode 100644 index 0000000..0d48f26 --- /dev/null +++ b/examples/abis_mofa.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "from mofapy2.run.entry_point import entry_point\n", + "from scipy.stats import trim_mean\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])\n", + "\n", + "data_mat = [[None for g in range(1)] for m in range(4)]\n", + "\n", + "for m in range(4):\n", + " data_mat[m][0] = Xs_norm[m]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -554078.45 \n", + "\n", + "Iteration 1: time=0.04, ELBO=-80263.41, deltaELBO=473815.045 (85.51407166%), Factors=12\n", + "Iteration 2: time=0.03, ELBO=-72448.31, deltaELBO=7815.094 (1.41046700%), Factors=12\n", + "Iteration 3: time=0.05, ELBO=-67384.30, deltaELBO=5064.013 (0.91395231%), Factors=12\n", + "Iteration 4: time=0.05, ELBO=-63970.45, deltaELBO=3413.847 (0.61613056%), Factors=12\n", + "Iteration 5: time=0.05, ELBO=-62161.20, deltaELBO=1809.254 (0.32653396%), Factors=12\n", + "Iteration 6: time=0.05, ELBO=-61281.89, deltaELBO=879.315 (0.15869856%), Factors=12\n", + "Iteration 7: time=0.03, ELBO=-60660.21, deltaELBO=621.672 (0.11219925%), Factors=12\n", + "Iteration 8: time=0.05, ELBO=-60037.75, deltaELBO=622.469 (0.11234304%), Factors=12\n", + "Iteration 9: time=0.03, ELBO=-59598.16, deltaELBO=439.582 (0.07933568%), Factors=12\n", + "Iteration 10: time=0.03, ELBO=-59364.67, deltaELBO=233.496 (0.04214138%), Factors=12\n", + "Iteration 11: time=0.03, ELBO=-59204.15, deltaELBO=160.515 (0.02896971%), Factors=12\n", + "Iteration 12: time=0.04, ELBO=-59080.27, deltaELBO=123.882 (0.02235817%), Factors=12\n", + "Iteration 13: time=0.04, ELBO=-58999.55, deltaELBO=80.719 (0.01456811%), Factors=12\n", + "Iteration 14: time=0.03, ELBO=-58949.40, deltaELBO=50.155 (0.00905203%), Factors=12\n", + "Iteration 15: time=0.04, ELBO=-58913.41, deltaELBO=35.983 (0.00649417%), Factors=12\n", + "Iteration 16: time=0.04, ELBO=-58883.82, deltaELBO=29.589 (0.00534023%), Factors=12\n", + "Iteration 17: time=0.04, ELBO=-58857.76, deltaELBO=26.060 (0.00470338%), Factors=12\n", + "Iteration 18: time=0.03, ELBO=-58833.88, deltaELBO=23.887 (0.00431114%), Factors=12\n", + "Iteration 19: time=0.07, ELBO=-58811.25, deltaELBO=22.625 (0.00408333%), Factors=12\n", + "Iteration 20: time=0.06, ELBO=-58789.14, deltaELBO=22.108 (0.00399001%), Factors=12\n", + "Iteration 21: time=0.05, ELBO=-58766.88, deltaELBO=22.266 (0.00401863%), Factors=12\n", + "Iteration 22: time=0.04, ELBO=-58743.80, deltaELBO=23.080 (0.00416554%), Factors=12\n", + "Iteration 23: time=0.04, ELBO=-58719.23, deltaELBO=24.565 (0.00443343%), Factors=12\n", + "Iteration 24: time=0.04, ELBO=-58692.46, deltaELBO=26.771 (0.00483167%), Factors=12\n", + "Iteration 25: time=0.04, ELBO=-58662.65, deltaELBO=29.812 (0.00538040%), Factors=12\n", + "Iteration 26: time=0.04, ELBO=-58628.74, deltaELBO=33.911 (0.00612020%), Factors=12\n", + "Iteration 27: time=0.04, ELBO=-58589.24, deltaELBO=39.500 (0.00712890%), Factors=12\n", + "Iteration 28: time=0.03, ELBO=-58541.91, deltaELBO=47.333 (0.00854274%), Factors=12\n", + "Iteration 29: time=0.04, ELBO=-58483.39, deltaELBO=58.519 (0.01056151%), Factors=12\n", + "Iteration 30: time=0.04, ELBO=-58409.34, deltaELBO=74.048 (0.01336420%), Factors=12\n", + "Iteration 31: time=0.04, ELBO=-58316.53, deltaELBO=92.808 (0.01674999%), Factors=12\n", + "Iteration 32: time=0.04, ELBO=-58209.32, deltaELBO=107.211 (0.01934936%), Factors=12\n", + "Iteration 33: time=0.03, ELBO=-58107.17, deltaELBO=102.146 (0.01843532%), Factors=12\n", + "Iteration 34: time=0.10, ELBO=-58034.88, deltaELBO=72.291 (0.01304710%), Factors=12\n", + "Iteration 35: time=0.04, ELBO=-57997.19, deltaELBO=37.690 (0.00680233%), Factors=12\n", + "Iteration 36: time=0.03, ELBO=-57980.63, deltaELBO=16.558 (0.00298839%), Factors=12\n", + "Iteration 37: time=0.03, ELBO=-57972.92, deltaELBO=7.719 (0.00139310%), Factors=12\n", + "Iteration 38: time=0.03, ELBO=-57968.42, deltaELBO=4.497 (0.00081157%), Factors=12\n", + "Iteration 39: time=0.04, ELBO=-57965.26, deltaELBO=3.162 (0.00057070%), Factors=12\n", + "Iteration 40: time=0.04, ELBO=-57962.82, deltaELBO=2.440 (0.00044033%), Factors=12\n", + "Iteration 41: time=0.04, ELBO=-57960.85, deltaELBO=1.962 (0.00035412%), Factors=12\n", + "Iteration 42: time=0.04, ELBO=-57959.24, deltaELBO=1.615 (0.00029146%), Factors=12\n", + "Iteration 43: time=0.04, ELBO=-57957.89, deltaELBO=1.352 (0.00024403%), Factors=12\n", + "Iteration 44: time=0.04, ELBO=-57956.74, deltaELBO=1.149 (0.00020738%), Factors=12\n", + "Iteration 45: time=0.03, ELBO=-57955.75, deltaELBO=0.990 (0.00017864%), Factors=12\n", + "Iteration 46: time=0.04, ELBO=-57954.89, deltaELBO=0.863 (0.00015584%), Factors=12\n", + "Iteration 47: time=0.04, ELBO=-57954.12, deltaELBO=0.762 (0.00013755%), Factors=12\n", + "Iteration 48: time=0.03, ELBO=-57953.44, deltaELBO=0.680 (0.00012273%), Factors=12\n", + "Iteration 49: time=0.03, ELBO=-57952.83, deltaELBO=0.613 (0.00011059%), Factors=12\n", + "Iteration 50: time=0.03, ELBO=-57952.27, deltaELBO=0.557 (0.00010056%), Factors=12\n", + "Iteration 51: time=0.04, ELBO=-57951.76, deltaELBO=0.511 (0.00009218%), Factors=12\n", + "Iteration 52: time=0.03, ELBO=-57951.29, deltaELBO=0.472 (0.00008513%), Factors=12\n", + "Iteration 53: time=0.03, ELBO=-57950.85, deltaELBO=0.439 (0.00007915%), Factors=12\n", + "Iteration 54: time=0.04, ELBO=-57950.44, deltaELBO=0.410 (0.00007402%), Factors=12\n", + "Iteration 55: time=0.03, ELBO=-57950.06, deltaELBO=0.386 (0.00006960%), Factors=12\n", + "Iteration 56: time=0.04, ELBO=-57949.69, deltaELBO=0.364 (0.00006576%), Factors=12\n", + "Iteration 57: time=0.03, ELBO=-57949.35, deltaELBO=0.346 (0.00006241%), Factors=12\n", + "Iteration 58: time=0.04, ELBO=-57949.02, deltaELBO=0.329 (0.00005946%), Factors=12\n", + "Iteration 59: time=0.03, ELBO=-57948.70, deltaELBO=0.315 (0.00005685%), Factors=12\n", + "Iteration 60: time=0.03, ELBO=-57948.40, deltaELBO=0.302 (0.00005453%), Factors=12\n", + "Iteration 61: time=0.03, ELBO=-57948.11, deltaELBO=0.291 (0.00005245%), Factors=12\n", + "Iteration 62: time=0.04, ELBO=-57947.83, deltaELBO=0.280 (0.00005059%), Factors=12\n", + "Iteration 63: time=0.04, ELBO=-57947.56, deltaELBO=0.271 (0.00004891%), Factors=12\n", + "Iteration 64: time=0.04, ELBO=-57947.30, deltaELBO=0.263 (0.00004739%), Factors=12\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "ent = entry_point()\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(4)])\n", + "ent.set_model_options(\n", + " factors = 13, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + ")\n", + "ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + ")\n", + "ent.build()\n", + "ent.run()\n", + "factors = ent.model.nodes[\"Z\"].getExpectation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[787.6095590397154]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "mds = MDS(n_components=2, random_state=0)\n", + "n_marker_genes = 915\n", + "\n", + "stress = []\n", + "w4_gfa = factors\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "\n", + "stress.append(mds.stress_)\n", + "\n", + "# m0_mds = mds.fit_transform(normalize(m0[:n_marker_genes,:]))\n", + "# stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13 12.13\n", + "0.986\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(16)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(16):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + " \n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(cell_label, xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', n_std=2, **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " \n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " # print(l,list_cell_types[l])\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " if l==14:\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=.8, edgecolor='black')\n", + " else:\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=n_std, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4)) \n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=16\n", + "\n", + "# plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes)\n", + "# plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (16-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (16-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[1])\n", + "# plot_scatter(w4_ntf_mds[:, 0], w4_ntf_mds[:, 1], title=\"NTF Reduced Data (16-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[4])\n", + "# plot_scatter(Xs_pca_reduced_mds[:, 0], Xs_pca_reduced_mds[:, 1], title=\"PCA Reduced Data (16-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[12])\n", + "# plot_scatter(m0_mds[:, 0], m0_mds[:, 1], title=\"Original Data (16-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[10])\n", + "\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"ISM Reduced Data (16-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes)\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/abis_mofa_screeplot.ipynb b/examples/abis_mofa_screeplot.ipynb new file mode 100644 index 0000000..cf110a3 --- /dev/null +++ b/examples/abis_mofa_screeplot.ipynb @@ -0,0 +1,2351 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "from mofapy2.run.entry_point import entry_point\n", + "from scipy.stats import trim_mean\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -180060.33 \n", + "\n", + "Iteration 1: time=0.01, ELBO=-82808.81, deltaELBO=97251.517 (54.01051845%), Factors=1\n", + "Iteration 2: time=0.01, ELBO=-81284.48, deltaELBO=1524.330 (0.84656651%), Factors=1\n", + "Iteration 3: time=0.01, ELBO=-81074.24, deltaELBO=210.245 (0.11676381%), Factors=1\n", + "Iteration 4: time=0.01, ELBO=-80994.47, deltaELBO=79.763 (0.04429780%), Factors=1\n", + "Iteration 5: time=0.01, ELBO=-80950.31, deltaELBO=44.161 (0.02452584%), Factors=1\n", + "Iteration 6: time=0.02, ELBO=-80920.83, deltaELBO=29.479 (0.01637199%), Factors=1\n", + "Iteration 7: time=0.01, ELBO=-80897.48, deltaELBO=23.349 (0.01296705%), Factors=1\n", + "Iteration 8: time=0.01, ELBO=-80875.66, deltaELBO=21.819 (0.01211769%), Factors=1\n", + "Iteration 9: time=0.01, ELBO=-80852.09, deltaELBO=23.573 (0.01309169%), Factors=1\n", + "Iteration 10: time=0.00, ELBO=-80823.69, deltaELBO=28.399 (0.01577220%), Factors=1\n", + "Iteration 11: time=0.00, ELBO=-80787.06, deltaELBO=36.630 (0.02034302%), Factors=1\n", + "Iteration 12: time=0.01, ELBO=-80738.34, deltaELBO=48.727 (0.02706138%), Factors=1\n", + "Iteration 13: time=0.01, ELBO=-80673.57, deltaELBO=64.766 (0.03596904%), Factors=1\n", + "Iteration 14: time=0.00, ELBO=-80589.68, deltaELBO=83.893 (0.04659147%), Factors=1\n", + "Iteration 15: time=0.00, ELBO=-80486.00, deltaELBO=103.677 (0.05757896%), Factors=1\n", + "Iteration 16: time=0.00, ELBO=-80368.21, deltaELBO=117.787 (0.06541527%), Factors=1\n", + "Iteration 17: time=0.01, ELBO=-80253.96, deltaELBO=114.250 (0.06345122%), Factors=1\n", + "Iteration 18: time=0.00, ELBO=-80166.37, deltaELBO=87.597 (0.04864844%), Factors=1\n", + "Iteration 19: time=0.01, ELBO=-80113.64, deltaELBO=52.729 (0.02928390%), Factors=1\n", + "Iteration 20: time=0.00, ELBO=-80086.70, deltaELBO=26.936 (0.01495962%), Factors=1\n", + "Iteration 21: time=0.00, ELBO=-80073.85, deltaELBO=12.849 (0.00713593%), Factors=1\n", + "Iteration 22: time=0.02, ELBO=-80067.76, deltaELBO=6.090 (0.00338242%), Factors=1\n", + "Iteration 23: time=0.00, ELBO=-80064.78, deltaELBO=2.979 (0.00165418%), Factors=1\n", + "Iteration 24: time=0.01, ELBO=-80063.24, deltaELBO=1.546 (0.00085833%), Factors=1\n", + "Iteration 25: time=0.00, ELBO=-80062.37, deltaELBO=0.867 (0.00048170%), Factors=1\n", + "Iteration 26: time=0.01, ELBO=-80061.84, deltaELBO=0.532 (0.00029524%), Factors=1\n", + "Iteration 27: time=0.01, ELBO=-80061.48, deltaELBO=0.356 (0.00019786%), Factors=1\n", + "Iteration 28: time=0.00, ELBO=-80061.22, deltaELBO=0.259 (0.00014406%), Factors=1\n", + "Iteration 29: time=0.01, ELBO=-80061.02, deltaELBO=0.203 (0.00011254%), Factors=1\n", + "Iteration 30: time=0.00, ELBO=-80060.85, deltaELBO=0.167 (0.00009290%), Factors=1\n", + "Iteration 31: time=0.01, ELBO=-80060.71, deltaELBO=0.144 (0.00007982%), Factors=1\n", + "Iteration 32: time=0.00, ELBO=-80060.58, deltaELBO=0.127 (0.00007051%), Factors=1\n", + "Iteration 33: time=0.00, ELBO=-80060.47, deltaELBO=0.114 (0.00006343%), Factors=1\n", + "Iteration 34: time=0.02, ELBO=-80060.36, deltaELBO=0.104 (0.00005775%), Factors=1\n", + "Iteration 35: time=0.00, ELBO=-80060.27, deltaELBO=0.095 (0.00005298%), Factors=1\n", + "Iteration 36: time=0.02, ELBO=-80060.18, deltaELBO=0.088 (0.00004885%), Factors=1\n", + "Iteration 37: time=0.00, ELBO=-80060.10, deltaELBO=0.081 (0.00004518%), Factors=1\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -211701.49 \n", + "\n", + "Iteration 1: time=0.01, ELBO=-82599.17, deltaELBO=129102.316 (60.98318740%), Factors=2\n", + "Iteration 2: time=0.00, ELBO=-81320.30, deltaELBO=1278.877 (0.60409462%), Factors=2\n", + "Iteration 3: time=0.02, ELBO=-81041.84, deltaELBO=278.454 (0.13153136%), Factors=2\n", + "Iteration 4: time=0.00, ELBO=-80891.22, deltaELBO=150.627 (0.07115059%), Factors=2\n", + "Iteration 5: time=0.01, ELBO=-80747.44, deltaELBO=143.776 (0.06791438%), Factors=2\n", + "Iteration 6: time=0.01, ELBO=-80576.60, deltaELBO=170.844 (0.08070049%), Factors=2\n", + "Iteration 7: time=0.00, ELBO=-80324.91, deltaELBO=251.689 (0.11888885%), Factors=2\n", + "Iteration 8: time=0.01, ELBO=-79847.19, deltaELBO=477.712 (0.22565343%), Factors=2\n", + "Iteration 9: time=0.00, ELBO=-79155.07, deltaELBO=692.120 (0.32693190%), Factors=2\n", + "Iteration 10: time=0.02, ELBO=-78707.62, deltaELBO=447.454 (0.21136085%), Factors=2\n", + "Iteration 11: time=0.00, ELBO=-78526.66, deltaELBO=180.966 (0.08548149%), Factors=2\n", + "Iteration 12: time=0.01, ELBO=-78402.88, deltaELBO=123.776 (0.05846719%), Factors=2\n", + "Iteration 13: time=0.00, ELBO=-78272.38, deltaELBO=130.499 (0.06164309%), Factors=2\n", + "Iteration 14: time=0.01, ELBO=-78143.21, deltaELBO=129.167 (0.06101355%), Factors=2\n", + "Iteration 15: time=0.00, ELBO=-78042.86, deltaELBO=100.358 (0.04740525%), Factors=2\n", + "Iteration 16: time=0.01, ELBO=-77983.16, deltaELBO=59.696 (0.02819813%), Factors=2\n", + "Iteration 17: time=0.00, ELBO=-77953.61, deltaELBO=29.554 (0.01396023%), Factors=2\n", + "Iteration 18: time=0.00, ELBO=-77939.69, deltaELBO=13.913 (0.00657207%), Factors=2\n", + "Iteration 19: time=0.02, ELBO=-77932.70, deltaELBO=6.990 (0.00330185%), Factors=2\n", + "Iteration 20: time=0.00, ELBO=-77928.70, deltaELBO=4.001 (0.00189009%), Factors=2\n", + "Iteration 21: time=0.02, ELBO=-77926.07, deltaELBO=2.632 (0.00124343%), Factors=2\n", + "Iteration 22: time=0.00, ELBO=-77924.14, deltaELBO=1.926 (0.00090954%), Factors=2\n", + "Iteration 23: time=0.02, ELBO=-77922.64, deltaELBO=1.503 (0.00071012%), Factors=2\n", + "Iteration 24: time=0.00, ELBO=-77921.42, deltaELBO=1.217 (0.00057499%), Factors=2\n", + "Iteration 25: time=0.01, ELBO=-77920.42, deltaELBO=1.006 (0.00047524%), Factors=2\n", + "Iteration 26: time=0.01, ELBO=-77919.57, deltaELBO=0.842 (0.00039782%), Factors=2\n", + "Iteration 27: time=0.01, ELBO=-77918.86, deltaELBO=0.711 (0.00033600%), Factors=2\n", + "Iteration 28: time=0.00, ELBO=-77918.26, deltaELBO=0.605 (0.00028583%), Factors=2\n", + "Iteration 29: time=0.00, ELBO=-77917.74, deltaELBO=0.518 (0.00024468%), Factors=2\n", + "Iteration 30: time=0.00, ELBO=-77917.29, deltaELBO=0.446 (0.00021067%), Factors=2\n", + "Iteration 31: time=0.00, ELBO=-77916.91, deltaELBO=0.386 (0.00018241%), Factors=2\n", + "Iteration 32: time=0.02, ELBO=-77916.57, deltaELBO=0.336 (0.00015879%), Factors=2\n", + "Iteration 33: time=0.00, ELBO=-77916.28, deltaELBO=0.294 (0.00013899%), Factors=2\n", + "Iteration 34: time=0.01, ELBO=-77916.02, deltaELBO=0.259 (0.00012231%), Factors=2\n", + "Iteration 35: time=0.00, ELBO=-77915.79, deltaELBO=0.229 (0.00010822%), Factors=2\n", + "Iteration 36: time=0.02, ELBO=-77915.59, deltaELBO=0.204 (0.00009628%), Factors=2\n", + "Iteration 37: time=0.00, ELBO=-77915.40, deltaELBO=0.182 (0.00008613%), Factors=2\n", + "Iteration 38: time=0.03, ELBO=-77915.24, deltaELBO=0.164 (0.00007749%), Factors=2\n", + "Iteration 39: time=0.00, ELBO=-77915.09, deltaELBO=0.148 (0.00007010%), Factors=2\n", + "Iteration 40: time=0.01, ELBO=-77914.96, deltaELBO=0.135 (0.00006378%), Factors=2\n", + "Iteration 41: time=0.03, ELBO=-77914.83, deltaELBO=0.124 (0.00005835%), Factors=2\n", + "Iteration 42: time=0.01, ELBO=-77914.72, deltaELBO=0.114 (0.00005369%), Factors=2\n", + "Iteration 43: time=0.01, ELBO=-77914.61, deltaELBO=0.105 (0.00004968%), Factors=2\n", + "Iteration 44: time=0.00, ELBO=-77914.52, deltaELBO=0.098 (0.00004622%), Factors=2\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -246185.54 \n", + "\n", + "Iteration 1: time=0.00, ELBO=-82064.64, deltaELBO=164120.903 (66.66553199%), Factors=3\n", + "Iteration 2: time=0.02, ELBO=-80051.96, deltaELBO=2012.686 (0.81754824%), Factors=3\n", + "Iteration 3: time=0.00, ELBO=-79577.71, deltaELBO=474.251 (0.19263965%), Factors=3\n", + "Iteration 4: time=0.01, ELBO=-79249.40, deltaELBO=328.308 (0.13335805%), Factors=3\n", + "Iteration 5: time=0.01, ELBO=-78767.53, deltaELBO=481.864 (0.19573194%), Factors=3\n", + "Iteration 6: time=0.02, ELBO=-78001.16, deltaELBO=766.378 (0.31130100%), Factors=3\n", + "Iteration 7: time=0.01, ELBO=-77332.79, deltaELBO=668.363 (0.27148732%), Factors=3\n", + "Iteration 8: time=0.01, ELBO=-76988.97, deltaELBO=343.827 (0.13966171%), Factors=3\n", + "Iteration 9: time=0.00, ELBO=-76740.25, deltaELBO=248.720 (0.10102960%), Factors=3\n", + "Iteration 10: time=0.00, ELBO=-76482.49, deltaELBO=257.759 (0.10470116%), Factors=3\n", + "Iteration 11: time=0.02, ELBO=-76245.11, deltaELBO=237.379 (0.09642274%), Factors=3\n", + "Iteration 12: time=0.02, ELBO=-76050.49, deltaELBO=194.621 (0.07905443%), Factors=3\n", + "Iteration 13: time=0.00, ELBO=-75911.40, deltaELBO=139.083 (0.05649539%), Factors=3\n", + "Iteration 14: time=0.01, ELBO=-75832.30, deltaELBO=79.107 (0.03213303%), Factors=3\n", + "Iteration 15: time=0.01, ELBO=-75795.39, deltaELBO=36.902 (0.01498932%), Factors=3\n", + "Iteration 16: time=0.00, ELBO=-75779.08, deltaELBO=16.315 (0.00662693%), Factors=3\n", + "Iteration 17: time=0.00, ELBO=-75771.13, deltaELBO=7.948 (0.00322827%), Factors=3\n", + "Iteration 18: time=0.02, ELBO=-75766.53, deltaELBO=4.602 (0.00186931%), Factors=3\n", + "Iteration 19: time=0.01, ELBO=-75763.41, deltaELBO=3.121 (0.00126755%), Factors=3\n", + "Iteration 20: time=0.01, ELBO=-75761.07, deltaELBO=2.344 (0.00095209%), Factors=3\n", + "Iteration 21: time=0.01, ELBO=-75759.20, deltaELBO=1.862 (0.00075635%), Factors=3\n", + "Iteration 22: time=0.00, ELBO=-75757.68, deltaELBO=1.524 (0.00061916%), Factors=3\n", + "Iteration 23: time=0.02, ELBO=-75756.41, deltaELBO=1.270 (0.00051570%), Factors=3\n", + "Iteration 24: time=0.01, ELBO=-75755.34, deltaELBO=1.069 (0.00043433%), Factors=3\n", + "Iteration 25: time=0.01, ELBO=-75754.43, deltaELBO=0.908 (0.00036873%), Factors=3\n", + "Iteration 26: time=0.01, ELBO=-75753.66, deltaELBO=0.775 (0.00031499%), Factors=3\n", + "Iteration 27: time=0.01, ELBO=-75752.99, deltaELBO=0.666 (0.00027048%), Factors=3\n", + "Iteration 28: time=0.00, ELBO=-75752.42, deltaELBO=0.574 (0.00023331%), Factors=3\n", + "Iteration 29: time=0.02, ELBO=-75751.92, deltaELBO=0.497 (0.00020208%), Factors=3\n", + "Iteration 30: time=0.01, ELBO=-75751.49, deltaELBO=0.433 (0.00017571%), Factors=3\n", + "Iteration 31: time=0.00, ELBO=-75751.11, deltaELBO=0.377 (0.00015333%), Factors=3\n", + "Iteration 32: time=0.02, ELBO=-75750.78, deltaELBO=0.331 (0.00013428%), Factors=3\n", + "Iteration 33: time=0.00, ELBO=-75750.49, deltaELBO=0.291 (0.00011801%), Factors=3\n", + "Iteration 34: time=0.02, ELBO=-75750.23, deltaELBO=0.256 (0.00010407%), Factors=3\n", + "Iteration 35: time=0.01, ELBO=-75750.01, deltaELBO=0.227 (0.00009210%), Factors=3\n", + "Iteration 36: time=0.00, ELBO=-75749.80, deltaELBO=0.201 (0.00008179%), Factors=3\n", + "Iteration 37: time=0.02, ELBO=-75749.63, deltaELBO=0.179 (0.00007289%), Factors=3\n", + "Iteration 38: time=0.00, ELBO=-75749.46, deltaELBO=0.160 (0.00006519%), Factors=3\n", + "Iteration 39: time=0.02, ELBO=-75749.32, deltaELBO=0.144 (0.00005853%), Factors=3\n", + "Iteration 40: time=0.00, ELBO=-75749.19, deltaELBO=0.130 (0.00005274%), Factors=3\n", + "Iteration 41: time=0.02, ELBO=-75749.07, deltaELBO=0.117 (0.00004771%), Factors=3\n", + "Iteration 42: time=0.01, ELBO=-75748.97, deltaELBO=0.107 (0.00004332%), Factors=3\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -285543.94 \n", + "\n", + "Iteration 1: time=0.01, ELBO=-80743.70, deltaELBO=204800.234 (71.72284484%), Factors=4\n", + "Iteration 2: time=0.01, ELBO=-76978.08, deltaELBO=3765.626 (1.31875549%), Factors=4\n", + "Iteration 3: time=0.00, ELBO=-76009.11, deltaELBO=968.970 (0.33934174%), Factors=4\n", + "Iteration 4: time=0.02, ELBO=-75560.09, deltaELBO=449.015 (0.15724895%), Factors=4\n", + "Iteration 5: time=0.00, ELBO=-75382.06, deltaELBO=178.034 (0.06234920%), Factors=4\n", + "Iteration 6: time=0.02, ELBO=-75301.64, deltaELBO=80.419 (0.02816333%), Factors=4\n", + "Iteration 7: time=0.01, ELBO=-75252.00, deltaELBO=49.637 (0.01738338%), Factors=4\n", + "Iteration 8: time=0.01, ELBO=-75214.45, deltaELBO=37.555 (0.01315197%), Factors=4\n", + "Iteration 9: time=0.01, ELBO=-75178.04, deltaELBO=36.405 (0.01274932%), Factors=4\n", + "Iteration 10: time=0.01, ELBO=-75129.89, deltaELBO=48.153 (0.01686370%), Factors=4\n", + "Iteration 11: time=0.01, ELBO=-75050.65, deltaELBO=79.235 (0.02774876%), Factors=4\n", + "Iteration 12: time=0.02, ELBO=-74918.97, deltaELBO=131.685 (0.04611729%), Factors=4\n", + "Iteration 13: time=0.01, ELBO=-74739.60, deltaELBO=179.373 (0.06281783%), Factors=4\n", + "Iteration 14: time=0.01, ELBO=-74565.87, deltaELBO=173.723 (0.06083934%), Factors=4\n", + "Iteration 15: time=0.01, ELBO=-74439.25, deltaELBO=126.624 (0.04434484%), Factors=4\n", + "Iteration 16: time=0.01, ELBO=-74350.81, deltaELBO=88.435 (0.03097060%), Factors=4\n", + "Iteration 17: time=0.01, ELBO=-74288.45, deltaELBO=62.360 (0.02183912%), Factors=4\n", + "Iteration 18: time=0.01, ELBO=-74249.15, deltaELBO=39.299 (0.01376269%), Factors=4\n", + "Iteration 19: time=0.01, ELBO=-74226.94, deltaELBO=22.217 (0.00778045%), Factors=4\n", + "Iteration 20: time=0.01, ELBO=-74213.90, deltaELBO=13.035 (0.00456485%), Factors=4\n", + "Iteration 21: time=0.01, ELBO=-74204.67, deltaELBO=9.238 (0.00323515%), Factors=4\n", + "Iteration 22: time=0.01, ELBO=-74196.59, deltaELBO=8.073 (0.00282733%), Factors=4\n", + "Iteration 23: time=0.00, ELBO=-74188.45, deltaELBO=8.138 (0.00284997%), Factors=4\n", + "Iteration 24: time=0.02, ELBO=-74179.51, deltaELBO=8.943 (0.00313184%), Factors=4\n", + "Iteration 25: time=0.00, ELBO=-74169.09, deltaELBO=10.423 (0.00365019%), Factors=4\n", + "Iteration 26: time=0.01, ELBO=-74156.35, deltaELBO=12.734 (0.00445961%), Factors=4\n", + "Iteration 27: time=0.01, ELBO=-74140.14, deltaELBO=16.211 (0.00567724%), Factors=4\n", + "Iteration 28: time=0.00, ELBO=-74118.75, deltaELBO=21.393 (0.00749194%), Factors=4\n", + "Iteration 29: time=0.01, ELBO=-74089.69, deltaELBO=29.060 (0.01017722%), Factors=4\n", + "Iteration 30: time=0.01, ELBO=-74049.49, deltaELBO=40.201 (0.01407868%), Factors=4\n", + "Iteration 31: time=0.00, ELBO=-73993.75, deltaELBO=55.740 (0.01952061%), Factors=4\n", + "Iteration 32: time=0.02, ELBO=-73917.85, deltaELBO=75.897 (0.02657964%), Factors=4\n", + "Iteration 33: time=0.01, ELBO=-73818.53, deltaELBO=99.320 (0.03478283%), Factors=4\n", + "Iteration 34: time=0.05, ELBO=-73696.47, deltaELBO=122.065 (0.04274811%), Factors=4\n", + "Iteration 35: time=0.02, ELBO=-73561.79, deltaELBO=134.682 (0.04716681%), Factors=4\n", + "Iteration 36: time=0.02, ELBO=-73439.50, deltaELBO=122.283 (0.04282468%), Factors=4\n", + "Iteration 37: time=0.01, ELBO=-73354.96, deltaELBO=84.538 (0.02960606%), Factors=4\n", + "Iteration 38: time=0.01, ELBO=-73309.48, deltaELBO=45.488 (0.01593032%), Factors=4\n", + "Iteration 39: time=0.01, ELBO=-73288.02, deltaELBO=21.456 (0.00751415%), Factors=4\n", + "Iteration 40: time=0.01, ELBO=-73277.93, deltaELBO=10.091 (0.00353387%), Factors=4\n", + "Iteration 41: time=0.00, ELBO=-73272.76, deltaELBO=5.166 (0.00180901%), Factors=4\n", + "Iteration 42: time=0.02, ELBO=-73269.76, deltaELBO=3.000 (0.00105054%), Factors=4\n", + "Iteration 43: time=0.02, ELBO=-73267.80, deltaELBO=1.967 (0.00068875%), Factors=4\n", + "Iteration 44: time=0.00, ELBO=-73266.39, deltaELBO=1.410 (0.00049378%), Factors=4\n", + "Iteration 45: time=0.02, ELBO=-73265.32, deltaELBO=1.070 (0.00037470%), Factors=4\n", + "Iteration 46: time=0.01, ELBO=-73264.48, deltaELBO=0.840 (0.00029435%), Factors=4\n", + "Iteration 47: time=0.01, ELBO=-73263.80, deltaELBO=0.675 (0.00023639%), Factors=4\n", + "Iteration 48: time=0.01, ELBO=-73263.25, deltaELBO=0.551 (0.00019283%), Factors=4\n", + "Iteration 49: time=0.01, ELBO=-73262.80, deltaELBO=0.455 (0.00015928%), Factors=4\n", + "Iteration 50: time=0.01, ELBO=-73262.42, deltaELBO=0.380 (0.00013306%), Factors=4\n", + "Iteration 51: time=0.01, ELBO=-73262.10, deltaELBO=0.321 (0.00011238%), Factors=4\n", + "Iteration 52: time=0.01, ELBO=-73261.82, deltaELBO=0.274 (0.00009595%), Factors=4\n", + "Iteration 53: time=0.00, ELBO=-73261.59, deltaELBO=0.237 (0.00008285%), Factors=4\n", + "Iteration 54: time=0.02, ELBO=-73261.38, deltaELBO=0.207 (0.00007235%), Factors=4\n", + "Iteration 55: time=0.01, ELBO=-73261.20, deltaELBO=0.182 (0.00006390%), Factors=4\n", + "Iteration 56: time=0.01, ELBO=-73261.03, deltaELBO=0.163 (0.00005708%), Factors=4\n", + "Iteration 57: time=0.01, ELBO=-73260.89, deltaELBO=0.147 (0.00005154%), Factors=4\n", + "Iteration 58: time=0.01, ELBO=-73260.75, deltaELBO=0.134 (0.00004703%), Factors=4\n", + "Iteration 59: time=0.00, ELBO=-73260.63, deltaELBO=0.124 (0.00004333%), Factors=4\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -325441.90 \n", + "\n", + "Iteration 1: time=0.00, ELBO=-80473.38, deltaELBO=244968.520 (75.27258206%), Factors=5\n", + "Iteration 2: time=0.01, ELBO=-75531.62, deltaELBO=4941.757 (1.51847599%), Factors=5\n", + "Iteration 3: time=0.01, ELBO=-74265.08, deltaELBO=1266.541 (0.38917570%), Factors=5\n", + "Iteration 4: time=0.01, ELBO=-73848.62, deltaELBO=416.462 (0.12796828%), Factors=5\n", + "Iteration 5: time=0.01, ELBO=-73639.81, deltaELBO=208.811 (0.06416216%), Factors=5\n", + "Iteration 6: time=0.02, ELBO=-73480.16, deltaELBO=159.647 (0.04905547%), Factors=5\n", + "Iteration 7: time=0.01, ELBO=-73323.97, deltaELBO=156.193 (0.04799403%), Factors=5\n", + "Iteration 8: time=0.03, ELBO=-73133.93, deltaELBO=190.037 (0.05839354%), Factors=5\n", + "Iteration 9: time=0.01, ELBO=-72856.02, deltaELBO=277.915 (0.08539620%), Factors=5\n", + "Iteration 10: time=0.01, ELBO=-72435.29, deltaELBO=420.730 (0.12927974%), Factors=5\n", + "Iteration 11: time=0.02, ELBO=-71964.81, deltaELBO=470.480 (0.14456652%), Factors=5\n", + "Iteration 12: time=0.01, ELBO=-71663.07, deltaELBO=301.740 (0.09271707%), Factors=5\n", + "Iteration 13: time=0.00, ELBO=-71505.62, deltaELBO=157.444 (0.04837856%), Factors=5\n", + "Iteration 14: time=0.02, ELBO=-71400.10, deltaELBO=105.521 (0.03242401%), Factors=5\n", + "Iteration 15: time=0.02, ELBO=-71323.96, deltaELBO=76.141 (0.02339625%), Factors=5\n", + "Iteration 16: time=0.01, ELBO=-71274.16, deltaELBO=49.802 (0.01530279%), Factors=5\n", + "Iteration 17: time=0.00, ELBO=-71244.96, deltaELBO=29.193 (0.00897026%), Factors=5\n", + "Iteration 18: time=0.02, ELBO=-71228.33, deltaELBO=16.633 (0.00511086%), Factors=5\n", + "Iteration 19: time=0.02, ELBO=-71217.86, deltaELBO=10.469 (0.00321691%), Factors=5\n", + "Iteration 20: time=0.01, ELBO=-71209.93, deltaELBO=7.935 (0.00243826%), Factors=5\n", + "Iteration 21: time=0.00, ELBO=-71202.75, deltaELBO=7.175 (0.00220469%), Factors=5\n", + "Iteration 22: time=0.02, ELBO=-71195.41, deltaELBO=7.337 (0.00225462%), Factors=5\n", + "Iteration 23: time=0.02, ELBO=-71187.27, deltaELBO=8.140 (0.00250112%), Factors=5\n", + "Iteration 24: time=0.00, ELBO=-71177.71, deltaELBO=9.565 (0.00293901%), Factors=5\n", + "Iteration 25: time=0.02, ELBO=-71165.99, deltaELBO=11.724 (0.00360250%), Factors=5\n", + "Iteration 26: time=0.02, ELBO=-71151.20, deltaELBO=14.788 (0.00454392%), Factors=5\n", + "Iteration 27: time=0.01, ELBO=-71132.30, deltaELBO=18.900 (0.00580758%), Factors=5\n", + "Iteration 28: time=0.01, ELBO=-71108.30, deltaELBO=24.000 (0.00737467%), Factors=5\n", + "Iteration 29: time=0.01, ELBO=-71078.81, deltaELBO=29.491 (0.00906192%), Factors=5\n", + "Iteration 30: time=0.02, ELBO=-71044.93, deltaELBO=33.878 (0.01040990%), Factors=5\n", + "Iteration 31: time=0.01, ELBO=-71009.99, deltaELBO=34.935 (0.01073452%), Factors=5\n", + "Iteration 32: time=0.00, ELBO=-70978.86, deltaELBO=31.130 (0.00956548%), Factors=5\n", + "Iteration 33: time=0.02, ELBO=-70955.37, deltaELBO=23.490 (0.00721778%), Factors=5\n", + "Iteration 34: time=0.02, ELBO=-70940.20, deltaELBO=15.176 (0.00466326%), Factors=5\n", + "Iteration 35: time=0.00, ELBO=-70931.42, deltaELBO=8.780 (0.00269774%), Factors=5\n", + "Iteration 36: time=0.02, ELBO=-70926.55, deltaELBO=4.870 (0.00149654%), Factors=5\n", + "Iteration 37: time=0.01, ELBO=-70923.76, deltaELBO=2.787 (0.00085631%), Factors=5\n", + "Iteration 38: time=0.01, 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deltaELBO=0.186 (0.00005711%), Factors=5\n", + "Iteration 50: time=0.01, ELBO=-70916.48, deltaELBO=0.165 (0.00005063%), Factors=5\n", + "Iteration 51: time=0.02, ELBO=-70916.34, deltaELBO=0.147 (0.00004525%), Factors=5\n", + "Iteration 52: time=0.01, ELBO=-70916.20, deltaELBO=0.133 (0.00004076%), Factors=5\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -360073.29 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-79742.48, deltaELBO=280330.809 (77.85381961%), Factors=6\n", + "Iteration 2: time=0.01, ELBO=-73224.56, deltaELBO=6517.922 (1.81016541%), Factors=6\n", + "Iteration 3: time=0.01, ELBO=-71327.33, deltaELBO=1897.225 (0.52689980%), Factors=6\n", + "Iteration 4: time=0.01, ELBO=-70643.16, deltaELBO=684.172 (0.19000904%), Factors=6\n", + "Iteration 5: time=0.01, ELBO=-70285.33, deltaELBO=357.827 (0.09937627%), Factors=6\n", + "Iteration 6: time=0.02, ELBO=-70032.38, deltaELBO=252.949 (0.07024928%), 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ELBO=-68886.69, deltaELBO=0.181 (0.00005014%), Factors=6\n", + "Iteration 152: time=0.05, ELBO=-68886.51, deltaELBO=0.180 (0.00005004%), Factors=6\n", + "Iteration 153: time=0.00, ELBO=-68886.33, deltaELBO=0.180 (0.00004994%), Factors=6\n", + "Iteration 154: time=0.02, ELBO=-68886.15, deltaELBO=0.179 (0.00004984%), Factors=6\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -395353.66 \n", + "\n", + "Iteration 1: time=0.03, ELBO=-80148.56, deltaELBO=315205.101 (79.72737609%), Factors=7\n", + "Iteration 2: time=0.01, ELBO=-73687.08, deltaELBO=6461.484 (1.63435532%), Factors=7\n", + "Iteration 3: time=0.01, ELBO=-71326.64, deltaELBO=2360.438 (0.59704474%), Factors=7\n", + "Iteration 4: time=0.02, ELBO=-70242.29, deltaELBO=1084.344 (0.27427198%), Factors=7\n", + "Iteration 5: time=0.02, ELBO=-69524.49, deltaELBO=717.802 (0.18155949%), Factors=7\n", + "Iteration 6: time=0.02, ELBO=-68937.65, 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deltaELBO=0.205 (0.00005177%), Factors=7\n", + "Iteration 184: time=0.00, ELBO=-66675.47, deltaELBO=0.204 (0.00005165%), Factors=7\n", + "Iteration 185: time=0.02, ELBO=-66675.27, deltaELBO=0.204 (0.00005152%), Factors=7\n", + "Iteration 186: time=0.02, ELBO=-66675.06, deltaELBO=0.203 (0.00005140%), Factors=7\n", + "Iteration 187: time=0.01, ELBO=-66674.86, deltaELBO=0.203 (0.00005127%), Factors=7\n", + "Iteration 188: time=0.02, ELBO=-66674.66, deltaELBO=0.202 (0.00005115%), Factors=7\n", + "Iteration 189: time=0.04, ELBO=-66674.46, deltaELBO=0.202 (0.00005102%), Factors=7\n", + "Iteration 190: time=0.01, ELBO=-66674.26, deltaELBO=0.201 (0.00005089%), Factors=7\n", + "Iteration 191: time=0.01, ELBO=-66674.05, deltaELBO=0.201 (0.00005077%), Factors=7\n", + "Iteration 192: time=0.01, ELBO=-66673.85, deltaELBO=0.200 (0.00005064%), Factors=7\n", + "Iteration 193: time=0.02, ELBO=-66673.65, deltaELBO=0.200 (0.00005051%), Factors=7\n", + "Iteration 194: time=0.01, ELBO=-66673.46, deltaELBO=0.199 (0.00005038%), Factors=7\n", + "Iteration 195: time=0.00, ELBO=-66673.26, deltaELBO=0.199 (0.00005025%), Factors=7\n", + "Iteration 196: time=0.01, ELBO=-66673.06, deltaELBO=0.198 (0.00005012%), Factors=7\n", + "Iteration 197: time=0.02, ELBO=-66672.86, deltaELBO=0.198 (0.00004999%), Factors=7\n", + "Iteration 198: time=0.02, ELBO=-66672.66, deltaELBO=0.197 (0.00004986%), Factors=7\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -428343.35 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80130.64, deltaELBO=348212.712 (81.29289483%), Factors=8\n", + "Iteration 2: time=0.02, ELBO=-72541.06, deltaELBO=7589.586 (1.77184621%), Factors=8\n", + "Iteration 3: time=0.02, ELBO=-69592.66, deltaELBO=2948.395 (0.68832525%), Factors=8\n", + "Iteration 4: time=0.01, ELBO=-68431.01, deltaELBO=1161.654 (0.27119687%), Factors=8\n", + "Iteration 5: time=0.06, ELBO=-67728.20, deltaELBO=702.804 (0.16407500%), Factors=8\n", + "Iteration 6: time=0.02, ELBO=-67244.14, deltaELBO=484.062 (0.11300802%), Factors=8\n", + "Iteration 7: time=0.02, ELBO=-66938.53, deltaELBO=305.607 (0.07134626%), Factors=8\n", + "Iteration 8: time=0.02, ELBO=-66752.63, deltaELBO=185.899 (0.04339944%), Factors=8\n", + "Iteration 9: time=0.01, ELBO=-66620.06, deltaELBO=132.571 (0.03094974%), Factors=8\n", + "Iteration 10: time=0.02, ELBO=-66503.28, deltaELBO=116.784 (0.02726408%), Factors=8\n", + "Iteration 11: time=0.02, ELBO=-66389.04, deltaELBO=114.237 (0.02666956%), Factors=8\n", + "Iteration 12: time=0.01, ELBO=-66277.71, deltaELBO=111.334 (0.02599176%), Factors=8\n", + "Iteration 13: time=0.02, ELBO=-66176.22, deltaELBO=101.492 (0.02369406%), Factors=8\n", + "Iteration 14: time=0.02, ELBO=-66091.59, deltaELBO=84.625 (0.01975634%), Factors=8\n", + "Iteration 15: time=0.02, ELBO=-66025.11, deltaELBO=66.476 (0.01551944%), Factors=8\n", + "Iteration 16: time=0.02, ELBO=-65970.88, deltaELBO=54.238 (0.01266234%), Factors=8\n", + "Iteration 17: time=0.02, ELBO=-65917.62, deltaELBO=53.259 (0.01243372%), Factors=8\n", + "Iteration 18: time=0.04, ELBO=-65850.36, deltaELBO=67.256 (0.01570145%), Factors=8\n", + "Iteration 19: time=0.01, ELBO=-65753.54, deltaELBO=96.818 (0.02260281%), Factors=8\n", + "Iteration 20: time=0.01, ELBO=-65624.10, deltaELBO=129.446 (0.03022005%), Factors=8\n", + "Iteration 21: time=0.02, ELBO=-65492.53, deltaELBO=131.570 (0.03071601%), Factors=8\n", + "Iteration 22: time=0.02, ELBO=-65403.56, deltaELBO=88.970 (0.02077069%), Factors=8\n", + "Iteration 23: time=0.01, ELBO=-65361.03, deltaELBO=42.530 (0.00992904%), Factors=8\n", + "Iteration 24: time=0.02, ELBO=-65342.44, deltaELBO=18.591 (0.00434027%), Factors=8\n", + "Iteration 25: time=0.02, ELBO=-65332.70, deltaELBO=9.736 (0.00227291%), Factors=8\n", + "Iteration 26: time=0.02, ELBO=-65326.20, deltaELBO=6.497 (0.00151680%), Factors=8\n", + "Iteration 27: time=0.02, ELBO=-65321.23, deltaELBO=4.972 (0.00116081%), Factors=8\n", + "Iteration 28: time=0.02, ELBO=-65317.22, deltaELBO=4.016 (0.00093752%), Factors=8\n", + "Iteration 29: time=0.01, ELBO=-65313.90, deltaELBO=3.311 (0.00077299%), Factors=8\n", + "Iteration 30: time=0.02, ELBO=-65311.15, deltaELBO=2.757 (0.00064357%), Factors=8\n", + "Iteration 31: time=0.02, ELBO=-65308.84, deltaELBO=2.309 (0.00053913%), Factors=8\n", + "Iteration 32: time=0.02, ELBO=-65306.89, deltaELBO=1.944 (0.00045386%), Factors=8\n", + "Iteration 33: time=0.01, ELBO=-65305.25, deltaELBO=1.644 (0.00038379%), Factors=8\n", + "Iteration 34: time=0.01, ELBO=-65303.85, deltaELBO=1.396 (0.00032593%), Factors=8\n", + "Iteration 35: time=0.01, ELBO=-65302.66, deltaELBO=1.191 (0.00027800%), Factors=8\n", + "Iteration 36: time=0.02, ELBO=-65301.64, deltaELBO=1.020 (0.00023815%), Factors=8\n", + "Iteration 37: time=0.00, ELBO=-65300.77, deltaELBO=0.878 (0.00020492%), Factors=8\n", + "Iteration 38: time=0.01, ELBO=-65300.01, deltaELBO=0.759 (0.00017715%), Factors=8\n", + "Iteration 39: time=0.01, ELBO=-65299.35, deltaELBO=0.659 (0.00015387%), Factors=8\n", + "Iteration 40: time=0.02, ELBO=-65298.77, deltaELBO=0.575 (0.00013431%), Factors=8\n", + "Iteration 41: time=0.02, ELBO=-65298.27, deltaELBO=0.505 (0.00011783%), Factors=8\n", + "Iteration 42: time=0.01, ELBO=-65297.82, deltaELBO=0.445 (0.00010391%), Factors=8\n", + "Iteration 43: time=0.02, ELBO=-65297.43, deltaELBO=0.395 (0.00009212%), Factors=8\n", + "Iteration 44: time=0.05, ELBO=-65297.08, deltaELBO=0.352 (0.00008212%), Factors=8\n", + "Iteration 45: time=0.01, ELBO=-65296.76, deltaELBO=0.315 (0.00007361%), Factors=8\n", + "Iteration 46: time=0.01, ELBO=-65296.48, deltaELBO=0.284 (0.00006635%), Factors=8\n", + "Iteration 47: time=0.00, ELBO=-65296.22, deltaELBO=0.258 (0.00006014%), Factors=8\n", + "Iteration 48: time=0.01, ELBO=-65295.98, deltaELBO=0.235 (0.00005483%), Factors=8\n", + "Iteration 49: time=0.02, ELBO=-65295.77, deltaELBO=0.215 (0.00005026%), Factors=8\n", + "Iteration 50: time=0.01, ELBO=-65295.57, deltaELBO=0.198 (0.00004633%), Factors=8\n", + "Iteration 51: time=0.02, ELBO=-65295.39, deltaELBO=0.184 (0.00004293%), Factors=8\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -459964.46 \n", + "\n", + "Iteration 1: time=0.01, ELBO=-80534.93, deltaELBO=379429.534 (82.49105448%), Factors=9\n", + "Iteration 2: time=0.02, ELBO=-73189.49, deltaELBO=7345.435 (1.59695705%), Factors=9\n", + "Iteration 3: time=0.02, ELBO=-69769.28, deltaELBO=3420.213 (0.74358199%), Factors=9\n", + "Iteration 4: time=0.06, ELBO=-67905.27, deltaELBO=1864.008 (0.40525052%), Factors=9\n", + "Iteration 5: time=0.04, ELBO=-65877.24, deltaELBO=2028.027 (0.44090945%), Factors=9\n", + "Iteration 6: time=0.01, ELBO=-64436.06, deltaELBO=1441.185 (0.31332529%), Factors=9\n", + "Iteration 7: time=0.02, ELBO=-64011.12, deltaELBO=424.941 (0.09238564%), Factors=9\n", + "Iteration 8: time=0.02, ELBO=-63824.82, deltaELBO=186.295 (0.04050194%), Factors=9\n", + "Iteration 9: time=0.02, ELBO=-63700.04, deltaELBO=124.778 (0.02712785%), Factors=9\n", + "Iteration 10: time=0.01, ELBO=-63608.05, deltaELBO=91.991 (0.01999955%), Factors=9\n", + "Iteration 11: time=0.02, ELBO=-63540.97, deltaELBO=67.083 (0.01458449%), Factors=9\n", + "Iteration 12: time=0.01, ELBO=-63493.89, deltaELBO=47.082 (0.01023607%), Factors=9\n", + "Iteration 13: time=0.01, ELBO=-63461.28, deltaELBO=32.607 (0.00708908%), Factors=9\n", + "Iteration 14: time=0.01, ELBO=-63438.02, deltaELBO=23.263 (0.00505758%), Factors=9\n", + "Iteration 15: time=0.02, ELBO=-63420.54, deltaELBO=17.478 (0.00379988%), Factors=9\n", + "Iteration 16: time=0.01, ELBO=-63406.79, deltaELBO=13.750 (0.00298931%), Factors=9\n", + "Iteration 17: time=0.02, ELBO=-63395.64, deltaELBO=11.154 (0.00242487%), Factors=9\n", + "Iteration 18: time=0.04, ELBO=-63386.41, deltaELBO=9.223 (0.00200512%), Factors=9\n", + "Iteration 19: time=0.01, ELBO=-63378.68, deltaELBO=7.735 (0.00168159%), Factors=9\n", + "Iteration 20: time=0.02, ELBO=-63372.10, deltaELBO=6.581 (0.00143084%), Factors=9\n", + "Iteration 21: time=0.02, ELBO=-63366.38, deltaELBO=5.715 (0.00124249%), Factors=9\n", + "Iteration 22: time=0.01, ELBO=-63361.25, deltaELBO=5.132 (0.00111583%), Factors=9\n", + "Iteration 23: time=0.02, ELBO=-63356.37, deltaELBO=4.879 (0.00106079%), Factors=9\n", + "Iteration 24: time=0.02, ELBO=-63351.30, deltaELBO=5.072 (0.00110276%), Factors=9\n", + "Iteration 25: time=0.02, ELBO=-63345.36, deltaELBO=5.942 (0.00129186%), Factors=9\n", + "Iteration 26: time=0.01, ELBO=-63337.45, deltaELBO=7.904 (0.00171835%), Factors=9\n", + "Iteration 27: time=0.02, ELBO=-63325.79, deltaELBO=11.659 (0.00253486%), Factors=9\n", + "Iteration 28: time=0.01, ELBO=-63307.50, deltaELBO=18.298 (0.00397803%), Factors=9\n", + "Iteration 29: time=0.02, ELBO=-63278.26, deltaELBO=29.231 (0.00635507%), Factors=9\n", + "Iteration 30: time=0.02, ELBO=-63232.77, deltaELBO=45.491 (0.00989000%), Factors=9\n", + "Iteration 31: time=0.02, ELBO=-63167.31, deltaELBO=65.463 (0.01423217%), Factors=9\n", + "Iteration 32: time=0.01, ELBO=-63086.08, deltaELBO=81.230 (0.01765999%), Factors=9\n", + "Iteration 33: time=0.02, ELBO=-63006.16, deltaELBO=79.917 (0.01737454%), Factors=9\n", + "Iteration 34: time=0.01, ELBO=-62947.39, deltaELBO=58.776 (0.01277832%), Factors=9\n", + "Iteration 35: time=0.02, ELBO=-62914.12, deltaELBO=33.273 (0.00723381%), Factors=9\n", + "Iteration 36: time=0.02, ELBO=-62897.50, deltaELBO=16.613 (0.00361186%), Factors=9\n", + "Iteration 37: time=0.02, ELBO=-62888.69, deltaELBO=8.812 (0.00191581%), Factors=9\n", + "Iteration 38: time=0.01, ELBO=-62883.13, deltaELBO=5.563 (0.00120950%), Factors=9\n", + "Iteration 39: time=0.02, ELBO=-62879.05, deltaELBO=4.079 (0.00088675%), Factors=9\n", + "Iteration 40: time=0.01, ELBO=-62875.82, deltaELBO=3.233 (0.00070286%), Factors=9\n", + "Iteration 41: time=0.02, ELBO=-62873.17, deltaELBO=2.648 (0.00057575%), Factors=9\n", + "Iteration 42: time=0.02, ELBO=-62870.97, deltaELBO=2.200 (0.00047831%), Factors=9\n", + "Iteration 43: time=0.06, ELBO=-62869.13, deltaELBO=1.841 (0.00040034%), Factors=9\n", + "Iteration 44: time=0.01, ELBO=-62867.58, deltaELBO=1.549 (0.00033685%), Factors=9\n", + "Iteration 45: time=0.02, ELBO=-62866.27, deltaELBO=1.310 (0.00028477%), Factors=9\n", + "Iteration 46: time=0.02, ELBO=-62865.15, deltaELBO=1.113 (0.00024187%), Factors=9\n", + "Iteration 47: time=0.02, ELBO=-62864.20, deltaELBO=0.950 (0.00020644%), Factors=9\n", + "Iteration 48: time=0.01, ELBO=-62863.39, deltaELBO=0.815 (0.00017711%), Factors=9\n", + "Iteration 49: time=0.02, ELBO=-62862.69, deltaELBO=0.703 (0.00015278%), Factors=9\n", + "Iteration 50: time=0.01, ELBO=-62862.08, deltaELBO=0.610 (0.00013256%), Factors=9\n", + "Iteration 51: time=0.02, ELBO=-62861.55, deltaELBO=0.532 (0.00011572%), Factors=9\n", + "Iteration 52: time=0.02, ELBO=-62861.08, deltaELBO=0.468 (0.00010168%), Factors=9\n", + "Iteration 53: time=0.02, ELBO=-62860.66, deltaELBO=0.414 (0.00008996%), Factors=9\n", + "Iteration 54: time=0.01, ELBO=-62860.30, deltaELBO=0.369 (0.00008014%), Factors=9\n", + "Iteration 55: time=0.02, ELBO=-62859.96, deltaELBO=0.331 (0.00007192%), Factors=9\n", + "Iteration 56: time=0.01, ELBO=-62859.67, deltaELBO=0.299 (0.00006502%), Factors=9\n", + "Iteration 57: time=0.02, ELBO=-62859.39, deltaELBO=0.272 (0.00005921%), Factors=9\n", + "Iteration 58: time=0.02, ELBO=-62859.14, deltaELBO=0.250 (0.00005433%), Factors=9\n", + "Iteration 59: time=0.02, ELBO=-62858.91, deltaELBO=0.231 (0.00005020%), Factors=9\n", + "Iteration 60: time=0.02, ELBO=-62858.70, deltaELBO=0.215 (0.00004672%), Factors=9\n", + "Iteration 61: time=0.02, ELBO=-62858.50, deltaELBO=0.201 (0.00004376%), Factors=9\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -490385.10 \n", + "\n", + "Iteration 1: time=0.07, ELBO=-79676.91, deltaELBO=410708.191 (83.75217564%), Factors=10\n", + "Iteration 2: time=0.03, ELBO=-71159.28, deltaELBO=8517.626 (1.73692595%), Factors=10\n", + "Iteration 3: time=0.01, ELBO=-67093.62, deltaELBO=4065.668 (0.82907648%), Factors=10\n", + "Iteration 4: time=0.02, ELBO=-65066.90, deltaELBO=2026.713 (0.41329015%), Factors=10\n", + "Iteration 5: time=0.05, ELBO=-63975.03, deltaELBO=1091.868 (0.22265528%), Factors=10\n", + "Iteration 6: time=0.01, ELBO=-63283.67, deltaELBO=691.367 (0.14098450%), Factors=10\n", + "Iteration 7: time=0.02, ELBO=-62875.78, deltaELBO=407.885 (0.08317647%), Factors=10\n", + "Iteration 8: time=0.02, ELBO=-62606.50, deltaELBO=269.280 (0.05491199%), Factors=10\n", + "Iteration 9: time=0.02, ELBO=-62326.47, deltaELBO=280.031 (0.05710439%), Factors=10\n", + "Iteration 10: time=0.01, ELBO=-61959.39, deltaELBO=367.082 (0.07485586%), Factors=10\n", + "Iteration 11: time=0.01, ELBO=-61620.23, deltaELBO=339.159 (0.06916171%), Factors=10\n", + "Iteration 12: time=0.02, ELBO=-61442.95, deltaELBO=177.280 (0.03615115%), Factors=10\n", + "Iteration 13: time=0.03, ELBO=-61373.45, deltaELBO=69.498 (0.01417203%), Factors=10\n", + "Iteration 14: time=0.02, ELBO=-61340.61, deltaELBO=32.845 (0.00669789%), Factors=10\n", + "Iteration 15: time=0.02, ELBO=-61319.41, deltaELBO=21.200 (0.00432311%), Factors=10\n", + "Iteration 16: time=0.02, ELBO=-61303.34, deltaELBO=16.072 (0.00327743%), Factors=10\n", + "Iteration 17: time=0.02, ELBO=-61290.33, deltaELBO=13.006 (0.00265216%), Factors=10\n", + "Iteration 18: time=0.02, ELBO=-61279.49, deltaELBO=10.835 (0.00220945%), Factors=10\n", + "Iteration 19: time=0.01, ELBO=-61270.33, deltaELBO=9.165 (0.00186896%), Factors=10\n", + "Iteration 20: time=0.02, ELBO=-61262.50, deltaELBO=7.827 (0.00159613%), Factors=10\n", + "Iteration 21: time=0.03, ELBO=-61255.77, deltaELBO=6.731 (0.00137262%), Factors=10\n", + "Iteration 22: time=0.02, ELBO=-61249.95, deltaELBO=5.821 (0.00118704%), Factors=10\n", + "Iteration 23: time=0.02, ELBO=-61244.89, deltaELBO=5.058 (0.00103149%), Factors=10\n", + "Iteration 24: time=0.02, ELBO=-61240.48, deltaELBO=4.414 (0.00090017%), Factors=10\n", + "Iteration 25: time=0.02, ELBO=-61236.61, deltaELBO=3.867 (0.00078866%), Factors=10\n", + "Iteration 26: time=0.02, ELBO=-61233.21, deltaELBO=3.401 (0.00069349%), Factors=10\n", + "Iteration 27: time=0.01, ELBO=-61230.21, deltaELBO=3.001 (0.00061192%), Factors=10\n", + "Iteration 28: time=0.04, ELBO=-61227.55, deltaELBO=2.657 (0.00054172%), Factors=10\n", + "Iteration 29: time=0.00, ELBO=-61225.19, deltaELBO=2.359 (0.00048110%), Factors=10\n", + "Iteration 30: time=0.03, ELBO=-61223.09, deltaELBO=2.102 (0.00042858%), Factors=10\n", + "Iteration 31: time=0.02, ELBO=-61221.21, deltaELBO=1.878 (0.00038294%), Factors=10\n", + "Iteration 32: time=0.02, ELBO=-61219.53, deltaELBO=1.683 (0.00034317%), Factors=10\n", + "Iteration 33: time=0.02, ELBO=-61218.02, deltaELBO=1.512 (0.00030843%), Factors=10\n", + "Iteration 34: time=0.02, ELBO=-61216.65, deltaELBO=1.363 (0.00027800%), Factors=10\n", + "Iteration 35: time=0.02, ELBO=-61215.42, deltaELBO=1.232 (0.00025129%), Factors=10\n", + "Iteration 36: time=0.02, ELBO=-61214.31, deltaELBO=1.117 (0.00022780%), Factors=10\n", + "Iteration 37: time=0.02, ELBO=-61213.29, deltaELBO=1.016 (0.00020710%), Factors=10\n", + "Iteration 38: time=0.00, ELBO=-61212.36, deltaELBO=0.926 (0.00018882%), Factors=10\n", + "Iteration 39: time=0.02, ELBO=-61211.52, deltaELBO=0.847 (0.00017264%), Factors=10\n", + "Iteration 40: time=0.02, ELBO=-61210.74, deltaELBO=0.776 (0.00015831%), Factors=10\n", + "Iteration 41: time=0.02, ELBO=-61210.03, deltaELBO=0.714 (0.00014559%), Factors=10\n", + "Iteration 42: time=0.02, ELBO=-61209.37, deltaELBO=0.659 (0.00013429%), Factors=10\n", + "Iteration 43: time=0.02, ELBO=-61208.76, deltaELBO=0.609 (0.00012422%), Factors=10\n", + "Iteration 44: time=0.02, ELBO=-61208.19, deltaELBO=0.565 (0.00011524%), Factors=10\n", + "Iteration 45: time=0.02, ELBO=-61207.67, deltaELBO=0.526 (0.00010723%), Factors=10\n", + "Iteration 46: time=0.02, ELBO=-61207.18, deltaELBO=0.491 (0.00010006%), Factors=10\n", + "Iteration 47: time=0.01, ELBO=-61206.72, deltaELBO=0.459 (0.00009365%), Factors=10\n", + "Iteration 48: time=0.03, ELBO=-61206.29, deltaELBO=0.431 (0.00008789%), Factors=10\n", + "Iteration 49: time=0.02, ELBO=-61205.88, deltaELBO=0.406 (0.00008273%), Factors=10\n", + "Iteration 50: time=0.02, ELBO=-61205.50, deltaELBO=0.383 (0.00007808%), Factors=10\n", + "Iteration 51: time=0.04, ELBO=-61205.14, deltaELBO=0.362 (0.00007390%), Factors=10\n", + "Iteration 52: time=0.02, ELBO=-61204.79, deltaELBO=0.344 (0.00007013%), Factors=10\n", + "Iteration 53: time=0.02, ELBO=-61204.47, deltaELBO=0.327 (0.00006673%), Factors=10\n", + "Iteration 54: time=0.06, ELBO=-61204.15, deltaELBO=0.312 (0.00006365%), Factors=10\n", + "Iteration 55: time=0.01, ELBO=-61203.85, deltaELBO=0.298 (0.00006087%), Factors=10\n", + "Iteration 56: time=0.04, ELBO=-61203.57, deltaELBO=0.286 (0.00005834%), Factors=10\n", + "Iteration 57: time=0.02, ELBO=-61203.29, deltaELBO=0.275 (0.00005605%), Factors=10\n", + "Iteration 58: time=0.02, ELBO=-61203.03, deltaELBO=0.265 (0.00005397%), Factors=10\n", + "Iteration 59: time=0.02, ELBO=-61202.77, deltaELBO=0.255 (0.00005207%), Factors=10\n", + "Iteration 60: time=0.02, ELBO=-61202.53, deltaELBO=0.247 (0.00005034%), Factors=10\n", + "Iteration 61: time=0.02, ELBO=-61202.29, deltaELBO=0.239 (0.00004876%), Factors=10\n", + "Iteration 62: time=0.01, ELBO=-61202.06, deltaELBO=0.232 (0.00004732%), Factors=10\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -522301.62 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80174.18, deltaELBO=442127.439 (84.64983095%), Factors=11\n", + "Iteration 2: time=0.01, ELBO=-72643.69, deltaELBO=7530.490 (1.44178943%), Factors=11\n", + "Iteration 3: time=0.02, ELBO=-68223.26, deltaELBO=4420.435 (0.84633759%), Factors=11\n", + "Iteration 4: time=0.02, ELBO=-65303.95, deltaELBO=2919.311 (0.55893206%), Factors=11\n", + "Iteration 5: time=0.02, ELBO=-63540.86, deltaELBO=1763.085 (0.33756070%), Factors=11\n", + "Iteration 6: time=0.01, ELBO=-62339.31, deltaELBO=1201.553 (0.23004965%), Factors=11\n", + "Iteration 7: time=0.02, ELBO=-61445.24, deltaELBO=894.067 (0.17117832%), Factors=11\n", + "Iteration 8: time=0.02, ELBO=-60953.60, deltaELBO=491.644 (0.09413035%), Factors=11\n", + "Iteration 9: time=0.02, ELBO=-60704.83, deltaELBO=248.769 (0.04762945%), Factors=11\n", + "Iteration 10: time=0.05, ELBO=-60537.62, deltaELBO=167.207 (0.03201358%), Factors=11\n", + "Iteration 11: time=0.02, ELBO=-60423.57, deltaELBO=114.051 (0.02183619%), Factors=11\n", + "Iteration 12: time=0.02, ELBO=-60351.79, deltaELBO=71.778 (0.01374269%), Factors=11\n", + "Iteration 13: time=0.02, ELBO=-60302.79, deltaELBO=48.997 (0.00938092%), Factors=11\n", + "Iteration 14: time=0.02, ELBO=-60263.85, deltaELBO=38.943 (0.00745596%), Factors=11\n", + "Iteration 15: time=0.02, ELBO=-60229.62, deltaELBO=34.227 (0.00655310%), Factors=11\n", + "Iteration 16: time=0.01, ELBO=-60197.74, deltaELBO=31.888 (0.00610529%), Factors=11\n", + "Iteration 17: time=0.02, ELBO=-60166.71, deltaELBO=31.027 (0.00594044%), Factors=11\n", + "Iteration 18: time=0.02, ELBO=-60135.35, deltaELBO=31.359 (0.00600395%), Factors=11\n", + "Iteration 19: time=0.03, ELBO=-60102.54, deltaELBO=32.814 (0.00628252%), Factors=11\n", + "Iteration 20: time=0.02, ELBO=-60067.06, deltaELBO=35.475 (0.00679209%), Factors=11\n", + "Iteration 21: time=0.02, ELBO=-60027.44, deltaELBO=39.626 (0.00758682%), Factors=11\n", + "Iteration 22: time=0.02, ELBO=-59981.58, deltaELBO=45.852 (0.00877883%), Factors=11\n", + "Iteration 23: time=0.01, ELBO=-59926.45, deltaELBO=55.132 (0.01055563%), Factors=11\n", + "Iteration 24: time=0.02, ELBO=-59857.78, deltaELBO=68.671 (0.01314782%), Factors=11\n", + "Iteration 25: time=0.02, ELBO=-59771.07, deltaELBO=86.713 (0.01660203%), Factors=11\n", + "Iteration 26: time=0.02, ELBO=-59666.09, deltaELBO=104.976 (0.02009870%), Factors=11\n", + "Iteration 27: time=0.02, ELBO=-59555.81, deltaELBO=110.278 (0.02111395%), Factors=11\n", + "Iteration 28: time=0.01, ELBO=-59466.71, deltaELBO=89.099 (0.01705892%), Factors=11\n", + "Iteration 29: time=0.02, ELBO=-59414.57, deltaELBO=52.149 (0.00998446%), Factors=11\n", + "Iteration 30: time=0.02, ELBO=-59390.53, deltaELBO=24.035 (0.00460167%), Factors=11\n", + "Iteration 31: time=0.05, ELBO=-59379.71, deltaELBO=10.824 (0.00207239%), Factors=11\n", + "Iteration 32: time=0.02, ELBO=-59373.79, deltaELBO=5.920 (0.00113341%), Factors=11\n", + "Iteration 33: time=0.02, ELBO=-59369.77, deltaELBO=4.016 (0.00076897%), Factors=11\n", + "Iteration 34: time=0.02, ELBO=-59366.72, deltaELBO=3.055 (0.00058497%), Factors=11\n", + "Iteration 35: time=0.00, ELBO=-59364.28, deltaELBO=2.437 (0.00046656%), Factors=11\n", + "Iteration 36: time=0.02, ELBO=-59362.29, deltaELBO=1.988 (0.00038055%), Factors=11\n", + "Iteration 37: time=0.02, ELBO=-59360.65, deltaELBO=1.645 (0.00031492%), Factors=11\n", + "Iteration 38: time=0.02, ELBO=-59359.27, deltaELBO=1.377 (0.00026366%), Factors=11\n", + "Iteration 39: time=0.06, ELBO=-59358.10, deltaELBO=1.165 (0.00022305%), Factors=11\n", + "Iteration 40: time=0.03, ELBO=-59357.11, deltaELBO=0.995 (0.00019052%), Factors=11\n", + "Iteration 41: time=0.03, ELBO=-59356.25, deltaELBO=0.858 (0.00016419%), Factors=11\n", + "Iteration 42: time=0.01, ELBO=-59355.51, deltaELBO=0.745 (0.00014268%), Factors=11\n", + "Iteration 43: time=0.03, ELBO=-59354.85, deltaELBO=0.653 (0.00012496%), Factors=11\n", + "Iteration 44: time=0.02, ELBO=-59354.28, deltaELBO=0.576 (0.00011024%), Factors=11\n", + "Iteration 45: time=0.02, ELBO=-59353.77, deltaELBO=0.511 (0.00009792%), Factors=11\n", + "Iteration 46: time=0.02, ELBO=-59353.31, deltaELBO=0.457 (0.00008754%), Factors=11\n", + "Iteration 47: time=0.02, ELBO=-59352.90, deltaELBO=0.411 (0.00007874%), Factors=11\n", + "Iteration 48: time=0.02, ELBO=-59352.53, deltaELBO=0.372 (0.00007122%), Factors=11\n", + "Iteration 49: time=0.02, ELBO=-59352.19, deltaELBO=0.338 (0.00006477%), Factors=11\n", + "Iteration 50: time=0.02, ELBO=-59351.88, deltaELBO=0.309 (0.00005920%), Factors=11\n", + "Iteration 51: time=0.02, ELBO=-59351.59, deltaELBO=0.284 (0.00005438%), Factors=11\n", + "Iteration 52: time=0.02, ELBO=-59351.33, deltaELBO=0.262 (0.00005018%), Factors=11\n", + "Iteration 53: time=0.02, ELBO=-59351.09, deltaELBO=0.243 (0.00004651%), Factors=11\n", + "Iteration 54: time=0.02, ELBO=-59350.86, deltaELBO=0.226 (0.00004329%), Factors=11\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -554078.45 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80263.41, deltaELBO=473815.045 (85.51407166%), Factors=12\n", + "Iteration 2: time=0.01, ELBO=-72448.31, deltaELBO=7815.094 (1.41046700%), Factors=12\n", + "Iteration 3: time=0.02, ELBO=-67384.30, deltaELBO=5064.013 (0.91395231%), Factors=12\n", + "Iteration 4: time=0.02, ELBO=-63970.45, deltaELBO=3413.847 (0.61613056%), Factors=12\n", + "Iteration 5: time=0.02, ELBO=-62161.20, deltaELBO=1809.254 (0.32653396%), Factors=12\n", + "Iteration 6: time=0.02, ELBO=-61281.89, deltaELBO=879.315 (0.15869856%), Factors=12\n", + "Iteration 7: time=0.02, ELBO=-60660.21, deltaELBO=621.672 (0.11219925%), Factors=12\n", + "Iteration 8: time=0.02, ELBO=-60037.75, deltaELBO=622.469 (0.11234304%), Factors=12\n", + "Iteration 9: time=0.02, ELBO=-59598.16, deltaELBO=439.582 (0.07933568%), Factors=12\n", + "Iteration 10: time=0.03, ELBO=-59364.67, deltaELBO=233.496 (0.04214138%), Factors=12\n", + "Iteration 11: time=0.02, ELBO=-59204.15, deltaELBO=160.515 (0.02896971%), Factors=12\n", + "Iteration 12: time=0.02, ELBO=-59080.27, deltaELBO=123.882 (0.02235817%), Factors=12\n", + "Iteration 13: time=0.02, ELBO=-58999.55, deltaELBO=80.719 (0.01456811%), Factors=12\n", + "Iteration 14: time=0.02, ELBO=-58949.40, deltaELBO=50.155 (0.00905203%), Factors=12\n", + "Iteration 15: time=0.02, ELBO=-58913.41, deltaELBO=35.983 (0.00649417%), Factors=12\n", + "Iteration 16: time=0.01, ELBO=-58883.82, deltaELBO=29.589 (0.00534023%), Factors=12\n", + "Iteration 17: time=0.03, ELBO=-58857.76, deltaELBO=26.060 (0.00470338%), Factors=12\n", + "Iteration 18: time=0.04, ELBO=-58833.88, deltaELBO=23.887 (0.00431114%), Factors=12\n", + "Iteration 19: time=0.02, ELBO=-58811.25, deltaELBO=22.625 (0.00408333%), Factors=12\n", + "Iteration 20: time=0.02, ELBO=-58789.14, deltaELBO=22.108 (0.00399001%), Factors=12\n", + "Iteration 21: time=0.02, ELBO=-58766.88, deltaELBO=22.266 (0.00401863%), Factors=12\n", + "Iteration 22: time=0.02, ELBO=-58743.80, deltaELBO=23.080 (0.00416554%), Factors=12\n", + "Iteration 23: time=0.02, ELBO=-58719.23, deltaELBO=24.565 (0.00443343%), Factors=12\n", + "Iteration 24: time=0.02, ELBO=-58692.46, deltaELBO=26.771 (0.00483167%), Factors=12\n", + "Iteration 25: time=0.02, ELBO=-58662.65, deltaELBO=29.812 (0.00538040%), Factors=12\n", + "Iteration 26: time=0.02, ELBO=-58628.74, deltaELBO=33.911 (0.00612020%), Factors=12\n", + "Iteration 27: time=0.02, ELBO=-58589.24, deltaELBO=39.500 (0.00712890%), Factors=12\n", + "Iteration 28: time=0.02, ELBO=-58541.91, deltaELBO=47.333 (0.00854274%), Factors=12\n", + "Iteration 29: time=0.02, ELBO=-58483.39, deltaELBO=58.519 (0.01056151%), Factors=12\n", + "Iteration 30: time=0.02, ELBO=-58409.34, deltaELBO=74.048 (0.01336420%), Factors=12\n", + "Iteration 31: time=0.02, ELBO=-58316.53, deltaELBO=92.808 (0.01674999%), Factors=12\n", + "Iteration 32: time=0.02, ELBO=-58209.32, deltaELBO=107.211 (0.01934936%), Factors=12\n", + "Iteration 33: time=0.02, ELBO=-58107.17, deltaELBO=102.146 (0.01843532%), Factors=12\n", + "Iteration 34: time=0.06, ELBO=-58034.88, deltaELBO=72.291 (0.01304710%), Factors=12\n", + "Iteration 35: time=0.02, ELBO=-57997.19, deltaELBO=37.690 (0.00680233%), Factors=12\n", + "Iteration 36: time=0.04, ELBO=-57980.63, deltaELBO=16.558 (0.00298839%), Factors=12\n", + "Iteration 37: time=0.02, ELBO=-57972.92, deltaELBO=7.719 (0.00139310%), Factors=12\n", + "Iteration 38: time=0.02, ELBO=-57968.42, deltaELBO=4.497 (0.00081157%), Factors=12\n", + "Iteration 39: time=0.02, ELBO=-57965.26, deltaELBO=3.162 (0.00057070%), Factors=12\n", + "Iteration 40: time=0.03, ELBO=-57962.82, deltaELBO=2.440 (0.00044033%), Factors=12\n", + "Iteration 41: time=0.02, ELBO=-57960.85, deltaELBO=1.962 (0.00035412%), Factors=12\n", + "Iteration 42: time=0.02, ELBO=-57959.24, deltaELBO=1.615 (0.00029146%), Factors=12\n", + "Iteration 43: time=0.02, ELBO=-57957.89, deltaELBO=1.352 (0.00024403%), Factors=12\n", + "Iteration 44: time=0.03, ELBO=-57956.74, deltaELBO=1.149 (0.00020738%), Factors=12\n", + "Iteration 45: time=0.01, ELBO=-57955.75, deltaELBO=0.990 (0.00017864%), Factors=12\n", + "Iteration 46: time=0.02, ELBO=-57954.89, deltaELBO=0.863 (0.00015584%), Factors=12\n", + "Iteration 47: time=0.03, ELBO=-57954.12, deltaELBO=0.762 (0.00013755%), Factors=12\n", + "Iteration 48: time=0.02, ELBO=-57953.44, deltaELBO=0.680 (0.00012273%), Factors=12\n", + "Iteration 49: time=0.02, ELBO=-57952.83, deltaELBO=0.613 (0.00011059%), Factors=12\n", + "Iteration 50: time=0.02, ELBO=-57952.27, deltaELBO=0.557 (0.00010056%), Factors=12\n", + "Iteration 51: time=0.02, ELBO=-57951.76, deltaELBO=0.511 (0.00009218%), Factors=12\n", + "Iteration 52: time=0.02, ELBO=-57951.29, deltaELBO=0.472 (0.00008513%), Factors=12\n", + "Iteration 53: time=0.03, ELBO=-57950.85, deltaELBO=0.439 (0.00007915%), Factors=12\n", + "Iteration 54: time=0.02, ELBO=-57950.44, deltaELBO=0.410 (0.00007402%), Factors=12\n", + "Iteration 55: time=0.02, ELBO=-57950.06, deltaELBO=0.386 (0.00006960%), Factors=12\n", + "Iteration 56: time=0.04, ELBO=-57949.69, deltaELBO=0.364 (0.00006576%), Factors=12\n", + "Iteration 57: time=0.01, ELBO=-57949.35, deltaELBO=0.346 (0.00006241%), Factors=12\n", + "Iteration 58: time=0.02, ELBO=-57949.02, deltaELBO=0.329 (0.00005946%), Factors=12\n", + "Iteration 59: time=0.03, ELBO=-57948.70, deltaELBO=0.315 (0.00005685%), Factors=12\n", + "Iteration 60: time=0.02, ELBO=-57948.40, deltaELBO=0.302 (0.00005453%), Factors=12\n", + "Iteration 61: time=0.02, ELBO=-57948.11, deltaELBO=0.291 (0.00005245%), Factors=12\n", + "Iteration 62: time=0.02, ELBO=-57947.83, deltaELBO=0.280 (0.00005059%), Factors=12\n", + "Iteration 63: time=0.02, ELBO=-57947.56, deltaELBO=0.271 (0.00004891%), Factors=12\n", + "Iteration 64: time=0.01, ELBO=-57947.30, deltaELBO=0.263 (0.00004739%), Factors=12\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -585197.23 \n", + "\n", + "Iteration 1: time=0.03, ELBO=-80405.65, deltaELBO=504791.589 (86.26007768%), Factors=13\n", + "Iteration 2: time=0.02, ELBO=-71662.97, deltaELBO=8742.675 (1.49397071%), Factors=13\n", + "Iteration 3: time=0.02, ELBO=-65404.79, deltaELBO=6258.183 (1.06941434%), Factors=13\n", + "Iteration 4: time=0.02, ELBO=-61082.29, deltaELBO=4322.501 (0.73864001%), Factors=13\n", + "Iteration 5: time=0.02, ELBO=-59423.91, deltaELBO=1658.377 (0.28338769%), Factors=13\n", + "Iteration 6: time=0.03, ELBO=-58864.74, deltaELBO=559.171 (0.09555254%), Factors=13\n", + "Iteration 7: time=0.02, ELBO=-58605.61, deltaELBO=259.130 (0.04428072%), Factors=13\n", + "Iteration 8: time=0.02, ELBO=-58438.71, deltaELBO=166.898 (0.02852002%), Factors=13\n", + "Iteration 9: time=0.02, ELBO=-58314.20, deltaELBO=124.508 (0.02127626%), Factors=13\n", + "Iteration 10: time=0.05, ELBO=-58218.41, deltaELBO=95.793 (0.01636936%), Factors=13\n", + "Iteration 11: time=0.01, ELBO=-58145.01, deltaELBO=73.398 (0.01254250%), Factors=13\n", + "Iteration 12: time=0.03, ELBO=-58088.96, deltaELBO=56.054 (0.00957867%), Factors=13\n", + "Iteration 13: time=0.02, ELBO=-58045.99, deltaELBO=42.967 (0.00734235%), Factors=13\n", + "Iteration 14: time=0.07, ELBO=-58012.68, deltaELBO=33.314 (0.00569284%), Factors=13\n", + "Iteration 15: time=0.02, ELBO=-57986.33, deltaELBO=26.343 (0.00450156%), Factors=13\n", + "Iteration 16: time=0.04, ELBO=-57964.99, deltaELBO=21.338 (0.00364626%), Factors=13\n", + "Iteration 17: time=0.02, ELBO=-57947.31, deltaELBO=17.680 (0.00302112%), Factors=13\n", + "Iteration 18: time=0.02, ELBO=-57932.40, deltaELBO=14.915 (0.00254873%), Factors=13\n", + "Iteration 19: time=0.02, ELBO=-57919.65, deltaELBO=12.752 (0.00217915%), Factors=13\n", + "Iteration 20: time=0.02, ELBO=-57908.64, deltaELBO=11.012 (0.00188180%), Factors=13\n", + "Iteration 21: time=0.03, ELBO=-57899.05, deltaELBO=9.584 (0.00163768%), Factors=13\n", + "Iteration 22: time=0.02, ELBO=-57890.66, deltaELBO=8.394 (0.00143438%), Factors=13\n", + "Iteration 23: time=0.02, ELBO=-57883.27, deltaELBO=7.392 (0.00126325%), Factors=13\n", + "Iteration 24: time=0.02, ELBO=-57876.72, deltaELBO=6.543 (0.00111800%), Factors=13\n", + "Iteration 25: time=0.02, ELBO=-57870.91, deltaELBO=5.816 (0.00099388%), Factors=13\n", + "Iteration 26: time=0.02, ELBO=-57865.71, deltaELBO=5.192 (0.00088719%), Factors=13\n", + "Iteration 27: time=0.02, ELBO=-57861.06, deltaELBO=4.652 (0.00079500%), Factors=13\n", + "Iteration 28: time=0.05, ELBO=-57856.88, deltaELBO=4.184 (0.00071497%), Factors=13\n", + "Iteration 29: time=0.02, ELBO=-57853.10, deltaELBO=3.776 (0.00064521%), Factors=13\n", + "Iteration 30: time=0.02, ELBO=-57849.68, deltaELBO=3.419 (0.00058416%), Factors=13\n", + "Iteration 31: time=0.03, ELBO=-57846.58, deltaELBO=3.105 (0.00053056%), Factors=13\n", + "Iteration 32: time=0.02, ELBO=-57843.75, deltaELBO=2.828 (0.00048332%), Factors=13\n", + "Iteration 33: time=0.02, ELBO=-57841.17, deltaELBO=2.584 (0.00044158%), Factors=13\n", + "Iteration 34: time=0.02, ELBO=-57838.80, deltaELBO=2.368 (0.00040458%), Factors=13\n", + "Iteration 35: time=0.02, ELBO=-57836.62, deltaELBO=2.175 (0.00037169%), Factors=13\n", + "Iteration 36: time=0.03, ELBO=-57834.62, deltaELBO=2.004 (0.00034239%), Factors=13\n", + "Iteration 37: time=0.02, ELBO=-57832.77, deltaELBO=1.851 (0.00031623%), Factors=13\n", + "Iteration 38: time=0.02, ELBO=-57831.06, deltaELBO=1.713 (0.00029280%), Factors=13\n", + "Iteration 39: time=0.02, ELBO=-57829.47, deltaELBO=1.590 (0.00027178%), Factors=13\n", + "Iteration 40: time=0.02, ELBO=-57827.99, deltaELBO=1.480 (0.00025289%), Factors=13\n", + "Iteration 41: time=0.02, ELBO=-57826.61, deltaELBO=1.380 (0.00023587%), Factors=13\n", + "Iteration 42: time=0.02, ELBO=-57825.32, deltaELBO=1.290 (0.00022052%), Factors=13\n", + "Iteration 43: time=0.02, ELBO=-57824.11, deltaELBO=1.209 (0.00020663%), Factors=13\n", + "Iteration 44: time=0.03, ELBO=-57822.97, deltaELBO=1.136 (0.00019406%), Factors=13\n", + "Iteration 45: time=0.02, ELBO=-57821.90, deltaELBO=1.069 (0.00018265%), Factors=13\n", + "Iteration 46: time=0.03, ELBO=-57820.89, deltaELBO=1.008 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(0.00004952%), Factors=13\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -615307.48 \n", + "\n", + "Iteration 1: time=0.04, ELBO=-80589.10, deltaELBO=534718.371 (86.90262875%), Factors=14\n", + "Iteration 2: time=0.02, ELBO=-72596.29, deltaELBO=7992.815 (1.29899520%), Factors=13\n", + "Iteration 3: time=0.03, ELBO=-67510.49, deltaELBO=5085.800 (0.82654606%), Factors=13\n", + "Iteration 4: time=0.02, ELBO=-63999.43, deltaELBO=3511.065 (0.57061952%), Factors=13\n", + "Iteration 5: time=0.02, ELBO=-61788.12, deltaELBO=2211.306 (0.35938231%), Factors=13\n", + "Iteration 6: time=0.03, ELBO=-59763.05, deltaELBO=2025.070 (0.32911510%), Factors=13\n", + "Iteration 7: time=0.02, ELBO=-58514.85, deltaELBO=1248.195 (0.20285711%), Factors=13\n", + "Iteration 8: time=0.02, ELBO=-57966.07, deltaELBO=548.785 (0.08918881%), Factors=13\n", + "Iteration 9: time=0.03, ELBO=-57502.26, deltaELBO=463.806 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"\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view1' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view2' group='group0' with N=915 samples and D=16 features...\n", + "Successfully loaded view='view3' group='group0' with N=915 samples and D=16 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -645535.19 \n", + "\n", + "Iteration 1: time=0.02, ELBO=-80045.18, deltaELBO=565490.011 (87.60018376%), Factors=15\n", + "Iteration 2: time=0.04, ELBO=-69744.16, deltaELBO=10301.020 (1.59573328%), Factors=14\n", + "Iteration 3: time=0.01, ELBO=-63151.18, deltaELBO=6592.979 (1.02131987%), Factors=13\n", + "Iteration 4: time=0.03, ELBO=-59310.89, deltaELBO=3840.287 (0.59489976%), Factors=13\n", + "Iteration 5: time=0.02, ELBO=-57762.93, deltaELBO=1547.965 (0.23979556%), Factors=13\n", + "Iteration 6: time=0.02, ELBO=-57255.89, deltaELBO=507.036 (0.07854501%), Factors=13\n", + "Iteration 7: time=0.06, ELBO=-57035.37, deltaELBO=220.523 (0.03416126%), Factors=13\n", + "Iteration 8: time=0.03, ELBO=-56898.02, deltaELBO=137.347 (0.02127652%), Factors=13\n", + "Iteration 9: time=0.03, ELBO=-56796.95, deltaELBO=101.070 (0.01565670%), Factors=13\n", + "Iteration 10: time=0.02, ELBO=-56715.73, 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ELBO=-55735.44, deltaELBO=0.316 (0.00004895%), Factors=13\n", + "Iteration 66: time=0.03, ELBO=-55735.14, deltaELBO=0.305 (0.00004727%), Factors=13\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "total_variance = np.zeros(17)\n", + "for k in range(2,17):\n", + " data_mat = [[None for g in range(1)] for m in range(4)]\n", + "\n", + " for m in range(4):\n", + " data_mat[m][0] = Xs_norm[m]\n", + "\n", + " ent = entry_point()\n", + " ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(4)])\n", + " ent.set_model_options(\n", + " factors = k, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + " )\n", + " ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + " )\n", + " ent.build()\n", + " ent.run()\n", + "\n", + " total_variance[k] = np.sum(ent.model.calculate_variance_explained())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(2,17), total_variance[2:17], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/abis_mvmds_screeplot.ipynb b/examples/abis_mvmds_screeplot.ipynb new file mode 100644 index 0000000..4d6d454 --- /dev/null +++ b/examples/abis_mvmds_screeplot.ipynb @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "# from sklearn.decomposition import NMF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "import os\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "\n", + "RESULTS_PATH = './'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 4 views.\n", + "There are 915 observations\n", + "The feature sizes are: [16, 16, 16, 16]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(RESULTS_PATH + r'abis_915.csv', na_values=' ', index_col='gene_id')\n", + "# df = pd.read_csv(RESULTS_PATH + r'\\abis_915_1000_random_genes.csv', na_values=' ', index_col='gene_id')\n", + "\n", + "# df_cell_type_connection = pd.read_csv(RESULTS_PATH + r'cell_type_connection.csv', na_values=' ', index_col='cell_type')\n", + "# cell_type_connection = df_cell_type_connection.values.astype(np.float_)\n", + "\n", + "max_rows = df.iloc[:,1:].apply(lambda x: x.sort_values(ascending=False).values, axis=1, result_type='broadcast').iloc[:,:4].mean(axis=1).values\n", + "df_norm = df.iloc[np.nonzero(max_rows)[0],1:].divide(max_rows[np.nonzero(max_rows)[0]], axis='rows')\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(df.iloc[:,0])\n", + "\n", + "m0 = df_norm.values.astype(np.float_)\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "# df_norm.to_csv(RESULTS_PATH + r'\\abis_915_norm.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "list_columns = df.columns[1:].to_list()\n", + "score_pref = ['9JD4', '925L', 'DZQV', 'G4YW']\n", + "n_scores = len(score_pref)\n", + "list_items = df.columns[1:].str[5:].to_list()[0:int((df.shape[1]-1)/n_scores)]\n", + "\n", + "n_items = [len(list_items) for i in range(n_scores+1)]\n", + "Xs=[m0[:,i*n_items[0]:(i+1)*n_items[0]] for i in range(n_scores)]\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "mvmds = MVMDS(n_components=16)\n", + "Xs_mvmds_reduced = mvmds.fit_transform(Xs)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.25888715 0.7012003 0.9441707 1.24957556 1.95817956 2.45557595\n", + " 2.70811699 2.92816196 3.13355755 3.24058277 3.28982273 3.3577562\n", + " 3.49285628 3.53937728 3.56071788 3.57073439]\n" + ] + } + ], + "source": [ + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "\n", + "p = Xs_concat.shape[1]\n", + "variance_explained = np.zeros(16)\n", + "\n", + "for k in range(16):\n", + " variance = 0\n", + " for i in range(p):\n", + " variance += np.var(np.dot(Xs_concat[:,i], Xs_mvmds_reduced[:,k])*Xs_mvmds_reduced[:,k])\n", + "\n", + " if k==0:\n", + " variance_explained[k] = variance\n", + " else: \n", + " variance_explained[k] = variance_explained[k-1]+variance\n", + "\n", + "print(variance_explained)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(1,17), variance_explained, 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/easi3.ipynb b/examples/easi3.ipynb new file mode 100644 index 0000000..47945ef --- /dev/null +++ b/examples/easi3.ipynb @@ -0,0 +1,949 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coucou\n" + ] + } + ], + "source": [ + "# from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "\n", + "DATA_PATH = r'C:\\Users\\paul_\\OneDrive\\Pro\\Galderma\\Vevey\\NEMO Phase 3\\AD\\EASI\\data'\n", + "RESULTS_PATH = r'C:\\Users\\paul_\\OneDrive\\Pro\\Galderma\\Vevey\\NEMO Phase 3\\AD\\EASI\\results\\ISM'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(DATA_PATH + r'\\adeasi3_by_zone.csv', na_values=' ', index_col='USUBJID')\n", + "\n", + "m0 = df.values[:,3:].astype(np.float_)\n", + "\n", + "list_columns = df.columns[3:].to_list()\n", + "score_pref = ['Head And Neck', 'Limb, Lower', 'Limb, Upper', 'Trunk']\n", + "list_items = ['Area of Involvement', 'Erythema', 'Excoriation', 'Induration/Papulation', 'Lichenification']\n", + "\n", + "n_scores = 4\n", + "n_items = [5, 5, 5, 5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "condition number(4, 6) = 2.33\n" + ] + } + ], + "source": [ + "\n", + "n_embedding, n_themes = [4,6]\n", + "h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0=True, update_h4_ism=True,\n", + " max_iter_integrate = 20, max_iter_mult=100, fast_mult_rules=False, sparsity_coeff=1)\n", + "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated), 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save ISM results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calinski_harabasz_score: 217.20840085533604\n", + "condition number(w4_ism) = 10.317792797819772\n" + ] + } + ], + "source": [ + "# Save\n", + "df_h4_updated_sparse = ilsm.format_loadings(h4_updated_sparse, list_columns)\n", + "df_h4_updated_sparse.to_csv(RESULTS_PATH + r'\\h4_updated_sparse.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "df_h4_updated = ilsm.format_loadings(h4_updated_sparse, list_columns)\n", + "df_h4_updated.to_csv(RESULTS_PATH + r'\\h4_updated.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "df_q4_ism = pd.DataFrame(q4_ism)\n", + "df_q4_ism.columns = ['theme_' + str(i) for i in range(1, n_themes + 1)]\n", + "df_q4_ism.insert(loc=0, column='score', value=score_pref)\n", + "df_q4_ism.to_csv(RESULTS_PATH + r'\\q4_ism.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "cluster = np.argmax(w4_ism, axis=1)+1\n", + "# cluster = np.argmax(normalize(w4_ism, norm='max', axis=0), axis=1)+1\n", + "\n", + "calinski_harabasz_score = metrics.calinski_harabasz_score(w4_ism, cluster)\n", + "print(f'calinski_harabasz_score: {calinski_harabasz_score}')\n", + "# silhouette_score = metrics.silhouette_score(w4_ism, cluster, metric='euclidean')\n", + "# print(f'Silhouette Score: {silhouette_score}')\n", + "\n", + "df_w4_ism = pd.DataFrame(np.column_stack((w4_ism, cluster)))\n", + "df_w4_ism.columns = ['theme_' + str(i) for i in range(1, n_themes + 1)] + ['nmf_cluster']\n", + "\n", + "def calculate_value(row):\n", + " for i in range(1, n_themes+1):\n", + " if row.iloc[n_themes] == i:\n", + " return row.iloc[i-1]\n", + "\n", + "# Apply the function to each row\n", + "df_w4_ism['nmf_cluster_loading'] = df_w4_ism.apply(lambda row: calculate_value(row), axis=1)\n", + "\n", + "for k in range(1,7):\n", + " df_w4_ism['pred_'+str(k)] = df_w4_ism['theme_'+str(k)] / df_w4_ism[['theme_' +str(i) for i in range(1,6) if i != k]].max(axis=1)\n", + "\n", + "df_w4_ism.columns = ['theme_' + str(i) for i in range(1, n_themes + 1)] + ['nmf_cluster', 'nmf_cluster_loading'] + ['pred_' + str(i) for i in range(1, n_themes + 1)]\n", + "\n", + "df_w4_ism.insert(0, 'TRTP', df['TRTP'].to_list())\n", + "df_w4_ism.insert(0, 'ASEX', df['ASEX'].to_list())\n", + "df_w4_ism.insert(0, 'AGEGR1', df['AGEGR1'].to_list())\n", + "df_w4_ism.insert(loc=0, column='USUBJID', value=(df.index.to_list()))\n", + "df_w4_ism.set_index('USUBJID', inplace=True)\n", + "df_w4_ism.to_csv(RESULTS_PATH + r'\\w4_ism.csv', sep=',', na_rep='.',index=True)\n", + "\n", + "df_easy_by_time = pd.read_csv(DATA_PATH + r'\\adeff_easi_no_dtype_no_doubles.csv', na_values=' ', index_col='USUBJID')\n", + "merged_df = pd.merge(df_w4_ism, df_easy_by_time, on='USUBJID')\n", + "merged_df.to_csv(RESULTS_PATH + r'\\w4_ism_adeff_easi.csv', sep=',', na_rep='.',index=True)\n", + "\n", + "print('condition number(w4_ism) = ' + str(np.linalg.cond(w4_ism)))\n", + "\n", + "# Save the tensor\n", + "df_tensor_score = pd.DataFrame(data=tensor_score)\n", + "df_tensor_score.columns = [score_pref[j] + ':theme_' + str(i) for j in range(len(score_pref)) for i in range(1, int(tensor_score.shape[1]/n_scores) + 1)]\n", + "df_tensor_score.insert(loc=0, column='wise_id', value=(df.index.to_list()))\n", + "df_tensor_score.set_index('wise_id', inplace=True)\n", + "df_tensor_score.to_csv(RESULTS_PATH + r'\\tensor_score.csv', sep=',', na_rep='.', index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additional tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "calculate difference in %success between treatment and placebo as a function of predictor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df_W16 = merged_df[(merged_df['AVISIT'] == 'Week 16')].copy()\n", + "ncols = 3\n", + "nrows = int(np.ceil(n_themes/ncols))\n", + "irow = 0\n", + "icol = -1\n", + "xmin, xmax = 0, 1.1\n", + "ymin, ymax = .25, .55\n", + "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12, 8))\n", + "\n", + "for k in range(1,7):\n", + " merged_df_W16['PRED_'+str(k)] = merged_df_W16['theme_'+str(k)] / merged_df_W16[['theme_' +str(i) for i in range(1,6) if i != k]].max(axis=1)\n", + " count_Y_Nemo = np.zeros(len(merged_df_W16))\n", + " count_Y_Placebo = np.zeros(len(merged_df_W16))\n", + " count_Nemo = np.ones(len(merged_df_W16))\n", + " count_Placebo = np.ones(len(merged_df_W16))\n", + " \n", + " for i in range(len(merged_df_W16)):\n", + " cutoff = merged_df_W16['PRED_'+str(k)].iloc[i]\n", + " count_Y_Nemo[i] = merged_df_W16.loc[(merged_df_W16['PRED_'+str(k)] >= cutoff) & (merged_df_W16['TRTP'] == 'Nemolizumab') & (merged_df_W16['CRIT1FL'] == 'Y'), 'CRIT1FL'].count()\n", + " count_Y_Placebo[i] = merged_df_W16.loc[(merged_df_W16['PRED_'+str(k)] >= cutoff) & (merged_df_W16['TRTP'] == 'Placebo') & (merged_df_W16['CRIT1FL'] == 'Y'), 'CRIT1FL'].count()\n", + " count_Nemo[i] = merged_df_W16.loc[(merged_df_W16['PRED_'+str(k)] >= cutoff) & (merged_df_W16['TRTP'] == 'Nemolizumab'), 'CRIT1FL'].count()\n", + " count_Placebo[i] = merged_df_W16.loc[(merged_df_W16['PRED_'+str(k)] >= cutoff) & (merged_df_W16['TRTP'] == 'Placebo'), 'CRIT1FL'].count()\n", + "\n", + " pct_respY_Nemo = np.divide(count_Y_Nemo, np.where(count_Nemo == 0, 1, count_Nemo))\n", + " pct_respY_Nemo_err = np.sqrt(np.divide(pct_respY_Nemo * (1-pct_respY_Nemo), np.where(count_Nemo <= 1, 1, count_Nemo-1)))\n", + " pct_respY_Placebo = np.divide(count_Y_Placebo, np.where(count_Placebo == 0, 1, count_Placebo))\n", + " pct_respY_Placebo_err = np.sqrt(np.divide(pct_respY_Placebo * (1-pct_respY_Placebo), np.where(count_Placebo <= 1, 1, count_Placebo-1)))\n", + "\n", + " merged_df_W16['COUNT_NEMO_'+str(k)] = count_Nemo\n", + " merged_df_W16['COUNT_PLACEBO_'+str(k)] = count_Placebo\n", + " merged_df_W16['PCT_RESPY_NEMO_'+str(k)] = pct_respY_Nemo\n", + " merged_df_W16['PCT_RESPY_NEMO_ERR_'+str(k)] = pct_respY_Nemo_err\n", + " merged_df_W16['PCT_RESPY_PLACEBO_'+str(k)] = pct_respY_Placebo\n", + " merged_df_W16['PCT_RESPY_PLACEBO_ERR_'+str(k)] = pct_respY_Placebo_err\n", + "\n", + " # Drop duplicates, sort and drop values with counts smaller than 10\n", + " merged_df_W16_sorted = merged_df_W16.drop_duplicates(subset=['PRED_'+str(k)]).sort_values(by='PRED_'+str(k))\n", + " merged_df_W16_sorted = merged_df_W16_sorted[(merged_df_W16_sorted['COUNT_NEMO_'+str(k)] > 10) & (merged_df_W16_sorted['COUNT_PLACEBO_'+str(k)] > 10)]\n", + "\n", + " # Superiority test\n", + " super_test_delta = .05\n", + " super_test = (merged_df_W16_sorted['PCT_RESPY_NEMO_'+str(k)] - merged_df_W16_sorted['PCT_RESPY_PLACEBO_'+str(k)] - super_test_delta).values\n", + " super_test_err = np.sqrt((((merged_df_W16_sorted['COUNT_NEMO_'+str(k)]-1) * merged_df_W16_sorted['PCT_RESPY_NEMO_ERR_'+str(k)]**2 + \\\n", + " (merged_df_W16_sorted['COUNT_PLACEBO_'+str(k)]-1) * merged_df_W16_sorted['PCT_RESPY_PLACEBO_ERR_'+str(k)]**2) / \\\n", + " ((merged_df_W16_sorted['COUNT_NEMO_'+str(k)] + merged_df_W16_sorted['COUNT_PLACEBO_'+str(k)]-2))).values)\n", + " \n", + " merged_df_W16_sorted['SUPER_TEST_'+str(k)] = np.divide(super_test, super_test_err, out=np.zeros_like(super_test), where=super_test_err!=0)\n", + "\n", + " icol = icol+1\n", + "\n", + " merged_df_W16_sorted.plot.scatter(x='PRED_'+str(k), y='PCT_RESPY_NEMO_'+str(k), s=2, ax=axes[irow, icol], color='blue')\n", + " merged_df_W16_sorted.plot.scatter(x='PRED_'+str(k), y='PCT_RESPY_PLACEBO_'+str(k), s=2, ax=axes[irow, icol], color='red')\n", + " \n", + " axes[irow, icol].fill_between(merged_df_W16_sorted['PRED_'+str(k)], merged_df_W16_sorted['PCT_RESPY_NEMO_'+str(k)]-merged_df_W16_sorted['PCT_RESPY_NEMO_ERR_'+str(k)], \n", + " merged_df_W16_sorted['PCT_RESPY_NEMO_'+str(k)]+merged_df_W16_sorted['PCT_RESPY_NEMO_ERR_'+str(k)], alpha=0.2, color='blue')\n", + " axes[irow, icol].fill_between(merged_df_W16_sorted['PRED_'+str(k)], merged_df_W16_sorted['PCT_RESPY_PLACEBO_'+str(k)]-merged_df_W16_sorted['PCT_RESPY_PLACEBO_ERR_'+str(k)], \n", + " merged_df_W16_sorted['PCT_RESPY_PLACEBO_'+str(k)]+merged_df_W16_sorted['PCT_RESPY_PLACEBO_ERR_'+str(k)], alpha=0.2, color='red')\n", + " axes[irow, icol].set_title('PCT_RESPY_'+str(k)+' by PRED'+str(k), fontdict={'fontsize': 8})\n", + " axes[irow, icol].set_xlabel('PRED_'+str(k), fontdict={'fontsize': 6})\n", + " axes[irow, icol].set_ylabel('PCT_RESPY_'+str(k), fontdict={'fontsize': 6})\n", + " axes[irow, icol].axhline(y=merged_df_W16_sorted['PCT_RESPY_NEMO_'+str(k)].iloc[0], linestyle='--', color='grey')\n", + " \n", + " X = merged_df_W16_sorted['SUPER_TEST_'+str(k)].values\n", + " Y = merged_df_W16_sorted['PRED_'+str(k)].values\n", + " test_X = np.nonzero(X < 1.645)\n", + " if len(test_X[0]) > 0:\n", + " x = Y[test_X[0][0]]\n", + " axes[irow, icol].axvline(x=x, linestyle='--', color='grey')\n", + " axes[irow, icol].text(0.95, 0.95, \"Cutoff=\"+str(round(x,2)), transform=axes[irow, icol].transAxes, \\\n", + " fontsize=8, ha=\"right\", va=\"top\")\n", + " \n", + " if icol==ncols-1:\n", + " icol = -1\n", + " irow+=1\n", + " \n", + "for ax in axes.flat:\n", + " ax.set_xlim(xmin, xmax)\n", + " ax.set_ylim(ymin, ymax)\n", + "\n", + "plt.rc('xtick',labelsize=6)\n", + "plt.rc('ytick',labelsize=6)\n", + "plt.subplots_adjust(wspace=0.3, hspace=0.3)\n", + "fig_name=RESULTS_PATH + r'\\predictors.png'\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predict on phase2b data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df2b = pd.read_csv(DATA_PATH + r'\\easi2b_by_zone.csv', na_values=' ', index_col='USUBJID')\n", + "\n", + "m02b = df2b.values[:,1:].astype(np.float_)\n", + "m02b_nan_0 = m02b.copy()\n", + "\n", + "# create m0_weight with ones and zeros if not_missing/missing value\n", + "m02b_weight = np.where(np.isnan(m02b), 0, 1)\n", + "m02b_nan_0[np.isnan(m02b_nan_0)]=0\n", + "\n", + "max_values = np.max(m02b_nan_0, axis=0)\n", + "# Replace maximum values equal to 0 with 1\n", + "m02b = np.divide(m02b, np.where(max_values == 0, 1, max_values))\n", + "m02b_nan_0 = np.divide(m02b_nan_0, np.where(max_values == 0, 1, max_values))\n", + "\n", + "my_nmfmodel = NMF(n_components=n_themes, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=0)\n", + "estimator_ = my_nmfmodel.fit_transform(m02b.copy(), h=h4_updated_sparse, update_h=False)\n", + "w42b = estimator_.w\n", + "h42b = estimator_.h\n", + "scale = np.linalg.norm(h4_updated_sparse, axis=0) / np.linalg.norm(h42b, axis=0)\n", + "h42b *= scale\n", + "w42b /= scale\n", + "\n", + "cluster = np.argmax(w42b, axis=1)+1\n", + "# cluster = np.argmax(normalize(w4_ism, norm='max', axis=0), axis=1)+1\n", + "\n", + "calinski_harabasz_score = metrics.calinski_harabasz_score(w42b, cluster)\n", + "print(f'calinski_harabasz_score: {calinski_harabasz_score}')\n", + "# silhouette_score = metrics.silhouette_score(w4_ism, cluster, metric='euclidean')\n", + "# print(f'Silhouette Score: {silhouette_score}')\n", + "\n", + "df_w42b = pd.DataFrame(np.column_stack((w42b, cluster)))\n", + "df_w42b.columns = ['theme_' + str(i) for i in range(1, n_themes + 1)]+ ['nmf_cluster']\n", + "\n", + "def calculate_value(row):\n", + " for i in range(1, n_themes+1):\n", + " if row.iloc[n_themes] == i:\n", + " return row.iloc[i-1]\n", + "\n", + "# Apply the function to each row\n", + "df_w42b['nmf_cluster_loading'] = df_w42b.apply(lambda row: calculate_value(row), axis=1)\n", + "df_w42b.insert(0, 'TRTP', df2b['TRTP'].to_list())\n", + "df_w42b.insert(loc=0, column='USUBJID', value=(df2b.index.to_list()))\n", + "df_w42b.set_index('USUBJID', inplace=True)\n", + "df_w42b.to_csv(RESULTS_PATH + r'\\w42b.csv', sep=',', na_rep='.',index=True)\n", + "\n", + "df_easy2b_by_time = pd.read_csv(DATA_PATH + r'\\easi2b_by_time.csv', na_values=' ', index_col='USUBJID')\n", + "merged_df = pd.merge(df_w42b, df_easy2b_by_time, on='USUBJID')\n", + "merged_df.to_csv(RESULTS_PATH + r'\\w42b_easi2b_by_time.csv', sep=',', na_rep='.',index=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot word clouds of the survey items\n", + "ncols = 2\n", + "nrows = int(np.ceil(n_themes/ncols))\n", + "\n", + "irow = 0\n", + "icol = -1\n", + "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8, 6))\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_h4_updated=df_h4_updated_sparse[df_h4_updated_sparse.columns[[0,i]]].set_index('label').T.to_dict('list')\n", + " for k in sub_df_h4_updated:\n", + " sub_df_h4_updated[k] = sub_df_h4_updated[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, relative_scaling=1).generate_from_frequencies(sub_df_h4_updated)\n", + "\n", + " icol = icol+1\n", + " title = 'theme ' + str(i)\n", + " axes[irow, icol].imshow(wc)\n", + " axes[irow, icol].axis('off')\n", + " axes[irow, icol].set_title(title)\n", + " if icol==ncols-1:\n", + " icol = -1\n", + " irow+=1\n", + " \n", + "fig_name=RESULTS_PATH + r'\\word_clouds_items.png'\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot individual word clouds of the survey items\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_h4_updated=df_h4_updated_sparse[df_h4_updated_sparse.columns[[0,i]]].set_index('label').T.to_dict('list')\n", + " for k in sub_df_h4_updated:\n", + " sub_df_h4_updated[k] = sub_df_h4_updated[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, relative_scaling=1).generate_from_frequencies(sub_df_h4_updated)\n", + "\n", + " title = 'theme ' + str(i)\n", + " plt.imshow(wc)\n", + " plt.axis('off')\n", + " plt.title(title)\n", + " fig_name=RESULTS_PATH + r'\\word_clouds_items_theme '+str(i)+'.png'\n", + " plt.savefig(fig_name, dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot word clouds of the surveys\n", + "ncols = 2\n", + "nrows = int(np.ceil(n_themes/ncols))\n", + "\n", + "irow = 0\n", + "icol = -1\n", + "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8, 6))\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_q4_ism=df_q4_ism[df_q4_ism.columns[[0,i]]].set_index('score').T.to_dict('list')\n", + " for k in sub_df_q4_ism:\n", + " sub_df_q4_ism[k] = sub_df_q4_ism[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, relative_scaling=1).generate_from_frequencies(sub_df_q4_ism)\n", + "\n", + " icol = icol+1\n", + " title = 'theme ' + str(i)\n", + " axes[irow, icol].imshow(wc)\n", + " axes[irow, icol].axis('off')\n", + " axes[irow, icol].set_title(title)\n", + " if icol==ncols-1:\n", + " icol = -1\n", + " irow+=1\n", + " \n", + "fig_name=RESULTS_PATH + r'\\word_clouds_surveys.png'\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DEkf6iFfvx3rbUjaPaFLHSW8HLsooHHGAxtn+bsT089/rYocHHlPqysea1PGnxpN46NxBhPTzzagCQLEjDTuyS7AxowB5NhdMioKYrqMt5MOhnhbs6ahHc7DP2E9Nxu/C08w2fHj5tninfMp3HZsmJbrDQZzo68C3T+2CdyC4TSLet7jCk42lzgykW2wABPqiIdT6e3DO14M6fw9CWgzr0wumfNeyoEJBCAGzUJFmUZGW4uduswWP1B5BbAYveANaFEe9bdieVYwvrr/amD29ypODArsbnz38F/QNXNHoA806Mamjwp2Fr254M4odacaBX+bKwD+//ueENtbj3jZENA120/hu8tqGdPZtzCjAPZU7sNKTDdOwL/Ka9DxcmbcMJ/s68N3Tu3Gst834mQ6JPzaewCU5pViXnrdoRqIsFFJKvNB2Fn9sPJEQCCah4Or8cnxg2UUocaYl9ROsSweuzi9HS7Afj9QewZNNp4zS8zGp4ze1b6DclYlr8suTjolcmwtZVrsRClFdQ52/F9uyilPuY0TXcLKvw/jvtWl5qPX3xK/AAZzu64QmdZhEcvu9BFDj70noFyl1psNuSm5uklLiWG8bfn729YRAsKkm3FyyBrcuWYdcmzPp91mL+PejtXQ9Hjp3EH9uPmNcXGpS4rH649iYUYCLs5dMaRj4mf5OvNReg/aQHwJApScb/2fpBmzJLEJ6ioEfUkr4YhE0BrzY1VGHizIKR+3vGY8F13w0F9gUE25fugHpZpvxmBAC69LzsCWzKGHbmNRhEgpuX7ohIRAAIM/mwqW5pQnbtwT7jav+iVjpycEX1l6JNWm5SYEwSFUUrEnLxefXXIFlroyEn/VFw/hDwzFEZ/Aui2ZGRziA/605lDC4QIHA24tW4l9WXYZSV8aIJxJl4Kr/4yt24o6lGxOahEJ6DD8/eyBhpb9BFkVFuStxLZVqX/eI+9ge8qE1eP51tmYVJTT/nPN1G6N0hovoWsKoIwGg0p26MzyoxfCLcwcTmlLNQsFd5Zvx4Ypt8fb4EU7qihAodHhw78pLcH1hRcIdQUCL4uGawwhoIzepjcffWs/iSE8LBICr8svxzY3X4835y5FpdaQcCSiEgNtsxaq0XHywfAs2ZRZO6f0BhsKMyLO5sCotJ+ngMisqtmQVJW2fa3Nia4rhekIIrE7LTTj4wrqWsjNuNFZFxfuXbUKB3T3mVb4QYmCZ0s1JZcb3dTaiYUjzF819upR4rrUatcM+txWebNy1fAtcZsuYzR1CxPsZbi/bgO3DrvTr/L34c/OZpAEVYuA9hqrxdadsupVSoj7gNU7UFkXF6rRcLB0SCi3B/hGP+5AWQ33g/O+nCJH03oPvs7+7EYe6mxMevzinFLeUrIVlnKOI7KoJ7192EfJsroTHj/W24Y2e1INFxiuqa5AANmcW4pMrL0HuKCE1nBBiWoaQMxRmQIkzDc4R2vWWuTKTOr7LXBkJdxVD5dqcCR+0puspO+NGU+bKSBk6IxFCYEd2SdIXyxsNYX9XEzuc55GgFsXfWqoTTtoKBG4tXYesCRSGFELAqZrxD0vXwz5k1JEE8LfW6oR+qEFlrgxYlfPbdoUDKbcDkDAU266asNSZkXD8RXQNVX1dKZ/bHvLBO+R1Myx25KcYuq1JiWebqxAeMkTcpprw7iVrUzY1jUQIgQK7GzuzlyQ8HpU6Xm6vSTnicCJcJgs+tHwrMiz2WVn/i6EwA4ocHozU3eQyWZKG8pU6M0ZMeJtigmXIF0tHfALaRGzOHH2M90j7uWPYQS8RnweSchw3zTmDHbB1w+4SCh1ubM0smnDfkBACa9JysXzYEruNfi9O9rUnXCwIIVDiTEvo9OyJBNEZ9mM4Teo47j2/amO21YlMqx0rPDnGyJ+Y1HG6vzPphCulRJ2/N+FEX2j3pLzIagv1J/SVAcASRzpWpeWM59dPoEBgQ0Z+0sikU32d8E+ieXeozZmFWJM2e313DIUZkGVxjJjwJqEkzZ7OtTpH3F4VSsKIHzkw1X28TEJBhTtrwlccQghsyihICqsaXw+CU2w3pQvnZF9HUjv3Sk/OpNeGtqtmbMgoSHgsKnUc7mlJGjmUZXEg13Z+VcOgFkuarQwA3ZEgGgNe47/LXBmwKCrybC5kWs7vZ1VfZ9Ia6xLxuUFDm6XKXBlJF15SSlT3dyc1Qa30ZMMxwQsmIP79yLe7E+6EAKA12G8MJJkMAeDS3KWjTvKbaQyFGeA2jzwkTBEioflIAHCNsj0Ekq4YJtJ8Y1ZUFNo9k7rqyLW5kDbsissXC6MzPLE+DZodEvET6XArPTmTbpZQhEClOyvppHW2vzvphG1S4hckQ6XqbG4J9qN9YJScQLyJ1STiZVeGzuCv9fckXYVrUsc5X+IM6FWe5P48ADjT35V0p1Hk8CCkxxDUohP+Zx6oCzWULxaZ0p2C02TBMlfGrI7wW1BDUucK6wRmHCtCwKrM3OpvJqEg0zq5RYVcJgs8ZmvCSI2Qphnjp2muk2hMMemrxJlqwPb4Fdo9UIUCTZ4PgZZgP6K6lnCFrkBg+cBd6uCp+OzAiXnoHehxb7txpW8SinFStCoqlrkyjZn+/bEIav09CbPro7qG2iGhYFFUlA8LokH1w8pnAMAjtUfwRMPJCf8NgPgM5OEjASXkiKOkxsOumhPujmYDQ2GaCUyskqKAmNFbRVWISd0eA/Fwsw8LuKiuTbnNlC6MsK4lDUowCQUek21KFyHpFltSs2JvNISontisKYTAMlcmrKrJGA7bEvLBFwvDM3AHquk6jvee70+wqCrKBoZDx0ff5eCJxoHfR4uhur8rYRJbRziQcNGSZXUgb0iT1SAJpLzD9UbD8E6huSfV+8SkNuZ2IzErStJ37kJj89E0E8AkhoXNYCgoyqQns5iEkjRZSJMyocAfzV1RXU+aV2JSFFgmWRF4kFmoMA87LmK6joiePABiiTM94aKkI+RPGIHUFwujxn++SSnf5k5YLrfclWncfUjEO3IHBzpIAI0Bb0KfSb7NlXK53aiuJXRGz6SpjMMQEFCmOPlsqninMN3m2GTfqRxeQqSanCmhc/DRvCClTDpDCST3UU2YSHXhI1OOSnObLCh2pKF74Gq+PxpGe8hvTEzrDAfQPKSExnJ3Fhzq+T62fLsb2VYHGgc6qM8MdDabFAUYGHk0tC+jwp2VspaXjtQDNEwp+gWmQhHKlOcKzPYphKGwwE1l+KgmZYpJSYL1j+YJVVGSrjr1CY5eS0VKmTQJTQiRsoS0RVVRPqRfQEe88N3gJM4zfZ2IaPGTukB8NNDQk3S6xYZiR5oRCl3hAFqD/VjmzoSExLlhHderBtYyGU6FSLpjFgA+WrEdGzMLUj5nskYrxDcfMBQWuJjUEU5xWz8eUV1DeNicCJOiTKgjnWaPVVFhUxNP1BFdQ0iLQUo56TuGoBZDbFhTTHw+TXIoKBAod8cnbA5OTjvn6zFG0FX1dxqPm4QSn8E/ZL8UxOdGvNbZACA+uqcu0IsyVwY0KVEzpJPZaTKj1Jm64J5JUWEbNnxUAsi2OUYcrbRYsU9hgdOknHRHWkCLwj9sjLtFUeE2p64+SXOLKhTkWBM7XSWAjhQTyCaiM+xPuoPMtDpgTtFXIUR8BNLQnzUH+xHRNYT1WMJJPdvmRKHdk/QaKzw5RpNQTOo4MzDMti8aRnf4fCdzutk+4iJUAkBOig7otuD41lFYTBgKC1xU14yVnSaqLxJC77A1GObCkDkaHwGkHJ55rj95aOZ4Dc4gHt4sWTxQcjuVIrsHniEXEu1hH4JaFIFYNGG2dakzHWnDLjgGa3ENff7pvvjdRUfID1/s/AXPaOWyASQVeQSAKl9XynLdixlDYYGLj+PunVS9onO+nqSSGtlWBzIsqes00dyzashV9qAT3vakiWbjpUmJE96OhDLVg1VJR1qsyWmyoMSRbvx3ZyiAQCyK1pAvYbGflZ6clP0SeTZXwh1PY8ALfyyCzrA/YeTRCk/OqJ28Kz05SUUeT/d1oo/zbhIwFBY4CeCN3tYJLyykSR37uhqTHl+dlptUpoPmJiEEVqblIHtYSYtqXxcahpSVGC8pJXoiQRzuSawyalVN2JSZegEcIF50rmzIVXpQi6IrEkSNr8cY3qxAjLhWh1lREorj9UZDaAv50RzsN+5YVCGSZk8PJQZ+PrxQXlOgL16ig/W8DPx2LwJHe1vRFOib0IHfEuzH/u7EUDArCrZkFk3b8qZyYHRTqn/8kk6PbKsD27MTF6/vi4bxTPOZCY9CkgBebq9JWj9hpScnZdPMoMET8uBxIyHRHOhLaIZKs9iwZMjdxFAK4uE2eNT5ohG0BfsTgs2hxpffHK3DON1ixyU5ieuTxKSO39Udm9QaJQsVh5EsAj2RIB5vOIGPr9gBFWPPto7qGp5oOImOUGKHZKHdgw0Z+dM2UqMl2I/7j76QsPgLANhNZnxq1aUod2VyVMgUKRB4W9EKvNh6Dn1D2t+fbjqNy3KXYkP6+D5PKSUaAl78tu5oQn+CKgTeVrhi1FnzAkCFJwuqIqDr8dBvDHgT+hOK7G5k21L3VQkhUO7KhF01I6BFoUPinK8nobhemsWKwhE6mYfux1sLK/HXlipj3gQAHOltwa9rj+ADyy6CRTVN6pJHSgmJaZoHMst4p7AISABPNZ3CU82nx2xGiuk6Xmg9hycaTyR0wCkQuL6gAukTqME/lqZgH472tuF0f2fCv6r+rqShsDQ5Qgis9OTg2mErhfVGQ/j2yV041dcxZv1/KSWagn341olXk5qdNqQX4PK8pWOeCHOsLmRb4v0CEvHPvm5ILaIKT3bSkNGhljiGdzZ3JJStKHNmJlVGHU4IgWXuTNxYvCphLoQmJR6tPYKfnT2A3khw3HepcmB2f3vIh7+0VOHpptPjet5cN2N3CprUcaC7KSGRJ2qwA2vpKLemYxlMcE3qiOp60kgDHRIRXYM6ZGbjfE/6oQbHhwe0KL5/ag/O9nfjnSWrUWBzw6oOFOIbmIzUFQ7gz82n8Zu6o0m308vcmXhr0YppWdlpUI2vBzLF2I8Cmwuls1wpcqaFtRhe7ahNWZJhvFQIbMosTCgQl4pJUfGepRtx0tuesG5BVX8X/vXI33Bb6TpckbsM6RYbLIoKIUT8hCfjCzq91tmAX9ceThg+CsTrDH24Yivco4z4GeQxW1Hk8KAlFJ+9fMLbgdYhzVDr0/NHfb7TZEGZK8N4zlFvG2JDai1VerLGVc5FFQr+oXQ9TvZ1GHMfgHidqEdqj+BAVxNuKKzEtqxiZFrtMAsVihCQAHQZLxsS0mJoCfTjTH8n3uhtxbHeNnSGA7i+sBJvK1ox5j7MdTMWClFdx8M1h0dcbGY8FCHw0crtY4aClBLNwX7UDJTW9cciCMSi5/9/LYqgFkNHyJ9wIAHAqb4O3H/0BThNFthVExyqGQ6TBU6TBU6T2fjf1Wm5RhGv+eSSnFL0RoM42tuGgBbFH+qP4a8t1ahwZ6HY4YHLZEVM6mgP+XDS24G2kA/Do9NtsuIjy7cmjXmfCinjs1FTXZNdlFk46SJ+80V/LIIHT++Z0vfDoqj4j03XjxkKAvEV/D656lJ88Y0XEpaubAn24/un9+CR2jdQ6c5Cnt0Nu2pCWNPQEfajqr8LrcH+pDtMt8mKeyp3YN04m58GK54e6G4CEC+DPchlsmDZGE2FJkXBCk8O9gycyNuHNG2ahRJ//ph7EZdmtuKTKy/Fl469aMy0BuJ3DCf7OnC6vxMO1Yy8gTpKFkWFDomQFkNfNISucBAhLYqorid9VxaCGe1TiLc9Tv6PJiTGvbTdn5tP46FzB41SLzLlNWiynkgIuzrqEt/X+F8Rr/8DgW9fdENSh91cZxIKriuswFJnOu4/+gKq+uNjsr3REA50Nxlf0NE4VDM+UrENO7KXTOuVe0iLpRwBY1VUbM8umfX6LxfCVL8fihTjPikJIbDKk4P71l2Jr594BVX955e21KREW8iX1IE8kiyLA/dUbse1BcvH/TkNjoRKJcfqHFd/QKU7C2ahJBX5s6mmMUNl+L4UOzy4b91V+O6p3djdWZfQT6JLCV8sAl+KtR8WgwXTpxCv6SKhI/5vKvk9+FXVB4p8xeT8vB5wmixY5clGuSsTD6y/BjuySyZU/KvA7sZn11yOG4tXTfswVL8WTbkKV77NhTXDSh3Q9IiXos7FNzZeh7cWrhizDX44VQhszizEVze+GdcVVkKBmNDntGzYms3G4+7MEdc0H7rvy91ZKfc52+pMGnY7FiEECu1ufHH9Vfinyh0osLsnfSEyeCe2wp095rbzwTTdKQiYFCVpYshUKWL8aw0oYvrf35CyKqTxI5iHvLcilFGHbArEV0Mb3N6kqFBG+RXjBejOb28eaPNNua2IDxsd3LbSnYUMiwNCCJS5MvDl9dfgb61n8XTTaVT1dyGqa0mTkFShIMfmxOW5S/GukjVY4kibkRN0e8iXUAd/0I6cJQuyjIZ5Bo5P8xjHWiqDi85/ZvXluK6wAk82nsKhnmb0RILxocBDt0X8uHeZLFidlovrCypxcc4SuEyWSR0T2VYnCuxuNCV1VueP67fItDhQ4kxDVV9XwuMV7qxJ1eMSQsBpsuAfS9fjitwyvNB2Dq+216La142QFk36ewDn/yYmoSDX5kKlOws7cpZgc0Yh8u3uCf1dhp87AKQsFXKhCTnuAeF1I/5ElxLtId+M1CvPsNjGbMuXUsIbDU3rYhnD5dqcsKdo547qGtpCvoTbz2yrY8Qrn+Hbi4HtHaNs3xo8384vEO/gS/X6MV1HW8hntP86VDOyrY6EA1UO3Bo3BLw43deJlmA/gloUJqEgy+rAMlcmlrszkWV1THodhrFIKfFk0yl8/cQrCc2DdtWEb226AReNMhFqPkp1jEyHwStU2xT6X6K6hs5wAGf7u1Hn70F3JIiwrsGiqMiw2LHEkYZlrkzk2JwTvrMYTh9opho+mzrb6oBDNY/5metSoiPsTxrC7DJZkGmxT/mYkTI+IKMj5EeNPz7ktWfg7yEQPz7TzHbk210ocaQhy+pAmtk26bvo4d9XIN7kW2B3zeCaCqVjbjEtoUA0EbqU+O6pv+N39ccSHl+XnofvXPTW0desJqIpGDsUZv9ehRad+LKKiZ14CgSuylsGp2lhjzoimusYCnTB+WKRhNmsAJBlteOy3LEnQRHRzGIo0AUlEZ+0Nnxy3MU5pci3jT4skYhmHkOBLiwpcaa/M2GRd7tqwg2FldO6Vi4RTQ5DgS6omNRx0tuRMNRvY0YBVnqy2XRENAewSipdUBFdQ3vYZ9T8UYTAjcWrpzSskoimD4ek0gWlS4lALIKhhQqcqhnqHJi0Q7TwjT0klXcKdEEpQsC1AGcsEy0UvDwjIiIDQ4GIiAwMBSIiMjAUiIjIwFAgIiIDQ4GIiAwMBSIiMjAUiIjIwMlrRItAU70XJ460Q+qJBQyEEFizKQ+FJZ5Z2jOaaxgKRIvA8UNt+N4Du6BpiaGgqAKfvP9yhgIZ2HxERESGRXunIKVER6sfAX90tnclgRBAVo4DLg/rAxHRhbeIQwH44Tf2YP+rDbO9KwkUVcHH/u1iXHtj5WzvChEtQos2FABA12RSG+tsk1LHuKuZExFNM/YpEBGRgaFAREQGhgIRERkWdZ9CeqYNeQWuST03GtPR3RFI+TOTWUFmtgOTWYZeUQXsDq5XTESzY9GGghDA3Z/ajmhEH3vjFOrP9uDz9/wl5fNLlqbhyz+4Doo6uRsxh5OhQESzYxGHgoDLPfm5AD2dAWCEewHVpCA9yw51kqFARDRbeNYiIiIDQ4GIiAwMBSIiMjAUiIjIsGg7mimRlBK6JtHfF4avP4JwKAZNk1AUAZNJgc1ugsttgcNpgRi4lBBiMoNuF5fBkiVBfxT9fWEEAzHEYjqkLqGoAharCofTAneaFWZz/A/LvyvNJobCAhGL6ThzvAOhYCzh8cxsB0qXpUMoyScaKeO1nxpqevH67kYc2d+C1qZ+eHtCCAZi0GI6FFXAbFbhcJmRlm5DboELK9fnYtX6XJRVZMCdFh/BNd4TWTSq4dTRDkQjWtLPXB4ryldkTtuorVhUQ9XJLgQDyZVwbXYTVqzNGfd7+fsjOHOiA0PLUgkBLC3PQHqWPeH3HwyC7s4g3jjQgoN7mlBb3YOeriD8/RFEoxp0XcJkUmC1meDyWJCV60Tl6mxs3lmEFety4HRZ5l04SCnRWOtFR5t/xG0ysuxYujxj3v1uiwlDYYEIBaL47v27UH+uN+Hxi68qxWe/diUsVjXhcV2XqDnTjccfPob9uxrQ1xtO+bq6LhGL6ggGouhqD+DcmW689nI9zBYFBcUevOO21XjLLSsx3u+4vz+Cr3/mRXSlmPi3bnM+HnjwWtgd0xMKfl8U3/vSLtRW9ST9rKQsHd//9TvG/V4NNb34/Ef/klRA8f9+Zifecdsq47+llOjpDOKZx0/jhaer0dLYD11PXeAwommIhDX0e8NoaejHsddb8eRvTmBpRSbe9u6VuOzNZbA7zfPiBCp1iTdeb8V3vvgKWpt8KbcpKHbj3vsuu8B7RhPFUFjgejqD0DQdQDwUpJSIRjT85Ykz+M3Pj6CrPfWs7LFEIzoaanoRjWjjDoSFqLa6G1IHhBqvunv0YAt+9p39OHuqa8QwGE00qqPqRCce/OpuvPZyPe68dxuKSz1zOhh0XeKNAy347v27RgyEkrI0/PN9l2HVhtw5/bsQQ2HB6+kKIhaLz7qWUiIUjOHXPzmEJ39zAuFQchPORNjsZqzfUrCov+S11T3QdB0QCv7+Qi1+9I096O4MTvl1Y1Ede16qR2d7AJ984HKUlqfPyb+zrksc2d+M796/C23NqQOhrDIT/3zfpahYnT0nfwdKxNFHC5zfF0FwYHW5SETDwz86iCcePj7lQACA0vJ05Be5p/w681lnmx/enhAO7W3CD78+PYEwVNWJTvzga7vR2x2cc+tsSF3i0N5mfOe+kQOhck02/uXLb2IgzCO8U1jgYlEdPV1BZOU68NRvT+JPvz1h3DkMZbGqSM+0wem2wukyw2xREY1o8PVH4OuLwNsTSuocXntRPhyuxV2nydcXwaHXmvH4r46hpys5EGwOEwqKPFi6PAP5xW443RYIEX9eU50XNWe60dLUj1h05Bpcxw624olfH8f77tkyZ5rqdF3i4GtN+O79r6KzLXUT5KoNufjn+y5DSVkaA2EeYSgscFosHgqH9zbj0Z8dTijgp6gCpeUZuPjKUmzcVoj8Ihc8GTaYzec7paMRDb3dQbQ1+3DiSDsO7mnC2VNdCIdiuGhn0aL/sgcDUfzk23vR703sqHe4zLjsmjK8+R0VWLYiEza7OemELiXg6wvjyP4W/OF/j+LMscTRTUO3++v/V4Wr37YcS5ZlzOBvMz66LvH67kZ89/5dKQcMAMD6Lfm4977LUFDsXvTHyHzDUFjgYjEdp492YN+uBvj6IsbjuQUu3PK+dXjTdcvgSR95WKnVZkJeoRu5BS6s25yPd96xBg01Xhw72IryFZkX7PeYy4YHwpJl6bj7k9uxcXshVFWMeFIUAvCk23DpNUuxZmMefvbdfXjxz2dTBkNPVxAv/6UGd3xkdvsWdF3iwN8b8b0HUgeCEMCmHUW49wuXIiffyUCYhxgKC5yuSzzxyDEEA+fnL6xan4t/+txOlK/ISjl/IZXBL7fVasLylVlYvjJrRvZ3vluyLB2f/uoVKF+ROe4TohACmTkO3P3/tqOj1Y+jr7em3O7A3xvxzjvWwuW2TOcuj5uuS+zf1YDvf/nvqQNBAbZftgT3/OvFyMp1MBDmKXY0LwJDA2H5qix88kuXo3zl+AOBxsfltuDDn9o+oUAYKi3Tjts+uBE2e+prtYaaXnSOMjFsJum6xL5XG/D9L/095TBmRRG49JoyfPzfL2EgzHMMhUXEk27Fh/9lB4qWzO1x7/PV1W9bjg3bCif9txUCWLspD8tXpb4LCwZjaKjpncIepnpPAVUdfX91XeK1l+rw/S+lbjJSVYEr31KOez53MTKGze6m+YehsIi8+R0VWLMhj1/aGZCWYcP171oBk2lqXymLVcXGbYUpfyalRGOdd0qvP5yiCFhHuDMB4oGw58U6fP/Lu1MOt1VVgTffWImPfHoHPOlWHlsLAPsUFon0TBuuvbHSKGZH02vtpjyUlKVPy2stW5EFRRHJM6Il0N0RgJRy2k6+iiJgt6ceVqxrErtfrMWDX9kNb08o6eeqSeCtt6zC+z+2GXbH/CjHQWNjKCwSqzfkoaiU48VnytbLSsZshhkPIQTSM2yw2tSEvqBBAV8UkBhpJdgJU1WRsg9D03T8/fla/OBre1IGgtms4Mbb1+COj2yC1WbicbWAMBQWASFgDI+k6edwmuMd99N0YrTYTDCZVADJoRCLadClhDpNqaAoAjZH4mlgrECwWFXc8r51+Ic7N8BiVRkICwxDYRGw2kxztnbOQpCeaUd2rnPaXk9VxYjNfLqO+J3CNFFUAduQ5iNdl3jt5Xr84OupA8FmN+G2uzbgne9ZC6uVp4+FiJ/qImCzm5CdN30nLUrkSbca60rMN4pyvvlISomjr7fiR994Dd7u5ECwO8x4z//dhLfdujqpFDstHAyFRcBsVuFJs832bixYaZn2eds0Z7aoRlmTc6e78eBX/p5yLoTDacadn9iK695ZmVAGhRYejkVZBFSTgNnCj3qmuD2zM8N4OtgdZggFaK7vw3cf2IXG2tRDXpdVZuKK65dNecgtzX38hBcBVVXYnzCDLPO4bd3mMKOnK4gHv/p3VJ3oHHG7k0fb8ceHj6WssEsLC0NhMWAgzKj52nQExNex/um39+Hw3uZRt9NiEo/96hief6p6YCU/WqgYCkSLWG11D/a+Up+yMutwoWAMDz34Og7vbZ5zC/7Q9GEo0AImISexTvJiomsyKRBcHgvSMlIPTOjtDuJH33wNNVXdDIYFiqFAC5aux5s9aHxUVWDjtkLc951r8P/uv2zEYGis9eIHX9uDzvYAg2EBYijQgqVpOsKh5FnBlCw714m77t2Gz3/7aqy9KB9bLinG++/ZPGIZ7+OH2/DTb+9FwB8dV9MTzR/zd9gE0RiiEQ2BQHS2d2PO23JJMe66dyuWLs8wRqmpqsA176hAR5sfv/35EWhacnG+Xc/VIjffhff+02ZOZltAeKdAC5a3J8Q7hTEoqsAV1y/D0uXJCwOZzSre/b71uOqty1MOYNN1iT/99gSeffwUNA5VXTAYCnRBCSFGrPCZVCp6iprr+6b9NRciIcSIo5btTjPu/MQWbNpRlPLnkbCGX/34EPa92sC/9QLBUKALymRSoIxwBgoFY9N2YpG6xNnT3dCHN3vQhKVn2vFPn92J8hHW5e73hvHf39qLM8c72PG8ADAU6IJSTQIWW+r2Z293ENGINi3vEwrGcPpo+7S81mInhEDhEg/u+dxO5Ba4Um7T2tSPH359D1qb+hkM8xxDgS4oRRFIz7Sn/JnfF0V7i2/Ko1kGl62sqeqe2guRQQiBFetycfcnt8M1Qq2nM8c78eP/eA2+vjCDYR5jKNAFpagKCorcKX8WDsVw/HAbprpggK5JvPjnswj4OfJoOimKwM4rl+COD18EiyX13d7+Vxvxyx8cRCQ8PXd8dOExFOiCUlWB0uUZI45m2fW32imdzKWUOHu6Cy88c5bj52eAqip4yy0r8LZbV0FRkj9EXZd49o+n8affnmDxvHmKoUAXlBACK9flwGpLPUXmzPEOvPTM2Ul1OEsp0dnmx8++sx+9XcGp7iqNwGI14R/v3ohLr1maMtxjUR2P/vQwdj1XyxFJ8xBDgS64sopMFC9NS/mzaFTHwwNDHCdSjVPXJeqqe/Cf972Ko6+3TNeu0gjcHivu/uR2rNmUn/LnAV8UP/vPvTh2sJX9C/MMQ4EuOIfLgiuuL0/Z/AAAPV1B/Od9r+A3PzuCtub+EcNBSolIWENTnRe//Z8j+MLH/opDe5uNZiMhAJOZh/hMycp14J5/vRhLlqWn/HlnewA//MYeNNR4GQzzCMtc0AWnKAJX3LAMzz1ZhdrqnpTb9PWG8fCPD+KvT5zBqo25qFiVjZx8F6w2FbGYjv7eMJob+nDuTDeqT3bB2xNM6kPYckkx0jPt+Nufqi7Ab7X4CCFQWp6Oj352J775by+huyO5ya62qgc/+uYefPorVyA908bFnuYBhgLNiqwcB+74yCZ8+wuvIjhCfSIpgbYWH9pafHjpmXPAwGRoCYw5QKmoNA133bsNNVXdeO7JKnY6zxAhBNZvKcBdn9iGH3xtd8pBAof3NeMX39+P//uZnbDZTQyGOY731jQrhBDYcUUp/s+HN43Y6ZxExoNirEDIL3LjE/9+CUrL07GkLH3E4ZM0PRRF4E3XL8Otd65P2VwndeCFp8/isf89ylLm8wBDgWaNyaTgxttX4+5PbUNmjmPKr6coAqs25OJz37gSay/KhxAC6Zk2ZOZO/bVpdCaTghv/cQ2uu6kyZV9RLKbjD788ihf+XM0RSXMcm49oVpnNKm545wpUrMrGY786htd3N8LXF5nQayiqQE6eE9fdVIkbbl6Z0HbtSbchN9+Flob+mdh9GsLuMOO9H92MrvYAXnu5PunnoWAM//O9/cjKceCinUVsRpqjhBz3sIC6md2TecbbE8Krf6tJOTImPdOOy95cNuLompkQiWjY9VwN+r3hpJ+53FZccf0yqKa5e2MopUQ0oqP2bDf2vdqAI/ta0NLYj2Agikg4hlgsvrSmogioJgVWmwqn24JllVnYcnERtl5WguwcBxRVSXrdg3ua0FjnTXjc7bHi8muXjXt0Uk9nALueTz3uvqwiE+s250/bSa6vN4S/v1CHSDi57HfhkjRs3lk04WOr/lwvjuxvTtp/RQhs3F6IkrL0qeyyYXCuyN5XRh5SXFDsxuadxXP6eFy4SsfcgqFAc4qU8TWDtZiOvt4QOtsD6OuNr4sQi0mYzApsNhPSM23IznPC5bZCNQledRKNC0OBiIgMY4cC79+IiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIymGZ7By40KSUkACkBAUAIQAgx27s1bXQpIWXiY0IAygL6HYlo5iyqUJBSoq7dh4dfqMbpxl44bWZcv7kYb9lWAotJne3dmxa/e+Uc/ri7FhgIBiGA97+5EtdvKZnV/SKi+WFRhUIwrOE//vAGdp9oMx47WtONDJcVl6/LXxB3DJ3eEM40eo3/FgB6fJHZ2yEimlemLRRO1PfghcPNCY+VF3pw3UXFUJS5cbLt9oXwRk13wmOBcAwHqjpw2dp8LIBMoDnk9apOHD7XhaHteRvKs7B5efakL0B8wSh+/+o5+EMx4zGHzYRbLi2Dx2GZ8j4TTVsonGny4ud/OZ3w2DWbinDtpiLEr1dnXzQmoel68uOaDplie6Kp2HuqHT/7yykMPbjuum4FLlqePelvhD8UxSMvnUWnN2Q8lu2x4bqLihkKNC0W1eijdJcF+RmOhMdMisDK4nTMkZsZWkAGBzRIJP4jmssWVSikOSy4+4aVKMlxwawqSHNa8M5LynD1xsIF0Z9ARDRVi6qjWVEErt1cjIuWZ6PHF4bNrKIgy7FgRh4REU3VogoFID5ePzfdjtx0+2zvChHRnLOomo+IiGh0DAUiIjIwFBYBdqET0Xgtuj6FC00OTFwaOhQxXnMp9al6otuPSSDl5MFU7zP4XpjK+9G8JYdMspOD/0dc2GNirH24EPsx1nfjQuxDKiP9bYDp/YwWXCiEoxp2n2hDNJY8SS0VRRHYvDwbGW7rpN5PSomT9b1o7PQDiM8u3bkqF6qiQEqJ9t4Qnn29AQfOdMAXjCInzY5tK3Jx5YYCZLqtxoc4WKjvbHMf/vp6I47WdSMU0ZDtseGi5dm4ckMh8jPsE/7QBQRM6vn3AIC+QBRHa7tx+GwXzrX2o9cXhqZLuO1mlOS4sL4sExvLs5CXbl9wBQMXo9aeAI7W9Biff5bHhouWZyUce5GYjtMNvTh0tgunGnvR4Q0hEtPhsKgozHJiTWkGNpVnYUmuC6oiJnxMBCMx7DvVgXBUAwCYTQouXp0Hq1k19kGXEg0dfhw624Vjtd1o6gogEIrBbFKQm2ZDRXEaNpVnYWVJOmxmdVqPSyklYlq8Ntrxum6cauhFU1cA/YEodCnhsJqQn+lAZVEa1pZmYHmRZ8r7IKVEVZMXtW0+47HyQg+W5bsTXldKiUA4hlMNvThyrhvVzX3o7AshFNVgNSnIdFtRkuPCypJ0LC/0oCjLCZM68c9o0IILhb5ABPf/+iC8/vHV+7FZVPzgny6ZdCgAwOO7a/HYrhoAQG66DQ9/+kpke2w4VteDr/7mME439iZULn3+cDP+9Fod/vW2jVhRnAYhBDRd4ul99fjhUyfQ3htKeP0XjjTjD7tq8Mmb12HnqrwJVTwVArCY4q2EwYiGvx5sxG9fOodzrX2IpAzONvz+1XPIS7fjhq0luPXyZchJszEY5rE3arrx+YcOIKrFP++1SzPwk49fBrvVhJim4/DZLvzyuTM4fLYLviHlM87rwBO7a5HhtuLytfm446oKlBW4J3Qc9voi+MpvDqFjYCa2w2rCQ598EyqK0iClRFOXHw8/X40XjjSjsy+UVOkXAJ59vRFOmwlrSjPwnqsrsH1lLszq1FrAB8Pg9aoO/P7Vczh0tgu9/kjK9x/kspmwoiQdN19ShjetL4DdMvlw+PP+BvzyuSrjv99z1XLc+851ECK+b9GYjlePt+KRF6txqsGLQDjV5xOnKgLpLgt2rsrDv922CTbL5IbaL7hQmG3+UAzNXQFEYxJffuQQzjR5k7bRpcTR2m589beH8e0PbUe2x4YXjzTjW48dhS8YTdpeSqCmtR9f+81h/OeHd6Ki0DPug1ARAlaziq6+EL73/x3DXw40jhAG52m6RHN3AP/z19PYc7Idn7l1A9YtzWAwLBAd3hB6fRGoisAfdtXgp8+cQu8YF1ESQHd/GE/sqcOBqk7ce9NaXLGhAKoyuZNyNKajrt2H8kIPXq/qxDd+dxjnWvrHnPHtD8Ww73QHTtT14LYrluP9b66A02ae1D5IKdHVH8bPnj2Fp/fWjxCIyXyhGF6v6sSx2m5cdqQAH3vHGpTkOKfl+9HuDUEiXv7eF4rhJ38+icf/XjtqGAzSdImuvjAiUW1K9eYWXEezogjkpNmQ4bLAZTPBalYuaAmLYDg2UJ67yggEIVJ39h6v68FTe+vR1BXAD586YQSCAFLuc1NXAL97+Sw0ffzFEhQl/gX8jz+8gaf31icFgiLiVxipSBkvdPj5h/bjWG1PQpsmzV/dfWG0e4P4w64a/NefjicFghg4Jkb62jR2+vGlRw/h+UPN0Cd5TEQ1HTWt/Thc3YX7fvU6zqYIBFURI353faEYHvrbafzoqZMIRsZ3Mh8qfncSwOd/eQC/e+XciIGgKmLE70c4quO5Q0343C/2oaq5b1q+H+29QUgZL9T5n4+9gUdfOjuuQBi6v1tX5MKsTv6kt+DuFDKcVvzgny6BLxiFPxSDPxT/375gFN19IfzptXrUd/jGfqFJ0iXwlwMNONXohdNmwnWbi7G1MgfRmI5nDjRg76l2DJ7TdV3imQON6PSGUNfug9mk4NLVebhifSFsVhX7T3fgqX31CEU04/V3nWhDd3943JPvdB14/O+1OHyuC/rAwkIFmQ5srsjGmtIMZHlsUBUBrz+CY3U92HOyDS1dgYQvaEOnH9/4/RH85907OOlvAYhqOn77yjnsPtGG4MCxleWxYlN5NtaXZSIv3Q6zSUEgHMOZJi92n2jDuZb+hADw+iP49uNHUZzjxOolGZPaj72n2vHcoSa0dAcAAHaritVLMrC5IhulOS7YrSZENR2NHX68dqodR2u7E74LMU3i96+eQ0mOE+++bNmEro67+8P4yqOHsO9Ue8KxbjYpKC/wYEtFNpYXeuAeKDLY54/gdKMX+890oLatP+HC7ER9L7786CF8865tA/1wkz8h9/jCCIZj+OVzVXhqb73xPjaLiqIsJ5YVuJGf4YDNoiIUiaGlO4i6tn60dAfgC8XgsJpwUXnWlPZhwYWCoiTPWB5M8HBUx5Ga7hkNBQDYfbIdFpOCT797A27cWWpcaVy6Nh+f+8V+7D3Vbmx7ttmL+vZ+CCHw3qsr8MHrV8Jqjt/AXbm+EHarCb96/nybY68vjNONveNu549qOl6v7gQAWM0Kbrl0GW6/shz5GY6kUuHv2FGKlu4Afv6X03hybx1i2tADvwePvFSNj719DdQptuPS7HtmfwOA+JXlmy8qwl3XrURZvtu4MhdCQEqJt2wtwfvfXInfv1qDX/7tTMJVa3tvED9++iS+fuc2OKwTP5UMHpcAsLI4Dfe8Yw22VOYYfWCD+wAAt1+5HLuOt+J7TxwzBnUAQCSm46G/ncHWFTkoy3OP7zsR0/HTZ09h7+nEQCjOduLDb12FN60rgMtmStoHAOj1R/D03nr87C+nE/otj9Z046fPnMJnb90Is2nyJ+RAWMOTe+vxyEvViOkSFpOCy9cV4JbLyrB6SQacNlPCHZzE+daJ1062o7UnkFT0c6IWxbdbCDHw78K95+aKbLxt+xKYVMV4/3SnBbdfUW6MBgLidxbhqI4VxWl4z9UVsA10WgkhYDYpePv2JXDbz7eZhqN6wmiF8TIpAh948wp8/KY1KMh0QFHEkL9L/J+iCBRmOfCpW9bjpp1LE/5eUgJP761HbbuPzUgLhBDADVtK8G+3bUJ5gdsYVTR4Yh38/zNcVtx5bSU++rbVxgl70N5T7dh7qn1Kx8TyQg++9oFtxmikVPtgs6i4emMh7n/PZuSm2RKe39YTxGO7asbVlCWlxP4zHXhqb31CZ3JprgvfuGsb3rq1BC6bKeU+DP4tbr9yOT7z7g1JQfjsgcb4+hlT0NUXwg+ePIFgWIPbbsYnb16PL713C7ZV5sBtN0MZ/p0VAk6bGatK0vGBayvx6XdvgN06tVpuiyIUZsMV6wuSvkBCCKwsSUdeiiaYazYWweNI7jDLy7CjICsx+Zu7AphAtwIAYN2yTNx+1XJYTKOPlBBCwGE14YPXr0R5vifhZ119YfztYBPLPy8QhZkO3P2WlXDZzWNeYZtUBe+6ZCkuWZOf8HgkpuOpFH1V42U1K7j7hpUozXONuQ9CCGxcloX3XlOZ0NcgAbx4pCVp1F4q4aiOR1+qTlikyGZR8fGb1mJVSXpCGIxEUQSu2VSE6zYXJzweCMfwx7/Xjns4fCqaHh9+ajEp+OjbV+PmS5YaF4qjGdzvwYvQqWAozACbRcXywrSUP8t0W5GTZk/afl1ZZsrtXTYzMpyJi6f0+MITujJTFYEbd5Qat8TjkZtuw40XL026VX3laAtCYW2kp9E8ctXGIhRmOce9vc2i4t2XlSUNdTx0thNtPcFJ7cPywjRcvDpv/KPpBiodL8l1Jzze3hvE61Wdo34vpJQ41dCLw2cTr+a3VGTj4lXj3wcAMKkCb922JOlu4UBVB9q9k/tbDHXp2vx40/MsNNUyFGaAy2ZClsea8iBTFYEsT+KcCJfdPOLENCGQtKKWPxSd0NV6utMSX+1rQvMbBC5ZnYd0V+J7N3X5UdveP4F3p7nIrCq4bG3+iCNrUhFCYE1pBpbmuhIeH5wMORk7V+XBPsH+iGyPFTtX5SY8punxZqGxrpV2HW9NGGkkAFy7uXjCY/qFEKgs8iBnWFNWd38YVSmGoU+Ezazi1suXwW6ZnS5fhsIMsFlMcNtHXhrRMeyK3W5RkeEaefKcddgBG43pE1rCqyDTkXR3Mh6FWQ4sGXYC6A9GUdPaz36FeS4+C3b8dwmDnDYz1ixNvKvV9PgV+ESHp5pNSrzJZoL7IITA1sqcpOedbuxFJDbyXWw0puPAmY6Ex9wOM1YWp09wD+KsZhXFw/6Gui5xunFqoVCSO/kRXdOBoTADLCZl1M6e4bMw7RaTMd0/leEzRyfan7Ak15XQuT1eFpOCsvzE23Qpgdo23inMd7npdrgmMelLEUB5gTtp/kBDh3/Cbek2s4rCLMek2sCLs51w2RP3v8cXRld/eMTndPaF0DqsmSvdZUWWxxYvMzPBf0IIpA27i5eIlxWZykVTZVHahJp6p9uCG5I6F5hUZdTp98O/AnarOurIqKmOmspJt0+oJMFQxdnJV5OD48pp/kp3Wka9EBmJEAKFmU4oioA+ZMhyhzeIqKZP6DXNA3V7JsPjtMDtMKN/SAWAQFiD1xdB0Qj9JJ3eEPqCiRP12nuC+MSPdk9qBrCUSBgeO8gfikHT5aQuxABgSY5r7I1mEENhBphNyoRuiU0z3JnksZsnHSyZLlvSYz394aEFGmkesltNkz5ppbssAxcZ50OhLxCFpk3s6tikign3JwxyWNSkNvdIVIN/hNm/Ukp4A9GEyW8AEIpqOFbXM6l9GElM0yc90xsA0pwjNz1fCGw+mgGqMkJdixGMXFBgesSv3ib3HqmawYIRjj6a7yzmyQ9dTNUpG45qE24yMauTL0FjNilJoRbT5KhDY32h6Jgd0dNiiu9hNs3uaZl3CjNgLl1BC8TrH032TiFVwbOYLsFbhVk0HSe2KbxGfJJb4mOaLi/o/JXBiVtDSSmhj9LhFo0mB4bAyLW/JmsqxeiA2f9aMRQWOAlA0853jE1UTEv+IpnVid0JLVapWgW1KQZqdOCzHEoREw/9mK5P4ZiQSVfcoxXQG4mmJ7/OeOm6TGqiEaMUrwOQsrlsWYEbH3nr6sntxAhy0mwwTbJ67FzAUFgEptLc4wsll/J2WCdXqnixsZiSm1kCodiUrqiD4Vj8Tm0Ik6pM+Go3HNWgS2Ay3Qr+FL+D3Wqa8BVyTNMnPfs3EtOTmorMqjDqhqUyfCg4EB/5d+X6AtbzGoJ/iUXA649MeBjroK6+5NIBmW4rbxTGIVXZkvgCMpP7MKSU6PHF6+UPZbeqKQNoNL5gDNFRxvSPpseX/DukOcwTDqaYJtGXYv2Q8QiEYwgO61S2mtUR11YYHD46fHSUPxQb9zoKiwVDYRFo7QmM2tY6mvqO5CF3RVlTq8K4WOSk25PCs7HTb6yANhmNnX6EhoWCx2GZcBG0nv7wpO4gpZRo6gwkremRm26f8CpokZiOTu/Y9YpS6fWF0RdIHF7qsJqQ4Rp55E52mi0pqHt8YXSmuPBZzBgKi0Btm29St+mBcAx1wyaqKYpImtBGqeVn2JNmo7f3BtHU6Z/U3YKU8aU1hz+1KNs54TkHbb3BcS9ZO5SmS1S39CXtw5Jc14RHzYQiGuo7Jld1t6bNh8CwGlxZHtuolQFy0+3Iy0ic2e/1R3BmijOQFxqGwiLQ4Q1O6svX2OlPulPwOMwoyx9f3frFLttjS6qI6w1EsO90xwjPGJmUQFd/CAeHrEEwaH1Z5oQ/D384hpP1vRM+JvqDURwbVufIpAqsWTLx5Vp1KfHGua4Jj+nXdYm9p9uTHl9TmjHqnB+LScH2FYk1kySA5w41jVoeY7FhKCwC/YEo/n6ibULP0aXES0daktaMXprrRnH27M64nC8yXFasKE6slisl8NTe+oHF4SdyMpR4/lAzmrsSZ5O77WZsGKHC7mh0XeKlN1omdAcppcTrVZ1J+5DptmJ16eRq9eyv6kRn38ilKVJp7Qlg/7BgNatKvB7SGLl0+bqChPVJAGDv6XYcOdfNel4DGAqLgATw1N46tPYEx3XgSynR2OHHk/vqEx5XBHDlhoJRR3hMaL+kxCMvVuP//feepH+/e+XcvP+SChEvTz28A/Z0oxePvFidsLLdaKSUONvSh4dfqEpqy1+7NANL8ybXnLfnZBuO1Y1v7W0pJXyhGH7/6rmkUT9bK3OQ7Ume+T4eLV0BPL2vftx3C5qu40+v1aN1WKmVgkwHNo6xDKUQAiuL07BtRU7C4/5QDD948vjA+siTHQQAoybSfMdQWCTq2nz4rz8dhy8YG7PmfF8gih88eQJNw+q65GU4cNXGomnbp6im4+WjLXjxjeR/h84mN5PMN0IIbFuRg/KCxMWKdCnx8AvVePiFKgTCI38eUsbH4lc19+GBXx9E07ArdKtZwc2Xlk16Bmx/MIrvPXEMzd2jF3CTUiIa0/Hw81U4WJX4udgtKt6+o3TSJTN0KfHw89XYc7J9zGDQdYndJ9rxm5fPJgyJFQK4bksxMkfpTxhkNil479UVSTWXjpzrxgOPHEJduw/6OE/ugyGgaTqaOv14el8DukcpyDdfzOg8hV5fGIfOdU26GBsQn1K/ojgt5cza8Rj8cCXiB1XSZy3jB6auS+PWcyG2l0vElwv0BaP44PUrUVmcllSjKaZJ1LT14yd/PokXj7QkPF9VBG6+tAwFmZOraplKfyCK9t7kBUmEALYNa/udr9KdFtx+ZTm++pvDCVfYoYiGHz11EgerO/HOi8uwriwDHke8ppBE/Iq4uTOAl4+24LFdNWhOUYTw0rX5E1qgJpWjNd34zM/34cNvWYXNFdmwmePFGQfXJtZ0iQ5vCA+/UIXHdtUkzZG4ckMhNi6b2kLxPb4w7vvVAdx57Qpcv6UYaQ7LwJyHeH0lKeNriPztUBN+9PTJpA7y0lwXbtpZOq55EkIIrFmaifddU4H/evJEQvPZ7hNtuOeHu3HzJUtx+boCFGU5YVIHZ2+LgRCIny98wSgaO/041ejF3lNtOF7bg6im4+f/fDmyJnnXNFfMaCgcOtuFT/xoz5ReY2meCz/5xGVw2kYPhWhMR1OXH/3BqDGGORCOIRDWEAjFEAhH0ReI4mxLX+LzNB2/eeksdp9og9NmhsNqgsOqwm41wW4xwWE1wW6Nr3cw0kI4c5nTZsKOlbl4+Y0WxHSJV4614vC5LqwpzcCa0gzkpNlhUgX6AlGcrO/B69Wd6ErRxrulMgc3X7J0WksCeP0RdKRYQjHdacX6skwshGnTQghce1ExDlR14ul9iesCRzUdu463Yd/pjoEV+WzwOCzQdImuvhA6+0IDfQ/Jr1te6ME/vW01bJOodAoAO1bm4mhtN/yhGI7X9eAzP9+LiqI0bFiWhYJMB6xmBYFwDGeb+7D/TMfA3UTia5TmuvCh61dOqtoqAKwsToMvFENjpx9dfWF85/Gj+P2r57CpPBtL89xw2U2IaRJNXX4cONOB6ua+pKYrh9WEu29YhfzM8Q+TVhWBWy5bhrbeIH73yrmEZrymTj8e/NNx/Or5KhRkOlGS44THEV+uNBLV4A1E0dEbRGdfKH6uGTKRb7YL2U2XGQ2FwfVGpyIY1sY1Fb61J4B7f7wHnd4QtIErf12X0OToU+k1XeL5w80JjymKgCri/6sMLGi/tTIH/3n3jin9LrOhoigNn373BjhtZjy9rx6aHm8e2nOyHXtOtsevCjH6Gg0rS9Lx6VvWT/tBX9/hQzCSfHxUFHlQkuOccsnwucJmUfHxG9fAH4ri5aOtSXNGIjEdrT3BpFr/Iykv8OALt2/C0rzJjwJ70/oCbK3MwU+fPYVQREMwouGNmm68URMfWaSI0Y+JvAw7PvsPG1GaN/lBB8U5Lrx9+xJ89beH0dYTREyXqG3zobbNZ+yDlCOXabKaVdx5bSWu3lQ44dYIh9WEj75tNRxWMx59sTqhuqqUQI8vgh5fBCfqp7eC6nywYPoU9IFbTH84hlBEQySmIzbJ2iq6LhHVJMJRHcGIBn8oNuVwmy3rl2Yi22PDJ29eh3+8ojxpklP8djj1c1VFYMeqXHzl/VumfRiqlMCZRm/SewsBvGldwYQnQs1lQghke2z499svwu1XlCeNfhkvq1nB1RsL8c27tmHd0okPQx2qPxDFHVctx8dvXJNyTYORjgkBYEVxGr783i3YtiJnSvvQ549g64ocfOk9W1BZlJZ0X6iPEgiZbis+cdMa3HF1xYRncw9y2sy4+4aVeOC9m7GqJH1Kd8EmVaA42zlrS2hOp/n/G9CIzKoYGJERn/X6sRvXYGtlDn79YjWO1faMGHQmVWBpnhs37VyKt+9YArfdPO3NZpquo7q5L+nxdKcFO1YujP6EoYQQSHda8Imb1uKqjYV4fFdtfJH33uCoV+QCQIbbirWlGbjp4qXYsSp3oN1/ap+H1x+Bxazi1svLsaY0Ew8/X4V9ZzpGnNCmCCA/04FrLyrGrZcvm5am1L5gFJDAlspsfO8jO/HbV87hbwcb0dIdGPFvku60YOuKHLznqgqsKc2YckVSs0mJ94uUZ+PFI8348/4GVDd70RcYu/yGzawiN8OONUsycMX6AmypzBl1RvV8IeS4x1DVjfrTDm8QNa3Tv0yj3WLC6iXpYxasCkViONnQO+kCW2PxOCxYUZyW9EWQUqKu3ZfQYeqym+Nrz6b40qTafqTXHty+tq0fHUPKAXgcFlQWp6W8ZW7s9KO5Kz5qSFEEVpWkG/Vg4p+0RDiqoaq5D0drulHd3IduXxi6LuG2m7Ek14V1ZZlYVZKO9IHmopnoR/H6I7j7e6/izLBFzt+0rgDf/OC2SV/9zQfxUUVAW08Q51r6cLqxF01dAfT6w4hEdZhNCtwOMwoyHagsSkN5gQdF2U6YFDGpz+KvBxvx+YcOJJTXuHFnKb54x2Zjf2K6RF1bP47WduN0oxcdvSFEYhocVhOKspxYszTeB5U7ULpjovvR0h3A+771UsJxvCTHiUc+exWcNnO8ExfxGd/H63pwvK4HTZ1+BMIxmE0qctNsWFGcjnVlGSjNc0/6bzGSwdNgOKqjocOH6uY+VLd40dodhD8URUyTMJsUeBwW5KbbUZrrRGmuG4XZDmS4ztcCm8g+SSnR2OlPWsmwLN+NbI9thvovS8fcYtruFHLS7JNaHH662CwmbCrPvuDvK0T8qnq8Y8Uns31Zvgdl+Z6xN0Z8+cxUS2jGXwsABGwWE9YtzcS6pROf9DRden1hI7wGmVSBazYVLaimo1SEiPdZFWY5UJjlwKVr8y/4Pgyd7yCEgFkVWF6YhuWFaaM8a5r3QSbugwCQn+FAfoYDV0/j0OfxGDwB2ywqKorSUFGUBqBkxt+zJMeFkllefnO4hf3tozmruqUvqbBbQaYD21dOrZ2a5pH5P89rQWIo0AUnpcTJ+t6EoYAC8THvme75PcabaL5jKNAFF47qOD2sMqXLbsZ1m4unfWlEIpoYhgJdcPFJUYmhsLUyuRwEEV14DAW64Grb+tE7ZOijWVXwjh2lsEyyhg8RTR/OU6ALSkqJqiYvdCmNUUZrl2bgoopsdjATzQEMBbrgrt1cjO1DJqg5bSa4UiyqTkQXHr+JdEEJIZDhso66bCIRzR424hIRkYGhQEREBoYCEREZ2KdAtMDlpdtx1cZCxIYUxFuzJOOC7oPNouLSNfnoC5wfipzlsU16RUWaOdNWJZWIiOa6saukMqaJiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMjAUCAiIgNDgYiIDAwFIiIyMBSIiMggpJRytneCiIjmBt4pEBGRgaFAREQGhgIRERkYCkREZGAoEBGRgaFAREQGhgIRERkYCkREZGAoEBGR4f8Hm1LiHYUAe5oAAAAASUVORK5CYII=", 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", 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5ykjxjvl7Y4xU29KjDw40aufhZtU09agvFFFqkkezC9J07rw8rZ6fp+x0vyRHbpcjR1PjQwyGo7rl0rn68GCTekMRdfeG9ds3K7Vybu4Z7UJsjFEwHNWhmnbtqGjWgaPtqm8NqC8Ukd/r0rSsZC0qztI583I1vyhTPo/rI49zGfgs27r7tP9Iu/YdbVVVXaca23oVCEbkcqRkv0e5GUkqyktRaWGGSgrTNTMvTT6vS64zOM4mFI5qW3mT2vtnMpBiVXsr5uYoLzNpVH/7lA6FSDSqPZWtevy1Q9p6oFHtPaFTPu6lrceUmuTR8pIcfW79fJ2/MD924iXwRhlj1BeKat+RVr26rUbv7GtQfX/oDMfjdlSYk6Lr1szSLevmKjvNN6oPxhipNxjW7987oic3VehIQ5ciJ41q/eBAo57dUqXC3FR9Zl2JbrqkRF6Pa0LmSxkrY4z2HW3TsaZuu212QZpmTUuT2+VoflGGDYWGtoB2Hm7R+lUzEn4vjzZ06f5H35cxsZPkW587V9etmaXu3rCeebtK//HmYR1v7hny3krS6ztr5fO4VDojQ5//xAJdfs4MedyJfW9if1usX/9/vHFYG96uUn1Lj07ezbv7GvT0W5WaU5CuO9bP11XnzZTf55bX45KmQBNKdyCk8xfma1lJjrYebJQkbdlbr31H2rRibs4Z2Wc4EtW2Q016/LVy7axoVmfg1Of9C+8fVXqKV+fOy9Pn1s/XytLcuDaQ0Ygao9rmHj2zpUqvbqtRbXOPgiNUk7ldjtJTvJpTkK67rl6oS5ZNH9O+RxIKR/WbNw7r4Y1ldhS82+XoujWzdP7C/FG/3pQNhb5QRBveqtSjLx1IqAGyuzesd/c1qKy6VXdcMU93rJ+vZN/If37USI++uF9PvV4xqn7Y4YjR0cZuPfLCfu063KL/eccqFWQlJ3xB6QqE9ONny/Tslqphv3xRExtl+uNny3TgaLu+eO0ipfin1se6eU9d3AjZtUsK5PO65Ei6eNl0vdt/px+OGL25q1ZXnDNDY7npikSNKus61d4d1A+f3q0XPzg6Yo+mYDiqfUfa9J3Ht6m2uVt3rJ8vjzuxnTe1B/Tgr3fqjV21pwydAeGIUfnxDv3zkztUfrxDn1wzSz7P1KjZ7e4LK9nv1k2XztGuw80KhqPqCoS0YXOllszOkmecJ2Ls6Q3riT+W6/HXDqnzNDeBg3X2xAbb7TrcrLuvWaRbLi2Rzzu6m6ZQOKpNO4/rJxv36khDlxKtFYr0Tzmzt7f1jJUSguGIfvPGYf1k414bCB63oxsvmqO/vnGp0pJHX8KdWlePfqFwVE+8Vq6fv7BfvYMuJsl+twqyklWYm6IUv1fdvSEdb+5RQ1vA1ld39oT08xcPKBiK6kvXLR7x5HM5UumMjCFd7VKTPMpM9akgO1nZ6X45klq7gqpp6lZTe6+9CESjRu/sq9fPnt+nr91+TkJfyGAookdf2q/fvV0Zd9HyeVyakZui4mlpSvK61d4d1LGmbjW29SoUierlrUftyPCpouOkqqMUv0fnL8y3lSer5+cpI8Wn9u5YsXhHeZMa23tVkD22yeEOHG3XT5/fpxfeP6pI1MhxpOw0v4ryUpWfmSQjqbEtoGNN3XZSQUnq6Qvr5y8e0Jzp6frYisIRT7SuQEg/eHq3Nu08HncRSfbHqv2K8lLlcbvU0tGrY03dau7oU18ooqc2launN6QpUnuk3mBYxkgXLSnQollZ2nW4RZL0xq5a3fbxuVo8K3sc9xXRIy/s05N/rIj7nqcmeVSQnazCnBQl+TzqCoRU09ythtaAvaFq7Qrqx8+UKRKJ6rOXz0s4rMKRqJ5+q1IPbyxTVyD+ptDlSOkpPqX4PfJ7XTJG6gtH1NMbVldv2E7VMq8o84yUmoLhiH79+mH9dONeW3Phdbt086Ul+soNS5Sa5BlTldmUCwVjjDbtOK5fvHTABoLb5eiiJQX67OWl/XXRHjlOrPge6AtrR0WzHvvDwf7eK/2h8sdylc7I0FXnzRz2jXMcR2uXFGhhcaaq6rq0sDhT65YX6pzSXM0uSFNqktfetUaNUUtHn17dXqNfvnzQlmCMkf64/bg+ffEcLS/JGXZ/xsRC5LdvxgfCnIJ03XPdIq1dUqDUJI993Y6eoLaU1evfXz6gqvouvbat5nQvPSkdPNauIw1d9veivBQtKMq079HsgjTNLUzX9vJmSVJta0B7qlpUkF00pv19cLBR7x9oUCRqlJXq020fL9VV581UYW6KrVqIRI2ONnTpqdcrtPHdI7Y3TU9fWI+/Vq7zFuQPO0dPNGr0/LvV+uP2GhsIjqTlJTn60nWLdE5prvz9NwdRY9Tc3qfXttfo/756SI3tvfrd21UJ341OtGA4qkjUKD3Zq09fPEd7q1sVjhi1dwf1zNtVml+UOS6lhYH39KnXTwSCx+3ospUzdOvH52rhzCz5vW573nf1hrT1QKN++cpB7TvSJknqDUX08xcPaO6MjITm0jLG6PWdx/Xwc2XqGlRL4HY5Wl6So+vWFGvZnBxNz0lRks8tmdh3pL4toMraTn1wsFHbDjXp6vNm2nN2vAxUGZ0cCLddNld/dd0SJfvH1nYqTbFQMMaovi2gn/1+X2y+ecXS+uZLS/TVTy09ZTJ6PT5duny6Fs3K0rce+1Dv9U+j0BuM6BcvHdB5C/JHnHwvI8Wrv/n0crldjpbMzrIn9Kne9Ok5KfqLy+apMDtF3/rVh/Y4OwMhvbO3QctKcoa9CQz0RfR//1BunydJxfmp+qe7z9fCmZlD9pmTnqRPXjhLi2Zl6f6fv6/Kus5h/5bJxBijt3bXxlWPXbBwmpIHVX/5vG6tXVygHeXNMopdHF7fWavLVs4YUwP+wAU+J92vb31utS5eWmAb6Ad43LHS4d9+ZoXcLpd++9Zhe5Euq2pRxfEOrSzNPe0+GtsDeur1w3GhvrwkR/909/kqzEkZ8hlOz0nRX1wxTwuKM/X1X3yg5o4p0JjQLxw2ikSNvB6X1i0v1FMzKnTgaLsk6bXtx3Xrx0o1tzD9IzXyGmNU3dClX7wcK+FLsQvznZ9YoLuuXqgk39ALYHaaX+vPLdKi4ix9/d8/0J6qVkmx8/DRFw9oeUnOsBP4GWNU1xrQwxv3xQVCit+ju69ZqM+sm3vK643f51Z2ul8LZ2bqqvNmqrWrz14vxoMxRuGoOVFlNBAIHpfuuGKevnTtov5wHPv7Pa4VftvKm/TqtppR/eyoaB5VN8Pn3z2iqvoTF74Vc3P15euHLyo5jqP8zCR95YYlSk85cYd3uK5Tb+2uHbHrmOM4Om9BnlbNy+0vhQzf2OhyOVq3olAXLpkWt31vdavMMHXLxhjtPNysvUda7TaP29EXr1l0ykCIHVvs+ObNyNC9n1wca6CcItp7Qnr/QKP93e916YJF0+J6TzmSLliUr+RBd1o7KmJVSGPlcTv6wlULdPGyArlczinbJxzHUZLXrTuumGd7K0lSMBTVzsPNp31tY4ze3F2nmkEN5yl+j75yw5JTBkJsX5LLcXTegnzdccX8MbWXTJRINGrP36w0n268aI79/Fo7+/TcO9UfedpvY6QNb1WqbtAUKGuXTNMXrlpoz8dTcRxHM/NT9eXrl8Tu5PvtrW7V1gONI573z22pVtWgmyyvx6V7P7lYn1s/f8S6esdx5HI5ys1IGlO9vhRbXOlkkajRf5zUqOzzuvSFKxfonusWf+RAkMa5pPCz3+8fz5cbor07pFe2HrN3bV63S59ZNzeh7oKO42jBzEytmpenN3fVSorddb65u07XXjBrxB47o26scTu6eOl0/XH7cbutvi2gqJGG29Nbu+vi2i/mTs9IqA7bcRytWTRNC4oyVVbdOuxjJ4uKmva4ky4/M1lLZmfF/a2O42huYYZmT0uz1QC1LQGVfYQqpDkF6br2glkjNv45jqNp2claNidbm3bGvjNGUmVdp4wxp/xMguGoNu+ui2tYPnd+nlbMzR3xM3Q5jj6xuki/fqMi7gI4mUWN7MXVcRxdfs4M/ccbh3W4rlNG0isfHtPNl5aoOD9tzGHX2N6rTTtPnEd+r1u3fby0v4pk+Oc6Tqxb5qLiLO2oiIV5MBzVm7vrtG5F4Wk7DbR2BeMWvJGkNYum6eZLSsa98fx0kk+6Jp2ql1GSz627r16oO9bPl88z8vuRiKlzWympvKZd1YPqn3Mz/Fq9IC/hC7bP49LyOdlx1Tf7jrSps3fkXgxjUZiTHPelC4YiCg/TENwbigy5C71g8bS40s1wMlK8WjXv9NUak0nUGL21uy6u6mjV/Dxlpg4t0qf4Pbpg0YlSVzRqYj16xtiofvHSglPu51R8HpdmTUuL29bRHVT4NHe/Hd1B7T/aFrftkmUFCVchTM9O0aKptEreSW9DXmaSrl87255jDW0B/f69I2MedGiM0Z6qFjW0ngjJotyUEdvmBkvxe7R0dnyD9+7KlrhOKierON6u6oYTNyw+r0ufvniOkv1np7u3I8W1W8V6GVXEBUKK321LLgPtKeNhSoXCzsrmuCH0c6anKyst8YU9HMdR8bS0uGJZZyAY94UbL47jyOdxx91VGA05h+I0tvXGVYu4HEfL5yTee8NxHC2ZnT0lqh86e0J6b1DVkcflaO3iaafsR+7q/z+/98R7ua28Sc1jmAvJ63Fp2ZychN8jx3Fixf9B20Lh6GmrRI40dqlr0E2G1+Ma1WfiONKS2Yl/5pON4zi66ryZmpmfKilW9fPC+0dV39ozwjNPb0dFc1wIzyvKVFpS4ovxOI6jWQXxwd7a1aeWYdpudh5uiWsTystI0srSkUt742mgcdqWEJ470e00Ncmjr9ywVJ+9rHTUXWxHMq7VR59ZV6Lik+6qRlJV16lntoxc72iMUXlNR9y2ZJ9bFcc7RtUHuK2rL+7KHIkYtY5xorWBxbZ7gxEFw1GFI7GLRaT/p6ape1R3SK2dfXF9r5P9bjtSOVFFealyOY4ik7z7yuHajriqo+x0v1bMPf3d3/yZmSrKS9Xh2thz6ltivZAuP2d0VUg+j0sz8k5dt386bpcrduvW/5YO987WtwRsY6gkZaX6lJ3mH9UxzsxLHdXjJ5uC7GRdu2aWHnl+n4yk2uZuvbz1mO68csGo++sbach5n+Rz61BN+6he5+QxDaFwVG3dQc06xWOjxuhwbfw+S2dkjHsPomE5UmqSV9Go0YsfHNXPnt9nG5VTkzz66xuX6dMXzzkjbYjj+ldesapo1CPotpTVa+M7RxQd9lSLfYhNJzUuvr6zVm/0tw8kykhx3f2MNGwx0j6u/0nt3UEdqunQzsPNOnisXQ1tsWkubDD0h0MoYhSORIcdtHTy63cGQnGDuPxetzISrOYYkJrkkc/rsncUk1E0arR5T52Cg/7WaVnJqq7vUm3zqe8oI8aoICvZhkI4arR5d53WLS8cVR2vx+1SVuroLtKJMsaorTsY95mnJXvjelMlIiPVJ4/bmdJThV93QbGe3VKlupZYO9rGd6r1yQtnKS8jaUhvr+H0BSNDBqdufKdaz79bParjOfmdjBoT9/0bLBiKDrlRnJGbctbaEgak+D16u6xOP/rdnriBs3deuUA3XTLnjB3PuIaCo9E3yCYqGI4OmVri5Av8mBglVEppbO/Vc+9U65Wtx1Td0HVGZoI8eaFuj9uJ6zWRCI/bJZ/HPalDobsvNrp88LteVt2q//zQ26N6na0Hm9TWFRzVet4uR3HVUOOt56TP0O91jWp0suM48ntc8rhcCkcm72c4HMdxNCMvVVefV6zHXjkoI+lIQ7de/bBGt19WKklyuxMLht5gZMjF+0yf9+FIVD0nnT8ZKb6zOqeYy3FUUduhx145OCSg3tvXoE+tnT3qOY0SNWXGKUSNGdKw6HL0kSabkySPxxn2NaLG6MODTfrBb3fpUE173JfRUewi7PW4lJbsVVqSR0k+t/xet3xet3r6QtpT1ZrwF/jkxkvHcUZd3HY5GvP8LmeDMUYVNR2qru8a+cEjqG8LaHdliz6+cuTeWYOdqqvfeDm5ZDiau+IBp+smO5U4kj554Sw9/94RNbX3KmqMntlSpavOL1ZOul9etyuhSf8iUTOkKtTlcj7yBdrjPv0EeVFjhnyOic6VNl6ixujh5/bakfyDbStv0r8+s0f333aOUocZRDlWUyYUXI4z5GK3/tyZunzVjI80I4DjcrRszqmHoBtjtOtwi771q61xXQS9bpcWzMzURUsLtKwkR7PyU5Wa5JXHHQsYtyt2Mf/wUJP+60/fSbgawHtScdD0T8k7GkYasf/1RDJGemdf/ZBS0ViEwlG9XVanS5dPT3g+ojPNe9JxRPtn1x0NY4Zvt5gKHMfRrGlpWr+qSE+9XiFJqqjt1Os7YyP7fR5XQuE8cC4NduNFc7Rm0egneot7XbdLpTPST/l/LseR56RjC0eip+2GfCYYIxsIRXmxHmlv7qpTKBKVMdLLW49pRm6q7r5moXyeSdzQfCb5PK4hdbOFuclav6roI5cWTqe7N6yfbtwbFwjpKV7de91iXbdmltJTvMPeyY+2ESg2AO9E0TgcMSPOxnqycMSMOHvjRAoEI9pSVh930bt81QzNTrCDQmNbr154/4i90H5woFEd3UHlZCRehXQmpZzUK6YvGFUoFJFGcUcXDEcSbouazNwuR5+6aLZe3npUrV1BRaNGGzZX6opVRfJ63And7Sf53Eo6qXfNrGmpWn9u0Rm7QHvdQ6817d3B2Bijs3jvkeR16/JVM3TX1Qs1LStZP9qwR797u1LR/skhn3jtkGbkpuj6C2eP6zVwyoSC1+NSflb8iR9rxDJynaHZw8qqW+0EX1Ksauauqxbq1o/NHbGRxxijSMQkXHXkOI7Sk71K8p1oD+gNRtTe3Sfp1Hc0p9LTG5606+TGepC1x41Iz0z16a+uW6y5hRkJvUZ9W0Dbypt0vL9Bur4toF2VLQkN8DvTHMdRdpo/rpG4MxBUd19YiU6HZoxRZ0/oT2L1soGBhx9bUahntsQahg8cbdfmPbEOAom0tfh9buVm+KVB48iON/fI6MzNGejzumL7HKSmqUfhSFRu19mblv66NbP0t59ZIb83VtX1lU8tUV1rj94uq5ck9fRF9ONnyzQ9O0UXLMoft+//lBmn4DiOFp80qKf8eEdcb53xZIzRnpMGuBTkpOiq82Ym3Orf2tU3qju+nAx/3HwsgWBYx5t6RlUddGyU3WDPJqPY+gGDe1LML8rQrGlpsXriBH4KspLjqvuCoajeLqsfdRXNmTIjJzluoFp7V1DN7aPr8ny0sXvkB00RHrejT19SovT+klIkavT0W5Xq7g3J7x35ntSRtGhWVty2A8fahx0E+lE5jqPSGfE3KRW1HeroGVq/fyblZvjj5nXKSvXpv9+2UgsHXQebO/r04K936nBt57hVG0+ZUJCklaW5cb1xapt7tO9I2xmpQ48aDZlfpyg3ZVSD5cqqRjfdRF5mUtyU0MbIzg6aCGOMyqoTb9g+2/qCEb1dVhe37eJl00dVzeY40qXLp8c1xA5UIU0GM/PT4roRh6NGOyoSX8zeGGlPVcvID5wiBqaXuWhpgd1WVtWqDw40JtyzbtW8vLhSRVVdpyrH8SJ4KrGV407ss6WjT1sPJP45ngmO46goL1Vfu32lpuecuE5U1Xfqe0/tUHNH37gc35QKhXkzMrVwZqb9vacvtnpWKBw9Ax/W0DVWXU7iyyQ2tvfqnX31o9qj1+0aMs7j/QMNCU/+1t4d0ocHG0d+4AQwJraQzOABa+nJXp2/YHQNho7jaEVJTtwkdXUtPdpddfbWBR5OWrJXK0riK4ve3F0XN9PmcGpbeuwcT38q/F63br60xE4REYpEtWFzZUIlWsdxtGxOdtxUI+3dQT37TvUZbXcpnZGhuYUnqm1D/esqdPSEJjwYlpfk6L/evEIZg6a/2XaoSf9nw271jGIhsNOZUqGQ7Hfr05eUxPXS+eOO42OaW8UYY3sUnIrLcZR10kjUxvbeuCkMTie2+EVF3DoBibp02XRb1JZi1UHPJXACRPunoT559Odk8u6++riLY8n09DFNq1yYmxJXhB6YhG7iIyFWXfLxlYVxd5ll1a16c1ftiN/RSNTo9+8dOSPTrky0ZXNytGbhifmrdle1xt0gDCcj1afr186O6334/DvV2rTj+Oh7541w3tt9pnh17fmz4kqkOw8365evHFRfKDLhwfDxlYX64jWL5OsfczMw+eBjfzh42kF5iZpSoTAwC+OaxSe+XL3BiH70uz164rVytXYNX3wyxqgvGFF9a49e3npM//bc3tOu7yo5ml+UGRdARxu7tKWs/rRfRGOMuntD+n+vVeipTRWjrsZxHEeLirN0/qDudsZIT7xWrte215y2HjUSNdp2qEmP/H7fpF15rTcY0eY98SWntUsTnyhuMLfL0aXL4quQth5sjFspbaLY2WoHlWhD4ah+unGvth1qOm24h8JRvbatRk9uqpgU4Tbe/F63blk398TiQtHEe8m5nNh6w8sHlcC6esN68Nc79fTmSnX0BEc87wPBsI43d2vju0f0yAv7R+yM4TiOrl1TrCWDVo6LRI2e/GO5fvDb3app6h72Ri22tntE9a0Bvba9Ro1t4xv0HrdLt6wr0c2XlNiwjPVIKtfv+1cVHPNrj9dBni2pSR79l08v0/HmbjvlQUdPSA89U6aXtx7TJcuma3lJjgqyk+V2OQqFo+ruDet4c4+q6zt14Fi7Dh5rU0tnn2bmp+lz6+efcj+OI60szdGMvBQ70CocMfrxs2XqC0V06fLpykjxye12FA7Hpqg4cLRNT2+u1Lt7GxSKRDUtK1mdgeCoRhd7PS7d+YkF2lnRbBdbae8O6rtPbNfOimZdfX6xpmXFZl8NhqNqbAto085aPf9utZo7+pSe7JUxJuHqirPlcG2nKgbNJ5Oa5NEFY1hUfMCqebnKTPXZIDjW1KO91a120ZyJlJnq019euUDffGyr/exrmnv09V98oJsuLdFlKwuVleaXy3EUDEdU09Sjl7ce1SsfHlNXIKycdL+6e0PqC03OgB8Lx4l9ZufOy9U7g5ZfTVR2mk/33bRMX3v0A9W2xHqetXT26fu/2akX3juii5dN19LZ2crPTJKr/7zvDIRU09St6vouHTjapoM17WrrCmrJ7Gzd+YlTn/eD5aT79dc3LtX//PcP1NR/LgbDsaqvd/bW66KlBTp3Xp6Kp6Up2edWNGrU0xdWbUuPKuti+zxwtE0dPSH97L5LlZ81tiVkT8fv9eie6xarrjWgTTtiU4sHghH927NlKsxJ1ppF0/48luN0HEelhen65h2r9Z0ntqnieOxCE4ka7TvSpv1H2uT0D3hxOSdGmJpTDiIaPk1z05P02ctK9YPf7rZdBBvbe/Xgr3fql68cVEF2snwet3pDETW2BdTQFrBdEWfkpuhbn1utn7+wXx+Mop5/YKbTe69brB8+vduOU+gMhPTkpgpt2FypnPQkO79Ra1efPbYUv0d/c9Mybdpx3HZbmwyMMXpnb31cfWdRXmrcspujMTDbbWlhhj481CQpNrjojV21unhQg+ZEcRxHly6frs9eNk+/+sNB+51obO/VI8/v06/+cFA56UnyuBx194bV1t1nH5Od5tfX/2KVfvL83kldFTgWSb5YaeHDQ02jHkvjOI6WleToG3es0j/9vx061r+IUThitPNwi3YdbpHjcuTuH0H+Uc77wfs8b0G+/ttt5+h//WannXvNmFi32N++WakNb1XK6R9YGzWxLugn79PrccmcgfKf48Squf72luVqaAvYji2xsNylB794gUpnZIz6HJtS1UcDYo0t2frf96zRJ84tsvVqkuySjeFI1K4fG4kO/WI4TuwEHG5KCJfL0Q1r5+gvLpsXN19OJGp0vLlH28ub9d7+Bu2saNbx5h6FI0aOpAUzM/WPf3m+Vi/I0zljWN8gNuhnjr5647Ih8/73haKqbelRdX2XGtoCNhCyUn36m08v0w1rZ2teUWJ9/s+WQF9Eb++ND6kLF08b9URxg/k8rtg6u4O2fXioacwz3o43nye2+MnnP7EgbrEUo9j7UdPUreqGLjV19NpAmJ6TrK//p1W6dPl0lRQkPjZlqhioWltWkuiojXiu/uf/73sv1CXLpsdV7Q6c96ERzntX/3mf6PQxLpejy1fN0INfvEDnzssdcr2Imtj1IDZL8mmuNVLCHVRGy3EcFeak6H989hwV5aXY7ZV1nfrer3eOqUfSmM9Kx3Hk97rjLpZjmVPG5XLk97nk6r+JjA3USGz/swvS9cCdq/XBgUZtfOeIdlQ0qa07NnJy8AczsNyh2+UoLzNJC2dm6ZLl07U2gQVsknxuffn6xVpQnKmnNlXo4LF2hcKRuNePzTfk0rSsJF1xbpFu+3ippvd3LV1ekqP0ZK+C4cioJkbzely69WNztag4U4+/Vq6tBxrV0xeOqyt0uxwl+912GceVpbEv7aLiLPu5+LxuuSY4+o81detIQ5c9Jr/XrQsXj61oO8BxHJ2/MF/Z/VUtktTYFtCh4x1aM6hnkssVW1ZzoJHX73WP+vT0uGPf9YGTK9EutMl+j+69brFWlOToyU0VdmGXgTYpp//4MlK8umTZdN1xxXwb6AuLM/Xm7tgMwL5xWGJxvLj6z/uB9i3vKCcXTPa7dculJTpwtC2ujczncSc0Gm2gi+v37r5AW/bW6/fvHdGuw83qDIQUicYPFh047z1uR9OykrWoOEvrVhTqgoX5o5po0uU4Oqc0Vz/4q7V6c3etXv7gmMqqW9XdGxp6rZFsiSUj1asFRZn6+MoZmjN95BH7Xo9r0PXUSXg81EBb5H+7daX+4fHt6grEqlT3VrfqJxv36u9vXaEkX+KXesckHCPxU9WGI1E1tAXiLlL5mcmjntUzEAyrub3PFq/cLqe/zjzxL5sxJja1dkevDtd2qrq+Uy2dQYXCEfm8bqX4PSrITlZxfqoKc1KUmeaLTcg1ihNtYGrrytpO7T/aZu/SfV63ctL9KpmertIZGcpN98s96Nj7QhE7IZjX7dK07ORRT3LXG4yopqlLZdVtqmnqVqAvrCSfR0V5KVoyO1vF+alxa7P2BsO2G6vLia1PPd4LcYzu+MNqau+1BWiXE/uMP+pc8Cd/Bx3F1mVIHTTVRDAUUUNbwO7b7XJUkJ0yqkkDO3qC6ugO2tdI9nmUm+FP+PtjTKyuubq+S/uOtKq2JaBgKKLUJK+Kp6Vq6exsFeam2O9krMNCWK1dsVKPxxUbzX+2p24+lWA4osa23hMh63Err78eP+HXCEXU2N4b1xvL63YpPyt5VJ9LrDE3qqb2gMqPd+hoY7fa+qtT/V63UpM8KshJUXF+qqZnpygz1feRv3MDa6g0tfeqorZDx5q61doZ26fH7VKy3628jCTNyEtVcX6qstPiB6AN97qtnX3qHjQnWEaKL+EVAqVYiaWxLRDX2WTod2f2iK8z5lAAAEw1I4fCxN96AAAmDUIBAGARCgAAi1AAAFiEAgDAIhQAABahAACwCAUAgEUoAAAsQgEAYBEKAACLUAAAWIQCAMAiFAAAFqEAALAIBQCARSgAACxCAQBgEQoAAItQAABYhAIAwCIUAAAWoQAAsAgFAIBFKAAALEIBAGARCgAAi1AAAFiEAgDAIhQAABahAACwCAUAgEUoAAAsQgEAYBEKAACLUAAAWIQCAMAiFAAAFqEAALAIBQCARSgAACxCAQBgEQoAAItQAABYhAIAwCIUAAAWoQAAsAgFAIBFKAAALEIBAGARCgAAi1AAAFiEAgDAIhQAABahAACwCAUAgEUoAAAsQgEAYBEKAACLUAAAWIQCAMAiFAAAFqEAALAIBQCARSgAACxCAQBgEQoAAItQAABYhAIAwCIUAAAWoQAAsAgFAIBFKAAALEIBAGARCgAAi1AAAFiEAgDAIhQAABahAACwCAUAgEUoAAAsQgEAYBEKAACLUAAAWIQCAMAiFAAAFqEAALAIBQCARSgAACxCAQBgEQoAAItQAABYhAIAwCIUAAAWoQAAsAgFAIBFKAAALEIBAGA5xhgz0QcBAJgcKCkAACxCAQBgEQoAAItQAABYhAIAwCIUAAAWoQAAsAgFAIBFKAAArP8PB/oSLi60tWUAAAAASUVORK5CYII=", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot individual word clouds of the surveys\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_q4_ism=df_q4_ism[df_q4_ism.columns[[0,i]]].set_index('score').T.to_dict('list')\n", + " for k in sub_df_q4_ism:\n", + " sub_df_q4_ism[k] = sub_df_q4_ism[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightyellow', relative_scaling=1).generate_from_frequencies(sub_df_q4_ism)\n", + "\n", + " title = 'theme ' + str(i)\n", + " plt.imshow(wc)\n", + " plt.axis('off')\n", + " plt.title(title)\n", + " fig_name=RESULTS_PATH + r'\\word_clouds_surveys_theme '+str(i)+'.png'\n", + " plt.savefig(fig_name, dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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nFRcXpxMnTujkyZO2ERyXL1+Ws7OzDhw4IGdnZ7t2bizkuLu7lzsBq5ubW4njCAAAAABVgWEFjmvXrmnFihVKSUnR008/bfdZ9+7d9fe//12vvPJKqdveOAHijbc83CgjI0ORkZF67bXXbMt+O4Lgt8LDw7V+/XpZrVbbRV5GRoa8vb1Vu3btW9m9SomPj9eqVatUu3ZtOTk52T02Nzw8XBkZGXbrZ2RkqGHDhiUuWItFRkaqTp06Wrt2rTZv3qxevXrZLtwr0oeVcafaNZvNpY5MqUp2795d4n14ePgdabt+/foym83KyMhQ3bp1JV2/fWTfvn1KTEy0rRcVFaXt27dr7969Sk5Olr+/v8LDw5WcnKygoCA1bNhQktSyZUtZLBbl5ubqiSeeuCMZAQAAAKAqMazAsXHjRv30008aPHiw3UgNSerRo4dSU1PLLHB4e3tr9OjRGjFihIqKivT4448rLy9PGRkZ8vHx0cCBA/XQQw9pxYoV2rJli8LCwrRy5Urt27dPYWFhZWZ67bXXNGfOHA0bNkxDhw5VVlaW3njjDY0cOVJOTnf/bp74+HhNmjRJycnJ6tmzp91fykeNGqXWrVtr6tSpiouL065duzR//nwtXLiw3Db79u2rxYsX68SJE9q+fbtteUX6sDLuVLuhoaE6c+aMDh06pNq1a8vb21tubm4aN26czp8/f9PbVM6fP69Dhw7ZLSsuFNwpGRkZmj17trp3766tW7fqgw8+0KZNm265neKnx9yoadOmevXVVzVmzBj5+/srJCREs2fPVkFBgW2eFkmKjo7WvHnzVKNGDTVu3Ni2bP78+erVq5dtvYYNGyo+Pl4DBgxQSkqKWrZsqR9//FHbtm1T8+bN7YppAAAAAOCIDCtwpKamqlOnTiWKG9L1Asfs2bP19ddfl7n91KlTVaNGDc2YMUPffvut/Pz81KpVK40fP16S9PLLL+vgwYOKi4uTyWRSnz599Nprr2nz5s1ltlmrVi19+umnGjNmjFq0aCF/f38NHjxYf/7zn29/hyugQYMGatOmjfbu3as5c+bYfdaqVSutW7dOEydO1NSpUxUUFKQpU6aUOsHojeLj45WcnKy6deuWmMPjZn1YWXei3R49eujDDz9Uhw4d9PPPP2vZsmUaNGiQcnJyKvTo0rfeesvuEaqStHLlyjs6EmfUqFHav3+/Jk+eLB8fH7399tuKiYmxfT5o0CCdPXvW7lGspendu3eJZefOndPMmTNVVFSk/v3769KlS4qIiNCWLVv0wAMP2NZ74oknVFRUZDdaJjo6WnPnzi0xUe+yZcs0bdo0jRo1SufPn9eDDz6oxx57TN26datcBwAAAABAFWKylvZMVgC3LSoqSh06dNCkSZOMjnLX5Ofny9fXV3US18nJzcPoOIDDOTuT0VO4ueLftXl5efLx8TE6DgAAVZbhj4kF7kd5eXk6ffp0pW5ZAQAAAADcOgocwF3g6+t7248WBgAAAABU3N2fORMAAAAAAOAuo8ABAAAAAAAcHgUOAAAAAADg8ChwAAAAAAAAh0eBAwAAAAAAODwKHAAAAAAAwOFR4AAAAAAAAA6PAgcAAAAAAHB4FDgAAAAAAIDDo8ABAAAAAAAcHgUOAAAAAADg8FyMDgAAAO48i8WiwsJCo2OgAlxdXeXs7Gx0DAAAHB4FDgAA7iNWq1UXL17Uzz//bHQU3AI/Pz8FBgbKZDIZHQUAAIdFgQMAgPtIcXGjZs2a8vDw4IK5irNarSooKFBubq4kKSgoyOBEAAA4LgocAADcJywWi624Ub16daPjoILc3d0lSbm5uapZsya3qwAAUElMMgoAwH2ieM4NDw8Pg5PgVhUfM+ZNAQCg8ihwAABwn+G2FMfDMQMA4PZR4AAAAAAAAA6POTgA3Lajk2Pk4+NjdAwA95G0tDR16NBBP/30k/z8/IyOAwAAHAAFDgAAfgdCkzbd0+93dmbXW1o/OjpajzzyiObMmXN3At1DS5cu1erVq5WZmalLly5RpAEA4B7hFhUAAIA7qKCgQF26dNH48eONjgIAwO8KBQ4AAGCoQYMGKT09XXPnzpXJZJLJZNLZs2clSQcOHFBERIQ8PDwUGRmprKwsu20/+ugjtWrVStWqVVO9evU0efJkXbt2zfa5yWTSkiVL1K1bN3l4eCg8PFy7du3SqVOnFB0dLU9PT0VGRur06dO31G55EhMTlZSUpMcee+z2OgYAANwSChwAAMBQc+fOVbt27fTSSy8pJydHOTk5qlOnjiTp9ddfV0pKivbv3y8XFxe9+OKLtu127NihAQMGKCEhQceOHdOSJUu0fPlyJScn27U/depUDRgwQIcOHVLjxo3Vt29fvfzyyxo3bpz2798vq9WqoUOH3nK7AACgaqHAAQAADOXr6yuz2SwPDw8FBgYqMDBQzs7OkqTk5GRFRUWpSZMmSkpK0ldffaWrV69KkiZPnqykpCQNHDhQ9erVU+fOnTV16lQtWbLErv0XXnhBsbGxatiwocaOHauzZ88qPj5eMTExCg8PV0JCgtLS0mzrV7RdAABQtTDJKAAAqLKaN29u+zooKEiSlJubq5CQEB0+fFgZGRl2IyssFouuXr2qgoICeXh4lGgjICBAktSsWTO7ZVevXlV+fr58fHwq3C4AAKhaKHAAAIAqy9XV1fa1yWSSJBUVFUmSLl++rMmTJ+uPf/xjie2qVatWbht3ol0AAFC1UOAAAACGM5vNslgst7RNq1atlJWVpQYNGtzRLHerXQAAcHdR4AAAAIYLDQ3Vnj17dPbsWXl5edlGU5Rn4sSJ6tatm0JCQtSzZ085OTnp8OHDOnr0qKZNm1bpLLfb7sWLF3Xx4kWdOnVKknTkyBF5e3srJCRE/v7+lc4FAADKxySjAADAcKNHj5azs7OaNGmiGjVqKDs7+6bbxMTEaOPGjfr888/VunVrPfbYY3rnnXdUt27d28pyu+0uXrxYLVu21EsvvSRJevLJJ9WyZUt9/PHHt5ULAACUz2S1Wq1GhwDgmPLz8+Xr66u8vDz5+PgYHQf43bt69arOnDmjsLAw5opwMOUdO37XAgBQMYzgAAAAAAAADo8CBwAAQAWtWrVKXl5epb6aNm1qdDwAAH7XmGQUAACggp577jm1bdu21M9ufPQsAAC49yhwAAAAVJC3t7e8vb2NjgEAAErBLSoAAAAAAMDhUeAAAOA+U1RUZHQE3CKOGQAAt49bVAAAuE+YzWY5OTnpwoULqlGjhsxms0wmk9GxUA6r1apff/1VP/74o5ycnGQ2m42OBACAw6LAAQDAfcLJyUlhYWHKycnRhQsXjI6DW+Dh4aGQkBA5OTG4FgCAyqLAAQDAfcRsNiskJETXrl2TxWIxOg4qwNnZWS4uLoy2AQDgNlHgAADgPmMymeTq6spjSwEAwO8K4yABAAAAAIDDo8ABAAAAAAAcHgUOAAAAAADg8JiDA0ClWa1WSVJ+fr7BSQDg/lX8O7b4dy4AACgdBQ4Alfbf//5XklSnTh2DkwDA/e/SpUvy9fU1OgYAAFUWBQ4Alebv7y9Jys7Odpj/dOfn56tOnTo6d+6cfHx8jI5TIY6W2dHySmS+Vxwtc1XJa7VadenSJQUHBxuWAQAAR0CBA0ClOTldn8bH19fXIS5WbuTj40Pmu8zR8kpkvlccLXNVyOsoRWQAAIzEJKMAAAAAAMDhUeAAAAAAAAAOjwIHgEpzc3PTG2+8ITc3N6OjVBiZ7z5HyyuR+V5xtMyOlhcAgN87k5VnjgEAAAAAAAfHCA4AAAAAAODwKHAAAAAAAACHR4EDAAAAAAA4PAocAAAAAADA4VHgAFBpCxYsUGhoqKpVq6a2bdtq7969Rkcq04wZM9S6dWt5e3urZs2a6t69u7KysoyOVWEzZ86UyWRSYmKi0VHKdf78efXr10/Vq1eXu7u7mjVrpv379xsdq0wWi0UTJkxQWFiY3N3dVb9+fU2dOlVVaf7tL7/8Us8++6yCg4NlMpn0z3/+0+5zq9WqiRMnKigoSO7u7urUqZNOnjxpTFiVn7ewsFBjx45Vs2bN5OnpqeDgYA0YMEAXLlwwLK908z6+0SuvvCKTyaQ5c+bcs3wAAKBiKHAAqJS1a9dq5MiReuONN5SZmakWLVooJiZGubm5RkcrVXp6uoYMGaLdu3dr69atKiws1NNPP60rV64YHe2m9u3bpyVLlqh58+ZGRynXTz/9pPbt28vV1VWbN2/WsWPHlJKSogceeMDoaGWaNWuWFi1apPnz5+v48eOaNWuWZs+erXnz5hkdzebKlStq0aKFFixYUOrns2fP1rvvvqvFixdrz5498vT0VExMjK5evXqPk15XXt6CggJlZmZqwoQJyszM1IcffqisrCw999xzBiT9fzfr42IbNmzQ7t27FRwcfI+SAQCAW8FjYgFUStu2bdW6dWvNnz9fklRUVKQ6depo2LBhSkpKMjjdzf3444+qWbOm0tPT9eSTTxodp0yXL19Wq1attHDhQk2bNk2PPPJIlf3LcVJSkjIyMrRjxw6jo1RYt27dFBAQoNTUVNuyHj16yN3dXX/7298MTFY6k8mkDRs2qHv37pKuj94IDg7WqFGjNHr0aElSXl6eAgICtHz5cvXu3dvAtCXzlmbfvn1q06aNvvvuO4WEhNy7cGUoK/P58+fVtm1bbdmyRV27dlViYmKVH1EFAMDvDSM4ANyyX3/9VQcOHFCnTp1sy5ycnNSpUyft2rXLwGQVl5eXJ0ny9/c3OEn5hgwZoq5du9r1dVX18ccfKyIiQr169VLNmjXVsmVLvffee0bHKldkZKS2bdumEydOSJIOHz6snTt36plnnjE4WcWcOXNGFy9etPv58PX1Vdu2bR3qXDSZTPLz8zM6SpmKiorUv39/jRkzRk2bNjU6DgAAKIOL0QEAOJ7//Oc/slgsCggIsFseEBCgf//73walqriioiIlJiaqffv2evjhh42OU6Y1a9YoMzNT+/btMzp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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot horizontal bar charts of the themes\n", + "for i in range(0,n_themes):\n", + "# for i in range(0,1):\n", + " # plot the horizontal bar chart with ordered values\n", + " df_temp = df_h4_updated_sparse[['description','theme_'+str(i+1)]].sort_values(by='theme_'+str(i+1), ascending=False).iloc[0:hhii_updated[i],:]\n", + " df_temp.plot.barh(x='description', y='theme_'+str(i+1))\n", + " fig_name=RESULTS_PATH + r'\\theme_' + str(i+1) + '.png'\n", + " plt.tight_layout()\n", + " ax = plt.gca() # get the current axes object\n", + " ax.invert_yaxis() # invert the y-axis\n", + " # fig = plt.gcf()\n", + "\n", + " # Insert word cloud\n", + " img = image.imread(RESULTS_PATH + r'\\word_clouds_surveys_theme '+str(i+1)+'.png')\n", + " # create a new axis for the image\n", + " # ax = plt.gca()\n", + " newax = ax.inset_axes([1.1, 0.25, 1.2, 1.2]) # adjust the position and size of the image\n", + " newax.imshow(img)\n", + " newax.axis(\"off\") # turn off the axis\n", + "\n", + " plt.savefig(fig_name, dpi=300, bbox_inches=\"tight\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot individual word clouds of the survey items\n", + "\n", + "for i in range(1,n_themes+1):\n", + " sub_df_h4_updated=df_h4_updated[df_h4_updated.columns[[0,i]]].set_index('label').T.to_dict('list')\n", + " for k in sub_df_h4_updated:\n", + " sub_df_h4_updated[k] = sub_df_h4_updated[k][0]\n", + "\n", + "# wc = WordCloud(width=800, height=800, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df)\n", + " # wc = WordCloud(width=800, height=800, min_font_size=4, background_color='lightgrey', color_func=lambda *args, **kwargs: \"dimgrey\", relative_scaling=1).generate_from_frequencies(sub_df_h4)\n", + " wc = WordCloud(width=800, height=800, min_font_size=4, relative_scaling=1).generate_from_frequencies(sub_df_h4_updated)\n", + "\n", + " title = 'theme ' + str(i)\n", + " plt.imshow(wc)\n", + " plt.axis('off')\n", + " plt.title(title)\n", + " fig_name=RESULTS_PATH + r'\\word_clouds_items_theme '+str(i)+'.png'\n", + " plt.savefig(fig_name, dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot horizontal bar charts of q4\n", + "for i in range(0,n_themes):\n", + " # plot the horizontal bar chart with ordered values\n", + " # df_temp = df_q4[['score','theme_'+str(i+1)]].sort_values(by='theme_'+str(i+1), ascending=False)\n", + " df_temp = df_q4_ism[['score','theme_'+str(i+1)]]\n", + " df_temp.plot.barh(x='score', y='theme_'+str(i+1))\n", + " fig_name=RESULTS_PATH + r'\\score_' + str(i+1) + '.png'\n", + " plt.tight_layout()\n", + " ax = plt.gca() # get the current axes object\n", + " ax.invert_yaxis() # invert the y-axis\n", + " # fig = plt.gcf()\n", + " plt.savefig(fig_name, dpi=300)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot interaction network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weight_cutoff = .6\n", + "show_edge_cutoff = .6\n", + "\n", + "rows_with_nonzeros = np.where(np.amax(h4_updated_sparse, axis=1) > 0)[0]\n", + "\n", + "list_network_items = [list_columns[i] for i in rows_with_nonzeros]\n", + "list_themes = ['Theme '+str(i) for i in range(1, n_themes+1)]\n", + "\n", + "# create a new list of lists with items that start with each search sequence\n", + "new_list = [[item for item in list_network_items if item.endswith(seq)] for seq in score_pref]\n", + "\n", + "# get the number of items in each sublist\n", + "n_network_items = [len(sublist) for sublist in new_list]\n", + "row_labels = list_themes+ list_network_items + score_pref\n", + "influence_matrix = np.vstack((h4_ism, h4_updated_sparse[rows_with_nonzeros], q4_ism)) @ w4_ism.T\n", + "\n", + "weight = np.corrcoef(influence_matrix)\n", + "\n", + "# Define a color map\n", + "node_colors = []\n", + "for i in range(0, len(row_labels)):\n", + " if i < len(list_themes):\n", + " node_colors.append('lightyellow')\n", + " elif i < len(list_themes)+n_network_items[0]:\n", + " node_colors.append('lightgreen')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]:\n", + " node_colors.append('lightblue')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]:\n", + " node_colors.append('orange')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]:\n", + " node_colors.append('red')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]+len(score_pref):\n", + " node_colors.append('yellow')\n", + "\n", + "G = nx.Graph()\n", + "G.add_nodes_from(row_labels)\n", + "\n", + "for i in range(len(row_labels)):\n", + " for j in range(i + 1, len(row_labels)):\n", + " if np.abs(weight[i][j]) > weight_cutoff:\n", + " G.add_edge(row_labels[i], row_labels[j], weight=np.round(weight[i][j],2))\n", + "\n", + "weights = nx.get_edge_attributes(G, 'weight')\n", + "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if np.abs(d['weight']) > show_edge_cutoff]\n", + "\n", + "plt.figure(figsize=(16, 12))\n", + "pos = nx.spring_layout(G, iterations=200)\n", + "nx.draw_networkx_nodes(G, pos, node_color=node_colors)\n", + "nx.draw_networkx_edges(G, pos, edgelist=elarge, width=[.25*weights[edge] for edge in elarge])\n", + "\n", + "# nx.draw_networkx_labels(G, pos)\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [score_pref[0]]}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='lightgreen', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [score_pref[1]]}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='lightblue', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [score_pref[2]]}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='orange', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [score_pref[3]]}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='red', edgecolor='none', boxstyle='round,pad=0.2'))\n", + " \n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in list_themes}, font_size=16, font_weight='bold', font_color='grey', bbox=dict(facecolor='lightyellow', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node not in score_pref and node not in list_themes})\n", + "\n", + "fig_name=RESULTS_PATH + r'\\interaction_network.png'\n", + "plt.title('Interaction network')\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot interaction network including patients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weight_cutoff = .6\n", + "show_edge_cutoff = 1\n", + "\n", + "list_themes = ['Theme '+str(i) for i in range(1, n_themes+1)]\n", + "\n", + "# Sort the DataFrame based on 'nmf_cluster' in ascending order and 'nmf_cluster_loading' in descending order\n", + "df_w4_ism = df_w4_ism.sort_values(['nmf_cluster', 'nmf_cluster_loading'], ascending=[True, False])\n", + "w4_ism_ordered = df_w4_ism[['theme_' + str(i) for i in range(1, n_themes + 1)]].values\n", + "w4_nmf_cluster = df_w4_ism[['nmf_cluster']].values\n", + "\n", + "list_network_items = df_w4_ism.index.values.tolist()\n", + "# get the number of observations in each nmf cluster\n", + "unique_values, n_network_items = np.unique(w4_nmf_cluster, return_counts=True)\n", + "\n", + "row_labels = list_themes + list_network_items\n", + "influence_matrix = np.vstack((h4_ism, w4_ism_ordered)) @ w4_ism.T\n", + "\n", + "# save influence_matrix\n", + "df_influence_matrix = pd.DataFrame(influence_matrix)\n", + "df_influence_matrix.columns = df.index.to_list()\n", + "df_influence_matrix['nmf_cluster'] = np.vstack((np.zeros((len(list_themes),1)), w4_nmf_cluster))\n", + "df_influence_matrix.insert(loc=0, column='wise_id', value=(row_labels))\n", + "df_influence_matrix.set_index('wise_id')\n", + "df_influence_matrix.to_csv(RESULTS_PATH + r'\\influence_matrix.csv', sep=',', na_rep='.',index=True)\n", + "\n", + "weight = np.corrcoef(influence_matrix)\n", + "\n", + "# Define a color map\n", + "node_colors = []\n", + "for i in range(0, len(row_labels)):\n", + " if i < len(list_themes):\n", + " node_colors.append('lightyellow')\n", + " elif i < len(list_themes)+n_network_items[0]:\n", + " node_colors.append('green')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]:\n", + " node_colors.append('magenta')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]:\n", + " node_colors.append('orange')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]:\n", + " node_colors.append('red')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]+n_network_items[4]:\n", + " node_colors.append('brown')\n", + " elif i < len(list_themes)+n_network_items[0]+n_network_items[1]+n_network_items[2]+n_network_items[3]+n_network_items[4]+n_network_items[5]:\n", + " node_colors.append('indigo')\n", + "\n", + "G = nx.Graph()\n", + "G.add_nodes_from(row_labels)\n", + "\n", + "for i in range(len(row_labels)):\n", + " for j in range(i + 1, len(row_labels)):\n", + " if np.abs(weight[i][j]) > weight_cutoff:\n", + " G.add_edge(row_labels[i], row_labels[j], weight=np.round(weight[i][j],2))\n", + "\n", + "weights = nx.get_edge_attributes(G, 'weight')\n", + "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if np.abs(d['weight']) > show_edge_cutoff]\n", + "\n", + "plt.figure(figsize=(16, 12))\n", + "pos = nx.spring_layout(G, iterations=200)\n", + "nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=[10 for node in G.nodes()])\n", + "nx.draw_networkx_edges(G, pos, edgelist=elarge, width=[.25*weights[edge] for edge in elarge])\n", + "\n", + " \n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[0]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='green', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[1]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='magenta', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[2]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='orange', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[3]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='red', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[4]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='brown', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "nx.draw_networkx_labels(G, pos, labels={node: node for node in G.nodes() if node in [list_themes[5]]}, font_size=16, font_weight='bold', font_color='lightgrey', bbox=dict(facecolor='indigo', edgecolor='none', boxstyle='round,pad=0.2'))\n", + "\n", + "fig_name=RESULTS_PATH + r'\\interaction_network_patients.png'\n", + "plt.title('Interaction network')\n", + "plt.savefig(fig_name, dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/getting_started_mofa.ipynb b/examples/getting_started_mofa.ipynb new file mode 100644 index 0000000..0f29d79 --- /dev/null +++ b/examples/getting_started_mofa.ipynb @@ -0,0 +1,593 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MOFA+: training a model in Python\n", + "Author: Ricard Argelaguet. \n", + "Affiliation: European Bioinformatics Institute, Cambridge, UK. \n", + "Date: 13/11/2019 \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook contains a detailed tutorial on how to train MOFA using Python.\n", + "A template script to run the code below can be found [here](https://github.com/bioFAM/MOFA2/tree/master/inst/scripts)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Load libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:16.898360Z", + "start_time": "2020-02-06T20:25:16.836870Z" + }, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "from mofapy2.run.entry_point import entry_point\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# initialise the entry point\n", + "ent = entry_point()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Load data\n", + "\n", + "To create a MOFA+ object you need to specify four dimensions: samples (cells), features, view(s) and group(s). MOFA objects can be created from a wide range of input formats:\n", + "\n", + "### 2.1) pandas data.frame format\n", + "A pandas data.frame with columns `sample`, `group`, `feature`, `view`, `value`. This is the most intuitive format, as it summarises all omics/groups in a single data structure. Also, there is no need to add rows that correspond to missing data.\n", + "\n", + "For example:\n", + "```\n", + "sample group feature value view\n", + "sample1 groupA gene1 2.8044 RNA\n", + "sample1 groupA gene3 2.2069 RNA\n", + "sample2 groupB gene2 0.1454 RNA\n", + "sample2 groupB gene1 2.7021 RNA\n", + "sample2 groupB promoter1 3.8618 Methylation\n", + "sample3 groupB promoter2 3.2545 Methylation\n", + "sample3 groupB promoter3 1.5014 Methylation\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we load a simulated data set with the following dimensions:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:20.952029Z", + "start_time": "2020-02-06T20:25:18.586476Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sample group feature view value\n", + "0 sample_0_group_0 group_0 feature_0_view_0 view_0 -2.05\n", + "1 sample_1_group_0 group_0 feature_0_view_0 view_0 0.10\n", + "2 sample_2_group_0 group_0 feature_0_view_0 view_0 1.44\n", + "3 sample_3_group_0 group_0 feature_0_view_0 view_0 -0.28\n", + "4 sample_4_group_0 group_0 feature_0_view_0 view_0 -0.88" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D = [1000,1000] # Number of features per view\n", + "M = len(D) # Number of views\n", + "K = 5 # Number of factors\n", + "N = [100,100] # Number of samples per group\n", + "G = len(N) # Number of groups\n", + "\n", + "data_dt = pd.read_csv(\"http://ftp.ebi.ac.uk/pub/databases/mofa/getting_started/data.txt.gz\", sep=\"\\t\")\n", + "data_dt.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2) List of matrices\n", + "A nested list of numpy arrays, where the first index refers to the view and the second index refers to the group. Samples are stored in the rows and features are stored in the columns. All views for a given group G must have the same samples in the rows. If there is any sample that is missing a particular view, the column needs to be filled with NAs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading the same data above in matrix format:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:21.324421Z", + "start_time": "2020-02-06T20:25:21.058184Z" + }, + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "data_prefix = \"http://ftp.ebi.ac.uk/pub/databases/mofa/getting_started\"\n", + "data_mat = [[None for g in range(G)] for m in range(M)]\n", + "for m in range(M):\n", + " for g in range(G):\n", + " data_mat[m][g] = np.loadtxt(\"%s/%d_%d.txt.gz\" % (data_prefix,m,g), delimiter=\"\\t\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "### 2.3 Define data options\n", + "- **scale_views**: if views have different ranges/variances, it is good practice to scale each view to unit variance. Default is False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:21.371541Z", + "start_time": "2020-02-06T20:25:21.359823Z" + } + }, + "outputs": [], + "source": [ + "ent.set_data_options(\n", + " scale_views = False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4) Add the data to the model\n", + "\n", + "This has to be run after defining the data options\n", + "- **likelihoods**: a list of strings, either \"gaussian\" (default), \"poisson\" or \"bernoulli\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:21.862319Z", + "start_time": "2020-02-06T20:25:21.805782Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=100 samples and D=1000 features...\n", + "Successfully loaded view='view0' group='group1' with N=100 samples and D=1000 features...\n", + "Successfully loaded view='view1' group='group0' with N=100 samples and D=1000 features...\n", + "Successfully loaded view='view1' group='group1' with N=100 samples and D=1000 features...\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# option 1: data.frame format\n", + "ent.set_data_df(data_dt, likelihoods = [\"gaussian\",\"gaussian\"])\n", + "\n", + "# option 2: nested matrix format\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\",\"gaussian\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) Set model options\n", + "\n", + "- **factors**: number of factors\n", + "- **spikeslab_weights**: use spike-slab sparsity prior in the weights? default is TRUE\n", + "- **ard_weights**: use ARD prior in the weights? Default is TRUE if using multiple views.\n", + "\n", + "Only change the default model options if you are familiar with the underlying mathematical model!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:26.955109Z", + "start_time": "2020-02-06T20:25:26.914220Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: True\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: True \n", + "\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n" + ] + } + ], + "source": [ + "ent.set_model_options(\n", + " factors = 10, \n", + " spikeslab_weights = True, \n", + " ard_weights = True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4) Set training options\n", + "\n", + "- **convergence_mode**: \"fast\" (default), \"medium\", \"slow\".\n", + "- **dropR2**: minimum variance explained criteria to drop factors while training\n", + "- **gpu_mode**: use GPU? (needs cupy installed and a functional GPU, see https://biofam.github.io/MOFA2/gpu_training.html)\n", + "- **seed**: random seed" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:25:29.519460Z", + "start_time": "2020-02-06T20:25:29.509712Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "GPU mode is activated, but GPU not found... switching to CPU mode\n", + "For GPU mode, you need:\n", + "1 - Make sure that you are running MOFA+ on a machine with an NVIDIA GPU\n", + "2 - Install CUPY following instructions on https://docs-cupy.chainer.org/en/stable/install.html\n", + "\n" + ] + } + ], + "source": [ + "ent.set_train_options(\n", + " convergence_mode = \"fast\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = True, \n", + " seed = 1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6) Build and train the MOFA object \n", + "\n", + "After training, the model will be saved as an hdf5 file" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-06T20:27:21.866240Z", + "start_time": "2020-02-06T20:27:18.843347Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -5381458.89 \n", + "\n", + "Iteration 1: time=0.06, ELBO=-575232.56, deltaELBO=4806226.336 (89.31084362%), Factors=9\n", + "Iteration 2: time=0.07, ELBO=-475536.26, deltaELBO=99696.298 (1.85258867%), Factors=8\n", + "Iteration 3: time=0.06, ELBO=-467044.81, deltaELBO=8491.451 (0.15779088%), Factors=7\n", + "Iteration 4: time=0.05, ELBO=-462583.09, deltaELBO=4461.713 (0.08290899%), Factors=6\n", + "Iteration 5: time=0.06, ELBO=-459116.18, deltaELBO=3466.915 (0.06442334%), Factors=5\n", + "Iteration 6: time=0.04, ELBO=-458285.22, deltaELBO=830.956 (0.01544108%), Factors=5\n", + "Iteration 7: time=0.04, ELBO=-457814.07, deltaELBO=471.158 (0.00875520%), Factors=5\n", + "Iteration 8: time=0.04, ELBO=-457487.58, deltaELBO=326.485 (0.00606685%), Factors=5\n", + "Iteration 9: time=0.06, ELBO=-457202.99, deltaELBO=284.587 (0.00528829%), Factors=5\n", + "Iteration 10: time=0.04, ELBO=-456926.93, deltaELBO=276.062 (0.00512988%), Factors=5\n", + "Iteration 11: time=0.05, ELBO=-456666.10, deltaELBO=260.835 (0.00484693%), Factors=5\n", + "Iteration 12: time=0.11, ELBO=-456437.43, deltaELBO=228.665 (0.00424912%), Factors=5\n", + "Iteration 13: time=0.08, ELBO=-456246.66, deltaELBO=190.774 (0.00354502%), Factors=5\n", + "Iteration 14: time=0.04, ELBO=-456087.59, deltaELBO=159.067 (0.00295584%), Factors=5\n", + "Iteration 15: time=0.05, ELBO=-455950.33, deltaELBO=137.263 (0.00255067%), Factors=5\n", + "Iteration 16: time=0.06, ELBO=-455826.84, deltaELBO=123.490 (0.00229474%), Factors=5\n", + "Iteration 17: time=0.04, ELBO=-455710.86, deltaELBO=115.979 (0.00215515%), Factors=5\n", + "Iteration 18: time=0.05, ELBO=-455597.76, deltaELBO=113.095 (0.00210156%), Factors=5\n", + "Iteration 19: time=0.05, ELBO=-455485.19, deltaELBO=112.574 (0.00209190%), Factors=5\n", + "Iteration 20: time=0.05, ELBO=-455372.67, deltaELBO=112.518 (0.00209085%), Factors=5\n", + "Iteration 21: time=0.06, ELBO=-455259.13, deltaELBO=113.542 (0.00210987%), Factors=5\n", + "Iteration 22: time=0.05, ELBO=-455141.61, deltaELBO=117.515 (0.00218370%), Factors=5\n", + "Iteration 23: time=0.05, ELBO=-455017.31, deltaELBO=124.306 (0.00230989%), Factors=5\n", + "Iteration 24: time=0.05, ELBO=-454890.60, deltaELBO=126.711 (0.00235458%), Factors=5\n", + "Iteration 25: time=0.06, ELBO=-454777.30, deltaELBO=113.294 (0.00210527%), Factors=5\n", + "Iteration 26: time=0.07, ELBO=-454687.38, deltaELBO=89.924 (0.00167099%), Factors=5\n", + "Iteration 27: time=0.08, ELBO=-454621.75, deltaELBO=65.626 (0.00121948%), Factors=5\n", + "Iteration 28: time=0.06, ELBO=-454575.85, deltaELBO=45.908 (0.00085308%), Factors=5\n", + "Iteration 29: time=0.24, ELBO=-454540.91, deltaELBO=34.939 (0.00064925%), Factors=5\n", + "Iteration 30: time=0.06, ELBO=-454510.81, deltaELBO=30.094 (0.00055922%), Factors=5\n", + "Iteration 31: time=0.06, ELBO=-454483.09, deltaELBO=27.720 (0.00051509%), Factors=5\n", + "Iteration 32: time=0.07, ELBO=-454457.65, deltaELBO=25.444 (0.00047280%), Factors=5\n", + "Iteration 33: time=0.09, ELBO=-454435.35, deltaELBO=22.304 (0.00041445%), Factors=5\n", + "Iteration 34: time=0.04, ELBO=-454416.89, deltaELBO=18.454 (0.00034292%), Factors=5\n", + "Iteration 35: time=0.06, ELBO=-454402.44, deltaELBO=14.450 (0.00026852%), Factors=5\n", + "Iteration 36: time=0.06, ELBO=-454391.52, deltaELBO=10.926 (0.00020303%), Factors=5\n", + "Iteration 37: time=0.04, ELBO=-454383.33, deltaELBO=8.189 (0.00015217%), Factors=5\n", + "Iteration 38: time=0.05, ELBO=-454377.13, deltaELBO=6.200 (0.00011521%), Factors=5\n", + "Iteration 39: time=0.06, ELBO=-454372.32, deltaELBO=4.807 (0.00008932%), Factors=5\n", + "Iteration 40: time=0.06, ELBO=-454368.46, deltaELBO=3.858 (0.00007169%), Factors=5\n", + "Iteration 41: time=0.04, ELBO=-454365.23, deltaELBO=3.230 (0.00006002%), Factors=5\n", + "Iteration 42: time=0.05, ELBO=-454362.40, deltaELBO=2.828 (0.00005256%), Factors=5\n", + "Iteration 43: time=0.05, ELBO=-454359.82, deltaELBO=2.585 (0.00004804%), Factors=5\n", + "Iteration 44: time=0.05, ELBO=-454357.37, deltaELBO=2.452 (0.00004556%), Factors=5\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "No output file name provided as a training options or to the save method. Saving to /tmp/mofa_20210223-153612.hdf5 .\n", + "Saving model in /tmp/mofa_20210223-153612.hdf5...\n" + ] + } + ], + "source": [ + "ent.build()\n", + "\n", + "ent.run()\n", + "\n", + "# Save the output\n", + "ent.save(outfile=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7) Downstream analysis\n", + "\n", + "This finishes the tutorial on how to train a MOFA model from python. To continue with the downstream analysis you can either use the [mofax](https://github.com/gtca/mofax) python package or the [MOFA2](https://www.bioconductor.org/packages/release/bioc/html/MOFA2.html) R package. Please, visit our [tutorials](https://biofam.github.io/MOFA2/tutorials.html) webpage for more information." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "latex_metadata": { + "affiliation": "European Bioinformatics Institute, Cambridge, UK", + "author": "Ricard Argelaguet", + "title": "MOFA+: training a model with Python" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/mofa_template_script_matrix.py b/examples/mofa_template_script_matrix.py new file mode 100644 index 0000000..9f6147b --- /dev/null +++ b/examples/mofa_template_script_matrix.py @@ -0,0 +1,153 @@ + +###################################################### +## Template script to train a MOFA+ model in Python ## +###################################################### + +from mofapy2.run.entry_point import entry_point +import pandas as pd +import io +import requests # to download the online data + +############### +## Load data ## +############### + +# The data format is a nested list of matrices, where the first index refers to the view and the second index refers to the group. +# samples are stored in the rows and features are stored in the columns. +# Missing values must be explicitly filled using NAs, including samples missing an entire view + +datadir = "/Users/ricard/data/mofaplus/test" +views = ["0","1"] +groups = ["0","1"] +data = [None]*len(views) +for m in range(len(views)): + data[m] = [None]*len(groups) + for g in range(len(groups)): + datafile = "%s/%s_%s.txt.gz" % (datadir, views[m], groups[g]) + data[m][g] = pd.read_csv(datafile, header=None, sep=' ') + +########################### +## Initialise MOFA model ## +########################### + +## (1) initialise the entry point +ent = entry_point() + + +## (2) Set data options +# - scale_views: if views have very different ranges, one can to scale each view to unit variance +ent.set_data_options( + scale_views = False +) + + +## (3) Define names +views_names = ["view1","view2"] +# groups_names = ["groupA","groupB"] + +# samples_names nested list with length n_groups. Each entry g is a list with the sample names for the g-th group +# - if not provided, MOFA will fill it with default samples names +samples_names = (...) + +# features_names nested list with length NVIEWS. Each entry m is a list with the features names for the m-th view +# - if not provided, MOFA will fill it with default features names +features_names = (...) + + +## (4) Set data matrix +ent.set_data_matrix(data, + views_names = views_names, + groups_names = groups_names, + samples_names = samples_names, + features_names = features_names +) + + +## (5) Set model options +# - factors: number of factors. Default is 15 +# - likelihods: likelihoods per view (options are "gaussian","poisson","bernoulli"). Default and recommended is "gaussian" +# - spikeslab_weights: use spike-slab sparsity prior in the weights? (recommended TRUE) +# - ard_weights: use automatic relevance determination prior in the weights? (TRUE if using multiple views) + +# using default values +ent.set_model_options() + +# using personalised values +ent.set_model_options( + factors = 5, + spikeslab_weights = True, + ard_weights = True +) + +## (5) Set training options ## +# - iter: number of iterations +# - convergence_mode: "fast", "medium", "slow". Fast mode is usually good enough. +# - dropR2: minimum variance explained criteria to drop factors while training. Default is None, inactive factors are not dropped during training +# - gpu_mode: use GPU mode? this functionality needs cupy installed and a functional GPU, see https://biofam.github.io/MOFA2/gpu_training.html +# - seed: random seed + +# using default values +ent.set_train_options() + +# using personalised values +ent.set_train_options( + iter = 100, + convergence_mode = "fast", + dropR2 = None, + gpu_mode = False, + seed = 42 +) + +#################################### +## Build and train the MOFA model ## +#################################### + +# Build the model +ent.build() + +# Run the model +ent.run() + +#################### +## Save the model ## +#################### + +outfile = "/Users/ricard/data/mofaplus/hdf5/test.hdf5" + +# - save_data: logical indicating whether to save the training data in the hdf5 file. +# this is useful for some downstream analysis in R, but it can take a lot of disk space. +ent.save(outfile, save_data=True) + +######################### +## Downstream analysis ## +######################### + +# Check the mofax package for the downstream analysis in Python: https://github.com/bioFAM/mofax +# Check the MOFA2 R package for the downstream analysis in R: https://www.bioconductor.org/packages/release/bioc/html/MOFA2.html +# All tutorials: https://biofam.github.io/MOFA2/tutorials.html + +# Extract factor values (a list with one matrix per sample group) +factors = ent.model.nodes["Z"].getExpectation() + +# Extract weights (a list with one matrix per view) +weights = ent.model.nodes["W"].getExpectation() + +# Extract variance explained values +r2 = ent.model.calculate_variance_explained() + +# Interact directly with the hdf5 file +import h5py +f = h5py.File(outfile, 'r') +f.keys() + +# Extract factors +f["expectations"]["Z"]["group_0"].value +f["expectations"]["Z"]["group_1"].value + +# Extract weights +f["expectations"]["W"]["view_0"].value +f["expectations"]["W"]["view_1"].value + +# Extract variance explained estimates +f["variance_explained"]["r2_per_factor"] +f["variance_explained"]["r2_total"] diff --git a/examples/simulation_biomed.ipynb b/examples/simulation_biomed.ipynb index 623da93..48e7a1a 100644 --- a/examples/simulation_biomed.ipynb +++ b/examples/simulation_biomed.ipynb @@ -4,21 +4,43 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "# !pip install -e .." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Relative error: 0.08\n" + "coucou\n", + "error ism before straightening: 0.09\n", + "error ism after straightening: 0.12\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "import adilsm.adilsm as ilsm\n", - "import pandas as pd\n", "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import adilsm.adilsm as ilsm\n", "\n", - "max_noise_level = 0.01\n", + "max_noise_level = 0.1\n", "# Generate a random non-negative matrix with 100 rows and 10 columns\n", "A = np.random.rand(100, 10)\n", "# Swap the columns of the A and add some noise to generate B\n", @@ -26,40 +48,39 @@ "# Add noise to A\n", "A += np.random.uniform(low=0, high=max_noise_level, size=A.shape)\n", "\n", - "# ISM is expected to recognize that A and B convey the same information up to some noise, albeit with the columns of B swapped around.\n", + "# ISM is expected to recognize that A and B convey the same information up to some noise,\n", + "# albeit with the columns of B swapped around. Heatmaps of the loadings of A and B columns\n", + "# on ISM components show the effective permutation. \n", "\n", - "m0 = np.hstack((A, B))\n", - "\n", - "n_items = [A.shape[1], B.shape[1]]\n", - "n_scores = len(n_items)\n", + "Xs = [A, B]\n", "n_embedding, n_themes = [10,10]\n", "\n", - "h4_updated, h4_updated_sparse, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0=False, update_h4_ism=True,\n", - " max_iter_mult=200, sparsity_coeff=.8)\n", - "error = np.linalg.norm(m0 - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0)\n", - "print('Relative error: ',round(error, 2))\n" + "ilsm_result = ilsm.ism(Xs, n_embedding, n_themes, norm_columns=False, update_h4_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "hv = ilsm_result['HV']\n", + "hv_sparse = ilsm_result['HV_SPARSE']\n", + "hhii = ilsm_result['HHII']\n", + "w_ism = ilsm_result['W']\n", + "h_ism = ilsm_result['H']\n", + "q_ism = ilsm_result['Q']\n", + "Xs_emb = ilsm_result['EMBEDDING']\n", + "Xs_norm = ilsm_result['NORMED_VIEWS']\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 5), constrained_layout=True)\n", + "ax[0].imshow(hv[0], cmap='viridis', aspect='auto')\n", + "# Add labels and title\n", + "ax[0].set_xlabel('Component')\n", + "ax[0].set_ylabel('Column')\n", + "ax[0].set_title('Loadings of A columns on ISM components')\n", + "ax[1].imshow(hv[1], cmap='viridis', aspect='auto')\n", + "# Add labels and title\n", + "ax[1].set_xlabel('Component')\n", + "ax[1].set_ylabel('Column')\n", + "ax[1].set_title('Loadings of B columns on ISM components')\n", + "\n", + "# Show the plot\n", + "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/uci_digits_all.ipynb b/examples/uci_digits_all.ipynb new file mode 100644 index 0000000..6b04a75 --- /dev/null +++ b/examples/uci_digits_all.ipynb @@ -0,0 +1,2061 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from mofapy2.run.entry_point import entry_point\n", + "\n", + "RESULTS_PATH = r'C:\\Users\\paul_\\OneDrive\\Pro\\George\\Wise\\analysis\\results\\uci_digits'\n", + "\n", + "list_solutions = None\n", + "predefined_solution = ''\n", + "\n", + "# ISM algorithmic options\n", + "embed = True\n", + "max_iter_integrate = 20\n", + "update_h4_ism = True\n", + "\n", + "# Grid search limits\n", + "min_embedding = 8\n", + "max_embedding = 10\n", + "min_themes = 10\n", + "max_themes = 10\n", + "\n", + "# list_solutions contains one ore more solutions selected because of their low condition numbers\n", + "list_solutions = [[9,10]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " scaler = StandardScaler()\n", + " Xs_norm[i] = scaler.fit_transform(Xs[i])\n", + "\n", + "data_mat = [[None for g in range(1)] for m in range(6)]\n", + "\n", + "for m in range(6):\n", + " data_mat[m][0] = Xs_norm[m]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "Successfully loaded view='view4' group='group0' with N=2000 samples and D=47 features...\n", + "Successfully loaded view='view5' group='group0' with N=2000 samples and D=6 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "- View 4 (view4): gaussian\n", + "- View 5 (view5): gaussian\n", + "\n", + "\n", + "\n", + "Warning: some view(s) have less than 15 features, MOFA won't be able to learn meaningful factors for these view(s)...\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -9709296.32 \n", + "\n", + "Iteration 1: time=0.35, ELBO=-1404936.80, deltaELBO=8304359.518 (85.52998325%), Factors=9\n", + "Iteration 2: time=0.31, ELBO=-1304107.77, deltaELBO=100829.032 (1.03847930%), Factors=9\n", + "Iteration 3: time=0.31, ELBO=-1292170.50, deltaELBO=11937.273 (0.12294684%), Factors=9\n", + "Iteration 4: time=0.33, ELBO=-1285195.38, deltaELBO=6975.120 (0.07183961%), Factors=9\n", + "Iteration 5: time=0.33, ELBO=-1279820.72, deltaELBO=5374.658 (0.05535579%), Factors=9\n", + "Iteration 6: time=0.32, ELBO=-1274512.59, deltaELBO=5308.129 (0.05467058%), Factors=9\n", + "Iteration 7: time=0.33, ELBO=-1268085.65, deltaELBO=6426.940 (0.06619368%), Factors=9\n", + "Iteration 8: time=0.32, ELBO=-1260996.51, deltaELBO=7089.137 (0.07301392%), Factors=9\n", + "Iteration 9: time=0.52, ELBO=-1255267.26, deltaELBO=5729.254 (0.05900792%), Factors=9\n", + "Iteration 10: time=0.73, ELBO=-1251797.77, deltaELBO=3469.488 (0.03573367%), Factors=9\n", + "Iteration 11: time=0.77, ELBO=-1249936.84, deltaELBO=1860.927 (0.01916645%), Factors=9\n", + "Iteration 12: time=0.61, ELBO=-1248892.38, deltaELBO=1044.460 (0.01075732%), Factors=9\n", + "Iteration 13: time=0.63, ELBO=-1248210.98, deltaELBO=681.405 (0.00701807%), Factors=9\n", + "Iteration 14: time=0.73, ELBO=-1247693.58, deltaELBO=517.396 (0.00532887%), Factors=9\n", + "Iteration 15: time=0.78, ELBO=-1247260.66, deltaELBO=432.928 (0.00445890%), Factors=9\n", + "Iteration 16: time=0.76, ELBO=-1246878.89, deltaELBO=381.764 (0.00393194%), Factors=9\n", + "Iteration 17: time=0.68, ELBO=-1246532.61, deltaELBO=346.282 (0.00356650%), Factors=9\n", + "Iteration 18: time=0.58, ELBO=-1246213.39, deltaELBO=319.223 (0.00328781%), Factors=9\n", + "Iteration 19: time=0.42, ELBO=-1245916.12, deltaELBO=297.270 (0.00306170%), Factors=9\n", + "Iteration 20: time=0.35, ELBO=-1245637.38, deltaELBO=278.732 (0.00287078%), Factors=9\n", + "Iteration 21: time=0.34, ELBO=-1245374.72, deltaELBO=262.661 (0.00270525%), Factors=9\n", + "Iteration 22: time=0.35, ELBO=-1245126.25, deltaELBO=248.476 (0.00255915%), Factors=9\n", + "Iteration 23: time=0.36, ELBO=-1244890.45, deltaELBO=235.793 (0.00242853%), Factors=9\n", + "Iteration 24: time=0.35, ELBO=-1244666.11, deltaELBO=224.346 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"Iteration 369: time=0.43, ELBO=-1234875.48, deltaELBO=5.059 (0.00005210%), Factors=9\n", + "Iteration 370: time=0.56, ELBO=-1234870.44, deltaELBO=5.038 (0.00005189%), Factors=9\n", + "Iteration 371: time=0.64, ELBO=-1234865.43, deltaELBO=5.018 (0.00005168%), Factors=9\n", + "Iteration 372: time=0.65, ELBO=-1234860.43, deltaELBO=4.997 (0.00005147%), Factors=9\n", + "Iteration 373: time=0.60, ELBO=-1234855.45, deltaELBO=4.977 (0.00005126%), Factors=9\n", + "Iteration 374: time=0.66, ELBO=-1234850.50, deltaELBO=4.957 (0.00005105%), Factors=9\n", + "Iteration 375: time=0.67, ELBO=-1234845.56, deltaELBO=4.937 (0.00005085%), Factors=9\n", + "Iteration 376: time=0.54, ELBO=-1234840.64, deltaELBO=4.917 (0.00005064%), Factors=9\n", + "Iteration 377: time=0.38, ELBO=-1234835.74, deltaELBO=4.897 (0.00005044%), Factors=9\n", + "Iteration 378: time=0.47, ELBO=-1234830.87, deltaELBO=4.877 (0.00005023%), Factors=9\n", + "Iteration 379: time=0.40, ELBO=-1234826.01, deltaELBO=4.858 (0.00005003%), Factors=9\n", + "Iteration 380: time=0.42, ELBO=-1234821.17, deltaELBO=4.838 (0.00004983%), Factors=9\n", + "Iteration 381: time=0.37, ELBO=-1234816.35, deltaELBO=4.819 (0.00004963%), Factors=9\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "ent = entry_point()\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(6)])\n", + "ent.set_model_options(\n", + " factors = 10, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + ")\n", + "ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + ")\n", + "ent.build()\n", + "ent.run()\n", + "model_mofa = ent.model.nodes[\"Z\"].getExpectation()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "X_car_p = Xs[2].copy()\n", + "X_car_p[X_car_p<0] = 0\n", + "X_car_n = -Xs[2].copy()\n", + "X_car_n[X_car_n<0] = 0\n", + "\n", + "Xs_concat = Xs[0]\n", + "Xs_concat = np.hstack((Xs_concat, Xs[1], X_car_p, X_car_n))\n", + "\n", + "# Xs_concat = np.hstack((Xs[0], Xs[1]))\n", + "\n", + "\n", + "for X in Xs[3:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "\n", + "m0 = Xs_concat\n", + "m0_nan_0 = m0.copy()\n", + "\n", + "# create m0_weight with ones and zeros if not_missing/missing value\n", + "m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "\n", + "max_values = np.max(m0_nan_0, axis=0)\n", + "# Replace maximum values equal to 0 with 1\n", + "m0 = np.divide(m0, np.where(max_values == 0, 1, max_values))\n", + "m0_nan_0 = np.divide(m0_nan_0, np.where(max_values == 0, 1, max_values))\n", + "\n", + "\n", + "list_columns = [str(i) for i in range(m0.shape[1])]\n", + "score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-kar-p', 'mfeat-kar-n', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", + "n_items = [Xs[i].shape[1] for i in range(2)] + [X_car_p.shape[1], X_car_n.shape[1]] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", + "# score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", + "# n_items = [Xs[i].shape[1] for i in range(2)] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", + "n_scores = len(n_items)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def format_loadings_merged(h4, list_solutions, list_columns):\n", + " # Format loadings\n", + " df_h4 = pd.DataFrame(data=h4)\n", + " list_themes = []\n", + " for i_solution in range(0, len(list_solutions)):\n", + " list_themes = list_themes + ['theme_' + str(i) + '_' + str(list_solutions[i_solution][0]) + '_' + str(list_solutions[i_solution][1]) for i in range(1, list_solutions[i_solution][1] + 1)]\n", + " \n", + " df_h4.columns = list_themes\n", + " df_h4.insert(loc=0, column='label', value=(list_columns))\n", + "\n", + " # Add description index\n", + " df_h4['description'] = df_h4['label']\n", + " \n", + " return df_h4\n", + "\n", + "def format_loadings(h4, list_columns):\n", + " # Format loadings\n", + " df_h4 = pd.DataFrame(data=h4)\n", + " n_comp = len(df_h4.columns)\n", + " df_h4.columns = ['theme_' + str(i) for i in range(1, n_comp + 1)]\n", + " df_h4.insert(loc=0, column='label', value=(list_columns))\n", + "\n", + " # Add description index\n", + " df_h4['description'] = df_h4['label']\n", + " \n", + " return df_h4\n", + "\n", + "def generate_h4_sparse(h4, q4_ism, n_items, n_comp, n_scores):\n", + " # Calculate hhii of each h column and generate sparse loadings\n", + " hhii = np.zeros(n_comp, dtype=int)\n", + " h_threshold = np.zeros(n_comp)\n", + "\n", + " if q4_ism is not None:\n", + " i1 = 0\n", + " for i_score in range(0,n_scores):\n", + " i2 = i1+n_items[i_score]\n", + " h4[i1:i2,:] *= q4_ism[i_score]\n", + " i1 = i2\n", + "\n", + " for i in range(0,n_comp):\n", + " # calculate inverse hhi\n", + " if np.max(h4[:,i]) > 0:\n", + " hhii[i] = int(round(np.sum(h4[:, i])**2 / np.sum(h4[:, i]**2)))\n", + " # hhii[i] = np.count_nonzero(h4[:, i])\n", + " \n", + " # sort the dataframe by score in descending order\n", + " h_threshold[i] = np.sort(h4[:, i], axis=0)[::-1][hhii[i]-1] * .8\n", + " \n", + "\n", + " h4_sparse = np.where(h4 < h_threshold[None,:], 0, h4)\n", + " \n", + " return h4_sparse, hhii\n", + "\n", + "def integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes):\n", + " EPSILON = np.finfo(np.float32).eps\n", + "\n", + " # Generate w for each score, based on sparse loadings and create tensor_score\n", + "\n", + " # Extract score-related items\n", + " i1 = 0\n", + " for i_score in range(n_scores):\n", + " i2 = i1+n_items[i_score]\n", + " w4_score = w4_ism.copy()\n", + " h4_score = h4_sparse[i1:i2, :].copy()\n", + " m0_score = m0_nan_0[:, i1:i2]\n", + " m0_weight_score = m0_weight[:, i1:i2]\n", + " i1=i2\n", + " # # Normalize w4_score by max column and update h4_score\n", + " # max_values = np.max(w4_score, axis=0)\n", + " # # Replace maximum values equal to 0 with 1\n", + " # w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " # h4_score = np.multiply(h4_score, max_values)\n", + " # h4_score0 = h4_score.copy()\n", + "\n", + " # Apply multiplicative updates to preserve h sparsity \n", + " for _ in range(0, 200):\n", + " # Weighted multiplicative rules\n", + " m0_score_est = w4_score @ h4_score.T\n", + " h4_score *= ((w4_score.T @ m0_score) / (w4_score.T @ (m0_score_est*m0_weight_score) + EPSILON)).T\n", + " w4_score *= (m0_score @ h4_score / ((m0_weight_score*m0_score_est) @ h4_score + EPSILON))\n", + " # if i % 10 == 0:\n", + " # # Normalize w4_score by max column and update h4_score\n", + " # max_values = np.max(w4_score, axis=0)\n", + " # # Replace maximum values equal to 0 with 1\n", + " # w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " # h4_score = np.multiply(h4_score, max_values)\n", + " # if np.linalg.norm(h4_score-h4_score0)/max(np.linalg.norm(h4_score0),EPSILON) < 1.e-10:\n", + " # print(i)\n", + " # break\n", + " # else:\n", + " # h4_score0 = h4_score.copy()\n", + "\n", + " # Normalize w4_score by max column and update h4_score\n", + " max_values = np.max(w4_score, axis=0)\n", + " # Replace maximum values equal to 0 with 1\n", + " w4_score = np.divide(w4_score, np.where(max_values == 0, 1, max_values))\n", + " h4_score = np.multiply(h4_score, max_values)\n", + "\n", + " # Generate embedding tensor and initialize h4_updated\n", + " if i_score == 0:\n", + " tensor_score = w4_score\n", + " h4_updated = h4_score\n", + " else:\n", + " tensor_score = np.hstack((tensor_score, w4_score))\n", + " h4_updated = np.vstack((h4_updated, h4_score))\n", + "\n", + " # Apply NTF with prescribed number of themes and update themes\n", + " my_ntfmodel = NTF(n_components=n_themes, leverage=None, init_type=2, max_iter=200, tol=1e-6, verbose=-1, random_state=0)\n", + "\n", + " if q4_ism is None:\n", + " estimator_ = my_ntfmodel.fit_transform(tensor_score, n_blocks=n_scores)\n", + " else:\n", + " estimator_ = my_ntfmodel.fit_transform(tensor_score, w=w4_ism, h=h4_ism, q=q4_ism, update_h=update_h4_ism, n_blocks=n_scores)\n", + "\n", + " w4_ism = estimator_.w\n", + " h4_ism = estimator_.h\n", + " q4_ism = estimator_.q\n", + "\n", + " # Update loadings based on h4_updated (initialized by multiplicative updates)\n", + " h4_updated = h4_updated @ h4_ism\n", + " h4_updated_sparse, hhii_updated = generate_h4_sparse(h4_updated, q4_ism, n_items, n_themes, n_scores)\n", + "\n", + " return h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "condition number(9, 10) = 7.99\n", + "condition number (primary NMF): 4.57\n" + ] + } + ], + "source": [ + "if predefined_solution != '':\n", + " max_iter_integrate = 0\n", + " # Read pre-defined themes\n", + " df_h4_updated = pd.read_csv(DATA_PATH + predefined_solution)\n", + " h4_updated = df_h4_updated.values.astype(np.float_)\n", + " h4_updated_sparse = h4_updated.copy()\n", + " list_solutions = [[h4_updated.shape[1],h4_updated.shape[1]]]\n", + "\n", + "if list_solutions is not None:\n", + " perform_grid_search = False\n", + "else:\n", + " perform_grid_search = True\n", + "\n", + "if perform_grid_search:\n", + " # Perform grid search first to select solutions with low condition numbers\n", + " cond = np.ones((max_embedding+1, max_themes+1))*999\n", + " list_solutions = []\n", + " for n_embedding in range(min_embedding, max_embedding+1):\n", + " for n_themes in range(min_themes, max_themes+1):\n", + " list_solutions += [[n_embedding, n_themes]]\n", + "else:\n", + " h4_updated_merged = None\n", + "\n", + "for n_embedding, n_themes in list_solutions:\n", + " if predefined_solution == '':\n", + " # Initial Embedding\n", + "\n", + " my_nmfmodel = NMF(n_components=n_embedding, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=0)\n", + " # estimator_ = my_nmfmodel.fit_transform(m0.copy(), sparsity=.5, regularization='components')\n", + " estimator_ = my_nmfmodel.fit_transform(m0.copy())\n", + " \n", + " w4 = estimator_.w\n", + " h4 = estimator_.h\n", + " \n", + " h4_sparse, hhii = generate_h4_sparse(h4, None, n_items, n_embedding, n_scores)\n", + "\n", + " if embed:\n", + " # Embed using scores w4 found in preliminary NMF and initialize themes through NTF \n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_sparse, w4, None, None, n_scores, n_items, n_themes) \n", + " else:\n", + " h4_updated = h4\n", + " h4_updated_sparse = h4_sparse\n", + " hhii_updated = hhii\n", + " w4_ism = w4\n", + " h4_ism = np.identity(n_themes)\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + "\n", + " else: \n", + " w4_ism = np.ones((m0.shape[0], n_themes))\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + " w4 = w4_ism\n", + " h4 = h4_updated.copy()\n", + " h4_sparse = h4\n", + " n_themes = list_solutions[0][1]\n", + " h4_updated_merged = None\n", + " if embed:\n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes)\n", + " else:\n", + " h4_updated = h4\n", + " h4_updated_sparse = h4_sparse\n", + " hhii_updated = hhii\n", + " w4_ism = w4\n", + " h4_ism = np.identity(n_themes)\n", + " q4_ism = np.ones((n_scores, n_themes))\n", + "\n", + " if embed:\n", + " # Iterate embedding with themes subtensor until sparsity becomes stable \n", + " flag = 0\n", + " for iter_integrate in range(0, max_iter_integrate):\n", + " # print(iter_integrate, hhii_updated)\n", + " # indices = np.nonzero(q4_ism[:, 0])[0]\n", + " # non_zero_elements = q4_ism[indices, 0]\n", + " # print(iter_integrate, np.column_stack((indices, non_zero_elements))) \n", + " hhii_updated_0 = hhii_updated.copy()\n", + "\n", + " if iter_integrate == 0: \n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, np.identity(n_themes), q4_ism, n_scores, n_items, n_themes)\n", + " else:\n", + " h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score = \\\n", + " integrate_scores(m0_nan_0, m0_weight, h4_updated_sparse, w4_ism, h4_ism, q4_ism, n_scores, n_items, n_themes)\n", + " \n", + " if (hhii_updated == hhii_updated_0).all():\n", + " flag+=1\n", + " else:\n", + " flag=0\n", + " \n", + " if flag==3:\n", + " break\n", + " \n", + " if perform_grid_search:\n", + " cond[n_embedding, n_themes] = np.linalg.cond(h4_updated)\n", + " # cond[n_embedding, n_themes] = np.linalg.cond(normalize(h4_updated, axis=0, norm='l2'))\n", + " elif len(list_solutions) > 1:\n", + " # Construct merged solutions\n", + " if h4_updated_merged is None:\n", + " h4_updated_merged = h4_updated\n", + " else:\n", + " h4_updated_merged = np.hstack((h4_updated_merged, h4_updated))\n", + " \n", + " print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated), 2)) \n", + "\n", + "if perform_grid_search:\n", + " row, col = np.unravel_index(np.argmin(cond), cond.shape)\n", + " print('minimum condition number achieved for '+ str(row) + ' embeddings and ' + str(col) + ' themes')\n", + "\n", + "if len(list_solutions) == 1:\n", + " # print the condition number achieved by NMF alone\n", + " print('condition number (primary NMF): ', np.round(np.linalg.cond(h4_sparse),2))\n", + " # print(h4_ism)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/100: 142 iterations with final cost -1337315.771801\n", + "Run 2/100: 142 iterations with final cost -1337269.733267\n", + "Run 3/100: 142 iterations with final cost -1337257.988570\n", + "Run 4/100: 142 iterations with final cost -1337252.456298\n", + "Run 5/100: 141 iterations with final cost -1337452.024950\n", + "Run 6/100: 141 iterations with final cost -1337308.567830\n", + "Run 7/100: 141 iterations with final cost 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-1337333.665984\n", + "Run 98/100: 142 iterations with final cost -1337243.838716\n", + "Run 99/100: 142 iterations with final cost -1337318.634972\n", + "Run 100/100: 142 iterations with final cost -1337311.633908\n" + ] + } + ], + "source": [ + "model_gfa = gfa_experiments(Xs_norm, K=9, Nrep=100, rotate=False, verbose=1)\n", + "# model = gfa_experiments(Xs_norm, K=7, Nrep=10, rotate=False, verbose=1)\n", + "# model = gfa_experiments(Xs_norm, K=6, Nrep=10, rotate=False, verbose=1)\n", + "w4_gfa = model_gfa['Z']\n", + "n_marker_genes = list_cell_codes.shape[0]\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[491.4730131240549, 3097.7486277939233, 224757.75543296538, 126240799704.80855, 1863.659405336969, 1968.7974287208208]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "# n_comp_pca_mvmds = 12\n", + "# n_comp_pca_mvmds = 10\n", + "# n_comp_pca_mvmds = 9\n", + "n_comp_pca_mvmds = 10\n", + "\n", + "# MVMDS reduction\n", + "mvmds = MVMDS(n_components=n_comp_pca_mvmds)\n", + "Xs_mvmds_reduced = mvmds.fit_transform(Xs)\n", + "\n", + "# PCA reduction concatenated views \n", + "pca = PCA(n_components=n_comp_pca_mvmds)\n", + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "Xs_pca_reduced = pca.fit_transform(Xs_concat)\n", + "\n", + "# NMF reduction concatenated views \n", + "\n", + "my_nmfmodel = NMF(n_components=n_themes, leverage=None, max_iter=200, tol=1.e-6, verbose=-1, random_state=1)\n", + "estimator_ = my_nmfmodel.fit_transform(m0.copy())\n", + "\n", + "w4_nmf = estimator_.w\n", + "h4_nmf = estimator_.h\n", + "\n", + "stress = []\n", + "w4_ism_mds = mds.fit_transform(w4_ism[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "Xs_mvmds_reduced_mds = mds.fit_transform(Xs_mvmds_reduced[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "w4_nmf_mds = mds.fit_transform(w4_nmf[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "Xs_pca_reduced_mds = mds.fit_transform(Xs_pca_reduced[:n_marker_genes,:])\n", + "stress.append(mds.stress_)\n", + "w4_mofa = model_mofa\n", + "w4_mofa_mds = mds.fit_transform(normalize(w4_mofa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "w4_gfa = model_gfa['Z']\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "# stress = []\n", + "# w4_ism_mds = mds.fit_transform(normalize(w4_ism[:n_marker_genes,:], axis=0, norm='l2'))\n", + "# stress.append(mds.stress_)\n", + "# Xs_mvmds_reduced_mds = mds.fit_transform(normalize(Xs_mvmds_reduced[:n_marker_genes,:], axis=0, norm='l2'))\n", + "# stress.append(mds.stress_)\n", + "# w4_nmf_mds = mds.fit_transform(normalize(w4_nmf[:n_marker_genes,:], axis=0, norm='l2'))\n", + "# stress.append(mds.stress_)\n", + "# Xs_pca_reduced_mds = mds.fit_transform(normalize(Xs_pca_reduced[:n_marker_genes,:], axis=0, norm='l2'))\n", + "# stress.append(mds.stress_)\n", + "# m0_mds = mds.fit_transform(normalize(m0[:n_marker_genes,:]))\n", + "# stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# n_comp_pca_mvmds = 13\n", + "\n", + "# # MVMDS reduction\n", + "# mvmds = MVMDS(n_components=n_comp_pca_mvmds)\n", + "# Xs_mvmds_reduced = mvmds.fit_transform(Xs)\n", + "\n", + "# stress = []\n", + "# Xs_mvmds_reduced_mds = mds.fit_transform(Xs_mvmds_reduced[:n_marker_genes,:])\n", + "# stress.append(mds.stress_)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 5.81\n", + "0.9233\n", + "8 4.01\n", + "0.8983\n", + "9 5.84\n", + "0.9261\n", + "4 1.91\n", + "0.881\n", + "7 2.91\n", + "0.867\n", + "9 4.39\n", + "0.8998\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(10)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(10):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + "\n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(int(cell_label), xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " # if p1[l] > .5 and p2[l] > .5:\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " inter_distance = distance_matrix(xy_mean, xy_mean)\n", + " mean_inter_distance = np.mean(inter_distance[inter_distance>0])\n", + " norm_distance = np.max(inter_distance) - inter_distance\n", + " # print(p1)\n", + " # print(p2)\n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", + " \n", + "fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=10\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (10-class)\", k=k, ax=ax[0,0], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(Xs_mvmds_reduced_mds[:, 0], Xs_mvmds_reduced_mds[:, 1], title=\"MVMDS Reduced Data (10-class)\", k=k, ax=ax[0,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_nmf_mds[:, 0], w4_nmf_mds[:, 1], title=\"NMF Reduced Data (10-class)\", k=k, ax=ax[1,0], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(Xs_pca_reduced_mds[:, 0], Xs_pca_reduced_mds[:, 1], title=\"PCA Reduced Data (10-class)\", k=k, ax=ax[1,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_mofa_mds[:, 0], w4_mofa_mds[:, 1], title=\"MOFA+ Reduced Data (10-class)\", k=k, ax=ax[2,0], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"GFA Reduced Data (10-class)\", k=k, ax=ax[2,1], list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/uci_digits_biomed.ipynb b/examples/uci_digits_biomed.ipynb index 4fb1519..c85e5cb 100755 --- a/examples/uci_digits_biomed.ipynb +++ b/examples/uci_digits_biomed.ipynb @@ -2,50 +2,60 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "!pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "# !pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "# !pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "\n", "# scipy==1.12.0 not used (due to changes in SVDS) to reproduce presented results in ref paper" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "from adnmtf import NMF, NTF\n", + "# !pip install -e .." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coucou\n" + ] + } + ], + "source": [ "import pandas as pd\n", "import numpy as np\n", - "from IPython.display import clear_output\n", - "import time\n", - "from wordcloud import WordCloud, ImageColorGenerator\n", + "\n", "import matplotlib.pyplot as plt\n", - "import matplotlib.image as image\n", "from matplotlib.colors import ListedColormap\n", "import matplotlib.patches as mpatches\n", "import distinctipy\n", "from matplotlib.patches import Ellipse\n", "import matplotlib.transforms as transforms\n", "\n", - "import sys\n", - "import networkx as nx\n", - "\n", "from sklearn.preprocessing import normalize\n", - "from sklearn import metrics\n", - "\n", "from mvlearn.datasets import load_UCImultifeature\n", - "from mvlearn.embed import MVMDS\n", - "import seaborn as sns\n", - "from sklearn.decomposition import PCA\n", + "\n", "from sklearn.manifold import MDS\n", "from sklearn.cluster import KMeans\n", - "import umap\n", "from scipy.spatial import distance_matrix\n", - "import hoggorm as ho\n", - "import adilsm.adilsm as ilsm" + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "import adilsm.adilsm as ilsm\n", + "\n", + "RESULTS_PATH = './'" ] }, { @@ -57,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -101,46 +111,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "X_car_p = Xs[2].copy()\n", - "X_car_p[X_car_p<0] = 0\n", - "X_car_n = -Xs[2].copy()\n", - "X_car_n[X_car_n<0] = 0\n", - "\n", - "Xs_concat = Xs[0]\n", - "Xs_concat = np.hstack((Xs_concat, Xs[1], X_car_p, X_car_n))\n", - "\n", - "# Xs_concat = np.hstack((Xs[0], Xs[1]))\n", - "\n", - "\n", - "for X in Xs[3:]:\n", - " Xs_concat = np.hstack((Xs_concat, X))\n", - "\n", - "m0 = Xs_concat\n", - "\n", - "# m0_nan_0 = m0.copy()\n", + "Xs_ism = [None] * 7\n", + "Xs_ism[0] = Xs[0].copy()\n", + "Xs_ism[1] = Xs[1].copy()\n", "\n", - "# # create m0_weight with ones and zeros if not_missing/missing value\n", - "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", - "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "Xs_ism[2] = Xs[2].copy()\n", + "Xs_ism[2][Xs_ism[2]<0] = 0\n", "\n", - "# max_values = np.max(m0_nan_0, axis=0)\n", - "# # Replace maximum values equal to 0 with 1\n", - "# m0 = np.divide(m0, np.where(max_values == 0, 1, max_values))\n", + "Xs_ism[3] = -Xs[2].copy()\n", + "Xs_ism[3][Xs_ism[3]<0] = 0\n", "\n", - "# df_m0 = pd.DataFrame(m0)\n", - "# df_m0.to_csv(RESULTS_PATH + r'\\m0.csv', sep=',', na_rep='.', index=True)\n", - "\n", - "\n", - "list_columns = [str(i) for i in range(m0.shape[1])]\n", - "score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-kar-p', 'mfeat-kar-n', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", - "n_items = [Xs[i].shape[1] for i in range(2)] + [X_car_p.shape[1], X_car_n.shape[1]] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", - "# score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", - "# n_items = [Xs[i].shape[1] for i in range(2)] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", - "n_scores = len(n_items)" + "for i in range(4,len(Xs_ism)):\n", + " Xs_ism[i] = Xs[i-1].copy()\n" ] }, { @@ -152,26 +138,38 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "condition number(9, 10) = 7.65\n", - "error: 0.52\n" + "error ism before straightening: 0.39\n", + "error ism after straightening: 0.52\n", + "condition number(9, 10) = 7.62\n" ] } ], "source": [ "n_embedding, n_themes = [9,10]\n", "\n", - "h4_updated, h4_updated_sparse, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0=True, update_h4_ism=True,\n", - " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", - "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated_sparse), 2))\n", - "error = np.linalg.norm(m0_norm - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0_norm)\n", - "print('error: ',round(error, 2))" + "ilsm_result = ilsm.ism(Xs_ism, n_embedding, n_themes, norm_columns=True, update_h4_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "hv = ilsm_result['HV']\n", + "hv_sparse = ilsm_result['HV_SPARSE']\n", + "hhii_updated = ilsm_result['HHII']\n", + "w4_ism = ilsm_result['W']\n", + "h4_ism = ilsm_result['H']\n", + "q4_ism = ilsm_result['Q']\n", + "Xs_emb = ilsm_result['EMBEDDING']\n", + "Xs_norm = ilsm_result['NORMED_VIEWS']\n", + "\n", + "h4_updated_sparse = hv[0].copy()\n", + "for h in hv_sparse[1:]:\n", + " h4_updated_sparse = np.vstack((h4_updated_sparse, h))\n", + "\n", + "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated_sparse), 2))\n" ] }, { @@ -183,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -214,14 +212,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "10 5.81\n" + "10 5.81\n", + "0.9233\n" ] }, { @@ -230,13 +229,13 @@ "" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -246,7 +245,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -351,7 +350,7 @@ " n_matched_cell_types = 0\n", " \n", " for l in np.unique(labels_matched):\n", - " if l not in skip_ellipse:\n", + " if not flag[l]:\n", " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", " p1[l] /= np.sum(labels_matched == l)\n", @@ -371,6 +370,7 @@ " # print(p1)\n", " # print(p2)\n", " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", "\n", "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", @@ -389,13 +389,6 @@ "ax2.set_facecolor(face_color)\n", "plt.show" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/uci_digits_biomed_bck.ipynb b/examples/uci_digits_biomed_bck.ipynb new file mode 100644 index 0000000..afb12ee --- /dev/null +++ b/examples/uci_digits_biomed_bck.ipynb @@ -0,0 +1,446 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "# !pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.12.0\n", + "\n", + "# scipy==1.12.0 not used (due to changes in SVDS) to reproduce presented results in ref paper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -e .." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coucou\n" + ] + } + ], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "from sklearn.metrics.cluster import rand_score\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X_car_p = Xs[2].copy()\n", + "X_car_p[X_car_p<0] = 0\n", + "X_car_n = -Xs[2].copy()\n", + "X_car_n[X_car_n<0] = 0\n", + "\n", + "Xs_concat = Xs[0]\n", + "Xs_concat = np.hstack((Xs_concat, Xs[1], X_car_p, X_car_n))\n", + "\n", + "# Xs_concat = np.hstack((Xs[0], Xs[1]))\n", + "\n", + "\n", + "for X in Xs[3:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "\n", + "m0 = Xs_concat\n", + "\n", + "# m0_nan_0 = m0.copy()\n", + "\n", + "# # create m0_weight with ones and zeros if not_missing/missing value\n", + "# m0_weight = np.where(np.isnan(m0), 0, 1)\n", + "# m0_nan_0[np.isnan(m0_nan_0)]=0\n", + "\n", + "# max_values = np.max(m0_nan_0, axis=0)\n", + "# # Replace maximum values equal to 0 with 1\n", + "# m0 = np.divide(m0, np.where(max_values == 0, 1, max_values))\n", + "\n", + "# df_m0 = pd.DataFrame(m0)\n", + "# df_m0.to_csv(RESULTS_PATH + r'\\m0.csv', sep=',', na_rep='.', index=True)\n", + "\n", + "\n", + "list_columns = [str(i) for i in range(m0.shape[1])]\n", + "score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-kar-p', 'mfeat-kar-n', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", + "n_items = [Xs[i].shape[1] for i in range(2)] + [X_car_p.shape[1], X_car_n.shape[1]] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", + "# score_pref = ['mfeat-fou', 'mfeat-fac', 'mfeat-pix', 'mfeat-zer', 'mfeat-mor']\n", + "# n_items = [Xs[i].shape[1] for i in range(2)] + [Xs[i].shape[1] for i in range(3, len(Xs))]\n", + "n_scores = len(n_items)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ISM workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error ism before straightening: 0.39\n", + "error ism after straightening: 0.52\n", + "condition number(9, 10) = 7.65\n", + "error: 0.52\n" + ] + } + ], + "source": [ + "n_embedding, n_themes = [9,10]\n", + "\n", + "h4_updated, h4_updated_sparse, hhii_updated, w4_ism, h4_ism, q4_ism, tensor_score, m0_norm = ilsm.ism(m0, n_embedding, n_themes, n_scores, n_items, norm_m0=True, update_h4_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "print('condition number('+str(n_embedding)+', '+str(n_themes)+') = ', np.round(np.linalg.cond(h4_updated_sparse), 2))\n", + "error = np.linalg.norm(m0_norm - w4_ism @ h4_updated_sparse.T) / np.linalg.norm(m0_norm)\n", + "print('error: ',round(error, 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[491.4730131240673]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1)\n", + "\n", + "n_marker_genes = list_cell_codes.shape[0]\n", + "\n", + "stress = []\n", + "\n", + "w4_ism_mds = mds.fit_transform(normalize(w4_ism[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 5.81\n", + "0.9233\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(10)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(10):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + "\n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(int(cell_label), xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " inter_distance = distance_matrix(xy_mean, xy_mean)\n", + " mean_inter_distance = np.mean(inter_distance[inter_distance>0])\n", + " norm_distance = np.max(inter_distance) - inter_distance\n", + " # print(p1)\n", + " # print(p2)\n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=10\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (10-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/uci_digits_gfa.ipynb b/examples/uci_digits_gfa.ipynb new file mode 100644 index 0000000..5044b00 --- /dev/null +++ b/examples/uci_digits_gfa.ipynb @@ -0,0 +1,1151 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/100: 132 iterations with final cost -1369484.296532\n", + "Run 2/100: 133 iterations with final cost -1369466.989440\n", + "Run 3/100: 133 iterations with final cost -1369407.701255\n", + "Run 4/100: 133 iterations with final cost -1369500.475922\n", + "Run 5/100: 134 iterations with final cost -1369414.021918\n", + "Run 6/100: 133 iterations with final cost -1369473.535264\n", + "Run 7/100: 133 iterations with final cost -1369485.893255\n", + "Run 8/100: 133 iterations with final cost -1369459.946172\n", + "Run 9/100: 134 iterations with final cost -1369416.110993\n", + "Run 10/100: 133 iterations with final cost -1369456.055691\n", + "Run 11/100: 133 iterations with final cost -1369467.927528\n", + "Run 12/100: 132 iterations with final cost 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"cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1640.2007565428682]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1)\n", + "\n", + "n_marker_genes = list_cell_codes.shape[0]\n", + "\n", + "stress = []\n", + "\n", + "w4_ism = model['Z']\n", + "\n", + "w4_ism_mds = mds.fit_transform(normalize(w4_ism[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9 4.45\n", + "0.9001\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(10)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(10):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + "\n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(int(cell_label), xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " inter_distance = distance_matrix(xy_mean, xy_mean)\n", + " mean_inter_distance = np.mean(inter_distance[inter_distance>0])\n", + " norm_distance = np.max(inter_distance) - inter_distance\n", + " # print(p1)\n", + " # print(p2)\n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=10\n", + "\n", + "plot_scatter(w4_ism_mds[:, 0], w4_ism_mds[:, 1], title=\"ISM Reduced Data (10-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/uci_digits_gfa_screeplot.ipynb b/examples/uci_digits_gfa_screeplot.ipynb new file mode 100644 index 0000000..5c7bc33 --- /dev/null +++ b/examples/uci_digits_gfa_screeplot.ipynb @@ -0,0 +1,1062 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# GFA wrapper\n", + "\"\"\"\n", + "GFA (Group Factor Analysis)\n", + "This is a Python implementation of the file ./R/CCAGFA.R in the R package CCAGFA\n", + "https://github.com/mladv15/gfa-python\n", + "\"\"\"\n", + "\n", + "from __future__ import division, print_function\n", + "import numpy as np\n", + "import scipy as sp\n", + "import scipy.special\n", + "import scipy.linalg\n", + "import scipy.optimize\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "def gfa_experiments(Y, K, Nrep=10, verbose=1, **opts):\n", + " \"\"\"\n", + " A wrapper for running the GFA model `Nrep` times\n", + " and choosing the final model based on the best\n", + " lower bound. This is the recommended way of applying\n", + " the algorithm.\n", + " See GFA() for description of the inupts.\n", + " \"\"\"\n", + " opts[\"verbose\"] = verbose\n", + " lb = [] # lower bounds\n", + " models = [] # the best one will be returned\n", + " for rep in range(Nrep):\n", + " model = gfa(Y, K, R=2, **opts)\n", + " models.append(model)\n", + " lb.append(model['cost'][-1]) # not defined yet\n", + " if verbose == 1:\n", + " # TODO: this is just a placeholder, will add real values after gfa() is finished\n", + " print(\"Run %d/%d: %d iterations with final cost %f\" % (rep+1, Nrep, len(model['cost']), lb[rep]))\n", + " k = np.argmax(lb)\n", + " return models[k]\n", + "\n", + "\n", + "def gfa(Y, K,\n", + " R=\"full\", lambda_=0.1, rotate=True,\n", + " opt_method=\"L-BFGS\", opt_iter=10e5, lbfgs_factr=10e10, bfgs_crit=10e-5,\n", + " init_tau=1000,\n", + " iter_crit=10e-6, iter_max=10e5,\n", + " addednoise=1e-5,\n", + " prior_alpha_0=1e-14, prior_alpha_0t=1e-14,\n", + " prior_beta_0=1e-14, prior_beta_0t=1e-14,\n", + " dropK=True, low_mem=False,\n", + " verbose=2):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " Y : list\n", + " List of M data ndarrays. Y[m] is an ndarray (matrix) with\n", + " N rows (samples) and D_m columns (features). The\n", + " samples need to be co-occurring.\n", + " NOTE: All of these should be centered, so that the mean\n", + " of each feature is zero\n", + " NOTE: The algorithm is roughly invariant to the scale\n", + " of the data, but extreme values should be avoided.\n", + " Data with roughly unit variance or similar scale\n", + " is recommended.\n", + " K : int\n", + " The number of components\n", + "\n", + " Returns\n", + " -------\n", + " The trained model, which is a dict that contains the following elements:\n", + " TODO: (could make the model an object later)\n", + " Z : The mean of the latent variables; N times K matrix\n", + " covZ : The covariance of the latent variables; K times K matrix\n", + " ZZ : The second moments ZZ^T; K times K matrix\n", + "\n", + " W : List of the mean projections; D_i times K matrices\n", + " covW : List of the covariances of the projections; D_i times D_i matrices\n", + " WW : List of the second moments WW^T; K times K matrices\n", + "\n", + " tau : The mean precisions (inverse variance, so 1/tau gives the\n", + " variances denoted by sigma in the paper); M-element vector\n", + "\n", + " alpha: The mean precisions of the projection weights, the\n", + " variances of the ARD prior; M times K matrix\n", + "\n", + " U,V,u.mu,v.mu: The low-rank factorization of alpha.\n", + "\n", + " cost : Vector collecting the variational lower bounds for each\n", + " iteration\n", + " D : Data dimensionalities; M-element vector\n", + " datavar : The total variance in the data sets, needed for\n", + " GFAtrim()\n", + " addednoise: The level of extra noise as in opts$addednoise\n", + "\n", + " They use getDefaultOpts() in the R package,\n", + " but I guess specifying default argument values like this is more standard Python,\n", + " like scikit learn https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L723.\n", + " \"\"\"\n", + " # check that data is centered\n", + " for m, Y_m in enumerate(Y):\n", + " if not np.all(np.abs(np.mean(Y_m, axis=0)) < 1e-7) and verbose == 2:\n", + " print(\"Warning: data from group %d does not have zero mean\" % m)\n", + "\n", + " # check that there is more than one group of data\n", + " if len(Y) < 2:\n", + " print(\"Warning: the number of data sets must be larger than 1\")\n", + "\n", + " # store dimensions\n", + " M = len(Y)\n", + " D = [Y_m.shape[1] for Y_m in Y] # Data dimensions for each group. D = [D_1, ..., D_M]\n", + " D = np.array(D)\n", + " Ds = sum(D) # total nr of features\n", + " N = Y[0].shape[0] # total number of samples\n", + " datavar = [] # total variance of the data for each group\n", + " for Y_m in Y:\n", + " # Y_m is NxD_m, so take variance along column (axis=0), total variance <- sum\n", + " datavar.append(sum(np.var(Y_m, axis=0)))\n", + "\n", + " if isinstance(R, int) and R >= min(M, K):\n", + " if verbose == 2:\n", + " print(\"The rank corresponds to full rank solution.\")\n", + " R = \"full\"\n", + " if R != \"full\":\n", + " if verbose == 2:\n", + " print(\"NOTE: optimization of the rotation is not supported for low rank model\")\n", + " rotate = False\n", + "\n", + " # Some constants for speeding up the computation\n", + " const = - N*Ds/2*np.log(2*np.pi) # constant factors for the lower bound\n", + " Yconst = [np.sum(np.vectorize(pow)(Y_m, 2)) for Y_m in Y]\n", + " id_ = np.ones(K)\n", + " alpha_0 = prior_alpha_0 # Easier access for hyperprior values\n", + " beta_0 = prior_beta_0\n", + " alpha_0t = prior_alpha_0t\n", + " beta_0t = prior_beta_0t\n", + "\n", + " #\n", + " # Initialize the model randomly; other initializations could\n", + " # be done, but overdispersed random initialization is quite good.\n", + " #\n", + "\n", + " # Latent variables Z\n", + " Z = np.random.randn(N, K) # The mean\n", + " covZ = np.diag(np.ones(K)) # The covariance\n", + " ZZ = covZ + covZ*N # The second moments\n", + "\n", + " # ARD and noise parameters (What is ARD?)\n", + " alpha = np.ones((M, K)) # The mean of the ARD precisions\n", + " logalpha = np.ones((M, K)) # The mean of <\\log alpha>\n", + " if R == \"full\":\n", + " b_ard = np.ones((M, K)) # The parameters of the Gamma distribution\n", + " a_ard = alpha_0 + D/2 # for ARD precisions\n", + " # psi is digamma, derivative of the logarithm of the gamma function\n", + " digammaa_ard = sp.special.psi(a_ard)\n", + " tau = np.repeat(init_tau, M) # The mean noise precisions\n", + " a_tau = alpha_0t + N*D/2 # The parameters of the Gamma distribution\n", + " b_tau = np.zeros(M) # for the noise precisions\n", + " digammaa_tau = sp.special.psi(a_tau) # Constants needed for computing the lower bound\n", + " lgammaa_tau = -np.sum(np.vectorize(math.lgamma)(a_tau))\n", + " lb_pt_const = -M*np.vectorize(math.lgamma)(alpha_0t) + M*alpha_0t*np.log(beta_0t)\n", + "\n", + " # Alpha needs to be initialized to match the data scale\n", + " for m in range(M):\n", + " alpha[m, :] = K*D[m]/(datavar[m]-1/tau[m])\n", + "\n", + " # The projections\n", + " # No need to initialize projections randomly ,since their updating\n", + " # step is the first one; just define the variables here\n", + " #low_mem = True\n", + " W = [None]*M # the means\n", + " if not low_mem:\n", + " covW = [None]*M # the covariances\n", + " else: \n", + " covW = np.diag(np.ones(K))\n", + "\n", + " WW = [None]*M # the second moments\n", + " for m in range(M):\n", + " # I think the more standard way would be to let W[m] be KxD_m\n", + " # but they apparently set it to (D_m x K)\n", + " W[m] = np.zeros((D[m], K)) # So each W[m] is actually W[m].T\n", + " if not low_mem:\n", + " covW[m] = np.diag(np.ones(K))\n", + " # matrix crossproduct of W is W.T %*% W\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # Rotation parameters (full rank only)\n", + " if(rotate):\n", + " Rot = np.diag(np.ones(K)) # The rotation matrix R (in ICML11 paper)\n", + " RotInv = np.diag(np.ones(K)) # Its inverse\n", + " r = np.array(Rot).flatten() # Vectorizd version of R, will be passed to optimization function\n", + "\n", + " # parameter dict for the optimization function\n", + " # scipy.optimize takes these optional parameters as a tuple and passes them to the objective function \n", + " # but store them as dict first for easier modification\n", + " par_dict = {'K': K, 'D': D, 'Ds': Ds, 'N': N, 'WW': WW, 'ZZ': ZZ, 'M': M}\n", + "\n", + " \n", + " # Use R-rank factorization of alpha\n", + " if R != \"full\":\n", + " U = np.abs(np.random.randn(M, R))\n", + " lu = U.size\n", + " u_mu = np.repeat(0, M)\n", + " V = np.abs(np.random.randn(K, R))\n", + " lv = V.size\n", + " v_mu = np.repeat(0, K)\n", + " \n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " x = np.random.randn(len(x)) / 100\n", + "\n", + " par_uv = {'getu': range(0, lu), \\\n", + " 'getv': range(lu, lu + lv), \\\n", + " 'getumean': range(lu + lv, lu + lv + M), \\\n", + " 'getvmean': range(lu + lv + M, len(x)), \\\n", + " 'M': M, \\\n", + " 'K': K, \\\n", + " 'R': R, \\\n", + " 'D': D, \\\n", + " 'lambda': lambda_}\n", + " \n", + " par_uv['w2'] = np.zeros((M, K))\n", + "\n", + "\n", + " cost = [] # for storing the lower bounds\n", + " \n", + " #\n", + " # The main loop\n", + " #\n", + " for iter_ in range(int(iter_max)):\n", + " \n", + " # Check if some components need to be removed\n", + " # remove columns which have most elements approaching 0\n", + " # np.where() returns a tuple\n", + " (keep,) = np.where(np.power(Z, 2).mean(axis=0) > 1e-7) # column indices to keep\n", + " if len(keep) != K and dropK:\n", + " K = len(keep)\n", + " if K == 0:\n", + " raise ValueError(\"All latent factors in Z are 0, shut down all components, no structure found in the data\")\n", + " id_ = np.ones(K)\n", + " # in R, when selecting only one column from the matrix, the result is defaulted to\n", + " # a normal (row) array. Since we're indexing with an array (`keep`), the Python default\n", + " # is to return a column vector, so no need for a drop argument.\n", + " Z = Z[:, keep]\n", + " # covZ = covZ[keep, keep] in R\n", + " covZ = covZ[keep][:, keep]\n", + " # ZZ = ZZ[keep, keep] in R\n", + " ZZ = ZZ[keep][:, keep]\n", + " for m in range(M):\n", + " W[m] = W[m][:, keep]\n", + " if not low_mem:\n", + " # covW[m] = covW[m][keep, keep] in R\n", + " covW[m] = covW[m][keep][:, keep]\n", + " # WW[m] = WW[m][keep, keep] in R\n", + " WW[m] = WW[m][keep][:, keep]\n", + "\n", + " alpha = alpha[:, keep]\n", + " logalpha = logalpha[:, keep]\n", + "\n", + " if R != \"full\":\n", + " V = V[keep, :]\n", + " v_mu = v_mu[keep]\n", + " x = np.hstack((U.flatten(), V.flatten(), u_mu, v_mu))\n", + " lv = V.size\n", + " par_uv['K'] = K\n", + " par_uv['getv'] = range(lu, lu + lv)\n", + " par_uv['getumean'] = range(lu + lv, lu + lv + M) \n", + " par_uv['getvmean'] = range(lu + lv + M, len(x))\n", + " par_uv['w2'] = np.zeros((M, K))\n", + " else:\n", + " b_ard = np.ones((M, K))\n", + " if rotate:\n", + " par_dict['K'] = K\n", + " # endif len(keep) != K and dropK\n", + "\n", + " #\n", + " # Update the projections\n", + " #\n", + " lb_qw = np.empty(M) # Computes also the determinant of covW needed for the lower bound\n", + " for m in range(M):\n", + " # Efficient and robust way of computing\n", + " # solve(diag(alpha) + tau * ZZ^T)\n", + " tmp = 1/np.sqrt(alpha[m, :])\n", + " # Cholesky decomposition\n", + " # R package uses upper triangular part, as does scipy (but NOT numpy)\n", + " # diag_tau = np.diag(np.tile(tau, K)[:K])\n", + " diag_tau = np.diag(1/(np.ones(K) * tau[m]))\n", + " cho_before = np.outer(tmp, tmp) * ZZ + diag_tau\n", + " cho = sp.linalg.cholesky(cho_before, lower=False)\n", + " det = -2*np.sum(np.log(np.diag(cho))) - np.sum(np.log(alpha[m, :])) - K*np.log(tau[m])\n", + " lb_qw[m] = det\n", + " if not low_mem:\n", + " # chol2inv calculates the inverse of the matrix whose Cholesky decomposition was given.\n", + " # Python doesn't have this function, so I'll just take the inverse of the matrix itself\n", + " # without going through its Cholesky decomposition\n", + " covW[m] = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW[m]) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " covW = 1/tau[m] * np.outer(tmp, tmp) * np.linalg.inv(cho_before)\n", + " W[m] = np.dot(Y[m].T, Z).dot(covW) * tau[m]\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " # \n", + " # Update the latent variables\n", + " #\n", + " \n", + " # Efficient and robust way of computing\n", + " # solve(diag(1,K) + tau * WW^t)\n", + " covZ = np.diag(np.ones(K))\n", + " for m in range(M):\n", + " covZ = covZ + tau[m]*WW[m]\n", + " cho = sp.linalg.cholesky(covZ, lower=False)\n", + " covZ = np.linalg.inv(covZ)\n", + " det = -2*np.sum(np.log(np.diag(cho)))\n", + " lb_qx = det\n", + "\n", + " Z = Z*0\n", + " for m in range(M):\n", + " Z = Z + Y[m].dot(W[m])*tau[m]\n", + " Z = Z.dot(covZ)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " #\n", + " # Optimization of the rotation (only start after the first\n", + " # iteration)\n", + " #\n", + "\n", + " if R==\"full\" and rotate and iter_ > 0:\n", + " #Update the parameter list for the optimizer\n", + " par_dict[\"WW\"] = WW\n", + " par_dict[\"ZZ\"] = ZZ\n", + "\n", + " # par <- list(K=K,D=D,Ds=Ds,N=N,WW=WW,ZZ=ZZ,M=M)\n", + " par = tuple([par_dict[key] for key in ['K', 'D', 'Ds', 'N', 'WW', 'ZZ', 'M']])\n", + "\n", + " # Always start from the identity matrix, i.e. no rotation\n", + " r = np.diag(np.ones(K)).flatten()\n", + " if opt_method == \"BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='BFGS', jac=gradE,\n", + " options={'maxiter': opt_iter}) # no reltol in SciPy\n", + " if opt_method == \"L-BFGS\":\n", + " r_opt = sp.optimize.minimize(fun=E, x0=r, args=par, method='L-BFGS-B', jac=gradE,\n", + " options={'maxiter': opt_iter}) # factr deprecated\n", + "\n", + " # print(r_opt)\n", + " if not r_opt.success:\n", + " # sometimes work, indicating that the loss function E and the gradient gradE are correct?\n", + " # mostly doesn't work though because the code is not complete yet.\n", + " print(\"\\n=============================================================\")\n", + " print(\"Failure in optimizing the rotation. Turning the rotation off.\")\n", + " print(\"=============================================================\\n\")\n", + " rotate = False\n", + " else:\n", + " # Update the parameters involved in the rotation:\n", + " Rot = r_opt.x.reshape(K, K)\n", + " U, d, V = np.linalg.svd(Rot)\n", + " det = np.sum(np.log(d))\n", + " RotInv = np.dot( V*np.outer(id_, 1/d), U.T )\n", + "\n", + " Z = np.dot(Z, RotInv.T)\n", + " covZ = np.dot(RotInv.dot(covZ), RotInv.T)\n", + " ZZ = np.dot(Z.T, Z) + N*covZ\n", + "\n", + " lb_qx = lb_qx - 2*det\n", + "\n", + " for m in range(M):\n", + " if not low_mem:\n", + " W[m] = W[m].dot(Rot)\n", + " covW[m] = np.dot(Rot, covW[m].T).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW[m]*D[m]\n", + " else:\n", + " # covW[m] is not stored, so it needs to be computed before rotation\n", + " covW = (WW[m] - np.dot(W[m].T, W[m]))/D[m]\n", + " W[m] = W[m].dot(Rot)\n", + " covW = np.dot(Rot.T, covW).dot(Rot)\n", + " WW[m] = np.dot(W[m].T, W[m]) + covW*D[m]\n", + "\n", + " lb_qw[m] = lb_qw[m] + 2*det\n", + " # endif rotate\n", + "\n", + " # Update alpha, the ARD parameters\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " tmp = beta_0t + np.diag(WW[m]) / 2\n", + " alpha[m, :] = a_ard[m] / tmp\n", + " b_ard[m, :] = tmp\n", + " else:\n", + " for m in range(M):\n", + " par_uv['w2'][m, :] = np.diag(WW[m])\n", + "\n", + " minBound = np.hstack((np.repeat(-np.sqrt(500/R), M*R+K*R), np.repeat(-50, M+K)))\n", + " maxBound = np.hstack((np.repeat(np.sqrt(500/R), M*R+K*R), np.repeat(50, M+K)))\n", + " res = sp.optimize.minimize(x0=x,\n", + " fun=Euv, \n", + " jac=gradEuv, \n", + " args=par_uv, \n", + " method='L-BFGS-B',\n", + " options={'maxiter': opt_iter},\n", + " bounds=tuple(zip(minBound, maxBound)))\n", + "\n", + " if not res.success:\n", + " cost[iter_] = None\n", + " raise ValueError(\"Problems in optimization. Try a new initialization.\")\n", + " # terminate the algorithm (next model to learn)\n", + " \n", + " x = res.x\n", + " U = x[par_uv['getu']].reshape(par_uv['M'], par_uv['R'])\n", + " V = x[par_uv['getv']].reshape(par_uv['K'], par_uv['R'])\n", + " u_mu = x[par_uv['getumean']]\n", + " v_mu = x[par_uv['getvmean']]\n", + " alpha = np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(K)) + np.outer(np.ones(M), v_mu)) \n", + " \n", + " #\n", + " # Update tau, the noise precisions\n", + " #\n", + " for m in range(M):\n", + " b_tau[m] = prior_beta_0t + (Yconst[m] + np.sum(WW[m] * ZZ) - 2 * np.sum(Z * Y[m].dot(W[m]))) / 2\n", + " \n", + " tau = a_tau / b_tau\n", + "\n", + " #\n", + " # Calculate the lower bound.\n", + " # Consists of calculating the likelihood term and KL-divergences between the\n", + " # factorization and the priors\n", + " #\n", + " logtau = digammaa_tau - np.log(b_tau)\n", + " if R == \"full\":\n", + " for m in range(M):\n", + " logalpha[m, :] = digammaa_ard[m] - np.log(b_ard[m, :])\n", + " else:\n", + " logalpha = np.log(alpha)\n", + "\n", + " lb_p = const + N * np.dot(D.T, logtau) / 2 - np.dot((b_tau - beta_0t).T, tau)\n", + " lb = lb_p\n", + "\n", + " # E[ ln p(Z) ] - E[ ln q(Z) ]\n", + " lb_px = -np.sum(np.diag(ZZ)) / 2\n", + " lb_qx = -N * lb_qx / 2 - N * K / 2\n", + " lb = lb + lb_px - lb_qx\n", + "\n", + " # E[ ln p(W) ] - E[ ln q(W) ]\n", + " if R == \"full\":\n", + " lb_pw = 0\n", + " for m in range(M):\n", + " lb_pw = lb_pw + D[m] / 2 * np.sum(logalpha[m, :]) - np.sum(np.diag(WW[m]) * alpha[m, :]) / 2\n", + " else:\n", + " lb_pw = Euv(x, par_uv) # TODO: Correct?\n", + "\n", + " for m in range(M):\n", + " lb_qw[m] = - D[m] * lb_qw[m] / 2 - D[m] * K / 2\n", + "\n", + " lb = lb + lb_pw - np.sum(lb_qw)\n", + "\n", + " # E[ ln p(alpha) ] - E[ ln q(alpha) ]\n", + " if R == \"full\":\n", + " lb_pa = M * K * (-sp.special.gammaln(alpha_0) + alpha_0 * np.log(beta_0)) + (alpha_0 - 1) * np.sum(logalpha) - beta_0 * np.sum(alpha)\n", + " lb_qa = -K * np.sum(sp.special.gammaln(a_ard)) + np.sum(a_ard * np.sum(np.log(b_ard), axis=1)) + np.sum((a_ard - 1) * np.sum(logalpha, axis=1)) - np.sum(b_ard * alpha)\n", + " lb = lb + lb_pa - lb_qa\n", + "\n", + " # E[ln p(tau) ] - E[ ln q(tau) ]\n", + " lb_pt = lb_pt_const + np.sum((alpha_0t - 1) * logtau) - np.sum(beta_0t * tau)\n", + " lb_qt = lgammaa_tau + np.dot(a_tau.T, np.log(b_tau)) + np.dot((a_tau - 1).T, logtau) - np.dot(b_tau.T, tau)\n", + " lb = lb + lb_pt - lb_qt\n", + "\n", + " # Store the cost function\n", + " cost.append(lb)\n", + "\n", + " if verbose == 2:\n", + " print(\"Iteration: %d/ cost: %d/ K: %d\" % (iter_, cost[len(cost)-1], K))\n", + " # Convergence if the relative change in cost is small enough\n", + " if iter_ > 0:\n", + " diff = cost[iter_] - cost[iter_-1]\n", + " if abs(diff)/abs(cost[iter_]) < iter_crit or iter_ == iter_max:\n", + " break\n", + "\n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that\n", + " # have effectively been turned off\n", + " Z += addednoise*np.random.randn(N, K).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " if R == \"full\":\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R}\n", + " else:\n", + " return {'W': W, 'covW': covW, 'ZZ': ZZ, 'WW': WW, 'Z': Z, 'covZ': covZ, \\\n", + " 'tau': tau, 'alpha': alpha, 'cost': cost, 'D': D, 'K': K, \\\n", + " 'addednoise': addednoise, 'datavar': datavar, 'iter': iter_, 'R': R, \\\n", + " 'U': U, 'V': V, 'u_mu': u_mu, 'v_mu': v_mu}\n", + "\n", + "\n", + "def E(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) cost function valule wrt the transformation\n", + " matrix R used in the generic optimization routine\n", + "\n", + " `r` is the flattened array of the rotation matrix R (see ICML11 paper)\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " (U, d, V) = np.linalg.svd(R)\n", + "\n", + " tmp = U*np.outer(np.ones(K), 1/d)\n", + " val = -np.sum(ZZ*np.dot(tmp, tmp.T))/2\n", + " val = val + (Ds-N)*np.sum(np.log(d))\n", + " for m in range(M):\n", + " val = val - D[m]*np.sum( np.log( (R*(WW[m].dot(R))).mean(axis=0) ) )\n", + " return -val\n", + "\n", + "\n", + "def gradE(r, K, D, Ds, N, WW, ZZ, M):\n", + " \"\"\"\n", + " Evaluates the (negative) gradient of the cost of the function E()\n", + " \"\"\"\n", + " R = np.array(r).reshape(K, K)\n", + " U, d, V = np.linalg.svd(R)\n", + " Rinv = np.dot( V*np.outer(np.ones(K), 1/(d**2)), U.T )\n", + " gr_tmp = np.dot( U*np.outer(np.ones(K), 1/(d**2)), U.T ).dot(ZZ) \\\n", + " + np.diag(np.ones(K)*(Ds-N))\n", + " gr = np.dot(gr_tmp, Rinv.T).flatten()\n", + "\n", + " tmp1 = WW[0].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[0] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " for m in range(1, M):\n", + " tmp1 = WW[m].dot(R)\n", + " tmp2 = 1/(R*tmp1).mean(axis=0)\n", + " tmp1 = D[m] * (tmp1*np.outer(np.ones(K), tmp2)).flatten()\n", + " gr = gr - tmp1\n", + " return -gr\n", + "\n", + "def Euv(x, par):\n", + " #\n", + " # Evaluates the cost function value wrt the low-rank\n", + " # factorization of alpha used in the generic optimization routine\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " logalpha = np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)\n", + " E = np.sum(np.dot(par['D'].T, logalpha)) - np.sum(par['w2'] * np.exp(logalpha))\n", + " if par['lambda'] != 0:\n", + " E = E - par['lambda'] * (np.sum(V ** 2) + np.sum(U ** 2))\n", + "\n", + " return -E / 2\n", + "\n", + "def gradEuv(x, par):\n", + " #\n", + " # Evaluates the gradient of the cost function Euv()\n", + " #\n", + " U = x[par['getu']].reshape(par['M'], par['R'])\n", + " V = x[par['getv']].reshape(par['K'], par['R'])\n", + " u_mu = x[par['getumean']]\n", + " v_mu = x[par['getvmean']]\n", + " alphaiAlphaw2 = np.outer(par['D'], np.ones(par['K'])) - np.exp(np.dot(U, V.T) + np.outer(u_mu, np.ones(par['K'])) + np.outer(np.ones(par['M']), v_mu)) * par['w2']\n", + " gradU = alphaiAlphaw2.dot(V)\n", + " gradV = np.dot(alphaiAlphaw2.T, U)\n", + " if par['lambda'] != 0:\n", + " gradU = gradU - par['lambda'] * 2 * U\n", + " gradV = gradV - par['lambda'] * 2 * V\n", + "\n", + " grad_umean = np.sum(alphaiAlphaw2, axis=1)\n", + " grad_vmean = np.sum(alphaiAlphaw2, axis=0)\n", + " grad = np.hstack((gradU.flatten(), gradV.flatten(), grad_umean, grad_vmean))\n", + " \n", + " return -grad / 2\n", + "\n", + "def gfa_prediction(pred, y, model, sample=False, nSample=100):\n", + " # Function for making predictions with the model. Gives the\n", + " # mean prediction and the mean and covariance of the latent\n", + " # variables. The predictive distribution itself does not have\n", + " # a closed-form expression, so the function also allows drawing\n", + " # samples from it.\n", + " #\n", + " # Inputs:\n", + " # pred: Binary vector of length 2, indicating which of the\n", + " # two data sets have been observed. (1,0) indicates\n", + " # we observe the first data set and want to predict\n", + " # the values for the latter, and (0,1) does the opposite.\n", + " # Using (1,1) allows computing the latent variables\n", + " # for new test samples where both views are observed.\n", + " # Y : The test data as a list of length 2, given in the\n", + " # same format as for the function GFA(). The data\n", + " # matrix for the missing views can be anything, e.g.\n", + " # zeros, but it needs to exist\n", + " # model: A model learned from training data using GFA()\n", + " # sample: Should we sample observations from the full predictive\n", + " # distribution?\n", + " # nSample: How many samples to draw if sample==TRUE\n", + " #\n", + " #\n", + " # Outputs:\n", + " # A list containing:\n", + " # Y : The mean predictions as list. Observed data sets are retained\n", + " # as they were.\n", + " # Z : Mean latent variables of the test samples, given the observed\n", + " # data; N times K matrix\n", + " # covZ : Covariance of the latent variables; K times K matrix\n", + " # sam : Samples drawn from the predictive distribution, only\n", + " # returned if sample==TRUE. A list of Z, W and Y.\n", + " # Z is nSample times N times K matrix of the samples values.\n", + " # W and Y are M-element lists where only the predicted\n", + " # views are included (to avoid storing nSample identical\n", + " # copies of the observed data), each being a multidimensional\n", + " # array of nSample times the size of W and Y, respectively.\n", + " \n", + " (tr, ) = np.where(pred == 1) # The observed data sets\n", + " (pr, ) = np.where(pred == 0) # The data sets that need to be predicted\n", + " \n", + " Y = map(np.copy, y)\n", + " \n", + " N = Y[tr[0]].shape[0]\n", + " M = len(model['D'])\n", + "\n", + " if isinstance(model['covW'], np.ndarray): # R: if (!is.null(dim(model$covW))) ?\n", + " model['covW'] = [];\n", + " for m in range(M):\n", + " model['covW'][m] = (model['WW'][m] - np.dot(model['W'][m].T, model['W'][m])) / model['D'][m]\n", + "\n", + " # Estimate the covariance of the latent variables\n", + " covZ = np.eye(model['K'])\n", + " for m in tr:\n", + " covZ = covZ + model['tau'][m] * model['WW'][m]\n", + "\n", + " # Estimate the latent variables\n", + " (eV, eW) = np.linalg.eigh(covZ)\n", + " covZ = np.dot(eW * np.outer(np.repeat(1, model['K']), 1 / eV), eW.T)\n", + " Z = np.zeros((N, model['K']))\n", + " for m in tr:\n", + " Z = Z + Y[m].dot(model['W'][m]) * model['tau'][m]\n", + "\n", + " Z = Z.dot(covZ)\n", + " \n", + " # Add a tiny amount of noise on top of the latent variables,\n", + " # to supress possible artificial structure in components that \n", + " # have effectively been turned off\n", + " Z = Z + model['addednoise'] * np.random.randn(N, model['K']).dot(sp.linalg.cholesky(covZ, lower=False))\n", + "\n", + " # The prediction\n", + " # NOTE: The ICML'11 paper has a typo in the prediction formula\n", + " # on page 5. The mean prediction should have W_2^T instead of W_2.\n", + " for m in pr:\n", + " Y[m] = np.dot(Z, model['W'][m].T)\n", + " \n", + " # Sample from the predictive distribution\n", + " # Note that this code is fairly slow fow large nSample\n", + " if sample:\n", + " sam = {}\n", + " sam['Z'] = np.zeros((model['K'], nSample, N))\n", + " sam['Y'] = [None] * M\n", + " sam['W'] = [None] * M\n", + " cholW = [None] * M\n", + " for m in pr:\n", + " cholW[m] = sp.linalg.cholesky(model['covW'][m], lower=False)\n", + " sam['W'][m] = np.zeros((model['K'], nSample, model['D'][m]))\n", + " sam['Y'][m] = np.zeros((model['D'][m], nSample, N))\n", + " \n", + " cholZ = sp.linalg.cholesky(covZ, lower=False)\n", + " for i in range(nSample):\n", + " Ztemp = Z + np.random.randn(N, model['K']).dot(cholZ)\n", + " # TODO: A bit unsure of this step, indexing in R and python are different\n", + " # Used transpose of what the R code said since dimensions were different in python\n", + " sam['Z'][:, i, :] = Ztemp.T \n", + " for m in pr:\n", + " Wtemp = model['W'][m] + np.random.randn(model['D'][m], model['K']).dot(cholW[m])\n", + " sam['W'][m][:, i, :] = Wtemp.T\n", + " var = 1 / np.sqrt(model['tau'][m])\n", + " sam['Y'][m][:, i, :] = (np.dot(Ztemp, Wtemp.T) + var * np.random.randn(N, model['D'][m])).T\n", + " \n", + " if sample:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ, 'sam': sam}\n", + " else:\n", + " return {'Y': Y, 'Z': Z, 'covZ': covZ}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 1/10: 51 iterations with final cost -1667706.262056\n", + "Run 2/10: 51 iterations with final cost -1667717.480977\n", + "Run 3/10: 51 iterations with final cost -1667738.247669\n", + "Run 4/10: 51 iterations with final cost -1667766.018791\n", + "Run 5/10: 51 iterations with final cost -1667761.424712\n", + "Run 6/10: 51 iterations with final cost -1667746.082755\n", + "Run 7/10: 51 iterations with final cost -1667772.658538\n", + "Run 8/10: 50 iterations with final cost -1667713.089674\n", + "Run 9/10: 49 iterations with final cost -1667711.213274\n", + "Run 10/10: 51 iterations with final cost -1667729.890125\n", + "Run 1/10: 84 iterations with final cost -1593176.097569\n", + "Run 2/10: 84 iterations with final cost -1593186.563296\n", + "Run 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iterations with final cost -1203364.204412\n", + "Run 8/10: 184 iterations with final cost -1203307.579001\n", + "Run 9/10: 179 iterations with final cost -1203445.088287\n", + "Run 10/10: 184 iterations with final cost -1203276.771754\n", + "Run 1/10: 191 iterations with final cost -1185487.070112\n", + "Run 2/10: 187 iterations with final cost -1185768.193736\n", + "Run 3/10: 192 iterations with final cost -1185422.264815\n", + "Run 4/10: 192 iterations with final cost -1185573.091691\n", + "Run 5/10: 195 iterations with final cost -1185509.193873\n", + "Run 6/10: 190 iterations with final cost -1185623.100158\n", + "Run 7/10: 189 iterations with final cost -1185588.334943\n", + "Run 8/10: 188 iterations with final cost -1185640.824238\n", + "Run 9/10: 188 iterations with final cost -1185595.136359\n", + "Run 10/10: 192 iterations with final cost -1185440.233444\n", + "2 0.029687702574105454\n", + "3 0.03393038485414468\n", + "4 0.04555350844521267\n", + "5 0.05664384961059846\n", + "6 0.06879185561736835\n", + "7 0.08266301622764172\n", + "8 0.09814874862641586\n", + "9 0.11200661509068298\n", + "10 0.1292666861504051\n", + "11 0.14910991700527848\n", + "12 0.16992858676032446\n", + "13 0.1904716017887359\n", + "14 0.21206792427828847\n", + "15 0.23364475961582268\n", + "16 0.25460850083774605\n" + ] + } + ], + "source": [ + "gfa_cov = np.zeros(17)\n", + "for k in range(2,17):\n", + " model = gfa_experiments(Xs_norm, K=k, Nrep=10, rotate=False, verbose=1)\n", + " gfa_cov[k] = np.trace(model['covZ'])\n", + "\n", + "for k in range(2,17):\n", + " print(k, gfa_cov[k])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca = PCA(n_components=16)\n", + "\n", + "# Concatenate views then PCA for comparison\n", + "Xs_concat = Xs_norm[0]\n", + "for X in Xs_norm[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "Xs_pca_reduced = pca.fit_transform(Xs_concat)\n", + "\n", + "\n", + "# Plot the scree plot\n", + "plt.plot (np.arange (1, pca.n_components_ + 1), pca.explained_variance_ratio_, 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(2,17), gfa_cov[2:17], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/uci_digits_mofa.ipynb b/examples/uci_digits_mofa.ipynb new file mode 100644 index 0000000..7b999f1 --- /dev/null +++ b/examples/uci_digits_mofa.ipynb @@ -0,0 +1,845 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics.cluster import rand_score\n", + "\n", + "from mofapy2.run.entry_point import entry_point" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])\n", + "\n", + "data_mat = [[None for g in range(1)] for m in range(6)]\n", + "\n", + "for m in range(6):\n", + " data_mat[m][0] = Xs_norm[m]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "Successfully loaded view='view4' group='group0' with N=2000 samples and D=47 features...\n", + "Successfully loaded view='view5' group='group0' with N=2000 samples and D=6 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "- View 4 (view4): gaussian\n", + "- View 5 (view5): gaussian\n", + "\n", + "\n", + "\n", + "Warning: some view(s) have less than 15 features, MOFA won't be able to learn meaningful factors for these view(s)...\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -9709296.32 \n", + "\n", + "Iteration 1: time=0.37, ELBO=-1404936.80, deltaELBO=8304359.518 (85.52998325%), Factors=9\n", + "Iteration 2: time=0.35, ELBO=-1304107.77, deltaELBO=100829.032 (1.03847930%), Factors=9\n", + "Iteration 3: time=0.33, ELBO=-1292170.50, deltaELBO=11937.273 (0.12294684%), Factors=9\n", + "Iteration 4: time=0.35, ELBO=-1285195.38, deltaELBO=6975.120 (0.07183961%), Factors=9\n", + "Iteration 5: time=0.33, ELBO=-1279820.72, deltaELBO=5374.658 (0.05535579%), Factors=9\n", + "Iteration 6: time=0.33, ELBO=-1274512.59, deltaELBO=5308.129 (0.05467058%), Factors=9\n", + "Iteration 7: time=0.33, ELBO=-1268085.65, deltaELBO=6426.940 (0.06619368%), Factors=9\n", + "Iteration 8: time=0.33, ELBO=-1260996.51, deltaELBO=7089.137 (0.07301392%), Factors=9\n", + "Iteration 9: time=0.33, ELBO=-1255267.26, deltaELBO=5729.254 (0.05900792%), Factors=9\n", + "Iteration 10: time=0.33, ELBO=-1251797.77, deltaELBO=3469.488 (0.03573367%), Factors=9\n", + "Iteration 11: time=0.35, ELBO=-1249936.84, deltaELBO=1860.927 (0.01916645%), Factors=9\n", + "Iteration 12: time=0.39, ELBO=-1248892.38, deltaELBO=1044.460 (0.01075732%), Factors=9\n", + "Iteration 13: time=0.82, ELBO=-1248210.98, deltaELBO=681.405 (0.00701807%), Factors=9\n", + "Iteration 14: time=0.43, ELBO=-1247693.58, deltaELBO=517.396 (0.00532887%), Factors=9\n", + "Iteration 15: time=0.43, ELBO=-1247260.66, deltaELBO=432.928 (0.00445890%), Factors=9\n", + "Iteration 16: time=0.42, ELBO=-1246878.89, deltaELBO=381.764 (0.00393194%), Factors=9\n", + "Iteration 17: time=0.37, ELBO=-1246532.61, deltaELBO=346.282 (0.00356650%), Factors=9\n", + "Iteration 18: time=0.37, ELBO=-1246213.39, deltaELBO=319.223 (0.00328781%), Factors=9\n", + "Iteration 19: time=0.44, ELBO=-1245916.12, deltaELBO=297.270 (0.00306170%), Factors=9\n", + "Iteration 20: time=0.45, ELBO=-1245637.38, deltaELBO=278.732 (0.00287078%), Factors=9\n", + "Iteration 21: time=0.45, ELBO=-1245374.72, deltaELBO=262.661 (0.00270525%), Factors=9\n", + "Iteration 22: time=0.47, ELBO=-1245126.25, deltaELBO=248.476 (0.00255915%), Factors=9\n", + "Iteration 23: time=0.63, ELBO=-1244890.45, deltaELBO=235.793 (0.00242853%), Factors=9\n", + "Iteration 24: time=0.48, ELBO=-1244666.11, deltaELBO=224.346 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"Iteration 369: time=1.01, ELBO=-1234875.48, deltaELBO=5.059 (0.00005210%), Factors=9\n", + "Iteration 370: time=0.92, ELBO=-1234870.44, deltaELBO=5.038 (0.00005189%), Factors=9\n", + "Iteration 371: time=0.89, ELBO=-1234865.43, deltaELBO=5.018 (0.00005168%), Factors=9\n", + "Iteration 372: time=1.36, ELBO=-1234860.43, deltaELBO=4.997 (0.00005147%), Factors=9\n", + "Iteration 373: time=1.64, ELBO=-1234855.45, deltaELBO=4.977 (0.00005126%), Factors=9\n", + "Iteration 374: time=1.16, ELBO=-1234850.50, deltaELBO=4.957 (0.00005105%), Factors=9\n", + "Iteration 375: time=1.11, ELBO=-1234845.56, deltaELBO=4.937 (0.00005085%), Factors=9\n", + "Iteration 376: time=1.17, ELBO=-1234840.64, deltaELBO=4.917 (0.00005064%), Factors=9\n", + "Iteration 377: time=1.31, ELBO=-1234835.74, deltaELBO=4.897 (0.00005044%), Factors=9\n", + "Iteration 378: time=1.12, ELBO=-1234830.87, deltaELBO=4.877 (0.00005023%), Factors=9\n", + "Iteration 379: time=1.01, ELBO=-1234826.01, deltaELBO=4.858 (0.00005003%), Factors=9\n", + "Iteration 380: time=0.96, ELBO=-1234821.17, deltaELBO=4.838 (0.00004983%), Factors=9\n", + "Iteration 381: time=1.36, ELBO=-1234816.35, deltaELBO=4.819 (0.00004963%), Factors=9\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "ent = entry_point()\n", + "ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(6)])\n", + "ent.set_model_options(\n", + " factors = 10, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + ")\n", + "ent.set_train_options(\n", + " convergence_mode = \"medium\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + ")\n", + "ent.build()\n", + "ent.run()\n", + "factors = ent.model.nodes[\"Z\"].getExpectation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1863.659405336067]\n" + ] + } + ], + "source": [ + "# 16-16 with .8\n", + "# generate N visually distinct colours\n", + "\n", + "# MDS projection\n", + "mds = MDS(n_components=2, random_state=0)\n", + "# mds = umap.UMAP(random_state=0, n_jobs=1, min_dist=1)\n", + "\n", + "n_marker_genes = list_cell_codes.shape[0]\n", + "\n", + "stress = []\n", + "\n", + "w4_gfa = factors\n", + "\n", + "w4_gfa_mds = mds.fit_transform(normalize(w4_gfa[:n_marker_genes,:], axis=0, norm='l2'))\n", + "stress.append(mds.stress_)\n", + "\n", + "print(stress)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 2.91\n", + "0.867\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = distinctipy.get_colors(10)\n", + "cmap = ListedColormap(palette)\n", + "\n", + "patches = []\n", + "for code in range(10):\n", + " patches.append(mpatches.Patch(color=palette[code], label=list_cell_types[code]))\n", + "\n", + "# Define a function to plot the confidence ellipse\n", + "def confidence_ellipse(x, y, cell_label, ax, n_std=2, facecolor='none', **kwargs):\n", + " # Create a plot of the covariance confidence ellipse of `x` and `y`\n", + " # Adapted from [1](https://matplotlib.org/stable/gallery/statistics/confidence_ellipse.html)\n", + " \n", + " # Calculate the covariance matrix and the Pearson correlation coefficient\n", + " cov = np.cov(x, y)\n", + " pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1])\n", + " \n", + " # Use a special case to obtain the eigenvalues of the covariance matrix\n", + " ell_radius_x = np.sqrt(1 + pearson)\n", + " ell_radius_y = np.sqrt(1 - pearson)\n", + " \n", + " # Create the ellipse object\n", + " ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n", + " facecolor=facecolor, **kwargs)\n", + " \n", + " # Scale and translate the ellipse according to the mean and standard deviation of the data\n", + " scale_x = np.sqrt(cov[0, 0]) * n_std\n", + " mean_x = np.mean(x)\n", + " scale_y = np.sqrt(cov[1, 1]) * n_std\n", + " mean_y = np.mean(y)\n", + " transf = transforms.Affine2D() \\\n", + " .rotate_deg(45) \\\n", + " .scale(scale_x, scale_y) \\\n", + " .translate(mean_x, mean_y)\n", + " ellipse.set_transform(transf + ax.transData)\n", + " \n", + " # Annotate centroid\n", + " x_mean = np.mean(x)\n", + " y_mean = np.mean(y)\n", + " ax.annotate(int(cell_label), xy=(x_mean, y_mean))\n", + "\n", + " # Add the ellipse to the axes\n", + " ax.add_patch(ellipse)\n", + " return x_mean, y_mean\n", + "\n", + "# define a function to plot scatter with clusters and confidence ellipses\n", + "def plot_scatter(x, y, title=None, k=None, ax=None, list_cell_codes=None, skip_ellipse=[], face_color='lavender', **kwargs):\n", + " # Perform k-means clustering\n", + " kmeans = KMeans(n_clusters=k, random_state=0).fit(np.c_[x, y])\n", + " # Get the cluster labels and centroids\n", + " labels = kmeans.labels_ # Get cluster labels\n", + " labels_matched = np.empty_like(labels)\n", + " unique_labels = np.unique(labels)\n", + " truth_label = np.zeros(len(unique_labels))\n", + "\n", + " # For each cluster label...\n", + " for l in unique_labels:\n", + " # ...find and assign the best-matching truth label\n", + " match_nums = [np.sum((labels==l)*(list_cell_codes==t)) for t in np.unique(list_cell_codes)]\n", + " truth_label[l] = np.unique(list_cell_codes)[np.argmax(match_nums)]\n", + " labels_matched[labels==l] = truth_label[l]\n", + "\n", + " ax.scatter(x, y, c=list_cell_codes[:n_marker_genes], cmap=cmap, alpha=0.5, s=20)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"MDS-1\")\n", + " ax.set_ylabel(\"MDS-2\")\n", + " ax.set_facecolor(face_color)\n", + "\n", + " p1 = np.zeros(k)\n", + " p2 = np.zeros(k)\n", + " unique_labels_matched = np.unique(labels_matched)\n", + " xy_mean = np.zeros((len(unique_labels),2)) \n", + " mean_intra_distance = np.zeros(len(unique_labels))\n", + " mean_inter_distance = np.zeros(len(unique_labels))\n", + " flag = np.zeros(len(unique_labels))\n", + "\n", + " for l in unique_labels_matched:\n", + " indices = np.where(truth_label == l)[0]\n", + " if indices.shape[0] > 1:\n", + " # calculate mean distance inter-clusters pointing to same class\n", + " xy_mean2 = np.zeros((indices.shape[0],2))\n", + " for l2 in range(len(indices)):\n", + " xy_mean2[l2,0] = np.mean(x[labels == indices[l2]])\n", + " xy_mean2[l2,1] = np.mean(y[labels == indices[l2]])\n", + " xy_intra = np.column_stack((x[labels == indices[l2]], y[labels == indices[l2]]))\n", + " D = distance_matrix(xy_intra, xy_intra)\n", + " mean_intra_distance[l] += np.mean(D[D>0])\n", + " \n", + " D = distance_matrix(xy_mean2, xy_mean2)\n", + " mean_inter_distance[l] = np.mean(D[D>0])\n", + " if mean_inter_distance[l] / mean_intra_distance[l] > 1:\n", + " flag[l] = 1\n", + "\n", + " n_matched_cell_types = 0\n", + " \n", + " for l in np.unique(labels_matched):\n", + " if not flag[l]:\n", + " p1[l] = np.sum(labels_matched[labels_matched==l] == list_cell_codes[labels_matched==l])\n", + " p2[l] = p1[l] / np.sum(list_cell_codes == l)\n", + " p1[l] /= np.sum(labels_matched == l)\n", + " if p2[l] > .5:\n", + " n_matched_cell_types += 1\n", + " xy_mean[l,0], xy_mean[l,1] = confidence_ellipse(x[labels_matched == l], y[labels_matched == l], list_cell_types[l], ax, n_std=2, edgecolor='black')\n", + " else:\n", + " p1[l] = 0\n", + " p2[l] = 0\n", + " else:\n", + " xy_mean[l,0] = 0\n", + " xy_mean[l,1] = 0\n", + " \n", + " inter_distance = distance_matrix(xy_mean, xy_mean)\n", + " mean_inter_distance = np.mean(inter_distance[inter_distance>0])\n", + " norm_distance = np.max(inter_distance) - inter_distance\n", + " # print(p1)\n", + " # print(p2)\n", + " print(n_matched_cell_types, round(np.sum(p1*p2),2))\n", + " print(round(rand_score(labels_matched, list_cell_codes),4))\n", + "\n", + "# fig, ax = plt.subplots(3, 2, figsize=(14, 18), constrained_layout=True)\n", + "fig, ax = plt.subplots(figsize=(8, 7), constrained_layout=True)\n", + "\n", + "face_color = 'lavender'\n", + "k=10\n", + "\n", + "plot_scatter(w4_gfa_mds[:, 0], w4_gfa_mds[:, 1], title=\"ISM Reduced Data (10-class)\", k=k, ax=ax, list_cell_codes=list_cell_codes, skip_ellipse=[])\n", + "\n", + "plt.show\n", + "\n", + "# Add the legend to a new figure\n", + "fig2, ax2 = plt.subplots()\n", + "ax2.legend(handles=patches, loc='center')\n", + "plt.gca().set_axis_off()\n", + "ax2.set_facecolor(face_color)\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/uci_digits_mofa_screeplot.ipynb b/examples/uci_digits_mofa_screeplot.ipynb new file mode 100644 index 0000000..a199860 --- /dev/null +++ b/examples/uci_digits_mofa_screeplot.ipynb @@ -0,0 +1,2222 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from mofapy2.run.entry_point import entry_point" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -3500167.92 \n", + "\n", + "Iteration 1: time=0.07, ELBO=-1640429.31, deltaELBO=1859738.611 (53.13283966%), Factors=1\n", + "Iteration 2: time=0.13, ELBO=-1638157.42, deltaELBO=2271.898 (0.06490825%), Factors=1\n", + "Iteration 3: time=0.13, ELBO=-1637485.81, deltaELBO=671.608 (0.01918788%), Factors=1\n", + "Iteration 4: time=0.15, ELBO=-1636892.57, deltaELBO=593.235 (0.01694876%), Factors=1\n", + "Iteration 5: time=0.15, ELBO=-1636321.26, deltaELBO=571.317 (0.01632255%), Factors=1\n", + "Iteration 6: time=0.15, ELBO=-1635738.53, deltaELBO=582.725 (0.01664848%), Factors=1\n", + "Iteration 7: time=0.18, ELBO=-1635117.89, deltaELBO=620.642 (0.01773179%), Factors=1\n", + "Iteration 8: time=0.17, ELBO=-1634438.67, deltaELBO=679.214 (0.01940519%), Factors=1\n", + "Iteration 9: time=0.15, ELBO=-1633690.47, deltaELBO=748.204 (0.02137623%), Factors=1\n", + "Iteration 10: time=0.15, ELBO=-1632881.16, deltaELBO=809.313 (0.02312211%), Factors=1\n", + "Iteration 11: time=0.16, ELBO=-1632043.82, deltaELBO=837.338 (0.02392281%), Factors=1\n", + "Iteration 12: time=0.15, ELBO=-1631233.51, deltaELBO=810.310 (0.02315059%), Factors=1\n", + "Iteration 13: time=0.13, ELBO=-1630505.42, deltaELBO=728.089 (0.02080155%), Factors=1\n", + "Iteration 14: time=0.17, ELBO=-1629876.76, deltaELBO=628.657 (0.01796077%), Factors=1\n", + "Iteration 15: time=0.19, ELBO=-1629287.82, deltaELBO=588.943 (0.01682614%), Factors=1\n", + "Iteration 16: time=0.14, ELBO=-1628565.54, deltaELBO=722.285 (0.02063572%), Factors=1\n", + "Iteration 17: time=0.16, ELBO=-1627355.84, deltaELBO=1209.693 (0.03456100%), Factors=1\n", + "Iteration 18: time=0.16, ELBO=-1624994.29, deltaELBO=2361.551 (0.06746964%), Factors=1\n", + "Iteration 19: time=0.16, ELBO=-1620473.47, deltaELBO=4520.820 (0.12916010%), Factors=1\n", + "Iteration 20: time=0.15, ELBO=-1613215.26, deltaELBO=7258.212 (0.20736754%), Factors=1\n", + "Iteration 21: time=0.12, ELBO=-1604951.73, deltaELBO=8263.526 (0.23608941%), Factors=1\n", + "Iteration 22: time=0.13, ELBO=-1598764.34, deltaELBO=6187.397 (0.17677430%), Factors=1\n", + "Iteration 23: time=0.19, ELBO=-1595359.92, deltaELBO=3404.414 (0.09726431%), Factors=1\n", + "Iteration 24: time=0.17, ELBO=-1593649.24, deltaELBO=1710.681 (0.04887424%), Factors=1\n", + "Iteration 25: time=0.16, ELBO=-1592742.77, deltaELBO=906.468 (0.02589783%), Factors=1\n", + "Iteration 26: time=0.16, ELBO=-1592225.05, deltaELBO=517.719 (0.01479127%), Factors=1\n", + "Iteration 27: time=0.14, ELBO=-1591912.29, deltaELBO=312.759 (0.00893556%), Factors=1\n", + "Iteration 28: time=0.15, ELBO=-1591715.55, deltaELBO=196.743 (0.00562094%), Factors=1\n", + "Iteration 29: time=0.16, ELBO=-1591587.47, deltaELBO=128.083 (0.00365935%), Factors=1\n", + "Iteration 30: time=0.15, ELBO=-1591501.17, deltaELBO=86.303 (0.00246567%), Factors=1\n", + "Iteration 31: time=0.13, ELBO=-1591440.80, deltaELBO=60.363 (0.00172458%), Factors=1\n", + "Iteration 32: time=0.16, ELBO=-1591396.83, deltaELBO=43.978 (0.00125644%), Factors=1\n", + "Iteration 33: time=0.16, ELBO=-1591363.38, deltaELBO=33.445 (0.00095553%), Factors=1\n", + "Iteration 34: time=0.09, ELBO=-1591336.84, deltaELBO=26.543 (0.00075833%), Factors=1\n", + "Iteration 35: time=0.08, ELBO=-1591314.92, deltaELBO=21.915 (0.00062613%), Factors=1\n", + "Iteration 36: time=0.08, ELBO=-1591296.19, deltaELBO=18.728 (0.00053507%), Factors=1\n", + "Iteration 37: time=0.08, ELBO=-1591279.73, deltaELBO=16.463 (0.00047035%), Factors=1\n", + "Iteration 38: time=0.08, ELBO=-1591264.94, deltaELBO=14.795 (0.00042269%), Factors=1\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -4186825.51 \n", + "\n", + "Iteration 1: time=0.15, ELBO=-1586060.00, deltaELBO=2600765.506 (62.11783850%), Factors=2\n", + "Iteration 2: time=0.12, ELBO=-1578871.40, deltaELBO=7188.596 (0.17169561%), Factors=2\n", + "Iteration 3: time=0.13, ELBO=-1576311.75, deltaELBO=2559.658 (0.06113601%), Factors=2\n", + "Iteration 4: time=0.13, ELBO=-1572730.91, deltaELBO=3580.841 (0.08552639%), Factors=2\n", + "Iteration 5: time=0.13, ELBO=-1565479.56, deltaELBO=7251.349 (0.17319443%), Factors=2\n", + "Iteration 6: time=0.12, ELBO=-1551338.48, deltaELBO=14141.072 (0.33775165%), Factors=2\n", + "Iteration 7: time=0.12, ELBO=-1535958.97, deltaELBO=15379.513 (0.36733112%), Factors=2\n", + "Iteration 8: time=0.12, ELBO=-1528282.26, deltaELBO=7676.715 (0.18335408%), Factors=2\n", + "Iteration 9: time=0.13, ELBO=-1525060.86, deltaELBO=3221.400 (0.07694134%), Factors=2\n", + "Iteration 10: time=0.12, ELBO=-1522582.22, deltaELBO=2478.638 (0.05920090%), Factors=2\n", + "Iteration 11: time=0.11, ELBO=-1520218.78, deltaELBO=2363.434 (0.05644930%), Factors=2\n", + "Iteration 12: time=0.13, ELBO=-1517942.04, deltaELBO=2276.745 (0.05437879%), Factors=2\n", + "Iteration 13: time=0.12, ELBO=-1515771.89, deltaELBO=2170.147 (0.05183276%), Factors=2\n", + "Iteration 14: time=0.12, ELBO=-1513729.99, deltaELBO=2041.900 (0.04876966%), Factors=2\n", + "Iteration 15: time=0.12, ELBO=-1511854.75, deltaELBO=1875.239 (0.04478905%), Factors=2\n", + "Iteration 16: time=0.12, ELBO=-1510199.46, deltaELBO=1655.291 (0.03953570%), Factors=2\n", + "Iteration 17: time=0.12, ELBO=-1508809.00, deltaELBO=1390.461 (0.03321039%), Factors=2\n", + "Iteration 18: time=0.12, ELBO=-1507697.05, deltaELBO=1111.951 (0.02655832%), Factors=2\n", + "Iteration 19: time=0.12, ELBO=-1506841.63, deltaELBO=855.420 (0.02043124%), Factors=2\n", + "Iteration 20: time=0.11, ELBO=-1506198.26, deltaELBO=643.367 (0.01536646%), Factors=2\n", + "Iteration 21: time=0.13, ELBO=-1505717.74, deltaELBO=480.520 (0.01147694%), Factors=2\n", + "Iteration 22: time=0.12, ELBO=-1505357.67, deltaELBO=360.074 (0.00860016%), Factors=2\n", + "Iteration 23: time=0.12, ELBO=-1505085.77, deltaELBO=271.895 (0.00649406%), Factors=2\n", + "Iteration 24: time=0.11, ELBO=-1504878.60, deltaELBO=207.170 (0.00494814%), Factors=2\n", + "Iteration 25: time=0.12, ELBO=-1504719.13, deltaELBO=159.477 (0.00380901%), Factors=2\n", + "Iteration 26: time=0.13, ELBO=-1504594.82, deltaELBO=124.308 (0.00296903%), Factors=2\n", + "Iteration 27: time=0.12, ELBO=-1504496.40, deltaELBO=98.419 (0.00235069%), Factors=2\n", + "Iteration 28: time=0.13, ELBO=-1504417.00, deltaELBO=79.399 (0.00189641%), Factors=2\n", + "Iteration 29: time=0.12, ELBO=-1504351.57, deltaELBO=65.433 (0.00156283%), Factors=2\n", + "Iteration 30: time=0.12, ELBO=-1504296.41, deltaELBO=55.159 (0.00131745%), Factors=2\n", + "Iteration 31: time=0.12, ELBO=-1504248.84, deltaELBO=47.569 (0.00113615%), Factors=2\n", + "Iteration 32: time=0.12, ELBO=-1504206.92, deltaELBO=41.921 (0.00100125%), Factors=2\n", + "Iteration 33: time=0.12, ELBO=-1504169.24, deltaELBO=37.677 (0.00089988%), Factors=2\n", + "Iteration 34: time=0.11, ELBO=-1504134.79, deltaELBO=34.448 (0.00082277%), Factors=2\n", + "Iteration 35: time=0.12, ELBO=-1504102.84, deltaELBO=31.954 (0.00076321%), Factors=2\n", + "Iteration 36: time=0.11, ELBO=-1504072.84, deltaELBO=29.995 (0.00071642%), Factors=2\n", + "Iteration 37: time=0.12, ELBO=-1504044.42, deltaELBO=28.426 (0.00067894%), Factors=2\n", + "Iteration 38: time=0.12, ELBO=-1504017.28, deltaELBO=27.143 (0.00064830%), Factors=2\n", + "Iteration 39: time=0.13, ELBO=-1503991.20, deltaELBO=26.071 (0.00062270%), Factors=2\n", + "Iteration 40: time=0.14, ELBO=-1503966.05, deltaELBO=25.157 (0.00060086%), Factors=2\n", + "Iteration 41: time=0.20, ELBO=-1503941.69, deltaELBO=24.361 (0.00058184%), Factors=2\n", + "Iteration 42: time=0.16, ELBO=-1503918.03, deltaELBO=23.654 (0.00056497%), Factors=2\n", + "Iteration 43: time=0.14, ELBO=-1503895.01, deltaELBO=23.017 (0.00054975%), Factors=2\n", + "Iteration 44: time=0.13, ELBO=-1503872.58, deltaELBO=22.434 (0.00053582%), Factors=2\n", + "Iteration 45: time=0.12, ELBO=-1503850.69, deltaELBO=21.893 (0.00052290%), Factors=2\n", + "Iteration 46: time=0.18, ELBO=-1503829.30, deltaELBO=21.386 (0.00051080%), Factors=2\n", + "Iteration 47: time=0.12, ELBO=-1503808.39, deltaELBO=20.908 (0.00049938%), Factors=2\n", + "Iteration 48: time=0.11, ELBO=-1503787.94, deltaELBO=20.453 (0.00048852%), Factors=2\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -4868144.53 \n", + "\n", + "Iteration 1: time=0.17, ELBO=-1494687.66, deltaELBO=3373456.868 (69.29656357%), Factors=3\n", + "Iteration 2: time=0.14, ELBO=-1479768.09, deltaELBO=14919.570 (0.30647344%), Factors=3\n", + "Iteration 3: time=0.16, ELBO=-1477188.52, deltaELBO=2579.566 (0.05298869%), Factors=3\n", + "Iteration 4: time=0.15, ELBO=-1474637.62, deltaELBO=2550.904 (0.05239992%), Factors=3\n", + "Iteration 5: time=0.15, ELBO=-1470398.11, deltaELBO=4239.511 (0.08708679%), Factors=3\n", + "Iteration 6: time=0.14, ELBO=-1462624.22, deltaELBO=7773.894 (0.15968905%), Factors=3\n", + "Iteration 7: time=0.14, ELBO=-1452784.13, deltaELBO=9840.086 (0.20213217%), Factors=3\n", + "Iteration 8: time=0.19, ELBO=-1445839.86, deltaELBO=6944.271 (0.14264719%), Factors=3\n", + "Iteration 9: time=0.16, ELBO=-1442297.62, deltaELBO=3542.240 (0.07276366%), Factors=3\n", + "Iteration 10: time=0.13, ELBO=-1440323.50, deltaELBO=1974.115 (0.04055169%), Factors=3\n", + "Iteration 11: time=0.15, ELBO=-1438917.04, deltaELBO=1406.461 (0.02889110%), Factors=3\n", + "Iteration 12: time=0.13, ELBO=-1437665.42, deltaELBO=1251.624 (0.02571049%), Factors=3\n", + "Iteration 13: time=0.14, ELBO=-1436369.41, deltaELBO=1296.004 (0.02662214%), Factors=3\n", + "Iteration 14: time=0.13, ELBO=-1434909.93, deltaELBO=1459.482 (0.02998024%), Factors=3\n", + "Iteration 15: time=0.15, ELBO=-1433214.89, deltaELBO=1695.042 (0.03481906%), Factors=3\n", + "Iteration 16: time=0.12, ELBO=-1431276.94, deltaELBO=1937.949 (0.03980877%), Factors=3\n", + "Iteration 17: time=0.13, ELBO=-1429181.01, deltaELBO=2095.933 (0.04305405%), Factors=3\n", + "Iteration 18: time=0.15, ELBO=-1427086.16, deltaELBO=2094.854 (0.04303187%), Factors=3\n", + "Iteration 19: time=0.14, ELBO=-1425150.37, deltaELBO=1935.788 (0.03976438%), Factors=3\n", + "Iteration 20: time=0.13, ELBO=-1423474.66, deltaELBO=1675.707 (0.03442188%), Factors=3\n", + "Iteration 21: time=0.13, ELBO=-1422099.89, deltaELBO=1374.773 (0.02824018%), Factors=3\n", + "Iteration 22: time=0.15, ELBO=-1421017.48, deltaELBO=1082.408 (0.02223451%), Factors=3\n", + "Iteration 23: time=0.14, ELBO=-1420184.80, deltaELBO=832.676 (0.01710458%), Factors=3\n", + "Iteration 24: time=0.13, ELBO=-1419546.09, deltaELBO=638.712 (0.01312024%), Factors=3\n", + "Iteration 25: time=0.14, ELBO=-1419049.00, deltaELBO=497.092 (0.01021112%), Factors=3\n", + "Iteration 26: time=0.13, ELBO=-1418652.04, deltaELBO=396.962 (0.00815429%), Factors=3\n", + "Iteration 27: time=0.13, ELBO=-1418325.20, deltaELBO=326.834 (0.00671373%), Factors=3\n", + "Iteration 28: time=0.14, ELBO=-1418047.69, deltaELBO=277.518 (0.00570070%), Factors=3\n", + "Iteration 29: time=0.12, ELBO=-1417805.13, deltaELBO=242.560 (0.00498259%), Factors=3\n", + "Iteration 30: time=0.13, ELBO=-1417587.43, deltaELBO=217.694 (0.00447180%), Factors=3\n", + "Iteration 31: time=0.15, ELBO=-1417387.26, deltaELBO=200.174 (0.00411192%), Factors=3\n", + "Iteration 32: time=0.12, ELBO=-1417199.00, deltaELBO=188.260 (0.00386717%), Factors=3\n", + "Iteration 33: time=0.13, ELBO=-1417018.11, deltaELBO=180.886 (0.00371570%), Factors=3\n", + "Iteration 34: time=0.14, ELBO=-1416840.64, deltaELBO=177.471 (0.00364555%), Factors=3\n", + "Iteration 35: time=0.12, ELBO=-1416662.83, deltaELBO=177.813 (0.00365257%), Factors=3\n", + "Iteration 36: time=0.13, ELBO=-1416480.79, deltaELBO=182.037 (0.00373935%), Factors=3\n", + "Iteration 37: time=0.14, ELBO=-1416290.23, deltaELBO=190.558 (0.00391440%), Factors=3\n", + "Iteration 38: time=0.12, ELBO=-1416086.24, deltaELBO=203.993 (0.00419036%), Factors=3\n", + "Iteration 39: time=0.13, ELBO=-1415863.36, deltaELBO=222.884 (0.00457841%), Factors=3\n", + "Iteration 40: time=0.14, ELBO=-1415616.38, deltaELBO=246.979 (0.00507337%), Factors=3\n", + "Iteration 41: time=0.14, ELBO=-1415342.71, deltaELBO=273.667 (0.00562159%), Factors=3\n", + "Iteration 42: time=0.13, ELBO=-1415047.14, deltaELBO=295.576 (0.00607164%), Factors=3\n", + "Iteration 43: time=0.14, ELBO=-1414747.56, deltaELBO=299.576 (0.00615381%), Factors=3\n", + "Iteration 44: time=0.13, ELBO=-1414474.35, deltaELBO=273.205 (0.00561210%), Factors=3\n", + "Iteration 45: time=0.13, ELBO=-1414254.77, deltaELBO=219.584 (0.00451064%), Factors=3\n", + "Iteration 46: time=0.14, ELBO=-1414094.27, deltaELBO=160.499 (0.00329692%), Factors=3\n", + "Iteration 47: time=0.13, ELBO=-1413979.61, deltaELBO=114.661 (0.00235533%), Factors=3\n", + "Iteration 48: time=0.12, ELBO=-1413895.46, deltaELBO=84.148 (0.00172854%), Factors=3\n", + "Iteration 49: time=0.15, ELBO=-1413831.61, deltaELBO=63.851 (0.00131160%), Factors=3\n", + "Iteration 50: time=0.13, ELBO=-1413781.72, deltaELBO=49.890 (0.00102483%), Factors=3\n", + "Iteration 51: time=0.13, ELBO=-1413741.62, deltaELBO=40.106 (0.00082384%), Factors=3\n", + "Iteration 52: time=0.15, ELBO=-1413708.51, deltaELBO=33.101 (0.00067995%), Factors=3\n", + "Iteration 53: time=0.12, ELBO=-1413680.60, deltaELBO=27.919 (0.00057350%), Factors=3\n", + "Iteration 54: time=0.13, ELBO=-1413656.65, deltaELBO=23.944 (0.00049185%), Factors=3\n", + "Iteration 55: time=0.14, ELBO=-1413635.85, deltaELBO=20.800 (0.00042727%), Factors=3\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -5549941.88 \n", + "\n", + "Iteration 1: time=0.48, ELBO=-1446396.32, deltaELBO=4103545.557 (73.93853206%), Factors=4\n", + "Iteration 2: time=0.28, ELBO=-1423932.62, deltaELBO=22463.707 (0.40475572%), Factors=4\n", + "Iteration 3: time=0.17, ELBO=-1419380.32, deltaELBO=4552.292 (0.08202414%), Factors=4\n", + "Iteration 4: time=0.17, ELBO=-1413166.32, deltaELBO=6214.005 (0.11196522%), Factors=4\n", + "Iteration 5: time=0.25, ELBO=-1400843.50, deltaELBO=12322.820 (0.22203512%), Factors=4\n", + "Iteration 6: time=0.22, ELBO=-1382058.12, deltaELBO=18785.381 (0.33847889%), Factors=4\n", + "Iteration 7: time=0.16, ELBO=-1368209.03, deltaELBO=13849.087 (0.24953571%), Factors=4\n", + "Iteration 8: time=0.15, ELBO=-1362557.56, deltaELBO=5651.469 (0.10182934%), Factors=4\n", + "Iteration 9: time=0.17, ELBO=-1359826.36, deltaELBO=2731.203 (0.04921138%), Factors=4\n", + "Iteration 10: time=0.20, ELBO=-1357713.61, deltaELBO=2112.746 (0.03806789%), Factors=4\n", + "Iteration 11: time=0.33, ELBO=-1355783.65, deltaELBO=1929.964 (0.03477448%), Factors=4\n", + "Iteration 12: time=0.22, ELBO=-1354058.62, deltaELBO=1725.030 (0.03108194%), Factors=4\n", + "Iteration 13: time=0.16, ELBO=-1352681.55, deltaELBO=1377.066 (0.02481227%), Factors=4\n", + "Iteration 14: time=0.17, ELBO=-1351709.03, deltaELBO=972.528 (0.01752321%), Factors=4\n", + "Iteration 15: time=0.18, ELBO=-1351038.15, deltaELBO=670.872 (0.01208792%), Factors=4\n", + "Iteration 16: time=0.19, ELBO=-1350525.54, deltaELBO=512.613 (0.00923637%), Factors=4\n", + "Iteration 17: time=0.16, ELBO=-1350076.65, deltaELBO=448.891 (0.00808822%), Factors=4\n", + "Iteration 18: time=0.17, ELBO=-1349640.33, deltaELBO=436.323 (0.00786176%), Factors=4\n", + "Iteration 19: time=0.17, ELBO=-1349184.84, deltaELBO=455.489 (0.00820710%), Factors=4\n", + "Iteration 20: time=0.18, ELBO=-1348687.22, deltaELBO=497.619 (0.00896621%), Factors=4\n", + "Iteration 21: time=0.17, ELBO=-1348137.58, deltaELBO=549.634 (0.00990342%), Factors=4\n", + "Iteration 22: time=0.17, ELBO=-1347557.99, deltaELBO=579.591 (0.01044320%), Factors=4\n", + "Iteration 23: time=0.19, ELBO=-1347018.78, deltaELBO=539.216 (0.00971570%), Factors=4\n", + "Iteration 24: time=0.17, ELBO=-1346602.36, deltaELBO=416.421 (0.00750315%), Factors=4\n", + "Iteration 25: time=0.20, ELBO=-1346327.14, deltaELBO=275.219 (0.00495896%), Factors=4\n", + "Iteration 26: time=0.24, ELBO=-1346150.23, deltaELBO=176.904 (0.00318749%), Factors=4\n", + "Iteration 27: time=0.21, ELBO=-1346027.61, deltaELBO=122.626 (0.00220950%), Factors=4\n", + "Iteration 28: time=0.21, ELBO=-1345934.66, deltaELBO=92.950 (0.00167480%), Factors=4\n", + "Iteration 29: time=0.36, ELBO=-1345859.02, deltaELBO=75.631 (0.00136274%), Factors=4\n", + "Iteration 30: time=0.25, ELBO=-1345794.00, deltaELBO=65.030 (0.00117172%), Factors=4\n", + "Iteration 31: time=0.26, ELBO=-1345735.67, deltaELBO=58.322 (0.00105086%), Factors=4\n", + "Iteration 32: time=0.26, ELBO=-1345681.75, deltaELBO=53.923 (0.00097160%), Factors=4\n", + "Iteration 33: time=0.82, ELBO=-1345630.84, deltaELBO=50.909 (0.00091730%), Factors=4\n", + "Iteration 34: time=0.30, ELBO=-1345582.10, deltaELBO=48.737 (0.00087816%), Factors=4\n", + "Iteration 35: time=0.30, ELBO=-1345535.02, deltaELBO=47.086 (0.00084841%), Factors=4\n", + "Iteration 36: time=0.27, ELBO=-1345489.25, deltaELBO=45.765 (0.00082461%), Factors=4\n", + "Iteration 37: time=0.30, ELBO=-1345444.59, deltaELBO=44.658 (0.00080466%), Factors=4\n", + "Iteration 38: time=0.78, ELBO=-1345400.90, deltaELBO=43.694 (0.00078729%), Factors=4\n", + "Iteration 39: time=0.24, ELBO=-1345358.07, deltaELBO=42.830 (0.00077172%), Factors=4\n", + "Iteration 40: time=0.22, ELBO=-1345316.03, deltaELBO=42.037 (0.00075743%), Factors=4\n", + "Iteration 41: time=0.22, ELBO=-1345274.74, deltaELBO=41.296 (0.00074409%), Factors=4\n", + "Iteration 42: time=0.23, ELBO=-1345234.14, deltaELBO=40.596 (0.00073148%), Factors=4\n", + "Iteration 43: time=0.23, ELBO=-1345194.21, deltaELBO=39.929 (0.00071944%), Factors=4\n", + "Iteration 44: time=0.28, ELBO=-1345154.92, deltaELBO=39.287 (0.00070788%), Factors=4\n", + "Iteration 45: time=0.19, ELBO=-1345116.26, deltaELBO=38.667 (0.00069671%), Factors=4\n", + "Iteration 46: time=0.23, ELBO=-1345078.19, deltaELBO=38.066 (0.00068587%), Factors=4\n", + "Iteration 47: time=0.32, ELBO=-1345040.71, deltaELBO=37.481 (0.00067534%), Factors=4\n", + "Iteration 48: time=0.27, ELBO=-1345003.80, deltaELBO=36.911 (0.00066507%), Factors=4\n", + "Iteration 49: time=0.19, ELBO=-1344967.44, deltaELBO=36.355 (0.00065505%), Factors=4\n", + "Iteration 50: time=0.19, ELBO=-1344931.63, deltaELBO=35.811 (0.00064525%), Factors=4\n", + "Iteration 51: time=0.21, ELBO=-1344896.35, deltaELBO=35.279 (0.00063566%), Factors=4\n", + "Iteration 52: time=0.21, ELBO=-1344861.60, deltaELBO=34.758 (0.00062627%), Factors=4\n", + "Iteration 53: time=0.21, ELBO=-1344827.35, deltaELBO=34.247 (0.00061707%), Factors=4\n", + "Iteration 54: time=0.25, ELBO=-1344793.60, deltaELBO=33.746 (0.00060804%), Factors=4\n", + "Iteration 55: time=0.30, ELBO=-1344760.35, deltaELBO=33.255 (0.00059919%), Factors=4\n", + "Iteration 56: time=0.25, ELBO=-1344727.58, deltaELBO=32.773 (0.00059051%), Factors=4\n", + "Iteration 57: time=0.32, ELBO=-1344695.28, deltaELBO=32.300 (0.00058199%), Factors=4\n", + "Iteration 58: time=0.23, ELBO=-1344663.44, deltaELBO=31.836 (0.00057362%), Factors=4\n", + "Iteration 59: time=0.20, ELBO=-1344632.06, deltaELBO=31.380 (0.00056541%), Factors=4\n", + "Iteration 60: time=0.21, ELBO=-1344601.13, deltaELBO=30.932 (0.00055734%), Factors=4\n", + "Iteration 61: time=0.24, ELBO=-1344570.64, deltaELBO=30.493 (0.00054942%), Factors=4\n", + "Iteration 62: time=0.24, ELBO=-1344540.58, deltaELBO=30.061 (0.00054164%), Factors=4\n", + "Iteration 63: time=0.24, ELBO=-1344510.94, deltaELBO=29.636 (0.00053399%), Factors=4\n", + "Iteration 64: time=0.27, ELBO=-1344481.72, deltaELBO=29.219 (0.00052648%), Factors=4\n", + "Iteration 65: time=0.28, ELBO=-1344452.91, deltaELBO=28.810 (0.00051910%), Factors=4\n", + "Iteration 66: time=0.30, ELBO=-1344424.50, deltaELBO=28.407 (0.00051185%), Factors=4\n", + "Iteration 67: time=0.28, ELBO=-1344396.49, deltaELBO=28.012 (0.00050472%), Factors=4\n", + "Iteration 68: time=0.19, ELBO=-1344368.87, deltaELBO=27.623 (0.00049771%), Factors=4\n", + "Iteration 69: time=0.19, ELBO=-1344341.63, deltaELBO=27.241 (0.00049083%), Factors=4\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -6213151.75 \n", + "\n", + "Iteration 1: time=0.24, ELBO=-1388191.99, deltaELBO=4824959.762 (77.65720128%), Factors=5\n", + "Iteration 2: time=0.22, ELBO=-1350821.48, deltaELBO=37370.512 (0.60147431%), Factors=5\n", + "Iteration 3: time=0.23, ELBO=-1342787.54, deltaELBO=8033.941 (0.12930540%), Factors=5\n", + "Iteration 4: time=0.22, ELBO=-1336563.50, deltaELBO=6224.039 (0.10017523%), Factors=5\n", + "Iteration 5: time=0.40, ELBO=-1331356.61, deltaELBO=5206.894 (0.08380439%), Factors=5\n", + "Iteration 6: time=0.33, ELBO=-1326957.24, deltaELBO=4399.366 (0.07080731%), Factors=5\n", + "Iteration 7: time=0.24, ELBO=-1323511.59, deltaELBO=3445.654 (0.05545743%), Factors=5\n", + "Iteration 8: time=0.27, ELBO=-1320584.62, deltaELBO=2926.966 (0.04710919%), Factors=5\n", + "Iteration 9: time=0.28, ELBO=-1317658.59, deltaELBO=2926.027 (0.04709409%), Factors=5\n", + "Iteration 10: time=0.44, ELBO=-1314494.38, deltaELBO=3164.212 (0.05092765%), Factors=5\n", + "Iteration 11: time=0.89, ELBO=-1311039.41, deltaELBO=3454.971 (0.05560739%), Factors=5\n", + "Iteration 12: time=0.31, ELBO=-1307536.62, deltaELBO=3502.792 (0.05637706%), Factors=5\n", + "Iteration 13: time=0.28, ELBO=-1304566.06, deltaELBO=2970.560 (0.04781084%), Factors=5\n", + "Iteration 14: time=0.29, ELBO=-1302537.05, deltaELBO=2029.007 (0.03265665%), Factors=5\n", + "Iteration 15: time=0.35, ELBO=-1301264.63, deltaELBO=1272.415 (0.02047938%), Factors=5\n", + "Iteration 16: time=0.41, ELBO=-1300438.82, deltaELBO=825.810 (0.01329133%), Factors=5\n", + "Iteration 17: time=0.38, ELBO=-1299874.13, deltaELBO=564.695 (0.00908871%), Factors=5\n", + "Iteration 18: time=0.40, ELBO=-1299466.62, deltaELBO=407.506 (0.00655876%), Factors=5\n", + "Iteration 19: time=0.30, ELBO=-1299155.55, deltaELBO=311.069 (0.00500662%), Factors=5\n", + "Iteration 20: time=0.36, ELBO=-1298904.97, deltaELBO=250.583 (0.00403311%), Factors=5\n", + "Iteration 21: time=0.27, ELBO=-1298693.45, deltaELBO=211.520 (0.00340439%), Factors=5\n", + "Iteration 22: time=0.28, ELBO=-1298508.03, deltaELBO=185.420 (0.00298432%), Factors=5\n", + "Iteration 23: time=0.39, ELBO=-1298340.67, deltaELBO=167.362 (0.00269368%), Factors=5\n", + "Iteration 24: time=0.33, ELBO=-1298186.22, deltaELBO=154.449 (0.00248583%), Factors=5\n", + "Iteration 25: time=0.40, ELBO=-1298041.28, deltaELBO=144.936 (0.00233273%), Factors=5\n", + "Iteration 26: time=0.35, ELBO=-1297903.54, deltaELBO=137.746 (0.00221700%), Factors=5\n", + "Iteration 27: time=0.29, ELBO=-1297771.35, deltaELBO=132.191 (0.00212761%), Factors=5\n", + "Iteration 28: 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Factors=5\n", + "Iteration 39: time=0.29, ELBO=-1296359.60, deltaELBO=114.407 (0.00184138%), Factors=5\n", + "Iteration 40: time=0.29, ELBO=-1296244.33, deltaELBO=115.268 (0.00185522%), Factors=5\n", + "Iteration 41: time=0.27, ELBO=-1296127.82, deltaELBO=116.504 (0.00187511%), Factors=5\n", + "Iteration 42: time=0.33, ELBO=-1296009.68, deltaELBO=118.149 (0.00190159%), Factors=5\n", + "Iteration 43: time=0.30, ELBO=-1295889.43, deltaELBO=120.245 (0.00193534%), Factors=5\n", + "Iteration 44: time=0.34, ELBO=-1295766.59, deltaELBO=122.843 (0.00197714%), Factors=5\n", + "Iteration 45: time=0.37, ELBO=-1295640.59, deltaELBO=125.999 (0.00202794%), Factors=5\n", + "Iteration 46: time=0.21, ELBO=-1295510.81, deltaELBO=129.783 (0.00208884%), Factors=5\n", + "Iteration 47: time=0.24, ELBO=-1295376.53, deltaELBO=134.274 (0.00216113%), Factors=5\n", + "Iteration 48: time=0.28, ELBO=-1295236.96, deltaELBO=139.567 (0.00224631%), Factors=5\n", + "Iteration 49: time=0.29, ELBO=-1295091.20, deltaELBO=145.766 (0.00234609%), Factors=5\n", + "Iteration 50: time=0.28, ELBO=-1294938.20, deltaELBO=152.996 (0.00246246%), Factors=5\n", + "Iteration 51: time=0.24, ELBO=-1294776.81, deltaELBO=161.396 (0.00259766%), Factors=5\n", + "Iteration 52: time=0.30, ELBO=-1294605.68, deltaELBO=171.124 (0.00275422%), Factors=5\n", + "Iteration 53: time=0.26, ELBO=-1294423.33, deltaELBO=182.354 (0.00293497%), Factors=5\n", + "Iteration 54: time=0.33, ELBO=-1294228.05, deltaELBO=195.276 (0.00314295%), Factors=5\n", + "Iteration 55: time=0.25, ELBO=-1294017.96, deltaELBO=210.092 (0.00338140%), Factors=5\n", + "Iteration 56: time=0.33, ELBO=-1293790.96, deltaELBO=227.003 (0.00365359%), Factors=5\n", + "Iteration 57: time=0.25, ELBO=-1293544.76, deltaELBO=246.202 (0.00396259%), Factors=5\n", + "Iteration 58: time=0.27, ELBO=-1293276.91, deltaELBO=267.844 (0.00431093%), Factors=5\n", + "Iteration 59: time=0.28, ELBO=-1292984.89, deltaELBO=292.022 (0.00470006%), Factors=5\n", + "Iteration 60: time=0.22, ELBO=-1292666.18, deltaELBO=318.714 (0.00512967%), Factors=5\n", + "Iteration 61: time=0.25, ELBO=-1292318.44, deltaELBO=347.731 (0.00559669%), Factors=5\n", + "Iteration 62: time=0.24, ELBO=-1291939.80, deltaELBO=378.644 (0.00609424%), Factors=5\n", + "Iteration 63: time=0.30, ELBO=-1291529.09, deltaELBO=410.709 (0.00661031%), Factors=5\n", + "Iteration 64: time=0.33, ELBO=-1291086.30, deltaELBO=442.793 (0.00712671%), Factors=5\n", + "Iteration 65: time=0.28, ELBO=-1290612.96, deltaELBO=473.341 (0.00761836%), Factors=5\n", + "Iteration 66: time=0.29, ELBO=-1290112.57, deltaELBO=500.384 (0.00805363%), Factors=5\n", + "Iteration 67: time=0.28, ELBO=-1289590.92, deltaELBO=521.655 (0.00839598%), Factors=5\n", + "Iteration 68: time=0.35, ELBO=-1289056.12, deltaELBO=534.802 (0.00860757%), Factors=5\n", + "Iteration 69: time=0.33, ELBO=-1288518.39, deltaELBO=537.722 (0.00865458%), Factors=5\n", + "Iteration 70: time=0.41, ELBO=-1287989.43, deltaELBO=528.965 (0.00851363%), Factors=5\n", + "Iteration 71: time=0.24, ELBO=-1287481.32, deltaELBO=508.110 (0.00817797%), Factors=5\n", + "Iteration 72: time=0.36, ELBO=-1287005.31, deltaELBO=476.011 (0.00766135%), Factors=5\n", + "Iteration 73: time=0.36, ELBO=-1286570.52, deltaELBO=434.784 (0.00699780%), Factors=5\n", + "Iteration 74: time=0.22, ELBO=-1286183.04, deltaELBO=387.483 (0.00623650%), Factors=5\n", + "Iteration 75: time=0.35, ELBO=-1285845.48, deltaELBO=337.565 (0.00543306%), Factors=5\n", + "Iteration 76: time=0.41, ELBO=-1285557.20, deltaELBO=288.279 (0.00463982%), Factors=5\n", + "Iteration 77: time=0.27, ELBO=-1285314.99, deltaELBO=242.208 (0.00389830%), Factors=5\n", + "Iteration 78: time=0.36, ELBO=-1285113.95, deltaELBO=201.039 (0.00323570%), Factors=5\n", + "Iteration 79: time=0.30, ELBO=-1284948.37, deltaELBO=165.586 (0.00266509%), Factors=5\n", + "Iteration 80: time=0.30, ELBO=-1284812.41, deltaELBO=135.955 (0.00218817%), Factors=5\n", + "Iteration 81: time=0.33, ELBO=-1284700.64, deltaELBO=111.775 (0.00179900%), Factors=5\n", + "Iteration 82: time=0.43, ELBO=-1284608.23, deltaELBO=92.411 (0.00148735%), Factors=5\n", + "Iteration 83: time=0.52, ELBO=-1284531.10, deltaELBO=77.125 (0.00124132%), Factors=5\n", + "Iteration 84: time=0.29, ELBO=-1284465.91, deltaELBO=65.185 (0.00104914%), Factors=5\n", + "Iteration 85: time=0.43, ELBO=-1284409.99, deltaELBO=55.924 (0.00090009%), Factors=5\n", + "Iteration 86: time=0.33, ELBO=-1284361.22, deltaELBO=48.771 (0.00078497%), Factors=5\n", + "Iteration 87: time=0.25, ELBO=-1284317.96, deltaELBO=43.255 (0.00069618%), Factors=5\n", + "Iteration 88: time=0.24, ELBO=-1284278.97, deltaELBO=38.995 (0.00062762%), Factors=5\n", + "Iteration 89: time=0.32, ELBO=-1284243.27, deltaELBO=35.694 (0.00057450%), Factors=5\n", + "Iteration 90: time=0.24, ELBO=-1284210.15, deltaELBO=33.121 (0.00053309%), Factors=5\n", + "Iteration 91: time=0.23, ELBO=-1284179.05, deltaELBO=31.099 (0.00050054%), Factors=5\n", + "Iteration 92: time=0.33, ELBO=-1284149.56, deltaELBO=29.492 (0.00047468%), Factors=5\n", + "Iteration 93: time=0.27, ELBO=-1284121.36, deltaELBO=28.200 (0.00045387%), Factors=5\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -6859324.64 \n", + "\n", + "Iteration 1: time=0.35, ELBO=-1325704.79, deltaELBO=5533619.850 (80.67295457%), Factors=6\n", + "Iteration 2: time=0.42, ELBO=-1267546.18, deltaELBO=58158.607 (0.84787658%), Factors=6\n", + "Iteration 3: time=0.34, ELBO=-1258463.81, deltaELBO=9082.376 (0.13240918%), Factors=6\n", + "Iteration 4: time=0.24, ELBO=-1253346.83, deltaELBO=5116.974 (0.07459880%), Factors=6\n", + "Iteration 5: time=0.24, ELBO=-1249654.55, deltaELBO=3692.279 (0.05382860%), Factors=6\n", + "Iteration 6: time=0.27, ELBO=-1246521.99, deltaELBO=3132.563 (0.04566868%), Factors=6\n", + "Iteration 7: time=0.23, ELBO=-1243804.37, deltaELBO=2717.620 (0.03961935%), Factors=6\n", + "Iteration 8: time=0.30, ELBO=-1241871.78, deltaELBO=1932.587 (0.02817460%), Factors=6\n", + "Iteration 9: time=0.34, ELBO=-1240672.94, deltaELBO=1198.845 (0.01747759%), Factors=6\n", + "Iteration 10: time=0.35, ELBO=-1239841.24, deltaELBO=831.698 (0.01212508%), Factors=6\n", + "Iteration 11: time=0.21, ELBO=-1239198.51, deltaELBO=642.734 (0.00937023%), Factors=6\n", + "Iteration 12: time=0.22, ELBO=-1238673.58, deltaELBO=524.931 (0.00765281%), Factors=6\n", + "Iteration 13: time=0.24, ELBO=-1238229.40, deltaELBO=444.176 (0.00647551%), Factors=6\n", + "Iteration 14: time=0.22, ELBO=-1237844.26, deltaELBO=385.143 (0.00561489%), Factors=6\n", + "Iteration 15: time=0.21, ELBO=-1237504.36, deltaELBO=339.892 (0.00495518%), Factors=6\n", + "Iteration 16: time=0.28, ELBO=-1237200.26, deltaELBO=304.107 (0.00443348%), Factors=6\n", + "Iteration 17: time=0.20, ELBO=-1236925.03, deltaELBO=275.223 (0.00401240%), Factors=6\n", + "Iteration 18: time=0.21, ELBO=-1236673.48, deltaELBO=251.556 (0.00366735%), Factors=6\n", + "Iteration 19: time=0.20, ELBO=-1236441.57, deltaELBO=231.911 (0.00338096%), Factors=6\n", + "Iteration 20: time=0.22, ELBO=-1236226.16, deltaELBO=215.411 (0.00314042%), Factors=6\n", + "Iteration 21: time=0.22, ELBO=-1236024.76, deltaELBO=201.397 (0.00293610%), Factors=6\n", + "Iteration 22: time=0.21, ELBO=-1235835.39, deltaELBO=189.365 (0.00276069%), Factors=6\n", + "Iteration 23: time=0.23, ELBO=-1235656.47, deltaELBO=178.927 (0.00260853%), Factors=6\n", + "Iteration 24: time=0.27, ELBO=-1235486.68, deltaELBO=169.784 (0.00247522%), Factors=6\n", + "Iteration 25: time=0.23, ELBO=-1235324.99, deltaELBO=161.696 (0.00235732%), Factors=6\n", + "Iteration 26: time=0.22, ELBO=-1235170.51, deltaELBO=154.477 (0.00225208%), Factors=6\n", + "Iteration 27: time=0.20, ELBO=-1235022.53, deltaELBO=147.978 (0.00215732%), Factors=6\n", + "Iteration 28: time=0.21, ELBO=-1234880.45, deltaELBO=142.078 (0.00207131%), Factors=6\n", + "Iteration 29: time=0.19, ELBO=-1234743.77, deltaELBO=136.682 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"Iteration 62: time=0.52, ELBO=-1231906.56, deltaELBO=56.425 (0.00082261%), Factors=6\n", + "Iteration 63: time=0.55, ELBO=-1231851.32, deltaELBO=55.245 (0.00080540%), Factors=6\n", + "Iteration 64: time=0.41, ELBO=-1231797.22, deltaELBO=54.101 (0.00078872%), Factors=6\n", + "Iteration 65: time=0.34, ELBO=-1231744.23, deltaELBO=52.992 (0.00077255%), Factors=6\n", + "Iteration 66: time=0.46, ELBO=-1231692.31, deltaELBO=51.916 (0.00075687%), Factors=6\n", + "Iteration 67: time=0.52, ELBO=-1231641.44, deltaELBO=50.873 (0.00074166%), Factors=6\n", + "Iteration 68: time=0.68, ELBO=-1231591.58, deltaELBO=49.860 (0.00072690%), Factors=6\n", + "Iteration 69: time=0.66, ELBO=-1231542.70, deltaELBO=48.878 (0.00071257%), Factors=6\n", + "Iteration 70: time=0.65, ELBO=-1231494.77, deltaELBO=47.924 (0.00069866%), Factors=6\n", + "Iteration 71: time=0.56, ELBO=-1231447.78, deltaELBO=46.997 (0.00068515%), Factors=6\n", + "Iteration 72: time=0.50, ELBO=-1231401.68, deltaELBO=46.097 (0.00067203%), Factors=6\n", + "Iteration 73: time=0.48, ELBO=-1231356.46, deltaELBO=45.222 (0.00065928%), Factors=6\n", + "Iteration 74: time=0.54, ELBO=-1231312.09, deltaELBO=44.372 (0.00064689%), Factors=6\n", + "Iteration 75: time=0.49, ELBO=-1231268.54, deltaELBO=43.546 (0.00063484%), Factors=6\n", + "Iteration 76: time=0.52, ELBO=-1231225.80, deltaELBO=42.742 (0.00062312%), Factors=6\n", + "Iteration 77: time=0.57, ELBO=-1231183.84, deltaELBO=41.960 (0.00061173%), Factors=6\n", + "Iteration 78: time=0.51, ELBO=-1231142.64, deltaELBO=41.200 (0.00060064%), Factors=6\n", + "Iteration 79: time=0.52, ELBO=-1231102.18, deltaELBO=40.460 (0.00058985%), Factors=6\n", + "Iteration 80: time=0.51, ELBO=-1231062.44, deltaELBO=39.740 (0.00057935%), Factors=6\n", + "Iteration 81: time=0.54, ELBO=-1231023.40, deltaELBO=39.038 (0.00056913%), Factors=6\n", + "Iteration 82: time=0.54, ELBO=-1230985.05, deltaELBO=38.356 (0.00055918%), Factors=6\n", + "Iteration 83: time=0.54, ELBO=-1230947.36, deltaELBO=37.691 (0.00054948%), Factors=6\n", + "Iteration 84: time=0.50, ELBO=-1230910.31, deltaELBO=37.043 (0.00054004%), Factors=6\n", + "Iteration 85: time=0.50, ELBO=-1230873.90, deltaELBO=36.412 (0.00053084%), Factors=6\n", + "Iteration 86: time=0.54, ELBO=-1230838.10, deltaELBO=35.797 (0.00052187%), Factors=6\n", + "Iteration 87: time=0.55, ELBO=-1230802.91, deltaELBO=35.198 (0.00051314%), Factors=6\n", + "Iteration 88: time=0.56, ELBO=-1230768.29, deltaELBO=34.613 (0.00050462%), Factors=6\n", + "Iteration 89: time=0.43, ELBO=-1230734.25, deltaELBO=34.044 (0.00049631%), Factors=6\n", + "Iteration 90: time=0.31, ELBO=-1230700.76, deltaELBO=33.488 (0.00048821%), Factors=6\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -7533633.70 \n", + "\n", + "Iteration 1: time=0.35, ELBO=-1462136.02, deltaELBO=6071497.678 (80.59188862%), Factors=7\n", + "Iteration 2: time=0.34, ELBO=-1413126.58, deltaELBO=49009.434 (0.65054177%), Factors=7\n", + "Iteration 3: time=0.33, ELBO=-1399190.24, deltaELBO=13936.340 (0.18498829%), Factors=7\n", + "Iteration 4: time=0.33, ELBO=-1331202.69, deltaELBO=67987.557 (0.90245371%), Factors=7\n", + "Iteration 5: time=0.37, ELBO=-1229392.66, deltaELBO=101810.028 (1.35140667%), Factors=7\n", + "Iteration 6: time=0.34, ELBO=-1209296.96, deltaELBO=20095.702 (0.26674646%), Factors=7\n", + "Iteration 7: time=0.35, ELBO=-1200636.41, deltaELBO=8660.545 (0.11495841%), Factors=7\n", + "Iteration 8: time=0.39, ELBO=-1195935.07, deltaELBO=4701.342 (0.06240471%), Factors=7\n", + "Iteration 9: time=0.43, ELBO=-1193570.43, deltaELBO=2364.643 (0.03138781%), Factors=7\n", + "Iteration 10: time=0.46, ELBO=-1192288.75, deltaELBO=1281.677 (0.01701273%), Factors=7\n", + "Iteration 11: time=0.38, ELBO=-1191476.75, deltaELBO=812.004 (0.01077838%), Factors=7\n", + "Iteration 12: time=0.34, ELBO=-1190887.60, deltaELBO=589.149 (0.00782025%), Factors=7\n", + "Iteration 13: time=0.36, 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"Iteration 24: time=0.33, ELBO=-1187680.29, deltaELBO=175.965 (0.00233572%), Factors=7\n", + "Iteration 25: time=0.33, ELBO=-1187511.56, deltaELBO=168.726 (0.00223964%), Factors=7\n", + "Iteration 26: time=0.50, ELBO=-1187349.45, deltaELBO=162.110 (0.00215181%), Factors=7\n", + "Iteration 27: time=0.60, ELBO=-1187193.44, deltaELBO=156.005 (0.00207078%), Factors=7\n", + "Iteration 28: time=0.35, ELBO=-1187043.11, deltaELBO=150.331 (0.00199546%), Factors=7\n", + "Iteration 29: time=0.37, ELBO=-1186898.09, deltaELBO=145.026 (0.00192504%), Factors=7\n", + "Iteration 30: time=0.36, ELBO=-1186758.05, deltaELBO=140.042 (0.00185889%), Factors=7\n", + "Iteration 31: time=0.32, ELBO=-1186622.70, deltaELBO=135.344 (0.00179652%), Factors=7\n", + "Iteration 32: time=0.32, ELBO=-1186491.80, deltaELBO=130.900 (0.00173754%), Factors=7\n", + "Iteration 33: time=0.32, ELBO=-1186365.11, deltaELBO=126.687 (0.00168162%), Factors=7\n", + "Iteration 34: time=0.31, ELBO=-1186242.43, deltaELBO=122.685 (0.00162850%), Factors=7\n", + "Iteration 35: time=0.32, ELBO=-1186123.55, deltaELBO=118.877 (0.00157795%), Factors=7\n", + "Iteration 36: time=0.32, ELBO=-1186008.31, deltaELBO=115.247 (0.00152977%), Factors=7\n", + "Iteration 37: time=0.32, ELBO=-1185896.52, deltaELBO=111.783 (0.00148379%), Factors=7\n", + "Iteration 38: time=0.34, ELBO=-1185788.05, deltaELBO=108.474 (0.00143986%), Factors=7\n", + "Iteration 39: time=0.30, ELBO=-1185682.74, deltaELBO=105.309 (0.00139786%), Factors=7\n", + "Iteration 40: time=0.32, ELBO=-1185580.46, deltaELBO=102.280 (0.00135765%), Factors=7\n", + "Iteration 41: time=0.34, ELBO=-1185481.08, deltaELBO=99.378 (0.00131913%), Factors=7\n", + "Iteration 42: time=0.69, ELBO=-1185384.48, deltaELBO=96.597 (0.00128221%), Factors=7\n", + "Iteration 43: time=0.64, ELBO=-1185290.56, deltaELBO=93.928 (0.00124678%), Factors=7\n", + "Iteration 44: time=0.57, ELBO=-1185199.19, deltaELBO=91.366 (0.00121278%), Factors=7\n", + "Iteration 45: time=0.75, ELBO=-1185110.28, deltaELBO=88.906 (0.00118012%), Factors=7\n", + "Iteration 46: time=0.68, ELBO=-1185023.74, deltaELBO=86.541 (0.00114873%), Factors=7\n", + "Iteration 47: time=0.52, ELBO=-1184939.48, deltaELBO=84.267 (0.00111854%), Factors=7\n", + "Iteration 48: time=0.66, ELBO=-1184857.40, deltaELBO=82.080 (0.00108951%), Factors=7\n", + "Iteration 49: time=0.74, ELBO=-1184777.42, deltaELBO=79.974 (0.00106156%), Factors=7\n", + "Iteration 50: time=0.67, ELBO=-1184699.48, deltaELBO=77.947 (0.00103465%), Factors=7\n", + "Iteration 51: time=0.62, ELBO=-1184623.48, deltaELBO=75.993 (0.00100872%), Factors=7\n", + "Iteration 52: time=0.66, ELBO=-1184549.37, deltaELBO=74.111 (0.00098374%), Factors=7\n", + "Iteration 53: time=0.37, ELBO=-1184477.08, deltaELBO=72.296 (0.00095965%), Factors=7\n", + "Iteration 54: time=0.35, ELBO=-1184406.53, deltaELBO=70.546 (0.00093641%), Factors=7\n", + "Iteration 55: time=0.31, ELBO=-1184337.67, deltaELBO=68.857 (0.00091399%), Factors=7\n", + "Iteration 56: time=0.31, 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time=0.26, ELBO=-1183624.11, deltaELBO=52.515 (0.00069707%), Factors=7\n", + "Iteration 68: time=0.25, ELBO=-1183572.69, deltaELBO=51.421 (0.00068256%), Factors=7\n", + "Iteration 69: time=0.23, ELBO=-1183522.33, deltaELBO=50.362 (0.00066849%), Factors=7\n", + "Iteration 70: time=0.22, ELBO=-1183473.00, deltaELBO=49.334 (0.00065485%), Factors=7\n", + "Iteration 71: time=0.22, ELBO=-1183424.66, deltaELBO=48.338 (0.00064163%), Factors=7\n", + "Iteration 72: time=0.24, ELBO=-1183377.29, deltaELBO=47.372 (0.00062881%), Factors=7\n", + "Iteration 73: time=0.23, ELBO=-1183330.85, deltaELBO=46.435 (0.00061637%), Factors=7\n", + "Iteration 74: time=0.22, ELBO=-1183285.32, deltaELBO=45.526 (0.00060430%), Factors=7\n", + "Iteration 75: time=0.25, ELBO=-1183240.68, deltaELBO=44.643 (0.00059259%), Factors=7\n", + "Iteration 76: time=0.23, ELBO=-1183196.89, deltaELBO=43.787 (0.00058122%), Factors=7\n", + "Iteration 77: time=0.23, ELBO=-1183153.94, deltaELBO=42.955 (0.00057018%), Factors=7\n", + "Iteration 78: time=0.25, ELBO=-1183111.79, deltaELBO=42.147 (0.00055946%), Factors=7\n", + "Iteration 79: time=0.23, ELBO=-1183070.43, deltaELBO=41.363 (0.00054904%), Factors=7\n", + "Iteration 80: time=0.23, ELBO=-1183029.83, deltaELBO=40.600 (0.00053892%), Factors=7\n", + "Iteration 81: time=0.25, ELBO=-1182989.97, deltaELBO=39.859 (0.00052908%), Factors=7\n", + "Iteration 82: time=0.22, ELBO=-1182950.83, deltaELBO=39.139 (0.00051953%), Factors=7\n", + "Iteration 83: time=0.24, ELBO=-1182912.39, deltaELBO=38.439 (0.00051023%), Factors=7\n", + "Iteration 84: time=0.22, ELBO=-1182874.63, deltaELBO=37.758 (0.00050119%), Factors=7\n", + "Iteration 85: time=0.21, ELBO=-1182837.54, deltaELBO=37.096 (0.00049240%), Factors=7\n", + "Iteration 86: time=0.23, ELBO=-1182801.09, deltaELBO=36.452 (0.00048385%), Factors=7\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -8206618.17 \n", + "\n", + "Iteration 1: time=0.28, ELBO=-1451650.54, deltaELBO=6754967.628 (82.31122114%), Factors=8\n", + "Iteration 2: time=0.27, ELBO=-1393570.54, deltaELBO=58079.999 (0.70772148%), Factors=8\n", + "Iteration 3: time=0.26, ELBO=-1378547.54, deltaELBO=15022.995 (0.18305951%), Factors=8\n", + "Iteration 4: time=0.27, ELBO=-1305496.70, deltaELBO=73050.840 (0.89014547%), Factors=8\n", + "Iteration 5: time=0.26, ELBO=-1198832.18, deltaELBO=106664.521 (1.29973783%), Factors=8\n", + "Iteration 6: time=0.25, ELBO=-1178674.59, deltaELBO=20157.596 (0.24562610%), Factors=8\n", + "Iteration 7: time=0.55, ELBO=-1170835.16, deltaELBO=7839.431 (0.09552572%), Factors=8\n", + "Iteration 8: time=0.26, ELBO=-1166321.79, deltaELBO=4513.367 (0.05499668%), Factors=8\n", + "Iteration 9: time=0.27, ELBO=-1163580.89, deltaELBO=2740.902 (0.03339868%), Factors=8\n", + "Iteration 10: time=0.26, ELBO=-1161732.63, deltaELBO=1848.262 (0.02252161%), Factors=8\n", + "Iteration 11: time=0.25, ELBO=-1160338.27, deltaELBO=1394.352 (0.01699058%), Factors=8\n", + "Iteration 12: time=0.26, ELBO=-1159215.57, deltaELBO=1122.701 (0.01368044%), Factors=8\n", + "Iteration 13: time=0.26, ELBO=-1158292.37, deltaELBO=923.201 (0.01124947%), Factors=8\n", + "Iteration 14: time=0.27, ELBO=-1157532.70, deltaELBO=759.667 (0.00925676%), Factors=8\n", + "Iteration 15: time=0.25, ELBO=-1156909.63, deltaELBO=623.075 (0.00759235%), Factors=8\n", + "Iteration 16: time=0.27, ELBO=-1156398.03, deltaELBO=511.594 (0.00623392%), Factors=8\n", + "Iteration 17: time=0.25, ELBO=-1155974.44, deltaELBO=423.589 (0.00516156%), Factors=8\n", + "Iteration 18: time=0.48, ELBO=-1155618.33, deltaELBO=356.115 (0.00433936%), Factors=8\n", + "Iteration 19: time=0.30, ELBO=-1155312.96, deltaELBO=305.374 (0.00372107%), Factors=8\n", + "Iteration 20: time=0.29, ELBO=-1155045.43, deltaELBO=267.522 (0.00325983%), Factors=8\n", + "Iteration 21: time=0.28, ELBO=-1154806.23, deltaELBO=239.204 (0.00291476%), Factors=8\n", + "Iteration 22: time=0.27, ELBO=-1154588.48, deltaELBO=217.754 (0.00265339%), Factors=8\n", + "Iteration 23: time=0.26, ELBO=-1154387.30, deltaELBO=201.180 (0.00245143%), Factors=8\n", + "Iteration 24: time=0.29, ELBO=-1154199.25, deltaELBO=188.051 (0.00229145%), Factors=8\n", + "Iteration 25: time=0.28, ELBO=-1154021.89, deltaELBO=177.361 (0.00216120%), Factors=8\n", + "Iteration 26: time=0.26, ELBO=-1153853.47, deltaELBO=168.416 (0.00205220%), Factors=8\n", + "Iteration 27: time=0.26, ELBO=-1153692.73, deltaELBO=160.735 (0.00195860%), Factors=8\n", + "Iteration 28: time=0.28, ELBO=-1153538.75, deltaELBO=153.989 (0.00187640%), Factors=8\n", + "Iteration 29: time=0.28, ELBO=-1153390.80, deltaELBO=147.950 (0.00180282%), Factors=8\n", + "Iteration 30: time=0.29, ELBO=-1153248.33, deltaELBO=142.461 (0.00173593%), Factors=8\n", + "Iteration 31: time=0.32, ELBO=-1153110.92, deltaELBO=137.411 (0.00167439%), Factors=8\n", + "Iteration 32: time=0.33, ELBO=-1152978.20, deltaELBO=132.721 (0.00161724%), Factors=8\n", + "Iteration 33: time=0.49, ELBO=-1152849.87, deltaELBO=128.333 (0.00156377%), Factors=8\n", + "Iteration 34: time=0.54, ELBO=-1152725.66, deltaELBO=124.206 (0.00151348%), Factors=8\n", + "Iteration 35: time=0.52, ELBO=-1152605.36, deltaELBO=120.307 (0.00146598%), Factors=8\n", + "Iteration 36: time=0.52, ELBO=-1152488.74, deltaELBO=116.612 (0.00142095%), Factors=8\n", + "Iteration 37: time=0.47, ELBO=-1152375.64, deltaELBO=113.102 (0.00137818%), Factors=8\n", + "Iteration 38: time=0.46, ELBO=-1152265.88, deltaELBO=109.759 (0.00133745%), Factors=8\n", + "Iteration 39: time=0.44, ELBO=-1152159.31, deltaELBO=106.572 (0.00129861%), Factors=8\n", + "Iteration 40: time=0.45, ELBO=-1152055.78, deltaELBO=103.528 (0.00126152%), Factors=8\n", + "Iteration 41: time=0.45, ELBO=-1151955.17, deltaELBO=100.618 (0.00122606%), Factors=8\n", + "Iteration 42: time=0.46, ELBO=-1151857.33, deltaELBO=97.832 (0.00119212%), Factors=8\n", + "Iteration 43: time=0.53, ELBO=-1151762.17, deltaELBO=95.164 (0.00115960%), Factors=8\n", + "Iteration 44: time=0.87, ELBO=-1151669.56, deltaELBO=92.606 (0.00112844%), Factors=8\n", + "Iteration 45: time=0.36, ELBO=-1151579.41, deltaELBO=90.153 (0.00109853%), Factors=8\n", + "Iteration 46: time=0.30, ELBO=-1151491.61, deltaELBO=87.797 (0.00106983%), Factors=8\n", + "Iteration 47: time=0.50, ELBO=-1151406.08, deltaELBO=85.533 (0.00104225%), Factors=8\n", + "Iteration 48: time=0.38, ELBO=-1151322.72, deltaELBO=83.358 (0.00101574%), Factors=8\n", + "Iteration 49: time=0.47, ELBO=-1151241.46, deltaELBO=81.266 (0.00099025%), Factors=8\n", + "Iteration 50: time=0.61, ELBO=-1151162.20, deltaELBO=79.253 (0.00096572%), Factors=8\n", + "Iteration 51: time=0.68, ELBO=-1151084.89, deltaELBO=77.315 (0.00094210%), Factors=8\n", + "Iteration 52: time=0.68, ELBO=-1151009.44, deltaELBO=75.448 (0.00091935%), Factors=8\n", + "Iteration 53: time=0.61, ELBO=-1150935.79, deltaELBO=73.649 (0.00089743%), Factors=8\n", + "Iteration 54: time=0.73, ELBO=-1150863.88, deltaELBO=71.914 (0.00087629%), Factors=8\n", + "Iteration 55: time=0.57, ELBO=-1150793.64, deltaELBO=70.241 (0.00085591%), Factors=8\n", + "Iteration 56: time=0.43, ELBO=-1150725.01, deltaELBO=68.627 (0.00083624%), Factors=8\n", + "Iteration 57: time=0.28, ELBO=-1150657.94, deltaELBO=67.069 (0.00081725%), Factors=8\n", + "Iteration 58: time=0.28, ELBO=-1150592.38, deltaELBO=65.564 (0.00079891%), Factors=8\n", + "Iteration 59: time=0.26, ELBO=-1150528.27, deltaELBO=64.110 (0.00078120%), Factors=8\n", + "Iteration 60: time=0.27, ELBO=-1150465.56, deltaELBO=62.705 (0.00076407%), Factors=8\n", + "Iteration 61: time=0.26, ELBO=-1150404.22, deltaELBO=61.346 (0.00074752%), Factors=8\n", + "Iteration 62: time=0.27, ELBO=-1150344.19, deltaELBO=60.032 (0.00073151%), Factors=8\n", + "Iteration 63: time=0.27, ELBO=-1150285.42, deltaELBO=58.761 (0.00071602%), Factors=8\n", + "Iteration 64: time=0.26, ELBO=-1150227.89, deltaELBO=57.531 (0.00070103%), Factors=8\n", + "Iteration 65: time=0.25, ELBO=-1150171.55, deltaELBO=56.340 (0.00068652%), Factors=8\n", + "Iteration 66: time=0.26, ELBO=-1150116.37, deltaELBO=55.186 (0.00067246%), Factors=8\n", + "Iteration 67: time=0.25, ELBO=-1150062.30, deltaELBO=54.069 (0.00065885%), Factors=8\n", + "Iteration 68: time=0.40, ELBO=-1150009.31, deltaELBO=52.986 (0.00064565%), Factors=8\n", + "Iteration 69: time=0.25, ELBO=-1149957.38, deltaELBO=51.937 (0.00063286%), Factors=8\n", + "Iteration 70: time=0.31, ELBO=-1149906.46, deltaELBO=50.919 (0.00062046%), Factors=8\n", + "Iteration 71: time=0.28, ELBO=-1149856.52, deltaELBO=49.932 (0.00060843%), Factors=8\n", + "Iteration 72: time=0.35, ELBO=-1149807.55, deltaELBO=48.974 (0.00059676%), Factors=8\n", + "Iteration 73: time=0.44, ELBO=-1149759.51, deltaELBO=48.045 (0.00058544%), Factors=8\n", + "Iteration 74: time=0.44, ELBO=-1149712.36, deltaELBO=47.143 (0.00057445%), Factors=8\n", + "Iteration 75: time=0.52, ELBO=-1149666.10, deltaELBO=46.267 (0.00056377%), Factors=8\n", + "Iteration 76: time=0.54, ELBO=-1149620.68, deltaELBO=45.416 (0.00055341%), Factors=8\n", + "Iteration 77: time=0.37, ELBO=-1149576.09, deltaELBO=44.589 (0.00054334%), Factors=8\n", + "Iteration 78: time=0.29, ELBO=-1149532.31, deltaELBO=43.786 (0.00053355%), Factors=8\n", + "Iteration 79: time=0.31, ELBO=-1149489.30, deltaELBO=43.006 (0.00052404%), Factors=8\n", + "Iteration 80: time=0.28, ELBO=-1149447.05, deltaELBO=42.247 (0.00051479%), Factors=8\n", + "Iteration 81: time=0.26, ELBO=-1149405.54, deltaELBO=41.509 (0.00050580%), Factors=8\n", + "Iteration 82: time=0.27, ELBO=-1149364.75, deltaELBO=40.792 (0.00049706%), Factors=8\n", + "Iteration 83: time=0.35, ELBO=-1149324.66, deltaELBO=40.093 (0.00048855%), Factors=8\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -8875938.10 \n", + "\n", + "Iteration 1: time=0.35, ELBO=-1445930.59, deltaELBO=7430007.516 (83.70954631%), Factors=9\n", + "Iteration 2: time=0.38, ELBO=-1380536.37, deltaELBO=65394.211 (0.73675831%), Factors=9\n", + "Iteration 3: time=0.62, ELBO=-1359583.75, deltaELBO=20952.627 (0.23606098%), Factors=9\n", + "Iteration 4: time=0.60, ELBO=-1216072.23, deltaELBO=143511.515 (1.61686025%), Factors=9\n", + "Iteration 5: time=0.28, ELBO=-1156133.94, deltaELBO=59938.294 (0.67528967%), Factors=9\n", + "Iteration 6: time=0.30, ELBO=-1145420.38, deltaELBO=10713.555 (0.12070335%), Factors=9\n", + "Iteration 7: time=0.63, ELBO=-1139743.65, deltaELBO=5676.737 (0.06395647%), Factors=9\n", + "Iteration 8: time=0.34, ELBO=-1136450.15, deltaELBO=3293.499 (0.03710593%), Factors=9\n", + "Iteration 9: time=0.32, ELBO=-1134320.07, deltaELBO=2130.078 (0.02399835%), Factors=9\n", + "Iteration 10: time=0.32, ELBO=-1132792.21, deltaELBO=1527.860 (0.01721351%), Factors=9\n", + "Iteration 11: time=0.33, ELBO=-1131606.22, deltaELBO=1185.991 (0.01336186%), Factors=9\n", + "Iteration 12: time=0.33, ELBO=-1130637.36, deltaELBO=968.859 (0.01091557%), Factors=9\n", + "Iteration 13: time=0.32, ELBO=-1129821.03, deltaELBO=816.335 (0.00919716%), Factors=9\n", + "Iteration 14: time=0.32, ELBO=-1129120.14, deltaELBO=700.883 (0.00789644%), Factors=9\n", + "Iteration 15: time=0.30, ELBO=-1128511.05, deltaELBO=609.095 (0.00686232%), Factors=9\n", + "Iteration 16: time=0.45, ELBO=-1127977.05, deltaELBO=533.998 (0.00601624%), Factors=9\n", + "Iteration 17: time=0.32, ELBO=-1127505.40, deltaELBO=471.653 (0.00531384%), Factors=9\n", + "Iteration 18: time=0.66, ELBO=-1127085.83, deltaELBO=419.569 (0.00472704%), Factors=9\n", + "Iteration 19: time=0.68, ELBO=-1126709.87, deltaELBO=375.955 (0.00423567%), Factors=9\n", + "Iteration 20: time=0.49, ELBO=-1126370.49, deltaELBO=339.386 (0.00382367%), Factors=9\n", + "Iteration 21: time=0.41, ELBO=-1126061.82, deltaELBO=308.666 (0.00347756%), Factors=9\n", + "Iteration 22: time=0.49, ELBO=-1125779.05, deltaELBO=282.772 (0.00318583%), Factors=9\n", + "Iteration 23: time=0.46, ELBO=-1125518.21, deltaELBO=260.841 (0.00293874%), Factors=9\n", + "Iteration 24: time=0.63, ELBO=-1125276.06, deltaELBO=242.145 (0.00272811%), Factors=9\n", + "Iteration 25: time=0.58, ELBO=-1125049.97, deltaELBO=226.088 (0.00254720%), Factors=9\n", + "Iteration 26: time=0.56, ELBO=-1124837.79, deltaELBO=212.181 (0.00239052%), Factors=9\n", + "Iteration 27: time=0.44, ELBO=-1124637.76, deltaELBO=200.031 (0.00225363%), Factors=9\n", + "Iteration 28: time=0.59, ELBO=-1124448.44, deltaELBO=189.322 (0.00213298%), Factors=9\n", + "Iteration 29: time=0.70, ELBO=-1124268.64, deltaELBO=179.801 (0.00202571%), Factors=9\n", + "Iteration 30: time=0.84, ELBO=-1124097.37, deltaELBO=171.265 (0.00192955%), Factors=9\n", + "Iteration 31: time=0.63, ELBO=-1123933.82, deltaELBO=163.555 (0.00184267%), Factors=9\n", + "Iteration 32: time=0.72, ELBO=-1123777.28, deltaELBO=156.539 (0.00176363%), Factors=9\n", + "Iteration 33: time=0.65, ELBO=-1123627.17, deltaELBO=150.113 (0.00169124%), Factors=9\n", + "Iteration 34: 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Factors=9\n", + "Iteration 45: time=0.38, ELBO=-1122192.43, deltaELBO=99.294 (0.00111869%), Factors=9\n", + "Iteration 46: time=0.40, ELBO=-1122096.05, deltaELBO=96.384 (0.00108590%), Factors=9\n", + "Iteration 47: time=0.40, ELBO=-1122002.44, deltaELBO=93.605 (0.00105459%), Factors=9\n", + "Iteration 48: time=0.38, ELBO=-1121911.50, deltaELBO=90.949 (0.00102467%), Factors=9\n", + "Iteration 49: time=0.38, ELBO=-1121823.09, deltaELBO=88.409 (0.00099605%), Factors=9\n", + "Iteration 50: time=0.40, ELBO=-1121737.11, deltaELBO=85.976 (0.00096864%), Factors=9\n", + "Iteration 51: time=0.39, ELBO=-1121653.47, deltaELBO=83.644 (0.00094237%), Factors=9\n", + "Iteration 52: time=0.41, ELBO=-1121572.06, deltaELBO=81.408 (0.00091717%), Factors=9\n", + "Iteration 53: time=0.41, ELBO=-1121492.80, deltaELBO=79.261 (0.00089299%), Factors=9\n", + "Iteration 54: time=0.59, ELBO=-1121415.60, deltaELBO=77.200 (0.00086976%), Factors=9\n", + "Iteration 55: time=0.43, ELBO=-1121340.38, deltaELBO=75.218 (0.00084744%), Factors=9\n", + "Iteration 56: time=0.44, ELBO=-1121267.07, deltaELBO=73.313 (0.00082597%), Factors=9\n", + "Iteration 57: time=0.38, ELBO=-1121195.59, deltaELBO=71.479 (0.00080532%), Factors=9\n", + "Iteration 58: time=0.44, ELBO=-1121125.87, deltaELBO=69.714 (0.00078543%), Factors=9\n", + "Iteration 59: time=0.39, ELBO=-1121057.86, deltaELBO=68.014 (0.00076628%), Factors=9\n", + "Iteration 60: time=0.61, ELBO=-1120991.48, deltaELBO=66.376 (0.00074782%), Factors=9\n", + "Iteration 61: time=0.46, ELBO=-1120926.69, deltaELBO=64.796 (0.00073002%), Factors=9\n", + "Iteration 62: time=0.46, ELBO=-1120863.42, deltaELBO=63.272 (0.00071285%), Factors=9\n", + "Iteration 63: time=0.62, ELBO=-1120801.61, deltaELBO=61.802 (0.00069628%), Factors=9\n", + "Iteration 64: time=0.67, ELBO=-1120741.23, deltaELBO=60.382 (0.00068029%), Factors=9\n", + "Iteration 65: time=0.62, ELBO=-1120682.22, deltaELBO=59.010 (0.00066484%), Factors=9\n", + "Iteration 66: time=0.53, ELBO=-1120624.54, deltaELBO=57.685 (0.00064991%), Factors=9\n", + "Iteration 67: time=0.62, ELBO=-1120568.13, deltaELBO=56.405 (0.00063548%), Factors=9\n", + "Iteration 68: time=0.62, ELBO=-1120512.97, deltaELBO=55.166 (0.00062152%), Factors=9\n", + "Iteration 69: time=0.61, ELBO=-1120459.00, deltaELBO=53.968 (0.00060803%), Factors=9\n", + "Iteration 70: time=0.66, ELBO=-1120406.19, deltaELBO=52.809 (0.00059496%), Factors=9\n", + "Iteration 71: time=0.67, ELBO=-1120354.50, deltaELBO=51.686 (0.00058232%), Factors=9\n", + "Iteration 72: time=0.72, ELBO=-1120303.90, deltaELBO=50.600 (0.00057008%), Factors=9\n", + "Iteration 73: time=0.80, ELBO=-1120254.36, deltaELBO=49.547 (0.00055822%), Factors=9\n", + "Iteration 74: time=0.50, ELBO=-1120205.83, deltaELBO=48.527 (0.00054672%), Factors=9\n", + "Iteration 75: time=0.44, ELBO=-1120158.29, deltaELBO=47.538 (0.00053559%), Factors=9\n", + "Iteration 76: time=0.42, ELBO=-1120111.71, deltaELBO=46.580 (0.00052479%), Factors=9\n", + "Iteration 77: time=0.41, ELBO=-1120066.06, deltaELBO=45.650 (0.00051431%), Factors=9\n", + "Iteration 78: time=0.40, ELBO=-1120021.31, deltaELBO=44.749 (0.00050416%), Factors=9\n", + "Iteration 79: time=0.40, ELBO=-1119977.44, deltaELBO=43.874 (0.00049430%), Factors=9\n", + "Iteration 80: time=0.42, ELBO=-1119934.41, deltaELBO=43.024 (0.00048473%), Factors=9\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -9537298.64 \n", + "\n", + "Iteration 1: time=0.75, ELBO=-1282147.16, deltaELBO=8255151.483 (86.55649562%), Factors=10\n", + "Iteration 2: time=0.72, ELBO=-1176812.19, deltaELBO=105334.967 (1.10445285%), Factors=10\n", + "Iteration 3: time=0.70, ELBO=-1162675.93, deltaELBO=14136.266 (0.14822085%), Factors=10\n", + "Iteration 4: time=0.78, ELBO=-1147749.13, deltaELBO=14926.801 (0.15650974%), Factors=10\n", + "Iteration 5: time=0.70, ELBO=-1131438.32, deltaELBO=16310.804 (0.17102121%), Factors=10\n", + "Iteration 6: time=0.66, ELBO=-1120597.36, deltaELBO=10840.965 (0.11366914%), Factors=10\n", + "Iteration 7: time=0.70, ELBO=-1114652.08, deltaELBO=5945.283 (0.06233718%), Factors=10\n", + "Iteration 8: time=0.67, ELBO=-1110790.54, deltaELBO=3861.540 (0.04048882%), Factors=10\n", + "Iteration 9: time=0.48, ELBO=-1107986.88, deltaELBO=2803.661 (0.02939681%), Factors=10\n", + "Iteration 10: time=0.43, ELBO=-1105870.62, deltaELBO=2116.257 (0.02218927%), Factors=10\n", + "Iteration 11: time=0.67, ELBO=-1104133.53, deltaELBO=1737.084 (0.01821359%), Factors=10\n", + "Iteration 12: time=0.53, ELBO=-1102637.25, deltaELBO=1496.287 (0.01568879%), Factors=10\n", + "Iteration 13: time=0.44, ELBO=-1101345.48, deltaELBO=1291.770 (0.01354440%), Factors=10\n", + "Iteration 14: time=0.44, ELBO=-1100247.70, deltaELBO=1097.775 (0.01151033%), Factors=10\n", + "Iteration 15: time=0.41, ELBO=-1099333.42, deltaELBO=914.284 (0.00958641%), Factors=10\n", + "Iteration 16: time=0.35, ELBO=-1098584.69, deltaELBO=748.723 (0.00785048%), Factors=10\n", + "Iteration 17: time=0.32, ELBO=-1097976.68, deltaELBO=608.013 (0.00637510%), Factors=10\n", + "Iteration 18: time=0.31, ELBO=-1097481.80, deltaELBO=494.882 (0.00518891%), Factors=10\n", + "Iteration 19: time=0.33, ELBO=-1097074.02, deltaELBO=407.776 (0.00427559%), Factors=10\n", + "Iteration 20: time=0.33, ELBO=-1096731.42, deltaELBO=342.603 (0.00359224%), Factors=10\n", + "Iteration 21: time=0.32, ELBO=-1096436.88, deltaELBO=294.545 (0.00308835%), Factors=10\n", + "Iteration 22: time=0.32, ELBO=-1096177.69, deltaELBO=259.183 (0.00271757%), Factors=10\n", + "Iteration 23: time=0.39, ELBO=-1095944.76, deltaELBO=232.935 (0.00244236%), Factors=10\n", + "Iteration 24: time=0.32, ELBO=-1095731.66, deltaELBO=213.103 (0.00223442%), Factors=10\n", + "Iteration 25: time=0.40, ELBO=-1095533.91, deltaELBO=197.745 (0.00207338%), Factors=10\n", + "Iteration 26: time=0.63, ELBO=-1095348.41, deltaELBO=185.500 (0.00194499%), Factors=10\n", + "Iteration 27: time=0.49, ELBO=-1095172.98, deltaELBO=175.435 (0.00183946%), Factors=10\n", + "Iteration 28: time=0.47, ELBO=-1095006.06, deltaELBO=166.918 (0.00175016%), Factors=10\n", + "Iteration 29: time=0.64, ELBO=-1094846.54, deltaELBO=159.522 (0.00167262%), Factors=10\n", + "Iteration 30: time=0.69, ELBO=-1094693.57, deltaELBO=152.961 (0.00160382%), Factors=10\n", + "Iteration 31: time=0.80, ELBO=-1094546.53, deltaELBO=147.040 (0.00154174%), Factors=10\n", + "Iteration 32: time=0.54, ELBO=-1094404.91, deltaELBO=141.627 (0.00148498%), Factors=10\n", + "Iteration 33: time=0.55, ELBO=-1094268.28, deltaELBO=136.629 (0.00143258%), Factors=10\n", + "Iteration 34: time=0.40, ELBO=-1094136.30, deltaELBO=131.981 (0.00138384%), Factors=10\n", + "Iteration 35: time=0.34, ELBO=-1094008.66, deltaELBO=127.633 (0.00133825%), Factors=10\n", + "Iteration 36: time=0.39, ELBO=-1093885.11, deltaELBO=123.549 (0.00129543%), Factors=10\n", + "Iteration 37: time=0.33, ELBO=-1093765.42, deltaELBO=119.700 (0.00125507%), Factors=10\n", + "Iteration 38: time=0.33, ELBO=-1093649.35, deltaELBO=116.062 (0.00121693%), Factors=10\n", + "Iteration 39: time=0.34, ELBO=-1093536.74, deltaELBO=112.618 (0.00118081%), Factors=10\n", + "Iteration 40: time=0.31, ELBO=-1093427.39, deltaELBO=109.349 (0.00114654%), Factors=10\n", + "Iteration 41: time=0.40, ELBO=-1093321.14, deltaELBO=106.242 (0.00111397%), Factors=10\n", + "Iteration 42: time=0.36, ELBO=-1093217.86, deltaELBO=103.286 (0.00108297%), Factors=10\n", + "Iteration 43: time=0.35, ELBO=-1093117.39, deltaELBO=100.468 (0.00105342%), Factors=10\n", + "Iteration 44: time=0.32, ELBO=-1093019.61, deltaELBO=97.779 (0.00102523%), Factors=10\n", + "Iteration 45: time=0.51, ELBO=-1092924.40, deltaELBO=95.211 (0.00099830%), Factors=10\n", + "Iteration 46: time=0.33, ELBO=-1092831.65, deltaELBO=92.755 (0.00097255%), Factors=10\n", + "Iteration 47: time=0.31, ELBO=-1092741.24, deltaELBO=90.405 (0.00094791%), Factors=10\n", + "Iteration 48: time=0.30, ELBO=-1092653.09, deltaELBO=88.153 (0.00092430%), Factors=10\n", + "Iteration 49: time=0.33, ELBO=-1092567.09, deltaELBO=85.994 (0.00090166%), Factors=10\n", + "Iteration 50: time=0.46, ELBO=-1092483.17, deltaELBO=83.923 (0.00087994%), Factors=10\n", + "Iteration 51: time=0.32, ELBO=-1092401.24, deltaELBO=81.933 (0.00085908%), Factors=10\n", + "Iteration 52: time=0.32, ELBO=-1092321.22, deltaELBO=80.022 (0.00083904%), Factors=10\n", + "Iteration 53: time=0.32, ELBO=-1092243.03, deltaELBO=78.183 (0.00081977%), Factors=10\n", + "Iteration 54: time=0.32, ELBO=-1092166.62, deltaELBO=76.414 (0.00080122%), Factors=10\n", + "Iteration 55: time=0.32, ELBO=-1092091.91, deltaELBO=74.711 (0.00078336%), Factors=10\n", + "Iteration 56: time=0.34, ELBO=-1092018.84, deltaELBO=73.070 (0.00076615%), Factors=10\n", + "Iteration 57: time=0.32, ELBO=-1091947.35, deltaELBO=71.487 (0.00074956%), Factors=10\n", + "Iteration 58: time=0.32, ELBO=-1091877.39, deltaELBO=69.961 (0.00073355%), Factors=10\n", + "Iteration 59: time=0.32, ELBO=-1091808.90, deltaELBO=68.488 (0.00071811%), Factors=10\n", + "Iteration 60: time=0.32, ELBO=-1091741.84, deltaELBO=67.065 (0.00070319%), Factors=10\n", + "Iteration 61: time=0.32, ELBO=-1091676.14, deltaELBO=65.691 (0.00068878%), Factors=10\n", + "Iteration 62: time=0.32, ELBO=-1091611.78, deltaELBO=64.362 (0.00067485%), Factors=10\n", + "Iteration 63: time=0.32, ELBO=-1091548.70, deltaELBO=63.078 (0.00066138%), Factors=10\n", + "Iteration 64: time=0.32, ELBO=-1091486.87, deltaELBO=61.834 (0.00064834%), Factors=10\n", + "Iteration 65: time=0.33, ELBO=-1091426.24, deltaELBO=60.631 (0.00063573%), Factors=10\n", + "Iteration 66: time=0.40, ELBO=-1091366.77, deltaELBO=59.466 (0.00062351%), Factors=10\n", + "Iteration 67: time=0.44, ELBO=-1091308.44, deltaELBO=58.337 (0.00061167%), Factors=10\n", + "Iteration 68: time=0.32, ELBO=-1091251.19, deltaELBO=57.243 (0.00060020%), Factors=10\n", + "Iteration 69: time=0.37, ELBO=-1091195.01, deltaELBO=56.183 (0.00058908%), Factors=10\n", + "Iteration 70: time=0.36, ELBO=-1091139.86, deltaELBO=55.154 (0.00057830%), Factors=10\n", + "Iteration 71: time=0.54, ELBO=-1091085.70, deltaELBO=54.156 (0.00056783%), Factors=10\n", + "Iteration 72: time=0.57, ELBO=-1091032.51, deltaELBO=53.187 (0.00055767%), Factors=10\n", + "Iteration 73: time=0.71, ELBO=-1090980.27, deltaELBO=52.246 (0.00054781%), Factors=10\n", + "Iteration 74: time=0.52, ELBO=-1090928.93, deltaELBO=51.332 (0.00053823%), Factors=10\n", + "Iteration 75: time=0.53, ELBO=-1090878.49, deltaELBO=50.445 (0.00052892%), Factors=10\n", + "Iteration 76: time=0.56, ELBO=-1090828.91, deltaELBO=49.582 (0.00051987%), Factors=10\n", + "Iteration 77: time=0.53, ELBO=-1090780.17, deltaELBO=48.743 (0.00051108%), Factors=10\n", + "Iteration 78: time=0.59, ELBO=-1090732.24, deltaELBO=47.927 (0.00050253%), Factors=10\n", + "Iteration 79: time=0.58, ELBO=-1090685.10, deltaELBO=47.134 (0.00049421%), Factors=10\n", + "Iteration 80: time=0.51, ELBO=-1090638.74, deltaELBO=46.362 (0.00048611%), Factors=10\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -10192024.40 \n", + "\n", + "Iteration 1: time=0.44, ELBO=-1275674.10, deltaELBO=8916350.294 (87.48360429%), Factors=11\n", + "Iteration 2: time=0.36, ELBO=-1158772.43, deltaELBO=116901.676 (1.14699172%), Factors=11\n", + "Iteration 3: time=0.55, ELBO=-1141754.49, deltaELBO=17017.939 (0.16697310%), Factors=11\n", + "Iteration 4: time=0.61, ELBO=-1121944.39, deltaELBO=19810.099 (0.19436864%), Factors=11\n", + "Iteration 5: time=0.74, ELBO=-1103706.66, deltaELBO=18237.731 (0.17894121%), Factors=11\n", + "Iteration 6: time=0.41, ELBO=-1094358.19, deltaELBO=9348.468 (0.09172337%), Factors=11\n", + "Iteration 7: time=0.38, ELBO=-1089581.13, deltaELBO=4777.058 (0.04687055%), Factors=11\n", + "Iteration 8: time=0.34, ELBO=-1086483.91, deltaELBO=3097.220 (0.03038867%), Factors=11\n", + "Iteration 9: time=0.39, ELBO=-1084264.09, deltaELBO=2219.820 (0.02177997%), Factors=11\n", + "Iteration 10: time=0.36, ELBO=-1082585.89, deltaELBO=1678.202 (0.01646584%), Factors=11\n", + "Iteration 11: time=0.37, ELBO=-1081224.16, deltaELBO=1361.732 (0.01336076%), Factors=11\n", + "Iteration 12: time=0.34, ELBO=-1080077.07, deltaELBO=1147.090 (0.01125478%), Factors=11\n", + "Iteration 13: time=0.36, ELBO=-1079100.39, deltaELBO=976.675 (0.00958274%), Factors=11\n", + "Iteration 14: time=0.37, ELBO=-1078264.39, deltaELBO=836.007 (0.00820256%), Factors=11\n", + "Iteration 15: time=0.57, ELBO=-1077542.10, deltaELBO=722.283 (0.00708675%), Factors=11\n", + "Iteration 16: time=0.33, ELBO=-1076908.19, deltaELBO=633.915 (0.00621972%), Factors=11\n", + "Iteration 17: time=0.35, ELBO=-1076340.12, deltaELBO=568.072 (0.00557369%), Factors=11\n", + "Iteration 18: time=0.35, ELBO=-1075819.35, deltaELBO=520.766 (0.00510955%), Factors=11\n", + "Iteration 19: time=0.38, ELBO=-1075331.71, deltaELBO=487.644 (0.00478456%), Factors=11\n", + "Iteration 20: time=0.39, ELBO=-1074867.04, deltaELBO=464.664 (0.00455910%), Factors=11\n", + "Iteration 21: time=0.39, ELBO=-1074418.59, deltaELBO=448.450 (0.00440001%), Factors=11\n", + "Iteration 22: time=0.36, ELBO=-1073982.23, deltaELBO=436.362 (0.00428141%), Factors=11\n", + "Iteration 23: time=0.37, ELBO=-1073555.80, deltaELBO=426.426 (0.00418392%), Factors=11\n", + "Iteration 24: time=0.38, ELBO=-1073138.61, deltaELBO=417.194 (0.00409334%), Factors=11\n", + "Iteration 25: time=0.44, ELBO=-1072731.01, deltaELBO=407.598 (0.00399919%), Factors=11\n", + "Iteration 26: time=0.43, ELBO=-1072334.21, deltaELBO=396.806 (0.00389330%), Factors=11\n", + "Iteration 27: time=0.40, ELBO=-1071950.10, deltaELBO=384.109 (0.00376872%), Factors=11\n", + "Iteration 28: time=0.36, ELBO=-1071581.20, deltaELBO=368.897 (0.00361947%), Factors=11\n", + "Iteration 29: time=0.36, ELBO=-1071230.45, deltaELBO=350.754 (0.00344146%), Factors=11\n", + "Iteration 30: time=0.39, ELBO=-1070900.79, deltaELBO=329.656 (0.00323445%), Factors=11\n", + "Iteration 31: time=0.42, ELBO=-1070594.64, deltaELBO=306.152 (0.00300384%), Factors=11\n", + "Iteration 32: time=0.42, ELBO=-1070313.27, deltaELBO=281.363 (0.00276062%), Factors=11\n", + "Iteration 33: time=0.40, ELBO=-1070056.57, deltaELBO=256.708 (0.00251872%), Factors=11\n", + "Iteration 34: time=0.42, ELBO=-1069823.07, deltaELBO=233.494 (0.00229094%), Factors=11\n", + "Iteration 35: time=0.35, ELBO=-1069610.48, deltaELBO=212.589 (0.00208584%), Factors=11\n", + "Iteration 36: time=0.49, ELBO=-1069416.14, deltaELBO=194.342 (0.00190680%), Factors=11\n", + "Iteration 37: time=0.35, ELBO=-1069237.45, deltaELBO=178.692 (0.00175326%), Factors=11\n", + "Iteration 38: time=0.37, ELBO=-1069072.09, deltaELBO=165.358 (0.00162243%), Factors=11\n", + "Iteration 39: time=0.56, ELBO=-1068918.10, deltaELBO=153.988 (0.00151087%), Factors=11\n", + "Iteration 40: time=0.40, ELBO=-1068773.86, deltaELBO=144.248 (0.00141531%), Factors=11\n", + "Iteration 41: time=0.39, ELBO=-1068638.00, deltaELBO=135.854 (0.00133294%), Factors=11\n", + "Iteration 42: time=0.36, ELBO=-1068509.43, deltaELBO=128.573 (0.00126151%), Factors=11\n", + "Iteration 43: time=0.37, ELBO=-1068387.21, deltaELBO=122.219 (0.00119916%), Factors=11\n", + "Iteration 44: time=0.49, ELBO=-1068270.57, deltaELBO=116.640 (0.00114442%), Factors=11\n", + "Iteration 45: time=0.48, ELBO=-1068158.86, deltaELBO=111.711 (0.00109607%), Factors=11\n", + "Iteration 46: time=0.40, ELBO=-1068051.53, deltaELBO=107.331 (0.00105309%), Factors=11\n", + "Iteration 47: time=0.62, ELBO=-1067948.12, deltaELBO=103.412 (0.00101463%), Factors=11\n", + "Iteration 48: time=0.74, ELBO=-1067848.23, deltaELBO=99.882 (0.00098000%), Factors=11\n", + "Iteration 49: time=0.54, ELBO=-1067751.55, deltaELBO=96.683 (0.00094861%), Factors=11\n", + "Iteration 50: time=0.60, ELBO=-1067657.79, deltaELBO=93.763 (0.00091997%), Factors=11\n", + "Iteration 51: time=0.72, ELBO=-1067566.71, deltaELBO=91.082 (0.00089366%), Factors=11\n", + "Iteration 52: time=0.58, ELBO=-1067478.10, deltaELBO=88.605 (0.00086936%), Factors=11\n", + "Iteration 53: time=0.61, ELBO=-1067391.80, deltaELBO=86.304 (0.00084678%), Factors=11\n", + "Iteration 54: time=0.63, ELBO=-1067307.64, deltaELBO=84.154 (0.00082569%), Factors=11\n", + "Iteration 55: time=0.72, ELBO=-1067225.51, deltaELBO=82.137 (0.00080590%), Factors=11\n", + "Iteration 56: time=0.70, ELBO=-1067145.27, deltaELBO=80.236 (0.00078724%), Factors=11\n", + "Iteration 57: time=0.89, ELBO=-1067066.83, deltaELBO=78.437 (0.00076959%), Factors=11\n", + "Iteration 58: time=0.53, ELBO=-1066990.10, deltaELBO=76.728 (0.00075282%), Factors=11\n", + "Iteration 59: time=0.54, ELBO=-1066915.00, deltaELBO=75.100 (0.00073685%), Factors=11\n", + "Iteration 60: time=0.51, ELBO=-1066841.46, deltaELBO=73.544 (0.00072158%), Factors=11\n", + "Iteration 61: time=0.83, ELBO=-1066769.41, deltaELBO=72.053 (0.00070695%), Factors=11\n", + "Iteration 62: time=0.56, ELBO=-1066698.79, deltaELBO=70.620 (0.00069290%), Factors=11\n", + "Iteration 63: time=0.56, ELBO=-1066629.55, deltaELBO=69.240 (0.00067936%), Factors=11\n", + "Iteration 64: time=0.51, ELBO=-1066561.64, deltaELBO=67.908 (0.00066629%), Factors=11\n", + "Iteration 65: time=0.54, ELBO=-1066495.02, deltaELBO=66.620 (0.00065365%), Factors=11\n", + "Iteration 66: time=0.48, ELBO=-1066429.65, deltaELBO=65.372 (0.00064140%), Factors=11\n", + "Iteration 67: time=0.52, ELBO=-1066365.49, deltaELBO=64.160 (0.00062951%), Factors=11\n", + "Iteration 68: time=0.53, ELBO=-1066302.50, deltaELBO=62.982 (0.00061795%), Factors=11\n", + "Iteration 69: time=0.47, ELBO=-1066240.67, deltaELBO=61.834 (0.00060669%), Factors=11\n", + "Iteration 70: time=0.48, ELBO=-1066179.96, deltaELBO=60.714 (0.00059570%), Factors=11\n", + "Iteration 71: time=0.47, ELBO=-1066120.34, deltaELBO=59.621 (0.00058497%), Factors=11\n", + "Iteration 72: time=0.47, ELBO=-1066061.78, deltaELBO=58.552 (0.00057448%), Factors=11\n", + "Iteration 73: time=0.48, ELBO=-1066004.28, deltaELBO=57.505 (0.00056422%), Factors=11\n", + "Iteration 74: time=0.47, ELBO=-1065947.80, deltaELBO=56.480 (0.00055416%), Factors=11\n", + "Iteration 75: time=0.47, ELBO=-1065892.33, deltaELBO=55.475 (0.00054429%), Factors=11\n", + "Iteration 76: time=0.47, ELBO=-1065837.84, deltaELBO=54.488 (0.00053462%), Factors=11\n", + "Iteration 77: time=0.47, ELBO=-1065784.32, deltaELBO=53.521 (0.00052512%), Factors=11\n", + "Iteration 78: time=0.47, ELBO=-1065731.75, deltaELBO=52.570 (0.00051580%), Factors=11\n", + "Iteration 79: time=0.46, ELBO=-1065680.11, deltaELBO=51.637 (0.00050665%), Factors=11\n", + "Iteration 80: time=0.47, ELBO=-1065629.39, deltaELBO=50.721 (0.00049766%), Factors=11\n", + "Iteration 81: time=0.47, ELBO=-1065579.57, deltaELBO=49.822 (0.00048883%), Factors=11\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -10847689.95 \n", + "\n", + "Iteration 1: time=0.56, ELBO=-1348351.06, deltaELBO=9499338.894 (87.57015488%), Factors=12\n", + "Iteration 2: time=0.65, ELBO=-1246775.46, deltaELBO=101575.603 (0.93638004%), Factors=12\n", + "Iteration 3: time=0.63, ELBO=-1214550.06, deltaELBO=32225.401 (0.29707155%), Factors=12\n", + "Iteration 4: time=0.57, ELBO=-1114159.63, deltaELBO=100390.428 (0.92545444%), Factors=12\n", + "Iteration 5: time=0.55, ELBO=-1085485.04, deltaELBO=28674.585 (0.26433817%), Factors=12\n", + "Iteration 6: time=0.63, ELBO=-1075312.51, deltaELBO=10172.533 (0.09377603%), Factors=12\n", + "Iteration 7: time=0.55, ELBO=-1069134.19, deltaELBO=6178.322 (0.05695518%), Factors=12\n", + "Iteration 8: time=0.56, ELBO=-1064835.88, deltaELBO=4298.310 (0.03962420%), Factors=12\n", + "Iteration 9: time=0.58, ELBO=-1061688.40, deltaELBO=3147.484 (0.02901524%), Factors=12\n", + "Iteration 10: time=0.51, ELBO=-1059488.50, deltaELBO=2199.894 (0.02027984%), Factors=12\n", + "Iteration 11: time=0.50, ELBO=-1058001.44, deltaELBO=1487.065 (0.01370859%), Factors=12\n", + "Iteration 12: time=0.51, ELBO=-1056933.55, deltaELBO=1067.888 (0.00984438%), Factors=12\n", + "Iteration 13: time=0.50, ELBO=-1056075.12, deltaELBO=858.428 (0.00791346%), Factors=12\n", + "Iteration 14: time=0.50, ELBO=-1055317.91, deltaELBO=757.211 (0.00698039%), Factors=12\n", + "Iteration 15: time=0.50, ELBO=-1054608.66, deltaELBO=709.252 (0.00653828%), Factors=12\n", + "Iteration 16: time=0.50, ELBO=-1053919.14, deltaELBO=689.518 (0.00635636%), Factors=12\n", + "Iteration 17: time=0.54, ELBO=-1053233.70, deltaELBO=685.439 (0.00631875%), Factors=12\n", + "Iteration 18: time=0.57, ELBO=-1052544.35, deltaELBO=689.355 (0.00635485%), Factors=12\n", + "Iteration 19: time=0.53, ELBO=-1051848.91, deltaELBO=695.441 (0.00641096%), Factors=12\n", + "Iteration 20: time=0.53, ELBO=-1051150.50, deltaELBO=698.408 (0.00643831%), Factors=12\n", + "Iteration 21: time=0.56, ELBO=-1050457.33, deltaELBO=693.163 (0.00638996%), Factors=12\n", + "Iteration 22: time=0.53, ELBO=-1049782.08, deltaELBO=675.250 (0.00622483%), Factors=12\n", + "Iteration 23: time=0.58, ELBO=-1049140.13, deltaELBO=641.952 (0.00591787%), Factors=12\n", + "Iteration 24: time=0.57, ELBO=-1048546.58, deltaELBO=593.549 (0.00547167%), Factors=12\n", + "Iteration 25: time=0.56, ELBO=-1048012.81, deltaELBO=533.771 (0.00492059%), Factors=12\n", + "Iteration 26: time=0.51, ELBO=-1047544.05, deltaELBO=468.758 (0.00432127%), Factors=12\n", + "Iteration 27: time=0.48, ELBO=-1047139.07, deltaELBO=404.986 (0.00373339%), Factors=12\n", + "Iteration 28: time=0.45, ELBO=-1046791.67, deltaELBO=347.392 (0.00320245%), Factors=12\n", + "Iteration 29: time=0.54, ELBO=-1046493.08, deltaELBO=298.599 (0.00275265%), Factors=12\n", + "Iteration 30: time=0.41, ELBO=-1046233.92, deltaELBO=259.154 (0.00238903%), Factors=12\n", + "Iteration 31: time=0.42, ELBO=-1046005.68, deltaELBO=228.244 (0.00210408%), Factors=12\n", + "Iteration 32: time=0.39, ELBO=-1045801.26, deltaELBO=204.420 (0.00188446%), Factors=12\n", + "Iteration 33: time=0.48, ELBO=-1045615.13, deltaELBO=186.124 (0.00171579%), Factors=12\n", + "Iteration 34: time=0.39, ELBO=-1045443.17, deltaELBO=171.965 (0.00158527%), Factors=12\n", + "Iteration 35: time=0.46, ELBO=-1045282.34, deltaELBO=160.829 (0.00148261%), Factors=12\n", + "Iteration 36: time=0.53, ELBO=-1045130.46, deltaELBO=151.876 (0.00140008%), Factors=12\n", + "Iteration 37: time=0.60, ELBO=-1044985.96, deltaELBO=144.500 (0.00133208%), Factors=12\n", + "Iteration 38: time=0.63, ELBO=-1044847.69, deltaELBO=138.272 (0.00127467%), Factors=12\n", + "Iteration 39: time=0.52, ELBO=-1044714.80, deltaELBO=132.889 (0.00122504%), Factors=12\n", + "Iteration 40: time=0.41, ELBO=-1044586.66, deltaELBO=128.140 (0.00118127%), Factors=12\n", + "Iteration 41: time=0.37, ELBO=-1044462.79, deltaELBO=123.874 (0.00114194%), Factors=12\n", + "Iteration 42: time=0.36, ELBO=-1044342.81, deltaELBO=119.982 (0.00110606%), Factors=12\n", + "Iteration 43: time=0.36, ELBO=-1044226.42, deltaELBO=116.384 (0.00107289%), Factors=12\n", + "Iteration 44: time=0.58, ELBO=-1044113.40, deltaELBO=113.018 (0.00104187%), Factors=12\n", + "Iteration 45: time=0.46, ELBO=-1044003.56, deltaELBO=109.840 (0.00101257%), Factors=12\n", + "Iteration 46: time=0.36, ELBO=-1043896.75, deltaELBO=106.814 (0.00098467%), Factors=12\n", + "Iteration 47: time=0.36, ELBO=-1043792.84, deltaELBO=103.913 (0.00095793%), Factors=12\n", + "Iteration 48: time=0.39, ELBO=-1043691.72, deltaELBO=101.116 (0.00093215%), Factors=12\n", + "Iteration 49: time=0.39, ELBO=-1043593.31, deltaELBO=98.409 (0.00090719%), Factors=12\n", + "Iteration 50: time=0.35, ELBO=-1043497.53, deltaELBO=95.780 (0.00088296%), Factors=12\n", + "Iteration 51: time=0.57, ELBO=-1043404.31, deltaELBO=93.223 (0.00085938%), Factors=12\n", + "Iteration 52: time=0.38, ELBO=-1043313.57, deltaELBO=90.733 (0.00083643%), Factors=12\n", + "Iteration 53: time=0.39, ELBO=-1043225.27, deltaELBO=88.309 (0.00081408%), Factors=12\n", + "Iteration 54: time=0.36, ELBO=-1043139.32, deltaELBO=85.949 (0.00079233%), Factors=12\n", + "Iteration 55: time=0.38, ELBO=-1043055.66, deltaELBO=83.655 (0.00077118%), Factors=12\n", + "Iteration 56: time=0.37, ELBO=-1042974.23, deltaELBO=81.428 (0.00075065%), Factors=12\n", + "Iteration 57: time=0.36, ELBO=-1042894.96, deltaELBO=79.270 (0.00073076%), Factors=12\n", + "Iteration 58: time=0.37, ELBO=-1042817.78, deltaELBO=77.183 (0.00071151%), Factors=12\n", + "Iteration 59: time=0.39, ELBO=-1042742.61, deltaELBO=75.167 (0.00069293%), Factors=12\n", + "Iteration 60: time=0.35, ELBO=-1042669.39, deltaELBO=73.223 (0.00067501%), Factors=12\n", + "Iteration 61: time=0.36, ELBO=-1042598.04, deltaELBO=71.351 (0.00065776%), Factors=12\n", + "Iteration 62: time=0.38, ELBO=-1042528.49, deltaELBO=69.552 (0.00064116%), Factors=12\n", + "Iteration 63: time=0.37, ELBO=-1042460.66, deltaELBO=67.822 (0.00062522%), Factors=12\n", + "Iteration 64: time=0.39, ELBO=-1042394.50, deltaELBO=66.162 (0.00060992%), Factors=12\n", + "Iteration 65: time=0.36, ELBO=-1042329.93, deltaELBO=64.569 (0.00059523%), Factors=12\n", + "Iteration 66: time=0.67, ELBO=-1042266.89, deltaELBO=63.040 (0.00058114%), Factors=12\n", + "Iteration 67: time=0.63, ELBO=-1042205.32, deltaELBO=61.573 (0.00056762%), Factors=12\n", + "Iteration 68: time=0.63, ELBO=-1042145.15, deltaELBO=60.166 (0.00055464%), Factors=12\n", + "Iteration 69: time=0.67, ELBO=-1042086.34, deltaELBO=58.814 (0.00054218%), Factors=12\n", + "Iteration 70: time=0.60, ELBO=-1042028.82, deltaELBO=57.515 (0.00053021%), Factors=12\n", + "Iteration 71: time=0.65, ELBO=-1041972.56, deltaELBO=56.268 (0.00051871%), Factors=12\n", + "Iteration 72: time=0.60, ELBO=-1041917.49, deltaELBO=55.068 (0.00050764%), Factors=12\n", + "Iteration 73: time=0.62, ELBO=-1041863.58, deltaELBO=53.913 (0.00049700%), Factors=12\n", + "Iteration 74: time=0.67, ELBO=-1041810.77, deltaELBO=52.801 (0.00048675%), Factors=12\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -11497424.02 \n", + "\n", + "Iteration 1: time=0.63, ELBO=-1245003.47, deltaELBO=10252420.551 (89.17145729%), Factors=13\n", + "Iteration 2: time=0.47, ELBO=-1094697.17, deltaELBO=150306.297 (1.30730411%), Factors=13\n", + "Iteration 3: time=0.43, ELBO=-1078353.30, deltaELBO=16343.869 (0.14215244%), Factors=13\n", + "Iteration 4: time=0.41, ELBO=-1069597.79, deltaELBO=8755.516 (0.07615198%), Factors=13\n", + "Iteration 5: time=0.45, ELBO=-1062300.17, deltaELBO=7297.614 (0.06347173%), Factors=13\n", + "Iteration 6: time=0.40, ELBO=-1055840.81, deltaELBO=6459.368 (0.05618100%), Factors=13\n", + "Iteration 7: time=0.43, ELBO=-1050180.84, deltaELBO=5659.966 (0.04922813%), Factors=13\n", + "Iteration 8: time=0.44, ELBO=-1045201.52, deltaELBO=4979.319 (0.04330813%), Factors=13\n", + "Iteration 9: time=0.42, ELBO=-1040859.66, deltaELBO=4341.863 (0.03776379%), Factors=13\n", + "Iteration 10: time=0.39, ELBO=-1037204.84, deltaELBO=3654.820 (0.03178816%), Factors=13\n", + "Iteration 11: time=0.49, ELBO=-1034308.92, deltaELBO=2895.920 (0.02518755%), Factors=13\n", + "Iteration 12: time=0.50, ELBO=-1032171.56, deltaELBO=2137.358 (0.01858989%), Factors=13\n", + "Iteration 13: time=0.52, ELBO=-1030670.10, deltaELBO=1501.465 (0.01305914%), Factors=13\n", + "Iteration 14: time=0.58, ELBO=-1029617.68, deltaELBO=1052.415 (0.00915349%), Factors=13\n", + "Iteration 15: time=0.46, ELBO=-1028848.31, deltaELBO=769.375 (0.00669172%), Factors=13\n", + "Iteration 16: time=0.43, ELBO=-1028249.05, deltaELBO=599.256 (0.00521209%), Factors=13\n", + "Iteration 17: time=0.50, ELBO=-1027752.30, deltaELBO=496.755 (0.00432058%), Factors=13\n", + "Iteration 18: time=0.47, ELBO=-1027319.35, deltaELBO=432.945 (0.00376558%), Factors=13\n", + "Iteration 19: time=0.45, ELBO=-1026928.00, deltaELBO=391.349 (0.00340380%), Factors=13\n", + "Iteration 20: time=0.42, ELBO=-1026565.04, deltaELBO=362.958 (0.00315686%), Factors=13\n", + "Iteration 21: time=0.50, ELBO=-1026222.17, deltaELBO=342.875 (0.00298219%), Factors=13\n", + "Iteration 22: time=0.49, ELBO=-1025893.76, deltaELBO=328.408 (0.00285637%), Factors=13\n", + "Iteration 23: time=0.44, ELBO=-1025575.72, deltaELBO=318.040 (0.00276619%), Factors=13\n", + "Iteration 24: time=0.42, ELBO=-1025264.85, deltaELBO=310.874 (0.00270386%), Factors=13\n", + "Iteration 25: time=0.46, ELBO=-1024958.52, deltaELBO=306.328 (0.00266432%), Factors=13\n", + "Iteration 26: time=0.43, ELBO=-1024654.56, deltaELBO=303.960 (0.00264372%), Factors=13\n", + "Iteration 27: time=0.39, ELBO=-1024351.20, deltaELBO=303.357 (0.00263848%), Factors=13\n", + "Iteration 28: time=0.46, ELBO=-1024047.14, deltaELBO=304.060 (0.00264459%), Factors=13\n", + "Iteration 29: time=0.43, ELBO=-1023741.62, deltaELBO=305.521 (0.00265730%), Factors=13\n", + "Iteration 30: time=0.38, ELBO=-1023434.52, deltaELBO=307.098 (0.00267101%), Factors=13\n", + "Iteration 31: time=0.39, ELBO=-1023126.45, deltaELBO=308.075 (0.00267951%), Factors=13\n", + "Iteration 32: time=0.38, ELBO=-1022818.71, deltaELBO=307.739 (0.00267659%), Factors=13\n", + "Iteration 33: time=0.38, ELBO=-1022513.23, deltaELBO=305.477 (0.00265691%), Factors=13\n", + "Iteration 34: time=0.49, ELBO=-1022212.35, deltaELBO=300.882 (0.00261695%), Factors=13\n", + "Iteration 35: time=0.41, ELBO=-1021918.52, deltaELBO=293.829 (0.00255561%), Factors=13\n", + "Iteration 36: time=0.39, ELBO=-1021634.04, deltaELBO=284.477 (0.00247427%), Factors=13\n", + "Iteration 37: time=0.40, ELBO=-1021360.84, deltaELBO=273.202 (0.00237621%), Factors=13\n", + "Iteration 38: time=0.43, ELBO=-1021100.36, deltaELBO=260.477 (0.00226552%), Factors=13\n", + "Iteration 39: time=0.38, ELBO=-1020853.63, deltaELBO=246.737 (0.00214602%), Factors=13\n", + "Iteration 40: time=0.46, ELBO=-1020621.33, deltaELBO=232.297 (0.00202043%), Factors=13\n", + "Iteration 41: time=0.39, ELBO=-1020403.99, deltaELBO=217.344 (0.00189037%), Factors=13\n", + "Iteration 42: time=0.46, ELBO=-1020201.98, deltaELBO=202.003 (0.00175694%), Factors=13\n", + "Iteration 43: time=0.47, ELBO=-1020015.53, deltaELBO=186.456 (0.00162172%), Factors=13\n", + "Iteration 44: time=0.59, ELBO=-1019844.51, deltaELBO=171.018 (0.00148745%), Factors=13\n", + "Iteration 45: time=0.58, ELBO=-1019688.36, deltaELBO=156.147 (0.00135810%), Factors=13\n", + "Iteration 46: time=0.55, ELBO=-1019546.01, deltaELBO=142.348 (0.00123808%), Factors=13\n", + "Iteration 47: time=0.39, ELBO=-1019415.98, deltaELBO=130.038 (0.00113102%), Factors=13\n", + "Iteration 48: time=0.41, ELBO=-1019296.53, deltaELBO=119.449 (0.00103892%), Factors=13\n", + "Iteration 49: time=0.46, ELBO=-1019185.93, deltaELBO=110.601 (0.00096196%), Factors=13\n", + "Iteration 50: time=0.49, ELBO=-1019082.58, deltaELBO=103.347 (0.00089887%), Factors=13\n", + "Iteration 51: time=0.49, ELBO=-1018985.13, deltaELBO=97.448 (0.00084756%), Factors=13\n", + "Iteration 52: time=0.44, ELBO=-1018892.49, deltaELBO=92.642 (0.00080576%), Factors=13\n", + "Iteration 53: time=0.46, ELBO=-1018803.80, deltaELBO=88.685 (0.00077135%), Factors=13\n", + "Iteration 54: time=0.45, ELBO=-1018718.43, deltaELBO=85.374 (0.00074255%), Factors=13\n", + "Iteration 55: time=0.45, ELBO=-1018635.88, deltaELBO=82.546 (0.00071796%), Factors=13\n", + "Iteration 56: time=0.45, ELBO=-1018555.80, deltaELBO=80.083 (0.00069653%), Factors=13\n", + "Iteration 57: time=0.45, ELBO=-1018477.91, deltaELBO=77.895 (0.00067750%), Factors=13\n", + "Iteration 58: time=0.46, ELBO=-1018401.99, deltaELBO=75.919 (0.00066031%), Factors=13\n", + "Iteration 59: time=0.44, ELBO=-1018327.88, deltaELBO=74.107 (0.00064455%), Factors=13\n", + "Iteration 60: time=0.43, ELBO=-1018255.45, deltaELBO=72.428 (0.00062995%), Factors=13\n", + "Iteration 61: time=0.43, ELBO=-1018184.60, deltaELBO=70.857 (0.00061628%), Factors=13\n", + "Iteration 62: time=0.42, ELBO=-1018115.22, deltaELBO=69.376 (0.00060340%), Factors=13\n", + "Iteration 63: time=0.38, ELBO=-1018047.25, deltaELBO=67.973 (0.00059120%), Factors=13\n", + "Iteration 64: time=0.38, ELBO=-1017980.61, deltaELBO=66.637 (0.00057959%), Factors=13\n", + "Iteration 65: time=0.39, ELBO=-1017915.25, deltaELBO=65.362 (0.00056849%), Factors=13\n", + "Iteration 66: time=0.39, ELBO=-1017851.11, deltaELBO=64.140 (0.00055786%), Factors=13\n", + "Iteration 67: time=0.43, ELBO=-1017788.14, deltaELBO=62.967 (0.00054766%), Factors=13\n", + "Iteration 68: time=0.44, ELBO=-1017726.30, deltaELBO=61.839 (0.00053785%), Factors=13\n", + "Iteration 69: time=0.48, ELBO=-1017665.55, deltaELBO=60.752 (0.00052840%), Factors=13\n", + "Iteration 70: time=0.41, ELBO=-1017605.85, deltaELBO=59.704 (0.00051928%), Factors=13\n", + "Iteration 71: time=0.41, ELBO=-1017547.15, deltaELBO=58.692 (0.00051048%), Factors=13\n", + "Iteration 72: time=0.61, ELBO=-1017489.44, deltaELBO=57.713 (0.00050196%), Factors=13\n", + "Iteration 73: time=0.68, ELBO=-1017432.68, deltaELBO=56.766 (0.00049373%), Factors=13\n", + "Iteration 74: time=0.53, ELBO=-1017376.83, deltaELBO=55.848 (0.00048575%), Factors=13\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -12133355.00 \n", + "\n", + "Iteration 1: time=0.44, ELBO=-1262182.20, deltaELBO=10871172.804 (89.59741806%), Factors=14\n", + "Iteration 2: time=0.44, ELBO=-1109537.71, deltaELBO=152644.487 (1.25805672%), Factors=14\n", + "Iteration 3: time=0.42, ELBO=-1083550.97, deltaELBO=25986.742 (0.21417606%), Factors=14\n", + "Iteration 4: time=0.48, ELBO=-1050628.55, deltaELBO=32922.420 (0.27133815%), Factors=14\n", + "Iteration 5: time=0.43, ELBO=-1030062.82, deltaELBO=20565.726 (0.16949744%), Factors=14\n", + "Iteration 6: time=0.42, ELBO=-1022132.84, deltaELBO=7929.976 (0.06535683%), Factors=14\n", + "Iteration 7: time=0.46, ELBO=-1017885.52, deltaELBO=4247.327 (0.03500538%), Factors=14\n", + "Iteration 8: time=0.47, ELBO=-1015245.19, deltaELBO=2640.322 (0.02176086%), Factors=14\n", + "Iteration 9: time=0.42, ELBO=-1013399.01, deltaELBO=1846.183 (0.01521576%), Factors=14\n", + "Iteration 10: time=0.45, ELBO=-1011953.67, deltaELBO=1445.343 (0.01191215%), Factors=14\n", + "Iteration 11: time=0.45, ELBO=-1010738.57, deltaELBO=1215.103 (0.01001456%), Factors=14\n", + "Iteration 12: time=0.46, ELBO=-1009679.23, deltaELBO=1059.335 (0.00873077%), Factors=14\n", + "Iteration 13: time=0.45, ELBO=-1008740.85, deltaELBO=938.379 (0.00773388%), Factors=14\n", + "Iteration 14: time=0.44, ELBO=-1007904.15, deltaELBO=836.700 (0.00689587%), Factors=14\n", + "Iteration 15: time=0.47, ELBO=-1007155.34, deltaELBO=748.815 (0.00617154%), Factors=14\n", + "Iteration 16: time=0.54, ELBO=-1006482.35, deltaELBO=672.983 (0.00554655%), Factors=14\n", + "Iteration 17: time=0.42, ELBO=-1005873.94, deltaELBO=608.416 (0.00501441%), Factors=14\n", + "Iteration 18: time=0.61, ELBO=-1005319.74, deltaELBO=554.195 (0.00456753%), Factors=14\n", + "Iteration 19: time=0.50, ELBO=-1004810.77, deltaELBO=508.977 (0.00419486%), Factors=14\n", + "Iteration 20: time=0.46, ELBO=-1004339.73, deltaELBO=471.038 (0.00388218%), Factors=14\n", + "Iteration 21: time=0.88, ELBO=-1003901.33, deltaELBO=438.403 (0.00361320%), Factors=14\n", + "Iteration 22: time=0.63, ELBO=-1003492.31, deltaELBO=409.013 (0.00337098%), Factors=14\n", + "Iteration 23: time=0.49, ELBO=-1003111.34, deltaELBO=380.976 (0.00313990%), Factors=14\n", + "Iteration 24: time=0.67, ELBO=-1002758.45, deltaELBO=352.888 (0.00290841%), Factors=14\n", + "Iteration 25: time=0.42, ELBO=-1002434.28, deltaELBO=324.169 (0.00267171%), Factors=14\n", + "Iteration 26: time=0.58, ELBO=-1002139.08, deltaELBO=295.203 (0.00243298%), Factors=14\n", + "Iteration 27: time=0.47, ELBO=-1001871.96, deltaELBO=267.118 (0.00220152%), Factors=14\n", + "Iteration 28: time=0.45, ELBO=-1001630.71, deltaELBO=241.249 (0.00198831%), Factors=14\n", + "Iteration 29: time=0.41, ELBO=-1001412.13, deltaELBO=218.577 (0.00180145%), Factors=14\n", + "Iteration 30: time=0.46, ELBO=-1001212.67, deltaELBO=199.466 (0.00164395%), Factors=14\n", + "Iteration 31: time=0.42, ELBO=-1001028.93, deltaELBO=183.734 (0.00151429%), Factors=14\n", + "Iteration 32: time=0.49, ELBO=-1000858.05, deltaELBO=170.886 (0.00140840%), Factors=14\n", + "Iteration 33: time=0.55, ELBO=-1000697.70, deltaELBO=160.348 (0.00132155%), Factors=14\n", + "Iteration 34: time=0.61, ELBO=-1000546.10, deltaELBO=151.594 (0.00124940%), Factors=14\n", + "Iteration 35: time=0.63, ELBO=-1000401.91, deltaELBO=144.196 (0.00118843%), Factors=14\n", + "Iteration 36: time=0.51, ELBO=-1000264.08, deltaELBO=137.829 (0.00113595%), Factors=14\n", + "Iteration 37: time=0.63, ELBO=-1000131.83, deltaELBO=132.252 (0.00108999%), Factors=14\n", + "Iteration 38: time=0.53, ELBO=-1000004.54, deltaELBO=127.290 (0.00104909%), Factors=14\n", + "Iteration 39: time=0.47, ELBO=-999881.72, deltaELBO=122.815 (0.00101221%), Factors=14\n", + "Iteration 40: time=0.43, ELBO=-999762.99, deltaELBO=118.732 (0.00097856%), Factors=14\n", + "Iteration 41: time=0.45, ELBO=-999648.02, deltaELBO=114.973 (0.00094758%), Factors=14\n", + "Iteration 42: time=0.57, ELBO=-999536.53, deltaELBO=111.486 (0.00091884%), Factors=14\n", + "Iteration 43: time=0.64, ELBO=-999428.30, deltaELBO=108.231 (0.00089201%), Factors=14\n", + "Iteration 44: time=0.66, ELBO=-999323.12, deltaELBO=105.178 (0.00086685%), Factors=14\n", + "Iteration 45: time=0.78, ELBO=-999220.82, deltaELBO=102.303 (0.00084315%), Factors=14\n", + "Iteration 46: time=0.95, ELBO=-999121.23, deltaELBO=99.586 (0.00082076%), Factors=14\n", + "Iteration 47: time=0.85, ELBO=-999024.22, deltaELBO=97.011 (0.00079954%), Factors=14\n", + "Iteration 48: time=0.77, ELBO=-998929.66, deltaELBO=94.567 (0.00077939%), Factors=14\n", + "Iteration 49: time=0.89, ELBO=-998837.42, deltaELBO=92.240 (0.00076022%), Factors=14\n", + "Iteration 50: time=0.90, ELBO=-998747.39, deltaELBO=90.023 (0.00074194%), Factors=14\n", + "Iteration 51: time=0.99, ELBO=-998659.49, deltaELBO=87.906 (0.00072450%), Factors=14\n", + "Iteration 52: time=0.69, ELBO=-998573.61, deltaELBO=85.882 (0.00070782%), Factors=14\n", + "Iteration 53: time=0.71, ELBO=-998489.66, deltaELBO=83.946 (0.00069186%), Factors=14\n", + "Iteration 54: time=0.57, ELBO=-998407.57, deltaELBO=82.090 (0.00067657%), Factors=14\n", + "Iteration 55: time=0.57, ELBO=-998327.26, deltaELBO=80.311 (0.00066190%), Factors=14\n", + "Iteration 56: time=0.59, ELBO=-998248.66, deltaELBO=78.603 (0.00064782%), Factors=14\n", + "Iteration 57: time=0.59, ELBO=-998171.69, deltaELBO=76.962 (0.00063430%), Factors=14\n", + "Iteration 58: time=0.58, ELBO=-998096.31, deltaELBO=75.384 (0.00062129%), Factors=14\n", + "Iteration 59: time=0.65, ELBO=-998022.45, deltaELBO=73.865 (0.00060878%), Factors=14\n", + "Iteration 60: time=0.86, ELBO=-997950.04, deltaELBO=72.403 (0.00059672%), Factors=14\n", + "Iteration 61: time=0.72, ELBO=-997879.05, deltaELBO=70.994 (0.00058511%), Factors=14\n", + "Iteration 62: time=0.65, ELBO=-997809.41, deltaELBO=69.635 (0.00057391%), Factors=14\n", + "Iteration 63: time=0.67, ELBO=-997741.09, deltaELBO=68.323 (0.00056310%), Factors=14\n", + "Iteration 64: time=0.63, ELBO=-997674.03, deltaELBO=67.057 (0.00055267%), Factors=14\n", + "Iteration 65: time=0.60, ELBO=-997608.20, deltaELBO=65.834 (0.00054259%), Factors=14\n", + "Iteration 66: time=0.60, ELBO=-997543.55, deltaELBO=64.652 (0.00053284%), Factors=14\n", + "Iteration 67: time=0.59, ELBO=-997480.04, deltaELBO=63.508 (0.00052342%), Factors=14\n", + "Iteration 68: time=0.64, ELBO=-997417.64, deltaELBO=62.402 (0.00051430%), Factors=14\n", + "Iteration 69: time=0.62, ELBO=-997356.31, deltaELBO=61.331 (0.00050547%), Factors=14\n", + "Iteration 70: time=0.61, ELBO=-997296.01, deltaELBO=60.293 (0.00049692%), Factors=14\n", + "Iteration 71: time=0.66, ELBO=-997236.73, deltaELBO=59.287 (0.00048863%), Factors=14\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n", + "\n", + " #########################################################\n", + " ### __ __ ____ ______ ### \n", + " ### | \\/ |/ __ \\| ____/\\ _ ### \n", + " ### | \\ / | | | | |__ / \\ _| |_ ### \n", + " ### | |\\/| | | | | __/ /\\ \\_ _| ###\n", + " ### | | | | |__| | | / ____ \\|_| ###\n", + " ### |_| |_|\\____/|_|/_/ \\_\\ ###\n", + " ### ### \n", + " ######################################################### \n", + " \n", + " \n", + " \n", + "View names not provided, using default naming convention:\n", + "- view1, view2, ..., viewM\n", + "\n", + "Features names not provided, using default naming convention:\n", + "- feature1_view1, featureD_viewM\n", + "\n", + "Groups names not provided, using default naming convention:\n", + "- group1, group2, ..., groupG\n", + "\n", + "Samples names not provided, using default naming convention:\n", + "- sample1_group1, sample2_group1, sample1_group2, ..., sampleN_groupG\n", + "\n", + "Successfully loaded view='view0' group='group0' with N=2000 samples and D=76 features...\n", + "Successfully loaded view='view1' group='group0' with N=2000 samples and D=216 features...\n", + "Successfully loaded view='view2' group='group0' with N=2000 samples and D=64 features...\n", + "Successfully loaded view='view3' group='group0' with N=2000 samples and D=240 features...\n", + "\n", + "\n", + "Model options:\n", + "- Automatic Relevance Determination prior on the factors: False\n", + "- Automatic Relevance Determination prior on the weights: True\n", + "- Spike-and-slab prior on the factors: False\n", + "- Spike-and-slab prior on the weights: False\n", + "Likelihoods:\n", + "- View 0 (view0): gaussian\n", + "- View 1 (view1): gaussian\n", + "- View 2 (view2): gaussian\n", + "- View 3 (view3): gaussian\n", + "\n", + "\n", + "\n", + "\n", + "######################################\n", + "## Training the model with seed 1 ##\n", + "######################################\n", + "\n", + "\n", + "ELBO before training: -12772449.96 \n", + "\n", + "Iteration 1: time=0.63, ELBO=-1339249.43, deltaELBO=11433200.531 (89.51454548%), Factors=15\n", + "Iteration 2: time=0.62, ELBO=-1210283.89, deltaELBO=128965.539 (1.00971653%), Factors=15\n", + "Iteration 3: time=0.60, ELBO=-1153269.11, deltaELBO=57014.785 (0.44638879%), Factors=15\n", + "Iteration 4: time=0.62, ELBO=-1042558.73, deltaELBO=110710.374 (0.86679043%), Factors=15\n", + "Iteration 5: time=0.63, ELBO=-1023541.38, deltaELBO=19017.358 (0.14889358%), Factors=15\n", + "Iteration 6: time=0.65, ELBO=-1014396.72, deltaELBO=9144.653 (0.07159670%), Factors=15\n", + "Iteration 7: time=0.70, ELBO=-1008412.02, deltaELBO=5984.705 (0.04685636%), Factors=15\n", + "Iteration 8: time=0.64, ELBO=-1004161.65, deltaELBO=4250.363 (0.03327758%), Factors=15\n", + "Iteration 9: time=0.60, ELBO=-1001172.78, deltaELBO=2988.878 (0.02340098%), Factors=15\n", + "Iteration 10: time=0.66, ELBO=-999098.53, deltaELBO=2074.243 (0.01623998%), Factors=15\n", + "Iteration 11: time=0.67, ELBO=-997609.75, deltaELBO=1488.781 (0.01165619%), Factors=15\n", + "Iteration 12: time=0.63, ELBO=-996455.75, deltaELBO=1154.007 (0.00903512%), Factors=15\n", + "Iteration 13: time=0.67, ELBO=-995489.07, deltaELBO=966.672 (0.00756841%), Factors=15\n", + "Iteration 14: time=0.60, ELBO=-994633.20, deltaELBO=855.879 (0.00670097%), Factors=15\n", + "Iteration 15: time=0.62, ELBO=-993848.67, deltaELBO=784.525 (0.00614232%), Factors=15\n", + "Iteration 16: time=0.62, ELBO=-993114.09, deltaELBO=734.578 (0.00575127%), Factors=15\n", + "Iteration 17: time=0.60, ELBO=-992416.89, deltaELBO=697.201 (0.00545863%), Factors=15\n", + "Iteration 18: time=0.61, ELBO=-991748.91, deltaELBO=667.978 (0.00522983%), Factors=15\n", + "Iteration 19: time=0.66, ELBO=-991104.23, deltaELBO=644.687 (0.00504748%), Factors=15\n", + "Iteration 20: time=0.67, ELBO=-990478.03, deltaELBO=626.195 (0.00490270%), Factors=15\n", + "Iteration 21: time=0.57, ELBO=-989866.15, deltaELBO=611.877 (0.00479060%), Factors=15\n", + "Iteration 22: time=0.46, ELBO=-989264.83, deltaELBO=601.324 (0.00470797%), Factors=15\n", + "Iteration 23: time=0.48, ELBO=-988670.66, deltaELBO=594.171 (0.00465197%), Factors=15\n", + "Iteration 24: time=0.55, ELBO=-988080.67, deltaELBO=589.992 (0.00461925%), Factors=15\n", + "Iteration 25: time=0.87, ELBO=-987492.47, deltaELBO=588.202 (0.00460524%), Factors=15\n", + "Iteration 26: time=0.48, ELBO=-986904.50, deltaELBO=587.965 (0.00460339%), Factors=15\n", + "Iteration 27: time=0.45, ELBO=-986316.39, deltaELBO=588.109 (0.00460451%), Factors=15\n", + "Iteration 28: time=0.63, ELBO=-985729.32, deltaELBO=587.077 (0.00459643%), Factors=15\n", + "Iteration 29: time=0.50, ELBO=-985146.35, deltaELBO=582.969 (0.00456427%), Factors=15\n", + "Iteration 30: time=0.47, ELBO=-984572.62, deltaELBO=573.730 (0.00449193%), Factors=15\n", + "Iteration 31: time=0.45, ELBO=-984015.11, deltaELBO=557.503 (0.00436489%), Factors=15\n", + "Iteration 32: time=0.45, ELBO=-983482.00, deltaELBO=533.116 (0.00417395%), Factors=15\n", + "Iteration 33: time=0.45, ELBO=-982981.48, deltaELBO=500.523 (0.00391877%), Factors=15\n", + "Iteration 34: time=0.45, ELBO=-982520.47, deltaELBO=461.003 (0.00360936%), Factors=15\n", + "Iteration 35: time=0.52, ELBO=-982103.51, deltaELBO=416.963 (0.00326455%), Factors=15\n", + "Iteration 36: time=0.49, ELBO=-981732.13, deltaELBO=371.377 (0.00290764%), Factors=15\n", + "Iteration 37: time=0.47, ELBO=-981405.02, deltaELBO=327.110 (0.00256106%), Factors=15\n", + "Iteration 38: time=0.45, ELBO=-981118.64, deltaELBO=286.383 (0.00224219%), Factors=15\n", + "Iteration 39: time=0.45, ELBO=-980868.11, deltaELBO=250.527 (0.00196146%), Factors=15\n", + "Iteration 40: time=0.44, ELBO=-980648.08, deltaELBO=220.030 (0.00172269%), Factors=15\n", + "Iteration 41: time=0.45, ELBO=-980453.34, deltaELBO=194.741 (0.00152470%), Factors=15\n", + "Iteration 42: time=0.46, ELBO=-980279.22, deltaELBO=174.122 (0.00136326%), Factors=15\n", + "Iteration 43: time=0.45, ELBO=-980121.76, deltaELBO=157.460 (0.00123281%), Factors=15\n", + "Iteration 44: time=0.45, ELBO=-979977.74, deltaELBO=144.020 (0.00112758%), Factors=15\n", + "Iteration 45: time=0.47, ELBO=-979844.61, deltaELBO=133.132 (0.00104233%), Factors=15\n", + "Iteration 46: time=0.47, ELBO=-979720.38, deltaELBO=124.227 (0.00097261%), Factors=15\n", + "Iteration 47: time=0.45, ELBO=-979603.53, deltaELBO=116.847 (0.00091483%), Factors=15\n", + "Iteration 48: time=0.46, ELBO=-979492.90, deltaELBO=110.634 (0.00086619%), Factors=15\n", + "Iteration 49: time=0.45, ELBO=-979387.58, deltaELBO=105.318 (0.00082457%), Factors=15\n", + "Iteration 50: time=0.55, ELBO=-979286.89, deltaELBO=100.694 (0.00078837%), Factors=15\n", + "Iteration 51: time=0.47, ELBO=-979190.28, deltaELBO=96.612 (0.00075641%), Factors=15\n", + "Iteration 52: time=0.57, ELBO=-979097.32, deltaELBO=92.959 (0.00072781%), Factors=15\n", + "Iteration 53: time=0.55, ELBO=-979007.67, deltaELBO=89.652 (0.00070191%), Factors=15\n", + "Iteration 54: time=0.46, ELBO=-978921.04, deltaELBO=86.629 (0.00067825%), Factors=15\n", + "Iteration 55: time=0.45, ELBO=-978837.20, deltaELBO=83.842 (0.00065643%), Factors=15\n", + "Iteration 56: time=0.54, ELBO=-978755.94, deltaELBO=81.257 (0.00063619%), Factors=15\n", + "Iteration 57: time=0.86, ELBO=-978677.09, deltaELBO=78.845 (0.00061730%), Factors=15\n", + "Iteration 58: time=0.45, ELBO=-978600.51, deltaELBO=76.583 (0.00059960%), Factors=15\n", + "Iteration 59: time=0.43, ELBO=-978526.06, deltaELBO=74.455 (0.00058294%), Factors=15\n", + "Iteration 60: time=0.50, ELBO=-978453.61, deltaELBO=72.447 (0.00056721%), Factors=15\n", + "Iteration 61: time=0.48, ELBO=-978383.06, deltaELBO=70.545 (0.00055232%), Factors=15\n", + "Iteration 62: time=0.46, ELBO=-978314.32, deltaELBO=68.740 (0.00053819%), Factors=15\n", + "Iteration 63: time=0.46, ELBO=-978247.30, deltaELBO=67.025 (0.00052476%), Factors=15\n", + "Iteration 64: time=0.48, ELBO=-978181.91, deltaELBO=65.391 (0.00051197%), Factors=15\n", + "Iteration 65: time=0.49, ELBO=-978118.08, deltaELBO=63.832 (0.00049976%), Factors=15\n", + "Iteration 66: time=0.48, ELBO=-978055.73, deltaELBO=62.343 (0.00048810%), Factors=15\n", + "\n", + "Converged!\n", + "\n", + "\n", + "\n", + "#######################\n", + "## Training finished ##\n", + "#######################\n", + "\n", + "\n" + ] + } + ], + "source": [ + "total_variance = np.zeros(17)\n", + "for k in range(2,17):\n", + " data_mat = [[None for g in range(1)] for m in range(4)]\n", + "\n", + " for m in range(4):\n", + " data_mat[m][0] = Xs_norm[m]\n", + "\n", + " ent = entry_point()\n", + " ent.set_data_matrix(data_mat, likelihoods = [\"gaussian\" for _ in range(4)])\n", + " ent.set_model_options(\n", + " factors = k, \n", + " spikeslab_weights = False, \n", + " ard_weights = True\n", + " )\n", + " ent.set_train_options(\n", + " convergence_mode = \"fast\", \n", + " dropR2 = 0.001, \n", + " gpu_mode = False, \n", + " seed = 1\n", + " )\n", + " ent.build()\n", + " ent.run()\n", + "\n", + " total_variance[k] = np.sum(ent.model.calculate_variance_explained())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(2,17), total_variance[2:17], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/uci_digits_mvmds_screeplot.ipynb b/examples/uci_digits_mvmds_screeplot.ipynb new file mode 100644 index 0000000..f655ccd --- /dev/null +++ b/examples/uci_digits_mvmds_screeplot.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from adnmtf import NMF, NTF\n", + "import pandas as pd\n", + "import numpy as np\n", + "from IPython.display import clear_output\n", + "import time\n", + "from wordcloud import WordCloud, ImageColorGenerator\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from matplotlib.colors import ListedColormap\n", + "import matplotlib.patches as mpatches\n", + "import distinctipy\n", + "from matplotlib.patches import Ellipse\n", + "import matplotlib.transforms as transforms\n", + "\n", + "import sys\n", + "import networkx as nx\n", + "\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn import metrics\n", + "\n", + "from mvlearn.datasets import load_UCImultifeature\n", + "from mvlearn.embed import MVMDS\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.manifold import MDS\n", + "from sklearn.cluster import KMeans\n", + "import umap\n", + "from scipy.spatial import distance_matrix\n", + "import hoggorm as ho\n", + "import adilsm.adilsm as ilsm\n", + "from sklearn.metrics.cluster import rand_score\n", + "from sklearn.preprocessing import StandardScaler\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data prep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 views.\n", + "There are 2000 observations\n", + "The feature sizes are: [76, 216, 64, 240, 47, 6]\n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# Load Data\n", + "# ---------\n", + "# Data comes from UCI Digits Data. Contains 6 views and classifications of\n", + "# numbers 0-9\n", + "\n", + "Xs, list_digits = load_UCImultifeature()\n", + "\n", + "sample_rate = 1\n", + "\n", + "if sample_rate < 1:\n", + " num_rows = list_digits.shape[0]\n", + " num_rows_to_select = int(num_rows * sample_rate)\n", + " selected_rows = np.random.choice(num_rows, num_rows_to_select, replace=False)\n", + "\n", + " for i in range(len(Xs)):\n", + " Xs[i] = Xs[i][selected_rows]\n", + "\n", + " list_digits = list_digits[selected_rows]\n", + "\n", + "list_cell_codes, list_cell_types = pd.factorize(list_digits)\n", + "\n", + "# Check data\n", + "print(f'There are {len(Xs)} views.')\n", + "print(f'There are {Xs[0].shape[0]} observations')\n", + "print(f'The feature sizes are: {[X.shape[1] for X in Xs]}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Xs_norm = Xs.copy()\n", + "scaler = StandardScaler()\n", + "for i in range(len(Xs)):\n", + " Xs_norm[i] = Xs[i] - np.mean(Xs[i], axis=0)\n", + " Xs_norm[i] = scaler.fit_transform(Xs_norm[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "mvmds = MVMDS(n_components=16)\n", + "Xs_mvmds_reduced = mvmds.fit_transform(Xs)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5044781.8612027 7944222.32409934 12568748.05447858 13366678.6347922\n", + " 14098489.67788737 14605600.54982345 14799708.65795875 14892973.51596771\n", + " 14954847.57334482 14987699.70407477 15019119.01472625 15044878.81515035\n", + " 15057565.31688737 15067358.69493423 15079793.61560008 15090013.68605973]\n" + ] + } + ], + "source": [ + "Xs_concat = Xs[0]\n", + "for X in Xs[1:]:\n", + " Xs_concat = np.hstack((Xs_concat, X))\n", + "\n", + "p = Xs_concat.shape[1]\n", + "variance_explained = np.zeros(16)\n", + "\n", + "for k in range(16):\n", + " variance = 0\n", + " for i in range(p):\n", + " variance += np.var(np.dot(Xs_concat[:,i], Xs_mvmds_reduced[:,k])*Xs_mvmds_reduced[:,k])\n", + "\n", + " if k==0:\n", + " variance_explained[k] = variance\n", + " else: \n", + " variance_explained[k] = variance_explained[k-1]+variance\n", + "\n", + "print(variance_explained)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Figures" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the scree plot\n", + "plt.plot (np.arange(5,17), variance_explained[4:], 'o-', linewidth=2, color='blue')\n", + "plt.xlabel ('Principal Component')\n", + "plt.ylabel ('Variance Explained')\n", + "plt.title ('Scree Plot')\n", + "plt.grid ()\n", + "plt.show ()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/setup.py b/setup.py index d6e9b88..1dec3fd 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,7 @@ from setuptools import find_packages, setup requirements = """ adnmtf==0.1.164 +scipy==1.9.1 """ description = "Integrated Longitudinal Multi Source Model"