notebooks/Immunoregulatory_Geneset-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Dependenices\" data-toc-modified-id=\"Dependenices-1\">Dependenices</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-2\">Functions</a></span></li><li><span><a href=\"#Paths\" data-toc-modified-id=\"Paths-3\">Paths</a></span></li><li><span><a href=\"#Main\" data-toc-modified-id=\"Main-4\">Main</a></span></li><li><span><a href=\"#Heatmap\" data-toc-modified-id=\"Heatmap-5\">Heatmap</a></span></li><li><span><a href=\"#Deprecated\" data-toc-modified-id=\"Deprecated-6\">Deprecated</a></span></li><li><span><a href=\"#Enrichment-analysis-with-MSigDB\" data-toc-modified-id=\"Enrichment-analysis-with-MSigDB-7\">Enrichment analysis with MSigDB</a></span></li><li><span><a href=\"#Using-ReactomePA\" data-toc-modified-id=\"Using-ReactomePA-8\">Using ReactomePA</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependenices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: grid\n",
      "\n",
      "========================================\n",
      "ComplexHeatmap version 2.6.2\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "  genomic data. Bioinformatics 2016.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(ComplexHeatmap))\n",
      "========================================\n",
      "\n",
      "\n",
      "========================================\n",
      "circlize version 0.4.12\n",
      "CRAN page: https://cran.r-project.org/package=circlize\n",
      "Github page: https://github.com/jokergoo/circlize\n",
      "Documentation: https://jokergoo.github.io/circlize_book/book/\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. circlize implements and enhances circular visualization\n",
      "  in R. Bioinformatics 2014.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(circlize))\n",
      "========================================\n",
      "\n",
      "\n",
      "Loading required package: AnnotationDbi\n",
      "\n",
      "Loading required package: stats4\n",
      "\n",
      "Loading required package: BiocGenerics\n",
      "\n",
      "Loading required package: parallel\n",
      "\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:parallel’:\n",
      "\n",
      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
      "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n",
      "    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n",
      "    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n",
      "    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n",
      "    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n",
      "    union, unique, unsplit, which.max, which.min\n",
      "\n",
      "\n",
      "Loading required package: Biobase\n",
      "\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "\n",
      "Loading required package: IRanges\n",
      "\n",
      "Loading required package: S4Vectors\n",
      "\n",
      "\n",
      "Attaching package: ‘S4Vectors’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    expand.grid\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#library(ggplot2)\n",
    "library(ComplexHeatmap)\n",
    "library(circlize)\n",
    "#library(clusterProfiler)\n",
    "library(org.Hs.eg.db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/OneDrive - UHN//R_src/ggplot2_theme.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_hm.fx <- function(class_vector_ordered){\n",
    "\n",
    "    colors = structure(c(\"#d7191c\", \"#fdae61\", \"#abd9e9\", \"#2c7bb6\"),\n",
    "                       names = c(\"C1\", \"C2\", \"C3\", \"C4\"))    \n",
    "    \n",
    "    class_mat <- t(as.matrix(class_vector_ordered))\n",
    "    rownames(class_mat) <- \"Cluster\"\n",
    "    class_hm = Heatmap(class_mat,\n",
    "                       #titles and names\n",
    "                       name = \"Immune cluster\",\n",
    "                       show_row_names = TRUE,\n",
    "                       show_column_names = TRUE,    \n",
    "                       #clusters\n",
    "                       cluster_columns = FALSE,\n",
    "                       cluster_rows = FALSE,\n",
    "                       #aesthestics\n",
    "                       col = colors,\n",
    "                       column_names_gp = gpar(fontsize = 15),\n",
    "                       height = unit(1, \"cm\"),\n",
    "                       row_names_gp = gpar(fontsize = 20))\n",
    "    return(class_hm)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_hm.fx <- function(genes_mat){\n",
    "\n",
    "   # col_fun= colorRamp2(c(-1, 0, 1), c(\"blue\", \"white\", \"red\"))\n",
    "    \n",
    "    gene_hm = Heatmap(genes_mat,\n",
    "                       #titles and names   \n",
    "                       name = \"Gene expression z score\",   \n",
    "                       show_row_names = TRUE,\n",
    "                       show_column_names = FALSE,     \n",
    "                       #clusters and orders  \n",
    "                       cluster_columns = TRUE,\n",
    "                       cluster_rows = TRUE,\n",
    "                       show_column_dend = TRUE,\n",
    "                       show_row_dend = TRUE,                      \n",
    "                       #row_dend_width = unit(8, \"cm\"),\n",
    "                       #aesthestics\n",
    "                 #      col = col_fun,\n",
    "                       column_names_gp = gpar(fontsize = 20),\n",
    "                       row_names_gp = gpar(fontsize = 20),\n",
    "                       height = unit(nrow(genes_mat), \"cm\"),\n",
    "                       column_title_gp = gpar(fontsize = 20),\n",
    "                       column_title = NULL,\n",
    "                       row_title = NULL)\n",
    "    return(gene_hm)   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cohorts_hm.fx <- function(cohorts_mat){\n",
    " \n",
    "colpal <- c(\"MB_PDX\" = \"#878787\", #PDX\n",
    "            \"ETMR\" = \"#76afa9\", #ped\n",
    "            \"MNG\" = \"#a6cee3\", \n",
    "            \"MB\" =  \"#1f78b4\",\n",
    "            \"SCHW\" =  \"#678ba5\",\n",
    "            \"ATRT\" =  \"#33a02c\",\n",
    "            \"EPN\" = \"#e31a1c\",\n",
    "            \"pedHGG\" = \"#fdbf6f\",\n",
    "            \"pedLGG\" = \"#8763ae\",\n",
    "            \"NFB\" = \"#cab2d6\",\n",
    "            \"CPH\" = \"#cccc7a\",\n",
    "            \"CP\" = \"#a9a9a9\",\n",
    "            \n",
    "            \"NBL\" = \"#b2df8a\",\n",
    "            \n",
    "            \"SKCM\" = \"#7c9c60\", #Adult\n",
    "            \"LGG\" = \"#753a00\",\n",
    "            \"GBM\" = \"#6a3d9a\",\n",
    "            \"OV\" = \"#afaf78\",\n",
    "            \"PRAD\" = \"#a85426\",\n",
    "            \"LUAD\" = \"#ad3837\",\n",
    "            \"COAD\" = \"#726579\")\n",
    "\n",
    "    cohort_hm = Heatmap(cohorts_mat,\n",
    "                        #titles and names\n",
    "                        name = \"Cohort\",\n",
    "                        show_row_names = TRUE,\n",
    "                        show_column_names = FALSE,    \n",
    "                        #clusters\n",
    "                        cluster_columns = FALSE,\n",
    "                        cluster_rows = FALSE,\n",
    "                        #aesthestics\n",
    "                        col = colpal,\n",
    "                        column_names_gp = gpar(fontsize = 15),\n",
    "                        height = unit(1, \"cm\"),\n",
    "                        row_names_gp = gpar(fontsize = 20))\n",
    "    return(cohort_hm)   \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Data/\"\n",
    "plotpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Plots/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(paste0(datapath,\"exp_mat/tpm_matrix_ped_final.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>20105</li><li>925</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 20105\n",
       "\\item 925\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 20105\n",
       "2. 925\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 20105   925"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(tpm_matrix_ped_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "immunoreg <- read.csv(file = paste0(datapath, \"Geneset/Riaz_Immunoreg_geneset.csv\"), \n",
    "                      header = T, stringsAsFactors = F, check.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_genes <- trimws(immunoreg$Gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'TNFRSF9'</li><li>'IDO1'</li><li>'TDO2'</li><li>'FASLG'</li><li>'KLRC2'</li><li>'IL6'</li><li>'CXCL8'</li><li>'IL23A'</li><li>'STAT3'</li><li>'FGF2'</li><li>'CXCL1'</li><li>'IL1RN'</li><li>'IL10'</li><li>'IL11'</li><li>'IL13'</li><li>'TGFB1'</li><li>'TGFB2'</li><li>'TGFB3'</li><li>'YAP1'</li><li>'CXCL5'</li><li>'PRKCI'</li><li>'PDCD1'</li><li>'CD274'</li><li>'CTLA4'</li><li>'CEACAM1'</li><li>'CD47'</li><li>'LAG3'</li><li>'TIGIT'</li><li>'PDCD1LG2'</li><li>'CD276'</li><li>'IL4I1'</li><li>'VEGFA'</li><li>'VEGFC'</li><li>'VEGFD'</li><li>'FOXP3'</li><li>'ADORA2A'</li><li>'ARHGEF5'</li><li>'BTLA'</li><li>'CD160'</li><li>'CD244'</li><li>'CD27'</li><li>'CD80'</li><li>'GEM'</li><li>'HAVCR2'</li><li>'ICOS'</li><li>'TNFSF4'</li><li>'VTCN1'</li><li>'CSF2'</li><li>'LGALS3'</li><li>'CCL2'</li><li>'PTGES2'</li><li>'ARG1'</li><li>'PDGFC'</li><li>'TNFRSF4'</li><li>'SIRPA'</li><li>'HMGB1'</li><li>'MRC2'</li><li>'VSIR'</li><li>'ENTPD1'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'TNFRSF9'\n",
       "\\item 'IDO1'\n",
       "\\item 'TDO2'\n",
       "\\item 'FASLG'\n",
       "\\item 'KLRC2'\n",
       "\\item 'IL6'\n",
       "\\item 'CXCL8'\n",
       "\\item 'IL23A'\n",
       "\\item 'STAT3'\n",
       "\\item 'FGF2'\n",
       "\\item 'CXCL1'\n",
       "\\item 'IL1RN'\n",
       "\\item 'IL10'\n",
       "\\item 'IL11'\n",
       "\\item 'IL13'\n",
       "\\item 'TGFB1'\n",
       "\\item 'TGFB2'\n",
       "\\item 'TGFB3'\n",
       "\\item 'YAP1'\n",
       "\\item 'CXCL5'\n",
       "\\item 'PRKCI'\n",
       "\\item 'PDCD1'\n",
       "\\item 'CD274'\n",
       "\\item 'CTLA4'\n",
       "\\item 'CEACAM1'\n",
       "\\item 'CD47'\n",
       "\\item 'LAG3'\n",
       "\\item 'TIGIT'\n",
       "\\item 'PDCD1LG2'\n",
       "\\item 'CD276'\n",
       "\\item 'IL4I1'\n",
       "\\item 'VEGFA'\n",
       "\\item 'VEGFC'\n",
       "\\item 'VEGFD'\n",
       "\\item 'FOXP3'\n",
       "\\item 'ADORA2A'\n",
       "\\item 'ARHGEF5'\n",
       "\\item 'BTLA'\n",
       "\\item 'CD160'\n",
       "\\item 'CD244'\n",
       "\\item 'CD27'\n",
       "\\item 'CD80'\n",
       "\\item 'GEM'\n",
       "\\item 'HAVCR2'\n",
       "\\item 'ICOS'\n",
       "\\item 'TNFSF4'\n",
       "\\item 'VTCN1'\n",
       "\\item 'CSF2'\n",
       "\\item 'LGALS3'\n",
       "\\item 'CCL2'\n",
       "\\item 'PTGES2'\n",
       "\\item 'ARG1'\n",
       "\\item 'PDGFC'\n",
       "\\item 'TNFRSF4'\n",
       "\\item 'SIRPA'\n",
       "\\item 'HMGB1'\n",
       "\\item 'MRC2'\n",
       "\\item 'VSIR'\n",
       "\\item 'ENTPD1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'TNFRSF9'\n",
       "2. 'IDO1'\n",
       "3. 'TDO2'\n",
       "4. 'FASLG'\n",
       "5. 'KLRC2'\n",
       "6. 'IL6'\n",
       "7. 'CXCL8'\n",
       "8. 'IL23A'\n",
       "9. 'STAT3'\n",
       "10. 'FGF2'\n",
       "11. 'CXCL1'\n",
       "12. 'IL1RN'\n",
       "13. 'IL10'\n",
       "14. 'IL11'\n",
       "15. 'IL13'\n",
       "16. 'TGFB1'\n",
       "17. 'TGFB2'\n",
       "18. 'TGFB3'\n",
       "19. 'YAP1'\n",
       "20. 'CXCL5'\n",
       "21. 'PRKCI'\n",
       "22. 'PDCD1'\n",
       "23. 'CD274'\n",
       "24. 'CTLA4'\n",
       "25. 'CEACAM1'\n",
       "26. 'CD47'\n",
       "27. 'LAG3'\n",
       "28. 'TIGIT'\n",
       "29. 'PDCD1LG2'\n",
       "30. 'CD276'\n",
       "31. 'IL4I1'\n",
       "32. 'VEGFA'\n",
       "33. 'VEGFC'\n",
       "34. 'VEGFD'\n",
       "35. 'FOXP3'\n",
       "36. 'ADORA2A'\n",
       "37. 'ARHGEF5'\n",
       "38. 'BTLA'\n",
       "39. 'CD160'\n",
       "40. 'CD244'\n",
       "41. 'CD27'\n",
       "42. 'CD80'\n",
       "43. 'GEM'\n",
       "44. 'HAVCR2'\n",
       "45. 'ICOS'\n",
       "46. 'TNFSF4'\n",
       "47. 'VTCN1'\n",
       "48. 'CSF2'\n",
       "49. 'LGALS3'\n",
       "50. 'CCL2'\n",
       "51. 'PTGES2'\n",
       "52. 'ARG1'\n",
       "53. 'PDGFC'\n",
       "54. 'TNFRSF4'\n",
       "55. 'SIRPA'\n",
       "56. 'HMGB1'\n",
       "57. 'MRC2'\n",
       "58. 'VSIR'\n",
       "59. 'ENTPD1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"TNFRSF9\"  \"IDO1\"     \"TDO2\"     \"FASLG\"    \"KLRC2\"    \"IL6\"     \n",
       " [7] \"CXCL8\"    \"IL23A\"    \"STAT3\"    \"FGF2\"     \"CXCL1\"    \"IL1RN\"   \n",
       "[13] \"IL10\"     \"IL11\"     \"IL13\"     \"TGFB1\"    \"TGFB2\"    \"TGFB3\"   \n",
       "[19] \"YAP1\"     \"CXCL5\"    \"PRKCI\"    \"PDCD1\"    \"CD274\"    \"CTLA4\"   \n",
       "[25] \"CEACAM1\"  \"CD47\"     \"LAG3\"     \"TIGIT\"    \"PDCD1LG2\" \"CD276\"   \n",
       "[31] \"IL4I1\"    \"VEGFA\"    \"VEGFC\"    \"VEGFD\"    \"FOXP3\"    \"ADORA2A\" \n",
       "[37] \"ARHGEF5\"  \"BTLA\"     \"CD160\"    \"CD244\"    \"CD27\"     \"CD80\"    \n",
       "[43] \"GEM\"      \"HAVCR2\"   \"ICOS\"     \"TNFSF4\"   \"VTCN1\"    \"CSF2\"    \n",
       "[49] \"LGALS3\"   \"CCL2\"     \"PTGES2\"   \"ARG1\"     \"PDGFC\"    \"TNFRSF4\" \n",
       "[55] \"SIRPA\"    \"HMGB1\"    \"MRC2\"     \"VSIR\"     \"ENTPD1\"  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycluster <- as.character(metadata_IC$immune_cluster)\n",
    "names(mycluster) <- rownames(metadata_IC)\n",
    "cluster_hm <- class_hm.fx(mycluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycohort <- metadata_IC$cohort\n",
    "names(mycohort) <- metadata_IC$sample_id\n",
    "mycohorts <- t(as.matrix(mycohort))\n",
    "rownames(mycohorts) <- \"Cohort\"\n",
    "cohorts_hm <- cohorts_hm.fx(mycohorts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg_genes[!reg_genes %in% rownames(tpm_matrix_ped_final)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_genes <- reg_genes[reg_genes %in% rownames(tpm_matrix_ped_final)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>59</li><li>925</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 59\n",
       "\\item 925\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 59\n",
       "2. 925\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  59 925"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mygen_mat <- tpm_matrix_ped_final[ reg_genes, metadata_IC$sample_id]\n",
    "dim(mygen_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygen_mat_log <- log2(mygen_mat + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 925</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TARGET-30-PAIFXV-01A</th><th scope=col>TARGET-30-PAIPGU-01A</th><th scope=col>TARGET-30-PAISNS-01A</th><th scope=col>TARGET-30-PAITCI-01A</th><th scope=col>TARGET-30-PAIVHE-01A</th><th scope=col>TARGET-30-PAIXIF-01A</th><th scope=col>TARGET-30-PAKYZS-01A</th><th scope=col>TARGET-30-PAKZRH-01A</th><th scope=col>TARGET-30-PALBFW-01A</th><th scope=col>TARGET-30-PALCBW-01A</th><th scope=col>⋯</th><th scope=col>ICGC_PA89</th><th scope=col>ICGC_PA9</th><th scope=col>ICGC_PA91</th><th scope=col>ICGC_PA92</th><th scope=col>ICGC_PA93</th><th scope=col>ICGC_PA94</th><th scope=col>ICGC_PA95</th><th scope=col>ICGC_PA96</th><th scope=col>ICGC_PA97</th><th scope=col>ICGC_PA99</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TNFRSF9</th><td>1.2203300</td><td>1.4905701</td><td>0.22650853</td><td>0.43295941</td><td>0.7048720</td><td>0.5058909</td><td>3.16832112</td><td>0.07038933</td><td>0.7990873</td><td>0.9781956</td><td>⋯</td><td>0.13750352</td><td>0.27500705</td><td>0.04264434</td><td>0.02856915</td><td>0.16349873</td><td>0.0000000</td><td>0.07038933</td><td>0.27500705</td><td>0.33342373</td><td>0.07038933</td></tr>\n",
       "\t<tr><th scope=row>IDO1</th><td>2.5509007</td><td>2.2357271</td><td>0.94860085</td><td>0.77399633</td><td>1.5655972</td><td>2.3868109</td><td>0.21412481</td><td>1.20163386</td><td>1.4698860</td><td>2.1699250</td><td>⋯</td><td>0.00000000</td><td>0.12432814</td><td>0.22650853</td><td>0.00000000</td><td>0.15055968</td><td>0.0000000</td><td>0.15055968</td><td>0.01435529</td><td>0.09761080</td><td>0.12432814</td></tr>\n",
       "\t<tr><th scope=row>TDO2</th><td>1.1634987</td><td>3.7949357</td><td>0.61353165</td><td>2.28392177</td><td>2.6016965</td><td>1.7865964</td><td>1.74416110</td><td>1.32768736</td><td>1.2750070</td><td>1.1634987</td><td>⋯</td><td>0.22650853</td><td>0.15055968</td><td>0.04264434</td><td>0.55581616</td><td>0.18903382</td><td>0.0000000</td><td>0.71369581</td><td>0.07038933</td><td>0.15055968</td><td>1.12432814</td></tr>\n",
       "\t<tr><th scope=row>FASLG</th><td>0.1375035</td><td>0.5849625</td><td>0.02856915</td><td>0.07038933</td><td>0.4541759</td><td>0.1505597</td><td>0.07038933</td><td>0.05658353</td><td>0.3448285</td><td>0.3448285</td><td>⋯</td><td>0.01435529</td><td>0.04264434</td><td>0.01435529</td><td>0.01435529</td><td>0.08406426</td><td>0.0000000</td><td>0.11103131</td><td>0.02856915</td><td>0.01435529</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>KLRC2</th><td>1.2078929</td><td>0.2387869</td><td>0.32192809</td><td>0.46466827</td><td>0.3448285</td><td>0.4114262</td><td>0.45417589</td><td>0.08406426</td><td>0.9927684</td><td>0.5945485</td><td>⋯</td><td>2.68929916</td><td>1.65535183</td><td>2.17312743</td><td>3.40053793</td><td>1.76553475</td><td>3.0635029</td><td>2.23878686</td><td>2.67807191</td><td>4.06781078</td><td>3.36737107</td></tr>\n",
       "\t<tr><th scope=row>IL6</th><td>5.5604093</td><td>1.1309309</td><td>1.57531233</td><td>4.19770816</td><td>4.7527486</td><td>6.5248159</td><td>4.39574833</td><td>1.26303441</td><td>3.7886857</td><td>2.0942361</td><td>⋯</td><td>0.16349873</td><td>0.67807191</td><td>0.36737107</td><td>0.18903382</td><td>0.27500705</td><td>0.1375035</td><td>0.26303441</td><td>0.27500705</td><td>0.41142625</td><td>0.00000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 925\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & TARGET-30-PAIFXV-01A & TARGET-30-PAIPGU-01A & TARGET-30-PAISNS-01A & TARGET-30-PAITCI-01A & TARGET-30-PAIVHE-01A & TARGET-30-PAIXIF-01A & TARGET-30-PAKYZS-01A & TARGET-30-PAKZRH-01A & TARGET-30-PALBFW-01A & TARGET-30-PALCBW-01A & ⋯ & ICGC\\_PA89 & ICGC\\_PA9 & ICGC\\_PA91 & ICGC\\_PA92 & ICGC\\_PA93 & ICGC\\_PA94 & ICGC\\_PA95 & ICGC\\_PA96 & ICGC\\_PA97 & ICGC\\_PA99\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tTNFRSF9 & 1.2203300 & 1.4905701 & 0.22650853 & 0.43295941 & 0.7048720 & 0.5058909 & 3.16832112 & 0.07038933 & 0.7990873 & 0.9781956 & ⋯ & 0.13750352 & 0.27500705 & 0.04264434 & 0.02856915 & 0.16349873 & 0.0000000 & 0.07038933 & 0.27500705 & 0.33342373 & 0.07038933\\\\\n",
       "\tIDO1 & 2.5509007 & 2.2357271 & 0.94860085 & 0.77399633 & 1.5655972 & 2.3868109 & 0.21412481 & 1.20163386 & 1.4698860 & 2.1699250 & ⋯ & 0.00000000 & 0.12432814 & 0.22650853 & 0.00000000 & 0.15055968 & 0.0000000 & 0.15055968 & 0.01435529 & 0.09761080 & 0.12432814\\\\\n",
       "\tTDO2 & 1.1634987 & 3.7949357 & 0.61353165 & 2.28392177 & 2.6016965 & 1.7865964 & 1.74416110 & 1.32768736 & 1.2750070 & 1.1634987 & ⋯ & 0.22650853 & 0.15055968 & 0.04264434 & 0.55581616 & 0.18903382 & 0.0000000 & 0.71369581 & 0.07038933 & 0.15055968 & 1.12432814\\\\\n",
       "\tFASLG & 0.1375035 & 0.5849625 & 0.02856915 & 0.07038933 & 0.4541759 & 0.1505597 & 0.07038933 & 0.05658353 & 0.3448285 & 0.3448285 & ⋯ & 0.01435529 & 0.04264434 & 0.01435529 & 0.01435529 & 0.08406426 & 0.0000000 & 0.11103131 & 0.02856915 & 0.01435529 & 0.00000000\\\\\n",
       "\tKLRC2 & 1.2078929 & 0.2387869 & 0.32192809 & 0.46466827 & 0.3448285 & 0.4114262 & 0.45417589 & 0.08406426 & 0.9927684 & 0.5945485 & ⋯ & 2.68929916 & 1.65535183 & 2.17312743 & 3.40053793 & 1.76553475 & 3.0635029 & 2.23878686 & 2.67807191 & 4.06781078 & 3.36737107\\\\\n",
       "\tIL6 & 5.5604093 & 1.1309309 & 1.57531233 & 4.19770816 & 4.7527486 & 6.5248159 & 4.39574833 & 1.26303441 & 3.7886857 & 2.0942361 & ⋯ & 0.16349873 & 0.67807191 & 0.36737107 & 0.18903382 & 0.27500705 & 0.1375035 & 0.26303441 & 0.27500705 & 0.41142625 & 0.00000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 925\n",
       "\n",
       "| <!--/--> | TARGET-30-PAIFXV-01A &lt;dbl&gt; | TARGET-30-PAIPGU-01A &lt;dbl&gt; | TARGET-30-PAISNS-01A &lt;dbl&gt; | TARGET-30-PAITCI-01A &lt;dbl&gt; | TARGET-30-PAIVHE-01A &lt;dbl&gt; | TARGET-30-PAIXIF-01A &lt;dbl&gt; | TARGET-30-PAKYZS-01A &lt;dbl&gt; | TARGET-30-PAKZRH-01A &lt;dbl&gt; | TARGET-30-PALBFW-01A &lt;dbl&gt; | TARGET-30-PALCBW-01A &lt;dbl&gt; | ⋯ ⋯ | ICGC_PA89 &lt;dbl&gt; | ICGC_PA9 &lt;dbl&gt; | ICGC_PA91 &lt;dbl&gt; | ICGC_PA92 &lt;dbl&gt; | ICGC_PA93 &lt;dbl&gt; | ICGC_PA94 &lt;dbl&gt; | ICGC_PA95 &lt;dbl&gt; | ICGC_PA96 &lt;dbl&gt; | ICGC_PA97 &lt;dbl&gt; | ICGC_PA99 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TNFRSF9 | 1.2203300 | 1.4905701 | 0.22650853 | 0.43295941 | 0.7048720 | 0.5058909 | 3.16832112 | 0.07038933 | 0.7990873 | 0.9781956 | ⋯ | 0.13750352 | 0.27500705 | 0.04264434 | 0.02856915 | 0.16349873 | 0.0000000 | 0.07038933 | 0.27500705 | 0.33342373 | 0.07038933 |\n",
       "| IDO1 | 2.5509007 | 2.2357271 | 0.94860085 | 0.77399633 | 1.5655972 | 2.3868109 | 0.21412481 | 1.20163386 | 1.4698860 | 2.1699250 | ⋯ | 0.00000000 | 0.12432814 | 0.22650853 | 0.00000000 | 0.15055968 | 0.0000000 | 0.15055968 | 0.01435529 | 0.09761080 | 0.12432814 |\n",
       "| TDO2 | 1.1634987 | 3.7949357 | 0.61353165 | 2.28392177 | 2.6016965 | 1.7865964 | 1.74416110 | 1.32768736 | 1.2750070 | 1.1634987 | ⋯ | 0.22650853 | 0.15055968 | 0.04264434 | 0.55581616 | 0.18903382 | 0.0000000 | 0.71369581 | 0.07038933 | 0.15055968 | 1.12432814 |\n",
       "| FASLG | 0.1375035 | 0.5849625 | 0.02856915 | 0.07038933 | 0.4541759 | 0.1505597 | 0.07038933 | 0.05658353 | 0.3448285 | 0.3448285 | ⋯ | 0.01435529 | 0.04264434 | 0.01435529 | 0.01435529 | 0.08406426 | 0.0000000 | 0.11103131 | 0.02856915 | 0.01435529 | 0.00000000 |\n",
       "| KLRC2 | 1.2078929 | 0.2387869 | 0.32192809 | 0.46466827 | 0.3448285 | 0.4114262 | 0.45417589 | 0.08406426 | 0.9927684 | 0.5945485 | ⋯ | 2.68929916 | 1.65535183 | 2.17312743 | 3.40053793 | 1.76553475 | 3.0635029 | 2.23878686 | 2.67807191 | 4.06781078 | 3.36737107 |\n",
       "| IL6 | 5.5604093 | 1.1309309 | 1.57531233 | 4.19770816 | 4.7527486 | 6.5248159 | 4.39574833 | 1.26303441 | 3.7886857 | 2.0942361 | ⋯ | 0.16349873 | 0.67807191 | 0.36737107 | 0.18903382 | 0.27500705 | 0.1375035 | 0.26303441 | 0.27500705 | 0.41142625 | 0.00000000 |\n",
       "\n"
      ],
      "text/plain": [
       "        TARGET-30-PAIFXV-01A TARGET-30-PAIPGU-01A TARGET-30-PAISNS-01A\n",
       "TNFRSF9 1.2203300            1.4905701            0.22650853          \n",
       "IDO1    2.5509007            2.2357271            0.94860085          \n",
       "TDO2    1.1634987            3.7949357            0.61353165          \n",
       "FASLG   0.1375035            0.5849625            0.02856915          \n",
       "KLRC2   1.2078929            0.2387869            0.32192809          \n",
       "IL6     5.5604093            1.1309309            1.57531233          \n",
       "        TARGET-30-PAITCI-01A TARGET-30-PAIVHE-01A TARGET-30-PAIXIF-01A\n",
       "TNFRSF9 0.43295941           0.7048720            0.5058909           \n",
       "IDO1    0.77399633           1.5655972            2.3868109           \n",
       "TDO2    2.28392177           2.6016965            1.7865964           \n",
       "FASLG   0.07038933           0.4541759            0.1505597           \n",
       "KLRC2   0.46466827           0.3448285            0.4114262           \n",
       "IL6     4.19770816           4.7527486            6.5248159           \n",
       "        TARGET-30-PAKYZS-01A TARGET-30-PAKZRH-01A TARGET-30-PALBFW-01A\n",
       "TNFRSF9 3.16832112           0.07038933           0.7990873           \n",
       "IDO1    0.21412481           1.20163386           1.4698860           \n",
       "TDO2    1.74416110           1.32768736           1.2750070           \n",
       "FASLG   0.07038933           0.05658353           0.3448285           \n",
       "KLRC2   0.45417589           0.08406426           0.9927684           \n",
       "IL6     4.39574833           1.26303441           3.7886857           \n",
       "        TARGET-30-PALCBW-01A ⋯ ICGC_PA89  ICGC_PA9   ICGC_PA91  ICGC_PA92 \n",
       "TNFRSF9 0.9781956            ⋯ 0.13750352 0.27500705 0.04264434 0.02856915\n",
       "IDO1    2.1699250            ⋯ 0.00000000 0.12432814 0.22650853 0.00000000\n",
       "TDO2    1.1634987            ⋯ 0.22650853 0.15055968 0.04264434 0.55581616\n",
       "FASLG   0.3448285            ⋯ 0.01435529 0.04264434 0.01435529 0.01435529\n",
       "KLRC2   0.5945485            ⋯ 2.68929916 1.65535183 2.17312743 3.40053793\n",
       "IL6     2.0942361            ⋯ 0.16349873 0.67807191 0.36737107 0.18903382\n",
       "        ICGC_PA93  ICGC_PA94 ICGC_PA95  ICGC_PA96  ICGC_PA97  ICGC_PA99 \n",
       "TNFRSF9 0.16349873 0.0000000 0.07038933 0.27500705 0.33342373 0.07038933\n",
       "IDO1    0.15055968 0.0000000 0.15055968 0.01435529 0.09761080 0.12432814\n",
       "TDO2    0.18903382 0.0000000 0.71369581 0.07038933 0.15055968 1.12432814\n",
       "FASLG   0.08406426 0.0000000 0.11103131 0.02856915 0.01435529 0.00000000\n",
       "KLRC2   1.76553475 3.0635029 2.23878686 2.67807191 4.06781078 3.36737107\n",
       "IL6     0.27500705 0.1375035 0.26303441 0.27500705 0.41142625 0.00000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mygen_mat_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygen_mat_z <- t(scale(t(mygen_mat_log)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The automatically generated colors map from the minus and plus 99^th of\n",
      "the absolute values in the matrix. There are outliers in the matrix\n",
      "whose patterns might be hidden by this color mapping. You can manually\n",
      "set the color to `col` argument.\n",
      "\n",
      "Use `suppressMessages()` to turn off this message.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gene_hm <- gene_hm.fx(mygen_mat_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H1wPr+o5pfIbViEAuBphmAHAPCUyTq2cHCjhv1n/npNR0TkXHfIJ1vjrh9aMrZp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4YhISHqTgwhhEorQSePAZa+22IBQHTG72jS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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_hm %v% cluster_hm %v% cohorts_hm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"heatmap_immunoregulatory_genes.pdf\"),\n",
    "   width = 20, height = 40)\n",
    "gene_hm %v% cluster_hm %v% cohorts_hm\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get median of log2 gene expression for each immune cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_mat <- matrix(nrow = 59, ncol = 4,\n",
    "                    dimnames = list(rownames(mygen_mat_log), c(\"C1\", \"C2\", \"C3\", \"C4\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for( g in rownames(mygen_mat_log)){\n",
    "    median_mat[g,1] <- median(mygen_mat_z[g,metadata_IC$sample_id[metadata_IC$immune_cluster == \"C1\"]])\n",
    "    median_mat[g,2] <- median(mygen_mat_z[g, metadata_IC$sample_id[metadata_IC$immune_cluster == \"C2\"]])    \n",
    "    median_mat[g,3] <- median(mygen_mat_z[g, metadata_IC$sample_id[metadata_IC$immune_cluster == \"C3\"]])\n",
    "    median_mat[g,4] <- median(mygen_mat_z[g, metadata_IC$sample_id[metadata_IC$immune_cluster == \"C4\"]])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "   # col_fun= colorRamp2(c(-1, 0, 1), c(\"blue\", \"white\", \"red\"))\n",
    "    \n",
    "    median_gene_hm = Heatmap(t(median_mat),\n",
    "                       #titles and names   \n",
    "                       name = \"Median z score gene expression\",   \n",
    "                       show_row_names = TRUE,\n",
    "                       show_column_names = TRUE,     \n",
    "                       #clusters and orders  \n",
    "                       cluster_columns = TRUE,\n",
    "                       cluster_rows = TRUE,\n",
    "                       show_column_dend = TRUE,\n",
    "                       show_row_dend = TRUE,                      \n",
    "                       #row_dend_width = unit(8, \"cm\"),\n",
    "                       #aesthestics\n",
    "                 #      col = col_fun,\n",
    "                       column_names_gp = gpar(fontsize = 20),\n",
    "                       row_names_gp = gpar(fontsize = 15),\n",
    "                       width = unit(nrow(median_mat)/2, \"cm\"),\n",
    "                       height = unit(ncol(median_mat), \"cm\"),\n",
    "                       column_title_gp = gpar(fontsize = 20),\n",
    "                       column_title = NULL,\n",
    "                       row_title = NULL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MEOAABAEgQ7AAAASRDsAAAAJEGwAwAA\nkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEO\nAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEAS\nBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJOHw4MbrxH3yja+TJkugi3DQ\npJx3bzQoJ2fJCNsLEUK8f6fNSt09l8r2QtJmVGwvRAhRtmWI7YXcPprG9kKEEM6ptFkpRw02\nsMiUN1KDUoR4eV+bYyo8RIPqFxmpTR32yKXBxtHqgPJ7qM0WTp9ZZ3shD647216IEKJqu1BN\nygl9rcF1/rs32nQWhAVrsMdf+WmzMIo2x7dI565BtSlY5b3thQghzu9MbXsh74JtL0MIIaq0\nDNeknDda7PGIcG1aG9e02pykQl4leHnosQMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQ\nBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4A\nAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIE\nOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAA\nSRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwA\nAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEk4rP46PHGf\nzJhZ0WQJIt9rUowIC3WwvZA0btqsVEiQBgsjhGgz6IXthfz9R2bbCxFCZM4TaXshzq7abOG3\nL7W5Jol4r8GeCg/RZneHa1GHhRChwRqU8z7M9jK0o82GEa6pNWq1IrTYwolsek2lSa/NSqXV\nohydzvYyhBDivRbHgqLNhhGv/bRpbbRZHo2OhYyZNdhVfk+12TKVmmtzMLx86GR7IY7Otpch\nhBCuabWpf2FvE7zL6bEDAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAA\nAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDs\nAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAk\nQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMA\nAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATB\nDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASzim9ACIy0kGTcpxTaVDI21faLMy7EG3K\nSev3yPZC6uU+Y3shQgglfWbbC3EICrS9ECGEyJFBk2IiXdPaXsh13/K2FyKEcEqlaFLO3Qta\nHAwaeR+mwbGg02bDCCcnbcp591aDlXLPrrO9ECFE7uIRmpTj/1iDrRP+TpumLyJcg3Ky5ou0\nvRAhRDp3berfi4ca9KT4PtamO6Z21zDbC3F01mbLBD7SJoq4ZdbgmHJ21Walwt6mWMcZPXYA\nAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg\n2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAA\nSIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAH\nAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJ\ngh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAA\ngCQIdgAAAJIg2AEAAEiCYAcAACAJB0VRUnoZAAAAoAF67AAAACRBsAMAAJAEwQ4AAEASzim9\nAAC05X/rxD8vhRAiS8k6JTKn9NLgwxAaGpo6dWr7KQcfBHa3XYr34YnTM9pPPyWEELXG7Bhd\nU/sFUMt3yFR71M+janlo/wX/JyLfPLx88fqjwIg0Htnzl65YMptrwj7/PvD22ePnbz3xC3gr\n3DLnLFS+dp1Ked2id+j+P+ypsBc3zp698fBliGOG7IUq1KpWOKNTnO/3vbjngo8QQuSs0rpy\njmRZRIM4DswtHR06bRVCCE9vZUvHZF2qxIq/+pmnCw14cu/uvccvXgcHB4eEi1Sp06Z188iR\nr0DBAnmypI1759mhkEcntqzfduRc5s+2j6tv+uK2ntnG3qlSu6lnt16fNC6c3kGr73x4Yvva\nNWvWbnb/4dUmG2qLVuXEIyLYz/fFq7choRGOrmnSpnPPmj1zWm26J2xtQjWmyZlXF/ToyqWb\nj/3fOabPkrtYhXL502t1TCRmd1u9hTVthyMDbp/868TZsxeu3XseEBj4+p3imi69e7Z8RYqX\nLF+jQeM6ZWzb0XZTbZSQp1fPX7vvGxDm4p413kPi6bmdO3cKIYRzjyRZGmP5O/cdvzBvy7L+\n5dMnyffYQHnnc/XEY4+m1fIJIcSRMVVGH47nE28e3/B3zZ81ra3f3HzerbnN9P/UvX14/viZ\nW4/9gx0y5i1Vs17NQhkNp72QG6tGDPxm5XGfMMPnHNLlq9N11JRJn9fLGbWDI/3OrZ07Z9X2\nY1cqLQhc28H497B7u2d8PfbnrTde62J8uUvO2j2GfTdhaNP8LkII7fbU+Xk95p5L1CdjqjZ0\n3ZCqGpSj9/rv1ZPH/bBk/523URc6zlmqdB09Y9rwhrksNSsnfmyjj1D1OtZ38NNgMepPPDox\n1hndrKQ+MLVma/WL4e3dQ2uX/7b7wB9/XX4SrIv9uhDCMV3usjXrNmzZsUf3NpWzmynDzry5\ntmps/5GLz/jrhBCerc28Qxfi9++Zff+e2bdy6tjq/ab+Mq1v5YyJ/z4l6O5R77Vr1qzd8tc9\nfZ33TJJydMHPHzx89sIxT40SWaP/PezenrkzF20+eOmub5Cje868xWu27fb5571q5TJzSorw\nv75/02/b9h09ee7K3RchkTFedEqXNV+xslWq16zbpHX7ltXzprWUeG1vQpOVVQd4hP/1/TsP\nXn742sE9f4WGrZtXyGZYWp3v0dkjR83YdOFFhPHdqTxKNfca/f3XPStnTuxlQZy7W4MtbGM7\nHPNSNujGpp+nzV265fSzUIsr5JCuUIMufQcNHeRZNqPZjWIH1eZ9aGikEEI4uaROZe5iN8Ln\nr0Xjxk7bcCraeirx8DbsN0/v+N6aKN4xmgHXwh1nn/LXJck3JZwu4OrmbztWzOoaffW9E9f+\nJYr+SyMe7P22ZcE00V9wcCvWafbZV4qihN6c1zSL+U87ZG06/ewb/Zo8/X1YtUyGehttVwad\nn1o/zpt1GcoP3Hw3IvaKJ35PabUBtauQodd+aZs/lYWvccze6KdzQfGtS40ayb1ScRyYSX3M\nJpgW1c/4kWvL+lTImJCxwc45an6+5Fxgsq+19SIebe9bKvoBbnbHmRw4rsU/XXsnLOHfFXhr\n35JvutbOl0aYSFhtibec8Md//OTVtFQWFzOFvzwwpGy62HvKIUudCUdfxvia0Hvbx7UqZO0l\nskO6go0HzzvyONx0YW1vQpNdvEfxu2vLe5Zyi764qYt2WnI9VFEU3YO1HXNaOkac87SedS6h\nKxXf7tZiC2vQDhu3VeSTAxOa5rVUmJniM1fpv/rGW5OVtpNqE2dNCDo/s1m22JH0fDymN1Lf\n2Gi66Ut3tVhm4y5x91Dv7jnlqD9y8z+WdmGyefnnhHqZoraXdcHOMW2OkrXb9GxVKv6aZA1P\nb0V56t0tr/kLlYyNF955OLdu1JWBg6t7jry5M6eJdkRn6bDuiRJ+YXzZ6P3DHbeo6+K/o0fu\naH93yVqyzkeen3Rq36x6EY+oYp3yf7rtqXZ7yt6C3XPvLrmiHxeOrhmz58qR0TXaVszcavUj\nsxlW82DXaFp8B6QVB+aCNhpvIttoU/0URVEU3519isYez+PgkiFbngKFi5UsWbxooXx5cmZ2\ni92gO+frsPhmaEptgji9OTq4aNQR7pSlfPtB62+Zed+Lc2t+HNS+YrS7LI6F+u71t/Jb3r+8\nsnvBiE7Vcpq7U+SYoXCDnstvaFeO38Hh5WIkt+hV8eHCxpY7+zO3WvPYcKwFHv+muuVOSccM\n2XKkN3vqdivZbenVqJO0Bk2oNgLuWnt0x3PmDVB095a3MhcrHHL33Ol/e2a1aHsnVYachYoW\nzZ81bbSVytTy1zuR2u1uLbawJu2wvppFPtrSq3DMHjMH10wFKzZo7dmlx2f9+/fp1qFFgxpl\n8qQzyb5pyg7Z/9xYqN1Um7iC3aO17WNUAxf33IWKFcmbyexyW8lTi2WO2iXr/tvoVcbQGKTK\n0/Rr79vBWnxDYgTuG1gs+t0bhzQ995gucN1xu7es+/Wn0T2bFnfXV0jHQr22++gURfH9e19i\n7F4xplGuaC2Vc5cd/t6eUfsoda6KTdq1b1W/TDb1Te4VKhZQ31rkkwXHn4YoiqIoYb6nl35a\n0nD+y/jJyl9aGv7DrXyfX47eD9IfHO9PjyhoWL9sdYatu/TyfdQWCHt2cnEf483WzJ9sC9Bq\nT52a3k4T009psafDTnyVz7h9i3X8ac+NAP3lfujz87+NqZ9DPVTTNZj/wMynzQU7xwyF63f/\net6aTZs2btzw29q1a1avWrXSKoOrxHG0JYBxYewi2D1frkn1U5SIf+fUz2BYRwePki37T/p1\n+7Frj1+FxWrsI4L9Hl475r3g28/q5Io6jvP02O6XbKttraA9vY2DgjLVGuF95208H3hzc/OY\nhjkNp8CMbVY/jfPtYb4Xt/08tH35bOYikGuOyh2G/Lz5zNN38S5nAsqJuDGzRsz+uFTuffcZ\nCgo92D+b4e/pSnf+YdWuQwd2rPi+c3HDR7L03BWsKIryZMVHhrbPwb2857Bpi9ds2rxx9aIf\nv/Is7+4ghHDK2WTS0Ye+/54/sG7uhIEdquaIHj7S15h6MVhRFEWLJnSbNt0v2l3TPl3e1LiF\nXXJWbdO5U/PyWdSgkbtUKfUwcS32ydw/7gWpCS7c79KGIdXdDZu+xarncSxqQqqNFlt480FN\n2mFPb0UJOzu2ZFTmcsnX5Iu52y76muvbDvO7cWTN953KRLsHm7ryD1fCtFopjaqN5WD38re2\nxksk98oDlx5/Eqy2hWb2mtU0DnbeiqKE3Fr3eSVj4+2Su96gBYcfJft1tt+2bsa2NlXeJqNX\nnXwSYjx3mOkhj/S/sLhbsVRCCOFSavABa6+ho4t8efqX3uWirk4ds9YcvObq60dz6jga6k+f\nLQ8MdxjC7m/tWzzaFYlj+e9uvI9Rns5nYwc1yTuVKKu+NVtn72jLFrH3M7XnzbXm1Jtm7+m8\nOTWstHrNUnX6AjvcUzbTHeyvbiaHgl6x99z7hxs/yaM/6tO3XuUT6+PGylCzlsm1nWOGIg0+\nHbdoz1U/09tClmnV7ttVsAte3SaVFtXvv5Cdn6rHh2Pejr9cDLByGMC7e5v6lzHcTck9+E87\nq6P3Z1VXV9Gt3vQr8ecrvfA7KzoYOjvzf3nSXBULeXJmw/RBLUtnMtfp4FakyWffLz/076sI\nM5+0tZzHCxsZbp06ZavZb+7+68/fRe2s0G3djHnd0zvafdeAff3y6w+1VA0W+ygh+7zUMXnO\nxQfsfRZzb0c+2+lVyFEIIdxqzrhh+PKIwBve41vkMyb5AgOPBiuaNKENFmjS+6JZsJs9saz6\nT48GM/9W75m8OjOxRvQ7s9k7evvGOkiCjg0urMaYcpNuxl7ERFQbTbZw+dpqjLKtHfb0jrwx\nqZIhjLoU91pvRZ9DZMCZuR3yGRbVueKUWzq7qjYWg90/UyoY1rTihPMxrpH0CE8AACAASURB\nVAiFSJ29RPk4FDCEjcwlapvo1NXVdsbBgMaFDn/4+/fNo45O4ZKr9oA5v995E3dDvk2LhXF1\nde26LeLMVwXUr05dcfhRP5PvtTD0Qfds0yc5hRBCZGy5/GGCdtvrK6sHVc9i7MV1yFj+s4Vn\n/XWKorxZ1kL9c85+B2I2+mHHhxr6O4Rjk8UvYhf73/SqakeielrLO+ps9NcvjS6k/3v6Xrst\nDtUJ3tpVf2XoUK+PYUC3jXtKE1rt7lafqHfNU7dbG2B+C5wcWVy/E3L2OWjamxJVGTb4/r1z\nwZiudQu6mQx3cPYo2cxr4vKDtwLiPYneWdmhUNTWTPyB6VnPQkOQElu4Wes8mlS/mYs+VuNC\n8VFnrQ/LiqIoypPlH6khMpVjKi1Wqm5dLUpxde0639A0O9eceT9Bx82rvT3Uq8/Unb1DjH/W\nvX1wbO0Ur6bFMsQ1DDH+ipHocnQXxxZWX83x8Zq7ppVed8jL0BFS5OsLMV97sbiJvvo7Nl3y\nZEsXNaQU+uqE2eC6z0vf3poeuc/3DzScep3qzbupTROqTbVp/YU2B/iwL+qoK1hnTvRzTeSl\ncaUMzY9j7dnm+rYUxW95S3UZ6s71tXl3a3aSUtO+re3wxv19DRM2ZOu82cfqQyry3sq2hnua\nmbz2vbSnatPV8Ey0yfZ/+rPhhcIjTpscaEIItwr9190OUSywZoy2JmKWH3Rj/YhGuaN1Azuk\nL9J80M87r1s6PWp2MbRhdy9DR3btufdiVwzLG8RvQzv9sZi5xx4rb00G/+s9ulG052fSlug0\n45iP8RLg9FD1sryAaRuoKMrZUYa+65ITzY6OuTGxpLrt9P/30ZoYe3lPD7XPuP6iOG5Q+S1S\nH9LM0aKBNntKE1rt7rqN1VsTdX+xeGPi3bEh+gziWGHStZgVwkxlCHly1nvWMM/qeUxHGbtk\nLd/686lrj959E8fgltcXFnxSWL2jZPuB2XZ1YKKtaqvNFq5dT90QNla/Lj2K6P9VZoq5AWhx\nizw+NJc2q6On1ZDKtgOa6c8fzq1XJfjpjn8ml9eXkrnfAV3kmzuHV3zbq0GsCwshhJN7scZ9\nvlv+x7/LDA8jWw52NpdzdpR62nNrtdLMSe/69yXUz+Yfc970xcBlLfTf6tp5+ogC+rcVGX/Z\nwqKeHqG/ZEjVYlnMLBB+dnRx9Uvqj+ivSROqDU9vbQ7wPweoTxxVmnHP0uYt/+MdCwU/nl1d\n/5bsg49rUG00OkmpOcrGdvjjbweo9/nTNFn8zFJJ5j1f1kK9dPTwmj3EnqqNhTswR/qr10iF\nvrlkugjqJ9zKfLbmhtkwEkf1+m/HhM/bV82l0bQtZirN21vbJniWzhCjyrlkr9B64A+r/7wd\n8D5JFsZzwufqHYCsXvvMXSnGsUEijn6hb2qcP1oR7/3YsAf7JrUqGLW8rgU++v73+zH7Lown\nv+YrYz+iELi0sfrRFqvMjssJWfNRjFUzKeSQl3pJ2G7De3MfVxdzvToS36VOLW32lCa02t21\n6qiRtM36OB4wDNzeWd+Spm+9LkYKiaMy6ILuH18/bXCbirGm2kids+rHQ2ZuOvUo2Pz15Ou/\nl3QppqahRB2YWqXeQtU02cLq+ttY/Ro0VDdJ63WJuJv6dlUL/aeditbQYKVajNam+jVqrZ4i\nKkz7L+ErdekbtdMza7mKec08OJo6V9WOw2ZvO+9j2GBxP2h5b/vYbjVtLcd3dm39S66fbDZz\nY/n1ylZqI5HxswOxL3CuTiimf7V019ZqtbG8u4NXt9S/JeeXJ01e+mdyaf1LmVo01aQJdSle\nS4uTi35z2XyA7+ii9gW0/c3kFGWs53E0aOG/qddsjvlLaFBttD1J2dgON+9cTv8P59arXlku\nyLzQ7d3VzVG2e1t7qjYWgt3OrurJy+SaWVEUxXlpz1JD19x8e31lz6pnDs/f9Esfc4+hm1e4\n3cRF7SYueHl+1fDeX669+U4IIUSj6VGP81jj8JiqXx+x8Fq64h0mbmn31VXvWRMnzdt+840i\nhAh/fnnPost7Fo1zTJ+vQq26dWrXrFmjRrWq5TRZGCHEuz0DtwohhHBr1aGJ9U9LCyGEcKr7\nUbP0C1YEiYgjvx8K+6yzpT0a6Xt8zlcDvtv0T7D+v51zNvxqzuKJnxQzPcocHNRWUKeLPVmX\nu7thHGzatHFPB+AghCKEuHb5sk7UMXa1FypUSIi/hRA+Pj5C5LXw2SdPnuj/4eGW6rnFb0jI\nnirkHnOzJmpSVq1298Pl7U+deCqEeP78ueWN4N5+2oR6O4b+FRa0Z9yEIx8vbBTrmf/YHNwK\n1Ok2pk63MfNe/3t0+6ZNGzdtPXzDP0IIEepzftu889vmjUqXr1abTzp37tKpZeXo474zVOi/\n4VKtxoM6J/bA1ErFUec2NbB1Cz9Z6dnhxCNhe/VzDdL/KzAwUIiETgYdFKR+2r3Rj+d+KWPr\nSmUqXKWQhwbV7/qsBvrGL1euRPQo5s+fX4h7Qgi/q39H+7OTR4nGH3ft1q3bxw2KpE/AvDAX\n1/3422lby/n3zh39P8rXqGHm9wgunj+vdiZUrV4tdpl58uQR4l8hxLPAMHWQV2RkZKy36Tka\nWkd/f3+TlwoVKiTEDSHEm5D3amtjWxMqKn99bk1N26uNEEKDAzx16tRCvBXqxonenAYHq+cU\nER4eLoTZ6RuN79E9vPU46s+JrTbanqRsbIdDA330/yhTp06CJ3l0rVy5jFh/Tgjx0C9Ek5XS\nqNpMqrrV3N+zZMkihI8QIiIiItaLihJ8Y7XxGcfUxbstuhzjUQ6r5sTSPV3aJG38bzPHyjm3\nIgOu7Zg5qIWlIQAOrt13abAwSrTuzRpzzA98jHOBn85V73oXH3fN7Kd1/ucWe1VwNwYUxyzV\nP1952dK1xUXDbY08I07HfvWviY31Jv5l9tO3f1BH2KZWr+8ytVsbvZ/75iT91Y1zw18sjvF8\nMKuWfos7Nuwba4ydWQnYU8rrqysH1cjsGEexUZ1PTlmrf770QuwtZdvuNg5TcG6yOPaY3GjC\nz48s6iCEEE6lxp4zdkUkaMa49y+v71syvnejou6xHrQo3LDnN4v3mjxokegDU+MJZWzbwqGb\nP1E/bFv1mzCqjL4Yl2a/xrmnzPFb01bdkBV/uKvBSkWxrZzd3dVAn6huyNB1JvMYO2atO2bD\nxecW+zzirq5R1caGcnZ1U8NEq7VmOuweTjdMKV5o9DkzBb/f0E7/snOzdmpfppk7tipjt1y2\nQaYNoM98td8w08edNWlCDZ2CWlUbRVFsOMCvjC2qf6nYhKsxXgg3Pgsmcgw9bv6GgO7PwTFm\nira12mh0klKrjY3tcE21vTDb0xYvY0+bQ5N29lRtLGz/16vUbu2CYy+afkSdoDjk1m/9DJML\npC7S8ZeoE6iVp64wwyNrSRTsDN7eP7x0XI+GxT1M53WO/vFEL4wSrXvTUlO774vCel/si/2i\nsalN/9mBWC++ubbuy1rZovJOhrK95p9+GddsQm/WtFUb/jx998c3DYKpBz/XVPNjmRqG8coZ\nao2LNgGo385u+n6CnF13mBvn9P6/ZS3UwRxO9ecv0XZPaTcpqy27W7nzY2W17FyfbImzSQnc\n1VM/fMOxcN8DL00WL0FfHeZ7aef80d3qFIj1oEWm/ibVJlEHpvGlOuMSNe2Owd+GsdU2beF3\n+7wMJxtbqt/jMyMNY5azfLTsvwQ8PvH+3oZOhj6AklEjtmxaqWhsKcc4Pqn67McJ/+Ync2IP\n9XPMWLRZvynrTzwyN3bL2mBnQzl7P1WPaXP31N5tbK+2rs4dN5prXgN+VTsz3D4d21EtKJfX\nPrO3Km//WFl/+Dg0/MWk+gQa280qE2dp0oRGPWegVbVRJeoAj9jRQ32ypPCQv6LlZ/81baMe\ni83abdtrM1/4ylu9nRmjO8+GaqPRSaqA4RC1qR22cUIAYzntvrSnamNp+wds8NRXnSIjz5g+\nPBH1z9D/Ng2urD434FLw4zlnAxXF+lNX0Jq2jolZ7kTOkv8+4PbhdTO+6tq0UgH9nb0YH0/s\nwiiKsq+XegnVcnUiZtELWtlc/2nnLjui/z3kv21jm0Z7wiBNMc8fjzyNf+zZmw2eavPmkLvd\nwsvWX4SE313eyjClf5nv/1zd1jgnj3POOoMXH/5PP4L/9V/j9bs8Temev572jVqgiIDLG8fU\nM6bQvJ8fDdF0T2k6KasNu1tRnixtbOhOT5Wn6fiNZ59YGPqmKC/Wd1A3Y7oKg3c9DLf5Nx5C\nHp/x/nloi2gjLc2Vk+ADU/tfnrBpCyvPtKh+ivJgUX3jjY+MFfrM2XvrVXxzrIY9Pbboi1rZ\nDeWkqb8o2kOEtq2UJuXcN1xWpO22LcFddhEH+6sPALoXLZrZ5PrJMUORxn0nrz3+MHorFnfF\nuLNpSNuytpZzaoh6T7nm3Njz6/01JLv6QQtD+29OUh9Sz/PVoY0dDdOi5Gy35IZJ4Ag8M7Gm\nmmGcas26H/0lne+eXgUNaWHkGY2a0OvGv2tVbaIk/Mz7bn9ftdfNIVPNr1af/Pd5gO+1bWPr\negghhKOTk371s3204GrM7fbq7LTGhkMxWwvP1jbvbkXR6iQ1fKIW7bBWwc5zjT1VG8NSOaXL\nXqRSvVZd+w3/fuaSDbuPXrp9emZDdyGEcK0y5UqMBsTkJ8XC7m77qqphZoACbX867b/Z2pOE\nOu41mYJdNLp3frdP7z0TY4qRRC6MoigXRhbQL4/lp4ricP8ntaHO1P+I+qfwRwd/aFs4ariJ\nS74WE3bfs7YVD78+tbqxW8vJo2SLvmMmz1p82OJ8KpEhz2+d8J45oHpWQ2dQmibLninK65Pf\n1ogx5iB1jnKNPPuO/H76tK8a51AjlmP6fOVq1m/csE6lQu7RDniXksP/fKXpntJ6UtbE725F\nUZRHq9pEn6jbIU324pVqNW413Uwf/Mt9nxUwpAS3Ym2Hdy6f+G3y7snZrbOGdaqWO95uSyWB\nB2YS/KSYbVtYi+qnKErkvdXts0cvxTVrybodvIZ9++O8pWs3em/buef3vXt2bvdev2zBjAlf\n9W5Xq2CG6De9s3ZYF3PeehtXSoty/jI8fZepx44EXkhGHh+q3q1M1337+zDf894zB31U2sPk\nNr9D+sKNPpu0+tiDtzqrKoat5QQubqLON9Zrt2mv6vXv1bvpIlO/w+a+PGBpc7UTrsniAN0/\nU6sYr3hc8zX0+m7+6s3bt21eNXdcz9o5jRfJ2Xv9HnXWvbZyQLtymQxHaKpqs/7Trgk10qra\nxJDQM+/DX5sbBnqZcO+0bFV3Qx+5S546PcfOXLJqzarF00d1rpzVuFfdGv7yQLF9dyuKotkW\n1qId1izYedtTtbFmaI1DriEnon3EzG/Fhj/YPbym+iPBznnzGM7A8S1Q8N5htWvXrj3+z4Qt\nc9L8rmUiF0ZRlJcLG6lz0DRf+jL+t8cUuq2betVRasJNRYl4fnJ2t9JRneNO2esP/+2fBHbs\n6h5s7lnCZIimpW21o5uLyc29zM0X3VI7aUP/8x7RMFcCnwcRGSt9ueNhpKJouac0n5Q18btb\nL+jKsl6lTIcum1/NsH9Xe+aLPXmn9dsk/MWVPQvH9qhXyHSAskO6vLU6L74axyetPTCT4Jiy\ndQvbXv30wu5sGljB/I91xyl9+QHrb5nWNNtXyuZyIozTbjlV+OFGQiYI8lnRTK2xqT5aEdVS\nhT47u+HHz5sVdzetXW4FG/Y2/tRh/BUj0eUYhk6KjO3XxexZv2OYnkW4tFtrrhV8PL++muRK\njL+sKIru6bZPC8VZY1xLjzoW/X5j9LvJGRvOu6nfoJo1oXpaVZtYEnbmffnnqKoZhAmHrB3W\nPlKUx6vaWPgZU/27sn20MuZMXjZWG422sO3tsHbBzp6qzYWta36ZMWF4/+7tGlcvXSBLGvM/\ndBZj2cwEO0VRlPeP935dN4uDxY9px+5+sPy/aeoPO7nU+tn8DI8WhRknS3fv431uaf9KHsYt\n6JCpav/llxL7Q+Rvb64d2jh/mnj3RcwRaelK9Vh6xaQbIPj+kSVjutQuFOc8lPolTlewkdf0\n/Q/CYhVu6566pfWkrJoIubv/50GtyuUwbmWLqxn58sy8z6rF/K2deLdJRODtP1ZM7NusZCbT\nMYeuOSq1+2LGhhMPLd56iMaqA9PujikjG6pflIjnp5eObB/rLpIFqfM1HDDr0H07+8EJo/eX\nvi6h7s5UJb/808opGt5dm9nQ0LuR+dO9Zg6Fd49Prp/s1aSw+ecbKwzZc83fqhiZ8HIez6+t\n7pnMTX86Z4hd7+8tb2u4Q+Xc7NfYM9xFPlznaeiOLWWcHkwXcH5ej3KmcUMIIUS6oh1mnTFp\nUdWa75i5Ut+V12JsFe2a0KSVoDNv6P29U3vVLZzRSQghXLKWbffNdsPg09BbSz3zmz1C0pfq\nvtLirybbUG002sK2tcNaBjsNV0pburDXT25fPLZ384p5U78Z6tW5VYPKJfK4u3ayItgpiqJE\nPDnwbX3j8JSkOkn8OV6dTVv7C6BEujW1nHpcZem0yTf+9xvdnV1DHY/qWr5EtqhUnb50j7kn\nXlj1k8txiXh978T25bMmfT38y2Wx5iPU01evVJmK1Ppk9K/HH8dxMgv3v3Nm/4bFM74b9YVX\n904d2rRq0axZi9YdOnXr1feLsdMWbz544VGQSc7Qak+9+lWrSVltWgxLQv3vXzlxaM+2zScf\nxfm+MJ9Lu36dPKRn2/qVSxXM5bXL/Lt0QfdPrJ/+hZnZ7JwzlW7ed9LKP+7EO1DMVLwHpv0G\nO4NEVL9Y3gf8c2Tj/EnDPvNsXrdK6cL5cmXPkilT5my58hUsVrpKgzZdB4z6Yemuc4/fJsPv\noNjk9a6exqcU05bssfxKPD8w+e7u9pF1jJ9IU2fW7bjWMPjBsdUTe9UvmC5WL6drjsodR87f\nc926gJeAct6dGl3K0Po5Z63QutfnAz5tUTKqmzV9h00mx/3bO7umtjf2zWVouTzm6Hndm9sH\nlk0Z2r1ts/q1atZp9FGnfmPnbrn4wsxy39gyddbSrScfWKg8mjahSSfBZ96Id29eh8TeHG9v\n75k7vGvTqiUL5slToFi5ao0/+fLHddGHslqW2Gqj3RZOXDvcyDDZaoff3iXCb2anYv4wqk1k\nZPQTiYMS5+/F6t48u/8iRBFCCLecRXIk+0xaKSJoe/dCH//2Uggh3GpNPXZwbCUr1jvwr9GN\nW/70d4gQQuRuVv/pwWPqC47ZyjaomOBbUHp1x+8fVycB73/z5La/Y7acOT1SJ/xmVbI5PyJ/\ntVmPhBAVpv3395jC8b4/pr/HFa409Z4QouzE28bpTO1PqM+F3zdv3Lhx854zj99Ff8ExfZF6\n7Tp37tK5Y7OyWRJXK4SI58Dc0tGh01YhhPD0VrZ0TPR3IJkEHh5a96N5N8L1/+XoXqaN14Ae\nrerVrFomt5vx9B4Z9PjqmaN7flv668bjT0LVP7o3+eXiwUGF4j/albf3jm5etXLF6q0nH4XE\nfMklR5U2PXr37t21RelYvcmJLCf9ncUdGg/a62Pu1JKh4fxzf3xR3LBef31Tsffifx4Ehhne\nm7HZ8qsH+uQz88lkYS9NqH2cebWqNtEl6RY2Nn02SmjLaS/VJkoKREv7d39ZU8MPzom0pfos\nvxQQV6/K+6d/zuwYNbmwW9MlC7WdSEwuxueOzcyXHT//JQ31n449eZUdCPe7tnfxuE/NzPCZ\nNm+NTiPmbD3/zNohhTaw/x476715+o/e03g6sj7ohdE92dy7aKz5zB1SpXPPlrtA4cL5c2ZJ\n72paoxxytJiT4Ps9kW/+Pfjr2K41c8eePNi1m4Ve50SV8/7hngmtC5lMIOucu+XU4zEH3sW8\nh+VR54ez5ubogJYSXo21qjZJTuMpPD9c8Wbt0zPaTz8lhBC1xuwYXTO+d4vIgNsn/zpx9uyF\na/eeBwQGvn6nuKZL754tX5HiJcvXaNC4Tplscf/ARtiLG2fP3nj4MsQxQ/ZCFWpVU8cPWOR7\ncc8FHyGEyFmldeWETkdvUQGvTVuu1Gw+/3aEECE3V3hV2jy1Wfee7RrVqVW5eO4sHu5uqcKD\nXgX63r164cyxPRvW7jjnE2b4aKZW81f2dxs6QKtFSYCIIJ9HT/yCHTLkzJ8/SxrjlYO6Bx0y\n1R7186haHnGVoJ04qo1xmmxHxwTMb26QLp0aCwMDA63+kIUtoyG/c6uXLNu4cesfNwJiTALu\nkr1C845dunTp0q52/ti3NUyc+KHFlONCJLyn1kS5Xj/9VEMIIYqXS3whZkQGXP99y84jJ0+f\nu3znmX9g4OtQJzePTJmy5C5eqWbt2g1atmtayiMRuzROB4aUTGjvoy7o0ZVLNx/7v3NMnyV3\nsQrl8qePuw1J0oWxjkPuTisvFav1Rbdhq28GG/+qvA9+9SL41YvY709doPmQWYumdCiY0D5f\nx/RFm/ab2rTf5Fe3D25YsXLl2l3nDW1XWFhY3J9NUDnO+VpN3N1i2J2/9h86f/uJf6hL5nxl\nG7RrVS2XmZ+jEEI4pMlVreOwH38c2lCdFEqrY8Gu2MlKJbwaa1VtLLKTLSOPeIPd03M7d+4U\nQgjnHnG/MejGpp+nzV265fSzUIvvcUhXqEGXvoOGDvIsG+vJttd/r5487ocl+++8jerAd85S\npevoGdOGN8xlqWk+8WMbfR1t9Fn3nOHxrYwVqg1dN6SqEB6N5hzZndaz2/QzgUII8fbuwSXf\nHVwSz2ed87efs2tt79zibt/58xtosDBFKpr8QRcZ6WCYpSjaX32PLxj/7ZzNJ+4H6X9+xyVL\nuRa9hn83rmclD4eoPbhz3/EL87Ys62+YDzMpxVFtsmTJIsRTIYSfn58QeRJY8MuXL/X/cHNz\ni/73xGwZc3TBzx88fPbCMU+NEjHmZw+7t2fuzEWbD1666xvk6J4zb/Gabbt9/nmvWrn0R9Cx\nGb2/jXYLwDlTyYYfd+7SuevHDYuZ/s6EZb5/HzhwQAgh3PqaeTXslW9gqBBCpPbI4R7n5VGx\nNiNHtrH2S62iBFxY/M2Iqav/emJycL8NCvB99N/NS8e2L5s2Mm2BRt2HfTtxcIOcWiUpsyL8\nr+/fefDyw9cO7vkrNGzdvEI2Qyum8z06e+SoGZsuvIhK16k8SjX3Gv391z0rZ9Yu0fttatBg\ngQbl1J94dKLhZ/TcyvdbdaXDUO9ffp6/ctfZh0Gxf8pIiFQeRas1atV1yMh+9XKb/bkoazm5\nF285cHrLgVMDbuxfv2LFynV7/jaTH20tx8m9aMMuRRvGVUD1EVt3D8iat3SVcrliXHFpdSwY\nhT/9a+WcJd4Hjl++/zzwnaNb1vxlajZp3/uLAW1KZEiu22ear1Rys7LaKO98rp547NG0Wj4h\nhDgypsrow/EUHHjv4r1AIWzeMnXH79tnroSEymF65tVIwreMEKLxjAsJ/RWy+G/FWnNbJ/LJ\ngQlN81p/8eiYuUr/1TeiPfAeeu2Xtvktfd4xe6OfzlmaIDD2zIQ2irGaYfe2j25ROJ6fg9Mv\npEfZLtP/fJFEA7VfX/vt2251i2ZN7SgcnNPlLN24z9Tdd9UBmrr76zrlNnsidcrZZtGNMJPe\nadfCHWef8k/yAeVxVBuNJmUt9s1VxcYtE0P44z9+8mpaKouLmcV+eWCIuR9ydMhSZ4L6SwrR\nNrKTe8mmPb8cPmzokC8HD/y8f1+vPp/17t3LGg3yW9xocW/SpBV4fHL97FZHNQePqoPW39bs\nhrPJWr+7trxnqRiJPnXRTkuuhyqKonuwtmNOSx2Gznlazzpn8/3TpG1tool4df/cvk2rlsz/\neerE7ydPn71w2ZpNOw5deBhk8yNYFrz3u7z9z0RM3JlU5Vj7IxnWHQvvH2zuW9r87zs75Wj8\n7SHfZHrARtOVSqrFSAjT3a0LuLr5244Vs7pGLzxBt0ftq+nTji1bJjFr7XzhwoU4C71nuOEV\neM/0rZmqVCkkhO7x1j4Nu6y+G+0OlINrpgKlypUulMM9Q4a0Dm/9nj3zefLftZtPgtWLUJ3/\nhV97VTl9cfsfc5tnE+LFlt7Nv9j1zNhR5+iaMWvmNKH+L16H6YQQuudHRrXsku3v3T3zJvfA\nRJeC7afvazPy4qaFizf8/sfxiw9em/4gtbN7kWoNmrXpPnigZ6lETK9lhchHW/s06bbmjqE7\nUhfsc+Pwim8Oe28fd+DwlJovFvTo7/3U7O9kR/rsHtS4u/tEw3+7e3i8Cgy8u2VY3RM7hs1b\n/F2nEm7mPmZJ4L0LdwOsfXNc1aZc7dzi4lMhQvZv2x/SoZ01udlAd3rvfn3J6apWLfZoay+b\ntswF7y7qxHgvD41o3GHW1WBzbxaPFnXuOO+amdeUlycmefYqcnn3p9H7HSNf/XNozT+HErBW\nWjs/r8fccxqUU23ouiElzk5q8dF3Z4OMf3R2L1ylfoPaFQrlyOyRTgkODHj+4NqFMyfPXHuu\n78xTAs8v7F7v/utjOwYWt6lTKTbl/oqODb32vozxx9A73p83T5Pj6rg7n/Td4mPo5kqVIWfe\n7G6Rr54+9gvRCSEinuwZ3qKz29k9/YpofbM4KThlLFC1RYGq8b9RK85ZyrdvYFsRQc9uPX0j\nhGuJYtnjf3Nyent0ePOuy26bbQlEpO/hya2aBB84+XODWJPCIT4xqo3/0e8+9pz8V0BcT2PG\n4pg2R/GKVau6312z96bmi2cvbNkyVatWa1s94V/pXLWqlc3HkTGm7/RUlC3h58Y3j0p1Lvma\n9B8x6LPOrSrFmt1BhL+8eXKf96IZs7dcf60IId5dm9f+o+xnT40M+nHERjXVpS7WcfKsiV7N\nSnmkEiLsxYVts0YO/+mYr0747x3U85f6f36R37RUo9RF2rXTokGpltv0L05ZK3f7bmm370Tk\nmye37zz28/f3fxUi0npkzpI5W75ixXO5Jc2ZQgl+8TDELd+Ln9p2M4Jg7AAAIABJREFUWXMn\nIvbrQed/8PyiwpxUU0+GCCGES5F2304Z2aF6/rRBT64cXvHDDysuvNQJxXfLsA2GIQuN55/v\n9Gd7r+XXg32Pzfyk3MamI2Yv+DbaUx/xODy6aiIeODJTbTbtb+kxb1mgEAEbvp8ztvU3pay/\nV7l68rJHQgghUtVvlHVa2wa2bZlx+1uvauEmIm/+3CZmqkvl7mHMvGGHfvj+sJpr0pXu/M2o\n7tVy6h6f3jD9p023g4Xw3zt83B7P1VZvj+Tw8K/167V4NCy0/bw8P7UypjqHrDUHTPxhbJ+G\n+WLfDYl89c/+tfOn/vDrqeeRQojn+wY3/jTT+U2dc2qwGAbPVg4YYkh1LjmrNq9XIPzWn4ev\nvIxQnq4ZVPdC0M0wIYRwLfbJjIXT+jQs6OYohHj/8u+tkwcOnHf2lRAB+4Z9ubbdvl7ZNFwm\nIRwzFK7bppNn84rZXRSd7n1EhC5Sp4tzqoFoNMludjI+yeLIrZQYmhlFd3nagPlqqnNwL/Ox\nV8/mFfJnFK8eXNq/etn2m0FCiPDrs/v/2PXmj1US9HSnZGwc3f5q/6Ba7Rb9axwN5ZAmXawu\n0rrjdg8t6XP/6omDO3f9cfuVIoRjoU/Xn1zZPofDlo7SBjsbt0zyLqwQQghPJfLGpEqGO6gu\nxb3W347/Ia3IgDNzO+QzHEHOFSev6K/Oke1Q0OuAv+nb3z/c+Eke9TdqWq+K/ePAH0QnbXjQ\niwf/XLtxz/d1uHV9/pFB/+4aUT2t8Fy1NqpDy6Pip1OWb/19/+4NC8a2LZZGCCEcXFz0mz9z\nhw0+MYoOv/VLY/UnZxyyRd9EIbfWfV7JeG3qkrveoAWHH1l1R1S7B460mJR103Lbt4xj0yV+\nivJ4YSNDSU7Zavabu//683dRnwjd1s0wJDGzp3e03yIJ2Ncvv34lUjVY7HNj03e261Qy+p6y\nuP3jrepa7akOk78yzkaTttKXux/HOwPW62u/di1saBGyfrrTyl1rzbp4zp5Y1rC/G8z8Wx2b\n8erMxBrR+52zd/SOfV8t6Njgwmp9KzfpphYLU7OWyVnPMUORBp+OW7Tnqp/p72jF6fiU5npT\njmuwVGYrRmigj15gEk+uZWYxdP7nFw6ol8f88xJGaQs06jf3z2cR8ZVm9avR6Q4NMIyVzdJm\n5d0Y4y/Cb883dtMl2byY0dnnrdhXl1aNaFnULUaEcM5S5dMZR57GMcVhjEL8tnUzPrmYKm+T\n0atOPgnRWfg6RVGUSP8Li7sVSyWEEC6lBh/w12zLaHJAacnmLZNIQqTOXqJ8HAoYfuIxc4na\nJsa/N/4ejsjWebOP1QdG5L2VbQ2/eJKxojplUep2awPMvjv45Eh11qOcfQ6a/hKNvQW7mE+S\nB15cMbpjxaxRowcd3Uu2Gbb4dNRYvPCnp9dNH9mva4ePmjWqV7t6lQplSxbJmzWdetpo/5Xh\nl4idyn9zKfrkIK//HFg46kgsa5ypPYru5iR1Ll8n000U/vD375vni+pTdclVe8Cc3++8iXsH\n3lnZoVDUZ2yoNn9qMCnrld+02DKpP/4t+OJYQ3jJ8fGau6Ytme6QlyFMFvn6QszXXixuot8e\njk2XxJ5JPxG0at1OTW+nhZYdq6n72yFft63WTtWte76zV0H1Y0VGnkvIr2SZY1zr1p+pnVFO\ndeZE/8HGyEvjShn2t2Pt2eZ/LMZveUt1VerOTcic45YWxnOD7987F4zpWregm8kltbNHyWZe\nE5cfvBVgzZprksnsMy7YODRTq5W68a3haqnWrNi/8xlx4ivDTaDi465ZLGRbV1dN1KmpzUrZ\nKNoXaTC63dM74sxXBdT/SF1x+FE/ncV3Rv+z7tmmT/Qd+hlbLl+k0e7W6iJHqz3erLWtW8bi\nj9PGTQjhVqH/utsWZxSLY0tFHhqgdgalabL4mbkPW/Z8WQu1m8TV0OL+8tzSm98dG1JIX9Mq\nTLoWc9vYW7CLWp7ND7f0LG7+WjVVAc9Vt8IUJejMpNpxDuxo1EZtdlw9N5geYC8WNTacU3KP\nMPM7yYpyeXyRGKXF3ERBN9aPaJQ72jHtkL5I80E/77wexxnp9YUFnxRW78MlutroBfwxpHRU\nTHR0L9NuxHzvI1eexBgeHvHm0aWDayb1rhv9ot+9yS93dedHaLJlCo9ZO0pNIW6tVppJZ9e/\nL6EWlX/MedMXA5e10H+Ra2dvWwOMotjNGVoVtK69WjvcPb3NX3NZ8Hpfr1z6T2b/8piN/SDG\nta7XVP1Fqkoz7sV8S9Q+Kv+jpXH7j2er41SyD9a2byzkyVnvWcM8q+cxvb3ikrV868+nrj16\n900cDzxossftpNrE+KI3ZyZWj/HwvbN74RrtvEZ898NP8xYunPvTD9+N7Pdxg7LZYzaQ2Vsu\nvBWm7Urt76126JYy31d7dlRe/esZ+hyMf9VsFPcPXiX/nmo9okuu6Bcmjq4Zs+fKkTH6lImZ\nW61+ZPYQjnaRs7uXehZzrT33Xuw3W14vvw3t9B0AmevV1mbLaFVttNrjhl8jS/yW6bEnUb9N\n5ijE28u/9qhcrc/amyHml82iu6dP6591dm7co0sCB9Nk6zGggz7Zhb3X/8Xd3d3Sm1PXm/hz\n58xCCN3ln8b+9tLS2+zKu0N9eq+5bX7ml/cPtvbt+P2J/WM6Tjj5Jo4ynCLe+un/VbZmTdMH\nHbI2alRG/WeRIkWEGQULFoyjcLdS3WYevn1t2wTP0vrH/ZWg/w4sHNGuTI7cFdsMmrrm6L+B\nscavZag8eNOlM0u6FEtjQ7XR82g858A646SsulfXd/78ZadG5fNkSO3mkT1PwSJFCuTKmiGd\ne75KzXpOWGWcat8hR4s5x3YOKuTg56fJlnn5+NSp+0IIIVw/6tUla6w3vjl//rb+XxkbNapk\n+qp7tWpFhRBChF2/fsfK9f5gRBw5cER/aBbsO8ozQdMfZmgxtLf+N0Ge79lzXqPleR/yWv+P\nPHlMpsgpUKCA+q98+Sz9YkH27OoA3IAAqx8Askqa3NU6Dpu15cyjF/ePr582OOq348L9ruxZ\n/M2nDQpny1PNc+jPm08/DknQ2OkPWcA2rxhDMz9feOSu73+ndyyb+f03I78cOHDIyG++/+nX\nrX9efepzc8+8gbUMHXvP9w1u/OkmHy0X5e3bt/p/5M4da/S0EELkz69eIb55Y7kprvjphM/b\nV81ln1OQ2ODW6mij23/ac+Nl0Cvfpz6vXvuc/21M/RyOQgj96PaHcZbyz97f9Zsua4/xAwsm\nZFBYlk7D9M+d+Z+8m5jlTzpa7XFf/c8O2bBlNi7clJgWy3Fpz1JphXh7fWXPqlV6rzD37J9F\nPj7qIVimTp2Mcb81NtfKlcvE+MPz588tv9u9/bQJ9VyFEEF7xk048s7yG61zekZ7vRmnbS3K\nklsbD6stSoYKPX5cd/DMlRvX/z65Z8V3HUu4CSEirs9o0v7XJ/p3pMpasdWng0eO/37K9J/n\nLVy6cu3Grbv2/3nmH59BmdXUlCNH7MmXjecqS5k4Q4Z4n/NKV7zDxC1XH1zeOP7jUobZnMKf\nX96zaFyvhsWzZMpfuUWPoZN/2Xjo4r1X7w2lVui/4dI5G6qNgUPuTisvnf21V6kYE4ko74Nf\nvXj64O7dhz4vg8Kiz+WVukDz0VtP7RpaLq0QIiREky3z+tk9NZGVr1HDTPfqxfPn1bNx1erV\nYg/wNmaMZ8+emV3HD9ij69f1zXWm5i2qJnQIb8VWrfRXevdPntToPO2USt0/kZEmzzcGBxuq\nX3i4paksje9JmzYhT2Fbz8GtQJ1uYxbsuvTsxe3DKyf1b146szqQONTn/LZ5IzvXyp+tQJ2u\no+bvuOirxYyudkx3a/Job3/9v9NW+nLXpb8WDTT3wI0Qwsm9ZKsvF57897JhaKbydPOXY3a9\n1m5hjI+xWGgNs2XLZvrO2Aq3m7ho+7lHT84t+7SUsWe20fTzCdUj9qVjinqs7yRxKOi187T3\nyFalPPRd9K7ZqnSd9sfZ3/Sj24OPfvPFal/LhYTcvaC/yHZr1aFJAifMdqr7UbP0QggR6Zfw\npU9KmuzxXV+U1B/qtmyZiCO/H0pEg+Hcd/X5Wo2HdB60/HrwP6u9qp05PHvT4s+tm8LW3189\neqNOpAmQJYs6zE49ni79/rvvsAEWfzyiwMCfv1xQbeYd5eGvX07+7OLUqvGMyI2T1bMuJ94z\n/fWqS4XxR09PrmhY2NIVarX6uLZXuWYrHkWqM3bnbLvsyEavEmZnWXpsaGtcXWO3ixkzGtK0\ns7NNj3M5epTrPHlrp+HXd69YtPjXdQf/faPPU7qgR5cOrL90YP08IYSDa/edoevUaW/TlbGh\n2kSX6ElZFY22THjwK/0/smY10+g+On9enX+zUKVKZiJi+vTqGgc92v7FF9uEEKJi3wVeFeL+\nTjMODS8z7KB48zjBH0w6xj7REqVKJfyZxQIFCgjhI4Tw9fUVQotnY1N75BTijhDizp07QpSN\neuH9lSv/qP+8cvmyIuqYSaHK5ctX9f+K1d+nNceMxRr1/rZR729/8b/xx9aNmzZt2nHszqtI\nIZTgRyc3zjy5ceZXGQrXb9+5c+cunk2TdlFSyCPvhfr+F4d83db+Pq91/CeHDGX6rT+V3aVG\nu9X3hfBbN2LKl60SMcNDojg4WH3R4pSlqtfqQ4pP0X5/hAghPApVqVIlYV/2QOP5f2ylPwOl\nbjvpp2aZTF9zztd55aYLV+rOvK0L2jN23KH/sXedAU0sXfvQURCpCmIHwS5YEBFsIKLYKNIU\nQcHerr33fu2iXgtKV6oooggoglhB7KA0UUCQ3nvIfj+yCSFsQjY7aO793ucXZHd2Z+acmT1z\nqvX1qcQFa2sKvjH+GKqjQ/o7JDpixFCAlwDEyWj+MChS/Mk1XFdFaWbqP31KB9uh7bdo3Ryg\n8+CF7glJN5eM6AJQn3pzhf7IeZeS+DkzNTfj32GOIgD8oVMnpizDMPPQHh1YF8LjYCAxeucp\nx24A0Jxy3H5NVAn5N/5WMGyH0nN3bNflEEG7Tt2+kqUCkZp59AYXqe43Q1Rh6JyNFyNS8zIf\nX9u5YLK2QmtWxBrqW58bBGYbTogr69rv9X3xvayUOClraXHas+Azqyim2ucCEVHcCkRU36z+\n5cv3eB9HjiSqzlVVhZubpBveX2QgIoPgvq8hhxgI+UrcjYrs5OTk5BxedvnfDdaxTUlJiXxr\n1rGtsFCgigZtITdgLGOXSfNzj2dzcSj1v8Dyzvh18587RHNYEXLZnyGmKhoYaKPpT7sQVxpi\ntvSgx+O0gp9v77ptcTDsiwda0Cszn3gfWW4+XE31Cg8rxb8W+ckMvam85akLlvwe+UW6zT5/\nieGaiWX4+DwXUrO1SI+Fa6xIm6eEG2NMTYk9LTobHLy6uj8AQL7nltOfuZCEVo9vgqw1zwFZ\nNQ0G1AjEBIF2l98JChRnfR6ozYxA5iD8691J2/7qaz2TjbZLLiZVZgSvMkh6/Lf/9XV6XN3e\nUKLfsM5ln2oB8gLtx5RtPXlo+awx6p0JzlHys06esQifH1pKz3S3NJG6dffMrBaXmsaC9lIt\ns4FX+lyN0f3R1VMdaWhIYPnRnDF9wLbENACA4ZMnCxljy/Sd4npoiushWlna0wd3792Pfvoy\n8dN3liW2FZCyzW9PygoAICGrAFAHeKmyHq0vJr54iQ97yKhRRKakX7/wk4i8jEQ1j7d8vrWb\nUXDMauAuq4E8buwwkC9lU56FS091dXUAZIVqlumTnnNRU3MVydbsD2Ke9UR0Ftip+LoXAWS6\nzZ0ieubkSrNBcoWx51auC6sGEBUTw5qbsaKby+0NNINXDWM7KlUkHLdaFsCQUpV63l8xQFPg\n3FA1PA6e3CHZXXf2at3Zq4/X5b6+H3jr+oXLD7MaAABopdUEaRj/9ShiaF8Ec830OpIGUBAe\nntnGo1VIIDll6kRRnzAi28Jvw8M1mqsjBG/Owcbtebf7WQSU0N+f2H5zxb35BAKKqLgkQBMQ\n+EjgMHPLIDrtMtDURPhlESYITHFJSSQzU1d3XlNzJcl3s6llpDRsLrwYbbzVzuVsYlnW7b8M\n38YcueXBPSUwMvRbdUl9h/O9UoCm3OhDdtGHO3XXGqTRU23OofAtrUv3qDhc83v0ztzjO73m\n/cXZQ6JnL9Vl+nUW+Y4Zs5r0qwnS5yIt781FVFdXVwdIAwBQU0OZwhUlxBW0pszfPGX+ZgCs\nvjj9fUJZD8L7SLMNnU4nUo+RBKKNVa7nYBHIwwA+v33bBD1aOUIkx8TgChVFPT2iIIyy588Z\nRkCRgeqyuWg6BAAAlTmfP3/m/JGlzyO8yo6uvYf2YjoUYWWfgs8cOHr53ruiBqsgLLg3AEBp\nZlJSEr+dyc/PByB7ZGWV9FXpXJOZicQzWmLa7sPTgpdGlgNW+vKs0/izbNfkra6cldzm7FcM\nhQ9Wj9byt3OcO167m1R9fvKTQM/gJFzUkJ1sof7EncIXkQrqfyZEBAUEBgTHZf3HfewY3yNB\nXTOPpOUDZD3/Ohj/jeJaKEEuN8jq6GhCWBqlZ1Ac1I9sRGsKAHDv9l5cLsrPPbZnwp11Txuq\nwnfuibG8NKWNdUlSTgWgBnDNEmGwGg+wNgocQkduEJziKipIZkZOroo8uTlMv5L9Lc48H2m8\nzd75zMuS72GbjXR6EisRkULJ6eYT2mr7dV4pNQAAWF1B6tuCVFBbTnCr2eUoj0qTxSHZzVCd\nFnaa2vrqcEhIEPpMKioq8r5BuCAiraylP4PHDaTY5t7Oxc1rPCx7CKw3gfo0/+tPBG7dChKD\nJwyDRx8Bqu5cDyydOZ/N1SQjNBTfQySNJhC5/OT6+cYyzBPa+gMkH6HpEAAARG8YNkzQq8B2\nOKFSymZgQ9z1qB8AkBIXV7xrILltoCYhAfd7U5WX5h1TRwK9l/iFpk+fcyKxtblVRMXC7ZTL\nAhHxkEjne8UAjbnPvI8+8+ZoLNJthtt10/D+7qh6wx+aij5GBfv7+wfcffatlQupiEyvcTOH\ndw549Xv789tA0TWznGltp7gWUFX1ZYPGWPPx41VgEIUvo1ANiqJ3u0z3/iLwHQP4FBtbsk2T\nnAGq4d27L61+EKqZYUJAivftj2RmVFWlycfFEvj0SfSZeerpe+PdDk7H44tzcgWyPpCF7HAX\nzzeT7f855eZ1O+bjL15hr5IDFga+1b64ee0h34TCFvm86yiL/m9Dv+FRcdLdB2qrco9Vrvj+\n4XsFAIDSwPEDW883leX6/xp8s01TupeDsdTtuCszBantVPvFa63dqutcyrqSh/qiJeMPrHlO\ng4o76+xOagVvGiMHAEDLurHx7AfGLeKTzKe18RumZ/v9dSiOoXsZbG/f9+MhRB1CBqqlbOLW\nPIi6kA+APb3p/3PZasJkEVxQ/zgyjrE0+xjOWbNAbzr10YCmLgAoTfo7/sOk0/uOXb/zIrOi\nGSRVhk1fcuDUvrm9AMAp6FnT/GkrQn60tW92GTz/fPB15365qm5uk1B1hheay9NiQ/39/f1v\nx3wpbdUfKdWRZtZ2dna2sw16dw6xDriKoDvCCIqumRWo1ZrclD18aoPYtOCdZ5x+xuuY2/GQ\nN3Zzm0/5KUXR5w+EpePe7XMCrLiJdhKjd55y9J7tXdicctx+zaTX10xbUVZyhOkoiHkDQH/i\n4fXDdQMZG1/jk/vRjPQGEkxNrzBCQIormaKYGXl9/b9WuM0i+W4uwRriPWccffJ+8v75Cw/H\nFbRr9aI31dcTZ2zjhSaO53bqP23DxWkbLjaUfk/9kpFTWKbALQJFVGnsmhuvlx15F3nv/qNn\niR+SM7Lze067Hbjo4hLbjYGZDQD1NdJjNwecna9FHJYQbC3CKGs46eAzhIbXjsOzw2ZmbVQN\nBe95XWW/gSG/drzMyi/bNH29am0ife/JuamkNv+qD+4r7Nb5fWXPnEdxZgB6up7Y8I/R3ynN\nUBK92UDTz2yGvjr9+9OwyC94JEiXWS7zWkfM1mTcO7/5r713GIZauekblw4G8m4AHYvi0FWL\nmFKdRC+T9Qf3r7UZ10Y466Yz08oaYP6STUdLk66tcVhzM+2bl62x7L34C6bjZs1UunCtBKDx\n6bED0U5XpvId85x56bA/w4tVYdq0uWZT+S4/wA+k+s7Y7jljOzTXV9VgneU6sT1cSts1OHnC\n/etX/MLj32fkV2GdFbv1GjjWZLbDIlv97uIAoGG2ukMJhVV/fxEW6O/vHxT5rqBV6hVxxSHG\nlrZ2dvYWkzW7dlxlVCpA67lF0TWT32q7fKNdZQ/vG9C66FCEjM7q1Qg6YyH9/NGSx1S926H7\nXMvh2998xKDxxdHNgfMDbfjOkfHt0n4fRlCTZDdo/El5QMIGDQQz08nE1Gi6LemjMY8oXDF1\n0wMxacuzChmJNWW5O4OFOnRCGNcppdh3+Pi+REGIrSGpqjtrie6sJWw/rQp4O954me1f/mnV\n768uGPXi8aWAC46DOyZx1W9FwfvISEGvwu+VX/lkm4ZP5+eYSj14/Pck/iItKt5cWGq3KTCT\n8yBPcWYAQHrcoeALycYr7+djQCt6H+71nv2q3OQjR61buvh0h67z5S/fyxqY352upmcuL1aF\nYL4G0T5mXM65Yk71IZ2Vml/v2HCT8aGV1t3wMOrkROV2LN+iiqOW+cYq0EbZBuanXLRZMPLj\nA+cVrlrux9MwgJ9XHZ0mvQmx78mH+bw26aDjvgTG6VvTZclkpFIdG8SkuxAlJpPRMl97ynxt\nB72UG+rz3zwI9Pf3Dwx/ldPK3CDaRXPCHFtbO1tr02HKHe11IVSeWxRdM3UOPPPVQtCNR2t6\nCY+x2/xqTs7Z9m9rF53RBNypu3pcCtOh6t0OoL16n/1py5vFAMVBLpZHNaO2jyTOjNIKZU+3\nWO98xTj9qDtdfraHfEqQthAqciOZGec1lgLkHAEQpFwFZ0EMikBdQaX6EyN9LgBA50FO1z9y\nFpjtwOItHHPC7flkiwUJ2QxTwvv9w1heGrJjdz1vp0wshtGLX56Y3Zf9qyg9QH8E2plp+hG+\nZ2Z/juOJuPr0I/GtyzC3poiC4eHXFa1/F6yUDWKGbEBQ5GdBeA1WEjqfZS4X72V+JqGM93vr\nMu9sMmB9dOSsbhWjGI3worHo0/3LOx0naXbh1MF17qU/b+PZkMS8Op4PYBFi6ulPbXF6Kq+r\nLJxClA4PrX+SiMnlIrLzWc2qYjf2RI7AVCGeYYqgsjBbFxAXLlR9cOdIEc91sA1pXla9257T\nrIIwDMtyn8qKgO48ePH1t6U8CulhTT+fnLTWYilcZKdeIS7xTB5IFtSnT5+yKyh1g43if2hm\nxAEaCpNfv07+UVwrKte9v46BnkZX3ofsX0nhb/IBAL7xuSZIo7k09fnTZ69fv/n0raC0rKyi\nDpOS6SLfrbem9qAR+pOMDYd241nqg1L6XLT2CIoYbLN3L9nMhITPGdzyt8DkVhs9cxSnG4Yg\nlBqxOzo4e4LF9bQmgOrXh6bPkH70cOcYLgcZemHc8YUL9kTmsvyUuui4nvc7q/f5RGAKn6Pn\nBebMiPc233/PbH3604fRiam5JfWSSr2HTZpjrteDOA+2SKceetbrjx5dN1ldCINfsHhKRX42\n372QCyX+lwLOmS+64L38tfnljGYAWs799eM0PC1Xrl9mM23cUFV2kw299ueHx0HXL5z3iMrC\n7eQiPW2unLPt4FQ+FDcKgNrsZ8F+t2MSlBaF7pzIefH2wm7b00ePn2rl4GRjrNGFfRqLEryu\nuPv7hzxKbu1AJ9ldZ5q1nZ2d3ZzxfWTIzDtFn3EkQOO5lRG099zTUsqumYaI8kgLg5Iscu0g\nhudPR9lzBWVjQOHdDgAAfV0Cgj+Mm+aWSgOoTbnhMjLwiOn8hXOmGBqM0lZXVpCXlWisKi/7\nlfnxzau48Fs+dxLyWaYXRXM3j6XIk2+gij8TDK0ojnxm+CE3TB8g24rU4sqjHf+O+cmjpnlL\nCeF3EUjw7leLpPvZf++CcVy+pjhEZPpPdjkS9LG8vfLitV8Z6XMBAKQ1rS++KW8zhLZHE1Qn\nPG7Pb7cDHYfyt54bKZC7dVepUao5O8i+D0ugVJhy6l1t2zfT8qL2GHdn04WIKhtsuvOtgfpM\nCI7sFyH3op6+/1nLMSSh0th9P4Z7p8o6hzeRfV3zg8WMNSNt7V+PYRgt6+aCAZy+UhLyvQcO\nH20w0dh4gv7Iwb27chpR5I2OfcB1VQnn5iPBuQT2XlLfKCo+eqzUVxLlh2ogpjJ2+bWW7aPN\nLiGuOGiq677r0allPBYST0IIAxBpiyOXMGUg9aVRZFRUGaf08FOSwtIokhMp1OjI3Z4SG3Og\nviTrw7Po8NuBz7N5vrMh/23Y1YNrF86eOGpwvx4uYfjPzT8jtuqTywEr3mfuhQ9Vgo+ex2Cp\ngSKlOCiObmb4Jrd4RHrrzKq04jc+W0yiHh6/d3fTGN7GXSkdMzNSneUJ+s+o/Ytcj0bntBcc\ng9V8e3J9xxOPU6NdT3udXthGj8zCH826zEDhp0ePiF73qbCdGxjoPtxkmCCho23R8PmSzcy/\nwn5wTi4JcrOAgFKivay9H9+om+B8Jw8DKIvZONVCKvbuqiEsBUtTzoPdDs5/Pyti+rKJ9zLb\n4+WxY7JqR/ls8Yde4yy5ZXwSHnz7hqLID17KRqyvvc9rjXEbluzw/MgqLNJUnv21PJu4vbLh\nRnffQ3P64MLgj6d+fiECDIIT9XN9144BQMJ+zTl3lps5uKfwW3S6uej15SXjnzx1D3dfoMkh\n5IrJD5oy22SYkmjp+ztnE4OammjNzXR+ff/Fy9q/hx8gcs1E0RVJE6F2zfwvASUbA1DybgcA\nEO1hdiwuSX/3yk0XHmbWErZkv1thmM228+c3T1IRPO/VvwVoZoYUufF9X1Sqq4pSp/qSwooG\nOgDQC2I2T7fr9u7ewl6/Z9bpOSGLJ9t5ZbLZNUSkFPsOHj7OM/SNAAAgAElEQVSkv6q8nFxn\nkeqivLz83IxPKbk1eLQlveTNVafRL5NCH52bxl34IZ0+V9cVQUKE99fXXH8PABB/YCpvBxje\nN6BS3RcGO09bHZbH+thQIDcySolrLAx4VDt74orIIgAojlxjYiMVF+yqJQGN30K327ucSShj\n9rfTALvjvv+s+Z0SuXCAXlPw/UdeoWhP/YGt4nIbvoWfO/lPYNTbzF9VovJqvbTHzXZYvtzJ\noAdjOfNXygYAoP1SNkMBABT0VnokWS7xPHv2H487bznNMExIqhnYrVq/cYXlcMUOC/tEwX5V\nceuMrd3T8WTwYsojZtlYEhlmJm71Ptr9dmBQ+LsiGgA0pPo4TZNSTLw2o1VdzebyL9HeX6IF\nG85cj5wcL8GatkJnpZ6KlOPXSr4+e1YMAKA8yHCg4EKeqO6W4w4eFn6FAFAQutBgVkGI719j\neK3d+m93dzu6nHzJ4Fo5q4PbRrc9jwi4FoQbVAaFlo0FRn19vbQ0S3Mu2W/u8YhZm5ICLl2+\n9eBRfNL3Cs6aC+LymnqTTGfNX7XCanBX7p8bAWdGGCzvxKA6MyTJDdJa1ifCk0sbMQzDsPqC\nxJtbJ6ri27LMJDciz70WNSMlbSUbGl5vH9TCsZK9TVafu530i8jk1lCUHOO9b95QtnFLjzr8\noX3rXOP3exvGKTFaiffqyfQV6yBLKCqF8ET7aQwciqfQm4Znf7GC06mQO6gDKFX77uQE1p4v\nqm59LdJ35Uj2aEc5nSU+yW2iX4QOiE2xjTmPTrhMHawsSdCqOHLtMAIttYiy4Z5YRrBChBN+\nfbpXDfmhVHlMY7QWt7vT9mpjacar+95n929Zt8LF0d5uvvOyNRt3HLpwK/p9Xg2hV/CL43OQ\n4PgLNOxXFe7M8hRVNNgYlN4ec1WmBG6drMZ8TtdZXj+FxuKDFggthmUPl2u26NzEFEfM2+X5\n6GN+TWujeHNN7tuwM6tM+7XkLRDpaXMrt/Vd1NZCypNoXo4m/IBemuhmv+u+4A8gmFhqg8IQ\nsTEV1HyP9z24xFSrqw13bqFV5CS/eREbeS8kICDkXlTsy6SUn1W8YgcwBDMjDGh3KZGdGdLk\nhsgSzp+afvjb4MrzLjM983l0muNCzY94ryPrneYeiiV4UYijipb+9EW73aMzKjmWd/KBkUwP\ndEltF7/U9j9GzaWvzln0Zm7x4rqHvrbnb4dhGNaUc3+bUeu0D0Iu2LHi1Aj7WV+Wz0BZPa/O\n0KOW4nobkX4uVMgd1DGUqny1X4/QCCyqYrT57ne+XerotXnvo17/wP97vGUUP3DetIoB93f8\nvocdUeuHMDDTCO/0zGv5bXFtJq+r7DdYBWFYUdSG4a22L3bq/7hkzD0ISMncO4eOYW829WX8\nP+JoOvkxZZ0YxWituDRGkDnpMCBhv2+nx+ISh+yE4x94x6y2oDH9hgUzDKDPmueNyQF7kSAg\nmdqUIAVSVzCqrpk4KK+FICsRRf11Aen8UppzGLnR+03VxanNCefEUh4UhmUhYWMBxkKvzIi5\nvtd5Yn+WrzbKbyiCmREKoPaqJE9u4nQnNc83aTP0OGqLozhlQyLBjoILZ9NDV6ZbYTfbwHy+\nidP8zWM209Ck6BLB5/eflhu5e2KLQ34HCXbNjXVI4G/Jq598ck/KHjzyU3qOTynhHXyS27/D\nKFUau3lEa0uSRK/ph5/kt3O4w0Ev/Ri421pXRYp9KviUrXmLzl+CDzIQ/IX41ay3oEoVYRWQ\nfFK/9aFUQt41gvm++qilLGO2zBDbw55h0ZF3buyz1WY2UV4YVoMVX5rC2HVFp10jfZCtv+2A\nK1AG70kh27gjgWSjsHbC8+SIjzuZRepDUH5/AX5mlrYNIgj1+fcDuY9/6euLzsP5TGUnqmy4\n+Q7HKY6GYC3gg5IZPP/yW+5hA4SoTfNfM1ZBhPqctJpYFIPCvh7S+c1sTCv7GnFlh/343m0s\n/oi4pa6uGsXMCAfQLSV6dUFW4Xvy5OaWx64ubm1/xoLTOfCp9aM4BTtadqjrYHZytyPYAQCA\nlLajT3oDhmHN0ctwenUyuZzH93gxDMOwAncz/Buk4BLBnxCAYRjWXPEzI52BfOE28lE08DHw\nyBW3dRpdLOB2Dz/kttzdkZQqiFg5sOWA33/Dcz5PocVP9kxQbNHC8ifYiXZWHTR+1sK/DlzY\nO4OzIeHY2zWhohLsZiyZwrRLiXUbt+Tcw88FdS30qL/twDyzKlkFsclspRFL+jAmQWLS5Xws\ngxkWK2lwimQepIYIF9ynRX7xA05K0Uq+xoW6/71tuaONhfnUSYaGE42nzbRyXLn1kNvNR58K\neCqOqQLNRtFZHXfHmOnZTk6+tvhyEBcKlZZECo9ygBACaa/nTGiH2wVBU/7za1vnjezGPSuQ\npJrBwkNBH0rabAs5lxCsBbZ9QKKX+eGYX/zZZcveXLIfyJ7ZHpVgh2RQ5VdNRX8TGzcVf7h3\nYeM8PTWixEGichqTFl6npnVmmXRnIdn6hAOEH47GqsLvXz4lf/tV0cjf9tFclRa2cWxnsFpG\nntzcExSXheJZqLrM9G2VabK1YFcZu2oAmzeF8oi5K/2+EjyuMMH76Mq5uiot2nfR/q73S7C0\nA3gUjvhMT5InKgyrD52P88Kw/alkG7cC9xySzdW/MpPfvvxSyPnuzHvHV8wYpaEqLyOjqK45\nYorjbvfnPwmzSwgMJILdXXt8U53lx0Ot2T65p9l2LKXoubed+zPZQ0Rttkd6u5NZFrFCi93c\nI9JpYThnt4123gv2vXpiy8Kp2vK4Hqu/UyhT4YPKNw6VYKfNLDqjaumdyfkNoke7MB2eNbe9\naX2t8LIJYyZEp14pxL4eGY4Lu8rzAn5h/CPzjD4+oZ2s/dmi7ZHmIRIIiDYKfC3oHMsg34W3\nOxinH8q7DRN1dW2PLwLJZFseM1t3mPaaGki7ZmL0pO0aCNZCgo/9IDYBTUTFYH1oJs8TCC0v\n+oBZT3YrseyQhZcSSfNcC1q2kUA0g4rc0Luj2bjhV9LtU+vmjiCUyKVUR1msPRX46qeABm6M\nyKSLaOtr+yZKC0owtP5wlCXd2GKtq9Iyk6Lyg2atv/yykLVRNv586Xt80xJ7ixmmUyaMHzta\nZ9ggzV4qMrhgZTWTPLl5VZ7IOjeBIab3WfGYTWnLLthRd+EMWaFCgUezT+sxWsstjiLfmg0d\n4d9KHUgEu5frcLcKvdM80xO1R+6Jph1OKVqWr01Ppp1crLdtwA9eetii2w4s5pPoZbLF83lu\nS3o5gslpLnlz2UFLAgBAcvAqhrchKsFurkcOAtxbjsdry5p7EGxRn/cNxF/XZ2si58UydzPG\nspKyDaJhlbcdmOZHWYMjSfwppkvjNusyP4LqK+JwF5zm3Mg9U3vxn4dZVGn0Ui/08S5xaDYK\nfOuZ4S2ANbXkymRG624rn5JvzQZCx3MqMhnj/g7VXv9uvN7cj9EbymuhPuP2FqPubPlTZIcs\nuvaOMMNeXVrQ2rFKbG7Y4uomO8L59/MlRss2chTNoHYulOkoNq7NfXXr+MrpQxSJ8s3Iapos\n2nc9Oq1c8HAU7iZdGUTkZoH6ghIYbKL8j+CF2sTnYYm+Vp5fGzCs6tWB8UTVEVs6M4k8uXmW\nFGtM3DRABABAbPD2BJZwzibYIXDhnDEF/5BP8xAgUWGtN74ZicwLId+aDR3g34oASAS7n6fG\nMW4TN+Gtqm6H3OMMfgelGr9encnypZAYsOgeN2cq2qu/+uK3SetuiC3iL2MwPS/AhlG9tuv0\n6z+ELLHwrzPjGU+TsgkkWEwVHub4J6frosi2Eu/HPXhtzSF7v2Coivw0Zwc7abT2cReRUuyn\nO2mmld2CRUuXLnawMJukP7SnDEeCk07D1j7kavgXCLdtkLAfjpm+AtiN633xGBcJ+7vkW/N0\nPKcok8Vkd7j2+jcD6VrAMHrxq7N27LZVyX6zj8UXsLcsf/OP4yD2PV9u2KIrSRQUdSy0pPR3\nRjMo+1lSiNmYXv09zueQy1QtOV6ZiigwRjsm3b6a3VCSG8WCogLWW2YsMeaVHVZ86Pb4iJXt\nFFkRs51AntwQzPNbXxa2kDHhohqukcUcnbZC4cIpwRyVYEyDajNC79+KAkgEOyz9KB7mCD1s\nKJCboqWG9ZwJFmN5Q6cX2wYs1WMo4U162sPwuyiVQxUqwe7pSpyxiHWrMczLYHKZwNWi9OoU\nxlWFJQw7QunjNdpsMpmshumy/RcDHr/LyC0sr2mkNVaXFeZ8eXnf58wW29b7raK5Ry6GYb8l\nDxHfoDjVHBv62DMC1CLNPavfeob5RHuO59RlMrTa69leZVRQwx5tKWCqBNRrAcMwDKtLD9ls\n2K1FchHtZrT1blYDhtHyYw5O782mlJboNW13RLYgUaNEaDF6TEMzqDmW6ojYuLky/fGN3U6T\n+rUuSAQAAGLyWsaL915/lOZu0YZ/+AafJl205EawoKiB9Zb+LA2QnM6Co75Rrz4kf373PPzG\nXuuBDIFPTEqKucNKqOiaO67atGvfoeOnzl+65uHjHxL28MmrL4UN68iTGyR6Tt3l/zq3htuA\nCv0scOO2jM6qsB+N7OsfiQsnkwDCI9gh8W+l1BuiXpG9yobca8bMAVEgNyrBDpU7Gi6KqLhE\nEG3APCaHFruacZoQn3HDQ5gEuzAHfDGY+xCcWn8cH4O/rP+WhLaXsaZbcxiXW/LPUStl89vy\nEPEFtIJdZ4fbpHUdtKil+Fxq7fjITwP+HM8RyGSItdcUwXwLpWpXyNcCc/aKXp6x1WaTsLvo\nWNvps1tfu45wvfaudQ34iNUaVKDKPPIbGKIZ1CxH/KhOjY0Vh4xgP0MzId1jjPX6M7cT85mP\nFmTpkTTpoiQ3ikMOKorjRlhJnV1vOcZVHrWYlV0WANRmu3/hamc9Sp7c+GNFOnXXHmlgbH78\nZdtbiiMW9WWecmS1Zm+wxSUymILGhZM807ABvWCHyL+VwE+ASq8oCXYYlu05iz3PuEDkFjbB\nDu8itXKo63keRn+zYHffEf/gEIW51PnPxaUscUYRV06wjq2yzg/Zfm74FrrFTINg/24DUYVh\ndsefsDx6f28eonaBSrDDp1hxwR2SavXm+HX4ficzP5RnXA8Zx3MUMlnDPSfcRweN9poirIKo\npkrAOmgtsNqnBW8a362t0VGi9/R9UTltz4moZmb8BESDOrkW1+FQYWOOLIJiCgNNXfZ7Pk6v\n5LR1klh6gpp00ZFbuA45eEdsAgmolH5sDPM8ITXTk5dr/lPy5MaJi9UVpL4tSAW15W17pWR2\nOcqj0mRxSHYzVKeFnU5jXmgsKWH80aNHD/KD7dOnD8A38u06Gpn+/pkAACBrfvSyY3/OA0fK\n8+eljL/6WFmNan1NxdZuyopHDzGgP42Ja15qLTz1Dns53XxCW22/ziulBkBAcqOCvLGb23yq\nD0n333r+eS1QLof6o7r92wUEVpf/8VmOwlS93gAAMVtHb3ncTouaHLxSVnFxMQDHmkp88RIv\n5TVk1CgiFdCvX78Yf8jLs9dvErSUDT02NIxRybSTyYEz81T5ri0o2s/52rGAR64PawFKbwfH\nXDUz67AKYwJAVQayagBKb+07u33mjsF8r9FfXgfdGdVxJSYaGxHyXN3P13f9vL29/aOSSzkn\nGWQ1TeYtsHdwsJo8oGvLSxsj/rnxHQAApMYfDzk5UZm/aRZRs7l4+mak/d2KiogtO09aRFYC\nAIDKgl0r+pEpAqk8b73j5rsXcqHE/1KAMeWiZEwgqHaloKAAUAdo1wIT0gOsTkT3khprdPhT\nI+tHqVE7Yp8c1ufuPU0dErKIBmUwY7rCefcySmwspgzNjAeKqhhtPn92g+XIbgQFZPkDvSoj\nNtjb29s3JC6rmqNOspi81iRLeweH+dhFLddQ4vbIyC2DYkHdt5vDVyt+MdLQkOBkrTlj+oBt\niWkAAMMnT1bi0V4AcoufWmnudTvm4y9elWUlBywMfKt9cfPaQ74JbIUi6TT8OyQqKsDOLSND\nEFb659GQ+iILAACkZjjZqbS5XJmYmMr4q+uUKSM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imJai8jYCjwIMhfYaDZCkSmivaBt/oFcXZOjFHx4AACAASURBVD28cSGBdHRuK9Dy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\nb6FbzDT4+YSIKgyzO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VLtew66g5aw7d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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "median_gene_hm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"heatmap_median_immunoregulatory_genes_IC.pdf\"),\n",
    "   width = 20, height = 40)\n",
    "median_gene_hm\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deprecated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enrichment analysis with MSigDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(fgsea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hs.H <- read.table(paste0(datapath, \"DEG/h.all.v7.1.symbols.gmt\"), \n",
    "                   header = F, check.names = F, sep = \"\\t\", fill = T, stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(Hs.H) <- Hs.H$V1\n",
    "Hs.H$V1 <- NULL\n",
    "Hs.H$V2 <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hs.H.list <- as.list(as.data.frame(t(Hs.H)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head(Hs.H.list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#shrunken_res_C4 <- shrunken_res_C4[order(shrunken_res_C4$log2FoldChange),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks <- shrunken_res_C1$log2FoldChange\n",
    "names(ranks) <- rownames(shrunken_res_C1)\n",
    "ranks <- ranks[!is.na(ranks)]\n",
    "head(ranks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks <- sort(ranks, decreasing = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "barplot(ranks, decreasing = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fgseaRes <- fgseaMultilevel(Hs.H.list, ranks, minSize=15, maxSize = 500, eps = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head(fgseaRes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(fgseaRes, aes(reorder(pathway, NES), NES)) +\n",
    "  geom_col(aes(fill=padj<0.2)) +\n",
    "  coord_flip() +\n",
    "  labs(x=\"Pathway\", y=\"Normalized Enrichment Score\",\n",
    "       title=\"Hallmark pathways NES from GSEA\") + myplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using ReactomePA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "library(clusterProfiler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(scipen = 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "myentrez <-  clusterProfiler::bitr(rownames(shrunken_res_C1), \n",
    "                                   fromType=\"SYMBOL\", toType=\"ENTREZID\", OrgDb= \"org.Hs.eg.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shrunken_res_C1$Entrez <- myentrez$ENTREZID[match(rownames(shrunken_res_C1), myentrez$SYMBOL)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ReactomePA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "myentrez <-  clusterProfiler::bitr(rownames(shrunken_res_C1), \n",
    "                                   fromType=\"SYMBOL\", toType=\"ENTREZID\", OrgDb= \"org.Hs.eg.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shrunken_res_C1$Entrez <- myentrez$ENTREZID[match(rownames(shrunken_res_C1), myentrez$SYMBOL)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygenelist <- shrunken_res_C1$log2FoldChange\n",
    "names(mygenelist) <- shrunken_res_C1$Entrez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary(mygenelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "de <- names(mygenelist)[abs(mygenelist) > 1.5]\n",
    "head(de)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x <- enrichPathway(gene=de, pvalueCutoff = 0.05, readable=TRUE)\n",
    "head(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(enrichplot)\n",
    "barplot(x, showCategory=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf(paste0(plotpath, \"Reactome_C1_barplot.pdf\"),\n",
    "   width = 20, height = 20)\n",
    "\n",
    "barplot(x, showCategory=20)\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygenelist <- mygenelist[order(mygenelist, decreasing = T)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y <- gsePathway(mygenelist, \n",
    "                pvalueCutoff = 0.05,\n",
    "                pAdjustMethod = \"BH\", \n",
    "                verbose = FALSE)\n",
    "head(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y <- as.data.frame(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "myenrichplot <- ggplot(y, aes(reorder(Description, NES), NES)) +\n",
    "  geom_col(aes(fill=p.adjust<0.01)) +\n",
    "  coord_flip() +\n",
    "  labs(x=\"Pathway\", y=\"Normalized Enrichment Score\",\n",
    "       title=\"Hallmark pathways NES from GSEA\") + myplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf(paste0(plotpath, \"Reactome_C1_enrichplot.pdf\"),\n",
    "   width = 20, height = 40)\n",
    "\n",
    "myenrichplot\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf(paste0(plotpath, \"Reactome_C1_dotplot.pdf\"),\n",
    "   width = 20, height = 20)\n",
    "\n",
    "dotplot(y, showCategory=20)\n",
    "dotplot(x, showCategory=20)\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edox <- setReadable(x, 'org.Hs.eg.db', 'ENTREZID')\n",
    "p1 <- cnetplot(edox, foldChange= mygenelist)\n",
    "## categorySize can be scaled by 'pvalue' or 'geneNum'\n",
    "p2 <- cnetplot(edox, categorySize=\"pvalue\", foldChange=mygenelist)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf(paste0(plotpath, \"Reactome_C1_cnet.pdf\"),\n",
    "   width = 20, height = 20)\n",
    "\n",
    "p2\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edo <- pairwise_termsim(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "p1 <- emapplot(edo)\n",
    "p2 <- emapplot(edo, cex_category=1.5)\n",
    "p3 <- emapplot(edo, layout=\"kk\")\n",
    "p4 <- emapplot(edo, cex_category=1.5,layout=\"kk\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf(paste0(plotpath, \"Reactome_C1_emap.pdf\"),\n",
    "   width = 20, height = 20)\n",
    "p1\n",
    "p2\n",
    "p3\n",
    "p4\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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