From 3bd34a600e34c49816420207fc47458b61f3949d Mon Sep 17 00:00:00 2001 From: Trang Le Date: Thu, 10 Jun 2021 11:50:41 -0400 Subject: [PATCH] Raw probabilities (#16) * use raw probabilities --- 10.visualize-gender.Rmd | 36 ++- 11.visualize-name-origins.Rmd | 34 +-- 12.analyze-affiliation.Rmd | 2 +- 14.us-name-origin.Rmd | 23 +- _output.yaml | 1 + docs/091.draw-roc.html | 293 +++++++++++----------- docs/092.save-raws-to-Rdata.html | 19 +- docs/093.summary-stats.html | 14 +- docs/10.visualize-gender.html | 183 +++++++------- docs/11.visualize-name-origins.html | 312 ++++++++++++------------ docs/12.analyze-affiliation.html | 110 +++++---- docs/13.us-race-analysis.html | 44 ++-- docs/14.us-name-origin.html | 229 +++++++++--------- docs/15.analyze-2020.html | 94 +++++--- figs/enrichment-plot.png | Bin 150904 -> 317799 bytes figs/gender_breakdown.png | Bin 55286 -> 117911 bytes figs/gender_breakdown.svg | 72 +++--- figs/region_breakdown.png | Bin 173042 -> 362608 bytes figs/region_breakdown.svg | 360 ++++++++++++++-------------- figs/us_name_origin.png | Bin 169206 -> 356831 bytes figs/us_name_origin.svg | 358 +++++++++++++-------------- figs/versions.pdf | Bin 51294 -> 51615 bytes figs/versions.png | Bin 786394 -> 779401 bytes figs/versions.pptx | Bin 45768 -> 45805 bytes 24 files changed, 1113 insertions(+), 1071 deletions(-) diff --git a/10.visualize-gender.Rmd b/10.visualize-gender.Rmd index a005ea7..bef07f7 100644 --- a/10.visualize-gender.Rmd +++ b/10.visualize-gender.Rmd @@ -65,21 +65,13 @@ iscb_pubmed <- iscb_gender_df %>% values_to = "probabilities" ) %>% filter(!is.na(probabilities)) %>% - group_by(type, year, gender) %>% - mutate( - pmc_citations_year = mean(adjusted_citations), - weight = adjusted_citations / pmc_citations_year, - weighted_probs = probabilities * weight - # weight = 1 - ) + group_by(type, year, gender) iscb_pubmed_sum <- iscb_pubmed %>% summarise( # n = n(), - mean_prob = mean(weighted_probs), - # mean_prob = mean(probabilities, na.rm = T), - # sd_prob = sd(probabilities, na.rm = T), - se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), + mean_prob = mean(probabilities, na.rm = T), + se_prob = sd(probabilities, na.rm = T), # n = mean(n), me_prob = alpha_threshold * se_prob, .groups = "drop" @@ -102,7 +94,7 @@ fig_1 <- iscb_pubmed_sum %>% # group_by(year, type, gender) %>% gender_breakdown("main", fct_rev(type)) fig_1 -ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5) +ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5, dpi = 600) ggsave("figs/gender_breakdown.svg", fig_1, width = 5, height = 2.5) ``` @@ -131,8 +123,8 @@ fig_1d <- iscb_pubmed %>% ) %>% group_by(type2, year, gender) %>% summarise( - mean_prob = mean(weighted_probs), - se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), + mean_prob = mean(probabilities), + se_prob = sd(probabilities)/sqrt(n()), me_prob = alpha_threshold * se_prob, .groups = "drop" ) %>% @@ -173,7 +165,7 @@ iscb_pubmed_sum %>% ```{r echo = F} get_p <- function(inte, colu) { broom::tidy(inte) %>% - filter(term == "weighted_probs") %>% + filter(term == "probabilities") %>% pull(colu) %>% sprintf("%0.5g", .) } @@ -181,7 +173,7 @@ get_p <- function(inte, colu) { ```{r} iscb_lm <- iscb_pubmed %>% - filter(gender == "probability_female", !is.na(weighted_probs)) %>% + filter(gender == "probability_female", !is.na(probabilities)) %>% mutate(type = as.factor(type)) %>% mutate(type = type %>% relevel(ref = "Pubmed authors")) ``` @@ -189,33 +181,33 @@ iscb_lm <- iscb_pubmed %>% ```{r} scaled_iscb <- iscb_lm %>% filter(year(year) >= 2002) -# scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F) +# scaled_iscb$s_prob <- scale(scaled_iscb$probabilities, scale = F) # scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F) -main_lm <- glm(type ~ year + weighted_probs, +main_lm <- glm(type ~ year + probabilities, data = scaled_iscb, # %>% mutate(year = as.factor(year)) family = "binomial" ) broom::tidy(main_lm) inte_lm <- glm( - # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F), + # type ~ scale(year, scale = F) * scale(probabilities, scale = F), # type ~ s_year * s_prob, - type ~ year * weighted_probs, + type ~ year * probabilities, data = scaled_iscb, # %>% mutate(year = as.factor(year)) family = "binomial" ) broom::tidy(inte_lm) anova(main_lm, inte_lm, test = "Chisq") # mean(scaled_iscb$year) -# mean(scaled_iscb$weighted_probs) +# mean(scaled_iscb$probabilities) ``` The two groups of scientists did not have a significant association with the gender predicted from fore names (_P_ = `r get_p(main_lm, 'p.value')`). Interaction terms do not predict `type` over and above the main effect of gender probability and year. ```{r include=FALSE, eval=FALSE} -# inte_lm <- glm(type ~ (year * weighted_probs), +# inte_lm <- glm(type ~ (year * probabilities), # data = iscb_lm, # family = 'binomial') ``` diff --git a/11.visualize-name-origins.Rmd b/11.visualize-name-origins.Rmd index 2b9de29..b2de730 100644 --- a/11.visualize-name-origins.Rmd +++ b/11.visualize-name-origins.Rmd @@ -96,17 +96,12 @@ iscb_pubmed_oth <- iscb_nat_df %>% values_to = "probabilities" ) %>% filter(!is.na(probabilities)) %>% - group_by(type, year, region) %>% - mutate( - pmc_citations_year = mean(adjusted_citations), - weight = adjusted_citations / pmc_citations_year, - weighted_probs = probabilities * weight - ) + group_by(type, year, region) iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% summarise( - mean_prob = mean(weighted_probs), - se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), + mean_prob = mean(probabilities), + se_prob = sd(probabilities)/sqrt(n()), me_prob = alpha_threshold * se_prob, .groups = "drop" ) @@ -127,7 +122,7 @@ for (conf in my_confs) { iscb_nat[[i]] <- iscb_pubmed_oth %>% filter(region != "OtherCategories", type != "Pubmed authors" & journal == conf) %>% group_by(type, year, region, journal) %>% - summarise(mean_prob = mean(weighted_probs), .groups = "drop") + summarise(mean_prob = mean(probabilities), .groups = "drop") } ``` @@ -169,7 +164,7 @@ fig_4b <- iscb_pubmed_sum_oth %>% fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1)) fig_4 -ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5) +ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5, dpi = 600) ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5) ``` @@ -185,7 +180,7 @@ iscb_lm <- iscb_pubmed_oth %>% type = as.factor(type) %>% relevel(ref = "Pubmed authors") ) main_lm <- function(regioni) { - glm(type ~ year + weighted_probs, + glm(type ~ year + probabilities, data = iscb_lm %>% filter(region == regioni, !is.na(probabilities), year(year) >= 2002), family = "binomial" @@ -193,9 +188,9 @@ main_lm <- function(regioni) { } inte_lm <- function(regioni) { - glm(type ~ year * weighted_probs, + glm(type ~ year * probabilities, data = iscb_lm %>% - filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002), + filter(region == regioni, !is.na(probabilities), year(year) >= 2002), family = "binomial" ) } @@ -215,7 +210,7 @@ Interaction terms do not predict `type` over and above the main effect of name o ```{r echo = F} get_p <- function(i, colu) { broom::tidy(main_list[[i]]) %>% - filter(term == "weighted_probs") %>% + filter(term == "probabilities") %>% pull(colu) } @@ -326,21 +321,16 @@ iscb_pubmed_oth_lag <- iscb_nat_df %>% values_to = "probabilities" ) %>% filter(!is.na(probabilities), year(year) >= 2002) %>% - group_by(type, year, region) %>% - mutate( - pmc_citations_year = mean(adjusted_citations), - weight = adjusted_citations / pmc_citations_year, - weighted_probs = probabilities * weight - ) + group_by(type, year, region) iscb_lm_lag <- iscb_pubmed_oth_lag %>% ungroup() %>% mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors")) main_lm <- function(regioni) { - glm(type ~ year + weighted_probs, + glm(type ~ year + probabilities, data = iscb_lm_lag %>% - filter(region == regioni, !is.na(weighted_probs)), + filter(region == regioni, !is.na(probabilities)), family = "binomial" ) } diff --git a/12.analyze-affiliation.Rmd b/12.analyze-affiliation.Rmd index e700614..e85fc4f 100644 --- a/12.analyze-affiliation.Rmd +++ b/12.analyze-affiliation.Rmd @@ -293,7 +293,7 @@ enrichment_plot_right <- plot_obs_exp_right %>% enrichment_plot <- cowplot::plot_grid(enrichment_plot_left, enrichment_plot_right, rel_widths = c(1, 1.3)) enrichment_plot -ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5) +ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5, dpi = 600) ``` diff --git a/14.us-name-origin.Rmd b/14.us-name-origin.Rmd index 06ca206..ef737ed 100644 --- a/14.us-name-origin.Rmd +++ b/14.us-name-origin.Rmd @@ -70,17 +70,12 @@ iscb_pubmed_oth <- iscb_nat_df %>% values_to = "probabilities" ) %>% filter(!is.na(probabilities)) %>% - group_by(type, year, region) %>% - mutate( - pmc_citations_year = mean(adjusted_citations), - weight = adjusted_citations / pmc_citations_year, - weighted_probs = probabilities * weight - ) + group_by(type, year, region) iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% summarise( - mean_prob = mean(weighted_probs), - se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), + mean_prob = mean(probabilities), + se_prob = sd(probabilities)/sqrt(n()), me_prob = alpha_threshold * se_prob, .groups = "drop" ) @@ -119,7 +114,7 @@ fig_us_name_originb <- iscb_pubmed_sum_oth %>% fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1)) fig_us_name_origin -ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5) +ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5, dpi = 600) ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5) ``` @@ -134,17 +129,17 @@ iscb_lm <- iscb_pubmed_oth %>% type = relevel(as.factor(type), ref = "Pubmed authors") ) main_lm <- function(regioni) { - glm(type ~ year + weighted_probs, + glm(type ~ year + probabilities, data = iscb_lm %>% - filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002), + filter(region == regioni, !is.na(probabilities), year(year) >= 2002), family = "binomial" ) } inte_lm <- function(regioni) { - glm(type ~ weighted_probs * year, + glm(type ~ probabilities * year, data = iscb_lm %>% - filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002), + filter(region == regioni, !is.na(probabilities), year(year) >= 2002), family = "binomial" ) } @@ -165,7 +160,7 @@ Interaction terms do not predict `type` over and above the main effect of name o ```{r echo = F} get_exp <- function(i, colu) { broom::tidy(main_list[[i]]) %>% - filter(term == "weighted_probs") %>% + filter(term == "probabilities") %>% pull(colu) } diff --git a/_output.yaml b/_output.yaml index ea2b6fe..0681b68 100644 --- a/_output.yaml +++ b/_output.yaml @@ -3,3 +3,4 @@ html_document: toc: true toc_float: true code_download: true + dpi: 600 diff --git a/docs/091.draw-roc.html b/docs/091.draw-roc.html index 524ce17..387df08 100644 --- a/docs/091.draw-roc.html +++ b/docs/091.draw-roc.html @@ -1741,35 +1741,22 @@

Plotting ROC curves

Name origin prediction method performance

-
library(tidyverse)
-
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
-
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
-## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
-## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
-## ✓ readr   1.4.0     ✓ forcats 0.5.0
-
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
-## x dplyr::filter() masks stats::filter()
-## x dplyr::lag()    masks stats::lag()
-
# still need to install caret for the calibration function because tidymodels's 
-# probably hasn't published this yet
-library(caret)
-
## Loading required package: lattice
+
library(tidyverse)
+# still need to install caret for the calibration function because tidymodels's 
+# probably hasn't published this yet
+library(caret)
+
+source('utils/r-utils.R')
+theme_set(theme_bw())
+
roc_df <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/models/NamePrism_roc_curves.tsv') %>%
+  rename('region' = category) %>%
+  # recode_region_letter() %>%
+  recode_region() %>% 
+  group_by(region) %>%
+  mutate(Sensitivity = tpr, Specificity = 1-fpr, dSens = c(abs(diff(1-tpr)), 0)) %>%
+  ungroup()
## 
-## Attaching package: 'caret'
-
## The following object is masked from 'package:purrr':
-## 
-##     lift
-
source('utils/r-utils.R')
-theme_set(theme_bw())
-
roc_df <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/models/NamePrism_roc_curves.tsv') %>%
-  rename('region' = category) %>%
-  # recode_region_letter() %>%
-  recode_region() %>% 
-  group_by(region) %>%
-  mutate(Sensitivity = tpr, Specificity = 1-fpr, dSens = c(abs(diff(1-tpr)), 0)) %>%
-  ungroup()
-
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   fpr = col_double(),
 ##   tpr = col_double(),
@@ -1780,38 +1767,38 @@ 
Name origin prediction method performance

 ## ℹ Unknown levels in `f`: OtherCategories
 ## ℹ Input `region` is `fct_recode(...)`.
## Warning: Unknown levels in `f`: OtherCategories
-
auc_df <- roc_df %>%
-  group_by(region) %>%
-  # add_count() %>%
-  summarise(auc = sum((1 - fpr) * dSens),
-            n = n()) %>%
-  arrange(desc(auc)) %>%
-  mutate(auc_pct = 100 * auc,
-         reg_auc = paste0(region, ', AUC = ', round(auc_pct, 1), '%'))
+
auc_df <- roc_df %>%
+  group_by(region) %>%
+  # add_count() %>%
+  summarise(auc = sum((1 - fpr) * dSens),
+            n = n()) %>%
+  arrange(desc(auc)) %>%
+  mutate(auc_pct = 100 * auc,
+         reg_auc = paste0(region, ', AUC = ', round(auc_pct, 1), '%'))
## `summarise()` ungrouping output (override with `.groups` argument)
-
# region_levels <- c('Celtic English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Muslim', 'Israeli', 'African')
-region_levels <- paste(c('Celtic/English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Arabic', 'Hebrew', 'African', 'Nordic', 'Greek'), 'names')
-region_levels_let <- toupper(letters[1:8])
-region_cols <- c('#b3de69', '#fdb462',  '#bc80bd', '#8dd3c7', '#fccde5', '#ffffb3', '#ccebc5', '#bebada', '#80b1d3', '#fb8072')
-
-fig_3a <- roc_df %>%
-  left_join(auc_df, by = 'region') %>%
-  ggplot(aes(x = Sensitivity, y = Specificity, color = fct_relevel(reg_auc, as.character(auc_df$reg_auc)))) +
-  scale_color_manual(values = region_cols) +
-  geom_step(size = 1, alpha = 0.8) +
-  coord_fixed() +
-  scale_x_reverse(breaks = seq(1, 0, -0.2), labels = scales::percent) +
-  scale_y_continuous(breaks = seq(0, 1, 0.2), labels = scales::percent, limits = c(NA, 1.05)) +
-  theme(legend.position = c(0.62, 0.42),
-        legend.title = element_blank(),
-        legend.text.align = 1,
-        legend.text = element_text(size = 7),
-        legend.margin = margin(-0.2, 0.2, 0.2, 0, unit='cm'))
-
predictions_df <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/NamePrism_results_test.tsv') %>%
-  mutate(y_true = as.factor(truth)) %>%
-  select(-truth)
+
# region_levels <- c('Celtic English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Muslim', 'Israeli', 'African')
+region_levels <- paste(c('Celtic/English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Arabic', 'Hebrew', 'African', 'Nordic', 'Greek'), 'names')
+region_levels_let <- toupper(letters[1:8])
+region_cols <- c('#b3de69', '#fdb462',  '#bc80bd', '#8dd3c7', '#fccde5', '#ffffb3', '#ccebc5', '#bebada', '#80b1d3', '#fb8072')
+
+fig_3a <- roc_df %>%
+  left_join(auc_df, by = 'region') %>%
+  ggplot(aes(x = Sensitivity, y = Specificity, color = fct_relevel(reg_auc, as.character(auc_df$reg_auc)))) +
+  scale_color_manual(values = region_cols) +
+  geom_step(size = 1, alpha = 0.8) +
+  coord_fixed() +
+  scale_x_reverse(breaks = seq(1, 0, -0.2), labels = scales::percent) +
+  scale_y_continuous(breaks = seq(0, 1, 0.2), labels = scales::percent, limits = c(NA, 1.05)) +
+  theme(legend.position = c(0.62, 0.42),
+        legend.title = element_blank(),
+        legend.text.align = 1,
+        legend.text = element_text(size = 7),
+        legend.margin = margin(-0.2, 0.2, 0.2, 0, unit='cm'))
+
predictions_df <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/NamePrism_results_test.tsv') %>%
+  mutate(y_true = as.factor(truth)) %>%
+  select(-truth)
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   African = col_double(),
 ##   CelticEnglish = col_double(),
@@ -1825,83 +1812,79 @@ 
Name origin prediction method performance

 ##   SouthAsian = col_double(),
 ##   truth = col_character()
 ## )
-
regs <- predictions_df %>% select(African:SouthAsian) %>% colnames()
-cal_dfs <- list()
-for (reg in regs) {
-  pred_reg <- predictions_df %>%
-    mutate(y_true_bin = as.factor((y_true == reg))) %>%
-    rename(prob = reg) %>%
-    select(y_true_bin, prob)
-
-  cal_dfs[[reg]] <- calibration(y_true_bin ~ prob,
-                                data = pred_reg,
-                                cuts = 11,
-                                class = 'TRUE')$data %>%
-    mutate(region = reg)
-}
-
## Note: Using an external vector in selections is ambiguous.
-## ℹ Use `all_of(reg)` instead of `reg` to silence this message.
-## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
-## This message is displayed once per session.
-
cal_dfs$EastAsian
-
##    calibModelVar            bin   Percent      Lower     Upper Count  midpoint
-## 1           prob     [0,0.0909]  0.973038  0.9061138  1.043559   777  4.545455
-## 2           prob (0.0909,0.182] 12.715105 10.7555376 14.887108   133 13.636364
-## 3           prob  (0.182,0.273] 20.620843 16.9791523 24.652952    93 22.727273
-## 4           prob  (0.273,0.364] 29.924242 24.4643714 35.841394    79 31.818182
-## 5           prob  (0.364,0.455] 35.897436 29.7515540 42.405681    84 40.909091
-## 6           prob  (0.455,0.545] 38.536585 31.8402892 45.569554    79 50.000000
-## 7           prob  (0.545,0.636] 45.637584 37.4635833 53.988516    68 59.090909
-## 8           prob  (0.636,0.727] 56.953642 48.6544756 64.974492    86 68.181818
-## 9           prob  (0.727,0.818] 61.421320 54.2394760 68.253900   121 77.272727
-## 10          prob  (0.818,0.909] 71.764706 66.6571343 76.488532   244 86.363636
-## 11          prob      (0.909,1] 97.209555 96.8524649 97.536348  8953 95.454545
-##       region
-## 1  EastAsian
-## 2  EastAsian
-## 3  EastAsian
-## 4  EastAsian
-## 5  EastAsian
-## 6  EastAsian
-## 7  EastAsian
-## 8  EastAsian
-## 9  EastAsian
-## 10 EastAsian
-## 11 EastAsian
-
fig_3b <- bind_rows(cal_dfs) %>%
-  recode_region() %>%
-  ggplot(aes(x = midpoint/100, y = Percent/100, color = fct_relevel(region, as.character(auc_df$region)))) +
-  geom_abline(slope = 1, linetype = 2, alpha = 0.5) +
-  scale_y_continuous(labels = scales::percent_format(accuracy = 20L), breaks = seq(0, 1, 0.2), limits = c(-0.005, 1.045)) +
-  scale_x_continuous(labels = scales::percent_format(accuracy = 20L), breaks = seq(0, 1, 0.2), limits = c(0, 1)) +
-  coord_fixed() +
-  geom_point() +
-  geom_line() +
-  scale_color_manual(values = region_cols) +
-  theme(legend.position = 'None') +
-  labs(x = 'Predicted probability', y = 'Fraction of names')
+
regs <- predictions_df %>% select(African:SouthAsian) %>% colnames()
+cal_dfs <- list()
+for (reg in regs) {
+  pred_reg <- predictions_df %>%
+    mutate(y_true_bin = as.factor((y_true == reg))) %>%
+    rename(prob = reg) %>%
+    select(y_true_bin, prob)
+
+  cal_dfs[[reg]] <- calibration(y_true_bin ~ prob,
+                                data = pred_reg,
+                                cuts = 11,
+                                class = 'TRUE')$data %>%
+    mutate(region = reg)
+}
+cal_dfs$EastAsian
+
##    calibModelVar            bin   Percent      Lower     Upper
+## 1           prob     [0,0.0909]  0.973038  0.9061138  1.043559
+## 2           prob (0.0909,0.182] 12.715105 10.7555376 14.887108
+## 3           prob  (0.182,0.273] 20.620843 16.9791523 24.652952
+## 4           prob  (0.273,0.364] 29.924242 24.4643714 35.841394
+## 5           prob  (0.364,0.455] 35.897436 29.7515540 42.405681
+## 6           prob  (0.455,0.545] 38.536585 31.8402892 45.569554
+## 7           prob  (0.545,0.636] 45.637584 37.4635833 53.988516
+## 8           prob  (0.636,0.727] 56.953642 48.6544756 64.974492
+## 9           prob  (0.727,0.818] 61.421320 54.2394760 68.253900
+## 10          prob  (0.818,0.909] 71.764706 66.6571343 76.488532
+## 11          prob      (0.909,1] 97.209555 96.8524649 97.536348
+##    Count  midpoint    region
+## 1    777  4.545455 EastAsian
+## 2    133 13.636364 EastAsian
+## 3     93 22.727273 EastAsian
+## 4     79 31.818182 EastAsian
+## 5     84 40.909091 EastAsian
+## 6     79 50.000000 EastAsian
+## 7     68 59.090909 EastAsian
+## 8     86 68.181818 EastAsian
+## 9    121 77.272727 EastAsian
+## 10   244 86.363636 EastAsian
+## 11  8953 95.454545 EastAsian
+
fig_3b <- bind_rows(cal_dfs) %>%
+  recode_region() %>%
+  ggplot(aes(x = midpoint/100, y = Percent/100, color = fct_relevel(region, as.character(auc_df$region)))) +
+  geom_abline(slope = 1, linetype = 2, alpha = 0.5) +
+  scale_y_continuous(labels = scales::percent_format(accuracy = 20L), breaks = seq(0, 1, 0.2), limits = c(-0.005, 1.045)) +
+  scale_x_continuous(labels = scales::percent_format(accuracy = 20L), breaks = seq(0, 1, 0.2), limits = c(0, 1)) +
+  coord_fixed() +
+  geom_point() +
+  geom_line() +
+  scale_color_manual(values = region_cols) +
+  theme(legend.position = 'None') +
+  labs(x = 'Predicted probability', y = 'Fraction of names')
## Warning: Problem with `mutate()` input `region`.
 ## ℹ Unknown levels in `f`: OtherCategories
 ## ℹ Input `region` is `fct_recode(...)`.
## Warning: Unknown levels in `f`: OtherCategories
-
n_obs <- sum(auc_df$n)
-short_regs <- auc_df$region %>% 
-  as.character() %>% 
-  gsub(' names', '', .)
-
-heat_dat <- predictions_df %>%
-  group_by(y_true) %>%
-  summarise_if(is.numeric, mean, na.rm = T) %>%
-  ungroup() %>%
-  pivot_longer(- y_true, names_to = 'region', values_to = 'pred_prob') %>%
-  recode_region() %>%
-  rename('reg_hat' = region, 'region' = y_true) %>% 
-  recode_region() %>%
-  rename('y_true' = region, 'region' = reg_hat) %>% 
-  left_join(auc_df, by = 'region') %>%
-  mutate(scale_pred_prob = log2((pred_prob)/(n/n_obs)),
-         region = region %>% gsub(' names', '', .) %>% fct_relevel(short_regs),
-         y_true = y_true %>% gsub(' names', '', .) %>% fct_relevel(short_regs))
+
n_obs <- sum(auc_df$n)
+short_regs <- auc_df$region %>% 
+  as.character() %>% 
+  gsub(' names', '', .)
+
+heat_dat <- predictions_df %>%
+  group_by(y_true) %>%
+  summarise_if(is.numeric, mean, na.rm = T) %>%
+  ungroup() %>%
+  pivot_longer(- y_true, names_to = 'region', values_to = 'pred_prob') %>%
+  recode_region() %>%
+  rename('reg_hat' = region, 'region' = y_true) %>% 
+  recode_region() %>%
+  rename('y_true' = region, 'region' = reg_hat) %>% 
+  left_join(auc_df, by = 'region') %>%
+  mutate(scale_pred_prob = log2((pred_prob)/(n/n_obs)),
+         region = region %>% gsub(' names', '', .) %>% fct_relevel(short_regs),
+         y_true = y_true %>% gsub(' names', '', .) %>% fct_relevel(short_regs))
## Warning: Problem with `mutate()` input `region`.
 ## ℹ Unknown levels in `f`: OtherCategories
 ## ℹ Input `region` is `fct_recode(...)`.
@@ -1910,31 +1893,31 @@

Name origin prediction method performance

## ℹ Unknown levels in `f`: OtherCategories ## ℹ Input `region` is `fct_recode(...)`. 
## Warning: Unknown levels in `f`: OtherCategories
-
fig_3c <- ggplot(heat_dat, aes(y_true, region,
-                               fill = scale_pred_prob)) +
-  geom_tile() +
-  scale_fill_gradientn(
-    colours = c("#3CBC75FF","white","#440154FF"),
-    values = scales::rescale(
-      c(min(heat_dat$scale_pred_prob),
-        0,
-        max(heat_dat$scale_pred_prob)))
-  ) +
-  coord_fixed() +
-  labs(x = 'True region', y = 'Predicted region', fill = bquote(log[2]~'FC')) +
-  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
-        legend.position = 'top',
-        legend.key.height = unit(0.2, 'cm'),
-        legend.title = element_text(vjust = 1),
-        legend.margin = margin(0, 0,0, -1, unit='cm'),
-        axis.title.x = element_text(margin = margin(t = 27, r = 0, b = 0, l = 0)),
-        axis.title.y = element_text(margin = margin(t = 0, r = 15, b = 0, l = 0)))
-
-fig_3 <- cowplot::plot_grid(fig_3a, fig_3b, fig_3c, labels = 'AUTO', nrow = 1,
-                            rel_widths = c(2,2,1.6))
-fig_3
+
fig_3c <- ggplot(heat_dat, aes(y_true, region,
+                               fill = scale_pred_prob)) +
+  geom_tile() +
+  scale_fill_gradientn(
+    colours = c("#3CBC75FF","white","#440154FF"),
+    values = scales::rescale(
+      c(min(heat_dat$scale_pred_prob),
+        0,
+        max(heat_dat$scale_pred_prob)))
+  ) +
+  coord_fixed() +
+  labs(x = 'True region', y = 'Predicted region', fill = bquote(log[2]~'FC')) +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
+        legend.position = 'top',
+        legend.key.height = unit(0.2, 'cm'),
+        legend.title = element_text(vjust = 1),
+        legend.margin = margin(0, 0,0, -1, unit='cm'),
+        axis.title.x = element_text(margin = margin(t = 27, r = 0, b = 0, l = 0)),
+        axis.title.y = element_text(margin = margin(t = 0, r = 15, b = 0, l = 0)))
+
+fig_3 <- cowplot::plot_grid(fig_3a, fig_3b, fig_3c, labels = 'AUTO', nrow = 1,
+                            rel_widths = c(2,2,1.6))
+fig_3

-
# ggsave('figs/fig_3.png', fig_3, height = 4, width = 10)
+
# ggsave('figs/fig_3.png', fig_3, height = 4, width = 10)
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
diff --git a/docs/092.save-raws-to-Rdata.html b/docs/092.save-raws-to-Rdata.html index 6a6034e..c479ccb 100644 --- a/docs/092.save-raws-to-Rdata.html +++ b/docs/092.save-raws-to-Rdata.html @@ -4300,7 +4300,7 @@

Save raw data into .Rdata to be loaded in by analys

General data read-in

all_full_names <- readr::read_tsv('data/names/full-names.tsv.xz') %>% distinct()
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   fore_name = col_character(),
 ##   last_name = col_character(),
@@ -4334,7 +4334,7 @@ 
General data read-in

          publication_date = ymd(publication_date, truncated = 2)) %>% 
   filter(year(publication_date) < 2020)
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   pmid = col_double(),
 ##   pmcid = col_character(),
@@ -4358,7 +4358,7 @@ 
General data read-in

   left_join(select(all_full_names, - full_name), by = c('fore_name', 'last_name')) %>% 
   filter(year(year) < 2020, conference != 'PSB') # remove PSB, exclude ISCB Fellows and ISMB speakers in 2020 for now
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   year = col_double(),
 ##   full_name = col_character(),
@@ -4371,9 +4371,10 @@ 
General data read-in

 ## )
keynotes %>% filter(is.na(fore_name_simple))
## # A tibble: 0 x 11
-## # … with 11 variables: year <date>, full_name <chr>, fore_name <chr>,
-## #   last_name <chr>, conference <chr>, source <chr>, affiliations <chr>,
-## #   afflcountries <chr>, publication_date <date>, fore_name_simple <chr>,
+## # … with 11 variables: year <date>, full_name <chr>,
+## #   fore_name <chr>, last_name <chr>, conference <chr>,
+## #   source <chr>, affiliations <chr>, afflcountries <chr>,
+## #   publication_date <date>, fore_name_simple <chr>,
 ## #   last_name_simple <chr>
large_jours <- articles %>%
   count(journal, sort = T) %>% 
@@ -4385,7 +4386,7 @@ 
General data read-in

   left_join(all_full_names, by = 'full_name')
## Warning: Missing column names filled in: 'X1' [1]
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   X1 = col_character(),
 ##   African = col_double(),
@@ -4406,8 +4407,8 @@ 
General data read-in

 
corr_authors %>% 
   count(year, name = 'Number of articles with last authors') %>% 
   DT::datatable(rownames = F)

-

-
+

+
 
If we set a threshold at least 200 articles a year, we should only consider articles from 1998 on.

 
corr_authors <- corr_authors %>% 
   add_count(year, name = 'n_aut_yr') %>% 
diff --git a/docs/093.summary-stats.html b/docs/093.summary-stats.html
index 54e91c6..4db9784 100644
--- a/docs/093.summary-stats.html
+++ b/docs/093.summary-stats.html
@@ -4317,8 +4317,8 @@ 
Honorees

   count(fore_name, last_name) %>% 
   arrange(desc(n)) %>% 
   DT::datatable()

-

-
+

+
 
Number of keynote speakers/fellows across years:

 
keynotes %>%
   select(year, conference) %>% 
@@ -4364,7 +4364,7 @@ 
Authors

 
Gender analysis

 
gender_df <- read_tsv('data/gender/genderize.tsv')

 
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   fore_name_simple = col_character(),
 ##   n_authors = col_double(),
@@ -4412,8 +4412,8 @@ 
Gender analysis

   pull(pred.asi) %>% 
   mean(na.rm = T)

 
## [1] "Proceeding with surname-only predictions..."

-
## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 1305
-## surnames were not matched.

+
## Warning in merge_surnames(voter.file, impute.missing =
+## impute.missing): 1305 surnames were not matched.

 
## [1] 0.8174599

 
Honorees that didn’t receive a gender prediction: Chung-I Wu.

 
In summary, the NA predictions mostly include initials only, hyphenated names and perhaps names with accent marks.

@@ -4492,8 +4492,8 @@ 
Name origin analysis

 ## ℹ Unknown levels in `f`: OtherCategories
 ## ℹ Input `region` is `fct_recode(...)`.
## Warning: Unknown levels in `f`: OtherCategories
-
- +
+ 
 pubmed_nat_pmids %>% count(!is.na(African), is.na(fore_name_simple.x))
## # A tibble: 2 x 3
 ##   `!is.na(African)` `is.na(fore_name_simple.x)`      n
diff --git a/docs/10.visualize-gender.html b/docs/10.visualize-gender.html
index 1f524c3..19daa4c 100644
--- a/docs/10.visualize-gender.html
+++ b/docs/10.visualize-gender.html
@@ -1682,7 +1682,7 @@ 
Load data

 alpha_threshold <- qnorm(0.975)
 gender_df <- read_tsv("data/gender/genderize.tsv")
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   fore_name_simple = col_character(),
 ##   n_authors = col_double(),
@@ -1730,21 +1730,13 @@ 
Prepare data frames for later analyses

     values_to = "probabilities"
   ) %>%
   filter(!is.na(probabilities)) %>%
-  group_by(type, year, gender) %>%
-  mutate(
-    pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations / pmc_citations_year,
-    weighted_probs = probabilities * weight
-    # weight = 1
-  )
+  group_by(type, year, gender) 
 
 iscb_pubmed_sum <- iscb_pubmed %>%
   summarise(
     # n = n(),
-    mean_prob = mean(weighted_probs),
-    # mean_prob = mean(probabilities, na.rm = T),
-    # sd_prob = sd(probabilities, na.rm = T),
-    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+    mean_prob = mean(probabilities, na.rm = T),
+    se_prob = sd(probabilities, na.rm = T),
     # n = mean(n),
     me_prob = alpha_threshold * se_prob,
     .groups = "drop"
@@ -1760,15 +1752,15 @@ 
Figure 2: ISCB Fellows and keynote speakers appear more evenly split between
   # group_by(year, type, gender) %>%
   gender_breakdown("main", fct_rev(type))
 fig_1
-

-
ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5)
+

+
ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5, dpi = 600)
 ggsave("figs/gender_breakdown.svg", fig_1, width = 5, height = 2.5)

 
## `summarise()` ungrouping output (override with `.groups` argument)

 
## # A tibble: 2 x 2
 ##   type                     prob_female_avg
 ##   <chr>                              <dbl>
 ## 1 Keynote speakers/Fellows           0.303
-## 2 Pubmed authors                     0.268

+## 2 Pubmed authors                     0.277

Supplementary Figure S2

@@ -1784,8 +1776,8 @@

Supplementary Figure S2

) %>% group_by(type2, year, gender) %>% summarise( - mean_prob = mean(weighted_probs), - se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), + mean_prob = mean(probabilities), + se_prob = sd(probabilities)/sqrt(n()), me_prob = alpha_threshold * se_prob, .groups = "drop" ) %>% @@ -1794,8 +1786,8 @@

Supplementary Figure S2

labels = scales::date_format("'%y"), expand = c(0, 0) ) -
## Scale for 'x' is already present. Adding another scale for 'x', which will
-## replace the existing scale.
+
## Scale for 'x' is already present. Adding another scale for
+## 'x', which will replace the existing scale.
@@ -1809,58 +1801,59 @@

Mean and standard deviation of predicted probabilities

) + theme(legend.position = c(0.88, 0.2)) 
## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
-
## Warning: Removed 13171 rows containing non-finite values (stat_smooth).
-

+
## Warning: Removed 13171 rows containing non-finite values
+## (stat_smooth).
+

Hypothesis testing

iscb_lm <- iscb_pubmed %>%
-  filter(gender == "probability_female", !is.na(weighted_probs)) %>%
+  filter(gender == "probability_female", !is.na(probabilities)) %>%
   mutate(type = as.factor(type)) %>% 
   mutate(type = type %>% relevel(ref = "Pubmed authors"))
scaled_iscb <- iscb_lm %>%
   filter(year(year) >= 2002)
-# scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F)
+# scaled_iscb$s_prob <- scale(scaled_iscb$probabilities, scale = F)
 # scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F)
 
-main_lm <- glm(type ~ year + weighted_probs,
+main_lm <- glm(type ~ year + probabilities,
   data = scaled_iscb, # %>% mutate(year = as.factor(year))
   family = "binomial"
 )
 
 broom::tidy(main_lm)
## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)    -2.06     0.478         -4.30 1.67e- 5
-## 2 year           -0.000271 0.0000320     -8.47 2.42e-17
-## 3 weighted_probs  0.155    0.0921         1.69 9.19e- 2
+## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) -2.06 0.478 -4.30 1.67e- 5 +## 2 year -0.000271 0.0000320 -8.47 2.46e-17 +## 3 probabilities 0.193 0.146 1.33 1.85e- 1 
inte_lm <- glm(
-  # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F),
+  # type ~ scale(year, scale = F) * scale(probabilities, scale = F),
   # type ~ s_year * s_prob,
-  type ~ year * weighted_probs,
+  type ~ year * probabilities,
   data = scaled_iscb, # %>% mutate(year = as.factor(year))
   family = "binomial"
 )
 broom::tidy(inte_lm)
## # A tibble: 4 x 5
-##   term                  estimate std.error statistic  p.value
-##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)         -1.91      0.523        -3.65  2.59e- 4
-## 2 year                -0.000281  0.0000351    -7.99  1.30e-15
-## 3 weighted_probs      -0.445     0.901        -0.494 6.21e- 1
-## 4 year:weighted_probs  0.0000402 0.0000593     0.679 4.97e- 1
+## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) -1.77 0.568 -3.11 1.85e- 3 +## 2 year -0.000291 0.0000383 -7.59 3.22e-14 +## 3 probabilities -0.992 1.29 -0.771 4.41e- 1 +## 4 year:probabilities 0.0000787 0.0000846 0.930 3.52e- 1 
anova(main_lm, inte_lm, test = "Chisq")
## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    153942     4582.3                     
-## 2    153941     4581.8  1  0.47034   0.4928
+## 1 153942 4582.8 +## 2 153941 4582.0 1 0.86975 0.351 
# mean(scaled_iscb$year)
-# mean(scaled_iscb$weighted_probs)
-

The two groups of scientists did not have a significant association with the gender predicted from fore names (P = 0.091898). Interaction terms do not predict type over and above the main effect of gender probability and year.

+# mean(scaled_iscb$probabilities) +

The two groups of scientists did not have a significant association with the gender predicted from fore names (P = 0.18469). Interaction terms do not predict type over and above the main effect of gender probability and year.

sessionInfo()
## R version 4.0.3 (2020-10-10)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
@@ -1878,55 +1871,73 @@ 
Hypothesis testing

 ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
 ## 
 ## attached base packages:
-## [1] stats     graphics  grDevices utils     datasets  methods   base     
+## [1] stats     graphics  grDevices utils     datasets  methods  
+## [7] base     
 ## 
 ## other attached packages:
-##  [1] gdtools_0.2.2       wru_0.1-10          rnaturalearth_0.1.0
-##  [4] lubridate_1.7.9.2   caret_6.0-86        lattice_0.20-41    
-##  [7] forcats_0.5.0       stringr_1.4.0       dplyr_1.0.2        
-## [10] purrr_0.3.4         readr_1.4.0         tidyr_1.1.2        
-## [13] tibble_3.0.4        ggplot2_3.3.2       tidyverse_1.3.0    
+##  [1] broom_0.7.2         DT_0.16             epitools_0.5-10.1  
+##  [4] gdtools_0.2.2       wru_0.1-10          rnaturalearth_0.1.0
+##  [7] lubridate_1.7.9.2   caret_6.0-86        lattice_0.20-41    
+## [10] forcats_0.5.0       stringr_1.4.0       dplyr_1.0.2        
+## [13] purrr_0.3.4         readr_1.4.0         tidyr_1.1.2        
+## [16] tibble_3.0.4        ggplot2_3.3.2       tidyverse_1.3.0    
 ## 
 ## loaded via a namespace (and not attached):
-##   [1] colorspace_2.0-0        ellipsis_0.3.1          class_7.3-17           
-##   [4] rprojroot_1.3-2         fs_1.5.0                rstudioapi_0.12        
-##   [7] farver_2.0.3            remotes_2.2.0           DT_0.16                
-##  [10] prodlim_2019.11.13      fansi_0.4.1             xml2_1.3.2             
-##  [13] codetools_0.2-16        splines_4.0.3           knitr_1.30             
-##  [16] pkgload_1.1.0           jsonlite_1.7.1          pROC_1.16.2            
-##  [19] broom_0.7.2             dbplyr_2.0.0            rgeos_0.5-5            
-##  [22] compiler_4.0.3          httr_1.4.2              backports_1.2.0        
-##  [25] assertthat_0.2.1        Matrix_1.2-18           cli_2.1.0              
-##  [28] htmltools_0.5.0         prettyunits_1.1.1       tools_4.0.3            
-##  [31] gtable_0.3.0            glue_1.4.2              rnaturalearthdata_0.1.0
-##  [34] reshape2_1.4.4          Rcpp_1.0.5              cellranger_1.1.0       
-##  [37] vctrs_0.3.4             svglite_1.2.3.2         nlme_3.1-149           
-##  [40] iterators_1.0.13        crosstalk_1.1.0.1       timeDate_3043.102      
-##  [43] gower_0.2.2             xfun_0.19               ps_1.4.0               
-##  [46] testthat_3.0.0          rvest_0.3.6             lifecycle_0.2.0        
-##  [49] devtools_2.3.2          MASS_7.3-53             scales_1.1.1           
-##  [52] ipred_0.9-9             hms_0.5.3               RColorBrewer_1.1-2     
-##  [55] yaml_2.2.1              curl_4.3                memoise_1.1.0          
-##  [58] rpart_4.1-15            stringi_1.5.3           desc_1.2.0             
-##  [61] foreach_1.5.1           e1071_1.7-4             pkgbuild_1.1.0         
-##  [64] lava_1.6.8.1            systemfonts_0.3.2       rlang_0.4.8            
-##  [67] pkgconfig_2.0.3         evaluate_0.14           sf_0.9-6               
-##  [70] recipes_0.1.15          htmlwidgets_1.5.2       labeling_0.4.2         
-##  [73] cowplot_1.1.0           tidyselect_1.1.0        processx_3.4.4         
-##  [76] plyr_1.8.6              magrittr_1.5            R6_2.5.0               
-##  [79] generics_0.1.0          DBI_1.1.0               mgcv_1.8-33            
-##  [82] pillar_1.4.6            haven_2.3.1             withr_2.3.0            
-##  [85] units_0.6-7             survival_3.2-7          sp_1.4-4               
-##  [88] nnet_7.3-14             modelr_0.1.8            crayon_1.3.4           
-##  [91] KernSmooth_2.23-17      utf8_1.1.4              rmarkdown_2.5          
-##  [94] usethis_1.6.3           grid_4.0.3              readxl_1.3.1           
-##  [97] data.table_1.13.2       callr_3.5.1             ModelMetrics_1.2.2.2   
-## [100] reprex_0.3.0            digest_0.6.27           classInt_0.4-3         
-## [103] stats4_4.0.3            munsell_0.5.0           viridisLite_0.3.0      
-## [106] sessioninfo_1.1.1
+## [1] colorspace_2.0-0 ellipsis_0.3.1 +## [3] class_7.3-17 rprojroot_1.3-2 +## [5] fs_1.5.0 rstudioapi_0.12 +## [7] farver_2.0.3 remotes_2.2.0 +## [9] prodlim_2019.11.13 fansi_0.4.1 +## [11] xml2_1.3.2 codetools_0.2-16 +## [13] splines_4.0.3 knitr_1.30 +## [15] pkgload_1.1.0 jsonlite_1.7.1 +## [17] pROC_1.16.2 dbplyr_2.0.0 +## [19] rgeos_0.5-5 compiler_4.0.3 +## [21] httr_1.4.2 backports_1.2.0 +## [23] assertthat_0.2.1 Matrix_1.2-18 +## [25] cli_2.1.0 htmltools_0.5.0 +## [27] prettyunits_1.1.1 tools_4.0.3 +## [29] gtable_0.3.0 glue_1.4.2 +## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4 +## [33] Rcpp_1.0.5 cellranger_1.1.0 +## [35] vctrs_0.3.4 svglite_1.2.3.2 +## [37] nlme_3.1-149 iterators_1.0.13 +## [39] crosstalk_1.1.0.1 timeDate_3043.102 +## [41] gower_0.2.2 xfun_0.19 +## [43] ps_1.4.0 testthat_3.0.0 +## [45] rvest_0.3.6 lifecycle_0.2.0 +## [47] devtools_2.3.2 MASS_7.3-53 +## [49] scales_1.1.1 ipred_0.9-9 +## [51] hms_0.5.3 RColorBrewer_1.1-2 +## [53] yaml_2.2.1 curl_4.3 +## [55] memoise_1.1.0 rpart_4.1-15 +## [57] stringi_1.5.3 desc_1.2.0 +## [59] foreach_1.5.1 e1071_1.7-4 +## [61] pkgbuild_1.1.0 lava_1.6.8.1 +## [63] systemfonts_0.3.2 rlang_0.4.8 +## [65] pkgconfig_2.0.3 evaluate_0.14 +## [67] sf_0.9-6 recipes_0.1.15 +## [69] htmlwidgets_1.5.2 labeling_0.4.2 +## [71] cowplot_1.1.0 tidyselect_1.1.0 +## [73] processx_3.4.4 plyr_1.8.6 +## [75] magrittr_1.5 R6_2.5.0 +## [77] generics_0.1.0 DBI_1.1.0 +## [79] mgcv_1.8-33 pillar_1.4.6 +## [81] haven_2.3.1 withr_2.3.0 +## [83] units_0.6-7 survival_3.2-7 +## [85] sp_1.4-4 nnet_7.3-14 +## [87] modelr_0.1.8 crayon_1.3.4 +## [89] KernSmooth_2.23-17 utf8_1.1.4 +## [91] rmarkdown_2.5 usethis_1.6.3 +## [93] grid_4.0.3 readxl_1.3.1 +## [95] data.table_1.13.2 callr_3.5.1 +## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 +## [99] digest_0.6.27 classInt_0.4-3 +## [101] stats4_4.0.3 munsell_0.5.0 +## [103] viridisLite_0.3.0 sessioninfo_1.1.1
-
---
title: "Representation analysis of gender"
---

## Setups

```{r message = F, warning=F}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
library(wru)
source("utils/r-utils.R")
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

## Load data

Only keep articles from 2002 because few authors had gender predictions before 2002.
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
load("Rdata/raws.Rdata")

alpha_threshold <- qnorm(0.975)
gender_df <- read_tsv("data/gender/genderize.tsv")

pubmed_gender_df <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  left_join(gender_df, by = "fore_name_simple")

iscb_gender_df <- keynotes %>%
  left_join(gender_df, by = "fore_name_simple")

start_year <- 1993
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_gender_df$journal)
my_confs <- unique(iscb_gender_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
```

## Prepare data frames for later analyses

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed <- iscb_gender_df %>%
  rename("journal" = conference) %>%
  select(year, journal, probability_male, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1
  ) %>%
  bind_rows(
    pubmed_gender_df %>%
      select(year, journal, probability_male, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors")
  ) %>%
  mutate(probability_female = 1 - probability_male) %>%
  pivot_longer(contains("probability"),
    names_to = "gender",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities)) %>%
  group_by(type, year, gender) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations / pmc_citations_year,
    weighted_probs = probabilities * weight
    # weight = 1
  )

iscb_pubmed_sum <- iscb_pubmed %>%
  summarise(
    # n = n(),
    mean_prob = mean(weighted_probs),
    # mean_prob = mean(probabilities, na.rm = T),
    # sd_prob = sd(probabilities, na.rm = T),
    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
    # n = mean(n),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  )
# https://stats.stackexchange.com/questions/25895/computing-standard-error-in-weighted-mean-estimation
```


```{r}
# save(iscb_pubmed, file = 'Rdata/iscb-pubmed_gender.Rdata')
```


## Figures for paper

### Figure 2: ISCB Fellows and keynote speakers appear more evenly split between men and women than PubMed authors, but the proportion has not reached parity.

```{r fig.height=3}
fig_1 <- iscb_pubmed_sum %>%
  # group_by(year, type, gender) %>%
  gender_breakdown("main", fct_rev(type))
fig_1
ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5)
ggsave("figs/gender_breakdown.svg", fig_1, width = 5, height = 2.5)
```

```{r echo=FALSE}
iscb_pubmed_sum %>%
  # group_by(year, type, gender) %>%
  # summarise(mean_prob = mean(probabilities, na.rm = T), .groups = 'drop') %>%
  filter(year(year) > 2016, grepl("female", gender)) %>%
  group_by(type) %>%
  summarise(prob_female_avg = mean(mean_prob))
```

### Supplementary Figure S2 {#sup_fig_s1}

Additional fig. 1 with separated keynote speakers and fellows

```{r}
fig_1d <- iscb_pubmed %>%
  ungroup() %>%
  mutate(
    type2 = case_when(
      type == "Pubmed authors" ~ "Pubmed authors",
      journal == "ISCB Fellow" ~ "ISCB Fellows",
      type == "Keynote speakers/Fellows" ~ "Keynote speakers"
    )
  ) %>%
  group_by(type2, year, gender) %>%
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  ) %>%
  gender_breakdown("main", fct_rev(type2)) +
  scale_x_date(
    labels = scales::date_format("'%y"),
    expand = c(0, 0)
  )
```

<!-- Increasing trend of honorees who were women in each honor category, especially in the group of ISCB Fellows, which markedly increased after 2015.  -->

```{r eval=FALSE, include=FALSE}
# By conference:
# fig_1d <- bind_rows(iscb_gender) %>%
#   gender_breakdown(category = 'sub', journal) +
#   theme(legend.position = 'bottom')

# fig_1d
ggsave("figs/fig_s1.png", fig_1d, width = 7, height = 3)
ggsave("figs/fig_s1.svg", fig_1d, width = 7, height = 3)
```

## Mean and standard deviation of predicted probabilities

```{r}
iscb_pubmed_sum %>%
  filter(gender == "probability_male") %>%
  gam_and_ci(
    df2 = iscb_pubmed %>% filter(gender == "probability_male"),
    start_y = start_year, end_y = end_year
  ) +
  theme(legend.position = c(0.88, 0.2))
```

## Hypothesis testing

```{r echo = F}
get_p <- function(inte, colu) {
  broom::tidy(inte) %>%
    filter(term == "weighted_probs") %>%
    pull(colu) %>%
    sprintf("%0.5g", .)
}
```

```{r}
iscb_lm <- iscb_pubmed %>%
  filter(gender == "probability_female", !is.na(weighted_probs)) %>%
  mutate(type = as.factor(type)) %>% 
  mutate(type = type %>% relevel(ref = "Pubmed authors"))
```

```{r}
scaled_iscb <- iscb_lm %>%
  filter(year(year) >= 2002)
# scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F)
# scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F)

main_lm <- glm(type ~ year + weighted_probs,
  data = scaled_iscb, # %>% mutate(year = as.factor(year))
  family = "binomial"
)

broom::tidy(main_lm)
inte_lm <- glm(
  # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F),
  # type ~ s_year * s_prob,
  type ~ year * weighted_probs,
  data = scaled_iscb, # %>% mutate(year = as.factor(year))
  family = "binomial"
)
broom::tidy(inte_lm)
anova(main_lm, inte_lm, test = "Chisq")
# mean(scaled_iscb$year)
# mean(scaled_iscb$weighted_probs)
```

The two groups of scientists did not have a significant association with the gender predicted from fore names (_P_ = `r get_p(main_lm, 'p.value')`).
Interaction terms do not predict `type` over and above the main effect of gender probability and year.

```{r include=FALSE, eval=FALSE}
# inte_lm <- glm(type ~ (year * weighted_probs),
#    data = iscb_lm,
#    family = 'binomial')
```

```{r}
sessionInfo()
```

+
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
diff --git a/docs/11.visualize-name-origins.html b/docs/11.visualize-name-origins.html index 34d453b..f8c841c 100644 --- a/docs/11.visualize-name-origins.html +++ b/docs/11.visualize-name-origins.html @@ -1694,7 +1694,7 @@

Representation analysis of name origins

rename("region" = Region) %>% recode_region() 
## 
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
 ## cols(
 ##   Country = col_character(),
 ##   Region = col_character()
@@ -1747,17 +1747,12 @@ 
Descriptive statistics

     values_to = "probabilities"
   ) %>%
   filter(!is.na(probabilities)) %>%
-  group_by(type, year, region) %>%
-  mutate(
-    pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations / pmc_citations_year,
-    weighted_probs = probabilities * weight
-  )
+  group_by(type, year, region)
 
 iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
   summarise(
-    mean_prob = mean(weighted_probs),
-    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+    mean_prob = mean(probabilities),
+    se_prob = sd(probabilities)/sqrt(n()),
     me_prob = alpha_threshold * se_prob,
     .groups = "drop"
   )
@@ -1777,7 +1772,7 @@ 
By conference keynotes/fellows

   iscb_nat[[i]] <- iscb_pubmed_oth %>%
     filter(region != "OtherCategories", type != "Pubmed authors" & journal == conf) %>%
     group_by(type, year, region, journal) %>%
-    summarise(mean_prob = mean(weighted_probs), .groups = "drop")
+    summarise(mean_prob = mean(probabilities), .groups = "drop")
 }
save(my_world, iscb_pubmed_oth, iscb_nat, file = "Rdata/iscb-pubmed_nat.Rdata")
@@ -1815,8 +1810,8 @@

Figure 4

fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1)) 
## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
fig_4
-

-
ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5)
+

+
ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5, dpi = 600)
 ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5)

 
 
@@ -1830,7 +1825,7 @@ 
Hypothesis testing

     type = as.factor(type) %>% relevel(ref = "Pubmed authors")
   )
 main_lm <- function(regioni) {
-  glm(type ~ year + weighted_probs,
+  glm(type ~ year + probabilities,
     data = iscb_lm %>%
       filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
     family = "binomial"
@@ -1838,122 +1833,120 @@ 
Hypothesis testing

 }
 
 inte_lm <- function(regioni) {
-  glm(type ~ year * weighted_probs,
+  glm(type ~ year * probabilities,
     data = iscb_lm %>%
-      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
     family = "binomial"
   )
 }
 
-main_list <- lapply(large_regions, main_lm)
-
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-
names(main_list) <- large_regions
+main_list <- lapply(large_regions, main_lm)
+names(main_list) <- large_regions
 lapply(main_list, broom::tidy)
## $CelticEnglish
 ## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)    -2.17     0.481         -4.52 6.06e- 6
-## 2 year           -0.000268 0.0000320     -8.37 5.64e-17
-## 3 weighted_probs  0.194    0.0561         3.46 5.46e- 4
+##   term           estimate std.error statistic  p.value
+##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)   -2.62     0.489         -5.36 8.47e- 8
+## 2 year          -0.000250 0.0000321     -7.78 7.40e-15
+## 3 probabilities  0.869    0.139          6.26 3.97e-10
 ## 
 ## $EastAsian
 ## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)    -2.30     0.480         -4.80 1.58e- 6
-## 2 year           -0.000242 0.0000321     -7.54 4.82e-14
-## 3 weighted_probs -1.67     0.286         -5.82 6.02e- 9
+##   term           estimate std.error statistic  p.value
+##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)   -2.29     0.479         -4.77 1.81e- 6
+## 2 year          -0.000241 0.0000320     -7.51 6.02e-14
+## 3 probabilities -1.75     0.250         -7.00 2.51e-12
 ## 
 ## $European
 ## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)    -2.08     0.480        -4.32  1.53e- 5
-## 2 year           -0.000272 0.0000319    -8.53  1.46e-17
-## 3 weighted_probs  0.0713   0.0822        0.867 3.86e- 1
+##   term           estimate std.error statistic  p.value
+##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)   -2.15     0.484         -4.45 8.72e- 6
+## 2 year          -0.000271 0.0000320     -8.46 2.62e-17
+## 3 probabilities  0.222    0.137          1.62 1.05e- 1
 ## 
 ## $OtherCategories
 ## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)    -2.06     0.479        -4.30  1.73e- 5
-## 2 year           -0.000273 0.0000319    -8.57  1.03e-17
-## 3 weighted_probs  0.0724   0.0948        0.763 4.45e- 1
-
inte_list <- lapply(large_regions, inte_lm)
-
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-
lapply(inte_list, broom::tidy)
+## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) -2.07 0.479 -4.33 1.52e- 5 +## 2 year -0.000274 0.0000319 -8.58 9.22e-18 +## 3 probabilities 0.159 0.151 1.05 2.95e- 1 +
inte_list <- lapply(large_regions, inte_lm)
+lapply(inte_list, broom::tidy)
## [[1]]
 ## # A tibble: 4 x 5
-##   term                  estimate std.error statistic  p.value
-##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)         -2.11      0.507      -4.17    3.06e- 5
-## 2 year                -0.000272  0.0000338  -8.05    8.48e-16
-## 3 weighted_probs       0.00264   0.525       0.00502 9.96e- 1
-## 4 year:weighted_probs  0.0000131 0.0000353   0.370   7.11e- 1
+##   term                 estimate std.error statistic  p.value
+##   <chr>                   <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)        -2.48      0.627        -3.96  7.57e- 5
+## 2 year               -0.000259  0.0000414    -6.26  3.96e-10
+## 3 probabilities       0.451     1.19          0.380 7.04e- 1
+## 4 year:probabilities  0.0000283 0.0000796     0.355 7.23e- 1
 ## 
 ## [[2]]
 ## # A tibble: 4 x 5
-##   term                 estimate std.error statistic  p.value
-##   <chr>                   <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)         -2.53     0.506        -5.01  5.54e- 7
-## 2 year                -0.000227 0.0000338    -6.71  1.94e-11
-## 3 weighted_probs       1.96     2.45          0.800 4.24e- 1
-## 4 year:weighted_probs -0.000239 0.000164     -1.46  1.44e- 1
+##   term                estimate std.error statistic  p.value
+##   <chr>                  <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)        -2.45     0.500        -4.90  9.36e- 7
+## 2 year               -0.000229 0.0000334    -6.87  6.37e-12
+## 3 probabilities       0.853    2.19          0.389 6.97e- 1
+## 4 year:probabilities -0.000172 0.000146     -1.18  2.38e- 1
 ## 
 ## [[3]]
 ## # A tibble: 4 x 5
-##   term                  estimate std.error statistic  p.value
-##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)         -1.87      0.538        -3.47  5.20e- 4
-## 2 year                -0.000286  0.0000358    -8.01  1.19e-15
-## 3 weighted_probs      -0.587     0.779        -0.753 4.51e- 1
-## 4 year:weighted_probs  0.0000441 0.0000511     0.862 3.89e- 1
+##   term                estimate std.error statistic  p.value
+##   <chr>                  <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)        -1.66     0.614         -2.71 6.75e- 3
+## 2 year               -0.000303 0.0000410     -7.39 1.44e-13
+## 3 probabilities      -1.28     1.20          -1.07 2.85e- 1
+## 4 year:probabilities  0.000101 0.0000796      1.27 2.05e- 1
 ## 
 ## [[4]]
 ## # A tibble: 4 x 5
-##   term                  estimate std.error statistic  p.value
-##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)         -1.80      0.527         -3.42 6.25e- 4
-## 2 year                -0.000290  0.0000351     -8.29 1.17e-16
-## 3 weighted_probs      -0.880     0.849         -1.04 3.00e- 1
-## 4 year:weighted_probs  0.0000628 0.0000544      1.15 2.48e- 1
+## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) -1.55 0.598 -2.60 9.42e- 3 +## 2 year -0.000309 0.0000401 -7.70 1.31e-14 +## 3 probabilities -1.76 1.35 -1.30 1.92e- 1 +## 4 year:probabilities 0.000127 0.0000882 1.44 1.50e- 1 
for (i in 1:4) {
   print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
 }
## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    163886     4627.2                     
-## 2    163885     4627.1  1  0.14187   0.7064
+## 1    163886     4599.0                     
+## 2    163885     4598.9  1  0.12594   0.7227
 ## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    163886     4576.6                     
-## 2    163885     4574.6  1   2.0829    0.149
+## 1    163886     4554.1                     
+## 2    163885     4552.7  1   1.3867    0.239
 ## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    163886     4634.1                     
-## 2    163885     4633.4  1  0.74565   0.3879
+## 1    163886     4632.2                     
+## 2    163885     4630.6  1    1.607   0.2049
 ## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    163886     4634.3                     
-## 2    163885     4633.1  1   1.2103   0.2713
+## 1 163886 4633.7 +## 2 163885 4631.6 1 2.0781 0.1494

Interaction terms do not predict type over and above the main effect of name origin probability and year (p > 0.01).

Conclusion

-

A Celtic/English name has 1.2141832 the odds of being selected as an honoree, significantly higher compared to other names (\(\beta_\textrm{Celtic/English} =\) 0.19407, P = 0.00054646). An East Asian name has 0.1889996 the odds of being selected as an honoree, significantly lower than to other names (\(\beta_\textrm{East Asian} =\) -1.666, P = 6.0164e-09). The two groups of scientists did not have a significant association with names predicted to be European (P = 0.38583) or in Other categories (P = 0.44544).

+

A Celtic/English name has 2.3850497 the odds of being selected as an honoree, significantly higher compared to other names (\(\beta_\textrm{Celtic/English} =\) 0.86922, P = 3.9713e-10). An East Asian name has 0.1731846 the odds of being selected as an honoree, significantly lower than to other names (\(\beta_\textrm{East Asian} =\) -1.7534, P = 2.5132e-12). The two groups of scientists did not have a significant association with names predicted to be European (P = 0.10527) or in Other categories (P = 0.29469).

Alternative approach

@@ -2059,21 +2052,16 @@

Time lag

values_to = "probabilities" ) %>% filter(!is.na(probabilities), year(year) >= 2002) %>% - group_by(type, year, region) %>% - mutate( - pmc_citations_year = mean(adjusted_citations), - weight = adjusted_citations / pmc_citations_year, - weighted_probs = probabilities * weight - ) + group_by(type, year, region) iscb_lm_lag <- iscb_pubmed_oth_lag %>% ungroup() %>% mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors")) main_lm <- function(regioni) { - glm(type ~ year + weighted_probs, + glm(type ~ year + probabilities, data = iscb_lm_lag %>% - filter(region == regioni, !is.na(weighted_probs)), + filter(region == regioni, !is.na(probabilities)), family = "binomial" ) } @@ -2083,35 +2071,35 @@

Time lag

lapply(main_list, broom::tidy) 
## $CelticEnglish
 ## # A tibble: 3 x 5
-##   term           estimate std.error statistic   p.value
-##   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
-## 1 (Intercept)    29.1     1.18         24.7   2.46e-134
-## 2 year           -0.00210 0.0000749   -28.0   2.09e-172
-## 3 weighted_probs  0.0199  0.102         0.196 8.45e-  1
+##   term          estimate std.error statistic   p.value
+##   <chr>            <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)   29.0     1.19         24.5   4.51e-132
+## 2 year          -0.00209 0.0000750   -27.9   5.63e-171
+## 3 probabilities  0.130   0.170         0.767 4.43e-  1
 ## 
 ## $EastAsian
 ## # A tibble: 3 x 5
-##   term           estimate std.error statistic   p.value
-##   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
-## 1 (Intercept)    29.0     1.18          24.6  5.89e-134
-## 2 year           -0.00209 0.0000749    -27.8  1.41e-170
-## 3 weighted_probs -0.708   0.292         -2.43 1.52e-  2
+##   term          estimate std.error statistic   p.value
+##   <chr>            <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)   28.9     1.18          24.6  3.99e-133
+## 2 year          -0.00208 0.0000749    -27.7  2.99e-169
+## 3 probabilities -1.01    0.280         -3.60 3.20e-  4
 ## 
 ## $European
 ## # A tibble: 3 x 5
-##   term           estimate std.error statistic   p.value
-##   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
-## 1 (Intercept)    29.1     1.18        24.7    4.84e-135
-## 2 year           -0.00210 0.0000748  -28.0    7.61e-173
-## 3 weighted_probs  0.00832 0.106        0.0788 9.37e-  1
+##   term          estimate std.error statistic   p.value
+##   <chr>            <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)   29.1     1.18         24.7   1.06e-134
+## 2 year          -0.00210 0.0000748   -28.0   8.91e-173
+## 3 probabilities  0.0595  0.166         0.358 7.20e-  1
 ## 
 ## $OtherCategories
 ## # A tibble: 3 x 5
-##   term           estimate std.error statistic   p.value
-##   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
-## 1 (Intercept)    29.1     1.18          24.7  3.41e-135
-## 2 year           -0.00210 0.0000748    -28.1  3.25e-173
-## 3 weighted_probs  0.170   0.106          1.60 1.09e-  1
+## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) 29.1 1.18 24.8 2.82e-135 +## 2 year -0.00210 0.0000748 -28.1 7.58e-174 +## 3 probabilities 0.432 0.183 2.36 1.80e- 2

Supplementary Figure S5

@@ -2148,55 +2136,73 @@

Supplementary Figure S5

## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: -## [1] stats graphics grDevices utils datasets methods base +## [1] stats graphics grDevices utils datasets methods +## [7] base ## ## other attached packages: -## [1] gdtools_0.2.2 wru_0.1-10 rnaturalearth_0.1.0 -## [4] lubridate_1.7.9.2 caret_6.0-86 lattice_0.20-41 -## [7] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 -## [10] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 -## [13] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 +## [1] broom_0.7.2 DT_0.16 epitools_0.5-10.1 +## [4] gdtools_0.2.2 wru_0.1-10 rnaturalearth_0.1.0 +## [7] lubridate_1.7.9.2 caret_6.0-86 lattice_0.20-41 +## [10] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 +## [13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 +## [16] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 ## ## loaded via a namespace (and not attached): -## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-17 -## [4] rprojroot_1.3-2 fs_1.5.0 rstudioapi_0.12 -## [7] farver_2.0.3 remotes_2.2.0 DT_0.16 -## [10] prodlim_2019.11.13 fansi_0.4.1 xml2_1.3.2 -## [13] codetools_0.2-16 splines_4.0.3 knitr_1.30 -## [16] pkgload_1.1.0 jsonlite_1.7.1 pROC_1.16.2 -## [19] broom_0.7.2 dbplyr_2.0.0 rgeos_0.5-5 -## [22] compiler_4.0.3 httr_1.4.2 backports_1.2.0 -## [25] assertthat_0.2.1 Matrix_1.2-18 cli_2.1.0 -## [28] htmltools_0.5.0 prettyunits_1.1.1 tools_4.0.3 -## [31] gtable_0.3.0 glue_1.4.2 rnaturalearthdata_0.1.0 -## [34] reshape2_1.4.4 Rcpp_1.0.5 cellranger_1.1.0 -## [37] vctrs_0.3.4 svglite_1.2.3.2 nlme_3.1-149 -## [40] iterators_1.0.13 crosstalk_1.1.0.1 timeDate_3043.102 -## [43] gower_0.2.2 xfun_0.19 ps_1.4.0 -## [46] testthat_3.0.0 rvest_0.3.6 lifecycle_0.2.0 -## [49] devtools_2.3.2 MASS_7.3-53 scales_1.1.1 -## [52] ipred_0.9-9 hms_0.5.3 RColorBrewer_1.1-2 -## [55] yaml_2.2.1 curl_4.3 memoise_1.1.0 -## [58] rpart_4.1-15 stringi_1.5.3 desc_1.2.0 -## [61] foreach_1.5.1 e1071_1.7-4 pkgbuild_1.1.0 -## [64] lava_1.6.8.1 systemfonts_0.3.2 rlang_0.4.8 -## [67] pkgconfig_2.0.3 evaluate_0.14 sf_0.9-6 -## [70] recipes_0.1.15 htmlwidgets_1.5.2 labeling_0.4.2 -## [73] cowplot_1.1.0 tidyselect_1.1.0 processx_3.4.4 -## [76] plyr_1.8.6 magrittr_1.5 R6_2.5.0 -## [79] generics_0.1.0 DBI_1.1.0 mgcv_1.8-33 -## [82] pillar_1.4.6 haven_2.3.1 withr_2.3.0 -## [85] units_0.6-7 survival_3.2-7 sp_1.4-4 -## [88] nnet_7.3-14 modelr_0.1.8 crayon_1.3.4 -## [91] KernSmooth_2.23-17 utf8_1.1.4 rmarkdown_2.5 -## [94] usethis_1.6.3 grid_4.0.3 readxl_1.3.1 -## [97] data.table_1.13.2 callr_3.5.1 ModelMetrics_1.2.2.2 -## [100] reprex_0.3.0 digest_0.6.27 classInt_0.4-3 -## [103] stats4_4.0.3 munsell_0.5.0 viridisLite_0.3.0 -## [106] sessioninfo_1.1.1 +## [1] colorspace_2.0-0 ellipsis_0.3.1 +## [3] class_7.3-17 rprojroot_1.3-2 +## [5] fs_1.5.0 rstudioapi_0.12 +## [7] farver_2.0.3 remotes_2.2.0 +## [9] prodlim_2019.11.13 fansi_0.4.1 +## [11] xml2_1.3.2 codetools_0.2-16 +## [13] splines_4.0.3 knitr_1.30 +## [15] pkgload_1.1.0 jsonlite_1.7.1 +## [17] pROC_1.16.2 dbplyr_2.0.0 +## [19] rgeos_0.5-5 compiler_4.0.3 +## [21] httr_1.4.2 backports_1.2.0 +## [23] assertthat_0.2.1 Matrix_1.2-18 +## [25] cli_2.1.0 htmltools_0.5.0 +## [27] prettyunits_1.1.1 tools_4.0.3 +## [29] gtable_0.3.0 glue_1.4.2 +## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4 +## [33] Rcpp_1.0.5 cellranger_1.1.0 +## [35] vctrs_0.3.4 svglite_1.2.3.2 +## [37] nlme_3.1-149 iterators_1.0.13 +## [39] crosstalk_1.1.0.1 timeDate_3043.102 +## [41] gower_0.2.2 xfun_0.19 +## [43] ps_1.4.0 testthat_3.0.0 +## [45] rvest_0.3.6 lifecycle_0.2.0 +## [47] devtools_2.3.2 MASS_7.3-53 +## [49] scales_1.1.1 ipred_0.9-9 +## [51] hms_0.5.3 RColorBrewer_1.1-2 +## [53] yaml_2.2.1 curl_4.3 +## [55] memoise_1.1.0 rpart_4.1-15 +## [57] stringi_1.5.3 desc_1.2.0 +## [59] foreach_1.5.1 e1071_1.7-4 +## [61] pkgbuild_1.1.0 lava_1.6.8.1 +## [63] systemfonts_0.3.2 rlang_0.4.8 +## [65] pkgconfig_2.0.3 evaluate_0.14 +## [67] sf_0.9-6 recipes_0.1.15 +## [69] htmlwidgets_1.5.2 labeling_0.4.2 +## [71] cowplot_1.1.0 tidyselect_1.1.0 +## [73] processx_3.4.4 plyr_1.8.6 +## [75] magrittr_1.5 R6_2.5.0 +## [77] generics_0.1.0 DBI_1.1.0 +## [79] mgcv_1.8-33 pillar_1.4.6 +## [81] haven_2.3.1 withr_2.3.0 +## [83] units_0.6-7 survival_3.2-7 +## [85] sp_1.4-4 nnet_7.3-14 +## [87] modelr_0.1.8 crayon_1.3.4 +## [89] KernSmooth_2.23-17 utf8_1.1.4 +## [91] rmarkdown_2.5 usethis_1.6.3 +## [93] grid_4.0.3 readxl_1.3.1 +## [95] data.table_1.13.2 callr_3.5.1 +## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 +## [99] digest_0.6.27 classInt_0.4-3 +## [101] stats4_4.0.3 munsell_0.5.0 +## [103] viridisLite_0.3.0 sessioninfo_1.1.1
-
---
title: "Representation analysis of name origins"
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
source("utils/r-utils.R")
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata).
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
alpha_threshold <- qnorm(0.975)
load("Rdata/raws.Rdata")

pubmed_nat_df <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  left_join(nationalize_df, by = c("fore_name", "last_name")) %>%
  group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
  summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
  ungroup()

iscb_nat_df <- keynotes %>%
  left_join(nationalize_df, by = c("fore_name", "last_name"))

start_year <- 1992
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_nat_df$journal)
my_confs <- unique(iscb_nat_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names")
region_levels_let <- toupper(letters[1:8])
region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5")
```

Names grouping:
```{r warning=FALSE, fig.height = 3}
our_country_map <- read_tsv("https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/country_to_region_NamePrism.tsv") %>%
  rename("region" = Region) %>%
  recode_region()

my_world <- world %>%
  select(-geometry) %>%
  rename(Country = "name") %>%
  left_join(our_country_map, by = "Country")

(gworld <- ggplot(data = my_world) +
  geom_sf(aes(fill = fct_relevel(region, region_levels))) +
  coord_sf(crs = "+proj=eqearth +wktext") +
  scale_fill_manual(
    values = region_cols,
    na.translate = FALSE
  ) +
  theme(
    panel.background = element_rect(fill = "azure"),
    legend.title = element_blank(),
    legend.position = "bottom",
    panel.border = element_rect(fill = NA)
  ))

ggsave("figs/2020-01-31_groupings.png", gworld, width = 7.2, height = 4.3)
ggsave("figs/2020-01-31_groupings.svg", gworld, width = 7.2, height = 4.3)
```


## Descriptive statistics
Prepare data frames for later analyses:

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed_oth <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1,
    pmid = -9999
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors")
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities)) %>%
  group_by(type, year, region) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations / pmc_citations_year,
    weighted_probs = probabilities * weight
  )

iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  )

iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
  filter(region != "OtherCategories")
```

## Prepare data frames for analysis

### By conference keynotes/fellows
```{r fig.height=6}
i <- 0
iscb_nat <- vector("list", length = n_confs)

for (conf in my_confs) {
  i <- i + 1
  iscb_nat[[i]] <- iscb_pubmed_oth %>%
    filter(region != "OtherCategories", type != "Pubmed authors" & journal == conf) %>%
    group_by(type, year, region, journal) %>%
    summarise(mean_prob = mean(weighted_probs), .groups = "drop")
}
```

```{r}
save(my_world, iscb_pubmed_oth, iscb_nat, file = "Rdata/iscb-pubmed_nat.Rdata")
```

## Figures for paper

### Figure 4

Compared to the name collection of Pubmed authors, honorees with Celtic/English names are overrepresented while honorees with East Asian names are underrepresented.

```{r fig.height=7, fig.width=9, warning=FALSE}
fig_4a <- iscb_pubmed_sum %>%
  filter(year < "2020-01-01") %>%
  region_breakdown("main", region_levels, fct_rev(type)) +
  guides(fill = guide_legend(nrow = 2)) +
  theme(legend.position = "bottom")

large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories")
## Mean and standard deviation of predicted probabilities:
fig_4b <- iscb_pubmed_sum_oth %>%
  filter(region %in% large_regions) %>%
  recode_region() %>%
  gam_and_ci(
    df2 = iscb_pubmed_oth %>%
      filter(region %in% large_regions) %>%
      recode_region(),
    start_y = start_year, end_y = end_year
  ) +
  theme(
    legend.position = c(0.88, 0.83),
    panel.grid.minor = element_blank(),
    legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"),
    legend.text = element_text(size = 7)
  ) +
  facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)

fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
fig_4
ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5)
ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5)
```


## Hypothesis testing

```{r}
iscb_lm <- iscb_pubmed_oth %>%
  ungroup() %>%
  mutate(
    # year = c(scale(year(year))),
    # year = as.factor(year),
    type = as.factor(type) %>% relevel(ref = "Pubmed authors")
  )
main_lm <- function(regioni) {
  glm(type ~ year + weighted_probs,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
    family = "binomial"
  )
}

inte_lm <- function(regioni) {
  glm(type ~ year * weighted_probs,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)
names(main_list) <- large_regions
lapply(main_list, broom::tidy)

inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, broom::tidy)
for (i in 1:4) {
  print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
}
```
Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01).

```{r echo = F}
get_p <- function(i, colu) {
  broom::tidy(main_list[[i]]) %>%
    filter(term == "weighted_probs") %>%
    pull(colu)
}

print_p <- function(x) sprintf("%0.5g", x)
```

## Conclusion
A Celtic/English name has `r exp(get_p(1, 'estimate'))` the odds of being selected as an honoree, significantly higher compared to other names ($\beta_\textrm{Celtic/English} =$ `r print_p(get_p(1, 'estimate'))`, _P_ = `r print_p(get_p(1, 'p.value'))`).
An East Asian name has `r exp(get_p(2, 'estimate'))` the odds of being selected as an honoree, significantly lower than to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_p(2, 'estimate'))`, _P_ = `r print_p(get_p(2, 'p.value'))`).
The two groups of scientists did not have a significant association with names predicted to be European (_P_ = `r print_p(get_p(3, 'p.value'))`) or in Other categories (_P_ = `r print_p(get_p(4, 'p.value'))`).


## Alternative approach
_Sincere thanks to the reviewers, Byron Smith and Katie Pollard, for their detailed suggestion with code._

The question of what unit one should use to perform this type of analyses is a difficult one.
We present here an alternative analysis that treats _names_ as units instead of _honors_ and _authorships_.
We caution that this approach does not distinguish scientists who were honored 4 times vs. one time
and hence may yield a conservative estimate of disparity.
Further, different authors may have the same names, 
and to sum `adjusted_citations` across them may not be optimal.

Nonetheless, the finding here is consistent with what we have seen above
where East Asian names are underrepresented in the honoree group.

```{r}

keynotes_post_2002 <- keynotes %>%
  filter(year(year) >= 2002) %>%
  separate_rows(afflcountries, sep = ",") %>%
  filter(afflcountries == "United States") %>%
  group_by(fore_name_simple, last_name_simple) %>%
  summarise_at("year", n_distinct, na.rm = T)

# nationalize_df was not unique, so the left join to corr_authors resulted
# in (mostly) duplicate rows.
# FIXME: I was getting occasional crashes on this line, and it's slow.
# TRANG: fixed on June 3, 2021 using distinct().
# Also, the duplication was intentional.
# Please see our extensive discussion on the merge/join on full names vs
# fore_name and last_name here:
# <https://github.com/greenelab/iscb-diversity/issues/6>

distinct_nationalize_df <- nationalize_df %>%
  distinct(fore_name_simple, last_name_simple, .keep_all = TRUE)

# Calculate sum of adjusted citations for all publications for a first-name/
# last-name pair in db since 2002
# where the author countries include US.
authors <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  separate_rows(countries, sep = ",") %>%
  filter(countries == "US") %>%
  group_by(fore_name_simple, last_name_simple) %>%
  summarise_at(vars(adjusted_citations), sum, na.rm = T) %>%
  left_join(
    keynotes_post_2002[c("fore_name_simple", "last_name_simple", "year")],
    by = c("fore_name_simple", "last_name_simple")
  ) %>%
  left_join(distinct_nationalize_df, by = c("fore_name_simple", "last_name_simple")) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African)

for (large_region in large_regions) {
  glm(
    as.formula(paste("honoree ~ adjusted_citations +", large_region)),
    data = authors %>% mutate(honoree = !is.na(year)),
    family = "binomial",
    control = list(epsilon = 1e-12, maxit = 55, trace = FALSE)
  ) %>%
    broom::tidy() %>%
    print()
}
```

<!-- We argue that honors and authorships are the appropriate units. -->
<!-- Although this approach may not satisfy the independent and identically distributed assumption -->

<!-- honor vs not honored. -->
<!-- But then this is considering scientists who have 4 rows of honorees as one  -->
<!-- Is this fair? -->
<!-- 195 rows/honors of keynotes post 2002 to 145 names/scientists. -->

## Time lag

In this section, we show that a 10-year lag model results in a similar result for East Asian scientists' names,
even though the effect size is less striking.
For example, if we assume that honors accrue 10 years after their most prolific year with respect to authorships, 
the proportion of honor associated with East Asian name origins in 2019 is still substantially less than the proportion of senior authorships associated with East Asian names in 2009.

```{r}
year_lag <- period(10, "years")
iscb_pubmed_oth_lag <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1,
    pmid = -9999
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors", year = year + year_lag)
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities), year(year) >= 2002) %>%
  group_by(type, year, region) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations / pmc_citations_year,
    weighted_probs = probabilities * weight
  )

iscb_lm_lag <- iscb_pubmed_oth_lag %>%
  ungroup() %>%
  mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors"))

main_lm <- function(regioni) {
  glm(type ~ year + weighted_probs,
    data = iscb_lm_lag %>%
      filter(region == regioni, !is.na(weighted_probs)),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)
names(main_list) <- large_regions
lapply(main_list, broom::tidy)
```

```{r include=FALSE, eval = FALSE}
checkdf <- iscb_lm %>%
  filter(
    year(year) == 2010,
    adjusted_citations > 3.1, 
    adjusted_citations < 3.32, 
    region == "EastAsian", 
    probabilities > 0.5
  )
```


## Supplementary Figure S5 {#sup_fig_s5}
It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction.
There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field.
However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented.

```{r fig.height=6, warning=FALSE}
# df2 <- iscb_pubmed_oth %>%
#   filter(region != "OtherCategories") %>%
#   recode_region()
# 
# fig_s5 <- iscb_pubmed_sum %>%
#   recode_region() %>%
#   gam_and_ci(
#     df2 = df2,
#     start_y = start_year, end_y = end_year
#   ) +
#   theme(legend.position = c(0.8, 0.1)) +
#   facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)
# fig_s5
# ggsave("figs/fig_s5.png", fig_s5, width = 6, height = 6)
# ggsave("figs/fig_s5.svg", fig_s5, width = 6, height = 6)
```


```{r}
sessionInfo()
```

+
---
title: "Representation analysis of name origins"
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
source("utils/r-utils.R")
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata).
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
alpha_threshold <- qnorm(0.975)
load("Rdata/raws.Rdata")

pubmed_nat_df <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  left_join(nationalize_df, by = c("fore_name", "last_name")) %>%
  group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
  summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
  ungroup()

iscb_nat_df <- keynotes %>%
  left_join(nationalize_df, by = c("fore_name", "last_name"))

start_year <- 1992
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_nat_df$journal)
my_confs <- unique(iscb_nat_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names")
region_levels_let <- toupper(letters[1:8])
region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5")
```

Names grouping:
```{r warning=FALSE, fig.height = 3}
our_country_map <- read_tsv("https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/country_to_region_NamePrism.tsv") %>%
  rename("region" = Region) %>%
  recode_region()

my_world <- world %>%
  select(-geometry) %>%
  rename(Country = "name") %>%
  left_join(our_country_map, by = "Country")

(gworld <- ggplot(data = my_world) +
  geom_sf(aes(fill = fct_relevel(region, region_levels))) +
  coord_sf(crs = "+proj=eqearth +wktext") +
  scale_fill_manual(
    values = region_cols,
    na.translate = FALSE
  ) +
  theme(
    panel.background = element_rect(fill = "azure"),
    legend.title = element_blank(),
    legend.position = "bottom",
    panel.border = element_rect(fill = NA)
  ))

ggsave("figs/2020-01-31_groupings.png", gworld, width = 7.2, height = 4.3)
ggsave("figs/2020-01-31_groupings.svg", gworld, width = 7.2, height = 4.3)
```


## Descriptive statistics
Prepare data frames for later analyses:

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed_oth <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1,
    pmid = -9999
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors")
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities)) %>%
  group_by(type, year, region)

iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
  summarise(
    mean_prob = mean(probabilities),
    se_prob = sd(probabilities)/sqrt(n()),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  )

iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
  filter(region != "OtherCategories")
```

## Prepare data frames for analysis

### By conference keynotes/fellows
```{r fig.height=6}
i <- 0
iscb_nat <- vector("list", length = n_confs)

for (conf in my_confs) {
  i <- i + 1
  iscb_nat[[i]] <- iscb_pubmed_oth %>%
    filter(region != "OtherCategories", type != "Pubmed authors" & journal == conf) %>%
    group_by(type, year, region, journal) %>%
    summarise(mean_prob = mean(probabilities), .groups = "drop")
}
```

```{r}
save(my_world, iscb_pubmed_oth, iscb_nat, file = "Rdata/iscb-pubmed_nat.Rdata")
```

## Figures for paper

### Figure 4

Compared to the name collection of Pubmed authors, honorees with Celtic/English names are overrepresented while honorees with East Asian names are underrepresented.

```{r fig.height=7, fig.width=9, warning=FALSE}
fig_4a <- iscb_pubmed_sum %>%
  filter(year < "2020-01-01") %>%
  region_breakdown("main", region_levels, fct_rev(type)) +
  guides(fill = guide_legend(nrow = 2)) +
  theme(legend.position = "bottom")

large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories")
## Mean and standard deviation of predicted probabilities:
fig_4b <- iscb_pubmed_sum_oth %>%
  filter(region %in% large_regions) %>%
  recode_region() %>%
  gam_and_ci(
    df2 = iscb_pubmed_oth %>%
      filter(region %in% large_regions) %>%
      recode_region(),
    start_y = start_year, end_y = end_year
  ) +
  theme(
    legend.position = c(0.88, 0.83),
    panel.grid.minor = element_blank(),
    legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"),
    legend.text = element_text(size = 7)
  ) +
  facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)

fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
fig_4
ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5, dpi = 600)
ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5)
```


## Hypothesis testing

```{r}
iscb_lm <- iscb_pubmed_oth %>%
  ungroup() %>%
  mutate(
    # year = c(scale(year(year))),
    # year = as.factor(year),
    type = as.factor(type) %>% relevel(ref = "Pubmed authors")
  )
main_lm <- function(regioni) {
  glm(type ~ year + probabilities,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
    family = "binomial"
  )
}

inte_lm <- function(regioni) {
  glm(type ~ year * probabilities,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)
names(main_list) <- large_regions
lapply(main_list, broom::tidy)

inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, broom::tidy)
for (i in 1:4) {
  print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
}
```
Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01).

```{r echo = F}
get_p <- function(i, colu) {
  broom::tidy(main_list[[i]]) %>%
    filter(term == "probabilities") %>%
    pull(colu)
}

print_p <- function(x) sprintf("%0.5g", x)
```

## Conclusion
A Celtic/English name has `r exp(get_p(1, 'estimate'))` the odds of being selected as an honoree, significantly higher compared to other names ($\beta_\textrm{Celtic/English} =$ `r print_p(get_p(1, 'estimate'))`, _P_ = `r print_p(get_p(1, 'p.value'))`).
An East Asian name has `r exp(get_p(2, 'estimate'))` the odds of being selected as an honoree, significantly lower than to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_p(2, 'estimate'))`, _P_ = `r print_p(get_p(2, 'p.value'))`).
The two groups of scientists did not have a significant association with names predicted to be European (_P_ = `r print_p(get_p(3, 'p.value'))`) or in Other categories (_P_ = `r print_p(get_p(4, 'p.value'))`).


## Alternative approach
_Sincere thanks to the reviewers, Byron Smith and Katie Pollard, for their detailed suggestion with code._

The question of what unit one should use to perform this type of analyses is a difficult one.
We present here an alternative analysis that treats _names_ as units instead of _honors_ and _authorships_.
We caution that this approach does not distinguish scientists who were honored 4 times vs. one time
and hence may yield a conservative estimate of disparity.
Further, different authors may have the same names, 
and to sum `adjusted_citations` across them may not be optimal.

Nonetheless, the finding here is consistent with what we have seen above
where East Asian names are underrepresented in the honoree group.

```{r}

keynotes_post_2002 <- keynotes %>%
  filter(year(year) >= 2002) %>%
  separate_rows(afflcountries, sep = ",") %>%
  filter(afflcountries == "United States") %>%
  group_by(fore_name_simple, last_name_simple) %>%
  summarise_at("year", n_distinct, na.rm = T)

# nationalize_df was not unique, so the left join to corr_authors resulted
# in (mostly) duplicate rows.
# FIXME: I was getting occasional crashes on this line, and it's slow.
# TRANG: fixed on June 3, 2021 using distinct().
# Also, the duplication was intentional.
# Please see our extensive discussion on the merge/join on full names vs
# fore_name and last_name here:
# <https://github.com/greenelab/iscb-diversity/issues/6>

distinct_nationalize_df <- nationalize_df %>%
  distinct(fore_name_simple, last_name_simple, .keep_all = TRUE)

# Calculate sum of adjusted citations for all publications for a first-name/
# last-name pair in db since 2002
# where the author countries include US.
authors <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  separate_rows(countries, sep = ",") %>%
  filter(countries == "US") %>%
  group_by(fore_name_simple, last_name_simple) %>%
  summarise_at(vars(adjusted_citations), sum, na.rm = T) %>%
  left_join(
    keynotes_post_2002[c("fore_name_simple", "last_name_simple", "year")],
    by = c("fore_name_simple", "last_name_simple")
  ) %>%
  left_join(distinct_nationalize_df, by = c("fore_name_simple", "last_name_simple")) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African)

for (large_region in large_regions) {
  glm(
    as.formula(paste("honoree ~ adjusted_citations +", large_region)),
    data = authors %>% mutate(honoree = !is.na(year)),
    family = "binomial",
    control = list(epsilon = 1e-12, maxit = 55, trace = FALSE)
  ) %>%
    broom::tidy() %>%
    print()
}
```

<!-- We argue that honors and authorships are the appropriate units. -->
<!-- Although this approach may not satisfy the independent and identically distributed assumption -->

<!-- honor vs not honored. -->
<!-- But then this is considering scientists who have 4 rows of honorees as one  -->
<!-- Is this fair? -->
<!-- 195 rows/honors of keynotes post 2002 to 145 names/scientists. -->

## Time lag

In this section, we show that a 10-year lag model results in a similar result for East Asian scientists' names,
even though the effect size is less striking.
For example, if we assume that honors accrue 10 years after their most prolific year with respect to authorships, 
the proportion of honor associated with East Asian name origins in 2019 is still substantially less than the proportion of senior authorships associated with East Asian names in 2009.

```{r}
year_lag <- period(10, "years")
iscb_pubmed_oth_lag <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1,
    pmid = -9999
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors", year = year + year_lag)
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities), year(year) >= 2002) %>%
  group_by(type, year, region) 

iscb_lm_lag <- iscb_pubmed_oth_lag %>%
  ungroup() %>%
  mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors"))

main_lm <- function(regioni) {
  glm(type ~ year + probabilities,
    data = iscb_lm_lag %>%
      filter(region == regioni, !is.na(probabilities)),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)
names(main_list) <- large_regions
lapply(main_list, broom::tidy)
```

```{r include=FALSE, eval = FALSE}
checkdf <- iscb_lm %>%
  filter(
    year(year) == 2010,
    adjusted_citations > 3.1, 
    adjusted_citations < 3.32, 
    region == "EastAsian", 
    probabilities > 0.5
  )
```


## Supplementary Figure S5 {#sup_fig_s5}
It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction.
There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field.
However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented.

```{r fig.height=6, warning=FALSE}
# df2 <- iscb_pubmed_oth %>%
#   filter(region != "OtherCategories") %>%
#   recode_region()
# 
# fig_s5 <- iscb_pubmed_sum %>%
#   recode_region() %>%
#   gam_and_ci(
#     df2 = df2,
#     start_y = start_year, end_y = end_year
#   ) +
#   theme(legend.position = c(0.8, 0.1)) +
#   facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)
# fig_s5
# ggsave("figs/fig_s5.png", fig_s5, width = 6, height = 6)
# ggsave("figs/fig_s5.svg", fig_s5, width = 6, height = 6)
```


```{r}
sessionInfo()
```

diff --git a/docs/12.analyze-affiliation.html b/docs/12.analyze-affiliation.html index 9f5c206..1a725a0 100644 --- a/docs/12.analyze-affiliation.html +++ b/docs/12.analyze-affiliation.html @@ -4338,8 +4338,8 @@

Country enrichment table

formatPercentage('Author proportion', 2) %>% formatRound(c('Observed', 'Expected', 'Observed - Expected', 'Enrichment', 'Log2(enrichment)'), 1) -
- +
+

Compute enrichment from proportion comparisons

Adapted from epitools::riskratio().

@@ -4434,7 +4434,7 @@

Log enrichment figure

rel_widths = c(1, 1.3)) enrichment_plot

-
ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5)
+
ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5, dpi = 600)
sessionInfo()
## R version 4.0.3 (2020-10-10)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
@@ -4452,56 +4452,74 @@ 
Log enrichment figure

 ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
 ## 
 ## attached base packages:
-## [1] stats     graphics  grDevices utils     datasets  methods   base     
+## [1] stats     graphics  grDevices utils     datasets  methods  
+## [7] base     
 ## 
 ## other attached packages:
-##  [1] DT_0.16             epitools_0.5-10.1   gdtools_0.2.2      
-##  [4] wru_0.1-10          rnaturalearth_0.1.0 lubridate_1.7.9.2  
-##  [7] caret_6.0-86        lattice_0.20-41     forcats_0.5.0      
-## [10] stringr_1.4.0       dplyr_1.0.2         purrr_0.3.4        
-## [13] readr_1.4.0         tidyr_1.1.2         tibble_3.0.4       
-## [16] ggplot2_3.3.2       tidyverse_1.3.0    
+##  [1] broom_0.7.2         DT_0.16             epitools_0.5-10.1  
+##  [4] gdtools_0.2.2       wru_0.1-10          rnaturalearth_0.1.0
+##  [7] lubridate_1.7.9.2   caret_6.0-86        lattice_0.20-41    
+## [10] forcats_0.5.0       stringr_1.4.0       dplyr_1.0.2        
+## [13] purrr_0.3.4         readr_1.4.0         tidyr_1.1.2        
+## [16] tibble_3.0.4        ggplot2_3.3.2       tidyverse_1.3.0    
 ## 
 ## loaded via a namespace (and not attached):
-##   [1] colorspace_2.0-0        ellipsis_0.3.1          class_7.3-17           
-##   [4] rprojroot_1.3-2         fs_1.5.0                rstudioapi_0.12        
-##   [7] farver_2.0.3            remotes_2.2.0           prodlim_2019.11.13     
-##  [10] fansi_0.4.1             xml2_1.3.2              codetools_0.2-16       
-##  [13] splines_4.0.3           knitr_1.30              pkgload_1.1.0          
-##  [16] jsonlite_1.7.1          pROC_1.16.2             broom_0.7.2            
-##  [19] dbplyr_2.0.0            rgeos_0.5-5             compiler_4.0.3         
-##  [22] httr_1.4.2              backports_1.2.0         assertthat_0.2.1       
-##  [25] Matrix_1.2-18           cli_2.1.0               htmltools_0.5.0        
-##  [28] prettyunits_1.1.1       tools_4.0.3             gtable_0.3.0           
-##  [31] glue_1.4.2              rnaturalearthdata_0.1.0 reshape2_1.4.4         
-##  [34] Rcpp_1.0.5              cellranger_1.1.0        vctrs_0.3.4            
-##  [37] svglite_1.2.3.2         nlme_3.1-149            iterators_1.0.13       
-##  [40] crosstalk_1.1.0.1       timeDate_3043.102       gower_0.2.2            
-##  [43] xfun_0.19               ps_1.4.0                testthat_3.0.0         
-##  [46] rvest_0.3.6             lifecycle_0.2.0         devtools_2.3.2         
-##  [49] MASS_7.3-53             scales_1.1.1            ipred_0.9-9            
-##  [52] hms_0.5.3               RColorBrewer_1.1-2      yaml_2.2.1             
-##  [55] curl_4.3                memoise_1.1.0           rpart_4.1-15           
-##  [58] stringi_1.5.3           desc_1.2.0              foreach_1.5.1          
-##  [61] e1071_1.7-4             pkgbuild_1.1.0          lava_1.6.8.1           
-##  [64] systemfonts_0.3.2       rlang_0.4.8             pkgconfig_2.0.3        
-##  [67] evaluate_0.14           sf_0.9-6                recipes_0.1.15         
-##  [70] htmlwidgets_1.5.2       labeling_0.4.2          cowplot_1.1.0          
-##  [73] tidyselect_1.1.0        processx_3.4.4          plyr_1.8.6             
-##  [76] magrittr_1.5            R6_2.5.0                generics_0.1.0         
-##  [79] DBI_1.1.0               mgcv_1.8-33             pillar_1.4.6           
-##  [82] haven_2.3.1             withr_2.3.0             units_0.6-7            
-##  [85] survival_3.2-7          sp_1.4-4                nnet_7.3-14            
-##  [88] modelr_0.1.8            crayon_1.3.4            KernSmooth_2.23-17     
-##  [91] utf8_1.1.4              rmarkdown_2.5           usethis_1.6.3          
-##  [94] grid_4.0.3              readxl_1.3.1            data.table_1.13.2      
-##  [97] callr_3.5.1             ModelMetrics_1.2.2.2    reprex_0.3.0           
-## [100] digest_0.6.27           classInt_0.4-3          stats4_4.0.3           
-## [103] munsell_0.5.0           viridisLite_0.3.0       sessioninfo_1.1.1
+## [1] colorspace_2.0-0 ellipsis_0.3.1 +## [3] class_7.3-17 rprojroot_1.3-2 +## [5] fs_1.5.0 rstudioapi_0.12 +## [7] farver_2.0.3 remotes_2.2.0 +## [9] prodlim_2019.11.13 fansi_0.4.1 +## [11] xml2_1.3.2 codetools_0.2-16 +## [13] splines_4.0.3 knitr_1.30 +## [15] pkgload_1.1.0 jsonlite_1.7.1 +## [17] pROC_1.16.2 dbplyr_2.0.0 +## [19] rgeos_0.5-5 compiler_4.0.3 +## [21] httr_1.4.2 backports_1.2.0 +## [23] assertthat_0.2.1 Matrix_1.2-18 +## [25] cli_2.1.0 htmltools_0.5.0 +## [27] prettyunits_1.1.1 tools_4.0.3 +## [29] gtable_0.3.0 glue_1.4.2 +## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4 +## [33] Rcpp_1.0.5 cellranger_1.1.0 +## [35] vctrs_0.3.4 svglite_1.2.3.2 +## [37] nlme_3.1-149 iterators_1.0.13 +## [39] crosstalk_1.1.0.1 timeDate_3043.102 +## [41] gower_0.2.2 xfun_0.19 +## [43] ps_1.4.0 testthat_3.0.0 +## [45] rvest_0.3.6 lifecycle_0.2.0 +## [47] devtools_2.3.2 MASS_7.3-53 +## [49] scales_1.1.1 ipred_0.9-9 +## [51] hms_0.5.3 RColorBrewer_1.1-2 +## [53] yaml_2.2.1 curl_4.3 +## [55] memoise_1.1.0 rpart_4.1-15 +## [57] stringi_1.5.3 desc_1.2.0 +## [59] foreach_1.5.1 e1071_1.7-4 +## [61] pkgbuild_1.1.0 lava_1.6.8.1 +## [63] systemfonts_0.3.2 rlang_0.4.8 +## [65] pkgconfig_2.0.3 evaluate_0.14 +## [67] sf_0.9-6 recipes_0.1.15 +## [69] htmlwidgets_1.5.2 labeling_0.4.2 +## [71] cowplot_1.1.0 tidyselect_1.1.0 +## [73] processx_3.4.4 plyr_1.8.6 +## [75] magrittr_1.5 R6_2.5.0 +## [77] generics_0.1.0 DBI_1.1.0 +## [79] mgcv_1.8-33 pillar_1.4.6 +## [81] haven_2.3.1 withr_2.3.0 +## [83] units_0.6-7 survival_3.2-7 +## [85] sp_1.4-4 nnet_7.3-14 +## [87] modelr_0.1.8 crayon_1.3.4 +## [89] KernSmooth_2.23-17 utf8_1.1.4 +## [91] rmarkdown_2.5 usethis_1.6.3 +## [93] grid_4.0.3 readxl_1.3.1 +## [95] data.table_1.13.2 callr_3.5.1 +## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 +## [99] digest_0.6.27 classInt_0.4-3 +## [101] stats4_4.0.3 munsell_0.5.0 +## [103] viridisLite_0.3.0 sessioninfo_1.1.1
-
---
title: 'Affiliation analysis'
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
library(epitools)
source('utils/r-utils.R')
library(DT)
theme_set(
  theme_bw() + 
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank(),
      legend.title = element_blank()
    ))
```

## Country level analysis

Observed vs. expected

```{r}
load('Rdata/raws.Rdata')

iscb_aff_country <- 
  keynotes %>% 
  separate_rows(afflcountries, sep = '\\|') %>% 
  filter(!is.na(afflcountries)) %>% 
  add_count(year, conference, full_name, name = 'num_affls') %>%
  mutate(probabilities = 1 / num_affls,
         publication_date = ymd(year, truncated = 2),
         year = ymd(year, truncated = 2)) %>%
  left_join(nat_to_reg, by = c('afflcountries' = 'country_name'))

pubmed_aff_country <- corr_authors %>%
  filter(!is.na(countries)) %>% 
  add_count(pmid, name = 'num_corr_authors') %>% # number of corresponding authors per pmid
  select(pmid, journal, publication_date, year, countries, fore_name_simple, last_name_simple, num_corr_authors) %>%
  separate_rows(countries) %>%
  add_count(pmid, fore_name_simple, last_name_simple, name = 'num_affls') %>% 
  mutate(probabilities = 1 / num_corr_authors / num_affls)
  
country_rep <- iscb_aff_country %>% 
  group_by(countries) %>% 
  summarise(Observed = sum(probabilities)) %>% 
  arrange(desc(Observed)) 

num_papers <- corr_authors %>% 
  filter(!is.na(countries)) %>% 
  pull(pmid) %>% 
  unique() %>% 
  length()

num_papers

num_honorees <- sum(iscb_aff_country$probabilities)

obs_vs_exp_all <- pubmed_aff_country %>%
  group_by(countries) %>%
  summarise(num_authors = sum(probabilities)) %>%
  ungroup() %>% 
  mutate(freq_affi = num_authors / sum(num_authors)) %>%
  arrange(desc(num_authors)) %>%
  mutate(Expected = freq_affi * num_honorees) %>%
  left_join(country_rep, by = 'countries') %>%
  left_join(nat_to_reg, by = 'countries') %>%
  select(country_name, everything()) %>%
  select(-c(region, countries)) %>%
  mutate(
    Observed = replace_na(Observed, 0),
    over_rep = Observed - Expected,
    other_honorees = num_honorees - Observed,
    other_authors = num_papers - num_authors
  )
```


### Fisher's exact test and 
#### Table of representations
Null hypothesis: For each country, the proportion of honorees affiliated with an institution/company from that country is similar to the proportion of authors affiliated with an institution/company from that country.

```{r warning=FALSE, fig.width = 7*1.5, fig.height = 2.5*1.5}
my_fish <- function(df) {
  res <- df %>%
    unlist() %>%
    matrix(ncol = 2, byrow = TRUE) %>%
    my_riskratio(correction = TRUE)
  
  res_fish <- df %>%
    unlist() %>%
    matrix(ncol = 2) %>%
    fisher.test()
  
  or <- res_fish$estimate
  l_or <- res_fish$conf.int[1]
  u_or <- res_fish$conf.int[2]
  p0 <- df[2]/(df[2] + df[1])
  fish_rr <- or/(1 - p0 + p0*or)
  fish_rr_lower <- l_or/(1 - p0 + p0*l_or)
  fish_rr_upper <- u_or/(1 - p0 + p0*u_or)
  
  res$measure[2, 1:3] %>% 
    c(p_value = res$p.value[2,2]) %>% 
    as.matrix() %>% t() %>% data.frame()
}

nested_obs_exp <- obs_vs_exp_all %>%
  filter(!is.na(country_name)) %>% 
  select(country_name, other_authors, num_authors, other_honorees, Observed) %>% 
  group_by(country_name) %>% 
  nest() 

fish_obs_exp <- nested_obs_exp %>% 
  mutate(fish = map(data, my_fish)) %>% 
  dplyr::select(-data) %>% 
  unnest()

```

```{r eval=FALSE, include=FALSE}
# manual check
x = nested_obs_exp %>% 
  filter(country_name == 'Finland') %>% 
  ungroup() %>% 
  select(data) %>% 
  unlist() %>%
  matrix(ncol = 2, byrow = TRUE) 

a0 <- x[1, 2]
b0 <- x[1, 1]
a1 <- x[2, 2]
b1 <- x[2, 1]
n1 <- a1 + b1
n0 <- a0 + b0
m0 <- b0 + b1
m1 <- a0 + a1

log2(n0/n1*(a1+1)/(a0)*qf(1-0.025, 2*(a1+1), 2*a0))
log2(n0/n1*(a1)/(a0 + 1)/qf(1-0.025, 2*(a0 + 1), 2*a1))
```


### Country enrichment table {#enrichment_tab}
```{r}
fish_obs_exp %>% 
  mutate(ci = paste0('(', round(log2(lower), 1), ', ', 
                     round(log2(upper), 1), ')'),
         lestimate = log2(estimate)) %>% 
  left_join(obs_vs_exp_all, by = 'country_name') %>% 
  select(country_name, freq_affi, Observed, Expected, over_rep,
         # log2fc, 
         estimate, lestimate, ci) %>% 
  rename('Country' = 'country_name',
         'Author proportion' = 'freq_affi',
         'Observed - Expected' = 'over_rep',
         'Enrichment' = 'estimate',
         'Log2(enrichment)' = 'lestimate',
         '95% Confidence interval' = 'ci') %>% 
  datatable(rownames = FALSE) %>% 
  formatPercentage('Author proportion', 2) %>% 
  formatRound(c('Observed', 'Expected', 'Observed - Expected', 
                'Enrichment', 'Log2(enrichment)'), 1)
```


```{r include = F, eval = F, fig.width = 7*1.5, fig.height = 2.5*1.5}
# world map of enrichment
enrich_df <- world %>% 
  left_join(
    fish_obs_exp  %>% 
      mutate(log_lower = case_when(
        lower > 1 ~ log2(lower),
        upper < 1 ~ log2(upper),
        TRUE ~ 0)
      ), by = c('name' = 'country_name'))

enrichment_map <- enrich_df %>% 
  ggplot() +
  geom_sf(aes(fill = log_lower)) +
  scale_fill_gradientn(
    colours = c('#3CBC75FF','white','#440154FF'),
    na.value = NA,
    values = scales::rescale(
      c(min(enrich_df$log_lower, na.rm = T),
        0,
        max(enrich_df$log_lower, na.rm = T)))
  ) +
  coord_sf(crs = '+proj=eqearth +wktext')
enrichment_map
# ggsave('figs/enrichment-map.png', enrichment_map, width = 7, height = 2.5)
# equal earth map projection:
# http://equal-earth.com/equal-earth-projection.html
```

#### Compute enrichment from proportion comparisons
Adapted from `epitools::riskratio()`.

Presentation enrichment/depletion of 20 countries that have the most publications.

```{r fig.width = 7, fig.height = 3.5}
filtered_obs_exp <- obs_vs_exp_all %>% 
  left_join(fish_obs_exp, by = 'country_name') %>% 
  top_n(25, num_authors) %>% 
  mutate(
    distance_to_null = case_when(
      lower > 1 ~ lower - 1,
      TRUE ~ upper - 2
    ),
    presentation = case_when(
      lower > 1 ~ '#377EB8', 
      upper < 1 ~ '#E41A1C',
      TRUE ~ 'grey20'
    ),
    country_name =  as.factor(country_name) %>% fct_reorder(num_authors)) %>% 
  arrange(desc(num_authors))

plot_obs_exp <- filtered_obs_exp %>%
  mutate(lestimate = log2(estimate),
         llower = log2(lower), 
         lupper = log2(upper)) %>% 
  select(country_name, Expected, Observed, lestimate, llower, lupper, presentation, over_rep) %>% 
  pivot_longer(- c(country_name, presentation, over_rep), names_to = 'type') %>% 
  mutate(subtype = ifelse(type == 'Expected' | type == 'Observed', 'Sqrt(number of honorees)', 'Log2 enrichment, 95% CI')) 

```

```{r}
save(plot_obs_exp, file = 'Rdata/affiliations.Rdata')
```

### Log enrichment figure {#enrichment_plot}
```{r}
plot_obs_exp_right <- plot_obs_exp %>% filter(subtype == 'Sqrt(number of honorees)')
plot_obs_exp_left <- plot_obs_exp %>% filter(subtype != 'Sqrt(number of honorees)')

enrichment_plot_left <- plot_obs_exp_left %>%
  ggplot(aes(x = country_name)) +
  coord_flip() +
  labs(x = NULL, y = bquote(Log[2] ~ 'enrichment, 95% CI')) +
  theme(
    legend.position = c(0.9, 0.3),
    axis.title = element_text(size = 9),
    plot.margin = margin(5.5, 2, 5.5, 5.5, unit = 'pt')
  ) +
  scale_color_brewer(type = 'qual', palette = 'Set1') +
  geom_pointrange(
    data = plot_obs_exp_left %>%
      pivot_wider(names_from = type),
    aes(y = lestimate,
        ymin = llower,
        ymax = lupper,
        group = country_name),
    color = filtered_obs_exp$presentation,
    stroke = 0.3, fatten = 2
  ) +
  scale_x_discrete(position = 'top', labels = NULL) +
  scale_y_reverse() +
  geom_hline(data = plot_obs_exp_left, aes(yintercept = 0), linetype = 2)

overrep_countries <- plot_obs_exp_right %>% 
  filter(over_rep > 0) %>% 
  pull(country_name)

enrichment_plot_right <- plot_obs_exp_right %>%
  ggplot(aes(x = country_name, y = value)) +
  geom_line(aes(group = country_name),
            color = rev(plot_obs_exp_right$presentation)) +
  geom_point(aes(shape = type), color = 'grey20') +
  labs(x = NULL, y = 'Number of honorees') +
  theme(
    axis.title = element_text(size = 9),
    legend.position = c(0.75 , 0.3),
    axis.text.y = element_text(
      color = rev(filtered_obs_exp$presentation),
      hjust = 0.5),
    plot.margin = margin(5.5, 5.5, 8.5, 2, unit = 'pt')
  ) +
  scale_y_sqrt(breaks = c(0, 1, 4, 16, 50, 120, 225)) +
  # scale_color_brewer(type = 'qual', palette = 'Set1') +
  coord_flip() +
  geom_text(
    data = . %>% 
      filter(type == 'Expected', !(country_name %in% overrep_countries)),
    nudge_y = 1.2, aes(label = round(over_rep, 1)), size = 2.5) +
  geom_text(
    data = . %>% 
      filter(type == 'Expected', country_name %in% overrep_countries), 
    nudge_y = -1.2, aes(label = round(over_rep, 1)), size = 2.5)

enrichment_plot <- cowplot::plot_grid(enrichment_plot_left, enrichment_plot_right,
                                rel_widths = c(1, 1.3))
enrichment_plot
ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5)

```


```{r}
sessionInfo()
```


+
---
title: 'Affiliation analysis'
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
library(epitools)
source('utils/r-utils.R')
library(DT)
theme_set(
  theme_bw() + 
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank(),
      legend.title = element_blank()
    ))
```

## Country level analysis

Observed vs. expected

```{r}
load('Rdata/raws.Rdata')

iscb_aff_country <- 
  keynotes %>% 
  separate_rows(afflcountries, sep = '\\|') %>% 
  filter(!is.na(afflcountries)) %>% 
  add_count(year, conference, full_name, name = 'num_affls') %>%
  mutate(probabilities = 1 / num_affls,
         publication_date = ymd(year, truncated = 2),
         year = ymd(year, truncated = 2)) %>%
  left_join(nat_to_reg, by = c('afflcountries' = 'country_name'))

pubmed_aff_country <- corr_authors %>%
  filter(!is.na(countries)) %>% 
  add_count(pmid, name = 'num_corr_authors') %>% # number of corresponding authors per pmid
  select(pmid, journal, publication_date, year, countries, fore_name_simple, last_name_simple, num_corr_authors) %>%
  separate_rows(countries) %>%
  add_count(pmid, fore_name_simple, last_name_simple, name = 'num_affls') %>% 
  mutate(probabilities = 1 / num_corr_authors / num_affls)
  
country_rep <- iscb_aff_country %>% 
  group_by(countries) %>% 
  summarise(Observed = sum(probabilities)) %>% 
  arrange(desc(Observed)) 

num_papers <- corr_authors %>% 
  filter(!is.na(countries)) %>% 
  pull(pmid) %>% 
  unique() %>% 
  length()

num_papers

num_honorees <- sum(iscb_aff_country$probabilities)

obs_vs_exp_all <- pubmed_aff_country %>%
  group_by(countries) %>%
  summarise(num_authors = sum(probabilities)) %>%
  ungroup() %>% 
  mutate(freq_affi = num_authors / sum(num_authors)) %>%
  arrange(desc(num_authors)) %>%
  mutate(Expected = freq_affi * num_honorees) %>%
  left_join(country_rep, by = 'countries') %>%
  left_join(nat_to_reg, by = 'countries') %>%
  select(country_name, everything()) %>%
  select(-c(region, countries)) %>%
  mutate(
    Observed = replace_na(Observed, 0),
    over_rep = Observed - Expected,
    other_honorees = num_honorees - Observed,
    other_authors = num_papers - num_authors
  )
```


### Fisher's exact test and 
#### Table of representations
Null hypothesis: For each country, the proportion of honorees affiliated with an institution/company from that country is similar to the proportion of authors affiliated with an institution/company from that country.

```{r warning=FALSE, fig.width = 7*1.5, fig.height = 2.5*1.5}
my_fish <- function(df) {
  res <- df %>%
    unlist() %>%
    matrix(ncol = 2, byrow = TRUE) %>%
    my_riskratio(correction = TRUE)
  
  res_fish <- df %>%
    unlist() %>%
    matrix(ncol = 2) %>%
    fisher.test()
  
  or <- res_fish$estimate
  l_or <- res_fish$conf.int[1]
  u_or <- res_fish$conf.int[2]
  p0 <- df[2]/(df[2] + df[1])
  fish_rr <- or/(1 - p0 + p0*or)
  fish_rr_lower <- l_or/(1 - p0 + p0*l_or)
  fish_rr_upper <- u_or/(1 - p0 + p0*u_or)
  
  res$measure[2, 1:3] %>% 
    c(p_value = res$p.value[2,2]) %>% 
    as.matrix() %>% t() %>% data.frame()
}

nested_obs_exp <- obs_vs_exp_all %>%
  filter(!is.na(country_name)) %>% 
  select(country_name, other_authors, num_authors, other_honorees, Observed) %>% 
  group_by(country_name) %>% 
  nest() 

fish_obs_exp <- nested_obs_exp %>% 
  mutate(fish = map(data, my_fish)) %>% 
  dplyr::select(-data) %>% 
  unnest()

```

```{r eval=FALSE, include=FALSE}
# manual check
x = nested_obs_exp %>% 
  filter(country_name == 'Finland') %>% 
  ungroup() %>% 
  select(data) %>% 
  unlist() %>%
  matrix(ncol = 2, byrow = TRUE) 

a0 <- x[1, 2]
b0 <- x[1, 1]
a1 <- x[2, 2]
b1 <- x[2, 1]
n1 <- a1 + b1
n0 <- a0 + b0
m0 <- b0 + b1
m1 <- a0 + a1

log2(n0/n1*(a1+1)/(a0)*qf(1-0.025, 2*(a1+1), 2*a0))
log2(n0/n1*(a1)/(a0 + 1)/qf(1-0.025, 2*(a0 + 1), 2*a1))
```


### Country enrichment table {#enrichment_tab}
```{r}
fish_obs_exp %>% 
  mutate(ci = paste0('(', round(log2(lower), 1), ', ', 
                     round(log2(upper), 1), ')'),
         lestimate = log2(estimate)) %>% 
  left_join(obs_vs_exp_all, by = 'country_name') %>% 
  select(country_name, freq_affi, Observed, Expected, over_rep,
         # log2fc, 
         estimate, lestimate, ci) %>% 
  rename('Country' = 'country_name',
         'Author proportion' = 'freq_affi',
         'Observed - Expected' = 'over_rep',
         'Enrichment' = 'estimate',
         'Log2(enrichment)' = 'lestimate',
         '95% Confidence interval' = 'ci') %>% 
  datatable(rownames = FALSE) %>% 
  formatPercentage('Author proportion', 2) %>% 
  formatRound(c('Observed', 'Expected', 'Observed - Expected', 
                'Enrichment', 'Log2(enrichment)'), 1)
```


```{r include = F, eval = F, fig.width = 7*1.5, fig.height = 2.5*1.5}
# world map of enrichment
enrich_df <- world %>% 
  left_join(
    fish_obs_exp  %>% 
      mutate(log_lower = case_when(
        lower > 1 ~ log2(lower),
        upper < 1 ~ log2(upper),
        TRUE ~ 0)
      ), by = c('name' = 'country_name'))

enrichment_map <- enrich_df %>% 
  ggplot() +
  geom_sf(aes(fill = log_lower)) +
  scale_fill_gradientn(
    colours = c('#3CBC75FF','white','#440154FF'),
    na.value = NA,
    values = scales::rescale(
      c(min(enrich_df$log_lower, na.rm = T),
        0,
        max(enrich_df$log_lower, na.rm = T)))
  ) +
  coord_sf(crs = '+proj=eqearth +wktext')
enrichment_map
# ggsave('figs/enrichment-map.png', enrichment_map, width = 7, height = 2.5)
# equal earth map projection:
# http://equal-earth.com/equal-earth-projection.html
```

#### Compute enrichment from proportion comparisons
Adapted from `epitools::riskratio()`.

Presentation enrichment/depletion of 20 countries that have the most publications.

```{r fig.width = 7, fig.height = 3.5}
filtered_obs_exp <- obs_vs_exp_all %>% 
  left_join(fish_obs_exp, by = 'country_name') %>% 
  top_n(25, num_authors) %>% 
  mutate(
    distance_to_null = case_when(
      lower > 1 ~ lower - 1,
      TRUE ~ upper - 2
    ),
    presentation = case_when(
      lower > 1 ~ '#377EB8', 
      upper < 1 ~ '#E41A1C',
      TRUE ~ 'grey20'
    ),
    country_name =  as.factor(country_name) %>% fct_reorder(num_authors)) %>% 
  arrange(desc(num_authors))

plot_obs_exp <- filtered_obs_exp %>%
  mutate(lestimate = log2(estimate),
         llower = log2(lower), 
         lupper = log2(upper)) %>% 
  select(country_name, Expected, Observed, lestimate, llower, lupper, presentation, over_rep) %>% 
  pivot_longer(- c(country_name, presentation, over_rep), names_to = 'type') %>% 
  mutate(subtype = ifelse(type == 'Expected' | type == 'Observed', 'Sqrt(number of honorees)', 'Log2 enrichment, 95% CI')) 

```

```{r}
save(plot_obs_exp, file = 'Rdata/affiliations.Rdata')
```

### Log enrichment figure {#enrichment_plot}
```{r}
plot_obs_exp_right <- plot_obs_exp %>% filter(subtype == 'Sqrt(number of honorees)')
plot_obs_exp_left <- plot_obs_exp %>% filter(subtype != 'Sqrt(number of honorees)')

enrichment_plot_left <- plot_obs_exp_left %>%
  ggplot(aes(x = country_name)) +
  coord_flip() +
  labs(x = NULL, y = bquote(Log[2] ~ 'enrichment, 95% CI')) +
  theme(
    legend.position = c(0.9, 0.3),
    axis.title = element_text(size = 9),
    plot.margin = margin(5.5, 2, 5.5, 5.5, unit = 'pt')
  ) +
  scale_color_brewer(type = 'qual', palette = 'Set1') +
  geom_pointrange(
    data = plot_obs_exp_left %>%
      pivot_wider(names_from = type),
    aes(y = lestimate,
        ymin = llower,
        ymax = lupper,
        group = country_name),
    color = filtered_obs_exp$presentation,
    stroke = 0.3, fatten = 2
  ) +
  scale_x_discrete(position = 'top', labels = NULL) +
  scale_y_reverse() +
  geom_hline(data = plot_obs_exp_left, aes(yintercept = 0), linetype = 2)

overrep_countries <- plot_obs_exp_right %>% 
  filter(over_rep > 0) %>% 
  pull(country_name)

enrichment_plot_right <- plot_obs_exp_right %>%
  ggplot(aes(x = country_name, y = value)) +
  geom_line(aes(group = country_name),
            color = rev(plot_obs_exp_right$presentation)) +
  geom_point(aes(shape = type), color = 'grey20') +
  labs(x = NULL, y = 'Number of honorees') +
  theme(
    axis.title = element_text(size = 9),
    legend.position = c(0.75 , 0.3),
    axis.text.y = element_text(
      color = rev(filtered_obs_exp$presentation),
      hjust = 0.5),
    plot.margin = margin(5.5, 5.5, 8.5, 2, unit = 'pt')
  ) +
  scale_y_sqrt(breaks = c(0, 1, 4, 16, 50, 120, 225)) +
  # scale_color_brewer(type = 'qual', palette = 'Set1') +
  coord_flip() +
  geom_text(
    data = . %>% 
      filter(type == 'Expected', !(country_name %in% overrep_countries)),
    nudge_y = 1.2, aes(label = round(over_rep, 1)), size = 2.5) +
  geom_text(
    data = . %>% 
      filter(type == 'Expected', country_name %in% overrep_countries), 
    nudge_y = -1.2, aes(label = round(over_rep, 1)), size = 2.5)

enrichment_plot <- cowplot::plot_grid(enrichment_plot_left, enrichment_plot_right,
                                rel_widths = c(1, 1.3))
enrichment_plot
ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5, dpi = 600)

```


```{r}
sessionInfo()
```


diff --git a/docs/13.us-race-analysis.html b/docs/13.us-race-analysis.html index 50c5b40..111e87d 100644 --- a/docs/13.us-race-analysis.html +++ b/docs/13.us-race-analysis.html @@ -1674,8 +1674,8 @@

Race/ethnicity predictions

rename('surname' = last_name_simple) %>% predict_race(surname.only = T, impute.missing = F) 
## [1] "Proceeding with surname-only predictions..."
-
## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 5166
-## surnames were not matched.
+
## Warning in merge_surnames(voter.file, impute.missing =
+## impute.missing): 5166 surnames were not matched.
pubmed_us_race <- pubmed_race_pmids %>% 
   group_by(pmid, journal, publication_date, year, adjusted_citations) %>% 
   summarise_at(vars(contains('pred.')), mean, na.rm = T, .groups = 'drop') %>% 
@@ -1685,8 +1685,8 @@ 
Race/ethnicity predictions

   rename('surname' = last_name_simple) %>%
   predict_race(surname.only = T, impute.missing = F)
## [1] "Proceeding with surname-only predictions..."
-
## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 100
-## surnames were not matched.
+
## Warning in merge_surnames(voter.file, impute.missing =
+## impute.missing): 100 surnames were not matched.
my_jours <- unique(pubmed_us_race$journal)
 my_confs <- unique(iscb_us_race$conference)
 n_jours <- length(my_jours)
@@ -1795,10 +1795,14 @@ 
Hypothesis testing

 ## -0.8113 -0.5414 -0.0942  0.2651 16.6148 
 ## 
 ## Coefficients:
-##                               Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                   0.612841   0.004475 136.937   <2e-16 ***
-## year                         -0.041522   0.004554  -9.118   <2e-16 ***
-## typeKeynote speakers/Fellows  0.083082   0.038991   2.131   0.0331 *  
+##                               Estimate Std. Error t value
+## (Intercept)                   0.612841   0.004475 136.937
+## year                         -0.041522   0.004554  -9.118
+## typeKeynote speakers/Fellows  0.083082   0.038991   2.131
+##                              Pr(>|t|)    
+## (Intercept)                    <2e-16 ***
+## year                           <2e-16 ***
+## typeKeynote speakers/Fellows   0.0331 *  
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
@@ -1820,10 +1824,14 @@ 
Hypothesis testing

 ## -0.2870 -0.2596 -0.2303  0.1112  6.2454 
 ## 
 ## Coefficients:
-##                               Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                   0.247463   0.003178  77.861  < 2e-16 ***
-## year                          0.043328   0.003234  13.397  < 2e-16 ***
-## typeKeynote speakers/Fellows -0.099391   0.027691  -3.589 0.000332 ***
+##                               Estimate Std. Error t value
+## (Intercept)                   0.247463   0.003178  77.861
+## year                          0.043328   0.003234  13.397
+## typeKeynote speakers/Fellows -0.099391   0.027691  -3.589
+##                              Pr(>|t|)    
+## (Intercept)                   < 2e-16 ***
+## year                          < 2e-16 ***
+## typeKeynote speakers/Fellows 0.000332 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
@@ -1845,10 +1853,14 @@ 
Hypothesis testing

 ## -0.1573 -0.1143 -0.0837  0.0159  9.6671 
 ## 
 ## Coefficients:
-##                               Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                   0.139696   0.001617  86.395   <2e-16 ***
-## year                         -0.001807   0.001645  -1.098    0.272    
-## typeKeynote speakers/Fellows  0.016309   0.014088   1.158    0.247    
+##                               Estimate Std. Error t value
+## (Intercept)                   0.139696   0.001617  86.395
+## year                         -0.001807   0.001645  -1.098
+## typeKeynote speakers/Fellows  0.016309   0.014088   1.158
+##                              Pr(>|t|)    
+## (Intercept)                    <2e-16 ***
+## year                            0.272    
+## typeKeynote speakers/Fellows    0.247    
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
diff --git a/docs/14.us-name-origin.html b/docs/14.us-name-origin.html
index c71358d..c5d0a57 100644
--- a/docs/14.us-name-origin.html
+++ b/docs/14.us-name-origin.html
@@ -1720,17 +1720,12 @@ 
Organize data

     values_to = "probabilities"
   ) %>%
   filter(!is.na(probabilities)) %>%
-  group_by(type, year, region) %>%
-  mutate(
-    pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations / pmc_citations_year,
-    weighted_probs = probabilities * weight
-  )
+  group_by(type, year, region)
 
 iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
   summarise(
-    mean_prob = mean(weighted_probs),
-    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+    mean_prob = mean(probabilities),
+    se_prob = sd(probabilities)/sqrt(n()),
     me_prob = alpha_threshold * se_prob,
     .groups = "drop"
   )
@@ -1768,8 +1763,8 @@ 
Figures for paper

 fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
fig_us_name_origin
-

-
ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5)
+

+
ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5, dpi = 600)
 ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5)

 
 

@@ -1782,17 +1777,17 @@ 
Hypothesis testing

     type = relevel(as.factor(type), ref = "Pubmed authors")
   )
 main_lm <- function(regioni) {
-  glm(type ~ year + weighted_probs,
+  glm(type ~ year + probabilities,
     data = iscb_lm %>%
-      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
     family = "binomial"
   )
 }
 
 inte_lm <- function(regioni) {
-  glm(type ~ weighted_probs * year,
+  glm(type ~ probabilities * year,
     data = iscb_lm %>%
-      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
     family = "binomial"
   )
 }
@@ -1803,108 +1798,108 @@ 
Hypothesis testing

 lapply(main_list, broom::tidy)
## $CelticEnglish
 ## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)     4.91     0.523         9.39  6.14e-21
-## 2 year           -0.000616 0.0000346   -17.8   6.39e-71
-## 3 weighted_probs  0.0434   0.0837        0.519 6.04e- 1
+##   term           estimate std.error statistic  p.value
+##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)    4.76     0.535          8.89 5.86e-19
+## 2 year          -0.000611 0.0000348    -17.6  3.87e-69
+## 3 probabilities  0.269    0.185          1.46 1.46e- 1
 ## 
 ## $EastAsian
 ## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)     4.75     0.520          9.13 6.68e-20
-## 2 year           -0.000595 0.0000347    -17.2  5.24e-66
-## 3 weighted_probs -1.77     0.501         -3.52 4.28e- 4
+##   term           estimate std.error statistic  p.value
+##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)    4.73     0.519          9.12 7.71e-20
+## 2 year          -0.000592 0.0000346    -17.1  9.97e-66
+## 3 probabilities -1.89     0.455         -4.14 3.43e- 5
 ## 
 ## $European
 ## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)     4.88     0.521          9.36 7.82e-21
-## 2 year           -0.000616 0.0000345    -17.8  3.48e-71
-## 3 weighted_probs  0.173    0.109          1.58 1.14e- 1
+##   term           estimate std.error statistic  p.value
+##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)    4.78     0.524          9.12 7.75e-20
+## 2 year          -0.000614 0.0000345    -17.8  6.78e-71
+## 3 probabilities  0.446    0.194          2.30 2.16e- 2
 ## 
 ## $OtherCategories
 ## # A tibble: 3 x 5
-##   term            estimate std.error statistic  p.value
-##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)     4.94     0.519         9.52  1.79e-21
-## 2 year           -0.000618 0.0000345   -17.9   1.17e-71
-## 3 weighted_probs  0.0518   0.136         0.381 7.03e- 1
+## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) 4.93 0.520 9.48 2.58e-21 +## 2 year -0.000618 0.0000345 -17.9 1.18e-71 +## 3 probabilities 0.0957 0.212 0.451 6.52e- 1 
inte_list <- lapply(large_regions, inte_lm)
 lapply(inte_list, broom::tidy)
## [[1]]
 ## # A tibble: 4 x 5
 ##   term                  estimate std.error statistic  p.value
 ##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)          5.01      0.563         8.89  5.91e-19
-## 2 weighted_probs      -0.228     0.591        -0.386 7.00e- 1
-## 3 year                -0.000623  0.0000377   -16.5   2.45e-61
-## 4 weighted_probs:year  0.0000201 0.0000425     0.473 6.36e- 1
+## 1 (Intercept)         4.81       0.730        6.60   4.16e-11
+## 2 probabilities       0.128      1.32         0.0969 9.23e- 1
+## 3 year               -0.000615   0.0000479  -12.8    9.81e-38
+## 4 probabilities:year  0.00000953 0.0000883    0.108  9.14e- 1
 ## 
 ## [[2]]
 ## # A tibble: 4 x 5
-##   term                  estimate std.error statistic  p.value
-##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)          4.70      0.540         8.72  2.89e-18
-## 2 weighted_probs      -0.524     4.08         -0.128 8.98e- 1
-## 3 year                -0.000592  0.0000359   -16.5   6.46e-61
-## 4 weighted_probs:year -0.0000794 0.000261     -0.304 7.61e- 1
+##   term                 estimate std.error statistic  p.value
+##   <chr>                   <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)         4.71      0.536         8.78  1.57e-18
+## 2 probabilities      -1.29      3.81         -0.339 7.34e- 1
+## 3 year               -0.000591  0.0000357   -16.5   1.68e-61
+## 4 probabilities:year -0.0000380 0.000243     -0.157 8.76e- 1
 ## 
 ## [[3]]
 ## # A tibble: 4 x 5
-##   term                  estimate std.error statistic  p.value
-##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)          5.07      0.573         8.85  9.11e-19
-## 2 weighted_probs      -0.462     0.821        -0.563 5.73e- 1
-## 3 year                -0.000629  0.0000382   -16.5   7.11e-61
-## 4 weighted_probs:year  0.0000437 0.0000549     0.796 4.26e- 1
+##   term                 estimate std.error statistic  p.value
+##   <chr>                   <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)         5.17      0.674         7.67  1.77e-14
+## 2 probabilities      -0.809     1.40         -0.577 5.64e- 1
+## 3 year               -0.000640  0.0000449   -14.3   3.42e-46
+## 4 probabilities:year  0.0000840 0.0000926     0.906 3.65e- 1
 ## 
 ## [[4]]
 ## # A tibble: 4 x 5
-##   term                  estimate std.error statistic  p.value
-##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
-## 1 (Intercept)          5.04      0.587         8.58  9.33e-18
-## 2 weighted_probs      -0.320     1.04         -0.308 7.58e- 1
-## 3 year                -0.000625  0.0000393   -15.9   6.63e-57
-## 4 weighted_probs:year  0.0000251 0.0000690     0.364 7.16e- 1
+## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) 5.22 0.670 7.79 6.76e-15 +## 2 probabilities -0.964 1.59 -0.605 5.45e- 1 +## 3 year -0.000637 0.0000447 -14.2 4.57e-46 +## 4 probabilities:year 0.0000701 0.000104 0.673 5.01e- 1 
for (i in 1:4) {
   print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
 }
## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ weighted_probs * year
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ probabilities * year
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1     26398     2066.7                     
-## 2     26397     2066.5  1   0.2267    0.634
+## 1     26398     2064.9                     
+## 2     26397     2064.9  1 0.011635   0.9141
 ## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ weighted_probs * year
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ probabilities * year
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1     26398     2043.8                     
-## 2     26397     2043.7  1 0.089951   0.7642
+## 1     26398     2036.7                     
+## 2     26397     2036.7  1 0.024177   0.8764
 ## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ weighted_probs * year
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ probabilities * year
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1     26398     2064.9                     
-## 2     26397     2064.2  1  0.63868   0.4242
+## 1     26398     2061.9                     
+## 2     26397     2061.1  1  0.82361   0.3641
 ## Analysis of Deviance Table
 ## 
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ weighted_probs * year
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ probabilities * year
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
 ## 1     26398     2066.8                     
-## 2     26397     2066.7  1  0.13195   0.7164
+## 2 26397 2066.3 1 0.45537 0.4998

Interaction terms do not predict type over and above the main effect of name origin probability and year (p > 0.01).

Conclusion

-

An East Asian name has 0.1709525 the odds of being selected as an honoree, significantly lower compared to other names (\(\beta_\textrm{East Asian} =\) -1.7664, P = 0.00042758). The two groups of scientists did not have a significant association with names predicted to be Celtic/English (P = 0.60355), European (P = 0.11373), or in Other categories (P = 0.70348).

+

An East Asian name has 0.1516018 the odds of being selected as an honoree, significantly lower compared to other names (\(\beta_\textrm{East Asian} =\) -1.8865, P = 3.4282e-05). The two groups of scientists did not have a significant association with names predicted to be Celtic/English (P = 0.14566), European (P = 0.021596), or in Other categories (P = 0.65199).

Supplement

@@ -1948,7 +1943,8 @@

Supplementary Figure S7

## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: -## [1] stats graphics grDevices utils datasets methods base +## [1] stats graphics grDevices utils datasets methods +## [7] base ## ## other attached packages: ## [1] broom_0.7.2 DT_0.16 epitools_0.5-10.1 @@ -1959,45 +1955,62 @@

Supplementary Figure S7

## [16] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 ## ## loaded via a namespace (and not attached): -## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-17 -## [4] rprojroot_1.3-2 fs_1.5.0 rstudioapi_0.12 -## [7] farver_2.0.3 remotes_2.2.0 prodlim_2019.11.13 -## [10] fansi_0.4.1 xml2_1.3.2 codetools_0.2-16 -## [13] splines_4.0.3 knitr_1.30 pkgload_1.1.0 -## [16] jsonlite_1.7.1 pROC_1.16.2 dbplyr_2.0.0 -## [19] rgeos_0.5-5 compiler_4.0.3 httr_1.4.2 -## [22] backports_1.2.0 assertthat_0.2.1 Matrix_1.2-18 -## [25] cli_2.1.0 htmltools_0.5.0 prettyunits_1.1.1 -## [28] tools_4.0.3 gtable_0.3.0 glue_1.4.2 -## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4 Rcpp_1.0.5 -## [34] cellranger_1.1.0 vctrs_0.3.4 svglite_1.2.3.2 -## [37] nlme_3.1-149 iterators_1.0.13 crosstalk_1.1.0.1 -## [40] timeDate_3043.102 gower_0.2.2 xfun_0.19 -## [43] ps_1.4.0 testthat_3.0.0 rvest_0.3.6 -## [46] lifecycle_0.2.0 devtools_2.3.2 MASS_7.3-53 -## [49] scales_1.1.1 ipred_0.9-9 hms_0.5.3 -## [52] RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3 -## [55] memoise_1.1.0 rpart_4.1-15 stringi_1.5.3 -## [58] desc_1.2.0 foreach_1.5.1 e1071_1.7-4 -## [61] pkgbuild_1.1.0 lava_1.6.8.1 systemfonts_0.3.2 -## [64] rlang_0.4.8 pkgconfig_2.0.3 evaluate_0.14 -## [67] sf_0.9-6 recipes_0.1.15 htmlwidgets_1.5.2 -## [70] labeling_0.4.2 cowplot_1.1.0 tidyselect_1.1.0 -## [73] processx_3.4.4 plyr_1.8.6 magrittr_1.5 -## [76] R6_2.5.0 generics_0.1.0 DBI_1.1.0 -## [79] mgcv_1.8-33 pillar_1.4.6 haven_2.3.1 -## [82] withr_2.3.0 units_0.6-7 survival_3.2-7 -## [85] sp_1.4-4 nnet_7.3-14 modelr_0.1.8 -## [88] crayon_1.3.4 KernSmooth_2.23-17 utf8_1.1.4 -## [91] rmarkdown_2.5 usethis_1.6.3 grid_4.0.3 -## [94] readxl_1.3.1 data.table_1.13.2 callr_3.5.1 -## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 digest_0.6.27 -## [100] classInt_0.4-3 stats4_4.0.3 munsell_0.5.0 +## [1] colorspace_2.0-0 ellipsis_0.3.1 +## [3] class_7.3-17 rprojroot_1.3-2 +## [5] fs_1.5.0 rstudioapi_0.12 +## [7] farver_2.0.3 remotes_2.2.0 +## [9] prodlim_2019.11.13 fansi_0.4.1 +## [11] xml2_1.3.2 codetools_0.2-16 +## [13] splines_4.0.3 knitr_1.30 +## [15] pkgload_1.1.0 jsonlite_1.7.1 +## [17] pROC_1.16.2 dbplyr_2.0.0 +## [19] rgeos_0.5-5 compiler_4.0.3 +## [21] httr_1.4.2 backports_1.2.0 +## [23] assertthat_0.2.1 Matrix_1.2-18 +## [25] cli_2.1.0 htmltools_0.5.0 +## [27] prettyunits_1.1.1 tools_4.0.3 +## [29] gtable_0.3.0 glue_1.4.2 +## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4 +## [33] Rcpp_1.0.5 cellranger_1.1.0 +## [35] vctrs_0.3.4 svglite_1.2.3.2 +## [37] nlme_3.1-149 iterators_1.0.13 +## [39] crosstalk_1.1.0.1 timeDate_3043.102 +## [41] gower_0.2.2 xfun_0.19 +## [43] ps_1.4.0 testthat_3.0.0 +## [45] rvest_0.3.6 lifecycle_0.2.0 +## [47] devtools_2.3.2 MASS_7.3-53 +## [49] scales_1.1.1 ipred_0.9-9 +## [51] hms_0.5.3 RColorBrewer_1.1-2 +## [53] yaml_2.2.1 curl_4.3 +## [55] memoise_1.1.0 rpart_4.1-15 +## [57] stringi_1.5.3 desc_1.2.0 +## [59] foreach_1.5.1 e1071_1.7-4 +## [61] pkgbuild_1.1.0 lava_1.6.8.1 +## [63] systemfonts_0.3.2 rlang_0.4.8 +## [65] pkgconfig_2.0.3 evaluate_0.14 +## [67] sf_0.9-6 recipes_0.1.15 +## [69] htmlwidgets_1.5.2 labeling_0.4.2 +## [71] cowplot_1.1.0 tidyselect_1.1.0 +## [73] processx_3.4.4 plyr_1.8.6 +## [75] magrittr_1.5 R6_2.5.0 +## [77] generics_0.1.0 DBI_1.1.0 +## [79] mgcv_1.8-33 pillar_1.4.6 +## [81] haven_2.3.1 withr_2.3.0 +## [83] units_0.6-7 survival_3.2-7 +## [85] sp_1.4-4 nnet_7.3-14 +## [87] modelr_0.1.8 crayon_1.3.4 +## [89] KernSmooth_2.23-17 utf8_1.1.4 +## [91] rmarkdown_2.5 usethis_1.6.3 +## [93] grid_4.0.3 readxl_1.3.1 +## [95] data.table_1.13.2 callr_3.5.1 +## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 +## [99] digest_0.6.27 classInt_0.4-3 +## [101] stats4_4.0.3 munsell_0.5.0 ## [103] viridisLite_0.3.0 sessioninfo_1.1.1
-
---
title: "Representation analysis of name origin in the US"
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
source("utils/r-utils.R")
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata).
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
load("Rdata/raws.Rdata")

alpha_threshold <- qnorm(0.975)

pubmed_nat_df <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  separate_rows(countries, sep = ",") %>%
  filter(countries == "US") %>%
  left_join(nationalize_df, by = c("fore_name", "last_name")) %>%
  group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
  summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
  ungroup()

iscb_nat_df <- keynotes %>%
  separate_rows(afflcountries, sep = "\\|") %>%
  filter(afflcountries == "United States") %>%
  left_join(nationalize_df, by = c("fore_name", "last_name"))

start_year <- 1992
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_nat_df$journal)
my_confs <- unique(iscb_nat_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names")

region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5")
```

## Organize data

Prepare data frames for later analyses:

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed_oth <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors")
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities)) %>%
  group_by(type, year, region) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations / pmc_citations_year,
    weighted_probs = probabilities * weight
  )

iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  )

iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
  filter(region != "OtherCategories")
```

## Figures for paper

```{r fig.height=7, fig.width=9, warning=FALSE}
fig_us_name_origina <- iscb_pubmed_sum %>%
  filter(year < "2020-01-01") %>%
  region_breakdown("main", region_levels, fct_rev(type)) +
  guides(fill = guide_legend(nrow = 2))

large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories")

## Mean and standard deviation of predicted probabilities:
fig_us_name_originb <- iscb_pubmed_sum_oth %>%
  filter(region %in% large_regions) %>%
  recode_region() %>%
  gam_and_ci(
    df2 = iscb_pubmed_oth %>%
      filter(region %in% large_regions) %>%
      recode_region(),
    start_y = start_year, end_y = end_year
  ) +
  theme(
    legend.position = c(0.88, 0.83),
    panel.grid.minor = element_blank(),
    legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"),
    legend.text = element_text(size = 6)
  ) +
  facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)

fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
fig_us_name_origin
ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5)
ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5)
```

## Hypothesis testing

```{r}
iscb_lm <- iscb_pubmed_oth %>%
  ungroup() %>%
  mutate(
    # year = c(scale(year)),
    # year = as.factor(year),
    type = relevel(as.factor(type), ref = "Pubmed authors")
  )
main_lm <- function(regioni) {
  glm(type ~ year + weighted_probs,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
    family = "binomial"
  )
}

inte_lm <- function(regioni) {
  glm(type ~ weighted_probs * year,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)

names(main_list) <- large_regions
lapply(main_list, broom::tidy)

inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, broom::tidy)
for (i in 1:4) {
  print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
}
```
Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01).

```{r echo = F}
get_exp <- function(i, colu) {
  broom::tidy(main_list[[i]]) %>%
    filter(term == "weighted_probs") %>%
    pull(colu)
}

print_p <- function(x) sprintf("%0.5g", x)
```

## Conclusion

An East Asian name has `r exp(get_exp(2, 'estimate'))` the odds of being selected as an honoree, significantly lower compared to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_exp(2, 'estimate'))`, _P_ = `r print_p(get_exp(2, 'p.value'))`).
The two groups of scientists did not have a significant association with names predicted to be Celtic/English (_P_ = `r print_p(get_exp(1, 'p.value'))`), European (_P_ = `r print_p(get_exp(3, 'p.value'))`), or in Other categories (_P_ = `r print_p(get_exp(4, 'p.value'))`).

## Supplement

### Supplementary Figure S7 {#sup_fig_s7}
It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction.
There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field.
However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented.

```{r fig.height=6, warning=FALSE}
df2 <- iscb_pubmed_oth %>%
  filter(region != "OtherCategories") %>%
  recode_region()

fig_s7 <- iscb_pubmed_sum %>%
  recode_region() %>%
  gam_and_ci(
    df2 = df2,
    start_y = start_year, end_y = end_year
  ) +
  theme(legend.position = c(0.8, 0.1)) +
  facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)

fig_s7
ggsave("figs/fig_s7.png", fig_s7, width = 6, height = 6)
ggsave("figs/fig_s7.svg", fig_s7, width = 6, height = 6)
```


```{r}
sessionInfo()
```

+
---
title: "Representation analysis of name origin in the US"
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
source("utils/r-utils.R")
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata).
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
load("Rdata/raws.Rdata")

alpha_threshold <- qnorm(0.975)

pubmed_nat_df <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  separate_rows(countries, sep = ",") %>%
  filter(countries == "US") %>%
  left_join(nationalize_df, by = c("fore_name", "last_name")) %>%
  group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
  summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
  ungroup()

iscb_nat_df <- keynotes %>%
  separate_rows(afflcountries, sep = "\\|") %>%
  filter(afflcountries == "United States") %>%
  left_join(nationalize_df, by = c("fore_name", "last_name"))

start_year <- 1992
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_nat_df$journal)
my_confs <- unique(iscb_nat_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names")

region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5")
```

## Organize data

Prepare data frames for later analyses:

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed_oth <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors")
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities)) %>%
  group_by(type, year, region)

iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
  summarise(
    mean_prob = mean(probabilities),
    se_prob = sd(probabilities)/sqrt(n()),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  )

iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
  filter(region != "OtherCategories")
```

## Figures for paper

```{r fig.height=7, fig.width=9, warning=FALSE}
fig_us_name_origina <- iscb_pubmed_sum %>%
  filter(year < "2020-01-01") %>%
  region_breakdown("main", region_levels, fct_rev(type)) +
  guides(fill = guide_legend(nrow = 2))

large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories")

## Mean and standard deviation of predicted probabilities:
fig_us_name_originb <- iscb_pubmed_sum_oth %>%
  filter(region %in% large_regions) %>%
  recode_region() %>%
  gam_and_ci(
    df2 = iscb_pubmed_oth %>%
      filter(region %in% large_regions) %>%
      recode_region(),
    start_y = start_year, end_y = end_year
  ) +
  theme(
    legend.position = c(0.88, 0.83),
    panel.grid.minor = element_blank(),
    legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"),
    legend.text = element_text(size = 6)
  ) +
  facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)

fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
fig_us_name_origin
ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5, dpi = 600)
ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5)
```

## Hypothesis testing

```{r}
iscb_lm <- iscb_pubmed_oth %>%
  ungroup() %>%
  mutate(
    # year = c(scale(year)),
    # year = as.factor(year),
    type = relevel(as.factor(type), ref = "Pubmed authors")
  )
main_lm <- function(regioni) {
  glm(type ~ year + probabilities,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
    family = "binomial"
  )
}

inte_lm <- function(regioni) {
  glm(type ~ probabilities * year,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)

names(main_list) <- large_regions
lapply(main_list, broom::tidy)

inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, broom::tidy)
for (i in 1:4) {
  print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
}
```
Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01).

```{r echo = F}
get_exp <- function(i, colu) {
  broom::tidy(main_list[[i]]) %>%
    filter(term == "probabilities") %>%
    pull(colu)
}

print_p <- function(x) sprintf("%0.5g", x)
```

## Conclusion

An East Asian name has `r exp(get_exp(2, 'estimate'))` the odds of being selected as an honoree, significantly lower compared to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_exp(2, 'estimate'))`, _P_ = `r print_p(get_exp(2, 'p.value'))`).
The two groups of scientists did not have a significant association with names predicted to be Celtic/English (_P_ = `r print_p(get_exp(1, 'p.value'))`), European (_P_ = `r print_p(get_exp(3, 'p.value'))`), or in Other categories (_P_ = `r print_p(get_exp(4, 'p.value'))`).

## Supplement

### Supplementary Figure S7 {#sup_fig_s7}
It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction.
There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field.
However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented.

```{r fig.height=6, warning=FALSE}
df2 <- iscb_pubmed_oth %>%
  filter(region != "OtherCategories") %>%
  recode_region()

fig_s7 <- iscb_pubmed_sum %>%
  recode_region() %>%
  gam_and_ci(
    df2 = df2,
    start_y = start_year, end_y = end_year
  ) +
  theme(legend.position = c(0.8, 0.1)) +
  facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)

fig_s7
ggsave("figs/fig_s7.png", fig_s7, width = 6, height = 6)
ggsave("figs/fig_s7.svg", fig_s7, width = 6, height = 6)
```


```{r}
sessionInfo()
```

diff --git a/docs/15.analyze-2020.html b/docs/15.analyze-2020.html index 237c854..2211437 100644 --- a/docs/15.analyze-2020.html +++ b/docs/15.analyze-2020.html @@ -1693,8 +1693,10 @@

Gender

n_confs <- length(my_confs) 
table(iscb_gender_df$afflcountries)
## 
-##          China          Italy          Japan United Kingdom  United States 
-##              1              1              1              1             13
+## China Italy Japan United Kingdom +## 1 1 1 1 +## United States +## 13 
mean(iscb_gender_df$probability_male, na.rm = T)
## [1] 0.584375

Proportion of US affiliation: 76.47%. Mean probability of being male: 58.44%.

@@ -1803,7 +1805,8 @@

Name origins

## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: -## [1] stats graphics grDevices utils datasets methods base +## [1] stats graphics grDevices utils datasets methods +## [7] base ## ## other attached packages: ## [1] broom_0.7.2 DT_0.16 epitools_0.5-10.1 @@ -1814,40 +1817,57 @@

Name origins

## [16] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 ## ## loaded via a namespace (and not attached): -## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-17 -## [4] rprojroot_1.3-2 fs_1.5.0 rstudioapi_0.12 -## [7] farver_2.0.3 remotes_2.2.0 prodlim_2019.11.13 -## [10] fansi_0.4.1 xml2_1.3.2 codetools_0.2-16 -## [13] splines_4.0.3 knitr_1.30 pkgload_1.1.0 -## [16] jsonlite_1.7.1 pROC_1.16.2 dbplyr_2.0.0 -## [19] rgeos_0.5-5 compiler_4.0.3 httr_1.4.2 -## [22] backports_1.2.0 assertthat_0.2.1 Matrix_1.2-18 -## [25] cli_2.1.0 htmltools_0.5.0 prettyunits_1.1.1 -## [28] tools_4.0.3 gtable_0.3.0 glue_1.4.2 -## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4 Rcpp_1.0.5 -## [34] cellranger_1.1.0 vctrs_0.3.4 svglite_1.2.3.2 -## [37] nlme_3.1-149 iterators_1.0.13 crosstalk_1.1.0.1 -## [40] timeDate_3043.102 gower_0.2.2 xfun_0.19 -## [43] ps_1.4.0 testthat_3.0.0 rvest_0.3.6 -## [46] lifecycle_0.2.0 devtools_2.3.2 MASS_7.3-53 -## [49] scales_1.1.1 ipred_0.9-9 hms_0.5.3 -## [52] RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3 -## [55] memoise_1.1.0 rpart_4.1-15 stringi_1.5.3 -## [58] desc_1.2.0 foreach_1.5.1 e1071_1.7-4 -## [61] pkgbuild_1.1.0 lava_1.6.8.1 systemfonts_0.3.2 -## [64] rlang_0.4.8 pkgconfig_2.0.3 evaluate_0.14 -## [67] sf_0.9-6 recipes_0.1.15 htmlwidgets_1.5.2 -## [70] labeling_0.4.2 cowplot_1.1.0 tidyselect_1.1.0 -## [73] processx_3.4.4 plyr_1.8.6 magrittr_1.5 -## [76] R6_2.5.0 generics_0.1.0 DBI_1.1.0 -## [79] mgcv_1.8-33 pillar_1.4.6 haven_2.3.1 -## [82] withr_2.3.0 units_0.6-7 survival_3.2-7 -## [85] sp_1.4-4 nnet_7.3-14 modelr_0.1.8 -## [88] crayon_1.3.4 KernSmooth_2.23-17 utf8_1.1.4 -## [91] rmarkdown_2.5 usethis_1.6.3 grid_4.0.3 -## [94] readxl_1.3.1 data.table_1.13.2 callr_3.5.1 -## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 digest_0.6.27 -## [100] classInt_0.4-3 stats4_4.0.3 munsell_0.5.0 +## [1] colorspace_2.0-0 ellipsis_0.3.1 +## [3] class_7.3-17 rprojroot_1.3-2 +## [5] fs_1.5.0 rstudioapi_0.12 +## [7] farver_2.0.3 remotes_2.2.0 +## [9] prodlim_2019.11.13 fansi_0.4.1 +## [11] xml2_1.3.2 codetools_0.2-16 +## [13] splines_4.0.3 knitr_1.30 +## [15] pkgload_1.1.0 jsonlite_1.7.1 +## [17] pROC_1.16.2 dbplyr_2.0.0 +## [19] rgeos_0.5-5 compiler_4.0.3 +## [21] httr_1.4.2 backports_1.2.0 +## [23] assertthat_0.2.1 Matrix_1.2-18 +## [25] cli_2.1.0 htmltools_0.5.0 +## [27] prettyunits_1.1.1 tools_4.0.3 +## [29] gtable_0.3.0 glue_1.4.2 +## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4 +## [33] Rcpp_1.0.5 cellranger_1.1.0 +## [35] vctrs_0.3.4 svglite_1.2.3.2 +## [37] nlme_3.1-149 iterators_1.0.13 +## [39] crosstalk_1.1.0.1 timeDate_3043.102 +## [41] gower_0.2.2 xfun_0.19 +## [43] ps_1.4.0 testthat_3.0.0 +## [45] rvest_0.3.6 lifecycle_0.2.0 +## [47] devtools_2.3.2 MASS_7.3-53 +## [49] scales_1.1.1 ipred_0.9-9 +## [51] hms_0.5.3 RColorBrewer_1.1-2 +## [53] yaml_2.2.1 curl_4.3 +## [55] memoise_1.1.0 rpart_4.1-15 +## [57] stringi_1.5.3 desc_1.2.0 +## [59] foreach_1.5.1 e1071_1.7-4 +## [61] pkgbuild_1.1.0 lava_1.6.8.1 +## [63] systemfonts_0.3.2 rlang_0.4.8 +## [65] pkgconfig_2.0.3 evaluate_0.14 +## [67] sf_0.9-6 recipes_0.1.15 +## [69] htmlwidgets_1.5.2 labeling_0.4.2 +## [71] cowplot_1.1.0 tidyselect_1.1.0 +## [73] processx_3.4.4 plyr_1.8.6 +## [75] magrittr_1.5 R6_2.5.0 +## [77] generics_0.1.0 DBI_1.1.0 +## [79] mgcv_1.8-33 pillar_1.4.6 +## [81] haven_2.3.1 withr_2.3.0 +## [83] units_0.6-7 survival_3.2-7 +## [85] sp_1.4-4 nnet_7.3-14 +## [87] modelr_0.1.8 crayon_1.3.4 +## [89] KernSmooth_2.23-17 utf8_1.1.4 +## [91] rmarkdown_2.5 usethis_1.6.3 +## [93] grid_4.0.3 readxl_1.3.1 +## [95] data.table_1.13.2 callr_3.5.1 +## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 +## [99] digest_0.6.27 classInt_0.4-3 +## [101] stats4_4.0.3 munsell_0.5.0 ## [103] viridisLite_0.3.0 sessioninfo_1.1.1 diff --git a/figs/enrichment-plot.png b/figs/enrichment-plot.png index ea7f96d460b2702bfc69f3a0cf5001ffe8e4790e..c9d3ccba0287f734dcf196e71e41e433a6aff8ac 100644 GIT binary patch literal 317799 zcmeFZbySs6_dj?Q6$AlA5d{U6ZlwE)ba$78#HFM=6cp+1?(S{@>5fZxcXwRops(+I zf3xO~S+mysF>C$KQs7>m`#k6Dv(Jvt{%pLYBm~h>aZw=<2)eKkp9}=@XbA#AVMMtL z-l0Ow%7bqYzX&N?LLh1axBu@v3+3g3K;A%v`FP~)Vz;LCo#Z~x-0U+rlXRq#ddR6{ zCVAYu!xIX-LzP?nv+`YMr^-lqMNZdFc1`}a&9poY2mj+IVf^>8r9a(ILN*nUDN38y^hKM;v98BbYj9)RS2%7)#8vNLQ z_(TEee|gL9wHDL=@`l^!|GoqWnzL>6EWz(tTCAEW3-9s zgyqFYvrFNs{h;~@q6G5{A8PxKP|$yCC?lXD=kYzBz%gTH*U>j9-wK^CPoP)xn27va zn*xDY^&a*?p^hiUu8MacA8ZeLId*f^ss{g<8VJN82uIV&d?Z&Hk^k}W*n?+(KJxF& z9Fhn8-_L))YX~Z#|1a;%SzEZ`%#CHs=e&FO?n?bWCs;8Y4rk*e3hDUf?(VFH*)Ph8 zI9t?oy)J9!%~33fr~mBzXYD>M$wTX}@MXxJ@$m3)=OeEXy@b*_h=`9OZT8+(Zh860d?nwzhv?|X=I=jX_K%Gj&_}((Ux*X?B^n(Q zqpG6v3Lg~}6-))(=QKMfCxS|OVVC07TC!GIWMt&pngNgYu$fX0AA{FPF(8NQFB(;bK# z8WGmRUHF-RsHi9}uX{5ut{({4e`uJcXb-?z%%*r`xpzFGUPROclBdv+=p zKG?uHpc<@-*_&s>l;g%tRWj4yA99VwN@^RdM3tT0AUU_VPlRoD1#)|h@QJ)N-y}y=dFOG4gjG=E(nvq2JHhwV1IaGyk}K?KfRARAbP94qCeM#lCJaRYk~6 zd!)TSU(Yi?(JPT)V=lkuhG8QQwX*AtLiXgHXU%sqjM8CU-w2Z)gE70HCMq;GU~3IzrPpOBHvGn&&2~bS>v(UnX+H$ zjuO4mke7$IM9{_bP%7p-AF4(>{xfX0#`afY%uY5(ce66$RmzQ{_}-c8v)P`_ zdEJMg{8xDV_cE74kOhI;DJ9RSo%i9z*=|EV89-PLjAec*JCsHDAE>vMya?B6DKO?T zDOW3E$%N}UUQgzRiISe}O3H!xe26${!O^seAeM;dWOGGaS+4e4+v&NuAlxyzEA0iAovAD6Guu0kQyc zo#QTRoxZEn~t8T$kEjL!K z{Gz3!lX4hwwbzWf*y_h&xA#d~JNfdyl{aR*5`^nVb1H7^sGh3Je|Dqtw?X+1hbB30 zs4#I~FW$O}%yzJ0^W~If59D%R67yLlCn>nH-@R_X6;E7TT;0{{HHXcV&7{eRFjJla zhviCl3s4Z1QiIM=a*;F{tB=ac<6Gk;*6eXH_pQQjA6a*Yjak26`9JRM12V?4+@|02 zfjQsM$VO-P>DcplqYGEZ+RECQeuq6PX=g2K%a43+F(J1xFjt4yQ&J6w^|bs3J}s^J zZ}-ut!YMU)9=&4S10t_o4p)_b*duIXiFG7 z8KRghH#wYBfjH$hv*N~lwfWimPZim}5EV?g?6nRQ?eyg;v;!$u5I{p^mk}U$fP4rj z0v`V*gWlTlMSjEY;?G^#+4*PAh?7Y(sPo0q)%l?+Df}J}3I>MDTGFl3k%&e1J2(xc zi(j5>Tb4W`wLkF^mz3P4Yqw~=Sq;LzlQ{YIKc*9sh!j@+{Ic<_X@z{v(nQk0i90`2 z4}R1u@YuBx3QEe+e02_^k=)IK28~e2X0+%lZl|+Jvj!6`di~y5KRgz1ZCD<^fWXdJ zk>b~Tn`v7Ifh9AN2?4M`dzdNXD)4?8ct2KR@OR|w zCimZXgMU(tYHs(AU}d?4AkB~>~4f2Q|_sA}OzyyeYq#dK-g*>API;;8JTQcs^ zom`#JtME*YNuKFNHm+ZpI5ln>r&7w#htikt^I;jk8B%eDy@#WbhM%k#m->lfOMUd5HTQFL(s1 zMzW1{S`o0Tzf@h%M+7plqUR?!(O8fI;vgX4^|gT%T3go1 z68%0&=(W{$*{DMy^R6>|=bmh9070MsSBL{PFmg`W)kVuSPl;_xmBs zsf|Ihg1^N?{5SkvJHUe>ZZqli& znBH9}+Mizjt|8CcHBo2JAsHdh^wVAWubWgGa$+nHceeoUZ~Db| z=J3PhlNDejK5#+?j`dFx$JJa_ZK$w3g_vvUxS7}LJOZ4HrwOs;# z$VHnc@>y~3h?7%W_$VGT0r^z4v~ff180i*v=7`yRMsg6*K`(U?1YqbL`i1 zY#P4HI5@D|GbL_r4r6jaN4MYPKKLbgB+yk|XODNX3e3r6igiz`nSX?nWv#A~@m_Pu zkJMw#Q|Ir0L@jE1*|syte!c9O^0axTW;eSFQ~dcRa!oN5YBw;ySu=u~9j$S3adaY^ z=2~x}n-J(<^9|03Yj!cKp4^Dkr%8(&b#!IplfpXiS<#N(L_C~ z)&6zdxH$ESjYLiEz$e6p!N7>`g8d?v3m3I=j?KP!c?iy?#*77fMRHLy1JH+}t7x5b zCKi8x=Y@1Fnu0a@?@o}PBQElYrxSwY&~5q7O|4R zp+xy6HEVovAT+@8lDHu@@aR`54@LsPE_`c|V+2;_?2tGIrjop4yG<9JH;Yd@Zc}`e zpL2Qb0CVia1d?zFzExujqf*gu5Qkqa2XGz;Z5B?f^u)|M9f}K&BCbL*m@f%O?^>B} zZLRYFA9?oid+#1JK{u9i`^;pvgt*!h%3f_rT3JCDz z9Y(zB$+7rlHAD{#p$tw3{^oa#amCSA zCt`m6GcmIXm%5tumxr%<^AJO;q#89w zxtFrm?QOQXGY0ln(*v^Kt!ZdUQJTDb%DSK&`x{Mi+z}wXn@a_*aYWmEYS;!u1Q`-> z8^xlb;o;>jth()iy}g0q;Wli!Bq&Yu&7NE@6b^5L6hRBNC=rdGx4@v)=H!o`K@(nC za%b6$SG0*PG$VzparEQK|A~g#b8=_)V&I-I+?&e_8b&EL`!90FktFm&aP4x1wMT7e zFb1EESLLW(G-hWD{Vn&OhBzq}Jq1PyGoM>dCdX{kqjJw}?UVIS0qLP(8EG@J92Qp2 zo=?SBJ0ZTR_iT-cph=MQ@RtWIBB0=8cZxg{8MU=&Ul90%mlud|iK1w@wxO6-Yu3M$7(0ew^ z$y|TE4EFm=$B-m+dL*g4TP#2~yS9{%pPzrz6L^uhxVP-=)yK`bxm2SR=Ite#Wh^K6 zh(_-wvfP@j8jXt$EAvUSr1D*N^>M@NW*8cD%Hu1>{i>*Ffa{6i}3{8dhwsK zyyIRvNQCQggvws`><7P&`aGf_{Hyz2MYOv0h{uvpm6&$<&y=65BGx`LGpE1(cr*Nd zt_zryh&|t&ZD8BnlXAG(n`|SMc=gP%!6a?QU_3#+Y`woF#eRXr^4pu-3-Y!cEvDJo zB2wBB6E+eFGSqL0(w!jKx>-K^UAYI5{+{8p$vqWj`KG25k$q~b*M~c<*rS&SB*LX) zd5F#BCZ5g=-5q#db>*!o#f1mbW1Pw;F=7u`Kp`%KmfgJQBqP_`zt70MhorV>d#W?- zY4t$N^sPku3G>eOo0hM}dvq2PY7H};{a1s79wre`$D9>xuQG4ZK7O5TL?2I= z`@6I8)8^G;R;9Hwd#$I+U1q|D`qDY@lZpa*Y6ax!RRPi;TpVV)#*qSyG+{y{kGx~) z$PP|&u;M4JvP*kg0z9h7-w81}^L3w+if&`dvy@ZaT>Va*t~zWb?#3OjXs(Rs;5?qb znU_VT3IJoMSiwX|qw z*S7rW7^7J-^MkoeXdm9oTz5bj4u%WXcX1RqW=t0 z-PSfUH~CI2L(4J2P3$Nl{uxrT`@3&)rZo4!F8pG`#>V!DEH3( zAU&y}lA`F^sM$S~cIrL*$DE5TH`c(4Wu<-ER^swQ`jbOmoDz}Wl0I)xSbY7j4^7Ai zZ4$-@ytIrW#IKG!dF{jewiVZi8{)%jIdVRlm9lb1tIzChj4<4cg@=vMIvoUNdbz*j zH>s!&ty_KNRA~w}-6j~8H_ggE@~ODCc-hy0ldxfgJ9p(teS=PnN;k6QZ&&44_&5ih zaaqOw7laaG{#VDtJi9xO8)%OX&pYuSD^OBUSlqZe(Okiw18=GN*kob7$tIapBG%x# zu(oz;I=VWEHh^{Q%cH~+6&00W*aL?4mc^< z=M5E_!yj{t8|VB`)eB9{;+@xE>%(7Puei_z33-s3zEYVUD<+hk%7bmkPvN+-wa&{k z#}p3GP!pjvdGG|e)~30qP7tg3EfCmdOy4wq&3=Ha-dIabBX;=PuNiS7ug9?(**$_Z zE+1j@bqg=M3ij!@Jkq;CWIm63tOmcA1xe9bVlpxO2e|2tejYMRMTZyKNXBOlsE4JP zn&8i0a-0}w_yLoS4u1c|Q@BiYr%LZvgrHAnzLSgpD#sXi*(4jtx_oHs*H(xO4Gro=)+tTF`pECj6Gx{O*k zP~2n6(KVb}=$QU!ZJ4tmfq_$o()9Q`p#*#R*q^~3*)m%M))Hd}WAa{gc`rj_dE({w zP#S8}+%ExN=do;S<#MPi@yZ7wsG^`xxU0*;4@KLV!yN=RG!`1GYEs$-J=d`G8Ry<+ zv9=>vH8bOROoXL%m54I%$P@0(~)=$`B)Aq9&o5o4axsgt`yG%r&Fg zEYl5^Kp<_4I2hG*opveA%Ie6AuKhJRdDnrC012Z07?h7@hP-U;{&;NlDX^X7`wMHr zmjaX9XNF}qUVTi?w#lhgEmXKy+vvP;>n0;Ag#zY%ObexfaJ@;}q`~=f3|kBPPZNp# zZoNzoBZIJX2#^k(R9F8i1D9fD+(y=?Ah<>M&1!3zQ(_=v${*SBC%Yr114r>(w--@VcfQUqx|UJKom*I@#^7%kW{JyQ{Rr5 z{uEc$jVvC$T4g_BdcaR%anzeV=(sAq_qMIKal(KxSE#HrS%yOk=0^iJ3vG*~?x6RI z=s&IMD`Js_h!_MUBZb10!<(Y25Vg z?S65!hDdT(+3hcMVQy}0Jf~5sG?^?1Hqj;@Ao8`_<&%#dAd9;`1fVe{FQ^GPB2khO|~4CF8kJ-IQa^>;OMi~xBI zBkRT`WrYE!s*IVdo!dcD=gyro553<;rcWX@N0K`?( z8=61~uc8xHFWCnv>}(tvzznY}J`P^(3-iD)kq@QnYr%WXXX0TL2g*~sV+hFj8 z6?+n&V~{j1y_L?V1~`wKhW0=*KfuI4+Sr^d2XQ%`h$V14Utb&}Jz$+_cL8t=fQ)Ep zX>IM~NH)D`sqY*aYfSjDaB*F?2NBLhRyp3{BwYQMyA3xTqzSB?r6!Zif8j%Rqc38G zhufiuoDj!n7p+kX4x3?N?Z=yjuEd`f7UA zrT-;r)X{{NbGhEL8k?#NzI$2EC0`cWb-EQhG$(Ck^H#wH4`3UwOp3?rFZ6~kIZ9Zz zXzg@Y=3<5x>ZG$-)X1Ldx7Rjm+8TfKaIeR4y*?PuPJ6o0G->1ol+R5=f+QqQxkPVc zIE!S+6*_G`_v@=x^N(rg)9H#Cht0_MI?2h&j~?)voA0|K4inJvn75{DD&ig@AtBXi zGC{5)B7$L+xrb)-{{f35UOr##{Nb)HtEUxbKvWgLtVC$~Zn^e}e|bQ60oJapWwlVP zX`#UT!hn%wYe5o^YNx!m7LQCb2_M&a|AE=-zG3Tzs*8$I%qTiCndy!Z2Xu!aAI${B z^$*{b=MC1rmNSyd*n$Vy&V_*uJ7dGCbXn>%rS;=hv&hH|tX2csWw(m@0gpsH`siY_ zz~_OVz+z_@9`_8MW3Q7_bVyw4p0x_>r z)a|UHAuZ9$a;~fy7hg<|!4{J)ibFrkb5`%HL3bt4xIjwq$>PN2$Ak*JO zXzG~i>gw2Bx~Ys9D=)RyhF!FTgSMSNy?gsDK=$@Tr}PxB;|!tvH-omDz4@4w=AdZ>q}jbv&oT(OMX#VtQJd$yj01 zaV0o+U5@J6^GQ+e*XnospT6*G9l3l`k*<*QnZE6MZuYD-hKc9#J#ciVo+JiCxor73 zNFVu*Ag(7%cBr1jnDk97>P6141Sn>g}Wi`>l0@}A+* zf!gR}=;|z2RRgV0>A1(Y?DWIW)l=TBSDooi(JKqldhQ(yUASB4oBr7Mv_4t&Gq41l zb*;#5t0{q`vbub2Q-}ZCo1=%)87F9x;;48~x@2VJ(TmgF*@lK1+ZB%8+QlGku-Da< zhC}I}_4M*J>MrU|XEWywthTp9=TYRstRt=9ITB%)PQb7?Jp{e}UhoA$rVe=Q{Q2Ry%$ zAdk4Hxfd`{HGOyLWlt+5mV};`j^pAw+Mi`QK%_J| z`|AYY6_XO7#vIb`P?~Vp6;%cLieC4p*`{^)smy*ZmJq6F6R+_DM#-Uajo;MsH zAI~GO885_ji*M66fz!~A8Z~Eet@0K@s?&?En-VI`BhheLe;8`0& zY{>%XRCDtPvY%nhd=II-4j%2|A=eBP@~9y_bv2n+^4x_uIo6IjijFejMSy@XD8S!} z`4-_ILHBn+;H2!t(f5m%sjn1pC=ij;@0HcGx>Q7KYw7h$CR_Duin$&A|#VvOAyDSOt6i4Kl_SjqL}m-+9-KS05lS!r=+rvglAtr2hs zBonx&9dyU&6r;5vA1eBdVsZfGX%4L-y^WOre*M+)`nPNh?p&%c5mS7;(=l{jc-(pH zUm{g+pa}6Dktze(-i96mE@wX9iUFc1{4IO(XQnXFq*Gv)|@=B*|eUev}($NG7l3-<4 z<~6pvriO-9S5kQjlJT750moKrAm+i}Y8~cap!}dX*H#XzV<-?%LU#*DCHd}r(4Sfm zfNjF9W(RCK;+~%jv%S!8uo3b}0P2CW{q6qE>Bt~YenfqqR6itmif_8? zu}>YP$_Z;~8s^=$4jWmD8ul$wXy^}`FOD}=1E!n~d+=#`Ii~t@g5;2{+5>m(mEt(PL} zd3xTpost@yADMVC`g*fiC~w1m-wT9E1sCM{Jv(Xx1E_KHV&>QF9#Rd%w=55WNII>L zphVE>ho|Xs{@LqkgK>dsMSk)^gLoV^Y72-8>SU5}Dnk@OnK@D4=T1L)9W~hqcK>gH zI)m{Gv+4CV1AwHK8i{iiVw~fJDL}oCg0oVjLMI`(BWsU;!^pt+r$K7{c&O~a+JOft z%}BLkP|1bh{RvFHB}bRz1IkNJ^+8g;?QwaGRc{uEJh|=-gQMN3SoT9ijm~sxsAz<^ zWIPK^Bcgx*F2Qs)c<7#wQ9)h#ar3ut-vBgXYbWpQe2qq1+wZV6n1qkb&1(Od?3GY@LleMvI zr9eRm-r7O7u(joprh=D5D4q|hf+XIIpw&d-*r!XQia0WRgOyJ>&rRMd+vv+%ImMIm zA?ENUP%1H-nUU>p4|)%QT9#Owv;P1Hekx-lt0@;1dvN%E?*T>jHNy7=RCl zm2ImX|3bo413<_DZ<OIak6e7t>|o~S_4^;{Wm%iHW3J)zl8-^F0XO0)ry)a7v* zU1s6|wkAyx6|ub(Roc9L0N&qfhF!lyavwerXvW@8-<1fx(sL-5n^C#%UeK)As3Vj4u*{N%`;D!MsF8wl6Bsw-y&POJ0?KJ7jH` zDnc&F(YT=zwVFT4$gpO9n<3qa)r7=XRq>!MOEv_^BGEBO1cW0r&L`vgv;k_BU*^GX zt2emRiKG=nXLaeh&gR_lSWJz%>}H)$L6lP7&@aw19U5lcVwML~dzrdoYC&KdUuRZZ zJY&c!*&0M3Md)PU#vr{T@VG|?a9pnkF{<6WyWJ=4SAf7Y`a}GgQbP^1p3In8&3|jjejpc-yR5@}x9gRnUS$WsCtQi5vqJ z^Y5MxM>jyyd4o%yGgNRz-u69fi&saa%E~yH^@m*taD6B4yF1>#Plw00_6S-YVp57g-B3psMxd ztowzxLAj&1Gm!_)(n&?x>*jSa8MUNz)c{z3HV{9hZZj!WZg$;wE_m>JKMq)f2c<

zA!S)oGqw=Zlxrj%~+`e22iqzK(=Gcqzd-5Ngx(8$(ASx#xGoK-1^mH-jdy20hzN%|e+ zm*js}?I}R+_@(vJrFq@J!C7?*-pO(db?<{n8V*o2QQX}D5sJR6f$qn(;}p)>jBGS| zo8e7SilC|Y+`^0oB@bQ4oi>!}OX^bUxiqv1a6^PV?mxXKvxKWFa z0sOaGhB7=CyCA!!M;<$^QTn+3KjPnCss}3vBySh_-F$c zCGI1sjy6nRE0P7@V4#}q$mOOp8=lLRISevch!^K{2jg^etM5bsy($KhO2wpqQ)z_ zH5{Wa3d(J67kd^pibsuEz;=M>H8!eo?giP#MZ)3DiF~@V%K8uKqU+>|O%+ADJiEUVKg550B3OVoqJJ<)hO@D?SL0fiVhA3jCtM?mAZD*yr;U-Ut3DP}!5oU&gJcEPPzV@>*D2ph zVf=wA__@;UsaSOb+H}e=T-Ae|=mW|aj8z5w=kPMPD2+^x11LM?V~@|h^|_m&5eB~Nim8&=mr`JK4zLaBzDV>(xPukAGlDU)eW ze62_gT0;jJ;#_TcTaj4o^%I6S6hfsXg#*UZwn+ye&gZ1G?3D~~)bYVA_lX-3fWY+E z1EkOxKrMLG?=6~ zW9NE59PmIU2N5!=QS@fCuSr-^pm0JQi%ibW*>pL;w=jEN9kiBHUGh4ld>%^jiNR0@ zXIbataMGPlz!p-jo6)kZ^Z4nOvGV8=UUSKzHsIch>1$_>Skq_5H_ItgGK{M~szgjK z64TH=d6KG9WTz%qD64wEYHwPxJxw$N!(n}MC>d=t+Rx0z)fCw56fEAwrn}nBjHpqi zaB!HRE=5%9?qgNk6XSg+U)MXFc{w7MZVy;r zVDRlIb~*>_{kxDo=sy?h@*F2!xyWvc017ov%6(cx#?G4~edW@-2dD?}apj`I{sTMg zvNX7y>TL3QW0ltV0WmCX7J_esJN>Vk6g{N=yABWCl9b z5xpB5Ijmh=TeJ94m6Wj}(vot*IE)nKBLMaZdsi22Ja!eB=E&Zn#CttK4G(LtIzP_5f4v&V@X>NtTZ{(BR?gmv1T$7Sr4IYSM5 zpI6dcHLI+?U2``~uldJuFeA=dCTx;Dk9*Q(dwNe!VxVu=d%a#W2Hs|9ARUT#k3aeI z6J)Ol6wKut;sM|e$r{$VlM3K^sQunXe4O0VdvBS@yW?&HNmm38AvXQ!EHN6oT)oZc z7{GYgt>FvgNcfS=LJNbo9Doy%=h)ifiCk$>L@I_ivsyAM5zkayfV>(i>I%|j z7wKLS_&vpjOPQ=CIEU<|;C%oAy+L`O6b&dz8yC{f*!1f^S8=7 z-9Au>5F!q4k$u2vp5mig^9P4_i}wH}?%VMp0x*<8qM=b`)pYdaJ^krBBTz;M1?YiT4}FO;A8|GyL=)GP)j;LnipO~7O4EPT{nv1bW^{Nl)`}% z6&-|iI8f{b;=jdYcerbn^RVNoI#a4yVT`OW(MMRr9_9|gji;)B#{)7B>%6X6=R6ez z;(9T|+QL!IPsRo)Eej{3;oi+n_HN9?`fsh%Y8`;xQKHEJl0&4*TT3lAp2rW42kKpK z7J8Q>fSu)V^mWgBG>L_udduUdp*mVS<~<_mvLzMCRX}zmWG8(4Zq5}a9Ul;r z?9670u2e!=CJ^VSQC0#X-LDs)dfL_(DHstx{WywP+33U2m;xZ}wKMcS&kKAUi?*}h zBQ?CO-;pa%eC<2eeiyD{a+0r0% zI=c8Q`cA@Ys`eZMsyr6CVLV+KVKR2Y@Ool7l5wby)+SP^CHvzsWt}=|l|h$o>!r*v6SrSlB>pKG0yHa+sc}t@UmlWOw!%$Hht% z^F|R8M)5*G&S5*1q1kj zP#z3mZ+n%2(hs%p&k=tQCjO6q2Xhn~Keg7z;HZpOeSEi|Lii_&-9ft6PT$kaU`a`@ zsCcYJgk0Pplwz%Zvs(LAkG=E9VSp~3eKrJ3;?;2nrk^%?c($wy`FMc)#z#7?E}LVf zh(3#`Ow$h5@P#1vZ^w)SQC0)mAGP@Jmm{Zt;pM-#{%=~i{@+|edTI+?f1l&gfZ%FR z%<5i0s2)10J^J?)@ZIghZCyAO6d2K8(EXZE3JiQqfb-Yp{=Mo=;eRsMe;@jV{4XK+ z_fD<P~D$&CT^`%hr#35?p7l&R07JQoh~n`N9r# z&7S>5s~|@&{?Fc^Gf%Bc%;h2dVQz#6nsC{OFR6qAwGzN{q2osmlJEzRwI0yi0?J;u z0<^miil2YRkwEp0bKb=a@e0~_d{nQVkxE^<_6WM^$_xZj87fCuaEq!X3Iee6&*g42 zEqctO+$Vg^(AmAS?v<&jY|R`wJ~y5wxdsOTC3kRF|L}1{x#2A|Z=_)*De#Vk}{`+c5=jhUZjD zbmj|u1QGWjdszS75B`k==ls0X=v*_+?Pg5FmF{o>0dLIv_u92}un7e+^?RS)YgADj zy;^T=4d5+9=+{;&3Ju_2HIn6a_ba+sn*frdJG zszYsBT3UAY253Op94)|n`0#w&%=NOHq0fL3H-M@dH0Chq_o`i-W@l3bbgvVYjMrK$ zcdjP5*54-4WR$nVsLlViS^c>lNTuTei2J!KT$l(;GaMPR$NJwjP#>vQFgw@IG~V$d z`ZAClxa=Be;2s+#2>L8bx4!5D5CPAsRr z1p;VoZ`|fy_tn)E$a=qk>v9zGyaNJGc}yS>+JDU@ZW@giMTI-IlH&oQr94k^6teT{ zt4p4WEFFM}N~t?r_XJ-W3#6aP&D8yIDlfF%=T zjY}a{X&rqw}|COa*z_BCX}t79`B$& zHc5Fe>&%(i(xu*|OCciT*6{Y{7*CY02MOXANoEt1lJ-S28O_w%^Lk>0b?7FigoTB% z%-HF;PgBN8*f09AL`O$YMK#_Bxltux(QJjlRPt;F%9e3+ahqGi?`x0(IM+1w= zgw@HGa-78JD)dGSnuFuAYBk={ejU_GYmWv+X*74Do0^*Lqdixm3LkMtLdF1M z)cO2*8Ax2QK%)*cI05#4R&nux=FNp7A0OW)0~ogrwUfbjAo#ajp+AEw4nA=4*2Ku6N;;f=)-Hr50&O!IGv8ic2 zXYzmn_w~^b=vhlkO)ceSc=KlNd^G{;a#cxIdkp&NR+g433y;8@^t&UIMmN=Sroa)Q z&dJV(aamk$R{)-m1JKiCGL0`^_INS5Lc_wCw3<9d7Gs!ACMwJlXkqsucDVnwmR~M* z>ltvK{d}-|eQ}@b3{R5!Ejl{9yQasb)_zexUga*NU-k(f5~N^54z-6isI1IrRHO0> z592|XCZag+5FmaR1A+v?M8REzU=7WfP%D>~&dyG7S#LDcCg85%d~bD{16==EmrIk) zAU4YtmF7h;R#%IvS&CUDG-NDp{@8my3C(5lqe)rhn;bW1H4t*r! z2Z(k@`_1lPbsH)R=qGpmB#}l>NEgA_c)(&G_J3dy63JJNdXnvC_xY4S~ z*XUakexB>9U|=voa4A<|mXo5xI3OKeX&2?`S!-wBvRS|Z&s&@-{A!sZ@Qt&X#%@J$ZSbr6dGta6sQ^|7?;f_ixw!^0H-FPgF z`}@&ncH&Oo66HEMId$>zEof;yQO(%uughiqaC`l5Q88+tqb7dJ0loNNz7*D#K-})O zmUc3zaxdw9{@fGCp~yo2W$stLdd&)VHYlHf7P;%?h#S|t2VJxc-3d_5lZ}SbTXhGf z%JFpP8j!fOw6yfnP5jRfS2yu5E-p;L4#bZRy<}mj0H23vN)u*aPOT4RNXD@r1I%8T zQX1lBy?6Ikou!Df;L4cz*@W*H-wzyU$6G)(JyEfjTD-?OsKKB0pj~Wg>CRE)=gDxe zR_C^xEk}UMFdG8J=snuqZo{FREUl5fXcKla-!fgoPy+fdBc*QswbI&x)CY z+bdTyRzh<>$6k=0XJEg?p+SL_9py5==_dA>ohFf$u1H{tw&ZcV1x zyTpIz7p_6h1IUO@uTE~y2E5#O{NQYVF_u?fe-(6yzND9u%YFXjiTUnKok$o(-NwaR z-XCOPA`J9pkJ4o9m)f69w}Vzc{+j4|k?*M34=`R;8jlypwu4Evec!>kJV(@oc2BMmH<66&4vu{XGFS?KhqU2sA|4gp@j@x{dsqE^3ZE4FG;mIh}>Yy^UY3PvyP zU% zat|LpVsx?tDllWW=m)BOp9ya3A%KT-yg6E->kWZWru^{(WLWgH8s4uN#{yYu;q#J< zKOR}`I=33ljM5{=(?cMr%%z2;j@kf+;vlOT^RLZ&&NEwKT(rqmA{LAOm%LK3P*RJJ0(7(GkR|Z`!%RB(O>v zWPV{aRrrLv!I;jK0z&6$`2894WH<%{m8Q#vuVe#hcOXU3deS$HOu8v^>Duqyw=-9{ zWVm2WtQMG&{(hY7Ck#)W7sgop7)xEs3w~EM*UW<(VIn~ydcJ7Js zxwBm~e3&9^E*XL)w#s|BWQ zo=leiPdPyW7#5a>b=ASzz>)K%I!9@rr8=bDGgA@CO-)H>S7MWE4*~SK+OsEyB|>$( ztg<77v|(cyH1`@Sa1*C9-N5tXyL)l{#ME?_r}y80D!SCqKgPoiGXXdOHg*VgV$(t&4dX!cl{j!6Y} z@F`MA#*1j2Ij_KT28Dwou65nDnet>_UMk=R0^;1EGeE3R8QJM|@MoArgQB-LuIg z+n)utCGrf)Mvsm8cG;I^3EeDWTFaF(YB)7I&A$FWX!{Dbs@5*rZ68q#L=;d_1VmB^ z=`cV+x*H`01*A(rF#$0Mk&p&Sk(RbVTHTa@fFj+}l6NdUp6}itaM$xa-|={0@3r1F z-#Nz|bIkd+>$T@R(MW+V%HG-8s=e8{-)Wp7q;lVWDf8^HPF1mMPZtYGk8G5OHNZ8Q7PH~csi_9B`~Lp?PIm?dRz>+=rH>yzPP)Ey|9A7b7T>!Q#*i_C0H;HG;y{kk+3( zr>N+)Fr93DMlJ10aIhm}&A{`3obTVi4`NkjJa{nLsGKM>J32Y#S!K~2ZhPNwYxHb} zeo>Q)IZA1u+L6bu|kzwBVyl*GQdHb69gU?dAM1JUYZq}b1&Kgl;R)FxdgHw?+#$u!Rw8tGwU-_dOvCF-hz z$qno7;_6zk*=~`m^DH!U5CY;%>NO+{=}gc7u$ZhX+I_jhv-N8?7)++9&nmpRBr~f( zx_w{O|DbP^0W%X*5A@8(?E?@xRMR$I{-m46GB=s!!BP=WP_WzZ*vE-s1TBSJOCPqv zMhz~FMMEC#pEi5`*>;@Hqedqwr|fY>K|=dW6|S4>D{PdT`bmoysY z?=9tu)^u}Qu$QTkHr}v)y%rO#^#%1 zUEOQv?CcyYn|t;9TU2MwO-za{FUZPPBdnJK93BvJ<1j468~?CSr6cD@!+GX0|K;Y| zaPxtNC`^?&e=|8bnHr4{YfiO_z?N`P9FdQRWLZDytwcN6oZuI$b~Nse;0Ei<+#HHa?Q(N8h& z2toAtcvNukOn12-dUaNieVKoZY}&tf&z=@2b3ncnwEd8|vDpn-!Ay&xgv-RiBX$eZ zgAk@%MGBe0Rv>b1{wf~FptR92DqV>+Er6BuT9^R1r&vWrMR|Bs#*=k&ErVY+JRb!` zN_yQqZT0MM&D>~igk=+t=~Fqg!84P6TDd8;RaFBQohsEZtYEm0bCF{!_7M0C*61_k zmW7xEQ(#sX2G*+YjGn%}n3x!H!_k=2ccHJae|>$;(W6I&%#m*k16UQ<_v;84Z&7BY z#=0+kySW`xKJ2{raOoxK71$xw6~_QBkP-{OejN?2MyijEY&YDTju{C5e}?YeZ0Ow6s~yW#|!(k#ZQxAv+v z%$Ep`o^hQ&SK+m&NV@L-S%Z>vFjhAuA=PVjMPhZyo~z?+q2usu1-%WQRU03k;>arq zvVZ>g;6Ch56||k-`YT$D%HyQ{$&_Xgr|ae`$z7AKfO zmo7kkw{6=t+#H6CZQN$-KTgbv&nYeWw-g^4+TXh=wj>an0= z7xDAzsp@B?lHa{kSA47f@IdmIL}jt3JiK>F!&;JVv;ie)<(^CH@lU=COBl@3OusY` z_i!t%0h})2Wyifvs#NClPXvX9>3F__UqkoRJduV~;`LJ~WriL62jo(^R%SOC1EgZ% z{pI|YjavEE`hcR4QKis3J2^RBjUHJ;qM$lJjEAD&om*Mfi;W$9wqfLKs=Ps_O8C=D zeSFqp61J>!ccgf-I1*HnHBx6H?HnD0YkE)AZ@T?7Ha@CEwXzIwXa&qrxR6gqMh3Ip zn)f~8;^gRGWwhz?2DR@xkM5NnvuFV-)0p{ovAHf>+j%_YfT+@KF%`_$YHIyw^UFK1 z;{6hyUu$JRtvK|eq}y|I?He=t>%z~(nSJq$Zf9e?80jZtn3+K&x*3LY+4VO>caSo z@Dq0^>A|z^r=p5&YS~tRJayg1t$TURn{8a>H6E+bX@`W=OmH9 zfb<`yQF(xG6F?Kb10`R7R&b_TPlTf)nNYM5>)qD*4rs0D6) zPFkk0cglMg%N9J@QG}-u`o4@X?O95oV7KTfS;W`^$HP2l&$_;` z_@dodx&Gv=@ADkc8oo^zoHGg~OzniWb39=jO>0h+r;jt+;o%~epaNy+M>8q%lkRWd zj$Qf`4f};l+XwCtVG1q$w6wGs`f}sO4VZJ9K}q4*4Ak16#nRN01FWVtke>PxPf3bC zWGXU6H}6*v=8sk zNi~E{i*iP0WTD7S)aDqT@@TQ_nx3&Bky6>N?p_BEyJ6EN-N750;|HS60`k_UYG)@Z z^WIB&wf>~buPl_b;lhA3JJS$FCS0T-TrxTdZXR= zT&R76GiT&(66pkEpit9`XSOTF)^g!bo;Z$28uwOf=%U{IUp`)Fd4^hx9+-Trbj{>T3eVbUu-13OwmZ! z8SE$^?;x|WOGrq>nN3Vi+FiY>l&mK77$g1~0**=&x9I)g&!RoDPf>k7pWpTVVbp~y z>^u9NyEw%c*@u}nz|9E?STnI{Koh{*V>rA!)wH9KnNk?kRPYma#+>Bj%#uYDgQCd( zV4h8zqz1{`Y>K~6^o2hNW)oIR?$%1ks{O3I<5o{XOVOZUL?W zv7C{X?mNRr=da9LX~LtN8Rl^L^C=ci&QajY!pGGb?sl6`JxZ%tJmfW-}3d$0xejJvYs{D@69R<)y!)w^2&oh=%EFs6);wfFjwU ztgL_T+&$%YXz$_lUB7<))9}C<%r*Nk^odivrUCN91otn+u2ZMFx0#me zWhF%(%^CIY_(HX|K-ScaIX!Q$!5B{0R5dw0lnNlbLD4=vPz!ULJ+0 z#~b&vsYPoi5AuaI&d|qSY}q!tyeg!AWtz1hBV#mh?RC-C)O0%1!SIB(813A2xbA1d zj$|>uJpMj{WY~@|k?d+o7jERF0jOA)3Y*nOSbUR&9NM&orsEul=)rz+bj_x5-D@$ir(H*$4*fJ%{*-GKP9?#)r{~&^J(4S?qum?zF`ZOgywj{^ zjpWJ4%Su=5W?otysDiX>V0?TPP;TTM$|rkE?(2WIMW+l$K0d`aP5Mf-R4LK>r=ejzWjoy*yt^ zG!;X(OT?pjOi8O?W`JvMmS%(c#fuj0Gt^H)4-Z%PrwzxDU!8Byd!nCou1e5egZgp7 zE9xkLhq8oo+xs>rj5oY#Urp~Mmww@ZG>c+nT9YLuEfEQj4Gu^PJB?fdH`Rxxw+69m zaMFM!f&EFy5i`nGbOgv~fn1w8dXyx2tHpt^SmgIltG<2}6=yi;f22M#&gVhqw^PZw zo`D;8C&&sl{gd^LP0D|$l~?(F!C;Y{glgVOV`8EXBZx{8^)ckMclJclI%Z1AMF@Uw zZ58UD0Fa3d;3xrX<8>O*>Np2htk7xH^4HSVSrlT5R)YfqV&|#}*>j_pgn$UVd{0l$ z*M0^blXEUEF7+{ifekqp4Yi8&qzmrIGJfA-^dtN6F2%kvE(%MkUAxTca~i2q&ORhN zjRjQbx94RxQeo^>tG)93pQHKm5kmzo5KWS(_rF>Fa4TCw9i*;uob;eHOic*G+w>Mv z^$YAkN_6cB_O6Q%QjJm4Vn4qQ-`O|1O70j|4bkIb;Jft5x@Tp1kr+W#S^3G`Lqnq( z(Fm{h1^v>})W;gQjpUv+q`Wgk5=dMWLasl%;dEqw3@&2s$*$H=3pxvTH9nHk8QtTWR1UDhw&GnqEBNDXE{PxvFuKQ}0GLoro3 zS*`HrhldOg=>=XuSHNvp+UW0$$#N(><3<~D8EuwTqgO(5y>rEU8J zKgG)gqSi800XVx*C(!kU5#0T5!7n0zR+IF42n4u%d!Bw^5R)(7`Qggm1M4IN%qQc^ zB}@!TSp19T2F50ub^B_!hK4+!=$iY^XKTw+8(8cR$|Bx*N{0BDCq1L1NPougB9Ecq zb!6|peM_kM{90JRa9xzA4l*Tf+d&Viq) zPzdn+COP@=JZA_o{P)5|`7pj;L@8}?gjZP<{-yDXTs$HtO6c78f^|pR^H(Yjub85KtqGytoCV?*Gg-7dHL&Mbj#4w(dm?VdZe0dAiX^C z-%6>uD1;55G}WmZdgzccL$1tI@x$l4Dufw_o_`G3v$|vE;?#ZB)q#@NV4{q6mGQA_ z{sT&!t1e9D=H^|cN_Qpd?9*5d9rCPwb*oA>mo0c@3zG;1acfSEsBs|q+6tvhT^;8f zdpMtk-Rwv!a`eKYqIMPmc3Q!!-;iEnj$g>4DU7l6<;8chAPjAnX9KXQqrpmTybF4ZO-)&`4D|j>U!DRE<=N?_!CEE_V0}G+!^CG zEB9;e^L<|P_;#PZtE{?-?>}BP*;YJhwnl-Ho^@%tiQ8~!BY#zuvQ_(TdZD)^UFJOI z=7X$`CMH7}x+={tnpmFq@VHha)C^5L_vK08jNl_IfsKJ7%5k`vQ@?Qfp~T9Ue48%h zMFzz#J85YlZJ^z;LkTPc4W~}d<(3y<6KV7ukW(Uv@!JQqOFO01E0?&K@Nj>W=eO&o=1wWv+3$yf zeN_@TM67o9ugE-{7IOy?{3uf8tkiP`(V+gVJbOaAySN>!T292Om|96!c$vA(a(FJ? z8d;qadHE4_4Zog$wNoWMV9WkoFZn}LFZ-wyC7|33M>vtgU7H(ARH>V!_N5wb5mMu` zjXErqtm2vFAu#<{3*a?kJ3O6P`}uRShI~!pzjR8R(qADDqYTT`UU80ZZwrhLpRN4* zwKnuaZ=8nb;wtM3>b!Ary_4@&1Kr&gmNQ#&t#sq05ea?$8T!4tpBm>{wmydc*vu2U zn`-^<8@zt-2csw$p&Qnh)# zo}kU7mi~d$8{N0kUYVbb9V^iy_0uujT!R%T&91U%cQM@1E=~CrZ&R2KFxuKNfhdlL{rzDil}R zf>8uI0`8zS|7fQ7D#>m7T;QWT?5|J0; zf}%7`I*Kwp7u3QZADWwWa+#8U-ZlE)XLU-!T5@9ufM4MPwZ=;+ue zBE>!0Fq7Up6)f&GOcsp&hB9p!pM}vIbq;0@jxZduoqe&jI*L>CZ0vQO~pz^9nsKXNb%l7VF&>dQ^-b(~{Ym@Sp4+>t_*r{N3ljgNqZ}OFi68OyaHM0mgf4Lh?1m zk5%8^vd2I@`2-kRFbJ*U%-AhU59tW_9H{(v)^Edp@oR#7d};zJ*=F*-O%%7FNP55^ zVPKQchEOFAF0cLhdL)&xO_qd5+%(}xOMg+Xjg`$6b{u+1i>}4%-~Y2w-w1R?mZYdyVXZ*ey!3$aQy;X< zbweJ3w4>N@_{o48YCE_tT^c~3*-aq@t}9V4l!}_#Bt&$>NkPvg7lnZ1$B)m-5`Lia zcRd1sK%r>I*RNkYJ?8Y?&DZFnOgo8?#KXfA|C|`zlwnvVYW~f74T;(mFL|^nwFM%N zsbZCM4IAJHOgJ|5m}!D6tH-W@lDw-G@q2U+=lf}Ek}SAdto(lqcyh-r%twN->`kZk z>!BBV1(PYVxk&QnM%!B+$-IJITZ)K}VJEEU=;<%d?+BLV$D}Y!1RwcYK!9#;f#mr3 zc#N(7Vkd0^{eH=Z2L9R@wI@D*Ke#x|-J;hfe=>g-VrZt@W55_zc|wS#=kS+(2vg^HJ5cDCKGwIi-)q z$g#9Tz1CYDZ0J~`$3^hsNVy$u5@Wire)rB*p}sXRFaX2N1zv&H493;0TbXsA^JKP1 zRoIOh06 zS;qZfhzU|eDrVUr4sG(sj~Uo>fGTX-S@EWtIiXOLVwSxMwk^tRt#N0!w?7XO3GeuV z0kU_uGpx*Yc^yVNarp4AUAq9Jae*Oi0(u1u^Gk~j@fgpan3%wgb-uDaeB$?oB!DYq zaef{()wdr$JjW}?ilECYZHB5c@$)4JrEIZ+Fl4n@448auT%6Q%qH!u90JrmG*)#s`-Lt3%qB^9C8Z7S~^<*`oQy-uf*RasRVs5MhZWV903;C_hB%8`?Nf!?b|_N3HpRc+2c*TB__~Z2vXoqurJ2HWS+M9_4oytfREt<eVZZgYxwzJGTwBWMpWh+J~CIy|p>^9HuCn*M$Yw?8wzAft->MjwUAdg+4#{ z4=QQyaB2BAh9O+~MDYM+$J5d>h+9^W@!sJ(`1ulF>vB|Ej>S}Oh+$Z9tbYf*53H9w zo+8JBL{9}lLO!zfb&*s^iS?D8=^8~43_@<|Q{5Pk&4Kygh7LFhf*dbHar@McTae6N zLvv(dOPW?@EH=PKE~~D^(VnV61l;E3ojqKlqSt_tLdOCL3R8CkP{N{|Mi+3qW~=MhyxzBS=g!!3 zupY{N^wiYxx@l(famTkl-tPBq@*;sTf88YjrsQ{9fDnae!r0n!HiZ9m9`9U6vM=mB zc5UR#ar%A&X+&Q=CnvY>xY2ve+L+=PsM@)2A9n5Qq$Ji<7PAL9EeAO{hfYrAYuVg= zt<2VlaIQ_+FTWBuh-q^b*1?YW5*W?7cB9_Yc-3?ShVsk0!V`K0A*N3)MLVL-`U+;S zppz6#NUwJC6JwY@?4sckpEM_v>4!!6bd8TskN<$={8-EhMm9D}>_7PSV@o1Ww)jg= zq0lQIA$WWaH;|c`dHT-Q&6_t@iOb2z&ebO)q?b4{DQ*CvzgGXT`&SAK)rjjHH(O2<#Q^cSFH z-Yaf7iq?3IbRMg6ICZ#4`uTmI46M6KmjNA6+;A16S`kY?QaC+z zR%+Df%K)i1p9jcEhY@bD-fl?xC4b>UG%MBq{mUqd)AMMk#j`)FmdiG)b5lPw4lOk- zj^$9<;Jbb77Q@2n`^0#=tHUP3==8P+*a`F6-a+-ZUb;vENpDD zZK^}iXuut7vIXDaFn0%vrbVe2l|WXmz$*B{S)luNX7-XRPR5sW+lc0TF?Wq|?LB)! z@U5uEB&ns~0T58kO-%<4CUCq3qGfv}_n24fPtOL+Tw*FmD6hGJ;|-i7h4m{dE0bgK z^Ytb4<*8xq;P5Q)z7SFPTqWutn;+0f`87XZ473J1Xm)Z^_T0HwcsY484OOAi(o#Se6!(POXVV-4ORwN0twc;;3N>&aA&1+x&%N~C(VxfTs9#_Ua5%P8rBsZYGYZBD!+)@s0E zZ0HcxPpTkwK%Mhjkk7QG|H4e5Ei@djpc8TkK#lPlShru#-(+i~KEpIuz)nNMF6?&% z1Gz@$9#r+cAen%ZVzJu$BrI$Jj_*oWsn|!#g0YUGIqb_pvEIJEW+IPN z5z(><@M>R6Jr<{(jJ9mv+*<6Ky}1jC0$PlUQ8^k}JSchW;mfeud~EmfR8T+Fo}g}JE?W;x^S>(7GGxWKn}NX#VS6eNb6bsGZnOCL z0bl673X)%&?225ImWqneA2T`ZvA`hb0>&PxBTYfJNz0-=h_kbo^Ts-`9na5 zLr%PqJpuyW_?-nnf`JDmEEY&WFZRsl!7spQx9;Ah50?R>SU*<=1dF!}*pxfHNgwls z#!D9oP+IV@51c&i4gd3DYs#9!NQnUuuro9dl(2%k%adR(JlI0FWhK8b8QjHD`tz$@|p&XRMJt%B{2(!fW zSbdbf>`I_c;5DlSA|C53VHb9Udvfk{{h;GR_X8hI?#2mnD+tX_;2GvJf#Pu@)s2jd zajeLh=hzr6z@>U8)^)%*!L}#-1mGp`LV|^5%qql^-TVr5BMP z;D_@)WD8#Q`j^=C%62@H&IXELpDSdA)(UP-L`Z0~HT#GN-21bd83H|)F3;)?Mn)PK z7ywmrgOeRIM*B-4RtqG%U#U%KXec(nR)JlDa%j)jK=@rl{JE&eoof#<*adN>*uMWk zXSTJqHB5}?ZL$l+0JS6tzVTr@%Wb~Cx+vG2K}}D;IPv{7?~%uOch@T&oF40NCu)PE zM}Xo}KVQQ1R;Cp9dMC`?#isW=P=&V~v3U4mjCqroBvJ;G7PF{&xzkwQFwM1Lb>Pi;ZNL8DxSD~>NuVUe(hc)-CkWwW3jTO z$@9oqS&4HfMBzQFOtQF3=NV6&(8v*rKKKtIy29L_nFnOcTtwI43VMD^KKnko(~<11 zZf>Yb<^cR4b-dGKaen&&(Y+_GXyK@<&TJI4fYu-47X7oUZ`WCm0K5Wq`gkKHLXv#{ zkgGx%pG^LX`m1t=fz1HOJ|HH7`AT2$i{P6kUf)<3>$MH(*&Xw6zi;2ZVR=G@WZ|??h z(w*g&t^rjhNL`?3+h_nESdPT+-fSyW%DJa3iIWo-VS7?-m&AD=h8f26lf{IYbH;0+A z4&6c<8!k4@7hh2vfls>EjZ_O-r{c_`j;Kn)9Pj9XXRy-L)U*)0jPwRMgmL9om|CiS z(JaEH`qT^oU0B7cLw$l|}D*Z3JH z2)Bd!j3ELtUrP0v9k&2#eHlTH*wKcLVOIu?DG=3`b$)m4T-@Abhufhg$%#?R14KA~ z6Us48L|o-zlwf#RSu2wcY3!z@?@&6o_LiBG{)_l18OI!;sTtR zCx-G+mWhY&AJ0b`hCqDJTU=Zm+m_ezz)6BQ9q#WxLUBOUs=l$9@91&6EUn`3hd2|_l!0s2P(I; z&v_Qv_4?+xWWp%m+$y+6ySo|dgpMDFdDTeQsm7ezU@@b`B^$!U=?i*{&-&|^3EKNQ z+emWXAS7}zSHdx_XV0EZ?IQvRHT7Rgy~i0hfKbNo6!g7A{K`>&ewa{E>;)hPxo}WF z=`w*VvEK-vui05(f3|H4QG2S$v#)7}`+_lr9{ZF9H!!1-0wUfT=^WYl6Cx;NLFTC= zlV&&R7s0~B-U|r{$q~?vL^b8IS^~l6gbUcn-o(y&y!reV?@w8#=Pi`R%d+(Lwku2P z1i-zU#(A#)m8Y{u^3YXe$?(Vb4~PjfGncJ<)_H z^N_!EoaY8~F^!K9_P944c$qA%O>Exga}{y|Hl5T~ZLA?R5RhD&yft1t^?*n%o&^Ol zQGi=IEu4p^OXM6H7?^*viQ(yqX75v{P8k^+OT9rTt&I@kWn~?}J`dR@V^p?0+a4P|O&?wa&Lu3* zZ5tdy+>4J<+pm)-ZlJLmrCQyb>^wa#jEE#orYl3;0xhc+NM8VosKl;fN;29qQ{jc7 zugjCC#luw^?i&FW|9TSZpJ1bDlB$(yy7&6>3x(eAUvlJ~uQp^lU~> z$3{s|dzdFGz{q)Ea(en2P79MW$OkOMBJ;Yw3I-K(vEn{$tEn&w*S`o0mXZQ21Ytgl z$&h9b*RQ_YD3F(PB^B$MD6bxK>NZ+%_qt?yj0-^Aouba`Iylk0X_T z_>syAgKMzCcH1ZzNX+X!Wi`uh6V8l26Ey8NLb8}mKQrI~@C%_O|IC`xfgV`7~K z4(oi!o%dszhJk^*gEkKa-j`;8t)RH&(3=U`+OFWIDceX+7zWt_IYh_VGW^hf z!K)YW^lpo1t4*>HTye)<3xm%Kf5N>fLUc1U$Y^N9OB_~vb_;itX_i$?>wq?zcWvfCS=O& z>WN_4;K)d>!ipu?quaMXTvAGFhIfT0E=jQu2EB(TJQ8({aiKm{Ed>=A)B(z~q9S>G zb8W7a2!vNHD${kYR+P?#chd3jhR+S`j|>Pfs*gYy;7DhQZcf!ajERx~6#&RTUL)b} zI|FU9RpZv%GFPu2E4qlJj@tCMRB}T*20V;?|9)|D0wj+Y#)JNObEHY$Tpdof!`W29 zvI*`<`R_kOnA<-IdvgsBP1GyK+&OSco*i^;S12K`z51<`EWWotk)!e|Y8_bdEZ|vl~J?^Q>N=?2X zEq!N{cMZvX^Oh|QVsMCgnY`!jVyu5>H>LSgQ%>Xb(}JJI+Aha|*(%LYE29 zNcv-<#hBEih|Sd(GP3tnqjnv*r#Ej-I{5$ch#4uRG^z?a+37uW>8VamTIl?%&_K?nB$0XTxHQAn#miQ~Qq3OaAg}qDihx^O<8Y%4Oc!fNVY~ zp?CvEW*VQ=mD*BTZIuDguAN$n-3<9FfXU3)1E=|ikoA% zEBIPhC$A|UExa&xY==9dAsImj1y5lUUI2E zmPJtTi}wcI4XG-e<`%-WIEOsehhQ~n;Zq=!DmnD=3ZnEpSOgWQY0>NvqW7X-eP^zY zRjc~6LXz|8lGxKa-;B&Y9tmv0XyW7MrxH{wvi0RT6F{`U5JM{9c{34i+WU>I+}!Uo zGc$*t2v^SafoGg}E60{?U0zLm1UTm;gX3E-pS}j?*ke90p#ZjsgN9nA@t_QF|^SkWM$is1ualyyglh2TIlH{|U00sJFeHZk)+&F~DA;^OqLbOG5*V=DJ;GyeXYE59V}z zsF+cV!)c_ zL%-U|aQjOi=G^hPrWYY2{~Ox|1tq0Ntb{SGQ%_crqLz`9<1-&bgbfXO+<|1l6mB?*(GM3 zZ%~pxpXV$KoAkDR| z`rsyf!ss`9D9rKt%#TEco?l&zlKsXw$g?_efkA8+>S1Bz$(Cxj6!iFhhtutdh#^P<)qPt zDxHbd%##Gu{&M5rI;`M?=g+kp>^&wXC-a(p>4 zpXw)Sc5Wg=y@TjrB2S8!oJaNP1ZqWi>&Tt@&!A2b`1tX0>`%P|?eE^cg^e+`DMJ2w z10SOxC)X%~G1B5YbEm$7rGRv{?tIznXbSX{M}J}`GxYpP=2g4hnwS zCM%$421jG3k#K04ta#T{RVBlL#ePH84^Tt?=_o>PEWuMEd15b7;R?yW^!XI^{Ra;S zg@nJpTHI&I5I%oV{)KXjDWQQS>a*P1S?_0mY001%v47wDsFvNq8MLQtMlHBT+{>Dh zRnO*V;+$L*coh^C1)OGTYim(p_4P({0*E^9#71HrxaQ%3Ubll3*z5U_91EkC* z2u6rP=zQfilVZJujZKUMaGo?(+X9uxML~2A;7x$~bV1!2OVf!o95iR~FHG(M z#&z!UI5LzqI5vio;TmLb^k#Uoq#o{n6Y1-WLr|2?PEGCFz)x3){QmT*Q*x0gep{i? z5?dFZMadwp#}XJSY9I3%lodRzKJ{1p0pQp_%pBY!5&@bG?YWodQh379g&YEtqL!1D zB~GZ*fn@PB1Q5t(J|~GzWg{(<)xEme`xp?<{Jaa|8zsE+9x0SyLCoanq#t^RdqBCH zJ6)_U5Xm;`r!qGQ8Wq*G?}1-S+%;`f^O2@JeMCi~-UdQpzo^Sgz87k2A0vdavGbr3 zS%ush#2xuE>;O^95QUN9=LA#PWBA__S2r~|q8*X!;*{G8$P5}K(;j8JT&x|ib4Vlw z1%)N}@1s66d`Mag3=f9XxK9rpqOi3-DqLGlZ33?5!Kp@#gPwJzAqPK3>Qi$QB#iVX%5=s zrbR5sOubz-KtpT|{v@Ral!9j=7J!H?I5&5Oev>mMN*p_OjE}FewY6Za8^KVq{XJMA zx{u1Q9Q3Y+ctF+{NPuX#4G3lvrAgFyM4%eNryf3hnk^X9wIJg`YxFlL4I~gSu(QD% zIZXD*1RrJhIYSHOaWOz&0K}dQ|2rieFRM z#UOE45QfvPJu*U%6yBERf_=HsPfbIQga}SR52QqRbU?_x5cl^E6Ru zx%cp)W^og0xnu@S_4&m`5n*Ar7B6H!{LbG~dJvBjCo2i|6MV-C^05sYHu%nrjEtcE zgO+F_0>%^cUvI`2@;l3gWw*39g-?tBr(ERe{@Svb+1i{9}#+KZRCMG#3B%VKCBXD8~DLLUZ zfbx_}gTuq!q2d6QjNRUZauoswW*|Uq!RFio2oNgawSsY=o%8RjKa$ClD47%G;tSb@ zpcAm4tU3zS8Ivb%T%RFhLoH@)7R9pyod z2XsLPGOp5c5MAG3m|1=)gI%g{}S!vz*1C#@kR z&C1EkBT%+O=;I+kxAjOk^z}zS5zti|0pJRer0m0f#Qc@BPNS!V@hJ%DU3tCagj7sOP zmJFpM|2`~zpS6YpuW-}0eR%%6H1>KQdMOEsuG{a`6J5r_i~fU1ve7%Tbzisiq)7z5~_IaoZz+j z36ugMPfuR?3i#(d+)U`8!>l`)c0fG(WRMxN)Bw;VXFjyIwcWXW+u~<5UNpoM1QE!l zIHh*i&#wlK4yXGO<3j;pup!7F=xJ%8HGzCL<$jbUJP@430elQd?}Vs#tW-W2G9hHy z$XY%n2Q$iDyl)W$(tGy=2Yfd~fvLPIei<|#9v8Pjw|W_j4;FQK@Ocy_iJ&uIeH+@` z=<#Xbl2H%xWd|Yeef-XyJ41Jy&Md6B4MVvB>%;#R!EA{GKl%S4Dy78fM$kR_Q8rbt zthS+rxT~v-gWbi|RsD+%arhV@fcW1Lrl;Ubf5FCZenuQrhIn5fABRz`qg@RS4b=uv z!XYES=b@$&nO;#5Xu#(3a;Yx=;k%hQs-f!|F9I(NTEcab30hWkw*nahf_HXyf)lE# zIseR&kmVwULp;EHT1Kq4wY9}r1yBXTgW&854Gj%UQvgviiB+ALVD;G%{1_Z+6mfNd zaf8FfAll!e&;W3yoSd9+rnqpRYWkXW8~GrlIih&R6m^#xV7(9T-$w}Ac0+4}uR-+{ z+X_{Blw0xXdw~oK3JSV|o<80Ch7q^*dHUOhWzUk(r%%yMiU;xe7$qSAOl4wX0!M;N zR@V0%Q&w(nE^5g6`6$=cVZ4c|@v0;d)=8vZ-+!kkzrNhs)`k}dvOjXcI-cUHt?kht z3g^me>*_qN5m$9-kTg0XPVGxiM1Bwku}9cV$gmG3U9wFv+l+faUHv}2fP$goA4NKU zsM+|^pD!U@ryBsk@b*njO*Ihy<5ZFgzx?e-kemq$47C3>X?ijf#Zf$N7?HMicY=s{ta+fFsSS`%aAIFH{NClZOD>aPnSH9Y|p6>Wz! z6Kv&-rVEsOAbd7Rq2(>hiD1Cc1w8e4vN}o0ijtP+j1q{7rEWiNG&Kk}Ag+U=W8Qn^ z^5x;t(e!MZKlfp|^?y&24xFBMxkW*VnBEH%vwPPr)rGGpwPUr|w}*>3zrf4p!g1fk zl|;LBrUTx14zPQ4bX2|55;e{Z0>78|x+mDbtH^B#;8GqP9ZgKqW{3j(M?*TUwzjsu z-dSL~%lNwQ z_&tPGwvd|L!#trQ({M)V{~Ton zn|kx^9igy-08kXp5%Iq<)X373kiKB~+S}VhoZWxl4yozDe{YrlHa7iT22Vj0ov>9A zWMFTdWnK~ht+vsV9_V-35%OOL*fB!xv1oS~8$xD5X zi7It93!4kS3px@Ah8P&!!IN?*Q)>jt$l##%wWAn;$;)eRZ=ar?4hS>d{+r<=N%G@> z#6LIL%Z2GC;5^Wr0TCalFt!gpy}gi?QLiol_tN;B*|>4z*w`3Ir7Jg?Oi7*uR5qkO+?0wNI98lE0-C~$dEd=>#EB_6JP|Mne4 zuRS*n3;TVg5bh4u2tqM|_pHOzL=6ooGE<@r2}_RiS_+4D;75bcR&6GJDXHS`*mcP{ z#28_SbK&}iuh!vn2u9}Vu|rV9+^vC$NRFSwa1m1Dj=xjoqm7{-TSR~GkM8bj_P$3*T^kj(M zb3cE&LePLWHq&v~p%!fy92|08`naL!;=jgly*?;X%^oLpO@Zf44M8i$q(0;)pgSOt zC-x8%mDJg@n>KEQo|z9Nf7h!zI&BygIg2D7vXA|R+6gWw3M@kL-qjB8R!>xW8$ znORCo3TmtmZEXt>edt2~mYi%Ml=~BDCdSj{aj>Jb3Ze8#&1JRoBSaUcQrv%g!{P9; zV-t=t{JVAqB5obP`L!rhvISaJ6A=e4o+Op8DTeS188D$o zh6)I*CAcmSEolCr8u_vScnQ_3M*i~qiVC3HAt!lO#>d1uuA?0y=yKvxvqHGXw4Ul2 zbb(>9)Lh4Rr6Xc-GcukM3d;lK0M?e4Zc=s+iKMQjto#`tx?4bwpP!#l0>XD}I1JNA zN=_VaO>hrKj)XKqD=2{Z_c*=%ye8-7deSJC42c}S{+YYWL7(XZK`UL{-5`@GF#7|J z>J2CEC3@`y6+yJ%sy0F5QEl!BjCvXAxv*TyZfH0wur)WhM=&ul-MVuJpg}eNBewN! z3nW_G8N{j8K$gxH{=|Z#hUbDTR@TXgosF#x_y*Y5p^1#NnwlHz8d0*>=QOz3*j19| zVvpAV=0?~nB0YT+YYS!W(Ii8#TO1ep3-k`q(z@a{ z5R4%6#YIrx0Ds!rMCJ)v7d#QaHFHtKX9zj{M};&eEe z1FKL**$>4KT74iTQB_slv}u#O`yv!4(~zFm*T0w^gdz-H8U`qjWDA$PJpb|IM_1Hx zz+@sTL#os`beg)ls;a>DhqQ~!Ja!KxAH+o_gN1|n096460_$SPLjynn23!q^&a*xW z3E2aaK`#a-DEHUz2ZW%f4@aVajn#}_2iwj;6LJ}YmEJ*-h5S}6lLwk9l>p0f46r%| zSBLKoQbRA%Ph>iSL))$1)LSBHZK}%JE#qf@f}JI+0wqf9Etk{{|V=tqlC6%r; zr?sLRr+Ux_7+!rFG4VsHs0pAI{a+|xp+vPIA4wH1gVJl-OYd~B8v!xU&@d!=3yv=v zG&zxNni%hWd=9ijPj@#D6psE+*jQN!WQvpus@)f_Jb&)AKskdDOal0q&3A9X0{0FQ zNdU+a2ts4N_?%;6RuJ*-Vy?5^mjGH*V0pvgE=gYIUY_HIuiE%Pe+L-c$PIh#E zyoM2O2#g9*;+#}IY3b=G1Hx?DQQJyqSP+IEBhqrn4)KmvQ#t?FU zh-{`%)7quI?z&wT5HKw1iC);#r)US(d*L(omb@u?{XG}zA{Oq;x7q2WqE z+3N~KD0t>N@$DpLu|S`MwgC_w2&+hbp@_gJ`WNHGN#h6_KK*n?6MIILs(S7z4CJg^bxpnJ4=#nv}%EZDVc_kaW8%=+z zcCAR(u!(3^g#jH9B+vtRW0IryU<>4)Mn*=mT-ajy@Z|wD&@>S>8|2IeT_-adA6JzqsKrYZr|6Cd!EDbILWuLCql{q*fVv7C>jdOxm1;(8cW7DxB<*OoW=>K)VoJXuE218`V#l=ltM}h@f z^dDcy`#uY|5xvt}7@q?C3A#-uIblj>uQSF!me;_vQhr$x;}*Qj_Ue^h3w;@OZjWwA zu%YaIE26X6k_SjyNdwy#8fOW+o_pOjUCaPxgboF%5w?}aH5+2(m0rZg#!?HMJZc6! zJ3OrW;-eVOwzqp!gLCYccgykt zUA!C)yKwRg!bsXE0UHBMhh9)c3Xb;VEXQGu-la2${b(#x2G^kl${D#<08I&8IBTA(+y_<%-KEuV#c-ii+&fS1($;&psbApxd485a?lh1CV92oX!x(9#kF{Rixn z8mh(~ntSPUEPNeALSUyX!_?UG06NeYkSa9)9CAAJjpZn$1fzB}gg#X53Mmu~5<6Dk zK+XVVW?E9xIi>uZ9JD@XNACrVsqYROqIrM3+{hLrf37YiiLDHo4&Zjcd0kyy|11r# z{FJ-3bak;!!A^n=2&e($s}cMz+OfzcwBGrou`c?tuhTui0m+i*?FvK>9wbPq>hV5J zp?S1M5tS8iW7*mE8*+XpvAZQro1Y^m-QDH0fI2173RLL4&*uyyhTQ{7;;Ycb0GfMC zJw4~+kPrca!Jrq&Lq)Ji0U;b54MwuD=|JWSBrVG|Ix<4QR8;NGH@H zF8i#7iSnD zc#Q$iP*C8CMpn2XTqrybR~X{_V5ATKhqm{Q=X!tt$6uXjmyT7GhLEkyY)({W$Q}_X ztCX#*G!Z3x%gCNlk$d{fY9d*e zM{QG7gyRIrrdl!=GA#%s=CDEVAgVKX$kt5Xhcp*TIo~9l*MQF^8*fSUIn07M)8=UT z`t2Ku;&@6}B3!Ki^5%e0e(!#al2BzIyQJhr5L1Be(CP55Cle^08DJ& z7LYa;Cc5kOeym&s!;8F+pN&nyy>!=hvy&$w%6)`o9|OCDR&-+#!x;<^>YujlMm;Df zScro&RoFmZ{}HrkVGp5qA{ZRnpm8p&IosdYhw?)f`&?@_WC&%v)fU~F`g$T3Qqsyn zGaIsuQMi>jaDT!|0GRFu4e>ZD!Uh`Sz{5-~o? zS&8rK$fM=fa&mGyemp+09d3~;SFS9dNDcxXpV77PZGF8xPCVZ$x&;v1;Wp0zNkIFV zwoJ=@3?K%FhorSJJrElgPfWdJw672F9uL}>fYJUdO`Q2a%kbt&7?W$?M~i60TlBVc}S`{s23`9=hl8*C}+w44o*hTv|lWi9rdm{s-Gr zel{F{+C%c0_M_X27Dxj8x=rIH;c?mGBDv~ytU>It&)x?HkAsP4pwGYL@ndjL1VPjd zVgSSg<#00AobNUK{tI7fnX1eoCb@T_7;M!#ZgA)0Q&X*0e^b&8sZKjSE-f9-qEpOU zp9KyC4MN6H5ugMFvInI&!U^hRAnh@Xy^&5?rDrkr0wn?&rD(+~An+Z)tlq%H(J*A% zlZgG0L>oRdA*s9^^+hxmI5a$ep8QEt^O&k?Es9QRMLz{Ww2lEpijZk*ZFND#1V#u) z*OsnzIN(u|9lCz$+V$(}FASm;4j4sysk80mmcRWJ*jHPMS~s3Vs6yqCx&gE{+B8ze z!%eCXUD1vP+39C3UFbr|af~2JHHl>w(KCP)M`!AovX+`A0_M;0@zBqns8*s+{e@TO zfz9^Gl!jS&5Rxv&fU4A+V*B>Ze7#?fr0RQbW!}7#yuKP6EwJG=&uoyKU7Q_H1%w#Q z0&yyqX{toN;>`7p)%> zSg)CntG|_kfoERBVn? zvlo5v1V`7(FgNMvS&2xUr;`hSj>Jv=EhpHu;QKluH!pycrK?%1XiPaVc+d$6)>>42^6&|yI^DK?~@Eic2#jDTBD33>+2W2(&z?J%)~kIM6WBc_G#H`h4kb z6_Q&sxR1oP+X5=UT5U0S+Spi#V6}VqZvQF-ZmoN;v)~*kMRTx-1ivmXBQmO46ukab zFlCt3+nkM#&c7zNS%HN^*-o9Ooi5dm6rc!ax91h34baj=br(2=Y1(b*o>D~PTxC~wccGXD;D~N6d9LU*ibYHW- zeE**JctiG#q}Ar4>3o9iV(i)Pfkl_+(-Yy7c!8YdQ{P`BXnjbRyX zuE`OC2FCLMG@@OK(Z?#t(elE)08GQ&XIveS9ZpV^M!LvrT^@63f(Wd33kxp9A7w7R zFh{^)mVQW&em<1M4OYqskyIx{qEzoWq(HL2i%jbP2A%v?vK3itm?;et($3cJ5-WYU z<$rAKeD>@g?l1zaU{w#RM%45pcva%CMGlkk{2@V@x$K=Yf|1@M2!ZCo9JAp}9{S}` zff}j*;YFgMVuQ3{yR(V45s328)SUqBar~5x=8n^p0nP{Klu>mVEKoV1;5CI8P%cJa zYr)X1rP0UVZ5n)7Az$sE(B}eNylYJs6EiceL1%$M_(Ck8*nlbk+WriF3XM3j0vQoL zuU(V4$nFShd|>gTZ5mui{1xJ&^fpjOOry(KCngcIjI z{tA5n0Ho+#A#|aAW7bF+x@1~?Hw&mDkc}|QtBvSvS$h1vqRGl%xtfdr+tzK{P=y_A zG8ne^KUP-pq2^#+e)PD~as)AILdScG|v41q_a&4NV`-*otTcL?r@e z29mVkEJdd2PX`O%YnxaufQgCwdXnS7%A$7g`NT^EnJ*_JA1^Nf6aU_G2nb)F>C#*q zSp{ycIH++eZC2Z~(=XSNDlL;=^!9H?$VU_l)dA&q-X}@-WSq8ZHk}DQ!iL3YD1aLO zqCETl{hGAX5Y9z1=a>^ zs$REnW7v(t!-`5VXHOVK<0@xDlgYETHX^7%kOrM$4Wk^?Ul_F)6Z0N?B;*?44XjR_ zxO-p`xUq!NkpP7x)Y@yg2@MZz*X|>kT!ivKq=wWoLrN2qb5cmon#jECk6Fg$PAnNNAqQ z*kFmMsHyQ?)B`_sb{)EO-xE<>;C*Olz9z&s%LYep+t z)pujO7aV?0Oh!1qD4DBW;K6Nkgd;@B^q`)==yJWv&}qOYBXe8>4*dKzzTB5DFnfNr zRILen*LF9yU!Ff=F9!zzjGwqBLNJ4(LlHJX91h+;;FFa#)jOIIITJA+WCbl#P(Z-N z?wKoBu0Wh9A1oxdqIom-a~s)(3CK-@B?#ly1LNS-UjM^yV-3(mx1qa54<07D&b50qzkC@tG#gM)Xc+u^jrKf|)>EX?1yUj-S! z&kEQBRwhD@7t$HhMGxcyD%GC);-Vtn?}u+FBtgRhU}$Z{58!gIZZai1%p+V-edoVl zT>K4cA@K)m&Hxla5}tK40GJc2l$7z_1xkw#t~Rj|K@5LCL%jRR6ElcMk?C`1kV*!) zvX9(VVS0T~*1GOia! zamu|WWvx(SAQ{$qsffiGgG_%Ixy0h)qHhysXUB*U>#9Ud1^|;R;65y${5BPex6;xEOs{AI6J@Gx4B+i zDOD@b{G=hEa4qkAusO5NTGl48w?fJR_Nl=SgH=ql2_6Ks zkbVIrTR#}+NvDGcpL;Kim`Z$I4#*QpU@)uSUnbV)FJ5RrX=;(R7ly9GSdxo?P>W`P zF4wYFp?84HzXg=5hUtSgEzmu`$09?mYOG!!MhCB9fG<`4#{4v!HtFubYf+M~YH2AZ z+LQL}6m1UVxLdR#b`Umv3Ksg^4{w*#um|F`HL|ogbxIe+O_j2jS4Bm1hMV1TeWgY4 z)H5Ck1cp1cg;A0nKTqH*jg2_l!?q>B+z(euQts{6`1||c>p@tBLjiflPrNd0;U=7zM*Cm64&PQ_9fGu#084D?uTK^(mo)vQERqy$ zQ4oMgpLMcftskhnIiSiH2WdkRgDQW-`bBe%H1#-tcPIC+`*mlh`S$FbVKWnbIky8&aKND zioK^K;$T76D+{uccUmYhT|oijz-uf2z;9+|hWB2J6EMP+d=o|E1nwfySPG2*s;V~u zL6^wPp%@sZc~DeDf2D;gf>V~nbX3q`Bap8FSigYVqCRb@yBB;4l28Bdjyy7Ji8~Q{ z7|F*^Ax`Ohu3t+@?;9F&$9uX{gQy!g6{#JRzy5(uQ!`-`A+T|ng&rRr8po3v@J-2X zT#Ny5P|yw!7p7%oyqEVPaz}_we0+SE_iDly-yj02>Ss@%o&Y#OM$KaT5A8w1QD)o5 zf5(R0NhXGNiuG#;!rc#vT8WIYXgM?ED)-l;b4O*0NzR& z7t#vZX#D_BB&NI3T7d(CC8(JkwVmo3N?08R87I1Bc*r;mQC;VS`~cWOcxnO)QNCmO zC*RPj=^b$Va`RIv+5zI?ObiSgLvziIba)o4?w!T;==_BqJGm%iaWF0)g;E2Q!=b)T8WNzRgpRsY5A!i5(IUT4AMAB4J z8<9%z=%VG{DBP>)*vo~MqJRvYoO7hIpGZ1X#zsZyufv8x?u=aKF)CK?#h(et96^P(~qT2J-V9EnV!Kr%s<~HF#Y=5&H=IwbU0)!PT-W5Mw5A;7B3^u4vI-I(&g8&2=s}C8C-em zR>)n=fTaN7H`qD4ohR+!a%yI0&g6_YKSPf%QjXA9bVx1u+iwmBhCa7J^$PvxyVC2h z^zvL%ncYWDb!4^j{m}2WjtyVoIdg@S@`Z z-2R;!BwOHj%Fnmry#gyv)j#j-%mSx|k(ZFO^(`)8g0R*OL=KMj5Iewn;HEu(Uf)ft zc<9hNLuLslE2Lx1QAX;*%JJ55_9SO%ez89h|H6lp4#5-43sJBtNtMG0I5J=dUS3|O znQYYkqi7atG62O3!13zUtL$MQ0Z>mtIb_xL65SbFF!xa(qXT~eZ97|Rqbi==ingn7 z;r;;uJdTocA3>{Qz-d)Ajt2yDl*oD**skw-{(OTy2KP;*BXlOfOA1W23OC77xVh($ z@ll#jReIF8V(8Tdq^aGSvo=aKn4cv+ihXa`Keiystd%#D8`V!tf`oWI4Dz# zI{5kVqrEv~k=`>~9w3W53vB^Tideizbkoupv`4fhul9&Lahh8|F5>MF!6#0jgS zw}>Ppm~fKG5D*iM^kf#G*k8{LUOt*1$$k+SI~M1Zog{?%k8g3rOGg+p(qvg7$4)c{ zaAGwni3c`?Zv_TLmZfSY^dqP$^bHJ#@|nO)V7DOo=k8TjQ2~`MiB1Vvmw~nc@qxJ; zXbDtjnIceSxoHt_E_7DbMvJKIYa_D_p2#gYqYfl; z5XBPXMRX6t2rH}PHJ%-@6p+{CWduZPxS(zmijc9E8`Ve?=Pu9_ABmENBZRB>PEEh1 zP3~VJD7CAqQYZGuu2@H@LXkZE-FwL>d43d4`uvbk#wrU);y!S~5!pXC-E1C)ycc8R z;y39e6L)I4jf4VP{*;b^?%PB}h0txyV|}1A#|_6bN&ubH#aOl|`z(Rc5Vq}PSZrJz zv=Qwf=L0**L62{wt^(5snXeVpHr$^z*de3<>{E=Bwcn>1X&rIt4Mbgo*0fAxTOjxF{MeW0()T{29Ea zz&eX)1?3nXZkB@_T8r@ba7X&6NKhdWIY-a(2S2;L{Qwj^Kwd%OJsk~lz z&QKYHBxDgYkwLJ*sXtZv7zd50h)B+BFG}Ja@B!}uH{c)WX<%S51NktHUo=>QC&HhK zTD^Pn>eXqqWC9;!=G%rN!9*6ZNdnIeD+Pc3P<$E`p#ThJV`+Bo56X0R)OzR9J{kAcVL)m8x=e zq@bwQ?wePw>ozq zIM0nLJW3Md0gfY1Vzp%<4}fh6Ko2oNL73hu!I=@+Hqyheh+#BPAc11wL77cZ0tsgu z99$bVZbV4yeYE1%4iR9kz#CEOX}U)MSCC_e9u#B7@bMHIUqVSA2v_D=v-9L$hySQJ zDJ<}hg-mk+sj&XsTYcug1FDb`;&Al4`*F!z5k65ahS@^@>oSp=KDOLtf~v0aJPU3L zq#P7MngKg(La|ifM<572*{m)~CiwMk(}N$0!J~6`!MQ;hzNei*;P-)7Rk>-+8Y`?- z^$Yxa0c#RfR8|ZkG!Qr&k@5Ap>D#OP`P8n z8k~;Zx~%LiMvegIzgmbdM!Y~p0}Sf~GHzS`Pu*_xvY>u|oY+dN9O7j_BjkVx4%TF^ zPbv>aSzJBmvtp&w_)GctvN%*=CH5IPR4UDb!ouOEw0iQ~zkOpE!PAIE zN&%Wnd&kF2tqxg>s^4uQPYUxsfBiZS$F(YdB*_@d)}cqrY%ub)f7Ex0UASd@psHHH z!iKmH{h|#>N@!_m3n7wlbPT+C6G%E(63|p|U~2~C1ERsu(6His2Ocl%i|+CU{%)R7 zM}PrtGl%p8JQZYRT3T8lf}n!S$^Eq~ z;(@{i$g05{ zk+W0H)!EtPfnC&Dqs*IZ}`=e=;v4tH=3{b%YHu zPVvuCPjN~F6AX7cx6k{4CF8W!mRq*Lg6fxOdeg7r$}nCxbvztq2LE<2Q0VKTgkHV7 zz2($$Jdsm-Vap28hva!EZMp3YjEwZo2`&Gs#1}Z($T9z&|M#z+I7p;TzdwnGW>ASj zS(fqNSFim^>hV;O(*TwqB0<1)&+or6r|`cEf70CSgmVv2H`bpTTqpMJv-<5xgtw@q z`a74A`#*o73WaD>)3Ie$IDVYA7Okh)zgP0_ztR`~Yjn|3ni}o4Hswu^JZ&IgXtm$d zuu2NhJo%(g{r6pXQPE_mNQDvkf=}E3UK&M%<+e6?O&V|hkZI(v%G}5%&n&&@&&I|7 z(RM%CjDJzIz5dVZq)twEapmSMS zE5nHt(7HLL7pHSS=-MrRPM+*TDG3$WoZipi=79OZN02uN94 zRda^i2X1d}0~_ngmEB8w8ApDtTnPc!i#Fnegku6;-kRbW9B9Uaf~yxzrvRX8P8RxG zA5D#oh3@2KC>ts3G#37-_Q{WXc057_#cA#bbq#m);I}=0{+U2oN4ZdGqR;gD#%(}4 zYVryi>*A$<;oO|NK(;tH<-h$4ewPfoX?0zn?gU9J+~Ip+jMZ<1Ee$8Tw}33Vy1OxZ zBUS1H&=lf*HangEkD1vA?7YcwEBr9+tq1?lYVmRSh?!Y{v%d3ZZUwB`Z{9K|>;*ez zFfQ8JP!^K^2BCiL+&NAz7@HB6I$DGWXZiUb2)x%eFnxlyQU04ha1Ty?{CTOgDVoZYmdLy4ojRI z$Zw3#Gt|)dfm4uM_a2xeASq(%U8r*~4upu4`>|Bu2j1WrpbVfBW5`Wc(BM3gvy}T2 zq~5J?cX62;|6b@iO8cgty{b!gN!YHjeKIOB%%fD1EB-aE;82i!WfRn#V>iRK@mnJ~{aFnVyxzMG?_cD{0lgCnvE#iQ|k z36;BO{Wf5(Nu+2?m%Wn#t8HOuXsvT)->Oy0pFdMSOvyX0WLl0?JBe3#Gi$<(RE9uM&av`J%DW>D@&}p?ljz zt+m4U{w2q*X&s_7r}MJNbZnw}%Fh!t0vx`~tLp2I#vzvq0yM5EB|8BfmBd-~FkfF^ z>}s?}F_=Oj28fH$Aokt2)ej6cRna^HJ%GBrDDslI=LU1goU zqD`|OuZYOS>G;@{tLxHKAB-pLML(0Hx$$X%p@0bXh?j8^65FM<9JmB{YmFByTBj&g zG{;gwDL2knzLs_=o2s|n7#k~Wa~#jSZyZ0QDS|H|^8J(c!Ms937wdbz;&;FDvg()3ySZDO!buaoD(7|9TJ1K^ zA=qM4hr!7rL%glK+hl(>BOP7x{RhYGF>MF_1iU52fLKu7tR?20+7|Qqz$oW;LM!U> zB&c@dwv&EJ)vouw-tU6IG*Ym`yi>cN#J%bviNMvyr;m|e=#0el73{If6&MsgVD)Ep*Q`Iiikn5g^(CN?T9-} zJpB6{<__@9&!5>L-4wyA14S|ij0*G@EIb~Lx}SclYi3K$n9{ji5C|pIpS!^t{vR)z={;4IQDw9cVD+6L-zc*k7@-o6_ojMr@%^7t)Wu9VzAI zJNYb+@18o>_m{WhVkC5y|YlWdG%#>=Fv?P7)b z)ltKiesq_YFS)cN$}$mu?+krN#f7i~PLZD;>$17H6wkSBD}8iixH)*BU&+E^eXcRz zjT@U6reD4BUzeOz_;7Xl&%BlHXU{fX$JdS@^Th9X*U|Chjgq+qpNa~PzZb9C5e@Nu zvcfPC)qc#fg7wX|o&X-tN$&IQ=Z1c6$=hZI70RV!(f|~NxeS=;_MivRISv58;JF`( z!53p16V^}|615bQGn2+=sC45OT4F(%esJpRwyzIZ8X2{XBjoiS3AP4&S0HViix%vfEs zUUJ5lhtIsfC!79O4|O;6!|SRIn_k9Y>aD^#3I zJ1f0 zwD99f^5YZD-dd2v>ibutnOX|BE-#Q9Cwu-EeMO$|NvudPS zy>6d3uD-s-gToE*w5C2E>;k#@)?xH1x+B`bw0`UdWdp|Aw!9xt+Dw*Ea6aSez)8oi z)7@0bTsS)1$2B`pZ&b#PuO}`~$o5z)@iNivt}i*dvp@7rJZyCMOux*}jU;Vzm>SjD zOMiY>#DMQ?kK_Z%>92Pa;9)QSX?VCRD`NWX&hqeUN_`FuS=3@@qtDT2>{^30M*pSW z_}Zp1X^+<#Mh{snPGbaTZ||nkz-?(ucfPy{DRUIKA=77C8e4KU&a1n`)xLMn$I?R^5%S94IT1Sxs}GIX6dkpT(?eRbFKA`D4MWWAOKlhFBbGXcAT^&jxy**#h8hXY@2vG#D+{j~RzpBeZJ>1Fp#-#WLD^!`yn?w-S23v+9H z7CXCQo9p+EOgeVnRcASPiZiA3ywH;OZlQ>$m&+;)Cwwd4OCE4jjk)7Gto^hlD@kws zXqHnHQ;!4&tmfX7<-4UR_?lxVTOcQ zd|@j+zxfGgxc@=x#ztO2ny*c0#KUyBae(^_x&Z5v8?4AEL0BU#{K~_KJm>(?LOa4( z31?df|7?rWbhAKD#Lrfm9zjPl2qZ|%4J`QZXxeuZq%hRh-FqqS*MRAlT8(q;g4lKg z6}uUS5hf^{VJXU~ZT{Z@iU{|8@hJb<))&a^t{P)AD(pmvw@X-m4g%5y#JL2ciBy0W933Iu4j*F<`xym;kOuD4hE=Pdbn$APVlX$Nx* zcigWyvM?c}Debs$ZQyE)V1?1{br0A2CeGLI6;RDw*y;DeVv5)KAM>}13!kDY7jmCP z>#S1qEgS?|57YUvrB+r*EUH=SC>*v;sWm+_uO5s(JCvzi=)77tO@q@5U67y+fO1OZ zVCW1GB%<8M@G4AXwnkLjPkX(k>;Kypmn$Y>&9DhAg7 zdt#34Q^PqTLsUxbDXxViY+iT7Px@279?3n?G!AS9W@K zYAoyY!VlB2=yg}|5)|Xb4B6V=-4*{eJ-{NFYg8-gX!y>GDnr}pz9!mFr6!(gu``EGJq#_Wl zqZg#3dOBGrX6fa1h;O(tFTrXD`F(XU3vbawFci6>>>^Mp4oT;cg3ZF#1vI_zj-9M!bk4i7(8@75s z^y5}%G1UQ8oUdwN-r(KGeU>vw!#c5pzao>Gv*thy(n#&Xqni%bp9Y-W~UXk-Mb zzg_@>>>3{*$_O7xQ-A#j%Iu39Nf!$&?Y(;{a2-#Dkhh7NP3CL}rgWcoavJXG9@OOL zzt$V;{Utj+$*QCAiiNOc7*EYe8_%Qj#~!8!1u}Hst9#A+h+F1$i{%G7i-?QLICPqe zRi@$-oCByM@*+jIQQ8?q^?2R`8zT}}tPUobKCl>wL;(GV0ZY=ayK4Ds7CtzTFlxII zkr=$T^j(O}Lg>2xKs-97uCCY%6Zvl%sh5oJzm^(56v)Qm!OXXRMm>;YY1~q?t|ZNG zsgc{G@=!=@wowC3dB15Xr&YXkh&G4g*O^Rfo^`&7ZnLII`O$M@7HWpk&Cj3riLZFV z+4^2Lu*}fF8I?MK^#JuHm==R}0oHB;Hq^w#1dRLHo-)ABi!)@f5{- zTEb$BR9Y7FU8Om7we$j=nW@(I#hWwsz&&SPdUgxHKliu~B_l0u`O2+&j4RViomHL& zdr*|AR{K2D2fm9suR$$SDYX$qiCVl=2^h+xm}0|kxDQY&I~W*r^rhf53V|1Z<_)s{ z6m3O>0>z2hZCrq%OEB!Rem;ui&&)ukd<3mf|3LM?JDv(GgJ>c|k zl+fMgHoeMp^&`Tk)hYQq?Q&1agB3>1jE(hxR{Ed|i=(qM(Xmcm2dSSnynPEyI~Iu> zrQ6%%`Lup1-b;&sAGe}49@n#O1X#!-dP)#(>>3YBSwLX`@RF-+yC4~Id9XHt5m%c^ zoUJ%76BfNBon|GKJbFk4KAs{slDNUqZHS%cZEi~@9c}ae{h7b^XFhh-d3bifv?SYW zvu%fBN_?wyjn=|oa5`(2mSU#1;rXgZ2Q;hh>hd{qgkEqT+9LAiXl)c-YJ^q>eA@3u zg`iJRKZPlDHT(UX=rZdNTK%dW+v%^w`>lO2`iYv~v_c9R(SxI8+A4+!rxP6n+RwHe z$5{;iTvi}M_jOejR4II@Y%pjcW>gzom6A?5-NQf!WRpnQ8IMEvcLs;m7YL^)K8jC#%?(m{#&f68oby;-`#j@`;baeD{8Kf$= zn(XIO(|)7>K+2-SVSU~>TSp;XQjrzx-DhK3N$LO|fN^Mb{ta9^V+U=v6KB(_On~YF zri-;Z-2q+`$?|RsOTiA{3P3z#;^G#H?j$7aWMo7SCu_*BH+6NGJ6DHZkP86CFeGPC zM|&NSO!t#*;8T*Rz*ruzP5EkU`Nd;nfqS# z6!n<1pG;q~(|#29rY`0&EFX+~QP<)>lA(?KmFMk}Y^Sd;tv)qHSrwD-q)+B3%R#Bg zq`m?UuO`!i7-S1I@}atS;3~TF4dH{};ZgQx?(R1em{%n)m?-WB4QI`j5zO4n4*ZOaOAZSj_`3ZrYh7G@YG*(YSEW$U7WKo2e zH80JYbLpA~Z@*D~AiqB3g4+%5&e|Q{=mP&}0XFdD?hR4uGA?GSkEJp^F{wPzQ=apP zlM>3XeU;6)U3hf0Dmd}d2m8^bJ-^JQp72}`TgU*$AvpG(hg+b zQ&M?2>aR>QdH;lnrprdkS7U?jiJBgL?z~g7q$I<1rRVyvDPRcs z1#4JC@clPgoYCJ7UBa^((z|NkV?2EgVxE|;r@wc)R`I^I`iVIPhhN6(OPR*Nzl?eo*H!%!G=WKIP)jfg4c z(u@S1h(p|FJ8koFaESF0_Z2gH!sqxb9qmcr9XO`M)i{pgS}AzOdB3;9#6YlE3KQ!_L#W zUfr8JegCRMPi-h0twFI(eYXB%-KWL2r3^X~CUi(`jrNN!rRZj%|Ht1$i9h1v;MHq) zYhufy%h_9~`2oyn-cHs z$~%@GoK@8A&Kn;(R-fITsqXerN@DE3-Fc_3?=MaR7L1YFrEkNNAi9u+ zufwGn;$6}Yhk?c%^l!a-#)n^**tM(_@ZWR(gFT_#e5%sTEfg7EC2rE%X-{{r@sy=O z(w?l4Iky@cZM|1g$UYYruh)z$s7CTFJ)_oC%-wR~SP|{3&+GV)H>ccFmX=o2*W7%? zmnYc$bp5dINgZy<>0!%GI|@Z|LfGTfk&|_2Eo_dy=^y_+)4C|%0l63k!kME%QDA#a zc(^F^1%Zc->BC-vcp>*JfN#V()0U&1rHdn>F6AZ3VS;{){iZxA8vo#}D>C45a;Q&g zNQHH471>3hLST_dsjI&8sFYb!r>DUp$^W^EfFJibhZ>th$%Dki{3M+WhfVXJBs?Mu zYNW$Z)luuQI`f?U{E`;QQQ={+8yxF$JKPVcwLvg|sVYQip&x}7_T$HU09&Ni_r4L4 zkSImQf>+zdhj&eerqpk1M6+ze0z^@cs5a{cS@1yn|A~?|#lOiXUo85Cf~Ml9uXfL* zI_KxRDuWD@lJ9)zx_>=A3HR2ym|XCa-nde8GXpz!^)@j^9X25R^72XMFUReR>qi&O z6U!`KDK4We?;i@C;HJoN(T?#NJ+z>*xBrg$s5ksE1j!DK!!Ue?b>N;0ToMv<;3w^2 zVS_WnX#^*R$VpjIT*?#u|0t!<&3@Ol+~)NkCMl_lo`^@#72)Dyw5cABy?oiSLT32N zY2OP>y}aj{-kA=6JTW5`1;=jwHz(D*ONV=hBy3KOetcfa;B062^3J~gfdr~xHFB(vGMg+#Krufhj6B**`h1{mJ3T65R=m+1tftzwZ22eu!3B?dLI(mCr6P~uT zOhS%=^O`#&eEDVn$4)BW$WD5w{3zAvjil$xb|r^R+h6OK);g#eBwW5+^X_g^ya<{{ zgera*mcDo*zAh=L&~=fGTjGKBo*WJNrBi7^EG#yy8r`=V_)hUI>GnJ-aLkLpCKg{} z+Yaw88yD+VypHaIjJsvp{!=>(};%B*pbv&e+L5 z4uO9x6n$t}?df-A(kmSLGVk#22UFn#J0f%KN7(wf9eiZr*CO%C5;+)~^b>g^rtIvl#2N9K&cnNg|pI;`in zNwz)#Es+m->;rTZ6JUplDgP|YDnMLdp3uouf_GE?h{5F6o*(WqsEnL@Pe(bQgAs4h62ryg04k zyn3mWf1%f{hx6h7WQMvJKmSfZ`|<}HE9VSlr>APsM-`Q&o8~c|kr=V$IJ9)_vUj)n z%fEnc9=f^pJP2}Rvlq6W8+AeTj9Bja=>-|#K|4s;rGCc$xKVb6hAB%Vti!KvIR`}F zym{q#dAF=vuXA$@?cu`n*2yZucyOtIC6C%a|>Tr(bY%gA{+w| zmuCmw`VAY%&WlYC?tfTdFFyT^=jUXDrxMGSh~mlXppODn@4Lf`{q@OulcO9z4VlB= zEr^^s-Xr83$g#)H`fZOV|H12zHf-PZbJFum)x<>X!fL_rD;WXGi*LB~rXq!YQKJdN zM|x~(DjuLD>a|-el8msxf>r_|7Ie>{t5FA|%i7wAlQS5~AsnIT772_N=jN_fmbNDn z{c9U_qe*rwdiW1n$+?^M>a&Fqzl;RG>8(v{d(-OTLrQGIlALV~0+~07*dFOy=d5*S zt|588>GV4Pxqa;!Mi09VUU%ZbYj|<#awwf_xY!xi^S5ho;%;>xk8oB>J$|B>?b<|> zlsoGMMV4(bQ&rhT;*KmfEq8P>SSQCDPo-p;??_KdP7sS4iP;lZ>dNCKe;uBVr|MMW zgcXk3g*ar@p?~Nj;##Rc-d1ouaC^IKi=yw56|( zW(n9#0TXsyn~H5_=Zl1_f0zLR{97QaB~#K>8*M4)cD&leB6Yf6@T6kf-+QS1gq_Ff z<&(KSSq2_r*^(9%aPgxQZAsa)PLZH*olJ$Cc*OX`A70LY$$$EcD{y4xd&M1;_NF`h zYj*Z)_vi5ovVO+7Rt;T+IboFR7Zza-{e}#LTLmnXq0Lj^z}7iF} zB^6RWtR|5%3lG`>I*jVn7-=+qL^#wQY{ospe?ik$vJdfBy=PD9jU7u^m_ z4}D&I^GeXvw7b;eu}ihz&NP9wJaac|lC)(!t6u~q^b4L#ta);YgC^opiMwjT7m1Tc z=I*eUe7s(4U{KX7pu>$gk`iNIffF=*#^&US@0qp;d!MY z6OeS_oz{3X>~)jlV}ZnO&6p^z$#GQcN}fDie;J8CuuZdS(Mr;YtW2`FzA1=B!b(Wc zacNFyy1ufR=CiQh+Tj;$Y8OhiGeR-l)3XeBAh|eV>hE0~eBnZL<-hmh^uK#i>M8Oo zv!;9K+PnX}+w*J3d-)&Qsjw?0X&i9=2Jp-FP0rxxm#(y1jtMgCe;!5rozBu_Eamr; z!T-v2{I~J{{WCM?|In^~|3vryuW#A(0J1eRcSzykWM>d8z!g?a`>%EF?_VuPlmV8F z0P5gPq2H$hi73#Y4T778$Q(gS~C;uP!GhjyQ(B;oTYvE|3SC#RU z;NakZq8qwyxDmqPhBX+XRD5{Cs?j40GZ%n_9B6$NjH+$rBZ@A3W@P_7E3@z8KYwE2 z1coKNfpc3*YAS;}nN-P`d^(S)qNHJ z{mLN!QasgUQw-Q^&Bl!)LZn`;&QDVk87JCqpUMBO(G!z|0kT)&eBqOEgm(_O{pr3p zbOH7MzN-&cx7!iJS@QX@gqB86NQTkK>)EqWE29X&HTv?908ZjFHU_I|WoW5XRETsH zYFlWjwlgtBB_x>k{YY~!Mu*Sw3~j@2yODO>gjS4HWF7a(yZ|)Z98pmL@(_*h#cje0 zp%XqzF;1zvS;PbZQ;<-4Ph(;NBojdRppZr~^%WpFN=_*$DZ9qVC?_%P^&2+aym2G{ z1WDuN4E~cyx|{^gSFe&eJ2w{u4;ws925#-m<$Yhj8fR#wBKd)j^IBg_aU>(`VPgBT zs%oenLd-b$=3($@)2F(X6f-VJYM~)E#CTR{WzH_(ssT=jV>~cO@5;(0u(qHd4a`ko zDu#ZIb2y)&Pj1c`fUWSdz3c*zbnF5!m61~JMA7RfLY8u3(TA;MI3_;r0e~7{urShw zMpU#+lb;XtZy_aGCRIz^{KXA?4edNytigl5`6v@`i3W)l6iPNRkOwJ6ruTAYaFB5Y zw{PD@6C%7^Xa|Sk5(G(YB>b)M#XG2m&~OT<9Ef5vRcRr8_nDS>JrY{X{Z?WNV1_qal@g&4?Q+s-P8j8wa+w994U{o2U%5ecUq6O&hs;|8 zh06yIvBn`6^Y@2t6P`_H8S zHVoRkk`lTaiUC-v`JYY24WV&r{iaQ=d>7o@FkWH^OdL!iN+-eXXnEh#63Wodgq(Bu z%K#dJps%ce1OzQIBRxYM5iFP zLpz7RKLP;Ed{R=wHMqlw1UL^S2fbPa# z!c3$ZmtwROKrQXZO>B#Lh+8>2(?S$jxw65<;>8+1;Gv78Q`_^7Y$E;Jm;VG$IroPc z5x4LOhy)lc-bcy+f3YAHh)Gq{J{_39VD-Y!$s5XJ><@g<(gpE4CUS!BhASsRA#w3V zjNZ2olbH;X76uS?A-*p=2V`sg7O=uQul@t|C`yblFecQiRuKu=q3l1LYj>~;-&?d z2;z5{4a|QGwojNI$Y`CBm}my|XlWSSKG4^dIHQSW&kO@HXi?Yk5H?ZPyCQW_N^pfW z34Tw!{)_N@(t-MZ{wVLf`@{5jA8Vj*gj+lPb}Tq9G-0`*i%=^=_}m@9Ihg-61RMgR z3YS{24Do>g9gQf9D6@TcLHCF0wU+RUz)_}JWwwPmMy-k? zN=5_w10$Y|&GJQo%5JimJIn^F85qV-gmDD{EC--)hcV?0tTt#0#HX#IcKXmPh}R4) zKN;}?bdPt}kiG`A7+g)pecs!q3mX<`n6d3%xp54w7=w#cn41@i0I_?e;)&uk*?q_d z?1OV}nrB2Bnwgow^iz=_@04ospcTI*`#yYRV0qDuK z97E%KECS_g%CTVF9|3`_-X(%w=^q%_)?f41IC@J!xD}r52gnvaTz$?iovi!z z-Nc=x5q6}chaAipTp!hymC`YYwJbynRxjl`#Rv2EAQZ8IVv2{n{U16=GwTyS&%o zp+({J1f^PH*CDtdum{IAeA3(jjsX})ukv7TWhJcP)qZDIE8(%J;r%KP=QU06W_ zq{aN0jE9XS+N+`~=io($xoU-IZFJ-y{{59p3*T{JTP4Y;FpN5Zk#@l4%kq`9MIVpB z3*H4O);+$6+qZELQgz=S*l=3UUf5@xxRjJeARiK$zcGvhZ|FFZ(w8q@L~ei2N55l7 zZcIX!#$8{{u{=OiQ5`-kta;>KB5#3=3*}%A`~r(UVih&E8uOm}5McY!Xb7&mrS~@^ z`IR)gO$H!RJG^lnmm=p1#}l?NWrXD+G|mhUM`Ox74t;zujJoU>*1;nBve9eUU33ae zGjZR<4}Rv4=3BUEX6oh)fBO~}5it$Y4sjhV>Wn3OJMch|dBThqJ`;8`%~;R;G8u42 z(?-$*Y8vf(aKBg(5<}|_2OQ@f$KBe!{C+p#v8YWqrpvCwc;+vIgOT_x`Xv~+H+!NO zbji?hJdl?8?oQxeEyO!9c!(4aYiE{ZAOLa;Y8=xfR`dE8pG;TQl_+`Mt5 zcGmH7VieqkcIkz|Cm^L^u5u1$eG5v^(d`50`>AP_sK+pF)C-+GiWRWYCU)s)q8S_w zOv8XsL^CW25J25|&Ui=_=lD!qCJ(LTOi(yc48ns+M)$4;25$erV!axM;|eKAU2Ql9 z^5kCZKmseYmv_IeTIRAtE5+=d)=&1W6wZMn!*3W$u;FN?v<|`uwjG+J8kCpKEPbMo zJMPLnATAzN$#gRnx%N(CR+9%m3~RJgBj;?T*|*mB1FjRt7!vQgcxfa4h=AjPK|vK5 z#k#f*Cfa5DyjyX8<0x89P0he0X^X&)y@)9h5^gtYb5 zL;wOLcJ02CZg4Y$Ys|5Q3&GbD7zKbb1+0AyVkOB2b$|dW{g01j;Aw>}DChpMcYK7RNAO!&qI?kB;?zUPq#L>SDksyu&Y@ zRixMD;pALHLt_d77ZF69hqdFBXg{PwgT<8-AQXrIZ+%5*qI@T zged*$!IOd=_|=DRZr$CP#fw~Y=8~wpO^)tk*y1kC@#K{WTD;tFa6CXc)d4<@;Lim= zn&vH(HROAvO5qdiwhF~&3=qC7xQ7qCJeka>DRkqR%^V%Gima?2zCUg09JFb96>~Ot zu#gALt?cgVf{E2$wdj*s3R{!}$^E@WKIx-nhHu2{{Ze}ljp^82jEjesZ|84mHA7_eFb_l5;KQ@~#l<^{!qEheVt=E3B3>=bFX-Zm4g1_9 zuHx*6l!TGJoJt;PAleyS&h49Ujl@=r2Qe4Qb!>yZPJWFT^VC-~>cCW3%<&HPl>rnH zOmL?y#eq?aak=SCDDs>H?iUu;+9^$3hC3WOB{2z^+#K+SD!T-D*_I%KMumi0at6^f zN53E`Dy}^Z+AEmc7`c-CK2#Y+e<(9#FZ-7wcvH1e$VCZR2}je&%sc_@d|ROR;ASf5 z!r`p{yw!yKg};!s7{N(BDgZ>I;&^uP<#2eE8yf7T++5DbvU>&@ z>7!wT2~;<{Y*!}tVVcRn!28kwt>-_M&Dug8(K~oGjV@Z%>Co=#TddD*UXHR`NJANe zj~`3zSqv0>@HR!Zx>Zs^R$fOcM#>o?B_t{eir|3I9lrH(?yfb&^lop0hxyEc z%M3!Gy+Y-blY@hx%PP%^y4qvRROOFiva&|-=+BsG<-9|vLMBa0*otsGNlN18%714= z`J8E=eT&GV6N8*}_GjeIK@a7-8dTKOOp?{@-`wrSo56nxaB^;DKtT;p7&ZMl)Pd(+ zUBx^?94=lYq38=nnn`A1+!~Y%O+x^!QHQUTsKOOUjywe!DS{5LxP)wFc*caliicNk92Uhw`5^VqBF{PT-i zKwqvy9>?D&~;Uez1)U)S-#tp71nD|3ddd<;!1a!tXR4e_r@8|lX1;AtD z-1eu>@QEnw;3fmE+27FNp2wj9eB%CVZTKxRMx{}0{nb3Tyi+mRGGv<-%sSp!s98Kk z5ESnl9?raIa5D0-TheZ*WQy9N3*sZ3w;=@w!i4-`wv6=j6GI`zU+F+qc-h&jV!giV zLO_zU+!U$I^oIn>55)Sh{KAVK+;*gRs&Nu6^9v5~$R?r{zZ7sc#bfxDq4s{ED2#9P zbiB5$>eK(@?Y-l<-v7Vxcc-D#(n2VeL&+>7*%}ljGka7*DUy{eAeZI_xMq7$r)gqU-GaW$ zL$#%zoHuSrK+id3XWM&{eeB5=dTJ%0url7mR((@s%L%1(nvmFp<`^?S$>C>v3M+1&TJ2w zX^{rJ93;+|V;lAkhYJbO zj=6I+t?wbNQ+OAdp$V;gC*c(wh2JHzfa!*MZS8U*>Y20dV;3z zNX>ioVaXAKJt*)9#JxW*C9dbQfTRbFl4(C6gIb=?AAqhw5ySX?EaZEj!*tr~1UafJ zudm;@@e^=HovYVAm7o*ri4qK8A3f&(2{L()5d1)9lpOb9)hXk~LAaq3K=Q8?w6Pby>p&a))#s*++L-0+^Y{5I+ z1}P~no-8!gAXD)0#Nv^$vz|FpLPCT#gV=-Lb|=PJ!D*os2Ekt>9JPM>X3=b+4G4Dg zIqo03t*8CwbCc$zQ}~k4f8tv~1&56N^CS!oSQ>7;{$|aGIm1*SH%~p2X#^eZ?0`4` zFnKKxI6IIUVsb3Su8`iN^yJV@42XAx+S1GUVP@Ri06aFUZ(7!d0H$_zJo1s_`9E{M zTm)DNBAW#gU51l=5c7Zy!*|pnh6S4pxH>-2(O!Q8aoMAKt3SEgqHQfLH%2lpI|#kJ zy-%$4a9G`2bR}EPQ*0v<9%P+KgQtg>@9TnOq&`R=v*^NYjktWWvmFuS0xXZn_EZ*t z1Yzaft25y;iEq?*UrQlV4k)Lgj}D0v3__BXks(5?3-=xiM-04W>Gsre|F342A zn^dHme3B<0IR(mR2*YU~`AC2gm+{seV$ zpX0j#(IdR%$PtH*T$>>pxp}9Op_D5zq7!G3pJ%SgvvcRGm^M5avY0HK2F}11|6r>4 zG9_m{H3~*Vgk&AEEvW7q_vw@UP;eH@g*=RHYVR)uzX-|zz|kAPY&pUyJ$bH{*k1GG8)W1U&~F2#YtLEcsI0F|MOi8=*D2d)VRkZqgWgWm&b zi?$Ag?-+IvV&3%g5CEo{+ys?KPnp-IFb)7bd2)bc9YWn?&2~_H+2hnOm}mMKxjjEy zr`78z@@s(+K)1yK5?D^RNKHaIaA!a%QoCK8^qV(hUP1v=sfT2_YaG%#{=gGz)#nAt zETY*mL|-jH&!CQ8a?Y(-T`_U{32w`!;)P@<#n&)dnoxJ`6P$TbF);>xWR~6Lsp>hf zo4xugXlQgI*eiLX--b-CN5bsbGcaJ5E08C1$#O+ZaKbv=#%HPabrhu_OznXRDBwaf zWH@}C`)O$Qmy;rjl4;IVZ~Rmd4!NdbB0PK_1HLETKpgXs03seK0t8Tj$?1qEnR4rw z>PXmN8yumVnX$WViJ3A>hqyy83sS^T7HCQ~Jo=>s0qI&sFxfaf@7o}cX61p9korVx zIiNCTmx4&`M35?T%_EWZIW5PGFbcJzrNmSdtGXM)Z2%7d$-oz2+7Ki-t&ygp{~c)p z4mk$TBm+0oNv8(cE3ncPI8ZS1)?R3eK*+iJ&p(q}4}rYG!T$#dxo6rD`W>Oex}cKf zKS0}HHdYPK|5P6H?GR`oqcCi^ff+ITzn2eB$}UY(#PgC#w5$05OF5#Hz)Blei(?xH z{?_YK0FsEdv?8`6`U6uHo_w=9nHJvtai_&Ukk3_Q!{!SK3SvxQbdn0O#mx@!KTkpU zqP|rN@QzD!88U4$M#_c@6V)GLUblL@bMrk&)S$MYyx9$d;oMK`;>t_jc>35f?}t?5 zXtr3>G?b4V#`eg`qLXD=#uKxSpuRkl=@!S39MgmNGz4H>L+Y8>ZFEIC{aEOnbk8CK zuXrpHt}{JarHHNvp2kne?l8*MnhRNWzQrFti;o*&^fDxVU?}dC1!C-Cu z*4BTIg>01y>Zb@id+340V?J0i5Y45ucfCA3w6d&YmHQqL7a=h+F);^iI7o%YtH-bQ z0#2x(a!zhvco2qUO|R`d-;#$j>^j%^=(1-ofE!$+XbmC%q4er6AL-9T%I8W}d(u{%L>8L%_9VPMqj8uDTx76=g)fB942)`I`c_xR1O zOTVg7$%fomtji2gTMV8YM>gWc=!l38S9$mLZC;Y%ed-gq!efC`^Xbv$JT0eXWiUcm zLF_UpaVFayhvmvcH5%?yKSLc}0LWh^z#!6wd6k5_VbyJmrQ+4GYFU^^flBA*z+vV1 z8dUlbP~1ZK90>xHRWZ8*lcYJ-6ChF>c<&=@ZU(P~24{LK283`)SHxE6L;@r9O4Dcg>;;w*SJOfFdis~?|FnY!wB@L)T zs0jJTI_CW+q<-1J9YAnw6v-3fFnFk&Hi_zvzyNWiFy8D~YC4%x&-&M2UOip0Jw042yKxu zop||Vh7>RFAWBN|ktvw&j7)L}I}ryLQJ#W6Nrt2Y7mK^L-+D|&K$GA$rb$C4i_AP| zfBvmo?;!Yx6x%Cb;fd1i?NTmC<(@j2nwn~Ae#VtBYoHUu1QWF}0W$>Tg?slb@UX13 zoNJ*S3?j5ShR1cm4YCNE>%vVz;ul^{?GLf`Ew4Kj^?+%kDUHY@PHjTi(~p`wtOHa} z0fa$y6ykOsckfmd7gy$I!nWca0rh@3BgIeITv1guLiVDI@W+kNq-LUiO#7&rFcr_j zvmq`vfMy_8{uf4dLx57pwiCaG^hMzw_I>LFb~KBVgZ`9{u05dSpaN~*wgx<4#4uv8Q)pDMC|C z7?vZf_GjW1>!!bWa!!9}KK_gh(C0VH z#Ct-aa5k+@AEnMnj;H5n6(!*DAZuIqJvHzdOfkBPQgATbX_IdqC1n$&w!0UPHH#dZ z4|XhR*MfKyNN$FU!Krt9HW=igzEYm5IQCY4U3i3-qpphVZ)i9 zmgC=l->K2!2=32f7yyDR!Olwx2#lb9jcw-z0XlpX6cq4JMa8RBJPOnld;pkXMwATt z$>2;(V^?6hBiX7TCSbqn<=E{*RSOflQM`osX=5YV508HQs^>1r%u_4H4 zBEx+e9*)`+Mq%Mp!4=d;$U)^;>iF0Nt^Ka1Q01N9%iFd&19Zj}P+r0ZGOkdl+D<;C zzjL&=ru5rk@qvMb{k4C=cLLY|oK5{)N^!tSAhkPyZpA8hj4C5>ehQC2#nG-eK;NNQ z<%;0p&xL;x=Y`-xPDSUxWWzXqEo`Uxr3R;Fu}$vxR|9+R7!8>KYuUBMM7t6ejO=( z|FYBNO6y0gAS07S_in!#t|A>9mHp&52Vm`^gN#u%rtqfrh#5$E&+hvUgcguZ#rAD( zZiWLOyBtzqh}25$!BC+j=`4iO_{)dam7yFW5=n#ZZPfLDcvo%GD2ahdT!}p*)|iEC?jpj zp*rV#GmVakV~wqu1>YW}9XgCWf#^?yRfZtrp=1)Qt8lYj_^wovmtS3!CB|!!_rDza zw$t<^!-d!N+qC)<)0?x9YMznd^am1-@eBeO77Shza!L_Qqd0S>4W>QBuKmyOHb6!# zs8YU5{~y1TY4D*_+)hr9s7wvh0TR8U7kmvD>;Xn6OkZEV8Zyso`pzb2z=UCt#$%K{ zE7{=wt9z_jsBLLYEB}4xZoTciPdM(Laamu+!+H^cykO2|k0UM46MAT*l&SD4YTu(8 zg*m;<^duhub%)lRB=P1)di&=+6JQE?10^Mg_6SZ%eVexHyt|JE_=cAj)5*&padBal zlDcv_ELBTYtMq}sS)+s0Na~Ib)q|9zrF9h|HG;J>cf_~&-@A8u$hKcPQU%Linly|y zHHnEPt5fTAG1?TEJ&>IePQ-h5oq;CeDP#j=Fi1Y2R$nQUCe5CpT9rSmQG>)HN|>*HcIKS)D_b z*xta-&k(9l=Nc&Lf}>$B$|OWD7$nc~ zo@r!zxMzFOnKXLdm~+Q^LVwsNC-;tc(AzvME#9L$cepaDEN|c3pASc`y)P|&+UC#I zkSfykW#YEylXrKHVVtBUO{a8dyHSbGKTXg>jr<0(itTVhISR(X7iKUP?yD77E zNEy5(iCRgq-E%W;G022EMD6>umD8I`FAEFHs|Q*};$TH4S1{J+IC^b((NK z)#B3NAAiGVqlqx#_ErQi{E{0??eV`{V|)k9gck;`g}gixJJzlIKG;wbqnUFt-R1?| z+_dkH`Y6`>XWy?U_av6W`{|qML5_;n`usRhk7WH3O$3UJlN3&|V;|Kzs3|VRzna4)2HoUvR6NN9`pU>5mvL>>TD|$`xZ&b z3LD=v^jEBkJNMFO=9kx3+lFKb`%y}`=Ty5)*;t?YiBqX&x0H*1#YVHO?bWH<0%og8 zba<;uem?R^S${+bk3?#_ixJYg^j-TNI8>Kr-MC>o zK&mf75A+kue|-&CxrJi_m^@*-Mvy&!f#v-@OmX`%? z%drp7F)+A)_im3Kw3c&R=URClneynf`4rzCjN^-ZIQNzD)J7C`Vrb_^Sm# zuRsC@N$f_biX`z$CRD&(^8nsp7@T_%f|Z{QLlsI4ROZBj$<$SA>*VRppL^+$F>4xU zUaDUFJ)r+HdEsIFB3JZlvus*#Rh5Oly87+?G++Lw<}5SKY9r%@Cz*2{RWEKG0N*$mXkTF#6SR4~j zScc3px?VxF8^)Y36rZ!K4bQl3a9`6lZfwk zcQ(ejF^(=@-jIFO+Y{GZ7Rw$w>~Ta=FPr|^+^eLhT#fR|r|O&bY?hR8q#tU!pvm2w zRyv)eUAh0OfHZqsTXTiDVdyM{c{2k;OQzMkw{k2F-8-`dm!?N$Ua49V-@W``mz&XJ zcJAu>Z3m7)H5DacA{XmAH)odQ07MI3HGlXTiWN8;uE=699-^LG1`;KihoA3z*+jcb zBztwiMiG(TE{B-qPYUY2DMI%ogl{EtJzqC)*mjHS#5RkI*b<@6 zSSJg*U3I;R;DNQpy=BCbwThyTUd1WQOZu3knd6w=()TrftByl!7_V?$Ow1P1Hz6}i z+-~+iQd^qvmv1qf@nzOQ(E5FgM zm%-@v0v7-*8cCy)%}D2hK2og_Ndl;!Fk@wd>Bc4T@595rUz^6g=t@mDKh9MB{G1=J zRQ|X077V{)xK*DoMcJz}W)_clvhx6Rx7F1ADwQ9dP5xQDJG37yh<0`v&z}#WYJyI5 z%o1^(7@V77z%*S<$FN6!24yScdyVsZJDV!|NiJT!gWYlb0gKe2+s&NNzFp%Jbsb&I z^j=qo#+m1|*~8^+2WsQh-03i`y|OJD-@X-~G9i22zUfiajw~JFa2gcYWoi4yrhtvP zg53qw(WnSe+#O6!z|QLot?RL+`Yi8xpQY|_aJ@)2L5Zc;FFR=URkrf2!g8)Wx={xj z?%h7c5S-rAkWz$YjQ7O;e>osa7pkB8=GwLKAzI1@NGZli;C+YiaB=k^dG2WDZXj?Ok3iNdZ$GO z5*ybI9%7Lw;7EuPwUTx0x)Y(FYZ+P>6%9pPuo?K)B|e*_gmKmctNr~B#iZ`4=({3{ zvPX_U*X~WsPq461p$O2!RAuBb>YG<{g)4CY`$EAPxI0T>ESc;I0^}W5<-MZ`Bb8FFqD|Jcvs}Ft$!m*t~n~x-LulA>QDkg9k+W#9mN&0nNn#SA!7aB)Xz!!IahZ8HlGDprU84Y(R>l?X zH+QkIX-Ytn0pORPR@qwneIiSgs6n)aZm1C=o;*Yr3-03Too zn2{?Hg90|hz?^}xuu$bln$}_OyEWdce#UcgvmqL$LMqh9?)mHPf$Zf4gnL8>z~n0%azEP(D`Kt)2KgMm<@wT# zPh$2KFIQ>(7r(=7{w2T0c&4xXjZ@q8%=5Krl!+UszTLrl!;HHqU8i+~7(lm0fOQni zNw~heGe$_tI%y&8MF4j|>RLts#F25Nb!;!?rMk1Gb=yY~BDh0{Skn2@OubQo)5MCr zu#|yY=VcOEbM{YnLt=OR`qR&!QMN$8Ocm)J$qwm7lneF|zC18)xP|H?k?-zwxct1| zIZStPh1Zco%dcI|_B~3wcU63B`Qyr{NVyZ`(<-~!V|{(SJuyor%>AF+x2C*u{1?^5y(&OZhXPJ{}6 zQCFbfuoH^hQw~erVpnB?PIdzOJ}a1!Z;}gKNNV99P`gDsom3Lr!YGBWwsw zwYI6+sqtJt&3YpPl{0F7i)>Bl$sEyCjhq7?dfVFboZoB{Ye**LIv4=wGiPeSm>X)+ z>hNVnJu@R&abnQL@Lg@tgv@)s>_Q*j+R0h5qE?0@cf=hvSnp5tT~y<0PHX(RFZh|= zJ4IQcqWpxp)fjwIdnK4>5X4<`J1uaij++3s7@h6fJ_S&9B#D~#*bqmL{m9fG*Uq^;Koh=Q_y!#V`QA$vdf=yMQ-cEbxR8Vd9h#Q>s}=;Ha0f9 z-Y9E8nBP?r)pbPwJ{gUcLU%fJpPZRrEvkH_^vxs9%{F;D12s=_Wy0T>HN1boOXuVi z7t4j4s@A-DF`rN3l9TMi9V^@KwYnAizAk^fsygbCaSB@o7DC@dni+9r+zq=87{O0oQh#3k?M@OxXo4o(<0g65D{(Jw9 z$S|4ruZKwoP#Ax^sLgfZ%fs6wM?*@h!#^cj?|7l5DIfRe zbwYkDECq+80)$oj%$IYca^t!A>6m7DVo2A<~$_U-0+EYBhl)*31&cIwb|v`x#M zkJ+)3J%1*uf^TmQ7qTu7Ps{UOl<)bvJju%h>g`x!R?p^5^wwMieqeKA=h&qO$*`muI;t$!IxRubX z+iY(yl5&{hMw7(*yz~0LG#bbTY+S!SOpUXYb316$$O@nZE>eh)JRit*VF_lth(D*o z2it^yvaLoAVDrz#I`y0b+}$q}`x3Oa4OfkeUR0-X9*=F0bj=*}AaD8m=Ag!OZ_Z{t zQLn=ZlU^~1m{_)H9b)4gV*99feMh&Y)p6Qcb89b5+@aKBV9#DDC*#V(JG*_0^_|B~ z6_&u~OV0i@w0evAI)2;;-~0r-1{Qhy{_FR`fj`=97Kr@&^_4 z$UOl+{R}%M?Lej9{XU;6vJpZ_IeBtFH(E3R?5GJwpdijO*Ms_gxfua;LKfTcTUS_5 z3^JZNVwp)d(tRopo?@W#zEKW~tL~?yS5oq#dc}hVo-YUXI;iZsZH92z*7r1lwsssi zV2Abr*&@K_=v6RBWdV@6Y`Tgn#LGgOz<`8%W~wbLEJU%hBbNHKxyPv17njGl1CKwJ z-pIbXmMg3O5?5!jL5f;{uVHi)clegQJLck_ujN`EUw2+bb?(Q#V-Z{UKR+>BLTP>7 zZ1>eWAHY-}>i#s{$X-xb@BI386Luy&J^o+?DhS#=BAI4lIib-AdLAaNa|sGo|NU>y zLat89SRxmE+S{T&Nrh=HAkug9P=kNIoySa2%5Cw60|~~DQNtNJ8O%1;E0VN_Lec$# z(izeuPdcvKy&xu`&oJKUl7+>0P-q&{E_VH(KAqkoDBbEb_32Z|_IqjOMrz8tchO@X zPqVM8ODwhj%5yN9%fu$PdD=c!kdkr;~FB)UKm8!8?~?+!|Oq`@s;*?x&~saG1%;woPqcrT(nC zi$RUwzQ@D2;ZZuiF0bo>}*YrlG?LQ zu2(FQ!s>cbtlnU%y!*O6j?$3zpKH!W9}e@{YUptnxu_&v3e@;D?&H2T(woWioPNWv z3Am@YpZV1t&O`Ol8qa-NTm%xjy*vlkI__)z;*qldu15sF+~>ors};xieQ`c7yod@a z)hNl0wL7E1wlb|FZxo8Xg`sR^MaV0# zCLUcbzdq%AUhU_Y5ooqPWY#HB^{0Y|lhgH1=bf&3t`1%+oiFrA8D}_)0X<}K+Lppq z91bzHF~Y#CY$iPn~56 zI%+EUF7GSEHuJ67SbR=@fF<30V`FhRjh~Ljnr*EA99g%ZZg6K&`qV;jC^~c)E6laR zbLk(4TxR8(^YdlGkF2^mm%Zt*tJDt1(MlS+xvRTHxfpjrnv{OS8EuUXE>Aec?&EOPT&voIJ%Uyk;UA-V4(nf$-pq{`f9n!t>{>h? zoBqkkG3C*1m1;}8ekxvQPe5AwWw;b*`+n=-k*~Q7oJJh1soF6*nbhhUQ zrf#@ic&1m?(b+pP3?REbYp2h>tQ7h(XNRNqQe%EQc;*D8@vKv$1FGL^>U#Z38+6h~ zBO}|j(<1JQw~u|>^hwq`t}Al+!S&W6BJ4ElnCR-&=Uaxub}gop2fQ(}Elg1=*&!pL z%n(~^GFP&>q;127v^-tYvEJG}4Ot4$RT$L~@C5$FH@@zT^MYbVf^ z1Ch{3BA)|W7Se6XoiIoNPW70CKb^kGu*~Dlo|*Y?DR{NGr!8(=zpT-_dbKxF?}d3& zVNr{Z8P*VcGKBl5aiDS~wk%2kCwoXfi_e|iHj7Up?x`QXr#@Q)Vf3GC>E4_*)7HMy zR6gBNI>yc}t2i}la$EJcc!ZnkVs-Dd2Ir~qj31{Y+0B|XC0p%jQh&b~hF1&Qjq-TM z-f_eGe%qs#Q?D6w@ZbAREl@n`oON{`)W?6d;Bc|rfMgL>6ws|s4JINh6Umr=C%2cm zh*u&0D5sLm$;}fX3h{e~p4>ae*3s-^SwL`GN`d{kyOK=bXR{@99YnIJ0)Kd&OYYBK zoI)9AQ{lLteu=XoRm*1l@wmDBG4A{O%{a|!)ta-@6*upjM>*VrNYCK>M{ESi#*j(K z6>8}LU$fvjYw}+e-S19|Qe<#+=(92bv@2T#+6oy5xv*pumVd`pp|H`5N=Wb*s(((}BV$e6fNh#J%>gF^D_ z75WT+>UW zG0y*yhJaOAYv=tmkaI9+F(o78^BFGN-s&DO5MF)=t30o7*LD;1FNusb>(^cdgC4_n_HLkRB1>6mwvFuhVa+sCbUj&&vbP_dl9c&=O zQRYl4uVsseq5pYIOgB&-ux}6ys{p>l2ci)GRyoK(ZV=ajnV2JUM^=mnh3v+d4HPnL zhh1Z9deKXZC4*33J}4Rhrx?77Y;{X*nzh&8z(hT?cfdP?MLk8vV(IHc2L-hjaG0PJ zaH;ND@G-WyT5KA10V!McmzhCGnX4FeYq6}MV8!_OSfXLLx?0U7-7peyAoLO*=#lmy zU}F|%$@n%4`h~sbCP7-8^s>hR(uoF_kAg#~M$Ot$!bV|-ZrM%<-hm{Gjz80nVb`3+ zL=YW-G&48V#}~fhnSL(k%Gcm80rUVX>N)t#Xv=EMc+i)VMy1!1i4qv*ird#pj)*uC zs|MQB2$@ZbVk3I&q5u5;;NL4YXf37Cp2n<}OBq~UHTsSNX+Ee?T|MKA#&FOB>QEpDPbGk*KJcEq%(9iE&Wef%^;_VKh z0uP)KwV{dO;W*1^bGH7=tk^1O2pdB_#;_Gk>if+c!2ya8hAB)U96ia%~z)}mh!3KCTU05Vv}l&rgV z6%p>D0N#K;6iKQmqmr)~7`}Lq=&sFbGfkU%wbKB&dHRn~4WLDHct<7s`C@%kVsCqU z&wy@;=e+20zJE!b{l$ap!7G)-5KHW>M^bDcu0jkBJUHybv@13gH@(m7_QqqMzSYDP z8o17&=QSs#6lC}z52QWw`-T_$#w~6JL?HzF3UYG$a!;wKSmMoslSp{XkON>AJbzD< z+t%q8o{Q3|3%wmEx8@FvJVx_1&H|eJQ_VBb9}HpVeT8WNDgrdO$Z!j zulKPyv}IXN157Sg$xUcV#Flug1s@NuL}UN6 ze!uT9fkZ;+ z&#_>XuTA_?N`H|@V_aqm$N*~AeK2agEb3%Ol7f*F&qfb0{M!(+H&J60km3l;=9DL)3?$OmHR=cTLc8Pfyr1qax^;pvPba?R*Hhg@V6qs|-?jPIpFrqINy(C^frnHa~`U?3OrC7C^k5nh0LG+!BjEn+nA+InD z)p#OCAozlP<@G~P&>8U#gU&HV%ZNX)@(UDLV1_lTW##2dFgT-x7b4*|{@jF)Yca4; z3nPMdtVH1$JGT*A3{O-VYziVcdIS9Vx>zsI6o8UiI&6Z6#7!82kzL{)oOYqg7){L` zgJBQQc?Dr6E_E8p=hcmI_91opj)@r=KVQ|sAi;vznp5rVauP3aka>nMh^N1N26XF; zLKj&gXObLKC=Ax$-fyTP6OtfLWm^6nO;-MXeh*!Sh-C(0mOp=O;a>$2Dm`!rVwezI zJYt#Yg!2TJs-T`IAKzv0MM33{IhWx@Y%f3v>^8^mHrCdox3;@zPBKQJsTvGYN!V{R z_n**kZTHyCt-Vc13FDh#{VqLb6Be%HEu+Njf(||dx&TEjTBYkd&;^LD?%>a~WKoDX zhO`Kp5mAXWDqVxu3T^vicoPFudJ!cf3YLiZ6zJaOm5M_--(RmL+!h>54a;^soU#NglLz?OX=)pqS zX#Yu~^k%8JW|^*mvhw{4uVdjB(epVicZ9W4RZ#-Lh;89n^qJW3prI$1h|@@I-?V8H z#H_9VKzjwOqXFv0T7&BuoG#u!CTP>EHo|-!b{wH2JiVDF_rz&cirdnAY9rzZr?R0l zRNsMiO$9{?+?sS5GDB%^0Zc|Cj{U0gU(=*}a&9*JMRv?VRAfNXZ0qO6H+mCI2`p&C zfnu;05fK^(xKL=ggmr76Q*pd&p9CGurDo;b6T2RvwjUXe-j*3qoBOOq3(tZSD04(JjccM`(Ls_$|TLU{l6Prp5o0liM9|de+e2?ch#;S$Lsz4qSD5i=0t)5(4kG(3~-Utn0d z1@WRsd0g&~KmGvo`uvyA_{QbSmTjQ}ICS~Zqqy68Bu0Z*v$MC?5`_qOy%Ag|54mHs z0Wlvyi zZ3Ag2t*6;)#1&Ef$NH7<67D^m|L$>;YB9W7$g|w6pgST4S~^bm$G)+b6@+Kcd0RfO}zA{Po1s%z#Ow#75b8HUT@a5MxUqx$C zy?Qczuj`F5uGAWYO-Vo24J%RDQu_biE$9>Z{>SFo~Xk0Zju3Se){%LG!i z{Qs-;7?JfW)*Q|N22Qo!2psvr`$yZ=txB-vFoxUU%W;Yk1f`6A6~BNT^41Gl zqNISS*pI(l*FnSIbM%1Mcfk8ZsN`)R(5>P0whhwWBWObszVPWyy`CD(hwZ7&>W&CI zU=MXW)}X+IIp{&&W)LarBUPs>8z0`yMU~49$PF7R5gQ}+msMj$|d+qC^ufJzqO^qHXGxNj>VBN)G^3#K|-SCEeSU%2I<;XlZ5eu21 zg8;oz$Tktmpa?JI90eaj`UD^{QBe;Ui0yeRQY_N{!2|8lX(UwWH#AZM^WqobUHQ3g z^?$Tq0E`$PL}j zgdLYfBDDZoxixYLa}(i`5fgZx3l}a#*?7G+CF*3ZGKV`lza=Dr8q)XLl=OG<1VPYd z3h(!{=YgO}7>$6(k1uJGd0K=bP_7xCf9S@|n`yB0A!P(!`I;m`0%m-g^f9L8gclO| zy{_&N5o}f7d2l7UIXRq6KG>jYKYsk!=UT+i>EBx+HJ5reEVjkvS7Ot9y$+PwK?;-% zKMQ#}+`@_Nwy)B0|K%x|R9#pK>mwGAS_~y)v7%$)2!+7CJRpFCc5WB6N27ApSr{-* z#`dA1%W4=LBxEIW80WGWq|CEn(2Pk#^W*WOgfYV8{Wj*n=>iH=i>8uR=K)Q$ScRT6 zjNvYnhwBNhA*a7-acFl&Zghx)Joo^QJ7o5qYbr`hsRtu>I4P*d)*PVEeiMwc}#Dbw+a7}r_vcV zZ-xirs+vM>3H(EKQZxAE1X|!2=;@hi(7uN{k6Lj7ifGKIe0+V;7kuiohi)=0Hlca517AM>* z?lrCEs5+~xT#4qu%i!-+#-O$Y_0g?}P)2J}_)@sKK|syik7xh>{X9I0CFcI}9Z2iD zJ_H&%%yurt7Cm)RWrM%`7^prlV97rH8gg>Q5G-P0X1+Le8x3vnWL!+$Da0Rb{m{L6 z3?>|MnWC{I-_2MX`+|rIkWYUYkSr{51`Lfd`in?M5L%{g-?{S)zFJEH$rBh?zzfSD zC=sl{8vsZmYnU$7uH6GD9e$RP9;|rTyDT#$u6R^JU!#dj9rq=Nh5$Ax~099PIdNrE)@5@KxZ2=|$685y7 zG)|LbD(`7PKpg@X$zS~f;ST2JgWxYBPpCu4iDOt7uc5fh*9en2jg3P@tU9qrqEu~q zK6Q}ZS$`Nn{F7I|IG2j&Hq@Mo~#t+Jg zS1nHfk~82v$Tzt~C8{baE~EJe9LY!#xip%8FU3ox43Vb^i2|7r4pZEswE%Iz)t*n0 zoS_JINslJYS|*fXjfyORMI7U2cLe9=42rNFbVX|B5bv=NIH`XNCmw$n79*p zGWx~STm0Z}A_hb|=?E|o2+y9gejgQB_OMXvGCGz`F2r{@5S!^bYoARO%)yNaEcytV zjb26Qs0LJA2NWCuH3D$pjESISH8trbsxxnu6UZi@ zXFxV4-i&-Czm${#M7(Hd$dqWT3qo!ZPytcT6aex!nAsJggHbJLr$u8gjv*{R0BaV< z{$y-*o$3=vAaFjtJU!QKmvjQQioJ^Mm5{(4tHy*|+qe|#mk^XL z1qD-^bg;V?tJu`%N%A!NQ20$gy(sF}p7Au)qQ?9D@v zgF%luIG7e*LVd_IHGggvJ+F!Io_w5~ zoD6vn{5s5C6so0X@sMZA1`Aj6G%E!k}9!dC~D~<$>M;EDX|)B-U8t znAB8jpsc7lAmqE7>#&qkPz2de(QIn5a?mpvi3Is5y+Gz*HU0AktonG+UUYtw5Z`HX z7>)gVHhT06@lYtd|4>Q46rF`~l=NDZmd0@_8i8t%;a&%V;lxB;k2Tci@j;mnfA(La zg;R3!WH~^+{stf_yb;{7^w7`;*|f=+Tsq^gg*_LuXVt1zP#*_X`-|legw&u$+=Uq5 z44wyAK;;+s2tPkR!1+Xemu?#i6GRLkC<5QvQ0}}G#JczK_U@SWgOqpnRUJ%uh^S4R z6%~~G_r(UCMJSSPf7UHag!CIJHgZ#|wFg!# zAYRog;fMjh%N8N`Ifp6(HkbN|E!(yY^z~uVky`i#*jl0)iTqQmCH|H}_mf-nyt>^4 ztgAg;!BZgs1tIh$gMAAJ#dLJhRJfT&tHj>fE@iIfcZ>~_32NDESO~O@c4$*=qP&tA zf;d+A&_87g;C?}o@^!r)9zAT=VhmQnuzkCMn%X%vko3`bebZYYACLwVQDuv;ZrEg1 zZ!urhVuQKgF)z?~!_9-)p&Sim;rUDNFqpXpjIDw{dLNEiG$K{9Zr7n+a8J=7#l}jNJgiN zZD0=a+NdJ@qu$68iCd`ZegOg3O+qhq1fbOZ3Et^I_GQn^D`TBx`T%qUPaBw~i9(?f5i{bPA;dhe$VF!zXZM;S8pY9r7!4wwNK^*# zfir^ih!O`B4xr`$AKHZZX=tv;!30Pr#%q@Se^idOw7pAdbxl8=t%4*?LmDn7IvA7*8!SN@R zBS1h_9|X0yAG-vm6BhLJ*H14?kzpXl zL}iZjc6x$A2l*bjRZ_15r;f$tCuxIS$0YdAdnSt7rdu+vE)`cuY|%0N>ms^=(bR18 z!vT}9V1c(Gbk8KhCt*>GqZKLu${1;%P*8A2J)xg}4&D*L;2JJCj>PUZ7ztQ+=Cx?H z|6hx&Pya`e_0-qb|GT;^-$X#2nysxZ0!8KI;WyIO)~RiL{fNHv034+pPt(qYfy7=w z(tRp#6bmo)tN#Te3E(Y+g80~c357Q!F-T84E?Fl*Ay{+_6=9yc&p^|Xi zZb3~Fc|gY1p%DG&wCmQn;J99D!Pdm#Z2XgeBABXGu74(tG-TNZ9$kNfbd~@s>%9CS zN{thtDe&|O&@zJ zVFV&rWo$mo{Qc5doy_=?JSbybbaR%$PZ?LgE?evdW}0hpW`8Ft%bZsylU)(bxWe zE3upM(G`(skl*Z?cKdhj*1cuU)(uA_XQW(A_UQAVza%7PP*TzdGBueG;+e+jmI!Ty zMqM5;v0vyMsqGu1<%NFInyZ||!)^6YJcqzXHoj&SegcJ|U}P4#@o=U%VLm>2z}ErB zky#TD%*Dz3F4zV(%{V|Ce$LL$&XZ_GL|u;8M}sK4UQkisrl*0LuDd&_)1Eu$_lqx> z<_>DfkjlZ^_dN2HvyN1-*h!tX38&%Sz4h=h*qa}ypOS!P^n3Sg2(4lJ>n}rOI#4J! z@&^!X15sJ%0UtP!k(x@RW}wfbaN-2_uV*<=P`@OYGFq4K^w3k9?$;FW6<&2MQ#t-6 zu^Y&cOL$SJ*4fX9$8~!_JhR&r4rEW*CNm)tjzPXZ;w1ksYO((ZrCyhdMAPWbpYedn;8~NoV{5|>{*6XY zCzL?jhALR)Vi&|>nXBgakSgt zVRY>+mDsHU#-~gJn%gAJDP#t2LXdX#S?xWYNV~LZ&{AX z$)R|1vObbpoqCN#1?kHeJT-*r-SU8GpDBfqP@@S>7p0iMkT^6 zPF0Z2sv!PIFt$DCLr){Jr#JM`-|XjZJiPLijB(QgEb=_jB2I?SeKb zl?;M7iGU71!Oh<+C*gn)^%^^H%~y5(`OuvDZ)x~sI4`2+xNd^x{-@;}M zqpf|At0MzxnsoITA+-lK6Dh-tK3JNy+>NO;Z)Z}_`aSWxP;+RR3(fs?=W>ef+<{p( zg-;S39}h7uzoxSPW=ep{1HYaBto(zM zg%FPzg&y44S;z#=a&6fyAn@WRA4+^+w@5gfo7-4eXac?mT(Wu9O$X?i7qjxbZbl3M zeY96^WKgUw`?TitpPlmVa2++=eVRe+ygZ2qBqzW5VjzXjJ0#ud*qZyP#l-a_DeFJ% z;4p<3a6+pMr2v#Fjv(szKrNHItvZh>EEvn-(Cjh>bPRbXV1nap8-=h5fsNoBfsrSk z*!4p97Cacs0gJHNuOQ@%L2BHanTKvw5y&c#=MA@#2i?r}BwBgUBX@AlS%y^^5i7n? zW9+P{Y1}N=!$WKvvE3Bs+>ubBmIg9YqJFjg22Q1Ch~06(A7vava73QX)>ypo!>GZY zy(B_n;L%na-m-Z!8YZ{bKih`oYjwp#xka+=-hFX8wZIfSBDB?6=UHC4vakCct$z_B z2>FvQgik_K16TnjTwHqgK&es097qV7}DZc2*e5&i$;KWso96)J4=t zp}r>(jXpeLMnSSbO^-j2n}Z|fKn6W}Oe%`?r_ip9qQ+k9S}-{DT_%l?FX}fTl)G?7 zDENK~oZgnK%gE0KIU{GHW9mtGhp1_V!UeoXSxtqnLzwt5&c?-c5nG5o+EY|S1gV7r zr+>psE<7A{oZJH|P(X+*RIOeCWuAMNnwaetX+6;3PcqC0>o;up0dE(S@g9flBjko& zj4tgCN9i7m_X{8;AyEdDt_{LJAP_{f$q%kafSHBGMYR;GD#vF0Q&I$Z*q>`jOksd?NDnB~9 z3+V*J*o@j4uaUM{^RwsA$KY+Pzk%Gmdrdq%lnFfUTeApW6qZJ1Ztw6dhJ@sWZf?Ma z$B}UBRuoeaf?UQgw z*7VC>y3`MdOr8Zr0i5qQLx1D)F$WH40T3piQ+lW%gg6p($nj*P3Yv2I5O-m$*xfsK zASPYsdLNMhf*f+*5LiJk&3*hMs^|zS4x7+n$Kg1NU?7k~<(evg`}Ert<+3 zVcznQ4rnNav6tt>dJ44rT)`y^EEfTpIbeM-F!+E1rlY9|^iOrGZZ@L&Lr*@M+{Eug zh-`+cBW9niFx$3e%XziZxs!D(Q~AqR3oNC_5N4p(0+-qHSg z21@^ycX6k-g5?7g<;VB$924Av8X9$owJ&ZC;L#a}$?+2hZw7dka_11COzvH>kb+3l zpp%h+T>}jYf&37b+s(<@ix-1E@AC8enfgtb?Xcyc_|0evMfILqyb{Kz2|_r_Sr&6U z_;>A+H5>%TKx0@Fb`Dnv$|U8Uf^!5+T8DBQ9X)-oQwNk$?|-Lt#ebpfy&WHa5hW1Z zkO{mmli!FJAts$e;Iyk{T{JUl*LYo#SDEUcRKX|lxu-k{^88pmeh@c`n2zk{81 zqUV+9IsU*z9clm0wze}nTB!)qwqd{exI)`C26n)xPZSnhCdOL zH1CKw)?&?*{IKuyhD*5i*At1U$eT4V9hy;dgetW(kUZk~tPHjcq|pfrNRO7JhbRX5*KGIeLG6s2-O zGYjWHX^QOqiY^mI&2rhUAWM;knjkYE%KOC&(m)FOV^!WvKAsyTsS zM`>P0y6u6OZ-k8kl=$;UZ!mBH++)Y#Dl^C^nMJozl&+{f{aV6pouR#aO@+kB2LUj{ znwy&uFxTdiMW-inRHtd7nJj=CLM820HyuP>rB#Sca=QX&Ad1>If|HBYAil#TV(-OQ zuy%x8SKl8j?DD`?TL+~Y;B%uQVcM+se@;%K1Y6P4l8@8~TJL1q=#{TWvuAwCH zJYuUkpi4D(BpMBVh!_~j4-~&IzR^X_jtD9!mz~@f-7Dbqj8MMT-{4gQ?yk@fFA(wu z#8W8m$<6?#-@hilCxVxBVb9jVo2$p^3p#j$N?o$Zzzxl6rVG0f@`~V zu=uM1|v%q>;RY-I}sat+G#m%$Aoxv^0 zG9!p_#B@AB4MK|L_^&4235a?TZ}^Gt2aN&Srgtd{l;_>4Hz-8h;dnTZAedUx904OW z`WXD!7q~&h@|vmC8{8i;Vx5dpWG{}Uz+^_~!7Wr<5ygmKkP8?cL`5cFOFGyr{Q%vFRjK~_(+5i zPHW&zr=#EpS?vDCbVZOI_o?78Vcs|n{$pzE%V*)>Xs<7rpNRwA`Q?3my;e4Q_anQk zgD(io=;gcUg@Z|uI8zT8YwUa5Vehl3m>^(#V-reIvQg_zec)lACO=b84vtvC)iAt( zw_m=@079OIG%wf+HnU0?Ihcq|jqm{ayno6G;adv^CB~{%8;bI0r%L;o(G+lz0iAP^ zU89HvI5w~P`cges4HMg{Ibg69egZ@$=H`XG>mP>Kq`w{BBQ(2RFaAtDD3q+%D4z7Fa*0yiEqW&p8wuRmHX08Rt`datYVzGE0= zM$^q0VKF^3GiE>t&TntlAjOQ`>%On+ zyw3ADkK;INkelwXW|R??Mz7n1+dV8GP;*BF0sg5%e-f{AZP@N%>oDlxJnCyS3gd7p zX=EJ$1_RJjav%iCqHEWxU3|cp2NRbA*b2UUc|0N*u?rIh-?ms()t-@;KMVeV zwG(W}mn7~8DnrF>+TM$e&&SPO$psCNI`D=y);2b^v5_$`tho`}mm&Fnj|MoR^V~%f z{L>0Zu@nr!xX7ywBX|ZlTn_Ep^~S#h)IKOCl0(11p}MOsHR5SJpn7t{ChmR3jYguC z*@ZY&C0d9Gyg8t~{nB(ck2T;#ESV3VH0FQFU$szO5r{h2!AJ5iH9#0)4hIWM_GJj0 zFjMvEP-p7wRGS3a8j=Y)poAY|)r|%uIfrMZ0hc z9Hj41*s%LBkTkBszmTsEkaAhF`w- zCINO7iSORU{Zs+MXlwfm`Jaqu5IL(sW{XPvf`3PR+{wuqw-K~H^qN~h@wUR#!UzTj zRrLDt0`%c@Mg!sy1stb-U1iBzi0!41!xRfA!-|kDVm1F{zhSg?bdBMVzaIg;^1~7b zbhD5t;*)zfg`+Es9EfltPG83P3q;o*_XSrg_ys_>2S!FPB@*`iSjBu7d;n_VUgUZ$ zz$(P?=ecoDO>9g|3~YPJEMny7aaaY&;Ukrd&;Z6qMX4M$^q?9!Bc^~IEAR~p5`n-3 z&=$ybayU~bCUT!W^B6ZQ)R?vGkax%}30N~Ngx~$P_Jhv1mv0|m;H8@Skbt z{1R9P@fdr?gt;F;L>o~0{)h(uS#Cl?31>dz@gu!AuSx#3_~6!eMj&M2QW@ z;J-Y;;Rr!^R-K@;z@K#gC41C^OTqg9QVaK9%S;YtPmtv65ruJDZ#if!_rq6y70N*f z0tj0YCH~6BxA*|y`pu%I1#7?&@h@#@P?jR-7?>OR%z4@#yXRvxvN9hZpCuzXAfw%Q z9b|yEFVb$oGNt(swD)gIhI&>n^#B*SE+M@HwXnn|6TFo9*}eGx_wVIG5XixcG>!CMa!StyX*z@}K}BT#iZ z;PHAtBoq@=z#(%gG~Adv&eXp#Y5qQ8F3O*C;P+op{>P2%_ur)aB}x4M;U#lXHTWyO z8TQe&LgV_hsDOv_3v&MB5q!?|EShO&Ax^tiIEZI$@mr}q|AN2fXT_ykwr)i|9j%DT z2x-#4U*t;vdz%wvc$wkQQZ_fwkNj{AQ7O+|pZvv)WOQtwNv5_I9!d+_4ohQs>4nEz zg81XrXy)}l8P|tfUito2WP^iAA#JGrY1D)H6a9$(&cBEI{5w%7lmCnOaS4?ZuB^F; zOQGcb-PQo!ug^{DpcJqF6Nm8zjZ*W}r1w&KT_jbRyN)bAc4Sq`;hV?Sv9N&f_!ML* z3=YC1NgpZd901vwaj8NN-~nMeS>40WxUyVH`yEMuelXPw7l#7@B&;2TGVH8GbO^w) z!FgN?m-N!oT#%fBE+Ow|M41nQ8#uxMVKg#Au>RcTfQ9Q2OwffQVVK*Z?;tQ1#rssYHLixPn=CO$0*47&QCZspSISfY6?Ci(*gW}iVtODDL@YZhMzHO%k zE(;E1;l2mtzWpy*cUVH?W`(EzqnVoSgZ>y?_6PzTNu@;GJ)-2n2GzpcgH&~GZ4@BT zK6W8-0Hz=#se=i*7>j`ybl0*0Y4e`suIjAZWw)UeeELwY2V*T|%AOQw{B4 z?Cp<>j&4G?1QoU1&Z_wb=-;vRJ{7{4W)$9s)p2s2aQvfAHO#p|4la*K@8I|itM8xcfzEJJ)-jRT8Pm& zuNx4;QJMKR03JKR^6YD)~(q%NIPX6Y}`qB#o-4s(iOK zGcWeIJ-WU6YV6t1ysROo147SUnQ;?mKjz%xA;>2m5*RHfyK!*nq?sAJh)8NoOr-P$ zqjO;i@~oIxy=eK@x<#(E7ta`sJq=afzioiyOf_3UK>^G+mGOjvJw4070II`M_kAcv zyuOjo0K5%}7x4C@Z^kHtqyXG1*Y%EPekYnK+w?UxM<@C$3O|RvFXi4%8yxiS=r{>Q z^`is3m$^k=y^t-v;jHdv(`y&yRjB?$jkg~4NH89N~G}#kK z0A;wb$T;Td-cgSvqJD?XBWDIe^TcV&$v<7BU*3u@Qek2?aGua`jN#YdQH$F}&tPgS z3_Uy+`qL+Z@g5s%#jU9qHzS>=_c1$%hf=E|smzZKJrs04)}Uq1vu~ewootAI!iPMZLs|xlp^QpdTk<3^{iDi_NqlH{uw@=J@ z%JyBwr4k+c4$f(U(Vv~ZiYNeRR6KNUmAb0@3C*vScXHI_E)KI$h>nW#+8jDUucbZO zawvxJk*J?gx7|s{{G+V;i8}b_@fStrn}v96+V;4A{Li1wY+wANaxLFe-u(GnE_}JB za{l~iZi;`9u1ZVmgKU8?ngDyLaz&*?imbtuV7!7Zne3zDg7FJ+$;rtxRhX1hiFT9k z8}uGSC0v@c3*v5*@v9IVzRwiIl2;%FDY-rcar0=UPA?tt81xZ%b9jM@d`0L%=^M8g zPdwUpEu4M-e*Nw;S~&ZmH-?GY0psIJ&to^JG7b#9`=O&69l1%Mb{PwUg!Gfy$6;fq zc;uhI^NTajHwp}NrNte)Y3sV)dMbcrlrJ0xT8{GZ_NoJjU0oyNE` zcOspgQt#d4me5_dahHUR&bF&z2G0Nlkm|8*&U4;r?eAFiOr5Bs2gY!_6`oLxo-FF-JAG< zuO-(by>?MAg>>OF|Ez}=g?B9-efbabei$BIyGY;#m=sz(+^gf@k^91=R`Bqob{mgRe0*8|cm6$v+Zk3& zWYegvPY#6ST{zX$RXUGDYERwESsT(=X0ug;9ko#bH_fA}_~ zkqVKgZTV(DUJ%ojX~@Vj6FI`^m-DmAcPgZ;T~WvLaK+v8On!@(PHy?~B=wT?X!cj* ze5L3}M(sP-y1&F#n=*Q2z>ZrdZg*GRa|b&2?THZ~-8&_x&x~2v=LSvD)szk2D~Nm1 zC-;SLn~#~D)bq7&?5g5S+H;S`H1I8*E-F+lDkOxh(qTChVLy0k@}|pI-dxL5m9akK zpFxyMFs^ZI_HTv~@VJ_kjD2%N0o1o#-YV7%3=e#B2rGdFP`_P%F?pMZu2wPgT5!up7g;39d>m8I>#h z*&U{BFmW|D!JlQeY^l^kk>sr5Aq!vQ5)NNuo|E0Qj}zUVZP`P%4;OuW#V zN8)ya1!F6JS>oH$L{0nvk_EtAJ^67BOKcSA<<}((G2{q16#8{~7f|M@x`2SVd7putBg^a(_GPF$ z2?qwL;UUmz!12B_Jg!EX%v8G;m*hEH9|~2GTctJTNqz3}u)z)P`G;qz_uD69B%{h7 z9$aFwM1xUUthbyu&}Hhs=IgC?yY$X{G>%tuA8x9CdV1@}dlHqFDZ8ol^(zt=p2KO_ zIXiX=X1*L2sKFytUw>t%Fbh}vR|XlS9>rnG4DtmqnpCHu5GT|T%R#9S>;WO zY*2FVSMz3eAO_Hd9c&$JmO38OE~OW6j5DZ>O_V0u_~wn(7xm*JOpBgRm&fhiptDae z{NEEFj^7p^;AJjr$Pln)xp7)=;kon2O(uKqR*4I(J7NTP`Duu41I5AQl0$lhK9~NR z{B4IF55UVC3a1{m=qCx*W4&j=)jXT5n%DrD3RWG;*A2S5x=~7CFs}taciT47=q_!s z7mXfyiRO_c&ISJ89K`Sk_LesH(Y5utOT(1bi;5wqGVJHwxbe^_=GU)cS1<^B zeE7I#wp-y=nS*=a9I6NQmTny5Fm!!m);AdEHYXkIg37kI*kZy+D5(zhHtD z>TFPx%`k9X$^FiqO@|$Ak!-^d^22WQB*@gl4H-$5{H^Dz`zAYudMSLP@XkP7sKIcT zf_79BAf{pWLlAfc^bAljlwBs_*+NFM6S~!$4{e=5QptQC*1{gQWRrA5_)V#u`)nUH z6}A4XsAxDDwC*Eo{)K@orMRl}g&ygsNXs}jrC+Z-{t5LUr%(Dg?)`XfrcYagL6vl zafje=PKkZ)&EG0Bkln=O*W#dC_fO46DXw#{lU}&cTSuX$u)t`m#_^fw#}~((`JBrT zrj+(6)wtwVP0iSf3lyO7T2LuVD)I*r_!V8Tfw3_g(BB3J!Bs9nodT&*oUZ_?N$^3c zf-l(oR)&v_XJ$k=>oF0hjo^|GlrDpXmj&aOcCLE7tj2Lh+vLZULPkfQ4l%L~754_n_yot=_J_64+z59V z^Dt`+)Gds(-DJ|0r9fA~$qBx|PJYZgy&P!39riTSY0duCDOMYEcL$VKChcVOECD2d zPN)}^TgAv|7s$~*)T}3q8VilOR+plOEqcyXXQN^EWzvC<&d5$KoG=E~1SxjB6GKcE zdi#!JjW);=$@IqRF7$P>rd>q$?uE4D8W38}0LbN_ZIPKSBhfifxqfEDVoKO;Kn6I; zLzB^$WDk%@{6cmbg{q;q#^g5rRpHLFFLzm7W6a5+rKQPhyxPRAQqgk|Ym<3q+NPrC z^GTaNrpBV=(z0}G)+2Ukw8~37xr{{t*jz+bVj{gE)mYg1i&?1mN)-i#`;giW1pt}u z$mVK*2b`3wY}GhGN_%_FQ5*K5^q;lH$O>DSMNWejaQdfpnx^-uQ z_kPDbAnw&sMvk1dv5GHc3IfeQ!|FVr35*ZrHw-sg_s`9aU?MX1l;fl(AMMF4;=_^l z99XPY4jcJ?vAJ8e@?~eyVx?g%>`t)hl z-lehZ*hMmVZ{C!Ypx25IUOa;&zErayx?7+}%z9HpxBdo^LE)b5Tw$e39b)WK-DVc* zULyk;Zm&-P`o|3@bs8AYOEmL>;g>@N~06gu253f87xxZHSBMU6_O z-hZ4l6hPf+IkI=8?Iz_Kcj?=;D;TT_E#g$mfvT5IQ>>+(JCvReY|vkM34XR#h#U29<5y?JEU{0@FXZjH;y?gpO3^=VaLJ z^PzGtJC@US_#dwGexKIdj(-}S`f$%%Fd@+grKzFHHi$Qn0oyHn*X&J9t$(9hj=L1* z_6K~D;WVvy@gp=RCg!@bW(qAHp<3-e|lq?j~c5e|-R)?H$ZEhU6gIV40wU7c&08wM8x zfU@kxmP(qbi#fxF?0>_g1{xp`F1-7Jf*FN?;inawZx^_q2w$>lb8sDLhIBLi?$#;n z^oeaKZCO3irJtz2J65P~fy$R(pLfbH;M$p_c>Grz+lYtw&e#5n8IpBtX9CRj%jkc& z<=vp)_V0~?!?SYT-_OT6U8j|{9%UOe-s{t0m+3Li^y>w~9!QZheqIIWPZZCGH$d`^ zE$=W=3Jrr?UmH4BX#4TmdAYlj3Dhe!zXNxqx~yEWf`*oEhc^lA{C@&_%Q|;)gO9$M zMxljQe6I98PRv`-zsD#(aB!E)N?qIfj1&xBYFQ(wI#UsG@{DBHqN7(G4Qf&GR$&(1^nYIF?pU(orLhn1FDs_ZPjb??QN6>$~;XjHr>>Hp~{nYg{(40~mQG8Uhsc_RFEGIU4 zX|=>>4oP|F!-rZ*IL5Iu&)28p(Ed=~vc#f29z>Tdf_m zjIF-DLfq_zhC6B$-?ac~cu4LVr)thW#+#91_q4Ue=+tMM>w-@QWnZ(%fheUMh$|5h zpwiPRXhJHBF423hX(Ek=%%L*?J~Xf+S+!ZH9SC9NrUWWCxUC|vZG_G>Iy#zGGy%iG z19M)x`hIn}^XrX5d2PuuqW59!v#>|VlRZ}3#HG4>5@*G74m$Ja+tU*gjn>=QFI&F6 zlc~i|;(Lv9O{Z{ahR{CY#T3_7&vJA5bqk3|mpgzKX$yoNQ;1=;MCQu_KLGj-j_@7u zK7zYINvWDRR2SLxN9S@kvpwVp@phE|lo474-tcT)>3>Q_Y?fV8$y5`w> zM%>R&e;YNiuP*j9NN*P5&vnu&h<*Q7>fwOuh&=0Jcb%^k-dvkK7b^71gnsS0-6`zx zpH^pp+a1eW{Jyw}H8gsw2{zhiuzZSf=LnVD8}V!cBXDIOwsdvnVI7cIR=|ci+{mDy zCYb~|x`8ji!L1RDK#cp41XY3?3XV)&!AwKwc|CdxqLK^*CtnxlT-iw z0iH)HJ54LJBa}96@Y`6ZJIiE|WhU=nnfqZ)l(QgOP602_@FK>5?d`E`zaYtn7bm!) z8nNV;qZIrYEO0zrx2_=g_U$)lEfmg7nA|YH;|eSND;K1iB}JTi=XN8r3pvaKq2`<6 zlWR(Ywbowczgk=zJ~_r$(^&M#eyoN+`s3pW+y0a`R%D9?;SY|mr zDR3^}=E?IaVfdh{Aa2{PQ1m$D;Sx&m3mnj+sKLMMW+s zM^K-_Ezo3#Dp7u1<%6ZfaX)sACM8KptG+s{)Y59a zCQ#^d`$$LgtC^3Q;nF^Gt%n^{C%)JSb(U6Fd)<6;@%GJkV%M(3>gqP@>T(3^_Vr^L z7(heiX0G$6-s)`gneQ(+G>C1`lPmb#uhQhHo z`Z4Oeh^98&psAsG;<_VIaDO zrQ5RK^=Z1-j?wU(>jf^6}{n;E=5|{TJ%ToL9JQH~IY9YRVRcdJvUk zX2xpUp=!%LSIaj~teLS1ynTDj<>lauF~j7uyNYa6A3cBk_`{8*c^@BleESihW7Azz zexc_5dnTU6QI(&5f0=U|{;o zjj^S=y7;DTaQ@uCDn;eqho=iek1u7`xx4#-Q||U=?TP}2(&1|=lDFQL%Y)|oH2PkK zk*o5o^WD&c4C@=ezP|QExT>RmLA`D=U%s8}K>t%(*b(i-2a$?9G$yUrySVVUY?;3= z{(mjB&t-EgkpHr<2`y@UAFMIc#?l_|-IAxxYZ4mNOy<=-_!-_4m;a|BJ^vn~|F zYt+v_1oshV=R!K%#llt)g0>YQu2N_#{_OXZ5SYaR?hO*yE+R4pM=8~=rSJoeJ`gwW zOYUkVb-9uJ2rgO@!a{l!`l(6AlxpV;R{Q_SA={bLI-jlJ_JqI)16Cc7-E6>$PFnh{=~uEE01f*qD4^YC`Z^Le}kH!Nz>`6%$2KGJ5Zh{=E0#) zNkTO-^2K*!Cg`$yp>BdbPhxr_v_J{zByRpMM&+td8&~MAZ$s@>{{B66k52Bj-TeGX zXbRLAuNnowsfk<~_zp$k`+c=|1&qmy+uEGHW$qrO{6my<z}APm)Ft@Rfr19<@a4wt4ZUBBije*herkb($iahocmu$MwdwDG0{~%^Jb99s zbE62y1x*&laKXfZPNz3E7My>1i*PETG{%iY))6J)$x=UXYN4w~@f{Os9;=Hz;9U0Q z58WLZqyM_GXp14)r3`;MXOwuD8{&gha^>da5dWQ@0+8Und9zDEK+#Fg(Qyg{#~W^L z2^CTLSUuo}?t!#M`t<1<=Z6o-MG8phV2-Z6D5wGK4}EN+O&U2Z4C}@uaCeZA&t>M| zhJwWg5_)EL4qT$ZB_}S?kkpyu_Tc)_+>EcFF?I8MM+eLL^@5O}f%q9RrW_o))kTOh zDl_{{ZLz3!`JKoI6JI>~*R5m^Dub7^FT|cxK*oVjUmN@>&2w3PZk5*j9rLZq1j7=4 z|42pf=aZ2@+QEGc?7(`gvezkC4x84g=0V>Jx=^$Z@frn!z3MDv`pC*R?s_M~tC2>g zF+ps@9RR;{jMU>9AF#~%X#x4cFQTi&t+yMzI8r3h3DRDq0#~WJA!5Q;sfc4V@8YBTPRzE?${kIw*TekieQP}3+! z%kA#D@sLYcSY>kd!1TUVOydy?TyyHgyY*$15-;8?4N!v*R0Uvd*==eG>cA$9=J))r zzoI%<+R3)9VfdoevF&oMB<{z?zGXMnP(sY{->s~gq#fs?l=z=g975F)stlMu@K$K$ zpzXNx%;SW8ajID$43B$@H0>4opc0;vas{+wbOo$+bRJ*jQNKI)Y|4kbgM>rkKg$fq zoq2R%9W%4)APDT=V3^WqoEQIx(2$Tt@>d!o`Go$2dyQtuG6n{%w2SYMMmmE63j$Eu znYp}4;g*}58$5K5N3^%K!Glc8TMxfC(?#Y%NQhnQdlrUK!v*NOf?Mr*_wIguA8e7r z$=(JHR zjjl=$Nbi7U0cPV(%UUc#cYrlSXxa!YnhDBV;plNzawtj^)jlGUi~|p|C+tU%dv|np zY69;@^@a=hS;H^rA!gw9Jn<#dc(L_V1@tr|M+cMG_YIyKI!SMg^DLK=D4cMM0K^Zl zT+^}Dz+tUi0&@Q>ZWuxl;-DI&nA@2xm3HH`VaXWV+0zWtS-fS+hZGEdDMtY6F3@-Z zJ-l^aQ{q`0YwK+|NzkF{ghok6EshGU;W$*~R@=5CtmOg{KPW1yL2KM~^27-cxf?%e z0PR4WqmR?Tt=p5|e+(}Jfl^opp@S&+aAw~1@~VCej_!oV{r0pU_m&j*hFD~%BhGzr zanrcVx%yc8Ow3*)uuTCj;rj-bK;@+a*XH-YX7b0B>{A6^-NGp(5|xHmKwgKp7YK$h zVxwv}pC$=%ohl6ZF|$fC6><;AwrWKWZ{J@0^{eiW)umX3s7_aHIq*s8>;pR50>Z&)08F%I zR`!UCh}b|W+h7H@K0e8!B}?pWZ2>jIH~P}bUx@S26J+cn3wnp77nkWloS<+P#08Y! z1x9ALZ=L4E_-uB@$;HLxxWrAn`G6mxq+M^ML!KZuv&=Ru5qM6SwQQ4Nh{x&zp8QzU zlAK7GaEYf^TT5sKh%{{?dvGH*&EMm;-)fl)$K2JWrfm8ab<9XUfB>XL zGwKw1-pid6R#S>+=ayikW-5XXmN0aOU%N^3jbd$p6KyjSM6aMC2u4HLjEsyQcMKDU z-%p2Vp>af#yTa+w&n-LY9^Pyc&ft^*XzDD1XeE6lM#RQu42Px-#^XGBj{9E|yJ*p( z{E-^133HP9L+W+Nc}l$?_|c<;*%i!sZ`f8%kY*Bgx|}wLfItOrIb~vR?NNR7HInlO zJv0K{azTWTJJk+nwF)Tky0Wj%Q6r%$5V-If9f2T$791XANHf-98}c8+4_kbsE`HyJ zuXAn}sfYlZu4QAJrK{<(9GWIRR(sDKLC|?WHwyNgAxPy{-~sNfhi=%lrdt}}NGZ_B z%z^&YUNMe}zB)1X6LJYM2)KVgO!2CvB~FP-OZ$OyOB2L8PeN8G7kaLbMAjIB4juqMuxi==Y_uk?#`%7p2>~4jcoD!oLuP8?o7* zyN`1a{zjG~H)b#N9bOoooAsve<4VDQl-NDacE6Hj442gV*Uc?;d*F6GX`vOJDtgjh zo0BDZ6(dp`>ykV+Z^%o1wLW5#Y{Iz(mt*VJF7nyQZq;h{eD=G@*vjT%mfL-8Dj&oQ z`wBj={Bs~(ofRLTwGF(2(&uSo6h2ksu1qDveZr+%(-Fexod+wS4ELOcYSce8G_gwP zD;Z|Otx9umQm~=pJhR$J$oSip)YW5qIdb)1XD=!CVlilIx4Dxv@xJ4Pwe=_p0vT&5 zk{RHs!c%?K6KU8tgmuN_65mT0h37j@YpU(WGcNY3fP&8J2Og}qyL&~|Ps9h#zN^dB zH8o{8sN4(?NyAJ{rc4_8(OLL02Q+T@$3Tuhs3@*Uk=+Ncx^?Ro=75$JW6tL}3OKq4 zEL*(-{RkZaM@O~v;l28$4z6Mc4nSD)J#ittVDWf3ml2H~2M1DyQ-Ua;(vchq^asjIaC`{EH<72(7jSDM0&^V5 zo4HN!Nb=)t4Hr?o;7ZntGx+rfnDTd2a$bm`RE=N>E1*W(D+|p=!z{~dSi{BnT|8F` z4bGFpPTb3&1g9xIBZe9X^qA74yq19Lc*0hJ;oG>(Z=tUYnbu7th^(A!s~kaqFg~k` zU4(wVNs*bVYT&3}dmszu%39K4Dsv&mRipv99yzX8qpT5!zwHIi9d!#c)Dd5R<*FoV z3YUZ>CYqv(f$AS_w@8V`pv32TOlCh27xXV;q_hk^{Gbbe;5yRh|Cxr47l7@!1KOV| zXu%zQi#ofz6SlyC#`@SXx9@%xuHN4AC_cQ>u3=n`VvR6mA8dwQJ)k#7&s=u()-78| z%2y-#Jr`pq4jugoddJrShwLyVc}4VXFl-%?CH0o;<_*IHRN#_KB|8n*FbbG{fconf zb-|+(tOX{+b_|yt-1Q7WBiCs%YxQ6iEA;8^Cw|RnLB+OF*D=+y?R3>FS2lv1WPSP| zd9;q>Y2Rz-KU4IrEaRQBU~{0Uf{KRI2LY_&+4L`D+l|!cV_#h)khWys^_Aj4ZM$~Z zlc;5J??(bjI`V$O=ii)-BSrYjAoRqSA&*fWVF9g)$Qm(R>FipJtCT{Dm|1?tyt}7I zV$OYK*->#AIHCALX$Kc}GT#ZHh+?G!Mno*PLf8B|;0LgqE~8crBMmz&Fvdl-;6FI?I;US_*jKJnTQ6(7?Va;Ezwk0Pk;apU))2Y(HV3V zaP~X+NfH^%>H#_!xx#d@sc?FsE{GL&2j**$3VrpPLZHqqz53BRI5>V1Ty^o{(ikwD zFlYzLLN#pDcrfBg0}>q{m8Q_Eh;A0K%Gi>&QKJi{y;hpARqUVrHL;T0MgHY>w^ZH~~<5uUON!Oz+7>+y`cX9|B4ww5D7!sje zz}mcn*gz@vSNsEV9D-~E-!)_VB_ktIml;Q^HpSJ4U1r3&xoP-(587f3r8uhp`T~IC z|3Kh^WW9qCN@CQyfDERgB16_$qGEzVTg3kR1JLJ_qT!eTa-f%|XC7Wch=SldAn$qj zFg||V`>mJxswk3fLFkb2=qJb;NF~JHEmbf`$ue%3EmEMtE2{H?rZQd+zgT~ihK^j! zo+J%LvxP;3r)PY8d^dA^R7}j*9LqQT)8sq#Cx!jNHS8JJty3I)df6RaEF3o-7iZ9g z3ARXm0E-pOZM%811X-01aQ+%k)NuHm+Fv}AAhdrBCGE@%I!=`?528{sGwBw$P=w$e zP;HSC=PIR%dQ6IFMjBd zcksO=D3ztxqSTI_`+(-huoZ)!;+_=R4un@L!+!*h2}?c@1;G(JYuJz%d|+F*P5`7U zFt}<}GODp-&_scQQa2V7?Oraq>yu*;RUQ%-N33aUZVoS6DXVX4npobCJbW4?OW1mU zI+vi%8(7xTkdUNi;Alc=vRYyik3$^iSA^eL;l=3m!ht+u$mpb4!$K-61fK&}=KBDO zHtSmy5s3?7T9Wn7=&0S-{+~ZDMqEVZ=xXy%i81Be7TnNoxYaz~`2;*p6qXvDaW0s= zj3rQ8mD|Ukqm4B+3XNNbWydGNl#~*_6qVUSeE3TIYT@FT9P-DsUZx(* z6L7(N4j2lg1OD|LF@qsrg^`ixzf{PH&I>E@nCQ( zuoI1;ipB6RUOv8)>=jrCBTwQQ-s2<=UV1!BrHaQ%NR8+ANvkDLfx~c%X$R2KeX69V zve$``q>}4_pG=N6qEUxBNQl+U&~OZBT<3mHyrg&~lu~t+cpOLj4<7tnQ&U4R>4+Tt zfPa8+9g`A)6z$(XI=^!feDGI$9L{0p6fx0E(%$VOm2u`c8*orEr7PNk-~9yPJEMpo zR#*7YNB{-uSrzAkviPQWr9{X5YB$2MUgm;om>BtEEe^;POx(PH*w7(|Z)E3Pu&A&b zG25#8lpgt%c{~_|gT3Gu2ESIU!ngsQK%|(cgy1*f@f_P9)>+V(uG*k-+r#@4_2T*S z6>|pans9W5xwHgID45_t6ARj?n*;AvjDo@sFI}{#eebHR zB|^y^E?(4u>5K0ERrI8@pJCGRGbJqSoBYXsSQ2tRyh=uF0_Wt60UrvIc#{%O%*G3o zSCtLqRs6yqMjG$Ey~gENe!N~)avZX+B4;O4C9=2dA`l>4XJZhGP}$jOBR=Z9 zI5#W+?F|Qkltt;kD`xTYj>V_XpS6C-2>VDuY)k}7*c-c(^@H^?VQ+n?oHsybPBY#J zpC{OGY+ko66?3Xe*2KsX$qE7pGc&V&>4^)_R->m`dXpZ*-{A1{nxKwzF1k}m~*!^hVXBO$eBYqh6jcbkZgh% z-vavpglmPPtt6emB>;^E28^2Wb5aF~mxtU}v{V9aV47*e#<9B3Br}=;y5#l?KR0qV zfti&xexd~VqW!P_r+O!NnHe4g#j=Am3M>QXh(NHfZ+NfxlKcMt9CsXHCWqg4?z^%@q#xDR#k#M8aGjbpI7UZS-)=Gd2{pRnsCGiJZ87t zY;3B`odPRcJdzM-P4?3P24|c;6-0u;x3h{$@+y*a1Y{2!3Z$klAv764M-h%K$Ssit z$gXa&AVySBbhh9Z2MEl91BzmS(KCgI8oSrBRW5M@&K%c}xd@PbUnUAz2(eyBh5HAE z(!_%Gpk&6@(%lW4V&%~HvnU&-qLhxNul124LqqXc;suXm;GQ-=TnT7)}MmBuJA z_d8=HT!b11Q6u07PCdR6`@tc zk2Mt6c3&T#r_aS<3E|o}<168PK00b` zVF3j_arIvmU<`)Z`pe5npVi+(kD%7L+LZty#T7(c%UpU%04jo(bI^;!4ap)Osd?#h zoQZLT+BnL9ol%Q)IM#zbN^Uio$I{vRZ5%lO&l<(6*~$x*oj>50E86&iW&_`aqo-Ip zyhzzdVfT4kQ89qB70`gvVesalwg!Os0pSzF1GsEhncPnRF#`$AbEXR0%~kYo>Y9t` zJ*na0>_<}|&&S53f9J+eWu&Gm-enWDI3cQ@X{^%n4PTC$AcC4;3Q2#pG0E!iIQ?y= zVB~^V1a$fV`_sc=<`#x0fC5|P2~1PZjCf|knVJ0#nIMPVFv5cRU>^whN^F^7PM+68 z1`@HI-s7VK3xHcV6E{`C$-kh0xJ042Gt894S_~5>OLXqarFL!$%QYpxom(}by`v*; z%PjIO|E}u^iHUGD*}1H=We}760Pm%3ME7^i^5xPpGTb3DBOJ2jR{$Gf6MZNzck1c~ zbcc8&;WDGWwy!eE)8b>6pnt=HlWc&Y!?vtsV33uQbL^_aS41Cb8YaTXv!ZnFBgmLK zYwpEiMG5;4EPTXHNGvc-V5~-WKRyBuoh@)3vLC+mGLj^8Lr4D9z}H%FK7&uTb`gd# z0nhkQ%Wd@m+*PbZ?l&keH;^^)0*M_lJu+re1i1@o13+XMZaShcG5e+O8y*@Wj)oRf zU<;w*)S_b$Dg zYx7emp}Va%egMAF`QyfWyaz5ref`~S-@bqUcNx5_NdHHr@E0I;6!64~6$^@0*cj}a z`+j~ykU>D%hl=#5Ulx?b;PKHOpT`M?1}2f&J948+pL1YJw7ha97RWXJ1C<*ygrza} zRaTbgg6>-!k)$5>IvHR>1Bg@ftC<1hHGI;y7t=rnICYBj++v+~a3ik^gk|NeU=3`sU^mu-`);aPvXZhyGvH{k|yX_zuT4pY)L2!4;t%viI#X2m=>7ZKm&rs`LSxWwbaF1@VHN~d ziT@7(|FBXBUJ|dar3J?u_yrxik*b$!3jhzr@MbR+4j(B=uT2BMv5H1yY;0`!YAbJ@ zVAn{y2tD{JVOAa7O%zB-V;a$Ut6q^c7o^FDSb6Jjyp@bIrIV4GCMNMQOei^jfU3BK zY|FS>*m$3Y&<2g=!V`Ey2vx%6*#sFFJb}BP2@xzQEtN@Ufy^Wg%NVPKW|5kg=K#wm z1BQFrl|IyK5gpv0X78v|ti(89L3>(MJh|^CFCr*^;L&l;7i$aW#!Qn?N zsYgrjG*M5kTD>|6AhI4AeE|@W%+2<&`PWCP+TMt?(QJ2nTM$STm%)ucch!Fn$hpDu z!-60pwZyMOmSzazje&uthKA_uEKc%nAOoMROsZoA;Gqoo1y&8fLx-@(fe+Y@59$M+ zMl&f=DGmp2Vq&77pE5Ct!MmdOqBQxsAb`pp3g{Sl63GA&=rEuMPyp&lR@R5uvSJ4h zI@sE_kJiY*!xI-ytVB2?p$r&7goU{!|0&erkc;6G>CZ(8gF*_|gbv*D?9iT? z&}m7WV=`)_W5F2h&qt|=ML{=}$<_O_62f+^zp!imZvOfI`9$*1zmY$O@BX>ZzyH>? z=`UjT`)@uS`~v{~{tJrzsXyn-@4q?H$Mu`h_2(t+TY)M3A6LQOm;7_%g5B{e;h5Ms_w&w7 z*(wn^bLe-z^y0tIzVUup3seL|;(_&_YAN7`L<|16asrju+lVaWa~-ie?mhpLOn)9+ z>kp(V2%4LKS*30ea6^=T{puB$B+h?%JpW=Y;gv{p)g) zhuCJP{3cJiYSd#p0%q_E1+%M#!IWO=4oe=4vEDg2I|i}tbRs;*4Vz|}s^7(kb+0`5Am zf=wUJ-CNp5SmjwsyqT*RX%msF|3f?=vrCuSK`_{U2Zm`0s2vbW z1Y}f!xne>TR@i0ZERQ&ya5cx4Jm*O?tVk#8`o;px18F{wsD|_4{I@yEtJ9xky?6^I zz4bvHgu=l?Z!cWN(TxD;D5R?Dfjb1T$yX>`5j%nBiGIC$Bc9mx3RLlOC9zT3N`8AE+}c07szQW(9H+ z8rt*yy8s#%@U3%7K%H#~L;+o8bgOV0kPCLnZEc_H>Zmm8yAL0d(8a*1O_*k&r}Ogi z4*MK;$74g0jV}&=Jwob%dm>_TJTh)%xmdeDT3dHAds?M^!g8H~^)EIF=@XFPT^57_ zAMm0jN-sDAfb|ZT@><$`tlDrqW~h#_LqIyasIQM{EoqqljG`JJ1Bu&j0v!;Rkzvh+ zttS#6B-#|5iw`yk{n^yvn~4u8hcuwSYlT?6FlILN6W0>T#GgdoRTP{+_`@$-x$^zT zk7`~xy_?aUn!!|SFaIDZj`f)DW!8|<0mf?SByw7EadL1ly0`*p_zWNwCp3;Q)*V$! zXuAM0y8Jn-2eq`Da>+Xhct)Y3Kz9QL`_ZFEk=xg%T`XtZriSDRnHg9Qd_iThZXksa z#>_$M1hd*)^c8Wo1E<2dKUm+absG8sPtQrX*1-Z9i7#dv!h#3ODgLt@5H+;d5Le-^ zh|#-e*T2UEn$l8xSPUWi0ij(D9SZE`$;n9*?KD+&^=Bwe@jqxR6t19;0o3>qMpi+n zppMCX41*!=-R@{4ZX;3- zB~Cv*GA;{zCyZcBE-e9T3iBYo1Jfn$-#F80g)++v4)b3}ld$dQPya2}oz+Hg`!AUd z1-gj~rZ?iWYI{L%&^7h(A5bAFnNV?qfqfv^IRKMj0+KXo!2y1cfI#Zc6yz#coO?tu za6ld1U%W9Bn`|8M9)LRqzM3VX6^ICGJa)iXaBL!|mArX_G&n>KgkoHD+)#vS$%nHy zfs7!f%tG$uYwwv2HJb!l>=5|hTD=v62KCbT)-2eelx@*kFft#lE*&>y-9T130(U!(p;lDv%OJUeI_gXRyU6HhGU*4TBkn+*0IsC^ z&8U&FBZvJTa>Isw+onyidQqD0s3O6W<`FsRz_Y{@?D7i<8BcbZHbI}&Jdw%+$O09}TMQ`6WNv_@3UON{j;eeOP&|$S?(Fl* zir5EGC=;7J`zX!jA#$WmiX^?YrNt;qj4~AFCnFqsCdwQOlq>Ehv-=5r%x$18K37%a zRZY*zN>Ygrd>%*w^n8LB_80~G6moM0X5L`fDFHu$T{j{L4bXN#V-*l`$}OCE1Mub6 zvSTg9R$zJNBMnIjsA?H;b}~{@M5}`M75*}Tx^csXedwMJ4_{mL8uq4y1!pY$?>?V5 zD(dQp5+_<#ov|W47R2l78MbwJ1P=~7`SGeQkyykFCH36;=9n|k9w9)>jW_Z zN*>`~Ztvf{BQEiQx|;yeJ%rVq*<|N}KCyD{q^750d?))ZHE7Dn%1(m3C0l3&s@(3; zAJy6O&XT?_34EA8CWP;D&Umg$i#@>~1h~KgzbeYvIqT{VNknlMmsyf!=sS?K$Nv(f zIoRAT<1HJHI>Mm@D10m!s0%D?N4qt^p=%EG{0HYII!zC5+yDxwiF<L?70DB>;) z`S3$CRqrGUSAz>GU!FA|YzeB_({YyIYwhFWYENV!JTk-&eeHAl`YNb~ zP#5eG78Zt>4_M`uqxb^odR6z_9fevlVBmqKmKI^~gNu#PAs8x&xLDf7d=Qx(37vpo zYdT$XxSy10i4C8=ykzs1EmM$a`o5f<87m?UvAWLg1q2@4H^+~#otygfapYzoM1c>wnOJXM(ktLnJq46e?txGT{0pdN#ufl` zPl3_OKoq!;HPSvy}I?iCZ8xXtGL45ENR4nVAEZys>@jP!(MdGew z&Hx5IY%O`F&416^8ybXJ2h2rjQADvhIx>=(mZrqBl=4sCiO@jyLn^Hyzbu!nS@RX% zw(q`^{7^fQr*7|qxe6?J14%}UwZMmfKa56d)e#mv7!c^LBKJYCNjrpx;+jfLP#=Kd zAS-h6?S%;JII=tPWXZrtnMTXKaCk+&1T4RlwB*>fYyraU3fOF`gtPXQ{jlZ&sq}%U zW%PDuOi(L%i6_P&V~1*|8Ib04J}y-n5>28^MZp=R12;unsRrenTM`Kgn0ezyG~ks+ zHJ3PO>zBQI7XogwMLvjCB(*`W4RnZr0J@fWTOxMFXmbfe%P+vIMEek}h>z4XccF*& z81t`=9=+aNkLC^Fa-2M88ZV-ks-fgk11bohwfwk|Z%f%6$-#w`i>G!7{0Hoza@gX^ z#{()uC*D7|%?Q~ArXpb#a^8{U@NM3c=<4b!LKmdz0XHjM^&QPQAtX8oGA)G76(8RP zXbFd($oUtGo)Bk7$DJ^ z^{60@)Wah9Gul!|m7|)&Lx=~pe3V)PFkfV%QQLvh6ppt(gg!Z;I~94R#8m%r>|7+w z{0FQ6*%9G#@x$vF{5IbN%GTMB>=^3>tP_T20=?zsmR;Kh?+O3yOVOJ9_xLTYtjS7u zG{Sh6LJ(0JOI0RArZEt*B{vs+Z7YdL)V0XH><3&X$Hy_QD5AS48#IPra4JFqg)bbD zZ_iRpOuMfL_bB$4qz$RS4t9fZ*>!GAIBU zPc~?%Sma$s6+mF{MK#y1T)y1Sq(em@RhQZ2#L6$qm_YF|QJSfE zj2Og1{p3!oX4dnN8@h^~G2W#QrA*Cb+j_vai`Ric3a1&F5tfP3o;(;49ZeT_UYo-$ zGtq`9M;vEp*KTEj7J?9mYDxPubq{aR%wbllA_=PND@+vXt@yfJF{a&vm@w6 zSzFhPd!RE6ck6YF(2}PodZXrQz(ZXeC5nVc?)2#-X#BD?Xs;U@>~Vqlr3PrSVtPinRL{@Bq-j8RJW2PUh1_AGsKB_8O}NUx2W3RJbrh~j+( zV~;gGCw~2!8y&n#vG-Lg0BO~TQ1lZtr?A7>%k9k#mW6@yixuUuH(Cs;ZK2 zhx_cf6NhT)%@4+8V$ZH8j^SSl$S&Kv_?qfMAZ3W|ggsbO2A|}n;Z(nyHy3aH_B0&e z1eZBWL=wUSbQA3yY#Vk3m3QF5JYJxGis3+o8UBoV@knh8{NUTAmGW9C%MP{Wm}p!U=@p}`TsEY z)=^!xUAOShPAmiz1DggVM5GM51f*LeloSMMP%H!$B_#x;8)+m(Z$#3dTS2aSe$Zzj`UF%wF&bj6)`-o#IbU5tT%U)ElNK#Wbu9G-{iod5v zB2tbbc_-2k=*RVi-UrFV>Q7^8G%hWRZ0(}8l}>a!B?tlFX4gYz$5xdL%b4erG8pU z3X~U}s01nQZ(L|i#U=r&qF@b*4tDy1-JSFBOXrZdAOtoEL8)MRqStVQ7cB(uZW~yM zsHmyM+jN22fogtz5y43{^CmZd12;A>przwwzCS)}2AwBD{?{MAYu*n+$z_j2_vB-o z@!(TGiFf0Plo6g%29U zK(@r6#3YLKI3uXAzn{@ESy_LUvwzkbv?gF;^X7{sdC@FqtCikUQw}#ito;Fi`dXw(q6CfF5m^WVE4szK_w6xGaYes zFi24lbJTz`*bUG<5lq!7-gp53tKa4TBo1zP z1adPiL08}9-P9mv1>8_}2MTgS_GmZYt&{l=w(zs8^X|P#|L>aP`qBqjLv^LRW_xI8 zSY2<-y&zJATGq_0tQEjh$BrFaKL&F?WTA*#l`uj@Kt$Hs6tUiWQ@R4X`G2ocE>r+_ zh^wDXU`p!M>7z_clK1(-nVFbS*rN&Kb}@}N&cp>kJC*@Qa5te8f3V!Lhe&)dSYZ(4 z4{=(gW2Dhdgo&mv0F9YkZ#4Qu)a=Ot+iKD+k8|vGf#2z(l1RObAk)(SQII^u(3GBf z_2Ky01Ve>DdT63jl$jLs6N`+xMq8 zR((I6Yi2~x#s;m)aGW}dLWzq8)i?y5kw2V#jAyF}p~LeD_Jsecb9OZ&4(!v1{_p|b z+x9%sMMi1al9w~s6a9;DndK$k1#;md< ze&tC0kuq6hg(FFaAp0Dk6fQ2M_JRIfhB2&A7gt}Y1uzKDo>d}Ilg}$aAOj>xXFrjR zBov7=g1Xj;J%pEEwqYUy_THrYCupR`O|J7Nz%jG=M`mzq zmRga|uj{{gyZrpi|4j&usju=txjOYD`2da=>kwlrfc(93T9LIWE{2ov?<8Ww#oVZUcX!RQc&OR)6@Q0|2?Kluklg8jVH3xgTQ?DN+mQors=BOp&t9 zdC&8l>;i_%SI!?KD^lx(7+CPvQ z6H&%AGT^LQ(|*a=lX^``>kUs|MkxzOFit&JCKIm&>}v{$d3+K?#eSBp7c}(M$@No# z+IIW|=v&ONYwuDrP=#CfU}1LA?DCUwGrO`t58`MN76d6~u{XmP(3p<25;Jc2L9C+~pZ_fN7^<9@gEh-Xw=I8BAeusaSv080$%*m|y`{il zVhiw_dtgFJbmkx-?8~4!q;0rei?0r9>F|-Kv65FRYb|Y}Ap;C>G~-Z0xr!{vE%#>p zvI)W2ohBi={Ll#NFtE{Ei1+fttrP&x%1iy!L5@R@ z6dR50g0lGD#2V6jZDSLY@=_1`+a1Hhn9Pk9mYG%AB$7hR!cl^pgRC3!Crq!~I2+Tz zdp_Q$rvwZKIW=`$_E1wGlpJqxh$E>=M5%|x_j^{JS4Z*}FdY2?S4}`dE5yiL9NmLd zPOkiBM&GO9e&Pyv{rb@E-R1~^$U|e#!Kkho-99jY0pc-RPevN@*Om(A#!GMs;a}7v zM3j79aHXua702Cu-J+(}jK6^nQe_uPbYk=dq49WCT~#G5<9PDigRt?rY$!b(e}34| zl^q^_U0XY}u&^*ZJp57M?kF9F29YK)*PJzH5 zS*}vTC%P1b1SEeL3fp^}jCAa(Nz$qBviSjGt*XDJ)iJ{{2%;d0V}HHHsfXjF?wTS* zR0MzYe-cxZEbkEkbd(Y>hAHM9q0U2BKSMn+Cki1N`|`WB4sQ4BcT}fP0U_Is+y{F@ zLYs_Kq2_fW^iv3gZZG8+HDnSF@+Sd3-pY^#s_3_Ry3$Im6vwQvhzO%Fl9Wi?>Tt_9 zmg7G1=zUvlvUWzu!9lt=2*NHTrk{XQY2TG3T|X1+!r&FAmruP@5iZS#-HHuSG?xH- z0Yj14F^a+)@Mf_qx~5nri0GQGA8fV`yQy`2Y6`6h_IT@VTt{q(DU>Xj)D2=sJ`R7K zM-E{Oilmr>S}6E*bi#7d=&6!h3Z1n+R+Qbt>jGQCK9NApAYAhs*P_?kQVmBC0 zTta7>M()#^BR=B9bJu=P>beRfpIw=nBq@ci;EkLH0nZphu!Z;T#8HE&88<(QXz~di zx(6{Dmuz`0J_6}0<9R};;R8d$8DOcKckJK?Wr!>ny-w;a_bxN|C<5(9b_XRDYKCN) zd+jXvv9^;4ar~fGAb{C>AAy$uKTvGd28h3#??;Y{fCX7lKo7RD$Loyj_;-CaUYAO%5PZmR3^IC^?!6DCVSm-S z_Z54Uf@8nyJ)_Rp1Cs1B!J#%ZHi|{c73raw=4NY7M#q;)WQwskupxXh(>QkOls-n% zxV^+W&c&(p66+4K69+w?&Ef$Dt zl-2o1GJv=cui%SZ`~zqk9$WN9fp>^rv6_+A_-Qbx+gz~o`V}uXH8h;#;n~*Tw%&fz zI=U?Jt>3)k;lF*g4!Z>#lTp|S;5=3}z1Dt8N&rks7HBF0|2F}W*U!!zLTzdEebX&R z;Hde42CNKeLI&{lOxjS&PH+3v(9`HW1wyc}vs|NA?{)%yQ#c zSQx~mTg8*X`U2$Qd{xHbgV*Wnd&*T=Mdjw{E5Jb@Ww3hrvJJM=T_0Q^L?3BP6KRb_ z;KA0DFKjC<72~!c%(&|zAI6^EBRK1Kgmh2zt3&bNwLV;bl&}6`g@(e^=PEoOU zEFzc)2g4$2Nxjsv=1x!;I3O|D3Ih1ye7YX48iUXwG4AR7DgP)Jj zNWzFwc?OF0a2XFD-VuyyG;4fU7tdN!uWxvGCqPibK9tb59%o~-K!`?R9d-o31ZAQOmp`idS9`te)aUEoT;9~BjYA3Comz>$9zpL7AY#%%c{1e!hS_AI zIuM8%fyb4N0k+4GU|zJOK23obWj~v)C>o+S4&c@pHsOTDBN&8MC2I$!xf6y4$T2sJ z;ZUQSFW4u4LP{!!qF~fINy$i+8+iY~uu<#b;*;NpT0gl9Ccd{nURiIA=VB-vUY!gZ;Fg zBlOO_g#HsoxiPPxV~)h}`pyMtbNsnce4g=@T8uMQ*oaYFI;dy?$fDzU0Y1z`%N9)R z5K^JlBu0*Y{CFK$DbyfLrjJ!3#VMFg2|u6H>jOx{BSad!g41E@?wWPDv&hO5gme~~ zcL+g39r_8X2ZlXZ>kzraO{cH7_f_6rZ{wH2?#$% zQYppC37zuN)73q9{(RhQ(c7YZ1LpgKsug-UaD2<@SwV}|*xU?nH5uD==ET!`{dWy6e?iQ_ z=ViXp9g&1n)xEd_jIejk%Sg#nsH_$Bwn#{n118EaX;$V96rf!ty`)&WS`TmFh zvyR;GoBSD!QHLwajm&D|5o3dBWSMGY2xIocZx0y5w_(Sg2(sb3z*MQ0+y4G6e-5?x zE4{EvL{>RH0qPO2AsjMb>Zf?i14#E_2$MX&3+K<>yCkwYw%r?@)IaGM7+^m4M$Grj z5C+b>Y15_=^bVk|XMO&Zno~S2jehF5nMy4Zk*kj1YW+8v5sK=<5HFMm!W?g;csZl>D73k^><-uk8j)!V+GO)zl( z_PK0AXMQtaMda8^G$h9HptIi;jnThN+Rzp#+(?Di4lH#}nC0PhyN!W&eU1f>+&!+a zF30W-p}+=C{_L?cLNO%r%>-qQSvk`MA<)4=-h*S>&AuKTCL*l{pLJ#4{LuoiP`aax zgW^075gMNiGNm{+@nbJh14C{B^IxDvml7$6qZ}FV-*cr`3BoJvJdDze#|wImOqkFn z$Ht$#Rk67}$xJj|=t>oSH<;#+C-;g#67J9){4KAWQ~!9i=&567DlTHHf(LaoO4B;FqK44$2sS5on%^9rKMXgR#l~g#zCQGpaQbiLi?W^z_eJyzdeu zHwZe2=3I`fn14X5JjdiPGw2UKVFolI;71KBnf4PIyg!e1lIc~p2l$ zS-HEy%EQer7N!4a)V6TCD0rZy%T4%xDc20y`;q@_HM{s45`tCu@&s+4@IN-;H}v zvnrW?|M)+?<9?qXpUOuO(NkWX@t?<%g%=l!SN|~yN*Q7RinzFVXuz7a9}F{yV2}S$ z{SYDqq~gbq9fJcY(T#&d3sf77Nf^HJ5nK(|iB)?z;$z24;tbH2Gx;#w87Bmq6d z<4L7II2;%PJ~(%{%@ly&!*}v-S6+UxhzIF-W0nqn0+MKHBc(=5|m^D1R(x_?-b` z;Ym`A?ym-!j6{^`s1fwjR1^U&IH7SESv_!r2zdrQGoXow?FUm_T-LUqJudbgd5lBu zDdNMOkiTb2+%&GuhQ)`EmnGvSCDNV@f|g0t39%E5bWxikYzIX zLR~Q5WYO_puI8rNMW=>!hr-$&MRrqk^*e|$@LR_83aBv~d};drJ>h0wW`(3|yn>(z zqfyP~n{7-HLhfHT@wmQu!#}9DxIbldM#bkw^vp+ zZhoppOzfys;G?#BVVSXf8Pcd*t{CUSlZ$~$?17a4?6Q)d9^_69ifFRJEIW`&gw9$_ z55zqVoIOI{Xf<&ANFOFjQ*rATPX9PSXk2mKRUdnE+5i4V3A_Bx_+EeZ^ea0iN?ANh zBUX&Xeg<&*RvR2_SDmy2sC;pkTjnCI+5C2e*XMuc+xG?As&3yoQ#!n2^ilhBAb0uO z{@XuSq+NHfe{-YyltSzIm*k(6&b0;vC#~GL_ARQiE}dhp%+@wDkGH(oJ<4%m{Y4o; z!5cZ&)z6%S6|$22_yX^&$i2PwvhV$xB10<&#;)$I@7~pw`&E{BP;EJY@BZhX8=kqn z!G{v#G@8p3?=h4n?vgvb$>Rs@;gXs`rOj1W0+BpuW#4-93$BTlSk+0A-Vfmbk_#<* zEKb<@2*%Zj6!T&4e{3d}Vv*;%WCxij&Yh?gJTpI}QXn3;Kg(2NU{rg#dE}f_9GyK`t;7dNor;x47ZDGH;O44wrGwQCR|(Lp{xoB4*r&wb*+nm)~LytVoLWLt4hF~ zm38xxG@{N465a1FB5=JvRU2gX+UxqP zj+Ncx$K(1H2l`z>fWFgg7Hfau7Qjh^k{3RQm<}=%Bql z?XkL1WqkgQ0v{D97Fn*a(+N!sy|8>~Z+N?Z!6+}BGeoC>S%$JW)<1W|q3 zYchKjO5J~6>|#BT()CMsy3c^#(ZN0bbwsoQ*(jLHH#q_jBWjfxiOO}&pR@A)p~x$$sWvaJ zu5o!ammIFLu1qwp(y4@2jI-3Z%1%GsKrCa=rx2$y>iGzH`CkgOloEAb$9W~=H1Y#5 z?wnREW99lgn=|1?*40j2JKmJS4AOes=rCqSzM38^b(*$irCJlW-Q%`sk*zqTLsgG+ z`2Bu%19VOc-O-Xnoz!Z`di3ZU-`5ZvhFJOl6GI2cxH3j#%Ir)l^C=Kdkk)?angnaoYgShs0bd#6~Llc9IY3P+r`!dYCo<$>Rz&N5 zTyyl-i__J^T9YBnW7=Uy#JE|Pn{UcVspRJjH+~CX@t`-$tt>Ew0QlC&osYiwo;$2U zOVOJaOs%riGvwy#IzG)2IHBqMu`m8>rb)(?9oy`CFP>qNI^lFkBYWQc^7lVw$sCM0Gw6TiH^W?oL zXMEas{YcdpvaRur#+e|=N6-?Fnyay~5&CIZH)ge1NTB>fAxd$VaF!r>{8zoZV5e}bFI(k&-%^RN< zkDZOiX9GXh`wcLO>&e<=8pHokaiRt|wDYxli*#|@YG+S^q;340EL_p^r z)LYO?0Wuhdut&z(=|d2vi!Y}D--Za0P@rN0kuI5E_CyuW!qfm4dI%9wRyc{an%VV# z$EcOwsIapTR1xD0-FiBN*S~RO3lf0ptcL^j1HuBGHiZ$Jn0l)A1Nv3md-+maqUui_ zO>u>I{gya|{cv3q5}LNEZp-g3yb^oh>}bLnAzj^yu!q-n-npfBtLTFelD33>b7kce zr)rl4WS<61`*QMy-{=&cE2Ck?NGY$>ZVI0D)HT+v6jSp?EG7&X{dHp#8N=+2H&fsB z7Q&eMTBj)P4lJYdpkDKK=j|`CP_NBL>^GYoRn8bR2%f5yyQf3BzDG9sK#0n&piPtN z<`OPDdA3|4ak+u z&V5xn=-gPHni(_rLEBi1f5Cdqtoz)rcK@Z| zw|WMCobN5>C6_By_#4aL&P$YpnNG4FWme5DaGf7MwfhVAmWO+ty4p$&PYS+faq8Si zlEQ^IGB+|*-HWlg&A5!}0EFsEdtt&~@6rPU*|Hb3}vE)YwWUuw5U9vj=y4H~J%)nceI(7A7^+~aL^q7ud05j10 z`M2OKf9Y@}G7;i`FePr{As@HmPS)=voZMS#ENCSy@*FpgJ{|0K-BawM{bPK?dx5g~ z9_d{VESKM{bGXPxCGn7A(c5TgoPv=5`W;}#H~`d|nk;Lxi}6Nb6^&r3qOpC;OE-53 zpA+=fgW3H8W=jA3W0R*bRvCMJAx3%lBXH(sd`962qHqBv?`;2-G-yh1&4=WiIdcYJ z9hQv}{(wr+_^cWlJJFZ}B#}_=Q~51dm!qi_xe#HKI~%cT=9e|Qk&N^s>J$Y<)A;z> z)(vq5Y}Fj3#YfJ=cg3$7pM`+L{+(;B;glHrU3aFQF9y>C2IVhkYdO z@IAAUo!#(*Cn+H;(>5g?$3+gh2b9>`WClcuSgR;Y?OH#f<^1k|Vl_W^#Z-~py*7uz z?1X)7S;=ZVzPmD--ijP%QCDKvrM*1C8hu|O!|)T!coDrvW{dg=k|a_HP5d1V54AmK z)xFvqwykGxEQNeXLk(zX>2=`u;23g%Dk9E7c%RnjfFc7ax8qnb6*LkVA+I31hnN$> zIu!FGxt^_S>@>gwP@z34fAVD0`PakGd4_v|N28ZP)8^+x=zSXtHS+DOpv>C1<6y5N z(1L#lK1^9qT_^PIPt~zmaXK^6{V3gd|5(Sq5_+M)ti4VPuS<9+X?!2o);yz*<>hk` zQpnBB5hI_YbYAYOI~FEQHQjJM$%B>OLiuf@xQ}<)*Bv{vGh*{!*pIwaNVs{^Y02c+ z#MsKbnfVXlpV1N2w~l*0tQ`cC(T5`wEeE}Lvj9Xnsi8yA1q+L2GiR6xqXSJby1LN= zKDeKO@Hu?11y`)+gQ_|SA~j$6G$oum0R4DpT5dllpL@NF*c(Uxz$4j>4+XvnbmzZw zxpZ{%rC)_wmUbC>!w0S=tf-G0FLfM$tmpHBvXW0NE}>8Fkc49QR_k`=oZHO$V@Ky6 zhnm)i?mjeB7Zpi;qpl+52Df8V=Z12FmYhoY`6CBO{Pmr_4=!YpvWtw*@+3kZ=|D_( z08U_}17ZoP19~12lOmT5(0jL8>v2$nY6+Qij*@;o-yLYCc#iP&SAP2gP)QbRFc?6H z+@}hDFWJA7@whmL-mm#KQbj2^wjvNOe};xdbYwC{m!oN?*W~;l)ykp5X`YL*mPU?h zioTsb;r+S8<6(LiTYIHdiUR@+%c~Us`IM96l6FCp``|~zxbqpUsuzWS4nEjdLOzmb z@XjV>bifP>hf|_0fnhlfq8H?^C*Q#oDFz&0)Fh#2660f7}|mgVc*&TWWq zV_MFttQymXQlaT!`nDf9TTw0q14YO0lDqoNB|dz~x7(DnttUaz$lN@oS_$Ju6BU&# z1o%&gd8Syt&v|g^&1N#Psavfh^>%Gn^`ND)MH9Ei{zmi#LqyS}6!fUJ8+wHf+K=pC zc+mzK1c8UQJlZEa9F;2%;QIj?pzDPpmhkE~%usxlPn2Q|e~10FM5N!}BvQyO;VZwe zk{opO@K93Js!REBN4i>DF!@(MD^owpP?mVe z#MU-d99`(!C>?t1j!}$w`V4)#A(k4%ZG`%Aai-mu-2my08jGg4o0~)Po+H%|J%NL< zdG{I7byZ!zlP}Cfu^*ZGeF2#>>U`nJ7DxG#(li zat*Z_l#dUeI7@mjRWYWU0g603;}Qr2Zm;M5O3x<7Fm6yJ5^}IspM^qxgVwfg=jd-#cQHxKPY&Wh-v;_Oy%_5o#Kch~# z%g~Ed0dfD%JOfYj>jMg=YtL-scU2j^7@B)QK|4hm!xUc^Y(<$dxM9}?3SSJaV(AoV z!ucs4nd-T)ChoABv~)k8Ntr-zOnY^>rq{49}W-x(5aEE4$ti_p>eM>dr?cq zsQ8F)@m9m-ojK_tG;8I;(pYbMe(SB;qj!+L=V@Z#%Gbr5mCb+7 zu1-=O417(Jmd?NE9aN$+-k^C%UjMf5F$F)eP*Zr&ghb>zyqA}fA@4a}m? z70A~g1^t{d>1Tu7u5N1>U7UQR^ut?us_C9$p1L%__+H(@Q4b1lcQ8D|3Dm^;?BLw^~2WG>bO< zuG|{)GEQIWUbVs+o;BN_neV4NxS=_ka28t~0t6beH6W>e;8~;xr9V&B$i z+IT&^Emi||EuNK)hbG-_6h5q7e{uJIJ?2A-UU@tRtE?XR#7pKnn}3&c2r~U=GtM!$ z?Ci3>^(0g2f}HIf4Mf1l+BZaeIO&0%iFrGL1Jx*6j>exj&-1L-3bZFaAo==>;on@t zhyhfPu4ZR^fWBggMo1RMN|{`%qfYq$%%%+G?dKUum~M>Lc~I4-h9f}ULg2y$!Gg0Y z3dzCO`K-$VecdS*uZFrlzrz%;vfLc#R6+Y%QfYQXm(#$#d3<6i>vikdv-dAc%IsP% zHDkJ^aPYYeK_!JATvO=I{)6njQr;K;4cCbB8vuWk08;4M#6GaFSCVRZ3qsI%li(ajx&KTmq}c5EV|kYBKxzmzOz1VI z#mg$Tib`BKN&fMJ4yD73Z~2d9<_(!kwLkBl&{@pM=u+p4b#=e`&!?u;v?2Fy%)*GG znMmnOb&IK{U$UKg6(?;n;Vdy_%uTyOfCmXVy!sc}!|RtsJvFai?`4`AHQE$9mI^N2Nxd zJM^_bpX#{3X&b+)a#4$Z<*&3IG}BT3L`L0Y)L%cUC|d5D>gMmP#HSAnPC$xf4J415KF4ifGh>Fh(Sg^yUEei5Xe6p1ymF3 z2vTdR4;dMry?SXlo!h#N+ecGZ^Q)e};G*F6E#G)KFSRBmo_3gBi4z|`mi#z(s$)v==cx8~P^k~=~Y z6TB!t&a9d_s!d!G#t(-TUU$S0s8}}8IvPd<4)itiCa&mHc+Rf%l z(z5sVjXN=vSXcamL08BH%8LD*_5h5uax7y@b#s=p(XgKyPdPDtrM#reu3cR)8N(qjg7U&MH z8#GIbTrVLrza<1r=bvjemq(#unu}+PZ2Rs94M4f9)Tvc;CBNe4s)tx zSQ$oy$;A>mNfkl(ACZ|IG9Z>G7*d6}9INoCbKEAy7Q^ao>3w>YTTSdkjxmTUayTqM z)SbDkM?o2{PCfxI~k3}gl8kjLOYT1*-DP0Ks2Xs-&QQw^ne|KlPLeSV5lR)`SFd8~2VjO` zKN-V&lX>$cq@SSX73$6yAGQG?L8#T;qfCBBO<2(rG8_yup>xAD0W^-6-kH`j2e^~U zNQhzY{wVr;yY^u{`OygG2|Z8k8-0DcEZyoF?=jAM2+*U{uAkwg;pyHtv1k279!u8& zcN?aJ%bOGu15$O09@9{mv~|>-pT9yGI6dq`@o`~#x7GQQqB2ACRJXqOW~L^zn=b(n z-ax~2%bJq(qx&0ZF2ZRcCD`Bp9cpdNh1LNC0RRe$#T6+1poDd7fTL>#y7wF4QC*@0 zr8iXO?NC>9wL46pg@H#Zjt>QDykhCjYlJ4jWz(NwpYQp~`fEWVbtz}*>2~Lh$5qqE zZJfH?oZ&@r(u>~cvkbyblCdH9mfNGdVq-)VcR~#J+WS}8);2l1^pB6r-95c0zAINR zFR>)aX6s9}J@p@tqw5!bZh2nQQR%o!vBPAH+Ss{|Js~~30>W<_4{C{d{+d6JNuVZ> zQxPU(l9HB=W#m_QQ_%m4&_0{z4|yGM1$fcAGx!`(Iw2%1?1at*6gHMdl=$#6rGI53 zrqz}Mlx^|R*IQ>huRLNcv^am#Zn{}V`BlP=^0KntZ)u=t%iF86t<;1)N=)|qr{?UV z?B+PzLz}bv5g?;EawSbXRl7n=_2E z0Gtq33Lt^PM;mu~@54R4SfcxgSO*@L;FS zu{<*xy{Z04&F&s5lNR~WWrz5*Td&y8oH5Ks#S)^LXN9JtFgWT?ESr3V*5y@oRLxI~a31_U&nd zio(u%XI46&RQjCgV7B2Fy{0AiPQy z4Hsu>He&#esZ4D=)l^;JJ91i{f>qLZU}(`=pOKv0C#zQGQ;Ayjcjlfi&8ewns#5at z*C{KM1ciOMkDNHs(PjRwllF6e=HOu7&FZNThdtxT&h$og^0jgFahDVP{Xx}stSj_? zt+)2W`8yoTlU|NO3``Zh&8!wJ%2TaUfXkFRI} zr&^sxGeXlVOe`9jBY!3f+0dn%sI`8M2J7~+)_vz82IbLf|1|jhBH!y~Snj9iVfQKf z`s3;iCL1!>**;_RTg-RQ z5%W@G1+?!QWmJzl`R1G%F`CQin`XdxFgZ@^N^^3^onKcEAB?#(5bHH8ZT=8W+jx2Y z(}VQ0vtAvJ3n$awfr3&^?E9v{<6tA27BxH^>H3sleegOa-TCj>>1xCl$;t+pwjR?w z9G{@sT5uS4Kfkh5=0?6)JKZB%gK#ZAB_@~>*RFj!%F6$|qB`~|Pn+AiDto8|*>Ymv z{>+N)M#51?HkZww6LPQuBk{tQ`Qv$Ox!PFrUZ>aFOxpR=9qnT;n6+e7srWr|O6?9cjN|0>Q@1B}VK)`v>}YhsnQOi_Pspv|I^q+`$PGc=C9 z%@8^f*N?4#*{9!MfJDmik3{OvSDgOTseiuk!}|Z%-x9sm5?Eh5`Xv;rqoiIJxIKNE z@Js*ChbR7A5AaFmB++gL{g|{C`fwC1*#G?-*8WKoT6llUB3t86?%gUR_+TGJtm{*` z+PM9g(I^`4+aDz*MA2>#za2znq`>6c1m(`MNH$vKL?y#I-`YLXOL3TuSdSS8#1*RJ zK>n{+cFVs~A654Fc*2L16Dik$^KW(uP0-ZfSri+3QRMdO)rFbgDsIwx*8g}zrp9&f zvu+%T$}GqljZa?Jx{Wj|2^bztU=$$Kh)ZGXpHKdNQ^bc4K}wpHPTeY{+M6v|SN|R| zslTgbRubu!%9UrH72OxO){->oxvxI^_!~drk63+%#E)F?(P>Jf?TJiD*(R{n@;K?o z(Y1!z{wj$O17{_{;0-4f8f*#E8Vs6110w(gq|2+OQ+TYZ?4V5BP#_T0C_L<7dV)?+ zc?QT)e&?xmXa+&?Sc~q6@2ll(-rv>6l`%>?Uy{d@+hOi`f&z5f!^}m8^2W68&K2gS z>{5sEi0Txf0j|APePLq59i5tMj_ksdKD=G|`j^<*C(x>o zwlPc(w*!|$D-{s&?Z#qL1=?)D1Wbaa;Vxj?w;e3ad6)}8IH16w08Rm;jK3niESx3~ zWuH@M&4ExtIB(LdI+*XBmsVB~PtUn?W}lwEeeU6U z^k~8GOjOj@n;#F`p7^RkQaD%=_p^HvXz+H7I04~$t#;O62erDxXy~BN zREUI-fbsT`YMvv{6coy+IpY%55BIX%4cz@W{*58b;eTSh0$gg`JK2kh+JEVdH|E+##i`&^|4nCAdci8B=!klopq?nh$}A) zO>qvLRwX&+_3#m4S0$0Mf2OCB_iFN5qMrstWtpcT8or46oIs?3uVg~TiH1wK6~j^L zBnL;0Qv-kvRK)!s^`XuMAH%0I_VY0fVNj1>3<{(pc zEC)}6{(K1rB*>U(qKSFW=#w83!L0B?%>PA6MtE&HI)d3ky)dO9K;tla5AQ9x0{#Cm z6NG3k?UHOitz8)1bYFE37CKgRN^-MtX1$j6o>1Z`{af*)rXwoa>u7RtppF=LZMvOH z=Kbb-9d^5hgr6iJob;oEB2^qoRylQuw|BZlm$>X+2?i_zn85Pi>;@AB0wOIrZ}h`9 zaOIip(x=dph(_tuiQ2QKEw8-1I=xocrL)Y2oZqpJ6v`k|7xPm&eXb+A7-v11mZJ%` z`3Hyo^u!|qgZFlP8z~5Pb+gb*il`5CS_z+hf3M?ui7_|#s%|t%o5St=Yanf5VVDOS z>*aYV;4E2w^0Uy6+=nEFuV~E&N_D~dURk%wIMZhVwF>JINi^4)aY+_tE> z(4@o^n*f79?5-}sGWB${P%Jcyl9Q7YZ*Z#tQEs6J1?!(VRynbbXh8;&qxpN+Z)yLR zSr5tK0?5gTeN-bb-Yt5;b=&t^ZI4Uqm1Os`CUUijf42N_S3Bdp z3OAS1=gJJt0%LxsJkbX5Z2;4e3Cs-MKvmN=x&4Q*<>iAe4=;>$S9i|0{CJv>2#XeY z;GOh)((tdzYE3J_Q|`rRGM{NoPepw?FbMAyUXpi@B*4GpQ?IsCjD~7PHeTjk^+VHaQ>Hr4E(TL?TkMmz`P^moKUN>n)EsSM%JtV{}|ePW7 z>rT}{hUd^mx@)n1&Rk`bIb{9*`xCeI@yVw>&@&VZUo1v)9?M>0X}+sb9?z zs&6+Ogx83syuAE4rmqatl`w95Y|Dy%X2mBS)}5!!(;AIe_K}l!-Ver2lUDxJG%ZIc z_BIHp?l?s0>9wwQ`!3$6fmxz<`3W(O7BL3#U0+_Q3@%@pbU$mx!WtVTAJ<&=tl-|U!c0|NRd`@au7ZO)=vdMjJqTFdj9)Dp_OCHu~G5iKj^L?}nB_fAy z1>eZ5kJ}GDw7WYYf$H^2-nSxh3v?>aEM0gTMG=^EL|9C2I-DytV+5R~UtKE_KbUTfehm~jX@@tB;#EPRhco?sWt7s&O#YW2 zoj+BZe)-Xj!y`X`29VUdy~5L6Xzkb2#EqB4p*tLXfu#kJ4lDWkmB0U>F-gMX>S(rn=cWa`eQHnB} z2__&(^e%8XGgEO7wZU| zI&%gi2Zyr8%TM5@H=8=N|1MU%V}E?~Na$O3iz6vK&5;f7Z8UqFe&eTCZCHD7mkt=b z^+t-m*YTdzthp3Uor@qBU)kHDg!ZvtQh_Xt3}IoAfxdBd8!vedOEJJI>CQEMmWuHi zFvKPrruA}8h{1UZ(S4DywLyShnWURRLK+wQQ-fGI!-|; zu78`L=&z?KM=Ux-Cr7;4rPc@JY}U;?M60gwyUEsvpY^!z)+g-uf23DlPIFaV-dpPv z)y4Wg%k#z09A+qQY4qmL&~?Ty7{-oj^0oROHMFxNw%!8{lm6*XJ{A%FbH7Rldo*|H za@EDkr-U#ojvc=fDmS~h@#EpF6D_^Sv;}uMAC9Lyv3=wHrEtY~$8LWEb;&rNbF1SY zOb8DPB$xB)JX!R6-QXkQfl;w1gpvt#effr`lWj zyuQ)mcD`1G3o{#A+9Q7YzAk6s=fT{3>50uEMQcXye(^GVPbP9F6}!G3W;Ckf`}XW% zL&p=mld>@D`{+@KuP|}R3fQC;+Sf62p=JKrssrpP6AdY>_E)n{PqI^A|fIPBXWv&TXY0;X`KsK`;9)AuwQp@o;c^!wKWBk&s5l>A2xiwMh^)6YOj^9%BYz0V-xwQ6Y zLA<4j#Rz%xNmn;{>PlV7TAuLe>Jadq=wXijvUg=F8e(pNVLFJr(Z-~8dS#u8?Ct{x zvMeI^y}5mU!`&})Q_2~HRN@wUQ29e`3~wAqXI>6lD$Jt5&SoT^Ba5#L!8B6E+|(cc zSw%MTR#gR)U!ChUtg#0$%r!XzyLAv|S?*!`UJ%vIe;n8-I;F+kw6e~kLE>S%6Ro|% z+u5s9$-(_dA(e3M&Lyz_Ptvf=0wcGpauy}|OpI=ZRvD2J-kfI10IOIL^qe3f(PT-* zy1loDwCHRfL9YP}|JTKa= zUg1-a<3^1RlmStR@&)nY{{3d7Pv*1>1{mqX@|I<@igj8t z;%^PjMCC7_)ub({`_MpSxyo^2@#5lCJM<1+R$XB{!n~#p{`)ihwR`vM>G0Bbsm{;0 z-*B0_^kuE?o^4x$3giqq7#>f4ClgFpEe<*uoxImDqikurCAxMaBM+?mFc#AB@8YtG z!=GjUXaRl;&1R`gV26NFDcxP=D?AKDgYRnYzANZI8s%cP!qdobo}=hzSz_3;z=*Z^ z2L^2X?nCZu)O7q1nr~U>uZ1^@acZ?wokDZYBeL7Z$2;bFJ}gX44QZ0fWHauIWZzpa z_Tmf+i?kv!?Lv(aGeiUx#U!@<+K>&=7oyX&5BT#9aYP{#>JWrjuJ9FH5DYp;CTjua z5MxfnhiMz=bgm;bZ_L>y&*^yrJsZ)Bbn(h!2;H zg!SjHTQ-e?G_-j~>{*KqM?_-|aBelJuSz%YaFk{92WSar%l!Cwn}B_3dImkxPWcE9 z*0gTaQoW8dHxG|o z5=dRPe|rg9UvCBUMYpORWnjQELvyUBxUldegUZ=54eM6D717mv@1i?m>HlKxOyhc7 z-*%sIna46jSjLpfP$49Xl#=FABtnz93@KSzhO$C5EM-WFW|az+T83y4X^;Z_ZB|V2kN~;)6 zO*1x$=`=^y;{B=Ki}O4-MS})e8a|FRtkoLc?Yr7<2Hx#A#!7EX8FGB~+V(w9=zD`2 zp`D6n)?ppXEH8@zhLS67Rn{qXfeXRzOFid~){Tl&+GlE*Xw&na!hh>#GjCO5hvv^b zciK3|l)g&MGwOPJwd@>HvZbQhBKgeOZ?49<%X6h0U5+kL**&uIT{`;H#!N{;ayC7G z=4q?;1tt-%HAp3eCAqn$1ms`NcqJ)eF#WrqdG}#F^5otz5$R0(4USz~B(TP*{WPsrZie{> zy(-hAQa;LI5q|W`#hk7$)=hVxxZ}f(9Pj?hxw+A5u5!Oett!7H|G6M4DGd4o4W@>B zi<>Xckl5a?#}LzJ$BGNC?OlDU)15@evRw7qy=BxY?|lS2%aNlh7&dj3Pnh#p)=T-4 z(FzI$Eq6oS3)!-#=0B@ujid8Y(kROFYU=-7+wtOtePKt!@=kspa^IP>OD3P9vEPmd zGT;fC*2ji#*|MzVo5`p1cebF{eBBrsD#e}4vVTe-gwAB- zFfR4${#e^<=yBcr$SwAsCJ8$EL9kikaj2mD^^)`@Xu zZ;b zT+0c%+1Az=-Y{W(AgK7U1uAp;(8XX>;jKJx&3|-mz$GRCrWt=7h=Ij>)k6w1s?pDR zyI(1Dy~kHN*rcl-Xa*`SO%-eEOV?k+U6VB3t;@GOnWxY)&+x+L(CzKKf*r~fZ-%Ai zK5lt*ccbO7Ufqd4tLvvleY|t&alnjsRm=Ap{GnZ(zCwNLlcu+k5<7?NU7UM<*@38l zL9=(b+uPgczJUJ=C&}rV}iy&e_x4Jl4_<~2e|6F%FWByQcHSzcw&`fzrBQ) zPG0MHPmPtSm5C&F$nM?9oV+s6pM2;1u@9xahRwW>>X_w2XIw!E{;}gJq8G@mq1C@g ztCzHLPRpcGqh2K595%zV#l2~GHmNj{Exz+ld(>mpqv$z+TkfOr>y}^nFX-9far-{X z69O})p6Q|*5<7IW=XS%0h9{{oo-8#>>}OhN$tkM7d-&F}x8{%?IiBlItQb;iHo@5J z+`9+CEc8I5JsO{@PT=8z;proD4)c4Q=Bzu*39Sb&e>)J9*3#PQ*-Llk!oGbpH@ukF z@lVOV`LpgC?Oz{$CbINVxx+QnRgIe?B38Y-wI+CmRPp5g9gX(%lN~9iYgf17OY@ai zH$P|C?APM#)}s(GBCpX8#jHrWvcqBTZ880 zEL_Qfd#>&}^k6KSAW4 znhO?u)za<0uEIZSx!R^PW+x}4P8`@=H}!UegM-}1+=>iMX7jIJ4J$1C*wlTVmxulJ z=VL>%R4RK+me7#7hl>+JUMryz(D9QJs#^GhqP&FS9*VPy8SHDD@HV)T8T%MC5v{cE2DW|icmpY+?CZgw*MNlDr~$x(~PXGldlANhWM_4%yF zu~&$AOHMD^dx%QXmyi9|+*y7?=qgtZ1ns(zPY(IXZSKS z2lG09i|88vvNB`q>fn#tqC*u2f8O)JQ{BLwk9DzU6EKvKc>cVxyZ5_HpuE z%fR}b?WWJz($!~h_kn|_Pp_Vl9K9n>dDN&)+0hv(xdUxB%w0)rxX7fK(PO!DvEo0lpgrEEl=(mGFCf&y=|7<> ziJd5(4H&L8#bezfg;@nTInK|#{vI>DYfVvd@~zU7ubiK(t$Y_>_jFm|!#U3OtbD6r ze-jt^go{aa$A|PDX1%^C$vI+zQeD$uQws*(np*p8wCco?b!*3|s+2wt;rX9sJ$`Sy zTxR98ZrCWUqDH|K-8uHZ;!t`1liRnS@4qvo_i(e2-YWGT@~xG*SF_gT+Q{i`V=cP- z%3Qy3Bk)bjqpc-5*B33x>(;&X?6u;v)!hdKwmy7W>wxC8@2wx&M>{)f>z9e#NL-D7 zX2$tu=SdZ;t%$4&55}*v`px3E6)T@cmS3@JUFVt~sG4=cKSSNj+1|dg$oTx}pOHuE z{x9hvgJ47pFOSvx7_5AE-Mw3ppXGfsGX6f6w*RBcr_-n0UcTf5@+^`^x0X-OHuoOW zulT|FTQ6@7&>dYRb-vSw?oZqno0y$BKIzBj7KZ-%${KTRE1xfKt@VyS`(?O7*lFcb zojc{_jyFGj-}Guz>RS11`RKzsPVQli z&vrTL#m<~H#HmW%>+4t7x1+>+<;@=Y^*bbL4&E50r95nU*t7{5@6Tm*Th}RYrrYR( z9NGL^k|Du04<5dLbYFV&M&)5@;f8e;BX{LxsoJM5%dCHIJN?Nzg#!o1do+Z{CK#Ms z(0|vbx4v$dq?6Y!$zS)AsnGZgU(4s`pCtckJN){o|Ih!)Z4I&Fl=VmYp}lYBmRneu5V-f} zQfm7OeRwB3-5jX!g(~&O!@GBDD)bhMH1r*6{0Jjck5C|c;c}=}FF4`l z>09(ulW^ElQc_w{@MBxm{1Bnx?i+K$D z5}|7k7esCtB+IAo?DjKnlc)*6pM^Q3y!D!HsJ+N;d zyo*0f_wFUCr@`gGzw7S%awTsRbLsczn<3)2fU43ZGBi}c+GHyu{p=y8pLB2Vjvv?d zJxsiRZW@itNwln)Q*KK0_RbXv@WOwV+1p!`K187!AyF>IdnO^)4;c#mz=jKSySL!d zf%0+iF{B3qtnq2!Jg&+@ceb=*m%vPgm3=BagIVybhZlT`?Ou7X#coquO;YvW;~y?e zZn3O(zsy_!!jh?2bz}sfRSY!GDUZ^(oWrYS%ksxK4Z!0r7BSyd zX7vQgsgOGy4Iq(fWoBgc#svLs_YS+r_5ZQ3Lyje8bt;|bWu7OEmu3xFz=h&{}f zpxF2bGnM$Wr2J#>q$u)NbX{`zXML;--y3wwS3hzC@CugzD{vmkg^e0 z=`qzc!S2o2oyvXgD4{Vb6BSCV`IhLntvCb889#{}CH-01VmL~2Z;!#kM?__Yeu(N@ zB;O$9CS|i@zkJz>il<6Q2Z_*N_8zCVS($NU1aCiW*t1rX6q(?p?Wasj&pwy?acez? z{;a5Wx`h?hwIFC_-FzGC>J>NsMks%8DBijPRSE4cyke(qSk8QB+$@173z=~j+41AI zF{zEput`hN!-ro{?=8M=A04BtVRM7*c&lbc5Rv<*OVg-Kz)#pR~&Ebd^IlZt%h6L=M zZQMl5ioGkY$&(TN9^H>}!u#k^OEPwN28Ah3=2NFm_3-dmbIagVXy^!r9F^NHeg}C` zQBe}rI4;tdQm`SIgBjJ^4U!O>o?nbe{t5Oxb06F}abm@aV#Zz|5IubO(6MrkMx2S~zX(TK}uv89$)RRGwfuCsRzHT}Wb`M%@KGr@EdbCnXY!5Ne!%36Zl4>HSsJNCv zWTA`;&xtPDnUeRI_m0(&`?kkF&Xx zpTGa_ziaQF-aArHuKDGK=MIDU=+Md)lh~#o_;m=?JFecttSFrJMG2RUF_rPkzjpO%w3?MN zwog|Q9s(v-?e94yHt^W7nY3uHJd4xWMg&4IgKrzVC_XGa+>Q~zltU(C3XEJ zB|j3N%xk-z4TdLqCuFMr?kC?VyO`8?4?l16M1$A+FVF`>RzJO=gAY3e0dbggpWi+@ z`*36Ey;v*+qx;mbYH{9l+BPf#PTQAn-^`tV7oI6J;A`yq^&DI37}wSl{JNLpKQiLt z#V?>_)T>#L*+42ZFC*KH`3F;%>$hwfxIbI%m=NaBxc02J*1e`={2VZ-6}ZWATF0kk z1Ovs4h#i0gIkFXN)+~AP@X(<{(Tc*xePd-rWKu#f4xSy#XUL5opZnyA&H9Zov21O^ zpOuA0ctiv)p5w-hF?Cx!XU-MQ4%kf<5xN6Q$3DOn&cfm!M<=O)efk*a8A0Vb$Gd}~ zHg@b-6qCVtxP(d4|6q(0#xD41MJ56S)605}s>RpXl{CHsw&e(r3!e@WT=qtVm^%%uxMf7RyQ~4L4)|!U24$Wr4#xIYFtiZ($PtW(#<;+A1;xkK0&R3_l{E~4>f)&x2_a=YX5hMW#hD??NYv<{0 zMmXtt-nf1}XDX@eK{U<3_p@Y1kgkJAM9}Ddxfri;wms%J^a(>NzekUmB-AWXIn{0b zyyv;jeBB|V1fztD4Cea;VJ)_mRKs=ZftQJA+xqM)Mf5Sn`%MrXQ7ciYVCZnV)4d5c zHct?uH)$Qy#@075CSwIkb4RGNNf(Z}1@az*D)AOvUK>b_)biQ1d9&)2DM%p%V*6nx zFDD1G6ItVEYir9mn(tmi$L~E1t_(iI=ky=F9rBgnx738-H{$_|SMJUs3Jyw{rdf%I ze8*UABxLCv5h@x)=0NSy4MCiEFY4-0v-%=hiGY4yG30uu@YtdM;u8@om)}K$a2y7>Hzfa8=)6biH&sV=pZqud@AZ{_KsmaMM#Gr<(DKMOe zsJYlj=yHDX)kn(KR97o3&De_FPwDL2d3lZ?g}7w8(e-+M^xshGom2%fMQdixqq-Y7%F0N81zv3uro4 zdnV^~`ZymRICN-jU7bxqPXbhftw2)C@ReNh=}Bvgc|v*!%l7?y(9H#=x6_|;xMRB^z_kz4oT zM(shv$gALnk+E^W@zE4m%XjLlH_h|gwWYDK(LB~-(zXV>E2<6{dz^DzU}^b)47u_R zaYUFoRF12n2IngZ(-*0V6&*g4pLsUqj&oG4E{zGAzh};pz>_DHzWwG(n`HKvryXQ& z8wVv`5>cWsG~r5b3E9;o2kZ@_HE)p|6Jj<6x7$F6_PC;vC78c|iHHJGl*jE%|KP~j z1)iy=esKZx9)bqo^wjt$Wc=0QVqcbfsGn-B?Te4&cv+rk0P6sJ09-Gynkg<^y$BxCCoZN`l zC$+MbPBaEoxv$dD__8QnXBcsP`34?cC(S1PaoOn%3@of~H&jLjwf;`oI=xjDw&1qa zG|dR?{$%X~q@)CKj#gQnKzfP&tnXItIWQm~YT`~j zOn9svHJjdEv4ilrTfh#OevzO|SUg@)F@D;zL?cf00VaG2DhQ_&7Z;@g#js&JX|jE&+)vacHkj0$qOz|h*)-CXkbTacOZsNuwtYLc z!B%@eh;OR#_{yYE4UfR%$D5JIUZQV!4x>+l7Woz(?}G=ibG(r189N)sPPA{Ig;?pd z4tDY_cqoKC)yW|FgGs)qk2XcCGVZT8E%Jz23ed? zI8Ooa@gufjui2MjC0S#|etdTR>C(E@7Q?0k_vt_=)YRXn*@2cCe0beD#18Xg0N4y9mU4$yIm?lgzr z+%jX0u0jUrP7qE{JJje9$R|bcYq*7{gL=uz(;>O0l_?c+lYH9%79??I<(Uv8_yDz@O(ElxgD zi|(ODWHm{9r;sImPAOYj{Ord@I=j9{Ta_fC;c<`rUij+)if|k<6HawfO7wS7as~I)5l6VM=|2L%fCQ+HYZ0rb=cs+mZb;E$POF+3<;VP z%Mwe53WorTvl`_A z5q|WS4AZ{=(%#<&A#Qq({FlGS^67KuUJI(~&@p3z8MZZm3H2MZeWcWe&No?eJ3W4z zrRtv|XomLs)nBvkQ(Ie$X_$EHJT^{ukVY;HiV*704uKIU)DE#Jwg4uZH!`Z_C8J^8 z4zHnICk&*%ABTU&pnpKz$C^^z(fw2$OOX9su(>#eF)d;UM@imPzt&Y57-nMI**N!} z3a1s_N`L)PxMdSA&fTc;I&BWFiJ2$aY~BYGw|`cF8nDYHYXxxAbJ)44S?t;)v*(+s zM9ZW_PixZeP2}k%qZV0Sn44Qoo>SAa)6>>@nwkXd$mdmT(#c!jFOv}mL`@ghmL;7v zRHz2Ko#?&O>c;J#J{LXrTTIyO;&Ke878RgKiFpW^7qAD1VSC$c^F=0YU}78!30d5{ zhRWVchg5RMj?C}J$s*(@Oz@BM-&+cJvUl&^r!S*7@AK1_&h2gRxtURavY>vFLIQ!& ze$8In{0~l=K5bg$6n)aqs{L#UpDl%(H|lzjFA*T#j63OWWM+28Zx0Jty4pAZ1;06( z1y3qjNm1vA?u@-NyVZL0USuc+)~x?0sP7NNu(3k-{ajz(C0AiT82H$95xfYJuU@_+ zq3PJE6WE9ir9;#XTMLW(L;{gn>UrfcYmk#8!1ypDYwPcPcuw8^vFTex3v7QjTe2!^ z^Oh}7P)eyS)w$+KF-ED{SJEbJiiYq7-}GwHoV!Iu8~G{}aXv2Eixv$q5p99n+U)v< zY+SsvXG9(iERGesu%v`Tne-|9=1nnYhs6}RSg?mr>V<`eX74dANVX)%Twk7eCdsXF zsdzeblk*$_PeeZO0f30XEt3o;51YtO;;(I6YD5rIx_dW5d~5$-FtG$qYIgX$y%pDG z`@3Oe(Z6-<_#F{f4&#`b7W(_-+6#|Pj))!tGDsFmrSD837I`B<*q_Qj7w-WClG;eh z9IXnPBOPB&V~n-SCuab~ja#;yb4*o(i0(|R^9nn4>KU)iD%t+jvi?njim61{0dape z*@d4yE5GA>uiok7<>f(d_8&N~1ExHS+1>p+G~NKU&t@tD<9mDZ_;H0u^8Lu-LZZS* zzmkjQBlB4OQCS~~36-SR@qXJ3&I;@>A|gUc2~TSqpG}9_8W^hecSeiv58)n>Avclc zpV3R_NTxsqt(3dG21-6h9M3;G`XHs8yM^9*eYzZ!;?5Dzdl>j>a|Ash9R&tCA2u>T zht+fW^5vC5IVaoIka0RYTbC|#)uYg5(X8BZO8zrhHH~89di4fdl{JS7dfn6RBOlv| zI}y;lyN8dNf zoXdSF_M*ltv#Lp=7U8mgN|u4n4jh_k=tcq9HVpyJwYxJzlvraTflYY?wA_Y-5YrH+9?r#!>dUbA7Cp*W)%*ZJ0OF@czxvhc0 zA&3YZQ%XzH!kvxu@ZWQg>lv+VbY{oMgilj;Ycy|q`fs}jSxyv@~#f?deh*>j* z1{?OoBGDk&M77SXp&KVy3rEk&5w9>l(~Jlrvvb|rK3^9Ls@HDd0z1?4@7*(N_S`~_ z;d;zs!U+hstoi{HEJmC(QiC6f-E_2-0QGo*RG1gThz6X!Pz-Bw1SeBBhn_ytYOGnhXphVpHD5bYCi$8 z5xa-+=cJ_W;L4B7%lGt>`ATYwzqjKG)t(qc`MDc5KdDSgnyfNIf+^s=wYqq_FVQm^ zuWBxi3j-~(O&kPPzXI~8ar1E-Gh)P3tWAv0?xOr6dJ^i+M)}F9fC z8*|KXOIx>c<(Tv(dNeE9IoWI2X;MQ&arLubz4~n7km3{orDjqCFL2uz18v+-ojqF~ z)dlbYGv+^Pw4=~=)&ds?Un4U!e{sBX=gtaN$+8CxQ^|=zkO}%U0wlfXv4`)qgx5?4 zotikYk9!vqgHq>^BS%iJ`YrTNtauOWnH>!d7AA*#Ka})tD~)%$iQb#6E!YepAgFbl zM>f`M_A{accof?1E4Ov$_>6Ij?-mz-LvvR7ne{vdbAzCrAn#ljzqxh9m#9eh^BW>Yjw(o z&IJf~q_`3moy38+m+hvVb1lv9bm2lGqifIp<t1N9zkYtpIXKNNN&EO1f!+xdOl*;LED#gX zzWO4KlmJNQi0&`RgCgrv{vrtk0lY3#-g8{qW%=^1dG%-Yrv4ykygpDcDt zN=o{lb~Gh$_z+}+ddAROB3e6M6~1sW&k9P~5n%+X9BOZ8mkbgJj1hIOnAz_>X8ki_ zgXnea5XDm(Vgl@G)@~e_uBY7FNmHhvctWLG>VUVlfz#@o_KilggGU55GwW3C_)pDc zmu)IPmUqp`pl^G-v1T=u2uWN zEK4U$nmBQ5Re*WNZ1SZB8xFdhdx(n>_TBpvB<4gsUtC5=_fkliprDWrEL))MI)~Oe z>m3jJ&`j(R31czk_h>O17<%4`Q(Gh#Fl_q9d70|$d}x^qPpV&TY1YlaE6Pj9#Kd2% zC*j@k*++}eVd($`wSSgz0@HCVeKxan)Hm@OG%g5sioYs9PdOlF@ggI&uLymMCe|Ns zKDP9Vcw5RA4SmP>X#>1fRaZwn_|4QU@+fs+V+?hT@a|pQwMUPkQ`a8TR*V%&06OU) z6+gy(PK9A8Pmzq1suaokK6j4H()vxqXsi$i#$~;FK!$F8c8#OXlj>QNs7kdqp9?no%SdiPE-(%&=7x+VRk>-QdoJTp-9+|G4* z3^wKfbQiot)Ub~U2xjD}J8Fws`rPN2D=bM%&KxUZ6)V0|0Bg}zy^yeEx^86XsTrtF z*5*^|y>aZ94SpWeXU3?uaG>E)3+Z33=I-g6?AxEFo;se3mO#LG`_PqBoKeMd&Gq7Y zD9fg%rc$}HsgkmR5^4M7|EqDtrq$cmH$VvQ zgwX}StBcilex}7?9 z>_Ve2oC825zOkX4pZpa7P=nfmFz3p#z zH}AYt#}e_f?FCz^aHr6pKv=HKxb4ph5&s&SHLHl|F7i7>M|@{K2d^XUcs+MIclnHV z9{d9*j<2KuzA=rD2RaOHQjZ#${>Fj;@XJ5pI%xQCPYzqo^((s^ht4-=QfBlQy*}Ra z2yT4!gw)h~Iim>IOSe@82<%`0N!}F45?S>0R;G9A+_~Ff$FMx3U1?+P8yJl4*H!(T zBwg;0?^-)ajvz>chp>TG@!}8|AFtd)6 z+BhRt5g+v*igzk%&>CFSz8f9{@;~YE$&u0%8t*Xn%5S+h1MtA*N230aZ+fzH)Tq;070K1$Vl@OcOHsw-=aB>au6tmJZjD@&Qz zrjk>l!m-$P)pX#K_^$b77{j-q$yWLDWpwdOMsKmV2t3$;N8lgo8fN1YEy{^U4|Mo8 zPlsNUHa2MJDsd&=irB;DbIUlNn}d=mds5oH?(*l(JqCq7SWTgSv!Y_DK{>D&l!1Vn z4}!jw>9N>gsEVUMamGnq@5mEh{XSkM*THWnOCwHsJMO~f6J7I;*>o}K@&*M4%H1Cg zR9MteP9l5vo;^%q=9}f+DHNS436tHwNJ6tFITVd<`tGAW8iDHi&z}!^dmF~R1waM= zH?BUljID96tjs(?-*@|oOBXMic+Lga`uH*W+s42P7cO{Rvo`=1Zk$?KRV60oT9WgS zhFKV*s`ugps1y{e#a_#Seto3m+0l~D8(Dq;Io*AhCw_ryaPQxJ(p9ft%SMttj}N#t zgEd1(6q;3)QrFE_V~hoiZ^JfuNqCa;P-+hfLiqFWRz1?8X6%)A<;xQ<-pFd384dk& zN@3%VS0ho_1JHxs{jC$T7OSf_f^2?mYU(*?;xZeZ`!8<~mA zG$=A8ho2*ClMs@W1T0+xo9pQ@gKeCt&~$u7`>1oo%e#_IlEME%PXus;2PCX{+AH(AkGS@$S4 zF1Uk}HQ77#iqW!x!{Z-v=G2sNo?`pmLYiv&nEAx8Kl`Sv0F2-)?A5({{}MLX$$$R2 z$dp4<&rfoH?b);A(}UGCpsY^xjckQoH1=B(xhw^^T5wxBwzSuO3%B>Uac2okN)`zk zE!T4?3U{_$Ld}^gEsdyikL|%l9%5rd;x8_yKNJ<;aZ&+evCmB3PFT5O#d#)vBUVkB zG^rYxA!0_dx8$@A$;bBY)iSkJA?UAOy*grk(LTmRn!bKD+cY=AFBEWz6&u~k;}F*z zLw)^ql+ZNRy!1OnnWXV{`R4@5Rn!MGc)a|885`JAX2FF?DJk&%62EqEItJU?*SCO0 zx^h2VPe^h{!M~s=I@sBb&cYY63aEhh6Wj7vIZ_q6Q8DnD$Xz^18ob=*&%ePD zBD6gy-bBm((W4>K(x9sm;o)N3IN*fOo40S9nIyu3TvSOEMT3HGO`9=e$l$@bbwkW6tatnQ z6EU_n)|k$jMB%$R>jI1N%}kMnkW_eq^T+=ttp-o!^TbpM3PaMh;|J`& z$!}(@vA?44ouW+wy4TRi|FW{Q9A9(=2#&0vfmZD-eSQ7XGe$mMr_$7(`%bMu^;L;R zg6X?L$PW;9KD{Z^-(~D@j8dB>kSMhuHjr!6P}&ZsbkCkWt_u6l>;s#=b99PUO0p#_ zEwQIza{a8$Bn*D3qDV9ziHm#7OW6Fb`~loQmBrrK%RvpPUOfpYfq{WF$VVjcbmS%cvk1*>wcyc9_v%L5pTwH_S{$u5?rjcC^ zl)*i%E5BGz+i}*`_Zm@EoS#f9)0CY@i(7f}9g(UlScNazivk2sSFd>i2cg%lU8WY( zNRU~jtXNaGj58UI2kc{I_-QhpF5S94%<}Zq*4BoI)cBdLAcnQd%HaRR=c&IzKG?wZ zR;v~-WOr}hqemZzo+sgo<&P$mndkXSmzuEe_}<(Mn!WN3D~zv3$Uf^e+}cmu zm0EEnjA2kEL^L!22+@vuPeYl<5ZzL)@26b?o^4}e^Wd5z+d1lCtIUsO_4V)MlZl%ZILhRs60^^vyce&j;J157U z>XoN413Dfs1IrqSh2AWk_mq^BUAyK2x46t&AuJxaaH-%y3Fai^oe1!8D!|pNL*Qs zT3TApUH=%3AbSIa2t@x0RJ8RKQj{X6Qf<{mg;+;lqAk-=1B%T!bBiM;aGS z=G+BX6~zFRfRH?E@QeEKs2qRESrzUiK-IEe3L7OG|D;I^QNm@`?jnGq&hU}J|CjU* zTOznJMG^V_es!IY?QhdJ6g+fa%Y7n5&_iqwZ}U)8Bo*D;d^ zlLhwd(|7MUZzA6K{Ycy;N`#(D$z1l6MT(^uMbkH8i<0tIoIqR7HE-GznyV20-%Bf+{d%@^_ z9v;m)N`nWzAJ=A|oc5Z)$0G9e@iE$LOEE+}Vq#JPv+n7O7wpBMBb|P{5RJ#-XW#t1 z-NMk7tF>ou-usISu=*TBo8+Z(qf70mUx&@u?95OX^qi6sXCRalP>bWW#K*i(TJHIC zgT7t}f=}Pi*vRO=0Rvxe@201_dW-JGLuL#%j&3UzB6t1Fv?W|d{r(}j&)$2cY;Y4NSSjROGd!RXR;Zik7Mh24A z8S2%hN15N=%@{avAjCnv+dU-;ce7f+1gU`)X3SWe+5)eP+XPzhAe;fwT9dAWcRJY$ zBDTohtWxAAO)^|R9te?Z#E4}`FcPyY8Rp|Py}6lN;?+UpK^w(sXSa_ov&8*!NgpFk z912f9vjTPsc?_Z#rbkTruXk|~)HxEto<07W9XLKfrz~!$UfMz~QP>+(*)3;-E5I8?trm{$UQ~O^AoDwC`;97!+ z@oU&YbXf(&Gi}X7<)*I$0@HV0B?{yI{a0i7@RM|X=UnDhh-rw4wqEJ!=`4Y#H30u) zQY???`fcsow_obn?AkQ?7)K;*L$U>L>i`a}^aOV!N4S-jSM)_&O>18pc4ze8 zw>P8HCJ*XsFm$q=Ha}TXJCpYYtlQOxc%<}gm8wMH#03#XR0~&Z2D2B~n13HUm<1$5 z;z#jK{f+m_VzmWb4& zW7D;J_akxSBF}-ah?ap0@L(qZ3wpM;@YNSWbB$E8$t=Hf=ME{k@dpai87eBKsgt4p z@m!T1OSIRYC70mkwSq?b4x9Cr-6qut=3AbzjSRrpcvMu>jV@MAHV;%PIDyToZQhC7 z8*%XXPdvgP21U}!{ha;xv_HV=G0pr^ncLr2`8SD@;PUcv>=G8HGMRFggjDm8NlYQ( z0G6R;D%&TCr>9r2iG!7bD&LG;#TD1;rf{oHPEK!j?vx+}fAG90LRUrQuo@pF`Cx+H zl}GcOFN=7jVJlEfaqYBfe4gEXjwYu)__VacJ32Bn4#C=+`@h*snfdbY4L5oxvfUoykGfp?FeC6{s5q z##f|dczJp8(eM+vNQ_u=Fd8`)VE(4FvM*lTm%V(=9f^_^j5Cl6_{ZVE&t!&THuZfVK;fh}zSxvG*Se|Bn+CFZ*|LX^58(IC@mbm=YmhRaTlvcJJ1W$BiHh zi*yTQ6S86WUo^G?bEHFqg(yd6EyVDM?W?;9+GCP!3S1^$P0wL7EedSAb?YWvG9Kgs zyO764=RD*mZ^Ly2g_Q~bpnW43d!{s8dheeiQ#bfOqKeLi3?E-#siFR7gpLyBZ;lxR zsSy`n(S@<`Y)%P9(&h#&oAF=#w>VtFrWh%yRP`Y!^&2a-=iFudpE{y6 zR!%_ywEZ>Er5N+trt?2dStRLHP#2VOzzi9(_{DLfDCM@Q+YUmtXgoG~CpmstBA7Tk zTKiF9jo>?Tu}<3`{Iz> z>h{8SYRAu8X>%&|=+@2XeP8(RJd-2m=xbA4$lVVwdb>JWKcC&Q2)Z{*uG^|qIsd(v z$LgQ>{`5+Rhyj6*NR_a(l3Wm-s|L*qU->avJwuXQ8sn>gxigU>WDUnniZc6s+O(}i zYmjW|!4me<$OuLZ=~Y}-Mq-xX%1LZnsZI8`pCXNhsQWkbo6m!dUX=G)^W|R0Sbd2Jz(T+qi<`fwO?*W z`{3Fev!$ZD#*hHsfzD4i=t@fiV@p6cC2YM2v$vE^WSCSDax*858MBg{48Tt=R^e>S z7KJ*R1JR8O7!ik0qpPD+GE~Ceprhvqyn0R-g>H)Y`suCwx5GBn~+(` z+l-BAP%RYaZiT}p+i}|xHN8YKY0e-Jlj5L#kYi>ttQZ=qQPy94ubpP#4fzAzggU3j#J0xr5&<9=`N>D+9d{%_JFuDlCi1(><7;b5aQS}6~U%<=Z9`@`=t zi1DBo{fuClsM{k4iDD+ytu5b|P6OU&Gd6-8aJ(jI^#j;}sfAMIQmjG!pT22JnBNy%BK6R@}BVruZ)ly?=EY50hqiN{_7 z?dWmiy8DnL22@y^nWefnHHa}(U7{*`WZ1-IJD+N#kr@H?$;{k3aojkqgwa!{Zn+yK zz|l!6Dsek<5YXT-%q-G{LCeq?U!LEvUDHr5fH!u6bQH>cQtQ>8y?dW)nG;Y{njVOH z?=*IS&*+r^9>M{%sw#r?X~EO~X=p(rpxuJrGn3P(d>&Wc2Y1ZHR`e0f{RS-nv>4{~1=jb>~hUDxD46Mip#qn^ov9NiyST zZ2`x$Z(qYwMuh*+OtRED-VR6iM4cBKz(}*cH{|m(osKGi9GuY`0;VhgCV-cQ?o{?u zo$nho|J6Et`)3)0m5*CJzpL#X-+rXm*!(}k{+QNIYu}g!FK-+A7zKBjux`^US-pI} zD(y`}p5HP(Kw?dt$@4l6PO_8z@cFkVN?9K+$6tFkVcLj0H*HRQZX8@U_wnXU zTkD$`q@tr()~QpcA}x}@3MO7e3=-zwIaLju6^dAy6RiMPqRW(y%&AB*68hS?CmY{B z+5$Mi*U(w9nw?S(u@&0YM?+3UCJ2)6%S7(sq-SU~+`^$S^w0V7``(4zn>Bb9>L6bl z0GNa05Bd9te=!O=KTK@XP#sZi?C}c>L|ObIeUhN2;vjY-;xhWd$`Gz%mb;09erLmg ze*Ko)+8RDA=J`*szBrNUfLBcu$&Od<-P;V3hzMAqx*D>>HIdf=J^XtK9>!72 zWHLB`?sMkJ-H2QjxkaIoB8E~$s`V130OFXt!8Cm8=g*%nx?Qj*!Vj9159_XRmk*qm zb)mYlGW>2gMo@i}TzkiOE7q4h|2GE)f_$d`uz1TwU{3O@M)g(fAmDuiXAkLjvP*^wiclL59P19qz`b?yN zj;m|-XowHU%gK>#*%cBZCPPauh28_iqGqP+5Vom$-YdCNK53wDgdbPy@C^L}u{Bi7 zno?R?TE1p|c0YpO;p)7=<&Yhtw}gz6*>wtI$If?2bl~SX&tX~~@D*!EBS84w$~`1U z;5dynG>W@V@kOWR1EDVZgR%HG5|Nd(T0cMwqb81;GL1BWE)7}okz6}qaWOs1|DXnr zdvUO72K3vYd_XO!{Xi(vyGuC$PR`sGc2OHRf_VqiV_D+$qq=BlSS=F@`4?8Hl$4l8 zUVgocl#EWnKa*Z$wKkuVow}Jy4(IFBcTAI-&AJ=Wq_AagHwy-e&53 z)twbrv^d1{d}5-Zd(=0NPDPJmNC&Bi+*QuXU*0v1ahB3vF>oZJHkGt#7}V^!*g?W1 zna_1_d!_9Su2KtiDyvQ8Zs#e=Q5Pb&^l?FtcOiwLzb6Jc#9S23yfmI30oGgbZ{s5z zQjlXKw>CAblrjcx)Vz>Iy{!VHC-HDl(73dBdX}5tz#YBlqM$v>t+S>bMTMf_9z#<4cDr-(GPd7V76%TJfTF-$_f{T;h z=T6b%3@zd)Yl7pKu?UvWQJ6Mk#{QOmg1{Vn!Q=^KaB9$^w0C{g+FrX4wW-E#_NIm^v4myR1U$;JR_8Io37)^7=8}&b zN37f*n(%BzREdR*a{J~@t+ahUdJ(oNG_I*)s;lkm*|NnvajS!H(sXbbFLz?b zmPKllr%pAux^VvdkZJ2f2YlOs&2{jiNjd3eam0_{MJOm0yL8*RJk0s1?6xz#3^VnV zhM&UpzE3r&d1Bpv}3y^KfFFiakPhdeIQ)2V9@1~09??ot##%N+J6&Nwy2 zBIHIKf;*OiUA*Nq5Iq)V(_pyL1bXW7C7OcaXy7s<+#pXT6o8K}&|eB^bkm1<(Uuk# zJY|mHg^7_LE9(g8B=p`s6b*$Rmz`gJT+Nj_$gSo3rQjDm8{#2^F7J`{*pmpr2Ap5DVx96A*G(Ltr@?*sW7 zB0B7Y0Xc$7vYCO6J@48}PXmadknH)mQ{Mi)d)v$e4R%`tI$`K)^&rDkxV?$v?1`RUVNhqa8wAE-L&M31W7`(l!^gF; zZo6p+%e0!)WF-ms1W+;l#x^K`C3Id$1*zPN{6DOoN^(vPv?pY~G~;>92N+AGhq0h|g)1!$!bb?QGXC4t=WG92aucKfxj{CM`7141WO zCVqPa_8P*NMq_5gm{$MECFy2!(uZibv)A)~?$*>0aYlY${A2(8DyyM)#x~vmCwHgoFXcM{fTx92413N-E&Ukx??+zI|F_xGHQ~?AR_| zA9q$3t(}!l0&=vcBEWHzPl-hM>1>|raO#x((>&|tBw|Di(ICE zt{t_}_?Gh!MC=oa608V_AFr!KY}wh(i#hTU(MZmXmx($7Ni!#szXgBiG9E!HUVrZ{@rBjq)2|;CRLSEHRyjJ#^-PoEjr;*$ ze(qe|?V}#DhowvfxD$Q<(W5s>yGc0s28gVu!vm#PEY?P;>Uy4C#Cil z&GSro_~1eHg)!p&MauoR@SRLpOXW5Pxow78b-L4MSJ(En5$xbc9BfPmVC#*!h;_C7Kvs1TsOjR^5foPl9H4CE9%CtjNH;Q z)W`u18*4@Lj4RWD;e!%_2{IMIM9-t~y`_`H_^(#jzV-7yd4_iN?qfW&2PI`WXG1HJU-R!``wCPg_9v=&K-a z^^8Vgnog@N$fInd$FRubR zk&YYDEk#yzz z*75Gbg-~muqFboI?c0aa)~3&1x}k68m_LVs;W%(&5hbKb`%`bNSaJN=F`W>&Q1WS~ z-_l2E*RI_dB~9BDGS_`ArLL~7bf8VYZ?0uk0`iQxMog#E7BelopaA@2sqw>uEWU+f zjiDB~37)|ptu4(xAOE4Uk)A#={B@F7R8%$C zG?|5CG&gfOaC%$g^eOAs|(P`q)i|LR$tht0M=Nz@!YH ztFU7{+YJ6e@{Y=lmv{iLjc!A0xQyCFgx=kO0NcsQ%Go-*cbE3~65q~54#t6x=W)4~ z-PUJBbh5MCXI8v;wN(@8hE1lsWkV6$HTy|x)hVN@wmyN$CUN{qMblR!D5k}V3D?pE zDq=ZCCeuK;-C{#yV1SJ3nHiU?-__NfbJ-$wp7m#ZewAK!p723YJdwWr^I8-{pBp<6 zqB_##G1RR8KFGmBXdK8{9Uk_#g3gA3{?(cECDSH+p(oH-_gT3ly|Y1->@er?UH-7M z3;%lIv(Mo*NFlgQzcGQV>}dP!_m)Q`cWv<+A=Gc6t)*Qs(d>&_7KW%oe$wDBu4|4E zfFGAb@_Dm$q6h&JgAOV09UbZMGH^aakrA4A=gb(%8ljLIC`HiT-`auPbje%kes5jIT*_{?y#I6RC7C zD=pGqaDiDOG|$fTcPfoeVUXjl(pT{fHKaxs{N7dVw^RN`e^2vIpe&oE>o$IIZvD)# zgkYF%MWsv-Y3{Y1?fxa<%e)KBY4L!3Qjg@DdZ>-NoUBZRTRNoh2xa(wIe4yjS6<=X z+A;xYjR+&SCqnik5st+G*Rf;AjFF++nOGEKu5>xMgtR?72%fOflsyb2+@sn*90cF- zYu+7)=wC}_@r^xec@3wDt>;*xa7?1>LJceN@3qVASFQC)ya|Kd$_#{BeXsLueT4j3 z^I9?nV~2XNk7wn>9>rsEPw$ZDcPPC9q%Fa5sNQRAW?wN3^LS<*(%U!+I4gNmP$oo@{ z5;*z>vz$1)dixgXD)SlarHI`V{r= zBEElUwY23d4ycw-FM1>f@x^pLrK>=9>Qa+o;35yp$f}SM1^xjcFKSTHTi_Ode zpKD9ms-W={VVeR(COd%gSaa`3?G&UwOfE4WLRNI|$uJN$E&nuO!LEGc_nsVB>!~z| z=uU~%01G(8^lrR4q-IHnNM|v|#YDfRme#;5AlE85ZDZ4(8&{GbBHv~byAU@`5fJ>D z>gB(1E)I{Hj-W9_oQP3O6cVwC+q!a`$_M`b+r;gft2Yzv*)M)akPRka#B%rSLgl3S zMr}>fVM3~9*M{}$5j|eHb?X=ErE}Jo4u^!uOD{t{=FOXLceRG29dT=fj7Z0JR?yz_ z3Daf$)7&X8Q(5CR0u1VenyZdUazcXahnd6X^l|s_kh%tt_}AXO`@LKyAC2XFiHPHX zR)}ViFibfc=2zl<>KH1D=(T4c2D4gyv^v7#1#L;9-5NbEa21LgVcloZ{fbD81G=Ib zCU27)AX1IB<I5l;&@~RqGc<}7bhBwgo1`Veqd`$CCO_kdY5`|K z9w~|x*K`7Apog0sBG0%Gt21_Mx_$lpm8vTo9R(kYuswbN#c2s4K_PQ7eyvHe=f}wI zzt`1kL{L6qfx$TS?BzyAMGQYo=D_e(g<#U7d-q~{AF&H{>{*Y9#b0E_E~q+xN1eLw zfhZVy{UMLk1ipj`6wiH~OlUTbTvDtv?GB3_cl4gdcQSOAS6AOb zjUE`uJM4=lcB`$!0 zgfo%IWV0wqI}|5QOaRp7wh$5kQYd4`YHs33{}*p>9+zYK{{P>0!q{5KzNgJrgDe#a zWx1=nA}S&(drD-f$=J%S+meh*r5&kB5yJ?Tt=*O-qQ#aelGOKk)?^-^_kX{Ce%Ir@ zm{IqAUFUV4$MQN}$LkPV@W&S%>RljfB)+UidTqv`khSK;*?y-(+hhIueefIbvH-bG z{Cjtp;h|M~Z;B`Wl<}bYX~QG{o_;x1bb!rlPmZ4#JqWY~+A7~qPd^Sc=nx)!Y}>YN z`}gnH(*PeF^K8SE^Wt!(biQyW;&9~)?X*hLYIdcIU5f(n0WPz(BpSl>F7w>Y6WArY z8p+;KJR=Ort^iSE=!BvYRk5Y_|FDIy`zD+YK9gbqImdjc?tsxWCkfa3JSF2rEtLD4HeT8ama*zq%! znamf@Eba7;%03j@f2e)FKQhb2M3rNZU4DnaKwUvELdxJQl1{UULHH=w)MsHKzg4X~w?AOjpj-wvY6D4D8!-``G8^FS;W#C$mBs+6&AZKCM z4Bfkb!m|F39?3>JLSQH9-tq#6gQ=@j*ez-r$o(TM%+xVcqaOhNQ9{BD9<>nMG>J4( zNV15_VEd?%BPkN@1MsiRqGU&jNFT+ytMzsBf3!LUe+BFWNc;KajTSY4gmHqw=_Ptz znFo3FO9BU(d@ag4V%~*tyrFM1)vD3Z8B1LtO|X1CaBSJrxvN%vz(v2kd*aN7#kS{( zVuiey`WtR2iItN^7QKCtz6cI=4&QnbS!mxefcD4nQ$^A;X1YM=vlw~+D- zkRM2QX5uM6Y5UHdg3MWUjwp$RMsaZpHPGV6jj&Gtu&o~&c_0%1Zm?1Zn;w}-5e5LW zl0_*Yy~BjMu36*676bIA)VPE)FBhbaB}@D%UU4yrqId5fl>JD;rx_W|SUe*Q;EUR4 zh}mj9$qMk?>~4=+>VA-Dy?OQOw9dGpZNUdbxi9zQH0`4(>kS_w*Uxww$#h(T|M!Do^z{~Ipd-w0h_8k0$7R=)W!R(LBr)CEm;X z$zIiU8(9AeBPT2(A%$a!u{a<>8BNmElE3M#J3%di!;0Kj@Yggp2qEK>Aj1|~Z;;^- z2EOz(5kS<1$-H=IGhuIUDh-b_GKG15~`i;1bm-CRCc9+3p=HJ~y zOdx`AYAe~T@&82B(liC08YRYUpZlt&RRhQ zPEI9wQD+=ern$+5SuTB|t!9>vvqOd_MSCwDkq|jD@86%}mrMoj3-1g@PEguYY(ZUR z&KxvQRqraaZA(OTBK(4X+~NZs|DqGAuT&e5Op+`+u&1OIk6Zzx2^CKAZUjrsDFxLG zA#qSy@{(-%=vI1gsQD?{TEWOCtR7gIy=MRc4MSo$&8r{94+heO=2x|WUB&){Z1k|O z(DvvSc<;%LMhr;OzAE*UPrr+oE(r=4*wA1l!Y|ZpVm;2@_So|2h2hF4wfaI|NTe^C z4V_K6#HP*TZDOc>G^vsPUk_a#?Kt0o%K;dH;o!dTzX0F7oFny_>~~Re1ptSz6Ogp- z%BY4wY%|#*?3gvQZ{Y}sh}bW5f>+&B$k515a?)w`O0%CsD{ATr7hrJ66WzND;Ggqb zn7odOCgRf7&Epq{2e|U`M6)NH!2Ha816ptVcXK@$NLtZwMY!=W1@s_H@i2RoN6iz| zfHxieVhcgjg4t_z+`ALoH~gr9$Il(v_`4FxhN>pH)~0{sKfdXc|NS@KuK&wamwsq+ ztxdm_(YtAz{rhhWl7BLJ|NS>=9!>VA>6eQCS9w3^>HmK>(~y$}c@a6(G9ptx$pydC zYK&N?7jhL<)fBMO*fX>)JR0_iib_>gVnRah;++O*ZGZViq&6{Fgp0Vl9c7I~|Dbr$ z#dWqH8pwwk|NMEBo0vo-iBfNRT@j%i5ar=^ zy%ZJQUft|CMa-Y9)BoJCO%g=4y5W*PCVVj7*9*Hmd&-#R5(D>X#trG%zC9fa8-GM1 z-GR&Ej7}Oe<}2v7j@xjGEicJa1bu{G{G)C-*ek9=JV$kP^|x=>37)EZ%3W(9DeI|I ze-FMSK1-U`6v24ciH;v2yr^50{%P_M=VH?rO*Z?00Yw-acrWTJYw7Z&sw*0@KQ3)1 zu}p`tbM^a&OP@ZiDLswtBF3~QS)`AEu$X-7I4K)VC~0H9nH0@!0HIQTZo40cGmBl5 zFW*^2j};gGuyf1){rUwFOkj99w}Nq%UBNq~O>!&VsOqUUqvWY#)?(PG5=P&90lyye zgT^9-nysF^hm1=})_-KfU2Fhm9>^Y@>fTr?kSI+Vau`ur2l5R*5_Hm1Zz&_~u^LKG zyG#Ch0S-Dp(IsrDdF)hjGU9bC;r!XQA}f*T{6wPV`#|&vEa;Kwx5fY9!SiSj`L1p! zS!h7;f!xSf5VWxUsya>&@DJEvF;yNLVRFB!wk-NAef$gRR$hshEs$gs1~q*0^?zrQ zNh0>(>F~aKjFlpn-__?NE|#c@wwl(?9VF5U!kYE}yqRTop4EV&SP5jWNhY>}sFu)V zIC8f!`+JUwYv_?mQ2T1vF&L02y9l4GzSN_UMG`b=c2mGQ*wMvBsFetruNo)pk<#-{ zlE3hW2m^8t+idY}Xk*Le_V;54@;35RIu1Vkv zx!sggn{PgH7Ef3Cvz0-8IEwP*TBhZ+B3u^K%C9XX5gTwfN|qE?i26@E__DO8yy_l- zOs5s`vmDEq`nnFmW5ccbY)kxbW(i*vaP1nK@H%d`Z|EL#QL@ zD=%GYRhtJSC?{E?VrEx!=ia?5aI&HObJ*dxhzo_Sb?eBxUGig`9Ub%NDFaByQEu9} z5vNw_Xc39K6hIiUD~rh1C*XQT>oK5Irt^5oVD$2}?Jh^mrTTtb#Abm`^zTyOgW`|5 zI8;RbVLE=Pr zrStE+6doUXVWYrmdS0F`nzI~ho39(`{le)}ru41LQqic}snoZx7pEDTOnV<`w3QZ& zI@(caT#g+hr~io@xELd*^!y{wYr9~xyvT+eZD_sAM%0JJ#NvjLN3F@shRcpgwjggCBFfs_RlgdJcu zI?^g+Y!T(V22Bc`^Ev=BFle30Q;yF_>WS{m1+N`*BGMI;vWK3;x z@0Q-^p;ZQHn)w)lp8Ss0C_khJslNB5%^PO8nl2~c&R%sApG`l9roO)Yr7hHw8S8Qm zt?I18laBWSxKT=EOg_D|ZMq#W$ZcX(l8p>BX((kffCueH@{JNMaC z-dNZSQZ4Xq?%l8Lb$Sp$7!J{C@HTKRAg6re}XGW|T_agCZ?O4>6m8dp%^r zzhh6nWp28!M`s2dn?*m?pxhS>!omt9CF*xR>nt(P9g?L7u1ah^k> zN6HRX!Q=%1+Dz&wT<}V>Zwh5sTHV^VZL7A4ujK=UDD{0H^OllO%<7{54qzLEY^(1))`g936(D1K&?JKF&vLf8QaK1E zw@7|IZRyf*C&9*|OAxo^Nao~RkE3Pv9cGx{ee`H!(T&|Ki)(WAmuEb3tw@HyqyoNrf#}Rd4{R!{BsMh@tVVO5Z3fa6GbK=t2?dlmL=3{ z=h7G@Xn2hKG1$uv}Z|&FpJ*XgprosC54gBDt`T%bqD912n*7r&7dJeKETug<7K&c>_BDx-}zyQ zq{NyWGBgd2txk(bj*cl&v z`nCO!7)=zBU1E72S`hj(?-AHO@Fom0E0A?QJFM#K7OKqY?G{n*<#%MNzr~+FKM#do zA}}PU!yOWNl~iBE(3cVTyoeODz`}^r#vM?fo#`V`$1tQ^3;K($h-`L_12rv+kJEZ)dW% z$_C{rR+VJE62!^_^C)SDFG{5OEbS;c3l@k-W)ldPuFbSW3;~>{W5>FiQO|fq-6Q1a#QH2Wz&IaNrjIesAp<<9iGV%ouuB?4pE)XNFxls&@$$_ZmiCl9*>*QX)8WXsxfL%b%&5_r4qFpRpsT#J6wLv0R-jie z&56S^a&rD*6*T|p+pUApQ|AO_uRVyhDGlr4UkVE|{oXyRn3kuHuv2OCA`?X2LA;YSo49!`>~O82N>D!?Btk7E?4Z zK}s`p4eTj`(7&XwW+iNr(wv5b%iCH=e&uCiFhakci5N!whHgX6`vzQH zZ0{DduU}hQsSjl8exG><9&W&DbcX?`E*qD(^?85+M;2mlcmle-sv5G?vo1`PaC&$yI6p`3yMKRQ{t2*SY863Q z%!1@>!hHg@ynJ*eL`;zeJ6jA$(rYt+vb}nBbM(pVdH@j~w(K{{E4Y0`+2zz!qk0Ya zu;+qLMA=w| z>Mfb&_5^&6ja~M>bw0a8Kl#q_K zzmn^?ov4y<>k6t@H7=2XqLi9o-wQ8XP@*7rDM2B1lv{hA+cF)S$8;BZ7@)in0q2|Q zZzv|Io8smlQ28za#jJVRrxT$$hZSC-B8AJPXl|h?r~s#pKmrmkZk;{6*^pTme#!oQ z0g$(jUZrWyo|7n^LZ;OT2=Vv*6cSFL9eOs*7kSM=W5-tGd_z)z=L_m9570IBA7|VOgouDX76LLwZ8G?h z!MFzLji7^6c+h+At-=_jZi{pyN->HdCKQh3`!2heA1O(h*+@Z-O~SXus>Lw+4{T)7 z%9XKi6!@vpz@={XsZZw?fbsXXj<7!ahw-b9?pe=hm#d23ho9Q&Wvzt@E7g?#{S9Rw z>2!t6M~YW@QAIlO2c&K6z@a}P;TAEC3>WH_tk|GF5j4CBvy|$>HE1A*gC&`?F zkuPhL8R~C0qet)FVl?%+K{B4QjFlK2^e5O`|y&|H$ zabs%fX?(?DSqnE&3<2fgkxg6NCG)3g@&v|a=8))+MTvBlM{Vt<)&MujCi6*q5Lb(# z-(^ARw7%ZYn&47{yaRIiV67uZ^bh~x`@X0sB=bXqb=OU4U67S!cC@uy@ORq~KHRf3 zG?1P}#7x#uY9&PSoG0C%ew_rFyFxNPY!0!lU>#>1~`|L+NlST(aU8^PMVGnTTY_;K?XXailXrlkEu(WCl9ud~tAiW!o+It%UgqlNN5+}x4yI)#+L z^6-IEY%}KdL4=tN{Hmx3UMKGSAMhV`C%v?@uU;zYzEZOb+73(*Pzt^&qMZfFu2^2r zN53cq0gI%xnUi?2OLucJe*x1YxuYh+z>%=w0q@tkE;HMwBmuedR-1zJmvwqmsi_6d0~bb2_Q7lKhHgdy z2Eoi{m1jI^OqAn@tnd8&j1Z~-*AGrn?l{eV3{q!>bINS+O1#pl>^X^vf>v#D#$?V@ zXwBrABncy4EIMMDNp`IMCcx|$n4JcK_bjiyv0NhM00_$lm%1Z0j04JWIsAV)vLi2J z3Y@u`ZR1R5Y{B0Wyf7cLgyNfyNy{=n$g_6&T?XetfS@n^|Ljc)fl`ObZ(g(+(6G`5prEWKAUJ}G~{sd zrAwyMkjIowpEemlFl~iw7P{S{=?RidmQLhSs@G74NR|M7%!i-6y<}8sj0LSGo>&M# zMWBw}1m122)Vp|Fm6wKn^4$m&iEl6xbG}|--kt+V=FN^eSkYY4-M65Al-odww{@GL z(al=Jz`DwNZm`v$@+n?1IRHzjnw5l|`V5xB_@ubXgA}y*GL4IUx$(=_5oV)iH>|p(`S9rn`9hpFBAc6$es)qvNsx(TSEHJxZp6OKpuX z02IZ!qIUq3{)(SR-dwR_1ydN_qu)^L%JS0env5kaf{gW)*L`+=((I4p#=Xd+6P`If z_0<)N(?Y1b3fkKH_bVgff`+l#ywwezb!MHvZU06hu?3&+pxLkAUk|io14StgH>rKq zZZ&i!)K~$7Ca$;a11l_rQK>&^tgBUiByMs3G+xG{S99tpXnTj!a?J znD+jssMqyj{;Z%$o&`}kcH%_OsKMJFEWWs9ZpTEaaveRg=yh2Dr;KAsFp+km1N@H9eP0)Zj%p(pQKl&!<0!u`oG^3mv5b+_Zgi+N;DjgZel^tqxo+uZwfsZu(m}x>n zMqykGK~&1_E!7ocWQZ4h}79Gq~tsdv)RY4&=rsp07_wvJcmuvm( zL%{dw=tBZZ48IJSHrW0YJ83I@zhIoie6JP%F$vS#I=oGg9!TMLZVc*$=nl)8%KoR` zg!GHQ#bU9j5+%F}ZRu+2ty{Ka-1m`R)0j@e&h?cS%~3aN2hC@;+oeyUny&a##M{!n__bP5V z(YI^Yc$^#^n8WS=K;kERak3J-fuzNS5CL4Z2LhDFh3Xmi@7uGu$SFYr%k6RMk3X)E zuwr8l#nHq&mg*D-lS=LuMr$kBDYlz9L@aJDHAeWyt*dk4=4=omfs3>B1+3`*xD zwV@8a!B?iT@jLzUi`CoT1K4bh<&j^RjYulABjX>#P#3TK7809tNlBgBxButUC#jbS zVIMyU**4>>hFER`XG{-6@tQerp+Qr9X#`8AF*K7I_f+!RzyIr(FJf84KN4Kj zlloQ?Uiu0~CmfpeH?^kpbsB*lTq!Gu1)RfV^wD=h4S(B|vxg z(vd4h?6R(X)kh+QVE|{R3XEV30o%mv+*}kJP}%ZtG>hG*+SxrL9pJmBx|4(CEo$XF z_dOp&OH@>e$mZ(pK`^sDb=AoFR4m141?Vej=1O0JFJA$1Jx|~j;;>+W5W?z^-AVcg zQHOdumVA{;H+IUipiErza`fKTEhJH$ehj&DJ37_V*DFL_ebUz24b;oahBKPkIqh_o z@2tSFS!k#KFrw@|sjm7Ja0u+CfuY)8NQ?>nP%_a&QQ#-srY< zYaL~u?_WQjwH=KKVbsk&hruw+PVX-;Q>Ebn3;I|K{T_>xPa>t9Cf@ROButD@I!J;$ zmP*Xsx>O-zf>2=N`iv@248H@eW1}gjX^1JoZGBQxQ=iq0PAF7G}*<6+^Ml+2SGfAA@B}m%afSGD;o*?T#6lPPsRc z-3~o!1~(az52$a50O`*?^jj|mcYWR%z9403Z)#T*(6|a#sKB=6^|2U)LiqBk68C+rt^-^ z+%(5lXF7Jdzk=Xvq^e!j7@ypsGH>z9mA#dfhv*&F{PoxM^b|%7<@mc|gF}jtvp_#L z@A(h#a7rUu8)5P?6|M+@FtVrJR}K=8zJywcvJg>B_b}5f_;nsZW8E#Pfj{sS*)^P= z51<_g*qL=O?fg6WyceSrS%Sk`75cZm&d$uNwV?|GNlQu?hBke5oTP|6;AXT183J+4 zI%s_$+M?lGZ!H9j5R8o7K_B?MyIQ{^L@%j7)W>OP_YzZ?R5}8}du{FJ(t+@ADJ@WT zFdi96*Y5&tFUW=>>=5;xThBNA_8(j@So9h|#&@MDNhk~HE#xF{46T+$(yFGXr|0WS z=ApNxCc#*1WnoH{&{S~XM37$U*t7{dSl5E0a8$ToAXEgHG1(T=lHA0i^Y!Z~{F?E6`xLjx*AGWP z>=R;lRE*PB{y}yj#5tHz$8T9J}wuWB^YMb%U+|s#wHvZ3yIRGsy?+A4GIQyOicr6+=|Xd**j8rYH2$O z?d)r&S9z?*V?h($5z)PK=X11;yMI}k9h#0I40G2#v-}1jE`zYcZrwwraw6!a_VDr5 zGQJo8X)@=`w&4FbUku&OG3|)8`GA%*ks7u=1qXfc;#j5S_~NzDJXI4%3MTs7Ql7Dq z#TlEu!eTcGEexl74BiS@?}^N_W&LAqozJw>Pxk{Lv-t6G%6Y1z#OtDhSh}*` zTe9JUJ3>TafcHE4t0RUm#Fu~(AIgl%+^23F4{S^~C5ZcT@=k4X%3#-#4~Rj*Ar2Ix z!|G>8y(lV*XVA^wnyKHKWZ^&6KfLtuqI!9K{i~fVdKKU9DwQb?y*MW5qWW6L0F#Ju z|D4{jG~iGR4XvceaWiI3F&l-FXF@N>HwJ5pOr~5r)PKsCNwsw^zQ0;GsleU)!i!!n z*4AhKlk)6UnmkE!^XIL9KO~^OL(GCMPrz({lQ80S+JJphCI`3e(4n%T;t?|Ql&xQg zo(7JNKm&-1Fge$9Fp`X;vd)<;^&vasM4C!bE$qq2F^oEXS9T8(6c+3W=Ee)9z^=*+ zlRu&tJZABOINR}bey-g@j*lfTe%@(e2Sh#;DwzUYj$5RnyrV8aA{jB_`jQ8;**BBw zYu6dB#WncU4>X-E1nuG%KE%yi_6ZLga(pJayZWqUE16rh`HdB!(%M%{9vN7tf0kDn zwQkMPQG_vTTS|kaf%JFk~`0^ zFhH%}g%SkJ^WwFWz>z8E{z(xpu1Cb6!|C*_rB#7%tc2qaQHSj38k~LYF4afl^mXQo?ZEJJW=r-wPaUN_db2Lq8}lDm3HP@ z)yL=dm)5+cJBX00r{VdFgYsFVR&7#F96J^)LOh+=Z_}im^iWSaXgEr5&x3v4R_xTV57C*;)Bdgp+48G*G*+G<0a{buG@cn zoES}B=~VqIJK55A>Cud(My3ka&k$}rq4GSYd?nl)spaxaAvphybOC{8C$ z9Wc(l%>PBnC??3#ibVdGFXgl^(or)G4)L06+rIQ zj4T1#m-_+Pskag7j*p3mXlTkW4M(5>v!>iDOqtH&QoXd1xf&d%9o2tT!J~}wnidsL zG@Zk6zj1c$w!K*LFB05c1)zZ79PfhnJdI+8-jF;67H~K!aAZQ|O8B#6QaMgt*|QEe zLd{D#nL?wau#(h^SY;itKI?fi(8yvZ^Bb!-U&O9)%$V${8ai04h1%<~GC_JaB!R7F z&+>p>PaU5KWuOM3AuyF~mdUozCR2E*{w`*TnUI3c&qiFSxsMFjNifnW!MECjro3~t^NAv>v zq7&fQ&_KG0GsDYKv3llJzlGppl+(G|>hIs-1shfnM&PnWO&u>D{*yhxBrO?dgT@jm z;|}Vv$r)+v8!=7&)_Wx19hQa{pZtxEQs6S5kc@KyW4nmC1DKWfU*yvVQ70@af<0!d zxkWTalhu>tQj?+fZhk)!9jA|^Zb0q~b<~*kRc~wU#Xncp?QiV!i3m&*h6a1&HjinD z7cOedHd*NC2+|ToEjXOq#!#G(f>+Db zYRs#Wl8CcM#IpQ`s3v}Libu1(g1wCd%1AdA1|h-(?W(9K^aG18DS}r*`-quv=lXRA z-c(AjHFgm=@|2k$b0c$DO1TD_Lpb+Q$7d$1&7O*!Zb|%VEX|EH0i+pijtQ7LKnQJV z&p+B_N@@t9M|*X>x}Gy{JvV#M{wHymYLla8QrcK|$ zCeW)DZ9+8@3p*HF)fExN;z1Jw@xZig$+ zaZDL|+@F(n;D8juTd`1`X!W;H!_B+`VW>#tgsqgW&2wYg7@tZ5QOXKi;|^86H&nKj z9s6}T`c=Dk&oDInP2104+RqxKl3?uu0~cK~T{wsdxpYBh;sBP9DJ1yjA8J;%6E`Py z!b%8T6q+G3&Op`?1eD5nF%DTf2RqbH`-*3k3F8ijhCnAnRY<}1Pi2dtyDb5Uf2@ic zE`OuM9}6=wn1f?<)F^0R;`TG! z@9t*i*k4O4Hg`6Rhm7EETHRkO6~B2Ce&$U3n{(r}T=BZCSiu0A+?&N^6_n8T&VVheqo6^>Ze=7|*@? zqN%9YaElUy7LB1=w*or0I2-foeh6QC!|2)~+oB7XG8i(vFX+(0gZ(39Sh~#tS-XPm z4w9m7puoC&-z7yw#gdE7-G0(4#fN&UhidLKcr`aMYAFyndRtuzzyY2!v2yJQ)A2qQ z4|E1!S!OXnZ*FFGw)xlE!+JMb&HWPC#03}{J#YT}_UgsT?Q_IeEzss*sq8WPc3(D7 zuP)r@|8~?!6HCU-H#U!r!qd$|0&MLZw(%uqC8e&ADKAPS#Zvdu(bZM5zam=ynr{)a zKutbOHNTT~wDq#TtOs3*KYY$+ut8HHuW?CSU`uvqGy$n(yTzd5y&>Lyw}WV%GIPJ5 zUpJMsE+gslHVoT5d2!Kfw>b`Sj^lueD@0-`n#Z6%QNPVpc<_H4eWH48>Amyjc^AvO zz>@gJ#lcdt!j8gr56kFux`h->wYlORqdWFdE`uKm(Yrg>73M7a;8bgCf!R<-(71Dg01(p z+KW?1E6bn=WcN$sgEDFdWC*>D|E?(Q_sy5x$c+ku5H7$s-@aQa&9HP#eSe9tzy?7& zL$#(f{*_1@uB=XAPN)D#F1>GxVMeFxs~#^uTblpE zt4Em6TIO zN`>G~V5r5sq>{+V!N-6Q0kM!92`=0x0;NXoi}E_^F!h*x1z+K+4i*ivVrC5}CAgUK zcauSfm;!7#n{`P?UvR{otgQ3I__$X~ZQ2W0U{7pTK!dczUb~*iXT@Z7rlq(eT3Y1F zCjhF1hMwQ<|G54QxBcSkFdccbE~8v5pOP#=6AoT*&?Jo9Oz1V#VqJ*?Zt|aD`-N~G zAzfcsH&vp_m8Tu^Cw!l^X2t0)_zE=8@E}HVvO=OaW`Vh!KRa~*?Sj59yb}OPcORaM zAthG!IrkVpN+Q94RNcZ#g32y3q+N7vL&st)VE@9-m@#{{mA0T`QWpU63s~jJ#)Xxo zm9l;7+Kxi6vgxWmE4A!UsMw)YMA^)n4jN02N5`U?wU}>D)Bm+|}Ozn^Ko-Gd(gjuPV{)) zLLJKL{K3b8cvRQ+jJRcPkQqgt6-Co2JAT#}OEpE&i4uN6s{B6|FOkRx*N2(aP+>SF zw9X-YF7rDa>S7c0?T9c?piX=;as2ovpy@gD0I+V~y{n`+bmlS0da`||s7eHN?a?C{ zS(aH}32KU_%0TAqH@(-DZ=D*r3M*byFr!_SZmdossD!&83cf4@Je3LEU@wK=yA% zY|pV9KD{7}&g)x`Aj!#zo2(YV82IXUV${OFym8uXeQ^^Lz+7v-E3eX;WW0)Z2qdb0 z3rt){SV|G<4}^48b3!zWfW7&M;6zMZun(27uH4+ptdQ+N$Nc>fp1FAW@&w&6Tlfru zD4nuyjys!1t~B1U>>}?H{G*k~_leDv#uV$yyndoYNp!Jkmlg!{1C<<}li8J|4{RDbiq#;%y;jf9IWOpro0G|Lyo!2ue;-yE-j&h zzUFOt`TVD`Zz-V-d%INJzk%k237aXjro{A9 zQ-s#v$*_VPL-1Fp>klRfq8s=3Ab>lU0}US*6m?w4tw&m9I;{onE&5O*GJaZ+`?bwO zX-=-anTS~ordD8cH7l6jD=jJ9{OorGddcR3&2RswH*8INaqUT^N zXXl>3PM@;W{?8Tjn2F4@`HgpFq8xRb6}J!c2O#T`ORMlW-gehtm1q&sa^FKx&O|g) zA<%Hm2y|J8jv6K2UeUnkz6&mISj*>&^7W!E{-fz^^EQcqin>}w$UN=%@QYa5N`qi2 z0VOr+BJg`G*mGD39JDqaRTCiDpsqN@qm5+5dRogzG!XZc{sN#CwW4B^m#uF5A-0|tJ-%In=v*!f-+aoy`!|)uYoK*S%2p!1Op=7;8V(cr* zjoy0u`b-ow6T`J|=0ID@W`3=zn#cq^?nv$jD+Zw89uE_{w3FBO$)}y4V0zv?AefC%cj}p@yQ?oO8_}F0O5zMU1)W2%#oCmih}D^!{J zma@P>L**u7s$A^+F%>SvJrrhn8O{vTA&Gd8j%ve@`5H~K0*_ETEcbic05k9mXzBs?EHY3i&2 z2dT_6rB4W$hsBAHWQQKni=i3>$3a<9sx+*&`EZG4 zK0yTpGzDKGTgofJ0UD5R;KF#}r^;4G#qgK_bGem6ACU_~D%f;}^7#SFU3CG(1 zXqqPHoiU6qFxr9PM@be=ZK>kz+Slou-=009s*#lBK~oJ>J!nq!R}%TYd-+n(7jYVw zJ*#|4zeilevx?grE}$||NaJ+XSq;lyH&=hc{C;KbqhB(TJ!Ir;>5;6YlLMs=7#Ee)`Xas^vBTF_ z4cw+qRH`-i+zU1wZY0YopZYKwraC9LkNVkAJVJr@zP_FHIK^S?Bmzf7t4vLWbdz8i z@PDjwM6bg&-_^U4ZCbMRV5m&;|tbVq_*}1g5oK<`$qReYN z{(tQotWO1`_P^d~pAyY?{KHzQVp0AaVJ0gweDujZ|-l6bII?9D)4VVdL zm$(tIE>360 z4FqGA32{oa)nnlu(Kp73cN$eu(|Ph_6K9xV3r3bspeqrK%XFbqA|RjpvpBkBfkLk2 z^HbS?gEHpLF-n7CwfZG3+z?~U-0W{*{_)3v*Bi&JR`|Ig3>>Av@4SI;pjfwVweKV1 zLgM5g-*Qs@;Q0~0bZ-G}#*9+VKeuQK)*zCF6m@4JyCm8qqdxT z|NdEAYDOAOko=Aa3Fr__o`fm(@c2rQU{xewL2q>5Iq~C`@#fO2l0wEUH z3brt|GAc$(^qyOGq^nfiCZcc9EcabJ78n>)<-^$|!W4W`0CQ!?B($(I?x(>NmxEez zoOoJhHq9Dxxb#6{9B)E6NALr3R)SXwz>P8R8L&IvwhQEzW}8OaC~O}9Oi7WPc6RbO z`Yym=(X1+Ynk+b~xLwmf`lzUQaD+g2S!FJwM}lIdx;Q6>itXIFlZO0noBK=Do5e0( zw!Ir)8(XlxvL7RKsKIB*;UF;DIy`qbyB}j&@#&KqmwF%c{qY0xvkgnA@Y4mEP=hyK zpsBkd)SPLP96;`B=oKR!$a&N)*pg7*$@8dM;GqRQn!Zluo~01NMG6YX9J_87hmsZ- z96h0wf+ao<#6SjXcQ3U^ELjAh3SlZgq$IT6Xy+c(H60eCU{|rcHQ&D%P#2u=gTxpQ zol5=B)y0rN0YES;3<6CNB^`)MoP?9398K{5nKhZsq|NtEX4Jx}?e$i}m!veG_PKctS{A2k9%pT+bb$dZWHBkBq*~xt~rsT@{Bw6{;A&6Vu z41x_DQ?`e(x8vbVuq~gHW_6lW{TEH~r{5=7NTufhSK&Gu&YUGuCJqp!9~=xxc-P&p zDJTnVs6H2n++fibSs@j|7_?JCb0>$s0^fj4XdcHTKDfA327isrCar@2MhQ z$8Hnlk*r&sG@8?tx1gYP9aCs>rN^((`uJOPaX*j<_}>r9%*=d3aUPHX!oBJHT5uaN zy@Tc(N;&QA$4g#W*KO3@Vq5IIVq+qC^Dr;BwQHT(Ks7l4r~Zc*0c{DbsP*gRF^yZ| zkm}F++bQ7tmU1?obEQW*s?czF=OX|YFe7om_AW`lG4Oz=p5AS9J0l~2WRnQrbXJto zHwsz$-`|biKG`gsEx3L1c^W$Tk33MOap=H-5!6kHVidRHK2LBsSdY9JQA*<==#iTx zI;(i!tCY9U7=QD5a6U89<@%_oOHY@QV<^k~EpE%h|7YiB41`6zdB8qgL5&7%RWXZ< zwC`yCM4{?R_^4(wR=%Aa*~_b*ZRM<2fm=cpQqAwk9(%%BvMfAeOAn7Ddgc7WoLRFN zg)V59FJygm1$7e^zs<~+LG5GLEi6{C@`*x}(sfrWriwr+V95(=mbRm0kseM=x764?!h z4BVC`PC@{Pf6)^ba#D&eEQ<2MQrlhGoLq7mn~EFDrw58K5Ic?>+$W3%)%Bv?G|I3I zr_Y*|3d_zlmGG84Z4D|lYt|r5w`1KjyUfxGcHs6RmeR}cW<)Rc55g;%+??Xbd zg>;?NOJkJ*>5iaY;RGNG@Eu9e2Z|r{5j$)UWITHOx}NeBBaL-CwU{JIIG4tYVh&!| zP%}~l4dgAHau9stX9|8~d<0m6hgP$4r5e6Ck&srJpdAUZHQuVcyCjwH|y!82e3*)ievvWX2 zV|+48#3wKi0rG&le21HLwNO(eg2-`0Wfpn)A$OB??t2<6w5jHR)-9 zvP|yP{IE$F$ck2#vKggRHoxB3vG1NF&G{r+fjMmjuN;H0h)g3#oHIo<6 z>hExT=6WGMlflWY$~#nHI%CGLS|hd+YKJzT??2qb&{SZSt<|4`GtNLTgS8>>l||b* zuu!Z&15vBwBJQJGs%l&&d}q6MS!)k6mM&FiTTw~*gLuIp8(wZ;flZ}{KSPPUXNs=H z?Y8qp2%@egYMBO!5F)owkT1$yZt{s(g#sys$aU?+k7UU3cz~F2cv0?wO~vN;idbpI z2*NJ`fYHizQkS(l3GY{pL0nI zfT)&t969#BUAnA=*^#Pa@sczE`an!>P7rH_izL|!`CKZbu4hLn1+ZheHJ}c;A82ox zNBh%|y;5mwD7iSDl0sE>2U+&3s(_UG3p>`+Fgc|9Wjg3D9oLhMA7UNka|q1YwCZY) zpFHUq+C$Qas7hb*p}mCjo zEa-Q4Prp$=)qlzkghQf6$FqG1&5Axb0LipQFy@{Di;gEk!{f4R1$X1}r-H=tk?_bE ztzr|Nx8tTg$GiO5QeqIc#KlDqi41DuL3bjnR%&wBVE~wd+jH`XzNKgNr$uYloZH&) z&U4?wsUSU^GwIt@?W57-irHSu^=&@koN_z-rgZ+#Kb7A;QB+7{Geu_UrHxx#^m|Y? z9s+zj;_77BjUtogU7aW9WOv&9sK6DdsOV;VBX?}Cjz|H`wXm20J~OXzt>suW{NMILe_+y0&kazNK1p(n~% z6uGjHPJBoSQ+nC0^pLV?I|dTGAOKNT#H8rjE`z0%GRER1#jssDiw8R~XD^80KOd4< zmGuLs@e%`1&l*5gjN0H`Uj+S>4DfV=ch45eTJjqNHbkOJOSJbG(ed=c?$`jR0Xf^6 zNn6=o-e7i12^BlGW5AfzaYwhRx7X9C@ zu>#Kvp8vp@oEpFGzF-LnHMlkn(cV)BJR)zTzZ-3&5j0r2Z(SaS1UT)i(q6zk>n~*% zf2N!RdS;VcEmVQ6EZ7)BBCx2$ErvPnPYVmtkjtMo%vu`I=g-$KUi6DdKwJICg&J9$ zBq(NWj7^$5h%M>^B=WM52yDVrB4JY(91&3%Tj)v&45`uJ?uXv~&^g zRQBN9V=`^`35P8Q9Hgl?@SJL;X#+*A3?4FtR0Kg~>D$nK8#-OzG|Zgd{8$fY6^Hx9 zd;zjVK?B-DYyqbpKuAS~z;|5Q{ekIA;^|O8gVONmQuRRagtCi1xOU@L>33UrH`EUZ z<^1-KCGf|_FlAA@-s`05qQ;xP{!)*F$Q2QYjuB9}IB1Fyr*P_f zeNO8DtSco{?f90>3=nDfK8f)n0%wXx51a(q896}uMtGsf)9=R{`QECwBB2G=U<02@+Wy_5LtzC_wE8%hGf*X4?;6< zp=7Vfk2$`<%EZJsNSH(nU|CWU4KTLhXTrmM{cP-KrtdxCov#X~yefEy5neLt=u&CK z(|wlQR9hOQU%AxSL^T&R39=GOHLcw7MdS~$RAMNwH(&)Bv#JUk6*8fXU^{Cl(NjK} zAw{r9^lFVW>Sd+T9{Zj$)A~_pzV&=w%ao%_C6b%(@QyoH8E)selRC~pXRBBHa&8^jN?Nap$9GXY;%6LClH+A)yTT>qIv4=KJR!QSY%PK5zFNAJM5?GeT+{E{1dwa&s zyu3UlCZu=L)7y3Gq@qa3lX)f#Ph}gv%+awly4&ul1LP*{qMLcI&luO|%rFdeK8C1y zm7o^>?oMOqQ|^zzM@{AKojXjL^}@G$Jli!S^Sfwv z57TZP&by)F zvDDF#%HaFD%b5tPs41pYwvgQX>-M_EVG3-5k4G2&uHwz{Lq`y{;DY3bcp=f&&`aZN zd?N^>0G&eqJ1GQL@TC#TU}ujmd7RCdMtCRc@Z-L2Q#eDA1wWISwgsN8fN7cAMu1X& zUSl96DqvC#`F_UTz+oHrwS}w;Xmi?0b1_qf+L!BxHW2|pFt%Y`%OVVb-EO2+S8p&h zQkTQ>x>gk=pjFVDjC14eQas%4#}O{-d_wO=L9prt*IiwkN9^iwOa_xDr(p0Op7*1B z$a!{PV+Dsr)R}7dQ-`Y9uK(^em$A-RtG2Ydv zeb+5CK32@cCq)%F#R>CcFIxPz*CMSd5h=t*tma)2n3EsL#}5^d!}VT#|_x=snUJB(>z3Eu71A77IUh! z4Ub>=d*e3k>~*vQllMr3Vg*1zl!RW$#EGfDAH>h#rr&;VjH#~lGboG?fPgXXVgmi8F?^t5WH6I^5vfhh3%7zGe^#*{<33L zGf9T!qPm#Z7WsR|owibAh8cxB>o_q9bk7%0S|6dPxzg{O4xPoj;TF}J31X7;5a*u} z@IcewDK&$9UIr@qoHm8ng}N!5)|2k>u~OkzZ@waOLkct?2y`=2{&%x!^5 zBbthyar_AD5}#~=3`5)}jT3R^@I{G42I|&W9P%B@t!9JU($H2_Cf|40$?#H(Y$C~^ zud^k8Rrm1f6}yCObMOBB;Wcx~FR8NtIhMtT6L4hIfO*2oidZm?iUwWx9Ykgz@$UNR zXa<0+8);=PYOnY(>?ha)$RVAYR2jfY)5VCBzaCOeJWD^*w=`{71LgoPm@b4IVka8e;H`0ik9)90f*C3wTwwKZ%(KMsaaW3bV49^ zQ>6lxD;?m%ZFRV;NNR&XDce@pW(iSZ(S!ahZ@WW_ZvOSJKN#|#HwT^t0=cgg=)?r6v*H51U2PtB~lT(_nnf|Z^^&M<3C*z8CVKQA?ckS<#`nNbTBLO zx8Y0);c1SX+b2`+Jf$);1Vh^t_^3ff*>o|rXhFLdmETIv`B~BW}ttTMUC>7Y# z)o*Wlx`&HCIraO28tqxCq9Ou!=0pbHt7MCBMkB0ObZdzc<^90m`JG2yZwN1nanMyig zTeRsv4j^qPJQWH4KM+=`FjlW$s}-;-C|sj;=~NtQ!+TG&kKFpV3s5%d5GpE6n-GN$ zs3V5Y;-pOHA&=)yZ2e`hJHCGXTCjSt#5CZ?5 zc-od#7!V2xf~Ue>gOgLli?aJ*)`?(wSN95_2_qx@89sJZpOE(s zdB_J&`Qtxc2HgEj+n2%si|vXEMi892BFhO%Ae}rW`a4~iH%M+WX2ru1lte(-d`}@< zL9T4DrhaJ`8j`?a@J|)jmeLlLv-%Xs8n4CvyuL^Mfj4Q8%(dTEe~alAKMue5XX3FC*Fc5SvmpWFds_du6aNx$HZr0JiQ)x8v{Btj za>|;lo>w|e=R1)%gL=7PR^KcqnR4-QeXlFvi`pF{{-!KS;>2zIfDR=4qs7n{uU{vS z0uf11A9RSpLKWWeZKG!lI<$XM&FyQ~UQ$3(erjyv!{5i)Bu0@oIA#f~n5)B2S?`Sb zKpE(FcPuoTf#NE*w7{qs*bE#&MR zO7!zEcCN=3Xwzl;0PD@|w$~uv@EHkPXz#C3v2aiF@{>qo_as8ag@ zdX!!{EC{{Rs-e;1O=-N(nKNe&)(>e+Zq&S*O0SZ<|BsytMrP++^Qi+7 zb6h2dUe;lbhaW63H8+dZM`VzVjmm}MBBSq-Jeqzq=)#tO1>9Je#ffNK}3cpXsZj@ z2BTFwA{XV6=e;AzuJr*!$cm2qZP)k-BPKgmzdF?TXUU!iZ0lA^Dk>`5+!{-*-s;;A zgY$~ZTcIC_Ha)Zr>`Op)U0Xq;^f4iOPWVW)B`#^FCc1S{^ATW{U%v{md2JL=Hh6lv zB-|sT7eZ4S*7W0Vf*P??x$)$Bcm2uMl{SM?LDPCWU#4R33@_gd!tCQqHI=vho< zYVdvSf1kzS*v|$zNtq2}0zoHep!D|x4-idQ+}iUEuR4k;Thpcie8UJu1kbDf#~5nO zq^Y7dwjohWQ=wRN*|Tw18d(0E+z~N?Hb+sXA!Hx|QY%nWNW)oETRB~4PB1wSLB=aR z8#EP(fzz7$k_?PKLrWM=k1Zx({3bk3`wkrvl9CFnts5WNAhOAPN|f%v&P%0y#JO`B znFI=rLxmzD#;ZN+`S0#C`RnILr{$1aktx0;OvHX}t*cGev!HYir$L9_dGg%&RsULq z&OfI*$fm<25G_78_q0QDzg@dzB5*=4YI6Q<9zK4|*v4CT@5aK%=U(wHV;271c%u@P8TH{EXd1SK#jS%xp0?gIz+bW;j_jcmpC@P%7vi}-{{@B^TLQi!JUNU`}z48 z3gq-I`#9iI0&>tAN2{2ts!^kw;8ufZP1+7q8H0&e7gG#l0#E$A4;#MV08P@~CXrtC zb%M;8F%9=n{G@&3cdLh9|Gqs>{}Jjj93k@=Kj(5Z{FMV<^I3_RFb5kq-c;;$VeVVCCVPzL@3I7Xjw&esgP{4 zN9JQ?mVIa$$%qnKc0^JV8UO3n>74WZ{h!zOb-taE?&rQg_j_E|^}gPKFo2Nb7W0B7 z;O0h)iiOV-2M!RgHI5e=JD9)n59MOF8DZcHqU!EqGcqYw+yJ=4xBvnWOe7L=a?VLy zUWp&AqxbL4d5S63^F0UDEHTs0jRonYW~QcJL6z8)5oUR5+E89Ve6nWkS~Aw%c4D6r zB&sfx{Yqc&0hS;HvjFj-AA?;Hw%~wkM>rC`nM;(0s7e7$e7%RqgR3+~x&y|Aas-tQ z&=UexlWQx5UAq@Ir6J7RgLn6dc>HgH> zo=hMdUVz}n&nL~TU#EY~iJ9I9G(F~|#iGN3TtMXX4(uUHQ33yJ$H8t6e)`0^apS$# zY#?nHE_{Ivl^bT(e<$M%$$!o$iR7E|_)JG#mdqLym6}mH_6`m}=XLS002yJ)CiL@A zn65lS5aS?FVScPrEr<~*cp;uJ*UCZ4LeNL!iseOct%q<5t*3|uq)d3b?!t?~y?p7b zto!p-@V%ZYN=TjfSnG{DyGfx5(*&4GAUM_m0V$Y$L-KhDXa{QX-Rn&-G)4>~mO0uS zb|OQ70iB`DB^)Q9PdF@ELO5w~ezk)J4ZT9c6z)2#j1l6GOilTL0c-jSk(gIqiWocgCilTsAn!Qea^0%u((Wqyh%; zE@@}yp9m|GtK0GS-~{jEAB{a{v{Q%j&Gv$6Z1dxe-VxFC6Xd zx5B?}a#J#@ZOqh%{@*W=$c&qM0$>UHH@vQRCQ!7;!>S$)Dg+lGoJO9*ID1Ltv%Ah` z8|Fyy3VQEFiUmBB9$ksV`3aA~JK0F68hm&3v%6?gh}nN{!o~=m-zGg_ldb{ixc)!& z54Lt-L1H%~0N}fnO@;u(fE+o3@@R$ z_7o@xKRHv68^i=qj9GeBlDwS%{VWo=)=11Yh#crZU>NuMQXD87Vt1~vAi4*9S#j$& zS&a%{TG21R^pu7|3^N%#tN#lL!q{k!P_~6m-d;>`pqIqMZE-MjaQF`P8x~;>wj>h0 zZtwGi118X+3gUVef(XoZhf9cJJZ#YC^6~DmsS(bOiOG4)+i^u2ssG5&6Nxn#=xVil zv@>J^K8RWj>I#iqfRGS)9z1X$OcKRf z_Cp0Ak)UC8(wAL2(S&Uk(Dd~N%TKYP;l!N!JQTW1-p7#l87#Rr-b9N6Mv{2tK_9?~ z_0T-DYq))wwJ35bUm>qJS`mg~X7;OI*m^>-bMWBFi*y-ALi!$MA0CT_dpxw-`LBrQ z$xRzLM~-xDB);C%AFGxw=+ea;)<_$`)XEs@m<9!inbAmqm$-`WNfU@eie)o)5S@p! z-T}CE_(lizoveI<4DBdH7Wk-8aE?`` zlC4_)g31(n7Ol*Q<$jQ!AWcl->-5S2b$qwh(Zr6n|Flo$^dkZ3smuc z>jgQY-3^ZW6q-(!Q$Q~0$H8Q2gVc~-$V$> zr2lLN%fRm1GpOQ#dshJ;NxHhMfl$IfA#B5b{rs7+&kcjX!0k7^R(a^H&4v7de%3o# z(zF=W2QEKML^|&3{NpnyhY$>(a0LvJ%Qb1K!A?Hnpr!-uoE-dH%8JGvC9PZ0d`LyR@5M7Pe_}H9WKN#JhfyM z6o^-WKkpHyYGCf7A@#Ub6%;2j1GTyqZx6oWRPU2D17>iR*%6J1y@9|J-Dd_i4-mvJ z3Ow|tIo`a(XsljAvwRuZpb5Z^?YEW^`>hh&?o5ynbS*`;2p%`hKLHMU9RoxE*DJ|i zVSZ$N`KA*w>WP~hVIK^gCV#)kIa%BeYM`{gGzZ7LF^idK)L7<{-5z!yQAe~Db5c^y z;#3`hki|;g5pQ#Z@TuHd?bYyo@oF6KlLuOuN&VfexV8sjo8a$H$i)L_7V!xCZ&h|d zDZ`VnN?PKfkq~*OH43ghEdC3e4788lzMY4zBe@2;jg_&19CY5`15b-axk{Z9+p>A{ zF5QA??71~Di67=f!iDZ{1NBa&`+W4Q@vX6CU9=atuGO3e60{tSB8mG;LXD-mWdP7N%4fJ{#G({2u-GR#J?p=hb-I8fI zhOjsa1ZGHG$LD}^iR$^5Jn$RM-(0`6xZ>Yy;z*trUuWh}e2Yb9(ze|3ZSt{mbPRXe zPMPO!JNR`HR+OKCE`Xu7!eWqD@84IK@=Z-mrF?}03YrVd3~ZK%{tSOK(pdD#e#DU! zx@$F9*qCz1_S%C>py*I9hf*3mm#t8&r=-xxcm}R>(eQ3?!xkfiwos7|fSzFkMXW^% zb4nQ`*3{tqCo|Dr%g0_2$xV_=!NimP570?!$wqmqQK&_dT8X=kc|)<8x%|a8mnQvx~lvI54(EiuWLNJvms$`R4L?u2lGLl?RQS(;}H|=|E^hXPj{VH9Z z9l>&>PW;N&X8~JxjfFYA%4Z@a2AmO%!f`>1t2rfe3%?n|SLn{}GQ&bO9MFdHouzKQ zWgW*K1qJDGR$ktFFsoGcWr=JcEKoCsd2C;IlWUQ41Dpei{W4-&#>R1I0FAziXsy?P zydAS@nVHUj7+bI4g|2uoQU{S3;?eQF%L|+5x*w zc3V742%9oxtCZ1CpOjpV9R|#B;J{tUf}|v(BoKquj4C4>Bjr&8%lhYjy;6ZH+i&cW zykHrClys!;de;ROz6o)t_yo>w_YB>rjvDzK&?XYFEhL#N|P>RYP78@cbMheNLv5{Q8@esJfOlSx2GhmsC z@vrQnjWezbD93l8(zSgfimDc=8a?ep@^Ly62W!aRz#`5TGv-F7A(KKnv&p5)SPmdA zIHUtO5ag2?O#;MJJ`73Otd;e+=xUpq0@`#;{ztSZOcf* znDcR-{mvH`459&bkCxyIv;63)7CZzj-z_S__E%$=tw6d+C~9YSzC)b|9Q`>gFR4@J z7a_g@Qvt`z2$xJKQ$ROGCIyQj3z`8HCGcBB8tfIY6T+&%MIeCz2!}v2K&sPAK=rHk zPcX#LTbwkrws?Wb1PHF1oAtYvA;c-F9oovq239sR(;~$|jP}~c;o;8j z#aUUSAbw=?{Bq)2Iy)8Bj5HM2YYgqA0{X*)&BCEwu1`w}BK;FENhF8WG~R?gk;@eSzJ1^D=iqHX zZfLhKZAihG-S9Ac`vll-`}^DMHE06IbGM=cArWv|YkUQ`?F=dv!m~|rlgMw;(QK`B zF-=ZL55k%MWQ3v>TXPyT96E0% zT?v>74$v&GL3@VW-Nokr?Mek94oXdpldz|)-n-1x?%zmEN?dtp#}^QoDCnnw!C^j8 z$88%{kqJ`@*sN6%@3G;?O15)F7zZ7T{{V*THg4p)Jux!}Ik8sbKFWH+cL}&+^pMWX zxwD0D8s0N^u_A5H*c_+Z%2A?%FMO_WQW@FTKD886VZ{1yXBZ+~VJ-_jjTRc!$_rRK z$fLbVpEz3#77lgn@UYbg4=^V{Z2k+0G^C$mz6(W1FOhXKv$A4wHHa-`YlRm8Y?{Vf9 z5*ml*6~{QlROng_BOML>kqy2;1ih?8E0| z;NfB)MZwX9cK9*0r9i0Vn{@8Ay?O;0qZgP0jyPr~J@U^$heP;$B$2j~JdY+4@|xct z&n-qmml48HOkKE?5*-~~TI#I*^ez}#;5XQ#seMnnl`4|cfEEeoCYg{nV#CSDV_F4p z_%8-yX7w1W$~!wBVgPxIlDc|cMh4?L6=h{=Uf$+7V*3G(po&!IGsmsVx_H7XUfymH z;7|HXmF%per*~)T{^MdW9O29Ud{i65tg`H&dHFt^rIBHjy>y8XuPeQ|fFcv(4KVt; z?O5i*UOu?6xa>%8xHY0Gk`>EUYg{~h@j@X!WoSgEz=|JJ99=L>ys!| zSJC_gE6J1hcXfg0WK}|QXj^t1CD|Vs+Ad~0%)+b&$v?K7*$6J*tNVi#8S^eKg zuAKY=0w@xmn6$lqZJB>US!%uXFYOa2eu6~TKJ}294CNH0<#nZ+fPDImmPvnP+qxA& zvB$`7Q)oT0!_MBm5VtBnAHe#W(9GU-w9vOAYi$g`ToZlu;MXgd^4nCYJhDugk>*;> z#Y>l90rUHQFo=34Z5|)V?pHsnQ2pSZ~#y>IG z^9tP`fR!KMjf33>H_#ABE+Ml=vl(FtQet&b9&9A#A)Ko}bQwMffceaX5%uJMimxE; z)<86~7mg`-V_VU)x_lBo1cfsH89#bSKZ`{6p zez=v3($m>_r6G(D0!CE$dJCD{nEh{WZOzQhWswK=5E&YJ#@bqm@iEejVw_P%J%vUF zBK|)iLU^Y^6Nv5KH>PDFLu503xc;!l=_it4d$%9 zoJ7saQd-%@uy!rcX?#>k3hW!Yukk^4ho=a=aRajM08Gu1t>`TkCTSNCFOO zL@|sC%@}S2R*xS&>O*)V;)Z951>PKB70FML^l~!rTYv-~b1Cr%3Gg5Ez5mt~1em933qO%}~;l-iF`+?K2S^I^mGT6n2&j;P2S6 zVOZs7(ybW3zo0;LWxRqr zUOx;hFiN3npt!jmj%iAhs7y}oMKDQbn)&r&BMnFm)S&{d8IZnsEV`4)7233)>QWC2ru#cDTdx&EB4s-X4B43nXz|(oy ze6g(I+qZjFn>)+P}sLzxaw1Y?7h}PQ9R|DlBX@-)s3on$ga3FL0t57SQVu6@Q>gI3OQE-EygavP0R(|q35h?& zg+C%Z92TNjd4dnzSuY|OC^qRVh3TaPCB0^DejZ+G1rV>Jb!_7q8BHgRNz2GA;}gXs z{~MDR&^h}ZU=l}+mhYDNb3TXWI%Lg0m+K43u%F_4R-Z+r7Xmko+-)CFk^8PwXf5jVs+11#-~6p4j5oZi>E@OBw|ntc zRQ;`6Ar(1{_c%k%Yj)(rnwA$~5fLGr1;q&&d3hcfY~(yHyE*P`F*+ROVOldN+5m8{ zVOvZU-s(@q7e?fD*uR{kAz7{IeWE#)l)|`W3uH%(t%~RvLqiF>?a? z3ya@VVm8(6G2KjaXjmBc?0p1oB#5{HPkBw$3~VsgQeAsIMmz_MT~3*BXnpKH%XMV; zkF_hUBMF%cCdE)b4y#*RSz&Mh^M)Ja#CVg?_F|g>VsU-MKna>r{8<1J6c2*^_^YGC z5d{9WbqZ91VFYv?gL8%JD~i#=PGJUC(SD3$h1_)f)P_A-H<{5_UPS@${3PU#2`RUL zbx2D~+xO`@KEoI~UNKYxsnMHQS%KicX9?|=KK^h2!gn_+%2&#^l9!ppj~(p_esw?c zi6I3*9lLvF9ni)G&)TAI3Jc5TOpG=jh9++9^zQo**y*Xn*n zg)0ndkPX3*W3jU8>C>mBrT;>>L5meVk|!oE?so?+zrs^*HQodfK!>MniKzAN+_Z~o zhZ&VHC6aeN2P;efNGR6erU&!KE0@6g5qU?qWCfuCL#+qY;NHH6R4N6`F0hF3b9uZt zVNk?wX4!dP0qN9sQ?~sJqi-ku^(4@>38zB;PQd z+?RlLS`NcO-Z*hfkSudRJM72NjmsOV>fLi%&eNediUWlL?Bt>6&#t0&lHQ42MVR9Q zxEM(VV2C(}97!zIAhgWg}2~h{wgk?1xohuIXp|OFO~D+X(LFyz|Q0IgwZrgN5s2f*n56_U?CKF5VsKXJS{zu z6o4fgs@y^_uj@TK`4(rYeYd0u8vsd`!MJuY# zC+-*pGa(2_6`ox%WIh<{mn;F5UbeAJ;qq9knW5miU^#(U~kdO%Tsx? z2%Z1y*O8K1x|jFx@ma%E%}n^y$Oy560a1yXn|oYE#UGwgb#))?N>T2@VTAruco5zm zrS7B5!^i$Py`;ZZJ+6`_tR10@U>QP2fQ*#btCJ#caWrS%0y@}oR4MlQguj*1g>^}3 z&50^oq`x(V1kMugc``6rE?!;>%tk^MYkf%sP{s7L2bgih|BSf0elPZIBvMoGk#2?= ziXZ2X!o%#E1%{4}#S=F0RzjA9-w=Q%)MozcaChKkWW0p91a&&XU7a1^km)@jXWB=q z#efpyu>eCnVPFs}w08>whQq5N0)p=W$RLN}#KaHyWk@yIn*)RPm-CGkHt#tx0Z5*h z1OXbEqJo15p$W0a5B{i1ad$&QzhQ9a!9!ssU0q^1TPCr$4!fx@0Jw$4CFB_?uM+%5 zXl60&h?^Mf4c*{bkuB{t{H13O?GVPov(Eyn(bGcQf!o6hFB+@Z`m@jr5wjha$9q1( z(5Qb3gE{|jS}4?UsIZ+%>po%?@+1BX5Ibg;$SC7D3&7#pI6fkR_h$>PH1iSI3L1I@#KeaO!L48q>CTedp`xK3$as+7 zE7bsBv&KH%)>5}$C7rSSKn*Ivk>lQfX;%Rq01DaqJm<|vXx}CIhDSzRK#8y%i6+Br z26AG}z7UUjZaRViKn<4y$JIIFu3vZ$oI$`8wfiRmU~W!OuXMl+xpj;e(5;h2oNV~- z(8;hvHWVICcpZW*dyXZe92}nQl1_lP+U?9m9hRp?=z=d=od4pH$%WkJ{%dT1{tECj zPN?M|Ia0hkIxr9(6vQ#}=xXBbCigmG5@+f2;pJnCLfjz?`Y6H6iO^h;MMc|cYLbAE zp|k;}0FVwNsmNgf?AlY&`Y0xF<9eg;!eWvghY?){s%xbL(a40zNPYVYyINHB&^^F_ zFCJ%5lHZTJ}3W9 z+mdV7j@jB~TLnG(2oM+5L5tD#`m+GFYCAf@Uwi-|%FM!oG4cpzSi9i;$qfu#@7RD^ z7uTy;gWMA}C``pSAdJIspyaNZt?~5j!hS8Qw-0%dbmCBk#Hq0|5JtX;I+sV<`Pv9b zq&`UO)#H|y37m(f;{BUaGc#upu-PT-WW_q$SBJR^?nUX<`*^k1Md+x2_bt5y%e_tm z&uRY&fE}k^b!3t+A~?7ec){5CxQT{_^iV9vJ_2|8`xAt%;0t&gk1HxBn(*`SF?GlK z$R0a(2Cnv~lCWx2B-8H%s>6v#J&061ZoLNXc)KvDlGUjqX48SGwEJ&t^Pe;$6Avf+ zKL5l|Zop`as-K3x?gMcYHI(})DUW-4Vh8C#=0Yu-RNaT>9%%}56DzdlkiP&eLYb+= z1cUT^t|w+9Ti|5H$&;m&+ik3?3t8D-t(IrcED=-1_Mbzu5D&Y4EtYoF$WV(u9=xgq zyUOW_kH=sy9Nh;Y1PE3d8Swttm~d;59i$a0D4^uzckRlks)~Q~G00oYqQ$8SD z4eN|-Ft@dZF@ly>Q1j3!tUIMTMk8e2bWa1x3n567#WrQ#_5c8Om~(>Ku00t2E-I6I zv?yWT=bur?`~l)+5@Vk!(aYQZEuTo9TR>*u>9a{XWawaMqv+_dW9rzcgAOIF4zF2? z%Uh$J7y-$Yi8k+uzICpv4U!YQx0%N$HQ%FOB0d9*F^sR0j;-QhKZcgKxc{U*GzEMm z;xT7IC4q(5wG#OlIZn=U7RwMa0%Uj~qLf+TtaOV|-;svv-zdsi`ooDTq20Um+uoIOns1=AS!k?QOOTo;=8i!9_Mr7sl7XZ~E2XMx0r3bX z9pUF}>LGS8GvnktfE_w-^j>y!z>Y*LQ#Ojh!=`Hf$4OSF^>lPUku()$qT{6D#ClZF zVOPnj4C?p^RChqB_zqxoDT_bPqbhU>fKixFcCTk%{vRqV1pE^|;EZ5KdjN>T!GlFU z9T+s^ASubp=)^qw2vi(J0I|dZp#TAvQ2Y@Pa-J8e)^%uZFywJ%e%29=X&~~^=YoqO z&~^#-(oPm6q!N#pVUta?=r#*A4y+&@$k?TOb}qLSg?$X6n}e>>hEaZyn~ z+Z)$n-{xJ(wx>^Tuwj1CFCd_}pdjSj<<-5ty^0BU&GJtm$3;5JKz6G&q6>kqfdMOd zb)E!{Yx_1SN#sV@Lt=<)C)**nS+TgYWXk#$SRlUX?$%bIj@%(saFYA6^Rh(kjqMNI zcTA*lb~{)x{fEjm#9l~z_QRs%OjULEXu{kH>*& zs#)R{PJjLjkTW1VM0sCYxbCi8z6|Kx+S#8!MWR%-Bz2UOFb(<(LC*sFD%DRuTG9)Wf8*M0w4rV6Yg<(f$VVM7 zV^?5=*i*n==paPD5bp2XS;afjsDFv~R2k5A) z2fi@E=!SENlbs!>95B6%A;=UroYc>L`0xq}1;8wLC(&O74RI%D@M)N-l()Wybvb1M zw9a?^prO1@j0R=afW)gW1pcS5tA8|89pJ}9ImM#p8gl& z*r_9&NoC*7_@Af!`;xkj41^s%c8UB3QvD!KGwo-F;hobR?%$t4QLuAm%;+-ZjPW~~)0nNgFdpP!MWM8*zNBIO4|V(ZEn}vgp@=3r zEf?hDOL~<_A^Mt>TWM()5ONGZCyR&#S=u1{QZx{0Wf)PX9&!47_s*Slap6dh$QuaX z2;J|{w!k#$thssXp-tqKR5FAgt?YY#$hQuC`I48GCQq+COyh#$V@KdFw*11vdl8l! zwd%J+7z13JHz4?%Jd@p#RGB4B%)gHkzbdj<)d|&lawbh20 z+ek=Y=eq*?=fS7iS3PfBzb>}_gzjb4&);MhbWq6@@AywO^ZT-jEE}42j-8J!f~)t@ zwfy@G#&Aa4q5WDBUU(4bJuMCfkLstl<AU|K5(XJN2ufRe5B*3dJh4DuzJ9B!*)qx=xDa%{!iBh zz+?CtT84)FPM*NC=2ojaDC!!hxr3!j!~^PH{T=0DN*&sD%$r0nT?ncm#AC6v(j5ig z>u17fSO2Hk`F$}h=QF8wtbSQasVF}Y*|}GuN`p!z_xjsXqj9vGnU_*|vQTMgUc>Pf zOW(R>3lt{%BqfOz?+BYvSzmXfPo8RStk9Ouzysn$F2sl(=5Ztyo`;7;UMB zlZ?4$h;-n03{Z8vtGE%elFxN0?@>}{^|jkd%#1n~uS70uKOdiRJh8oif_%EcZXr5j zo-`&t)cxN1;Fo1OVov?8+e!WYcK!=u@WB6!`5$Cic;?+ zge0S5SAhd9HOpvBVPu_DU56MkAn^##MTrT`FUo}G=wyH(7_!-l{po->F+|hW0htfz z?)7Y9x0oNm;aFLDLjbTxfe}Uil(e+LFJFkwWd5`Wx$|)B7IPD1q`Sj@M93u^LKVn& z9u`o?(a#fJ6tY}cc@S&~`*+!y<1``vq5S`O>iH+Kfw5}#L5~k^H>b$j+HaM3=49zeI9yN!L#s7XfFk%GOiPH1_{d?^9>mpXw@t!Q`f{0iNa?_8{=g~umGKR4# zu9d*EQ`D|Q_5{_y&VfoIC@4ruNaVly36MrtY#AB3BrjB9|yzjblo5z!N(KsJ6j<2J&K6UJoIe_ zn9vc3vX44p=VLp9I@tEiNZcALQ`4P~q9G!}V6>JR6CDj$ONJ2 zip5+qi~ya{+BmT6q9f$PDRn>`Fsr_6ER9A+Lpu>)7%k17M_TlthDQ7QW$EP=6p&Z_ z=KHMfP{%tlo|0LEDuH?PW|iVk5cETq$KSym#L@*hebF&+lN3ttqEF=5;+}sn1mb&0 zOTeEF0#rDp9nlgNG^scMWe$iutP&@Q3Qy(Dq4Bpwg5>XjOVsZq( z3oy6!ol{FgCDV#B&~W6d?E?FSi-oD2+%=Fm^9b_TTszD@iR0X!mVh^gKqJ z$oBDED2O1Iu^oSF9|FW5#lg!X+;z0!pg+2j@cYj-S&e%X65sasCsEKIZ}Fu|np1qZ z0ZN$Eya_zmxAHqgE|_Wsn`wB%9AujW)&#M`kJB~{GNmb$e)VS?;+2pw!6pND{KT+; zi7fP*#{vk0IkTAK=(;dR5#)_)1a6(~QH$b@*!+-^h(QWWeRqCU<+aBYVM-#jHK?XQ z>sp^@gPcIk3(yIC>k>tOm1*CYqY9y%eyj&_xhS zzVONcGUs(tMTLj4k<(UIyI+^CPNq^Do;@1^3$@`X0DJ&26y=RFjKJJB#@Mx=1tQ!2 z0p$&bZ5|q(_qV(XPVnYU1$TETKFmJKf+8QZ06^Ickc}CQ{SJ7bVTLB-u&P|gt5-k{ z$N+me7=wh6N}J&2`()n(en!&!e9)(ag*o80gs(T|cF`_h!Z$-q*0)!IIq&;YdxgQCOo$!)ScVbVkRv9sO9m#q zc;kobP)LGzf~1*{)0eFat?z*!R9Tse{Zp_)2f@5-FCaQOCDjtAT@OLg$b=N=kt4#9AKI0sc={E+_o2 z(%{7b{|6$gs^HZLd9Wg?b)^0u$PDksm@c&9Knegu$S@K(aO%iC?rWe4WaZ?@ZUl!0 z%~Fx>0~_edK#gJ83=-=gz_o^kut~#fj=PWIWprq0zw6J75bE&6(n!;$sFESU2JWr9 z9rGw*AbN%n3!q-^20CYdW@K~}Nr4d8M_s;zr7c((k6iU!03KHDFJIb&g-)&^=GMv_ z{hKUK0)sel0u7KkJYW#&PFMo?3E6iYSQlQzeUM0vqylqTq6EuJ0DllaxFzWtdM?2A zXpY}id#E>^-uSi%u_>fl22-kpwgSG{?F6s{P#r4s!u$7!U**KWU=j%mVF=)zeHQX2 zQtym^C*Nc6%@AH`pFAn>gQBQ-%eE0YacZplGQJY<+QU;?j*e6474Y)sq^G|ww$aDD z0MtK#=|j5W#Q_Pv!3-YO2l*fLX851icS5Ng4<3S-n5u}mA4zi%HUQGl0)rt)r< zCw*VhP-E{;NJNBu*>}`c5CCBe3;`?piJ2RgzXQgAZ;giyJK;O!DaGOsZ?W@m@+KgO zCTg(#R{;OO0p*YG-4_j~o*#I{@mqp>(J7&Lb0`zQm{IhLXImhcz{7<{RPgf zW_fKb3SAIkcfmRX)#ARW=de4vfvPTVa%K)^O3PX(zti>yII;GM_^`@}|}i zqgxy+u+9We8Kr*U_JevaQi0vViD7zf#vV|$Pi&5_msbta9e9`2hK#}o52|tI5K=y= zYiesFO7tM+6%`k!9^r);76Ac`7&~sk*kjn%5i8ArEsNUD0*u5sV-Mz#Zj|Q0P(%|= zQVNIvlV~iQoAb{axdSiGUNn!eg$bv;0uBHRbYp zY}>gpG!KdcLyYYzyu7>s%1~Ap!}$aB*t!F_KG-97&sx%0MO~+-?9PSu&bk0!;^N0z zfc^N_GYN}x3e;#_h^w#|iP%Adw+ag6zs@$?-J5*x9@oznWX2W;-mbq43S;zyA6Q{=^vJ292Rft;A_ndf);uqG0>U$<>H$N?XwgwpHhc@L~7(pv(OfIxBbg0__< zW??3-FM}u_QqnW{F^EW6+1VAK%h1e0HiX?8m<54$3zZ7qc|5UBm{j1PBdlW2HsJeC zIFL#_gN-vj@KL!9)qg+$^lwd#jf5lzWiI-7A0IhTaHxV%>-G0!0Eb?c@N0Hj6Fdg(wcglt54gV9(bZoaR^ zkOQG1#HbOOOb7@uF8bpK{!Xthjh!6cvlq7pp$I2!0uyauQ_-ekgB0<>sELa>CWvu^ zfEWiIKe}}{{r?_RIpkP?@EBRstTjP|$F!4jXGT3TZe{ zzHJ3{b?H+62B#imH9$P!_k&xYM3-Y1-EtdGRcl|zbqItYIz-nYdkqLKCaHp#s}F#} z&>ash>Pw1Cnn@4bMPvk$tC7aPvJh#ZLsc9S<)01C%TdnDo#%T{P~xTivZZe0FopWb zCi8Mj$NdK)_rI8ZXnShwfJ@>&Uo~`K%PGyPLQ+FoW+s=+w@*{da`NfWnhbpj^4i$w z8<%Omju`UOFt*b>UZpESU%VUXLi&dDI0$mCLIlsqy&_14?q>ryK0Rov3sGpoNYgCdqA29`} z#z}_1kcE>i-Y4H5>tV;QQy1Ky?dIcJtR7S(_j7`QX!|?^SN`W+NWqmJKjc!rXd!=Y zMc`oAto%ZF`iy?)n&pSeZm!GMOV<~)Rs4B`Jhj+en-y9Aijoq?&zYF)U=)1!rAHbg>IkX6a1cSmVIXar8-K}__d;? ztM0kJQ|flYJcDlI6xI=&d+ENBV#}m>QG}=grIP9aP>+0f@}NOZVxlKAgiq_6O7Rai ztLb5YEpqV?{in8Dw%kDs&5Xox{&^zNU5fYx@$qzc=os*Rhg-$BE|u9R%2C%Uj!BbO zJx6*+QxXJlVH0Dm&&>99a39Zj3_lrH*5bpfGLhJm5 zSLfKv*lp!o8!PDkO1DNQb144jwwD05(o#}(yFP6TU3 zRydYZ;-#-+p#L~?pdFcD;ayg;M||bhR}c0zy?w9S{z76y+%etaRU-qz(=)B=2Yct-4SKg4zKoE%nF0DT&fGeTb z)A}i7hW;&-6U8y1(vK^}FE+aYh)KGC=5OpHq{DOFdE>rEw|gHx4>@}L+)nhrPlhwn zO|7%XS{ond$Kf}I*~3Ey2`0o1%t( z3llk25iW`P>leL@Glk9n+>4?kOw%TdrxeN;zmAv}jC^ySH5_UG%0S!t!2e^O*NgQp z{XKr}^J-o^ZFkB1?X~Y_r><(xuKMumo4B!J>eD8M;?LzaTh2wC`5sF;xm>yPPz7n{)5D|Fb)H z8jri4ln+p6Z9i9%7r=7zxq!-B$&u>}`3pV8qa^~?i)mF{#%`xH%lo?g2l{@MJS3BL zp91EKw$o=lp7wEiYhH3+!G70IEzNBj)|P+jI*lqkq0Z+Uv|8TPp8lqP zXOG32;-OV<92mBnRZhQm(3)Gw;>--`*znF(6MT6$$YEiH^L@eOf8SxGOU+WoliD^y*)b30%I zB`bR!>{LkLUKo&KixWV{HC&j4(}toDcMBkOW*-FAm|6ZdJ7v@Q0lqZJ_g>mb4&u51P)4_QGBQ@b zz2f|NVQX~Xdwsn1vsd0cl;Ya{{Ny*78T|fGzvGoKba#(GmB#{6`}2u$=LMzl^2zmv zrc!QPX_dTcF42_HYKMyt#uY6u+8vkRYs)Hpcc}W^_XjZ_Y+0E?btgW~_&?FL;qtAB zl$$@)pS@F0(1J=f6}k0Ap({eCWB%7|qf>&MgHuoIR}Al2p12zm34rS&?U|q-@Y$Aj z&Je{dqz%{CV_oT?!-vB?7{p`TFpv-E2o4%)if7LB0km~RI|9!mXnNmtc0zOpaS|4T zp!+@oE7l^S%LK3t)F@oxHcj&QnXPD!L$y@!Fy=Zqe4PW7F1BcM4)N zuadHx0ZOCW{va~aPv|9zQ*NOi@I3!iEPdY=*&`|#nKaZiZ>%>lv$o0D9y@S6eI=me zp0cNA4f4l6t68VLA-<>AtL-nI4f)ZkoK)7!${aFQ6XIC8y_~ga;**kxjWJC z>Ni4#0VNY*?bfi0=BIR@x${QTr8h;2UR9T!)D?R-(r49q^E#2>=FQoM;*hd*e+K_~ zz+KBJRgw93Xpi|h2qy>nEc35toDY1qS*N3+E%99amaEZG5P^m1zSh1bboiiRxwXP! zJHr!Pr#PG&-Yd=YNKWoQ;Bj40o4Qqj5us;aMg7b=Z&Nrpp{p<8x z`ukOVQ4F>3YNfl}I#Mzpzv@4FREV$l{M*(SMyI+Q!BF&B<$tk|pJsKh^;DoW81;o`8yC5T|gjOAM4*-FPvrVZ1nAX zdZkCi$#)O@laha1dEh3u$PZhGyxff922m*Q9j~%f{pRz=scaWaQ;HY-7bd?nW?BbP zTpf-3vb>eGF1C~G-#EQCD?D^H%IMeqOe;foP+7}7&9MBNaAByu2^1|39=-IM4`6c9sm)sTQl) zgO9E&jq+ir^1rpeec}C=iCXFxZU)+G^|reLtkj#QEtD5)%$81xrxUR)2v}WptUqK- z#JHf}fpyZO1)meu*5)2bu)7jb?vsC4B}ew;v2*w1GTTZvhA-TSb>@BZt%82xCV4JO z^+vg)LezSZ#26+%gPey2xdmx5>TAq{u;3IU7NEU;wm_i?{ELCFe4Zaj@iau+6pGZ( zE8?N`4i_(8w717blERV_Cn$W7I?$HFD%6KSE77XDK@kfN7g&$%p8xRsT#vz5qCZ?; z?`3>Q2Xk`S9ev|n`pzYR>*9YuxQTiw$zL=!hS~?;p#4N4FU#;dv!9FinoL6#^@BJK z`>nOCX5`iwzGj-MT)zOY02n*#DPji{IVx|+&&;j4TRygK)mSmEsRcZl6O~dGSfJ$`Idi^I7krInPS!b><8I=u)v>$z7 zay+JD)TF;MyGQH14f~@PMyj=XSq%aMcF{3n~*$&1n(z z)6B5E04d=66km|okfV1*NZQ7Zpl^N`*ixPil;6()e|7L-$gi3%$nbiWZqgt)u;AX; z@nyeB)K=@#@53KD-`3^FXj&c<^hrp{a-A^=SxqCWq-*SMpk~6lKm9rcpZtOe+8RP< znC7Qr4YgAIUFWsd7Cq517s$$uv{WNy5@S(-EbN3hgT%iUt_8(vST*P2m;|H*YOkEy zZ(=i`gy`uPUW?vcQW~3K3OYPI#Dx`nn1Z}K1{L9qrt6Rid(fJmo{CL@0-;H6l5472 z=xC;Dux?2yPbmYCeWU^VWl8^d=ZvZT&&DJY^ zhSdc(ditk_-@aD(Ql-~EtJV;lO{!Z882_GrDK|xlhzb@y+ss(qJfb7I>J3sxx zGkO|t7a_ekw({>6#>Or3bSX^@M%~lj{kYpacI=9;+P-yLc=qcQeV6k|3%tU?IklUY zdrEBshz2B{ClUgUR`+g+G1y*kV>Ma{;BwcFoA9ClyygVE3!ZQwOu2@|%Db6IvgLA^ z4iPCu!_k-FEr~yTq;TdCaR*4A3*XO=m)X>oZ{I4JMIKQ)I+)uSd~Lr`e95$htdW62 zYj{&_yJBBHMg`>0w%uy-Xr3eY8=Z(PQz929v&n6+b~2H>-a*e(k-2iq4cW=As5&C0 zQ%_?=s^FeGrWQ(9!E`WHjqpz_h=e2@ZKJl$4cN-nnLv!)xPzZ6$!Wg(n9sQ^i=YAG zf<*5ZvYpjU_FyFtXSKkQt4OZk}{9LxpV0>bOnKoCF21mzYuBDIM=WN;!9`I!4 zzxt`SH+h@^2OK>#z)ha;jEoFWYSt~PU@FgFF5wyk3C3K z=@vTt#_4N*MChDIK~wqKSid6?%BetdmD%@+iqYl#OwgB4_3FA;AOueBAsqFACe|YVDln%6{!}Oj;b9;_AL7JNtH`>b|1n^j|q` zAr#J}`}r|y-DP(IdTiO+@8_CdND6hAYls2L1&Ss%#>6{F5ST~sN0RZsU7eJ>Km(h`v{ zFcq4u5{?kgKn;d-c^|mJQ*q^Ga=REi*&3 zoVJuNaDP&qDBL-A=zCXZ@YcPr<9E+YUtgz}#$Gulkr+^3nVKo}s#rX7eOA5k@{BfY zsUc%R+ff0IqA;}#Ga@XdFtAv#z5))ZT@Jd1gEqdM7}cR3gxvI~W~0XnF)H!{bdByo8A- zK^lwN;&15O^Iy_etEoQF+=$9K zkPazYnHm{Sr#mqYUofPjf2mHj@zw^V;*pvUN@Q_;lu@2FOnTQU@M2Ayr0iJZoK({p z?}k&vM=r?y%7`NCg~5AyvDk6k}n z(z%eVlk+2hpJG|QjR zybHa~kgC4iGZz~7uFlRny((;0vF5YfD*$ET3sec_0#G5m)V;uV_60b~rTsaijN_dK zYnAVA@zq;rQ#v)*cUUHvIwxFF>f1WEOzo<3`A9W+={Aub-8%l<@A&hOoLkpx?*znl z#0?#(WZPajW5nq^b~sX>)w%13e%ZtJZKcZPWp{8S*;K>h^4{%?_cJZVt!%TWx80*E zcEXs0@%pp0_jjBHJBMt~ylcw3VNtj&!w_;^7z>#TfKG%#UC7nv&<$dkw)?*6F(sbe zyN5B@g-sXkr7G}|as#Q0moCK&qAH8$TgKQt)RZQ(dyHSz5i5&G84+|@G7`ZW2Qn3| z<(iP)XZIcbdg}ecm%YLFFRXd+_3cP`=Ux4?SAT!0O`p5^9y_Mj4)|FPIce{|Y;H=? z>6Y0Od9|@l)1ChA@e0+(@T~T%oe5(aniaJ=<~IG5GiKcUyJt^*zIR{I<)cZ()9!D1 zNQH;QjY~v78>`n+{@R)h4aLWgd2Z}dkR^I9Tfv-w*^7@zWdD!6H;>A3|HFSD^RUgz zCi4(^l1R!d)7C(eCM82Eii$+3&}OrDWlAbCMM{H8DJe3CN`?j$B`GwSOGP@bPvQGp zXPrOJzh|9$eb;wedz0t6@6Y`nuJ?6aZxxjviGHuJr)CWs!&SrEkGv4$Foy6qv!GDN z%lJ{Uv;5st;%m5!Ti;0`J?m+vb#`Oj_j;>;Jq!BAN4zSsUA4kuRNR>HpPb^>zPPse z@wfSVbw8U=IM+w3`8nxTO&sagszmGZ@PBcGOKbc?e~+`=qB)MC-!Is`9{!iyy+wKo zN%a#JMhy!kZY)B(Lz;u!J)y$)6=IJ>8kxgHYmOe}1iS z)1aIeMp$U3F!Mmn4DWTo^sW8+_m7&eBP660z5be<qeWjqg=@ z^@{w{_v*(xZ+nt$dTh(1mN$FKbZz+J6qhr@JLFuV>K<1mrQsK=Rp%6F-GAZztdGJ8 z*+r?7tB={{nO)XQ^HF;DLov=La=!7#z3;zQzdv(*VVYy=6{Uh(9iQd2(1?X93H_wL zt}ZPXIwZqoqZ$+T?9m`{>a`sT#8bc4*K4IlQ_(8%_;mP4?*#p)w-l)XGV10=EI4rR zaN@g?R!OIx-m9&0)fMks+woDQ(yq%wmt7thQ{QQ>{S>qB&-!~BM3fwl3mnBe@WpOH z-3gx_n+)s_3UO*^RAap(3G?{gNeZ!2+NK}ayg6?*{hAh(G6o1GaK zH&z%?^Hk^b7LV_8+Hb>l=|{WDQzla!U*#pvz3KReS9<@gUl*5Ow0lybTSB2=9(eJP zT*XJbvonXb_7F3JOwMx{7|;L%rLPlAl1Lv@XanildaG|KF-(7psfk{~NO_KC&ReCNufI z(ae`?oIn2|Z#*C+-?_Bx*dc?|iJ9|v-`*G%bM<(emNV8Ya2v5^T+;p}*7h%ZUyVGm z$#sq9Tx{(Pdst3E!O7i(v|UuYJ6xBn@r zkG6B`X?5c&tr{P{d!MjwL!6p#$`KzkmzPVQ*XS%b`1<8hyL)H8+UeguB{4`u3c`%W z_^G3y93*Wx)3(*XDf?d>)}MnL2lf9E-ZjW`eV{4z>n9xR+1gZDxz^3ijk6;xqxe^l zrRE2I*n_*2MDoS^+)rn*pdRCT2Cp|SSL%B4jEgu#&q78}PQ}U|ahYJ6Z+hsEoOh3` z&r3sJ4_j3_a_aQ+Pycz>c}Z2ewyVv9TPCW}YZ_!b+MTg}<1pp(hbLE0sC_*bX{EgB zeBzj~Jwhx?ucqHksa7pr@4lvr=xF|#k34=^hp9U0Kgu5br}SpP#AkVM_ zWuFaF8a;kh-lKEH`x5uLoe976X-CbpwPt3+WTK~SU3O~Jr4c^bud*FqWv5R!$hoB% zZ(*WyBC%ikmY?!nzdZ-~|7d<;B?|H~9eZ_J{rcI{IT@R0&wRQ1MRN9wHD`UJ)0gQE zThgEvnRqlxweQmG`^8sJBvuqW&%LFY6Yu|NRG^e(uiU57&f;{P+ks8~p=;*GzkB*{ z@hedcb1m2SwO&IqzPIN;k-HKCA0>JwdHPt7%ZXW0rGBB{*1OJ5&rI^pX6|XYeWie; zY8pIh-k#f=!V?=ax^yZF-xd=^MX{Vz%qNcON!QotqiZ zd`mrMkaU{=(n7tA>Gt7y8YlI}e6ic7yT^QQ+v$_HR?M;Z>$o&{_K=s8tB=+vhUbR+ zc>8GPUVD<-W~%BMeV>G+kDvCLExkOjQAaPQ;ZK{=DQj0`1>0mkxRfK_f~aqoOFwK5 zsGmG~>O$ARGg8~hUQaqX(0kZ8=o#G2KDd2|R^Edny**u3BPV{b>#Dp_ZDUW*e~bgZ zJU%l)ydX|-aCFD#Bjn%uQ z&0k2;yVLJ)wBfHIE69&gt#%dmd_E7#w9h<^TyAm`tQ>i?s^W!2iZ%}q7iOw2E? z6RYe^bu9oKL5tv$0mQJm7w#<68W=4Vr)O=!gEKZx3OtwJGbWA5qb1kT<~WGbrX&OE&skod7ARIurD5+Fe^J3h)o4tdUpIF z58E(SsbMH)?OCv#;%}1Q1 zOVbTh)hbhhVA;{W=}SiJhfxKPtF-LOWvmQ#y=x=swC{3Lq}NECy~|$wajvl0(6*rU zxj+e=rQTAqtK`+IpzM^73ZdXYXQ%#` zV<{;$TOT^VfifmdFmt7z}0+g`6<{b;VUOrwLwlp zq6M?C5XZ5ycxLttsUwOWaNG87n0_)E&t|w=XwD35^yljWQIkp`GS;B&G1hPpw^G{o z*VlBJ)nVri%6EJ*9$=tT){2%lKy#Svb6JVO%wbRU0{QS4RzDJ7$###nmR8+|n_Z#( zpE?sQevX0ZuNUpZUp>830lwE3UQQ~G5~|Dn`@Pr!*ePVUp4{f~3h8(6zGaGmn|rYm z8ZWv^Ag4EQ=+}e%`VEL|KtCX4$&F2qA#C=qs;p@KD%N`QAA4iuS(uDvTI$zU5)Ifp zC3nxA8<~aQ)tSdsY3>9vi^*z1s3nyb;3USQH;A(j|DBDRh@wp=g zuN3>Qo37r+VO{g*b&pOMY>X${ZMa&UvU*4i&N+V7W&2}?E?uGzSw|!~E-ROQoP+>n z_@F@=7c6XTlUcq+&8;}C66Y+=68xGyea8KzcFagmZww$Q2i+!b9?wB$CBH7z-l`SmVJ=uLu{MbT5D=jeU953kJWVw;H6eTJXz=CfnDL<*qT zd#uImKC__GVSV+T<;_!Y!8q;0Ap#@dG1_MD_VY6otDO(8pihw38vnP(xs`Xi7#kbg z4HpcjAerA0c~H0ZIKf1jjH1rGf4`S^YQ%${J$r7z(+FEjDlsn9T|Sy603ie%{-4#k z6%`d_W#N}@k3V?qSbsS=G#Qz~J`Uj%7ZS2JZ@g12;6M_j5(`};;TM7=V&bd)Kr{Cw zhn`1);FF7AJ;2$6HxC^WIx&6^J3M=I+&O9STka}s**8pDq5k=Cl6-t#@(G}CS5`iU zr3~ft(#4B!Ee8jsFPu8tFs-M7pTAekx#Iolb=&XUx+ONg#`!InR(HDcv?(I^OU!UC zta+j<_4AkM+JDk%E0g&;&=r9)l#+z|34Xk*u5IP!c-Zd#RbX;+TEi-A2J{@Ob8-v} zZ`fiq35lH(^-txMQSx}+onX17AqTc%SDW-fQ|evi4BP?V(8W$qT*G~C7adu^d5VIH#&Pk`>+G5 zcNfl3%D;TWZ`>dktT&RKcN_s&W{+$n#nvu4+0ZD zqtc~>-B-Zs=yhxXaFGb<1sW`pDk)VV6&kqZ%OlL5F_ko27ERMIO$Fx^MX`>i2*oHH zl3p+#vh=m}0&cA@aDD-rFcQ1<>RcUoVW>=A!u&Iy7m?e0%~GRZT3mzK<&c>`8CZqO%xHUGVg60_(Tu(nz2YEgisCVns?6zuiTq8(JLVCm#$MmX95k538Mk$b{d)zW-pJ?p z_uCX8VYD%q`e7K0BS%<#2dMh&bKorgGjjzuTX5~4&8Z~J^LG>Gyg{1U9t)^|mm2rKwV?{#3&}G2 zeZRrWP2}F3AVvd3ZhY1$-Ei~LO-Z9-*s5WAQ#LUzJ}xfn!ief9Hh02HPaXKj&Y39d zpfY(h4E~H&smn4NQ#r3R+EU}f0fvyk7*=8a!R!qbtm~|k2>og5TFjr z=fG)G4iYBck8zUm(frZ=Kic-QQ2Y@Mdihq;z%$|Wnq`T5+jqfCckoOtCgM?uY{hy| zaisRJ1LQAeTH?3Ln^m@Ny`5VaQ5)XEaA8P(cw~L}ZDj|@cDQ8VzoeCleSTCQq$DNM z@wPb+XRIoBAu#ZLbw-1XKO>fOd?yZwORHavo8FG+U1sr)?kaEnFbZ8T?iBj5Kgfa# zTc)Y0iCswjy)4)M!d^5dqtkY1Bcd{}vqtIgEpZn3sW<>%*^^wQR=o@eiz3-UV50yWA*w0uj zRhmA18xW|}IVyr5tJsS)J`Fcc!(H&>g$=h?HPp9=XIhq)B``$2r(+S?NR;z?U7}4b zwgu)7@1wqRH4yJK_EN_dqGED9znZZRshvu8V}?uHumRFpufyeDl`74Go-JayA|ExT zXaG*?E)?kpGRXY6)f%o7L!xU(Jbwa&8g(2CvERIJ-!*Ulk`!d5r7dPBH%Ok01DvXO zwpqmv=-E@aW@pIal22v%p0XtU6lkN^p2llkvPtnW)uN^4X?tIN&?lcrRg~qCF<11r z>>~bf&~@HT*08WRm3#~Ze1PgXG|ngWOx|?^F+@1C54&XhBk?dg{M2Sk{rK|BVzEs7 z#^?3h;<9=y(kYT8;n8%*gYLP7NX7hqv{>YAAkEbp=lA!%eGA}Kb}HDZ&E_e*wzAAP zLbq>!Pd;6Y9A)0<4?>3MwRX^m5vxcK@&RINCJQs>sBss7Bx|oWkDK)Nl6sLbDB2H^ z@75lmSMka~jek*MC8J%->hhu=?nEf!Ea=;|8IQtQHVeD*>JWASe_}Ie6B^6Mn1u07 z?wgTjghPwyNCG6EC1Sx4nT{_uzd44L(A{3}_{Oi%QjocSM14VZTUPl#C4I7cE$>v; zj``k#moRqmnw2ZhqjSi;ses+M$gl)kL-zp=dkLbPESrg1klLKGcB?sEd>>Gw+&-uD z)vKQU`mKFCTJ?({1E9u>^TR&a;KO}eqJ&?=BX(E3P#oYQva7~|32mF^{E~z6Q>M7{ zK5F~g(uz_VZvWOIo?5d36DUE{DoCytTNJrjHd>ghB(m>YGZq)Lb?nbhLiqEFiYV9a zHP*cwH*NA)#M13le9AV}AXWXx7wpV7zF9b>-~6)h@CX;)#9Zxa{6UZdr5o~?Y`SLb zEccp!bi$+i_jRM@xH0}_uYAHTtj{*_7UQ@%^8z=G!X+$y>5=45XbgC_{FXpxs3CdY zFcL7+wfoJ4lOO{GMOZQfOb+c9ww7Fp;>Nn*{>Wb5pnf>eM7Rh`bDXLjQ^m`bYO&Fn za&MrDuFi*2Lr8V3=Jb}8&pqzdYm~`;`UFRz z+BPUD7pLxL!{wpR9;Xq52VZ|^E|JZ|WsDaE#lL~;SBh@0RG$0l3r2jPk{di9D!X>c z^_g2J+{L!BhA9Xo^A{iir5eO#HMhSN(Z1LhLV?$}EOd~@!?H^L^MopohJ2N!ix`Y6+Mb9yhW>!oTESoCMUk62jonUbY^=CP^ zFi-_c063tCj*f39bJLa&{N=-khij84oVkxC(JqZYwlHiufll+-y=(@TA>VTS=2yRZ ziTF~+8~K`{bE*ZE;6J@38hz=fF&-+-%w($y>W-?wmeTGQ3}@}c zlw>hWaRA3Hyy(5OCp0j33&cQr(?3h}1`7NZTxb9(fY8-A5;oD(NZbXRhX6>Fd z;l__t20Y;w+U;tk5rQYOjGSCVx-Wi8?=OGeYwC-Yq!TqI$}6u#N`3ru&s_I;nE#DM z-|{`Q(n`TTq*s~{+27E??Xl1uke(+bMDa!mCfEhL$l{u>o}qmBqf>v1_ol zRp>?QoNzcBJKfZsPMT6b-Xm4^!sE$FYs5#{42jt0{mb(G+=QiXjNJ=xAW1mkH5bf`a&vd#X^oswoqzPT6w*X zn60aOe_P|X7YB4;QiB5YJvWfgR5bsuUp<(fUsli#Ql8od>3q$%D_12Dd1DrzA1;+@ z?}#-lR+ty!qoQ7D3g--o)?wJXTx=nS>>K3_bSYaNws6#8m{n?a7$K1EM!Ih(A6wrYgGREV~ zz(^$@jF#OVlD{-f-+_Blb{Xp-~egLYmWOi4%PYmO)Q zDVsKIh=jg*=FBQO8#W?iH`9uap&9%!uMVD$F*v;{_djk<)z`4>Y@f3lJICI=U|W*g zx;}47R9s2~zFb~f2^1FS#IN!S?kd;Tb#b{^Sz&Eum5Uo>s~Qo1FTdo7dvuvFdGeD8 z!;FXK-}lWPGP3H#9Ix3!sv74{l+4i&HyGlhkixiV&X9#|zP7qNIK9^AjFY#Vq&%I( zs--pZTOaVDsz#M1{dTs9XdTigj&BAayXTETU}%=1Nz}P>>nMssRyj z+jf?_czJix-15r5o`<#tdrc-`2E`W=IxNmvs4D0^ujR09+AzM<~t_+qWu9 zAt^w@?3CN&U=QpW)C!oAZL~2wQiZC^y~#yeySIN!Uvctz%q?wplo~}OuKv3mN;6V3 zjWQ+1k)LcmYQS!ZD*?xkFTFkPg!`v7(9~rslv=bsqdLp~z=7hIFHe3vs@?2 z*mI8}F*SL|j++7*V##$;QIPLWX$Qdjq^pNV3?FVgZUyIzcW2Z57P~C3ot8NgS+>N= zZ7SBJn_l1Rlb7CZpgT01Wmq;8XwR3|NFca6*L|`INo3(d z7D+WqNQZ>)#VvOk<`vI(I9sgzkD5<{c5R4Q@Ja&H8I9PHcS>p|y1Kh5^I5XI z3S)s;uG+)$JOPrMpy^^ABIrEPj-aizm zF$nqj!EZIRtQu3JFxIdBu#rR|-HS_==kp(d%~^Oyq*Gs&ruxbw{dPvi8UT7wqJ_J2 zPKSi_IW2$9gZV+IiCD}4Q)c|3Dw1gr&)I(RszJStB9~pgV!a4w8=C^%IbqCPh|f#* zzQodi?Dly1^dzP`5eKqb>Fqq|CS4tp8RmD9BSV#y7IOITVpv-Q$b`e^F%8tN5KaP< zmA_HM(`D;f30v8Wr6@Iw?pM7K&q=-G&r(Kj8qOg=4R8%VF<8;o)Ewh9*<&!4vb_8> zbQ!&H0##vi1l;_DP&R#k{rWXP(JQrBrAH@QEnQvzNF*z3#w^&n^~dza@4?=CzvvF@ zQC*oQTFL;WD}+IAb6*^zOc${i9Q2?pZlYxW!n&R|R{JLpnMgW=;F&*f_1d*dU`iS; zNEe81O&pm2q-A1|g^RCUPk;-HlC-c`4r2KiW;;`P-LaLQr_3lZX`B)MITwgQlGCXcznxSw(6w?Lkh%b1G$a4V?@Ql?0jk{1IuW zOFBod9_L5xBp1;%TK`>&jBmelW?sgw&VA(~dS3G&jGi_T?+3gL!xJSC=d{(|$zcO~ zdrw+yVq!of(0thBG`5kmAtX9@uXKB2Vq$UI>C|HEJUcKoK6)+cVwl4Z*RPW3nG2hW zhe`*PhYcGxbm*)Lry}?02*_sjj$b%>&~Xr80lr6LtR=|$AZYXa`mhoG$BD-8Aa2Id zg?wltYelGz)%q+J3nQctn|716(HGXeoas6Hl#7nk&o=kyGUA~VMz(| z3A6$s9qopc(@7@Y-BV{^L@>w;9$%DU1!C^Q9Zw6I&Oa-SwqxGGfYqc*0 z8$u$bD1*!SkAOMZD|`XV^hU8ym9%={d93*2SV+ZM01ltbYA4;GzXEX!^_THk+%Ci- zc=YEAE7xjj-UY~xycIe_vRuKhZmxzWp)zf$8Y{^$snAH_u1IPJ{u;Y^xS3!Qcv>&*8&+&oifGgsU`y;Vjh|I8fOw1Ki+hRZ9&+U(|rP;r#tsj|G7J{HAyfL(;@+xYrdor=OGkz(t?7 z{zSz+9O}p)>e;d$cFC|lQyeKSg-5LfgGH#Q7aPdbC5&;k)~) zM`Ni#Ndt7)%{w)@YFS?z_9I9~IBzlzr6naeH*tBV78AN~V>)yCG+RtmMvR#IaRrka z4*1^QV`nxhH3dD%|3NC44|kPjGV}P<$#`m&wc=jWz;c!9ma>zK(B-4~_0FJ9y~h+0 z;xOj$N<>l13`p-?C(e+NHc;MyoQ_KD^Gv7*N1K~||40KXLa6WYjaIajBvQb&(N%wA zZ;!T8#Tr==*y;{Fg`8Q=;-t`U4B6YFiS#6qs9G{?WWdPK)OK8&+xwJ6>r6Q!iTOr( zonbw;fTQqvbJt#EO&CK2W`Q{L=;y_)j=;V89@9)!og=x?=58d=rZH2~*pa_r)bGRi z8h0iP1!MVc3<@{iVdGrX;`6WBx;h@S{<#jRXQQG(_c(6O34i@uxd$W;{wIwVPeS@` zYz>ogsG%u5ZreT7)lc}%x&52JfJfXf09O*R8@nb})I$o82Uizn7=--(h_Nw6eE#Nz zYef_zLr&DZg3i=`K5b(>Fj#}=AZBbpSNzYf;T$f)9E#if2EJ!8m6^k{3sn?|M3Lb> zng&bM8P66N`PCQg=sK;osMVf-nFzV#HB6;H1-r<+H(NWlZb|%RTni+Tc)A!D{fUh^ zB5X0xp7^UPmGmDoZrnF^xJ={!m}m1g7o@nU1KDsfF&fkjr~C;RB~F~k`cKh6vi_xb za;13F>M^(ZO&BBd|98fXo|WdX z0~DzL?%c@}7mmW@04^5L&}v)6J_R(oeLGaVkjIY$&~X05=A@Hm0+{F0Tl}kW)2IVC zW--=F08+*-id%vvTioVNuF7K9|y|41)3 zRRxB|;Ltp6;zVfa6wVrBAe1FX#;*Je3WQS@tP?rKA1kmEwGk8>E{A@E3BAt3?-WG> zyr0L9FEsOh^yYB3=N+9ecJSaC#yxSC7=`H$qav!lULAKP2li_I1{#LHC#atok?}zd z?DaFjutn;W`35#r&zuhZ7w85lbQQ$)$=-x!wQL*+mHWOL;0u~u~=+Hs6 z$2V?(k(03E1tVkj>|3NT24T^)PoZ`^jWASnaBwiM=`7LMtbM_7$238bhYFiIkk-%W z-YJmb#+UyNj6Xb>R)Ii}T;7By=KX??inf7KxW9ckIMtQ<7zpz%3Zr@h;^K2KY4&;% zDJX2M9H>o+=|Xa+AmcxFhsmgSIpMzLiY+C2Z9}vYK$%$kW8^+ASLi4ew?3l#Gx&Z= zd_~pYH?lEZA#BVqF2H&G4?JfNF+OV?BWx-06>Qs+6+thuvK*7@3hT(YuP<>_QbII~#H+eTUHrw#o{(Pg3d$ob2EalF1)kYT{Q z98-lglx>b?#^LEiX{r=9kY)5joDVt(St|Z0Ymt=1nu&JpD%?7xp1Sg9)EE+(sOC~* zzHt+&CcKt$%p@MSf8YCM+S{yt6uP~)nSMSgsSfyn7P6!q+?1$Q9et^23HH^b(%5zG>{6Jk{HX zKYLi0(ltm`Iy~3uN>ZR{hos_vBaJMpxVfC$d~P*|&n9euz^mLCb7ugO{jBaI{*NCl zFP~;ec}4raQDrk@7A(0inaNlhd!yv_Ytpbf#rd#dCHx8_2679iCdc-}$B#C-5q?x0 zX)}pzOs=m`MNqH2DlSHMYs&-nb6jCqo7Nu^h`F~v)kSK7aU73#^^q zpKU#2FDp0ys%y3OJEM<(zGF<8Im_Mf6_^G^_#U zr%?EDaukywt~0fB|Gr*a%Q)t;SA(h8Vq-{Cj%3G^bLVI0a65h7mS1=GZuIO(^I zv=t0Dv@WRN+QvKKw`AAOoud8*|5b83PG>0`5uhT1Y75;%q}^gs{j($a`H5&1h$3ql zU(({AP_fbmD$Dg6#_%xxM_pMn2(I^iJyY_7BNs9Gl9@ZQbC+~fA$a6IB;o?EhiLbu z7rEN_!;J*Me1c4BX=fMpX_Z9dYW%zizj^vLpKbk`0g|BfUko6RZ6l|Ke&FCIcZ?Jr z^oY-^hjUnYy=h${y25T8z*pqIn=e^nFwpPFk;$y5NvgV)z6l~5GY?AAH5-PDMX&&| z^rn#f_4M@IFFxaY#FTDb$t@yK>?wct8jll9V875EynJmREaqhAK4-CC+@>niA#xr=IyHi`%zL8#wqeaU_Po94wYqbe5JQ$4MuJq59 zEBy+!*aRCmZq;5>tpp3sGDgpbr)^12Zsi>Xe}$(JxW{iJZz1P6V6ALuG({d?yj+2heOq!G2=S(HXwr2*B#{{VorhIMet<$vStcCj4$JDvQ>2=SP;5^ zc6`V5qw>HVLN6z1byDMwmtU&hk1SIQH=3w!YOz@|v|ox>7n8&FaaSK)NKAZB{%1Wm9G;m0>ew@|~IHgLd4lnhN}Lg>ijY zT^jn!>)e$L%?K(3>PftCbaXNRKLw5RpMN+bS;rfx4OuK5=^}}gUg3m-TM)$^9V4*M z=7ZoqnWrNR2y-J0BTjcdtzSlV0cFs3(-_qYRqo@kg|if7;K4!iI2HeD8I{yDtMr2u zoiZnGMf}ZuE5vkp$KPZRlDzUO$s%WGXGl@ZeevCat;HIakbR}EU$YCgiouM1)@jeS zc9LBEHV%%V`#^G++R<@^6mT)c?YW!>V!GiQ~t&ViQc`n`R%LV3F!Y(HvQGxFIqqXgMH; zwnG9#061qnEqYMjOK=4n2mt^ZihE|P#j0k388c}8iSo(<*!MD(Fc zCXF1Qv{Cyb7FAiiY|IPCVo^;IV|-Hee?+W9Q{66DI7XT@)38_=-!`>qp|-X*{wsN_ zMo}YRnxhr_gIXISv~0$y(e^7>jw7UU^2K39PM|QF)`R}r%U0e>vMxO#F;y)=qE_nVtkhk*U`jyO_p$vlQNvFMH*^kSIXpKR=r@7a%1Iw!C zR(UeeI1V4)>1Wxi03XlM8JL~50f}f^6Q;G1mV_g?NPG96Pn^!9op!+uC=y725+~k~ie9iCNz#tcOCnH!oj~B~~mNN zlA$fHyf^#wY9K3H#8FXmK7?00`-e8L?qR#C=j%?3U0v(>-K|rB&#uBL&HplyWp}wt zj_Cq)wUNb?U+Jro8e10#q#Z^FyJKWKAXDZW6wUD?aPuzCwA%!qEZBi{oZzcpSsCmuCbQ>IkEJ)9HW)062m}kU7MnOP4N$c^g1X zwH*)&mG4hsljfCsNC?6WCh--4gzz6;_92V9Q@9V<_4<1NfQInIYxSq-r4|PM9lnt# zdfCilxIn|VFx~0Rk>cj6&P&3(nj^*hrdOXn6Zvu4&Pfw_PxRW{O&59o!$63kOh8=@ zYbEp`v_ac&Km7cMFxGEhC zc`M^1cbX=zn@sM~gvAzAH`~{leeAwi<*?C=ZLT^{XtO2Pa*rA)f`Ew?Gax0?H-EaN8YR-P0<;n$&wep7CY&Z;8DU^zA}qqm`8_v+ zFnj#@kfDOuV4kgI!yt_MMeKnk5CXyrjSRW?B~P9I6k4%JhtHQi^7b$v&08NwQWvfG z&Lnzp*grdWmN{t{^L-}4d2Xn+ux+e+2()JYq|@Y5bVc(QS%=j zv7%>4n5XVfhb!~u*+K1)+LDQqFbisn)~mLc+mdm3$ZFp$mht^1lSC=6(dH9IAvSfb zf?7i2Ap1V~)+7hlI19$i|N5)0-oIq@la0|cPqO?>FwRH~z2xNT!Q~rq<*(m)Vz=;a zNY?HCZ=x!N`1Yc8e|pYVVJd8{-ha$;kLX3PtDbJd9y}O?I5~QyjZJukOv21rv(TVy zJXQ*8x?jIh3Sx;6xJmJK;vRu|doe_G(tuOJliRmn3w?kL3#N5XOpB_}2U?`L&e?hy zy#Qe8c!+pUw!i@cXauWhlS;Ozh?UW6v#x!f_bX5X+g2Y*JH5%A@NCf_{D7wqV6`O0 zbtCDWG%C~RN7*zE-<0t>SH+$Av2XpA4xE5a&Twj`7Iq)$bL<%7rgEkVaq93A7x zMPdYAU@uxBF1iz@j!E@$%2=QU*xNYd>bg^swEdl$5AF(l2?ZSJ7&7=ff#M+wOeLmBe0s!AmO{EVub&oeZ3~vTCQUXbUN?6!g$V5ft0`N`k@~^LU zNPbtrBx&P^TJf#x3B=bv8KsbqWLI6dr%~0%IvFI0iZ^QOhRIHEk)SEm>QvJKD^3g2 z?Pmm)z53IqEQc49HkQ}c)pgi@s8~778jwlgCtDdhzG;(~o^DA?nO_zkE(HsjVNwMU z3g&@HwrflWiR^Q*wl;nc<}5f_a-lA6ZtqFDD2&WwEp{$17G2Hs!6FKM9uyaf#KS`&CV?(8dk-nr3oC$Pw!4SdLuL?8+Tq(!m`@xj~~ZsPahK=u+&vl zM^reXkpv ziB(>mC%#Ua4K<~D>zNnn=@H=1WU@s{5uNwQj~L`l#F*wx{W6DgSCkAMP=ba`(CVld=$}f!(cNCyZf`Q#rj|Zyu^UZ%fZ&viq%6N`mslz`~vx2t37` zMVc1jg+NA+P8Wvl;@IL|(EP_(Cf80#rqPD<27~2*SJ833;L6BNu0}@wH7mxO2V1ii zV7nI!Xmu6ai|FGG=CkXpXBMMIHQSuK*XG>Y5MA;|>XbVXHnmw5u0w)#S@EzZPLmot zzK?)8KepI!xSK^rw1hvXj}D}>zK*^(se@n}jcN7k369x|gBJ5FF6iN<)LjfnStJlp zn-kju-&@FY84w!g=le0+(t5HDw$Q~ewJE1Nr;O+*BP&}#wMrfU6&H(XD72_CBC~f! zx$3!H38|cdA8S3XC?;%G}84xICALFn-~8$ zWkOm3Us_c(1ah9t(V_oovkT>qkkZ>1|B25kL-A&7qSV|wT3?(5s4tN;Jvsi_`$S^m zS7HyUr!DyI@?@5uU=Y^Bj7OV27p_G>U2_hD%cPXH}B-hhOu z{O^n9A^FV;joc&Kapp?fah7ejX65K^tN#*SqxiP|@5qMnE1eiASwC$P)?PYOZxp75 zL*iRhk!Q8^wC2y3Z^APV{cWSXSbQ=Ll=nQTALfX}y!iw}Bew+qnJ1rO<5Smn+>rI| z>FkSqe`!9*GzMQ=HvqT;_%W;A!LC(vUt3p)h;&rCGgi;DGs(QRC3jb@)1<}o&#bke zn%`_;$phnqC^%d1{qOK}AM1mYH3kpAa(%J;T%pXAEc!JVkYpdw4k2kSx$F`f5)!!b zhoSPzh~2y05<}+=&mOAo&%P7tP-=B%5^q%>G0-H0Eu;U(jw-w&^en zKo?z3GQ3^BAG-ax@BfW+GGs21@79+=$6ra$+Wci~V&Vcj^X7}_)xZ{@(+f?v>)v`% zJZi9?cJA>(Ew8QP>4Y@gPaC-T;ejJZ%r+<^KmJ(S>DSWtZ8m#&p zoDk6rvNRhMBIW&v++8DY*lu4ClmDQG!hm^)P1cq@n!|lL{T;461g3wFw#{=>>*Y=% zrMV(r96B*H+*%*hrfIt2;duR9sHD@7AAh?Tv=$3fs&ZVJ9VdK^2pVz-xepccin|?Q zBTxpt(pfT^dYCYrdu3QaM*sf(GoN?;u>Ed&y0mI*B%duTCF>85M|@zQtjNokJi0R* zWL{*VXP_YCnDqR=OLr~fkaW{`c{{5YkG2l2IU9rNPs0t{#ac{u#74hPoC6!n{q=ebdR76(3oyJPJ=n$c-NdV$XmVs%njWs{UhN-G8H%! z8?$}6^vJ;t%R}^&(EF^PN?}FkHr)m>Yo9=eZWF zvdIa}jCZD5ospWCxA=o%c3xf*rT6wldpbY=$_!w;7gI8CZ|}itY+sj^ZHA_tn@*aT zpV(hdzO4HjS0Q+~t|#L0-kYQL3>METzkSD!`kf=iHaT)9!={IhuAu4U1w$blRJYzH z9+|drmlyF=BO!?4XH(r)A9`C_I-Y*ear0#1(`Gme6hAc;QO=#WeLHkHD6PrTQSP&4 z==pHUP`ZQGb7shBxiN%zwDkj}Y#Uju3ixNhlj)t|0WwvuK4NBI;6(195la9s_Cajk z{e9$%(A3uI*JT}6WIb3xJJLq>*i0{pbSKN}rdq5TmK&c#6_b01v;tpb&GaqQVFE+g z%$=OItJ7H14dSD#7K_E{(r(}h3|YbrlAYf#ty+Z#$!OE~yQc$k_$@){8!*Y=v`Kft z0%Ij?%T|%o{QYl#?{c8r?WX*fOJmO$uYP>?@SwF$pezK+HAPzC20u{J9?P|Z+jl3TwVa)Td%weyVF_;dM`Ga;r-U9;#ifpUWHTwxf)$=YtQ z^wR^S-&_Fe%r7R|ZpG*FsiblBtNyX7fYnN^7fvTyq7Iz;t<~}=sV~Rb=`J1`YE(&~ z({bm_1c>k}92}H;;{-9{qUT1UJqS=A=un5v*l3jkP!QpV{_=3Wvv6FHG|^P`V(*rI z;=8i@cO4)O?6|;oIw67!%7cka(H7_Xp%w!Q@e-IKqNmL6)o-Z<%+~d|ugpNPcmLV5 zXC`I`y=iI0V$ldc#u6nZmg|RWokyL0y-h%%C%P~>)rU5Bde|JZsNcCJ>a_I}f$LBkPWjlOuSPN1AmVotb}P{2?+=?q|KR_O8yvYhQdyLQ={h=rTH zcgTV3Lr-dob z?o$-I#l<*+Knz%WR*A7VPp3^P9i@qxS>H9@j*m{xYWJ~j!}i)hHGxrNyIj|D>EU^c z#QbDFIM2+J56fn&cZSz-;=qAZ6=#`$-oACKy%!5ZPlPYGe-v?QZUlvSVa|$8LR}YMti>j_CsLHkIzq(gvvB7D=pA1W&idU zVnu`zO%AVxE5iIMcb0@3fa=@Qz`{}8K7A^SD9!d73g+28PeB=$J?QPNgW{N)6{JiJ z7A$=_Pv#0=8EV53vLSsG5cy*mf1m7o@&^s)VzN7-)xpDuC-PD?sHnDts|8z6_DSxp z5-CHX0OD*%0miW0<3f$Mz!RVYPO8F1X;9c(r!i)pP-qajl#N}8P)bnZo-7vj1n72y zoWuhI;rBDToenDye_iu+s!4A}u*YNh;OvKLr)`b-y6K znq=OdH8>`QJp`CQg@`rJvcf(5>!5Pqj8X^%wYaypu4ZKC{9=nUZyN7Tw%bRFZ_(`> z{m;?a*&#c0Pho~pKMq{4qn(XR#0d5V{Am<3DrXe^(pe$*-}~U4vkWe_r_+!+na&vE z(+{lxocgSd{;%5v2L~rS7$h1AW^cvyuwk^1sDQ%~LJaku)5Zgs)J)I9HAa5@J)I`B zLNi0Q)1zqf$#4!S4a0lUBe3^#M3-r{AMhOj{V~<6EVz`|=hG&M-aGIaX7mqv0*qWb zEKP?!@wqk`F+;?jN8(tF8L4X|{z*~Y$Z~fVSoEpYOdaWN&8hRRT2Wfm(s4&_xd}C7 zI(7&T9r8Ms`WY|ne&;k^4>9xsHsiKnt?2r^-J6OE#V`tD#$-m{WJ!KMz|_J!r**i0 z{81Xccf#(>1{NFGF0cT~*}?WwoAc#QVwCSskji)gO26mDH9rs(E#X@>Q=8i%z ze(`k;mBW?5Y=6q|wj*3WynlaCsc=f~zO9C7=l;R7c0U{Gmfb^05O4 z^sN&q5SiN@Rd-lwlY)T9FG3EDJ9{QlWHG|GM!!(ZO;e+v$-X=XO?~a3 zwh#$v6*fas%iO?#WD4&pt@@0D{QOC*Ir+R~C`;aEgF>>v$98QxUar z=h{!J3)z_~-s#+k>JKkxQR+KGHXIfPFd!Dc^0laH zSHoH^%41nA$eMvc4gndsX1!oL zbsYc_K(^+>xZ)H)7ETFV%(iMGV_)m`n>sa+@J8`{g3xcXN{}45_-J$AE77YYAxZ#B zOfe2SL>Ssg!GrD8j0O<<6I*HylXXy_&`ZI(plc+_v>q8kF1;5xK9Wj&dl~niIUVT$ z1aD~ADr@V}i{nZJ2jJ1Eo!l*4S5UgUy1JU0=9BG%YbHIxRDq#egc0BuZ_;!|%ly5I zO--S_^FG-8_(0f}zj#BXiRxWOzv)Ctwqb)Ch7|(rnHv~xOIY$;l#qIQP(S_WDV69gU zc1K>V_FAd+?AGlZbLPYIj9zO(8u3lz?5c8%=^`Tge?UA&?-9{p+P-z0Hkl`;FU#1n zXAO!~{ctC4vF-Z?1F0V<7?)M_N-L{sObX)d3-Vh}6&NIl&-{t*jm^peiR?0TH*nji zr0s8wN7usOhft><`NK%totMp5}lUWD8c#$p$xyl2k>)E07cP!c2>Ra!^T7xa0V9E-p?w&f91>L>J&KioY`T6nMHi{-YMAjESG;nB-1Ip@gZgGC51! z5{57NiiEaG!)rlMH&=cC@^o zc0)tM6E;^Xxf`~r&%}mU-nmKR zF?g8_3kbMY6p$BHASEE&&mLky3xFfzA>O>h&buO5qI-goY~zow(~ljnP4(*O$ZIJU zzeI1qG*YYuXOCB0yth&2aMq>L=hT;er&PJXv1OBCuygZmU+%P|m1woN<})1hgO_}i zS({*oSUhJbv_`1+`!Ej&JtcrELyld03%PX>7sC49!Au0{#2Jy6b zgoV%GpH_QCV5B~wDEl%&uL>B~wc4aDQW1c5aPp=i&~P7Hmzj zd4BODdiedATX2g2bMV#~G=6+8a6bqsHYpH_a9Kb4@4&j_!9MXRnx9cJ;!&wGxbovZt#J%J=rlh1` zl0eTMo_$A5`$#LVOG-p^L;49d(uDxcdko>1oX3w6c8>U%f!vsy%Dpi=2(6#!3R~(6 z*_~LUV)R+#`pSYJdM7@1G|P4SVZ)$%>&a z2}9MfMDv)nAH3%}+LA9v6s=dSdLqVXQ{Cj&%&@~3qpabjaW}rxADzXe)a;~Azcnv% zVEWI>V|iO@hi(QC>(WC+^TBoP?Sx;ObH`}nUjS;^CY5pwaIGvkhf21!EKps;1-GbsHzT^QFbzHG`%4%Po&?&Rj5xxCA-H&8P4 zLrsma3S?Ni*udblsj<|rxqmuQqr>EB8-vQ*_T8HQn8K>x!L0XO8bfUS2K)B;GBuGL z%?u@(gLk|9XS53&^eg8|{>tViin0sohJ^h#oLqi&-M}2>Ov-JK?JeqTs8eSi9NWOUSQ1aBD_vZ5NXR&+U=m1_JceH`b+v&gnXcXo+kCD*v$bAUtt!0Uho*uK5OR`5u7;8olJ!LE-T zGq0<$9t8Xas?$+)|H=VEq0jZdS4$%|2QsGU zx=3$s{`+Oap<%d4Pfu)+@7xN02~1eq3xG(eT9zJtIx}5YTsNPZ3BWI`pN^`qP z3@mZdBU6<&nfXWzX8$@;dbz8ww2FVqYjDiZFLmFS)gzJqjU1_!@QFh&o|j<+^VC6` z6Np4$mBgOUyv3lBY3b>o0J6S(VNZA|eIQNTqQcHU1Pw_@UW*&C@my|cX<657uW{(t z0SnUJ%=}5s*c}{&hI~2ok@e~hu%Cxx8pimHIT?@A)3!l$e{|avpJY%1Q?JoWKrK#u zveh?qcmGaBf0SY1_q#izxJ4S9|98Vn39&X&<|I&1a(5}FUt|#~taqN#5=D;KBqqf#WOJ6*Bjfu_te!IoHj~<9ChF*EQ>%xVP0L|{@ z%1MfLE~=inmY7sdYdIm@h+i>$fIt^TNkgpfdD#_WCr=-KTVP~nBWnDwvV#eH_(=#Y1m1KkUeE*|McHwMvQX%H~kPEU!e01%8u zZH|mf0XYO1f{`#nUS8;L;YyHT(Uif~A>U{*H7s zyb64UcMt%^F7#!HOTwS8sd*8}5tT6)W<4`ca_DK80&%6}P6_)Q+DS%$35t{j9u~vce zvH_WQ?+S-7et(ePA1sG(@rI=V$n!}KI|TBA zS6xhdVL@pe?IPeJU;*t^-v1P^uAjIAr8bohPNpImwJ*d&6NsrjeOKuI(Vi=)!5-)q zYFaLI_b2VFYuB$k07(UIqrrr(DxLM;yb5wcq##f(CTH7MFh}xMY&45U@f-{ZfIxj{D8UE5(DB`vKRO1w*vR_dU9L+~)P z$-$%NhhyW19MxgDW%dR7q#B?+tHKaWMnqiV+yT!FM~*DK5ZuCOu)LyTRKw%QO`9?i z6-mpVuuo#fHBu9Co^e6%DEn-0t7AV@cqQIdTD#v0EGH`-%D$X=#aO?_G4P}2RNVdG zQCnrQ>?x_K9RuM;kjoES;HR>KugaN&Z|HI6CUqMge?{dze_Q%ukWa6ydZ0DaHEGeU zT|B&kcj{74|=W(`fYsPKv1AAQ4;Ov-Hhhpbf;BhWoWCCyqqd^X2l=#^ny z;bulU-7{MKzKpa7%M%r+Zj$^s^R>yOC$s8xaM}5KF(IuskQQ0za_XH~OQ#-&XW;Hn zcmap(2WO;TyheO!_r*}TE0LcP7>{Dyz8?2^zbnkX>ObOZdXJ^6C_EDZ!DoN zA^oEroFBdbt+nY_6GI({tHB{5N;4>ZBo`eY=&v+wQ{c`6|Mri6K%;W?tJ~$GrA6A9 z;SF6uM{UoOe=340P%ru1dQMFY#B4ZonaF^QTm>F#K&nBFg&gm8b~Z*4(Q$Eck4hek zXZ1(u+7scAf&xV)IDn`%X^0^q@Sb+Q%|-^wjv6!OiIu%_0cc|C&`}z~0Rkyd?AX3Y zW=7Ed2*?yO9;lbJ{=yamy-IWLC5;#Nyp6V66NjOhkEEN&Ke}e#`?%kA>&M$j3Jh{}uQazO9gAdT9FHMPbx2WG~Z6IE{ zdNLPP2X!t}LcDTxj9eUtTYc