VanTineEtAl_Fig1_LinePlot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# This notebook is to generate the plot in Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import os\n",
    "from numpy import ma\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "from natsort import natsorted\n",
    "from glob import glob\n",
    "import glob\n",
    "from statistics import mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set working directory\n",
    "\n",
    "os.chdir('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/vanTine_001_FinalDataWithControls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load this data file from the GitHub page\n",
    "\n",
    "dfSarcoma = pd.read_excel('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/vanTine_001_FinalDataWithControls/MQ/vanTine_001_90_samples/Protein/Model/Protein_pVals_Final_WithMetaData.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dfSarcoma\n",
    "\n",
    "df = df.loc[:,~df.columns.str.contains('CON_')] # remove contaminants\n",
    "df = df.query(\"dataType == 'Intensity_Raw'\")\n",
    "\n",
    "totalCols = df.columns\n",
    "numCols = df._get_numeric_data().columns\n",
    "catCols = list(set(totalCols)-set(numCols))\n",
    "\n",
    "df = pd.melt(df,\n",
    "    id_vars = catCols,\n",
    "    value_vars = numCols,\n",
    "    var_name = 'protAcc',\n",
    "    value_name=\"area\",\n",
    "    ignore_index=False,\n",
    "    )\n",
    "\n",
    "#remove rows without areas & duplicates\n",
    "df.dropna(subset=['area'], inplace=True)\n",
    "df.drop_duplicates(inplace = True)\n",
    "\n",
    "# add a column 'sarcoma' for clarity\n",
    "dataset = 'sarcoma'\n",
    "df.insert(loc=1, column='dataset', value=dataset)\n",
    "\n",
    "dfSarcoma = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jasonheld/Python/env_221209_3-10/lib/python3.10/site-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    }
   ],
   "source": [
    "# This file is on the GitHub page\n",
    "\n",
    "dfUniprot = pd.read_excel('/Users/jasonheld/Manuscripts/2022_Sarcoma-Exosomes/Figure2_Data_CancerVsNormal/uniprot-download_true_fields_accession_2Creviewed_2Cid_2Cprotein_nam-2023.03.17-19.33.16.44.xlsx')\n",
    "dfUniprot = dfUniprot.rename(columns={'Entry' : 'protAcc'})\n",
    "dfUniprot = dfUniprot.rename(columns={'Gene Names (primary)' : 'geneNamePrimary'})\n",
    "dfUniprot = dfUniprot.drop(columns=['Length','Entry Name','Organism','Gene Names'])\n",
    "dfUniprot['protAcc'] = dfUniprot['protAcc'].astype(str)\n",
    "dfUniprot.drop_duplicates(subset=['protAcc'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# annotate uniprot info by merging with dfUniprot\n",
    "\n",
    "dfSarcoma = dfSarcoma.merge(dfUniprot, on='protAcc', how='left')\n",
    "\n",
    "totalCols = dfSarcoma.columns\n",
    "numCols = dfSarcoma._get_numeric_data().columns\n",
    "catCols = list(set(totalCols)-set(numCols))\n",
    "\n",
    "dfSarcoma['area'] = dfSarcoma['area'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove CRAPome\n",
    "\n",
    "df = dfSarcoma\n",
    "\n",
    "crapome =['KRT1', 'KRT2', 'KRT3', 'KRT4', 'KRT5', 'KRT6', 'KRT7', 'KRT8', 'KRT9', 'KRT10', 'KRT11', 'KRT12', 'KRT13', 'KRT14', 'KRT15', 'KRT16', 'KRT17', 'KRT18', 'KRT19', 'KRT20', 'KRT21', 'KRT22', 'KRT23', 'KRT24']\n",
    "\n",
    "df = df.loc[~df['geneNamePrimary'].str.contains('|'.join(crapome))] \n",
    "\n",
    "dfSarcoma = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# proteins detected per samples (e.g. number of non zeros)\n",
    "\n",
    "dfSarcomaPivot = pd.pivot_table(\n",
    "    data=dfSarcoma,\n",
    "    index = ['sample','cancer','timing'],\n",
    "    columns = 'geneNamePrimary',\n",
    "    values = 'area'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "exoGeneList = ['CD9', 'CD63', 'CD81', # classical exosomes\n",
    "              'HSPA13', 'MTHFD1', 'IHD1',# exomere\n",
    "              'ANXA1', 'ARF6', 'TSG101', # microvesicle\n",
    "              'PDCD6IP', # ALIX\n",
    "              'SDCBP', # syntenin-1, universal biomarker\n",
    "              'FLOT1', 'CD40', # misc\n",
    "               'ANXA6',\n",
    "               'HSPA8', 'ANXA2', 'GAPDH', 'ENO1', 'HSP90AA1', 'PKM' # http://exocarta.org\n",
    "              ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medianIntensity</th>\n",
       "      <th>meanIntensity</th>\n",
       "      <th>medianIntensityRank</th>\n",
       "      <th>meanIntensityRank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geneNamePrimary</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG</th>\n",
       "      <td>5773000.0</td>\n",
       "      <td>6.576969e+06</td>\n",
       "      <td>126.0</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AADAT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>3419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAGAB</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.980589e+04</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>2036.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS1</th>\n",
       "      <td>18792.5</td>\n",
       "      <td>2.317269e+05</td>\n",
       "      <td>1147.0</td>\n",
       "      <td>1026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABAT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.489000e+02</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>3247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF385B</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3456.0</td>\n",
       "      <td>3458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSWIM9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.611611e+04</td>\n",
       "      <td>3457.0</td>\n",
       "      <td>2329.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZW10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.636654e+04</td>\n",
       "      <td>3458.0</td>\n",
       "      <td>2320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX</th>\n",
       "      <td>2848565.0</td>\n",
       "      <td>4.835775e+06</td>\n",
       "      <td>184.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>58757.0</td>\n",
       "      <td>1.343538e+05</td>\n",
       "      <td>998.0</td>\n",
       "      <td>1279.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3458 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 medianIntensity  meanIntensity  medianIntensityRank  \\\n",
       "geneNamePrimary                                                        \n",
       "A1BG                   5773000.0   6.576969e+06                126.0   \n",
       "AADAT                        0.0   0.000000e+00               1196.0   \n",
       "AAGAB                        0.0   2.980589e+04               1197.0   \n",
       "AARS1                    18792.5   2.317269e+05               1147.0   \n",
       "ABAT                         0.0   7.489000e+02               1198.0   \n",
       "...                          ...            ...                  ...   \n",
       "ZNF385B                      0.0   0.000000e+00               3456.0   \n",
       "ZSWIM9                       0.0   1.611611e+04               3457.0   \n",
       "ZW10                         0.0   1.636654e+04               3458.0   \n",
       "ZYX                    2848565.0   4.835775e+06                184.0   \n",
       "ZZEF1                    58757.0   1.343538e+05                998.0   \n",
       "\n",
       "                 meanIntensityRank  \n",
       "geneNamePrimary                     \n",
       "A1BG                         138.0  \n",
       "AADAT                       3419.0  \n",
       "AAGAB                       2036.0  \n",
       "AARS1                       1026.0  \n",
       "ABAT                        3247.0  \n",
       "...                            ...  \n",
       "ZNF385B                     3458.0  \n",
       "ZSWIM9                      2329.0  \n",
       "ZW10                        2320.0  \n",
       "ZYX                          170.0  \n",
       "ZZEF1                       1279.0  \n",
       "\n",
       "[3458 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calc median intensity of each protein\n",
    "\n",
    "dfProteinIntensity = pd.DataFrame()\n",
    "\n",
    "#dfProteinIntensity.index = dfTemp.T.index\n",
    "dfProteinIntensity['medianIntensity'] = dfSarcomaPivot.median(axis = 0)\n",
    "dfProteinIntensity['meanIntensity'] = dfSarcomaPivot.mean(axis = 0)\n",
    "\n",
    "# add rank columns\n",
    "dfProteinIntensity['medianIntensityRank'] = dfProteinIntensity['medianIntensity'].rank(method = 'first', ascending = False)\n",
    "dfProteinIntensity['meanIntensityRank'] = dfProteinIntensity['meanIntensity'].rank(method = 'first', ascending = False)\n",
    "dfProteinIntensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "sns.scatterplot(data = dfProteinIntensity,\n",
    "        x= 'meanIntensityRank',\n",
    "        y = 'meanIntensity',\n",
    "        size=5,\n",
    "        facecolor = 'grey',\n",
    "        edgecolor = 'black',)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/39/4j31xtfn0k1gbr7xx3tcxqgw0000gr/T/ipykernel_26741/3079745642.py:19: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  (dfProteinIntensity[dfProteinIntensity.index.isin(exoGeneList)].meanIntensityRank[i], # x\n",
      "/var/folders/39/4j31xtfn0k1gbr7xx3tcxqgw0000gr/T/ipykernel_26741/3079745642.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  dfProteinIntensity[dfProteinIntensity.index.isin(exoGeneList)].meanIntensity[i], # y\n"
     ]
    },
    {
     "data": {
      "image/png": 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NGzbkAHMiIqJKoNJXprS0tNCuXTuEhYXJyqRSKcLCwookGGXl5eWF6OhoXLhwQdEwK0zhO/qsra0xefJkuLq64tChQ3J1rl27hq5du6JTp044cOCAXCIFvBpPFhQUJFvOysrC7t27MXr06Co5BiIioppG6cnU2x7TnzlzJn744Qds27YNt27dwuTJk5GRkYGxY8cqMeqqoauri9zcXNnymTNn8P7772PIkCH46aefoKFR9MLiqFGjcOrUKVn//fLLL7CxsUHbtm2rLG4iIqKaROnJ1MWLF9GmTRu0adMGwKvkqU2bNvDx8QEAeHh44JtvvoGPjw9at26NqKgoHD16tMig9HeJEAJ//PEHjh07hp49e8rKBw0ahP79+2PDhg0ljn8yMzNDnz59EBISAgAICgrCuHHjqiJsIiKiGknpyZSLiwuEEEU+hckAAHz++eeIj49HTk4Ozp07B2dnZ+UFXIGePXuGP44cwf379wH8/wuPdXR00KdPH3h4eMDPz09Wf8CAAdi/fz9OnTr1xnbHjRuHkJAQ/P3334iMjMTIkSMr8zCIiIhqNKUnU8oSEBAAR0dHdOjQQSn73/zll/ipbVsY9e2LPzt2RFR4OFxcXBAVFYW7d+8iKysL27Ztk3vP4Pfff4/hw4ejT58+iIiIKLHtPn36ICsrC+PHj0f//v1Rp06dqjgkIiKiGkmln+arTF5eXvDy8pJN+lWVzp86BdOAAAz936tsOj57hv8+e4Z72tpvfBmxRCLBli1boKamhr59++Lw4cN4//33i9TT0NDAJ598gpUrV+LIkSOVdhxERERUg69MKVNYYCAG/eudgIYAcp4/f+u2EokEmzdvxieffIK+ffsiPDy82HpfffUVnj59WqqJTImIiKj8mEwpgYaODnKLW6H26tsxZswYudfnFDp9+jQkEglSUlIQEBCAjh07omfPntDV1cV//vMfAMDq1asBvJpWYsOGDVBTU4NEIsGQIUMAAN7e3khPT5dr97PPPoO6ujr27t1bbLxr166Fg4MDdHV1YWVlBW9v7yITgxIREdVUTKaUYPCsWfihXj25snH6+liyZEmx9QsH6RsYGMjKgoODcf78efzwww+4fv06zp07h507dxaZhNTJyQkJCQmIj4/H7t27sWXLFsyaNUu2PjMzE7t378acOXPk5qcqtHPnTsybNw++vr64desWAgMDERoaii+//FKRLiAiInpn1NgxU8rUuEkTOKxeDZ9vvoHl48d4WqsW6g0dik8nTix1G4cOHcKwYcMwfvx4WZmTk1ORehoaGrLZ4j08PBAWFoZDhw7h+++/BwDs3bsXjo6OmDdvHiwtLfHw4UNYWVnJtj9z5gy6dOmCjz/+GABgY2ODESNG4Ny5c+U6diIiondNjb0ypeyn+dyGD8fiCxfgcfMm5l+9ik8XLy7T9hYWFjh79izi4+PLtN2/JwINDAyEp6cnjI2N5eanKtS5c2dcunQJ58+fBwD8/fff+O2339C3b98y7ZeIiOhdVWOTKVV4nUzhi4mLm8m8cM6p1z99+vSRrff19YWJiQlsbGzg4OCAMWPGYM+ePZBKpSXu79KlS9i5c6dsItC7d+/i7Nmz8PDwAAB4enoiODgYQgjZNh9//DH8/f3RtWtXaGpqonHjxnBxceFtPiIiov+pscmUKiooKJAlQz169JC9Zqfws3XrVlndevXqITIyEtevX8f06dORn5+P0aNHw93dXS6hun79OgwMDKCrq4uOHTuiU6dO2LBhA4BXs6O7ubnB1NQUANC3b1+kpKTgxIkTsu3Dw8OxbNkybNy4EZcvX8a+fftw+PBhfPXVV1XRJURERCqPY6ZUwJMnT7B+3DjoREdDqqGBawUFqO/oWGTOqUePHhXZtnnz5mjevDmmTJmCSZMmoVu3bvjzzz/Ro0cPAICDgwMOHToEDQ0NWFpaQktLC8CrxG3btm1ITEyUuzJWUFCAoKAgfPDBBwCARYsWYdSoUfj0008BAC1atEBGRgYmTpyIBQsWQE2N+TgREdVsTKaUTAiBlYMH46u//oLu/8puALiWk1PmthwdHQEAGRkZsjItLa1iJwL97bffkJaWhitXrkBdXV1WfuPGDYwdOxbJyckwMTFBZmZmkYSpsP7rtwOJiIhqKiZTSnbjxg10vnpVlkgBgC4A9RcvkJmZCT09vWK3mzx5MiwtLdGzZ080aNAACQkJWLJkCerWrYtOnTq9db+BgYHo168fWrVqJVfu6OgIb29v7NixA15eXujfvz9Wr16NNm3awNnZGbGxsVi0aBH69+8vl4QRERHVVDU2mQoICEBAQECReZmqWsrLl6idmVmkXF0qRXZ2donJlKurK4KCgrBp0yY8f/4cpqam6NSpE8LCwt76Lr6kpCQcPnwYO3fuLLJOTU0NgwYNQmBgILy8vLBw4UJIJBIsXLgQ//zzD+rWrYv+/ftj6dKl5TtgIiKid4xE1PB7NYXv5ktJSYGRkVGV7z83NxdftW6Nr27dkpVJASzo2hXLT52q8niIiIiqA2Wfv1/H0cNKpqWlhZ7LlsGnSRNcBvCXhgbmtGiBMRs3Kjs0IiIiKgVemVKRzDYzMxMn/vtf6Ojrw8Xdvdi5p4iIiOgVVTl/AzV4zJSq0dPTw3/+N3kmERERVR+8zUdERESkACZT1VBiYiKmTp0KW1tbaGtrw8rKCv3790dYWBiAVy8jlkgkkEgk0NXVhY2NDYYNGyY3s3mhsLAwdO7cGYaGhrCwsMDcuXORn59f1YdERERUbTGZqmbi4uLQrl07nDhxAqtWrcL169dx9OhR9OjRA15eXrJ6/v7+SEhIQExMDLZv3w4TExO4urrKTWlw9epV9O3bF+7u7rhy5QpCQ0Nx6NAhzJs3TxmHRkREVC3V2DFTqjLPVFlNmTIFEokE58+fh76+vqzcyckJ48aNky0XXmkCgIYNG6J79+6oV68efHx8MHToUDg4OCA0NBQtW7aEj48PAMDOzg4rV67EsGHD4OvrC0NDw6o9OCIiomqoxl6Z8vLyQnR0NC5cuKDsUErtxYsXOHr0KLy8vOQSqUImJiZv3H769OkQQuDgwYMAgJycHOjo6MjV0dXVRXZ2Ni5dulRhcRMREb3LamwyVR3FxsZCCIGmTZuWa/vatWvDzMwMcXFxAAA3NzecOXMGu3btQkFBAf755x/4+/sDABISEioqbCIioncak6lqpCKmBBNCQCKRAAB69+6NVatWYdKkSdDW1oa9vT369u0LAEVebkxERETF4xlTxZ2LiMCyMWPw7eefQ0dbGxKJBLdv3y5XW8+fP8fTp0/RqFEjWdnMmTORnJyMBw8e4NmzZxgwYAAAwNbWtkLiJyIietfV2AHo1UHAnDmw+P57zE1NRRaAzQcPokPr1ggICMC0adOKjJtKTk5+47ipdevWQU1NDQMHDpQrl0gksLS0BADs2rULVlZWaNu2bQUfDRER0buJyZSKSkxMhHTHDgxJTQUAGACY/egR4urUwYOCAnTs2BH+/v5o2bIl8vPzcfz4cWzatAm3/vfC5LS0NCQmJiIvLw/379/HTz/9hK1bt2L58uWws7OT7WfVqlVwd3eHmpoa9u3bhxUrVmDPnj1QV1dXxmETERFVO0ymVNSlU6fQ/fHjIuWNnz3D6dOnsWbNGsyaNQsJCQmoW7cu2rVrh02bNsnq+fj4wMfHB1paWrCwsMB7772HsLAw9OjRQ669I0eOYOnSpcjJyUGrVq1w8OBB9OnTp9KPj4iI6F3BFx2r0IsSX3fr5k1Ede2KEcnJcuV+zZvD5+pVDhAnIqIaTZXO3zX2jBwQEABHR0d06NBB2aEUq5mTE6537YqHr5Wd1NeHtYcHEykiIiIVwitTKpTZ/ltubi6+X7gQKefOoUBTEy1HjMCg8eOVHRYREZHSqdL5m8mUCn0ziIiIqHRU6fzN+0VERERECmAyRURERKQAJlNERERECmAyRURERKQAJlNERERECmAyRURERKQAJlNERERECmAyRURERKQAJlNERERECqixyZSqv5uPiIiIqge+TkaFpqMnIiKi0lGl83eNvTJFREREVBGYTBEREREpgMkUERERkQKYTBEREREpgMkUERERkQKYTBEREREpgMkUERERkQKYTBEREREpgMkUERERkQKYTBEREREpgMkUERERkQKYTBEREREpgMkUERERkQKYTBEREREpgMkUERERkQJqbDIVEBAAR0dHdOjQQdmhEBERUTUmEUIIZQehTKmpqTA2NkZKSgqMjIyUHQ4RERGVgiqdv2vslSkiIiKiisBkioiIiEgBTKaIiIiIFMBkioiIiEgBTKaIiIiIFMBkioiIiEgBTKao3MaMGYOBAwcWKQ8PD4dEIkFycjIA4IcffkCrVq1gYGAAExMTtGnTBsuXL5fV9/Pzg0QigUQigYaGBmxsbODt7Y309HRZnbCwMHTu3BmGhoawsLDA3LlzkZ+fL7ffa9euoVu3btDR0YGVlRVWrlxZbNyPHj2ClpYWmjdv/sbji4yMhLq6Ovr161fs+mnTpqFdu3bQ1tZG69at39gWERG9u5hMUaUKCgrCjBkzMG3aNERFReGvv/7CnDlz5BIlAHByckJCQgLi4uLw9ddfY8uWLZg1axYA4OrVq+jbty/c3d1x5coVhIaG4tChQ5g3b55s+9TUVPTu3RvW1ta4dOkSVq1aBT8/P2zZsqVITCEhIRg2bBhSU1Nx7ty5EmMPDAzE1KlTERERgcePHxdbZ9y4cfDw8ChP1xAR0TtCQ9kB0Lvt0KFDGDZsGMaPHy8rc3JyKlJPQ0MDFhYWAAAPDw+EhYXh0KFD+P777xEaGoqWLVvCx8cHAGBnZ4eVK1di2LBh8PX1haGhIXbs2IHc3FwEBQVBS0sLTk5OiIqKwurVqzFx4kTZfoQQCA4OxsaNG9GgQQMEBgbC2dm5SDzp6ekIDQ3FxYsXkZiYiJCQEHz55Zdydb777jsAwNOnT3Ht2jXFO4uIiKolXpmiSmVhYYGzZ88iPj6+TNvp6uoiNzcXAJCTkwMdHZ0i67Ozs3Hp0iUAr27Jde/eHVpaWrI6bm5uiImJwcuXL2VlJ0+eRGZmJlxdXeHp6Yndu3cjIyOjyP737NmDpk2bwsHBAZ6enggKCkINf1kAERGVgMkUKeTXX3+FgYGB3KdPnz6y9b6+vjAxMYGNjQ0cHBwwZswY7NmzB1KptMQ2L126hJ07d6Jnz54AXiVFZ86cwa5du1BQUIB//vkH/v7+AICEhAQAQGJiIszNzeXaKVxOTEyUlQUGBmL48OFQV1dH8+bNYWtri7179xaJITAwEJ6engAAd3d3pKSk4M8//yxPFxER0TuOyRSVSXp6OsKOHsWN69cBAD169EBUVJTcZ+vWrbL69erVQ2RkJK5fv47p06cjPz8fo0ePhru7u1xCdf36dRgYGEBXVxcdO3ZEp06dsGHDBgBA7969sWrVKkyaNAna2tqwt7dH3759AQBqaqX/EU5OTsa+fftkSRIAeHp6IjAwUK5eTEwMzp8/jxEjRgB4dQvSw8OjSD0iIiKAY6aoDPb/8ANurlyJD2JjcdvICBd1dWHdrh3s7Ozk6j169KjIts2bN0fz5s0xZcoUTJo0Cd26dcOff/6JHj16AAAcHBxw6NAhaGhowNLSUu52HQDMnDkT3t7eSEhIQK1atRAXF4f58+fD1tYWwKvbiUlJSXLbFC4XjsXauXMnsrOz5cZICSEglUpx584d2NvbA3h1VSo/Px+WlpZy9bS1tbFhwwYYGxuXq/+IiOjdxCtTVCpPnjxB7FdfYWFsLDoBGJqailZJSfj76tUyt+Xo6AgAcmOVtLS0YGdnBxsbmyKJVCGJRAJLS0vo6upi165dsLKyQtu2bQEAnTp1QkREBPLy8mT1jx8/DgcHB9SqVQvAqyRp1qxZclfRrl69im7duiEoKAgAkJ+fj+3bt+Pbb78tUs/S0hK7du0q8/ESEdG7jVemqFQOh4Tg44cP5co0ASA19Y3bTZ48GZaWlujZsycaNGiAhIQELFmyBHXr1kWnTp1Kvf9Vq1bB3d0dampq2LdvH1asWIE9e/ZAXV0dAPDxxx9j8eLFGD9+PObOnYsbN25g3bp1WLNmDQAgKioKly9fxo4dO9C0aVO5tkeMGAF/f38sWbIEv/76K16+fInx48cXuQI1ZMgQBAYGYtKkSQCA2NhYpKenIzExEVlZWYiKigLwKlksKSEkIqJ3D69MUanoGRkhvbgVEskbt3N1dcXZs2fx0Ucfwd7eHkOGDIGOjg7CwsJQp06dUu//yJEj6NatG9q3b4/Dhw/j4MGDchOGGhsb4/fff8f9+/fRrl07zJo1Cz4+PrJpEQIDA+Ho6FgkkQKAQYMG4cmTJ/jtt98QGBgIV1fXYm/lDRkyBBcvXpRNg/Dpp5+iTZs2+P7773Hnzh20adMGbdq0KXFOKiIiejdJRA1/3js1NRXGxsZISUmBkZGRssNRWRkZGVjSrh2WxcSgMH26p6aGX6dNw/T/Xf0hIiKqKqp0/uZtPioVfX19eAQH48tZs2AZH480PT1o9ugB71WrlB0aERGRUjGZolJr3akTWp85g5SUFOjq6nJcEBEREZhMUTlwagAiIqL/xwHoRERERAp4J5Kp+/fvo0ePHnB0dESLFi2KfdcaERERUWV4J27zjRkzBkuWLEG3bt3w4sULaGtrKzskIiIiqiGqfTJ18+ZNaGpqolu3bgCA2rVrKzkiIiIiqkmUfpsvIiIC/fv3h6WlJSQSCQ4cOFCkTkBAAGxsbKCjowNnZ2ecP39etu7u3bswMDBA//790bZtWyxbtqwKoyciIqKaTunJVEZGBlq1aoWAgIBi14eGhmLmzJnw9fXF5cuX0apVK7i5ueHJkycAXr1L7dSpU9i4cSMiIyNx/PhxHD9+vMT95eTkIDU1Ve5DREREVF5KT6b69OmDJUuWYNCgQcWuX716NSZMmICxY8fC0dERmzdvhp6enuzFtPXr10f79u1hZWUFbW1t9O3bV/aOtOIsX74cxsbGso+VlVVlHBYRERHVEEpPpt4kNzcXly5dgqurq6xMTU0Nrq6uiIyMBAB06NABT548wcuXLyGVShEREYFmzZqV2Ob8+fORkpIi+zz818t7iYiIiMqiXAPQMzIyoK+vX9GxFPHs2TMUFBTA3Nxcrtzc3By3b98GAGhoaGDZsmXo3r07hBDo3bs3/vOf/5TYpra2Np/2IyIiogpTritT5ubmGDduHE6fPl3R8ZRLnz59cP36ddy4cQOrV69WdjhUicaMGQOJRFLkExsbizFjxmDgwIElbpuVlQVfX1/Y29tDW1sbpqam+Oijj3Dz5k1ZHRsbm2LbL/yMGTMGALB06VJ07twZenp6MDExqdyDJiIilVauZOqnn37Cixcv0LNnT9jb22PFihV4/PhxRccGU1NTqKurIykpSa48KSkJFhYWFb4/qh7c3d2RkJAg92nUqNEbt8nJyYGrqyuCgoKwZMkS3LlzB7/99hvy8/Ph7OyMs2fPAgAuXLgga/OXX34BAMTExMjK1q1bB+DVLeiPPvoIkydPrtyDJSIilVeuZGrgwIE4cOAA/vnnH0yaNAk7d+6EtbU1/vOf/2Dfvn3Iz8+vkOC0tLTQrl07hIWFycqkUinCwsLQqVMnhdoOCAiAo6MjOnTooGiYVMW0tbVhYWEh91FXV3/jNmvXrkVkZCR+/fVXDBs2DNbW1ujYsSN++eUXNGvWDOPHj4cQAnXr1pW1WThnmZmZmays8L2Eixcvhre3N1q0aFHpx0tERKpNoQHodevWxcyZM3Ht2jWsXr0af/zxB4YOHQpLS0v4+PggMzPzrW2kp6cjKipK9gTe/fv3ERUVhQcPHgAAZs6ciR9++AHbtm3DrVu3MHnyZGRkZGDs2LGKhA4vLy9ER0fjwoULCrVD1cPOnTvRq1cvtGrVSq5cTU0N3t7eiI6OxtWrV5UUHRERVWcKzYCelJSEbdu2ISQkBPHx8Rg6dCjGjx+PR48e4euvv8bZs2fx+++/v7GNixcvokePHrLlmTNnAgBGjx6NkJAQeHh44OnTp/Dx8UFiYiJat26No0ePFhmUTjXHr7/+CgMDA9lynz59sHfv3jduc+fOHbmfs9cVPv15584dtG7dusLiJCKimqFcydS+ffsQHByMY8eOwdHREVOmTIGnp6fcQNzOnTu/cYqCQi4uLhBCvLHO559/js8//7w8odI7qEePHti0aZNsubRPlr7t54yIiKg8ypVMjR07FsOHD8dff/1V4pgjS0tLLFiwQKHgKlNAQAACAgJQUFCg7FDoDZ48eYIt3t6QxMYiz9AQ/+TlQb9WLdjZ2ZWpHXt7e9y6davYdYXl9vb2CsdLREQ1T7mSqYSEBOjp6b2xjq6uLnx9fcsVVFXw8vKCl5cXUlNTZYOKSbXk5uZiubs7ll65gsKfNhctLSSVY9D38OHDsWDBAly9elVu3JRUKsWaNWvg6OhYZDwVERFRaZQrmTI0NERCQgLMzMzkyp8/fw4zMzNe7aEKcWj7doy/dg2vp+02ubk4GxdX4jYpKSlFXidUp04deHt74+DBg+jfvz++/fZbODs7IykpCcuWLcOtW7fwxx9/QCKRlDq2Bw8e4MWLF3jw4AEKCgpk+7Szs5Mbz0VERO++ciVTJY09ycnJgZaWlkIBERWKu3YN/YtJzNXy8krcJjw8HG3atJErGz9+PLZu3YoTJ05g2bJl+PLLLxEfHw9DQ0P06NEDZ8+eRfPmzcsUm4+PD7Zt2yZbLtznyZMn4eLiUqa2iIioepOIMozK/e677wAA3t7e+Oqrr+T+Ay8oKEBERATi4uJw5cqVio+0khTe5ktJSYGRkZGyw6HXnD91Cgn9+mFAWpqsLB/AVx9+iMUHDyovMCIiUjpVOn+XaZ6pNWvWYM2aNRBCYPPmzbLlNWvWYPPmzcjMzMTmzZsrK1aqYTp07YqrH36II/r6kAJ4CGB+s2YY8+235W7z9dfRaGlpwc7ODv7+/sjPz0d4eLhsnZqaGoyNjdGmTRvMmTMHCQkJRdpKTU3FggUL0LRpU+jo6MDCwgKurq7Yt2+f7Oqti4uLrE1tbW3Ur18f/fv3x759+4qN7/Dhw3B2doauri5q1aol93qcuLg4SCQS2S3FwuXCT506ddC7d+9q9c8MEdE7QZSDi4uLePHiRXk2VRkbNmwQzZo1E/b29gKASElJUXZIVAypVCpOHT8ulo0fL7YuXSpSU1MVam/06NHC3d1dJCQkiLi4OLFixQoBQNSqVUtoaGgIAMLFxUXs2bNHxMTECFNTUwFAABBaWlrC2tpafPTRR+LgwYPCyclJNGjQQISEhIibN2+KmJgYMWbMGKGlpSW0tLRE3bp1haWlpZgwYYJISEgQ4eHhok2bNkJPT08AEIaGhmLBggUiNzdXCCHEzz//LGrVqiU2bdokYmJixM2bN0VoaKgs9vv37wsA4sqVK3LLf/zxh0hISBAXLlwQnTp1Eubm5uLly5cK9RMRkapLSUlRmfN3uZKpd4kqfTOo8o0ePVoMGDBACPEqGbG0tBT6+vrC3t5ebN++XQAQS5YsEQ4ODkIIIaytrcWiRYtE48aNRfv27cWff/4pJkyYIEuu/vnnH1nb3377rbC0tBSBgYHi9u3b4urVq6J58+Zi+vTpQggh7t27J4KCgkRUVJRYuXKlACBMTEzE/PnzRV5enqhfv77YunVribGXlEwVLgshxF9//SUAiKNHj1ZovxERqRpVOn+XegD6zJkz8dVXX0FfX182S3lJVq9eXdYLZERVbsqUKZBIJHBxcUFCQgKsrKwA/P+0GYVq166Nzz//HN7e3mjatCk2b96MH3/8EdnZ2Uj733iuly9fYuHChfjvf/+LDz74QLZtnTp1ZF/b2trC1tYWANCiRQssX74cVlZWOHXqFC5fvox//vkHampqaNOmjWy2/1WrVpVpcLyuri6AV9NKEBFR1Sh1MnXlyhXk/e8pqjeNySjL4+VEyvLixQscOXIE48aNw44dOzB16lS59a/P5g8ATZs2BfBqnBIAZGdnAwAOHjyIOXPm4Pjx45BKpfjnn3/QrFkzpKWloXPnzsjJySl2/2pqamjYsCFiY2MxY8YM/P333wAAPz8/rF69GjY2Nvj222/h4uKCO3fuyF66/CbJycmyB0M6duxYlu4gIiIFlDqZOnnyZLFfE1U3v/76Kxo0aAAA2LZtG0aOHAk/P783vvRa/G9AuUQikX1tYGAgS67+/vtvSKVSLFu2DOvWrYOxsTEWLlyIq1evon379nJtde7cGZcvX0ZOTg6srKzg7++P3bt3AwAWLFiAIUOGAACCg4PRoEED7N27F5999lmJsXXu3BlqamrIyMiAra0tQkND+e5KIqIqpNCLjgulpqbixIkTaNq0qew/eCJVkJOTg03z5iHt3DkIdXXczchAjx498Nlnn+Gjjz7C7t27MXTo0Le2U/jKGRsbG9SpUwcmJibIzs6WXYmVSqXIy8vDd999h969ewMAdu3aBTMzMzx69EiurdDQUCQnJ6NTp0548eIFvvnmG9lrmRwdHWX1tLW1YWtriwcPHrwxttDQUDg6OsriIiKiqlWmqREKDRs2DBs2bAAAZGVloX379hg2bBhatGiBX375pUIDrCwBAQFwdHQs8d2C9G5YMnw4hq5di0WRkfA5fRq1rlzBrTNnUJCSAolEgrt37761jdzcXGzZsgXdu3dH3bp1oaamhoEDByI7O1t2+61evXoAXiVD6enpyM/PR926daGpqSkbV1XIysoKFy5cQEZGBqZNmwY/Pz+0bt0a2traiImJkdXLy8tDXFwcrK2t3xiflZUVGjduzESKiEhJypVMRUREoFu3bgCA/fv3QwiB5ORkfPfdd1iyZEmFBlhZvLy8EB0d/cZbO1S93b9/H7anT6PBa2WmAIwyM2Hy+edwsLJCQEAAMjIy5LZ78uQJYmJicPfuXWRkZOC7777Ds2fPsGnTpv9vx9QUALB161Zs375ddltt7dq1aNOmDdLT0/HixQvk5eVBS0sLiYmJePToEc6ePYu5c+di0qRJmDx5Mpo2bYq8vDwYGBhg0qRJ8PX1xe+//46YmBhMnjwZAPDRRx9Vaj8REZFiynWbLyUlRfYf+dGjRzFkyBDo6emhX79++OKLLyo0QKLyehgfD7tnz4qUawNonZ2NYXl52FxQgI4dO8Lf3x9ZWVkAAAcHBwCv3kGZnZ2Njh07YuPGjTA0NERERAR++uknbN26FX5+fsjOzsaSJUsQHx8PbW1tbN68GQsXLsTDhw/x5ZdfQk9PD0eOHEG9evWgoaEBQ0NDtGrVCuvWrZO9M9DDwwOamppYtWoVNDQ0MGrUKGRlZcHZ2RknTpxArVq1qrLbiIiorMozn0KTJk1EaGioSE9PF3Xr1hVhYWFCCCGioqJEnTp1Km7ihiqgSvNUUMVKSUkRy21thQDkPgsAIQXEKUDs27dPeHl5CWtra6GlpSXq168vPvzwQ3Hy5EkhxKt5pvDapJ0NGzYUw4YNEydOnCh2f+PGjRMmJiaidu3aYtCgQeLBgwey9bt37xZt27YVBgYGQl9fXzg6Ooply5aJrKysquoSIqJ3hiqdv8v0br5CGzduxPTp02FgYABra2tcvnwZampqWL9+Pfbt21etnvZTpXf7UMX7ceVKZK9ahVHPniEHwPcA2gH4AEBA/foYeukSn3wjIqqGVOn8Xa5kCgAuXryIhw8folevXrIXHh8+fBgmJibo0qVLhQZZmVTpm0GV4/bNm/jm00+hc/ky5uXmogGAs9raODV6NL74/ntlh0dEROWgSufvcidT7wpV+mZQ5Tr53//iVGAg1AoK0GzAAAweP56TzBIRVVOqdP4uVzJVUFCAkJAQhIWF4cmTJ5BKpXLrT5w4UWEBVjZV+mYQERFR6ajS+btcT/NNnz4dISEh6NevH5o3b14t/7sPCAhAQEAACgoKlB0KERERVWPlujJlamqK7du3o2/fvpURU5VSpcyWiIiISkeVzt/lmrRTS0sLdnZ2FR0LERERUbVTrmRq1qxZWLduHWr42HUiIiKi8o2ZOn36NE6ePIkjR47AyckJmpqacuv37dtXIcERERERqbpyJVMmJiYYNGhQRcdCREREVO2UK5kKDg6u6DiIiIiIqqVyjZkCgPz8fPzxxx/4/vvvkZaWBgB4/Pgx0tPTKyw4IiIiIlVXritT8fHxcHd3x4MHD5CTk4NevXrB0NAQX3/9NXJycrB58+aKjpOIiIhIJZXrytT06dPRvn17vHz5Erq6urLyQYMGISwsrMKCq0wBAQFwdHREhw4dlB0KERERVWPlmrSzTp06OHPmDBwcHGBoaIirV6/C1tYWcXFxcHR0RGZmZmXEWilUadIvIiIiKh1VOn+X68qUVCot9jUsjx49gqGhocJBEREREVUX5UqmevfujbVr18qWJRIJ0tPT4evr+068YoaIiIiotMp1m+/Ro0dwc3ODEAJ3795F+/btcffuXZiamiIiIgJmZmaVEWulUKXLhERERFQ6qnT+LlcyBbyaGiE0NBRXr15Feno62rZti5EjR8oNSK8OVOmbQURERKWjSufvciVTERER6Ny5MzQ05GdWyM/Px5kzZ9C9e/cKC7CyqdI3g4iIiEpHlc7f5Roz1aNHD7x48aJIeUpKCnr06KFwUERERETVRbmSKSEEJBJJkfLnz59DX19f4aCIiIiIqosyJVODBw/G4MGDIZFIMGbMGNny4MGDMWDAALi5uaFz586VFSsRvWbMmDGQSCSYNGlSkXVeXl6y31OJRPLGj5+fH+Li4iCRSBAVFVWkLRcXF8yYMUNuubh28vPzi6zX1tZG/fr10b9/f+zbt69I20uXLkXnzp2hp6cHExOTiuoaIqIqVaZkytjYGMbGxhBCwNDQULZsbGwMCwsLTJw4ET/99FNlxUpE/2JlZYXdu3cjKytLVpadnY2dO3eiYcOGAICEhATZZ+3atTAyMpIrmz17dpn3O2HCBLk2EhIS5MZQFq6/d+8efvnlFzg6OmL48OGYOHGiXDu5ubn46KOPMHny5HL2ABGR8pXp3XzBwcEAABsbG8yePZu39IiUrG3btrh37x727duHkSNHAgD27duHhg0bolGjRgAACwsLWX1jY2NIJBK5MgB49uxZmfarp6dXpI2S1jdo0ADvvfcemjZtinHjxmHYsGFwdXUFACxevBgAEBISUqb9ExGpknKNmfL19a32iRTfzUfvinHjxsn+0QGAoKAgjB07VokRFW/06NGoVatWsbf7iIiqs3IlU0lJSRg1ahQsLS2hoaEBdXV1uU914OXlhejoaFy4cEHZoRApxNPTE6dPn0Z8fDzi4+Px119/wdPTs1xtde7cGQYGBnKfU6dOFam3ceNGuTqzZs16a9tqamqwt7dHXFxcuWIjIlJVZbrNV2jMmDF48OABFi1ahHr16hX7ZB8RVY26deuiX79+CAkJgRAC/fr1g6mpabnaCg0NRbNmzeTKCm8f/rtswYIFsuXSDh4v6UlgIqLqrFzJ1OnTp3Hq1Cm0bt26gsMhopJIpVIELlmChOPHIZFKcSs1FRa2tgBe3er7/PPPAby6hV2cyMhIjBs3rsjV47i4ONn4qg8//BC1atVCixYtsGTJEnTr1k32VgM/Pz/ZGKc///xTNs7p+PHjsjFQycnJuHv3Li5cuIBNmzbB2toaa9euRd++fVFQUIC7d+/y1joRvXPKlUxZWVmhnG+hIaJyWj1lCvoEBsLpf1MQRAGIfvkSAODu7o7c3FxIJBK4ubkVu31gYCBcXV1x/PhxPH78GJaWlkXqHD9+HBYWFli6dCn+85//4M6dO3LrnZycYGRkBCcnJ3z11VcAgNq1awN49WRer169kJ2djX79+mHlypWIj4+XXbXatm0bXr58iSFDhlRAbxARqY5yjZlau3Yt5s2bx7EPRFUkPT0d+P13WSIFAIYAtJ4/R2JiItTV1XHr1i1ER0cXO24xPT0doaGh6NGjBzQ1NUt8es7U1BTNmzfHl19+idTUVJw7d05uvYaGBrS0tKCvrw8LCwtYWFhAS0sLwKuB7y9evEDz5s1hYmICHR0dNG7cGFlZWZg7dy4mTZqEyZMny70l4cGDB4iKisKDBw9QUFCAqKgoREVFvTpeIqJqolxXpjw8PJCZmYnGjRtDT08PmpqacuuLe9UMEZXf06dPUT85uUi5fk4OHj16BAsLize+m2rPnj1o2rQp6tWrBy0tLQQFBWH+/PnFjl/KysrC9u3bAUCWKJXGoUOH0KlTJ5w4cQIRERH44YcfoK6uDjMzM7Rr1w6hoaEYNGiQ3DY+Pj7Ytm2bbLlNmzYAgJMnT8LFxaXU+yYiUqZyvej49T9+xRk9enS5A6pqqvSiRKKSFBQUwL9tWyy+dk2ufIm9PWZFRcnGNZWkS5cuGDZsGKZPn478/HzUq1cPe/fuhYuLi2zMlK6uLtTU1JCZmQkhBNq1a4fIyEjZP0t+fn746quv5Pbl6OiI8+fPAwCaNm2KuLg4jBw5ElOmTEFsbCymTJmCadOmwdfXt4J7hIhqOlU6f5frylR1SpaI3gXq6uroOHs21sybhwmPH0MdwDYzMzTx8ioxkcrPz4dUKsX9+/dx/vx57N+/H8CrW3UeHh4IDAyUu/oTGhqKpk2b4saNG5gzZw5CQkKKXHV2cHDAoUOHZMva2tqyr6VSKczMzLBlyxaoq6ujXbt2+Oeff7Bq1SomU0T0TitTMpWamlqqesrOEIneRf1GjULzbt2wZc0aFOTnY+C0aWji4FCkXnZ2NlZ/9hnEmTPQzMvDfrxKrF4fcC6EgLa2NjZs2CArs7KyQpMmTdCkSRPk5+dj0KBBuHHjhlzCpKWlBTs7u2Ljq1evHjQ1NeXGbDVr1gyJiYnIzc0t0y1DIqLqpEzJlImJyRvniCmcQ6agoEDhwIioKGsbG8xct+6NdVZPnIgxP/4ISwD5AFYD6N6gAdYfPgw1tf9/5mTgwIHYtWsX3N3di7QxdOhQ+Pj4YOPGjfD29i5VbF26dMHOnTshlUpl+7lz545snBYR0buqTMnUyZMnKysOIqoA+fn5QGQkCq9B/QrgJYAZT58iIyUFnbp1k9UdMmQIAgMDi02mJBIJpk2bBj8/P3z22WfQ09N7674nT56MDRs2YPr06Zg6dSru3r2LZcuWYdq0aRVzcEREKqpMydT7779fWXEQUQXIz8+HVm6ubDkQgCuA+rm5ePmvp2yHDBmClStXlnj7fvTo0ViwYAE2bNiAOXPmvHXfVlZWOHbsGLy9vdGyZUvUr18f06dPx9y5cxU5JCIilVeup/mAV4NNY2Nj8eTJE0ilUrl13bt3r5DgqoIqPQ1AVBEWuLpicViY3H9Kvvb2mFeKp/6IiKoLVTp/l+tpvrNnz+Ljjz9GfHx8kZnQOWaKSLkmbtmCuR99hL43b8IoNxeH7ezQdckSJlJERJWkXFemWrduDXt7eyxevLjYFx0bGxtXWICVTZUyW6KKIpVKcSY8HOkpKeju5laqMU9ERNWJKp2/y5VM6evr4+rVqyU+Il2dqNI3g4iIiEpHlc7f5Xo3n7OzM2JjYys6lioVEBAAR0dHvsGeiIiIFFKuK1P79+/HwoUL8cUXX6BFixZFZklu2bJlhQVY2VQpsyUiIqLSUaXzd7mSqdcn/pM1JJFUy0k7VembQURERKWjSufvcj3Nd//+/YqOg4iIiKhaKlcyZW1tXdFxEBEREVVL5UqmCkVHR+PBgwfIfW3GZQD48MMPFQqKiIiIqLooVzL1999/Y9CgQbh+/bpsrBQA2XxT1WnMFBEREZEiyjU1wvTp09GoUSM8efIEenp6uHnzJiIiItC+fXuEh4dXcIhEREREqqtcyVRkZCT8/f1hamoKNTU1qKmpoWvXrli+fDnfEE9EZSKRSN748fPzA/BqSpb33nsPxsbGMDQ0hJOTE2bMmCHXVm5uLlatWoW2bdtCX18fxsbGaNWqFRYuXIjHjx/L6kVERKB///6wtLSERCLBgQMHisQlhICPjw/q1asHXV1duLq64u7du3J1li5dis6dO0NPTw8mJiYV3DNEVF2UK5kqKCiAoaEhAMDU1FT2R8ra2hoxMTEVFx0RvfMSEhJkn7Vr18LIyEiubPbs2QgLC4OHhweGDBmC8+fP49KlS1i6dCny8vJk7eTk5KBXr15YtmwZxowZg4iICFy/fh3fffcdnj17hvXr18vqZmRkoFWrVggICCgxrpUrV+K7777D5s2bce7cOejr68PNzQ3Z2dmyOrm5ufjoo48wefLkyukcIqoWyjVmqnnz5rh69SoaNWoEZ2dnrFy5ElpaWtiyZQtsbW0rOkYieodZWFjIvjY2NoZEIpErA4D//ve/6NKlC7744gtZmb29PQYOHChbXrNmDU6fPo2LFy+iTZs2svKGDRvi/fffl3spe58+fdCnT58SYxJCYO3atVi4cCEGDBgAANi+fTvMzc1x4MABDB8+HACwePFiAEBISEjZD5yI3hnlujK1cOFCSKVSAIC/vz/u37+Pbt264bfffsN3331XoQESEVlYWODmzZu4ceNGiXV27dqFXr16ySVSr/v3C9nf5P79+0hMTISrq6uszNjYGM7OzoiMjCx94ERUI5QrmXJzc8PgwYMBAHZ2drh9+zaePXuGJ0+eoGfPnhUaIBHR1KlT0aFDB7Ro0QI2NjYYPnw4goKCkJOTI6tz584dODg4yG03aNAgGBgYwMDAAJ07dy71/hITEwEA5ubmcuXm5uaydUREhcqVTBWKjY3FsWPHkJWVhdq1a1dUTERUQwghUJo3Wunr6+Pw4cOIjY3FwoULYWBggFmzZqFjx47IzMwscbuNGzciKioK48aNe2M9IiJFlCuZev78OT744APY29ujb9++SEhIAACMHz8es2bNqtAAiejdk5+fj2+nTIG/kxOW2dlhUd+++OfRo7du17hxY3z66afYunUrLl++jOjoaISGhgIAmjRpUuQBmHr16sHOzq7M/+wVjtlKSkqSK09KSioynouIqFzJlLe3NzQ1NfHgwQPo6enJyj08PHD06NEKC46I3k1rvLww+Pvv4XvrFhb8/TcWHTmCbwcNko3FLA0bGxvo6ekhIyMDADBixAgcP34cV65cUTi+Ro0awcLCAmFhYbKy1NRUnDt3Dp06dVK4fSJ6t5Trab7ff/8dx44dQ4MGDeTKmzRpgvj4+AoJjIjeTVKpFFl//olGryVOWgD63riBE7dvF7uNn58fMjMz0bdvX1hbWyM5ORnfffcd8vLy0KtXLwCv/sk7fPgwPvjgA/j6+qJbt26oVasW7ty5gyNHjkBdXV3WXnp6OmJjY2XL9+/fR1RUFGrXro2GDRtCIpFgxowZWLJkCZo0aYJGjRph0aJFsLS0lHuC8MGDB3jx4gUePHiAgoICREVFAXg1ltTAwKDiOo2IVFq5kqmMjAy5K1KFXrx4AW1tbYWDIqJ3l1QqhdZrczUVqpudjcy0tGK3ef/99xEQEIBPPvkESUlJqFWrFtq0aYPff/9dNuhcR0cHYWFhWLt2LYKDgzF//nxIpVI0atQIffr0gbe3t6y9ixcvokePHrLlmTNnAgBGjx4tm+Zgzpw5yMjIwMSJE5GcnIyuXbvi6NGj0NHRkW3n4+ODbdu2yZYLnyQ8efIkXFxcytdBRFTtSERpRn/+S9++fdGuXTt89dVXMDQ0xLVr12BtbY3hw4dDKpXi559/roxYK0VqaiqMjY2RkpICIyMjZYdDVCMs6NULi//4Q+6/uaW2tph65Qp/D4moVFTp/F2uK1MrV67EBx98gIsXLyI3Nxdz5szBzZs38eLFC/z1118VHSMRvWM+3bQJc4cOxeDoaNTKy8MBGxu0WLBA6X8QiYjKo1xXpgAgOTkZAQEBuHr1KtLT09G2bVt4eXmhXr16FR1jpVKlzJaoJikoKMDJ335D6vPn+GDQIBgbGys7JCKqRlTp/F3uZCo7OxvXrl3DkydPijyB8+GHH1ZIcFVBlb4ZREREVDqqdP4u122+o0ePYtSoUXjx4kWRCfckEgkKCgoqJDgiIiIiVVeueaamTp2KYcOG4fHjx5BKpXIfZSRSNjY2aNmyJVq3bi33hA4RERFRZSvXlamkpCTMnDmzyHurlOnMmTOc14WIiIiqXLmuTA0dOhTh4eEVHAoRERFR9VOuZGrDhg3Yt28fxowZg2+//Rbfffed3KcsIiIi0L9/f1haWkIikeDAgQNF6gQEBMDGxgY6OjpwdnbG+fPn5dZLJBK8//776NChA3bs2FGeQyIikomMjIS6ujr69esnVx4XFweJRAIzMzOk/WuC0datW8PPzw/Aq4mNGzduLJsM9PXtjYyM8MMPPxTZZ9OmTaGtrY3ExES58ry8PMydOxctWrSAvr4+LC0t8cknn+Dx48cVcKREVBHKdZtv165d+P3336Gjo4Pw8HBIJBLZOolEgmnTppW6rYyMDLRq1Qrjxo3D4MGDi6wPDQ3FzJkzsXnzZjg7O2Pt2rVwc3NDTEwMzMzMAACnT59G/fr1kZCQAFdXV7Ro0QItW7Ysdn85OTnIycmRLaemppY6ViKqGQIDAzF16lQEBgbi8ePHsLS0lFuflpaGb775BosXLy52e319fQQHB+ODDz7AoEGD0K1bNwghMHbsWHTp0gUTJkyQq3/69GlkZWVh6NCh2LZtG+bOnStbl5mZicuXL2PRokVo1aoVXr58ienTp+PDDz/ExYsXK/7giajsRDmYm5uLpUuXioKCgvJsXiIAYv/+/XJlHTt2FF5eXrLlgoICYWlpKZYvX15sG7NnzxbBwcEl7sPX11cAKPJJSUmpiEMgomouLS1NGBgYiNu3bwsPDw+xdOlS2br79+8LAOKLL74QBgYGIikpSbauVatWwtfXV64tb29v0bhxY5Geni7WrFkjTExMxKNHj4rsc8yYMWLevHniyJEjwt7e/q0xnj9/XgAQ8fHx5T9QomouJSVFZc7f5brNl5ubCw8PD6iplWvzMu3n0qVLcHV1lZWpqanB1dUVkZGRAF5d2Sq83J6eno4TJ07AycmpxDbnz5+PlJQU2efhw4eVegxEVL3s2bMHTZs2hYODAzw9PREUFFRkCpgRI0bAzs4O/v7+b2xr6dKl0NDQgKenJ7788kusX78e9evXl6uTlpaGvXv3wtPTE7169UJKSgpOnTr1xnZTUlIgkUhgYmJSrmMkoopVrmxo9OjRCA0NrehYinj27BkKCgqKPDVobm4uG1eQlJSErl27olWrVnjvvffwySefoEOHDiW2qa2tDSMjI7kPEVGhwMBAeHp6AgDc3d2RkpKCP//8U66ORCLBihUrsGXLFty7d6/EtnR1dbFu3TocOHAALi4usnZft3v3bjRp0gROTk5QV1fH8OHDERgYWGKb2dnZmDt3LkaMGMG/X0QqolxjpgoKCrBy5UocO3YMLVu2hKamptz61atXV0hwpWFra4urV69W2f6I6N1SUFCAe/fuwdTUFE+fPsX58+exf/9+AICGhgY8PDwQGBgIFxcXue3c3NzQtWtXLFq0CDt37iyx/cDAQOjp6eH69etISUkp8tqcoKAguSTL09MT77//PtavXw9DQ0O5unl5eRg2bBiEENi0aZOCR05EFaVcydT169fRpk0bAMCNGzfk1r0+GF1RpqamUFdXR1JSklx5UlISLCwsKmw/RFQzHd+7FxFLlqBlXBweGxlhr4EB8vPz5QacCyGgra2NDRs2FNl+xYoV6NSpE7744oti2w8NDcWvv/6KyMhIjBgxAt7e3ggKCpKtj46OxtmzZ3H+/Hm5QecFBQXYvXu33ED1wkQqPj4eJ06c4FUpIlWi5DFbclDCAPTPP/9ctlxQUCDq169f4gD00tqwYYNo1qyZsLe3V5kBbERUdZKSksRSa2shACEAkQeIuoD4sGtXcf36dblP48aNxaZNm2QD0K9cuSJrZ+jQoaJ3797C0dFRdOjQQTRq1EhoaWkJS0tLoampKT777DMhhBD16tWTPfCio6MjrK2thb29vWjRokWR/c2cOVM4OzuLZ8+eifr16wsAom/fvsLJyUk8efJECCHEyZMnRZs2bYSWlpZo3LjxGx+8IXoXqdIAdKUnU2lpaeLKlSviypUrAoBYvXq1uHLliuwpld27dwttbW0REhIioqOjxcSJE4WJiYlITEyskP2r0jeDiKrOZj8/kfC/REoAYj8gtADxZevWRerOmTNHtG/fvthkKiYmRqirqwsAom7duuLnn38WMTExwsXFRTRq1Eg4ODgIIYSwtrYW3bp1E/Xq1RPXr18XYWFhQkdHR0gkErFkyRK5/UVHRwsAomfPnsLd3V0AEJaWliIqKkokJCSIc+fOCV1dXTF9+nQRHR0t1q9fL9TV1cXRo0crtc+IVIkqnb+VnkydPHmy2KkKRo8eLauzfv160bBhQ6GlpSU6duwozp49W2H7V6VvBhFVnU0+PiLptWTqP4DoCwj/tm2L1D137pwAIK5evVokmRJCCCsrKwFAzJ8/XwghxLZt24Senp64e/euePnypRDiVTK1atUq0bx5czF27Fjx888/CzU1NTFz5kyhpqYmbt++LdemhYWFaNCggdixY0exfyMBiJMnT8rqe3h4CDc3twrtIyJVpkrnb6UnU8qmSt8MIqo6iYmJYoWVlSyZEoD4BxDfvDavXWk8f/5cSCQSsWzZsjfWs7a2FmvWrClx+6+//lpWdvPmTWFhYSHi4+Nl/3AWJmVCCNGtWzcxffp0uXaCgoKEkZFRmWInqs5U6fxduRNFERGpKHNzc7Rcvhw+Tk44qK+PjfXq4fshQ/D5t9+WqZ3Y2FgIIdC0adNyxVG7dm2YmZkhLi4OwKu3NIwYMQKrVq1Cw4YNi90mMTGx2CljUlNTkZWVVa44iKj8yvU037sgICAAAQEBKCgoUHYoRKQkfUaOhOuwYbh9+zbeMzMrkqCUJC0tDXsDApB07x5ql/DqqrIQQsiehJ4/fz6aNWtW7JxURKSaamwy5eXlBS8vL6SmphaZ94WIag5NTU20aNGi1PXj7t1DwIcfYmp0NBoA2KunBwmA27dvl2v/z58/x9OnT9GoUSMAwIkTJ3D9+nX8/PPPACCbfd3U1BQLFizA4sWLYWFhUeyUMUZGRtDV1S1XHERUfjU2mSIiKo+QWbOwPDpa9sfTIzMTK9XVsXbNGkybNg36+vpy9ZOTk9/42pd169ZBTU0NAwcOBAD88ssvcrfqLly4gHHjxuHUqVNo3LgxAKBTp0747bff5No5fvw4OnXqpPDxEVHZMZkiIioD7fj4In84dxQUoGNGBjp27Ah/f3+0bNkS+fn5OH78ODZt2oRbt24BeHV7MDExEXl5ebh//z5++uknbN26FcuXL4ednR0AyBKmQs+ePQMANGvWTJaUTZo0CRs2bMCcOXMwbtw4nDhxAnv27MHhw4cr9diJqHhMpoiIyiCnmJnHC9TVscrPD9fj4zFr1iwkJCSgbt26aNeundxrX3x8fODj4wMtLS1YWFjgvffeQ1hYGHr06FGmGBo1aoTDhw/D29sb69atQ4MGDbB161a4ubkpfHxEVHYSIf71OvQa4vUB6Hfu3EFKSgpfz0BEb/Xbjh3InDYNQ1+8AABkAfiydWusOHsW2trayg2OqAYpHPOsCufvGptMFVKlbwYRVQ9Hd+/G2R9+gFZaGqR2dpiwZk2pnwQkooqhSudvJlMq9M0gIiKi0lGl8zcn7SQiIiJSAJMpIiIiIgUwmSIiIiJSAJMpIiIiIgXU2GQqICAAjo6O6NChg7JDISIiomqMT/Op0NMAREREVDqqdP6usVemiIiIiCoCkykiIiIiBTCZIiIiIlIAkykiohouMjIS6urq6Nevn1x5XFwcJBIJzMzMkJaWJreudevW8PPzAwBkZGSgcePGmDlzZpHtjYyM8MMPPxTZZ9OmTaGtrY3ExMQi6/bt24fevXujTp06kEgkiIqKUuwAiSoZkykiohouMDAQU6dORUREBB4/flxkfVpaGr755psSt9fX10dwcDDWr1+PU6dOAQCEEBg7diy6dOmCCRMmyNU/ffo0srKyMHToUGzbtq1IexkZGejatSu+/vprBY+MqGrU2GSKUyMQEQHp6ekIDQ3F5MmT0a9fP4SEhBSpM3XqVKxevRpPnjwpsZ3u3btj6tSpGDt2LDIyMrBu3TpERUVh69atReoGBgbi448/xqhRoxAUFFRk/ahRo+Dj4wNXV1eFjo2oqtTYZMrLywvR0dG4cOGCskMhIlKaPXv2oGnTpnBwcICnpyeCgoLw7xlzRowYATs7O/j7+7+xraVLl0JDQwOenp748ssvsX79etSvX1+uTlpaGvbu3QtPT0/06tULKSkpsqtZRNVVjU2miIjo1VUiT09PAIC7uztSUlLw559/ytWRSCRYsWIFtmzZgnv37pXYlq6uLtatW4cDBw7AxcVF1u7rdu/ejSZNmsDJyQnq6uoYPnw4AgMDK/agiKoYkykiohpGKpUCAGJiYnD+/HmMGDECAKChoQEPD49ikxs3Nzd07doVixYtemPbgYGB0NPTw/Xr15GSklJkfVBQkFyS5enpib179xYZ4E5UnTCZIiKqIU799hsWdu6Mlba28GnTBrMnT0Z+fj4sLS2hoaEBDQ0NbNq0Cb/88kuxidCKFSsQGhqKK1euFNt+aGgofv31V5w5cwaGhobw9vaWWx8dHY2zZ89izpw5sv299957yMzMxO7duyvlmImqgoayAyAiosoXHxeHyM8+w5JHjwAA+fHxqCuR4NORIzF93jy5ugMHDsSuXbvg7u4uV96xY0cMHjwY8/5VHwCSkpLg5eWFJUuWoFWrVggJCUHnzp3x0UcfoU+fPgBeXbXq3r07AgIC5LYNDg5GYGBgkaf+iKoLJlNERDXA3pUrMeV/iRQA/AogUwjUefwYzZs3l6s7ZMgQBAYGFkmmgFeDzJ2cnKChIX/6mDhxIpo1a4YZM2YAeJV4ffHFF5g4cSJu3LgBPT09/Pjjj/D39y+yv08//RSrV6/GzZs34eTkhBcvXuDBgweyaRpiYmIAABYWFrCwsFC0K4gqHG/zERHVADmpqdB7bTkQgCsAg/+Nn3rdkCFDcPHiRaSmphZZZ29vj3HjxiE7O1tWtn37dvzxxx8IDg6Gmtr/n1YWL14MExMTeHt749ChQ3j+/DkGDRpUpM1mzZqhWbNmsrFahw4dQps2bWSTiA4fPhxt2rTB5s2by3fwRJVMIv79DGwNo0pvnSYiqix/Hj2KjKFD0TcjQ1b2CMC+L77AtJUrlRcYUTmp0vm7xl6Z4qSdRFSTdHdzw63Ro/GDmRnuAPjFyAgb3N3x2VdfKTs0omqPV6ZUKLMlIqps8XFxuPDHH2jSujVatW+v7HCIyk2Vzt8cgE5EVINY29jA+tNPlR0G0Tulxt7mIyIiIqoITKaIiIiIFMBkioiIiEgBTKaIiIiIFMBkioiIiEgBTKaIiIiIFMBkioiIiEgBTKaIiIiIFMBkioiIiEgBNTaZ4rv5iIiIqCLw3Xwq9G4fIiIiKh1VOn/X2CtTRERERBWByRQRERGRAphMERERESmAyRQRERGRAphMERERESmAyRQRERGRAphMERERESmAyRQRERGAp0+fYvLkyWjYsCG0tbVhYWEBNzc3/PXXXwAAGxsbSCQSSCQS6OrqwsbGBsOGDcOJEyeKbe+XX36Bi4sLjI2NYWBggJYtW8Lf3x8vXrwAAISEhMjak0gkMDAwQLt27bBv3z65dlxcXGR1dHR04OjoiI0bN1ZuZ1CZMJkiIiICMGTIEFy5cgXbtm3DnTt3cOjQIbi4uOD58+eyOv7+/khISEBMTAy2b98OExMTuLq6YunSpXJtLViwAB4eHujQoQOOHDmCGzdu4Ntvv8XVq1fx448/yuoZGRkhISEBCQkJuHLlCtzc3DBs2DDExMTItTdhwgQkJCQgOjoaw4YNg5eXF3bt2lW5HUKlJ2q4lJQUAUCkpKQoOxQiIlKSly9fCgAiPDy8xDrW1tZizZo1Rcp9fHyEmpqauH37thBCiHPnzgkAYu3atSXuSwghgoODhbGxsdy6goICoampKfbs2SMre//998X06dPl6jVp0kQMHz787Qf2DlOl8zevTBERUY1nYGAAAwMDHDhwADk5OWXadvr06RBC4ODBgwCAHTt2wMDAAFOmTCm2vomJSbHlBQUF2LZtGwCgbdu2b9ynrq4ucnNzyxQnVR4mU0REVONpaGggJCQE27Ztg4mJCbp06YIvv/wS165de+u2tWvXhpmZGeLi4gAAd+/eha2tLTQ1Nd+6bUpKiiyR09LSwuTJk7FlyxY0bty42PoFBQX46aefcO3aNfTs2bNMx0iVh8kUERERXo2Zevz4MQ4dOgR3d3eEh4ejbdu2CAkJeeu2QghIJBLZ16VlaGiIqKgoREVF4cqVK1i2bBkmTZqE//73v3L1Nm7cCAMDA+jq6mLChAnw9vbG5MmTy3R8VHmYTBERUY0jhMBve/Zg6ccfY/W0aXj8+DEAQEdHB7169cKiRYtw5swZjBkzBr6+vm9s6/nz53j69CkaNWoEALC3t8fff/+NvLy8t8ahpqYGOzs72NnZoWXLlpg5cyZcXFzw9ddfy9UbOXIkoqKicP/+fWRkZGD16tVQU+MpXFXwO0FERDWO/8iRMPzkE3y5axfGr1+PrV26ICoyskg9R0dHZGRkvLGtdevWQU1NDQMHDgQAfPzxx0hPTy9x+oLk5OQ3tqeuro6srCy5MmNjY9jZ2aF+/fpMolSQhrIDUJaAgAAEBASgoKBA2aEQEVEVunL+PJr/9hu6/W+guTGAKXFxaN+vH5Z89x1atmwJQ0NDXLx4EStXrsSAAQNk26alpSExMRF5eXm4f/8+fvrpJ2zduhXLly+HnZ0dAMDZ2Rlz5szBrFmz8M8//2DQoEGwtLREbGwsNm/ejK5du2L69OkAXl0hS0xMBABkZWXh+PHjOHbsGHx8fKq2U0ghNTaZ8vLygpeXF1JTU2FsbKzscIiIqIqcO3wYw1JS5MoMAdioqWHNmjW4d+8e8vLyYGVlhQkTJuDLL7+U1fPx8YGPjw+0tLRgYWGB9957D2FhYejRo4dce19//TXatWuHgIAAbN68GVKpFI0bN8bQoUMxevRoWb3U1FTUq1cPAKCtrQ1ra2v4+/tj7ty5ldcBVOEkoiwj5d5BhclUSkoKjIyMlB0OERFVsrD//hcaw4bh/exsuXLfLl2w+PRpJUVFZaVK52/eeCUiohqlR79+ONihA57+b1kA2FGnDpz5dByVU429zUdERDWTmpoa/H/7DT8sWIDMa9eQr6eHHlOmoHu/fsoOjaop3uZTocuEREREVDqqdP7mbT4iIiIiBTCZIiIiIlIAkykiIiIiBTCZIiIiIlIAkykiIiIiBTCZIiIiIlIAkykiIiIiBTCZIiIiIlIAkykiIiIiBTCZIiIiIlIAkykiIiIiBTCZIiIiIlIAkykiIiIiBTCZIiIiIlIAkykiIiIiBTCZIiIiIlIAkykiIiIiBbwzyVRmZiasra0xe/ZsZYdCRERENcg7k0wtXboU7733nrLDICIiohrmnUim7t69i9u3b6NPnz7KDoWIiIhqGKUnUxEREejfvz8sLS0hkUhw4MCBInUCAgJgY2MDHR0dODs74/z583LrZ8+ejeXLl1dRxERERET/T+nJVEZGBlq1aoWAgIBi14eGhmLmzJnw9fXF5cuX0apVK7i5ueHJkycAgIMHD8Le3h729vZVGTYRERERAEAihBDKDqKQRCLB/v37MXDgQFmZs7MzOnTogA0bNgAApFIprKysMHXqVMybNw/z58/HTz/9BHV1daSnpyMvLw+zZs2Cj49PsfvIyclBTk6ObDk1NRVWVlZISUmBkZFRpR4fERERVYzU1FQYGxurxPlb6Vem3iQ3NxeXLl2Cq6urrExNTQ2urq6IjIwEACxfvhwPHz5EXFwcvvnmG0yYMKHERKqwvrGxsexjZWVV6cdBRERE7y6VTqaePXuGgoICmJuby5Wbm5sjMTGxXG3Onz8fKSkpss/Dhw8rIlQiIiKqoTSUHUBFGjNmzFvraGtrQ1tbu/KDISIiohpBpa9MmZqaQl1dHUlJSXLlSUlJsLCwUFJURERERP9PpZMpLS0ttGvXDmFhYbIyqVSKsLAwdOrUSaG2AwIC4OjoiA4dOigaJhEREdVgSr/Nl56ejtjYWNny/fv3ERUVhdq1a6Nhw4aYOXMmRo8ejfbt26Njx45Yu3YtMjIyMHbsWIX26+XlBS8vL9nTAERERETlofRk6uLFi+jRo4dseebMmQCA0aNHIyQkBB4eHnj69Cl8fHyQmJiI1q1b4+jRo0UGpRMREREpg0rNM6UMqjRPBREREZWOKp2/VXrMFBEREZGqq7HJFAegExERUUXgbT4VukxIREREpaNK5+8ae2WKiIiIqCIwmSIiIiJSAJMpIiIiIgXU2GSKA9CJiIioInAAugoNYCMiIqLSUaXzd429MkVERERVKzIyEurq6ujXr59ceVxcHCQSCczMzJCWlia3rnXr1vDz8wMAZGRkoHHjxrK3pby+vZGREX744Yci+2zatCm0tbWRmJhYbEy3bt3Chx9+CGNjY+jr66NDhw548OBBmY6LyRQRERFVicDAQEydOhURERF4/PhxkfVpaWn45ptvStxeX18fwcHBWL9+Pc6cOQMAEEJg7Nix6NKlCyZMmCBX//Tp08jKysLQoUOxbdu2Iu3du3cPXbt2RdOmTREeHo5r165h0aJF0NHRKdNxMZkiIiKiSpeeno7Q0FBMnjwZ/fr1Q0hISJE6U6dOxerVq/HkyZMS2+nevTumTp2KKVOmAAA2bdqEqKgobN26tUjdwMBAfPzxxxg1ahSCgoKKrF+wYAH69u2LlStXok2bNmjcuDE+/PBDmJmZlenYmEwRERFRpduzZw+aNm0KBwcHeHp6IigoCP8etj1ixAjY2dnB39//jW0tXboUGhoaAAB/f3+sX78e9evXl6uTlpaGvXv3wtPTE7169UJKSgpOnTolWy+VSnH48GHY29vDzc0NZmZmcHZ2xoEDB8p8bEymiIiIqNIFBgbC09MTAODu7o6UlBT8+eefcnUkEglWrFiBLVu24N69eyW2paurixUrVgAAunbtKmv3dbt370aTJk3g5OQEdXV1DB8+HIGBgbL1T548QXp6OlasWAF3d3f8/vvvGDRoEAYPHlwkrrepsckUp0YgIiKqPEIIREVF4WxkJG7evInz589jxIgRAAANDQ14eHjIJTeF3Nzc0LVrVyxatOiN7f/4448AgOjoaKSkpBRZHxQUJJdkeXp6Yu/evbIB7lKpFAAwYMAAeHt7o3Xr1pg3bx7+85//YPPmzWU61hqbTHl5eSE6OhoXLlxQdihERETvlIfx8fiiUyc86NIF6e+/j/EuLsjPz4elpSU0NDSgoaGBTZs24Zdffik2EVqxYgVCQ0Nx5cqVYtsPDQ3FsWPHAAAGBgbw9vaWWx8dHY2zZ89izpw5sv299957yMzMxO7duwEApqam0NDQgKOjo9y2zZo149N8REREpFybxo3D8nPn8GFmJlzy8hD37Bl6Wljg4sWLiIqKQlRUFK5evQpLS0vs2rWryPYdO3bE4MGDMW/evCLrkpKS4OXlhYULF77a16ZN2L59O44cOSKrExgYiO7du+Pq1auy/UVFRWHmzJmyq2FaWlro0KEDYmJi5Nq/c+cOrK2ty3bAooZLSUkRAERKSoqyQyEiIqr2UlJSxLr69YUAhADEfkBoAeKguro4HREhV3fOnDmiffv24v79+wKAuHLlimxdTEyM0NDQEDo6OsLX11dW/uGHH4quXbuKly9fys7f8+bNEw0aNBDJyckiNzdX1K1bV2zatKlIbNHR0QKAuHHjhhBCiH379glNTU2xZcsWcffuXbF+/Xqhrq4uTp06VaZj5pUpIiIiqjDq6urI+9+TdgAQCMAVgLqWFrT/NX/TkCFDcPHiRaSmphZpx97eHuPGjUN2drasbPv27fjjjz8QHBwMNbX/T2EWL14MExMTeHt749ChQ3j+/DkGDRpUpM1mzZqhWbNmsqtTgwYNwubNm7Fy5Uq0aNECW7duxS+//IKuXbuW6Zj5OhkVmo6eiIjoXeA7aBC8DxyAyf+W8wHMa98eq86fh0QiqZB9qNL5W+PtVYiIiIhKb9a2bfjmk09gcuUKtPPz8ahJE3weGFhhiZSqqbHJVEBAAAICAlBQUKDsUIiIiN4pRkZG8D9wAGlpacjLy0Pt2rWVHVKl4m0+FbpMSERERKWjSudvDkAnIiIiUgCTKSIiIiIFMJkiIiIiUgCTKSIiIiIFMJkiIiIiUgCTKSIiIiIFMJkiIiIiUgCTKSIiIiIF1NhkKiAgAI6OjujQoYOyQyEiIqJqjDOgq9AMqkRERFQ6qnT+rrFXpoiIiIgqQo190XGhwgtzqampSo6EiIiISqvwvK0KN9hqfDKVlpYGALCyslJyJERERFRWaWlpMDY2VmoMNX7MlFQqxePHj2FoaAiJRPLGuqmpqbCyssLDhw+Vfn+2OmM/Vhz2ZcVhX1Yc9mXFYD++mRACaWlpsLS0hJqackct1fgrU2pqamjQoEGZtjEyMuIPdgVgP1Yc9mXFYV9WHPZlxWA/lkzZV6QKcQA6ERERkQKYTBEREREpgMlUGWhra8PX1xfa2trKDqVaYz9WHPZlxWFfVhz2ZcVgP1YfNX4AOhEREZEieGWKiIiISAFMpoiIiIgUwGSKiIiISAFMpoiIiIgUwGSqlAICAmBjYwMdHR04Ozvj/Pnzyg5Jpfj5+UEikch9mjZtKlufnZ0NLy8v1KlTBwYGBhgyZAiSkpLk2njw4AH69esHPT09mJmZ4YsvvkB+fn5VH0qVi4iIQP/+/WFpaQmJRIIDBw7IrRdCwMfHB/Xq1YOuri5cXV1x9+5duTovXrzAyJEjYWRkBBMTE4wfPx7p6elyda5du4Zu3bpBR0cHVlZWWLlyZWUfWpV7W1+OGTOmyM+pu7u7XB32JbB8+XJ06NABhoaGMDMzw8CBAxETEyNXp6J+p8PDw9G2bVtoa2vDzs4OISEhlX14Vao0feni4lLk53LSpElyddiXKk7QW+3evVtoaWmJoKAgcfPmTTFhwgRhYmIikpKSlB2ayvD19RVOTk4iISFB9nn69Kls/aRJk4SVlZUICwsTFy9eFO+9957o3LmzbH1+fr5o3ry5cHV1FVeuXBG//fabMDU1FfPnz1fG4VSp3377TSxYsEDs27dPABD79++XW79ixQphbGwsDhw4IK5evSo+/PBD0ahRI5GVlSWr4+7uLlq1aiXOnj0rTp06Jezs7MSIESNk61NSUoS5ubkYOXKkuHHjhti1a5fQ1dUV33//fVUdZpV4W1+OHj1auLu7y/2cvnjxQq4O+1IINzc3ERwcLG7cuCGioqJE3759RcOGDUV6erqsTkX8Tv/9999CT09PzJw5U0RHR4v169cLdXV1cfTo0So93spUmr58//33xYQJE+R+LlNSUmTr2Zeqj8lUKXTs2FF4eXnJlgsKCoSlpaVYvny5EqNSLb6+vqJVq1bFrktOThaamppi7969srJbt24JACIyMlII8eokqKamJhITE2V1Nm3aJIyMjEROTk6lxq5K/p0ASKVSYWFhIVatWiUrS05OFtra2mLXrl1CCCGio6MFAHHhwgVZnSNHjgiJRCL++ecfIYQQGzduFLVq1ZLry7lz5woHB4dKPiLlKSmZGjBgQInbsC+L9+TJEwFA/Pnnn0KIivudnjNnjnBycpLbl4eHh3Bzc6vsQ1Kaf/elEK+SqenTp5e4DftS9fE231vk5ubi0qVLcHV1lZWpqanB1dUVkZGRSoxM9dy9exeWlpawtbXFyJEj8eDBAwDApUuXkJeXJ9eHTZs2RcOGDWV9GBkZiRYtWsDc3FxWx83NDampqbh582bVHogKuX//PhITE+X6ztjYGM7OznJ9Z2Jigvbt28vquLq6Qk1NDefOnZPV6d69O7S0tGR13NzcEBMTg5cvX1bR0aiG8PBwmJmZwcHBAZMnT8bz589l69iXxUtJSQEA1K5dG0DF/U5HRkbKtVFY513+2/rvviy0Y8cOmJqaonnz5pg/fz4yMzNl69iXqq/Gv+j4bZ49e4aCggK5H2IAMDc3x+3bt5UUlepxdnZGSEgIHBwckJCQgMWLF6Nbt264ceMGEhMToaWlBRMTE7ltzM3NkZiYCABITEwsto8L19VUhcdeXN+83ndmZmZy6zU0NFC7dm25Oo0aNSrSRuG6WrVqVUr8qsbd3R2DBw9Go0aNcO/ePXz55Zfo06cPIiMjoa6uzr4shlQqxYwZM9ClSxc0b94cACrsd7qkOqmpqcjKyoKurm5lHJLSFNeXAPDxxx/D2toalpaWuHbtGubOnYuYmBjs27cPAPuyOmAyRRWiT58+sq9btmwJZ2dnWFtbY8+ePfwlJpUxfPhw2dctWrRAy5Yt0bhxY4SHh+ODDz5QYmSqy8vLCzdu3MDp06eVHUq1V1JfTpw4UfZ1ixYtUK9ePXzwwQe4d+8eGjduXNVhUjnwNt9bmJqaQl1dvchTKklJSbCwsFBSVKrPxMQE9vb2iI2NhYWFBXJzc5GcnCxX5/U+tLCwKLaPC9fVVIXH/qafPwsLCzx58kRufX5+Pl68eMH+fQtbW1uYmpoiNjYWAPvy3z7//HP8+uuvOHnyJBo0aCArr6jf6ZLqGBkZvXP/hJXUl8VxdnYGALmfS/alamMy9RZaWlpo164dwsLCZGVSqRRhYWHo1KmTEiNTbenp6bh37x7q1auHdu3aQVNTU64PY2Ji8ODBA1kfdurUCdevX5c7kR0/fhxGRkZwdHSs8vhVRaNGjWBhYSHXd6mpqTh37pxc3yUnJ+PSpUuyOidOnIBUKpX9Ue7UqRMiIiKQl5cnq3P8+HE4ODi8c7elyuLRo0d4/vw56tWrB4B9WUgIgc8//xz79+/HiRMnitzWrKjf6U6dOsm1UVjnXfrb+ra+LE5UVBQAyP1csi9VnLJHwFcHu3fvFtra2iIkJERER0eLiRMnChMTE7knK2q6WbNmifDwcHH//n3x119/CVdXV2FqaiqePHkihHj1GHXDhg3FiRMnxMWLF0WnTp1Ep06dZNsXPvrbu3dvERUVJY4ePSrq1q1bI6ZGSEtLE1euXBFXrlwRAMTq1avFlStXRHx8vBDi1dQIJiYm4uDBg+LatWtiwIABxU6N0KZNG3Hu3Dlx+vRp0aRJE7nH+ZOTk4W5ubkYNWqUuHHjhti9e7fQ09N7px7nF+LNfZmWliZmz54tIiMjxf3798Uff/wh2rZtK5o0aSKys7NlbbAvhZg8ebIwNjYW4eHhco/rZ2ZmyupUxO904eP8X3zxhbh165YICAh45x7nf1tfxsbGCn9/f3Hx4kVx//59cfDgQWFrayu6d+8ua4N9qfqYTJXS+vXrRcOGDYWWlpbo2LGjOHv2rLJDUikeHh6iXr16QktLS9SvX194eHiI2NhY2fqsrCwxZcoUUatWLaGnpycGDRokEhIS5NqIi4sTffr0Ebq6usLU1FTMmjVL5OXlVfWhVLmTJ08KAEU+o0ePFkK8mh5h0aJFwtzcXGhra4sPPvhAxMTEyLXx/PlzMWLECGFgYCCMjIzE2LFjRVpamlydq1eviq5duwptbW1Rv359sWLFiqo6xCrzpr7MzMwUvXv3FnXr1hWamprC2tpaTJgwocg/RexLUWwfAhDBwcGyOhX1O33y5EnRunVroaWlJWxtbeX28S54W18+ePBAdO/eXdSuXVtoa2sLOzs78cUXX8jNMyUE+1LVSYQQouqugxERERG9WzhmioiIiEgBTKaIiIiIFMBkioiIiEgBTKaIiIiIFMBkioiIiEgBTKaIiIiIFMBkioiIiEgBTKaIiIiIFMBkioioBC4uLpgxY4aywyi1kJAQmJiYKDsMohqHyRQRVanw8HBIJBIkJyeXepsxY8Zg4MCBlRZTSfbt24evvvpKtmxjY4O1a9eWuR2JRCL7GBkZoUOHDjh48GAFRkpEysRkioioBLVr14ahoWGFtBUcHIyEhARcvHgRXbp0wdChQ3H9+vUKaZuIlIvJFNE7ysXFBVOnTsWMGTNQq1YtmJub44cffkBGRgbGjh0LQ0ND2NnZ4ciRI7Jtbty4gT59+sDAwADm5uYYNWoUnj17Jlt/9OhRdO3aFSYmJqhTpw7+85//4N69e7L1cXFxkEgk2LdvH3r06AE9PT20atUKkZGRJcZZeGvq2LFjaNasGQwMDODu7o6EhAQAgJ+fH7Zt24aDBw/Kru6Eh4cDAB4+fIhhw4bBxMQEtWvXxoABAxAXFydru/CK1jfffIN69eqhTp068PLyQl5enqzOxo0b0aRJE+jo6MDc3BxDhw6V68PC23wuLi6Ij4+Ht7e3LI6MjAwYGRnh559/ljumAwcOQF9fH2lpabIyExMTWFhYwN7eHl999RXy8/Nx8uTJSu3bp0+fon379hg0aBBycnJKrEdEimEyRfQO27ZtG0xNTXH+/HlMnToVkydPxkcffYTOnTvj8uXL6N27N0aNGoXMzEwkJyejZ8+eaNOmDS5evIijR48iKSkJw4YNk7WXkZGBmTNn4uLFiwgLC4OamhoGDRoEqVQqt98FCxZg9uzZiIqKgr29PUaMGIH8/PwS48zMzMQ333yDH3/8EREREXjw4AFmz54NAJg9ezaGDRsmS7ASEhLQuXNn5OXlwc3NDYaGhjh16hT++usvWSKWm5sra/vkyZO4d+8eTp48iW3btiEkJAQhISEAgIsXL2LatGnw9/dHTEwMjh49iu7duxcb4759+9CgQQP4+/vL4tDX18fw4cMRHBwsVzc4OBhDhw4t9qpWfn4+AgMDAQBaWlqV1rcPHz5Et27d0Lx5c/z888/Q1tYusf+JSEGCiN5J77//vujatatsOT8/X+jr64tRo0bJyhISEgQAERkZKb766ivRu3dvuTYePnwoAIiYmJhi9/H06VMBQFy/fl0IIcT9+/cFALF161ZZnZs3bwoA4tatW0IIIU6ePCkAiJcvXwohhAgODhYARGxsrGybgIAAYW5uLlsePXq0GDBggNy+f/zxR+Hg4CCkUqmsLCcnR+jq6opjx47JtrO2thb5+fmyOh999JHw8PAQQgjxyy+/CCMjI5GamlpiH06fPl22bG1tLdasWSNX59y5c0JdXV08fvxYCCFEUlKS0NDQEOHh4bI6AISOjo7Q19cXampqAoCwsbERz58/L3a/QpSvb4ODg4WxsbG4ffu2sLKyEtOmTZPrHyKqHLwyRfQOa9mypexrdXV11KlTBy1atJCVmZubAwCePHmCq1ev4uTJkzAwMJB9mjZtCgCy2013797FiBEjYGtrCyMjI9jY2AAAHjx4UOJ+69WrJ9tHSfT09NC4cWO5bd5UHwCuXr2K2NhYGBoayuKtXbs2srOz5W6POTk5QV1dvdi2e/XqBWtra9ja2mLUqFHYsWMHMjMz37jff+vYsSOcnJywbds2AMBPP/0Ea2vrIle41qxZg6ioKBw5cgSOjo7YunUrateuLVtfUX2blZWFbt26YfDgwVi3bh0kEkmZjoeIyk5D2QEQUeXR1NSUW5ZIJHJlhSdaqVSK9PR09O/fH19//XWRdgpP2v3794e1tTV++OEHWFpaQiqVonnz5nK31f6939f3UZY4hRBvPLb09HS0a9cOO3bsKLKubt26b2y7MBZDQ0NcvnwZ4eHh+P333+Hj4wM/Pz9cuHChTFMMfPrppwgICMC8efMQHByMsWPHFkliLCwsYGdnBzs7OwQHB6Nv376Ijo6GmZkZgIrrW21tbbi6uuLXX3/FF198gfr165f6OIiofHhliogAAG3btsXNmzdhY2MjO+kXfvT19fH8+XPExMRg4cKF+OCDD9CsWTO8fPmySmLT0tJCQUFBkXjv3r0LMzOzIvEaGxuXum0NDQ24urpi5cqVuHbtGuLi4nDixIlSxwEAnp6eiI+Px3fffYfo6GiMHj36jfvs2LEj2rVrh6VLlwJAhfatmpoafvzxR7Rr1w49evTA48ePy9UOEZUekykiAgB4eXnhxYsXGDFiBC5cuIB79+7h2LFjGDt2LAoKClCrVi3UqVMHW7ZsQWxsLE6cOIGZM2dWSWw2Nja4du0aYmJi8OzZM+Tl5WHkyJEwNTXFgAEDcOrUKdy/fx/h4eGYNm0aHj16VKp2f/31V3z33XeIiopCfHw8tm/fDqlUCgcHhxLjiIiIwD///CP3lGOtWrUwePBgfPHFF+jduzcaNGjw1n3PmDED33//Pf75558K71t1dXXs2LEDrVq1Qs+ePZGYmFjutojo7ZhMEREAwNLSEn/99RcKCgrQu3dvtGjRAjNmzICJiQnU1NSgpqaG3bt349KlS2jevDm8vb2xatWqKoltwoQJcHBwQPv27VG3bl389ddf0NPTQ0REBBo2bIjBgwejWbNmGD9+PLKzs2FkZFSqdk1MTLBv3z707NkTzZo1w+bNm7Fr1y44OTkVW9/f3x9xcXFo3Lix3K1EABg/fjxyc3Mxbty4Uu3b3d0djRo1wtKlSyulbzU0NGTH0rNnz7eOQSOi8pOItw1MICKit/rxxx/h7e2Nx48fy015QETvPg5AJyJSQGZmJhISErBixQp89tlnTKSIaiDe5iMiUsDKlSvRtGlTWFhYYP78+coOh4iUgLf5iIiIiBTAK1NERERECmAyRURERKQAJlNERERECmAyRURERKQAJlNERERECmAyRURERKQAJlNERERECmAyRURERKSA/wMZd4a+jnY/pgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exomarkers\n",
    "\n",
    "mask = dfProteinIntensity.index.isin(exoGeneList)\n",
    "\n",
    "sns.scatterplot(data = dfProteinIntensity.loc[mask],\n",
    "        x = 'meanIntensityRank',\n",
    "        y = 'meanIntensity',\n",
    "        size=5,\n",
    "        facecolor = 'red',\n",
    "        edgecolor = 'black',)\n",
    "plt.yscale('log')\n",
    "\n",
    "# Loop for annotation of all points\n",
    "\n",
    "annotations = dfProteinIntensity[dfProteinIntensity.index.isin(exoGeneList)].index # gene names\n",
    "\n",
    "for i in range(len(dfProteinIntensity[dfProteinIntensity.index.isin(exoGeneList)].meanIntensityRank)):\n",
    "    plt.annotate(annotations[i],\n",
    "        (dfProteinIntensity[dfProteinIntensity.index.isin(exoGeneList)].meanIntensityRank[i], # x\n",
    "             dfProteinIntensity[dfProteinIntensity.index.isin(exoGeneList)].meanIntensity[i], # y\n",
    "        )\n",
    "    ) "
   ]
  },
  {
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}