{"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"4.0.5"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"

Biologically interesting associations in two CITEseq datasets

","metadata":{}},{"cell_type":"markdown","source":"

What we do here:

\nThe analysis workflow includes comparisons of correlations, expression and scatter plots for data from two different CITEseq datasets.\n
\n \n
","metadata":{}},{"cell_type":"markdown","source":"
\n\n

Data

\n\n
\n
    \n\n
  1. RDS files with sparse matrices of normalised counts data for Open Problems - Multimodal Single-Cell Integration (CITEseq2022). The dataset for this competition comprises single-cell multiomics data (n = 70988 cells) collected from mobilized peripheral CD34+ hematopoietic stem and progenitor cells (HSPCs) isolated from four healthy human donors.
    2. \n\n**Proteins (Targets dataset):** for the surface protein levels (n = 140), each row corresponds to a cell (e.g. \"45006fe3e4c8\") and each column to a protein (e.g. \"CD86\").\n\n**RNA (Inputs dataset):** For the RNA counts (n = 22050), each row corresponds to a cell (e.g. \"45006fe3e4c8\") and each column to a gene. The column format for a gene is given by {EnsemblID}_{GeneName} where EnsemblID refers to the Ensembl Gene ID and GeneName to the gene name (e.g. \"ENSG00000159840_ZYX\").\n\n**Metadata:** Donor and cell types. The train data consists of both gene expression (RNA) and surface protein data for days 2,3,4 for donors 1-3 (donor IDs: 32606,13176, and 31800), the public test data consists of RNA for days 2,3,4 for donor 4 (donor ID: 27678) and the private test data consists data from day 7 from all donors.\n\n
  2. CSV files with log-normalised counts from the Multi-modal CITE-seq Prediction competition (CITEseq2023). The dataset consists of single-cell multiomics data: 25 proteins (Antibody-Derived Tags, ADT) and 639 highly variable genes (single-cell RNA-sequencing) in 4000 cells.
    3. \n \n
  3. RDS files with sparse matrices of all RNA-RNA Spearman correlations (calculated here and stored in the dataset) \n
\n
\n\n

Output

\n \n
\n
    \n \n
  1. All unique RNA, differentially expressed by gender in the CSV file
    2. \n
\n
\n
","metadata":{}},{"cell_type":"code","source":"# Libraries\nsuppressPackageStartupMessages({\n \n library(Matrix)\n library(dplyr) \n library(tidyr)\n library(ggplot2)\n library(tictoc) #time measuring\n library(ggpubr) #ggscatter\n library(ggcorrplot) #ggcorrplot\n library(psych)\n library(grid)\n library(patchwork)\n library(corrplot)\n library(\"gridExtra\") \n})\n\n# Function for figure size adjusment\nfig <- function(width, heigth) {\n options(repr.plot.width = width, repr.plot.height = heigth) }","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Load the data**","metadata":{}},{"cell_type":"code","source":"#CITEseq2022\n\n# Load Metadata (day, donor, cell type, and technology)\nmetadata <- read.csv('../input/open-problems-multimodal/metadata.csv',row.names=1)\n\nmetadata <- \n metadata %>% \n filter(technology == \"citeseq\" &\n donor %in% c(\"13176\", \"31800\", \"32606\") &\n day %in% c(\"2\",\"3\",\"4\")) %>%\n mutate_all(as.character)# %>%\n #mutate(\"Row.names\" = row.names(.))\n\n# Load RNA normalized data\n#path <- \"/kaggle/input/sparse-raw-counts-data-open-problems-multimodal/citeseq/sp_train_cite_inputs_raw.rds\"\npath <- \"/kaggle/input/sparse-measurement-data-open-problems-multimodal/sp_train_cite_inputs.rds\"\nsmat_RNA <- readRDS(path)\n\n# dgCMatrix to matrix\nmat_RNA <- as.matrix(smat_RNA)\n\n# Load protein normalized data\n#path <- \"/kaggle/input/sparse-raw-counts-data-open-problems-multimodal/citeseq/sp_train_cite_targets_raw.rds\"\npath <- \"/kaggle/input/sparse-measurement-data-open-problems-multimodal/sp_train_cite_targets.rds\"\nmat_prot <- readRDS(path)\n\n#dgCMatrix to matrix\nmat_prot <- as.matrix(mat_prot)\n\n#CITEseq2023\n\nmat_RNA23 <- t(as.matrix(read.csv(\"../input/machine-learning-challenge-2-prediction/training_set_rna.csv\", row.names = 1)))\nmat_prot23 <- t(as.matrix(read.csv(\"../input/machine-learning-challenge-2-prediction/training_set_adt.csv\", row.names = 1)))\n\ngc()","metadata":{"_uuid":"051d70d956493feee0c6d64651c6a088724dca2a","_execution_state":"idle","_kg_hide-output":true,"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Function to subset needed columns, merge with metadata, and calculate sample sizes by cell_type\nmake_subset <- function(RNA_names, prot_names) {\n \n my_RNA <- mat_RNA[, RNA_names]\n \n #remove Ensembl Gene IDs for easy comparison between datasets\n colnames(my_RNA) <- gsub(\".*_\",\"\", colnames(my_RNA))\n \n my_RNA <- merge(metadata, my_RNA, by=0, all.y = TRUE)\n my_RNA$day <- factor(my_RNA$day, levels = c(\"2\",\"3\",\"4\"))\n \n mat_prot <- mat_prot[, prot_names] %>% as.data.frame %>% mutate(\"Row.names\" = rownames(.) )\n\n dat <- left_join(my_RNA, mat_prot, by = \"Row.names\")\n\n # add sample sizes\n sample_size <- dat %>%\n group_by(cell_type) %>%\n summarize(ss = n(),.groups = \"keep\" )\n\n dat <- dat %>%\n left_join(sample_size, by = c(\"cell_type\")) %>%\n mutate(sample_size_ct = paste0(cell_type, \"\\n\", \"n=\", ss)) %>%\n select(-ss)\n return(dat)\n}","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# to draw a ggscatter of RNA vs protein use the function:\n\nmy_ggscatter <- function(set, prot, RNA, color = \"#033E8C\") {\n \n plt <- suppressMessages(ggscatter(set, x = prot, y = RNA, color = color,\n shape = 20, alpha = 0.2, size = 3,\n add = \"reg.line\", conf.int = TRUE, \n cor.coef = TRUE, cor.coef.size = 8,\n cor.coeff.args = list(method = \"spearman\", label.sep = \"\\n\", color = \"black\",\n label.y.npc = \"bottom\",label.x.npc = \"right\", hjust = 1, vjust = -0.5),\n add.params = list(fill = \"lightgray\"),\n title = paste0(\"Spearman correlation,\\n(n = \", nrow(set), \")\"),\n ggtheme = theme_bw(base_size = 22) + theme(aspect.ratio = 1) ) )\n return(plt)\n} \n\n#to draw scatter plots for the two datasets we use the following functions\n\n# all data for both\ncomb_paris <- function(name1, name2) {\n \n sbst1 <- set22 %>% select(all_of(c(name1,name2)))\n names(sbst1) <- c(\"RNA\", \"Protein\")\n sbst1 <- sbst1 %>%\n mutate(data = \"CITEseq 2022\")\n\n sbst2<- as.data.frame(set23) %>% select(all_of(c(name1, name2)))\n names(sbst2) <- c(\"RNA\", \"Protein\")\n sbst2 <- sbst2 %>%\n mutate(data = \"CITEseq 2023\")\n\n plt_pair <- rbind(sbst1, sbst2)\n plt_pair$pair_name <- paste(name1, \"vs\", name2)\n \n plt <- ggscatter(plt_pair, x = \"Protein\", y = \"RNA\", color = \"#033E8C\",\n shape = 20, alpha = 0.2, size = 3,\n add = \"reg.line\", conf.int = TRUE, \n cor.coef = TRUE, cor.coef.size = 8,\n cor.coeff.args = list(method = \"spearman\", label.sep = \"\\n\", color = \"black\",\n label.y.npc = \"bottom\",label.x.npc = \"right\", hjust = 1),\n add.params = list(fill = \"lightgray\"),\n facet.by = c(\"data\"), scales = \"free\", ncol = 4,\n title = paste0(\"Spearman correlation for \", unique(plt_pair$pair_name), \",\\n CITEseq22 (n = \", nrow(set22), \")\",\n \" vs CITEseq23 (n = \", nrow(set23), \")\"),\n ggtheme = theme_bw(base_size = 22) + theme(aspect.ratio = 1) ) \n \n g <- ggplot_gtable(ggplot_build(plt))\n strip_both <- which(grepl('strip-t', g$layout$name))\n fills <- c(\"#DFABA0\",\"#DFC79C\")\n k <- 1\n for (i in strip_both) {\n j <- which(grepl('rect', g$grobs[[i]]$grobs[[1]]$childrenOrder))\n g$grobs[[i]]$grobs[[1]]$children[[j]]$gp$fill <- fills[k]\n k <- k+1\n }\n grid.draw(g)\n}\n\n# by cell type for 2022\nplot_2ds <- function(protein, RNA) {\n \n comp_pair <- \n rbind(set22 %>% \n select(all_of(c(RNA, protein)), \"sample_size_ct\"),\n set23 %>% as.data.frame %>% \n select(all_of(c(RNA, protein))) %>%\n mutate(sample_size_ct = paste0(\"CITEseq23 (n = \", nrow(set23), \")\") ) )\n comp_pair$sample_size_ct <- factor(comp_pair$sample_size_ct, levels = unique(comp_pair$sample_size_ct))\n\n fig(25,22)\n plt <- ggscatter(comp_pair, x = protein, y = RNA, color = \"#033E8C\",\n shape = 20, alpha = 0.2, size = 3,\n add = \"reg.line\", conf.int = TRUE, \n cor.coef = TRUE, cor.coef.size = 8,\n cor.coeff.args = list(method = \"spearman\", label.sep = \"\\n\", color = \"black\",\n label.y.npc = \"bottom\",label.x.npc = \"right\", hjust = 1),\n add.params = list(fill = \"lightgray\"),\n facet.by = c(\"sample_size_ct\"), scales = \"free\",\n title = paste0(\"Spearman correlation, CITEseq22 data (n = \", nrow(set22),\n \") by cell type and CITEseq23 data (n = \", nrow(set23), \")\"),\n ggtheme = theme_bw(base_size = 26) + theme(aspect.ratio = 1) )\n\n #Change facet label text and background colour: \n #https://stackoverflow.com/questions/41631806/change-facet-label-text-and-background-colour\n g <- ggplot_gtable(ggplot_build(plt))\n strip_both <- which(grepl('strip-t', g$layout$name))\n strip_23 <- strip_both[strip_both %in% c(48)]\n strip_both <- strip_both[!strip_both %in% c(48, 49)]\n\n for (i in strip_both) {\n j <- which(grepl('rect', g$grobs[[i]]$grobs[[1]]$childrenOrder))\n g$grobs[[i]]$grobs[[1]]$children[[j]]$gp$fill <- \"#DFABA0\"\n }\n\n j <- which(grepl('rect', g$grobs[[strip_23]]$grobs[[1]]$childrenOrder))\n g$grobs[[strip_23]]$grobs[[1]]$children[[j]]$gp$fill <- \"#DFC79C\"\n\n grid.draw(g)\n}","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

LGALS family

","metadata":{}},{"cell_type":"markdown","source":"Accorging to [genecards.org](http://www.genecards.org/cgi-bin/carddisp.pl?gene=LGALS1): The galectins are a family of beta-galactoside-binding proteins implicated in modulating cell-cell and cell-matrix interactions. This gene product may act as an autocrine negative growth factor that regulates cell proliferation. Lectin that binds beta-galactoside and a wide array of complex carbohydrates. Plays a role in regulating apoptosis, cell proliferation and cell differentiation. Inhibits CD45 protein phosphatase activity and therefore the dephosphorylation of Lyn kinase. Strong inducer of T-cell apoptosis.","metadata":{}},{"cell_type":"markdown","source":"**Subset data from the two datasets**","metadata":{}},{"cell_type":"code","source":"#CITEseq2022\nRNA_pattern <- c(\"LGALS\", \"PTPRC$\")\nRNA_names22 <- colnames(mat_RNA)[which(grepl(paste0(RNA_pattern, collapse = \"|\"),\n colnames(mat_RNA),\n ignore.case = T))]\nprot_pattern <- c(\"CD45\",\"CD45RO\",\"CD45RA\")\n\nset22 <- make_subset(RNA_names22, prot_pattern)\nRNA_names22 <- gsub(\".*_\",\"\", RNA_names22)\n\ncat(\"CITEseq2022: Head of the data frame with\", ncol(set22)-6, \"RNA/proteins in columns X\", nrow(set22), \"cells in rows\")\nhead(set22)\n\n#CITEseq2023\nRNA_names23 <- colnames(mat_RNA23)[which(grepl(paste0(RNA_pattern, collapse = \"|\"),\n colnames(mat_RNA23),\n ignore.case = T))]\nprot_names <- colnames(mat_prot23)[grep(paste(prot_pattern, collapse = \"|\"), colnames(mat_prot23))]\n\nset23 <- cbind(mat_RNA23[, RNA_names23],\n mat_prot23[, prot_names]) %>% as.data.frame\ncat(\"CITEseq2023: Head of the data frame with\", ncol(set23), \"RNA/proteins in columns X\", nrow(set23), \"cells in rows\")\nhead(set23)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Correlogram**","metadata":{}},{"cell_type":"code","source":"#calculate correlations\nset22_corr <- cor(select(set22, c(all_of(c(RNA_names22, prot_pattern)))), method = \"spearman\")\ncat(\"The correlation matrix CITEseq2022\")\nset22_corr\n\n#matrix of p-values\nset22_p <- corr.test(select(set22, c(all_of(c(RNA_names22, prot_pattern)))),\n method = \"spearman\", adjust = \"BH\")$p # Apply corr.test function\n#set22_p \n\nset23_corr <- cor(set23, method = \"spearman\")\ncat(\"\\nThe correlation matrix CITEseq2023\")\nset23_corr\n\n#matrix of p-values\nset23_p <- corr.test(set23, method = \"spearman\", adjust = \"BH\")$p # Apply corr.test function\n#set23_p ","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Check expression levels**","metadata":{}},{"cell_type":"code","source":"fig(15,7)\n\nsum_expr_RNA <- apply(select(set22, c(all_of(c(RNA_names22)))), 2, sum) %>% sort(decreasing = TRUE)\nsum_expr_RNA <- data.frame(\"Sum_expression\" = sum_expr_RNA,\n \"Name\" = names(sum_expr_RNA))\nsum_expr_RNA$Name <- factor(sum_expr_RNA$Name, levels = unique(sum_expr_RNA$Name ))\n\np1 <- ggplot(sum_expr_RNA,aes(x = Name, y = Sum_expression)) +\n geom_bar(stat = 'identity', fill=\"#BF9039\", col=\"grey\") + \n theme_bw(base_size = 22) +\n xlab(\"\") +\n ggtitle(paste0(\"CITEseq 2022\")) +\n theme(axis.text.x = element_text(angle = 45, hjust=1))\n\nsum_expr_RNA <- apply(select(set23, grep(\"LGALS|PTPRC\", colnames(set23))), 2, sum) %>% sort(decreasing = TRUE)\nsum_expr_RNA <- data.frame(\"Sum_expression\" = sum_expr_RNA,\n \"Name\" = names(sum_expr_RNA))\nsum_expr_RNA$Name <- factor(sum_expr_RNA$Name, levels = unique(sum_expr_RNA$Name ))\n\np2 <- ggplot(sum_expr_RNA, aes(x = Name, y = Sum_expression)) +\n geom_bar(stat = 'identity', fill=\"#BF9039\", col=\"grey\") + \n theme_bw(base_size = 22) +\n xlab(\"\") +\n ggtitle(paste0(\"CITEseq 2023\")) +\n theme(axis.text.x = element_text(angle = 45, hjust=1))\n\np1+p2 +\n plot_layout(widths = c(4, 1)) +\n plot_annotation(\n title = \"Overall expression levels of LGALS** and PTPRC\") & \n theme(text = element_text(size = 22) ) ","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CITEseq2022**","metadata":{}},{"cell_type":"code","source":"fig(25,14)\nggcorrplot(set22_corr, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2022, Spearman cor., insignificant are blank\", lab_size = 6,\n p.mat = set22_p,insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Comparison of the two datasets**","metadata":{}},{"cell_type":"markdown","source":"LGALS1 and LGALS2 are common in the two datasets. \n[proteinatlas.org](http://www.proteinatlas.org/): LGALS1 in single cell data is mostly expressed in macroghages, monocytes, T-cells; LGALS2 and 12 in platelets. \n![image.png](attachment:55cd9840-5cfe-4e43-a388-3cda333c0e88.png)\n![image.png](attachment:78f52a34-48d3-4cc2-a0ff-74c3b9d910b9.png)","metadata":{},"attachments":{"55cd9840-5cfe-4e43-a388-3cda333c0e88.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABVMAAAETCAYAAAA7ypaaAAAgAElEQVR4nOy9f1Ac15nv/ZUlGcnGtgqjfZGNRa7fCnMLvGVArkQy68ZsoSkswZYqsgT7boxiiaqbUcwyCeNkXUWh0pTq+hf4QuFl7t0CKbB5S4O1zqtkcEKNuEZqBYu31mJIxSg7Sq0rkonFG43JL/xbYt4/+sd093TP9AyD+KHvp6qr6T6nz3nO6dPNM0+f5zlrotFoFIQQQgghhBBCCCGEEEIScsdSC0AIIYQQQgghhBBCCCErARpTCSGEEEIIIYQQQgghxAY0phJCCCGEEEIIIYQQQogNaEwlhBBCCCGEEEIIIYQQG6yzk2loaAhvv/02fv3rX+Mvf/kLEq1ZtWbNGqYznelMZzrTmc70W5a+Zs0ay+usWLduHe666y5kZ2dj/fr1WLt2bVrlGLn77rvx8MMP42tf+xoEQVhweXahrsZ0pjOd6UxnOtOZzvTlnr5a9PY10QSt/c1vfoNXXnkFly5dWrCQhBBCCCGLwXJSyrR89atfxbPPPosHH3wwo+Vqoa5GCCGEEEJWCqtFb7c0pv7mN79BU1MT/vznP2dUQEIIIYSQTHLHHalHLboVStknn3yCtWvX4tVXX8VDDz2U0bIB6mqEEEIIIWRlsVr0dstWvPzyy1TOCSGEELLqybQypuUPf/gDurq6FqVs6mqEEEIIIeR2Yrno7aYxU4eGhjA1NZVRoQghhBBClhtahSxRnKd0UOJC/fu//zveeust7N69O2NlU1cjhBBCCCG3E8tJbzedmToyMqITziik8TzTmc50pjOd6Uxn+lKnL4RMlmXG22+/ndHyqKsxnelMZzrTmc50pjN9JaYvlEyVY4Udvd10ZurU1BSi0ahpR2hX6GI605nOdKYznelMXw7pqRKNRtVtsYhGo5ifn8f8/Dx+/etfZ7Rs6mpMZzrTmc50pjOd6UxfaenKuVRQrkvn2lTqSEVvN52Z+uc//zmpkExn+mKltxwfwZn/5xXsW6L6mc50pjOd6Ss33S7RaBQ3b97El19+iZs3b+qUtIVsihJ28+ZN3Lx5E/Pz84hGo/jTn/6UEbkVqKsxnemrN526MNOZznSmM321pqfDctTb10RNWvq1r30t440nhBBCCFkM1q0zdbRJyB133IH169djw4YN2LBhA9avX49169bhjjvuwJo16QW2VxSy+fl53LhxA5988gk+/fRTfPnll4hGo3jnnXfSKtcM6mqEEEIIIWSlsVr0dtOZqYQsJZ4T/xv/+yftCb/GE0IIIQthfn4eX375JT755BP85S9/wSeffILPP/98QV+75+fn8cUXX2Bubg5/+tOf8Mknn6gKGSGE2IW6MCGEEBJjOertpiZh20r/0+346eEyZGvPXf0Z/vZb7QCA/R0/xbdLs00vvTL8t3j2feX6K/jZ3z6LdjnN88O3sWvrHCZ6/g4emNRhqEvKb5I+N4H/+XcevKE5FZNJLv/fYmm6ev/NWJhJu+X61TI1bQc8OPH2LhTIMsCqL3Qy7kf7T7+NMm02szLNmhr6n/i7ljf098SybEMbv38Cb1dLpV4Z/ls8+wrM722sNkz0/Aho+DbKss3unVX7YumqvCb9KY2+KBCNgj8/CSGELBaqa8+NG7h58yZu3LiBL7/8EnfeeSfWrl2LtWvXYs2aNZZfvBVlTHEN+uKLL/DFF1/gs88+w40bNzA/P79osifT1RTdRP2/DsBKN0mUZ6vm/7oun0Z30OsqGn0jTg9T9Bi9HmIuqz7NiKneYyCuPK3MUikxOZLqPX+HmRoLfVOXrpSZelvlHOb9Fye7rqX4Wc9HEDS6lCVmOp8Boy5nnk+RU9YDn16ITq/vI7u6ZHwxSj5rnVc7nnS6qGm/mOjl2upCF3D5qzvidOGE40wrQ4LfDdSFCSGEED3LTW9P25i6/7UAXKXZmAv5UPk9xSjWgcB3duHtn+bBV9ui/vO/8vNKfMtMYXxa+aMAQsc+vPq9N/TpWgXi6s9QeeDVBBLNYeKfa9GiGgh/iNGnyvDNjn0YVMvdj699VdGIslG4Yx+ip96ILyoaRcIu0KRFtTJuFdC+99WYDJo8CpZ9gefxw1FJifJVtkgKo6E/VUnnJnTHz/ePYlfpN9G+dxAtWrmz78dWRb6nv4ZCrTKoaePzRTGFr6DIg2j0VeBUC2pPSeeUe62XfT86GjRlYT86Ai5Joaz8Fl7VtOnbb5/AVvWcLFrpU/BEB6Vzhv7U/k0FkhBCSDIWOvPzZjSK6BdfqIrVnXfeqW7r16/HHXeYO/IobkGff/45Pv/8czWO02IbUgEb8apiGU11Gt31dvIAuP//2IdoVNI+tLqDLq9W38guxNf2RjEY94E6G2U1HkRPvZpUVjNdUtJ7vo0Tzw/iW+/LGXR6oqyTVJ+A52VJ/zDTZZ7vH8Wuw2/jxH+pxLdeSab3AM/XAHH6JozpxnbYbysA6/57+VuofFkj91aDHE93QFCLth4bpjqfhv2vBbBrq9LfSj/+FO3/aWyz1fhZiE6fii6ZSO/XKcO4/78o8ml/B0htUPrKvF8G0VI7KJ18ugOB70jGav0426FrX/JxhpR+N1AXJoQQstpYLXp7em7+T3fgGfmLaq1WWfq3FtRWVqJSa/izSXbpM+h4Onk+27wyhSsAsr/6dexXzj39dUlBvXolPi0jZKPsQEdaZT7fL89e6Nf03b+1oPbnV4DsMjzzmnWpr05dAZCNwseNeQpQ/H35z4fvRzbmMDcXVzOKtwKYu4IrcwC2FuP5NOTf/9ozKMsGrvxcq+i+im/98wTmUIBd/cZSzc4RQgghqWPHlWfNmjVYt26dqmytW7dOt6qoEtT+s88+w8cff6zGTfrss8/wxRdfqIqWEqRe+ZL9ySefxOVVgtcryuIdd9yRVnyo5UZMb9qPvBwAc3MwqhX7Hy9ENoArV610E5mtu/DD75snJePVwATmABQUW+kRb+D//c0cVD1Io7dqjaOvHvBhYg4oeOqHaek+tkmhrbb7Ly2S63wF90veWx+9DwBvoKW2EpWV5sZjK9LV6VPXJTWY6f0ysXFSgPuzgbk4ZTgzunDq4yz93w2EEELISmW16O2mxtRkDdunKHpTryTOGysxSToAZKOs1hN/XbIyEtRjbE9M7gN47yqA7EJ8/ekU5LXKp2tGGXZ936T+hGV7ZCXuMsZPGdJefk9SDu/fatomy/6SDcYFxVKfeooLAHyEj2YNcny/GAUA5n7zFt5Sf3ik2r/78PWvZgO4gvdeNlx7ahyX5wDk5GGfsa+2Cuh42ro/04l7wY0bN27cuCmbghK0/u6778a9996Le+65B3fddRfWr1+vcwWKRmPK2aeffoq5uTn85S9/wccff6zGZVK+aH/88cdq2qeffoov5C/kikIGAGvWrMHatWtx5513YsOGDcgkSdtv/N9q6JPU8szhytW5mN4kf5yem/3IcL1GH2h4TzVu7TMpGwAKnugw6AY226E9Z3Wt5ry13jqI8d/MAbgfeXb0wURymqSn3FYb/WerT6zGhG2dLxtl3wlIOpqN50tfv3x9yjp9OrqktTwSV3DlKoCtxfBo2v+RcdxmRBdOYZzpuirx74alfody48aNGzdut2pTWCl6e1ozU/VfrW3kf+oszp7Vbv36L7NXJzAxB2BrBV6zirS+dbehjLPo/0GCSn/wiKwYjasxQ7erSlqi2ZzpcyUkz5R44rXUvjLv24L7AWD2mvWM3pwtlmU+X1wAYA6X39Fe/RE+mlOu248tOQCuvof3Elz7xjuXk8z0sEL60o+5j3DFKkv2/ZpYr3OYCEn9X1bL2amEEEIWj7Vr1yIrKwt333037rvvPmzatAn33Xcf7rnnHtx9991qnCWjcnbjxg31a/fc3JzuK/Ynn3yiKmyffvopbty4oVMCAei+qGdlZWHjxo23uukAjDrYbtO463b46KOPAGTj/oehertcnvpIn2nfdtkD6D28ilfVD9fbjbrd3AQmrgLILsPuRLqcBc/XlslGK6vwT3qdL7neKrfLFtko+45Brx1KoPel0la7/ZcmdnQ+Zdav2s5EbTPDjk5vSqq6pIE4vV/io48kI+aWfcD+vPsBXMF7U/pLM6MLpz7O0v7dQAghhKxSVpLevrAFqKJRSFm/j4Fzu/XGstd3Y1wu5srPK9Dwktn1yh/X8N0fTuCt58pQduA1TCizJ6NRzUzLt1DxjGmwURlZ6ftO7MzcRA92f1eOdaRRUF+JRoGX3sOVpwpQ8NXt2B8dxKBpu6zarc0ajR3PfBcDE2/hcFkZGl6bwEexTGqegqfO4uxTseuv/LwCDf9pKC9ZvdllOHz2LA7HWoqJ13fju6eA2LKf1zB+eQ5lZYXY/gNIM0guXwFytW38Ph5RZsS+EUUU47h8oAxlWx/B89EoXjHWC4t+0Z7T3rO4fJqUsQa8df857N66G/07JnTtf+WZCiS604QQQkgylC/LGzduxF133YWsrCzceeedahyltWvXYv369cjKysLnn3+urgpq/D+srB6qrPgZjUbVmEtmKMrY+vXrsX79eqxbtw7r1q2zjN+ULkl1NVMdTKOvafUXy//v2v/b7+FKaQEKip/H8ygAcAXX/hMo1MhTt0M7Mw94ZeoKdm8tQOGO/Yi+odO0MP7MW7j/3G4UPNWP7aoaYCJHIv3pJcT0nq27cfbsbs2FV/BWRQNeAaB62Jvpd2Z9kLBfNDqXAat67LY1lf6Lq8ci/rxWOls63xvfxe43NOMkuwyHz/ajQO5LU3TttaHTG+/V1bdQ8YyhvAR1SSTS++vUc9fGLmOutAyFO54HvpoNzF3GFWkKA6T1nZ5PURe26N+oLkPCcWbndwN1YUIIIbcTK1Fvt3TzT7T/bWQO0tdV5fzLaKiogCAIeOuqppxYgeblaTWTU9/FOfnLfdlWxKcnkEdiDhdfF1BR8Zb6RfujGb+ar87gegNcgdSMQnx9n6EeJG5/nFyadg5+95zkllVWphqXtXJe+bnUTxVyfzW8BETfuCYpUDl5qLNsn0ZPm7uInooKCD9TW4prp+Lz+2ekWSSFTxQiG3MIv+PXl6d8xb98AYMAolE/rs0CQAEe+Sdte/UCxJ//rTQLVp4xEN9fxgZIvPzTi9IX+TL9yq7Jxh/33HPPPffcG/fRqBxfae069Yv2Pffcg3vvvRd33303NmzYoCpHivvQhg0bcM8996jb3XffjaysrLi4TIpi9tlnn+Hzzz9PGF8pKysLGzZswIYNG5CVlaUGwrdaWTRdkvZJLKPuvPH6+PLUM/p0yP/rcypQsRXSx2nd9fuxvVCeDfqSXN5/fgRJ1dqO/XH1vYy3JiSX6jLNculW7bjys5juVFFRgWdeVNLlHFffQkVFBf55Qo6HORfBFbmcK4re+n+m30/Jzsenx3oOeAVDxrbGXW+v/yzlSKYz29X5opK8zwgCKl5XZqkWoOJ/7E/c3lR0+qtv6XThimdeQTRlXXIOE69XQBA0ev//NxifX9axswsr5IkFF3QTKOzrwlb9HpMrlXGm4Hcbfzcs/buUe+6555577hdzr2wrXW+3NKYqlZnt/c1n5Xic349Ll2VDNKpdDcuqPLVCzM/P46WfXNQvZBCNYj5WYAK5tOW8hG/KRsaCXf+K5+fnEY3W4evy0qgFT52DKIo4d+4wtmUDQDYcj9eZymOvPmM7X5IV5tg5RDUqWNSsvJfwqysAsh34+tOGdI2S51crBqLz84i++E0MXQGAAuweeF7Kr+2vF38lKWjZ2QA+wrVB/f35frGktmWXfQfnzkn9sltWeguKnlflML+PGlmifly4LP1IeOQHBvmf/joc2QCu/AovGcZHdNCNs1pfrqT9zj333HPPPffxeyAWX2njxo249557VZegjRs3mq7sqY2LtHHjRmRnZ+O+++7Dvffei40bN+q+Ss/PxwLYK/GVlPqB2NdyozKmdUNS8mYKO30i59ScVxUYIBrFyRl5LpxBN1GvnJ+Heqj8r8/ORjaAuchv9TqH4gGEAuyWdYpzz8kfS7MLsX2fSf2yPqmp0GY7tHu1QzA/Pw9/cz8uzgHI3oaG/7Ef0WgUJ8fC0sdbjW4j7WMGzF+9GDtvpbNFtTqMSb8b07X96De21dgeW/2XSA59Pxjls6vz6faDzXjqZzGpjf2uVmlSf+o6fZq6ZFSj9z81gO/rxjkQ07GlcfvRjB+x5GgK/WLWv9p67I8z/fgy/m5Q+nN5vFu555577rnnPtN7YHXo7Wn6nL0s/eMvqMHPu+rVs/VdP0dNusG4AOANN/oNCkVaaIyMNT/6AbB/u6SEzV3EPz/xBJ5Qtu6L6hf/+sQlpoSqyKfAy98cwhVkY1vTj6CG1NrfiZ/vKgDmLqK/2Z/gOgAFNfjRC8ZUefYtAFz5FV7Wpf0Af10g5RnS9skTSnl/jVTCmCltLtj1c3SqgZ9+gB81bUM2rmDomy+bXqfKTwghhKSJEuPoro0bcc892bj3PilY/YYNG+LiKlldv3HjRvWLeHZ2NjZu3Ggal0mL1jUoKytLdUkyUwKXJfJH1wKhM6YHvfDX8kfccRg1D79ifMUcwmP61Ppyh2RknfhnjU7xhDxTNBuOcjNN62XVEJY5/HCfkPW7sgOSTqLolwa99Qc/+g62ZQNXfvZNmGspmSRxW9PrP7vY1fl+gB+dP4/z52O6aH2e5BKveHvZJg2dPl1dMk7vN3DlI1UZxq9e1KZUZlQXTnecpfO7gRBCCFmprBa9Pe2YqSf/sRon67ow3PQdnD+vCViEOVzsrkbzIFBfLp0p2HUe53fpr5+beB3VY/F1nvzHs9j9ixp8RTob+3pbUIPz52sMhVzE69XNWsmhiP7SPwzhkV/U4CsFNfjJ/zUnKaiXL+Cktm2DFxB+dhu2ZTuwfX8U1wAA2djWdB7nm+LrMVMjo9GYjNGoUv9JNJ/bjV/s/oqubfo8Rl7CP/zNb9E1/Bxqzp+H2tIrQ/ibf4gFnNXVpVz31iP4xe6voGDXv+IH3bHFIKLRk7hw+QC2lWXjt++9pK+3Sprxqnzl18rxqys1KCgowCP/FEX0RZi0TyeJPPP2JJqrT+Kf/u9foEbbf5Z9p5T1EgITT+I52e3NztgjhBBCtGzatElVjNJVipQv3llZWerX8s8++wyffvqpuuqnFiWP4l60bt06Wwpgpkj2/9L8f7dRj3gJ//A3wI9+UYPvnD8PRZubm3gd1f9o/M8dRfS//wq/3VWAr8yFcWEwiqgamvJheebdHMJjJ3X1nRwL40DZNmQXbkdd9Fq8HvPfA7goPIdt2eY6klU7TNusZBhsxg/Lh/FcWTa2PduJusFmSW994Uf4xW6t3hrTWe3XaaInAvjtW3+jWeRTus5+W+tt9t9Jgz5lIp+Jvvzb6d/a1Pli40Gri/72rb/BN3VGSF0v6T2wYEOnh9X4TVeX1Ov9//pPr8vxR+UZ0EofKu1XLirYYlsX1tUa1bTd0Ad2xpmd3w3UhgkhhKxWVoveviZqos08+uijaRdICCGEEHIrKSoq0ilFypYOkoFDckX68ssvMTc3hz//+c/4+OOPVSPKmjVrVIXsrrvu0sVWSlTvT37yk7RkMoO6GiGEEEIIWWmsFr3ddGbq/Py8rtBoNMpjHvOYxzzmMY95vCyPldU+jWlG1qwBdKtyx+VQ8klfu6PRqBpDKRqN6upX8qxdu1Z3rVm9iwF1NR7zmMc85jGPecxjHq+049WityecS2ssmMc85jGPecxjHvN4uR3bdQ2KRoEoYlsy1qyJ/1purHupWeq+5zGPecxjHvOYxzzmMY/tHq8WvT1hzFTuueeee+655577lbDXKk4LRSkXgOnX9eVgXF0Ofc4999xzzz333HPPPfep7KPR1aG3m8ZMfeSRRzJWASGEEELIYrJt27Y4d6GFoKhGN2/exMcff4w//OEP+NOf/qSev+OOO3DnnXdi48aN2LhxI4DEMZcUAoFARuQDqKsRQgghhJCVx2rR2xPOTCWEEEIIWe4oX7kXo8zlqhMtV7kIIYQQQgixYrXo7faCFRBCCCGE3CbQUEkIIYQQQsjyZ6n0ds5MJYQQQsiKZn5+HoD9gPbJUL5sz8/Px22KW9DNmzfVc4A9d6FMQl2NEEIIIYSsNFaL3m4qvVFB5zGPecxjHvOYxzxersdmylOmtps3b+LGjRv44osv8OWXX+KLL75Q/75x40ZKWyZZLn3PYx7zmMc85jGPecxjHts9Xi16u+nM1H/5l39JeiEh6bBp06alFoEQQghZ8VBXIysZ6oOEEEIIWcmYGlMJWQw2bdqErKyspRaDEEIIIYQsEdQHCSGEELLS4QJUhBBCCCGEEEIIIYQQYgMaUwkhhBBCCCGEEEIIIcQGNKYSQgghhBBCCCGEEEKIDWhMJYQQQgghhBBCCCGEEBvQmEoIIYQQQgghhBBCCCE2oDGVEEIIIWTVMYuRIwIEQdlaMXJdThlphSAIGJhaeC1T/fqyFxttfZlsB5nCgCBAODKC2aUWhRBCCCFkmUNjKlk+TPrgcPgQsp3XoduahiKW2UM9CfLYrDcy1GSoswmBGev8oZ7EMhFCCCGLxVT/HnhHBbS9KUIUfWiECO/eAdxOdsfZkVYaWgkhhBBCSMahMXUhXB9BK2dE3GIiCDQ74KgD/OEwwurmR1FLORzNAcSbL0MY7wKc1U4EW07ZM9YaS+hxoPyME2PaOgeL4KlwwDdpnr++K42KCCGEkAUzi2vvA0AhtmwGgGI0iCJEsQHFAHKqjkEURTQUL6mQi8rsSCv2eMWlFmMFIY+Ro1XIWWpRCCGEEEKWOTSmLoTNVTi2yn+MLDciQ0fhGXbDH3ahVJdSClfYD/ewB6eMxs3JcXTCjYMvOOFEJ8ZNjJ+JkYyx7mdrkas9XeKCvxnofCeky+tzOFDf5YSzOtV6CCGEkEzSC5cQPxtV7x4fc+8e6Y+FBdB+KFbyC4IAoX8gsTv41IBpaAHTrP3mYQjiQhT0p/jVempANaT2ugQI/YF4mWU5W0dm1Y/jatsEAYKu31KQx6L9ah/K1yptH5hC0vqV0AYD/a0xmRPIpLtfav5kafFu/sa8sTGRfMwQQgghhKxmaExdCJyZeouJ4MKZIJwd+wyGVIVSuMJhuEr01wROdALN21GatwPOaqPx0z6XpuPnvJYeDiN8OCZNZOg4Opv9CIePwJlWLYQQQshCyUHV/kb571647BgkR88Cu5SQAEDvG7JR7foIXvOKwCEfRFHE6Qcvo9eqjKkBCK5eCG2nIYoifIesQwtM9Qtw9SlhCE6jrVLJO4uRI3vgHW2ETxQhvtkGoc+lMwgmpbgBp9sEAECjT4R4oBY1bQIwehYTsnFz6t1eAAKefFQzD7MP2CYqfdAL15ERzKYiT4L251R9D22VAPpOYmRkAK4+AId8+g/ypvUriLj84PcgiiKOVcFaJvl+xWQARO9rklE3UZoBZWZvo0+EKIoQfY3odekNs5ZjhhBCCCFklZO2MdX4tZpfpMmiM3MBwWEnnI/lJs+ruwZwP14KIBe1z7qBrvEUXf1Lsa/DiWBLuRwr1Tq+am5Nt864SgghhCwJxQ2SkU057nMlXlyo8kmUbQaALXigEsDoh7gGYPaXZyECaHxMsvrlVP09Gi2KMBooix9rBNCLi3H64RQu9mnrzEHVUTkMwfUJnB0FcGgbigFgcxmerATE8xMLMtTlPPokBIg4+8tZk/pllDpRjG2HIBlf/8O+PInbn4Oq59ogQITX2wugEb4DBtcms/pVQ6fG8Gujj0TvHghHRrDlgAhRPIYqTTsTpUnMYuK8CFS2oUYRsbgGbcZ2W4wZQgghhJDVThrGVMmtaI8X8mwCaTvdJsR/sSZk0ZHc6nULQ/XETJ2Rd4MIVrdjnzJbtWQ73OjE8RQXhsqt6UY47IcbANCJeqUu0xithBBCyDJADkck+mTz56gXQyl++L72OxGAgAf+yu4VIrx75Q/tLmkO6+VrBt3w+jVcBoCHt1jH5+xzyR/r98A7ioUb6rQGx6mL6AUgPFFmWf+WBwUAIj5U/snblidB+zdX4e8PydlUw2mS+n+fIJOZTNo6Rr3Yow0ZkChNxzV8OGpRJw2mhBBCCCGpG1NnR16DdxRo9Om/ZCuLGRyrYth6ciuRXPuVRaH8zdo0OSzAzh2aWKel2N4MBM9cSMMIqq9rrMMJDHtQ3pNe2ABCCCHklqBxe08VW0Y9HYLuY7upbrh5CwoB4P1r1rNN5bACsa0hofExOTkoe0Jy9X/tDRMXfwOqEVlRIGzLk6D9U7J7PwD0uRJ6dNkyYlvIVHxAOfbJs4h74ZLDPCRKiyHPNDWj8gFsSSBSRpkJoMnwwdxs0c/lTwShSUXrlCYBLGo7JkMxD6qZAJocTQjMLGJ9S4WubYvfr5GhJv3kjWRjM+n4lRfVTfQ7YtK3Qsd95u9HZDKk/nYL9STpt0wx6VvUiTOptSOVPg3Bt1qfe0KWESkaU2W3HzRiW1KN1jowvhJIf2RkwDTd7uIFycqxCrKvLrwwIgf8V77Ma+vVusJd1+ZjOIMlI28HnNVBBN+1+S9t8hQ8w9C450tbfReA4SAuLPAfTG5Nt2RQTTlsACGEELKIKHqLRpdJfYaphOQeD/S+Kyk/syMnLWOmSm7tiiu9Rk+L0+Pi3dilvAOYkmeQou+iNGNSXZwpNeUrZ0uhRVtEiKOId/EHpHim1wFdGID/al+exO2fwoBLdu+Xwy/0ugyzQs3qj3PBhzrL1lQm7cJaKEaDXJfw4JbEafqekg3PmpnMU0PwjiaezZtJQj0OOCqCcJ6LfcQOn2vHpToHmlL0LlpaIgg0l+P4tHJsFt8/g0z64Kgbjx3n1aI73I3avEWq73ajuh1jmokV2k17T1MZv87wcYsxHYLvxKUVugZDZsd5ZKgJ5SfUhyhuzYrFIQRf3SW0v2BYgDiDLF47SuEaLILnRXpQErKYrEstu+z2k1vNa5YAACAASURBVPSrtDZYfwOKr4+gda8LrQ+e1sxOEHEW34MoNsiLEJzEyK5jqPp9LHj/saocKW3vALZYzgCwLkcJnN9QLC904H0NI48eQ5l8Ze/vtkAURTnIfi9cb7ThtCgCI63Y4/XitSfKcOzRCbTu9UI85IN4oFjK62rFA28eQ1VqnUcWTC527HTC03IKoRqXxSJUMULvdEpKT5fxn2AIPkc9PD8OodbGP7DIUBPKW4rgD8fXmZtflFoTCCGEkMVmcxWOvQm07vViz6hXPimg7U3JqyilgEybq/C9trMQvS4IfYDQ1oZGiOYG1eIGiD5AcO2BIFdr9GRSsx4Q4YMA114BXlU+eWbl0dMA9sAlyLVUtuG0Mb5oMoq3oRG96HUJ6K1sw+mjVchRXP2tjIKHngReFyCMAkAjfEerkAOgyq48Cdo/1e9CL4BGXwOKN0Pu0164+rdB3JW4/ni3+pwEMhXHyYBDPohVOQDi5Yul6WvJqTqG02jFHpeg3mtFpzaTKKNM+lDf5UT7OYMRMK8W3YPTcNQdReAxGgjJMiXV8bvTiaIzFxCpMfxemRzHpZ1OFA0Hb53sREVaVPggwiv1PVOyD+0ox6nJ2sX7eEPIbU6KxlQT5JVLVSrbcPo5mAbG956fwGyVYoKMuVdpXchMg/f39eLiVAOKTfVo83JQ3ABRbJBmobpEU9GVL/HS7AUxpljLx4DJwguPPgkBXpz95SyqqqpwTKRJ9VaSW9MN/1UH6h2XDEqKZCDthBPt50oBhDDeBbgHzb4mSq7+6BpH6HBpUqNsbs1BuFvqUd+cbzDMRhA40Qlnx1jSMgghhJBbymZrHSWn6hhiScVoEEU0xFJRdVRE1VGr/FMY8EKNd5pzQIR4QFO4rH/Zodh4bQIZTK/RyRWX09AuLVYu/lss6rWWJ75a8/Yb26rr0+uKcdK8fvN+SiBTontgmRbfXzmW/Zt8zCyE0DudQLPf3Fha4kI4bMjfI3sdAQD0RqzIUBPKzzjh3xlEfYtilHLHPpDPBNBUEYRz0IlgnQdqjsH4mYZWdQCQZ4R2xo6b/QgffgiB5nJ4hgEMl8Nxph1jXfk45agHBsNw5cl1G8oK9ThQD786Yy1p3SYy1Ds6pTYY6zCVU9FiIzF54+qS9Gzo+sXknKF8Z8cYumsSzetT9HcFt37yQsrl2Sx3EUl1/AI7sN1hNHpFEDhxCc4XtmPamD0O433TtjUEn+M48juK4Gnp1KTp+8fZ0Y6iFo/mXhrLNJSb9LnRjg3jvVDQjC/juETsXkuTWoIAgih3BNF+rhv5P9Y/I8n7oB4Y9AN12jYnGkshnGoJwj3YHetl+V3S7vDAozyPzX6ED0PXPt27YyaApopY/8Su0TzbSjtsvou05RYlfB6lhZcdJwLYFzexiBCSCVJ08zdZrbO4wRB7SUPaiwfYWLwgGbIr0x5vIXyiCN+h+CyFW+w7KvW6ZHn2eiECEH/H8PuLg2ZxJ4sYQ6WHwwgPFsFToc1Tj0sdYwjLrkyRoePohBvbLb7ElX6jHU50YlwtN1G9kquK3+FBuS69HNPPhm0qdYQQQsgKRAmBpIQMkBePincNXwFcn5A+9lu5z5NlQATTYcC59SFbeQPNDtSHY67XYx2Ap8IQK3DYg+M4Irtbj6G9uhP1ujiFQXhOAEc0MfE763xyCCe5Dvhj7tqDRfo6ZEOQe1Bx6fbD3VWPpiGgtmsM7dWS4SZsNGjI4as8P9bKIk8GeLzUXt1aSlwID7ohGZIsDDB1l9B+Ti+nomOHesrhcfg1awMgJTfhyFATHHWAPxwrv6ilPEFYBtnI1uzXrH3QiXo5RmXq5amSINCs/C7QlHtL1jhIZfzGKH3cjc53NPLNXEAQTuxIOisyBJ9Df9/8zZ2od/g0IciC8JzJl58RF0rl/on1+xicZzw6Y6dxLEiL8Kby3Ohap1tzQnoGATQflAypceNSKivYchSBGU1Ytep2jJmGrLDTB0Bn3Ti2q8+RWy3flMlx89+Rwx4Et46pYRucXfVwOGLl6vsgBJ9s8Axr6oXmmYvHZp/m7YCzGvoxYyZzXj6cGQhtRwgxJ8WZqTmo2t8I72iimaIaZNd4I8mjXsVc0dJFmeGquIwtNMxpzLWJLBolLoTDrozkza3pRrgmwfV5tegO18oH9uqV4trYEw/IRW1XGLXJMxJCCCHLl+IG+A71wtWnCRmguoavHKSQTlLc2Lbnqm5J3E+SPkX5Nj5Uz1xAcNiJ9nMxI2VuzRG0nylH8N0IatWP3W4cVP+WQkbhzDQiKFWvcz+rKeMxJ5wIYnoGKMUFBIfd8Hdp5jSWuOBvdqBeDhelzESMGS8l45FEIsOfNHPMU6fxlJINIv4SpX2J606JmWkEAU0MTr2c02EADo10yXRpHdKir+7Bbs3sz1K4Bt1w1FmE51LaqmlH6eEwwnJ5gVTLU/kA08MAdsbOxMpdIMMelDs88ecNYcVsjV8tJdvh1oyDyLtBYOcR5OKDxNeZ9qFkJB+fdKFUHpO6xXgnT8GjG1e5qH2hHcFhj6YMY3/JXn0GLJ+bBEbgUE85PGjHmCKz7jeZvixb2O2Djn2xMVOyHW50YnoGgImskelLQLUT8SZxzbtENmhiZ6xcffg37fOlrTcx9vpUfo+1xMaM8h7SPRd5+SiSrzdrJyFkYaQ4MxVAcQ3aKoFel3ZBgVmMHHHF4mctYPEA+4sX2EF2+b8+gpN9STObEr/wQisXoSKEEELIbUNsBXh5SzV+6TIgp+qYLL/Jx/rNVTi2lO1a6vqXIZembcyHnJlGEEXINzEyBK9qjFDV+SZGES1OQxnGOuK9l2Ju9+nNRFSRjTqKp5RkENkuu1InqzvVuvahvToY8+zSzTKUDLvoqpfrSXUlcMmA2Vln8PKqi3fuVpAMVlb3JvXyYpRiX4dTswCt2WzJNLFagMow69jW+DXIvL1ZGQcRXDgDOB9LbpA170PJ8KmVQWvcNb0mLx9mq0BEhpoSjLsEz42VvENNqO9yw2/qdi6tVu9wOOAwusYnKjONPkibpO8SM6QZ5oonZeIRbL9PJUOrMma0M9q1PIT86nTGIyHEDmnETJXiIpU90Yo96qIBMkqAf6QQrN9ICosXJCzmgA+NfS5p4QEAjYcaIfb14sPfQ12AyhbqIg7SwgsAILSd5ixVQgghhBBCMkou8h2QjaFmxg8pNuL0s2FY+RR9cDUI4GBGpIlMX0LieJsLNVJIRp/6d0JwlQDjXUrsfzt1p4rGa2rSB0ddPRxdiMVwVL2+5PiTFQ54EsVo1TIzjUuwiO+YDgssLzarVgolUO/QxgvVE4vJKWE/LqtpzfbHr6FdpY+7pXGQN40gnDiS9kxCeZbx1nSv18bpdcMflmYHS/E9F8CkD+UtQPs5wz3QxBV1dowhXJOrxg9Nn4X3wcLRxIpt9iPcVaqeywh5tTjY7JHGDDQz2gkht4y0F6CyDkqv5rC3eIBZWTYXL0hcTnwg/YYD8lFxkvqMxwkWcSCEEEIIIakwi5Eje+B92DwcFLm9Kf1GO5wVxxH4hokRb/IUPMNOtL8AAPkmLrDyTNGdac4UNSC57V5K4LqczHiWnNLH3UDdOAJbL6Gz2omxPLt1LwDFcDrpg6POuCCrYnSVDH9SyASTMmSDZxGgzmy8NB0BSuz1Q25+ETA8DdOeS6M8cyRXa5dsxNK6fatypBTOwEaNtsevAdnVP7D1kuzinxzzPpRm9RY9mwuYhAkwvUZ7L9VFfMO6BbGMYSBSYiaAJjmusLFPIu8GETSESYgPSWFNOn1wS1DCD2gN+Lp+Xjilj7uBE9MIwMTFH4C+HwghmSZ1N39CCCGEEELSRvrgTkMqMSWvFgebg/ELLckGGWfHEckgoyzgpFkkKTJ0FJ5hpy0XaVsorvG6hZgkd2RlIaTSx91A13GdrKEexY1ea2xNVEcnPC1BfWxLG3XHkZcPp2yAjWPSF+e+HwsrILsi9xgWQVL7UnIX7jwRkyX0Y60rtuJafzS+H5otFrGSQxwc17Zl0ie75adRXixXfB9ZLSi0GNgdv3E8hPzqTnha7Ln4A1D7ULswVKinPnFb5fEWuyaCwIvxbvVa1/BQTzk8w/ZEikdaiCmoiytsQDaGAlD7SYvWYBpHOn2QhNzHnHBa1ZcS2mdR7ocFl6mhZDvcwx54TF38IRtvUw/HQAixR9ozUwkhhBBCCEkdzkwliSk9HEb4cR8cFQ5ol/vRz5aTZlDm9zg0iwLZdEu3TS5qu8aA5nLdwkM6V/ASF8KDBlkV13koM0/r4ehywx/eblrHjp1OYNhoRLNRt5G8HXBWe+CpcCDYMYbuxzRpJS6MdTShXCtndTvG5IWIarv8mHbIrv8ysZmEuWq6Iot7UFrkR5W2phtjMCvfLD4mIM0a9cPnKIejRa0xNpMv5fI05Z5rR1OFtlxpXCw4XILVAlTQ3xd749eIPA7gxA7b47cUrvAY8pvL4VBnjSYLDZEbd6+dHe1wqwtQmfRfsx9jHcdR3mKcxZycyNBxydW9Sz+2ALk/arrhv+qQQzEA0r3yo6iiPraQXMl2uCGFa3APhqF/itLpgyTk7YCzutx0JrNtTJ439+AY2k+Uw/NOCK6STATvkBcG6zI3HEuzfhcSMoIQkog10Wg0ajx5/vz5pZCFrHI2bdqErKyspRaDEEIIWRIKCwszVla6utpUvwBXn4C2N1OPR585aEy9naE+SMhyIwSf4zjyM/ohYmUTGWpC+dWD6keR5YoUy9ZvIqd1fF5CiD2S6e108yeEEEIIISkz1d+KketLLQUhhBDbmIV76KlHZ3UqM2JXP7k1B+E2hO9YfoSkReu+YWLwnTwFD9qxj4ZUQhYNGlMJIYQQQkhKSDNcl1oKQgghKVHiwlgH4KlwwOGQtvqwnfAJtxulcA0WGWIWLx8iQ01wOOpxyTQGbwi+uktof4H3lJDFhDFTyS3jj3/8IzZt2rTUYhBCCCGrHr2x08Kt//oIWvd6IR5S3O2nMCC40FvZhtNHq5CjpCv55fMYaZXLFuHdK+Bs22kcq8rR16mUAWB2pBV7vCIaDzWit68XOPQy2ha7AwghhJiSW9ONcM1SS7ECKHEh3JU821KQ+B6WwhXuvpXiEHJbQmMquWUwRhYhhBCy+OjjosrxSfcOYIvYkEIpsxh53QtRMYpODUBwefHaE2U4VnUMvt/pY6/q65SMsnse3qKLidqLbRDFBqnsIxlvNiGEEEIIIbcEuvkTQgghhKwapnCxD0DlkyjbDAA5qDoqQhQbkNZST6Ne7BFaMfJXDRBFEceqcmzUWYxthwD0XcSUJlfjY4oEskxcfIoQQgghhKxAaEwlhBBCCFktXL+GywDw8BaYmT3tk4Oq/Y3y35I7vyAIGJhKcMmoF3sEKZ/k7n8Z17hAFSGEEEIIWWXQzZ8QQgghZLWweQsKAYjvX8MsihdmUC1ukN3yYzFYe10D2GYVLkATJ1XL7EJkIIQQQgghZJnBmamEEEIIIasG2cV+9Cwm5FmhU/0CBGEAcZNKZcMr3r8mGTynLqJXTZzCgCBAODKCWQDFB06jrRJA5QPYAmDLg0KCOvXXEkIIIYQQsprgzFRCCCGEkFVE8QERPghw7RXgBQAIaHtTipmqN6gWo6ZNQK/Xiz2jXghtbWgEZINqMRpEHyC4sGfUK+dvhE+UZ54++iQEeOHdK8B7yAcxrs5G+ORZqjSoEkIIIYSQ1cSaaDQaNZ48f/78UshCVjmbNm1CVlbWUotBCCGELAmFhYUZK4u6GlmpUB8khBBCyHInmd5ON/9VwlQ/3ekIIYQQQgghhBBCCFlMaExdJRQfECGaLPqwUgn1OOBwaLcmBGZSKyMy1ARHcwCRzEmF0KT850wATWnIZFlyjwNNQ5mTlBBCCCGEEEIIIYRkHhpTVwmrZmbqTABNDgfq4Uc4HI5tg0XwVCylwTEEn6Me48phXi26w92ozctAyT0O1HctvBxCCCGEEEIIIYQQsrikbUydHWmFIAi6bSBumdhbiyLTrZRjQXVeH0GrIEDoX+KOWzZEEHjRg2CzH+HDpfqkEhfCg24EW04htDTCLQIh+BwO1Hc54axealkIIYQQQgghhBBCSDLWpX7JLEaO7IF3VEDbmyKqNstnR1qxxyXgcttpHKtaLc7mi8D1EbT+bAuOHSheakmWHzMXEBx2ov2FUvP0EhfCYe2JCALN5fAMa8+54Q+7YF6CMX983shQE8pbguqxs2MM3TUfwOeoRycA1DnQ2exH+BvTaKoIwnlOmp0a6pFm0/pRH5tlWt2Osa5a5Fo0NzJ0XCrr8EMINAcRtMhHCCGEELJa+OMf/4hNmzYttRiEEEIIIWmTsjF1duQ1eEeBRt8x1ZAKADlVxyBWZVK0lUFK7b4+gta9XoiHfNLx5iocux07zYLIu0EEq504YtN1PtRTDo/Dj3CXYg6VXPHre7bHz2yV0y51jCHcJZk3I0NNKHf4VIOqZEgF2s+FJff9mQCaKsrhyw/DFfYDjnpgMAxXCYCZ6XiBuuoxPhhG+HCsvqNDO9BdY25Oza3phmQbZqxUQgghhNwebNq0CVlZWUstBiGEEEJI2qTo5j+LifMigEZsSzqxchYjRzRhADSu7FP9AgShFSMjA6bpmNKcF1oxct2qjikMqPkGMBGXbF2OJINFiALF/d4om+qWPyDXO4ALOjd/WZ4jAxg4Yqx3CgN7vRABoM8FQRjAlImbv14urcxK2SMY6V8+oRUWnUmfYSEqB3zyIlClh8MGo2kptjdblTOOzup2HNEYNnNrjqC9uhPHhyIAIrhwJghnx5FYHNS8WnSHZeOpHarbsU/NK8kSvPqB3ZYSQgghhBBCCCGEkGVOisbUa/hwFEDlA9iSMJ8SCqARPlGE+GYbhD4XWke0yyOJOIsaiKII3yEAfSclw+HUAARXL4S203KaCO/eAZjZDKf6XeiFFG5AFLfhQ6+oSUxQztQAXH1Ao0+EKJ5GWyXQ65LTNLNHRVGE6GsEjLL3AdtEEaLYAIdZ80cv44HnlLKVeovR8GYbBAA45IMoNsBoj57qF+DqU9oj4nQb4N1rMJiOngV2iRBFHxoB9L6xChadSkSJS7MQlR9ukyyRoSbV0Gq1kFNk+hIw7EG5zjCrdfn/ANPDQFG+lVO+DRz5li79hBBCCCGEEEIIIWTlk/YCVCq62Z/yivLXJ3B2FMChbZLBcHMZnqwExPMTGsOfgCcflWKrbnlQACDiw98DU+/26tKKH2sE0IuLcdbUKVzsA1D5JMo2A0AxatqEWKqNcnpdAoT+a6g6KqrGzdlfnoUIoPEx2dRZ3ABRFPVxYJV2WaHKlIOyJwQL+S3ac+jv1fAJOVV/LxlM39VcrJa9BQ9UAhj9ENcAFB8QIR6twkqOVpv7mBPO4SAuzNjLH+qRjKLlLUXwywZXv8XM1A+uBqUYpqphNrZZueETQgghhBBCCCGEEKIlRWOq3oAHQDU2KjMldfS5ZCPrHnhHDdclRIR3r2ycdfUCAC5fM8y/vH4NlwHg4S0JDIgW5RTXoK3SIOMRaYbntd+JAAQ88Fe2BE1KzpZCc/mNKO0x4/1rq3v2qULeDjirg/D8OGQjcwjjXYB7MIywuohUBNNh89wPbXUCw9Owdrp/CPnVwKVpxi8lhBBCCCGEEEIIIeakaEzNQdV+q5miJiiu8uoW79puTszVXdl0M0MBYPMWFAJJDI1W5eTIs1HlEAQAMOrFayOzulmymWD2mmQiLdySZM6o0h4zEhqMVxO5qO3yw91VD0dzQL8s06QPDkc9OnVxSfXGz1CP1m3fUHLNQbjRifoejaF2JoAmNQZrLnbsdCLYcgqxHBEEmh1oGoqAxlZCCCGEEEIIIYQQkrqbvzyrs9elXRxpFiNHXOhVDmW3fvRdjMUhNS4yZVX8Y40ARJz9pWQiVReriluEqhjbDgEYPYsJeYGnIU3M1ETlzGoXjdpchWM+aU5t4ZYc5Dz6JARoXOtl2fXxXpMw6sXQlNQvugW7EhlMlfYosWMBzI6cRC80IQduC0rhCofhdxjim9ZdQvu5MMJdtXJc0lK4zrUDLeWxeKnwY6zDCXSNI35uaylcYdlQq5RZ4UHRYGyBqdyabox1XEK9JqZqcOeYHAZAMbaWxxt6CSGEEEIIIYQQQshtwbrUL5FmdZY90Yo9ewV4tUmVbTgtx+2sOnoawB64hN5Y2gEbRsHiBog+QHDtgSAX3ug7psYR1WU9cBpt7++Bd68ALxrR1iYAikE1UTlVx3AardjjElQDsNB2Gg3FAFCFY28CrXtdEPrkxEM+iFU5QJxB14LKNjzwrgDBBUizY5UZuZLBtLfPBaFPQNu/PGlojwgfBLjUfpVm1pq1fbVTejiM8OEkmfJq0R2uNZzsRrhG/rNG87dUKlzhMFwJisyNu8Y6TVt36eEwjBEGzM5ZlIzarjCMLSGEEEIIIYQQQgghy4s10Wg0ajx5/vz5pZBlFTCFAcGFXo1RmcTYtGkTsrKylloMQgghZEkoLLT2T0kV6mpkpWJbH5wJoKnCg6DmlFvjUWSHyFATys84MdZVi9yZAJoqgnCe60ZtnkX+yRBQUoqVujSprr1LLUwKhHokD7Pw4dJEueBzHEf+uW7U5oXgc9QDKY6H5cBKvUfW6O9FRts36YOjDvCr62NosPE8x6F5pzg7/heKWv7bihxDqaJ7vtLpN+uS4XPUo9M0zW1+31YcIYQmS1Faggz3XWZY9PfJMmzzrSSZ3p66mz8hhBBCCCGELBKhHgccFUE4z4URDsvbuXZcqlNi2WeeyFATyk9ML0rZZOGEeupxqePIbfmDnmSG0I89CDb7EQ6H0V1z31KLs2qQFoTWb/7mTtSv+NB4krF4XDnMq0V3+DYzKt6ObU4BGlMJIYQQQgghy4NJH+q7nGg3zoTJq0X3oBvBlqMIzCyZdGQpmAngeJcbB2tWx1xOshREMB0GnFsfWmpBbgtKv9EO53AQF/iuJqsYGlMzSjEaRBEiXfwJIYQQQghJmdA7nUDzQfOZMCUuhA2zZEI9mgVLHU1pGVojQ00obwkCwx6UO5oQ+FUATQ4HfJM6yeBTz8l/D0n5lPr1+SG5KWvSk8+qjSDQrG2PT7OoqlK/tE9U5gVtnxhnhxlkMpYR6nHA0RPS92uyMnr0S78muyfGspPNB5ZmFG5P0WXY2Jf6/owMNcHRHEBoqMmivyG5uGrv71AATcb2JLjHkaEmOHoCqhzatIT3yFCvsY+T3yNj25M/FxFdPxjHVaJxuZByM4yx39Q+C8HnKIdnGNJiwlr5pxM9w/bGkK6nTM4leh5CPQ40DQXUZzruHaKwwGfOkiTlZoxFfe+E4HM0ITDki7tHifsl0f2NhTDorJPrmol//lMb34mfIzt9EDG8ry6kWEdCmWcCaHL4EFDSmwOI6Nps839Qyv/3Vi40phJCCCGEEEKWAanMHpN+NNbDH3MvHSyCpyJ1g2puTTfGOpxAdTvGwt2o/esdcFYDne9ofoZOjqMTbmzXxFfsbNGEIhh0o7MuVndkqEmO96i4vvpR1FKe4IelZPAJ7hxT2zPWcQn1hh/DnXXj2B6O1Rk3U3fYg+BWpYwxtMODcsXoMBNAU90ltGvCJ4x1OOPL6KrH+OMxud3DHhxV5J70wVHXqXHt9cPdVS+3K/k9iQw1ob7LrfbL2M4gPF2J7k4I412A+/HUTKmhnnJ4HBo5wn640Yl6rbFo2IPjOBLrq2ptegi+Cg/QMaZejxZ9DF9b97jLg+lnw7JreW7yeyTXW6R1nR50A131ekNbgntkbPtYB+B50drlWvqYgNi4ONcOtJRrPhwkH5emTPpQ3lIU659z7cBizSyf9MGh6zdpXEoGt1K4wmNorwacHWMIa2J5drZM46Cmn7XPsK0xlBB776hgSxD5ct+bxm9d4DNnSdz7QCrX0qBrmwgCL3oQrHZiR55ZPZl+7wBAEJ4z+RgLh+X7m7xfEt/fUrjCfrghhzEwieec+LkxYvM5StIHuvoGAU9LMK4ObZv8zZ26OpLL3AnP1YNSmkUc1kT/g1L/v7eyoTGVEEIIIYQQsmwoyrfhzj1zAcFhN/zaH7klLvibg/D8eKGzq3KxY6cT6BqPzXB6pxMwzI50amN46uqO4MKZINyD2gVYSuEadCPYcsrcCDU5js7qdhzRuLLn1hxBe3Unjmt+iDo79sXKLNkON4KY1hlNtO7wcjvC05IhzST+Xe5jTjiNslS3Y59q1CnF9mYgePUDTT/4NUafUrgUQ2HSexLCqRZ9v0htNOsQmZlpXIIT+SnG7Cs9bDSASO3QY91XkaHjhvsh3b8Ydu+x3gCfrF6lP3VGtZLtcBuLsLxH0gcJLbk13ZaGEaUdurGcV4tuRQab49K05OlL+hNpxV/sRL1xlq7DAYdhcTrTcTno1j3DZujun+H9YW8MJcDuO0oxOlqwsGcukXzTuj40HXs26Kwz3p9yeNAeWxRpsd87Ms6dO2Jj3Ea/LOz+JnlujNh9jhI814ETnSb/cwx1QN/m0sOSgXh80r7MyT5cWf8PSuP/3gpn3VILQAghhBBCCCEKl6YjQEkSg+rMNILoRNBhspZ0KgYPC6Qf+x6MT7pQWiLPjhzU/8g0Gn0f2uoEzkhGselhoHPYYbLSdZxZDIBseBruRLnDE5fm3GldZxzV+Ug+r9e4CrfBsOHItzS8TYcB506LGpLdE9kw6tQZjnKR70gqcNqoIRy0cigk6KsPrgYBx0F9P+TlwwnFQPiBvXtsVoetexRBoFlyTTcpNcE9ykXts2546urh6AIAk/jDOqR2FD1rccdtjktTSWoOwt1Sj3p5PDg7xnTGL3tYrAovrzIuS2k+Lku2w43jmJ4BSk3bH2+kjz3DpWr/JhxDibD7jrK8l8CCn7lElOxDe3U5PBUOeACg2W86AzMZ7kG7BthFeu/I6N6NKfRLnZfHQgAAIABJREFUevc38XNjxPZzZNkH5vU9tNUJhDV1VDsN7xbJIHt8OgKU2JE5+Ycr6/9BNt+JqwgaUwkhhBBCCCHLAMmwJs3EMfvBJhmYpp8NY9/0JVgaWjJBXi0ONntQ/04ILsgzfuzO2JqZxiWkYmSQjXfVmtlc8TlsVp5IrgCa5Bl9zo4xhGtyDUaphRFJdk9mkkVHzRyhHgfquyDL041S5VymKrBxj9NzbNUYnJr9CHeVqudsU+JCOOyCapCtcMCT1KhqzsLGpTzTEYrBqhyOlnSNqmkg36P8NC9f6BhK+jxkgIXVkYvarjBqAdmNXjbAp2lUtWSR3ztm2OmXRX9HaEj+HC0W8kz1rbegqjT+7610aEwlt4w//vGP2LRp01KLQQghhBBCliml32iHs+I4At8wMfxMnoJn2In2F4BcFAG4lGDWWQZkedwNnJhGAJKLqfFHuXEGrTqbMQ+SdHZm2Mo8tNUJdE3DyoycCSLvBhE0/qCXXX3jXG5NSWzszs1Pck/y8lGkuISq6fKP/YzOTlVmEmt/1KdWj9kMRV1f5eWnfI9tobjqao1AspGiKOXCFGOZZFQNvhtBbZwR8yHkVwNBi3Zkalzm1nQjXCMbVc9cQKQmk0Yli3E5M40ginDQ8v1gHIvaGckhnEpjDH1wNfY0JX0ebLHAZ84uigF+0gdH3ThCh0szZgBe7PeO6RVJ+2Wh74jEz01c7gU/R+b1xY23YWMd+tmoqcicMov1TlzGMGYquWVs2rQJWVlZ3Lhx48aN2225EUJskFeLg83B+AVUZgJoqtPEjCvZh/bqoGFhHWmV4XQWu9D+EFUp2Q73sAceiwWQdAuoTPpQ3+VE+zdKAZRin8kCK6EekxWqlfprDsYvbiOvTr7wBWE0aNso92kqlD7uBrqOx7erJ2Tjnkj90lmnXRDlqM6VPY68HXBWG+PC2uPStHa18PLE9RjIrTmoX/wFIfh0fZX6PU5Bck17pQWp7M/hk1fz1o2jCwgOO+F8zMzAIcVs1cc0lMpoGoosaFxKq4b7dOVeOBPUx7bMENK41C6eJN8vQ5xjI9qxqH+GJRKNIemd4cEppc6ZAI5rF1PL0DtqYc9cAiZ9cSvcm8WGzgiL+d4xw2a/JH5HSAZMbZ4YiZ+buNwLfr9L4Tvi/+do27w9ro5QT71m4cTUZE6dxXwnLk84M5UQQgghhBCybCg9HEb4cR8cSiw/Gf0solzUdo0BzeW6OHRpuxCXbIcbUnzHWD3ygiRdZosIAe4OJ4KqjHo36tyaboyhCeXaNiR085RWj4ZDiXVp1uaFkVvTDf9VhxrDUpLZj6KKeotZiyaUuBAeNNwbjVtwsnui9otDnv5V3Y725iDiIwmqUmPHTifK3wnBVaI38XTWmcTmk2VxnWtHU4XkVq6cH+s4jvIWadZd8pil8WW4O9rh1MRWTHaP0zIelLgw1qEv0z04hvYT5fCY9EE8uajt8mPaZBxZufjH3RNo71lu2uMyfrxBuj/KOJv0ySt/Z8ANvsSF8Ll8NFU4YhMLbbiruzvycdzhkI3V2mc4+RgqLXFhrOMSypVxWN0Of4cTwTNqD2TmHbXAZy5RucaxJo1fuc9kV3xnGuEhtCz+e8fMoJqs73OT31/53eNpKYfjTDvGXohvl/VzYyQD7/e4PnCjvcMJjzreSuEKjyG/uRwxkfSz3BPKnMYHKyOp/99b2ayJRqNR48nz588vhSxklaPMTCWEEEJuRwoLCzNWFnU1slJZafqgFEPPaJSRY1jeRrHhlpSZAJoqpnFwEWNPrig5VhGhHh9wmP25XIkM+XDhMdeCjKmErFSS6e108yeEEEIIIYSkzvURtAoCBqaWWpDFIoRxg9svWQLyanGwuRPHM+KKag8zF/XAix4EF8MF+rYlhPFwvo1ZwmRpiODCGSRd3Z2Q2xW6+RNCCCGEEEJSZ3MVjolVSy3FoiCtPh6Es2MMLhoTlpzSw34UOY4i8NjCXI7tYuminslVzm93ZqaBRYifSjLFB5h2bEftUotByDKFbv7klrHS3LoIIYSQTEI3f7LquD6C1r1eFPpENBTbu4T6ICGEEEKWO3TzJ4QQQgi57ZjFyBEBgqBsrRi5LqeMtELIkGv2VL++7MVGW18m27FSMfa/0ieCIKD1x6fRKggQ+pN30K2+j4QQQgghKxkaU28J8g8aG8rsbc2kDw6Hw3zr0UQsGmqCozmQ3gqZq4zIZMh+P8wE0ORoQiADK/URQghZ3kz174F3VEDbmyJE0YdGiPDuHcDtpInMjrTeZobWKQx5RaCyDadFEceeyE6/qOsjaKXeSgghhBBiCmOm3hJyUHVUxOqMKJVp3PDHrZAprZjatHUM3TWMqqMQGWpC+Rknxrpsxm7Kq0V3mFFvCCFk9TOLa+8DQCG2bAaAYjSIIhqU5KpjWKVhLlVmR1qxxyui0bfUkiwuxQdEiAcsElOIZ6orR3bdFw+t8s4jhBBCCEkTzky9JXBm6sIoxb4OJ4JnLnA2KiGEEGKbXriE+Nmoevf4KQwIAoQjIxjpj4UF0M7o1LqOC/0Dav5ZsyqnBkxDC5hm7TcPQxAXoiBV/WlqAHu8otQDLgFCfyBeZlnO1pFZdUV6tW2CAEHXb/bl0bdJ048LrMOqr2Lu+VMYEFzoBYBRL/YIrRj5j5E4N39b5ez1QgSAPhcE4X/hh0cMsl6PL5cQQggh5HYiZWOqTqHWbK0jpip1xlmc+FhTiX8YkOWNSXiApiGt2TUEny7dBzVogOL6PuSLSw/1mIcZACIINBtDEWjKNGJah9bdPgSfLj1Wlk4GzTXKCrMY9qBcW9ZMAE0W4RGMbv6hHildV4cufIKxnQwRQAghK4McVO1vlP/uhcuO4Wv0LLBLCQkA9L4h60TXR/CaVwQO+SCKIk4/eFky2JkxNQDB1Quh7TREUYTvkHVogal+Aa4+JQzBabRVKnlnMXJkD7yjjfCJIsQ32yD0uVLTM4sbcLpNAAA0+kSIB2pR0yYAo2cxoRgP3+0FIODJR3Ni1/UB20SlD3rhOjKC2VTkmRqAq0+uUzyNtkqg12Vofxp1WPeVrtFokO+d5OZ/DFX32+1zQzlvtkEA5Hv+3/Ct/Y0AenFRzjj7y7MQATQ+ZnPFKUIIIYSQVUbaM1MlRVGUlWVA9O5ZkIFzqp9B74kFMwEcbQnCuXMH4pz8ZwJoqruE9nNhhMPSNtbhRLDlqGz4iyDQXI9LHWNqur+5E/U642gQnjP5GAuHEQ6Pob26E/UOB8Yfl8s81w5nVz18k1LuUE85PA6/Wl447IcbxjKNBOFpAfyqjICnQmuA1crgQqlsyKwPt8vnlGskg2ZuTTfGOpxAdTvGwt2ozYNkVK4Iwqn2xRjaw/UGQ7CBrvpYO8N+uIc9OCoboo3tHOsAPC8yVi0hhKwIihsko5xy3OdK/NG48kmUbQaALXigEsDoh7iGeMNZTtXfo9GiCKOBsvgxvRFOkxMX+7R1SuGQRLEBxdcncHYUwKFtKAaAzWV4shIQz08s6IN3zqNPQoCIs7+cNalfRqkTxdh2CJLx9T9Sl0eaDXst1iZtYsp1JOirlHpgAeUUb5MM7O9OAZjFxHkRQCO2FUMOJSCigXZVQgghhNxGZCRmavGuNgh9XvS+O4WG4tS1KfVL+a7keXNugzhftzedqHd0xp11dljESzWJA5r7mBNOBOWjDzA9DGBnLL30cBhhQzHuZ2tlQ20udux0AnBiX4lSRz6KkOj6UmxvTtYuwD0YiwWbW3ME7WfKcXxoH7pr5DZqjcUzFxAcdqL9XK16Trkm+G4EtSZ9EXqnE86OMcmwKrel9oV2BCuOI/CNbphGS61uj7VTbkfn1Q8AANNhAI5Y1tyaboRrkreTEELIMkGJmSnPGMWoF0P7q5DKq/za70QAAh74K7tXiPDuFeDVnLl8bRYo1swAvX4NlwHg4S3IgQV9Lgh92hMf4tpRuzKYIBsovecnMLvlQ/QCEJ4os6x/y4MCABEfRp5MKI/u+uIatFX2wjuqyV/ZhtNHq0zrsVXHc0jeV3aw0+eWSIbf3r6LmNr1gN7wSwghhBByG5L5mKlxMaFk9yHlvCEW1OxIK1x9gKJ8a12a1LyamRSpxvnSM6WJU5XIrd8Qt0oXx8tQhlkMqaTlE2vc6uxN7ZZ84SmNK3+FRzWlqvFWW8oTuOM7kZ8XdzIpkaEm1QW+vitZbmMduch3AEHZcAkARfmaNs5MI4iiuGt27HTqrtFIg+kwNO006wsTHPnxs33lumqfdQNd9XTxJ4SQlY7G7T1VVIPf7+1eobiRx7ZjVQbz3eYtKASA969Z60lyWIHYlupMTCM5KHtCcvV/7Q0TF38DqhFZ+SdpSx5ltqcYmxU86sVrFiEKbNVhp6/ssMBylFnGJ19f7i7+kj6oeBNFhpoMIYwWgElYKesQUwuTe8mYDFmHrULq/amElEqXyGRo+XlEGcJmZRb9OFho/8VjEqosU8/HggkhpIz/Re3jW0Pm710KWIR2S44cem4F9zsht5KMGFOnfuaNV6zUmFANKNasCioqcaJkdzNUHYPvEKAo38eqcgwxnXxoHPViT6JYX1ZxvvRSSoH5K9twWlFyLcqd6tfErRJ9aIQI7+tSmVP9LvRCE9MKvXD1S25PI697ISrl+xoTKtAkQ6gxQjWu/Ofa4dRkya3p1rvjJ4txmgQlxmh5S5Fq+PXbmJmaCT64amUalWbgOjXhDGJbt2a2agqUuGLhAqqD8FTQqEoIISsC5eOu5qNu6jNMJST3eMXFG5gdOWkZM1UyuCmu9NpFjeJyxlzcdQsgDWBKnkGKvov6j/EpLnaUs6XQoi0ixFHEu/gDQN9JWVaNS/x/tS+P7oP/5ioc80kBEQq3aOOyplpHgr5KqUdSKEcxvOoul1z9xVGNi/9tiflHf3sf/pc5kz446sYTZsmt6Ua4q9biQ3xmiQw1ofzE9C2o6XYhBJ+jHMGd+t8KfocH5Qv4XZQ52eqRePSRxacUrsEihnUjxCZpu/n3ugSdMt3ok+MlKQqzxv1HircloG2XcqYYNW0Cer1nMfFcFbboSjbGdNK4Fh0oNuSVMYvzZXS9mrqod+lSXN/kOrUUHxAhHpBmp7pGTVsPl9CLRp+oKUP+uTLqxZ7Rs2h78xhEsUG9ouqoCEYnyDyRd4MIVrdjTKvYzUwjCOgMqhKlcIXDcCn/sCddKE3ZyBjCeBfgHgzDpbrHR+Jc4uMJYnoGgFqfdI1z50MATGaa5uXDKV9TanqNkYeQX6246Gdaxc1FbVcYtYgg0GwdZoAQQsgyYXMVjr0JtO71Ys+o4nAvoO3NY6jajNRmJm6uwvfazkL0Si7oQtv/z979hLaRpXvj/87QTJjbw8Xjqwx22ulFX0iBxaXtJIukTZcTUIQZ7BczIWP3xqHb3lS4xmKsTYORsTD0Ru5XxpfURk4Tb0buJv2a1+ZiFIGTCu5kkY7cC5m3sphFtzs2N7r+hbuZxb3Qv0X90alSlVSS/8b+fsDQrj/nnDqSZ5RH53lOAiPQvAOq4SFoKiAr/ZDNbkdUo8+KS29rUCFDsUsCyEg8MFZ7hqeWAPRDkc1eriewdLvO6F34EkaQMT6vWqn2Vi3SNZ8U/+FrwL/JkNcAYASqmZ4fCTie5sg0ljCBfuEzspxYcn0+rr+PanNVT0A1eDvWZ28F8rz1vrGOgSn+RG+jjWdII4as6zN8550sYrPmv4s6fO6l06PjFlLowjcbfcK/dYnIS8PBVDt4GoC1GqKSkTbmGSBdE/8BAAAvsf3a59oAdrdfAnCtDvBhrIyFkW41BWNFq3nOqg+roRxQlhNLmI4YO+cm1zIQ64XVM0/UoNUt2CHEnWWMDqRRDqUagdNNseaq9WGiA0CDqyw3t0pAh9Fe4W4X4quoEUwF0gMqruhG3dTSyhTiqzFkZ0PwDqZeRbQnjvgXy7hqBoqNe6JIfW5Wd21rF57dSMuPDwxC/agc6C2tjJoraMv1WoMxAqdxKQv9jnmnWcc1+jkDqUREx57jS2MnZ/35MIY0DeWvf41U9ciU3/VFLCRh195svq1Buy00Hh5yfJlcTdh9b5UxeN5TtY6++7lEfin+rT79+o+n4sqatf0b68NvrpzHXc/s8R4I1k611+ZwUvyNzy/lbBxn7XzzM8qqdTYW+HNO9Xb3R2llFF0Po8jeyGHQ7ss9RuPzqbVLQHQm5ajPD8BcKVreR0Acq9VHSoojbpWaGstCvwNHu84v/43sqnJpqihSj4VNTM2+BqW0cV/LMka7txCd2UR8PAf0pPB/b+Twvx5Gy4sYdpYx6i4pNSZ8dgwyVx6vR/l4Dl1SDqnHc7j6fBRdP0aR0uOIrwrz4R6D2P/OMka7c4jOtCM+bs8KsroCiHPhGLP7/VW+J+hn6eP7/t10LdQArMUmjj72871l8X0/C38LAxLSY1nofzIveq5Csl83jzYFhbsSBpGy3x92H5efCu8PoQ3rvSG2WXHM+Xda6++46mtZ7X3qyf0+cT1/3e0FbNf8N6X01TJuHdIqdKK31f7XTPVg1NvyUiXdzEqXt3+8VzYEZaV7vdyutR7DWhnrswrC3LVU0zS7/piW/NJI2QoP2eM1ShcAGaXeNCyqR6h3DtkxK3VfMnezzyKGHHLPSwA6oTxOAWIt0YFNpB7XG1y0VLY3iCzWZ6LA7LMqKTJRpGZgj7N2gNNYDWqk3lj3wPl/dh1X7LIF6gaMtPzFGNID5TpIjQVSrf6Nb6rF+qvtiw2WDCAiordTccFZB97cyEh+r9Gvt4/Q6xfG5kleKf5Ug/n5+BBS/I2AEZB6rNvlmzDeZdaRrExVXp/ZxGCQNOUN1VGiyWh36mDKF63GcQ+TQrmkNAbtmoUlLI8NGkEj83z0YRzi9qullVHz86pzDhx1WVfjyL0vlLiaHYQkPcMVe16iSA9Y82LUyhxEtpzivdiOeLdZvsn8DGmVMSgHydKI//iZcf1sH/7J8ZAFqN1xtC8K5Q4WjXr7gWq/Vnk9Qr1zxmfrnhTWxXJVs3FsfSqUVdhQzc+n1hjMz66O+pA5xB+2YV18LSQJzz7SHXNXrlNqLibQhTYhvn7VHdv3b8ctoWxXjRqajby39JQ5xzrWZ1B+b6HW+7kTip5FDEaAthwQFF83q80a8yS8P/TFmLGXxBfApF2WLVdHCrvxd7oplFDLjonvAzOQOpZ1nveqQbuhmv9Gtd5T60jp7vepu+8uxOGeU/P5626vzP3+Xp9B5Zy0tCG6msNTlnYjqqrhlan1MGpUJZH89yIit41kopWkBlxPGB9mHcFWK43IKAEQOSvUOp3aQ6K8le715AV2IxE0WzVUh1Xvb97NUgFw1AbbRX7Sqqc6hHBkGurPMpT5C2h1jTN8ewmJv/UjiXMNr6Y9dToUCF+M+nLvKt95R4d+x3WR2FBLH+Z0z73sPc9V7lrv/MbWu70AO91fVqDriscJV/viGa9nc93naLHDrw9UjLvzjg53r85jHu0TEdHpEh6COpyBMi9kDA2r0NybSh1zu/kJ9CeNTKnEv0Ya2NH+FCsuQFaMT8Mj6l43AqulhKcPc4jOrJcDaOLnl41nSPeksC6sxgv1TiL1sAv3Vm5hrspnsdLWJiCu/6z2+dBXGoNS2uO4e0VaDJ/ZYzQ2EMXDLZTQidDGN2Z2Uqd9vu/zFHKrcWuknnMwOZND18OnKPX2VfbRchXRHgA3btljCLUJz7rzFDlHnwA6FGTHJAx+W0BflRVtsY/8znl8fu24gphvS06NvR4xXBFXQ36XBsayQvC3E8piDOmBZyjc6bTnIvaptcLOfC0QxS3rnpY2x6rgys/HnbgSeG+E4/z+Nct2WStEZwchmStJK1e41vveiiL1uLyK0XomozQYar6f/VY/ll83IHQ56lECzaUnVX5drfei0Mb596Oo+MePL2M/CtwoH3G8N6xMR+FvRzwv5h0Wvks7n9/6m+++h+U/eSxU8ZzT8r81a7bn+0yVpekq/90L82/CXaKOiNwOJZhart1l1NsCYHwQt1Z+WsHWmzKSwyq0ippO5ZpSjW/nFMaQUcir/I8Be/Vp0XmdOoKMkjHrVo1gZFhDZv4VtqcilTWtxBpf7vYxAlXjB3YiIiJ6e1VL+X5bVE2/r1ISYd8cRh8HpY7SDXtnBDDaP/UOr5S2NoHVNLqkeMW56A2PGwSh3s8QGx+0g6GNpfgHTMnuaYNXhXvAfIaeqPO8I6DnPQehy1FEx43yTudr9FFhZws5pJHzCgRXDRRG0VYzmFKZFh8koNrQ6+F4Zp+9BDquIIZ7RtAt8DNUcqfUV58ny3F//8K18MJ87ca7oLYJK5Lrfm+14zPHHBtB6/iPRkix1vvZ+ykaeN2ktn1MS+/ErZkousa7II0D7r99z79jT8b7NDdrtSOKeuzxAZ853UN7tnJZOCOQ7lc6wdiLIyeUtSOiSnUHU2vWgvL7sFjtQ2QdNZ3qrfPl5Fc/y3W84kPjEIbssVTro1p9LiIiIiIiatRPPxq1O9d9a/l51KG3lTNujECZEYw4iLqpB8LcYPWzBm41VjXWV/ezNqFe5FgW+mynfSyYA3o9drawCaCtwdvLtT9jyOpz6LSONT4i2/F7/4bQN7sOjHUh/l0BSsf+vTt++rHGu3UP7+fDUl61abyvjUC28XcUONhsBtj3739nArRXLT3fDqabgfRuCfEa9WiJyNuh1EwlOtVa+jCn8/+giIiIiKozVkRtbnlXNTz/ftTefHMvQr1zdu3H3MOnAWso7g9xA1GbGQA0eM+BsRKujhWD7j7NzYf2jZXmrAt1Lh3PUcf4Gno9QmiTgNyPrneDuaqvkdWoQAHPZs3anXbg2UyNDuS4vn+NuqbeNTWNeWxYSxuiFe8tc9Xw++dxEO/nA2MGeCsZgWyrfu6zDZ+/Y0/mKk/3+7QazzndQ3uejLIPRh1ha68Ri7nCuu0t+JKJ6AgxmEpERERERMeAkR6cG/9G2GjGCASNrpSMVGf3ZkA7yxi1NuOsorQyCsmx0Y9Z3/LG1cPdsbrjVuWGVF8Iu3LbcyBsLrSzjKnxPYzV2nzIsdFMAaoklTe1qhrA8SNeb2xI5R2MqlTr9QgSrOr8yL3hVQHqQBoYu7KnFbhi4K9w11nCoLrj+v410rsxO+jcxMzs/95sFKk/NThjLVcRdb23SitTiK9GEb0cQrD3c/Ug9IEw64LGvxX+Dr8Sy2C4/j4A+wuEKx2wNwK+5zivul4jQJx78TWufD3FsZlz+q37fTKK5Z0G2itfVRlUN+uzGq+VdWwLmw2WxyA6TQ6nZioREREREVENod45rGMUXVJ5uVw5pTUERc8CUnnzHMBYSah0VLblbjf7o+TcQGosC91Kld1QIQ2gRiq83wZUZltVNnISRmKnVluPGJ1JIbZarqMZ6p2D3qZC6pZgHd2vdG6xXqejzZariPbEEe+WkJtZx9zlGk12KFifGUWXMMbY4jpSXwVLGa/5enRcQQxGanVsUcctnzHoj9sw2i2V99QJ/Dp46YTyOIXRbqEe5VgW6zP30DXu3NSq2nMdy/dvhwJdvwJVctfaLJczaIyxwrHtriS8t5xp47Xfz2aN1fEuSA9TWP+84cHUwXitN7uF+qGLKURXc47zjveC+Vyd1nk965rPcikNR1CzQ4G+CEgDEsqvXrWyG5X/G+GY05Z62xPbzWLL4/0nZlCWnueQ64liksFUoqp+9csvv/ziPvjkyZOjGAudcE1NTThz5sxRD4OIiOhIXLhwYd/a4mc1elsd58+DhbsqcGc/64oSHR6+f2nvjFqqW5/WDvATnXS1PrczzZ+IiIiIiE65Ap7px6yGI1FgfP/SPtj4BnGkcIuBVKKamOZPRERERESn284WcNj1U4n2C9+/tGcFqAObSD1W+D4iCoBp/nSompqajnoIRERER+Jf/uVf9q0tflajt9VxTvMnIiIiAmqn+XNlKh0afngmIiIiIiIiIqK3GWumEhEREREREREREQXAYCoRERERERERERFRAAymEhEREREREREREQXAYCoRERERERERERFRANyAioiIiIiIDsWbN2/Q1NR01MMgIiIiahiDqUREREREdCiamppw5syZox4GERERUcOY5k9EREREREREREQUAIOpJ11xAbI8gfzrox5IADvLGJVGsbxTz00lFDZKBzWiQAp3JUh3C8YvdT6D4979GxFUSYIk/ux7Hw3aKGBvIzGeTd3Yp/EQEREREREREdWBwdSTLjwETZtG5OxRD+QglLA81oV7W0c9jmNkZxmj0iCwqEPXrZ91pPRBSGPLONKw84YKaeDZUY6AiIiIiIiIiGhPGEw96d6mlam0Z6XnOeR6UrjVIR4Noe/zFKKrOTyta9UvERERERERERGJ6g6m7uYnIMtyxc9EfnefhlTEgixDnsxjF0DxvnwIwUBnn8HPHcV4ThErZX5jGaNCyrqR4m2sSo2vArnxLnvVZeGuhNGVZTvNvXytK+1dUp3p5htq1bT4wl3x3qBp/F4p6TXS1N3jkCSMrpSEe0exvKJ6P4NldQs/uY+19GFOn0Nfi3BsxzmvlaUA3OUCyv15z3Pl+O2xb6iQBtIA0hh0PL/7tXE/k3MMoytchkxERERERERER6fhlakjqgZNM37UYUBL9mOhuJ9Da8DrPCbuH/UgGlXEwmkPnnrKIf4VMGmmrK/PRJEeUFFACH2z60j1ANGZdeizfQhZd4zn0PbYuF7pAAp3uxCXskLaexYxpDFoBQ/NQF9sUTg/O2gGAo1g3yCE+xfbEe+ut7ZrADvLGB3YROpxOUV/fSaK3PiU0FcO8YdtWNd16LqCTlcTod7PjGerCMS6bKiQunOIPnaVArADqgWo0iDSY+Xnzo6lMSiUCnDPc2llFNIAkBXmuX28yxhDhwJ9MQYghqx5vdFHF3I31oXn3cSgHVAtYXlMHMM6og9tYgffAAAgAElEQVTjSO/jlBMRERERERER1WNf0vzDf0xABpB5vv+BzPBtLVjNz9d5TNxMQtv3ERyGIhZkBRn79zCGNA3aVATNRziq4yL2aTlQGrocRRSb2KoWyOyJ4qqwArPzjg79jhh27MSVsfJvhe/SwFjWDPAZ5xVdx1xvCNh5itxqDFnx/g4F2bEc4t/u86ZOHqtHjed1it64as9HJWPs6zPGXbnxLteKXkPhuzSiM5NCX2YpgNl7RuB24xnScD535x3dEbR2znMJTx/mEFsUA7ydUBZjyI1/472CduMZ0j0pTPaWnybUO4lUTxr3VkrAxjeIO+beHKPvsxMRERERERERHaz9r5n6Oo8JWYZ8f8FIVZcXUATM2p1WWQBn2r5YOmAiv+1oriLN32rf+rlfBFDEghVInVfKfbquFVfOVuuzFnepA+eKXDNF3/oRV5s65sAa+y7yk2YgdS2JfnkC+deVaf7V+rTnKL/gavskiKKtpfZVDlKbZ7CxtDJqBxYHZ+2j2NKB6Pvnvdva2UJOWOlZef9BEFLbu+PIuc62t/mHUi2h3jnXSlwgPWCtpjWeWQy0uvsqbW0CPW3wmRWDY55/wtYqkB5wlVMY8F9HWtraBFbj6HLMrVG6wXcMLW1or/n0REREp4D5OffIM8MOgqtskG95JC9WmSjXF+/Okk0+pZKIiIiIAnhnPxop/rsRyBy5HAZgBibngUuahiHACCIqGciJJUxHmlG8L0O5uYBWbQjh13l8mdSAYRXa7TB28xPo9+vIWn1qXmu0q2DivSVMP0jgpXjOde1ufgL9ygTOPZhGBHX06bKbn0B/UsOIqmEobD2bjJeJJUxHto0VptcTWJqKoNkcQ/8HrdD+uI0JYQ6MdhQsXNYwNKXi1ZpwH4pYCNyntXZVwyP8BZo2ZMzv/F+R/+M0InW+lidR4a4V/Iwhq8+h0zoW4N7S1qZ5X2VK/b7bWcaoGdCMzqxD7w2Zx9zh1Hp1QtGzgDSI3PMS+nqNwGd0Zt1YfevBpzhAlbFvYRNAbFEXVvhW99OPOaAnhXVxtas4hpV6B0FERHSKnI1gWjuBn/R2ljFqll9SOmCWYxpF22NX7XdPJSx/EUcOzsye0sooBmfLn+cKdyUMjrX5fgYhIiIiqqbhlakZpbxKUplHOdBnGb4E69fi8wwAGdc+NAJ/4csjADL4vgjs/vBICMQCzZFPMOLTp/tahIegaZoQUPS/tvnDa5Ch4dEPu3X16WoVL55owPUEeq2HC/cicR3QnrzAbvF7ZADIH1800vPPRjCtaUZw1/zv6dYVyLKM/mTQggQ1+rSvK89v63syAA2v/sOaowBlEk6sAp7NwqyHagVEjZWZhhDaJCD3Y8WWTcbZtnagVlmBephBRy+l5znkelJYt0oMmNcHD6UaK1q966SeR1uP87/9nhkwn9trIys/5orRza3gYdjz70er9uE5hirzR0REdKqc0JWphW/jyInll+oor1RamUIc7hJJBXwz7ixF1Hkni9hqHN/Us+KViIiIyLQvG1BpmiuQ6klD8qYZgFWM6qAvt3ex/bMGQMa5P9Tus55rLXbQ1ywDoP283VA75gjwas3n1Nor6NsvAQAXWj0qnVolB5SXSDzQsJSQ96XP+goUnDTVA6EiMchXuFtOJQeAzo9igFUr1L7G3N2+4xZSPTnEv1gWVmtWC1qKjKBl+qvyvYVvK9P2HcTgobkyI7hO3JqJIjfeVZkOZ9Yf/aw3BCCEvk9jwOyg4zqjDIKZ9tZxBTGYtUvtNtQqaXFW31OV82htWtXS5qh3a2+WdVdocWcZo1Y6X8ctpHrE89ZqEyIi8uUuKeT4McsgvYWsckd24LDoLG1UURYqaDt0zHiXX+r8KAboW9UzZzZUdI23I/u5q7r6zhY2K8pGGfXz6/kSmIiIiMiyL2n+wchIPKhcIbmbF1ZR1lg96VhxGXClZcWK2Tr7dI0A5677nLp+DlIrAGh4ub0LhJ0BVXs1rGrMwa5XGw302Qqc6oBq50cxYGAQkpm65XEFlMcpjHZ3QRo3D41lsT5zD13jz1C404nODgX6ogqpW0Ic5WusTav6ZteBsS50SfbZqinyZSH0zWaxJQ3a98YWs4jNehcYCPXOIfujhEHJCqBGkXqcRXu3lZ7vcdOGCmkAdtpaqHcO+uVljHZLkMTrelJY14VUtg4F+iIgDUgoh2vFcgZGaQBVEuZNOO8VUA31zmEdo+gS51FM42+5imhPHPFuCbmZdcz1lssPSEIN2nKpgJA995L5MNGZFGKrcRARkY/wEDRtCADMsj/en7/ebrvIf50BMAJVG0IYQPF+o20VsTC5jV5u+nlMGKWI2j/1+IxlfuHs/emrANUsDdCJZeepnS3k0I7PPEoEGF/IM9GfiIiI6nMowdTw5RFgPoNHP+wiYtVMtT7cm+n3medFDIXD2M3/VdjV3qnZda1VFxWJJUxHWnEBgOZzraPuaB19ukaAix/LQDKJlT9HzPqlK0iuAXLiIprDrRhBBpknL7AbMWufygoywyqW3jNaMAKt21hxpPlXCZjW6hMnKJja0oc5vc//d69jHQp0MYh6R4cOVL8HADAHXQxOuttxCKFvVoe7BUun2GdFX51QdB2OlnXd+17r9zuuDuzrQ1B019N1KMiOqc5jns/roeoz+4zdZ9yWUK9rXp1nPebRvw/fe3oDPBsREZ0ozZFpVCsPGr6tQbtdbztFu9a97/910VuhcHcQ6bEs9A4A+1WaiYiIiMhHw2n+dQkPQVNHoCX7hRqr5iqJsxFMqyPAvAJZlvElrvnXLz0bwfSDBGTzWtncYMqomRrGpWGY7UwgD+e1/UkNcmLJCEbW06dLc2QaSwm5XD5AyWBE1ewxDGkqRtaS6JdlyNZmVLfDaI78xahzmuyHLCt4OTwCGWZw1QqYmve5U8+q90mnWwHPdNeO90RERH7cZQDu23nzWJBlyJN5M3tmF/lJ/9IAVrq89TOR33W1s4CFSeu8M/3eSMk3f+z+vNu1PhOV0/N3kZ/sR3INADJQzGvcaf7B2lGML9PXkuiXJ7D0f9wlAKrPgeM5XJ/f/Pqvdc56joX7E+V5tUpFeVxPADZUY3OpOwe+VSgRERERgAZWptZaGeC7s6iQdlbzXCQC67dm90qDKjuXVq5KqLLLaZU+XRdiSNMc56rPQeX15l2ITGmITAmHbpevqmizjj7dz13zNaKTY2cLuHGVCWpERFTb6zwmlAzkxBKmI81m1o6ChctBat872/nS/JJ62so4Sn6J/IdCOYG1lzj3QIM2ZQY/by6gVRsCHKUHjJWh/R+0QrvtyiIKm2UKlAmcezCNi3bnzYhMLQHoR3JNSPN/Xh5e8HZUvFozv/ieMjKK/ut/ixlQL/BoDY5NVW3FBWEDVvMZlQVc0obQWq3/H5znUFyArMh4ac6lQcPL95agadN2FpY2rJbnyGzrZH7UMzfJ3CoBHa5PNz3eXx4XvjMKFpXLJBlyVlmhy22IIoetHaDTlervrs1KREREFMQh1kwlon3X0geFuYlERBSE9YV0cQGyHKzAUTVash/ykwSWpjxS7K9fw8WzQDn7JoPvi5eAefGckVWUmf8exdut2H6iARjBJTNyKX5ZHLzW/C5eNNyOOJ4wWq1695f9I80ZRUZmWIVmf2G+i7xv/+a56wn0Wk2Ge5G4nkHSLBFlkHHtQyOwuusag1HGKmmWzqqyaOCtJW4uWg6mFr5LA1LW88vjihJJO8sY7c4h+ngOfS0A0IZ2M5gKO5hawLNZoH2RX0cTERFR/Q4nzZ+IiIiIjpaVLq68ROKBhqWE3Fg7ZyP4ZNj8b7u0kXcqPAA0t14AALzc/i/XPUbpJ+Altl9v49Ua7M01G7e3dsKXRwBk8H2xMijrvLAXCavevVV+ajKP3ar9m+e8rL2qWgPfLvV0MwkNgPbziamYX6HzTylEZwehbpgHNlQMzkaR+lOjafyduDUTRXpAtTfQLNwdRLonhVsd+zBgIiIiOnW4MpWIiIjoFLBXOZp164Ov9qxUXm1pbuKEDJT7lzw3gdrdfgkAuND6j8YBO63ecRXy12EEFaeAxqvCm5t6NtpO+JKxmejXX0L2S/EH4CjfZKXiryXx5cdf4Zpv/1U2HDWDr34hUrsswGnQ0oe5xS1IAxKsxP3Yom6uMoXHytPaQr1zyP4oCaUAYsjqfSyTRERERA3hylQiIiKiU8TY/LKIlaQmHBWCkEC5XqgXcxMrY9OpMIYeJCADkN8T1mKuJbFSBJxp9+ZmoWuP8OI14Nz0yiwHgAy+d2wW5dy8qrZ62vEKblpj1Kqm+Jc3skJ5Y1MAF1r/uUr/5Q1HV+x9v1aQXAPkjy96Bn6NtH4g87xY2e9J1qFA13X7RxFXkLb0YU6vEkj1Od95RxfaVMDtqoiIiKhRXJlKREREdAo0R/6CxBMNyWQ/5CQgD49Ans8YwdVwMyJ/HkFyLQNFzgDDCSSuA5pXQDU8BE0FZMVoBwAwrEKLNMNeW3k9gXPPZcgKAMhIPDA2isJtDSpkKDdlGLeOQLVWqUamsYQJ9CsyrIquI6pW9yra5sDtmMHNZBL9a0l79Wf48ggwn4Fvir9PH3JiyVg9Gvbv3+8ZfVedno1g+gEwcVOBPO/qh4iIiIiOxK9++eWXX9wHnzx5chRjoROuqakJZ86cOephEBERHYkLFy7sW1vH97Oamfbvmcr/liguQFYyRoD4NqOW+42fB4mIiOi4q/W5nStT6dC8efMGTU1NRz0MIiIiIl/F58aaUb8UfyIiIiI63RhMpUPDlQhERER0fFmbaQEYVplKT0RERESeGEwlIiIion0SxpCmYeioh9GQt3nsRERERHRYfn3UAyAiIiIiIiIiIiJ6GzCYSkRERERERERERBQAg6lEREREREREREREAbBmKhERERERHYo3b96gqanpqIdBRERE1DAGU4mIiIiI6FA0NTXhzJkzRz0MIiIiooYxzZ+OqQJUaRTLO0c9DiIiIiIiIiIiIgODqdSY13lMyDIWigfReAGqNIj0QTRNRERERERERETUIKb5U2PORjCtRfa/3Q0V0kAa6Ikiurr/zRMRERERERERETWKK1OpMQeyMrWE5a/SiC3q0D+P7mfDREREREREREREe1Z3MHU3PwFZliHfF6NoRSzIMuTJPHb3cXAAULwvQ5YnkH+9zw0HYD+rLGMiv99PdkjMoKfz9TquQuib1aF0HPU4iIiIiIiIiIiIKjW+MnX+r0cS4Dw8RawkNeB6AkuahulIc8UVu/mJA6oZepSKWDiAoDgREREREREREdHbbg9p/hqS/3Z6g267+Qn0J7WjHkZtZyOY1jRot8MBLi5iQVaQOfBBERERERFVKq2MQhpbRulIei9AlSSoG0fS+VujcFeCdLdg/LKzjFFpFMs7RzsmTx5jK9yVIEkSJEnFovgcNRTc124UEOxOIiI6ifZWM3UtiRWPlZlWery9arO4IKTKl0sC5O/LQhp9EfnJ8u8VKz5/WLDPOVLWrTR29312evuC0Z+8AK9FpEVhDOVyAkJQcS2JfneZgeKCHUjNKOZ4PPvbdTyTWK7AnqO8MH5hRahYYqCyzIA5hxX3WXO7gIVJs7//50zzt8omLNx3lzDYRX7S45ld83sQpRyIiIiI6JAcSN37k6ITis6SUydGSx/m9Dn0tZi/7yzj3mwUqcc6dF3BwB0d+p3OQE11itduqJAGnh3MmImI6K3QcDB1JJGADCDzdYPBtbVHwB81aJqKEQBaUsGrP5d/zyhi8FPDI/RC0zQsJWRgXjE+AL7OY+JmEtqwap/LKK7A5zxwSdOgaUNwr80s3pehzMtIPNDM+4HkTRkLxTCGzHEYaf7TiJwVbgwPGeMAMKK6Vn0K/eF+P5JrI1A167kqV/Nmfm7FtPVcVnD6dR5fJjXIiSVomgZ1GNCSXzoDvWb5Ae1BAvJaEv1igHntJc79qwZNm0bkn7wmX8PL9/5iz5mW7MdCsRmRKfcz7yL/b0loVl/qCLCWxJdva/1YIiIiotPOzFoaCpK0RHSS7Gwhh3a0tdS+lIiIqJrGV6a2RvAXMwD4ZX67gQYuoPUsALTi3HUAGMGlsPi7SMa1D42apc0fXjOCuM+L2P3hETQAI5fDwjkNj34Qgn3DlyqCqIYivp8HMPyJHShtjnxiBHKf7+GreqG/8G0NmtaL7UkZsk/6vD321gsAgJfb5bFryX7Ik3m03jYDo2cBFL9HBoD88UU0A95p/Nev4aIY/K3gPZ++rJWqfxiCZtWP5QdxIiIiorfPSViZurOMUclK1xbTr0tYHpMwulJyXeuR6u2Z3u1M8zeuW8byWLmv0ZWSq3+hbauvFbU8NklFAWJ6ubNvr7GIx6yyB4WV0Yo2K+7xGpOfDXGMPmOopz1fxpx6jt1zvir7qjWWkmNuhNdfeO1LK6OQBtIA0hg0r6mce/+x2tduqI521A3jPVftNSQiopNnT2n+VvBRSybrr7N5/RxaG+n0bCsuAMDftvH/mYcyipmCfjMJDYD2c4Dg7uttvPQ797ftfUllN1Lq+5H8QLVX3AZyNoJPhs3/XkuiXyhTsLttjPpCa+WGWA0R5rPymZsR+bM1ag3Jmz4lGIiIiOiYsEoMeZc3Oi2cJafKJaaOQ26NXzkso5TVcuCxVrRzWmyokLrjaF/Uoes6dD2L2OygGbgK4eqNKHIPn9o1V0vPc8ghhy07AFfAs1kg9lGw9G7MxrH1qdnXYgy58S5IXwCTunEsO5ZD/AuxxmsO8YdtWNd16Po6Uj1G0O3ZR2Ybj1OIzg7WV5d1NY57mDSf12zTFUAeRNY8r0NfbEe8u0oA1AwIxlxzaAQhG2jPVwnLY4PYnFm328qOiWM352scyJrn12eAeLcVxKw9ltLKKLrGYabuG/OL8a6K+Q31zkFfjAGIIavrmOsNucZagCoNIj2WdY7VXb+3Q3G0o3QY7znMPhMC3HW+x4iI6K2zt5qpCKPXTHc/NFYQ9INW/N48NKIaafr2T5DNlqwgopcPWrH3UKW58vV6AkuBNn9yMla1akIQNgPlftFzBeueCPPp+czhIXteVTPA6yzBcEDcNY6IiIiITpxd5L/OADDLQt3+YA9tFbFwTALGB6nwXRoYywp1TTuhLMbsYFbochTR1Rye7gBACU8fAqmZGNLfmaGujWdII4YrQeui9qRwy7q24wpiAGKf9sEKxZ1/P1pxS/m8GWgT22hpQ3t9jwwghs/s4J/Zpr5lBPl2niK3GkNWrP3ZoRhB3m+9V0Z6zqEVYGygPX8/YWvVeaTTo05pbFGBdSTUO4lUTxr3VkoBxlLC04c5RGcmy/9maOnDXCN1b833hdhX5x0d+mz5tfYTuhxFFGk8swK49b7HiIjorVMRTP3v//7vuhpojvwFCVdavjvgV3y+1/3hNST/3Qjfian97hT1+r6hD+PSMID5vwqbQv0VGZRT76uxnrGmtVfYFtoOxLFhVxhDD4z6tPJ7rUD4krEa+MkL56ZT9+sJb3rPZ2WJBedKjvDtJeO1bnRVMREREdEp1xyZhuZbKimMIU2DNhWp+cW+sx1h89QTrYQtHYi+f955uOMKYtg0Vp+2XEW0x1qJ+hO2VtvRdrkNUTPYWtraBMauIPCaQamtZjDNKbr/NTl72nDe79zOFnJmyrmY6j4463eDzxw23F41nbg1EzVW8/qUJ6icrxDaJCD3408BxmIEa9vb6nuFvJS2NqvPczUtVxHtgR2wN4LVdbzHiIjorVMRTH3nnXfwzjvv1NGEmApuCvcicd2s+SlPYPu9wAnuPmSM4K+QZRn95sZMQ2EYdTsfJCDPK5XnAgjf1qAOl9PX+5NA4kHAOqBmUDOj+KVihTGkjgDIQJFl9CcvYGQYdnC1ettD0NQRc/7M8gXDqlGrFObmWHb6v7kZVV2rX2Uk3vveY86acfFj2S4tYG/EZfdlbqgV4AM+ERERlf3613tMBmqQ/UVz3qiTKVulg8TUcvNzTD3XVqbOO8sLeLbl+LxkXW+lt1f5Uvi1OB7ntUZJJa/2658j62fC3mjTesYFLNhjdW50Wq1/d7vWl/3lL/93kZ/sR3INsD4vLhQrSxIEa8cMpJp17pf+j3uBwQkv/7CzhU37FyMYl/6uYKwQHLuCzpariPZsYmvHWMl4ktKvS1ubsFLOdfdPwJ3qD7K9UO9cuZSAHRj1Cqoe/FgOTgh9n1qro5niT0R0GlRETX/1q1+hqakJpVLJ63rjW/CI62B4yNi9vnwVIlMaIlPlI5HIkH1uSNMw5Hut6/fbGrTbxn8O3fYY0NkIpisGVOW4e+hC+64zrnHWPl/RX8W8DJWfwT2P7msr7g0yNo/jfvPwoXf7la9vrXkgIiKiWt59990j7T/zcys0TcNufgL9yQyUrxNY0jQgP4H+ZBJffnwRf6nj2unaH7E82kpi5c8RDIWtIOIIVG0I4dd5TNxUMPHekvnFseB1HhPml8ra7bCRvaMY137ycz+UeRmJB9OInDVWZvZ/0Bqs3JPQ/pfmF8vTkWYU78tQkl8i/+G0vUEp1l7i3AMN2pQ57psLaNWGgPuyb//G82oYUY0v6Yv3ZSjKBM49mMZFu/NmRKaWAAhzgSIWhOEFb0fFqzXzC/apCJpRxH/9bw2Z50UMhcPA6xd4tAZzo9RGNo49LoRVi+J6UXOX9s/MFY6dH8WAgWdQkUbsIx0AcPUGMPXtN8BqFNHPD3/kwRkrRyEFuzrU1g6Yq3I7A62I9ZnDhtsLyigloJi1SZ9tKGb75ipiuy9z5eyN8wHGch5tPUBuqwR07G11aqitHVjdgvesBNBxBTHcw9YKjHIBTPEnIjrRPJdJNDU1HfY4iIiIiE6s3/72t0fav/yeUaDHKlMkf3wRzfAuW1TPtbVYpZMcJaAcgT0AZy/i2nWxhFGZsxwR7Fru05Ftszb9NVw8C5TLN33f0MpLLdkPeTKP1tsaNE0IpELsw8zgQQbfF4tV+t/FiycagBFcsobt1W5Ne2nHOR+OeTwbwbRvmYHjr/OjGODYwKkAdcCVVt1xBTGkkZ4tp5CH2tqRm00j1xPF1WNUk//8+1Fg9l55c6eNbxBfrXqLU8ctpHrcm2AZu9Lbu9q7GHN4z7GhlL37fAPt+fO4z6OeaHqgvFK1tDKF+KpZI7bmWMwNx8a/EVa6GptW1T1W8z1zzzFW1XsVbUsbolZZCVsnrozlEB9nij8R0Wngmc//T//0T9jd3cXu7kkvYU9ERER0sN5991387ne/O9IxXGgNXqCnnmv3ZF6BPC8eeIXtKThKCW3/rAGQce4PPm2sJdG/lhQOvMT2awSv7X42gk+Gk9DmxbasVaKVjKCwhpfb/2VsZOrZ/zZerWEfaszvrZ3w5RFgPoPvi7045wrKHnurcXRJcdfBKFKP59DXoUB/3IbRbqm8eHMs60r77sSVMQC6EDg1N4/avHG1sZWHByTUO4fsjxIGuyXEAWAsi+wYMBi8BfTNrgNjXY45i86se+xYb+pQoC+qkKw+zX6tOay7PV+dUB6nMNrdBWncbgmpx3NCsDGK1AwwKFmvZgxZ3dqQqvazhXrnsI5RdElS5XlHsDPAWPUsVEkca3ksjoBqy1VEe+KId0vICWMxgtRppvgTEZ0CnsHU3/zmNzh37hz+/ve/4+9///thj4kOkH9ZAyIiItpvv/nNb/D73/++znr0p4SVul9F63syAA2v/gOA12pMO63dqZ7lAOXPRtYmThko9y95fl7a3X4JALjQ+o9V+t9F/jqMOvmu4HB9zI1BG20nfAkjyCDz9ZeQxZXAx1yodw56b42LWvowp/dVvaTzjg7deQSKrvtc7X1NkDYc4/UYV+XzVLZh7HAvHin36zUflcdC6JvVUX1GXDoU6Lric7J6e455qfVaBHitcLnxsQBV3jPuvjsUiFPv9/p6jcR5bbUxOVfdEhHRyeS7G8Lvf/97/PM//zOam7nVEBEREVG93n33XfzhD3/AP/zDPxz1UA6AEOgDyqn7QZhp/XZKvrXBlMcmVM0fXoMMIPPcPGdeO5FvNdLY1x7hxWugckOsgMzNtYxNp8IYepCAjHKpAwDAWhIrRcCZdh+u0r9YDsBowtgsyrl5VW31tGO+Hg7WGDVnqQQiOhCF79KIztxiij8R0SlQdZnE73//e7z77rv4z//8T7x58wZv3rzB//zP/xzW2IiIiIjeKr/+9a/x7rvv4re//S1+97vfneAVqc2I/HkEybUMFDkDDCeQuA5ogQKq5Y2XFDljHLqewJLXKtWzEUw/ACZuCiUBhlVokWYAGlTIUG7KMBLtR6Caq0QDB1TDQ9BUQFb6IVvZ+nb72/bYzj2XISsAICPxwCwBcNu/f0SmsYQJ9CsyzCfEiKohcra+VbPNgdsxA69Jo+yAtWGVler/VqX4E71tdpYx2h1HrieF9TvHqYgEEREdlF/98ssvvxz1IIiIiIgouCdPnhz1EE4BM+3fp5TAW6G4AFnJBCqpcFiamppw5syZox4GERERka8LF6pvvOqb5k9ERERERG+v4nNjPStT/ImIiIj2z0nNPSMiIiIiOqWszbQADKsYYiyViIiIaN8wzZ+IiIjoLcM0f3pbMc2fiIiIjjum+RMRERERERERERHtAwZTiYiIiIiIiIiIiAJgzVQiIiIiIjoUb968QVNT01EPg4iIiKhhDKYSEREREdGhYM1UIiIietsxzZ+IiIiIiIiIiIgoAAZTiYiIiIiIiIiIiAJgMJWIiIiIiIiIiIgoAAZTiYiIiIiIiIiIiAJgMJWIiIiIiIiIiIgoAAZTiYiIiIiIiIiIiAJgMJWIiIiIiIiIiIgoAAZTiYiIiIjIUFyALE8g//poui+tjEKSpMqfsWWUGmlwo4DCfg5wz+0VoEoS1A3vs6WV0cafNYidZYxKo1jeqT0WIiIi8vbOUQ+AiN/SilsAACAASURBVIiIiIiOifAQNO2Ix9CTwvpsH0LCocJdCV3SFrK6gs6g7WyokAaArB74jsNtz0Oodw5674E1T0RERPuAK1OJiIiIiMhwxCtT/XTeWUeqJ43Bu/u6zpSIiIiobgymEhEREZ0Yu8hPypDlBRSPeihHaDc/AVmWsVAEgCIWZBnyZB67te90zJ+znb1yjuMg2z6ZQuj7NAbMPnOk2RfuiuUArPR1mKtI0wDSGBRT2XeWMSqWD6gIzpawPObRZsPtGan01vnRla2qT+lI87dS8jecfVRPy3ePX4Ikqftb6oCIiOiUYzCViIiIiGjPilg4zGDm6zwm7p+ykHlLG6LYxNYOYAUNB5GFruvGz2I74t1m8LNDgb4YAxBDVtehdMAIiHbnEH1sXq+vI6UPCgHQEpbHuhBHCutmm+szQLxbRaHh9gaRHsva56MP40jX9dA5xL8CJu3xRJEe8A+OFu52IS4Jc6JnEQNX9BIREe0nBlOJiIiIiDw0R6ahaRqGwrWuLGJBVpCpek0YQ5oGbSqC5r0O7HUeEzeTKJc23ce23xY7T5FbjSF7R6hf2qEgO5ZD/FvvwGHhuzSiM5Poa7GOhND3eQrR2XtGAHbnKXKrUaQ+L9drDfXOQfep01qzvY1vEHeM0Txf56PGPhXGczkqBJQrdd7RoYtzgk5cGauzQyIiIqqKwVQiIiKiE8pOJc/nMSHLkGUzhb24YP63R9p5gGsr08p90uPFthyrNq3rzZ9qKyxfi+NxXlu8L3uMrU7i891/4T1/Refv1s9Eftd8FjOQupZEv1lv1BjbBBbuT5jXPvVOxd/2niPrfqt2aXksRSxYgdR5xZzzyjR/91jL5QTK1+bve51/S+xsIWem3Isp7YOzfjeUsKUDufEuZwp8dxw5R5vtaGvxa6O+9kpbm0BPG86Lt7W0ob2uB40GHI9rdCujAeaEiIiIGsFgKhEREdEJl/m5FdOahqWEDCAD5etzWLJ+X0viy/xuQ9fW1e9aEitFwAg+9iO5NgJV06A9SECeV8zApIu1AnNYhaZp0NQRwLy2eF+GMi8j8UCDpqkYWUuiv+609yIWlAxwPYElTYN2+RWSaz6Xvs7jy6QGObEETdOgDgNa8kvkXzcjMqViBDDbmUbkrHWThpfv/QWapmE68o9V50hTRwLObxhDDxKQAWBYhaYNwb1wdjc/gf6khhFVs+cto8jOOV57BPzRnDsAma/fgnqrO1vI9URxtcUMVJop97r7547XOtKfsLUKRGfWK6/X54TVpUHtd3v7w6oh2zXebs9NlitTiYiI9hWDqUREREQnnPxeKwCgufWC8fvHF9Es/N7otbWMXA472nq5vQu8foFHawCGLxlBwLMXce06oD15URHM2/3hETShHYSHzMDkNr6fB3D9Gi6eBYAwLg0DmP++vo23it8jIzwjwr1IXK9+i5bshzyZR+ttDZojcOpFxrUPqyfel5/tEkbgPQ/12cWLJxpwPYFeK8pqPpejbXvuWnHuOoC1V9gGzDmu9VxHoYTlr9KI3riKEIBQWztQJd290nm09QC5H3/yv8RRk3Xv7YXa2oHVLTiu2NnCZsAR16+AZ7NAbFEXShMYK2iJiIho/zCYSkRERHTCXWgNXkmznmv3ZF4xU8z7jdWgVjBPsP2zBkDGuT/4tLGWRL+Zpq7MA8BLbL8OPoTd7ZcAAj7z2Qg+GXb3u1Bf8LYqV1CzYdt45be6ds9tH53C3S7EV2P4rNesHtpxC6meHOJfmDvfG1dBlSSMrphHHMHREPo+jQGzg1A3yu0a6fDmhk4tVxHtcdVc3VnGqGRualVvex23kOoRN38qYfkLoazAAdncEmbkbhfiqwfcIRER0SnzzlEPgIiIiIhOoWEV2u3qOzu1vicD0PDqPwB4rZS8nsCSx6ZLQVd2GitmNWPFbLh2QDV8W4N2GyhvOJWBcv+SeWyvzCDo9XNoBfYQ9DSDsl7Mto+91Ti6pLjzWE8K63p5IyYghL7ZdWCsy3FtdGYdc1bAteUqoj1xxLsl5GbWMderQF8EpAEJafuOGLL2Ks5ym5Jkt4jUYyttf+/tRWdSiK26nm3fdEJ5nMJodxekcfPQWBbrM/fQNf4MhTudnhtpERERUX0YTCUiIiKiOgmrKKeAZit1PwgrrX/+exRvhxEW66K6gqvNH16DDA2Z50UMhcN2DVUklvDJMJCZf4QX/xpB5KwZ3DSDq4GFL2EEGWSevMBuJILm4op/zdTiAmQlAzmxhOmIUbf05c0kYJZF8A1g1pD5Oo/eqQia3SUHHIFkM3XfcrYVRhjYSzMufiwDySRW/hzBUBiA+Vxy4iKaj/na1FDvHPTewFejb1ZHXz3nOxToutJgm/vUXm+VEYvP39KHOd11rdexmuf92uyEorMGABERUb0YTCUiIiKiOjUj8ucRJNcyUOQMMJwwanIGCqg2IzK1BKDfuBcwVph6rVI9G8H0A2DipgJ53jw2rEKLNAPQoEKGclNGEgAwAtVcpRq85mg5KNq/lrSfwzOgGh6CpgKy0g856R4L7ABm/1oSI6qGSwFHMPLBK/TLst3etNlec+QTjCQ1ZBQZGYwgkZCBNSt8atSIzcwrkOdlJB584mizOTKNJUygX5GRsfpRNSOwesyDqURERETH3a9++eWXX456EEREREQU3JMnT456CEQNaWpqwpkzZ456GERERES+LlyovvEqN6AiIiIiIiIiIiIiCoDBVCIiIiIiIiIiIqIAGEwlIiIiIiIiIiIiCoDBVCIiIiIiIiIiIqIAGEwlIiIiIiIiIiIiCoDBVCIiIiIiIiIiIqIA3jnqARARERER0enw5s0bNDU1HfUwiIiIiBrGYCoRERERER2KpqYmnDlz5qiHQURERNQwpvkTERERERERERERBcBgKhEREREREREREVEADKYSERERERERERERBcBgKhEREREREREREVEADKYSERERERERERERBcBgKhEREREREREREVEADKYSEREREdGxUFoZhSRJvj/qRoBGdpYxKo1iecfvggLUoG0dZxsFFI59v3uc65qv5f4q3JUg3T2SWSUiorfIO0c9ACIiIiKik2kX+cl+JD9Qod0OH/Vg3h49KazP9iHU6P0tfZjT+/ZzRMfPhgppAMjqnce8304oun6gQyIiIjpsDKYSERERER2IZkSmNESOehhEREREtG+Y5k9EREREdCB2kZ+UId8vHvVATpYNFZKkulLNS1gekzC6UvJIDTdSza1SAaMrW5Vt7ixjVCwpIKZ6W+1tOK9xpq4b/ZdLErhS06u178V9vXjPhgppIA0gjUG/FHprzCuq75gKdyWMrizbc2O3s6E6+h1dKdXo1/3s4mvjTPO30ugLd4Xrx5ZRqj4bTn7j8ykJ4E7dd/R9iCUEiIjo5GAwlYiIiOgEKd6XIcuVPwuHFM8z+p9A/vXh9IfXeUzI/gHL3fzEoT4/HYKOK4ghjXsrQghu5ylyq1FEL7uLA5SwPDaI9FgWuq5D19cRfRhHWrxkQ4XUnUP0sW5fk9IHXQHPHOJfAZO6cc36TBTpAStoWMLyWBfiSGHdPg/Eu83zgdoXFaB2x9G+aF2vQ1+MAbODRlCyQzF+RwxZXYfS4TdROcTHgazXmKwrxnNoM8eldJg1awc2kbLG+jgFjHcZAUvPfgtQpS7kbqzbY12f2cRgRbBbMDuIZx9Zz5ZFbDWOqZVg4VRjfOVn0vUs2q3xtVxFtCeH+LdizwU8mwViH3XCCvoOIivMazvi3QyoEhFRfRhMJSIiIjpxZCQeaNA0DZq2hMR1IKPsJcBZxMJkHrv7OUQiP6txdHltQGWvYOzErZkocg+f2isaS89zyPVEcbXF1dbGN4ivxpC9Y9X4DKHv8xSiwiWF79KIzkyiz77XvGb2niPIFvu0XMc1dDmKKDaxtQM7kJv6XDjfOwddV9BZR/tlRp1RR5C04wpiQebOJbZojMEY0yRSPa4gtGPOSnj6MOcca0sfJl1z7bDxDOmeFCZ7y0Fsz35EPSncsp+tE1fGgNyPPwV4GmN84jMBnVAWY8iNf4MCQuj7NAbMPisHcjeeIY0YrnTAfJ3E9wKADgXZMXcAloiIqDrWTCUiIiI60ZoR+fMIkmsZPPphF5FIc533F7EgK8hcT6A3wNXh2xq0242Ms0FnI5jWWJX0RAmwAVXochTR8Rye7vShr8UMAt6YrLintLUJ9ERxXjzY0ob28hXY0oHcbBekcXcvUSHoGkWbO1Br2dlCDu34zPN80Pa9710e60J8tXykvoCqe8whtElW4NKcKalNmLOfsLUKtH/qnEVjrrcg3FUe4dYmsJpGlxSv7P2Gz7AcfdbDGF96VXKuLAZgz0zHFcQwiGcbCjo7jEA2xrJG8HVnCzmkkZMq78ZYQwMiIqJTiitTiYiIiE4RO+39/oJRAsBMj7eOO8sC7CI/qSADAGtJ9Nvp+2YtUOt6IcXekeZvp+DnheurrJAtLghjcPUlrowVU/sr0vyLWLDbWMCLQH0EG6v3HFWfDzogZkp37nnJXnH4WW8jITojQBedKaepl3/mhNWkjWqkfavGaxfiUracDr/XoTRqZws5n1M//Zgzgt8Vz6ZjrqHXo/o4NgHEFiv7slYBWytd098VYKT4R5H6k3GmtLUJq0RBxf3ialUiIqIaGEwlIiIiOtF2kf86A0DGtQ/Lq1IzuGSUAbgdxm5+Av1JDSOqWRpAHUFGkTGRByJTKkYA4HoCS9o0Imd3kZ/sR3JtBKqmQXuQgDyvYCJfpQjA/Cu0TpnXQkPy3z2CjcUFyEoGcmIJmqZBHdaQvLmAIppx8WMZWEtixbxt94dH0CAj8cdwZTP3FWTsMgeX8CqpBeij9ljdc6QOW6UTGpgP2gchXL1hpJ8vfxtHbuwKvMJhobZ2YNVYVWkzg3KG82jrCZpm7qOlrZzyX6GB9s3U9KwY5HOMOaica0zGKtno++d9rjfGurnlTM83Vve2weuu8+9HK+f3oJgrit3jc+v8yEj1X165h7RQxiDU1g74vk5ERETBMZhKREREdOJoSN60Vkr2I7kmI/FgGpGz5StGLluByF28eKIB1xPotQ6Fe5G4DmhPXlTWSX39Ao/WAAxfQhgAzl7ENb9rLfa1rbgAAH/brri2+NwZ8A1fHgGQwfdFoPnDa5ABZJ4XhfFew8WzcLeC7+chnAujNyEH6qP6WM0+MYJL5hyFb2vQtGlEUG0+mhGZMgLWtP9CvZ8hthpH3N5gyEPHLaR60hi0N3sqYfmLuLDS0qqzOSjsTm9udFRtEyWR18ZH9s7yjbYvBv2MDakcq0OrBnDLyptkAaWVKcSrruA1A9TjU+VarjvLmBrPIXrjqpGa7+o31PsZYhDn13p2yfG8+8OslSuOD0DhrlhPF/ZrHhfHbR/PIf6FcK25Cng04AZYREREAGumEhEREZ1AlcFTf9t4tQbguseptVfYnmr1vm1egTwvHniF7al6x+lmBIGTwpGX27tAOIJPhpPQ5r9H8Y/n8GgNkBMXUVH99fU2XgLAB62V52r18Ydq47Lm6Bx8ZsN3PuqtUEswN6CqrMEJGOny5fRxI6Ubs+YGQ55C6JtdB8a6IElWGynEVoX2OxToi4A0INbijCGrK56rXYP0AUSRemym8bfU2X6HgvWZUXR1S7BGGVtcR+qrLsS/K0Dp6DQDuHHEuyXkHHMiiiI1Awzag6r9TKHeOehtKiShb8ecV/TbCUXPAtIgpNlyO7FF1wZa+yTUO4d1OOemssauERTGKhC9LM5L+XUS319R3/kjIiLyxmAqERER0anWinNegVSgevBwWPVccbm3aqH+QeDw5RFgPoO//ptspPh/6BGmNFeTan/bxi7CPoFMnz786rgCsOdorUqA1Gc+qD6h3jnoQXY6M3Xe0aHfcR1s6cOc3ie2ir5ZHeIR9PY57+lQoOuKdycV7QXsI2j7HjznwdF+jf4sl/377byjQ697rF79dkLRdfj0AkUv9+LVp+84AM+5D/Ie8b+m+rxVHQsREZGJaf5EREREp1plTVIUV5BcA+SPL6LZHWw109gx/70ROK3YAKoxRsq9hkc/GAUAHBtZAUD4EkYAaGsaMPyJz6rbMC4NA1h7hBevAaCIFaFmas0+fJlzJJQEMDajmkAeBzMfRERERHQ8cWUqERER0SnXHJnGEibQr8jImMdGVA1D5kLLix/LQDKJ/rWkcXxqCUA/FNm8+noCS3tdlRkegqYCstIPOWmNQVxBagRKM/NivVePZm4vIfG3fjOVfwSJhAxYAdVqfdQIqPrNUeQsgIOYDyIiIiI6ln71yy+//HLUgyAiIiKi4J48eXLUQyBqSFNTE86cOXPUwyAiIiLydeHCharnmeZPREREREREREREFACDqUREREREREREREQBMJhKREREREREREREFACDqUREREREREREREQBMJhKREREREREREREFMA7Rz0AIiIiIiI6Hd68eYOmpqajHgYRERFRwxhMJSIiIiKiQ9HU1IQzZ84c9TCIiIiIGsY0fyIiIiIiIiIiIqIAGEwlIiIiIiIiIiIiCoDBVCIiIiIiIiIiIqIAGEwlIiIiIiIiIiIiCoDBVCIiIiIiIiIiIqIAGEwlIiIiIiIiIiIiCoDBVCIiIiIiIiIiIqIAGEwlIiIiInrbFRcgyxPIvz7qgexNaWUUkiR5/oyulPbewUYBhcAXF6BKEtSNvXd70Eoro5DGlmHMUL3jLqCwx2cs3JUg3a02s3vrw/l8J1kJhY29PeXpmSsioqPzzlEPgIiIiIiI9ig8BE076kHsk54U1mf7EBKP7SxjtLsLapsOpaPBdjdUSANAVu8MeEMnFF1vsLOjVM+4C1ClQWBRR9BZqd9h9HESlLA81oXcjXXMNfoeJyKiQ8GVqUREREREb7sTsjLVV0sfPhsD0t8FX1dKREREdBAYTCUiIiI6QYr3Zciy18/xDbTt5icgyzIWigBQxIIsQ57MY7fmnfVcW8PrPCZkGfL94l5bqsM+jv+UKdyVMLqyDNUsAWCntG+o3qUBNlRIA2kAaQw6UuBLWB4TywmoQhkAZ7q8lT5dcJQiUKuUDTDvX1nGqDAmMf2+7uewRu0aw1Ovfjf8rrfaM1aMpgGkB5xp+oW74pyMYnmncv7t82PL2Ko6B4314eWpq1/XrFR5Lf3GVu362u15zyuMldSSimXrvD1WvzaNVanxVSA33uV8tj29F4iI6CAwmEpERER04shIPNCgaeLPNCJna9+5m58wg5p0IF7nMXGoAdsTYkPF4CwQ+6icKJ4bz6HtsQ5dN1L/SyujkAY2kTKP6Y9TwHiXEXzqUKAvxgDEkNWtUgEFqJKRVq3rxj3rM5sYrBaEW43jHibN69eR6kljsGqtUCA9nkPUGtNiDOkBZ+DQ+zmArDkmXc+i3XoOcy66xlF+zkUgPp7z7b+0Muq83pwXdaMTip5FDEBsUYd+pxNWsG8QWXtO9MV2xLvLYy6tjGJwNmaPb/1GDvFZv96r9KGnsG7POxx9+M197v318twjji577ut9LUtYHhvE5kz5+uyY+FrWbs9/Xq0+0oj/+JlxbrYPoapthtA3u45UDxCdWTev3//3AhER7Q8GU4mIiIgIgBFI7U+elMKbx9DrPCZuJlGe4TCGNA3aVATNRzisY2c1ji73BlQDacQWXfVSe6K42mL9UsLThzlEZybRZx1r6cPkTBS5h0+9N+PZeIZ0TwqTveXqrKHeSaR60rjnu9lVDJ/Z14dw9UYU0LeqbvbjGFOHguxYDvFvhRCfx3PEFhWhvmgnlMUYcuPfoIASlr9Ke7Tp17v3vMzpPrVnd54itxpD9o5Q3dQx5gK+GXeOz5izKhPg2UcUqc/LdXGNNnLIPa8yk47XKoS+z1OIzt4zArB1v5Y/YWvVeaTzjhXsDdJesHkVg//1j3G/3wtERLRfGEwlIiIiOjV2kZ+UK1Pq5QUUiwt2IDWjmOnudur7Qvk6oQ2v8gFWyr71I65ydZQgCJjabtxT7sNZEsD/+dxjs++7v2CcC7A61FkywVUmwZob60dozz0HE/ldY66tQOq8Ys5lZZq///yVr83f957fE6OnvGJR/KkI/kltwiZVRnCsvc2xbRVCl6OIrm7hJ49uSlubHoFbI9Xaf2xtOF/n47jHdP59VwDW4znSA5XB5GrPef79qE/v3tf72tlCziyFIPY/OFs+v4ko2lrEm0Jok4I1X+6jvaKNqzeiyP3o9UqZHPMEoKUN7chha6eR17ITt2aiRkq9Rwp/7faCzKtznuof436/F4iIaL+8c9QDICIiIqL9piF5U0ZSPHQ9gaWpCCL/msCjteT/394dhDaZ5nEc/00ZrDUV0qLFamcEkQlMDqvWw7plX0fIBFkaKCu7o5eUNbm8g2IZe5QsDYW51EXpMu8lOejF3VkKBWVZOoXqK4KIOu4hQjzsQbtWttKbB0HoHt43ydvkfZO32k7Vfj8nMc/7vP/nSQ7lz//5Pyr8OKvd+/IqSMpaacXj0nTuiYbytrKWrXRc0uKC82xR6rdtpeUkGPNzWVl2WnGVdNUwlf/rrA6NJSS3srXyfOmKIdO8oN1T4+r9pyGzaCg3Na7ETue5oX29sofja7bq0pWh4NhcBfXLttMh5vLG61btnjD03LKV7nErTDOWE3/pqgzT1IU90xrvvdG4B/m/aPZX40pP5fTE+5xKeuB551Ld/jnzGnqSm9Z4ZQlzN6UztuxhZ32FH2c1SGVrsBfzmpF02uejZ09nnMTt5ZT8U2JNEnvr5cW8HkuNVbhVP+vuOr7+5fxjOa0QvNWQK+NbL8+eBn1TIZ9t+l022jE4qfKgVOnrejJ2SZW1ax1+G61jrKtO3eDfAgAgGJWpAAAAHx2fnqmVhNvOhL7LGdJcXvmipIzlJO6ayfSrMiQ+bMu2B7XwZ0OGYapQHbSkh7dtSVn1x71jneTpg6KkY1/p0E5Jiqs/I6n4QGtZWBkcW032cJjkrRtv5lS1z2x34pSykgr3S1r6903Z3rniadm2rfFEd/XfgwtOhalZDBu9u3/Hchqsbvagcsck+/bDWhVvdQ97tfuYpLnnWqjGEK4v7sfpM/Udlx7P113OM/84sJr0s8+TUkDV6lqqj+nZ05nGKsuKXX360ueZGv91OonI8OOD7Oj7UtJjzQf1LvVUg9a81Hw51PTVOZIN73DmSH7epO63vp2Cp0r23b7LgzLdfqQjuqS7j8LMt7p9ld4ixjX/LQAA1grJVAAAgE2mkhiUwiYXa5yj70PK77Nk21Z1HmlBz+ckHdut3qCH5/Iaco+nO0nGJ1pYDBq8esGxrdLigp4EffafBZX/a0sytLvHLwinjcBQ/gtZti0rE/al7v75qSRM0YR7TPz8WO0SoxfXNXZ+RsmvjziJy7ok3o7B0xpR3QVSL67rbCzmuUTo3a2I6ZGlk5eTmvi9b92nasfPx1ZcxvTzD5Ub4Xco9acRnzmD3l7Zl394jrE7F0A5lxjVJeQO/EETx2c0+r3nNnn31ntnvBPfpW+8FzGNNW+NUP+OXUeUrHuHM0dSycNN6kr/Naqxam/Rl7r+/ahmzp1WatfbfJfeNbke3dUljejXB8LM12pfG4WZsy8mT6uDtf4tAADWCsf8AQAANpnSlVrVZsG8qn47rXAp1UqFaU7T7jH1Gk+l5Jj8j527rQbqPwvTO/XdYlulnb36QpLvVVz7ehXbY0iy9fx/kuoqQUv3C3Iqg509DR+Fu39+miWoUbVjcFLlPkuxozGNuv+XvHhHk5ULf3YdUfL4qEaPxjRz8Y4mB52b5hU7qZgnARV8rPrtjFxMaqYaU1ITtyZrFwYFrOOOzmrAs44Vx8MPmCr/3bvOEU1cTGr0pxbzxWqNTb37cuTrpEbPDyj2k/OO1OU70rkBDcRGfcc3zHd8QhPnZmqxNkbg846y+n6Ied7Rel90fELJpwOqLuPc32oXRmm13+VBmbcmdPbogGLnq6vUxK1Jt71B6/ma7qtvZW/rOQ/+ZkT65qRil91WC2v8WwAArA2SqQAAAJtJ6apTFZqxNL3nmobyBZlX+mUPx9XdG5hCXMlNmGr2mucofbcO/daQ5gp6UHJ6sDo9QKXc1Lj6M1KheFMPzySqPVMLbnK1md4VictKK4HVxrZacTfea5r9XaVnqjNf9nBc3T1fyZCtwv2S0vG4cxnVibyUm9YpqRavZnXNe8y/WZK2sn/5vG78MeH2TL2h/Jxk5A6pe5PUptb6WDZ38NuyfE+WHzBVLptBsyt1uazUyplklsvyf8L5rFlsoeLtS2mynPL9KGgdLef1WWeqOn5l3K3ma/zMb59aPZNa5Xh37d82ecj3+ZRSgc80+y597Ar+XsLOF7ivgXO3mNPne3233wIAYD2QTAUAAPiALC8vhxjlcwGVpKxlSWZBUlbWcFzd+k6527byRVNXD9tKx/uVVUEF03ASnWfq540rbWVVMAsyDWeebMZWoegkMOOJcU3rgoZMo5rIzFq208dz2JYlQ2Y1rqwst0q1WWVqd+KUsnnbiUlZ5XKGNOeXjmwe22orO+MN8Tp9aJ2epAmNT0kXTpgyKsnSjCU70S3JUrZouvFK2UxWdrHgJoMrSVpTRtFQbupU3Vr998/pabs5kqkAAADvu0+Ww/1FDgAAgPfEvXv39Pr1640OA1i1aDSq9vb2jQ7jF+LcEq81bhsAAADWT1tbm/bv3990DJWpAAAAH5jt27eTTAXee43H7QEAwPstEom0HNP2C8QBAACANRSNRjc6BAAAAOCj09HR0XIMyVQAAIAPTFdXl7q6ujY6DAAAAOCjEYlE1NnZ2XIcyVQAAIAPzNatW9XX1xfqjz0AAAAAzW3ZskVdXV369NPWHVFJpgIAAHyAotGo9u7dS4UqAAAA8A4ikYh6enq0bdu2UOM/WV5eXl7nmAAAALBOUor0NgAAAINJREFU3rx5o8XFRS0tLenVq1dcTIX3WjQaVXt7+0aHAQAANrm2tjZFIhF1dHSos7MzVEVqBclUAAAAAAAAAAiBY/4AAAAAAAAAEALJVAAAAAAAAAAIgWQqAAAAAAAAAIRAMhUAAAAAAAAAQiCZCgAAAAAAAAAhkEwFAAAAAAAAgBD+DzLSuWhXq4AqAAAAAElFTkSuQmCC"},"78f52a34-48d3-4cc2-a0ff-74c3b9d910b9.png":{"image/png":"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"}}},{"cell_type":"code","source":"gene_set <- c(\"PTPRC\", \"LGALS1\", \"LGALS2\", \"CD45RO\", \"CD45RA\")\n\nfig(15,6)\nsbst <- set22_corr %>% as.data.frame %>%\n select(all_of(gene_set)) %>%\n filter(row.names(set22_corr) %in% gene_set)\n\nsbst_p <- set22_p %>% as.data.frame %>%\n select(all_of(gene_set)) %>%\n filter(row.names(set22_corr) %in% gene_set)\n\nplt1 <- ggcorrplot(sbst, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2022\", lab_size = 6, p.mat = as.matrix(sbst_p),insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)\nplt2 <- ggcorrplot(set23_corr, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2023\", lab_size = 6, p.mat = set23_p,insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)\nplt1 + plt2","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(12,8)\ncomb_paris(name1 = \"LGALS1\", name2 = \"CD45RO\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein = \"CD45RO\"\nRNA = \"LGALS1\"\ncat(RNA, \"vs\", protein)\n\nplot_2ds(protein = protein, RNA = RNA)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(12,8)\ncomb_paris(name1 = \"LGALS1\", name2 = \"CD45RA\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein = \"CD45RA\"\nRNA = \"LGALS1\"\ncat(RNA, \"vs\", protein)\n\nplot_2ds(protein = protein, RNA = RNA)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

RUNX1, P300, AP-1, IRF7/8

","metadata":{}},{"cell_type":"markdown","source":"**Subset data from the two datasets**","metadata":{}},{"cell_type":"code","source":"#CITEseq2022\nRNA_pattern <- c(\"PTPRC$\", \"RUNX1\", \"EP300\", \"JUN\", \"IRF7\", \"IRF8\")\nRNA_names22 <- colnames(mat_RNA)[which(grepl(paste0(RNA_pattern, collapse = \"|\"),\n colnames(mat_RNA),\n ignore.case = T))]\nprot_pattern <- c(\"CD45\",\"CD45RO\",\"CD45RA\")\n\nset22 <- make_subset(RNA_names22, prot_pattern)\nRNA_names22 <- gsub(\".*_\",\"\", RNA_names22)\n\ncat(\"CITEseq2022: Head of the data frame with\", ncol(set22)-6, \"RNA/proteins in columns X\", nrow(set22), \"cells in rows\")\nhead(set22)\n\n#CITEseq2023\nRNA_names23 <- colnames(mat_RNA23)[which(grepl(paste0(RNA_pattern, collapse = \"|\"),\n colnames(mat_RNA23),\n ignore.case = T))]\nprot_names <- colnames(mat_prot23)[grep(paste(prot_pattern, collapse = \"|\"), colnames(mat_prot23))]\n\nset23 <- cbind(JUNB = mat_RNA23[, RNA_names23],\n mat_prot23[, prot_names]) %>% as.data.frame\ncat(\"CITEseq2023: Head of the data frame with\", ncol(set23), \"RNA/proteins in columns X\", nrow(set23), \"cells in rows\")\nhead(set23)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Correlogram**","metadata":{}},{"cell_type":"code","source":"#calculate correlations\nset22_corr <- cor(select(set22, c(all_of(c(RNA_names22, prot_pattern)))), method = \"spearman\")\ncat(\"The correlation matrix CITEseq2022\")\nset22_corr\n\n#matrix of p-values\nset22_p <- corr.test(select(set22, c(all_of(c(RNA_names22, prot_pattern)))),\n method = \"spearman\", adjust = \"BH\")$p # Apply corr.test function\n#set22_p \n\nset23_corr <- cor(set23, method = \"spearman\")\ncat(\"\\nThe correlation matrix CITEseq2023\")\nset23_corr\n\n#matrix of p-values\nset23_p <- corr.test(set23, method = \"spearman\", adjust = \"BH\")$p # Apply corr.test function\n#set23_p ","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Check expression levels**","metadata":{}},{"cell_type":"code","source":"fig(15,7)\n\nsum_expr_RNA <- apply(select(set22, c(all_of(c(RNA_names22)))), 2, sum) %>% sort(decreasing = TRUE)\nsum_expr_RNA <- data.frame(\"Sum_expression\" = sum_expr_RNA,\n \"Name\" = names(sum_expr_RNA))\nsum_expr_RNA$Name <- factor(sum_expr_RNA$Name, levels = unique(sum_expr_RNA$Name ))\n\np1 <- ggplot(sum_expr_RNA,aes(x = Name, y = Sum_expression)) +\n geom_bar(stat = 'identity', fill=\"#BF9039\", col=\"grey\") + \n theme_bw(base_size = 22) +\n xlab(\"\") +\n ggtitle(paste0(\"CITEseq 2022\")) +\n theme(axis.text.x = element_text(angle = 45, hjust=1))\n\nsum_expr_RNA <- apply(select(set23, -c(\"CD45RA\",\"CD45RO\")), 2, sum) %>% sort(decreasing = TRUE)\nsum_expr_RNA <- data.frame(\"Sum_expression\" = sum_expr_RNA,\n \"Name\" = names(sum_expr_RNA))\nsum_expr_RNA$Name <- factor(sum_expr_RNA$Name, levels = unique(sum_expr_RNA$Name ))\n\np2 <- ggplot(sum_expr_RNA, aes(x = Name, y = Sum_expression)) +\n geom_bar(stat = 'identity', fill=\"#BF9039\", col=\"grey\") + \n theme_bw(base_size = 22) +\n xlab(\"\") +\n ggtitle(paste0(\"CITEseq 2023\")) +\n theme(axis.text.x = element_text(angle = 45, hjust=1))\n\np1+p2 +\n plot_layout(widths = c(4, 1)) +\n plot_annotation(\n title = \"Overall expression levels\") & \n theme(text = element_text(size = 22) ) ","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CITEseq 2022**","metadata":{}},{"cell_type":"code","source":"fig(25,12)\nggcorrplot(set22_corr, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2022, Spearman cor., insignificant are blank\", lab_size = 6,\n p.mat = set22_p,insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Comparison of the two datasets**","metadata":{}},{"cell_type":"markdown","source":"JUNB is common in the two datasets. \n[proteinatlas.org](http://www.proteinatlas.org/): JUNB - Immune cell enhanced (neutrophil). In single cell data is mostly expressed in B-cells.\n![image.png](attachment:a4635619-8b9e-487d-a60a-a6f78ed0d9d7.png)","metadata":{},"attachments":{"a4635619-8b9e-487d-a60a-a6f78ed0d9d7.png":{"image/png":"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"}}},{"cell_type":"code","source":"gene_set <- c(\"JUNB\",\"PTPRC\", \"CD45RO\", \"CD45RA\")\n\nfig(15,8)\nsbst <- set22_corr %>% as.data.frame %>%\n select(all_of(gene_set)) %>%\n filter(row.names(set22_corr) %in% gene_set)\n\nsbst_p <- set22_p %>% as.data.frame %>%\n select(all_of(gene_set)) %>%\n filter(row.names(set22_corr) %in% gene_set)\n\nplt1 <- ggcorrplot(sbst, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2022\", lab_size = 6, p.mat = as.matrix(sbst_p),insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)\nplt2 <- ggcorrplot(set23_corr, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2023\", lab_size = 6, p.mat = set23_p,insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)\nplt1 + plt2\n","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(12,8)\ncomb_paris(name1 = \"JUNB\", name2 = \"CD45RO\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein = \"CD45RO\"\nRNA = \"JUNB\"\ncat(RNA, \"vs\", protein)\n\nplot_2ds(protein = protein, RNA = RNA)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(12,8)\ncomb_paris(name1 = \"JUNB\", name2 = \"CD45RA\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein = \"CD45RA\"\nRNA = \"JUNB\"\ncat(RNA, \"vs\", protein)\n\nplot_2ds(protein = protein, RNA = RNA)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# fig(20,35)\n\n# for (rna in c(\"RUNX1\", \"EP300\", \"JUNB\", \"JUND\")) {\n \n# sbst <- select(set22, all_of(rna), day, cell_type)\n# sbst[, 1] <- as.numeric(sbst[, 1])\n# names(sbst)[names(sbst) == rna] <- \"Expression level\"\n\n# plt <- \n# ggplot(data = sbst, aes(x = `Expression level`))+\n# geom_histogram(position=\"dodge\", fill = \"#033E8C\", color = \"gray\") +\n# facet_wrap(~cell_type + day, ncol = 3, scales = \"free\") +\n# theme_minimal(base_size = 24) + \n# theme(aspect.ratio = 1) +\n# ggtitle(paste(rna, \"expression level by cell type and day\"))\n \n# print(plt)\n# }","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Correlograms for several RNA groups vs CD36

","metadata":{}},{"cell_type":"markdown","source":"**CD36 and S100A1-8,10,11,13,14,16**","metadata":{}},{"cell_type":"code","source":"prot_of_interest <- \"CD36\"","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"RNA <- colnames(mat_RNA)[which(grepl(paste0(\"_\", \"S100A\"),\n colnames(mat_RNA),\n ignore.case = T))]\nord <- c('ENSG00000160678_S100A1',\n 'ENSG00000196754_S100A2',\n 'ENSG00000188015_S100A3',\n 'ENSG00000196154_S100A4',\n 'ENSG00000196420_S100A5',\n 'ENSG00000197956_S100A6',\n 'ENSG00000143546_S100A8',\n 'ENSG00000163220_S100A9',\n 'ENSG00000197747_S100A10',\n 'ENSG00000163191_S100A11',\n 'ENSG00000163221_S100A12',\n 'ENSG00000189171_S100A13',\n 'ENSG00000188643_S100A16')\n\nmy_RNA <- mat_RNA[, RNA] #select the needed column\nmy_RNA <- mat_RNA[, ord] \n\n#Merge with the protein of interest\nmy_RNA <- cbind(my_RNA, mat_prot[, prot_of_interest]) \ncolnames(my_RNA)[ncol(my_RNA)] <- prot_of_interest\n\n#calculate correlations\nset_corr <- cor(my_RNA, method = \"spearman\")\n\nfig(15,8) # figure width, heigth\ncorrplot::corrplot(set_corr, type=\"upper\", order=\"original\",\n tl.col=\"black\", tl.srt=45,\n #p.mat = p.mat,\n sig.level = 0.05, insig = \"blank\")\ncat(\"Correlogram for\", prot_of_interest, \"protein and S100A... RNA.\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CD36 and HB**","metadata":{}},{"cell_type":"code","source":"RNA <- colnames(mat_RNA)[which(grepl(paste0(\"_\", \"HB\"),\n colnames(mat_RNA),\n ignore.case = T))]\nmy_RNA <- mat_RNA[, RNA] #select the needed column\n\n#Merge with the protein of interest\nmy_RNA <- cbind(my_RNA, mat_prot[, prot_of_interest]) \ncolnames(my_RNA)[ncol(my_RNA)] <- prot_of_interest\n\n#calculate correlations\nset_corr <- cor(my_RNA, method = \"spearman\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(15,8) # figure width, heigth\ncorrplot::corrplot(set_corr, type=\"upper\", order=\"original\",\n tl.col=\"black\", tl.srt=45,\n #p.mat = p.mat,\n sig.level = 0.05, insig = \"blank\")\ncat(\"Correlogram for\", prot_of_interest, \"protein and HB RNA.\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CD36 and RNA related to heme biosynthesis chain**","metadata":{}},{"cell_type":"code","source":"RNA_names <- c(\"BLVRA\",\"BLVRB\",\"UROD\",\"UROB\",\"HMBS\",\"ALAD\",\"FECH\",\"HMOX1\",\"HMOX2\",\"COX10\",\"COX15\",\"CPOX\",\"PPOX\",\"FXN\",\"CP\")\nRNA <- colnames(mat_RNA)[which(grepl(paste0(\"_\", RNA_names,'$', collapse = \"|\"),\n colnames(mat_RNA),\n ignore.case = T))]\nmy_RNA <- mat_RNA[, RNA] #select the needed column\n\n#Merge with the protein of interest\nmy_RNA <- cbind(my_RNA, mat_prot[, prot_of_interest]) \ncolnames(my_RNA)[ncol(my_RNA)] <- prot_of_interest\n\n#calculate correlations\nset_corr <- cor(my_RNA, method = \"spearman\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(15,8) # figure width, heigth\ncorrplot::corrplot(set_corr, type=\"upper\", order=\"original\",\n tl.col=\"black\", tl.srt=45,\n #p.mat = p.mat,\n sig.level = 0.05, insig = \"blank\")\ncat(\"Correlogram for\", prot_of_interest, \"protein and RNA related to heme biosynthesis chain.\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

MALAT1 and NEAT1 vs CD45 proteins

","metadata":{}},{"cell_type":"markdown","source":"The leukocyte common antigen (CD45) is an abundant glycoprotein expressed exclusively on the surface of all leukocytes and their precursors and plays an important role in the development and function of T and B lymphocytes. The extracellular region of the protein shows structural heterogeneity as a result of alternative splicing of exons 4, 5 and 6 (also called A, B and C), which can give rise to a number of different isoforms ranging in size from 180,000 to 220,000 MW. Although the physiological significance of this heterogeneity is not fully understood, there is evidence that the alternatively spliced forms play different roles in signalling ([Timón & Beverley 2001](http://doi.org/10.1046%2Fj.1365-2567.2001.01177.x)).","metadata":{}},{"cell_type":"markdown","source":"**Subset data from the two datasets**","metadata":{}},{"cell_type":"code","source":"#CITEseq2022\nRNA_pattern <- c(\"PTPRC$\", \"MALAT1\", \"NEAT1\")\nRNA_names22 <- colnames(mat_RNA)[which(grepl(paste0(RNA_pattern, collapse = \"|\"),\n colnames(mat_RNA),\n ignore.case = T))]\nprot_pattern <- c(\"CD45\",\"CD45RO\",\"CD45RA\")\n\nset22 <- make_subset(RNA_names22, prot_pattern)\nRNA_names22 <- gsub(\".*_\",\"\", RNA_names22)\n\ncat(\"CITEseq2022: Head of the data frame with\", ncol(set22)-6, \"RNA/proteins in columns X\", nrow(set22), \"cells in rows\")\nhead(set22)\n\n#CITEseq2023\nRNA_names23 <- colnames(mat_RNA23)[which(grepl(paste0(RNA_pattern, collapse = \"|\"),\n colnames(mat_RNA23),\n ignore.case = T))]\nprot_names <- colnames(mat_prot23)[grep(paste(prot_pattern, collapse = \"|\"), colnames(mat_prot23))]\n\nset23 <- cbind(mat_RNA23[, RNA_names23],\n mat_prot23[, prot_names]) %>% as.data.frame\ncat(\"CITEseq2023: Head of the data frame with\", ncol(set23), \"RNA/proteins in columns X\", nrow(set23), \"cells in rows\")\nhead(set23)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Correlogram**","metadata":{}},{"cell_type":"code","source":"#calculate correlations\nset22_corr <- cor(select(set22, c(all_of(c(RNA_names22, prot_pattern)))), method = \"spearman\")\ncat(\"The correlation matrix CITEseq2022\")\nset22_corr\n\n#matrix of p-values\nset22_p <- corr.test(select(set22, c(all_of(c(RNA_names22, prot_pattern)))),\n method = \"spearman\", adjust = \"BH\")$p # Apply corr.test function\n#set22_p \n\nset23_corr <- cor(set23, method = \"spearman\")\ncat(\"\\nThe correlation matrix CITEseq2023\")\nset23_corr\n\n#matrix of p-values\nset23_p <- corr.test(set23, method = \"spearman\", adjust = \"BH\")$p # Apply corr.test function\n#set23_p ","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CITEseq2022**","metadata":{}},{"cell_type":"code","source":"fig(25,8)\nggcorrplot(set22_corr, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2022, Spearman cor., insignificant are blank\", lab_size = 6,\n p.mat = set22_p,insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Correlation of MALAT1 and NEAT1 vs CD45 by days in the CITEseq22 dataset**","metadata":{}},{"cell_type":"code","source":"fig(25,8)\n\nggscatter(set22, x = \"CD45\", y = \"MALAT1\", color = \"#033E8C\",\n shape = 20, alpha = 0.5, size = 3,\n add = \"reg.line\", conf.int = TRUE, \n cor.coef = TRUE, cor.coef.size = 6,\n cor.coeff.args = list(method = \"spearman\", label.sep = \"\\n\", color = \"red\",\n label.y.npc = \"bottom\"),\n add.params = list(fill = \"lightgray\"),\n facet.by = c(\"day\"), scales = \"fixed\", ncol = 3,\n title = paste0(\"Spearman correlation for MALAT1 vs CD45 in CITE-seq2022 (n = \", nrow(set22), \")\",\n \" by day\"),\n ggtheme = theme_bw(base_size = 20) + theme(aspect.ratio = 1) ) ","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(25,8)\n\nggscatter(set22, x = \"CD45\", y = \"NEAT1\", color = \"#033E8C\",\n shape = 20, alpha = 0.5, size = 3,\n add = \"reg.line\", conf.int = TRUE, \n cor.coef = TRUE, cor.coef.size = 6,\n cor.coeff.args = list(method = \"spearman\", label.sep = \"\\n\", color = \"red\",\n label.y.npc = \"bottom\"),\n add.params = list(fill = \"lightgray\"),\n facet.by = c(\"day\"), scales = \"fixed\", ncol = 3,\n title = paste0(\"Spearman correlation for NEAT1 vs CD45 in CITE-seq2022 (n = \", nrow(set22), \")\",\n \" by day\"),\n ggtheme = theme_bw(base_size = 20) + theme(aspect.ratio = 1) ) ","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Comparison of the two datasets**","metadata":{}},{"cell_type":"code","source":"gene_set <- c(\"MALAT1\", \"NEAT1\",\"PTPRC\", \"CD45RO\", \"CD45RA\")\n\nfig(15,8)\nsbst <- set22_corr %>% as.data.frame %>%\n select(all_of(gene_set)) %>%\n filter(row.names(set22_corr) %in% gene_set)\n\nsbst_p <- set22_p %>% as.data.frame %>%\n select(all_of(gene_set)) %>%\n filter(row.names(set22_corr) %in% gene_set)\n\nplt1 <- ggcorrplot(sbst, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2022\", lab_size = 6, p.mat = as.matrix(sbst_p),insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)\nplt2 <- ggcorrplot(set23_corr, hc.order = TRUE, lab = TRUE,\n title = \"CITEseq 2023\", lab_size = 6, p.mat = set23_p,insig = \"blank\",\n ggtheme = theme_minimal(base_size = 24), tl.cex = 18)\nplt1 + plt2","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(12,8)\ncomb_paris(name1 = \"MALAT1\", name2 = \"CD45RO\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein = \"CD45RO\"\nRNA = \"MALAT1\"\ncat(RNA, \"vs\", protein)\n\nplot_2ds(protein = protein, RNA = RNA)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(12,8)\ncomb_paris(name1 = \"MALAT1\", name2 = \"CD45RA\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein = \"CD45RA\"\nRNA = \"MALAT1\"\ncat(RNA, \"vs\", protein)\n\nplot_2ds(protein = protein, RNA = RNA)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(12,8)\ncomb_paris(name1 = \"NEAT1\", name2 = \"CD45RO\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein = \"CD45RO\"\nRNA = \"NEAT1\"\ncat(RNA, \"vs\", protein)\n\nplot_2ds(protein = protein, RNA = RNA)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(12,8)\ncomb_paris(name1 = \"NEAT1\", name2 = \"CD45RA\")","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"protein = \"CD45RA\"\nRNA = \"NEAT1\"\ncat(RNA, \"vs\", protein)\n\nplot_2ds(protein = protein, RNA = RNA)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Correlogram with scatter plots, histograms, and the Spearman correlation**","metadata":{}},{"cell_type":"code","source":"fig(10,10)\ncat(\"CITEseq2022\")\n\npairs.panels(select(set22, all_of(gene_set)),\n smooth = FALSE, ellipses = FALSE,\n method = \"spearman\",\n pch = 20, lm = TRUE,\n hist.col = \"#BF5841\", stars = TRUE, \n cex.cor=1, cex.labels = 2)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cat(\"CITEseq2023\")\n\npairs.panels(set23,\n smooth = FALSE, ellipses = FALSE,\n method = \"spearman\",\n pch = 20, lm = TRUE,\n hist.col = \"#BF9039\", stars = TRUE, \n cex.cor=1, cex.labels = 2)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

Markers of T cells vs CD45, CD45RA, CD45RO

","metadata":{}},{"cell_type":"markdown","source":"Reference: https://doi.org/10.3390%2Fcancers8030036","metadata":{}},{"cell_type":"code","source":"#CITEseq2022\nRNA_pattern <- c(\"PTPRC$\", \"SELL\",\"_CD8$\",\"ENSG00000153563\",\n \"_CD4$\", \"_CD3$\",\"ENSG00000167286\",\n \"_CD25$\",\"ENSG00000134460\",\n \"CD127\", \"ENSG00000168685\")\nRNA_names22 <- colnames(mat_RNA)[which(grepl(paste0(RNA_pattern, collapse = \"|\"),\n colnames(mat_RNA),\n ignore.case = T))]\nRNA_names22\nprot_pattern <- c(\"CD45\",\"CD45RO\",\"CD45RA\", \"CD8\", \"CD4\", \"CD3\", \"CD62L\", \"CD25\", \"CD127\")\n\nset22 <- make_subset(RNA_names22, prot_pattern)\nRNA_names22 <- gsub(\".*_\",\"\", RNA_names22)\n\ncat(\"CITEseq2022: Head of the data frame with\", ncol(set22)-6, \"RNA/proteins in columns X\", nrow(set22), \"cells in rows\")\nhead(set22)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(20,6)\np1 <- ggplot(set22, aes(x = `CD45RO`, y = `PTPRC`, color = `CD45`)) +\n geom_point() +\n theme_classic(base_size = 22) +\n scale_color_gradient(low='darkblue', high='yellow') +\n theme(aspect.ratio = 1)\np2 <- ggplot(set22, aes(x = `CD45RA`, y = `PTPRC`, color = `CD45`)) +\n geom_point() +\n theme_classic(base_size = 22) +\n scale_color_gradient(low='darkblue', high='yellow') +\n theme(aspect.ratio = 1)\n\np1+p2","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(20,6)\np1 <- ggplot(set22, aes(x = `CD45`, y = `CD45RO`, color = `SELL`)) +\n geom_point() +\n theme_classic(base_size = 22) +\n scale_color_gradient(low='darkblue', high='yellow') +\n theme(aspect.ratio = 1)\np2 <- ggplot(set22, aes(x = `CD45`, y = `CD45RA`, color = `SELL`)) +\n geom_point() +\n theme_classic(base_size = 22) +\n scale_color_gradient(low='darkblue', high='yellow') +\n theme(aspect.ratio = 1)\n\nfig(20,6)\np3 <- ggplot(set22, aes(x = `CD45`, y = `CD45RO`, color = `CD62L`)) +\n geom_point() +\n theme_classic(base_size = 22) +\n scale_color_gradient(low='darkblue', high='yellow') +\n theme(aspect.ratio = 1)\np4 <- ggplot(set22, aes(x = `CD45`, y = `CD45RA`, color = `CD62L`)) +\n geom_point() +\n theme_classic(base_size = 22) +\n scale_color_gradient(low='darkblue', high='yellow') +\n theme(aspect.ratio = 1)\n\np1+p2+p3+p4 + plot_layout(ncol = 4)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#

RNA and proteins with many intersections in top correlated RNA

","metadata":{}},{"cell_type":"markdown","source":"**Subset data from the two datasets**","metadata":{}},{"cell_type":"markdown","source":"We seach assososiations between the following pairs:","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:4c45a8ea-d8b8-4463-90de-ba8b06f3d73e.png)\n![image.png](attachment:e4acb281-f3cd-4a4f-ba10-9863ca3a3dcf.png)","metadata":{},"attachments":{"4c45a8ea-d8b8-4463-90de-ba8b06f3d73e.png":{"image/png":"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"},"e4acb281-f3cd-4a4f-ba10-9863ca3a3dcf.png":{"image/png":"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"}}},{"cell_type":"code","source":"#CITEseq2022\nRNA_pattern <- c(\"_CD7$\", \"TFRC\", \"ITGA2B\",\"CD38\", \"SELL\", \"CD44$\", \"CD274\")\nRNA_names22 <- colnames(mat_RNA)[which(grepl(paste0(RNA_pattern, collapse = \"|\"),\n colnames(mat_RNA),\n ignore.case = T))]\nprot_pattern <- c(\"CD19\",\"CD18\",\"CD41\",\"CD335\", \"CD162\", \"CD45RA\", \"CD40\", \"CD88\", \"CD226\")\n\nset22 <- make_subset(RNA_names22, prot_pattern)\nRNA_names22 <- gsub(\".*_\",\"\", RNA_names22)\n\ncat(\"CITEseq2022: Head of the data frame with\", ncol(set22)-6, \"RNA/proteins in columns X\", nrow(set22), \"cells in rows\")\nhead(set22)\n\n#CITEseq2023\nRNA_names23 <- colnames(mat_RNA23)[which(grepl(paste0(\"CD7$|\", paste0(RNA_pattern, collapse = \"|\")),\n colnames(mat_RNA23),\n ignore.case = T))]\nprot_names <- colnames(mat_prot23)[grep(paste(prot_pattern, collapse = \"$|\"), colnames(mat_prot23))]\n\nset23 <- cbind(mat_RNA23[, RNA_names23],\n mat_prot23[, prot_names]) %>% as.data.frame\ncat(\"CITEseq2023: Head of the data frame with\", ncol(set23), \"RNA/proteins in columns X\", nrow(set23), \"cells in rows\")\nhead(set23)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(25,20)\np1 <- my_ggscatter(set = set22, prot = \"CD335\", RNA = \"ITGA2B\")\np2 <- my_ggscatter(set = set22, prot = \"CD41\", RNA = \"CD7\")\np3 <- my_ggscatter(set = set22, prot = \"CD18\", RNA = \"TFRC\")\np4 <- my_ggscatter(set = set22, prot = \"CD19\", RNA = \"CD7\")\n\np1+p2+p3+p4 + plot_layout(ncol = 2)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(25,20)\nsbst <- set22[set22$ITGA2B !=0, ]\np1 <- my_ggscatter(set = sbst, prot = \"CD335\", RNA = \"ITGA2B\")\nsbst <- set22[set22$CD7 !=0, ]\np2 <- my_ggscatter(set = sbst, prot = \"CD41\", RNA = \"CD7\")\nsbst <- set22[set22$TFRC !=0, ]\np3 <- my_ggscatter(set = sbst, prot = \"CD18\", RNA = \"TFRC\")\nsbst <- set22[set22$CD7 !=0, ]\np4 <- my_ggscatter(set = sbst, prot = \"CD19\", RNA = \"CD7\")\n\np1+p2+p3+p4 + plot_layout(ncol = 2)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(25,20)\np1 <- my_ggscatter(set = set22, prot = \"CD226\", RNA = \"ITGA2B\")\np2 <- my_ggscatter(set = set22, prot = \"CD88\", RNA = \"CD44\")\np3 <- my_ggscatter(set = set22, prot = \"CD40\", RNA = \"CD274\")\np4 <- my_ggscatter(set = set22, prot = \"CD162\", RNA = \"CD38\")\n\np1+p2+p3+p4 + plot_layout(ncol = 2)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(25,20)\nsbst <- set22[set22$ITGA2B !=0, ]\np1 <- my_ggscatter(set = sbst, prot = \"CD226\", RNA = \"ITGA2B\")\nsbst <- set22[set22$CD44 !=0, ]\np2 <- my_ggscatter(set = sbst, prot = \"CD88\", RNA = \"CD44\")\nsbst <- set22[set22$CD274 !=0, ]\np3 <- my_ggscatter(set = sbst, prot = \"CD40\", RNA = \"CD274\")\nsbst <- set22[set22$CD38 !=0, ]\np4 <- my_ggscatter(set = sbst, prot = \"CD162\", RNA = \"CD38\")\n\np1+p2+p3+p4 + plot_layout(ncol = 2)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig(25,20)\np1 <- my_ggscatter(set = set22, prot = \"CD45RA\", RNA = \"SELL\")\np2 <- my_ggscatter(set = set23, prot = \"CD45RA\", RNA = \"SELL\")\n\nsbst <- set22[set22$SELL !=0, ]\np3 <- my_ggscatter(set = sbst, prot = \"CD45RA\", RNA = \"SELL\")\nsbst <- set23[set23$SELL !=0, ]\np4 <- my_ggscatter(set = sbst, prot = \"CD45RA\", RNA = \"SELL\")\n\np1+p2+p3+p4 + plot_layout(ncol = 2)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n 💡 Note: \n \n
","metadata":{}},{"cell_type":"markdown","source":"#

RNA, differentially expressed by gender

","metadata":{}},{"cell_type":"code","source":"fem <- metadata %>% filter(donor == \"13176\") %>% rownames #select cells from female donor\nfem <- mat_RNA[rownames(mat_RNA) %in% fem, ] \n\nmale <- metadata %>% filter(donor != \"13176\") %>% rownames #select cells from female donor\nmale <- mat_RNA[rownames(mat_RNA) %in% male, ] \n\ncat(\"There are\", nrow(fem), \"cells from female donor and\", nrow(male),\n \"cells from male donors.\")\n\nfem_non_zero_RNA <- colSums(fem)\nfem_non_zero_RNA <- names(fem_non_zero_RNA[fem_non_zero_RNA !=0])\n\nmal_non_zero_RNA <- colSums(male)\nmal_non_zero_RNA <- names(mal_non_zero_RNA[mal_non_zero_RNA !=0])\n\ncat(\"\\nRNA with at least 1 non-zero value in females:\", length(fem_non_zero_RNA),\n \"\\nRNA with at least 1 non-zero value in males:\", length(mal_non_zero_RNA),\n \"\\nRNA detected only in female donor:\", length(fem_non_zero_RNA[!fem_non_zero_RNA %in% mal_non_zero_RNA]),\n \"\\nRNA detected only in male donors:\", length(mal_non_zero_RNA[!mal_non_zero_RNA %in% fem_non_zero_RNA]))","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Save all unique RNA, differentially expressed by gender in csv**","metadata":{}},{"cell_type":"code","source":"write.csv(rbind(data.frame(\n \"RNA\" = fem_non_zero_RNA[!fem_non_zero_RNA %in% mal_non_zero_RNA],\n \"sex\" = \"female\"),\n data.frame(\n \"RNA\" = mal_non_zero_RNA[!mal_non_zero_RNA %in% fem_non_zero_RNA],\n \"sex\" = \"male\") ) ,\n'unique_RNA_by_sex.csv', row.names = F)\n#remove unused variables\nfor (thing in ls()) { message(thing); print(object.size(get(thing)), units='auto') }\nrm(fem, fem_non_zero_RNA, mal_non_zero_RNA, male)\ngc()","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#","metadata":{"_kg_hide-input":true},"execution_count":null,"outputs":[]}]}