200330 TF analysis Figure 3.ipynb

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       "'/home/garret/Documents/R code/SCENIC/SCENIC_HB_neuron'"
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       "'/home/garret/Documents/R code/SCENIC/SCENIC\\_HB\\_neuron'"
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   "source": [
    "getwd(); setwd(\"/home/garret/Documents/R code/SCENIC/SCENIC_HB_neuron\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/html": [
       "<style> .container { width:95% !important; } </style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(Seurat)\n",
    "library(dplyr)\n",
    "library(magrittr)\n",
    "IRdisplay::display_html(\"<style> .container { width:95% !important; } </style>\")\n",
    "library(\"ggplot2\")\n",
    "library(\"xlsx\")\n",
    "library(tools)\n",
    "library(\"reshape2\")\n",
    "library(data.table)\n",
    "\n",
    "library(SingleCellExperiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "LHb.integrated<-readRDS(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/LHb_integrated_0.8.rds\")\n",
    "cntl<-readRDS(\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/cntl_0.8.rds\")\n",
    "stim<-readRDS(\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/stim_0.8.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "\\begin{enumerate*}\n",
       "\\item 17726\n",
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       "1. 17726\n",
       "2. 5558\n",
       "\n",
       "\n"
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       "[1] 17726  5558"
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       "[1] TRUE"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "$MulticoreParam\n",
       "class: MulticoreParam\n",
       "  bpisup: FALSE; bpnworkers: 10; bptasks: 0; bpjobname: BPJOB\n",
       "  bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE\n",
       "  bpRNGseed: ; bptimeout: 2592000; bpprogressbar: FALSE\n",
       "  bpexportglobals: TRUE\n",
       "  bplogdir: NA\n",
       "  bpresultdir: NA\n",
       "  cluster type: FORK\n",
       "\n",
       "$SnowParam\n",
       "class: SnowParam\n",
       "  bpisup: FALSE; bpnworkers: 10; bptasks: 0; bpjobname: BPJOB\n",
       "  bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE\n",
       "  bpRNGseed: ; bptimeout: 2592000; bpprogressbar: FALSE\n",
       "  bpexportglobals: TRUE\n",
       "  bplogdir: NA\n",
       "  bpresultdir: NA\n",
       "  cluster type: SOCK\n",
       "\n",
       "$SerialParam\n",
       "class: SerialParam\n",
       "  bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB\n",
       "  bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE\n",
       "  bpRNGseed: ; bptimeout: 2592000; bpprogressbar: FALSE\n",
       "  bpexportglobals: TRUE\n",
       "  bplogdir: NA\n",
       "  bpresultdir: NA\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "load(\"data/sceMouseBrain.RData\")\n",
    "library(SCENIC)\n",
    "exprMat <- counts(sceMouseBrain) \n",
    "# Optional: log expression (for TF expression plot, it does not affect any other calculation)\n",
    "#exprMat <- log2(exprMat+1)\n",
    "dim(exprMat)\n",
    "library(SCENIC)\n",
    "library(\"BiocParallel\") # require function \"register\"\n",
    "scenicOptions <- readRDS(\"int/scenicOptions.Rds\")\n",
    "scenicOptions@settings$verbose <- TRUE\n",
    "scenicOptions@settings$nCores <- 6# defauexprMat <- counts(sceMouseBrain) \n",
    "dim(exprMat)\n",
    "library(SCENIC)\n",
    "library(\"BiocParallel\") # require function \"register\"\n",
    "scenicOptions <- readRDS(\"int/scenicOptions.Rds\")\n",
    "scenicOptions@settings$verbose # lt is 20 \n",
    "scenicOptions@settings$seed <- 123\n",
    "library(BiocParallel) \n",
    "registered()\n",
    "regulons <- loadInt(scenicOptions, \"regulons\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
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    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Hgs'</li>\n",
       "\t<li>'Hmga1'</li>\n",
       "\t<li>'Ifrd1'</li>\n",
       "\t<li>'Lmna'</li>\n",
       "\t<li>'Mid1'</li>\n",
       "\t<li>'Mpv17'</li>\n",
       "\t<li>'Ndrg1'</li>\n",
       "\t<li>'Ociad2'</li>\n",
       "\t<li>'Sh2d5'</li>\n",
       "\t<li>'Stk40'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Hgs'\n",
       "\\item 'Hmga1'\n",
       "\\item 'Ifrd1'\n",
       "\\item 'Lmna'\n",
       "\\item 'Mid1'\n",
       "\\item 'Mpv17'\n",
       "\\item 'Ndrg1'\n",
       "\\item 'Ociad2'\n",
       "\\item 'Sh2d5'\n",
       "\\item 'Stk40'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Hgs'\n",
       "2. 'Hmga1'\n",
       "3. 'Ifrd1'\n",
       "4. 'Lmna'\n",
       "5. 'Mid1'\n",
       "6. 'Mpv17'\n",
       "7. 'Ndrg1'\n",
       "8. 'Ociad2'\n",
       "9. 'Sh2d5'\n",
       "10. 'Stk40'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Hgs\"    \"Hmga1\"  \"Ifrd1\"  \"Lmna\"   \"Mid1\"   \"Mpv17\"  \"Ndrg1\"  \"Ociad2\"\n",
       " [9] \"Sh2d5\"  \"Stk40\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "[1] 10"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "regulons$Fos\n",
    "length(regulons$Fos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# assgin clusters to cell types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "new.ident <- c(\"MHb1\",\"MHb2\",\"MHb3\",\"LHb1\",\"LHb2\",\"LHb3\",\"MHb4\",\"LHb4\",\"MHb5\",\"LHb5\",\"LHb6\",\"MHb6\",\"Surr1\",\"Surr2\",\"Surr3\",\"Surr4\")\n",
    "names(x = new.ident) <- levels(x =LHb.integrated)\n",
    "LHb.integrated<- RenameIdents(object =LHb.integrated, new.ident)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (i in 1:length(new.ident)){\n",
    "assign(paste(new.ident[i],\"_barcode\",sep=\"\"),colnames(LHb.integrated@assays$RNA@data[,which(Idents(object=LHb.integrated) %in% new.ident[i])]))# this gives all barcodes in cluster\n",
    "assign(paste(new.ident[i],\"_barcode_cntl\",sep=\"\"),intersect(colnames(cntl@assays$RNA@data),eval(parse(text = paste(new.ident[i],\"_barcode\",sep=\"\")))))\n",
    "assign(paste(new.ident[i],\"_barcode_stim\",sep=\"\"),intersect(colnames(stim@assays$RNA@data),eval(parse(text = paste(new.ident[i],\"_barcode\",sep=\"\")))))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "LHb.integrated<-subset(x=LHb.integrated,cells=c(MHb1_barcode,MHb2_barcode,MHb3_barcode,LHb1_barcode,LHb2_barcode,LHb3_barcode,MHb4_barcode,MHb5_barcode,LHb4_barcode,LHb5_barcode,MHb6_barcode,LHb6_barcode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TFs regulating markers in LHB vs MHb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "MHb_gene<-read.xlsx(\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/conserved_markers_MHB_overLHbgene_eachcombined.xlsx\",sheetIndex=1)     \n",
    "LHb_gene<-read.xlsx(\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/conserved_markers_LHB_overMHbgene_eachcombined.xlsx\",sheetIndex=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "MHb_gene<-as.character(MHb_gene[,1])\n",
    "LHb_gene<-as.character(LHb_gene[,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "[1] 170"
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       "[1] 289"
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     },
     "metadata": {},
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   ],
   "source": [
    "length(MHb_gene)\n",
    "length(LHb_gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# careful with [[]]\n",
    "unique_gene<-unique(MHb_gene)\n",
    "M.TF.list<-list()\n",
    "for (j in 1:length(unique_gene)){\n",
    "GOI<-unique_gene[j]\n",
    "temp<-vector(mode=\"character\", length=0)\n",
    "for (i in 1:length(regulons)){if (GOI %in% regulons[[i]]){temp<-c(temp,names(regulons[i]))}}\n",
    "if (length(temp)==0){M.TF.list[[GOI]]<-\"None\"}else{M.TF.list[[GOI]]<-temp}  }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# careful with [[]]\n",
    "unique_gene<-unique(LHb_gene)\n",
    "L.TF.list<-list()\n",
    "for (j in 1:length(unique_gene)){\n",
    "GOI<-unique_gene[j]\n",
    "temp<-vector(mode=\"character\", length=0)\n",
    "for (i in 1:length(regulons)){if (GOI %in% regulons[[i]]){temp<-c(temp,names(regulons[i]))}}\n",
    "if (length(temp)==0){L.TF.list[[GOI]]<-\"None\"}else{L.TF.list[[GOI]]<-temp}  }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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    "collapsed": true
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      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
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      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
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      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
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      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
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      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
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     "text": [
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (M.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (L.TF.list[[i]] == \"None\") {:\n",
      "“the condition has length > 1 and only the first element will be used”"
     ]
    }
   ],
   "source": [
    "M.TFs<-vector(mode=\"character\", length=0)\n",
    "for (i in 1:length(M.TF.list)){\n",
    "    if (M.TF.list[[i]]==\"None\"){M.TFs<-M.TFs}else{M.TFs<-c(M.TFs,M.TF.list[[i]])}\n",
    "}\n",
    "L.TFs<-vector(mode=\"character\", length=0)\n",
    "for (i in 1:length(L.TF.list)){\n",
    "    if (L.TF.list[[i]]==\"None\"){L.TFs<-L.TFs}else{L.TFs<-c(L.TFs,L.TF.list[[i]])}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "M.TFs<-as.data.frame(table(M.TFs))\n",
    "M.TFs_order<-M.TFs[order(-M.TFs$Freq),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(M.TFs_order)<-as.character(M.TFs_order[,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonDEG<-vector(mode=\"numeric\", length=0)\n",
    "DEGenrichment<-vector(mode=\"numeric\", length=0)\n",
    "for (i in 1:dim(M.TFs_order)[1]){\n",
    "nonDEG[i]<- length(regulons[[rownames(M.TFs_order)[i]]]) - M.TFs_order$Freq[i]\n",
    "DEGenrichment[i]<-   M.TFs_order$Freq[i]/length(regulons[[rownames(M.TFs_order)[i]]])\n",
    "}\n",
    "M.TFs_order$nonDEG<-nonDEG\n",
    "M.TFs_order$DEGenrichment<-DEGenrichment\n",
    "colnames(M.TFs_order)[2]<-\"DEG\"\n",
    "rownames(M.TFs_order)<-M.TFs_order$M.TFs\n",
    "M.TFs_order<-M.TFs_order[order(M.TFs_order$DEGenrichment),]# you may reverse order if normoal bar graph\n",
    "M.TFs_order$M.TFs<-factor(M.TFs_order$M.TFs,levels=as.character(M.TFs_order$M.TFs))\n",
    "write.xlsx2(M.TFs_order,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/MTF_rank_MHbrichgenes.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>105</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 105\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 105\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 105   4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(M.TFs_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#use this. This is remove the TFs if if has extended\n",
    "remove<-numeric()\n",
    "for (i in 1:dim(M.TFs_order)[1]){\n",
    "if(paste(M.TFs_order[,1][i],\"_extended\",sep=\"\") %in% M.TFs_order[,1]){\n",
    "    remove<-c(remove,i)}else{remove<-remove}\n",
    "}\n",
    "M.TFs_order<-M.TFs_order[-remove,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>M.TFs</th><th scope=col>DEG</th><th scope=col>nonDEG</th><th scope=col>DEGenrichment</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Gtf2f1_extended</th><td>Gtf2f1_extended</td><td> 1             </td><td>1184           </td><td>0.0008438819   </td></tr>\n",
       "\t<tr><th scope=row>Bmyc_extended</th><td>Bmyc_extended  </td><td> 3             </td><td>1944           </td><td>0.0015408320   </td></tr>\n",
       "\t<tr><th scope=row>Ets2_extended</th><td>Ets2_extended  </td><td> 1             </td><td> 644           </td><td>0.0015503876   </td></tr>\n",
       "\t<tr><th scope=row>Ep300_extended</th><td>Ep300_extended </td><td> 2             </td><td>1166           </td><td>0.0017123288   </td></tr>\n",
       "\t<tr><th scope=row>Setdb1_extended</th><td>Setdb1_extended</td><td> 1             </td><td> 424           </td><td>0.0023529412   </td></tr>\n",
       "\t<tr><th scope=row>Elk1_extended</th><td>Elk1_extended  </td><td> 1             </td><td> 390           </td><td>0.0025575448   </td></tr>\n",
       "\t<tr><th scope=row>E2f4_extended</th><td>E2f4_extended  </td><td> 1             </td><td> 385           </td><td>0.0025906736   </td></tr>\n",
       "\t<tr><th scope=row>E2f6_extended</th><td>E2f6_extended  </td><td> 5             </td><td>1882           </td><td>0.0026497085   </td></tr>\n",
       "\t<tr><th scope=row>Zfp369_extended</th><td>Zfp369_extended</td><td> 2             </td><td> 682           </td><td>0.0029239766   </td></tr>\n",
       "\t<tr><th scope=row>Zeb1_extended</th><td>Zeb1_extended  </td><td> 1             </td><td> 332           </td><td>0.0030030030   </td></tr>\n",
       "\t<tr><th scope=row>Rbbp5_extended</th><td>Rbbp5_extended </td><td> 7             </td><td>2299           </td><td>0.0030355594   </td></tr>\n",
       "\t<tr><th scope=row>Gabpa_extended</th><td>Gabpa_extended </td><td> 1             </td><td> 295           </td><td>0.0033783784   </td></tr>\n",
       "\t<tr><th scope=row>Mxi1_extended</th><td>Mxi1_extended  </td><td> 7             </td><td>1835           </td><td>0.0038002172   </td></tr>\n",
       "\t<tr><th scope=row>Etv3_extended</th><td>Etv3_extended  </td><td> 5             </td><td>1298           </td><td>0.0038372985   </td></tr>\n",
       "\t<tr><th scope=row>Nfyb_extended</th><td>Nfyb_extended  </td><td> 5             </td><td>1297           </td><td>0.0038402458   </td></tr>\n",
       "\t<tr><th scope=row>Zfp239</th><td>Zfp239         </td><td> 1             </td><td> 212           </td><td>0.0046948357   </td></tr>\n",
       "\t<tr><th scope=row>Phf8_extended</th><td>Phf8_extended  </td><td> 8             </td><td>1652           </td><td>0.0048192771   </td></tr>\n",
       "\t<tr><th scope=row>Pml_extended</th><td>Pml_extended   </td><td> 1             </td><td> 186           </td><td>0.0053475936   </td></tr>\n",
       "\t<tr><th scope=row>Suz12_extended</th><td>Suz12_extended </td><td> 7             </td><td>1088           </td><td>0.0063926941   </td></tr>\n",
       "\t<tr><th scope=row>Zfp523_extended</th><td>Zfp523_extended</td><td>10             </td><td>1517           </td><td>0.0065487885   </td></tr>\n",
       "\t<tr><th scope=row>Sin3a_extended</th><td>Sin3a_extended </td><td> 4             </td><td> 593           </td><td>0.0067001675   </td></tr>\n",
       "\t<tr><th scope=row>Taf1_extended</th><td>Taf1_extended  </td><td>27             </td><td>3993           </td><td>0.0067164179   </td></tr>\n",
       "\t<tr><th scope=row>Stat1_extended</th><td>Stat1_extended </td><td> 2             </td><td> 287           </td><td>0.0069204152   </td></tr>\n",
       "\t<tr><th scope=row>Yy1_extended</th><td>Yy1_extended   </td><td> 9             </td><td>1212           </td><td>0.0073710074   </td></tr>\n",
       "\t<tr><th scope=row>Maz_extended</th><td>Maz_extended   </td><td>30             </td><td>3607           </td><td>0.0082485565   </td></tr>\n",
       "\t<tr><th scope=row>Smc3_extended</th><td>Smc3_extended  </td><td> 3             </td><td> 351           </td><td>0.0084745763   </td></tr>\n",
       "\t<tr><th scope=row>Hdac2_extended</th><td>Hdac2_extended </td><td>37             </td><td>4315           </td><td>0.0085018382   </td></tr>\n",
       "\t<tr><th scope=row>Kdm5b_extended</th><td>Kdm5b_extended </td><td>20             </td><td>2245           </td><td>0.0088300221   </td></tr>\n",
       "\t<tr><th scope=row>Elf2_extended</th><td>Elf2_extended  </td><td>18             </td><td>1966           </td><td>0.0090725806   </td></tr>\n",
       "\t<tr><th scope=row>Kdm5a_extended</th><td>Kdm5a_extended </td><td>37             </td><td>3513           </td><td>0.0104225352   </td></tr>\n",
       "\t<tr><th scope=row>...</th><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><th scope=row>Npdc1_extended</th><td>Npdc1_extended  </td><td> 1              </td><td>  45            </td><td>0.02173913      </td></tr>\n",
       "\t<tr><th scope=row>Tef_extended</th><td>Tef_extended    </td><td>39              </td><td>1750            </td><td>0.02179989      </td></tr>\n",
       "\t<tr><th scope=row>Usf2_extended</th><td>Usf2_extended   </td><td>65              </td><td>2882            </td><td>0.02205633      </td></tr>\n",
       "\t<tr><th scope=row>Bhlhe41_extended</th><td>Bhlhe41_extended</td><td> 4              </td><td> 176            </td><td>0.02222222      </td></tr>\n",
       "\t<tr><th scope=row>Foxp2_extended</th><td>Foxp2_extended  </td><td>42              </td><td>1739            </td><td>0.02358226      </td></tr>\n",
       "\t<tr><th scope=row>Etv5_extended</th><td>Etv5_extended   </td><td> 6              </td><td> 233            </td><td>0.02510460      </td></tr>\n",
       "\t<tr><th scope=row>Pou2f2_extended</th><td>Pou2f2_extended </td><td> 2              </td><td>  72            </td><td>0.02702703      </td></tr>\n",
       "\t<tr><th scope=row>E4f1_extended</th><td>E4f1_extended   </td><td> 1              </td><td>  35            </td><td>0.02777778      </td></tr>\n",
       "\t<tr><th scope=row>Klf7_extended</th><td>Klf7_extended   </td><td> 3              </td><td>  98            </td><td>0.02970297      </td></tr>\n",
       "\t<tr><th scope=row>Nfix</th><td>Nfix            </td><td> 1              </td><td>  20            </td><td>0.04761905      </td></tr>\n",
       "\t<tr><th scope=row>Myef2_extended</th><td>Myef2_extended  </td><td> 2              </td><td>  39            </td><td>0.04878049      </td></tr>\n",
       "\t<tr><th scope=row>Mxd4_extended</th><td>Mxd4_extended   </td><td> 2              </td><td>  31            </td><td>0.06060606      </td></tr>\n",
       "\t<tr><th scope=row>Zic1</th><td>Zic1            </td><td>15              </td><td> 192            </td><td>0.07246377      </td></tr>\n",
       "\t<tr><th scope=row>Ybx1_extended</th><td>Ybx1_extended   </td><td> 4              </td><td>  48            </td><td>0.07692308      </td></tr>\n",
       "\t<tr><th scope=row>Lhx9_extended</th><td>Lhx9_extended   </td><td> 3              </td><td>  33            </td><td>0.08333333      </td></tr>\n",
       "\t<tr><th scope=row>Tcf7_extended</th><td>Tcf7_extended   </td><td> 3              </td><td>  33            </td><td>0.08333333      </td></tr>\n",
       "\t<tr><th scope=row>Klf9_extended</th><td>Klf9_extended   </td><td> 6              </td><td>  63            </td><td>0.08695652      </td></tr>\n",
       "\t<tr><th scope=row>Zic4_extended</th><td>Zic4_extended   </td><td> 1              </td><td>   5            </td><td>0.16666667      </td></tr>\n",
       "\t<tr><th scope=row>Tcf7l2</th><td>Tcf7l2          </td><td>20              </td><td>  93            </td><td>0.17699115      </td></tr>\n",
       "\t<tr><th scope=row>Nfib</th><td>Nfib            </td><td> 2              </td><td>   9            </td><td>0.18181818      </td></tr>\n",
       "\t<tr><th scope=row>Pou3f3_extended</th><td>Pou3f3_extended </td><td> 5              </td><td>  22            </td><td>0.18518519      </td></tr>\n",
       "\t<tr><th scope=row>Pbx3_extended</th><td>Pbx3_extended   </td><td> 5              </td><td>  15            </td><td>0.25000000      </td></tr>\n",
       "\t<tr><th scope=row>Sox6</th><td>Sox6            </td><td> 2              </td><td>   5            </td><td>0.28571429      </td></tr>\n",
       "\t<tr><th scope=row>Ebf1</th><td>Ebf1            </td><td>11              </td><td>  26            </td><td>0.29729730      </td></tr>\n",
       "\t<tr><th scope=row>Lef1_extended</th><td>Lef1_extended   </td><td> 5              </td><td>   9            </td><td>0.35714286      </td></tr>\n",
       "\t<tr><th scope=row>Pou3f1_extended</th><td>Pou3f1_extended </td><td>35              </td><td>  52            </td><td>0.40229885      </td></tr>\n",
       "\t<tr><th scope=row>Neurod2_extended</th><td>Neurod2_extended</td><td>19              </td><td>  14            </td><td>0.57575758      </td></tr>\n",
       "\t<tr><th scope=row>Irx3_extended</th><td>Irx3_extended   </td><td> 3              </td><td>   2            </td><td>0.60000000      </td></tr>\n",
       "\t<tr><th scope=row>Neurod1_extended</th><td>Neurod1_extended</td><td> 9              </td><td>   0            </td><td>1.00000000      </td></tr>\n",
       "\t<tr><th scope=row>Gpd1</th><td>Gpd1            </td><td> 7              </td><td>   0            </td><td>1.00000000      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & M.TFs & DEG & nonDEG & DEGenrichment\\\\\n",
       "\\hline\n",
       "\tGtf2f1\\_extended & Gtf2f1\\_extended &  1                & 1184              & 0.0008438819     \\\\\n",
       "\tBmyc\\_extended & Bmyc\\_extended   &  3                & 1944              & 0.0015408320     \\\\\n",
       "\tEts2\\_extended & Ets2\\_extended   &  1                &  644              & 0.0015503876     \\\\\n",
       "\tEp300\\_extended & Ep300\\_extended  &  2                & 1166              & 0.0017123288     \\\\\n",
       "\tSetdb1\\_extended & Setdb1\\_extended &  1                &  424              & 0.0023529412     \\\\\n",
       "\tElk1\\_extended & Elk1\\_extended   &  1                &  390              & 0.0025575448     \\\\\n",
       "\tE2f4\\_extended & E2f4\\_extended   &  1                &  385              & 0.0025906736     \\\\\n",
       "\tE2f6\\_extended & E2f6\\_extended   &  5                & 1882              & 0.0026497085     \\\\\n",
       "\tZfp369\\_extended & Zfp369\\_extended &  2                &  682              & 0.0029239766     \\\\\n",
       "\tZeb1\\_extended & Zeb1\\_extended   &  1                &  332              & 0.0030030030     \\\\\n",
       "\tRbbp5\\_extended & Rbbp5\\_extended  &  7                & 2299              & 0.0030355594     \\\\\n",
       "\tGabpa\\_extended & Gabpa\\_extended  &  1                &  295              & 0.0033783784     \\\\\n",
       "\tMxi1\\_extended & Mxi1\\_extended   &  7                & 1835              & 0.0038002172     \\\\\n",
       "\tEtv3\\_extended & Etv3\\_extended   &  5                & 1298              & 0.0038372985     \\\\\n",
       "\tNfyb\\_extended & Nfyb\\_extended   &  5                & 1297              & 0.0038402458     \\\\\n",
       "\tZfp239 & Zfp239          &  1              &  212            & 0.0046948357   \\\\\n",
       "\tPhf8\\_extended & Phf8\\_extended   &  8                & 1652              & 0.0048192771     \\\\\n",
       "\tPml\\_extended & Pml\\_extended    &  1                &  186              & 0.0053475936     \\\\\n",
       "\tSuz12\\_extended & Suz12\\_extended  &  7                & 1088              & 0.0063926941     \\\\\n",
       "\tZfp523\\_extended & Zfp523\\_extended & 10                & 1517              & 0.0065487885     \\\\\n",
       "\tSin3a\\_extended & Sin3a\\_extended  &  4                &  593              & 0.0067001675     \\\\\n",
       "\tTaf1\\_extended & Taf1\\_extended   & 27                & 3993              & 0.0067164179     \\\\\n",
       "\tStat1\\_extended & Stat1\\_extended  &  2                &  287              & 0.0069204152     \\\\\n",
       "\tYy1\\_extended & Yy1\\_extended    &  9                & 1212              & 0.0073710074     \\\\\n",
       "\tMaz\\_extended & Maz\\_extended    & 30                & 3607              & 0.0082485565     \\\\\n",
       "\tSmc3\\_extended & Smc3\\_extended   &  3                &  351              & 0.0084745763     \\\\\n",
       "\tHdac2\\_extended & Hdac2\\_extended  & 37                & 4315              & 0.0085018382     \\\\\n",
       "\tKdm5b\\_extended & Kdm5b\\_extended  & 20                & 2245              & 0.0088300221     \\\\\n",
       "\tElf2\\_extended & Elf2\\_extended   & 18                & 1966              & 0.0090725806     \\\\\n",
       "\tKdm5a\\_extended & Kdm5a\\_extended  & 37                & 3513              & 0.0104225352     \\\\\n",
       "\t... & ... & ... & ... & ...\\\\\n",
       "\tNpdc1\\_extended & Npdc1\\_extended   &  1                 &   45               & 0.02173913        \\\\\n",
       "\tTef\\_extended & Tef\\_extended     & 39                 & 1750               & 0.02179989        \\\\\n",
       "\tUsf2\\_extended & Usf2\\_extended    & 65                 & 2882               & 0.02205633        \\\\\n",
       "\tBhlhe41\\_extended & Bhlhe41\\_extended &  4                 &  176               & 0.02222222        \\\\\n",
       "\tFoxp2\\_extended & Foxp2\\_extended   & 42                 & 1739               & 0.02358226        \\\\\n",
       "\tEtv5\\_extended & Etv5\\_extended    &  6                 &  233               & 0.02510460        \\\\\n",
       "\tPou2f2\\_extended & Pou2f2\\_extended  &  2                 &   72               & 0.02702703        \\\\\n",
       "\tE4f1\\_extended & E4f1\\_extended    &  1                 &   35               & 0.02777778        \\\\\n",
       "\tKlf7\\_extended & Klf7\\_extended    &  3                 &   98               & 0.02970297        \\\\\n",
       "\tNfix & Nfix             &  1               &   20             & 0.04761905      \\\\\n",
       "\tMyef2\\_extended & Myef2\\_extended   &  2                 &   39               & 0.04878049        \\\\\n",
       "\tMxd4\\_extended & Mxd4\\_extended    &  2                 &   31               & 0.06060606        \\\\\n",
       "\tZic1 & Zic1             & 15               &  192             & 0.07246377      \\\\\n",
       "\tYbx1\\_extended & Ybx1\\_extended    &  4                 &   48               & 0.07692308        \\\\\n",
       "\tLhx9\\_extended & Lhx9\\_extended    &  3                 &   33               & 0.08333333        \\\\\n",
       "\tTcf7\\_extended & Tcf7\\_extended    &  3                 &   33               & 0.08333333        \\\\\n",
       "\tKlf9\\_extended & Klf9\\_extended    &  6                 &   63               & 0.08695652        \\\\\n",
       "\tZic4\\_extended & Zic4\\_extended    &  1                 &    5               & 0.16666667        \\\\\n",
       "\tTcf7l2 & Tcf7l2           & 20               &   93             & 0.17699115      \\\\\n",
       "\tNfib & Nfib             &  2               &    9             & 0.18181818      \\\\\n",
       "\tPou3f3\\_extended & Pou3f3\\_extended  &  5                 &   22               & 0.18518519        \\\\\n",
       "\tPbx3\\_extended & Pbx3\\_extended    &  5                 &   15               & 0.25000000        \\\\\n",
       "\tSox6 & Sox6             &  2               &    5             & 0.28571429      \\\\\n",
       "\tEbf1 & Ebf1             & 11               &   26             & 0.29729730      \\\\\n",
       "\tLef1\\_extended & Lef1\\_extended    &  5                 &    9               & 0.35714286        \\\\\n",
       "\tPou3f1\\_extended & Pou3f1\\_extended  & 35                 &   52               & 0.40229885        \\\\\n",
       "\tNeurod2\\_extended & Neurod2\\_extended & 19                 &   14               & 0.57575758        \\\\\n",
       "\tIrx3\\_extended & Irx3\\_extended    &  3                 &    2               & 0.60000000        \\\\\n",
       "\tNeurod1\\_extended & Neurod1\\_extended &  9                 &    0               & 1.00000000        \\\\\n",
       "\tGpd1 & Gpd1             &  7               &    0             & 1.00000000      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | M.TFs | DEG | nonDEG | DEGenrichment |\n",
       "|---|---|---|---|---|\n",
       "| Gtf2f1_extended | Gtf2f1_extended |  1              | 1184            | 0.0008438819    |\n",
       "| Bmyc_extended | Bmyc_extended   |  3              | 1944            | 0.0015408320    |\n",
       "| Ets2_extended | Ets2_extended   |  1              |  644            | 0.0015503876    |\n",
       "| Ep300_extended | Ep300_extended  |  2              | 1166            | 0.0017123288    |\n",
       "| Setdb1_extended | Setdb1_extended |  1              |  424            | 0.0023529412    |\n",
       "| Elk1_extended | Elk1_extended   |  1              |  390            | 0.0025575448    |\n",
       "| E2f4_extended | E2f4_extended   |  1              |  385            | 0.0025906736    |\n",
       "| E2f6_extended | E2f6_extended   |  5              | 1882            | 0.0026497085    |\n",
       "| Zfp369_extended | Zfp369_extended |  2              |  682            | 0.0029239766    |\n",
       "| Zeb1_extended | Zeb1_extended   |  1              |  332            | 0.0030030030    |\n",
       "| Rbbp5_extended | Rbbp5_extended  |  7              | 2299            | 0.0030355594    |\n",
       "| Gabpa_extended | Gabpa_extended  |  1              |  295            | 0.0033783784    |\n",
       "| Mxi1_extended | Mxi1_extended   |  7              | 1835            | 0.0038002172    |\n",
       "| Etv3_extended | Etv3_extended   |  5              | 1298            | 0.0038372985    |\n",
       "| Nfyb_extended | Nfyb_extended   |  5              | 1297            | 0.0038402458    |\n",
       "| Zfp239 | Zfp239          |  1              |  212            | 0.0046948357    |\n",
       "| Phf8_extended | Phf8_extended   |  8              | 1652            | 0.0048192771    |\n",
       "| Pml_extended | Pml_extended    |  1              |  186            | 0.0053475936    |\n",
       "| Suz12_extended | Suz12_extended  |  7              | 1088            | 0.0063926941    |\n",
       "| Zfp523_extended | Zfp523_extended | 10              | 1517            | 0.0065487885    |\n",
       "| Sin3a_extended | Sin3a_extended  |  4              |  593            | 0.0067001675    |\n",
       "| Taf1_extended | Taf1_extended   | 27              | 3993            | 0.0067164179    |\n",
       "| Stat1_extended | Stat1_extended  |  2              |  287            | 0.0069204152    |\n",
       "| Yy1_extended | Yy1_extended    |  9              | 1212            | 0.0073710074    |\n",
       "| Maz_extended | Maz_extended    | 30              | 3607            | 0.0082485565    |\n",
       "| Smc3_extended | Smc3_extended   |  3              |  351            | 0.0084745763    |\n",
       "| Hdac2_extended | Hdac2_extended  | 37              | 4315            | 0.0085018382    |\n",
       "| Kdm5b_extended | Kdm5b_extended  | 20              | 2245            | 0.0088300221    |\n",
       "| Elf2_extended | Elf2_extended   | 18              | 1966            | 0.0090725806    |\n",
       "| Kdm5a_extended | Kdm5a_extended  | 37              | 3513            | 0.0104225352    |\n",
       "| ... | ... | ... | ... | ... |\n",
       "| Npdc1_extended | Npdc1_extended   |  1               |   45             | 0.02173913       |\n",
       "| Tef_extended | Tef_extended     | 39               | 1750             | 0.02179989       |\n",
       "| Usf2_extended | Usf2_extended    | 65               | 2882             | 0.02205633       |\n",
       "| Bhlhe41_extended | Bhlhe41_extended |  4               |  176             | 0.02222222       |\n",
       "| Foxp2_extended | Foxp2_extended   | 42               | 1739             | 0.02358226       |\n",
       "| Etv5_extended | Etv5_extended    |  6               |  233             | 0.02510460       |\n",
       "| Pou2f2_extended | Pou2f2_extended  |  2               |   72             | 0.02702703       |\n",
       "| E4f1_extended | E4f1_extended    |  1               |   35             | 0.02777778       |\n",
       "| Klf7_extended | Klf7_extended    |  3               |   98             | 0.02970297       |\n",
       "| Nfix | Nfix             |  1               |   20             | 0.04761905       |\n",
       "| Myef2_extended | Myef2_extended   |  2               |   39             | 0.04878049       |\n",
       "| Mxd4_extended | Mxd4_extended    |  2               |   31             | 0.06060606       |\n",
       "| Zic1 | Zic1             | 15               |  192             | 0.07246377       |\n",
       "| Ybx1_extended | Ybx1_extended    |  4               |   48             | 0.07692308       |\n",
       "| Lhx9_extended | Lhx9_extended    |  3               |   33             | 0.08333333       |\n",
       "| Tcf7_extended | Tcf7_extended    |  3               |   33             | 0.08333333       |\n",
       "| Klf9_extended | Klf9_extended    |  6               |   63             | 0.08695652       |\n",
       "| Zic4_extended | Zic4_extended    |  1               |    5             | 0.16666667       |\n",
       "| Tcf7l2 | Tcf7l2           | 20               |   93             | 0.17699115       |\n",
       "| Nfib | Nfib             |  2               |    9             | 0.18181818       |\n",
       "| Pou3f3_extended | Pou3f3_extended  |  5               |   22             | 0.18518519       |\n",
       "| Pbx3_extended | Pbx3_extended    |  5               |   15             | 0.25000000       |\n",
       "| Sox6 | Sox6             |  2               |    5             | 0.28571429       |\n",
       "| Ebf1 | Ebf1             | 11               |   26             | 0.29729730       |\n",
       "| Lef1_extended | Lef1_extended    |  5               |    9             | 0.35714286       |\n",
       "| Pou3f1_extended | Pou3f1_extended  | 35               |   52             | 0.40229885       |\n",
       "| Neurod2_extended | Neurod2_extended | 19               |   14             | 0.57575758       |\n",
       "| Irx3_extended | Irx3_extended    |  3               |    2             | 0.60000000       |\n",
       "| Neurod1_extended | Neurod1_extended |  9               |    0             | 1.00000000       |\n",
       "| Gpd1 | Gpd1             |  7               |    0             | 1.00000000       |\n",
       "\n"
      ],
      "text/plain": [
       "                 M.TFs            DEG nonDEG DEGenrichment\n",
       "Gtf2f1_extended  Gtf2f1_extended   1  1184   0.0008438819 \n",
       "Bmyc_extended    Bmyc_extended     3  1944   0.0015408320 \n",
       "Ets2_extended    Ets2_extended     1   644   0.0015503876 \n",
       "Ep300_extended   Ep300_extended    2  1166   0.0017123288 \n",
       "Setdb1_extended  Setdb1_extended   1   424   0.0023529412 \n",
       "Elk1_extended    Elk1_extended     1   390   0.0025575448 \n",
       "E2f4_extended    E2f4_extended     1   385   0.0025906736 \n",
       "E2f6_extended    E2f6_extended     5  1882   0.0026497085 \n",
       "Zfp369_extended  Zfp369_extended   2   682   0.0029239766 \n",
       "Zeb1_extended    Zeb1_extended     1   332   0.0030030030 \n",
       "Rbbp5_extended   Rbbp5_extended    7  2299   0.0030355594 \n",
       "Gabpa_extended   Gabpa_extended    1   295   0.0033783784 \n",
       "Mxi1_extended    Mxi1_extended     7  1835   0.0038002172 \n",
       "Etv3_extended    Etv3_extended     5  1298   0.0038372985 \n",
       "Nfyb_extended    Nfyb_extended     5  1297   0.0038402458 \n",
       "Zfp239           Zfp239            1   212   0.0046948357 \n",
       "Phf8_extended    Phf8_extended     8  1652   0.0048192771 \n",
       "Pml_extended     Pml_extended      1   186   0.0053475936 \n",
       "Suz12_extended   Suz12_extended    7  1088   0.0063926941 \n",
       "Zfp523_extended  Zfp523_extended  10  1517   0.0065487885 \n",
       "Sin3a_extended   Sin3a_extended    4   593   0.0067001675 \n",
       "Taf1_extended    Taf1_extended    27  3993   0.0067164179 \n",
       "Stat1_extended   Stat1_extended    2   287   0.0069204152 \n",
       "Yy1_extended     Yy1_extended      9  1212   0.0073710074 \n",
       "Maz_extended     Maz_extended     30  3607   0.0082485565 \n",
       "Smc3_extended    Smc3_extended     3   351   0.0084745763 \n",
       "Hdac2_extended   Hdac2_extended   37  4315   0.0085018382 \n",
       "Kdm5b_extended   Kdm5b_extended   20  2245   0.0088300221 \n",
       "Elf2_extended    Elf2_extended    18  1966   0.0090725806 \n",
       "Kdm5a_extended   Kdm5a_extended   37  3513   0.0104225352 \n",
       "...              ...              ... ...    ...          \n",
       "Npdc1_extended   Npdc1_extended    1    45   0.02173913   \n",
       "Tef_extended     Tef_extended     39  1750   0.02179989   \n",
       "Usf2_extended    Usf2_extended    65  2882   0.02205633   \n",
       "Bhlhe41_extended Bhlhe41_extended  4   176   0.02222222   \n",
       "Foxp2_extended   Foxp2_extended   42  1739   0.02358226   \n",
       "Etv5_extended    Etv5_extended     6   233   0.02510460   \n",
       "Pou2f2_extended  Pou2f2_extended   2    72   0.02702703   \n",
       "E4f1_extended    E4f1_extended     1    35   0.02777778   \n",
       "Klf7_extended    Klf7_extended     3    98   0.02970297   \n",
       "Nfix             Nfix              1    20   0.04761905   \n",
       "Myef2_extended   Myef2_extended    2    39   0.04878049   \n",
       "Mxd4_extended    Mxd4_extended     2    31   0.06060606   \n",
       "Zic1             Zic1             15   192   0.07246377   \n",
       "Ybx1_extended    Ybx1_extended     4    48   0.07692308   \n",
       "Lhx9_extended    Lhx9_extended     3    33   0.08333333   \n",
       "Tcf7_extended    Tcf7_extended     3    33   0.08333333   \n",
       "Klf9_extended    Klf9_extended     6    63   0.08695652   \n",
       "Zic4_extended    Zic4_extended     1     5   0.16666667   \n",
       "Tcf7l2           Tcf7l2           20    93   0.17699115   \n",
       "Nfib             Nfib              2     9   0.18181818   \n",
       "Pou3f3_extended  Pou3f3_extended   5    22   0.18518519   \n",
       "Pbx3_extended    Pbx3_extended     5    15   0.25000000   \n",
       "Sox6             Sox6              2     5   0.28571429   \n",
       "Ebf1             Ebf1             11    26   0.29729730   \n",
       "Lef1_extended    Lef1_extended     5     9   0.35714286   \n",
       "Pou3f1_extended  Pou3f1_extended  35    52   0.40229885   \n",
       "Neurod2_extended Neurod2_extended 19    14   0.57575758   \n",
       "Irx3_extended    Irx3_extended     3     2   0.60000000   \n",
       "Neurod1_extended Neurod1_extended  9     0   1.00000000   \n",
       "Gpd1             Gpd1              7     0   1.00000000   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M.TFs_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>78</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 78\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 78\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 78  4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(M.TFs_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 12 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 12 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {
      "image/png": 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eWaNWss3kVkZOSZM2cs2xwAgGKyLe0CrCI9PV1ExMnRyeBqB0dHEZH0jPQCLx44\ncCAqKkpETp8+7e3tnZCQUOJlotguXLhQnAl6Ll68WGQbrVYbGRlZzL3UqVPH4s0BALCYMoJd\ndla2iIitreG3Y2dnV6BVnpUrV27YsCF3uVKlSgS7R0JsbKzFkUutVsfGxhbZ7ObNm2aNmXiQ\nKXsBAKAkKCPY2dnnJrdsw+Ne8163dyg4YNbJycnd3T137a1bt0q8RliDZfMM59LpdC4uLkU2\nM6VNSfcAAIBllHGPnbOzs0j+FdkHpOc+M9TZybnAi6GhoVu3bt26devYsWNPnDhR8kXCCgIC\nAmxsbCzbVqfTBQQEFNnMxcXFzc3Nsl3kMmUvAACUBGUEO18/P5WIJNy8aegqnS4+PkFEpEJF\nQ2Nm8Shp3bp1ca6Ttm3b1sRmFg/RsLe3b9KkiWXbAgBQTMoIdo5VAn1FJDsyMtrA2siLFzUi\n4hMY6GxgLR4lffr0sWxDlUrl6enZrl07E/di7iR2udRqdZcuXYp5wg8AAIspI9hJjSZN3UUk\nKiLixgProg8ciBURtxbNaz30umBlDRs27NSpkwWn03Q63fDhwwsbXnOfQYMGeXt7W7AXrVY7\nYsQIc7cCAMBaFBLs1PVDOvqK6M6vXnL43vvsUvcuXHtFRCqEhNRXyJt9zE2dOlVEzJpA2MbG\nxs/P78MPPzSxvbu7+7hx48w9aadSqZ566qmuXbuatRUAAFZUFrKOTqfV3KXNu01OW/DFAjNc\nXPjjjd69e/fuPSE8q0AfNrUGDmnnJhK/cerYOdsv3crWiS476cLWn8d8sz1JxOOJIf1rWXjP\nPcqW5s2bT5061fRJT9RqtVqtXrp0qVkjat95551+/fqZ3l6tVlesWPGPP/4wfRMAAKyuLEx3\ncmzWS59vTrvvxehF7/ddlP+v5u8vHRNiL8Z4PDF89NX4LxadO/PPjBH/fG1rb6PJytGJiMqt\n3kuh77XzsH7hKCWjRo2KjY2dMWOGWq02fl5NrVbb2trOmzevffv2Zu1CpVLNmzfv1q1bW7du\nNaVx+fLl161bV6EC43MAAKWpLJyxsxan4EFTZk18o1ebWhU8ndRatbNXQJ12fd6aNGvS83Uc\nS7s4WNf06dN/++23vHluDMm9Vlu5cuXt27c///zzFuzCxcVl48aNI0eOzD3nZ7BN7uvt27c/\ndOhQo0aNLNgLAABWpCrOo5OUISwsLPe+qOjoaH9//9IuB2aIj4+fNGnSn0UpPmoAACAASURB\nVH/++eCDQ4KCgt555523337bwcGhmHs5efLkhAkTVq1alZGRUfB1lUrVsmXLTz75pG/fvsXc\nBQAAVkGwI9g98jQazb///nv+/PmoqCgHBwd/f/+mTZvWq1fPuntJT0/fvn17ZGTk9evXPT09\n/f3927dvX7FiRevuBQCA4iDYEewAAIBCKOkeOwAAgMcawQ4AAEAhCHYAAAAKQbADAABQCIId\nAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACA\nQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDs\nAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAA\nFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJg\nBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAA\noBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAE\nOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAA\nAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg\n2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEA\nACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgE\nwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4A\nAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAh\nCHYAAAAKQbADAABQCFvLNtNoNDY2NgVfyY4/sn7NztPRKbY+NVo907N9FWczu8y+HrF25cZd\nh87FJKRk2bp5+Qc1btetT69WAfam95EZc2D96s17jp6Lik/O0Ng5lytfuWaj1l16Pt3C38HM\ncgAAAB41Kp1OZ9YGd04tHPvRuD+9J12f10+V91rm6bmv9nnr7/MZ+kZqv85jFy0e08Hb1F6T\nD8wOnbruaqaIiNre0U6TkakREbGv3P3jSW+39DChC03c9m8+/3779SwRW2dvvwqe9umJcTcS\n0zQidhWeHD5hZIcKNgY3DAsL69q1q4hER0f7+/ubWjIAAEAZY94Zu7T9X3TqMuZAikjH2ESR\n3NyWsnXUM6/9Hakt0E4bt3Vsr34e+8Pfr6My3NM94tZNnbbuaqbKo+HAt/7Tp1Wgm432zrWI\nVf+btfBw1IZpUwK/n9SjQhFd6KKWfvXN9usalW+bNz9+u3stD7WIiCbp1Jrvp/wWcX3HN1MC\ng74eWMmUagAAAB5N5txjl7139ItjD6SIiEhsbGzeq+e//2j2PakuT8qOzz/6+6YJ3WYdWbTw\neIZIQJ/QMYPbBrrZiIjapVLLF0NH9wkQyTyxcOGhzKI6Obt5/SWNiEuH/4x8Ji/ViYiNZ70+\nHwxtbS+iubhxy0WT3iUAAMAjyoxgd2vRlJ8v6a/bxsTE5C4cnff7IY2+icrZv27dAJe882Ip\na/+3MKbIbjVHw/fcElEH9+5b597b6ezr9O0VrBZJ3hN+ONt4J1nXYxNFRKrWqeN43yrXOrUr\niYjcuB6reWA7AAAA5TA92GVuWrUx7yY63+5TV41/UkRETq9adUHfwr3Lt0cvnzp19dQvT3uJ\niIhu1+Yt6UX1e/bI0TQRqdqkiecD6zybNA4UkYzDh08b78Te09NFRCQtLe2BdRkZGSIi7uU8\nDd9jBwAAoAymB7uzR47k5jqbkPG/f9SxsqOISMK2bcf1DXwHj367hr2IusrQ9551FxERzYkT\nZ4roNj3qWoKI2AdWNXQbXcWqgfYiknwtKsV4N7WaN3MTkat7d12997Kw7lrE4esi4tGsWY0i\nSgEAAHikmR7soqOjcxeqtWmjz2CaPbv36S/OOnbq2j7vlJhNzZrVcpfi4+OL6PZG/A0REW9v\nL4Or816Pi4sz3o1Ty5dfb+Ol0pxfMGHSgt3nr9/KyMpIvnFp3/JpXy48p1V5tX1tUPP7L9IC\nAAAoiumjYvOuaIp4eOTPPnJ83778S59NW7Wy0y87O+fNYmfo0ui90tPTRUScHJ0MrnZwdBQR\nSc8o8pKuX6ePp3r8s3DxmvAFU/YvyH/Z3q9Rt6H9n+vTuPx97U+cOHH9+nUROX78eLly5W7d\nulXUHgAAAMo004Ods7OzSKqIpKam5r0Ut3t3/kjTgBYtKua3zczMG8bq4FDUxMDZWdkiIra2\nhiuxs7Mr0MqonPjzx89djr2dIyp7t/IVvB3SE+JupmTdvHzm+IFjzeqGVLm3lIULF27YsCF3\nOTAwkGAHAAAedaYHOz8/v9xgd+nYsVSp7ypyc9Wqvfq1Du3bN7/bNioqKnehXLlyRXRrZ5+b\n3LINJ7e81+0dinj+hObqqrGjfz1+277yk29++vrTdcupRUS0yWc3z/tpzubV3546mzRpyoDq\nDJ8AAADKZfo9dnUaNMhNgdmrx7/9y6bta6a+OnaTfn45dZtOHfJvYUvbtmhN3q111atXL6Lb\nvKu2eVdkH5Ceey3X2cn4E8oSN/3v9+O3xb7Ba+NH9chLdSKi9qjd7b3xw5o7Stq5hb9vumdW\nvYkTJ0ZEREREREyePPno0aNFlAkAAFDmmR7sHJ96ulPuCa+cc3++2a1jr09W6+coFnXbZ3vl\n3cN29Z+R3Z7/8XLuP6q2bu1XRLe+fn4qEUm4edPQo8108fEJIiIVKhp99ET6wX3HNCIS/GRH\nnwceLuHRtm2wiGQdO3i86Ou5AAAAjywzJigu/9KIl3wNrvF6bsTL+mesxoQv2aUfCRs4YEBz\ngxsU4Fgl0FdEsiMjow2sjbx4USMiPoGBRs/YJSfd0omIjauroZGvDi6utiKii79hyoMwAAAA\nHlHmPFLM9Znpc1574DY1dcDAWTP6509WEhQUlLfk2XP8B62KvqmtRpOm7iISFRFx44F10QcO\nxIqIW4vmtYz24eLqIiKiiYtLMLA2JTExp0ArAAAAZTIn2In49vhl77aZ/9cu0EUlIioH30YD\nxvyz++8X/e82KR8U5C4i7s0/Wj53SMXCOipYQf2Qjr4iuvOrlxy+9z671L0L114RkQohIfWN\n1+lWt14lEZGLO8KjHnhsbdKef8+IiATUretuQj0AAACPKPOCnYja94kRv++KTMlMSbqdnh53\nZMn4XoH3jqytG/LG8O+2nNoztaO3aV3a1Bo4pJ2bSPzGqWPnbL90K1snuuykC1t/HvPN9iQR\njyeG9K9V4MTfhT/e6N27d+/eE8Kz7r5Y7ZkBTZ1ENJcWTp65/kyyPtzp7lze9tMXvx/NFnFs\n0r9HUQM5AAAAHmUqnc7QoIWHLf3k/M+/WHQuTUREZWtvo8nK0YmIyq3eS5+Pe75OwTvnLvzx\nxgfLros0/2DpmI4FJkFJOf7XhMlLzqboRFT2bj5+Ps7ZiXE3kjO0IuJco9+n4/6vseETdmFh\nYV27dhWR6Ohof39/g20AAADKPtPnsStRTsGDpsxqsH7F2vADZ64lpuQ4eAVUrtu8U89+3YI9\nTTyp6NZg8JQfW21ft37HwVOXrt2MuZJo4+jmV6Nu3abtuz/TuY4XU9gBAACFs/iMXWbMoW3b\n9h48fOxsdEJysnT6aumIRiIicnHfDscmTwYUMaFwGcIZOwAAoAwWnLG7c3LBhE8m/rrhVKIm\n/zXvih/nLaVsHdNh+KX+0xb89m5zD8MdAAAAoASYGexyLvz+UtdhSyKzCm1x8+ZNybiw7L3O\nUSnh2z9ramheOQAAAJQAs0bFJq8bFvK6sVQnIvHxudMTp+z/74tjI3KKURoAAADMYUawy9k/\naficq3enibPzrFq/mtf9rRIS9HMEa8/Nmrz0dnELBAAAgGlMD3a67b//cTF3oIV90MDpmy4m\n3rx8fGqn+5t1m7n96255z41NW7d8Q6Z16gQAAEARTA92p8LD40RExKnz12GLRnWt7mpwW7VX\n85FzJ4Tk3ruXfujQaSsUCQAAgKKZHuxiYmJytwh5dWhVldGmFdq0qZq7FBsba2llAAAAMIvp\nwS4jI0NERBw8PZ2LamtrmzfaNieH4RMAAAAPh+nBzssrd6BE+pEj54poGrt9+/l7NwIAAEBJ\nMz3Y1axVK3fh+Mz3vz+dXmi7tFOzXx23LW/u4ho1gopTHQAAAExm+gTFviEhDWTHcRG5uWF4\no6DFr/ynf5vgmOu5K3Pijm8Pux19NmLr0j/+Cr+qHwpbr3PnClYvGQAAAIaY86zYmF86V39z\nm+nzl9i3n3V+5ztVLKrrIeJZsQAAQBnMefKE/9CvP2/pZGprx6afzfxPmU91AAAAimHWI8Xs\nGof+s+CtBi5Ft3QO/s/8VWOb21taFgAAAMxlVrATkYp9ftx3YOGnvep6FDKVnbpcvV6fLIw4\n8Eu/AOOT3QEAAMCqTB88kc+p7vNfrXou9GrEjvBd+45dvJ6QdDtD7eRRztu/VuNWbTs80axS\nkfPcAQAAwOosCHYiIqJyrdLimVdaPGPVYgAAAGA5cy/FAgAAoIwi2AEAACiEBZdidamXdq3f\ntOvgqSs3bt1Oz9IanQev9ciFI1pZWhwAAABMZ2awSzrw7TtDP194MsXUWY1zBhDsAAAAHgpz\ngl3GofFPdRoXcafEigEAAIDlzLjH7vJP700k1QEAAJRVpp+xu7Zs0Z4cA6+r1DbqwqcitmF0\nBgAAwMNherA7ffr03X84VHvmg9DhA0Oa16ns5WTDIyYAAABKn+nBLjs7W7/oO3D+vsX9y5dI\nQQAAALCM6VdKK1eunLfk238YqQ4AAKCsMT3Y1QkJ8c9dcnVzK6FqAAAAYDHTg51d5xEjm9mJ\niERuD79SYgUBAADAMuYMWq01ctGc56rYiHbfxFe+PJBSYjUBAADAAmY9ecImaPDCg0HtR70z\n7q//tgve8p+RwwZ2aVm3kpeLXaH50NbJ1dGm+GUCAACgKKYHu91fPhW67lZycvLtlBSNjWRH\nbfvxg20/FrVV/yW6pQOKVSEAAABMYnqwiz28efueEqwEAAAAxcKDIQAAABSCYAcAAKAQpl+K\nLV+/c7dMDzcXJ0d7Gxu1qYGwRVWLygIAAIC5TA92HcZt6VCChQAAAKB4uBQLAACgEAQ7AAAA\nhTBrguKCMmMObdu29+DhY2ejE5KTpdNXS0c0EhGRi/t2ODZ5MsDeaiUCAADAFBYEuzsnF0z4\nZOKvG04lavJf8674cd5SytYxHYZf6j9twW/vNvewTo0AAAAwgZnBLufC7y91HbYkMqvQFjdv\n3pSMC8ve6xyVEr79s6aOxawPAAAAJjLrHrvkdcNCXjeW6kQkPj5eRERS9v/3xbEROcUoDQAA\nAOYwI9jl7J80fM5Vbf6/7Tyr1q/mdX+rhISEvCXtuVmTl94uboEAAAAwjenBTrf99z8u6kRE\nxD5o4PRNFxNvXj4+tdP9zbrN3P51t/K5y2nrlm/ItE6dAAAAKILpwe5UeHiciIg4df46bNGo\nrtVdDW6r9mo+cu6EkNx799IPHTpthSIBAABQNNODXUxMTO4WIa8Oraoy2rRCmzZVc5diY2Mt\nrQwAAABmMT3YZWRkiIiIg6enc1FtbW3zRtvm5DB8AgAA4OEwPdh5eeUOlEg/cuRcEU1jt28/\nf+9GAAAAKGmmB7uatWrlLhyf+f73p9MLbZd2avar47blzV1co0ZQcaoDAACAyUyfoNg3JKSB\n7DguIjc3DG8UtPiV//RvExxzPXdlTtzx7WG3o89GbF36x1/hV/VDYet17lzB6iUDAADAEJVO\npzO1bcwvnau/uc30+Uvs2886v/OdKhbV9RCFhYV17dpVRKKjo/39/Uu7HAAAAAuZ8+QJ/6Ff\nf97SydTWjk0/m/mfMp/qAAAAFMOsR4rZNQ79Z8FbDVyKbukc/J/5q8Y2t7e0LAAAAJjLrGAn\nIhX7/LjvwMJPe9X1KGQqO3W5er0+WRhx4Jd+AcYnuwMAAIBVmT54Ip9T3ee/WvVc6NWIHeG7\n9h27eD0h6XaG2smjnLd/rcat2nZ4olmlIue5AwAAgNVZEOxERETlWqXFM6+0eMaqxQAAAMBy\n5l6KBQAAQBlFsAMAAFAI0y/Fnl42afUVF2dHBztbGxu1SQMjVGo753Le5SvVada0hqelF30B\nAABgEtPj1skFoZ8ss3AvatfqnYZ+/MV//9PGl1OEAAAAJePh5Cxt6qUt3w97okX/X86Y/uAK\nAAAAmONhnkDTXP3n7b6jd6U/xF0CAAA8PkwPdnb2dsXfnebMtx/+fKX4/QAAAOB+pt9j1+fv\njMyfE2MO/f3ZG58uPJdu49u0z4sDnmrdoIa/t6uDNuP2zajzx/7duHThmmMJWudaz42d/G67\n8qqczJSEa2f2b5o/Z9HBeI2IiGbfr7+fGDGufom9IwAAgMeUSqfTmdw48/BXnZ8cvSfVptoL\n/1v969BgQw+NTTkye0ivt1dcE58ev/y76vWg3FOCuoRt7z351A+nckREFfJTQtgwT+vUbwVh\nYWFdu3YVkejoaH9//9IuBwAAwEJm3GOXs3t039A9qSLV3533h+FUJyJujYfNn/OfANHdXPtO\n/6kn815VeXea9sXA3E10Z86cLV7RAAAAeJDpwS5t1Q+/XdGJiGe33u3tjbV06tKri5OIZB39\n4ecd2vxXmzevl7uUlJRkUa0AAAAwwvRgd3rv3mQREVGpipqdWGVrm9vvtbCwM/mvZmRk5C5k\nZ2ebVSMAAABMYHqwi42NzV1IDFu/P8dYy+zdG7feyV2Mjo7Wvxq3Y8e53CV3d3czqwQAAECR\nTA92Li76u+rOffPyGwvOFTIdXcb5hW8MnXU5719arVZERJO4f+YroRvzJieuUaOGhdUCAACg\nUKZPd1Krdm2RvSIikn3u95eC103r0a/nk03qVqtYzsVelZ12Ky7yzKGda5etjrh+90prtWrV\nRLRrX6/Sc+4d/WuB7dpVsuI7AAAAgIiYE+wC+g5o8/HevZq8f+bEHV750+GVxrep06dPLRHx\n8vIQ0Qe7GoMHt7akUgAAABhlxnQn1d6a9k6QOY8gqzB4+qhGIiJVqlTRv1bxpakfNClq8AUA\nAADMZ05Qc2o3feO856uZ9GQxdYXuszb+3KOciIhUrFLFTkTEp8OXK2f39TK/SgAAABTJnGAn\nYhc0aOHx44tCBzTxcyisjY1nnaff/zXi1Lp3Gjrrd1Kzbf/nPvpl98mw0S3cilMtAAAACmXW\nI8UK0Ny6cGDvgSPHz165cet2aobO3sXNw7tSzfqNmrVpHVze6PzFZQ2PFAMAAMpg+uCJe9mU\nq9H66Rqtn7ZqMQAAALCceZdiAQAAUGYR7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABA\nIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABTC0keKiegykyJPnzwffTP5dmqW2snN3dOvat36\ntSq62FixPAAAAJjKgmCXcmLp99/+On/5llOJOfevUztXbtnrhaFvj3j1SX/LMyMAAADMZ+al\n2MQdYzvVbDww9NeNBlKdiGjTov5dNO3NDrUbDZl7LssqFQIAAMAk5gS77KMTunafEB6nKbpp\n6ql5rz75/F8xFtcFAAAAM5kR7K7+/P7EQ+kmN9fF/TN81LJbFtQEAAAAC5h+I1z00r93ZBf4\nt9o1sPkTbRrVquLr4Wyny0xNir18+vDu3UevZ+Q3SVo+d+Xt/kPcrVgvAAAACmF6sDt75oxO\nv+zdIXTOb6N7Bznf30iXfHLR+NeHzfw3WUREsg4ePCFD2lqjUAAAABhn+qXYjAz9qTjfwb+t\nnmgg1YmIyiP4ha83/DHYL+/fCQkJxa0QAAAAJjE92AUEBOQuOHYb2MvNWEuP3i/3zrv86ufn\nZ6wlAAAArMb0YFe7TRtPERGxd3IqYiuVm5uriIg4Nm5cx+LSAAAAYA7Tg51jj7dfq6oSkdt7\n95w03vTqvn2xIiJeLw4byMgJAACAh8OM6U5sWn+xdEJrZ5HjM4ZNPpRSWLP00z+9MXG3TtRB\nr/81/RknaxQJAACAohkMdpqMVAPupEmDTzfu++v9to57PmtX/6n3vl68/fjVpAytiIgu63bM\n6d0rZo3q1bDF2xtvV+k5cePOn572erhvBgAA4HGm0ul0D7y4dIBq4LLCtlDb2qpycjT3vGKn\n0mRr7u1Hbefs7untN+j3M1+HWK3aEhEWFta1a1cRiY6O9vf3L+1yAAAALGT6PHZ62pz7HxKr\nzck20Cw77daNtFtXkyyrCwAAAGYy51mxAAAAKMMIdgAAAAph8FJs1U5DhrgWo1OdVqvRZGdl\npKXeblC+GP0AAADAdAaDXfN35s59yHUAAACgmLgUCwAAoBAEOwAAAIUwf7oTKMLly5dXrly5\nc+fO2NjYxMTEihUrVqtWrXv37t27d3d35zlwAAA8kgxOUJzrxvGwY3HF7d+vYZcGvia1zL4e\nsXblxl2HzsUkpGTZunn5BzVu161Pr1YB9mbtMCt2/5qVG3YduhCTmCauPhUCard5ul+PdoGu\nqsK2eNwmKD537lxoaOiyZct0Op1ardbpdLkLIqLVal1cXD7++ONRo0a5uLiUdqUAAMA8RoKd\n0edPmKr/Et3SAUU3Sz4wO3TququZIiJqe0c7TUamRkTEvnL3jye93dLDtJ3pkvb/9Pn0DVcz\nRERl62Cvy8ztxaZC58+mvN/S03C2e6yC3d9//z106NDs7OzCvneVSqXT6WrWrLlmzZpatWo9\n5PIAAEBxlIl77OLWTZ227mqmyqPhc59+P3/Z0sVLli348b8vNimnyoraMG3K2usm9aK5sviL\nqRuuZtj4tHhl3OxFy5YsWbbop3GDGnmI5vrWqd9sSi7hd1H2fffdd4MHD87JySk8zUvuqosX\nL7Zo0eLYsWMPsToAAFBcZSDYZR1ZtPB4hkhAn9Axg9sGutmIiNqlUssXQ0f3CRDJPLFw4aHM\nortJDJuz5EKWONZ99YvQAU39HVUiaqeAps+HjujiIaI5u3tvfIm/lbJs/fr1I0eOFBGtVltk\nY61Wm5qa2rNnz5s3b5Z8aQAAwDpKP9hpjobvuSWiDu7dt869t9PZ1+nbK1gtkrwn/LCBp9He\nK2bTmsNZIn7dh/YIuOdNOTZ7/dvfFixZOKH7YzxXcnp6+uuvvy76E3Km0Gq1165dGz16dEnW\nBQAArMnIqNjAJwcNcjS86urO+Tuv5i1XbPN85+qFdtMysKgKzh45miYiVZs08XxgnWeTxoFy\n8nLG4cOnpXVDY73c2Lf/ioj4PvFknQeyqrPXYxzpcs2aNSsmJsbcrXQ63Zw5c0aNGlW7du2S\nqAoAAFiXkWDXYvhffxWyaumAu8Gu4Ru//vV/lj+ALD3qWoKI2AdWrWBgbcWqgfZyOSv5WlSK\nNHQrvBfN+QuXRcS+bp0giytRsrlz56rValMuwt5Ho9H8/fff48ePL4mqAACAdZX6PHY34m+I\niHh7exlc7e3tJXJd4uLiRIwEu9hr1zQi4lexoio7Zv/K5Wt3Hr4Ym5SucvEOqNXkyV79n2ns\na96sKUpy+fLlU6dOWbatWq1euXIlwQ4AgEdCqQe79PR0EREnRyeDqx0cHUVE0jPSjfaSkpIi\nIuJiEznvo++XXspQO3qU9/RWJd64fuHA+gsHtu98/vMvBgU7F9zk4sWLCQkJInLhwgVXV9fU\n1FQrvJsy6ezZsxZvq9Vqz5w5Y8ViAABAySn1YJedlS0iYmtruBI7O7sCrQqVkZkhIhK18qeL\nrk3/b8JrPRuVt1eJ5CSeXP/zzDl7b5xfNPnHmrM/bFlgzt3ff/99w4YNuctBQUFHjx4t/psp\nmyy4u66gzMzMxMRELy/Dp1QBAEDZUeqjYu3sc5NbtuHklve6vYPxK6kqUYmI3MkIHDLhk36N\ny9vnzkVs6xXc68MPe1UQkeSdSzbfsF7Zj5K8cFyqPQAAgIeg1IOds7OzSP4V2Qekp6WJiDg7\nORtcrefolDt+t17HzhXue76EXZ1e3WuKiO788RMFp8MLDQ3dunXr1q1bx44de+LECQvLfxRU\nrFixOJu7urq6uRm5vxEAAJQVpR7sfP38VCKScPOmoQnWdPHxCSIiFSoaGjN7l4+Pj4iI2sPD\nwPhcn4AAexHRJibeKvCqk5OTu7u7u7u7k5OTRqOxsPxHQcOGDXMfBWsBtVrdtGlT69YDAABK\nSKkHO8cqgb4ikh0ZGW1gbeTFixoR8QkMNH7Gzicw0EVEtLdupTy4UpedrRERsbN9PC8o+vr6\ntm7d2rJsp9Vq+/TpY/WSAABASSj1YCc1mjR1F5GoiIgHb4GLPnAgVkTcWjQv6mn0dZs0dhSR\nc0ePpD2wLibqmkZEHCtU9LBCvY+kYcOGWTCJnUqlcnZ2HjRoUEmUBDxatFptUlKSBf+PAOBh\nMjIq9nbUiavJhldF3b67nBp9+sQJw3OViIhHlfqV3Y1WoK4f0tF3/aob51cvOdztnSYFekrd\nu3DtFRGpEBJSv6gAat8spL3H7rDkvYsWn2/1fzULDLVIj1i27oqIODRpVt+miF4Ua9CgQdOm\nTTt58qRZv5Z0Ot3HH3/s5+dXcoUBZZlWq123bt3y5cs3btwYFxen0WhsbGwqVKjw1FNP9e3b\nt0ePHhbf5AAAJURV+MNDlw5QDVxW3P77L9EtHVBUo+SdU96etjtFTv9RAQAAIABJREFUXOo8\nO2xYv7bVytnmJF3cufjHn9deSBePJz6Z9VG7u+faLvzxxgfLros0/2DpmI4Fx8reDBvz7ndH\n0sSjwXPvvtWvZSVnlSb16oF/Zs9afOK22FQeOO27l2sYSnZhYWFdu3YVkejoaH9//+K+47Lq\n5MmTrVq1Sk9PNzHbqVSqNm3abNu2zd7+8Z3bGY+z7du3jxgx4siRIyrV/T8nc19p1KjRN998\n07Fjx1IqEAAMKPV57EREPJ4YPvpq/BeLzp35Z8aIf762tbfRZOXoRETlVu+l0PfamXYF1afL\nx6Exn3+59OLxxV++vdTexcU2KzUtWyciNr5t3xv9ksFU9/gIDg5esmRJv379srOzTRksUqtW\nreXLl5Pq8HiaOXPmhx9+mLv84F+/ua8cP348JCRkypQp+S0BoNSViWAn4hQ8aMqsButXrA0/\ncOZaYkqOg1dA5brNO/Xs1y3Y0/RLHa4NXpk+q/G6FWu3R5y5lnA7y8bVt3KNhm26PNurfRVn\nVdHbK93TTz+9e/fuPn36XLt27cGTELlyHynbs2fP+fPnu7sbv4oOKNPkyZM/++yzwv6P5NNq\ntSqV6qOPPsrMzAwNDX1o5QGAEWXiUmzpekwuxeZLS0v75ptvZsyYkZiYKCIqlUqtVuefw2vY\nsOHEiRN79epVqjUCpWbt2rW5x7/xVJdPpVKJyKpVq3r27FmylQGACYwEu11fdp+4s7j9P/Hf\nDaHti9tJyXrcgl0ujUaza9eunTt3xsTE3Lp1y8/Pr3r16t27d69Zs2ZplwaUmqysrNq1a1+9\netWsYUZqtbpKlSpnz57l1gUApc7Ipdj2ofpHqUJ5bGxsOnTo0KFDh9IuBChD5s6dGxkZae5W\nWq02MjJyzpw5w4YNK4GiAMAMjNUHgDyLFy+2bAYTtVq9ePFiq9cDAOYi2AGAiEh6evr27dst\nm4JYq9Xu2LHjzp07Vq8KAMxi5FKsJiM1PafY/Tu5Oj7e04wAeDRcu3YtJ8fyn3kajSYqKqpO\nnTpWLAkAzGUk2K0Y7PZYjIoFABG5cePBxxqaJy4ujmAHoHRxKRYARESKP3EjUz8CKHUEOwAQ\nESn+bEeVKlWySiUAYDGCHQCIiHh7e9euXduyUbEqlapWrVrly5e3elUAYBYTHymmdgmo37pd\n+3bt27Wo5anK0Zg2JbuIVGxucWkA8HD17dt38uTJFmyo0+meffZZq9cDAOYy+5Fijn4NOnTv\n0aNnr97dWwe6KuGE3+P55AkAD7p27VqNGjWysrJMfJ5YLpVKZW9vf/78+cqVK5dcbQBgCiPJ\nrO7Tr4TU8br/lF5G3PGNf0wePrBd1fKVWjz77pT54RdvWzLtEwCUNZUqVfrwww/NSnUiotPp\nRo0aRaoDUBYYOWMnIiI5SafDVy1fsWLFP5sOxqQbbuPg27hrv4EDBgzo07FWuUdv0jrO2AHI\nl52d3a1bt/DwcBPjnUql6tChw6ZNm+zs7Eq6NgAoUlHBLp/uztV9G1YsX758xdrdF5I1BtvY\n+TTo8uyAAQMGPBtS74FTfWUWwQ5AQYmJib179969e7cpjdu2bbtq1Spvb++SrgoATGFysLsr\nO/74lpUrVqxYvnLL0bhMg01svep26jNwwIABfbs0KG9f/CpLFMEOwH2ysrJGjRr1448/6nQ6\ngz8kcwfPvvXWWzNmzHBwcHjoBQKAYRYEu3zalMt71i5fseKflRv3XjR8Es/m+RU5C8v4UDGC\nHQCDTp48OWnSpNWrV6ekpBR83dXVtXfv3qNHjw4ODi6t2gDAoOIEu7tyks7t2rhu3Yo/f1t6\nKPGeoRSPwCPFCHYAjMjKyoqIiLhy5UpiYqKXl1dgYGDz5s3t7cv6tQgAj6di3wmnS48+vG3D\nxs1btmzdtud4IgNkASiLvb1927Zt27ZtW9qFAEDRLA122tsXd61ZunTZsuXrD0QXMlrWxqPW\nEy0DLS4NAAAA5jAz2OUknQ5fuWzpsqUrNh+9YXjghNh61u3Yp/+AAQP7dW1Y5kdOAAAAKIZp\nwS4r/mjYimVLly1dufV0Yo7hNnY+9Tv3GTBwwIBnQ4K9mc8JAADgYTMa7NJjDmxcvmzpsmWr\nd164bXjqOrH3bdT12QEDBg7s06m256M3OzEAAIBiGAl26/+vfI8/7hgeM6tyDmjWtU///gMG\n9n4yyIM8BwAAUPqMBLs7qfenOhuP6q26PPNMj2d6PN2pcQXHkq0MAAAAZjFx8ITaNaBBmyfb\nN6pkn3Unau+y73Ys1po2/12Ld+e+07w4BQIAAMA0JgY7bWr00c0Ljm42u//UngQ7AACAh0Jd\n2gUAAADAOgh2AAAACmHkUmyFJt26pRa3/yYVitsDAAAATGIk2LUP3bDh4RUCAACA4uFSLAAA\ngEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ\n7AAAABTCyCPF7nP599feWhSdu1z/nYXTe5UroZIAAABgCdOD3fHNf27cmC0iIk6+w0l1AAAA\nZYzpl2I9PDz0iw4ODiVSDAAAACxnerBr3aePX97irQMHLpRMOQAAALCU6cHOofv0ZWOe8FGJ\niByd+uaME2klVhQAAADMZ86o2HLtxoefO7RwwuB2ldO3ftiyfrf3v14QFnH22o3E26mFydCU\nWOkAAAAoyPTBE7u/fOq/65Nv305JSUnNtP1/9u47Lorj7wP47B4d6UqPooiKWCBiQUVFRGwg\n9tgToz9jjSXGxF4SY4st1jymWBJRRAHFigpWFAEFC3ZFjt67wO0+fxwSpN1eh+Pz/iOv43Zn\n70s4vA8zOzNqJP/NpV2LL+0S0WqUH3tytHQlAgAAAAAX3INdUvTl0FtyrAQAAAAApIIFigEA\nAABUBIIdAAAAgIrgPhTbrEP/gcX6ek20tTTU1GiugbCrjURlAQAAAIC4uAe7vmuu9JVjIQAA\nAAAgHQzFAgAAAKgIBDsAAAAAFcF9KLYapoAfGxH1nJ+emVNE6xqbmLXo0M25jbG67IoDAAAA\nAO4kCXaliTf/3PrLzr8uPc0u+/QIpfuZy9h5y5bPHmqrK5PyAAAAAIArcYdimcSLP/bt6PrN\n9nPVUh0hhC14f/uv74d1dJ76z/Ni2RQIAAAAANyIF+yKItYMGb7xTqao0+IOT+o3+WSS5GUB\nAAAAgLjECXbMw/VTfnr4gdvJSSe/+upgskQ1AQAAAIAExLjHriBoy944tuJLWq+li3vfLvYt\nzYx0NZjivIyEFzF3rlx7mFpSfkL+xZ9/vfvllu5SzM8AAAAAAM64py72xtlzOeWPdT6f/ee/\nm8a1bVL1pLLUO3vnTFp88nUZIYS8PX783pbuPWVTKQAAAADUiftQ7LPw8CzhI73BvwXvqSHV\nEULUTF3mH7+4wUVT+OX7GzfeSV8jAAAAAHDAPdglJZVPhtAcMm2CeV2XbD1zlmd5T+D79+8l\nrw0AAAAAxMA92OXm5gofmFhZadV9qn6bNuXJLzNT1AxaAAAAAJAN7sGuSZPysdfMlJTSuk9l\ns7PLQ6CuLhYqBgAAAFAM7sHO1NRU+KD4wokzuXWdWXDhRHBulUYAAAAAIGfcg1377t31hI8y\njs0ev/1+DlvTWWzO/V3jv/6r/HY8ve7d7aUuEQAAAAC44L7cCa/fUE/dIycLCCEk5dyi7rZ/\nDftitEePTnZWJk002A95GfwXMXcunTx+7lEmU95Ed+CQvljFDgAAAEAxKJatseetJmX3FrXt\nvv0152vbLo54ttWZJ1FdChQSEuLh4UEI4fP5lpaWyi4HAAAAQELibCmm1m3NH9+24dgFp26/\n+K/V9T/VAQAAAKgMcYIdIfr9tgYfmtJGxGonhGjbTzt6dpOrnsRlAQAAAIC4xAt2hKi1nnAo\nIvzQwkG2OjWfoGs35Lujd+/+MbYVeusAAAAAFEmSuQ36nadsOz9x9eu710Ku33sSn5KRVUTp\nGpmYNnfo0de9X7eW+uKmRQAAAACQXo3Brvhx8NE7SYQQrfZDJ/W0ED759srBkDeEEGLsPGak\nowHPoFVPn//19FFYpQAAAABQpxqD3Yt/F83Y8JwQ0mZVTEWwu79vxgx/QghxWN97pKOBwioE\nAAAAAE5qHDVNTEwUPigrK1NkMQAAAAAguRqDXWFhofDBa7+9QYkliqwHAAAAACRU41BskyZN\nCMklhJAXB4c3/8fY3NxIiy5ILj/6Yrtn67/VOV5/8O6Xvw2SRaEAAAAAULcag12HDh0IuV3+\nhaAok/8ms9LRksz4V5k1NatJUr401QEAAAAAZzUOxVqMmeimoehKAAAAAEAqNS85Zz3z9z3e\nVlhhGAAAAKABqWWBYl7r6adju53646/A6zFvU3OLyliS9TrydRYhhGhZOjhYiNxVrJytsYwK\nBQAAAIC6USzLcjvz5GhqTPk6dk8frWgnz6IUKyQkxMPDgxDC5/MtLS2VXQ4AAACAhLD7FwAA\nAICK4L5XrNP0337rRwghJj3M5VUNAAAAAEiMe7CzHTR3rhwLAQAAAADpYCgWAAAAQEUg2AEA\nAACoCAQ7AAAAABWBYAcAAACgIhDsAAAAAFQEgh0AAACAikCwAwAAAFARCHYAAAAAKoJ7sAvf\n/93us48yyuRYDAAAAABIjnuwSwj5dZ5XRwvrLiMX7wqMSSuVY1EAAAAAID5xh2JLU6JOb/vW\np7OVpZPPtztOR6WWyKUsAAAAABCXpPfYlaY/CNy1cGQXK8uO3vO2+t9PRsADAAAAUC5pJ0+U\nZTw6s3vJ6K7WFg5D52w+cS+xWCZlAQAAAIC4uAc79zUBe5d/5dmhmUZNRwWZT87tXTque3OL\n9oO/2eR7h4+ABwAAAKBYFMuy4rVg8l7fPh8YGBgYeO7mi2xBLWfRBnYDxk6dOnXyiF7NtaWu\nUq5CQkI8PDwIIXw+39LSUtnlAAAAAEhI/GD3n7LMp9fPBgUGBZ29fPd1bs0Rj9Jv7fbFzIXf\nzRxqp0dJXKVcIdgBAACAapAm2P2nLPtl+JVLly5dunw5NOJNTvWMRxt//uXaXb/M7mVa/1ZE\nRrADAAAA1SCbnKVm2Lr3qNnr9vx9+PCBdZOdjKtdlcmM+nNen07ua29myuQFAQAAAKAqNekv\nweS/vX3W/1RAYNDFO6+y69iZgkkJXTNosMadWz92lMHLAgAAAMAnJE9YTN7rm2dO+p08eep8\nRC2LnPBMOvbrWHo/LC6nYri34N7qb3+ffHW2tcSvCwAAAAA1Ensolsl9FXp00xwfZ6tmtn0n\nLt19uoZUR+nauE1b/++deH5MyLUnCU9Prx74WUWCLL3278kkacsGAAAAgKq499ilRRw++NdJ\nv1OXolM+1HaOulmX4V/OmDF9gkfrijmwVJO2PmvOd7Ub6TgpMJ0QQsijR48JsZCqbAAAAKhf\n8vLy3r9/n5KSYmBgYGlpaW5uruyKGiPuwS5s09Rl/rUcow3aeEyY/r8ZX3o5NVOv8QSriUun\nLAvcFk8IIfn5+eIXCgAAAPVQcXHxwYMHT5w4cevWLYZhKp5v1arVyJEj582b17x5cyWW19hI\nOStWy9plwso/Q18nPruwd8nIWlKdkI2NzcdGWlrSvSoAAADUBwEBAXZ2dvPmzauS6gghb968\n2bp1q52d3Q8//FBaWqqsChsbCSdP8Iw7DJ08fcaMyYMdjHncmuTk5JQ/MjU1reF4afL94MCL\nN6OeJ2bklajpGVvaOvbyHO7V3arGLcy4KHl+6Nsl/nyW6His8Z33uaSXAQAAgBqsXbt27dq1\nFEURQqqkOkKIcKHckpKSTZs23b59+9SpU02bNlVClY2MmMGO0m3Zd+zXM2Z8NcrFUlO8ptaT\n/rjRr4gQQvRsHKoezInYv3zzufgPhBBCa2ipf8hKfnn/wsv7V68O+n7D7G4G4r0UIYSQspfH\ndp3my2D1ZQAAAKhq48aNa9asoSiqeqSr7ubNm15eXqGhoZqaYqYHEBP3YGft8f2+/02f4GGn\nL9nWYE1suvS2qflQyrnNW87Ff6AMOo2ZNWN49xZ6PKYg4X7Q/+32jX5/YcumFr9tGCruDZiC\nl767TscTNTW6rEz0Gw4AAAC4u3LlyrJlyyiK6/5VLMuGh4cvWrRoz5498q6tkeN+j50g6/HZ\nXQsneA2bczxBxKmFJ7+0NDc3Nzfvvv6B6AuXPDjuG1tMiNXw5asm9WyhxyOE0LrW3cYvXzbc\nipAPj3x9o2qdh1tLra9P7PJ/yxj1H9Rd4oFcAAAAqAHDMAsWLKBpWtxdSffv3//48WM5VQVC\n3INd0v1gobAXoia16jTTK01JSUlJuRdyI1XUdQUPQ29nE0I7eI9o92kK02g3wsuBJiTndmi0\nODddCt6d2HXirUCn29RJTujxBQAAkKkzZ848evRIIKi+M7xov/zyi8zrgcpks1dsFcmPH2cI\nH718+UrUyc8ePCwkhNg4ORlVO2bk5NiCEFIcHf2U84sz7/x3+r0WaDt+9U1/Y86tAAAAgBN/\nf3+aliQ/MAwTFBRUUlIi85KgQt332CVGBNzjlz+OSCx/kPf0SkBAXM0NmA85/Njz+3ddK++c\nzcrKElFA0fuEDEKIRgubmm6js7BpoUHelOQkvM8jnfREXIoQQpj3p3cef1mm1WHGnIFNCXnD\noQkAAABwFxISwmXCRI3y8vIiIyNdXFxkWxJUqDvY3d40Yky1RYnj/5074l+Olxc9/J6alkoI\nISYmNfeumZgYE5JMUlJSCBEd7Fj+6V3HXpZqtJ86d5CZZHM8AAAAoFYCgSAlJUWaK7x//x7B\nTn4kXMeOK3t7exFnFBUVEUKItpZ2jYc1hYsZFxUXiX4xlh+469izEvU2U+d5WYqMdYmJicKl\n9fh8vra2dnkdAAAAULvs7GyJu+uE0tPTZVUMVFd3sLPu5Gxx9n6SmHNSKzQZNGdKKxHnlJaU\nEkKImlrNlairq1c6qy5s8tldR5+W8FpNnudjxaG3bu/evRcuXBA+btOmzcOHD0W3AQAAaNwM\nDQ1pmpYm22GZYrmq++bHHqsi3ic9Ob936fge1jX3qNVKu8WQtUFHvrYWdZ66hjC51bLZSPnz\nGpoili1hU87uOvK0hLYZM29kC457YQAAAIB4eDyeubm4i8t+AlvHypXIoViekf2gWRsHzdpw\nYiRv3GlCCGk+YffOMVa1NqBodW0jK3unTp814TJlRkdHh5BMUttIaFFhISGE6Gjr1HmVlPO7\njzwqpj8bNX+sLcdYN3v27IkTJxJC7t27N2vWLG6NAAAAGruBAwceOnRI3EXshPT19bt06SLz\nkqAC53vs/pvZrGfv7uPTTlYFmJqZUSSBzUhPZ4ldtRFUNi0tgxBCzC3q+usg7eLuQw+L1VqO\nXzyxNedvyNLS0tLSkhDC5/Nxgx0AAABHI0eO/PvvvyVoSNP08OHDy2+yAvngPnmi9eA5c8wJ\nIcSya/UF5ySn1byFKYlMKX37lk9cqg3cvn31SkAIadqiRR09dsUxdx8WEULeHFsw8lhNJxRe\nXuN9mRBi4rPlr2ltZVQ4AABAozR06NDOnTvHxsaKe6cdRVHLli2TU1UgxD3YOX69e7c8Kmjt\n9Ln+qfO57+/fTx1vbfrpMX5ERBIhRK+rc5s6rkBpGZqamtZ46ENuWk4xS2kZNNPXJMRID38k\nAAAASIem6Z07d/bv35/7XrFCc+fObddOZkN+UKMag52gOL+ojBBCCE9TV1udqvKkWNfXbqJV\n901vdAf3fqbng1JfnPGL9pzjVGmORv4d3+B3hBBzd/cOdd2up9lr/sFeNR+K2DF2/dVibdeF\nB+d9LnbtAAAAUJO+fftu3rz5u+++45jtKIpycXHZvHmzAmpr5GoMTKcn6ZXz2MOv/qQ4Jp0W\nWQKvzZipvfQISbu4efWfYa+zS1nClma9vHpg1Y6wLEIMXKeOalMpG7489D9vb29v73Wh2JIE\nAABASRYvXrxhwwZS+Sb8mlAURQjp37//2bNnNTRELHEB0pPzAsXcGLjOXxaftv7487iAXxcE\nbFPT4AlKylhCCKXXfsLyeb0MlF0gAAAAVPXjjz86OjrOnTv39evX1Re3Ez6jqam5dOnSFStW\n1LZgLchWPfm/rO0wcdPujudPB4dGxCVk5pVpGlt9Zu/sNmykp4ORJPsMAwAAgPwNHjw4Li7u\n0KFDx48fDwsLq7wqbdu2bUeOHDl79mzhGhSgGPUk2BFCeE07DZvRadgMUee1nvp70FSuF+26\n4ETQAunqAgAAgNqpq6tPnz59+vTpRUVFfD4/OTnZ0NDQ0tLS2LjmXeBBrmoMdjZuU6c2IYQQ\n0qa9bvUnxdHVRsLCAAAAoCHR1tZu3bp169atlV1IoybeRGWVFBIS4uHhQQjh8/noLgYAAICG\nCzewAQAAAKgI7sHu1q65W/zuJX6QYzEAAAAAIDnuwS7p+p7vx3b/zNzec+aGf26/L5RjUQAA\nAAAgPnGHYpnsuEu/L5/Uy8bczn3aukOhb/Ib+y16AAAAAPWEpPfYMXkvr/61+ks3W/OWfaes\n+CPkea54GwEDAAAAgIxJO3mCLXh3/cjP0z3amrdwGf/DvvNPsgQyqQsaPoFAEBoaumjRor59\n+7Zt27Z58+a9e/eeNm3a6dOnCwsxlA8AACB73Jc7yYk7d/yfkyf9A689zSyr/TQNC2evCVOm\nTBk/uFNTddnUKGdY7kQezp49+/333z99+pSiKIqihPvMVDxu1qzZmjVrZsyYoa7eMN4kAAAA\nDYL469iVZT0NDfT3P+UfcOlBcu1zZNWbdh48furUGV+N7GgoZY1yhmAnW2VlZfPnz9+3b1/1\nfQMrCA/16dPH39+/adOmCq4QAABAVUmxQDGT9+Z2sL//qVOnzoXHF9RylVF+7MnRElenEAh2\nMiQQCEaMGHHmzBkuJ1MU1apVq7t375qYmMi7MAAAgMZAinvsaL2Wvb/4bvuJ2+/SEiICdv84\nxd3eBONqjdyPP/7IMdURQliWff369ejRo8vK6hjcBwAAAK5ksvOEtqXz8DkbDoU8SUl7eWXP\nTBdTniyuCg3NgwcPtm7dKlYTlmVDQ0P/+OMPOZUEAADQqKjJ5CpM/tvwS8Hnzl24cPFaVEJt\nw7Kg4pYtW0ZRYg/u0zS9atWqqVOnamlpyakwAACARkKqYFec/CAkKCAwMCDoysNU7DXWuKWl\npV28eLG22RJ1YBgmNTX1ypUrQ4cOlUdhAAAAjYcEwa4s69mN4MCAgIDAC+Hv6uidow3s3EYO\nc5CiOGhAzp8/L0GqE6Io6uzZswh2AAAAUuIe7IoS7l44HRAQEHD2elxdC9kRDVOnweMmTZw4\n3qu7BcbWGo1nz55J3JaiKGmaAwAAgBD3YBe8oMcY/zqOU7ote/tMmDhx4tiB9kaYPdHoJCUl\n1bFwXd0YhklISJB5SQAAAI2N9JMn1EwcPMZOnDhx4oiezXUoGZQEDZKmpqbkayISgpkTAAAA\n0pM82Glbd/f+YuLESV8M6twMy9eBpaWlxMGOx+NZW1vLth4AAIBGSOxgxzNo03/UhImTJo3s\na6snk1XwQCV06dJF4rYCgcDZ2VmGxQAAADRO3IOdUccR83tNnDR+WFdzTTkWBA1U//79dXR0\nCgsLJWvu7e0t23oAAAAaIe59bu6rT+1cOAqpDmqmpaU1ffp0CRrSNN21a1dpOvwAAABAqMYe\nO0FxfpFwQROepq62OlXlSbGur91EC1NkG4kVK1b89ddfBQUFYs2NZVl269atFIWJNwAAANKq\nscfu9CS9ch57+NWfFMek04r7XkDJmjVrdvjwYZZlxUppP/zwQ58+feRXFQAAQOOB6Q8gSz4+\nPtu3byeE0DSnt9akSZN++uknORcFAADQWCDYgYx9++23AQEBenp6dZxDUZSamtrPP/98+PBh\njhEQAAAARMJnKsiet7f369evFy1aVDneVYzPqqurjxkz5smTJ8uWLcOtdQAAADJU4+QJG7ep\nU5sQQghp0163+pPi6GojYWHQsBkbG//666+//PJLWFhYdHQ0n88vKyszMzNr3779wIED9fX1\nlV0gAACACqKk2QZKNYSEhHh4eBBC+Hy+paWlsssBAAAAkBCGYgEAAABUBPdgd2vjMKE5xxNE\nnFp48ktLc3Nzc/Pu6x9IVx4AAAAAcMV9S7Gk+8HBwYQQ4tAjX8SpOs30SlNS0glJCbmRutLR\nVJoCAQAAAIAbuQzFJj9+nCF89PLlK3m8AAAAAABUU3ePXWJEwL2PW09EJJY/yHt6JSAgruYG\nzIccfuz5/buulc/IyMrKkkmZAAAAACBK3cHu9qYRY/yrPhn/79wR/3K8PObcAgAAACiKnGfF\n2tvby/cFAAAAAKBc3cHOupOzhabkF28yaM6UVpI3BwAAAAAx1B3seqyKeJ/05PzepeN7WGuL\nd2HtFkPWBh352lqK2gAAAABADJx3nmBOjOSNO00IIc0n7N45xqr2K9Lq2kZW9k6dPmvSMFY/\nxs4TAAAAoBo4r2NH0x9jmp69u49POznVAwAAAAAS4r5AcevBc+aYE0KIZVcjuZUDAAAAAJLi\nHuwcv969W46FAAAAAIB0JLoN7sP70IOrZyw9kfzJs4kHJ/UZ/c3qP28mlcqkNAAAAAAQh9jB\nLvPOVq82rd1mrDsYFJvxyZHs+Kgb/gfWfe1qa+/9yy3sOAEAAACgWOIFu4J7awcOWHI2voQQ\nQvLz8z85mJeXJ3xQ9OrMsgG9Zp5LkU2JAAAAAMCFOMGu5N7qyesiCz9+WWuwI4SQ4qe/T5n9\nb5q05QE0Infu3Fm6dGnXrl3Nzc21tbWtra379Onz008/PXnyRNmlAQBAwyBGsMs+sWHPc+a/\nr4uKij45rqZvrMer9HXGqe9+uiGQrjyARuH+/ft9+/bt2bPn5s2bIyMjU1JSiouL+Xz+rVu3\nVq5c2aFDh3Hjxr1+/VrZZQIAQH3HPdgVnzt1rlj4kGraZ+lNffwqAAAgAElEQVTxh+//HPbJ\nCf22vc7KenH+Z5/WWuXPJB3/NxTJDqBu+/fvd3FxuXnzpvDLymuGMwwjfMbPz8/R0fHcuXPK\nKREAABoI7sEuNjKyfLarxVeHgzeO7dRMo9o5PL3Wg5adurp7cPlKdyk3bryQRZUAqmrz5s2z\nZs0SCATCDFcblmULCgq8vLxOnTqlsNoAAKDB4R7sUlNThQ+0Bo4Y1KSOE6nPps4cWt5px+fz\nJa8NQMWdPXv2hx9+oChOO/sJk9/kyZNjY2PlXxoAADRIEqxjp29gQNV9hlrTpobCR1XvwwOA\ncsXFxbNmzeKY6oQYhikuLp47d65cCwMAgIaLe7AzMTERPki9efN5nWeyT65cS67SCAA+deDA\ngYSEhLpHYKtjGOb69esXL16UU1UAANCgcd9SrJ2DA034DCEkasPk5S5n1vczrSkVlvEvfD9p\n40PhF7S9fVuZlAmiFBYW3r17NyEhISsry8TEpEWLFt26ddPQqH4fJNQX//77L03T4gY7QghN\n08eOHfP09JRHVQAA0KBxD3aGHgO7UZfCWUJI/r0Nbq1PDhw7dnCvznafNTPQUWc/5Gclv42L\nvnHOzz/sbcXwq9OAAcbyqBoqiYiI+OWXX86fP19cXFz5+SZNmgwfPnzZsmXt27dXVm1Qm7S0\ntIiICO6DsJUxDHPmzBmZlwQAACpAjPt7SNbxETZfBORyvrbu0CNvz05qKlFdChQSEuLh4UEI\n4fP5lpaWyi5HDAUFBbNmzTp69ChFUTV2/FAURVHU3Llzt2zZgt67eiU8PNzFxUWaK2RlZRka\nGsqqHgAAUA3iTJ4wGrt16yDOPXD6/TZsHV/vU13DlZyc3KtXryNHjrAsW9twnvDQrl27BgwY\nkJ2dreAKoQ4pKdLuuJeUlCSTSgAAQJWINSuWsp1xPGh932aiGxl2X3rKf347nsgTQSJFRUXe\n3t4xMTEcz7958+a4ceMEAiwXXV/o6Ogo/QoAAKB6xF3uRL/XiisPLm/90sWyloE9nonjmLVn\nom5sdMfddfKzZMkSse7QYln20qVLGzZskGtVwJ2Ug/4URVlYWMiqGAAAUBni3GP3idL0p7dD\nw25Hxr1Py8wuEGjqG5lY2jm59HFzdbLUlnGR8tXg7rF79uyZg4ODuN1vFEVpa2u/fv3azMxM\nToUBd6Wlpc2aNcvJyZGgLU3Tjo6OkZGRMq8KAAAaOu6zYqtQb2rfd7R939GyLAY42bVrlwRr\nZLAsW1hYeODAgVWrVsmjKhCLurr60KFDfX19JfhRMgzj4+Mjj6oAAKChk2DnCVAmlmUDAgIk\na0vTNHYarT/mz58vQX+5sOd1+vTp8igJAAAaOomD3YfEqAv/7Pn5u5lTxo8ePmT0jocfD7y6\ne51fIpvioLq3b98mJiZKvP5ZTExMQUGBzKsCCXTv3n306NEUJWKDvipYll2yZAlusAMAgBpJ\nMBRb8PjYuqU/HbzwJPO/m7xMLL4vf5R3dVXf+a9HbTn2x1xnA9nUCJUkJiZK05xl2cTERDs7\nO1nVA9LYv39/ZGTk27dvOQ7IUhTl6uq6YsUKeRcGAAANlJg9dmUv/xrb4fMJm4Mrp7pPpKen\nk+KX/vP6D/wlqrjmU0AKeXl5Ul4hN5f7GtMgX8bGxsHBwZaWlhz77RwdHf39/dXV1eVdGAAA\nNFBiBbucc9+4T/d7W+dAa1paGiGEkLx7K8avvl8mRWlQE+nntJqbm8ukEpCJdu3aRUZG9uvX\njxBC0zX/PtI0TVHUlClTbt261bQpVv0GAIBaiRHsyu5tmP9n/H8jRupGNh1aVlusLiMjo/wR\n83z3xpPoHJKxFi1aiHtXVmVaWlpY7qS+MTU1vXr1amBgYNeuXav/cHk83oABA27dunXo0CFt\n7Ya1lBAAACga93vs2LC/Dr0S3rKvYTtmw76NM91bNTk1mhrj/8lpntvDtqUMWXQxjRBSeO7U\nhQ9fjNWUZcGNnbGxcbdu3SIiIiRYJoOmaU9PTzU1ide4ATny9vb29vZOTEwMCwvj8/kZGRlm\nZmbNmzd3c3MzMjJSdnUAANAwcP+MfxIaKtzdUrv/tpDjc2xq6TWijZ0X/r0u+LNZV8oIKYqK\nekrGOsqiUKgwbty4u3fvStCQYZixY8fKvB6QIUtLy/Hjxyu7CgAAaKi4D8V+nI5Ju0/7qrZU\nV87cxcVG+AgblcvBN998Y2FhUdv9WLWhadrBwWHcuHFyqgoAAACUjns4KC4WTnLVNDISufl4\nxWhfWRmmT8ictrb2b7/9xrIs95vtKIqiKGrPnj08Hk+utQEAAIAScQ92xsbCiRJFDx48F3Fq\nUljYi08bgWyNGjVq+fLlHLMdTdMsy+7cubNv374KqA0AAACUhXuws2vTRvggdvu3vz0tqvW8\nwif7p625Vr7KXevWttJUB7Vbt27dhg0bSO1rZAjRNM3j8Q4cODBnzhxFlQYAAADKwX3yhKm7\ne0dyPZYQkn5hfmfbE1NmjHJxSEwWHixLiQ0LyeU/u3/15KGjofEfytu0798fa6bJCUVRP/74\nY/fu3RcuXBgTE0NRVOV9xoQ9eSzL9ujRY8eOHV27dlVepQAAAKAglBi7jib+3r/VzGsfRJ9Y\nTqP37hc35jSXqC4FCgkJ8fDwIITw+XxLS0tllyM2hmGCg4MDAgIuX76clJRUVlamoaFhbW09\ncODAUaNGDRgwQNkFAgAAgIKIE+xI6YOfe/dcca/2YdjKtD5ffePOGmcNSStTmIYe7CpjWTYn\nJ8fQ0FDZhQAAAIASiLVkhrrj8oBjszrqij5Tx2HGP0GrG0CqUzEURdXDVJeVlfXkyZOYmJjk\n5GRx/pAAAAAA8Yi3FhohFsP33o3w/cHL3qC2BYoN23st9b0f8ftIK8k3vgJV8Pjx4wULFrRo\n0cLY2NjBwaFz584WFhYmJiZTp04NCQlRdnUAAAAqSKyh2ErY/Pj710Nv3o15lZyRlVtMaxsY\nmli2cezes69rF2uR69zVK6o0FFtPZGdnL168+O+//xa+u6q8x2iaZhjG3d197969bT7OtQYA\nAADpSRrsVAiCnWy9fPly6NChL168qPutRdN0kyZN/Pz8Bg4cqLDaAAAAVJu4Q7EAdUlJSXFz\nc3v58qXIPxgYhsnPzx86dGhYWJhiagMAAFB5Na5jJyjOL5LRVmBq2k20sIlVI8EwzMiRI/l8\nPsduYIZhCCFjxox5/Phxs2bN5FwdAACA6qsx2J2epDfGXzbXH+XHnhwtm0tBfefr63v79m2x\nmjAMk5aWtm7dut9++01OVQEAADQeGIoFmVmzZk3d+5vVZv/+/SkpKTKvBwAAoLFBsAPZePTo\n0YsXL4Sjq+IqKys7c+aMzEsCAABobBDsQDauXLkicVuapi9fvizDYgAAABqnGu+xs3GbOrWJ\nbK7f1UY214H6Lj4+XuK2LMtK0xwAAACEagx2znP+/lvBdUBDl56eLlx5WIK2LMviHjsAAADp\nYSgWZMPY2FiyVEcIoSiqadOmsq0HAACgEUKwA9mwtraWuC1N082bN5dhMQAAAI1TjUOxXHxI\njLp27U5kdMwzfkZODnH75eSCzoQQQl7dva7l1MdKQ2YlQoPQv39/idsKBAJ3d3cZFgMAANA4\nSRDsCh4fW7f0p4MXnmQKKp4zsfi+/FHe1VV9578eteXYH3OdDWRTIzQEjo6OzZs3T0hIkGBA\nlqZpLy8veVQFAADQqIg5FFv28q+xHT6fsDm4cqr7RHp6Oil+6T+v/8Bfooqlrw8aCoqiVqxY\nIUGqoyjqyy+/lGYkFwAAAITECnY5575xn+73tqSuc9LS0gghhOTdWzF+9X0Z7TgLDcJXX33V\nqVMnsTafoGlaT09v3bp18qsKAACg8RDjM7js3ob5f8b/1yGjbmTToaVx1bMyMjLKHzHPd288\nmSttgdBwqKmpBQYGGhkZccx2wtNOnDhhZWUl59IAAAAaBe7Bjg3769ArlhBCiIbtmK2XXmWm\nv4nd7Fb1NM/tYds8mwkfF547deGDbOqEhsHGxuby5cvm5uYURdV9Jk3T6urq//77r6enp2Jq\nAwAAUHncg92T0FDhErLa/beFHF/s0apJjW1pY+eFf69zF07KKIqKeiqDIqEhcXJyioyM9Pb2\nJh/75KoQPtmpU6dbt26NGzdO0fUBAACoLu7BLjExUdjCfdpXNnX3xpi7uNgIHyUlJUlaGTRc\n5ubmAQEB169f/+KLL/T19Ssf0tDQ8PT0PHbsWGRkZJcuXZRVIQAAgErivtxJcbFwkqumkZGO\nyKuqlV+3rAzTJxotV1dXV1fXsrKyV69eJScnFxcXW1tb29jY6OrqKrs0AAAA1cQ92BkbGxOS\nTEjRgwfPyZA2dZ2aFBb2olIjaMzU1NTatm3btm1bZRcCAACg+rgPxdq1KQ9zsdu//e1pUa3n\nFT7ZP23NtfJV7lq3tpWmOgAAAADgjHuPnam7e0dyPZYQkn5hfmfbE1NmjHJxSEwWHixLiQ0L\nyeU/u3/15KGjofEfp8K279/fXOYlAwAAAEBNKJZluZ6b+Hv/VjOvcV+/RKP37hc35tT7vd1D\nQkI8PDwIIXw+39LSUtnlAAAAAEhInJ0nLL/atrKbNteztT7/cfuMep/qAAAAAFSGWFuKqTsu\nDzg2qyOHOY06DjP+CVrtrCFpWQAAAAAgLrGCHSHEYvjeuxG+P3jZG9SylB1t2N5rqe/9iN9H\nWonYegAAAAAAZIn75IkK2vbjfgkauzz+/vXQm3djXiVnZOUW09oGhiaWbRy79+zr2sVa5Dp3\nAAAAACBzEgQ7QgghVJPmXYdM6TpEpsUAAAAAgOTEHYoFAAAAgHpK0h67T7CF/Jj7j95lFGsY\nW7bu6NTaWCZXBQAAAABxcOqxE6RHndi6aH1QavVDRXHHFw5oaWLt2HfQ8JE+g/t1szO3dJqw\n5XoaI+tKAQAAAKBOIvvWSp4fnT1y9p+P81jrBaNWeptWPlb25LfBveeHZX3SoDTtwbHv3W5F\nHw//Z7SFGBNjS5PvBwdevBn1PDEjr0RNz9jS1rGX53Cv7lZirJnC5r0KO3vm2r3Hr/mZ+aW0\nln7Tz+w69fDwHtLdSov7VQAAAAAaJBE7T6T4T3AcfUy4bxjtc7T49ET1imNs3M/OnVZEldbS\nVMttT9zV2S24lZETsX/55nPCrchoDS11QfEHASGEaHw26PsNs7sZcLlG0YuTG9YfeZgt/H5o\nmmaY8m5DDYv+i37+tmfTmmMmdp4AAAAA1VDnUOyHKyvmlKc6Qgjz9m18pYMFZ3/5tdZURwgp\nvrZufUgxpyJSzm3eci7+A2XQaewPv/3jf/KEn/+xvSvGOxlSJe8vbNkUnCz6EiTnxs61hx9m\ns5Rx59GLf/3T9+Rp/+N/7/px/OdNaVKSdHXb9vPpnGoBAAAAaKjqCnYFgQeOplT6+u3bt5UP\n/uVXaQxWp93wb9dv/mXJuE7/9a6l+B0NKRFdQsmD476xxYRYDV++alLPFno8Qgita91t/PJl\nw60I+fDI1zdK5Aa1b8/+czuXELXW41etntLXrqmOGsXTNrZxGb/8B5/PKEJKYi+EJoquBQAA\nAKDhqiPYsTcvX6noceO1HLHNb0WvioOlV4MvF1VcpN2C8xEBO1Ys+WGz792L3zt8HPLMPX/+\nlsgKBA9Db2cTQjt4j2j36e10Gu1GeDnQhOTcDo2uo2uQEEIyn73K1qAJ3WHI0Faf3jao3qZ3\n92aEEBL/7l1dg84AAAAADV0dwY4fG5v58SynNWePLxzQ4r8ZCBHXQvM/PtYf+dPaPk3Kv9Dq\n/v23bh+vmvr4scgB0GcPHhYSQmycnIyqHTNycmxBCCmOjn5a90WMPVf5njzt77vcTa/aMQN9\nYSciwyDYAQAAgCqrI9i9e/fu40mu02a0V6987O3t2xUDm7pDx3vpVzpm4upq//Hxq1evRBRQ\n9D4hgxCi0cLGvIajFjYtNAghOQnv80RchxBCqWtp8qo9W/LmDZ8QQpq3bInVmAEAAECV1bHc\nSU5OTvkjs06dzD45VHL37oOPj6lebv0+HUK1trYm5DEhhJDc3FwRBaSmpRJCiImJcY2HTUyM\nCUkmKSkphFTvjBONTT7nf6uQEI0Onv0+nfCamZlZVFQkfKChoVFSwuF2QJBUaWlpWVmZtra2\nsgsBAABQZXUEuw8fPk5ZaNas2aeHHty9WzGdwa5HjyqZrNLHd0FBgYgChOGKaGvV/JGvqaVF\nCCFFxUU1HhahOO7o5qNPS4iG7bgZg5p+emzbtm0XLlwQPra3t3/48KEkrwC1y8zMPH78eEBA\nQFRUVHp6OiFEX1/f3t7e29v7iy++aNWqlbILBAAAUDV1jE5qamqWPxIIBJ8cSbh16/3Hx9rO\nzu2rNCzPaoQQoqEhannh0pJSQghRU6s5Yqqrq1c6SxxsTvTfK9b4vSyhTHrNXza6ZfVBWpCX\nsrKyzZs3t2zZcvbs2SEhIcJURwjJzc2NiIhYvnx527Zt586dm5mZWfd1AAAAQCx19Njp6ekR\nUkQIIfyEBEI6VBzIuhwSWfFFNxeXqpHpv5vzhNeok7qGMLmV1pzcyp/X0BRj/wlCSEnC5e1r\n995KEfBM+3y7fmGfZtXXJv7qq6+8vb0JIVFRUUuWLBHr8vVNfHx8UFDQ1atX+Xx+Wlqaubm5\ntbW1h4eHt7e3mZmZ6PYylZWVNXr06KtXr1IURQipWCZaSPhlWVnZnj17zp07d/bs2fbtq/5l\nAAAAAJKpI9i1bNmSkFRCCMm+diWSHdSlPBzFH/rzckUPnn2fPlWGOElSaOizj4+bN28uogAd\nHR1CMiv38n2iqLCQEEJ0tHVEXOc/bE7M4Z9+8X9WQHTsRv6wcqqjYY07Ttja2tra2hJCcnNz\n8/PzazqlAYiPj1+1atXhw4dZlqVpmmVZlmXfvn1LUZSfn9/s2bO/+eablStXmpqair6WLBQU\nFLi5ucXExBBC6t7UhBDy7t27nj17RkRE2NnZKaQ6AAAAFVfHUKx1x44fFyB5vX/hz/dyWELY\n7IiNX629WdG7ZjpgQMdPW+VeXb/j5scPdF17e1HBztTMjCKEZKSn15QC2LS0DEIIMbeoac5s\nDQTJYVsWr/Z/VsAz7z1/yy9f1pLqVMP58+c7dOggTHWEEIZhhA9Ylq3oGNu9e7eDg8OtW6IX\nFJSJadOmPXz4UGSkE2IYJi8vb9iwYaJvxQQAAAAO6uixI30Geeoc9C0khJD8Gyt7WO5qbsKk\nJGQU//ehbT5yVM9Kwano7cWNX32x72XFxd3c+4jKVVrNW5iSyJTSt2/5xMW66tG3r14JCCFN\nW7Tg0mPHZtzZuXL7zVRGt+2oZSumdDRQ4VBHfH19J06cSDh0jGVmZvbv3z8oKMjT01OuJV2+\nfPnEiRNiNWEY5vnz59u2bVu5cqWcqgJQsKKiopCQkLCwsMTExMzMTDMzMxsbm4EDB7q4uNA0\nFl0CADlj61B4aVpdHWWU089PK519Z3GLT2Oi9pDD6XVdXkjwYO9ELy8v70WnU6odSzg+18vL\ny2vC3gcC0RcqiNk/a4SXl9eEH0++KhJ9eoXLly8L6+Xz+WI0U6p79+5pampy/5CgaVpfX//Z\ns2dyrapr164SfG5RFKWrq5udnS3X2gAUIDs7e9myZTo65X+H8ng8mqYrfiksLCwOHDhQWlqq\n7DIBQJXV+TGs7bHm5yEGtRzktZyxaU67Sk+YGhuXVT6h1TfLxpuI/lynO7j3MyWEfXHGL/rT\n++zy7/gGvyOEmLu7dxAZF/LC9/8anFDGs/JatnJUKy1RpzdkDMN8/fXXpaWlVeYl1N0kPz9/\n9uzZ8qvq3bt3ERER3EuqwLJsQUHB+fPn5VEVgMKEh4e3bdt2w4YNFXcMCwQChmEqfilSUlJm\nzpzZo0cPPp+vvDIBQMWJCEyfTfvn1A/dqmc7NbOBO4J+8/jkgE379v+tRafu8N3Rn3rVNdBb\ngddmzNReeoSkXdy8+s+w19mlLGFLs15ePbBqR1gWIQauU0e1qTTx9uWh/3l7e3t7rwuttKKw\n4JnfwdBMQpoNmTO5nYagRgy3G7/qv2PHjsXGxooboRiGuXLlSkhIiJyqkiaZ0TSNYAcNWnBw\ncN++fYUr+7C1/FMj/J2Nior6/PPPnz9/rtD6AKDREBm9DPv/cuuR58GdB05ciX6dnMvoWbTp\nNnTGj0vGta+6kAltb9+WkAeEUEbdFx8P3OzCeSKrgev8ZfFp648/jwv4dUHANjUNnqCkjCWE\nUHrtJyyf16u2TsP/PLh4KZUQQtLOLBt7ppZzaNelAUt6ca2pHjty5AhN0xL0jdE0feTIkQED\nBsijqjdv3kjclmVZaZoDKFdsbOy4cePKysq4/FayLJuenu7l5XX37l1DQ0MFlAcAjQqXPjU1\n637fbOn3jcjzbB26tu5hN2rGkiVfdjUR71YrbYeJm3Z3PH86ODQiLiEzr0zT2Ooze2e3YSM9\nHYy4XKm0TOwFjBuo/Pz8a9euSZDqCCEMw5w5c4ZlWeEKc7KVkpIiWdwkhLAsm5iYKPo8gPqH\nYZjJkycXFRWJdWvE8+fPly1btnfvXrnWBgCNEFXbqEHjERIS4uHhQQjh8/mWlpYiz1eu2NjY\nTp06SXOFlJQUeSxrN2/evD179kj8dnJycoqKipJtSQAK8O+//wrnp4tLTU3t8ePHbdq0kXlJ\nANCYYe59vRMREfHDDz84OjqamJhQFGVgYNChQ4f58+dfvXqVZdnk5GQpry+nvjErKyuJUx2P\nx7O2rrbWDUBDcPDgQckWMSkrKzt06JDM6wGARg7Brh558uTJkCFDunXrtmnTppiYGOFWqrm5\nuU+ePNm9e7e7u7uLi4v091yXb78ra716SX7/okAgcHV1lWExAIqRlZV1/fp1ye5AoCgqICBA\n5iUBQCPHad4qKMDJkycnT55cUlI+17dy71fF44iIiIiICClfyMrKSsor1Khnz54mJiaZmZmS\n9dsJ9+0FaFiePn0qEAhEn1cTlmXj4uLKysrU1PDvMADIDHrs6oV//vln7NixJSUldf/pX7Fp\nmGQoirKxsZHTRDwej7do0SIJyqNpeujQoW3btpVHVQBylZSUJE1zhmGkv7kCAKAyBDvlu3//\n/rRp0yiK4rhWgjSvNWLECGma123BggVWVlY8Hk/0qR/RNM3j8TZt2iS/qgDkR/rJZ5i+BgCy\nhWCnZCzLzp49m+MKWFKiaXrGjBnyu76Ojs7p06fV1NQ43ksuzLJ79uxxcHCQX1UA8mNhYSFN\nc5qmzc3r2rcRAEBcCHZKdubMGcl24pLAtGnT7O3t5foSXbt29fPz47KPrfCEn376Sa5ZE0Cu\n2rVrJ9mUWEIIRVF2dnZymswEAI0Wgp2S+fr6SvzBwB1N023btt26dau8X4gQ4uXldefOHeE9\nczV+a8LlkY2NjU+ePLl8+XIFlAQgJyYmJr169ZLsV5hlWR8fH5mXBACNHIKdMrEsGxwcLE13\nHZdPFIqiLCwszp49q6+vL/ELiaVz586xsbH/93//5+joWH2XixYtWqxevfr169cjR45UTD0A\n8jNt2jTJfoVpmp4yZYrM6wGARg47Tyhz54n09PRmzZpJc4XWrVu/fPmytr28hM+7urr6+fmZ\nmZlJ80ISS0hIiI6O5vP5JSUl1tbWbdu2xR11oEoEAkGnTp3i4uLEincURU2bNu3gwYPyKwwA\nGiesn6RMKSkpUl5h06ZNGRkZP/30U3x8vPCZyiHPzs5uzZo148aNk8fmsBxZW1tjVwlQYTwe\n79ChQ7179y4tLeWY7Wia/uyzzzAZHADkAcFOmZo0aSLlFQwMDEaOHDljxozo6Ohr167Fx8dn\nZmY2bdrUxsZmwIAB7du3l0mdAFAHZ2fnw4cPC3eMFZntaJo2NDQ8e/asiYmJQqoDgMYFwU6Z\nzM3NKUqq0fCKsWMnJycnJycZ1QUA4hk7dmyzZs1GjRqVlZVV2znCX3Z7e/ugoKBWrVopsjwA\naDwweUKZNDU1nZycJJ4Va2RkZGdnJ9uSAEAybm5uz58/nz9/vnAFE+Hi2xRFCf9LCDE0NPz1\n118jIyOR6gBAftBjp2QjRoyIioqSoCFFUcOHD8cukwD1R9OmTXfu3Ll27doLFy6Ehoby+fzM\nzExTU9OWLVt6enq6ublpaGgou0YAUHGYFavMWbGEkOTkZFtb26KiInF/EBRFRUVFOTo6yqkw\nAAAAaHAwFKtk5ubmS5culSBeT5kyBakOAAAAKkOwU75ly5b179+f+4okNE23a9du165dcq0K\nAAAAGhwEO+VTU1Pz8/Pr3r07l5MpimrVqtW5c+cUto0EAAAANBQIdvWCsbFxaGjo//73P4qi\napskK3zey8vr3r17LVu2VGyBAAAA0AAg2NUXmpqaBw4ciIyMHD58uKamZpWjPB7Pzc3typUr\ngYGBRkZGSqkQAAAA6jksllG/ODk5nTp1qrCw8Pr16/Hx8WlpaYaGhs2bN+/Vq5exsbGyqwMA\nAIB6DcGuPtLR0Rk0aJCyqwAAAIAGBkOxAAAAACoCwQ4AAABARSDYAQAAAKgIBDsAAAAAFYFg\nBwAAAKAiEOwAAAAAVASCHQAAAICKQLADAAAAUBEIdgAAAAAqAsEOAAAAQEUg2AEAAACoCAQ7\nAAAAABWBYAcAAACgIhDsAAAAAFQEgh0AAACAikCwAwAAAFARCHYAAAAAKgLBDgAAAEBFINgB\nAAAAqAgEOwAAAAAVgWAHAAAAoCIQ7AAAAABUBIIdAAAAgIpAsAMAAABQEQh2AAAAACoCwQ4A\nAABARSDYAQAAAKgIBDsAAAAAFaGm7AIAalBYWJiYmEhRlKWlpba2trLLAQAAaBjQYwf1yMOH\nDxcuXGhjY6Orq2tnZ9e6dWsdHZ2WLVsuWrQoJiZG2dUBAADUdwh2UC+kpqZOnjzZyclp586d\n8fHxlQ+9e/dux44djo6OU6dOTU9PV1aFAAAA9R+CXSvHsfwAACAASURBVCMVHR09YcKEli1b\nGhkZ6erqmpqadujQYcWKFZmZmYovJiYmxtnZ+Z9//mE/qny04skjR444OzvHxsYqvkIAAIAG\ngaryIdoIhYSEeHh4EEL4fL6lpaWyy5G7mzdvTpky5c2bNzUepShq8ODBR44cMTY2Vkw9b968\ncXZ2zs7OZhhG5Mk0Tevr60dERLRu3VoBtQEAADQs6LFrXJYsWeLq6lpbqiOEsCx77tw5S0vL\nsLAwBdRTXFw8ZMgQjqmOEMIwTG5uro+PT0lJibxrAwAAaHAQ7BqRSZMmbd26lcuZHz58cHNz\nCw4OlndJe/bsiYuL45jqhBiGefz48b59++RXFQAAQAOFodjGMhS7efPmpUuXitVEXV396dOn\ntra2wi9LSkrCwsLCw8OTk5Pz8/MtLCxat249ePBgKysryUoqKSkxNzfPzs4W901I07SRkVFS\nUpK6urpkLw0AAKCSsI5do5Cdnb1s2TJxW5WWlvr4+MTGxmZnZ2/atGnPnj15eXmEEIqiKIoS\ndrNRFOXq6rphw4ZevXqJe/1r165lZWWJ24oQwjBMRkbG9evX3d3dJWgOAACgqjAU2yh8+eWX\nAoFAgoaPHj3asmWLra3txo0b8/PzhU+yLFsxeMqy7M2bN3v37j1jxgxx73sLCQmhKEqCqoQu\nXbokcVsAAACVhB67RuHixYsSt126dClN04SQ2gZMhSHv4MGDcXFxFy5c0NXV5Xjl+Ph4mqYl\nS5w8Hq/KcndQr7x48SIoKOjRo0epqakURZmbm3fu3Nnb27tFixbKLg0AQJUh2Km+27dvFxcX\nS9ycZVmO2evmzZtffvnliRMnOPbDpaamSnyLJ8uyqampkrUFubp58+bSpUtv375NCKFpWvhm\nYBiGZdn58+d7eHhs3Ljx888/V3aZAACqCUOxqu/q1asKe62TJ0/+/fffHE82MjKSeCiWoigj\nIyPJ2oKcCASChQsXurq6hoeHC59hGEYgEAgEgooEf+XKFWdn5zVr1mDaFgCAPCDYqb46Vq2T\nOZqmly9fXlRUxOVka2trsRY6qYxhmM8++0yytiAPZWVlPj4+O3bsIB9H52skPLR27dopU6Yg\n2wEAyByCnepTU1PcgDvDMElJSRwXwOvXr580Q7Fubm6StQV5WLx48dmzZ7mcKfyhHz16dP36\n9XIuCgCg0UGwU30VC9EpBk3TgYGBXM4cOHCglpaWBKOxFEVpa2sPGDBA/OpALkJDQ3ft2iVW\nE4qi1qxZExMTI6eSAAAaJwQ71TdkyBBFvhzDMJGRkVzObNKkyfz58yXotGNZduHChTo6OuJX\nB3Lx/fffC6dOc8eyLEVR4i6aDQAAdcPOE6q280RJSYmGhkaVJ/X09CpWoVMAfX39nJwcLmdm\nZWW1a9cuIyOD+6InNE2bmprGxcUZGBhIUSPIzKNHjzp27ChZW4qi3r9/L/HmJQAAUAV67Bo8\nlmUvXbr0zTff2NnZaWtra2pq6urqtm/ffsGCBTdu3BCeM3bsWEWWVFpayvFMIyOj06dP83g8\njv09NE2rqakFBAQg1dUfQUFBErdlWfbMmTMyLAYAoJFDsGvYbty40a1bN09Pz99///3ly5fC\n9eoKCwvj4uJ27drVp08fNze36Ojoffv2aWpqKqwqsTo+e/bs6e/vr62tLTLb0TSto6Nz+vTp\n7t27S1cgyNKjR494PJ5kbSmKevTokWzrAQBozBDsGrDt27f369cvKiqKVNsWgmVZ4TPXr1/v\n0aPHsWPHDh06pJiqaJp2cHAQq8mwYcPCw8OFi9bWGO+ETzo7O9+9e1fBtwyCSImJiRLf0UHT\nNJ/Pl209AACNGYJdQ/XTTz8tWrSo8ratNWIYpqys7Msvv8zJyfnxxx8VUBjDMMOGDRO3VYcO\nHe7du3fixAkPD48q9whqaGgMHDjQz88vPDy8ffv2sqsUZAP36QIA1B/YUqxBCgwMXLVqFUVx\nmvvCMAxFUXPmzAkNDW3VqtXMmTNFLgtsYmIyePDgo0ePilsYRVE6OjojRowQt6Gw7ZgxY8aM\nGZOXl/f8+fPExERCiKWlZZs2bfT09CS4ICiGpaWlxDuIMAyjAjOWAADqD/TYNTzFxcVz587l\nmOqEhB17s2bNmjZtWkJCgqura22fxBoaGgsXLkxNTd2yZYuOjo64H9gsy37//fdNmzYVq1UV\nenp6Xbp08fLy8vLy6tKlC1JdPdehQwfuk5qrYFlW4hm1AABQHYJdw/P7778nJCSIuxkXwzCx\nsbF+fn4WFhbXr1/Pzc39+eefe/fubWtra2lp6eDgMGTIED8/vw8fPmzbto2maXNz8x07dog1\nykbTtKOj45IlS8T8hqBh8/b2lrgtRVFeXl4yLAYAoJHDOnYNbx27Xr16hYeHS7DLKk3Tw4cP\nP3XqFPcmCxcu3LFjB5feQZqmzczM7t69iy1cG6GuXbtGR0eL229H07SHh8eFCxfkVBUAQCOE\nYFcfg11CQkJISMi7d+/S0tJMTEysrKzc3Nzs7OwIIVlZWSYmJhL/1LS1tfPy8sRanGLr1q3C\n7QFqi5I0TTMM07Vr14CAgHryPxAU7Nq1a/379xeriXCUPzo6unPnzvIpCgCgMcLkifrl/Pnz\na9asiYiIqB7d7O3tly9fbm9vL00WLyoqSklJESt+fffdd25ubkuWLLl27RohhMfjCTtmhEuQ\nMAxjYGCwatWq2bNnV9/xAhoJNze3OXPm7Nmzh3sTlmVXr16NVAcAIFvosasvPXY5OTkTJ04M\nDg4WdoBVP0H4fJs2bZ4/fy7NC92/f79Lly4SNIyNjQ0KCgoPD09ISCgoKLCwsGjbtu3gwYM9\nPT2xbSuUlZV5e3ufP3+e4/kTJkw4evSoxNNpAQCgRuixqxf4fL67u7swsdU24il8/sWLF1K+\nlsSTTDt27IgJjFAbNTW1oKCgRYsW/fbbb7X9cUIIoWmaZdkVK1asXbsWqQ4AQOYwK1b5ioqK\nvL29X7x4waX3VPoeVmy4DnKipqa2a9eu69evd+3aVfgMXYnwmX79+t27d2/dunVIdQAA8oAe\nO+Vbvny5cFswBXBwcNDV1VXMa0Hj5OrqGh4eHhcXd+bMmUePHiUmJvJ4PHNzc0dHR29v71at\nWim7QAAAVYZgp2Rv374V65ZzKVlYWCjstaAxa9euXbt27ZRdBQBAo4OhWCU7ePBgSUmJwl4u\nMzNTYa8FAAAACoZgp2T+/v4Vtx8pwIMHD4qKihT2cgAAAKBICHbKVFRUFBcXJ8EeEhJjGCYx\nMVFhLwcAAACKhGCnTElJSYp/0aysLMW/KAAAACgAgp0yFRcXK/5FTU1NFf+iAAAAoACYFatM\nit/oQkNDQzgx9u7duwEBATdv3nz//n1ubq65uXmrVq0GDRrk4+NjbW2t4KoAAABAJhDslMnQ\n0NDExCQjI0MxL8fj8fr37x8dHf3dd9/duHGDEEJR5XvKZWVlPXv2LDg4eOHChTNnzly9enWz\nZs0UUxUAAADICoZilczb21thS/ALBAJDQ0MXF5dbt24Jn6m8j4VwDkdZWdmePXs6deoUERGh\nmKoAAABAVhDslGzixInS7xLGBU3TBgYGvr6+LMuKnIeblpbm6up6+/ZtBRRWWUFBgZ+f3+TJ\nkzt37mxqampqauro6Dh16tSTJ08WFhYquBgAAIAGh1JMqqjPQkJCPDw8CCF8Pl/xN70RQtzd\n3UNDQ8Va9KRiCFUsYrWiadrY2Dg6Orr6LXelpaXFxcV6enriFlAHgUBw4MCBNWvWpKWlVdlC\nXvilmZnZ2rVrp0+fzuPxZPi6AAAAqgTBTvnBLiIiokePHuKuZmdlZcXn87mfr6amxjCMuPHx\niy+++Pfffwkh+fn5x48fDwwMvHPnTnp6OiFES0vLzs5u2LBhX3zxRadOncQqvors7Oxx48Zd\nunSpSqSrTHho0KBBvr6+BgYG0rwcgLxlZWXduXOHz+dnZ2ebmZk1b97cxcVFU1NT2XUBgGjx\n8fGBgYH37t1LTEwsKioyMzNr27btkCFDevXq1TB6FthG7/Lly8L/FXw+XykFfP311+LeZqew\n2/IoioqOjt63b59wLkWVTTKEXwrzX3x8fJXvKz8//59//hkzZoytra2WlpaGhoaVlZWnp+fu\n3buTkpIqTsvNze3YsSP376hTp055eXmK/REBcHX58uUBAwaoqVWdl6arqzthwoSnT58qu0AA\nqNWTJ098fHwqPuBomqYoisfjCT+hrK2tDx06JBAIlF2mCAh2Sg52jx8/VuSWYhIQbuVed5EU\nRZmYmFy/fl34TQkEgv3795uZmVVvKPxSS0tr2bJleXl5DMN4e3uLW9KIESMU/5MCqFt6evqQ\nIUPq+GURfkIsWrSotLRU2cUCQFX79+9XU1Oro5dB+Kvt7u6ekZGh7GLrgmCn5GC3ePFihXW/\nyRVN0xoaGrdu3crLyxP+xVNHFhR+y+3bt9+7d69kL3f69Gnh/8DCwsKAgICvv/66d+/eLVu2\nbNu2bf/+/YXrudT/v6tAZbx48aJVq1Ycf5fd3d3R6wxQr6xcuZJwGw2jKKp169apqanKLrlW\nCHZKDna2traqEewIITRNN23a1NnZmfv5PB5Pgg5LmqbbtWv34cOHPXv2CMeIKYqq+N8o7Dwn\nhHTo0OH8+fOK/5lCY5OZmWlnZyfWO9nHxwd/eADUE4cPHxbrM4iiKFdX15KSEmUXXjNMnlDm\n5InCwkJdXV1FvqIq+fzzz6OiokTOt5g3b962bduq3/MEICvjxo3z8/MT99/S7du3L1iwQE4l\nyVBhYeGbN2+SkpLU1dXNzMxsbW3V1dWVXRSAzKSlpbVq1aqwsFDcKYy//vrrokWL5FSVNBDs\nlBnsXr58aWdnp8hXVCXcV2/x8fHx9/ev5/cyQgMVHh7es2dPcf8hpSjKwMDgzZs3hoaGcipM\nSizL+vv7Hzp06NKlSyUlJRXP6+npDRkyZObMmW5ubkosD1RAcXFxSEhISEjIu3fvMjMzDQ0N\nra2t3dzcBg0a1KRJkzoasiybmpqak5NjYmJiYmIiZRkLFizYuXOnuK2Ev8Lv3r3T19eXsgDZ\nU2JvYT2hxKHYp0+fKven33j8+OOPCv7hQiMxduxYif9m2L59u7LLr9nDhw+F91TU+K0Jn/T0\n9Kw+Fx4aM4FAEBkZefr06QMHDhw9ejQsLCw/P7/GMwsKCn7++WfhYqgV004rJp9qaWktXbo0\nKyureqt9+/a5u7tXHoHR0tLy8fE5cuSIZHOSSktLpfnj6siRIxK8qLwh2Ckz2OXk5Ej8fgKx\n0DQdExOj4J8v1EO5ublnzpz57bffVq5cuX37dl9fX2l+8YuLi+vuWqj7Pdm3b1/ZfWcyExgY\nqKOjIzKt0jRtamp6584dZdcLysfn8+fNmydcCaEyTU1NLy+vsLCwyic/ffrU1taW1D5TQfi8\nmZlZeHh4RatDhw41bdq0jjekjY3NuXPnxK08NDSU++9s9V+BMWPGyOB/n6wh2Ckz2OXl5Un8\nlgKx0DQ9bNgwaX5YAoEgOjo6MDDw999/9/X1vXHjRmFhoazeCaAAkZGRw4cP19DQqPLeoCiq\ne/fuFfOsxb2mNG9LDQ0NhmFk/p1KIzQ0VE1NjWMfJE3T+vr6z549U3bV9UJpaenZs2dnzJjh\n4OBgbGysqanZvHnzAQMGbNu27d27d8quTl4Yhtm0aZOWllblGWxV3iSEEB8fH+ESIREREfr6\n+lzeYMKVFs6dO1daWjpz5kwub0iKotavXy/W75TEKzMI2dvby+1/reQQ7JQZ7J49eybNWwrE\nwuPx0tPTJfgx1fbHqHAI4NatW7W12rdv38iRI7t162ZjY+Ps7Ozj47Nr1y4V/ie+3vrw4cPM\nmTMpiqrt40T4fJ8+fRITE8W6clBQkJRvy3q1aEJKSoqxsbFYa+vTNN2mTZsPHz4oss6YmJjV\nq1e7uLiYm5urq6s3a9bMyclp8eLFFUtpKl5AQECbNm1ItV4o4VtLXV193rx5kv37U5+VlJRM\nmDCh+nddHUVRrVq1unnzprm5Ofc3GE3Tenp6w4cP5/6GJISsXLmS+7ewYsUKsS5ehb6+vvz+\n90oMwQ7BrhER3g/BMMy9e/dWrVo1adKkgQMHent7z5o16++//67+zy7DMBs3bhT5x+iYMWMq\n3w6SlJT0zTffCP/x+v/27jMgiuNtAPizSz16kyIgLRRFxYoKFlRs0bMCgr3EXoiaGGOJBY0t\nrxo1lsRojEFQsf3tioKKnQRUDIgiKqJ0qQIHd/t+OMoe3B3Xl5zP7xO3Ozs7u7PcPjc7O1M3\n9grQJuqYPHkyhncqU1RU5OvrK8nlQRCEjY3N06dPJc+cP+GePNLS0pR37NKaN2+ebEexY8cO\n1ZTwxYsXY8aM4f9P0cP0uv9QX19f+vM7Faiurl64cCE0NYo7ANjb2ycmJqqybMomYUMaH0mS\n+vr6qplm6fz58xIewg8//CBD/nVMTEyUeoZlg4Hd5/IoFt8JBYCVK1dGRkY6OzvzP/JnjKnr\nsUuSJD3k4nA4ISEhINmPUVdXV/4d+tatW02+pUUQhKGh4YULF1R8vX2GqqurhwwZIvkVQpKk\nvb295K1oN27ckDxzocrKypR6BiSXm5sr26hA/M521dXVyi7h+fPnDQwMxP8/8v+jd+7cqezC\n8PF4PP63hIQnSk9PLyEhQTVlU7a//vpLhqtFNczNzSUcZG7fvn3y7MjT01PZ51kGGNgxPECx\npaWlIi5j1DSSJO3s7KCpKTF0dXUjIiIoipoxY4ZUmbu6ukZFRWlpaUnYfYQkyaNHj6r+kvus\nbN68WfJKrLsGJO+O+fr1a2nzp7O0tFTq4Uvl0KFD8hzL7du3lVo8/ohFkvxz8SO/sLAwpZaH\nb8OGDVKdJf63UG5urgrKplTl5eX29vbNub3gl19+keRA7t69K/MuSJKcMGGCsk+1DDCwYziw\na926tYIuY6QY/IenwcHB0m5IEITkvc6htmvww4cPVX/VfSby8vKMjIxke5QTExMj4V6cnJxk\nyJ9v0qRJyjwB0pk0aZI89+l169Ypr2yPHz9msViSF4//ZPbMmTPKKxJFUenp6dra2jJcYPPm\nzVNqwVRA2qkaVM/Dw0OSA+FyufI0rxw/flzZp1oGzTfc/ky8f/+e6SIgAfzBxyMjI6W9yVEU\nVV1dLfnY5Twer7q6+quvvpJ2uHMkocOHDxcXF1PSj8FOkqTkA5YaGxtLm3+dZjU68bt372Te\nliCIjIwMBRamgfnz51dWVkr+n0JRFEEQc+fOLS8vV16p1q5dyx87TdoN9+/f/+bNG2UUSWVO\nnz7dnJvrAOD58+dcLrfJZCRJfvXVVzLkT5KktbX10KFDZdhW2ZrPPEtVWfEXzl6J+yf1fX4J\nR9PQrKVLB99BI9jdbBsOTqD0TFSnoKAAh7IDgFatWmVkZBAEIf6LW8zsYYrF/6ZWwb54PN6T\nJ08iIyP5b5YhxeLfe2SoRx6Pd/ny5U+fPunp6YlPyeVy5blDp6amyrytwuXn58t8zZMkmZ+f\nr9jy1Lly5crt27el3YrH471//37v3r1KmvSJw+FERUXJENUBQHV1dVRU1JIlSxReKpW5detW\nM/9FSlFUZmZmq1atmky5dOnSffv2FRYWSnVEPB5v3bp1TX5FMKKZRNxFj/YtWrDu4IUHqR8+\nfgJNovJj1sv4y4c3hC7a81DiwEchmahUVlYW00VoFvT19cPDw5v8D9HX1586dapqiqQyJEk2\n5z7I/10URT169Ejme09FRUVSUlKTyZKSkj5+/CjbLgBAnsFRFc7S0lLmNhgej9eiRQvFlqdO\nRESEbAUjSTI8PFzh5eG7fft2aWmpbNuSJHn+/HnFlkeVOBxOQUEB06VomiT/wgBgbGz8xx9/\n8Ft5Jc982LBh06dPl7VoytUsArvsi1u2XnxbSRi3D1q2K/xk1PETJyP2rAzpaEJwMi5v3XxB\nouhHIZmomK6uLtNFaBaSk5O7deuWnp4eGhoq9OGUiYnJokWL0tPT1W/2cR6Pd/369bKyMqYL\nom4KCgoqKyvlySEzM1MhacSoqKhoPjdIe3t7mbelKEqSphHZnD9/XrYAncfjJSQkZGdnK7xI\nAPDy5UuZt+XxeC9evFBgYVSspKREtqZKFZP8gRibzd6+fTtIPHxEp06dwsPDm+3D6GZQLE7i\nscinFQC2I1b8MMHHwVADAEh9O++QFctH2AJUJkVG/tPk97NCMlE5GxsbpovQXPzwww8WFhY7\nduzIzc2NiYnZuXPnsmXLli1btnPnzpiYmNzc3G3btpmbm0dHRzNdUsXjcDhyvlyJGpM/VpYk\nh8LCQjn3Ik+Dn2INGDBAnodrAwcOVGBh6hQVFcnzkJeiqLS0NAWWp46c8WJ2dvZ/IjYSytTU\n9D/xG9vMzEzyxKGhoSdOnOCPWioqDT+SCw4Ovn37tpGRkQKKqBzMB3bcx7F3CwFIz+GjPAR7\nwml7jGJ7kgBFd2MTqlSQierl5uYyXYTm4vLly/w/NDU1/fz8FixYsHHjxo0bNy5YsMDPz69u\neC05G0iaLXyHRuGsra1lex+2TsuWLZtMI/9wRc1nwKMhQ4bo6OjIcNL4g/917txZGaWSv7/K\nhw8fFFKSBuTsXMViseS8PhnEr/HmX353d3ep0o8ZM+bly5czZsyoC1s1NDTqBjoFgHbt2l26\ndCkiIqJ5dq2rw3xg9zzx8ScAcOzY0bTROtOOHRwAoCIhIVkFmaheTEwM00VoLiRsM6+urlZ2\nSRjReAJTJCdtbW1bW1t57j38ecrFc3BwkDl/ADA1NTU0NJQnBwUyNjb++uuvZWhG4vF4q1ev\nVtJtXl9fn/EchJIk7lfe5oz78ssvmS5CEwiCsLW1lXYrGxub/fv35+XlHTt2bNGiRYGBgUOG\nDJk5c+bWrVuTk5MTExMHDx6sjNIqFuNvxZZnvMsHAG0HR2sha20cHbQhnVP0LqME2ov+/lNI\nJgyQ4VUvdSXJe+lQ+76q+vmvf8s3T8OGDdu/f78MG5Ik6eHhIUnQ5urq6ujoyJ+tRIa9SDUr\nhgosW7bsyJEjWVlZkj+TJUmyQ4cOU6ZMUVKRrK2t5XwdXkn/XD4+PjJvSxCEhNPcNVtBQUG7\nd+9muhTifPHFFzI/LzYyMgoKCgoKClJskVSG8Ra7nNwcAABzc+HPwmuWN9GdQZZMysvLi4uL\ni4uLy8vLpZr0WoHwrVhpNf/GfxmYmJjIM8gtEiUkJES2XwI8Hk/yAWgCAwNl3ktAQIAMGyqP\niYnJ+fPndXR0JOwVTpKkubn5mTNnlPcVqqmp2alTJ5l7qRsaGnp4eCi2SHyOjo5t2rSRrWAU\nRQ0fPlzhRVKlXr16+fv7S3v4/IGjpRpoWvqi1Zg5c6bM2/7XMR7Y1YwfydJlCV2tw39vtLxC\n7CiTsmSyYcOGfv369evXb+3atW3btpW64IqA007UkfCnlZKeqjCIJMlhw4bJNkcnEq93794D\nBw6U9t5DkmSLFi1CQ0MlTL9kyRI9PT1p70AkSXp5eY0YMUKqrVSgY8eOly5dMjExafK8EQTh\n4OAQGxsrz+u0khg5cqRsLXb8fy7l9XNYtGiRDAUjSdLFxaX5P8ps0vbt2yX/DcBHUVRoaKix\nsbGE8y7q6OjI9pvByMgIAzsGVXGqAABE3dhq7vc1qZSaCQPGjx/PdBGaCwkHwZK2M2zzx+Px\n5syZw3Qp1NbPP/+sp6cn7b1n9+7dBgYGEqa3srLiz6Yl+S747Ra7du1qnsMl9OnTJz4+ftCg\nQSCsyYSoNXHixPj4+DZt2ii7PNOnT5chdIbaMEIZReKbMmVK69atpa1EHo+3ZcuW/8RbpeK1\nbdv2yJEjFEVJfgZCQkK2bdv26NEjfjOqqA35dW1ra3vv3j3ZxpfetGlTc35rVdkY/1rR0uYH\nXVXCg66a5do6Yn9zyZJJcHDwpk2bNm3aNH78eKZmd2GqpbAZmjBhgiTJVDxAsbT3EhnSjxo1\nSp7OOkg8Dw+PyMhICZ/+8Ktv+fLl0vatWbx4seQ/0giCoChqx44dvXr1kmovquTk5HTx4sXY\n2NgpU6aYm5vTV9na2s6bNy8hIeHw4cNSDSchM2tr62+//Vba590EQQQGBnbr1k1JpQIATU3N\nqKgoaX85zJ07d/To0corlSqNGTPm+PHjTbbb8dfOmjXr8OHDBEG4uLgkJCTs2LHD1LTmbUcN\nDQ2SJOsa5/T19detW5ecnNyhQ4cNGzb07t1bqlKNHz/+c/+1rKpJaUV589ccNpvNnnEoVejq\n5wen81enKS2Ta9eu8U9FZmamtKWXH1Pd+ySngm5tBEHk5uZKcro+ffqkmhdISZI0MDCQqjtI\nUFCQu7u75OlJkrSzs8vKylL2NYauXLlibGws/komSZIkya1bt8q2i8rKSv6Mk03e4TQ0NHbt\n2qXYA1S2rKysxMTEp0+f5ufnM1IADofj5+cn+XcRSZLOzs6qKe21a9cMDAwk/McfPXo0h8NR\nQalU6enTp/7+/lDbmtugIgDA3t7+6NGjjTesqqq6fv36999/P2HChAEDBoSEhHzzzTcXL16s\nqKigJ8vPz5f8V1BAQEB5ebmqDr2ZYjywK7+2ZjibzR699S5PyFpe3OaRbDabvepymdIyYTaw\na+ZzZBEEwWKxJIw+vb29ZdtLQECA5GdsxYoVsu1F8rsC/+vpzJkzEREREv4YnTNnDofDefHi\nhZ2dnSQ74vc6T0xMVN6lhejS09ODg4P5VUOv0LpbUbdu3eLi4uTcy6FDh6ytrUFYeMdf4uXl\ndevWLYUc0ecmLy+vS5cuEv7/2tnZpaSkqKxsz549a/LZopaW1tq1a3k8YfcotXDr1q358+c7\nOzvX9YkyNzcfPXr04cOH5Y+0KisrFy9erKmpKerblX+r2rRpkxqfYckxHthRzw99xWaz2fOO\nZghZ+erwTDabzZ56IFl5mTAb2BUXF6usn40MjYcujQAAIABJREFUDwqnTp365MkTNzc3aOo7\nKywsjMvlyvBwWU9PT6of1hwOx9XVVdq9BAcHu7m5SRhyEQRR13KTmJjo5+cHEv8YzcrK4v+4\nFHW6+Ms7duzIHyMDqVJSUtLatWt9fHysrKxIkjQzM2vfvn1oaGhMTIyi7gdlZWX79u0bNGiQ\njo5OXaWbmJiEhIScPHmSy+UqZC+fp/Ly8qlTp4ppR+cv79+/f05OjorLVlVVtX//fqFv4Orp\n6U2cOPHly5cqLhJTeDxebm5ug1Y3hUhNTZ0xY4aFhUWDM2xvb79o0aL3798rfI//UcwHdtzE\nPePZbPbwxaezG617d2w+m81mj9uT2MS3oTyZMBvYURQVFhbWZLTRmLTPcPm9j6V6ltGyZUv+\ng0IOh7Nnzx6h4RSLxZo4cWJaWs1T7vz8fKm63ZAk+fDhQ2nPWHZ2dl3nDEl4e3tXV1d//PiR\n3x9cTCRNEISurm5ERESDPcbExMyZM8fBwaHutJuamo4aNUroj1EejxcZGUn/iqfv0cnJ6eDB\ng9XV1TJdLOi/JDc3Ny0tjT+xJlKUBw8eDB06tPHLBwRB9OjR49y5c8wW78WLF3/++efGjRtX\nrly5e/fuK1eu4JNBxeJyuUlJSRcuXAgPD79y5Yoqm2b/KwiK8RFfuakHZn3zvxxoMWjt7nkd\naQOWlN77v3kbb34E65E/7Z3mJj6MkSOT6OjoAQMGAEBmZiZT48T279//xo0bkqd3cXHZsWPH\nqFGjJJyJQVdXNzY2NjY2dtmyZfy+2+LTkyTJYrFu3brVqVMn+vKUlJQHDx68e/euvLzcysrK\nzc2tT58+uvzRZGq9efPGz89PkslP9fT0Ll++LFsX8uzsbD8/v5SUlCZTBgUFHTlyhN8zj6Ko\n48ePL1++/NWrVwCgoaFBURRBEPzWGpIkJ0yYEBYWJmY6cy6Xm5eXZ2xs3OCohUpJSbl27dqb\nN2/y8/NNTU0dHBz8/Py8vLykOVCEkBBFRUU3b958/fp1dna2hYVFy5Yt+/Tpw38OjtDnjtGw\nskbhrU3j2Gw2O/jb32PTPnJ4FI9T8OL6vkVBbDabPWFLXCE98Ys/ZrDZbDZ7bUyl7JnQMd5i\nR1FURUUFP7iUhKenZ0FBAUVR9+/fl6R5zN7e/tmzZ/wdHThwQEtLS3yTFQA4Ojo+ffpU5sMp\nLS0NDQ0V/4h52LBhr1+/luekVVZWbt68mcUSPnghANjY2Jw5c6bxhjwe78GDBz/88MOECRP6\n9u07bNiwWbNmHTp0SMIXOBBCCKFmqxm02AEAlD8LXxV2LPUTAAChqa3B5VRTAEAYthm3as1Y\nD3rbyMvDMxefzALosjjqBz9tGTOhaw4tdgBAUVRYWNj69etFDdoCACRJzpgxY9euXXWPIQoL\nCzdt2rRt2zahW+nr63///feLFi2iz1j87NmzpUuXXrx4kZ8hf4BN/lNaHo+no6OzaNGiZcuW\nGRsby3lEr169Onjw4PHjx1+8eFG30MLCIigoaOLEid27d5czf77S0tKLFy9GREQ8e/YsOztb\nU1PTxsamR48ewcHBfn5+zf+lY4QQQkiBmklgBwDcvCeXTl+IfZTyrqCkWsuohX3rLn2HjR7k\nadqg2Ud0YCdFJnTNJLDjy8nJCQsLi4yMzMvLoy83NDQcPnz4unXrnJ2dG29VWlp6+fLlmJiY\njIyM/Px8CwsLR0dHf39/f39/UQ1a//7779mzZ2/fvv3mzZvi4mIbGxsnJ6fBgwez2ezGXVPl\nxOFwPnz4UFFRYW9vTw8xEUIIIaRYzSewY0yzCuzqpKenv337Nicnx9TU1M7Ozt3dXS2nSUUI\nIYSQAuEMlc2Uk5MTTgyPEEIIIakwPqUYQgghhBBSDAzsEEIIIYTUBAZ2CCGEEEJqAgM7hBBC\nCCE1gYEdQgghhJCawMAOIYQQQkhNYGCHEEIIIaQmMLBDCCGEEFITGNghhBBCCKkJDOwQQggh\nhNQEBnYIIYQQQmoCAzuEEEIIITWBgR1CCCGEkJrAwA4hhBBCSE1gYIcQQgghpCYwsEMIIYQQ\nUhMY2CGEEEIIqQkM7BBCCCGE1IQm0wVoRt68eVNRUcF0KRBCCCGERNLS0rK3txe1FgO7ej4+\nPkwXASGEEEJIHGdn57S0NFFrMbCDPn36pKamurm5KXUv1tbW1tbWlZWVycnJSt0Ram4cHBxM\nTU2Li4tfvXrFdFmQSrm5uenp6eXl5b17947psiCVateunYaGxrt37/Ly8pguC1IdTU3Ntm3b\nAkBaWlpJSQlTxSAoimJq380Hh8O5deuWUncRHR0dHR1tamr63XffKXVHqLk5duxYQkKCm5vb\ntGnTmC4LUqlffvklIyOjR48eI0aMYLosSKXWrl1bXl7OZrN9fX2ZLgtSndLS0vXr1wPA1KlT\n3d3dlbcjFosl5tLCFjsAAG1tbX9/f6Xugt9aw2KxlL0j1NzcvHkTAMzNzbHqPzdHjhwBAHt7\ne6z6z82GDRsAwN3dHav+s1JQUMD/o2PHjgx27sK3YhFCCCGE1AS22KmIi4uLv7+/mZkZ0wVB\nqubp6VlVVaXsTpyoGfL29raxsfHw8GC6IEjV/Pz8ysvLW7VqxXRBkErVPf2zsLBgsBjYxw4h\nhBBCSE3go1iEEEIIITWBgR1CCCGEkJrAwE7ZKtMu/t/8kd1dW5rp6bBMbFy6DJ29+WxqOdPF\nQgrHeXs1bLiLLkEQhMeaJDEJ8ZJQF1TBP+Frpgzq7GxpqKulrW/a0sNn+NwtZ5+XCk2N9a5G\nKtIv//z1mJ6edqb62lo6BmZ2nj1Hzf/pf6llQlNj1autiofLPDQIgiBMvrosbD1DVU8hJco5\nN9dTr+ZMa7AM9GrfVWG1nnU2m+nCIYWpzrq5LaS1Qd1/lfvqp6KS4iWhLoof/tjfqu6XsYaG\nRl31634xKSqDJ5ga6119VKUfm+zOqq1tQkOzvn1ExznkaHq1YHKsevVVGf+dZ80/vvH0S41W\nM1b1GNgp0atf+uoDAGHZb0XU0zwORVHVH5PPrhloRQKAXp9dL5kuIFIATsrR6Z1MCQBguYyZ\nMsBSbGCHl4S6yIkYYwEAQLbsvyz84dvCSl5Vcebjk6sH22kAALD6/pJBS431rj6qEle11wIA\nMO7w1S/RqTllXF5lUWbi6XVsB00AAF3f7em05Fj16osTv6K9Bmhoa2sIDewYrHoM7JSm/Oo0\nKwAA98V3PgmuuLPYDQCgxeRLZQyVDSlO7t7+ABrWvZecTP1EPfrOQUxgh5eE2ni8yhUAQLvL\n2oRKgRUV95e2JgAA2v+YWrsM612NlEQFGwAA2E45nye4hvPPirYAANBuw/PaZVj16ouTsLq9\nJoD11AWB+kICO0arHvvYKUtVdHhUNoBG79BvfVgCa3R9vgntpQGQG/XX1UqGSocUhjBqN3nP\nvX9jfxrtyhKfEi8JtfH+/t85uhqg0WfO/A7aAmt0ugWNcAAAeJaUVDOSFNa7Okl++kLPmKXp\nMHbuUHPBNVodR3zZCgAgPT29ZhFWvdqqTvpx2o9Pqo3Zm9cP1BOyntmqx8BOWR5ERxcDQLuB\nA60brbMZOLAtAJRdvXpH5eVCCmY+bvsfc7qaEk2nxEtCbbSceaGwnFNReGZS4xHHW7RoAQAA\nXC6PvwDrXZ10XROfXfiJk/5T18brNDU1AQCsrWsrGqteTXGTNk/9MaHKcMCWPZNaCk3BbNVj\nYKckJcnJmQDAatfORcjaL9q10wWA3OTkfBWXCzEGLwk1Q+rU94auV56Y+BwAwNPLi9+rGutd\nDRFE419ypTEHIl8BgP2gga35S7Dq1RM3+aepYfEc/T6b98+wE56E4arHwE5J3rx5AwBga2sr\ndHXN8vo2e6T28JL4DPDS9m45UQyg22fGRFf+Iqx39VZdlvf239tHVwd2H7HnFeh7fn1gXa+a\nx/NY9eqI9/ynaWviK3V7rv9ttpOoJzUMVz3OFaskJSUlAAAGBgZCV+vr6wMAlJYKH/EKqSG8\nJNRe6d1VY1fcqQDdTqt+nlX7Ux7rXW0lrvTouOE5/28ti7b+M8JmLlkysn4kFKx69cN7vn36\nmvsVOt6bDix0Fd0Bh+GqxxY7JamoqAAA0NbWFrpaR0eHlgp9DvCSUGtUztXv+g/58e8K0jbw\n4OnvvbRqV2C9fxa45R+z3qb+nZD+qX4ZVr26odJ2T191p0Kr4w+/L3EXFz0xXPUY2CmJrq4u\nAEBlpfDXXmqWs1hNvEiJ1AdeEuqrIuX3sd2HbnlYrOkYcjg2PKQV7ac81rvaarfy0ceP+TmZ\naY9vRm2b6pZ948j6kM4dJkVk1LwPjVWvXqj0XdOX3ynXaP/9gW/bin/ayXDVY2CnJEZGRgB1\nDbKNFBcX01KhzwFeEuqJyrmxrG+Pr06kVxt1XXrxfviEL7QE1mO9qy0NXUMTE7MWLZ3b9x4T\nuut6wskpraDixZGv5hzOAQCsejWTvnfG8ptlGm2++X1FJ60m0jJc9RjYKYmjkxMBAJkZtT/e\nBPDevs0EAHBxcVZtsRBz8JJQQ9WvjgZ3G7z5fqGWc9DB+zc3D7Bq1O0G6/0zQViywxZ0A4BP\nl4+fLwHAqlcrb3+bsex6mbbXqr/WdRH+fJWO4arHwE5J9Nu2dQSAyidPngtZ+/Sff6oAwL5t\nW2PVFgsxBy8JdcPLPDXFf9Lx11Um3b+7cj9yamuhz1Ww3tVJdWlOxoun8f+8Ftrr3a5VKxIA\nuFlZuQBY9eqk9MbZ6yUAnMdrOukQgtiHywCg6PchBEEQhN0394HxqsfATlk6DxpkAQD/Xrz4\nptG65+cvvAQA86FDu6m8XIgxeEmolaKYUP+Q8HSuWZ9NN6I39W0h8g05rHc1krTep5Vb+67d\n550oFrI2MyODBwBgbs6flQKrXm2QBlYOIljqEwBAGFg6ODg4ONib6QAwXvXKmqsMVd3/2hEA\noNWMK8UCKwpOjbMGAHBe/KCKobIhJRE/VyxeEuoj//T4lgCg6b7wZnFTabHe1Ud1zDxbAADt\nDqv/Lm+wrjx+eRsSAKDtmqSaRVj1n4Fzk4XNFcto1WNgp0TZkQHmAAAmPovD/8mq4FG8ig/x\nh+d3MQQAaBF8IofpAiL58bjVVfXuL+UHdqsSaQu5vLrUeEmoB869xfwv7YWxpVUiVGO9q6X0\nvX30AQAIK9/QX2+kvC/icCsL3/0bvWd2VwsCAMBo6JGs+uRY9WpPeGDHaNVjYKdUxbdWdat9\niE5q62rXPK0hzHqG3S1lunBIEa5Nb7KXxNBDtJ/2eEmog4vTmn6bTWPsCdoWWO/qI/vKt12M\n65+806cXI0y7fn89XzA5Vr2aExXYMVj12MdOqQx7rYtLur5z4ahuLtaGJE/DuKWbT8CiPbFJ\nsSt76DNdOMQEvCTUgajhqUTDelcflgO33H9+/4/V04Z4u1kbszRJTZax1RddBk1e8fvdZ3d/\n7GcmmByr/rPFWNUTFCXsbVyEEEIIIfRfgy12CCGEEEJqAgM7hBBCCCE1gYEdQgghhJCawMAO\nIYQQQkhNYGCHEEIIIaQmMLBDCCGEEFITGNghhBBCCKkJDOwQQjI6P8WAqDf4j1KmC6Qqyjjw\n5nsyo2eb0Et2oJDpAiGExMHADiGEEEJITWBgh5DiVRwL1CEIgnBe/o/A4qOjdQiCIKzm3ZYo\nm9IDg4mGDMaeLhezSdW5yaaNtqE3/zTI02NNkrgSnJmgS0tsMjtaonIjhBBiiibTBUBIDT2I\nu8MBAH0fn/b0xY/u3OUAgLavb1eZsy67ePJKxaiRusLXVsecPo9PypTOc9yW7R04tZ8cvXWY\nLAxCCNFhYIeQwqXFxX0AAPD26UH/D3sRF5cNANDJ11dEXCaJ0nNRlzkjR2oLW8eNPXWuQPas\nkYScBs79eiDThUAIIWHwUSxCilYSF/cUAMDdx8ectrj4zp0kAADbnj0d5Mm++PzJaxyha6hb\np8/myZM1Qgih/zgM7BBSMN7duHtcADDw8WlHW0zdu3OPBwCsnr6dZMnW1ta25q+icyejhUV2\n1N1TZ7L4f5K2ttay7AQhhNB/GwZ2CClY0p07RQBAdPPpoUFbnHznTgEAQFdfHy1ZsjXr3duz\n5s+is1HR1Y0SUA9On3lf83dnX1+WLDuRB0mSAFD14cFfayb7d3SzN9fT0Te3bd0zcMkvMR8a\nF1cynPd3/gibF+jX3qmlhaGONsvEyqGN75h568Mf5fGEJP+4tz/tZY+BvxYDAEBZ2pWd89nd\nWjtY6Ovomdq49xi1cGd0RmXjzQv29Gu0eWH87q/6uLXQ19ExtJt+hv/iiiRDk1CFT09tWzpp\ncFc3eysTlpa2nomlk1ffMXM3RTzMrmryuDU1NetKPrxbmyZLLvMZq9vs7bVdi4N6tbazMNBl\nmbR08eo/afWfD7K5AEAQRJPlFaMi/eL2rwN6trazMNRhGdu4tPMbv/LQ3ffVAMA7PVa7/jx2\n+jFVEUck5zUg835rlb2K/n3trNH9OrvamhvpaWtqsQzNbd27DggO3RLxMEfWfwSEpEAhhOQW\nM89K2n+9EUfKm8q15LdB9emdvt37rXPtB7NpV6sapn5Qv9plxc5QWoEGHSoRnieA++qn4kpw\nejz9vQDjWdcE1p6brE8/oIiqj3Hr+1kIiwJMun4XUyD56eTLv7VhaCtRvREJkw6zItMbnoTq\n8FG0NF03v6Gq08LHuwjrkGjcbfntjw02rzoygpbCe8tbburPfYxoJ/K3EiEHTj+9fOVJv09t\nZyIyHDJoPfaX+KIG2wjkSQSelK7kMp8xiqIobsbJ6W30hWxCWg/cfL8oZh69R8Gg34TvW6jq\nV0cnuAr7iUG08N94ryh3bx/asp473iniiOS8BuQ5k9z3l5b3thH3u43lGrDzUaHkJxAhGWBg\nh5ACqCCws5x7PXa+Te0n81nXGtxYEpd9UbvSau6tyzMM6zdVSWBHBu0NH2Uq+niN+u15LcUZ\n/XhpjluDO6SmLktbIFgi7YOPfxDc7NQ42g3cfcXDe9+1Fv2GmPX40w1u6ydDaPtsvSruWKAh\nfQOJAruK+B99jKAp+p1W3y8TeTL1Jp0UX/IJZxoFJDKeMar87hIPMbFIy6nfTWwhcA4kDuzK\nYue7iHk/z2bqzu870j7335vbHK4BmfdbFrfETZLXEY16/vxvtaTnECHpYWCHkAK8jd6zle/r\nfmYAAOAWsG4rzWJ/SwAA8Aj+sWbJmaa/3AWCMOPpl6ouTjOu/Wg56xqXnvbJD+51KSefqzo/\nhdbcoJLATsPGpgWAgWfQ6l9PXYm+evrgunFeAlERmIaclLStIvtYIK2ViLAesPZCysdqiuKV\nvo3dFfJF/Y3bbExENn3DsxNp7UM2g4Z20gDQsvEOmLN02bezx3RsQX84DkB2+ekVJXJz+8Bg\nX356UtfYzISlKUlgVxK70Jnew0XfM2TDnxdv3r5+9s8fJ3cwpgUHhMP8WxUiTqaZn38TJe/6\nf+kKOWO8J2u9BLLWcw9cd+TKvfhHcRd+/36ogzaAhgY9gcSBHS9hladAXx8t+0Hf7D11/fbt\n6KhdC/taawCwWPRGsYG/CjTqMnUNyLrfzB096w9Xx3V02J+X7z9JTkt/+Swh7uyer3tb1te9\n/pcHG8SwCCkQBnYIKVLpkREaAAA2i+7QF1dGjNEGAHBY8kiKzASCMIMp56nyk4F6tZ+tZsfS\nIrtna+te1GCNPVFGnZtMi8jEBHZSEf8oFkCn4+pH9FaoTw+WtaffRbUHH5DoeSzv6YrWtM2s\np10VaNvipv5E66bouTpZTJHAqPf6B3VPPXnZF6Y4CTS8NGglEthcU1MTwNJv1f9Si7gURVGc\nwoISnpC90E/v2+2+9EYb5/k36I9cP91d6kk7IdqDD2QL37UEJfffl6eAM8a9Pa8lPVe7aRfz\n6Vt+ODG2QVu0pIFddexsgfd3LEYfzaQXKf3gMDMxOTN1Dci8X07EiPpcjWdcEfjZRVFUZewC\nR4MW9q7tvfsMGjlhZzy22SGlwcAOIUW6tbAlAIDWyEiBB60PF7cCANAOiKyUIjOBIEx/8jmK\nKjkyoq6Nw2rBzbqbx8swr9rFuiMiSihGAju7eTGcBkdQGhVgQEuhNfq4JMf/cGkr2kZtfnjS\n6MSEs+vvrs7L/xFZJFbfXxr028r9bRA98rJZcFPMEYHjnNhSIQUUGdglh3nSVmgN/DWvwZbZ\n+/rp6BgYm1vZOri4e3ZdepUjPE8pSy7zGbv/jR1tQ8JnW6Nubhn/10Og3U3SwO7eElv6Zh3X\nv2yY4uWGToTInJm6BmTeb9HvA+q30/TdkiqsNyNCqoBvxSKkQO8fPnwPANC+Wzf6Q6bshw/f\nAgC079JF6MDCEjNgjxlYm0P2qag4iv9n+qnTj2uW6g4KGGYgbFOlazEioE/Dvlr6g4f0ot28\nqx4//rfpjN7Fxb2t/6TRpo17wxQGnTvXL3t19262iJx0h82eYiu4yMLf34v2sbBQ3EQd3ecv\n7SPspQJR8m7epB+f95Ah5g1SWM66XlFRUpiX9e71y5Skh5sHiOjdJl3JZT5jeQkJ72ip2g4a\n1GCnAHajRnURXkax8hITM2kfWw9juzRM4hIY0LHhslpMXQOy79fI1dWybnn1ne86t/1y9to9\nx2MeZ5RwRRQNIeXAwA4hxal++CgRAMCyWzdH2mLuo0cJAABmXbs2urtJyXh44IDayC7z1Mn7\nFADAm1Onauek1RkcOJyZuA7aeXk1fg9U382N/qgvMzOzUZJGXqal0T5xo4J1Gs1+KzDF7ZMn\nT0Tk5Ontrddwmb29Pe0ThyN8rGcAAHDo08ex6eLSpCQnU/WfWK6udqLTiiddyWU+Y69fv6Zt\nSLi5fQGNOHl6NipK0968eUPPuXXrRhESwBft2okalIepa0CO/facMbtt/Q2VKnl+af+aeWP7\ndWhlYtTSq3/wwvW/XXqSI+ZqQ0hRMLBDSHGePnpUAQCkt7dAI8e/jx6VAQDRuUtnuXdhPDyg\nPrI7dy4BALLOnHlQs0RnQAC76Tcyady+f1giRsRYiadBJc3Nhb0Ta2xsTPtUUVzc9BhuHz9+\nlHSn/PT5+SLGFTMzM2u0TENHR0NIUmFatWrVdCK6ggL6hG5GAocuHelKLvMZKykpoS1mGRsL\na1A2NTWRKncAACguLqbnbGIi5EIihF8yAMxdA3Lsl+iw6vRvAQ6Nm2B5nz48uXFs16qZX3q1\ntO08YeuN9+KGwUNIbjhXLELyytrtZ7PgJm0B7/wUI2JKo3TUtekmxHQAAHBfkZCyvoNMezMa\nETBA68KFKgCA9CtXXvzodPvCvZp2IqnjOiC0WQYGopv4dCX/hiBIoT8TKYqifyTJpge7JQVy\nIlnGpnpNFINTBmAoZDkpvEyS0tOTsqmKy6U/dWtw6FKRruQKOmMiRiIWPCoJCR68iJx5PFEh\nDlPXgFz71fxi2omkbse2hP18+Nz9t5+E1D4375/wpQOi7/x+79QUJ2xWQUqCgR1C/zGmIwL6\naV24UgUAkHj9eoZT9K2aG6/WgIAR0sV1CsQtKCgGaLR7wSYQPSOjpr9zTExMALJqP5lNP5u7\nq4+49M2HqakpQF1nr6KPHykAuWZtkJDMZ8zQ0BAgp/bTp8LCKoCGTU7cvDzpWrHqc84VnzNP\ndM5MXQNy79fAc+y6yLHrKnOe3Ym9GXfn3oNHjx4lpOZW0KI8XvbZOXP+HHp5SgvR2SAkBwzs\nEJKXoc/MjRsHA7w99+PeuyVAthu3fly7+vv563Pr998tA3APWDOlc80DKXPfliIyk4DpiIB+\nWleuVAEA7+7VVeY3aiZG0uofMEKGh2aKkvT0KYBvg4WFyclZtI+Ojo5NZ+Tm5ka7uRZkZpYD\nqHyCNJm4urrSArvKlJR0GO4smOThEuc+v340MDQ0NDQ0NOi46H9/NezaLwOZz5i9vT1AXa8y\n6vnzVADPBmlSHj+WoWeYnZ0dwKu6nFNSngO0bZDm+ZMnoub0YuoaUNR+dSw9+wV59guaCwBQ\nVZBy68yBDSu3xXyoie8qrp44XzRlquyP6hESAxuDEZKXfqdxy5YtWza3q0EpAID7yO++X1Zv\nZif9MgAA22FLVtctnNHLUnyeYpmOCuxf85us/OyRqCL+n1r+gUzGdfDhf6fuNXxmV3Thwh3a\nR72OHd2azsjGx8ex/hPv9o2bTffLax5se/emx3H/XLrU8F3NlzdupFeUFuZ9yEhP/ffJWwM7\nOQL8ejKfMSsvL/owdc8uXcpokIJKPh6VBNKz8fKiN0g9u3DhdcMkqSeiRL3xwNg1oJT9apl5\n9J/20+WjM+smjgEqM/OD/BkjJBQGdggpyN/xf1MAwOrSRaDNIz7+bwAALW9vkWM7SM18ZIBf\nTWRX101J05/R9joAeLX/25+T6ffBsri1Gy5U1H82GDainyQvLnQeP4E2SGxe+Jrt/wq0GXHi\nl7fVN7V2cG3Xxaff4KDtD5rNu4YdJ09pX99Wy43dvPIK/WFj7tHvfkqs/2g/fnwvxTyplfmM\ndR86lDYiC/Vg2/IzubTtuC/3he6QJa4D6D5kCP1yjN/2beR7es5p+xduFxnXMXcNyLjfqsfh\nK0OnBw/z6+phb9ZmcZyQ0mhqa9PutxYWFoopMEKNMTyOHkLqIn1rNwAA6PEzfYB9Km0DP57r\nvDFd6iwbD1BcJ2+fv2A/Cs0Bv9OnDJB0gGK5phQTmLsJtLS0gLDoPnv78ev3Hty9enTjxAZT\nitnNj21yetwaHwSnnSUsfRfuOX8/6XnK47v/2xvas0V9OES6fH1X1MRcDWdxrTmmsbTgUmPs\nCfo6CTZvIln+2UkCEy7oeYwNO3L5zr3YswdXDBV4ZdJo6KEs6XYtruSynrHqe98IDsGj6zp6\n9R8X4x7cizm5c16vFiRosFj0YjeY+Eu0qlsLHQRy1rIfsHjX8UvXoy/8tXWmjwUBpJ4e/fJq\nMPQxU9eAbPt9s7N3/VkizHsu3Hv2dsLawD9cAAAENUlEQVTz9Hfv32e+fZl079LhlV+2qt8n\n2W37G8nOIkLSw8AOIYWoOjFWFwDAZoHAXGIVx0ZpAQC0mHNd+jzFBHZU9j5/euOX5oD9AlNB\nqSSwE5htnRy1Zr2Y4ZeJluNP5YvYizB5Z6c5Nd0F2KBr2KNPgsfNcGBHUcUx37Sjj04tlLbH\nnGt5kufZdMllPmPUx3MTbES3G5Ju3+5YQB/1pe8v2ZSE8k43nI+MjnAO3Tyb3r+w0ZwWzFwD\nMu6Xk7iui4QvUet2XvOogkJIWfBRLEIK8ezvvysAQKNLF4Enro/j46sAgGgwsp38LEcH9Kq/\nOWn0DRjVeLguJeNVVNCeOBma+X534q+v2grraq5lz9599XepSmg+/MD9iyv72YiYmQEA9NwC\nd8VcW9mlub1XYei3Ne7mtjEuIu/yul+M2ng1drd/w1kp5CTrGTMZ9tulHQOshT0j13YJOXA6\n7Esb+jPVT58+SVyikYcub/e3EpqzU9CvZzf66oodSIWpa0Cm/Wp5rbp8ZcNQhyamljH0GLsj\n9vrqLhKPD4mQ1PCtWIQUoejvv9MAANp0EbjJ5MTHZwAAeHh7K3ockhajA3vPi43hAgBo9A4Y\npfqxE0oExqC1srLSdPT/7b7XyAO79h+9lvAyI+eTlmnLLzr3Hz1l4cLAtlLPh0FYDgi7/uqr\nG4cPRF66cSch7X1eQUmVpr6pjVObTj0Hjpo4LaibVTP9AjPyXhSVMv7Rsd//Onft5sOUd7kF\nxdW65ja2jm17DQscPzGwV6smm/RkIesZ0/VaeOWZ75//t+PQmdinr3PKNEys7Zw7+AdOnT19\nRBtjKHloA1DXHU5wTOMmsDp8ffXfXn9u+/nw2dgn6dmlpIm1nZNX/8Cps6eP9DSBm9XVtMRa\nWo0iKaauAdn2a95z+fnUqfdP/RV1Kfb+4+ev3mZ/LC3nUFp6BiaWrVw9O/kOGBEcwvaykHSA\nbIRkQ1ByjKGJEEIIyYZ7ahxrTETt6zY6k89V/DGM0QIhpBaa6Q9ehBBC/3m8ysKsjLdv377N\nN+k+tFODF0ETHj6kvUTt7i5kOlmEkNQwsEMIIaRwRUfHtZ4dlVVSxX8o5Pbtg8Qt3rR+Cvmn\ntx5Mq//oNGiQq4pLiJB6wpcnEEIIKZyxX992VVV1XX1Sf2IPXHzgauKLdx8ynt8//dO43hOO\n1w+Zp9N/6cJOjBQTIbWDfewQQggpQXXKDv/ui24WNZnQin3w/umpjvhSAUKKgC12CCGElEDT\n4+tzsb+Ma60vJg1p3n3h8bjjGNUhpDDYYocQQkh5uLn/nP4z/PTVuH/+TcvMK/pUralnbNqi\nlUdH716DgqaM6+ckLvBDCEkLAzuEEEIIITWBj2IRQgghhNQEBnYIIYQQQmoCAzuEEEIIITWB\ngR1CCCGEkJrAwA4hhBBCSE1gYIcQQgghpCb+H3ounyl/pawiAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(M.TFs_order,aes(x=DEG,y=DEGenrichment))+geom_point(stat = \"identity\",size=5)+xlab(\"# MHb enriched genes\")+ylab(\"Selectivity for MHb genes\")+ scale_y_continuous(breaks=seq(0,1.0,0.2))+theme(axis.text.x=element_text(size=16,color=\"black\"),axis.text.y=element_text(size=16,hjust = 1),axis.title=element_text(size=20,face=\"bold\"),plot.title = element_text(size=24),,panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+xlim(0,40)+ggtitle(\"DEG and DEG enrichment\")\n",
    "#+xlab(\"TF\")+ylab(\"DEG enrichment\")+theme(axis.text.x=element_text(size=10,angle = 50, hjust = 1))+ggtitle(\"correlation_DEG_Ar\") \n",
    "             \n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/MHb_TFs_scatter_tight.pdf\",width=15,height=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.xlsx2(M.TFs_order,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/MTF_rank_MHbrichgenes_tight.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.TFs<-as.data.frame(table(L.TFs))\n",
    "L.TFs_order<-L.TFs[order(-L.TFs$Freq),]\n",
    "rownames(L.TFs_order)<-as.character(L.TFs_order[,1])\n",
    "nonDEG<-vector(mode=\"numeric\", length=0)\n",
    "DEGenrichment<-vector(mode=\"numeric\", length=0)\n",
    "for (i in 1:dim(L.TFs_order)[1]){\n",
    "nonDEG[i]<- length(regulons[[rownames(L.TFs_order)[i]]]) - L.TFs_order$Freq[i]\n",
    "DEGenrichment[i]<-   L.TFs_order$Freq[i]/length(regulons[[rownames(L.TFs_order)[i]]])\n",
    "}\n",
    "L.TFs_order$nonDEG<-nonDEG\n",
    "L.TFs_order$DEGenrichment<-DEGenrichment\n",
    "colnames(L.TFs_order)[2]<-\"DEG\"\n",
    "rownames(L.TFs_order)<-L.TFs_order$L.TFs\n",
    "L.TFs_order<-L.TFs_order[order(L.TFs_order$DEGenrichment),]\n",
    "L.TFs_order$L.TFs<-factor(L.TFs_order$L.TFs,levels=as.character(L.TFs_order$L.TFs))\n",
    "write.xlsx2(M.TFs_order,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/MTF_rank_MHbrichgenes.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove<-numeric()\n",
    "for (i in 1:dim(L.TFs_order)[1]){\n",
    "if(paste(L.TFs_order[,1][i],\"_extended\",sep=\"\") %in% L.TFs_order[,1]){\n",
    "    remove<-c(remove,i)}else{remove<-remove}\n",
    "}\n",
    "L.TFs_order<-L.TFs_order[-remove,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 26 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 26 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {
      "image/png": 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ACQCIIdAACARBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAiCHYAAAAS\nQbADAACQCIIdAACARBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAiCHYAAAASQbAD\nAACQCIIdAACARBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAiCHYAAAASQbADAACQ\nCIIdAACARBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAiCHYAAAASQbADAACQCIId\nAACARBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAiCHYAAAASQbADAACQCIIdAACA\nRBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAiCHYAAAASQbADAACQCIIdAACARBDs\nAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAiCHYAAAASQbADAACQCIIdAACARBDsAAAA\nJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAiCHaQiJycnJiYmPj4eHMXAgCA2RDsULqdO3du\nypQptWrVsra29vLyKl++vI2NTatWrb788svo6GhzVwcAQLEi2KG0ioqKGjRoUOPGjefNm3fj\nxg2FQqF6PDMz88SJEx9++KGvr+/HH3/8+PFj89YJAECxIdihVDp+/HjTpk3XrFmjVCqFEKr/\n5lGFvMePH8+ePbtdu3b37t0zT5UAABQvgh1Kn0uXLgUFBcXFxT2T556hWnv8+PEXX3zxwYMH\nxVUdAABmQ7BDKfPgwYOXXnopPT09b+xVO6VSeePGjSFDhpi6MAAAzI5gh1Jmzpw5EREROTk5\num+iVCr//fffrVu3mq4qAABKAoIdSpOEhIQFCxbIZDJ9N5TL5Z988okpSgIAoOQg2KE02bp1\n6+PHj7WfWqeRQqE4e/bsrVu3TFEVAAAlhKW5CwD0sHPnTrlcruPZdRo3f+utt4xbEgCoxMbG\nbt68+ejRo7GxsampqV5eXj4+Pt26dQsICJDL6UZBMSHYoTS5c+eOAd11ecLDw41XCwDkun37\n9scff7xmzRqFQiGXy2UymWohJydnzpw5VatWnT179qBBg4h3KAYcZChNYmJiDA52crk8NjbW\nuPUAwKpVq+rUqbN69WrVYIJCocjJyVEqlXnXeEVGRg4dOjQ4ODgxMdGslaJMINihNHF1dTV4\nW4VC4ezsbMRiAGDu3LlDhw7Nzs7W8ptTFfj27dvXokWL+/fvF2N1KIsIdm9v4fwAACAASURB\nVChNKleubGFhUZTNjVgMgDJuw4YNH3zwgVBHN+2USuWtW7f69OmTmZlp+tJQdhHsUJq8+OKL\nes1g94x27doZsRgAZdmDBw9GjRolk8l0Pz9EqVQePnz4u+++M2lhKOMIdihNevXqZcAkdkII\nuVxeqVKlZs2aGb0kAGXT3Llzk5KS9L1IXyaTzZ49OykpyURVAQQ7lCZ+fn59+vQxINspFIoP\nPviAS9IAGIVSqVy+fLlhG6akpGzYsMHoJQEqfM+hlPniiy+srKz0imhyudzPz48Z7AAYy8mT\nJ+/du2fYtnK5fPPmzcatB8hTcuaxy7p38t9NOw+evhadkJpp6ejm5ftCYJdePVp6Wxu6x8xr\nv707ZX2UUtgHz1gzrokxi4X51K5de8WKFUOGDNHx1Ba5XG5vb79x40Zra4MPJQB4ysWLFw3e\nVqFQnDt3zojFAPkZGOxycnKeuTgxK+7s9q3/XY5KtfTwa9m1e5uq9vrsL/nEkmlfb7v7WAgh\n5Na2Vo+T7t04uePGyb17X3r/i7dbGDJHRfaN1Qs3RBk+ly1KrkGDBsXHx0+cOFE1C6iWlnK5\n3MnJacOGDfXr1y+28gBIXnR0dFE2j4mJMVYlwDP0Hop9eGnNe93qeI3YlC8yPb786+D6VRv3\nGjl+6ifT3nvrlbY+NYI+25+g8z5jt309d9vdxzLnhgOmLlq1ft1ff69fvfjjVxu7yDIjdsz9\n6l8DurtzbqxZuOGusLRkrFmaxo8fv337dm9vbyGExmFZ1YMtW7Y8ceJE+/bti7k8ANJm2FVc\nxtoc0EK/3PPo+OcdWr36zbar9yNi8ubPTt07ueuoP69n5GuniN07vcfLC67o1F+WeXbtmgsZ\nQnj3mvbpkNbVHC2EEHKHyi1enfZRL28hHoetWXP6sV5lipxbfy1cH65w7fhSS0bfJKtz587X\nrl379ttvGzZs+MwqS0vLDh06rF+//tChQ35+fmYpD4CEeXl5mXFzQAt9gl3WkY9enX4iVQiR\nvx/5+qIpS8I1jIalHvhkyp/xhe8151zo4QdCyOv37FPn6RRmXadPj/pyIZIPh57J0qPOnDt/\nLfwrPMe+xfAhjW302A6ljq2t7cSJE8+cORMREbFnz57ff/999erV//33X1xcXEhIyMsvv8zP\nYgCm0KBBA4O3lcvljRo1MmIxQH56nGP3YO1XP91S98FFR0cL4S+EOPf7itN5E8bK7L3qVJPd\nvRL1UCmESP335zXRg8cW8rvk6tlzj4QQ1Rs3fv5mUa6NX6gmLt7OOHPmsmj1bK9MARR31i/4\n+1aO3QsjxnR0u31et41QylWuXJm7SgAoNo0bN/by8jLs7tUKhaJXr16mqAoQ+vTYPd61eWfu\ncKvnS19vnvmiEEKIy5s331C3cOq04NztS5fuXlr6PzchhBDKg7v3pBey2/SIyAQhhHW16hU1\nrK1UvZq1ECI5MiJVtyoVERsWrL2Rbev/2judPXTbBAAAvchkstdff92AVCeTyVxcXHr37m2K\nqgChT7C7evasKtdZBM1cMaV9FVshhEjYt++CuoHnkI/e9rMWQl51xLjeTkIIIXLCwq4Ustv7\ncfeFEMLd3U3j6tzHY2NjdalRGbVh4eobWdb1hox9qUIhY3Dp6ekpKSkpKSnp6elFuf0oAKAM\neu+99zw8PPSd9lypVH766afOzoZM9gDoQveh2KioKNWCT0CAunMt5/ChY+rfK7YdgtvkxiOL\nmjV9hDgnhIiLiytkt+np6UIIYWdrp3G1ja2tEEKkZxTW8yeEUEZtWrj6aqZVreHjengVemrV\n7Nmzd+zYoVr29/dnViEAgO4cHR1//fXXnj176n67WJlM1q5du7Fjx5q6NpRluv/UyMjIHYjN\n90vjwrFjj9TLTVq2tFIv29vnzmL36FHe+gJkZWYJIYSlpeaIaWVlla+VNsp7WxeuvJxpUWPg\nuN7enDEPADCxbt26zZ8/XxQw6dLz6tSps27dutzvNcA0dO+xs7e3FyJNCJGWlpb7UOyhQzfV\nq72bN6+U1/bx49z5SWxsCrss1cpaldyyNCe33MetbQqZtkQZu3XhH5cz5dUHjnu5mk7jqiNG\njOjZs6cQ4vTp01OmTNFlEwAA8hs/fry3t/ewYcPS09ML6reTy+UKhaJbt25//vmnk5NTMVeI\nskb3HrsKFSqoFm6dP69KdvGbNx9Rr7Vp06bZk7YRERGqBRcXl0J2m9u5lzsi+5x0VZefvZ32\nG1nEbv/+j7AMeZU+4wf46ni6nK+vb4sWLVq0aOHn5/ckqwIAoI++ffteu3Zt1KhRqqEnmUxm\nYWFhYWGR143n6+u7bt26LVu2kOpQDHTvsavToIGluJktRNaWmW8v9Rjldfab6bvUEwfLAzq0\ns1W3fLRv7dbcU+tq1KhRyG49K1SQiUhlQny8UtR8bgRVGReXIIQQFStpumZWLW7n97+dy7D0\neXXyYL+Sc/NbAEDZ4O3t/fPPP3/99dfbt28/evRoZGTko0ePKlas6Ofn17Vr1yZNuFk5io/u\nOci28/86WGzcnSNE9rU/Rnf5I/86eevePcqrFu9unDj4zcW3VX9Ub9WqQmG7rVrNU5yKzQoP\njxIBz01EFn7zZo4QwqNaNS09dhnnj51LF0LcXj3h5dWaGjzaPaPnbiGEe++5K0bWLqQgAAAM\n4OrqOmjQoEGDBpm7EJRpelynXX7QhEGeGte4DZgwVD0NcXTo3wfVV8JW69evmcYN8vNr3MRJ\nCBFx8uT959ZFnTgRI4RwbN6slpY9yGxdPAvgbCsTQshsnT09PT09PRw5YxUAAEiXPiOX5brO\nWz7qUK9lt3LyPyr37v/9N33zZqHz9fUVIkoIIVy7z5zUsvAT3uT+Qe09t2++f33L32e6vNM4\n36wnaUfW/HtHCFExKMhfWwC1CRz/S6DmVSe+G/D53gy7thN/GUdPOAAAkDj9Zlb07Lb0yL75\nrwVWc5AJIWQ2no36fbrx0J+v5rtrWHlfXychhFOzKf/8OrxSQTvKz6JW/+GBjkLE7fx6+vL9\ntx5kKYUyK+nG3p8+/W5/khDObYf3rZUvH9747c2ePXv27PlZaKZetQMAAEicvtcayD3bTlhx\ncMLyrLTkDCtnR5vnJ4yrG/Tm+Mb/e39MR2+dxz2d247/6G7c52uvXdn4zYSN31paW+RkZiuF\nEDLHeoOmjQtkhm4AAIDCGXgRqcyqnEsBuc1n8NwFeu/Prv7gr75vsH3Dv6EnrkQmpmbbuHlX\nqdusQ/eXu9R31a9TEQAAoKzS9UYoEhYSEhIcHCyEiIqK8vLyKrQ9AABAyWTwtG+Po0/v23fk\n1JnzV6MSkpNFhznrJjQSQghx89gB28YvehdypwgAAAAYmQHB7uHF1Z99MOuXHZcSn1wd617p\n/dyl1L2ftht/q+/c1cvGNuPcOAAAgOKjZ7DLvrFiUPCYv8MLviA1Pj5eZNxYP65jRGro/g+b\n2BbYEAAAAEal15UJydvGBL2uLdUJIeLiVNMTpx7/+NXpJ7OLUBoAAAD0oUewyz7+xfjldxV5\nf1u5Vvf3cXu2VUJCQu6S4tr3X65LKWqBAAAA0I3uwU65f8VvN1VX0Fr79p+362Zi/O0LX3d4\ntlmX+fu/7ZJ739hH2/7Z8dg4dQIAAKAQuge7S6GhsUIIIew6fhuydnJwjXIat5W7NZv462dB\nqnP30k+fvmyEIgEAAFA43YNddHS0aougkSOqP3/DifwqBgRUVy3FxMQYWhkAAAD0onuwy8jI\nEEIIYePqal9YW0vL3Ktts7O5fAIAAKB46B7s3NxUF0qknz17rZCmMfv3X396IwAAAJia7sGu\nZq1aqoUL899ddDm9wHaPLi0ZOWNf7tzFfn6+RakOAAAAOtN9gmLPoKAG4sAFIUT8jvGNfP8a\n9kbfgPrR91Qrs2Mv7A9Jibp6cu+631aG3lVfCluvY8eKRi8ZAAAAmsiUSqWubaOXdqwxep/u\n85dYt/n++n/vVDWormIUEhISHBwshIiKivLy8jJ3OQAAAAbS584TXiO+/aSFna6tbZt8OP+N\nEp/qAAAAJEOvW4pZvTBt4+q3GjgU3tK+/hurNk9vZm1oWQAAANCXXsFOCFGp1+JjJ9ZM7VHX\nuYCp7OQu9Xp8sObkiaUve2uf7A4AAABGpfvFE3ns6r4yZ/OAaXdPHgg9eOz8zXsJSSkZcjtn\nF3evWi+0bN2ubdPKhc5zBwAAAKMzINgJIYSQlavavOuw5l2NWgwAAAAMp+9QLAAAAEoogh0A\nAIBEGDAUq0y7dXD7roOnLt25/yAlPVOhdR68VhPXTGhpaHEAAADQnZ7BLunEgndGfLLmYqqu\nsxpn9yPYAQAAFAt9gl3G6ZmdO8w4+dBkxQAAAMBwepxjd/vHcbNIdQAAACWV7j12kevXHs7W\n8LhMbiEveCpiC67OAAAAKB66B7vLly8/+cPGp+ukaeP7BzWrU8XNzoJbTAAAAJif7sEuKytL\nvejZf9Wxv/qWN0lBAAAAMIzuI6VVqlTJXfLsO4ZUBwAAUNLoHuzqBAV5qZbKOTqaqBoAAAAY\nTPdgZ9VxwsSmVkIIEb4/9I7JCgIAAIBh9LlotdbEtcsHVLUQimOzhs0+kWqymgAAAGAAve48\nYeE7ZM0p3zaT35mx8uPA+nvemDimf6cWdSu7OVgVmA8t7crZWhS9TAAAABRG92B3aHbnadse\nJCcnp6Sm5liIrIh9iyftW1zYVn3/Vq7rV6QKAQAAoBPdg13Mmd37D5uwEgAAABQJN4YAAACQ\nCIIdAACAROg+FFvev2OXx86ODna21hYWcl0DYfPqBpUFAAAAfeke7NrN2NPOhIUAAACgaBiK\nBQAAkAiCHQAAgEToNUFxfo+jT+/bd+TUmfNXoxKSk0WHOesmNBJCCHHz2AHbxi96WxutRAAA\nAOjCgGD38OLqzz6Y9cuOS4k5eY+5V3o/dyl176ftxt/qO3f1srHNnI1TIwAAAHSgZ7DLvrFi\nUPCYv8MzC2wRHx8vMm6sH9cxIjV0/4dNbItYHwAAAHSk1zl2ydvGBL2uLdUJIeLi4oQQQqQe\n//jV6Sezi1AaAAAA9KFHsMs+/sX45XcVeX9buVb393F7tlVCQkLukuLa91+uSylqgQAAANCN\n7sFOuX/FbzeVQgghrH37z9t1MzH+9oWvOzzbrMv8/d92Ka9afrTtnx2PjVMnAAAACqF7sLsU\nGhorhBDCruO3IWsnB9cop3FbuVuzib9+FqQ6dy/99OnLRigSAAAAhdM92EVHR6u2CBo5orpM\na9OKAQHVVUsxMTGGVgYAAAC96B7sMjIyhBBC2Li62hfW1tIy92rb7GwunwAAACgeugc7NzfV\nhRLpZ89eK6RpzP7915/eCAAAAKame7CrWauWauHC/HcXXU4vsN2jS0tGztiXO3exn59vUaoD\nAACAznSfoNgzKKiBOHBBCBG/Y3wj37+GvdE3oH70PdXK7NgL+0NSoq6e3Lvut5Whd9WXwtbr\n2LGi0UsGAACAJjKlUqlr2+ilHWuM3qf7/CXWbb6//t87VQ2qqxiFhIQEBwcLIaKiory8vMxd\nDgAAgIH0ufOE14hvP2lhp2tr2yYfzn+jxKc6AAAAydDrlmJWL0zbuPqtBg6Ft7Sv/8aqzdOb\nWRtaFgAAAPSlV7ATQlTqtfjYiTVTe9R1LmAqO7lLvR4frDl5YunL3tonuwMAAIBR6X7xRB67\nuq/M2Txg2t2TB0IPHjt/815CUkqG3M7Zxd2r1gstW7dr27RyofPcAQAAwOgMCHZCCCFk5ao2\n7zqseVejFgMAAADD6TsUCwAAgBKKYAcAACARug/FXl7/xZY7Dva2NlaWFhZynS6MkMmt7F3c\ny1eu07SJn6uhg74AAADQie5x6+LqaR+sN/BZ5OVqdBjx/ucfvxHgSRchAACAaRRPzlKk3dqz\naEzb5n2XXtH9xhUAAADQR3F2oOXc3fh2n48OphfjUwIAAJQdugc7K2uroj9dzpUF7/10p+j7\nAQAAwLN0P8eu158Zj39KjD7954dvTl1zLd3Cs0mvV/t1btXAz8u9nI0iIyU+4vr5ozvXrdl6\nPkFhX2vA9C/HBpaXZT9OTYi8cnzXquVrT8XlCCFEzrFfVoRNmOFvslckHRkZGXFxce7u7vb2\nTPkMAAAKJ1MqlTo3fnxmTscXPzqcZuEz8Octv4yor+mmsalnlwzv8faGSOHRbenRza/7qroE\nlQn7xr3Y+YdL2UIIWdCPCSFjXI1TvxGEhIQEBwcLIaKiory8vMxbjEKh2Lx587p163bs2JGQ\nkKB60MnJKSgoqHfv3gMHDrS25ga8AABAMz3Oscs+9FGfaYfThKgx9vffNKc6IYTjC2NWLX/D\nWyjj/32n79cXcx+VuXeY+3l/1SbKK1euFq1oqQoNDW3cuHGfPn1Wr16dl+qEECkpKZs2bRo+\nfHjt2rXXrzf00mQAACB1uge7R5t/WHZHKYRw7dKzjdZeI7tOPTrZCSEyz/3w0wFF3qPNmtVT\nLSUlJRlUq7QtWLAgKCgoLCxMCKFQKJ5Zq3rk7t27/fr1e++993JycsxQIgAAKNl0D3aXjxxJ\nFkIIIZMVNjuxzNJStd/IkJAreY9mZGSoFrKysvSqsQz49ttvJ0yYoFQqn490+anWfvPNN+PH\njy+u0gAAQKmhe7CLiYlRLSSGbD+era1l1qGdex+qFqOiotSPxh44cE215OTkpGeV0rZnz54p\nU6bIZHqc77h48eJly5aZtCoAAFDq6B7sHBzUZ9Vd+27om6uvFTAdXcb1NW+O+P527l+5HVA5\nicfnD5u2M3dyYj8/PwOrlaCcnJyxY8cKIfS5ikXI5fL3338/OTnZZHUBAIDSR/fpTmrVri3E\nESGEEFnXVgyqv21ut5e7v9i4rk8lFwdrWdajB7HhV07/9+/6LSfvPRlp9fHxEULx7+tVu//6\nUP1YtcDAykZ8BaXc2rVrr1y5Uni7pykUisTExIULF37yySemqAoAAJRGugc77z79At4/ckR9\n0n527JlNP57ZpH2bOr161RJCuLk5C6EOdn5DhrQypFKJWrdunVwu135qnUZyufyvv/4i2AEA\ngDx6THfi89bcd3z1uQVZxSHzJjcSQoiqVauqH6s06OtJjQu7+KLMUCgUO3bsMCDVqbYNCwuL\njo42elUAAKCU0ieo2QXO2/n7Kz463VlMXvGl73f+1M1FCCFEpapVrYQQwqPd7E1L+rjpX6VU\nxcfHp6cX6d654eHhRqoFAACUevoEOyGsfAevuXBh7bR+jSvYFNTGwrXO/9795eSlbe80VN8I\nS16zdd8BU5YeuhjyUXPHolQrNffv3y/iHmJjY41SCQAAkADdz7FTc6g9YNbfA2Y+uHHiyImz\nF67euf8gJS1Dae3g6OxeuaZ/o6YBreqXf3b+Yv93V681Tr3S4uzsXMQ9uLi4GKUSAAAgAfoH\nOxULF79W//Nr9T+jFlPmVKxY0bArJ/JUrswVxgAAIJd+Q7EwLisrq4CAALnckE9BJpNVrFiR\nOQEBAEAeQ3vsYCR9+vQ5dOiQwdsWfn83AEV2//79yMjIxMRENze3ypUre3p6mrsiANCMHjsz\nGzVqlKurqwH5zMLCYuLEiaYoCYBKQkLCjBkz/P39K1So0LRp0+Dg4KZNm1aoUKFBgwYzZsxI\nSEgwd4EA8CyCnZm5uLjMmDFDr/uJqYwdO7ZmzZqmKAmAUqn87rvvfHx8Zs6cefny5WfWXrp0\naebMmT4+PvPnzzfgf14AMB2CnfmNGzduwIABureXyWQBAQFffvml6UoCyrLHjx8PHTp04sSJ\naWlpIu+W1/moHklLS5s0adKgQYMyMjLMUCUAaEKwMz+ZTLZixYqePXuqlgtt37x5840bN9rY\nFDiVIICieP3111etWiWE0N4bp1q7Zs2aoUOH0m8HoIQg2JUI9vb2GzZsmDlzprW1tUwm0xjv\n5HK5XC5/++239+/fz7nbgIksWrRo5cqVem2ybt26RYsWmageANCLjB+aISEhwcHBQoioqCgv\nLy/zFhMREfH111+vW7fu3r17+R93cXHp2bPnBx98UK9ePXPVBkheUlJSjRo1kpOT9fqHUSaT\nOTk53bp1y82NOyYCMDOmOylZqlSpsmjRooULF168eDE8PPz+/fseHh7e3t6NGjWytOTDAkzr\nxx9/fPDggb5bKZXK5OTkxYsXf/zxx6aoCgB0R1YoiWQymb+/v7+/v7kLAcqW9evXG3YzGLlc\nvn79eoIdALMzPNgpHyeFX754PSo+OSUtU27n6ORaoXpd/1qVHCyMWB4AFJPk5OQzZ84YdnaK\nQqE4d+5cUlKSq6ur0QsDAN0ZEOxSw9YtWvDLqn/2XErMfnad3L5Kix4DR7w9YeSLXnQGAihF\noqKiinLOsVKpjIyMJNgBMC89r4pNPDC9Q80X+k/7ZaeGVCeEUDyKOLp27uh2tRsN//VaplEq\nBIDikJiYWMQ9cC8KAGanT7DLOvdZ8EufhcbmFN407dLvI198ZWW0wXUBQPHy8PAo4h7Kly9v\nlEoAwGB6BLu7P70763S6zs2VsRvHT16v9/VlAGAW3t7ecrnhU3vKZLLKlSsbsR4AMIDuJ8JF\nrfvzQFa+v+XlqjVrG9CoVlVPZ3sr5eO0pJjbl88cOnTu3pOb6yT98+umlL7DnYxYLwCYhqOj\nY/PmzU+cOGHYVbHNmjVzdnY2RWEAoDvdg93VK1fyTit2bzdt+bKPevraP9tImXxx7czXx8w/\nmiyEECLz1KkwMby1MQoFAFPr16/fsWPHDNhQoVD07dvX6PUAgL50H3d4cp9rzyHLtszSkOqE\nEDLn+gO/3fHbkAq5f3MuMYDSY/To0R4eHvoOyMpkMnd39zFjxpioKgDQne7/fnl7e6sWbLv0\n7+GoraVzz6E9c4dfK1SooK0lAJQgjo6OX331lb5DsUql8quvvnJy4qwTAOane7CrHRCgmp/J\n2s6ukK1kjo7lhBBC2L7wQh2DSwOAYjdy5Eh9+95GjBgxatQoE9UDAHrR/Rw7225vj6q+cl64\nMuXI4YsiuL6WpnePHYsRQgi3V8f05zeslMTFxR0/fjw2NjY1NdXLy8vX17dx48YymczcdQHG\ntHDhQiHEkiVLtN9eTLV29OjRixYtKsbqAEArpR7ST37eyl4I4dhmzqmUgho9urS4i7sQQu77\n+rYEffZuLrt371a9Fap55/E8hULx999/BwYGPn/uUcWKFSdNmnT//n1z1wgY2YoVKzw9PYUQ\nzx/2qkfKly+/fPlyc5cJAE+RKTXcQicnIy39+ftKyOSWVtaZ19Z+OnriosOplYNff/f1fsGt\nGtas7GorF8rMlJibF47t+Wf5gp+23sis2n36sqUfdKpUGm4rFhISEhwcLISIiory8vIydzkl\nTnh4+MCBA48dO6ax90ImkymVynLlys2dO5eTxyExaWlpP//88z///HP48OG8g18ulwcEBPTt\n2/eNN94oV66ceSsEgGdoDHbr+sn6ry9oC7mlpSw7O+epR6xkOVk5T+9HbmXv5OpeYfCKK98G\nGa1akyDYaXH06NHu3bsnJSVpP51cFe/eeuutH374gZFZgymVypMnT+7bty86OjopKcnDw6N6\n9erBwcF16nCqqpmlp6dHRkYmJia6ublVrlzZzs7O3BUBgGb6d6kpsp/tzFNkZ2lolvXowf1H\nD+4mGVYXSoBbt2517do1OTm50IsEVT8PfvzxRw8Pj88++6xYqpOUrKyspUuXzpkzJyoqSvVI\n/v7RunXrfvbZZ3379iU0m4udnV3NmjXNXQUAFM7w++dA2lQTruqS6vKbNWvWnj17TFeVJF2/\nfr1hw4Zjx46NiYnJezD/23716tX+/fsHBQXFx8ebo0AAQKlBsINmK1euPHv2rL4TeslkssmT\nJxtwR6Yy69ixYy1atLh27Zp4Oszlp3o8NDS0adOm4eHhxVkeAKB00TgUW73D8OFFOSVYqVDk\n5GRlZjxKS2lQvgj7gRnNnTtX+1wPGikUinPnzu3du7dTp04mKkxKIiIievTokZKSosv7rFQq\nIyMje/ToceTIEc7ZBwBopDHYNXvn11+LuQ6UKOHh4WFhYYZtK5PJNm7cSLDTxciRIxMSEnRP\nzwqFIiws7KOPPlJNtAYAwDMYioUGBw8eNOPmZcSuXbtCQkIMGLZesmTJzZs3TVESAKC0I9hB\ng+joaIO3VSqVERERRixGqn766Sd9bzavkpWVtWzZMqPXAwCQAIIdNEhNTS3K5g8fPjRWJVKV\nkZGxfft2w64yUQ12G70kAIAEaJnH7v6FkPOxRd1/hYadGngWdScobhUqVCjK5uXLc81MIa5d\nu5aenm7Ytkql8sqVKxkZGba2tsatCgBQ2mkJdgdmBhd8/wld9f1bua5fUXeC4ubj42PwtnK5\n3M/Pz4jFSFJRBruFEEqlMiYmpigfEwBAkhiKhQbt27e3sbExbFuFQtG1a1fj1iM9WVkabtei\nl8zMTKNUAgCQEoIdNHBwcOjRo4dhp/ZbWFj07dvX6CVJTKVKlYq4B29vb6NUAgCQEoIdNJsx\nY4YBW8lkspEjR9aoUcPY5UiNn5+fhYWFYdvKZLJKlSoxRzEA4HlazrGr9uLgwQWcm333v1X/\n3c1drhTwSscaBe6mRbUiFAczql+//oQJE7799lvdN5HL5R4eHp999pnpqpIMFxeXNm3aHDx4\nMCcnx4DNe/XqZfSSAAASoCXYNR+/cmUBq9b1exLsGr75y8rX6DuQoq+++urChQu7d+/WpbGF\nhYWFhcWmTZsqVqxo6sKkYdiwYfv37zdgQ6VSOXToUKPXAwCQAIZiUSBLS8tNmzYNHDhQCKH9\nfDuZTObq6rp3795WrVoVV3Wl3rBhw2rXrq3viYxyubxnz56tW7c2UVUAgFKNYAdt7Ozs/vzz\nz59//tnT01M8F+9Uf8pkskGDBp09ezYwMNA8VZZOlpaWy5Yts7S0Tekb8QAAIABJREFU1D3b\nyeVyZ2fnBQsWmLQwAEDpRbBDIWQy2euvv37jxo0VK1b06tXL09NTFUTs7e0bNmw4bdq08+fP\nr1y5kos0DRAYGPjTTz8plUpdsp1cLreystq4cWP16tVNXxoAoFTSco4d8ISDg8Nrr7322muv\nCSGys7MzMjK4KtMoXnvtNTc3t0GDBj169EipVGppWaFChc2bNzdr1qzYagMAlDr02EFvlpaW\npDoj6tmz58WLFwcPHiyTyYQQcrlctSCTyVQ9edbW1pMmTQoLCyPVAQC0o8cOML9q1ar98ccf\ns2bN2rx5c2hoaERERHx8fKVKlapWrdq5c+fu3btz+10AgC4IdkBJUa1atXHjxo0bN87chQAA\nSiuGYgEAACRCS49dSkTY3WTNqyJSniynRV0OC7MraCfOVf2rOBlaHAAAAHSnJdjtmtig//rC\n93Do4xYNPi5wbd+/lev6GVAXAAAA9MRQLAAAgEQQ7AAAACSCYAcAACARWs6xq9i4S5e0ou6/\nccWi7gEAAAA60RLs2kzbsaP4CgEAAEDRMBQLAAAgEQQ7AAAAiTDFLcXi108c8vNl1XLbj3dM\na2OC5wAAAMAzTBHsMm4f2bnzmGq53OsmeAIAAAA8j6FYAAAAiSDYAQAASATBDgAAQCIIdgAA\nABJBsAMAAJAIgh0AAIBEEOwAAAAkgmAHAAAgEQQ7AAAAiSDYAQAASISWW4qlRITdTTZkn7H3\n0g0tBwAAAIbSEux2TWzQf33xVQIAAIAiYSgWAABAIgh2AAAAEkGwAwAAkAgt59hVbNylS1pR\n99+4YlH3AAAAAJ1oCXZtpu3YUXyFAAAAoGgYigUAAJAIgh0AAIBEEOwAAAAkgmAHAAAgEQQ7\nAAAAiSDYAQAASATBDgAAQCIIdgAAABJBsAMAAJAIgh0AAIBEaLml2DNurxj11too1bL/O2vm\n9XAxUUkAAAAwhO7B7sLuP3buzBJCCGHnOZ5UBwAAUMLoPhTr7OysXrSxsTFJMQAAADCc7sGu\nVa9eFXIXH5w4ccM05QAAAMBQugc7m5fmrf+0rYdMCCHOfT36m7BHJisKAAAA+tPnqliXwJmh\n106v+WxIYJX0ve+18O/y7rerQ05ejbyfmJJWkIwck5UOAACA/HS/eOLQ7M4fb09OSUlNTU17\nbGkp0m7vWjh518JCtur7t3Jdv6KVCAAAAF3oHuxizuwOPWTCSgAAAFAkTFAMAAAgEQQ7AAAA\nidB9KLa8f8fOGU6O5exsrS0t5boGwubVDSoLAAAA+tI92LWbsaedCQuBmlKpPH369K5duyIj\nI+Pi4lxcXCpXrty+ffvAwEALCwtzVwcAAEou3YMdTC4nJ+f333+fOXPmnTt3hBAymUwulysU\nCqVSKYTw8PCYOnXqO++8Y2tra+5KAQBAScQ5diVFdHR0QEDAyJEjIyIiVI8olcqcnBxVqhNC\nJCQkvPfee/7+/pcuXTJfmQAAoOQqQo+d4mHUhROnr0XFJyanyx3c3CtU82/RrJablfGKKzuu\nX7/erl27e/fuCSEUCoXGNqqEd/v27ZYtW27fvr1NmzbFWiIAACjxDAl2WdEHl8+bs2DFrssP\nsp9eI3OoEjBg3EfT3u7m62CU8sqEBw8edOvWLTY2Nq9zTguFQvHo0aNevXqdOnWqevXqpq8O\nAACUGvoOxSqid37YrkHbMfO3PZfqhBDKhxGHV7zfvUGz4auuZRinwDJgypQpN27cKKij7nkK\nheLBgwcjR440aVUAAKDU0S/YpZ+Y0bXXl0cSC2t25fch7YeuizG8rLLj0qVLy5cv16WvLj+F\nQrFv377t27ebqCoAAFAa6RPsFOc+Hzbr3GPdGsesGzHil3sG1VSmGJDqVORy+dKlS41eDwAA\nKL30OMfu4ea5i688iSByR5+AoHZN6/pUcHWwVmSkJkReP39kz75z9zNzG6TtnP3NsdfmtmRG\nFW02btxo2IYKhWLHjh3p6el2dnbGLQkAAJRSuqcu5X9btyXnLts3eXv5n1+9Urvcs42y7x9Z\n/M6QyetuZQshRPjatcfntmxtnEql6NGjRzdv3jR484yMjOvXrzds2NCIJQEAgNJL96HYq0eP\nJqmWHP+36N8fNKQ6IYSlZ8D4tTu/CLBR/Rnx3393il6jdEVHR5t9DwAAQDJ0D3YxMbkXQ9h0\nHTmoorZd+o1+q0tuT2DeZLvQJDv7+SuLi3sPAABAMnQPdikpKaoFd2/vQm5p5VSrVm7yS0ws\n7AraMs3Ly8vsewAAAJKhe7ArVy537DUxNjZLe1Plgwe5IdDBgYmKtXBycqpUqZJMJjNsc0tL\nSz8/P+OWBAAASi/dg52np6dqIWPHX1tStLV8uOOvf1Oe2QgF6NWrl2EbyuXy9u3bOzk5Gbce\nAABQeuke7Oq1bOmoWkpY/far808ma5x8TZl8cuGro1bkno7n2LJl3SKXKG3Dhg0zbB47hULx\n2muvGbscAABQiuk+3YlF+25dHP5Y91AIIWK3TWrpu6L7wH7BrRrW9HYvZ618nJoQdf38kV3r\n1m4LS1TfG8uhc9d2zGKnXUBAQO/evTdv3qz7LcWEEHK53N/f/9VXXzVdYQAAoNSR6dFdlH18\nUu2W82/pvG/fySeuzmtmYVBdxSgkJCQ4OFgIERUVZZZrEe7evdusWbPExMScnBxd2svlcmtr\n64MHDzZt2tTUtQEAgFJEn1uKWbaYsezdWjp2wVnVnbxieslPdSVB1apVN2zYYG1tLZcX/nHI\n5XKZTPbbb7+R6gAAwDP0CXZCOLWf9+9vw2oVMtuJEHZ1R67c+lVbR4PLKmsCAwOPHDni7e0t\nhNB+kayTk9OOHTsGDBhQXKUBAIBSQ79gJ4Sl36DfThz9beJLvvaaGzjU7PreymPHlg2oQW+d\nXho1ahQWFvbRRx/Z2NgIIeRyuaoDL2/BwsJizJgxly9f7tSpk5lrBQAAJZI+59g9JSf51rF9\nIQeOX7obm5CULnNwdfesWr9Vu6D2LXyc9E2L5mX2c+yekZycvGPHjl27dv2fvfsOiOL42wA+\nuxTpSK8iKhZUUJREBRVRUTCi2Av2Ek3sJcZYEktiosbyGmOM0ajRGI2KFLuooIDYsEWsCAJH\nkypVuNt9/zjlh0rZ22tw93z+Om53dp/jgPuyszPz8uXLrKwsExOTpk2benl5+fv7Y/YYAAAA\nqEW1hV3Zw1MHr6UTQnTafjbOw0b8ZNLF3eGJhBBi6j5iaEdjRYaUr/pW2AEAAADwU+1QiGeH\nFk5f95QQ0urb+5WF3a3fpk8/Tggh7dZ2V6nCDgAAAEA1VNtrmpaWJn6AJeYBAAAAGoxqC7uS\nkhLxgxdHd4SmlSsyDwAAAADwVG1XrIGBASGvCSHk2e7BDn+bWlub6NDFGW+3PtvS32mfFsfj\n+21//ouvLIICAAAAQO2qLezat29PSMzbL0SluYLE3Cpby3OTE3Kra1ad9CJp0gEAAAAAZ9V2\nxdqMCPTWVnQSAAAAAJBK9VPO2c/Y9esgO8wwDAAAANCA1LDyq4bTtBMPPg3aszfkyv2krNel\nQpbkvbj9Io8QQnRs27WzqXNVsbdamMooKAAAAADUjvvKE8eGUyPezmP36L8VbeQZSrEwQTEA\nAACohoa1+hcAAAAA1KiGrthquE375ZdehBBi1tVaXmkAAAAAgDfuhV0L39mz5RgEAAAAAKSD\nrlgAAAAAFYHCDgAAAEBFoLADAAAAUBEo7AAAAABUBAo7AAAAABWBwg4AAABARaCwAwAAAFAR\nKOwAAAAAVAT3wi525+LtJ//LEcoxDAAAAADwx72wSw3fNMffxca+89BF20Luv6qQYygAAAAA\nkJykXbEVmXEnNs8L6GBn6xYwb+uJuKxyucQCAAAAAEnxvceuIvtuyLYFQzvb2boMmvPz8VsZ\nKPAAAAAAlEvawRPCnP/Ctn81/BN7m3afzdrw7420MpnEAgAAAABJcS/s+qwK3rF8cv/2FtrV\nbRXlxp/e8fWoLg42bf1mrj98TYACDwAAAECxKJZlJWvBFL6IORMSEhIScjrqWb6ohr1o45Z9\nR06cOHH8EE8HXalTylV4eLiPjw8hRCAQ2NraKjsOAAAAAE+SF3b/I8x9dOVkaEho6MkL11+8\nrr7Eo4ycvEfPWLB4xmctDSneKeUKhR0AAACoBmkKu/8R5j+PvXj+/PnzFy5E3Ews+LjGo007\nTVq97ccvPS3r34zIKOwAAABANcimztJs7NR92Jdrft3311+/rxnvZvrRUZncuD/n9HTtszoq\nVyYnBAAAAIAPaUp/CKYoKebk8aDgkNBz1xLya1mZgsmMWOXrp30t+hsXGZwWAAAAAN7Dv8Ji\nCl9EhR07euxY0JmbNUxyomHm0sul4lbk44LK7t7iG9/N2zX+0pf2vM8LAAAAANWSuCuWeZ0Q\ncXD9rAB3O4sWXoFfbz9RTVVH6Tt6T1l76Fqy4H745fjURye+69eksoKsuHzoWLq0saEBePPm\nTXBw8OTJk11cXCwsLIyNjZ2dnYcMGfLnn3/m5qJLHgAAQPa4D554dfOv3XuPHQ06fyfzTU37\naFl1Hjxp+vRpY32c3h8Dywj+HtpxXEg2IYQQ46kX8nf3lSK0bGHwhMyxLHvw4MHly5enpKTQ\nNM0wTOUm8ZcGBgZLly5duHChrm49nwwHAACgIeF+xS5y/cRlv4VVX9XRxq36f7HheJwg5dbR\nn2b0c/poZhPaLvDrCQ5vvygqKuIbF+q/0tLSwMDACRMmCAQCQkjVqq7yy+Li4hUrVnh6eqam\npionJQAAgCqSchSDjn23oZOnfz51lFdTvTp2dXR0JCSZEEJ0dHSkOyvUWxUVFQMGDIiIiCAf\nlXRVia8T3717t0uXLjdv3sSFUgAAAJngWdhpmLb/bPy06dPH+7Uz1eDWpKCg4O0jS0tLfmeF\nem/u3Lniqo4LlmXT09MDAgKioqK0tatdqg4AAAAkIGFhR+k38xo5dfr0ycO62TaSrKn9uD1X\ne5USQoihYzvJmkLDEBsb+/vvv0vUhGXZmzdv7tixY/78+XJKBQAAoD64D56I/f3ru82mjfVp\naVRPlwbjC4MnZKV3796RkZG19MBWi6bpxo0bp6Sk6OnV1Z0PAAAAteI+eEKU9/DktgVj/QfO\nOlLX/e4lxybZWltbW1t3WXtXunjQYKSnp0dEREha1RFCGIbJzc09f/68PFIBAACoFe6FXfqt\nU2KRz+oa1KpnYViRmZmZmXkj/GqWdPmgoThz5gzvdYdpmj516pRs8wAAAKgh2awV+4GMhw9z\nxI+eP0+Qxwmg/nn27JkSmwMAAACpa/BE2s3gG4K3j2+mvX1Q+OhicPDj6hswbwoED87s3Hb5\n7aWbvLw8mcSEei8jI+ODuYi5YxhGPOkdAAAASKP2wi5m/ZARxz98MvnQ7CGHOB6ed+ccNDR6\nenrSvNv6+voyDAMAAKCepJyguC7Ozs5cd63IuHUq5FxU3NO0nMJyTUNT2xYdPfsP9u9iJ8H8\nZmxhQuTJsMs3Hr4Q5BZV0DpG5k1aunb1GTSgix3mRJYzW1tb3oWdhoaGnZ2dbPMAAACoodoL\nO3tXd5uTt9JrXBu2Dga+syY057Rnwc2dyzecTn5DCCG0to7Wm7yM57fOPr916ZLvknVffmrM\n5Rilz46tW3vgXr64uKBpuqI4T/D4huDxjYvnei/8YZ6HuYrN01K/fPrpp7zbikSirl27yjAM\nAACAeqp98ETXb2+mpMef2fH1mK72Ei7Wrtt0wOrQA1PtueybeXrDxtPJbyhj15FLf/n7+LF/\njx7/Z8eKMW6NqfKUsxvXn8rgcIyCq/+3+q97+Sxl2mH4ok1/Hj524viRfdu+GdPJnCbl6Zc2\nbzmTLdkLAMl4eXkZGhpSFM/qedCgQbLNAwAAoIY4T1DM/DtUY9QJQghxGLv9/0bU3G9G0Vq6\nJnbObq5NDLgNuS2/u23at+H5xC5gw/9NaVOl47X88Z/zlgQLiHHvVbvnd6p9oYukv2fOPZJG\nNJ3G/rxhdPMq1yErnu6b/1VQCkscJ+zcNry66YcxQbGsLF26dP369ZK2omm6e/fukZGR8ogE\nAACgVjjfY0fT78o0Q+c+AQFtZJZAdC8iJp8Qut2gIW3ev51Ou80Q/3ahOx8WxETcmdWpq1Yt\nB8l9kpCvTRNh+wGfNX//JWm16t7FIigliyS/fMkSW/TGytHSpUv/+OOP/Px87mNjKYpiWXbj\nxo1yDQYAAKAmuA+ecPKbNcuaEEJsPzGRZYInd++VEEIc3dw+PqyJW8em5GFi2Z07j0hX11oO\nYtr/28P92YqycvrjQRLGRsaEZBHCMCwhKOzkqHHjxocPH/bz8xOXa1yasCy7bt06ae7PAwAA\ngErcC7uOU7dvl0OA0pTUHEKIdlNH62q22jg21SaJ5QWpKYXE1bCOQ1FaOtX015YnJgoIIcSh\nWTO5zMYMVfn4+Pzxxx+ff/45y7IikaiWPcXF36xZs5YuXaqweAAAAKqt2sJOVFZUKiSEEKLR\nSF9Xi/rgSYmOr2ugo1HbDlmvsgghxMzMtNrNZmamhGSQzMxMQuoq7KrDZpw+Hl1CiHb7/r1w\n+5xCTJ482dHRccyYMZmZmTVduqMoqlGjRps2bfryyy8VnxAAAEBVVVvYnRhn+HZeYs8tKVHz\n7T94UhLDjrLHhte2Q2lpKSGE6OpUP+q2kY4OIYSUlpVKfGpCSNnjgxsOPion2i1GTfc1f3/b\nlStXEhMTCSHPnz+3tLTMysKqtjLj7e2dkJCwefPmnTt3pqWlfbDV0NBw7Nix3377LYaqAAAA\nyJacJyiuW0V5BSGEaGpWn0RLS6vKXpJgC+7sX7s+6Hk5ZeY5d9nwZh9eNzx//vzZs2fFj21s\nbFDYyZa+vv7KlStXrFhx+/btO3fuCAQCoVBoZWXVtm3bHj16aGtLMO00AAAAcKT0wk5LW1y5\nVVRfub19XruRZIVAeeqFLat3RGeKNCx7zlu7oKfFx4MmdHV1jYyMxKfIz8+XNDdwQVGUu7u7\nu7u7soMAAACoBaUXdnp6eoTkvuuR/UhpSQkhhOjp6nE+Iltw/6/vfzz+pJjotRy6dOXEjo2r\nHQq7fPny5cuXkyrz2AEAAAA0aNUWdo7eEycaEEIIadVW/+MnJfGJYx07WFpZUSSVzcnOZknL\njyow9tWrHEIIsbapbsxsNUQZkZtWbI3KEmlYd5+1cn7fJujyAwAAAHVRbWHnPmvfPm5PSk/H\noakluZ1ZkZQkIN0+WoAsKSFBRAgxb9qUyxU7Nufa/63cEpXF6LcetmzFBBdjzFoHAAAAakT5\nU7s5uXUyIoSk3Lr18egFwc2b6YQQw0/cW9V9oJIHu1ZujMhkDNtP/GHtRFR1AAAAoG64F3bR\n22ZvPHoj7Y3ME7Tv08uSEPZZ2NE7799nV3Tt8KmXhBDrPn3a15mzMHbnplOpQg07/2UrhzX/\neP0JAAAAAFXHvbBLv/LrkpFdmlg795+x7u+YlBKZRdBoNWKipyEhr85t+O7PyBf5FSxhK/Ke\nX/r9262ReYQY95g4rFWVuUqe7/980KBBgwatiSj/35OiJ0d3R+QSYjFg1vg22qJqMdwWuQIA\nAABooCQdFcvkPz6/a/n5XSsNnXoNHz9hwvhhXs0MpO30NO4xd1nyq7VHnj4O3jQ/eLOmtoao\nXMgSQijDtmOXz/E0rvMId8+dzyKEkFdhy0aG1bAP3ePr4K88pYwKAAAAUG/xvceOKXx+ae93\nk7xbWDfzmrBiT/jT14w0MXTbBa7f/v3n/t1aWZvo0gytZ2rXxnPwF+u2rxvVhku3aoVQ4gmM\nAQAAAFRM9Ut5VufYcKq2JcV07bsODpwwYcLofm1Nal0ctt6pnMdOIBBgkSsAAABouLhfsfP5\n/tTvKyb7OJtW33tbmhp7eP2XA9pZN/lk+OJfQu9n4xIaAAAAgEJxv2L3jjDvUUTI8eNBx4PP\n382oeYyslnkHvzETJ06fPNSlsZQZ5QxX7AAAAEA1SF7YVWIKE2NOHT8eFBR0Oja5uIajDDvK\nHhvOO51CoLADAAAA1SDFBMW0YbPuoxdv+Tfm5avUm8Hbv5nQx9lMS3bJAAAAAEAiMll5QtfW\nffCsdfvD4zNfPb/464xulg1r9AQAAACASpB0HrvqMUVJsedPnT599uy5y3GpNXXLAgAAAIAc\nSVXYlWXcDQ8NDgkJDr14L0vma40BAAAAgCR4FHbCvCdXT4UEBweHnI19WcvVOdq4pffQge2k\nCAcAAAAA3HEv7EpTr589ERwcHHzyyuNcYS07alu6+Y0aFxg4xr+LDZdVIwAAAABAFrgXdqfm\nd61t5QlC6TfrHjA2MDBwZD/nBrb2BACAzGRnZ4eFhV25ciUtLS0vL8/KysrBwaF///4+Pj66\nurrKTgcAKk76wROaZu18RgYGBgYO8XDQo2QQCQCgQUpPT1+9evXu3btFIhFN04QQhmE0NDQY\nhtmxY4ehoeE333wzb948PT09ZScFAJXFv7DTte8yaHRg4LjRvh0sMH0dAKi5c+fOjRw5srCw\nUDzrO8Mw4udFIpH4QVFR0bJly/bu3Xvy5MlWrVopLSgAqDSJCzsN41a9h40NHDduqFcLQ5nM\nggcA0MAdOnRo/PjxhJBa1vIRb0pISPj0008jIiI6duyouHwAoDa4F3YmLkPmegaOGzPwE+tG\ncgwEANCwxMbGTp48mVS5SlcLhmEKCwv9/f1v375taWkp/3QAoF64F3Z9vgvqI8cgAAANkFAo\nnDRpklAo5FLViTEMIxAIlixZsm/fPnlGAwB1VG1hJyorKhVPaKLRSF9Xi/rgSYmOr2uggyGy\nAKCq9u/f/+TJE0lbsSz7119/LVq0yMXFRR6pAEBtVXuX3Ilxhm/5/Cr4+ElJjDuhuNcCAKBo\ne/fuFQ+AlRTLsgcOHJB5HgBQcxj+AADAU3Z29rVr17h3wlZFUVRwcLDMIwGAmkNhBwDA05Mn\nT/hVdYQQlmUTEhIqKipkGwkA1BwKOwAAntLS0qRpzjBMenq6rMIAAJAaBk84ek+caEAIIaRV\nW/2Pn5TEJ448gwEA1Hf87q6rSkMDo8sAQJaqLezcZ1UzCL/aJwEA1Jetra00zWmatrKyklUY\nAAAii7ViQS6SkpLCw8OfPn3avHnzXr16tWnTRtmJAOBDzs7OmpqaQqHkM0ERQtN027ZtNTXx\nRxgAZIn735Tonwb+GEUIIU3H7/x1lH1tu5Ycm+Q0+yxDSNNZZ6+vxLI5EkhJSfH39//vv/8q\n15cUo2m6WbNmR44c6dy5s7KyAcAHGjdu7OXldfnyZR5DKBiGGTJkiDxSAYA6436DSPqtU2KR\nz4rq2FXPwrAiMzMzM/NG+NUs6fKplX79+jk4ONy7d++Dqo4QwjBMQkKCu7u7i4vLx1sBQFmm\nT5/Ob2CslpbWxIkTZZ4HANScXEbFZjx8mCN+9Px5gjxOoHLKy8utra0vXLhQ557//fefkZFR\nSkqKAlIBQJ1GjhzZqVMnHqMoZs6c2aJFC3lEAgB1VntXbNrN4Bvvlp64+W5Yf+Gji8HBj6tv\nwLwpEDw4s3PbZVb8dV5enkxiqrqmTZtmZmZy3LmkpKRVq1YFBQXa2tpyTQUAdaIo6sCBA126\ndCkpKeF46Y6m6datW//www/yzgYAaqj2wi5m/ZARxz98MvnQ7CGHOB6eZVk+qdTLZ599lpGR\nIVGTsrIyFxcXHitUAoDMtW3b9ujRowEBAUKhsM47JSiKsrKyCgsLMzQ0VEw8AFArcp6g2NnZ\nWb4naPAyMzNPnz7No+HTp08vXbok8zwAwIOvr29UVJR47hKKoqrdR9xd27Vr17i4OHTCAoCc\n1F7Y2bu62zTif3AD31kTmvNvrhYGDx7Mu+24ceNkmAQApOHu7v7kyZNVq1YZGLydyp2maQ0N\njcrb7+zt7ffu3RsVFWVtba28mACg4qi6OktFeY8uHN7/119/B8emlkpwYN2mA5bu3f+tt7l0\n+RQgPDzcx8eHECIQCKScbpQHbW1t3otFUhTFe51KAJCT8vLyy5cvR0RECASCvLw8S0tLR0dH\nX19fd3f3mi7mAQDISp2F3TvMv0M1Rp0ghBCHsdv/b4RdzUektXRN7JzdXJsYNIyFaJVY2JWW\nlurp6UlzhOPHjw8dOlRWeQAAAKBB4zxB8f9G8xs69wkIwDoIshAdHS3lEaKiolDYAQAAgBj3\nlSec/GbNsiaEENtPTOQWR83Ex8dLeYSkpCRZBAEAAABVwL2w6zh1+3Y5BlFLbdu2lfIIjo6O\nsggCAAAAqoDXbXBvUiJ2fzf963/fn3stbfe4nsNnfvdnVDrPsQDqx9PTU8ojeHh4yCQJAAAA\nqACJC7vcaz/7t3Lynr5md+iDnPe25CfHXT3++5qpPVo4D/oxGitOcKCrq6ulpcW7OUVRw4cP\nl2EeAAAAaNAkK+yKb6zu1/erk8nlhBBSVFT03sbCwkLxg9KEsGV9PWec5rpEllrr2LEj77bi\n2VABAAAAxCQp7MpvfDd+ze2Sd1/WWNgRQkjZo10Tvjz0Stp4qi84OJh32/3798swCQAAADR0\nEhR2+f+u+/VplelwS0vfn7BY08jUUKPK1zlBi7+/WseyiWBra9uvXz8eDVu0aMGvIQAAAKgq\n7oVd2emg02Xih5R5z6+P3Ev5c+B7O/Ta/CIv79mZHwKcdN4+k37kUAQquzqdO3fO3FyyFTq0\ntbUfPHggpzwAAADQQHEv7B7cvv12tKvN5L9O/TTS1UL7o300DJ18lwVd2u73dqa7zKtXn8ki\npcpLTk42MzPjuLOOjs6TJ090dXXlGgkAAAAaHO6FXVZWlviBTr8hvga17Eg1mTjjs7cX7QQC\nAf9sakRXVzc7O9vLy6vOPdu0aVNUVITp6wAAAOBjPOaxMzI2rmMda01z88biRx/ehwe1iYiI\nSEhIcHZ21tDQ+GATRVEODg7R0dGPHj36eCsAAAAAkWTlCTOXr7DuAAAgAElEQVQzM0IEhJCs\nqKinpEermvdk4y9ezqjSCLhr3ry5eJ2x+Pj48PDwhw8ftmzZskePHl26dFF2NAAAAKjvuBd2\nbdq1o4mAIYTErRu/vFvY2l6W1V3uEwrOLhn30z3xF7Szc2uZxFQ/bdu2lX7BMQAAAFAr3Au7\nxj79PqXOx7KEkKIb67ydjvUbOdLPs0PLJhbGelrsm6K8jKTHd66ePno8Mqmy+9Wtb19TeaQG\nAAAAgI9QLMty3TfvyBDH0cGvOR9b/7MDSSfHSTaPhxKEh4f7+PgQQgQCga2trbLjAAAAAPAk\nyeAJk5E//+zL+QqcUa91P4+p91UdAAAAgMqQaFQs1WL6kdC1XhZ1N2rc5eug43PbYPQmAAAA\ngMJIOt2JkeeKi3cv/Dypm+3H0xMTQgjRMOs4YnVY3NWf+uDuOgAAAABF4j54opKGbe9Fe2Pm\nbnwUExEZc/txyqvc/GJRIyMTM9uWbt16evdws8WSCAAAAACKx6OwE9Myd/Ya7uw1XJZhAAAA\nAIA/HitPAAAAAEB9xLuwe5MWd/bvX39YPGPCmOGDBwzfeu/dhoTrVwTlsgmnnkJCQnr37m1s\nbKypqUlRlIaGhoGBQbdu3Xbv3q3saAAAAFCv8eiKLX74z5qvv999Nj5XVPmcmc2St48KL33r\nNffFsI3/7JntbiybjGrj4sWLkyZNSk1NrfokwzDFxcWxsbGxsbFfffXVL7/8Mm7cOGUlBAAA\ngPpMwit2wud7R7bvNHbDqapV3Xuys7NJ2fPjc3r3+zGuTPp86mPVqlU+Pj4fVHUfyM/PHz9+\nPAo7AAAAqJZEhV3B6Zl9ph1NqrWj9dWrV4QQQgpvrBjz3S2hFNHUyeLFi1evXs1xFZC///57\n4MCB8o4EAAAADY4EhZ3wxrq5fyYzlV9rmTi2b/bRZHU5OTlvHzFPt/90jPsCZOrr6NGjmzZt\nkqjJqVOn1q5dK6c8AAAA0EBxL+zYyL37E8RXlLRbjPj5fEJuduKDDd4f7tZ/S+Tm/hbixyWn\ng86+kU1OVTZt2jQerdasWfP6NepmAAAA+B/uhV18REQmIYQQ3d6bw48s8mluUG1b2tR9wb41\nfcSDMkrj4h7JIKQqW716Nb/6TCgU8qsIAQAAQFVxL+zS0tLELfpMmexI1bqrdbdujuJH6enp\nfJOpif379/Nue+bMGRkmAQAAgIaOe2FXViYe5NrIxESvrn01Nd9OoyIUYvhE7ZKSkni3LSoq\nevr0qeyyAAAAQMPGvbAzNRUPlCi9e7euWiI9MvLZ+42gWs+ePeM4ErYm165dk1UYAAAAaOi4\nF3YtW7USP3iwZd4vj0pr3K8kfueUVZffznLn5NRCmnSq7vHjx1Ie4cmTJzJJAgCgwqKiohYt\nWtShQwdzc3Oapi0sLDp37rx06dKbN28qOxqAjHFfecKyTx8XcuUBIST77NwOLf6dMH1Yt3Zp\nGeKNwswHkeGvBU9uXTq2/2BE8ruhsG1797aWeWQVYmJiIuURjI2xvAcAQI1u3bq1aNGiK1eu\nUBRFCBF3kmRnZ+fk5MTFxa1fv75///4///xz+/btlZ0UQDYoCboC03b1bj7jMvf5S7S7b392\ndZYDr1wKFB4e7uPjQwgRCAS2traKPPXr16+lrMxCQ0P9/f0JIYWFhTExMWlpaa9evbKysrK3\nt/fw8NDV1ZVRUgCAhuePP/6YNWuWSCRiGKamfWia1tLS+vPPP8eOHavIbAByIslasbaTN6/c\n47HiRs3dsFXpdPpmy/R6X9Upl5GRkY6OzrthKRKjKMrHxycmJmbdunUXLlwoL39vTRAdHR1/\nf//ly5d36NBBFmEBABqSrVu3LliwgKbpWqo6QgjDMBUVFePGjSstLZ06darC4gHIiURLiml1\nXB78zxcu+nXvqddu+t+h37lr842lPrp06cK7rYODw6RJkzw9Pc+cOfNBVUcIKSsrO378uJub\n2xdffPHmDSaKBgA1cv78+UWLFlEUVXtVJybe54svvsBwNFABEhV2hBCbwTuu3zy81N/ZuIap\n7OjGbf2/Pnzr5q6hdrVPdgeEEEIkXUysKqFQeOTIEfLur9LHGIZhWXbnzp3e3t65ubm8TwQA\n0IBUVFR88cUX5N0ddVywLCsSiWbOnMmlEASozyS5x64qtij51pWIqOv3EzJy8l6X0brGjc1s\nW3Xs4uHVo7N9nfPc1StKvMdOzNPTMyYmRtJW2traQqGQ498giqK8vb3PnTtXOcMgAICq2rVr\n14wZM/i1PXz48KhRo2SbB0CR+BZ2KkTphV1RUZGdnZ1EC4vVeddItb777rtVq1ZJ2goAoGHx\n9va+cuUKjz+SNE0PGjToxIkT8kgFoBiSdsWC7BkYGFy7do37CFYNDQ3xuH2JUBS1YcMGrPEG\nAKqtsLDw6tWr/HpUGYY5e/ZsRUWFzFMBKEy1hZ2orEhWykSKfkUNUtu2bZOTkx0dHevc09TU\ndPjw4Tz+ZrEsW1pa+vvvv/PJBwDQQCQlJYlE/D95ysrKMjIyZJgHQMGqLexOjDOUlXG4os2R\nubl5YmLizp07zczMqt3BwMBgxYoV2dnZV65c4XcKmqaDgoKkyAgAUN9lZmZKeQT0bECDhlvp\n65cZM2bMmDEjMTHxt99+e/bsWUZGhrm5uaOj49SpUzt27EgISUxM5P1Hh2GYBw8eFBcX6+tz\nmLIGAKABkv7vG/5CQoOGwq4+atas2YYNG6rdlJaWJuXBBQJBq3fr/gIAqBg7OzulHwFAiTB4\nooEpLCxU+hEAAOqtJk2a2Nra8hhhRgihKKpNmzaNGzeWeSoAhan2ip2j98SJBrI5/ieOsjkO\nvGVlZSXlEaytrWWSBACgHqIoavDgwTt37uTRlmXZgIAAmUcCUCTMY6f8eewkkpeXZ2Zmxvtd\n09XVLSws1NDQkG0qAID6Iz4+3sXFhcfsAdra2k+fPm3atKk8UgEoBrpiGxgTE5OuXbvSNJ83\njqZpX19fVHUAoNratm07bdo0Hg3nz5+Pqg4aOhR2Dc/o0aN5z72JpXIAQB1s3rzZxcWF+//A\nFEV169ZtzZo1ck0FoAC8u2LfpMVdvnzt9p37TwQ5BQXE+8dj8zsQQghJuH5Fx62nnbYsU8pV\nw+qKJYSUlZWZmZmVlJRI2tDCwiIjI4Pf1T4AgIYlOTm5X79+T5484bJzhw4dzp07J/1NzABK\nx+MzvvjhP18PbGfr0Nlv3OwVm3YdOHw89EzE47y3Wwsvfevl1G749lsFMs0J//PkyZPS0lIe\nDfPz81NTU2WeBwCgHnJwcLh+/fqQIUMIITX9Q0vTNEVR48ePj4mJQVUHqkHCwk74fO/I9p3G\nbjgVn1vDii3Z2dmk7PnxOb37/RhXJn0++Ngvv/zCr2FFRQWWFAMA9WFsbBwUFHT58mVvb++P\nby/W0tLy9fWNiYn566+/9PT0lJIQQOYkmqC44PTMPtOOJtd6e9erV68IIYQU3lgx5jufh+vd\nMQWyTDEMExoayq8DnaKoEydO/PDDDzJPBQBQb/Xq1atXr155eXlXr159+fJlbm6uubl506ZN\ne/bsaWRkpOx0ADImQdklvLFu7p9VqjotE8fWjV//l5j73l45OTlvHzFPt/90bPmx0fi1kaXE\nxMR3tbPEWJZ99OhRYWGhoaGhbFMBANRzJiYmgwYNUnYKALnj3hXLRu7dnyC+TqTdYsTP5xNy\nsxMfbPD+cLf+WyI397cQPy45HXT2jWxywlvSLynG+wj3799fu3btwIED3dzcWrVq5eXlNX36\n9KCgoOLiYikjAQAAgExwv2IXHxGRSQghRLf35vAjsxxrWK6FNnVfsG/NqSZfXBQSUhoX94iM\n7CiLoCAmfRXF4wg3btxYsmRJZGQkIURDQ0MkEhFCEhISoqKidu/e3bhx45UrV86aNatRo0ZS\nZgMAAABpcL9i9+5CD91nyuSaqrq3rLt1cxQ/Sk9P55sMqmVjYyPlESSd0mXDhg1du3a9evWq\n+EtxVUcIYRhGPJ1eQUHBokWLPDw8BAKBlNkAAABAGtwLu7Iy8SDXRiYmdQ4e0tR8eyVQKBTy\nywU1cHR0lGYiOn19fQsLC+77L1iw4OuvvyaE1DIlsngkx927d93d3ZOTk3lnAwAAAClxLxFM\nTU0JIYSU3r37tI5d0yMjn73fCGTF2Ni4e/fuvJcUGzBgAPclxX7//fetW7eSd6Vb7RiGycrK\n8vf35zFzMgAAAMgE9/qgZatW4gcPtsz75VHN8+OWxO+csury2+46J6cW0qSD6owZM4b3kmJj\nxozhuHN6evqCBQsoqvZe9w+Pf//+/Y0bN/LIBgAAANLjXthZ9unjIn6UfXZuhxY9pn23dc+/\nNzLETwkzH0SGhx769dtp3s6dvjj7bjqOtr17W8s2LxAyZcqUZs2aSXrRjqbpTz75JCAggOP+\n33//fVlZmaQT5lEUtWHDBt4TsgAAAIA0JFkrNm1X7+YzLnOfv0S7+/ZnV2c58MqlQA1urVhC\nyPnz5/38/FiW5fj20TStqakZHR3t7u7OZX+hUGhubl5QwHNduN9///3zzz/n1xYAAAB4k+Sq\nj+3kzSs/1eW6t06nb7ZMr/dVXQPVr1+/jRs3sizL5bodTdMsy+7Zs4djVUcIiYqK4l3V0TR9\n8uRJfm0BAABAGhJ152l1XB78zxcu+nXvqddu+t+h37lr840FdVq4cOGuXbs0NDRqr+0oimrU\nqNHRo0fHjRvH/eCPHz/mHYxhmPj4eN7NAQAAgDdJB1faDN5x/ebhpf7OxjVNUNy4rf/Xh2/d\n3DXUToLb7oGP6dOnx8bGenl5ib+sOtChstobNGjQnTt3hg0bJtGRMzIypAkm/fIYAAAAwIME\na8W+o+s86sfQkcuTb12JiLp+PyEjJ+91Ga1r3NjMtlXHLh5ePTrb1znPHchKp06dLl26dO3a\nteDg4IsXLyYnJ4vXt3Z0dPTx8Rk2bFjHjnwW/tDS0pIm1cfN8/Pz37x5Y2lpKdEwWwAAAJAI\nj8KOEEIIZeDwyYAJnwyQaRjgqVu3bt26dZPhAaUcRCJunp6efuDAgeDg4Li4uDdv3hBCNDU1\nnZycAgICAgMD27dvL5usAAAA8A7/NQxAhbm5ufFuS9O0m5vbypUrW7Ro8fXXX1+/fl1c1RFC\nhELh06dPf/rpJ1dX14kTJ0rZ4QsAAAAfkGS6kxqxJYL7t/57mVOmbWrr5OLmZMr3OqBSNMTp\nThTAwcEhNTWV349Hq1atnj59SlG1/XRRFGVjYxMaGtq5c2cpYgIAAMD/cLpiJ8qO+/fnhWtD\nsz7eVPr4yIK+zczsO3r5Dh4a4Nfr05bWtm5jN155xWdlBKhHvvzySx5VnXjCvGfPnpG6FiJj\nWTYzM7Nnz54PHjzgnxIAAACqqLOwK396cFqH5u6jvtqy6/KzD7YJ43/x8xi99eLLsipPVry6\n+88Sb/dxx9KlvxQIyjN37lwbGxvuC8uKMQwjEok4VoQikaisrMzf3//169e8MgIAAMB76ijs\nMo9P8hq/52EhSwhJS0qqqLqNfbx+/KLIvGrbMcn/jA/87aWsUoLi6enpHT58mKZpScexSnSd\nj2GYly9fbtiwQcJ0AAAAUI1aC7s3F1fM+qfy/nYmKSm5ysbikz9uiquoptVbZZfXrA0vq3k7\n1Hs9e/bcvXs3TdMcr9vp6+tLuoItIYSiqE2bNuXlVf8vAgAAAHBX28dwccjvBzOrfJ2UlFR1\n496jVT6K9doMnrd2w49fjXI1rnwu8+jB8HJZBQWlmDBhwvnz5xs3bkzenwC5KvFVvdGjRxcX\nFzOMxDdXsixbVlZ26tQpabMCAACovVoKOzbqwsXKK24azYZsPrrCs3JjxaVTF0orD9Jm/pmb\nwVtXfLV0w+Hr55a0e/f5//rMmWjZRwbF6t27d0JCwjfffGNoaCh+hqKoyitzFEX16NEjOjq6\nR48evE9B0/S5c+dkkBUAAEC91TIxieDBg9y3D2m3VSePLGhbZTmBm5cjit49Nhr6/eqeBm+/\n0OmyZJ73z59fYgghJOvhw2zibS7r0KBgxsbG69atW716dWRkZGxsbHp6emFhoZ2dXYsWLQYM\nGCCeI+bEiRO8j8+ybGJiouzyAgAAqKlaCruXL98NfqB7TJne9r1FopJiYipXA9X/bIy/UZVt\nZj16OJNLDwkhhCQkJBCCwk41aGlp9e3bt2/fvtVuzcrKommaR1csIYRlWSwvCwAAIL1aumIL\nCgrePrJydbV6b1P59et33z2mPL17ab+31d7e/t1DzGOhNoyNjaWZ7NrU1FSGYQAAANRTLYVd\n5UJQxMLC4v1Nd69ff7eNtOza9YOPZF1d3XcPi4uLpY4IDYKtrS3vwk5DQ6PKfwMAAADAUy2F\nXaNGjd4+EolE721JjY5OefdY19297QcNS0srh1Voa2sTUAs9e/bk3VYkEnl5eckwDAAAgHqq\npbCrHARJBKmpVTfkXQi/XfnFp926fTjH2f9uzqtyDFBxXbp0sbS0lHQ2YzGKogYNGiTzSAAA\nAOqmlsKuWbNmbx/lX754+3+dbMn7/7xQeQXPuWfPD8dGpEdEPHn32MHBQSYxod6jaXrJkiU8\nemMpiho6dGiLFi3kkQoAAECt1FLY2bu4mLx9+GLngh9uFLCEsPk3f5q8OqpywQnLvn1d3m/1\n+tLarVHvPtz1nZ1R2KmPWbNmNWvWTKLFJ2ia1tbW/vHHH+WXCgAAQH3U9hnc07e/3tuHRVdX\ndrW1cnSwsOnyzaX8yj2shw7zqNL1Vpp07rvBw357/u5rTe8+Pfn0zEHDpKOjExwcrKOjw7G2\noyiKZdk///yzZcuW8s4GAACgDmr7ANYdMGW0deVXbMmrlyk5ZVV62ii3OfO8/jcRXuxi55a+\nayL+V/bp9hs5sLEsw0K95+rqGhoaamBgUGdtJ16IbMuWLWPHjlVMNgAAAJVX66evrs+qHwYY\n17BRo9n09bPaVHnC0tRUWHWH5jOXjTGTNh80OH369Llx40anTp0IIdWWd+InbW1tT58+PW/e\nPEXnAwAAUF11XFZpMuXvoKWfflzbaVr12xr6i897Gxzbtq2cv45otVt88HvPWta1ABXWunXr\nGzduHD58uGfPnhoaH46abteu3c8///zs2bP+/fsrJR4AAICqqrP0atz7x+j/+u/+v9//vXjn\nRcZrxtCm1aefTf/mq1FtP5zIhHZ2bk3IXUIoky6LjoRs6KZX7QFBLVAUNWrUqFGjRuXm5j54\n8CA1NVUoFNrY2LRp0wZDpQEAAOSEkmYZqA8Igz93Xp8/bPpXX036xEyCkZFKFh4e7uPjQwgR\nCATi9ewBAAAAGiJZdpZqBux6FiDD4wEAAACABHAXHKipR48eJSQkZGVl6ejo2Nvbd+zY0cjI\nSNmhAAAApILCDtRLXl7epk2b/v7776SkpKrPa2tr9+7de968eb6+vkqKBgAAIK2GcyscgNT2\n7dvXvHnzH374ocp6xm+Vl5efP3/ez8+vb9++aWlpSokHAAAgJRR2oBZYll28ePHkyZNfv34t\n/vLjfRiGIYRcunSpc+fOd+7cUXREAAAAqaGwA7Wwbt26TZs2kXfVWy1Yls3KyvL19U1NTVVI\nNAAAAJlBYQeq7/LlyytXrqQorgsXMwzz6tWrESNGyHAyIAAAAAVAYQcqjmXZRYsWUZRkUzay\nLBsbG3v06FH5BQMAAJA5FHag4i5dunTnzp06e2A/RtP0hg0b5BEJAABATlDYgYoLDg7m3glb\nFcMwcXFxKSkpMo8EAAAgJyjsQMVduXKFd1uWZaOiomQYBgAAQK5Q2IGKEwgE0oyBEAgEMgwD\nAAAgVyjsQMUVFhZK07ygoEBWSQAAAOQNhR2oOAsLC2maW1lZySoJAACAvKGwAxXXvHlzmub/\nc96sWTMZhgEAAJArFHag4vz8/HjMdSKmra3dq1cvmcYBAACQIxR2oOKGDRvGb7oTmqYHDhyo\nr68v80gAAABygsIOVFyrVq3GjRvHr7ZbtWqVrOMAAADIEQo7UH3r1q0zMTGR9E67uXPnuri4\nyCkSAACAPKCwA9Vnb28fFBRE0zT32s7b23vjxo1yTQUAACBzKOxALXh5eZ09e9bIyKj2Pllx\n5Td06NCwsDBNTU1FpQMAAJANFHagLvr06XP79u2AgABCCEVRH1R44pLOxMRk+/btx44dw5gJ\nAABoiHBNAtRI8+bNg4KCbt68efjw4bCwsKSkpIqKCkKIkZGRh4fH4MGDx44da2RkpOyYAAAA\nPFHSLKOpGsLDw318fAghAoHA1tZW2XFAcViWzcrKMjQ01NPTU3YWAAAAGcAVO1BfFEVhxTCo\nJ169ehUbG5uenl5UVGRlZeXk5OTu7q6hoaHsXADQwKCwAwB1kZKSEhIScu7cuRcvXmRmZpqZ\nmdnb2/fs2TMgIKBDhw7KShUUFLR58+Zr1659sESKqanpqFGjli9fbmdnp6xsANDgoCsWXbEA\nqi8zM3PVqlW7d+8WCoU0TVeWUJWP+/fvv2HDBldXV0WmSklJGT16dExMTNVIVVEUpa2tvXbt\n2sWLF/ObZBsA1A1GxQKAiouNjXV1dd25c6dQKCSEVC2hKh9fuHDB3d19z549Ckt1+/Ztd3f3\n2NjYDyJVxbJseXn5kiVLxo8fLw4PAFA7FHYAoMoiIyO9vLyys7Nr341hGKFQOG3aNMVMTJ2S\nkuLn55eTk1NTSVdJ3Kny999/L1q0SAHBAKChQ2EHACorMTFx6NChIpGozvqJEMKyLEVRX3/9\n9cmTJ+WaimXZ0aNH5+TkiEQi7q22bdt24sQJ+aUCANWAwg4AVNbChQvz8/O510/i2m7GjBml\npaXySxUUFBQTE8Ol1qyKpumvvvpKPPMiAEBNUNgBgGq6fv16cHCwpPUTwzBpaWk7duyQUypC\nyKZNm7gvW1yJYZiEhISwsDB5RAIAlYHCDgBU06FDh/iNJKVp+sCBAzLPI5aVlRUbGytpuSlG\n03RISIjMIwGAKkFhBwCqKTQ0lF9DhmHu37+fmpoq2zxi165d4z3JFMMwV69elW0eAFAxKOwA\nQAVVVFQkJyfzLqFYln327JlsI4mlpaUpsTkAqDwUdgCggjIzM/l1d1aSUwlVWFgoTfPy8vLy\n8nJZhQEA1YPCDgBUUKNGjaQ8go6OjkySfMDa2lqa5sbGxtra2rIKAwCqB2vF1kfl5eU3b95M\nSkrKyckxMTGxt7fv0qWLnp6esnMBNBjm5uZaWlrSTA4ipxVamzVrxrstTdNOTk4yDAMAqgeF\nXf3y+PHjdevWBQcHf9Bf06hRIz8/v6VLl3bp0kVZ2QAaEIqiOnfufOPGDX4dstra2u3atZN5\nKkJI165djY2NCwoKeLRlGOazzz6TeSQAUCXoiq0vhELhwoUL27dvf/DgwY/vwnnz5k1oaGi3\nbt0CAwOLioqUkhCgYQkICOA9q4iPj4+hoaHMIxFCtLS0hg8fzm8eFoqiRowYIfNIAKBKUNjV\nC4WFhf3799+yZYtIJKppHB/DMCzLHjp0qGvXrnKaiAFAlUyePFlfX59HCcUwzOzZs+URSWzF\nihWamhL3llAUNXbsWDldRwQAlYHCTvkYhhkzZsylS5c47v/o0aOBAwcWFxfLNRVAQ2dpabl4\n8WJJZzyhKKp3796+vr5ySkUIcXR0dHNz49Fw8uTJMg8DACoGhZ3ybdy48dSpU9z3Zxjm3r17\nCxculF8kANWwbNmy7t27c79oR9O0ubn5/v375Zrq/v37N2/elLQVRVEbNmyQRx4AUCUo7JQs\nOzv7hx9+4NFbtHv37ocPH8ojEoDK0NbWDgoKcnFx4bIzTdPGxsYnT560t7eXa6q1a9fy6yA+\nf/58bGysPCIBgMpAYadku3fvLiws5DE/PsuyW7dulUckAFViYWERHR09cuRIQghNV/8XT/y8\ni4vLrVu3Pv30U7nmKS0tPXXqFL9RHRRFHTt2TOaRAECVoLBTsqCgoJo+bGrHsmxwcLBIJJJ5\nJAAVY2BgcOTIkUuXLnl4eFR7qax58+Z79uy5fft28+bN5R0mOjq6tLSUd/Nz587JMAwAqB7M\nY6dM5eXlt2/f5r3wUXZ29vPnz1u3bi3bVAAqydvb++rVq+np6RcuXEhKSsrMzDQ1NbW1tfXy\n8mrbtq3CYiQnJ/Nuy7Lsy5cvZRgGAFQPCjtlysjIkHI5S4FAgMIOgDsbG5sJEyYoMcCrV6+k\naV5YWFheXo5VxQCgJuiKVabXr18r/QgAoEgmJibSNNfV1UVVBwC1QGGnTFIuBy6TIwCAIkk5\n5FbeI3YBoKFDYadMZmZmBgYGvJtTFCXNguIAoHienp5aWlr82lIU1bdvX9nmAQAVg8JOmSiK\n8vPz471qZIcOHaysrGSeCgDkx9jYuFevXrzHwgcEBMg8EgCoEhR2SjZo0CAek9gRQliWleuq\nRwAgJ8uWLeMxaoqmaXd3dx8fH3lEAgCVgcJOyXJycni3LSgokGESAFCMXr16DR48WKJL9eKd\nN2/ezO8CPwCoDxR2SsZ7gmKKosLCwvhd7QMA5dq3b5+TkxP3332WZdevX9+jRw+5pgIAFYDC\nTpmEQmF0dDS/qexYlk1NTX3x4gW/U0dHRy9ZssTDw8POzq5x48atW7f29fXdunUrpj8FUIDG\njRufP3+eyySUNE1TFPXtt98uXrxYAcEAoKFDYadMmZmZUq4JlpqaKmmTa9eueXh4dO/efePG\njbGxsWlpaQUFBU+fPr1w4cKCBQtatGgxc+bMzMxMaVIBQJ0cHR2vX78+ceJEiqKqvXQnftLK\nyurYsWOrV69WeEAAaJBQ2ClTXl6elEfIzc2VaP9NmzZ17979+vXr4i+r9uSKLxyKRKJdu3a5\nurrGxsZKmQ0AamdoaLhv3764uLjx48ebmppW3URRlJub288///z8+fOhQ4cqKyEANDhYUkyZ\nLCwsZHWEkpKS69evp6am5uXlmZmZNW3a9NNPP/1ghh4ORt8AACAASURBVPrly5evW7eOoqja\n78xjWTYnJ8fLyys8PBz39ADIW8eOHfft2ycSiZ4+fSoQCIqKiqysrJycnKT/+wAAaqiOz3h1\nEB4eLp5BQCAQ2NraKvLUDMPo6em9efOG9xGSk5MzMjJ+/PHHM2fOlJWVVd1kYGAwePDgZcuW\niRc4P3ToUGBgIPcj0zRtYmISFxfn4ODAOx4AAAAoEgo7ZRZ2hJCWLVs+f/6cX1t9ff2hQ4ce\nPHiQvN+pWomiKIqiZs+evWLFijZt2uTn50s0UIOiqJEjRx4+fJhfPJBUdnb2yZMnIyIiBAJB\nXl6elZVVkyZNfHx8+vfvL80KJQAAoD5Q2Cm5sDM3N+c9lV2dnaqVHBwckpOT+Z0iLi6uY8eO\nPNoCdxkZGWvWrPnjjz+EQqH4lnmGYTQ0NBiGYVlWT09vyZIlixYtQnkHAAC1w+AJZWIYRtLR\nD1VxL8r5VXXiU/zzzz/82gJHly5dcnZ2/u2334RCISGEYZjKgSzit7i0tHTVqlUuLi4PHz5U\nclYAAKjfUNgpU0JCQj2/YiqeBrnqMxkZGffu3YuKinrx4oU0dweC2LFjx/r16/f69eta9hH/\nkCQnJ3fp0uXGjRuKigYAAA0PCjtlio+PV3aEOrAs+/z5c5Zlnz59On/+/KZNm9rY2HTs2LFH\njx4tWrQwNjb29/c/dOgQvzmW4fbt2+PHj2dZlss3kGGY0tLSQYMGpaWlKSAbAAA0RCjslKlB\nTGdQUVExbdq0du3abdu2LSUlpeqmN2/enD59OjAw0NXVNTo6WlkJGyiGYSZPnlxeXs69LGYY\nJisra8GCBXINBgAADRcGTyhz8ERRUZGhoaEizygnNE1raGjs2LFj2rRpVZ8XCARhYWFxcXHp\n6ekikcjS0tLZ2XngwIHt2rVTVtT64+DBg+PHj+fRkKKoGzduuLu7yzwSAAA0dJigWJkMDAz0\n9PRKSkqUHURa4mtOn3/+uZ6e3tixYwkh8fHx33zzTVhYGMuylSsmifscly5d6ubmtn79enE9\nrbb27dtH0zS/Xuz9+/ejsAMAgI+hK1bJPDw8lB1BNhiGoShqypQp8fHxW7dudXV1FVd1hBCW\nZUUikUgkqixi7t27169fvylTpqjt8IvXr19HRkbyvjcxODhYtnkAAEA1oLBTsi1btig7gsww\nDFNRUTFw4MAFCxaIJ2CrZU9CyN69e318fD5YMENNPHv2TDy5CQ8sy6amphYVFck2EgAAqAAU\ndkrWvn373r17KzuFzDAMk5iYSDjPsXf16tUPbstTE+np6VIeAWNjAQDgYyjslO/UqVOmpqbK\nTqE0f//9d1BQkLJTKJqGhobSjwAAAKoHhZ3y6ejo3Lp1SzWGx/JA0/TSpUtFIpGygyiUjY2N\nlEdQ/PJ3AABQ/6GwqxeaNWu2Y8cO8dDROuno6PA+EU3THM+iMAzDPHv2LCoqStlBFKpVq1aN\nGjXi15aiKCcnJ11dXdlGAgAAFVC/PuPV1j///DNhwgQue1IUJc1og1mzZrm4uBBCairvaJqm\nKKpTp068T8EDRVEhISGKPKPS6enp9evXj1+RzbLskCFDZB4JAABUAAo75bt9+/bkyZMpiuIy\n+YV4Wjh+J9LR0Vm2bFl0dPSIESNqOoiBgcGWLVvGjRvH7xT8UBT133//KfKM9cH06dP5TXei\noaExZcoUmecBAAAVgAmKlYxl2VmzZlVUVHD/jBfXdjyWDFm2bFlGRkaXLl2Sk5NrKuyKiorm\nz5+v4MlvGYb5YLEydeDv79+9e/eYmBiJyjuKoqZOndqmTRv5BQMAgIYLV+yU7OTJk9evX5f0\nyo24quN+6Y6iqL59+7q7u3t4eKSmppKapyMRJ7l165ZEeYCfvXv3GhkZcR/fStO0k5PT+vXr\n5ZoKAAAaLhR2Snb48GF+N1rRNM399nlPT8+1a9eOGjXqzZs33ItI3n2+kqJpukmTJoo5V73i\n5OQUFBTUqFEjLrUdTdMWFhYnT55s3LixArIBAEBDhMJOmViWPXXqFL8brRiGKS0tHT58OEVR\nNVVg4kVa58yZc+HChZkzZxYXF0t0Lh69vfywLNu+fXvFnKu+8fb2vnbtmriurel9FJf+nTt3\njouLa9WqlULzAQBAg4LCTplycnIKCgp4N2dZ9quvvoqNjR00aNDHc2fo6+uPGjXq/v3727Zt\nO378+L1793ivTCpvLMsOGjRI2SmUxtXVNT4+/scffzQ2NhY/Q9O0hoZGZZ1nZ2e3Z8+ea9eu\nYe46AACoHZ978FVMeHi4j48PIUQgECj4g/Phw4dSXqkKDg4ePHgwIaSkpOTatWupqanZ2dmW\nlpZNmzbt0qVLZbXXvXv3a9eu8SvsOA7UEO/GY1QHTdPNmzd/9OiRpqa6D+URCoWRkZGRkZHJ\nycn5+fkWFhZNmzbt16/fJ598orBucQAAaNDU/aNUufT19WVyhMTExJCQkMuXL6ekpOTm5lpY\nWDRp0sTHx2fw4MG2travXr3iXdWJ7+QrLi6uczeaplevXr1q1SqhUChRbccwzE8//YSqjhCi\nqanZp0+fPn36KDsIAAA0VPg0VSYbGxt+E5dUYll23Lhxhw4dYlmWpmlx9fby5cu4uLgTJ07M\nmTNn+vTpvXv35t0JK244c+bMP/74Q/z4g7Tik7Zu3XrXrl3du3c3MDCYN2+eRKcYM2bM0KFD\n+cWTEsuyr169MjQ0xCoOAACgGtAVq8yuWEKIpaXlq1ev+LXV1NRs1KhRSUlJ7W+igYFBUVER\nv1OIvX79WiAQ/Prrr8HBweLZUsS0tbX79OkzZsyYsWPHVo7rnD179q+//sqxYPXw8AgPD1dw\nXXXjxo3Dhw+Hhoa+fPlSKBQSQgwNDT08PAICAsaOHWtkZKTIMAAAADKEwk7JhZ2JiUl+fj7v\n5pVX6Woh5UVBQkhOTo6pqan4cVpamkAgKCkpsba2btq06ccL1zIM07dv38uXL9d5WDs7u7i4\nOEtLS2mySSQhIWHRokUhISHiW9aqflvE30lTU9NVq1bNnj0b97QBAEBDhMJOmYVdUVGRoaGh\nIs/IQ6NGjbivTlvZNcxx/3bt2kVHR1eOBpWrixcvDh8+vKCgoJafeXF5N2TIkL/++svAwEAB\nqQAAAGQI050oU3p6urIj1IGmaYlWr1qzZg33qo4Q8vDhwzFjxihgHpbIyEhfX9/CwsLa/5MR\nJzlx4oS/v39FRYW8UwEAAMgWCjtlqrcTy1ViGEY8nQoX8fHxa9askbQT88yZMwcOHJA8mgRS\nUlKGDh3KsqxIJOLYJCIiYvHixXJNBQAAIHPoilVmV2xhYWE9v1Wfpun4+PjWrVtz2XnIkCGh\noaGSVqs0TVtbWycmJmpra/PKWLcJEyYcPHiQxwR7d+/edXFxkVMqAABVIhQKY2NjExMTs7Ky\njI2NbW1tu3btWnl/NigMpjtRJkNDQwsLC96jYuWNoqgpU6ZwrOpyc3PDwsJ4XINkGCYtLe3y\n5cv9+/cXz9AbFhb24MGDtLQ0QoidnZ2Li8ugQYN69OjBb667J0+e8KjqxFatWnX8+HEeDQFU\nSXl5eVZWFk3TVlZWXNY1BnWTmJj4/fffHz9+/IO1lDQ0NHr27PnVV1/5+fkpK5s6YtXehQsX\nxN8KgUCg+LO3a9dOuT8ANaFpukmTJq9eveL4Qg4ePMj7XBRFzZo16/Dhw82bNyfvlritjCHu\n223RosXRo0cZhpH0O/zDDz/wDqatrS2+LQ9ADd25c2fhwoUtWrSo+mfBxcVl5cqVCQkJyk4H\n9YJIJPr222+1tLRqX+q6b9++GRkZyg6rLlDYKbmwU/xlai73wNE0bWRkdO/ePe4vZMWKFbwj\nia8EkHd/AmrahxAyZswY8bx93PXo0aOWw9bp5MmTEr6lAA1eWlra2LFjqXc+/k3U0tKaO3cu\n/u1Rc2VlZQEBAYTDxwpFUXZ2dv/995+yI6sFdMUqU0FBQW5uroJPqqmpWcuqX+JJ75o3bx4W\nFibReNi0tDQuk+pVi2GYzMxMUutoEvGmf/75Jzk5+eLFi5XL4NbpxYsX0gxSSUxM5N0WoCGK\ni4sbOHCg+PrKx1vFv00VFRXbtm27ePFiWFhYs2bNpDxjdnb2hQsXnj9/Lr43y9ra2tPT083N\nTcrDgrzNmDEjODiYfLQi0cdYlk1PT/fz87t165Yi5y5VU0otK+sFJV6xe/TokeLf8Zs3bw4b\nNkz8D1bl7TKVvZ96enqrVq2q8x9xkUj0wTMzZsxQ2KS+EydO5P5NlnJMxooVKyR9WwEarkeP\nHhkZGXG8yC2+YSMzM5P36WJjY318fKo9XZMmTbZt2/bmzRsZvjqQoT179kj655SiKD8/P2UH\nV324YqdMWlpaCj6jmZmZu7v7sWPHEhISQkJCrl69KhAI8vLybGxsmjdv7uvr6+fnV+10wW/e\nvAkODg4JCYmJicnIyCgvLzc3N3dycho4cOCIESNatmxpY2PDKmqE9f79+8eOHduvXz8uOxsa\nGubk5PA+l2ImT+YtIyMjLCwsOjo6PT399evXlpaWLVq08PX17dWrl/xGGYOqKioq+uyzz4qK\nijhe5GYYJjU1dfjw4REREZLe8FBeXj579uzdu3dTFFXt6QQCwdy5c//v//7vxIkTGJxe35SU\nlCxfvlzSXhqWZc+cORMeHt63b1/5ZQNcsVPmFbvi4mIFv93m5uY8ch44cKBJkybko3vgxF/S\nND1t2rT9+/cr7FXQNN2xY0eO4V1dXaW5lHjo0CEe3zEFSEpKGj9+fOVbIB5loqGhIX6x5ubm\nv/zyS3l5ubJjQkOyatUqfr8m4oHn3BUWFnp6enI5Mk3T+vr658+fl9NLBn7+/PNPfj8qNE3j\nop28obBTZmFXUlLC73eDN4qiysrKuCcsKyubNGkSqXVYg/iw1tbW3O97k4kHDx5weQlz5szh\nXdhRFJWSksL37ZWjo0eP6ujo1PK6xO9Xp06dUlNTlR0WGobCwkJdXV1+vynNmjXjfiKRSMR9\n2nPxD7OhoWF8fLz8XjtIyt/fn/egNG1tbfHSjiAnWHlCmcRTtSkSy7LcT8owzPDhw/ft20fq\nWiSDZdmsrCyhUCh9Qu5OnjxJCMnMzNyxY4efn1/Tpk11dHQMDAzatGkzduzYI0eOiOvmgIAA\nllcfMU3TnTp1sre3l3Fuqe3YsWPkyJHiq3E17SN+v+7evdu5c2eM/wAuzpw5U1payq9tYmLi\n3bt3Oe68Y8eOkJAQ7gdnGKa4uHj06NFKXKqnvLxcWaeun6Kioni/HeXl5bdv3+bXNjs7e9++\nfZMmTerbt6+7u7ufn9/nn39+9OjRwsJCfgdUTcqtK+sDJV6xi4+PV/w7fu7cOY7xvvnmG4mO\nLO4NlGZuEYnOFRgYuGzZMh0dHVJDH7GNjc3u3btFIlGnTp34zar677//yvUHgIezZ89Wzu3H\n8RvVtm3b169fKzs41Hfjxo3j8TtSad26dVzOUlhYaGFhwe+vxP79++X9TagkFAqDg4OHDRtm\nbm4unhpdR0fH0dFxwYIFt2/fVliM+qmsrEzKoXIHDhyQ9KQZGRmzZs0Svxfim08IIZU3n+jq\n6q5YsQJ/6MRQ2CmzsMvPz5fmd4OfU6dOccn2+PHj+jzFvPjOG1Lr/Eni3/xhw4adOXPm4+m4\nakdRlIeHB4/5kKtKS0s7cODAunXrFi9e/P333+/fvz85OVmaAxYXF9vY2PD4UFy8eLE0563P\nRCJRenr63bt3nz17Jukch1CVlJOlDx48mMtZdu/eze/4NE1/8skn8v4miIWFhdW+vKSXl9fT\np08VE6YekmY4mtiOHTskOuOlS5dMTExqOaD4z7uDg4NE06+qKhR2Sp6gWE9PT8rfEEnduXOH\nS7DRo0fz+5+s8nZ+LjtL828f97aenp4S3RVO07SlpaU0d9edO3fOw8Pj44QURXXu3PnEiRP8\nDvvTTz9xfxVVaWtrS1lT1jcMwxw7dmz48OEf/Aa5urp+9913Svldbuhq/+CsU9u2bbmcZeDA\ngbwv6ivgnleRSPTll19yCaOlpRUUFCTXMPUWwzBSTukg0bcuKChIU1OTy48NTdN6enrR0dHy\ne+0NAgo7JRd2jRs3lubXg4e8vLw6U5WUlIi7OPkJDAys89Z+iqJcXV0V029LCJk+ffqCBQsI\nh4pTPBAkLi6O3xuan5/v7+9fy4nEz3t7e/OY/atVq1a8S+ENGzbwe0X1UExMTMeOHav9Jouf\n0dHRWblyJQYFS0TKwq5169ZcziJeY4a30NBQuX4TRowYIVGeejtqXt5atmwpzb/ld+/e5Xii\nuLg4XV1d7p8UNE2bm5ur2P+xkkJhp16jYs3MzLgEO336NO9T0DTdp0+f1NTUqVOnVk6lRtN0\n1cmQ+/fvHxcX9+uvv8rgJXEWHR29b98+cSVd7Z+kyjUNef8kpKamtm7dmsvfO4qi7O3tnzx5\nwv3gT5484f3aaZr29PTk96Lqm71792ppadU5TJsQ0qtXr+zsbGXnbTCkLOycnZ3rPIVQKJTy\nf7nffvtNft+B9evXS5pHQ0OD4/B8FTN//nx+hR1FUba2thzvchGJRB07dpT0Z4aiqCFDhsj7\nO1CfobBTZmGXkJDA4xdDGnPmzOESbPv27dKcxcHBQXycwsLCY8eOrVixYuLEiYGBgYsXL96z\nZ0/lUtApKSkKW6yCpulevXqxLJubm7t8+XJHR8cPdtDS0urfv/+ZM2d4v5vFxcVubm4S/Wfp\n5OTE5QKq2IkTJ6T5DhgbG/N+afXH0aNHuQ/QoSiqU6dO0t94JxQKb9++HRISsmfPntDQ0Nu3\nb4sX5VMxCrjHjveo20pbtmyR08vPzs4W35gvKY6XKlVMTEwM7zdx4cKFHM/yzz//8D5LTEyM\nXL8D9RkKOzUaFUvTNMe5oFasWCHNiRo1asTxOyDNZEiSoigqLS2t8tSPHj0KCwvbvXv3wYMH\nIyMjpZ9Xac6cOTwiBQYGcjy+9Bc4G/rYgnv37jVq1EjSH5hp06bxPuPTp0+nTp1qamr6wTFN\nTU2nTp2qxNvnhUJheHj4nDlzPDw8mjRpYmlp6ebmNnr06IMHD+bn5/M7ZmBgoDQ/XRxHxUq5\nlMuRI0f4vbo6SdoJWxXHEWkqZsCAATyupenr63O/C8XX15ffBwRFUTNnzpTry6/PUNgps7DL\ny8vj8SPL2+effy4+r0gkOn/+/BdffNGuXTsTExMNDQ3xqtvff//9o0ePWClu0hczMTHh+B34\n77//KserK8CePXvk9FY+e/aM393EFEVxnD3ht99+k+a1UxRVWloqp5evGDUtKiqr73BVFRUV\nixYt0tTUrOmHk6IoTU3NhQsXKv5OvrCwMGdn5/9v774Dmrj+AIB/LwmBsPdGlgIKqCii4kBR\nQGvBBW4rDqy7VotWrQUFtVr3ruvXui3OqnVRlYoKiuIsiCCKoKKALGUEkt8fUbhAAsllHMTv\n5y9y49337h3ky7t37wliIIcnuDi6urorVqygkMTHxMRQvbkAJO415eXlJcv/cpQ7vzaK8uDM\nANCjRw8FRdWUpaamamtrS1ubGzdulLD8kpISyvMiCrpKK/T0mzJM7Gh+ecLExITajSvtXe7s\n7CxolIqNjW3Xrh2I+VYgCGLMmDHr16+X5XBt27aV/AqsXbtWxrOT3IIFCxRUj+Hh4dRCYjAY\nEyZMkOQQUo3pWp/k2XbTdPPmTcpXWDBIteQKCwt9fX0lLL9Xr16SP0+XEZfLnTFjBjT4DpDg\nl9rDw0PaF0iLi4tlmXlCwl5TUVFR1A5BEISVlZWMIxCJQ3m8XAENDQ1FRNX0nT59umZIOUlI\n+LdOQPIhr8VR2i9mU4OJHc2J3cSJEyl3QVVTU5O8n356ejqPx1uyZEmjXZQIgpDxXd1Ro0ZJ\ndRFmzZoFEgxfImPDHoPBCA0NVVA9tmzZknJ4BgYGNX22SktLL1269Mcff6xfv37//v3x8fE1\nDUKyTCDBYDB69+6toHNXjrlz51I+fTabLfnIpVVVVQEBAZLXJkEQffv25XK5Cj19Pp9fXV09\ndOhQyaOysLCQNreLiIigdoUlH282LS2N8gCZ8+fPl/6ySWTVqlXUQqqhhBugaTp79qyOjk7D\nvy+Cb5w5c+ZI1Tn13LlzMlbK48ePFXfiTRkmdjQndqdPn6Z8186fP18w6EPDfHx8BH0aZs+e\nDZKlRzIOTTx+/Hhpr8PmzZvZbHYDsREEwWazZZngiyCI6dOnK6AO5TDQdHp6+o0bNwIDA+s/\netDR0Rk7dmxqaiqfz3dzc6P8GGvDhg2KOHeladu2rSyZ/fnz5yU8kFRDHtaIiIhQ5Nnz+dL3\nfCUIwsPDQ6rn78XFxfb29tIO5d29e/fq6mrJjxIWFiZtVTIYDD09vfz8fOkvm0SmTp0qVTz1\nSfWGu4p5+vTpoEGDau6HOhUHAK1atTp58qS0xV68eFHGShH82fwCYWJHc2JH7VtEIDAwUJIn\nuXZ2dkVFRbt376Z8IGn17NmTwqV4/vz52LFjazIb8pNiNps9bty4Fy9e9O/fX5bAJOzfLS3Z\nX4IRPPgTl7QRBMFkMufOnUvtETlBEJqamhSGzWtSZOx0/9tvv0lylFevXnE4HGnTDoIgNDQ0\nFPoH5PHjx9Ry+pUrV0p1oJSUFB0dHQmPxWAwzM3NpT3x3Nxca2traf97VOh8YtOnT6dwbcnS\n09MVF16zcOfOnfnz57u7uwvmBFJTU7OxsRk7duzRo0epNWc+evRIxkopKSmR+2k2C5jY0ZzY\ntWjRQsZ7VxLq6uqNtpbLkampKeULUlJScuzYscjIyMmTJ0+ePDkyMvLYsWOlpaWCtaGhobIE\n9vfff8up3oTI2EFHcr169dLW1qawI4U21CalqqpKxrs3OjpakgMtWrSI8iEU14OTz+cPGTKE\nwhUgCEJXV1fa92Rv3bplbm4uyeHatGmTkZFB4XTu3r2rpaUlSfooCCM8PJzCUSS3Zs0aaa9t\nHSo5Ag5lNX+xZVFWVka50ydBEPb29rLH0ExhYkdnYldZWSnjX5OmiclkKuiKyTgiw+HDhxUR\n1atXr+R16RTEw8NDQb3OlUZHR0eWKyDhwLYuLi6Uh1dQ3HhmhYWFsszgRGF2hJycHMGkgvUn\nWRZcHxaLNWPGDFnmXH/w4IGtrW3DkQs65v/666+UjyIhGRuHNDU1FR3hl2nIkCGUfx8lHy1P\n9ShpCDEkUnZ2Nt0hKITihqZ78+aNLC03KSkpcgymhqmpKbWGNKVJTk4+f/483VHIxNraWpaq\nl6R3ZkFBQWpqKo/Ho1A+n89/8uRJXl4ehX0bdenSJS6XS21fBoNx5swZafeytLQ8dOhQUlLS\nrFmz6qRfLi4uCxcuTElJ2bRpkyzZtru7+71798LDwwW9L8hPZmv+gHh7e9+4ceOHH36gfBQJ\nubq6yvIr7O3tLcdgUI2wsDBqv48EQUyYMEHu8TQXmNjRKT09ne4QmhlBqyq1fQUDFMs3HgEm\nkzlgwACljbRMAUEQMk5cQbs+ffpQ3pfFYnXv3r3RzXJycigfQkBB/6rJMpscj8ejvHuHDh3W\nr1+fmZlZVlaWkZHx4sWLioqKx48fL1u2rGXLlpRDqqGvr79q1aqsrKwdO3YEBQW5u7sbGRk5\nODj06NFj4cKFiYmJ165d69y5s+wHksSwYcMo7yvjiO5InH79+vXu3ZvCMMjjx4+XcSaVZo3K\nDCpIXiiPvtjEUfsfS9EU2sVw1KhRR44cUVz5MuKTuhw0U4MHD6Y20x2DwfD19ZVkBJ+ioiIK\n5ZPJ/n60SLm5ubLsLnu6qaGh4eDgIGMh4piZmYWFhYWFhSmofAmtW7du//79FLrHeHh4+Pj4\nKCIkBAB79uzx9PQsLCysrq6WZHsGg+Hg4PDrr78qOrCmrOm2MXwJ6k9VpBp0dXUVVLK1tTXl\nhjEej2dlZSXfeGoEBgZ27ty5KTfaCaZTozsK6nr37t2lSxcKV5jH40n4SoSZmZn0cQkxNzeX\nsQSRNDQ0ZNldU1NTXpGoMF1d3U2bNkm7F5vNPnbsmCLiQQJ2dnYnT57kcDgSvmpjbGx8+vRp\nAwMDJcTWZDXd7yHUfDk6OiqoZDc3N1myE3d3dzkGQ0YQxPbt29lsdpPN7SorK0tKSuiOgjqC\nINasWVO/L3+jhg4d2rNnT0m2tLKykmUERyaTKcs4iw2wtLSkvK9gfHI5BqPCJk+e/O2330q+\nPUEQp06dsre3V1xICAC6d++emJgouM7ifv0Ff3g7dux49+5dFxcXpcbX9DTRL6EvhJGREd0h\nKETfvn0VVHJQUBDlxI7NZvv7+8s3HrL27dvv3buXz+dLPoiDMrHZbBlfLKWdt7f3mjVr+Hy+\nhFePwWA4OztLPoKjpqZmz549qeV2DAaje/fuCnqHxtPTU5bdvby85BWJytu+fXtkZKQkNxiH\nw7l69Wq/fv2UEBVq06bNo0ePVq1aVdMUx2AwyPOMW1pa7tq1KyEhQXGPZZoT+l7IbSpoHO6k\nqqpKxjkemqaEhAQFXTEul9uyZUsKF40giHHjxikoKrIzZ85oa2s3/MUgmA5OhgssNYIgHBwc\nlHD6SrBs2bJGp8UTcHNze/HihVSFb926lfJF3rx5s4JOuaqqytjYmPI/A3FxcQoKTFXduHGj\ndevW4q4nQRAhISG0jHuKuFzu5cuXIyIixo8fP2jQoLCwsKioqMTExOY+nJN8YWJH8wDFfn5+\n1P5YN02CjuoKvWIxMTHSRiWYjkza73jKnj17JhgDDIRHfqn5OSgo6NChQ/K65hJegalTpyrn\n9JXgr7/+srGxAVED6wie1TIYjEmTJlEYd768vLxFixbSPk9nMBg2NjZSTd4lrZ9//plCvTMY\njLZt20o13xeqce3atRkzZjg5Oenq6qqpqRkbG3fo0GHFihXUxmRGSGkIflPpT819k3T21IX4\nu2mv8ksqWTqGlo7tuwUMDOxsJc2Lo1QKiY2NI13M5AAAIABJREFUFWRXOTk5svRloWb79u2y\nT1PYRAjaxhMTEz08PBR3FD6fP2bMmIMHD0q117Zt26ZMmaKgkER68uTJiRMnLl269Pz58/z8\nfAMDA1tbW19f38GDB7u7u79588bS0lKZv30XL15Upf8iKioqduzYcfjw4Zs3b5Ivo6mp6cCB\nA7/77jvKgx2cOnVq8ODBACB57RAEcfz48ZrpMhWhpKTE0dExPz9f2lfOL1y4oNAeCAihpqaJ\nJHZFt7cvWvV3VgUAAIOtoVZdXlENAMC26Tdv+TQvySaJpFgIvYldWVlZy5YtX79+3TQqgjqC\nIPh8/s6dOydNmqToY5WVlfn7+8fHx0sY1ffff7927VpFRyUtDw+PBw8eKGFoGAaD4enpmZCQ\noPyOfUpQUFDw4sWLnJwcPT09CwsLBwcH2d9fiYiIWLp0qeDmaXhLwTY///zzkiVLZDxoo65c\nueLv7y9ofpNwl7lz565evVqhUSGEmhx6GgqFvTm7MCQwMDBozKJ9158XV/H5/OrSl4kHfx4b\nFBgYGLzgzGuFFkLvo1g+n3/gwAF674EGEAShr6/faEIgmPlH0LFdOcrLywXzxjbwLU4QBJPJ\n3Lhxo9KiksqKFSvkW1niLgKLxRI0ayEJ8Xg8SXryMRgMgiCio6OV1r/n999/Z7FYEvYxHTFi\nBE5gitAXqAkkdhXJG8YGBgYGTtmdUiG8ImX3lMDAwMAx6+6UK7AQ2hM7Pp8/c+ZMOXyHK8b5\n8+fHjBkjbpgJwTefvb39hQsXlH/dLl682L59+5pIyO9JEQQxaNCg1NRU5UcloZKSEhMTEwrd\nuRp9OaOGYLMtW7bQfa7N0sWLFwU96OvXkWBJ69atlX/bX7lyxdTUFMS/WC1I5aOiorA7OUJf\nJvqHO6m+f/VGIQDDNWiwi3BPOLbL4EBXBkDRjavJjcyTKJdCaLR27dpp06ZBY7OsKvlRGoPB\n8Pf3DwgI2LdvX1JSUmhoaJ3xWRgMhre399atW1NTU2npx+Pn55ecnPzo0aNffvll7Nixfn5+\n/fv3DwsL2759e3Z29okTJ5ydnZUflYS0tbU3bNgg1aNYQeZ67NgxwcBsDd8PgvakNWvWCG4t\nJC0/P7+HDx/GxMQMHz7c2Ni4ZrmRkdHw4cNjYmIePnyo/Nu+V69eGRkZkZGRIgdLYrPZw4YN\n+++//3766SeVfPKOEGoU/VOKPbl3/yMA2Hl41B8p2sCjvS08zixPTk6BLm0VXQiNWCzWli1b\nOnXqFB4enpeXx2Aw6nzfC5ZoamouXrx4586dz5494yu4Tx6DwdDQ0Fi3bp3gY4cOHfbs2VNd\nXZ2env7mzZvi4mIrKyt7e/umMMC3q6trM50WcOTIkffu3Vu1apUk3bkE98DOnTv9/f179+69\nZMmS1atXV1RUiLtbWrZsuWXLFsWNKfglYDKZwcHBwcHBAFBeXp6Xl2dsbCzjPBCy09bWjoiI\nWLx4cUJCQlJSUk5OTnl5uaWlpZOTk5+fn4LG0kMINRe0J3ZlL7PzAYBtaydqLh4LO1s2ZFYW\nZb8sgbbiB1eVSyH0Cw0NHTp06Pbt22NiYpKSksjf9C4uLkOGDJk5c6apqem7d+8U/SoAg8Hg\n8/kHDhxo06YNeTmTyXR2dm7KzWDNzi+//CL4nq6fn5ExGAwWi7V79+4xY8YAgJqaWnR09JQp\nU7Zu3XrixInU1NSaLdlstq+v7/Dhw8eMGcNi0f4Lrjo0NDSa1BQOgvZyb29vugNBCDUttD+K\nffvuLQCAkZHoWVM/LW9kEmy5FNIk6OjohIeH37p1q6Cg4OHDh7GxsXfv3n379u3jx4+joqIE\nfWtmz57NZrOlfc7CYDAMDAwEQ3402iVcQ0ND0cM3IAGCIBYvXnzhwgVBDl2namq6Nvr4+CQm\nJgqyuhrW1tbLly9PSUnJz89/+PBhfHx8RkZGUVHRuXPnQkNDMatDCKEvEO1/+svKygAAOBoc\nkavVBU89ysrL5F3I1q1bb968CQDFxcWtWrV6+vSptKErlL6+vr6+vpubW/1V1tbW4eHh0dHR\nUhXI4/GioqKmT5/ep0+fefPmZWZm1mkiEiQQfD4/ICBg7dq1ON2eMvn5+d2/f//MmTMnT56M\njY198+YNl8tVV1e3tbUNCAgIDg5ueLZTQ0NDQ0PR/9UghBD6otCe2HEruQAA4loXPs289Gkr\neRby6tWrlJQUwc+amppSBU27iIiIxMTE2NhYyXvajRo1StCJPjg4OCgo6Pjx4ydPnoyLi3v7\n9i2Px1NXV7ezs/vqq69CQkK6du2qyNiRaAwGIygoKCgoSPCxqKhIT0+yARwRQgihz2hP7NTY\ngqSLKzpz+7Scrd7g/BNUCunUqZMgn8vJyfnrr7+kjZteLBYrJiYmODg4Nja24S0FvfJDQkJ2\n7dpV8/SWzWaPGDFixIgRAFBdXV1aWoo5RFODNYIQQogC2vvYfWos+/QwtZ6yjx8BADQ5DTap\nUSlk4MCBCxcuXLhw4eDBg7Ozs6UOnG56enrnzp1bsGCBoL9d/S53giUcDmflypVHjhzhcEQ/\np2YymZhDIIQQQqqB9sTO1MyMAID8vDxRzxT5797lAwCYW4h63VW+hTQ/LBZr+fLlT548mTZt\nmoWFRZ21tra24eHhGRkZ8+bNwxGtEEIIoS8B7Y9iNVrYmsKdXO7z5znQtd5YAs8zMqoBwNjW\ntsEWO7kU0lzZ2dlt3rx506ZNaWlp2dnZBQUFJiYmNjY2jo6OdIeGEEIIIaWiPbGDlh4ddI+f\nK36ZlPR2pLWp8Lqc27dfA4BOJ08nJRTSrBEEgSPMIYQQQl842h/FAsOtTy9TAP7T0zHJwl3k\nSm8ePvsCAMz79HFrJE65FIIQQggh1Kw1gVyH6RQyrpsOwLsLqyL2xD0r5PKBz32ffvm3n9fH\nvQfQ6zFuqBOzdvP0PyYHBQUFBS29Wkm9EIQQQggh1UP/o1gA0Osxa2HWu6gjaakn18w+uZbF\nZlZXVvEBgNBpM2rRzG4SvbMpl0IQQgghhJqvJpHYAXBcR6/c7H7uxNmrt1OzC0qq1A2tbFp7\n9v56SICrgcSNinIpBCGEEEKouSIkn7pAVcXGxvr5+QFATk6OpaUl3eEghBBCCFGELVkIIYQQ\nQioCEzuEEEIIIRWBiR1CCCGEkIrAxA4hhBBCSEVgYocQQgghpCIwsUMIIYQQUhGY2CGEEEII\nqQhM7BBCCCGEVAQmdgghhBBCKgITO4QQQgghFYGJHUIIIYSQisDEDiGEEEJIRWBihxBCCCGk\nIlh0B9CEvHjxory8nO4oEEIIIYTEUlNTs7GxEbcWE7ta3t7edIeAEEIIIdQQBweHjIwMcWsx\nsQMfH5+0tDQnJyeFHsXc3Nzc3LyioiIlJUWhB0JNja2trYGBQXFx8bNnz+iOBSmVk5OTpqZm\nXl5ednY23bEgpXJ3d2cymdnZ2Xl5eXTHgpSHxWK5ubkBQEZGRklJCV1hEHw+n65jNx2VlZX/\n/vuvQg8RGxsbGxtrYGAwf/58hR4INTVHjhxJTk52cnKaMGEC3bEgpdqyZcvLly+7du06cOBA\numNBSrVkyZKysrLAwMBu3brRHQtSntLS0ujoaAAYP368s7Oz4g7E4XAauLWwxQ4AgM1m9+3b\nV6GHELTWcDgcRR8INTVxcXEAYGRkhFX/pdm3bx8A2NjYYNV/aZYtWwYAzs7OWPVflIKCAsEP\nHh4eNHbuwrdiEUIIIYRUBLbYKYmjo2Pfvn0NDQ3pDgQpm6urK5fLVXQnTtQEeXl5WVhYuLi4\n0B0IUrZevXqVlZW1aNGC7kCQUtU8/TM2NqYxDOxjhxBCCCGkIvBRLEIIIYSQisDEDiGEEEJI\nRWBip2gVGX+vmTGoSytLQ011jr6Fo+eAKStPpZXRHRaSu8qsi1FBjhoEQRAukY8a2BBvCVXB\nL7h7IDI0oKODqY6GGlvLwNLFO2jaqlNPSkVujfWuQsozz2+YPbS7q7WBFltNXdvQ2rX74Bmr\n/0r7IHJrrHqVVX7rRxcmQRCE/qTzotbTVPV8pEBvT09z1fx0pZkcbc3P76pwWn97Kpfu4JDc\nVL2JWzuytXbNb5VzxENxm+ItoSqKby3vY1bznzGTyaypfo2W3xx9yRPeGutddXAzj4xz5nyu\nbYLJqm0fUXcYeTCzSnhzrHrVVZE03/XTL77exHP1VtNW9ZjYKdCzLb21AIAw9V109GFeJZ/P\nr3qfcirS34wBAJo+m9LpDhDJQWXqwYkdDAgA4DgODfUzbTCxw1tCVbw9NNQYAIBh2efHA7ey\nCit43OKc+8ci+lkzAQA4vbe8JG2N9a46uPcWt1UDANBrP2lLbNrbD9W8iqKceyeWBtqyAAA0\nuq3LJG2OVa+6KpMWtWUCk81mikzsaKx6TOwUpuziBDMAAOc51z8Kr7g+xwkAwGTcuQ80xYbk\n5922PgBM855zj6V95N+eb9tAYoe3hMq4v7gVAADbc0lyhdCK8oR5rQkAgLbL0z4vw3pXISVH\nR2gDAFiFnskTXlN5d5EbAAC4L3vyeRlWveqqTI5oywIwHz8zREtEYkdr1WMfO0Xhxh44mgvA\n7PlduDdHaI2G9w/f9WACvDu6/2IFTdEhuSF03cdtvfnf1dVDWnEa3hJvCZXxKuHOWw0mMH2m\nzmjPFlqj3nnYQFsAgMePHn0aSQrrXZWkPHyqqcdh2Q6fNsBIeI2ax8CvWgAAZGZmflqEVa+y\nqh4tn7D8QZVe4Mpof00R6+mtekzsFCUxNrYYANz9/c3rrbPw93cDgA8XL15XelxIzoxGrft9\naicDovEt8ZZQGZaTzxaWVZYXnvym/ojjJiYmAABQXc0TLMB6VyWdIpNyCz9WZq7uVH8di8UC\nADA3/1zRWPUqqvrRyvHLk7k6fqu2fmMpcgt6qx4TOwUpSUnJAQCOu7ujiLUt3d01AOBdSkq+\nkuNCtMFbQsUw1Gt7Q9cqu3fvCQCAa7t2gl7VWO8qiCDq/ydXemXX4WcAYBPg31qwBKteNVWn\nrB4flVSp5bPytzBr0ZvQXPWY2CnIixcvAACsrKxErv60vLbNHqk8vCW+ALyMbatiigE0fMLG\nthIswnpXbVUf8rL+u3YwIqTLwK3PQMt19q6lPT49nseqV0W8J6snRCZVaHSP3jnFXtyTGpqr\nHueKVZCSkhIAAG1tbZGrtbS0AABKS0WPeIVUEN4SKq/0xuLhi66Xg0aHxRu+/fyvPNa7yrr3\nk4vHsieCn9WM3fqGRU2eO3dQ7UgoWPWqh/dk3cTIhHJ1r192zWolvgMOzVWPLXYKUl5eDgDA\nZrNFrlZXVydthb4EeEuoNP7bi/P79F9+p5xhFbLnxIJ2ap9XYL1/EarL3r/JSruTnPmxdhlW\nvarhZ2yeuPh6uZrHz7vnOjeUPdFc9ZjYKYiGhgYAQEWF6NdePi3ncBp5kRKpDrwlVFd56u7h\nXQasulXMshv5x9UDI1uQ/pXHeldZ7j/dfv8+/21Oxv24o2vHO+Ve3hc9smP7bw69/PQ+NFa9\nauFnbpq48HoZs+2CXeFuDT/tpLnqMbFTEF1dXYCaBtl6iouLSVuhLwHeEqqJ//byj727TorJ\nrNLtNO/vhANjWqoJrcd6V1lMDR19fUMTS4e2PYd+t+mf5GOhLaD86b5JU/94CwBY9Somc1vY\nwrgPzDY/7F7UQa2RbWmuekzsFMTO3p4AgJyXn/95E8LLysoBAHB0dFBuWIg+eEuooKpnB0d0\n7rcyoVDNYdiehLiVfmb1ut1gvX8hCNPAqJmdAeDj+T/PlABg1auUrJ1hP/7zgd1u8f6lnqKf\nr5LRXPWY2CmIlpubHQBUPHjwRMTah3fvcgHAxs1NT7lhIfrgLaFqeDnHQ/t+8+dzrn6X+RcS\nDo9vLfK5Cta7Kqkqffvy6cOku89F9nq3btGCAQDVb968A8CqVyWll0/9UwJQeT+ygzohLPCP\nDwBQtLs/QRAEYf1DAtBe9ZjYKUrHgABjAPjv779f1Fv35MzZdAAwGjCgs9LjQrTBW0KlFF35\nru/IA5nVhj6/XI79pbeJ2DfksN5VyKNo7xZObTt1mR5TLGJtzsuXPAAAIyPBrBRY9SqDoW1m\nK4apFgEAhLapra2tra2NoToA7VWvqLnKEDdhth0AQIuwC8VCKwqOjzIHAHCYk8ilKTakIA3P\nFYu3hOrIPzHaEgBYzrPiihvbFutddVRdmW4FAMBuH3GnrM66sqSFbRgAAG6Rjz4twqr/Apwe\nJ2quWFqrHhM7Bco9HGwEAKDvPefA3TflPD6v/HXSHzM8dQAATEbEvKU7QCQ7XnUVt1bCPEFi\nt/geaWE1r2ZrvCVUQ+XNOYI/2rOulnLFqMJ6V0mZ23y0AAAIs27f7bic+qqosrqiMPu/2K1T\nOhkTAAC6A/a9qd0cq17liU7saK16TOwUqvjfxZ0/P0RnsDXYn57WEIbdo26U0h0ckodLExvt\nJTHgf6R/7fGWUAV/T2j8bTbm8BjSHljvqiP3QrinXu2Td/L0YoRBpwX/5AtvjlWv4sQldjRW\nPfaxUyidHkvjH/2zcdbgzo7mOgweU8/SyTv4+61XH139qasW3cEhOuAtoQrEDU8lHta76jD1\nX5XwJOH3iAn9vZzM9TgsBoujZ9bSM2Dcot03Ht9Y7msovDlW/ReLtqon+HxRb+MihBBCCKHm\nBlvsEEIIIYRUBCZ2CCGEEEIqAhM7hBBCCCEVgYkdQgghhJCKwMQOIYQQQkhFYGKHEEIIIaQi\nMLFDCCGEEFIRmNghhKRwJlSbqNXv91K6A1IWRZx4072YsVP0yZHtKqQ7IISQpDCxQwghhBBS\nEZjYIaRA5UdC1AmCIBwW3hVafHCIOkEQhNn0axIVU7qrH6n9hHCJfCRVFNLtfnKMBmlj/Smx\nUh0LIYQQnVh0B4CQKkuMv14JAFre3m3Ji29fv1EJAOxu3TrRExeSmuuoVevaV37+ZOelTmcw\nCCEkDiZ2CClORnz8awAAL++u5F+1p/HxuQAAHbp106AlLiQ9e/9ps/3pDgIhhBqDj2IRUpiS\n+PiHAADO3t5GpMXF168/AgCw6t7dlpa4EEIIqSpM7BBSFN6N+JvVAKDt7e1OWsy/ef0mDwA4\n3bt1oCkyhBBCKgoTO4QU5dH160UAQHT27sokLU65fr0AAKBTN281egKTHwaDAQDc14n7I8f1\n9XCyMdJU1zKyat09ZO6WK6+rKBZa+er671HTQ3q1tbc01lFnc/TNbNt0Gzo9+sDtPJ6Izd9v\n60N62cN/RzEAAHzIuLBxRmDn1rbGWuqaBhbOXQfP2hj7sqL+7gVbfevtXpi0eZKPk4mWurqO\n9cSTZQAg2dAk/MKHx9fO+6ZfJycbM32OGltT39S+Xe+h0345dCuX2+h5s1ismsiDOrdpNHLK\nV6xmt6xLm+YM69Ha2lhbg6Nv6diuzzcRexNzqwGAIIhG421Aeebf62YHd29tbayjztGzcHTv\nNfqn/914VQUAvBPD2bXXscPyNHmckYz3AOXjfvbhWezuJd8O8e3YyspIV5PNUuPoGFk5d/Ib\n8d2qQ7feUv1FQIgiPkJIfq5MN5P2d3DgvrLGSi3ZGUDewznioVRBSbf7idHk9wL0vr0ktPb0\nOC1y7Ie47+OjfY1FZQH6neZfKZAqTj6fn//vsgEtxHU8JPTbf3s4k1tnl6oDg0nbdFr5gl+V\ncWC0I1tEAXqdF157X2d37r6BpC28VmVVp23w0a1dErCzRMSJB/yvpE45ZY92j3fXF5sOabce\nviWpqM4+QmUSIceki5zyFePz+fzql8cmttESsQvD3H9lQtGV6eTOAwE7RR9bpKpnB8e04ogK\nxqTviptF77b5kJZ1X58tjzOS8R6Q5UpWvzq3sKdFQ/+icVoFb7xdKPkFREhGmNghJE9fVmLH\nGLbtwGAD8aem67v1uRRxvj831anONyRLg8MWSpYYNiP+fC282/FRpC9w50W3bs5vLf6tMPPR\nJ+p8rR8bSTpm68XxR0J0yDtIlNiVJy331oXGaHWISPgg9mJqfnOs4cjHnKyXkFC8YvyyG3Nd\nGshFLMfPH2sidA0kTuw+XJ3h2MA7eRbjNy7wIH3us+1dU7gHKB/3Q/xcJ0leQdTtvuG/Kkmv\nIUKywcQOIXnKit36q8BsX0MAAHAKXvoryZy+pgAA4DJi+aclJxv/i99UEzumhYUJgLbrsIgd\nxy/EXjyxZ+modkJZERiMPCZpW0XukRBSKxFh7rfkbOr7Kj6fV5p1ddPIlrVf3IZDD+WSdzw1\nltQ+ZBEwoAMTQM3CK3jqvB/Dpwz1MCE/BwdgeK5+xhe7u03IiG6C7Rkaeob6HJYkiV3J1VkO\n5F4tWq4jl+39O+7aP6f2Lh/XXo+UHBC2M/4tF3MxDXv1bSTyTmsy5XLFeA+WtBMqWtM5ZOm+\nCzeTbsef3b1ggC0bgMkkbyBxYsdLXuwq1L9HzSbgh23H/7l2Lfboplm9zZkAHA65Ucx/h1Cj\nLl33ANXj5qzvXnu66q2GRO09n/AgJSMz/XFy/Kmts3ua1ta91ld76uSwCCkIJnYIKUTpvoFM\nAACL76+TF1ccGsoGALCde1uKwuSb2Eml4UexAOoeEbfJrVAfE39sS/4WZffbJdHzWN7DRa1J\nu5lPuCjUtlWdtprUI9E1IqWBkEC3Z3RizVNPXu7ZUHuhhpc6rURCu7NYLADTXov/Siuq5vP5\n/MrCghKeiKOQE7usdd3IjTYOMy6TH7l+vDHPlXRB2P125Yo+tASR992eJ4crVn1tuiW5VOsJ\nf+eT93wdM7xOs7OkiV3V1Snm5P2MhxzMIYeUuedrwwZKpuseoHzcykMDa0vVC7tQXed6VFyd\naadtYtOqrZdPwKAxG5OwzQ4pBSZ2CCnEv7MsAQDUBh0WetB6a04LAAB28OEKKQpruomd9fQr\nlXWOVno0WJu0hdqQPyU51VvzWpB2avPzg3oncSCw9tvVYeFdsSFxem+p02/r3c4AcuZlMTOu\ngTMCu6lXS0UEKDaxS4lyJZ+u/468OnvmbvdVV9fWMzKzsnV0du0072Kl6DKljJzyFUv4wZq0\nI+G9tl43t5drugq1u0ma2N2ca0XezSM6ve4W6cs6EGJLpuseoHzcot1+tfuxuq1KE9WbESFl\nw7diEVKEV7duvQIAaNu5M/nJU+6tW1kAAG09PUV17G52TAYG+9Ttq6XVr38P0pc39/79/xov\nKDs+Pqv2E7NNG+e6W2h37Fi77NmNG7liStL4ekqolfAi475925E+FhY2NKV9lxnzfES9VCBO\nXlwc+fy8+vc3qrOF6bf/lJeXFOa9yX6envro1ko/Mb3bpIuc8hXLS07OJm3lFhBQ56AA1oMH\ne4qOsUF59+7lkD62/jrQse4mjiHBHnWXfUbXPUD9uLqtWpnWLK+6Pr+j21dTlmz988r9lyXV\nYkJDSPEwsUNIAapu3b4HAGDaubMdaXH17dvJAACGnTrV+8prltzbtav/HqiWkxP5UV9OTk69\nTepJz8ggfao+OkKdqEtoitsHDx6IKcnVy0uz7jIbGxvSp8rKyrob1LL18bFrPFyS1JQUfu0n\nTqtW1uK3bZh0kVO+Ys+fPyftSDg5tawfir2ra71QGvfixQtyya1b18uQAFq6u4t6YxaAvntA\nhuN2D5viVvslyi95cu63yOnDfdu30Ne1bNdnxKzonecevG3gbkNIETCxQ0gBHt6+XQ4ADC8v\noZaP/27f/gAAREfPjvTEBQAATgtulTTg0HCJp0FlGBmJeidWT0+P9Km8uLjxMdzev38v6UEF\n2+fnixlXzNDQsN4ypro6U8SmorRo0aLxjcgKCgpIn3SFTl060kVO+YqVlJSQFnP09ES1HRsY\n6EtVOgAAFBcXk0vW1xdxIxGibxkA+u4BGY5LtF98Ymewbf0mWN7H1w8uH9m0ePJX7SytOo75\n9fKrhobBQ0iucK5YhOTmzeZeFjPjSAt4Z0J1idB62/EvTdQnJgIAgPOi5NTo9soJ7zOCzdHW\n1ha7WkPyvwoEQ+S/hnw+n/yRwWh8sFuGUEkMjp6BZiNhVH4A0BGxnCE6JklpakrZVFVdTX7q\nVufUpSJd5HK6YmJGIhY+KwkJn7yYknk8cSkOXfeATMdltZwQ86jzkVVRG/44nZD1UUTtV+fd\nPTDPL/b67pvHQ+2xKQUpASZ2CCGqqgsKigHqDeAm3ASiqavb+N8ZfX19gDefPxlOPPVuk09D\n2zcdBgYGADWdvYrev+cDyDRrg4QoXzEdHR2At58/fSws5ALUbXKqzsuTrhWrtuR3DZfME18y\nXfeAzMfVdh2+9PDwpRVvH1+/Ghd//Wbi7du3k9PelZOyPF7uqalT9w44H2oivhiE5AQTO4Tk\nRsd78ooV/QCyTi/fdqMEGO6joke5137JPz8d/duNDwDOwZGhHT89pTLqZimmsGbh0cOHAN3q\nLCxMSXlD+mhnZ9d4QU5OTqQv14KcnDIAcX2xmpZWrVqREruK1NRMCHIQ3uTWXAefHe+1dXR0\ndHR0tD2+/2t/3a79FFC+YjY2NgA1vcr4T56kAbjW2Sb1/n0KPcOsra0BntWUnJr6BMCtzjZP\nHjwQN6cXXfeAvI6rburqO8zVd9g0AABuQeq/J3ct+2ntldef8rvyizFnikLHU39Uj5CEMLFD\nSG60Ooz6sQNA8f+uLNwGAM6D5i/4sW3N2oIdlxcAAFh9PTfixy50xShfr/86fnN1N6GpcKHo\n7NnrpI+aHh5OjRdk4e1tB/8+//SJd+1yHHdUv2Yxla5Vz54OEF+Tztw9dy53nvD0I+mXL2eW\nl0J5aWHeawDjHtZyyeUpXzGzdu3M4GpNKvr43LmXEa7kFwuAn/Ln0Uf1d2w8pHbtTODfmia7\nx2fPPo90sxPaJC3mqLg3Hmi7BxRyXDVDlz4TVvdwKLXr/dtrwSJ+Ts5rAEzskMLhE3+E5O1O\n0h0+AHA8PYUaQpKS7gAAqHl5iR3wofnlet6yAAAF8klEQVR59lv4hhTyuxEf4pcsO1te+1n7\n64G+kry40HH0GNIgsXkHItf9J9RmVJm00E3LwNy2lbunt2+/YesSm8y7hh7jQtvWNstWX135\n0wXyw8Z3B+evvlf70Wb06B7yeVJL+Yp1GTCANCILP3HtwpPvSPtVp2//bj2VvA6gS//+5Hcu\nktaGH35FLjnjt1nrxOZ19N0DFI/LvX/gp+8mjvi6VycXG8M2c+JFRMNis0nfscbGxvIJGKEG\nYYsdQnL2/M6dfACA9p6e5IzmWVJSAQBAWy8vid86FePFrtFdzjf6sKjrwivrguT+KEu477ua\nWuX1H3r2fLpoTkgXW05JRty+lcv3kYf/sA6dFijZ2whu038avGX0iU85UXnij719cyIXjOrZ\nSr86P+3GkVU/b3z8kQ9ZhblZ6QzH2cvaN51xAJ2mR43dOnDv54d5L3cN8S5ZsGi8b0uNvJRL\nO5asOptfs6nugKULesha/Z9RvWIs37DxjntW1zyNfb1/ZLeP8xdN8mttwM2+fXR91LZrHzgc\ntbKymoRd0ldC2H6TvrHdu7Fm0JO3R7/p4n97XphfG0Pe6+RTW1fuvFGsqan+8aOYp7F03QPU\njqtmUHBt255/P12l9YP6VEeHh3RxsTbRUSeqPr7PeXrn7KbFW2vG+mF0/vorTOyQUtA7PjJC\nKocbM1wDAMBiptBcYuVHBqsBAJhM/Uf6MilNHfF5klP5zhUrNNs6Y3BkdAMjLROWo4/nizmK\nKHmnJtg3/r+mdqeo2x/JuzUwi2vNOQ0n5djM4TFS7t7oZsVXfnAnD0QtEttl6qU8yctsPHLK\nV4z//vQYC/Hthgyn8PUzyaO+9N6Sy5dQ3om685GREQ7frZxC7l9Yb04Leu4BisetvLfUU8KX\nqDU6Rt4u5yOkDPgoFiH5enznTjkAMD09hZ643k9K4gIAUWdku+aGV15OeuKkY9htfsz+SW6i\n2gXVbAI3X9w9uP6AYuIZBe1K+PsnXwvx/Zs0nUI2Xbn0k2dTe69Cp9ev8XFrhzqK/ZbXaDl4\nxcWrm/vWnZVCRlSvmP7XO8+t9zMX9Yyc7Thy14moryzIz1Q/fvwocUSD/nd+XV8zkSXbD9tx\nakU3jQYHUqHrHqB0XLV2i89fWDbAtpGWQx2X4euv/hPhKa+mWoQaho9iEZKrojt3MgAA2ngK\nffO8TUp6CQDg4uVVb3SQ5qREaAxaMzMzll3fnQntBu3a9NvBS8npL99+VDOwbNmxz5DQWbNC\n3MQPlicGYeoX9c+zSZf/2HX43OXryRmv8gpKuCwtAwv7Nh26+w8eO2FYZ7Mm+kdL1+v7o6mj\nbx/Zvf/0pbhbqdnvCoqrNIwsrOzcenwdMnpsSI8WjTbpUUH1imm0m3Xhcbe9a9b/7+TVh8/f\nfmDqm1s7tO8bMn7KxIFt9KDklgVATXc44TGNG8FpP/vifz32rt3wx6mrDzJzSxn65tb27fqE\njJ8ycZCrPsRVVZE2VlOrl0nRdQ9QO65R94Vn0sYnHN9/9NzVhPtPnmXlvi8tq+SraWrrm7Zo\n5dqhm9/AESMD2xlLOkA2QrIj+DKMp4kQQghJrvr4KM7QQ59776mPO13++9e0BoSQymmi//wi\nhBBqlngVhW9eZmVlZeXrdxnQoc77Asm3bpFeonZ2FjGdLEJIJpjYIYQQkouig6NaTzn6poQr\neBDkFJ54b5UXqUtC/olf92TUfrQPCGil5AgRUn348gRCCCG50OvV253Lrenek7Y60H/Orov3\nnma/fvkk4cTqUT3H/Fk7ZJ56n3mzOtASJkIqDfvYIYQQkpOq1PV9u3wfV9TohmaBexJOjLfD\nlwoQkjdssUMIISQnLJfZp69uGdVaq4FtGEZdZv0Z/ydmdQgpBLbYIYQQkq/qd3dP7D1w4mL8\n3f8ycvKKPlaxNPUMTFq4eHj1CBgWOsrXvqHEDyEkC0zsEEIIIYRUBD6KRQghhBBSEZjYIYQQ\nQgipCEzsEEIIIYRUBCZ2CCGEEEIqAhM7hBBCCCEVgYkdQgghhJCK+D8gCDq82H6NIgAAAABJ\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(L.TFs_order,aes(x=DEG,y=DEGenrichment))+geom_point(stat = \"identity\" ,size=5)+xlab(\"# LHb enriched genes\")+ylab(\"Selectivity for LHb genes\")+ scale_y_continuous(breaks=seq(0,0.6,0.2))+theme(axis.text.x=element_text(size=16,color=\"black\"),axis.text.y=element_text(size=16,hjust = 1),axis.title=element_text(size=20,face=\"bold\"),plot.title = element_text(size=24),,panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+xlim(0,40)+ggtitle(\"DEG and DEG enrichment\")\n",
    "#+xlab(\"TF\")+ylab(\"DEG enrichment\")+theme(axis.text.x=element_text(size=10,angle = 50, hjust = 1))+ggtitle(\"correlation_DEG_Ar\") \n",
    "             \n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/LHb_TFs_scatter_tight.pdf\",width=15,height=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.xlsx2(L.TFs_order,file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/LTF_rank_LHbrichgenes_tight.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# testing coverage of marker genes by TFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "M.TFs_order<-M.TFs_order[rev(order(M.TFs_order$DEGenrichment)),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>M.TFs</th><th scope=col>DEG</th><th scope=col>nonDEG</th><th scope=col>DEGenrichment</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Gpd1</th><td>Gpd1            </td><td> 7              </td><td>   0            </td><td>1.00000000      </td></tr>\n",
       "\t<tr><th scope=row>Neurod1_extended</th><td>Neurod1_extended</td><td> 9              </td><td>   0            </td><td>1.00000000      </td></tr>\n",
       "\t<tr><th scope=row>Irx3_extended</th><td>Irx3_extended   </td><td> 3              </td><td>   2            </td><td>0.60000000      </td></tr>\n",
       "\t<tr><th scope=row>Neurod2_extended</th><td>Neurod2_extended</td><td>19              </td><td>  14            </td><td>0.57575758      </td></tr>\n",
       "\t<tr><th scope=row>Pou3f1_extended</th><td>Pou3f1_extended </td><td>35              </td><td>  52            </td><td>0.40229885      </td></tr>\n",
       "\t<tr><th scope=row>Lef1_extended</th><td>Lef1_extended   </td><td> 5              </td><td>   9            </td><td>0.35714286      </td></tr>\n",
       "\t<tr><th scope=row>Ebf1</th><td>Ebf1            </td><td>11              </td><td>  26            </td><td>0.29729730      </td></tr>\n",
       "\t<tr><th scope=row>Sox6</th><td>Sox6            </td><td> 2              </td><td>   5            </td><td>0.28571429      </td></tr>\n",
       "\t<tr><th scope=row>Pbx3_extended</th><td>Pbx3_extended   </td><td> 5              </td><td>  15            </td><td>0.25000000      </td></tr>\n",
       "\t<tr><th scope=row>Pou3f3_extended</th><td>Pou3f3_extended </td><td> 5              </td><td>  22            </td><td>0.18518519      </td></tr>\n",
       "\t<tr><th scope=row>Nfib</th><td>Nfib            </td><td> 2              </td><td>   9            </td><td>0.18181818      </td></tr>\n",
       "\t<tr><th scope=row>Tcf7l2</th><td>Tcf7l2          </td><td>20              </td><td>  93            </td><td>0.17699115      </td></tr>\n",
       "\t<tr><th scope=row>Zic4_extended</th><td>Zic4_extended   </td><td> 1              </td><td>   5            </td><td>0.16666667      </td></tr>\n",
       "\t<tr><th scope=row>Klf9_extended</th><td>Klf9_extended   </td><td> 6              </td><td>  63            </td><td>0.08695652      </td></tr>\n",
       "\t<tr><th scope=row>Tcf7_extended</th><td>Tcf7_extended   </td><td> 3              </td><td>  33            </td><td>0.08333333      </td></tr>\n",
       "\t<tr><th scope=row>Lhx9_extended</th><td>Lhx9_extended   </td><td> 3              </td><td>  33            </td><td>0.08333333      </td></tr>\n",
       "\t<tr><th scope=row>Ybx1_extended</th><td>Ybx1_extended   </td><td> 4              </td><td>  48            </td><td>0.07692308      </td></tr>\n",
       "\t<tr><th scope=row>Zic1</th><td>Zic1            </td><td>15              </td><td> 192            </td><td>0.07246377      </td></tr>\n",
       "\t<tr><th scope=row>Mxd4_extended</th><td>Mxd4_extended   </td><td> 2              </td><td>  31            </td><td>0.06060606      </td></tr>\n",
       "\t<tr><th scope=row>Myef2_extended</th><td>Myef2_extended  </td><td> 2              </td><td>  39            </td><td>0.04878049      </td></tr>\n",
       "\t<tr><th scope=row>Nfix</th><td>Nfix            </td><td> 1              </td><td>  20            </td><td>0.04761905      </td></tr>\n",
       "\t<tr><th scope=row>Klf7_extended</th><td>Klf7_extended   </td><td> 3              </td><td>  98            </td><td>0.02970297      </td></tr>\n",
       "\t<tr><th scope=row>E4f1_extended</th><td>E4f1_extended   </td><td> 1              </td><td>  35            </td><td>0.02777778      </td></tr>\n",
       "\t<tr><th scope=row>Pou2f2_extended</th><td>Pou2f2_extended </td><td> 2              </td><td>  72            </td><td>0.02702703      </td></tr>\n",
       "\t<tr><th scope=row>Etv5_extended</th><td>Etv5_extended   </td><td> 6              </td><td> 233            </td><td>0.02510460      </td></tr>\n",
       "\t<tr><th scope=row>Foxp2_extended</th><td>Foxp2_extended  </td><td>42              </td><td>1739            </td><td>0.02358226      </td></tr>\n",
       "\t<tr><th scope=row>Bhlhe41_extended</th><td>Bhlhe41_extended</td><td> 4              </td><td> 176            </td><td>0.02222222      </td></tr>\n",
       "\t<tr><th scope=row>Usf2_extended</th><td>Usf2_extended   </td><td>65              </td><td>2882            </td><td>0.02205633      </td></tr>\n",
       "\t<tr><th scope=row>Tef_extended</th><td>Tef_extended    </td><td>39              </td><td>1750            </td><td>0.02179989      </td></tr>\n",
       "\t<tr><th scope=row>Npdc1_extended</th><td>Npdc1_extended  </td><td> 1              </td><td>  45            </td><td>0.02173913      </td></tr>\n",
       "\t<tr><th scope=row>...</th><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><th scope=row>Kdm5a_extended</th><td>Kdm5a_extended </td><td>37             </td><td>3513           </td><td>0.0104225352   </td></tr>\n",
       "\t<tr><th scope=row>Elf2_extended</th><td>Elf2_extended  </td><td>18             </td><td>1966           </td><td>0.0090725806   </td></tr>\n",
       "\t<tr><th scope=row>Kdm5b_extended</th><td>Kdm5b_extended </td><td>20             </td><td>2245           </td><td>0.0088300221   </td></tr>\n",
       "\t<tr><th scope=row>Hdac2_extended</th><td>Hdac2_extended </td><td>37             </td><td>4315           </td><td>0.0085018382   </td></tr>\n",
       "\t<tr><th scope=row>Smc3_extended</th><td>Smc3_extended  </td><td> 3             </td><td> 351           </td><td>0.0084745763   </td></tr>\n",
       "\t<tr><th scope=row>Maz_extended</th><td>Maz_extended   </td><td>30             </td><td>3607           </td><td>0.0082485565   </td></tr>\n",
       "\t<tr><th scope=row>Yy1_extended</th><td>Yy1_extended   </td><td> 9             </td><td>1212           </td><td>0.0073710074   </td></tr>\n",
       "\t<tr><th scope=row>Stat1_extended</th><td>Stat1_extended </td><td> 2             </td><td> 287           </td><td>0.0069204152   </td></tr>\n",
       "\t<tr><th scope=row>Taf1_extended</th><td>Taf1_extended  </td><td>27             </td><td>3993           </td><td>0.0067164179   </td></tr>\n",
       "\t<tr><th scope=row>Sin3a_extended</th><td>Sin3a_extended </td><td> 4             </td><td> 593           </td><td>0.0067001675   </td></tr>\n",
       "\t<tr><th scope=row>Zfp523_extended</th><td>Zfp523_extended</td><td>10             </td><td>1517           </td><td>0.0065487885   </td></tr>\n",
       "\t<tr><th scope=row>Suz12_extended</th><td>Suz12_extended </td><td> 7             </td><td>1088           </td><td>0.0063926941   </td></tr>\n",
       "\t<tr><th scope=row>Pml_extended</th><td>Pml_extended   </td><td> 1             </td><td> 186           </td><td>0.0053475936   </td></tr>\n",
       "\t<tr><th scope=row>Phf8_extended</th><td>Phf8_extended  </td><td> 8             </td><td>1652           </td><td>0.0048192771   </td></tr>\n",
       "\t<tr><th scope=row>Zfp239</th><td>Zfp239         </td><td> 1             </td><td> 212           </td><td>0.0046948357   </td></tr>\n",
       "\t<tr><th scope=row>Nfyb_extended</th><td>Nfyb_extended  </td><td> 5             </td><td>1297           </td><td>0.0038402458   </td></tr>\n",
       "\t<tr><th scope=row>Etv3_extended</th><td>Etv3_extended  </td><td> 5             </td><td>1298           </td><td>0.0038372985   </td></tr>\n",
       "\t<tr><th scope=row>Mxi1_extended</th><td>Mxi1_extended  </td><td> 7             </td><td>1835           </td><td>0.0038002172   </td></tr>\n",
       "\t<tr><th scope=row>Gabpa_extended</th><td>Gabpa_extended </td><td> 1             </td><td> 295           </td><td>0.0033783784   </td></tr>\n",
       "\t<tr><th scope=row>Rbbp5_extended</th><td>Rbbp5_extended </td><td> 7             </td><td>2299           </td><td>0.0030355594   </td></tr>\n",
       "\t<tr><th scope=row>Zeb1_extended</th><td>Zeb1_extended  </td><td> 1             </td><td> 332           </td><td>0.0030030030   </td></tr>\n",
       "\t<tr><th scope=row>Zfp369_extended</th><td>Zfp369_extended</td><td> 2             </td><td> 682           </td><td>0.0029239766   </td></tr>\n",
       "\t<tr><th scope=row>E2f6_extended</th><td>E2f6_extended  </td><td> 5             </td><td>1882           </td><td>0.0026497085   </td></tr>\n",
       "\t<tr><th scope=row>E2f4_extended</th><td>E2f4_extended  </td><td> 1             </td><td> 385           </td><td>0.0025906736   </td></tr>\n",
       "\t<tr><th scope=row>Elk1_extended</th><td>Elk1_extended  </td><td> 1             </td><td> 390           </td><td>0.0025575448   </td></tr>\n",
       "\t<tr><th scope=row>Setdb1_extended</th><td>Setdb1_extended</td><td> 1             </td><td> 424           </td><td>0.0023529412   </td></tr>\n",
       "\t<tr><th scope=row>Ep300_extended</th><td>Ep300_extended </td><td> 2             </td><td>1166           </td><td>0.0017123288   </td></tr>\n",
       "\t<tr><th scope=row>Ets2_extended</th><td>Ets2_extended  </td><td> 1             </td><td> 644           </td><td>0.0015503876   </td></tr>\n",
       "\t<tr><th scope=row>Bmyc_extended</th><td>Bmyc_extended  </td><td> 3             </td><td>1944           </td><td>0.0015408320   </td></tr>\n",
       "\t<tr><th scope=row>Gtf2f1_extended</th><td>Gtf2f1_extended</td><td> 1             </td><td>1184           </td><td>0.0008438819   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & M.TFs & DEG & nonDEG & DEGenrichment\\\\\n",
       "\\hline\n",
       "\tGpd1 & Gpd1             &  7               &    0             & 1.00000000      \\\\\n",
       "\tNeurod1\\_extended & Neurod1\\_extended &  9                 &    0               & 1.00000000        \\\\\n",
       "\tIrx3\\_extended & Irx3\\_extended    &  3                 &    2               & 0.60000000        \\\\\n",
       "\tNeurod2\\_extended & Neurod2\\_extended & 19                 &   14               & 0.57575758        \\\\\n",
       "\tPou3f1\\_extended & Pou3f1\\_extended  & 35                 &   52               & 0.40229885        \\\\\n",
       "\tLef1\\_extended & Lef1\\_extended    &  5                 &    9               & 0.35714286        \\\\\n",
       "\tEbf1 & Ebf1             & 11               &   26             & 0.29729730      \\\\\n",
       "\tSox6 & Sox6             &  2               &    5             & 0.28571429      \\\\\n",
       "\tPbx3\\_extended & Pbx3\\_extended    &  5                 &   15               & 0.25000000        \\\\\n",
       "\tPou3f3\\_extended & Pou3f3\\_extended  &  5                 &   22               & 0.18518519        \\\\\n",
       "\tNfib & Nfib             &  2               &    9             & 0.18181818      \\\\\n",
       "\tTcf7l2 & Tcf7l2           & 20               &   93             & 0.17699115      \\\\\n",
       "\tZic4\\_extended & Zic4\\_extended    &  1                 &    5               & 0.16666667        \\\\\n",
       "\tKlf9\\_extended & Klf9\\_extended    &  6                 &   63               & 0.08695652        \\\\\n",
       "\tTcf7\\_extended & Tcf7\\_extended    &  3                 &   33               & 0.08333333        \\\\\n",
       "\tLhx9\\_extended & Lhx9\\_extended    &  3                 &   33               & 0.08333333        \\\\\n",
       "\tYbx1\\_extended & Ybx1\\_extended    &  4                 &   48               & 0.07692308        \\\\\n",
       "\tZic1 & Zic1             & 15               &  192             & 0.07246377      \\\\\n",
       "\tMxd4\\_extended & Mxd4\\_extended    &  2                 &   31               & 0.06060606        \\\\\n",
       "\tMyef2\\_extended & Myef2\\_extended   &  2                 &   39               & 0.04878049        \\\\\n",
       "\tNfix & Nfix             &  1               &   20             & 0.04761905      \\\\\n",
       "\tKlf7\\_extended & Klf7\\_extended    &  3                 &   98               & 0.02970297        \\\\\n",
       "\tE4f1\\_extended & E4f1\\_extended    &  1                 &   35               & 0.02777778        \\\\\n",
       "\tPou2f2\\_extended & Pou2f2\\_extended  &  2                 &   72               & 0.02702703        \\\\\n",
       "\tEtv5\\_extended & Etv5\\_extended    &  6                 &  233               & 0.02510460        \\\\\n",
       "\tFoxp2\\_extended & Foxp2\\_extended   & 42                 & 1739               & 0.02358226        \\\\\n",
       "\tBhlhe41\\_extended & Bhlhe41\\_extended &  4                 &  176               & 0.02222222        \\\\\n",
       "\tUsf2\\_extended & Usf2\\_extended    & 65                 & 2882               & 0.02205633        \\\\\n",
       "\tTef\\_extended & Tef\\_extended     & 39                 & 1750               & 0.02179989        \\\\\n",
       "\tNpdc1\\_extended & Npdc1\\_extended   &  1                 &   45               & 0.02173913        \\\\\n",
       "\t... & ... & ... & ... & ...\\\\\n",
       "\tKdm5a\\_extended & Kdm5a\\_extended  & 37                & 3513              & 0.0104225352     \\\\\n",
       "\tElf2\\_extended & Elf2\\_extended   & 18                & 1966              & 0.0090725806     \\\\\n",
       "\tKdm5b\\_extended & Kdm5b\\_extended  & 20                & 2245              & 0.0088300221     \\\\\n",
       "\tHdac2\\_extended & Hdac2\\_extended  & 37                & 4315              & 0.0085018382     \\\\\n",
       "\tSmc3\\_extended & Smc3\\_extended   &  3                &  351              & 0.0084745763     \\\\\n",
       "\tMaz\\_extended & Maz\\_extended    & 30                & 3607              & 0.0082485565     \\\\\n",
       "\tYy1\\_extended & Yy1\\_extended    &  9                & 1212              & 0.0073710074     \\\\\n",
       "\tStat1\\_extended & Stat1\\_extended  &  2                &  287              & 0.0069204152     \\\\\n",
       "\tTaf1\\_extended & Taf1\\_extended   & 27                & 3993              & 0.0067164179     \\\\\n",
       "\tSin3a\\_extended & Sin3a\\_extended  &  4                &  593              & 0.0067001675     \\\\\n",
       "\tZfp523\\_extended & Zfp523\\_extended & 10                & 1517              & 0.0065487885     \\\\\n",
       "\tSuz12\\_extended & Suz12\\_extended  &  7                & 1088              & 0.0063926941     \\\\\n",
       "\tPml\\_extended & Pml\\_extended    &  1                &  186              & 0.0053475936     \\\\\n",
       "\tPhf8\\_extended & Phf8\\_extended   &  8                & 1652              & 0.0048192771     \\\\\n",
       "\tZfp239 & Zfp239          &  1              &  212            & 0.0046948357   \\\\\n",
       "\tNfyb\\_extended & Nfyb\\_extended   &  5                & 1297              & 0.0038402458     \\\\\n",
       "\tEtv3\\_extended & Etv3\\_extended   &  5                & 1298              & 0.0038372985     \\\\\n",
       "\tMxi1\\_extended & Mxi1\\_extended   &  7                & 1835              & 0.0038002172     \\\\\n",
       "\tGabpa\\_extended & Gabpa\\_extended  &  1                &  295              & 0.0033783784     \\\\\n",
       "\tRbbp5\\_extended & Rbbp5\\_extended  &  7                & 2299              & 0.0030355594     \\\\\n",
       "\tZeb1\\_extended & Zeb1\\_extended   &  1                &  332              & 0.0030030030     \\\\\n",
       "\tZfp369\\_extended & Zfp369\\_extended &  2                &  682              & 0.0029239766     \\\\\n",
       "\tE2f6\\_extended & E2f6\\_extended   &  5                & 1882              & 0.0026497085     \\\\\n",
       "\tE2f4\\_extended & E2f4\\_extended   &  1                &  385              & 0.0025906736     \\\\\n",
       "\tElk1\\_extended & Elk1\\_extended   &  1                &  390              & 0.0025575448     \\\\\n",
       "\tSetdb1\\_extended & Setdb1\\_extended &  1                &  424              & 0.0023529412     \\\\\n",
       "\tEp300\\_extended & Ep300\\_extended  &  2                & 1166              & 0.0017123288     \\\\\n",
       "\tEts2\\_extended & Ets2\\_extended   &  1                &  644              & 0.0015503876     \\\\\n",
       "\tBmyc\\_extended & Bmyc\\_extended   &  3                & 1944              & 0.0015408320     \\\\\n",
       "\tGtf2f1\\_extended & Gtf2f1\\_extended &  1                & 1184              & 0.0008438819     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | M.TFs | DEG | nonDEG | DEGenrichment |\n",
       "|---|---|---|---|---|\n",
       "| Gpd1 | Gpd1             |  7               |    0             | 1.00000000       |\n",
       "| Neurod1_extended | Neurod1_extended |  9               |    0             | 1.00000000       |\n",
       "| Irx3_extended | Irx3_extended    |  3               |    2             | 0.60000000       |\n",
       "| Neurod2_extended | Neurod2_extended | 19               |   14             | 0.57575758       |\n",
       "| Pou3f1_extended | Pou3f1_extended  | 35               |   52             | 0.40229885       |\n",
       "| Lef1_extended | Lef1_extended    |  5               |    9             | 0.35714286       |\n",
       "| Ebf1 | Ebf1             | 11               |   26             | 0.29729730       |\n",
       "| Sox6 | Sox6             |  2               |    5             | 0.28571429       |\n",
       "| Pbx3_extended | Pbx3_extended    |  5               |   15             | 0.25000000       |\n",
       "| Pou3f3_extended | Pou3f3_extended  |  5               |   22             | 0.18518519       |\n",
       "| Nfib | Nfib             |  2               |    9             | 0.18181818       |\n",
       "| Tcf7l2 | Tcf7l2           | 20               |   93             | 0.17699115       |\n",
       "| Zic4_extended | Zic4_extended    |  1               |    5             | 0.16666667       |\n",
       "| Klf9_extended | Klf9_extended    |  6               |   63             | 0.08695652       |\n",
       "| Tcf7_extended | Tcf7_extended    |  3               |   33             | 0.08333333       |\n",
       "| Lhx9_extended | Lhx9_extended    |  3               |   33             | 0.08333333       |\n",
       "| Ybx1_extended | Ybx1_extended    |  4               |   48             | 0.07692308       |\n",
       "| Zic1 | Zic1             | 15               |  192             | 0.07246377       |\n",
       "| Mxd4_extended | Mxd4_extended    |  2               |   31             | 0.06060606       |\n",
       "| Myef2_extended | Myef2_extended   |  2               |   39             | 0.04878049       |\n",
       "| Nfix | Nfix             |  1               |   20             | 0.04761905       |\n",
       "| Klf7_extended | Klf7_extended    |  3               |   98             | 0.02970297       |\n",
       "| E4f1_extended | E4f1_extended    |  1               |   35             | 0.02777778       |\n",
       "| Pou2f2_extended | Pou2f2_extended  |  2               |   72             | 0.02702703       |\n",
       "| Etv5_extended | Etv5_extended    |  6               |  233             | 0.02510460       |\n",
       "| Foxp2_extended | Foxp2_extended   | 42               | 1739             | 0.02358226       |\n",
       "| Bhlhe41_extended | Bhlhe41_extended |  4               |  176             | 0.02222222       |\n",
       "| Usf2_extended | Usf2_extended    | 65               | 2882             | 0.02205633       |\n",
       "| Tef_extended | Tef_extended     | 39               | 1750             | 0.02179989       |\n",
       "| Npdc1_extended | Npdc1_extended   |  1               |   45             | 0.02173913       |\n",
       "| ... | ... | ... | ... | ... |\n",
       "| Kdm5a_extended | Kdm5a_extended  | 37              | 3513            | 0.0104225352    |\n",
       "| Elf2_extended | Elf2_extended   | 18              | 1966            | 0.0090725806    |\n",
       "| Kdm5b_extended | Kdm5b_extended  | 20              | 2245            | 0.0088300221    |\n",
       "| Hdac2_extended | Hdac2_extended  | 37              | 4315            | 0.0085018382    |\n",
       "| Smc3_extended | Smc3_extended   |  3              |  351            | 0.0084745763    |\n",
       "| Maz_extended | Maz_extended    | 30              | 3607            | 0.0082485565    |\n",
       "| Yy1_extended | Yy1_extended    |  9              | 1212            | 0.0073710074    |\n",
       "| Stat1_extended | Stat1_extended  |  2              |  287            | 0.0069204152    |\n",
       "| Taf1_extended | Taf1_extended   | 27              | 3993            | 0.0067164179    |\n",
       "| Sin3a_extended | Sin3a_extended  |  4              |  593            | 0.0067001675    |\n",
       "| Zfp523_extended | Zfp523_extended | 10              | 1517            | 0.0065487885    |\n",
       "| Suz12_extended | Suz12_extended  |  7              | 1088            | 0.0063926941    |\n",
       "| Pml_extended | Pml_extended    |  1              |  186            | 0.0053475936    |\n",
       "| Phf8_extended | Phf8_extended   |  8              | 1652            | 0.0048192771    |\n",
       "| Zfp239 | Zfp239          |  1              |  212            | 0.0046948357    |\n",
       "| Nfyb_extended | Nfyb_extended   |  5              | 1297            | 0.0038402458    |\n",
       "| Etv3_extended | Etv3_extended   |  5              | 1298            | 0.0038372985    |\n",
       "| Mxi1_extended | Mxi1_extended   |  7              | 1835            | 0.0038002172    |\n",
       "| Gabpa_extended | Gabpa_extended  |  1              |  295            | 0.0033783784    |\n",
       "| Rbbp5_extended | Rbbp5_extended  |  7              | 2299            | 0.0030355594    |\n",
       "| Zeb1_extended | Zeb1_extended   |  1              |  332            | 0.0030030030    |\n",
       "| Zfp369_extended | Zfp369_extended |  2              |  682            | 0.0029239766    |\n",
       "| E2f6_extended | E2f6_extended   |  5              | 1882            | 0.0026497085    |\n",
       "| E2f4_extended | E2f4_extended   |  1              |  385            | 0.0025906736    |\n",
       "| Elk1_extended | Elk1_extended   |  1              |  390            | 0.0025575448    |\n",
       "| Setdb1_extended | Setdb1_extended |  1              |  424            | 0.0023529412    |\n",
       "| Ep300_extended | Ep300_extended  |  2              | 1166            | 0.0017123288    |\n",
       "| Ets2_extended | Ets2_extended   |  1              |  644            | 0.0015503876    |\n",
       "| Bmyc_extended | Bmyc_extended   |  3              | 1944            | 0.0015408320    |\n",
       "| Gtf2f1_extended | Gtf2f1_extended |  1              | 1184            | 0.0008438819    |\n",
       "\n"
      ],
      "text/plain": [
       "                 M.TFs            DEG nonDEG DEGenrichment\n",
       "Gpd1             Gpd1              7     0   1.00000000   \n",
       "Neurod1_extended Neurod1_extended  9     0   1.00000000   \n",
       "Irx3_extended    Irx3_extended     3     2   0.60000000   \n",
       "Neurod2_extended Neurod2_extended 19    14   0.57575758   \n",
       "Pou3f1_extended  Pou3f1_extended  35    52   0.40229885   \n",
       "Lef1_extended    Lef1_extended     5     9   0.35714286   \n",
       "Ebf1             Ebf1             11    26   0.29729730   \n",
       "Sox6             Sox6              2     5   0.28571429   \n",
       "Pbx3_extended    Pbx3_extended     5    15   0.25000000   \n",
       "Pou3f3_extended  Pou3f3_extended   5    22   0.18518519   \n",
       "Nfib             Nfib              2     9   0.18181818   \n",
       "Tcf7l2           Tcf7l2           20    93   0.17699115   \n",
       "Zic4_extended    Zic4_extended     1     5   0.16666667   \n",
       "Klf9_extended    Klf9_extended     6    63   0.08695652   \n",
       "Tcf7_extended    Tcf7_extended     3    33   0.08333333   \n",
       "Lhx9_extended    Lhx9_extended     3    33   0.08333333   \n",
       "Ybx1_extended    Ybx1_extended     4    48   0.07692308   \n",
       "Zic1             Zic1             15   192   0.07246377   \n",
       "Mxd4_extended    Mxd4_extended     2    31   0.06060606   \n",
       "Myef2_extended   Myef2_extended    2    39   0.04878049   \n",
       "Nfix             Nfix              1    20   0.04761905   \n",
       "Klf7_extended    Klf7_extended     3    98   0.02970297   \n",
       "E4f1_extended    E4f1_extended     1    35   0.02777778   \n",
       "Pou2f2_extended  Pou2f2_extended   2    72   0.02702703   \n",
       "Etv5_extended    Etv5_extended     6   233   0.02510460   \n",
       "Foxp2_extended   Foxp2_extended   42  1739   0.02358226   \n",
       "Bhlhe41_extended Bhlhe41_extended  4   176   0.02222222   \n",
       "Usf2_extended    Usf2_extended    65  2882   0.02205633   \n",
       "Tef_extended     Tef_extended     39  1750   0.02179989   \n",
       "Npdc1_extended   Npdc1_extended    1    45   0.02173913   \n",
       "...              ...              ... ...    ...          \n",
       "Kdm5a_extended   Kdm5a_extended   37  3513   0.0104225352 \n",
       "Elf2_extended    Elf2_extended    18  1966   0.0090725806 \n",
       "Kdm5b_extended   Kdm5b_extended   20  2245   0.0088300221 \n",
       "Hdac2_extended   Hdac2_extended   37  4315   0.0085018382 \n",
       "Smc3_extended    Smc3_extended     3   351   0.0084745763 \n",
       "Maz_extended     Maz_extended     30  3607   0.0082485565 \n",
       "Yy1_extended     Yy1_extended      9  1212   0.0073710074 \n",
       "Stat1_extended   Stat1_extended    2   287   0.0069204152 \n",
       "Taf1_extended    Taf1_extended    27  3993   0.0067164179 \n",
       "Sin3a_extended   Sin3a_extended    4   593   0.0067001675 \n",
       "Zfp523_extended  Zfp523_extended  10  1517   0.0065487885 \n",
       "Suz12_extended   Suz12_extended    7  1088   0.0063926941 \n",
       "Pml_extended     Pml_extended      1   186   0.0053475936 \n",
       "Phf8_extended    Phf8_extended     8  1652   0.0048192771 \n",
       "Zfp239           Zfp239            1   212   0.0046948357 \n",
       "Nfyb_extended    Nfyb_extended     5  1297   0.0038402458 \n",
       "Etv3_extended    Etv3_extended     5  1298   0.0038372985 \n",
       "Mxi1_extended    Mxi1_extended     7  1835   0.0038002172 \n",
       "Gabpa_extended   Gabpa_extended    1   295   0.0033783784 \n",
       "Rbbp5_extended   Rbbp5_extended    7  2299   0.0030355594 \n",
       "Zeb1_extended    Zeb1_extended     1   332   0.0030030030 \n",
       "Zfp369_extended  Zfp369_extended   2   682   0.0029239766 \n",
       "E2f6_extended    E2f6_extended     5  1882   0.0026497085 \n",
       "E2f4_extended    E2f4_extended     1   385   0.0025906736 \n",
       "Elk1_extended    Elk1_extended     1   390   0.0025575448 \n",
       "Setdb1_extended  Setdb1_extended   1   424   0.0023529412 \n",
       "Ep300_extended   Ep300_extended    2  1166   0.0017123288 \n",
       "Ets2_extended    Ets2_extended     1   644   0.0015503876 \n",
       "Bmyc_extended    Bmyc_extended     3  1944   0.0015408320 \n",
       "Gtf2f1_extended  Gtf2f1_extended   1  1184   0.0008438819 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M.TFs_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumulative<-numeric()\n",
    "genes<-numeric()\n",
    "for (i in 1:dim(M.TFs_order)[1]){\n",
    "    genes<-unique(c(genes,intersect(regulons[[as.character(M.TFs_order[i,1])]],MHb_gene)))\n",
    "    cumulative<-c(cumulative,length(genes))\n",
    "}\n",
    "cumulative<-(100/max(cumulative))*cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "M_coverage<-data.frame(TFs=M.TFs_order[,1],cumulative=cumulative)\n",
    "M_coverage$TFs<-factor(M_coverage$TFs,levels=M_coverage$TFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\t<li>4.60526315789474</li>\n",
       "\t<li>10.5263157894737</li>\n",
       "\t<li>11.8421052631579</li>\n",
       "\t<li>19.0789473684211</li>\n",
       "\t<li>30.2631578947368</li>\n",
       "\t<li>31.5789473684211</li>\n",
       "\t<li>32.8947368421053</li>\n",
       "\t<li>32.8947368421053</li>\n",
       "\t<li>34.8684210526316</li>\n",
       "\t<li>35.5263157894737</li>\n",
       "\t<li>35.5263157894737</li>\n",
       "\t<li>42.7631578947368</li>\n",
       "\t<li>42.7631578947368</li>\n",
       "\t<li>44.7368421052632</li>\n",
       "\t<li>44.7368421052632</li>\n",
       "\t<li>46.7105263157895</li>\n",
       "\t<li>48.0263157894737</li>\n",
       "\t<li>50</li>\n",
       "\t<li>50</li>\n",
       "\t<li>51.3157894736842</li>\n",
       "\t<li>51.3157894736842</li>\n",
       "\t<li>52.6315789473684</li>\n",
       "\t<li>53.2894736842105</li>\n",
       "\t<li>53.2894736842105</li>\n",
       "\t<li>53.2894736842105</li>\n",
       "\t<li>63.1578947368421</li>\n",
       "\t<li>63.1578947368421</li>\n",
       "\t<li>71.0526315789474</li>\n",
       "\t<li>76.3157894736842</li>\n",
       "\t<li>76.3157894736842</li>\n",
       "\t<li>77.6315789473684</li>\n",
       "\t<li>77.6315789473684</li>\n",
       "\t<li>80.2631578947368</li>\n",
       "\t<li>86.1842105263158</li>\n",
       "\t<li>86.1842105263158</li>\n",
       "\t<li>90.1315789473684</li>\n",
       "\t<li>94.0789473684211</li>\n",
       "\t<li>94.0789473684211</li>\n",
       "\t<li>97.3684210526316</li>\n",
       "\t<li>97.3684210526316</li>\n",
       "\t<li>97.3684210526316</li>\n",
       "\t<li>97.3684210526316</li>\n",
       "\t<li>97.3684210526316</li>\n",
       "\t<li>98.0263157894737</li>\n",
       "\t<li>98.0263157894737</li>\n",
       "\t<li>98.0263157894737</li>\n",
       "\t<li>98.0263157894737</li>\n",
       "\t<li>98.6842105263158</li>\n",
       "\t<li>98.6842105263158</li>\n",
       "\t<li>98.6842105263158</li>\n",
       "\t<li>98.6842105263158</li>\n",
       "\t<li>98.6842105263158</li>\n",
       "\t<li>98.6842105263158</li>\n",
       "\t<li>98.6842105263158</li>\n",
       "\t<li>99.3421052631579</li>\n",
       "\t<li>99.3421052631579</li>\n",
       "\t<li>99.3421052631579</li>\n",
       "\t<li>99.3421052631579</li>\n",
       "\t<li>99.3421052631579</li>\n",
       "\t<li>99.3421052631579</li>\n",
       "\t<li>99.3421052631579</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "\t<li>100</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 4.60526315789474\n",
       "\\item 10.5263157894737\n",
       "\\item 11.8421052631579\n",
       "\\item 19.0789473684211\n",
       "\\item 30.2631578947368\n",
       "\\item 31.5789473684211\n",
       "\\item 32.8947368421053\n",
       "\\item 32.8947368421053\n",
       "\\item 34.8684210526316\n",
       "\\item 35.5263157894737\n",
       "\\item 35.5263157894737\n",
       "\\item 42.7631578947368\n",
       "\\item 42.7631578947368\n",
       "\\item 44.7368421052632\n",
       "\\item 44.7368421052632\n",
       "\\item 46.7105263157895\n",
       "\\item 48.0263157894737\n",
       "\\item 50\n",
       "\\item 50\n",
       "\\item 51.3157894736842\n",
       "\\item 51.3157894736842\n",
       "\\item 52.6315789473684\n",
       "\\item 53.2894736842105\n",
       "\\item 53.2894736842105\n",
       "\\item 53.2894736842105\n",
       "\\item 63.1578947368421\n",
       "\\item 63.1578947368421\n",
       "\\item 71.0526315789474\n",
       "\\item 76.3157894736842\n",
       "\\item 76.3157894736842\n",
       "\\item 77.6315789473684\n",
       "\\item 77.6315789473684\n",
       "\\item 80.2631578947368\n",
       "\\item 86.1842105263158\n",
       "\\item 86.1842105263158\n",
       "\\item 90.1315789473684\n",
       "\\item 94.0789473684211\n",
       "\\item 94.0789473684211\n",
       "\\item 97.3684210526316\n",
       "\\item 97.3684210526316\n",
       "\\item 97.3684210526316\n",
       "\\item 97.3684210526316\n",
       "\\item 97.3684210526316\n",
       "\\item 98.0263157894737\n",
       "\\item 98.0263157894737\n",
       "\\item 98.0263157894737\n",
       "\\item 98.0263157894737\n",
       "\\item 98.6842105263158\n",
       "\\item 98.6842105263158\n",
       "\\item 98.6842105263158\n",
       "\\item 98.6842105263158\n",
       "\\item 98.6842105263158\n",
       "\\item 98.6842105263158\n",
       "\\item 98.6842105263158\n",
       "\\item 99.3421052631579\n",
       "\\item 99.3421052631579\n",
       "\\item 99.3421052631579\n",
       "\\item 99.3421052631579\n",
       "\\item 99.3421052631579\n",
       "\\item 99.3421052631579\n",
       "\\item 99.3421052631579\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\item 100\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 4.60526315789474\n",
       "2. 10.5263157894737\n",
       "3. 11.8421052631579\n",
       "4. 19.0789473684211\n",
       "5. 30.2631578947368\n",
       "6. 31.5789473684211\n",
       "7. 32.8947368421053\n",
       "8. 32.8947368421053\n",
       "9. 34.8684210526316\n",
       "10. 35.5263157894737\n",
       "11. 35.5263157894737\n",
       "12. 42.7631578947368\n",
       "13. 42.7631578947368\n",
       "14. 44.7368421052632\n",
       "15. 44.7368421052632\n",
       "16. 46.7105263157895\n",
       "17. 48.0263157894737\n",
       "18. 50\n",
       "19. 50\n",
       "20. 51.3157894736842\n",
       "21. 51.3157894736842\n",
       "22. 52.6315789473684\n",
       "23. 53.2894736842105\n",
       "24. 53.2894736842105\n",
       "25. 53.2894736842105\n",
       "26. 63.1578947368421\n",
       "27. 63.1578947368421\n",
       "28. 71.0526315789474\n",
       "29. 76.3157894736842\n",
       "30. 76.3157894736842\n",
       "31. 77.6315789473684\n",
       "32. 77.6315789473684\n",
       "33. 80.2631578947368\n",
       "34. 86.1842105263158\n",
       "35. 86.1842105263158\n",
       "36. 90.1315789473684\n",
       "37. 94.0789473684211\n",
       "38. 94.0789473684211\n",
       "39. 97.3684210526316\n",
       "40. 97.3684210526316\n",
       "41. 97.3684210526316\n",
       "42. 97.3684210526316\n",
       "43. 97.3684210526316\n",
       "44. 98.0263157894737\n",
       "45. 98.0263157894737\n",
       "46. 98.0263157894737\n",
       "47. 98.0263157894737\n",
       "48. 98.6842105263158\n",
       "49. 98.6842105263158\n",
       "50. 98.6842105263158\n",
       "51. 98.6842105263158\n",
       "52. 98.6842105263158\n",
       "53. 98.6842105263158\n",
       "54. 98.6842105263158\n",
       "55. 99.3421052631579\n",
       "56. 99.3421052631579\n",
       "57. 99.3421052631579\n",
       "58. 99.3421052631579\n",
       "59. 99.3421052631579\n",
       "60. 99.3421052631579\n",
       "61. 99.3421052631579\n",
       "62. 100\n",
       "63. 100\n",
       "64. 100\n",
       "65. 100\n",
       "66. 100\n",
       "67. 100\n",
       "68. 100\n",
       "69. 100\n",
       "70. 100\n",
       "71. 100\n",
       "72. 100\n",
       "73. 100\n",
       "74. 100\n",
       "75. 100\n",
       "76. 100\n",
       "77. 100\n",
       "78. 100\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]   4.605263  10.526316  11.842105  19.078947  30.263158  31.578947\n",
       " [7]  32.894737  32.894737  34.868421  35.526316  35.526316  42.763158\n",
       "[13]  42.763158  44.736842  44.736842  46.710526  48.026316  50.000000\n",
       "[19]  50.000000  51.315789  51.315789  52.631579  53.289474  53.289474\n",
       "[25]  53.289474  63.157895  63.157895  71.052632  76.315789  76.315789\n",
       "[31]  77.631579  77.631579  80.263158  86.184211  86.184211  90.131579\n",
       "[37]  94.078947  94.078947  97.368421  97.368421  97.368421  97.368421\n",
       "[43]  97.368421  98.026316  98.026316  98.026316  98.026316  98.684211\n",
       "[49]  98.684211  98.684211  98.684211  98.684211  98.684211  98.684211\n",
       "[55]  99.342105  99.342105  99.342105  99.342105  99.342105  99.342105\n",
       "[61]  99.342105 100.000000 100.000000 100.000000 100.000000 100.000000\n",
       "[67] 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000\n",
       "[73] 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dfAyKyhdWsnVw/Pod5e/RzN8TkFIAUWp/ZcrgAAAAAAxMDiCQAAAAAVgWAHAAAA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WA7N15swZEbGysmrYsKHatQAAYLZ0X4o9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\n2AEAREKwAwCIhGAHABAJwQ4AIBJ/Ao5A6ovOxCxUAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data=M_coverage, aes(x=TFs, y=cumulative,group=1)) +geom_line()+xlab(\"TF\")+ylab(\"regulated marker genes (%)\")+geom_hline(yintercept=80, linetype=\"dashed\", color = \"red\")+\n",
    "theme(axis.text.x=element_blank(),axis.text.y=element_text(size=20,face=\"bold\",hjust = 1),axis.title=element_text(size=20,face=\"bold\"),plot.title = element_text(size=24),,panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "axis.ticks.x=element_blank(),panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+ggtitle(\"Cumulative MHb markers\")\n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/MHb_cumulative.pdf\",width=12,height=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Pou3f1_extended'</li>\n",
       "\t<li>'Ybx1_extended'</li>\n",
       "\t<li>'Etv5_extended'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Pou3f1\\_extended'\n",
       "\\item 'Ybx1\\_extended'\n",
       "\\item 'Etv5\\_extended'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Pou3f1_extended'\n",
       "2. 'Ybx1_extended'\n",
       "3. 'Etv5_extended'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Pou3f1_extended\" \"Ybx1_extended\"   \"Etv5_extended\"  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "62"
      ],
      "text/latex": [
       "62"
      ],
      "text/markdown": [
       "62"
      ],
      "text/plain": [
       "[1] 62"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "78"
      ],
      "text/latex": [
       "78"
      ],
      "text/markdown": [
       "78"
      ],
      "text/plain": [
       "[1] 78"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "100"
      ],
      "text/latex": [
       "100"
      ],
      "text/markdown": [
       "100"
      ],
      "text/plain": [
       "[1] 100"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intersect(M.TFs_order[,1][1:33],L.TFs_order[,1][1:33])\n",
    "length(intersect(M.TFs_order[,1],L.TFs_order[,1]))\n",
    "length(M.TFs_order[,1])\n",
    "length(L.TFs_order[,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "80.2631578947368"
      ],
      "text/latex": [
       "80.2631578947368"
      ],
      "text/markdown": [
       "80.2631578947368"
      ],
      "text/plain": [
       "[1] 80.26316"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative[33]\n",
    "#M: 78, overlap is 62, but till 33 only 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.TFs_order<-L.TFs_order[rev(order(L.TFs_order$DEGenrichment)),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumulative<-numeric()\n",
    "genes<-numeric()\n",
    "for (i in 1:dim(L.TFs_order)[1]){\n",
    "    genes<-unique(c(genes,intersect(regulons[[as.character(L.TFs_order[i,1])]],LHb_gene)))\n",
    "    cumulative<-c(cumulative,length(genes))\n",
    "}\n",
    "cumulative<-(100/max(cumulative))*cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "L_coverage<-data.frame(TFs=L.TFs_order[,1],cumulative=cumulative)\n",
    "L_coverage$TFs<-factor(L_coverage$TFs,levels=L_coverage$TFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v+76ZchbetrSL/b+8dPijMXy6lDe92Sr7Ls27c1ET4renrvQX6VdwQAAACQY3Ix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OEYkO2XPnvVbVylIgDCc1NVXosQMAwHLp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\nEHYAACGEHQBACGEHABDiBS08XrRQmL0wAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data=L_coverage, aes(x=TFs, y=cumulative,group=1)) +geom_line()+xlab(\"TF\")+ylab(\"regulated marker genes (%)\")+geom_hline(yintercept=80, linetype=\"dashed\", color = \"red\")+\n",
    "theme(axis.text.x=element_blank(),axis.text.y=element_text(size=20,face=\"bold\",hjust = 1),axis.title=element_text(size=20,face=\"bold\"),plot.title = element_text(size=24),,panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "axis.ticks.x=element_blank(),panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+ggtitle(\"Cumulative LHb markers\")\n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/class_genes/LHb_cumulative.pdf\",width=12,height=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "80.7692307692308"
      ],
      "text/latex": [
       "80.7692307692308"
      ],
      "text/markdown": [
       "80.7692307692308"
      ],
      "text/plain": [
       "[1] 80.76923"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative[36] # has 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Pbx3_extended'</li>\n",
       "\t<li>'Tcf7l2'</li>\n",
       "\t<li>'Zic4_extended'</li>\n",
       "\t<li>'Zic1'</li>\n",
       "\t<li>'Pou2f2_extended'</li>\n",
       "\t<li>'Tef_extended'</li>\n",
       "\t<li>'Npdc1_extended'</li>\n",
       "\t<li>'Jund_extended'</li>\n",
       "\t<li>'Crebl2_extended'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Pbx3\\_extended'\n",
       "\\item 'Tcf7l2'\n",
       "\\item 'Zic4\\_extended'\n",
       "\\item 'Zic1'\n",
       "\\item 'Pou2f2\\_extended'\n",
       "\\item 'Tef\\_extended'\n",
       "\\item 'Npdc1\\_extended'\n",
       "\\item 'Jund\\_extended'\n",
       "\\item 'Crebl2\\_extended'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Pbx3_extended'\n",
       "2. 'Tcf7l2'\n",
       "3. 'Zic4_extended'\n",
       "4. 'Zic1'\n",
       "5. 'Pou2f2_extended'\n",
       "6. 'Tef_extended'\n",
       "7. 'Npdc1_extended'\n",
       "8. 'Jund_extended'\n",
       "9. 'Crebl2_extended'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Pbx3_extended\"   \"Tcf7l2\"          \"Zic4_extended\"   \"Zic1\"           \n",
       "[5] \"Pou2f2_extended\" \"Tef_extended\"    \"Npdc1_extended\"  \"Jund_extended\"  \n",
       "[9] \"Crebl2_extended\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intersect(M.TFs_order[,1][1:33],L.TFs_order[,1][1:36])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# genes regulated by top regulons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>M.TFs</th><th scope=col>DEG</th><th scope=col>nonDEG</th><th scope=col>DEGenrichment</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Gpd1</th><td>Gpd1            </td><td> 7              </td><td> 0              </td><td>1.0000000       </td></tr>\n",
       "\t<tr><th scope=row>Neurod1_extended</th><td>Neurod1_extended</td><td> 9              </td><td> 0              </td><td>1.0000000       </td></tr>\n",
       "\t<tr><th scope=row>Irx3_extended</th><td>Irx3_extended   </td><td> 3              </td><td> 2              </td><td>0.6000000       </td></tr>\n",
       "\t<tr><th scope=row>Neurod2_extended</th><td>Neurod2_extended</td><td>19              </td><td>14              </td><td>0.5757576       </td></tr>\n",
       "\t<tr><th scope=row>Pou3f1_extended</th><td>Pou3f1_extended </td><td>35              </td><td>52              </td><td>0.4022989       </td></tr>\n",
       "\t<tr><th scope=row>Lef1_extended</th><td>Lef1_extended   </td><td> 5              </td><td> 9              </td><td>0.3571429       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & M.TFs & DEG & nonDEG & DEGenrichment\\\\\n",
       "\\hline\n",
       "\tGpd1 & Gpd1             &  7               &  0               & 1.0000000       \\\\\n",
       "\tNeurod1\\_extended & Neurod1\\_extended &  9                 &  0                 & 1.0000000         \\\\\n",
       "\tIrx3\\_extended & Irx3\\_extended    &  3                 &  2                 & 0.6000000         \\\\\n",
       "\tNeurod2\\_extended & Neurod2\\_extended & 19                 & 14                 & 0.5757576         \\\\\n",
       "\tPou3f1\\_extended & Pou3f1\\_extended  & 35                 & 52                 & 0.4022989         \\\\\n",
       "\tLef1\\_extended & Lef1\\_extended    &  5                 &  9                 & 0.3571429         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | M.TFs | DEG | nonDEG | DEGenrichment |\n",
       "|---|---|---|---|---|\n",
       "| Gpd1 | Gpd1             |  7               |  0               | 1.0000000        |\n",
       "| Neurod1_extended | Neurod1_extended |  9               |  0               | 1.0000000        |\n",
       "| Irx3_extended | Irx3_extended    |  3               |  2               | 0.6000000        |\n",
       "| Neurod2_extended | Neurod2_extended | 19               | 14               | 0.5757576        |\n",
       "| Pou3f1_extended | Pou3f1_extended  | 35               | 52               | 0.4022989        |\n",
       "| Lef1_extended | Lef1_extended    |  5               |  9               | 0.3571429        |\n",
       "\n"
      ],
      "text/plain": [
       "                 M.TFs            DEG nonDEG DEGenrichment\n",
       "Gpd1             Gpd1              7   0     1.0000000    \n",
       "Neurod1_extended Neurod1_extended  9   0     1.0000000    \n",
       "Irx3_extended    Irx3_extended     3   2     0.6000000    \n",
       "Neurod2_extended Neurod2_extended 19  14     0.5757576    \n",
       "Pou3f1_extended  Pou3f1_extended  35  52     0.4022989    \n",
       "Lef1_extended    Lef1_extended     5   9     0.3571429    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>L.TFs</th><th scope=col>DEG</th><th scope=col>nonDEG</th><th scope=col>DEGenrichment</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Pou3f2</th><td>Pou3f2         </td><td> 4             </td><td> 4             </td><td>0.5000000      </td></tr>\n",
       "\t<tr><th scope=row>Zfhx2_extended</th><td>Zfhx2_extended </td><td> 7             </td><td>10             </td><td>0.4117647      </td></tr>\n",
       "\t<tr><th scope=row>Pou2f2_extended</th><td>Pou2f2_extended</td><td>30             </td><td>44             </td><td>0.4054054      </td></tr>\n",
       "\t<tr><th scope=row>Tead1</th><td>Tead1          </td><td> 1             </td><td> 2             </td><td>0.3333333      </td></tr>\n",
       "\t<tr><th scope=row>Esrrg_extended</th><td>Esrrg_extended </td><td>19             </td><td>40             </td><td>0.3220339      </td></tr>\n",
       "\t<tr><th scope=row>Ar</th><td>Ar             </td><td> 4             </td><td> 9             </td><td>0.3076923      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & L.TFs & DEG & nonDEG & DEGenrichment\\\\\n",
       "\\hline\n",
       "\tPou3f2 & Pou3f2          &  4              &  4              & 0.5000000      \\\\\n",
       "\tZfhx2\\_extended & Zfhx2\\_extended  &  7                & 10                & 0.4117647        \\\\\n",
       "\tPou2f2\\_extended & Pou2f2\\_extended & 30                & 44                & 0.4054054        \\\\\n",
       "\tTead1 & Tead1           &  1              &  2              & 0.3333333      \\\\\n",
       "\tEsrrg\\_extended & Esrrg\\_extended  & 19                & 40                & 0.3220339        \\\\\n",
       "\tAr & Ar              &  4              &  9              & 0.3076923      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | L.TFs | DEG | nonDEG | DEGenrichment |\n",
       "|---|---|---|---|---|\n",
       "| Pou3f2 | Pou3f2          |  4              |  4              | 0.5000000       |\n",
       "| Zfhx2_extended | Zfhx2_extended  |  7              | 10              | 0.4117647       |\n",
       "| Pou2f2_extended | Pou2f2_extended | 30              | 44              | 0.4054054       |\n",
       "| Tead1 | Tead1           |  1              |  2              | 0.3333333       |\n",
       "| Esrrg_extended | Esrrg_extended  | 19              | 40              | 0.3220339       |\n",
       "| Ar | Ar              |  4              |  9              | 0.3076923       |\n",
       "\n"
      ],
      "text/plain": [
       "                L.TFs           DEG nonDEG DEGenrichment\n",
       "Pou3f2          Pou3f2           4   4     0.5000000    \n",
       "Zfhx2_extended  Zfhx2_extended   7  10     0.4117647    \n",
       "Pou2f2_extended Pou2f2_extended 30  44     0.4054054    \n",
       "Tead1           Tead1            1   2     0.3333333    \n",
       "Esrrg_extended  Esrrg_extended  19  40     0.3220339    \n",
       "Ar              Ar               4   9     0.3076923    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(M.TFs_order)\n",
    "head(L.TFs_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Gpd1</li>\n",
       "\t<li>Neurod1_extended</li>\n",
       "\t<li>Irx3_extended</li>\n",
       "\t<li>Neurod2_extended</li>\n",
       "\t<li>Pou3f1_extended</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'Gtf2f1_extended'</li>\n",
       "\t\t<li>'Nfyb'</li>\n",
       "\t\t<li>'Bmyc_extended'</li>\n",
       "\t\t<li>'Ets2_extended'</li>\n",
       "\t\t<li>'Ep300_extended'</li>\n",
       "\t\t<li>'Ep300'</li>\n",
       "\t\t<li>'Setdb1_extended'</li>\n",
       "\t\t<li>'Elk1_extended'</li>\n",
       "\t\t<li>'E2f4_extended'</li>\n",
       "\t\t<li>'E2f6_extended'</li>\n",
       "\t\t<li>'Zfp369_extended'</li>\n",
       "\t\t<li>'Yy1'</li>\n",
       "\t\t<li>'Zeb1_extended'</li>\n",
       "\t\t<li>'Rbbp5_extended'</li>\n",
       "\t\t<li>'Gabpa_extended'</li>\n",
       "\t\t<li>'Phf8'</li>\n",
       "\t\t<li>'Mxi1_extended'</li>\n",
       "\t\t<li>'Etv3_extended'</li>\n",
       "\t\t<li>'Nfyb_extended'</li>\n",
       "\t\t<li>'Zfp239'</li>\n",
       "\t\t<li>'Phf8_extended'</li>\n",
       "\t\t<li>'Etv3'</li>\n",
       "\t\t<li>'Pml_extended'</li>\n",
       "\t\t<li>'Sin3a'</li>\n",
       "\t\t<li>'Gabpa'</li>\n",
       "\t\t<li>'Taf1'</li>\n",
       "\t\t<li>'Suz12_extended'</li>\n",
       "\t\t<li>'Zfp523_extended'</li>\n",
       "\t\t<li>'Sin3a_extended'</li>\n",
       "\t\t<li>'Taf1_extended'</li>\n",
       "\t\t<li>'Stat1_extended'</li>\n",
       "\t\t<li>'Yy1_extended'</li>\n",
       "\t\t<li>'Maz_extended'</li>\n",
       "\t\t<li>'Elf2'</li>\n",
       "\t\t<li>'Kdm5a'</li>\n",
       "\t\t<li>'Smc3_extended'</li>\n",
       "\t\t<li>'Hdac2_extended'</li>\n",
       "\t\t<li>'Kdm5b_extended'</li>\n",
       "\t\t<li>'Stat1'</li>\n",
       "\t\t<li>'Elf2_extended'</li>\n",
       "\t\t<li>'Hcfc1'</li>\n",
       "\t\t<li>'Kdm5a_extended'</li>\n",
       "\t\t<li>'Creb1_extended'</li>\n",
       "\t\t<li>'Bclaf1'</li>\n",
       "\t\t<li>'Rfx3_extended'</li>\n",
       "\t\t<li>'Ctcf_extended'</li>\n",
       "\t\t<li>'Esrra_extended'</li>\n",
       "\t\t<li>'Smarca4_extended'</li>\n",
       "\t\t<li>'Rad21_extended'</li>\n",
       "\t\t<li>'Hcfc1_extended'</li>\n",
       "\t\t<li>'Chd2_extended'</li>\n",
       "\t\t<li>'Ubtf_extended'</li>\n",
       "\t\t<li>'Srebf2_extended'</li>\n",
       "\t\t<li>'Max_extended'</li>\n",
       "\t\t<li>'Etv1'</li>\n",
       "\t\t<li>'Zmiz1_extended'</li>\n",
       "\t\t<li>'Nr2c2_extended'</li>\n",
       "\t\t<li>'Ppargc1a_extended'</li>\n",
       "\t\t<li>'Bclaf1_extended'</li>\n",
       "\t\t<li>'Etv1_extended'</li>\n",
       "\t\t<li>'Usf2'</li>\n",
       "\t\t<li>'Crebl2_extended'</li>\n",
       "\t\t<li>'Jund_extended'</li>\n",
       "\t\t<li>'Npdc1_extended'</li>\n",
       "\t\t<li>'Tef_extended'</li>\n",
       "\t\t<li>'Usf2_extended'</li>\n",
       "\t\t<li>'Bhlhe41_extended'</li>\n",
       "\t\t<li>'Foxp2_extended'</li>\n",
       "\t\t<li>'Etv5_extended'</li>\n",
       "\t\t<li>'Chd2'</li>\n",
       "\t\t<li>'Esrra'</li>\n",
       "\t\t<li>'Pou2f2_extended'</li>\n",
       "\t\t<li>'E4f1_extended'</li>\n",
       "\t\t<li>'Klf7_extended'</li>\n",
       "\t\t<li>'Klf7'</li>\n",
       "\t\t<li>'Nfix'</li>\n",
       "\t\t<li>'Myef2_extended'</li>\n",
       "\t\t<li>'Mxd4_extended'</li>\n",
       "\t\t<li>'Zic1'</li>\n",
       "\t\t<li>'Ybx1_extended'</li>\n",
       "\t\t<li>'Lhx9_extended'</li>\n",
       "\t\t<li>'Tcf7_extended'</li>\n",
       "\t\t<li>'Klf9_extended'</li>\n",
       "\t\t<li>'Ctcf'</li>\n",
       "\t\t<li>'Npdc1'</li>\n",
       "\t\t<li>'Zic4_extended'</li>\n",
       "\t\t<li>'Tcf7l2'</li>\n",
       "\t\t<li>'Nfib'</li>\n",
       "\t\t<li>'Pou3f3_extended'</li>\n",
       "\t\t<li>'Pbx3_extended'</li>\n",
       "\t\t<li>'Bhlhe41'</li>\n",
       "\t\t<li>'Sox6'</li>\n",
       "\t\t<li>'Ebf1'</li>\n",
       "\t\t<li>'Lef1'</li>\n",
       "\t\t<li>'Lef1_extended'</li>\n",
       "\t\t<li>'Pou3f1'</li>\n",
       "\t\t<li>'Pou3f1_extended'</li>\n",
       "\t\t<li>'Pbx3'</li>\n",
       "\t\t<li>'Foxp2'</li>\n",
       "\t\t<li>'Neurod2_extended'</li>\n",
       "\t\t<li>'Irx3_extended'</li>\n",
       "\t\t<li>'Neurod2'</li>\n",
       "\t\t<li>'Neurod1_extended'</li>\n",
       "\t\t<li>'Gpd1'</li>\n",
       "\t\t<li>'Neurod1'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Gpd1\n",
       "\\item Neurod1\\_extended\n",
       "\\item Irx3\\_extended\n",
       "\\item Neurod2\\_extended\n",
       "\\item Pou3f1\\_extended\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'Gtf2f1\\_extended'\n",
       "\\item 'Nfyb'\n",
       "\\item 'Bmyc\\_extended'\n",
       "\\item 'Ets2\\_extended'\n",
       "\\item 'Ep300\\_extended'\n",
       "\\item 'Ep300'\n",
       "\\item 'Setdb1\\_extended'\n",
       "\\item 'Elk1\\_extended'\n",
       "\\item 'E2f4\\_extended'\n",
       "\\item 'E2f6\\_extended'\n",
       "\\item 'Zfp369\\_extended'\n",
       "\\item 'Yy1'\n",
       "\\item 'Zeb1\\_extended'\n",
       "\\item 'Rbbp5\\_extended'\n",
       "\\item 'Gabpa\\_extended'\n",
       "\\item 'Phf8'\n",
       "\\item 'Mxi1\\_extended'\n",
       "\\item 'Etv3\\_extended'\n",
       "\\item 'Nfyb\\_extended'\n",
       "\\item 'Zfp239'\n",
       "\\item 'Phf8\\_extended'\n",
       "\\item 'Etv3'\n",
       "\\item 'Pml\\_extended'\n",
       "\\item 'Sin3a'\n",
       "\\item 'Gabpa'\n",
       "\\item 'Taf1'\n",
       "\\item 'Suz12\\_extended'\n",
       "\\item 'Zfp523\\_extended'\n",
       "\\item 'Sin3a\\_extended'\n",
       "\\item 'Taf1\\_extended'\n",
       "\\item 'Stat1\\_extended'\n",
       "\\item 'Yy1\\_extended'\n",
       "\\item 'Maz\\_extended'\n",
       "\\item 'Elf2'\n",
       "\\item 'Kdm5a'\n",
       "\\item 'Smc3\\_extended'\n",
       "\\item 'Hdac2\\_extended'\n",
       "\\item 'Kdm5b\\_extended'\n",
       "\\item 'Stat1'\n",
       "\\item 'Elf2\\_extended'\n",
       "\\item 'Hcfc1'\n",
       "\\item 'Kdm5a\\_extended'\n",
       "\\item 'Creb1\\_extended'\n",
       "\\item 'Bclaf1'\n",
       "\\item 'Rfx3\\_extended'\n",
       "\\item 'Ctcf\\_extended'\n",
       "\\item 'Esrra\\_extended'\n",
       "\\item 'Smarca4\\_extended'\n",
       "\\item 'Rad21\\_extended'\n",
       "\\item 'Hcfc1\\_extended'\n",
       "\\item 'Chd2\\_extended'\n",
       "\\item 'Ubtf\\_extended'\n",
       "\\item 'Srebf2\\_extended'\n",
       "\\item 'Max\\_extended'\n",
       "\\item 'Etv1'\n",
       "\\item 'Zmiz1\\_extended'\n",
       "\\item 'Nr2c2\\_extended'\n",
       "\\item 'Ppargc1a\\_extended'\n",
       "\\item 'Bclaf1\\_extended'\n",
       "\\item 'Etv1\\_extended'\n",
       "\\item 'Usf2'\n",
       "\\item 'Crebl2\\_extended'\n",
       "\\item 'Jund\\_extended'\n",
       "\\item 'Npdc1\\_extended'\n",
       "\\item 'Tef\\_extended'\n",
       "\\item 'Usf2\\_extended'\n",
       "\\item 'Bhlhe41\\_extended'\n",
       "\\item 'Foxp2\\_extended'\n",
       "\\item 'Etv5\\_extended'\n",
       "\\item 'Chd2'\n",
       "\\item 'Esrra'\n",
       "\\item 'Pou2f2\\_extended'\n",
       "\\item 'E4f1\\_extended'\n",
       "\\item 'Klf7\\_extended'\n",
       "\\item 'Klf7'\n",
       "\\item 'Nfix'\n",
       "\\item 'Myef2\\_extended'\n",
       "\\item 'Mxd4\\_extended'\n",
       "\\item 'Zic1'\n",
       "\\item 'Ybx1\\_extended'\n",
       "\\item 'Lhx9\\_extended'\n",
       "\\item 'Tcf7\\_extended'\n",
       "\\item 'Klf9\\_extended'\n",
       "\\item 'Ctcf'\n",
       "\\item 'Npdc1'\n",
       "\\item 'Zic4\\_extended'\n",
       "\\item 'Tcf7l2'\n",
       "\\item 'Nfib'\n",
       "\\item 'Pou3f3\\_extended'\n",
       "\\item 'Pbx3\\_extended'\n",
       "\\item 'Bhlhe41'\n",
       "\\item 'Sox6'\n",
       "\\item 'Ebf1'\n",
       "\\item 'Lef1'\n",
       "\\item 'Lef1\\_extended'\n",
       "\\item 'Pou3f1'\n",
       "\\item 'Pou3f1\\_extended'\n",
       "\\item 'Pbx3'\n",
       "\\item 'Foxp2'\n",
       "\\item 'Neurod2\\_extended'\n",
       "\\item 'Irx3\\_extended'\n",
       "\\item 'Neurod2'\n",
       "\\item 'Neurod1\\_extended'\n",
       "\\item 'Gpd1'\n",
       "\\item 'Neurod1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Gpd1\n",
       "2. Neurod1_extended\n",
       "3. Irx3_extended\n",
       "4. Neurod2_extended\n",
       "5. Pou3f1_extended\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'Gtf2f1_extended'\n",
       "2. 'Nfyb'\n",
       "3. 'Bmyc_extended'\n",
       "4. 'Ets2_extended'\n",
       "5. 'Ep300_extended'\n",
       "6. 'Ep300'\n",
       "7. 'Setdb1_extended'\n",
       "8. 'Elk1_extended'\n",
       "9. 'E2f4_extended'\n",
       "10. 'E2f6_extended'\n",
       "11. 'Zfp369_extended'\n",
       "12. 'Yy1'\n",
       "13. 'Zeb1_extended'\n",
       "14. 'Rbbp5_extended'\n",
       "15. 'Gabpa_extended'\n",
       "16. 'Phf8'\n",
       "17. 'Mxi1_extended'\n",
       "18. 'Etv3_extended'\n",
       "19. 'Nfyb_extended'\n",
       "20. 'Zfp239'\n",
       "21. 'Phf8_extended'\n",
       "22. 'Etv3'\n",
       "23. 'Pml_extended'\n",
       "24. 'Sin3a'\n",
       "25. 'Gabpa'\n",
       "26. 'Taf1'\n",
       "27. 'Suz12_extended'\n",
       "28. 'Zfp523_extended'\n",
       "29. 'Sin3a_extended'\n",
       "30. 'Taf1_extended'\n",
       "31. 'Stat1_extended'\n",
       "32. 'Yy1_extended'\n",
       "33. 'Maz_extended'\n",
       "34. 'Elf2'\n",
       "35. 'Kdm5a'\n",
       "36. 'Smc3_extended'\n",
       "37. 'Hdac2_extended'\n",
       "38. 'Kdm5b_extended'\n",
       "39. 'Stat1'\n",
       "40. 'Elf2_extended'\n",
       "41. 'Hcfc1'\n",
       "42. 'Kdm5a_extended'\n",
       "43. 'Creb1_extended'\n",
       "44. 'Bclaf1'\n",
       "45. 'Rfx3_extended'\n",
       "46. 'Ctcf_extended'\n",
       "47. 'Esrra_extended'\n",
       "48. 'Smarca4_extended'\n",
       "49. 'Rad21_extended'\n",
       "50. 'Hcfc1_extended'\n",
       "51. 'Chd2_extended'\n",
       "52. 'Ubtf_extended'\n",
       "53. 'Srebf2_extended'\n",
       "54. 'Max_extended'\n",
       "55. 'Etv1'\n",
       "56. 'Zmiz1_extended'\n",
       "57. 'Nr2c2_extended'\n",
       "58. 'Ppargc1a_extended'\n",
       "59. 'Bclaf1_extended'\n",
       "60. 'Etv1_extended'\n",
       "61. 'Usf2'\n",
       "62. 'Crebl2_extended'\n",
       "63. 'Jund_extended'\n",
       "64. 'Npdc1_extended'\n",
       "65. 'Tef_extended'\n",
       "66. 'Usf2_extended'\n",
       "67. 'Bhlhe41_extended'\n",
       "68. 'Foxp2_extended'\n",
       "69. 'Etv5_extended'\n",
       "70. 'Chd2'\n",
       "71. 'Esrra'\n",
       "72. 'Pou2f2_extended'\n",
       "73. 'E4f1_extended'\n",
       "74. 'Klf7_extended'\n",
       "75. 'Klf7'\n",
       "76. 'Nfix'\n",
       "77. 'Myef2_extended'\n",
       "78. 'Mxd4_extended'\n",
       "79. 'Zic1'\n",
       "80. 'Ybx1_extended'\n",
       "81. 'Lhx9_extended'\n",
       "82. 'Tcf7_extended'\n",
       "83. 'Klf9_extended'\n",
       "84. 'Ctcf'\n",
       "85. 'Npdc1'\n",
       "86. 'Zic4_extended'\n",
       "87. 'Tcf7l2'\n",
       "88. 'Nfib'\n",
       "89. 'Pou3f3_extended'\n",
       "90. 'Pbx3_extended'\n",
       "91. 'Bhlhe41'\n",
       "92. 'Sox6'\n",
       "93. 'Ebf1'\n",
       "94. 'Lef1'\n",
       "95. 'Lef1_extended'\n",
       "96. 'Pou3f1'\n",
       "97. 'Pou3f1_extended'\n",
       "98. 'Pbx3'\n",
       "99. 'Foxp2'\n",
       "100. 'Neurod2_extended'\n",
       "101. 'Irx3_extended'\n",
       "102. 'Neurod2'\n",
       "103. 'Neurod1_extended'\n",
       "104. 'Gpd1'\n",
       "105. 'Neurod1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] Gpd1             Neurod1_extended Irx3_extended    Neurod2_extended\n",
       "[5] Pou3f1_extended \n",
       "105 Levels: Gtf2f1_extended Nfyb Bmyc_extended Ets2_extended ... Neurod1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'6030419C18Rik'</li>\n",
       "\t<li>'Adam11'</li>\n",
       "\t<li>'Gdpd5'</li>\n",
       "\t<li>'Impdh1'</li>\n",
       "\t<li>'Lsp1'</li>\n",
       "\t<li>'Pde2a'</li>\n",
       "\t<li>'Plch2'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '6030419C18Rik'\n",
       "\\item 'Adam11'\n",
       "\\item 'Gdpd5'\n",
       "\\item 'Impdh1'\n",
       "\\item 'Lsp1'\n",
       "\\item 'Pde2a'\n",
       "\\item 'Plch2'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '6030419C18Rik'\n",
       "2. 'Adam11'\n",
       "3. 'Gdpd5'\n",
       "4. 'Impdh1'\n",
       "5. 'Lsp1'\n",
       "6. 'Pde2a'\n",
       "7. 'Plch2'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"6030419C18Rik\" \"Adam11\"        \"Gdpd5\"         \"Impdh1\"       \n",
       "[5] \"Lsp1\"          \"Pde2a\"         \"Plch2\"        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'A830010M20Rik'</li>\n",
       "\t<li>'Gabbr2'</li>\n",
       "\t<li>'Gfra1'</li>\n",
       "\t<li>'Kcnip1'</li>\n",
       "\t<li>'Neurod1'</li>\n",
       "\t<li>'Nr4a2'</li>\n",
       "\t<li>'Rgs6'</li>\n",
       "\t<li>'St6galnac5'</li>\n",
       "\t<li>'Tmem56'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'A830010M20Rik'\n",
       "\\item 'Gabbr2'\n",
       "\\item 'Gfra1'\n",
       "\\item 'Kcnip1'\n",
       "\\item 'Neurod1'\n",
       "\\item 'Nr4a2'\n",
       "\\item 'Rgs6'\n",
       "\\item 'St6galnac5'\n",
       "\\item 'Tmem56'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'A830010M20Rik'\n",
       "2. 'Gabbr2'\n",
       "3. 'Gfra1'\n",
       "4. 'Kcnip1'\n",
       "5. 'Neurod1'\n",
       "6. 'Nr4a2'\n",
       "7. 'Rgs6'\n",
       "8. 'St6galnac5'\n",
       "9. 'Tmem56'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"A830010M20Rik\" \"Gabbr2\"        \"Gfra1\"         \"Kcnip1\"       \n",
       "[5] \"Neurod1\"       \"Nr4a2\"         \"Rgs6\"          \"St6galnac5\"   \n",
       "[9] \"Tmem56\"       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Lmo3'</li>\n",
       "\t<li>'Lsp1'</li>\n",
       "\t<li>'Rapgef4'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Lmo3'\n",
       "\\item 'Lsp1'\n",
       "\\item 'Rapgef4'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Lmo3'\n",
       "2. 'Lsp1'\n",
       "3. 'Rapgef4'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Lmo3\"    \"Lsp1\"    \"Rapgef4\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'3632451O06Rik'</li>\n",
       "\t<li>'6030419C18Rik'</li>\n",
       "\t<li>'A830010M20Rik'</li>\n",
       "\t<li>'Adam11'</li>\n",
       "\t<li>'Ano3'</li>\n",
       "\t<li>'Chat'</li>\n",
       "\t<li>'Fgf1'</li>\n",
       "\t<li>'Gdpd5'</li>\n",
       "\t<li>'Impdh1'</li>\n",
       "\t<li>'Klf13'</li>\n",
       "\t<li>'Lhfp'</li>\n",
       "\t<li>'Lmo3'</li>\n",
       "\t<li>'Napb'</li>\n",
       "\t<li>'Plch2'</li>\n",
       "\t<li>'Rbfox3'</li>\n",
       "\t<li>'Rgs6'</li>\n",
       "\t<li>'Slc18a3'</li>\n",
       "\t<li>'Tgfb2'</li>\n",
       "\t<li>'Zdhhc18'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '3632451O06Rik'\n",
       "\\item '6030419C18Rik'\n",
       "\\item 'A830010M20Rik'\n",
       "\\item 'Adam11'\n",
       "\\item 'Ano3'\n",
       "\\item 'Chat'\n",
       "\\item 'Fgf1'\n",
       "\\item 'Gdpd5'\n",
       "\\item 'Impdh1'\n",
       "\\item 'Klf13'\n",
       "\\item 'Lhfp'\n",
       "\\item 'Lmo3'\n",
       "\\item 'Napb'\n",
       "\\item 'Plch2'\n",
       "\\item 'Rbfox3'\n",
       "\\item 'Rgs6'\n",
       "\\item 'Slc18a3'\n",
       "\\item 'Tgfb2'\n",
       "\\item 'Zdhhc18'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '3632451O06Rik'\n",
       "2. '6030419C18Rik'\n",
       "3. 'A830010M20Rik'\n",
       "4. 'Adam11'\n",
       "5. 'Ano3'\n",
       "6. 'Chat'\n",
       "7. 'Fgf1'\n",
       "8. 'Gdpd5'\n",
       "9. 'Impdh1'\n",
       "10. 'Klf13'\n",
       "11. 'Lhfp'\n",
       "12. 'Lmo3'\n",
       "13. 'Napb'\n",
       "14. 'Plch2'\n",
       "15. 'Rbfox3'\n",
       "16. 'Rgs6'\n",
       "17. 'Slc18a3'\n",
       "18. 'Tgfb2'\n",
       "19. 'Zdhhc18'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"3632451O06Rik\" \"6030419C18Rik\" \"A830010M20Rik\" \"Adam11\"       \n",
       " [5] \"Ano3\"          \"Chat\"          \"Fgf1\"          \"Gdpd5\"        \n",
       " [9] \"Impdh1\"        \"Klf13\"         \"Lhfp\"          \"Lmo3\"         \n",
       "[13] \"Napb\"          \"Plch2\"         \"Rbfox3\"        \"Rgs6\"         \n",
       "[17] \"Slc18a3\"       \"Tgfb2\"         \"Zdhhc18\"      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'3632451O06Rik'</li>\n",
       "\t<li>'6030419C18Rik'</li>\n",
       "\t<li>'A830010M20Rik'</li>\n",
       "\t<li>'Adam11'</li>\n",
       "\t<li>'Ano3'</li>\n",
       "\t<li>'Cadps2'</li>\n",
       "\t<li>'Celf2'</li>\n",
       "\t<li>'Chat'</li>\n",
       "\t<li>'Chst2'</li>\n",
       "\t<li>'Coro2b'</li>\n",
       "\t<li>'Cpne4'</li>\n",
       "\t<li>'Etv1'</li>\n",
       "\t<li>'Fgf1'</li>\n",
       "\t<li>'Fzd7'</li>\n",
       "\t<li>'Gdpd5'</li>\n",
       "\t<li>'Gfra1'</li>\n",
       "\t<li>'Gnal'</li>\n",
       "\t<li>'Kcnd2'</li>\n",
       "\t<li>'Kcnip3'</li>\n",
       "\t<li>'Lix1'</li>\n",
       "\t<li>'Lmo3'</li>\n",
       "\t<li>'Nr4a2'</li>\n",
       "\t<li>'Pde2a'</li>\n",
       "\t<li>'Pld5'</li>\n",
       "\t<li>'Ppp1r1b'</li>\n",
       "\t<li>'Rbfox3'</li>\n",
       "\t<li>'Rgs6'</li>\n",
       "\t<li>'Robo3'</li>\n",
       "\t<li>'Slc18a3'</li>\n",
       "\t<li>'Snca'</li>\n",
       "\t<li>'St6galnac5'</li>\n",
       "\t<li>'Tac2'</li>\n",
       "\t<li>'Tgfb2'</li>\n",
       "\t<li>'Tmem56'</li>\n",
       "\t<li>'Zic1'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '3632451O06Rik'\n",
       "\\item '6030419C18Rik'\n",
       "\\item 'A830010M20Rik'\n",
       "\\item 'Adam11'\n",
       "\\item 'Ano3'\n",
       "\\item 'Cadps2'\n",
       "\\item 'Celf2'\n",
       "\\item 'Chat'\n",
       "\\item 'Chst2'\n",
       "\\item 'Coro2b'\n",
       "\\item 'Cpne4'\n",
       "\\item 'Etv1'\n",
       "\\item 'Fgf1'\n",
       "\\item 'Fzd7'\n",
       "\\item 'Gdpd5'\n",
       "\\item 'Gfra1'\n",
       "\\item 'Gnal'\n",
       "\\item 'Kcnd2'\n",
       "\\item 'Kcnip3'\n",
       "\\item 'Lix1'\n",
       "\\item 'Lmo3'\n",
       "\\item 'Nr4a2'\n",
       "\\item 'Pde2a'\n",
       "\\item 'Pld5'\n",
       "\\item 'Ppp1r1b'\n",
       "\\item 'Rbfox3'\n",
       "\\item 'Rgs6'\n",
       "\\item 'Robo3'\n",
       "\\item 'Slc18a3'\n",
       "\\item 'Snca'\n",
       "\\item 'St6galnac5'\n",
       "\\item 'Tac2'\n",
       "\\item 'Tgfb2'\n",
       "\\item 'Tmem56'\n",
       "\\item 'Zic1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '3632451O06Rik'\n",
       "2. '6030419C18Rik'\n",
       "3. 'A830010M20Rik'\n",
       "4. 'Adam11'\n",
       "5. 'Ano3'\n",
       "6. 'Cadps2'\n",
       "7. 'Celf2'\n",
       "8. 'Chat'\n",
       "9. 'Chst2'\n",
       "10. 'Coro2b'\n",
       "11. 'Cpne4'\n",
       "12. 'Etv1'\n",
       "13. 'Fgf1'\n",
       "14. 'Fzd7'\n",
       "15. 'Gdpd5'\n",
       "16. 'Gfra1'\n",
       "17. 'Gnal'\n",
       "18. 'Kcnd2'\n",
       "19. 'Kcnip3'\n",
       "20. 'Lix1'\n",
       "21. 'Lmo3'\n",
       "22. 'Nr4a2'\n",
       "23. 'Pde2a'\n",
       "24. 'Pld5'\n",
       "25. 'Ppp1r1b'\n",
       "26. 'Rbfox3'\n",
       "27. 'Rgs6'\n",
       "28. 'Robo3'\n",
       "29. 'Slc18a3'\n",
       "30. 'Snca'\n",
       "31. 'St6galnac5'\n",
       "32. 'Tac2'\n",
       "33. 'Tgfb2'\n",
       "34. 'Tmem56'\n",
       "35. 'Zic1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"3632451O06Rik\" \"6030419C18Rik\" \"A830010M20Rik\" \"Adam11\"       \n",
       " [5] \"Ano3\"          \"Cadps2\"        \"Celf2\"         \"Chat\"         \n",
       " [9] \"Chst2\"         \"Coro2b\"        \"Cpne4\"         \"Etv1\"         \n",
       "[13] \"Fgf1\"          \"Fzd7\"          \"Gdpd5\"         \"Gfra1\"        \n",
       "[17] \"Gnal\"          \"Kcnd2\"         \"Kcnip3\"        \"Lix1\"         \n",
       "[21] \"Lmo3\"          \"Nr4a2\"         \"Pde2a\"         \"Pld5\"         \n",
       "[25] \"Ppp1r1b\"       \"Rbfox3\"        \"Rgs6\"          \"Robo3\"        \n",
       "[29] \"Slc18a3\"       \"Snca\"          \"St6galnac5\"    \"Tac2\"         \n",
       "[33] \"Tgfb2\"         \"Tmem56\"        \"Zic1\"         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M.TFs_order[1:5,1]\n",
    "intersect(regulons[[as.character(M.TFs_order[1,1])]],MHb_gene)\n",
    "intersect(regulons[[as.character(M.TFs_order[2,1])]],MHb_gene)\n",
    "intersect(regulons[[as.character(M.TFs_order[3,1])]],MHb_gene)\n",
    "intersect(regulons[[as.character(M.TFs_order[4,1])]],MHb_gene)\n",
    "intersect(regulons[[as.character(M.TFs_order[5,1])]],MHb_gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Pou3f2</li>\n",
       "\t<li>Zfhx2_extended</li>\n",
       "\t<li>Pou2f2_extended</li>\n",
       "\t<li>Tead1</li>\n",
       "\t<li>Esrrg_extended</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'E2f4_extended'</li>\n",
       "\t\t<li>'Etv3'</li>\n",
       "\t\t<li>'Gabpa'</li>\n",
       "\t\t<li>'Elk1'</li>\n",
       "\t\t<li>'Elk1_extended'</li>\n",
       "\t\t<li>'Klf16_extended'</li>\n",
       "\t\t<li>'Etv5_extended'</li>\n",
       "\t\t<li>'Etv3_extended'</li>\n",
       "\t\t<li>'Sin3a'</li>\n",
       "\t\t<li>'Srf_extended'</li>\n",
       "\t\t<li>'Phf8'</li>\n",
       "\t\t<li>'Zfp319_extended'</li>\n",
       "\t\t<li>'Yy1_extended'</li>\n",
       "\t\t<li>'Ets2'</li>\n",
       "\t\t<li>'Stat1_extended'</li>\n",
       "\t\t<li>'Setdb1'</li>\n",
       "\t\t<li>'Elf2_extended'</li>\n",
       "\t\t<li>'Smc3_extended'</li>\n",
       "\t\t<li>'Elf2'</li>\n",
       "\t\t<li>'Phf8_extended'</li>\n",
       "\t\t<li>'Pou3f1_extended'</li>\n",
       "\t\t<li>'Sin3a_extended'</li>\n",
       "\t\t<li>'Bclaf1'</li>\n",
       "\t\t<li>'Elk4_extended'</li>\n",
       "\t\t<li>'Gabpa_extended'</li>\n",
       "\t\t<li>'Gtf2f1_extended'</li>\n",
       "\t\t<li>'Zfp369_extended'</li>\n",
       "\t\t<li>'E2f6_extended'</li>\n",
       "\t\t<li>'Mxi1_extended'</li>\n",
       "\t\t<li>'Kdm5b_extended'</li>\n",
       "\t\t<li>'Rad21_extended'</li>\n",
       "\t\t<li>'Setdb1_extended'</li>\n",
       "\t\t<li>'Ctcf_extended'</li>\n",
       "\t\t<li>'Yy1'</li>\n",
       "\t\t<li>'Pou3f1'</li>\n",
       "\t\t<li>'Ets2_extended'</li>\n",
       "\t\t<li>'Zfp239'</li>\n",
       "\t\t<li>'Creb1_extended'</li>\n",
       "\t\t<li>'Hcfc1'</li>\n",
       "\t\t<li>'Ubtf_extended'</li>\n",
       "\t\t<li>'Ybx1_extended'</li>\n",
       "\t\t<li>'Nfyb_extended'</li>\n",
       "\t\t<li>'Maz_extended'</li>\n",
       "\t\t<li>'Ep300_extended'</li>\n",
       "\t\t<li>'Nfyb'</li>\n",
       "\t\t<li>'Etv1'</li>\n",
       "\t\t<li>'Suz12_extended'</li>\n",
       "\t\t<li>'Chd2'</li>\n",
       "\t\t<li>'Gabpb1_extended'</li>\n",
       "\t\t<li>'Sp1_extended'</li>\n",
       "\t\t<li>'Esrra_extended'</li>\n",
       "\t\t<li>'Zfp523_extended'</li>\n",
       "\t\t<li>'Ep300'</li>\n",
       "\t\t<li>'Creb3_extended'</li>\n",
       "\t\t<li>'Mxd1_extended'</li>\n",
       "\t\t<li>'Hdac2_extended'</li>\n",
       "\t\t<li>'Max_extended'</li>\n",
       "\t\t<li>'Rbbp5_extended'</li>\n",
       "\t\t<li>'Taf1_extended'</li>\n",
       "\t\t<li>'Nr2c2_extended'</li>\n",
       "\t\t<li>'Stat1'</li>\n",
       "\t\t<li>'Ebf1'</li>\n",
       "\t\t<li>'Kdm5a'</li>\n",
       "\t\t<li>'Nrf1_extended'</li>\n",
       "\t\t<li>'Bhlhe41_extended'</li>\n",
       "\t\t<li>'Hcfc1_extended'</li>\n",
       "\t\t<li>'Etv1_extended'</li>\n",
       "\t\t<li>'Klf6_extended'</li>\n",
       "\t\t<li>'Foxp2_extended'</li>\n",
       "\t\t<li>'Nrf1'</li>\n",
       "\t\t<li>'Taf1'</li>\n",
       "\t\t<li>'Bclaf1_extended'</li>\n",
       "\t\t<li>'Srebf2_extended'</li>\n",
       "\t\t<li>'Smarca4_extended'</li>\n",
       "\t\t<li>'Kdm5a_extended'</li>\n",
       "\t\t<li>'Zfp160_extended'</li>\n",
       "\t\t<li>'Zmiz1_extended'</li>\n",
       "\t\t<li>'Trim28_extended'</li>\n",
       "\t\t<li>'Usf2_extended'</li>\n",
       "\t\t<li>'Pou3f3_extended'</li>\n",
       "\t\t<li>'Usf2'</li>\n",
       "\t\t<li>'Chd2_extended'</li>\n",
       "\t\t<li>'Sp4_extended'</li>\n",
       "\t\t<li>'Rfx5_extended'</li>\n",
       "\t\t<li>'Sp4'</li>\n",
       "\t\t<li>'Bhlhe40_extended'</li>\n",
       "\t\t<li>'Bmyc_extended'</li>\n",
       "\t\t<li>'Crebl2_extended'</li>\n",
       "\t\t<li>'Zfp35'</li>\n",
       "\t\t<li>'Jund_extended'</li>\n",
       "\t\t<li>'Atf2_extended'</li>\n",
       "\t\t<li>'Ezh2'</li>\n",
       "\t\t<li>'Foxk1_extended'</li>\n",
       "\t\t<li>'Pbx3_extended'</li>\n",
       "\t\t<li>'Tef_extended'</li>\n",
       "\t\t<li>'Mga_extended'</li>\n",
       "\t\t<li>'Foxn3_extended'</li>\n",
       "\t\t<li>'Zeb1_extended'</li>\n",
       "\t\t<li>'Jun_extended'</li>\n",
       "\t\t<li>'Meis3'</li>\n",
       "\t\t<li>'Foxn3'</li>\n",
       "\t\t<li>'Rfx3_extended'</li>\n",
       "\t\t<li>'Tef'</li>\n",
       "\t\t<li>'Npdc1_extended'</li>\n",
       "\t\t<li>'Esrra'</li>\n",
       "\t\t<li>'Onecut2'</li>\n",
       "\t\t<li>'Tbl1xr1_extended'</li>\n",
       "\t\t<li>'Pbx3'</li>\n",
       "\t\t<li>'Nfia'</li>\n",
       "\t\t<li>'Zic1'</li>\n",
       "\t\t<li>'Zmiz1'</li>\n",
       "\t\t<li>'Atf2'</li>\n",
       "\t\t<li>'Bhlhe40'</li>\n",
       "\t\t<li>'Rora_extended'</li>\n",
       "\t\t<li>'Zbtb7a'</li>\n",
       "\t\t<li>'Cebpz_extended'</li>\n",
       "\t\t<li>'Trps1'</li>\n",
       "\t\t<li>'Zic4_extended'</li>\n",
       "\t\t<li>'Tcf7l2'</li>\n",
       "\t\t<li>'Hlf_extended'</li>\n",
       "\t\t<li>'Ppargc1a_extended'</li>\n",
       "\t\t<li>'Gtf3c2'</li>\n",
       "\t\t<li>'Hlf'</li>\n",
       "\t\t<li>'Zeb1'</li>\n",
       "\t\t<li>'Ppargc1a'</li>\n",
       "\t\t<li>'Shox2_extended'</li>\n",
       "\t\t<li>'Ar'</li>\n",
       "\t\t<li>'Esrrg_extended'</li>\n",
       "\t\t<li>'Tead1'</li>\n",
       "\t\t<li>'Esrrg'</li>\n",
       "\t\t<li>'Pou2f2_extended'</li>\n",
       "\t\t<li>'Zfhx2_extended'</li>\n",
       "\t\t<li>'Pou3f2'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Pou3f2\n",
       "\\item Zfhx2\\_extended\n",
       "\\item Pou2f2\\_extended\n",
       "\\item Tead1\n",
       "\\item Esrrg\\_extended\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'E2f4\\_extended'\n",
       "\\item 'Etv3'\n",
       "\\item 'Gabpa'\n",
       "\\item 'Elk1'\n",
       "\\item 'Elk1\\_extended'\n",
       "\\item 'Klf16\\_extended'\n",
       "\\item 'Etv5\\_extended'\n",
       "\\item 'Etv3\\_extended'\n",
       "\\item 'Sin3a'\n",
       "\\item 'Srf\\_extended'\n",
       "\\item 'Phf8'\n",
       "\\item 'Zfp319\\_extended'\n",
       "\\item 'Yy1\\_extended'\n",
       "\\item 'Ets2'\n",
       "\\item 'Stat1\\_extended'\n",
       "\\item 'Setdb1'\n",
       "\\item 'Elf2\\_extended'\n",
       "\\item 'Smc3\\_extended'\n",
       "\\item 'Elf2'\n",
       "\\item 'Phf8\\_extended'\n",
       "\\item 'Pou3f1\\_extended'\n",
       "\\item 'Sin3a\\_extended'\n",
       "\\item 'Bclaf1'\n",
       "\\item 'Elk4\\_extended'\n",
       "\\item 'Gabpa\\_extended'\n",
       "\\item 'Gtf2f1\\_extended'\n",
       "\\item 'Zfp369\\_extended'\n",
       "\\item 'E2f6\\_extended'\n",
       "\\item 'Mxi1\\_extended'\n",
       "\\item 'Kdm5b\\_extended'\n",
       "\\item 'Rad21\\_extended'\n",
       "\\item 'Setdb1\\_extended'\n",
       "\\item 'Ctcf\\_extended'\n",
       "\\item 'Yy1'\n",
       "\\item 'Pou3f1'\n",
       "\\item 'Ets2\\_extended'\n",
       "\\item 'Zfp239'\n",
       "\\item 'Creb1\\_extended'\n",
       "\\item 'Hcfc1'\n",
       "\\item 'Ubtf\\_extended'\n",
       "\\item 'Ybx1\\_extended'\n",
       "\\item 'Nfyb\\_extended'\n",
       "\\item 'Maz\\_extended'\n",
       "\\item 'Ep300\\_extended'\n",
       "\\item 'Nfyb'\n",
       "\\item 'Etv1'\n",
       "\\item 'Suz12\\_extended'\n",
       "\\item 'Chd2'\n",
       "\\item 'Gabpb1\\_extended'\n",
       "\\item 'Sp1\\_extended'\n",
       "\\item 'Esrra\\_extended'\n",
       "\\item 'Zfp523\\_extended'\n",
       "\\item 'Ep300'\n",
       "\\item 'Creb3\\_extended'\n",
       "\\item 'Mxd1\\_extended'\n",
       "\\item 'Hdac2\\_extended'\n",
       "\\item 'Max\\_extended'\n",
       "\\item 'Rbbp5\\_extended'\n",
       "\\item 'Taf1\\_extended'\n",
       "\\item 'Nr2c2\\_extended'\n",
       "\\item 'Stat1'\n",
       "\\item 'Ebf1'\n",
       "\\item 'Kdm5a'\n",
       "\\item 'Nrf1\\_extended'\n",
       "\\item 'Bhlhe41\\_extended'\n",
       "\\item 'Hcfc1\\_extended'\n",
       "\\item 'Etv1\\_extended'\n",
       "\\item 'Klf6\\_extended'\n",
       "\\item 'Foxp2\\_extended'\n",
       "\\item 'Nrf1'\n",
       "\\item 'Taf1'\n",
       "\\item 'Bclaf1\\_extended'\n",
       "\\item 'Srebf2\\_extended'\n",
       "\\item 'Smarca4\\_extended'\n",
       "\\item 'Kdm5a\\_extended'\n",
       "\\item 'Zfp160\\_extended'\n",
       "\\item 'Zmiz1\\_extended'\n",
       "\\item 'Trim28\\_extended'\n",
       "\\item 'Usf2\\_extended'\n",
       "\\item 'Pou3f3\\_extended'\n",
       "\\item 'Usf2'\n",
       "\\item 'Chd2\\_extended'\n",
       "\\item 'Sp4\\_extended'\n",
       "\\item 'Rfx5\\_extended'\n",
       "\\item 'Sp4'\n",
       "\\item 'Bhlhe40\\_extended'\n",
       "\\item 'Bmyc\\_extended'\n",
       "\\item 'Crebl2\\_extended'\n",
       "\\item 'Zfp35'\n",
       "\\item 'Jund\\_extended'\n",
       "\\item 'Atf2\\_extended'\n",
       "\\item 'Ezh2'\n",
       "\\item 'Foxk1\\_extended'\n",
       "\\item 'Pbx3\\_extended'\n",
       "\\item 'Tef\\_extended'\n",
       "\\item 'Mga\\_extended'\n",
       "\\item 'Foxn3\\_extended'\n",
       "\\item 'Zeb1\\_extended'\n",
       "\\item 'Jun\\_extended'\n",
       "\\item 'Meis3'\n",
       "\\item 'Foxn3'\n",
       "\\item 'Rfx3\\_extended'\n",
       "\\item 'Tef'\n",
       "\\item 'Npdc1\\_extended'\n",
       "\\item 'Esrra'\n",
       "\\item 'Onecut2'\n",
       "\\item 'Tbl1xr1\\_extended'\n",
       "\\item 'Pbx3'\n",
       "\\item 'Nfia'\n",
       "\\item 'Zic1'\n",
       "\\item 'Zmiz1'\n",
       "\\item 'Atf2'\n",
       "\\item 'Bhlhe40'\n",
       "\\item 'Rora\\_extended'\n",
       "\\item 'Zbtb7a'\n",
       "\\item 'Cebpz\\_extended'\n",
       "\\item 'Trps1'\n",
       "\\item 'Zic4\\_extended'\n",
       "\\item 'Tcf7l2'\n",
       "\\item 'Hlf\\_extended'\n",
       "\\item 'Ppargc1a\\_extended'\n",
       "\\item 'Gtf3c2'\n",
       "\\item 'Hlf'\n",
       "\\item 'Zeb1'\n",
       "\\item 'Ppargc1a'\n",
       "\\item 'Shox2\\_extended'\n",
       "\\item 'Ar'\n",
       "\\item 'Esrrg\\_extended'\n",
       "\\item 'Tead1'\n",
       "\\item 'Esrrg'\n",
       "\\item 'Pou2f2\\_extended'\n",
       "\\item 'Zfhx2\\_extended'\n",
       "\\item 'Pou3f2'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Pou3f2\n",
       "2. Zfhx2_extended\n",
       "3. Pou2f2_extended\n",
       "4. Tead1\n",
       "5. Esrrg_extended\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'E2f4_extended'\n",
       "2. 'Etv3'\n",
       "3. 'Gabpa'\n",
       "4. 'Elk1'\n",
       "5. 'Elk1_extended'\n",
       "6. 'Klf16_extended'\n",
       "7. 'Etv5_extended'\n",
       "8. 'Etv3_extended'\n",
       "9. 'Sin3a'\n",
       "10. 'Srf_extended'\n",
       "11. 'Phf8'\n",
       "12. 'Zfp319_extended'\n",
       "13. 'Yy1_extended'\n",
       "14. 'Ets2'\n",
       "15. 'Stat1_extended'\n",
       "16. 'Setdb1'\n",
       "17. 'Elf2_extended'\n",
       "18. 'Smc3_extended'\n",
       "19. 'Elf2'\n",
       "20. 'Phf8_extended'\n",
       "21. 'Pou3f1_extended'\n",
       "22. 'Sin3a_extended'\n",
       "23. 'Bclaf1'\n",
       "24. 'Elk4_extended'\n",
       "25. 'Gabpa_extended'\n",
       "26. 'Gtf2f1_extended'\n",
       "27. 'Zfp369_extended'\n",
       "28. 'E2f6_extended'\n",
       "29. 'Mxi1_extended'\n",
       "30. 'Kdm5b_extended'\n",
       "31. 'Rad21_extended'\n",
       "32. 'Setdb1_extended'\n",
       "33. 'Ctcf_extended'\n",
       "34. 'Yy1'\n",
       "35. 'Pou3f1'\n",
       "36. 'Ets2_extended'\n",
       "37. 'Zfp239'\n",
       "38. 'Creb1_extended'\n",
       "39. 'Hcfc1'\n",
       "40. 'Ubtf_extended'\n",
       "41. 'Ybx1_extended'\n",
       "42. 'Nfyb_extended'\n",
       "43. 'Maz_extended'\n",
       "44. 'Ep300_extended'\n",
       "45. 'Nfyb'\n",
       "46. 'Etv1'\n",
       "47. 'Suz12_extended'\n",
       "48. 'Chd2'\n",
       "49. 'Gabpb1_extended'\n",
       "50. 'Sp1_extended'\n",
       "51. 'Esrra_extended'\n",
       "52. 'Zfp523_extended'\n",
       "53. 'Ep300'\n",
       "54. 'Creb3_extended'\n",
       "55. 'Mxd1_extended'\n",
       "56. 'Hdac2_extended'\n",
       "57. 'Max_extended'\n",
       "58. 'Rbbp5_extended'\n",
       "59. 'Taf1_extended'\n",
       "60. 'Nr2c2_extended'\n",
       "61. 'Stat1'\n",
       "62. 'Ebf1'\n",
       "63. 'Kdm5a'\n",
       "64. 'Nrf1_extended'\n",
       "65. 'Bhlhe41_extended'\n",
       "66. 'Hcfc1_extended'\n",
       "67. 'Etv1_extended'\n",
       "68. 'Klf6_extended'\n",
       "69. 'Foxp2_extended'\n",
       "70. 'Nrf1'\n",
       "71. 'Taf1'\n",
       "72. 'Bclaf1_extended'\n",
       "73. 'Srebf2_extended'\n",
       "74. 'Smarca4_extended'\n",
       "75. 'Kdm5a_extended'\n",
       "76. 'Zfp160_extended'\n",
       "77. 'Zmiz1_extended'\n",
       "78. 'Trim28_extended'\n",
       "79. 'Usf2_extended'\n",
       "80. 'Pou3f3_extended'\n",
       "81. 'Usf2'\n",
       "82. 'Chd2_extended'\n",
       "83. 'Sp4_extended'\n",
       "84. 'Rfx5_extended'\n",
       "85. 'Sp4'\n",
       "86. 'Bhlhe40_extended'\n",
       "87. 'Bmyc_extended'\n",
       "88. 'Crebl2_extended'\n",
       "89. 'Zfp35'\n",
       "90. 'Jund_extended'\n",
       "91. 'Atf2_extended'\n",
       "92. 'Ezh2'\n",
       "93. 'Foxk1_extended'\n",
       "94. 'Pbx3_extended'\n",
       "95. 'Tef_extended'\n",
       "96. 'Mga_extended'\n",
       "97. 'Foxn3_extended'\n",
       "98. 'Zeb1_extended'\n",
       "99. 'Jun_extended'\n",
       "100. 'Meis3'\n",
       "101. 'Foxn3'\n",
       "102. 'Rfx3_extended'\n",
       "103. 'Tef'\n",
       "104. 'Npdc1_extended'\n",
       "105. 'Esrra'\n",
       "106. 'Onecut2'\n",
       "107. 'Tbl1xr1_extended'\n",
       "108. 'Pbx3'\n",
       "109. 'Nfia'\n",
       "110. 'Zic1'\n",
       "111. 'Zmiz1'\n",
       "112. 'Atf2'\n",
       "113. 'Bhlhe40'\n",
       "114. 'Rora_extended'\n",
       "115. 'Zbtb7a'\n",
       "116. 'Cebpz_extended'\n",
       "117. 'Trps1'\n",
       "118. 'Zic4_extended'\n",
       "119. 'Tcf7l2'\n",
       "120. 'Hlf_extended'\n",
       "121. 'Ppargc1a_extended'\n",
       "122. 'Gtf3c2'\n",
       "123. 'Hlf'\n",
       "124. 'Zeb1'\n",
       "125. 'Ppargc1a'\n",
       "126. 'Shox2_extended'\n",
       "127. 'Ar'\n",
       "128. 'Esrrg_extended'\n",
       "129. 'Tead1'\n",
       "130. 'Esrrg'\n",
       "131. 'Pou2f2_extended'\n",
       "132. 'Zfhx2_extended'\n",
       "133. 'Pou3f2'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] Pou3f2          Zfhx2_extended  Pou2f2_extended Tead1          \n",
       "[5] Esrrg_extended \n",
       "133 Levels: E2f4_extended Etv3 Gabpa Elk1 Elk1_extended ... Pou3f2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Fxyd6'</li>\n",
       "\t<li>'Hpca'</li>\n",
       "\t<li>'Nr2f1'</li>\n",
       "\t<li>'Nrxn3'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Fxyd6'\n",
       "\\item 'Hpca'\n",
       "\\item 'Nr2f1'\n",
       "\\item 'Nrxn3'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Fxyd6'\n",
       "2. 'Hpca'\n",
       "3. 'Nr2f1'\n",
       "4. 'Nrxn3'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Fxyd6\" \"Hpca\"  \"Nr2f1\" \"Nrxn3\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Bhlhe40'</li>\n",
       "\t<li>'Elavl2'</li>\n",
       "\t<li>'Lgi2'</li>\n",
       "\t<li>'Tpbg'</li>\n",
       "\t<li>'Tuba1b'</li>\n",
       "\t<li>'Vamp1'</li>\n",
       "\t<li>'Zfhx4'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Bhlhe40'\n",
       "\\item 'Elavl2'\n",
       "\\item 'Lgi2'\n",
       "\\item 'Tpbg'\n",
       "\\item 'Tuba1b'\n",
       "\\item 'Vamp1'\n",
       "\\item 'Zfhx4'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Bhlhe40'\n",
       "2. 'Elavl2'\n",
       "3. 'Lgi2'\n",
       "4. 'Tpbg'\n",
       "5. 'Tuba1b'\n",
       "6. 'Vamp1'\n",
       "7. 'Zfhx4'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Bhlhe40\" \"Elavl2\"  \"Lgi2\"    \"Tpbg\"    \"Tuba1b\"  \"Vamp1\"   \"Zfhx4\"  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Ahi1'</li>\n",
       "\t<li>'Cacna1g'</li>\n",
       "\t<li>'Camk2a'</li>\n",
       "\t<li>'Cd47'</li>\n",
       "\t<li>'Cntnap5a'</li>\n",
       "\t<li>'Csrnp3'</li>\n",
       "\t<li>'Dlgap1'</li>\n",
       "\t<li>'Elavl2'</li>\n",
       "\t<li>'Elmod1'</li>\n",
       "\t<li>'Fam19a2'</li>\n",
       "\t<li>'Fign'</li>\n",
       "\t<li>'Fut9'</li>\n",
       "\t<li>'Gnaq'</li>\n",
       "\t<li>'Hpca'</li>\n",
       "\t<li>'Kcnip4'</li>\n",
       "\t<li>'Mal2'</li>\n",
       "\t<li>'Mast4'</li>\n",
       "\t<li>'Ncam1'</li>\n",
       "\t<li>'Ntng1'</li>\n",
       "\t<li>'Ntrk2'</li>\n",
       "\t<li>'Pak1'</li>\n",
       "\t<li>'Pcdh10'</li>\n",
       "\t<li>'Pcdh19'</li>\n",
       "\t<li>'Pcdh7'</li>\n",
       "\t<li>'Plxna4'</li>\n",
       "\t<li>'Runx1t1'</li>\n",
       "\t<li>'Sox1'</li>\n",
       "\t<li>'Spats2l'</li>\n",
       "\t<li>'Tshz2'</li>\n",
       "\t<li>'Zfhx3'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Ahi1'\n",
       "\\item 'Cacna1g'\n",
       "\\item 'Camk2a'\n",
       "\\item 'Cd47'\n",
       "\\item 'Cntnap5a'\n",
       "\\item 'Csrnp3'\n",
       "\\item 'Dlgap1'\n",
       "\\item 'Elavl2'\n",
       "\\item 'Elmod1'\n",
       "\\item 'Fam19a2'\n",
       "\\item 'Fign'\n",
       "\\item 'Fut9'\n",
       "\\item 'Gnaq'\n",
       "\\item 'Hpca'\n",
       "\\item 'Kcnip4'\n",
       "\\item 'Mal2'\n",
       "\\item 'Mast4'\n",
       "\\item 'Ncam1'\n",
       "\\item 'Ntng1'\n",
       "\\item 'Ntrk2'\n",
       "\\item 'Pak1'\n",
       "\\item 'Pcdh10'\n",
       "\\item 'Pcdh19'\n",
       "\\item 'Pcdh7'\n",
       "\\item 'Plxna4'\n",
       "\\item 'Runx1t1'\n",
       "\\item 'Sox1'\n",
       "\\item 'Spats2l'\n",
       "\\item 'Tshz2'\n",
       "\\item 'Zfhx3'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Ahi1'\n",
       "2. 'Cacna1g'\n",
       "3. 'Camk2a'\n",
       "4. 'Cd47'\n",
       "5. 'Cntnap5a'\n",
       "6. 'Csrnp3'\n",
       "7. 'Dlgap1'\n",
       "8. 'Elavl2'\n",
       "9. 'Elmod1'\n",
       "10. 'Fam19a2'\n",
       "11. 'Fign'\n",
       "12. 'Fut9'\n",
       "13. 'Gnaq'\n",
       "14. 'Hpca'\n",
       "15. 'Kcnip4'\n",
       "16. 'Mal2'\n",
       "17. 'Mast4'\n",
       "18. 'Ncam1'\n",
       "19. 'Ntng1'\n",
       "20. 'Ntrk2'\n",
       "21. 'Pak1'\n",
       "22. 'Pcdh10'\n",
       "23. 'Pcdh19'\n",
       "24. 'Pcdh7'\n",
       "25. 'Plxna4'\n",
       "26. 'Runx1t1'\n",
       "27. 'Sox1'\n",
       "28. 'Spats2l'\n",
       "29. 'Tshz2'\n",
       "30. 'Zfhx3'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Ahi1\"     \"Cacna1g\"  \"Camk2a\"   \"Cd47\"     \"Cntnap5a\" \"Csrnp3\"  \n",
       " [7] \"Dlgap1\"   \"Elavl2\"   \"Elmod1\"   \"Fam19a2\"  \"Fign\"     \"Fut9\"    \n",
       "[13] \"Gnaq\"     \"Hpca\"     \"Kcnip4\"   \"Mal2\"     \"Mast4\"    \"Ncam1\"   \n",
       "[19] \"Ntng1\"    \"Ntrk2\"    \"Pak1\"     \"Pcdh10\"   \"Pcdh19\"   \"Pcdh7\"   \n",
       "[25] \"Plxna4\"   \"Runx1t1\"  \"Sox1\"     \"Spats2l\"  \"Tshz2\"    \"Zfhx3\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Mal2'"
      ],
      "text/latex": [
       "'Mal2'"
      ],
      "text/markdown": [
       "'Mal2'"
      ],
      "text/plain": [
       "[1] \"Mal2\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Adcyap1r1'</li>\n",
       "\t<li>'Atp2b2'</li>\n",
       "\t<li>'Caln1'</li>\n",
       "\t<li>'Ckmt1'</li>\n",
       "\t<li>'Gabra1'</li>\n",
       "\t<li>'Hcn2'</li>\n",
       "\t<li>'Kcnip4'</li>\n",
       "\t<li>'Kcnj3'</li>\n",
       "\t<li>'Lingo1'</li>\n",
       "\t<li>'Nat8l'</li>\n",
       "\t<li>'Nr2f2'</li>\n",
       "\t<li>'Nrxn3'</li>\n",
       "\t<li>'Pde1b'</li>\n",
       "\t<li>'Plxna4'</li>\n",
       "\t<li>'Rnf152'</li>\n",
       "\t<li>'Slc24a3'</li>\n",
       "\t<li>'Sorl1'</li>\n",
       "\t<li>'Spats2l'</li>\n",
       "\t<li>'Stxbp6'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Adcyap1r1'\n",
       "\\item 'Atp2b2'\n",
       "\\item 'Caln1'\n",
       "\\item 'Ckmt1'\n",
       "\\item 'Gabra1'\n",
       "\\item 'Hcn2'\n",
       "\\item 'Kcnip4'\n",
       "\\item 'Kcnj3'\n",
       "\\item 'Lingo1'\n",
       "\\item 'Nat8l'\n",
       "\\item 'Nr2f2'\n",
       "\\item 'Nrxn3'\n",
       "\\item 'Pde1b'\n",
       "\\item 'Plxna4'\n",
       "\\item 'Rnf152'\n",
       "\\item 'Slc24a3'\n",
       "\\item 'Sorl1'\n",
       "\\item 'Spats2l'\n",
       "\\item 'Stxbp6'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Adcyap1r1'\n",
       "2. 'Atp2b2'\n",
       "3. 'Caln1'\n",
       "4. 'Ckmt1'\n",
       "5. 'Gabra1'\n",
       "6. 'Hcn2'\n",
       "7. 'Kcnip4'\n",
       "8. 'Kcnj3'\n",
       "9. 'Lingo1'\n",
       "10. 'Nat8l'\n",
       "11. 'Nr2f2'\n",
       "12. 'Nrxn3'\n",
       "13. 'Pde1b'\n",
       "14. 'Plxna4'\n",
       "15. 'Rnf152'\n",
       "16. 'Slc24a3'\n",
       "17. 'Sorl1'\n",
       "18. 'Spats2l'\n",
       "19. 'Stxbp6'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Adcyap1r1\" \"Atp2b2\"    \"Caln1\"     \"Ckmt1\"     \"Gabra1\"    \"Hcn2\"     \n",
       " [7] \"Kcnip4\"    \"Kcnj3\"     \"Lingo1\"    \"Nat8l\"     \"Nr2f2\"     \"Nrxn3\"    \n",
       "[13] \"Pde1b\"     \"Plxna4\"    \"Rnf152\"    \"Slc24a3\"   \"Sorl1\"     \"Spats2l\"  \n",
       "[19] \"Stxbp6\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L.TFs_order[1:5,1]\n",
    "intersect(regulons[[as.character(L.TFs_order[1,1])]],LHb_gene)\n",
    "intersect(regulons[[as.character(L.TFs_order[2,1])]],LHb_gene)\n",
    "intersect(regulons[[as.character(L.TFs_order[3,1])]],LHb_gene)\n",
    "intersect(regulons[[as.character(L.TFs_order[4,1])]],LHb_gene)\n",
    "intersect(regulons[[as.character(L.TFs_order[5,1])]],LHb_gene)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# combinatorial regulation of TFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MHb_gene[1] %in% regulons[[MHb_TF[1]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "170"
      ],
      "text/latex": [
       "170"
      ],
      "text/markdown": [
       "170"
      ],
      "text/plain": [
       "[1] 170"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(MHb_gene)\n",
    "MHb_TF<-as.character(rownames(M.TFs_order))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_TFs<-numeric()\n",
    "for (i in 1: length(MHb_gene)){\n",
    "    temp<-0\n",
    "    for (j in 1: length(MHb_TF)){\n",
    "        if (sum(MHb_gene[i] %in% regulons[[MHb_TF[j]]])){\n",
    "            temp<-temp+1\n",
    "        }else{temp<-temp}\n",
    "    }\n",
    "    number_TFs[i]<-temp\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "152"
      ],
      "text/latex": [
       "152"
      ],
      "text/markdown": [
       "152"
      ],
      "text/plain": [
       "[1] 152"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "140"
      ],
      "text/latex": [
       "140"
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      "text/markdown": [
       "140"
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      "text/plain": [
       "[1] 140"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(number_TFs>0)\n",
    "sum(number_TFs>1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.921052631578947"
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       "0.921052631578947"
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      "text/plain": [
       "[1] 0.9210526"
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     "metadata": {},
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    {
     "data": {
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       "0.823529411764706"
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      "text/plain": [
       "[1] 0.8235294"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "140/152\n",
    "140/length(MHb_gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "260"
      ],
      "text/latex": [
       "260"
      ],
      "text/markdown": [
       "260"
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      "text/plain": [
       "[1] 260"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "251"
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      "text/latex": [
       "251"
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      "text/markdown": [
       "251"
      ],
      "text/plain": [
       "[1] 251"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LHb_TF<-as.character(rownames(L.TFs_order))\n",
    "number_TFs<-numeric()\n",
    "for (i in 1: length(LHb_gene)){\n",
    "    temp<-0\n",
    "    for (j in 1: length(LHb_TF)){\n",
    "        if (sum(LHb_gene[i] %in% regulons[[LHb_TF[j]]])){\n",
    "            temp<-temp+1\n",
    "        }else{temp<-temp}\n",
    "    }\n",
    "    number_TFs[i]<-temp\n",
    "}\n",
    "sum(number_TFs>0)\n",
    "sum(number_TFs>1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.965384615384615"
      ],
      "text/latex": [
       "0.965384615384615"
      ],
      "text/markdown": [
       "0.965384615384615"
      ],
      "text/plain": [
       "[1] 0.9653846"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.868512110726644"
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       "0.868512110726644"
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       "0.868512110726644"
      ],
      "text/plain": [
       "[1] 0.8685121"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "251/260\n",
    "251/length(LHb_gene)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# disc plot visualization of TFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(scales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TFs MHb\n",
    "Cell_type<-factor(rev(c(\"MHb3\",\"MHb6\",\"MHb4\",\"MHb5\",\"MHb1\",\"MHb2\")),levels=rev(c(\"MHb3\",\"MHb6\",\"MHb4\",\"MHb5\",\"MHb1\",\"MHb2\")))\n",
    "gene_list<-rev(c(\"Neurod1\" ,\"Gpd1\" ,\"Pou3f1\",\"Neurod2\" ,\"Irx3\",\"Lef1\",\"Ebf1\",\"Sox6\",\"Pbx3\",\"Pou3f3\",\"Nfib\",\"Tcf7l2\",\"Zic4\",\"Klf9\",\"Tcf7\",\"Lhx9\",\"Ybx1\",\"Zic1\",\"Mxd4\",\"Myef2\",\"Nfix\",\"Klf7\",\"E4f1\",\"Pou2f2\",\"Etv5\",\"Foxp2\",\"Bhlhe41\",\"Usf2\",\"Tef\",\"Npdc1\",\"Jund\",\"Crebl2\",\"Etv1\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "\t<li>MHb2</li>\n",
       "\t<li>MHb1</li>\n",
       "\t<li>MHb5</li>\n",
       "\t<li>MHb4</li>\n",
       "\t<li>MHb6</li>\n",
       "\t<li>MHb3</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'MHb2'</li>\n",
       "\t\t<li>'MHb1'</li>\n",
       "\t\t<li>'MHb5'</li>\n",
       "\t\t<li>'MHb4'</li>\n",
       "\t\t<li>'MHb6'</li>\n",
       "\t\t<li>'MHb3'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\item MHb2\n",
       "\\item MHb1\n",
       "\\item MHb5\n",
       "\\item MHb4\n",
       "\\item MHb6\n",
       "\\item MHb3\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'MHb2'\n",
       "\\item 'MHb1'\n",
       "\\item 'MHb5'\n",
       "\\item 'MHb4'\n",
       "\\item 'MHb6'\n",
       "\\item 'MHb3'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. MHb2\n",
       "2. MHb1\n",
       "3. MHb5\n",
       "4. MHb4\n",
       "5. MHb6\n",
       "6. MHb3\n",
       "7. MHb2\n",
       "8. MHb1\n",
       "9. MHb5\n",
       "10. MHb4\n",
       "11. MHb6\n",
       "12. MHb3\n",
       "13. MHb2\n",
       "14. MHb1\n",
       "15. MHb5\n",
       "16. MHb4\n",
       "17. MHb6\n",
       "18. MHb3\n",
       "19. MHb2\n",
       "20. MHb1\n",
       "21. MHb5\n",
       "22. MHb4\n",
       "23. MHb6\n",
       "24. MHb3\n",
       "25. MHb2\n",
       "26. MHb1\n",
       "27. MHb5\n",
       "28. MHb4\n",
       "29. MHb6\n",
       "30. MHb3\n",
       "31. MHb2\n",
       "32. MHb1\n",
       "33. MHb5\n",
       "34. MHb4\n",
       "35. MHb6\n",
       "36. MHb3\n",
       "37. MHb2\n",
       "38. MHb1\n",
       "39. MHb5\n",
       "40. MHb4\n",
       "41. MHb6\n",
       "42. MHb3\n",
       "43. MHb2\n",
       "44. MHb1\n",
       "45. MHb5\n",
       "46. MHb4\n",
       "47. MHb6\n",
       "48. MHb3\n",
       "49. MHb2\n",
       "50. MHb1\n",
       "51. MHb5\n",
       "52. MHb4\n",
       "53. MHb6\n",
       "54. MHb3\n",
       "55. MHb2\n",
       "56. MHb1\n",
       "57. MHb5\n",
       "58. MHb4\n",
       "59. MHb6\n",
       "60. MHb3\n",
       "61. MHb2\n",
       "62. MHb1\n",
       "63. MHb5\n",
       "64. MHb4\n",
       "65. MHb6\n",
       "66. MHb3\n",
       "67. MHb2\n",
       "68. MHb1\n",
       "69. MHb5\n",
       "70. MHb4\n",
       "71. MHb6\n",
       "72. MHb3\n",
       "73. MHb2\n",
       "74. MHb1\n",
       "75. MHb5\n",
       "76. MHb4\n",
       "77. MHb6\n",
       "78. MHb3\n",
       "79. MHb2\n",
       "80. MHb1\n",
       "81. MHb5\n",
       "82. MHb4\n",
       "83. MHb6\n",
       "84. MHb3\n",
       "85. MHb2\n",
       "86. MHb1\n",
       "87. MHb5\n",
       "88. MHb4\n",
       "89. MHb6\n",
       "90. MHb3\n",
       "91. MHb2\n",
       "92. MHb1\n",
       "93. MHb5\n",
       "94. MHb4\n",
       "95. MHb6\n",
       "96. MHb3\n",
       "97. MHb2\n",
       "98. MHb1\n",
       "99. MHb5\n",
       "100. MHb4\n",
       "101. MHb6\n",
       "102. MHb3\n",
       "103. MHb2\n",
       "104. MHb1\n",
       "105. MHb5\n",
       "106. MHb4\n",
       "107. MHb6\n",
       "108. MHb3\n",
       "109. MHb2\n",
       "110. MHb1\n",
       "111. MHb5\n",
       "112. MHb4\n",
       "113. MHb6\n",
       "114. MHb3\n",
       "115. MHb2\n",
       "116. MHb1\n",
       "117. MHb5\n",
       "118. MHb4\n",
       "119. MHb6\n",
       "120. MHb3\n",
       "121. MHb2\n",
       "122. MHb1\n",
       "123. MHb5\n",
       "124. MHb4\n",
       "125. MHb6\n",
       "126. MHb3\n",
       "127. MHb2\n",
       "128. MHb1\n",
       "129. MHb5\n",
       "130. MHb4\n",
       "131. MHb6\n",
       "132. MHb3\n",
       "133. MHb2\n",
       "134. MHb1\n",
       "135. MHb5\n",
       "136. MHb4\n",
       "137. MHb6\n",
       "138. MHb3\n",
       "139. MHb2\n",
       "140. MHb1\n",
       "141. MHb5\n",
       "142. MHb4\n",
       "143. MHb6\n",
       "144. MHb3\n",
       "145. MHb2\n",
       "146. MHb1\n",
       "147. MHb5\n",
       "148. MHb4\n",
       "149. MHb6\n",
       "150. MHb3\n",
       "151. MHb2\n",
       "152. MHb1\n",
       "153. MHb5\n",
       "154. MHb4\n",
       "155. MHb6\n",
       "156. MHb3\n",
       "157. MHb2\n",
       "158. MHb1\n",
       "159. MHb5\n",
       "160. MHb4\n",
       "161. MHb6\n",
       "162. MHb3\n",
       "163. MHb2\n",
       "164. MHb1\n",
       "165. MHb5\n",
       "166. MHb4\n",
       "167. MHb6\n",
       "168. MHb3\n",
       "169. MHb2\n",
       "170. MHb1\n",
       "171. MHb5\n",
       "172. MHb4\n",
       "173. MHb6\n",
       "174. MHb3\n",
       "175. MHb2\n",
       "176. MHb1\n",
       "177. MHb5\n",
       "178. MHb4\n",
       "179. MHb6\n",
       "180. MHb3\n",
       "181. MHb2\n",
       "182. MHb1\n",
       "183. MHb5\n",
       "184. MHb4\n",
       "185. MHb6\n",
       "186. MHb3\n",
       "187. MHb2\n",
       "188. MHb1\n",
       "189. MHb5\n",
       "190. MHb4\n",
       "191. MHb6\n",
       "192. MHb3\n",
       "193. MHb2\n",
       "194. MHb1\n",
       "195. MHb5\n",
       "196. MHb4\n",
       "197. MHb6\n",
       "198. MHb3\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'MHb2'\n",
       "2. 'MHb1'\n",
       "3. 'MHb5'\n",
       "4. 'MHb4'\n",
       "5. 'MHb6'\n",
       "6. 'MHb3'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5\n",
       " [16] MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3\n",
       " [31] MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5\n",
       " [46] MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3\n",
       " [61] MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5\n",
       " [76] MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3\n",
       " [91] MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5\n",
       "[106] MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3\n",
       "[121] MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5\n",
       "[136] MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3\n",
       "[151] MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5\n",
       "[166] MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3\n",
       "[181] MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5 MHb4 MHb6 MHb3 MHb2 MHb1 MHb5\n",
       "[196] MHb4 MHb6 MHb3\n",
       "Levels: MHb2 MHb1 MHb5 MHb4 MHb6 MHb3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cell_number<- data.frame(Date=as.Date(character()),File=character(),User=character(),stringsAsFactors=FALSE)\n",
    "\n",
    "for (i in 1:length(gene_list)){\n",
    "L<-length(Cell_type)\n",
    "\n",
    "Cell_number_t<- data.frame(\"cluster\" =Cell_type, \"gene\"=c(rep(gene_list[i],L)))\n",
    "#used normalized uncorrected data\n",
    "for (p in 1:length(Cell_type)){\n",
    "Cell_number_t$pct[p]<-100*sum(LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\")))]>0)/length(eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\"))))\n",
    "Cell_number_t$avg[p]<-(mean(LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\")))])-mean(LHb.integrated@assays$RNA@data[gene_list[i],]))/sd(LHb.integrated@assays$RNA@data[gene_list[i],])\n",
    "#for avg only consider the expressed cell\n",
    " #t<-LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\")))]>0\n",
    "# Cell_number_t$avg[p]<-mean(LHb.integrated@assays$RNA@data[gene_list[i],t])/sd(LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\")))])    \n",
    "    }\n",
    "Cell_number<-rbind(Cell_number_t,Cell_number)}\n",
    "as.factor(Cell_number$cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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sPb2xpP9NltDiKKS1IUTE2etigzPFDW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\nN3j6jCQqx2Tx3Yt5HH8vHkTlZ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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(Cell_number, aes(gene, cluster)) + geom_point(aes(size = pct, colour=avg)) + \n",
    "scale_color_gradient(low = \"white\", high = \"darkblue\",limits = c(-1.5,1.5),oob=squish) + \n",
    "geom_point(aes(size = pct), pch=21,, lwd=0,stroke=0)+ scale_size_continuous(range = c(0,3.9))+\n",
    "theme(axis.title.y=element_text(size=15),axis.text.y=element_text(size=8),axis.text.x=element_text(size=6,angle = 50, hjust = 1),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+ggtitle(\"% cells\")\n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/discplots_TF_MHb.pdf\",height=4, width=10 , paper = \"letter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TFs LHb\n",
    "Cell_type<-factor(rev(c(\"LHb2\",\"LHb1\",\"LHb3\",\"LHb6\",\"LHb4\",\"LHb5\")),levels=rev(c(\"LHb2\",\"LHb1\",\"LHb3\",\"LHb6\",\"LHb4\",\"LHb5\")))\n",
    "gene_list<-rev(c(\"Pou3f2\" ,\"Zfhx2\",\"Pou2f2\" ,\"Tead1\" ,\"Esrrg\",\"Ar\",\"Shox2\",\"Gtf3c2\",\"Ppargc1a\",\"Hlf\",\"Tcf7l2\",\"Zic4\",\"Trps1\",\"Cebpz\",\"Zbtb7a\",\"Rora\",\"Zic1\",\"Nfia\",\"Tbl1xr1\",\"Onecut2\",\"Npdc1\",\"Rfx3\",\"Meis3\",\"Jun\",\"Zeb1\",\"Foxn3\",\"Mga\",\"Tef\",\"Pbx3\",\"Foxk1\",\"Ezh2\",\"Atf2\",\"Jund\",\"Zfp35\",\"Crebl2\",\"Bmyc\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "\t<li>LHb5</li>\n",
       "\t<li>LHb4</li>\n",
       "\t<li>LHb6</li>\n",
       "\t<li>LHb3</li>\n",
       "\t<li>LHb1</li>\n",
       "\t<li>LHb2</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'LHb5'</li>\n",
       "\t\t<li>'LHb4'</li>\n",
       "\t\t<li>'LHb6'</li>\n",
       "\t\t<li>'LHb3'</li>\n",
       "\t\t<li>'LHb1'</li>\n",
       "\t\t<li>'LHb2'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\item LHb5\n",
       "\\item LHb4\n",
       "\\item LHb6\n",
       "\\item LHb3\n",
       "\\item LHb1\n",
       "\\item LHb2\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'LHb5'\n",
       "\\item 'LHb4'\n",
       "\\item 'LHb6'\n",
       "\\item 'LHb3'\n",
       "\\item 'LHb1'\n",
       "\\item 'LHb2'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. LHb5\n",
       "2. LHb4\n",
       "3. LHb6\n",
       "4. LHb3\n",
       "5. LHb1\n",
       "6. LHb2\n",
       "7. LHb5\n",
       "8. LHb4\n",
       "9. LHb6\n",
       "10. LHb3\n",
       "11. LHb1\n",
       "12. LHb2\n",
       "13. LHb5\n",
       "14. LHb4\n",
       "15. LHb6\n",
       "16. LHb3\n",
       "17. LHb1\n",
       "18. LHb2\n",
       "19. LHb5\n",
       "20. LHb4\n",
       "21. LHb6\n",
       "22. LHb3\n",
       "23. LHb1\n",
       "24. LHb2\n",
       "25. LHb5\n",
       "26. LHb4\n",
       "27. LHb6\n",
       "28. LHb3\n",
       "29. LHb1\n",
       "30. LHb2\n",
       "31. LHb5\n",
       "32. LHb4\n",
       "33. LHb6\n",
       "34. LHb3\n",
       "35. LHb1\n",
       "36. LHb2\n",
       "37. LHb5\n",
       "38. LHb4\n",
       "39. LHb6\n",
       "40. LHb3\n",
       "41. LHb1\n",
       "42. LHb2\n",
       "43. LHb5\n",
       "44. LHb4\n",
       "45. LHb6\n",
       "46. LHb3\n",
       "47. LHb1\n",
       "48. LHb2\n",
       "49. LHb5\n",
       "50. LHb4\n",
       "51. LHb6\n",
       "52. LHb3\n",
       "53. LHb1\n",
       "54. LHb2\n",
       "55. LHb5\n",
       "56. LHb4\n",
       "57. LHb6\n",
       "58. LHb3\n",
       "59. LHb1\n",
       "60. LHb2\n",
       "61. LHb5\n",
       "62. LHb4\n",
       "63. LHb6\n",
       "64. LHb3\n",
       "65. LHb1\n",
       "66. LHb2\n",
       "67. LHb5\n",
       "68. LHb4\n",
       "69. LHb6\n",
       "70. LHb3\n",
       "71. LHb1\n",
       "72. LHb2\n",
       "73. LHb5\n",
       "74. LHb4\n",
       "75. LHb6\n",
       "76. LHb3\n",
       "77. LHb1\n",
       "78. LHb2\n",
       "79. LHb5\n",
       "80. LHb4\n",
       "81. LHb6\n",
       "82. LHb3\n",
       "83. LHb1\n",
       "84. LHb2\n",
       "85. LHb5\n",
       "86. LHb4\n",
       "87. LHb6\n",
       "88. LHb3\n",
       "89. LHb1\n",
       "90. LHb2\n",
       "91. LHb5\n",
       "92. LHb4\n",
       "93. LHb6\n",
       "94. LHb3\n",
       "95. LHb1\n",
       "96. LHb2\n",
       "97. LHb5\n",
       "98. LHb4\n",
       "99. LHb6\n",
       "100. LHb3\n",
       "101. LHb1\n",
       "102. LHb2\n",
       "103. LHb5\n",
       "104. LHb4\n",
       "105. LHb6\n",
       "106. LHb3\n",
       "107. LHb1\n",
       "108. LHb2\n",
       "109. LHb5\n",
       "110. LHb4\n",
       "111. LHb6\n",
       "112. LHb3\n",
       "113. LHb1\n",
       "114. LHb2\n",
       "115. LHb5\n",
       "116. LHb4\n",
       "117. LHb6\n",
       "118. LHb3\n",
       "119. LHb1\n",
       "120. LHb2\n",
       "121. LHb5\n",
       "122. LHb4\n",
       "123. LHb6\n",
       "124. LHb3\n",
       "125. LHb1\n",
       "126. LHb2\n",
       "127. LHb5\n",
       "128. LHb4\n",
       "129. LHb6\n",
       "130. LHb3\n",
       "131. LHb1\n",
       "132. LHb2\n",
       "133. LHb5\n",
       "134. LHb4\n",
       "135. LHb6\n",
       "136. LHb3\n",
       "137. LHb1\n",
       "138. LHb2\n",
       "139. LHb5\n",
       "140. LHb4\n",
       "141. LHb6\n",
       "142. LHb3\n",
       "143. LHb1\n",
       "144. LHb2\n",
       "145. LHb5\n",
       "146. LHb4\n",
       "147. LHb6\n",
       "148. LHb3\n",
       "149. LHb1\n",
       "150. LHb2\n",
       "151. LHb5\n",
       "152. LHb4\n",
       "153. LHb6\n",
       "154. LHb3\n",
       "155. LHb1\n",
       "156. LHb2\n",
       "157. LHb5\n",
       "158. LHb4\n",
       "159. LHb6\n",
       "160. LHb3\n",
       "161. LHb1\n",
       "162. LHb2\n",
       "163. LHb5\n",
       "164. LHb4\n",
       "165. LHb6\n",
       "166. LHb3\n",
       "167. LHb1\n",
       "168. LHb2\n",
       "169. LHb5\n",
       "170. LHb4\n",
       "171. LHb6\n",
       "172. LHb3\n",
       "173. LHb1\n",
       "174. LHb2\n",
       "175. LHb5\n",
       "176. LHb4\n",
       "177. LHb6\n",
       "178. LHb3\n",
       "179. LHb1\n",
       "180. LHb2\n",
       "181. LHb5\n",
       "182. LHb4\n",
       "183. LHb6\n",
       "184. LHb3\n",
       "185. LHb1\n",
       "186. LHb2\n",
       "187. LHb5\n",
       "188. LHb4\n",
       "189. LHb6\n",
       "190. LHb3\n",
       "191. LHb1\n",
       "192. LHb2\n",
       "193. LHb5\n",
       "194. LHb4\n",
       "195. LHb6\n",
       "196. LHb3\n",
       "197. LHb1\n",
       "198. LHb2\n",
       "199. LHb5\n",
       "200. LHb4\n",
       "201. LHb6\n",
       "202. LHb3\n",
       "203. LHb1\n",
       "204. LHb2\n",
       "205. LHb5\n",
       "206. LHb4\n",
       "207. LHb6\n",
       "208. LHb3\n",
       "209. LHb1\n",
       "210. LHb2\n",
       "211. LHb5\n",
       "212. LHb4\n",
       "213. LHb6\n",
       "214. LHb3\n",
       "215. LHb1\n",
       "216. LHb2\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'LHb5'\n",
       "2. 'LHb4'\n",
       "3. 'LHb6'\n",
       "4. 'LHb3'\n",
       "5. 'LHb1'\n",
       "6. 'LHb2'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6\n",
       " [16] LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2\n",
       " [31] LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6\n",
       " [46] LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2\n",
       " [61] LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6\n",
       " [76] LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2\n",
       " [91] LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6\n",
       "[106] LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2\n",
       "[121] LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6\n",
       "[136] LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2\n",
       "[151] LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6\n",
       "[166] LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2\n",
       "[181] LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6\n",
       "[196] LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2 LHb5 LHb4 LHb6 LHb3 LHb1 LHb2\n",
       "[211] LHb5 LHb4 LHb6 LHb3 LHb1 LHb2\n",
       "Levels: LHb5 LHb4 LHb6 LHb3 LHb1 LHb2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cell_number<- data.frame(Date=as.Date(character()),File=character(),User=character(),stringsAsFactors=FALSE)\n",
    "\n",
    "for (i in 1:length(gene_list)){\n",
    "L<-length(Cell_type)\n",
    "\n",
    "Cell_number_t<- data.frame(\"cluster\" =Cell_type, \"gene\"=c(rep(gene_list[i],L)))\n",
    "#used normalized uncorrected data\n",
    "for (p in 1:length(Cell_type)){\n",
    "Cell_number_t$pct[p]<-100*sum(LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\")))]>0)/length(eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\"))))\n",
    "Cell_number_t$avg[p]<-(mean(LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\")))])-mean(LHb.integrated@assays$RNA@data[gene_list[i],]))/sd(LHb.integrated@assays$RNA@data[gene_list[i],])\n",
    "#for avg only consider the expressed cell\n",
    " #t<-LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\")))]>0\n",
    "# Cell_number_t$avg[p]<-mean(LHb.integrated@assays$RNA@data[gene_list[i],t])/sd(LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],\"_barcode\",sep=\"\")))])    \n",
    "    }\n",
    "Cell_number<-rbind(Cell_number_t,Cell_number)}\n",
    "as.factor(Cell_number$cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n7BkqGRV4xoyJjUwuFJbocv1qiJ8mHJCXNylWrF4bJOQeC6WPW4mdnRPKA9aESy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dvdErKo+90FSRrs/99fXF7607VqR2jR42\nZ9lX/3s0yVloeodgYIeQDXqdedtPmVlnqpoN83S9udJdw8OmPNazA11ouwidxnT+qOzKpXp5\nnZZjwctPGtfTr9+IsM57NHIsNRlYSkEsYUSdHCggdFvTqIxatcnVXdyujx2rlUZNo9HNQ2JP\nZHxHaR3YPfnkk5s2bbIn+CGESKXSixcvBgdfb+NoT2BXd2jZjLmrfUfHHF1rfPd6YKff/3zS\n5NTRq359d0KI/MC7857YN2ZfzucjpHzT23+0lmRjYIcQn/JC5fmj5YWX65UNBgDw8XeOT/Hv\nd0+YQGMjhBBCXYdVYFdbWxsfH9+uyMeq0M6ewC7t078dHfZ/S83vBo84+1ZTYKdeO83vOZ+N\nVb9MdQYAMO5aEDxD/WXdpqnbbU+f00aTMD5dup4YoVsrPNbLnooGhBBCt4W9e/e2K6pjGGb3\n7t1WtbFtGvzK14MB4FjLqWdPnDAO+mjktYoRyYgRgw3/OJoGITzT54xt106vp7ljqyGEEEII\n3V4KCmx/BZ4Px3F5eXkO2bWurKzeLSzselmBe2ioR21pqZxneoc/yoGBHUIIIYTuCAqFws6R\nSpuoVCqHNFrT6/UglTZvOOfs7Aw6nYJnuu1P9dkBAzuEEEII3RH8/f3bG6X5+PAOpt0uLi4u\noNc3L4fT6/Xg6urLM73Dw0FhYIcQQgihO0JycnK7lmcYJiXF9jD77eUcFRWoLS+XN01QlJY2\nBkdHe/FM72inWAzsEEIIIXRnGD9+vFjcjsEmOY67//77HbPvgSNHuqYdPKi5+qd6//7TrqNH\nD+ad3lEY2CGEEELojuDh4fHkk0/auTDDML6+vnPnzm3nToxyWXFxcXFxlYoFQ0NZcXFxcWm9\nDlymLX0hbNNrT686WSgrPPXdwte2hy1e8oAL7/SOwsAOIYQQQneKN954Izg4WCRqe4R5juM+\n+OCD5t+Xt8/lj0bFxMTExDz0Ux2cXTY4JiYmps/fDwOIh77312/T5cunpcSmTFuhmLV+138G\nOQH/9A7CAYoRQggh1D3Z/FZsenr6/fffr9Vq+b4qZvms7Ysvvvjee+9Zzer634rFEjuEEEII\n3UH69et36NChhIQEAGCYFoEQIYQQIpFIli9f3jqquy1gYIcQQgihO0tiYuLJkyc///zzgQMH\nNh/NJCgo6Nlnn83IyHjmmWduYfJuhJ3FiY1FZy7IDM4R/QZGuXVughBCCCGEOpuTk9OCBQsW\nLFjQ2NhYXl6u1+uDg4ODg4MdMmrdLWRnYHd02aj71+gGLi8+sxQDO4QQQgh1Fx4eHj169LjV\nqXAYO6ti/f39AUCj0bS5JEIIIYQQujXsDOwGPb14qAtk/7R8Z/0d0YkWIYQQQuj2Y2dVLOn5\nWuq5mI/f+2TxkFFbn3pq+qh+iWF+7lJR63poZ59g7w5/BwMhhBBCyFFEIpGLS8cH+7W5QQdu\nrTPYGdjtWhT2xEatVqfT6Qw//OPoD/xLzvqDbnzQMWlDCCGEEOo4QkgXH3bO4ew8Wk1dRXV9\n56YEIYQQQgjdEDsDu1HvHDz8slQiFolEjHA3YN84B6QKIYQQQgi1m52BXUDK6IDOTQhCCCGE\nELoxHap4NqtKc3NKquUq58Txd8dIHJ0mhBBCCCHUAe36pBitT/vx1VkDQ9y9o1KGjLx30pR/\n7FQBAMDZjx564tODFeZOSSJCCCGEELKH/SV2hqwvZ4x9cVcN13rW5e2/bPw5e/PhjI0nf5kR\n5MDUIYQQQgghu9lbYmc6s2zG4l01nHvyA69/ufnwxYJvp1yfGfnoe6/f7c0VrX586Vb8NgVC\nCCGE0K1hZ4mdccenX+ZT99HLT+5emiIFAKhpNgqxe/LMj3Y71ybc/9PGVRu/nL7AqzNSihBC\nCCGEBNlZYpd+9KgaIp58/5UUvq9KeNz3t7lRYMjMvOKwtCGEEEIIoXawM7CrqakBSOnbV2jx\nmJhogMrKyhtPFEIIIYQQaj87AztnZ2cAlUoltIxSqQRwdXV1RLIQQgghhFB72RnYJSUlAaSt\nX1dE+ZagBZu3XARITk5yVNIQQgghhFB72BnYRU6fNZAxnXpzyoLv02pZ67lUmbnub7OWneNE\nQ2ZOC3N0EhFCCCGEkD3sHccu8fmPX/hx3MrLa54ZsuX9wXcP7E0uAkDRtn8tSqvJP3vgULaC\nA3HSK8ufi+/M1CKEEEIIIV6EUt7aVStsybZXZj/xeZrc5nZ8hyz+acMn0yJFDkycA6Wmpo4f\nPx4AZDJZaGjorU4OQgghhJDjteNbsaKoB1aeLFy0c83qrQdOXSyobFAZGVcvv7CEvkPGPjBv\n7qRkz3Z9nwwhhBBCCDlUOwI7AADGu+fUxR9OXWxrHmcyGFlG7CzuooV2CCGEEELdm52FbKmv\n3XXXwDdSBZc58UqUi8uQjwodkCqEEEIIIdRudgZ2iqILFzKKFYLLeHp6Aly5gl+eQAghhBC6\nJYSrYmsupV6sBgC4VANAqy+lpnrbXtCsrTjz3ep8ADeOc3gaEUIIIYSQHYQDuyP/Hv/Qpqa/\nDr07/lAbm3O5++67bjxRCCGEEEKo/YSrYu95dfWnb/1t9ti+oS5tbIdIvUL7TPn7hu+fDnZc\n4hBCCCGEkP2ES+yChz728tDHXgbY+CB5aOvs9frfZtpekDAiEUM6IX0IIYQQQshOdg53EjPu\nqaf8Bsc5ObVzeBSEEEIIIXSz2BmoDVj0ww9886hJZyAuzhjyIYQQQgjdUu35WITq8ub/LvrX\n1vqmCWZZ6rvTe/i4uLpIPWLHPP9bts7xCUQIIYQQQvaxO7DTnPn3xKGz/v7t76eKrk7Rn3ht\nzKRl23KULACnLjr01WNjn9rW0EnpRAghhBBCbbA3sCv9/pV3TzVKkh9+aVqUZUrZ929+ns+C\n15DX1h5NO7pp2YQgqPr9nysvdVpSEUIIIYSQEDtbxsk2bjjOkbve2bp2cZIlFqzYuO4oC8zd\ny9b9d040AAz6VX487OnUnX+VvNM7qtOSixBCCCGE+NhZYldcVASQPPWBpGvLK/fuOU2BjJw/\nL/rqlIB77kkCyM/P74RUIoQQQgihNtkZ2NXV1QEEBARc+9t89NBxFqDvuHFNk8DHxwdArVY7\nPI0IIYQQQsgOdgZ2Li4uAHK5/NrfJ/elqgGix42Lu76MXC6/uiBCCCGEELr57AzsEhMTAS7v\n3VMGAACNf32/TgYQeP/9g64vUn/sWC5AdHS04xOJEEIIIYTaZmfniegpU1PePHf89Xsn587s\nrUhd9VsNQOz8x0deiwuNJWtf/M9+E4SOGZPYaWlFCCGEEEIC7P1eRMrLK/7226Sv83d/+dFu\nAABx3PPf/HPg1bhO9vn4xBePGMF7zNsvjcQvxiKEEEII3RJ2fwjM694vT50auuLbHekVZt+e\nk//26sKh3tfmhURGSj3iJ7y1+rdn44S2gRBCCCGEOg+hlN74VjRVlVxQiEcXLqxLTU0dP348\nAMhkstDQ0FudHIQQQgghx7O7xE6QW3CIQ7aDEEIIIYQ6zM7A7vSKRz491dZClDOb9AOWbH/z\nnhtNFUIIIYQQajc7A7uyY+vXb7Jv0Uc7nhiEEEIIIdRx9g53MmbBAncb0zl9fXlhftbF3BqS\nMO25uYP83fre5dD0IYQQQgghO9kZ2A18/uef+ecaSvd/8eLTy/4smnfgp6lhDkkXQgghhBBq\nJzu/PCFMGnnv0o1bXmZ+WfDY1yWO2CBCCCGEkENwsoOfPjuxb0yQp4urX2SP4XP/s6NQb5lV\nuXKkE3GduaHld+7T/5lESMjiwywAADScWPHEiAR/V6mrf+LoZ785q9qxQErEj2272YdhJ4cE\ndgAATnc9PDNRd3DV2kJHbREhhBBC6MboT7x578Ql62oHvvDxmo3rvlgyltv19gPjXz2oAQAI\nmTN/nFi3a/2OxmZrnFu7Lg9i5j0+UgQAhV/MGv/Kz1kBsz/6ef3P/56i/XLqA59kGUEqld6a\nw2mTY4Y7AQAAT09PgJy8fIBYx20UIYQQQqijuLRtO+ulA/+xYdXSeAAAmDaSPR/+6po1x78Y\nMwEg4KH5kxbv+Wv9tsY58zwAAICe+n19IaS88/gAAgDnv/nkkDZg/sbUrya7AsDUKROiZiXM\nBnC7dUfUBoeV2IExK+sKAMuyDtsiQgghhNCNYEZ+lFnbeObv8U1TwpKT3UFVXNwAAABe0x+b\n5qHfvX67yjKXHl27vowMfHx+LwCAmlOnikA6duoE12tr+z34wpygm3kA7eWAwI41qMov7vpk\nzuK1CoDExMQb3yJCCCGEkEPoC3d89OzUYT0iArxcpRKxk2jqz2oAjuMss12nzZ/lbdi7fqsC\nAIA9vHZDudOox+fFAABAVVUVQFBYmOj65kjPnj1u+jG0g51VsZsfdprNM44dZVnu2r+dRz09\nN972YgghhBBCN1nl6tmDF+wgwxb9/aO3BkQHeDpLnE7/Z8Rzm68vIZ342EOBP/+yYaty/uNu\nB37fWC2Z9NG1QjlKKQDDtCgFs/qzq7EzsOPYtupYiVvMpJe//vH5GAckCiGEEELoxsk2fL2j\n3u3Bdfu+fvhas7jGdK2mxTJOY+bPifz+qw3bFPP8f99Y7z7t8Qd9r87y8/MDuFRdTQHItcVz\ncnJvUuI7xM7A7u6/b9kyz/YswoidPQKievVOCnQhthdBCCGEELr5jEYjgIufX1NnB+3p9z/d\nxzWrigUAcs/8ubGffbJ9x3bPbQ2+Dz4+tWnp8AEDAuH0oX0nzNOGWyIm5Y5v1sm6cucJOwO7\n0EHTpw/q3JQghBBCCDlU1OgxseTj3954vt9b06K40lMbPvvVNG/xoDdXXN6/9fzE6clxga4A\nAP0fm9frgxX/fl3SEPzo45Mk19cf/vSzKd/+56vZUywDZ7oAACAASURBVNzffXZUOJu74YOV\nDf0Hwu7sW3VAberS9cQIIYQQQh3HDHpn8w8LBzRuevWRGXNe/iY96t+7176x9LX5KXTXy/c+\n/E1TfNZj/mMDVQUFddeGr2tav9+ynZvfmOp/6Yvn5jz0xL93eS7Z8cvsQABCumolJaGU2prO\nmfTGDg1cIpI4i7tgtJiamjp+/HgAkMlkoaGhtzo5CCGEELotFXw0KP6NiiUnZB8Pu9VJsYUv\nBts8x6Vj5mzm2SJCCCGE0G2Fvbh6yZOPvpt6/Ztj5X/9dQkkAwak3MJUCXHglycQQgghhLoT\nUbhX3V/rPv2ztOat5yfGiGvPb/jvh0fMSUten+lxq5PGgy+wG/Xvgwdf6MgGA3rdQGoQQggh\nhLoO3wd+OLo55u3//f7B336o00kC4gfN/fib/3v5HudbnTA+fG3suhtsY4cQQgihbq+9/RxY\nZVWd3nqiquhSUSNna3GEEEIIIXSztCOwM5Vsf/3emKAhH1yynnNh+bi4sJRHv7+kdWTSEEII\nIYRQe9jdeUKx97nRM38oZsGtvt56nlKppI01vz8zzuyXuWFmgGNTiBBCCCGE7GJviV3G8pd+\nKGalCY98uvEfw61nTlpxZsOLA9yh5o/X/pd2RzTZQwghhBDqeuwM7LK3bMkBpu+y7WtfnpTY\nqoevk3/vhz5bv2wAA4V//tl1v7KBEEIIIdSt2RnYFRQUAERNmJjM/wWNuHvvjQYoKipyTMIQ\nQgghhG5LbNXBdyeGO5F7vqiyOb98+VDS0tAV5Y7ZtZ1t7BiGAXBxcRFaRiwWA4hEIqFlEEII\nIYRuBr3enJ+vcPhmfX2dw8LcBRaoO7RsxtzVvqMTPcHIs4hSqYTB75xevyDw2hSpb4hjkmdn\nYBcTEwOQd/x4LfTg6RtBC3btyQPoGR3tmIQhhBBCCHXc5cv1Awascfhmn3669/ffTxRYoDC9\nZvqmc0vN7wavPcuziFKphMDoXtHRbg5Pnp1VsT2mTYsH8/63HlueVs+2nq3O++P5We+cpZA4\nbVqyYxOIEEIIIXT7GPzK10uH+gouolQqRV5ejo/qwP7hTga8uvzR1TPW7nltaOSKASNH9k+O\nDPRyFVNDY115XsbxQ6cLVCyQsHnLX+3fGalECCGEEOomzEqlVlL9+/y7l+66WEWDeo554v0V\nb4wPs3sIOiF2b8TvgR9TfxY99PyaLNnZ3b+f3W0937PP419t+HaqcISKEEIIIXRTBAa6FhQs\nbD5l5cpzn312vl0beeml/i++OKD5lLS0yhtNmdbJr1e83LX/m5vf6eFSffiz55+7b7L5zLl3\n7hLf6Jbb/a1YU236jl/X7jh8JrOgqqHRQJw9fEPiUgaNnvro3Kl3+Tsk1Owc+K1YhBBC6I6S\nfr56yCDHt7F78qne33wn1MbuqmMvB484+1blsReC21qSZr7Vs/eqcYcqPx9148lrZywmDug3\n85V+M1+58R0jhBBCCHUeQkDM8A/T1lEi4uhtksTEBKiprGQBbnxokS5cyIYQQggh1FEESKcE\ndje8Ta7k4Kpt9UOffbC3FAAA2IyMTIh9OMEhA8ZhYIcQQgihbogQEIs6o8ROeL5RLqtQmgCq\nVCwYGsqKi/XAeARF+rn8+bj71My3is6+Ee1ctuWtpz851/DdPyZHkZLU/z33dc3oTxcNEN6u\nnTCwQwghhFA3RKBzqmLb2Oblj0b1+6jg6h/LBscsA/B6apfih0nXFwmavzZV+/o/Pnt4yEsK\nUWB8//tWHn7/6SjHJA8DO4QQQgh1Q7eojd1dH16hH9qaMeVndVN/Ve/Bi75LXeTglAEABnYI\nIYQQ6pYIgNjO7zC0RyfU7joSBnYIIYQQ6p5uvKNDa52wSUfCwA4hhBBC3RAhhOmEKIx07cgO\nAzuEEEIIdU9YYocQQggh1B0Q0kmBXZeO7DCwQwghhFD31ClVsRjYIYQQQgjdZASrYhFCVipK\nVZfP1zTUaQkhfoGuvfoHBYW53+pE3WZMRk5WqFQrDWYz5+ElDYn2dHUX3+pEIYS6P0JIpwR2\nXTuyw8AO3a44jnbq1ZV+omLt1+lFeXKr6Qm9/B59rl/vgcGdt+ubgGNpRbFK2aCnlHp4SUNj\nvMQSxw/3pGzQH99VnJtey5q5pomEQHic94j7Y0JjPB2+R4QQaq5zqmIdvklHwsCu4yiF6vJG\neZ3O2dkpKMIDCyFujtICxZ+/55w9Vi6v1QFDgkLchoyJmPpoD/8gN0ftgjVzqz4+u3dzns07\nQkF2w7+fT502t8e8F/rbXMCgM+ecrynNkysb9ADg5escmeiTPCBQ6twlLjeTgT29v+z8kXKj\nnm2a6CRmUgYH3z052oHZOOd8za61uZyZoy2nUwrlBcq1n6UPvjdixJTYTrpFsmZO5NQJI5Oi\nrkGnMVWWqAw6s5unJDTay8mOUWjLC5UXT1VWlqi0aqO7lzQy3rvv3aH+wQ67b9w4o96cf6m+\nokip1Zhc3SVhMZ7xvf0l0ra/C08pKOp0eo3J2U3s7e/SxcOOm6wzOjoQ6NKnuEs8aW47HEdP\n7i05sOWKUq63TCEM6TUg6L5HkwPv1Ho6SqFGpm6o1jAiJiTSw9PXuTN2sfbrjD9WXaIAhAIF\nCiytkqm3/np55/qcZ14fMmFmgsDqHEfLCxTVFRoCEBLlGRrlyXe9f/neySO7iiyr2NwOAGz/\nLVuvY5/5+2CruYVZ9Ud3FBoNLCGEUgoAeo25qrQx42jFyGmxMT19O3TobdBpTPXVWqDgE+Di\n5ikRWFJZr9/03aWGaq3VdLOJyzhRkXexdsbC3iGRHjbXZVl65kBp+rGKylIVy3IBoe4pg4Pv\nmRTj7GrjNpJ1pnrX2hxCiI0zCGA5M2n7y3Qa88RHEu07yraZTdyx3UWn9pdWFDdyHOft69xn\naMj4BxN9/F0ctYvminPluek1tTI1y3LuXs7RST4pQ4KlLt3hpmrUm6vK1Aa92cNbGhTm3nmj\ndlGOluQrKotVRgPr5iGOSvIJCG3jFiqv0x3YciX3fA1Hr2YuJ4lowMiwkffH8p18g8687ees\ny+eqLZc8pdBQoy3Jkx/bVTxsfNS4BxO6Qs3ahRMVh7YVGnRmIECAUKDpR2XOrk5jpsf1HhrC\nt5bZxJ0/Irt4okKrNlmmuLg59RkW2n9UmFjSdkTYMTq1Sa0yuLhJ3L2E7jZdASGdU2LXBTKM\ngKvPnpuo5Ne/Lc6Y8MPyGYFtTmePfjhjheRfm5YM0uz915wtPT//ek7rT+RSRca6r1btSq/Q\nu4SmTHhy8dx+PjbOeGpq6vjx4wFAJpOFhobeyAGYTdzPy8/mpNcQAs1PHiHESUzmLxnYo38g\n/9pXGfRmsUTUFe4mDpF/qW7nb9l1lZqmKfEp/lPn9/QLchVYi2XppZOVBVn1dZVqJyeRf6hb\nz4FBCX38+Zb/7qO0netzbM6y/BYLXx885ZFkmwtkna3evOpSfbOAJiTSY/aivjHJ1pHWvi35\n3354WiDZzb307j0jJkY3/ZmXUXt4WwFY5YxmKRz1QFziXQF2btweijrd4e2FxTkNTTsMj/Ue\n9UBsQKiNcgi91rzm43OqBgPfVU8IkTgzjy0d4N0qElI26H/6KK2ipPH6wREClHp4SR9/bWBE\nvHfzhesqNauXn6cctef2MnFOUu8hDqjXltfqvn73RIsUWj4oJBUtWDKw7zDeR2MHmIzsgc1X\nSnLlTeG75R9SZ9H4h5NCox1fxazXmbPPVteUq3Uak2+ga3QP38gE77ZXA9BrTVVlam2j0TfI\nNSjcvc3efAad+eDWgsy0ao67Wnvu5ikZcV9MHztOIMvShmqtptHo4+/i5evcZqFGRbFq12/Z\nijp984kxyb4TH01y87AdMZQVKNZ9kWHUs62zll+Q29yX+3v6SK2mGw3sjx+eri5X82XGngOC\nHlrU59b2czy8vfB0amlTdmpCACjAsIlRI+6Pab2WVm3atiqrtkJ9bcHr6/gFu05/KkX4Na8D\nCrLqT+8rabrVe/k5DxoT2XNQUJsrmoxsdZlaWa9z95IGRbg7u96kCq68rLpnpm91+Gbvn530\n2v+NEF6GrTr4fwsee3dv9IrKYy/YvMPpc399ffF7644VqV2jh81Z9tX/Hk1yTIHIbf9yKdv+\n/nv7A5577+d7/OoPrvzXB1+G/fTWmE4tW9/8w6Wc9BoA62c3pdRsoqs/OfvyRyP52tdzHM0+\nW5OZVqXXmBiGBIS5D7o3Qjj6aU6jMoqlIntK5i0aFYa0A2U15Y0SZ6eYZJ+7hocxnfCJu/Tj\nss3fX7K6KxZk1X217MTCfw4JjrBd/GPUs1tXZVaVNlruQwZgtXmmklx5cW7QuAcTWt9jTx8q\n44vqAIBSIISsWn6m98DgqHjrB17awbLfv0i3unFXlas/f+vEwn8O7tHveiCu15l//yaDIYSz\nIyJhGLLmi/NDx0RY3oyV9fqjOwoJz8sSpUAIHN1RFMxfnNkoN6TtL60oVlFKQ6I8B98b6eUn\ndJ3XVmj++PqC0cBCsx3KipTrVmbMfCYlLNbLavkjfxYq6/XAj1Jq1LN71+XNfqFv8+lGA/vD\n+6drZGponu0pBQC1yvDD+6df+nCEb+D1bHxkRxHl7DmFAABHdxQm9wu4wdIFo5798p3j1eUa\naHlhUgCTkV310ZmX3h8e19OPb3VKaWZadVF2g1FnDgx3HzAqXPiJuH/TldI8OVwremz6h9HA\n7fotZ/pTvfz4K/jMJi79qKw0X6HTmP2DXfuNDAsKb6OMqqq0cduqTK3aRAghAEU5DecOl/cY\nEDjhkSSBl0NKadr+suO7ikzGqyFaYLj75DnJwTwlsgCg15p/W3G+vloHzbKUptG0e12uvFY3\nalqsQCJzM2qP7CjQqK6WG4XFeI2dGSdwHipLVBu+vMCx1rmkKLdh/ecX5r7Sr3XxW6PCsP7L\nDKPebDNr1ddoN3x94ck3Blmdk11rcwSiOgC4fK76VGrpsPGtiw2uKitQ5JyrUckN3v7OvYeG\nOLxaJjej9nRqKTTLTk0oABA4uackJNIjvneLl17K0T9/ya6tVDct2GwdaKjW7fj58uwX+grk\nEJ3GlHO+RtWg9/CWJvULbDMKPHOg7OSe4ub3UVWDIXVjXlVZ49iZ8QIrFl6uP7KjUNt4NW+I\nJaLB4yL63n1DJSx2IgBMJ7TIaLNMpu7QshlzV/uOTvQEI88i+v1LJzyZOnrVzuwJIfID7857\nYtJrETmfj7B+MelQ8hywjVuIZv+5JT9l9rNjI90kbpETX/ni+9dGdyCqM+jNaamlO37K2rc+\nr7JYJbCkrEh55lAZb3IomE1056/ZfAsc2V549mCZXmsCAI6jNeWNf63OriptbDOFJbnynz88\n89MHZ75759Rfa3J010rdBVSXNX7/n9NnD5WVXVEWZNbv+yP/10/PsyzX5oocR7PP1aT+kX9o\na0FlidDZAACVXL/95ywAwrXcMKVg1LMbv7vIV2ZzeHvBtQNv8WjMPlt98URF6+V/+zJduPSb\nUkopXffdhdYp3PjdRQBiVa9KOUopt3ZlukFvbpp49mi5SmGwMyThONpQo71wutLyZ/qRco5S\nCrzrUgoc5c4fkdmc21Cj/fWT89nnqhV1OmW9Pie99tdPztfK1AJb2/17rqllVAcAlFKOo7vW\n5phNLX4SjcqYebKqzYOiFEqvKCqKWvzuR/4s5Hs0Ugp6nfnPNdfzfKPCUJhdb39NgFZtKsis\ntzmLY2lpnrwgs16naSPD7992papMzRdSU6Drvsrgy4qUo398dXH32pz8CzXFefLT+0u/fy+t\nhv/MF16uL23VpebavijH0mM7i/nWNZu4P76+kLa/rKqsUVmvK7zcsOmbi/kX6wQOTa8zb/n+\nkk5rvrr9a1ks+1zNyd0lAise3Vl0aFuBqVk2qJWp1352vqHGuiK+yeEdhVZRneWoAOD0gdLy\nQiXfipfPVu/6LafpyQ0AFSXK9V9eUMl5XiQo7FmX+//s3Xd8XNWZP/7n3Omj3nuxiuXeLYxx\nw8bGGLBDxyG0LCSBDckuLFmSkITNkmz2FzZfCGRJaClLMzU0myLjBnKTuyxbsro06qPpvZzf\nHyPJo9HMnZHQ2GLyeb/0Auvq3HvP3HLuc08br8cb5KRw0g1Y938S5KPt/bDZbgse1fny2dNu\nPLm/23+Zttd6vLpL/GpkjPZ80DQS/gY4+Fn72388VXekr6NJX3uw57XfHz99KPx9REQtZwa3\nPXPiuccOvPm/Jzub9CHTcdr9XpNYdSEnxtju95oCTsvZY/097cZQ5Q3nvE9jPlPTG2qr3a3G\nv/22Zt+HLSequ7/Y3vrX/+9I69ngV7VPb4dp/6etNLo1wnf6ag92i1zDbQ26T16r939muVye\nL7e3Hv8iSDk/+RgThMn/CVu/23ys7xtvH3nvvnkhg2XzO0+9YNj6xJ9uX1qUlb9g6zO/vcXw\n/JPvhCx2xuVrFNhJTLWvPnbf7TffvPWeh363o8VORNRzum6wfPGS4YYPZUKSYtw1Ul4v//S1\nhlP7u/s05o5zuh0vnxGJZmr2dnIeJrw4e7TPbAwSpHe3GdvqdUTni03OiXN+qCpkpOij67dt\nf/msZbjQbDmj/fSNBvFViNP7fzntdnuJD0U8vgwELS4DHPy0/cCnbZ1Nhpazuo9fre9oDF0k\nER2v7nI5g5XORJzznnZT0IeBzeI6e6w/1DaP7gsMfbrajW1Neh6sx9voPVLNvk6nw+O/8Mg+\njdMRpOHGl95kcJw+fL7sO1bdNb7OE4wdP9BNRF4Pbz2rCx3UjeySWs8MBu26V/XGOY/bL6Tk\nnHPvJ9vqQ22pu9Wo7bGECLa42eBsOTPov7C5ThtpHRqjxtrzxTTndHBnu0izGudUd6R35JqP\n6Dj4742x5tFZ9XG7vNv/dmbP35urt7f+/bnTY/sF+tv/SatYDr28u93Udi74lXxkr6atQUdE\nXi9xLydObqfnw7/WhfoUZ4/2iZTqnPPeTpOu3xb0r4c/7xjssxENFQK+y3LXu012qztoeiKq\nPdBtt7qDXvxH92rcIcIRu9V18LOOkR0N541cLh6qEHA5PbWHegKjumGM2Ikvgz+JuZd/8VFL\nQAcE7iWnw3P48+CFW3e7UdtjDR2iUe2h7oDbxOP2nj7cI35pMcZOHRwV2NUd6Q3bH4Bzslvd\nzWeCvF30dpgOfNZGbOjC8JXYn7/bZNI7xLfZ1WLY8fIZbbfZ5fT0a8wf/Lmuv8sSNGV3u9Gg\ntYvnkXM+2GfrHf2yceZIHxN9gDOBzhzpC/onj4fvePWsy3H+4vG4PZ9uqxe5Doci5mD5ZIxO\nBXsb9zn4WTsRG/UBOTGiw593BLx8RgMjEhib9J+w7faV//rsQ8tEe1TXVFc7l65aNdwmI1+5\nstKxb9+hSfnUUyuwa/jzPZv9XPfbar8/anZXs1v+66VtLz91Z17dH3/3bgcRabValmg79ocf\n3fvNm2+54/6fP1/dPeq6/NWvfrV27dq1a9f+x3/8x5w5c4LuVNtt6es0EZHvviWiusMh33K6\nWowhuoOf5+U8aGjYFawukHPS9VtFbiciaqrVetzekZKdc+o4p7eKVtoN9FoG+2yBDwNG9ceD\n3+cj3C5v/fF+8j1yOCdGpw+FPBpE1NVqFB9zFFD349PXaRaJ0kw6R8Cn6whdVRDA6fAEFH+d\nTQaRxghG1Nl8/nnf120eV1AiMOrrMhOR2ehwOT1h0xORy+kxGwKfClazq7fTFPAE4l7S9dlC\nNZ72doap6O3tGJVgKJ6IgMDIvzOixeQ0DNrFD4vXyzUtQ+fIoI10Rz6cKOhnbD6tHQmPPG7v\niS+7x6bxMRudgwO2sCeu/VzwColzJ/oDymnOubbXGqpmq0+0Xc8nVFVrc90g8YB9kcvp0YS+\nwrtajaFeNtwuj+/yG0vTbPR6gz01OW9rCBJGE9FAj9V/Vpox6/Hu9uBvvNpeq9XsCnpM2kJU\nbfZrgkc5Ixw2j3Fw1FWhG7AFvLMFzWRX26gj2d9ljrDzXF9nkCPZdFrL+ehohpPH7W09G/wY\njqir6RuJZnyv1vVHg5e9oQK+YClH5bBPY+KicRH3Um+wD0VE2h6L2eD0D5190a3Ia3xPhyl0\n7SB1dwQvjpwOz0C3JWgTs8vpifyzfxUXpcYuLFtHhzYuL+98f5n43NyE/vZ2sb4yEZtafexK\nb/2fn191vhuB9+Azdz8/8pts6ZabZyZLiDJXbl711wePn9RtLfB43OxE9bkHH3n6/kzeufuZ\n//jtfyX+/ve3FAyvY7PZjMahwkgiCd6JJ6CVhxMTaei0W0M3BPjxb90b4XJ4R3Vx9eN0uIMO\nLfSxmp1jV7OanCIzUzhswSJFTg5rmJLRZnGNugk5iUeQYYva4IciXAzkcnqIzn86X+N1hGyj\nEzsdbvGXfLvtfGZsVrdIW+pYnA/tLlQ7TlBjEzuDHSXxP4nvkY05NU6HJ/jFNwbn5PJbVyRv\nQfPpdHpCXOYh9+c/8coIu+X8fjmRzRSqq0qkOXQE2wsR2W3uoJU69qA3EVEk1QyhrnCn3R30\n0AS9TYY35RU5mK4QN6DIjRnqOIS9K4OeJpE8EA/5p0jeggLShNp7ALeLc85HnrsOuzvYWKYg\nnI4gpyDUsXLYwmTGanKOOtGMWUJcwCGP3tgc+meGU6jKWn8ej9fr4WO7Vvs3mvsL/uAgIiKX\nU+wu83i8QScWFT/REb4MfxXJacq/fHyj/5L3Xjnz3it149rIlttmbbltpv+S+lqx7hORsNvt\npFD4d6hTKpVks9mIvvoAiqkV2ElUSSkpKSO/euL8A5ektLThyCwpMYmMRiNRQkKit2jtjZdm\nK4moYO03r3z3vsPHB28pGK4B3bBhQ3l5ORE1NjY+99xzQXeanhMnCL4uYkOvV1kFIfvGJqcr\nu9qNYdsEk9OCTK+QmKoIWqZLpEKoIWA+mXnx/qsxYhKZkJIpNuQiNUPNBBaQTyaw9GCDJf0l\nJCvU8TKbZej9mzHKDn00iCgpRSne0hG0+39CslgHUUFgAQckJSPS8SVElJox6uAnpSopdAHI\niaekn89hSrqyrXE8Q8XZ0LmOSxjHIK+xpzshWSGRCmPrSwSBhRo/IT7RACdKGD08MC5BHnms\n5d+NOiFJIQiBPRTHGrnm4xLk42yKDf5ZsosTTnw5/CrEee6YsSDnc5isEAQheAWVn5SM4JOe\npGXHjW3UFhhLzQyePi5RZtKHjDJ94kNc4akZaqvFODbQSA19OyemKEQCk1ADcZLTgy9nLOTd\nJH5XMhZyX4lpyqChPBMoJT34vuLCTpPBKD5pVH4Sxgx3DbpWQqLcvzYlIVkZ4d2ckBLk04U6\nL2nZYUqkrIJ4/9ov7uWhxqwEfEyxHPqfIEZxifKwLcLqeFnQAXPpOcEfBCIj+RKTlRaDM/jB\nZBSXIA/aMKKKk0nlEneIAC4pCrNiBTAM2n+49cOAheOdj+KD18588NqorvNXXl++emOQccqR\nU6lUZLf718/Z7XZSq8fxqAttajXFijIZDMPXlNFopOSkZKL8sjKFfnBweLnX4yWZzO8Ju2rV\nqjvvvPPOO+9cvXp1X1/wmnB1gnz5VcUjZzojL37ByrxQmaiYnxEmqmMUlygPOt/BtJmpUplk\nbBVu6Zw08ZlUpy/IyC5KJCLGiDHijK+6dppEdHyrKl62eHV+QMaI0/KNxWKZJyKi1d8olQ9P\npZuWHbd4Tb5I4unzM0IeDkaCRCibHWT6ksz8hPgkefDKbEbFM1ICphudPic9ku9FYALLzosP\nmKl41uIs8Uq4mYvPj9UvqUgNG7X7415eOiOViJRqWSTRJ2MsJVM9tnZWIhUWrQ5y1c2/LFce\nYlrjoukpghB6mkxGATO55JeGDIwCcE4FpedHFssUktLZaSJND4yx+ET5yCDcrPyQgy5D7S7o\nMy8zL375VcXxSQqFWlqxKHPu8pDTbcjkkooF6eKNI4IgzJgffKKZZesLBYkQMM5t8eX5oWZk\nKJqeSqIzeUikQqgZTxZfns9GX42MsfzS5KwQg8cp9C3GBMrIjQ8VrWYXJqRlxY1tw+WcQk0u\nk5KuSslQhTqMnFPp7ODDiuMS5NMqUseux70UaiKMovIUsQ4SAsvMS1CNerGnhCRFek6c+Flm\nxErnjCpwps1IjfA9bezMR0Q0c3FmUprS/zAyxrKLEosqUsYm9rdoVX6qX5CUXZQ4J8RhLyhP\njqRfryCwgEmFiipSxCeUYYwVVwTv5hWfJJ/tOzV+WyidnZZTFHKmnmkz00QOZEmIIeeCwKYH\nm8GKMcrMTxAf9T85GAnC5P989blxlEVFmdbOzvNdFfTt7abs4uLJGBR7kQI7r9Nq8RNZVbT9\n0IcftNs5efWHPtrXV7JoQRKRfMmm9eyTl147pXU4Tc07/u/j3tnLFo3vsUJEVLEo85YfzL/i\n5unX3DXr2rtni8y8sHh1fnyiXOw+5HT5ltJQ7y5rvlEiVwpE52+nvJKkpZcXjE3sTxDYdffO\nWb2lpHxexuxLsm/47rxI5g1as7lkwWW5I+WgSi295o6Z+aGrPUZkFyTcdP/8q26bsfnuWdfc\nOUt8wtUZCzMKypKDX+WcVmwsDjqEnjFavaWUxnwxC2Mkk0uWX1UckF6pkq65ujTstFjcyzfe\nVBGwcOairOKK1KAREGO0YHlu/rTzx+SSywvD7GPMFipXD52+SE4K53x2iGSV6wpnL80+f0AY\nVSzMvPTKkFMwxCXKF67MCxWyzlyUGfBSnl+alJiqiKQ8ksmF8vmjyuJ115cThRw0xDm/4obz\nU7wWlCcr1dJx9UEpnxt88sLSuWnXfWfOzd+fX3lFgfibzMabKoi4yHTwKzcVh6oaycyL33L3\n7Pjhjg0CY4tW5a28JuTr+LzlORKpWCebeZfmhCpD8kuTVm8ulfq9peROS1x/U5D5fUYUVaRU\n+Gbk8UvDBCYIwhU3hZyRmzF27V2zFErJyGq+02a0vgAAIABJREFUjvbl89IXhn5xXbO5lAc7\n0YyxuET5otArrruxzPeQ9n0Q38GZuThzdmXwaEYVL5t/WcjZLriXX7YxyJV/2cZikSiNMWJE\nl1wx6haumJ+RmKIQ7wfMGBVXpGYEq8SSKyRb7p7tPx9NUXnyNbfPCFvlI1NIbvn+giu3Vlyy\nvvCqb824/jtzQ729q+NlsxZnhtkco9mV2QEvhItW5QlM5JJnjGjRqpCnbPWW0kWr8iQSgYgk\nApu7LGf9zeUiZey85TmJKUEKEMaYQildujZkyblsQ1FKpor8S3tGCpV03Q1iM6RMnoszKja8\nJatWqQ/t2jXcy9C8c+dB9Zo1gTPeT8zFaYptfOUHW185/+vKf3//4ctEV/B6vVSy7lLTSw/9\nU5POocitfODBzblERLI5d//iOy/879Pff2dQSCqYe+XP/u3q8LMDB6NOkBdVhJ/LUaGUfvMH\nC5//9aGxDZ0+pbPSVm4KOdtTXknS9d+Z11Q7oBuwKZTS7MKEsZONBSWRsLnLckTmHw+yilS4\n8taK5RuL+zRmhVKamR8f+QR4MrkgMtmVP8bYbT9Y9LffHelqNTCBfJ15fZ1aFq7Iu+LGkA+e\n0tlpV26dsevdc/69VRJTlRu3VgRt/rjtvgUHd7cHdPgNyEnBtKSrx0xQzBjd88jSZ3+5X9Ni\nHDlrvn+UzUnf+v0F/olLKlIXXZp37IAmopd8xpZfUZg7/I47Y3Fm/fH+AZHhF4wycuMrQsxf\nLQhs7Q1lC1fldbcZOec5hYmp4SY4XLGp2G51nT7ce35eU0bEqXR22robAo+8ILA1W0rf/3P4\nziWXXlkcUFlSMjP1ylsqPn69fswEqoyIz78059INxSOLJBJ2yRWFe95vDrsjImKMSmanZYab\nyC2s0tlpm74586NXzoztUMUYFZQmb7lzttjqc9K+M2PZQLfFbnNn5MaLf69afJLi8uvKPn/7\nHFGQNtLcaUnildyzK7NKZqd2t5kcNndaljqSz75xa0VKhurI7s6RPkkZufHrbigTv0mz8uO/\n8/Nl+z9pazuntxod6Tlx8y7NnbkoU+ThXT4vfd11ZZ//vYk4Z8OTL3JOCcnyG74zT+QdLz5J\ncftDi49/2dXeoDcZnGlZqllLsqbNFBsVuHpzia7P1lo/OGpCaYE4pxWbppUEqx2cW5nTcKL/\nTLBRCL7crruhLOArwqQy4erbZr3+h+OhetoJjElkbNNtwSc2J6LULPWt318w2G816RzJ6arI\nK5kECSsL8cYSYPXmktZ6ndXsDDoYggksLlG26trAN43kdNXKa0v2vNcUpBmcMeL8squniZQh\nUpmw4uppl11VbDY64xLkYac4lckl37h37od/OT3YZxv+Ri3GOY9LlF19+yyRniFKtfTm++ef\n+LKrsVar19rjkxSFZUlL1xZcmO9oYVH6SrEwm3TqNF0GF1GP0UOOwY7WVjsJCVmFaaoP74q/\ntvbRlppHilWbH/p+3uqH73kx51cbs/o++unD7+c98MWWyfmOnAv/zRMXxyR+8wQRNZzof/X3\nx8wmp+85x3yNK5wWrsi76bvzIo+fYobHw09Ud9Ue7tH2WASJkFuUsGhVvsh8sCNcTo+m2ajt\ntUilQlpOXF5xokhtaEPtwGP/XGUzu4JM28EoMyf+8T9tCDU1tMftPej7OqwOE2OUPy1pyer8\nRSvzxr549XSaHr5ju8PmFu9VJjAWlyj/7f9dne5XdFpNzu3/d1bXH3w0ZWqm+qrbZ076dwpr\nmg11R/p8vcRSM1UVCzOLp4dso9n3YcvBqnaRwQ0VCzKuvXNW0NWP7tN8+H91/lP5yOTC2m+U\nr72uNOAwetze154+ITLJlg9jTKGS3P5viyerq83+qrZ3X6y1+g2HYowtu6LwpnvnyZWTfFd2\ntxn3ftDiPwRYkAjzlmUvubwgGtOAE5HXw3X9VpvFnZqpUov2yv2KtL3W4192dbUY7DZ3Yqqy\ndFbavEtzolGscU6nDnQf/6Krv9tMnCQSoaA8uXJdQcGYOcZHeNzeT7bVH/1C45ucYOT6kkiF\nK64vX7o2eOtHze6Oj145S4wHRE6MMZlCuPm+BWUhWpkvmIFuy1t/PGnUBekzl5SmvPG7c0NN\n9Vx3uHfPB80uh8f3sup7JEllklXXTgvV+PtVeD288dRA+zmd2ehUx8vypiVVLMyM5It6L5bm\n+sEf3bF90je7bkvZfT9ZFvrvxx8pW/jfTaMWJf3TDv0LG88HdkTkan7rkft/9re9zZb40tV3\n/vp/f/2NaZPzdEBgN0EOu/vwrs76E32DfValWpZblLh4VX5xuI4X8BX1aswvPHH40J5Rk2MJ\njK3dXHrXvyxOiLgbsrhTh3v++992O0LMfufbo0ItffTJtRXzArttuZyeo3s0pw/2+M8FLZEK\ncy7JXrQ6fyqUgMe+6NrzfpPb6T1f6zzcfFa5tmDFpmKR2Nrt8p6rHehpM3o8PCM3bvq8jIC6\nvREWo/ON/z3h+wbboHxfX3b9vXMjrLSOkN3mrqvp7WwxOB2ejJy42Uuyovct75xTX6epv8vi\ncXvjkxX5JUmx8UWxF57L6XU63Cp18J7+Y3W1Gk9Ud3U0GxxWV0Kyomh6yqJV+eKvB53Nhk/f\nbPCf8kYQ2IyFmVfcWJ46nrFZ0eOwuQ/t7Dh5oNsy/O4UnySfd2lO5dqCUB1tfWwW15kjfV0t\nRqvZqY6X5RYnzlycpZrsF8ivqZb6wX+/a8ekb3bd5rLv/viSSd/sZEFgB18/3R2mY/u7ejUm\niVTIyU9YvCJv0ovm9ib9Uz//oq1RHzAg1BcMlc5K/eF/rMgtDNnR2O3ydrUYfO/fiSmK3GlJ\nUyGkG2ExOk9UdzfXDRq0Ns55Qopy2oyUuctyQvXEnxinw/P5242nD/fw4ZayIYwR5/mlSRu3\nVoz9UlqA6DEM2rvbjFazKz5Jnl+SPOnV55OAk0Fnt1lc6jhZYkr479sFcS31gz/+9seTvtm1\n15Z+5xEEdhcbAjsYL+7l1Tvbv/ys9VRNj2+yw7hE+bylOSs2FFeuLrio3xj+dTLQbamr6W05\nqzMN2t0enpisyCtJrFiQWTwD1dsAEF0tDYM/jUJgd/m1pff++9QN7NBwABAcE9hl64suW19E\nvtl9GX3FL6r/x5SeE7fq2pJV117sfADAPx5GbHzfEhnhZqf2mz0CO4Dw/gEHxAAAxIDxTkcc\nCQR2AAAAABccuyjTnVxkCOwAAAAgBjHU2AEAAADEjGgEdlHY5GRCYAcAAACxiEWn2RSBHQAA\nAMAFxoihKRYAAAAgRkSnKRaBHQAAAMAFxqJTuzal4zqaQl9zBAAAADBZfKNiJ/0nfLBor3/5\nBxtmZKoV6vSKdQ+8Wm8fk6LziWVstGVPdk7Op0aNHQAAAMSmizEq1r7zoQ3frlrz4kdnNuTo\nPv/lt+7e+HDB2adXKvzTGAwGqnzs4LY7M4eXKFJzJid7COwAAAAgNl2EUbHmd556wbD1rT/d\nvlRJVLT1md9+nH3dk+/818qt8X6JDAYDZRbPLi6Om/TcoSkWAAAAYhBjk98OG74ptqa62rl0\n1Srl0K/ylSsrHfv2HRqdyGAwSJKSJj+qIwR2AAAAEKuiEdiJj4q1dXRo4/LykkYWxOfmJvS3\nt4/qZ+c2GKzyptfuWF6aER+XXrr0psc/07gn6SOjKRYAAABiUHyS4qd/uMJ/yb4dLfu2N49r\nIys3lay8apr/kvYmnUh6u91OCoV/hzqlUkk2m41IObLIKk2bXaZTL/rxO4/NVPXueeqf7990\nlfvwkccWyMaVt6AQ2AEAAEAMshgdTz6yL2DheGeh+3JHy5c7WvyXXLK2cOHyvFDpVSoV2e3+\n9XN2u53UarV/osQbX6y9cfiXkjue+VvD53P/9GL1Y0+vHlfegkJgBwAAALEpOt88IfZXZVFR\nprW2U0eU4lugb283Zc8sVoisw6ZPL6e+7m4PkeQrZw997AAAACAWMWICm/SfMPOdLFm1Sn1o\n1y7L0K/mnTsPqtesqfRP4m3b9fzv3zrlGP7dc/x4LZWUl3/1qI4Q2AEAAEBM8n1X7AUePEGq\nzQ99P+/th+95cX+zpvnAc/c+/H7eAw9uURHRh3fFsyW/aSUSlB3vPvrNm7/z3L76jvaGL176\n3j3P9q156HuLJ+VToykWAAAAYtOFb4olki17fPsr7vt/tnnO/Zb40tV3btvxn0sDoq2sO16t\nsv7oJ0/dcskP9ZLMskWbfr/n1/cUTU72ENgBAABALIrWd8WG26as5Mb/+fjG/wlcfM1fzHz4\n38mV33uu6nuTnjVCYAcAAAAxiV2crxS7yBDYAQAAQGyKTlPslI7sENgBAABALGLEojFGdErH\ndQjsAAAAIBb5RsVO+majsc1JhMAOAAAAYtN4v2ciElM6rENgBwAAALGJEYtG7Rr62AEAAABc\nYNEaFTu1v9sBgR0AAADEItTYAQAAAMSMqMR1k7/JyYTADgAAAGISi0aNXVRqASfP1G4oBgAA\nAJgQxkgQ2KT/hG+Jtde//IMNMzLVCnV6xboHXq23TzDNhCCwAwAAgNjEokN0n/adD2349ic5\nP/3oTHtD1WOlu7698eF9jgmkmSAEdgAAABCbolNjJxrYmd956gXD1if+dPvSoqz8BVuf+e0t\nhueffMc87jQT/siTshUAAACAKYUxYgKb/B/xCrua6mrn0lWrlEO/yleurHTs23do3GkmCoMn\nAAAAIDZFY8458cDO1tGhjcvLSxpZEJ+bm9Df3m4nUo4nzYQhsAMAAIAYpIqT3/7QYv8lJ/d3\nn9zfPa6NzLs0Z96lOf5L+jRibaZ2u50UCoXfEqVSSTabzS9oiyTNhCGwAwAAgBhkt7reevZk\nwMLxfhdF7cGe2oM9/ktmLs4sm5seKr1KpSK73X+Mq91uJ7VaPc40E4bADgAAAGLThf/mCWVR\nUaa1tlNHlOJboG9vN2XPLFaMM82EYfAEAAAAxKZojIoVxDvZLVm1Sn1o1y7L0K/mnTsPqtes\nqRx3mgl/5EnZCgAAAMCUEq0JisVDJ9Xmh76f9/bD97y4v1nTfOC5ex9+P++BB7eoiOjDu+LZ\nkt+0iqb56tAUCwAAALEpTBA2wY2K/1m27PHtr7jv/9nmOfdb4ktX37ltx38uDYy2Ikkz0dxx\nzidnS1NbVVXV+vXriUij0eTm5l7s7AAAAEB0aXusH/z59KRvdvr8jOWbiid9s5MFNXYAAAAQ\ng3xNsZO/2andiw2BHQAAAMSmCz8q9qJDYAcAAACxKRo1dmFGxV5sCOwAAAAgBkWrKXZKx3UI\n7AAAACBGRaM/HPrYAQAAAFxwjEWnxm5KV9khsAMAAIDYFJ3BE5O/yUmEwA4AAABiEMPgCQAA\nAIAYwaLTbIrADgAAAODCw6hYAAAAgFjAojQqdmoHdlN7zC4AAADAxDAmCJP/M+7mXXv9yz/Y\nMCNTrVCnV6x74NV6+5gUnU8sY6Mte7Jzgh8aNXYAAAAQm6bAd8Xadz604dtVa1786MyGHN3n\nv/zW3RsfLjj79EqFfxqDwUCVjx3cdmfm8BJFas4Es4fADgAAAGJTNAZPsHHNd2J+56kXDFvf\n+tPtS5VERVuf+e3H2dc9+c5/rdwa75fIYDBQZvHs4uK4r549NMUCAABADPJ9pdhFboqtqa52\nLl21Sjn0q3zlykrHvn2HRicyGAySpKRJiOoIgR0AAADEKiawKPyMIwO2jg5tXF5e0siC+Nzc\nhP729lH97NwGg1Xe9Nody0sz4uPSS5fe9PhnGvdEPzKaYgEAACAGyZWSVVtK/Je0N+ja63Xj\n2khhRUrh9BT/JYZBW+Sr2+12Uij8O9QplUqy2WxEypFFVmna7DKdetGP33lspqp3z1P/fP+m\nq9yHjzy2QDaurPogsAMAAIAY5HJ4D37aFrBwvF8y1nFO33FO778kryQpuzAx5Apv3cpu2ub7\n5y1v8r+oVGS3+9fP2e12UqvV/qsk3vhi7Y3Dv5Tc8czfGj6f+6cXqx97evW4suqDwA4AAABi\nUzS+/itMH7sNT5w69ajvn8mFpDxRlGmt7dQRDdX66dvbTdkzixWhN0Bs+vRy6uvu9hBJxp89\n9LEDAACAWMSi0scuzKDYxPw5w/ITiZasWqU+tGuXZeiv5p07D6rXrKn0X8Pbtuv53791yjH8\nu+f48VoqKS+fQFRHCOwAAAAgJjGKyqjY8dUCqjY/9P28tx++58X9zZrmA8/d+/D7eQ88uEVF\nRB/eFc+W/KaVSFB2vPvoN2/+znP76jvaG7546Xv3PNu35qHvLZ7Yp0ZTLAAAAMSm8faoi2yj\n40otW/b49lfc9/9s85z7LfGlq+/ctuM/lwYEX1l3vFpl/dFPnrrlkh/qJZllizb9fs+v7yma\naO445xNc9Wulqqpq/fr1RKTRaHJzcy92dgAAACC6zAbH8T2aSd9sdlFC2fyMSd/sZEGNHQAA\nAMQkFp0auyhsc/IgsAMAAIAYxKL0XbEI7AAAAAAuNBaVPnZTO65DYAcAAAAxCjV2AAAAALGA\nRanGbmrPFIfADgAAAGKTEIUgbGpX2CGwAwAAgNgUlVGxaIoFAAAAuNAYQx87AAAAgFgRlXns\n0McOAAAA4EKLTo3d+L4r9oJDYAcAAAAxKGoTFE/6JifT1K5PBAAAAJgoxib/h8Yf2Hl6dv3y\nynwpW/FMT4gU9vqXf7BhRqZaoU6vWPfAq/X2CX9kBHYAAAAQixgxgU3+zzir7AZ2/2LN4m8f\nSZ+eGDKJfedDG779Sc5PPzrT3lD1WOmub298eJ9jgh8agR0AAADEJCYIk/8z3sCu+VjfN94+\n8t598+ShUpjfeeoFw9Yn/nT70qKs/AVbn/ntLYbnn3zHPLHPjMAOAAAAYhCjKNXYjS8blf/6\n7EPLUsVS1FRXO5euWqUc+lW+cmWlY9++QxP71Bg8AQAAALHoazKPna2jQxuXl5c0siA+Nzeh\nv73dTqQUWS0EBHYAAAAQgyRSoWhmiv8Sw4DdMDC+cQlJ6cqk9FHxlcPqnoTM+bHb7aRQKPyW\nKJVKstlsCOwAAAAAfDxur6bJELCQjbMPmnHQbhwcFQsmpCjikxWh0tNbt7Kbtvn+ecub/PUb\nw+9CpVKR3e6/D7vdTmq1enw5HYLADgAAAGJQlL5SLMwExRueOHXqUd8/kwsj2qCyqCjTWtup\nIxqqXtS3t5uyZxaHDh7FILADAACAmMSi85ViottMzJ8zJ398G1yyapX6pV27LPdeH0dEZN65\n86B6zX2VE8sdAjsAAACIRdEaPDGu5E6dpsvgIuoxesgx2NHaaichIaswTfXhXfHX1j7aUvNI\nsWrzQ9/PW/3wPS/m/GpjVt9HP334/bwHvtiimlj2ENgBAABADPJNdzL5mx1fZFf336sX/nfT\n0C+/qJz2C6Kkf9qhf2GjXxrZsse3v+K+/2eb59xviS9dfee2Hf+5dKIBGuOcT3DVr5Wqqqr1\n69cTkUajyc3NvdjZAQAAgOhyOT39Gsukb1adIEtOn2B12gWAGjsAAACITeMdAxvRNqPQbW8S\nIbADAACA2PS1mKB4ciGwAwAAgBjEGItKYBeNkbaTB4EdAAAAxKboDJ6Y9E1OJgR2AAAAEJvQ\nFAsAAAAQCxibCtOdXGgI7AAAACA2CRgVCwAAABAbolK7hsAOAAAA4AKL0qhYYWpX2SGwAwAA\ngNgUlalJpnRch8AOAAAAYhKLyqjYaGxzEkWhVyFADDEM2FvP6M7W9NUf6Ws7ozMO2i92jr6W\nHDa3SecwDtptZtc/xtdTA8CUwAQ26T8TGD3h6dn1yyvzpWzFMz1B/975xDI22rInOyf4kVFj\nB19XbpeXCSSRROvlxGn3NJ0cMBucRESMGCejztHXaU5MVZbMTZXJJVHa7wXjtHtMOruXU0Ky\nXKmWRWkvNrOrX2Nx2NwjSyQyIS1bnZSmjNIeLxhNs+Hk/u7udqPL5UlOU5XPTZ+7LCcGLgyA\nmMGmxqjYgd2/uO62v6WumZ5IzhBJDAYDVT52cNudmcNLFKk5E8zehQ/s2l6+74HjG1544rrM\nsMs9+35z3ZPyn7394FLLpz/b+u6sp5/dWhRqs46Tz/7zozumPfzuT1de2HLV4/ZKpKj4vHA8\nHu/BqvZTB7oNWgdjlJqtXrwqf8Hy3Mnt9OByeOoO9rqdnqHfOXEi4kRExkFH3cG+2cuypDKx\n827SOcwGBxHFJysSkhWTmbmvzG511e7v6es0jyxJyVDNWZ4TST69Hq7X2jxunpyuDBvEmHSO\n3nZTQBWd1837OswOmzszP35C2Z8COFW93VCzu5MY810c+gF7y5nBw7s6bvre/NQsdTT2abe6\ne9qNNrMrNTsu6+t76IKxml1WkzMxVSlXRFp8u5yegW6rzeyKT5Jn5MaNqx8V51N3ugqb2dXT\nYbIaXXGJsqzCBFVctN64/lFEax678aVvPtb3jbePPOT+ZfarNSGSGAwGyiyeXVwc95VzFys1\nds6zr/zxoCvxq23E7fJKpOMY7NJWrzvxZbdZb5dIJQXlSQtX5UVyE2p7LMf2dQ10mxVKWfGM\nlLmX5ojHB19RY+1AZ7NBoZBWLMiI0vNmhNng7O00SWVCblGiLLIC2mZxnfiiq6fTLJWy/NLk\nOZdkix8Nj9u77Q8nOhr1jBHnxDlpu62fvF7f1WLY9K2ZYXfHvVyvtQsCS0xVip/oltODLpcn\nxB+5y+5uO6MrnZcW9M9ul/fciQFdn3VkSWqWumxeeiQnurfD1HpWxzkVVSTnFI3jiraZXZxz\ndYI8bEqn3V29vc1hcfkv1A3Yqre3XrapOF40tms6rd3/Savd4iYiiVRYuDJ3wYq80Dvy9Lab\nxza8cs6JyDBgV6qlialh6u2sJpfL5UlMVkReOnec07fW69xOb0Zu3IzFmZHfXy6n1+X0qOPD\n38XVn7bW7O4kIhr+eNzLiUivtb/+h2P3PrpMPOQ16RyNpwZsVldWQULJrLRIypxTB7r3vN/k\nsA1dkwXlKRu3ViSmROuFoafd1FSrtVvdKRmqmYszVREck4nRD9j2b2/t1ZiJiDEqm5exdG2B\nXBmm9Og4p6/ZpXFYh67hpDTVJVcWpGaGKd88Ht5yWtvZaHDa3co4+bRZKfllyREW+C6Hx2Jy\nJiQrovoa31SrrTvc5/V4fb8KB3rnXJI1bVZq2BV1/bbWMzqryalOkBXNSAl7KL4Ki8lZf7Tf\nrHeoE+Tl89MjrHq3GJ11h3sMWntcorxiUWZUczjalPiu2Mp/fbaSiL4QSWIwGCRJSZMQ1VGM\nBHaeptefqZ57y4azz7ZObAO6ftvu95oHusxSmWTx6rx5y8NXgLae1X35UQtjjHNyuzwtZwYH\ne61XfWuG+G0/0G3Z/rcznBPn3G5xn/jS1qcxb7i1IpLCxaC1t54dVMXJSuekRVS4cPrgb3Vn\nj/X5fvvyk9Zrb59ZsTBTfCUfs8HRelanUEqmzUqL8Ll47uTAoZ0dvsebQiVZe31ZWnaYa9Ru\nc7//Uq3dOtRIN9Bt6W41brytQmTaoZo9nR2Nejr/PB2KEk4d7Jk+P6NsbrrI7gxa++6/N5t0\ndiJKyVRffl2pOiH448pqdBq0Yn3pONFgrzXPmqRUB7mDzp0Y0PtFdUSk67U2nhyYsTjMwT+6\nV7P/0zZfveCRPZ1LLs+/5IpC8VWIyGF3H/6sY6DbQr4n3IYC8fDu3PEBh8UVGG5x8ri8dYd6\nKzeE3GNXq3H3u40j48E8bm/Nrk6JVJi7LPj9Mthr9VV0hqLttooEdm6nd+fb51rrdUSUlKZa\nf1N5Wnb4h8GxvZpTB3oYIyLW0ahvOq29+o6ZYa9h7uX7Pmypq+nlnFKz4tbfXCby4LHb3NUf\ntxExGvPpuJcbBx1H92pETlxHo/7jV+s9bt/Duzu/NPnq22eIPycaTw18uq3Bv066s1H3znMn\n7/i3JYIkTNnR2aQ/trfLZnHmFCUuWVsQyctnw/H+ml2djDFOvLfD3HRau+lbM+ISw78z9LSZ\nju3rspocmXnxiy/PD/ua4bS7P3293mYeis84p3Mn+h0299obykTWGui2VG9v8z/4xkH77nea\nr75rhkIp9kQ7faCnu9XoO292s7PuUK/L4SmZE/z1zN+J6u6juzu9Xi6VS5ZfWVQ+X6ycOf/p\nHJ4TX3Zpe6yZefHzlod/ge9pN9UeGNX9yuvxnqzujkuSZ+aJVdD2dpiP7dUwxjjnJoOjt8M8\nf0VuTlFCJJkkIu7lkYcp+gH7Z9saPB6v7+pvOq1dvaUkuzDMvgxa2wd/PuNxe4iIODUcH1h3\nY1l+WXKEO/0qovbNE5O9RbfBYJX3vnbH8od2nOzhWbMuv/vXTz6yPm9iIdrXqA1RYqp99bH7\nbr/55q33PPS7HS0jz11v61vPfF707dsXBHl5ra2traqqqqqqOnXqVHJyyMvoszfOabstROR2\new5Wtbc36MPm5theDWNDUQURESeD1t54ckB8rSO7Oznn3K8So7vVqGk2hN2dQWt778XTx/Zq\nqne0Vr15Lmx6ImqsHRiJ6oiIc/7x6/We4XdBEWaD86O/nj3xRdehqo7P3zwXSVd3q8l1eGcH\neYeSOu3eL3e0hV2r9kC3zeLxVbz59tLbaWo9qxNb5WB30FuKMao9FLxL6ogvPmo16R2+f+v6\nbQer2kOl1A9ENELCMGAbu9Budev6rAHHzBcIjoSwQZl0joOftvuHCkd2dw6ODhCDOlXdo+2x\nDGVJazu6WyOeXtNiDHVKtT0Wpz1UPSUdqmonIv+rlxEd2a0ZjlECWQxO0biO3C6vyDE5vLuj\ntWHoYjAO2j6L4LI36R21B3uIfFcUJyKD1n72SF+49ejUgZ7Th3t9n0zXZ/309QaRxG31OrfL\nMzaq82GMnTslVg7serfR63cbdjbp64/3i2fvyO5Oxpj/DjknbY+1tX5QfEWD1r73vWbDoM1p\n97Q16Kq3t4qnJyK3y3t0j4Z8hRsnIu69oeDTAAAgAElEQVR2eI5/0RV2RYvRuee9ZsOA1Wn3\naJqNX34Ufl91NX1WU+BgmvYGnX8ngbEajvXzUZchcc6ddndrnVjRYdI5uluNREPnzbd240mt\n0xHygvfpajXWfN7h9XIicjs9X3zYIv7WN+LztxqP7dN0NOprdnXs+7A5bPqWusGx77SMUcvp\nMGe57nAvIzZ0RDgxorrDvZHkkHv5vvebX3/q+M43G0PdxQGO7dV4PZx8JTYnzunwzvA9/A/t\n7PB4hot6IiJe/XFrJLubFILAJv1n8ic9tkrTZpfJ1Yt+/s7B08c++EVl0282XfX4cVf4FYOZ\nWoFdw5/v2eznut9W+/1Rs7ua3fJfL217+ak78+r++Lt3O4iIiGvee+bD5DvuWR70jeb1119/\n5JFHHnnkkVdeeaWoKHgHPbPBYdDa/O4K5qsTEuGwua0m59iIZ7AvyJPe30C3ZexavphSXMsZ\n3cjDoLvVaDWHP98a37vpCE5Oh0fbHT5Q6GjUu4dbIfu7Lb4qLnEDPRav9/wn45wbB+3+/eVD\nrGVlLOBwsAHRo6Hrt1OwSJNz0vaKfTSnw6Prs/pX9PW0Bfb9Op/YHibnPiPtYv6s5lB9Y8X+\nRES9nSbv6AxxTj3tprDZ6Osc1dw52Gv1ekLGUy6n53zHwTE4F8vkYJ8t4IhxIrfLY9Q5xib2\nuL1er2hYR0REIpnpbDLS+dNFBq3NYhQ7gESk7bEG5JAx1h/B/dXRZBgpqTnnun6bxRRyX4ZB\nsducc67vD3kpmg2OwFCGhT/L/d1mHuxiFb9ZiKi7zej1DlebcuppN4V9fuv7bedXGVov/I6I\nqLfD5PUMXcKc84FuiyPcfaTrswZ9QIq/z+j67WODasZIrxU7L4Zg49k55+ZgV6+/rhaj/69e\nzrvbjKESj3A7vR2NOuJDbfTNpwfDviEbBx1jzzLnZBwUy6HD5nbY3P4njBO5HB5bBM+I3k5z\nZ5OBiPo6Tb5/hKXtsQbE1BajI3xRrzFzv+uOc7KaXJE8xb6u3rp1ZGjrrW9FtkrijS/Wntzz\n53/ZOL+kaPqldzzzt38pO/WnF6vDrxjM1GqKLb31f35+1flabu/BZ+5+fuQ32dItN89MlhBl\nrty86q8PHj+p21qQ3PvhH96S3/I/a1OIuie4U7li1EHgxBWqMIdFKhOYwPjo5xYjCttCKldK\nXGNeEMN2KCEihV8aQWCRjLxTqqRji7+wHy1gX4yRXLR1w2dsl2eBsbBND3KFhHx95c7j4r2n\nJVLmCVaGMCLxYyKVCoLA/EMNmSJkf0omsCDNbGMEbQWThK72F/kTEQVtRYrkfMkUEv9IVJAI\nIs1zYbubiCSQSlnQMCzoiY7wjVaklUSpkvhal4Y3SGE7bga9eCLpj69QSvxbVpnARNaShrvN\npfKQCeQK6dgm3EgKnKBvEVLZ+A6IRCoI4UaRBz3IEygEBIHJwmUvVBEhXnTIZAIjFtDKz8Od\nF1mIbYYttBVjyudIjoZEygSJ4HV7ua8UDV3a+K8SdLkQYrmPVCYEFqK+5aEvwhGqOJkvV5xT\nhN0oZQqJe3TnY0FgYfclV0oco5sCGIvoKfZ1teGJU6ce9f0zOXxvmmDY9Onl1Nfd7SGawGGa\nWjV2ElVSip/kUd1BktLShj9gUmISGY1GGvjs2Tec199/dXaoDT7++OM1NTU1NTW/+c1vTpw4\nETSNXCkpn5dORL74WiqTTF8QpguFRCpk5MQFPLo4UUFZkviK+SWjEjAiQWA5xeH7yJfPz/B1\nsxAEtvSKQlkEN23pnDRBIoxkUmAsqyAhko6uxTNSs4sSiYgJbMGKvKDdyAKk58TFJ8n9jgcr\nrEgOW2LmlyQFBseM8krEjmHetKSgEQNnlCt6GAUJK5k93JmGERGVz8sIlVgdL4tkqrWgvezj\nkxWCJEgeBQkTH5eQXZgQlygbWZMxplRL86aFuaKIaNrMFCJiNNQ6MG1WikhiiVSIT1KwEEOI\nfX8NtW5BeXLAeoxRUpoq6FhaQRJRwS0S08xcnMk5Z8OXcNnc9LAhWnpunEI16uhzzgvLw3fl\nmbU0i4iYMBSPVsxPF8l8hmiHJ4FRVn7ILkdypSS/JNn/LDPGSsL1ji+anhqs/wGF/WgF5cnx\nSXJfYiKatTQzbHiRmKqIT1YEJMsvDX8d5hQnJqYqRvY1fWFG2P5/eaXJgXVUjAkSQXzYUGZB\nfJC+m5yyCsR6eiVnqASJMOrKZ0yhlg3lObRps9JkCsHXA4QxFpcoj+RoMIFVrivgbGhHlVeE\nnNBhRHpuXJCzw1hGrlhnZYlUSMlU+3dQYYxS0lWR3H1JacpLryoumJ5Sub5QvBvfiPzS0aeG\nUXZhQtg5pwqnj77CGcsuSojkKfZ1lZg/Z1h+ZEPgvG27nv/9W6dGKmc9x4/XUkl5+cSC36lV\nYyfKZDDwoTLDaDRSclIy1Z88ZW059cMbXyUi4l43eX938w1ffP+VH18+vimyVl1bkpYd19Vq\nUMfL5y7LTkwJv/qyK4s+frXeaXePvPFULMwIG6Itvrygr9OiG2mpYWzJ2oLkdFXY3UllwqY7\nZhp1DoVSEkktDhGlZ8dd860Zn77Z4OvGlFWYcM0d4ceNEpEgYetuLDPrHTJFpPuSyoTLry87\n+Gl7f5dZYKx4ZuqSy/PDrjV9QUZPh6n5tHZov4wtWp2XniNWil2yrrDl7GBAnQdjJEiExavD\n7HHpunxlnLS9QS8IbNqs1FlLQg5lSM5UCfV6sZZExiQCS0oPcp1IpELh9JTWM4Pn88iIOBVO\nTxGPdGUKyVW3zfzsjQZfD574JPkVN5VHElX7xtt2nNN7vTyvJClsZ/DimSkBfbRHFFYkizyM\nl20o6mox2CxDtYOMEWNszTdKQqVPTFVoe0K2qTEiVYJMpG6mbG46MXbmSK/L7imcnrJgRW6o\nlCPkCsnqLaV73m92WN1ExAQ2pzKrIILALrc4cfNds04e6HbY3IXlyfMuFRs+lT8tKSVDpR+w\nB20e9XIKNZrEZ+31pVVvNna1GohIKheWbywWj0iIaPnG4uY6rd3m9l1SvhqaBctzxW8WIpLJ\nJZtun3XuRL/V7MouSiiIoLs6Y2zlNdP2vNdsHW6MLihLnl2ZFXZFiVTYsLWi6ZTWbHBm5scX\nTg+/r5JZqa1nBtsbdL57xNdruXJdofhAjZlLMtvq9Tazy3eH+Y5GVkFCnmi8pVBJZ1dm1h7o\nZcN97ASBzV+RE7ZnfXyS/Jo7Zx3f16UfsKfnqBeuyotwTpa5l+bklSRpe60ZuXGRFPLT52d0\nNRtd/rXijMnkQtixGnMvzT5U1Wkb7kShVMvmLg9Z2RGgqCKlqELsVTDA/MtyTYOOno6hzgMp\n6eqlVxSEXWvR6jy91qYZbu1NyVCtuGZa5Dv9+nPqNF0GF1GP0UOOwY7WVjsJCVmFaaoP74q/\ntvbRlppHipUd7z56z++ODD73k6uKWFvVb+9/tm/N//ve4ontjwUtm6Kp7eX7Hji65vf/ec35\n+hKpMk4hCTuP3f+2Lvn2r350baHccOiZh3/VvOHZJ28aVdJ3v/XD79bfGGIeu6qqqvXr1xOR\nRqPJzQ3/hIiE0+6uP96v67MpVNKC8mTxGqMRXi9vqRsc7LXIldLC8uSUKI/6dru82h6LQiVN\nTlNdgK+387i9gm9i7oj1dph6O80SqZA3LTGS4u/oXk3V2+e4lwsC40Tcy2VyYfPds8vmRDRU\nLUJdzUaNaKeTworkrNBjwQa6LK1ndb4WUoVKWlSRki762j3C6+X6ARvnlJKuClvhMTGc08kv\nuzRNhvMtOIwR52nZcUuuKJCI7tRudR3dq+lqNXhcPDM/fuHKPJFT5vXyjnq9yxGsxx8jgbH8\n6UniwxgnxuX09HaYXU5Pek5clGYQ7GjUv/b0ce7lY8vPWUuyNt81O+wWTDqHzepKzVBH0mRG\nRCa9Y98Hzc1ntE6HNzldVbm2YHZldvQmY/O4vT3tJt90J1GdJolzXn+0v/XsoMXkTE5TzVqa\nFUkLht3qPvllV8c5g8vpUaql5fPTZyzOjGSuALPe0dVitFtc6kR5fllyJC9OF5JZ7zhZ3dPf\nNTR2JKsgfu6ynLik8OORvV7e12m2mlzqBFlmXnyUio4R2h6rSWePS5Sn58ZHfhEOdFsMA7a4\nJEVWQfzkDz6Y0o4/Urbwv5tGLUr6px36FzaeD+yI9If++KOfPP1hTbNeklm2aNMDv/n1PYtT\nJnaYLk5g98bocXsr//39hy8TDexMH//0tk/LH1jc+vedTTqHIrfy1h9874rC0UX2BQ/s4GIZ\n7LPW1fT2d1kkEpaZnzCnMkukAXGCODXVagd7rEH7r2TkxRVHMLmU7+V7anYl6WoxdjbqjYMO\nr5cnpihyihMLK5InvbR1OT1dzUan3RNQySpIWE5xYqjpZr4WOhr1H718Ru83MlqQCJWXF6y8\ndlr0vg2FiDweLh58/0Nxu7xRnQf0onA5PFazS50gm5pFB0x9Fz6wuzgQ2MEE9HWYNU0Gt+v8\ngC6pXJJflpSRNznTSP4j4F6uH7AbB+2+iVSkUiE+RZGSqYqB5zH38rZz+p52o9vlTUpTlsxK\ni4tggmgAgKiaWhXRAFNKZkF8Rn6cSed0WF3EmFItHduvHMQxgaVkqlIyVZwT8XFMhTr1MYEV\nV6QUj6eLEgBAtCGwAxDDGEtMVVC4oXMQFmPRmK8dAABG+dq3hgAAAACADwI7AAAAgBiBwA4A\nAAAgRiCwAwAAAIgRCOwAAAAAYgQCOwAAAIAYgcAOAAAAIEYgsAMAAACIEQjsAAAAAGIEAjsA\nAACAGIHADgAAACBGILADAAAAiBEI7AAAAABiBAI7AAAAgBiBwA4AAAAgRiCwAwAAAIgRCOwA\nAAAAYgQCOwAAAIAYgcAOAAAAIEYgsAMAAACIEQjsAAAAAGIEAjsAAACAGIHADgAAACBGILAD\nAAAAiBEI7AAAAABiBAI7AAAAgBiBwA4AAAAgRiCwAwAAAIgRCOwAAAAAYgQCOwAAAIAYgcAO\nAAAAIEYgsAMAAACIEQjsAAAAAGIEAjsAAACAGIHADgAAACBGILADAAAAiBEI7AAAAABiBAI7\nAAAAgBiBwA4AAAAgRiCwAwAAAIgRCOwAAAAAYgQCOwAAAIAYgcAOAAAAIEYgsAMAAACIEQjs\nAAAAAGIEAjsAAACAGIHADgAAACBGILADAAAAiBEI7AAAAABiBAI7AAAAgBiBwA4AAAAgRiCw\nAwAAAIgRCOwAAAAAYgQCOwAAAIAYgcAOAAAAIEYgsAMAAACIEQjsAAAAAGIEAjsAAACAGIHA\nDgAAACBGILADAAAAiBEI7AAAAABiBAI7AAAAgBiBwA4AAAAgRiCwAwAAAIgRCOwAAAAAYgQC\nOwAAAIAYgcAOAAAAIEYgsAMAAACIEQjsAAAAAGIEAjsAAACAGIHADgAAACBGILADAAAAiBEI\n7AAAAABiBAI7AAAAgBiBwA4AAAAgRiCwAwAAAIgRCOwAAAAAYgQCOwAAAIAYgcAOAAAAIEYg\nsAMAAACIEQjsAAAAAGIEAjsAAACAGIHADgAAACBGILADAAAAiBEI7AAAAABiBAI7AAAAgBiB\nwA4AAAAgRiCwAwAAAIgRCOwAAAAAYgQCOwAAAIAYgcAOAAAAIEYgsAMAAACIEQjsAAAAAGIE\nAjsAAACAGIHADgAAACBGILADAAAAiBEI7AAAAABiBAI7AAAAgBghvdgZAJjSOOc9HSa91s4Y\nJaersvISGLvYeQIAAAgBgR1AcEadffu2+kOft5sMjpGFSanKZeuKNt5cEZ8oF1nXbnM312l1\n/TYiSslQlcxKU6r+Qe81/YCtrqa3rUFv0tvdLp6QrMibljh9QUZ+SdLFzhoAQAxinPOLnYcL\noaqqav369USk0Whyc3MvdnZgqjuyr/OlJw677J6gt4dKLb3nx5fMq8wZ+yfDoP2TNxqO7NN4\nPd6RhRKpsGhl3sabpyemKKOW5XEb7LMatHbu5QkpyvQcNZvsqkiPh3+xveVUdbeXOCPylTS+\nfXCioukp628qVyeIxccAEDmP2yuRonsVILADGGPfjpb/e+oIMeLe4AmYwBinex6pXLqmwH95\ny9nBvzxRY7O4gt1VTB0vvevflkybkRqNPI9LS91gze5Os/58TaRSLVuwImfG4qzJiu7cLu97\nL53WtBhCJWCM1AnyG747LzltCgW7E2AxOT1uro6XSWV4pk5pZqOz7nBPV6vRYXOrE+QFZcmz\nlmTJFZKLna/z3C5vy5nBnnaj3epWxcmyCxOmzUwNG6u1nB08sruzrUHndHhkCqGoPGXx6vyS\nWWkXJs8wBV34wK7t5fseOL7hhSeuywy73LPvN9c9Kf/Z2w8utXz6s63vznr62a1FAVtrfOme\nB//ed/532ZqfvP3gsrF7nfTAzmJyHqpqb2vQaXutSpU0Mz9+wYq88rnpX33L/4BcTo9EIgiS\nccQUFpNT12cVBCE1Wx15K6fH49X2WCUSlpKhDrW7lvrB3/zrbu71it8ZjJFEJvzsmStyixJ9\nS3o6TE//7EuXwxvqnhIEJpMLDzx+WVZ+QoQZjlBvh2mg28I5pWapc4oSxYOzA5+0nTnSxxj5\nZ5MRcaLC8uTLry8TPxEOu1vXZ3O7vKlZanW8LFSyT16vrz/eL55txlhyuvLWBxbI5JP8cHXY\n3E67JyFZQVHrEOlxe08d6Dl7rN9ucRGRILC8ksSFq/JSM9XR2uUF1N9t6e0wWU2ulAxVfmmS\nKi7kiR7L6+GR38tOh6f+WF9fp9licianqfJLk6bNSgv7dtHTbjp1oGegy2w1uRJTldmFCfNX\n5IhXh9ce6tn7QbPH7WUCcS/5/qtUS6+8dUZheXKEuY2qjkb9rnfO2SxuYjR0e3JSx8suv748\nvzR4vwWvh3/8ev2J6i7/21kQmNfL516SfdU3Z0SjAs+kd3S3GQ1ae3ySIqcoITldNem7gK/o\n697vx2qzxl/585f+ac7wAsnEW3ZsFpdCKY2kSOrvsmz7wzGr2UXEiHOLydlar2s5M7hwZd76\nG6eLP0s45x2NBl2fVa6Q5E5LSvqaV1eMsJqcJ6u7B3osUpmQU5Q4uzI7fAUGp7PH+47s6jDq\nHQJjqdlxl15ZFLbrldXsOvBJW1PtgO9XgbHZl2QvvjxfPDhwOT0HPm0/9HmHr4VUJpcs31i8\nZE3+2NO97Y8niIeJ6oiIc/K4vG8+d/KHv1rh+3XbsyfdzpBRHRF5vdzp9Lz+7MkfPH5ZqEeX\ny+EZ6LFyztNz4iKpSzAM2j9/p0nTrB9ZkpkXv+6GsrTsuKDpaw/2nDnSR0QB2fT91n5Of/Cz\n9ks3Br4/+bhd3iN7Oo9/0TXUysxo+vyM5VcWq8aEd51N+rBRHRFxznX9tqN7NZdcURg2cYRa\nzw7u/6TNoLUTkVQumb88Z+HKvEmvS3M5PZ+81jDQbRk5jV4v72wyaJqNa28oC/UY/iqsJueh\nnR0djXqbxZ2aqZq3PGf6vIxIwlazwdFWr7OaXWnZ6mkzUpkQZh2n3fPpGw0tZwZHlkikwopN\nxfMuDdLxIGBH+z5qaT6jdVjdiSnK+ZflLlqVLxEtTvu7LB/+tc5idPq6AbSf05/c3507Lema\nO2bKlSEv/pP7uw991k7D17B+wGYYsDWe0m64tTwvROlx+nDPrncbfTedrxre91+HzfPBX05/\n4545edMuco/Pjkb9x6+cHbor+fnb02Z1b3/5zNV3zAyaw6q3z52o7qLRt7PXy4no1MEeQSJs\num1G2F27nJ5I36w4HaxqP7pX49sFERGjWYuzVm0uET/RRNRxTn/maJ9+wJaQpCiZnTZ9QQZG\noUXP1z6ws9rUmclK5VeLjhprB/Z+0Gw2OAWBzViUufLqaYrQlUAej/fdF07azG7iNPxAJO7l\nRHRsnyYrP0GkBHTY3FVvnNP1W32/Ht2rWbgyb9bSrLA5NAzaq3e0drUY5ErJrKXZC1fkRf5O\n7LC5pTIhqn0v+rssH/ylzu3yMuJE1N6gP3u077p758iVYhfY3g+aTx/uYYyIk5dzbbflgz+f\nXr2ldNaSkAfE7fJ+8OfTpkH7yBIv56cOdA/2WTfdPjPknji983xte4PObzuePe83DXSbN31r\n1FrtjfqmOm0En5iIiHOqrenp05gz8+LrT/R3+kVXIVfxUmeTvuFkf8X8jLF/barV7vuw2WHz\nEJFcKbnsquLpC4IkG+F0eN59/pTF6PJf2N9leef5U9/84aK4McM77Fb3sb2a4eq54OqP9VUs\nygha7VT15qjnPXE6d7y/r9N80/3zAyKno3s1jEXUGsCIjn2hWXp5wbjqa0OpPdDzxfaWkc6C\nHqfnyO7O7hbjtXfPiiSgaawdcNrd+aXJ6TnBw+IRR3Z1DnRbaPRx9H3cPe833/DduUp1yCvf\n6+Enq7vqj/fbre7MvPjKKwrTssNU8un6bW//6ZTL7vbtrr/LXPXmuc4mw9rry8RXPHu0b9e7\nTR73UJeCtGz1lm/PFu/XuP2Vsx2No65kj5vveb9ZqZKKXI2DfdZXnzrqsHt8R8QwaN/7QXNz\n3eBN980TQhx5p93z/p9P2y1uIvK/VLpaDJ+92XB1iNu5u8148NP2gIWcyOP2fvZGwy0PLBhb\nuWgxOfd+0BL0guScM6Kqtxq+9eASkdDEZnHVHe416uwpmepxtd6aDQ5dvy0tSy1+zD1u7973\nmzkPcsdwL2cC7Xmv6dYHFgbcI12txiN7O0U2e6K6a96y7PzSkPWRpw/1HKxqt5ldCrVs8eq8\nhSvyxF8VjuzprNndOSoNp7qaXibQmi2lIise/rzj2F4NExj3coPW1tGobzs7uOHWiuhVqP+D\n+xp1CpGYal997L7bb7556z0P/W5Hi52IyGG1elTqr1YT3N1m/PiVet/T0evldUf6dr7dKJK+\n8eSAfsAe9KHFiA7tDCx0/B34tE0/HNUREffyY3s7+zrN4jl02NzvPneqqXbAanbptY7qHa37\nP20TX8XH7fJWbWvY9vvjr/+/Y6cP9USyChFxTjWfd7z+1PG3/3iyrV4XPr2X73yrwevyEOd8\n+F3ToLVXfyyWyf4u8+maHvJ71+ScE6Mvd7Q6HZ5Qa52p6TUO2sceek2zof1cyLjqXO2Af1Q3\nstPTh3u724z+y08e7BbJc1AnDnYTUe3hHorwDZSx2mDnQttr/fydJqd96LM77Z49f28WvzZO\n7e82G5wBlyLn3GH3Ht2rGZu+9eyg2+UVieqIiHNqOhUktO1qNY6K6nyJfYNeD/f6L3S7vO2N\nhgj7eHAip93TNfosjBjste5849z2v51pqg0fbTvtnv2ftRE7HyX4/tfVZmwMt7rV7Hrz2RPV\nO1prdnW+92Jt/TGx6kaX03PuxEDQP3HOXQ5P48ngf/Wp2dVxdK/GYnR63N6edtOOV85YjE7x\n7H35UYvLcX4cj+/znT3ap2kO2YWRiHT9ts/fafL4DeIZ7LV99qZY4aZpNgREdb4dMkZfftwq\ncuHseb/ZafcGJOhs0gdcG/5OH+6xmV1Br5OWukFtjyXoWiere4KO8uGcuxze+qNBTtyZI31u\nlyfUBenlZBx0BJQP/uxW13svnv7/2TvvgCiOLoDP3h299947Ku0AO4Il1jSTmGJEicFeYjdo\nxF6wd2NvsZeoqNgVsSCCgCC993rHwTWuzPfHAVJ29/awfmR+f3xfhH3MzNvZmTcz771JflZe\nkMF+FV0SeexNi6FMTkps+dkdSVGnMk5vT8pMIutRJbl1DXVCoi8GSkE9S1ia3/4bSXxSipFa\nRhgGXsWUEv02M6nq4b85fK4IANDIEz29mZ/8nGz0EzVK4h4UYwBn/HgTV1HXarHdjvLC+sTH\nJaB5+0O2V5qfwUpPqCQSQbwjn5dhl3nk969a8e3Gp61+WfLwKfbjusNnT24fb/Fm35bLRQAA\nHp8HeEknl04ZN+aHX377Y82Jl5VtbII9e/aMGzdu3LhxO3fudHJywi00Na4Ctl4yQpidUi3g\niYkqWZxXBwg+JwhAbSVP9ql0pFEgLs5mw/YiWMupIhF5abX17ObPHkIAQNKT0reb4cS8eVEu\nGw4kUhj/oJhD/O21piCDlfGqSiKWCnjipzfyhXxCVcioKuVyaoUdq5OTUkMyuRdksHDmCQjE\njZKyDkNYC8U5dUTBm8U4E1ITeWm1RFZXO2OlsrSBptAJAQYqSxsAABVF9Rg1UwaDsLy4vuPP\nc1NrpG0d+6QAklskRUTagKAwG2eiqi7jym0chgHZXlT7sgjsZgzDCrPalMVhCVpHBFNBlhem\nI4+uZFeW1LOr+bG3C1iVPNxnWigr4EhE7W0LAACGAZJpW0bK8zJB82cLIYi9Q7Y8qynnSYi/\nPowGKksIzXEIoewovOWfjQJJbirZWxaLpEW5eIYyBvLTydqVn14rlbRRCISwKItFYkcW5eBb\nihCChrpGdg3+a5JKYH56bccaYjRAsv9dmsch2UYtycUfBMoL64lMNAwD5UU4X1ZZAUduxHcZ\nwdICAJCZWM3nigBo2k+rqxGQq12GUCCOvVMoE4FSacz1fCl+hD0AAFSV4luxbZ9p36mKc9lQ\n3iKNJHopNbYcA7IjFgABABh4TWrYlRc1SMT4/R5CQLLGyH1TAzvOmDQsh7TbI96Fz+so1uGn\nzcuGv40/kMbuCjnQ8i8lv6/HuOnSATDu/1XAsbmJyayfrYC+c08XqceIWTOcNLnZUXs2rFmt\ntG37TzbNvai0tDQtLU323+rq+Ocd9a1iA1vgsASq6pq4zwt5YvJTJllAE87P+WK8wRmQGJFN\nNWS1r6FELOVzRRryUkVwWMLWTrWcWoG2vvxD67f2HwRSCBvqhCQH04C4/hKxVNwoUSI4tiCx\nFwU8fMsYACAUiPH3SjGM7A9yRTQM4IxJGOC3rTyPKyI9qGwPDcP4DSKZIEUhCACvAWdmbWAL\nMYC1HqkxgHV89a0R8vG1AQAU4vngMyUAACAASURBVL2URoFEbuMgBAI8TQr5YnzNQCgUtHle\nKCDccCUC9901CiX8hrc/r6sR6JHGJeBWGwAAAEb8qybaDQKNQrFQIFYhcCQg2VEGAABI9oCA\nK+6439M6USJOcQIxxJtPMdIvBcgahffKeA2ijsf0MshXcURqFArEuOtMCDEBwSq3qSziYZSo\nLBGxbiEEjURdl3TRhZE2vL5O2O4Yl/x9yWioa2zRieykmNfQqKmjgvtwo1BO/wQANAraPyPk\nieWOVES7DACAulpBG7sQ4kw0bSpA3jeIZzEhT4zTDaWQpG6Id+Tz2rGjq+notUK3jXmkY2DQ\nbCLoaOsADocDgF7viUsWhg7vbqyhom7cbfTvIy3yY1uth4Gfn9/o0aNHjx7ds2fPmhr89YG+\niXqb/o0BGg3TMyI839XUUSEZJTAMIxo01TWVafT2CocQaOnif+0ttHfBwYCqOkNDU36YiImV\nVlPyMAyjK9EM5HkOyTC11mqSwTBVdSUdAzlH3Vp6OPXHAFBRVyKy6gAARGMcAGQK0dBWJjqI\nIfuDeqr40RAQaLetvLYu2cvtiFQKZX9BS1eF8kkspqWDY17rGau3P1QFUN+ETPmaOvjawDAM\nVxtqGkpyW4dhGG6sq4aOMv4sggGttmWpKxJB2SSCV6KyCt3AVAMDAGCAzqAZWeIvtFrQ1CH8\nIuR+YobmGi2awWhAW0+FyKoDAMgNESV5QE1TSUWN0W7/gjyQVlVdiaGE8x1BiP/ptfmzHV4Z\njYZpE0uRp90m+sRUCb50DACSlaSGtjLR0QdJTdQ0CN+LLIEOTkFayuQTHWyqDD4GJu0/TAMT\n+YHPOgaqKqp02deJYUBdS0lDm1DtVIKO1TTa11BDR0WO+wdGNswammq0Hj0wTE67SFQESD8x\nLV2VjjuLGIZ9Vkk9uxifl2FHSn1dXcvWE4cDdHV0gaQy9cHD1Lfb4iKxCKiotOpgX3/9dVhY\nWFhY2LfffltcjO9n6tPfotUXiAEIfIOsSKKEbInzkGEYsLDTIXKtZSjR3JjG7Z5nKNFcfIxx\nn2/Bzk3f3E4HyNKnYQAA0H+UPRW3UydPQ6/+Flp6qoZmGgO/c6SYs8DYUrPfKDszay1rZ91B\nPzjKjSjUM1KzsNdpZ2FAADx6m5JIOXQ3oNFp7VqBYZimjrKptTahVDcDos1SO3fC9+Lqjef0\njQEajebs0eZXFrY6iiYAsrTTAQBYE3sodwBaO+I87OJtpKbBaNEiRsNUVOkkcSQAAHsCbUAI\nHbvjZLEytdGS2zYIoZkNjv4d3A1wD84gBA5ty9LSUyHf4u2IkTm+0RY42qFbLzMnD6MvfnKW\nuz9tZq2toa3U0dKFEDrKy0PU3d/UvDnqUEWVMYDUE9zAVF1ZlUE0p0IIzO0IOzAAwH+wNYAA\nw4CsqrqGqk4eZNWj0TFnz/YPYBig0TAnD7LYGsfuBupaSu2q2aO3GcnbsXPXx20WhgETSy0i\nYwvDgBveIAYhxP25DFtXfZLEQNbOeri/snLSJRr6IASWjjiho9bOukQJKd8+44RfHADA2dPI\nxOptfiI7d30rvO+3HQwGbfAYZ5kxpK2v9sUYZxIbzMyWrMPI6Nip7Nz05R4ukGTN9B9sjWFN\nvVj2Zfcaih8OL8PYUrNjd5LBUKKT6MTZ25ihRO/wYcJu/vKjBhGd49MYdtJGHrcV5CcbzQhe\nRF4rFEAgZb+4/rjS3sdLB9Cl2dd3rN95Jb1G0MireH320A1Wj/5+CuYk0tJV+XG6l7OnkY6B\nqqm11qDvnMiTL1g76dq46OMMLhgGMKz/l3Yksp79zJ09jVq6uJqm8oCv7eVuJ2A07JuJ3QK+\ntLd313f3Nf1ukoerPFuwBY8+Zt9O6j58nCvubE2EjYvewO8d+42yo5ijKGi0o37b1Z6jh6FX\nX7J8gZo6KgFf2tEwDMMAaJ7kGMq0wT84kwRI2rkb2LjoAfB2VpUJevQxJ4lkNLPR9h9o3fIw\nAIBGAwCAwK/t26Wb8epjrtAFDDQ6rUdPMwCAZx8FkiN64T2spqE0aoK7ma0OjYbRaJipldao\n8e7kq2RXLyOcJAgYMDDV8MArwtpJV10Lf5OvWRTQGTRHPDtD10jNN9AStNKh7P/t3Q0curV5\nnkbD7CmkImv+I5i2voqROf67U1FlePQx8xtkRX4I21QuHRv4nRON8dahHKMBAIBHHzNzeRMn\nnUEb8avr6Ek9Rga7/TTLu/VEjlMQDfPsa4Z7uifbh7DvRpYb1snDcNhYVzt3A3M7be/+Fl9O\n6CY3Yr33UBtjSy3Z38dkTqAY1n+UHcnBAgBAVUNp1Hj3FtVhNKx7T9M+BLlsZOgbq3v3swCg\njdWK0TAanTbga3sSwf6j7Jtr2GyzYsA3yIpkGezsaWhipdW+m2AAAOAzwIJo/9WrvzmD0dFK\nAAADOgZqTh0sYACAi5cx0U6/TNDCTqfpmAIPGh0bPtZ18PdO/oOth/3iQh7+2RpzW+2fZ3uH\n/Ok3ZrqHkQXZfrOJhZaJpRZRDTEMmNlqdxzfmP0t6HRil2AMo9Fosm8WF2NLze+neTh0N9Q3\nUbd1M/g2tAe5wUqjYYFfOwCsTTVlH1vf4TYkYeDaeiqB3zg0dfImOxLzG2RNlJsG8e58mgTF\n59pG7PVfdHVBX9IExfVRS8bedprJzP/3Xg5LqGLu/9OsKYOtVQCA1QlnDpy897qQLdU0svMZ\nNn78V646OD39/SYoFvDElw++Lspmy3wvZP9LZ9CG/ezSzY9sm0oGr0HEruKpqDJ0jdS6zA0w\nEIKCjNrqUi5DmW5mo0U+NbbAquInxpRUlXIZSjRTay3v/hZytxVl7udJT0oa2I0YhukaqfoG\nWtm6yb/OIfdNTeydworiehodM7XS7jvcFndkObjhReyDQipudhgAA0Y5jJ3pLfvnye0JSc/K\n5a2hMc/eZr/O9iZ5QiqBsu4kvwYAiEXShOji+EclMs8tGh3z6GXmP8iaKA1YSW7dnbNZEOB4\nXcs8/HoPsyFZNhRmsmLvFdWWcyGE2vqqXv0s3PAuq2BX809uSaAS3wMAGDLGmWRfR1Ea6hrj\nHxaVFdQ3CsUGphqefcw/RFY5AEDsncK0+MoW7yuZM6umjvIXP32Qi+MgBBmvKotz6gQ8kZ6R\nWjd/U4qLLiiFrGo+v0Gkb6zeMeMgngB4FVPy4n5xi1OXoZnmwG8dTKzkHIVDKXwdW56fXtvA\nadQzUvPobSY3OZyAJ757PrN1LAKNhvkMsOg5xIZkYVCax7l7PlPIlzQPvwBCoGuoOuwXV6Kz\n6bICzuWDKVIpbO+tiAF1DaUx073krq4/NHW1gn8PvBbyO0TvYkBNQ+nb33vgNi3xSenNU+nt\nko2D5t74xRhn5gBCw65zFGSyHl/La4mBVddS6jPU1gX3SKQtvPrG7Nc1dbUCTR1lWxd9PWOU\n1vgDgq4U6yQQwuzX1ZlJVexqgbIq3dxW26OXObnLC+L90iiU0OgY431bxqxq/qppdxvqG3E9\n1lvAaJiOvuqyPYNbPMx4DaIdYTE1VXxCT20MMzBSm72un0J5/KkglUBWNV8qgfrG8pcKuak1\njyPzYdvATtk86hNoKTcPraw4qRSSn9EnRBfH3MiX84cwYO+mP3Kc+/9pqtKyfE5GYlVVKVcs\nkmjrqVo767n6GL33WzQ+CVIpZFfzeQ0iPSM1uYfg70hNOa+iqJ7LadQ1UjO31SbfpZYh4Isz\nX1WVFXCEPLGGtrKVk65DDwN6Bw/m1lQWN9y5kFlb0Sa22tJed/APTp/cqpPRUCeMicwryGqV\nLgADti76/Ubakegk+XnZ7XMZIqFUZszJcsUxlOhDfnAiPzN5F+pZwrpagZauira+6v/px9u1\nQYYdAtGenLSa7WGPcaOYZWA0TENDae6GAKu2rnUcluDopviiHLbsVp+3z2MAQmDtqDt+vq/2\nZzCLcFjCpJjSgkyWLMaQzqBZOuh49jUjuq+ic8TcyEuILum4nQBAU45kKwfdkcFun9VlnYgu\nDISgOJddls8R8MTqWsrWjrrG8iJyPj71bGF5Yb2AJ1JTVzK10SKJfmiBV9+Y/KysMJvNrW/U\n0FK2ctT16GVGxT5GdFWQYYdA4FBeXH9wfWxBFptGA9JWntcyM8XB3SB0sb+BCY4ZJJXCl4+K\nn94uKM2vaw5JBua2On2/sGEOsCTKwv9JgBDyuWIohWqaSh+oYlmvq2Ou58nybtAABpuKBUrK\ndN9AS2bg56UQBAKB6AIgww6BwAdCkBBT/OJBUXpiJY8rAgBoaCm7eRv3GmTj2Uv+eSW3vpFd\nzQcA6Bp+8MOszxmJWFqUzS7IYnNqBVIp1NRWNrfTsXfTVzRyFoFAIBBUQGMrAoEPhgFmf0tm\nf0sgS4uKAYXcpzS0lP/L9lwLdAbN1lWfJDoSgUAgEO8RZNghEPIhybSMQCAQCMTnQxfJtYFA\nIBAIBAKBQIYdAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYd\nAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYdAoFAIBAIRBcB\nGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYdAoFAIBAIRBcBGXYIBAKBQCAQ\nXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYdAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQC\ngUB0EZBhh0AgEAgEAtFFQIYdAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0Ag\nEAgEAtFFQIYdAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYd\nAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYdAoFAIBAIRBcB\nGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYdAoFAIBAIRBcBGXYIBAKBQCAQ\nXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYdAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQC\ngUB0EZBhh0AgEAgEAtFFQIYdAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0Ag\nEAgEAtFFQIYdAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFQIYd\nAoFAIBAIRBcBGXYIBAKBQCAQXQRk2CEQCAQCgUB0EZBhh0AgEAgEAtFFYHzqCnxsdu/era2t\n/alrgUAgEAgEog1Dhw718vL61LX4v+e/Ytjp6+sHBARER0evXbsW9wFlZWVVVVWpVNrQ0ED9\nz8qkIIT19fXUpZSUlNTU1D6mFACAw+FQl2IwGOrq6gCA+vp6COFnKEWn0zU0NAAADQ0NUqn0\nM5Si0WiampoAAC6XK5FIPkMpDMO0tLQAADweTywWf4ZSAADZMozP54tEog8tpaWlhWFY56QE\nAkFjY+OHltLU1KTRaJ2TEgqFQqHwQ0tpaGjQ6fTOSTU2NgoEgg8tpa6uzmAwOiclEon4fP6H\nllJTU1NSUuqclFgs5vF4H1pKVVVVWVm5c1ISiYTL5RI9o6Ojgwy7d+e/Ytj5+PhcvHjR2dmZ\n6AEdHR0DAwOJRFJQUED9z8qkpFJpfn4+dSltbW1DQ0MIYV5eHnUpLS0tIyMjRaU0NTWNjY0B\nAHl5edTNJg0NDRMTk05L5efnUzeA1NTUzMzMAAAFBQXUjZIWqcLCQuqGgqqqqrm5OQCgqKiI\n+uStoqJiYWGhqJSysrKlpSUAoKSkhPokp6SkZGVl1Wmp0tJS6tMVg8GwtrYGAJSVlVGfQuh0\nuo2NDQCgvLyc+rBOo9FsbW0BABUVFSTDejswDLOzs1NUCgBgb28PAKisrFRonWZnZ4dhWFVV\nlUJrJ5lUdXW1QmsnW1tbGo1WU1NTV1dHXcrGxoZOpysqZW1tzWAwWCwWi8WiLmVlZaWkpNQ5\nKTabXVtbS13KwsJCRUWlrq6upqbmQ0uZm5urqqrW19dXVVVRlzIzM1NTU1NUytTUVF1dvaGh\nobKykrqUiYmJhoYGl8utqKhQVIrH45WXl1OXMjY21tTU5PP5ZWVl1KWMjIy0tLQUlTI0NNTW\n1hYIBKWlpUTPqKioUP+DCEIgAkII4YkTJ5hM5uDBgxWSOnPmDJPJHDBggEJSFy9eZDKZffr0\nUUjqypUrTCbTz89PIambN28ymUwmkykWi6lL3b17VybF5/OpSz169EgmxeFwqEs9efJEJlVT\nU0NdKi4uTiZVXl5OXSoxMVEmVVRURF0qNTVVJpWbm0tdKjMzUyaVkZFBXSovL08mlZKSQl2q\nuLhYJvXq1SvqUhUVFTKpFy9eUJeqra2VST158oS6VH19vUzq4cOH1KUEAoFM6s6dO9SlJBKJ\nTOr69evUpSCE/v7+TCbzypUrCkn17duXyWReuHBBIanAwEAmk3n69GmFpIYMGcJkMo8dO6aQ\n1IgRI5hM5qFDhxSS+vrrr5lM5t9//62Q1Pfff89kMnft2qWQ1M8//8xkMrds2aKQVHBwMJPJ\n3LBhg0JSEydOZDKZa9asUUhq6tSpTCYzPDxcIamZM2cymcywsDCFpObOnctkMhcsWKCQ1KJF\ni5hM5pw5cxSS+uuvv5hM5vTp0xWSWrFiBZPJnDRpkkJSa9euZTKZISEhCkkhOgEKnkAgEAgE\nAoHoIiDDDoFAIBAIBKKLgEHKHlRdm7KysqKiIgaD4ePjQ12qoqKioKCATqczmUzqUpWVlfn5\n+RiG+fn5UZeqqqqSedf5+/tTl6qpqcnJyQEA+Pn5YRhGUaq2tjY7OxsA4OvrS6NRtf5ZLFZW\nVhYAgMlk0ul0ilJ1dXUZGRkAAG9vbyUlJYpSHA4nPT0dAODl5aWsrExRqr6+Pi0tDQDg6elJ\n3ZmDy+WmpqYCAHr06CGLRKECj8dLSUkBAHTv3l0WHUIFPp//+vVrAEC3bt1kERtUEAqFSUlJ\nAAA3NzdZjAIVGhsbExMTAQCurq7UQ8VFItGrV68AAC4uLjo6OhSlJBJJfHw8AMDJyUlPT4+i\nlFQqffnyJQDA0dFRX1+fohSEMC4uDgDg4OBgYGBAUQoA8OLFCwCAnZ2dkZERdamXL19KpVJb\nW1uZMytF4uPjJRKJjY2NzC2VIgkJCWKx2MrKSuZgSpHExMTGxkZLS0uZgylFkpKShEKhhYWF\nzMGUIsnJyQKBwMzMTOb0SZGUlBQej2dqaipz+qRIamoql8s1MTGROX1S5M2bNw0NDUZGRjL3\nTYqkp6dzOBxDQ0OZ+yZFMjIy6urqDAwMHBwcqEtlZmay2Ww9PT0nJyfqUtnZ2bW1tbq6uiSu\n5B3JycmpqanR0dFxcXGhLpWbm1tdXa2lpeXm5kZdKj8/v7KyUlNT093dnboUohMgww6BQCAQ\nCASii4COYhEIBAKBQCC6CMiwQ/y3EJWkZdWhXWoEoiuAPmcEoiPIsEP8p8g7s/54ulAqECiQ\n3xWBQHyWoM+5NaL6egWSQjdDPXko4v8G+vLlyz91HT4LRFUpD+/HFkFja0M1qiEGH1fqowOr\nUpPZeqbaVKMg/i+0wUmLuppQkRfzktGzpxXFmIvO1ZD94uyVMrNu5uqf8StGvOUjf5WNFa+i\nIh/mSM3sTdSpL687J/Ux+bg17Mzn/DFr+FGHRPaj9bP35Fj6eSsy5rBjdyy7ohTob055mH+H\nGiI+Fp/n4PCxkRbfXL36TGZN8Z2IuRsfU02z/jGlQMm1FX8dTWJ38tCh+NLqNf9mK3A9DQBS\naea1A9dzKV8Y81G10QwUcFh8yutNVl4uy/qHaQNq7j6vEjfWU7uuorM1hHlxNy9v/XP1NUXU\n/k5vWZD1IrFGcVHFdNg5qXfrvR+hrI/8VUqKrq9ad6WSJozdsfYKYRL+9yPVjJSTn/Qqh0X1\nkpZONe3datiJrmij6OfcyRp+xE7V+SFRd+CCpQOL9yxYfZXqmNOYe2HdaaVfxzOpphV4xxoi\nPhZoxw4A0BizdxcM3hA6yH8As/HikXzvoe6an5cUYD0+cauc8+jCU2UPfyddhS6Cg+w3UTde\nZsT9e+Exy9LXx0qDijEvyT66YGeShuug4V76lFZkH1UbAAAAYPWzvat2Xom6dCkZuDBdDeQm\nSqmJPxi+8T6te9BXY7+1er13U2Sdk38PYxXy5nW6hpiBKCvFeJRb4q4j6YZMHzNeFVtNU87q\n9l3ecsW9TeFbb/K8vvYxVEBKUR12Tupd2vVRyvrIXyXv4c79Kr+vG9/P27ru5WshrEjNFRlY\nGcvZP+qcVHNVn2xdfiQxK/rs1Xw9Hx9bTXlCnWrau9Swc10RAKDhOuJLyp9zJ2v4ETvVOw2J\nvLLs0vraxKvnH9VQGOpZz7aui/FdND/AkAa4uQ8vX777qoRmbmeqQb55926D9otj69fvOnmn\n1nGgl7FCm4QIRfiv79iJStKy6rhslgijAQAA3dLXS0jxslieolKdKqvJbaKmBvMeuyxisvGt\n8MUHXlRTvYcVAFARuemf2n6TNhw9tmpYw/HFy8+lU7k8k+7464Ix9qxbOw4mUHNNVlgb7yAF\nAACw5MqOO1YzN4aH+OiV3l69YMfjcgmJt0hdWny2Wr/5EdMsH+4+eDdPecDcjYu80jbNj7hb\nSOydw8rLZXWubwAAgLKnFyNH8OWKtWPg6bD5C9cfTZF7u2en3zIv9ejWx91n/qz/+nWJvGdF\n5UXlsvtuFdRhE52QUrhdrLxcFvxIZQHQ6X7Y2ffFrqqkqWvQQEPCtcf55QWV/IrozXNX3Cwh\n13znpAAAQCqtfPhUa+qmFWt3bhgDLoQt/SeFI+er7lTTOl/DznVFSX1JHkfXwthyIJXP+R1q\n+HE61TtMRgAAAMojN57iDpyx7tDRVYNqDy9cfj6DdKgXVOSXcgV1dXwgyjwevjMeMzXDkvcs\nWH27nLhvvNuQCNj3916mj994YOeqMe7KAFC/1hqhKJ/4SrNPTO7xGYuvVojLYq6/qJZCCCEs\nPTNrdbQYQghrn508l8QlEWbF31FEqlNlse4tH//HwbhqqZDDEUIIIT//5prfQ8IvZ3BlUokN\n5C0U3l814e8UWXlQWhG55Ifvpm6NqZDgtygzPq2SL235Nzfz4rKQ0HU3CwTkhUCosDbeSQpC\nmHtm/ekcKTt684rz+WJO7KYJX/04dd6ifbH4Na1/tW/aL9N3RJeLobDwzvrQkKXn0xqguDxm\n1/SQ9U8JVFj9OGLixJUnzl/tXA0hrLoYFvFcBBvzziya/PvEaZsfFDfKkejcWxYm7lm8O5ED\nYdbR0L+i6iGEEPLr6nALq3265fdvp5zMgVBRHTbTGSkF21X9eO+eh9UikegjlCWDFX8ntkrx\nt9zZr7L8aVQ8WyplJz94WSmFEEJpyYV54/YkfQgpKMk6MmfGtOWni5v+zc24HP7bpPX3ikkv\nj+5U0zpZw850KmnVs92LF6yOWDJ5/MKL+RII5X3O71TDznaqDz5BtIJ/b+VvB940jd7isqth\nP3w3ZdOjUpy3LCxPiDx96kZSVX3mhcUhM/bsCV9yuQJCCKG07NKC0MOEd1u/65CYuHvKnqTm\n+UUUv3Pq9pfyhkREp/iP79jRlLDS5+e37Y7XczeQ7eHTGXSBQAgac8+tO81ytCO6MECQdvVW\npc9gPwWkOlVWi9tEVAVDGQAAVG2G/blptt3TtQs3RKw9VetgT3I5AaxKTarq3tcu5sDx1AYA\nAMCMe/ccNnyo9MTaszjLLHHC0ZU7di6fNnnRxmPXX+TViYG60+jwiN/0ov5acrGAbJXaCW00\nt4+6VFs3F+vv5v1oX3rzHG/ANzZ0Lf8Bfb4ImTNp7gR//NskNL0mb1wxqObwgr9OZ+sOXrhp\nrmv8+gXbHkt7Tt+4fZo/jgpF5UXl2v3mR0yzTLh2+2VmlUTBdgEAADD0dq999ez5zl35I1fv\n2xCiHbn3ahm5hLKWlmJvGfLK01/nGo1dMcVTCwD7Ht0Kk1MlAFbe37TqbGaHzYvG3PPrz6pO\nnukS9ygTKqpDAGQ9iv6VolKKtUtUklbbY/LUfrXH5865bj7uB0VrmJXLwRTSIai9f+BsZsbd\nW1lSABR7ywq/LyD7WOp6D/XRwTCdHoFMIwwAADB9Y0NVedeumCgsBfMurI6Ion+/cIxz5bXN\nR5LqIABA3fmbZRsm9zEhjIqC3LK017lcOkOhpkk5+Umlxv4eCrZL5v5rSKkrwrfZ9OsfH75l\nOmPdzEATbfcRAdawro5LN+lL9DnL6JzmBekXIlMVftFSTn5SnraP94eeIJrLepXDYvj0tX60\n7+jregAAoJv26RUQNKKnkWb7t9zay/C2xncrwkdUveL17Cm7NAXT0dNRUSI8azbo5JAI886u\nPvS6Ebgw7V8caqohYNja6JSXUzk/QijMf9zHTtfNjnXhwEOeFbOPn8zvRLny5aVyPb27e5/4\nLVwQYERk+PIT/tmdbTfSQ1f2TypSnSsLx20CUzX18qA/PJE9YNmiAYQ1BFJp5rl1V2iDg0f3\nqDqz4UCCxNAA5Fy9Wd4rdKzhwyt1vQc5tpseaUZaNfdSuy36a4hmcXzU8X1HopLLGoC+58gx\nA3uYWuhqEHuvdEIbHZ0t5Ei1c3Oh0WgYEGTdP5uA+fqZFty4Ve333SArEu8XJX1rnZIHN+/d\nfZir12vk11/11Xyxd1OktNfwHrodGsZ6tiPsr0tcv1E93b2DfJUfbd96W+jS092cpWC7nFXz\n92187LEgbKSVkrq5z5AgNy38W91gbdzRtRv2P5X6Bjg3+atQecu1T7eH//0iN+HSydvF+t29\nbXUMeSnHM7QdEw/ccJz5Rz/9NlKQ9XTrumf+i+cO6KaWefiR+lB/M4ZiOpT1KHrAABtV6lLt\nX7S8duWdWnK4oW+QjZK2EfMehgAAIABJREFUp5da9K7tt4U2+uX3UhSpIa1ffxtVqjoEAKgI\nk/ZtuUf3COzbw1CZ6gigaLtaaPex1Ka/yKgse374bPnwSd8RzIwduwcVKQAApmOi/uZoxNma\nXjPmDJPe2Lw3VtXTz1GHgamb2Ri3+/xZebkCXT01rPbp9vD9cXkJF49HFel397bVplNpWgcf\nPkrtanH/fVJn7etjVPqApFOJ8i4s25nvMcBZAwAAsm9d5fsx8/f/w5iw6Esr4cNNi5+bj/TS\nV1JRIVa9Ippv1gYA9U+Pnef1HujYHF9Aog3Iys6q1zHQoLGebF1xNKmVNqh0Kh1XO9ZFxSej\nt5ov9/n9R/XrG/fHCfV0pBmXr5b1/v1Hr/aWZ3svw+piWq9fg/uZ0VkFGUV5zw6frRwe+pUN\n4b2JmJqZokMiAABTk6QeOFzgPXLoAKfac+v3PK1XU+a+vnSzfuDYIcRlId6BT71l+Glo2Ytu\nrMrILCu6t3lK8OKTr+ukEMKayLDvf5ixL7Ge/C+wby5dcKmi5Z8kUo2Vr+9evfowrVbUibLK\nri4JO5svhJK6tMurJo5fei69HkJp4cWFM/fEc8jqJ846PGfGrCVHs6QQQiiqeHl+x4oly3fd\nyObysk6FLb9eiSvFiV4zYUlUFYQQsqLXT/lzx741c4J/Col4UPW+tNEE6+7Kxcez+VJuPVcK\nIeTz+fKkso4tPZYlqXqxf3bw7P2xVbKzZHHxw+3zJoyduPTMG7xTgNpXD+KbzgzExTeWTllx\nt5ybc31NyNh5x17VSiE3P6es40GFMOfcwtl74u5vnX04QyYrrXt9cvH4qVsfpFxWtF2svNJa\nwoebkRZeXBp+sUAIIRRUpMbGpleJoPy3LJEUnl57KEsEIeTn34r4fcLyu2XSqn8XjPl5Np6U\nNOfchsNJspMjScaRGUtuNHUBOTpsoW2PoirV8UXLaVf+6ZnBYbs2Ld3+tAFCaV3KycUTQuct\nXzxb4RpCSO1LgVBSnZGR9eroguCpWx+UiqiNAAq36y1tPhZ24sn1y8I3HospFhI9j9M9KEiJ\nRKKm/xLk31gbGhJ+MZNT9Wzf7OB5F/LxHDGqH0dMnLjianbmqVX702SdKmqDrFNR6IoVlyL2\np4shFJVH75wRsujk68JXcmsIy68uCTuZVS8Vs9IiN0wOWXo2JYOwU9U+2zJ98cX85jZBfuym\n334cv+yG7Pxckrhn9sE0woKaoa552ZljZA4PQhi3fdaxrFa/I9ZG7bPtoSFLzqflX9p4IKO1\nNuqk5J2q5t7+fad3rdp6LV3BCaKj5l9lvbywa/WyVXuicnh4EiVnZv5xsRzC+vhtob8vP3Tu\n/IHwkHFLbxTlRm7488+1Bx4WEr8vKG0ofZOcVsaVKDIkNtHwbHPo8js1EEJxTcrNk/t2H7gY\nVyaSJ4XoJP9Fw05cGLl0dvjhs0fCJ865WCL7WX3qmSXjQ9ffKRBCfu7TuGJ8HzQIpeV3IuYv\nDF+/ff+usPHTN16MvP3w6cvk9JyKmhxcKUnRjWWzluw5smdJyIT10bUKlQUJ3SaEVeVVxA4y\n0sqUxOJG2FgavW3azzP3x7NbOc3F7ZsZOn9fbI2USLj44rzgiJjavIt/zjuQwoUQQimXU09Q\nmKLaeAuuswW5NhR3c+FnXlw8bsKKy1m8+sQ9M2YfSZWNdNLaV8fmjZ34dxKOA4+09knEjGU3\nSsRQnLB9+q6kloaLyh5unTZ+zvazL/BqWJubUyslbJc8ck/OWBctlNQmn10+Zepfm1dOm7j5\nORdCMdlbFmccnB0auuxsWXPFy67+GbwtTsxOvPm4iNR5CkIIIevOX+NXR7PlPtfcEnZhVl4V\np6Rjj8KjRRsQVyF8gnbVpcVncSEUpx2a/M33kzc/bBr2ZZr/82ohWaH8N1eiMmDHPk+qQwgh\nlOaeX7Xhei4fQigsvrdpSvDCYy9en5z91128+a2Rw2nuMwq0CzYWv8lkSwk/Fi5ew1rpkKB7\nkCLOODhj1r7Yyqa+Kq2KCv/5h+lbo8u4xQUEDrZQWhN/aE7wb7/9eaqo+SdNnYpcjQr58ImK\n07ObTCLq7r/CnPPzfpiwO5Hf+ofc1H/mjR3/556LUbfPRyxae7eCsH8opvlmoZr4w3PGz9z3\nrLL4wvyZEacv/Bt5617007hX2VWELxpCad3rU4tCQkPDO2gjJ4tkcJOkHfrtyx8WnkprgFCB\nCaJT3pMKexk2d8WaJ1tnz/4rfM74X2fveFAklDMkyipYFv3PiZvxRQ0SCCH70app2+L5BI8i\n3iv/waNY3Ih3h27+Qb31k/dvjdYbNqq3rTbRsSOmaevXm+lmb2FgoFrzPJ3WzURUkJWcUm3g\nG+Bn11EKNzJc2aj7ACplAQAYepLEvf/kOfXzNlEBNE1DXgXXybe7s52NPmGAfstplJ2hTa8g\nD/7NLXtjVTz9HHUYAACGoXvgyJG9rYnTbmg7WFQc37I/Fo4O+6OnHgYAwJRUlAkKU0gbbfKC\n6krT9h/PdOjnbaICAE2lIuY212+Qu5mVObE26CoqdAAAYOg69htgW3hq47GEzOdPxQHf9zMj\ncLliGLgF9TZ6fWTjgX8TlL8Pn91LduiKqZl6BvbxsbXVb5cOQFSSevfKXclXc35wVAE0E73K\nk6cq/AY6aQAAAE3TtleQt5Njr+44mTxr4s9eLbf3tlSm6QOcdjnLOWxQU6r9d/um0/cLTL6Z\nv3jC0H5aqVcruw9x01LXJE7DQDPo1k0t8/rpu2zX3h7GKhimIcm9k2U0IsjX2Vpbvuesqq2j\n9PrWfxuZ/Z3lPQ2rn+xYvvvR6+cXTsVgQ2eMN32yvVWPwuGtNmg0gPOie37pbYBTZmNJzN/r\n9qfq+TDdfUe1SmChpmXbK8jbztBCnyRHR9Mpm5dVuz5PI9MhAC2HlacLzH393Lr1HeDCuXfo\nTGHvicFeRh2a1zoBLPV2tRwu61j27u+P87F49TDt0IFb65Cge+B4d7FeXLrFtnA1VgE0g27d\naPe3bbsvdvN3M1DC1A0a2I1uHs4ODg7GRD4VmJqZdyBTPfPmmbsslz6eJm87VW9rTRI1Yvrd\nPbVyb566WenYy8tUFVMycA3oawGAuW37014Ais7/NW97VKmRt6+dtrKeJPnvUwXO/byMlQGm\nYcitFzvwb54o6DGsh26bVj3bsuah7XC7+MPnSy19fSyb6q9k1GNQkDMszshn6fQPDuljTFxB\nomEKT/OttRHEVHq4Y+tTDo1Nt3LVa2RVlhbkc018PO0NiLSBqRj3COypm3fj9O0qp16eJiot\n2rDy9sGZIJqAmIb7gF7C6xuPpOr5+Drbe1CbIBTRfAuaVo5mqhimamJrLtMkplSTcIfl/qWP\nEe7zNfEHwzfew7qZp10r/XLl/O+HD2NiT3bvfKDRZ2TQYMIhUYZIwK0tSLh96sCJu+k1DJcA\nq1enkm0Ge1DLoIV4Fz61ZfnxIdiLLhZDCHkcDvFiR1KXl5iQVdO8fSyJ2zZtfyr53gXr6qJJ\nR7NkfzLnyLRNsa1+R1oWlPLranliCCEn+dicsRNXnYpOTLy1ed7mWNz99RY6nkZJyM5fcGE/\nWPPL3Itl8h6T7eI0x9DK0UbHXVJuysl5Y0PCj956EnN53dy10fJPLNtSfnXB2D+vFcrfnILc\nrEvhE4KXnE2Tc1zQFJXG5zdv1lDa1hLlXt13MUMIxZmHZs7Y87SCo1i7pNyytOS00no+Xyhb\nPnOS/1687pE8qaYzEVbKycXjQsL2nrt8Yv2SiLul5L3x7UkKhBBKy26vDA7ZGitn84f9YO3S\ns0ViCCE/L2r9xHHL75WR9Kj22hAr9KLFlU/2zJr456lUDmyzb0GBNqds1Po8zmGlvI0wbtbV\n5SEhK69k8RRoV5vDZVn9KAwdsLUOpUL53UOYc37BlCmTfg6NuNN0nCZlJR6ZHzx145W4pHtb\nF+6MJ1WkmFOcnVPBlUApO/nEwl+pdaqmjVIIIeRmXFgWErouijyCXlp25c8pYctn/7bgWCJb\nCutfH5/za+jac09TUx7sDNvzqrH68vywqDaHnKzbK2bsjKuDEHLenA0bF/zXhXQ5IcdtC2zT\n5ylqvlW7pHWvTy7+JXhVVKn8Y0N+ZuyrJs8PbsaFv0ImrY8iOdhsn4IAttozznpy5koah2yC\naLXTTE3zMinZaNOsjZq02FepcZdXz151j8zXhmQrlxhBwd2DW7Ydu5/PgxCK6/Ljbhzb/Oe0\nsd99tySqRm49Ee/Kf3DHTstIF6ObOZjCGoH91+NH+nXzGeAnvbEtwfxbP5OOvrfE/rCYoST1\nSKLhKLKEsKom+mp0UwcLdQwAQcbtOPUvAmxogPX8n8haR09roh2INrk6ewcMHNbPsib+3sMk\nAfO30AHGpKFzNP3uHlrZt89Eldr18TFTxQDA1C19A/1dyaMf2lbaxl56a1e01tC+hJf0vN3F\nORlVpNvd206HQaoNnF1S4Dry5xHOjbnJaWXq/UN+603ZSx0AAGDRpVUnlELDx7qqym+Pkr5b\nQH+LnFMbj7/R9/a1JQwG7HA9EYVtLXbsjjVHY18LHb/u2a17d+VHO7Y+0P4hbFovKL9dALSK\nfjhxp8yoh3Px0YURV8o8f/9jqCXeW+7o3v5AOGDGNK/iS/+81P4pbHZ/Y7w3TOQUr6Pj0MfP\nxdTKTJ807XzGzROV3X/qbU4DDF3HPj6S63tS3KaGfo/bozpq47bQpV/gwOGBjhReNAA0DSu/\nIHde5KY9LzS9+vr3Gdi0bzG4H1FPhBV3N634+86rtCJuQ/qLTCnkVtaw6/k0q/7fBJFH/Egy\nj8xZHWfo62OhgTF0nfr1Vn20edOVIlMfPxvCncE2kUy9h438aiCVdnWMm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dseavYN8lOVv1HabLtvOVHUfeyU4O9H9rFSBdJ6ob5nN2OS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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(Cell_number, aes(gene, cluster)) + geom_point(aes(size = pct, colour=avg)) + \n",
    "scale_color_gradient(low = \"white\", high = \"darkblue\",limits = c(-1.5,1.5),oob=squish) + \n",
    "geom_point(aes(size = pct), pch=21,, lwd=0,stroke=0)+ scale_size_continuous(range = c(0,3.9))+\n",
    "theme(axis.title.y=element_text(size=15),axis.text.y=element_text(size=8),axis.text.x=element_text(size=6,angle = 50, hjust = 1),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),\n",
    "panel.background = element_blank(), axis.line = element_line(colour = \"black\"))+ggtitle(\"% cells\")\n",
    "ggsave(file=\"/media/garret/New Volume/scRNAseq_data/Yoshiko/LHb_integrate/clean/Neurons/gene2000_30/discplots_TF_LHb.pdf\",height=4, width=10 , paper = \"letter\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}